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Demographic and
Health Survey 2014
Kenya
Republic of Kenya
Demographic and Health Survey
2014
Kenya National Bureau of Statistics
Nairobi, Kenya
Ministry of Health
Nairobi, Kenya
National AIDS Control Council
Nairobi, Kenya
Kenya Medical Research Institute
Nairobi, Kenya
National Council for Population and Development
Nairobi, Kenya
The DHS Program, ICF International
Rockville, Maryland, USA
December 2015
KENYANS AND AMERICANS
WORLD BANK IN PARTNERSHIP TO FIGHT HIV/AIDS
The 2014 Kenya Demographic and Health Survey (2014 KDHS) was implemented by the Kenya National
Bureau of Statistics from May 2014 to October 2014 in partnership with the Ministry of Health, the National
AIDS Control Council (NACC), the National Council for Population and Development (NCPD), and the Kenya
Medical Research Institute (KEMRI). Funding for the KDHS was provided by the Government of Kenya with
support from the United States Agency for International Development (USAID), the United Nations Population
Fund (UNFPA), the United Kingdom Department for International Development (DfID), the World Bank, the
Danish International Development Agency (DANIDA), the United Nations Children’s Fund (UNICEF), the
German Development Bank (KfW), the Clinton Health Access Initiative (CHAI), the World Food Programme
(WFP), and the Micronutrient Initiative (MI). ICF International provided technical assistance for the survey
through The DHS Program, a USAID-funded project that helps implement population and health surveys in
countries worldwide.
Additional information about the 2014 KDHS may be obtained from the Kenya National Bureau of Statistics
(KNBS), P.O. Box 30266-00100 GPO Nairobi, Kenya; telephone (Nairobi): 3317586/8, 3317612/22, 3317623,
3317651; fax: 3315977; e-mail: directorgeneral@knbs.or.ke, info@knbs.or.ke; website: www.knbs.or.ke.
Information on The DHS Program may be obtained from ICF International, 530 Gaither Road, Suite 500,
Rockville MD, 20850, USA; telephone: 301-407-6500; fax: 301-407-6501; e-mail: info@DHSprogram.com;
website: www.DHSprogram.com.
CONTENTS
TABLES AND FIGURES ........................................................................................................................... ix
FOREWORD ............................................................................................................................................xvii
MILLENNIUM DEVELOPMENT GOAL INDICATORS ................................................................... xix
SUMMARY OF FINDINGS ..................................................................................................................... xxi
MAP OF KENYA .................................................................................................................................... xxvi
1 INTRODUCTION ........................................................................................................................... 1
1.1 History, Geography, and Economy ..................................................................................... 1
1.1.1 History ................................................................................................................... 1
1.1.2 Geography .............................................................................................................. 1
1.1.3 Economy ................................................................................................................ 1
1.2 Population ............................................................................................................................ 1
1.3 Population and Health Policy Frameworks ......................................................................... 2
1.3.1 Population Policy Framework................................................................................ 2
1.3.2 Health Priorities and Programmes ......................................................................... 3
1.4 Objectives of the Survey...................................................................................................... 3
1.5 Survey Organisation ............................................................................................................ 4
1.6 Sample Design ..................................................................................................................... 5
1.7 Questionnaires ..................................................................................................................... 5
1.8 Training ............................................................................................................................... 7
1.8.1 Training of Trainers ............................................................................................... 7
1.8.2 Pretest Activities .................................................................................................... 7
1.8.3 Main Training of Field Staff .................................................................................. 7
1.9 Fieldwork............................................................................................................................. 8
1.10 Data Processing ................................................................................................................... 8
1.11 Response Rates .................................................................................................................... 9
2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION .............................. 11
2.1 Household Characteristics ................................................................................................. 11
2.1.1 Water and Sanitation ............................................................................................ 12
2.1.2 Housing Characteristics ....................................................................................... 14
2.1.3 Household Possessions ........................................................................................ 16
2.2 Household Wealth ............................................................................................................. 17
2.3 Food Security..................................................................................................................... 18
2.4 Hand Washing ................................................................................................................... 19
2.5 Household Population by Age and Sex ............................................................................. 20
2.6 Household Composition .................................................................................................... 21
2.7 Birth Registration .............................................................................................................. 22
2.8 Children’s Living Arrangements, Orphanhood, and School Attendance .......................... 23
2.9 Education of the Household Population ............................................................................ 25
2.9.1 Educational Attainment ....................................................................................... 25
2.9.2 School Attendance Ratios .................................................................................... 27
3 CHARACTERISTICS OF RESPONDENTS ............................................................................. 31
3.1 Characteristics of Survey Respondents ............................................................................. 31
3.2 Educational Attainment by Background Characteristics ................................................... 34
3.3 Literacy .............................................................................................................................. 39
Contents • iii
3.4 Access to Mass Media ....................................................................................................... 44
3.5 Employment ...................................................................................................................... 49
3.5.1 Employment Status .............................................................................................. 49
3.5.2 Occupation ........................................................................................................... 51
3.5.3 Earnings, Employers, and Continuity of Employment ........................................ 53
4 MARRIAGE AND SEXUAL ACTIVITY .................................................................................. 55
4.1 Current Marital Status ....................................................................................................... 55
4.2 Polygyny ............................................................................................................................ 56
4.3 Age at First Marriage......................................................................................................... 58
4.4 Age at First Sexual Intercourse ......................................................................................... 60
4.5 Recent Sexual Activity ...................................................................................................... 62
5 FERTILITY ................................................................................................................................... 65
5.1 Current Fertility ................................................................................................................. 65
5.2 Fertility Differentials ......................................................................................................... 66
5.3 Fertility Trends .................................................................................................................. 69
5.4 Children Ever Born and Living ......................................................................................... 70
5.5 Birth Intervals .................................................................................................................... 71
5.6 Postpartum Amenorrhoea, Abstinence, and Insusceptibility ............................................. 74
5.7 Menopause......................................................................................................................... 75
5.8 Age at First Birth ............................................................................................................... 75
5.9 Teenage Pregnancy and Motherhood ................................................................................ 78
6 FERTILITY PREFERENCES..................................................................................................... 81
6.1 Desire for More Children .................................................................................................. 81
6.2 Desire to Limit Childbearing by Background Characteristics ........................................... 82
6.3 Ideal Family Size ............................................................................................................... 84
6.4 Fertility Planning ............................................................................................................... 86
6.5 Wanted Fertility Rates ....................................................................................................... 87
7 FAMILY PLANNING .................................................................................................................. 89
7.1 Introduction ....................................................................................................................... 89
7.2 Knowledge of Contraceptive Methods .............................................................................. 89
7.3 Current Use of Contraception ............................................................................................ 93
7.4 Current Use of Contraception by Background Characteristics .......................................... 94
7.5 Trends in Current Use of Contraception ............................................................................ 95
7.6 Timing of Sterilisation ....................................................................................................... 97
7.7 Source of Contraception .................................................................................................... 97
7.8 Informed Choice ................................................................................................................ 98
7.9 Contraceptive Discontinuation Rates ................................................................................ 99
7.10 Reasons for Discontinuation of Contraceptive Use ......................................................... 100
7.11 Knowledge of Fertile Period............................................................................................ 101
7.12 Need and Demand for Family Planning Services ............................................................ 101
7.13 Future Use of Contraception............................................................................................ 104
7.14 Exposure to Family Planning Messages .......................................................................... 105
7.15 Contact of Nonusers with Family Planning Providers..................................................... 106
7.16 Men’s Knowledge of and Attitudes towards Contraceptive Use ..................................... 107
8 INFANT AND CHILD MORTALITY ...................................................................................... 111
8.1 Data Quality..................................................................................................................... 112
8.2 Levels and Trends in Infant and Child Mortality ............................................................ 113
8.3 Socioeconomic Differentials in Infant and Child Mortality ............................................ 115
iv • Contents
8.4 Demographic Differentials in Infant and Child Mortality ............................................... 116
8.5 Perinatal Mortality ........................................................................................................... 117
8.6 High-Risk Fertility Behaviour ......................................................................................... 118
9 MATERNAL HEALTH ............................................................................................................. 121
9.1 Antenatal Care ................................................................................................................. 121
9.1.1 Number and Timing of Antenatal Visits ............................................................ 123
9.1.2 Components of Antenatal Care .......................................................................... 124
9.2 Tetanus Toxoid Vaccination ............................................................................................ 126
9.3 Place of Delivery ............................................................................................................. 127
9.4 Assistance during Delivery .............................................................................................. 129
9.5 Postnatal Care .................................................................................................................. 132
9.5.1 Timing of First Postnatal Checkup for the Mother ............................................ 132
9.5.2 Provider of First Postnatal Checkup for the Mother .......................................... 133
9.5.3 Timing of First Postnatal Checkup for the Newborn ......................................... 134
9.5.4 Provider of First Postnatal Checkup for the Newborn ....................................... 135
9.6 Problems in Accessing Health Care ................................................................................ 136
9.7 Fistula .............................................................................................................................. 137
10 CHILD HEALTH........................................................................................................................ 139
10.1 Child’s Weight and Size at Birth ..................................................................................... 140
10.2 Vaccination Coverage...................................................................................................... 141
10.3 Acute Respiratory Infection............................................................................................. 145
10.4 Fever ................................................................................................................................ 147
10.5 Diarrhoeal Disease........................................................................................................... 150
10.6 Knowledge of ORS Packets and Zinc Tablets ................................................................. 154
10.7 Disposal of Children’s Stools .......................................................................................... 155
11 NUTRITION OF CHILDREN AND WOMEN ........................................................................ 157
11.1 Nutritional Status of Children.......................................................................................... 158
11.1.1 Measurement of Nutritional Status among Young Children .............................. 158
11.1.2 Data Collection .................................................................................................. 158
11.1.3 Measures of Child Nutritional Status ................................................................. 159
11.1.4 Trends in Children’s Nutritional Status ............................................................. 163
11.2 Breastfeeding and Complementary Feeding .................................................................... 163
11.2.1 Initiation of Breastfeeding ................................................................................. 163
11.2.2 Breastfeeding Status by Age .............................................................................. 165
11.2.3 Duration of Breastfeeding .................................................................................. 167
11.2.4 Types of Complementary Foods ........................................................................ 168
11.2.5 Infant and Young Child Feeding (IYCF) Practices............................................ 169
11.3 Micronutrient Intake among Children ............................................................................. 172
11.4 Iodisation of Household Salt ........................................................................................... 176
11.5 Nutritional Status of Women ........................................................................................... 177
11.6 Micronutrient Intake among Mothers .............................................................................. 180
12 MALARIA ................................................................................................................................... 183
12.1 Introduction ..................................................................................................................... 183
12.2 Ownership of Mosquito Nets ........................................................................................... 185
12.3 Access to Insecticide-Treated Nets.................................................................................. 188
12.4 Use of Mosquito Nets ...................................................................................................... 189
12.4.1 Overall Use of Mosquito Nets ........................................................................... 189
12.4.2 Use of Mosquito Nets by Children Under Age 5 ............................................... 194
12.4.3 Use of Mosquito Nets by Pregnant Women....................................................... 197
Contents • v
12.5 Preventive Malaria Treatment During Pregnancy ........................................................... 198
12.6 Fever among Children Under Age 5 ................................................................................ 201
12.6.1 Prevalence and Treatment of Fever among Children ......................................... 201
12.6.2 Type and Timing of Antimalarial drugs ............................................................ 204
13 HIV/AIDS-RELATED KNOWLEDGE, ATTITUDES, AND BEHAVIOUR ....................... 207
13.1 Introduction ..................................................................................................................... 207
13.2 HIV and AIDS Knowledge, Transmission and Prevention Methods .............................. 208
13.2.1 Awareness of HIV and AIDS............................................................................. 208
13.2.2 Knowledge of HIV Prevention Methods ........................................................... 210
13.2.3 Rejection of Misconceptions about HIV/AIDS ................................................. 213
13.2.4 Knowledge of Mother-to-Child Transmission of HIV....................................... 218
13.3 Attitudes towards People Living with HIV and AIDS .................................................... 221
13.4 Attitudes towards Negotiating Safer Sexual Relationships ............................................. 224
13.5 Attitude towards Condom Education for Youth .............................................................. 225
13.6 High-Risk Sex ................................................................................................................. 227
13.6.1 Multiple Partners and Condom Use ................................................................... 227
13.6.2 Point Prevalence and Cumulative Prevalence of Concurrent Sexual Partners ... 229
13.6.3 Transactional Sex ............................................................................................... 231
13.7 Coverage of HIV Counselling and Testing ..................................................................... 234
13.7.1 General HIV Testing .......................................................................................... 234
13.7.2 HIV Counselling and Testing During Pregnancy .............................................. 239
13.8 Male Circumcision .......................................................................................................... 241
13.9 Self-Reporting of Sexually Transmitted Infections ......................................................... 242
13.10 Prevalence of Medical Injections..................................................................................... 244
13.11 HIV/AIDS Knowledge and Sexual Behaviour among Youth ......................................... 245
13.11.1 HIV/AIDS-Related Knowledge among Youth .................................................. 246
13.11.2 Trends in Age at First Sex ................................................................................. 247
13.11.3 Abstinence and Premarital Sex .......................................................................... 249
13.11.4 Multiple Sexual Partners among Youth ............................................................. 250
13.11.5 Cross-generational Sexual Partners ................................................................... 251
13.11.6 Voluntary HIV Counselling and Testing among Youth..................................... 252
14 NONCOMMUNICABLE DISEASES AND OTHER HEALTH ISSUES ............................. 255
14.1 Introduction ..................................................................................................................... 255
14.2 Knowledge of and Screening for Cancer ......................................................................... 255
14.2.1 Breast Cancer ..................................................................................................... 255
14.2.2 Cervical Cancer.................................................................................................. 256
14.2.3 Prostate Cancer .................................................................................................. 257
14.3 Screening for Hypertension and Diabetes ....................................................................... 258
14.4 Knowledge and Attitudes Concerning Tuberculosis ....................................................... 259
14.5 Use of Tobacco ................................................................................................................ 261
14.6 Alcohol Consumption ...................................................................................................... 263
14.7 Physical Activity ............................................................................................................. 265
14.8 Unintentional Injury ........................................................................................................ 267
14.9 Health Insurance Coverage .............................................................................................. 269
15 WOMEN’S EMPOWERMENT AND DEMOGRAPHIC AND HEALTH OUTCOMES ... 273
15.1 Employment and Form of Earnings................................................................................. 273
15.2 Control over and Relative Magnitude of Women’s and Husbands’ Earnings ................. 274
15.2.1 Control over Wife’s Earnings ............................................................................ 275
15.2.2 Control over Husbands’ Earnings ...................................................................... 276
vi • Contents
15.3 Control over Women’s Earnings and Relative Size of Husband’s and Wife’s
Earnings ........................................................................................................................... 277
15.4 Ownership of Assets ........................................................................................................ 277
15.5 Women’s Participation in Decision Making.................................................................... 279
15.6 Attitudes towards Wife Beating ...................................................................................... 283
15.7 Women’s Empowerment Indices..................................................................................... 286
15.8 Current Use of Contraception by Women’s Status.......................................................... 286
15.9 Ideal Family Size and Unmet Need by Women’s Status ................................................. 287
15.10 Reproductive Health Care and Women’s Empowerment ................................................ 288
15.11 Differentials in Infant and Child Mortality by Women’s Status...................................... 289
16 DOMESTIC VIOLENCE ........................................................................................................... 291
16.1 Measurement of Violence ................................................................................................ 291
16.1.1 Use of Valid Measures of Violence ................................................................... 291
16.1.2 Ethical Considerations in the 2014 KDHS ........................................................ 293
16.1.3 Subsample for the Violence Module .................................................................. 293
16.2 Experience of Physical Violence ..................................................................................... 293
16.3 Perpetrators of Physical Violence.................................................................................... 296
16.4 Experience of Sexual Violence........................................................................................ 297
16.5 Perpetrators of Sexual Violence ...................................................................................... 300
16.6 Age at First Experience of Sexual Violence .................................................................... 301
16.7 Experience of Different Forms of Violence .................................................................... 302
16.8 Violence during Pregnancy ............................................................................................. 303
16.9 Marital Control by Spouse ............................................................................................... 303
16.10 Forms of Spousal Violence.............................................................................................. 306
16.11 Spousal Violence by Background Characteristics ........................................................... 309
16.12 Violence by Spousal Characteristics and Women’s Empowerment Indicators ............... 311
16.13 Recent Spousal Violence ................................................................................................ 314
16.14 Onset of Spousal Violence .............................................................................................. 315
16.15 Physical Consequences of Spousal Violence .................................................................. 316
16.16 Violence by Women and Men against Their Spouse ....................................................... 317
16.17 Violence against Spouses by Spousal Characteristics and Women’s Empowerment
Indicators ......................................................................................................................... 320
16.18 Help-seeking Behaviour by Women and Men Who Experience Violence ...................... 322
17 ADULT AND MATERNAL MORTALITY ............................................................................. 327
17.1 Data and Assessment of Quality ...................................................................................... 327
17.2 Estimates of Adult Mortality ........................................................................................... 328
17.3 Estimates of Maternal Mortality ...................................................................................... 329
18 FEMALE GENITAL CUTTING ............................................................................................... 331
18.1 Knowledge of Female Circumcision ............................................................................... 331
18.2 Prevalence of and Age at Circumcision........................................................................... 333
18.3 Aspects of Circumcision among Circumcised Girls and Women ................................... 337
18.4 Religious and Community Attitudes towards FGC ......................................................... 339
18.5 Support for the Continuation of FGC .............................................................................. 342
REFERENCES ......................................................................................................................................... 345
APPENDIX A SAMPLE IMPLEMENTATION ................................................................................. 351
APPENDIX B ESTIMATES OF SAMPLING ERRORS ................................................................... 357
APPENDIX C DATA QUALITY .......................................................................................................... 385
APPENDIX D PERSONS INVOLVED IN THE 2014 KENYA DEMOGRAHIC AND
HEALTH SURVEY....................................................................................................... 391
APPENDIX E QUESTIONNAIRES ..................................................................................................... 399
Contents • vii
TABLES AND FIGURES
1 INTRODUCTION ........................................................................................................................... 1
Table 1.1 Basic demographic indicators ................................................................................ 2
Table 1.2 Results of the household and individual interviews ............................................... 9
2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION .............................. 11
Table 2.1 Household drinking water .................................................................................... 12
Table 2.2 Household sanitation facilities ............................................................................. 14
Table 2.3 Household characteristics .................................................................................... 15
Table 2.4 Household possessions......................................................................................... 16
Table 2.5 Wealth quintiles ................................................................................................... 18
Table 2.6 Food security status.............................................................................................. 19
Table 2.7 Hand washing ...................................................................................................... 20
Table 2.8 Household population by age, sex, and residence................................................ 21
Table 2.9 Household composition ....................................................................................... 22
Table 2.10 Birth registration of children under age five ........................................................ 23
Table 2.11 Children’s living arrangements and orphanhood ................................................. 24
Table 2.12 School attendance by survivorship of parents ...................................................... 25
Table 2.13.1 Educational attainment of the female household population ............................... 26
Table 2.13.2 Educational attainment of the male household population .................................. 27
Table 2.14 School attendance ratios ...................................................................................... 28
Figure 2.1 Population pyramid .............................................................................................. 21
Figure 2.2 Age-specific school attendance rates of the de-facto population age
5 to 24 years ......................................................................................................... 29
3 CHARACTERISTICS OF RESPONDENTS ............................................................................. 31
Table 3.1 Background characteristics of respondents .......................................................... 32
Table 3.1C Background characteristics of respondents .......................................................... 33
Table 3.2.1 Educational attainment: Women .......................................................................... 35
Table 3.2.1C Educational attainment: Women .......................................................................... 36
Table 3.2.2 Educational attainment: Men ............................................................................... 37
Table 3.2.2C Educational attainment: Men ............................................................................... 38
Table 3.3.1 Literacy: Women ................................................................................................. 40
Table 3.3.1C Literacy: Women ................................................................................................. 41
Table 3.3.2 Literacy: Men ....................................................................................................... 42
Table 3.3.2C Literacy: Men ....................................................................................................... 43
Table 3.4.1 Exposure to mass media: Women ........................................................................ 45
Table 3.4.1C Exposure to mass media: Women ........................................................................ 46
Table 3.4.2 Exposure to mass media: Men ............................................................................. 47
Table 3.4.2C Exposure to mass media: Men ............................................................................. 48
Table 3.5.1 Employment status: Women ................................................................................ 50
Table 3.5.2 Employment status: Men ..................................................................................... 51
Table 3.6.1 Occupation: Women ............................................................................................ 52
Table 3.6.2 Occupation: Men.................................................................................................. 53
Table 3.7 Type of employment among women ................................................................... 54
Figure 3.1 Women’s employment status in the past 12 months ............................................ 49
Tables and Figures • ix
4 MARRIAGE AND SEXUAL ACTIVITY .................................................................................. 55
Table 4.1 Current marital status ........................................................................................... 56
Table 4.2.1 Number of women’s co-wives ............................................................................. 57
Table 4.2.2 Number of men’s wives ....................................................................................... 58
Table 4.3 Age at first marriage ............................................................................................ 59
Table 4.4 Median age at first marriage by background characteristics ................................ 59
Table 4.4C Median age at first marriage by county ............................................................... 60
Table 4.5 Age at first sexual intercourse.............................................................................. 61
Table 4.6 Median age at first sexual intercourse by background characteristics ................. 61
Table 4.7.1 Recent sexual activity: Women............................................................................ 63
Table 4.7.2 Recent sexual activity: Men ................................................................................. 64
5 FERTILITY ................................................................................................................................... 65
Table 5.1 Current fertility .................................................................................................... 66
Table 5.2 Fertility by background characteristics ................................................................ 66
Table 5.2C Fertility................................................................................................................. 68
Table 5.3.1 Trends in age-specific fertility rates..................................................................... 69
Table 5.3.2 Trends in age-specific and total fertility rates ...................................................... 70
Table 5.4 Children ever born and living .............................................................................. 71
Table 5.5 Birth intervals ...................................................................................................... 72
Table 5.5C Birth intervals ...................................................................................................... 73
Table 5.6 Postpartum amenorrhoea, abstinence and insusceptibility ................................... 74
Table 5.7 Median duration of amenorrhoea, postpartum abstinence and postpartum
insusceptibility ..................................................................................................... 75
Table 5.8 Menopause ........................................................................................................... 75
Table 5.9 Age at first birth ................................................................................................... 76
Table 5.10 Median age at first birth ....................................................................................... 76
Table 5.10C Median age at first birth ....................................................................................... 77
Table 5.11 Teenage pregnancy and motherhood ................................................................... 78
Table 5.11C Teenage pregnancy and motherhood ................................................................... 79
Figure 5.1 Trends in total fertility rate, 1978-2014 ............................................................... 69
Figure 5.2 Trends in age-specific fertility rates..................................................................... 70
6 FERTILITY PREFERENCES..................................................................................................... 81
Table 6.1 Fertility preferences by number of living children .............................................. 82
Table 6.2.1 Desire to limit childbearing: Women ................................................................... 83
Table 6.2.2 Desire to limit childbearing: Men ........................................................................ 83
Table 6.3 Ideal number of children by number of living children ....................................... 85
Table 6.4 Mean ideal number of children ............................................................................ 86
Table 6.5 Fertility planning status........................................................................................ 87
Table 6.6 Wanted fertility rates............................................................................................ 87
7 FAMILY PLANNING .................................................................................................................. 89
Table 7.1 Knowledge of contraceptive methods .................................................................. 90
Table 7.2 Knowledge of contraceptive methods by background characteristics ................. 91
Table 7.2C Knowledge of contraceptive methods by county ................................................. 92
Table 7.3 Current use of contraception by age .................................................................... 93
Table 7.4 Current use of contraception by background characteristics ............................... 94
Table 7.4C Current use of contraception by county ............................................................... 95
Table 7.5 Trends in the current use of contraception ........................................................... 96
Table 7.6 Timing of sterilisation .......................................................................................... 97
Table 7.7 Source of modern contraception methods ............................................................ 98
x • Tables and Figures
Table 7.8 Informed choice ................................................................................................... 99
Table 7.9 Twelve-month contraceptive discontinuation rates ........................................... 100
Table 7.10 Reasons for discontinuation ............................................................................... 100
Table 7.11 Knowledge of fertile period ............................................................................... 101
Table 7.12 Need and demand for family planning among currently married women ......... 103
Table 7.13 Future use of contraception ................................................................................ 104
Table 7.14 Reason for not intending to use contraception in the future .............................. 105
Table 7.15 Exposure to family planning messages .............................................................. 106
Table 7.16 Contact of nonusers with family planning providers ......................................... 107
Table 7.17 Husband/partner’s knowledge of women’s use of contraception ...................... 108
Table 7.18 Men’s attitudes towards contraception .............................................................. 109
Figure 7.1 Trends in contraceptive use among currently married women ............................ 96
Figure 7.2 Trends in unmet need for family planning ......................................................... 104
8 INFANT AND CHILD MORTALITY ...................................................................................... 111
Table 8.1 Early childhood mortality rates .......................................................................... 114
Table 8.2 Early childhood mortality rates by socioeconomic characteristics .................... 115
Table 8.3 Early childhood mortality rates by demographic characteristics ....................... 117
Table 8.4 Perinatal mortality.............................................................................................. 118
Table 8.5 High-risk fertility behaviour .............................................................................. 119
Figure 8.1 Trends in childhood mortality, 1999-2014 ........................................................ 114
Figure 8.2 Under-5 mortality by background characteristics .............................................. 116
9 MATERNAL HEALTH ............................................................................................................. 121
Table 9.1 Antenatal care .................................................................................................... 122
Table 9.1C Antenatal care .................................................................................................... 123
Table 9.2 Number of antenatal care visits and timing of first visit .................................... 124
Table 9.3 Components of antenatal care ............................................................................ 125
Table 9.4 Tetanus toxoid injections ................................................................................... 127
Table 9.5 Place of delivery ................................................................................................ 128
Table 9.5C Place of delivery ................................................................................................ 129
Table 9.6 Assistance during delivery ................................................................................. 130
Table 9.6C Assistance during delivery ................................................................................. 131
Table 9.7 Timing of first postnatal checkup for the mother............................................... 133
Table 9.8 Type of provider of first postnatal checkup for the mother ............................... 134
Table 9.9 Timing of first postnatal checkup for the newborn ............................................ 135
Table 9.10 Type of provider of first postnatal checkup for the newborn ............................. 136
Table 9.11 Problems in accessing health care ...................................................................... 137
Table 9.12 Fistula................................................................................................................. 138
Figure 9.1 Trends in components of antenatal care ............................................................. 126
Figure 9.2 Mother’s duration of stay in the health facility after giving birth ...................... 132
10 CHILD HEALTH........................................................................................................................ 139
Table 10.1 Child’s size and weight at birth.......................................................................... 140
Table 10.2 Vaccinations by source of information .............................................................. 142
Table 10.3 Vaccinations by background characteristics ...................................................... 143
Table 10.3C Vaccinations by county ...................................................................................... 144
Table 10.4 Prevalence and treatment of symptoms of ARI ................................................. 146
Table 10.4C Prevalence of symptoms of ARI ........................................................................ 147
Table 10.5 Prevalence and treatment of fever ...................................................................... 148
Tables and Figures • xi
Table 10.5C Prevalence and treatment of fever ...................................................................... 149
Table 10.6 Prevalence of diarrhoea...................................................................................... 150
Table 10.6C Prevalence of diarrhoea...................................................................................... 151
Table 10.7 Diarrhoea treatment ........................................................................................... 152
Table 10.8 Feeding practices during diarrhoea .................................................................... 153
Table 10.9 Knowledge of ORS packets ............................................................................... 155
Table 10.10 Disposal of children’s stools .............................................................................. 156
Figure 10.1 Trends in vaccination coverage during the first year of life among children
12-23 months ..................................................................................................... 142
Figure 10.2 Trends in childhood vaccination coverage......................................................... 145
11 NUTRITION OF CHILDREN AND WOMEN ........................................................................ 157
Table 11.1 Nutritional status of children ............................................................................. 160
Table 11.1C Nutritional status of children ............................................................................. 162
Table 11.2 Initial breastfeeding ........................................................................................... 164
Table 11.3 Breastfeeding status by age ................................................................................ 166
Table 11.4 Median duration of breastfeeding ...................................................................... 168
Table 11.5 Foods and liquids consumed by children in the day or night preceding the
interview ............................................................................................................ 169
Table 11.6 Infant and young child feeding (IYCF) practices .............................................. 171
Table 11.7 Micronutrient intake among children: Vitamin A, iron, and deworming
medication .......................................................................................................... 173
Table 11.8 Micronutrient intake among children: Vitamin A and iodised salt .................... 174
Table 11.8C Micronutrient intake among children: Vitamin A and iodised salt .................... 175
Table 11.9 Presence of iodised salt in household ................................................................ 176
Table 11.9C Presence of iodised salt in household ................................................................ 177
Table 11.10 Nutritional status of women ............................................................................... 178
Table 11.10C Nutritional status of women ............................................................................... 179
Table 11.11 Micronutrient intake among mothers ................................................................. 181
Figure 11.1 Nutritional status of children by age .................................................................. 161
Figure 11.2 Trends in nutritional status of children under 5 years ........................................ 163
Figure 11.3 Infant feeding practices by age .......................................................................... 166
Figure 11.4 IYCF indicators on breastfeeding status ............................................................ 167
Figure 11.5 IYCF indicators on minimum acceptable diet ................................................... 172
12 MALARIA ................................................................................................................................... 183
Table 12.1 Household possession of mosquito nets............................................................. 186
Table 12.1C Household possession of mosquito nets............................................................. 187
Table 12.2 Access to an insecticide-treated net (ITN) ......................................................... 188
Table 12.3 Use of mosquito nets by persons in the household ............................................ 190
Table 12.3C Use of mosquito nets by persons in the household ............................................ 191
Table 12.4 Use of existing ITNs .......................................................................................... 192
Table 12.4C Use of existing ITNs .......................................................................................... 193
Table 12.5 Use of mosquito nets by children....................................................................... 195
Table 12.5C Use of mosquito nets by children....................................................................... 196
Table 12.6 Use of mosquito nets by pregnant women ......................................................... 197
Table 12.7 Use of Intermittent Preventive Treatment (IPTp) by women during pregnancy .. 199
Table 12.7C Use of Intermittent Preventive Treatment (IPTp) by women during pregnancy .. 200
Table 12.8 Prevalence, diagnosis, and prompt treatment of children with fever ................. 202
Table 12.8C Prevalence, diagnosis, and prompt treatment of children with fever ................. 203
Table 12.9 Source of advice or treatment for children with fever ....................................... 204
xii • Tables and Figures
Table 12.10 Type of antimalarial drugs used ......................................................................... 205
Figure 12.1 Percentage of the de facto population with access to an ITN in the household . 189
Figure 12.2 Ownership of, access to, and use of ITNs .......................................................... 194
Figure 12.3 Trends in ITN ownership and use ...................................................................... 198
Map 12.1 Malaria prevalence in Kenya ............................................................................. 184
13 HIV/AIDS-RELATED KNOWLEDGE, ATTITUDES, AND BEHAVIOUR ....................... 207
Table 13.1 Knowledge of AIDS .......................................................................................... 208
Table 13.1C Knowledge of AIDS .......................................................................................... 209
Table 13.2 Knowledge of HIV prevention methods: condom use and limiting sexual
partners............................................................................................................... 210
Table 13.2C Knowledge of HIV prevention methods by county: condom use and
limiting sexual partners ...................................................................................... 211
Table 13.3 Knowledge of HIV prevention methods: abstinence ......................................... 212
Table 13.4.1 Comprehensive knowledge about AIDS: Women ............................................. 214
Table 13.4.2 Comprehensive knowledge about AIDS: Men .................................................. 215
Table 13.4.1C Comprehensive knowledge about AIDS: Women ............................................. 216
Table 13.4.2C Comprehensive knowledge about AIDS: Men .................................................. 217
Table 13.5 Knowledge of prevention of mother to child transmission of HIV.................... 218
Table 13.5C Knowledge of prevention of mother to child transmission of HIV.................... 220
Table 13.6.1 Accepting attitudes towards those living with HIV/AIDS: Women .................. 221
Table 13.6.2 Accepting attitudes towards those living with HIV/AIDS: Men ....................... 223
Table 13.7 Attitudes towards negotiating safer sexual relations with husband ................... 225
Table 13.8 Adult support of education about condom use to prevent AIDS ....................... 226
Table 13.9.1 Multiple sexual partners: Women ...................................................................... 228
Table 13.9.2 Multiple sexual partners: Men ........................................................................... 229
Table 13.10 Point prevalence and cumulative prevalence of concurrent sexual partners ...... 230
Table 13.11 Payment for sexual intercourse and condom use at last paid sexual
intercourse .......................................................................................................... 232
Table 13.11C Payment for sexual intercourse .......................................................................... 233
Table 13.12.1 Coverage of prior HIV testing: Women ............................................................. 235
Table 13.12.1C Coverage of prior HIV testing: Women ............................................................. 237
Table 13.12.2 Coverage of prior HIV testing: Men .................................................................. 236
Table 13.12.2C Coverage of prior HIV testing: Men .................................................................. 238
Table 13.13 Pregnant women counselled and tested for HIV ................................................ 239
Table 13.13C Pregnant women counselled and tested for HIV ................................................ 240
Table 13.14 Male circumcision.............................................................................................. 242
Table 13.14C Male circumcision.............................................................................................. 242
Table 13.15 Self-reported prevalence of sexually-transmitted infections (STIs) and STIs
symptoms ........................................................................................................... 243
Table 13.16 Prevalence of medical injections........................................................................ 245
Table 13.17 Comprehensive knowledge about AIDS and of a source of condoms
among youth ...................................................................................................... 246
Table 13.18 Age at first sexual intercourse among young people ......................................... 248
Table 13.19 Premarital sexual intercourse and condom use during premarital sexual
intercourse among youth .................................................................................... 249
Table 13.20.1 Multiple sexual partners in the past 12 months among young people: Women. 250
Table 13.20.2 Multiple sexual partners in the past 12 months among young people: Men ...... 251
Table 13.21 Age-mixing in sexual relationships among women and men age 15-19 ............ 252
Table 13.22 Recent HIV tests among youth .......................................................................... 253
Tables and Figures • xiii
Figure 13.1 Trends in knowledge of HIV prevention methods: Women .............................. 213
Figure 13.2 Trends in knowledge of HIV prevention methods: Men.................................... 213
Figure 13.3 Accepting attitudes towards those with HIV: Women ....................................... 222
Figure 13.4 Accepting attitudes towards those with HIV: Men ............................................ 224
Figure 13.5 Women and men seeking treatment for STIs ..................................................... 244
Figure 13.6 Comprehensive knowledge about AIDS and source of condoms among youth ... 247
Figure 13.7 Trends in age of first sexual intercourse ............................................................ 248
14 NONCOMMUNICABLE DISEASES AND OTHER HEALTH ISSUES ............................. 255
Table 14.1 Breast cancer screening...................................................................................... 256
Table 14.2 Cervical cancer knowledge and screening ......................................................... 257
Table 14.3 Prostate cancer knowledge and screening .......................................................... 258
Table 14.4 Hypertension and diabetes screening ................................................................. 259
Table 14.5.1 Knowledge and attitudes concerning tuberculosis: Women .............................. 260
Table 14.5.2 Knowledge and attitudes concerning tuberculosis: Men ................................... 261
Table 14.6.1 Use of tobacco: Women ..................................................................................... 262
Table 14.6.2 Use of tobacco: Men .......................................................................................... 263
Table 14.7.1 Alcohol consumption: Women .......................................................................... 264
Table 14.7.2 Alcohol consumption: Men................................................................................ 265
Table 14.8.1 Physical activity: Women .................................................................................. 266
Table 14.8.2 Physical activity: Men........................................................................................ 267
Table 14.9.1 Unintentional injury: Women ............................................................................ 268
Table 14.9.2 Unintentional injury: Men.................................................................................. 269
Table 14.10.1 Health insurance coverage: Women .................................................................. 270
Table 14.10.2 Health insurance coverage: Men ........................................................................ 271
15 WOMEN’S EMPOWERMENT AND DEMOGRAPHIC AND HEALTH OUTCOMES ... 273
Table 15.1 Employment and cash earnings of currently married women and men ............. 274
Table 15.2.1 Control over women’s cash earnings and relative magnitude of women’s
cash earnings ...................................................................................................... 275
Table 15.2.2 Control over men’s cash earnings ...................................................................... 276
Table 15.3 Women’s control over their own earnings and over those of their husbands .... 277
Table 15.4.1 Ownership of assets: Women............................................................................. 278
Table 15.4.2 Ownership of assets: Men .................................................................................. 279
Table 15.5 Participation in decision making ........................................................................ 280
Table 15.6.1 Women’s participation in decision making by background characteristics ....... 281
Table 15.6.2 Men’s participation in decision making by background characteristics ............ 283
Table 15.7.1 Attitude towards wife beating: Women ............................................................. 284
Table 15.7.2 Attitude towards wife beating: Men................................................................... 285
Table 15.8 Indicators of women’s empowerment ................................................................ 286
Table 15.9 Current use of contraception by women’s empowerment .................................. 287
Table 15.10 Ideal number of children and unmet need for family planning by women’s
empowerment..................................................................................................... 287
Table 15.11 Reproductive health care by women’s empowerment ....................................... 288
Table 15.12 Early childhood mortality rates by women’s status ........................................... 289
Figure 15.1 Number of decisions in which currently married women participate ................ 282
16 DOMESTIC VIOLENCE ........................................................................................................... 291
Table 16.1.1 Experience of physical violence: Women .......................................................... 294
Table 16.1.2 Experience of physical violence: Men ............................................................... 295
Table 16.2.1 Persons committing physical violence: Women ................................................ 297
Table 16.2.2 Persons committing physical violence: Men...................................................... 297
xiv • Tables and Figures
Table 16.3.1 Experience of sexual violence: Women ............................................................. 298
Table 16.3.2 Experience of sexual violence: Men .................................................................. 299
Table 16.4.1 Persons committing sexual violence: Women ................................................... 300
Table 16.4.2 Persons committing sexual violence: Men ......................................................... 301
Table 16.5.1 Age at first experience of sexual violence: Women........................................... 301
Table 16.5.2 Age at first experience of sexual violence: Men ................................................ 302
Table 16.6.1 Experience of different forms of violence: Women ........................................... 302
Table 16.6.2 Experience of different forms of violence: Men ................................................ 302
Table 16.7 Experience of violence during pregnancy .......................................................... 303
Table 16.8.1 Marital control exercised by husbands............................................................... 304
Table 16.8.2 Marital control exercised by wives .................................................................... 305
Table 16.9.1 Forms of spousal violence: Women ................................................................... 307
Table 16.9.2 Forms of spousal violence: Men ........................................................................ 308
Table 16.10.1 Spousal violence by background characteristics: Women ................................. 310
Table 16.10.2 Spousal violence by background characteristics: Men ...................................... 311
Table 16.11.1 Spousal violence by husband’s characteristics and empowerment indicators ... 312
Table 16.11.2 Spousal violence by wife’s characteristics and empowerment indicators ......... 313
Table 16.12.1 Physical or sexual violence in the past 12 months by any husband/partner:
Women ............................................................................................................... 314
Table 16.12.2 Physical or sexual violence in the past 12 months by any wife/partner: Men ... 315
Table 16.13.1 Experience of spousal violence by duration of marriage: Women .................... 316
Table 16.13.2 Experience of spousal violence by duration of marriage: Men .......................... 316
Table 16.14.1 Injuries to women due to spousal violence: Women ......................................... 317
Table 16.14.2 Injuries to men due to spousal violence: Men .................................................... 317
Table 16.15.1 Women’s violence against their spouse by background characteristics............. 318
Table 16.15.2 Men’s violence against their spouse by background characteristics .................. 319
Table 16.16.1 Women’s violence against their spouse by spouse’s characteristics and
empowerment indicators .................................................................................... 321
Table 16.16.2 Men’s violence against their spouse by spouse’s characteristics and
empowerment indicators .................................................................................... 322
Table 16.17.1 Help seeking to stop violence: Women.............................................................. 323
Table 16.17.2 Help seeking to stop violence: Men ................................................................... 324
Table 16.18.1 Sources for help to stop the violence: Women................................................... 325
Table 16.18.2 Sources for help to stop the violence: Men ........................................................ 326
Figure 16.1 Percentage of ever-married women age 15-49 who have experienced
specific types of spousal physical and sexual violence by the current or
most recent husband/partner .............................................................................. 309
17 ADULT AND MATERNAL MORTALITY ............................................................................. 327
Table 17.1 Adult mortality rates .......................................................................................... 328
Table 17.2 Adult mortality probabilities .............................................................................. 328
Table 17.3 Maternal mortality ............................................................................................. 329
Figure 17.1 Maternal mortality ratio (MMR) with confidence intervals for the seven
years preceding the KDHS................................................................................. 330
18 FEMALE GENITAL CUTTING ............................................................................................... 331
Table 18.1 Knowledge of female circumcision ................................................................... 332
Table 18.2 Prevalence of female circumcision .................................................................... 333
Table 18.3 Age at circumcision ........................................................................................... 335
Table 18.4 Prevalence of circumcision and age at circumcision: Girls 0-14 ....................... 335
Table 18.5 Circumcision of girls age 0-14 by mother’s background characteristics ........... 337
Tables and Figures • xv
Table 18.6 Infibulation among circumcised girls age 0-14 .................................................. 338
Table 18.7 Aspects of circumcision among circumcised girls age 0-14 and women
age 15 49 ............................................................................................................ 339
Table 18.8 Opinions of women and men about whether circumcision is required by
religion ............................................................................................................... 340
Table 18.9 Opinions of women and men about whether circumcision is required by
the community ................................................................................................... 341
Table 18.10 Opinions of women and men about whether the practice of circumcision
should continue .................................................................................................. 343
Figure 18.1 Percentage of women age 15-49 circumcised by ethnic group .......................... 334
Figure 18.2 Percentage of women age 15-49 and girls age 0-14 circumcised by age ........... 336
APPENDIX A SAMPLE IMPLEMENTATION ................................................................................. 351
Table A.1 Enumeration Areas and households ................................................................... 351
Table A.2 Population .......................................................................................................... 352
Table A.3 Sample allocation of clusters and households.................................................... 353
Table A.4 Sample allocation of completed interviews with women and men .................... 354
Table A.5 Sample implementation: Women ....................................................................... 355
Table A.6 Sample implementation: Men ............................................................................ 356
APPENDIX B ESTIMATES OF SAMPLING ERRORS ................................................................... 357
Table B.1 List of selected variables for sampling errors, Kenya 2014 ............................... 359
Table B.2 Sampling errors: Total sample, Kenya DHS 2014 ............................................. 361
Table B.3 Sampling errors: Urban sample, Kenya DHS 2014 ........................................... 363
Table B.4 Sampling errors: Rural sample, Kenya DHS 2014 ............................................ 365
Table B.5 Sampling errors: Coast sample, Kenya DHS 2014 ............................................ 367
Table B.6 Sampling errors: North Eastern sample, Kenya DHS 2014 ............................... 369
Table B.7 Sampling errors: Eastern sample, Kenya DHS 2014 ......................................... 371
Table B.8 Sampling errors: Central sample, Kenya DHS 2014 ......................................... 373
Table B.9 Sampling errors: Rift Valley sample, Kenya DHS 2014 ................................... 375
Table B.10 Sampling errors: Western sample, Kenya DHS 2014 ........................................ 377
Table B.11 Sampling errors: Nyanza sample, Kenya DHS 2014 ......................................... 379
Table B.12 Sampling errors: Nairobi sample, Kenya DHS 2014 ......................................... 381
Table B.13 Sampling errors for adult and maternal mortality rates, Kenya 2014 ................ 383
APPENDIX C DATA QUALITY .......................................................................................................... 385
Table C.1 Household age distribution ................................................................................ 385
Table C.2.1 Age distribution of eligible and interviewed women ......................................... 386
Table C.2.2 Age distribution of eligible and interviewed men .............................................. 386
Table C.3 Completeness of reporting ................................................................................. 387
Table C.4 Births by calendar years ..................................................................................... 387
Table C.5 Reporting of age at death in days ....................................................................... 388
Table C.6 Reporting of age at death in months .................................................................. 388
Table C.7 Nutritional status of children based on the NCHS/CDC/WHO International
Reference Population ......................................................................................... 389
Table C.8 Completeness of information on siblings........................................................... 390
Table C.9 Sibship size and sex ratio of siblings ................................................................. 390
xvi • Tables and Figures
FOREWORD
T
he 2014 Kenya Demographic and Health Survey (KDHS) provides information to help monitor and
evaluate population and health status in Kenya. The survey, which follows up KDHS surveys
conducted in 1989, 1993, 1998, 2003, and 2008-09, is of special importance for several reasons.
New indicators not collected in previous KDHS surveys, such as noncommunicable diseases, fistula, and
men’s experience of domestic violence, are included. Also, it is the first national survey to provide
estimates for demographic and health indicators at the county level. Following adoption of a constitution in
Kenya in 2010 and devolution of administrative powers to the counties, the new 2014 KDHS data should
be valuable to managers and planners.
The 2014 KDHS has specifically collected data to estimate fertility, to assess childhood, maternal,
and adult mortality, to measure changes in fertility and contraceptive prevalence, to examine basic
indicators of maternal and child health, to estimate nutritional status of women and children, to describe
patterns of knowledge and behaviour related to the transmission of HIV and other sexually transmitted
infections, and to ascertain the extent and pattern of domestic violence and female genital cutting. Unlike
the 2003 and 2008-09 KDHS surveys, this survey did not include HIV and AIDS testing. HIV prevalence
estimates are available from the 2012 Kenya AIDS Indicator Survey (KAIS), completed prior to the 2014
KDHS.
Results from the 2014 KDHS show a continued decline in the total fertility rate (TFR). Fertility
decreased from 4.9 births per woman in 2003 to 4.6 in 2008-09 and further to 3.9 in 2014, a one-child
decline over the past 10 years and the lowest TFR ever recorded in Kenya. This is corroborated by the
marked increase in the contraceptive prevalence rate (CPR) from 46 percent in 2008-09 to 58 percent in
the current survey. The decline in fertility accompanies a marked decline in infant and child mortality. All
early childhood mortality rates have declined between the 2003 and 2014 KDHS surveys. Total under-5
mortality declined from 115 deaths per 1,000 live births in the 2003 KDHS to 52 deaths per 1,000 live
births in the 2014 KDHS. The maternal mortality ratio is 362 maternal deaths per 100,000 live births for
the seven-year period preceding the survey; however, this is not statistically different from the ratios
reported in the 2003 and 2008-09 KDHS surveys and does not indicate any decline over time.
The proportion of mothers who reported receiving antenatal care from a skilled health provider
increased from 88 percent to 96 percent between 2003 and 2014. The percentage of births attended by a
skilled provider and the percentage of births occurring in health facilities each increased by about 20
percentage points between 2003 and 2014. The percentage of children age 12-23 months who have
received all basic vaccines increased slightly from the 77 percent observed in the 2008-09 KDHS to 79
percent in 2014. Six in ten households (59 percent) own at least one insecticide-treated net, and 48 percent
of Kenyans have access to one. In malaria endemic areas, 39 percent of women received the recommended
dosage of intermittent preventive treatment for malaria during pregnancy. Awareness of AIDS is universal
in Kenya; however, only 56 percent of women and 66 percent of men have comprehensive knowledge
about HIV and AIDS prevention and transmission.
The 2014 KDHS was conducted as a joint effort by many organisations. The Kenya National
Bureau of Statistics (KNBS) served as the implementing agency by providing guidance in the overall
survey planning, development of survey tools, training of personnel, data collection, processing, analysis,
and dissemination of the results. The Bureau would like to acknowledge and appreciate the institutions and
agencies for roles they played that resulted in the success of this exercise: Ministry of Health (MOH),
National AIDS Control Council (NACC), National Council for Population and Development (NCPD),
Kenya Medical Research Institute (KEMRI), Ministry of Labour, Social Security and Services, United
States Agency for International Development (USAID/Kenya), ICF International, United Nations Fund for
Foreword • xvii
Population Activities (UNFPA), the United Kingdom Department for International Development (DfID),
World Bank, Danish International Development Agency (DANIDA), United Nations Children’s Fund
(UNICEF), German Development Bank (KfW), World Food Programme (WFP), Clinton Health Access
Initiative (CHAI), Micronutrient Initiative (MI), US Centers for Disease Control and Prevention (CDC),
Japan International Cooperation Agency (JICA), Joint United Nations Programme on HIV/AIDS
(UNAIDS), and the World Health Organization (WHO). The management of such a huge undertaking was
made possible through the help of a signed memorandum of understanding (MoU) by all the partners and
the creation of active Steering and Technical Committees. The Bureau is grateful to all the staff from
various institutions and agencies who worked tirelessly to ensure the success of this exercise.
Special thanks go to all the KNBS staff, survey personnel, and ICF International staff who worked
long hours to collect data and most important, to the respondents who gave time to provide the information
from which this report is developed.
Zachary Mwangi
Director General
Kenya National Bureau of Statistics
xviii • Foreword
MILLENNIUM DEVELOPMENT GOAL INDICATORS
Kenya 2014
Sex
Indicator Male Female Total
1. Eradicate extreme poverty and hunger
1.8 Prevalence of underweight children under 5 years of age 12.1 9.8 11.0
2. Achieve universal primary education
2.1 Net attendance ratio in primary education1 85.5 87.6 86.6
2.3 Literacy rate of 15-24 year-olds2 94.6a 92.8 93.7b
3. Promote gender equality and empower women
3.1 Ratio of girls to boys in primary, secondary and tertiary education
3.1a Ratio of girls to boys in primary education3 na na 1.0
3.1b Ratio of girls to boys in secondary education3 na na 1.1
3.1c Ratio of girls to boys in tertiary education3 na na 0.9
4. Reduce child mortality
4.1 Under five mortality rate4 60 52 52
4.2 Infant mortality rate4 44 37 39
4.3 Percentage of 1 year old children immunised against measles 87.9 86.2 87.1
5. Improve maternal health
5.1 Maternal mortality ratio5 na na 362
(CI: 254,471)
5.2 Percentage of births attended by skilled health personnel6 na na 61.8
5.3 Contraceptive prevalence rate7 na 58.0 na
5.4 Adolescent birth rate8 na 96.3 na
5.5 Antenatal care coverage
5.5a At least one visit9 na 95.5 na
5.5b Four or more visits10 na 57.6 na
5.6 Unmet need for family planning na 17.5 na
6. Combat HIV/AIDS, malaria and other diseases
6.2 Condom use at last higher-risk sex11 75.0a 58.7 66.8
6.3 Percentage of the population age 15-24 years with comprehensive correct knowledge of HIV/AIDS12 63.7 54.2 59.0
6.4 Ratio of school attendance of orphans to school attendance of non-orphans age 10-14 years 0.98 1.01 0.99
6.7 Percentage of children under 5 sleeping under insecticide-treated bednets13 55.0 53.5 54.3
6.8 Percentage of children under 5 with fever who are treated with appropriate antimalarial drugs14 27.0 27.0 27.0
Urban Rural Total
7. Ensure environmental sustainability
7.8 Percentage of population using an improved water source15 85.7 57.0 66.9
7.9 Percentage of population using an improved sanitation facility16 30.5 21.6 24.7
na = Not applicable
1
The ratio is based on reported attendance, not enrolment, in primary education among primary school age children (6-13 year-olds). The rate also includes
children of primary school age enrolled in secondary education. This is a proxy for MDG indicator 2.1, Net enrolment ratio.
2
Refers to respondents who attended secondary school or higher or who could read a whole sentence or part of a sentence
3
Based on reported net attendance, not gross enrolment, among 6-13 year-olds for primary, 14-17 year-olds for secondary and 18-22 year-olds for tertiary
education
4
Expressed in terms of deaths per 1,000 live births. Mortality by sex refers to a 10-year reference period preceding the survey. Mortality rates for males and
females combined refer to the 5-year period preceding the survey.
5
Expressed in terms of maternal deaths per 100,000 live births in the 7-year period preceding the survey
6
Among births in the five years preceding the survey
7
Percentage of currently married women age 15-49 using any method of contraception
8
Equivalent to the age-specific fertility rate for women age 15-19 for the 3-year period preceding the survey, expressed in terms of births per 1,000 women
age 15-19
9
With a skilled provider
10
With any healthcare provider
11
Higher-risk sex refers to sexual intercourse with a non-marital, non-cohabiting partner. Expressed as a percentage of men and women age 15-24 who had
higher-risk sex in the past 12 months.
12
Comprehensive knowledge means knowing that consistent use of a condom during sexual intercourse and having just one uninfected faithful partner can
reduce the chance of getting the AIDS virus, knowing that a healthy-looking person can have the AIDS virus, and rejecting the two most common local
misconceptions about transmission or prevention of the AIDS virus
13
An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment, or (2) a net that has been soaked with insecticide
within the past 12 months
14
Measured as the percentage of children age 0-59 months who were ill with a fever in the two weeks preceding the interview and received any anti-malarial
drug
15
Percentage of de jure population whose main source of drinking water is a household connection (piped), public tap or standpipe, tubewell or borehole,
protected dug well, protected spring, rainwater collection, or bottled water
16
Percentage of de jure population whose household has a flush toilet, ventilated improved pit latrine, pit latrine with a slab, or composting toilet and does not
share this facility with other households
a
Restricted to men in sub-sample of households selected for the male interview
b
The total is calculated as the simple arithmetic mean of the percentages in the columns for males and females
Millennium Development Goal Indicators • xix
SUMMARY OF FINDINGS
HOUSEHOLD POPULATION AND 10 years for both women and men. Six percent of
CHARACTERISTICS currently married men are in a polygynous union;
11 percent of currently married women have co-
Housing wives. The percentage of women married by age
15 appears to be declining; 9 percent of women age
The majority (71 percent) of households in
45-49 were married by age 15, as compared with 2
Kenya have access to an improved source of drink-
percent among those age 15-19. Fifteen percent of
ing water. Twenty-three percent of households
women age 20-49 had first sexual intercourse by
have an improved toilet facility that is not shared
age 15, 50 percent by age 18, and 71 percent by
with other households. The majority (64 percent)
age 20. Twenty-two percent of men age 20-49 had
of households in Kenya do not have electricity.
first sexual intercourse by age 15, 56 percent by
Almost half (46 percent) of households live in
age 18, and 76 percent by age 20.
dwellings with cement floors. More than half (53
percent) of households use one room for sleeping.
More than half (56 percent) of households use FERTILITY
wood as their main source of cooking fuel.
Fertility Levels and Trends
Education, Media, and Mobile Phones
The total fertility rate for the three years pre-
The percentage of women and men with no ceding the survey is 3.9 births per woman, with
education has dropped by half over the last 10 rural women having at least one child more than
years, from 13 percent and 6 percent in 2003 to 7 urban women. Fertility has decreased from 4.9
percent and 3 percent, respectively, in the 2014 births per woman in 2003 to 3.9 births per woman
KDHS. Over the same period, the percentage of in 2014, a one-child decline in the past 10 years.
women and men with at least some secondary Half of births occur within three years of a previ-
education increased from 29 percent and 37 per- ous birth, with 18 percent occurring within 24
cent in 2003 to 43 percent and 49 percent, respec- months. Childbearing begins early in Kenya, with
tively, in 2014. Eighty-eight percent of women and almost one-quarter of women giving birth by age
92 percent of men are literate. Twenty-three per- 18 and nearly half by age 20. Eighteen percent of
cent of women and 10 percent of men are not ex- adolescent women age 15-19 are already mothers
posed to any source of mass media. Eighty-six or pregnant with their first child. In the last five
percent of households own mobile phones. years, teenage pregnancy has remained unchanged.
Employment Fertility Preferences
Sixty-one percent of women and 80 percent of Half of currently married women age 15-49
men are currently employed. Women are mostly and 42 percent of currently married men age 15-49
employed in agricultural or domestic service posi- want no more children or are sterilised. The mean
tions, while men are mostly employed in agricul- ideal number of children among all women age 15-
tural, unskilled manual, or domestic service posi- 49 is 3.6, while that of all men is 3.9. The mean
tions. ideal number of children among women has de-
clined marginally in the last 10 years from 3.9 in
the 2003 KDHS to 3.6 in 2014. The gap between
MARRIAGE AND SEXUAL ACTIVITY actual fertility and ideal family size has narrowed
The median age at first marriage among wom- in the last 10 years, from 1.3 children in 2003 to
en age 25-49 is 20.2 years; the median age at first 1.0 in 2014.
marriage among men age 30-49 is 25.3 years. Me-
dian age at marriage has remained stable in the past
Summary of Findings • xxi
Family Planning ever, only 1 percent of these women reported hav-
ing ever experienced fistula-like symptoms.
More than half of currently married women
(58 percent) use a contraceptive method. The most
popular modern contraceptive methods used by CHILD HEALTH
married women are injectables (26 percent), im-
Childhood Mortality
plants (10 percent), and the pill (8 percent). Use of
modern methods has increased over the last decade The infant mortality rate is 39 deaths per 1,000
from 32 percent in the 2003 KDHS to 53 percent in live births, and under-5 mortality is 52 deaths per
2014. The public sector remains the major provider 1,000 live births. At these levels, about one in
of contraceptive methods; 60 percent of modern every 26 Kenyan children dies before reaching age
contraceptive users obtain their contraception from 1, and about one in every 19 does not survive to his
a government source. Thirty-one percent of family or her fifth birthday. All early childhood mortality
planning users discontinue use of a method within rates declined between the 2003 and 2014 KDHS
12 months of starting its use. Side effects and surveys. Neonatal mortality has exhibited the
health concerns (11 percent) are the main reason slowest rate of decline (33 percent). A child born
for discontinuation. Eighteen percent of currently in the Nyanza region is almost twice as likely to
married women have an unmet need for family die before age 5 as a child born in the Central re-
planning services, with 9 percent in need of spac- gion. Nairobi has the second highest under-5 mor-
ing and 8 percent in need of limiting. tality rate, following Nyanza (72 deaths per 1,000
live births). Male children are more likely than
MATERNAL HEALTH female children to die during their first year of life
(44 deaths versus 37 deaths per 1,000 live births).
Antenatal Care Once past infancy, male and female children one to
four years of age experience the same level of
Ninety-six percent of women with a live birth mortality (16 deaths per 1,000 live births). The
in the five years preceding the survey received neonatal mortality rate for the five years preceding
antenatal care from a skilled provider, an im- the survey is 22 deaths per 1,000 live births, 1.4
provement from 92 percent in the 2008-09 KDHS times the postneonatal rate. The perinatal mortality
and 88 percent in the 2003 KDHS. Fifty-eight rate for the same reference period is 29 deaths per
percent of women make the recommended four or 1,000 pregnancies.
more antenatal care visits during their pregnancy,
an increase of 11 percentage points from the 2008- Childhood Vaccination Coverage
09 KDHS (47 percent).
Seventy-nine percent of children age 12-23
Delivery, Postnatal, and Newborn Care months have received all basic vaccines, slightly
higher than the 77 percent observed in the 2008-09
Sixty-one percent of live births in the five KDHS.
years preceding the survey were delivered in a
health facility; 62 percent were assisted by a Childhood Illness and Treatment
skilled provider. More than half (53 percent) of
women who gave birth in the two years before the Nine percent of children under age 5 showed
survey received a postnatal care checkup in the symptoms of acute respiratory infection in the two
first two days after delivery. Thirty-six percent of weeks before the survey; 66 percent of these chil-
infants born in the two years before the survey had dren were taken to a health facility or provider for
their first postnatal checkup within the first two advice or treatment. Twenty-four percent of chil-
days after birth. One in three newborns received dren under age 5 had a fever in the two weeks
postnatal care from a doctor, a nurse, or a midwife. before the survey; 63 percent of these children
were taken to a health facility or provider for ad-
Fistula vice or treatment. Fifteen percent of children under
age 5 had diarrhoea in the two weeks before the
More than half (54 percent) of the women in- survey. The proportion of children with diarrhoea
terviewed in the survey had heard of fistula. How- taken to a health provider for advice or treatment
xxii • Summary of Findings
increased from 49 percent in the 2008-09 KDHS to MALARIA
58 percent in the 2014 KDHS. The proportion of
children with diarrhoea given fluid from ORS Net Ownership and Use
packets has increased over the past five years, from
Six in 10 households (59 percent) own at least
39 percent in 2008-09 to 54 percent in 2014. The
one insecticide-treated mosquito net (ITN), while
percentage of women who know that ORS can be
34 percent of households have at least one net for
used to treat diarrhoea in children has increased
every two people. Forty-eight percent of Kenyans
from 78 percent in 2008-09 to 93 percent in 2014.
have access to an ITN. Two-fifths of the household
The percentage of children whose stools are dis-
population (42 percent) slept under an ITN the
posed of safely has increased from 78 percent in
night prior to the survey, and two-thirds (67 per-
2008-09 to 83 percent in 2014.
cent) of members of households with at least one
ITN slept under an ITN the night prior to the sur-
NUTRITION vey. Fifty-four percent of children under age 5
slept under an ITN the night before the survey,
Nutritional Status of Children and, among those living in households with an
Twenty-six percent of children under age 5 are ITN, 77 percent slept under an ITN the night be-
stunted, 4 percent are wasted, and 11 percent are fore the survey. Fifty-one percent of pregnant
underweight. women overall slept under an ITN the night before
the survey, and, among those living in households
Nutritional Status of Women with an ITN, 77 percent slept under an ITN the
night before the survey.
Nine percent of women age 15-49 are thin or
undernourished (BMI <18.5 kg/m2); 33 percent of Pregnant Women and Children
women are either overweight or obese (BMI ≥25
kg/m2), with 10 percent of them being obese (BMI Seventeen percent of women received in-
≥30 kg/m2). termittent preventive treatment (IPTp) for malaria
during pregnancy; that is, they received two or
Breastfeeding Practices more doses of SP/Fansidar, at least one during an
antenatal care visit. In malaria endemic areas, 39
Ninety-nine percent of children have ever been percent of women received IPTp. Twenty-three
breastfed; however, only 61 percent of children percent of children under age 5 who had a fever
less than age 6 months are exclusively breastfed. took ACT, and 13 percent took ACT within 24
Complementary foods are generally introduced at hours of fever onset.
the recommended age; 81 percent of breastfed
children age 6-9 months received complementary
foods in the 24 hours preceding the survey. Only HIV/AIDS
22 percent of children are fed in accordance with Awareness of and Knowledge about AIDS
the three recommended infant and young child
feeding practices. Awareness of AIDS is universal in Kenya.
However, only 56 percent of women and 66 per-
Supplements and Deworming for Children cent of men have comprehensive knowledge about
and Women
HIV and AIDS prevention and transmission; that
Seventy-two percent of children age 6-59 is, they know that both condom use and limiting
months received vitamin A supplements in the past sexual intercourse to one uninfected partner can
six months. Fifty-one percent of children age 12-59 prevent HIV, they are aware that a healthy-looking
months received deworming medication in the person can have HIV, and they reject the two most
same time period. Among women, only 8 percent common local misconceptions about HIV: that
took iron tablets daily for 90 or more days during HIV can be transmitted by mosquitoes and by
the pregnancy of their last birth. Thirty-one percent sharing food. Seventy-two percent of women and
of women took deworming medication during their 62 percent of men know both that HIV can be
last pregnancy. transmitted through breastfeeding and that the risk
Summary of Findings • xxiii
of mother-to-child transmission can be reduced by sions pertaining to their own health care, major
taking special drugs during pregnancy. household purchases, visits to their family or rela-
tives, and major household purchases. Thirty-nine
HIV-related Behavioural Indicators percent of women have the main say in their own
health care.
Among respondents who had more than one
sexual partner in the past 12 months, 40 percent of Contraceptive use increases with women’s
women and 44 percent of men reported using a empowerment. In general, unmet need for family
condom during their last sexual intercourse. planning decreases with improvements in women’s
empowerment. Access to antenatal care, delivery
HIV Testing
assistance from a skilled provider, and postnatal
Since the 2008-09 KDHS, there has been an care within the first two days of delivery increases
increase in the percentage of both women (from 29 with increasing women’s empowerment.
percent to 53 percent) and men (from 23 percent to
46 percent) who were tested for HIV in the past 12 GENDER-BASED VIOLENCE
months and received their results. Sixty-eight per-
cent of women who gave birth in the two years Violence Since Age 15
before the survey received HIV counselling during
antenatal care. Almost 7 in 10 women (69 percent) Forty-five percent of women and 44 percent of
were tested for HIV during antenatal care and re- men age 15-49 have experienced physical violence
ceived the test results and post-test counselling, since age 15, and 20 percent and 12 percent, re-
while 23 percent received results but did not re- spectively, experienced physical violence within
ceive post-test counselling. the 12 months prior to the survey. The main perpe-
trators of physical violence against women are
husbands, whereas the main perpetrators against
OTHER HEALTH ISSUES men are parents, teachers, and others.
Ten percent of women have had both a breast Sexual and Partner Violence
exam from a health provider and a breast self-
exam. Three-quarters (76 percent) of women have Fourteen percent of women and 6 percent of
heard of cervical cancer, and 14 percent have had a men age 15-49 report having experienced sexual
cervical cancer screening exam. Approximately violence at least once in their lifetime. Overall, 39
two-thirds (65 percent) of men have heard of pros- percent of ever-married women and 9 percent of
tate cancer, and 3 percent have been examined by a men age 15-49 report having experienced spousal
doctor or health care provider for prostate cancer. physical or sexual violence. Among women and
Tobacco use is more common among Kenyan men men who have ever experienced spousal violence
than women (83 percent of men don’t use tobacco (physical or sexual), 39 percent and 24 percent,
compared with 99 percent of women). Sixteen respectively, reported experiencing physical inju-
percent of men smoke cigarettes. Among men who ries. Forty-four percent of women and 27 percent
smoke cigarettes, 28 percent smoked more than 10 of men have sought assistance to stop the violence
cigarettes in the past 24 hours. Most Kenyans do they have experienced.
not have health insurance; 82 percent of women
and 79 percent of men are not covered by any Female Genital Cutting
health insurance.
Twenty-one percent of women age 15-49 have
been circumcised. There is some evidence of a
WOMEN’S EMPOWERMENT trend over time to circumcise girls at younger ages.
Twenty-eight percent of circumcised women age
Nearly half (49 percent) of currently married 20-24 were circumcised at age 5-9, as compared
employed women who earn cash make inde- with 17 percent of circumcised women age 45-49.
pendent decisions about how to spend their earn- With respect to type of circumcision, 2 percent of
ings, an increase from the figure of 42 percent circumcised women age 15-49 had cutting with no
reported in the 2008-09 KDHS. Fifty-four percent flesh removed, 87 percent had cutting with flesh
of currently married women participate in deci- removed, and 9 percent had their genital area sewn
xxiv • Summary of Findings
closed after cutting (a procedure known as infibu-
lation). Girls age 0-14 are more likely to be cir-
cumcised if their mother is circumcised. Likewise,
girls age 0-14 are more likely to be infibulated if
their mother is also infibulated. Eight percent of
girls age 0-14 have had their genital area sewn
closed. Eleven percent or less of women and men
believe that the practice of female genital cutting is
required by their community or their religion or
that the practice should continue.
ADULT AND MATERNAL MORTALITY
Fourteen percent of women and 18 percent of
men are likely to die between exact ages 15 and 50.
Maternal deaths account for 14 percent of all
deaths to women age 15-49. The maternal mortali-
ty ratio was 362 maternal deaths per 100,000 live
births for the seven-year period preceding the sur-
vey. When comparing the estimate of an MMR of
362 with the MMR estimated in the previous DHS
(2008-09 KDHS estimate of 520 maternal deaths
per 100,000 live births), the differential is not large
enough to conclude whether or not there has been
any change over time between the two surveys.
Summary of Findings • xxv
xxvi • Map of Kenya
INTRODUCTION 1
Macdonald Obudho, James N. Munguti, John K. Bore, Mutua Kakinyi
1.1 HISTORY, GEOGRAPHY, AND ECONOMY
1.1.1 History
K
enya is a former British colony. The independence process was met with resistance and an armed
struggle by Kenyans against the British colonial rulers. The Mau Mau rebellion in the 1950s
paved the way for constitutional reform and political development in the following years. The
country achieved self-rule in June 1963 and gained independence on December 12, 1963. The country was
a multi-party state until 1981, when it was converted to a single-party state by amending the constitution.
Kenya reverted to the multi-party state in 1992. The Kenya African National Union (KANU) ruled the
country from independence to 2002, when the National Alliance of Rainbow Coalition (NARC) was
elected to power. To date, the multi-party state remains, with the Jubilee Coalition currently in power.
1.1.2 Geography
Kenya is situated in the eastern part of the African continent. The country lies between 5 degrees
north and 5 degrees south latitude and between 24 and 31 degrees east longitude. The equator passes at the
middle, separating the upper and lower parts almost equally. Kenya borders Ethiopia (north), Somalia
(northeast), Tanzania (south), Uganda (west), and South Sudan (northwest). The Indian Ocean is on the
eastern side. The coastline houses the port of Mombasa, which enables Kenya and several other countries,
including Uganda, Rwanda, and South Sudan, to engage in global trade.
The country is administratively divided into 47 counties. It has a total of 582,646 square
kilometres, of which 571,466 square kilometres are the dry land area. Most of the land area (80 percent) is
arid or semi-arid, and only 20 percent is arable. The country has diverse physical features: Mount Kenya,
the second highest mountain in Africa; Lake Victoria, the largest freshwater lake on the continent; the
Great Rift Valley, which runs from north to south; and Lake Nakuru, a major tourist attraction due to the
presence of flamingos.
The country falls within two regions: lowlands, including the coastal and lake region lowlands,
and highlands, which fall on both sides of the Great Rift Valley. Rainfall and temperatures are influenced
by altitude and proximity to the Indian Ocean. The coastal region has a tropical climate, with both rainfall
and temperatures higher than the rest of the country throughout the year.
1.1.3 Economy
The Kenyan economy is predominantly agricultural with a strong industrial base. The
performance of the Kenyan economy since the country gained independence has been mixed. Recent years
have seen an estimated 5-6 percent growth. From the demand side, growth has mainly been driven by an
increase in private consumption and rapid growth in capital investment. From the supply side, the major
drivers of the economy have been agriculture, forestry, and fishing; construction wholesale and retail trade;
education; and finance and insurance.
1.2 POPULATION
Kenya’s population was enumerated at 38.6 million in the 2009 census (Table 1.1). The trend data
from population censuses indicate that the total population more than tripled between 1969 and 2009.
Introduction • 1
These data also suggest that the population increased by approximately one million people per year
between 1999 (28.7 million) and 2009. The inter-censal growth rate, which was 3.3 percent per annum in
1969, increased to a peak of 3.8 percent per annum in 1979 before declining to 2.9 percent per annum in
1999. At a growth rate of 2.9 percent per annum, the population may increase to 77 million by 2030.
Table 1.1 Basic demographic indicators
Selected demographic indicators for Kenya, 1969, 1979, 1989, 1999, 2009, and 2014
Indicator 1969 1979 1989 1999 2009 2014
Population (millions) 10.9 16.2 23.2 28.7 38.6 43.0a
Density (pop/km2) 19.0 27.0 37.0 49.0 66.4 73.9a
Percent urban 9.9 15.1 18.1 19.4 32.3 32.3c
Crude birth rate 50.0 54.0 48.0 41.3 34.8 30.5c
Crude death rate 17.0 14.0 11.0 11.7 10.4 10.4b
Inter-censal growth rate 3.3 3.8 3.4 2.9 2.9 2.9b
Total fertility rate 7.6 7.8 6.7 5.0 4.8 3.9c
Infant mortality rate (per 1,000 births) 119 88 66 77.3 54.0 39.0c
Life expectancy at birth 50 54 60 56.6 58.0 58.0b
a
Projected figures
b
Assumed to remain constant over the inter-censal period
c
2014 KDHS results (see later chapters)
Source: CBS, 1970; CBS, 1981; CBS, 1994; CBS, 2002a; KNBS & ICF Macro, 2010; KNBS, 2012
1.3 POPULATION AND HEALTH POLICY FRAMEWORKS
1.3.1 Population Policy Framework
In 2012, the government of Kenya launched a new policy on population and national
development. The policy is described in the Sessional Paper No. 3 of 2012; it outlines the goal of attaining
a high quality of life for the people of Kenya by managing population growth to a level that can be
sustained with the available resources. The principal objective of the policy is to provide a framework to
guide national population programmes and activities for the next two decades (National Council for
Population and Development [NCPD], 2012). Overall, the policy seeks to:
• Reduce the population growth rate in order to achieve harmony with the economic growth and
social development goals envisioned in Vision 2030;
• Reduce fertility and mortality rates and at the same time assist individuals and couples who
desire to have children but are unable to;
• Provide equitable and affordable quality reproductive health services, including family
planning;
• Contribute to the planning and implementation of socioeconomic development programmes as
a long-term measure to influence population dynamics, with a special focus on poverty
reduction, technology and research, the environment, education, health and gender equity, and
equality and empowerment of women; and
• Mobilise resources through government budgetary allocations, international cooperation, and
public/private partnerships to ensure the sustainability of population programmes and
significant impacts on population dynamics.
The policy has the following targets:
• Reduce the natural growth rate of the population from 2.5 percent in 2009 to 1.5 percent by
2030.
2 • Introduction
• Reduce the infant mortality rate from 52 per 1,000 live births in 2009 to 25 per 1,000 live
births by 2030.
• Reduce the under-5 mortality rate from 74 per 1,000 live births in 2009 to 48 per 1,000 live
births by 2030.
• Reduce the maternal mortality rate from 488 deaths per 100,000 live births in 2009 to 200
deaths per 100,000 live births by 2030.
• Reduce the crude death rate from 13 deaths per 1,000 people in 2009 to 8 deaths per 1000
people by 2030.
• Improve life expectancy at birth for both sexes from 57 years in 2009 to 64 years by 2030.
• Reduce the total fertility rate from 4.6 children per woman in 2009 to 2.6 children per woman
by 2030.
1.3.2 Health Priorities and Programmes
The government of Kenya emphasises the health of its citizens and the improvement of health
service delivery. The Ministry of Health plays a coordinating and capacity-building role in ensuring that all
services offered are in line with established policies and standards. The government recognises that good
health is a prerequisite to socioeconomic development. A number of government policy documents and
successive national development plans, including Vision 2030, have stated that health services should meet
the basic needs of the population, that health facilities should be situated so that they are within reach of all
Kenyans, and that there should be a focus on preventive, promotive, and rehabilitative services without
ignoring curative services.
Under the 2010 Kenya constitution, the health function has been devolved to the county
governments, with distinct functions being assigned to the national and county governments. The national
government is responsible for leadership in health policy development, management of national referral
health facilities, capacity building and technical assistance to counties, and consumer protection, including
the development of norms, standards, and guidelines. The county governments are responsible for county
health services and pharmacies; ambulance services; promotion of primary health care; licensing and
control of establishments that sell food to the public; cemeteries, funeral parlours, and crematoria; and
refuse removal, refuse dumps, and solid waste disposal. With regard to their functions, the county
governments have undertaken new strategies and initiatives to address the health needs of their
populations, including the construction of more health facilities, the acquisition of new equipment and
medication at these facilities, and the addition of ambulances and more medical staff.
The Kenya Health Policy 2014-2030 takes into account the objectives of devolution and adheres
to the following principles:
• Equity in the distribution of health services and interventions;
• A people-centred approach to health and health interventions;
• A participatory approach to delivery of interventions;
• A multisectoral approach to realising health goals;
• Efficiency in the application of health technologies; and
• Social accountability.
1.4 OBJECTIVES OF THE SURVEY
The 2014 Kenya Demographic and Health Survey (KDHS) was designed to provide information
to monitor and evaluate the population and health situations in Kenya and to be a follow-up to the previous
Introduction • 3
KDHS surveys. In addition, it provides information on indicators previously not collected in KDHS
surveys, such as fistula and men’s experience of domestic violence. Finally, the 2014 KDHS is the first
such survey to provide estimates for selected demographic and health indicators at the county level.
The specific objectives of the 2014 KDHS were to:
• Estimate fertility and childhood, maternal, and adult mortality;
• Measure changes in fertility and contraceptive prevalence;
• Examine basic indicators of maternal and child health;
• Collect anthropometric measures for children and women;
• Describe patterns of knowledge and behaviour related to transmission of HIV and other
sexually transmitted infections; and
• Ascertain the extent and pattern of domestic violence and female genital cutting.
1.5 SURVEY ORGANISATION
The 2014 KDHS was a joint effort of many organisations, including the following:
• Kenya National Bureau of Statistics (KNBS)
• Ministry of Health (MOH)
• National AIDS Control Council (NACC)
• National Council for Population and Development (NCPD)
• Kenya Medical Research Institute (KEMRI)
• Ministry of Labour, Social Security and Services
• United States Agency for International Development (USAID/Kenya)
• ICF International
• United Nations Population Fund (UNFPA)
• Department for International Development (DFID)
• World Bank
• Danish International Development Agency (DANIDA)
• United Nations Children’s Fund (UNICEF)
• German Development Bank (KfW)
• World Food Programme (WFP)
• Clinton Health Access Initiative (CHAI)
• Micronutrient Initiative (MI)
• U.S. Centers for Disease Control and Prevention (CDC)
• Japan International Cooperation Agency (JICA)
• Joint United Nations Programme on HIV/AIDS (UNAIDS)
• World Health Organization (WHO)
The Kenya National Bureau of Statistics (KNBS) served as the implementing agency and, as such,
had a primary role in the planning of the survey and in the analysis and dissemination of the survey results.
As the implementing agency, the bureau took responsibility for operational matters including planning and
conducting fieldwork and processing collected data. Staff from the KNBS and other partners were
responsible for overseeing day-to-day technical operations, including recruitment and training of field and
data processing staff and supervision of office and field operations. The bureau was also responsible for
organising the writing and distribution of reports. With funding from USAID/Kenya, ICF International
staff provided technical assistance, mainly through short-term visits to Kenya, in the areas of survey and
sample design, questionnaire design, field staff training, fieldwork monitoring, data processing, and report
writing and dissemination. NACC, as the body mandated to coordinate the national HIV and AIDS
multisectoral response, assisted in reviewing the protocol and survey instruments to ensure that the
information collected is relevant to the national HIV and AIDS programmes. USAID/Kenya provided
4 • Introduction
funding for survey field transport in addition to other logistical support. WHO-Kenya helped in mobilising
logistical and financial support from member organisations. The Ministry of Health (MOH) assisted in
reviewing the survey instruments in addition to participating in report writing.
1.6 SAMPLE DESIGN
The sample for the 2014 KDHS was drawn from a master sampling frame, the Fifth National
Sample Survey and Evaluation Programme (NASSEP V). This is a frame that the KNBS currently operates
to conduct household-based surveys throughout Kenya. Development of the frame began in 2012, and it
contains a total of 5,360 clusters split into four equal subsamples. These clusters were drawn with a
stratified probability proportional to size sampling methodology from 96,251 enumeration areas (EAs) in
the 2009 Kenya Population and Housing Census. The 2014 KDHS used two subsamples of the NASSEP V
frame that were developed in 2013. Approximately half of the clusters in these two subsamples were
updated between November 2013 and September 2014. Kenya is divided into 47 counties that serve as
devolved units of administration, created in the new constitution of 2010. During the development of the
NASSEP V, each of the 47 counties was stratified into urban and rural strata; since Nairobi county and
Mombasa county have only urban areas, the resulting total was 92 sampling strata.
The 2014 KDHS was designed to produce representative estimates for most of the survey
indicators at the national level, for urban and rural areas separately, at the regional (former provincial1)
level, and for selected indicators at the county level. In order to meet these objectives, the sample was
designed to have 40,300 households from 1,612 clusters spread across the country, with 995 clusters in
rural areas and 617 in urban areas. Samples were selected independently in each sampling stratum, using a
two-stage sample design. In the first stage, the 1,612 EAs were selected with equal probability from the
NASSEP V frame. The households from listing operations served as the sampling frame for the second
stage of selection, in which 25 households were selected from each cluster.
The interviewers visited only the preselected households, and no replacement of the preselected
households was allowed during data collection. The Household Questionnaire and the Woman’s
Questionnaire were administered in all households, while the Man’s Questionnaire was administered in
every second household. Because of the non-proportional allocation to the sampling strata and the fixed
sample size per cluster, the survey was not self-weighting. The resulting data have, therefore, been
weighted to be representative at the national, regional, and county levels.
1.7 QUESTIONNAIRES
The 2014 KDHS used a household questionnaire, a questionnaire for women age 15-49, and a
questionnaire for men age 15-54. These instruments were based on the model questionnaires developed for
The DHS Program, the questionnaires used in the previous KDHS surveys, and the current information
needs of Kenya. During the development of the questionnaires, input was sought from a variety of
organisations that are expected to use the resulting data. A two-day workshop involving key stakeholders
was held to discuss the questionnaire design.
Producing county-level estimates requires collecting data from a large number of households
within each county, resulting in a considerable increase in the sample size from 9,936 households in the
2008-09 KDHS to 40,300 households in 2014. A survey of this magnitude introduces concerns related to
data quality and overall management. To address these concerns, reduce the length of fieldwork, and limit
interviewer and respondent fatigue, a decision was made to not implement the full questionnaire in every
household and, in so doing, to collect only priority indicators at the county level. Stakeholders generated a
list of these priority indicators. Short household and woman’s questionnaires were then designed based on
the full questionnaires; the short questionnaires contain the subset of questions from the full questionnaires
required to measure the priority indicators at the county level.
1
Former provinces were Coast, North Eastern, Eastern, Central, Rift Valley, Western, Nyanza, and Nairobi.
Introduction • 5
Thus, a total of five questionnaires were used in the 2014 KDHS: (1) a full Household
Questionnaire, (2) a short Household Questionnaire, (3) a full Woman’s Questionnaire, (4) a short
Woman’s Questionnaire, and (5) a Man’s Questionnaire. The 2014 KDHS sample was divided into halves.
In one half, households were administered the full Household Questionnaire, the full Woman’s
Questionnaire, and the Man’s Questionnaire. In the other half, households were administered the short
Household Questionnaire and the short Woman’s Questionnaire. Selection of these subsamples was done
at the household level—within a cluster, one in every two households was selected for the full
questionnaires, and the remaining households were selected for the short questionnaires.
It is important to note that the priority data collected in the short questionnaires were collected
from all households and from all women since the short questionnaires were subsets of the full
questionnaires. Therefore, data collected in both the full and the short questionnaires can produce estimates
of indicators at the national, rural/urban, regional, and county levels. Data collected only in the full
questionnaires (i.e., in one-half of households) can produce estimates at the national, rural/urban, and
regional levels only. Data collected only in the full questionnaires are not recommended for estimation at
the county level. A list of topics included in the full and short questionnaires is presented in Appendix E.
In this report, county-level data are tabulated for nearly all of the indicators for which they are available;
county-level tables are not presented for indicators with insufficient cases for evaluation (less than 50
unweighted cases) within each county. In the case of indicators not collected at the county level, the tables
include data at the regional level only.
The Household Questionnaire was used to list all of the usual members of the household and
visitors who stayed in the household the night before the survey. One of the main purposes of the
Household Questionnaire was to identify women and men who were eligible for the individual interview.
Some basic information was collected on the characteristics of each person listed, including age, sex,
education, and relationship to the head of the household. The Household Questionnaire also collected
information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet
facilities, materials used for the floor and roof of the house, ownership of various durable goods, and
ownership and use of mosquito nets. In addition, this questionnaire was used to record height and weight
measurements of women age 15-49 and children under age 5.
The Woman’s Questionnaires were used to collect information from women age 15-49. The full
questionnaire covered the following topics (see Appendix E for a side-by-side comparison of topics
included in the full and short questionnaires):
• Background characteristics (education, marital status, media exposure, etc.)
• Reproductive history
• Knowledge and use of family planning methods
• Fertility preferences
• Antenatal and delivery care
• Breastfeeding and infant feeding practices
• Vaccinations and childhood illnesses
• Marriage and sexual activity
• Women’s work and husbands’ background characteristics
• Childhood mortality
• Awareness and behaviour regarding HIV and other sexually transmitted infections
• Adult mortality, including maternal mortality
• Domestic violence
• Female circumcision
• Fistula
6 • Introduction
The Man’s Questionnaire was administered to men age 15-54 living in every second household in
the sample. The Man’s Questionnaire collected information similar to that contained in the Woman’s
Questionnaire but was shorter because it did not contain questions on maternal and child health, nutrition,
adult and maternal mortality, or experience of female circumcision or fistula.
Both the Woman’s and the Man’s Questionnaires also included a series of questions to obtain
information on respondents’ experience of domestic violence. The domestic violence questions were
administered in the subsample of households that received the full Household Questionnaire, the full
Woman’s Questionnaire, and the Man’s Questionnaire. Additionally, the violence questions were
administered to only one eligible individual, a woman or a man, per household. In households with more
than one eligible individual, special procedures were followed in order to ensure that there was random
selection of the respondent to be interviewed for the domestic violence module.
After finalisation of the questionnaires in English, they were translated into 16 other languages,
namely Borana, Embu, Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Maasai, Maragoli, Meru, Mijikenda,
Pokot, Somali, Swahili, and Turkana. The translated questionnaires were pretested to detect any possible
problems in questionnaire translation or flow, as well as to gauge the length of time required for
interviews.
1.8 TRAINING
1.8.1 Training of Trainers
Training of trainers was conducted by ICF International from January 20-25, 2014, with 18
trainers drawn from the KNBS and the Ministry of Health. The objectives of the training were to
harmonise concepts related to survey design and questionnaire content, to review effective adult teaching
techniques, and to familiarise trainers with the training materials and equipment. The trainers participated
in leading the pretest and the main training and later served as fieldwork coordinators during data
collection.
1.8.2 Pretest Activities
The pretest took place from January 17 to February 15, 2014. The objectives of the pretest were
(1) to train interviewers, editors, and supervisors to fulfil their respective roles and to conduct high-quality
household and individual interviews, (2) to pilot the questionnaires in the field, and (3) to review and
modify the questionnaire translations based on field experience. Classroom training addressed all aspects
of the questionnaire content and interviewing procedures and included anthropometry practice with
children from neighbouring child care centres. Training concluded with two days of local field practice,
after which field teams were formed and sent throughout Kenya (to clusters not included in the KDHS
sample) to pilot the translated questionnaires. After the fieldwork, a two-day debriefing workshop was held
to look at the issues emanating from the pretest. The resolutions from the debriefing were used to enrich
the questionnaires and improve field logistics before implementation of the main training and the actual
survey.
1.8.3 Main Training of Field Staff
Several categories of personnel were recruited and trained to undertake the 2014 KDHS. These
included 48 supervisors, 48 field editors, 144 female interviewers, 48 male interviewers, 28 quality
assurance personnel, and 20 reserves.
The training for these personnel took place from March 24 to April 17, 2014, in Nakuru. Trainees
were divided into six classrooms, each managed by three trainers. The training consisted of a detailed,
question-by-question explanation of the questionnaires, accompanied by explanations from the
interviewer’s manual, demonstration through role-plays, group discussions, and in-class practice
Introduction • 7
interviewing in pairs. Several graded take-home assignments and quizzes were administered, the results of
which were used both to enhance understanding of key terms and concepts and to identify candidates for
further strengthening or elimination from the field teams. A number of guest speakers were invited to give
lectures on specific topics relevant to the KDHS.
Anthropometry training provided all trainees with instruction, demonstration, and practice in
length/height and weight measurements for children and adults. Trainees completed a standardisation
exercise measuring children, intended to gauge and improve measurement accuracy and precision. In this
exercise, 175 children age 0-59 months and their caregivers were invited to the training site in groups of 50
child-caregiver pairs assigned throughout the day to one of three classrooms. Fifteen nutrition specialists
from partnering organisations were trained to support the exercise; they provided a reference measurement
for children and monitored the standardisation activity. Each of the 336 trainees served as both measurers
and assistants and measured the same 10 children twice. Results were recorded and analysed using
Software for Emergency Nutrition Assessment (ENA for SMART); more than 70 percent of trainees’
scores were acceptable or higher. A debriefing session was held the following day to provide feedback and
correction to trainees.
Three field practice sessions were held throughout the main training. Trainees were organised into
teams with a team leader selected from the pretest trainees. Team leaders assisted with logistics, guided
trainees through fieldwork, monitored trainees’ performance, edited trainees’ questionnaires for errors, and
debriefed their team on errors/corrections. The first field practice occurred early in the training and focused
only on the Household Questionnaire. The final two days of field practice occurred at the end of training
and covered the full KDHS protocol: all questionnaires, salt testing, and anthropometry.
1.9 FIELDWORK
Fieldwork for the main survey took place from May 7 to October 20, 2014. Field staff were
divided into 48 teams according to counties and languages spoken in the areas where they conducted the
interviews. Each team had one supervisor, one field editor, three female interviewers, one male
interviewer, a driver, and a vehicle. Data collection was overseen by 18 coordinators who had also served
as trainers during the pretest and main training and by a staff of 28 quality assurance personnel.
Coordinators were each assigned two to three teams for which they were responsible for observing and
monitoring data collection quality, ensuring uniformity in data collection procedures and fidelity to the
survey protocol, providing moral support to the field teams, and replenishing field team supplies.
Coordinators met in person and via phone with teams throughout the fieldwork, spending a total of 70 days
in the field. Quality control staff fulfilled similar responsibilities and spent a total of 60 days in the field.
1.10 DATA PROCESSING
Completed questionnaires were sent to the KNBS Data Processing Centre in Nairobi. Office
editors who received the questionnaires verified cluster and household numbers to ensure that they were
consistent with the sampled list. They also ensured that each cluster had 25 households and that all
questionnaires for a particular household were packaged together.
Data entry began on May 28, 2014, with a four-day training session and continued until November
21, 2014. All data were double entered (100 percent verification) using CSPro software. The data
processing team included 42 keyers, three office editors, two secondary editors, four supervisors, and one
data manager. Secondary editing, which included further data cleaning and validation, ran simultaneously
with data entry and was completed on January 28, 2015, in collaboration with ICF International. The
KDHS Key Indicators Report was prepared and launched in April 2015.
8 • Introduction
1.11 RESPONSE RATES
Table 1.2 presents the summary response rates for the 2014 KDHS. A total of 39,679 households
were selected for the sample, of which 36,812 were found occupied at the time of the fieldwork. Of these
households, 36,430 were successfully interviewed, yielding an overall household response rate of 99
percent. The shortfall of households occupied was primarily due to structures that were found to be vacant
or destroyed and households that were absent for an extended period of time.
Table 1.2 Results of the household and individual interviews
Number of households, number of interviews, and response rates, according to
residence (unweighted), Kenya 2014
Residence
Result Urban Rural Total
ALL HOUSEHOLDS
Household interviews
Households selected 15,419 24,260 39,679
Households occupied 14,177 22,635 36,812
Households interviewed 13,914 22,516 36,430
Household response rate1 98.1 99.5 99.0
Interviews with women age 15-49
Number of eligible women 12,157 20,015 32,172
Number of eligible women interviewed 11,614 19,465 31,079
Eligible women response rate2 95.5 97.3 96.6
HOUSEHOLDS SELECTED FOR FULL QUESTIONNAIRES
Household interviews
Households selected 7,394 11,636 19,030
Households occupied 6,790 10,835 17,625
Households interviewed 6,645 10,764 17,409
Household response rate1 97.9 99.3 98.8
Interviews with women age 15-49
Number of eligible women 5,772 9,545 15,317
Number of eligible women interviewed 5,472 9,269 14,741
Eligible women response rate2 94.8 97.1 96.2
Interviews with men age 15-54
Number of eligible men 5,676 8,541 14,217
Number of eligible men interviewed 4,915 7,904 12,819
Eligible men response rate2 86.6 92.5 90.2
HOUSEHOLDS SELECTED FOR SHORT QUESTIONNAIRES
Household interviews
Households selected 8,025 12,624 20,649
Households occupied 7,387 11,800 19,187
Households interviewed 7,269 11,752 19,021
Household response rate1 98.4 99.6 99.1
Interviews with women age 15-49
Number of eligible women 6,385 10,470 16,855
Number of eligible women interviewed 6,142 10,196 16,338
Eligible women response rate2 96.2 97.4 96.9
1
Households interviewed/households occupied
2
Respondents interviewed/eligible respondents
As noted, the 2014 KDHS sample was divided into halves, with one half of households receiving
the full Household Questionnaire, the full Woman’s Questionnaire, and the Man’s Questionnaire and the
other half receiving the short Household Questionnaire and the short Woman’s Questionnaire. The
household response rate for the full Household Questionnaire was 99 percent, as was the household
response rate for the short Household Questionnaire.
In the households selected for and interviewed using the full questionnaires, a total of 15,317
women were identified as eligible for the full Woman’s Questionnaire, of whom 14,741 were interviewed,
generating a response rate of 96 percent. A total of 14,217 men were identified as eligible in these
households, of whom 12,819 were successfully interviewed, generating a response rate of 90 percent.
Introduction • 9
In the households selected for and interviewed with the short questionnaires, a total of 16,855
women were identified as eligible for the short Woman’s Questionnaire, of whom 16,338 were
interviewed, yielding a response rate of 97 percent.
Response rates are lower in the urban sample than in the rural sample, more so for men. The
principal reason for non-response among both eligible men and eligible women was failure to find them at
home despite repeated visits to the household. The lower response rates for men reflect the more frequent
and longer absences of men from the household.
10 • Introduction
HOUSING CHARACTERISTICS AND
HOUSEHOLD POPULATION 2
Macdonald Obudho, James N. Munguti, John K. Bore, Mutua Kakinyi
Key Findings
• The majority (71 percent) of households in Kenya have access to an
improved source of drinking water.
• Twenty-three percent of households have an improved toilet facility that
is not shared with other households.
• The majority (64 percent) of households in Kenya do not have electricity.
• Almost half (46 percent) of households live in dwellings with cement
floors.
• More than half (53 percent) of households use one room for sleeping.
• More than half (56 percent) of households use wood as their main source
of cooking fuel.
• Eighty-six percent of households own mobile phones.
• Three in 10 Kenyans are below age 10.
• One-third of households are headed by women.
• The average household size in Kenya is 3.9 members.
• The births of two out of every three children below age 5 are registered
with the civil authorities.
T
his chapter provides an overview of the demographic and socioeconomic characteristics of the
households sampled in the 2014 KDHS. In the 2014 KDHS, a household is defined as a person or
group of persons, related or unrelated, who usually live together, who acknowledge one adult
member as the head of the household, and who have common cooking arrangements. Information was
collected on all usual residents of a selected household (de jure population) as well as persons who had
stayed in the selected household the night before the interview (de facto population).
This chapter presents information on the conditions of the households in which the survey
population lives, including the source of drinking water, availability of electricity, sanitation facilities,
building materials, and possession of household durable goods. Also included are findings on birth
registration among children, living arrangements, orphanhood status, school attendance, and educational
attainment. The background information presented in this chapter is intended to facilitate the interpretation
of the demographic, socioeconomic, and health indices presented in later chapters.
2.1 HOUSEHOLD CHARACTERISTICS
The characteristics of a household determine the socioeconomic and health status of its members.
The household is where decisions about health, education, and general welfare are made and acted upon.
The 2014 KDHS asked respondents about their household environment, including the source of drinking
water; type of sanitation facility; building characteristics such as type of material used for the roofing,
flooring, and walls; and number of rooms used for sleeping. Many of these measures help to assess
Kenya’s progress towards Millennium Development Goal 7, which focuses on environmental sustainability
and targets sustainable access to safe drinking water, basic sanitation, and adequate housing.
Housing Characteristics and Household Population • 11
2.1.1 Water and Sanitation
Table 2.1 includes a number of indicators that are useful in monitoring household access to
improved drinking water. Improved water sources include piped water into the dwelling, yard, or plot; a
public tap/standpipe or borehole; a protected well or protected spring water; rainwater; and bottled water.
Lack of easy access to an improved water source may limit the quantity of suitable drinking water that is
available to a household as well as increase the risk of illness. Unimproved water sources increase the
spread of waterborne disease and the burden of service delivery through increased demand for health care;
these sources include unprotected wells or springs, water delivered by tanker trucks, and surface water.
Table 2.1 Household drinking water
Percent distribution of households and de jure population by source of drinking water, time to obtain drinking water, treatment of
drinking water, and person who usually collects drinking water, according to residence, Kenya 2014
Households Population
Characteristic Urban Rural Total Urban Rural Total
Source of drinking water
Improved source 88.2 59.1 71.3 85.7 57.0 66.9
Piped water into dwelling/
yard/plot 45.5 15.0 27.8 43.2 12.1 22.8
Public tap/standpipe 24.8 9.3 15.8 22.6 9.6 14.0
Tube well or borehole 3.8 8.2 6.3 4.3 8.4 7.0
Protected well 3.9 10.3 7.6 4.5 10.7 8.6
Protected spring 3.4 11.6 8.2 4.5 12.3 9.6
Rain water 2.6 4.5 3.7 2.8 3.9 3.5
Bottled water 4.3 0.2 1.9 3.8 0.1 1.4
Non-improved source 10.1 39.2 26.9 12.5 41.5 31.6
Unprotected well 1.7 8.8 5.8 2.4 9.8 7.3
Unprotected spring 1.2 5.5 3.7 1.8 5.8 4.4
Tanker truck/cart with drum 3.1 0.8 1.8 3.0 0.7 1.5
Surface water 4.1 24.0 15.6 5.4 25.2 18.4
Other 1.7 1.7 1.7 1.8 1.4 1.5
Total 100.0 100.0 100.0 100.0 100.0 100.0
Time to obtain drinking
water (round trip)
Water on premises 53.7 27.0 38.2 52.1 23.6 33.4
Less than 30 minutes 33.4 32.7 33.0 32.2 33.4 33.0
30 minutes or longer 11.1 39.9 27.8 13.9 42.8 32.9
Don’t know/missing 1.9 0.4 1.0 1.9 0.3 0.8
Total 100.0 100.0 100.0 100.0 100.0 100.0
Water treatment prior to
drinking1
Boiled 25.5 22.5 23.7 25.9 21.0 22.7
Bleach/chlorine added 21.7 22.5 22.2 24.0 23.8 23.8
Strained through cloth 0.4 1.1 0.8 0.6 1.3 1.0
Ceramic, sand or other filter 1.2 3.5 2.6 1.6 3.9 3.2
Solar disinfection 0.0 0.0 0.0 0.0 0.0 0.0
Other2 0.7 2.0 1.5 0.9 2.0 1.6
No treatment 54.5 54.1 54.3 51.9 54.1 53.3
Percentage using an
appropriate treatment
method3 44.9 44.2 44.5 47.5 44.1 45.3
Number of all households 15,290 21,140 36,430 48,946 93,762 142,708
Person who usually collects
drinking water
Adult female 15+ 27.7 56.8 44.6 34.3 64.2 53.9
Adult male 15+ 16.4 11.8 13.7 10.9 7.7 8.8
Female child under age 15 0.8 2.5 1.8 1.3 2.7 2.2
Male child under age 15 0.6 1.2 1.0 0.8 1.3 1.1
Other 0.8 0.8 0.8 0.8 0.6 0.7
Water on premises 53.5 26.7 38.0 51.8 23.3 33.2
Total 100.0 100.0 100.0 100.0 100.0 100.0
Number of households
selected for full questionnaire 7,280 10,080 17,360 23,176 44,073 67,249
Note: Totals may not add up to 100 percent because households with missing information are not shown separately.
1
Respondents may report multiple treatment methods; therefore, the sum of all treatment methods may exceed 100 percent.
2
Other water treatment methods include covering the water container, and letting the water stand and settle.
3
Appropriate water treatment methods include boiling, bleaching/adding chlorine, filtering/straining, and solar disinfecting.
12 • Housing Characteristics and Household Population
Table 2.1 indicates that the majority of households in Kenya (71 percent) obtain drinking water
from an improved source, while 27 percent use non-improved sources. This is an improvement since the
2008-09 KDHS, when 63 percent of households obtained drinking water from an improved source. Use of
improved sources is more common among households in urban areas (88 percent) than among those in
rural areas (59 percent). The most common source of drinking water in urban areas is water piped into the
dwelling/yard/plot, with almost half (46 percent) of households using this source. In rural areas, the most
common source of drinking water is surface water (24 percent), followed by water piped into the
dwelling/yard/plot (15 percent).
Nearly 4 in 10 households have the source for their drinking water on their premises, but nearly 3
in 10 households (28 percent) spend 30 minutes or longer to obtain their drinking water. In rural areas, 4 in
10 households spend 30 minutes or more to obtain their drinking water, as compared with only 1 in 10
urban households.
Over half of households (54 percent) do not treat their drinking water, and this is true in both
urban and rural areas. The most commonly used methods of water treatment are boiling and adding
bleach/chlorine (24 percent and 22 percent of households, respectively). Overall, 45 percent of households
use an appropriate treatment method.
When water is not on the premises, the responsibility of collecting drinking water usually rests on
adult women. Forty-five percent of households reported that a female adult age 15 and above usually
collects the drinking water for the household. An even higher percentage of rural households delegate
collection of drinking water to women (57 percent), as these households are much less likely to have their
water source on the premises.
Table 2.2 presents the percent distribution of households and the de jure population by the type of
toilet/latrine facilities usually used by household members. Twenty-five percent of household members
usually use an improved (and not shared) toilet/latrine facility. About 4 in 10 urban dwellers (43 percent)
use an improved facility that is shared by two or more households, as compared with only about 1 in 10
(12 percent) rural dwellers. Approximately two-thirds of rural Kenyans usually use a non-improved toilet
facility (66 percent), most commonly a pit latrine without a slab or an open pit (48 percent). One-half of
urban Kenyans use a shared facility of which a pit latrine with a slab is the most common (17 percent).
Housing Characteristics and Household Population • 13
Table 2.2 Household sanitation facilities
Percent distribution of households and de jure population by type of toilet/latrine facilities, according to residence, Kenya 2014
Households Population
Type of toilet/latrine facility Urban Rural Total Urban Rural Total
Improved, not shared facility
Flush/pour flush to piped
sewer system 8.0 0.1 3.4 8.5 0.1 3.0
Flush/pour flush to septic
tank 8.0 0.8 3.8 8.7 0.6 3.4
Flush/pour flush to pit latrine 0.9 0.3 0.6 1.2 0.3 0.6
Ventilated improved pit (VIP)
latrine 4.2 8.6 6.8 6.1 9.2 8.1
Pit latrine with slab 4.0 10.5 7.8 5.7 11.1 9.2
Composting toilet 0.3 0.3 0.3 0.4 0.3 0.3
Total 25.5 20.6 22.7 30.5 21.6 24.7
Shared facility1
Flush/pour flush to piped
sewer system 11.9 0.1 5.1 9.6 0.1 3.3
Flush/pour flush to septic
tank 5.2 0.3 2.4 4.4 0.2 1.6
Flush/pour flush to pit latrine 3.7 0.2 1.7 3.0 0.2 1.1
Ventilated improved pit (VIP)
latrine 12.1 6.4 8.8 11.2 5.1 7.2
Pit latrine with slab 17.3 8.1 12.0 14.9 6.4 9.3
Composting toilet 0.2 0.1 0.2 0.2 0.1 0.2
Total 50.4 15.3 30.1 43.3 12.1 22.8
Non-improved facility
Flush/pour flush not to sewer/
septic tank/pit latrine 1.5 0.0 0.6 1.4 0.0 0.5
Pit latrine without slab/open
pit 19.4 47.7 35.9 21.4 48.3 39.1
Bucket 0.2 0.0 0.1 0.3 0.0 0.1
Hanging toilet/hanging latrine 0.5 0.2 0.3 0.4 0.3 0.3
No facility/bush/field 1.4 16.0 9.9 1.7 17.6 12.2
Other 0.9 0.1 0.5 0.7 0.1 0.3
Total 24.1 64.1 47.3 26.2 66.3 52.5
Total 100.0 100.0 100.0 100.0 100.0 100.0
Number 15,290 21,140 36,430 48,946 93,762 142,708
Note: Totals may not add up to 100 percent because households with missing information are not shown separately.
1
Facilities that would be considered improved if they were not shared by two or more households.
2.1.2 Housing Characteristics
Table 2.3 presents information on housing characteristics in Kenya. These characteristics are
usually a function of the household’s socioeconomic situation and have a direct bearing on the health and
welfare of household members. The table includes information on access to electricity, type of flooring
material, number of rooms used for sleeping, the place used for cooking, the type of fuel used for cooking,
and the frequency of someone smoking in the home. The majority of households in urban areas have
electricity (68 percent), while the vast majority of rural households do not (only 13 percent have
electricity). Nationally, 36 percent of households have access to electricity, as compared with 23 percent in
2008-09.
Cement is the most common household flooring material; 46 percent of households have cement
floors, up from 41 percent in 2008-09. Not surprisingly, cement floors are much more common in urban
households (70 percent) than in rural households (28 percent). The most common flooring in rural
households is earth/sand (43 percent).
The number of rooms used for sleeping provides an indication of the extent of crowding in
households. Overcrowding increases the risk of contracting infectious diseases such as acute respiratory
infections and skin diseases, which particularly affect children and the elderly population. The proportion
of households using one room for sleeping has increased from 48 percent to 53 percent in the last five
years. The presence and extent of indoor pollution are dependent on cooking practices, the cooking
location, and types of fuel used. According to the 2014 KDHS, 50 percent of households cook inside the
14 • Housing Characteristics and Household Population
Table 2.3 Household characteristics
Percent distribution of households by housing characteristics, percentage using
solid fuel for cooking, and percent distribution by frequency of smoking in the
home, according to residence, Kenya 2014
Residence
Housing characteristic Urban Rural Total
Electricity
Yes 68.4 12.6 36.0
No 31.6 87.4 64.0
Total 100.0 100.0 100.0
Flooring material
Earth, sand 10.9 43.1 29.6
Dung 5.0 26.9 17.7
Wood/planks 0.1 0.2 0.2
Palm/bamboo 0.0 0.0 0.0
Parquet or polished wood 0.4 0.1 0.3
Vinyl or asphalt strips 1.4 0.0 0.6
Ceramic tiles 7.3 0.8 3.5
Cement 70.3 28.4 46.0
Carpet 4.4 0.3 2.0
Other 0.1 0.1 0.1
Total 100.0 100.0 100.0
Rooms used for sleeping
One 68.4 42.0 53.1
Two 21.3 36.5 30.2
Three or more 9.9 21.1 16.4
Total 100.0 100.0 100.0
Place for cooking
In the house 77.0 30.4 50.0
In a separate building 14.5 61.1 41.5
Outdoors 6.2 7.4 6.9
No food cooked in household 2.3 1.0 1.5
Other 0.0 0.0 0.0
Missing 0.0 0.0 0.0
Total 100.0 100.0 100.0
Cooking fuel
Electricity 0.9 0.1 0.4
LPG/natural gas/biogas 24.5 2.0 11.5
Paraffin/kerosene 26.6 1.3 11.9
Coal/lignite 0.1 0.1 0.1
Charcoal 27.6 9.7 17.2
Wood 17.2 84.2 56.1
Straw/shrubs/grass 0.7 1.4 1.1
Agricultural crop 0.0 0.1 0.0
Animal dung 0.0 0.0 0.0
Other 0.0 0.0 0.0
No food cooked in household 2.3 1.0 1.5
Total 100.0 100.0 100.0
Percentage using solid fuel
for cooking1 45.6 95.5 74.6
Number of all households 15,290 21,140 36,430
Frequency of smoking in the
home
Daily 10.9 12.9 12.1
Weekly 1.8 1.9 1.9
Monthly 0.2 0.5 0.4
Less than monthly 0.6 0.5 0.5
Never 86.3 84.1 85.0
Total 100.0 100.0 100.0
Number of households
selected for full questionnaire 7,280 10,080 17,360
LPG = Liquid petroleum gas
Note: Totals may not add up to 100 percent because households with missing
information are not shown separately.
1
Includes coal/lignite, charcoal, wood, straw/shrubs/grass, agricultural crops,
and animal dung.
home, while 42 percent cook in a separate building and 7 percent cook outdoors. The percentage of
households that cook within the dwelling unit is much higher in urban areas (77 percent) than in rural areas
(30 percent). Using solid fuels for cooking increases indoor pollution. Solid fuels are defined as
coal/lignite, charcoal, wood, straw/shrubs/grass, and agricultural crops. Nationally, 75 percent of
households use solid fuels, mostly wood (56 percent) and charcoal (17 percent). While this is a decrease
Housing Characteristics and Household Population • 15
from the 84 percent of households using solid fuels reported in the 2008-09 KDHS, over 9 in 10 rural
households continue to use solid fuels. Households that do not use solid fuels mostly use gas or kerosene
(12 percent of households each, compared with 7 percent and 8 percent, respectively in 2008-09).
A major concern for the government of Kenya is the effect of secondhand smoke on the health of
children and neonates. The purpose of the Tobacco Control Act of 2007, followed in 2014 by the Tobacco
Control Regulations (2014), is to control tobacco and tobacco-related product use. Secondhand smoke is a
risk factor for children and adults who do not smoke. Children who are exposed to secondhand smoke are
at a higher risk of respiratory and ear infections and poor lung development (U.S. Department of Health
and Human Services, 2006). Pregnant women who are exposed to secondhand smoke have a higher risk of
giving birth to a low birth weight baby (Windham et al., 1999). To measure the extent of smoke exposure
among household members, respondents were asked how often anyone smokes inside the house. In Kenya,
someone smokes in the house on a daily basis in 12 percent of households (11 percent in urban areas and
13 percent in rural areas).
2.1.3 Household Possessions
The availability of durable consumer goods is a useful indicator of a household’s socioeconomic
status. Moreover, particular goods have specific benefits. For instance, having access to a radio or a
television exposes household members to innovative ideas; a refrigerator prolongs the wholesomeness of
foods; and a means of transport allows greater access to services away from the local area. Table 2.4 shows
the availability of selected consumer goods by residence.
Table 2.4 Household possessions
Percentage of households possessing various household effects, means of
transportation, agricultural land and livestock/farm animals, a dwelling, and land
on which the dwelling is built, by residence, Kenya 2014
Residence
Possession Urban Rural Total
Household effects
Watch 24.9 15.0 19.2
Radio 73.5 63.1 67.5
Television 56.0 18.9 34.5
Mobile telephone 94.2 80.0 86.0
Non-mobile telephone 0.7 0.2 0.4
Refrigerator 12.7 1.5 6.2
Solar panel 4.0 14.0 9.8
Table 87.8 83.1 85.1
Chair 79.4 86.3 83.4
Sofa 64.5 47.5 54.6
Bed 94.1 92.7 93.3
Cupboard 51.9 41.7 46.0
Clock 27.3 14.4 19.8
Microwave oven 7.2 0.7 3.4
DVD player 40.6 9.5 22.5
Cassette or CD player 19.5 5.8 11.6
Means of transport
Bicycle 16.2 24.8 21.2
Animal drawn cart 1.1 2.5 1.9
Motorcycle/scooter 6.0 8.2 7.3
Car/truck 7.2 2.7 4.6
Boat with a motor 0.2 0.2 0.2
Ownership of agricultural land 47.7 79.2 66.0
Ownership of farm animals1 43.2 80.4 64.8
Number of all households 15,290 21,140 36,430
Ownership of dwelling 25.5 85.2 60.2
Ownership of land on which
dwelling is built 24.4 81.9 57.8
Number of households selected
for full questionnaire 7,280 10,080 17,360
1
Local cattle, exotic/grade cattle, horses, donkeys, camels, goats, sheep or
chickens
16 • Housing Characteristics and Household Population
Possession of mobile phones has significantly increased from 62 percent in 2008-09 to 86 percent
in 2014; rural areas have registered a greater increase (from 53 percent to 80 percent), as ownership of
mobile phones among urban households was already relatively high. More than 9 in 10 urban households
(94 percent) own a mobile phone. Sixty-eight percent of households have a radio, and about one-third (35
percent) have a television. Urban households are somewhat more likely to possess a radio (74 percent) than
rural households (63 percent). Fifty-six percent of urban households and 19 percent of rural households
possess a television, and television ownership has increased nationally from 28 percent to 35 percent since
2008-09. A refrigerator is available in 13 percent of urban households and only 2 percent of rural
households.
Bicycles are still the most common means of transport owned by households. Twenty-one percent
of households own a bicycle (25 percent in rural areas and 16 percent in urban areas).
The agricultural sector plays a large role in the Kenyan economy, and a substantial proportion of
the population is engaged in this sector. The 2014 KDHS indicates that two of every three households own
agricultural land, with 79 percent of rural households and 48 percent of urban households owning land.
Two in three households (65 percent) own farm animals, 80 percent in rural areas and 43 percent in urban
areas. In urban areas, ownership of agricultural land and farm animals has increased since 2008-09 (from
35 percent to 48 percent and 27 percent to 43 percent, respectively), while the national figure has remained
at two-thirds.
More than 8 in 10 rural households own their dwelling (85 percent) and the land on which the
dwelling is built (82 percent). About one-quarter (26 percent) of urban households own their dwelling and
the land on which it is built (24 percent). Thus, nationally, 6 in 10 Kenyan households own their dwelling
(60 percent), and nearly 6 in 10 (58 percent) own the land on which the dwelling is built.
2.2 HOUSEHOLD WEALTH
The wealth index used in this report and in many other DHS survey reports serves as a proxy for a
household’s long-term standard of living. It has been demonstrated to be consistent with expenditure and
income measures (Rutstein, 1999; Rutstein and Johnson, 2004). The index is constructed using household
asset data collected in the Household Questionnaire and is generated via a principal components analysis.
The wealth index has been improved to better take into account urban-rural differences in scores
and indicators of wealth by performing the first and second steps of its creation separately for urban and
rural areas prior to creating a national wealth index in the last step. In the first step, a subset of indicators
common to urban and rural areas is used to create wealth scores for households in both areas. Categorical
variables to be used are transformed into separate dichotomous (0-1) indicators. These indicators and those
that are continuous are then examined using a principal components analysis to produce a common factor
score for each household. In the second step, separate factor scores are produced for households in urban
and rural areas using area-specific indicators. The third step combines the separate area-specific factor
scores to produce a nationally applicable combined wealth index by adjusting area-specific scores through
a regression on the common factor scores. The resulting combined wealth index has a mean of zero and a
standard deviation of one. Once the index is computed, national-level wealth quintiles (from lowest to
highest) are obtained by assigning the household score to each de jure household member, ranking each
person in the population by his or her score, and then dividing the ranking into five equal categories, each
comprising 20 percent of the population.
Thus, throughout this report, wealth quintiles are expressed in terms of quintiles of individuals in
the overall population rather than quintiles of individuals at risk for any one health or population indicator.
For example, quintile rates for infant mortality refer to infant mortality rates per 1,000 live births among all
people in the population quintile concerned, as distinct from quintiles of live births or newly born infants,
who constitute the only members of the population at risk of mortality during infancy.
Housing Characteristics and Household Population • 17
Table 2.5 presents percent distributions of the de jure population across the five wealth quintiles
by residence and region. Three-quarters of urban residents (75 percent) are in the two highest wealth
quintiles, while more than three-quarters of rural residents (78 percent) are in the lowest three quintiles
(and are nearly equally distributed across these quintiles). By region, the most skewed distributions are
seen in Nairobi and North Eastern. Nine in 10 people in Nairobi are in the two highest wealth quintiles,
and 7 in 10 people in North Eastern are in the lowest wealth quintile. Populations in the other regions are
more spread out across the quintiles.
Table 2.5 Wealth quintiles
Percent distribution of the de jure population by wealth quintiles, and the Gini Coefficient, according to residence and region, Kenya 2014
Residence/ Wealth quintile Number of
region/county Lowest Second Middle Fourth Highest Total persons Gini coefficient
Residence
Urban 6.0 8.3 10.6 26.1 49.0 100.0 48,946 0.18
Rural 27.3 26.1 24.9 16.8 4.9 100.0 93,762 0.19
Region
Coast 40.0 10.4 10.6 14.5 24.5 100.0 13,972 0.34
North Eastern 72.9 4.5 4.5 9.0 9.1 100.0 4,164 0.36
Eastern 19.5 27.7 22.4 19.7 10.7 100.0 20,960 0.21
Central 2.4 12.0 21.9 32.0 31.7 100.0 16,297 0.21
Rift Valley 26.5 20.2 19.7 19.1 14.5 100.0 37,746 0.29
Western 12.3 30.9 33.8 16.8 6.2 100.0 16,692 0.20
Nyanza 16.6 31.2 23.8 17.5 10.9 100.0 20,050 0.28
Nairobi 0.2 0.8 6.0 25.6 67.4 100.0 12,827 0.15
Total 20.0 20.0 20.0 20.0 20.0 100.0 142,708 0.27
Table 2.5 also includes information on the Gini coefficient, which indicates the level of
concentration of wealth (0 being an equal distribution and 1 a totally unequal distribution). This ratio is
expressed as a proportion between 0 and 1. The coefficient indicates the distribution of wealth independent
of the level of wealth. The coefficient is lowest in Nairobi, indicating that people in that region are more
similar to each other with regard to wealth than people in any other region. With the highest Gini
coefficient of 0.36, the most unequal distribution of wealth is seen in the North Eastern region.
2.3 FOOD SECURITY
The National Food and Nutrition Security Policy of 2011 states that all Kenyans should at all
times have access to safe food of sufficient quantity and quality to satisfy their nutritional needs for
optimal health. Household respondents in the 2014 KDHS were asked on how many days during the seven
days preceding the survey members of their household had consumed items from various food groups
(staples, pulses, vegetables, fruits, meat, dairy, oil, and sugar). They were also asked if there were any days
in the seven days preceding the survey when their household did not have food or enough money to buy
food. Respondents who answered ‘yes’ to the latter question were asked to indicate how many days in that
week their household had to rely on less preferred food, rely on borrowed food, reduce the number of
meals, reduce the size of meals, and/or reduce what adults ate in order for small children to eat. These
questions and the three measures described below were developed by the World Food Programme.
The first measure, the food consumption score (FCS), derived from the household consumption
history questions, is a composite calculation including dietary diversity (the number of food groups
consumed by a household over a seven-day period), food frequency (the number of days a particular food
group is consumed), and the relative nutritional importance of different food groups. The FCS is intended
to describe short-term food security at the time of data collection. Food consumption scores are divided
into poor, borderline, and acceptable food consumption groups.
The second measure is the percentage of households that report lacking food or money to purchase
food in the seven days preceding the survey. The third measure is the coping strategy index (CSI). The CSI
is a composite calculation of the frequency and severity of coping strategies that households adopt when
facing lack of food or money to purchase food. A higher CSI score indicates a more serious food security
18 • Housing Characteristics and Household Population
situation. The minimum possible CSI score (among households reporting any of the provided list of coping
strategies) is 7.0, and the maximum possible score is 56.0.
Table 2.6 presents the percent distribution of households with poor, borderline, or acceptable food
consumption; the percentage of households that report lacking food or money to purchase food; and the
mean CSI score, according to background characteristics.
Table 2.6 Food security status
Percent distribution of households with poor, borderline or acceptable food consumption, percentage of households that report lacking food or money to purchase
food in the seven days preceding the survey, and the mean coping strategy index, according to background characteristics, Kenya 2014
Number of
Percentage of households
Number of households with total
households that report coping
with valid food lacking food or strategy index
Background Food consumption score groups consumption money to Number of Mean coping greater than
characteristic Poor Borderline Acceptable Total score purchase food households strategy index zero
Residence
Urban 1.4 7.3 91.3 100.0 7,217 23.0 7,280 17.4 1,658
Rural 1.7 11.4 86.9 100.0 10,041 36.2 10,080 19.6 3,645
Region
Coast 1.1 10.7 88.2 100.0 1,651 24.5 1,688 16.1 414
North Eastern 1.9 8.5 89.6 100.0 342 37.8 344 15.4 130
Eastern 0.8 8.0 91.2 100.0 2,510 37.5 2,516 20.0 942
Central 1.3 5.8 92.9 100.0 2,391 17.4 2,400 16.7 418
Rift Valley 2.3 11.4 86.4 100.0 4,387 25.4 4,406 22.1 1,119
Western 1.4 10.8 87.8 100.0 1,720 44.6 1,726 17.3 769
Nyanza 1.6 12.9 85.5 100.0 2,174 41.9 2,187 18.4 915
Nairobi 1.6 7.5 90.8 100.0 2,085 29.1 2,093 18.3 597
Wealth quintile
Lowest 3.8 17.9 78.3 100.0 2,888 54.4 2,894 21.7 1,574
Second 1.8 14.0 84.2 100.0 3,152 41.6 3,166 19.5 1,316
Middle 1.1 9.8 89.1 100.0 3,244 32.6 3,262 17.4 1,061
Fourth 0.8 6.2 93.0 100.0 3,913 21.1 3,948 16.3 824
Highest 0.8 3.7 95.5 100.0 4,062 13.0 4,091 16.1 528
Total 1.5 9.7 88.8 100.0 17,258 30.7 17,360 18.9 5,303
Note: The food consumption score reflects the quantity and quality of people’s diet. The coping strategy index measures behaviours adopted by households when
they have difficulties in covering their food needs.
The majority of households (89 percent) in Kenya had acceptable food consumption scores. Two
percent of households had poor food consumption scores and 10 percent had borderline scores. Rural
households were more likely to have borderline scores (11 percent) than urban households (7 percent).
Households in Nyanza were most likely (13 percent) to have borderline scores, followed closely by
households in Rift Valley, Western, and Coast (all 11 percent). The proportion of households with
borderline scores decreased with increasing household wealth.
Three in 10 (31 percent) households in Kenya reported not having enough food or money to buy
food in the seven days preceding the survey. More than 3 in 10 rural households (36 percent) and
households in Western (45 percent), Nyanza (42 percent), North Eastern (38 percent), and Eastern (38
percent) reported lacking food or money to purchase food. As expected, the likelihood of lacking food or
money to purchase food decreased with increasing household wealth. However, 13 percent of households
in the highest wealth quintile did report not having enough food or money to buy food.
Among households that reported not having food or enough money to purchase food, the mean
CSI score was 18.9. The mean score was highest in Rift Valley (22.1) and lowest in North Eastern (15.4).
Mean CSI scores decreased slightly with increasing household wealth.
2.4 HAND WASHING
Environmental management at the household level is a key indicator of a household’s intention to
manage its health. Hand washing is one of the most effective ways to prevent the spread of germs, and it is
used here as an indicator of personal and household hygiene. Table 2.7 provides information on places
designated for hand washing and the availability of water and cleansing agents by residence, region, and
wealth quintile.
Housing Characteristics and Household Population • 19
Table 2.7 Hand washing
Percentage of households in which the place most often used for washing hands was observed, and among households in which the place for washing hands was
observed, percent distribution by availability of water, soap and other cleansing agents, Kenya 2014
Percentage
of Among households where place for washing hands was observed, percentage with: Number of
households Water and No water, no households
where place cleansing Cleansing soap, no with place for
for washing agent2 other agent other other washing
Background hands was Number of Soap and than soap Soap but no than soap cleansing hands was
characteristic observed households water1 only Water only water3 only2 agent Total observed
Residence
Urban 42.7 7,280 60.1 0.2 21.8 3.5 0.0 14.2 100.0 3,111
Rural 27.1 10,080 37.5 0.1 26.0 2.7 0.1 33.2 100.0 2,729
Region
Coast 30.3 1,688 24.5 0.1 23.2 6.0 0.0 45.9 100.0 512
North Eastern 23.6 344 26.5 2.4 18.1 1.7 0.0 50.7 100.0 81
Eastern 39.1 2,516 37.5 0.0 17.4 6.3 0.1 38.4 100.0 985
Central 55.7 2,400 45.2 0.5 41.7 1.7 0.0 10.8 100.0 1,337
Rift Valley 29.5 4,406 62.4 0.1 20.4 0.9 0.0 15.8 100.0 1,300
Western 25.6 1,726 38.1 0.1 10.3 3.8 0.0 47.3 100.0 442
Nyanza 16.5 2,187 51.8 0.0 25.2 6.1 0.1 16.2 100.0 360
Nairobi 39.3 2,093 73.4 0.0 15.1 2.1 0.0 9.5 100.0 823
Wealth quintile
Lowest 18.0 2,894 18.5 0.6 18.8 2.7 0.0 59.0 100.0 520
Second 24.8 3,166 33.2 0.1 20.8 3.7 0.3 41.5 100.0 784
Middle 27.9 3,262 40.7 0.0 23.4 3.7 0.0 31.8 100.0 911
Fourth 36.6 3,948 48.5 0.0 30.6 2.9 0.0 17.9 100.0 1,445
Highest 53.3 4,091 67.2 0.3 21.7 3.0 0.0 7.7 100.0 2,180
Total 33.6 17,360 49.5 0.2 23.8 3.2 0.0 23.1 100.0 5,840
Note: Totals may not add up to 100 percent because households with missing information are not shown separately.
1
Soap includes soap or detergent in bar, liquid, powder or paste form. This column includes households with soap and water only as well as those that had soap and
water and another cleansing agent.
2
Cleansing agents other than soap include locally available materials such as ash, mud or sand.
3
Includes households with soap only as well as those with soap and another cleansing agent
Interviewers collected data by observing the place household members use for hand washing. A
place for handwashing was only observed in one-third of households. A place for hand washing was
observed in about 4 in 10 urban households (43 percent) and fewer than 3 in 10 rural households (27
percent). The ability of interviewers to observe a place for hand washing varied substantially across
regions, from a low of 17 percent in Nyanza to a high of 56 percent in Central. It is especially interesting to
note that the ability of interviewers to observe a place for hand washing steadily increased with increasing
wealth, from a low of only 18 percent among households in the lowest quintile to a high of 53 percent
observed among households in the highest quintile.
Both water and soap were available in 50 percent of the households where a place for hand
washing was observed (60 percent of urban households and 38 percent of rural households). The presence
of soap and water increases steadily with increasing wealth, from 19 percent in the lowest quintile to 67
percent in the highest quintile. Approximately half of households in Coast, North Eastern, and Western
where a place for hand washing was observed had neither water nor soap available.
2.5 HOUSEHOLD POPULATION BY AGE AND SEX
The distribution of the de facto household population in the 2014 KDHS is shown in Table 2.8 by
five-year age groups, according to sex and residence. The age and sex structure of the population is key in
all demographic analyses. The 2014 KDHS de facto household population constitutes 137,780 persons, of
whom 51 percent are female and 49 percent are male. Among this population, 34 percent live in urban
areas and 66 live in rural areas. Half of the population is below age 20 (52 percent). At 14 percent, the
under-5 population constitutes the largest age group in urban areas, while the 5-9 population is the largest
five-year age group in rural areas (17 percent).
20 • Housing Characteristics and Household Population
Table 2.8 Household population by age, sex, and residence
Percent distribution of the de facto household population by five-year age groups, according to sex and residence, Kenya 2014
Urban Rural Total
Age Male Female Total Male Female Total Male Female Total
<5 13.8 13.9 13.9 15.6 13.9 14.7 15.0 13.9 14.4
5-9 12.6 12.5 12.5 17.4 16.3 16.8 15.7 15.0 15.3
10-14 10.3 10.4 10.4 16.0 14.8 15.4 14.0 13.3 13.7
15-19 7.3 8.3 7.8 10.5 8.9 9.7 9.4 8.7 9.0
20-24 10.0 12.0 11.0 6.3 6.8 6.6 7.6 8.6 8.1
25-29 11.8 12.8 12.3 5.8 7.0 6.4 7.9 8.9 8.4
30-34 9.8 9.0 9.4 5.2 5.5 5.4 6.8 6.7 6.7
35-39 7.1 6.1 6.6 4.7 5.2 4.9 5.5 5.5 5.5
40-44 5.3 4.2 4.7 3.8 4.2 4.0 4.3 4.2 4.3
45-49 3.3 2.8 3.1 2.9 3.5 3.2 3.1 3.2 3.2
50-54 3.0 2.9 3.0 2.9 3.7 3.3 3.0 3.4 3.2
55-59 2.3 1.6 1.9 2.2 2.8 2.5 2.2 2.4 2.3
60-64 1.3 1.1 1.2 2.2 2.3 2.3 1.9 1.9 1.9
65-69 0.8 0.8 0.8 1.6 1.7 1.7 1.3 1.4 1.4
70-74 0.6 0.7 0.6 1.1 1.2 1.2 0.9 1.0 1.0
75-79 0.3 0.4 0.3 0.7 0.9 0.8 0.6 0.7 0.6
80 + 0.4 0.6 0.5 0.9 1.4 1.2 0.7 1.1 0.9
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number 23,574 23,871 47,445 43,865 46,470 90,335 67,439 70,341 137,780
Figure 2.1 depicts the age-sex structure of the Kenyan population in a population pyramid. The
broad base depicts the youthfulness of the population. The drop in the female population between ages 10-
14 and 15-19 is a bit steep and could partially be due to some interviewers estimating ages of women to be
under the interview cutoff age of 15 to reduce their workload. Similarly, there is an increase in the female
population between ages 45-49 and 50-54, which might be due to pushing some of the women out of the
age eligibility category. The drop in population between ages 5-9 and under 5 among both males and
females reflects a fertility decline, addressed in the chapter on fertility.
Figure 2.1 Population pyramid
2.6 HOUSEHOLD COMPOSITION
Information on key aspects of the composition of households is presented in Table 2.9. Nationally,
one-third of households are headed by women. A higher proportion of rural than urban households are
headed by women (36 percent and 27 percent, respectively).
Housing Characteristics and Household Population • 21
Table 2.9 Household composition
Percent distribution of households by sex of head of household and by household
size; mean size of household, and percentage of households with orphans and
foster children under 18 years of age, according to residence, Kenya 2014
Residence
Characteristic Urban Rural Total
Household headship
Male 72.7 64.2 67.8
Female 27.3 35.8 32.2
Total 100.0 100.0 100.0
Number of usual members
0 0.1 0.0 0.0
1 27.3 13.5 19.3
2 16.6 10.2 12.9
3 16.5 14.1 15.1
4 15.6 17.0 16.4
5 10.7 15.2 13.3
6 6.1 10.9 8.8
7 3.4 7.8 6.0
8 1.8 5.2 3.8
9+ 1.9 6.2 4.4
Total 100.0 100.0 100.0
Mean size of households 3.2 4.4 3.9
Percentage of households with
orphans and foster children
under 18 years of age
Foster children1 10.8 21.3 16.9
Double orphans 1.3 1.9 1.6
Single orphans2 5.4 10.8 8.5
Foster and/or orphan children 13.5 26.4 21.0
Number of households 15,290 21,140 36,430
Note: Table is based on de jure household members, i.e., usual residents.
1
Foster children are those under age 18 living in households with neither their
mother nor their father present.
2
Includes children with one dead parent and an unknown survival status of the
other parent.
The data also show that the mean size of a Kenyan household is 3.9 people, lower than the mean
size of 4.2 recorded in the 2008-09 KDHS. As expected, rural households are larger on average (4.4
people) than urban households (3.2 people).
Nationally, 17 percent of Kenyan households are fostering a child under age 18 (1 in every 10
urban households and 2 in every 10 rural households). Nine percent of all Kenyan children under age 18
have had one parent die. The percentage of households housing a single or double orphan is higher in rural
areas (13 percent) than in urban areas (7 percent).
2.7 BIRTH REGISTRATION
Birth registration is the inscription of the facts of the birth into an official log kept at the registrar’s
office. A birth certificate is issued at the time of registration or later as proof of the registration of the birth.
Birth registration is basic to ensuring a child’s legal status and, thus, basic rights and services.
Table 2.10 presents the percentage of the de jure population under age 5 whose births are
registered with the civil authorities, according to background characteristics. Two-thirds of children in
Kenya have their births registered (67 percent). This is an improvement of 7 percentage points since the
2008-09 KDHS, which reported a figure of 60 percent. However, only about one-quarter (24 percent) of
children are reported to have a birth certificate.
22 • Housing Characteristics and Household Population
Table 2.10 Birth registration of children under age five
Percentage of de jure children under five years of age whose births are registered with the
civil authorities, according to background characteristics, Kenya 2014
Children whose births are registered
Percentage who Percentage who
Background had a birth did not have birth Percentage Number of
characteristic certificate certificate registered children
Age
<2 19.8 48.2 68.0 7,662
2-4 26.8 39.4 66.2 12,291
Sex
Male 24.9 42.5 67.4 10,170
Female 23.3 43.1 66.4 9,784
Residence
Urban 37.4 41.4 78.8 6,603
Rural 17.5 43.4 61.0 13,351
Region
Coast 21.3 53.5 74.8 2,019
North Eastern 44.7 17.1 61.8 682
Eastern 18.1 57.0 75.1 2,458
Central 36.3 53.4 89.7 1,789
Rift Valley 20.1 42.8 62.9 5,727
Western 19.6 33.6 53.2 2,585
Nyanza 19.5 34.6 54.1 2,929
Nairobi 42.6 36.9 79.5 1,765
Wealth quintile
Lowest 12.3 39.8 52.1 4,924
Second 15.4 43.2 58.6 4,277
Middle 19.7 45.4 65.0 3,652
Fourth 27.5 49.6 77.1 3,430
Highest 51.4 37.3 88.7 3,670
Total 24.1 42.8 66.9 19,954
There is little age or sex differential nationally in the percentage of children registered. However,
only slightly more than half of children in Western and Nyanza are registered, as compared with 9 in 10
children in Central. The percentage of children registered and the percentage having a birth certificate both
increase steadily with increasing wealth.
2.8 CHILDREN’S LIVING ARRANGEMENTS, ORPHANHOOD, AND SCHOOL ATTENDANCE
Children’s living arrangements affect their development and well-being. Table 2.11 presents the
percent distribution of children by their living arrangements and the survival status of their biological
parents. For 22 percent of children, both parents are alive but their father is living elsewhere; 10 percent of
children are not living with either parent although both are alive.
There is not a great deal of variation in living arrangements by sex of the child. Children in urban
areas are slightly more likely to be living with both parents (59 percent) than children in rural areas (53
percent).
Nationally, only 55 percent of children age 0-17 live with both of their biological parents and
living arrangements vary by region. Two-thirds of children in Nairobi are living with both of their parents,
the highest percentage in the country, while only half of children in the Eastern and Western regions are
living with both parents. Children in the Western region are most likely (16 percent) to not be living with
either parent despite both of them being alive. Nyanza has the highest percentage of children who have
experienced the death of their father; 9 percent of these children are living with their mother, and 3 percent
are living with neither parent. Children in the Eastern region are most likely to be living with their mother
but not their father even though their father is alive (28 percent).
A notable pattern by wealth quintile is seen among children living with their mother but not their
father. The percentage of children who are living with their mother and whose father has died decreases
with increasing wealth.
Housing Characteristics and Household Population • 23
Table 2.11 Children’s living arrangements and orphanhood
Percent distribution of de jure children under age 18 by living arrangements and survival status of parents, the percentage of children not living with a biological parent, and the
percentage of children with one or both parents dead, according to background characteristics, Kenya 2014
Living with mother Living with father
but not with father but not with mother Not living with either parent Percent- Percent-
Missing age not age with
informa- living with one or
Living Only Only tion on a both Number
Background with both Father Father Mother Mother father mother father/ biological parents of
characteristic parents alive dead alive dead Both alive alive alive Both dead mother Total parent dead1 children
Age
0-4 63.8 25.1 2.2 0.9 0.2 5.2 0.4 0.4 0.1 1.6 100.0 6.2 3.4 19,954
<2 67.0 27.6 1.7 0.3 0.0 1.9 0.2 0.1 0.0 1.2 100.0 2.2 2.0 7,662
2-4 61.8 23.6 2.5 1.2 0.2 7.3 0.5 0.6 0.2 1.9 100.0 8.7 4.2 12,291
5-9 55.6 21.4 4.5 2.7 0.6 10.4 0.8 1.2 0.9 1.9 100.0 13.3 8.2 21,331
10-14 49.1 20.3 7.4 3.2 1.2 11.3 1.4 2.2 1.9 2.0 100.0 16.8 14.5 19,914
15-17 44.1 17.8 9.1 3.6 1.7 13.1 1.6 3.0 2.9 3.0 100.0 20.6 18.7 9,058
Sex
Male 54.5 21.9 5.2 2.9 0.9 9.1 0.8 1.5 1.2 1.9 100.0 12.6 9.8 35,442
Female 54.6 21.5 5.3 2.0 0.7 9.9 1.1 1.5 1.3 2.1 100.0 13.8 10.1 34,815
Residence
Urban 58.6 20.5 4.2 2.5 0.8 7.7 0.9 1.4 1.3 2.2 100.0 11.2 8.6 20,440
Rural 52.9 22.2 5.7 2.4 0.8 10.3 1.0 1.5 1.2 1.9 100.0 14.0 10.5 49,817
Region
Coast 54.2 24.1 5.7 3.1 0.5 8.6 1.0 1.0 0.8 1.1 100.0 11.4 9.1 6,802
North Eastern 63.9 16.4 3.5 3.1 0.8 9.3 1.3 0.9 0.5 0.4 100.0 12.0 6.9 2,593
Eastern 49.4 27.6 4.5 2.0 0.8 8.3 0.8 1.1 1.1 4.4 100.0 11.4 8.7 9,875
Central 58.2 23.2 4.1 2.0 0.9 6.3 0.6 1.1 0.5 3.2 100.0 8.4 7.6 6,606
Rift Valley 55.8 22.9 5.1 2.1 0.6 9.9 0.7 1.0 0.7 1.3 100.0 12.3 8.1 19,358
Western 49.5 19.7 3.8 3.7 0.7 16.1 1.6 2.1 1.3 1.6 100.0 21.0 9.5 9,309
Nyanza 52.1 16.6 8.8 2.0 1.2 9.2 1.4 3.2 3.3 2.1 100.0 17.2 18.2 11,010
Nairobi 67.0 17.6 4.7 2.3 1.1 4.1 0.5 0.7 0.8 1.2 100.0 6.1 7.8 4,704
Wealth quintile
Lowest 54.3 21.7 7.3 2.8 0.8 9.0 0.7 1.3 1.0 1.2 100.0 12.0 11.1 16,700
Second 52.3 21.7 6.4 2.5 1.0 9.7 1.2 1.6 1.4 2.2 100.0 13.9 11.8 15,350
Middle 51.1 23.2 5.3 2.0 0.7 10.6 1.1 1.8 1.5 2.7 100.0 15.1 10.8 14,448
Fourth 53.8 21.7 3.8 2.5 0.6 10.9 1.1 1.6 1.3 2.6 100.0 15.0 8.7 12,658
Highest 63.7 19.7 2.4 2.3 0.9 7.1 0.7 1.2 0.8 1.4 100.0 9.7 5.9 11,100
Total <15 56.1 22.3 4.7 2.3 0.7 9.0 0.9 1.3 1.0 1.8 100.0 12.1 8.6 61,199
Total <18 54.6 21.7 5.3 2.4 0.8 9.5 1.0 1.5 1.2 2.0 100.0 13.2 9.9 70,257
Note: Table is based on de jure members, i.e., usual residents.
1
Includes children with father dead, mother dead, both dead and one parent dead but missing information on survival status of the other parent.
Table 2.12 presents the percentage of children age 10-14 who are attending school, by the
survivorship of their parents. The results show a high level of school attendance overall among both boys
and girls, regardless of whether or not a parent is deceased (96 percent and 98 percent respectively). It is
sometimes assumed that becoming an orphan jeopardises a child’s chances of attending school, but the
data in Table 2.12 do not strongly support this conjecture. In fact, the greatest differential is seen in the
lowest wealth quintile, in which only 89 percent of children living with at least one parent are attending
school, as compared with 94 percent of double orphans (both parents have died).
24 • Housing Characteristics and Household Population
Table 2.12 School attendance by survivorship of parents
For de jure children 10-14 years of age, the percentage attending school by parental survival and
the ratio of the percentages attending school, by parental survival, according to background
characteristics, Kenya 2014
Percentage attending school by survivorship of parents
Both parents
alive and living
Background Both parents with at least
characteristic deceased Number one parent Number Ratio1
Sex
Male 95.0 185 97.0 7,309 0.98
Female 97.4 198 96.5 7,144 1.01
Residence
Urban 96.2 118 98.3 3,982 0.98
Rural 96.2 265 96.1 10,471 1.00
Region
Coast (78.9) 28 95.5 1,430 (0.83)
North Eastern * 6 72.2 616 *
Eastern 92.2 47 98.6 2,135 0.94
Central * 13 99.5 1,514 1.01
Rift Valley 95.2 62 95.9 4,046 0.99
Western (100.0) 48 99.3 1,743 (1.01)
Nyanza 99.0 169 99.4 2,053 1.00
Nairobi * 10 99.2 916 *
Wealth quintile
Lowest 94.4 69 88.7 3,420 1.06
Second 97.1 93 99.1 3,101 0.98
Middle 97.6 100 99.4 2,984 0.98
Fourth 98.8 81 99.1 2,671 1.00
Highest (88.8) 41 99.2 2,277 (0.90)
Total 96.2 383 96.7 14,453 0.99
Note: Table is based only on children who usually live in the household. Figures in parentheses
are based on 25-49 unweighted cases. An asterisk denotes that a figure is based on fewer than
25 unweighted cases and has been suppressed.
1
Ratio of the percentage with both parents deceased to the percentage with both parents alive
and living with at least one parent
2.9 EDUCATION OF THE HOUSEHOLD POPULATION
Education is a key determinant of the lifestyle and status an individual enjoys in a society. Studies
have consistently shown that educational attainment has a strong effect on health behaviours and attitudes.
Results from the 2014 KDHS can be used to look at educational attainment among household members
and school attendance ratios among youth.
2.9.1 Educational Attainment
Tables 2.13.1 and 2.13.2 present data on the educational attainment of household members age 6
and older. Continuing a trend found in earlier KDHS surveys, the data show a slight decrease in the
proportion of women and men with no education. Compared with the 2008-09 KDHS, the 2014 KDHS
shows a decline from 19 percent to 16 percent among women and from 13 percent to 11 percent among
men. As expected, more men (13 percent and 8 percent, respectively) than women (10 percent and 7
percent, respectively) have completed a secondary education and more than a secondary education.
Housing Characteristics and Household Population • 25
Table 2.13.1 Educational attainment of the female household population
Percent distribution of the de facto female household population age six and over by highest level of schooling attended or completed and median years
completed, according to background characteristics, Kenya 2014
Background Completed Some Completed More than Median years
characteristic No education Some primary primary1 secondary secondary2 secondary Total Number completed
Age
6-9 33.5 66.3 0.0 0.0 0.0 0.0 100.0 8,598 0.0
10-14 3.9 92.9 0.8 2.2 0.0 0.0 100.0 9,376 3.8
15-19 2.6 38.1 12.0 36.4 7.9 2.7 100.0 6,118 7.5
20-24 5.3 17.4 24.7 15.7 23.0 13.8 100.0 6,027 8.5
25-29 7.9 20.3 27.2 9.8 17.7 16.9 100.0 6,293 7.8
30-34 7.9 25.3 28.7 8.4 15.8 13.8 100.0 4,699 7.6
35-39 8.9 26.9 28.5 8.7 15.8 11.1 100.0 3,888 7.5
40-44 10.5 31.3 25.3 8.8 15.2 8.6 100.0 2,950 7.3
45-49 12.8 26.1 26.6 10.7 14.6 8.8 100.0 2,272 6.8
50-54 23.1 24.5 24.2 8.4 12.9 6.8 100.0 2,400 6.1
55-59 35.9 26.2 20.5 6.4 6.4 4.1 100.0 1,660 3.4
60-64 46.7 26.8 16.9 3.3 2.9 2.6 100.0 1,342 1.1
65+ 67.3 23.0 5.7 1.4 0.9 1.1 100.0 2,979 0.0
Residence
Urban 8.9 31.6 17.6 11.2 16.6 13.8 100.0 19,931 7.4
Rural 19.5 48.1 14.6 8.8 5.9 3.0 100.0 38,677 4.7
Region
Coast 26.5 39.8 14.3 6.3 9.0 3.8 100.0 5,591 4.1
North Eastern 69.0 23.4 2.3 2.1 1.5 1.1 100.0 1,562 0.0
Eastern 14.4 45.8 19.0 9.0 6.9 4.8 100.0 8,731 5.5
Central 7.2 35.0 21.1 13.1 14.2 9.3 100.0 7,104 7.2
Rift Valley 18.2 44.0 14.0 8.8 8.4 6.4 100.0 15,121 5.4
Western 12.5 55.2 12.0 10.8 5.3 4.1 100.0 6,920 5.1
Nyanza 13.7 49.1 14.9 10.2 7.6 4.2 100.0 8,334 5.5
Nairobi 4.5 24.0 18.3 11.3 22.7 18.9 100.0 5,245 8.8
Wealth quintile
Lowest 40.2 47.0 7.7 3.4 1.3 0.2 100.0 11,197 1.1
Second 16.2 55.4 15.6 8.0 3.9 0.8 100.0 11,628 4.7
Middle 11.8 48.9 18.4 11.3 6.9 2.6 100.0 12,066 5.7
Fourth 8.1 38.0 20.6 13.1 13.9 6.2 100.0 11,618 7.0
Highest 4.8 23.9 15.3 11.8 21.2 22.7 100.0 12,099 9.4
Total 15.9 42.5 15.6 9.6 9.6 6.7 100.0 58,608 5.8
Note: Totals may not add up to 100 percent because individuals with missing information on education are not shown separately. Total includes seven women for
whom information on age is missing.
1
Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are
considered to have completed primary if they completed grade 7
2
Completed Form 4 at the secondary level
With the exception of children age 6-9, fewer males than females have never been to school. In
that age group, boys (38 percent) are more likely than girls (34 percent) to have never attended school.
Meanwhile, 4 percent of both boys and girls age 10-14 have never been to school, indicating that boys
enrol in school slightly later than girls. However, the proportion of the population with no education
steadily increases thereafter with age, as does the gap between the proportion of males and females with no
education, indicating a gender differential in educational attainment as students age.
Nationally, the median number of years of schooling completed is slightly higher among males
(6.3 years) than females (5.8 years). Over the years, median number of years of schooling completed has
been increasing among both men (from 5.0 in 2003 and 6.0 in 2008-09 to 6.3 in 2014) and women (from
4.3 in 2003 and 5.2 in 2008-09 to 5.8 in 2014).
About twice as many women and men in rural areas as in urban areas have no education. The
proportion of respondents who have never been to school varies rather dramatically across regions. For
example, the proportion of women who have never been to school varies from a low of 5 percent in
Nairobi to a high of 69 percent in North Eastern. As expected, the proportion of women and men with no
education decreases dramatically as wealth increases.
26 • Housing Characteristics and Household Population
Table 2.13.2 Educational attainment of the male household population
Percent distribution of the de facto male household population age six and over by highest level of schooling attended or completed and median years completed,
according to background characteristics, Kenya 2014
Background Completed Some Completed More than Median years
characteristic No education Some primary primary1 secondary secondary2 secondary Total Number completed
Age
6-9 37.9 61.9 0.0 0.0 0.0 0.0 100.0 8,615 0.0
10-14 3.7 93.5 0.6 2.0 0.1 0.0 100.0 9,441 3.5
15-19 2.1 46.3 9.5 35.6 5.0 1.3 100.0 6,342 7.1
20-24 2.6 16.5 20.6 17.5 25.5 17.3 100.0 5,133 9.9
25-29 3.5 17.7 26.3 8.1 24.7 19.5 100.0 5,336 9.0
30-34 4.3 21.9 27.0 7.2 22.6 16.6 100.0 4,589 7.9
35-39 4.7 22.6 29.5 7.6 21.9 13.5 100.0 3,723 7.8
40-44 6.0 21.7 26.8 7.8 24.6 12.8 100.0 2,923 7.8
45-49 5.5 18.4 26.3 7.7 28.2 13.7 100.0 2,071 8.0
50-54 8.9 15.8 28.4 8.4 24.0 13.7 100.0 1,997 7.0
55-59 12.7 19.9 27.9 9.6 20.2 9.6 100.0 1,492 6.7
60-64 19.3 26.1 25.9 6.8 13.7 8.1 100.0 1,286 6.2
65+ 32.4 30.9 20.1 5.1 6.2 4.9 100.0 2,422 3.6
Residence
Urban 6.3 29.8 17.0 10.2 20.9 15.5 100.0 19,729 7.7
Rural 13.7 49.3 14.7 9.2 8.7 4.2 100.0 35,652 5.2
Region
Coast 15.9 38.2 17.1 8.1 13.7 6.5 100.0 5,574 6.1
North Eastern 49.2 35.0 4.8 4.8 3.0 2.8 100.0 1,640 0.0
Eastern 8.5 49.0 17.5 9.0 10.2 5.8 100.0 8,388 5.8
Central 3.8 36.3 19.1 12.7 18.9 9.2 100.0 6,637 7.3
Rift Valley 13.8 44.4 14.7 8.2 11.2 7.6 100.0 14,341 5.8
Western 8.8 54.7 11.8 11.3 8.5 4.7 100.0 6,114 5.3
Nyanza 10.7 45.3 14.8 11.1 10.6 7.2 100.0 7,405 6.1
Nairobi 3.1 21.9 17.0 9.1 26.4 22.0 100.0 5,281 10.5
Wealth quintile
Lowest 28.7 52.4 9.9 4.8 3.3 0.7 100.0 10,439 2.5
Second 10.5 55.0 16.9 8.9 6.7 1.7 100.0 10,942 5.1
Middle 7.9 46.8 18.7 11.6 10.9 4.0 100.0 11,217 6.3
Fourth 5.9 35.3 19.5 11.9 18.8 8.3 100.0 11,865 7.3
Highest 3.7 23.1 11.9 10.1 24.5 26.3 100.0 10,918 11.0
Total 11.1 42.3 15.5 9.6 13.0 8.2 100.0 55,381 6.3
Note: Totals may not add up to 100 percent because individuals with missing information on education are not shown separately. Total includes nine men for
whom information on age is missing.
1
Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are
considered to have completed primary if they completed grade 7
2
Completed Form 4 at the secondary level
2.9.2 School Attendance Ratios
Table 2.14 presents the primary school and secondary school net and gross attendance ratios
(NAR and GAR) by household residence, region, and wealth quintile. The NAR for primary school is the
percentage of the primary-school-age population (age 6-13) that is attending primary school. The NAR for
secondary school is the percentage of the secondary-school-age population (age 14-17) that is attending
secondary school. By definition, the NAR cannot exceed 100 percent. The GAR for primary school is the
total number of primary school students of any age, expressed as a percentage of the official primary-
school-age population. The GAR for secondary school is the total number of secondary school students of
any age, expressed as a percentage of the official secondary-school-age population. If there are significant
numbers of over-age and under-age students at a given level of schooling, the GAR can exceed 100
percent. Youth are considered to be attending school currently if they attended a formal academic school at
any point during the given school year. Note that the NAR and GAR values reported here are not
comparable with those from previous DHS surveys due to an improvement in the precision of calculation.
The NAR is 86 percent at the primary school level. It is slightly higher for girls (87 percent) than
for boys (85 percent). Note, however, that differentials in attendance ratios are much greater across regions
than between girls and boys. Sixty percent of boys age 6-13 in the North Eastern region are attending
primary school, while 94 percent are attending in the Central region. Similarly, only 51 percent of girls age
6-13 in North Eastern are attending primary school, as compared with 95 percent of girls in Central. Large
regional differentials also exist in secondary school attendance rates. As might be expected, the NAR for
primary school is higher in urban (89 percent) than in rural (85 percent) areas, and it increases with
increasing wealth.
Housing Characteristics and Household Population • 27
Table 2.14 School attendance ratios
Net attendance ratios (NAR) and gross attendance ratios (GAR) for the de facto household population by sex and level of
schooling; and the Gender Parity Index (GPI), according to background characteristics, Kenya 2014
Net attendance ratio1 Gross attendance ratio2
Background Gender Gender
characteristic Male Female Total Parity Index3 Male Female Total Parity Index3
PRIMARY SCHOOL
Residence
Urban 88.1 90.2 89.2 1.02 106.6 103.2 104.9 0.97
Rural 83.6 85.3 84.5 1.02 110.1 106.4 108.2 0.97
Region
Coast 76.6 80.3 78.5 1.05 103.4 100.2 101.8 0.97
North Eastern 59.6 50.5 55.5 0.85 81.0 61.5 72.2 0.76
Eastern 90.8 92.3 91.5 1.02 118.8 114.9 116.8 0.97
Central 93.7 95.0 94.3 1.01 111.9 108.4 110.2 0.97
Rift Valley 84.1 85.9 85.0 1.02 108.4 105.5 107.0 0.97
Western 86.1 89.5 87.9 1.04 118.0 114.3 116.0 0.97
Nyanza 83.8 85.3 84.5 1.02 105.8 102.9 104.3 0.97
Nairobi 92.3 93.2 92.8 1.01 105.7 101.2 103.3 0.96
Wealth quintile
Lowest 71.0 71.1 71.0 1.00 97.5 90.5 94.1 0.93
Second 86.9 89.5 88.2 1.03 115.6 112.9 114.2 0.98
Middle 89.4 91.6 90.5 1.02 116.7 114.5 115.6 0.98
Fourth 91.0 93.0 92.0 1.02 112.5 108.4 110.4 0.96
Highest 91.6 92.6 92.2 1.01 103.9 101.3 102.6 0.97
Total 84.8 86.7 85.7 1.02 109.2 105.5 107.3 0.97
SECONDARY SCHOOL
Residence
Urban 44.2 42.9 43.6 0.97 67.9 61.5 64.7 0.91
Rural 26.8 30.4 28.5 1.13 51.3 49.4 50.4 0.96
Region
Coast 22.1 22.2 22.1 1.00 44.8 38.3 41.7 0.85
North Eastern 21.4 16.3 19.3 0.76 40.7 26.1 34.7 0.64
Eastern 27.0 35.0 30.7 1.30 52.4 55.1 53.7 1.05
Central 50.6 58.1 54.2 1.15 80.7 82.6 81.6 1.02
Rift Valley 26.6 28.7 27.6 1.08 48.4 49.1 48.8 1.02
Western 24.3 28.0 26.1 1.15 50.6 46.2 48.5 0.91
Nyanza 38.7 36.3 37.5 0.94 64.0 52.8 58.5 0.83
Nairobi 51.7 45.2 48.3 0.87 70.9 61.8 66.1 0.87
Wealth quintile
Lowest 12.8 13.6 13.1 1.06 27.6 23.9 25.9 0.86
Second 22.9 27.7 25.2 1.21 46.0 46.1 46.0 1.00
Middle 31.4 33.9 32.6 1.08 60.7 55.2 58.1 0.91
Fourth 41.9 47.0 44.3 1.12 66.7 69.6 68.0 1.04
Highest 64.4 52.7 58.0 0.82 97.5 75.1 85.4 0.77
Total 31.3 33.9 32.6 1.08 55.6 52.8 54.3 0.95
1
The NAR for primary school is the percentage of the primary-school age (6-13 years) population that is attending primary
school. The NAR for secondary school is the percentage of the secondary-school age (14-17 years) population that is attending
secondary school. By definition the NAR cannot exceed 100 percent.
2
The GAR for primary school is the total number of primary school students, expressed as a percentage of the official primary-
school-age population. The GAR for secondary school is the total number of secondary school students, expressed as a
percentage of the official secondary-school-age population. If there are significant numbers of overage and underage students
at a given level of schooling, the GAR can exceed 100 percent.
3
The Gender Parity Index for primary school is the ratio of the primary school NAR(GAR) for females to the NAR(GAR) for
males. The Gender Parity Index for secondary school is the ratio of the secondary school NAR(GAR) for females to the
NAR(GAR) for males.
With the exception of North Eastern, GARs are quite high in all regions, indicating that a
substantial number of boys and girls who are not of official primary school age are attending primary
school.
Table 2.14 also shows the gender parity index (GPI), which assesses sex-related differences in
school attendance rates. The GPI is calculated by dividing the GAR for the female population by the GAR
for the male population. A GPI of less than 1 indicates a gender disparity in favour of the male population;
that is, a higher proportion of males than females attend that level of schooling. A GPI greater than 1
indicates a gender disparity in favour of females. A GPI of 1 indicates parity or equality between the rates
of participation for the sexes.
28 • Housing Characteristics and Household Population
The GPI for NAR shows close to gender parity at the national level in both primary and secondary
school; however, the GPI for GAR at the primary (0.97) as well as the secondary (0.95) level is skewed to
favour male children. Differentials across the country exist, especially at the secondary school level.
Among the regions, North Eastern has the lowest NARs, GARs, and GPIs for both primary and secondary
school.
Figure 2.2 illustrates age-specific attendance rates, that is, the percentage of a given age cohort
attending school regardless of the level attended (primary, secondary, or higher). At age 5-10, attendance
rates are higher among girls than they are among boys. Between age 10 and age 14, the peak ages of school
attendance, boys and girls attend in similar proportions. At age 15 and older, attendance rates decline
among both boys and girls, and the gender differential in favour of boys increases with increasing age.
Figure 2.2 Age-specific school attendance rates of the de-facto population
age 5 to 24 years
Percent
100
90
80
70
60
50
40
30
20
10
0
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Age (years)
Male Female
KDHS 2014
Housing Characteristics and Household Population • 29
CHARACTERISTICS OF RESPONDENTS 3
Michael M. Musyoka, John K. Bore, Godfrey Odhiambo Otieno
Key Findings
• The percentage of women and men with no education has dropped by
half over the last 10 years, from 13 percent and 6 percent in 2003 to 7
percent and 3 percent, respectively, in the 2014 KDHS. Over the same
period, the percentage of women and men with at least some secondary
education increased from 29 percent and 37 percent in 2003 to 43
percent and 49 percent, respectively, in 2014.
• Eighty-eight percent of women and 92 percent of men are literate.
• Twenty-three percent of women and 10 percent of men are not exposed
to any source of mass media.
• Sixty-one percent of women and 80 percent of men are currently
employed. Women are mostly employed in agricultural or domestic
service positions, while men are mostly employed in agricultural,
unskilled manual, or domestic service positions.
T
his chapter provides a description of the respondents who were interviewed in the 2014 KDHS.
Women age 15-49 and men age 15-54 were interviewed in the course of the survey. This
information is useful for understanding the context of the reproductive and health status of women
and men discussed in later chapters of this report. Percent distributions of various demographic and
socioeconomic characteristics are shown for the full sample. Data are provided on the main background
characteristics discussed in subsequent chapters, including age at the time of the survey, marital status,
urban/rural residence, region, educational level, and the wealth quintile to which respondents belong. In
addition, information is provided on employment and work status.
3.1 CHARACTERISTICS OF SURVEY RESPONDENTS
Table 3.1 presents the percent distribution of women and men age 15-49 by the following
background characteristics: age, religion, ethnic group, marital status, residence, region, education, and
wealth quintile.
The distribution of both women and men tends to decline with increasing age, reflecting the
comparatively young age structure of the Kenyan population. Thirty-seven percent of women and 39
percent of men are in the 15-24 age group. Thirty-four percent of women and 32 percent of men are in the
25-34 age group. The remaining respondents (29 percent of both women and men) are age 35-49.
The majority of both women (71 percent) and men (68 percent) are Protestant or another Christian
denomination. Twenty percent of women and 21 percent of men are Roman Catholic, 7 percent of both
women and men are Muslim, and 2 percent of women and 4 percent of men have no religion.
Ethnic affiliation is associated with various demographic behaviours because of differences in
cultural beliefs. For example, in Kenya, certain ethnic groups encourage initiation to some rites while
others consider them as taboo. The largest ethnic groups are the Kikuyu (women, 22 percent; men, 21
percent) and Luhya (women, 15 percent; men, 16 percent). Eleven to 12 percent of women and 11 to 13
percent of men are Luo, Kamba, or Kalenjin. Six percent or less of both women and men belong to other
ethnic groups.
Characteristics of Respondents • 31
Table 3.1 Background characteristics of respondents
Percent distribution of women and men age 15-49 by selected background characteristics, Kenya 2014
Women Men
Background Weighted Weighted Unweighted Weighted Weighted Unweighted
characteristic percent number number percent number number
Age
15-19 18.7 5,820 6,078 21.1 2,540 2,811
20-24 18.5 5,735 5,405 17.6 2,125 1,981
25-29 19.6 6,100 5,939 17.4 2,104 1,942
30-34 14.5 4,510 4,452 14.8 1,785 1,701
35-39 12.1 3,773 3,868 12.3 1,483 1,486
40-44 9.3 2,885 2,986 10.1 1,224 1,198
45-49 7.3 2,257 2,351 6.6 800 895
Religion
Roman Catholic 20.3 6,315 6,229 21.4 2,583 2,551
Protestant/other Christian 71.1 22,091 20,072 67.5 8,141 7,500
Muslim 6.8 2,107 4,161 6.5 784 1,460
No religion 1.5 466 506 4.1 492 449
Other 0.2 65 73 0.5 59 51
Ethnic group
Embu 1.0 312 398 1.0 118 170
Kalenjin 12.0 3,718 4,335 12.2 1,467 1,729
Kamba 11.4 3,543 2,950 12.6 1,521 1,275
Kikuyu 21.9 6,798 5,033 20.9 2,523 1,946
Kisii 5.7 1,771 1,788 5.9 712 680
Luhya 15.0 4,667 3,653 16.0 1,927 1,555
Luo 11.1 3,453 3,060 10.9 1,311 1,179
Maasai 1.9 589 655 1.8 220 235
Meru 5.6 1,749 1,593 5.9 717 682
Mijikenda/Swahili 5.3 1,642 1,708 5.2 623 648
Somali 2.6 816 1,815 2.2 260 616
Taita/Taveta 0.9 295 452 1.1 134 199
Turkana 1.3 394 717 0.9 106 191
Samburu 0.5 143 620 0.1 12 45
Other 3.8 1,186 2,294 3.3 399 848
Marital status
Never married 28.9 8,997 8,575 44.4 5,350 5,384
Married 54.6 16,961 17,751 48.4 5,839 5,748
Living together 5.1 1,588 1,285 2.1 256 241
Divorced/separated 7.7 2,394 2,277 4.7 567 585
Widowed 3.7 1,139 1,191 0.4 50 56
Residence
Urban 40.8 12,690 11,614 43.9 5,300 4,648
Rural 59.2 18,389 19,465 56.1 6,762 7,366
Region
Coast 9.9 3,076 3,902 10.4 1,260 1,505
North Eastern 2.1 648 1,664 1.9 227 591
Eastern 14.1 4,375 5,247 15.1 1,825 2,144
Central 12.9 3,994 3,114 13.0 1,564 1,248
Rift Valley 25.6 7,953 9,059 25.3 3,050 3,484
Western 10.4 3,225 2,840 9.6 1,164 1,130
Nyanza 13.0 4,038 4,254 11.6 1,405 1,542
Nairobi 12.1 3,770 999 13.0 1,568 370
Education
No education 7.0 2,176 4,183 2.9 345 663
Primary incomplete 25.7 7,989 8,431 25.5 3,071 3,466
Primary complete 24.6 7,637 7,182 22.7 2,734 2,720
Secondary incomplete 15.8 4,922 4,537 16.2 1,960 1,850
Secondary complete 15.7 4,880 4,058 18.9 2,282 1,980
More than secondary 11.2 3,475 2,688 13.9 1,671 1,335
Wealth quintile
Lowest 15.6 4,838 7,262 14.0 1,691 2,504
Second 17.6 5,457 5,970 17.8 2,145 2,443
Middle 19.4 6,032 5,946 19.7 2,370 2,466
Fourth 21.1 6,550 5,958 24.5 2,959 2,579
Highest 26.4 8,203 5,943 24.0 2,897 2,022
Total 15-49 100.0 31,079 31,079 100.0 12,063 12,014
50-54 na na na na 756 805
Total 15-54 na na na na 12,819 12,819
Note: Totals may not add up to 100 percent because women and men with missing information are not shown separately.
na = Not applicable
32 • Characteristics of Respondents
Table 3.1C Background characteristics of respondents
Percent distribution of women and men age 15-49 by county, Kenya 2014
Women Men
Weighted Weighted Unweighted Weighted Weighted Unweighted
County percent number number percent number number
Coast 9.9 3,076 3,902 10.4 1,260 1,505
Mombasa 2.9 912 598 4.0 481 270
Kwale 2.0 619 671 1.9 226 250
Kilifi 3.4 1,043 824 3.0 359 304
Tana River 0.6 197 686 0.5 65 204
Lamu 0.3 89 600 0.3 37 227
Taita Taveta 0.7 215 523 0.8 93 250
North Eastern 2.1 648 1,664 1.9 227 591
Garissa 0.8 261 609 0.8 94 208
Wajir 0.7 212 532 0.6 72 187
Mandera 0.6 175 523 0.5 60 196
Eastern 14.1 4,375 5,247 15.1 1,825 2,144
Marsabit 0.4 115 575 0.3 40 199
Isiolo 0.3 104 606 0.3 35 196
Meru 3.6 1,110 682 4.1 495 320
Tharaka-Nithi 0.9 275 528 0.8 102 215
Embu 1.5 459 645 1.4 164 266
Kitui 2.4 759 747 2.5 303 318
Machakos 2.8 873 718 3.6 436 335
Makueni 2.2 680 746 2.1 250 295
Central 12.9 3,994 3,114 13.0 1,564 1,248
Nyandarua 1.4 436 562 1.6 198 242
Nyeri 2.1 650 708 1.9 229 275
Kirinyaga 1.5 451 560 1.5 184 250
Murang’a 2.4 735 633 2.4 284 250
Kiambu 5.5 1,722 651 5.5 669 231
Rift Valley 25.6 7,953 9,059 25.3 3,050 3,484
Turkana 1.0 320 514 0.6 76 118
West Pokot 0.9 267 534 0.9 103 234
Samburu 0.4 123 579 0.3 35 159
Trans-Nzoia 2.5 768 695 2.7 329 322
Uasin Gishu 2.5 784 689 2.9 355 335
Elgeyo Marakwet 0.8 250 630 0.7 86 234
Nandi 2.0 628 742 2.2 264 338
Baringo 1.1 335 598 1.0 125 229
Laikipia 1.1 342 631 1.0 124 234
Nakuru 5.1 1,574 741 4.9 589 280
Narok 2.1 642 702 2.0 240 265
Kajiado 2.2 670 642 2.0 241 226
Kericho 1.8 563 654 1.8 215 227
Bomet 2.2 687 708 2.2 267 283
Western 10.4 3,225 2,840 9.6 1,164 1,130
Kakamega 3.6 1,108 725 3.4 411 312
Vihiga 1.2 368 634 1.2 140 252
Bungoma 3.9 1,203 805 3.4 413 307
Busia 1.8 546 676 1.6 199 259
Nyanza 13.0 4,038 4,254 11.6 1,405 1,542
Siaya 1.8 572 654 1.8 213 264
Kisumu 2.6 820 696 2.6 309 272
Homa Bay 2.6 798 716 2.0 243 238
Migori 2.1 650 770 1.7 211 251
Kisii 2.8 864 794 2.6 315 291
Nyamira 1.1 334 624 0.9 114 226
Nairobi 12.1 3,770 999 13.0 1,568 370
Total 15-49 100.0 31,079 31,079 100.0 12,063 12,014
50-54 na na na na 756 805
Total 15-54 na na na na 12,819 12,819
na = Not applicable
Sixty percent of women and 51 percent of men are married or living in an informal union. About 4
in 10 men (44 percent) have never been married, as compared with about 3 in 10 (29 percent) women.
Women (11 percent) are more likely than men (5 percent) to be divorced, separated, or widowed.
Fifty-nine percent of women and 56 percent of men live in rural areas. The Rift Valley region has
a quarter of all women (26 percent) and men (25 percent). The North Eastern region has 2 percent of all
Characteristics of Respondents • 33
women and men. The remaining regions each have between 10 percent and 15 percent of the remaining
population.
Slightly more women (7 percent) than men (3 percent) have no education. Twenty-six percent of
both women and men did not finish primary school. Slightly more women (25 percent) than men (23
percent) ended their schooling by completing primary school, and thereafter slightly fewer women than
men obtained some secondary education, completed secondary education, or advanced beyond secondary
education. The smallest proportions of both women (16 percent) and men (14 percent) are in the lowest
wealth quintile. Almost half of the population (48 percent of women and 49 percent of men) is in the two
highest wealth quintiles.
The distribution of female and male respondents by county shows that more respondents live in
Nairobi, Kiambu, and Nakuru (between 5 percent and 13 percent) than the other 44 counties (Table 3.1C).
3.2 EDUCATIONAL ATTAINMENT BY BACKGROUND CHARACTERISTICS
Tables 3.2.1 and 3.2.2 show the percent distribution of women and men age 15-49 by educational
attainment and median years of schooling completed, according to background characteristics. Men have
achieved more education than women. In 2014, the proportion of women with no education declined
marginally to 7 percent from the 9 percent figure recorded in the 2008-09 KDHS, but this proportion
remains more than twice that of men with no education (3 percent).
Table 3.2.1 shows that 93 percent of women age 15-49 have attended school. Five in 10 women
either have some primary education (26 percent) or have completed primary education (25 percent). Three
in 10 have either some secondary education or a completed secondary education (16 percent each). One in
10 (11 percent) have gone beyond a secondary education, an increase from 7 percent in 2008-09.
The urban-rural difference in level of education is pronounced for women on either end of the
educational attainment scale. Four percent of urban women have no education compared with 9 percent of
rural women, and 14 percent of urban women have some primary education compared with 34 percent of
rural women. About a quarter of women in both rural and urban areas have completed primary education,
and 16 percent of women in both areas have some secondary education. The differences pick up again for
women who have completed secondary school (urban women, 23 percent; rural women, 11 percent) or
gone beyond secondary school (urban women, 19 percent; rural women, 6 percent). At the regional level,
Nairobi had the highest proportion of women with more than a secondary education (24 percent), although
this figure was a decline from the 31 percent reported in 2008-09. The North Eastern region had the most
women with no education at 75 percent, a slight improvement from 78 percent in 2008-09.
Education increases with wealth; 31 percent of women in the lowest wealth quintile have no
education, as compared with 2 percent of women in the highest wealth quintile. Almost one in three (29
percent) women in the highest quintile have more than a secondary education, compared with only one in
10 (10 percent) women in the fourth highest wealth quintile. Table 3.2.2 shows similar patterns in
educational attainment among men, although men are more educated than women. County level
differences are presented in Table 3.2.1C and Table 3.2.2C.
34 • Characteristics of Respondents
Table 3.2.1 Educational attainment: Women
Percent distribution of women age 15-49 by highest level of schooling attended or completed, and median years completed, according to background
characteristics, Kenya 2014
Highest level of schooling
Background Completed Some Completed More than Median years Number of
characteristic No education Some primary primary1 secondary secondary2 secondary Total completed women
Age
15-24 3.8 27.0 18.6 26.4 15.8 8.4 100.0 7.9 11,555
15-19 2.3 36.1 13.8 36.5 8.5 2.8 100.0 7.6 5,820
20-24 5.3 17.7 23.6 16.2 23.2 14.1 100.0 8.7 5,735
25-29 7.7 20.9 27.5 10.3 16.9 16.7 100.0 7.8 6,100
30-34 7.8 25.7 28.2 8.7 15.7 14.0 100.0 7.6 4,510
35-39 8.9 27.3 28.3 9.1 15.2 11.1 100.0 7.5 3,773
40-44 10.1 31.6 26.3 9.2 14.6 8.3 100.0 7.3 2,885
45-49 12.8 22.1 31.3 10.8 14.2 8.7 100.0 6.7 2,257
Residence
Urban 3.6 14.1 24.1 15.8 22.9 19.4 100.0 9.4 12,690
Rural 9.3 33.7 24.9 15.9 10.7 5.5 100.0 7.2 18,389
Region
Coast 16.3 29.0 23.1 11.0 14.5 6.0 100.0 7.2 3,076
North Eastern 74.9 9.9 4.8 4.1 3.5 2.7 100.0 0.0 648
Eastern 4.8 28.4 31.2 15.6 11.8 8.3 100.0 7.5 4,375
Central 0.9 14.7 29.7 17.8 21.8 15.1 100.0 8.8 3,994
Rift Valley 9.2 27.7 22.7 15.3 14.0 11.0 100.0 7.5 7,953
Western 2.8 40.8 19.5 20.0 9.6 7.3 100.0 7.2 3,225
Nyanza 1.4 33.4 25.3 18.6 13.5 7.7 100.0 7.5 4,038
Nairobi 1.7 8.9 23.4 14.7 27.9 23.5 100.0 11.0 3,770
Wealth quintile
Lowest 30.6 42.5 16.5 7.1 2.7 0.4 100.0 5.0 4,838
Second 3.9 44.1 27.6 15.3 7.5 1.6 100.0 7.0 5,457
Middle 3.0 29.6 30.5 20.1 12.0 4.9 100.0 7.5 6,032
Fourth 2.4 17.1 28.9 19.9 21.6 10.1 100.0 8.0 6,550
Highest 1.8 7.5 19.5 15.0 26.8 29.4 100.0 11.2 8,203
Total 7.0 25.7 24.6 15.8 15.7 11.2 100.0 7.6 31,079
1
Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are
considered to have completed primary if they completed Grade 7.
2
Completed Form 4 at the secondary level
Characteristics of Respondents • 35
Table 3.2.1C Educational attainment: Women
Percent distribution of women age 15-49 by highest level of schooling attended or completed, and median years completed, according to county, Kenya 2014
Highest level of schooling
Completed Some Completed More than Median years Number of
County No education Some primary primary1 secondary secondary2 secondary Total completed women
Coast 16.3 29.0 23.1 11.0 14.5 6.0 100.0 7.2 3,076
Mombasa 5.8 18.8 26.9 15.0 23.6 9.9 100.0 7.9 912
Kwale 21.7 35.0 23.5 6.9 8.5 4.4 100.0 6.1 619
Kilifi 20.4 34.1 19.3 10.5 11.4 4.4 100.0 6.4 1,043
Tana River 41.7 33.3 13.5 5.6 4.1 1.8 100.0 2.4 197
Lamu 17.0 39.1 21.0 10.4 6.2 6.3 100.0 6.4 89
Taita Taveta 2.3 22.6 34.5 13.3 21.1 6.2 100.0 7.7 215
North Eastern 74.9 9.9 4.8 4.1 3.5 2.7 100.0 0.0 648
Garissa 72.7 9.6 5.7 4.4 3.6 4.0 100.0 0.0 261
Wajir 76.9 9.9 4.6 4.2 2.5 1.7 100.0 0.0 212
Mandera 75.9 10.4 3.7 3.6 4.3 2.0 100.0 0.0 175
Eastern 4.8 28.4 31.2 15.6 11.8 8.3 100.0 7.5 4,375
Marsabit 61.9 16.0 10.3 5.0 4.3 2.4 100.0 0.0 115
Isiolo 39.7 22.5 18.1 6.0 8.7 5.0 100.0 5.0 104
Meru 4.1 37.4 27.5 11.1 11.0 9.0 100.0 7.2 1,110
Tharaka-Nithi 2.0 35.8 28.4 12.9 12.3 8.5 100.0 7.4 275
Embu 1.3 28.6 29.2 16.6 13.7 10.6 100.0 7.6 459
Kitui 3.9 35.4 34.4 12.9 8.7 4.6 100.0 7.3 759
Machakos 0.2 15.9 36.9 20.1 16.5 10.4 100.0 7.8 873
Makueni 0.9 21.6 34.6 24.0 10.4 8.5 100.0 7.7 680
Central 0.9 14.7 29.7 17.8 21.8 15.1 100.0 8.8 3,994
Nyandarua 0.8 17.1 39.1 18.0 18.6 6.4 100.0 7.8 436
Nyeri 1.1 11.5 27.8 21.1 24.2 14.3 100.0 9.2 650
Kirinyaga 0.8 28.0 29.8 15.0 21.1 5.4 100.0 7.7 451
Murang’a 1.6 19.4 29.9 20.8 20.7 7.6 100.0 7.9 735
Kiambu 0.5 9.9 27.9 15.9 22.4 23.3 100.0 10.1 1,722
Rift Valley 9.2 27.7 22.7 15.3 14.0 11.0 100.0 7.5 7,953
Turkana 64.1 24.1 3.1 1.2 5.5 2.0 100.0 0.0 320
West Pokot 33.8 41.0 12.0 5.2 4.7 3.4 100.0 4.6 267
Samburu 55.7 21.1 7.3 5.2 5.4 5.4 100.0 0.0 123
Trans-Nzoia 2.6 39.3 22.9 19.2 10.8 5.2 100.0 7.3 768
Uasin Gishu 1.5 25.1 21.9 18.6 17.2 15.7 100.0 7.9 784
Elgeyo Marakwet 1.2 27.7 28.6 14.4 15.0 13.0 100.0 7.7 250
Nandi 0.8 37.8 23.9 16.6 12.6 8.3 100.0 7.4 628
Baringo 9.3 30.0 24.5 15.0 12.1 9.0 100.0 7.4 335
Laikipia 13.4 19.8 24.7 14.9 14.9 12.3 100.0 7.6 342
Nakuru 1.9 14.7 30.1 19.0 19.2 15.2 100.0 8.3 1,574
Narok 15.5 38.2 17.3 12.0 10.4 6.6 100.0 6.6 642
Kajiado 18.0 12.8 17.9 14.1 17.0 20.2 100.0 8.0 670
Kericho 0.3 32.0 24.9 15.6 16.3 10.8 100.0 7.6 563
Bomet 0.4 39.8 25.7 14.3 11.5 8.2 100.0 7.3 687
Western 2.8 40.8 19.5 20.0 9.6 7.3 100.0 7.2 3,225
Kakamega 4.0 38.7 18.5 20.3 10.5 8.1 100.0 7.3 1,108
Vihiga 0.4 30.0 26.7 25.2 9.6 8.2 100.0 7.6 368
Bungoma 0.9 41.2 19.8 20.7 10.3 7.2 100.0 7.3 1,203
Busia 6.6 51.6 16.1 14.3 6.0 5.2 100.0 6.5 546
Nyanza 1.4 33.4 25.3 18.6 13.5 7.7 100.0 7.5 4,038
Siaya 1.9 35.7 28.6 18.6 9.5 5.6 100.0 7.4 572
Kisumu 1.2 23.7 24.7 20.8 16.2 13.4 100.0 7.9 820
Homa Bay 1.1 39.7 28.1 18.2 8.9 4.0 100.0 7.3 798
Migori 2.6 49.9 24.0 14.2 6.7 2.6 100.0 6.8 650
Kisii 0.9 28.8 22.0 19.1 19.2 10.0 100.0 7.8 864
Nyamira 0.8 17.8 25.6 21.9 23.9 10.0 100.0 9.1 334
Nairobi 1.7 8.9 23.4 14.7 27.9 23.5 100.0 11.0 3,770
Total 7.0 25.7 24.6 15.8 15.7 11.2 100.0 7.6 31,079
1
Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are considered
to have completed primary if they completed Grade 7.
2
Completed Form 4 at the secondary level
36 • Characteristics of Respondents
Table 3.2.2 Educational attainment: Men
Percent distribution of men age 15-49 by highest level of schooling attended or completed, and median years completed, according to background characteristics,
Kenya 2014
Highest level of schooling
Background Completed Some Completed More than Median years Number of
characteristic No education Some primary primary1 secondary secondary2 secondary Total completed men
Age
15-24 1.4 29.9 15.8 28.7 15.3 8.9 100.0 7.9 4,666
15-19 1.2 42.6 10.9 37.2 6.6 1.5 100.0 7.3 2,540
20-24 1.8 14.7 21.7 18.5 25.6 17.8 100.0 10.0 2,125
25-29 2.7 18.5 26.9 9.4 20.9 21.6 100.0 8.6 2,104
30-34 4.0 25.7 24.8 8.1 18.7 18.7 100.0 7.8 1,785
35-39 5.1 26.2 28.9 7.5 19.7 12.7 100.0 7.6 1,483
40-44 3.7 24.1 24.5 9.3 24.5 13.9 100.0 7.9 1,224
45-49 3.7 18.0 32.4 7.0 25.5 13.5 100.0 7.7 800
Residence
Urban 1.2 14.4 21.8 14.8 25.7 22.1 100.0 10.4 5,300
Rural 4.2 34.1 23.4 17.4 13.6 7.4 100.0 7.4 6,762
Region
Coast 4.2 25.0 26.9 14.3 18.9 10.7 100.0 7.7 1,260
North Eastern 36.9 20.9 13.8 12.7 9.8 5.9 100.0 5.7 227
Eastern 3.0 31.7 26.3 14.0 16.3 8.7 100.0 7.5 1,825
Central 0.3 16.1 25.2 21.2 24.2 13.0 100.0 9.1 1,564
Rift Valley 4.3 27.4 22.6 14.5 17.8 13.3 100.0 7.7 3,050
Western 0.9 42.9 17.4 20.0 10.6 8.2 100.0 7.3 1,164
Nyanza 0.5 28.3 22.7 19.5 15.9 13.2 100.0 7.8 1,405
Nairobi 0.0 9.4 17.7 13.7 29.1 30.1 100.0 11.3 1,568
Wealth quintile
Lowest 14.4 49.0 19.1 9.5 6.5 1.4 100.0 6.1 1,691
Second 1.4 41.5 25.8 17.1 10.7 3.4 100.0 7.2 2,145
Middle 1.3 28.4 27.5 19.5 17.2 6.2 100.0 7.7 2,370
Fourth 0.9 16.3 24.8 18.5 26.2 13.3 100.0 9.2 2,959
Highest 0.5 6.8 16.3 14.5 26.3 35.7 100.0 11.4 2,897
Total 15-49 2.9 25.5 22.7 16.2 18.9 13.9 100.0 7.9 12,063
50-54 7.0 19.3 28.6 9.2 23.8 12.2 100.0 6.9 756
Total 15-54 3.1 25.1 23.0 15.8 19.2 13.8 100.0 7.9 12,819
1
Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are
considered to have completed primary if they completed Grade 7.
2
Completed Form 4 at the secondary level
Characteristics of Respondents • 37
Table 3.2.2C Educational attainment: Men
Percent distribution of men age 15-49 by highest level of schooling attended or completed, and median years completed, according to county, Kenya 2014
Highest level of schooling
Completed Some Completed More than Median years Number of
County No education Some primary primary1 secondary secondary2 secondary Total completed men
Coast 4.2 25.0 26.9 14.3 18.9 10.7 100.0 7.7 1,260
Mombasa 2.5 10.0 31.3 16.8 25.9 13.4 100.0 9.1 481
Kwale 7.8 38.7 19.6 11.2 14.1 8.7 100.0 7.1 226
Kilifi 2.6 34.4 24.9 15.9 13.5 8.7 100.0 7.4 359
Tana River 18.2 33.3 25.2 5.1 13.6 4.7 100.0 6.8 65
Lamu 5.8 41.2 18.4 11.2 9.6 13.7 100.0 7.0 37
Taita Taveta 0.0 20.6 34.1 9.6 22.7 13.1 100.0 7.8 93
North Eastern 36.9 20.9 13.8 12.7 9.8 5.9 100.0 5.7 227
Garissa 33.2 16.2 17.7 12.6 12.1 8.2 100.0 7.0 94
Wajir 38.2 23.2 10.5 12.4 9.6 6.1 100.0 4.7 72
Mandera 41.2 25.3 11.8 13.0 6.5 2.1 100.0 5.2 60
Eastern 3.0 31.7 26.3 14.0 16.3 8.7 100.0 7.5 1,825
Marsabit 35.7 18.6 10.0 7.7 15.4 12.6 100.0 6.3 40
Isiolo 11.3 29.7 21.1 12.5 21.9 3.5 100.0 7.4 35
Meru 4.7 35.7 25.3 10.4 14.9 9.0 100.0 7.3 495
Tharaka-Nithi 2.0 38.9 18.0 17.3 12.5 11.4 100.0 7.4 102
Embu 1.0 33.6 27.4 16.6 12.2 9.3 100.0 7.5 164
Kitui 2.7 44.8 23.3 15.5 9.2 4.4 100.0 7.0 303
Machakos 0.3 21.4 31.6 12.5 24.3 10.0 100.0 7.8 436
Makueni 0.3 24.4 28.5 20.0 17.1 9.7 100.0 7.8 250
Central 0.3 16.1 25.2 21.2 24.2 13.0 100.0 9.1 1,564
Nyandarua 0.4 17.8 31.7 22.5 21.4 6.2 100.0 7.9 198
Nyeri 0.2 8.6 30.7 20.6 24.1 15.9 100.0 9.3 229
Kirinyaga 0.7 27.7 28.8 14.8 20.5 7.6 100.0 7.7 184
Murang’a 0.8 22.5 27.7 24.6 16.8 7.8 100.0 7.9 284
Kiambu 0.0 12.2 19.3 21.4 29.3 17.8 100.0 10.5 669
Rift Valley 4.3 27.4 22.6 14.5 17.8 13.3 100.0 7.7 3,050
Turkana 35.2 35.3 7.7 6.7 5.2 9.9 100.0 4.1 76
West Pokot 18.9 50.8 14.7 6.0 5.0 4.6 100.0 6.1 103
Samburu 25.9 25.2 13.4 11.9 12.5 11.1 100.0 6.8 35
Trans-Nzoia 2.0 34.9 24.3 15.5 14.6 8.7 100.0 7.5 329
Uasin Gishu 1.1 21.0 24.6 15.0 26.1 12.1 100.0 8.2 355
Elgeyo Marakwet 0.2 24.4 26.6 13.3 21.4 14.1 100.0 7.9 86
Nandi 0.3 33.3 26.3 9.7 18.9 11.5 100.0 7.6 264
Baringo 5.9 33.3 16.7 15.3 20.1 8.7 100.0 7.6 125
Laikipia 2.6 25.1 15.5 22.1 19.4 15.3 100.0 8.0 124
Nakuru 1.9 16.3 26.2 17.0 19.4 19.2 100.0 9.2 589
Narok 13.0 32.8 21.5 11.0 14.0 7.7 100.0 7.2 240
Kajiado 4.8 17.2 15.9 16.1 17.3 28.7 100.0 10.0 241
Kericho 0.0 32.9 22.2 16.8 16.2 11.9 100.0 7.7 215
Bomet 0.0 33.1 27.2 14.4 18.1 7.2 100.0 7.6 267
Western 0.9 42.9 17.4 20.0 10.6 8.2 100.0 7.3 1,164
Kakamega 1.6 41.3 15.8 21.5 9.9 9.9 100.0 7.3 411
Vihiga 0.3 36.4 24.3 24.5 7.5 7.1 100.0 7.4 140
Bungoma 0.4 45.2 16.9 17.9 11.5 8.1 100.0 7.2 413
Busia 1.1 45.7 16.9 18.1 12.4 5.8 100.0 7.1 199
Nyanza 0.5 28.3 22.7 19.5 15.9 13.2 100.0 7.8 1,405
Siaya 0.7 33.9 23.0 20.2 14.4 7.8 100.0 7.5 213
Kisumu 0.7 19.6 23.8 21.3 12.8 21.8 100.0 8.3 309
Homa Bay 0.0 35.0 27.1 18.2 12.0 7.8 100.0 7.4 243
Migori 0.8 42.5 24.3 14.4 12.9 5.3 100.0 7.2 211
Kisii 0.4 23.3 17.7 21.8 22.1 14.8 100.0 8.9 315
Nyamira 0.0 14.1 21.2 19.3 23.6 21.8 100.0 10.2 114
Nairobi 0.0 9.4 17.7 13.7 29.1 30.1 100.0 11.3 1,568
Total 15-49 2.9 25.5 22.7 16.2 18.9 13.9 100.0 7.9 12,063
50-54 7.0 19.3 28.6 9.2 23.8 12.2 100.0 6.9 756
Total 15-54 3.1 25.1 23.0 15.8 19.2 13.8 100.0 7.9 12,819
1
Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are considered
to have completed primary if they completed Grade 7.
2
Completed Form 4 at the secondary level
38 • Characteristics of Respondents
3.3 LITERACY
The ability to read and write empowers women and men. Literacy statistics are important for
policymakers to determine how best to reach the populations they serve. In the 2014 KDHS, literacy was
determined by respondents’ ability to read all or part of a simple sentence. During data collection,
interviewers carried a set of cards on which simple sentences were printed in 17 of the country’s major
languages (English, Swahili, Borana, Embu, Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Maragoli, Luo,
Maasai, Meru, Mijikenda, Pokot, Somali, and Turkana) for testing a respondent’s reading ability. Those
who had never been to school and those who had only a primary education were asked to read the cards in
the language they were most familiar with. Those with a secondary education or higher were assumed to be
literate.
Table 3.3.1 shows the percent distribution of women age 15-49 by level of schooling attended and
level of literacy, along with the percentage literate, according to background characteristics. The
proportion of literate women (88 percent) was slightly higher than in 2008-09 (85 percent). Eight percent
of women could read part of a sentence.
Literacy declines with age and varies by place of residence. Ninety-four percent of women
residing in urban areas are literate, as compared with 84 percent of rural women. Regional differences are
notable, with the proportion of literate women being highest in Nairobi (97 percent) and lowest in North
Eastern (24 percent). Literacy increases with wealth; virtually all women (97 percent) in the highest
quintile are literate, compared with 58 percent of women in the lowest quintile.
Literacy among women age 15-49 at the county level was highest in Nandi and Nyamira (98
percent each). The counties with the lowest proportion of literate women were Wajir (21 percent),
Mandera (24 percent), Turkana (25 percent), Garissa (26 percent), and Marsabit (36 percent). In most
counties, the proportion of literate women is above 80 percent (Table 3.3.1C).
Men are more likely to be literate than women. Table 3.3.2 shows that 92 percent of men age 15-
49 are literate, not much of a difference from the 91 percent figure reported in the 2008-09 KDHS. The
pattern of literacy among men is similar to that of women. However, there are marked differences between
men and women across age groups. Ninety-one percent of men age 45-49 are literate, as compared with 78
percent of women in the same age group. The absolute difference in urban-rural literacy among men (8
percentage points) is slightly lower than that among women (10 percentage points). Men in the North
Eastern region are more likely to be illiterate (32 percent) than those in the other regions. County level
differences for men are presented in Table 3.3.2C and are similar to those observed among women.
Characteristics of Respondents • 39
Table 3.3.1 Literacy: Women
Percent distribution of women age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to
background characteristics, Kenya 2014
No schooling or primary school
Secondary Can read a Can read No card with Blind/
Background school or whole part of a Cannot read required visually Percentage Number of
characteristic higher sentence sentence at all language impaired Total literate1 women
Age
15-24 50.6 36.0 6.1 6.8 0.1 0.1 100.0 92.8 11,555
15-19 47.8 41.1 6.0 4.5 0.1 0.1 100.0 95.0 5,820
20-24 53.5 30.8 6.3 9.2 0.1 0.1 100.0 90.5 5,735
25-29 43.9 35.5 7.6 12.4 0.0 0.2 100.0 87.0 6,100
30-34 38.3 38.8 9.6 12.8 0.0 0.3 100.0 86.8 4,510
35-39 35.4 40.1 9.4 14.8 0.0 0.1 100.0 84.9 3,773
40-44 32.0 38.9 12.0 16.5 0.0 0.6 100.0 82.8 2,885
45-49 33.7 31.9 12.2 21.4 0.0 0.9 100.0 77.7 2,257
Residence
Urban 58.2 29.5 6.0 5.8 0.1 0.2 100.0 93.6 12,690
Rural 32.1 41.8 9.9 15.8 0.0 0.3 100.0 83.8 18,389
Region
Coast 31.5 42.9 5.8 19.3 0.0 0.2 100.0 80.2 3,076
North Eastern 10.3 7.0 6.5 75.9 0.0 0.1 100.0 23.9 648
Eastern 35.7 43.1 10.7 10.3 0.0 0.2 100.0 89.4 4,375
Central 54.7 34.3 5.9 4.4 0.0 0.1 100.0 94.9 3,994
Rift Valley 40.3 35.8 8.3 15.2 0.0 0.1 100.0 84.5 7,953
Western 36.8 43.4 9.8 9.1 0.0 0.6 100.0 90.1 3,225
Nyanza 39.9 40.2 11.6 7.7 0.0 0.4 100.0 91.7 4,038
Nairobi 66.1 24.8 5.6 3.0 0.2 0.2 100.0 96.5 3,770
Wealth quintile
Lowest 10.3 36.3 11.7 41.2 0.0 0.3 100.0 58.3 4,838
Second 24.4 49.4 13.7 12.0 0.0 0.3 100.0 87.5 5,457
Middle 36.9 46.5 9.1 6.9 0.0 0.4 100.0 92.5 6,032
Fourth 51.6 36.6 6.2 5.2 0.1 0.1 100.0 94.5 6,550
Highest 71.2 21.6 3.8 2.9 0.0 0.1 100.0 96.6 8,203
Total 42.7 36.8 8.3 11.7 0.0 0.2 100.0 87.8 31,079
Note: Totals may not add up to 100 percent because women with missing information have been are not shown separately.
1
Refers to women who attended secondary school or higher and women who can read a whole sentence or part of a sentence
40 • Characteristics of Respondents
Table 3.3.1C Literacy: Women
Percent distribution of women age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to county,
Kenya 2014
No schooling or primary school
Secondary Can read a Can read No card with Blind/
school or whole part of a Cannot read required visually Percentage Number of
County higher sentence sentence at all language impaired Total literate1 women
Coast 31.5 42.9 5.8 19.3 0.0 0.2 100.0 80.2 3,076
Mombasa 48.5 39.2 4.9 6.5 0.0 0.2 100.0 92.6 912
Kwale 19.8 45.3 7.6 26.4 0.0 0.2 100.0 72.7 619
Kilifi 26.3 43.4 5.4 24.9 0.0 0.0 100.0 75.1 1,043
Tana River 11.5 34.9 7.0 45.0 0.0 1.4 100.0 53.5 197
Lamu 22.9 51.5 9.9 15.6 0.0 0.1 100.0 84.2 89
Taita Taveta 40.6 52.4 3.0 3.7 0.0 0.0 100.0 96.1 215
North Eastern 10.3 7.0 6.5 75.9 0.0 0.1 100.0 23.9 648
Garissa 12.0 7.3 7.1 73.2 0.0 0.2 100.0 26.3 261
Wajir 8.5 7.0 5.2 79.3 0.0 0.0 100.0 20.7 212
Mandera 10.0 6.7 7.4 75.7 0.0 0.0 100.0 24.0 175
Eastern 35.7 43.1 10.7 10.3 0.0 0.2 100.0 89.4 4,375
Marsabit 11.8 13.3 10.5 64.5 0.0 0.0 100.0 35.5 115
Isiolo 19.7 23.8 14.4 42.0 0.0 0.0 100.0 58.0 104
Meru 31.1 41.8 13.3 13.9 0.0 0.0 100.0 86.1 1,110
Tharaka-Nithi 33.7 40.2 10.1 15.2 0.0 0.2 100.0 84.1 275
Embu 40.9 47.5 7.0 4.4 0.0 0.0 100.0 95.4 459
Kitui 26.2 47.6 17.9 8.3 0.0 0.0 100.0 91.7 759
Machakos 47.0 40.8 7.5 3.9 0.0 0.8 100.0 95.3 873
Makueni 42.9 49.3 4.7 3.0 0.0 0.0 100.0 96.9 680
Central 54.7 34.3 5.9 4.4 0.0 0.1 100.0 94.9 3,994
Nyandarua 43.0 47.6 3.7 5.1 0.0 0.2 100.0 94.3 436
Nyeri 59.7 31.9 4.7 3.7 0.0 0.0 100.0 96.3 650
Kirinyaga 41.5 46.6 3.5 7.6 0.0 0.0 100.0 91.5 451
Murang’a 49.1 38.9 5.9 5.8 0.0 0.3 100.0 93.9 735
Kiambu 61.7 26.5 7.6 3.2 0.0 0.0 100.0 95.8 1,722
Rift Valley 40.3 35.8 8.3 15.2 0.0 0.1 100.0 84.5 7,953
Turkana 8.6 12.6 3.4 75.2 0.0 0.3 100.0 24.5 320
West Pokot 13.3 15.8 20.9 49.9 0.0 0.1 100.0 50.0 267
Samburu 16.0 12.9 11.2 59.3 0.3 0.0 100.0 40.1 123
Trans-Nzoia 35.2 43.9 6.5 14.2 0.0 0.1 100.0 85.6 768
Uasin Gishu 51.5 36.8 5.6 5.9 0.0 0.0 100.0 93.9 784
Elgeyo Marakwet 42.5 48.5 5.9 3.0 0.0 0.1 100.0 96.8 250
Nandi 37.5 47.3 13.6 1.4 0.0 0.2 100.0 98.4 628
Baringo 36.1 37.9 12.3 12.3 0.1 0.3 100.0 86.3 335
Laikipia 42.0 33.4 7.5 16.7 0.0 0.0 100.0 83.0 342
Nakuru 53.4 34.6 6.1 5.6 0.0 0.0 100.0 94.0 1,574
Narok 29.0 40.4 4.6 25.9 0.0 0.1 100.0 74.0 642
Kajiado 51.3 28.0 3.9 16.7 0.0 0.0 100.0 83.1 670
Kericho 42.7 34.3 12.3 9.8 0.0 0.7 100.0 89.3 563
Bomet 34.0 41.1 14.2 10.4 0.0 0.1 100.0 89.4 687
Western 36.8 43.4 9.8 9.1 0.0 0.6 100.0 90.1 3,225
Kakamega 38.8 48.5 4.8 6.8 0.0 1.1 100.0 92.1 1,108
Vihiga 43.0 43.6 6.5 6.7 0.0 0.0 100.0 93.1 368
Bungoma 38.1 38.4 12.2 10.5 0.1 0.3 100.0 88.7 1,203
Busia 25.6 44.2 17.2 12.3 0.0 0.7 100.0 87.0 546
Nyanza 39.9 40.2 11.6 7.7 0.0 0.4 100.0 91.7 4,038
Siaya 33.7 46.5 11.3 8.1 0.0 0.0 100.0 91.5 572
Kisumu 50.3 35.7 6.5 6.8 0.0 0.4 100.0 92.6 820
Homa Bay 31.1 45.1 17.6 5.6 0.0 0.6 100.0 93.8 798
Migori 23.5 51.8 10.3 13.2 0.0 0.8 100.0 85.7 650
Kisii 48.3 32.2 10.7 8.4 0.0 0.3 100.0 91.2 864
Nyamira 55.8 26.9 15.5 1.5 0.0 0.0 100.0 98.2 334
Nairobi 66.1 24.8 5.6 3.0 0.2 0.2 100.0 96.5 3,770
Total 42.7 36.8 8.3 11.7 0.0 0.2 100.0 87.8 31,079
Note: Totals may not add up to 100 percent because women with missing information are not shown separately.
1
Refers to women who attended secondary school or higher and women who can read a whole sentence or part of a sentence
Characteristics of Respondents • 41
Table 3.3.2 Literacy: Men
Percent distribution of men age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to background
characteristics, Kenya 2014
No schooling or primary school
Secondary Can read a Can read No card with Blind/
Background school or whole part of a Cannot read required visually Percentage Number of
characteristic higher sentence sentence at all language impaired Total literate1 men
Age
15-24 52.9 35.2 6.6 5.2 0.0 0.0 100.0 94.6 4,666
15-19 45.3 43.0 6.9 4.6 0.0 0.0 100.0 95.2 2,540
20-24 61.9 25.9 6.1 5.9 0.0 0.0 100.0 93.9 2,125
25-29 51.9 33.2 6.9 7.8 0.0 0.0 100.0 92.1 2,104
30-34 45.5 36.8 7.6 10.0 0.0 0.0 100.0 89.9 1,785
35-39 39.9 43.4 7.4 9.2 0.0 0.0 100.0 90.6 1,483
40-44 47.7 37.0 6.9 8.2 0.0 0.2 100.0 91.6 1,224
45-49 45.9 37.2 7.5 9.2 0.0 0.1 100.0 90.7 800
Residence
Urban 62.6 29.5 4.7 3.2 0.0 0.0 100.0 96.7 5,300
Rural 38.4 41.9 8.8 10.8 0.0 0.1 100.0 89.0 6,762
Region
Coast 43.9 45.8 4.2 5.9 0.0 0.1 100.0 93.9 1,260
North Eastern 28.4 29.2 9.6 32.3 0.4 0.0 100.0 67.2 227
Eastern 38.9 46.2 6.5 8.4 0.0 0.0 100.0 91.6 1,825
Central 58.4 32.3 5.0 4.2 0.0 0.1 100.0 95.8 1,564
Rift Valley 45.7 34.4 10.1 9.7 0.0 0.0 100.0 90.1 3,050
Western 38.8 41.5 6.5 12.8 0.0 0.0 100.0 86.9 1,164
Nyanza 48.6 36.9 9.6 4.6 0.0 0.2 100.0 95.0 1,405
Nairobi 72.9 22.4 3.3 1.4 0.0 0.0 100.0 98.6 1,568
Wealth quintile
Lowest 17.4 44.0 15.0 23.3 0.1 0.1 100.0 76.4 1,691
Second 31.2 49.3 9.7 9.5 0.0 0.1 100.0 90.2 2,145
Middle 42.8 43.4 7.1 6.6 0.0 0.0 100.0 93.3 2,370
Fourth 58.0 33.5 4.9 3.5 0.0 0.1 100.0 96.4 2,959
Highest 76.5 19.7 2.4 1.4 0.0 0.0 100.0 98.5 2,897
Total 15-49 49.0 36.4 7.0 7.4 0.0 0.0 100.0 92.4 12,063
50-54 45.2 33.4 8.0 12.1 0.0 1.3 100.0 86.6 756
Total 15-54 48.8 36.2 7.0 7.7 0.0 0.1 100.0 92.1 12,819
Note: Totals may not add up to 100 percent because men with missing information are not shown separately.
1
Refers to men who attended secondary school or higher and men who can read a whole sentence or part of a sentence
42 • Characteristics of Respondents
Table 3.3.2C Literacy: Men
Percent distribution of men age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to county, Kenya
2014
No schooling or primary school
Secondary Can read a Can read No card with Blind/
school or whole part of a Cannot read required visually Percentage Number of
County higher sentence sentence at all language impaired Total literate1 men
Coast 43.9 45.8 4.2 5.9 0.0 0.1 100.0 93.9 1,260
Mombasa 56.1 40.4 2.4 1.1 0.0 0.0 100.0 98.9 481
Kwale 33.9 42.1 11.6 12.4 0.0 0.0 100.0 87.6 226
Kilifi 38.1 54.1 1.9 5.5 0.0 0.0 100.0 94.1 359
Tana River 23.3 50.8 6.6 18.8 0.0 0.4 100.0 80.8 65
Lamu 34.5 48.3 4.2 12.4 0.0 0.6 100.0 87.1 37
Taita Taveta 45.3 46.3 3.2 4.3 0.0 0.4 100.0 94.8 93
North Eastern 28.4 29.2 9.6 32.3 0.4 0.0 100.0 67.2 227
Garissa 32.9 28.2 11.1 27.8 0.0 0.0 100.0 72.2 94
Wajir 28.1 27.7 8.9 33.9 1.3 0.0 100.0 64.7 72
Mandera 21.6 32.7 7.9 37.4 0.0 0.0 100.0 62.2 60
Eastern 38.9 46.2 6.5 8.4 0.0 0.0 100.0 91.6 1,825
Marsabit 35.7 7.1 20.8 36.4 0.0 0.0 100.0 63.6 40
Isiolo 37.9 27.7 22.3 12.1 0.0 0.0 100.0 87.9 35
Meru 34.3 52.1 3.9 9.6 0.0 0.0 100.0 90.4 495
Tharaka-Nithi 41.1 47.1 2.3 9.5 0.0 0.0 100.0 90.5 102
Embu 38.0 44.3 9.8 7.8 0.0 0.0 100.0 92.2 164
Kitui 29.1 43.7 11.9 15.3 0.0 0.0 100.0 84.7 303
Machakos 46.8 47.3 4.6 1.4 0.0 0.0 100.0 98.6 436
Makueni 46.8 45.4 3.3 4.5 0.0 0.0 100.0 95.5 250
Central 58.4 32.3 5.0 4.2 0.0 0.1 100.0 95.8 1,564
Nyandarua 50.1 36.9 10.1 2.9 0.0 0.0 100.0 97.0 198
Nyeri 60.5 29.4 5.6 4.0 0.0 0.4 100.0 95.6 229
Kirinyaga 42.9 45.0 5.8 6.3 0.0 0.0 100.0 93.7 184
Murang’a 49.1 37.5 4.1 9.3 0.0 0.0 100.0 90.7 284
Kiambu 68.5 26.2 3.5 1.8 0.0 0.0 100.0 98.2 669
Rift Valley 45.7 34.4 10.1 9.7 0.0 0.0 100.0 90.1 3,050
Turkana 21.8 6.8 24.2 47.2 0.0 0.0 100.0 52.8 76
West Pokot 15.6 47.0 8.7 28.7 0.0 0.0 100.0 71.3 103
Samburu 35.5 22.0 13.3 29.2 0.0 0.0 100.0 70.8 35
Trans-Nzoia 38.8 45.3 3.9 12.1 0.0 0.0 100.0 87.9 329
Uasin Gishu 53.3 38.2 5.9 2.7 0.0 0.0 100.0 97.3 355
Elgeyo Marakwet 48.7 41.8 5.0 4.1 0.0 0.0 100.0 95.5 86
Nandi 40.1 39.2 14.8 5.9 0.0 0.0 100.0 94.1 264
Baringo 44.1 35.7 5.1 14.5 0.0 0.7 100.0 84.9 125
Laikipia 56.8 29.7 2.2 11.4 0.0 0.0 100.0 88.6 124
Nakuru 55.6 31.2 8.6 4.7 0.0 0.0 100.0 95.3 589
Narok 32.7 22.6 25.9 17.9 0.0 0.0 100.0 81.1 240
Kajiado 62.1 21.3 5.9 10.7 0.0 0.0 100.0 89.3 241
Kericho 44.9 44.7 7.6 2.6 0.0 0.0 100.0 97.1 215
Bomet 39.7 35.8 17.5 7.1 0.0 0.0 100.0 92.9 267
Western 38.8 41.5 6.5 12.8 0.0 0.0 100.0 86.9 1,164
Kakamega 41.4 47.0 5.7 5.9 0.0 0.0 100.0 94.1 411
Vihiga 39.0 28.8 27.7 3.7 0.0 0.0 100.0 95.5 140
Bungoma 37.5 39.3 2.7 19.8 0.0 0.0 100.0 79.5 413
Busia 36.3 43.7 1.4 18.7 0.0 0.0 100.0 81.3 199
Nyanza 48.6 36.9 9.6 4.6 0.0 0.2 100.0 95.0 1,405
Siaya 42.3 46.6 6.5 4.4 0.0 0.0 100.0 95.4 213
Kisumu 55.8 28.0 14.0 1.9 0.0 0.2 100.0 97.9 309
Homa Bay 37.9 49.4 1.5 9.8 0.0 1.0 100.0 88.8 243
Migori 32.5 51.9 8.5 7.1 0.0 0.0 100.0 92.9 211
Kisii 58.7 27.8 10.1 3.4 0.0 0.0 100.0 96.6 315
Nyamira 64.7 13.6 21.4 0.0 0.0 0.3 100.0 99.7 114
Nairobi 72.9 22.4 3.3 1.4 0.0 0.0 100.0 98.6 1,568
Total 15-49 49.0 36.4 7.0 7.4 0.0 0.0 100.0 92.4 12,063
50-54 45.2 33.4 8.0 12.1 0.0 1.3 100.0 86.6 756
Total 15-54 48.8 36.2 7.0 7.7 0.0 0.1 100.0 92.1 12,819
Note: Totals may not add up to 100 percent because men with missing information are not shown separately.
1
Refers to men who attended secondary school or higher and men who can read a whole sentence or part of a sentence
Characteristics of Respondents • 43
3.4 ACCESS TO MASS MEDIA
Information access is essential in increasing people’s knowledge and awareness of the world
around them, which may eventually influence their perceptions and behaviour. Exposure to media was
assessed by asking respondents how often they read a newspaper, watched television, or listened to a radio.
It is important to know the types of persons who are more or less likely to be reached by the various types
of media to plan programmes intended to spread information about health and family planning. Tables
3.4.1 and 3.4.2 show the percentage of women and men age 15-49 exposed to different types of mass
communication media by background characteristics. Tables 3.4.1C and 3.4.2C show these data by county.
Women are less likely than men to have access to mass media; this is true for all types of media.
Radio is the most popular medium for both women and men (accessed at least weekly by 70 percent of
women and 86 percent of men), while newspapers are the least popular medium (accessed at least weekly
by 18 percent of women and 41 percent of men). Only 11 percent of women and 33 percent of men have
weekly exposure to all three media sources. Twenty-three percent of women and 10 percent of men have
no weekly access to mass media.
There are no overarching patterns in media consumption by age group, although newspaper
reading declines among women with age (as does literacy, noted above) and men age 15-19 are much less
likely to have weekly exposure to any of the media than men in other age groups.
Urban women and men have more access to all forms of mass media than their rural counterparts;
only 13 percent of women and 28 percent of men in rural areas read a newspaper at least once a week, as
compared with 25 percent of women and 58 percent of men in urban areas. Although 66 percent of women
and 78 percent of men in urban areas watch television at least once a week, only 20 percent of women and
44 percent of men residing in rural areas do so. Access to all three forms of mass media is highest among
women in the Central region (18 percent) and men in Central and Nairobi (52 percent each) and lowest
among residents of the North Eastern region (2 percent for women and 4 percent for men).
Access to mass media increases with increasing education and wealth among both women and
men. The proportion of women who listen to the radio at least once a week increases from 28 percent
among those with no education to 79 percent among those with at least some secondary schooling.
Similarly, the proportion of women who watch television at least once a week increases from only 3
percent among those in the lowest wealth quintile to 90 percent among those in the highest quintile.
Across counties, women in Kiambu (27 percent), Nakuru and Nyeri (21 percent each) are most
likely to have access to all three media at least once a week. Counties where the highest proportions of
women have no access to any of the three media sources at least once a week are Turkana (80 percent),
Garissa (77 percent), and Wajir (72 percent).
Men in Mombasa and Nyeri (71 percent each) and in Machakos and Kajiado (62 percent each)
have higher access to all three media services at least once a week than their counterparts in the other
counties. The counties with the highest proportions of men with no access to the three media services at
least once a week are Turkana (84 percent), Wajir (51 percent), and Garissa (48 percent).
44 • Characteristics of Respondents
Table 3.4.1 Exposure to mass media: Women
Percentage of women age 15-49 who are exposed to specific media on a weekly basis, by background characteristics, Kenya 2014
Reads a Accesses all Accesses none
newspaper at Watches Listens to the three media at of the three
Background least once a television at least radio at least least once a media at least Number of
characteristic week once a week once a week week once a week women
Age
15-19 21.7 33.2 66.0 9.5 25.2 5,820
20-24 19.4 44.2 73.5 12.2 18.6 5,735
25-29 18.2 45.2 71.6 11.8 20.3 6,100
30-34 16.8 41.1 70.5 10.9 22.5 4,510
35-39 14.9 36.8 68.1 9.5 25.2 3,773
40-44 15.0 31.4 67.4 9.5 28.1 2,885
45-49 15.1 32.0 68.5 9.3 25.7 2,257
Residence
Urban 24.9 66.4 75.6 17.9 11.2 12,690
Rural 13.2 19.9 65.6 5.7 31.0 18,389
Region
Coast 13.6 38.2 50.2 9.0 37.8 3,076
North Eastern 4.0 11.4 20.9 1.5 72.4 648
Eastern 17.4 24.7 67.0 8.1 29.0 4,375
Central 25.5 57.1 84.1 18.1 9.0 3,994
Rift Valley 19.1 34.6 70.3 10.9 23.4 7,953
Western 14.1 21.2 73.3 5.9 22.8 3,225
Nyanza 14.9 25.3 68.6 6.7 25.7 4,038
Nairobi 20.7 80.3 78.7 16.5 6.1 3,770
Education
No education 0.3 9.7 28.0 0.2 68.1 2,176
Primary incomplete 5.8 17.1 61.9 1.9 34.4 7,989
Primary complete 10.3 36.6 73.0 5.0 20.5 7,637
Secondary + 32.6 58.1 79.3 20.9 10.1 13,277
Wealth quintile
Lowest 4.1 3.2 37.6 0.7 61.1 4,838
Second 9.9 7.5 63.3 2.2 34.2 5,457
Middle 14.6 16.5 74.6 4.3 21.5 6,032
Fourth 19.6 48.3 80.9 11.0 12.1 6,550
Highest 32.7 89.8 80.4 26.5 2.7 8,203
Total 17.9 38.9 69.7 10.7 22.9 31,079
Characteristics of Respondents • 45
Table 3.4.1C Exposure to mass media: Women
Percentage of women age 15-49 who are exposed to specific media on a weekly basis, by county, Kenya 2014
Reads a Accesses all Accesses none
newspaper at Watches Listens to the three media at of the three
least once a television at least radio at least least once a media at least Number of
County week once a week once a week week once a week women
Coast 13.6 38.2 50.2 9.0 37.8 3,076
Mombasa 26.3 70.3 66.6 20.0 12.7 912
Kwale 7.6 21.3 38.5 3.4 53.5 619
Kilifi 6.7 23.9 37.8 3.6 53.2 1,043
Tana River 7.8 16.5 38.4 3.5 55.4 197
Lamu 8.4 35.5 65.0 4.9 25.6 89
Taita Taveta 18.1 41.4 78.8 11.9 13.6 215
North Eastern 4.0 11.4 20.9 1.5 72.4 648
Garissa 3.1 15.1 12.8 1.3 76.9 261
Wajir 4.4 8.8 23.1 1.3 71.6 212
Mandera 5.0 9.0 30.4 2.0 66.6 175
Eastern 17.4 24.7 67.0 8.1 29.0 4,375
Marsabit 1.9 19.2 20.7 0.8 69.2 115
Isiolo 16.1 34.7 46.7 11.7 45.0 104
Meru 13.4 27.0 58.7 6.8 37.8 1,110
Tharaka-Nithi 12.5 20.6 54.6 6.1 41.2 275
Embu 18.3 27.5 72.5 8.5 22.5 459
Kitui 2.8 11.7 56.6 1.5 40.9 759
Machakos 17.2 33.4 82.2 10.2 14.4 873
Makueni 44.7 23.7 85.2 15.8 10.1 680
Central 25.5 57.1 84.1 18.1 9.0 3,994
Nyandarua 17.4 32.3 86.1 8.5 10.2 436
Nyeri 29.7 61.6 90.5 21.3 3.7 650
Kirinyaga 17.6 37.4 64.5 8.7 26.7 451
Murang’a 11.9 36.8 84.1 6.4 12.9 735
Kiambu 33.9 75.5 86.4 26.7 4.5 1,722
Rift Valley 19.1 34.6 70.3 10.9 23.4 7,953
Turkana 4.1 7.1 18.8 2.7 79.5 320
West Pokot 9.3 12.3 31.2 6.7 67.3 267
Samburu 3.4 16.3 28.1 1.8 67.2 123
Trans-Nzoia 14.9 22.0 67.4 4.8 25.8 768
Uasin Gishu 22.1 45.6 78.0 14.5 17.0 784
Elgeyo Marakwet 10.1 24.5 71.3 7.0 26.6 250
Nandi 7.9 16.6 67.1 3.4 30.1 628
Baringo 9.8 16.3 50.9 4.3 44.1 335
Laikipia 15.3 33.9 74.3 10.7 20.7 342
Nakuru 33.5 59.0 81.4 21.4 8.8 1,574
Narok 13.1 26.1 82.3 5.9 13.0 642
Kajiado 19.1 55.7 64.5 13.4 19.4 670
Kericho 30.3 33.3 79.3 14.7 15.6 563
Bomet 17.1 22.8 83.6 7.1 14.6 687
Western 14.1 21.2 73.3 5.9 22.8 3,225
Kakamega 15.2 23.4 73.3 6.3 21.8 1,108
Vihiga 22.0 29.8 84.9 12.0 12.5 368
Bungoma 12.1 18.0 71.3 4.8 25.8 1,203
Busia 10.8 17.9 69.9 3.6 25.4 546
Nyanza 14.9 25.3 68.6 6.7 25.7 4,038
Siaya 16.8 16.9 76.7 6.1 19.3 572
Kisumu 21.5 50.8 83.3 14.5 9.8 820
Homa Bay 16.4 15.6 66.8 5.5 29.8 798
Migori 8.4 18.4 70.0 4.5 27.3 650
Kisii 14.2 23.9 50.6 4.0 38.4 864
Nyamira 6.2 17.1 66.7 3.0 30.8 334
Nairobi 20.7 80.3 78.7 16.5 6.1 3,770
Total 17.9 38.9 69.7 10.7 22.9 31,079
46 • Characteristics of Respondents
Table 3.4.2 Exposure to mass media: Men
Percentage of men age 15-49 who are exposed to specific media on a weekly basis, by background characteristics, Kenya 2014
Reads a Accesses all Accesses none
newspaper at Watches Listens to the three media at of the three
Background least once a television at least radio at least least once a media at least
characteristic week once a week once a week week once a week Number of men
Age
15-19 29.4 43.6 77.9 19.0 16.2 2,540
20-24 44.4 65.1 88.0 36.1 7.5 2,125
25-29 44.7 66.5 88.0 37.7 6.4 2,104
30-34 46.1 63.9 86.5 37.7 8.2 1,785
35-39 43.1 60.8 86.7 34.5 8.7 1,483
40-44 45.5 58.4 88.1 37.1 8.2 1,224
45-49 42.1 57.5 87.9 33.1 9.6 800
Residence
Urban 58.3 78.3 89.0 49.2 3.9 5,300
Rural 28.0 43.8 82.8 19.8 14.1 6,762
Region
Coast 51.0 67.6 83.6 42.2 10.6 1,260
North Eastern 7.9 22.5 42.6 4.2 47.5 227
Eastern 33.5 59.5 87.4 27.6 9.5 1,825
Central 61.3 76.5 95.6 51.9 1.8 1,564
Rift Valley 31.6 49.2 84.4 23.9 11.3 3,050
Western 32.8 45.7 83.2 23.7 14.3 1,164
Nyanza 29.6 41.5 81.5 19.7 13.4 1,405
Nairobi 63.3 83.2 88.3 51.6 1.1 1,568
Education
No education 1.1 20.8 50.4 0.8 47.3 345
Primary incomplete 15.1 38.1 79.3 10.1 17.6 3,071
Primary complete 35.2 57.5 89.6 27.6 6.7 2,734
Secondary + 60.1 72.7 88.9 48.7 4.6 5,913
Wealth quintile
Lowest 13.1 22.1 64.8 6.8 32.0 1,691
Second 23.8 37.9 85.2 15.0 11.3 2,145
Middle 35.2 49.1 88.5 24.8 8.2 2,370
Fourth 48.6 69.8 91.3 38.9 4.4 2,959
Highest 68.3 92.9 89.4 61.2 1.6 2,897
Total 15-49 41.3 58.9 85.5 32.7 9.6 12,063
50-54 40.9 51.8 84.4 30.9 12.2 756
Total 15-54 41.3 58.5 85.4 32.6 9.7 12,819
Characteristics of Respondents • 47
Table 3.4.2C Exposure to mass media: Men
Percentage of men age 15-49 who are exposed to specific media on a weekly basis, by county, Kenya 2014
Reads a Accesses all Accesses none
newspaper at Watches Listens to the three media at of the three
least once a television at least radio at least least once a media at least
County week once a week once a week week once a week Number of men
Coast 51.0 67.6 83.6 42.2 10.6 1,260
Mombasa 72.7 96.8 97.6 70.8 0.7 481
Kwale 28.6 44.0 64.7 19.4 26.0 226
Kilifi 48.0 49.0 77.4 29.6 13.9 359
Tana River 18.7 43.5 75.8 11.7 20.2 65
Lamu 22.9 49.7 84.3 14.5 6.9 37
Taita Taveta 38.4 69.0 86.0 30.9 6.4 93
North Eastern 7.9 22.5 42.6 4.2 47.5 227
Garissa 3.1 26.5 37.1 1.5 47.5 94
Wajir 9.7 13.5 40.6 2.8 51.4 72
Mandera 13.1 26.9 53.7 10.3 43.0 60
Eastern 33.5 59.5 87.4 27.6 9.5 1,825
Marsabit 10.6 52.1 56.1 8.6 39.3 40
Isiolo 18.5 39.3 51.6 11.4 36.4 35
Meru 32.2 81.8 91.7 29.1 5.4 495
Tharaka-Nithi 21.0 40.3 87.4 14.9 8.3 102
Embu 20.1 41.1 84.1 12.7 12.4 164
Kitui 11.9 15.2 68.5 3.8 26.6 303
Machakos 66.8 90.0 98.9 61.9 0.2 436
Makueni 23.9 40.0 93.9 13.5 2.9 250
Central 61.3 76.5 95.6 51.9 1.8 1,564
Nyandarua 34.9 54.2 88.5 22.7 5.6 198
Nyeri 71.7 93.9 97.4 70.6 0.7 229
Kirinyaga 41.0 69.9 91.0 32.4 4.6 184
Murang’a 66.4 74.7 98.2 59.1 0.6 284
Kiambu 68.9 79.8 97.2 56.4 0.7 669
Rift Valley 31.6 49.2 84.4 23.9 11.3 3,050
Turkana 5.2 7.8 14.0 4.2 83.6 76
West Pokot 15.5 30.4 74.2 11.1 23.8 103
Samburu 13.8 27.5 55.1 8.6 40.6 35
Trans-Nzoia 29.7 53.4 84.4 19.8 6.4 329
Uasin Gishu 45.2 63.6 93.1 35.5 5.0 355
Elgeyo Marakwet 26.8 60.1 92.1 19.9 4.2 86
Nandi 30.4 33.7 89.4 20.2 7.6 264
Baringo 21.7 37.9 80.3 14.8 16.6 125
Laikipia 21.2 37.7 78.2 11.2 13.5 124
Nakuru 24.1 46.5 83.9 17.4 10.4 589
Narok 21.2 48.9 83.8 19.0 14.2 240
Kajiado 64.8 84.3 92.9 61.9 3.8 241
Kericho 44.0 44.6 88.4 27.8 7.6 215
Bomet 30.1 47.7 89.3 22.2 7.3 267
Western 32.8 45.7 83.2 23.7 14.3 1,164
Kakamega 39.9 57.6 91.7 31.0 4.9 411
Vihiga 67.0 54.0 97.3 44.6 1.1 140
Bungoma 14.0 25.0 66.7 8.2 31.6 413
Busia 33.2 58.2 90.3 25.9 6.8 199
Nyanza 29.6 41.5 81.5 19.7 13.4 1,405
Siaya 50.3 57.8 93.7 37.6 4.3 213
Kisumu 41.1 61.3 86.6 31.0 4.3 309
Homa Bay 27.1 32.6 90.6 15.5 7.4 243
Migori 19.2 38.5 87.5 12.4 11.5 211
Kisii 16.4 25.1 57.8 5.6 32.0 315
Nyamira 20.4 27.5 79.4 16.9 19.9 114
Nairobi 63.3 83.2 88.3 51.6 1.1 1,568
Total 15-49 41.3 58.9 85.5 32.7 9.6 12,063
50-54 40.9 51.8 84.4 30.9 12.2 756
Total 15-54 41.3 58.5 85.4 32.6 9.7 12,819
48 • Characteristics of Respondents
3.5 EMPLOYMENT
3.5.1 Employment Status
The 2014 KDHS asked respondents a number of questions regarding their employment status,
including whether they were working in the seven days preceding the survey and, if not, whether they had
worked in the 12 months before the survey. The results for women and men are presented in Tables 3.5.1
and 3.5.2.
At the time of the survey, 61 percent of women were employed and 5 percent were not currently
employed but had worked sometime during the past 12 months (Figure 3.1). In general, the proportion of
women who are currently employed increases with age, peaking at age 40-44 (84 percent) before dropping
slightly at age 45-49 (80 percent). Employment increases with the number of living children. The North
Eastern region has the lowest proportion of women who are currently employed (13 percent). Women with
no education are less likely to be currently employed (43 percent) than those with any degree of education
(60 percent and above). Similarly, women in the lowest wealth quintile are less likely to be currently
employed (50 percent) than women in the other quintiles (61 percent or higher).
Men are more likely to be currently employed (80 percent) than women. The 15-19 age group has
the lowest employment (35 percent); thereafter, employment rises more sharply than for women. Urban
men are more likely to be currently employed (84 percent) than rural men (77 percent). Patterns are similar
to those found with women by region and wealth quintile, but men at the highest level of education are less
likely to be employed than men with no education.
Since the 2008-09 KDHS, current employment has improved among women (from 57 percent to
61 percent) but has declined among men (from 86 percent to 80 percent).
Figure 3.1 Women’s employment status in the past 12 months
Did not work in
past 12 months
34%
Currently
employed
61%
Not currently
employed, but
worked in past 12
months
5% KDHS 2014
Characteristics of Respondents • 49
Table 3.5.1 Employment status: Women
Percent distribution of women age 15-49 by employment status, according to background characteristics, Kenya 2014
Employed in the 12 months Not employed in
preceding the survey the 12 months
Background Currently Not currently preceding the Number of
characteristic employed1 employed survey Total women
Age
15-19 18.8 3.6 77.5 100.0 2,717
20-24 53.1 6.9 40.0 100.0 2,691
25-29 71.1 5.2 23.7 100.0 2,932
30-34 75.9 5.0 19.0 100.0 2,162
35-39 78.7 3.8 17.4 100.0 1,780
40-44 83.9 3.8 12.3 100.0 1,292
45-49 79.5 5.9 14.6 100.0 1,052
Marital status
Never married 37.1 4.1 58.8 100.0 4,255
Married or living together 69.4 5.3 25.3 100.0 8,710
Divorced/separated/
widowed 82.1 5.3 12.6 100.0 1,660
Number of living children
0 32.8 4.6 62.5 100.0 3,890
1-2 69.0 5.4 25.6 100.0 5,000
3-4 74.6 5.2 20.2 100.0 3,381
5+ 73.9 4.1 22.0 100.0 2,354
Residence
Urban 63.3 5.9 30.8 100.0 5,929
Rural 60.2 4.3 35.5 100.0 8,696
Region
Coast 52.8 4.3 42.9 100.0 1,421
North Eastern 13.3 0.4 86.3 100.0 299
Eastern 66.4 6.1 27.5 100.0 2,066
Central 73.3 6.3 20.4 100.0 1,905
Rift Valley 59.9 3.9 36.2 100.0 3,714
Western 56.4 5.3 38.3 100.0 1,571
Nyanza 62.0 3.0 35.0 100.0 1,908
Nairobi 65.3 7.6 27.0 100.0 1,742
Education
No education 42.7 1.4 55.8 100.0 1,015
Primary incomplete 59.8 5.0 35.1 100.0 3,793
Primary complete 70.0 6.3 23.7 100.0 3,543
Secondary + 60.6 4.7 34.6 100.0 6,274
Wealth quintile
Lowest 50.4 3.6 46.1 100.0 2,236
Second 62.5 5.3 32.2 100.0 2,590
Middle 60.8 5.2 34.0 100.0 2,859
Fourth 63.7 4.5 31.8 100.0 3,113
Highest 65.9 5.7 28.4 100.0 3,827
Total 61.4 5.0 33.6 100.0 14,625
1
“Currently employed” is defined as having done work in the past seven days. Includes persons who did not work in the
past seven days but who are regularly employed and were absent from work for leave, illness, vacation, or any other
such reason.
50 • Characteristics of Respondents
Table 3.5.2 Employment status: Men
Percent distribution of men age 15-49 by employment status, according to background characteristics, Kenya 2014
Employed in the 12 months Not employed in
preceding the survey the 12 months
Background Currently Not currently preceding the
characteristic employed1 employed survey Total Number of men
Age
15-19 34.7 5.5 59.6 100.0 2,540
20-24 75.8 3.7 20.5 100.0 2,125
25-29 94.9 2.1 3.0 100.0 2,104
30-34 98.1 1.3 0.6 100.0 1,785
35-39 98.2 1.3 0.5 100.0 1,483
40-44 96.5 2.8 0.7 100.0 1,224
45-49 97.8 1.0 1.2 100.0 800
Marital status
Never married 57.8 4.6 37.6 100.0 5,350
Married or living together 98.2 1.4 0.4 100.0 6,095
Divorced/separated/
widowed 95.1 3.5 1.2 100.0 618
Number of living children
0 59.3 4.5 36.1 100.0 5,540
1-2 97.9 1.2 0.9 100.0 3,206
3-4 97.9 1.8 0.3 100.0 2,032
5+ 97.3 1.8 0.9 100.0 1,285
Residence
Urban 84.1 2.2 13.7 100.0 5,300
Rural 77.0 3.4 19.5 100.0 6,762
Region
Coast 76.1 2.8 21.2 100.0 1,260
North Eastern 60.0 0.1 39.9 100.0 227
Eastern 79.8 2.6 17.6 100.0 1,825
Central 85.9 2.3 11.8 100.0 1,564
Rift Valley 81.6 4.0 14.4 100.0 3,050
Western 73.1 1.2 25.6 100.0 1,164
Nyanza 77.0 3.0 19.9 100.0 1,405
Nairobi 86.0 3.4 10.7 100.0 1,568
Education
No education 85.4 8.1 6.5 100.0 345
Primary incomplete 73.4 3.6 22.9 100.0 3,071
Primary complete 93.0 1.7 5.3 100.0 2,734
Secondary + 77.3 2.8 19.9 100.0 5,913
Wealth quintile
Lowest 71.5 4.2 24.3 100.0 1,691
Second 79.8 3.2 17.0 100.0 2,145
Middle 77.8 3.2 19.0 100.0 2,370
Fourth 82.6 2.9 14.4 100.0 2,959
Highest 84.7 1.6 13.6 100.0 2,897
Total 15-49 80.1 2.9 17.0 100.0 12,063
50-54 96.1 2.1 1.8 100.0 756
Total 15-54 81.0 2.8 16.1 100.0 12,819
Note: Totals may not add up to 100 percent because men with missing information are not shown separately.
1
“Currently employed” is defined as having done work in the past seven days. Includes persons who did not work in the
past seven days but who are regularly employed and were absent from work for leave, illness, vacation, or any other
such reason.
3.5.2 Occupation
Respondents who were currently employed or had worked in the 12 months preceding the survey
were asked to specify their occupation. Table 3.6.1 and Table 3.6.2 show the percent distribution of
women and men age 15-49 by occupation according to background characteristics.
Most of the women (a combined 59 percent) in Kenya are employed in either agriculture or
domestic service. The other notable occupations include professional, technical, or managerial (14
percent); sales and services (14 percent); and unskilled manual labour (10 percent). There is a great deal of
variation by background characteristics.
Employed men age 15-49 are mostly engaged in agricultural, unskilled manual, or domestic
service occupations (24 percent, 22 percent, and 21 percent, respectively). As among women, 14 percent of
men work in professional, technical, or managerial occupations.
Characteristics of Respondents • 51
Table 3.6.1 Occupation: Women
Percent distribution of women age 15-49 employed in the 12 months preceding the survey by occupation, according to background characteristics, Kenya 2014
Profes-
sional/
Background technical/ Sales and Skilled Unskilled Domestic Number of
characteristic managerial Clerical services manual manual service Agriculture Missing Total women
Age
15-19 7.5 0.8 15.2 0.2 15.2 28.6 28.5 4.1 100.0 609
20-24 13.0 1.5 20.6 0.7 9.7 30.1 23.7 0.7 100.0 1,614
25-29 16.9 0.9 18.9 0.9 10.8 26.8 23.9 0.8 100.0 2,237
30-34 15.4 1.6 12.9 0.2 9.5 29.6 30.2 0.7 100.0 1,750
35-39 14.8 1.4 9.6 0.2 10.5 26.9 36.1 0.5 100.0 1,470
40-44 12.6 0.8 10.0 0.9 9.6 25.9 39.7 0.5 100.0 1,133
45-49 12.9 0.2 5.8 0.4 7.5 23.3 48.8 1.1 100.0 899
Marital status
Never married 19.2 2.3 20.0 0.5 11.2 30.2 14.9 1.7 100.0 1,754
Married or living together 13.5 0.8 12.5 0.5 9.6 25.1 37.3 0.7 100.0 6,508
Divorced/separated/
widowed 11.4 1.4 15.1 0.7 11.3 35.2 24.2 0.7 100.0 1,450
Number of living children
0 21.6 2.5 19.8 0.6 10.7 28.7 14.2 2.0 100.0 1,459
1-2 16.5 1.5 17.7 0.7 9.7 30.0 23.3 0.7 100.0 3,719
3-4 12.4 0.6 11.5 0.5 10.0 25.3 38.5 1.2 100.0 2,698
5+ 6.4 0.1 6.7 0.4 11.0 24.9 50.4 0.1 100.0 1,836
Residence
Urban 19.6 2.0 20.4 0.5 9.4 37.2 10.0 0.8 100.0 4,104
Rural 10.3 0.5 9.7 0.6 10.7 20.4 46.9 0.9 100.0 5,608
Region
Coast 13.9 0.9 20.9 0.5 11.7 34.3 16.6 1.3 100.0 811
North Eastern 21.1 2.0 29.4 0.0 5.5 32.9 9.2 0.0 100.0 41
Eastern 8.8 1.1 12.2 1.0 7.9 19.5 48.7 0.9 100.0 1,497
Central 12.7 1.5 15.7 0.7 8.0 24.2 35.9 1.2 100.0 1,516
Rift Valley 14.0 1.2 12.7 0.4 13.5 26.1 31.1 1.1 100.0 2,369
Western 16.2 0.8 10.4 0.5 13.3 19.6 38.6 0.7 100.0 969
Nyanza 13.4 0.6 9.7 0.8 7.3 28.3 39.6 0.4 100.0 1,239
Nairobi 22.0 1.5 20.3 0.0 8.9 44.7 2.0 0.6 100.0 1,271
Education
No education 4.9 0.0 13.6 0.5 11.0 32.1 36.9 0.8 100.0 448
Primary incomplete 4.3 0.0 10.6 0.4 11.5 27.8 44.5 0.9 100.0 2,461
Primary complete 5.4 0.1 15.0 0.3 12.3 27.3 38.5 1.1 100.0 2,702
Secondary + 26.9 2.6 16.0 0.8 7.9 27.1 17.9 0.7 100.0 4,100
Wealth quintile
Lowest 4.6 0.0 8.1 0.6 12.4 24.8 49.0 0.6 100.0 1,206
Second 6.7 0.2 8.1 0.5 11.4 20.5 51.6 1.2 100.0 1,756
Middle 9.6 0.3 11.2 0.7 11.1 22.1 44.4 0.6 100.0 1,887
Fourth 13.3 1.0 16.1 0.5 11.8 31.2 24.8 1.4 100.0 2,123
Highest 27.1 3.0 21.5 0.6 6.6 34.2 6.5 0.6 100.0 2,740
Total 14.2 1.1 14.2 0.5 10.2 27.5 31.3 0.9 100.0 9,712
52 • Characteristics of Respondents
Table 3.6.2 Occupation: Men
Percent distribution of men age 15-49 employed in the 12 months preceding the survey by occupation, according to background characteristics, Kenya 2014
Profes-
sional/
Background technical/ Sales and Skilled Unskilled Domestic Number of
characteristic managerial Clerical services manual manual service Agriculture Missing Total men
Age
15-19 3.7 0.0 5.8 3.5 26.7 15.8 39.8 4.7 100.0 1,023
20-24 13.6 0.4 8.7 9.5 22.2 23.0 21.1 1.5 100.0 1,690
25-29 13.9 1.4 8.3 12.8 23.7 21.5 17.6 0.8 100.0 2,042
30-34 16.9 1.0 6.4 11.5 20.8 21.4 21.7 0.3 100.0 1,774
35-39 14.1 1.6 7.4 9.4 21.2 23.8 22.3 0.4 100.0 1,476
40-44 15.9 0.9 4.8 7.5 21.8 21.0 27.7 0.5 100.0 1,215
45-49 18.5 1.5 4.4 7.4 17.3 16.8 33.2 0.8 100.0 791
Marital status
Never married 12.4 0.6 7.6 6.4 23.1 21.5 26.1 2.4 100.0 3,335
Married or living together 15.4 1.3 6.6 11.2 21.6 20.6 23.0 0.4 100.0 6,067
Divorced/separated/
widowed 9.1 0.2 6.0 9.2 22.5 23.1 28.6 1.3 100.0 609
Number of living children
0 13.2 0.9 7.7 6.1 22.4 22.0 25.4 2.2 100.0 3,535
1-2 15.6 0.9 6.8 12.7 23.4 21.8 18.1 0.6 100.0 3,178
3-4 13.9 1.2 6.5 11.7 20.6 20.3 25.2 0.5 100.0 2,025
5+ 12.0 0.9 5.5 7.6 20.5 17.8 35.5 0.2 100.0 1,273
Residence
Urban 18.4 1.6 9.9 12.7 23.4 26.4 6.8 0.9 100.0 4,573
Rural 10.3 0.5 4.4 6.8 21.1 16.5 39.1 1.3 100.0 5,438
Region
Coast 15.5 1.4 7.7 12.0 24.4 27.6 10.9 0.6 100.0 993
North Eastern 21.0 0.2 11.9 8.9 15.1 28.9 13.2 0.9 100.0 136
Eastern 10.8 0.6 4.9 7.2 22.5 22.9 29.2 1.8 100.0 1,504
Central 13.7 0.1 8.0 10.0 18.3 21.1 27.3 1.4 100.0 1,379
Rift Valley 12.5 1.0 6.0 9.6 22.2 16.9 31.0 0.9 100.0 2,610
Western 11.7 1.3 6.2 9.1 23.6 14.7 33.1 0.3 100.0 864
Nyanza 15.4 0.7 5.0 8.2 19.3 17.4 33.2 0.7 100.0 1,124
Nairobi 18.9 2.0 10.7 10.9 25.8 28.0 1.9 1.7 100.0 1,401
Education
No education 4.2 0.0 5.8 7.3 23.8 25.0 33.3 0.7 100.0 323
Primary incomplete 3.8 0.2 4.1 7.7 24.1 23.1 36.1 1.0 100.0 2,364
Primary complete 6.2 0.2 6.6 11.4 26.5 21.8 26.5 0.7 100.0 2,588
Secondary + 24.0 1.9 8.6 9.5 18.6 19.3 16.7 1.4 100.0 4,737
Wealth quintile
Lowest 6.5 0.0 4.2 3.6 23.8 19.7 41.2 1.0 100.0 1,280
Second 6.2 0.3 4.6 6.4 23.4 18.5 39.0 1.6 100.0 1,779
Middle 9.0 0.6 5.2 10.7 22.1 19.1 32.5 0.9 100.0 1,919
Fourth 14.6 1.2 8.2 12.0 23.1 22.8 17.2 1.0 100.0 2,530
Highest 26.6 2.1 9.9 11.3 19.5 23.2 6.4 1.1 100.0 2,503
Total 15-49 14.0 1.0 6.9 9.5 22.1 21.0 24.3 1.1 100.0 10,012
50-54 17.1 0.9 3.1 5.7 19.6 16.1 37.4 0.1 100.0 743
Total 15-54 14.2 1.0 6.6 9.2 22.0 20.7 25.3 1.0 100.0 10,754
3.5.3 Earnings, Employers, and Continuity of Employment
Table 3.7 shows the percent distribution of women age 15-49 by type of earnings, type of
employer, and continuity of employment, by whether their work is agricultural or nonagricultural.
Almost two in every three women (66 percent) are paid cash only for their work. This is mostly in
nonagricultural work (83 percent, compared with 29 percent for agricultural work). Forty-three percent of
those who work in agriculture are not paid, while 9 percent are paid in-kind.
Half of Kenyan women (52 percent) are self-employed. Most of those in agricultural work are
self-employed (68 percent), while women doing nonagricultural work are slightly more likely to be
employed by a nonfamily member (50 percent) than to be self-employed (45 percent).
Sixty-five percent of women are employed year-round, and 28 percent are employed on a seasonal
basis. Nonagricultural work is more continuous than agricultural work, with 73 percent of women doing
nonagricultural work employed all year compared with 49 percent of women doing agricultural work.
Characteristics of Respondents • 53
Table 3.7 Type of employment among women
Percent distribution of women age 15-49 employed in the 12 months preceding the survey
by type of earnings, type of employer, and continuity of employment, according to type of
employment (agricultural or nonagricultural), Kenya 2014
Nonagricultural
Employment characteristic Agricultural work work Total
Type of earnings
Cash only 29.0 82.8 65.7
Cash and in-kind 19.7 10.2 13.1
In-kind only 8.8 1.2 3.6
Not paid 42.5 5.7 17.5
Total 100.0 100.0 100.0
Type of employer
Employed by family member 18.0 4.7 9.1
Employed by nonfamily member 14.0 50.3 38.7
Self-employed 68.0 45.0 52.2
Total 100.0 100.0 100.0
Continuity of employment
All year 48.8 73.2 65.4
Seasonal 45.9 19.2 27.6
Occasional 5.1 7.6 6.8
Total 100.0 100.0 100.0
Number of women employed
during the last 12 months 3,038 6,587 9,712
Note: Total includes women with missing information on type of employment who are not
shown separately.
54 • Characteristics of Respondents
MARRIAGE AND SEXUAL ACTIVITY 4
Ben Obonyo Jarabi, Samwel Ogola, Henry Z. Osoro
Key Findings
• The median age at first marriage among women age 25-49 is 20.2 years;
the median age at first marriage among men age 30-49 is 25.3 years.
Median age at marriage has remained stable in the past 10 years for both
women and men.
• Six percent of currently married men are in a polygynous union; 11
percent of currently married women have co-wives.
• The percentage of women married by age 15 appears to be declining; 9
percent of women age 45-49 were married by age 15, as compared with
2 percent among those age 15-19.
• Fifteen percent of women age 20-49 had first sexual intercourse by age
15, 50 percent by age 18, and 71 percent by age 20. Twenty-two percent
of men age 20-49 had first sexual intercourse by age 15, 56 percent by
age 18, and 76 percent by age 20.
T
his chapter discusses the principal factors, other than contraception, that affect a woman’s risk of
becoming pregnant. These factors include marriage and sexual activity. Marriage signals the onset
of exposure to the risk of pregnancy for most women and is an important fertility indicator. In the
context of the 2014 KDHS, the term married refers to legal or formal marriage, and the phrase living
together designates an informal union in which a man and a woman live together, whether or not a formal
civil or religious ceremony has occurred. In later tables that do not list living together as a separate
category, these women and men are included in the currently married group. Respondents who are
currently married, divorced, separated, or widowed are referred to as ever married. This chapter also
includes information on more direct measures of the beginning of exposure to pregnancy and the level of
exposure, for example, age at first sexual intercourse and frequency of recent sexual intercourse.
4.1 CURRENT MARITAL STATUS
Marriage is a primary indication of regular exposure of women to the risk of pregnancy and
therefore is important in the understanding of fertility. Populations in which age at first marriage is low
tend to have early childbearing and high fertility.
Table 4.1 shows the percent distribution of women and men by marital status, according to age.
Sixty percent of women and 51 percent of men age 15-49 are currently in a union; the majority report
being married, while 5 percent of women and 2 percent of men report living together as if married. A
higher proportion of men (44 percent) than women (29 percent) have never been married. A lower
proportion of men are divorced, separated, or widowed compared with women (5 percent and 11 percent,
respectively).
Marriage and Sexual Activity • 55
Table 4.1 Current marital status
Percent distribution of women and men age 15-49 by current marital status, according to age, Kenya 2014
Percentage
of
Marital status respondents
Never Living currently in Number of
Age married Married together Divorced Separated Widowed Total union respondents
WOMEN
15-19 86.8 10.5 1.5 0.3 0.8 0.1 100.0 11.9 5,820
20-24 38.8 47.6 7.0 1.0 5.0 0.6 100.0 54.6 5,735
25-29 14.0 69.2 5.5 2.0 7.4 1.9 100.0 74.7 6,100
30-34 8.0 73.4 5.7 2.5 7.1 3.3 100.0 79.1 4,510
35-39 6.7 70.3 6.4 3.5 7.5 5.6 100.0 76.7 3,773
40-44 5.0 67.0 5.5 3.7 8.1 10.7 100.0 72.5 2,885
45-49 4.8 66.8 4.8 4.2 5.5 14.0 100.0 71.6 2,257
Total 15-49 28.9 54.6 5.1 2.1 5.6 3.7 100.0 59.7 31,079
MEN
15-19 99.3 0.5 0.1 0.0 0.1 0.0 100.0 0.6 2,540
20-24 79.6 15.1 2.7 0.6 2.1 0.0 100.0 17.7 2,125
25-29 37.0 53.8 3.3 1.5 4.4 0.1 100.0 57.1 2,104
30-34 12.6 75.4 3.0 2.0 6.5 0.6 100.0 78.3 1,785
35-39 4.8 83.9 2.2 3.1 4.7 1.3 100.0 86.1 1,483
40-44 3.2 88.6 1.3 1.6 4.8 0.5 100.0 89.9 1,224
45-49 2.7 87.5 3.3 1.9 2.9 1.6 100.0 90.8 800
Total 15-49 44.4 48.4 2.1 1.3 3.4 0.4 100.0 50.5 12,063
50-54 1.5 86.6 1.6 3.0 3.9 3.5 100.0 88.1 756
Total 15-54 41.8 50.7 2.1 1.4 3.4 0.6 100.0 52.7 12,819
Eleven percent of women age 15-19 are currently married, as compared with just 1 percent of men
age 15-19. The proportion of women who are married increases rapidly between age 15-19 and age 20-24
(from 11 percent to 48 percent); the highest proportion of women married are age 30-34 (73 percent).
Among men, the percentage married increases most rapidly between age 20-24 and age 25-29 (from 15
percent to 54 percent). After age 35, 84 percent or more of men are married.
4.2 POLYGYNY
Polygyny, the practice of having more than one wife, has implications for the frequency of sexual
activity and fertility. The extent of polygyny was measured in the 2014 KDHS by asking all currently
married female respondents whether their husband or partner had other wives (co-wives) and, if so, how
many. Currently married men were also asked whether they had one or more wives or partners with whom
they were living.
Table 4.2.1 shows the percent distribution of currently married women by the number of co-wives
they have, and Table 4.2.2 shows the percent distribution of currently married men by the number of wives
they have, according to background characteristics. The results show that the majority of Kenyan women
(86 percent) and men (95 percent) are in monogamous unions. Eleven percent of married women and 6
percent of married men are in polygynous unions. Three percent of women age 15-19 report that they have
co-wives, and this proportion rises with age to 18-19 percent among women in the 40-44 and 45-49 age
groups. The percentage of men having more than one wife rises from 1 percent among men age 20-24 to
11 percent among men age 45-49. Seven percent of urban women and 4 percent of urban men are in
polygynous unions, as compared with 14 percent of rural women and 6 percent of rural men.
56 • Marriage and Sexual Activity
Table 4.2.1 Number of women’s co-wives
Percent distribution of currently married women age 15-49 by number of co-wives, according to background
characteristics, Kenya 2014
Background Number of co-wives Number of
characteristic 0 1 2+ Don’t know Missing Total women
Age
15-19 95.7 2.9 0.3 1.1 0.0 100.0 301
20-24 91.9 5.6 0.5 1.6 0.4 100.0 1,465
25-29 89.5 7.7 1.0 1.8 0.1 100.0 2,171
30-34 86.6 9.1 2.0 2.4 0.0 100.0 1,717
35-39 82.2 12.6 2.7 2.5 0.1 100.0 1,365
40-44 77.9 14.3 4.6 3.0 0.2 100.0 923
45-49 78.4 13.4 4.1 4.0 0.1 100.0 768
Residence
Urban 90.3 6.4 0.7 2.3 0.2 100.0 3,445
Rural 83.5 11.4 2.9 2.2 0.1 100.0 5,265
Region
Coast 84.0 11.6 2.0 2.1 0.3 100.0 850
North Eastern 67.8 27.5 4.5 0.0 0.2 100.0 209
Eastern 92.3 5.1 0.7 1.9 0.1 100.0 1,268
Central 91.4 3.9 0.0 4.7 0.0 100.0 1,113
Rift Valley 84.5 10.7 2.5 2.3 0.0 100.0 2,171
Western 81.8 11.4 4.0 2.7 0.2 100.0 929
Nyanza 79.5 14.9 3.7 1.9 0.0 100.0 1,203
Nairobi 94.5 4.1 0.4 0.5 0.5 100.0 968
Education
No education 66.8 25.0 6.8 1.3 0.0 100.0 795
Primary incomplete 82.3 12.4 2.8 2.3 0.1 100.0 2,274
Primary complete 88.9 7.1 1.5 2.3 0.2 100.0 2,465
Secondary+ 91.7 5.1 0.6 2.5 0.1 100.0 3,177
Wealth quintile
Lowest 74.6 18.9 4.9 1.7 0.1 100.0 1,457
Second 85.4 10.1 2.1 2.3 0.1 100.0 1,567
Middle 87.9 6.9 2.5 2.4 0.3 100.0 1,663
Fourth 87.5 9.0 1.3 2.2 0.1 100.0 1,885
Highest 92.3 4.8 0.2 2.6 0.1 100.0 2,138
Total 86.2 9.4 2.0 2.3 0.1 100.0 8,710
Women in the North Eastern region are most likely to be in polygamous unions (32 percent),
followed by those in Nyanza (19 percent). Six percent or less of women in Nairobi and the Eastern and
Central regions are in polygamous unions. Eighteen percent of men in the North Eastern region report
being in a polygynous union, followed by 12 percent in Nyanza and 8 percent in Coast. Education is
negatively associated with polygyny for both women and men, with the proportion of women in a
polygynous union decreasing from 32 percent among those with no education to 6 percent among those
with at least some secondary education and, similarly, from 16 percent to 4 percent among men. Among
both women and men, wealth quintile is also negatively associated with polygyny. The proportion of
women in a polygynous union ranges from 5 percent among those in the highest wealth quintile to 24
percent among those in the lowest wealth quintile, and for men, from 4 percent to 11 percent.
Marriage and Sexual Activity • 57
Table 4.2.2 Number of men’s wives
Percent distribution of currently married men age 15-49 by number of
wives, according to background characteristics, Kenya 2014
Background Number of wives Number of
characteristic 1 2+ Total men
Age
15-19 * * 100.0 16
20-24 98.7 1.3 100.0 377
25-29 97.8 2.2 100.0 1,201
30-34 96.3 3.7 100.0 1,398
35-39 92.9 7.1 100.0 1,277
40-44 92.7 7.3 100.0 1,100
45-49 89.1 10.9 100.0 727
Residence
Urban 95.6 4.4 100.0 2,894
Rural 93.6 6.4 100.0 3,201
Region
Coast 91.9 8.1 100.0 617
North Eastern 81.9 18.1 100.0 103
Eastern 97.0 3.0 100.0 835
Central 97.6 2.4 100.0 773
Rift Valley 94.5 5.5 100.0 1,523
Western 94.1 5.9 100.0 561
Nyanza 88.2 11.8 100.0 767
Nairobi 98.5 1.5 100.0 916
Education
No education 83.6 16.4 100.0 234
Primary incomplete 93.7 6.3 100.0 1,370
Primary complete 94.4 5.6 100.0 1,677
Secondary+ 96.0 4.0 100.0 2,814
Wealth quintile
Lowest 88.9 11.1 100.0 813
Second 93.7 6.3 100.0 1,036
Middle 95.5 4.5 100.0 1,110
Fourth 95.4 4.6 100.0 1,481
Highest 96.5 3.5 100.0 1,655
Total 15-49 94.5 5.5 100.0 6,095
50-54 89.0 11.0 100.0 667
Total 15-54 94.0 6.0 100.0 6,762
Note: An asterisk denotes a figure based on fewer than 25 unweighted
cases that has been suppressed.
4.3 AGE AT FIRST MARRIAGE
The start of marriage is an important social and demographic indicator and, in most societies,
represents the point in a person’s life when childbearing first becomes acceptable. The duration of
exposure to the risk of pregnancy depends primarily on the age at which women first marry. Women who
marry early, on average, are more likely to have their first child at a young age and give birth to more
children overall, contributing to higher fertility. Age at first marriage is defined as the age at which the
respondent began living with her or his first spouse/partner.
Table 4.3 shows the percentage of women and men age 15-49 who were first married by specific
ages, according to current age. Marriage occurs relatively early in Kenya; among women age 25-49, 29
percent were married by age 18, and 48 percent were married by age 20. The median age at first marriage
among women age 25-49 is 20.2 years.
The median age at first marriage does not vary much across the age cohorts from 25-29 to 45-49,
hovering around age 20. However, the proportion of women married by age 15 increases with age from
about 2 percent among those currently age 15-19 to 9 percent among those currently age 40-49. This is an
indication of rising age at first marriage.
Men tend to marry later than women. The median age at first marriage among men age 30-49 is
25.3 years. Eleven percent of men age 25-49 were married by age 20, and less than half (48 percent) were
married before age 25. The median age at first marriage for men is almost constant across the age cohorts,
reflecting stability over time.
58 • Marriage and Sexual Activity
Table 4.3 Age at first marriage
Percentage of women and men age 15-49 who were first married by specific exact ages and median age at first marriage,
according to current age, Kenya 2014
Percentage Median age
Percentage first married by exact age: never Number of at first
Current age 15 18 20 22 25 married respondents marriage
WOMEN
15-19 1.6 na na na na 86.8 5,820 a
20-24 4.4 22.9 40.7 na na 38.8 5,735 a
25-29 8.4 28.4 45.9 62.3 79.1 14.0 6,100 20.5
30-34 6.3 28.6 48.2 63.9 78.5 8.0 4,510 20.2
35-39 6.6 25.5 46.0 64.2 79.8 6.7 3,773 20.4
40-44 8.9 30.5 51.1 67.6 80.3 5.0 2,885 19.9
45-49 9.3 32.5 52.2 68.4 82.2 4.8 2,257 19.8
20-49 7.0 27.4 46.3 na na 15.6 25,259 a
25-49 7.8 28.7 48.0 64.5 79.6 8.8 19,524 20.2
MEN
15-19 0.1 na na na na 99.3 2,540 a
20-24 0.3 2.5 7.9 na na 79.6 2,125 a
25-29 0.1 3.9 12.2 24.0 48.1 37.0 2,104 a
30-34 0.2 4.0 11.3 24.5 49.5 12.6 1,785 25.1
35-39 0.2 3.5 9.6 24.1 51.0 4.8 1,483 24.9
40-44 0.3 3.3 9.5 21.3 43.1 3.2 1,224 25.6
45-49 0.1 2.9 9.3 21.1 44.3 2.7 800 25.8
20-49 0.2 3.4 10.1 na na 29.7 9,522 a
25-49 0.2 3.6 10.7 23.4 47.8 15.4 7,397 a
30-49 0.2 3.5 10.1 23.1 47.7 6.8 5,293 25.3
20-54 0.2 3.3 10.0 na na 27.6 10,279 a
25-54 0.2 3.6 10.6 23.1 47.6 14.1 8,153 a
30-54 0.2 3.4 10.0 22.8 47.5 6.1 6,049 25.3
Note: The age at first marriage is defined as the age at which the respondent began living with her/his first spouse/partner.
na = Not applicable due to censoring
a = Omitted because less than 50 percent of the women or men began living with their spouse or partner for the first time
before reaching the beginning of the age group
Table 4.4 shows the median age at first marriage Table 4.4 Median age at first marriage by background
among women age 25-49 and men age 30-54, according to characteristics
background characteristics. Urban women marry two years later Median age at first marriage among women age 25-
49, and median age at first marriage among men age
than rural women (21.5 years and 19.5 years, respectively). 30-54, according to background characteristics,
Kenya 2014
Women from Nairobi, the region with the highest median at
Background Women age Men age
22.1 years, marry about three years later than women from the characteristic 25-49 30-54
North Eastern, Nyanza, and Western regions. Median age at Residence
first marriage increases with increasing education. Women with Urban 21.5 25.9
Rural 19.5 24.8
at least some secondary education marry about five years later Region
than those with no education (22.7 years and 17.9 years, Coast 19.7 26.4
North Eastern 18.6 24.9
respectively). Also, women from the highest wealth quintile Eastern 20.5 25.5
marry more than four years later than those from the lowest Central
Rift Valley
21.4
20.0
26.0
25.3
quintile. Western 19.2 24.1
Nyanza 18.6 23.8
Nairobi 22.1 26.1
Although some variation exists in the median age at Education
first marriage for men, the range in age at marriage is not as No education
Primary incomplete
17.9
18.3
24.5
24.0
broad by background characteristics as it is for women. Urban Primary complete 19.7 24.5
Secondary+ 22.7 26.3
men marry one year later than rural men. Men from the Nairobi,
Wealth quintile
Coast, and Central regions have the highest median age at first Lowest 18.3 24.3
Second 19.1 24.6
marriage (26.0 years or greater), while those from the Nyanza Middle 19.6 25.0
and Western regions have the lowest median age (23.8 and 24.1 Fourth 20.6 25.1
Highest 22.6 26.6
years, respectively). Wealth quintile has a positive association Total 20.2 25.3
with men’s age at first marriage, as it does for women, but the
Note: The age at first marriage is defined as the age
age range is only 2 years for men, whereas it is 5 years for at which the respondent began living with her/his first
women. spouse/partner.
Marriage and Sexual Activity • 59
At the county level, median age at first marriage for Table 4.4C Median age at first marriage by county
women is highest in Nairobi (22.1 years), Nyeri (21.8), Kiambu Median age at first marriage among women age
and Embu (21.6 each), and Mombasa (21.5) and lowest in Migori 25-49, and median age at first marriage among
men age 30-54, according to county, Kenya 2014
(17.1), Tana River (17.3), Homa Bay (17.5), Wajir (18.1), and
Women age Men age
Marsabit (18.3). The median age at first marriage for men is County 25-49 30-54
highest in Mombasa (27.7 years) and Marsabit (27.6), and lowest Coast 19.7 26.4
in Migori (22.2) and Busia (22.3) (Table 4.4C). Mombasa
Kwale
21.5
19.1
27.7
25.3
Kilifi 18.9 24.8
4.4 AGE AT FIRST SEXUAL INTERCOURSE Tana River
Lamu
17.3
19.4
24.5
25.1
Taita Taveta 21.4 26.7
Age at first marriage is often used as a proxy for first North Eastern 18.6 24.9
Garissa 18.7 24.8
exposure to sexual intercourse, but the two events do not Wajir 18.1 24.0
necessarily occur at the same time. In the 2014 KDHS, women Mandera 19.0 26.6
and men were asked how old they were when they first had Eastern
Marsabit
20.5
18.3
25.5
27.6
sexual intercourse. Table 4.5 shows the percentage of women and Isiolo 18.5 25.3
Meru 20.3 25.4
men who had first sexual intercourse by specific ages and the Tharaka-Nithi 21.0 25.1
median age at first intercourse, irrespective of marital status. This Embu 21.6 26.5
Kitui 19.8 24.3
information allows an assessment of the age at which women and Machakos 21.0 26.4
Makueni 20.4 24.5
men start having sexual intercourse and the trends across age
Central 21.4 26.0
cohorts. Nyandarua 20.7 25.6
Nyeri 21.8 25.3
Kirinyaga 21.1 25.3
Fifteen percent of women age 20-49 had first sexual Murang’a 21.3 26.6
intercourse by age 15, 50 percent by age 18, and 71 percent by Kiambu 21.6 26.3
age 20. Older women are slightly more likely to have had their Rift Valley
Turkana
20.0
18.9
25.3
24.2
first sexual encounter at an earlier age. Men have an earlier West Pokot 19.0 24.5
Samburu 18.4 26.2
sexual debut than women, a pattern that holds true for most age Trans-Nzoia 19.6 24.6
groups. For example, 22 percent of men age 20-49 had first Uasin Gishu 20.9 25.9
Elgeyo Marakwet 20.5 24.2
sexual intercourse by age 15, 56 percent by age 18, and 76 Nandi 20.7 25.4
Baringo 20.7 26.1
percent by age 20. The median age at first sexual intercourse Laikipia 20.6 26.0
among men age 20-49 (17.4 years) is also slightly lower than that Nakuru 20.6 25.6
Narok 18.6 24.9
among women (18.0 years). Kajiado 21.3 26.7
Kericho 19.5 25.0
Bomet 18.9 24.8
Three percent of both women and men age 20-49 have
Western 19.2 24.1
never had sexual intercourse, while 37 percent of women and 38 Kakamega 19.2 24.7
Vihiga 20.6 24.6
percent of men age 15-24 have never had sexual intercourse. Bungoma 19.2 23.9
There does not appear to be a change in age at first sex compared Busia 18.4 22.3
with the 2008-09 KDHS. Nyanza 18.6 23.8
Siaya 19.1 24.3
Kisumu 19.1 24.5
Homa Bay 17.5 23.8
Migori 17.1 22.2
Kisii 19.3 23.4
Nyamira 19.7 24.5
Nairobi 22.1 26.1
Total 20.2 25.3
Note: The age at first marriage is defined as the
age at which the respondent began living with
her/his first spouse/partner.
60 • Marriage and Sexual Activity
Table 4.5 Age at first sexual intercourse
Percentage of women and men age 15-49 who had first sexual intercourse by specific exact ages, percentage who never
had sexual intercourse, and median age at first sexual intercourse, according to current age, Kenya 2014
Percentage
who never Median age
Percentage who had first sexual intercourse by exact age: had at first
Current age 15 18 20 22 25 intercourse Number intercourse
WOMEN
15-19 10.7 na na na na 62.7 5,820 a
20-24 13.6 46.7 71.1 na na 10.7 5,735 18.2
25-29 14.9 49.0 69.8 81.8 90.6 1.7 6,100 18.1
30-34 13.8 49.1 69.4 80.8 88.6 0.7 4,510 18.1
35-39 14.7 50.1 69.6 81.8 89.1 0.4 3,773 18.0
40-44 17.0 52.2 72.2 83.7 89.6 0.4 2,885 17.8
45-49 16.4 54.3 73.4 83.0 90.7 0.3 2,257 17.6
20-49 14.7 49.5 70.6 na na 3.1 25,259 18.0
25-49 15.1 50.3 70.4 82.0 89.7 0.9 19,524 18.0
15-24 12.1 na na na na 36.9 11,555 a
MEN
15-19 19.6 na na na na 59.4 2,540 a
20-24 22.6 57.2 78.6 na na 11.5 2,125 17.3
25-29 25.0 60.3 78.5 88.3 93.9 2.8 2,104 17.0
30-34 23.1 55.7 76.1 88.3 93.9 0.6 1,785 17.4
35-39 17.3 54.4 73.6 86.7 92.2 0.3 1,483 17.6
40-44 20.7 52.9 74.5 85.6 90.3 0.3 1,224 17.7
45-49 16.2 54.0 74.1 84.7 91.9 0.3 800 17.6
20-49 21.6 56.3 76.4 na na 3.4 9,522 17.4
25-49 21.3 56.1 75.8 87.2 92.8 1.1 7,397 17.4
15-24 21.0 na na na na 37.6 4,666 a
20-54 21.3 56.0 76.1 na na 3.2 10,279 17.4
25-54 20.9 55.7 75.5 86.8 92.6 1.0 8,153 17.4
na = Not applicable due to censoring
a = Omitted because less than 50 percent of the respondents had sexual intercourse for the first time before reaching the
beginning of the age group
Table 4.6 shows the median age at first Table 4.6 Median age at first sexual intercourse by background
sexual intercourse among women age 20-49 and characteristics
25-49 and men age 20-54 and 25-54 by Median age at first sexual intercourse among women age 20-49 and
age 25-49, and median age at first sexual intercourse among men age
background characteristics. Women in rural areas 20-54 and age 25-54, according to background characteristics, Kenya
2014
initiate sexual activity slightly earlier than their
Women age Men age
urban counterparts. Among women age 20-49, Background
characteristic 20-49 25-49 20-54 25-54
sexual activity begins earliest in the Nyanza Residence
region (16.4 years) and latest in Nairobi (19.3 Urban
Rural
18.8
17.4
18.8
17.3
17.6
17.3
17.6
17.3
years). With respect to education, women with at Region
least some secondary education begin sexual Coast
North Eastern
18.3
19.0
18.2
18.9
18.2
a
18.3
24.1
activity three years later than those with no Eastern 18.0 17.9 16.2 16.1
Central 19.1 19.0 18.3 18.4
education. Similarly, women in the highest wealth Rift Valley 17.7 17.7 16.9 16.9
Western 17.1 16.9 17.1 17.1
quintile tend to initiate sexual activity three years Nyanza 16.4 16.3 17.1 17.2
later than those in the lowest. Nairobi 19.3 19.6 17.8 17.9
Education
No education 16.4 16.5 18.2 18.3
The data for men age 25-54 show fewer Primary incomplete 16.2 16.2 16.5 16.6
Primary complete 17.5 17.6 17.2 17.3
and less dramatic patterns than those seen among Secondary+ 19.5 19.7 17.8 17.9
women. There are minimal differences in median Wealth quintile
Lowest 16.6 16.6 17.3 17.3
age by residence, education, and wealth. By Second 16.9 16.9 16.9 16.9
region, however, differences in median age at first Middle
Fourth
17.4
18.2
17.3
18.1
17.1
17.4
17.2
17.4
sex are more substantial. The median age at first Highest 19.6 19.7 18.0 18.1
sex for men in the Eastern region is 16.1 years, as Total 18.0 18.0 17.4 17.4
compared with 24.1 years for men in the North a = Omitted because less than 50 percent of the respondents had
intercourse for the first time before reaching the beginning of the age
Eastern region. group
Marriage and Sexual Activity • 61
At the county level, the five counties with Table 4.6C Median age at first sexual intercourse by county
the highest median age at first sexual intercourse for Median age at first sexual intercourse among women age 20-49 and
women are Mandera (19.4 years), Tharaka-Nithi age 25-49, and median age at first sexual intercourse among men
age 20-54 and age 25-54, according to county, Kenya 2014
(19.4), Kiambu (19.4), Mombasa (19.3), and
Women age Men age
Nairobi (19.3). The counties with the lowest median County 20-49 25-49 20-54 25-54
age include Migori (15.5), Homa Bay (15.7),
Coast 18.3 18.2 18.2 18.3
Samburu (15.7), Kisumu (16.4), and Siaya (16.6). Mombasa 19.3 19.3 18.1 18.1
Kwale 16.6 16.3 18.5 18.5
For men, Garissa (23.6) and Wajir (22.7) counties Kilifi 18.4 18.3 17.7 17.8
recorded the highest median age at first sexual Tana River 17.0 17.0 18.9 19.6
Lamu 18.8 18.6 18.8 18.9
intercourse, while Meru (14.4) and Samburu (15.0) Taita Taveta 19.0 19.0 17.8 17.8
had the lowest median age at first sexual intercourse North Eastern 19.0 18.9 a 24.1
Garissa 19.1 18.9 a 23.6
(Table 4.6C). Wajir 18.5 18.5 a 22.7
Mandera 19.4 19.3 a a
4.5 RECENT SEXUAL ACTIVITY Eastern 18.0 17.9 16.2 16.1
Marsabit 17.5 17.5 17.9 17.9
Isiolo 18.2 18.5 17.1 17.1
In the absence of contraception, the Meru 17.1 17.0 14.6 14.4
Tharaka-Nithi 19.4 19.5 16.3 16.0
probability of pregnancy is related to the regularity Embu 19.1 18.9 18.0 18.3
of sexual intercourse. Thus, information on sexual Kitui
Machakos
17.7
18.3
17.6
18.2
16.9
15.8
17.0
15.6
activity can give added perspective to measurement Makueni 18.1 18.0 17.9 17.8
of exposure to pregnancy. The 2014 KDHS asked Central 19.1 19.0 18.3 18.4
Nyandarua 18.9 18.8 18.3 18.4
all women and men how long ago their last sexual Nyeri 18.7 18.6 19.0 18.9
intercourse occurred. Tables 4.7.1 and 4.7.2 show Kirinyaga
Murang’a
18.9
19.1
18.9
19.0
17.5
19.0
17.3
19.2
the percent distribution of women and men age 15- Kiambu 19.4 19.3 18.0 18.2
49 by the timing of their last sexual intercourse, Rift Valley 17.7 17.7 16.9 16.9
Turkana 17.6 18.1 18.0 18.0
according to background characteristics. West Pokot 17.0 17.2 15.8 15.9
Samburu 15.7 15.7 14.9 15.0
Trans-Nzoia 17.8 17.7 17.5 17.6
Fourteen percent of women and 15 percent Uasin Gishu 17.3 17.2 16.1 16.2
of men age 15-49 have never had sexual Elgeyo Marakwet
Nandi
17.9
16.9
17.8
16.7
16.3
16.7
16.4
16.7
intercourse. Twelve percent of women and 10 Baringo 17.6 17.7 16.6 16.6
Laikipia 18.1 18.1 18.2 18.3
percent of men report that their last sexual Nakuru 18.7 18.6 17.2 16.9
encounter occurred more than one year before the Narok 16.7 16.7 17.8 17.6
Kajiado 18.3 18.3 16.6 16.3
survey. Slightly more than half of women (51 Kericho 17.4 17.5 17.0 17.0
Bomet 17.5 17.5 16.6 16.7
percent) and men (54 percent) reported that they had
Western 17.1 16.9 17.1 17.1
a recent sexual encounter (within the last four Kakamega 17.0 16.8 16.7 16.5
weeks). The proportion of both women and men Vihiga
Bungoma
18.1
17.1
18.0
16.9
17.7
17.1
17.5
17.3
who reported having a recent sexual encounter is Busia 16.6 16.5 17.4 17.4
similar to that observed in the 2008-09 KDHS. Nyanza 16.4 16.3 17.1 17.2
Siaya 16.6 16.5 16.6 16.7
Kisumu 16.4 16.2 17.9 18.0
The proportion of women who were Homa Bay 15.7 15.6 15.6 15.8
Migori 15.5 15.4 17.0 17.2
sexually active within the last four weeks is lowest Kisii 17.2 17.3 17.7 17.8
among those in the youngest age group of 15-19 (11 Nyamira 17.1 17.1 18.1 18.1
percent) and highest among those age 30-34 (69 Nairobi 19.3 19.6 17.8 17.9
percent). Similarly, the proportion of men sexually Total 18.0 18.0 17.4 17.4
active in the last four weeks ranges from 10 percent a = Omitted because less than 50 percent of the respondents had
intercourse for the first time before reaching the beginning of the age
among those age 15-19 to 80 percent among those group
age 40-44. By marital status, recent sexual activity
is most common among those currently married or living together, with 80 percent of married women and
84 percent of married men having had sex in the four weeks before the survey. Male-female differences are
greatest among those who have never been married and those who were formerly married. The proportion
of never-married men who reported having a recent sexual encounter is about three times that of women
(20 percent and 7 percent, respectively), and the proportion among formerly married men is more than
twice that among women (37 percent and 17 percent, respectively). These patterns are similar to patterns in
the 2003 and 2008-09 KDHS surveys.
62 • Marriage and Sexual Activity
Recent sexual activity for both women and men differed by rural-urban residence, with women
and men in urban areas somewhat more likely than those in rural areas to report being sexually active in
the four weeks preceding the survey. By region, women in Nairobi and Central are most likely to have
been sexually active in the four weeks before the survey (55 percent), although regional differences are
minimal. Regional variations are wider for men, with Nairobi having the highest proportion of men with
recent sexual activity (61 percent) and North Eastern having the lowest (37 percent). There are no patterns
evident in recent sexual activity by educational level or wealth among women and no evident pattern for
education among men. The proportion of men with recent sexual activity is higher among those in the
highest wealth quintile (61 percent) than among those in the other quintiles (50-53 percent).
Table 4.7.1 Recent sexual activity: Women
Percent distribution of women age 15-49 by timing of last sexual intercourse, according to background characteristics, Kenya
2014
Timing of last sexual intercourse Never had
Background Within the past One or more sexual Number of
characteristic 4 weeks Within 1 year1 years intercourse Total women
Age
15-19 11.4 15.3 9.6 63.7 100.0 2,717
20-24 49.2 28.0 12.3 10.5 100.0 2,691
25-29 63.0 25.9 9.0 1.9 100.0 2,932
30-34 69.0 22.3 7.8 0.8 100.0 2,162
35-39 64.9 20.3 14.2 0.6 100.0 1,780
40-44 60.3 20.0 19.1 0.4 100.0 1,292
45-49 57.1 17.4 25.0 0.4 100.0 1,052
Marital status
Never married 6.8 24.7 18.8 49.5 100.0 4,255
Married or living together 79.5 18.1 2.3 0.0 100.0 8,710
Divorced/separated/widowed 17.3 35.3 47.3 0.0 100.0 1,660
Marital duration2
0-4 years 79.7 18.9 1.3 0.0 100.0 1,992
5-9 years 80.5 18.3 1.1 0.0 100.0 1,747
10-14 years 79.5 18.6 1.8 0.0 100.0 1,593
15-19 years 81.9 15.1 2.8 0.0 100.0 1,260
20-24 years 76.0 20.1 3.9 0.0 100.0 807
25+ years 72.6 20.3 6.9 0.0 100.0 700
Married more than once 83.7 14.1 2.2 0.0 100.0 612
Residence
Urban 53.9 22.4 11.8 11.7 100.0 5,929
Rural 49.5 21.6 12.5 16.3 100.0 8,696
Region
Coast 52.5 21.1 10.3 16.1 100.0 1,421
North Eastern 48.7 19.0 9.0 22.4 100.0 299
Eastern 52.8 20.3 10.2 16.7 100.0 2,066
Central 54.8 18.3 13.0 13.8 100.0 1,905
Rift Valley 49.2 23.4 14.1 13.2 100.0 3,714
Western 46.5 23.1 11.8 18.6 100.0 1,571
Nyanza 50.6 23.2 12.9 13.2 100.0 1,908
Nairobi 55.0 23.7 11.5 9.4 100.0 1,742
Education
No education 55.0 25.4 14.8 4.3 100.0 1,015
Primary incomplete 51.2 19.6 11.3 17.8 100.0 3,793
Primary complete 59.6 21.5 12.4 6.5 100.0 3,543
Secondary+ 46.1 23.1 12.3 18.4 100.0 6,274
Wealth quintile
Lowest 48.0 24.6 13.7 13.4 100.0 2,236
Second 50.7 20.4 12.5 16.3 100.0 2,590
Middle 49.3 22.3 12.2 16.2 100.0 2,859
Fourth 53.9 21.5 11.6 13.1 100.0 3,113
Highest 53.0 21.7 11.6 13.5 100.0 3,827
Total 51.3 22.0 12.2 14.4 100.0 14,625
Note: Totals may not add up to 100 percent because women with missing information are not shown separately.
1
Excludes women who had sexual intercourse within the last 4 weeks
2
Excludes women who are not currently married
Marriage and Sexual Activity • 63
Table 4.7.2 Recent sexual activity: Men
Percent distribution of men age 15-49 by timing of last sexual intercourse, according to background characteristics, Kenya 2014
Timing of last sexual intercourse Never had
Background Within the past One or more sexual Number of
characteristic 4 weeks Within 1 year1 years intercourse Total men
Age
15-19 9.6 15.8 15.0 59.4 100.0 2,540
20-24 38.0 34.8 15.6 11.5 100.0 2,125
25-29 62.2 26.6 7.9 2.8 100.0 2,104
30-34 75.2 19.0 5.1 0.6 100.0 1,785
35-39 77.8 16.4 5.4 0.3 100.0 1,483
40-44 79.6 15.7 4.2 0.3 100.0 1,224
45-49 77.2 16.5 5.8 0.3 100.0 800
Marital status
Never married 20.1 27.7 17.7 34.3 100.0 5,350
Married or living together 84.4 14.5 0.9 0.0 100.0 6,095
Divorced/separated/widowed 37.3 38.9 23.8 0.0 100.0 618
Marital duration2
0-4 years 84.6 13.7 0.8 0.0 100.0 1,483
5-9 years 82.7 16.7 0.5 0.0 100.0 1,282
10-14 years 85.2 13.5 1.2 0.0 100.0 1,089
15-19 years 84.1 14.7 1.1 0.0 100.0 735
20-24 years 82.4 15.9 1.7 0.0 100.0 358
25+ years 87.4 12.2 0.4 0.0 100.0 127
Married more than once 85.7 13.5 0.7 0.0 100.0 1,021
Residence
Urban 56.7 24.1 8.4 10.4 100.0 5,300
Rural 51.0 19.6 10.4 19.0 100.0 6,762
Region
Coast 48.8 23.1 11.0 16.7 100.0 1,260
North Eastern 37.1 9.9 9.8 43.1 100.0 227
Eastern 51.7 20.6 11.9 15.9 100.0 1,825
Central 54.4 20.4 9.5 15.5 100.0 1,564
Rift Valley 54.0 22.1 9.6 14.3 100.0 3,050
Western 48.3 19.1 11.1 21.4 100.0 1,164
Nyanza 56.1 20.6 7.5 15.6 100.0 1,405
Nairobi 61.0 26.3 6.2 5.9 100.0 1,568
Education
No education 58.4 21.0 9.4 11.1 100.0 345
Primary incomplete 48.6 17.9 9.4 24.1 100.0 3,071
Primary complete 64.0 21.1 8.2 6.4 100.0 2,734
Secondary+ 50.8 23.8 10.2 14.9 100.0 5,913
Wealth quintile
Lowest 49.6 18.3 11.5 20.4 100.0 1,691
Second 50.9 21.4 10.0 17.7 100.0 2,145
Middle 50.1 21.3 10.8 17.6 100.0 2,370
Fourth 52.7 24.0 8.8 14.4 100.0 2,959
Highest 61.1 21.5 7.6 9.3 100.0 2,897
Total 15-49 53.5 21.6 9.5 15.2 100.0 12,063
50-54 75.1 17.0 7.2 0.1 100.0 756
Total 15-54 54.7 21.3 9.4 14.3 100.0 12,819
Note: Totals may not add up to 100 percent because men with missing information are not shown separately.
1
Excludes men who had sexual intercourse within the last 4 weeks
2
Excludes men who are not currently married
64 • Marriage and Sexual Activity
FERTILITY 5
Andrew Kyalo Mutuku, Samwel Ogola, Michael Musyoka
Key Findings
• The total fertility rate for the three years preceding the survey is 3.9 births
per woman, with rural women having at least one child more than urban
women.
• Fertility has decreased from 4.9 births per woman in 2003 to 3.9 births
per woman in 2014, a one-child decline in the past 10 years.
• Half of births occur within three years of a previous birth, with 18 percent
occurring within 24 months.
• Childbearing begins early in Kenya, with almost one-quarter of women
giving birth by age 18 and nearly half by age 20.
• Eighteen percent of adolescent women age 15-19 are already mothers or
pregnant with their first child. In the last five years, teenage pregnancy
has remained unchanged.
O
ne of the major objectives of the 2014 KDHS was to examine fertility levels, trends, and
differentials in Kenya. Fertility is a principal component of population change that contributes to
the size, structure, and composition of the population in a country. This chapter focuses on a
number of fertility indicators including levels, patterns, and trends in both current and cumulative fertility;
the length of birth intervals; and the age at which women begin childbearing. Birth intervals are important
because short intervals are associated with high childhood mortality. The age at which childbearing begins
can have a major impact on the health and well-being of both the mother and the child.
To generate data on fertility, a birth history was collected from each woman interviewed in the
2014 KDHS. Women were asked to report on the total number of sons and daughters to whom they had
given birth in their lifetime. To ensure that all information was reported, women were asked separately
about children still living at home, those living elsewhere, and those who had died. The sex, date of birth,
and survival status of each child were obtained, and age at death for deceased children was recorded.
5.1 CURRENT FERTILITY
Measures of current fertility are presented in Table 5.1 for the three-year period preceding the
survey, corresponding to the calendar period 2011-2014. A three-year period was used for calculating these
rates to provide the most current information while also allowing the rates to be calculated for a sufficient
number of cases so as not to compromise the statistical precision of the estimate. Age-specific fertility
rates (ASFRs), expressed as the number of births per thousand women in a specified age group, show the
age pattern of fertility.1 The total fertility rate (TFR) is the number of live births a woman would have if
she were subject to the current age-specific fertility rates throughout her reproductive period (age 15-49).
More generalised indicators of fertility include the general fertility rate (GFR), expressed as the annual
number of live births per 1,000 women age 15-44, and the crude birth rate (CBR), expressed as the annual
number of live births per 1,000 population.
1
Numerators for ASFRs are calculated by summing the live births that occurred in the three-year period preceding the
survey classified according to the age of the mother (in five-year age groups) at the time of the child’s birth. The
denominators of the rates represent the number of woman-years lived by the survey respondents in each of the five-
year age groups during the specified period.
Fertility • 65
Table 5.1 shows that the TFR is 3.9 births per woman. Table 5.1 Current fertility
This means that a Kenyan woman would bear about four Age-specific and total fertility rates, the general fertility
children in her lifetime if fertility were to remain constant at rate, and the crude birth rate for the three years
preceding the survey, by residence, Kenya 2014
current levels. This represents a decrease since the 2008-09
Residence
KDHS, when the TFR was 4.6 births per woman. The TFR is Age group Urban Rural Total
higher among rural women than urban women (4.5 and 3.1, 15-19 81 106 96
respectively), and this trend is evident across all age groups. 20-24 164 248 206
25-29 149 214 183
The largest absolute difference is seen among women age 20- 30-34 119 170 148
24; the ASFR for rural women of this age is 248 births per 35-39
40-44
73
23
116
45
100
38
1,000, compared with 164 per 1,000 among urban women. 45-49 6 10 9
Rural-urban differences appear to be narrowing over time. In TFR (15-49) 3.1 4.5 3.9
GFR 118 158 141
the 2008-09 KDHS, the TFR was 5.2 in rural areas and 2.9 in CBR 31.0 30.3 30.5
urban areas. The overall age pattern, as reflected in the ASFRs,
Note: Age-specific fertility rates are per 1,000 women.
indicates that fertility is low among adolescents, increases to a Rates for age group 45-49 may be slightly biased due
to truncation. Rates are for the period 1-36 months
peak of 206 births per 1,000 among women age 20-24, and prior to interview.
declines thereafter. The table also shows a GFR of 141 live TFR: Total fertility rate expressed per woman
GFR: General fertility rate expressed per 1,000
births per 1,000 women and a CBR of 30.5 live births per 1,000 women age 15-44
CBR: Crude birth rate, expressed per 1,000
population. This is a decrease from the figures of 161 and 34.8, population
respectively, reported in the 2008-09 KDHS.
5.2 FERTILITY DIFFERENTIALS
This section examines the association between a woman’s background characteristics and her
fertility. Table 5.2 presents the TFR, the percentage of women age 15-49 who are currently pregnant, and
the mean number of children ever born to women age 40-49 by background characteristics. Fertility is
lowest in Nairobi and Central region (TFRs of 2.7 and 2.8, respectively) and highest in North Eastern (6.4).
Table 5.2 Fertility by background characteristics
Total fertility rate for the three years preceding the survey, percentage
of women age 15-49 currently pregnant, and mean number of children
ever born to women age 40-49 years, by background characteristics,
Kenya 2014
Mean number
Percentage of of children
women age ever born to
Background Total fertility 15-49 currently women age
characteristic rate pregnant 40-49
Residence
Urban 3.1 6.0 3.9
Rural 4.5 6.4 5.6
Region
Coast 4.3 6.6 5.5
North Eastern 6.4 12.0 7.1
Eastern 3.4 4.6 4.7
Central 2.8 4.8 3.7
Rift Valley 4.5 7.0 5.5
Western 4.7 6.7 6.1
Nyanza 4.3 5.9 5.8
Nairobi 2.7 6.8 3.1
Education
No education 6.5 11.0 6.5
Primary incomplete 4.8 6.3 6.0
Primary complete 4.2 6.3 5.1
Secondary+ 3.0 5.4 3.7
Wealth quintile
Lowest 6.4 9.4 6.7
Second 4.7 6.5 5.9
Middle 3.8 5.7 5.5
Fourth 3.1 5.7 4.3
Highest 2.8 5.0 3.4
Total 3.9 6.3 5.0
Note: Total fertility rates are for the period 1-36 months prior to
interview.
66 • Fertility
Fertility rates decrease as women’s education and wealth increase. Table 5.2 shows that the TFR
decreases from 6.5 among women with no education to 4.8 among women with some education and further
to 3.0 among women with a secondary or higher education. Fertility is also closely associated with wealth,
with women in the lowest quintile (6.4) having more children than those in the highest quintile (2.8).
Table 5.2 shows that 6 percent of women were pregnant at the time of the survey. This may be an
underestimate as women in the early stages of pregnancy may be unaware or unsure that they are pregnant,
and some may choose not to declare that they are pregnant. Differentials in pregnancy rates are generally
consistent with the pattern of fertility depicted across the various subgroups. The proportion of women
who are pregnant is highest in North Eastern region (12 percent) and lowest in Eastern and Central (5
percent each). The findings show that the proportion of women who are currently pregnant declines as the
level of education increases, from 11 percent among those with no education to 5 percent among those
with a secondary or higher education.
Comparison of the mean number of lifetime births to older women with the current TFR can
provide some insight into changes in fertility over the previous two decades or so. For example, the 2014
KDHS data show that the mean number of children ever born to women age 40-49 is 5.0, a decline from
the figure of 5.6 reported in the 2008-09 KDHS and approximately one child more than the current TFR
(3.9). On average, rural women age 40-49 have given birth to 5.6 children, as compared with only 3.9
among their urban counterparts. Women age 40-49 in Nairobi have the lowest mean number of children
ever born (3.1), while those in the North Eastern region have the highest (7.1). The largest absolute
differences between completed fertility at age 40-49 and the level of current fertility occur in Nyanza and
Western regions (1.5 and 1.4, respectively). The mean number of children born to women age 40-49
decreases as education and wealth quintile increase.
Table 5.2C shows indicators of fertility by county. The county with the lowest TFR is Kirinyaga
(2.3), followed by Nyeri, Kiambu, and Nairobi (2.7 each). The counties with the highest TFR are Wajir
(7.8), West Pokot (7.2), Turkana (6.9), and Samburu (6.3). Counties with higher TFRs tend to be in
northern Kenya.
Fertility • 67
Table 5.2C Fertility
Total fertility rate for the three years preceding the survey,
percentage of women age 15-49 currently pregnant, and mean
number of children ever born to women age 40-49 years, by county,
Kenya 2014
Mean number
Percentage of of children
women age ever born to
Total fertility 15-49 currently women age
County rate pregnant 40-49
Coast 4.3 6.6 5.5
Mombasa 3.2 5.4 4.1
Kwale 4.7 7.5 5.8
Kilifi 5.1 7.1 6.4
Tana River 5.8 10.2 7.4
Lamu 4.3 5.6 5.0
Taita Taveta 3.2 3.7 4.3
North Eastern 6.4 12.0 7.1
Garissa 6.1 11.7 6.8
Wajir 7.8 13.6 7.9
Mandera 5.2 10.6 6.4
Eastern 3.4 4.6 4.7
Marsabit 5.0 12.7 6.0
Isiolo 4.9 6.2 6.1
Meru 3.1 4.8 4.3
Tharaka-Nithi 3.4 4.4 4.3
Embu 3.1 4.5 4.1
Kitui 3.9 4.1 5.3
Machakos 3.4 3.9 4.3
Makueni 3.3 4.0 5.5
Central 2.8 4.8 3.7
Nyandarua 3.5 6.0 4.8
Nyeri 2.7 4.8 3.3
Kirinyaga 2.3 4.1 3.4
Murang’a 3.0 4.3 3.9
Kiambu 2.7 5.0 3.6
Rift Valley 4.5 7.0 5.5
Turkana 6.9 10.6 6.4
West Pokot 7.2 10.7 6.4
Samburu 6.3 11.6 6.5
Trans-Nzoia 5.2 6.3 6.6
Uasin Gishu 3.6 8.4 5.3
Elgeyo Marakwet 4.1 5.9 5.8
Nandi 4.0 4.8 6.1
Baringo 4.8 7.8 6.2
Laikipia 3.7 7.9 4.9
Nakuru 3.7 5.3 4.7
Narok 6.0 10.2 6.7
Kajiado 4.5 7.7 4.3
Kericho 4.0 5.7 5.0
Bomet 4.3 5.5 5.7
Western 4.7 6.7 6.1
Kakamega 4.4 7.3 5.4
Vihiga 4.5 6.2 5.3
Bungoma 5.0 6.2 6.9
Busia 4.7 6.8 6.5
Nyanza 4.3 5.9 5.8
Siaya 4.2 5.9 5.9
Kisumu 3.6 5.3 5.6
Homa Bay 5.2 6.4 6.2
Migori 5.3 9.0 7.0
Kisii 3.7 5.0 5.1
Nyamira 3.5 3.2 4.7
Nairobi 2.7 6.8 3.1
Total 3.9 6.3 5.0
Note: Total fertility rates are for the period 1-36 months prior to
interview.
68 • Fertility
5.3 FERTILITY TRENDS
Fertility trends can be further investigated using Table 5.3.1 Trends in age-specific fertility rates
retrospective data from the birth histories collected in the Age-specific fertility rates for five-year periods preceding
2014 KDHS. Table 5.3.1 shows age-specific fertility rates the survey, by mother’s age at the time of the birth, Kenya
2014
for successive five-year periods preceding the 2014 KDHS.
Number of years preceding survey
Because women age 50 or above were not interviewed in the Mother’s
age at birth 0-4 5-9 10-14 15-19
survey, the rates for older age groups are successively 15-19 101 120 132 120
truncated for periods more distant from the survey date. For 20-24 206 228 247 253
25-29 193 216 238 243
example, rates cannot be calculated for women age 35-39 for 30-34 154 192 198 [214]
the period 15-19 years before the survey because these 35-39
40-44
106
40
127
[76]
[160]
women are currently over age 50 and therefore not eligible 45-49 [10]
to be interviewed. Fertility rates are lower in every age group Note: Age-specific fertility rates are per 1,000 women.
during the period 0-4 years before the survey than they are in Estimates in brackets are truncated. Rates exclude the
month of interview.
periods more distant from the survey, suggesting a decline in
fertility over time.
Kenya has undertaken many surveys that have collected data on fertility, allowing for examination
of trends over the last few decades. Figure 5.1 and Table 5.3.2 compare age-specific and total fertility rates
from estimates obtained since 1977. There was a sharp decline in the TFR between the 1977-78 Kenya
Fertility Survey (8.1), the 1989 KDHS (6.7), and the 1993 KDHS (5.4), after which time there was further
decline to 4.7 in the 1998 KDHS. Fertility seemed to rise, albeit marginally, afterwards, to a TFR of 4.9
children reported in 2003. The decrease in the TFR from 4.9 and 4.6 in the 2003 and 2008-09 KDHS
surveys, respectively, to the current 3.9 indicates that Kenya’s fertility is again on the decline. The TFR of
3.9 is the lowest ever recorded in Kenya.
Figure 5.1 Trends in total fertility rate, 1978-2014*
Births per woman
8.1
6.7
5.4
4.7 4.9
4.6
3.9
KFS 1977-78 KDHS 1989 KDHS 1993 KDHS 1998 KDHS 2003 KDHS 2008-09 KDHS 2014
* Data from 2003 and later are nationally representative while data before 2003 exclude North Eastern
region and several northern districts in the Eastern and Rift Valley regions.
Fertility • 69
Table 5.3.2 Trends in age-specific and total fertility rates
Age-specific and total fertility rates (TFR) for the three-year period preceding several surveys
Mother’s 1977/78 KFS1 1989 KDHS1 1993 KDHS1 1998 KDHS1 2003 KDHS 2008-09 KDHS 2014 KDHS
age at birth 1975-78 1984-88 1990-92 1995-97 1999 Census 2000-02 2006-08 2011-2013
15-19 168 152 110 111 142 114 103 96
20-24 342 314 257 248 254 243 238 206
25-29 357 303 241 218 236 231 216 183
30-34 293 255 197 188 185 196 175 148
35-39 239 183 154 109 127 123 118 100
40-44 145 99 70 51 56 55 50 38
45-49 59 35 50 16 7 15 12 9
TFR 15-49 8.1 6.7 5.4 4.7 5.0 4.9 4.6 3.9
Note: Age-specific fertility rates are per 1,000 women. Rates refer to the three-year period preceding the surveys except for the 1989 KDHS,
which used a five-year period and the 1999 census, which used a period that varied with the age groups used to make the adjustment.
Sources: NCPD et al., 1999; Central Bureau of Statistics, 2002b.
1
Data exclude North Eastern region and several northern districts in the Eastern and Rift Valley regions.
Table 5.3.2 and Figure 5.2 show the ASFRs for recent surveys. The largest decline in fertility is
seen among women of peak childbearing ages (20-34).
Figure 5.2 Trends in age-specific fertility rates
Births per 1,000
women
300
250
200
150
100
50
0
15-19 20-24 25-29 30-34 35-39 40-44 45-49
Age group
1998 KDHS 2003 KDHS 2008-09 KDHS 2014 KDHS
Data collected before 2003 exclude North Eastern region and several northern districts in the Eastern and Rift Valley.
5.4 CHILDREN EVER BORN AND LIVING
Information on children ever born (or parity) is useful in looking at how average family size varies
across age groups. The percentage of currently married women in their 40s who have never had children
also provides an indicator of the inability to bear children in societies in which voluntary childlessness is
rare. Comparisons of differences in the mean number of children ever born and surviving reflect the
cumulative effects of mortality levels during the period in which women have been bearing children.
Table 5.4 shows the percent distribution of all women and currently married women by number of
children ever born, mean number of children ever born, and mean number of children living. In Kenya,
childbearing starts early and is nearly universal. Eighty-five percent of women age 15-19 have never given
birth, as compared with only 35 percent of women age 20-24 and 12 percent of women age 25-29. In the
subsequent age groups, the percentage of women who have never given birth drops to 4 percent or lower.
A similar pattern is observed among currently married women. The proportion of women who have never
given birth declines from 32 percent among those age 15-19 to 5 percent or less among those age 25 and
above.
70 • Fertility
Table 5.4 Children ever born and living
Percent distribution of all women and currently married women age 15-49 by number of children ever born, mean number of children ever born and
mean number of living children, according to age group, Kenya 2014
Mean Mean
Number number of number of
Number of children ever born
of children living
Age 0 1 2 3 4 5 6 7 8 9 10+ Total women ever born children
ALL WOMEN
15-19 85.3 12.1 2.3 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 5,820 0.18 0.17
20-24 35.3 32.6 21.4 7.9 2.3 0.3 0.1 0.0 0.0 0.0 0.0 100.0 5,735 1.11 1.05
25-29 11.5 24.0 26.2 19.2 11.5 4.9 1.9 0.7 0.1 0.0 0.0 100.0 6,100 2.22 2.09
30-34 3.7 11.3 23.0 22.8 16.3 9.9 6.9 4.1 1.5 0.3 0.3 100.0 4,510 3.27 3.07
35-39 3.3 6.7 15.0 19.6 16.1 13.5 10.0 7.1 5.0 2.3 1.5 100.0 3,773 4.13 3.81
40-44 2.6 4.7 10.2 15.5 15.5 14.0 12.6 10.0 6.7 3.7 4.6 100.0 2,885 4.85 4.40
45-49 1.9 3.8 9.8 12.9 15.0 13.2 11.9 10.8 7.8 5.0 7.9 100.0 2,257 5.27 4.73
Total 26.1 16.2 16.3 13.3 9.5 6.3 4.6 3.3 2.0 1.0 1.2 100.0 31,079 2.48 2.29
CURRENTLY MARRIED WOMEN
15-19 31.6 51.5 15.5 1.4 0.1 0.0 0.0 0.0 0.0 0.0 0.0 100.0 695 0.87 0.84
20-24 12.5 36.9 33.2 12.8 3.8 0.5 0.2 0.0 0.0 0.0 0.0 100.0 3,133 1.61 1.51
25-29 5.0 21.1 28.9 22.1 13.5 6.1 2.4 0.8 0.1 0.0 0.0 100.0 4,556 2.51 2.37
30-34 1.5 8.0 22.1 23.9 17.9 11.3 8.1 4.7 1.8 0.3 0.3 100.0 3,566 3.54 3.32
35-39 0.9 4.1 13.2 20.9 16.9 14.4 11.2 7.9 5.9 2.7 1.6 100.0 2,894 4.46 4.12
40-44 1.5 2.2 8.2 15.4 16.0 14.9 13.2 11.6 7.2 4.3 5.5 100.0 2,091 5.19 4.73
45-49 0.9 1.9 8.0 12.1 15.5 13.4 12.8 11.0 8.7 5.5 10.1 100.0 1,615 5.65 5.06
Total 5.2 15.9 21.2 18.3 13.2 8.9 6.5 4.6 2.9 1.5 1.8 100.0 18,549 3.37 3.12
Parity increases with age. On average, a woman in Kenya has given birth to more than two
children by her late 20s and to more than four children by her late 30s. These figures are slightly lower
than those reported in the 2008-09 KDHS. In all age groups, the mean number of children ever born is
higher among currently married women than among women overall. The largest difference is in the
youngest age group (15-19). Currently married women age 15-19 have an average of 0.9 children,
compared with 0.2 children among all women age 15-19.
Because voluntary childlessness is rare in Kenya, it might be assumed that most married women
with no births are unable to physiologically bear children. The percentage of women who are childless at
the end of the reproductive period is an indirect measure of primary infertility (the proportion of women
who are unable to bear children at all). Table 5.4 shows that primary infertility is less than 2 percent.
Primary infertility has changed little since 2003.
The last column in Table 5.4 shows the mean number of living children. The difference between
the mean number of children ever born (2.5) and living (2.3) is an indicator of the level of mortality in the
population.
5.5 BIRTH INTERVALS
The length of time between births affects the overall level of fertility and also affects the health of
both the mother and the child. Examining birth intervals provides insights into birth patterns and maternal
and child health. Studies have shown that children born fewer than 24 months after a previous sibling are
at greater risk of having poor health and that such births threaten maternal health. Table 5.5 shows the
percent distribution of non-first births in the five years before the survey by the number of months since
the preceding birth and the median number of months since the preceding birth, according to background
characteristics.
Eighteen percent of Kenyan children are born less than 24 months after a previous birth. The most
common birth interval category is 24-35 months (32 percent), while the least common birth interval is 7-17
months (7 percent). The median birth interval is 36.3 months, a slight increase from 33.1 months in the
2008-09 KDHS. The median birth interval increases with the age of the mother, is longer for children
whose preceding sibling is living (36.8 months) than for those whose preceding sibling is dead (26.9
months), decreases with birth order, and is longer for urban children (41.0 months) than for rural children
(34.7 months). There are regional variations; Central region has the longest median birth interval (53.9
months) and North Eastern the shortest (27.7 months). Median birth intervals increase with increasing
mother’s education and wealth.
Fertility • 71
Table 5.5 Birth intervals
Percent distribution of non-first births in the five years preceding the survey by number of months since preceding birth, and median
number of months since preceding birth, according to background characteristics, Kenya 2014
Median
number of
months
Number of since
Background Months since preceding birth non-first preceding
characteristic 7-17 18-23 24-35 36-47 48-59 60+ Total births birth
Age
15-19 16.3 25.7 40.9 12.2 2.9 2.1 100.0 157 26.3
20-29 8.2 14.1 36.0 18.4 9.9 13.4 100.0 6,860 32.7
30-39 4.9 9.0 27.8 16.9 13.5 27.9 100.0 6,057 40.9
40-49 3.3 6.7 24.1 17.0 12.9 36.0 100.0 1,314 47.4
Sex of preceding
birth
Male 6.3 11.2 31.4 17.9 11.6 21.6 100.0 7,041 36.5
Female 6.6 11.6 31.6 17.3 11.6 21.3 100.0 7,346 36.1
Survival of
preceding birth
Living 5.4 11.0 31.8 18.0 11.9 21.9 100.0 13,546 36.8
Dead 24.0 17.3 27.3 10.8 7.0 13.5 100.0 842 26.9
Birth order
2-3 6.5 11.1 28.3 17.2 11.2 25.7 100.0 7,651 38.0
4-6 6.5 10.7 33.5 17.8 12.7 18.8 100.0 4,902 35.7
7+ 6.3 14.4 39.6 18.6 10.1 11.0 100.0 1,835 32.3
Residence
Urban 6.8 9.9 25.1 16.7 12.1 29.4 100.0 4,540 41.0
Rural 6.3 12.1 34.4 18.0 11.4 17.8 100.0 9,848 34.7
Region
Coast 7.1 10.9 33.5 17.2 10.7 20.6 100.0 1,516 35.2
North Eastern 15.3 18.9 37.5 16.9 5.6 5.8 100.0 574 27.7
Eastern 4.1 8.2 31.0 18.8 11.2 26.8 100.0 1,680 39.8
Central 3.6 6.9 17.5 15.3 14.6 42.1 100.0 1,216 53.9
Rift Valley 6.0 12.0 35.4 18.0 11.3 17.2 100.0 4,255 34.4
Western 6.6 13.7 33.8 17.8 13.8 14.3 100.0 1,780 34.3
Nyanza 7.6 12.7 32.8 17.9 11.1 17.9 100.0 2,184 34.5
Nairobi 6.9 9.3 21.4 16.7 11.8 33.9 100.0 1,182 43.7
Education
No education 8.8 14.2 41.0 18.4 8.1 9.5 100.0 2,060 31.6
Primary incomplete 6.0 12.6 35.9 18.3 10.8 16.3 100.0 4,655 34.2
Primary complete 5.9 11.2 28.3 17.2 12.6 24.8 100.0 4,063 38.0
Secondary+ 6.3 8.5 24.0 16.6 13.5 31.1 100.0 3,610 43.0
Wealth quintile
Lowest 8.1 13.6 42.1 17.9 8.7 9.6 100.0 3,864 31.7
Second 6.1 13.3 34.7 19.0 10.1 16.8 100.0 3,186 34.0
Middle 6.0 10.5 28.2 18.6 14.4 22.1 100.0 2,626 39.1
Fourth 5.2 8.5 25.6 15.5 14.6 30.6 100.0 2,318 43.9
Highest 6.0 9.1 19.5 16.0 12.3 37.0 100.0 2,394 47.5
Total 6.5 11.4 31.5 17.6 11.6 21.4 100.0 14,388 36.3
Note: First-order births are excluded. The interval for multiple births is the number of months since the preceding pregnancy that ended
in a live birth.
By county (Table 5.5C), the median birth interval is longest in Kirinyaga (65.4 months), Nyeri,
Kiambu, Tharaka-Nithi, and Meru (each over 50 months) and shortest in Garissa, Wajir, West Pokot, and
Narok (all less than 30 months).
72 • Fertility
Table 5.5C Birth intervals
Percent distribution of non-first births in the five years preceding the survey by number of months since preceding birth, and median
number of months since preceding birth, according to county, Kenya 2014
Median
number of
months
Number of since
Months since preceding birth non-first preceding
County 7-17 18-23 24-35 36-47 48-59 60+ Total births birth
Coast 7.1 10.9 33.5 17.2 10.7 20.6 100.0 1,516 35.2
Mombasa 6.5 5.9 27.1 14.9 14.4 31.2 100.0 354 41.6
Kwale 5.6 16.3 32.2 18.2 9.2 18.4 100.0 331 34.4
Kilifi 8.5 11.1 37.7 17.1 9.0 16.6 100.0 548 33.4
Tana River 8.4 12.0 37.1 23.0 10.4 9.2 100.0 145 33.4
Lamu 9.9 14.0 34.0 17.2 11.4 13.5 100.0 47 32.1
Taita Taveta 2.4 6.6 30.8 14.1 12.1 34.0 100.0 91 43.0
North Eastern 15.3 18.9 37.5 16.9 5.6 5.8 100.0 574 27.7
Garissa 15.0 21.8 39.7 12.8 4.0 6.8 100.0 207 26.9
Wajir 15.4 19.2 38.3 19.8 3.4 3.8 100.0 230 27.5
Mandera 15.4 13.9 33.0 18.3 11.7 7.7 100.0 138 31.1
Eastern 4.1 8.2 31.0 18.8 11.2 26.8 100.0 1,680 39.8
Marsabit 2.9 13.0 37.6 22.1 12.5 11.9 100.0 73 34.9
Isiolo 10.9 10.0 35.3 18.0 12.1 13.7 100.0 70 34.0
Meru 1.6 5.2 23.1 16.2 10.8 43.1 100.0 348 50.5
Tharaka-Nithi 1.9 6.3 21.2 14.3 15.4 41.0 100.0 107 50.8
Embu 5.4 10.1 19.0 21.2 10.6 33.7 100.0 130 43.9
Kitui 5.2 10.6 44.1 21.0 5.7 13.4 100.0 342 32.9
Machakos 5.2 6.8 26.9 19.2 15.2 26.8 100.0 343 40.4
Makueni 3.5 8.7 36.6 18.9 11.4 20.9 100.0 266 37.0
Central 3.6 6.9 17.5 15.3 14.6 42.1 100.0 1,216 53.9
Nyandarua 3.7 6.9 21.8 20.6 17.3 29.6 100.0 189 46.2
Nyeri 2.4 6.7 16.0 12.3 17.2 45.5 100.0 157 56.6
Kirinyaga 1.9 3.3 15.7 14.3 10.2 54.5 100.0 130 65.4
Murang’a 2.2 5.5 18.6 20.9 12.0 40.7 100.0 227 49.8
Kiambu 5.1 8.4 16.3 11.9 15.2 43.2 100.0 512 54.7
Rift Valley 6.0 12.0 35.4 18.0 11.3 17.2 100.0 4,255 34.4
Turkana 8.6 8.6 41.8 19.3 8.9 12.9 100.0 278 32.4
West Pokot 5.0 20.6 46.6 12.9 6.9 8.0 100.0 255 29.7
Samburu 4.4 13.7 37.4 23.7 10.0 10.8 100.0 95 33.6
Trans-Nzoia 5.6 7.0 39.2 15.8 13.2 19.2 100.0 381 35.5
Uasin Gishu 4.5 9.8 26.4 23.0 12.9 23.3 100.0 332 40.7
Elgeyo Marakwet 5.6 12.8 39.5 16.6 9.9 15.6 100.0 129 32.4
Nandi 6.8 11.3 26.7 20.1 13.4 21.6 100.0 301 38.9
Baringo 7.9 18.0 37.3 13.7 12.0 11.2 100.0 190 30.6
Laikipia 1.9 8.1 28.0 23.0 15.7 23.3 100.0 155 40.7
Nakuru 4.5 12.9 30.7 18.1 13.2 20.6 100.0 665 37.2
Narok 6.1 16.7 46.2 14.7 6.6 9.5 100.0 499 29.9
Kajiado 10.1 11.6 31.2 18.8 8.9 19.4 100.0 322 35.4
Kericho 5.0 8.3 29.4 17.6 15.3 24.4 100.0 268 41.0
Bomet 7.0 9.8 36.2 19.7 12.0 15.4 100.0 385 34.3
Western 6.6 13.7 33.8 17.8 13.8 14.3 100.0 1,780 34.3
Kakamega 6.1 14.3 33.3 17.0 13.8 15.4 100.0 582 34.5
Vihiga 9.1 9.5 31.1 14.6 14.0 21.7 100.0 179 36.1
Bungoma 6.4 15.4 32.0 19.0 15.3 11.9 100.0 703 34.6
Busia 6.7 11.3 40.3 18.5 10.1 13.0 100.0 317 32.7
Nyanza 7.6 12.7 32.8 17.9 11.1 17.9 100.0 2,184 34.5
Siaya 7.7 14.4 32.7 19.3 10.3 15.5 100.0 311 33.9
Kisumu 8.8 7.9 31.0 18.0 13.2 21.1 100.0 379 36.8
Homa Bay 7.4 13.0 34.1 18.3 10.0 17.2 100.0 527 33.8
Migori 8.1 16.8 40.0 16.8 7.9 10.5 100.0 471 30.1
Kisii 6.3 11.8 25.7 16.2 15.1 24.9 100.0 349 38.8
Nyamira 6.5 9.6 26.1 20.8 12.7 24.3 100.0 148 39.5
Nairobi 6.9 9.3 21.4 16.7 11.8 33.9 100.0 1,182 43.7
Total 6.5 11.4 31.5 17.6 11.6 21.4 100.0 14,388 36.3
Note: First-order births are excluded. The interval for multiple births is the number of months since the preceding pregnancy that
ended in a live birth.
Fertility • 73
5.6 POSTPARTUM AMENORRHOEA, ABSTINENCE, AND INSUSCEPTIBILITY
Postpartum amenorrhoea refers to the interval between childbirth and the return of menstruation.
The length and intensity of breastfeeding influence the duration of amenorrhoea, which offers protection
from conception. Postpartum abstinence refers to the period between childbirth and the time when a
woman resumes sexual activity. Women are considered to be insusceptible to pregnancy if they are not
exposed to the risk of conception, either because their menstrual period has not resumed since giving birth
or because they are abstaining from intercourse after childbirth.
Table 5.6 shows that the median duration of amenorrhoea among women who gave birth in the
three years preceding the survey is 6.2 months and the median duration of postpartum abstinence is 3.0
months. The two factors, postpartum amenorrhoea and abstinence, taken together indicate that the median
duration of postpartum insusceptibility to pregnancy is 8.6 months. Almost all women are insusceptible to
pregnancy (98 percent) within the first two months following childbirth. The contribution of abstinence is
greatly reduced after the second month. At 8-9 months, 41 percent of women are still amenorrhoeic, but
only 18 percent are still abstaining. At 26-27 months after birth, insusceptibility drops to 10 percent or less.
Table 5.6 Postpartum amenorrhoea, abstinence and insusceptibility
Percentage of births in the three years preceding the survey for which
mothers are postpartum amenorrhoeic, abstaining, and insusceptible, by
number of months since birth, and median and mean durations, Kenya 2014
Percentage of births for which the mother is:
Months Number of
since birth Amenorrhoeic Abstaining Insusceptible1 births
<2 93.8 91.4 97.6 224
2-3 78.6 51.2 85.7 304
4-5 59.7 30.1 67.4 289
6-7 44.0 20.3 54.5 311
8-9 41.2 17.8 49.2 288
10-11 38.0 16.8 47.2 331
12-13 24.0 13.1 32.4 331
14-15 20.9 7.0 26.1 323
16-17 19.9 6.8 25.5 323
18-19 10.2 3.5 13.2 322
20-21 6.1 9.3 14.9 303
22-23 4.8 8.3 12.8 284
24-25 11.2 9.3 18.3 300
26-27 3.4 3.4 6.6 322
28-29 5.9 4.4 9.9 346
30-31 6.5 3.2 9.7 281
32-33 1.4 1.1 2.5 276
34-35 3.3 4.6 7.7 302
Total 25.2 15.5 31.3 5,462
Median 6.2 3.0 8.6 na
Mean 9.7 6.3 11.9 na
Note: Estimates are based on status at the time of the survey.
na = Not applicable
1
Includes births for which mothers are either still amenorrhoeic or still
abstaining (or both) following birth
Table 5.7 shows the median duration of postpartum amenorrhoea, abstinence, and insusceptibility
by background characteristics. Older women (age 30 and above) have a slightly longer median period of
insusceptibility, even though they have a lower median duration of abstaining, because of the longer
duration of postpartum amenorrhoea. Women living in urban areas report a shorter median duration of
amenorrhoea and, hence, have a shorter period of insusceptibility than rural women (5.1 months versus
10.8 months). The median duration of both amenorrhoea and insusceptibility declines as education
increases. Women in the lowest wealth quintile have the longest durations of amenorrhoea and abstinence,
and thus insusceptibility.
74 • Fertility
Table 5.7 Median duration of amenorrhoea, postpartum abstinence
and postpartum insusceptibility
Median number of months of postpartum amenorrhoea, postpartum
abstinence, and postpartum insusceptibility following births in the
three years preceding the survey, by background characteristics,
Kenya 2014
Background Postpartum Postpartum Postpartum
characteristic amenorrhoea abstinence insusceptibility1
Mother’s age
15-29 5.6 3.3 7.8
30-49 9.3 2.3 9.9
Residence
Urban 4.3 2.9 5.1
Rural 8.7 3.0 10.8
Region
Coast 7.9 3.4 9.3
North Eastern 5.4 2.8 8.2
Eastern 4.3 3.4 6.1
Central 4.8 * 4.9
Rift Valley 8.8 3.8 11.2
Western 5.3 * 10.8
Nyanza 7.5 (2.0) 9.4
Nairobi * * *
Education
No education 10.7 3.7 12.0
Primary incomplete 9.6 2.9 11.1
Primary complete 5.5 2.7 6.9
Secondary+ 4.3 3.0 5.4
Wealth quintile
Lowest 10.9 3.5 12.3
Second 7.3 2.6 10.7
Middle 6.1 2.7 10.5
Fourth 5.1 2.8 5.8
Highest 4.0 2.9 4.5
Total 6.2 3.0 8.6
Note: Figures in parentheses are based on 25-49 unweighted cases.
An asterisk denotes a figure based on fewer than 25 unweighted
cases that has been suppressed. Medians are based on the status at
the time of the survey (current status).
1
Includes births for which mothers are either still amenorrhoeic or still
abstaining (or both) following birth
5.7 MENOPAUSE
Another factor influencing the risk of pregnancy is menopause. Table 5.8 Menopause
In the 2014 KDHS, women were considered menopausal if they were Percentage of women age 30-49 who
neither pregnant nor postpartum amenorrhoeic and had not had a are menopausal, by age, Kenya 2014
menstrual period in the six months preceding the survey (Table 5.8). Percentage Number of
Age menopausal1 women
Prevalence of menopause increases with age, ranging from 5 percent
30-34 4.7 2,162
among women age 30-34 to 45 percent among women age 48-49. The 35-39 6.1 1,780
proportion of women age 30-49 who are menopausal (11 percent) has not 40-41
42-43
9.2
8.9
632
426
changed from the 2008-09 KDHS. 44-45 20.8 520
46-47 24.4 424
48-49 45.1 343
5.8 AGE AT FIRST BIRTH Total 10.7 6,286
The age at which childbearing starts has important consequences 1 Percentage of all women who are not
pregnant and not postpartum
for the overall level of fertility as well as the health and welfare of the amenorrhoeic whose last menstrual
mother and the child. Early age at initiation of childbearing lengthens the period occurred six or more months
preceding the survey
reproductive period. Table 5.9 shows the percentage of women age 15-49
who gave birth by exact ages, the percentage who have never given birth, and the median age at first birth,
according to current age. Medians for women age 15-24 are not presented because less than 50 percent had
given birth before age 15.
Fertility • 75
Table 5.9 Age at first birth
Percentage of women age 15-49 who gave birth by specific exact ages, percentage who have never given birth, and
median age at first birth, according to current age, Kenya 2014
Percentage
who have
Percentage who gave birth by exact age never given Number of Median age at
Current age 15 18 20 22 25 birth women first birth
15-19 1.4 na na na na 85.3 5,820 a
20-24 4.1 23.3 43.0 na na 35.3 5,735 a
25-29 4.9 25.7 46.8 64.4 81.3 11.5 6,100 20.3
30-34 3.7 24.8 48.3 67.7 82.9 3.7 4,510 20.2
35-39 4.4 21.7 44.4 65.5 83.6 3.3 3,773 20.5
40-44 4.7 25.0 47.5 68.4 85.2 2.6 2,885 20.2
45-49 5.4 29.8 50.7 69.5 85.0 1.9 2,257 19.9
20-49 4.5 24.7 46.3 na na 12.4 25,259 a
25-49 4.6 25.1 47.2 66.6 83.1 5.7 19,524 20.3
na = Not applicable due to censoring
a = Omitted because less than 50 percent of women had a birth before reaching the beginning of the age group
The median age at first birth for women age 25-29 is 20.3 years and is relatively unchanged from
that reported in the 2008-09 KDHS (19.8). One-quarter of Kenyan women age 25-49 (25 percent) have
given birth by age 18, while about half (47 percent) have given birth by age 20. The median age at first
birth does not vary much across age groups, indicating no change over time in median age at first birth.
Table 5.10 presents the median age at first birth among women age 25-49 by background
characteristics. In all age groups, women in urban areas have a slightly higher median age at first birth than
their rural counterparts. The highest median age at first birth among women age 25-49 was recorded in
Nairobi (22.2), while the lowest was observed in Nyanza (18.9). This implies that, on average, women in
Nyanza have their first birth about three years earlier than those in Nairobi. Table 5.10C reflects these
regional findings in that the counties of Homa Bay, Migori, and Siaya, all located in Nyanza, report some
of the lowest median ages at first birth.
Table 5.10 Median age at first birth
Median age at first birth among women age 25-49 years, by background characteristics,
Kenya 2014
Background Women age
characteristic 25-29 30-34 35-39 40-44 45-49 25-49
Residence
Urban 21.6 21.2 21.4 20.9 20.7 21.3
Rural 19.4 19.5 20.0 19.9 19.7 19.7
Region
Coast 20.2 20.5 20.8 20.2 20.0 20.3
North Eastern 19.6 19.2 20.7 21.4 25.3 20.2
Eastern 20.2 20.1 20.5 20.4 20.1 20.3
Central 21.1 21.1 21.1 20.6 20.2 20.9
Rift Valley 20.1 20.1 20.3 20.1 20.0 20.1
Western 19.4 19.3 19.9 19.6 19.5 19.6
Nyanza 18.7 18.6 19.4 19.1 18.9 18.9
Nairobi 22.7 21.9 21.9 21.4 22.9 22.2
Education
No education 18.4 18.9 19.7 20.2 19.4 19.2
Primary incomplete 18.1 18.6 19.1 18.9 18.3 18.6
Primary complete 19.6 19.8 19.9 19.8 19.4 19.7
Secondary+ 22.9 22.6 22.5 22.1 22.0 22.5
Wealth quintile
Lowest 18.8 18.9 19.4 19.8 19.5 19.1
Second 18.8 19.4 19.7 19.3 19.2 19.3
Middle 19.5 19.4 20.1 19.7 19.0 19.5
Fourth 20.4 20.2 20.6 20.6 20.3 20.4
Highest 23.0 22.6 22.6 21.6 22.1 22.6
Total 20.3 20.2 20.5 20.2 19.9 20.3
76 • Fertility
Age at first birth increases with increasing education; on average, women with at least some
secondary education begin childbearing more than three years after women with no education (22.5 and
19.2, respectively). Similarly, women in the highest wealth quintile have their first birth about three and
one-half years later, on average, than women in the lowest quintile.
Table 5.10C Median age at first birth
Median age at first birth among women age 25-49 years, by county, Kenya 2014
Women age
County 25-29 30-34 35-39 40-44 45-49 25-49
Coast 20.2 20.5 20.8 20.2 20.0 20.3
Mombasa 21.6 21.9 22.7 20.6 21.3 21.6
Kwale 18.7 19.7 19.3 20.6 19.8 19.5
Kilifi 20.4 19.9 20.8 19.4 18.7 19.9
Tana River 18.7 18.7 18.4 19.5 20.7 19.0
Lamu 19.3 21.2 20.9 21.6 24.2 20.6
Taita Taveta 20.3 20.5 21.0 21.5 21.5 21.0
North Eastern 19.6 19.2 20.7 21.4 25.3 20.2
Garissa 20.3 19.8 20.5 21.6 23.4 20.5
Wajir 19.4 18.8 19.1 19.5 26.2 19.6
Mandera 19.6 18.7 21.9 23.0 25.1 20.8
Eastern 20.2 20.1 20.5 20.4 20.1 20.3
Marsabit 18.7 18.8 20.1 22.4 23.0 19.6
Isiolo 20.0 19.9 20.0 20.1 21.5 20.2
Meru 20.0 19.5 20.1 20.6 20.2 20.0
Tharaka-Nithi 20.7 21.1 21.7 20.8 22.1 21.2
Embu 21.3 21.3 21.5 21.3 20.3 21.1
Kitui 19.9 19.7 20.2 20.0 18.6 19.8
Machakos 20.1 20.6 20.5 20.6 20.2 20.4
Makueni 20.8 20.3 20.6 20.0 20.3 20.4
Central 21.1 21.1 21.1 20.6 20.2 20.9
Nyandarua 20.8 20.5 19.8 20.1 19.6 20.1
Nyeri 21.7 21.3 20.7 20.4 20.9 21.1
Kirinyaga 20.6 20.5 20.8 20.0 19.8 20.4
Murang’a 20.6 21.1 21.7 20.6 20.0 20.8
Kiambu 21.5 21.3 21.7 21.3 20.5 21.3
Rift Valley 20.1 20.1 20.3 20.1 20.0 20.1
Turkana 20.6 20.2 19.7 21.4 20.9 20.5
West Pokot 19.2 19.5 19.8 21.3 21.8 20.0
Samburu 18.7 19.7 20.6 20.8 22.5 19.9
Trans-Nzoia 19.7 19.6 20.1 19.0 19.7 19.6
Uasin Gishu 20.4 20.4 21.1 19.7 19.8 20.3
Elgeyo Marakwet 20.9 19.9 21.0 21.2 20.6 20.6
Nandi 20.0 20.4 19.6 19.6 19.4 19.8
Baringo 19.9 21.1 21.5 19.8 20.0 20.4
Laikipia 21.3 18.4 20.2 20.5 19.8 20.2
Nakuru 20.9 20.9 20.9 19.8 19.8 20.6
Narok 19.0 19.6 18.8 20.2 20.3 19.5
Kajiado 21.6 20.8 20.9 21.3 22.1 21.3
Kericho 19.7 19.2 20.5 20.6 19.5 19.9
Bomet 19.2 19.2 19.7 19.1 18.9 19.3
Western 19.4 19.3 19.9 19.6 19.5 19.6
Kakamega 19.4 19.5 20.1 20.3 19.8 19.8
Vihiga 19.7 20.2 20.5 20.8 20.4 20.3
Bungoma 19.4 19.3 20.4 19.3 19.3 19.5
Busia 19.1 18.7 18.5 18.4 19.3 18.8
Nyanza 18.7 18.6 19.4 19.1 18.9 18.9
Siaya 18.4 18.8 20.2 18.1 18.4 18.7
Kisumu 19.6 18.9 19.4 20.2 19.1 19.4
Homa Bay 17.6 17.5 18.7 18.4 17.2 17.9
Migori 17.7 18.0 18.1 18.1 17.4 17.9
Kisii 19.5 19.4 20.5 20.0 19.8 19.9
Nyamira 19.2 19.5 20.0 20.9 18.6 19.6
Nairobi 22.7 21.9 21.9 21.4 22.9 22.2
Total 20.3 20.2 20.5 20.2 19.9 20.3
Fertility • 77
5.9 TEENAGE PREGNANCY AND MOTHERHOOD
Teenage pregnancy and motherhood has remained a major health and social concern because of its
association with higher morbidity and mortality for both the mother and the child. Childbearing during the
teenage years also frequently has other adverse social consequences, particularly for female educational
attainment, as women who become mothers in their teens are more likely to curtail education.
Table 5.11 presents the percentage of women age 15-19 who have had a live birth or who are
pregnant with their first child and the percentage of women who have begun childbearing by selected
background characteristics. Fifteen percent of women age 15-19 have already had a birth, and 3 percent are
pregnant with their first child. The percentage of women who have begun childbearing increases rapidly
with age, from about 3 percent among those age 15 to 40 percent among those age 19.
Table 5.11 Teenage pregnancy and motherhood
Percentage of women age 15-19 who have had a live birth or who are pregnant with their first
child, and percentage who have begun childbearing, by background characteristics, Kenya
2014
Percentage of women
age 15-19 who: Percentage who
Background Have had a live Are pregnant have begun Number of
characteristic birth with first child childbearing women
Age
15 1.7 1.6 3.2 1,226
16 5.9 2.0 8.0 1,206
17 10.3 4.7 15.0 1,078
18 21.5 4.4 25.9 1,185
19 35.3 4.6 39.9 1,125
Residence
Urban 14.0 3.3 17.3 1,859
Rural 15.0 3.5 18.5 3,961
Region
Coast 16.6 4.3 20.8 604
North Eastern 8.7 3.5 12.2 143
Eastern 12.1 2.3 14.4 849
Central 7.7 2.7 10.4 600
Rift Valley 17.0 4.3 21.2 1,492
Western 14.1 2.7 16.8 790
Nyanza 19.2 3.0 22.2 874
Nairobi 13.1 4.3 17.4 467
Education
No education 29.2 4.1 33.2 133
Primary incomplete 15.7 3.2 18.9 2,102
Primary complete 30.0 6.2 36.2 801
Secondary+ 8.8 2.7 11.5 2,783
Wealth quintile
Lowest 22.3 3.9 26.2 1,040
Second 14.5 3.9 18.4 1,220
Middle 15.8 3.4 19.1 1,331
Fourth 13.1 3.7 16.8 1,113
Highest 8.1 2.1 10.2 1,116
Total 14.7 3.4 18.1 5,820
While rural-urban differences are small, the prevalence of early childbearing varies by region,
ranging from 10 percent in Central region to 21 percent in Rift Valley and Coast and 22 percent in Nyanza.
One-third of women age 15-19 with no education (33 percent) have begun childbearing, as compared with
only 12 percent among those who have a secondary or higher education (Table 5.11). Similarly, teenagers
from the poorest households are more likely to have begun childbearing (26 percent) than teenagers from
the wealthiest households (10 percent). The proportion of teenagers who have begun childbearing has not
changed since the 2008-09 KDHS.
At the county level (Table 5.11C), early childbearing is lowest in Murang’a, Nyeri, Embu, and
Elgeyo Marakwet (less than 10 percent each) and highest in Samburu, Nyamira, Tana River, West Pokot,
Homa Bay, and Narok (more than 25 percent each).
78 • Fertility
Table 5.11C Teenage pregnancy and motherhood
Percentage of women age 15-19 who have had a live birth or who are pregnant with their first
child, and percentage who have begun childbearing, by county, Kenya 2014
Percentage of women
age 15-19 who: Percentage who
Have had a live Are pregnant have begun Number of
County birth with first child childbearing women
Coast 16.6 4.3 20.8 604
Mombasa 11.6 5.0 16.6 123
Kwale 18.9 5.3 24.2 132
Kilifi 18.8 3.0 21.8 252
Tana River 20.4 7.8 28.2 41
Lamu 8.2 1.9 10.0 20
Taita Taveta 10.0 3.4 13.4 36
North Eastern 8.7 3.5 12.2 143
Garissa 8.5 1.7 10.2 67
Wajir 13.8 3.5 17.4 41
Mandera 3.3 6.8 10.1 36
Eastern 12.1 2.3 14.4 849
Marsabit 11.9 4.8 16.6 25
Isiolo 18.0 0.9 18.9 18
Meru 18.3 1.5 19.9 185
Tharaka-Nithi 10.4 3.2 13.7 50
Embu 4.7 3.3 8.0 91
Kitui 11.8 3.0 14.8 169
Machakos 12.2 1.7 14.0 143
Makueni 9.3 1.8 11.1 168
Central 7.7 2.7 10.4 600
Nyandarua 4.0 5.7 9.7 67
Nyeri 4.2 2.7 6.9 101
Kirinyaga 9.2 2.1 11.3 54
Murang’a 2.6 3.8 6.3 137
Kiambu 12.7 1.4 14.1 242
Rift Valley 17.0 4.3 21.2 1,492
Turkana 17.6 2.6 20.2 51
West Pokot 22.8 5.9 28.6 38
Samburu 19.7 6.0 25.7 21
Trans-Nzoia 18.9 4.3 23.3 185
Uasin Gishu 16.4 5.9 22.2 137
Elgeyo Marakwet 7.4 1.2 8.7 51
Nandi 13.8 1.8 15.6 133
Baringo 10.5 2.7 13.2 83
Laikipia 14.8 3.9 18.7 65
Nakuru 13.4 5.0 18.4 295
Narok 33.0 7.4 40.4 107
Kajiado 16.2 4.0 20.2 106
Kericho 17.6 2.9 20.5 91
Bomet 19.6 4.5 24.0 129
Western 14.1 2.7 16.8 790
Kakamega 13.5 6.0 19.4 242
Vihiga 10.8 2.0 12.7 98
Bungoma 13.8 0.7 14.4 319
Busia 18.4 2.3 20.8 131
Nyanza 19.2 3.0 22.2 874
Siaya 13.6 3.6 17.2 130
Kisumu 12.4 3.1 15.4 179
Homa Bay 31.2 2.1 33.3 177
Migori 20.9 3.4 24.3 140
Kisii 15.9 2.5 18.4 191
Nyamira 23.5 4.3 27.8 58
Nairobi 13.1 4.3 17.4 467
Total 14.7 3.4 18.1 5,820
Fertility • 79
FERTILITY PREFERENCES 6
Samwel Ogola, Michael Musyoka, Andrew Kyalo Mutuku
Key Findings
• Half of currently married women age 15-49 and 42 percent of currently
married men age 15-49 want no more children or are sterilised.
• The mean ideal number of children among all women age 15-49 is 3.6,
while that of all men is 3.9. The mean ideal number of children among
women has declined marginally in the last 10 years from 3.9 in the 2003
KDHS to 3.6 in 2014.
• The gap between actual fertility and ideal family size has narrowed in the
last 10 years, from 1.3 children in 2003 to 1.0 in 2014.
I
nformation on fertility preferences is of considerable importance to family planning programmes
because it allows planners to assess the need for contraception, whether for spacing or limiting of
births, and also to assess the extent of unwanted and mistimed pregnancies. Data on fertility
preferences may also be useful as an indicator of the direction that future fertility efforts of a country’s
citizens may take.
The 2014 KDHS included questions to ascertain fertility preferences. Women who were either not
pregnant or unsure about their pregnancy status were asked the following question: Would you like to have
(a/another) child or would you prefer not to have any (more) children? A different question was posed to
women who were pregnant at the time of the survey. Pregnant women were asked After the child you are
expecting now, would you like to have another child or would you prefer not to have any more children?
Women who indicated that they wanted another child were asked how long they would like to wait before
the birth of the next child. Finally, women were asked the total number of children they would like to have
if they were to start childbearing afresh.
Given that ongoing family planning programmes seek to address both men and women and that
men play a crucial role in the realisation of reproductive goals, the 2014 KDHS also included questions
that elicited information on the fertility preferences of men.
6.1 DESIRE FOR MORE CHILDREN
Data on desire for more children can indicate future reproductive behaviour provided that the
required family planning services are available, affordable, and accessible to allow people to realise their
fertility preferences. Table 6.1 presents the distribution of currently married women and men by the desire
for more children and according to the number of living children.
Table 6.1 shows that there is widespread desire among Kenyans to control the timing and number
of births they have. Among all currently married women, almost half do not want to have another child (47
percent), and for an additional 3 percent, either they or their husband/partner are sterilised. Nearly one-
third (32 percent) of married women would like to wait two years or more for their next birth, and 13
percent would like to have a child soon (within two years). The remainder are uncertain about their fertility
desires (3 percent) or say they are unable to get pregnant (infecund; 1 percent). Proportions are similar
among currently married men, although men tend to be slightly more pronatalist than women.
Fertility Preferences • 81
Table 6.1 Fertility preferences by number of living children
Percent distribution of currently married women and currently married men age 15-49 by desire for children, according to number of living
children, Kenya 2014
Number of living children Total Total
Desire for children 0 1 2 3 4 5 6+ 15-49 15-54
WOMEN1
Have another soon2 72.6 25.2 12.8 7.5 4.6 4.5 4.3 12.9 na
Have another later3 18.2 64.8 47.0 27.4 16.0 12.3 7.2 31.9 na
Have another, undecided when 1.5 1.4 1.1 0.5 0.2 0.1 0.5 0.7 na
Undecided 2.1 1.3 2.8 3.4 4.1 2.6 4.7 3.1 na
Want no more 2.7 6.2 34.7 57.3 69.2 72.7 72.7 47.0 na
Sterilised4 0.0 0.0 0.6 2.8 5.0 6.9 8.7 3.3 na
Declared infecund 2.4 0.7 0.8 0.7 0.6 0.9 1.4 0.9 na
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 na
Number of women 312 1,439 2,020 1,676 1,225 825 1,214 8,710 na
MEN5
2
Have another soon 69.0 26.9 15.2 12.1 6.0 6.6 9.7 16.2 15.3
Have another later3 23.2 64.9 47.7 31.3 22.2 21.8 14.8 36.8 33.9
Have another, undecided when 2.8 0.8 1.4 1.4 1.0 1.1 1.6 1.3 1.2
Undecided 0.4 1.7 4.2 4.6 3.8 1.5 2.2 3.1 3.0
Want no more 0.8 5.6 30.9 49.6 64.3 67.7 70.1 41.4 45.2
Sterilised4 0.0 0.0 0.0 0.7 2.4 0.8 1.3 0.7 0.7
Declared infecund 1.6 0.0 0.1 0.0 0.0 0.2 0.2 0.1 0.3
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of men 244 1,086 1,468 1,209 796 505 786 6,095 6,762
Note: Totals may not add up to 100 percent because respondents with missing information are not shown separately.
na = Not applicable
1
The number of living children includes the current pregnancy.
2
Wants next birth within 2 years
3
Wants to delay next birth for 2 or more years
4
Includes both female and male sterilisation
5
The number of living children includes one additional child if respondent’s wife is pregnant (or if any wife is pregnant for men with more than
one current wife).
Fertility preferences are closely related to the number of living children a person already has
(Table 6.1). About three in four currently married women without a child want to have a child soon (73
percent). This proportion declines dramatically as women have more children, so that among women with
three or more children, less than 10 percent want to have another child soon. The proportion of women
who want no more children increases greatly after two children, from 35 percent among those with two
children to 73 percent among those with five or more children. Only 3 percent of childless women say they
do not want to have a child at all. These patterns are similar for men. There have been minimal changes in
these numbers since the 2003 and 2008-09 KDHS surveys.
6.2 DESIRE TO LIMIT CHILDBEARING BY BACKGROUND CHARACTERISTICS
Tables 6.2.1 and 6.2.2 provide information on women’s and men’s desire to stop childbearing, by
background characteristics. Overall, rural women are more likely than urban women to want no more
children; however, this is likely associated with the finding that rural women have more children than
urban women. When the number of living children is held constant, the pattern is reversed; that is, urban
women are more likely than rural women to want no more children. For example, at parities between one
and five, urban women are more likely to want to limit childbearing than rural women. Interestingly, at six
or more children, rural women are more likely to want to limit childbearing than urban women. By region,
the proportion of married women who want no more children is highest in Eastern (60 percent) and
Nyanza (58 percent) and lowest in North Eastern (6 percent).
In general, fertility preferences and education are positively associated. For women with two to
five children, the desire to limit childbearing increases as education increases. For women with one or no
children or women with six or more children, the relationship between desire to control fertility and
education is mixed. A similar pattern exists for wealth: for women with three to five children, the desire to
limit childbearing increases as wealth increases. However, this pattern is not observed for women with
lower or higher parities.
82 • Fertility Preferences
Table 6.2.1 Desire to limit childbearing: Women
Percentage of currently married women age 15-49 who want no more children, by number of living children, according to
background characteristics, Kenya 2014
Background Number of living children1
characteristic 0 1 2 3 4 5 6+ Total
Residence
Urban 0.7 6.4 38.2 65.8 81.4 82.3 75.3 42.7
Rural 6.5 6.1 32.3 56.8 71.1 78.7 82.4 55.1
Region
Coast 0.0 2.2 21.2 50.0 52.2 53.9 71.7 37.1
North Eastern (0.0) (1.4) 1.2 8.3 0.3 0.8 13.4 6.1
Eastern (0.0) 8.3 46.5 70.4 88.2 92.2 95.4 60.3
Central * 6.3 42.4 71.5 91.8 100.0 98.4 55.5
Rift Valley 14.5 5.1 29.2 48.0 65.0 81.1 83.0 48.1
Western * 7.1 26.4 57.3 72.2 73.9 94.6 56.4
Nyanza (0.0) 6.7 35.2 59.5 81.8 84.7 83.6 57.5
Nairobi * 8.6 38.9 (77.8) * * * 41.6
Education
No education 0.0 8.8 12.1 25.4 27.2 42.7 52.8 33.4
Primary incomplete (0.0) 10.1 26.3 52.3 71.8 78.5 84.2 57.3
Primary complete 3.8 7.3 35.5 60.9 79.6 88.3 96.3 55.3
Secondary+ 3.5 4.9 41.1 73.4 90.0 93.7 90.7 45.4
Wealth quintile
Lowest 0.0 5.6 19.5 30.8 42.7 58.0 67.4 41.9
Second (3.4) 8.9 30.3 59.7 75.3 82.2 91.0 58.4
Middle (16.1) 3.8 29.1 60.4 81.4 87.7 88.8 58.1
Fourth 1.6 6.1 37.3 63.3 83.3 89.3 87.9 49.1
Highest 1.3 6.6 44.3 73.6 90.4 88.6 72.8 44.7
Total 2.7 6.3 35.3 60.2 74.2 79.6 81.4 50.2
Note: Women who have been sterilised are considered to want no more children. Figures in parentheses are based on 25-
49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
1
The number of living children includes the current pregnancy.
Table 6.2.2 Desire to limit childbearing: Men
Percentage of currently married men age 15-49 who want no more children, by number of living children, according to
background characteristics, Kenya 2014
Background Number of living children1
characteristic 0 1 2 3 4 5 6+ Total
Residence
Urban 0.5 6.3 30.3 54.8 69.8 64.4 73.8 37.6
Rural 1.5 4.5 31.8 46.2 64.6 70.3 70.4 46.1
Region
Coast (2.9) 11.8 31.7 38.2 64.7 56.5 58.8 36.2
North Eastern * (2.1) (11.1) (0.5) (5.2) (1.3) 2.3 3.5
Eastern (0.0) 3.5 41.0 58.1 76.0 77.7 79.6 47.7
Central * 4.6 38.8 53.3 81.6 (84.4) (93.2) 43.2
Rift Valley 0.0 5.8 27.9 49.6 63.4 63.9 74.4 42.3
Western * 0.9 16.5 36.0 62.4 74.4 77.8 46.0
Nyanza (3.9) 3.9 31.4 42.8 58.7 72.0 75.2 46.9
Nairobi * (5.7) 26.4 (63.7) * * * 37.3
Education
No education * (4.2) (8.3) 18.4 34.6 35.3 20.3 21.1
Primary incomplete (3.1) 4.1 25.7 34.7 57.1 68.9 73.6 43.1
Primary complete (0.0) 9.0 27.9 51.2 71.0 66.3 79.8 44.7
Secondary+ 0.6 4.4 35.5 59.3 73.7 76.4 75.2 41.7
Wealth quintile
Lowest (0.0) 5.5 16.3 27.2 34.8 50.2 55.4 33.5
Second (0.0) 4.2 33.7 44.0 69.6 73.0 77.1 49.2
Middle (5.0) 2.4 27.8 46.9 71.0 78.9 81.4 47.6
Fourth (0.0) 6.0 31.0 58.5 73.4 73.4 70.2 42.5
Highest 0.8 7.0 35.1 58.8 75.7 (58.9) 80.6 37.6
Total 15-49 0.8 5.6 31.0 50.3 66.7 68.4 71.3 42.0
50-54 * * (81.0) 84.5 77.1 88.6 82.0 81.0
Total 15-54 0.8 5.9 32.1 52.5 68.3 71.6 74.3 45.9
Note: Men who have been sterilised or who state in response to the question about desire for children that their wife has
been sterilised are considered to want no more children. Figures in parentheses are based on 25-49 unweighted cases.
An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
1
The number of living children includes one additional child if respondent’s wife is pregnant (or if any wife is pregnant for
men with more than one current wife).
Fertility Preferences • 83
Fertility preferences among men show similar patterns to those for women, although the overall
proportions of men who do not want to have more children are lower. In terms of a rural-urban
comparison, 46 percent of rural men want no more children, as compared with 38 percent of urban men.
Men in the Eastern, Nyanza, and Western regions (48 percent, 47 percent, and 46 percent, respectively) are
more likely to want to limit childbearing than men in other regions. The proportion of married men in
North Eastern who want no more children is only 4 percent. The same relationships between fertility
desires and both education and wealth that were observed for women were also observed for men.
6.3 IDEAL FAMILY SIZE
Women and men, regardless of marital status, were asked about the number of children they
would choose to have if they could start afresh. Respondents who had no children were asked If you could
choose exactly the number of children to have in your whole life, how many would that be? For
respondents who had children, the question was rephrased as follows: If you could go back to the time you
did not have any children and could choose exactly the number of children to have in your whole life, how
many would that be? Responses to these questions are summarised in Table 6.3 for both men and women
age 15-49.
Table 6.3 indicates that almost 98 percent each of women and men provided a numeric response.
Among all women, the mean ideal family size is 3.6 children, a slight decline from 3.8 children recorded in
the 2008-09 KDHS. The mean ideal family size among all men (3.9 children) is slightly higher than for
women (3.6 children) and is also similar to that observed in the 2008-09 KDHS.
Ideal family size increases with the number of living children. For example, women with six or
more children have an ideal family size of 5.4 children (similar to the 2008-09 KDHS ideal family size), as
compared with 3.0 children for those with one child. This pattern can be attributed to two possibilities:
women achieve their desired family size or women adjust their ideal number of children to be the actual
number they have. Among men, the ideal family size ranges from 3.3 children for those with one child to
6.7 children for men with six or more living children.
The majority of women and men (77 percent of each) prefer two to four children, with men
slightly more inclined to want three or four. Only 2 percent of women and 1 percent of men say that having
one child is ideal.
84 • Fertility Preferences
Table 6.3 Ideal number of children by number of living children
Percent distribution of women and men 15-49 by ideal number of children, and mean ideal number of children for all respondents and for
currently married respondents, according to the number of living children, Kenya 2014
Number of living children
Ideal number of children 0 1 2 3 4 5 6+ Total
WOMEN1
0 2.1 0.6 0.5 0.7 1.0 0.3 0.6 1.0
1 1.9 4.4 2.9 2.8 1.7 0.7 0.6 2.4
2 31.9 30.7 28.0 17.8 16.6 14.9 7.0 24.0
3 31.3 37.8 28.6 27.2 14.4 15.3 12.7 26.8
4 20.3 18.1 29.9 31.7 38.4 22.6 28.0 25.9
5 5.6 4.0 4.6 9.8 9.7 21.4 10.3 7.7
6+ 4.8 2.9 4.5 8.2 16.6 21.6 34.9 10.1
Non-numeric responses 2.1 1.5 1.0 1.9 1.6 3.2 5.8 2.2
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of women 3,637 2,495 2,590 2,025 1,470 987 1,422 14,625
Mean ideal number of children for:2
All women 3.1 3.0 3.3 3.6 4.1 4.5 5.4 3.6
Number of women 3,560 2,459 2,564 1,988 1,446 955 1,339 14,311
Currently married women 3.5 3.1 3.3 3.7 4.2 4.6 5.4 3.9
Number of currently married women 300 1,408 2,002 1,649 1,205 801 1,138 8,503
MEN3
0 0.7 0.0 0.1 0.2 1.0 0.7 0.0 0.4
1 0.9 2.8 0.7 0.4 0.5 0.5 0.2 1.0
2 24.8 22.9 20.8 10.7 14.0 9.3 4.5 19.8
3 33.1 41.7 32.3 29.0 15.8 15.5 10.0 30.2
4 24.3 22.3 30.3 35.2 37.9 25.5 25.0 27.1
5 7.1 5.3 6.8 13.9 11.7 19.0 8.9 8.5
6+ 7.6 4.0 7.1 8.5 17.6 25.0 44.8 11.1
Non-numeric responses 1.4 0.9 1.8 2.1 1.5 4.6 6.6 2.0
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of men 5,396 1,557 1,662 1,278 842 526 803 12,063
Mean ideal number of children for:2
All men 3.6 3.3 3.6 4.0 4.4 4.9 6.7 3.9
Number of men 5,318 1,543 1,631 1,251 829 502 750 11,825
Currently married men 3.3 3.2 3.6 4.0 4.4 4.9 6.7 4.2
Number of currently married men 235 1,077 1,441 1,184 784 484 733 5,938
Mean ideal number of children for
men 15-54:2
All men 3.6 3.3 3.6 4.0 4.4 4.8 6.6 4.0
Number of men 5,334 1,558 1,673 1,349 980 598 1,052 12,544
Currently married men 3.3 3.2 3.6 4.0 4.4 4.8 6.6 4.3
Number of currently married men 239 1,084 1,472 1,268 924 574 1,010 6,573
1
The number of living children includes current pregnancy for women.
2
Means are calculated excluding respondents who gave non-numeric responses.
3
The number of living children includes one additional child if respondent’s wife is pregnant (or if any wife is pregnant for men with more
than one current wife).
Table 6.4 shows the mean ideal number of children for all women age 15-49 by background
characteristics. Ideal family size increases with age; for example, the mean ideal number of children for
women age 15-19 is 3.2, compared with 4.5 among women age 45-49. Women in rural areas have a higher
ideal family size (3.9 children) than women in urban areas (3.2 children). By region, North Eastern
recorded the highest ideal family size for women (9.3 children) and Nairobi the lowest (3.0 children).
Education and wealth are negatively associated with ideal family size. As education and wealth increase,
ideal family size decreases. Notably, there is a large decrease in ideal family size from 7.0 children among
women with no education to 3.9 children among those with at least some primary education. Similarly,
there is a large decrease in ideal family size from 5.1 children among women in the lowest wealth quintile
to 3.6 children among those in the second wealth quintile.
Fertility Preferences • 85
Table 6.4 Mean ideal number of children
Mean ideal number of children for all women age 15-
49 by background characteristics, Kenya 2014
Background Number of
characteristic Mean women1
Age
15-19 3.2 2,662
20-24 3.3 2,654
25-29 3.5 2,888
30-34 3.6 2,111
35-39 4.0 1,728
40-44 4.2 1,252
45-49 4.5 1,015
Residence
Urban 3.2 5,818
Rural 3.9 8,493
Region
Coast 4.2 1,336
North Eastern 9.3 270
Eastern 3.1 2,046
Central 3.2 1,887
Rift Valley 3.8 3,659
Western 3.7 1,544
Nyanza 3.4 1,856
Nairobi 3.0 1,712
Education
No education 7.0 935
Primary incomplete 3.9 3,671
Primary complete 3.5 3,490
Secondary+ 3.0 6,214
Wealth quintile
Lowest 5.1 2,136
Second 3.6 2,531
Middle 3.5 2,808
Fourth 3.3 3,067
Highest 3.1 3,769
Total 3.6 14,311
1
Number of women who gave a numeric response
6.4 FERTILITY PLANNING
Analysis of the level of fertility planning in a society provides some insight into the degree to
which couples are able to control their fertility. To measure the level of unwanted fertility, the KDHS
asked women a series of questions for each child born in the preceding five years and any current
pregnancy to determine whether the particular pregnancy was desired at the time (‘wanted then’), not
desired at the time but wanted at a later time (‘wanted later’), or unwanted at any time (‘wanted no more’).
The questions required the respondents to accurately recall their wishes at one or more points in the last
five years. Care has to be exercised in interpreting these results because an unwanted conception may well
have become a cherished child, leading to the rationalisation of responses to these questions. Such
rationalisation may result in an underestimate of the true extent of unwanted births.
Table 6.5 presents the percent distribution of births to women age 15-49 in the five years
preceding the survey, by planning status of the birth, according to birth order and mother’s age at birth.
Ten percent of births in Kenya are unwanted and 25 percent are wanted later. The percentage of unwanted
births increases with birth order and with the mother’s age at birth. The percentage of births wanted later
generally decreases with birth order and with mother’s age at birth. Overall, the proportion of births
considered unwanted has declined since the 2008-09 KDHS, from 17 percent to 10 percent, while the
proportion of births wanted later has remained stable.
86 • Fertility Preferences
Table 6.5 Fertility planning status
Percent distribution of births to women age 15-49 in the five years preceding the survey (including
current pregnancies), by planning status of the birth, according to birth order and mother’s age at
birth, Kenya 2014
Planning status of birth
Birth order and Wanted Wanted Wanted no Number of
mother’s age at birth then later more Missing Total births
Birth order
1 64.7 34.5 0.5 0.3 100.0 2,652
2 73.1 24.8 1.8 0.2 100.0 2,275
3 67.1 25.9 7.0 0.0 100.0 1,764
4+ 56.3 18.8 24.4 0.4 100.0 3,582
Mother’s age at birth
<20 52.7 46.4 0.5 0.4 100.0 1,479
20-24 67.1 29.3 3.5 0.2 100.0 3,201
25-29 71.4 20.1 8.2 0.3 100.0 2,710
30-34 64.0 17.8 17.8 0.4 100.0 1,677
35-39 56.1 13.5 30.3 0.0 100.0 916
40-44 45.9 7.6 45.9 0.6 100.0 258
45-49 (40.7) (2.0) (57.3) (0.0) 100.0 31
Total 64.1 25.4 10.3 0.3 100.0 10,273
Note: Figures in parentheses are based on 25-49 unweighted cases.
6.5 WANTED FERTILITY RATES Table 6.6 Wanted fertility rates
Total wanted fertility rates and total fertility rates
The wanted fertility rate measures the potential for the three years preceding the survey, by
background characteristics, Kenya 2014
demographic impact of avoiding unwanted births. It is calculated in
the same manner as the total fertility rate presented in Chapter 5 Background
Total
wanted Total
but excludes births classified as unwanted from the numerator. A characteristic fertility rates fertility rate
birth is considered wanted if the number of living children at the Residence
Urban 2.6 3.1
time of conception is less than the ideal number of children Rural 3.4 4.6
reported by the respondent. The gap between wanted fertility and Region
actual fertility shows how successful women are in achieving their Coast
North Eastern
3.8
6.4
4.4
6.7
reproductive intentions. This measure may also be an Eastern 2.5 3.6
Central 2.1 2.7
underestimate because women may not want to report an ideal Rift Valley 3.4 4.6
family size that is lower than their actual family size. Western
Nyanza
3.2
3.0
4.5
4.5
Nairobi 2.3 2.8
The total wanted fertility rates in Table 6.6 represent the Education
levels of fertility that would have prevailed in the three years No education
Primary incomplete
6.1
3.3
6.8
4.8
preceding the survey if all unwanted births had been avoided. Primary complete 3.1 4.1
Secondary+ 2.4 3.0
Overall, women have 1.0 child more than their ideal number (4.0
Wealth quintile
compared with 3.0). This implies that the total fertility rate is one Lowest 5.0 6.5
child higher than it would be if unwanted births were avoided. The Second
Middle
3.1
2.7
4.7
4.0
level of wanted fertility is less than the actual fertility for all Fourth 2.7 3.2
Highest 2.3 2.7
background characteristics. The gap between wanted and observed
Total 3.0 4.0
fertility is greatest among women living in rural areas, those with
less than a secondary education, and those in the lower wealth Note: Rates are calculated based on births to
women age 15-49 in the period 1-36 months
quintiles. preceding the survey. The total fertility rates are
the same as those presented in Table 5.2.
Fertility Preferences • 87
FAMILY PLANNING 7
Alfred Agwanda, Abdulkadir Amin Awes, Ruth Wayua Muia
Key Findings
• Knowledge of at least one contraceptive method is universal in Kenya.
• More than half of currently married women are using a contraceptive
method (58 percent).
• The most popular modern contraceptive methods used by married
women are injectables (26 percent), implants (10 percent), and the pill
(8 percent).
• Use of modern methods has increased over the last decade from
32 percent in the 2003 KDHS to 53 percent in 2014.
• The public sector remains the major provider of contraceptive methods;
60 percent of modern contraceptive users obtain their contraception from
a government source.
• Thirty-one percent of family planning users discontinue use of a method
within 12 months of starting its use. Side effects and health concerns
(11 percent) are the main reason for discontinuation.
• Eighteen percent of currently married women have an unmet need for
family planning services, with 9 percent in need of spacing and 8 percent
in need of limiting.
7.1 INTRODUCTION
T
his chapter presents information on knowledge of various contraceptive methods and discusses past
and current use of contraception. For users of periodic abstinence and withdrawal methods,
knowledge of the ovulatory cycle is examined; for those relying on sterilisation, the timing of the
procedure is assessed. Also discussed are sources of modern contraceptive methods, informed choice,
discontinuation rates and reasons for discontinuation, unmet need for family planning, demand for
contraception, non-use of contraception, and intention to use contraceptive methods in the future. In
addition, information is provided on exposure to family planning messages through the media and contact
with family planning providers. These topics are of practical use to policymakers in formulating efficient
and effective family planning strategies and policies. Although the main focus of this chapter is on women,
results from male respondents are also presented because men play an important role in the realisation of
reproductive goals. Wherever possible, comparisons are made with findings from previous surveys in order
to evaluate trends and progress made in family planning in Kenya over time.
7.2 KNOWLEDGE OF CONTRACEPTIVE METHODS
Development of a profile regarding knowledge of family planning methods was one of the
objectives of the survey, because knowledge of methods is a prerequisite for making a decision to initiate
contraceptive use. The 2014 KDHS collected information on knowledge of contraception by asking
respondents whether or not they had heard of 10 modern methods (female sterilisation, male sterilisation,
the pill, intra-uterine devices [IUDs], injectables, implants, male condoms, female condoms, lactational
amenorrhoea, and emergency contraception) and two traditional methods (rhythm or calendar method and
withdrawal). Provision was also made within the questionnaire to record any other methods named
Family Planning • 89
spontaneously by the respondent. Most emphasis is placed on women because they bear the risk of
exposure to pregnancy and most methods of contraception are designed for them.
Table 7.1 shows the level of knowledge of contraceptive methods among all women and men age
15-49. Knowledge is also presented for women and men who are currently married and who are sexually
active and unmarried. Knowledge of at least one family planning method is virtually universal: 98 percent
among women and 99 percent among men.
Women are more familiar with modern methods of contraception (98 percent) than with
traditional methods (84 percent). Similar to results from the 2008-09 KDHS, the most widely known
modern methods of contraception among women are male condoms (96 percent), injectables (95 percent),
and the pill (94 percent). The least known methods among women are the lactational amenorrhoea method
(LAM) (12 percent), male sterilisation (47 percent), and emergency contraception (59 percent). With
regard to traditional methods, about four out of every five women (79 percent) know of the rhythm method
and three out of every five (61 percent) know of the withdrawal method.
Similar to women, men are more familiar with modern than with traditional methods (99 percent
compared with 86 percent), and the most widely known methods among men include male condoms (99
percent), injectables (92 percent), and the pill (92 percent). Men are more likely than women to know
about male sterilisation and withdrawal, while women are more likely than men to know about IUDs,
implants, and LAM. On average, women and men know about the same number of methods (mean of 8.7
for women and 8.5 for men), an increase in number and a narrowing of the gap between women and men
from the 2008-09 KDHS (mean of 7.5 for women and 6.6 for men).
Table 7.1 Knowledge of contraceptive methods
Percentage of all respondents, currently married respondents and sexually active unmarried respondents age 15-49 who know
any contraceptive method, by specific method, Kenya 2014
Women Men
Currently Sexually active Sexually active
married unmarried Currently unmarried
Method All women women women1 All men married men men1
Any method 98.4 98.7 100.0 99.3 99.7 100.0
Any modern method 98.4 98.7 100.0 99.3 99.7 100.0
Female sterilisation 78.5 84.2 83.6 76.2 87.0 78.2
Male sterilisation 46.6 50.3 50.2 55.6 65.4 56.3
Pill 94.2 97.0 97.5 91.7 96.8 93.6
IUD 77.3 85.8 84.7 63.0 77.3 61.6
Injectables 95.3 98.0 99.3 92.4 97.9 96.2
Implants 85.6 92.4 93.1 65.3 80.7 64.7
Male condom 96.4 96.8 99.1 98.8 99.3 100.0
Female condom 75.6 78.9 84.6 79.0 87.1 84.8
Lactational amenorrhoea (LAM) 12.1 14.2 11.5 8.7 11.2 7.8
Emergency contraception 59.2 60.4 73.6 62.5 70.6 70.3
Any traditional method 83.8 88.7 91.8 85.9 94.9 90.8
Rhythm 78.9 83.5 87.1 80.8 90.8 84.7
Withdrawal 61.2 67.5 78.0 71.9 82.8 79.2
Other methods 4.6 5.7 4.6 3.4 4.4 2.8
Mean number of methods known
by respondents 15-49 8.7 9.1 9.5 8.5 9.5 8.8
Number of respondents 31,079 18,549 583 12,063 6,095 1,308
Mean number of methods known
by respondents 15-54 na na na 8.5 9.5 8.8
Number of respondents na na na 12,819 6,762 1,338
na = Not applicable
1
Had last sexual intercourse within 30 days preceding the survey
90 • Family Planning
Table 7.2 shows knowledge of contraceptive methods among currently married women and men
age 15-49 who have heard of at least one method and who have heard of at least one modern method by
background characteristics. Since knowledge of contraception is virtually universal, there is little variation
in knowledge according to background characteristics. Knowledge of contraceptive methods is universal in
almost all regions except North Eastern (71 percent of women and 89 percent of men). Women and men
with no education demonstrated slightly less knowledge of contraceptives (88 percent and 95 percent,
respectively) than women and men with at least some education (100 percent). Women and men in the
lowest wealth quintile had less knowledge of contraceptive methods than those in higher wealth quintiles,
although the differences are less striking among men than they are among women.
More than 95 percent of women demonstrate knowledge of at least one contraceptive method in
all counties other than Mandera, Wajir, West Pokot, Garissa, Turkana, Tana River, and Marsabit. Among
men, only two counties fall at or below 95 percent: Mandera and Turkana (Table 7.2C).
Table 7.2 Knowledge of contraceptive methods by background characteristics
Percentage of currently married women and currently married men age 15-49 who have heard of at
least one contraceptive method and who have heard of at least one modern method by background
characteristics, Kenya 2014
Women Men
Heard of Heard of
Background Heard of any modern Heard of any modern
characteristic any method method1 Number any method method1 Number
Age
15-19 98.2 98.2 695 * * 16
20-24 98.8 98.7 3,133 99.7 99.7 377
25-29 98.6 98.5 4,556 100.0 99.9 1,201
30-34 98.9 98.9 3,566 99.8 99.8 1,398
35-39 98.9 98.8 2,894 99.3 99.2 1,277
40-44 98.7 98.6 2,091 99.8 99.8 1,100
45-49 98.9 98.8 1,615 99.7 99.6 727
Residence
Urban 99.6 99.5 7,285 99.8 99.8 2,894
Rural 98.2 98.1 11,265 99.6 99.5 3,201
Region
Coast 99.4 99.3 1,821 100.0 99.8 617
North Eastern 70.8 68.8 451 89.2 89.2 103
Eastern 99.7 99.7 2,667 100.0 100.0 835
Central 99.9 99.9 2,323 99.4 99.4 773
Rift Valley 98.5 98.4 4,696 99.9 99.7 1,523
Western 100.0 100.0 1,950 100.0 100.0 561
Nyanza 99.9 99.9 2,525 100.0 100.0 767
Nairobi 99.8 99.8 2,117 100.0 100.0 916
Education
No education 87.8 86.9 1,692 94.7 93.5 234
Primary incomplete 99.6 99.6 4,694 100.0 99.9 1,370
Primary complete 99.9 99.9 5,389 99.7 99.7 1,677
Secondary+ 100.0 100.0 6,774 100.0 100.0 2,814
Wealth quintile
Lowest 93.7 93.2 3,174 98.5 98.1 813
Second 99.7 99.7 3,290 99.9 99.9 1,036
Middle 99.8 99.8 3,503 100.0 100.0 1,110
Fourth 99.8 99.8 3,957 100.0 100.0 1,481
Highest 99.8 99.8 4,626 99.7 99.7 1,655
Total 15-49 98.7 98.7 18,549 99.7 99.7 6,095
50-54 na na na 99.7 99.7 667
Total 15-54 na na na 99.7 99.7 6,762
Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
na = Not applicable
1
Female sterilisation, male sterilisation, pill, IUD, injectables, implants, male condom, female condom,
lactational amenorrhoea method (LAM), and emergency contraception
Family Planning • 91
Table 7.2C Knowledge of contraceptive methods by county
Percentage of currently married women and currently married men age 15-49 who have heard of at
least one contraceptive method and who have heard of at least one modern method by county, Kenya
2014
Women Men
Heard of Heard of
Heard of any modern Heard of any modern
County any method method1 Number any method method1 Number
Coast 99.4 99.3 1,821 100.0 99.8 617
Mombasa 99.8 99.8 537 100.0 100.0 254
Kwale 100.0 100.0 357 100.0 100.0 91
Kilifi 99.7 99.7 600 100.0 100.0 168
Tana River 94.0 94.0 144 100.0 97.5 40
Lamu 100.0 100.0 55 100.0 100.0 19
Taita Taveta 100.0 100.0 128 100.0 100.0 46
North Eastern 70.8 68.8 451 89.2 89.2 103
Garissa 86.7 86.1 165 100.0 100.0 40
Wajir 70.9 67.6 158 97.8 97.8 37
Mandera 50.3 48.1 128 59.7 59.7 26
Eastern 99.7 99.7 2,667 100.0 100.0 835
Marsabit 94.0 93.3 76 100.0 100.0 17
Isiolo 98.3 97.8 65 100.0 100.0 17
Meru 100.0 100.0 690 100.0 100.0 273
Tharaka-Nithi 99.8 99.8 169 100.0 100.0 52
Embu 100.0 100.0 266 100.0 100.0 69
Kitui 100.0 100.0 445 100.0 100.0 112
Machakos 99.8 99.8 553 100.0 100.0 186
Makueni 100.0 100.0 404 100.0 100.0 108
Central 99.9 99.9 2,323 99.4 99.4 773
Nyandarua 99.4 99.4 273 100.0 100.0 82
Nyeri 99.8 99.8 358 100.0 100.0 129
Kirinyaga 100.0 100.0 281 100.0 100.0 104
Murang’a 100.0 100.0 444 100.0 100.0 124
Kiambu 100.0 100.0 967 98.7 98.7 333
Rift Valley 98.5 98.4 4,696 99.9 99.7 1,523
Turkana 93.1 92.3 214 94.6 91.7 40
West Pokot 75.7 75.0 197 100.0 98.5 60
Samburu 99.6 98.8 83 100.0 100.0 19
Trans-Nzoia 99.9 99.9 467 100.0 100.0 139
Uasin Gishu 100.0 100.0 460 100.0 100.0 178
Elgeyo Marakwet 100.0 100.0 139 100.0 100.0 49
Nandi 100.0 100.0 335 100.0 100.0 119
Baringo 98.2 98.0 190 100.0 100.0 57
Laikipia 98.8 98.8 207 100.0 100.0 59
Nakuru 100.0 100.0 851 100.0 100.0 273
Narok 100.0 100.0 446 100.0 100.0 142
Kajiado 99.6 99.3 387 100.0 100.0 133
Kericho 100.0 100.0 327 100.0 100.0 115
Bomet 100.0 100.0 394 100.0 100.0 140
Western 100.0 100.0 1,950 100.0 100.0 561
Kakamega 99.9 99.9 697 100.0 100.0 212
Vihiga 100.0 100.0 212 100.0 100.0 48
Bungoma 100.0 100.0 696 100.0 100.0 201
Busia 100.0 100.0 345 100.0 100.0 100
Nyanza 99.9 99.9 2,525 100.0 100.0 767
Siaya 100.0 100.0 326 100.0 100.0 110
Kisumu 100.0 100.0 500 100.0 100.0 166
Homa Bay 100.0 100.0 520 100.0 100.0 132
Migori 99.3 99.3 432 100.0 100.0 128
Kisii 100.0 100.0 531 100.0 100.0 160
Nyamira 100.0 100.0 216 100.0 100.0 73
Nairobi 99.8 99.8 2,117 100.0 100.0 916
Total 15-49 98.7 98.7 18,549 99.7 99.7 6,095
50-54 na na na 99.7 99.7 667
Total 15-54 na na na 99.7 99.7 6,762
na = Not applicable
1
Female sterilisation, male sterilisation, pill, IUD, injectables, implants, male condom, female condom,
lactational amenorrhoea method (LAM), and emergency contraception
92 • Family Planning
7.3 CURRENT USE OF CONTRACEPTION
This section presents information on the prevalence of current contraceptive use among all
women, currently married women, and sexually active unmarried women age 15-49. Current use of
contraceptives is the most widely employed and valuable measure of the success of family planning
programmes. The contraceptive prevalence rate (CPR) is usually defined as the percentage of currently
married women who are currently using a method of contraception.
Table 7.3 shows the percent distribution by age of all women, currently married women, and
sexually active unmarried women who use specific family planning methods. Contraceptive methods are
grouped into modern and traditional methods.
Table 7.3 Current use of contraception by age
Percent distribution of all women, currently married women, and sexually active unmarried women age 15-49 by contraceptive method currently used, according to age, Kenya 2014
Modern method Any Traditional method
Any Female Male tradi- Not cur- Number
Any modern sterili- sterili- Inject- Im- Male Female tional With- rently of
Age method method sation sation Pill IUD ables plants condom condom LAM Other method Rhythm drawal Other using Total women
ALL WOMEN
15-19 10.1 9.3 0.0 0.0 0.5 0.0 4.9 1.2 2.5 0.1 0.0 0.0 0.8 0.6 0.2 0.0 89.9 100.0 5,820
20-24 42.0 38.5 0.0 0.0 4.6 0.9 20.6 7.2 4.9 0.1 0.0 0.0 3.5 2.9 0.4 0.2 58.0 100.0 5,735
25-29 54.2 51.0 0.4 0.0 5.9 2.7 27.7 11.0 3.2 0.0 0.1 0.1 3.2 2.6 0.4 0.2 45.8 100.0 6,100
30-34 57.5 53.8 2.0 0.0 7.9 3.6 26.5 10.8 2.7 0.0 0.1 0.1 3.7 2.7 0.6 0.4 42.5 100.0 4,510
35-39 55.7 51.0 4.2 0.1 9.1 3.7 21.6 9.2 3.0 0.0 0.1 0.0 4.7 4.0 0.6 0.2 44.3 100.0 3,773
40-44 48.4 43.4 7.1 0.0 7.3 5.2 15.3 5.8 2.5 0.0 0.0 0.1 5.0 4.1 0.6 0.4 51.6 100.0 2,885
45-49 37.3 31.0 9.5 0.0 5.9 2.0 9.4 2.4 1.8 0.0 0.0 0.0 6.3 5.2 0.5 0.6 62.7 100.0 2,257
Total 42.6 39.1 2.2 0.0 5.5 2.3 18.7 7.1 3.1 0.0 0.1 0.0 3.5 2.8 0.4 0.2 57.4 100.0 31,079
CURRENTLY MARRIED WOMEN
15-19 40.2 36.8 0.0 0.0 1.9 0.2 27.1 5.4 2.1 0.0 0.0 0.0 3.4 2.0 1.3 0.1 59.8 100.0 695
20-24 53.5 49.8 0.1 0.0 6.3 1.4 30.2 9.6 2.2 0.0 0.0 0.0 3.7 2.9 0.5 0.3 46.5 100.0 3,133
25-29 60.8 57.3 0.4 0.0 7.2 3.1 31.4 12.9 2.1 0.0 0.1 0.0 3.6 2.9 0.4 0.2 39.2 100.0 4,556
30-34 63.5 59.1 2.3 0.0 9.1 4.0 29.7 11.9 1.9 0.0 0.2 0.0 4.5 3.2 0.7 0.5 36.5 100.0 3,566
35-39 63.0 57.7 4.8 0.1 10.8 4.5 24.5 10.4 2.6 0.0 0.1 0.0 5.3 4.4 0.8 0.1 37.0 100.0 2,894
40-44 57.7 51.1 8.1 0.1 9.1 6.7 18.0 6.5 2.6 0.0 0.0 0.1 6.6 5.3 0.8 0.5 42.3 100.0 2,091
45-49 45.2 37.2 11.0 0.0 7.5 2.3 11.6 2.9 1.9 0.0 0.0 0.0 8.0 6.7 0.7 0.6 54.8 100.0 1,615
Total 58.0 53.2 3.2 0.0 8.0 3.4 26.4 9.9 2.2 0.0 0.1 0.0 4.8 3.8 0.7 0.3 42.0 100.0 18,549
SEXUALLY ACTIVE UNMARRIED WOMEN1
15-19 50.1 49.3 0.0 0.0 1.8 0.0 14.3 6.0 27.2 0.0 0.0 0.0 0.7 0.7 0.0 0.0 49.9 100.0 79
20-24 69.3 64.2 0.0 0.0 12.2 0.0 13.4 1.5 35.0 2.1 0.0 0.0 5.1 3.8 1.3 0.0 30.7 100.0 157
25-29 75.7 69.8 0.0 0.0 7.6 1.8 35.0 8.2 17.2 0.0 0.0 0.0 5.8 3.2 2.0 0.6 24.3 100.0 117
30-34 74.4 71.3 0.4 0.0 3.9 3.9 30.2 15.1 17.8 0.0 0.0 0.0 3.1 1.3 1.8 0.0 25.6 100.0 94
35-39 64.2 55.2 5.3 0.0 4.8 1.6 25.1 9.4 7.8 0.0 0.0 1.2 9.1 9.1 0.0 0.0 35.8 100.0 57
40-44 (39.0) (38.3) (5.0) (0.0) (5.5) (0.0) (18.1) (0.0) (9.7) (0.0) (0.0) (0.0) (0.7) (0.7) (0.0) (0.0) (61.0) 100.0 52
45-49 (64.6) (56.3) (20.6) (0.0) (0.0) (0.0) (17.2) (12.5) (6.0) (0.0) (0.0) (0.0) (8.3) (8.3) (0.0) (0.0) (35.4) 100.0 26
Total 65.4 60.9 1.9 0.0 6.6 1.1 22.3 6.8 21.4 0.6 0.0 0.1 4.5 3.3 1.1 0.1 34.6 100.0 583
Note: If more than one method is used, only the most effective method is considered in this tabulation. Figures in parentheses are based on 25-49 unweighted cases.
na = Not applicable
LAM = Lactational amenorrhoea method
1 Women who have had sexual intercourse within 30 days preceding the survey
Nearly 6 in 10 currently married women (58 percent) are using a method of family planning.
Modern methods of contraception are more commonly used (53 percent) than are traditional methods (5
percent). Of the modern methods, injectables are the most widely used (26 percent), followed by implants
(10 percent) and the pill (8 percent); all other modern methods are used by 3 percent or less of married
women.
Use of any contraceptive method is higher among sexually active unmarried women (65 percent)
than among currently married women (58 percent). More sexually active unmarried women use modern
(61 percent) than traditional (5 percent) contraceptive methods. Injectables are the most commonly used
form of modern contraception for sexually active unmarried women at 22 percent, followed by male
condoms at 21 percent.
Table 7.3 further shows that contraceptive use varies by age, peaking at age 30-34 among
currently married women and at age 25-29 among sexually active unmarried women.
Family Planning • 93
7.4 CURRENT USE OF CONTRACEPTION BY BACKGROUND CHARACTERISTICS
Analysing current use of contraception by background characteristics is important because it helps
identify subgroups of the population to target for family planning services. Table 7.4 presents the percent
distribution of currently married women age 15-49 by their use of family planning methods, according to
background characteristics. This table allows a comparison of levels of current contraceptive use across
major population groups and an examination of differences in use in the various subgroups.
Table 7.4 shows that contraceptive use is associated with the number of children a woman has.
Only 15 percent of currently married women with no living children use contraception; the percentage
increases to 61 percent among women with one or two children and 66 percent among women with three
or four children before declining to 52 percent among women with five or more children. A higher
percentage of urban women (62 percent) than rural women (56 percent) use some method of contraception,
although this gap is smaller than the one observed in the 2008-09 KDHS (53 percent urban, 43 percent
rural).
Table 7.4 Current use of contraception by background characteristics
Percent distribution of currently married women age 15-49 by contraceptive method currently used, according to background characteristics, Kenya 2014
Modern method Any Traditional method
Any Female Male tradi- Not cur- Number
Background Any modern sterili- sterili- Inject- Im- Male Female tional With- rently of
characteristic method method sation sation Pill IUD ables plants condom condom LAM Other method Rhythm drawal Other using Total women
Number of
living children
0 15.4 12.3 0.0 0.0 3.7 0.4 3.0 0.4 4.8 0.0 0.0 0.0 3.1 2.5 0.6 0.0 84.6 100.0 1,086
1-2 61.4 56.5 0.4 0.0 9.5 3.9 29.7 10.8 2.2 0.0 0.1 0.0 4.8 3.8 0.6 0.4 38.6 100.0 7,339
3-4 65.9 61.3 4.0 0.0 9.4 4.3 30.3 11.2 2.0 0.0 0.1 0.0 4.6 3.7 0.6 0.3 34.1 100.0 5,936
5+ 51.9 46.6 7.7 0.1 4.4 2.2 21.3 8.9 1.8 0.0 0.1 0.1 5.3 4.1 0.8 0.3 48.1 100.0 4,188
Residence
Urban 61.8 56.9 2.1 0.0 10.7 4.7 24.7 12.0 2.6 0.0 0.1 0.0 4.9 3.8 0.7 0.4 38.2 100.0 7,285
Rural 55.5 50.9 3.9 0.0 6.2 2.6 27.5 8.6 1.9 0.0 0.1 0.0 4.6 3.7 0.7 0.3 44.5 100.0 11,265
Region
Coast 43.9 38.3 1.6 0.0 4.7 2.2 18.7 9.4 1.5 0.0 0.1 0.0 5.6 4.2 1.4 0.1 56.1 100.0 1,821
North Eastern 3.4 3.4 0.0 0.0 0.6 0.1 1.9 0.6 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 96.6 100.0 451
Eastern 70.4 63.9 4.8 0.0 8.9 2.9 37.9 7.8 1.5 0.0 0.0 0.0 6.5 5.6 0.5 0.3 29.6 100.0 2,667
Central 72.8 66.9 3.5 0.0 19.5 9.0 21.6 10.7 2.4 0.0 0.2 0.0 5.9 4.9 0.7 0.3 27.2 100.0 2,323
Rift Valley 52.8 46.8 2.2 0.0 5.5 2.9 26.8 7.2 1.9 0.0 0.2 0.0 6.0 4.7 1.0 0.3 47.2 100.0 4,696
Western 58.6 56.9 5.9 0.0 4.6 1.3 27.5 15.2 2.5 0.0 0.0 0.0 1.7 1.1 0.3 0.3 41.4 100.0 1,950
Nyanza 56.4 53.9 3.6 0.0 3.4 2.0 29.3 12.4 2.9 0.0 0.1 0.1 2.5 2.0 0.3 0.2 43.6 100.0 2,525
Nairobi 62.6 58.3 2.0 0.1 12.5 4.5 23.6 12.1 3.3 0.0 0.0 0.0 4.4 3.2 0.3 0.9 37.4 100.0 2,117
Education
No education 17.7 15.3 1.2 0.0 1.3 0.2 8.3 3.7 0.5 0.0 0.0 0.0 2.4 1.5 0.7 0.1 82.3 100.0 1,692
Primary
incomplete 54.6 51.1 3.9 0.1 4.5 1.8 28.5 10.4 1.9 0.0 0.1 0.0 3.4 2.7 0.4 0.3 45.4 100.0 4,694
Primary
complete 64.3 59.6 4.2 0.0 9.7 3.6 30.7 9.9 1.6 0.0 0.1 0.0 4.7 3.7 0.7 0.3 35.7 100.0 5,389
Secondary+ 65.3 59.0 2.4 0.0 10.7 5.3 26.1 11.1 3.3 0.0 0.1 0.0 6.3 5.1 0.8 0.4 34.7 100.0 6,774
Wealth quintile
Lowest 32.3 29.2 1.9 0.0 1.4 0.5 19.0 5.7 0.8 0.0 0.0 0.0 3.1 2.2 0.8 0.1 67.7 100.0 3,174
Second 58.2 54.1 3.6 0.0 5.6 1.5 31.4 10.1 1.8 0.0 0.0 0.0 4.1 3.4 0.5 0.2 41.8 100.0 3,290
Middle 64.2 59.5 4.8 0.0 7.3 3.0 32.4 9.8 2.1 0.0 0.1 0.1 4.7 3.8 0.6 0.3 35.8 100.0 3,503
Fourth 65.9 60.9 3.1 0.1 9.5 3.7 30.4 11.1 2.9 0.0 0.1 0.0 5.0 4.0 0.7 0.3 34.1 100.0 3,957
Highest 63.9 57.7 2.7 0.0 13.4 7.0 19.9 11.7 2.9 0.0 0.1 0.0 6.2 4.9 0.7 0.6 36.1 100.0 4,626
Total 58.0 53.2 3.2 0.0 8.0 3.4 26.4 9.9 2.2 0.0 0.1 0.0 4.8 3.8 0.7 0.3 42.0 100.0 18,549
Note: If more than one method is used, only the most effective method is considered in this tabulation.
LAM = Lactational amenorrhoea method
Use of any method is highest in the Central (73 percent) and Eastern (70 percent) regions and
lowest in the North Eastern (3 percent) and Coast (44 percent) regions. There is a noticeable increase in
current use among women with at least some education and a higher degree of household wealth. Only 18
percent of currently married women with no education use contraception, while more than half of women
with at least some schooling use a method (55-65 percent). Thirty-two percent of women in the lowest
wealth quintile use a method of contraception, as compared with 58 percent to 66 percent of women in the
higher wealth quintiles.
94 • Family Planning
Twenty-two counties have a CPR above the national average (58 percent). Approximately three-
quarters of currently married women use a contraceptive method in Kirinyaga (81 percent), Makueni (80
percent), Meru (78 percent), Machakos (76 percent), and Tharaka-Nithi and Kiambu (74 percent each).
The counties with the lowest CPR include Mandera and Wajir (2 percent each), Garissa (6 percent),
Turkana (10 percent), and Marsabit (12 percent) (Table 7.4C).
Table 7.4C Current use of contraception by county
Percent distribution of currently married women age 15-49 by contraceptive method currently used, according to county, Kenya 2014
Modern method Any Traditional method
Any Female Male tradi- Not cur- Number
Any modern sterili- sterili- Inject- Im- Male Female tional With- rently of
County method method sation sation Pill IUD ables plants condom condom LAM Other method Rhythm drawal Other using Total women
Coast 43.9 38.3 1.6 0.0 4.7 2.2 18.7 9.4 1.5 0.0 0.1 0.0 5.6 4.2 1.4 0.1 56.1 100.0 1,821
Mombasa 55.0 43.6 0.2 0.0 6.5 3.2 17.7 12.6 2.9 0.0 0.4 0.0 11.4 9.0 2.4 0.0 45.0 100.0 537
Kwale 41.5 38.2 3.0 0.0 4.3 1.6 21.6 6.8 0.8 0.0 0.0 0.0 3.3 2.2 1.1 0.0 58.5 100.0 357
Kilifi 34.1 32.8 2.8 0.0 2.7 1.1 15.9 10.0 0.3 0.0 0.0 0.0 1.3 0.9 0.3 0.0 65.9 100.0 600
Tana River 28.7 20.5 0.2 0.0 1.1 0.4 13.1 2.7 3.0 0.0 0.0 0.0 8.2 3.9 4.3 0.0 71.3 100.0 144
Lamu 42.2 39.5 1.2 0.0 10.2 1.0 19.0 6.4 1.2 0.0 0.5 0.0 2.6 2.6 0.1 0.0 57.8 100.0 55
Taita Taveta 68.0 61.3 0.4 0.0 10.0 6.9 34.1 8.6 1.5 0.0 0.0 0.0 6.6 5.4 0.5 0.7 32.0 100.0 128
North Eastern 3.4 3.4 0.0 0.0 0.6 0.1 1.9 0.6 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 96.6 100.0 451
Garissa 5.5 5.5 0.0 0.0 1.1 0.2 2.4 1.5 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 94.5 100.0 165
Wajir 2.3 2.3 0.0 0.0 0.2 0.0 1.6 0.2 0.1 0.0 0.2 0.0 0.0 0.0 0.0 0.0 97.7 100.0 158
Mandera 1.9 1.9 0.0 0.0 0.4 0.0 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 98.1 100.0 128
Eastern 70.4 63.9 4.8 0.0 8.9 2.9 37.9 7.8 1.5 0.0 0.0 0.0 6.5 5.6 0.5 0.3 29.6 100.0 2,667
Marsabit 11.7 10.9 0.4 0.0 1.1 0.3 6.3 2.7 0.0 0.0 0.0 0.0 0.8 0.8 0.0 0.0 88.3 100.0 76
Isiolo 27.0 26.3 0.8 0.0 7.2 1.4 13.2 3.3 0.4 0.0 0.0 0.0 0.7 0.6 0.1 0.0 73.0 100.0 65
Meru 78.2 73.2 4.3 0.0 12.3 5.4 44.8 3.5 2.8 0.0 0.0 0.0 5.0 4.3 0.7 0.0 21.8 100.0 690
Tharaka-Nithi 74.0 67.2 1.8 0.0 7.0 7.2 44.1 5.5 1.3 0.2 0.0 0.0 6.8 4.3 0.6 1.9 26.0 100.0 169
Embu 70.6 67.2 3.8 0.0 15.2 4.6 31.2 11.0 1.5 0.0 0.0 0.0 3.4 3.2 0.2 0.0 29.4 100.0 266
Kitui 57.3 55.1 3.0 0.0 4.5 1.1 36.9 9.5 0.0 0.0 0.0 0.0 2.2 2.0 0.0 0.2 42.7 100.0 445
Machakos 75.9 67.5 5.5 0.0 9.1 0.5 41.6 10.4 0.5 0.0 0.0 0.0 8.3 7.5 0.5 0.3 24.1 100.0 553
Makueni 80.3 65.0 10.2 0.0 5.9 1.8 33.8 10.3 2.9 0.0 0.0 0.0 15.3 13.4 1.1 0.8 19.7 100.0 404
Central 72.8 66.9 3.5 0.0 19.5 9.0 21.6 10.7 2.4 0.0 0.2 0.0 5.9 4.9 0.7 0.3 27.2 100.0 2,323
Nyandarua 65.6 60.4 2.8 0.0 13.8 8.0 22.9 10.8 0.9 0.0 1.3 0.0 5.2 5.0 0.2 0.0 34.4 100.0 273
Nyeri 73.1 67.1 7.3 0.0 16.7 10.0 22.3 9.2 1.6 0.0 0.0 0.0 6.0 5.3 0.6 0.2 26.9 100.0 358
Kirinyaga 81.0 75.6 0.9 0.0 26.0 13.2 20.4 13.0 2.0 0.0 0.0 0.0 5.4 4.3 1.1 0.0 19.0 100.0 281
Murang’a 68.9 63.4 4.0 0.0 22.1 6.3 20.6 7.8 2.5 0.0 0.0 0.0 5.5 4.3 0.0 1.2 31.1 100.0 444
Kiambu 74.0 67.8 2.7 0.0 19.2 8.9 21.9 12.0 3.1 0.0 0.0 0.0 6.3 5.3 1.0 0.0 26.0 100.0 967
Rift Valley 52.8 46.8 2.2 0.0 5.5 2.9 26.8 7.2 1.9 0.0 0.2 0.0 6.0 4.7 1.0 0.3 47.2 100.0 4,696
Turkana 10.4 10.1 0.0 0.0 0.5 0.5 5.7 3.0 0.4 0.0 0.0 0.0 0.3 0.3 0.0 0.0 89.6 100.0 214
West Pokot 14.2 13.3 0.4 0.0 0.7 0.2 9.0 3.1 0.0 0.0 0.0 0.0 0.9 0.2 0.0 0.7 85.8 100.0 197
Samburu 22.7 20.0 0.5 0.0 2.9 0.6 10.9 4.4 0.8 0.0 0.0 0.0 2.7 2.4 0.3 0.0 77.3 100.0 83
Trans-Nzoia 63.9 56.4 4.0 0.0 4.9 0.7 38.7 4.6 3.2 0.1 0.2 0.0 7.5 5.9 1.1 0.4 36.1 100.0 467
Uasin Gishu 62.6 56.0 1.8 0.0 7.4 2.7 28.7 12.9 2.4 0.0 0.0 0.0 6.6 5.3 0.8 0.5 37.4 100.0 460
Elgeyo Marakwet 55.2 43.6 1.1 0.0 1.6 1.3 28.5 8.7 2.1 0.3 0.0 0.0 11.6 10.6 1.0 0.0 44.8 100.0 139
Nandi 64.5 59.2 1.8 0.0 5.5 0.9 40.3 9.1 1.6 0.0 0.0 0.0 5.4 3.9 1.5 0.0 35.5 100.0 335
Baringo 41.4 33.1 0.7 0.0 4.6 3.2 16.2 5.5 2.3 0.0 0.6 0.0 8.3 6.5 1.4 0.4 58.6 100.0 190
Laikipia 59.1 51.3 5.0 0.0 12.5 5.1 20.8 4.5 3.1 0.0 0.0 0.4 7.8 6.6 0.6 0.7 40.9 100.0 207
Nakuru 56.8 53.5 1.4 0.2 10.4 6.7 25.4 7.6 1.4 0.0 0.4 0.0 3.2 2.6 0.4 0.2 43.2 100.0 851
Narok 47.8 38.1 1.9 0.0 3.7 1.1 25.3 3.8 2.2 0.0 0.0 0.1 9.7 6.9 2.4 0.4 52.2 100.0 446
Kajiado 54.5 45.2 1.5 0.0 6.5 5.9 20.0 8.9 2.2 0.0 0.2 0.0 9.3 6.7 2.1 0.6 45.5 100.0 387
Kericho 62.9 56.9 3.5 0.0 3.2 2.1 35.8 9.9 1.9 0.0 0.3 0.2 6.1 5.1 1.0 0.0 37.1 100.0 327
Bomet 54.8 50.4 4.9 0.0 0.4 1.7 33.9 7.5 2.0 0.0 0.0 0.0 4.4 3.5 0.6 0.4 45.2 100.0 394
Western 58.6 56.9 5.9 0.0 4.6 1.3 27.5 15.2 2.5 0.0 0.0 0.0 1.7 1.1 0.3 0.3 41.4 100.0 1,950
Kakamega 62.1 60.3 6.9 0.0 5.4 1.0 30.4 14.1 2.6 0.0 0.0 0.0 1.7 1.0 0.5 0.3 37.9 100.0 697
Vihiga 59.5 56.6 3.9 0.0 4.8 3.3 25.3 16.2 3.1 0.0 0.0 0.0 2.9 2.9 0.0 0.0 40.5 100.0 212
Bungoma 55.5 53.9 5.1 0.0 4.4 0.8 29.0 11.8 2.9 0.0 0.0 0.0 1.6 0.9 0.2 0.5 44.5 100.0 696
Busia 57.5 56.5 6.5 0.0 3.5 1.8 20.2 23.6 1.0 0.0 0.0 0.0 0.9 0.9 0.1 0.0 42.5 100.0 345
Nyanza 56.4 53.9 3.6 0.0 3.4 2.0 29.3 12.4 2.9 0.0 0.1 0.1 2.5 2.0 0.3 0.2 43.6 100.0 2,525
Siaya 55.0 51.0 3.2 0.0 5.8 1.8 19.3 15.3 5.7 0.0 0.0 0.0 4.0 3.3 0.7 0.0 45.0 100.0 326
Kisumu 62.4 59.3 5.2 0.0 3.7 1.5 24.3 21.1 3.5 0.0 0.0 0.0 3.1 3.1 0.0 0.0 37.6 100.0 500
Homa Bay 46.7 45.5 3.8 0.0 2.1 1.1 26.1 8.6 3.5 0.0 0.0 0.3 1.2 1.2 0.0 0.0 53.3 100.0 520
Migori 44.6 43.9 1.9 0.0 2.3 1.1 24.6 10.6 3.1 0.3 0.0 0.0 0.7 0.2 0.3 0.3 55.4 100.0 432
Kisii 66.1 62.8 3.2 0.0 4.0 3.5 41.8 9.2 0.8 0.0 0.3 0.0 3.4 2.2 0.7 0.4 33.9 100.0 531
Nyamira 67.9 64.2 4.2 0.0 3.5 3.9 42.6 8.2 1.2 0.0 0.3 0.3 3.7 3.0 0.3 0.3 32.1 100.0 216
Nairobi 62.6 58.3 2.0 0.1 12.5 4.5 23.6 12.1 3.3 0.0 0.0 0.0 4.4 3.2 0.3 0.9 37.4 100.0 2,117
Total 58.0 53.2 3.2 0.0 8.0 3.4 26.4 9.9 2.2 0.0 0.1 0.0 4.8 3.8 0.7 0.3 42.0 100.0 18,549
Note: If more than one method is used, only the most effective method is considered in this tabulation.
LAM = Lactational amenorrhoea method.
7.5 TRENDS IN CURRENT USE OF CONTRACEPTION
Trends in current use of family planning can be used to monitor and evaluate the success of family
planning programmes over time. Table 7.5 and Figure 7.1 show trends in the use of modern contraceptives
among currently married women from 2003 to 2014. Data from the three DHS surveys conducted in Kenya
over the past 11 years show an impressive increase in the use of modern contraceptive methods.
Family Planning • 95
Table 7.5 Trends in the current use of contraception
Percent distribution of currently married women age 15-49 by contraceptive
method currently used, according to several surveys
Method 2003 KDHS 2008-09 KDHS 2014 KDHS
Any method 39.3 45.5 58.0
Any modern method 31.5 39.4 53.2
Female sterilisation 4.3 4.8 3.2
Male sterilisation 0.0 0.0
Pill 7.5 7.2 8.0
IUD 2.4 1.6 3.4
Injectables 14.3 21.6 26.4
Implants 1.7 1.9 9.9
Male condom 1.2a 1.8a 2.2
Other modern method 1.5 0.5 0.1
Any traditional method 7.0 5.3 4.8
Rhythm 6.3 4.7 3.8
Withdrawal 0.6 0.7 0.7
Other 1.9 0.7 0.3
Not currently using 60.7 54.5 42.0
Total 100.0 100.0 100.0
Number of women 4,919 4,928 18,549
a
The question did not specify male condom.
Figure 7.1 Trends in contraceptive use among currently married women
39
ANY METHOD 46
58
32
Any modern method 39
53
4
Female sterilisation 5
3
8
Pill 7
8
2
IUD 2
3
14
Injectables 22
26 2003 KDHS
2
Implants 2 2008-09 KDHS
10
1 2014 KDHS
Male condom 2
2
8
Any traditional method 6
5
6
Rhythm 5
4
1
Withdrawal 1
1
1
Other traditional method 6
<1
Percentage of currently married women
KDHS 2014
Currently married women’s use of modern contraceptives increased from 32 percent in 2003 to 39
percent in 2008-09 and again to the current 53 percent. With these increases, the government of Kenya’s
Population Policy for National Development has achieved its target of 52 percent of currently married
women using a modern contraceptive method by 2015. While the use of modern methods has increased,
uptake of traditional contraceptive methods has decreased slightly over the years (8 percent in 2003 to 5
percent in 2014). Among the various methods women use, the biggest change is reported for injectables
and for implants. Currently married women’s use of injectables increased from 14 percent in 2003 to the
current 26 percent. Use of implants increased from 2 percent in 2003 to the current 10 percent.
7.6 TIMING OF STERILISATION
Sterilisation is a very effective permanent method of family planning. In women, it involves
blocking or occluding the fallopian tubes to prevent eggs and sperm from uniting. This is one of the
96 • Family Planning
options that may be adopted by couples who do not want any more children; therefore, it is important to
know if the age at which women get sterilised is changing. Table 7.6 shows the percent distribution of
sterilised women age 15-49 by age at the time of sterilisation and median age at sterilisation, according to
the number of years since the operation. The median age at sterilisation for Kenyan women is 33.2 years,
which does not differ much from the median age (33.0 years) reported in the 2008-09 KDHS.
Table 7.6 Timing of sterilisation
Percent distribution of sterilised women age 15-49 by age at the time of sterilisation and median age at sterilisation, according
to the number of years since the operation, Kenya 2014
Years since Age at time of sterilisation Number of Median
operation <25 25-29 30-34 35-39 40-44 45-49 Total women age1
<2 (0.0) (18.6) (34.9) (30.1) (14.1) (2.3) 100.0 48 (33.4)
2-3 4.6 11.1 36.1 24.5 15.4 8.5 100.0 68 32.4
4-5 0.0 25.3 31.3 20.1 23.3 0.0 100.0 56 33.3
6-7 (0.0) (8.0) (25.4) (59.2) (7.5) (0.0) 100.0 46 (36.4)
8-9 (1.0) (12.3) (19.9) (63.3) (3.5) (0.0) 100.0 40 (36.3)
10+ 8.4 32.9 42.9 15.8 0.0 0.0 100.0 81 a
Total 3.0 19.4 33.4 31.7 10.4 2.0 100.0 339 33.2
Note: Figures in parentheses are based on 25-49 unweighted cases.
a = Not calculated due to censoring
1
Median age at sterilisation is calculated only for women sterilised before age 40 to avoid problems of censoring.
7.7 SOURCE OF CONTRACEPTION
Table 7.7 presents the main sources of contraception for users of modern methods. Information on
where users obtain their contraceptive method is important for programme managers and implementers in
designing family planning policies and programmes. All current users of modern contraceptive methods
were asked the most recent source of their method.
The public sector is the major source of contraceptive methods in Kenya, providing contraception
to 60 percent of current users. Within the public sector, 24 percent of users obtain their methods from
government dispensaries, 20 percent from government hospitals, and 16 percent from government health
centres. Thirty-four percent of modern contraceptive users obtain their methods from the private medical
sector, mainly from private hospitals/clinics (21 percent) and pharmacies (10 percent). Sources of
contraception remain stable compared with those reported in 2008-09, when 57 percent of users obtained
contraception from the public sector and 36 percent from the private sector.
Except for the pill and male condoms, the public sector is the primary provider of most types of
contraception used in Kenya. The majority of women who use the pill obtain it from the private sector (57
percent), and nearly half of women who use male condoms obtain them from other sources, largely from
shops (39 percent). These findings point to the continued reliance on government facilities as a major
source of contraceptives.
Family Planning • 97
Table 7.7 Source of modern contraception methods
Percent distribution of users of modern contraceptive methods age 15-49 by most recent source of method, according to method,
Kenya 2014
Female Male
Source sterilisation Pill IUD Injectables Implants condom Total
Public sector 74.5 39.6 64.3 62.7 78.2 23.7 59.9
Government hospital 50.8 13.3 32.1 15.2 28.4 9.4 19.9
Government health centre 14.7 11.0 19.6 15.6 23.5 6.4 15.9
Government dispensary 9.0 15.3 12.7 31.9 26.3 7.9 24.1
Other public 0.0 0.0 0.0 0.0 0.0 0.1 0.0
Private medical sector 21.4 57.0 34.5 36.4 18.2 19.4 33.7
Private hospital/clinic 9.3 10.4 29.0 29.3 15.6 1.9 20.7
Pharmacy/chemist 0.0 45.4 0.0 5.4 0.0 16.1 10.3
Nursing/maternity home 0.9 0.2 0.9 0.1 0.3 0.6 0.3
Faith-based church, mission
hospital/clinic 10.3 0.6 2.8 1.4 1.8 0.8 1.9
Family options/FHOK clinic 0.9 0.0 1.9 0.2 0.4 0.0 0.3
Other private 0.0 0.3 0.0 0.0 0.0 0.0 0.1
Other source 0.6 2.9 1.0 0.6 3.5 48.3 5.3
Shop 0.0 1.3 0.0 0.0 0.0 39.0 3.3
Mobile clinic 0.6 0.1 1.0 0.6 3.5 0.4 1.1
Community-based distributor 0.0 0.5 0.0 0.0 0.0 0.8 0.1
Community health worker (CHW) 0.0 0.5 0.0 0.0 0.0 0.4 0.1
Friend/relative 0.0 0.6 0.0 0.0 0.0 7.6 0.7
Other 0.3 0.1 0.0 0.0 0.0 7.2 0.6
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of women 691 1,703 712 5,818 2,213 975 12,131
Note: Total includes other modern methods but excludes lactational amenorrhoea method (LAM). Total includes contraceptive
methods with too few users to show separately, including one user of male sterilisation and six female condom users. Totals may
not add up to 100 percent because women with missing information are not shown separately.
FHOK = Family Health Organisation of Kenya
7.8 INFORMED CHOICE
Informed choice is an important principle in the delivery of family planning services. It is required
that all family planning providers inform potential users about the side effects of the method and what they
should do if they encounter such problems. This information assists users in making an informed decision
about what contraceptive method may work best for them and in coping with side effects. By making an
informed choice, users can choose the method that is right for them, and thereby decrease the likelihood
that they will discontinue use of the method. Women should be informed of all methods available to them.
Table 7.8 shows the percentage of current users of modern methods who were informed about side effects
or problems of the method used, about what to do if they experienced side effects, and about other methods
they could use. These data are also presented by method type and initial source.
Sixty percent of current users of modern contraceptive methods were informed about potential
side effects of their method, 52 percent were told what to do if they experienced side effects, and 79
percent were given information about other methods. Since the 2008-09 KDHS, only one of these
indicators, being informed about alternative methods (61 percent in 2008-09), has improved.
Users were slightly more likely to receive information about side effects or problems associated
with a method from a government medical facility (63 percent) than from a private facility (55 percent).
This pattern prevails for being informed about what to do when experiencing side effects (public sector, 56
percent; private sector, 45 percent) and being informed about alternative methods (public sector, 81
percent; private sector, 75 percent).
98 • Family Planning
Table 7.8 Informed choice
Among current users of modern methods age 15-49 who started the last episode of use within the five years
preceding the survey, the percentage who were informed about possible side effects or problems of that method,
the percentage who were informed about what to do if they experienced side effects, and the percentage who
were informed about other methods they could use, by method and initial source, Kenya 2014
Among women who started last episode of modern contraceptive method within
five years preceding the survey:
Percentage who
Percentage who Percentage who were informed by a
were informed were informed health or family
about side effects about what to do if planning worker of
or problems of side effects other methods that Number of
Method/source method used experienced could be used women
Method
Female sterilisation 51.2 44.1 77.4 142
Pill 50.8 42.6 77.4 661
IUD 81.4 74.9 88.8 246
Injectables 55.0 46.8 75.1 2,260
Implants 71.6 64.3 85.8 1,012
Initial source of method1
Public sector 62.9 56.0 81.2 2,800
Government hospital 67.2 59.6 83.1 944
Government health centre 61.8 56.6 82.3 759
Government dispensary 60.0 52.6 78.8 1,097
Private medical sector 54.9 45.3 75.4 1,407
Private hospital/clinic 58.8 49.3 79.9 918
Pharmacy/chemist 41.3 32.2 63.6 377
Nursing/maternity home * * * 16
Faith-based church,
mission hospital/clinic 68.3 51.3 72.1 85
Family options/FHOK clinic * * * 10
Other private * * * 1
Other source 52.4 39.2 85.4 73
Total 59.6 51.8 78.8 4,322
Note: Table includes users of only the methods listed individually. Total includes 39 cases for whom information
about informed choice is missing. An asterisk denotes a figure based on fewer than 25 unweighted cases that has
been suppressed.
FHOK = Family Health Organisation of Kenya
1
Source at start of current episode of use
7.9 CONTRACEPTIVE DISCONTINUATION RATES
Couples can realise their reproductive goals only when they use contraceptive methods
consistently and correctly. Discontinuation of contraception, for reasons other than to conceive, interferes
with family planning and increases the risk for unplanned pregnancies. In the “calendar” section of the
Woman’s Questionnaire, all segments of contraceptive use from 62 months prior to the survey are
recorded. When calculating discontinuation rates, the month of the interview and the two months prior to
the survey are ignored in order to avoid the bias that may be introduced by unrecognised pregnancies.
Twelve-month contraceptive discontinuation rates based on the calendar data are presented in Table 7.9.
Thirty-one percent of contraceptive users discontinue use of the method within 12 months of
starting its use. Discontinuation rates are highest for methods in the “other” category (e.g., female condom,
LAM, withdrawal) (46 percent), followed by the pill (45 percent) and male condoms (43 percent). The
lowest discontinuation rates are for IUDs (6 percent) and implants (8 percent). Users of the pill are most
likely (22 percent) to switch to another method, while users of IUDs and implants are least likely to switch
to another method (4 percent each). Eleven percent of episodes of discontinuation occurred because of side
effects or health concerns, and 5 percent because the woman wanted to become pregnant.
Family Planning • 99
Table 7.9 Twelve-month contraceptive discontinuation rates
Among women age 15-49 who started an episode of contraceptive use within the five years preceding the survey, the percentage of episodes
discontinued within 12 months, by reason for discontinuation and specific method, Kenya 2014
Other Side Wanted Other
Desire to fertility effects/ more method Switched Number of
Method become related health effective related Other Any to another episodes
Method failure pregnant reasons2 concerns method reasons3 reasons reason4 method5 of use6
Female sterilisation (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.9) (0.9) (0.0) 150
Pill 5.3 6.2 4.0 15.7 6.8 2.2 4.6 44.9 21.5 1,727
IUD 0.8 0.2 0.4 4.2 0.0 0.2 0.6 6.4 3.8 298
Injectables 1.7 5.4 2.8 14.4 1.9 1.2 3.4 30.9 10.2 4,054
Implants 0.3 0.3 0.2 6.6 0.1 0.1 0.3 8.0 3.7 1,164
Male condom 1.9 5.3 21.4 0.8 2.3 0.3 10.8 42.9 4.6 859
Rhythm 9.1 4.1 3.8 0.0 6.3 0.2 1.4 24.8 6.7 661
Other1 9.0 10.5 2.4 5.7 9.3 2.8 6.2 45.8 15.9 243
All methods1 3.0 4.7 4.4 10.5 3.1 1.1 3.7 30.5 10.5 9,158
Note: Figures are based on life table calculations using information on episodes of use that began 3-62 months preceding the survey. Figures in
parentheses are based on 125-249 unweighted cases.
1
Includes female condom, LAM, withdrawal, other modern and other traditional methods
2
Includes infrequent sex/husband away, difficult to get pregnant/menopausal, and marital dissolution/separation
3
Includes lack of access/too far, costs too much, and inconvenient to use
4
Reasons for discontinuation are mutually exclusive and add to the total given in this column
5
The episodes of use included in this column are a subset of the discontinued episodes included in the discontinuation rate. A woman is
considered to have switched to another method if she used a different method in the month following discontinuation or if she gave “wanted a
more effective method” as the reason for discontinuation and started another method within two months of discontinuation.
6
Number of episodes of use includes both episodes of use that were discontinued during the period of observation and episodes of use that were
not discontinued during the period of observation
7.10 REASONS FOR DISCONTINUATION OF CONTRACEPTIVE USE
Table 7.10 shows the percent distribution of discontinuations of contraceptive methods in the five
years preceding the survey by the main reason for discontinuation, according to method. The most
common reason for discontinuing a method is health concerns or side effects (29 percent), followed by
desire to become pregnant (26 percent) and pregnancy (11 percent). Health concerns or side effects are
most often cited as the reason for discontinuing use of implants (52 percent), IUDs (43 percent), injectables
(38 percent), and the pill (28 percent). Infrequent sex or absence of the husband is the reason most often
reported for discontinuing use of the male condom (38 percent).
Table 7.10 Reasons for discontinuation
Percent distribution of discontinuations of contraceptive methods in the five years preceding the survey by main reason stated for discontinuation, according to
specific method, Kenya 2014
Male All
Reason Pill IUD Injectables Implants condom LAM Rhythm Withdrawal Other methods
Became pregnant while using 14.1 4.7 6.2 1.3 6.2 (1.5) 38.5 35.7 (16.7) 10.8
Wanted to become pregnant 23.5 24.2 29.0 24.5 16.5 (8.7) 21.8 29.2 (34.7) 25.6
Husband/partner disapproved 0.6 4.2 0.9 1.1 5.6 (0.0) 0.7 1.6 (1.9) 1.3
Wanted more effective method 12.8 4.7 4.9 2.4 9.6 (60.1) 19.4 12.4 (6.4) 8.7
Health concerns/side effects 27.6 42.9 37.6 51.5 1.2 (0.0) 0.0 3.4 (15.6) 29.0
Lack of access/too far 0.5 0.0 1.4 0.1 0.2 (0.0) 0.0 0.0 (0.0) 0.8
Cost too much 0.9 0.0 1.2 0.2 0.0 (0.0) 0.0 0.0 (0.0) 0.8
Inconvenient to use 3.5 2.2 1.2 1.1 1.4 (7.7) 0.9 2.5 (3.8) 1.9
Up to God/fatalistic 0.1 0.9 0.1 0.0 0.0 (0.0) 0.1 0.0 (0.0) 0.1
Difficult to get pregnant/menopausal 0.6 2.9 0.4 0.2 0.2 (0.0) 0.7 0.0 (1.2) 0.5
Infrequent sex/husband away 6.2 1.9 6.5 3.9 38.0 (1.3) 5.8 1.4 (8.5) 8.9
Marital dissolution/separation 1.1 0.7 1.2 4.4 0.5 (0.0) 0.1 0.6 (0.0) 1.1
Other 3.4 7.7 3.6 5.7 4.2 (14.5) 2.6 1.1 (2.9) 3.8
Don’t know/missing 5.2 3.1 5.7 3.7 16.2 (6.2) 9.3 12.2 (8.3) 6.7
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of discontinuations 1,713 119 3,394 374 624 41 541 76 72 6,961
Note: Total includes contraceptive methods with too few users to show separately, including four users of female sterilisation and three female condom users.
Figures in parentheses are based on 25-49 unweighted cases.
LAM = Lactational amenorrhoea method
100 • Family Planning
7.11 KNOWLEDGE OF FERTILE PERIOD
A basic understanding of the reproductive cycle Table 7.11 Knowledge of fertile period
is important for successful use of coitus-related methods Percent distribution of women age 15-49 by knowledge of the
of contraception such as the rhythm method. The fertile period during the ovulatory cycle, according to current use
of the rhythm method, Kenya 2014
successful practice of such methods depends in large
Users of Nonusers of
part on understanding when during the ovulatory cycle a rhythm rhythm
woman is most likely to conceive. All women in the Perceived fertile period method method All women
survey were asked about their knowledge of a woman’s Just before her
menstrual period
fertile period. Specifically, they were asked whether begins 20.2 16.0 16.1
During her menstrual
there are certain days between two menstrual periods period 1.7 3.6 3.5
when a woman is most likely to become pregnant if she Right after her
menstrual period has
has sexual intercourse. Those who answered in the ended 32.6 32.3 32.3
Halfway between two
affirmative were further asked if this time is just before menstrual periods 35.8 25.2 25.5
the period begins, during the period, right after the Other 0.0 0.2 0.2
No specific time 4.2 10.8 10.6
period ends, or halfway between the two periods. Don’t know 5.5 11.9 11.7
Total 100.0 100.0 100.0
Table 7.11 shows the percent distribution of Number of women 425 14,200 14,625
women age 15-49 by knowledge of women’s fertile Note: Totals may not add up to 100 percent because women
period, according to current use of the rhythm method. with missing information are not shown separately.
Only 26 percent of all women correctly reported the
most fertile time as being halfway between two menstrual periods. Among users of the rhythm method, 36
percent were able to correctly identify the fertile period, while 33 percent incorrectly reported that the
fertile period is directly after menstruation has ended and 20 percent incorrectly reported that it is just
before menstruation begins. These numbers do not indicate any improvement in understanding of the
reproductive cycle since the 2008-09 KDHS. There is continued need for education about women’s
reproductive system and effective use of contraceptive methods.
7.12 NEED AND DEMAND FOR FAMILY PLANNING SERVICES
This section provides information on the extent of need and potential demand for family planning
services in Kenya. Unmet need for family planning refers to fecund women who are not using
contraception but who wish to postpone their next birth (spacing) or stop childbearing altogether (limiting).
Specifically, women are considered to have an unmet need for spacing if they are:
• At risk of becoming pregnant, not using contraception, and either do not want to become
pregnant within the next two years or are unsure if or when they want to become pregnant.
• Pregnant with a mistimed pregnancy.
• Postpartum amenorrhoeic for up to two years following a mistimed birth and not using
contraception.
Women are considered to have an unmet need for limiting if they are:
• At risk of becoming pregnant, not using contraception, and want no (more) children.
• Pregnant with an unwanted pregnancy.
• Postpartum amenorrhoeic for up to two years following an unwanted birth and not using
contraception.
Women who are classified as infecund have no unmet need because they are not at risk of
becoming pregnant.
Family Planning • 101
Women using contraception are considered to have a met need. Women using contraception who
say they want no (more) children are considered to have a met need for limiting, and women who are using
contraception and say they want to delay having a child, or are unsure if or when they want a (another)
child, are considered to have a met need for spacing.
Unmet need, total demand, percentage of demand satisfied, and percentage of demand satisfied by
modern methods are defined as follows:
Unmet need: the sum of unmet need for spacing plus unmet need for limiting
Total demand for family the sum of unmet need plus total contraceptive use
planning:
Percentage of demand total contraceptive use divided by the sum of unmet need plus
satisfied: total contraceptive use
Percentage of demand use of modern contraceptive methods divided by the sum of
satisfied by modern methods: unmet need plus total contraceptive use
In the past, the definition of unmet need used information from the contraceptive calendar and
other questions that were not included in every survey, which led to unmet need being calculated
inconsistently across surveys. The revised definition uses only information that has been collected in every
survey so that unmet need can be measured in the same way over time (see Bradley et al., 2012).
Table 7.12 shows need and demand for family planning among currently married women age 15-
49 by background characteristics. Eighteen percent of currently married women have an unmet need for
family planning, with 9 percent having an unmet need for spacing and 8 percent having an unmet need for
limiting.
Fifty-eight percent of women have a met need for family planning. If all currently married women
who say they want to space or limit their children were to use a family planning method, the contraceptive
prevalence rate would increase to 76 percent. Currently, 77 percent of the family planning needs of married
women are being met.
Unmet need is higher in rural areas (20 percent) than in urban areas (13 percent). Unmet need is
highest in North Eastern (30 percent) and lowest in Central (9 percent) and Nairobi (11 percent). Unmet
need decreases with increasing education; married women with no education have a higher unmet need for
family planning (28 percent) than their educated counterparts (23 percent or less). Unmet need declines
steadily as household wealth increases, from 29 percent in the lowest wealth quintile to 11 percent in the
highest quintile.
Total demand for family planning is highest among women age 35-39 (82 percent) and lowest
among women at the beginning (age 15-19) and end (age 45-49) of their reproductive years (each 61
percent). Demand for family planning does not vary much by urban-rural residence; however, there are
wide variations by region. North Eastern has the lowest demand (33 percent) and Eastern the highest (83
percent). Women with no education (47 percent) and women in the lowest wealth quintile (60 percent)
have a lower demand than their more educated or wealthier counterparts.
102 • Family Planning
Table 7.12 Need and demand for family planning among currently married women
Percentage of currently married women age 15-49 with unmet need for family planning, percentage with met need for family planning, the total demand for family
planning, and the percentage of the demand for contraception that is satisfied, by background characteristics, Kenya 2014
Percentage
Met need for family planning Total demand for family
of demand
Unmet need for family planning (currently using) planning1
Percentage satisfied by
Background For For For For For For of demand modern Number
characteristic spacing limiting Total spacing limiting Total spacing limiting Total satisfied2 methods3 of women
Age
15-19 21.8 1.3 23.0 37.1 1.3 38.4 58.9 2.5 61.4 62.5 56.2 301
20-24 17.1 1.8 18.9 43.5 9.5 53.1 60.6 11.3 71.9 73.8 68.3 1,465
25-29 11.1 3.7 14.9 37.7 23.0 60.7 48.9 26.7 75.6 80.3 76.5 2,171
30-34 7.5 8.3 15.9 24.5 39.2 63.7 32.0 47.6 79.6 80.0 74.4 1,717
35-39 5.1 13.4 18.5 12.0 51.2 63.2 17.1 64.6 81.7 77.3 70.3 1,365
40-44 3.3 18.5 21.8 5.0 54.0 59.0 8.2 72.5 80.8 73.1 65.3 923
45-49 1.8 15.0 16.7 0.9 43.7 44.5 2.6 58.6 61.3 72.7 59.5 768
Residence
Urban 7.3 6.1 13.4 32.3 30.2 62.5 39.5 36.4 75.9 82.4 76.7 3,445
Rural 10.4 9.7 20.2 20.8 34.3 55.1 31.2 44.0 75.2 73.2 66.7 5,265
Region
Coast 12.3 8.4 20.6 24.3 20.1 44.4 36.5 28.5 65.0 68.3 58.4 850
North Eastern 27.2 2.7 29.9 2.8 0.6 3.4 30.1 3.3 33.3 10.2 10.2 209
Eastern 4.0 8.4 12.4 27.4 43.1 70.5 31.4 51.5 82.9 85.1 77.2 1,268
Central 3.0 5.8 8.8 30.8 42.3 73.0 33.8 48.1 81.8 89.3 83.9 1,113
Rift Valley 11.1 9.7 20.8 25.2 27.4 52.6 36.2 37.1 73.4 71.6 62.3 2,171
Western 11.0 9.7 20.7 24.9 34.7 59.6 35.9 44.4 80.3 74.2 71.8 929
Nyanza 12.6 10.6 23.2 20.4 35.0 55.3 33.0 45.6 78.5 70.5 67.4 1,203
Nairobi 6.3 4.8 11.1 29.1 33.2 62.3 35.4 38.0 73.4 84.9 81.5 968
Education
No education 18.7 9.1 27.7 7.2 11.8 19.0 25.9 20.9 46.8 40.7 35.8 795
Primary incomplete 11.5 11.9 23.4 20.4 33.6 54.1 31.9 45.5 77.4 69.8 64.7 2,274
Primary complete 7.3 8.1 15.3 26.0 38.7 64.7 33.3 46.7 80.0 80.8 74.9 2,465
Secondary+ 6.6 5.8 12.4 32.8 32.6 65.5 39.5 38.4 77.9 84.1 76.7 3,177
Wealth quintile
Lowest 18.4 10.2 28.6 13.5 17.6 31.1 31.9 27.8 59.7 52.1 45.6 1,457
Second 11.4 11.7 23.1 22.4 35.8 58.3 33.8 47.6 81.4 71.6 66.4 1,567
Middle 7.2 9.9 17.1 25.3 38.0 63.4 32.5 48.0 80.5 78.7 72.8 1,663
Fourth 6.2 5.8 12.0 30.2 36.1 66.3 36.4 41.8 78.3 84.7 78.2 1,885
Highest 5.5 5.5 11.0 31.2 33.5 64.8 36.7 39.0 75.7 85.5 78.8 2,138
Total 9.2 8.3 17.5 25.3 32.7 58.0 34.5 41.0 75.5 76.8 70.7 8,710
Note: Numbers in this table correspond to the revised definition of unmet need described in Bradley et al., 2012.
1
Total demand is the sum of unmet need and met need
2
Percentage of demand satisfied is met need divided by total demand
3
Modern methods include female sterilisation, male sterilisation, pill, IUD, injectables, implants, male condom, female condom, and lactational amenorrhoea
method (LAM)
Figure 7.2 shows that unmet need decreased only marginally from 1998 (28 percent) to 2008-09
(26 percent) before a more substantial decrease to the current 18 percent.
Family Planning • 103
Figure 7.2 Trends in unmet need for family planning
Percent
28 27
26
18
1998 KDHS 2003 KDHS 2008-09 KDHS 2014 KDHS
Note: Estimates for all years are based on the revised definition of unmet need. Data collected before 2003
exclude North Eastern region and several northern districts in the Eastern and Rift Valley.
7.13 FUTURE USE OF CONTRACEPTION
An important indicator of the changing demand for family planning is the extent to which
nonusers plan to use contraceptive methods in the future. In the 2014 KDHS, currently married women age
15-49 who were not using any contraceptive method at the time of the survey were asked about their
intention to use family planning in the future. Women who stated that they did not intend to use a
contraceptive method in the future were also asked the reason behind this intention.
Table 7.13 shows that among currently married women not using contraception, 57 percent intend
to use a family planning method in the future, 4 percent are unsure of their intentions, and 38 percent have
no intention of using any method in the future. These data do not indicate any change from 2008-09. The
proportion of women intending to use family planning peaks at 69 percent among nonusers with one child
and declines to 45 percent among those with four or more children.
Table 7.13 Future use of contraception
Percent distribution of currently married women age 15-49 who are not using a contraceptive
method by intention to use in the future, according to number of living children, Kenya 2014
Number of living children1
Intention 0 1 2 3 4+ Total
Intends to use 64.9 68.8 68.3 58.4 45.4 57.3
Unsure 6.8 4.0 3.1 2.6 3.7 3.7
Does not intend to use 28.3 26.3 27.7 37.6 50.2 38.2
Missing 0.0 0.9 0.9 1.4 0.6 0.8
Total 100.0 100.0 100.0 100.0 100.0 100.0
Number of women 231 626 708 616 1,475 3,656
1
Includes current pregnancy
104 • Family Planning
Table 7.14 presents the distribution of currently married Table 7.14 Reason for not intending to use
nonusers who do not intend to use a contraceptive method in the contraception in the future
future by the main reason why they do not intend to use. Method- Percent distribution of currently married
women age 15-49 who are not using
related reasons, especially fear of side effects (17 percent) and health contraception and who do not intend to use in
concerns (12 percent), were commonly cited reasons for not intending the future by main reason for not intending to
use, Kenya 2014
to use family planning in the future. Fertility-related reasons were Reason Total
also common, including menopause (13 percent), desire for many Fertility-related reasons
children (9 percent), and infrequent sex (9 percent). Religious Infrequent sex/no sex 8.7
Menopausal/had hysterectomy 12.5
prohibition and opposition to use each accounted for 9 percent of Subfecund/infecund 5.7
women’s reasons for not intending to use a family planning method Wants as many children as
possible 9.1
in the future. Opposition to use
Respondent opposed 8.6
7.14 EXPOSURE TO FAMILY PLANNING MESSAGES Husband/partner opposed
Others opposed
4.7
0.1
Religious prohibition 9.3
The media play an important role in communicating Lack of knowledge
messages about family planning. In assessing the reach of family Knows no method 3.5
Knows no source 0.3
planning messages, the 2014 KDHS asked women and men age 15- Method-related reasons
49 whether they had heard or seen a message about family planning Health concerns 12.4
Fear of side effects 17.1
on the radio, on television, or in a newspaper or magazine in the last Lack of access/too far 0.1
few months before the survey (Table 7.15). Cost too much
Inconvenient to use
0.1
0.5
Interfere with body’s normal
Most women (75 percent) and men (82 percent) hear family process 3.0
planning messages on the radio; 46 percent of women and 58 percent Don’t know
Other 3.8
0.2
of men hear messages on television. A lower proportion of women Missing 0.2
(29 percent) and men (43 percent) access family planning messages Total 100.0
Number of women 1,398
through a magazine or newspaper. In general, men are more exposed
to family planning messages via mass media than women. One out of five women (20 percent) and about
one in 10 men (13 percent) have not been exposed to family planning messages through any media. This
trend has not changed substantially since the 2008-09 KDHS.
Not surprisingly, women and men residing in urban areas are much more likely to have been
exposed to family planning messages in any media than their rural counterparts. Twenty-six percent of
rural women have had no exposure compared with 11 percent of urban women, and 17 percent of rural
men have had no exposure compared with 7 percent of urban men. Education has a positive influence on
media exposure. For example, 69 percent of uneducated women have no exposure to family planning
information in any form of mass media, as compared with only 9 percent among those with a secondary or
higher education. A similar pattern is observed for men. Among both women and men, exposure to family
planning messages via media increases with increasing household wealth.
Family Planning • 105
Table 7.15 Exposure to family planning messages
Percentage of women and men age 15-49 who heard or saw a family planning message on radio, on television or in a newspaper or magazine in the past few
months, according to background characteristics, Kenya 2014
Women Men
None of None of
these three these three
Background Newspaper/ media Number of Newspaper/ media Number of
characteristic Radio Television magazine sources women Radio Television magazine sources men
Age
15-19 65.8 38.7 27.6 27.9 2,717 71.0 39.2 26.6 23.7 2,540
20-24 78.5 51.9 34.0 16.1 2,691 85.5 62.1 47.6 10.1 2,125
25-29 78.2 53.1 31.9 16.3 2,932 85.5 65.8 45.7 8.5 2,104
30-34 77.0 48.2 30.1 17.8 2,162 83.9 62.5 48.0 10.6 1,785
35-39 73.8 43.3 26.8 22.2 1,780 83.4 63.4 45.1 11.6 1,483
40-44 73.7 39.7 25.4 22.4 1,292 88.0 60.5 52.9 8.8 1,224
45-49 75.3 37.1 22.7 22.1 1,052 87.0 61.8 47.0 9.7 800
Residence
Urban 80.0 71.2 40.4 11.4 5,929 85.9 76.9 55.6 7.1 5,300
Rural 71.0 28.7 21.9 26.4 8,696 79.5 43.1 33.3 17.2 6,762
Region
Coast 63.4 42.8 20.4 30.1 1,421 77.8 60.9 36.5 17.9 1,260
North Eastern 13.8 8.4 4.6 81.2 299 32.8 20.5 5.5 53.3 227
Eastern 66.9 32.3 23.4 28.7 2,066 77.8 50.8 35.6 17.6 1,825
Central 78.3 57.1 32.9 15.7 1,905 84.3 68.1 52.3 10.6 1,564
Rift Valley 74.8 43.7 31.3 20.9 3,714 81.3 49.6 36.7 14.5 3,050
Western 78.3 31.7 22.6 18.9 1,571 84.7 48.9 41.1 12.6 1,164
Nyanza 83.4 36.1 26.3 13.5 1,908 91.1 52.6 42.8 5.1 1,405
Nairobi 86.1 87.3 49.6 4.6 1,742 88.7 86.9 67.5 3.1 1,568
Education
No education 29.5 7.7 1.2 69.2 1,015 41.1 11.8 2.6 57.4 345
Primary incomplete 67.2 23.4 11.5 30.1 3,793 74.3 31.9 16.4 23.3 3,071
Primary complete 79.8 44.3 23.4 16.2 3,543 84.9 57.0 38.0 10.8 2,734
Secondary+ 83.6 66.7 48.1 8.9 6,274 87.6 74.6 61.7 5.6 5,913
Wealth quintile
Lowest 46.5 8.1 8.8 52.4 2,236 63.1 21.7 16.8 34.6 1,691
Second 72.2 19.0 18.1 26.4 2,590 82.5 37.0 28.4 14.9 2,145
Middle 78.9 29.9 24.8 18.0 2,859 85.7 50.8 39.9 11.3 2,370
Fourth 83.4 57.5 33.1 12.4 3,113 86.6 68.0 49.2 8.6 2,959
Highest 82.4 88.9 49.4 5.7 3,827 86.2 90.2 65.6 4.0 2,897
Total 15-49 74.6 46.0 29.4 20.3 14,625 82.3 58.0 43.1 12.8 12,063
50-54 na na na na na 82.6 52.3 45.9 14.7 756
Total 15-54 na na na na na 82.3 57.6 43.2 12.9 12,819
na = Not applicable
7.15 CONTACT OF NONUSERS WITH FAMILY PLANNING PROVIDERS
When family planning providers visit women in their households or when women visit health
facilities, family planning fieldworkers and health providers are expected to discuss reproductive health
needs and available contraceptive options and to counsel women on adopting a method of family planning.
To get insight into the level of contact between nonusers and health workers, women age 15-49 who were
not using contraception were asked if they had been visited by a fieldworker during the 12 months
preceding the survey and if, during that visit, they had discussed family planning issues. In addition,
women were asked whether they had visited a health facility in the 12 months preceding the survey for any
reason and whether anyone at the facility had discussed family planning with them during the visit. This
information is especially useful for determining if nonusers of family planning are being reached by family
planning programmes.
Table 7.16 shows that only 6 percent of nonusers during the 12 months preceding the survey were
visited by a fieldworker who discussed family planning. Only 14 percent of nonusers who had visited a
health facility discussed family planning at the facility (45 percent visited a facility and did not discuss
family planning). Overall, 82 percent of nonusers did not discuss family planning with a fieldworker or
while visiting a health facility in the past 12 months, indicating missed opportunities to inform and educate
women about family planning. These data show a slight improvement in opportunities to discuss family
planning in these specific scenarios compared with the 2008-09 KDHS (88 percent).
106 • Family Planning
Table 7.16 Contact of nonusers with family planning providers
Among women age 15-49 who are not using contraception, the percentage who during the past 12 months were visited by a
fieldworker who discussed family planning, the percentage who visited a health facility and discussed family planning, the
percentage who visited a health facility but did not discuss family planning, and the percentage who did not discuss family
planning either with a fieldworker or at a health facility, by background characteristics, Kenya 2014
Percentage of Percentage of
women who were Percentage of women who visited a women who did not
visited by health facility in the past 12 months discuss family
fieldworker who and who: planning either with
Background discussed family Discussed family Did not discuss fieldworker or at a Number of
characteristic planning planning family planning health facility women
Age
15-19 3.4 4.9 39.4 92.3 2,443
20-24 6.1 17.1 45.0 79.1 1,585
25-29 8.0 20.2 48.4 75.5 1,329
30-34 9.0 22.3 44.7 72.3 898
35-39 7.1 17.7 48.1 78.4 775
40-44 5.2 15.2 49.0 81.2 654
45-49 6.7 10.0 49.3 85.1 654
Residence
Urban 6.6 13.2 41.8 82.5 3,164
Rural 5.6 14.4 46.6 82.1 5,174
Region
Coast 4.6 16.7 47.5 80.4 949
North Eastern 14.9 8.6 32.9 81.8 291
Eastern 3.7 14.2 54.5 82.7 995
Central 10.2 13.6 54.0 78.6 913
Rift Valley 5.1 13.7 42.9 83.6 2,275
Western 7.0 13.9 50.0 82.0 904
Nyanza 7.3 17.2 37.1 78.7 1,079
Nairobi 2.7 9.5 34.9 88.1 932
Education
No education 6.9 11.3 39.0 84.2 827
Primary incomplete 5.0 13.1 44.9 83.6 2,277
Primary complete 7.5 19.4 46.1 76.4 1,659
Secondary+ 5.8 12.5 45.4 83.6 3,575
Wealth quintile
Lowest 6.4 14.3 42.3 81.3 1,669
Second 6.1 14.7 47.1 81.8 1,476
Middle 5.4 15.3 46.7 81.2 1,548
Fourth 5.5 14.2 44.1 82.4 1,600
Highest 6.6 11.7 44.3 83.9 2,045
Total 6.0 13.9 44.8 82.2 8,338
This low level of contact of nonusers with family planning providers varies little by background
characteristics. However, adolescents are least likely to have discussed family planning either with a
fieldworker or at a health facility.
7.16 MEN’S KNOWLEDGE OF AND ATTITUDES TOWARDS CONTRACEPTIVE USE
Use of family planning methods is facilitated when couples discuss and agree on the issue. To
assess the extent to which women use contraception without telling their partners, married women
interviewed in the 2014 KDHS were asked whether their husbands or partners knew that they were using a
method of family planning. Table 7.17 shows that 92 percent of currently married women reported that
their husbands/partners knew they were using a method of family planning. There is no notable variation in
husbands/partners’ knowledge of use of a family planning method by age or residence; however, a
substantially lower proportion of women in the North Eastern region (53 percent) than in the other regions
(88 percent or more) report that their husband/partner knows they are using a method of family planning.
Husbands/partners’ knowledge increases gradually with increasing women’s education and household
wealth.
Family Planning • 107
Table 7.17 Husband/partner’s knowledge of women’s use of contraception
Among currently married women age 15-49 who are using a method, percent distribution by whether they report
that their husbands/partners know about their use, according to background characteristics, Kenya 2014
Background Unsure whether Number of
characteristic Knows1 Does not know knows/missing Total women
Age
15-19 92.9 7.1 0.0 100.0 116
20-24 92.8 7.0 0.2 100.0 778
25-29 92.3 7.5 0.2 100.0 1,319
30-34 92.2 7.8 0.0 100.0 1,093
35-39 91.3 8.5 0.2 100.0 863
40-44 90.6 8.9 0.5 100.0 545
45-49 91.8 7.5 0.7 100.0 342
Residence
Urban 93.3 6.5 0.2 100.0 2,154
Rural 91.0 8.7 0.3 100.0 2,900
Region
Coast 93.0 7.0 0.1 100.0 377
North Eastern 52.6 47.4 0.0 100.0 7
Eastern 94.1 5.7 0.1 100.0 894
Central 93.5 6.4 0.2 100.0 813
Rift Valley 91.6 8.0 0.4 100.0 1,141
Western 88.0 11.8 0.2 100.0 554
Nyanza 88.8 10.8 0.4 100.0 666
Nairobi 94.7 5.3 0.0 100.0 603
Education
No education 81.7 17.5 0.7 100.0 151
Primary incomplete 86.9 12.7 0.4 100.0 1,230
Primary complete 93.2 6.7 0.1 100.0 1,594
Secondary+ 94.8 5.0 0.2 100.0 2,080
Wealth quintile
Lowest 84.2 15.4 0.5 100.0 453
Second 89.4 10.5 0.2 100.0 913
Middle 90.7 9.0 0.3 100.0 1,054
Fourth 93.7 6.2 0.1 100.0 1,250
Highest 95.8 4.0 0.2 100.0 1,385
Total 92.0 7.8 0.2 100.0 5,054
1
Includes women who report use of male sterilisation, male condoms, or withdrawal
Additionally, men were asked whether they agreed or disagreed with two statements about family
planning use: (1) contraception is women’s business and a man should not have to worry about it, and (2)
women who use contraception may become promiscuous. The results in Table 7.18 indicate that only 13
percent of men believe that contraception is solely women’s business, while 29 percent believe that women
who use family planning may become promiscuous. A higher percentage of divorced, separated, and
widowed men agree with the above two statements than their married and never-married counterparts.
Accepting views on contraception are more likely to be expressed by men with a secondary or higher
education, and such views increase with increasing household wealth.
108 • Family Planning
Table 7.18 Men’s attitudes towards contraception
Percentage of men age 15-49 who agree with statements about
contraceptive use, by background characteristics, Kenya 2014
Woman may
Background Woman’s become Number of
characteristic business promiscuous men
Age
15-19 13.4 27.8 2,540
20-24 11.3 34.2 2,125
25-29 12.1 29.9 2,104
30-34 14.0 27.5 1,785
35-39 14.2 26.4 1,483
40-44 11.6 29.3 1,224
45-49 13.4 28.8 800
Marital status
Never married 11.9 30.5 5,350
Married or living together 12.7 27.1 6,095
Divorced/separated/
widowed 21.3 40.2 618
Residence
Urban 12.7 27.4 5,300
Rural 12.9 30.7 6,762
Region
Coast 14.9 34.1 1,260
North Eastern 16.7 16.9 227
Eastern 18.3 37.7 1,825
Central 10.7 21.2 1,564
Rift Valley 8.5 25.3 3,050
Western 4.7 32.0 1,164
Nyanza 16.1 29.3 1,405
Nairobi 17.6 31.2 1,568
Education
No education 19.9 38.1 345
Primary incomplete 19.0 35.0 3,071
Primary complete 15.4 33.3 2,734
Secondary+ 8.0 23.9 5,913
Wealth quintile
Lowest 17.1 35.1 1,691
Second 14.1 33.1 2,145
Middle 13.5 32.8 2,370
Fourth 11.9 27.6 2,959
Highest 9.6 21.9 2,897
Total 12.8 29.3 12,063
Family Planning • 109
INFANT AND CHILD MORTALITY 8
Ben Obonyo Jarabi, Andrew A. Imbwaga, John Anampiu
Key Findings
• The infant mortality rate is 39 deaths per 1,000 live births, and under-5
mortality is 52 deaths per 1,000 live births. At these levels, about one in
every 26 Kenyan children dies before reaching age 1, and about one in
every 19 does not survive to his or her fifth birthday.
• All early childhood mortality rates declined between the 2003 and 2014
KDHS surveys. Neonatal mortality has exhibited the slowest rate of
decline (33 percent).
• A child born in the Nyanza region is almost twice as likely to die before
age 5 as a child born in the Central region. Nairobi has the second
highest under-5 mortality rate, following Nyanza (72 deaths per 1,000
live births).
• Male children are more likely than female children to die during their first
year of life (44 deaths versus 37 deaths per 1,000 live births). Once past
infancy, male and female children 1-4 years of age experience the same
level of mortality (16 deaths per 1,000 live births).
• The neonatal mortality rate for the five years preceding the survey is 22
deaths per 1,000 live births, 1.4 times the postneonatal rate.
• The perinatal mortality rate for the same reference period is 29 deaths
per 1,000 pregnancies.
T
his chapter presents levels, trends, and differentials in early childhood mortality and, among
women in Kenya, high-risk fertility behaviour. This information is relevant for the planning and
evaluation of health policies and programmes, and it serves the needs of the health sector by
identifying vulnerable groups that are at high risk for early childhood deaths. Infant and child
mortality rates are also regarded as indices that reflect the degree of poverty and deprivation of a
population. Under-5 and infant mortality rates are two indicators used to monitor child health under
Millennium Development Goal (MDG) 4. The government of Kenya is undertaking a number of
interventions aimed at reducing childhood mortality. Targets for these programmes, including the Vision
2030 indicators and Sustainable Development Goals (SDGs), rely on data from census and from household
surveys, such as the KDHS. The data presented in this chapter will contribute to planning and assessment
of the progress of those interventions.
In the 2014 KDHS, data for child mortality estimations were collected in the birth history section
of the Woman’s Questionnaire. The birth history section began with questions about the respondent’s
experience with childbearing (i.e., the number of sons and daughters she has given birth to, the number
who are alive, and the number who have died). These questions were followed by a retrospective birth
history in which the respondent was asked to list chronologically each of her births, starting with the first
one. For each birth, data were obtained on sex, month and year of birth, survivorship status, and current
age or, if the child had died, the age at death. This information was used to directly estimate early
childhood mortality rates. Because the primary causes of childhood mortality change as children age—
from biological factors to environmental factors—childhood mortality rates are expressed by age
categories and are defined as follows:
Infant and Child Mortality • 111
Neonatal mortality (NN): the probability of dying within the first month of life
Postneonatal mortality (PNN): the difference between infant and neonatal mortality
Infant mortality (1q0): the probability of dying before the first birthday
Child mortality (4q1): the probability of dying between the first and the fifth birthday
Under-5 mortality (5q0): the probability of dying between birth and the fifth birthday
All rates are expressed per 1,000 live births except for child mortality, which is expressed per
1,000 children surviving to age 12 months.
8.1 DATA QUALITY
The quality of mortality estimates can be affected by both sampling and nonsampling error.
Estimates of sampling error can be found in Appendix B. Nonsampling error is affected by the accuracy
with which births and deaths are reported and recorded and the completeness with which births and deaths
are reported.
Nonsampling error arises from problems occurring during the collection or processing of mortality
data. Specifically, the reliability of mortality estimates depends upon full reporting of children who have
died, the absence of differential displacement of birth dates of surviving and dead children, and accurate
information on ages at death. Although the nonsampling error associated with the KDHS mortality data
cannot be evaluated statistically, Appendix C includes several tables that can be used to assess the extent to
which the KDHS mortality data may be subject to common reporting errors.
When age at death is misreported or misrecorded, this may distort the age pattern of mortality,
especially if the net effect of the age misreporting results in children moving from one age group to
another. For example, a net transfer of deaths from under one month to a higher age range will affect the
estimates of neonatal and postneonatal mortality.
Displacement of dates of birth can distort mortality trends. This can occur if an interviewer
knowingly recorded a death as occurring in a different year, which may happen if an interviewer were
trying to cut down on overall workload because live births occurring during the five years preceding the
interview are the subject of a lengthy set of additional questions. In the 2014 KDHS questionnaire, the cut-
off for asking these questions was January 2009. For possible misreporting of children’s birth dates, the
results are shown in Appendix Table C.4. The calendar year ratios for living and deceased children are 86
and 78, respectively, for 2009, compared with 113 and 118, respectively, in 2008. This suggests some level
of transference of births from 2009 to the previous year. This pattern has also been observed in the
previous KDHS surveys and could be due to some interviewers transferring births out of the five-year
reference period to reduce their workload.
Another potential data quality problem is selective omission from the birth histories of the births
of infants who did not survive, which can lead to underestimation of mortality rates. These omissions may
occur when mothers are reluctant to discuss their dead children because of grief or cultural stigma
surrounding discussing such deaths. When selective omission of childhood deaths occurs, it is usually most
pronounced for deaths occurring early in infancy. One way such omissions can be detected is by
examining the proportion of neonatal deaths to infant deaths. Generally, if there is substantial
underreporting of deaths, the result is an abnormally low ratio of neonatal deaths to infant deaths.
Appendix Table C.5 does not show any sign of severe underreporting of early neonatal deaths at the
national level. For the five-year period before the survey, the proportion of neonatal deaths occurring in the
first week of life is 71 percent, slightly lower than that recorded in the 2008-09 KDHS (82 percent) but
112 • Infant and Child Mortality
within the expected range.1 Moreover, this ratio is approximately stable over the 20 years preceding the
survey, a further indication that early infant deaths have not been grossly underreported.
Examination of the 2014 KDHS infant death data shows that the proportion of neonatal to infant
deaths ranges from 59 percent in the period 0 to 4 years prior to the survey to 46 percent during the period
15 to 19 years before the survey (Table C.6). This pattern conforms to the expectation that, as mortality
levels decline, a larger proportion of infant deaths will take place during the early neonatal period.
Although slightly lower than that in the 2008-09 KDHS (61 percent), the 59 percent observed in 2014 is
higher than those recorded in the 2003 (47 percent) and the 1998 (41 percent) KDHS surveys, an
indication that underreporting of deaths was minimal. This high percentage of neonatal deaths implies that
there was little if any selective omission of childhood deaths that could compromise the quality of the 2014
KDHS early childhood mortality rates.
Another potential data quality problem is heaping of the age at death. Errors in the reporting of the
age at death may result in the transference of deaths from one age bracket for which mortality rates are
being calculated to another. For example, heaping on age 1 year or 12 months can result in an
underestimate of the infant mortality rate and an overestimate of the child mortality level. Several steps
were taken in the training of the KDHS interviewers and in the structuring of the KDHS birth history to
reduce errors in reporting the age at death. Interviewers were instructed to record age at death in days if the
child died during the first month of life. They were to record age at death in months if the child died in the
first two years of life. Because heaping on “1 year” or “12 months” is very common, interviewers were
asked specifically to probe when the mothers gave these responses. The distribution of deaths under two
years during the 20 years prior to the survey by age at death in months can show if there is heaping at age
12 months during any of the periods before the survey, with corresponding deficits in adjacent months.
Table C.6 shows that there are 256 reported deaths at 12 months compared with 46 deaths at 11 months, 30
deaths at 13 months, and 33 deaths at 14 months. This is likely to somewhat underestimate infant mortality
and overestimate child mortality. However, this will have minimal effect on the mortality estimates for the
period 0 to 4 years before the survey since heaping of deaths at age 12 months is much less pronounced in
the most recent period of 0 to 4 years prior to the survey (37 deaths) than in the periods of 5-9 years, 10-14
years, and 15-19 years prior to the survey (49-77 deaths).
In addition to recall errors for the more distant retrospective periods, there are structural reasons
for limiting mortality estimation to recent periods, preferably to the periods 0-4, 5-9, and 10-14 years
before the survey. In fact, the periods other than the first (0-4 years) have slightly biased estimates because
they are based on the child mortality experiences of women age 15-44 and 15-39, respectively, instead of
women age 15-49 as in the period 0-4 years preceding the survey. Therefore, estimating mortality for
periods more than 10-14 years before the survey is not advisable.
In summary, while there is evidence of some omission or displacement of infant deaths from one
period to another, infant deaths in the 2014 KDHS do not appear to be severely underreported.
8.2 LEVELS AND TRENDS IN INFANT AND CHILD MORTALITY
Table 8.1 shows neonatal, postneonatal, infant, child, and under-5 mortality rates for three
successive five-year periods before the survey. For the five years immediately preceding the 2014 KDHS
(approximate calendar years 2010-2014), the infant mortality rate is 39 deaths per 1,000 live births and the
under-5 mortality rate is 52 deaths per 1,000 live births. This implies that about one in every 26 children
born in Kenya dies before age 1, while one in every 19 does not survive to age 5. Neonatal mortality is 22
deaths per 1,000 live births during the same period, while postneonatal mortality is 16 deaths per 1,000
live births. Fifty-six percent of infant deaths in Kenya occur during the first month of life.
1
There are no models for mortality patterns during the neonatal period. However, one review of data from several
developing countries concluded that, at neonatal mortality levels of 20 per 1,000 or higher, approximately 70 percent
of neonatal deaths occur within the first six days of life (Boerma, 1988).
Infant and Child Mortality • 113
Table 8.1 Early childhood mortality rates
Neonatal, postneonatal, infant, child, and under-5 mortality rates for five-year periods preceding the survey,
Kenya 2014
Neonatal Postneonatal Infant Child Under-5
Years preceding Approximate mortality mortality mortality mortality mortality
the survey calendar years (NN) (PNN)1 (1q0) (4q1) (5q0)
0-4 2010-2014 22 16 39 14 52
5-9 2005-2009 24 19 43 18 60
10-14 2000-2004 26 26 51 30 80
1
Computed as the difference between the infant and neonatal mortality rates
The 2014 KDHS documents a pattern of decreasing under-5 mortality during the 15 years prior to
the survey. Results from the three recent KDHS surveys conducted between 2003 and 2014 show a similar
decline in childhood mortality over the past 15 years (Figure 8.1).
Comparing data for the five-year period preceding each of the three KDHS surveys, under-5
mortality declined from 115 deaths per 1,000 live births in 1999-2003 to 74 deaths per 1,000 in 2004-08,
and further decreased by 30 percent to 52 deaths per 1,000 in the five years preceding the 2014 KDHS.
The infant mortality rate similarly declined from 77 deaths per 1,000 deaths in 1999-2003 to 52 deaths per
1,000 in 2004-08, and it declined further by 25 percent to 39 deaths per 1,000 in the five years preceding
the 2014 KDHS. Postneonatal mortality has also steadily declined, from 44 deaths to 21 deaths per 1,000
live births between 1999-2003 and 2004-08, and then to 16 deaths per 1,000 in the five years preceding
2014, a 24 percent decrease in the past five years. Similar declines are observed for neonatal and child
mortality rates.
Figure 8.1 Trends in childhood mortality, 1999-2014
Deaths per
1,000 live births
115
77 74
52 52
44 41
39
33 31
22 21 23
16 14
Neonatal mortality Postneonatal Infant mortality Child mortality Under-5 mortality
mortality
2003 KDHS (1999-2003) 2008-09 KDHS (2004-2008) 2014 KDHS (2010-2014)
The observed trends imply that the increase in mortality witnessed in the 1990s has potentially
reversed (Opiyo and Sawhney, 2014; Wafula et al., 2012). These findings are consistent with and may be
related to other improved health outcomes and behaviours presented elsewhere in this report, including
improvements in utilisation of maternal health care services, such as deliveries in a health facility,
deliveries by a skilled health provider, and uptake of postnatal care services for mothers and newborns
(Chapter 9); improved health care seeking behaviour for childhood illnesses such as pneumonia, diarrhoea,
and malaria (Chapter 10); and increased levels of ownership and use of insecticide-treated mosquito nets
(Chapter 12). The decline in childhood mortality reflects the recent global trend of under-5 mortality
reducing faster than at any other time in the past two decades (UNICEF, 2014).
114 • Infant and Child Mortality
However, accelerated change for child survival, health, and development needs more focus on a
healthy start of life. The neonatal mortality rate of 22 deaths per 1,000 live births indicates that progress
remains to be made before Kenya achieves the Every Newborn Action Plan’s goal of a neonatal mortality
rate below 10 deaths per 1,000 live births by 2035 (UNICEF, 2014).
8.3 SOCIOECONOMIC DIFFERENTIALS IN INFANT AND CHILD MORTALITY
The probability of dying in early childhood is higher in some population subgroups than in others.
Table 8.2 and Figure 8.2 show differentials in early childhood mortality rates by residence, region, level of
mother’s education, and household wealth. The childhood mortality rates by background characteristics are
calculated for the 10-year period before the survey (approximately 2005-2014) so that the estimates are
based on a sufficient number of births in each category to study mortality differentials across subgroups.
While postneonatal and child mortality are slightly lower in urban areas than in rural areas,
neonatal mortality is 24 percent higher in urban areas than it is in rural areas (26 deaths versus 21 deaths
per 1,000 live births).
Table 8.2 Early childhood mortality rates by socioeconomic characteristics
Neonatal, postneonatal, infant, child, and under-5 mortality rates for the 10-year period preceding the
survey, by background characteristics, Kenya 2014
Neonatal Postneonatal Infant Child Under-5
Background mortality mortality mortality mortality mortality
characteristic (NN) (PNN)1 (1q0) (4q1) (5q0)
Residence
Urban 26 16 43 15 57
Rural 21 18 40 16 56
Region
Coast 25 19 44 14 57
North Eastern 24 13 37 8 44
Eastern 24 12 36 9 45
Central 24 14 38 4 42
Rift Valley 20 14 34 12 45
Western 19 21 40 25 64
Nyanza 19 31 50 33 82
Nairobi 39 16 55 17 72
Mother’s education
No education 21 16 36 15 51
Primary incomplete 22 22 44 20 63
Primary complete 25 15 40 17 56
Secondary+ 23 17 40 11 51
Wealth quintile
Lowest 20 20 40 18 57
Second 23 19 42 23 63
Middle 21 17 39 16 54
Fourth 26 20 46 13 58
Highest 26 13 38 9 47
1
Computed as the difference between the infant and neonatal mortality rates
The under-5 mortality rate summarises the mortality rate from birth to age 5. As is true in each of
its component rates, the under-5 mortality differentials are most pronounced across regions. The range in
under-5 mortality ranges from a low of 42 deaths per 1,000 live births in Central region to a high of 82
deaths per 1,000 live births in Nyanza. Nairobi has the second highest under-5 mortality rate.
Focusing on the component parts of under-5 mortality, the highest infant mortality is experienced
in Nairobi and Nyanza (55 and 50 deaths per 1,000 live births). The highest neonatal mortality is
experienced in Nairobi (39 deaths per 1,000 live births) and the highest postneonatal mortality is
experienced in Nyanza region (31 deaths per 1,000 live births). Nyanza also has the highest child mortality
rate (33 deaths per 1,000 live births), followed by Western region (25 deaths per 1,000 live births).
Infant and Child Mortality • 115
The highest under-5 mortality rate by education is among those born to mothers with an
incomplete primary education (63 deaths per 1,000 live births). Children in these households experience
both the highest postneonatal and child mortality (20 and 22 deaths per 1,000 live births).
While infants born into the wealthiest households experience the lowest levels of both
postneonatal and child mortality, they, along with the second wealthiest households (fourth wealth
quintile), experience the highest neonatal mortality. The highest child mortality occurs in households in the
second wealth quintile (63 deaths per 1,000 live births).
Figure 8.2 Under-5 mortality by background characteristics
TOTAL 52
RESIDENCE
Urban 57
Rural 56
REGION
Coast 57
North Eastern 44
Eastern 45
Central 42
Rift Valley 45
Western 64
Nyanza 82
Nairobi 72
WEALTH QUINTILE
Lowest 57
Second 63
Middle 54
Fourth 58
Highest 47
Deaths per 1,000
live births
KDHS 2014
8.4 DEMOGRAPHIC DIFFERENTIALS IN INFANT AND CHILD MORTALITY
The demographic characteristics of both mother and child play an important role in the survival of
children. Table 8.3 presents early childhood mortality rates for the 10-year period preceding the survey by
demographic characteristics (i.e., sex of child, mother’s age at birth, birth order, previous birth interval,
and birth size).
Male children are more likely than female children to die during their first year of life (44 deaths
versus 37 deaths per 1,000 live births). Once past infancy, male and female children one to four years of
age experience the same level of mortality (16 deaths per 1,000 live births).
Both mother’s age at the time of the birth of the child and the child’s birth order exhibit a U-
shaped association with neonatal mortality. Babies born to the youngest and oldest mothers experience the
highest neonatal mortality rates, as do babies born after the shortest and longest birth intervals.
Shorter birth intervals have been demonstrated to be associated with higher mortality, both during
and after infancy. Kenya follows suit with a strong association as well during neonatal, postneonatal, and
child portions of life. Babies born after the shortest birth intervals, less than two years, are nearly twice as
likely to die (83 deaths per 1,000 live births) as babies born after three (42 deaths per 1,000 live births) or
four or more years (44 deaths per 1,000 live births). An anomaly to this pattern is the higher neonatal
mortality of births born after four or more years as compared with births born after an interval of two or
three years; a further analysis that accounts for other co-factors of mortality might elucidate why this is so.
116 • Infant and Child Mortality
Table 8.3 Early childhood mortality rates by demographic characteristics
Neonatal, postneonatal, infant, child, and under-5 mortality rates for the 10-year period preceding the
survey, by demographic characteristics, Kenya 2014
Neonatal Postneonatal Infant Child Under-5
Demographic mortality mortality mortality mortality mortality
characteristic (NN) (PNN)1 (1q0) (4q1) (5q0)
Child’s sex
Male 25 19 44 16 60
Female 21 17 37 16 52
Mother’s age at birth
<20 27 19 45 20 65
20-29 21 17 38 15 52
30-39 25 18 43 14 57
40-49 28 20 48 (15) (62)
Birth order
1 28 15 43 15 57
2-3 20 17 36 14 49
4-6 21 21 42 20 62
7+ 28 20 48 18 65
Previous birth interval2
<2 years 31 29 60 25 83
2 years 17 19 36 17 53
3 years 14 14 29 14 42
4+ years 22 13 36 9 44
Birth size3
Small/very small 41 15 57 na na
Average or larger 17 17 35 na na
Note: Figures in parentheses are based on 250-499 unweighted exposed persons.
na = Not applicable
1
Computed as the difference between the infant and neonatal mortality rates
2
Excludes first-order births
3
Rates for the five-year period before the survey
The 2014 KDHS reports size at birth according to the size of the baby as gauged by the mother.
Women were asked: When (NAME) was born, was he/she very large, larger than average, average,
smaller than average, or very small? Children whose birth size is small or very small are more than two
times as likely to die during the first month of life as children whose birth size is average or larger (41
deaths per 1,000 live births versus 17 deaths per 1,000 live births).
8.5 PERINATAL MORTALITY
Perinatal mortality is a good indicator of the state of health in general and the health status of the
mother at the time of delivery. Pregnancy losses occurring after seven completed months of gestation
(stillbirths) plus deaths to live births within the first seven days of life (early neonatal deaths) constitute
perinatal deaths. The distinction between a stillbirth and an early neonatal death may be a fine one, often
depending on observing and then remembering sometimes faint signs of life after delivery. The causes of
stillbirths and early neonatal deaths are closely linked, and examining just one or the other can understate
the true level of mortality at or near the time of delivery. For this reason, deaths around delivery are
combined into the perinatal mortality rate, defined as the number of perinatal deaths per 1,000 pregnancies
reaching seven months of gestation.
Table 8.4 presents the number of stillbirths and early neonatal deaths and the perinatal mortality
rate for the five-year period preceding the 2014 KDHS, by background characteristics. Of the 9,484
reported pregnancies of at least seven months’ duration, 126 ended in stillbirths and 146 were neonatal
deaths, thus giving a perinatal mortality rate of 29 deaths per 1,000 pregnancies, a decline from the 37
deaths per 1,000 pregnancies reported in the 2008-09 KDHS.
Babies born to mothers in their 20s experience the lowest perinatal mortality rate (22 deaths per
1,000 pregnancies). The shortest of birth intervals, those less than 15 months in duration, experience the
highest perinatal mortality rates of all birth interval lengths (37 deaths per 1,000 pregnancies). There is no
difference in the perinatal mortality rate by urban-rural residence, similar to the findings in the 2003 and
Infant and Child Mortality • 117
2008-09 KDHS surveys. There are regional variations in the level of perinatal mortality, although the
results should be interpreted with caution. The North Eastern region has the lowest perinatal mortality rate
(16 deaths per 1,000 pregnancies) and the Eastern region the highest (44 deaths per 1,000 pregnancies).
However, the small number of pregnancies of at least seven months’ gestation in North Eastern should be
kept in mind when interpreting these data.
There is no strong pattern of association between perinatal mortality rates and mother’s education
status or household wealth. The rate is highest among children whose mothers have a complete primary
education (33 deaths per 1,000 pregnancies) and children in the second wealth quintile (34 deaths per
1,000 pregnancies).
Table 8.4 Perinatal mortality
Number of stillbirths and early neonatal deaths, and the perinatal mortality rate for the five-year
period preceding the survey, by background characteristics, Kenya 2014
Number of
pregnancies of
Background Number of Number of early Perinatal 7+ months
characteristic stillbirths1 neonatal deaths2 mortality rate3 duration
Mother’s age at birth
<20 27 28 39 1,395
20-29 58 60 22 5,431
30-39 37 52 37 2,392
40-49 4 6 36 266
Previous pregnancy
interval in months4
First pregnancy 36 44 34 2,346
<15 25 20 37 1,214
15-26 15 25 21 1,891
27-38 14 19 24 1,362
39+ 36 39 28 2,671
Residence
Urban 48 52 29 3,436
Rural 78 94 28 6,048
Region
Coast 22 16 39 974
North Eastern 3 2 16 310
Eastern 27 24 44 1,173
Central 15 14 34 852
Rift Valley 22 36 22 2,711
Western 18 12 26 1,145
Nyanza 13 24 28 1,332
Nairobi 5 19 25 987
Mother’s education
No education 15 18 29 1,126
Primary incomplete 35 40 27 2,816
Primary complete 42 41 33 2,513
Secondary+ 34 47 27 3,029
Wealth quintile
Lowest 24 35 27 2,216
Second 33 33 34 1,923
Middle 19 34 31 1,745
Fourth 19 19 23 1,721
Highest 30 25 29 1,879
Total 126 146 29 9,484
1
Stillbirths are foetal deaths in pregnancies lasting seven or more months.
2
Early neonatal deaths are deaths at age 0-6 days among live-born children.
3
The sum of the number of stillbirths and early neonatal deaths divided by the number of
pregnancies of seven or more months’ duration, expressed per 1,000
4
Categories correspond to birth intervals of <24 months, 24-35 months, 36-47 months, and 48+
months.
8.6 HIGH-RISK FERTILITY BEHAVIOUR
The survival of infants and children depends in part on the demographic and biological
characteristics of their mothers. Because the probability of dying in early childhood is typically much
greater if children are born to mothers who are too young (under age 18) or too old (over age 34), if they
are born after a short birth interval (less than 24 months after the preceding birth), or if they are born to
118 • Infant and Child Mortality
mothers with high parity (birth order four or higher), the risk of mortality is examined for births occurring
in each of these categories and for births occurring under a combination of these categories. Table 8.5
shows three different measures related to risk that are meaningful to interpret together.
The first data column presents the percent distribution of births in the five years preceding the
survey by risk category. Since no birth carries zero risk, the lowest risk categories are classified into two
groups: births not in any high-risk category and births in an unavoidable risk category. The births that are
not in any high-risk category are those that meet all of these criteria: they are born to women between age
18 and 34, they follow a birth interval of longer than 24 months, and they are of second or third birth order.
Those in the unavoidable risk category are first-order births to women between age 18 and 34.
The second column in Table 8.5 denotes the relationship between the risk factors and mortality.
The risk ratio compares each risk category with the “not in any high risk” category. Being in multiple risk
categories usually places a baby at a higher risk of dying than does being in any one single risk category.
The last column in Table 8.5 looks to the future and addresses the question of what proportion of
currently married women have the potential for having a high-risk birth. Results were obtained by
classifying a currently married woman into the risk category she would fall into if she were to become
pregnant at the time of the survey.
Table 8.5 High-risk fertility behaviour
Percent distribution of children born in the five years preceding the survey by category of
elevated risk of mortality and the risk ratio, and percent distribution of currently married women
by category of risk if they were to conceive a child at the time of the survey, Kenya 2014
Births in the 5 years preceding the
survey Percentage of
Percentage of currently married
Risk category births Risk ratio women1
Not in any high risk category 30.3 1.00 25.6a
Unavoidable risk category
First order births between ages 18 and
34 years 21.0 1.34 4.5
Single high-risk category
Mother’s age <18 5.7 0.95 0.3
Mother’s age >34 1.6 0.72 5.4
Birth interval <24 months 6.4 0.93 9.1
Birth order >3 19.4 0.96 16.7
Subtotal 33.1 0.94 31.5
Multiple high-risk category
Age <18 and birth interval <24
months2 0.4 2.90 0.1
Age >34 and birth interval <24 months 0.1 * 0.3
Age >34 and birth order >3 8.8 1.31 26.5
Age >34 and birth interval <24 months
and birth order >3 1.2 1.84 3.2
Birth interval <24 months and birth
order >3 5.1 1.95 8.2
Subtotal 15.6 1.60 38.3
In any avoidable high-risk category 48.7 1.15 69.9
Total 100.0 na 100.0
Number of births/women 19,564 na 18,549
Note: Risk ratio is the ratio of the proportion dead among births in a specific high-risk category
to the proportion dead among births not in any high-risk category. An asterisk denotes a figure
based on fewer than 25 unweighted cases that has been suppressed.
na = Not applicable
1
Women are assigned to risk categories according to the status they would have at the birth of
a child if they were to conceive at the time of the survey: current age less than 17 years and 3
months or older than 34 years and 2 months, latest birth less than 15 months ago, or latest birth
being of order 3 or higher.
2
Includes the category age <18 and birth order >3
a
Includes sterilised women
Infant and Child Mortality • 119
Nationally, only 3 in 10 births were in no high-risk birth category at the time they occurred, and 2
in 10 fell into the unavoidable risk category. The other 49 percent were high-risk births, of whom 33
percent were in a single risk category and 16 percent were in more than one risk category. The most
common single risk category is being of birth order four or higher, with about one in five births born in the
preceding five years falling into this category. The most common multiple risk category is being of birth
order four or higher and being born to a mother over age 34; 9 percent of births fell in this category.
Births born into just one single high-risk mortality category actually experienced lower mortality
rates (an average risk ratio of 0.94) than births not born into any high-risk category. It is in the situation of
being in multiple risk categories that births experience higher mortality levels. Births born to women less
than age 18 and born less than 24 months after the preceding birth are at nearly three times the risk of
dying as compared with births born to women who are not in any high-risk category (risk ratio of 2.90). It
is, of course, an unusual scenario; less than 1 percent of births were born into this category. Those who
experience nearly double the risk of mortality (risk ratio of 1.95), are those births that are above birth order
three, born after a birth interval of less than 24 months.
The most common multiple risk category among women is being over age 34 and having already
given birth to at least three children. One-quarter of women fall in this category, and births born to women
in this category experience 30 percent higher mortality than births born to women who are not in any high-
risk category (risk ratio of 1.31).
Nationally, 70 percent of currently married women are in a high-risk birth category, such that if
they had given birth at the time of the survey, their baby would have been in a high-risk situation. Thirty-
two percent of women, and thus their births, would fall into a single high-risk category, while 38 percent
would fall into multiple high-risk categories.
120 • Infant and Child Mortality
MATERNAL HEALTH 9
Ruth Wayua Muia, Andolo Miheso, Annah W. Wamae, James N. Munguti
Key Findings
• Ninety-six percent of women with a live birth in the five years preceding
the survey received antenatal care from a skilled provider, an
improvement from 92 percent in the 2008-09 KDHS and 88 percent in the
2003 KDHS.
• Fifty-eight percent of women make the recommended four or more
antenatal care visits during their pregnancy, an increase of 11
percentage points from the 2008-09 KDHS (47 percent).
• Sixty-one percent of live births in the five years preceding the survey
were delivered in a health facility; 62 percent were assisted by a skilled
provider.
• More than half (53 percent) of women who gave birth in the two years
before the survey received a postnatal care checkup in the first two days
after delivery.
• Thirty-six percent of infants born in the two years before the survey had
their first postnatal checkup within the first two days after birth. One in
three newborns received postnatal care from a doctor, a nurse, or a
midwife.
• More than half (54 percent) of the women interviewed in the survey had
heard of fistula. However, only 1 percent of these women reported having
ever experienced fistula-like symptoms.
T
he health status of mothers and children is an important indicator of the overall economic health
and well-being of a country (United Nations, 2010). Maternal health is inextricably linked with the
survival of newborns. For every woman who dies, another 30 suffer long-lasting injuries and
illnesses such as obstetric fistula (UNDP, WHO, UNFPA, and World Bank, 2006). The International
Conference on Population and Development, held in Cairo, Egypt, in 1994, called for the development of
comprehensive reproductive health policies, programmes, and implementation plans (UNFPA, 1994). This
call defined the focus of the Kenya National Reproductive Health Programme, through which efforts
towards improvements in maternal health have been made.
Provision of a continuum of care during pregnancy, labour and delivery, and the postnatal period
results in reduced maternal and neonatal morbidity and mortality. The 2014 KDHS collected information
on the extent to which women in Kenya receive care during each of these stages by asking women age 15-
49 who had a live birth in the five years preceding the survey questions about the antenatal, labour and
delivery, and postnatal care they received. The findings can be used to identify populations underusing
maternal health services and to assist in the planning of improvements in services.
9.1 ANTENATAL CARE
Antenatal care (ANC) from a skilled provider is important to monitor pregnancy and reduce the
risk of morbidity for mother and baby during pregnancy and delivery. The quality of antenatal care can be
monitored through the content of services received and the kind of information mothers are given during
their visit. In the 2014 KDHS, women who gave birth in the five years preceding the survey were asked to
Maternal Health • 121
report on all persons they saw for antenatal care for their most recent birth. When a woman saw more than
one provider, only the provider with the highest qualifications was considered in the tabulation of results.
Table 9.1 shows the percent distribution of women age 15-49 who had a live birth in the five years
preceding the survey by source of antenatal care received during pregnancy, according to background
characteristics. Ninety-six percent of women received antenatal care from a skilled provider (a doctor, a
nurse, or a midwife) for their most recent birth in the five years preceding the survey. The majority of
women (64 percent) received care from a nurse or midwife, while 31 percent received care from a doctor.
Urban women (98 percent) were slightly more likely than rural women (94 percent) to receive antenatal
care services from a skilled provider. In all regions except North Eastern (67 percent), 94 percent or more
of women received antenatal care from a skilled provider. Receipt of antenatal care services from a skilled
provider increased with increasing education and wealth. Eighty-nine percent of women in the lowest
wealth quintile received antenatal care services from a skilled provider, compared with virtually all women
(99 percent) in the highest wealth quintile.
Table 9.1 Antenatal care
Percent distribution of women age 15-49 who had a live birth in the five years preceding the survey by antenatal care (ANC) provider during
pregnancy for the most recent birth and the percentage receiving antenatal care from a skilled provider for the most recent birth, according to
background characteristics, Kenya 2014
Percentage
receiving
Antenatal care provider antenatal
Community Traditional care from a
Background Nurse/ health birth skilled Number of
characteristic Doctor midwife worker attendant Missing No ANC Total provider1 women
Mother’s age at
birth
<20 28.6 66.3 0.1 0.0 0.0 5.0 100.0 94.9 1,871
20-34 31.8 64.2 0.4 0.0 0.2 3.3 100.0 96.0 10,641
35-49 29.8 63.4 0.5 0.0 0.2 6.0 100.0 93.2 1,930
Birth order
1 37.4 60.0 0.1 0.0 0.0 2.5 100.0 97.3 3,659
2-3 33.1 64.0 0.3 0.1 0.2 2.3 100.0 97.1 5,815
4-5 25.7 68.1 0.6 0.0 0.1 5.4 100.0 93.8 2,795
6+ 22.5 67.9 0.7 0.1 0.4 8.4 100.0 90.3 2,173
Residence
Urban 41.0 56.8 0.2 0.0 0.2 1.8 100.0 97.8 5,561
Rural 24.9 69.1 0.5 0.1 0.2 5.2 100.0 94.0 8,881
Region
Coast 46.3 51.2 0.0 0.0 0.2 2.3 100.0 97.5 1,471
North Eastern 9.2 57.4 7.7 0.2 0.4 24.9 100.0 66.5 372
Eastern 18.3 78.9 0.0 0.0 0.1 2.6 100.0 97.2 1,834
Central 61.7 35.6 0.2 0.0 0.0 2.4 100.0 97.3 1,528
Rift Valley 29.4 64.4 0.1 0.1 0.1 5.8 100.0 93.9 4,002
Western 20.3 76.8 0.0 0.1 0.2 2.5 100.0 97.2 1,590
Nyanza 13.0 83.5 0.6 0.0 0.4 2.4 100.0 96.6 1,988
Nairobi 44.8 52.8 0.5 0.0 0.2 1.7 100.0 97.6 1,657
Education
No education 18.9 63.2 2.1 0.3 0.5 15.1 100.0 82.1 1,409
Primary incomplete 26.1 68.7 0.1 0.0 0.2 4.9 100.0 94.7 3,846
Primary complete 31.5 65.4 0.5 0.0 0.1 2.5 100.0 96.8 4,024
Secondary+ 38.0 60.6 0.1 0.0 0.1 1.1 100.0 98.6 5,163
Wealth quintile
Lowest 20.5 68.0 1.1 0.2 0.2 9.9 100.0 88.5 2,947
Second 22.5 73.0 0.1 0.0 0.2 4.1 100.0 95.5 2,782
Middle 27.4 69.7 0.2 0.0 0.3 2.4 100.0 97.1 2,660
Fourth 33.8 63.6 0.4 0.0 0.0 2.2 100.0 97.4 2,777
Highest 48.8 50.0 0.2 0.0 0.1 0.9 100.0 98.8 3,277
Total 31.1 64.3 0.4 0.0 0.2 3.9 100.0 95.5 14,442
Note: If more than one source of ANC was mentioned, only the provider with the highest qualifications is considered in this tabulation.
1
Skilled provider includes doctor, nurse, or midwife.
Table 9.1C shows the percent distribution of women age 15-49 who had a live birth in the five
years preceding the survey by county and source of antenatal care. ANC from a skilled provider is virtually
universal in Mombasa, Embu, Machakos, and Nandi (99 percent). Less than 90 percent of women in
Garissa, Marsabit, West Pokot, and Samburu and less than 60 percent in Mandera and Wajir received ANC
from a skilled provider; 47 percent of women in Mandera received no ANC at all.
122 • Maternal Health
Table 9.1C Antenatal care
Percent distribution of women age 15-49 who had a live birth in the five years preceding the survey by antenatal care (ANC) provider during
pregnancy for the most recent birth and the percentage receiving antenatal care from a skilled provider for the most recent birth, according to
county, Kenya 2014
Percentage
receiving
Antenatal care provider antenatal
Community Traditional care from a
Nurse/ health birth skilled Number of
County Doctor midwife worker attendant Missing No ANC Total provider1 women
Coast 46.3 51.2 0.0 0.0 0.2 2.3 100.0 97.5 1,471
Mombasa 61.3 37.9 0.0 0.0 0.0 0.8 100.0 99.2 422
Kwale 15.2 80.5 0.0 0.0 0.9 3.4 100.0 95.7 304
Kilifi 70.0 28.2 0.0 0.0 0.0 1.8 100.0 98.2 503
Tana River 0.1 93.4 0.0 0.0 0.0 6.4 100.0 93.6 115
Lamu 6.3 89.4 0.0 0.0 1.0 3.3 100.0 95.7 36
Taita Taveta 24.5 73.4 0.0 0.0 0.0 2.1 100.0 97.9 90
North Eastern 9.2 57.4 7.7 0.2 0.4 24.9 100.0 66.5 372
Garissa 2.9 84.5 0.0 0.0 0.0 12.7 100.0 87.3 135
Wajir 4.0 53.6 19.9 0.0 0.0 21.9 100.0 57.6 141
Mandera 25.7 24.8 0.5 0.8 1.7 46.5 100.0 50.5 96
Eastern 18.3 78.9 0.0 0.0 0.1 2.6 100.0 97.2 1,834
Marsabit 12.9 62.7 1.4 0.0 0.0 23.1 100.0 75.6 64
Isiolo 20.6 75.4 0.0 0.3 0.0 3.5 100.0 96.0 58
Meru 24.2 73.0 0.0 0.0 0.2 2.5 100.0 97.3 442
Tharaka-Nithi 22.5 75.8 0.0 0.0 0.0 1.7 100.0 98.3 121
Embu 21.5 77.7 0.0 0.0 0.0 0.8 100.0 99.2 167
Kitui 11.2 86.3 0.0 0.0 0.0 2.5 100.0 97.5 313
Machakos 24.1 74.7 0.0 0.0 0.2 1.0 100.0 98.8 396
Makueni 5.7 92.3 0.0 0.0 0.0 2.0 100.0 98.0 274
Central 61.7 35.6 0.2 0.0 0.0 2.4 100.0 97.3 1,528
Nyandarua 40.4 56.3 0.0 0.0 0.0 3.3 100.0 96.7 195
Nyeri 62.3 34.4 0.0 0.0 0.0 2.8 100.0 96.7 216
Kirinyaga 36.8 59.6 0.0 0.0 0.0 3.6 100.0 96.4 174
Murang’a 74.3 23.1 0.0 0.0 0.0 2.6 100.0 97.4 255
Kiambu 69.3 28.7 0.4 0.0 0.0 1.7 100.0 97.9 688
Rift Valley 29.4 64.4 0.1 0.1 0.1 5.8 100.0 93.9 4,002
Turkana 0.6 90.4 0.0 0.0 0.0 9.0 100.0 91.0 214
West Pokot 14.7 70.5 0.0 0.2 0.0 14.6 100.0 85.2 180
Samburu 2.7 71.1 0.4 0.0 0.6 25.2 100.0 73.8 79
Trans-Nzoia 44.6 47.4 0.0 0.0 0.0 8.0 100.0 92.0 382
Uasin Gishu 30.4 65.6 0.2 0.3 0.0 3.4 100.0 96.1 363
Elgeyo Marakwet 20.8 77.3 0.0 0.0 0.0 1.9 100.0 98.1 114
Nandi 8.5 90.0 0.0 0.0 0.0 1.5 100.0 98.5 302
Baringo 11.5 81.4 0.0 1.1 0.0 5.9 100.0 92.8 160
Laikipia 23.8 69.9 0.0 0.0 0.0 6.3 100.0 93.7 165
Nakuru 39.9 55.7 0.0 0.0 0.5 3.9 100.0 95.6 674
Narok 13.5 78.1 0.0 0.0 0.3 8.1 100.0 91.6 403
Kajiado 59.9 36.7 0.0 0.0 0.0 3.3 100.0 96.7 335
Kericho 46.8 50.3 0.0 0.0 0.3 2.6 100.0 97.1 277
Bomet 30.0 63.6 1.1 0.0 0.0 5.4 100.0 93.5 354
Western 20.3 76.8 0.0 0.1 0.2 2.5 100.0 97.2 1,590
Kakamega 29.4 67.1 0.0 0.3 0.5 2.7 100.0 96.4 532
Vihiga 16.5 80.6 0.2 0.0 0.0 2.6 100.0 97.1 164
Bungoma 13.2 84.4 0.0 0.0 0.0 2.4 100.0 97.6 607
Busia 20.8 76.8 0.0 0.0 0.0 2.4 100.0 97.6 287
Nyanza 13.0 83.5 0.6 0.0 0.4 2.4 100.0 96.6 1,988
Siaya 1.6 96.2 0.6 0.1 0.0 1.5 100.0 97.8 268
Kisumu 21.9 76.6 0.3 0.0 0.0 1.3 100.0 98.4 378
Homa Bay 23.1 70.4 2.1 0.0 1.1 3.3 100.0 93.5 447
Migori 4.5 91.9 0.0 0.0 0.3 3.3 100.0 96.4 360
Kisii 10.7 87.0 0.0 0.0 0.0 2.3 100.0 97.7 384
Nyamira 7.7 88.6 0.0 0.0 0.7 2.9 100.0 96.4 152
Nairobi 44.8 52.8 0.5 0.0 0.2 1.7 100.0 97.6 1,657
Total 31.1 64.3 0.4 0.0 0.2 3.9 100.0 95.5 14,442
Note: If more than one source of ANC was mentioned, only the provider with the highest qualifications is considered in this tabulation.
1
Skilled provider includes doctor, nurse, or midwife.
9.1.1 Number and Timing of Antenatal Visits
Regular antenatal care is helpful in identifying and preventing adverse pregnancy outcomes when
it is sought early in the pregnancy and is continued until delivery. The World Health Organization
recommends that women have at least four antenatal care visits during each pregnancy. It is possible
Maternal Health • 123
during these visits to detect health problems associated with a pregnancy and to plan interventions. In the
event of any complications, more frequent visits are advised, and admission to a health facility may be
necessary (MOH, 2012).
Table 9.2 presents the percent distribution of women age 15-49 who had a live birth in the five
years preceding the survey by number of antenatal care (ANC) visits for the most recent live birth and by
the timing of the first visit, according to residence and region. It also shows median number of months
pregnant at first visit. Fifty-eight percent of pregnant women made four or more antenatal care visits
during their pregnancy. This is an increase from 47 percent in the 2008-09 KDHS.
Table 9.2 Number of antenatal care visits and timing of first visit
Percent distribution of women age 15-49 who had a live birth in the five years preceding the survey by number of antenatal care (ANC) visits for the most recent
live birth, and by the timing of the first visit, and among women with ANC, median months pregnant at first visit, according to residence and region, Kenya 2014
Residence Region
Number and timing North
of ANC visits Urban Rural Coast Eastern Eastern Central Rift Valley Western Nyanza Nairobi Total
Number of ANC
visits
None 1.8 5.3 2.5 25.2 2.7 2.4 5.9 2.6 2.6 1.7 4.0
1 2.1 4.1 3.2 6.6 3.2 2.1 4.1 4.0 3.0 2.0 3.3
2-3 27.9 39.1 32.0 31.2 37.3 31.7 38.1 41.8 35.5 22.6 34.8
4+ 67.7 51.3 62.3 36.8 56.3 63.4 51.7 51.3 58.7 73.1 57.6
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of months
pregnant at time of
first ANC visit
No antenatal care 1.8 5.3 2.5 25.2 2.7 2.4 5.9 2.6 2.6 1.7 4.0
<4 25.6 16.2 17.6 12.1 18.0 22.9 16.2 19.7 21.2 30.2 19.8
4-5 44.4 41.4 43.2 37.9 45.7 40.8 39.7 43.7 44.7 44.7 42.6
6-7 26.1 33.7 32.5 22.3 31.0 32.2 34.7 30.9 29.0 22.5 30.8
8+ 1.8 3.1 3.5 2.4 2.4 1.7 3.4 2.8 2.4 1.0 2.6
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of women 5,561 8,881 1,471 372 1,834 1,528 4,002 1,590 1,988 1,657 14,442
Median months
pregnant at first visit
(for those with ANC) 5.1 5.6 5.4 5.4 5.4 5.3 5.6 5.4 5.3 4.9 5.4
Number of women with
ANC 5,459 8,411 1,435 278 1,783 1,491 3,767 1,549 1,937 1,629 13,869
Note: Totals may not add up to 100 percent because women with missing information are not shown separately.
Urban women were more likely than rural women to have had four or more antenatal visits (68
percent and 51 percent, respectively). By region, the proportion of women having four or more ANC visits
ranges from 37 percent in North Eastern to 73 percent in Nairobi.
Forty-three percent of women made their first antenatal care visit between the fourth and fifth
months of pregnancy, and only 20 percent made their first visit before the fourth month of pregnancy. The
median duration of pregnancy at the first antenatal care visit is 5.4 months. The timing of the first ANC
visit is fairly consistent across regions.
9.1.2 Components of Antenatal Care
High-quality antenatal care operates on the principle that every pregnancy is at risk of
complications. Therefore, apart from receiving basic care, every pregnant woman should be routinely
monitored for complications. To assess the quality of antenatal care services, women who gave birth in the
five years preceding the survey were asked a number of questions about the components of care they
received when they were pregnant with their most recent live birth.
Table 9.3 presents information on the percentage of women age 15-49 with a live birth in the five
years preceding the survey who took iron tablets, iron syrup, or iron and folic acid supplementation and
intestinal parasite drugs during their most recent pregnancy in the five years preceding the survey. It also
shows the percentage of women receiving antenatal care who were informed about the signs of pregnancy
complications and who received specific routine services during ANC visits.
124 • Maternal Health
Table 9.3 Components of antenatal care
Among women age 15-49 with a live birth in the five years preceding the survey, the percentage who took iron tablets or syrup and drugs for intestinal parasites during the
pregnancy of the most recent birth, and among women receiving antenatal care (ANC) for the most recent live birth in the five years preceding the survey, the percentage
receiving specific antenatal services, according to background characteristics, Kenya 2014
Among women with a live birth in
the past five years, the percentage
who during the pregnancy of their Among women who received antenatal care for their most recent birth in the past five years,
last birth: the percentage with selected services
Took iron Given
tablets, Number of information Number of
iron syrup, women Informed of on iron women
or iron and Took with a live signs of Given and/or folic with ANC
folic acid intestinal birth in the pregnancy Blood Urine Blood information acid for their
Background supple- parasite past five compli- pressure sample sample Height on breast- supple- most
characteristic ments drugs years cations measured taken taken Weighed measured feeding mentation recent birth
Mother’s age at
birth
<20 65.0 27.4 867 58.1 91.5 86.7 95.8 96.6 46.4 63.7 64.2 827
20-34 71.1 31.6 5,074 59.5 94.3 89.5 96.4 97.5 46.3 70.3 70.5 4,910
35-49 64.4 33.2 935 52.6 94.4 86.3 93.7 97.1 40.9 62.5 66.8 888
Birth order
1 70.4 30.4 1,705 66.8 95.1 93.2 97.4 97.8 52.3 70.4 71.5 1,656
2-3 72.4 32.5 2,747 59.8 94.7 91.3 97.3 98.0 47.9 70.8 70.8 2,692
4-5 69.0 31.5 1,351 52.1 92.6 85.3 95.2 96.8 40.1 67.8 68.6 1,287
6+ 60.7 29.4 1,072 48.4 91.9 79.0 91.1 95.3 35.5 59.3 61.9 992
Residence
Urban 74.9 31.6 2,677 66.9 97.9 95.8 97.8 99.1 59.2 75.6 76.0 2,629
Rural 65.9 31.1 4,199 52.8 91.4 84.2 94.8 96.2 36.7 63.6 64.8 3,997
Region
Coast 82.4 50.5 698 55.1 95.3 93.3 96.5 97.8 51.3 66.9 75.9 690
North Eastern 40.4 7.4 178 36.0 83.6 77.6 82.8 84.6 41.2 53.3 51.0 135
Eastern 68.5 34.5 891 59.7 94.8 91.5 96.8 98.3 44.8 59.1 62.0 868
Central 71.3 34.2 715 62.3 98.9 96.7 99.3 98.0 53.5 70.4 75.7 694
Rift Valley 61.7 23.8 1,899 47.2 94.1 84.4 95.5 96.6 40.8 64.0 62.6 1,810
Western 60.9 33.5 790 62.4 83.8 80.0 92.7 95.2 32.5 72.8 66.3 768
Nyanza 83.2 32.9 934 67.9 94.3 86.8 96.7 98.3 37.7 74.8 80.4 910
Nairobi 74.7 27.4 771 71.3 98.7 97.9 97.7 100.0 69.6 79.5 74.2 751
Education
No education 57.1 21.5 675 35.9 87.4 76.6 89.5 91.3 31.6 50.6 55.4 578
Primary incomplete 65.0 30.4 1,901 50.6 90.4 82.3 95.3 96.3 38.1 61.6 62.4 1,818
Primary complete 71.4 34.1 1,856 57.5 94.9 90.4 96.4 97.9 46.1 71.5 69.7 1,818
Secondary+ 74.7 32.6 2,445 70.2 97.5 95.3 97.7 99.1 54.2 75.5 77.4 2,412
Wealth quintile
Lowest 62.8 27.5 1,381 44.7 89.3 77.1 91.8 93.4 34.4 55.3 58.8 1,249
Second 66.6 30.1 1,312 51.0 90.3 85.2 95.6 97.4 38.4 64.7 63.9 1,268
Middle 66.0 34.6 1,276 60.3 93.0 87.5 95.3 97.8 40.2 68.1 68.8 1,242
Fourth 74.0 32.8 1,372 61.2 97.0 93.6 98.0 98.2 49.0 74.0 73.4 1,350
Highest 76.4 31.6 1,536 71.7 98.9 98.1 98.5 99.3 62.4 77.6 79.0 1,516
Total 69.4 31.3 6,876 58.4 94.0 88.8 96.0 97.3 45.6 68.4 69.2 6,625
Sixty-nine percent of women with a live birth in the last five years took iron tablets, iron syrup, or
iron and folic acid supplementation during the pregnancy of their most recent birth, and 31 percent took
drugs for intestinal parasites. There are variations by background characteristics in intake of iron
supplements and anti-parasite drugs. Iron supplements were most commonly taken by women age 20-34
(71 percent), while the proportion of women taking anti-parasite drugs increased with age. Women having
a child of birth order six or higher (61 percent) were least likely to take iron supplements, but there was not
much variation in use of anti-parasite drugs by birth order. While urban women were more likely to take
iron supplements (75 percent) than rural women (66 percent), similar proportions of urban and rural
women took anti-parasite drugs (32 percent and 31 percent, respectively). Regions with high proportions of
women taking iron supplements (Nyanza, 83 percent; Coast, 82 percent; Nairobi, 75 percent) varied in the
proportion of women taking anti-parasite drugs (Coast, 51 percent; Nyanza, 33 percent; Nairobi, 27
percent). Women with no education and women in the lowest wealth quintile were less likely to take either
iron supplements or anti-parasite drugs than women with some education and those in wealthier
households.
Maternal Health • 125
Use of various antenatal care services shows a general pattern according to which use is more
common among women with births of lower order, women in urban areas, and women at higher levels of
education and wealth. There are small variations in receipt of some of these services.
At least 9 in 10 women receiving ANC had their blood pressure measured (94 percent), had a
blood sample taken (96 percent), and were weighed (97 percent); 89 percent had a urine sample taken.
Lower proportions of women were given information on iron supplements (69 percent) and breastfeeding
(68 percent), were informed of signs of pregnancy complications (58 percent), or had height measurements
taken (46 percent). Women having a first birth (67 percent) and urban women (67 percent) were more
likely than their counterparts to be informed about signs of pregnancy complications. Women in North
Eastern (36 percent) were least likely to be informed about signs of complications. The proportion of
women informed of signs of pregnancy complications increases with increasing education and wealth.
As shown in Figure 9.1, the proportion of women receiving all of the selected components of
antenatal care except iron supplements, which has remained stable, has increased since the 2008-09
KDHS.
Figure 9.1 Trends in components of antenatal care
Percentage
94 97 94 96
89
85 82
69 69 68 68
58
53
43 46
31 31
17
Took iron Took Informed of Weighed Height Blood Urine sample Blood sample Given
supplements intestinal signs of measured pressure taken taken information
parasite pregnancy measured on
drugs complications breastfeeding
2008-09 KDHS 2014 KDHS
9.2 TETANUS TOXOID VACCINATION
Neonatal tetanus is a leading cause of death among infants in developing countries, where a
considerable proportion of deliveries take place at home or at locations where hygienic conditions may be
poor. Tetanus toxoid (TT) vaccine is given to women during pregnancy to prevent infant deaths caused by
neonatal tetanus, which can occur when unclean tools are used to cut the umbilical cord after delivery. For
full protection, women should receive at least two doses of TT vaccine during each pregnancy. If a woman
has been vaccinated during a previous pregnancy or during maternal and neonatal tetanus vaccination
campaigns, she may require only one dose for her current pregnancy. Five doses are considered to provide
lifetime protection (Ministry of Health [MOH], 2012).
Table 9.4 presents the percentage of women age 15-49 with a live birth in the five years preceding
the survey who received two or more TT injections during their last pregnancy and the percentage whose
last live birth was protected against neonatal tetanus. More than half (51 percent) of pregnant women
received two or more tetanus injections during their last pregnancy, and 76 percent had their last birth
protected against neonatal tetanus. There has been a slight increase in the percentage of women whose last
birth was protected against neonatal tetanus since the 2008-09 KDHS (73 percent).
126 • Maternal Health
Table 9.4 Tetanus toxoid injections
Among mothers age 15-49 with a live birth in the five years preceding the
survey, the percentage receiving two or more tetanus toxoid injections during
the pregnancy for the last live birth and the percentage whose last live birth
was protected against neonatal tetanus, according to background
characteristics, Kenya 2014
Percentage Percentage
receiving two or whose last birth
more injections was protected
Background during last against neonatal Number of
characteristic pregnancy tetanus1 mothers
Mother’s age at
birth
<20 61.2 73.4 867
20-34 50.9 76.2 5,074
35-49 42.4 74.4 935
Birth order
1 68.7 74.9 1,705
2-3 50.8 79.3 2,747
4-5 41.7 73.0 1,351
6+ 35.7 70.8 1,072
Residence
Urban 56.5 76.4 2,677
Rural 47.6 75.1 4,199
Region
Coast 64.5 83.7 698
North Eastern 33.5 59.9 178
Eastern 54.7 79.1 891
Central 58.0 79.7 715
Rift Valley 45.3 74.3 1,899
Western 45.3 66.9 790
Nyanza 44.5 70.1 934
Nairobi 60.3 83.0 771
Education
No education 41.6 66.9 675
Primary incomplete 46.1 74.0 1,901
Primary complete 49.8 76.6 1,856
Secondary+ 58.5 78.6 2,445
Wealth quintile
Lowest 42.8 69.5 1,381
Second 45.3 75.3 1,312
Middle 50.6 74.5 1,276
Fourth 52.2 77.0 1,372
Highest 62.8 81.1 1,536
Total 51.1 75.6 6,876
1
Includes mothers with two injections during the pregnancy of her last birth,
or two or more injections (the last within 3 years of the last live birth), or three
or more injections (the last within 5 years of the last birth), or four or more
injections (the last within 10 years of the last live birth), or five or more
injections at any time prior to the last birth.
There are some differences in tetanus protection by region. More than 8 in 10 women in Coast and
Nairobi (84 percent and 83 percent, respectively) had their last birth protected against neonatal tetanus, as
compared with 6 in 10 women (60 percent) in North Eastern. Education and wealth have a positive
association with receipt of two or more TT injections and protection of births against neonatal tetanus.
Sixty-seven percent of births to women with no education were protected against neonatal tetanus,
compared with 79 percent of births to women with a secondary or higher education. Similarly, women in
the lowest wealth quintile (70 percent) were less likely than women in the highest quintile (81 percent) to
have their last birth protected against neonatal tetanus.
9.3 PLACE OF DELIVERY
Increasing the percentage of births delivered in health facilities is important for reducing deaths
arising from complications of pregnancy. The expectation is that if complications arise during delivery in a
health facility, a skilled birth attendant can manage them or refer the mother to the next level of care.
Kenya is promoting skilled care during pregnancy and childbirth for both mothers and newborns (MOH,
2009).
Maternal Health • 127
Table 9.5 presents the percent distribution of live births in the five years preceding the survey by
place of delivery and the percentage of births delivered in a health facility, according to background
characteristics. More than one-third of births (37 percent) took place at home. Sixty-one percent of births
were delivered in a health facility: 46 percent in a public-sector facility and 15 percent in a private-sector
facility. This is a large increase in facility deliveries from 43 percent in 2008-09.
Table 9.5 Place of delivery
Percent distribution of live births in the five years preceding the survey by place of delivery and percentage delivered in a health
facility, according to background characteristics, Kenya 2014
Percentage
Health facility delivered in
Background Public Private a health Number of
characteristic sector sector Home Other Missing Total facility births
Mother’s age at birth
<20 49.8 11.9 37.3 0.6 0.4 100.0 61.7 2,924
20-34 46.4 16.1 36.0 1.1 0.4 100.0 62.5 14,342
35-49 38.8 13.7 45.7 1.2 0.6 100.0 52.5 2,298
Birth order
1 57.8 20.7 20.6 0.4 0.4 100.0 78.6 5,176
2-3 47.6 18.4 32.4 1.2 0.4 100.0 66.1 7,651
4-5 38.8 8.6 50.7 1.2 0.6 100.0 47.5 3,785
6+ 30.2 5.4 62.5 1.4 0.6 100.0 35.6 2,952
Antenatal care visits1
None 12.4 5.3 79.4 1.2 1.7 100.0 17.7 573
1-3 44.6 12.6 41.4 1.2 0.1 100.0 57.3 5,505
4+ 54.1 20.9 23.8 1.2 0.1 100.0 75.0 8,319
Residence
Urban 54.8 27.2 16.7 0.9 0.3 100.0 82.0 7,024
Rural 41.1 8.4 48.9 1.1 0.5 100.0 49.5 12,540
Region
Coast 48.2 9.5 41.1 0.8 0.4 100.0 57.7 2,023
North Eastern 26.1 3.2 68.6 0.7 1.5 100.0 29.2 650
Eastern 45.0 17.7 35.3 1.8 0.3 100.0 62.7 2,321
Central 64.2 26.1 8.6 1.0 0.1 100.0 90.2 1,796
Rift Valley 38.5 11.7 48.8 0.5 0.5 100.0 50.2 5,677
Western 40.8 6.2 51.3 1.4 0.3 100.0 47.0 2,255
Nyanza 54.8 10.0 33.1 1.3 0.9 100.0 64.8 2,790
Nairobi 50.1 38.6 10.1 1.0 0.3 100.0 88.7 2,051
Mother’s education
No education 21.0 3.9 73.7 0.6 0.8 100.0 24.9 2,307
Primary incomplete 37.5 7.0 53.6 1.4 0.4 100.0 44.6 5,582
Primary complete 52.7 14.1 31.8 1.0 0.4 100.0 66.8 5,397
Secondary+ 57.0 27.4 14.3 0.8 0.4 100.0 84.4 6,277
Wealth quintile
Lowest 27.3 2.9 68.2 1.1 0.6 100.0 30.1 4,657
Second 42.0 7.1 49.1 1.3 0.5 100.0 49.1 3,987
Middle 50.8 11.5 36.1 1.1 0.6 100.0 62.3 3,525
Fourth 60.6 19.3 19.0 0.7 0.4 100.0 79.9 3,453
Highest 55.1 37.6 6.2 0.9 0.2 100.0 92.7 3,942
Total 46.0 15.2 37.4 1.0 0.5 100.0 61.2 19,564
Note: Total includes 51 births for whom information on ANC visits is missing.
1
Includes only the most recent birth in the five years preceding the survey
By age, delivery in a health facility is least common among births to mothers age 35-49 (53
percent), and it decreases as birth order increases. Delivery in a health facility increases with the number of
ANC visits the mother made. Children in urban areas (82 percent) are much more likely to be delivered in
a health facility than rural children (50 percent). The percentage of births delivered in a health facility
ranges from 29 percent in North Eastern region to 90 percent in Central.
Health facility delivery increases with increasing mother’s education and wealth. For example, 25
percent of births to mothers with no education are delivered in a health facility, as compared with 84
percent of births to mothers with a secondary or higher education. Similarly, 30 percent of births to
mothers in the lowest wealth quintile are delivered in a health facility, compared with 93 percent of births
to mothers in the highest quintile. Private-sector deliveries are more common among births to women in
the higher wealth quintiles and women at higher educational levels.
128 • Maternal Health
Table 9.5C shows the county-level percent distribution of live births in the five years preceding
the survey by place of delivery and the percentage of births delivered in a health facility. The proportion of
health facility deliveries ranges from 18 percent in Wajir to 93 percent each in Kiambu and Kirinyaga.
Table 9.5C Place of delivery
Percent distribution of live births in the five years preceding the survey by place of delivery and percentage delivered in a health
facility, according to county, Kenya 2014
Health facility Percentage
delivered in
Public Private a health Number of
County sector sector Home Other Missing Total facility births
Coast 48.2 9.5 41.1 0.8 0.4 100.0 57.7 2,023
Mombasa 58.6 23.2 17.6 0.3 0.4 100.0 81.8 512
Kwale 44.5 4.5 48.0 1.8 1.1 100.0 49.0 427
Kilifi 46.5 6.1 46.6 0.7 0.1 100.0 52.6 739
Tana River 30.5 1.1 67.6 0.0 0.8 100.0 31.6 175
Lamu 41.0 2.9 55.9 0.0 0.2 100.0 43.9 56
Taita Taveta 56.7 5.3 37.3 0.8 0.0 100.0 61.9 113
North Eastern 26.1 3.2 68.6 0.7 1.5 100.0 29.2 650
Garissa 35.5 1.1 62.7 0.0 0.7 100.0 36.7 237
Wajir 17.3 1.0 78.5 1.8 1.5 100.0 18.3 258
Mandera 26.1 9.9 61.2 0.0 2.7 100.0 36.0 155
Eastern 45.0 17.7 35.3 1.8 0.3 100.0 62.7 2,321
Marsabit 22.3 3.5 74.2 0.0 0.0 100.0 25.8 91
Isiolo 35.7 6.4 57.6 0.0 0.2 100.0 42.1 85
Meru 47.9 33.9 15.5 2.4 0.3 100.0 81.8 517
Tharaka-Nithi 58.7 19.0 19.0 2.4 0.9 100.0 77.7 141
Embu 60.3 21.2 16.2 2.3 0.0 100.0 81.5 201
Kitui 32.4 13.2 52.1 2.3 0.0 100.0 45.6 438
Machakos 48.8 14.1 35.1 1.3 0.7 100.0 62.9 493
Makueni 44.7 8.5 45.6 1.1 0.0 100.0 53.3 355
Central 64.2 26.1 8.6 1.0 0.1 100.0 90.2 1,796
Nyandarua 69.8 16.3 12.8 1.0 0.1 100.0 86.1 248
Nyeri 72.9 16.1 9.6 1.4 0.0 100.0 89.0 249
Kirinyaga 76.7 15.7 6.5 0.0 1.1 100.0 92.5 197
Murang’a 72.4 12.6 14.3 0.7 0.0 100.0 85.0 308
Kiambu 53.4 40.0 5.4 1.2 0.0 100.0 93.4 794
Rift Valley 38.5 11.7 48.8 0.5 0.5 100.0 50.2 5,677
Turkana 20.4 2.7 75.9 0.8 0.1 100.0 23.1 347
West Pokot 24.5 1.3 73.6 0.0 0.6 100.0 25.8 302
Samburu 16.8 7.7 74.0 1.1 0.4 100.0 24.5 117
Trans-Nzoia 34.9 6.6 57.7 0.8 0.1 100.0 41.5 528
Uasin Gishu 45.4 12.0 42.5 0.0 0.1 100.0 57.4 483
Elgeyo Marakwet 48.7 16.0 34.0 1.3 0.0 100.0 64.7 168
Nandi 39.3 7.2 53.1 0.4 0.0 100.0 46.5 402
Baringo 48.9 4.7 45.4 1.0 0.0 100.0 53.5 238
Laikipia 36.1 12.0 51.0 0.3 0.6 100.0 48.1 209
Nakuru 53.7 16.0 29.7 0.5 0.2 100.0 69.7 909
Narok 32.0 6.6 60.7 0.5 0.1 100.0 38.6 638
Kajiado 35.8 26.7 37.0 0.2 0.4 100.0 62.4 461
Kericho 44.4 17.8 35.3 1.0 1.5 100.0 62.2 373
Bomet 32.6 16.3 48.3 0.4 2.4 100.0 49.0 502
Western 40.8 6.2 51.3 1.4 0.3 100.0 47.0 2,255
Kakamega 37.7 9.2 50.6 2.1 0.3 100.0 47.0 747
Vihiga 42.8 7.3 47.0 2.1 0.7 100.0 50.2 229
Bungoma 36.3 4.5 58.1 0.9 0.2 100.0 40.8 870
Busia 54.7 3.7 40.3 1.0 0.2 100.0 58.4 409
Nyanza 54.8 10.0 33.1 1.3 0.9 100.0 64.8 2,790
Siaya 62.4 7.2 27.0 1.2 2.2 100.0 69.6 391
Kisumu 57.5 12.0 28.8 1.3 0.4 100.0 69.5 500
Homa Bay 57.1 4.8 35.7 1.5 0.9 100.0 61.9 658
Migori 41.7 11.6 43.9 2.1 0.6 100.0 53.3 565
Kisii 54.3 15.0 30.0 0.7 0.0 100.0 69.3 482
Nyamira 63.3 10.9 23.5 0.2 2.0 100.0 74.3 195
Nairobi 50.1 38.6 10.1 1.0 0.3 100.0 88.7 2,051
Total 46.0 15.2 37.4 1.0 0.5 100.0 61.2 19,564
9.4 ASSISTANCE DURING DELIVERY
Obstetric care from a health professional during delivery is recognised as critical in reducing
maternal and neonatal mortality. Table 9.6 shows the percent distribution of live births in the five years
preceding the survey by person providing assistance during delivery, the percentage of births assisted by a
skilled provider, and the percentage of births delivered via caesarean section, according to background
Maternal Health • 129
characteristics. Sixty-two percent of births were assisted at delivery by a skilled birth attendant (doctor,
nurse, or midwife), 13 percent were assisted by relatives or friends, and 19 percent were assisted by a
traditional birth attendant. Five percent of births were unassisted. The role of community health workers in
delivery assistance is very limited (less than 1 percent). Almost all health facility births (99 percent) were
assisted by a skilled provider, as compared with just 3 percent of births delivered outside of health
facilities.
Table 9.6 Assistance during delivery
Percent distribution of live births in the five years preceding the survey by person providing assistance during delivery, percentage of birth assisted by a skilled provider and
percentage delivered by caesarean-section, according to background characteristics, Kenya 2014
Person providing assistance during delivery Percentage Percentage
Community Traditional Don’t delivered delivered
Background Nurse/ health birth Relative/ know/ by a skilled by Number of
1
characteristic Doctor midwife worker attendant friend Other No one missing Total provider C-section births
Mother’s age at birth
<20 24.3 37.8 0.4 21.6 13.3 0.4 1.9 0.3 100.0 62.1 5.9 2,924
20-34 27.1 36.0 0.3 19.0 12.1 0.9 4.2 0.4 100.0 63.1 9.0 14,342
35-49 23.2 30.7 0.3 19.2 14.1 1.5 10.5 0.5 100.0 53.9 10.0 2,298
Birth order
1 36.0 43.1 0.2 11.9 7.3 0.4 0.7 0.4 100.0 79.1 13.1 5,176
2-3 29.5 36.8 0.3 17.9 11.6 0.6 2.9 0.3 100.0 66.3 9.7 7,651
4-5 17.1 31.4 0.5 25.6 17.2 1.7 6.1 0.4 100.0 48.5 4.2 3,785
6+ 12.4 24.8 0.4 28.7 17.7 1.6 13.8 0.5 100.0 37.2 3.9 2,952
Antenatal care visits2
None 8.6 10.0 0.0 39.7 26.1 1.3 12.5 1.7 100.0 18.6 1.4 573
1-3 23.4 34.3 0.3 20.7 14.0 1.0 6.2 0.1 100.0 57.7 6.5 5,505
4+ 33.9 41.8 0.3 12.4 8.2 0.7 2.7 0.0 100.0 75.7 12.3 8,319
Place of delivery
Health facility 42.5 56.7 0.2 0.1 0.1 0.1 0.3 0.1 100.0 99.2 14.2 11,969
Elsewhere 0.5 2.3 0.5 50.5 32.4 2.3 11.5 0.1 100.0 2.8 0.0 7,505
Residence
Urban 41.7 40.6 0.2 9.9 4.6 0.6 2.0 0.3 100.0 82.4 14.7 7,024
Rural 17.6 32.8 0.4 24.8 16.9 1.1 6.0 0.5 100.0 50.4 5.3 12,540
Region
Coast 32.0 26.3 0.1 18.4 18.7 0.4 3.8 0.4 100.0 58.2 7.0 2,023
North Eastern 7.2 25.1 0.5 64.2 1.5 0.1 0.1 1.2 100.0 32.4 2.9 650
Eastern 22.1 41.2 0.2 19.8 12.2 0.5 3.7 0.3 100.0 63.3 11.7 2,321
Central 55.4 34.3 0.2 0.9 5.8 0.4 2.9 0.1 100.0 89.7 15.7 1,796
Rift Valley 22.2 29.0 0.3 20.9 21.2 1.2 4.9 0.3 100.0 51.3 5.6 5,677
Western 11.2 36.6 0.2 30.9 9.1 1.2 10.5 0.3 100.0 47.8 4.3 2,255
Nyanza 11.4 53.6 0.9 19.5 7.2 1.5 5.0 0.9 100.0 65.0 5.1 2,790
Nairobi 53.4 35.7 0.3 5.5 2.8 0.8 1.3 0.3 100.0 89.1 20.7 2,051
Mother’s education
No education 9.2 17.2 0.2 39.5 25.8 1.3 6.1 0.7 100.0 26.4 2.2 2,307
Primary incomplete 15.3 29.8 0.4 25.6 18.6 1.4 8.5 0.4 100.0 45.1 4.4 5,582
Primary complete 29.9 37.5 0.4 17.3 9.9 1.0 3.6 0.4 100.0 67.4 8.2 5,397
Secondary+ 39.1 45.9 0.2 8.4 4.3 0.3 1.4 0.3 100.0 85.1 15.2 6,277
Wealth quintile
Lowest 10.3 20.9 0.4 34.3 25.7 1.2 6.8 0.5 100.0 31.1 2.1 4,657
Second 14.3 35.6 0.5 24.8 15.7 1.4 7.2 0.5 100.0 49.9 4.8 3,987
Middle 24.4 38.7 0.3 19.7 10.6 1.1 4.8 0.5 100.0 63.0 7.4 3,525
Fourth 36.2 44.4 0.1 11.2 4.7 0.2 2.8 0.3 100.0 80.6 11.5 3,453
Highest 50.1 42.6 0.2 3.5 2.1 0.6 0.7 0.1 100.0 92.7 19.0 3,942
Total 26.2 35.6 0.3 19.4 12.5 0.9 4.6 0.4 100.0 61.8 8.7 19,564
Note: If the respondent mentioned more than one person attending during delivery, only the most qualified person is considered in this tabulation. Total includes 51 births for
whom information on ANC visits is missing and 114 births for whom place of delivery is missing.
1
Skilled provider includes doctor, nurse, or midwife.
2
Includes only the most recent birth in the five years preceding the survey
The percentage of births assisted by a skilled birth attendant has increased in the last five years,
from 44 percent in 2008-09 to 62 percent in 2014. It is noteworthy that delivery assistance by a skilled
birth attendant in rural areas has increased from 37 percent to 50 percent.
Nine percent of births are delivered via caesarean section. The likelihood of this type of delivery
increases with mother’s age, decreases with birth order, and increases with the number of ANC visits the
mother made. Births in urban areas (15 percent) are more likely to be delivered via caesarean section. The
likelihood of caesarean-section deliveries increases with increasing mother’s education and wealth.
130 • Maternal Health
Caesarean deliveries are noticeably more common in Nairobi and among babies born to women in the
highest wealth quintile; about one in five of these births occurs via caesarean-section.
Table 9.6C shows the county-level percent distribution of live births in the five years preceding
the survey by person providing assistance during delivery, the percentage of births assisted by a skilled
provider, and the percentage delivered via caesarean section. The proportion of births assisted by a skilled
provider ranges from 22 percent in Wajir to 93 percent in Kiambu. Caesarean-section deliveries are least
common in Turkana and Wajir (1 percent). In five counties, caesarean deliveries exceed 15 percent:
Kirinyaga, Embu, Tharaka-Nithi, Kiambu, and Nairobi.
Table 9.6C Assistance during delivery
Percent distribution of live births in the five years preceding the survey by person providing assistance during delivery, percentage of birth assisted by a skilled provider
and percentage delivered by caesarean-section, according to county, Kenya 2014
Person providing assistance during delivery Percentage Percentage
Community Traditional Don’t delivered delivered
Nurse/ health birth Relative/ know/ by a skilled by Number of
County Doctor midwife worker attendant friend Other No one missing Total provider1 C-section births
Coast 32.0 26.3 0.1 18.4 18.7 0.4 3.8 0.4 100.0 58.2 7.0 2,023
Mombasa 54.2 28.6 0.0 13.9 3.3 0.0 0.0 0.0 100.0 82.8 13.3 512
Kwale 12.8 37.3 0.3 17.6 21.6 0.9 8.4 1.0 100.0 50.1 5.3 427
Kilifi 38.2 14.0 0.2 10.8 31.4 0.1 4.9 0.3 100.0 52.3 4.4 739
Tana River 2.0 30.2 0.0 62.9 3.3 0.8 0.5 0.3 100.0 32.2 2.4 175
Lamu 9.3 38.0 0.0 44.8 5.0 1.8 0.9 0.2 100.0 47.3 7.3 56
Taita Taveta 20.7 41.8 0.0 9.7 24.2 0.6 3.1 0.0 100.0 62.5 8.7 113
North Eastern 7.2 25.1 0.5 64.2 1.5 0.1 0.1 1.2 100.0 32.4 2.9 650
Garissa 5.5 34.2 1.0 55.0 3.9 0.0 0.4 0.0 100.0 39.8 5.5 237
Wajir 1.2 20.5 0.0 76.5 0.0 0.3 0.0 1.4 100.0 21.7 1.3 258
Mandera 20.0 18.8 0.4 57.9 0.2 0.0 0.0 2.7 100.0 38.7 1.8 155
Eastern 22.1 41.2 0.2 19.8 12.2 0.5 3.7 0.3 100.0 63.3 11.7 2,321
Marsabit 4.4 21.4 0.5 64.0 7.3 0.9 1.4 0.0 100.0 25.8 2.3 91
Isiolo 14.4 29.4 0.0 45.8 6.8 1.7 1.8 0.2 100.0 43.8 6.9 85
Meru 28.0 54.7 0.0 1.3 10.0 0.2 5.4 0.3 100.0 82.8 18.5 517
Tharaka-Nithi 37.6 39.0 0.0 2.5 14.8 0.4 4.5 1.2 100.0 76.6 14.5 141
Embu 34.1 47.4 0.0 0.3 16.4 0.0 1.9 0.0 100.0 81.5 15.9 201
Kitui 16.6 29.6 0.2 24.3 25.9 0.0 3.5 0.0 100.0 46.2 5.4 438
Machakos 22.3 41.0 0.5 26.1 6.6 0.5 2.4 0.7 100.0 63.4 11.4 493
Makueni 13.6 41.0 0.3 32.7 5.6 1.8 5.1 0.0 100.0 54.6 9.9 355
Central 55.4 34.3 0.2 0.9 5.8 0.4 2.9 0.1 100.0 89.7 15.7 1,796
Nyandarua 47.7 37.6 0.0 0.7 9.1 0.5 4.2 0.1 100.0 85.3 9.0 248
Nyeri 56.1 32.0 1.1 0.2 4.8 0.8 5.0 0.0 100.0 88.1 13.5 249
Kirinyaga 41.3 50.9 0.0 1.6 4.9 0.4 0.4 0.5 100.0 92.3 15.3 197
Murang’a 64.9 20.6 0.0 0.5 10.4 0.0 3.6 0.0 100.0 85.5 11.7 308
Kiambu 57.4 35.2 0.0 1.2 3.5 0.4 2.3 0.0 100.0 92.6 20.1 794
Rift Valley 22.2 29.0 0.3 20.9 21.2 1.2 4.9 0.3 100.0 51.3 5.6 5,677
Turkana 0.8 22.0 0.1 7.4 49.9 5.0 14.4 0.3 100.0 22.8 0.5 347
West Pokot 8.2 18.7 0.0 69.4 2.6 0.1 0.2 0.7 100.0 27.0 2.2 302
Samburu 3.8 25.3 0.0 5.7 52.7 2.4 9.7 0.4 100.0 29.0 2.9 117
Trans-Nzoia 26.5 15.3 0.6 34.3 13.6 0.3 9.5 0.1 100.0 41.8 5.3 528
Uasin Gishu 29.7 29.3 0.2 30.6 7.4 0.4 2.4 0.0 100.0 59.0 4.8 483
Elgeyo Marakwet 12.3 52.8 0.0 31.9 2.6 0.3 0.1 0.1 100.0 65.0 3.5 168
Nandi 5.6 41.1 0.0 44.9 6.0 0.0 2.4 0.0 100.0 46.8 2.8 402
Baringo 9.5 44.3 0.0 16.4 25.7 2.1 2.1 0.0 100.0 53.8 5.2 238
Laikipia 22.8 26.7 0.0 5.5 37.4 0.6 6.4 0.6 100.0 49.5 6.5 209
Nakuru 34.1 35.3 0.0 7.3 18.8 0.8 3.4 0.2 100.0 69.5 6.9 909
Narok 12.9 27.4 1.0 17.8 34.4 0.9 5.5 0.1 100.0 40.3 5.1 638
Kajiado 40.2 23.0 0.0 13.7 21.4 0.2 1.3 0.2 100.0 63.2 11.1 461
Kericho 35.6 28.7 0.1 18.7 7.3 6.5 2.4 0.6 100.0 64.4 10.3 373
Bomet 24.6 27.6 0.9 3.8 33.8 0.0 8.6 0.8 100.0 52.2 4.8 502
Western 11.2 36.6 0.2 30.9 9.1 1.2 10.5 0.3 100.0 47.8 4.3 2,255
Kakamega 15.3 33.4 0.7 30.1 11.2 0.9 8.1 0.3 100.0 48.6 3.1 747
Vihiga 16.6 33.7 0.0 27.5 8.7 0.0 12.8 0.7 100.0 50.3 6.6 229
Bungoma 6.6 34.8 0.0 35.4 8.5 2.1 12.5 0.2 100.0 41.4 5.0 870
Busia 10.7 47.8 0.0 24.4 7.2 0.3 9.4 0.2 100.0 58.5 4.0 409
Nyanza 11.4 53.6 0.9 19.5 7.2 1.5 5.0 0.9 100.0 65.0 5.1 2,790
Siaya 3.7 66.7 2.6 11.2 4.3 0.4 8.8 2.2 100.0 70.4 4.3 391
Kisumu 20.0 49.2 0.0 25.0 1.8 1.0 2.6 0.4 100.0 69.2 6.8 500
Homa Bay 12.7 47.6 1.1 28.9 2.0 1.0 5.6 0.9 100.0 60.4 3.1 658
Migori 5.7 47.7 0.7 28.6 7.0 3.2 6.7 0.4 100.0 53.4 6.5 565
Kisii 15.2 57.6 0.0 3.8 18.5 1.5 3.1 0.3 100.0 72.8 5.3 482
Nyamira 7.5 66.6 1.5 2.2 16.5 0.8 2.0 2.9 100.0 74.1 5.2 195
Nairobi 53.4 35.7 0.3 5.5 2.8 0.8 1.3 0.3 100.0 89.1 20.7 2,051
Total 26.2 35.6 0.3 19.4 12.5 0.9 4.6 0.4 100.0 61.8 8.7 19,564
Note: If the respondent mentioned more than one person attending during delivery, only the most qualified person is considered in this tabulation.
1
Skilled provider includes doctor, nurse, or midwife.
Maternal Health • 131
Figure 9.2 shows the percent distribution of mothers with a birth in the five years preceding the
survey who delivered their last birth in a health facility, by duration of stay in the facility and type of
delivery. The majority of women with a vaginal birth stayed at a health facility for two days or fewer (87
percent). In contrast, the majority of women who delivered via caesarean section (83 percent) stayed at a
health facility for three or more days.
Figure 9.2 Mother’s duration of stay in the health facility after giving birth
Percentage
83
56
15 13 13
10
6
1 2 1
Vaginal birth Caesarean birth
<6 hours 6-11 hours 12-23 hours 1-2 days 3+ days
KDHS 2014
9.5 POSTNATAL CARE
The postpartum period is particularly important for women, since during this period they may
develop serious, life-threatening complications. Evidence has shown that a large proportion of deaths occur
during the postpartum period, with postpartum haemorrhage being a major cause (UNDP, WHO, UNFPA,
World Bank, 2006). A postnatal care visit is an ideal time to educate a new mother on how to care for
herself and her newborn. The 2014 KDHS asked women age 15-49 who had a live birth in the two years
preceding the survey about what postnatal care they and their newborn received, including timing and
provider.
9.5.1 Timing of First Postnatal Checkup for the Mother
Table 9.7 presents, among women age 15-49 giving birth in the two years before the survey, the
percent distribution of mothers’ first postnatal checkup for their last live birth by time after delivery and
the percentage of women who received a postnatal checkup in the first two days after giving birth,
according to background characteristics. Fifty-three percent of women received postnatal care within the
critical two-day period following delivery. Thirty-eight percent of women received postnatal care within
four hours after delivery, 9 percent received care within 4-23 hours, and 6 percent were seen 1-2 days
following delivery. Overall, 43 percent of women did not receive a postnatal checkup within the first six
weeks after delivery. In the North Eastern region, 80 percent of women did not have any postnatal care.
132 • Maternal Health
Table 9.7 Timing of first postnatal checkup for the mother
Among women age 15-49 giving birth in the two years preceding the survey, the percent distribution of the mother’s first postnatal checkup for the last live birth by
time after delivery, and the percentage of women with a live birth in the two years preceding the survey who received a postnatal checkup in the first two days after
giving birth, according to background characteristics, Kenya 2014
Percentage of
women with a
postnatal
Time after delivery of mother’s first postnatal checkup checkup in the
Background Less than Don’t know/ No postnatal first two days Number of
characteristic 4 hours 4-23 hours 1-2 days 3-6 days 7-41 days missing checkup1 Total after birth women
Mother’s age at birth
<20 38.1 11.3 5.7 1.0 3.0 0.7 40.2 100.0 55.1 502
20-34 38.5 9.1 6.0 1.1 2.0 0.3 43.0 100.0 53.5 2,627
35-49 33.3 9.1 3.4 1.4 6.0 0.3 46.6 100.0 45.8 415
Birth order
1 45.1 12.4 8.2 0.3 2.6 0.7 30.6 100.0 65.7 946
2-3 39.0 10.7 6.8 1.6 1.7 0.4 39.8 100.0 56.5 1,413
4-5 35.7 6.0 2.0 1.0 3.2 0.1 52.0 100.0 43.7 662
6+ 23.9 4.9 2.6 1.5 4.1 0.2 62.9 100.0 31.3 523
Place of delivery
Health facility 52.1 13.5 6.9 1.0 1.7 0.6 24.2 100.0 72.5 2,314
Elsewhere 10.8 1.7 3.4 1.4 4.3 0.0 78.3 100.0 15.9 1,226
Residence
Urban 44.7 14.3 8.5 1.0 2.7 0.7 28.1 100.0 67.5 1,261
Rural 34.0 6.7 4.1 1.2 2.6 0.2 51.3 100.0 44.7 2,282
Region
Coast 37.8 7.3 4.4 2.3 1.9 0.0 46.3 100.0 49.5 374
North Eastern 7.0 4.3 3.6 0.8 3.8 0.5 80.0 100.0 14.9 108
Eastern 46.0 10.2 4.9 0.7 4.2 0.0 34.0 100.0 61.1 429
Central 45.9 15.4 10.6 0.8 3.7 0.0 23.7 100.0 71.9 312
Rift Valley 32.8 9.0 4.1 1.1 3.0 0.3 49.6 100.0 45.9 1,057
Western 26.4 4.2 4.1 2.1 2.5 0.0 60.8 100.0 34.6 414
Nyanza 47.6 10.8 2.7 0.4 1.2 0.8 36.7 100.0 61.0 484
Nairobi 44.9 12.2 14.5 0.8 0.9 1.6 25.1 100.0 71.6 366
Education
No education 15.3 2.6 3.4 1.3 2.5 0.4 74.6 100.0 21.3 414
Primary incomplete 31.1 5.5 4.1 1.3 2.4 0.3 55.3 100.0 40.7 999
Primary complete 42.1 8.8 6.5 0.7 2.7 0.3 38.9 100.0 57.5 914
Secondary+ 47.7 15.3 7.1 1.3 2.8 0.5 25.3 100.0 70.1 1,216
Wealth quintile
Lowest 23.9 3.6 3.4 1.5 2.5 0.2 64.8 100.0 31.0 879
Second 37.8 7.3 3.0 0.8 2.9 0.0 48.1 100.0 48.2 698
Middle 38.7 8.8 4.8 1.2 1.9 0.3 44.3 100.0 52.3 631
Fourth 43.3 13.0 9.0 1.1 3.2 0.8 29.7 100.0 65.2 648
Highest 49.5 16.1 8.8 0.9 2.6 0.7 21.4 100.0 74.4 687
Total 37.8 9.4 5.7 1.1 2.6 0.4 43.0 100.0 52.9 3,544
Note: Total includes six women for whom information on place of delivery is missing.
1
Includes women who received a checkup after 41 days
9.5.2 Provider of First Postnatal Checkup for the Mother
The skill level of the person who provides the first postnatal checkup has important implications
for maternal and neonatal health. Table 9.8 shows, among women age 15-49 giving birth in the two years
preceding the survey, the percent distribution by type of provider of the mother’s first postnatal health
check in the two days after the last live birth, according to background characteristics. Just under half (49
percent) of women received postnatal care from a doctor, a nurse, or a midwife, an increase of 12
percentage points from the 2008-09 KDHS (37 percent). At the same time, the role of traditional birth
attendants in providing postnatal checks declined from 10 percent in 2008-09 to 4 percent in 2014.
Postnatal care from a skilled provider is highest among women younger than age 35 (50 percent),
first-order births (64 percent), mothers who delivered in a health facility (72 percent), and mothers in urban
areas (66 percent). Across regions, postnatal care from a skilled birth attendant is highest in Central (72
percent) and Nairobi (71 percent) and lowest in North Eastern (14 percent). Skilled postnatal care increases
with increasing education and wealth, from 16 percent of women with no education and 25 percent of
women in the lowest wealth quintile to 68 percent of women with a secondary or higher education and 74
percent of women in the highest wealth quintile.
Maternal Health • 133
Table 9.8 Type of provider of first postnatal checkup for the mother
Among women age 15-49 giving birth in the two years preceding the survey, the percent distribution by type of
provider of the mother’s first postnatal health check in the two days after the last live birth, according to background
characteristics, Kenya 2014
Type of health provider of mother’s first No postnatal
postnatal checkup checkup in the
Background Doctor/nurse/ Community Traditional first two days Number of
characteristic midwife health worker birth attendant after birth Total women
Mother’s age at
birth
<20 50.3 0.3 4.6 44.9 100.0 502
20-34 50.0 0.3 3.3 46.5 100.0 2,627
35-49 41.6 0.8 3.3 54.2 100.0 415
Birth order
1 63.8 0.1 1.8 34.3 100.0 946
2-3 53.1 0.1 3.3 43.5 100.0 1,413
4-5 37.4 1.1 5.2 56.3 100.0 662
6+ 26.3 0.5 4.6 68.7 100.0 523
Place of delivery
Health facility 72.2 0.1 0.2 27.5 100.0 2,314
Elsewhere 5.5 0.9 9.6 84.1 100.0 1,226
Residence
Urban 65.8 0.0 1.7 32.5 100.0 1,261
Rural 39.8 0.5 4.4 55.3 100.0 2,282
Region
Coast 45.4 0.0 4.1 50.5 100.0 374
North Eastern 13.5 0.5 0.9 85.1 100.0 108
Eastern 58.7 0.0 2.4 38.9 100.0 429
Central 71.9 0.0 0.0 28.1 100.0 312
Rift Valley 40.3 0.6 5.0 54.1 100.0 1,057
Western 30.9 0.0 3.7 65.4 100.0 414
Nyanza 54.6 1.1 5.3 39.0 100.0 484
Nairobi 71.0 0.0 0.6 28.4 100.0 366
Education
No education 16.2 0.4 4.7 78.7 100.0 414
Primary incomplete 35.7 0.6 4.4 59.3 100.0 999
Primary complete 53.4 0.4 3.7 42.5 100.0 914
Secondary+ 68.0 0.1 2.1 29.9 100.0 1,216
Wealth quintile
Lowest 25.3 0.5 5.2 69.0 100.0 879
Second 43.3 0.9 4.0 51.8 100.0 698
Middle 47.8 0.3 4.3 47.7 100.0 631
Fourth 62.6 0.0 2.6 34.8 100.0 648
Highest 73.6 0.0 0.7 25.6 100.0 687
Total 49.1 0.4 3.5 47.1 100.0 3,544
Note: Total includes six women for whom information on place of delivery is missing.
9.5.3 Timing of First Postnatal Checkup for the Newborn
Newborn care is essential to reduce neonatal health problems and death. To identify, manage, and
prevent newborn health complications, the government of Kenya recommends at least three postnatal
checkups for the newborn within the seven days after delivery, which is considered a critical time period
for neonates and mothers (MOH, 2012).
Table 9.9 shows the percent distribution of last births in the two years preceding the survey by
timing of the first postnatal checkup, along with the percentage of births with a postnatal checkup in the
first two days after birth, according to background characteristics.
Thirty-six percent of newborns had a postnatal checkup within the critical first two days after
birth. The majority of newborns (62 percent) did not receive a postnatal checkup in the first week after
birth. Although the patterns seen above for mothers’ postnatal care are largely repeated for newborns’
postnatal care, it is noteworthy that more than half of infants born in a health facility (52 percent) did not
receive postnatal checkups.
134 • Maternal Health
Table 9.9 Timing of first postnatal checkup for the newborn
Percent distribution of last births in the two years preceding the survey by time after birth of first postnatal checkup, and the percentage of births with a
postnatal checkup in the first two days after birth, according to background characteristics, Kenya 2014
Percentage of
births with a
Time after birth of newborn’s first postnatal checkup postnatal
Don’t No checkup in the
Background Less than know/ postnatal first two days Number of
characteristic 1 hour 1-3 hours 4-23 hours 1-2 days 3-6 days missing checkup1 Total after birth births
Mother’s age at
birth
<20 8.6 16.0 4.7 5.1 2.8 0.0 62.8 100.0 34.3 502
20-34 10.7 16.8 4.9 4.5 2.5 0.4 60.2 100.0 36.9 2,627
35-49 8.0 10.8 7.1 2.6 2.0 0.0 69.6 100.0 28.4 415
Birth order
1 11.0 19.2 5.7 5.4 2.3 0.7 55.7 100.0 41.3 946
2-3 11.5 17.6 5.6 4.5 2.7 0.1 58.0 100.0 39.2 1,413
4-5 8.6 13.7 4.2 3.0 2.1 0.1 68.3 100.0 29.6 662
6+ 6.7 8.9 3.8 3.5 2.8 0.1 74.2 100.0 22.8 523
Place of delivery
Health facility 13.8 22.4 6.4 4.0 1.4 0.4 51.6 100.0 46.6 2,314
Elsewhere 3.1 4.0 2.7 4.9 4.6 0.0 80.6 100.0 14.7 1,226
Residence
Urban 13.2 21.1 6.4 4.5 2.0 0.7 52.2 100.0 45.1 1,261
Rural 8.4 13.2 4.4 4.3 2.8 0.0 66.9 100.0 30.3 2,282
Region
Coast 10.6 13.1 9.9 5.5 4.6 1.0 55.4 100.0 39.0 374
North Eastern 0.0 2.2 1.1 1.9 1.9 0.7 92.2 100.0 5.1 108
Eastern 8.6 17.2 5.1 5.5 2.5 0.1 61.1 100.0 36.3 429
Central 14.6 33.7 10.2 4.2 1.2 0.0 36.1 100.0 62.8 312
Rift Valley 9.6 8.7 2.5 2.0 2.0 0.1 75.2 100.0 22.7 1,057
Western 9.7 11.1 2.4 4.4 3.1 0.2 69.1 100.0 27.6 414
Nyanza 7.1 21.6 8.1 4.5 2.7 0.0 56.1 100.0 41.2 484
Nairobi 16.7 25.9 3.9 9.0 2.3 0.8 41.4 100.0 55.5 366
Mother’s education
No education 7.1 7.1 2.7 2.2 3.6 0.1 77.2 100.0 19.1 414
Primary incomplete 8.0 12.9 4.1 4.0 2.5 0.1 68.4 100.0 29.0 999
Primary complete 9.2 17.9 5.2 4.5 3.3 0.1 59.8 100.0 36.9 914
Secondary+ 13.6 20.2 6.7 5.1 1.5 0.6 52.3 100.0 45.6 1,216
Wealth quintile
Lowest 6.2 9.7 3.9 3.6 3.6 0.0 72.9 100.0 23.5 879
Second 8.1 12.5 3.3 4.2 2.2 0.0 69.6 100.0 28.2 698
Middle 9.7 19.0 5.0 4.0 1.9 0.3 60.2 100.0 37.7 631
Fourth 13.4 17.3 7.5 4.2 2.2 0.6 54.8 100.0 42.4 648
Highest 14.4 23.6 6.2 5.8 2.3 0.6 47.1 100.0 50.1 687
Total 10.1 16.0 5.1 4.3 2.5 0.3 61.7 100.0 35.6 3,544
Note: Total includes six births for whom information on place of delivery is missing.
1
Includes newborns who received a checkup after the first week
9.5.4 Provider of First Postnatal Checkup for the Newborn
Table 9.10 presents the percent distribution of last births in the two years preceding the survey by
type of provider of newborn care during the first two days after delivery, according to background
characteristics. Thirty-three percent of newborns received postnatal care in the two days following birth
from a doctor, nurse, or midwife, and 2 percent received care from a traditional birth attendant. As is the
case with postnatal care for mothers, practically no newborns received postnatal care from a community
health worker. Patterns of postnatal care for newborns by background characteristics are similar to the
patterns observed for their mothers.
Maternal Health • 135
Table 9.10 Type of provider of first postnatal checkup for the newborn
Percent distribution of last births in the two years preceding the survey by type of provider of the newborn’s first
postnatal health check during the two days after the last live birth, according to background characteristics, Kenya 2014
Type of health provider of newborn’s first No postnatal
postnatal checkup checkup in the
Background Doctor/nurse/ Community Traditional first two days Number of
characteristic midwife health worker birth attendant after birth Total births
Mother’s age at birth
<20 31.7 0.0 2.6 65.7 100.0 502
20-34 34.4 0.0 2.5 63.1 100.0 2,627
35-49 26.7 0.0 1.7 71.6 100.0 415
Birth order
1 40.1 0.0 1.2 58.7 100.0 946
2-3 36.9 0.0 2.3 60.8 100.0 1,413
4-5 25.5 0.1 4.0 70.4 100.0 662
6+ 20.0 0.0 2.9 77.2 100.0 523
Place of delivery
Health facility 46.2 0.0 0.4 53.4 100.0 2,314
Elsewhere 8.5 0.0 6.2 85.3 100.0 1,226
Residence
Urban 44.0 0.0 1.1 54.9 100.0 1,261
Rural 27.1 0.0 3.1 69.7 100.0 2,282
Region
Coast 37.5 0.0 1.5 61.0 100.0 374
North Eastern 4.2 0.5 0.5 94.9 100.0 108
Eastern 34.4 0.0 1.9 63.7 100.0 429
Central 62.8 0.0 0.0 37.2 100.0 312
Rift Valley 19.3 0.1 3.4 77.3 100.0 1,057
Western 24.8 0.0 2.8 72.4 100.0 414
Nyanza 37.4 0.0 3.9 58.8 100.0 484
Nairobi 54.3 0.0 1.2 44.5 100.0 366
Mother’s education
No education 14.7 0.1 4.3 80.9 100.0 414
Primary incomplete 26.0 0.1 3.0 71.0 100.0 999
Primary complete 34.6 0.0 2.3 63.1 100.0 914
Secondary+ 44.2 0.0 1.4 54.4 100.0 1,216
Wealth quintile
Lowest 19.5 0.1 4.0 76.5 100.0 879
Second 24.9 0.1 3.2 71.8 100.0 698
Middle 35.9 0.0 1.8 62.3 100.0 631
Fourth 40.8 0.0 1.6 57.6 100.0 648
Highest 49.2 0.0 0.9 49.9 100.0 687
Total 33.1 0.0 2.4 64.4 100.0 3,544
Note: Total includes six births for whom information on place of delivery is missing.
9.6 PROBLEMS IN ACCESSING HEALTH CARE
Many factors can prevent women from getting medical advice or treatment for themselves when
they are sick. Information on such factors is particularly important in understanding and addressing the
barriers women may face in seeking care during pregnancy and labour.
In the 2014 KDHS, women age 15-49 were asked whether or not each of the following factors
would be a significant problem for them in seeking medical care: getting permission to go for treatment,
getting money for treatment, distance to a health facility, and not wanting to go alone. Table 9.11 presents
the percentage of women who reported that they have serious problems accessing health care for
themselves when they are sick, according to type of problem and background characteristics. The most
often cited problem is getting money to go for treatment (37 percent), followed by distance to the health
facility (23 percent). Eleven percent of women cited not wanting to go alone as a problem, and 6 percent
reported that getting permission to go for treatment was a problem. Overall, 46 percent of women reported
that at least one of these problems would pose a barrier to seeking health care for themselves when they are
sick.
136 • Maternal Health
Table 9.11 Problems in accessing health care
Percentage of women age 15-49 who reported that they have serious problems in accessing health care for themselves when they are
sick, by type of problem, according to background characteristics, Kenya 2014
Problems in accessing health care
At least one
Getting problem
Background permission to go Getting money Distance to Not wanting to accessing health Number of
characteristic for treatment for treatment health facility go alone care women
Age
15-19 7.4 33.0 20.4 13.7 44.3 2,717
20-34 5.9 34.3 21.6 9.6 43.9 7,784
35-49 5.2 43.7 26.1 10.3 51.6 4,124
Number of living
children
0 6.3 29.3 17.8 12.2 38.9 3,890
1-2 5.0 30.7 18.0 7.7 40.1 5,000
3-4 6.0 42.5 26.8 11.5 52.5 3,381
5+ 7.3 53.3 34.6 12.8 61.9 2,354
Marital status
Never married 6.0 31.6 18.1 11.7 40.5 4,255
Married or living
together 5.8 37.3 24.4 9.8 47.4 8,710
Divorced/separated/
widowed 6.3 46.6 25.0 11.5 53.7 1,660
Employed last 12
months
Not employed 6.6 36.3 23.1 12.6 47.0 4,913
Employed for cash 4.5 34.0 19.3 8.2 42.3 7,655
Employed not for cash 9.7 47.8 34.0 14.4 58.5 2,040
Residence
Urban 4.3 26.7 12.4 7.8 34.0 5,929
Rural 7.1 43.5 29.7 12.5 54.4 8,696
Region
Coast 5.1 39.3 23.5 6.7 49.3 1,421
North Eastern 26.7 60.2 51.9 31.2 66.1 299
Eastern 4.2 37.7 31.7 14.2 51.5 2,066
Central 2.8 21.3 11.2 7.1 29.4 1,905
Rift Valley 9.1 32.2 22.5 12.4 41.7 3,714
Western 6.4 59.4 33.2 13.3 68.6 1,571
Nyanza 6.3 53.4 24.7 8.4 60.6 1,908
Nairobi 1.1 16.9 7.1 5.6 25.4 1,742
Education
No education 12.6 57.1 43.5 18.1 66.4 1,015
Primary incomplete 7.7 48.4 28.7 13.1 58.7 3,793
Primary complete 5.3 38.1 22.3 9.6 48.0 3,543
Secondary+ 4.2 25.5 15.8 8.3 34.2 6,274
Wealth quintile
Lowest 11.7 57.7 43.4 18.8 68.1 2,236
Second 7.4 50.6 31.0 12.6 61.1 2,590
Middle 5.8 42.0 25.0 10.9 52.8 2,859
Fourth 4.5 29.7 17.1 8.1 39.6 3,113
Highest 2.9 16.8 7.7 6.1 23.6 3,827
Total 6.0 36.7 22.7 10.6 46.1 14,625
Note: Total includes 11 women for whom information on employment in the last 12 months is missing.
Younger women, women with fewer children, and women who have never been married are less
likely than other women to report having any of these four problems in accessing health care. Women who
are employed but not for cash are more likely to report at least one problem than women who are either
unemployed or employed for cash. Although getting permission is not widely observed as a problem at the
national level, 27 percent of women in the North Eastern region cited this problem as a barrier to accessing
care.
9.7 FISTULA
The 2014 KDHS included a series of questions on fistula, a condition that may develop during
prolonged or obstructed labour when the blood supply to the tissues of the vagina, bladder, and/or rectum
is cut off, resulting in the formation of an opening through which urine and/or faeces pass uncontrollably.
Women who develop fistulas are often socially rejected and face a number of related health concerns. All
Maternal Health • 137
women were asked whether they had heard of fistula and, if they had, whether they themselves had
experienced fistula-like symptoms. Results are presented in Table 9.12 by background characteristics.
Slightly over half (54 percent) of women have heard of fistula. Urban women are more likely (63
percent) than rural women (48 percent) to have heard of fistula. Awareness of fistula is higher among
women in Nairobi (70 percent) and awareness increases with increasing wealth and education. One percent
of women have experienced fistula-like symptoms. Because of the low percentage of women who have
experienced symptoms indicative of fistula, any variations by background characteristics should be
considered with caution.
Table 9.12 Fistula
Percentage of women who have ever heard of fistula and percentage who
have experienced fistula, according to background characteristics, Kenya
2014
Percentage who Percentage who
Background have ever heard have ever had Number of
characteristic of fistula fistula women
Age
15-19 33.2 0.3 2,717
20-24 52.9 1.1 2,691
25-29 59.7 1.1 2,932
30-34 61.3 1.6 2,162
35-39 59.3 1.1 1,780
40-44 60.5 1.1 1,292
45-49 63.4 1.0 1,052
Residence
Urban 62.7 1.3 5,929
Rural 48.1 0.8 8,696
Region
Coast 52.0 0.7 1,421
North Eastern 23.2 1.8 299
Eastern 46.9 0.8 2,066
Central 55.8 1.8 1,905
Rift Valley 50.3 0.7 3,714
Western 49.7 0.5 1,571
Nyanza 62.9 1.3 1,908
Nairobi 69.6 1.6 1,742
Education
No education 30.0 1.1 1,015
Primary incomplete 44.6 0.6 3,793
Primary complete 56.5 1.4 3,543
Secondary+ 62.3 1.1 6,274
Wealth quintile
Lowest 36.6 0.7 2,236
Second 45.3 0.9 2,590
Middle 54.1 0.7 2,859
Fourth 57.1 1.8 3,113
Highest 67.6 0.9 3,827
Total 54.0 1.0 14,625
138 • Maternal Health
CHILD HEALTH 10
Andolo Miheso, Lucy Gathigi Maina, Annah W. Wamae
Key Findings
• The percentage of all births with a reported birth weight has increased in
the last five years from 47 percent to 66 percent.
• Seventy-nine percent of children age 12-23 months have received all
basic vaccines, slightly higher than the 77 percent observed in the 2008-
09 KDHS.
• Nine percent of children under age 5 showed symptoms of acute
respiratory infection in the two weeks before the survey; 66 percent of
these children were taken to a health facility or provider for advice or
treatment.
• Twenty-four percent of children under age 5 had a fever in the two weeks
before the survey; 63 percent of these children were taken to a health
facility or provider for advice or treatment.
• Fifteen percent of children under age 5 had diarrhoea in the two weeks
before the survey.
• The proportion of children with diarrhoea taken to a health provider for
advice or treatment increased from 49 percent in the 2008-09 KDHS to 58
percent in the 2014 KDHS.
• The proportion of children with diarrhoea given fluid from ORS packets
has increased over the past five years, from 39 percent in 2008-09 to 54
percent in 2014.
• The percentage of women who know that ORS can be used to treat
diarrhoea in children has increased from 78 percent in 2008-09 to 93
percent in 2014.
• The percentage of children whose stools are disposed of safely has
increased from 78 percent in 2008-09 to 83 percent in 2014.
T
he Division of Family Health in the Ministry of Health is supporting several child survival
interventions, including various operational initiatives, to improve the health of children in Kenya.
These include the Expanded Programme on Immunisation, the Integrated Management of
Childhood Illnesses Initiative, the Community-Based Newborn Care Programme, the Infant and Young
Child Feeding Programme, a micronutrient supplementation programme, and a vitamin A and deworming
campaign. Biannual child-mother health and nutrition weeks, called ‘Malezi Bora’ in Kiswahili, also are
held in May and November to deliver a specific package of health interventions targeting mothers and
children under age 5. The ultimate goal of the ‘Malezi Bora’ strategy is to improve delivery of routine
health and nutrition services targeting children, expectant women, and lactating mothers.
This chapter reviews information from the 2014 KDHS that is useful in managing the Ministry of
Health’s child health and survival programmes. Specifically, the chapter presents findings on infant birth
weight and size at birth, childhood vaccination coverage, and treatment practices and contact with health
services when a child is ill with respiratory infection, diarrhoea, and fever.
Child Health • 139
10.1 CHILD’S WEIGHT AND SIZE AT BIRTH
A child’s birth weight or size at birth is an important indicator of the child’s vulnerability to the
risk of childhood illnesses and chances of survival. For births in the five years preceding the 2014 KDHS,
birth weight was recorded in the questionnaire if available from the Mother and Child Health Booklet,
some other written record, or the mother’s recall. Since birth weight may not be known for many babies,
the mother’s estimate of the baby’s size at birth was also obtained. Such estimates, even though subjective,
can be a useful proxy for the weight of the child at birth. Children reported to be “very small” or “smaller
than average” at birth or children whose birth weight was less than 2.5 kilograms are considered to have a
higher than average risk of early childhood death.
Table 10.1 presents information on size and weight at birth for children born in the five years prior
to the 2014 KDHS according to background characteristics. Based on the mother’s assessment, 3 percent
of children were very small at birth, 12 percent were smaller than average, and 84 percent were average or
larger in size. Children were most likely to be reported by their mothers as very small or smaller than
average in the North Eastern region (25 percent) and least likely in Nyanza (8 percent). Twenty percent of
children born to mothers with no education were considered by the mother to be very small or smaller than
average, as compared with 14 percent of children born to mothers who had a secondary or higher
education.
Table 10.1 Child’s size and weight at birth
Percent distribution of live births in the five years preceding the survey by mother’s estimate of baby’s size at birth, percentage of live births in the
five years preceding the survey that have a reported birth weight, and among live births in the five years preceding the survey with a reported birth
weight, percentage less than 2.5 kg, according to background characteristics, Kenya 2014
Percentage Births with a reported
Percent distribution of all live births by size of child at birth of all births birth weight1
that have a Percentage
Background Smaller than Average or Don’t know/ reported Number of less than Number of
characteristic Very small average larger missing Total birth weight1 births 2.5 kg births
Mother’s age at birth
<20 3.5 12.4 82.0 2.0 100.0 66.3 1,368 9.0 906
20-34 3.1 11.3 84.1 1.4 100.0 66.7 6,892 7.0 4,596
35-49 4.1 12.6 81.8 1.6 100.0 58.6 1,098 9.4 643
Birth order
1 3.6 13.9 81.4 1.1 100.0 80.5 2,417 9.4 1,947
2-3 2.3 10.6 85.8 1.3 100.0 69.4 3,658 6.5 2,540
4-5 3.8 10.1 84.4 1.8 100.0 56.6 1,818 6.1 1,029
6+ 4.7 12.3 80.4 2.6 100.0 43.0 1,464 8.9 630
Mother’s smoking
status
Smokes cigarettes/
tobacco (12.2) (15.6) (72.1) (0.0) 100.0 (74.7) 30 * 22
Does not smoke 3.3 11.6 83.6 1.5 100.0 65.6 9,328 7.6 6,123
Residence
Urban 3.6 12.2 83.1 1.2 100.0 85.7 3,388 8.6 2,904
Rural 3.1 11.3 83.8 1.7 100.0 54.3 5,970 6.7 3,242
Region
Coast 5.3 13.2 80.5 1.0 100.0 73.2 952 12.7 697
North Eastern 11.3 13.5 65.6 9.6 100.0 27.4 307 7.9 84
Eastern 2.6 15.4 81.2 0.8 100.0 66.7 1,146 8.4 764
Central 3.1 17.5 79.1 0.3 100.0 95.6 837 9.2 800
Rift Valley 3.1 11.4 84.7 0.8 100.0 53.9 2,689 6.6 1,448
Western 3.4 8.5 87.5 0.7 100.0 51.1 1,127 4.8 575
Nyanza 1.8 6.3 88.5 3.4 100.0 69.2 1,319 3.5 913
Nairobi 2.3 11.4 84.4 1.8 100.0 87.9 982 8.9 864
Mother’s education
No education 5.5 14.4 76.3 3.8 100.0 30.4 1,111 9.4 338
Primary incomplete 3.5 10.2 84.2 2.1 100.0 51.7 2,781 8.9 1,436
Primary complete 3.4 12.1 83.5 1.1 100.0 72.1 2,472 7.8 1,781
Secondary+ 2.3 11.5 85.7 0.5 100.0 86.5 2,995 6.5 2,590
Wealth quintile
Lowest 3.5 11.7 81.9 2.8 100.0 35.9 2,192 8.5 787
Second 3.3 10.2 85.3 1.3 100.0 56.4 1,890 5.7 1,066
Middle 3.0 11.7 83.0 2.3 100.0 64.5 1,725 8.0 1,113
Fourth 3.1 13.6 82.7 0.6 100.0 82.8 1,702 8.8 1,410
Highest 3.5 11.2 85.0 0.3 100.0 95.7 1,849 7.0 1,770
Total 3.3 11.6 83.5 1.5 100.0 65.7 9,358 7.6 6,146
Note: Total includes one birth for whom information on mother’s smoking status is missing. Figures in parentheses are based on 25-49 unweighted
cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
1
Based on either a written record or the mother’s recall
140 • Child Health
Table 10.1 shows that birth weights were reported for 66 percent of children born in the five years
before the survey. This represents an improvement from the 47 percent of children for whom birth weights
were obtained in the 2008-09 KDHS. The increased availability of birth weight data is not surprising since
the proportion of births occurring in health facilities increased between the two surveys (see Chapter 9),
and children are more likely to be weighed at birth when delivered in an institutional setting. The variation
in the proportion of children for whom a birth weight was obtained in the KDHS in fact closely parallels
differences in the proportions of births delivered in health facilities.
Among children born in the five years before the survey with a reported birth weight, 8 percent
were of low birth weight (less than 2.5 kg). The percentage of low birth weight children varied from a high
of 13 percent in the Coast region to a low of 4 percent in the Nyanza region. Differences by other
background characteristics are generally small.
10.2 VACCINATION COVERAGE
Universal immunisation of children against six common vaccine-preventable diseases, namely
tuberculosis, diphtheria, whooping cough (pertussis), tetanus, polio, and measles, is crucial to reducing
infant and child mortality. Other childhood vaccines given in Kenya protect against hepatitis B and
Haemophilus influenzae type b (Hib). The pneumococcal vaccine introduced into Kenya’s routine
immunisation programme in February 2012 protects against Streptococcus pneumoniae bacteria, which
cause severe pneumonia, meningitis, and other illnesses. The 2014 KDHS collected information on the
coverage of all of these vaccines among children born in the five years preceding the survey. The information
obtained in the survey on differences in vaccination coverage among subgroups of children is useful for
programme planning and targeting resources towards areas most in need.
According to the guidelines developed by the World Health Organization, children are considered
to have received all basic vaccinations when they have received a vaccination against tuberculosis (also
known as BCG), three doses each of the DPT-HepB-Hib (also called pentavalent) and polio vaccines, and
a vaccination against measles. The BCG vaccine is usually given at birth or at first clinical contact, while
the DPT-HepB-Hib and polio vaccines are given at approximately age 6, 10, and 14 weeks. Measles
vaccinations should be given at or soon after age 9 months. The Kenyan immunisation programme
considers a child to be fully vaccinated if the child has received all basic vaccinations and three doses of
the pneumococcal vaccine (also given at age 6, 10, and 14 weeks).
Information on vaccination coverage was obtained in two ways in the 2014 KDHS: from written
vaccination records, including the Mother and Child Health Booklet and other health cards, and from
mothers’ verbal reports. The Ministry of Health introduced the Mother and Child Health Booklet in 2010
to replace the various cards used to record services offered in maternal and child health clinics, maternity
wards, and family planning clinics. In the KDHS, for each child born in the five years before the survey,
mothers were asked to show the interviewer the Mother and Child Health Booklet or health card used for
recording the child’s immunisations. If the Mother and Child Health Booklet or the card was available, the
interviewer copied the dates of each vaccination received. If a vaccination was not recorded in the Mother
and Child Health Booklet or on the card as being given, the mother was asked to recall whether that
particular vaccination had been given. If the mother was not able to present the Mother and Child Health
Booklet or card for a child, she was asked to recall whether the child had received BCG, polio, DPT-
HepB-Hib, measles, and pneumococcal vaccine. If she indicated that the child had received the polio,
DPT-HepB-Hib, or pneumococcal vaccine, she was asked the number of doses that the child received.
Table 10.2 presents data on vaccination coverage among children age 12-23 months by source of
information (i.e., vaccination record or mother’s report). Children age 12-23 months are the youngest
cohort who have reached the age by which a child should be fully immunised.
Child Health • 141
Table 10.2 Vaccinations by source of information
Percentage of children age 12-23 months who received specific vaccines at any time before the survey, by source of information (vaccination card or mother’s report),
and percentage vaccinated by 12 months of age, Kenya 2014
All
basic Fully No Number
DPT-HepB-Hib1 Polio2 Pneumococcal
vacci- vacci- vacci- of
Source of information BCG 1 2 3 0 1 2 3 Measles nations3 1 2 3 4
nated nations children
Vaccinated at any
time before survey
Vaccination card 73.2 74.2 73.5 70.9 57.4 74.5 73.7 71.3 65.4 63.0 71.2 69.8 67.1 59.6 0.0 2,820
Mother’s report 23.5 23.3 22.4 18.9 8.8 23.6 22.3 18.8 21.7 16.3 22.5 21.1 18.0 15.2 1.6 957
Either source 96.7 97.5 95.8 89.9 66.2 98.0 96.1 90.0 87.1 79.4 93.7 90.8 85.1 74.9 1.6 3,777
Vaccinated by 12
months of age5 95.9 97.0 94.9 88.3 66.1 97.5 94.9 88.1 78.9 71.3 93.0 90.0 83.2 67.2 2.0 3,777
1
DPT-HepB-Hib is also called pentavalent.
2
Polio 0 is the polio vaccination given at birth. The data on polio vaccination were adjusted for a likely misinterpretation of polio 0 and polio 1; for children whose
mothers reported that they received three doses of DPT-HepB-Hib and polio 0, polio 1, and polio 2, it was assumed that polio 0 was in fact polio 1, polio 1 was polio 2
and polio 2 was polio 3.
3
BCG, measles, and three doses each of DPT-HepB-Hib and polio vaccine (excluding polio vaccine given at birth)
4
BCG, measles, and three doses each of DPT-HepB-Hib, polio (excluding polio vaccine given at birth), and pneumococcal vaccine
5
For children whose information is based on the mother’s report, the proportion of vaccinations given during the first year of life is assumed to be the same as for
children with a written record of vaccination.
Table 10.2 and Figure 10.1 show that 79 percent of children age 12-23 months received all basic
vaccinations, and 75 percent are fully vaccinated. Only 2 percent of children had not received any
vaccinations. Ninety-seven percent of children received the BCG vaccination, 98 percent the first dose of
DPT-HepB-Hib, 98 percent the first dose of polio, and 94 percent the first dose of the pneumococcal
vaccine. Eighty-seven percent of children have received a measles vaccination. Coverage rates decline for
subsequent doses, with 90 percent of children receiving the recommended three doses of DPT-HepB-Hib,
90 percent the three doses of polio, and 85 percent the three doses of the pneumococcal vaccine. Dropout
rates, which represent the proportion of children who receive the first dose of a vaccine but do not go on to
get the third dose, were around 8 percent for both polio and DPT-HepB-Hib and 9 percent for the
pneumococcal vaccine.
Figure 10.1 Trends in vaccination coverage among children age 12-23 months*
All basic 79
vaccinations 77
BCG 97
96
DPT1-HepB-Hib 98
96
DPT2-HepB-Hib 96
93
DPT3-HepB-Hib 90
86 2014 KDHS
Polio 0 66 2008-09 KDHS
59
Polio 1 98
96
Polio 2 96
94
Polio 3 90
88
Measles 87
85
Percentage of children with specific vaccines
* Refers to vaccinations received at any time before the survey
142 • Child Health
Table 10.3 presents vaccination coverage (according to card information and mothers’ reports)
among children age 12-23 months by background characteristics. Vaccination records were available for
75 percent of these children. For the remaining children, information on vaccinations is based solely on the
mother’s report. There is no difference in the coverage rates between male and female children.
Immunisation coverage decreases as birth order increases, with 80 percent of first-born children and only
59 percent of children of birth order six and above being fully immunised. Immunisation coverage
increases with increasing mother’s education; more than three-quarters of children whose mothers have
completed primary or higher education are fully immunised, as compared with 55 percent of children
whose mothers have no education. Only 62 percent of children in the lowest wealth quintile are fully
immunised, compared with around 8 in 10 children in the other quintiles.
Coverage is highest in the Central (90 percent) region and lowest in the North Eastern region,
where only 51 percent of children are fully immunised. Eleven percent of children in North Eastern have
not received any of the recommended immunisations, as compared with 2 percent or less in the other
regions.
Table 10.3 Vaccinations by background characteristics
Percentage of children age 12-23 months who received specific vaccines at any time before the survey (according to a vaccination card or the mother’s report), and percentage
with a vaccination card, by background characteristics, Kenya 2014
Percent-
age
with a
All vacci-
basic Fully No nation Number
DPT-HepB-Hib1 Polio2 Pneumococcal
Background vacci- vacci- vacci- card of
characteristic BCG 1 2 3 0 1 2 3 Measles nations3 1 2 3 nated4 nations seen children
Sex
Male 97.1 98.3 96.3 89.6 67.2 98.5 96.2 89.9 87.9 79.3 94.3 90.8 84.5 74.1 1.2 75.3 1,966
Female 96.2 96.7 95.3 90.2 65.1 97.5 95.9 90.1 86.2 79.5 93.0 90.9 85.9 75.7 2.2 73.9 1,811
Birth order
1 97.4 97.8 95.8 93.1 73.4 98.2 96.6 93.7 91.9 84.8 95.2 91.7 88.0 80.2 1.6 71.2 1,015
2-3 97.7 98.5 97.3 91.6 68.8 98.7 97.2 92.1 90.1 83.0 94.3 92.5 87.1 77.9 1.0 74.7 1,511
4-5 96.3 97.4 95.8 90.3 62.5 97.8 95.8 89.7 84.9 77.2 93.7 90.8 85.2 73.2 1.7 78.7 709
6+ 92.9 94.6 91.7 78.5 50.6 96.0 92.3 77.8 72.4 62.2 89.1 84.7 74.3 58.5 3.6 75.8 542
Residence
Urban 97.7 98.0 95.9 91.2 74.1 98.4 96.6 91.4 91.7 83.0 94.1 90.3 86.3 77.8 1.3 67.2 1,330
Rural 96.1 97.3 95.8 89.2 61.9 97.8 95.7 89.3 84.6 77.4 93.5 91.1 84.5 73.3 1.8 78.7 2,447
Region
Coast 97.1 97.2 96.6 91.9 75.8 98.2 96.6 92.0 86.6 80.5 95.7 93.9 89.8 77.9 1.3 78.6 391
North Eastern 83.4 87.5 82.9 77.4 41.4 88.7 82.2 74.6 69.8 55.6 84.8 80.6 72.7 51.1 10.6 51.1 121
Eastern 98.7 99.0 98.2 93.6 71.8 99.2 97.5 92.1 92.1 85.9 94.6 93.7 89.6 81.5 0.5 85.3 431
Central 99.6 99.4 98.4 95.5 80.1 99.8 98.9 96.0 97.2 93.3 97.6 95.2 92.3 90.3 0.2 76.1 363
Rift Valley 96.7 97.4 95.1 87.9 59.9 97.6 94.7 86.8 83.1 74.0 92.3 89.7 81.7 68.7 1.8 77.3 1,083
Western 95.9 96.8 95.4 90.2 52.1 97.8 96.2 91.2 85.7 81.4 94.1 92.3 87.3 77.8 2.2 74.6 419
Nyanza 95.6 98.5 98.0 89.7 70.6 98.7 97.5 90.9 85.3 77.2 93.6 90.7 82.8 72.8 1.0 72.4 552
Nairobi 97.6 97.3 93.6 88.0 71.4 98.3 96.9 91.3 92.5 81.2 93.6 85.9 83.3 74.4 1.7 61.7 417
Mother’s education
No education 89.9 91.4 87.3 77.3 46.0 92.9 87.5 74.4 70.8 56.9 89.4 85.1 74.5 54.6 5.4 71.0 431
Primary incomplete 95.8 97.8 96.1 88.0 59.4 98.5 96.6 89.2 82.5 74.8 92.6 90.1 82.8 69.8 1.4 77.5 1,072
Primary complete 97.9 98.7 98.1 93.0 68.2 98.7 97.2 92.6 90.2 84.0 93.5 91.6 87.0 78.8 1.2 76.6 1,021
Secondary+ 98.7 98.4 96.7 93.3 77.4 98.9 97.6 94.0 94.1 87.3 96.3 92.9 89.3 83.0 0.9 71.9 1,253
Wealth quintile
Lowest 92.8 94.8 91.7 83.3 51.7 95.8 91.9 83.5 76.4 66.1 90.4 86.6 78.9 61.5 3.4 77.7 940
Second 96.7 98.4 97.7 90.1 62.6 98.7 98.0 90.8 86.9 80.0 93.9 92.2 84.2 75.3 1.1 79.4 765
Middle 98.1 98.3 97.2 92.0 67.8 98.5 96.6 90.4 88.3 81.7 94.8 92.5 87.4 78.3 1.3 74.5 667
Fourth 98.6 99.2 98.7 93.7 75.5 99.4 98.2 93.2 94.5 87.3 94.7 92.3 88.3 81.5 0.3 76.9 666
Highest 98.4 97.8 95.2 92.7 78.6 98.5 96.9 94.4 93.1 86.4 95.9 92.0 89.3 82.4 1.5 64.0 739
Total 96.7 97.5 95.8 89.9 66.2 98.0 96.1 90.0 87.1 79.4 93.7 90.8 85.1 74.9 1.6 74.7 3,777
1
DPT-HepB-Hib is also called pentavalent.
2
Polio 0 is the polio vaccination given at birth. The data on polio vaccination were adjusted for a likely misinterpretation of polio 0 and polio 1; for children whose mothers
reported that they received three doses of DPT-HepB-Hib and polio 0, polio 1, and polio 2, it was assumed that polio 0 was in fact polio 1, polio 1 was polio 2 and polio 2 was
polio 3.
3
BCG, measles, and three doses each of DPT-HepB-Hib and polio vaccine (excluding polio vaccine given at birth)
4
BCG, measles, and three doses each of DPT-HepB-Hib, polio (excluding polio vaccine given at birth), and pneumococcal vaccine
Child Health • 143
Differentials in immunisation coverage across counties are presented in Table 10.3C. The
proportion of children who are fully immunised is 90 percent or higher in Machakos, Nyamira, Vihiga,
Tharaka-Nithi, Nandi, and Kiambu. Less than half of children are fully immunised in Wajir and Mandera,
and only 31 percent are fully immunised in West Pokot.
Table 10.3C Vaccinations by county
Percentage of children age 12-23 months who received specific vaccines at any time before the survey (according to a vaccination card or the mother’s report), and
percentage with a vaccination card, by county, Kenya 2014
Percent-
age
with a
All vacci-
basic Fully No nation Number
DPT-HepB-Hib1 Polio2 vacci- Pneumococcal vacci- vacci- card of
County BCG 1 2 3 0 1 2 3 Measles nations3 1 2 3 nated4 nations seen children
Coast 97.1 97.2 96.6 91.9 75.8 98.2 96.6 92.0 86.6 80.5 95.7 93.9 89.8 77.9 1.3 78.6 391
Mombasa 100.0 100.0 98.3 95.6 78.9 100.0 98.3 93.8 89.1 84.6 96.7 93.7 92.3 78.6 0.0 66.4 93
Kwale 98.6 96.7 96.7 95.1 73.5 99.4 95.9 94.6 90.7 85.9 96.1 96.1 93.6 84.5 0.0 92.3 89
Kilifi 94.3 95.3 95.3 87.5 81.9 96.2 95.8 88.8 83.7 74.7 95.3 92.9 87.4 74.1 3.0 80.9 144
Tana River 97.1 97.1 95.0 89.9 41.8 97.1 93.8 89.4 77.6 73.3 94.3 92.8 85.2 70.2 2.5 72.1 32
Lamu 94.2 99.3 97.0 84.7 62.1 100.0 97.7 94.2 83.2 69.9 91.9 90.4 79.3 67.4 0.0 72.3 9
Taita Taveta 98.4 98.4 98.4 97.5 87.1 100.0 100.0 97.5 93.0 93.0 95.6 95.6 90.4 88.8 0.0 71.5 23
North Eastern 83.4 87.5 82.9 77.4 41.4 88.7 82.2 74.6 69.8 55.6 84.8 80.6 72.7 51.1 10.6 51.1 121
Garissa 80.3 94.4 93.9 91.6 49.8 94.4 88.2 84.1 81.2 62.1 94.4 91.3 84.7 57.9 5.6 61.2 43
Wajir 91.0 90.8 86.2 79.1 39.2 91.7 87.4 77.0 64.5 55.8 83.8 82.4 74.1 49.5 6.6 58.6 54
Mandera 71.8 68.3 56.5 48.8 31.3 71.8 60.1 52.3 61.7 43.8 70.3 57.9 48.7 42.7 28.2 16.5 24
Eastern 98.7 99.0 98.2 93.6 71.8 99.2 97.5 92.1 92.1 85.9 94.6 93.7 89.6 81.5 0.5 85.3 431
Marsabit 92.6 96.4 93.4 85.7 41.2 99.0 95.6 88.3 76.8 70.8 94.7 92.2 84.6 67.5 1.0 80.5 16
Isiolo 96.4 98.6 96.5 94.4 72.0 98.6 95.9 93.7 86.5 83.3 94.9 93.8 92.0 82.3 1.4 89.0 20
Meru 99.2 100.0 100.0 93.6 69.0 100.0 100.0 92.4 91.3 88.4 97.2 97.2 89.1 83.9 0.0 89.9 95
Tharaka-Nithi 100.0 98.3 98.3 96.8 95.3 100.0 100.0 98.3 98.5 95.3 100.0 100.0 100.0 95.3 0.0 92.2 33
Embu 100.0 100.0 100.0 99.2 89.9 100.0 100.0 92.7 92.8 85.5 100.0 100.0 99.2 85.5 0.0 83.4 42
Kitui 96.1 96.1 93.1 81.5 42.7 97.7 93.1 81.9 84.9 69.3 81.0 78.1 69.2 56.8 2.3 85.3 76
Machakos 100.0 100.0 100.0 97.8 86.0 98.4 98.4 96.2 97.2 93.4 98.0 97.2 95.9 90.0 0.0 72.7 86
Makueni 100.0 100.0 100.0 99.1 75.6 100.0 96.2 95.3 96.8 92.0 96.2 95.3 95.3 89.7 0.0 93.1 63
Central 99.6 99.4 98.4 95.5 80.1 99.8 98.9 96.0 97.2 93.3 97.6 95.2 92.3 90.3 0.2 76.1 363
Nyandarua 100.0 100.0 98.1 90.8 84.9 100.0 98.1 94.6 95.1 86.2 98.0 89.7 84.9 81.4 0.0 81.3 53
Nyeri 98.7 97.2 97.2 94.9 84.4 100.0 100.0 95.1 92.7 87.8 94.4 93.1 89.1 84.3 0.0 82.2 59
Kirinyaga (100.0) (100.0) (100.0) (100.0) (60.3) (100.0) (100.0) (100.0) (100.0) (100.0) (92.3) (92.3) (92.3) (92.3) (0.0) (60.3) 31
Murang’a 99.0 99.0 95.7 89.9 78.0 99.0 95.7 89.9 97.3 88.2 97.3 92.0 89.9 86.4 1.0 74.3 70
Kiambu 100.0 100.0 100.0 99.0 81.8 100.0 100.0 99.0 99.0 99.0 100.0 100.0 97.2 97.2 0.0 76.1 150
Rift Valley 96.7 97.4 95.1 87.9 59.9 97.6 94.7 86.8 83.1 74.0 92.3 89.7 81.7 68.7 1.8 77.3 1,083
Turkana 94.5 94.4 91.5 86.2 63.7 94.4 90.8 83.9 71.9 62.5 93.8 89.8 83.2 61.8 3.7 87.1 61
West Pokot 78.9 86.4 80.6 68.3 35.3 87.3 78.6 60.5 58.2 35.9 78.7 73.1 58.7 31.2 11.2 60.8 66
Samburu 96.8 93.2 89.7 86.9 48.4 96.6 92.6 87.9 71.7 66.6 94.5 89.6 84.8 64.1 1.4 79.4 23
Trans-Nzoia 100.0 100.0 98.1 77.8 56.8 100.0 98.3 89.8 84.8 71.5 93.3 91.3 73.0 63.9 0.0 64.7 105
Uasin Gishu 95.9 97.3 95.9 91.5 54.3 95.9 94.6 90.2 91.3 85.6 83.4 82.3 75.8 72.3 2.7 73.7 96
Elgeyo Marakwet 100.0 100.0 97.9 97.9 78.4 99.4 97.9 95.3 86.6 85.9 99.4 97.9 97.0 85.2 0.0 91.2 32
Nandi 99.4 100.0 100.0 99.4 69.1 100.0 100.0 99.4 97.5 96.3 99.4 99.4 99.4 96.3 0.0 92.1 82
Baringo 100.0 100.0 98.3 94.5 60.5 100.0 99.0 94.1 82.5 78.3 92.6 90.9 85.1 69.4 0.0 81.8 49
Laikipia 98.7 98.7 98.7 89.7 72.0 98.7 96.3 91.1 92.3 83.2 96.6 96.6 90.9 79.3 1.3 79.9 38
Nakuru 97.2 97.9 96.1 90.9 74.6 97.9 95.1 90.4 86.4 79.2 94.8 93.0 86.5 74.7 2.1 81.8 167
Narok 95.0 96.3 91.2 83.0 37.6 98.4 95.0 82.1 74.5 66.4 90.8 86.3 74.8 58.5 1.6 79.1 118
Kajiado 97.7 97.0 92.4 79.1 51.9 96.2 87.6 68.5 80.9 58.5 90.3 83.3 71.9 56.4 1.1 57.2 95
Kericho 100.0 100.0 100.0 95.7 81.0 100.0 100.0 93.0 82.9 76.3 94.2 92.9 88.3 71.8 0.0 84.5 67
Bomet 100.0 100.0 99.2 98.0 62.4 100.0 100.0 95.6 92.1 87.0 96.1 95.3 92.8 81.3 0.0 84.7 84
Western 95.9 96.8 95.4 90.2 52.1 97.8 96.2 91.2 85.7 81.4 94.1 92.3 87.3 77.8 2.2 74.6 419
Kakamega 94.9 98.9 94.9 89.5 43.4 98.9 94.9 89.5 80.1 77.3 97.6 94.6 86.2 73.1 1.1 77.0 125
Vihiga 98.3 98.3 98.3 97.0 75.0 98.3 98.3 94.4 98.3 94.4 98.3 98.3 97.0 94.4 1.7 72.9 48
Bungoma 95.3 95.0 95.0 88.9 51.3 96.2 95.7 90.5 84.3 80.8 89.1 88.2 83.7 75.9 3.8 71.7 187
Busia 98.0 96.9 95.3 90.4 54.1 100.0 98.4 94.1 92.2 81.6 98.9 95.5 92.7 80.4 0.0 80.0 59
Nyanza 95.6 98.5 98.0 89.7 70.6 98.7 97.5 90.9 85.3 77.2 93.6 90.7 82.8 72.8 1.0 72.4 552
Siaya 98.4 100.0 100.0 93.9 78.6 100.0 100.0 91.9 84.8 79.3 99.5 99.5 91.3 78.0 0.0 78.1 84
Kisumu 97.2 97.6 97.6 87.0 76.3 97.6 96.4 87.7 89.5 78.9 96.8 95.1 84.7 78.9 2.4 51.5 96
Homa Bay 94.5 97.9 97.2 82.3 64.8 97.9 95.3 84.7 80.3 69.0 90.9 84.6 74.7 64.4 2.1 73.9 131
Migori 87.6 99.6 97.7 89.9 39.9 98.9 96.9 92.4 82.0 70.3 87.4 82.1 71.2 57.2 0.4 69.0 102
Kisii 100.0 97.8 97.8 95.3 90.5 99.1 99.1 97.2 86.5 84.6 94.4 94.4 91.9 82.2 0.0 87.2 99
Nyamira 99.0 99.0 99.0 97.6 88.0 100.0 100.0 97.0 98.0 95.0 96.1 94.6 94.6 92.1 0.0 78.0 39
Nairobi 97.6 97.3 93.6 88.0 71.4 98.3 96.9 91.3 92.5 81.2 93.6 85.9 83.3 74.4 1.7 61.7 417
Total 96.7 97.5 95.8 89.9 66.2 98.0 96.1 90.0 87.1 79.4 93.7 90.8 85.1 74.9 1.6 74.7 3,777
Note: Figures in parentheses are based on 25-49 unweighted cases.
1
DPT-HepB-Hib is also called pentavalent.
2
Polio 0 is the polio vaccination given at birth. The data on polio vaccination were adjusted for a likely misinterpretation of polio 0 and polio 1; for children whose mothers
reported that they received three doses of DPT-HepB-Hib and polio 0, polio 1, and polio 2, it was assumed that polio 0 was in fact polio 1, polio 1 was polio 2 and polio 2 was
polio 3.
3
BCG, measles, and three doses each of DPT-HepB-Hib and polio vaccine (excluding polio vaccine given at birth)
4
BCG, measles, and three doses each of DPT-HepB-Hib, polio (excluding polio vaccine given at birth), and pneumococcal vaccine
144 • Child Health
Figure 10.2 compares the results of the 2014 KDHS with immunisation data from the KDHS
surveys carried out between 1993 and 2008-09 in order to explore the trends in childhood vaccination
coverage over the past 20 years in Kenya. Because the pneumococcal vaccine was introduced only in 2012,
Figure 10.2 focuses on changes in the proportion of children receiving all basic immunisations, that is, a
BCG vaccination, three doses each of the DPT-HepB-Hib and polio vaccines, and a vaccination against
measles. Some caution should be exercised in interpreting the trends in Figure 10.2 since some areas in
northern Kenya were not included in the 1993 and 1998 surveys, and there are minor differences in the
method used for calculating the polio immunisation rate.
Figure 10.2 shows that the proportion of children age 12-23 months who received all basic
immunisations declined from 79 percent in 1993 to 65 percent in 1998 and then dropped again to a low of
57 percent in 2003. By 2008-09, the rate had rebounded to 77 percent. Between 2008-09 and 2014,
however, the rate increased slightly to 79 percent.
Figure 10.2 Trends in childhood vaccination coverage
Percentage of children age 12-23
months who have received all basic
vaccinations
79 77 79
65
57
KDHS 1993 KDHS 1998 KDHS 2003 KDHS 2008-09 KDHS 2014
* Data from 2003 and later are nationally representative, while data before
2003 exclude North Eastern region and several northern districts in the
Eastern and Rift Valley regions.
10.3 ACUTE RESPIRATORY INFECTION
Acute respiratory infection (ARI) is a leading cause of childhood morbidity and mortality
throughout the world. Early diagnosis and treatment with antibiotics can reduce the number of deaths
caused by ARI. In the 2014 KDHS, the prevalence of ARI symptoms was estimated by asking mothers
whether, in the two weeks preceding the survey, their children under age 5 had been ill with a cough
accompanied by short, rapid breathing and difficulty breathing as a result of a problem in the chest. These
symptoms are consistent with pneumonia. It should be noted that the data collected on ARI symptoms are
subjective because they are based on a mother’s perception of the illness without validation by medical
personnel.
Table 10.4 shows that 9 percent of children under age 5 were ill with symptoms of an acute
respiratory infection in the two weeks before the survey. Differences in the prevalence of ARI symptoms
by background characteristics are generally minor, with the largest differentials observed by age and
region. Considering the age pattern, the proportion of children reported to have ARI symptoms was lowest
among those less than age 6 months (4 percent) and peaked among those age 6-11 months (11 percent).
With regard to regional differences, the highest prevalence of ARI symptoms was reported among children
in the Western region (13 percent) and the lowest among children in the North Eastern region (4 percent).
Child Health • 145
Table 10.4 Prevalence and treatment of symptoms of ARI
Among children under age five, the percentage who had symptoms of acute respiratory infection (ARI) in the two weeks
preceding the survey and among children with symptoms of ARI, the percentage for whom advice or treatment was sought
from a health facility or provider and the percentage who received antibiotics as treatment, according to background
characteristics, Kenya 2014
Among children under age five: Among children under age five with symptoms of ARI:
Percentage for
whom advice or
treatment was
sought from a
Background Percentage with Number of health facility or Percentage who Number of
characteristic symptoms of ARI1 children provider2 received antibiotics children
Age in months
<6 4.4 1,694 77.6 58.0 74
6-11 11.4 1,909 73.3 53.3 218
12-23 10.0 3,777 67.4 56.6 379
24-35 8.7 3,760 62.4 53.1 326
36-47 7.9 3,889 68.3 52.6 308
48-59 7.5 3,672 55.3 47.1 276
Sex
Male 8.7 9,477 67.7 54.1 820
Female 8.2 9,225 63.7 51.9 761
Mother’s smoking status
Smokes cigarettes/tobacco (11.4) 27 * * 3
Does not smoke 8.5 18,675 65.7 53.1 1,578
Cooking fuel
Electricity or gas 6.7 1,607 75.4 65.1 108
Paraffin/kerosene 6.2 1,358 (61.5) (34.2) 84
Coal/lignite * 18 * * 3
Charcoal 8.4 3,529 66.5 58.1 298
Wood/straw3 9.0 12,163 64.8 51.8 1,089
Animal dung * 6 * * 0
Other fuel * 4 * * 0
No food cooked in
household * 13 * * 0
Residence
Urban 7.3 6,677 63.6 50.0 490
Rural 9.1 12,025 66.7 54.4 1,091
Region
Coast 7.2 1,936 66.0 47.5 140
North Eastern 4.0 625 35.4 34.1 25
Eastern 8.7 2,235 67.6 55.7 195
Central 7.2 1,725 70.3 58.8 125
Rift Valley 8.3 5,457 68.1 61.9 453
Western 12.8 2,166 56.5 53.0 276
Nyanza 9.7 2,638 71.0 41.1 255
Nairobi 5.9 1,920 (65.2) (44.8) 113
Mother’s education
No education 6.2 2,218 57.1 45.2 138
Primary incomplete 9.3 5,304 63.2 45.8 491
Primary complete 9.6 5,164 68.1 56.6 493
Secondary+ 7.6 6,016 68.5 59.4 459
Wealth quintile
Lowest 8.7 4,457 62.6 47.9 388
Second 9.5 3,803 66.7 49.3 363
Middle 9.7 3,375 64.9 51.7 328
Fourth 8.3 3,285 63.4 61.6 271
Highest 6.1 3,782 73.5 59.3 231
Total 8.5 18,702 65.7 53.1 1,582
Note: Total includes one child for whom information on mother’s smoking status is missing and five children for whom
information on cooking fuel in the household is missing. Figures in parentheses are based on 25-49 unweighted cases. An
asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
1
Symptoms of ARI, considered a proxy for pneumonia, include cough accompanied by short, rapid breathing which was
chest-related and/or by difficult breathing which was chest-related.
2
Excludes pharmacy, shop, and traditional practitioner
3
Includes grass, shrubs, crop residues
Mothers who reported that their child had ARI symptoms were asked about the actions they had
taken to treat the illness. Table 10.4 shows that, among children with ARI symptoms, two-thirds were
taken to a health facility or health provider for advice or treatment. This is an increase from the 2008-09
KDHS, when treatment or advice was sought from a health provider for 56 percent of children ill with ARI
symptoms. Also, antibiotics were used to treat children with ARI symptoms somewhat more often in 2014
than in 2008-09 (53 percent and 50 percent, respectively).
146 • Child Health
As Table 10.4 shows, the treatment that children ill Table 10.4C Prevalence of symptoms of ARI
with ARI symptoms received differed across subgroups. The Among children under age five, the percentage who had
proportion of children with ARI symptoms for whom medical symptoms of acute respiratory infection (ARI) in the two
weeks preceding the survey, according to county, Kenya
treatment or advice was sought and the proportion receiving 2014
antibiotics generally decrease with child’s age and increase Among children under age five:
with increasing mother’s education and wealth. The greatest Percentage with Number of
County symptoms of ARI1 children
variations in treatment patterns were by region. The
Coast 7.2 1,936
proportion of children with ARI symptoms for whom Mombasa 6.0 493
Kwale 5.3 408
treatment or advice was sought from a health provider ranged Kilifi 8.7 705
from only 35 percent in North Eastern to 71 percent in Tana River
Lamu
7.6
8.9
166
52
Nyanza. The proportion of children with ARI symptoms who Taita Taveta 9.0 110
were treated with antibiotics was lowest in North Eastern (34 North Eastern
Garissa
4.0
2.9
625
223
percent) and highest in Rift Valley (62 percent). Wajir 6.5 252
Mandera 1.5 150
Eastern 8.7 2,235
Differentials in ARI prevalence across counties are Marsabit 10.7 88
presented in Table 10.4C. The counties with the highest Isiolo 10.7 81
Meru 7.1 490
proportion of children reported as ill with ARI symptoms Tharaka-Nithi 10.8 137
Embu 6.3 194
included Vihiga (17 percent), Bungoma (16 percent), Baringo Kitui 9.9 424
(14 percent), Homa Bay (13 percent), and Migori (13 Machakos
Makueni
7.8
10.3
474
346
percent), while ARI prevalence was lowest in Mandera (2 Central 7.2 1,725
percent) and in Turkana, Garissa, Kisumu, Kirinyaga, and Nyandarua
Nyeri
7.5
9.5
232
240
Nyamira (each 3 percent). Due to the small numbers of Kirinyaga 3.2 188
Murang’a 11.5 293
children with ARI symptoms in most counties, it is not Kiambu 5.9 772
possible to compare treatment patterns across counties. Rift Valley 8.3 5,457
Turkana 2.5 333
West Pokot 3.6 294
10.4 FEVER Samburu 4.4 114
Trans-Nzoia 9.3 516
Uasin Gishu 7.3 463
Fever is a major symptom of malaria and other acute Elgeyo Marakwet 9.4 164
Nandi 7.0 388
infections in children. In the 2014 KDHS, mothers were asked Baringo 13.6 230
Laikipia 11.7 206
whether their children under age 5 had a fever in the two Nakuru 7.9 849
weeks preceding the survey and, if so, whether any treatment Narok
Kajiado
11.7
10.8
614
452
was sought. Table 10.5 shows the percentage of children Kericho 7.7 359
Bomet 7.1 475
under age 5 with a fever during the two weeks preceding the
Western 12.8 2,166
survey and the percentage receiving antimalarial drugs and Kakamega 11.9 721
Vihiga 16.6 215
antibiotics, by background characteristics. Bungoma 15.6 842
Busia 6.2 388
Twenty-four percent of children under age 5 had a Nyanza 9.7 2,638
Siaya 11.4 378
fever in the two weeks preceding the survey. Fever was least Kisumu 3.2 478
Homa Bay 12.8 616
common among children under age 6 months (17 percent) and Migori 12.7 516
most common among children age 6-23 months (30-31 Kisii
Nyamira
10.0
3.1
463
187
percent). The prevalence of fever was highest in Nyanza (37 Nairobi 5.9 1,920
percent), Western (36 percent), and Coast (27 percent) and Total 8.5 18,702
lowest in North Eastern (9 percent). 1
Symptoms of ARI, considered a proxy for pneumonia,
include cough accompanied by short, rapid breathing
Advice or treatment was obtained from a health which was chest-related and/or by difficult breathing
which was chest-related.
provider for 63 percent of children with a fever. This
represents an increase from the 49 percent reported in 2008-09. The proportion of children for whom
advice or treatment was sought from a health provider was lowest in the North Eastern and Western
regions (50 percent and 52 percent, respectively). The likelihood that a child ill with fever received care or
treatment generally increased with increasing mother’s education and wealth.
Just over one-quarter of children with a fever received antimalarial drugs. Additional information
on the use of antimalarial drugs to treat fever in children is provided in Chapter 12, which presents data
Child Health • 147
from the KDHS relating to efforts to prevent and treat malaria. Slightly more than 4 in 10 children with a
fever were given antibiotics. Antibiotics were given most often to children age 6-23 months; urban
children; children from the Central, Rift Valley, and Eastern regions; children whose mothers had
completed primary or higher education; and children in the two highest wealth quintiles.
Table 10.5 Prevalence and treatment of fever
Among children under age five, the percentage who had a fever in the two weeks preceding the survey; and among children with fever,
the percentage for whom advice or treatment was sought from a health facility or provider, the percentage who took antimalarial drugs,
and the percentage who received antibiotics as treatment, by background characteristics, Kenya 2014
Among children under age five: Among children under age five with fever:
Percentage for
whom advice or
treatment was
sought from a Percentage who Percentage who
Background Percentage with Number of health facility or took antimalarial took antibiotic Number of
characteristic fever children provider1 drugs drugs children
Age in months
<6 16.9 1,694 58.3 11.9 38.4 286
6-11 31.2 1,909 67.3 18.0 48.3 597
12-23 29.9 3,777 63.0 24.3 45.7 1,131
24-35 24.8 3,760 60.8 29.5 41.5 933
36-47 21.2 3,889 62.7 32.7 41.7 826
48-59 21.5 3,672 61.6 34.0 40.7 789
Sex
Male 24.5 9,477 62.0 27.0 42.5 2,325
Female 24.2 9,225 63.1 27.0 43.8 2,237
Residence
Urban 21.7 6,677 62.3 20.4 46.0 1,447
Rural 25.9 12,025 62.6 30.0 41.8 3,114
Region
Coast 27.2 1,936 67.6 11.9 40.8 526
North Eastern 8.7 625 49.5 7.3 42.1 54
Eastern 18.2 2,235 68.7 18.1 47.5 406
Central 17.9 1,725 68.2 4.8 58.6 308
Rift Valley 20.9 5,457 61.6 13.3 50.3 1,139
Western 36.1 2,166 51.5 51.8 38.6 782
Nyanza 37.4 2,638 65.8 48.7 32.6 987
Nairobi 18.7 1,920 63.3 10.6 44.9 359
Mother’s education
No education 17.7 2,218 60.7 17.5 39.8 392
Primary incomplete 29.2 5,304 57.1 32.0 36.8 1,550
Primary complete 24.2 5,164 64.4 27.7 45.2 1,250
Secondary+ 22.8 6,016 67.5 23.3 49.5 1,369
Wealth quintile
Lowest 25.1 4,457 59.5 23.1 38.1 1,119
Second 28.5 3,803 63.3 36.3 39.5 1,082
Middle 26.1 3,375 60.4 31.9 40.4 881
Fourth 24.0 3,285 62.5 26.2 51.0 788
Highest 18.3 3,782 69.0 13.2 51.6 691
Total 24.4 18,702 62.5 27.0 43.2 4,562
1
Excludes pharmacy, shop, market, and traditional practitioner
As Table 10.5C shows, the prevalence of fever varied greatly across counties, from a high of 49
percent in Vihiga to a low of 5 percent in Mandera. Children with a fever were most likely to be taken to a
health facility or provider for advice or treatment in West Pokot (77 percent) and least likely in Kakamega
(42 percent). Children with a fever were most likely to have received antibiotics in Elgeyo Marakwet (73
percent).
148 • Child Health
Table 10.5C Prevalence and treatment of fever
Among children under age five, the percentage who had a fever in the two weeks preceding the survey; and among children with
fever, the percentage for whom advice or treatment was sought from a health facility or provider, the percentage who took antimalarial
drugs, and the percentage who received antibiotics as treatment, by county, Kenya 2014
Among children under age five: Among children under age five with fever:
Percentage for
whom advice or
treatment was
sought from a Percentage who Percentage who
Percentage with Number of health facility or took antimalarial took antibiotic Number of
County fever children provider1 drugs drugs children
Coast 27.2 1,936 67.6 11.9 40.8 526
Mombasa 22.5 493 75.1 14.9 32.3 111
Kwale 25.7 408 63.1 33.2 51.1 105
Kilifi 31.3 705 69.7 2.1 35.5 221
Tana River 26.8 166 62.9 12.3 49.0 45
Lamu 22.2 52 45.3 5.7 44.3 12
Taita Taveta 29.5 110 56.3 1.3 59.8 33
North Eastern 8.7 625 49.5 7.3 42.1 54
Garissa 7.0 223 (41.4) (5.6) (43.2) 16
Wajir 12.5 252 54.2 4.3 46.2 31
Mandera 4.8 150 * * * 7
Eastern 18.2 2,235 68.7 18.1 47.5 406
Marsabit 19.3 88 63.1 10.6 51.0 17
Isiolo 13.2 81 74.6 51.1 34.0 11
Meru 26.0 490 72.0 23.1 65.8 128
Tharaka-Nithi 28.1 137 75.2 27.2 19.2 39
Embu 13.8 194 (72.4) (21.7) (68.9) 27
Kitui 17.0 424 60.8 7.4 37.1 72
Machakos 13.6 474 (61.4) (12.8) (25.2) 64
Makueni 14.1 346 74.8 13.7 57.0 49
Central 17.9 1,725 68.2 4.8 58.6 308
Nyandarua 17.2 232 67.0 6.7 69.1 40
Nyeri 14.1 240 (65.6) (0.0) (47.4) 34
Kirinyaga 21.2 188 (74.8) (18.6) (47.6) 40
Murang’a 17.7 293 (83.3) (0.0) (66.1) 52
Kiambu 18.5 772 61.8 3.2 58.6 143
Rift Valley 20.9 5,457 61.6 13.3 50.3 1,139
Turkana 11.4 333 61.7 29.9 20.3 38
West Pokot 9.4 294 76.9 32.2 45.1 28
Samburu 19.1 114 53.4 6.8 31.3 22
Trans-Nzoia 21.9 516 56.2 12.4 64.6 113
Uasin Gishu 19.3 463 52.3 5.8 61.6 89
Elgeyo Marakwet 29.7 164 63.9 2.0 73.3 49
Nandi 19.7 388 50.9 8.8 60.1 76
Baringo 22.9 230 71.6 27.7 63.9 53
Laikipia 21.6 206 73.6 16.8 64.7 44
Nakuru 15.5 849 67.1 11.6 50.9 132
Narok 30.7 614 61.7 13.3 34.5 188
Kajiado 25.7 452 53.4 1.4 54.0 116
Kericho 25.6 359 65.3 19.4 40.5 92
Bomet 20.8 475 68.9 21.0 42.2 99
Western 36.1 2,166 51.5 51.8 38.6 782
Kakamega 28.9 721 42.2 38.7 33.3 209
Vihiga 49.2 215 52.5 40.7 55.5 106
Bungoma 35.8 842 52.6 58.7 42.5 302
Busia 42.7 388 60.9 62.9 27.4 166
Nyanza 37.4 2,638 65.8 48.7 32.6 987
Siaya 44.9 378 73.7 59.4 42.2 170
Kisumu 30.9 478 62.5 46.0 44.8 148
Homa Bay 45.6 616 64.7 51.7 19.1 281
Migori 48.2 516 61.4 42.4 26.0 249
Kisii 28.1 463 71.1 46.9 49.4 130
Nyamira 5.7 187 * * * 11
Nairobi 18.7 1,920 63.3 10.6 44.9 359
Total 24.4 18,702 62.5 27.0 43.2 4,562
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted
cases that has been suppressed.
1
Excludes pharmacy, shop, market, and traditional practitioner
Child Health • 149
10.5 DIARRHOEAL DISEASE
Dehydration caused by severe diarrhoea is a major Table 10.6 Prevalence of diarrhoea
cause of morbidity and mortality among young children, Percentage of children under age five who had diarrhoea in
although the condition can be easily treated with oral the two weeks preceding the survey, by background
characteristics, Kenya 2014
rehydration therapy (ORT). Exposure to diarrhoea-causing
Diarrhoea in the two
agents is frequently related to the use of contaminated weeks preceding the
water and to unhygienic practices in food preparation and survey
Background All Diarrhoea Number of
disposal of excreta. Reducing deaths from diarrhoea characteristic diarrhoea with blood children
largely depends on whether the children are able to access Age in months
lifesaving oral rehydration salts (ORS) and zinc tablets. <6
6-11
12.9
26.6
1.3
2.2
1,694
1,909
12-23 24.2 1.9 3,777
In the 2014 KDHS, mothers of children born 24-35
36-47
15.8
9.2
2.3
1.5
3,760
3,889
during the five years preceding the survey were asked a 48-59 6.7 1.0 3,672
series of questions about episodes of diarrhoea suffered by Sex
Male 15.9 1.6 9,477
their children in the two weeks before the survey, Female 14.4 1.8 9,225
including questions on feeding practices during diarrhoea, Source of drinking
water1
treatment of the condition, and their knowledge and use of Improved 14.8 1.5 12,024
ORS. Not improved 16.1 2.2 6,496
Other/missing 7.1 0.0 182
Toilet facility2
Table 10.6 shows the percentage of children under Improved, not shared 11.2 1.0 3,983
age 5 who had diarrhoea in the two weeks preceding the Shared3 16.2 1.2 4,854
Non-improved 16.4 2.2 9,855
survey, by background characteristics. Overall, 15 percent
Residence
of children under age 5 had diarrhoea, with 2 percent Urban 14.3 1.2 6,677
having diarrhoea with blood. Diarrhoea prevalence is Rural 15.7 2.0 12,025
Region
highest among children age 6-11 and 12-23 months (27 Coast 17.6 2.6 1,936
percent and 24 percent, respectively). Diarrhoea is less North Eastern 7.8 1.3 625
Eastern 14.3 0.7 2,235
common among children who used improved, private toilet Central 10.4 0.4 1,725
Rift Valley 13.2 1.8 5,457
facilities (11 percent) than among those who use non- Western 20.1 2.7 2,166
improved or shared improved facilities (16 percent each). Nyanza 18.9 2.7 2,638
Nairobi 15.6 0.7 1,920
The Western, Nyanza, and Coast regions have the highest
Mother’s education
prevalence of diarrhoea (18-20 percent), and North Eastern No education 14.1 2.8 2,218
Primary incomplete 18.4 2.4 5,304
has the lowest (8 percent). Primary complete 14.2 1.1 5,164
Secondary+ 13.7 1.2 6,016
Table 10.6C presents differences in diarrhoea Wealth quintile
Lowest 17.2 2.7 4,457
prevalence by county. The proportion of children reported Second 17.1 2.3 3,803
to have had diarrhoea was highest in Migori (28 percent), Middle 15.5 1.7 3,375
Fourth 15.4 1.1 3,285
Homa Bay (24 percent), and Vihiga (24 percent). The Highest 10.5 0.4 3,782
lowest proportions of children with diarrhoea were in Total 15.2 1.7 18,702
Nyamira (3 percent), Mandera (3 percent), and Nyeri (5 Note: Total includes 14 children for whom information on
percent). toilet facility is missing.
1
See Table 2.1 for definition of categories
2
See Table 2.2 for definition of categories
3
Facilities that would be considered improved if they were
not shared by two or more households
150 • Child Health
Table 10.6C Prevalence of diarrhoea
Percentage of children under age five who had diarrhoea in the two
weeks preceding the survey, by county, Kenya 2014
Diarrhoea in the two weeks
preceding the survey
Diarrhoea with Number of
County All diarrhoea blood children
Coast 17.6 2.6 1,936
Mombasa 11.8 0.5 493
Kwale 14.6 2.6 408
Kilifi 22.9 4.0 705
Tana River 21.3 3.5 166
Lamu 17.3 2.2 52
Taita Taveta 16.1 1.0 110
North Eastern 7.8 1.3 625
Garissa 5.5 2.3 223
Wajir 12.6 1.1 252
Mandera 3.0 0.1 150
Eastern 14.3 0.7 2,235
Marsabit 14.9 0.6 88
Isiolo 6.8 1.2 81
Meru 12.9 0.4 490
Tharaka-Nithi 20.5 1.5 137
Embu 11.5 0.5 194
Kitui 18.2 0.7 424
Machakos 15.1 1.3 474
Makueni 11.3 0.3 346
Central 10.4 0.4 1,725
Nyandarua 11.3 0.1 232
Nyeri 4.6 0.5 240
Kirinyaga 12.0 1.0 188
Murang’a 12.1 1.0 293
Kiambu 11.0 0.0 772
Rift Valley 13.2 1.8 5,457
Turkana 14.3 3.9 333
West Pokot 8.4 0.2 294
Samburu 18.1 3.4 114
Trans-Nzoia 14.9 1.7 516
Uasin Gishu 9.3 1.4 463
Elgeyo Marakwet 11.6 1.7 164
Nandi 10.9 0.6 388
Baringo 16.0 2.5 230
Laikipia 13.5 0.5 206
Nakuru 11.7 1.9 849
Narok 18.8 3.3 614
Kajiado 10.6 0.6 452
Kericho 16.3 2.3 359
Bomet 12.3 1.2 475
Western 20.1 2.7 2,166
Kakamega 20.8 3.9 721
Vihiga 23.6 2.5 215
Bungoma 19.5 2.0 842
Busia 18.2 2.0 388
Nyanza 18.9 2.7 2,638
Siaya 14.1 0.7 378
Kisumu 15.5 2.4 478
Homa Bay 23.5 3.7 616
Migori 27.9 3.6 516
Kisii 16.9 3.3 463
Nyamira 2.9 0.3 187
Nairobi 15.6 0.7 1,920
Total 15.2 1.7 18,702
Prompt treatment, including oral rehydration therapy, is important in treating diarrhoea. Table
10.7 shows the percentage of children with diarrhoea who according to the mother’s report received
specific treatments, by background characteristics. Advice or treatment was sought from a health provider
in the case of 58 percent of children with diarrhoea. Children age 6-11 months (65 percent), male children
(59 percent), and children with bloody diarrhoea (74 percent) were more likely than their counterparts to
have received advice or treatment from a health facility or provider. Considering regional differentials,
treatment or advice was least likely to be sought from a health facility or provider for children with
diarrhoea in North Eastern (44 percent) and Western (47 percent) regions and most likely in Coast (65
percent). Children of mothers with no education (63 percent) and children of mothers in the lowest wealth
quintile (61 percent) were more likely than others to be taken to a health facility or provider for advice or
treatment.
Child Health • 151
Table 10.7 Diarrhoea treatment
Among children under age five who had diarrhoea in the two weeks preceding the survey, the percentage for whom advice or treatment was sought from a health facility or provider, the
percentage given oral rehydration therapy (ORT), the percentage given increased fluids, the percentage given ORT or increased fluids, and the percentage who were given other
treatments, by background characteristics, Kenya 2014
Percentage
of children
with
diarrhoea for
whom advice Oral rehydration therapy (ORT) Other treatments
or treatment Fluid Either Number
was sought Fluid from ORS or ORT or of
from a health from ORS Home- home- In- in- Anti- Zinc Intra- Home children
Background facility or ORS packets made made creased creased Antibiotic motility supple- venous remedy/ No treat- with
characteristic provider1 packets and zinc fluids fluids fluids fluids drugs drugs ments solution other Missing ment diarrhoea
Age in months
<6 42.6 31.1 1.7 35.5 55.4 8.1 58.6 11.5 1.4 1.8 1.0 18.4 0.0 30.2 218
6-11 65.2 56.1 8.9 57.5 79.3 17.8 83.0 14.3 2.4 10.6 0.9 20.0 0.5 9.3 508
12-23 57.7 60.1 7.9 60.4 83.2 22.8 86.0 17.4 0.6 8.6 2.3 20.7 0.0 8.8 915
24-35 58.0 49.8 9.3 59.7 78.2 25.0 82.0 16.3 1.3 10.4 1.0 20.9 0.3 9.5 596
36-47 57.8 61.4 5.3 60.1 80.7 21.4 84.6 14.2 0.6 5.7 2.6 19.5 0.5 11.3 360
48-59 54.1 44.4 4.4 54.5 72.1 27.9 78.7 18.8 0.0 5.0 0.0 21.5 0.2 11.4 247
Sex
Male 59.1 56.3 7.9 57.1 78.3 23.4 82.5 16.5 1.3 8.1 1.3 21.3 0.2 10.5 1,511
Female 55.9 51.0 6.5 57.5 77.8 19.4 80.9 15.1 0.8 8.2 1.8 19.3 0.3 12.0 1,332
Type of diarrhoea
Non-bloody 55.7 53.0 6.9 56.8 77.7 21.7 81.3 15.8 0.9 7.8 1.4 19.1 0.1 12.0 2,487
Bloody 73.7 62.0 11.1 63.0 83.2 19.8 86.7 15.7 2.2 11.3 2.1 30.2 0.0 5.8 320
Residence
Urban 56.7 57.5 9.9 61.7 80.9 29.5 84.6 17.5 1.5 11.0 1.4 17.5 0.2 9.0 957
Rural 58.1 51.9 5.9 55.1 76.6 17.5 80.3 15.0 0.9 6.7 1.6 21.8 0.3 12.3 1,886
Region
Coast 64.7 63.1 12.1 56.8 80.4 32.6 85.2 16.3 0.1 12.5 5.5 17.9 0.0 8.5 341
North Eastern 44.2 55.3 7.2 39.2 72.6 10.1 74.8 11.8 0.2 8.2 3.9 14.8 0.2 15.4 49
Eastern 57.4 47.2 3.0 56.1 80.0 12.3 83.1 17.1 0.2 3.4 0.0 16.9 0.0 13.7 320
Central 63.2 50.6 8.6 65.5 80.6 27.7 85.5 17.3 1.7 9.2 0.0 25.8 0.0 5.9 180
Rift Valley 58.9 53.0 3.3 59.4 77.7 21.8 80.1 15.9 3.0 4.5 0.3 24.5 0.4 10.5 718
Western 47.3 45.6 4.7 58.8 75.5 16.8 80.8 9.8 0.0 5.1 0.8 22.5 0.2 14.1 436
Nyanza 59.7 55.3 11.7 45.9 75.3 11.8 77.2 19.7 1.0 12.7 1.1 20.2 0.5 13.9 500
Nairobi 57.4 63.4 11.1 68.9 82.0 39.3 88.0 16.3 0.0 12.8 3.6 11.8 0.0 6.9 300
Mother’s education
No education 63.3 51.6 5.7 47.9 73.7 19.9 77.6 12.8 0.0 7.9 3.2 28.6 0.0 11.1 312
Primary incomplete 57.6 51.5 4.5 56.3 77.4 17.4 81.4 15.2 0.6 4.7 1.5 18.8 0.3 13.3 975
Primary complete 53.7 53.7 5.6 59.0 77.4 19.1 80.1 14.9 1.0 6.0 1.0 19.1 0.2 12.2 734
Secondary+ 59.1 57.5 12.6 60.4 81.1 29.2 85.2 18.7 2.1 14.1 1.4 20.2 0.3 7.8 823
Wealth quintile
Lowest 60.8 52.1 5.0 51.7 75.3 18.2 79.4 13.0 0.8 6.1 3.0 23.3 0.1 13.1 767
Second 58.3 53.3 4.7 54.9 77.6 16.9 81.1 16.2 1.3 5.2 0.5 20.7 0.2 11.8 650
Middle 56.4 55.3 7.4 61.6 81.4 19.1 84.2 14.3 1.0 7.5 1.4 21.5 0.0 10.2 525
Fourth 54.8 55.0 11.5 58.9 77.5 26.9 81.6 21.0 1.1 12.2 0.1 15.8 0.3 9.9 506
Highest 55.6 54.5 10.3 64.1 80.3 31.8 84.4 16.3 1.4 12.5 2.2 18.6 0.8 9.4 396
Total 57.6 53.8 7.2 57.3 78.1 21.5 81.7 15.9 1.1 8.1 1.5 20.4 0.2 11.2 2,844
Note: ORT includes fluid prepared from oral rehydration salt (ORS) packets, homemade sugar-salt solution, and other homemade fluids. Total includes 56 children for whom information
on type of diarrhoea is missing.
1 Excludes pharmacy, shop, and traditional practitioner
Oral rehydration therapy (ORT) involves giving a child a solution prepared from oral rehydration
salts (ORS) or other homemade fluids. Table 10.7 shows that 54 percent of children with diarrhoea were
treated with an ORS solution and 57 percent with homemade fluids. Overall, 78 percent received some
form of ORT, either ORS or other homemade fluids. Simply increasing the fluids a child receives can help
prevent dehydration. Twenty-two percent of children were given increased fluids. Eighty-two percent were
given ORT or increased fluids.
Table 10.7 also shows the proportions of children receiving treatments other than ORT or
increased fluids. Eight percent of children with diarrhoea were given zinc supplements, while 7 percent
received both zinc supplements and ORS. Sixteen percent of children with diarrhoea received antibiotic
drugs, 1 percent were given anti-motility drugs, and 2 percent were treated with intravenous fluids. Twenty
percent were treated with home remedies. Eleven percent of children with diarrhoea did not receive any
treatment at all.
Treatment practices varied considerably across subgroups of children. For example, as Table 10.7
shows, use of ORT or increased fluids was lowest among children less than age 6 months (59 percent) and
highest among children age 12-23 months (86 percent). There are also marked regional differences in use
of ORT or increased fluids, from a high of 88 percent in Nairobi to a low of 75 percent in North Eastern.
152 • Child Health
Use of ORT or increased fluids is higher among children of mothers with a secondary or higher education
(85 percent) than among children of mothers at other educational levels.
Mothers are encouraged to continue feeding children with diarrhoea normally and to increase the
amount of fluids. These practices help to reduce dehydration and minimise the adverse consequences of
diarrhoea on the child’s nutritional status. To obtain information on the extent to which these feeding
practices are used, mothers of children under age 5 who had diarrhoea during the two weeks before the
survey were asked about the amount of fluids and food they gave during the diarrhoeal episode.
Table 10.8 shows that 37 percent of children with diarrhoea were given the same amount of fluid
as usual, 22 percent were given more, 25 percent were given somewhat less, and 14 percent were given
much less than the usual amount. Only 2 percent were not given any fluids. Regarding the amount of food
children with diarrhoea received, 32 percent were given the same amount of food as usual, and 3 percent
were offered more to eat. On the other hand, 31 percent were given somewhat less than the usual amount
of food, 18 percent were given much less than the usual amount, and 7 percent ate nothing during the
diarrhoeal episode. Only 15 percent of children received increased fluids with continued feeding. Fifty-
three percent of children with diarrhoea continued feeding and were given ORT and/or increased fluids.
Table 10.8 Feeding practices during diarrhoea
Percent distribution of children under age five who had diarrhoea in the two weeks preceding the survey by amount of liquids and food offered compared with normal practice, the percentage of children given
increased fluids and continued feeding during the diarrhoea episode, and the percentage of children who continued feeding and were given ORT and/or increased fluids during the episode of diarrhoea, by
background characteristics, Kenya 2014
Percent-
age who
con-
Percent- tinued
age feeding
given in- and were
creased given Number
Amount of liquids given Amount of food given
fluids ORT of
Don’t Some- Never Don’t and con- and/or in- children
Background Same as Some- Much know/ Same as what Much gave know/ tinued creased with diar-
characteristic More usual what less less None missing Total More usual less less None food missing Total feeding1 fluids1 rhoea
Age in months
<6 8.1 52.7 16.0 13.2 10.0 0.0 100.0 3.5 12.1 7.5 6.8 2.3 67.9 0.0 100.0 3.2 14.9 218
6-11 17.8 37.5 28.8 13.1 2.6 0.3 100.0 2.0 25.1 25.8 18.5 9.3 19.0 0.3 100.0 8.6 42.1 508
12-23 22.8 31.7 24.8 17.1 3.0 0.5 100.0 3.9 26.7 35.3 21.8 10.4 1.7 0.1 100.0 15.0 56.4 915
24-35 25.0 38.3 22.4 13.3 0.8 0.1 100.0 2.6 41.0 32.7 17.7 5.1 0.9 0.1 100.0 20.1 62.1 596
36-47 21.4 39.7 25.7 12.5 0.2 0.5 100.0 2.1 45.2 34.6 12.1 5.7 0.2 0.0 100.0 16.5 68.3 360
48-59 27.9 33.3 25.7 12.7 0.3 0.1 100.0 3.3 38.5 32.2 22.8 3.0 0.1 0.1 100.0 23.3 57.1 247
Sex
Male 23.4 37.9 23.1 13.4 2.0 0.2 100.0 3.7 33.8 30.1 16.8 7.3 8.3 0.0 100.0 17.0 55.5 1,511
Female 19.4 35.7 26.1 15.4 2.9 0.5 100.0 2.2 29.3 31.1 19.4 7.2 10.6 0.2 100.0 12.5 51.1 1,332
Type of
diarrhoea
Non-bloody 21.7 37.9 23.9 13.9 2.3 0.2 100.0 3.1 32.8 30.2 17.9 6.5 9.5 0.1 100.0 15.0 53.7 2,487
Bloody 19.8 29.5 30.6 16.7 3.4 0.0 100.0 2.0 24.0 32.8 19.5 13.0 8.7 0.0 100.0 14.6 52.0 320
Residence
Urban 29.5 37.3 20.3 10.9 1.6 0.5 100.0 2.7 35.7 32.9 16.4 5.1 7.2 0.1 100.0 22.0 61.8 957
Rural 17.5 36.7 26.7 16.1 2.9 0.2 100.0 3.1 29.6 29.4 18.9 8.3 10.5 0.1 100.0 11.3 49.2 1,886
Region
Coast 32.6 27.9 23.5 10.2 5.7 0.2 100.0 2.3 25.3 29.9 19.1 9.3 13.8 0.2 100.0 17.8 47.7 341
North Eastern 10.1 6.5 40.2 41.7 1.5 0.0 100.0 0.0 8.4 34.4 33.8 11.4 12.0 0.0 100.0 1.2 31.4 49
Eastern 12.3 38.0 33.2 16.1 0.4 0.0 100.0 2.7 27.8 36.9 18.9 3.2 10.5 0.0 100.0 7.7 57.8 320
Central 27.7 36.0 19.4 12.6 4.3 0.0 100.0 1.7 29.1 28.3 31.6 4.5 4.8 0.0 100.0 13.7 48.8 180
Rift Valley 21.8 39.2 24.1 11.7 2.7 0.5 100.0 1.0 34.8 33.4 10.6 11.7 8.2 0.2 100.0 15.9 55.6 718
Western 16.8 37.0 24.5 19.5 2.0 0.2 100.0 6.5 25.5 23.6 24.3 8.2 11.6 0.2 100.0 9.9 43.2 436
Nyanza 11.8 38.6 27.7 19.4 2.3 0.1 100.0 3.3 29.7 30.9 20.4 6.1 9.6 0.1 100.0 8.9 48.8 500
Nairobi 39.3 42.6 12.7 4.2 0.0 1.2 100.0 4.4 53.2 27.9 10.0 0.0 4.6 0.0 100.0 37.2 79.0 300
Mother’s
education
No education 19.9 33.0 27.7 18.4 1.0 0.0 100.0 1.4 27.7 28.3 15.2 14.5 12.8 0.0 100.0 9.2 42.7 312
Primary
incomplete 17.4 38.3 26.3 14.4 3.3 0.3 100.0 3.1 31.3 30.1 18.1 7.1 10.1 0.1 100.0 11.6 52.2 975
Primary
complete 19.1 37.5 24.5 14.9 3.3 0.7 100.0 3.0 32.1 30.1 18.5 7.4 8.8 0.2 100.0 14.2 52.1 734
Secondary+ 29.2 36.0 21.2 12.3 1.2 0.1 100.0 3.3 33.2 32.4 18.6 4.6 7.8 0.1 100.0 21.7 60.0 823
Wealth quintile
Lowest 18.2 34.5 28.6 15.0 3.5 0.2 100.0 1.7 29.2 30.5 16.8 9.8 11.7 0.2 100.0 10.6 48.2 767
Second 16.9 39.9 26.4 15.1 1.4 0.2 100.0 4.0 31.8 28.8 19.7 7.6 7.9 0.2 100.0 10.7 51.7 650
Middle 19.1 35.7 22.2 19.6 3.4 0.0 100.0 3.9 29.6 28.0 20.1 6.8 11.6 0.0 100.0 14.0 51.1 525
Fourth 26.9 37.7 21.9 11.4 1.9 0.1 100.0 3.1 30.1 35.5 17.7 5.6 7.9 0.1 100.0 20.1 58.0 506
Highest 31.8 36.9 20.1 8.5 1.4 1.4 100.0 2.2 41.0 30.7 15.6 4.5 6.1 0.0 100.0 24.8 63.6 396
Total 21.5 36.9 24.5 14.3 2.4 0.3 100.0 3.0 31.7 30.6 18.0 7.2 9.4 0.1 100.0 14.9 53.4 2,844
Note: It is recommended that children should be given more liquids to drink during diarrhoea and food should not be reduced. Total includes 56 children for whom information on type of diarrhoea is missing.
1 Continued feeding practices includes children who were given more, same as usual, or somewhat less food during the diarrhoea episode
Child Health • 153
Feeding practices during diarrhoeal episodes varied by background characteristics. The proportion
of children who were fed optimally, that is, who continued feeding and were given ORT and/or increased
fluids, generally increased with age, reaching a peak of 68 percent among children age 36-47 months. The
likelihood that a child was fed according to the optimal practice was greater among urban than rural
children and generally increased with both mother’s education and wealth quintile. Children with diarrhoea
were most often fed according to the optimal practice in Nairobi (79 percent) and least often in North
Eastern (31 percent). County-level data are not presented for Table 10.7 or Table 10.8 since there are
insufficient cases of children with diarrhoea.
A comparison of the results from the 2008-09 and 2014 KDHS surveys with respect to the actions
taken when children have diarrhoea highlights a number of improvements in treatment practices. The
proportion of children with diarrhoea taken to a health facility or provider for advice or treatment increased
from 49 percent at the time of the 2008-09 KDHS to 58 percent in the 2014 KDHS. The percentage of
children with diarrhoea treated with a solution prepared from an ORS packet also increased from 39
percent in 2008-09 to 54 percent in 2014. Use of zinc to treat diarrhoea in children increased from less than
1 percent (0.2 percent) in 2008-09 to 8 percent in 2014. While treatment practices improved in a number of
ways in the past five years, there are also a number of areas of concern. The proportion of children
receiving no treatment increased from 13 percent in the 2008-09 KDHS to 18 percent in the 2014 KDHS.
Also, there was no change in the proportion of children with diarrhoea given increased fluids with
continued feeding.
10.6 KNOWLEDGE OF ORS PACKETS AND ZINC TABLETS
To ascertain how widespread knowledge of oral rehydration salt (ORS) is in Kenya, women age
15-49 with a live birth in the five years preceding the survey were asked whether they knew about ORS
packets. Women were also asked whether they had heard of zinc tablets as a treatment for diarrhoea.
Table 10.9 shows that 93 percent of women age 15-49 with a live birth in the five years preceding
the survey have heard of ORS packets. This represents a substantial increase from the 78 percent of
mothers who knew about ORS packets at the time of the 2008-09 KDHS. With regard to differentials in
ORS knowledge, the proportion of women who had heard about ORS packets was below 90 percent only
among those age 15-19 (89 percent), those in North Eastern (81 percent), those with no education (82
percent), and those in the lowest wealth quintile (87 percent).
While most women with a birth in the five years before the survey knew about ORS packets, only
around 1 in 6 women were aware of zinc tablets (17 percent). Women in urban areas (21 percent), women
in Western and Nyanza (22 percent each), women with a secondary or higher education (24 percent), and
women in the fourth and highest wealth quintiles (21 percent each) were more likely to have heard of zinc
tablets than other women.
154 • Child Health
Table 10.9 Knowledge of ORS packets
Percentage of women age 15-49 with a live birth in the five years
preceding the survey who know about ORS packets and who know about
zinc tablets for treatment of diarrhoea, by background characteristics,
Kenya 2014
Percentage of Percentage of
women who women who
Background know about ORS know about zinc Number of
characteristic packets tablets women
Age
15-19 89.2 15.6 845
20-24 91.8 17.8 3,458
25-34 93.9 18.5 7,191
35-49 92.5 14.6 2,948
Residence
Urban 94.9 20.7 5,561
Rural 91.6 15.3 8,881
Region
Coast 89.9 15.2 1,471
North Eastern 81.2 11.3 372
Eastern 93.5 17.5 1,834
Central 93.4 17.8 1,528
Rift Valley 91.6 13.1 4,002
Western 94.7 22.3 1,590
Nyanza 94.7 22.3 1,988
Nairobi 95.8 19.7 1,657
Education
No education 82.3 8.9 1,409
Primary incomplete 90.5 13.0 3,846
Primary complete 94.2 16.0 4,024
Secondary+ 96.4 24.0 5,163
Wealth quintile
Lowest 86.7 10.2 2,947
Second 93.1 15.9 2,782
Middle 94.4 18.6 2,660
Fourth 95.2 20.7 2,777
Highest 94.9 21.3 3,277
Total 92.8 17.4 14,442
ORS = Oral rehydration salts
10.7 DISPOSAL OF CHILDREN’S STOOLS
Unsafe disposal of human faeces spreads disease, either by direct contact or through indirect
transmission. Hence, the proper disposal of children’s stools is extremely important in preventing the
spread of disease. Children’s stools are considered to be safely disposed of if the child uses a toilet or
latrine or the stools are put or rinsed into a toilet or latrine or buried.
Table 10.10 presents information on the disposal of stools of children under age 5. Overall, 83
percent of children had their last stool disposed of safely. This represents an improvement from the 78
percent reported in the 2008-09 KDHS. Children whose mothers had no education (49 percent), children in
the lowest wealth quintile (57 percent), and children in the North Eastern region (62 percent) were least
likely to have their last stool disposed of safely. Children in the Central and Western regions (95 percent
each) were most likely to have their last stool disposed of safely.
Child Health • 155
Table 10.10 Disposal of children’s stools
Percent distribution of youngest children under age five living with the mother by the manner of disposal of the child’s last faecal matter, and percentage of children whose
stools are disposed of safely, according to background characteristics, Kenya 2014
Percentage
of children
Manner of disposal of children’s stools whose
Child used Put/rinsed Put/rinsed stools are
Background toilet or into toilet or into drain or Thrown into Left in the disposed of Number of
characteristic latrine latrine Buried ditch garbage open Other Missing Total safely1 children
Age in months
<6 2.5 60.9 5.0 8.4 15.4 5.7 2.0 0.0 100.0 68.4 794
6-11 5.3 62.9 5.3 6.7 11.4 7.3 1.0 0.0 100.0 73.5 894
12-23 4.8 69.6 6.1 4.3 9.3 5.5 0.5 0.1 100.0 80.4 1,700
24-35 14.9 69.6 5.1 2.2 3.7 4.3 0.2 0.2 100.0 89.5 1,347
36-47 37.8 49.8 3.8 0.8 2.6 4.8 0.2 0.2 100.0 91.5 982
48-59 63.7 26.3 2.3 1.0 2.2 3.1 0.2 1.1 100.0 92.3 721
Toilet facility2
Improved, not shared 25.5 62.5 0.8 3.6 5.2 2.0 0.2 0.2 100.0 88.8 1,405
Shared3 15.1 70.9 1.0 2.6 8.2 1.8 0.2 0.3 100.0 87.0 1,755
Non-improved or
shared 17.0 52.5 8.6 4.5 7.8 8.3 1.0 0.2 100.0 78.1 3,272
Residence
Urban 19.9 65.8 1.1 2.7 9.2 1.0 0.2 0.1 100.0 86.8 2,398
Rural 17.4 56.1 7.1 4.4 6.2 7.6 0.9 0.3 100.0 80.6 4,041
Region
Coast 13.2 53.1 10.6 7.0 13.0 2.4 0.1 0.5 100.0 76.9 651
North Eastern 0.9 36.2 24.3 2.9 19.5 13.9 0.8 1.4 100.0 61.5 174
Eastern 24.5 60.8 4.1 1.3 4.4 4.0 0.8 0.0 100.0 89.4 849
Central 33.9 60.1 0.6 1.4 1.9 1.4 0.0 0.6 100.0 94.6 674
Rift Valley 13.7 59.6 2.2 4.2 7.8 11.5 1.0 0.0 100.0 75.5 1,805
Western 28.9 59.7 5.9 3.1 0.6 1.1 0.6 0.0 100.0 94.5 753
Nyanza 11.5 66.9 9.0 5.4 2.8 3.2 0.9 0.3 100.0 87.4 877
Nairobi 13.7 61.5 0.0 4.0 20.3 0.6 0.0 0.0 100.0 75.1 655
Mother’s education
No education 6.1 27.8 14.6 7.5 23.6 18.1 1.6 0.6 100.0 48.5 655
Primary incomplete 16.9 57.2 7.2 5.5 4.9 7.1 0.9 0.3 100.0 81.3 1,803
Primary complete 22.3 64.1 3.4 2.9 3.8 3.1 0.4 0.1 100.0 89.8 1,713
Secondary+ 20.0 67.7 1.2 2.0 7.2 1.4 0.3 0.2 100.0 88.9 2,267
Wealth quintile
Lowest 7.3 35.2 14.0 8.4 16.3 16.7 1.8 0.3 100.0 56.5 1,338
Second 19.0 63.3 6.1 2.6 2.5 5.5 0.7 0.1 100.0 88.5 1,267
Middle 21.6 67.4 2.7 4.2 1.6 1.9 0.3 0.4 100.0 91.7 1,207
Fourth 22.3 68.6 0.9 2.4 4.8 0.7 0.1 0.1 100.0 91.9 1,247
Highest 21.9 65.4 0.2 1.4 10.3 0.5 0.1 0.1 100.0 87.6 1,379
Total 18.3 59.7 4.9 3.8 7.3 5.2 0.6 0.2 100.0 82.9 6,438
Note: Total includes seven children for whom information on type of facility is missing.
1
Children’s stools are considered to be disposed of safely if the child used a toilet or latrine, if the faecal matter was put/rinsed into a toilet or latrine or if it was buried.
2
See Table 2.2 for definition of categories
3
Facilities that would be considered improved if they were not shared by two or more households
156 • Child Health
NUTRITION OF CHILDREN AND WOMEN 11
Lucy Gathigi Maina, Andolo Miheso, Annah W. Wamae, Henry Z. Osoro
Key Findings
• Twenty-six percent of children under age 5 are stunted, 4 percent are
wasted, and 11 percent are underweight.
• Ninety-nine percent of children have ever been breastfed and 61 percent
of children less than age 6 months are exclusively breastfed.
• Complementary foods are generally introduced at the recommended age;
81 percent of breastfed children age 6-9 months received complementary
foods in addition to being breastfed within the 24 hours preceding the
survey.
• Only 22 percent of children are fed in accordance with the three
recommended infant and young child feeding practices.
• Seventy-two percent of children age 6-59 months received vitamin A
supplements in the past six months.
• Fifty-one percent of children age 12-59 months received deworming
medication in the past six months.
• Nine percent of women age 15-49 are thin or undernourished (BMI <18.5
kg/m2); 33 percent of women are either overweight or obese (BMI ≥25
kg/m2), with 10 percent being obese (BMI ≥30 kg/m2).
• Only 8 percent of women took iron tablets daily for 90 or more days
during the pregnancy of their last birth.
• Thirty-one percent of women took deworming medication during their last
pregnancy.
G
ood nutrition is a prerequisite for the national development of countries and for the well-being of
individuals. The 2010 Constitution of Kenya recognises adequate food and nutrition as a human
right. It states that every person has the right to be free from hunger and the right to adequate food
of acceptable quality (Article 43) and that every child has the right to basic nutrition (Article 53).
Furthermore, the Government of Kenya’s 2011 Food and Nutrition Security Policy states that nutrition is
central to human development in the country (Government of Kenya, 2011).
Adequate nutrition is critical to children’s growth and development. The period from birth to age 2
years is especially important for optimal physical, mental, and cognitive growth, health, and development.
Unfortunately, this period is often marked with nutrient deficiencies that interfere with optimal growth and
may cause common childhood illnesses such as diarrhoea and acute respiratory infections.
A woman’s nutritional status has important implications for her health as well as for the health of
her children. Malnutrition in women results in reduced productivity, increased susceptibility to infections,
slowed recovery from illness, and a heightened risk of adverse pregnancy outcomes. For example, a
woman with poor nutritional status, as indicated by a low body mass index (BMI), short stature, or
micronutrient deficiencies, has a greater risk of obstructed labour, of having a baby with a low birth
weight, of death from postpartum haemorrhage, and of morbidity for both herself and her baby.
This chapter covers the nutritional concerns for children and women. Specifically, it presents
information on the nutritional status of children and women based on anthropometric measurements, infant
and young child feeding practices including breastfeeding and complementary feeding, micronutrient
intake among children and women, and salt iodisation.
Nutrition of Children and Women • 157
11.1 NUTRITIONAL STATUS OF CHILDREN
The nutritional status of children under age 5 is an important measure of children’s health.
Anthropometric data collected in the 2014 KDHS permit estimation of the overall nutritional status of
Kenyan children and analysis of nutrition status by background characteristics. This allows identification
of subgroups of the child population that are at increased risk of faltered growth, disease, impaired mental
development, and death.
11.1.1 Measurement of Nutritional Status among Young Children
In the 2014 KDHS, height and weight measurements were obtained for children born since
January 2009. The height and weight data are used to compute three summary indices of nutritional status:
height-for-age, weight-for-height, and weight-for-age.
For this report, the summary indicators of the nutritional status of children were calculated using
growth standards published by the World Health Organization (WHO) in 2006. These standards were
generated using data collected in the WHO Multicentre Growth Reference Study (WHO, 2006). The study,
whose sample included 8,440 children in six countries, was designed to provide a description of how
children should grow under optimal conditions. The WHO Child Growth Standards can therefore be used
to assess children all over the world, regardless of ethnicity, social and economic influences, and feeding
practices. Each of the three nutritional status indicators described below is expressed in standard deviation
units from the median of the WHO Multicentre Growth Reference Study sample.
The height-for-age index provides an indicator of linear growth retardation and cumulative growth
deficits. Children whose height-for-age Z-score is below minus two standard deviations (-2 SD) from the
median of the WHO reference population are considered short for their age (stunted) and are chronically
malnourished. Children who are below minus three standard deviations (-3 SD) are considered severely
stunted. Stunting reflects failure to receive adequate nutrition over a long period of time and is affected by
recurrent and chronic illness. Height-for-age, therefore, represents the long-term effects of malnutrition in
a population and is not sensitive to recent, short-term changes in dietary intake.
The weight-for-height index measures body mass in relation to body height or length and
describes current nutritional status. Children with Z-scores below minus two standard deviations (-2 SD)
are considered thin (wasted) and are acutely malnourished. Wasting represents the failure to receive
adequate nutrition in the period immediately preceding the survey and may be the result of inadequate food
intake or a recent episode of illness causing loss of weight and the onset of malnutrition. Children whose
weight-for-height index is below minus three standard deviations (-3 SD) are considered severely wasted.
The weight-for-height index also provides data on overweight and obesity. Children more than two
standard deviations (+2 SD) above the median weight-for-height are considered overweight or obese.
Weight-for-age is a composite index of height-for-age and weight-for-height. It takes into account
both chronic and acute malnutrition. Children whose weight-for-age is below minus two standard
deviations (-2 SD) are classified as underweight. Children whose weight-for-age is below minus three
standard deviations (-3 SD) are considered severely underweight.
11.1.2 Data Collection
Measurements of height and weight were obtained for all children born since January 2009 and
listed in the Household Questionnaire. The survey included children who were not biological offspring of
the women interviewed. Each interviewing team carried a scale and a measuring board. The scales were
electronic SECA scales with a digital screen. They were designed and manufactured under the authority of
the United Nations Children’s Fund (UNICEF). The scale allowed weighing of very young children
through an automatic mother-child adjustment that eliminated the mother’s weight while she was standing
on the scale with her baby. Weight was measured to the nearest 100 grams (g). Height measurements were
made using height/length (Shorr) boards, manufactured by Shorr Productions for use in survey settings.
158 • Nutrition of Children and Women
Children younger than 24 months and those whose age was unknown and who were less than 87
centimetres were measured lying down on the board (recumbent length). Standing height was measured for
older children.
A total of 21,435 children under age 5 were eligible for weight and height measurements. Of these
children, 3 percent had missing values for height or weight and 1 percent had height or weight data
considered to be out of range for their age and were not included in the final analysis. Thus, this chapter
includes data for 96 percent of the 21,435 (unweighted) children under age 5 who were present in sampled
households at the time of the survey.
11.1.3 Measures of Child Nutritional Status
Height-for-age
Table 11.1 and Figure 11.1 present the nutritional status of children under age 5 by various
background characteristics. Nationally, 26 percent of children are stunted, while 8 percent are severely
stunted. Analysis of this indicator by age group shows that stunting is highest in children age 18-23 months
(36 percent) and 24-35 months (34 percent). Similar results are observed for children who are severely
stunted; children age 18-23 months are the most affected (12 percent). Stunting levels are higher among
boys (30 percent) than girls (22 percent) and higher among children whose mothers reported they were
very small at birth (43 percent) than among those who were average or larger at birth (24 percent).
Stunting is higher among rural children (29 percent) than urban children (20 percent). At the
regional level, Coast (31 percent), Rift Valley and Eastern (each 30 percent) have the highest proportions
of stunted children, while Nairobi (17 percent) and Central (18 percent) have the lowest. Stunting in
children generally decreases with education of the mother. Children of mothers who did not complete
primary school (34 percent) or who have no education (31 percent) are more likely to be stunted than
children of mothers with a secondary or higher education (17 percent). Table 11.1 further shows that
stunting in children decreases as household wealth increases, from 36 percent to 14 percent.
Weight-for-height
Table 11.1 also shows the nutritional status of children under age 5 as measured by wasting or low
weight-for-height. Nationally, 4 percent of children are wasted and 1 percent are severely wasted. Wasting
levels are highest among children in the age groups 6-8 months and 9-11 months (each 7 percent).
Typically during this period, children are introduced to complementary foods, which may vary in quality
and quantity, and are more vulnerable to diseases. Wasting is higher (9 percent) among children whose
mothers are thin (BMI <18.5 kg/m2) than among other children. The North Eastern region (13 percent) has
the highest levels of wasting, while the Western, Nyanza, and Central regions have the lowest (each 2
percent). Children whose mothers have no education have a higher chance of wasting (10 percent) than
children whose mothers have some education (4 percent or less). Wasting in children generally decreases
with increasing household wealth.
Weight-for-age
Table 11.1 also shows that 11 percent of Kenyan children are underweight (low weight-for-age),
with 2 percent classified as severely underweight. Peak levels of low weight-for-age are found among
children older than age 12 months. The percentage underweight is slightly higher among boys (12 percent)
than girls (10 percent); also, it is higher among children whose mother is thin (24 percent) than children of
mothers with a higher BMI (11 percent or less) and among rural children (13 percent) than urban children
(7 percent). North Eastern has the highest proportion (19 percent) of underweight children, while Nairobi
has the lowest (4 percent). The proportion underweight decreases as mother’s educational level increases
and as household wealth increases.
Nutrition of Children and Women • 159
Table 11.1 Nutritional status of children
Percentage of children under five years classified as malnourished according to three anthropometric indices of nutritional status: height-for-age, weight-for-height, and
weight-for-age, by background characteristics, Kenya 2014
Height-for-age1 Weight-for-height Weight-for-age
Percent- Percent- Mean Percent- Percent- Percent- Mean Percent- Percent- Percent- Mean
Background age below age below Z-score age below age below age above Z-score age below age below age above Z-score Number
characteristic -3 SD -2 SD2 (SD) -3 SD -2 SD2 +2 SD (SD) -3 SD -2 SD2 +2 SD (SD) of children
Age in months
<6 2.8 10.0 -0.3 1.4 3.7 14.4 0.5 1.0 3.7 4.3 0.1 1,612
6-8 3.8 12.3 -0.4 2.0 6.5 6.9 0.1 1.5 8.0 2.9 -0.3 934
9-11 4.5 16.7 -0.7 1.6 6.5 4.7 -0.1 2.3 8.8 2.5 -0.5 966
12-17 8.5 26.5 -1.1 1.3 5.5 4.3 -0.1 2.6 11.1 1.9 -0.6 1,995
18-23 11.7 35.5 -1.4 1.1 4.7 3.9 -0.0 2.7 11.8 1.5 -0.7 1,786
24-35 10.4 33.6 -1.4 0.4 3.0 2.7 0.0 2.8 12.5 0.7 -0.7 3,921
36-47 8.7 28.6 -1.3 0.8 3.3 2.7 -0.0 2.2 12.2 0.8 -0.8 4,013
48-59 7.2 23.3 -1.2 0.6 3.7 1.8 -0.2 2.2 12.1 0.5 -0.8 3,759
Sex
Male 9.7 29.7 -1.3 1.0 4.4 4.7 0.0 2.7 12.1 1.5 -0.7 9,653
Female 6.3 22.3 -1.0 0.8 3.7 3.5 -0.0 1.8 9.8 1.3 -0.6 9,334
Birth interval in months3
First birth4 6.1 21.8 -1.0 0.7 3.6 5.7 0.1 1.5 8.2 1.6 -0.5 4,281
<24 10.9 32.6 -1.4 0.8 4.3 3.1 -0.1 3.0 13.7 1.4 -0.8 2,249
24-47 9.3 29.6 -1.3 1.4 4.7 3.4 -0.1 2.7 13.2 1.2 -0.8 6,404
48+ 5.7 21.0 -0.9 0.7 3.4 4.6 0.1 1.6 8.0 1.5 -0.5 4,267
Size at birth3
Very small 19.6 42.8 -1.7 1.4 7.7 1.4 -0.4 6.4 24.1 0.1 -1.3 255
Small 12.2 34.2 -1.5 0.6 6.0 4.9 -0.2 3.1 18.0 0.3 -1.0 956
Average or larger 6.7 24.0 -1.1 0.9 3.1 4.4 0.1 1.5 9.1 1.5 -0.6 6,943
Don’t know/missing 13.0 31.5 -1.4 6.0 10.3 4.0 -0.4 6.5 19.4 0.0 -1.1 109
Mother’s interview status
Interviewed 7.8 25.9 -1.1 1.0 4.1 4.2 0.0 2.2 10.7 1.4 -0.6 17,201
Not interviewed but in
household 7.1 20.7 -0.8 0.2 2.7 4.2 0.1 2.3 10.1 2.3 -0.4 365
Not interviewed and not in
the household5 11.5 28.9 -1.2 0.4 4.0 2.8 -0.1 3.5 14.4 0.9 -0.7 1,420
Mother’s nutritional status6
Thin (BMI <18.5) 10.3 30.6 -1.5 1.4 9.0 1.8 -0.6 5.0 23.6 0.1 -1.2 644
Normal (BMI 18.5-24.9) 8.6 28.1 -1.2 0.8 3.4 3.3 -0.0 2.0 11.1 1.0 -0.7 4,507
Overweight/obese (BMI ≥25) 5.0 18.7 -0.9 0.9 2.8 6.4 0.3 1.1 5.3 2.0 -0.3 2,145
Residence
Urban 5.7 19.8 -0.9 0.8 3.4 5.5 0.1 1.5 7.0 2.4 -0.4 6,206
Rural 9.2 29.1 -1.3 1.0 4.4 3.4 -0.1 2.7 12.9 0.9 -0.8 12,780
Region
Coast 10.4 30.8 -1.3 0.8 4.5 3.3 -0.1 2.4 13.6 1.2 -0.8 1,926
North Eastern 10.7 24.7 -0.9 2.6 13.3 2.6 -0.7 4.5 19.0 1.2 -1.0 604
Eastern 8.2 30.1 -1.3 1.2 4.4 4.3 -0.1 2.2 12.2 0.9 -0.8 2,409
Central 4.9 18.4 -0.9 0.2 2.3 6.2 0.2 1.2 5.3 2.4 -0.3 1,694
Rift Valley 9.3 29.8 -1.3 1.3 5.7 3.7 -0.2 3.6 15.3 1.0 -0.8 5,466
Western 8.2 25.2 -1.1 0.4 1.9 3.4 0.1 1.5 9.0 1.3 -0.6 2,476
Nyanza 7.6 22.7 -1.0 0.4 2.0 4.4 0.2 1.3 7.4 2.0 -0.4 2,769
Nairobi 3.9 17.2 -0.7 0.8 2.5 5.3 0.2 1.2 3.8 1.7 -0.2 1,643
Mother’s education7
No education 11.0 30.6 -1.3 2.3 10.2 2.2 -0.5 4.5 20.6 0.8 -1.1 2,110
Primary incomplete 10.8 33.5 -1.4 0.9 3.9 3.5 -0.0 2.8 13.3 0.8 -0.8 5,059
Primary complete 7.7 25.5 -1.2 0.9 2.8 3.3 0.0 1.7 9.5 1.0 -0.6 4,853
Secondary+ 3.9 17.2 -0.8 0.5 3.0 6.4 0.2 1.2 5.6 2.6 -0.3 5,544
Wealth quintile
Lowest 12.3 35.9 -1.5 1.6 7.3 2.1 -0.3 4.2 19.5 0.5 -1.1 4,608
Second 9.5 30.2 -1.3 0.8 3.0 3.6 0.0 2.7 12.1 0.8 -0.8 4,096
Middle 7.4 25.4 -1.2 0.8 3.7 4.0 0.1 1.9 9.1 1.2 -0.6 3,536
Fourth 6.2 20.7 -1.0 0.6 2.7 5.4 0.1 1.1 6.9 1.7 -0.5 3,299
Highest 3.3 13.8 -0.6 0.6 2.5 6.3 0.2 0.8 4.0 3.1 -0.2 3,447
Total 8.1 26.0 -1.1 0.9 4.0 4.1 0.0 2.3 11.0 1.4 -0.6 18,986
Note: Table is based on children who stayed in the household on the night before the interview. Each of the indices is expressed in standard deviation units (SD) from
the median of the WHO Child Growth Standards adopted in 2006. The indices in this table are NOT comparable to those based on the previously used
NCHS/CDC/WHO reference.
Table is based on children with valid dates of birth (month and year) and valid measurement of both height and weight.
1
Recumbent length is measured for children under age 2, or in the few cases when the age of the child is unknown and the child is less than 87 cm; standing height is
measured for all other children.
2
Includes children who are below -3 standard deviations (SD) from the WHO Child Growth standards population median
3
Excludes children whose mothers were not interviewed. Data only available from respondents to the full questionnaire.
4
First-born twins (triplets, etc.) are counted as first births because they do not have a previous birth interval
5
Includes children whose mothers are deceased
6
Excludes children whose mothers were not weighed and measured, children whose mothers were not interviewed, and children whose mothers are pregnant or gave
birth within the preceding 2 months. Mother’s nutritional status in terms of BMI (Body Mass Index) is presented in Table 11.10.
7
For women who are not interviewed, information is taken from the Household Questionnaire. Excludes children whose mothers are not listed in the Household
Questionnaire
160 • Nutrition of Children and Women
Figure 11.1 shows that Kenyan children are more likely to experience stunting than to be wasted
or underweight. Additionally, the risk of stunting peaks at age 18 to 28 months.
Figure 11.1 Nutritional status of children by age
Percent
40
35
30
25
20
15
10
5
0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58
Age (months)
Stunted Wasted Underweight
Note: Stunting reflects chronic malnutrition; wasting reflects acute malnutrition; underweight
reflects chronic or acute malnutrition or a combination of both. Plotted values are smoothed
by a five-month moving average. KDHS 2014
At the county level (Table 11.1C), West Pokot and Kitui have the highest proportions (46 percent
each) of stunted children. Other counties reporting high proportions of stunting include Kilifi (39 percent),
Mandera (36 percent), and Bomet (36 percent). Nyeri, Garissa, and Kiambu counties have the lowest
proportion of stunted children, each 16 percent or less. Wasting is concentrated in the north: more than 11
percent of children in Garissa, Wajir, Mandera, Marsabit, Turkana, West Pokot, and Samburu are wasted,
topping out at 23 percent in Turkana. The counties with the lowest proportion of wasted children are Siaya
and Kisumu (each 1 percent or less). The table also shows that one-quarter of children or more are
underweight in five counties: Mandera, Marsabit, Turkana, West Pokot, and Samburu. Four percent or less
of children in Nyeri and Nairobi are underweight.
Nutrition of Children and Women • 161
Table 11.1C Nutritional status of children
Percentage of children under five years classified as malnourished according to three anthropometric indices of nutritional status: height-for-age, weight-for-
height, and weight-for-age, by county, Kenya 2014
Height-for-age1 Weight-for-height Weight-for-age
Percent- Percent- Mean Percent- Percent- Percent- Mean Percent- Percent- Percent- Mean
age below age below Z-score age below age below age above Z-score age below age below age above Z-score Number
County -3 SD -2 SD2 (SD) -3 SD -2 SD2 +2 SD (SD) -3 SD -2 SD2 +2 SD (SD) of children
Coast 10.4 30.8 -1.3 0.8 4.5 3.3 -0.1 2.4 13.6 1.2 -0.8 1,926
Mombasa 7.1 21.1 -1.0 0.0 4.1 4.2 0.0 1.5 9.6 1.9 -0.6 456
Kwale 10.5 29.7 -1.4 0.8 4.4 3.8 0.0 1.5 11.8 1.5 -0.8 401
Kilifi 13.6 39.1 -1.5 0.9 4.1 2.8 -0.1 3.1 16.9 0.6 -1.0 737
Tana River 9.4 28.1 -1.4 0.9 5.7 1.3 -0.3 3.1 18.6 0.6 -1.0 164
Lamu 7.1 29.2 -1.2 0.3 4.2 2.0 -0.1 2.1 11.8 2.1 -0.8 53
Taita Taveta 5.8 23.8 -0.9 3.6 7.2 4.2 -0.1 3.2 7.8 1.8 -0.6 115
North Eastern 10.7 24.7 -0.9 2.6 13.3 2.6 -0.7 4.5 19.0 1.2 -1.0 604
Garissa 5.9 15.6 -0.7 1.3 11.4 2.3 -0.6 2.9 13.1 1.3 -0.8 228
Wajir 10.3 26.4 -1.0 3.1 14.2 0.7 -0.8 3.7 21.1 0.0 -1.1 228
Mandera 19.0 36.1 -1.3 3.8 14.8 5.9 -0.5 8.2 24.9 3.0 -1.1 148
Eastern 8.2 30.1 -1.3 1.2 4.4 4.3 -0.1 2.2 12.2 0.9 -0.8 2,409
Marsabit 10.7 26.5 -1.2 5.1 16.3 1.0 -0.9 7.3 30.1 0.4 -1.3 90
Isiolo 5.1 19.1 -0.7 2.4 9.1 1.6 -0.6 3.2 12.9 1.0 -0.8 80
Meru 6.2 25.2 -1.1 1.0 2.9 4.9 0.1 1.2 8.1 0.5 -0.6 529
Tharaka-Nithi 7.6 32.9 -1.4 0.0 3.3 3.6 0.0 4.1 10.8 0.8 -0.8 151
Embu 6.5 26.8 -1.3 0.2 3.0 3.6 0.1 1.3 11.1 1.3 -0.7 202
Kitui 12.7 45.8 -1.7 0.4 3.4 3.1 -0.2 2.9 19.7 0.7 -1.1 486
Machakos 7.1 26.5 -1.1 2.5 6.5 5.5 -0.1 1.7 8.1 1.2 -0.7 502
Makueni 7.8 25.1 -1.3 1.0 2.1 5.3 0.1 1.7 10.2 1.0 -0.7 369
Central 4.9 18.4 -0.9 0.2 2.3 6.2 0.2 1.2 5.3 2.4 -0.3 1,694
Nyandarua 8.1 29.4 -1.3 0.1 2.0 6.8 0.3 1.0 6.8 0.8 -0.6 248
Nyeri 5.6 15.1 -0.9 0.0 2.4 5.9 0.2 1.2 2.5 2.1 -0.3 268
Kirinyaga 3.7 17.2 -0.9 0.8 3.9 4.5 -0.0 1.3 7.7 0.0 -0.5 185
Murang’a 4.8 19.3 -1.0 0.0 1.4 3.1 0.1 1.6 5.6 1.2 -0.5 307
Kiambu 3.8 15.7 -0.7 0.3 2.3 7.9 0.4 1.1 5.1 4.3 -0.1 686
Rift Valley 9.3 29.8 -1.3 1.3 5.7 3.7 -0.2 3.6 15.3 1.0 -0.8 5,466
Turkana 7.1 23.9 -1.1 4.4 22.9 0.2 -1.3 9.8 34.0 0.0 -1.5 359
West Pokot 19.0 45.9 -1.8 2.4 14.3 1.4 -0.8 9.6 38.5 0.3 -1.5 286
Samburu 9.8 30.1 -1.3 2.8 13.6 0.6 -0.9 8.1 28.9 0.3 -1.4 112
Trans-Nzoia 10.7 29.2 -1.3 2.0 3.9 2.4 -0.1 3.9 15.3 0.6 -0.8 556
Uasin Gishu 11.1 31.2 -1.3 1.1 3.0 5.1 -0.0 2.8 11.5 0.7 -0.7 482
Elgeyo Marakwet 7.3 29.9 -1.4 1.2 4.3 4.5 -0.2 2.5 12.6 0.7 -0.9 170
Nandi 8.3 29.9 -1.3 1.0 3.9 3.7 -0.1 1.9 11.1 0.5 -0.8 405
Baringo 8.4 29.5 -1.4 1.2 6.9 2.0 -0.5 3.6 20.2 1.2 -1.1 233
Laikipia 7.4 26.9 -1.3 0.8 4.4 3.7 -0.1 2.8 13.9 0.8 -0.8 211
Nakuru 7.2 27.6 -1.2 0.6 4.5 5.7 0.1 2.8 10.2 1.5 -0.6 840
Narok 8.7 32.9 -1.4 0.7 2.4 3.0 -0.1 1.1 11.6 0.6 -0.9 628
Kajiado 7.1 18.2 -0.7 0.8 3.0 4.3 0.1 2.5 8.1 4.0 -0.4 400
Kericho 10.5 28.7 -1.3 1.1 5.6 6.6 0.1 3.5 12.4 1.4 -0.7 311
Bomet 10.7 35.5 -1.6 0.4 1.8 3.7 0.1 2.1 12.0 0.6 -0.8 472
Western 8.2 25.2 -1.1 0.4 1.9 3.4 0.1 1.5 9.0 1.3 -0.6 2,476
Kakamega 12.3 28.4 -1.3 0.5 1.8 4.3 0.2 2.2 10.1 1.2 -0.6 845
Vihiga 6.0 23.5 -1.1 0.4 2.6 4.0 0.2 1.4 5.9 0.9 -0.5 260
Bungoma 6.4 24.4 -1.1 0.2 1.8 2.9 0.1 0.9 9.0 1.8 -0.5 938
Busia 5.4 22.0 -1.1 0.7 2.2 2.4 0.1 1.8 9.0 0.8 -0.6 433
Nyanza 7.6 22.7 -1.0 0.4 2.0 4.4 0.2 1.3 7.4 2.0 -0.4 2,769
Siaya 7.1 24.7 -1.1 0.0 0.2 4.7 0.2 1.4 7.8 1.6 -0.4 423
Kisumu 6.9 18.0 -0.7 0.0 0.8 5.7 0.2 0.4 6.6 3.8 -0.2 492
Homa Bay 4.2 18.7 -0.7 0.9 2.3 4.1 0.2 1.3 5.4 2.6 -0.3 621
Migori 10.1 26.4 -1.1 0.9 4.0 4.4 0.2 1.6 8.6 1.5 -0.5 526
Kisii 9.3 25.5 -1.3 0.0 1.7 4.0 0.2 1.8 8.4 0.9 -0.6 511
Nyamira 10.1 25.5 -1.1 0.9 4.1 2.9 -0.0 2.0 9.6 0.5 -0.6 195
Nairobi 3.9 17.2 -0.7 0.8 2.5 5.3 0.2 1.2 3.8 1.7 -0.2 1,643
Total 8.1 26.0 -1.1 0.9 4.0 4.1 0.0 2.3 11.0 1.4 -0.6 18,986
Note: Table is based on children who stayed in the household on the night before the interview. Each of the indices is expressed in standard deviation units
(SD) from the median of the WHO Child Growth Standards adopted in 2006. The indices in this table are NOT comparable to those based on the previously
used NCHS/CDC/WHO reference.
Table is based on children with valid dates of birth (month and year) and valid measurement of both height and weight.
1
Recumbent length is measured for children under age 2, or in the few cases when the age of the child is unknown and the child is less than 87 cm;
standing height is measured for all other children.
2
Includes children who are below -3 standard deviations (SD) from the WHO Child Growth standards population median
162 • Nutrition of Children and Women
11.1.4 Trends in Children’s Nutritional Status
Figure 11.2 shows trends in children’s nutritional status since 1998. Comparison of KDHS data
over time indicates an overall improvement in children’s nutritional status in Kenya. Since 1998, stunting
has declined from 38 percent to 26 percent, wasting has declined from 7 percent to 4 percent, and the
proportion of underweight children has declined from 18 percent to 11 percent. Kenya has met the 2015
Millennium Development Goal (MDG) target of reducing the prevalence of underweight children under
age 5 to 11 percent (Ministry of Devolution and Planning, 2013).
Figure 11.2 Trends in nutritional status of children under 5 years
Percent
38
36 35
26
18
16 16
11
7 6 7 6 6 5 4
4
Stunting Wasting Underweight Overweight
(height-for-age) (weight-for-height) (weight-for-age)
1998 KDHS 2003 KDHS 2008-09 KDHS 2014 KDHS
Note: The data are based on the WHO Child Growth Standards adopted in 2006. Data for 1998 were collected only for children whose mothers were
interviewed; for other surveys, data were collected for all children listed in the Household Questionnaire. Data from 1998 exclude North Eastern region and
some northern districts in the Eastern and Rift Valley regions.
11.2 BREASTFEEDING AND COMPLEMENTARY FEEDING
Feeding practices play a critical role in child development. Poor feeding practices can adversely
impact the health and nutritional status of children, in turn resulting in direct consequences for their mental
and physical development. The duration and intensity of breastfeeding also affect a mother’s period of
postpartum infertility and, hence, the length of the birth interval and fertility levels.
11.2.1 Initiation of Breastfeeding
Early initiation of breastfeeding is important for both the mother and the child. Early suckling
stimulates the release of prolactin, which helps in the production of milk, and oxytocin, which is
responsible for the ejection of milk. It also stimulates contraction of the uterus after childbirth and reduces
postpartum blood loss. The first liquid to come from the breast, known as colostrum, is produced in the
first few days after delivery. Colostrum is highly nutritious and contains antibodies that provide natural
immunity to the infant. It is recommended that children be fed colostrum immediately after birth (within
one hour) and that they continue to be exclusively breastfed even if the regular breast milk has not yet
started to flow. Prelacteal feeding, giving food to newborns before the initiation of breastfeeding, is not
recommended.
Table 11.2 shows the percentage of last-born children born in the two years preceding the survey
according to whether they were ever breastfed, when they began breastfeeding, and whether they were fed
anything other than breast milk prior to the commencement of breastfeeding. Ninety-nine percent of
children have been breastfed for some period of time, with negligible differences by background
characteristics. Nearly two-thirds of children (62 percent) were breastfed within one hour of birth. The vast
majority (91 percent) of children were breastfed within one day of birth.
Nutrition of Children and Women • 163
Initiation of breastfeeding in the first hour after birth varies somewhat by background
characteristics. Eighty-one percent of children in the North Eastern region were breastfed within one hour
of birth, as compared with 48 percent of children in the Central region. Mothers with no education (76
percent) were more likely to initiate breastfeeding in the first hour than those with some education (65
percent or less). Mothers in the lowest wealth quintile (67 percent) were more likely to initiate early
breastfeeding than those in higher wealth quintiles (64 percent or less).
Table 11.2 Initial breastfeeding
Among last-born children who were born in the two years preceding the survey, the percentage who were ever breastfed and the
percentages who started breastfeeding within one hour and within one day of birth; and among last-born children born in the two years
preceding the survey who were ever breastfed, the percentage who received a prelacteal feed, by background characteristics, Kenya
2014
Among last-born children born in
the past two years who were ever
Among last-born children born in the past two years: breastfed:
Percentage who Percentage who
started started
breastfeeding breastfeeding Percentage who Number of last-
Background Percentage ever within 1 hour of within 1 day of Number of last- received a born children
characteristic breastfed birth birth1 born children prelacteal feed2 ever breastfed
Sex
Male 98.9 62.3 90.4 1,816 15.6 1,796
Female 98.5 62.1 90.9 1,728 15.4 1,702
Assistance at delivery
Health professional3 98.9 62.1 92.1 2,337 11.2 2,310
Traditional birth
attendant 99.3 65.5 91.0 616 23.6 612
Other 98.2 61.2 85.7 436 24.4 428
No one 96.2 53.8 81.4 153 23.5 148
Place of delivery
Health facility 98.8 62.1 92.2 2,314 11.1 2,287
At home 98.6 62.4 87.6 1,198 24.4 1,181
Other (98.2) (64.1) (96.8) 27 (3.3) 27
Residence
Urban 98.7 60.5 89.5 1,261 12.4 1,245
Rural 98.7 63.1 91.3 2,282 17.2 2,253
Region
Coast 99.1 62.1 83.7 374 21.0 370
North Eastern 98.7 80.8 95.3 108 16.4 107
Eastern 98.8 64.9 95.0 429 6.4 424
Central 99.3 48.1 92.4 312 7.7 309
Rift Valley 98.9 69.4 89.5 1,057 18.6 1,045
Western 98.9 52.8 92.3 414 25.0 409
Nyanza 97.6 58.4 95.3 484 11.6 473
Nairobi 98.3 60.8 85.1 366 12.4 360
Mother’s education
No education 99.1 76.0 91.1 414 15.5 410
Primary incomplete 98.6 58.5 88.5 999 20.3 985
Primary complete 98.4 64.6 93.2 914 15.0 900
Secondary+ 98.8 58.8 90.3 1,216 12.0 1,202
Wealth quintile
Lowest 98.9 66.7 90.9 879 18.4 870
Second 98.5 61.0 89.8 698 17.2 687
Middle 97.6 58.8 90.4 631 17.3 616
Fourth 99.0 64.4 92.5 648 10.9 642
Highest 99.3 58.8 89.8 687 12.9 683
Total 98.7 62.2 90.6 3,544 15.5 3,498
Note: Table is based on last-born children born in the two years preceding the survey regardless of whether the children are living or
dead at the time of interview. Total includes two children with missing information on assistance at delivery and six children with
missing information on place of delivery. Figures in parentheses are based on 25-49 unweighted cases.
1
Includes children who started breastfeeding within one hour of birth
2
Children given something other than breast milk during the first three days of life
3
Doctor, nurse or midwife
The practice of giving prelacteal feeds is discouraged because it limits the frequency of suckling
by the infant and exposes the baby to the risk of infection. Sixteen percent of children born in the last two
years were given prelacteal feeds within the first three days of life. Prelacteal feeding varies by assistance
at and place of delivery, residence, and region. Children whose birth was assisted by a health professional
and children who were delivered at a health facility are less likely to receive prelacteal feeds (each 11
percent) than their counterparts who were delivered without health professional assistance or who were
164 • Nutrition of Children and Women
delivered at home (each 24 percent). Prelacteal feeding is higher among children of mothers in rural areas
(17 percent) than among children of mothers in urban areas (12 percent). Among the regions, the highest
proportion of children receiving a prelacteal feed is observed in Western (25 percent), while the lowest is
seen in Eastern (6 percent).
The 2014 KDHS initiation of breastfeeding results are not fully comparable to those of the 2008-
09 KDHS as the sample included for reporting this indicator has changed from children born in the five
years to children born in the two years preceding the survey.
11.2.2 Breastfeeding Status by Age
UNICEF and WHO recommend that children be exclusively breastfed during the first six months
of life and that children be given solid and semisolid complementary foods in addition to continued
breastfeeding from six months until 24 months or more when the child is fully weaned. Exclusive
breastfeeding is recommended because breast milk is uncontaminated and contains all of the nutrients
necessary for children in the first several months of life. In addition, the mother’s antibodies in breast milk
provide immunity to disease. Early supplementation is discouraged for several reasons. First, it exposes
infants to pathogens and increases their risk of infection, especially disease. Second, it decreases infants’
intake of breast milk, and therefore suckling, which reduces breast milk production. Third, in low-resource
settings, supplementary food is often nutritionally inferior.
After six months, a child requires adequate complementary foods for normal growth. Lack of
appropriate complementary feeding may lead to malnutrition and illness, which in turn may lead to death.
However, even with complementary feeding, the child should continue to be breastfed for two years or
more.
Interviewers obtained information on complementary feeding by asking mothers about the current
breastfeeding status of all children under age 5 and, for the youngest child born in the two-year period
before the survey and living with the mother, foods and liquids given to the child the day and night before
the survey.
Table 11.3 shows the percent distribution of youngest children under age 2 living with their
mother by breastfeeding status and the percentage of children under age 2 using a bottle with a nipple,
according to age in months. Exclusive breastfeeding for the first six months in Kenya is 61 percent for
children under age 6 months (Table 11.3 and Figure 11.3). Among subgroups, the percentage of children
exclusively breastfed decreases sharply from 84 percent of infants age 0-1 month to 63 percent of infants
age 2-3 months and, further, to 42 percent of infants age 4-5 months.
In addition to receiving breast milk, 10 percent of children under age 6 months receive plain
water, 10 percent receive other milk, and 15 percent are given complementary foods. Contrary to
recommendations, 2 percent of children age 0-1 months, 13 percent of children age 2-3 months and 27
percent of children age 4-5 months receive complimentary foods. After age 6 months, a majority of
children are receiving complementary foods in addition to breast milk, as recommended; however, 19
percent of children age 6-9 months did not receive complementary foods the day or night preceding the
survey.
Eleven percent of children under age 6 months and 30 percent of children age 6-9 months used a
bottle with a nipple the day or night preceding the survey. Bottle feeding is a concern because of the
possible contamination due to unsafe water and lack of hygiene in its preparation; it also may reduce the
child’s interest in breastfeeding, with a consequential decline in the mother’s milk production.
It is recommended to continue breastfeeding a child until 2 years of age. The duration of
breastfeeding in Kenya is long, with at least half of children being breastfed until 2 years. The proportion
of children who are currently breastfeeding decreases with increasing child age with 88 percent among
children age 12-17 months and to 61 percent among children age 18-23 months.
Nutrition of Children and Women • 165
Table 11.3 Breastfeeding status by age
Percent distribution of youngest children under two years who are living with their mother by breastfeeding status and the percentage currently breastfeeding;
and the percentage of all children under two years using a bottle with a nipple, according to age in months, Kenya 2014
Breastfeeding status
Breast- Number of
Breast- Breast- feeding and youngest
feeding and feeding and Breast- consuming Percentage child under Percentage Number of
consuming consuming feeding and comple- currently two years using a all children
Age in Not breast- Exclusively plain water non milk consuming mentary breast- living with bottle with a under two
months feeding breastfed only liquids1 other milk foods Total feeding their mother nipple years
0-1 0.4 84.1 6.8 2.3 4.4 1.9 100.0 99.6 215 5.7 218
2-3 0.4 63.0 11.9 2.9 8.7 13.1 100.0 99.6 302 10.2 305
4-5 0.7 42.0 11.1 4.5 14.7 26.9 100.0 99.3 277 15.3 282
6-8 1.6 7.6 4.0 2.7 5.7 78.3 100.0 98.4 446 28.7 457
9-11 2.3 1.4 1.3 0.3 1.6 93.1 100.0 97.7 447 30.4 454
12-17 12.4 0.3 1.4 1.2 1.2 83.6 100.0 87.6 907 24.7 952
18-23 38.6 0.6 0.4 0.8 0.4 59.2 100.0 61.4 793 19.4 885
0-3 0.4 71.8 9.8 2.7 6.9 8.5 100.0 99.6 517 8.3 524
0-5 0.5 61.4 10.2 3.3 9.6 14.9 100.0 99.5 794 10.8 806
6-9 1.6 6.9 3.6 2.1 5.0 80.8 100.0 98.4 571 29.6 583
12-15 9.6 0.4 1.2 0.4 1.5 86.9 100.0 90.4 605 27.0 631
12-23 24.6 0.4 0.9 1.0 0.8 72.2 100.0 75.4 1,700 22.2 1,838
20-23 46.9 1.0 0.1 0.4 0.5 51.2 100.0 53.1 502 16.3 569
Note: Breastfeeding status refers to a “24-hour” period (yesterday and last night). Children who are classified as breastfeeding and consuming plain water only
consumed no liquid or solid supplements. The categories of not breastfeeding, exclusively breastfed, breastfeeding and consuming plain water, non-milk liquids,
other milk, and complementary foods (solids and semi-solids) are hierarchical and mutually exclusive, and their percentages add to 100 percent. Thus children
who receive breast milk and non-milk liquids and who do not receive other milk and who do not receive complementary foods are classified in the non-milk liquid
category even though they may also get plain water. Any children who get complementary food are classified in that category as long as they are breastfeeding
as well.
1
Non-milk liquids include juice, juice drinks, clear broth or other liquids
Figure 11.3 Infant feeding practices by age
100%
90%
80%
70%
60%
50%
Not breastfeeding
40%
Complementary foods
30% Other milk
Non-milk liquids/juice
20% Plain water only
Exclusively breastfed
10%
0%
<2 2-3 4-5 6-7 8-9 10-11 12-13 14-15 16-17 18-19 20-21 22-23
Age group in months
KDHS 2014
166 • Nutrition of Children and Women
Figure 11.4 shows the infant and young child feeding (IYCF) indicators of breastfeeding status.
As mentioned previously, 61 percent of children under age 6 months and 42 percent of children age 4-5
months are exclusively breastfed, and 75 percent of children under age 6 months are predominantly
breastfed.1 Figure 11.4 also shows that 90 percent of children continue breastfeeding at age 1 and that 53
percent continue doing so at age 2. Eighty percent of children age 6-8 months have been introduced to
solid, semisolid, or soft foods. Twenty-two percent of children age 0-23 months used a bottle with a nipple
the day or night preceding the survey.
Figure 11.4 IYCF indicators on breastfeeding status
Exclusive breastfeeding under age 6 months 61
Exclusive breastfeeding at age 4-5 months 42
Continued breastfeeding at 1 year 90
Introduction of solid, semisolid, or soft foods
80
(6-8 months)
Continued breastfeeding at 2 years 53
Age-appropriate breastfeeding (0-23 months) 73
Predominant breastfeeding (0-5 months) 75
Bottle feeding (0-23 months) 22
Percentage of children
KDHS 2014
The proportion of children younger than age 6 months who are exclusively breastfed has markedly
increased from 32 percent in the 2008-09 KDHS to the current 61 percent. The proportion of children less
than age 6 months using a bottle with a nipple has also noticeably decreased, from 25 percent in 2008-09 to
11 percent in 2014. However, use of a bottle with a nipple remains of concern among children age 6-23
months. Bottle feeding remains unchanged among children age 6-9 months at 30 percent, while the
proportion among children age 12-23 months has increased from 12 percent in 2008-09 to 22 percent in
2014.
11.2.3 Duration of Breastfeeding
Table 11.4 provides information on the median duration of any breastfeeding, exclusive
breastfeeding, and predominant breastfeeding among children born in the three years preceding the survey.
The median duration of any breastfeeding in Kenya is 21.0 months. Differences in the median duration of
breastfeeding by background characteristics are small except by region. The median duration of any
breastfeeding is longest in Eastern (24.5 months) and shortest in North Eastern region (19.4 months).
Table 11.4 also shows the median duration of predominant breastfeeding, which is defined as
exclusive breastfeeding or breastfeeding in combination with plain water and/or non-milk liquids only. The
median duration of predominant breastfeeding is 4.4 months.
Since the 2008-09 KDHS, the median duration of exclusive breastfeeding has increased from 0.7
to 3.3 months, and the median duration of predominant breastfeeding has increased from 2.2 to 4.4 months.
1
Children who are exclusively breastfed, children who breastfeed and consume plain water, and children who are
breastfed and consume non-milk liquids or juice.
Nutrition of Children and Women • 167
Table 11.4 Median duration of breastfeeding
Median duration of any breastfeeding, exclusive breastfeeding, and
predominant breastfeeding among children born in the three years preceding
the survey, by background characteristics, Kenya 2014
Median duration (months) of breastfeeding among
children born in the past three years1
Background Any breast- Exclusive Predominant
characteristic feeding breastfeeding breastfeeding2
Sex
Male 21.0 3.2 4.3
Female 21.1 3.4 4.6
Residence
Urban 20.5 3.7 4.6
Rural 21.2 3.0 4.3
Region
Coast 21.0 3.8 4.3
North Eastern 19.4 * 6.4
Eastern 24.5 (2.5) 4.1
Central 20.9 4.3 5.2
Rift Valley 21.2 3.1 4.1
Western 20.7 3.4 4.3
Nyanza 19.9 3.4 4.9
Nairobi (19.1) * *
Mother’s education
No education 21.3 2.8 5.0
Primary incomplete 20.7 3.4 4.8
Primary complete 20.9 3.3 4.4
Secondary+ 21.3 3.4 4.1
Wealth quintile
Lowest 20.9 2.9 4.6
Second 20.3 3.2 4.5
Middle 21.1 (2.4) 4.1
Fourth 21.7 4.1 4.7
Highest 21.0 3.5 4.2
Total 21.0 3.3 4.4
Mean for all children 21.3 4.3 5.5
Note: Median and mean durations are based on the distributions at the time
of the survey of the proportion of births by months since birth. Includes
children living and deceased at the time of the survey. Figures in
parentheses are based on 25-49 unweighted cases. An asterisk denotes a
figure based on fewer than 25 unweighted cases that has been suppressed.
1
It is assumed that non-last-born children and last-born children not currently
living with the mother are not currently breastfeeding.
2
Either exclusively breastfed or received breast milk and plain water, and/or
non-milk liquids only
11.2.4 Types of Complementary Foods
UNICEF and WHO recommend the introduction of solid or semi-solid food to infants around age
6 months because by that age breast milk alone is no longer sufficient to maintain a child’s optimal growth.
In the transition to eating the family diet, children from age 6 months should be fed small quantities of
solid and semisolid foods throughout the day while continuing to breastfeed up to age 2 or beyond. Table
11.5 presents the percentages of youngest children under age 2 who are living with their mother by types
of foods consumed in the day or night preceding the interview, according to their breastfeeding status.
Table 11.5 shows that, in Kenya, 80 percent of breastfed children age 6-8 months are fed solid or
semisolid foods in addition to being breastfed within the 24 hours before the survey. This is substantially
higher than the percentage of breastfeeding children fed complementary foods at age 4-5 months (27
percent) or age 2-3 months (13 percent) when the introduction of complementary foods is not
recommended.
Overall, 92 percent of breastfed children age 6-23 months receive solid or semisolid
complementary foods. The most common foods given to breastfeeding children age 6-23 months are foods
made from grains (80 percent), fruits and vegetables rich in vitamin A (64 percent), food made from roots
and tubers (38 percent), and other fruits and vegetables (33 percent). Children are also fed protein-rich
168 • Nutrition of Children and Women
foods such as legumes and nuts (25 percent); meat, fish, and poultry (21 percent); and eggs (17 percent).
Thirteen percent are fed cheese, yogurt, and other milk products, and 5 percent are given fortified baby
foods. Other than breast milk, liquids fed to children in this age group include other liquids such as juice or
clear broth (63 percent) and other milk (49 percent). Five percent of breastfeeding children age 6-23
months are also given infant formula.
Table 11.5 also presents data on the types of complementary foods consumed by nonbreastfeeding
children. Ninety-seven percent of nonbreastfeeding children age 6-23 months are fed solid or semisolid
foods. The percentage of children consuming each type of complementary food or liquid is higher among
nonbreastfeeding children than among breastfeeding children.
Table 11.5 Foods and liquids consumed by children in the day or night preceding the interview
Percentage of youngest children under two years of age who are living with the mother by type of foods consumed in the day or night preceding the
interview, according to breastfeeding status and age, Kenya 2014
Liquids Solid or semisolid foods
Fruits
and Food
vege- Other made Food Cheese,
Food tables fruits from made yogurt, Any solid
Fortified made rich in and roots from Meat, other or semi- Number
Age in Infant Other Other baby from vitamin vege- and legumes fish, milk solid of
months formula milk1 liquids2 foods grains3 A4 tables tubers and nuts poultry Eggs product food children
BREASTFEEDING CHILDREN
0-1 1.3 3.7 2.8 0.0 1.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.9 214
2-3 0.4 11.7 9.4 0.0 8.8 3.8 1.8 0.6 0.0 0.0 0.1 0.0 13.2 301
4-5 0.3 29.6 17.1 1.7 20.4 7.7 3.1 7.3 0.2 1.8 0.7 2.3 27.1 275
6-8 4.1 43.8 50.1 7.3 64.3 41.4 25.2 27.9 14.0 7.8 9.0 6.2 79.6 439
9-11 5.5 51.2 62.0 2.8 78.6 59.9 28.2 41.4 21.9 15.4 15.4 13.4 95.2 437
12-17 6.4 51.0 66.7 3.6 84.7 70.8 36.8 36.1 28.6 26.6 18.2 13.7 95.4 795
18-23 4.8 47.8 70.0 7.7 87.8 77.3 40.0 45.8 33.2 28.4 22.7 16.7 96.4 488
0-17 4.0 38.4 44.8 3.2 56.3 42.2 22.0 24.9 15.7 12.9 10.3 8.2 66.7 2,461
6-23 5.4 48.9 63.1 5.1 80.0 64.1 33.4 37.7 25.3 20.9 16.8 12.8 92.4 2,159
Total 4.1 40.0 49.0 3.9 61.5 48.0 24.9 28.3 18.6 15.5 12.4 9.6 71.6 2,949
NONBREASTFEEDING CHILDREN
0-17 10.6 54.8 67.8 8.0 83.5 75.0 42.5 35.9 29.1 34.4 26.3 22.0 93.4 133
6-23 6.6 54.6 73.8 6.6 86.1 77.3 40.3 44.7 32.9 31.9 24.5 19.2 96.6 435
Total 6.5 54.7 73.2 6.6 85.8 76.6 39.9 44.3 32.6 31.6 24.3 19.0 96.2 439
Note: Breastfeeding status and food consumed refer to a “24-hour” period (yesterday and last night).
1
Other milk includes fresh, tinned, and powdered animal milk.
2
Does not include plain water. Includes juice, juice drinks, clear broth, or other non-milk liquids.
3
Includes fortified baby food
4
Includes pumpkin, squash, carrots, yellow or orange sweet potatoes, dark green leafy vegetables, mangoes, papayas, and guavas.
11.2.5 Infant and Young Child Feeding (IYCF) Practices
Appropriate IYCF practices include breastfeeding through age 2 years, introduction of solid and
semisolid foods at age 6 months, and gradual increases in the amount of food given and frequency of
feeding as the child gets older. The minimum frequencies for feeding children in developing countries are
based on the energy output of complementary foods. The energy needs of children are based on age-
specific total daily energy requirements plus two standard deviations (to cover almost all children), minus
the average energy intake from breast milk. Infants with low breast milk intake need to be fed more
frequently than those with high breast milk intake. However, care should be taken that feeding frequencies
do not exceed the recommended input from complementary foods because excessive feeding can result in
displacement of breast milk (PAHO/WHO, 2003).
According to recommendations, breastfed children age 6-23 months should receive animal-source
foods and vitamin A-rich fruits and vegetables daily (PAHO/WHO, 2003). Because first foods almost
always include a grain- or tuber-based staple, it is unlikely that young children who eat food from less than
three groups will receive both an animal-source food and a vitamin A-rich fruit or vegetable. Therefore,
Nutrition of Children and Women • 169
three food groups are considered the minimum number appropriate for breastfed children (Arimond and
Ruel, 2004). Breastfed infants age 6-8 months should receive complementary foods two to three times a
day with one or two snacks; breastfed children age 9-23 months should receive meals three to four times a
day with one or two snacks (PAHO/WHO, 2003; WHO, 2008; WHO, 2010a).
Nonbreastfed children age 6-23 months should receive milk or milk products two or more times a
day to ensure that their calcium needs are met. In addition, they need animal-source foods and vitamin A-
rich fruits and vegetables. Four food groups are considered the minimum number appropriate for
nonbreastfed young children. Nonbreastfed children age 12-23 months should be fed meals four to five
times each day, with one or two snacks (WHO, 2005; WHO, 2008; WHO, 2010a).
Table 11.6 presents summary indicators of IYCF practices in the 24 hours preceding the survey
for the youngest children age 6-23 months living with their mother. Ninety-one percent of children
received breast milk or milk products. Forty-one percent had an adequately diverse diet—that is, they had
been given foods from the appropriate number of food groups—and 51 percent had been fed the minimum
number of times appropriate for their age. The feeding practices of only 22 percent of children age 6-23
months meet the minimum standards with respect to all three IYCF practices. The IYCF indicators for
minimum acceptable diet by breastfeeding status among Kenyan children age 6-23 months are summarised
in Figure 11.5.
The likelihood of children being fed according to the recommended IYCF guidelines increases
with age. Children in urban areas (31 percent) are more likely to be fed appropriately than their rural
counterparts (17 percent). By region, adherence to IYCF feeding practices is highest in Nairobi (39
percent) and lowest in North Eastern (3 percent). Appropriate feeding increases with increasing mother’s
education and household wealth.
In the period between the 2008-09 KDHS and the 2014 KDHS, the definition of standard IYCF
indicators changed to reflect more restrictive requirements. In order to compare the IYCF results presented
here with results from the 2008-09 KDHS, the 2014 data were recalculated according to the definitions
used in 2008-09. This comparison indicates that the percentage of children age 6-23 months fed in
accordance with the three recommended IYCF practices decreased between 2008-09 and 2014, from 39
percent to 31 percent (data not shown).
170 • Nutrition of Children and Women
Table 11.6 Infant and young child feeding (IYCF) practices
Percentage of youngest children age 6-23 months living with their mother who are fed according to three IYCF feeding practices based on breastfeeding status, number of food
groups, and times they are fed during the day or night preceding the survey, by background characteristics, Kenya 2014
Among breastfed children 6-23 months, Among non-breastfed children 6-23 months,
percentage fed: percentage fed: Among all children 6-23 months, percentage fed:
Both 4+
food Number Number
groups of of non- Number
and breastfed breastfed Breast of all
Minimum minimum children Milk or Minimum With 3 children milk, milk, Minimum With 3 children
Background 4+ food meal meal 6-23 milk 4+ food meal IYCF 6-23 or milk 4+ food meal IYCF 6-23
characteristic groups1 frequency frequency months products3
2
groups1 frequency practices5
4
months products6 groups1 7
frequency practices months
Age in months
6-8 21.1 62.0 17.0 439 * * * * 7 99.3 21.3 61.9 16.8 446
9-11 32.2 40.6 17.9 437 * * * * 10 98.7 32.2 41.4 17.8 447
12-17 45.5 46.7 24.9 795 52.0 63.4 58.0 17.4 112 94.1 47.7 48.1 23.9 907
18-23 47.3 51.2 28.1 488 40.3 51.7 55.6 18.9 306 77.0 49.0 52.9 24.5 793
Sex
Male 37.9 49.5 21.8 1,093 44.5 57.8 57.9 21.1 233 90.2 41.4 51.0 21.7 1,326
Female 38.6 49.6 23.3 1,066 42.5 49.6 55.4 14.5 202 90.8 40.4 50.5 21.9 1,268
Residence
Urban 53.8 57.1 32.2 752 54.0 67.3 66.4 26.9 185 90.9 56.5 58.9 31.2 937
Rural 29.9 45.6 17.4 1,406 35.9 44.2 49.5 11.6 251 90.3 32.1 46.2 16.5 1,657
Region
Coast 24.8 52.5 15.8 212 (26.9) (43.8) (45.9) (7.3) 41 88.1 27.9 51.4 14.4 253
North Eastern 10.7 21.0 2.4 60 (62.4) (14.7) (42.9) (4.0) 18 91.3 11.6 26.1 2.7 77
Eastern 31.8 66.6 21.7 284 (38.5) (48.0) (63.9) (15.2) 33 93.6 33.5 66.3 21.1 317
Central 57.5 58.6 37.4 190 (28.8) (54.3) (51.9) (10.7) 49 85.4 56.9 57.3 31.9 239
Rift Valley 34.5 48.8 21.3 678 52.9 48.8 60.5 14.5 110 93.4 36.5 50.4 20.4 789
Western 26.4 24.9 10.5 248 (20.3) (41.4) (23.4) (9.1) 51 86.4 29.0 24.6 10.3 298
Nyanza 40.2 51.7 25.7 274 28.2 67.9 63.5 19.8 68 85.7 45.7 54.0 24.5 342
Nairobi 74.1 52.5 37.0 213 * * * * 65 95.5 75.0 58.9 39.3 278
Mother’s
education
No education 16.3 33.4 7.9 278 53.8 22.8 42.3 6.1 39 94.3 17.1 34.5 7.7 316
Primary
incomplete 27.2 48.7 16.7 603 31.3 40.5 51.9 11.3 117 88.8 29.4 49.2 15.9 720
Primary
complete 39.3 52.5 22.0 550 34.4 49.7 43.0 8.5 105 89.5 41.0 51.0 19.8 655
Secondary+ 55.0 54.3 33.4 728 55.0 72.5 71.3 30.9 175 91.3 58.4 57.6 32.9 903
Wealth quintile
Lowest 19.2 40.8 10.7 543 42.6 33.0 50.1 8.3 93 91.6 21.2 42.2 10.3 636
Second 27.6 46.6 17.2 419 27.2 41.5 44.6 11.0 91 87.0 30.1 46.2 16.1 510
Middle 36.2 50.1 21.5 378 24.5 63.7 57.1 8.1 71 88.1 40.5 51.2 19.3 449
Fourth 51.9 54.0 29.7 409 48.3 71.1 52.0 29.5 78 91.7 54.9 53.7 29.7 487
Highest 62.7 59.4 37.8 409 68.6 64.4 76.8 31.3 103 93.7 63.0 62.8 36.5 512
Total 38.3 49.6 22.6 2,159 43.6 54.0 56.7 18.0 435 90.5 40.9 50.8 21.8 2,594
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
1
Food groups: a. infant formula, milk other than breast milk, cheese or yogurt or other milk products; b. foods made from grains, roots, and tubers, including porridge and fortified
baby food from grains; c. vitamin A-rich fruits and vegetables; d. other fruits and vegetables; e. eggs; f. meat, poultry, fish, and shellfish (and organ meats); g. legumes and nuts.
2
For breastfed children, minimum meal frequency is receiving solid or semi-solid food at least twice a day for infants 6-8 months and at least three times a day for children 9-23
months
3
Includes two or more feedings of commercial infant formula, fresh, tinned, and powdered animal milk, and yogurt
4
For non-breastfed children age 6-23 months, minimum meal frequency is receiving solid or semi-solid food or milk feeds at least four times a day
5
Non-breastfed children age 6-23 months are considered to be fed with a minimum standard of three Infant and Young Child Feeding Practices if they receive other milk or milk
products at least twice a day, receive the minimum meal frequency, and receive solid or semi-solid foods from at least four food groups not including the milk or milk products
food group
6
Breastfeeding, or not breastfeeding and receiving two or more feedings of commercial infant formula, fresh, tinned and powdered animal milk, and yogurt
7
Children are fed the minimum recommended number of times per day according to their age and breastfeeding status as described in footnotes 2 and 4.
Nutrition of Children and Women • 171
Figure 11.5 IYCF indicators on minimum acceptable diet
Percent
57
54
50 51
41
38
23 22
18
IYCF 5: Minimum dietary IYCF 6: Minimum meal IYCF 7: Minimum acceptable
diversity frequency diet
Among breastfed children Among non-breastfed children Among all children 6-23 months
KDHS 2014
11.3 MICRONUTRIENT INTAKE AMONG CHILDREN
Micronutrient deficiency is a major contributor to childhood morbidity and mortality. Children
can receive micronutrients from foods, food fortification, and direct supplementation. Table 11.7
summarises information collected on children’s intake of vitamin A and iron, and their receipt of
deworming medications.
Vitamin A is an essential micronutrient for the immune system that plays an important role in
maintaining the epithelial tissue in the body. Severe vitamin A deficiency (VAD) can cause eye damage.
VAD can also increase the severity of infections, such as measles and diarrhoeal diseases in children, and
slow recovery from illness. Vitamin A is found in breast milk, other milk, liver, eggs, fish, butter,
mangoes, papayas, carrots, pumpkins, dark green leafy vegetables, and some other fruits and vegetables.
The liver can store an adequate amount of the vitamin for four to six months. Periodic dosing (usually
every six months) with vitamin A supplements is one method of ensuring that children at risk do not
develop VAD.
Overall, 72 percent of children age 6-23 months consumed foods rich in vitamin A the day or
night preceding the survey. The percentage of children consuming foods rich in vitamin A increases with
age. Nonbreastfeeding children (83 percent) are more likely than breastfeeding children (70 percent) to
consume vitamin A-rich foods. Urban children are more likely to consume vitamin A-rich foods (84
percent) than children in rural areas (65 percent). Nairobi has the highest proportion (97 percent) of
children who consumed vitamin A-rich foods, while North Eastern has the lowest (25 percent).
Consumption of vitamin A-rich foods increases with increasing mother’s education and household wealth.
Iron is essential for cognitive development, and low iron intake can contribute to anaemia. Iron
requirements are greatest at age 6-11 months, when growth is extremely rapid. Table 11.7 shows that 33
percent of children age 6-23 months consumed foods rich in iron the day or night preceding the survey.
Consumption of iron-rich foods increases with age. Nonbreastfeeding children (43 percent) are more likely
than breastfeeding children (31 percent) to consume iron-rich foods. Urban children (41 percent) are more
likely to consume iron-rich foods than children in rural areas (29 percent). Nyanza (47 percent) and
Nairobi (46 percent) have the highest proportions of children consuming iron-rich foods, while North
Eastern and Eastern have the lowest (21 percent each). Consumption of iron-rich foods increases with
increasing mother’s education and household wealth.
172 • Nutrition of Children and Women
Table 11.7 shows that only 6 percent of children age 12-59 months received iron supplements in
the seven days preceding the survey. Iron supplementation does not vary much by background
characteristics. Infection with helminths or intestinal worms has an adverse impact on the physical
development of children and is associated with high levels of iron deficiency anaemia and other nutritional
deficiencies. In Kenya, policy requires that children age 12-59 months receive deworming medication once
every six months since regular treatment is a simple, cost-effective measure to address these infections.
Fifty-one percent of children age 12-59 months received deworming medication in the six months
Table 11.7 Micronutrient intake among children: Vitamin A, iron, and deworming medication
Among youngest children age 6-23 months who are living with their mother, the percentages who consumed vitamin A-rich and iron-
rich foods in the day or night preceding the survey, and among all children 12-59 months, percentages who were given iron
supplements in the past seven days, and who were given deworming medication in the six months preceding the survey, by
background characteristics, Kenya 2014
Among youngest children age 6-23 months living
with the mother: Among all children age 12-59 months:
Percentage who Percentage who Percentage Percentage
consumed foods consumed foods given iron given deworming
Background rich in vitamin A rich in iron in last Number of supplements in medication in Number of
characteristic in last 24 hours1 24 hours2 children last 7 days last 6 months3 children
Age in months
6-8 45.5 13.5 446 na na na
9-11 65.4 24.6 447 na na na
12-17 78.2 39.0 907 6.1 36.4 952
18-23 83.2 43.0 793 6.3 41.5 885
24-35 na na na 5.2 54.7 1,771
36-47 na na na 4.9 54.2 1,856
48-59 na na na 5.8 55.8 1,764
Sex
Male 70.8 33.2 1,326 5.8 51.5 3,634
Female 73.0 33.5 1,268 5.2 50.1 3,594
Breastfeeding status
Breastfeeding 69.6 31.3 2,159 6.8 40.2 1,588
Not breastfeeding 83.3 43.2 432 5.1 53.9 5,599
Mother’s age at birth
15-19 69.5 27.8 195 2.9 44.6 222
20-29 72.9 33.8 1,544 5.6 52.6 3,937
30-39 71.3 33.5 756 5.6 50.0 2,537
40-49 65.2 35.9 98 5.3 44.3 532
Residence
Urban 83.7 40.9 937 5.1 56.5 2,623
Rural 65.2 29.1 1,657 5.8 47.5 4,604
Region
Coast 66.1 28.3 253 4.3 43.7 718
North Eastern 24.5 20.7 77 9.2 19.7 240
Eastern 64.4 21.4 317 3.1 43.2 866
Central 83.7 39.6 239 7.0 70.8 672
Rift Valley 67.6 29.4 789 6.2 52.6 2,082
Western 66.2 31.8 298 5.8 48.7 869
Nyanza 80.4 46.7 342 5.2 49.2 1,006
Nairobi 96.6 46.2 278 5.2 57.6 774
Mother’s education
No education 38.2 20.4 316 8.5 25.2 861
Primary incomplete 71.4 31.1 720 5.0 46.0 2,163
Primary complete 72.8 32.2 655 5.4 54.6 1,930
Secondary+ 83.4 40.5 903 5.0 61.7 2,274
Wealth quintile
Lowest 51.6 24.1 636 6.9 34.3 1,666
Second 70.8 29.8 510 5.4 48.5 1,475
Middle 71.7 35.3 449 5.2 52.1 1,329
Fourth 84.7 37.0 487 5.0 60.6 1,322
Highest 86.1 43.2 512 4.8 61.9 1,435
Total 71.9 33.3 2,594 5.5 50.8 7,228
Note: Information on iron supplements and deworming medication is based on the mother’s recall. Total for children age 6-23 months
includes four children with missing information on breastfeeding status. Total for children age 12-59 months 53 children with missing
information on breastfeeding status.
na = Not applicable
1
Includes meat (and organ meat), fish, poultry, eggs, pumpkin, squash, carrots, orange or yellow sweet potatoes, dark green leafy
vegetables, mango, papaya, guava, and other locally grown fruits and vegetables that are rich in vitamin A
2
Includes meat (including organ meat), fish, poultry, and eggs
3
Deworming for intestinal parasites is commonly done for helminths and for schistosomiasis.
Nutrition of Children and Women • 173
before the survey. More children in urban Table 11.8 Micronutrient intake among children: Vitamin A and iodised salt
areas (57 percent) received deworming Among all children 6-59 months, the percentage who were given vitamin A
medication than in rural areas (48 percent). supplements in the six months preceding the survey, and among all children
age 6-59 months who live in households that were tested for iodised salt, the
Children in Central (71 percent) are more percentage who live in households with iodised salt, by background
likely to receive deworming medication than characteristics, Kenya 2014
those in other regions, and children in North Among children age 6-59
months living in
Eastern are least likely to receive deworming Among all children age households tested for
6-59 months: iodised salt:
medication (20 percent). The likelihood of
Percentage Percentage
receiving deworming medication increases given vitamin living in
A supplements households
with increasing mother’s education and Background in last 6 Number of with iodised Number of
household wealth. characteristic months children salt1 children
Age in months
For the purpose of comparison with 6-8
9-11
67.9
80.2
941
968
99.7
99.2
900
918
the 2008-09 KDHS, the data on receipt of 12-17 79.5 1,983 99.4 1,911
18-23 75.0 1,794 99.3 1,734
deworming medication were recalculated for 24-35 70.2 3,760 99.5 3,622
children age 6-59 months. The results indi- 36-47
48-59
69.7
68.5
3,889
3,672
99.6
99.4
3,738
3,537
cate that deworming among children age 6- Sex
59 months increased from 38 percent in Male 71.6 8,618 99.4 8,326
Female 71.9 8,390 99.6 8,034
2008-09 to 46 percent in 2014 (data not
Breastfeeding status
shown). Breastfeeding 77.1 2,479 99.5 2,398
Not breastfeeding 70.9 14,487 99.5 13,923
Table 11.8 shows that 72 percent of Mother’s age at birth
15-19 68.1 739 98.2 698
children age 6-59 months received vitamin 20-29 71.5 9,387 99.5 9,061
30-39 73.1 5,713 99.5 5,487
A supplements in the past six months. 40-49 69.0 1,168 99.7 1,113
Children who are still breastfeeding (77 Residence
percent) are more likely to be given vitamin Urban
Rural
75.4
69.7
6,104
10,904
99.4
99.5
5,940
10,420
A supplements than those who are not Region
breastfeeding (71 percent). More children in Coast 69.6 1,711 97.5 1,659
North Eastern 55.3 577 98.2 488
urban areas received vitamin A supplements Eastern 73.9 2,051 99.5 1,997
than in rural areas (75 percent and 70 Central
Rift Valley
79.2
67.5
1,579
4,956
99.9
99.8
1,538
4,695
percent, respectively). At the regional level, Western 77.8 1,967 99.4 1,912
Nyanza 69.9 2,413 99.9 2,342
the proportion of children receiving vitamin Nairobi 77.8 1,754 100.0 1,730
A supplements is highest in Central (79 Mother’s education
percent) and lowest in North Eastern (55 No education
Primary incomplete
60.0
68.8
2,034
4,821
99.1
99.2
1,779
4,660
percent). The proportion of children re- Primary complete 75.2 4,707 99.4 4,593
Secondary+ 75.8 5,446 99.9 5,328
ceiving vitamin A supplements increases
Wealth quintile
with increasing mother’s education and Lowest 64.3 4,018 98.7 3,661
household wealth. Second
Middle
71.3
72.5
3,476
3,063
99.5
99.8
3,385
2,987
Fourth 76.2 2,986 99.8 2,929
The percentage of children receiv- Highest 76.4 3,464 99.8 3,397
ing vitamin A supplements varies across Total 71.7 17,008 99.5 16,360
counties (Table 11.8C). In 11 counties, more Note: Information on vitamin A is based on both mother’s recall and the
than 80 percent of children receive vitamin immunisation card (where available). Total for children age 6-59 months
includes 53 children with missing information on breastfeeding status. Total for
A supplements (Laikipia, Bungoma, Kwale, children age 6-59 months living in households tested for iodised salt includes 47
children with missing information on breastfeeding status.
Kitui, Busia, Makueni, Mombasa, Kiambu, 1 Excludes children in households in which salt was not tested.
Nyandarua, Elgeyo Marakwet, and Isiolo).
Mandera has the lowest proportion (20 percent) of children receiving vitamin A supplements.
A comparison with the 2008-09 KDHS shows that the proportion of children receiving vitamin A
supplements has increased from 30 percent in 2008-09 to the current 72 percent.
174 • Nutrition of Children and Women
Table 11.8C Micronutrient intake among children: Vitamin A and iodised salt
Among all children 6-59 months, the percentages who were given vitamin A supplements in
the six months preceding the survey, and among all children age 6-59 months who live in
households that were tested for iodised salt, the percentage who live in households with
iodised salt, by county, Kenya 2014
Among children age 6-59 months
Among all children age 6-59 living in households tested for
months: iodised salt:
Percentage given
vitamin A Percentage living in
supplements in last Number of households with Number of
County 6 months children iodised salt1 children
Coast 69.6 1,711 97.5 1,659
Mombasa 81.7 438 100.0 423
Kwale 86.6 350 98.1 332
Kilifi 49.9 624 95.5 610
Tana River 67.6 151 99.6 148
Lamu 77.2 47 86.0 46
Taita Taveta 78.9 100 99.6 100
North Eastern 55.1 579 98.2 490
Garissa 75.5 206 96.6 180
Wajir 58.5 233 98.7 193
Mandera 19.5 140 100.0 118
Eastern 73.9 2,051 99.5 1,997
Marsabit 65.0 80 100.0 74
Isiolo 81.2 74 96.7 71
Meru 59.5 443 100.0 427
Tharaka-Nithi 74.3 125 98.7 117
Embu 78.5 179 100.0 176
Kitui 84.5 387 99.8 379
Machakos 71.6 442 99.0 442
Makueni 82.1 321 99.7 310
Central 79.2 1,578 99.9 1,538
Nyandarua 80.5 207 100.0 207
Nyeri 76.1 220 100.0 219
Kirinyaga 76.7 171 98.9 154
Murang’a 76.4 264 100.0 252
Kiambu 81.3 716 100.0 706
Rift Valley 67.5 4,956 99.8 4,695
Turkana 69.8 309 99.3 206
West Pokot 64.2 263 100.0 259
Samburu 72.6 105 99.3 98
Trans-Nzoia 70.2 452 100.0 441
Uasin Gishu 68.7 419 100.0 407
Elgeyo Marakwet 80.7 147 100.0 146
Nandi 66.6 351 100.0 349
Baringo 67.2 207 100.0 198
Laikipia 90.1 185 98.9 169
Nakuru 73.6 783 100.0 776
Narok 57.9 560 100.0 542
Kajiado 47.8 415 99.6 378
Kericho 67.1 326 98.7 309
Bomet 69.9 431 100.0 417
Western 77.8 1,967 99.4 1,912
Kakamega 65.0 674 99.3 655
Vihiga 76.3 202 98.5 198
Bungoma 87.6 746 99.4 724
Busia 82.6 346 100.0 336
Nyanza 70.0 2,411 99.9 2,339
Siaya 60.8 347 100.0 332
Kisumu 74.0 437 100.0 416
Homa Bay 79.2 561 99.7 553
Migori 73.3 466 99.8 449
Kisii 55.4 423 100.0 413
Nyamira 75.4 177 100.0 176
Nairobi 77.7 1,754 100.0 1,730
Total 71.7 17,008 99.5 16,360
Note: Information on vitamin A is based on both mother’s recall and the immunisation card
(where available).
1
Excludes children in households in which salt was not tested.
Nutrition of Children and Women • 175
Iodine deficiency, most frequently caused by inadequate iodine intake, has serious effects on body
growth and mental development. Fortification of salt with iodine is the most common method of
preventing iodine deficiency. To assess the use of iodised salt, the 2014 KDHS asked households to
provide a teaspoon of salt used for cooking. The salt was tested for iodine using a rapid test kit. Virtually
all children living in households tested for iodised salt have access to iodised salt (Table 11.8). There is no
difference in the availability of iodised salt across the households and children’s background
characteristics.
11.4 IODISATION OF HOUSEHOLD SALT
Table 11.9 shows the percentage of households with salt tested for iodine content, the percentage
of households without salt, and, among households with tested salt, the percentage with iodine present in
the salt. Survey teams tested salt in 94 percent of households; the remaining 6 percent had no salt available.
Virtually all households with tested salt have iodised salt. In all counties, over 95 percent of the households
tested for iodised salt have iodised salt except Lamu, with 89 percent (Table 11.9C).
Table 11.9 Presence of iodised salt in household
Among all households, the percentage with salt tested for iodine content and the percentage with no salt in
the household; and among households with salt tested, the percentage with iodised salt, according to
background characteristics, Kenya 2014
Among households with
Among all households, the percentage tested salt:
Background With no salt in Number of Percentage with Number of
characteristic With salt tested the household households iodised salt households
Residence
Urban 94.0 6.0 15,290 99.3 14,367
Rural 93.5 6.5 21,140 99.6 19,772
Region
Coast 91.7 8.3 3,569 98.5 3,275
North Eastern 79.1 20.9 724 98.3 573
Eastern 93.2 6.8 5,262 99.6 4,905
Central 94.6 5.4 5,012 99.5 4,740
Rift Valley 93.4 6.6 9,249 99.8 8,635
Western 94.5 5.5 3,604 99.1 3,405
Nyanza 95.4 4.6 4,559 99.7 4,348
Nairobi 95.7 4.3 4,451 99.7 4,260
Wealth quintile
Lowest 87.8 12.2 6,077 99.0 5,335
Second 94.2 5.8 6,557 99.5 6,175
Middle 94.6 5.4 6,967 99.7 6,590
Fourth 94.5 5.5 8,225 99.7 7,776
Highest 96.0 4.0 8,603 99.5 8,263
Total 93.7 6.3 36,430 99.5 34,139
176 • Nutrition of Children and Women
Table 11.9C Presence of iodised salt in household
Among all households, the percentage with salt tested for iodine content and the percentage with no salt in the
household; and among households with salt tested, the percentage with iodised salt, according to county,
Kenya 2014
Among households with tested
Among all households, the percentage salt:
With no salt in Number of Percentage with Number of
County With salt tested the household households iodised salt households
Coast 91.7 8.3 3,569 98.5 3,275
Mombasa 89.4 10.6 1,245 99.9 1,113
Kwale 91.4 8.6 704 98.7 644
Kilifi 93.0 7.0 999 96.9 930
Tana River 93.4 6.6 210 99.7 196
Lamu 93.7 6.3 104 89.2 97
Taita Taveta 96.1 3.9 307 99.9 295
North Eastern 79.1 20.9 724 98.3 573
Garissa 87.0 13.0 265 96.9 230
Wajir 71.6 28.4 242 99.3 173
Mandera 77.9 22.1 217 99.3 169
Eastern 93.2 6.8 5,262 99.6 4,905
Marsabit 87.1 12.9 146 100.0 127
Isiolo 93.0 7.0 122 98.2 113
Meru 90.7 9.3 1,406 100.0 1,275
Tharaka-Nithi 87.1 12.9 379 99.4 330
Embu 96.9 3.1 548 100.0 531
Kitui 92.7 7.3 856 99.8 793
Machakos 98.2 1.8 1,088 99.1 1,068
Makueni 93.1 6.9 717 99.5 668
Central 94.6 5.4 5,012 99.5 4,740
Nyandarua 97.5 2.5 593 100.0 578
Nyeri 97.9 2.1 792 99.9 776
Kirinyaga 85.5 14.5 622 95.9 532
Murang’a 92.3 7.7 968 100.0 894
Kiambu 96.3 3.7 2,037 100.0 1,961
Rift Valley 93.4 6.6 9,249 99.8 8,635
Turkana 66.5 33.5 448 99.4 298
West Pokot 98.2 1.8 319 100.0 313
Samburu 86.9 13.1 146 99.5 127
Trans-Nzoia 94.9 5.1 814 99.7 772
Uasin Gishu 95.3 4.7 962 99.7 917
Elgeyo Marakwet 97.8 2.2 301 100.0 295
Nandi 98.8 1.2 671 99.9 663
Baringo 87.9 12.1 391 99.7 343
Laikipia 92.2 7.8 406 99.8 375
Nakuru 98.1 1.9 1,950 99.9 1,912
Narok 94.8 5.2 752 100.0 713
Kajiado 88.9 11.1 770 99.5 684
Kericho 93.0 7.0 589 99.5 548
Bomet 92.2 7.8 732 100.0 675
Western 94.5 5.5 3,604 99.1 3,405
Kakamega 94.6 5.4 1,350 98.6 1,276
Vihiga 97.1 2.9 446 98.5 433
Bungoma 92.8 7.2 1,180 99.6 1,096
Busia 95.4 4.6 628 99.8 599
Nyanza 95.4 4.6 4,559 99.7 4,348
Siaya 92.7 7.3 725 99.8 672
Kisumu 94.3 5.7 943 99.6 889
Homa Bay 97.0 3.0 877 99.5 851
Migori 94.0 6.0 701 99.8 659
Kisii 96.6 3.4 904 99.8 874
Nyamira 98.6 1.4 409 100.0 404
Nairobi 95.7 4.3 4,451 99.7 4,260
Total 93.7 6.3 36,430 99.5 34,139
11.5 NUTRITIONAL STATUS OF WOMEN
Measurements of height and weight were obtained from women in one-half of households selected
in the 2014 KDHS in order to assess women’s nutritional status. There are, however, sufficient cases to
calculate county-level estimates of women’s nutritional status. The nutritional status of women was
assessed with two anthropometric indices: height and body mass index (BMI).
Nutrition of Children and Women • 177
Short stature reflects poor socioeconomic conditions and inadequate nutrition during childhood
and adolescence. In a woman, short stature is a risk factor for poor birth outcomes and obstetric
complications. For example, short stature is associated with small pelvic size, which increases the
likelihood of difficulty during delivery and the risk of bearing low birth weight babies. A woman is
considered to be at risk if her height is below 145 cm.
BMI is expressed as the ratio of weight in kilograms to the square of height in metres (kg/m2).
BMI is used to measure thinness or obesity. A BMI below 18.5 kg/m2 indicates thinness or acute
undernutrition, and a BMI of 25.0 kg/m2 or above indicates overweight or obesity. A BMI below 17 kg/m2
indicates severe undernutrition and is associated with increased mortality. Low pre-pregnancy BMI, as
with short stature, is associated with poor birth outcomes and obstetric complications.
To derive these indices, the 2014 KDHS took height and weight measurements among women age
15-49. Weight measurements were made using an electronic scale (SECA scale). Standing height
measurements were made using height/length (Shorr) boards. Respondents for whom there was no
information on height and/or weight and those who were pregnant or had given birth in the two months
preceding the survey were excluded from the analysis.
According to Table 11.10, less than 1 percent of women age 15-49 are shorter than 145 cm. There
are no apparent differences in height by background characteristics.
Table 11.10 Nutritional status of women
Among women age 15-49, the percentage with height under 145 cm, mean Body Mass Index (BMI), and the percentage with specific BMI levels, by background
characteristics, Kenya 2014
Height Body Mass Index1
<17
(Moderate- ≥25.0
Percentage Mean Body 18.5-24.9 ly and (Total over- 25.0-29.9
Background below Number of Mass Index (Total <18.5 17.0-18.4 severely weight or (Over- ≥30.0 Number of
characteristic 145 cm women (BMI) normal) (Total thin) (Mildly thin) thin) obese) weight) (Obese) women
Age
15-19 1.7 2,628 21.4 71.2 16.6 11.2 5.4 12.2 10.6 1.6 2,480
20-29 0.6 5,504 23.3 62.8 8.7 6.5 2.2 28.5 21.7 6.9 4,838
30-39 0.7 3,857 24.8 50.9 5.7 4.1 1.6 43.4 28.8 14.6 3,576
40-49 0.6 2,276 25.5 46.0 6.0 4.6 1.4 48.0 28.6 19.3 2,249
Residence
Urban 0.5 5,702 24.9 51.2 5.5 4.1 1.4 43.3 28.6 14.7 5,246
Rural 1.0 8,563 23.0 63.0 11.2 7.9 3.2 25.8 18.8 7.0 7,897
Region
Coast 2.1 1,391 23.5 56.8 11.0 8.6 2.5 32.1 20.6 11.6 1,262
North Eastern 0.1 283 21.5 52.3 28.7 17.3 11.4 19.0 14.2 4.7 239
Eastern 1.7 2,050 23.4 60.2 9.8 6.9 2.9 30.1 21.6 8.4 1,918
Central 0.7 1,810 25.3 46.7 6.2 4.1 2.1 47.1 29.4 17.6 1,697
Rift Valley 0.5 3,651 23.1 59.6 11.8 8.0 3.8 28.6 20.8 7.8 3,349
Western 0.4 1,551 23.0 67.0 8.6 6.8 1.8 24.4 18.6 5.8 1,431
Nyanza 0.5 1,865 23.3 67.2 6.3 5.0 1.3 26.5 19.8 6.8 1,729
Nairobi 0.3 1,662 25.4 49.7 2.8 2.6 0.2 47.6 31.1 16.5 1,517
Education
No education 0.9 992 21.6 57.1 25.3 15.2 10.1 17.6 11.9 5.8 855
Primary incomplete 1.6 3,725 22.7 63.8 12.3 8.5 3.8 23.9 17.0 6.9 3,443
Primary complete 0.7 3,453 24.3 56.6 5.6 4.6 1.0 37.7 26.3 11.4 3,170
Secondary+ 0.4 6,095 24.3 56.1 6.2 4.8 1.4 37.8 25.8 12.0 5,673
Wealth quintile
Lowest 1.8 2,198 21.1 66.4 21.5 14.4 7.2 12.1 10.2 1.8 1,934
Second 1.5 2,546 22.5 67.8 11.2 8.1 3.1 21.0 16.3 4.8 2,353
Middle 0.5 2,819 23.3 65.0 7.6 5.9 1.7 27.4 21.0 6.4 2,613
Fourth 0.5 3,048 24.6 53.6 5.3 3.9 1.4 41.1 28.6 12.5 2,829
Highest 0.3 3,655 25.6 45.9 4.2 3.2 0.9 50.0 30.7 19.3 3,415
Total 0.8 14,265 23.7 58.3 8.9 6.4 2.5 32.8 22.7 10.1 13,143
Note: The Body Mass Index (BMI) is expressed as the ratio of weight in kilograms to the square of height in metres (kg/m2).
1
Excludes pregnant women and women with a birth in the preceding 2 months
178 • Nutrition of Children and Women
Table 11.10C Nutritional status of women
Among women age 15-49, the percentage with height under 145 cm, mean Body Mass Index (BMI), and the percentage with specific BMI levels, by county,
Kenya 2014
Height Body Mass Index1
<17
(Moderate- ≥25.0
Percentage Mean Body 18.5-24.9 ly and (Total over- 25.0-29.9
below Number of Mass Index (Total <18.5 17.0-18.4 severely weight or (Over- ≥30.0 Number of
County 145 cm women (BMI) normal) (Total thin) (Mildly thin) thin) obese) weight) (Obese) women
Coast 2.1 1,391 23.5 56.8 11.0 8.6 2.5 32.1 20.6 11.6 1,262
Mombasa 1.0 400 25.4 47.0 4.8 4.7 0.1 48.2 26.9 21.3 362
Kwale 4.0 280 22.8 55.9 16.3 12.8 3.5 27.8 20.9 6.9 252
Kilifi 2.3 479 22.3 68.7 10.9 8.4 2.5 20.4 14.9 5.4 435
Tana River 1.1 89 21.5 51.2 29.1 20.1 9.0 19.7 15.7 4.0 81
Lamu 2.3 44 24.1 48.9 13.9 7.8 6.1 37.2 20.0 17.2 41
Taita Taveta 0.9 99 25.3 50.6 4.5 3.4 1.1 45.0 26.1 18.9 91
North Eastern 0.1 283 21.5 52.3 28.8 17.4 11.4 19.0 14.3 4.7 239
Garissa 0.3 116 21.1 49.0 33.3 19.2 14.1 17.7 13.2 4.5 96
Wajir 0.0 83 22.2 46.7 28.3 21.2 7.1 25.0 17.8 7.2 67
Mandera 0.0 84 21.4 61.3 23.5 11.7 11.8 15.2 12.4 2.8 76
Eastern 1.7 2,051 23.4 60.2 9.8 6.9 2.9 30.1 21.6 8.4 1,918
Marsabit 0.9 53 21.3 55.3 27.0 18.0 9.0 17.6 15.0 2.7 45
Isiolo 0.3 48 22.1 50.4 24.4 13.3 11.0 25.2 18.1 7.1 44
Meru 1.6 530 23.9 60.7 7.7 4.2 3.5 31.6 21.4 10.2 495
Tharaka-Nithi 0.5 130 22.9 64.6 10.9 9.4 1.5 24.5 17.5 7.0 125
Embu 0.7 209 23.7 53.7 12.8 8.8 4.0 33.5 21.4 12.1 197
Kitui 3.5 355 23.2 59.5 9.5 8.7 0.8 31.0 25.0 6.0 335
Machakos 0.8 398 23.6 64.3 6.3 4.0 2.3 29.4 20.7 8.7 371
Makueni 2.6 328 23.3 59.4 10.6 8.1 2.5 30.0 22.6 7.4 306
Central 0.7 1,810 25.3 46.7 6.2 4.1 2.1 47.1 29.4 17.6 1,697
Nyandarua 0.6 199 24.5 53.8 5.5 3.1 2.5 40.6 25.0 15.6 187
Nyeri 0.7 319 25.3 47.2 3.6 1.7 1.9 49.2 34.2 15.1 297
Kirinyaga 0.5 210 25.9 38.8 6.8 3.4 3.4 54.4 36.7 17.7 196
Murang’a 0.0 343 25.1 47.0 5.8 5.6 0.2 47.3 30.3 16.9 328
Kiambu 1.1 739 25.4 46.7 7.6 4.9 2.7 45.7 26.1 19.6 689
Rift Valley 0.5 3,651 23.1 59.6 11.8 8.0 3.8 28.6 20.8 7.8 3,349
Turkana 0.9 144 19.4 49.9 45.3 19.1 26.3 4.7 3.7 1.0 121
West Pokot 0.5 124 21.2 66.2 23.2 17.4 5.9 10.6 7.0 3.6 108
Samburu 0.6 57 19.9 50.9 41.0 22.5 18.4 8.1 5.3 2.9 51
Trans-Nzoia 0.2 343 23.0 63.9 7.4 5.9 1.5 28.7 21.5 7.2 316
Uasin Gishu 0.3 384 23.6 56.7 11.2 9.3 1.8 32.2 23.0 9.1 353
Elgeyo Marakwet 0.0 109 22.5 64.3 12.8 8.6 4.2 22.9 17.3 5.6 99
Nandi 0.3 286 22.8 67.9 8.4 6.1 2.2 23.7 16.7 7.0 269
Baringo 0.7 149 21.9 51.8 25.1 15.9 9.2 23.2 17.5 5.7 134
Laikipia 0.0 151 24.0 51.4 14.2 8.0 6.2 34.4 20.2 14.2 133
Nakuru 1.4 712 24.1 55.3 6.2 4.4 1.9 38.5 31.3 7.2 671
Narok 0.4 299 23.0 64.5 9.7 7.0 2.8 25.7 18.0 7.7 260
Kajiado 0.2 299 24.9 45.1 10.9 8.2 2.7 44.0 26.7 17.3 276
Kericho 0.0 266 23.1 69.7 6.4 4.3 2.0 23.9 16.8 7.1 249
Bomet 0.4 330 22.4 69.8 9.8 7.8 2.0 20.3 16.0 4.4 309
Western 0.4 1,551 23.0 67.0 8.6 6.8 1.8 24.4 18.6 5.8 1,431
Kakamega 0.8 540 23.3 65.1 7.7 6.5 1.2 27.3 19.8 7.5 500
Vihiga 1.0 175 23.4 65.4 8.3 6.9 1.4 26.4 19.3 7.0 163
Bungoma 0.0 561 22.9 66.9 9.0 6.8 2.1 24.1 19.1 5.0 513
Busia 0.3 275 22.2 72.0 9.8 7.2 2.5 18.3 14.9 3.4 256
Nyanza 0.5 1,865 23.3 67.2 6.3 5.0 1.3 26.5 19.8 6.8 1,729
Siaya 0.5 267 23.1 67.9 9.1 7.6 1.6 23.0 16.0 6.9 251
Kisumu 0.5 384 23.9 61.8 4.5 3.3 1.2 33.6 24.8 8.8 353
Homa Bay 0.3 353 22.6 74.1 7.1 5.4 1.7 18.8 14.7 4.1 330
Migori 0.2 291 22.9 65.0 8.1 6.3 1.8 26.9 22.4 4.5 256
Kisii 0.7 411 23.6 67.2 4.6 3.9 0.7 28.2 20.6 7.6 387
Nyamira 1.2 159 23.8 67.3 4.9 4.0 0.9 27.8 18.6 9.1 152
Nairobi 0.3 1,662 25.4 49.7 2.8 2.6 0.2 47.6 31.1 16.5 1,517
Total 0.8 14,265 23.7 58.3 8.9 6.4 2.5 32.8 22.7 10.1 13,143
Note: The Body Mass Index (BMI) is expressed as the ratio of weight in kilograms to the square of height in metres (kg/m2).
1
Excludes pregnant women and women with a birth in the preceding 2 months
The mean BMI among women age 15-49 is 23.7 kg/m2. Nine percent of women of reproductive
age are thin or undernourished (BMI <18.5 kg/m2). The proportions of mild thinness (17.0-18.4 kg/m2) and
moderate and severe thinness (<17 kg/m2) are 6 percent and 3 percent, respectively. Younger and rural
women are more likely to be thin. The North Eastern region has the highest proportion (29 percent) of
women who are thin, while Nairobi has the lowest (3 percent). Thinness is more common among women
Nutrition of Children and Women • 179
with no education (25 percent) than among women at other educational levels (12 percent or less).
Similarly, thinness is more common among women in the lowest wealth quintile (22 percent) and is
inversely related to wealth. Women in the lowest wealth quintile (22 percent) are 5 times as likely to be
thin as women in the highest wealth quintile (4 percent).
Overall, 33 percent of women are either overweight or obese (BMI ≥25 kg/m2) with 10 percent of
them being obese (BMI 30 kg/m2 or above). The risk of being overweight or obese increases with age.
Urban women are more likely to be overweight/obese (43 percent) than rural women (26 percent). Nairobi
has the highest proportion (48 percent) of women who are overweight or obese, followed by Central (47
percent); the lowest proportion is observed in the North Eastern region (19 percent). The proportion of
overweight or obese women increases steadily with increasing education and wealth. Women with no
education (18 percent) and in the lowest wealth quintile (12 percent) are less likely to be overweight or
obese compared to women with a secondary or higher education (38 percent) and women in the highest
wealth quintile (50 percent).
A comparison of the 2008-09 KDHS and 2014 KDHS results indicates that the proportion of thin
women (BMI <18.5 kg/m2) has decreased marginally from 12 percent to 9 percent. The proportion of
women who are overweight or obese, on the other hand, has increased from 25 percent to 33 percent, and
the proportion of obese women has increased from 7 percent to 10 percent.
11.6 MICRONUTRIENT INTAKE AMONG MOTHERS
Adequate micronutrient intake by women has important benefits for both women and their
children. Breastfeeding children benefit from micronutrient supplementation that mothers receive,
especially vitamin A. Iron supplementation of women during pregnancy protects the mother and foetus
against anaemia, which is considered a major cause of perinatal and maternal mortality. Anaemia also
results in an increased risk of premature delivery and low birth weight. Finally, iodine deficiency is related
to a number of adverse pregnancy outcomes, including foetal brain damage, congenital malformation,
stillbirth, and prenatal death. Table 11.11 includes a number of measures that are useful in assessing
micronutrient intake by women, especially during pregnancy and the postpartum period in the first two
months after birth.
The findings show that 54 percent of women received a vitamin A dose during the postpartum
period. The percentage of women receiving postpartum vitamin A is higher in urban areas (58 percent)
than in rural areas (51 percent). Women in the Central region are most likely to take vitamin A during the
postpartum period (65 percent), while women in the North Eastern region are least likely to do so (27
percent). The prevalence of postpartum vitamin A supplementation increases with increasing education.
Women in the lowest wealth quintile are less likely to receive a postpartum vitamin A dose (38 percent)
than their more wealthy counterparts (54 percent or higher).
Nutritional deficiencies such as anaemia are often exacerbated during pregnancy because of the
additional nutrient demands associated with foetal growth. Iron status can be enhanced through iron
supplementation, improving women’s diets, and controlling parasites and malaria infection. Iron
supplementation is necessary for pregnant women because their needs are usually too high to be met solely
through food intake. In Kenya, pregnant women are advised to take combined folic acid and iron tablets
daily from conception to delivery. Women may still access noncombined formulations of iron supplements
in the market and within the health care system.
Table 11.11 shows that only 8 percent of women took iron tablets for 90 or more days during their
last pregnancy. Five percent took iron supplements for 60-89 days, and 53 percent took the supplements
for fewer than 60 days. Thirty percent of women did not take iron supplements at all during their last
pregnancy. The proportion of women taking iron supplements for 90 or more days is slightly higher in
urban areas and among those in the Coast and Nyanza regions.
180 • Nutrition of Children and Women
Helminth infections are one of the factors contributing to anaemia among pregnant women.
Deworming during pregnancy is a cost-effective intervention against intestinal worms that allows better
absorption of nutrients and iron, thus reducing the prevalence of anaemia. In Kenya, the Ministry of Health
has approved and implemented a policy to provide 500 mg of mebendazole once during pregnancy.
Table 11.11 shows that 31 percent of women took deworming medication during their last
pregnancy. Women in the Coast region were most likely to take deworming medication (51 percent), while
women in North Eastern were least likely to do so (7 percent). Women with no education (22 percent)
were less likely than those with some education (30-34 percent) to take deworming medication.
Iodine deficiency has adverse effects on all population groups, but women of reproductive age are
often the most affected. As mentioned, iodine deficiency is related to a number of adverse pregnancy
outcomes. As a result, use of iodised salt by women of reproductive age is emphasised. Table 11.11 shows
that virtually all women with a child born in the five years preceding the survey live in households with
iodised salt.
A comparison with the 2008-09 KDHS data shows that the proportion of women who received
vitamin A postpartum increased from 46 percent to 54 percent. The proportion of women taking iron
supplements for 90 days or more during the pregnancy of their last birth increased marginally from 3
percent to 8 percent, and the proportion of women taking deworming medication increased from 17 percent
to 31 percent.
Table 11.11 Micronutrient intake among mothers
Among women age 15-49 with a child born in the past five years, the percentage who received a vitamin A dose in the first two months after the birth of the last
child, the percent distribution by number of days they took iron tablets, iron syrup, or iron and folic acid supplements during the pregnancy of the last child, and
the percentage who took deworming medication during the pregnancy of the last child; and among women age 15-49 with a child born in the past five years and
who live in households that were tested for iodised salt, the percentage who live in households with iodised salt, by background characteristics, Kenya 2014
Among women with a
child born in the last five
Percentage of years, who live in
Number of days women took iron tablets, iron syrup, or iron and women who households that were
folic acid supplements during pregnancy of last birth took tested for iodised salt
deworming Percentage
Percentage medication living in
who received Don’t during households
Background vitamin A dose know/ pregnancy of Number with iodised Number
characteristic postpartum1 None <60 60-89 90+ missing Total last birth of women salt2 of women
Age
15-19 41.7 36.6 48.5 5.3 6.4 3.2 100.0 26.5 386 98.9 365
20-29 55.7 28.9 54.2 4.7 7.8 4.4 100.0 30.9 3,669 99.6 3,568
30-39 54.5 29.4 52.9 5.0 7.8 5.0 100.0 32.8 2,313 99.4 2,236
40-49 47.0 36.6 51.2 2.3 4.7 5.3 100.0 31.0 507 99.7 488
Residence
Urban 57.9 24.7 54.3 6.4 9.6 4.9 100.0 31.6 2,677 99.4 2,617
Rural 51.3 33.5 52.5 3.5 6.1 4.4 100.0 31.1 4,199 99.5 4,039
Region
Coast 45.5 17.5 59.4 7.5 12.9 2.7 100.0 50.5 698 97.5 678
North Eastern 26.5 58.9 34.6 2.3 1.2 3.0 100.0 7.4 178 98.3 155
Eastern 59.2 30.9 50.3 5.3 6.3 7.2 100.0 34.5 891 99.9 868
Central 65.4 28.3 55.5 3.1 5.6 7.5 100.0 34.2 715 99.8 692
Rift Valley 47.8 37.6 51.4 2.8 4.3 3.9 100.0 23.8 1,899 99.8 1,828
Western 60.7 38.8 50.9 1.2 6.9 2.2 100.0 33.5 790 99.1 764
Nyanza 59.0 16.4 57.0 8.8 12.6 5.2 100.0 32.9 934 99.8 910
Nairobi 52.5 24.7 55.6 6.0 9.3 4.4 100.0 27.4 771 100.0 762
Education
No education 29.7 41.9 46.5 4.6 3.6 3.3 100.0 21.5 675 99.1 605
Primary incomplete 51.3 34.4 52.3 3.6 6.4 3.3 100.0 30.4 1,901 99.1 1,838
Primary complete 55.9 28.1 54.5 4.7 7.5 5.2 100.0 34.1 1,856 99.5 1,806
Secondary+ 60.9 24.9 54.8 5.4 9.3 5.5 100.0 32.6 2,445 99.8 2,407
Wealth quintile
Lowest 38.0 36.6 51.7 3.9 5.0 2.9 100.0 27.5 1,381 98.7 1,276
Second 54.0 33.3 52.1 4.1 7.3 3.2 100.0 30.1 1,312 99.4 1,280
Middle 57.4 33.0 52.4 3.1 6.0 5.4 100.0 34.6 1,276 99.7 1,251
Fourth 60.9 25.2 55.7 5.3 8.4 5.4 100.0 32.8 1,372 99.7 1,343
Highest 58.7 23.4 54.0 6.4 10.4 5.9 100.0 31.6 1,536 99.8 1,507
Total 53.9 30.1 53.2 4.6 7.5 4.6 100.0 31.3 6,876 99.5 6,657
1
In the first two months after delivery of last birth
2
Excludes women in households where salt was not tested.
Nutrition of Children and Women • 181
MALARIA 12
Rebecca Kiptui, Abdulkadir Amin Awes, James Muttunga
Key Findings
• Six in 10 households (59 percent) own at least one insecticide-treated
mosquito net (ITN), while 34 percent of households have at least one net
for every two people.
• Forty-eight percent of Kenyans have access to an ITN.
• Two-fifths of the household population (42 percent) slept under an ITN
the night prior to the survey, and two-thirds (67 percent) of members of
households with at least one ITN slept under an ITN the night prior to the
survey.
• Fifty-four percent of children under age 5 slept under an ITN the night
before the survey, and, among those living in households with an ITN, 77
percent slept under an ITN the night before the survey.
• Fifty-one percent of pregnant women overall slept under an ITN the night
before the survey, and, among those living in households with an ITN, 77
percent slept under an ITN the night before the survey.
• Seventeen percent of women received intermittent preventive treatment
(IPTp) for malaria during pregnancy; that is, they received two or more
doses of SP/Fansidar, at least one during an antenatal care visit. In
malaria endemic areas, 39 percent of women received IPTp.
• Twenty-three percent of children under age 5 who had a fever took ACT,
and 13 percent took ACT within 24 hours of fever onset.
12.1 INTRODUCTION
M
alaria is a leading cause of morbidity and mortality in Kenya, with more than 70 percent of the
population at risk of infection (MOH, 2015a). Malaria transmission varies across Kenya; the
four main epidemiological zones are described below.
Endemic areas: These are areas of stable malaria transmission (with altitudes ranging from 0 to
1,300 metres) around Lake Victoria in western Kenya and in the coastal regions. Rainfall, temperature, and
humidity are the determinants of perennial transmission of malaria. The vector life cycle is usually short
with a high survival rate due to the suitable climatic conditions. Transmission is intense throughout the
year, with annual entomological inoculation rates1 between 30 and 100.
Seasonal malaria transmission areas: This zone, in arid and semi-arid areas of the northern and
south-eastern parts of the country, experiences short periods of intense malaria transmission during the
rainfall seasons. Temperatures are usually high, and water pools created during the rainy season provide
the malaria vectors with breeding sites. Extreme climatic conditions such as the El Niño southern
oscillation lead to flooding in these areas, resulting in epidemic outbreaks with high morbidity rates due to
the population’s low immune status.
1
The entomological inoculation rate is the average number of inoculations with malaria parasites received by a person
over a period of time (usually annually). It is used to measure malaria transmission intensity and is dependent on the
frequency with which people living in an area are bitten by anopheline mosquitoes carrying sporozoites (WHO,
2015a).
Malaria • 183
Highland epidemic prone areas: Malaria transmission in the western highlands of Kenya is
seasonal, with considerable year-to-year variation. The epidemic phenomenon is experienced when
climatic conditions favour sustainability of minimum temperatures around 18°C. This increase in
minimum temperatures during periods of long rains favours and sustains vector breeding, resulting in
increased intensity of malaria transmission. The whole population is vulnerable, and case fatality rates
during an epidemic can be up to 10 times greater than what is experienced in regions where malaria occurs
regularly.
Low risk malaria areas: This zone covers the central highlands of Kenya, including Nairobi.
Temperatures are usually too low to allow completion of the sporogonic cycle of the malaria parasite in the
vector. However, increasing temperatures and changes in the hydrological cycle associated with climate
change are likely to increase the areas suitable for malaria vector breeding, with the introduction of malaria
transmission in areas where it did not previously exist.
The highest malaria burden is in the lake endemic region, while the lowest is in the low risk and
seasonal transmission areas. Map 12.1 shows the malaria burden across counties (Noor et al., 2012).
Map 12.1 Malaria prevalence in Kenya
The main malaria control interventions in Kenya are the following:
• Vector control via insecticide-treated nets (ITNs), indoor residual spraying (IRS), and larval
source management;
• Management of malaria in pregnancy by ensuring that pregnant women receive and use ITNs
and undergo intermittent preventive treatment (IPTp);
• Case management using artemisinin-based combination therapy (ACT) and improved
diagnosis and treatment;
• Epidemic preparedness and response (EPR);
• Surveillance, monitoring and evaluation, and operations research;
• Advocacy, communication, and social mobilisation; and
• Cross-cutting strategies including programme management, resource mobilisation, and
capacity building among counties.
184 • Malaria
Due to variation in disease patterns, not all interventions are carried out in all areas of the country. The
various interventions and in what areas they are implemented should be considered in interpreting results.
Case
Manage- Health
Epidemiological zone ITNs IPTp ment EPR Surveillance Education
Endemic ● ● ● ● ●
Highland epidemic prone ● ● ● ● ●
Seasonal transmission ● ● ● ●
Low risk ● ● ●
Source: (MOH, 2010)
Note: IRS, a vector control intervention, has not been implemented since 2012 in an effort to practice
insecticide resistance management. Accordingly, in this report, results for IRS are not presented.
12.2 OWNERSHIP OF MOSQUITO NETS
Nets and window screening have long been considered useful protection methods against
mosquitoes and other insects (Lindsay and Gibson, 1988). Nets reduce human-vector contact by acting as a
physical barrier and thus reducing the number of bites from infective vectors (Bradley et al., 1986).
However, nets and screens are often not well fitted or are torn, thus allowing mosquitoes to enter or feed
on the part of the body adjacent to the netting fabric during the night (Lines et al., 1987). The problem of
ill-used nets and screens provides one of the motives for impregnating them with a fast-acting insecticide
that will repel or kill mosquitoes (Lines et al., 1987; Hossain and Curtis, 1989).
Treatment of nets has been made possible by the availability of synthetic pyrethroids that mimic
the insecticidal compounds of natural pyrethrum. Treated nets have low mammalian toxicity; are repellent,
highly toxic to mosquitoes, and odourless; and have low volatility with consequent long persistence. ITNs
are regarded as a promising malaria control tool that, when used by all or most members of the community,
can reduce malaria transmission, morbidity, and mortality. Long-lasting insecticidal nets (LLINs) are a
subset of ITNs. An LLIN is a factory-treated mosquito net made with netting material that has insecticide
incorporated within or bound around the fibres. The net must retain its effective biological activity, without
re-treatment for repeated washes, for three years of use under field conditions (WHO/Global Malaria
Program, 2007). The current generation of LLINs last three to five years, after which the nets should be
replaced.
The National Malaria Control Programme (NMCP) in Kenya distributes only LLINs; however,
other varieties of treated and untreated nets may be found in markets or from other sources. In Kenya, an
ITN is a net that is either an LLIN or a net treated with insecticide in the past six months.2 The aim of the
National Malaria Strategy 2009-2017 is for 80 percent of people living in endemic and epidemic prone
areas to use a form of malaria prevention (MOH, 2015). Accordingly, the NMCP has undertaken efforts
towards universal coverage (one net for every two people). Delivery mechanisms include mass and routine
distribution of nets, social marketing, the commercial sector, and campaigns specifically targeting
vulnerable groups such as young children and pregnant women.
All households interviewed in the 2014 KDHS were asked whether they owned a mosquito net
and, if so, how many. Respondents were also asked to show the mosquito nets they owned to the
interviewer so that the interviewer could identify the brand and type. Table 12.1 shows the percentage of
households with at least one mosquito net (any net, an ITN, or an LLIN), the average number of nets per
household, and the percentage of households with at least one net for every two people who slept in the
household the previous night.
Sixty-five percent of households in Kenya own at least one mosquito net of any type, 59 percent
own at least one ITN, and 57 percent own at least one LLIN. Thirty-four percent of households have
reached universal coverage; that is, these households have at least one ITN for every two persons who
2
This differs slightly from the international definition, which includes nets that have been soaked with insecticide
within the past 12 months.
Malaria • 185
stayed in the household the night before the survey. Household ownership of at least one ITN has
improved slightly since the 2008-09 KDHS (56 percent) and the 2010 Kenya Malaria Indicator Survey
(KMIS) (48 percent) to the current level of 59 percent.
Slightly more urban households (67 percent) than rural households (64 percent) own at least one
mosquito net of any type. However, more rural households (61 percent) than urban households (56
percent) own an ITN. There is marked regional variation in ownership of mosquito nets. The percentage of
households that own an ITN is higher in the malaria prone Western (82 percent), Nyanza (81 percent), and
Coast (69 percent) regions than in other regions (56 percent or less). As in the 2008-09 KDHS, the 2014
KDHS shows that ownership of at least one ITN is not directly related to household wealth. However,
households in the lowest wealth quintile are less likely to own at least one ITN (51 percent) than
households in the other wealth quintiles.
Table 12.1 Household possession of mosquito nets
Percentage of households with at least one mosquito net (treated or untreated), insecticide-treated net (ITN), and long-lasting insecticidal net (LLIN); average
number of nets, ITNs, and LLINs per household; and percentage of households with at least one net, ITN, and LLIN per two persons1 who stayed in the
household last night, by background characteristics, Kenya 2014
Percentage of households with at
least one net for every two persons
Number of
Percentage of households with at Average number of nets per who stayed in the household last
households
least one mosquito net household night1
with at least
Insecticide- Long- Insecticide- Long- Insecticide- Long- one person
Any treated lasting Any treated lasting Any treated lasting who stayed in
Background mosquito mosquito insecticidal mosquito mosquito insecticidal Number of mosquito mosquito insecticidal the household
characteristic net net (ITN)2 net (LLIN) net net (ITN)2 net (LLIN) households net net (ITN)2 net (LLIN) last night
Residence
Urban 66.5 55.7 53.7 1.2 1.0 1.0 15,290 46.3 37.3 35.6 15,120
Rural 64.2 60.8 60.2 1.3 1.2 1.2 21,140 34.8 32.1 31.7 21,065
Region
Coast 76.8 69.1 67.9 1.5 1.4 1.3 3,569 50.9 43.3 42.2 3,531
North Eastern 51.2 48.8 48.8 1.1 1.0 1.0 724 24.2 21.9 21.9 722
Eastern 59.6 56.2 55.5 1.1 1.1 1.0 5,262 34.6 31.1 30.5 5,227
Central 43.2 37.7 37.3 0.8 0.7 0.7 5,012 29.7 25.0 24.4 4,990
Rift Valley 59.6 55.6 54.8 1.2 1.1 1.1 9,249 34.7 31.6 31.2 9,195
Western 85.4 81.5 79.6 1.9 1.8 1.7 3,604 48.6 45.3 44.2 3,581
Nyanza 84.9 81.1 80.6 1.7 1.6 1.6 4,559 48.1 45.3 44.8 4,542
Nairobi 64.3 43.3 39.2 1.0 0.7 0.6 4,451 44.4 28.5 24.9 4,397
Wealth quintile
Lowest 53.4 50.8 50.7 1.0 0.9 0.9 6,077 22.1 20.5 20.5 6,060
Second 64.7 61.1 60.5 1.2 1.1 1.1 6,557 31.2 28.5 28.0 6,528
Middle 67.5 63.6 62.7 1.4 1.3 1.3 6,967 39.3 36.2 35.6 6,910
Fourth 63.7 56.4 55.2 1.2 1.1 1.1 8,225 43.3 37.4 36.7 8,163
Highest 73.3 60.5 57.8 1.5 1.2 1.2 8,603 55.2 43.9 41.3 8,525
Total 65.1 58.7 57.4 1.3 1.1 1.1 36,430 39.6 34.3 33.3 36,185
1
De facto household members
2
An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN) or (2) a net that has been soaked with insecticide
within the past six months.
Overall, the average number of ITNs per household is 1.1. The average number of ITNs per
household is highest in the Western (1.8) and Nyanza (1.6) regions.
Table 12.1C shows that households in counties in the lakeside endemic zones (Nyanza and
Western) are more likely to own at least one ITN than households in counties in other malaria zones.
Ownership of an ITN is highest in the counties of Kisumu (88 percent), Kisii (86 percent), and Nyamira
(85 percent) in the Nyanza region, followed by Busia (84 percent), Bungoma (83 percent), and Vihiga (83
percent) in the Western region. ITN ownership is lowest in counties in the low risk or seasonal
transmission zone: Nyandarua, Laikipia, Samburu, Nyeri, and Elgeyo Marakwet (each less than 25
percent).
186 • Malaria
Table 12.1C Household possession of mosquito nets
Percentage of households with at least one mosquito net (treated or untreated), insecticide-treated net (ITN), and long-lasting insecticidal net (LLIN); average
number of nets, ITNs, and LLINs per household; and percentage of households with at least one net, ITN, and LLIN per two persons who stayed in the household
last night, by county, Kenya 2014
Percentage of households with at
least one net for every two persons
Number of
Percentage of households with at Average number of nets per who stayed in the household last
households
least one mosquito net household night1 with at least
Insecticide- Long- Insecticide- Long- Insecticide- Long- one person
Any treated lasting Any treated lasting Any treated lasting who stayed in
mosquito mosquito insecticidal mosquito mosquito insecticidal Number of mosquito mosquito insecticidal the household
County net net (ITN)2 net (LLIN) net net (ITN)2 net (LLIN) households net net (ITN)2 net (LLIN) last night
Coast 76.8 69.1 67.9 1.5 1.4 1.3 3,569 50.9 43.3 42.2 3,531
Mombasa†‡ 69.0 56.9 55.4 1.1 0.9 0.9 1,245 50.1 38.9 37.4 1,221
Kwale†‡ 82.2 81.0 80.1 1.8 1.7 1.7 704 50.6 47.8 47.1 703
Kilifi†‡ 81.7 73.1 71.9 1.9 1.6 1.6 999 49.8 41.5 40.7 993
Tana River†‡ 72.8 65.3 65.0 1.5 1.3 1.3 210 40.0 33.0 32.8 208
Lamu†‡ 79.5 71.1 70.5 1.8 1.5 1.5 104 58.6 51.2 50.4 102
Taita Taveta†‡ 82.5 80.4 79.1 1.7 1.6 1.6 307 63.1 60.6 59.2 304
North Eastern 51.2 48.8 48.8 1.1 1.0 1.0 724 24.2 21.9 21.9 722
Garissa 64.4 61.1 61.1 1.6 1.4 1.4 265 39.5 34.4 34.4 265
Wajir 55.8 53.6 53.6 1.1 1.1 1.1 242 19.1 18.1 18.1 241
Mandera 29.8 28.7 28.4 0.5 0.5 0.5 217 11.1 10.9 10.9 217
Eastern 59.6 56.2 55.5 1.1 1.1 1.0 5,262 34.6 31.1 30.5 5,227
Marsabit 31.7 24.7 24.5 0.4 0.3 0.3 146 10.5 7.0 7.0 146
Isiolo† 65.4 62.7 62.5 1.2 1.1 1.1 122 34.0 31.5 31.2 121
Meru† 57.1 53.5 52.1 1.1 1.0 0.9 1,406 33.3 30.6 29.6 1,398
Tharaka-Nithi† 71.1 67.2 66.7 1.4 1.3 1.3 379 48.9 45.8 44.8 375
Embu† 62.3 55.9 54.2 1.3 1.1 1.0 548 43.3 35.3 33.3 539
Kitui† 62.9 61.1 61.1 1.2 1.1 1.1 856 31.3 29.3 29.3 849
Machakos† 59.0 56.0 55.7 1.2 1.1 1.1 1,088 35.7 31.5 31.4 1,088
Makueni† 58.2 55.3 55.0 1.2 1.1 1.1 717 30.1 27.7 27.6 711
Central 43.2 37.7 37.3 0.8 0.7 0.7 5,012 29.7 25.0 24.4 4,990
Nyandarua 16.4 12.5 12.4 0.3 0.2 0.2 593 9.3 7.1 7.1 589
Nyeri 25.8 19.7 19.2 0.4 0.3 0.3 792 16.1 11.9 11.6 788
Kirinyaga†‡ 73.5 68.5 67.2 1.5 1.4 1.3 622 59.2 54.3 53.2 620
Murang’a† 45.4 43.7 43.7 0.9 0.8 0.8 968 28.9 27.1 27.1 965
Kiambu† 47.4 39.9 39.4 0.8 0.7 0.7 2,037 32.2 25.2 24.3 2,028
Rift Valley 59.6 55.6 54.8 1.2 1.1 1.1 9,249 34.7 31.6 31.2 9,195
Turkana‡ 46.2 46.2 46.2 0.7 0.7 0.7 448 16.5 16.3 16.3 446
West Pokot†‡ 60.3 60.2 60.2 1.1 1.1 1.1 319 21.3 21.2 21.2 319
Samburu 22.2 18.8 18.2 0.4 0.3 0.3 146 12.3 8.7 8.3 145
Trans-Nzoia†‡ 72.7 70.6 69.3 1.6 1.5 1.5 814 40.4 38.4 37.7 809
Uasin Gishu†‡ 72.3 72.0 71.8 1.5 1.5 1.5 962 50.2 49.7 49.5 950
Elgeyo Marakwet† 40.1 21.9 20.8 0.7 0.3 0.3 301 20.4 10.1 9.7 301
Nandi†‡ 79.2 78.8 78.8 1.6 1.6 1.6 671 42.3 41.9 41.8 667
Baringo†‡ 64.4 59.8 59.4 1.2 1.1 1.1 391 36.3 32.8 32.5 387
Laikipia 30.2 17.5 15.5 0.5 0.3 0.2 406 18.5 10.6 8.9 402
Nakuru 42.7 37.7 36.8 0.7 0.6 0.6 1,950 26.6 22.7 22.4 1,948
Narok†‡ 53.2 52.2 52.0 1.0 1.0 1.0 752 26.4 25.4 25.4 746
Kajiado† 56.8 49.8 48.1 1.0 0.9 0.8 770 39.3 33.3 32.7 759
Kericho†‡ 84.7 79.5 78.5 1.7 1.6 1.6 589 50.5 45.4 44.8 589
Bomet†‡ 81.6 77.6 77.3 1.9 1.8 1.8 732 49.2 46.8 46.5 727
Western 85.4 81.5 79.6 1.9 1.8 1.7 3,604 48.6 45.3 44.2 3,581
Kakamega†‡ 85.3 78.8 74.3 1.9 1.7 1.6 1,350 52.7 47.0 44.4 1,341
Vihiga†‡ 84.7 82.9 82.7 1.8 1.7 1.7 446 46.8 44.9 44.7 446
Bungoma†‡ 84.3 82.9 82.5 1.8 1.8 1.8 1,180 43.0 41.7 41.5 1,170
Busia†‡ 88.3 83.9 83.2 2.0 1.9 1.9 628 51.4 48.8 48.3 624
Nyanza 84.9 81.1 80.6 1.7 1.6 1.6 4,559 48.1 45.3 44.8 4,542
Siaya†‡ 84.8 78.8 78.2 1.6 1.5 1.5 725 45.4 41.6 40.9 720
Kisumu†‡ 88.9 87.6 87.4 1.7 1.7 1.7 943 54.1 52.6 52.5 939
Homa Bay†‡ 81.9 74.3 73.8 1.7 1.5 1.5 877 43.1 37.5 37.2 873
Migori†‡ 78.0 74.6 73.3 1.4 1.4 1.3 701 32.2 30.3 29.0 699
Kisii†‡ 87.8 86.1 85.8 2.0 1.9 1.9 904 55.8 54.4 54.1 903
Nyamira†‡ 87.3 84.5 84.5 1.7 1.7 1.7 409 60.3 57.1 57.1 409
Nairobi 64.3 43.3 39.2 1.0 0.7 0.6 4,451 44.4 28.5 24.9 4,397
Total 65.1 58.7 57.4 1.3 1.1 1.1 36,430 39.6 34.3 33.3 36,185
1
De facto household members
2
An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN) or (2) a net that has been soaked with insecticide
within the past six months.
†
Counties in which ITNs are distributed in some or all of the sub-counties routinely by the government of Kenya.
‡
Counties in which ITNs are distributed in some or all of the sub-counties by mass net campaigns by the government of Kenya.
Malaria • 187
12.3 ACCESS TO INSECTICIDE-TREATED NETS
Use of ITNs is one of the most effective measures for preventing malaria. The government of
Kenya, with support from several partners, has distributed millions of mosquito nets across the country. In
addition, increasing knowledge among the populace of the importance of using mosquito nets has led to
increased demand. The 2014 KDHS data can be used to show the proportion of the population that could
sleep under an ITN if each ITN in the household were used by up to two people. This population is
referred to as having access to an ITN. Coupled with data on actual mosquito net usage, ITN access data
provide useful information on the magnitude of the behavioural gap in ITN ownership and use or, in other
words, the population with access to an ITN but not using it. If the difference between these indicators is
substantial, the malaria programme may need to focus on behaviour change and identify the main drivers
of or barriers to ITN use to design an appropriate intervention. This analysis helps ITN programmes
determine whether they need to achieve higher ITN coverage, promote ITN use, or both.
Table 12.2 presents the percent distribution of the de facto household population by the number of
ITNs the household owns, according to the number of persons who stayed in the household the night
before the survey. Slightly more than one-third (37 percent) of the population slept in homes without any
ITNs the night before the survey and, therefore, were not able to use an ITN. About 2 in 10 individuals
stayed in households that own one ITN (20 percent) or two ITNs (23 percent), and 15 percent of the
population slept in homes with three ITNs. Few individuals slept in homes with more than four ITNs (4
percent or less). Overall, 48 percent of the population has access to an ITN. ITN access gradually decreases
as household size increases in households with four or more persons. For example, 51 percent of
households where two persons slept the night before the survey had access to an ITN, whereas 41 percent
of households where more than eight people slept had access to an ITN.
Table 12.2 Access to an insecticide-treated net (ITN)
Percent distribution of the de facto household population by number of ITNs the household owns, according to number of persons
who stayed in the household the night before the survey, Kenya 2014
Number of persons who stayed in the household the night before the survey
Number of ITNs 1 2 3 4 5 6 7 8+ Total
0 56.9 48.6 37.5 35.4 34.5 36.9 33.6 29.7 36.6
1 35.3 30.7 28.6 21.5 16.3 14.3 15.8 12.1 19.7
2 6.0 15.2 23.6 27.5 27.7 24.1 22.3 19.3 22.7
3 1.4 4.2 8.8 12.6 16.8 18.6 19.1 22.4 14.8
4 0.3 0.9 1.1 2.0 3.1 3.8 6.0 7.8 3.5
5 0.2 0.2 0.3 0.7 0.9 1.5 2.2 4.4 1.5
6 0.0 0.1 0.2 0.1 0.6 0.7 0.9 3.1 0.9
7+ 0.0 0.1 0.0 0.1 0.1 0.1 0.1 1.4 0.3
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number 7,049 10,100 17,382 23,872 23,297 18,496 13,806 23,778 137,780
Percent with
access to an ITN1 43.1 51.4 53.0 53.8 50.2 45.6 42.8 41.1 48.0
1
Percentage of the de facto household population who could sleep under an ITN if each ITN in the household were used by up to
two people
Figure 12.1 shows the percentage of the de facto population with access to an ITN in the
household by residence, region, and wealth quintile. Forty-eight percent of household members in Kenya
have access to an ITN. While there is little difference in ITN access between urban and rural areas (49
percent and 47 percent, respectively), there are wide regional variations in ITN access. The majority of
household members in Western and Nyanza, within the endemic zones, had access to an ITN (63 percent
and 62 percent, respectively). Access to ITNs is lower in Nairobi (39 percent), North Eastern (34 percent),
and Central (33 percent). Access to ITNs among household members increases with increasing household
wealth, from 36 percent in the lowest wealth quintile to 55 percent in the highest quintile.
188 • Malaria
Figure 12.1 Percentage of the de facto population with access to an ITN1
in the household
TOTAL 48
RESIDENCE
Urban 49
Rural 47
REGION
Coast 58
North Eastern 34
Eastern 44
Central 33
Rift Valley 44
Western 63
Nyanza 62
Nairobi 39
WEALTH QUINTILE
Lowest 36
Second 46
Middle 52
Fourth 50
Highest 55
Percent
1 An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN), or
(2) a net that has been soaked with insecticide within the past 6 months. KDHS 2014
12.4 USE OF MOSQUITO NETS
12.4.1 Overall Use of Mosquito Nets
Mosquito net coverage of the entire population is necessary to achieve a large reduction in the
malaria burden. Although vulnerable groups, such as children under age 5 and pregnant women, should
still be prioritised, the equitable and communal benefits of wide-scale ITN use by older children and adults
should be promoted and evaluated by national malaria control programmes (Killeen et al., 2007). The 2014
KDHS asked about use of mosquito nets by household members during the night before the survey.
Table 12.3 presents the percentage of the de facto household population that slept under a
mosquito net of any type, under an ITN, or under an LLIN the night before the survey. Two-fifths of the
household population (42 percent) slept under an ITN the night prior to the survey. Two-thirds (67 percent)
of members of households with at least one ITN slept under an ITN the night prior to the survey.
A higher percentage of women (45 percent) than men (40 percent) slept under an ITN the night
prior to the survey. Urban residents (46 percent) are more likely to sleep under an ITN than those in rural
areas (41 percent). The Nyanza region (61 percent) has the highest percentage of the household population
that slept under an ITN the night prior to the survey, and the Central region (27 percent) has the lowest
percentage. The percentage of the household population that slept under an ITN on the night before the
survey generally increases with increasing wealth.
Net usage among the population that owns at least one ITN (the final two columns of Table 12.3)
is greater than that of the general population, indicating that ITN ownership increases the likelihood of net
usage. Variations in ITN use among households that own at least one ITN are similar to those within the
general population, with the exception that Nairobi had the highest prevalence of ITN use (78 percent).
Malaria • 189
Table 12.3 Use of mosquito nets by persons in the household
Percentage of the de facto household population who slept the night before the survey under a mosquito net (treated or
untreated), under an insecticide-treated net (ITN), and under a long-lasting insecticidal net (LLIN); and among the de facto
household population in households with at least one ITN, the percentage who slept under an ITN the night before the survey,
by background characteristics, Kenya 2014
Household population in
Household population households with at least one ITN1
Percentage who Percentage who Percentage who Percentage who
Background slept under any slept under an slept under an slept under an
characteristic net last night ITN1 last night LLIN last night Number ITN1 last night Number
Age in months
<5 58.9 54.1 53.0 19,798 76.9 13,913
5-14 40.0 36.5 35.9 40,025 56.9 25,681
15-34 45.7 40.9 39.8 44,456 65.3 27,861
35-39 53.2 47.3 46.4 17,900 76.8 11,041
50+ 46.7 41.8 40.7 15,584 73.8 8,820
Sex
Male 44.4 40.1 39.2 67,439 64.3 42,070
Female 49.5 44.7 43.7 70,341 69.4 45,251
Residence
Urban 54.0 45.5 43.7 47,445 74.7 28,885
Rural 43.4 40.9 40.3 90,335 63.2 58,437
Region
Coast 61.3 54.9 54.3 13,581 74.3 10,048
North Eastern 32.9 30.1 30.1 3,976 61.3 1,957
Eastern 39.4 36.8 36.3 20,176 61.4 12,085
Central 30.5 26.8 26.2 15,922 66.8 6,385
Rift Valley 37.0 34.6 34.2 36,251 58.6 21,413
Western 60.8 57.5 56.0 16,118 68.5 13,520
Nyanza 63.7 60.5 60.0 19,231 73.2 15,908
Nairobi 55.0 37.3 33.5 12,524 77.9 6,007
Wealth quintile
Lowest 32.4 30.6 30.5 27,438 55.8 15,034
Second 43.8 41.1 40.5 27,673 62.7 18,141
Middle 49.3 46.5 45.9 27,735 67.3 19,144
Fourth 50.2 44.9 44.0 27,562 71.9 17,232
Highest 59.5 49.1 46.6 27,372 75.6 17,770
Total 47.0 42.4 41.5 137,780 67.0 87,321
Note: Total includes 16 household members for whom age is missing.
1
An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN), or (2) a net that
has been soaked with insecticide within the past six months.
Table 12.3C presents use of mosquito nets by the household population across counties. The
percentage of people who slept under an ITN the night prior to the survey ranges from 5 percent in
Nyandarua to 71 percent in Kisii. Generally, individuals in the at-risk counties in Western, Nyanza, and
Coast are more likely to have slept under an ITN the night before the survey than those in other counties or
regions.
190 • Malaria
Table 12.3C Use of mosquito nets by persons in the household
Percentage of the de facto household population who slept the night before the survey under a mosquito net (treated or untreated),
under an insecticide-treated net (ITN), and under a long-lasting insecticidal net (LLIN); and among the de facto household
population in households with at least one ITN, the percentage who slept under an ITN the night before the survey, by county,
Kenya 2014
Household population in
Household population households with at least one ITN1
Percentage who Percentage who Percentage who Percentage who
slept under any slept under an slept under an slept under an
County net last night ITN1 last night LLIN last night Number ITN1 last night Number
Coast 61.3 54.9 54.3 13,581 74.3 10,048
Mombasa 54.7 46.2 45.2 3,487 75.9 2,121
Kwale 67.2 64.0 63.5 2,953 75.6 2,499
Kilifi 63.5 55.6 55.2 4,782 72.8 3,655
Tana River 48.5 44.6 44.5 975 66.9 650
Lamu 62.4 53.1 52.6 414 77.2 285
Taita Taveta 68.8 67.0 65.6 971 77.6 838
North Eastern 32.9 30.1 30.1 3,976 61.3 1,957
Garissa 46.2 41.3 41.3 1,450 68.6 873
Wajir 32.2 30.2 30.2 1,349 54.1 753
Mandera 17.3 16.3 16.1 1,177 58.2 330
Eastern 39.4 36.8 36.3 20,176 61.4 12,085
Marsabit 8.7 7.1 7.0 636 28.2 159
Isiolo 43.3 40.8 40.7 510 64.3 324
Meru 42.9 39.1 37.9 4,924 68.7 2,806
Tharaka-Nithi 49.8 47.0 46.3 1,308 67.4 914
Embu 43.1 37.4 36.5 1,875 61.1 1,149
Kitui 31.4 30.8 30.8 3,714 47.0 2,437
Machakos 45.0 42.8 42.5 4,098 70.3 2,494
Makueni 35.0 33.1 32.8 3,111 57.1 1,802
Central 30.5 26.8 26.2 15,922 66.8 6,385
Nyandarua 6.5 5.2 5.1 2,115 41.9 260
Nyeri 13.0 9.3 9.1 2,419 43.1 521
Kirinyaga 64.0 60.7 59.5 1,853 81.5 1,379
Murang’a 32.3 31.1 31.1 3,186 65.4 1,513
Kiambu 34.6 28.6 27.7 6,350 67.0 2,711
Rift Valley 37.0 34.6 34.2 36,251 58.6 21,413
Turkana 15.3 15.3 15.3 1,835 31.3 897
West Pokot 27.9 27.7 27.7 1,594 43.0 1,027
Samburu 13.8 11.7 11.6 623 67.4 108
Trans-Nzoia 47.8 46.3 45.5 3,694 61.8 2,768
Uasin Gishu 55.7 55.5 55.2 3,496 71.6 2,707
Elgeyo Marakwet 24.0 11.5 10.6 1,195 48.6 282
Nandi 45.7 45.5 45.4 2,947 55.6 2,412
Baringo 38.7 35.7 35.6 1,556 55.6 998
Laikipia 17.6 10.3 9.0 1,470 63.0 240
Nakuru 26.1 23.5 22.9 6,490 63.3 2,405
Narok 27.6 27.0 26.8 3,218 50.9 1,711
Kajiado 38.0 33.0 31.8 2,552 67.4 1,251
Kericho 50.3 47.6 47.2 2,409 57.6 1,990
Bomet 51.6 48.5 48.1 3,172 58.7 2,616
Western 60.8 57.5 56.0 16,118 68.5 13,520
Kakamega 59.5 54.3 50.4 5,597 66.2 4,589
Vihiga 60.0 58.5 58.4 1,949 68.1 1,676
Bungoma 59.5 57.6 57.5 5,738 69.0 4,796
Busia 66.4 62.8 62.2 2,834 72.3 2,459
Nyanza 63.7 60.5 60.0 19,231 73.2 15,908
Siaya 62.0 57.4 57.0 2,890 69.5 2,387
Kisumu 71.0 70.3 70.1 3,685 78.1 3,316
Homa Bay 57.8 50.7 49.9 4,005 68.1 2,981
Migori 51.9 49.5 47.9 3,346 64.7 2,560
Kisii 72.5 71.3 71.2 3,759 80.2 3,341
Nyamira 68.6 66.5 66.5 1,548 77.8 1,322
Nairobi 55.0 37.3 33.5 12,524 77.9 6,007
Total 47.0 42.4 41.5 137,780 67.0 87,321
1
An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN), or (2) a net that has
been soaked with insecticide within the past six months.
Malaria • 191
Table 12.4 presents the percentage of ITNs used in the Table 12.4 Use of existing ITNs
household by anyone the night before the survey, by Percentage of insecticide-treated nets (ITNs) that
background characteristics. Overall, 77 percent of ITNs were were used by anyone the night before the survey, by
background characteristics, Kenya 2014
used by anyone in the household the night before the survey.
Percentage of
Net use was higher in urban areas (80 percent) than in rural Background existing ITNs1 Number of
areas (76 percent). Net use is lowest in Rift Valley (69 percent) characteristic used last night ITNs1
and highest in Nairobi (86 percent), Nyanza (85 percent), and Residence
Urban 79.8 15,166
North Eastern (85 percent). This finding is interesting given that Rural 75.8 26,120
Nairobi and North Eastern are in areas of decreased risk. Region
Coast 81.2 4,874
Households in the lowest wealth quintile are least likely to use North Eastern 84.6 753
existing ITNs (71 percent). Eastern
Central
72.8
75.2
5,533
3,383
Rift Valley 68.8 9,912
Table 12.4C shows that use of existing ITNs by county Western
Nyanza
78.8
84.6
6,372
7,429
ranges from 44 percent in Turkana to 92 percent in Mandera. Nairobi 85.7 3,031
The counties in the coastal and lakeside endemic zones report Wealth quintile
Lowest 71.4 5,602
higher use of existing ITNs; coastal county rates range from 73 Second 78.2 7,394
percent in Tana River to 86 percent in Kilifi, and lakeside Middle
Fourth
80.0
78.3
8,838
9,036
county rates range from 72 percent in Kakamega to 88 percent Highest 76.5 10,416
in Nyamira. Total 77.3 41,286
1
An insecticide-treated net (ITN) is (1) a factory-
treated net that does not require any further treatment
(LLIN), or (2) a net that has been soaked with
insecticide within the past six months.
192 • Malaria
Table 12.4C Use of existing ITNs
Percentage of insecticide-treated nets (ITNs) that were
used by anyone the night before the survey, by county,
Kenya 2014
Percentage of
existing ITNs1 Number of
County used last night ITNs1
Coast 81.2 4,874
Mombasa 78.6 1,099
Kwale 82.3 1,215
Kilifi 86.0 1,619
Tana River 73.0 279
Lamu 80.0 159
Taita Taveta 74.3 503
North Eastern 84.6 753
Garissa 86.1 381
Wajir 78.9 260
Mandera 92.3 112
Eastern 72.8 5,533
Marsabit 49.0 48
Isiolo 76.1 136
Meru 79.0 1,344
Tharaka-Nithi 76.7 487
Embu 70.3 575
Kitui 60.9 983
Machakos 78.3 1,179
Makueni 69.4 780
Central 75.2 3,383
Nyandarua 50.9 123
Nyeri 51.5 257
Kirinyaga 85.3 846
Murang’a 74.5 782
Kiambu 76.0 1,374
Rift Valley 68.8 9,912
Turkana 43.9 311
West Pokot 57.8 353
Samburu 82.6 44
Trans-Nzoia 68.3 1,239
Uasin Gishu 79.3 1,426
Elgeyo Marakwet 69.6 104
Nandi 71.1 1,048
Baringo 65.8 436
Laikipia 77.0 111
Nakuru 73.9 1,153
Narok 62.2 728
Kajiado 68.9 677
Kericho 67.1 955
Bomet 65.2 1,327
Western 78.8 6,372
Kakamega 71.9 2,343
Vihiga 86.4 757
Bungoma 81.9 2,082
Busia 82.1 1,190
Nyanza 84.6 7,429
Siaya 86.9 1,089
Kisumu 86.4 1,616
Homa Bay 80.1 1,324
Migori 84.5 963
Kisii 83.6 1,754
Nyamira 88.1 683
Nairobi 85.7 3,031
Total 77.3 41,286
1
An insecticide-treated net (ITN) is (1) a factory-treated
net that does not require any further treatment (LLIN), or
(2) a net that has been soaked with insecticide within
the past six months.
Malaria • 193
Figure 12.2 presents data on ownership of, access to, and use of ITNs. Although more than half of
households own at least one ITN (59 percent), only one-third (34 percent) own at least one ITN for every
two persons. Forty-eight percent of the household population has access to an ITN, and 42 percent slept
under an ITN.
Figure 12.2 Ownership of, access to, and use of ITNs1
Percent
59
48
42
34
Percent of households Percent of households Percent of the Percent of the
with at least one ITN with at least one ITN for household population household population
every two persons who with access to an ITN who slept under an ITN
stayed in the household within their household
last night
1 An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN), or
(2) a net that has been soaked with insecticide within the past 6 months. KDHS 2014
12.4.2 Use of Mosquito Nets by Children Under Age 5
Use of mosquito nets by vulnerable groups in highly endemic communities is one of the major
malaria control and prevention strategies adopted under the National Malaria Strategy (MOH, 2015).
Young children are especially vulnerable to malaria. For about six months following birth, antibodies
acquired from the mother during pregnancy protect children born in areas of endemic malaria. This
immunity is gradually lost, and children start to develop their own immunity to malaria. The pace at which
immunity is developed depends on their exposure to malaria infection, and, in highly malaria endemic
areas, children are thought to have attained a high level of immunity by their fifth birthday. Such children
may experience episodes of malaria illness but usually do not suffer from severe, life-threatening malaria.
Immunity in areas of low malaria transmission is acquired more slowly, and malaria illness affects all age
groups of the population.
Table 12.5 shows that 54 percent of children under age 5 slept under an ITN the night before the
survey. Children in urban areas are more likely to sleep under an ITN (59 percent) than those in rural areas
(52 percent). In households overall, children in Nyanza and Western (69 percent) are more likely to sleep
under an ITN than children in other regions; however, in households that own at least one ITN, the
percentage of children in Nairobi, Coast, and Central (81-86 percent) who slept under an ITN the night
before the survey is equivalent to or higher than the percentage of children in the at-risk areas of Western
and Nyanza (79 percent and 81 percent, respectively). The percentage of children who slept under an ITN
the night before the survey increases with increasing wealth.
Use of mosquito nets among children under age 5 by county is presented in Table 12.5C. The
percentage of children under age 5 who slept under an ITN the night before the survey ranges from 12
percent in Marsabit and Nyandarau to 82 percent in Taita Taveta and Kisumu. As expected, more children
sleep under an ITN in the counties in the lakeside and coastal endemic zones.
194 • Malaria
Table 12.5 Use of mosquito nets by children
Percentage of children under five years of age who, the night before the survey, slept under a mosquito net (treated or
untreated), under an insecticide-treated net (ITN), and under a long-lasting insecticidal net (LLIN); and among children under
five years of age in households with at least one ITN, the percentage who slept under an ITN the night before the survey, by
background characteristics, Kenya 2014
Children under age five in
Children under age five in all households households with at least one ITN1
Percentage who Percentage who Percentage who Percentage who
Background slept under any slept under an slept under an Number of slept under an Number of
characteristic net last night ITN1 last night LLIN last night children ITN1 last night children
Age in months
<12 67.1 62.8 62.0 3,700 82.3 2,825
12-23 62.8 57.6 56.4 3,919 81.2 2,780
24-35 58.9 54.3 53.0 4,011 77.5 2,813
36-47 54.1 48.3 47.5 4,209 71.6 2,839
48-59 52.3 48.3 46.9 3,959 71.9 2,657
Sex
Male 59.2 54.7 53.7 10,059 77.8 7,070
Female 58.5 53.4 52.2 9,740 76.0 6,843
Residence
Urban 68.3 58.9 56.7 6,563 84.5 4,570
Rural 54.2 51.7 51.1 13,236 73.2 9,343
Region
Coast 70.5 65.4 64.9 2,006 82.2 1,596
North Eastern 43.8 40.2 40.1 664 77.7 343
Eastern 55.7 53.1 52.7 2,464 73.5 1,779
Central 47.4 43.0 42.2 1,792 80.8 954
Rift Valley 45.7 43.0 42.4 5,713 68.9 3,564
Western 71.8 68.8 67.2 2,526 78.9 2,203
Nyanza 71.6 68.9 68.3 2,894 80.7 2,470
Nairobi 70.7 49.5 44.4 1,738 85.8 1,003
Wealth quintile
Lowest 42.3 40.4 40.2 4,850 66.5 2,949
Second 57.6 55.2 54.6 4,231 74.4 3,137
Middle 60.6 57.3 56.6 3,636 77.2 2,696
Fourth 66.0 59.6 58.5 3,411 83.3 2,441
Highest 73.8 62.5 59.2 3,670 85.3 2,691
Total 58.9 54.1 53.0 19,798 76.9 13,913
Note: Table is based on children who stayed in the household the night before the interview.
1
An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN), or (2) a net that
has been soaked with insecticide within the past six months.
Malaria • 195
Table 12.5C Use of mosquito nets by children
Percentage of children under five years of age who, the night before the survey, slept under a mosquito net (treated or untreated),
under an insecticide-treated net (ITN), and under a long-lasting insecticidal net (LLIN); and among children under five years of
age in households with at least one ITN, the percentage who slept under an ITN the night before the survey, by county, Kenya
2014
Children under age five in
Children under age five in all households households with at least one ITN1
Percentage who Percentage who Percentage who Percentage who
slept under any slept under an slept under an Number of slept under an Number of
County net last night ITN1 last night LLIN last night children ITN1 last night children
Coast 70.5 65.4 64.9 2,006 82.2 1,596
Mombasa 67.2 61.5 61.0 477 85.9 342
Kwale 74.6 72.4 71.9 421 83.4 365
Kilifi 70.9 63.6 63.3 759 79.1 611
Tana River 60.1 57.4 57.2 174 78.3 128
Lamu 68.8 57.7 57.6 55 81.0 39
Taita Taveta 83.2 82.0 80.0 120 88.8 111
North Eastern 43.8 40.2 40.1 664 77.7 343
Garissa 52.5 47.6 47.6 239 77.2 147
Wajir 46.1 42.8 42.8 260 76.1 146
Mandera 27.5 25.5 24.9 165 84.1 50
Eastern 55.7 53.1 52.7 2,464 73.5 1,779
Marsabit 15.0 12.1 11.8 93 43.7 26
Isiolo 60.6 57.7 57.6 83 80.9 59
Meru 64.3 59.3 58.5 541 81.8 393
Tharaka-Nithi 65.4 61.9 60.9 155 77.8 123
Embu 63.0 58.5 58.5 204 72.3 165
Kitui 39.8 39.6 39.6 494 54.2 361
Machakos 65.0 62.9 62.5 515 85.0 381
Makueni 52.5 50.8 50.6 379 70.9 272
Central 47.4 43.0 42.2 1,792 80.8 954
Nyandarua 14.8 12.3 12.0 259 69.7 46
Nyeri 24.1 17.4 17.2 268 62.6 75
Kirinyaga 81.1 78.9 78.0 196 89.6 173
Murang’a 58.0 57.0 57.0 315 81.3 221
Kiambu 53.6 47.5 45.9 755 81.3 441
Rift Valley 45.7 43.0 42.4 5,713 68.9 3,564
Turkana 21.0 21.0 21.0 372 40.8 192
West Pokot 43.4 42.9 42.9 306 61.4 214
Samburu 18.2 16.6 16.6 117 86.1 23
Trans-Nzoia 60.2 59.2 58.8 570 74.8 452
Uasin Gishu 69.8 69.3 69.2 498 84.1 410
Elgeyo Marakwet 39.1 16.9 16.5 179 56.5 53
Nandi 55.2 55.0 55.0 416 67.1 341
Baringo 52.5 49.1 49.1 235 69.2 167
Laikipia 22.2 13.6 10.9 216 78.7 37
Nakuru 34.1 30.7 29.7 880 74.6 363
Narok 31.5 31.1 30.7 640 56.1 355
Kajiado 47.7 42.8 40.9 447 81.4 235
Kericho 55.7 53.1 52.3 349 61.4 302
Bomet 63.0 60.0 59.8 487 69.7 419
Western 71.8 68.8 67.2 2,526 78.9 2,203
Kakamega 66.0 62.6 58.6 860 73.1 737
Vihiga 72.5 70.9 70.9 263 78.3 238
Bungoma 73.7 71.5 71.3 955 82.0 832
Busia 78.4 73.9 72.9 448 83.5 396
Nyanza 71.6 68.9 68.3 2,894 80.7 2,470
Siaya 71.3 67.8 67.5 428 77.1 376
Kisumu 82.2 81.7 81.4 529 88.4 489
Homa Bay 63.9 56.4 55.6 658 74.8 496
Migori 63.0 62.2 60.4 556 74.4 464
Kisii 80.4 79.5 79.5 516 88.5 464
Nyamira 70.5 69.6 69.6 207 79.8 181
Nairobi 70.7 49.5 44.4 1,738 85.8 1,003
Total 58.9 54.1 53.0 19,798 76.9 13,913
Note: Table is based on children who stayed in the household the night before the interview.
1
An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN), or (2) a net that
has been soaked with insecticide within the past six months.
196 • Malaria
12.4.3 Use of Mosquito Nets by Pregnant Women
In malaria endemic areas, adults usually have acquired some degree of immunity to severe, life-
threatening malaria. However, pregnancy leads to depression of the immune system, and thus pregnant
women, especially those in their first pregnancy, have a higher risk of malaria. Moreover, these infections
may be asymptomatic, may lead to malaria-induced anaemia, and may interfere with the mother-foetus
exchange, resulting in low birth weight births. During pregnancy, women can reduce their risk of adverse
malaria effects by sleeping under ITNs. Accordingly, the goal of the National Malaria Strategy is for 80
percent of pregnant women to sleep under an ITN (MOH, 2015).
Table 12.6 shows that 51 percent of pregnant women age 15-49 slept under an ITN the night
before the survey. Although there is almost no urban-rural difference, there are variations by region;
pregnant women in the malaria prone Nyanza (71 percent), Western (67 percent), and Coast (63 percent)
regions are more likely to have slept under an ITN than pregnant women in other regions (50 percent or
less). Pregnant women with no education and those in the lowest wealth quintile were substantially less
likely to have slept under an ITN than their more educated or wealthier counterparts. Not surprisingly,
pregnant women in households that own at least one ITN are 1.5 times more likely than pregnant women
in the general population to have used an ITN (77 percent compared with 51 percent). There are
insufficient cases to evaluate these indicators at the county level, so data at this level are not presented.
Table 12.6 Use of mosquito nets by pregnant women
Percentages of pregnant women age 15-49 who, the night before the survey, slept under a mosquito net (treated or untreated),
under an insecticide-treated net (ITN), and under a long-lasting insecticidal net (LLIN); and among pregnant women age 15-49 in
households with at least one ITN, the percentage who slept under an ITN the night before the survey, by background characteristics,
Kenya 2014
Among pregnant women age
15-49 in households with at least
Among pregnant women age 15-49 in all households one ITN1
Percentage who Percentage who Percentage who Percentage who
Background slept under any slept under an slept under an Number of slept under an Number of
characteristic net last night ITN1 last night LLIN last night women ITN1 last night women
Residence
Urban 60.5 51.1 49.4 750 80.8 474
Rural 52.8 50.1 49.7 1,188 74.0 804
Region
Coast 71.3 63.1 63.1 202 86.9 147
North Eastern 43.4 43.1 43.1 78 82.4 41
Eastern 52.3 49.8 49.3 204 72.3 141
Central 38.7 34.7 34.7 188 73.0 89
Rift Valley 42.1 40.4 40.1 562 67.6 336
Western 70.5 66.7 66.4 220 75.7 194
Nyanza 76.0 70.9 69.1 242 86.4 199
Nairobi 61.0 43.3 39.6 241 (78.8) 133
Education
No education 32.3 32.1 32.1 240 70.9 109
Primary incomplete 53.2 50.5 50.0 507 73.3 349
Primary complete 60.2 51.9 50.3 482 78.4 319
Secondary+ 62.5 55.8 54.8 708 78.7 501
Wealth quintile
Lowest 39.7 38.2 37.8 459 73.0 240
Second 59.6 56.6 56.0 358 74.6 272
Middle 61.0 58.1 58.1 349 76.3 265
Fourth 59.3 51.0 49.7 368 77.7 241
Highest 62.8 52.0 50.0 403 80.9 259
Total 55.8 50.5 49.6 1,937 76.5 1,278
Notes: Table is based on women who stayed in the household the night before the interview. Figures in parentheses are based on
25-49 unweighted cases.
1
An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN), or (2) a net that has
been soaked with insecticide within the past six months.
Malaria • 197
Figure 12.3 shows that ownership and use of mosquito nets have increased in the last decade, with
the largest changes from 2003 to 2008-09. The percentage of households that own at least one ITN
increased from 6 percent in 2003 to 59 percent in 2014. The proportion of children under age 5 who slept
under an ITN increased from 5 percent to 54 percent, and the percentage of pregnant women age 15-49
who slept under an ITN increased from 4 percent to 51 percent. It is important to note that the timing of
data collection can affect net use indicators, since fieldwork for the three surveys may or may not have
included the full malaria season.
Figure 12.3 Trends in ITN1 ownership and use
Percent
59
56 54
49 51
47
6 5 4
At least one ITN Slept under an ITN Slept under an ITN
Household ownership of nets Net use by children under age 5 Net use by pregnant women
2003 KDHS 2008-09 KDHS 2014 KDHS
1 An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment (LLIN), or
(2) a net that has been soaked with insecticide within the past six months.
12.5 PREVENTIVE MALARIA TREATMENT DURING PREGNANCY
Intermittent preventive treatment during pregnancy (IPTp), an important component of malaria
control, is intended to reduce malaria during pregnancy. The government of Kenya’s IPTp policy states
that all pregnant women living in malaria endemic areas should receive at least two doses of sulfadoxine-
pyrimethamine (SP), an effective antimalarial drug, during routine antenatal care (ANC) visits. Areas
outside of endemic zones do not implement IPTp (MOH, 2010). The first dose of SP should be given at 16
weeks of gestation, and subsequent doses should be administered at each ANC visit, a minimum of one
month later (MOH, 2014).
In the 2014 KDHS, women who had a live birth in the two years preceding the survey were asked
if they had taken any drugs to prevent them from getting malaria during the pregnancy for their most recent
birth and, if so, which drugs. If the respondent did not know the name of the drug she took, interviewers
were instructed to show her some examples of common antimalarials. If respondents had taken SP or
Fansidar, they were further asked how many times they had taken it and whether they had received it
during an antenatal care visit.
Table 12.7 shows the percentage of women who took various doses of SP/Fansidar, at least one
during an ANC visit, by background characteristics. Nationally, 17 percent of women took the
recommended two or more doses of SP/Fansidar, with at least one dose being administered during an ANC
visit. This is a slight increase from 15 percent in the 2008-09 KDHS. Only 10 percent received three or
more doses, with at least one dose being administered during an ANC visit. Table 12.7C presents these
data by county, with attention given to areas where the government of Kenya implements IPTp.
Specifically within these endemic focus areas, 39 percent of women received the recommended two or
more doses.
198 • Malaria
Table 12.7 indicates that rural women are slightly more likely to receive SP/Fansidar during ANC
than their urban counterparts. Eighteen percent of rural women received the recommended two or more
doses, as compared with 14 percent of urban women. Receipt of two or more doses is not clearly
associated with education or wealth at the national level.
Table 12.7 Use of Intermittent Preventive Treatment (IPTp) by women during pregnancy
Percentage of women age 15-49 with a live birth in the two years preceding the survey who, during the
pregnancy preceding the last birth, received one or more doses of SP/Fansidar at least one of which was
received during an ANC visit, received two or more doses of SP/Fansidar at least one of which was
received during an ANC visit, and received three or more doses of SP/Fansidar at least one of which
was received during an ANC visit, by background characteristics, Kenya 2014
Number of women
Percentage who Percentage who Percentage who with a live birth in
received 1 or more received 2 or more received 3 or more the two years
Background doses of doses of doses of preceding the
characteristic SP/Fansidar1 SP/Fansidar1 SP/Fansidar1 survey
Residence
Urban 26.8 14.1 7.8 2,618
Rural 31.0 18.4 11.4 4,739
Region
Coast 73.7 52.5 32.7 793
North Eastern 5.2 2.1 1.1 228
Eastern 24.3 9.8 5.0 872
Central 16.4 4.5 2.4 682
Rift Valley 14.6 6.9 4.5 2,167
Western 53.4 38.4 26.2 827
Nyanza 43.3 21.8 9.7 1,035
Nairobi 6.3 1.3 0.8 753
Education
No education 28.9 17.3 11.3 834
Primary incomplete 32.0 18.6 11.2 2,036
Primary complete 31.4 17.4 10.3 1,987
Secondary+ 26.3 14.9 8.6 2,499
Wealth quintile
Lowest 33.2 19.7 11.5 1,823
Second 29.4 16.5 10.1 1,461
Middle 32.4 20.5 13.5 1,332
Fourth 27.1 13.9 8.0 1,283
Highest 24.6 13.0 7.1 1,458
Total 29.5 16.9 10.1 7,357
1
Received the specified number of doses of SP/Fansidar, at least one of which was received during an
ANC visit
Table 12.7C shows that the areas in which the government of Kenya implements IPTp, in fact,
report higher rates of IPTp than other areas. More than half of women with a live birth in the preceding
two years received the recommended IPTp dosage in the Coast region (53 percent). Thirty-eight percent
received two or more doses in Western, and 25 percent received two or more doses in Nyanza’s focus
counties. In the areas of IPTp implementation, more than half of women in Kwale (79 percent), Lamu (60
percent), and Taita Taveta (55 percent) received two or more doses. Less than one-quarter of women are
receiving the recommended doses in Siaya and Homa Bay (both 23 percent).
Malaria • 199
Table 12.7C Use of Intermittent Preventive Treatment (IPTp) by women during pregnancy
Percentage of women age 15-49 with a live birth in the two years preceding the survey who, during the
pregnancy preceding the last birth, received one or more doses of SP/Fansidar at least one of which was
received during an ANC visit, received two or more doses of SP/Fansidar at least one of which was received
during an ANC visit, and received three or more doses of SP/Fansidar at least one of which was received
during an ANC visit, by county, Kenya 2014
Number of
women with a
Percentage who Percentage who Percentage who live birth in the
received 1 or received 2 or received 3 or two years
Background more doses of more doses of more doses of preceding the
1 1
characteristic SP/Fansidar SP/Fansidar SP/Fansidar1 survey
Areas of IPTp implementation 58.9 38.7 23.6 2,396
Coast 73.7 52.5 32.7 793
Mombasa 67.3 46.4 24.9 190
Kwale 91.8 79.1 51.9 181
Kilifi 65.2 41.8 28.5 293
Tana River 77.0 41.6 21.4 68
Lamu 81.0 60.0 35.7 19
Taita Taveta 75.0 55.1 31.9 42
Western 53.4 38.4 26.2 827
Kakamega 36.2 28.1 20.1 244
Vihiga 72.9 47.1 20.5 83
Bungoma 57.5 39.6 27.7 354
Busia 60.9 47.9 36.1 146
Nyanza (focus counties) 49.6 24.7 11.5 775
Siaya 56.9 23.4 15.0 142
Kisumu 58.2 26.9 5.9 177
Homa Bay 38.4 22.8 12.3 253
Migori 51.0 26.1 12.9 203
Areas IPTp not implemented 15.3 6.3 3.6 4,961
North Eastern 5.2 2.1 1.1 228
Garissa 2.7 1.0 0.8 86
Wajir 4.4 2.0 0.5 93
Mandera 11.3 4.3 3.0 49
Eastern 24.3 9.8 5.0 872
Marsabit 1.5 1.2 0.6 35
Isiolo 47.7 3.3 1.3 33
Meru 19.1 3.8 0.0 198
Tharaka-Nithi 25.9 10.3 7.1 56
Embu 15.0 1.1 0.0 81
Kitui 42.2 25.8 16.0 164
Machakos 16.5 8.3 2.5 190
Makueni 26.5 10.1 7.4 115
Central 16.4 4.5 2.4 682
Nyandarua 7.3 3.0 2.2 97
Nyeri 5.2 3.2 1.9 92
Kirinyaga 10.2 8.4 3.7 61
Murang’a 14.4 4.8 1.9 120
Kiambu 24.5 4.4 2.6 312
Rift Valley 14.6 6.9 4.5 2,167
Turkana 52.4 22.7 13.0 131
West Pokot 3.1 3.0 2.9 121
Samburu 6.3 2.9 2.8 46
Trans-Nzoia 10.5 3.7 3.2 218
Uasin Gishu 7.8 7.0 6.4 187
Elgeyo Marakwet 15.2 4.3 1.8 65
Nandi 11.3 3.4 1.9 153
Baringo 21.2 9.9 6.7 94
Laikipia 19.4 7.0 1.6 78
Nakuru 19.2 9.3 4.2 332
Narok 6.6 1.4 0.6 237
Kajiado 20.6 12.3 8.5 179
Kericho 7.5 4.4 3.4 139
Bomet 7.5 5.2 4.8 187
Nyanza (non-focus counties) 24.3 13.0 4.5 260
Kisii 25.5 11.3 3.7 193
Nyamira 20.7 18.1 7.0 67
Nairobi 6.3 1.3 0.8 753
Total 29.5 16.9 10.1 7,357
1
Received the specified number of doses of SP/Fansidar, at least one of which was received during an ANC
visit
200 • Malaria
12.6 FEVER AMONG CHILDREN UNDER AGE 5
The Kenya Malaria Strategy stipulates that, by 2018, 100 percent of all suspected malaria cases
presented to a health care provider will be managed according to the National Malaria Treatment
Guidelines (MOH, 2015a). According to these guidelines, the first line of treatment is artemisinin
combination therapy (ACT) with artemether-lumefantrine (AL).3 Following reported drug resistance to
sulfadoxine-pyrimethamine (SP), the government of Kenya in 2006 rolled out the use of ACT/AL. The
treatment guidelines further state that, before treatment is given, the patient should undergo testing either
by a rapid diagnostic test or microscopy, so that only patients who have a positive malaria test receive
malaria medication (MOH, 2014).
12.6.1 Prevalence and Treatment of Fever among Children
The 2014 KDHS asked mothers whether their children under age 5 had a fever in the two weeks
preceding the survey and, if so, whether any treatment was sought. Questions were also asked about blood
testing, the types of drugs given to the child, and how soon drugs were taken after onset of fever. Table
12.8 shows the percentage of children under age 5 who had a fever in the two weeks preceding the survey
and, among these children, the percentage for whom advice or treatment was sought from a health facility,
provider, or pharmacy; the percentage who had a drop of blood taken from a finger or heel prick
(considered a proxy for malaria testing); the percentage who received antimalarial treatment; and the
percentage receiving treatment the same or the next day.
Twenty-four percent of children under age 5 had a fever in the two weeks preceding the survey.
Children age 12-23 months (30 percent), children in rural areas (26 percent), and children in Nyanza (37
percent) and Western (36 percent) were more likely to have suffered fever.
Among children with a fever, 72 percent were taken for advice or treatment, and 35 percent had
blood taken from a finger or heel for testing. There do not appear to be differences in blood testing by age,
sex, or rural-urban residency. By region, the proportion of children who had blood tested ranged from a
high of 47 percent in Nyanza, a region with some malaria endemicity, to a low of 25 percent in Central. In
Western and Coast, also malaria prone regions, 38 percent and 35 percent of children with a fever,
respectively, had blood drawn for testing. Children whose mothers have a secondary or higher education
(38 percent) and those in the highest wealth quintile (44 percent) were most likely to have blood drawn.
Twenty-seven percent of children under age 5 who had a fever took antimalarial drugs, and only
16 percent took antimalarials the same or next day. This is a slight increase from the 2008-09 KDHS, when
23 percent of children with a fever took antimalarials and 12 percent took them within the recommended
time frame. Twenty-three percent of children took ACT, and 13 percent took ACT the same or the next
day, an increase from 2008-09 (8 percent and 4 percent, respectively). Children in the malaria prone
Western and Nyanza regions (28 percent and 25 percent, respectively) were substantially more likely to
take ACT the same or the next day than those in other regions (6 percent or less). Children whose mothers
have no education were less likely to take antimalarials or ACT and to take them promptly than were
children whose mothers have some education. Children in the highest wealth quintile were least likely to
take antimalarials or ACT and to take them promptly.
3
ACT/AL is considered the first line of treatment for uncomplicated malaria. At the time of the 2014 KDHS, a policy
change occurred for treatment of severe cases of malaria from quinine to parenteral artesunate, data for which were
not collected in the survey.
Malaria • 201
Table 12.8 Prevalence, diagnosis, and prompt treatment of children with fever
Percentage of children under age five with fever in the two weeks preceding the survey; and among children under age five with fever, the percentage for whom advice
or treatment was sought, the percentage who had blood taken from a finger or heel, the percentage who took any artemisinin-based combination therapy (ACT), the
percentage who took ACT the same or next day following the onset of fever, the percentage who took antimalarial drugs, and the percentage who took the drugs the
same or next day following the onset of fever, by background characteristics, Kenya 2014
Among children under
age five: Among children under age five with fever:
Percentage Percentage Percentage
with fever in Percentage for who had blood Percentage who took Percentage
the two weeks whom advice taken from a who took antimalarial Percentage who took any
Background preceding the Number of or treatment finger or heel antimalarial drugs same or who took any ACT same or Number of
characteristic survey children was sought1 for testing drugs next day ACT next day children
Age (in months)
<12 24.5 3,603 73.8 28.9 16.0 10.2 13.2 8.1 883
12-23 29.9 3,777 70.5 34.2 24.3 15.7 20.5 12.6 1,131
24-35 24.8 3,760 72.6 39.3 29.5 15.7 26.1 13.5 933
36-47 21.2 3,889 71.0 35.4 32.7 18.5 28.5 16.5 826
48-59 21.5 3,672 71.0 37.0 34.0 18.7 29.0 15.6 789
Sex
Male 24.5 9,477 71.6 35.0 27.0 16.3 22.7 13.5 2,325
Female 24.2 9,225 71.9 34.8 27.0 15.0 23.5 12.8 2,237
Residence
Urban 21.7 6,677 72.6 38.6 20.4 11.7 16.8 9.3 1,447
Rural 25.9 12,025 71.4 33.2 30.0 17.5 26.1 14.9 3,114
Region
Coast 27.2 1,936 78.0 34.9 11.9 4.4 10.2 3.6 526
North Eastern 8.7 625 59.3 31.4 7.3 3.9 5.0 3.9 54
Eastern 18.2 2,235 76.6 33.2 18.1 9.1 11.9 6.4 406
Central 17.9 1,725 71.5 24.7 4.8 3.2 3.7 3.0 308
Rift Valley 20.9 5,457 68.7 25.6 13.3 7.4 9.8 5.4 1,139
Western 36.1 2,166 67.5 37.9 51.8 29.8 49.5 28.3 782
Nyanza 37.4 2,638 75.6 46.9 48.7 30.1 42.2 24.9 987
Nairobi 18.7 1,920 67.4 36.0 10.6 7.5 6.9 3.8 359
Mother’s education
No education 17.7 2,218 68.3 28.7 17.5 8.8 14.7 7.0 392
Primary incomplete 29.2 5,304 67.9 33.2 32.0 18.7 27.3 15.5 1,550
Primary complete 24.2 5,164 74.1 36.0 27.7 15.5 23.7 12.8 1,250
Secondary+ 22.8 6,016 74.9 37.6 23.3 14.4 20.3 12.6 1,369
Wealth quintile
Lowest 25.1 4,457 68.2 30.4 23.1 12.9 19.3 10.5 1,119
Second 28.5 3,803 72.7 36.9 36.3 21.2 32.0 18.2 1,082
Middle 26.1 3,375 72.0 31.7 31.9 19.4 28.5 17.0 881
Fourth 24.0 3,285 71.7 34.0 26.2 14.3 22.5 11.9 788
Highest 18.3 3,782 75.7 44.2 13.2 8.2 9.4 5.9 691
Total 24.4 18,702 71.7 34.9 27.0 15.7 23.1 13.1 4,562
1
Excludes relative/friend and traditional practitioner
Table 12.8C shows the prevalence, diagnosis, and prompt treatment of children with a fever by
county. The percentage of children with a fever ranged from 5 percent in Mandera to 49 percent in Vihiga.
Among counties with sufficient cases of fever for evaluation, children with a fever in Mombasa (91
percent) were most likely to be taken for treatment, children in Siaya and Isiolo (both 61 percent) were
most likely to have blood taken, and children in Siaya and Busia (59 percent and 60 percent, respectively)
were most likely to have taken ACT.
202 • Malaria
Table 12.8C Prevalence, diagnosis, and prompt treatment of children with fever
Percentage of children under age five with fever in the two weeks preceding the survey; and among children under age five with fever, the percentage for whom
advice or treatment was sought, the percentage who had blood taken from a finger or heel, the percentage who took any artemisinin-based combination therapy
(ACT), the percentage who took ACT the same or next day following the onset of fever, the percentage who took antimalarial drugs, and the percentage who took
the drugs the same or next day following the onset of fever, by county, Kenya 2014
Among children under age
five: Among children under age five with fever:
Percentage Percentage Percentage
with fever in Percentage for who had blood Percentage who took Percentage
the two weeks whom advice taken from a who took antimalarial Percentage who took any
preceding the Number of or treatment finger or heel antimalarial drugs same or who took any ACT same or Number of
County survey children was sought1 for testing drugs next day ACT next day children
Coast 27.2 1,936 78.0 34.9 11.9 4.4 10.2 3.6 526
Mombasa 22.5 493 91.1 46.7 14.9 4.4 13.5 3.0 111
Kwale 25.7 408 69.5 42.8 33.2 14.2 30.0 11.8 105
Kilifi 31.3 705 78.6 31.0 2.1 0.0 1.0 0.0 221
Tana River 26.8 166 81.1 22.6 12.3 6.7 9.5 5.8 45
Lamu 22.2 52 61.5 11.8 5.7 2.1 1.2 0.0 12
Taita Taveta 29.5 110 58.6 21.7 1.3 1.3 1.3 1.3 33
North Eastern 8.7 625 59.3 31.4 7.3 3.9 5.0 3.9 54
Garissa 7.0 223 (44.0) (52.4) (5.6) (5.6) (5.6) (5.6) 16
Wajir 12.5 252 65.3 19.9 4.3 0.0 0.4 0.0 31
Mandera 4.8 150 * * * * * * 7
Eastern 18.2 2,235 76.6 33.2 18.1 9.1 11.9 6.4 406
Marsabit 19.3 88 63.1 22.3 10.6 1.8 10.1 1.8 17
Isiolo 13.2 81 78.7 60.7 51.1 51.1 43.4 43.4 11
Meru 26.0 490 73.3 54.0 23.1 11.0 14.9 7.8 128
Tharaka-Nithi 28.1 137 78.9 41.9 27.2 18.6 9.8 6.3 39
Embu 13.8 194 (72.4) (38.4) (21.7) (10.9) (21.7) (10.9) 27
Kitui 17.0 424 72.1 9.1 7.4 3.3 3.5 2.5 72
Machakos 13.6 474 (81.6) (18.4) (12.8) (6.2) (10.8) (6.2) 64
Makueni 14.1 346 89.7 21.7 13.7 1.1 7.8 0.0 49
Central 17.9 1,725 71.5 24.7 4.8 3.2 3.7 3.0 308
Nyandarua 17.2 232 70.8 22.6 6.7 4.2 5.3 2.8 40
Nyeri 14.1 240 (67.2) (17.5) (0.0) (0.0) (0.0) (0.0) 34
Kirinyaga 21.2 188 (82.4) (28.6) (18.6) (16.2) (18.6) (16.2) 40
Murang’a 17.7 293 (85.1) (3.7) (0.0) (0.0) (0.0) (0.0) 52
Kiambu 18.5 772 64.8 33.6 3.2 1.2 1.2 1.2 143
Rift Valley 20.9 5,457 68.7 25.6 13.3 7.4 9.8 5.4 1,139
Turkana 11.4 333 63.4 49.5 29.9 26.8 21.7 18.5 38
West Pokot 9.4 294 80.4 20.6 32.2 32.2 16.1 16.1 28
Samburu 19.1 114 54.5 16.6 6.8 2.5 5.0 2.5 22
Trans-Nzoia 21.9 516 65.9 45.2 12.4 4.6 10.9 4.1 113
Uasin Gishu 19.3 463 62.8 17.0 5.8 2.9 4.7 1.8 89
Elgeyo Marakwet 29.7 164 67.1 13.5 2.0 2.0 1.5 1.5 49
Nandi 19.7 388 52.8 12.2 8.8 5.3 8.8 5.3 76
Baringo 22.9 230 71.6 39.1 27.7 14.3 22.9 11.4 53
Laikipia 21.6 206 74.9 26.0 16.8 10.7 6.2 6.2 44
Nakuru 15.5 849 67.9 27.6 11.6 6.8 11.0 6.8 132
Narok 30.7 614 78.0 20.4 13.3 5.2 9.7 3.6 188
Kajiado 25.7 452 64.7 21.8 1.4 0.8 0.0 0.0 116
Kericho 25.6 359 70.7 37.9 19.4 10.0 14.4 6.9 92
Bomet 20.8 475 73.9 14.9 21.0 11.5 12.9 8.1 99
Western 36.1 2,166 67.5 37.9 51.8 29.8 49.5 28.3 782
Kakamega 28.9 721 56.0 34.9 38.7 19.6 38.4 19.6 209
Vihiga 49.2 215 69.1 34.4 40.7 18.0 39.5 18.0 106
Bungoma 35.8 842 74.6 41.6 58.7 36.0 55.1 33.5 302
Busia 42.7 388 67.9 37.2 62.9 38.9 59.6 36.5 166
Nyanza 37.4 2,638 75.6 46.9 48.7 30.1 42.2 24.9 987
Siaya 44.9 378 80.7 61.1 59.4 38.5 59.2 38.4 170
Kisumu 30.9 478 74.9 48.8 46.0 23.1 39.5 18.8 148
Homa Bay 45.6 616 67.5 50.7 51.7 35.1 46.5 29.9 281
Migori 48.2 516 80.4 42.6 42.4 24.9 37.2 21.0 249
Kisii 28.1 463 80.2 27.1 46.9 28.1 27.2 13.4 130
Nyamira 5.7 187 * * * * * * 11
Nairobi 18.7 1,920 67.4 36.0 10.6 7.5 6.9 3.8 359
Total 24.4 18,702 71.7 34.9 27.0 15.7 23.1 13.1 4,562
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 cases that has been suppressed.
1
Excludes relative/friend and traditional practitioner
Malaria • 203
Table 12.9 presents information on the source of Table 12.9 Source of advice or treatment for children with
advice or treatment for children under age 5 with a fever. fever
The first column refers to children with a fever in general Percentage of children for whom advice or treatment was
sought from specific sources among children under age five
and can give a sense of the overall coverage of various with fever in the two weeks preceding the survey and
sources of advice and treatment. The second column refers among children under age five with fever in the two weeks
preceding the survey for whom advice or treatment was
to children with a fever for whom advice or treatment was sought, by source, Kenya 2014
sought; from this column, the relative strength of each of Percentage for whom advice or
treatment was sought from
the treatment options can be seen. Treatment was sought each source:
from a public sector source for 50 percent of children with Among
children with
a fever, mostly from dispensaries (24 percent), government fever for whom
health centres (15 percent), and government hospitals (12 Among
children with
advice or
treatment was
percent). Twenty-one percent went to a private sector Source fever sought
source, mostly private hospitals or clinics (12 percent) and Any public sector source 50.1 69.1
Government hospital 11.9 16.4
pharmacies (8 percent). The second column, focusing on Government health
children with a fever for whom advice or treatment was centre 15.2 20.9
Government dispensary 23.7 32.6
sought, shows that 69 percent of children went to a public Other 0.0 0.0
sector source, with most visiting a dispensary (33 percent). Any private sector
source 21.2 29.2
Twenty-nine percent went to a private sector source for Private hospital/clinic 11.9 16.4
treatment and advice. Pharmacy 7.8 10.8
Mission hospital/clinic 1.4 1.9
Other private medical
12.6.2 Type and Timing of Antimalarial drugs sector 0.2 0.2
Any other source 3.0 4.1
Mobile clinic 0.1
0.1
Among children under age 5 with a fever in the Community health worker 0.3
0.4
two weeks prior to the survey who also took antimalarial Shop 1.6
2.2
Traditional practitioner 0.5
0.6
drugs, information was collected on the type of drugs taken Friend/relative 0.2
0.3
and the timing (same or next day). Table 12.10 shows the Number of children 4,562 3,310
type and timing of antimalarial drugs used and the
percentage of children who took specific antimalarial drugs the same or next day after developing a fever,
by background characteristics.
Among children with a fever who took antimalarial drugs, the majority took ACT (86 percent), the
recommended first line of treatment for uncomplicated malaria. This is a substantial increase from 34
percent in 2008-09. Two percent to 4 percent took other antimalarial drugs, which are no longer
recommended. There are marginal differences in the percentage of children taking ACT by age, sex, rural-
urban residence, and mother’s education. Children in the highest wealth quintile were less likely (72
percent) to take ACT than those in the lower wealth quintiles (84 percent or higher). There are insufficient
cases of children with a fever in the last two weeks to allow an analysis at the county level.
204 • Malaria
Table 12.10 Type of antimalarial drugs used
Among children under age five with fever in the two weeks preceding the survey who took any antimalarial medication, the percentage
who took specific antimalarial drugs, by background characteristics, Kenya 2014
Number of
children with
Percentage of children who took drug: fever who took
Background Other antimalarial
characteristic Any ACT Quinine SP/Fansidar Chloroquine Amodiaquine antimalarial drug
Age (in months)
<12 82.2 4.3 1.2 1.5 7.6 4.0 141
12-23 84.3 4.7 4.7 1.9 2.7 3.2 275
24-35 88.4 1.5 3.2 1.0 4.6 2.7 275
36-47 87.3 1.2 3.4 1.4 1.8 6.1 270
48-59 85.2 3.4 4.0 2.6 1.5 5.2 268
Sex
Male 84.3 4.0 4.1 2.2 2.7 4.5 627
Female 87.4 1.8 3.0 1.2 3.7 4.1 603
Residence
Urban 82.0 2.8 5.1 2.9 1.8 6.9 295
Rural 87.0 3.0 3.0 1.3 3.7 3.4 934
Region
Coast 85.3 0.7 10.2 0.5 0.0 3.3 63
North Eastern * * * * * * 4
Eastern 65.8 1.0 3.3 3.2 10.3 17.7 73
Central * * * * * * 15
Rift Valley 73.6 5.6 7.1 3.9 4.1 9.0 151
Western 95.5 2.1 0.1 0.0 1.5 1.6 405
Nyanza 86.7 2.8 3.5 1.4 4.0 3.4 481
Nairobi * * * * * * 38
Mother’s education
No education 84.3 2.4 6.2 0.0 5.1 2.1 69
Primary incomplete 85.4 2.3 4.5 1.5 3.3 4.0 495
Primary complete 85.5 3.8 1.5 2.6 3.4 4.4 346
Secondary+ 87.1 2.9 3.6 1.4 2.5 5.1 319
Wealth quintile
Lowest 83.5 1.9 7.0 3.0 2.6 4.2 259
Second 88.2 3.0 3.4 1.3 3.1 3.1 393
Middle 89.2 2.9 0.1 0.6 4.9 2.6 281
Fourth 85.8 2.3 3.6 0.2 2.5 6.1 207
Highest 71.5 6.9 4.5 6.6 1.8 10.8 91
Total 85.8 2.9 3.5 1.7 3.2 4.3 1,230
Note: An asterisk denotes a figure based on fewer than 25 cases that has been suppressed.
ACT = Artemisinin-based combination therapy
Malaria • 205
HIV/AIDS-RELATED KNOWLEDGE,
ATTITUDES, AND BEHAVIOUR 13
Lawrence Ikamari, John Wanyungu, James Muttunga, Mercy Khasiani, Abdulkadir Amin Awes
Key Findings
• Awareness of AIDS is universal in Kenya. However, only 56 percent of
women and 66 percent of men have comprehensive knowledge about
HIV and AIDS prevention and transmission; that is, they know that both
condom use and limiting sexual intercourse to one uninfected partner can
prevent HIV, they are aware that a healthy-looking person can have HIV,
and they reject the two most common local misconceptions about HIV:
that HIV can be transmitted by mosquitoes and by sharing food.
• Seventy-two percent of women and 62 percent of men know both that
HIV can be transmitted through breastfeeding and that the risk of mother-
to-child transmission can be reduced by taking special drugs during
pregnancy.
• Among those who had more than one sexual partner in the past 12
months, 40 percent of women and 44 percent of men reported using a
condom during their last sexual intercourse.
• Since the 2008-09 KDHS, there has been an increase in the percentage
of both women (from 29 percent to 53 percent) and men (from 23 percent
to 46 percent) who were tested for HIV in the past 12 months and
received their results.
• Sixty-eight percent of women who gave birth in the two years before the
survey received HIV counselling during antenatal care. Almost 7 in 10
women (69 percent) were tested for HIV during antenatal care and
received the test results and post-test counselling, while 23 percent
received results but did not receive post-test counselling.
13.1 INTRODUCTION
A
cquired immune deficiency syndrome (AIDS) is caused by the human immunodeficiency virus
(HIV), which weakens the immune system and makes the body susceptible to and unable to
recover from other opportunistic diseases that can lead to death. The predominant modes of HIV
transmission are through sexual contact; mother-to-child transmission, in which the mother passes the
virus to her child during pregnancy, delivery, or breastfeeding; use of contaminated blood supplies for
transfusions; and injections using contaminated needles or syringes.
The AIDS epidemic in Kenya has been severe and generalised since the mid-1980s. HIV
prevalence seems to have stabilised in Kenya at 6 percent, but new HIV infections have been estimated at
88,620 annually (National AIDS Control Council [NACC] and National AIDS and STI Control
Programme [NASCOP], 2014). The future course of Kenya’s AIDS epidemic depends on a number of
factors including levels of HIV- and AIDS-related knowledge among the general population, stigma
associated with being HIV positive, risk-behaviour modification, access to quality health care services for
sexually transmitted infections (STIs), and provision and uptake of HIV counselling and testing.
The principal objective of this chapter is to establish the extent of relevant knowledge,
perceptions, and behaviour at the national level as well as within geographic and socioeconomic
subpopulations. Prevention programmes can use these data to target groups most in need of information
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 207
and most at risk of HIV infection. In this chapter, information on indicators related to HIV and AIDS
knowledge, attitudes, and behaviour is presented for the general adult population age 15-49. The chapter
also focuses on HIV and AIDS knowledge and patterns of sexual activity among young people age 15-24,
as young adults are the main target of many HIV prevention efforts.
13.2 HIV AND AIDS KNOWLEDGE, TRANSMISSION AND PREVENTION METHODS
13.2.1 Awareness of HIV and AIDS
The 2014 KDHS asked respondents whether they have heard of an illness called AIDS. Those
who reported having heard of AIDS were asked other questions about whether and how AIDS can be
avoided. Table 13.1 shows the percentage of women and men age 15-49 who have heard of AIDS, by
background characteristics. In Kenya, knowledge of AIDS is virtually universal (above 99 percent among
both women and men). There is no noticeable variation in awareness by respondents’ background
characteristics. This is consistent with the 2008-09 KDHS.
Table 13.1 Knowledge of AIDS
Percentage of women and men age 15-49 who have heard of AIDS, by background
characteristics, Kenya 2014
Women Men
Background Have heard of Number of Have heard of Number of
characteristic AIDS respondents AIDS respondents
Age
15-24 99.6 11,555 99.6 4,666
15-19 99.4 5,820 99.4 2,540
20-24 99.8 5,735 99.8 2,125
25-29 99.7 6,100 100.0 2,104
30-39 99.8 8,283 100.0 3,268
40-49 99.9 5,142 99.8 2,024
Marital status
Never married 99.5 8,997 99.6 5,350
Ever had sex 99.9 4,541 99.8 3,512
Never had sex 99.2 4,456 99.1 1,838
Married/living together 99.8 18,549 100.0 6,095
Divorced/separated/
widowed 99.8 3,533 100.0 618
Residence
Urban 99.8 12,690 100.0 5,300
Rural 99.6 18,389 99.7 6,762
Region
Coast 99.8 3,076 99.6 1,260
North Eastern 95.9 648 99.3 227
Eastern 99.8 4,375 99.7 1,825
Central 100.0 3,994 99.8 1,564
Rift Valley 99.6 7,953 99.8 3,050
Western 99.8 3,225 99.9 1,164
Nyanza 99.9 4,038 99.9 1,405
Nairobi 99.8 3,770 100.0 1,568
Education
No education 97.4 2,176 97.9 345
Primary incomplete 99.7 7,989 99.6 3,071
Primary complete 99.9 7,637 99.9 2,734
Secondary+ 100.0 13,277 100.0 5,913
Wealth quintile
Lowest 98.8 4,838 99.3 1,691
Second 99.9 5,457 99.8 2,145
Middle 99.9 6,032 99.8 2,370
Fourth 99.9 6,550 100.0 2,959
Highest 99.8 8,203 99.9 2,897
Total 15-49 99.7 31,079 99.8 12,063
50-54 na na 100.0 756
Total 15-54 na na 99.8 12,819
na = Not applicable
208 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.1C shows county-level data on the percentage of women and men age 15-49 who have
heard of AIDS. Awareness of AIDS is very high in all counties. However, awareness among women is
slightly lower in Wajir (92 percent), Mandera, and West Pokot (both 95 percent); among men, there are no
apparent county-level differences.
Table 13.1C Knowledge of AIDS
Percentage of women and men age 15-49 who have heard of AIDS, by county, Kenya 2014
Women Men
Have heard of Number of Have heard of Number of
County AIDS respondents AIDS respondents
Coast 99.8 3,076 99.6 1,260
Mombasa 100.0 912 99.7 481
Kwale 100.0 619 99.3 226
Kilifi 100.0 1,043 100.0 359
Tana River 98.5 197 97.5 65
Lamu 100.0 89 100.0 37
Taita Taveta 99.0 215 100.0 93
North Eastern 95.9 648 99.3 227
Garissa 99.7 261 100.0 94
Wajir 91.8 212 97.9 72
Mandera 95.0 175 100.0 60
Eastern 99.8 4,375 99.7 1,825
Marsabit 95.5 115 100.0 40
Isiolo 99.8 104 100.0 35
Meru 100.0 1,110 100.0 495
Tharaka-Nithi 99.5 275 99.5 102
Embu 99.8 459 99.2 164
Kitui 100.0 759 99.5 303
Machakos 99.9 873 100.0 436
Makueni 99.7 680 99.5 250
Central 100.0 3,994 99.8 1,564
Nyandarua 100.0 436 100.0 198
Nyeri 99.9 650 99.7 229
Kirinyaga 100.0 451 99.9 184
Murang’a 99.8 735 100.0 284
Kiambu 100.0 1,722 99.6 669
Rift Valley 99.6 7,953 99.8 3,050
Turkana 98.5 320 97.7 76
West Pokot 95.1 267 100.0 103
Samburu 99.7 123 99.3 35
Trans-Nzoia 99.7 768 100.0 329
Uasin Gishu 100.0 784 99.8 355
Elgeyo Marakwet 100.0 250 100.0 86
Nandi 99.6 628 100.0 264
Baringo 99.7 335 99.7 125
Laikipia 99.6 342 100.0 124
Nakuru 99.9 1,574 100.0 589
Narok 99.9 642 99.1 240
Kajiado 99.3 670 100.0 241
Kericho 100.0 563 100.0 215
Bomet 100.0 687 99.7 267
Western 99.8 3,225 99.9 1,164
Kakamega 99.7 1,108 100.0 411
Vihiga 100.0 368 100.0 140
Bungoma 99.9 1,203 99.7 413
Busia 100.0 546 100.0 199
Nyanza 99.9 4,038 99.9 1,405
Siaya 100.0 572 100.0 213
Kisumu 99.8 820 99.6 309
Homa Bay 100.0 798 100.0 243
Migori 99.8 650 100.0 211
Kisii 99.9 864 99.8 315
Nyamira 100.0 334 99.7 114
Nairobi 99.8 3,770 100.0 1,568
Total 15-49 99.7 31,079 99.8 12,063
50-54 na na 100.0 756
Total 15-54 na na 99.8 12,819
na = Not applicable
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 209
13.2.2 Knowledge of HIV Prevention Methods
Among adults, HIV is mainly transmitted through sexual contact between an infected partner and
an uninfected partner. Accordingly, Kenya’s HIV prevention programmes focus on three aspects of
behaviour: consistent condom use during sexual intercourse, limiting the number of sexual partners or
staying faithful to one partner, and sexual abstinence. In the 2014 KDHS, men and women age 15-49 were
asked if it is possible to reduce the risk of acquiring HIV through these three prevention methods.
Knowledge of each of the methods is presented in Tables 13.2, 13.2C, and 13.3.
Table 13.2 Knowledge of HIV prevention methods: condom use and limiting sexual partners
Percentage of women and men age 15-49 who, in response to prompted questions, say that people can reduce the risk of getting the AIDS virus by
using condoms every time they have sexual intercourse, and by having one sex partner who is not infected and has no other partners, by background
characteristics, Kenya 2014
Women Men
Using Using
condoms and condoms and
Limiting sexual limiting sexual Limiting sexual limiting sexual
intercourse to intercourse to intercourse to intercourse to
Background Using one uninfected one uninfected Number of Using one uninfected one uninfected Number of
characteristic condoms1 partner2 partner1,2 women condoms1 partner2 partner1,2 men
Age
15-24 77.4 89.3 72.8 11,555 86.1 92.0 82.0 4,666
15-19 72.2 86.2 66.7 5,820 82.1 90.0 76.9 2,540
20-24 82.6 92.4 78.9 5,735 90.9 94.3 88.0 2,125
25-29 82.7 92.9 80.4 6,100 90.0 95.1 87.6 2,104
30-39 81.9 93.3 79.3 8,283 88.1 95.8 86.0 3,268
40-49 78.4 92.6 76.1 5,142 87.9 95.9 86.2 2,024
Marital status
Never married 77.0 89.1 72.4 8,997 86.5 92.4 82.7 5,350
Ever had sex 84.6 92.5 80.7 4,541 91.1 94.7 87.8 3,512
Never had sex 69.3 85.6 63.8 4,456 77.8 88.1 72.8 1,838
Married/living together 80.7 92.7 78.0 18,549 88.7 95.7 86.6 6,095
Divorced/separated/
widowed 82.3 92.6 79.6 3,533 87.1 95.3 85.0 618
Residence
Urban 83.7 93.3 80.7 12,690 90.4 95.0 87.8 5,300
Rural 77.1 90.4 73.7 18,389 85.5 93.6 82.4 6,762
Region
Coast 75.3 87.5 71.9 3,076 78.0 89.6 73.7 1,260
North Eastern 27.1 55.6 21.0 648 57.0 68.1 47.6 227
Eastern 73.5 94.3 71.3 4,375 85.7 95.4 83.2 1,825
Central 77.8 89.6 74.1 3,994 89.7 96.6 87.6 1,564
Rift Valley 80.4 92.9 77.7 7,953 87.3 93.5 84.5 3,050
Western 85.9 91.4 81.2 3,225 89.9 95.4 86.9 1,164
Nyanza 87.9 94.4 84.9 4,038 94.4 97.1 92.7 1,405
Nairobi 86.8 94.7 83.5 3,770 93.1 95.9 89.9 1,568
Education
No education 48.8 75.1 43.9 2,176 53.9 77.1 49.7 345
Primary incomplete 75.6 88.6 71.1 7,989 81.8 90.8 77.1 3,071
Primary complete 81.8 93.8 79.2 7,637 89.2 95.5 86.7 2,734
Secondary+ 86.3 94.9 83.7 13,277 91.9 96.4 89.9 5,913
Wealth quintile
Lowest 63.9 83.5 60.1 4,838 73.8 86.9 69.2 1,691
Second 78.6 91.1 75.1 5,457 87.3 94.3 83.9 2,145
Middle 82.2 92.8 79.0 6,032 89.5 94.7 86.7 2,370
Fourth 83.9 93.8 80.8 6,550 89.4 96.1 86.8 2,959
Highest 85.0 94.2 82.1 8,203 92.8 96.1 90.8 2,897
Total 15-49 79.8 91.6 76.6 31,079 87.6 94.2 84.8 12,063
50-54 na na na na 84.6 94.9 82.4 756
Total 15-54 na na na na 87.5 94.3 84.6 12,819
na = Not applicable
1
Using condoms every time they have sexual intercourse
2
Partner who has no other partners
Table 13.2 shows that knowledge about condom use and limiting sexual partners as methods of
avoiding HIV transmission is generally high and widespread. Eighty percent of women and 88 percent of
men know that the risk of getting HIV can be reduced by using condoms. Ninety-two percent of women
and 94 percent of men know that limiting sexual intercourse to one uninfected partner can reduce the
chances of contracting HIV. Seventy-seven percent of women and 85 percent of men are aware of both of
these prevention methods. Young women and men age 15-19 (67 percent and 77 percent, respectively) are
least likely among all age groups to be aware of both prevention methods.
210 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
As in the 2008-09 KDHS, women and men who have never had sex (64 percent and 73 percent,
respectively) and those in rural areas (74 percent and 82 percent, respectively) have less knowledge of HIV
prevention methods than their counterparts. Knowledge of HIV prevention methods varies by region and is
lowest in North Eastern (women, 21 percent; men, 48 percent) and highest in Nyanza (women, 85 percent;
men, 93 percent). Knowledge of condom use and limiting sexual partners as methods of HIV prevention
increases with increasing education and wealth among both women and men.
Table 13.2C Knowledge of HIV prevention methods by county: condom use and limiting sexual partners
Percentage of women and men age 15-49 who, in response to prompted questions, say that people can reduce the risk of getting the AIDS virus by
using condoms every time they have sexual intercourse, and by having one sex partner who is not infected and has no other partners, by county,
Kenya 2014
Women Men
Using Using
condoms and condoms and
Limiting sexual limiting sexual Limiting sexual limiting sexual
intercourse to intercourse to intercourse to intercourse to
Using one uninfected one uninfected Number of Using one uninfected one uninfected Number of
County condoms1 partner2 partner1,2 women condoms1 partner2 partner1,2 men
Coast 75.3 87.5 71.9 3,076 78.0 89.6 73.7 1,260
Mombasa 85.4 94.2 82.5 912 96.9 98.9 96.4 481
Kwale 62.1 65.3 56.0 619 70.9 89.5 67.2 226
Kilifi 75.9 93.1 73.0 1,043 54.1 75.1 43.8 359
Tana River 60.0 85.0 57.9 197 73.8 92.1 70.5 65
Lamu 70.2 92.5 67.6 89 88.0 94.7 84.0 37
Taita Taveta 84.1 95.8 82.6 215 88.2 93.7 85.1 93
North Eastern 27.1 55.6 21.0 648 57.0 68.1 47.6 227
Garissa 42.9 59.7 31.2 261 69.6 94.1 68.7 94
Wajir 18.5 52.3 15.1 212 38.0 33.6 11.4 72
Mandera 14.1 53.7 13.0 175 60.1 68.7 58.1 60
Eastern 73.5 94.3 71.3 4,375 85.7 95.4 83.2 1,825
Marsabit 54.2 63.5 45.3 115 97.3 96.3 95.0 40
Isiolo 76.2 90.9 73.9 104 70.8 97.6 70.8 35
Meru 81.2 95.3 78.5 1,110 87.3 99.1 86.4 495
Tharaka-Nithi 76.1 94.1 74.2 275 82.1 98.1 81.3 102
Embu 80.4 95.2 78.2 459 71.7 81.8 62.9 164
Kitui 51.5 93.5 50.2 759 82.6 98.3 81.7 303
Machakos 76.5 96.8 74.9 873 88.1 91.4 83.2 436
Makueni 78.9 95.5 76.6 680 93.0 98.4 92.2 250
Central 77.8 89.6 74.1 3,994 89.7 96.6 87.6 1,564
Nyandarua 83.2 91.2 80.3 436 91.6 90.7 85.2 198
Nyeri 81.0 92.5 76.8 650 86.1 97.3 85.0 229
Kirinyaga 91.9 97.8 90.6 451 87.6 97.0 86.1 184
Murang’a 56.3 74.4 53.7 735 80.3 97.9 78.2 284
Kiambu 80.6 92.4 75.9 1,722 95.0 97.5 93.6 669
Rift Valley 80.4 92.9 77.7 7,953 87.3 93.5 84.5 3,050
Turkana 50.5 91.2 49.2 320 7.7 30.7 2.4 76
West Pokot 53.8 76.9 47.8 267 79.3 95.0 76.8 103
Samburu 78.4 96.8 78.1 123 82.6 95.8 79.3 35
Trans-Nzoia 86.3 97.1 84.6 768 84.9 92.2 79.7 329
Uasin Gishu 85.7 94.8 82.8 784 86.9 92.7 82.8 355
Elgeyo Marakwet 85.6 97.5 84.7 250 97.1 99.2 97.1 86
Nandi 92.0 97.1 90.7 628 98.9 98.8 98.4 264
Baringo 74.7 89.6 72.6 335 91.7 96.3 89.9 125
Laikipia 86.3 96.9 84.8 342 77.6 89.5 72.3 124
Nakuru 84.2 94.4 80.9 1,574 92.8 96.6 91.4 589
Narok 67.9 85.3 62.4 642 81.2 92.8 78.4 240
Kajiado 78.2 92.2 76.3 670 87.4 96.5 85.2 241
Kericho 76.5 87.4 72.5 563 94.0 94.9 90.3 215
Bomet 87.9 95.9 85.5 687 92.2 96.5 90.1 267
Western 85.9 91.4 81.2 3,225 89.9 95.4 86.9 1,164
Kakamega 86.4 92.2 81.9 1,108 87.3 93.4 83.1 411
Vihiga 81.4 91.0 77.9 368 83.0 83.4 72.0 140
Bungoma 88.7 91.3 84.2 1,203 91.7 99.7 91.7 413
Busia 81.8 90.0 75.5 546 96.3 99.1 95.4 199
Nyanza 87.9 94.4 84.9 4,038 94.4 97.1 92.7 1,405
Siaya 88.5 96.6 86.5 572 96.6 98.8 95.6 213
Kisumu 88.9 92.5 84.4 820 99.1 98.7 98.2 309
Homa Bay 91.5 93.8 88.3 798 97.4 98.6 96.7 243
Migori 84.8 90.0 79.8 650 86.7 89.7 80.3 211
Kisii 81.7 96.5 80.1 864 89.8 97.4 88.5 315
Nyamira 98.2 99.8 97.9 334 98.3 98.5 98.3 114
Nairobi 86.8 94.7 83.5 3,770 93.1 95.9 89.9 1,568
Total 15-49 79.8 91.6 76.6 31,079 87.6 94.2 84.8 12,063
50-54 na na na na 84.6 94.9 82.4 756
Total 15-54 na na na na 87.5 94.3 84.6 12,819
na = Not applicable
1
Using condoms every time they have sexual intercourse
2
Partner who has no other partners
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 211
Table 13.2C presents information by county about knowledge of condom use and limiting sexual
partners as methods of HIV prevention. The results show that there is variation across counties in women’s
and men’s knowledge. Women in Garissa, Wajir, and Mandera (below 60 percent) and men in Turkana
and Wajir (below 40 percent) have the least knowledge of these two HIV prevention methods.
Table 13.3 presents the percentage of women and men age 15-49 who say that abstinence can
reduce the risk of HIV, by background characteristics. Eighty-four percent of women and 88 percent of
men know that abstaining from sex can reduce the risk of getting HIV. Women and men who have never
had sex are less likely to know that abstinence is an effective way to reduce the risk of contracting HIV (78
percent and 84 percent, respectively), as are women and men in the North Eastern region (43 percent and
64 percent, respectively). In general, knowledge of this HIV prevention method increases with increasing
education and wealth.
Table 13.3 Knowledge of HIV prevention methods: abstinence
Percentage of women and men age 15-49 who, in response to prompted questions, say
that people can reduce the risk of getting the AIDS virus by abstaining from sexual
intercourse, by background characteristics, Kenya 2014
Women Men
Abstaining Abstaining
Background from sexual Number of from sexual Number of
characteristic intercourse women intercourse men
Age
15-24 80.7 5,407 86.5 4,666
15-19 78.9 2,717 85.3 2,540
20-24 82.5 2,691 87.9 2,125
25-29 85.1 2,932 89.0 2,104
30-39 85.4 3,942 89.6 3,268
40-49 84.9 2,344 88.7 2,024
Marital status
Never married 81.6 4,255 87.1 5,350
Ever had sex 85.5 2,134 88.6 3,512
Never had sex 77.6 2,122 84.3 1,838
Married/living together 84.0 8,710 88.9 6,095
Divorced/separated/
widowed 85.9 1,660 90.1 618
Residence
Urban 83.9 5,929 87.9 5,300
Rural 83.2 8,696 88.3 6,762
Region
Coast 81.4 1,421 77.7 1,260
North Eastern 42.7 299 63.6 227
Eastern 86.1 2,066 92.8 1,825
Central 78.9 1,905 89.4 1,564
Rift Valley 84.7 3,714 86.6 3,050
Western 85.2 1,571 92.5 1,164
Nyanza 90.3 1,908 94.2 1,405
Nairobi 82.8 1,742 87.6 1,568
Education
No education 68.1 1,015 71.0 345
Primary incomplete 82.1 3,793 85.6 3,071
Primary complete 85.7 3,543 89.1 2,734
Secondary+ 85.7 6,274 90.0 5,913
Wealth quintile
Lowest 75.9 2,236 81.0 1,691
Second 83.5 2,590 89.3 2,145
Middle 86.0 2,859 88.8 2,370
Fourth 84.5 3,113 88.6 2,959
Highest 85.4 3,827 90.4 2,897
Total 15-49 83.5 14,625 88.1 12,063
50-54 na na 90.0 756
Total 15-54 na na 88.2 12,819
na = Not applicable
212 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Figures 13.1 and 13.2 show that both women’s and men’s knowledge of using condoms and
limiting sex as methods of HIV prevention increased from 2003 to 2014. Across time, men have
consistently reported higher levels of knowledge of HIV prevention methods than women.
Figure 13.1 Trends in knowledge of HIV prevention methods: Women
Percent
92 92
88
81 84
80 77 79
75
71
61 58
Using condoms Limiting sex to one Using condoms & Abstaining from sex
faithful partner limiting sex to one
faithful partner
2003 KDHS 2008-09 KDHS 2014 KDHS
Figure 13.2 Trends in knowledge of HIV prevention methods: Men
Percent
93 94
88 89 89 90 88
85
81 78
72 70
Using condoms Limiting sex to one Using condoms & Abstaining from sex
faithful partner limiting sex to one
faithful partner
2003 KDHS 2008-09 KDHS 2014 KDHS
13.2.3 Rejection of Misconceptions about HIV/AIDS
As part of the effort to assess HIV and AIDS knowledge, the 2014 KDHS investigated the
prevalence of common misconceptions about HIV transmission. Respondents were asked whether it is
possible for a healthy-looking person to have HIV and whether HIV is transmitted through mosquito bites,
supernatural means, or sharing food with a person who has HIV or AIDS. Results are presented in Tables
13.4.1 and 13.4.2 by background characteristics.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 213
Table 13.4.1 Comprehensive knowledge about AIDS: Women
Percentage of women age 15-49 who say that a healthy-looking person can have the AIDS virus and who, in response to prompted questions, correctly
reject local misconceptions about transmission or prevention of the AIDS virus, and the percentage with a comprehensive knowledge about AIDS by
background characteristics, Kenya 2014
Percentage who
say that a
Percentage of respondents who say that: healthy looking
A person cannot person can have
The AIDS virus become infected the AIDS virus
The AIDS virus cannot be by sharing food and who reject Percentage with
A healthy-looking cannot be transmitted by with a person the two most a comprehensive
Background person can have transmitted by supernatural who has the common local knowledge about Number of
characteristic the AIDS virus mosquito bites means AIDS misconceptions1 AIDS2 women
Age
15-24 85.0 85.3 93.2 91.0 69.3 54.2 11,555
15-19 81.4 85.8 92.9 90.1 66.9 49.0 5,820
20-24 88.6 84.7 93.5 91.9 71.7 59.6 5,735
25-29 89.9 82.8 92.2 90.7 71.5 60.7 6,100
30-39 90.4 79.2 91.9 89.6 69.0 58.3 8,283
40-49 91.5 74.2 89.7 88.5 64.5 52.3 5,142
Marital status
Never married 85.4 87.0 93.9 91.6 71.7 56.0 8,997
Ever had sex 88.8 87.4 95.0 93.8 75.0 63.8 4,541
Never had sex 81.9 86.5 92.7 89.3 68.2 48.2 4,456
Married/living together 89.5 79.2 91.5 89.6 67.6 56.2 18,549
Divorced/separated/
widowed 90.9 78.4 90.5 89.5 68.3 57.4 3,533
Residence
Urban 91.8 86.5 93.5 93.0 76.4 63.8 12,690
Rural 86.2 77.7 91.1 88.2 63.6 51.1 18,389
Region
Coast 89.9 79.2 85.9 86.1 66.3 50.3 3,076
North Eastern 50.6 67.0 64.7 60.6 27.1 10.9 648
Eastern 88.3 77.2 90.2 86.9 63.3 47.6 4,375
Central 93.5 81.4 94.1 90.6 71.4 54.9 3,994
Rift Valley 86.0 81.3 93.1 90.6 67.8 57.0 7,953
Western 89.1 80.2 96.5 93.8 69.2 59.1 3,225
Nyanza 89.4 85.6 94.9 93.5 74.5 65.4 4,038
Nairobi 92.2 86.8 93.0 94.3 77.7 66.7 3,770
Education
No education 68.9 57.3 70.4 67.6 33.7 19.9 2,176
Primary incomplete 83.1 72.9 89.4 86.1 56.2 43.0 7,989
Primary complete 90.7 81.5 93.9 92.4 70.0 57.2 7,637
Secondary+ 93.6 90.2 96.2 95.0 81.6 69.7 13,277
Wealth quintile
Lowest 75.7 67.9 81.1 78.5 47.1 33.8 4,838
Second 87.4 77.5 93.2 89.4 63.5 50.4 5,457
Middle 89.9 82.2 94.3 92.0 70.2 58.3 6,032
Fourth 91.3 84.3 94.2 93.3 74.0 61.5 6,550
Highest 93.3 88.8 94.6 93.7 80.1 67.8 8,203
Total 15-49 88.5 81.3 92.1 90.2 68.8 56.3 31,079
1
Two most common local misconceptions of source of disease: mosquito bites and sharing food with a person who has AIDS.
2
Comprehensive knowledge means knowing that consistent use of condoms during sexual intercourse and having just one uninfected faithful partner
can reduce the chance of getting the AIDS virus, knowing that a healthy-looking person can have the AIDS virus, and rejecting the two most common
local misconceptions about AIDS transmission or prevention.
The data indicate that some misconceptions regarding how AIDS is transmitted still exist in
Kenya. It is encouraging that about 9 in 10 women and men know that a healthy-looking person can have
the AIDS virus and know that AIDS cannot be transmitted by supernatural means or by sharing food with
a person who has AIDS. However, misunderstandings about transmission through insects are slightly more
widespread; about 8 in 10 respondents know that AIDS cannot be transmitted by mosquito bites.
Comprehensive knowledge about HIV is a composite measure defined as knowing that consistent
use of condoms during sexual intercourse and having just one uninfected faithful partner can reduce the
chance of contracting HIV, knowing that a healthy-looking person can have HIV, knowing that HIV
cannot be transmitted by mosquito bites, and knowing that HIV cannot be contracted by sharing food with
a person who has AIDS. Fifty-six percent of women and 66 percent of men have comprehensive
knowledge about HIV and AIDS. This is a slight increase since the 2008-09 KDHS, where comprehensive
knowledge was 48 percent among women and 55 percent among men.
214 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.4.2 Comprehensive knowledge about AIDS: Men
Percentage of men age 15-49 who say that a healthy-looking person can have the AIDS virus and who, in response to prompted questions, correctly
reject local misconceptions about transmission or prevention of the AIDS virus, and the percentage with a comprehensive knowledge about AIDS by
background characteristics, Kenya 2014
Percentage who
say that a
Percentage of respondents who say that: healthy looking
A person cannot person can have
The AIDS virus become infected the AIDS virus
The AIDS virus cannot be by sharing food and who reject Percentage with
A healthy-looking cannot be transmitted by with a person the two most a comprehensive
Background person can have transmitted by supernatural who has the common local knowledge about
characteristic the AIDS virus mosquito bites means AIDS misconceptions1 AIDS2 Number of men
Age
15-24 87.5 87.5 94.1 93.1 74.4 63.7 4,666
15-19 83.5 88.1 93.7 91.8 70.9 57.7 2,540
20-24 92.3 86.7 94.7 94.7 78.6 70.9 2,125
25-29 92.4 85.1 94.4 93.8 76.1 68.1 2,104
30-39 93.9 84.8 95.0 93.3 78.2 69.6 3,268
40-49 93.7 79.7 94.5 90.9 72.4 64.3 2,024
Marital status
Never married 88.2 87.1 93.9 93.0 74.9 64.7 5,350
Ever had sex 91.3 87.0 94.8 94.1 77.8 70.0 3,512
Never had sex 82.4 87.3 92.2 90.8 69.3 54.5 1,838
Married/living together 93.5 83.6 94.8 93.2 76.3 68.1 6,095
Divorced/separated/
widowed 92.7 81.0 96.5 89.2 70.6 60.7 618
Residence
Urban 93.2 87.3 95.2 95.1 79.5 71.3 5,300
Rural 89.5 83.3 93.9 91.2 72.2 62.2 6,762
Region
Coast 93.0 82.7 92.1 94.3 74.8 57.2 1,260
North Eastern 68.4 78.8 79.0 62.8 43.1 25.3 227
Eastern 91.9 84.8 95.3 92.3 74.8 64.8 1,825
Central 94.5 88.2 93.6 91.9 80.3 72.4 1,564
Rift Valley 88.4 84.4 95.4 92.2 72.8 64.3 3,050
Western 90.8 84.9 95.5 94.5 74.3 65.8 1,164
Nyanza 91.0 85.3 95.9 94.2 76.5 71.7 1,405
Nairobi 94.4 86.0 94.9 97.0 81.0 73.8 1,568
Education
No education 72.0 60.8 77.9 72.4 41.9 23.8 345
Primary incomplete 85.0 75.3 91.7 88.1 59.9 48.4 3,071
Primary complete 90.9 82.6 95.5 92.4 72.5 63.9 2,734
Secondary+ 95.5 92.6 96.4 96.8 86.7 79.0 5,913
Wealth quintile
Lowest 81.2 74.8 89.4 86.9 58.1 44.0 1,691
Second 89.0 82.2 94.6 91.7 70.8 61.4 2,145
Middle 91.2 85.0 95.0 92.3 74.6 66.7 2,370
Fourth 93.5 88.0 95.1 94.4 79.4 70.1 2,959
Highest 96.0 90.1 96.3 96.4 85.4 78.3 2,897
Total 15-49 91.1 85.0 94.5 92.9 75.4 66.2 12,063
50-54 94.1 78.2 94.6 88.0 70.1 61.2 756
Total 15-54 91.3 84.6 94.5 92.6 75.1 65.9 12,819
1
Two most common local misconceptions of source of disease: mosquito bites and sharing food with a person who has AIDS.
2
Comprehensive knowledge means knowing that consistent use of condoms during sexual intercourse and having just one uninfected faithful partner
can reduce the chance of getting the AIDS virus, knowing that a healthy-looking person can have the AIDS virus, and rejecting the two most common
local misconceptions about AIDS transmission or prevention.
Women with the highest comprehensive knowledge of AIDS are found in Nyamira (89 percent),
Nandi (78 percent), and Kisii (71 percent) counties (Table 13.4.1C). Women in Mandera (4 percent), Wajir
(8 percent), and Garissa (19 percent) are least likely to have comprehensive knowledge. Comprehensive
knowledge among men is highest in Nandi (93 percent), Nyamira (91 percent), and Kiambu (85 percent)
and lowest in Turkana (2 percent), Mandera (4 percent), and Wajir (9 percent) (Table 13.4.2C).
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 215
Table 13.4.1C Comprehensive knowledge about AIDS: Women
Percentage of women age 15-49 who say that a healthy-looking person can have the AIDS virus and who, in response to prompted questions,
correctly reject local misconceptions about transmission or prevention of the AIDS virus, and the percentage with a comprehensive knowledge about
AIDS by county, Kenya 2014
Percentage who
say that a
Percentage of respondents who say that: healthy looking
A person cannot person can have
The AIDS virus become infected the AIDS virus
The AIDS virus cannot be by sharing food and who reject Percentage with
A healthy-looking cannot be transmitted by with a person the two most a comprehensive
person can have transmitted by supernatural who has the common local knowledge about Number of
County the AIDS virus mosquito bites means AIDS misconceptions1 AIDS2 women
Coast 89.9 79.2 85.9 86.1 66.3 50.3 3,076
Mombasa 90.3 84.1 91.3 91.3 72.2 61.5 912
Kwale 92.0 85.1 95.4 87.4 72.1 39.5 619
Kilifi 92.6 73.1 74.6 80.1 60.1 46.9 1,043
Tana River 67.5 66.0 81.1 85.1 46.7 36.2 197
Lamu 84.9 77.3 88.8 88.4 63.4 50.1 89
Taita Taveta 92.2 84.5 93.6 90.1 73.5 63.9 215
North Eastern 50.6 67.0 64.7 60.6 27.1 10.9 648
Garissa 60.5 71.5 75.8 72.9 39.5 18.5 261
Wajir 48.4 58.2 57.6 65.2 26.5 7.6 212
Mandera 38.7 70.7 56.6 36.9 9.5 3.5 175
Eastern 88.3 77.2 90.2 86.9 63.3 47.6 4,375
Marsabit 65.7 62.7 77.4 74.8 39.3 24.6 115
Isiolo 93.2 90.7 63.1 63.8 54.5 39.0 104
Meru 83.6 70.7 89.1 88.8 56.8 48.1 1,110
Tharaka-Nithi 91.7 74.2 88.5 84.8 61.7 49.4 275
Embu 89.2 82.7 92.6 87.5 64.4 51.4 459
Kitui 83.1 73.8 88.0 83.7 57.5 29.8 759
Machakos 94.4 86.5 94.1 90.7 76.2 57.9 873
Makueni 95.3 77.6 95.1 88.2 69.0 55.5 680
Central 93.5 81.4 94.1 90.6 71.4 54.9 3,994
Nyandarua 93.1 72.2 90.8 89.9 63.1 52.3 436
Nyeri 91.9 78.7 92.5 90.6 69.1 54.6 650
Kirinyaga 96.2 76.4 95.6 85.4 67.7 62.6 451
Murang’a 91.2 72.9 93.0 88.2 61.5 36.2 735
Kiambu 94.5 89.6 95.6 93.1 79.7 61.7 1,722
Rift Valley 86.0 81.3 93.1 90.6 67.8 57.0 7,953
Turkana 74.4 64.8 88.0 80.8 47.6 23.9 320
West Pokot 63.4 70.1 73.7 65.3 37.3 24.0 267
Samburu 87.4 58.9 80.2 82.7 51.3 45.7 123
Trans-Nzoia 91.4 82.9 95.7 90.6 71.7 61.8 768
Uasin Gishu 88.2 89.2 95.7 95.5 76.9 67.0 784
Elgeyo Marakwet 91.2 84.4 97.7 93.2 74.7 64.8 250
Nandi 89.9 92.5 97.8 94.7 81.8 77.5 628
Baringo 76.9 85.8 96.3 93.7 65.2 51.5 335
Laikipia 93.8 73.8 95.4 87.4 64.1 57.3 342
Nakuru 92.8 84.2 92.8 93.0 74.9 63.0 1,574
Narok 75.6 66.7 89.1 84.7 48.1 36.8 642
Kajiado 83.5 79.7 90.1 87.6 66.1 55.8 670
Kericho 77.1 88.0 95.6 96.5 66.6 52.5 563
Bomet 90.2 79.6 95.9 94.1 69.8 61.4 687
Western 89.1 80.2 96.5 93.8 69.2 59.1 3,225
Kakamega 85.6 78.9 96.9 94.3 66.4 56.4 1,108
Vihiga 91.5 82.4 97.3 94.2 72.7 59.7 368
Bungoma 92.4 80.8 96.8 93.7 71.6 64.0 1,203
Busia 87.3 80.3 94.5 92.7 67.0 53.1 546
Nyanza 89.4 85.6 94.9 93.5 74.5 65.4 4,038
Siaya 87.7 82.9 97.7 92.9 69.5 62.8 572
Kisumu 86.7 87.1 97.7 93.8 73.6 64.8 820
Homa Bay 83.6 86.7 94.7 93.2 70.8 64.8 798
Migori 86.3 74.0 92.1 87.9 60.0 49.4 650
Kisii 97.6 90.6 91.4 96.4 86.8 71.1 864
Nyamira 98.2 93.4 98.2 98.2 90.8 89.4 334
Nairobi 92.2 86.8 93.0 94.3 77.7 66.7 3,770
Total 15-49 88.5 81.3 92.1 90.2 68.8 56.3 31,079
1
Two most common local misconceptions of source of disease: mosquito bites and sharing food with a person who has AIDS.
2
Comprehensive knowledge means knowing that consistent use of condoms during sexual intercourse and having just one uninfected faithful
partner can reduce the chance of getting the AIDS virus, knowing that a healthy-looking person can have the AIDS virus, and rejecting the two most
common local misconceptions about AIDS transmission or prevention.
216 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.4.2C Comprehensive knowledge about AIDS: Men
Percentage of men age 15-49 who say that a healthy-looking person can have the AIDS virus and who, in response to prompted questions, correctly
reject local misconceptions about transmission or prevention of the AIDS virus, and the percentage with a comprehensive knowledge about AIDS by
county, Kenya 2014
Percentage who
say that a
Percentage of respondents who say that: healthy looking
A person cannot person can have
The AIDS virus become infected the AIDS virus
The AIDS virus cannot be by sharing food and who reject Percentage with
A healthy-looking cannot be transmitted by with a person the two most a comprehensive
person can have transmitted by supernatural who has the common local knowledge about
County the AIDS virus mosquito bites means AIDS misconceptions1 AIDS2 Number of men
Coast 93.0 82.7 92.1 94.3 74.8 57.2 1,260
Mombasa 98.6 81.9 93.6 95.4 77.8 75.3 481
Kwale 81.3 88.1 91.7 95.5 71.7 53.9 226
Kilifi 93.1 77.3 89.4 94.4 71.2 30.2 359
Tana River 90.0 90.1 94.8 94.4 82.1 64.1 65
Lamu 95.0 84.4 93.7 93.1 79.3 69.0 37
Taita Taveta 93.9 88.4 94.1 85.7 74.4 66.7 93
North Eastern 68.4 78.8 79.0 62.8 43.1 25.3 227
Garissa 79.6 90.3 99.8 92.2 71.0 51.6 94
Wajir 59.8 67.1 64.3 68.1 35.9 8.7 72
Mandera 60.9 74.7 64.1 10.2 7.8 3.9 60
Eastern 91.9 84.8 95.3 92.3 74.8 64.8 1,825
Marsabit 98.3 84.2 89.7 83.6 73.5 70.9 40
Isiolo 97.3 89.5 99.4 88.9 79.0 60.6 35
Meru 97.3 84.7 95.6 94.0 79.3 69.9 495
Tharaka-Nithi 93.6 83.1 91.0 91.5 73.1 60.9 102
Embu 90.0 84.1 88.7 85.9 70.2 46.2 164
Kitui 96.3 85.5 97.8 89.4 76.3 63.8 303
Machakos 80.2 81.0 96.0 95.1 64.5 57.7 436
Makueni 94.8 91.3 96.4 93.5 85.7 81.9 250
Central 94.5 88.2 93.6 91.9 80.3 72.4 1,564
Nyandarua 88.7 81.6 90.0 88.1 67.9 63.0 198
Nyeri 95.6 85.0 93.9 94.6 80.1 69.1 229
Kirinyaga 95.3 90.1 92.1 79.6 69.0 62.6 184
Murang’a 89.8 82.9 93.8 91.1 72.6 57.4 284
Kiambu 97.7 92.9 94.8 95.8 90.5 85.3 669
Rift Valley 88.4 84.4 95.4 92.2 72.8 64.3 3,050
Turkana 71.4 88.4 95.2 93.2 62.8 1.7 76
West Pokot 81.1 65.1 77.3 82.3 49.6 42.8 103
Samburu 87.1 76.8 93.1 78.1 59.7 46.5 35
Trans-Nzoia 92.9 78.1 96.7 89.4 70.1 59.8 329
Uasin Gishu 91.4 91.9 98.1 94.7 81.0 67.8 355
Elgeyo Marakwet 97.6 86.3 99.1 98.7 82.6 80.5 86
Nandi 98.3 97.8 97.8 96.0 93.4 92.5 264
Baringo 84.0 87.6 95.2 90.9 70.4 66.1 125
Laikipia 91.0 82.1 96.4 91.8 72.1 53.5 124
Nakuru 96.8 84.0 98.3 94.5 78.8 72.1 589
Narok 70.1 79.1 82.8 89.1 57.6 51.6 240
Kajiado 91.0 81.5 95.0 85.7 71.6 66.5 241
Kericho 85.9 83.6 96.5 94.5 71.3 66.6 215
Bomet 72.0 83.9 97.8 94.8 59.5 54.4 267
Western 90.8 84.9 95.5 94.5 74.3 65.8 1,164
Kakamega 88.1 82.7 98.5 94.4 70.6 61.0 411
Vihiga 91.6 97.7 96.0 98.3 89.2 65.9 140
Bungoma 94.6 80.5 91.3 92.0 71.7 66.8 413
Busia 88.0 89.7 97.6 96.9 77.0 73.8 199
Nyanza 91.0 85.3 95.9 94.2 76.5 71.7 1,405
Siaya 90.5 75.0 94.4 93.9 67.9 66.1 213
Kisumu 86.4 96.6 98.6 98.2 83.0 81.6 309
Homa Bay 93.6 76.1 98.6 91.1 68.7 66.3 243
Migori 82.7 73.0 95.3 87.2 57.0 46.2 211
Kisii 96.8 92.8 91.8 97.5 89.8 79.8 315
Nyamira 98.2 95.9 97.9 94.1 91.5 91.4 114
Nairobi 94.4 86.0 94.9 97.0 81.0 73.8 1,568
Total 15-49 91.1 85.0 94.5 92.9 75.4 66.2 12,063
50-54 94.1 78.2 94.6 88.0 70.1 61.2 756
Total 15-54 91.3 84.6 94.5 92.6 75.1 65.9 12,819
1
Two most common local misconceptions of source of disease: mosquito bites and sharing food with a person who has AIDS.
2
Comprehensive knowledge means knowing that consistent use of condoms during sexual intercourse and having just one uninfected faithful
partner can reduce the chance of getting the AIDS virus, knowing that a healthy-looking person can have the AIDS virus, and rejecting the two most
common local misconceptions about AIDS transmission or prevention.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 217
13.2.4 Knowledge of Mother-to-Child Transmission of HIV
Increasing the level of general knowledge of how HIV is transmitted from mother to child and
reducing the risk of transmission by using antiretroviral drugs is critical to reducing mother-to-child
transmission of HIV (MTCT). To assess MTCT knowledge, respondents were asked whether HIV can be
transmitted from a mother to a child through breastfeeding and whether a mother with HIV can reduce the
risk of transmission to her baby by taking special drugs during pregnancy.
Table 13.5 shows MTCT knowledge among women and men age 15-49 by background
characteristics. Eighty-nine percent of women and 87 percent of men know that HIV can be transmitted
through breastfeeding, and 76 percent of women and 68 percent of men know that the risk of MTCT
Table 13.5 Knowledge of prevention of mother to child transmission of HIV
Percentage of women and men age 15-49 who know that HIV can be transmitted from mother to child by breastfeeding and that the risk of mother to child
transmission (MTCT) of HIV can be reduced by mother taking special drugs during pregnancy, by background characteristics, Kenya 2014
Percentage of women who know that: Percentage of men who know that:
HIV can be HIV can be
transmitted by transmitted by
breastfeeding breastfeeding
Risk of MTCT and risk of MTCT Risk of MTCT and risk of MTCT
can be reduced can be reduced can be reduced can be reduced
by mother taking by mother taking by mother taking by mother taking
HIV can be special drugs special drugs HIV can be special drugs special drugs
Background transmitted by during during Number of transmitted by during during Number of
characteristic breastfeeding pregnancy pregnancy women breastfeeding pregnancy pregnancy men
Age
15-24 88.1 69.1 65.0 5,407 86.4 59.9 54.6 4,666
15-19 86.8 63.0 59.4 2,717 84.9 55.2 49.9 2,540
20-24 89.5 75.3 70.7 2,691 88.2 65.7 60.3 2,125
25-29 89.7 80.8 76.0 2,932 88.5 68.9 64.0 2,104
30-39 88.5 81.0 75.7 3,942 88.2 73.4 67.6 3,268
40-49 88.2 78.7 73.8 2,344 86.0 73.8 68.0 2,024
Marital status
Never married 87.1 67.7 63.5 4,255 86.8 60.8 55.7 5,350
Ever had sex 89.3 74.1 70.0 2,134 88.2 64.5 59.2 3,512
Never had sex 84.9 61.2 57.0 2,122 84.2 53.7 49.0 1,838
Married/living together 88.9 79.5 74.6 8,710 87.6 73.3 67.4 6,095
Divorced/separated/
widowed 90.1 80.9 75.8 1,660 86.5 68.2 63.8 618
Currently pregnant
Pregnant 91.0 75.6 72.2 915 na na na na
Not pregnant or not
sure 88.4 76.2 71.5 13,711 na na na na
Residence
Urban 89.7 80.4 75.2 5,929 86.9 69.7 63.5 5,300
Rural 87.8 73.4 69.0 8,696 87.4 65.7 60.9 6,762
Region
Coast 91.0 76.9 73.8 1,421 92.2 70.4 68.3 1,260
North Eastern 64.4 31.2 30.6 299 62.1 58.5 50.2 227
Eastern 90.1 72.3 68.9 2,066 91.4 71.2 67.4 1,825
Central 90.1 79.6 74.7 1,905 84.9 67.6 61.1 1,564
Rift Valley 86.8 70.2 66.0 3,714 86.5 59.9 55.6 3,050
Western 90.6 77.8 73.1 1,571 92.8 66.4 62.8 1,164
Nyanza 86.3 86.3 78.5 1,908 86.4 81.4 73.5 1,405
Nairobi 91.4 84.7 78.8 1,742 82.2 65.0 55.1 1,568
Education
No education 75.4 46.2 43.4 1,015 60.9 45.4 39.8 345
Primary incomplete 86.2 73.2 68.3 3,793 85.9 61.3 57.0 3,071
Primary complete 89.7 80.3 75.1 3,543 87.5 67.5 61.8 2,734
Secondary+ 91.4 80.5 76.0 6,274 89.2 71.9 66.1 5,913
Wealth quintile
Lowest 83.1 60.8 57.6 2,236 80.3 54.6 49.7 1,691
Second 88.2 74.6 69.9 2,590 88.7 66.5 62.0 2,145
Middle 89.6 77.5 73.1 2,859 88.6 69.3 64.1 2,370
Fourth 89.0 79.5 74.4 3,113 89.2 69.9 64.3 2,959
Highest 90.7 82.6 77.2 3,827 87.1 71.7 65.2 2,897
Total 15-49 88.5 76.2 71.5 14,625 87.2 67.5 62.0 12,063
50-54 na na na na 83.9 70.7 63.7 756
Total 15-54 na na na na 87.0 67.7 62.1 12,819
na = Not applicable
218 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
can be reduced by the mother taking special drugs during pregnancy. Seventy-two percent of women and
62 percent of men know both that HIV can be transmitted through breastfeeding and that the risk of MTCT
can be reduced by taking special drugs during pregnancy. There has been a slight increase in knowledge
about HIV transmission through breastfeeding and MTCT-reducing drugs since 2008-09.
Knowledge of HIV transmission through breastfeeding and of MTCT-reducing drugs is lowest
among women and men who are age 15-19 (59 percent and 50 percent, respectively), who have never had
sex (57 percent and 49 percent, respectively), and who live in North Eastern region (31 percent and 50
percent, respectively). Among both women and men, knowledge of transmission through breastfeeding and
MTCT-reducing drugs increases with increasing education and wealth.
Table 13.5C shows that there is some variation in MTCT knowledge across counties. Among
women, the level of knowledge is highest in Bungoma (87 percent) and in Kilifi, Kitui, and Nandi counties
(86 percent each) and lowest in Wajir and Marsabit (29 percent each). Among men, the highest level of
knowledge is found in Mombasa (85 percent) and Machakos (84 percent) and the lowest in Wajir and West
Pokot (18 percent each).
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 219
Table 13.5C Knowledge of prevention of mother to child transmission of HIV
Percentage of women and men age 15-49 who know that HIV can be transmitted from mother to child by breastfeeding and that the risk of mother to child
transmission (MTCT) of HIV can be reduced by mother taking special drugs during pregnancy, by county, Kenya 2014
Percentage of women who know that: Percentage of men who know that:
HIV can be HIV can be
transmitted by transmitted by
breastfeeding breastfeeding
Risk of MTCT and risk of MTCT Risk of MTCT and risk of MTCT
can be reduced can be reduced can be reduced can be reduced
by mother taking by mother taking by mother taking by mother taking
HIV can be special drugs special drugs HIV can be special drugs special drugs
transmitted by during during Number of transmitted by during during Number of
County breastfeeding pregnancy pregnancy women breastfeeding pregnancy pregnancy men
Coast 91.0 76.9 73.8 1,421 92.2 70.4 68.3 1,260
Mombasa 91.1 80.0 75.0 416 92.4 88.2 85.1 481
Kwale 93.1 62.9 61.6 282 88.5 31.9 31.7 226
Kilifi 91.5 89.2 86.4 487 95.6 71.8 69.9 359
Tana River 83.6 39.4 37.9 91 89.8 71.4 69.9 65
Lamu 89.4 66.1 63.1 45 88.4 66.4 62.7 37
Taita Taveta 89.5 81.6 79.6 99 90.4 67.3 65.2 93
North Eastern 64.4 31.2 30.6 299 62.1 58.5 50.2 227
Garissa 73.0 32.3 32.0 118 94.2 73.4 71.7 94
Wajir 61.6 30.3 29.0 93 20.6 41.6 18.0 72
Mandera 55.7 30.6 30.3 88 61.5 55.4 55.4 60
Eastern 90.1 72.3 68.9 2,066 91.4 71.2 67.4 1,825
Marsabit 71.7 30.2 29.1 54 74.1 70.2 62.4 40
Isiolo 79.0 56.2 55.1 49 93.3 31.9 31.5 35
Meru 90.2 75.9 72.4 533 95.5 80.1 78.5 495
Tharaka-Nithi 92.1 75.6 73.5 131 84.8 63.0 55.5 102
Embu 89.8 82.7 75.7 212 83.4 64.5 57.7 164
Kitui 93.7 90.5 86.2 355 89.9 34.5 31.6 303
Machakos 91.8 61.4 59.8 400 96.6 84.1 83.8 436
Makueni 88.1 61.4 58.2 333 86.2 88.7 76.9 250
Central 90.1 79.6 74.7 1,905 84.9 67.6 61.1 1,564
Nyandarua 86.1 77.7 69.0 204 86.2 52.5 49.0 198
Nyeri 91.4 78.7 75.2 320 87.6 86.5 77.0 229
Kirinyaga 96.1 84.7 82.8 218 81.9 69.5 63.1 184
Murang’a 93.9 75.4 72.9 348 86.3 68.7 64.0 284
Kiambu 87.5 80.8 74.5 815 83.9 64.6 57.5 669
Rift Valley 86.8 70.2 66.0 3,714 86.5 59.9 55.6 3,050
Turkana 77.0 31.5 31.5 146 43.3 33.1 32.2 76
West Pokot 64.9 54.1 46.2 127 82.8 21.0 17.7 103
Samburu 92.2 41.4 40.6 58 49.2 46.9 42.8 35
Trans-Nzoia 70.8 58.4 52.7 346 87.7 56.5 51.0 329
Uasin Gishu 86.4 59.1 56.0 391 89.5 44.7 42.0 355
Elgeyo Marakwet 91.5 66.2 63.3 114 96.6 58.1 56.8 86
Nandi 96.1 87.6 85.8 290 99.3 79.9 79.5 264
Baringo 94.3 70.6 67.3 155 92.8 63.8 61.5 125
Laikipia 96.9 77.5 76.7 155 86.7 68.6 62.8 124
Nakuru 91.6 84.5 79.7 719 87.8 65.1 59.4 589
Narok 88.8 64.9 61.5 302 76.9 60.7 56.3 240
Kajiado 81.0 61.3 57.2 306 81.2 62.7 53.9 241
Kericho 94.3 79.8 77.8 271 88.5 61.1 57.4 215
Bomet 84.4 80.2 70.0 335 89.5 67.8 63.5 267
Western 90.6 77.8 73.1 1,571 92.8 66.4 62.8 1,164
Kakamega 86.5 69.8 62.7 544 93.5 57.8 55.4 411
Vihiga 85.9 68.9 62.8 176 97.8 67.0 66.1 140
Bungoma 95.7 88.9 86.8 575 93.7 65.9 63.2 413
Busia 91.1 76.1 71.9 276 85.8 84.6 75.1 199
Nyanza 86.3 86.3 78.5 1,908 86.4 81.4 73.5 1,405
Siaya 75.0 91.0 71.1 277 85.5 84.6 77.2 213
Kisumu 87.7 89.5 83.2 392 85.0 80.9 71.8 309
Homa Bay 89.6 87.3 82.3 362 82.6 81.8 70.3 243
Migori 89.8 80.6 76.0 297 86.0 76.7 67.1 211
Kisii 88.0 84.0 79.6 416 89.1 80.5 75.7 315
Nyamira 84.8 85.0 73.3 163 92.8 87.3 83.4 114
Nairobi 91.4 84.7 78.8 1,742 82.2 65.0 55.1 1,568
Total 15-49 88.5 76.2 71.5 14,625 87.2 67.5 62.0 12,063
50-54 na na na na 83.9 70.7 63.7 756
Total 15-54 na na na na 87.0 67.7 62.1 12,819
na = Not applicable
220 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
13.3 ATTITUDES TOWARDS PEOPLE LIVING WITH HIV AND AIDS
Widespread stigma and discrimination against those living with HIV and AIDS can adversely
affect people’s willingness to be tested and their adherence to antiretroviral therapy. Survey respondents
who had heard of AIDS were asked if they would be willing to care for a family member with AIDS in
their own household, if they would buy fresh vegetables from a shopkeeper who has the AIDS virus, if
they thought a female teacher who has the AIDS virus but is not sick should be allowed to continue
teaching, and if they would like to keep a family member’s positive HIV status secret. Table 13.6.1 and
Figure 13.3 show the results for women age 15-49 by background characteristics, and Table 13.6.2 and
Figure 13.4 show the results for men.
Table 13.6.1 Accepting attitudes towards those living with HIV/AIDS: Women
Among women age 15-49 who have heard of AIDS, percentage expressing specific accepting attitudes towards people with HIV/AIDS,
by background characteristics, Kenya 2014
Percentage of women who:
Say that a
female teacher
Are willing to who has the Would not want
care for a family Would buy fresh AIDS virus but is to keep secret Percentage
member with vegetables from not sick should that a family expressing Number of
AIDS in the shopkeeper who be allowed to member got accepting women who
Background respondent’s has the AIDS continue infected with the attitudes on all have heard of
characteristic home virus teaching AIDS virus four indicators AIDS
Age
15-24 91.6 75.7 88.1 33.3 23.0 5,385
15-19 90.3 73.4 86.6 30.4 20.5 2,699
20-24 92.8 78.0 89.6 36.2 25.4 2,686
25-29 91.4 76.1 87.2 35.4 24.3 2,927
30-39 92.6 79.4 88.1 40.4 29.3 3,935
40-49 93.6 78.9 87.4 43.0 30.2 2,341
Marital status
Never married 93.2 78.6 89.9 34.4 25.5 4,238
Ever had sex 94.9 82.7 92.7 36.9 29.2 2,133
Never had sex 91.4 74.4 87.0 31.9 21.8 2,105
Married/living together 91.5 76.3 86.9 38.6 26.3 8,691
Divorced/separated/
widowed 93.0 79.5 87.5 37.0 26.5 1,659
Residence
Urban 94.7 81.4 92.1 38.5 30.2 5,921
Rural 90.4 74.5 84.9 36.3 23.3 8,666
Region
Coast 93.5 71.2 89.1 25.2 16.1 1,420
North Eastern 28.4 22.2 27.3 50.5 2.8 284
Eastern 92.1 71.9 84.2 30.8 18.8 2,062
Central 95.0 81.9 92.3 40.2 29.8 1,904
Rift Valley 91.3 73.1 83.4 45.2 29.8 3,701
Western 93.0 82.0 92.8 32.1 24.5 1,568
Nyanza 94.6 86.9 93.0 32.9 26.0 1,906
Nairobi 96.8 87.0 95.3 41.3 36.3 1,742
Education
No education 66.0 41.4 54.5 38.1 6.8 989
Primary incomplete 88.8 69.5 81.7 33.7 19.2 3,784
Primary complete 95.0 78.6 91.4 36.7 26.8 3,539
Secondary+ 96.7 86.9 94.8 39.4 32.9 6,274
Wealth quintile
Lowest 79.8 58.1 70.5 33.4 12.5 2,210
Second 92.2 75.1 86.1 35.6 22.6 2,585
Middle 94.0 78.7 89.7 36.4 26.1 2,856
Fourth 94.7 82.6 93.0 39.0 30.8 3,111
Highest 95.7 84.5 93.4 39.6 32.5 3,824
Total 15-49 92.1 77.3 87.8 37.2 26.1 14,587
Ninety-two percent of women and 95 percent of men reported that they would be willing to care
for a relative sick with HIV, 77 percent of women and 84 percent of men said that they would be willing to
buy fresh vegetables from a vendor who has AIDS, and 88 percent of both women and men agreed that a
female teacher who has the AIDS virus but is not sick should be allowed to continue teaching. Since 2003,
there has been continuous improvement in these three measures of accepting attitudes.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 221
Figure 13.3 Accepting attitudes towards those with HIV: Women
Percent
90 92 88
84
77 76
68
60 57 59
54
37
33
27 26
Are willing to care Would buy fresh Believe an HIV- Would not want an Percentage
for relative with HIV vegetables from a positive female HIV-positive status expressing
at home vendor with AIDS teacher should be of family member to accepting attitudes
allowed to continue remain secret on all four
teaching measures
2003 KDHS 2008-09 KDHS 2014 KDHS
The proportion of respondents with accepting attitudes related to disclosure of a family member’s
HIV-positive status continues to decline, however. In 2014, 37 percent of women and 55 percent of men
reported that if a family member became infected with the HIV virus, they would not want it to remain
secret, a decrease from 54 percent of women and 69 percent of men in 2008-09 and 59 percent of women
and 72 percent of men in 2003.
The percentage of women and men expressing accepting attitudes on all four measures remains
low. Only one in four women (26 percent) and two in five men (44 percent) show acceptance on all four
measures, a decrease from the figures reported in 2008-09 (33 percent among women and 48 percent
among men).
Urban women (30 percent) are more likely than rural women (23 percent) to have accepting
attitudes on all four measures; the difference between urban and rural men is minimal. Accepting attitudes
remain lowest in the North Eastern region; only 3 percent of women and 31 percent of men currently
express accepting attitudes on all four measures. In 2008-09, a similarly low 11 percent of women and 14
percent of men in that region expressed acceptance.
222 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.6.2 Accepting attitudes towards those living with HIV/AIDS: Men
Among men age 15-49 who have heard of HIV/AIDS, percentage expressing specific accepting attitudes towards people with
HIV/AIDS, by background characteristics, Kenya 2014
Percentage of men who:
Say that a
female teacher
Are willing to who has the Would not want
care for a family Would buy fresh AIDS virus but is to keep secret Percentage
member with vegetables from not sick should that a family expressing
AIDS in the shopkeeper who be allowed to member got accepting Number of men
Background respondent’s has the AIDS continue infected with the attitudes on all who have heard
characteristic home virus teaching AIDS virus four indicators of AIDS
Age
15-24 94.1 80.4 86.6 48.5 36.9 4,648
15-19 93.1 77.6 84.3 44.8 32.9 2,526
20-24 95.3 83.7 89.3 52.9 41.7 2,122
25-29 96.0 86.4 88.2 56.2 44.6 2,104
30-39 96.4 85.8 87.2 60.0 48.3 3,267
40-49 96.2 86.6 89.8 62.6 50.8 2,020
Marital status
Never married 94.3 81.9 87.2 49.1 38.4 5,328
Ever had sex 95.7 85.4 88.8 50.4 40.7 3,507
Never had sex 91.6 75.2 84.3 46.7 33.9 1,821
Married/living together 96.6 85.8 88.3 60.0 47.9 6,093
Divorced/separated/
widowed 93.5 82.9 83.4 62.6 48.1 618
Residence
Urban 96.3 87.2 92.3 52.8 44.3 5,298
Rural 94.7 81.4 83.8 57.3 43.2 6,741
Region
Coast 89.8 82.3 82.8 53.8 43.8 1,256
North Eastern 75.8 50.8 66.5 75.9 31.0 225
Eastern 95.0 77.9 83.5 49.3 35.2 1,820
Central 97.8 92.2 91.7 60.2 52.5 1,560
Rift Valley 96.5 82.3 85.8 60.5 46.9 3,044
Western 97.6 84.9 88.6 55.1 43.7 1,162
Nyanza 95.8 89.9 92.1 54.3 46.0 1,403
Nairobi 96.9 85.8 93.9 46.8 38.1 1,568
Education
No education 79.8 47.4 46.9 56.2 18.2 338
Primary incomplete 92.6 71.9 76.4 49.4 31.2 3,058
Primary complete 95.8 85.0 88.4 59.8 47.0 2,732
Secondary+ 97.6 91.7 95.3 56.3 50.1 5,911
Wealth quintile
Lowest 89.8 68.0 70.5 53.7 30.5 1,679
Second 95.3 82.0 84.1 56.0 42.7 2,140
Middle 96.6 85.0 88.4 54.3 43.5 2,366
Fourth 96.7 87.3 92.1 58.1 48.5 2,958
Highest 96.5 90.3 94.8 53.7 47.3 2,896
Total 15-49 95.4 83.9 87.6 55.3 43.7 12,039
50-54 96.3 83.0 85.4 65.6 50.8 756
Total 15-54 95.5 83.9 87.5 55.9 44.1 12,795
Education and socioeconomic status remain strongly related to positive attitudes towards those
who are HIV positive. The proportion of women and men who express accepting attitudes on all four
measures increases with increasing education as well as wealth. Only 7 percent of women and 18 percent
of men with no education express acceptance on all four measures, as compared with 33 percent of women
and 50 percent of men with a secondary or higher education. Similarly, women in the highest wealth
quintile are more than twice as likely to express accepting attitudes on all four measures as those in the
lowest quintile (33 percent versus 13 percent). Forty-seven percent of men in the highest wealth quintile
express accepting attitudes on all four measures, compared to 31 percent of those in the lowest quintile.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 223
Figure 13.4 Accepting attitudes towards those with HIV: Men
Percent
94 95
88 88
84
80 80
74 72 69
60
55
48
44
40
Are willing to care Would buy fresh Believe an HIV- Would not want an Percentage
for relative with HIV vegetables from a positive female HIV-positive status expressing
at home vendor with AIDS teacher should be of family member to accepting attitudes
allowed to continue remain secret on all four
teaching measures
2003 KDHS 2008-09 KDHS 2014 KDHS
13.4 ATTITUDES TOWARDS NEGOTIATING SAFER SEXUAL RELATIONSHIPS
HIV transmission in Kenya mainly occurs through sexual intercourse. Safer sex is an important
prevention strategy and can reduce individuals’ risk of contracting HIV. Negotiating safer sex can be
difficult, especially for married women. Table 13.7 shows the percentage of women and men age 15-49
who believe that a woman is justified in refusing to have sex with her husband if he has sex with other
women or in asking that her husband use a condom if she knows he has an STI.
Seventy-nine percent of both women and men believe that a wife is justified in refusing to have
sexual intercourse with her husband if he has sex with other women. Higher proportions of women (89
percent) and men (92 percent) believe that a wife is justified in asking her husband/partner to use a
condom if she knows he has an STI.
Positive attitudes towards sexual negotiation follow similar patterns to other HIV-related attitudes.
They are lowest among women and men age 15-19, women and men who have never had sex, and rural
women and men. Women in North Eastern are much less likely than women in other regions to support
either refusing sex (32 percent) or asking for condom use (19 percent). Interestingly, men in North Eastern
are much more supportive than women in that region (76 percent support refusing sex and 72 percent
support asking for condom use). Support for women’s right to negotiate safer sex increases with increasing
education and wealth among both men and women.
224 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.7 Attitudes towards negotiating safer sexual relations with husband
Percentage of women and men age 15-49 who believe that a woman is justified in refusing to have sexual intercourse with her
husband if she knows that he has sexual intercourse with other women, and percentage who believe that a woman is justified in
asking that they use a condom if she knows that her husband has a sexually transmitted infection (STI), by background characteristics,
Kenya 2014
Percentage of women who believe that Percentage of men who believe that
a woman is justified in: a woman is justified in:
Refusing to have Refusing to have
sexual intercourse Asking that they sexual intercourse Asking that they
with her husband if use a condom if with her husband if use a condom if
she knows he has she knows that her she knows he has she knows that her
Background sex with other husband has an Number of sex with other husband has an Number of
characteristic women STI women women STI men
Age
15-24 76.9 87.0 5,407 74.8 89.6 4,666
15-19 73.1 82.0 2,717 72.1 86.7 2,540
20-24 80.7 92.0 2,691 78.0 93.0 2,125
25-29 80.7 90.8 2,932 77.9 93.0 2,104
30-39 78.8 90.1 3,942 82.8 93.4 3,268
40-49 79.2 90.4 2,344 80.4 93.1 2,024
Marital status
Never married 76.8 86.0 4,255 75.1 89.7 5,350
Ever had sex 80.5 92.6 2,134 77.3 93.1 3,512
Never had sex 73.1 79.3 2,122 70.8 83.2 1,838
Married/living together 78.9 90.1 8,710 80.8 93.5 6,095
Divorced/separated/
widowed 80.9 92.0 1,660 84.7 93.2 618
Residence
Urban 81.8 92.9 5,929 80.1 94.7 5,300
Rural 76.3 86.6 8,696 77.1 89.5 6,762
Region
Coast 74.1 87.4 1,421 78.8 87.8 1,260
North Eastern 32.1 19.0 299 76.1 71.8 227
Eastern 82.4 88.9 2,066 79.1 90.7 1,825
Central 78.6 89.6 1,905 81.5 92.1 1,564
Rift Valley 77.1 88.3 3,714 77.7 90.5 3,050
Western 80.4 91.2 1,571 77.7 94.6 1,164
Nyanza 81.2 93.8 1,908 80.4 96.0 1,405
Nairobi 83.7 97.2 1,742 75.2 95.7 1,568
Education
No education 57.3 58.7 1,015 68.7 74.1 345
Primary incomplete 74.5 85.8 3,793 74.5 86.9 3,071
Primary complete 79.1 92.5 3,543 78.8 93.1 2,734
Secondary+ 84.1 94.2 6,274 81.0 94.8 5,913
Wealth quintile
Lowest 67.0 74.1 2,236 72.4 83.9 1,691
Second 77.0 89.2 2,590 78.4 90.0 2,145
Middle 79.4 90.5 2,859 77.0 92.1 2,370
Fourth 81.7 92.6 3,113 78.4 93.5 2,959
Highest 83.2 94.0 3,827 83.3 95.8 2,897
Total 15-49 78.5 89.1 14,625 78.5 91.8 12,063
50-54 na na na 80.7 90.5 756
Total 15-54 na na na 78.6 91.7 12,819
na = Not applicable
13.5 ATTITUDE TOWARDS CONDOM EDUCATION FOR YOUTH
Condom use is one of the most effective strategies for combating the spread of HIV. However,
educating youth about condoms is sometimes controversial, with some people believing that it promotes
early sexual initiation (MOH, 2015b). To gauge attitudes towards condom education for youth,
respondents were asked if they thought that young people age 12-14 should be taught about using condoms
to avoid HIV infection. Results are tabulated for adult respondents (those age 18 and above). Table 13.8
shows the percentage of women and men age 18-49 who agree that children age 12-14 should be taught
about using a condom to avoid AIDS, by background characteristics.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 225
Table 13.8 Adult support of education about condom use to prevent AIDS
Percentage of women and men age 18-49 who agree that children age 12-14 years
should be taught about using a condom to avoid AIDS, by background
characteristics, Kenya 2014
Women Men
Background Percentage Percentage
characteristic who agree Number who agree Number
Age
18-24 58.5 3,727 64.3 3,134
18-19 57.1 1,037 59.6 1,009
20-24 59.1 2,691 66.6 2,125
25-29 60.5 2,932 63.1 2,104
30-39 56.1 3,942 60.6 3,268
40-49 57.8 2,344 60.6 2,024
Marital status
Never married 59.4 2,660 62.7 3,821
Married or living
together 56.9 8,634 61.9 6,094
Divorced/separated/
widowed 62.1 1,651 63.1 616
Residence
Urban 57.4 5,478 61.5 4,916
Rural 58.6 7,468 62.8 5,615
Region
Coast 53.0 1,271 46.3 1,106
North Eastern 8.8 260 14.4 179
Eastern 61.3 1,816 69.6 1,563
Central 54.1 1,727 52.1 1,401
Rift Valley 57.6 3,307 59.8 2,675
Western 71.4 1,305 72.0 944
Nyanza 70.3 1,620 78.2 1,164
Nairobi 49.0 1,639 67.3 1,500
Education
No education 39.2 981 30.2 328
Primary incomplete 61.4 3,059 64.3 2,243
Primary complete 60.1 3,349 63.3 2,600
Secondary+ 58.4 5,557 62.8 5,360
Wealth quintile
Lowest 50.9 1,945 53.3 1,372
Second 63.4 2,206 65.3 1,793
Middle 61.5 2,457 66.1 1,989
Fourth 57.4 2,784 62.8 2,634
Highest 56.9 3,553 61.4 2,742
Total 18-49 58.1 12,945 62.2 10,531
50-54 na na 56.1 756
Total 18-54 na na 61.8 11,288
na = Not applicable
Six in 10 women (58 percent) and men (62 percent) support teaching young people about
condoms for HIV prevention. Among men, this is a decline from the 2008-09 KDHS, when 72 percent
supported teaching young people about condoms. There is no clear trend in opinions across age or rural-
urban residence. More than 70 percent of women and men from the Nyanza and Western regions support
youth condom education. Residents of North Eastern are least likely to agree that young people should be
taught about condoms (9 percent of women and 14 percent of men).
Women and men with no education are less likely than their more educated counterparts to agree
that youth age 12-14 should be taught about condoms. Thirty-nine percent of women with no education
support condom education, as compared with 58 percent or more among those at higher educational levels.
Similarly, 30 percent of men with no education support condom education, compared with 63 percent or
more among those at higher levels of education. Women (51 percent) and men (53 percent) in the lowest
wealth quintile are least likely to support condom education.
226 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
13.6 HIGH-RISK SEX
Information on sexual behaviour is important in designing and monitoring programmes to control
the spread of HIV. The 2014 KDHS included questions on respondents’ sexual partners both during their
lifetime and over the 12 months preceding the survey. Men were also asked whether they paid for sex
during the 12 months preceding the interview. In addition, information was collected on women’s and
men’s use of condoms during their last sexual encounter with each type of partner. These questions are
sensitive, and some respondents may have been reluctant to provide information on recent sexual
behaviour.
13.6.1 Multiple Partners and Condom Use
Tables 13.9.1 and 13.9.2 show the percentage of women and men age 15-49 who had sexual
intercourse with more than one partner in the past 12 months and, among those with more than one partner
in the past 12 months, the percentage reporting that a condom was used during their most recent
intercourse. Also shown is the mean number of lifetime sexual partners among respondents who had ever
had sex. Men are much more likely than women to report having two or more sexual partners in the 12
months before the survey (13 percent and 1 percent, respectively). Among men, those in their 20s (17
percent); those who are divorced, separated, or widowed (19 percent); and those residing in Nairobi (19
percent) and Nyanza (18 percent) are most likely to have had multiple partners in the 12 months preceding
the survey. Men in the Central and North Eastern regions are least likely to report multiple partners (both 6
percent). There are no clear patterns by education or wealth.
Forty percent of women and 44 percent of men who had two or more partners in the 12 months
preceding the survey reported using a condom during their last sexual intercourse. Among men, this is a
decrease from the 62 percent reported in the 2008-09 KDHS. Condom use decreases with age among men
and is lowest among those who are married or living together with a partner (20 percent), those in the
Western (27 percent) and Coast (32 percent) regions, those with no education (18 percent), and those in the
lowest wealth quintile (31 percent).
Among respondents who have ever had sexual intercourse, the mean number of lifetime sexual
partners is considerably higher among men (6.8) than among women (2.1). Mean number of partners
increases with age among both men and women. For men, the mean number is higher among those who
are divorced, separated, or widowed; those in polygynous marriages; and those living in Nairobi.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 227
Table 13.9.1 Multiple sexual partners: Women
Among all women age 15-49, the percentage who had sexual intercourse with more than one sexual partner in the past 12 months;
among those having more than one partner in the past 12 months, the percentage reporting that a condom was used at last
intercourse; and the mean number of sexual partners during their lifetime for women who ever had sexual intercourse, by background
characteristics, Kenya 2014
Among women who had 2+ Among women who ever had
Among all women: partners in the past 12 months: sexual intercourse1:
Percentage who
Percentage who reported using a
had 2+ partners condom during Mean number of
Background in the past 12 Number of last sexual Number of sexual partners Number of
characteristic months women intercourse women in lifetime women
Age
15-24 1.5 5,407 37.5 83 1.8 3,387
15-19 1.0 2,717 (26.1) 28 1.5 984
20-24 2.0 2,691 (43.3) 55 1.9 2,402
25-29 1.3 2,932 (43.1) 39 2.1 2,855
30-39 1.6 3,942 (48.0) 64 2.2 3,867
40-49 0.9 2,344 * 20 2.5 2,309
Marital status
Never married 1.4 4,255 65.6 60 2.0 2,124
Married or living
together 1.0 8,710 12.5 89 2.0 8,647
Divorced/separated/
widowed 3.4 1,660 56.8 56 2.9 1,648
Residence
Urban 2.1 5,929 47.4 122 2.2 5,180
Rural 1.0 8,696 29.6 84 2.1 7,238
Region
Coast 1.1 1,421 (26.9) 15 1.7 1,190
North Eastern 0.0 299 * 0 1.2 232
Eastern 1.0 2,066 (41.8) 20 2.6 1,716
Central 1.3 1,905 * 24 2.3 1,638
Rift Valley 0.8 3,714 (41.4) 31 1.9 3,214
Western 0.9 1,571 * 15 2.2 1,268
Nyanza 1.4 1,908 (39.6) 27 2.2 1,621
Nairobi 4.2 1,742 * 73 2.3 1,539
Education
No education 0.7 1,015 * 8 1.7 968
Primary incomplete 1.4 3,793 33.7 52 2.3 3,092
Primary complete 1.2 3,543 (31.4) 43 2.2 3,284
Secondary+ 1.6 6,274 50.0 103 2.1 5,075
Wealth quintile
Lowest 1.0 2,236 (16.6) 22 1.9 1,927
Second 1.4 2,590 (29.7) 36 2.1 2,156
Middle 0.9 2,859 (48.2) 27 2.1 2,380
Fourth 1.8 3,113 (43.6) 55 2.3 2,678
Highest 1.7 3,827 (47.5) 65 2.1 3,278
Total 15-49 1.4 14,625 40.1 205 2.1 12,418
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted
cases that has been suppressed.
1
Means are calculated excluding respondents who gave non-numeric responses.
228 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.9.2 Multiple sexual partners: Men
Among all men age 15-49, the percentage who had sexual intercourse with more than one sexual partner in the past 12 months; among
those having more than one partner in the past 12 months, the percentage reporting that a condom was used at last intercourse; and the
mean number of sexual partners during their lifetime for men who ever had sexual intercourse, by background characteristics, Kenya
2014
Among men who had 2+ partners Among men who ever had sexual
Among all men: in the past 12 months: intercourse1:
Percentage who
Percentage who reported using a
had 2+ partners condom during Mean number of
Background in the past 12 last sexual sexual partners
characteristic months Number of men intercourse Number of men in lifetime Number of men
Age
15-24 9.6 4,666 68.9 449 4.4 2,901
15-19 3.7 2,540 64.1 95 2.8 1,028
20-24 16.7 2,125 70.2 354 5.2 1,873
25-29 17.3 2,104 50.1 365 6.3 2,012
30-39 14.6 3,268 28.2 477 7.6 3,189
40-49 11.9 2,024 22.2 241 9.8 1,962
Marital status
Never married 11.3 5,350 72.4 605 4.6 3,498
Married or living together 13.2 6,095 20.2 807 7.7 5,961
Divorced/separated/
widowed 19.4 618 66.6 120 11.6 605
Type of union
In polygynous union 69.0 333 13.7 230 11.7 316
In non-polygynous union 10.0 5,762 22.8 577 7.5 5,645
Not currently in union 12.1 5,968 71.4 725 5.6 4,103
Residence
Urban 14.4 5,300 46.6 761 7.6 4,666
Rural 11.4 6,762 42.3 771 6.2 5,398
Region
Coast 11.9 1,260 32.0 150 5.6 1,041
North Eastern 5.9 227 * 13 1.8 127
Eastern 12.5 1,825 50.3 229 6.5 1,514
Central 5.5 1,564 43.7 86 6.8 1,315
Rift Valley 11.5 3,050 51.2 351 6.5 2,579
Western 12.5 1,164 27.2 145 6.3 902
Nyanza 18.4 1,405 47.6 258 7.1 1,134
Nairobi 19.0 1,568 45.7 299 9.3 1,452
Education
No education 13.9 345 17.9 48 6.9 300
Primary incomplete 11.4 3,071 36.0 351 7.1 2,286
Primary complete 14.3 2,734 44.9 390 6.9 2,512
Secondary+ 12.6 5,913 49.8 743 6.7 4,967
Wealth quintile
Lowest 12.2 1,691 31.0 207 6.6 1,328
Second 11.6 2,145 45.6 249 6.6 1,735
Middle 11.9 2,370 46.9 282 7.0 1,923
Fourth 13.1 2,959 46.7 389 7.1 2,488
Highest 14.0 2,897 46.6 404 6.8 2,590
Total 15-49 12.7 12,063 44.4 1,531 6.8 10,064
50-54 11.4 756 14.5 86 11.7 725
Total 15-54 12.6 12,819 42.8 1,618 7.2 10,789
Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
1
Means are calculated excluding respondents who gave non-numeric responses.
13.6.2 Point Prevalence and Cumulative Prevalence of Concurrent Sexual Partners
The 2014 KDHS provides information on concurrent relationships by presenting the point
prevalence and the cumulative prevalence of concurrent sexual partners. The point prevalence of
concurrent sexual partners is defined as the percentage of respondents who had two or more sexual
partners concurrently at the point in time six months before the survey. The cumulative prevalence of
concurrent sexual partners is defined as the percentage of respondents who had two or more partners
concurrently at any time during the 12 months preceding the survey. Table 13.10 shows the point
prevalence and cumulative prevalence of concurrent sexual partners among women and men age 15-49
during the 12 months before the survey. It also shows, among women and men who had multiple sexual
partners in the 12 months before the survey, the percentage who had concurrent sexual partners.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 229
Table 13.10 Point prevalence and cumulative prevalence of concurrent sexual partners
Percentage of all women and men age 15-49 who had concurrent sexual partners six months before the survey (point
prevalence1), and percentage of all women and all men age 15-49 who had any concurrent sexual partners during the
12 months before the survey (cumulative prevalence2), and among women and men age 15-49 who had multiple
sexual partners during the 12 months before the survey, percentage who had concurrent sexual partners, by
background characteristics, Kenya 2014
Among all respondents who had
multiple partners during the
Among all respondents: 12 months before the survey:
Cumulative
Point prevalence prevalence of Percentage who
Background of concurrent concurrent Number of had concurrent Number of
characteristic sexual partners1 sexual partners2 respondents sexual partners2 respondents
WOMEN
Age
15-24 0.2 0.6 5,407 41.3 83
15-19 0.1 0.5 2,717 (51.9) 28
20-24 0.3 0.7 2,691 (35.9) 55
25-29 0.2 0.9 2,932 (71.4) 39
30-39 0.2 0.7 3,942 (42.4) 64
40-49 0.3 0.8 2,344 * 20
Marital status
Never married 0.2 0.9 4,255 62.9 60
Married or living together 0.2 0.5 8,710 52.7 89
Divorced/separated/
widowed 0.2 1.4 1,660 40.0 56
Residence
Urban 0.2 0.9 5,929 44.0 122
Rural 0.2 0.6 8,696 64.2 84
Total 15-49 0.2 0.7 14,625 52.2 205
MEN
Age
15-24 2.2 5.6 4,666 57.7 449
15-19 0.7 2.1 2,540 56.1 95
20-24 4.0 9.7 2,125 58.1 354
25-29 4.8 12.2 2,104 70.4 365
30-39 6.8 12.5 3,268 85.5 477
40-49 7.5 10.4 2,024 87.6 241
Marital status
Never married 2.4 6.0 5,350 52.8 605
Married or living together 7.0 12.1 6,095 91.6 807
Divorced/separated/
widowed 3.7 12.3 618 63.3 120
Type of union
In polygynous union 60.5 67.1 333 97.2 230
In non-polygynous union 3.9 9.0 5,762 89.4 577
Not currently in union 2.5 6.6 5,968 54.5 725
Residence
Urban 4.8 10.7 5,300 74.5 761
Rural 4.8 8.4 6,762 73.6 771
Total 15-49 4.8 9.4 12,063 74.1 1,531
50-54 8.2 10.7 756 93.6 86
Total 15-54 5.0 9.5 12,819 75.1 1,618
Note: Two sexual partners are considered to be concurrent if the date of the most recent sexual intercourse with the
earlier partner is after the date of the first sexual intercourse with the later partner. Figures in parentheses are based
on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been
suppressed..
1
The percentage of respondents who had two (or more) sexual partners that were concurrent at the point in time six
months before the survey
2
The percentage of respondents who had two (or more) sexual partners that were concurrent anytime during the 12
months preceding the survey
Among women, both the point prevalence and the cumulative prevalence are less than 1 percent.
Among men, the point prevalence is 5 percent and the cumulative prevalence is 9 percent. The point
prevalence for men increases with age. The cumulative prevalence is slightly higher among men age 25 to
39 and among men in urban areas. Both the point prevalence and the cumulative prevalence among
married men are equal to or higher than those of their non-married counterparts; to some extent, this is
attributable to polygamy among married men.
230 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Women with multiple sexual partners in the last 12 months are less likely than men to have
concurrent partners (52 percent versus 74 percent). Women who are married (53 percent) and urban
women (44 percent) are less likely to have concurrent partners than women who have never been married
(63 percent) and those who reside in rural areas (64 percent). The percentage of men with recent
concurrent sexual partners increases with age; the percentage is highest among married men (92 percent)
and men in polygynous unions (97 percent).
13.6.3 Transactional Sex
Payment for sexual intercourse is associated with risk of contracting HIV and other sexually
transmitted infections due to compromised power relations that result in inconsistent condom use. In the
2014 KDHS, men age 15-49 were asked if they had ever paid for sexual intercourse, if they had paid for
sexual intercourse in the 12 months preceding the survey, and, among those who had paid for sexual
intercourse in the 12 months preceding the survey, whether they used condoms the last time they paid for
sexual intercourse.
Table 13.11 shows that 9 percent of men had ever paid for sex and that 3 percent had paid for sex
in the 12 months before the survey. Seventy-four percent of men who reported having paid for sex in the
12 months before the survey said that they used a condom the last time they paid for sex.
Men age 30-39 are more likely than their counterparts to have ever paid for sex (13 percent), to
have paid for sex in the 12 months before the survey (3 percent), and to have used a condom during their
last paid sex (83 percent). A similar pattern prevails among men who are divorced, separated, or widowed
(21 percent had ever paid for sex, 8 percent paid in the previous 12 months, and 83 percent used a condom
during their last paid sex). Urban men are more likely than rural men to have ever paid for sex (11 percent
compared with 7 percent) and to have used a condom during their last paid sex (80 percent compared with
70 percent). Men in the Coast region are most likely to have ever paid for sex (14 percent), and men in the
Western and Nyanza regions are most likely to have paid for sex in the previous 12 months (both 4
percent). Men in North Eastern are least likely to report ever having paid for sex (1 percent) or having paid
for sex in the previous 12 months (1 percent). There are no apparent trends by education or wealth.
By county, men in Migori (11 percent), Kakamega (6 percent), Vihiga (6 percent), and Elgeyo
Marakwet (6 percent) are somewhat more likely to have paid for sex in the previous 12 months than men
in other counties (Table 13.11C).
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 231
Table 13.11 Payment for sexual intercourse and condom use at last paid sexual intercourse
Percentage of men age 15-49 who ever paid for sexual intercourse and percentage reporting payment for sexual
intercourse in the past 12 months, and among them, the percentage reporting that a condom was used the last
time they paid for sexual intercourse, by background characteristics, Kenya 2014
Among men who paid for sex in
Among all men: the past 12 months:
Percentage
Percentage who Percentage who reporting
ever paid for paid for sexual condom use at
Background sexual intercourse in the last paid sexual
characteristic intercourse past 12 months Number of men intercourse Number of men
Age
15-24 4.0 1.9 4,666 63.5 88
15-19 2.2 1.4 2,540 (67.7) 36
20-24 6.1 2.5 2,125 60.5 52
25-29 10.3 2.5 2,104 69.3 53
30-39 12.8 3.4 3,268 83.3 112
40-49 10.9 2.4 2,024 (77.9) 48
Marital status
Never married 5.6 2.2 5,350 73.0 117
Married or living
together 10.1 2.2 6,095 72.3 136
Divorced/separated/
widowed 20.7 7.8 618 82.5 48
Residence
Urban 10.6 2.3 5,300 79.8 121
Rural 7.1 2.6 6,762 70.4 179
Region
Coast 13.6 2.8 1,260 (59.0) 35
North Eastern 1.1 0.7 227 * 2
Eastern 11.0 1.2 1,825 (81.4) 22
Central 5.3 1.8 1,564 * 29
Rift Valley 5.6 2.5 3,050 84.2 75
Western 10.0 3.6 1,164 (59.2) 42
Nyanza 9.1 4.3 1,405 72.2 60
Nairobi 10.7 2.3 1,568 * 36
Education
No education 7.5 2.2 345 * 8
Primary incomplete 9.9 3.5 3,071 65.0 108
Primary complete 9.9 2.6 2,734 82.0 70
Secondary+ 7.4 1.9 5,913 79.2 115
Wealth quintile
Lowest 8.0 2.9 1,691 65.6 49
Second 7.5 2.4 2,145 68.2 52
Middle 8.1 3.3 2,370 71.8 78
Fourth 9.3 2.1 2,959 83.2 61
Highest 9.6 2.1 2,897 (80.5) 60
Total 15-49 8.6 2.5 12,063 74.2 300
50-54 12.9 2.6 756 * 20
Total 15-54 8.9 2.5 12,819 72.8 320
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer
than 25 unweighted cases that has been suppressed.
232 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.11C Payment for sexual intercourse
Percentage of men age 15-49 who ever paid for sexual intercourse and
percentage reporting payment for sexual intercourse in the past 12
months, by county, Kenya 2014
Percentage who Percentage who
ever paid for paid for sexual
sexual intercourse in the
County intercourse past 12 months Number of men
Coast 13.6 2.8 1,260
Mombasa 25.5 4.0 481
Kwale 4.4 2.3 226
Kilifi 7.0 2.1 359
Tana River 1.7 0.5 65
Lamu 3.3 1.9 37
Taita Taveta 11.9 1.9 93
North Eastern 1.1 0.7 227
Garissa 1.0 0.0 94
Wajir 1.2 1.2 72
Mandera 1.3 1.3 60
Eastern 11.0 1.2 1,825
Marsabit 2.0 2.0 40
Isiolo 2.3 2.3 35
Meru 0.4 0.1 495
Tharaka-Nithi 11.6 1.8 102
Embu 13.0 2.3 164
Kitui 11.9 0.3 303
Machakos 26.2 0.4 436
Makueni 5.3 4.7 250
Central 5.3 1.8 1,564
Nyandarua 9.3 4.3 198
Nyeri 10.3 0.7 229
Kirinyaga 4.5 2.6 184
Murang’a 3.2 1.8 284
Kiambu 3.5 1.3 669
Rift Valley 5.6 2.5 3,050
Turkana 0.6 0.6 76
West Pokot 12.1 4.4 103
Samburu 4.3 0.9 35
Trans-Nzoia 7.0 2.9 329
Uasin Gishu 3.5 2.5 355
Elgeyo Marakwet 12.3 6.0 86
Nandi 1.3 1.2 264
Baringo 12.7 4.5 125
Laikipia 5.6 3.4 124
Nakuru 3.2 2.2 589
Narok 2.2 0.0 240
Kajiado 10.7 3.3 241
Kericho 6.7 3.3 215
Bomet 7.7 1.9 267
Western 10.0 3.6 1,164
Kakamega 21.2 6.4 411
Vihiga 6.2 6.2 140
Bungoma 4.2 1.5 413
Busia 1.9 0.4 199
Nyanza 9.1 4.3 1,405
Siaya 8.1 2.7 213
Kisumu 0.9 0.9 309
Homa Bay 18.1 6.0 243
Migori 23.8 11.1 211
Kisii 4.4 4.4 315
Nyamira 0.0 0.0 114
Nairobi 10.7 2.3 1,568
Total 15-49 8.6 2.5 12,063
50-54 12.9 2.6 756
Total 15-54 8.9 2.5 12,819
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 233
13.7 COVERAGE OF HIV COUNSELLING AND TESTING
13.7.1 General HIV Testing
HIV counselling and testing is the entry point to HIV prevention, care, and support and treatment
services. Knowledge of HIV status helps HIV-negative individuals make decisions that can reduce their
risk. For those who are HIV positive, knowledge of their status allows them to take action to protect their
sexual partners, to access treatment, and to plan for the future. To assess awareness and coverage of HIV
testing services, respondents in the 2014 KDHS were asked if they knew a place where they could go to be
tested and further if they have ever undergone an HIV test and received the results of the test.
Tables 13.12.1 and 13.12.2 show, among women and men age 15-49, the percentage who know
where to get an HIV test, the percent distribution by testing status and by whether they received their
results, the percentage ever tested, and the percentage tested in the 12 months preceding the survey who
received the results of their last test, by background characteristics.
Knowledge of a place to get tested for HIV is widespread in Kenya; 91 percent of women and 97
percent of men know a place where people get tested for HIV. Knowledge generally increases with age
among both women and men. Relative to their respective counterparts, knowledge is lower among women
and men who have never been married and never had sex, women and men in rural areas, and women and
men in North Eastern. Among both women and men, knowledge of a place to get tested for HIV generally
increases with increasing education and wealth.
There has been a noticeable increase since 2008-09 in the percentage of women and men age 15-
49 who have ever been tested: from 58 percent and 42 percent, respectively, to 85 percent and 72 percent.
There has also been an increase, albeit less dramatic, in the percentage of women and men who were tested
in the past 12 months and received their results: from 29 percent and 23 percent, respectively, to 53 percent
and 46 percent. The proportion of women and men who were recently tested and received their results is
higher among those who are married or have been married, urban residents, those at higher educational
levels, and those who are wealthier.
By county, women in Wajir and Mandera and men in West Pokot and Mandera are least likely to
have ever been tested or to have been tested in the past 12 months. The counties with the highest levels of
lifetime and recent testing among women include Homa Bay, Uasin Gishu, Migori, and Kisumu, and the
counties with the highest levels among men include Migori, Kisumu, and Siaya (Tables 13.12.1C and
13.12.2C).
234 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.12.1 Coverage of prior HIV testing: Women
Percentage of women age 15-49 who know where to get an HIV test, percent distribution of women age 15-49 by testing status and by whether they
received the results of the last test, the percentage of women ever tested, and the percentage of women age 15-49 who were tested in the past 12
months and received the results of the last test, according to background characteristics, Kenya 2014
Percentage
who have
Percent distribution of women by testing status been tested for
and by whether they received the results of the HIV in the past
Percentage last test 12 months and
who know Ever tested Ever tested, received the
Background where to get and received did not receive Percentage results of the Number of
characteristic an HIV test results results Never tested1 Total ever tested last test women
Age
15-24 82.7 70.4 1.9 27.7 100.0 72.3 49.5 11,555
15-19 71.5 52.7 1.3 46.0 100.0 54.0 35.3 5,820
20-24 94.1 88.4 2.4 9.1 100.0 90.9 64.0 5,735
25-29 96.9 93.9 1.6 4.6 100.0 95.4 63.4 6,100
30-39 96.2 92.2 2.0 5.8 100.0 94.2 54.4 8,283
40-49 91.2 83.3 2.2 14.5 100.0 85.5 45.1 5,142
Marital status
Never married 78.5 63.3 1.4 35.3 100.0 64.7 42.3 8,997
Ever had sex 91.7 84.0 1.7 14.2 100.0 85.8 60.0 4,541
Never had sex 65.0 42.1 1.0 56.9 100.0 43.1 24.2 4,456
Married/living together 95.5 91.1 2.1 6.8 100.0 93.2 57.5 18,549
Divorced/separated/
widowed 95.1 90.4 2.1 7.5 100.0 92.5 54.9 3,533
Residence
Urban 92.9 87.1 1.6 11.3 100.0 88.7 57.8 12,690
Rural 88.8 80.1 2.2 17.8 100.0 82.2 49.4 18,389
Region
Coast 90.6 84.3 1.6 14.2 100.0 85.8 53.4 3,076
North Eastern 59.5 48.7 2.8 48.4 100.0 51.6 20.1 648
Eastern 90.7 83.2 1.1 15.7 100.0 84.3 51.0 4,375
Central 92.7 84.4 2.3 13.3 100.0 86.7 53.0 3,994
Rift Valley 89.9 81.4 2.4 16.1 100.0 83.9 51.8 7,953
Western 87.7 77.8 2.4 19.8 100.0 80.2 45.4 3,225
Nyanza 93.0 86.4 2.0 11.6 100.0 88.4 60.4 4,038
Nairobi 94.3 90.0 0.9 9.1 100.0 90.9 60.4 3,770
Education
No education 79.1 71.6 2.8 25.6 100.0 74.4 37.1 2,176
Primary incomplete 86.1 75.7 2.6 21.7 100.0 78.3 46.8 7,989
Primary complete 94.6 88.8 1.8 9.4 100.0 90.6 57.1 7,637
Secondary+ 92.7 85.8 1.4 12.8 100.0 87.2 56.5 13,277
Wealth quintile
Lowest 83.9 74.8 2.7 22.5 100.0 77.5 45.0 4,838
Second 89.9 81.2 2.2 16.6 100.0 83.4 51.7 5,457
Middle 91.1 83.1 1.9 15.1 100.0 84.9 52.0 6,032
Fourth 92.3 85.1 1.7 13.2 100.0 86.8 56.1 6,550
Highest 93.0 87.1 1.5 11.4 100.0 88.6 56.2 8,203
Total 15-49 90.5 83.0 1.9 15.1 100.0 84.9 52.8 31,079
1
Includes ‘don’t know/missing’
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 235
Table 13.12.2 Coverage of prior HIV testing: Men
Percentage of men age 15-49 who know where to get an HIV test, percent distribution of men age 15-49 by testing status and by whether they received
the results of the last test, the percentage of men ever tested, and the percentage of men age 15-49 who were tested in the past 12 months and
received the results of the last test, according to background characteristics, Kenya 2014
Percentage
who have
Percent distribution of men by testing status been tested for
and by whether they received the results of the HIV in the past
Percentage last test 12 months and
who know Ever tested Ever tested, received the
Background where to get and received did not receive Percentage results of the Number of
characteristic an HIV test results results Never tested1 Total ever tested last test men
Age
15-24 94.2 56.6 0.9 42.5 100.0 57.5 38.9 4,666
15-19 90.4 41.0 1.0 58.0 100.0 42.0 26.6 2,540
20-24 98.8 75.2 0.9 23.9 100.0 76.1 53.6 2,125
25-29 98.8 83.0 0.2 16.8 100.0 83.2 57.8 2,104
30-39 98.3 79.8 0.4 19.8 100.0 80.2 50.0 3,268
40-49 98.1 76.8 1.1 22.2 100.0 77.8 41.9 2,024
Marital status
Never married 94.7 58.1 0.8 41.1 100.0 58.9 39.4 5,350
Ever had sex 97.9 69.8 0.6 29.6 100.0 70.4 49.8 3,512
Never had sex 88.4 35.6 1.3 63.1 100.0 36.9 19.7 1,838
Married/living together 98.5 81.5 0.6 17.9 100.0 82.1 51.2 6,095
Divorced/separated/
widowed 98.2 77.0 0.6 22.4 100.0 77.6 45.8 618
Residence
Urban 98.6 77.7 0.6 21.7 100.0 78.3 51.3 5,300
Rural 95.3 65.5 0.7 33.7 100.0 66.3 41.3 6,762
Region
Coast 96.9 64.4 0.3 35.4 100.0 64.6 40.5 1,260
North Eastern 74.4 44.0 0.0 56.0 100.0 44.0 22.8 227
Eastern 95.4 66.6 0.4 33.0 100.0 67.0 39.8 1,825
Central 97.4 70.5 0.8 28.6 100.0 71.4 40.1 1,564
Rift Valley 96.2 70.5 0.6 28.9 100.0 71.1 47.1 3,050
Western 97.0 62.0 1.0 37.0 100.0 63.0 39.8 1,164
Nyanza 98.9 81.1 0.7 18.2 100.0 81.8 56.2 1,405
Nairobi 100.0 83.4 1.2 15.4 100.0 84.6 57.9 1,568
Education
No education 77.8 42.5 0.3 57.1 100.0 42.9 26.2 345
Primary incomplete 92.8 56.2 1.1 42.7 100.0 57.3 34.4 3,071
Primary complete 98.3 74.8 0.6 24.7 100.0 75.3 47.9 2,734
Secondary+ 99.3 78.4 0.5 21.1 100.0 78.9 51.7 5,913
Wealth quintile
Lowest 89.9 56.5 0.8 42.7 100.0 57.3 34.7 1,691
Second 96.6 65.8 1.0 33.3 100.0 66.7 41.1 2,145
Middle 97.2 69.1 0.9 30.0 100.0 70.0 45.7 2,370
Fourth 98.2 75.0 0.3 24.7 100.0 75.3 49.7 2,959
Highest 99.2 80.4 0.6 19.0 100.0 81.0 51.5 2,897
Total 15-49 96.8 70.9 0.7 28.4 100.0 71.6 45.7 12,063
50-54 97.4 72.2 0.5 27.2 100.0 72.8 37.0 756
Total 15-54 96.8 71.0 0.7 28.4 100.0 71.6 45.2 12,819
1
Includes ‘don’t know/missing’
236 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.12.1C Coverage of prior HIV testing: Women
Percentage of women age 15-49 who know where to get an HIV test, percent distribution of women age 15-49 by testing status and by whether they
received the results of the last test, the percentage of women ever tested, and the percentage of women age 15-49 who were tested in the past 12
months and received the results of the last test, according to county, Kenya 2014
Percentage
who have
Percent distribution of women by testing status been tested for
and by whether they received the results of the HIV in the past
Percentage last test 12 months and
who know Ever tested Ever tested, received the
where to get and received did not receive Percentage results of the Number of
County an HIV test results results Never tested1 Total ever tested last test women
Coast 90.6 84.3 1.6 14.2 100.0 85.8 53.4 3,076
Mombasa 91.5 86.7 0.9 12.3 100.0 87.7 57.8 912
Kwale 91.1 85.0 0.7 14.3 100.0 85.7 51.8 619
Kilifi 90.1 82.6 2.2 15.1 100.0 84.9 53.9 1,043
Tana River 85.9 77.5 4.1 18.4 100.0 81.6 41.6 197
Lamu 90.8 81.7 2.0 16.3 100.0 83.7 45.3 89
Taita Taveta 92.1 86.6 0.8 12.6 100.0 87.4 51.4 215
North Eastern 59.5 48.7 2.8 48.4 100.0 51.6 20.1 648
Garissa 62.4 53.4 1.8 44.8 100.0 55.2 27.3 261
Wajir 59.4 53.0 2.9 44.1 100.0 55.9 21.1 212
Mandera 55.4 36.6 4.4 59.1 100.0 40.9 8.1 175
Eastern 90.7 83.2 1.1 15.7 100.0 84.3 51.0 4,375
Marsabit 74.9 64.3 1.9 33.9 100.0 66.1 34.2 115
Isiolo 93.6 87.0 2.4 10.6 100.0 89.4 45.5 104
Meru 92.1 84.5 1.1 14.4 100.0 85.6 45.3 1,110
Tharaka-Nithi 90.3 82.8 3.6 13.6 100.0 86.4 51.6 275
Embu 90.0 83.6 0.1 16.3 100.0 83.7 51.4 459
Kitui 90.2 82.8 0.5 16.7 100.0 83.3 56.2 759
Machakos 92.5 86.5 0.7 12.9 100.0 87.1 55.4 873
Makueni 89.2 80.0 1.4 18.6 100.0 81.4 52.3 680
Central 92.7 84.4 2.3 13.3 100.0 86.7 53.0 3,994
Nyandarua 92.9 84.5 4.1 11.4 100.0 88.6 54.5 436
Nyeri 94.7 87.7 0.6 11.7 100.0 88.3 54.5 650
Kirinyaga 94.2 85.5 2.6 11.9 100.0 88.1 56.6 451
Murang’a 90.8 82.0 2.6 15.5 100.0 84.5 51.5 735
Kiambu 92.3 83.9 2.3 13.8 100.0 86.2 51.6 1,722
Rift Valley 89.9 81.4 2.4 16.1 100.0 83.9 51.8 7,953
Turkana 75.4 69.5 0.2 30.2 100.0 69.8 42.4 320
West Pokot 79.1 66.6 8.4 25.0 100.0 75.0 34.1 267
Samburu 84.3 67.8 5.6 26.6 100.0 73.4 45.0 123
Trans-Nzoia 85.9 72.1 5.3 22.6 100.0 77.4 43.1 768
Uasin Gishu 95.4 89.1 1.1 9.8 100.0 90.2 63.5 784
Elgeyo Marakwet 89.8 79.4 1.5 19.0 100.0 81.0 51.3 250
Nandi 88.5 81.8 0.6 17.6 100.0 82.4 53.3 628
Baringo 85.6 74.9 2.5 22.5 100.0 77.5 51.5 335
Laikipia 89.6 81.7 2.8 15.5 100.0 84.5 50.2 342
Nakuru 92.6 84.2 2.7 13.1 100.0 86.9 50.3 1,574
Narok 90.2 84.1 2.2 13.7 100.0 86.3 55.6 642
Kajiado 91.3 85.6 0.4 13.9 100.0 86.1 53.0 670
Kericho 94.4 84.8 2.8 12.4 100.0 87.6 59.1 563
Bomet 91.8 84.2 2.1 13.8 100.0 86.2 53.0 687
Western 87.7 77.8 2.4 19.8 100.0 80.2 45.4 3,225
Kakamega 87.9 77.9 2.8 19.3 100.0 80.7 44.4 1,108
Vihiga 89.6 79.7 3.0 17.4 100.0 82.6 44.8 368
Bungoma 85.6 74.6 2.0 23.4 100.0 76.6 44.4 1,203
Busia 90.9 83.3 2.2 14.6 100.0 85.4 50.0 546
Nyanza 93.0 86.4 2.0 11.6 100.0 88.4 60.4 4,038
Siaya 92.9 85.5 3.5 11.0 100.0 89.0 57.5 572
Kisumu 94.4 89.0 0.7 10.3 100.0 89.7 62.1 820
Homa Bay 96.2 93.0 1.0 6.0 100.0 94.0 70.9 798
Migori 93.6 87.7 3.1 9.2 100.0 90.8 64.5 650
Kisii 89.2 78.9 1.9 19.3 100.0 80.7 51.4 864
Nyamira 91.0 82.3 3.7 13.9 100.0 86.1 50.6 334
Nairobi 94.3 90.0 0.9 9.1 100.0 90.9 60.4 3,770
Total 15-49 90.5 83.0 1.9 15.1 100.0 84.9 52.8 31,079
1
Includes ‘don’t know/missing’
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 237
Table 13.12.2C Coverage of prior HIV testing: Men
Percentage of men age 15-49 who know where to get an HIV test, percent distribution of men age 15-49 by testing status and by whether they
received the results of the last test, the percentage of men ever tested, and the percentage of men age 15-49 who were tested in the past 12
months and received the results of the last test, according to county, Kenya 2014
Percentage
who have
Percent distribution of men by testing status been tested for
and by whether they received the results of the HIV in the past
Percentage last test 12 months and
who know Ever tested Ever tested, received the
where to get and received did not receive Percentage results of the Number of
County an HIV test results results Never tested1 Total ever tested last test men
Coast 96.9 64.4 0.3 35.4 100.0 64.6 40.5 1,260
Mombasa 98.8 67.4 0.2 32.4 100.0 67.6 39.9 481
Kwale 88.8 53.9 1.0 45.1 100.0 54.9 36.9 226
Kilifi 99.2 65.0 0.0 35.0 100.0 65.0 42.3 359
Tana River 95.3 57.6 0.0 42.4 100.0 57.6 35.6 65
Lamu 94.2 59.2 0.0 40.8 100.0 59.2 40.8 37
Taita Taveta 100.0 78.4 0.0 21.6 100.0 78.4 48.1 93
North Eastern 74.4 44.0 0.0 56.0 100.0 44.0 22.8 227
Garissa 96.5 60.3 0.0 39.7 100.0 60.3 28.3 94
Wajir 73.5 55.7 0.0 44.3 100.0 55.7 32.3 72
Mandera 40.8 4.3 0.0 95.7 100.0 4.3 2.5 60
Eastern 95.4 66.6 0.4 33.0 100.0 67.0 39.8 1,825
Marsabit 96.5 59.3 0.0 40.7 100.0 59.3 51.0 40
Isiolo 99.3 76.6 0.2 23.3 100.0 76.7 40.9 35
Meru 92.2 67.0 1.0 32.0 100.0 68.0 37.9 495
Tharaka-Nithi 95.3 72.9 0.0 27.1 100.0 72.9 42.7 102
Embu 94.6 63.4 0.5 36.1 100.0 63.9 36.1 164
Kitui 96.5 59.4 0.5 40.1 100.0 59.9 33.1 303
Machakos 96.6 73.0 0.0 27.0 100.0 73.0 45.8 436
Makueni 98.0 62.8 0.0 37.2 100.0 62.8 40.2 250
Central 97.4 70.5 0.8 28.6 100.0 71.4 40.1 1,564
Nyandarua 97.4 65.8 2.3 31.9 100.0 68.1 39.9 198
Nyeri 98.0 77.0 1.1 21.9 100.0 78.1 47.0 229
Kirinyaga 97.7 69.3 1.5 29.2 100.0 70.8 35.7 184
Murang’a 97.3 69.5 1.2 29.2 100.0 70.8 45.6 284
Kiambu 97.3 70.4 0.0 29.6 100.0 70.4 36.7 669
Rift Valley 96.2 70.5 0.6 28.9 100.0 71.1 47.1 3,050
Turkana 81.8 67.5 0.0 32.5 100.0 67.5 59.9 76
West Pokot 88.1 35.4 0.0 64.6 100.0 35.4 20.3 103
Samburu 96.9 70.5 0.0 29.5 100.0 70.5 51.2 35
Trans-Nzoia 92.2 62.8 1.2 36.0 100.0 64.0 41.0 329
Uasin Gishu 98.3 83.1 0.5 16.4 100.0 83.6 54.2 355
Elgeyo Marakwet 100.0 63.4 0.0 36.6 100.0 63.4 34.4 86
Nandi 98.6 70.1 0.0 29.9 100.0 70.1 53.7 264
Baringo 96.4 66.9 0.2 32.9 100.0 67.1 44.4 125
Laikipia 98.2 76.7 1.9 21.4 100.0 78.6 43.1 124
Nakuru 97.6 73.2 0.7 26.1 100.0 73.9 48.9 589
Narok 94.2 66.3 0.0 33.7 100.0 66.3 43.1 240
Kajiado 97.9 78.7 0.8 20.5 100.0 79.5 52.6 241
Kericho 97.3 74.4 0.0 25.6 100.0 74.4 51.4 215
Bomet 97.5 66.8 1.2 32.0 100.0 68.0 43.8 267
Western 97.0 62.0 1.0 37.0 100.0 63.0 39.8 1,164
Kakamega 97.7 65.7 0.0 34.3 100.0 65.7 41.5 411
Vihiga 92.6 54.9 0.7 44.5 100.0 55.5 34.5 140
Bungoma 97.3 55.9 0.6 43.6 100.0 56.4 36.9 413
Busia 98.0 72.2 4.4 23.5 100.0 76.5 46.2 199
Nyanza 98.9 81.1 0.7 18.2 100.0 81.8 56.2 1,405
Siaya 98.8 89.9 0.0 10.1 100.0 89.9 67.7 213
Kisumu 98.3 89.7 0.0 10.3 100.0 89.7 65.3 309
Homa Bay 99.4 83.2 2.2 14.6 100.0 85.4 58.9 243
Migori 98.1 84.8 1.1 14.0 100.0 86.0 62.4 211
Kisii 99.5 64.8 0.4 34.8 100.0 65.2 38.7 315
Nyamira 99.2 75.0 1.1 23.8 100.0 76.2 41.6 114
Nairobi 100.0 83.4 1.2 15.4 100.0 84.6 57.9 1,568
Total 15-49 96.8 70.9 0.7 28.4 100.0 71.6 45.7 12,063
50-54 97.4 72.2 0.5 27.2 100.0 72.8 37.0 756
Total 15-54 96.8 71.0 0.7 28.4 100.0 71.6 45.2 12,819
1
Includes ‘don’t know/missing’
238 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
13.7.2 HIV Counselling and Testing During Pregnancy
Table 13.13 presents information on HIV screening of women age 15-49 who gave birth in the
two years preceding the survey. The HIV screening process is a key tool in reducing mother-to-child
transmission of HIV. Sixty-eight percent of women who gave birth in the two years before the survey
received HIV counselling during antenatal care (ANC). Almost 7 in 10 women (69 percent) were tested for
HIV during antenatal care and received the test results and post-test counselling, while 23 percent received
results but did not receive post-test counselling. Less than 1 percent of women were tested for HIV during
an ANC visit but did not receive the test results.
Table 13.13 Pregnant women counselled and tested for HIV
Among all women age 15-49 who gave birth in the two years preceding the survey, the percentage who received HIV pretest counselling, the percentage
who received an HIV test during antenatal care for their most recent birth by whether they received their results and post-test counselling, and percentage
who received an HIV test at the time during ANC or labour for their most recent birth by whether they received their test results, according to background
characteristics, Kenya 2014
Percentage who had an HIV
Percentage who were tested for HIV during Percentage test during ANC or labour and
Percentage antenatal care and who: who received who:2
who received Received Received counselling on Number of
counselling on results and results and did HIV and an women who
HIV during received post- not receive HIV test during gave birth in
Background antenatal test post-test Did not receive ANC, and the Received Did not receive the past two
characteristic care1 counselling counselling results results results results years3
Age
15-24 67.2 68.7 24.3 0.6 66.6 93.8 0.6 2,864
15-19 65.1 65.7 25.9 0.8 64.2 93.8 0.8 695
20-24 67.8 69.7 23.8 0.5 67.3 93.8 0.5 2,169
25-29 68.6 72.6 20.7 0.9 67.9 93.6 0.9 2,135
30-39 67.8 68.0 23.3 0.8 67.1 91.6 0.8 2,057
40-49 62.2 62.2 25.2 0.4 61.7 88.0 0.4 301
Marital status
Never married 67.5 68.7 22.6 0.6 67.2 93.2 0.6 674
Married or living together 67.5 69.4 23.4 0.8 66.9 93.2 0.8 6,127
Divorced/separated/
widowed 67.8 69.5 19.4 0.4 66.9 89.7 0.4 556
Residence
Urban 75.3 78.2 18.0 0.4 74.8 96.7 0.4 2,618
Rural 63.3 64.5 25.8 0.9 62.6 90.9 0.9 4,739
Region
Coast 69.0 71.7 23.6 0.6 69.0 95.3 0.6 793
North Eastern 28.7 33.8 26.8 0.2 27.3 61.0 0.5 228
Eastern 69.2 67.6 27.0 0.3 68.4 95.6 0.3 872
Central 61.8 70.7 26.9 0.3 61.8 97.9 0.3 682
Rift Valley 58.4 61.7 26.7 1.4 57.3 89.1 1.4 2,167
Western 74.3 75.1 19.8 0.9 73.9 95.0 0.9 827
Nyanza 78.7 73.6 21.2 0.6 77.8 95.4 0.6 1,035
Nairobi 84.5 88.6 8.6 0.0 84.5 97.7 0.0 753
Education
No education 38.6 43.7 29.8 1.6 37.7 73.7 1.6 834
Primary incomplete 65.9 66.5 24.6 1.1 64.8 91.7 1.2 2,036
Primary complete 71.1 72.5 23.0 0.3 70.8 96.1 0.3 1,987
Secondary+ 75.8 77.8 19.5 0.4 75.3 97.9 0.4 2,499
Wealth quintile
Lowest 53.2 55.1 27.8 1.6 51.8 83.6 1.6 1,823
Second 66.3 69.4 23.6 0.6 65.7 93.5 0.6 1,461
Middle 69.3 69.4 25.8 0.3 69.3 95.6 0.3 1,332
Fourth 73.6 76.0 19.3 0.5 73.1 96.2 0.5 1,283
Highest 79.7 81.4 17.3 0.3 79.3 98.8 0.3 1,458
Total 15-49 67.5 69.4 23.0 0.7 66.9 92.9 0.7 7,357
1
In this context, “pretest counselling” means that someone talked with the respondent about all three of the following topics: 1) babies getting the AIDS
virus from their mother, 2) preventing the virus, and 3) getting tested for the virus.
2
Women are asked whether they received an HIV test during labour only if they were not tested for HIV during ANC.
3
Denominator for percentages includes women who did not receive antenatal care for their last birth in the past two years.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 239
Table 13.13C Pregnant women counselled and tested for HIV
Among all women age 15-49 who gave birth in the two years preceding the survey, the percentage who received HIV pretest counselling, the
percentage who received an HIV test during antenatal care for their most recent birth by whether they received their results and post-test
counselling, and percentage who received an HIV test at the time during ANC or labour for their most recent birth by whether they received their test
results, according to county, Kenya 2014
Percentage who had an HIV
Percentage who were tested for HIV during Percentage test during ANC or labour and
Percentage antenatal care and who: who received who:2
who received Received Received counselling on Number of
counselling on results and results and did HIV and an women who
HIV during received post- not receive HIV test during gave birth in
antenatal test post-test Did not receive ANC, and the Received Did not receive the past two
County care1 counselling counselling results results results results years3
Coast 69.0 71.7 23.6 0.6 69.0 95.3 0.6 793
Mombasa 84.7 84.4 13.5 0.0 84.7 97.9 0.0 190
Kwale 76.8 72.2 22.2 0.0 76.8 94.4 0.0 181
Kilifi 59.3 68.2 27.2 1.0 59.3 95.3 1.0 293
Tana River 44.7 46.9 42.2 1.5 44.5 89.1 1.5 68
Lamu 56.4 55.1 40.8 0.0 56.4 95.9 0.0 19
Taita Taveta 77.3 84.0 12.2 1.4 76.0 96.3 1.4 42
North Eastern 28.7 33.8 26.8 0.2 27.3 61.0 0.5 228
Garissa 45.9 42.6 35.6 0.0 43.3 78.3 0.5 86
Wajir 17.6 29.2 25.7 0.0 17.5 54.9 0.3 93
Mandera 19.7 27.3 13.6 0.8 18.0 42.5 0.8 49
Eastern 69.2 67.6 27.0 0.3 68.4 95.6 0.3 872
Marsabit 16.0 36.7 28.6 0.0 15.7 65.5 0.0 35
Isiolo 77.5 90.4 5.4 1.1 76.3 95.8 1.1 33
Meru 73.5 78.8 18.6 0.0 73.5 97.4 0.0 198
Tharaka-Nithi 71.4 63.3 13.7 3.3 61.0 90.1 3.3 56
Embu 72.2 78.8 21.2 0.0 72.2 100.0 0.0 81
Kitui 75.3 58.1 38.3 0.0 75.3 97.1 0.0 164
Machakos 57.8 57.6 38.6 0.0 57.8 96.2 0.0 190
Makueni 82.8 75.3 22.4 0.0 82.3 98.6 0.0 115
Central 61.8 70.7 26.9 0.3 61.8 97.9 0.3 682
Nyandarua 46.2 62.9 33.0 0.5 46.2 96.5 0.5 97
Nyeri 69.8 76.0 19.4 0.8 69.8 95.4 0.8 92
Kirinyaga 67.8 61.3 34.0 0.0 67.8 96.4 0.0 61
Murang’a 58.8 66.1 30.3 0.7 58.8 96.5 0.7 120
Kiambu 64.3 75.1 24.4 0.0 64.3 100.0 0.0 312
Rift Valley 58.4 61.7 26.7 1.4 57.3 89.1 1.4 2,167
Turkana 53.3 49.8 27.4 0.0 52.1 78.1 0.0 131
West Pokot 43.0 21.5 46.1 9.7 35.9 68.9 9.9 121
Samburu 36.7 39.0 27.4 2.1 34.9 66.9 2.1 46
Trans-Nzoia 68.4 67.8 22.0 0.2 68.0 90.3 0.2 218
Uasin Gishu 74.8 83.7 10.5 0.6 74.2 94.8 0.6 187
Elgeyo Marakwet 44.5 48.2 49.9 0.0 44.5 98.4 0.0 65
Nandi 74.6 80.1 15.6 1.5 74.6 95.7 1.5 153
Baringo 52.5 58.6 26.9 0.9 51.8 86.9 0.9 94
Laikipia 69.4 52.5 36.0 1.2 69.4 89.6 1.2 78
Nakuru 64.3 52.5 39.0 1.2 62.8 91.6 1.2 332
Narok 48.5 58.8 25.7 2.1 48.0 85.5 2.1 237
Kajiado 42.0 61.6 28.9 0.5 40.7 91.9 0.5 179
Kericho 59.8 72.0 17.9 1.4 59.1 89.9 1.4 139
Bomet 55.6 79.8 15.4 0.0 55.6 97.3 0.0 187
Western 74.3 75.1 19.8 0.9 73.9 95.0 0.9 827
Kakamega 65.0 76.4 17.8 0.0 65.0 94.2 0.0 244
Vihiga 64.3 64.9 27.6 3.0 62.9 92.5 3.0 83
Bungoma 82.2 72.2 22.6 1.4 81.7 94.8 1.4 354
Busia 76.3 86.0 12.1 0.0 76.3 98.1 0.0 146
Nyanza 78.7 73.6 21.2 0.6 77.8 95.4 0.6 1,035
Siaya 75.3 87.0 8.6 0.0 75.3 95.6 0.0 142
Kisumu 77.4 81.9 15.3 0.7 76.7 97.2 0.7 177
Homa Bay 87.6 54.3 39.5 0.3 86.2 94.1 0.3 253
Migori 67.3 67.4 24.0 0.9 66.1 92.7 0.9 203
Kisii 86.7 89.8 7.6 0.6 86.1 98.1 0.6 193
Nyamira 67.7 68.7 25.7 1.8 65.6 96.0 1.8 67
Nairobi 84.5 88.6 8.6 0.0 84.5 97.7 0.0 753
Total 15-49 67.5 69.4 23.0 0.7 66.9 92.9 0.7 7,357
1
In this context, “pretest counselling” means that someone talked with the respondent about all three of the following topics: 1) babies getting the
AIDS virus from their mother, 2) preventing the virus, and 3) getting tested for the virus
2
Women are asked whether they received an HIV test during labour only if they were not tested for HIV during ANC
3
Denominator for percentages includes women who did not receive antenatal care for their last birth in the past two years
240 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Overall, 67 percent of women received HIV counselling, an HIV test, and the results during ANC
for their most recent birth in the two years preceding the survey. By age, differences are marginal except
for women age 40-49, who were less likely to be counselled, tested, and given their HIV result during
ANC than were younger women. Women were more likely to have been counselled and tested and to have
received test results if they lived in urban areas (75 percent) or in Nairobi (85 percent). The likelihood of
HIV counselling and testing during ANC increases with increasing education and wealth. For example, the
proportion of women who were counselled about HIV during ANC, were tested, and received their test
results ranges from 38 percent among those with no education to 75 percent among those with at least
some secondary education. Likewise, women in the lowest wealth quintile (52 percent) were less likely
than women in the highest quintile (79 percent) to have been counselled and tested and to have received
their results. By county, 85 percent or more of women in Nairobi, Mombasa, Kisii, and Homa Bay were
counselled about HIV during ANC, were tested, and received their test results, as compared with less than
20 percent of women in Mandera, Wajir, and Marsabit (Table 13.13C).
13.8 MALE CIRCUMCISION
The risk of acquisition of HIV by the circumcised male is reduced by 60 percent (Bailey, 2007).
Circumcision is widely practiced in Kenya, mostly as a rite of passage to adulthood. Table 13.14 shows the
percentage of men age 15-49 who report having been circumcised, by background characteristics. Ninety-
three percent of Kenyan men are circumcised, an increase from both 2003 (84 percent) and 2008-09 (86
percent). Young men age 15-19 are least likely to be circumcised (87 percent), although this is still an
increase from 76 percent in 2008-09.
Men from Nyanza are less likely to be circumcised (72 percent) than their counterparts in other
regions. However, this is a noteworthy increase from the 45 percent of men in Nyanza who were
circumcised according to the 2008-09 KDHS. Muslim men are most likely to be circumcised (99 percent).
Circumcision is widespread among most ethnic groups except the Luo (59 percent) and Turkana (42
percent), although circumcision among Luo men has substantially risen (from 17 percent in 2003 and 22
percent in 2008-09). The counties with the lowest percentage of circumcised men include Turkana (26
percent), Siaya (56 percent), Homa Bay (56 percent), and Kisumu (59 percent) (Table 13.14C).
Circumcision is virtually universal among men in Garissa, Wajir, Mandera, Kitui, Makueni, and Nyamira.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 241
Table 13.14 Male circumcision Table 13.14C Male circumcision
Percentage of men age 15-49 who report having Percentage of men age 15-49 who report having
been circumcised, by background characteristics, been circumcised, by county, Kenya 2014
Kenya 2014
Percentage Number of
Background Percentage Number of County circumcised men
characteristic circumcised men
Coast 97.6 1,260
Age Mombasa 95.8 481
15-24 91.4 4,666 Kwale 97.4 226
15-19 87.1 2,540 Kilifi 99.5 359
20-24 96.5 2,125 Tana River 99.6 65
25-29 94.6 2,104 Lamu 97.8 37
30-39 93.4 3,268 Taita Taveta 97.6 93
40-49 91.9 2,024
North Eastern 100.0 227
Residence Garissa 100.0 94
Urban 93.7 5,300 Wajir 100.0 72
Rural 91.7 6,762 Mandera 100.0 60
Region Eastern 95.3 1,825
Coast 97.6 1,260 Marsabit 98.1 40
North Eastern 100.0 227 Isiolo 97.8 35
Eastern 95.3 1,825 Meru 92.3 495
Central 97.0 1,564 Tharaka-Nithi 91.5 102
Rift Valley 92.5 3,050 Embu 96.7 164
Western 96.6 1,164 Kitui 100.0 303
Nyanza 72.1 1,405 Machakos 92.7 436
Nairobi 95.4 1,568 Makueni 100.0 250
Religion Central 97.0 1,564
Roman Catholic 92.0 2,583 Nyandarua 93.1 198
Protestant/other Nyeri 99.2 229
Christian 92.1 8,141 Kirinyaga 98.1 184
Muslim 98.7 784 Murang’a 96.6 284
No religion 94.1 492 Kiambu 97.2 669
Other 91.3 59
Rift Valley 92.5 3,050
Ethnic group Turkana 26.0 76
Embu 98.2 118 West Pokot 97.9 103
Kalenjin 94.4 1,467 Samburu 86.1 35
Kamba 99.9 1,521 Trans-Nzoia 94.5 329
Kikuyu 97.4 2,523 Uasin Gishu 93.4 355
Kisii 99.0 712 Elgeyo Marakwet 91.0 86
Luhya 98.5 1,927 Nandi 95.0 264
Luo 58.5 1,311 Baringo 86.5 125
Maasai 94.0 220 Laikipia 94.6 124
Meru 93.9 717 Nakuru 94.0 589
Mijikenda/Swahili 99.5 623 Narok 91.5 240
Somali 100.0 260 Kajiado 96.9 241
Taita/Taveta 100.0 134 Kericho 96.5 215
Turkana 41.8 106 Bomet 97.1 267
Samburu (100.0) 12
Other 89.5 399 Western 96.6 1,164
Kakamega 98.7 411
Total 15-49 92.6 12,063 Vihiga 97.2 140
50-54 91.3 756 Bungoma 98.0 413
Busia 89.2 199
Total 15-54 92.5 12,819
Nyanza 72.1 1,405
Siaya 55.9 213
Note: Total includes three men for whom
Kisumu 58.8 309
information on religion is missing and 16 men for
Homa Bay 56.0 243
whom information on ethnic group is missing.
Migori 72.5 211
Figures in parentheses are based on 25-49
Kisii 98.1 315
unweighted cases.
Nyamira 100.0 114
Nairobi 95.4 1,568
Total 15-49 92.6 12,063
50-54 91.3 756
Total 15-54 92.5 12,819
13.9 SELF-REPORTING OF SEXUALLY TRANSMITTED INFECTIONS
Information about the prevalence of sexually transmitted infections (STIs) is useful not only as a
marker of unprotected sexual intercourse, but also because STI infection is a co-factor in HIV
transmission. The 2014 KDHS asked respondents who had ever had sex whether they had suffered from a
disease that they acquired through sexual contact in the past 12 months. They were also asked whether, in
the past 12 months, they had any genital discharge and whether they had a genital sore or ulcer. These
symptoms have been shown to be useful in identifying STIs in men. For women, however, discharge is
242 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
less easily interpreted as a symptom because women experience non-STI conditions of the reproductive
tract that also produce discharge. Table 13.15 shows the self-reported prevalence of STIs and STI
symptoms among women and men age 15-49, by background characteristics.
Two percent of both women and men reported having had an STI in the 12 months preceding the
survey. Six percent of women and 2 percent of men reported recently experiencing an STI or STI
symptoms. Women who are currently married or living together with a partner (6 percent) and those who
are divorced, separated, or widowed (7 percent) have a higher prevalence of STIs and STI symptoms than
women who have never been married (4 percent). Women in Coast (10 percent) and Western (11 percent)
are more likely to have an STI or STI symptoms than women in other regions.
The prevalence of STIs or STI symptoms is higher among divorced, separated, or widowed men
(6 percent) and uncircumcised men (6 percent) than among their counterparts. The highest prevalence of
STIs and STI symptoms occurs in Nyanza (6 percent).
Table 13.15 Self-reported prevalence of sexually-transmitted infections (STIs) and STIs symptoms
Among women and men age 15-49 who ever had sexual intercourse, the percentage reporting having an STI and/or symptoms of an STI in the past 12
months, by background characteristics, Kenya 2014
Percentage of women who reported having in the past 12 months: Percentage of men who reported having in the past 12 months:
Bad Number of Bad STI/ Number of
smelling/ women who smelling/ abnormal men who
abnormal STI/ genital ever had abnormal discharge ever had
Background genital Genital discharge/ sexual discharge Genital from penis/ sexual
characteristic STI discharge sore/ulcer sore or ulcer intercourse STI from penis sore/ulcer sore or ulcer intercourse
Age
15-24 1.8 4.4 2.3 6.0 3,385 1.7 2.1 1.5 2.9 2,909
15-19 1.2 3.8 2.9 6.3 982 0.8 1.3 0.9 2.0 1,029
20-24 2.0 4.6 2.1 5.8 2,403 2.3 2.6 1.8 3.4 1,880
25-29 2.4 4.7 2.3 6.3 2,870 1.7 1.7 1.2 3.0 2,040
30-39 2.3 4.5 2.5 6.0 3,906 1.6 1.1 1.0 2.1 3,245
40-49 1.3 3.4 2.4 5.2 2,333 1.0 0.9 0.4 1.6 2,016
Marital status
Never married 1.4 2.8 1.8 4.2 2,134 0.9 1.3 0.6 1.8 3,512
Married or living
together 2.1 4.5 2.3 6.2 8,701 1.7 1.4 1.1 2.4 6,079
Divorced/separated/
widowed 2.1 4.9 3.2 6.5 1,660 3.5 3.4 3.6 5.5 618
Male circumcision
Circumcised na na na na na 1.4 1.4 1.0 2.1 9,562
Not circumcised na na na na na 3.9 2.2 2.6 6.0 640
Don’t know/missing na na na na na * * * * 7
Residence
Urban 1.8 3.9 2.1 5.4 5,216 1.7 1.7 1.2 2.5 4,733
Rural 2.1 4.6 2.6 6.3 7,278 1.4 1.2 1.0 2.3 5,476
Region
Coast 3.9 7.8 4.3 10.4 1,188 1.5 2.0 1.1 2.8 1,049
North Eastern 0.2 1.3 0.2 1.4 232 0.5 0.0 0.0 0.5 129
Eastern 1.3 4.7 2.1 6.5 1,721 0.7 0.6 0.8 1.2 1,535
Central 1.7 4.7 2.5 6.6 1,641 0.2 0.5 0.5 0.8 1,322
Rift Valley 1.3 2.7 1.1 3.5 3,220 1.0 0.8 0.5 1.5 2,612
Western 3.4 7.4 5.4 10.8 1,278 1.5 2.2 1.4 2.4 914
Nyanza 2.7 4.0 2.7 5.4 1,654 4.1 2.1 2.1 5.6 1,184
Nairobi 1.3 2.4 1.1 3.4 1,559 2.8 3.2 1.9 4.0 1,464
Education
No education 2.0 3.7 2.9 5.7 971 1.0 1.2 0.9 2.0 307
Primary incomplete 2.9 5.8 3.4 8.1 3,110 1.8 2.3 1.4 3.1 2,328
Primary complete 1.9 4.0 2.3 5.7 3,311 2.2 1.6 1.9 3.4 2,556
Secondary+ 1.5 3.7 1.7 4.8 5,102 1.1 1.0 0.5 1.6 5,019
Wealth quintile
Lowest 2.0 4.2 2.7 5.5 1,934 1.4 1.3 1.3 2.6 1,344
Second 2.7 5.7 2.7 7.7 2,167 2.4 1.7 1.4 3.2 1,764
Middle 1.9 4.2 2.4 6.0 2,395 1.0 1.0 0.7 1.7 1,953
Fourth 2.3 4.8 2.3 6.3 2,706 1.3 1.5 1.0 2.1 2,528
Highest 1.3 3.1 2.0 4.5 3,291 1.7 1.7 1.0 2.5 2,621
Total 15-49 2.0 4.3 2.4 5.9 12,494 1.5 1.5 1.1 2.4 10,209
50-54 na na na na na 0.6 0.5 0.6 1.0 756
Total 15-54 na na na na na 1.5 1.4 1.0 2.3 10,965
Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
na = Not applicable
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 243
Figure 13.5 shows that most women and men who reported having an STI or STI symptoms
sought advice or treatment from a clinic, hospital, private doctor, or other health facility (68 percent and 70
percent, respectively). Twenty-five percent of women and 14 percent of men did not seek any treatment.
Figure 13.5 Women and men seeking treatment for STIs
Percentage
68 70
25
14
5 7
2 4
Clinic/hospital/private Advice or medicine from Advice or treatment No advice or treatment
doctor/other health shop/pharmacy from any other source
professional
Women Men
KDHS 2014
13.10 PREVALENCE OF MEDICAL INJECTIONS
Table 13.16 shows the percentage of women and men age 15-49 who received at least one medical
injection in the 12 months that preceded the survey, the average number of medical injections in the last 12
months, and, among those who received a medical injection, the percentage whose last injection was
administered with a syringe and needle taken from a new, unopened package.
Forty-seven percent of women and 32 percent of men reported having received a medical injection
in the last 12 months. Ninety-nine percent of women and 98 percent of men reported that the syringe and
needle used to administer their last injection were taken from a new, unopened package. Women in North
Eastern (83 percent) and women with no education (94 percent) were less likely than their counterparts to
report that their most recent injection was administered with a syringe from a new, unopened package;
similar results are seen for men with no education (94 percent).
244 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.16 Prevalence of medical injections
Percentage of women and men age 15-49 who received at least one medical injection in the last 12 months, the average number of medical injections
per person in the last 12 months, and among those who received a medical injection, the percentage of last medical injections for which the syringe
and needle were taken from a new, unopened package, by background characteristics, Kenya 2014
Women Men
For last For last
Percentage Average injection, Number of Percentage Average injection, Number of
who number of syringe respondent who number of syringe respondent
received a medical and needle s receiving received a medical and needle s receiving
medical injections taken from medical medical injections taken from medical
injection in per person a new, injections injection in per person a new, injections
Background the last 12 in the last Number of unopened in the last the last 12 in the last Number of unopened in the last
characteristic months 12 months respondents package 12 months months 12 months respondents package 12 months
Age
15-24 44.4 1.3 5,407 98.9 2,403 29.5 1.0 4,666 97.8 1,377
15-19 38.2 1.1 2,717 98.5 1,036 28.6 0.9 2,540 98.5 726
20-24 50.8 1.6 2,691 99.2 1,367 30.6 1.2 2,125 97.0 650
25-29 52.6 1.9 2,932 99.1 1,543 33.9 0.9 2,104 97.8 714
30-39 47.2 1.8 3,942 98.7 1,859 34.0 1.4 3,268 98.2 1,111
40-49 43.0 2.0 2,344 97.8 1,007 32.2 1.3 2,024 99.1 651
Marital status
Never married 37.3 1.2 4,255 98.7 1,586 29.9 1.0 5,350 98.0 1,599
Ever had sex 42.7 1.3 2,134 99.0 911 31.0 1.1 3,512 97.7 1,088
Never had sex 31.8 1.0 2,122 98.2 675 27.8 0.9 1,838 98.7 511
Married/living together 51.6 1.9 8,710 98.7 4,498 33.6 1.2 6,095 98.2 2,048
Divorced/separated/
widowed 44.0 1.7 1,660 98.6 729 33.4 1.5 618 99.1 206
Residence
Urban 45.9 1.8 5,929 99.1 2,720 32.1 1.2 5,300 98.7 1,702
Rural 47.1 1.6 8,696 98.4 4,093 31.8 1.1 6,762 97.7 2,151
Region
Coast 51.5 1.9 1,421 99.0 732 25.8 0.9 1,260 98.2 325
North Eastern 38.8 2.4 299 83.3 116 21.1 1.1 227 98.1 48
Eastern 49.1 1.8 2,066 98.8 1,015 32.2 1.1 1,825 96.9 588
Central 46.6 1.8 1,905 98.7 889 29.4 0.8 1,564 99.4 460
Rift Valley 49.2 1.5 3,714 98.6 1,828 31.3 1.0 3,050 96.9 954
Western 49.2 1.8 1,571 99.7 773 33.4 1.3 1,164 99.6 388
Nyanza 41.9 1.4 1,908 98.9 799 37.6 1.6 1,405 98.3 529
Nairobi 38.0 1.6 1,742 100.0 661 35.7 1.4 1,568 99.3 560
Education
No education 36.6 1.4 1,015 94.2 372 21.0 1.1 345 94.0 72
Primary incomplete 46.1 1.5 3,793 98.8 1,747 32.2 1.1 3,071 97.3 990
Primary complete 50.5 1.7 3,543 98.9 1,790 33.2 1.1 2,734 98.6 907
Secondary+ 46.3 1.8 6,274 99.1 2,905 31.9 1.2 5,913 98.5 1,884
Wealth quintile
Lowest 43.3 1.3 2,236 96.6 969 26.9 1.1 1,691 96.3 455
Second 47.7 1.6 2,590 98.8 1,234 33.2 1.1 2,145 98.4 711
Middle 48.7 1.6 2,859 99.1 1,391 30.9 1.0 2,370 97.5 731
Fourth 49.0 1.9 3,113 99.0 1,527 33.6 1.2 2,959 98.4 993
Highest 44.2 1.8 3,827 99.3 1,692 33.2 1.2 2,897 99.1 962
Total 15-49 46.6 1.7 14,625 98.7 6,813 31.9 1.1 12,063 98.1 3,853
50-54 na na na na na 27.9 1.7 756 98.6 211
Total 15-54 na na na na na 31.7 1.2 12,819 98.2 4,064
Note: Medical injections are those given by a doctor, nurse, pharmacist, dentist or other health worker.
na = Not applicable
13.11 HIV/AIDS KNOWLEDGE AND SEXUAL BEHAVIOUR AMONG YOUTH
This section addresses HIV- and AIDS-related knowledge and sexual behaviour among youth age
15-24. In addition to knowledge of HIV transmission, data are presented on age at first sex, condom use,
age differences between sexual partners, sex related to alcohol use, and voluntary counselling and testing
for HIV.
Younger people are often at a higher risk of contracting STIs, as they are more likely to
experiment with sex before marriage. Therefore, condom use among young adults plays an important role
in the prevention of transmission of HIV and other sexually transmitted infections, as well as unwanted
pregnancies. Likewise, knowledge of where to get condoms is an important prerequisite to their use.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 245
13.11.1 HIV/AIDS-Related Knowledge among Youth
Table 13.17 shows comprehensive knowledge about AIDS and knowledge of a source of condoms
among women and men age 15-24 by background characteristics. Fifty-seven percent of young women and
64 percent of young men have comprehensive knowledge about AIDS. Knowledge increases with age
among both women and men and is lowest among women (50 percent) and men (55 percent) who have
never had sex, women (15 percent) and men (25 percent) in North Eastern, rural women (52 percent) and
men (61 percent), and women (14 percent) and men (23 percent) with no education. Seventy-one percent
of young women and 88 percent of young men know a place where they can get condoms; trends by
background characteristics are similar to those observed for comprehensive knowledge among young
people.
Table 13.17 Comprehensive knowledge about AIDS and of a source of condoms among youth
Percentage of young women and young men age 15-24 with comprehensive knowledge about AIDS and percentage with
knowledge of a source of condoms, by background characteristics, Kenya 2014
Women Men
Percentage with Percentage with
comprehensive Percentage who comprehensive Percentage who
Background knowledge of know a condom Number of knowledge of know a condom Number of
characteristic AIDS1 source2 respondents AIDS1 source2 respondents
Age
15-19 51.7 61.7 2,717 57.7 80.9 2,540
15-17 48.8 54.0 1,680 52.5 74.8 1,531
18-19 56.3 74.2 1,037 65.6 90.2 1,009
20-24 61.6 81.3 2,691 70.9 96.1 2,125
20-22 61.8 80.3 1,688 70.8 94.9 1,365
23-24 61.4 82.9 1,002 71.2 98.4 760
Marital status
Never married 56.2 67.1 3,434 63.6 86.8 4,214
Ever had sex 64.5 84.3 1,417 69.8 95.3 2,457
Never had sex 50.3 55.0 2,016 54.9 75.0 1,757
Ever married 57.4 79.0 1,974 65.2 97.5 452
Region
Coast 53.2 74.3 535 50.3 89.1 493
North Eastern 14.5 29.8 108 25.3 69.1 108
Eastern 45.4 68.3 708 64.1 85.3 724
Central 59.7 71.3 565 71.0 89.2 549
Rift Valley 56.3 68.3 1,457 61.9 86.1 1,171
Western 59.8 62.9 671 65.5 84.0 533
Nyanza 64.3 78.6 741 70.6 92.6 601
Nairobi 65.1 87.8 622 70.9 95.6 485
Residence
Urban 63.4 80.9 2,140 68.1 93.7 1,751
Rural 52.2 65.2 3,267 61.1 84.3 2,915
Education
No education 14.0 34.3 205 22.6 57.9 67
Primary incomplete 41.9 56.5 1,430 45.1 75.1 1,395
Primary complete 59.8 76.1 971 59.2 92.0 738
Secondary+ 66.2 80.1 2,802 76.7 94.7 2,466
Total 56.6 71.4 5,407 63.7 87.9 4,666
1
Comprehensive knowledge means knowing that consistent use of condoms during sexual intercourse and having just one
uninfected faithful partner can reduce the chance of getting the AIDS virus, knowing that a healthy-looking person can have the
AIDS virus, and rejecting the two most common local misconceptions about AIDS transmission or prevention of the AIDS virus. The
components of comprehensive knowledge are presented in Tables 13.2, 13.4.1 and 13.4.2.
2
For this table, the following responses are not considered a source for condoms: friends, family members, and home.
Figure 13.6 shows trends in comprehensive knowledge about AIDS and knowledge of a source for
condoms among women and men age 15-24. Comprehensive knowledge and knowledge of a condom
source have steadily increased among young people since 2003.
246 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Figure 13.6 Comprehensive knowledge about AIDS and
source of condoms among youth
Comprehensive
knowledge of AIDS
34
Women 48
57
47
Men 55
64 2003 KDHS
2008-09 KDHS
Knows a condom 2014 KDHS
source
53
Women 65
71
75
Men 84
88
Percent
13.11.2 Trends in Age at First Sex
Because HIV transmission in Kenya occurs predominantly through heterosexual intercourse, age
at first sexual intercourse marks the time at which individuals first risk exposure to the virus. Table 13.18
shows the percentage of women and men age 15-24 who had their sexual debut before age 15 and before
age 18. Young men (21 percent) are almost twice as likely to engage in sexual intercourse before age 15 as
young women (12 percent). By age 18, nearly half (47 percent) of women and more than half (55 percent)
of men have had sexual intercourse.
Urban young women are less likely to initiate sexual activity before age 18 (39 percent) than rural
young women (53 percent); there are no apparent rural-urban differences among young men. Among
young women, the percentage who have had sexual intercourse before age 15 or before age 18 declines
with increasing education. Young men who know a source of condoms are much more likely to have had
sex by age 15 (23 percent) or age 18 (57 percent), although this pattern does not exist for young women.
Women in Nyanza (21 percent) and men in Nyanza and Eastern (27 percent each) are more likely than
their counterparts to have had sex before age 15.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 247
Table 13.18 Age at first sexual intercourse among young people
Percentage of young women and young men age 15-24 who had sexual intercourse before age 15 and percentage of young women and young men
age 18-24 who had sexual intercourse before age 18, by background characteristics, Kenya 2014
Women Men
Percentage Percentage Percentage Percentage
who had who had who had who had
sexual Number of sexual Number of sexual Number of sexual Number of
Background intercourse respondents intercourse respondents intercourse respondents intercourse respondents
characteristic before age 15 (15-24) before age 18 (18-24) before age 15 (15-24) before age 18 (18-24)
Age
15-19 10.7 5,820 na na 19.6 2,540 na na
15-17 10.6 3,510 na na 17.9 1,531 na na
18-19 10.8 2,310 46.3 2,310 22.2 1,009 50.4 1,009
20-24 13.6 5,735 46.7 5,735 22.6 2,125 57.2 2,125
20-22 14.0 3,529 48.4 3,529 22.0 1,365 57.1 1,365
23-24 12.8 2,206 44.1 2,206 23.6 760 57.3 760
Marital status
Never married 7.6 7,277 29.2 3,942 20.0 4,214 52.1 2,685
Ever married 19.8 4,278 63.3 4,103 29.9 452 72.6 450
Knows condom
source1
Yes 12.9 3,863 47.9 2,956 22.6 4,099 56.6 2,954
No 11.7 7,692 45.9 5,089 9.2 566 28.4 180
Region
Coast 9.3 1,179 43.3 831 16.0 493 49.9 339
North Eastern 7.2 241 37.1 152 6.1 108 16.2 60
Eastern 11.1 1,527 45.2 1,007 27.2 724 60.4 462
Central 5.8 1,248 26.0 908 18.3 549 45.7 386
Rift Valley 13.8 3,091 52.0 2,214 22.9 1,171 58.3 796
Western 11.7 1,343 54.9 814 14.6 533 55.2 314
Nyanza 20.9 1,577 65.4 984 26.5 601 59.1 360
Nairobi 8.7 1,349 35.2 1,136 18.6 485 57.5 417
Residence
Urban 10.3 4,628 38.8 3,654 20.4 1,751 54.5 1,367
Rural 13.3 6,927 53.1 4,391 21.3 2,915 55.4 1,768
Education
No education 30.3 434 70.5 362 17.4 67 48.1 50
Primary incomplete 19.1 3,117 71.5 1,481 22.9 1,395 60.9 567
Primary complete 14.5 2,154 60.5 1,763 24.6 738 61.0 604
Secondary+ 6.1 5,849 30.8 4,439 18.9 2,466 51.5 1,913
Total 12.1 11,555 46.6 8,045 21.0 4,666 55.0 3,134
na = Not available
1
For this table, the following responses are not considered a source for condoms: friends, family members and home.
Figure 13.7 compares trends in age at first sex between the 2008-09 KDHS and 2014 KDHS
surveys; there have been no substantial changes over time.
Figure 13.7 Trends in age of first sexual intercourse
Percent
58
55
47 47
22 21
11 12
Percentage of women Percentage of men Percentage of women Percentage of men
15-24 who had sexual 15-24 who had sexual 18-24 who had sexual 18-24 who had sexual
intercourse before intercourse before intercourse before intercourse before
exact age 15 exact age 15 exact age 18 exact age 18
2008-09 KDHS 2014 KDHS
248 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
13.11.3 Abstinence and Premarital Sex
Premarital sex and the interval between sexual initiation and marriage are among the factors that
may increase exposure to HIV infection. Table 13.19 shows the percentage of never-married young
women and men age 15-24 who have never had sex, the percentage who had sex in the 12 months
preceding the survey, and, among those who had sex in the 12 months preceding the survey, the percentage
who used a condom during their most recent sexual encounter.
Table 13.19 Premarital sexual intercourse and condom use during premarital sexual intercourse among youth
Among never-married women and men age 15-24, the percentage who have never had sexual intercourse, the percentage who had sexual intercourse in the
past 12 months, and, among those who had premarital sexual intercourse in the past 12 months, the percentage who used a condom at the last sexual
intercourse, by background characteristics, Kenya 2014
Women Men
Percentage Percentage
Percentage who had Percentage Percentage who had Percentage
who have sexual Number of who used a who have sexual Number of who used a
never had intercourse never condom at never had intercourse never condom at
Background sexual in the past married last sexual Number of sexual in the past married last sexual Number of
characteristic intercourse 12 months respondents intercourse respondents intercourse 12 months respondents intercourse respondents
Age
15-19 72.8 17.1 2,383 55.6 407 59.9 24.8 2,522 66.3 626
15-17 82.9 10.7 1,595 48.3 170 71.9 16.3 1,529 54.3 249
18-19 52.4 30.0 787 60.9 236 41.5 38.0 993 74.2 377
20-24 26.8 47.4 1,051 64.8 498 14.5 66.3 1,692 79.0 1,122
20-22 31.5 43.2 752 62.9 325 17.1 62.7 1,198 76.0 751
23-24 14.9 58.0 299 68.2 173 8.3 75.2 494 85.3 371
Knows condom
source1
Yes 48.1 34.9 2,303 62.7 803 36.0 45.9 3,658 76.4 1,679
No 80.3 9.0 1,130 44.3 102 79.1 12.5 555 27.4 69
Region
Coast 65.6 23.6 327 48.5 77 46.5 36.9 437 72.1 161
North Eastern 97.7 1.0 64 * 1 86.0 2.6 100 * 3
Eastern 68.3 18.8 480 52.0 90 42.4 36.8 663 72.5 244
Central 63.5 24.0 396 75.0 95 45.6 39.6 517 79.2 205
Rift Valley 53.8 29.6 889 51.8 263 38.6 45.3 1,058 67.9 479
Western 63.2 20.6 456 61.6 94 50.9 28.7 483 70.1 138
Nyanza 53.6 27.7 469 68.5 130 39.7 44.2 536 81.1 237
Nairobi 40.4 43.6 353 (71.1) 154 19.8 67.1 419 82.2 281
Residence
Urban 50.6 35.1 1,261 65.0 443 33.3 50.4 1,545 78.4 779
Rural 63.4 21.2 2,172 56.5 462 46.5 36.3 2,669 71.3 970
Education
No education 83.6 10.6 50 * 5 50.3 37.7 56 (48.7) 21
Primary incomplete 74.7 13.5 898 37.3 121 57.6 29.0 1,264 52.9 366
Primary complete 49.5 30.0 430 62.3 129 25.6 57.6 610 76.3 352
Secondary+ 53.1 31.6 2,056 64.9 649 37.0 44.2 2,283 82.2 1,009
Total 58.7 26.3 3,434 60.7 905 41.7 41.5 4,214 74.5 1,748
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been
suppressed.
1
For this table, the following responses are not considered a source for condoms: friends, family members and home.
Twenty-six percent of young women and 42 percent of young men had sex in the 12 months
before the survey. Sixty-one percent of women and 75 percent of men used a condom during their last
sexual encounter. Among both women and men, the proportion who had sex in the past 12 months and
used a condom during their most recent sexual encounter increases with age and knowledge of a condom
source. Young women (1 percent) and men (3 percent) from North Eastern are least likely to report having
had sex in the 12 months before the survey. Women and men in urban areas and those at higher
educational levels are more likely than their counterparts to have had sex in the past 12 months and to have
used a condom during their most recent sexual encounter.
In the last decade, the percentage of women and men age 15-24 who used a condom during their
last premarital sexual intercourse has steadily increased. Among women, condom use increased from 27
percent in 2003 and 40 percent in 2008-09 to 61 percent in 2014. Similarly, condom use among men
increased from 48 percent in 2003 and 64 percent in 2008-09 to 75 percent in 2014.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 249
13.11.4 Multiple Sexual Partners among Youth
Having multiple sexual partners and having unprotected sex increase one’s chances of both
contracting and transmitting HIV. The percentage of women and men age 15-24 who had more than one
sexual partner in the past 12 months and the percentage who reported using a condom during their last
intercourse are presented in Table 13.20.1 and Table 13.20.2.
More men than women reported having two or more sexual partners (10 percent and 2 percent,
respectively). Ever-married young women and men (3 percent and 15 percent, respectively) were more
likely than their never-married counterparts (1 percent and 9 percent, respectively) to have had two or
more partners in the past 12 months. The proportion of women (2 percent) and men (13 percent) with
multiple partners is higher in urban areas.
Among respondents reporting two or more sexual partners in the past 12 months, 38 percent of
women and 69 percent of men used a condom during their most recent sexual encounter. The proportion of
men who used a condom during their last sexual encounter was lowest among those who had ever been
married (37 percent) and highest among those residing in urban areas (71 percent).
Table 13.20.1 Multiple sexual partners in the past 12 months among young people: Women
Among all young women age 15-24, the percentage who had sexual intercourse with more
than one sexual partner in the past 12 months, and among those having more than one
partner in the past 12 months, the percentage reporting that a condom was used at last
intercourse, by background characteristics, Kenya 2014
Women age 15-24 who had 2+
Women age 15-24 partners in the past 12 months
Percentage who Percentage who
had 2+ partners reported using a
Background in the past 12 Number of condom at last Number of
characteristic months women intercourse women
Age
15-19 1.0 2,717 (26.1) 28
15-17 0.7 1,680 * 13
18-19 1.5 1,037 * 15
20-24 2.0 2,691 (43.3) 55
20-22 1.8 1,688 (61.7) 30
23-24 2.5 1,002 * 25
Marital status
Never married 0.9 3,434 (56.9) 32
Ever married 2.6 1,974 (25.1) 50
Knows condom
source1
Yes 2.0 3,863 38.4 78
No 0.3 1,545 * 5
Residence
Urban 2.3 2,140 (39.7) 50
Rural 1.0 3,267 (34.3) 33
Education
No education 0.7 205 * 2
Primary incomplete 1.5 1,430 (22.9) 21
Primary complete 1.3 971 * 13
Secondary+ 1.7 2,802 (50.1) 48
Total 15-24 1.5 5,407 37.5 83
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a
figure based on fewer than 25 unweighted cases that has been suppressed.
1
For this table, the following responses are not considered a source for condoms: friends,
family members and home.
250 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.20.2 Multiple sexual partners in the past 12 months among young people: Men
Among all young men age 15-24, the percentage who had sexual intercourse with more than
one sexual partner in the past 12 months, and among those having more than one partner in
the past 12 months, the percentage reporting that a condom was used at last intercourse, by
background characteristics, Kenya 2014
Men age 15-24 who had 2+
Men age 15-24 partners in the past 12 months
Percentage who Percentage who
had 2+ partners reported using a
Background in the past 12 condom at last
characteristic months Number of men intercourse Number of men
Age
15-19 3.7 2,540 64.1 95
15-17 1.8 1,531 (66.5) 28
18-19 6.7 1,009 63.1 67
20-24 16.7 2,125 70.2 354
20-22 16.6 1,365 65.0 226
23-24 16.8 760 79.3 128
Marital status
Never married 9.0 4,214 74.7 379
Ever married 15.4 452 37.4 70
Knows condom
source1
Yes 10.7 4,099 69.8 437
No 2.1 566 * 12
Residence
Urban 12.5 1,751 71.3 220
Rural 7.9 2,915 66.6 229
Education
No education 13.4 67 * 9
Primary incomplete 7.3 1,395 55.9 102
Primary complete 13.2 738 81.1 98
Secondary+ 9.7 2,466 70.3 240
Total 15-24 9.6 4,666 68.9 449
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a
figure based on fewer than 25 unweighted cases that has been suppressed.
1
For this table, the following responses are not considered a source for condoms: friends,
family members and home.
13.11.5 Cross-generational Sexual Partners
To examine age differences between sexual partners, women and men age 15-19 who had sex in
the 12 months preceding the survey were asked the age of their partners. If they did not know a partner’s
age, they were asked whether the partner was older or younger than they were and, if older, whether the
partner was 10 or more years older.
As shown in Table 13.21, women are more likely (14 percent) than men (1 percent) to have had
sex with a partner 10 or more years older. Among women, this is an increase from 2008-09 (4 percent).
Urban women (18 percent) and women with no education (42 percent) are most likely to have had sex with
a partner 10 or more years their senior.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 251
Table 13.21 Age-mixing in sexual relationships among women and men age 15-19
Among women and men age 15-19 who had sexual intercourse in the past 12 months, percentage who
had sexual intercourse with a partner who was 10 or more years older than themselves, by background
characteristics, Kenya 2014
Women age 15-19 who had sexual Men age 15-19 who had sexual
intercourse in the past 12 months intercourse in the past 12 months
Percentage who Percentage who
had sexual had sexual
intercourse with a intercourse with a
Background man 10+ years woman 10+ years
characteristic older Number of women older Number of men
Age
15-17 13.7 252 0.0 251
18-19 13.4 472 0.7 393
Marital status
Never married 3.1 407 0.5 626
Ever married 26.8 317 * 19
Knows condom
source1
Yes 12.2 572 0.5 600
No 18.5 152 (0.0) 45
Residence
Urban 17.7 270 1.5 193
Rural 11.0 454 0.0 452
Education
No education 41.5 24 * 7
Primary incomplete 17.5 197 0.0 249
Primary complete 10.0 155 0.0 99
Secondary+ 10.8 348 1.0 290
Total 13.5 724 0.5 644
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based
on fewer than 25 unweighted cases that has been suppressed.
1
For this table, the following responses are not considered a source for condoms: friends, family
members and home.
13.11.6 Voluntary HIV Counselling and Testing among Youth
People’s knowledge of their HIV status can motivate them to practice safer sexual behaviour to
avoid transmitting the virus to others. Table 13.22 shows, among women and men age 15-24 who reported
having sexual intercourse in the past 12 months, the percentage who were tested for HIV and received their
results in the past 12 months.
Young women are more likely (69 percent) than young men (54 percent) to have been tested for
HIV and to have received the results in the 12 months preceding the survey. These figures represent a
substantial increase from the 2008-09 KDHS, when 41 percent of women and 26 percent of men age 15-24
had been tested for HIV and received the results in the 12 months preceding the survey.
The likelihood of having been tested increases with age among both women and men. Ever-
married men (61 percent) are more likely than never-married men to have been tested. Both women (71
percent) and men (56 percent) who know a condom source are more likely to have been tested. The
prevalence of testing is highest among women and men in Nairobi (75 percent and 67 percent,
respectively) and Nyanza (75 percent and 70 percent, respectively). Urban women (75 percent) and men
(59 percent) are more likely to have been tested. Among both women and men, the likelihood of having
been tested increases with increasing education.
252 • HIV/AIDS-related Knowledge, Attitudes, and Behaviour
Table 13.22 Recent HIV tests among youth
Among young women and young men age 15-24 who have had sexual intercourse in the past 12
months, the percentage who were tested for HIV in the past 12 months and received the results of the
last test, by background characteristics, Kenya 2014
Women age 15-24 who have had
sexual intercourse in the past 12 Men age 15-24 who have had sexual
months: intercourse in the past 12 months:
Percentage who Percentage who
have been tested have been tested
for HIV in the past for HIV in the past
12 months and 12 months and
Background received the results received the results
characteristic of the last test Number of women of the last test Number of men
Age
15-19 64.6 724 40.1 644
15-17 60.9 252 33.3 251
18-19 66.6 472 44.5 393
20-24 70.5 2,076 60.4 1,548
20-22 69.9 1,221 57.6 915
23-24 71.2 856 64.4 633
Marital status
Never married 67.1 905 52.8 1,748
Ever married 69.9 1,896 60.8 444
Knows condom
source1
Yes 70.5 2,311 55.5 2,113
No 61.5 490 25.4 79
Region
Coast 69.0 281 42.5 216
North Eastern 35.2 38 (27.1) 9
Eastern 62.1 312 49.5 303
Central 66.7 254 52.1 236
Rift Valley 69.2 805 50.1 590
Western 65.2 300 45.2 187
Nyanza 74.7 394 69.6 303
Nairobi 75.3 416 67.3 347
Residence
Urban 75.3 1,292 59.1 982
Rural 63.5 1,509 50.6 1,210
Education
No education 45.2 147 19.6 30
Primary incomplete 60.2 617 38.0 495
Primary complete 72.5 652 55.3 477
Secondary+ 73.7 1,383 61.7 1,191
Total 69.0 2,800 54.4 2,192
Note: Figures in parentheses are based on 25-49 unweighted cases.
1
For this table, the following responses are not considered a source for condoms: friends, family
members and home.
HIV/AIDS-related Knowledge, Attitudes, and Behaviour • 253
NONCOMMUNICABLE DISEASES AND
OTHER HEALTH ISSUES 14
James Muttunga, John Anampiu, Abdulkadir Amin Awes
Key Findings
• Overall, 10 percent of women have had both a breast exam from a health
provider and a breast self-exam.
• Three-quarters (76 percent) of women have heard of cervical cancer, and
14 percent have had a cervical cancer screening exam.
• Approximately two-thirds (65 percent) of men have heard of prostate
cancer, and 3 percent have been examined by a doctor or health care
provider for prostate cancer.
• Tobacco use is more common among Kenyan men than women (83
percent of men don’t use tobacco compared with 99 percent of women).
Sixteen percent of men smoke cigarettes. Among men who smoke
cigarettes, 28 percent smoked more than 10 cigarettes in the past 24
hours.
• Most Kenyans do not have health insurance; 82 percent of women and
79 percent of men are not covered by any health insurance.
14.1 INTRODUCTION
A
healthy population is an end in itself, along with being one of the most basic requirements for
quality of life and a basic foundation for a country’s economic growth and development. It is
important for the population to live a healthy lifestyle, free from communicable and
noncommunicable diseases and free from use of destructive substances. Around the world, the rapid
increases in noncommunicable diseases such as cardiovascular diseases, diabetes, and cancer are becoming
a challenge in achieving global progress. Kenya, similar to other countries that are in an epidemiological
transition, is experiencing an increase in noncommunicable diseases, obesity, and other conditions
associated with urbanisation and modern, less active lifestyles, combined with new and re-emerging
infectious diseases such as HIV and AIDS and tuberculosis. This chapter presents information on health
issues in Kenya, including screening for noncommunicable diseases (NCDs), knowledge of tuberculosis,
use of tobacco and alcohol, physical activity, accidental injury, and health insurance coverage.
14.2 KNOWLEDGE OF AND SCREENING FOR CANCER
In Kenya, cancer is estimated to be the second leading cause of NCD-related deaths after
cardiovascular diseases, and it accounts for 7 percent of overall national mortality (WHO, 2014a). Cancer
mortality can be reduced if cases are detected and treated early. Individuals and their physicians can
participate in regular screening to identify abnormalities suggestive of cancer so that prompt diagnosis,
treatment, and care can be sought. In the absence of early detection or screening, patients may be
diagnosed at late stages when curative treatment is no longer an option (WHO, 2015b). The 2014 KDHS
asked respondents if they had heard of specific types of cancers and if they had been screened for them.
Women were asked about breast and cervical cancer, while men were asked about prostate cancer.
14.2.1 Breast Cancer
Breast cancer is one of the most common forms of cancer among women and is a leading cause of
death worldwide. Breast self-examinations—physical examinations of the breasts performed by women
Noncommunicable Diseases and Other Health Issues • 255
themselves—as well as examinations by medical professionals and mammography are methods for the
early detection of breast cancer. Table 14.1 presents the percentage of women age 15-49 who have
completed a breast self-exam for possible cancer and the percentage who have had a doctor or health care
provider examine their breasts for cancer, by background characteristics.
Table 14.1 Breast cancer screening
Percentage of women age 15-49 who have examined their breasts to detect or check for
cancer and the percentage who have had a doctor or health care provider examine their
breasts for cancer, by background characteristics, Kenya 2014
Percentage who
have had a Percentage who
Percentage who doctor or health have had both
have performed care provider an examination
a self- perform an for breast cancer
Background examination for examination for and a self- Number of
characteristic breast cancer breast cancer examination women
Age
15-19 14.0 3.4 2.6 2,717
20-24 22.8 12.2 8.4 2,691
25-29 29.7 17.3 12.8 2,932
30-34 29.0 16.0 12.1 2,162
35-39 29.4 16.1 12.4 1,780
40-44 29.7 17.9 13.5 1,292
45-49 30.6 17.7 13.7 1,052
Residence
Urban 34.2 18.6 14.7 5,929
Rural 19.5 10.1 6.9 8,696
Region
Coast 18.1 9.4 7.5 1,421
North Eastern 2.2 1.4 1.3 299
Eastern 30.3 10.9 7.9 2,066
Central 40.8 24.0 16.8 1,905
Rift Valley 25.0 13.5 10.2 3,714
Western 15.3 9.2 5.9 1,571
Nyanza 12.9 7.8 5.6 1,908
Nairobi 36.7 20.8 17.4 1,742
Education
No education 7.6 3.1 1.6 1,015
Primary incomplete 14.5 8.3 5.2 3,793
Primary complete 25.2 14.2 10.0 3,543
Secondary+ 35.1 18.0 14.4 6,274
Wealth quintile
Lowest 9.5 4.4 2.9 2,236
Second 17.8 9.1 5.8 2,590
Middle 19.5 10.6 6.9 2,859
Fourth 29.3 15.2 11.7 3,113
Highest 41.2 22.7 18.3 3,827
Total 25.4 13.5 10.1 14,625
One-quarter (25 percent) of women have performed a breast self-examination, and 14 percent
have had a doctor or health provider perform a breast exam. Overall, 10 percent of women have had both a
self-exam and a breast exam from a health provider. The percentage of women who have had both exams
increases with age and is most common among women age 25 and above. Urban women (15 percent) are
more likely to have had both exams than rural women (7 percent). By region, the proportion of women
who have had both exams ranges from 1 percent in North Eastern to 17 percent in Nairobi and Central. The
likelihood of having both exams increases with increasing education and wealth. Fourteen percent of
women with a secondary education have had both kinds of breast exam, as compared with 2 percent of
women with no education. Similarly, 18 percent of women in the highest wealth quintile have had both
kinds of breast exam, compared with 3 percent of women in the lowest wealth quintile.
14.2.2 Cervical Cancer
Worldwide, cervical cancer is the fourth most frequent cancer in women. When women are
regularly screened for cervical cancer, pre-cancerous lesions and cancer are often identified at stages when
they can easily be treated. Early treatment prevents up to 80 percent of cervical cancers. Because pre-
cancerous lesions and cancer may take many years to develop, screening is recommended for every woman
256 • Noncommunicable Diseases and Other Health Issues
age 30 to 49 at least once in her lifetime and ideally more frequently (WHO, 2015c). Two common types
of screening examinations are the pap smear test and visual inspection with acetic acid (VIA) or with
Lugol’s iodine (VILI).
Table 14.2 presents the percentage of women age 15-49 who have heard of cervical cancer and the
percentage who have had a cervical cancer screening exam. Among women who have had an exam, Table
14.2 presents the percent distribution by examination type.
About three-quarters (76 percent) of women have heard of cervical cancer and 14 percent have
had a cervical cancer screening exam. Among women who have had an exam, 62 percent have had a pap
smear, 32 percent have had visual inspection, and 1 percent have had both screening tests. Knowledge of
cervical cancer and likelihood of having a screening exam are lowest among young women age 15-19 (59
percent and 2 percent, respectively), rural women (71 percent and 11 percent), women in North Eastern (5
percent and less than 1 percent), women with no education (33 percent and 3 percent), and women in the
lowest wealth quintile (49 percent and 4 percent).
Table 14.2 Cervical cancer knowledge and screening
Percentage of women age 15-49 who have heard of cervical cancer and the percentage who have had a cervical cancer screening exam; and among women
who have had a cervical cancer screening exam, the percent distribution by examination type, according to background characteristics, Kenya 2014
Percentage Among women who have had a cervical cancer exam:
Percentage who have
who have had a Both pap
heard of cervical smear and
Background cervical cancer Number of Visual visual Don’t know / Number of
characteristic cancer exam women Pap smear inspection1 inspection1 not sure Missing Total women
Age
15-19 59.3 2.0 2,717 (54.7) (21.3) (0.9) (23.2) (0.0) 100.0 53
20-24 75.9 10.0 2,691 57.1 37.7 2.0 2.9 0.3 100.0 269
25-29 80.0 15.4 2,932 62.3 29.8 0.5 7.2 0.2 100.0 451
30-34 84.3 19.0 2,162 62.6 31.1 2.1 3.6 0.6 100.0 411
35-39 79.5 19.2 1,780 67.3 28.7 0.8 3.0 0.2 100.0 342
40-44 81.6 22.4 1,292 61.9 32.2 1.0 4.9 0.0 100.0 290
45-49 80.6 19.8 1,052 59.8 34.8 2.6 2.7 0.0 100.0 209
Residence
Urban 83.7 18.6 5,929 70.7 23.0 1.7 4.5 0.1 100.0 1,100
Rural 71.0 10.6 8,696 51.6 41.8 1.0 5.2 0.4 100.0 925
Region
Coast 64.7 7.5 1,421 53.7 33.5 0.0 12.5 0.3 100.0 107
North Eastern 5.0 0.4 299 * * * * * 100.0 1
Eastern 79.9 12.8 2,066 49.9 42.9 1.3 5.9 0.0 100.0 264
Central 86.8 21.8 1,905 56.9 40.8 0.1 2.2 0.0 100.0 415
Rift Valley 71.6 12.1 3,714 56.2 32.5 2.9 8.0 0.5 100.0 450
Western 68.2 8.1 1,571 49.2 46.4 1.2 2.7 0.6 100.0 127
Nyanza 84.5 13.1 1,908 62.6 34.0 0.2 2.5 0.6 100.0 250
Nairobi 89.5 23.6 1,742 86.8 7.6 2.2 3.5 0.0 100.0 411
Education
No education 32.5 2.9 1,015 (40.2) (44.3) (0.0) (14.7) (0.8) 100.0 30
Primary incomplete 64.8 9.4 3,793 49.2 44.6 0.3 5.4 0.4 100.0 358
Primary complete 81.0 15.1 3,543 57.5 36.0 0.8 5.3 0.5 100.0 535
Secondary+ 87.4 17.6 6,274 68.9 24.9 2.1 4.1 0.0 100.0 1,102
Wealth quintile
Lowest 48.7 4.4 2,236 55.8 36.2 2.1 3.9 2.0 100.0 99
Second 73.5 8.9 2,590 46.3 45.1 0.0 8.0 0.5 100.0 231
Middle 76.9 10.9 2,859 44.3 49.0 0.1 6.6 0.0 100.0 312
Fourth 82.0 16.3 3,113 63.1 30.8 1.0 4.8 0.3 100.0 508
Highest 88.8 22.9 3,827 72.5 21.7 2.3 3.5 0.0 100.0 875
Total 76.2 13.8 14,625 62.0 31.6 1.4 4.8 0.2 100.0 2,025
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been
suppressed.
1
Visual inspection with acetic acid (VIA) or with Lugol’s Iodine (VILI)
14.2.3 Prostate Cancer
Prostate cancer starts in the prostate gland, which is a small, walnut-sized structure that makes up
part of a man’s reproductive system. Prostate cancer can be detected through a digital rectal exam. Also,
the blood level of prostate-specific antigen, a protein that is produced by the prostate, can be tested. Table
Noncommunicable Diseases and Other Health Issues • 257
14.3 presents the percentage of men age 15-49 who have heard of prostate cancer and the percentage who
have been examined for prostate cancer by a doctor or health care provider. Among men who have had an
exam, Table 14.3 presents the timing of the exam and the exam results.
About two-thirds (65 percent) of men have heard of prostate cancer and 3 percent have been
examined by a doctor or health care provider for prostate cancer. Among men who have had an exam, 82
percent have had the exam in the last five years, and 2 percent were told they had a problem with their
prostate. Men age 15-19 (41 percent), men in rural areas (62 percent), and men in North Eastern (23
percent) were least likely to have heard of prostate cancer. Men with these characteristics were also less
likely to have had a prostate exam. Knowledge of prostate cancer and the likelihood of having had a
prostate exam generally increase with increasing education and wealth.
Table 14.3 Prostate cancer knowledge and screening
Percentage of men age 15-49 who have heard of prostate cancer, the percentage who have had a doctor or health care provider
examine them for prostate cancer; and among men who have had a prostate cancer exam, the timing of the exam and the results,
by background characteristics, Kenya 2014
Percentage who
have had a Among men who have had a
doctor or health prostate cancer exam:
care provider Were told they
Percentage who perform an Had the exam had a problem
Background have heard of examination for within the last 5 with their
characteristic prostate cancer prostate cancer Number of men years prostate Number of men
Age
15-19 41.1 0.8 2,540 (76.9) (3.8) 21
20-24 66.7 2.1 2,125 (82.8) (5.3) 44
25-29 70.9 3.8 2,104 91.1 0.0 79
30-34 71.6 2.8 1,785 (72.2) (0.0) 49
35-39 70.3 3.0 1,483 (76.6) (7.3) 45
40-44 75.1 4.3 1,224 (86.6) (1.5) 53
45-49 78.1 2.6 800 (67.5) (0.3) 21
Residence
Urban 69.0 3.3 5,300 89.3 0.8 175
Rural 61.6 2.0 6,762 71.7 4.3 138
Region
Coast 53.4 1.1 1,260 * * 14
North Eastern 22.6 0.0 227 * * 0
Eastern 66.0 4.0 1,825 74.6 0.0 73
Central 76.1 3.4 1,564 (62.6) (1.5) 53
Rift Valley 65.5 1.6 3,050 85.2 2.7 50
Western 56.5 0.7 1,164 * * 8
Nyanza 71.8 3.4 1,405 88.1 6.6 48
Nairobi 66.1 4.2 1,568 * * 66
Education
No education 30.5 1.6 345 * * 5
Primary incomplete 49.3 1.8 3,071 73.0 5.7 55
Primary complete 63.0 3.1 2,734 88.3 3.5 83
Secondary+ 75.7 2.8 5,913 82.9 0.7 168
Wealth quintile
Lowest 46.2 1.5 1,691 (81.2) (5.0) 26
Second 59.7 1.8 2,145 (69.7) (8.2) 39
Middle 64.4 2.5 2,370 72.0 1.9 60
Fourth 68.5 2.5 2,959 85.9 2.3 75
Highest 76.1 3.9 2,897 87.9 0.0 113
Total 15-49 64.8 2.6 12,063 81.6 2.3 312
50-54 75.7 7.0 756 (78.0) (9.2) 53
Total 15-54 65.5 2.8 12,819 81.0 3.3 365
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25
unweighted cases that has been suppressed.
14.3 SCREENING FOR HYPERTENSION AND DIABETES
Elevated blood pressure, commonly referred to as hypertension, is among the major risk factors
for cardiovascular disease and stroke. Diabetes is characterised by chronic hyperglycaemia or raised blood
sugar. It requires lifelong treatment and can damage the heart, blood vessels, eyes, kidneys, and nerves.
The KDHS asked respondents if they had ever been told by a doctor or health worker that they had high
258 • Noncommunicable Diseases and Other Health Issues
blood pressure or hypertension or that they had raised blood sugar or diabetes. Table 14.4 presents the
percentage of women and men age 15-49 who were told they had either of these health concerns, according
to background characteristics. It is important to note that these data do not represent the prevalence of
hypertension or diabetes in Kenya; rather, they reflect only the proportion of people who have visited a
health care provider, undergone an examination, and been informed that they have these health concerns.
Nine percent of women and 3 percent of men have been told by a health care provider that they
have hypertension. The percentage of women and men who have been told they have hypertension
generally increases with age, education, and wealth. Women and men in urban areas, women in Nairobi
and Central regions, and men in Nairobi and Nyanza regions are more likely to have been told they have
hypertension, although this pattern may reflect increased access to health care in these areas. One percent
of both women and men have been told by a health care provider that they have diabetes.
Table 14.4 Hypertension and diabetes screening
Percentage of women and men age 15-49 who were told by a doctor or health worker that they have raised blood pressure or
hypertension and the percentage who were told by a doctor or health worker that they have raised blood sugar or diabetes,
according to background characteristics, Kenya 2014
Women Men
Percentage who Percentage who Percentage who Percentage who
were told by a were told by a were told by a were told by a
doctor or health doctor or health doctor or health doctor or health
Background worker they had worker they had Number of worker they had worker they had Number of
characteristic hypertension diabetes women hypertension diabetes men
Age
15-19 2.7 0.5 2,717 1.1 0.2 2,540
20-24 6.2 1.0 2,691 1.8 0.7 2,125
25-29 9.8 1.4 2,932 2.0 0.6 2,104
30-34 10.7 1.2 2,162 4.9 0.6 1,785
35-39 12.0 1.3 1,780 3.6 1.1 1,483
40-44 16.5 2.3 1,292 4.9 1.2 1,224
45-49 17.5 2.6 1,052 7.1 2.1 800
Residence
Urban 11.6 1.6 5,929 3.9 0.9 5,300
Rural 7.8 1.0 8,696 2.3 0.7 6,762
Region
Coast 9.4 1.2 1,421 3.4 1.0 1,260
North Eastern 5.2 2.0 299 1.3 2.2 227
Eastern 8.1 1.3 2,066 1.7 0.5 1,825
Central 12.8 1.7 1,905 2.6 0.9 1,564
Rift Valley 8.3 1.1 3,714 2.8 0.3 3,050
Western 8.0 1.2 1,571 2.2 0.7 1,164
Nyanza 7.3 0.7 1,908 4.3 1.6 1,405
Nairobi 13.5 1.5 1,742 4.7 0.8 1,568
Education
No education 6.7 1.8 1,015 2.2 1.1 345
Primary incomplete 7.6 0.8 3,793 2.0 0.5 3,071
Primary complete 11.0 1.1 3,543 3.2 0.8 2,734
Secondary+ 9.9 1.6 6,274 3.5 0.9 5,913
Wealth quintile
Lowest 5.4 0.8 2,236 2.3 0.6 1,691
Second 7.9 1.4 2,590 2.8 0.6 2,145
Middle 8.6 1.1 2,859 2.2 0.6 2,370
Fourth 10.7 1.2 3,113 2.2 0.8 2,959
Highest 12.3 1.7 3,827 5.1 1.0 2,897
Total 15-49 9.4 1.3 14,625 3.0 0.8 12,063
50-54 na na na 9.8 2.4 756
Total 15-54 na na na 3.4 0.9 12,819
na = Not applicable
14.4 KNOWLEDGE AND ATTITUDES CONCERNING TUBERCULOSIS
The 2014 KDHS collected data on women’s and men’s knowledge and attitudes concerning
tuberculosis (TB). Tables 14.5.1 and 14.5.2 show the percentage of women and men who have heard of TB
and, among those who have heard of TB, the percentage who know that TB is spread through the air by
coughing.
Noncommunicable Diseases and Other Health Issues • 259
As in the 2008-09 KDHS, awareness of TB is almost universal in Kenya (97 percent among
women and 99 percent among men). Eighty-four percent of women and 87 percent of men age 15-49 who
have heard of TB know that it is spread through the air by coughing. Women and men in rural areas and
those in Western region are less likely than their counterparts to know that TB is spread through the air by
coughing. Correct knowledge of TB transmission increases with increasing education and wealth.
Table 14.5.1 Knowledge and attitudes concerning tuberculosis: Women
Percentage of women age 15-49 who have heard of tuberculosis (TB), and among women who
have heard of TB, the percentages who know that TB is spread through the air by coughing, by
background characteristics, Kenya 2014
Among women who have
Among all women heard of TB
Percentage who
report that TB is
spread through
Background Percentage who Number of the air by Number of
characteristic have heard of TB women coughing women
Age
15-19 96.0 2,717 83.7 2,608
20-24 97.1 2,691 84.8 2,613
25-29 97.1 2,932 84.7 2,848
30-34 97.4 2,162 84.1 2,106
35-39 96.9 1,780 85.2 1,724
40-44 96.9 1,292 82.7 1,251
45-49 97.8 1,052 78.6 1,029
Residence
Urban 98.3 5,929 88.9 5,830
Rural 96.0 8,696 80.4 8,349
Region
Coast 97.9 1,421 82.3 1,391
North Eastern 72.6 299 82.2 217
Eastern 98.2 2,066 80.8 2,028
Central 99.1 1,905 84.4 1,887
Rift Valley 96.1 3,714 83.9 3,568
Western 95.9 1,571 79.6 1,506
Nyanza 97.2 1,908 85.9 1,854
Nairobi 99.2 1,742 90.0 1,729
Education
No education 84.6 1,015 63.1 859
Primary incomplete 95.5 3,793 72.9 3,624
Primary complete 98.5 3,543 85.1 3,490
Secondary+ 98.9 6,274 92.6 6,206
Wealth quintile
Lowest 91.1 2,236 71.4 2,036
Second 97.1 2,590 79.2 2,516
Middle 97.2 2,859 84.0 2,778
Fourth 98.4 3,113 86.2 3,063
Highest 98.9 3,827 91.8 3,786
Total 97.0 14,625 83.9 14,179
260 • Noncommunicable Diseases and Other Health Issues
Table 14.5.2 Knowledge and attitudes concerning tuberculosis: Men
Percentage of men age 15-49 who have heard of tuberculosis (TB), and among men who have
heard of TB, the percentages who know that TB is spread through the air by coughing, by
background characteristics, Kenya 2014
Among men who have
Among all men heard of TB
Percentage who
report that TB is
spread through
Background Percentage who Number of the air by Number of
characteristic have heard of TB men coughing men
Age
15-19 97.2 2,540 83.7 2,469
20-24 99.3 2,125 88.1 2,110
25-29 98.9 2,104 87.6 2,082
30-34 98.8 1,785 86.3 1,763
35-39 99.0 1,483 86.2 1,469
40-44 98.2 1,224 89.1 1,202
45-49 99.2 800 85.7 794
Residence
Urban 98.6 5,300 91.5 5,227
Rural 98.5 6,762 82.6 6,663
Region
Coast 99.6 1,260 90.1 1,255
North Eastern 98.7 227 86.5 224
Eastern 98.5 1,825 87.0 1,798
Central 99.0 1,564 87.1 1,548
Rift Valley 99.0 3,050 83.9 3,020
Western 97.4 1,164 81.9 1,134
Nyanza 98.6 1,405 87.4 1,386
Nairobi 97.3 1,568 90.1 1,526
Education
No education 96.2 345 69.5 332
Primary incomplete 97.2 3,071 73.6 2,984
Primary complete 98.9 2,734 85.5 2,704
Secondary+ 99.3 5,913 94.5 5,869
Wealth quintile
Lowest 98.1 1,691 78.8 1,659
Second 98.8 2,145 82.4 2,118
Middle 98.4 2,370 84.8 2,332
Fourth 98.7 2,959 88.5 2,922
Highest 98.7 2,897 93.4 2,859
Total 15-49 98.6 12,063 86.5 11,889
50-54 99.4 756 79.9 752
Total 15-54 98.6 12,819 86.1 12,641
14.5 USE OF TOBACCO
Smoking and using other forms of tobacco can cause a wide variety of diseases and lead to death.
Smoking is a risk factor for NCDs, including cardiovascular disease, lung cancer, and other forms of
cancer, and it contributes to the severity of pneumonia, emphysema, and chronic bronchitis. Further,
secondhand smoke may adversely affect health and aggravate illnesses. In the 2014 KDHS, women and
men age 15-49 were asked whether they currently smoke cigarettes and, if so, how many cigarettes they
had smoked in the past 24 hours. Those who were not currently smoking cigarettes were asked whether
they used any other forms of tobacco, such as a pipe, chewing tobacco, or snuff.
Tables 14.6.1 and 14.6.2 show that tobacco use is more common among Kenyan men than women
(99 percent of women do not use tobacco compared with 83 percent of men). Sixteen percent of men age
15-49 smoke cigarettes, while a very small proportion of men chew tobacco, use snuff, or use other
tobacco products (each 1 percent). Use of tobacco increases with age and is more common among men
with no education and those in the lower wealth quintiles. In particular, cigarette smoking is most common
among men in Eastern and Central regions (30 percent and 25 percent, respectively), men with less than a
secondary education (20-21 percent), and men in the second wealth quintile (20 percent). Among men who
smoke cigarettes, 18 percent smoked 1-2 cigarettes, 36 percent smoked 3-5 cigarettes, and 14 percent
smoked 6-9 cigarettes in the past 24 hours. Twenty-eight percent of men who smoke cigarettes smoked
more than 10 cigarettes in the past 24 hours.
Noncommunicable Diseases and Other Health Issues • 261
Table 14.6.1 Use of tobacco: Women
Percentage of women age 15-49 who smoke cigarettes or a pipe or use other tobacco products, according to background
characteristics and maternity status, Kenya 2014
Uses tobacco
Background Chewing Does not use Number of
characteristic Cigarettes Pipe tobacco Snuff Other tobacco tobacco women
Age
15-19 0.0 0.1 0.1 0.0 0.1 99.6 2,717
20-24 0.3 0.0 0.4 0.2 0.1 99.0 2,691
25-29 0.7 0.0 0.6 0.3 0.1 98.4 2,932
30-34 0.6 0.0 0.4 0.3 0.2 98.5 2,162
35-39 0.6 0.0 1.1 0.5 0.0 97.9 1,780
40-44 0.0 0.0 1.4 0.6 0.0 98.2 1,292
45-49 0.6 0.0 1.3 1.5 0.0 96.7 1,052
Maternity status
Pregnant 0.6 0.0 1.1 0.4 0.5 97.4 915
Breastfeeding (not
pregnant) 0.2 0.0 1.1 0.6 0.0 98.3 3,220
Neither 0.5 0.0 0.4 0.3 0.1 98.8 10,491
Residence
Urban 0.7 0.1 0.1 0.1 0.2 98.9 5,929
Rural 0.2 0.0 1.0 0.6 0.0 98.4 8,696
Region
Coast 1.2 0.0 1.6 1.2 0.0 96.6 1,421
North Eastern 0.0 0.0 0.0 0.0 0.0 100.0 299
Eastern 0.1 0.0 0.4 0.1 0.0 99.4 2,066
Central 0.4 0.0 0.1 0.0 0.0 99.4 1,905
Rift Valley 0.2 0.1 1.5 0.9 0.0 97.3 3,714
Western 0.3 0.0 0.0 0.0 0.0 99.7 1,571
Nyanza 0.1 0.0 0.0 0.0 0.0 99.8 1,908
Nairobi 1.0 0.2 0.1 0.0 0.7 98.3 1,742
Education
No education 0.1 0.2 7.8 4.8 0.0 87.8 1,015
Primary incomplete 0.4 0.0 0.2 0.1 0.0 99.3 3,793
Primary complete 0.3 0.0 0.0 0.0 0.1 99.5 3,543
Secondary+ 0.5 0.1 0.0 0.0 0.2 99.3 6,274
Wealth quintile
Lowest 0.1 0.1 3.7 2.2 0.0 94.3 2,236
Second 0.3 0.0 0.1 0.1 0.0 99.5 2,590
Middle 0.3 0.0 0.0 0.1 0.1 99.5 2,859
Fourth 0.4 .0 0.0 0.0 0.1 99.4 3,113
Highest 0.8 0.1 0.1 0.0 0.2 99.0 3,827
Total 0.4 0.0 0.6 0.4 0.1 98.6 14,625
262 • Noncommunicable Diseases and Other Health Issues
Table 14.6.2 Use of tobacco: Men
Percentage of men age 15-49 who smoke cigarettes or a pipe or use other tobacco products and the percent distribution of cigarette smokers by number of cigarettes
smoked in preceding 24 hours, according to background characteristics, Kenya 2014
Percent distribution of men who smoke cigarettes by
Uses tobacco number of cigarettes smoked in the past 24 hours Number
Does not Don’t of
Background Cigar- Chewing Other use Number know/ cigarette
characteristic ettes Pipe tobacco Snuff tobacco tobacco of men 0 1-2 3-5 6-9 10+ missing Total smokers
Age
15-19 1.5 0.0 0.1 0.1 0.6 98.0 2,540 0.0 17.7 27.6 17.2 21.6 15.9 100.0 39
20-24 9.4 0.1 0.6 0.3 1.9 88.5 2,125 4.7 23.9 31.6 10.8 26.7 2.2 100.0 200
25-29 17.3 0.1 0.7 1.1 1.2 80.9 2,104 4.7 20.4 40.6 12.8 20.8 0.6 100.0 364
30-34 23.1 0.3 1.1 2.4 1.3 74.8 1,785 1.5 14.3 36.5 19.2 27.0 1.4 100.0 412
35-39 24.7 0.2 1.6 2.2 0.6 73.1 1,483 1.5 16.3 38.0 10.8 32.5 1.0 100.0 367
40-44 24.4 0.0 1.4 1.6 0.9 73.5 1,224 2.6 20.9 32.8 10.4 32.0 1.2 100.0 299
45-49 28.1 0.4 1.1 1.7 0.9 69.9 800 1.3 14.6 35.5 15.1 32.5 1.0 100.0 225
Residence
Urban 15.4 0.2 0.4 0.9 1.8 83.1 5,300 3.9 17.7 31.6 15.1 30.7 1.0 100.0 815
Rural 16.1 0.1 1.1 1.4 0.5 82.0 6,762 1.5 18.2 39.6 12.5 26.3 1.9 100.0 1,090
Region
Coast 19.7 0.6 0.9 0.4 4.3 77.5 1,260 3.7 16.9 22.9 15.6 38.7 2.2 100.0 249
North Eastern 11.5 0.0 3.8 0.0 0.0 87.3 227 0.9 16.9 38.9 16.5 26.7 0.0 100.0 26
Eastern 30.0 0.3 1.4 0.8 0.7 68.7 1,825 0.0 13.6 45.2 13.2 26.6 1.3 100.0 548
Central 24.9 0.1 0.1 1.2 0.6 74.1 1,564 3.2 15.1 32.3 15.4 34.1 0.0 100.0 390
Rift Valley 10.4 0.1 .4 2.2 0.5 86.9 3,050 1.3 18.4 38.4 11.5 28.9 1.5 100.0 317
Western 8.4 0.0 0.0 1.5 0.1 90.4 1,164 1.0 30.3 39.9 12.7 7.6 8.5 100.0 98
Nyanza 5.9 0.1 0.0 0.4 0.0 93.9 1,405 7.7 29.6 36.0 12.0 11.3 3.3 100.0 83
Nairobi 12.4 0.0 0.6 0.9 2.3 85.6 1,568 (7.6) (25.9) (29.8) (12.7) (24.0) (0.0) 100.0 195
Education
No education 20.6 0.5 10.0 4.6 1.8 66.3 345 1.2 20.1 29.3 15.5 34.0 0.0 100.0 71
Primary
incomplete 20.2 0.3 1.2 1.7 1.5 78.1 3,071 2.3 18.4 36.7 12.1 29.3 1.2 100.0 620
Primary complete 21.0 0.1 0.5 1.5 1.3 77.0 2,734 2.7 15.6 38.6 13.2 28.7 1.2 100.0 575
Secondary+ 10.8 0.1 0.2 0.6 0.7 88.3 5,913 2.9 19.5 34.3 15.1 25.9 2.2 100.0 638
Wealth quintile
Lowest 18.1 0.3 3.3 2.7 0.9 77.1 1,691 1.5 21.5 41.7 11.5 21.9 1.9 100.0 306
Second 19.9 0.1 0.7 1.2 1.0 79.0 2,145 1.9 16.5 42.0 11.5 26.7 1.4 100.0 426
Middle 17.4 0.2 0.5 1.1 1.2 81.3 2,370 1.4 17.0 32.5 16.4 32.0 0.6 100.0 412
Fourth 14.5 0.0 0.5 1.1 0.9 84.6 2,959 3.4 17.8 36.7 14.5 25.8 1.8 100.0 430
Highest 11.5 0.2 0.1 0.4 1.3 87.1 2,897 4.8 18.2 27.5 13.6 34.1 1.9 100.0 332
Total 15-49 15.8 0.2 0.8 1.2 1.1 82.5 12,063 2.6 18.0 36.2 13.6 28.2 1.5 100.0 1,905
50-54 29.2 0.5 2.1 3.1 0.4 66.9 756 3.1 18.2 37.1 12.5 28.1 1.0 100.0 221
Total 15-54 16.6 0.2 0.9 1.3 1.0 81.6 12,819 2.6 18.0 36.3 13.5 28.2 1.4 100.0 2,126
Note: Figures in parentheses are based on 25-49 unweighted cases.
14.6 ALCOHOL CONSUMPTION
Tables 14.7.1 and 14.7.2 show the percentage of women and men age 15-49 who drink alcohol
and, among those who drink alcohol, the number of days at least one alcoholic drink was consumed in the
past two weeks and the mean number of days on which alcohol was consumed in the past two weeks, by
background characteristics. Men are more likely to consume alcohol than women (29 percent and 5
percent, respectively). Women over age 20 (5-7 percent) and men over age 25 (36-45 percent) are more
likely than younger women and men to consume alcohol. Women and men in urban areas and in Nairobi
are more likely to consume alcohol than their counterparts living elsewhere. Alcohol consumption among
women is not clearly associated with education or wealth; among men, however, the proportion who drink
alcohol generally increases with increasing education and wealth.
Among respondents who drink alcohol, 48 percent of women and 19 percent of men reported that
they had not consumed alcohol in the past two weeks. Twenty-nine percent of women and 39 percent of
men reported that they had consumed alcohol on 1-2 days during the last two weeks; 10 percent and 17
percent, respectively, had consumed alcohol on 3-4 days during the last two weeks, and 13 percent and 26
percent, respectively, had consumed alcohol on 5 or more days. Among women who drink alcohol, daily
alcohol consumption in the past two weeks was higher among those age 45-49, those in rural areas, those
in Coast and Eastern regions, those at lower educational levels, and those in lower wealth quintiles. Among
men, daily alcohol consumption in the past two weeks was higher among those above age 30, those in
Nyanza and Coast regions, and those with no education. Men in the highest wealth quintile were least
likely to report daily consumption.
Noncommunicable Diseases and Other Health Issues • 263
Table 14.7.1 Alcohol consumption: Women
Percentage of women age 15-49 who drink alcohol, and among those who drink alcohol, the number of days at least one alcoholic drink was consumed in
the past two weeks, and the mean number of days alcohol was consumed in the past two weeks, by background characteristics, Kenya 2014
Among women who drink alcohol, the number of days at least one drink
Percentage was consumed in the last two weeks Mean
Background who drink Number of 14 Number of number of
characteristic alcohol women 0 1-2 3-4 5-9 10-13 (daily) Missing Total women days
Age
15-19 1.0 2,717 * * * * * * * 100.0 27 *
20-24 5.3 2,691 44.5 38.7 12.1 2.5 0.4 1.9 0.0 100.0 142 1.3
25-29 5.2 2,932 58.3 17.8 10.9 5.5 3.0 4.6 0.0 100.0 152 1.9
30-34 5.7 2,162 45.4 30.3 8.7 8.7 2.9 4.1 0.0 100.0 123 2.1
35-39 4.8 1,780 60.3 15.9 9.5 4.0 1.3 8.1 1.0 100.0 86 2.0
40-44 7.1 1,292 32.1 42.8 6.8 10.9 2.3 5.0 0.0 100.0 91 2.4
45-49 7.0 1,052 39.0 23.4 13.5 5.0 3.3 16.0 0.0 100.0 74 3.7
Residence
Urban 7.0 5,929 55.2 27.4 8.7 3.7 1.1 3.9 0.0 100.0 415 1.5
Rural 3.2 8,696 37.5 30.4 11.9 8.6 3.5 7.9 0.3 100.0 281 2.8
Region
Coast 3.6 1,421 39.2 24.6 16.1 9.1 1.7 9.2 0.0 100.0 51 2.9
North Eastern 0.0 299 * * * * * * * * 0 *
Eastern 3.2 2,066 44.6 29.5 6.9 10.2 0.3 8.5 0.0 100.0 66 2.5
Central 5.6 1,905 71.3 18.3 4.1 0.5 2.3 3.5 0.0 100.0 106 1.1
Rift Valley 4.6 3,714 35.3 32.1 12.9 10.7 4.3 4.8 0.0 100.0 172 2.6
Western 5.5 1,571 40.6 36.9 10.2 2.1 2.0 7.1 1.0 100.0 86 2.1
Nyanza 1.7 1,908 (43.3) (25.1) (17.1) (2.8) (4.9) (6.8) (0.0) 100.0 33 (2.4)
Nairobi 10.4 1,742 (54.8) (28.8) (8.7) (3.6) (0.0) (4.2) (0.0) 100.0 181 (1.5)
Education
No education 6.8 1,015 35.5 15.0 16.6 16.8 6.4 9.7 0.0 100.0 69 3.9
Primary incomplete 4.8 3,793 37.0 32.5 11.0 7.5 1.7 9.9 0.5 100.0 181 2.8
Primary complete 2.6 3,543 52.3 29.0 6.0 4.3 5.4 3.0 0.0 100.0 91 1.8
Secondary+ 5.7 6,274 55.0 29.2 9.2 3.0 0.5 3.1 0.0 100.0 356 1.3
Wealth quintile
Lowest 5.0 2,236 32.2 21.2 16.6 15.0 5.9 8.5 0.8 100.0 111 3.6
Second 2.7 2,590 34.1 27.2 15.6 11.7 3.8 7.8 0.0 100.0 70 3.2
Middle 2.5 2,859 38.0 34.3 11.7 6.3 2.3 7.4 0.0 100.0 72 2.4
Fourth 4.4 3,113 49.5 29.2 10.0 3.4 1.9 5.9 0.0 100.0 138 1.9
Highest 8.0 3,827 58.8 30.0 5.9 1.8 0.3 3.3 0.0 100.0 304 1.1
Total 4.8 14,625 48.1 28.6 10.0 5.7 2.0 5.5 0.1 100.0 696 2.0
Note: Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has
been suppressed.
264 • Noncommunicable Diseases and Other Health Issues
Table 14.7.2 Alcohol consumption: Men
Percentage of men age 15-49 who drink alcohol, and among those who drink alcohol, the number of days at least one alcoholic drink was consumed, and
the mean number of days, by background characteristics, Kenya 2014
Among men who drink alcohol, the number of days at least one drink
Percentage was consumed in the last two weeks Mean
Background who drink Number of 14 Number of number of
characteristic alcohol men 0 1-2 3-4 5-9 10-13 (daily) Missing Total men days
Age
15-19 5.5 2,540 38.6 47.2 5.9 1.2 3.2 3.9 0.0 100.0 141 1.9
20-24 24.0 2,125 23.7 42.3 16.3 12.0 1.4 4.3 0.0 100.0 510 2.7
25-29 35.8 2,104 22.6 41.9 17.0 10.0 3.0 5.5 0.0 100.0 754 2.9
30-34 44.7 1,785 15.3 36.5 19.1 11.7 3.5 13.7 0.3 100.0 798 4.3
35-39 39.9 1,483 13.9 34.0 17.6 14.1 4.8 15.6 0.0 100.0 591 4.8
40-44 37.8 1,224 14.7 34.8 18.2 12.5 5.0 14.3 0.5 100.0 463 4.5
45-49 36.5 800 14.9 39.6 16.4 11.5 3.5 13.9 0.1 100.0 292 4.3
Residence
Urban 34.6 5,300 21.9 38.5 15.8 10.0 3.4 10.5 0.0 100.0 1,832 3.6
Rural 25.4 6,762 15.2 38.6 瘣8.6 13.1 3.5 10.8 0.3 100.0 1,717 3.9
Region
Coast 26.4 1,260 20.5 33.5 15.1 11.5 4.2 15.1 0.1 100.0 332 4.3
North Eastern 2.0 227 * * * * * * * 100.0 5 *
Eastern 35.0 1,825 11.6 43.0 20.8 11.1 1.9 10.9 0.6 100.0 638 3.7
Central 36.7 1,564 23.5 41.5 12.8 10.7 4.4 7.2 0.0 100.0 574 3.3
Rift Valley 24.9 3,050 11.8 38.7 20.0 15.2 3.8 10.5 0.0 100.0 760 4.2
Western 25.7 1,164 14.2 37.6 21.1 12.0 6.1 9.0 0.0 100.0 299 4.1
Nyanza 21.9 1,405 18.5 29.7 15.2 14.1 4.2 18.2 0.0 100.0 307 4.9
Nairobi 40.3 1,568 30.8 38.2 14.1 6.5 1.9 8.5 0.0 100.0 633 2.9
Education
No education 23.6 345 5.2 32.5 20.9 14.2 3.0 24.3 0.0 100.0 82 5.9
Primary incomplete 26.1 3,071 12.8 38.4 20.2 12.3 3.4 12.4 0.5 100.0 802 4.2
Primary complete 31.8 2,734 16.0 38.4 14.5 13.0 3.2 14.8 0.0 100.0 869 4.3
Secondary+ 30.4 5,913 23.1 38.9 16.8 10.2 3.7 7.2 0.1 100.0 1,796 3.2
Wealth quintile
Lowest 25.2 1,691 9.6 36.2 21.5 14.5 4.5 13.7 0.2 100.0 425 4.6
Second 26.4 2,145 11.4 39.0 20.5 13.4 3.7 11.3 0.7 100.0 567 4.2
Middle 26.6 2,370 15.8 39.0 20.0 10.0 2.2 13.0 0.0 100.0 630 3.9
Fourth 29.1 2,959 21.0 38.8 12.4 10.7 4.3 12.7 0.0 100.0 860 4.0
Highest 36.8 2,897 25.9 38.6 15.7 10.7 3.1 6.0 0.0 100.0 1,066 3.0
Total 15-49 29.4 12,063 18.6 38.5 17.1 11.5 3.5 10.6 0.1 100.0 3,549 3.8
50-54 42.5 756 13.6 39.2 19.1 8.5 5.4 14.2 0.1 100.0 322 4.3
Total 15-54 30.2 12,819 18.2 38.6 17.3 11.2 3.6 10.9 0.1 100.0 3,871 3.8
Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
14.7 PHYSICAL ACTIVITY
The World Health Organization (WHO) defines physical activity as any bodily movement
produced by skeletal muscles that requires energy expenditure, including activities undertaken while
working, playing, carrying out household chores, travelling, and engaging in recreational pursuits. In order
to be beneficial for cardiorespiratory health, physical activity should be performed in bouts of at least 10
minutes in duration. WHO recommends regular and adequate levels of physical activity to reduce the risk
of NCDs, including hypertension, coronary heart disease, stroke, diabetes, and breast and colon cancer
(WHO, 2010b).
In the 2014 KDHS, women and men age 15-49 were asked if they engaged in exercise that causes
an increase in their heart rate for at least 10 minutes continuously at work or during other activities. Results
are shown in Tables 14.8.1 and 14.8.2 for women and men, respectively. Sixty-two percent of women and
59 percent of men are not involved in exercise that causes an increase in their heart rate for at least 10
minutes continuously. Among women, 15 percent are involved in exercise at work, 12 percent are involved
in exercise outside of work, and 12 percent are involved in exercise both at work and elsewhere. Among
men, 18 percent are involved in exercise at work, 24 percent in exercise outside of work, and 17 percent in
exercise both at work and elsewhere.
Noncommunicable Diseases and Other Health Issues • 265
Overall, women above age 35, women in rural areas, and women in Western region were more
likely to engage in 10 minutes of continuous physical activity than their counterparts. Women with no
education and women in the highest wealth quintile were less likely to be physically active for 10 minutes
continuously. In contrast, men age 29 and younger and those in Coast and North Eastern regions were
more likely to engage in 10 minutes of continuous physical activity than their counterparts. Men with a
secondary or higher education were more likely to be physically active, as were men in the highest wealth
quintile.
Table 14.8.1 Physical activity: Women
Percent distribution of women age 15-49 who are involved in exercise that causes an increase in their heart rate for at
least 10 minutes continuously, by background characteristics, Kenya 2014
During other
Background physical Number of
characteristic At work activities Both Neither Total women
Age
15-19 5.7 23.6 9.7 60.9 100.0 2,717
20-24 11.8 12.1 10.9 65.1 100.0 2,691
25-29 16.2 9.2 12.0 62.5 100.0 2,932
30-34 17.4 7.9 11.5 63.1 100.0 2,162
35-39 19.8 8.7 12.1 59.4 100.0 1,780
40-44 21.8 9.1 14.4 54.6 100.0 1,292
45-49 23.8 8.3 13.0 54.9 100.0 1,052
Residence
Urban 13.4 13.0 8.0 65.4 100.0 5,929
Rural 16.2 11.4 14.0 58.3 100.0 8,696
Region
Coast 5.9 15.3 6.7 71.9 100.0 1,421
North Eastern 9.8 1.5 4.1 84.6 100.0 299
Eastern 12.1 9.7 8.5 69.7 100.0 2,066
Central 15.1 10.4 11.8 62.6 100.0 1,905
Rift Valley 15.6 10.3 12.9 61.0 100.0 3,714
Western 21.8 13.6 25.5 39.1 100.0 1,571
Nyanza 20.7 16.0 11.8 51.4 100.0 1,908
Nairobi 13.6 14.2 4.6 67.5 100.0 1,742
Education
No education 12.9 8.9 11.7 66.3 100.0 1,015
Primary incomplete 17.4 11.7 12.8 57.9 100.0 3,793
Primary complete 19.0 8.0 11.7 61.3 100.0 3,543
Secondary+ 11.8 15.1 10.7 62.2 100.0 6,274
Wealth quintile
Lowest 14.2 10.6 13.8 61.4 100.0 2,236
Second 18.9 11.6 14.4 55.2 100.0 2,590
Middle 17.9 11.8 13.4 56.9 100.0 2,859
Fourth 14.2 11.8 11.3 62.5 100.0 3,113
Highest 11.6 13.7 7.3 67.2 100.0 3,827
Total 15-49 15.1 12.1 11.6 61.1 100.0 14,625
266 • Noncommunicable Diseases and Other Health Issues
Table 14.8.2 Physical activity: Men
Percent distribution of men age 15-49 who are involved in exercise that causes an increase in their heart rate for at
least 10 minutes continuously, by background characteristics, Kenya 2014
During other
Background physical
characteristic At work activities Both Neither Total Number
Age
15-19 4.3 46.7 12.6 36.0 100.0 2,540
20-24 13.7 30.3 18.7 36.9 100.0 2,125
25-29 23.3 17.8 20.3 38.2 100.0 2,104
30-34 22.6 14.9 18.5 43.8 100.0 1,785
35-39 25.7 12.5 17.8 44.0 100.0 1,483
40-44 24.5 10.3 17.6 47.3 100.0 1,224
45-49 25.8 12.0 16.5 45.4 100.0 800
Residence
Urban 19.4 23.6 17.0 39.6 100.0 5,300
Rural 17.1 24.1 17.5 41.1 100.0 6,762
Region
Coast 14.4 16.4 12.7 56.2 100.0 1,260
North Eastern 15.1 23.9 2.7 58.3 100.0 227
Eastern 16.9 18.6 24.5 39.8 100.0 1,825
Central 24.7 15.7 23.0 36.4 100.0 1,564
Rift Valley 17.9 21.0 17.1 43.3 100.0 3,050
Western 9.7 36.1 8.6 45.4 100.0 1,164
Nyanza 22.4 29.0 18.3 30.2 100.0 1,405
Nairobi 19.1 36.1 14.8 30.0 100.0 1,568
Education
No education 20.3 8.7 19.6 51.4 100.0 345
Primary incomplete 20.0 23.1 14.0 42.8 100.0 3,071
Primary complete 23.5 14.0 18.8 43.7 100.0 2,734
Secondary+ 14.6 29.7 18.2 37.1 100.0 5,913
Wealth quintile
Lowest 18.8 20.3 17.8 42.9 100.0 1,691
Second 20.7 21.1 16.3 41.7 100.0 2,145
Middle 17.5 24.5 17.7 40.0 100.0 2,370
Fourth 17.9 22.3 19.1 40.5 100.0 2,959
Highest 16.4 29.1 15.6 38.4 100.0 2,897
Total 15-49 18.1 23.9 17.3 40.5 100.0 12,063
50-54 29.0 10.5 13.0 47.5 100.0 756
Total 15-54 18.8 23.1 17.0 40.9 100.0 12,819
14.8 UNINTENTIONAL INJURY
Tables 14.9.1 and 14.9.2 present the percentage of women and men age 15-49 unintentionally
injured in the past 12 months and the percentage involved in a road traffic accident in the past 12 months.
Among those injured unintentionally (excluding involvement in traffic accidents), the cause of injury is
presented, according to background characteristics. One in five women (20 percent) and one in three men
(33 percent) experienced an unintentional injury in the past 12 months. Three percent of women and 9
percent of men were involved in a road traffic accident in the last 12 months. Excluding road traffic
accidents, the most common causes of injury among women were cuts (60 percent), falls (40 percent), and
burns (20 percent). Among men, the most common causes were cuts (66 percent) and falls (33 percent).
Although there are no apparent rural-urban differences, there are differences by region; overall,
women in Nyanza (29 percent) were more likely to have been injured in the past 12 months than women in
other regions. Also, women in Nyanza were more likely than those in other regions to have been involved
in a road traffic accident (5 percent) and to report having been cut (70 percent).
Noncommunicable Diseases and Other Health Issues • 267
Younger men age 15-19 and 20-24 (both 35 percent) were slightly more likely to have
experienced an unintentional injury in the past 12 months than older men (31-34 percent). Also, men in
rural areas (37 percent) were more likely to have experienced an injury than urban men. A higher
proportion of men in Nyanza (46 percent) and Central regions (43 percent) reported having been injured.
Men in Nyanza were more likely to report involvement in a road traffic accident (18 percent).
Unintentional injury does not appear to be related to education or wealth among either women or men.
Table 14.9.1 Unintentional injury: Women
Percentage of women age 15-49 unintentionally injured in the past 12 months and percentage involved in a road traffic accident in the past 12 months;
and among women injured unintentionally, the percentage by cause of injury, according to background characteristics, Kenya 2014
Percentage
involved in
Percentage a road Number of
unintention- traffic women
ally injured accident in Number Cause of injury: injured
Background in the past the past 12 of Poison- Near Animal unintention-
characteristic 12 months months1 women Fall Burn ing Cut drowning bite Shooting Other ally2
Age
15-19 22.5 2.3 2,717 40.8 18.1 0.3 60.1 0.0 2.9 0.0 4.1 570
20-24 20.6 4.1 2,691 38.6 26.4 0.3 58.6 0.5 2.8 0.0 2.3 485
25-29 18.4 3.1 2,932 37.4 22.6 2.3 59.7 0.2 2.5 0.0 1.2 471
30-34 17.6 2.0 2,162 35.2 17.4 1.9 61.4 0.0 1.7 0.0 3.2 352
35-39 17.4 3.7 1,780 37.5 17.8 1.0 63.9 0.1 1.8 0.0 5.2 272
40-44 20.0 3.4 1,292 47.3 16.9 0.4 56.0 0.4 2.3 0.0 6.5 229
45-49 21.3 4.9 1,052 48.8 15.0 0.9 60.1 0.0 1.6 0.3 3.1 186
Residence
Urban 19.6 3.7 5,929 38.7 21.4 1.6 57.4 0.1 0.8 0.0 3.3 1,010
Rural 19.8 2.9 8,696 40.5 19.2 0.7 61.6 0.2 3.4 0.0 3.5 1,554
Region
Coast 15.5 2.5 1,421 42.5 45.7 0.5 48.7 0.0 6.4 0.0 2.5 195
North Eastern 4.0 0.5 299 (48.3) (38.1) (0.0) (43.4) (0.0) (18.2) (0.0) (0.0) 11
Eastern 22.3 3.0 2,066 48.9 11.0 0.4 62.0 0.2 1.2 0.0 1.7 415
Central 14.9 2.4 1,905 37.5 15.1 0.5 63.6 0.0 0.9 0.0 3.3 245
Rift Valley 18.2 2.9 3,714 40.5 17.1 1.2 56.9 0.2 2.8 0.1 3.9 607
Western 21.4 4.2 1,571 31.7 16.7 0.8 66.2 0.2 2.6 0.0 5.0 294
Nyanza 28.5 4.6 1,908 35.6 29.6 1.0 69.9 0.4 2.9 0.0 2.4 497
Nairobi 20.0 3.7 1,742 40.8 12.6 2.8 45.8 0.0 0.0 0.0 5.3 301
Education
No education 10.2 1.3 1,015 42.0 25.2 0.3 61.8 0.0 5.4 0.5 2.6 95
Primary incomplete 19.8 3.0 3,793 40.0 18.2 0.8 57.6 0.2 3.4 0.0 6.2 676
Primary complete 19.2 3.4 3,543 43.4 21.6 0.8 58.3 0.2 1.9 0.0 3.2 599
Secondary+ 21.4 3.6 6,274 37.8 19.9 1.3 62.0 0.2 1.8 0.0 1.9 1,195
Wealth quintile
Lowest 17.9 1.9 2,236 47.6 21.8 0.6 59.7 0.0 4.8 0.1 2.8 378
Second 20.3 3.2 2,590 44.2 15.3 0.2 66.0 0.4 2.6 0.0 3.9 474
Middle 19.8 3.1 2,859 38.4 19.4 1.3 61.5 0.3 1.4 0.0 2.8 509
Fourth 20.6 4.2 3,113 37.5 23.3 0.8 55.6 0.1 3.5 0.0 4.7 549
Highest 19.5 3.3 3,827 35.2 20.3 1.7 58.1 0.2 0.6 0.0 2.7 654
Total 19.7 3.2 14,625 39.8 20.0 1.0 60.0 0.2 2.4 0.0 3.4 2,564
Note: Figures in parentheses are based on 25-49 unweighted cases.
1
As a driver, passenger, pedestrian, or cyclist
2
Excludes involvement in or injury caused by road traffic accidents
268 • Noncommunicable Diseases and Other Health Issues
Table 14.9.2 Unintentional injury: Men
Percentage of men age 15-49 unintentionally injured in the past 12 months and percentage involved in a road traffic accident in the past 12 months; and
among women injured unintentionally, the percentage by cause of injury, according to background characteristics, Kenya 2014
Percentage
involved in
Percentage a road Number of
unintention- traffic men
ally injured accident in Cause of injury: injured
Background in the past the past 12 Number Poison- Near Animal unintention-
characteristic 12 months months1 of men Fall Burn ing Cut drowning bite Shooting Other ally2
Age
15-19 35.0 6.5 2,540 41.6 1.9 0.6 58.3 0.1 1.9 0.0 8.5 792
20-24 35.0 10.2 2,125 35.8 4.2 1.2 56.4 0.2 1.1 0.0 12.6 616
25-29 32.7 11.3 2,104 27.6 2.6 0.7 71.9 0.4 0.3 0.2 10.1 561
30-34 32.8 10.4 1,785 34.5 2.8 0.9 69.1 0.0 1.2 0.2 5.2 447
35-39 30.9 7.2 1,483 22.6 2.4 0.1 76.7 0.3 0.6 0.0 7.4 397
40-44 33.5 9.3 1,224 26.3 2.4 0.4 68.4 0.4 0.6 0.0 11.8 324
45-49 32.2 8.7 800 29.3 2.8 0.6 68.9 0.0 1.3 0.0 11.2 210
Residence
Urban 29.5 7.9 5,300 35.6 3.3 0.2 62.6 0.1 0.9 0.1 10.2 1,293
Rural 36.5 10.0 6,762 30.9 2.4 1.0 67.3 0.3 1.2 0.1 9.0 2,055
Region
Coast 19.8 7.2 1,260 28.0 5.6 0.3 75.5 0.2 2.4 0.0 5.0 171
North Eastern 8.0 1.9 227 (50.8) (10.3) (0.0) (50.7) (0.0) (1.8) (0.0) (0.0) 15
Eastern 36.2 8.5 1,825 32.0 2.2 1.9 64.9 0.2 1.2 0.0 6.2 559
Central 43.2 7.0 1,564 27.6 1.6 0.0 72.3 0.1 0.7 0.0 6.5 616
Rift Valley 37.2 8.3 3,050 30.6 3.1 0.4 66.6 0.1 1.2 0.0 12.7 983
Western 29.3 9.5 1,164 33.9 0.5 1.8 68.2 0.0 1.1 0.0 2.6 268
Nyanza 46.3 17.5 1,405 39.0 2.8 0.8 62.3 0.6 1.2 0.4 8.7 525
Nairobi 19.2 7.9 1,568 (45.0) (5.8) (0.0) (39.2) (0.0) (0.0) (0.0) (26.3) 210
Education
No education 20.5 6.1 345 30.4 4.0 2.5 52.3 0.0 6.0 0.0 16.4 53
Primary incomplete 36.7 10.6 3,071 32.9 2.2 0.8 65.9 0.3 1.4 0.2 9.1 930
Primary complete 35.6 9.4 2,734 29.6 2.2 0.4 71.0 0.2 0.9 0.0 8.5 814
Secondary+ 31.5 8.3 5,913 34.4 3.3 0.8 62.8 0.1 0.9 0.1 9.9 1,551
Wealth quintile
Lowest 31.4 9.2 1,691 34.6 1.8 0.7 62.3 0.3 1.1 0.3 12.3 424
Second 38.2 10.0 2,145 32.0 2.7 1.7 66.0 0.4 1.4 0.0 8.6 705
Middle 36.6 10.6 2,370 31.2 2.3 0.5 65.3 0.0 1.0 0.2 9.3 716
Fourth 33.3 8.9 2,959 29.9 2.7 0.1 70.8 0.2 1.1 0.0 7.7 821
Highest 28.6 7.3 2,897 37.3 3.9 0.6 60.8 0.1 0.8 0.0 10.8 681
Total 15-49 33.4 9.1 12,063 32.7 2.7 0.7 65.5 0.2 1.1 0.1 9.4 3,347
50-54 26.7 3.7 756 24.8 3.2 0.0 70.6 0.0 1.8 0.0 10.3 186
Total 15-54 33.0 8.8 12,819 32.3 2.8 0.7 65.8 0.2 1.1 0.1 9.5 3,533
Note: Figures in parentheses are based on 25-49 unweighted cases.
1
As a driver, passenger, pedestrian, or cyclist
2
Excludes involvement in or injury caused by road traffic accidents
14.9 HEALTH INSURANCE COVERAGE
Health insurance can be crucial in disease management and access to quality health care since
insurance can reduce the costs associated with illness, treatment, and care substantially. The 2014 KDHS
asked respondents if they were covered by any health insurance and, if so, what type. Table 14.10.1 and
Table 14.10.2 show the percentage of women and men age 15-49 with specific types of health insurance.
Most Kenyans age 15-49 do not have health insurance (82 percent of women and 79 percent of men).
Among those who do have coverage, the national insurance scheme is the most common type for
both women (14 percent) and men (18 percent). Employer-based insurance is the next most common,
accounting for 2 percent of insured women and 3 percent of insured men. One percent or less of women
and men have some other type of insurance. Health insurance coverage is more common among women
and men age 25 or above, those living in urban areas, and those residing in Nairobi and Central regions.
Insurance coverage increases with increasing education and wealth.
Noncommunicable Diseases and Other Health Issues • 269
Table 14.10.1 Health insurance coverage: Women
Percentage of women age 15-49 with specific types of health insurance coverage, according to background characteristics, Kenya 2014
Mutual Health
Organisation/ Privately
National Employer community purchased
Background insurance based based commercial Pre-payment Number of
characteristic scheme insurance insurance insurance scheme Other None women
Age
15-19 5.6 0.6 0.1 0.7 0.0 0.4 92.6 2,717
20-24 10.8 1.5 0.1 0.7 0.3 0.0 86.8 2,691
25-29 17.9 3.4 0.7 1.5 0.0 0.0 77.3 2,932
30-34 19.0 2.6 0.4 1.5 0.0 0.2 77.2 2,162
35-39 18.1 3.3 0.4 1.4 0.0 0.3 76.7 1,780
40-44 18.0 3.8 0.5 0.9 0.3 0.4 77.1 1,292
45-49 15.2 3.4 0.3 1.0 0.1 1.2 79.7 1,052
Residence
Urban 19.5 4.2 0.5 2.0 0.1 0.2 74.6 5,929
Rural 10.8 1.2 0.3 0.6 0.1 0.3 87.0 8,696
Region
Coast 8.3 1.7 0.1 0.8 0.0 0.0 89.4 1,421
North Eastern 3.1 2.0 0.2 0.2 0.0 0.0 94.5 299
Eastern 15.3 1.6 0.2 0.5 0.1 0.0 82.4 2,066
Central 20.1 2.9 1.4 1.8 0.1 1.1 73.1 1,905
Rift Valley 14.1 2.6 0.3 1.5 0.0 0.2 81.6 3,714
Western 6.1 0.8 0.1 0.1 0.2 0.0 93.0 1,571
Nyanza 15.5 1.8 0.3 0.2 0.0 0.2 83.0 1,908
Nairobi 20.3 5.2 0.2 2.6 0.3 0.3 72.4 1,742
Education
No education 1.5 0.5 0.2 0.3 0.0 0.0 97.6 1,015
Primary incomplete 5.6 0.5 0.1 0.3 0.1 0.2 93.4 3,793
Primary complete 10.6 1.4 0.4 0.7 0.1 0.4 86.4 3,543
Secondary+ 23.8 4.5 0.5 2.0 0.1 0.3 70.0 6,274
Wealth quintile
Lowest 2.1 0.3 0.2 0.2 0.1 0.0 97.2 2,236
Second 4.9 0.8 0.1 0.4 0.0 0.2 93.7 2,590
Middle 10.6 1.1 0.2 0.6 0.1 0.2 87.3 2,859
Fourth 18.1 2.0 0.5 0.4 0.3 0.5 78.6 3,113
Highest 27.5 6.1 0.7 3.2 0.0 0.3 63.8 3,827
Total 14.3 2.4 0.4 1.1 0.1 0.3 82.0 14,625
270 • Noncommunicable Diseases and Other Health Issues
Table 14.10.2 Health insurance coverage: Men
Percentage of men age 15-49 with specific types of health insurance coverage, according to background characteristics, Kenya 2014
Mutual Health
Organisation/ Privately
National Employer community purchased
Background insurance based based commercial Pre-payment Number of
characteristic scheme insurance insurance insurance scheme Other None men
Age
15-19 7.2 0.3 0.3 0.6 0.0 0.2 91.4 2,540
20-24 10.7 1.8 0.1 0.6 0.0 0.2 86.8 2,125
25-29 19.9 4.1 0.2 1.4 0.5 0.0 75.9 2,104
30-34 24.8 6.0 0.0 1.7 0.3 0.1 70.0 1,785
35-39 23.9 3.2 0.0 1.5 0.1 0.3 72.5 1,483
40-44 25.2 6.4 0.1 1.7 0.2 0.3 69.4 1,224
45-49 22.2 5.6 0.2 1.5 0.7 0.8 71.2 800
Residence
Urban 24.4 6.0 0.0 1.9 0.3 0.2 69.8 5,300
Rural 12.2 1.4 0.2 0.6 0.2 0.2 85.9 6,762
Region
Coast 6.0 5.2 0.0 2.0 0.0 0.2 86.8 1,260
North Eastern 3.2 0.3 0.0 0.1 0.2 0.0 96.3 227
Eastern 14.6 1.3 0.0 0.6 0.8 0.0 83.0 1,825
Central 21.8 2.2 0.8 1.3 0.2 0.3 74.1 1,564
Rift Valley 18.4 2.6 0.1 1.1 0.0 0.4 78.4 3,050
Western 12.5 1.2 0.0 1.1 0.1 0.1 86.7 1,164
Nyanza 15.8 3.8 0.1 1.2 0.0 0.1 80.8 1,405
Nairobi 31.7 8.8 0.0 1.4 0.5 0.2 63.2 1,568
Education
No education 2.2 0.6 0.0 0.4 0.0 0.1 96.8 345
Primary incomplete 7.1 0.5 0.1 0.3 0.1 0.1 91.9 3,071
Primary complete 13.4 1.1 0.1 0.2 0.3 0.1 85.0 2,734
Secondary+ 25.7 6.1 0.2 2.1 0.2 0.3 68.2 5,913
Wealth quintile
Lowest 2.9 0.3 0.0 0.2 0.0 0.2 96.5 1,691
Second 8.2 0.9 0.1 0.2 0.0 0.1 90.6 2,145
Middle 12.9 1.4 0.3 0.4 0.1 0.2 85.2 2,370
Fourth 20.6 3.0 0.2 0.6 0.4 0.2 75.7 2,959
Highest 33.5 9.0 0.1 3.7 0.4 0.4 57.9 2,897
Total 15-49 17.5 3.4 0.1 1.2 0.2 0.2 78.8 12,063
50-54 19.0 6.8 1.2 3.9 0.3 0.6 70.9 756
Total 15-54 17.6 3.6 0.2 1.3 0.2 0.2 78.4 12,819
Noncommunicable Diseases and Other Health Issues • 271
WOMEN’S EMPOWERMENT AND DEMOGRAPHIC
AND HEALTH OUTCOMES 15
Robert C.B. Buluma, Rosemary Uside Kongani, Godfrey Odhiambo Otieno
Key Findings
• Nearly half (49 percent) of currently married employed women who earn
cash make independent decisions about how to spend their earnings, an
increase from the figure of 42 percent reported in the 2008-09 KDHS.
• Fifty-four percent of currently married women participate in four common
household decisions, including decisions pertaining to their own health
care, major household purchases, visits to their family or relatives, and
major household purchases. Thirty-nine percent of women have the main
say in their own health care.
• Contraceptive use increases with women’s empowerment.
• In general, unmet need for family planning decreases with improvements
in women’s empowerment.
• Access to antenatal care, delivery assistance from a skilled provider, and
postnatal care within the first two days of delivery increases with
increasing women’s empowerment.
W omen’s empowerment encompasses women’s sense of self-worth, access to opportunities,
access to and control of resources, choices and the ability to exercise them, control over their
own lives, and influence over the direction of social change (United Nations Population
Information Network, 1995).
Women’s empowerment is supported internationally and in Kenya. The 1994 International
Conference on Population and Development declared that “advancing gender equality and equity and the
empowerment of women and the elimination of all kinds of violence against women, and ensuring
women’s ability to control their own fertility are cornerstones of population and development-related
programs” (United Nations, 1994). Furthermore, Kenya is a signatory to many international conventions
on human rights, women’s rights, reproductive health rights, and children’s rights, as well as to agreements
on international goals regarding education, health, and poverty eradication. As a signatory to the United
Nations Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) and as
mandated by the Constitution of Kenya promulgated in 2010, the government of Kenya is committed to
ensuring nondiscrimination, gender equity, and social justice.
This chapter presents data on the status of women in Kenya, including information on
employment, access to and control over cash earnings, asset ownership, participation in household decision
making, relative earnings of husbands and wives, and attitudes towards wife beating. The chapter also
explores how demographic and health indicators are affected by women’s empowerment, as measured by
the number of decisions in which women participate and the number of situations in which they believe
wife beating is justified. The ranking of women on these indices has been found to be associated with
demographic and health outcomes, including contraceptive use, unmet need for family planning, access to
reproductive health care, and child survival.
Women’s Empowerment and Demographic and Health Outcomes • 273
15.1 EMPLOYMENT AND FORM OF EARNINGS
Employment, especially for cash, and control over how earnings are used are important indicators
of empowerment for both women and men. Table 15.1 shows the percentage of currently married women
and men age 15-49 who were employed at any time in the 12 months before the survey and the percent
distribution of employed women and men by the type of earnings they received (cash only, cash and in-
kind, in-kind only). Only 75 percent of currently married women age 15-49 were employed in the past 12
months, as compared with virtually all currently married men. The results show an improvement over time
in the proportion of employed women, from 67 percent in the 2008-09 KDHS to 75 percent in 2014. There
is no difference from 2008-09 to 2014 in the proportion of men who were employed. The majority of those
employed were earning cash only (women, 61 percent; men, 82 percent); 15 percent of women and 10
percent of men had cash and in-kind earnings, and 4 percent of women and 1 percent of men had in-kind
earnings only. A higher proportion of women (20 percent) than men (7 percent) were not paid for their
work.
Table 15.1 Employment and cash earnings of currently married women and men
Percentage of currently married women and men age 15-49 who were employed at any time in the past 12 months and the percent
distribution of currently married women and men employed in the past 12 months by type of earnings, according to age, Kenya 2014
Among currently married Percent distribution of currently married respondents employed in
respondents: the past 12 months, by type of earnings
Percentage
employed in
past 12 Number of Cash and Missing/ Number of
Age months respondents Cash only in-kind In-kind only Not paid don’t know Total respondents
WOMEN
15-19 48.0 301 60.3 9.6 9.0 21.1 0.0 100.0 145
20-24 61.8 1,465 62.5 11.1 6.1 20.2 0.1 100.0 905
25-29 72.4 2,171 65.2 12.6 4.2 18.0 0.1 100.0 1,573
30-34 78.1 1,717 63.9 14.8 3.2 18.1 0.0 100.0 1,341
35-39 80.3 1,365 60.6 15.8 3.3 19.6 0.6 100.0 1,096
40-44 85.4 923 56.7 18.3 4.7 20.3 0.0 100.0 788
45-49 86.1 768 51.3 20.7 4.0 23.7 0.3 100.0 661
Total 15-49 74.7 8,710 61.2 14.8 4.3 19.5 0.2 100.0 6,508
MEN
15-19 * 16 * * * * * * 14
20-24 98.9 377 85.2 7.6 1.9 5.3 0.0 100.0 373
25-29 99.8 1,201 84.3 8.2 1.1 6.3 0.1 100.0 1,198
30-34 99.9 1,398 82.9 9.4 1.3 6.3 0.1 100.0 1,396
35-39 99.7 1,277 82.5 10.0 1.2 6.3 0.0 100.0 1,273
40-44 99.4 1,100 80.1 10.7 1.7 7.4 0.1 100.0 1,093
45-49 99.0 727 75.7 13.6 1.3 9.3 0.0 100.0 720
Total 15-49 99.5 6,095 81.9 9.9 1.4 6.8 0.1 100.0 6,067
50-54 98.5 667 73.4 13.7 1.6 11.4 0.0 100.0 656
Total 15-54 99.4 6,762 81.0 10.3 1.4 7.2 0.1 100.0 6,724
Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
Employment increases with age. Younger women age 15-19 are less likely to be employed (48
percent) than older women age 45-49 (86 percent). The proportion who earn cash only is higher among
women age 25-29 (65 percent) and among men age 20-24 (85 percent), while women and men age 45-49
are least likely to earn cash only (51 percent and 76 percent of men).
15.2 CONTROL OVER AND RELATIVE MAGNITUDE OF WOMEN’S AND HUSBANDS’
EARNINGS
Access to and control of resources, including women’s control over their earnings, is one
dimension of women’s empowerment. Currently married women who earn cash for their work were asked
who mainly decides how the cash is used and the relative magnitude of their earnings compared with their
husband’s earnings. This information may provide some insight into women’s empowerment within the
family and the extent of their control over decision making in the household. It is expected that
employment and earnings are more likely to empower women if women themselves control their own
earnings and perceive their earnings as significant relative to those of their husbands or partners.
274 • Women’s Empowerment and Demographic and Health Outcomes
15.2.1 Control over Wife’s Earnings
Table 15.2.1 shows the percent distribution of currently married women age 15-49 who received
cash earnings in the 12 months preceding the survey according to the person who decides how their
earnings are used and according to whether they earn more or less than their husband, by background
characteristics. One-half (49 percent) of currently married women decide how their cash earnings are used,
while 41 percent of women report that decisions about their earnings are made jointly with their husbands.
Only a small proportion of women (9 percent) report that decisions about their earnings are made
exclusively by their husbands. Seventy-two percent of currently married women age 15-49 earn less than
their husbands, while 11 percent earn more and 13 percent earn about the same.
Table 15.2.1 Control over women’s cash earnings and relative magnitude of women’s cash earnings
Percent distribution of currently married women age 15-49 who received cash earnings for employment in the 12 months preceding the survey by person who decides
how wife’s cash earnings are used and by whether she earned more or less than her husband, according to background characteristics, Kenya 2014
Person who decides how the wife’s Wife’s cash earnings compared with
cash earnings are used: husband’s cash earnings:
Wife and Husband Don’t
Background Mainly husband Mainly About the has no know/ Number
characteristic wife jointly husband Other Missing Total More Less same earnings missing Total of women
Age
15-19 57.6 30.9 10.6 0.9 0.0 100.0 3.2 81.6 12.9 1.3 1.1 100.0 101
20-24 47.4 38.7 12.5 0.1 1.3 100.0 9.2 76.8 10.7 0.8 2.4 100.0 665
25-29 45.0 45.5 8.9 0.0 0.5 100.0 9.0 77.4 10.6 0.9 2.2 100.0 1,224
30-34 51.1 40.3 8.0 0.1 0.6 100.0 9.5 73.6 13.7 1.4 1.7 100.0 1,055
35-39 51.0 40.1 8.7 0.0 0.1 100.0 13.6 69.9 11.0 2.8 2.7 100.0 838
40-44 50.2 42.8 6.3 0.1 0.6 100.0 13.6 63.2 17.7 3.6 1.9 100.0 591
45-49 53.9 37.7 7.3 0.0 1.1 100.0 16.5 60.8 15.0 3.7 4.0 100.0 476
Number of living
children
0 43.9 48.9 6.3 0.0 0.8 100.0 8.2 76.9 9.3 3.9 1.7 100.0 262
1-2 46.6 44.1 8.8 0.1 0.4 100.0 10.8 74.4 12.0 0.9 1.8 100.0 2,009
3-4 49.9 40.5 8.6 0.0 0.9 100.0 10.7 71.6 13.2 1.8 2.6 100.0 1,634
5+ 55.3 34.7 9.5 0.1 0.5 100.0 12.9 66.9 13.7 3.5 3.0 100.0 1,046
Residence
Urban 53.8 38.3 7.2 0.1 0.6 100.0 10.9 76.5 8.9 1.2 2.4 100.0 2,184
Rural 45.9 43.4 10.0 0.1 0.6 100.0 11.2 68.5 15.6 2.5 2.3 100.0 2,767
Region
Coast 48.8 44.4 6.0 0.0 0.8 100.0 10.2 70.3 12.0 2.3 5.2 100.0 415
North Eastern 51.0 28.1 18.4 0.0 2.4 100.0 26.7 46.5 15.1 9.3 2.4 100.0 16
Eastern 32.6 59.3 7.4 0.0 0.8 100.0 12.4 70.8 13.2 0.9 2.7 100.0 620
Central 51.8 40.3 7.8 0.0 0.1 100.0 9.2 73.6 14.2 1.4 1.6 100.0 872
Rift Valley 44.6 44.7 10.1 0.0 0.6 100.0 12.8 69.0 14.5 2.3 1.4 100.0 1,163
Western 58.0 31.5 9.7 0.0 0.7 100.0 8.1 74.3 11.7 3.7 2.2 100.0 500
Nyanza 54.7 32.1 12.1 0.5 0.7 100.0 12.9 67.9 13.8 1.9 3.5 100.0 705
Nairobi 58.5 34.7 5.9 0.0 0.9 100.0 9.7 80.9 6.7 1.1 1.5 100.0 659
Education
No education 47.2 38.7 12.6 0.0 1.5 100.0 14.5 56.1 14.0 9.1 6.4 100.0 226
Primary incomplete 53.5 34.3 11.3 0.1 0.8 100.0 12.8 66.3 14.2 3.1 3.6 100.0 1,237
Primary complete 50.5 39.2 9.6 0.2 0.6 100.0 10.8 74.3 11.7 1.4 1.8 100.0 1,373
Secondary+ 46.5 46.8 6.2 0.0 0.4 100.0 9.8 75.6 12.3 0.8 1.5 100.0 2,115
Wealth quintile
Lowest 53.7 34.0 11.1 0.2 1.0 100.0 14.3 63.2 12.1 6.6 3.8 100.0 532
Second 49.2 38.6 11.6 0.0 0.6 100.0 11.4 69.1 15.1 1.6 2.8 100.0 816
Middle 46.1 43.5 9.4 0.0 1.0 100.0 10.9 72.0 12.9 1.7 2.5 100.0 943
Fourth 50.0 40.3 9.0 0.0 0.7 100.0 10.4 72.7 13.3 1.9 1.7 100.0 1,159
Highest 49.5 44.4 5.7 0.1 0.3 100.0 10.4 76.4 10.8 0.6 1.9 100.0 1,501
Total 49.4 41.2 8.7 0.1 0.6 100.0 11.1 72.0 12.6 1.9 2.3 100.0 4,951
The proportion of currently married women who make independent decisions on how their cash
earnings are used increased from 42 percent in 2008-09 to 49 percent in 2014. The data further show that
the proportion of wives who make decisions about their earnings jointly with their husbands declined from
49 percent to 41 percent over the same period.
There is no clear pattern in decision making on use of cash earnings according to the woman’s
age. Women age 15-19, women with more living children, urban women, and women in the lowest wealth
quintile are more likely than other women to act as the main decision makers in the use of their earnings.
Women in the Nairobi (59 percent) and Western (58 percent) regions are more likely to make decisions
alone about their earnings than those in other regions. Women in Eastern are least likely to be the main
Women’s Empowerment and Demographic and Health Outcomes • 275
decision makers regarding the use of their income; however, that region reported the highest proportion of
women who jointly make decisions with their husbands on the use of their earnings.
Regarding the relative magnitude of women’s earnings with those of their husbands, older women,
women with five or more children, and women who live in North Eastern region are more likely to earn
more than their husband. The likelihood a woman earns more than her husband decreases with education
and wealth.
15.2.2 Control over Husbands’ Earnings
Table 15.2.2 shows the percent distributions of currently married men age 15-49 who receive cash
earnings and currently married women age 15-49 whose husbands receive cash earnings, according to the
person who decides how the husband’s cash earnings are used. Fifty-five percent of currently married men
age 15-49 who earn cash decide jointly with their wives how their earnings are used, as compared with 49
percent of women who indicated that joint decisions are made. About 4 in 10 men and women (41 percent
and 42 percent, respectively) reported that the husband mainly decides how his cash earnings are used. A
small proportion (4 percent of currently married men and 9 percent of currently married women) reported
that the wife acts as the main decision maker in the use of the husband’s earnings.
Table 15.2.2 Control over men’s cash earnings
Percent distributions of currently married men age 15-49 who receive cash earnings and of currently married women age 15-49 whose husbands receive cash
earnings, by person who decides how husband’s cash earnings are used, according to background characteristics, Kenya 2014
Men Women
Person who decides how the husband’s Person who decides how the husband’s
cash earnings are used: cash earnings are used:
Husband Husband Number
Background Mainly and wife Mainly Number Mainly and wife Mainly of
characteristic wife jointly husband Other Missing Total of men wife jointly husband Other Missing Total women
Age
15-19 * * * * * 100.0 14 10.5 44.1 43.4 2.0 0.0 100.0 292
20-24 3.3 46.0 48.7 1.8 0.3 100.0 346 10.0 47.7 41.7 0.2 0.4 100.0 1,440
25-29 3.4 54.0 42.2 0.0 0.4 100.0 1,109 7.7 50.8 41.2 0.2 0.2 100.0 2,141
30-34 3.9 56.6 39.3 0.0 0.2 100.0 1,289 9.2 50.6 40.0 0.0 0.1 100.0 1,690
35-39 4.3 56.7 38.7 0.1 0.2 100.0 1,177 9.1 45.0 45.4 0.0 0.5 100.0 1,327
40-44 6.0 53.6 40.1 0.0 0.3 100.0 992 9.5 49.5 40.6 0.0 0.3 100.0 893
45-49 3.5 57.3 38.9 0.0 0.1 100.0 643 8.4 46.4 44.8 0.0 0.4 100.0 742
Number of living
children
0 1.2 49.6 47.5 0.6 1.1 100.0 367 11.2 58.0 30.2 0.2 0.3 100.0 469
1-2 3.9 57.7 38.0 0.2 0.2 100.0 2,410 8.4 51.6 39.5 0.3 0.2 100.0 3,408
3-4 5.4 56.3 38.1 0.1 0.2 100.0 1,751 8.4 47.4 43.6 0.1 0.4 100.0 2,732
5+ 3.8 48.1 47.8 0.1 0.3 100.0 1,041 10.2 42.5 47.1 0.0 0.2 100.0 1,917
Residence
Urban 3.7 55.0 41.1 0.0 0.2 100.0 2,816 8.5 50.0 40.9 0.1 0.5 100.0 3,406
Rural 4.6 54.9 39.8 0.3 0.4 100.0 2,754 9.2 47.6 42.8 0.2 0.2 100.0 5,120
Region
Coast 3.5 53.2 42.7 0.1 0.4 100.0 591 5.0 54.3 40.1 0.2 0.4 100.0 830
North Eastern 8.9 4.2 85.9 0.0 1.0 100.0 63 9.1 46.6 43.9 0.0 0.4 100.0 198
Eastern 2.0 69.1 28.6 0.0 0.3 100.0 762 5.9 62.7 31.2 0.0 0.2 100.0 1,252
Central 1.9 67.0 31.0 0.0 0.1 100.0 755 6.7 48.2 45.0 0.0 0.1 100.0 1,098
Rift Valley 2.4 48.8 48.1 0.1 0.5 100.0 1,343 7.9 48.3 43.4 0.1 0.3 100.0 2,121
Western 6.1 55.3 37.5 0.3 0.8 100.0 476 14.2 36.4 48.6 0.4 0.3 100.0 884
Nyanza 9.7 46.9 42.8 0.5 0.1 100.0 670 14.6 37.4 47.5 0.5 0.1 100.0 1,186
Nairobi 5.5 52.4 42.1 0.0 0.0 100.0 910 9.2 51.7 38.2 0.0 1.0 100.0 957
Education
No education 5.4 24.4 69.4 0.0 0.8 100.0 148 9.3 43.0 47.4 0.0 0.3 100.0 743
Primary incomplete 5.3 50.0 43.9 0.2 0.6 100.0 1,195 11.0 39.0 49.2 0.3 0.5 100.0 2,196
Primary complete 3.5 51.9 44.2 0.2 0.2 100.0 1,526 8.6 49.6 41.3 0.2 0.4 100.0 2,437
Secondary+ 4.0 60.5 35.2 0.1 0.2 100.0 2,701 7.7 55.8 36.3 0.1 0.2 100.0 3,149
Wealth quintile
Lowest 6.0 39.5 53.8 0.2 0.5 100.0 617 10.9 38.7 49.9 0.2 0.2 100.0 1,380
Second 6.6 56.4 36.2 0.3 0.5 100.0 892 10.2 46.3 43.0 0.3 0.3 100.0 1,530
Middle 3.9 51.5 44.1 0.2 0.4 100.0 989 10.8 48.3 40.3 0.0 0.5 100.0 1,638
Fourth 3.7 58.8 37.4 0.1 0.1 100.0 1,430 7.9 48.7 42.7 0.2 0.4 100.0 1,855
Highest 2.8 58.7 38.3 0.0 0.2 100.0 1,640 6.2 56.7 36.9 0.0 0.1 100.0 2,122
Total 15-49 4.2 54.9 40.5 0.1 0.3 100.0 5,570 8.9 48.6 42.0 0.2 0.3 100.0 8,526
50-54 5.1 52.9 41.8 0.0 0.3 100.0 572 na na na na na na na
Total 15-54 4.3 54.7 40.6 0.1 0.3 100.0 6,141 na na na na na na na
Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
na = Not applicable
276 • Women’s Empowerment and Demographic and Health Outcomes
There are few distinct patterns in how decisions about the use of a husband’s earnings are made.
However, younger men age 20-24, men with no education, and men in the lowest wealth quintile are more
likely than other men to have the main say in the use of their earnings. It follows that men with these
characteristics are less likely to engage in joint decision making in use of their earnings. Among women,
few patterns are discernible. The proportion of women who report that their husband mainly decides how
his earnings are used increases with increasing number of living children and, in general, decreases with
increasing education and with increasing wealth.
Regional variations are evident, with a much higher proportion of men in Eastern (69 percent) and
Central (67 percent) than in North Eastern (4 percent) indicating that the husband and wife jointly decide
how the husband’s cash earnings are used. In addition, women in Eastern (63 percent) are much more
likely than women in Nyanza (37 percent) and Western (36 percent) to report that they and their husbands
jointly decide how the husband’s cash earnings are used.
15.3 CONTROL OVER WOMEN’S EARNINGS AND RELATIVE SIZE OF HUSBAND’S AND
WIFE’S EARNINGS
Table 15.3 shows who decides how the woman’s cash earnings are used, according to the relative
magnitude of the woman’s and the husband’s cash earnings. Women whose cash earnings are less than
their husbands’ and women whose husbands do not have cash earnings are more likely to decide for
themselves how their earnings are used (53 percent and 60 percent, respectively). In contrast, women are
more likely to report joint decision making on the use of their earnings when they earn the same as their
husband (65 percent) or more than their husband (47 percent).
Table 15.3 Women’s control over their own earnings and over those of their husbands
Percent distribution of currently married women age 15-49 with cash earnings in the last 12 months by person who decides how the wife’s cash earnings are used and
percent distribution of currently married women age 15-49 whose husbands have cash earnings by person who decides how the husband’s cash earnings are used,
according to the relation between wife’s and husband’s cash earnings, Kenya 2014
Person who decides how the wife’s Person who decides how the husband’s
cash earnings are used: cash earnings are used:
Women’s earnings Wife and Number Wife and Number
relative to husband’s Mainly husband Mainly of Mainly husband Mainly of
earnings wife jointly husband Other Missing Total women wife jointly husband Other Missing Total women
More than husband 47.6 46.7 5.5 0.3 0.0 100.0 548 12.0 50.4 36.7 0.2 0.7 100.0 548
Less than husband 52.9 37.1 10.0 0.0 0.0 100.0 3,567 9.8 45.4 44.8 0.0 0.1 100.0 3,567
Same as husband 28.3 64.9 6.7 0.1 0.0 100.0 626 7.5 67.1 25.2 0.1 0.1 100.0 626
Husband has no cash
earnings or did not
work 59.9 39.1 1.0 0.0 0.0 100.0 95 na na na na na na na
Woman worked but
has no cash earnings na na na na na na na 10.4 51.1 38.3 0.1 0.1 100.0 1,517
Woman did not work na na na na na na na 6.2 47.6 45.8 0.4 0.1 100.0 2,153
Total1 49.4 41.2 8.7 0.1 0.6 100.0 4,951 8.9 48.6 42.0 0.2 0.3 100.0 8,526
na = Not applicable
1
Includes 122 cases where a woman does not know whether she earned more or less than her husband
Table 15.3 also shows who decides how the husband’s cash earnings are used, according to the
relative magnitude of the woman’s and the husband’s cash earnings. Women who earn less than their
husband are more likely to report that their husband has the main say in deciding use of his earnings.
Women who earn the same as their husband and women who earn more than their husband are more likely
to report joint decision making in the use of their husband’s earnings (67 percent and 50 percent,
respectively). Since the 2008-09 KDHS, these indicators show marginal improvements or have remained
stable.
15.4 OWNERSHIP OF ASSETS
Ownership and control of assets by women and men gives an enhanced picture of their access to
economic resources. Ownership of assets confers additional economic value, status, and bargaining power.
Women’s Empowerment and Demographic and Health Outcomes • 277
For women in particular, ownership of assets may provide protection in case of marital dissolution or
abandonment, positively influence their position in the home, and decrease their vulnerability to various
forms of violence or discrimination. Respondents were asked if they own a house or land alone, jointly
with someone else, or both alone and jointly. Table 15.4.1 shows the percent distribution of women age
15-49 by ownership of housing and land, according to background characteristics.
Table 15.4.1 Ownership of assets: Women
Percent distribution of women age 15-49 by ownership of housing and land, according to background characteristics, Kenya 2014
Percentage who own a house: Percentage who own land:
Alone Percentage Alone Percentage Number
Background and who do not and who do not of
characteristic Alone Jointly jointly own a house Total Alone Jointly jointly own land Total women
Age
15-19 0.8 4.9 1.1 93.2 100.0 0.7 4.3 1.2 93.7 100.0 2,717
20-24 4.2 21.4 3.1 71.3 100.0 3.4 20.0 2.7 73.9 100.0 2,691
25-29 6.3 35.2 4.4 53.9 100.0 5.5 32.5 3.7 58.2 100.0 2,932
30-34 7.1 40.8 5.4 46.7 100.0 6.6 37.3 4.9 51.1 100.0 2,162
35-39 12.8 43.0 4.8 39.3 100.0 11.3 40.1 4.2 44.4 100.0 1,780
40-44 19.0 43.6 3.6 33.7 100.0 17.6 39.9 3.3 39.2 100.0 1,292
45-49 20.2 49.1 5.6 24.9 100.0 18.4 46.1 5.3 30.2 100.0 1,052
Residence
Urban 4.7 18.3 2.9 74.2 100.0 5.0 17.5 2.5 74.9 100.0 5,929
Rural 10.1 39.0 4.4 46.5 100.0 8.5 35.5 4.0 51.9 100.0 8,696
Region
Coast 5.9 28.7 2.7 62.5 100.0 4.8 22.9 2.1 70.0 100.0 1,421
North Eastern 14.5 18.8 15.8 50.9 100.0 6.3 11.6 14.9 66.9 100.0 299
Eastern 6.0 47.9 2.8 43.3 100.0 5.4 44.3 2.7 47.4 100.0 2,066
Central 6.9 27.7 2.3 63.1 100.0 6.6 25.0 2.1 66.3 100.0 1,905
Rift Valley 11.1 30.2 4.3 54.3 100.0 10.9 28.8 3.6 56.6 100.0 3,714
Western 3.9 34.1 3.9 58.1 100.0 4.0 32.4 3.5 60.2 100.0 1,571
Nyanza 14.0 38.8 6.0 41.2 100.0 10.9 37.5 5.8 45.8 100.0 1,908
Nairobi 1.8 5.3 1.7 91.2 100.0 1.9 4.8 1.4 91.9 100.0 1,742
Education
No education 18.8 38.2 10.5 32.4 100.0 14.6 33.0 8.6 43.7 100.0 1,015
Primary incomplete 9.3 34.3 4.0 52.4 100.0 8.0 32.0 3.7 56.2 100.0 3,793
Primary complete 8.2 37.9 3.6 50.2 100.0 7.0 33.7 3.2 56.0 100.0 3,543
Secondary+ 5.2 22.9 2.6 69.2 100.0 5.3 22.1 2.4 70.1 100.0 6,274
Wealth quintile
Lowest 17.0 39.5 6.5 36.9 100.0 13.4 33.5 6.0 47.0 100.0 2,236
Second 10.6 38.4 5.0 45.8 100.0 8.4 35.7 4.1 51.6 100.0 2,590
Middle 7.7 36.2 3.0 53.1 100.0 7.0 32.9 2.7 57.5 100.0 2,859
Fourth 4.8 28.8 2.9 63.5 100.0 5.4 26.6 2.5 65.5 100.0 3,113
Highest 3.5 17.3 2.6 76.6 100.0 3.9 17.9 2.6 75.6 100.0 3,827
Total 7.9 30.6 3.8 57.7 100.0 7.1 28.2 3.4 61.3 100.0 14,625
Note: Totals may not add up to 100 percent because women with missing information are not shown separately.
Forty-two percent of women own a house and 39 percent of women own land (alone, jointly, or
both). Among women, joint ownership is the most common type of home or land ownership; 31 percent
own a house jointly, and 28 percent own land jointly. Eight percent of women own a house alone, and 7
percent own land alone. The percentage of women who own a house or land increases with age. Ownership
of either is also more likely among women in rural areas than among their counterparts in urban areas.
Likelihood of ownership decreases with increasing education and with increasing wealth.
Table 15.4.2 provides details of asset ownership among men age 15-49. Forty-nine percent of men
own a house and 44 percent own land (alone, jointly, or both). In contrast to women, sole ownership is
more common for men; 36 percent own a house alone, and 28 percent own land alone. Eleven percent own
a house jointly, and 12 percent own land jointly. Similar to women, ownership of a house or land increases
with age, and men in rural areas are more likely than those in urban areas to own a house; however, the
rural-urban relationship is not evident for land ownership.
278 • Women’s Empowerment and Demographic and Health Outcomes
Table 15.4.2 Ownership of assets: Men
Percent distribution of men age 15-49 by ownership of housing and land, according to background characteristics, Kenya 2014
Percentage who own a house: Percentage Percentage who own land:
Alone who do not Alone Percentage
Background and own a and who do not Number
characteristic Alone Jointly jointly house Total Alone Jointly jointly own land Total of men
Age
15-19 8.8 3.2 0.6 87.4 100.0 3.2 1.1 1.0 94.8 100.0 2,540
20-24 25.3 5.6 0.5 68.7 100.0 13.8 7.0 1.6 77.6 100.0 2,125
25-29 39.1 11.1 2.5 47.4 100.0 29.1 12.5 3.6 54.8 100.0 2,104
30-34 45.3 14.9 4.1 35.7 100.0 37.0 18.3 5.8 38.9 100.0 1,785
35-39 49.8 17.0 4.2 29.0 100.0 45.4 21.1 5.7 27.7 100.0 1,483
40-44 60.8 14.4 4.4 20.4 100.0 54.0 18.3 6.0 21.7 100.0 1,224
45-49 59.5 19.6 4.2 16.7 100.0 53.9 21.3 5.5 19.3 100.0 800
Residence
Urban 28.3 7.4 3.0 61.3 100.0 29.0 10.2 3.6 57.2 100.0 5,300
Rural 42.1 13.2 2.1 42.6 100.0 27.7 13.8 3.7 54.7 100.0 6,762
Region
Coast 40.7 2.7 1.4 55.2 100.0 28.2 3.8 2.3 65.6 100.0 1,260
North Eastern 36.2 1.3 1.4 61.0 100.0 37.5 2.0 1.7 58.8 100.0 227
Eastern 41.7 8.8 8.2 41.3 100.0 28.4 16.3 10.0 45.3 100.0 1,825
Central 44.6 6.6 1.2 47.7 100.0 29.3 9.3 4.8 56.6 100.0 1,564
Rift Valley 37.8 13.2 2.1 46.9 100.0 25.2 10.9 3.0 61.0 100.0 3,050
Western 32.4 19.4 0.2 47.8 100.0 24.1 18.6 0.2 57.2 100.0 1,164
Nyanza 30.0 22.7 2.2 45.0 100.0 30.2 20.5 2.2 47.0 100.0 1,405
Nairobi 22.0 2.2 0.8 75.0 100.0 33.1 9.2 1.7 56.0 100.0 1,568
Education
No education 58.8 10.0 1.6 29.6 100.0 43.4 12.2 1.0 43.2 100.0 345
Primary incomplete 36.5 11.0 1.9 50.7 100.0 26.3 12.4 3.4 57.9 100.0 3,071
Primary complete 44.2 12.8 3.8 39.2 100.0 33.4 14.3 5.1 47.2 100.0 2,734
Secondary+ 30.8 9.5 2.3 57.4 100.0 26.0 11.2 3.3 59.5 100.0 5,913
Wealth quintile
Lowest 48.4 10.3 2.0 39.3 100.0 31.8 11.0 3.7 53.4 100.0 1,691
Second 40.8 15.5 2.3 41.4 100.0 28.6 15.8 4.2 51.3 100.0 2,145
Middle 37.5 11.1 2.5 48.9 100.0 26.2 12.0 3.6 58.2 100.0 2,370
Fourth 34.2 10.6 2.7 52.6 100.0 27.9 11.0 3.3 57.7 100.0 2,959
Highest 26.2 7.0 2.7 64.2 100.0 28.0 11.7 3.5 56.8 100.0 2,897
Total 15-49 36.1 10.6 2.5 50.8 100.0 28.3 12.2 3.6 55.8 100.0 12,063
50-54 66.0 17.1 3.1 13.9 100.0 61.7 19.1 4.3 14.9 100.0 756
Total 15-54 37.8 11.0 2.5 48.6 100.0 30.2 12.6 3.7 53.4 100.0 12,819
Note: Totals may not add up to 100 percent because men with missing information are not shown separately.
15.5 WOMEN’S PARTICIPATION IN DECISION MAKING
Decision making can be a complex process, and the ability of women to make decisions that affect
their personal circumstances is an essential aspect of their environment and an indicator of their autonomy
and control in their daily life. The 2014 KDHS collected information on women’s participation in
household decision making. Currently married women were asked who usually makes decisions about four
specific issues: their own health care, major household purchases, visits to their family or relatives, and
what food to cook each day. Currently married men were also asked who makes decisions about two
specific issues: their own health care and major household purchases.
Table 15.5 shows the percent distribution of currently married women and men age 15-49
according to the person who usually makes decisions concerning these matters. Women are considered to
participate in decision making if they usually make decisions alone or jointly with their husbands.
Women’s involvement in decision making varies according to the type of decision. Eighty-three percent of
women decide solely what food should be cooked each day, a domain commonly relegated to women. A
much smaller percentage have the main say in other household decisions. Thirty-nine percent of women
act as the main decision maker in their own health care, while 40 percent say this decision is made jointly
with their husband and 21 percent say their husband mainly decides. Less than one-quarter of women are
the main decision makers about visits to their family or relatives (23 percent) and major household
purchases (20 percent). These decisions are mostly made jointly with the husband (50 percent and 53
percent, respectively) or mainly by the husband (26 percent and 27 percent, respectively).
Women’s Empowerment and Demographic and Health Outcomes • 279
Table 15.5 Participation in decision making
Percent distribution of currently married women and currently married men age 15-49 by person who usually makes decisions about various
issues, Kenya 2014
Wife and
husband Mainly Someone
Decision Mainly wife jointly husband else Other Missing Total Number
WOMEN
Own health care 38.6 40.1 20.9 0.2 0.1 0.2 100.0 8,710
Major household purchases 19.6 52.9 27.0 0.2 0.1 0.2 100.0 8,710
Visits to her family or relatives 23.2 50.2 26.1 0.2 0.1 0.2 100.0 8,710
What food should be cooked
each day 82.9 10.7 5.0 1.0 0.2 0.2 100.0 8,710
MEN
Own health care 5.9 42.2 51.3 0.2 0.2 0.3 100.0 6,095
Major household purchases 9.7 56.1 33.8 0.1 0.1 0.3 100.0 6,095
Men are more likely to be the main decision makers regarding their own health care (51 percent),
while decisions about major household purchases are more likely to be made jointly (56 percent). Less
than 10 percent of men reported that their wife was the main decision maker regarding either of these
specific issues.
Table 15.6.1 shows the percentage of currently married women age 15-49 who usually make
specific decisions either by themselves or jointly with their husbands, by background characteristics. In
general, women’s participation in decision making increases with age, education, and wealth quintile.
Women who reside in urban areas and women who are employed and earning cash appear slightly more
likely to be involved in decision making than their counterparts. Women in the Central and Eastern regions
are generally more involved in decision making than women in other regions, while women in the Western
region are least likely to have a say in household decisions.
280 • Women’s Empowerment and Demographic and Health Outcomes
Table 15.6.1 Women’s participation in decision making by background characteristics
Percentage of currently married women age 15-49 who usually make specific decisions either by themselves or jointly
with their husband, by background characteristics, Kenya 2014
Specific decisions
Making What food
Woman’s major Visits to her should be None of the
Background own health household family or cooked All four four Number of
characteristic care purchases relatives each day decisions decisions women
Age
15-19 67.0 55.8 60.2 79.3 33.8 7.2 301
20-24 71.9 65.2 64.5 89.1 43.5 4.4 1,465
25-29 76.4 71.2 70.9 93.9 50.8 2.1 2,171
30-34 81.6 74.9 75.8 95.5 57.4 1.7 1,717
35-39 81.8 74.3 77.2 94.4 58.5 2.2 1,365
40-44 83.4 80.6 81.8 96.7 65.5 1.1 923
45-49 85.0 79.0 80.1 97.6 65.2 1.0 768
Employment (past 12
months)
Not employed 69.3 61.5 67.1 90.8 45.1 4.0 2,201
Employed for cash 82.3 77.6 76.9 95.0 58.8 1.5 4,951
Employed not for cash 80.3 72.3 70.8 93.3 53.4 2.8 1,546
Number of living
children
0 75.1 74.9 74.5 87.9 51.7 3.4 491
1-2 79.1 71.9 72.5 93.0 52.8 2.2 3,441
3-4 79.9 74.2 75.2 94.8 57.9 2.5 2,790
5+ 77.2 70.6 72.0 94.3 52.6 2.3 1,989
Residence
Urban 80.6 75.0 76.5 93.9 57.4 1.7 3,445
Rural 77.4 70.9 71.3 93.5 52.3 2.8 5,265
Region
Coast 76.2 71.0 66.2 91.6 49.1 3.1 850
North Eastern 63.3 65.5 72.4 91.9 55.2 5.9 209
Eastern 85.5 83.2 76.3 96.2 62.6 0.9 1,268
Central 86.8 72.6 79.8 97.3 58.6 1.4 1,113
Rift Valley 78.2 68.6 73.6 94.0 53.0 2.4 2,171
Western 70.3 64.8 62.5 90.3 40.5 3.7 929
Nyanza 76.2 72.0 74.2 92.1 56.7 3.8 1,203
Nairobi 78.1 78.2 77.4 92.3 56.3 0.6 968
Education
No education 67.5 62.2 63.3 91.3 47.6 4.8 795
Primary incomplete 74.1 68.2 67.7 91.9 48.2 3.8 2,274
Primary complete 79.9 72.8 72.0 94.0 53.7 2.1 2,465
Secondary+ 83.9 77.9 81.0 95.1 60.9 1.0 3,177
Wealth quintile
Lowest 70.8 63.6 62.5 90.9 44.6 5.1 1,457
Second 76.4 70.4 70.0 92.6 49.9 2.7 1,567
Middle 78.2 73.1 70.9 93.3 52.9 2.6 1,663
Fourth 80.7 74.7 77.8 95.1 57.4 1.3 1,885
Highest 84.3 77.8 81.2 95.1 62.6 1.0 2,138
Total 78.7 72.5 73.4 93.6 54.3 2.4 8,710
1
Total includes six women for whom information on employment in the past 12 months is missing.
Women’s Empowerment and Demographic and Health Outcomes • 281
Figure 15.1 shows the number of decisions in which currently married women participate. More
than one-half of currently married women (54 percent) participate in all four decisions, about one-fourth
(23 percent) participate in three decisions, more than 1 in 10 (12 percent) participate in two decisions, and
less than 1 in 10 participate in one (9 percent) or no decisions (2 percent).
Figure 15.1 Number of decisions in which currently married women participate
Percent of women
54
23
12
9
2
0 1 2 3 4
Number of decisions
KDHS 2014
Table 15.6.2 presents the percentage of currently married men age 15-49 who usually make
specific decisions either alone or jointly with their wife, by background characteristics. There are no
obvious patterns evident in decision making for men by age, education, or wealth. However, unemployed
men are less likely to be involved in decision making than those who are employed, and men in the Central
and Eastern regions are generally more involved in decision making than men in other regions.
282 • Women’s Empowerment and Demographic and Health Outcomes
Table 15.6.2 Men’s participation in decision making by background characteristics
Percentage of currently married men age 15-49 who usually make specific decisions either
alone or jointly with their wife, by background characteristics, Kenya 2014
Specific decisions
Making
major Neither of
Background Man’s own household Both the two Number of
characteristic health purchases decisions decisions men
Age
15-19 * * * * 16
20-24 92.0 86.3 84.1 5.8 377
25-29 95.0 90.6 86.8 1.1 1,201
30-34 94.0 90.7 86.7 2.1 1,398
35-39 92.5 89.4 84.4 2.5 1,277
40-44 93.2 90.2 86.6 3.3 1,100
45-49 93.6 89.8 86.0 2.7 727
Employment (past 12
months)
Not employed 77.3 74.6 71.3 19.5 27
Employed for cash 93.5 89.7 85.8 2.5 5,570
Employed not for cash 94.0 92.0 88.3 2.3 494
Number of living
children
0 93.4 89.4 86.3 3.6 389
1-2 93.1 91.0 86.5 2.4 2,542
3-4 94.1 90.1 86.6 2.3 1,918
5+ 93.5 87.2 83.7 3.0 1,247
Residence
Urban 93.6 90.0 86.1 2.4 2,894
Rural 93.4 89.7 85.8 2.7 3,201
Region
Coast 97.2 91.7 90.6 1.7 617
North Eastern 87.3 87.7 85.8 10.8 103
Eastern 96.2 94.0 91.3 1.1 835
Central 98.5 93.4 92.7 0.8 773
Rift Valley 93.0 89.8 84.4 1.6 1,523
Western 91.3 88.7 86.0 6.0 561
Nyanza 92.2 83.8 79.9 4.0 767
Nairobi 88.3 88.0 79.7 3.4 916
Education
No education 92.9 86.8 85.1 5.4 234
Primary incomplete 90.9 87.9 82.7 3.9 1,370
Primary complete 95.1 91.6 88.7 1.9 1,677
Secondary+ 93.8 90.0 85.9 2.0 2,814
Wealth quintile
Lowest 92.0 87.1 83.3 4.2 813
Second 92.7 89.1 84.9 3.2 1,036
Middle 92.7 89.1 84.4 2.6 1,110
Fourth 92.6 90.8 85.3 1.9 1,481
Highest 96.1 91.3 89.4 2.1 1,655
Total 15-49 93.5 89.9 85.9 2.6 6,095
50-54 94.1 90.3 87.8 3.4 667
Total 15-54 93.6 89.9 86.1 2.7 6,762
Note: An asterisk denotes a figure based on fewer than 25 unweighted cases that has been
suppressed.
1
Total includes four men for whom information on employment in the past 12 months is
missing.
15.6 ATTITUDES TOWARDS WIFE BEATING
One of the most common forms of violence against women worldwide is abuse by a husband or
partner (Heise et al., 1999). The 2014 KDHS collected information on women’s and men’s attitudes
towards wife beating. Respondents were asked whether a husband is justified in beating his wife under a
series of circumstances: if the wife burns the food, argues with him, goes out without telling him, neglects
the children, or refuses sexual relations. Women and men who believe that a husband is justified in hitting
or beating his wife for any reason may believe women to be low in status, both absolutely and relative to
men. Such a perception could act as a barrier to accessing health care for women, affect attitudes towards
women’s rights to contraception, and influence women’s general well-being. Table 15.7.1 presents
women’s attitudes towards wife beating in regard to the five specific circumstances. The table also shows
the percentage of women who agree that wife beating is justified for at least one of the specified reasons.
Women’s Empowerment and Demographic and Health Outcomes • 283
Table 15.7.1 Attitude towards wife beating: Women
Percentage of all women age 15-49 who agree that a husband is justified in hitting or beating his wife for specific reasons, by background
characteristics, Kenya 2014
Husband is justified in hitting or beating his wife if she: Percentage
Refuses to who agree
Goes out have sexual with at least
Background Argues with without telling Neglects the intercourse one specified Number of
characteristic Burns the food him him children with him reason women
Age
15-19 7.5 21.8 20.9 34.0 11.2 44.5 2,717
20-24 6.4 20.0 22.0 32.2 13.2 39.4 2,691
25-29 6.7 20.7 22.1 32.7 14.5 41.0 2,932
30-34 6.0 19.3 19.6 30.3 14.2 38.3 2,162
35-39 7.7 20.6 22.2 33.6 18.9 42.3 1,780
40-44 7.8 21.9 22.8 36.7 19.7 44.8 1,292
45-49 8.5 24.9 24.7 37.1 21.7 45.9 1,052
Employment (past 12
months)
Not employed 7.6 21.0 22.0 33.9 14.1 42.0 4,912
Employed for cash 5.9 18.4 20.0 30.5 13.8 38.6 7,655
Employed not for cash 10.1 30.8 28.0 42.7 22.6 53.7 2,040
Number of living
children
0 5.8 15.9 16.4 28.1 9.1 36.1 3,890
1-2 5.6 19.2 20.1 29.4 12.0 38.1 5,000
3-4 7.5 22.7 23.9 36.6 18.7 44.6 3,381
5+ 11.4 30.6 31.4 45.3 26.6 55.1 2,354
Marital status
Never married 6.4 16.9 18.2 30.1 9.6 38.2 4,255
Married or living together 7.2 23.3 23.5 34.6 17.3 43.6 8,710
Divorced/separated/
widowed 7.9 19.3 21.8 34.6 17.5 41.5 1,660
Residence
Urban 3.5 13.8 13.9 24.2 8.1 31.0 5,929
Rural 9.5 25.9 27.1 39.5 19.9 49.1 8,696
Region
Coast 5.3 15.6 19.1 24.1 13.5 32.7 1,421
North Eastern 9.9 22.1 30.3 44.8 30.7 53.7 299
Eastern 4.8 23.1 20.3 31.1 16.2 41.6 2,066
Central 4.7 12.1 16.1 30.3 12.8 36.9 1,905
Rift Valley 11.0 24.2 31.6 46.0 18.2 53.6 3,714
Western 11.0 30.0 24.9 40.5 19.2 52.2 1,571
Nyanza 6.5 28.5 20.8 30.6 17.2 41.1 1,908
Nairobi 1.6 9.2 7.9 13.9 2.5 19.1 1,742
Education
No education 16.0 36.9 38.3 49.4 36.0 59.1 1,015
Primary incomplete 11.4 30.6 31.2 42.0 22.4 53.3 3,793
Primary complete 6.1 21.4 22.0 35.8 15.4 44.5 3,543
Secondary+ 3.5 12.4 13.3 24.0 7.2 30.5 6,274
Wealth quintile
Lowest 13.7 34.0 36.4 47.3 28.8 59.1 2,236
Second 9.6 28.5 29.4 41.3 20.4 51.6 2,590
Middle 8.0 25.1 24.7 39.0 18.3 49.4 2,859
Fourth 5.3 17.1 17.5 29.9 10.7 37.0 3,113
Highest 2.1 8.5 9.4 18.2 4.7 23.4 3,827
Total 7.0 21.0 21.8 33.3 15.1 41.8 14,625
Note: Total includes 13 women for whom information on employment in the past 12 months is missing.
Forty-two percent of women believe wife beating is justified for at least one of the specified
reasons. Overall, acceptance of wife beating ranges from a high of 33 percent for neglecting the children to
a low of 7 percent for burning the food. Women who are employed but not paid in cash and rural women
are more likely to justify wife beating than their counterparts. The proportion of women who justify wife
beating increases with increasing number of living children and decreases with increasing education and
wealth. Acceptance of wife beating varies by region, from 19 percent of women in Nairobi to slightly more
than one-half in North Eastern, Rift Valley, and Western (52-54 percent).
Table 15.7.2 presents the percentage of men age 15-49 who agree that a husband is justified in
hitting or beating his wife for specific reasons, by background characteristics. Compared with women, men
are less likely to agree that wife beating is justified. Thirty-six percent of men believe wife beating is
284 • Women’s Empowerment and Demographic and Health Outcomes
justified for at least one of the specified reasons. Overall, men’s acceptance of wife beating ranges from a
high of 27 percent for neglecting the children to a low of 5 percent for burning the food. Similar to women,
men who are employed but not paid in cash are more likely to say wife beating is justified. Acceptance of
wife beating decreases with increases in education and wealth. By region, the proportion of men who
justify wife beating ranges from 25 percent in Western to 52 percent in North Eastern.
Table 15.7.2 Attitude towards wife beating: Men
Percentage of all men age 15-49 who agree that a husband is justified in hitting or beating his wife for specific reasons, by background
characteristics, Kenya 2014
Husband is justified in hitting or beating his wife if she: Percentage
Refuses to who agree
Goes out have sexual with at least
Background Argues with without telling Neglects the intercourse one specified Number of
characteristic Burns the food him him children with him reason men
Age
15-19 5.3 19.3 16.8 26.8 10.8 36.7 2,540
20-24 5.1 22.3 19.7 29.2 10.4 40.9 2,125
25-29 4.9 20.6 19.7 28.8 9.6 36.8 2,104
30-34 4.1 21.3 18.1 28.3 9.6 35.5 1,785
35-39 3.4 20.9 19.5 27.9 8.7 36.3 1,483
40-44 3.7 18.4 18.5 24.0 9.8 31.8 1,224
45-49 3.7 19.9 16.9 22.4 9.3 31.3 800
Employment (past 12
months)
Not employed 5.4 17.3 15.2 23.5 10.4 33.1 2,047
Employed for cash 4.1 20.6 18.3 27.1 9.2 35.8 8,686
Employed not for cash 6.3 25.1 25.0 35.5 13.5 45.6 1,316
Number of living
children
0 4.9 19.2 17.8 26.9 9.9 36.7 5,540
1-2 3.9 20.2 17.5 27.2 9.1 35.5 3,206
3-4 4.7 20.7 17.8 25.8 9.1 33.7 2,032
5+ 4.3 26.5 25.5 32.2 12.8 41.6 1,285
Marital status
Never married 5.4 19.6 17.6 27.0 10.1 37.0 5,350
Married or living together 3.9 21.4 19.0 27.5 9.6 35.6 6,095
Divorced/separated/
widowed 3.4 20.0 22.1 29.2 10.1 38.9 618
Residence
Urban 3.8 19.8 16.1 26.9 8.6 35.6 5,300
Rural 5.1 21.1 20.4 27.7 10.9 37.0 6,762
Region
Coast 3.7 21.5 12.5 16.6 9.9 27.4 1,260
North Eastern 15.5 39.6 43.3 43.9 43.5 51.8 227
Eastern 5.2 26.2 29.0 39.0 14.9 47.0 1,825
Central 2.0 10.5 18.1 23.3 5.6 31.3 1,564
Rift Valley 5.3 17.2 18.8 26.9 8.1 34.2 3,050
Western 5.6 16.6 8.4 13.0 4.2 24.5 1,164
Nyanza 2.3 24.5 15.2 25.2 8.9 38.4 1,405
Nairobi 5.2 26.0 18.1 37.5 11.8 45.4 1,568
Education
No education 15.5 45.4 48.2 51.1 34.8 60.4 345
Primary incomplete 6.3 26.1 24.2 32.1 12.5 43.1 3,071
Primary complete 3.9 21.6 19.6 28.3 10.9 37.5 2,734
Secondary+ 3.3 15.6 13.3 23.1 6.6 31.0 5,913
Wealth quintile
Lowest 9.7 28.8 31.2 34.4 18.4 44.9 1,691
Second 5.2 22.7 19.8 29.6 11.2 39.0 2,145
Middle 4.2 19.5 17.2 27.1 8.7 36.7 2,370
Fourth 3.1 18.4 17.1 27.0 8.2 35.7 2,959
Highest 2.8 17.0 12.7 22.2 6.6 29.9 2,897
Total 15-49 4.5 20.5 18.5 27.4 9.9 36.4 12,063
50-54 4.4 18.3 18.5 26.4 9.3 33.7 756
Total 15-54 4.5 20.4 18.5 27.3 9.8 36.2 12,819
Note: Total includes 18 men for whom information on employment in the past 12 months is missing.
Women’s and men’s attitudes towards wife beating have improved somewhat since the 2008-09
KDHS. In 2008-09, 53 percent of women and 44 percent of men agreed with wife beating for at least one
of the specified reasons, compared with 42 percent of women and 36 percent of men in 2014.
Women’s Empowerment and Demographic and Health Outcomes • 285
15.7 WOMEN’S EMPOWERMENT INDICES
Women’s empowerment has important implications for demographic and health outcomes,
including women’s use of family planning, maternal health care services, and child health. Two summary
indices of women’s empowerment were used to assess the relationship of selected demographic and health
outcomes with women’s empowerment.
The first index is the number of decisions that currently married women participate in alone or
jointly with their husband/partner (see Table 15.6.1 for the list of decisions). The index ranges in value
from 0 (participates in none of the four specified decisions) to 4 (participates in all four decisions). It
reflects the degree of decision-making control that women are able to exercise in areas that affect their own
lives and environments. A high score on this index suggests that women are more empowered in this
domain. The second index ranges from 0 to 5 and corresponds with the total number of reasons for which
women feel that a husband is justified in beating his wife (see Table 15.7.1 for the list of reasons). A low
score on this index suggests that women have a greater sense of self-worth and higher status.
The relationship between the two indices is presented in Table 15.8. It is expected that women
who participate more in making decisions at the household level will be less likely to endorse wife beating.
The percentage of women who disagree with wife beating under any circumstance is highest among those
who participate in 3-4 household decisions (60 percent). On the other hand, women who do not participate
in any decisions and those who participate in one or two decisions have similar levels of support for wife
beating (43 percent and 44 percent, respectively). It is also expected that women who more strongly
endorse wife beating will be less likely to be involved in household decision making; however, there is no
clear pattern between the number of reasons for which women justify wife beating and their participation
in all four household decisions.
Table 15.8 Indicators of women’s empowerment
Percentage of currently married women age 15-49 who participate in all decision
making and the percentage who disagree with all of the reasons justifying wife
beating, by value on each of the indicators of women’s empowerment, Kenya 2014
Percentage who
disagree with all
Percentage who the reasons
participate in all justifying wife Number of
Empowerment indicator decision making beating women
Number of decisions in
which women participate1
0 na 42.7 206
1-2 na 43.9 1,785
3-4 na 60.1 6,719
Number of reasons for which
wife beating is justified2
0 21.0 na 4,911
1-2 25.3 na 2,194
3-4 26.1 na 1,280
5 21.7 na 325
na = Not applicable
1
See Table 15.6.1 for the list of decisions
2
See Table 15.7.1 for the list of reasons
15.8 CURRENT USE OF CONTRACEPTION BY WOMEN’S STATUS
A woman’s ability to have the number of children she wants and her use and choice of
contraceptive methods are likely to be affected by self-image and sense of empowerment. A woman who
feels that she is unable to control other aspects of her life may be less likely to feel she can make decisions
regarding her fertility. She may also feel the need to choose contraceptive methods that are less obvious or
do not need the approval or knowledge of her husband. Table 15.9 shows the relationship of each of the
empowerment indices with current use of contraception among currently married women age 15-49,
according to selected indicators of women’s status.
286 • Women’s Empowerment and Demographic and Health Outcomes
Table 15.9 Current use of contraception by women’s empowerment
Percent distribution of currently married women age 15-49 by current contraceptive method, according to selected indicators of women’s status, Kenya
2014
Modern methods
Temporary
modern Any Not
Any modern Female female Male traditional currently Number of
Empowerment indicator Any method method sterilisation methods1 condom method using Total women
Number of decisions in
which women participate2
0 37.8 35.5 1.3 32.8 1.5 2.2 62.2 100.0 206
1-2 54.9 51.4 2.6 47.2 1.6 3.5 45.1 100.0 1,785
3-4 59.5 54.4 3.5 48.4 2.5 5.1 40.5 100.0 6,719
Number of reasons for which
wife beating is justified3
0 60.2 55.9 3.3 49.8 2.9 4.3 39.8 100.0 4,911
1-2 57.7 52.3 3.4 47.5 1.4 5.4 42.3 100.0 2,194
3-4 52.3 47.5 3.3 42.3 1.9 4.8 47.7 100.0 1,280
5 49.5 44.8 1.8 42.3 0.6 4.7 50.5 100.0 325
Total 58.0 53.4 3.3 47.8 2.3 4.7 42.0 100.0 8,710
Note: If more than one method is used, only the most effective method is considered in this tabulation.
1
Pill, IUD, injectables, implants, female condom and lactational amenorrhoea method
2
See Table 15.6.1 for the list of decisions
3
See Table 15.7.1 for the list of reasons
As may be expected, contraceptive use is positively associated with participation in household
decision making and negatively associated with endorsement of wife beating. For example, the proportion
of currently married women using a modern method of contraception increases from 36 percent among
those not involved in any decision making to 54 percent among those involved in 3-4 decisions. Similarly,
the proportion of currently married women using a modern method decreases from 56 percent among those
who do not endorse wife beating to 45 percent among those who justify wife beating for all five specific
reasons. These same patterns are generally observed for each of the individual contraceptive methods
presented in Table 15.9.
15.9 IDEAL FAMILY SIZE AND UNMET NEED BY WOMEN’S STATUS
As a woman becomes more empowered, she is more likely to have a say in the number of children
(ideal family size) she desires and the time at which she has them. She has more control over her ability to
access and use contraceptives and to space and limit her family size. Women who have a desire to limit or
delay their births, but who are not using family planning, are considered to have an unmet need for family
planning. Table 15.10 shows how a woman’s ideal family size and her unmet need for family planning
vary by the two women’s empowerment indices.
Table 15.10 Ideal number of children and unmet need for family planning by women’s empowerment
Mean ideal number of children for women 15-49 and the percentage of currently married women age 15-49 with
an unmet need for family planning, by indicators of women’s empowerment, Kenya 2014
Mean ideal Percentage of currently married women
number of Number of with an unmet need for family planning2 Number of
Empowerment indicator children1 women For spacing For limiting Total women
Number of decisions in
which women participate3
0 4.8 199 15.9 8.8 24.8 206
1-2 4.2 1,731 12.8 7.7 20.5 1,785
3-4 3.8 6,573 8.0 8.4 16.4 6,719
Number of reasons for which
wife beating is justified4
0 3.4 8,348 7.9 7.5 15.4 4,911
1-2 3.6 3,627 9.9 9.3 19.2 2,194
3-4 4.1 1,873 12.3 9.5 21.8 1,280
5 4.6 463 11.3 9.3 20.6 325
Total 3.6 14,311 9.2 8.3 17.5 8,710
1
Mean excludes respondents who gave non-numeric responses.
2
See Table 7.12 for the definition of unmet need for family planning
3
Restricted to currently married women. See Table 15.6.1 for the list of decisions.
4
See Table 15.7.1 for the list of reasons
Women’s Empowerment and Demographic and Health Outcomes • 287
As indicated in Table 15.10, mean ideal number of children varies with both indices of women’s
empowerment. The ideal number of children decreases as the number of decisions in which a woman
participates increases, from 4.8 children among women who do not participate in any household decision
making to 3.8 among women who participate in 3-4 household decisions. Ideal number of children
increases with women’s justification of wife beating.
With respect to need for family planning, women who score well on the first index, household
decision making, are less likely to have an unmet need for spacing. Similarly, women who do not endorse
wife beating are also less likely to have an unmet need for spacing. The relationship between the
empowerment indicators and unmet need for limiting is mixed.
15.10 REPRODUCTIVE HEALTH CARE AND WOMEN’S EMPOWERMENT
Women’s status is less likely to affect their access to health care services in places where such
services are available. In other areas, increased empowerment of women is likely to enhance their ability to
seek and use health care services that better meet their own reproductive health needs. Table 15.11 shows
use of antenatal, delivery, and postnatal care services according to women’s scores on the two
empowerment indices. It is expected that empowered women will be more likely to seek out health care
services that better meet their reproductive health goals, including safe motherhood.
Table 15.11 Reproductive health care by women’s empowerment
Percentage of women age 15-49 with a live birth in the five years preceding the survey who received
antenatal care, delivery assistance and postnatal care from health personnel for the most recent birth, by
indicators of women’s empowerment, Kenya 2014
Percentage of
Percentage Percentage women with a
receiving receiving postnatal Number of
antenatal care delivery care checkup in the women with a
from a skilled from a skilled first two days child born in the
Empowerment indicator provider1 provider1 after birth2 last five years
Number of decisions in
which women participate3
0 94.4 51.4 38.1 155
1-2 95.7 59.2 47.5 1,294
3-4 97.3 68.8 55.6 4,135
Number of reasons for which
wife beating is justified4
0 97.3 73.5 60.2 3,823
1-2 96.0 64.2 50.4 1,741
3-4 94.2 51.2 43.2 1,027
5 92.2 46.7 36.2 285
Total 96.3 66.7 54.2 6,876
1
‘Skilled provider’ includes doctor, nurse, or midwife.
2
Includes women who received a postnatal checkup from a doctor, nurse, midwife, community health
worker or traditional birth attendant (TBA) in the first two days after the birth. Includes women who gave
birth in a health facility and those who did not give birth in a health facility.
3
Restricted to currently married women. See Table 15.6.1 for the list of decisions.
4
See Table 15.7.1 for the list of reasons
As expected, women’s empowerment is positively associated with their access to and use of
maternal health services. For example, the proportion of women who received delivery care from health
personnel for a live birth in the five years before the survey increases with the number of decisions in
which they participate, from 51 percent among those who do not participate in any decisions to 69 percent
among those who participate in 3-4 decisions. Similar trends are seen between decision making and
women’s receipt of antenatal and postnatal care.
Among women who do not justify wife beating for any of the specified reasons, 97 percent
received antenatal care, 74 percent received delivery care, and 60 percent received postnatal care within
the first two days after delivery. In contrast, the corresponding proportions among women who justify wife
beating for all five specified reasons were 92 percent, 47 percent, and 36 percent.
288 • Women’s Empowerment and Demographic and Health Outcomes
15.11 DIFFERENTIALS IN INFANT AND CHILD MORTALITY BY WOMEN’S STATUS
The ability of women to access information, make decisions, and act effectively in their own
interests or in the interests of those who depend on them is essential to their empowerment. If women, the
primary caretakers of children, are empowered, the health and survival of their children will be enhanced.
In fact, maternal empowerment fits into Mosley and Chen’s framework on child survival as an individual-
level variable that affects child survival through proximate determinants (Mosley and Chen, 1984).
Table 15.12 presents infant and child mortality rates for the 10-year period preceding the survey,
by indicators of women’s empowerment. The results appear mixed and do not follow any discernible trend.
Table 15.12 Early childhood mortality rates by women’s status
Infant, child, and under-5 mortality rates for the 10-year period preceding
the survey, by indicators of women’s empowerment, Kenya 2014
Infant Child Under-5
mortality mortality mortality
Empowerment indicator (1q0) (4q1) (5q0)
Number of decisions in
which women participate1
0 39 17 55
1-2 55 19 74
3-4 39 14 52
Number of reasons for which
wife beating is justified2
0 41 15 56
1-2 36 15 51
3-4 44 24 66
5 40 14 53
1
Restricted to currently married women. See Table 15.6.1 for the list of
decisions.
2
See Table 15.7.1 for the list of reasons
Women’s Empowerment and Demographic and Health Outcomes • 289
DOMESTIC VIOLENCE 16
Robert C.B. Buluma, Rosemary Uside Kongani, John Wanyungu,
Ruth Wayua Muia, Lawerence Ikamari
Key Findings
• Forty-five percent of women and 44 percent of men age 15-49 have
experienced physical violence since age 15, and 20 percent and 12
percent, respectively, experienced physical violence within the 12 months
prior to the survey. The main perpetrators of physical violence against
women are husbands; whereas, the main perpetrators against men are
parents, teachers, and others.
• Fourteen percent of women and 6 percent of men age 15-49 report
having experienced sexual violence at least once in their lifetime.
• Overall, 39 percent of ever-married women and 9 percent of men age 15-
49 report having experienced spousal physical or sexual violence.
• Among women and men who have ever experienced spousal violence
(physical or sexual), 39 percent and 24 percent, respectively, reported
experiencing physical injuries.
• Forty-four percent of women and 27 percent of men have sought
assistance from any source to stop the violence they have experienced.
S
ince the 1990s, there has been an increased focus on violence against women in general, and domestic
violence in particular, in both developed and developing countries. Not only has domestic violence
been acknowledged worldwide as a violation of basic human rights, but an increasing amount of
research continues to highlight the health burdens, intergenerational effects, and demographic consequences
of such violence. Domestic violence occurs in all population subgroups. In many countries, including Kenya,
women are often socialised into tolerating and rationalising a key component of domestic violence, namely
violence by husbands against wives and to remain silent about it when it occurs. Violence of any kind has a
detrimental impact on the economy of a country through increased disability, medical costs, and loss of
labour hours; however, because women bear the brunt of domestic violence, they disproportionately bear the
health and psychological burdens as well.
The Government of Kenya has enacted several laws and has policies and regulations to prevent and
control various forms of violence against women and children including in the Constitution of Kenya (2010),
the Sexual Offences Act (2006), the Children’s Act (2001), the Penal Code (2009), the Prohibition of Female
Genital Mutilation Act (2011), and the National Gender and Equality Commission Act (2011). In recognition
of domestic violence as a serious problem in Kenya, the 2014 KDHS included the domestic violence module
for both women and men.
16.1 MEASUREMENT OF VIOLENCE
The 2014 KDHS is the third DHS survey in Kenya to include questions on violence perpetrated
against women and the first survey to include questions on violence against men. Collecting valid, reliable,
and ethical data on domestic violence poses particular challenges. What constitutes violence or abuse varies
across cultures and among individuals. A culture of silence usually surrounds domestic violence and can
affect reporting. The sensitivity of the topic is another issue. Assuring the safety of respondents and
interviewers when asking about domestic violence in a familial setting, protecting individuals who disclose
Domestic Violence • 291
violence, and reducing the risk of double victimisation of respondents as they relive their experiences are all
specific ethical concerns. The responses to these challenges in the 2014 KDHS are described in the sections
that follow.
16.1.1 Use of Valid Measures of Violence
In the 2014 KDHS, information was obtained from ever-married respondents on violence committed
by their current and former spouses and by others. Information was collected from never-married
respondents on violence by anyone. Since international research shows that intimate partner violence is one
of the most common forms of violence, especially against women, information on spousal violence was
measured in more detail than violence by other perpetrators. This was done by using a shortened, modified
version of the Conflict Tactics Scale (Strauss, 1990). Specifically, violence by the current spouse/partner for
currently married respondents and by the most recent spouse/partner for formerly married respondents was
measured by asking all ever-married women and men the following set of questions.
Does (did) your (last) spouse/partner ever:
(a) Push you, shake you, or throw something at you?
(b) Slap you?
(c) Twist your arm or pull your hair?
(d) Punch you with his/her fist or with something that could hurt you?
(e) Kick you, drag you, or beat you up?
(f) Try to choke you or burn you on purpose?
(g) Threaten or attack you with a knife, gun, or any other weapon?
(h) Physically force you to have sexual intercourse with him/her even when you did not want to?
(i) Physically force you to perform any other sexual acts you did not want to?
(j) Force you with threats or in any other way to perform sexual acts you did not want to?
For every question to which the respondent answered ‘yes,’ she or he was asked about the frequency
of the act in the 12 months preceding the survey. An affirmative answer to one or more of items (a) to (g)
above constitutes evidence of physical violence, and an affirmative answer to item (h) to (j) constitutes
evidence of sexual violence.
Similarly, emotional violence among ever-married respondents was measured by the following
questions.
Does (did) your (last) spouse/partner ever:
(a) Say or do something to humiliate you in front of others?
(b) Threaten to hurt or harm you or someone close to you?
(c) Insult you or make you feel bad about yourself?
This approach of asking about specific acts to measure violence has the advantage of not being
affected by different understandings of what constitutes a summary term such as ‘violence.’ By including a
wide range of acts, this approach has the additional advantage of giving the respondent multiple opportunities
to disclose any experience of violence.
In addition to these questions that were asked only of ever-married respondents, all women and men
were asked about physical violence from persons other than the current or most recent spouse/partner.
Respondents who answered yes to this question were asked who committed violence against them and the
frequency of such violence during the 12 months preceding the survey. Respondents who reported
experiencing different forms of violence were asked for the perpetrators of the violence.
292 • Domestic Violence
Although this approach to questioning is generally considered to be optimal, the possibility of
underreporting of violence, particularly sexual violence, cannot be entirely ruled out in any survey, and this
survey is no exception.
16.1.2 Ethical Considerations in the 2014 KDHS
In recognition of the challenges in collecting data on violence, the interviewers in the 2014 KDHS
were given special training. The training focused on how to ask sensitive questions, ensure privacy, and
build rapport between interviewer and respondent. Rapport with the interviewer, confidentiality, and privacy
are all keys to building respondents’ trust so that they can safely share their experiences with the interviewer.
Also, placement of questions about violence at the end of the questionnaire provides time for the interviewer
to develop a certain degree of rapport that should further encourage respondents to share their experiences
of violence, if any. In addition, the following protections were built into the survey in keeping with the World
Health Organization’s ethical and safety recommendations for research on domestic violence (WHO, 2001).
• To maintain confidentiality, only one woman or man per household was administered the
questions on violence. In the one-third of the households selected for the male survey, one man
per household was randomly selected to receive the questions on domestic violence. In the
remaining two-thirds of households, one woman per household was selected for the questions
on violence. The random selection of one woman or man was done through a simple selection
procedure based on the Kish grid, which was built into the Household Questionnaire (Kish,
1965).
• As a means of obtaining additional consent beyond the initial consent at the start of the
interview, the respondent was informed at the start of the domestic violence module that the
questions could be sensitive and was reassured regarding the confidentiality of her/his
responses.
• The violence module was implemented only if privacy could be obtained. The interviewers
were instructed to skip the module, thank the respondent, and end the interview if they could
not maintain privacy.
• A brochure that included information on domestic violence and contact information for service
centres across the country was provided to all eligible respondents after the interview was
completed, irrespective of whether or not they were selected for the module. This was done to
safeguard against identifying the respondent selected for the module and to provide information
to all respondents so that they could access the services and be informed about what to do in
the event of domestic violence.
16.1.3 Subsample for the Violence Module
The domestic violence module for women and the module for men were implemented in separate
subsamples of households. Furthermore, in keeping with ethical requirements, only one woman or man per
household was selected for the module, as mentioned above. As a result of these restrictions, a total of 5,657
women age 15-49 (4,023 ever-married women) and 4,962 men age 15-54 (2,890 ever-married men)
completed the domestic violence module. In all, four women and four men eligible for the domestic violence
module could not be interviewed with the module because privacy was not possible, and another 11 women
and 29 men could not be interviewed with module due to other reasons.
16.2 EXPERIENCE OF PHYSICAL VIOLENCE
Tables 16.1.1 and 16.1.2 show the percentage of women and men age 15-49, respectively, who have
experienced physical violence since age 15 and the percentage who have experienced violence during the 12
months preceding the survey, by background characteristics. Forty-five percent of women and 44 percent of
men age 15-49 have experienced physical violence since age 15, and 20 percent of women and 12 percent
of men experienced physical violence in the 12 months prior to the survey including 5 percent of women
Domestic Violence • 293
and 2 percent of men, respectively, who reported that they had experienced physical violence often in the
past 12 months.
Table 16.1.1 Experience of physical violence: Women
Percentage of women age 15-49 who have ever experienced physical violence since age 15 and percentage who have
experienced violence during the 12 months preceding the survey, by background characteristics, Kenya 2014
Percentage who
have ever Percentage who have experienced physical violence
experienced in the past 12 months
Background physical violence Often or Number of
1
characteristic since age 15 Often Sometimes sometimes2 women
Age
15-19 31.6 1.4 16.4 18.1 1,009
20-24 43.9 3.8 14.2 18.1 1,065
25-29 47.7 5.3 17.2 22.5 1,176
30-39 47.5 6.6 15.7 22.2 1,492
40-49 52.0 7.2 12.0 19.2 916
Religion
Roman Catholic 45.2 5.6 15.7 21.3 1,129
Protestant/other Christian 46.4 5.0 15.6 20.6 4,025
Muslim 27.9 2.5 8.6 11.2 354
No religion 39.5 6.8 20.3 27.1 125
Other * * * * 24
Residence
Urban 43.9 5.7 12.6 18.4 2,251
Rural 45.3 4.5 17.0 21.6 3,406
Region
Coast 39.4 3.3 13.3 16.6 568
North Eastern 15.0 0.9 4.7 5.6 118
Eastern 48.8 2.9 17.8 20.8 792
Central 35.3 3.5 13.5 17.0 736
Rift Valley 37.8 4.6 11.3 16.2 1,435
Western 53.3 8.5 18.2 26.8 640
Nyanza 57.1 6.3 23.7 30.0 756
Nairobi 53.9 7.3 13.5 20.8 611
Marital status
Never married 31.7 0.6 11.0 11.6 1,634
Married or living together 47.2 5.9 17.6 23.7 3,352
Divorced/separated/
widowed 64.3 10.9 13.5 24.5 670
Number of living children
0 31.8 1.1 13.0 14.3 1,465
1-2 45.3 4.2 14.5 18.7 1,987
3-4 53.8 7.1 18.0 25.2 1,267
5+ 51.6 9.8 16.5 26.3 938
Employment
Employed for cash 50.4 6.4 15.1 21.6 3,017
Employed not for cash 49.3 5.4 18.4 23.8 780
Not employed 33.6 2.4 14.2 16.6 1,858
Education
No education 38.1 6.4 15.6 21.9 399
Primary incomplete 50.9 7.8 18.3 26.3 1,542
Primary complete 47.8 5.0 16.4 21.5 1,341
Secondary+ 40.2 2.9 12.6 15.5 2,375
Wealth quintile
Lowest 42.9 7.6 16.4 24.3 900
Second 51.3 5.9 20.2 26.1 1,062
Middle 49.1 4.7 17.7 22.5 1,118
Fourth 46.8 3.5 13.4 17.0 1,204
Highest 35.6 4.0 10.3 14.3 1,373
Total 15-49 44.8 5.0 15.2 20.3 5,657
Note: Total includes two women for whom information on religion is missing and two women for whom information on
employment is missing. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been
suppressed.
1
Includes violence in the past 12 months. For women who were married before age 15 and who reported physical
violence by a spouse, the violence could have occurred before age 15.
2
Includes women for whom frequency in the past 12 months is not known.
294 • Domestic Violence
Table 16.1.2 Experience of physical violence: Men
Percentage of men age 15-49 who have ever experienced physical violence since age 15 and percentage who have
experienced violence during the 12 months preceding the survey, by background characteristics, Kenya 2014
Percentage who
have ever Percentage who have experienced physical violence
experienced in the past 12 months
Background physical violence Often or
characteristic since age 151 Often Sometimes sometimes2 Number of men
Age
15-19 42.2 3.8 20.2 24.0 950
20-24 46.1 1.0 8.2 9.2 836
25-29 42.2 0.8 7.6 8.4 837
30-39 44.2 0.6 7.1 7.7 1,242
40-49 45.0 1.2 6.9 8.2 830
Religion
Roman Catholic 46.5 2.1 9.8 11.9 1,017
Protestant/other Christian 44.4 1.4 10.6 12.0 3,147
Muslim 38.9 0.5 6.5 7.0 302
No religion 29.9 1.5 6.9 8.4 212
Other * * * * 14
Residence
Urban 39.3 1.4 9.1 10.5 1,981
Rural 47.3 1.5 10.7 12.2 2,713
Region
Coast 41.9 0.5 8.5 8.9 481
North Eastern 32.0 0.7 9.4 10.1 83
Eastern 41.6 2.6 5.8 8.4 773
Central 44.1 2.1 9.7 11.9 566
Rift Valley 39.7 0.9 10.8 11.7 1,201
Western 60.9 1.1 12.3 13.3 445
Nyanza 56.3 1.8 13.5 15.2 568
Nairobi 33.5 1.6 10.4 11.9 577
Marital status
Never married 42.5 2.0 12.6 14.6 2,070
Married or living together 43.8 0.7 7.4 8.2 2,408
Divorced/separated/
widowed 57.7 4.4 14.0 18.4 216
Number of living children
0 42.4 2.1 12.8 14.9 2,132
1-2 43.7 1.0 6.8 7.8 1,215
3-4 45.3 0.4 7.1 7.6 812
5+ 48.3 1.5 10.5 12.0 536
Employment
Employed for cash 43.8 0.9 7.7 8.6 3,424
Employed not for cash 44.9 1.7 14.7 16.4 521
Not employed 43.5 3.9 17.3 21.2 747
Education
No education 33.6 0.3 7.3 7.5 131
Primary incomplete 45.4 1.6 12.9 14.5 1,200
Primary complete 44.8 1.6 8.6 10.2 1,121
Secondary+ 43.2 1.4 9.3 10.7 2,241
Wealth quintile
Lowest 41.8 0.9 10.4 11.3 675
Second 49.9 2.1 13.7 15.9 853
Middle 46.3 1.3 10.6 11.9 944
Fourth 45.8 2.1 9.4 11.5 1,136
Highest 36.4 0.8 7.0 7.8 1,086
Total 15-49 43.9 1.5 10.0 11.5 4,694
50-54 46.2 0.8 3.5 4.3 268
Total 15-54 44.0 1.4 9.7 11.1 4,962
Note: Total includes one man for whom information on religion is missing and two men for whom information on
employment is missing. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been
suppressed.
1
Includes violence in the past 12 months. For men who were married before age 15 and who reported physical violence
by a spouse, the violence could have occurred before age 15.
2
Includes men for whom frequency in the past 12 months is not known.
The experience of physical violence differs by background characteristics. It increases by age from
32 percent among women age 15-19 to 52 percent among those age 40-49. The percentage is highest among
women who are Roman Catholic or Protestant/other Christian (45-46 percent), women in Nyanza region (57
percent), women with three or more children (52-54 percent), and women who are employed (49-50 percent).
Currently married (47 percent) and formerly married (64 percent) women are much more likely than those
Domestic Violence • 295
who have never been married (32 percent) to have experienced physical violence. The percentage of women
who have experienced physical violence since age 15 is higher among those with an incomplete or complete
primary education (48-51 percent) and lower among those with no education (38 percent). There are no clear
patterns by wealth; however, women in the highest wealth quintile are less likely to have experienced
physical violence since age 15 (36 percent) than women in the lower quintiles (43-51 percent).
Men who are Roman Catholic or Protestant/other Christian (44-47 percent), men with five or more
children (48 percent), and formerly married men (58 percent) are more likely to have experienced physical
violence since age 15 than men in most other subgroups. While women’s experience of physical violence
since age 15 does not differ by residence, men in rural areas (47 percent) are more likely than men in urban
areas (39 percent) to have experienced physical violence. By region, the percentage of men who have
experienced physical violence since age 15 is lowest among those in North Eastern region (32 percent) and
highest among those in Western region (61 percent). Men with no education (34 percent) and those in the
highest wealth quintile (36 percent) are less likely than their counterparts in other subgroups to have
experienced physical violence since age 15.
The percentage of women who experienced physical violence in the past 12 months (often or
sometimes) also varies by background characteristics. It is highest among women age 25-39 (22-23 percent),
women who report having no religion (27 percent), women living in rural areas (22 percent), women in
Nyanza (30 percent), currently or previously married women (24-25 percent), women with three or more
children (25-26 percent), and women who are employed (22-24 percent). Women with a secondary or higher
education (16 percent) and those in the highest wealth quintile (14 percent) are less likely to have experienced
physical violence in the past 12 months than most other women.
Men age 15-19 (24 percent), men living in Nyanza (15 percent), formerly married men (18 percent),
men with no children (15 percent), men who are not employed (21 percent), men with an incomplete primary
education (15 percent), and men in the second wealth quintile (16 percent) are more likely to have
experienced physical violence in the past 12 months than other subgroups of men.
16.3 PERPETRATORS OF PHYSICAL VIOLENCE
Tables 16.2.1 and 16.2.2 show perpetrators of physical violence, according to marital status, among
women and men age 15-49 who have experienced physical violence since age 15. These tables show that,
although the percentages of women and men who report experiencing physical violence since age 15 are
very similar, the persons perpetrating the violence differs greatly by gender for ever-married respondents.
Among ever-married women, the most commonly reported perpetrator of physical violence is the
current husband or partner (57 percent) followed by the former husband/partner (24 percent). By contrast,
among ever-married men, the most common perpetrators are those in the “other” category (46 percent),
followed by teachers (29 percent). Only about 1 in 10 men who have experienced physical violence since
age 15 mention their current spouse as a perpetrator of physical violence.
Among the never-married respondents reported perpetrators are similar for women and men.
Among never-married women who have experienced physical violence since age 15, the most common
perpetrators are teachers (48 percent), followed by mothers or stepmothers (40 percent) and fathers or
stepfathers (19 percent). Among never-married men, the most commonly reported perpetrators are also
teachers (46 percent), followed by those in the “other” category (39 percent), and fathers or stepfathers (21
percent).
296 • Domestic Violence
Table 16.2.1 Persons committing physical violence: Women
Among women age 15-49 who have experienced physical violence since age
15, percentage who report specific persons who committed the violence,
according to the respondent’s current marital status, Kenya 2014
Marital status
Person Ever-married Never married Total
Current husband/partner 56.6 na 45.0
Former husband/partner 23.8 na 18.9
Current boyfriend 0.5 0.8 0.6
Former boyfriend 1.5 2.5 1.7
Father/step-father 11.1 19.2 12.8
Mother/step-mother 17.2 40.0 21.9
Sister/brother 7.1 7.9 7.2
Daughter/son 0.3 0.1 0.3
Other relative 5.1 12.5 6.6
Mother-in-law 0.3 na 0.3
Father-in-law 0.1 na 0.1
Other in-law 0.8 na 0.8
Teacher 11.9 48.2 19.3
Employer/someone at work 0.0 0.4 0.1
Police/soldier 0.1 0.0 0.1
Other 4.6 16.0 6.9
Number of women 2,015 518 2,533
Note: Women can report more than one person who committed the violence.
na = Not applicable
Table 16.2.2 Persons committing physical violence: Men
Among men age 15-49 who have experienced physical violence since age 15,
percentage who report specific persons who committed the violence,
according to the respondent’s current marital status, Kenya 2014
Marital status
Person Ever-married Never married Total
Current wife/partner 11.2 na 6.4
Former wife/partner 8.9 na 5.1
Current girlfriend 0.1 0.0 0.1
Former girlfriend 0.8 0.0 0.4
Father/step-father 19.3 20.8 19.9
Mother/step-mother 14.4 14.0 14.2
Sister/brother 7.6 10.0 8.6
Daughter/son 0.1 0.0 0.0
Other relative 9.1 7.3 8.3
Father-in-law 0.1 na 0.0
Other in-law 1.4 na 0.8
Teacher 29.3 46.0 36.4
Employer/someone at work 2.9 1.2 2.2
Police/soldier 6.3 5.0 5.7
Other 45.7 39.1 42.8
Number of men 1,180 880 2,061
Note: Men can report more than one person who committed the violence.
na = Not applicable
16.4 EXPERIENCE OF SEXUAL VIOLENCE
Tables 16.3.1 and 16.3.2 show the percentage of women and men age 15-49, respectively, who have
experienced sexual violence ever and in the past 12 months, according to background characteristics.
Table 16.3.1 shows that 14 percent of women age 15-49 have ever experienced sexual violence and
8 percent have experienced sexual violence in the past 12 months. There are notable variations in the
experience of sexual violence by age. The youngest women (age 15-19) are less likely than older women
age 30-49 to report sexual violence ever and in the past 12 months (7 percent and 3 percent, compared with
17-18 percent, respectively). Women’s report of sexual violence is also lowest, at 6 percent or less, among
Muslim, North Eastern, and never married women. The percentages of women who have experienced sexual
violence ever and in the past year is lower among those with a secondary or higher education (10 percent
and 5 percent, respectively) and those in the highest wealth quintile (11 percent and 6 percent, respectively)
than other women in these categories.
Domestic Violence • 297
Table 16.3.1 Experience of sexual violence: Women
Percentage of women age 15-49 who have ever experienced sexual violence and
percentage who have experienced sexual violence in the 12 months preceding the
survey, by background characteristics, Kenya 2014
Percentage who have experienced
sexual violence:
Background In the past Number of
characteristic Ever1 12 months women
Age
15-19 6.5 2.7 1,009
20-24 12.6 7.0 1,065
25-29 14.9 10.2 1,176
30-39 17.4 9.2 1,492
40-49 17.5 8.9 916
Religion
Roman Catholic 14.5 7.5 1,129
Protestant/other Christian 14.9 8.3 4,025
Muslim 5.7 3.2 354
No religion 10.3 6.7 125
Other * * 24
Residence
Urban 15.2 8.3 2,251
Rural 13.3 7.4 3,406
Region
Coast 8.3 5.2 568
North Eastern 0.6 0.3 118
Eastern 12.2 6.7 792
Central 9.8 4.2 736
Rift Valley 10.5 4.7 1,435
Western 21.9 14.2 640
Nyanza 22.0 12.9 756
Nairobi 20.0 11.6 611
Marital status
Never married 6.1 1.7 1,634
Married or living together 15.2 10.0 3,352
Divorced/separated/
widowed 28.2 11.7 670
Employment
Employed for cash 17.4 9.7 3,017
Employed not for cash 16.2 9.5 780
Not employed 7.7 4.0 1,858
Number of living children
0 7.5 2.9 1,465
1-2 13.7 8.3 1,987
3-4 18.9 10.4 1,267
5+ 18.6 10.9 938
Education
No education 11.7 6.7 399
Primary incomplete 17.9 10.2 1,542
Primary complete 18.1 10.5 1,341
Secondary+ 9.7 4.8 2,375
Wealth quintile
Lowest 13.7 8.1 900
Second 16.2 9.2 1,062
Middle 15.1 8.8 1,118
Fourth 15.1 7.2 1,204
Highest 11.0 6.2 1,373
Total 15-49 14.1 7.8 5,657
Note: Total includes two women for whom information on religion is missing and two
women for whom information on employment is missing. An asterisk denotes a
figure based on fewer than 25 unweighted cases that has been suppressed.
1
Includes violence in the past 12 months
298 • Domestic Violence
Table 16.3.2 Experience of sexual violence: Men
Percentage of men age 15-49 who have ever experienced sexual violence and
percentage who have experienced sexual violence in the 12 months preceding the
survey, by background characteristics, Kenya 2014
Percentage who have experienced
sexual violence:
Background In the past
characteristic Ever1 12 months Number of men
Age
15-19 2.7 0.9 950
20-24 4.1 0.9 836
25-29 8.4 3.6 837
30-39 8.0 3.5 1,242
40-49 5.5 2.1 830
Religion
Roman Catholic 6.6 3.2 1,017
Protestant/other Christian 5.9 2.3 3,147
Muslim 2.4 0.1 302
No religion 6.3 1.7 212
Other * * 14
Residence
Urban 6.4 2.6 1,981
Rural 5.5 2.1 2,713
Region
Coast 3.6 0.9 481
North Eastern 0.0 0.0 83
Eastern 4.9 1.7 773
Central 2.9 1.2 566
Rift Valley 4.7 2.1 1,201
Western 7.2 2.8 445
Nyanza 13.4 4.9 568
Nairobi 6.9 3.0 577
Marital status
Never married 2.5 0.5 2,070
Married or living together 7.7 3.5 2,408
Divorced/separated/
widowed 17.2 6.6 216
Employment
Employed for cash 7.1 2.7 3,424
Employed not for cash 3.1 1.6 521
Not employed 2.1 0.9 747
Number of living children
0 2.8 0.6 2,132
1-2 8.4 3.6 1,215
3-4 7.1 3.2 812
5+ 10.3 4.6 536
Education
No education 1.5 0.2 131
Primary incomplete 5.9 2.7 1,200
Primary complete 6.9 2.6 1,121
Secondary+ 5.5 2.0 2,241
Wealth quintile
Lowest 5.2 2.2 675
Second 7.2 3.4 853
Middle 5.6 1.7 944
Fourth 6.5 2.1 1,136
Highest 4.8 2.1 1,086
Total 15-49 5.9 2.3 4,694
50-54 6.7 2.1 268
Total 15-54 5.9 2.3 4,962
Note: Total includes one man for whom information on religion is missing and two
men for whom information on employment is missing. An asterisk denotes a figure
based on fewer than 25 unweighted cases that has been suppressed.
1
Includes violence in the past 12 months
Experience of sexual violence ever and in the past 12 months is highest among formerly married
women (28 percent and 12 percent, respectively), women who are employed for cash (17 percent and 10
percent, respectively), Western, Nyanza and Nairobi women (20-22 percent and 12-14 percent), and women
with three or more living children (19 percent and 10-11 percent, respectively).
Domestic Violence • 299
Table 16.3.2 shows that 6 percent of men age 15-49 have ever experienced sexual violence and that
2 percent experienced sexual violence in the past 12 months. Men age 25-40 are more likely than younger
or older men to report sexual violence. Sexual violence is highest at 17 percent ever and 7 percent in the past
12 months among formerly married men.
16.5 PERPETRATORS OF SEXUAL VIOLENCE
Tables 16.4.1 and 16.4.2 show perpetrators of sexual violence, according to marital status, among
women and men age 15-49 who have ever experienced sexual violence.
Among ever-married women and men, the most commonly reported perpetrators of sexual violence
are current spouses/partners (55 percent and 37 percent, respectively) and former spouses/partners (28
percent and 25 percent, respectively).
Among never-married women who have ever experienced sexual violence, the most common
perpetrators of violence are strangers (reported by 44 percent of women), followed by friends or
acquaintances (14 percent) and those in the “other” category (12 percent). Among never-married men, the
most common perpetrators of sexual violence are those in the “other” category (reported by 42 percent of
men), friends or acquaintances (19 percent), and strangers and other relatives (12 percent and 11 percent,
respectively).
Table 16.4.1 Persons committing sexual violence: Women
Among women age 15-49 who have experienced sexual violence, percentage who
report specific persons who committed the violence according to the respondent’s
current marital status, Kenya 2014
Marital status
Person Ever-married Never married Total
Current husband/partner 55.2 na 48.3
Former husband/partner 28.0 na 24.5
Current/former boyfriend 3.9 8.2 4.5
Father/step-father 0.1 4.7 0.7
Brother/step-brother 0.0 1.1 0.2
Other relative 3.2 1.6 3.0
In-law 0.5 na 0.5
Own friend/acquaintance 4.3 14.4 5.5
Family friend 2.3 6.9 2.9
Teacher 0.7 5.8 1.4
Employer/someone at work 1.4 0.0 1.2
Police/soldier 0.2 1.3 0.3
Priest/religious leader 0.9 0.0 0.8
Stranger 5.6 43.8 10.4
Other 2.6 12.2 3.8
Number of women 697 99 796
Note: Women can report more than one person who committed the violence.
na = Not applicable
300 • Domestic Violence
Table 16.4.2 Persons committing sexual violence: Men
Among men age 15-49 who have experienced sexual violence, percentage who
report specific persons who committed the violence according to the respondent’s
current marital status, Kenya 2014
Marital status
Person Ever-married Never married Total
Current wife/partner 36.5 na 29.7
Former wife/partner 25.4 na 20.7
Current/former girlfriend 15.5 7.5 14.0
Father/step-father 0.0 1.8 0.3
Other relative 0.8 10.8 2.7
In-law 0.5 na 0.4
Own friend/acquaintance 9.8 18.9 11.5
Family friend 3.9 0.7 3.3
Teacher 0.5 2.2 0.8
Employer/someone at work 6.1 4.0 5.7
Police/soldier 0.4 0.0 0.3
Stranger 7.5 12.3 8.4
Other 9.9 41.8 15.9
Number of men 223 52 275
Note: Men can report more than one person who committed the violence.
na = Not applicable
16.6 AGE AT FIRST EXPERIENCE OF SEXUAL VIOLENCE
Tables 16.5.1 and 16.5.2 show the percentage of women and men age 15-49, respectively, who
experienced sexual violence by specific exact ages, according to current age and current marital status. Four
percent of women and 2 percent of men had experienced violence by age 18, including 2 percent of women
and 1 percent of men who had experienced sexual violence by age 15. Overall, of those who reported sexual
violence about half of women and men had first experienced sexual violence by age 22.
Table 16.5.1 Age at first experience of sexual violence: Women
Percentage of women age 15-49 who experienced sexual violence by specific exact ages, according to
current age and current marital status, Kenya 2014
Percentage
who have not
Percentage who first experienced sexual violence experienced
Background by exact age: sexual Number of
characteristic 10 12 15 18 22 violence women
Age
15-19 0.3 1.0 4.1 na na 93.5 1,009
20-24 0.1 0.1 1.5 4.4 na 87.4 1,065
25-29 0.2 0.5 1.0 3.2 6.7 85.1 1,176
30-39 0.2 0.3 1.9 4.7 7.6 82.6 1,492
40-49 0.2 0.2 1.1 3.7 6.9 82.5 916
Marital status
Never married 0.3 0.7 2.8 4.2 5.6 93.9 1,634
Ever married 0.2 0.3 1.6 4.5 8.4 82.7 4,023
Total 0.2 0.4 1.9 4.4 7.6 85.9 5,657
na = Not applicable
Domestic Violence • 301
Table 16.5.2 Age at first experience of sexual violence: Men
Percentage of men age 15-49 who experienced sexual violence by specific exact ages, according to current
age and current marital status, Kenya 2014
Percentage
who have not
Percentage who first experienced sexual violence experienced
Background by exact age: sexual Number of
characteristic 10 12 15 18 22 violence men
Age
15-19 0.1 0.1 1.1 na na 97.3 950
20-24 0.3 0.6 1.0 2.1 na 95.9 836
25-29 0.3 0.5 1.0 1.6 3.4 91.6 837
30-39 0.4 0.8 1.0 1.6 3.1 92.0 1,242
40-49 0.2 0.2 0.3 0.4 1.2 94.5 830
Marital status
Never married 0.2 0.2 0.9 1.7 2.1 97.5 2,070
Ever married 0.4 0.7 0.9 1.5 3.3 91.5 2,624
Total 0.3 0.5 0.9 1.6 2.8 94.1 4,694
na = Not applicable
16.7 EXPERIENCE OF DIFFERENT FORMS OF VIOLENCE
Tables 16.6.1 and 16.6.2 present information on the experience of various forms of violence among
respondents age 15-49.
Table 16.6.1 Experience of different forms of violence: Women
Percentage of women age 15-49 who have ever experienced different forms of
violence by current age, Kenya 2014
Physical Sexual Physical Physical or
violence violence and sexual sexual Number of
Age only only violence violence women
15-19 28.1 3.1 3.4 34.7 1,009
15-17 29.5 2.5 2.3 34.3 639
18-19 25.8 4.2 5.4 35.4 371
20-24 34.3 3.0 9.6 46.9 1,065
25-29 34.9 2.0 12.9 49.8 1,176
30-39 32.9 2.8 14.7 50.3 1,492
40-49 36.6 2.1 15.4 54.2 916
Total 33.3 2.6 11.5 47.4 5,657
Table 16.6.2 Experience of different forms of violence: Men
Percentage of men age 15-49 who have ever experienced different forms of
violence by current age, Kenya 2014
Physical Sexual Physical Physical or
violence violence and sexual sexual Number of
Age only only violence violence men
15-19 40.3 0.8 1.9 43.0 950
15-17 41.7 1.0 1.8 44.5 578
18-19 38.0 0.4 2.1 40.6 371
20-24 43.2 1.2 2.9 47.3 836
25-29 36.8 3.0 5.4 45.2 837
30-39 38.1 1.9 6.1 46.0 1,242
40-49 40.0 0.6 4.9 45.6 830
Total 15-49 39.5 1.5 4.4 45.4 4,694
50-54 39.6 0.2 6.5 46.4 268
Total 15-54 39.6 1.4 4.5 45.5 4,962
Forty-seven percent of women age 15-49 reported that they have experienced either physical or
sexual violence. Thirty-three percent have experienced physical violence only, 3 percent have experienced
sexual violence only, and 12 percent have experienced both physical and sexual violence. The percentage of
women who have experienced physical or sexual violence increases steadily with age, from 35 percent
among those age 15-19 to 54 percent among those age 40-49.
302 • Domestic Violence
Overall, 45 percent of men age 15-49 reported Table 16.7 Experience of violence during pregnancy
that they have experienced either physical or sexual Among women age 15-49 who have ever been pregnant,
violence; 40 percent have experienced physical violence percentage who have ever experienced physical violence during
pregnancy, by background characteristics, Kenya 2014
only, 2 percent have experienced sexual violence only,
and 4 percent have experienced both physical and sexual Percentage who
experienced
Number of
women who
violence. For men there are no clear relationships Background violence during have ever been
characteristic pregnancy pregnant
between age and the various forms of violence.
Age
15-19 5.5 168
16.8 VIOLENCE DURING PREGNANCY 20-24 9.4 736
25-29 10.8 1,070
30-39 8.4 1,459
Female respondents who had ever been 40-49 9.0 897
pregnant were asked specifically whether they had ever Religion
experienced physical violence while pregnant. Table Roman Catholic
Protestant/other Christian
8.6
9.8
866
3,060
16.7 presents, among women age 15-49 who have ever Muslim 5.6 271
No religion 7.0 116
been pregnant, the percentage who have ever Other * 17
experienced physical violence during pregnancy Residence
according to background characteristics. Urban 9.7 1,739
Rural 8.8 2,591
Region
Nine percent of ever-pregnant women have Coast 5.1 455
experienced physical violence during pregnancy. By age, North Eastern 2.7 87
Eastern 7.0 615
this percentage is lowest among women age 15-19 (6 Central 4.0 559
percent) and highest among women age 25-29 (11 Rift Valley 7.9 1,097
Western 10.6 459
percent). Physical violence during pregnancy is highest Nyanza 14.4 586
among women residing in Nairobi (18 percent, and Nairobi 18.1 473
formerly married women (21 percent). Marital status
Never married 4.0 403
Married or living together 7.4 3,269
Women with three or more children (10-11 Divorced/separated/
widowed 20.9 659
percent), those with an incomplete primary education (12
Number of living children
percent), and those in the second or fourth wealth 0 4.4 138
quintile (11 percent each) are more likely than other 1-2
3-4
8.3
10.6
1,987
1,267
women in these categories to have experienced violence 5+ 9.9 938
during pregnancy. Education
No education 6.6 370
Primary incomplete 12.4 1,226
16.9 MARITAL CONTROL BY SPOUSE Primary complete 8.9 1,203
Secondary+ 7.4 1,532
Wives’ or husbands’ close control and Wealth quintile
Lowest 9.8 741
monitoring of the behaviour of their spouse is known to Second 10.8 812
be an important warning sign and correlate of violence Middle
Fourth
8.4
11.4
837
971
in a relationship. A series of questions were included in Highest 5.7 968
the 2014 KDHS to elicit the degree of marital control Total 15-49 9.2 4,331
exercised by husbands or wives over their spouses. Note: Total includes two women for whom information on religion
Controlling behaviours most often manifest themselves is missing. An asterisk denotes a figure based on fewer than 25
unweighted cases that has been suppressed.
in terms of extreme possessiveness, jealousy, and
attempts to isolate spouses from their family and friends.
To determine degree of marital control, ever-married women and men were asked whether their
current or former spouse exhibited each of the following controlling behaviours: (1) is jealous or gets angry
if she/he talks to other men/women, (2) frequently accuses her/him of being unfaithful, (3) does not permit
meetings with female/male friends, (4) tries to limit contact with her/his family, and (5) insists on knowing
where she/he is at all times. Because the concentration of such behaviours is more noteworthy than the
display of any single behaviour, the proportion of respondents whose spouses display at least three of the
specified behaviours is highlighted. Tables 16.8.1 and 16.8.2 present the percentage of ever-married women
and men age 15-49, respectively, whose spouses have ever displayed each of the listed behaviours, by
selected background characteristics.
Domestic Violence • 303
Table 16.8.1 Marital control exercised by husbands
Percentage of ever-married women age 15-49 whose husbands/partners have ever demonstrated specific types of controlling behaviours, by background
characteristics, Kenya 2014
Percentage of women whose husband/partner:
Is jealous or Frequently Does not Insists on Displays 3 or
angry if she accuses her of permit her to Tries to limit knowing more of the Displays none Number of
Background talks to other being meet her her contact where she is specific of the specific ever-married
characteristic men unfaithful female friends with her family at all times behaviours behaviours women
Age
15-19 60.1 15.1 18.0 5.1 46.1 20.2 32.0 111
20-24 52.4 22.6 22.5 10.5 41.8 25.9 36.6 647
25-29 55.4 24.5 22.4 11.7 43.3 26.0 34.5 1,002
30-39 50.9 22.9 19.7 11.7 41.4 23.4 36.4 1,395
40-49 51.7 21.7 19.2 13.5 38.1 24.1 40.7 867
Religion
Roman Catholic 52.3 20.5 18.6 10.8 37.9 21.7 39.0 783
Protestant/other Christian 52.3 23.3 21.0 12.4 42.3 24.8 36.0 2,853
Muslim 52.6 16.6 23.4 8.4 39.4 24.6 41.6 271
No religion 65.7 37.0 21.8 8.0 42.3 31.3 29.7 100
Other * * * * * * * 15
Residence
Urban 57.9 23.2 23.8 11.6 45.4 27.6 32.7 1,588
Rural 49.3 22.5 18.6 11.7 38.7 22.5 39.4 2,435
Region
Coast 66.8 26.4 26.7 11.9 50.5 32.6 28.5 439
North Eastern 31.3 9.9 9.4 2.6 18.5 10.5 66.9 97
Eastern 50.9 23.6 17.7 13.1 44.1 23.6 33.7 585
Central 51.9 18.8 17.1 9.7 43.3 20.7 37.1 518
Rift Valley 48.3 18.1 17.2 8.7 33.3 18.1 41.7 983
Western 54.0 29.3 25.8 16.7 40.3 29.8 36.4 433
Nyanza 47.5 26.4 23.4 16.3 43.8 26.7 36.6 553
Nairobi 62.3 25.2 24.5 9.9 47.8 31.6 31.2 414
Marital status
Married or living together 49.3 20.1 16.5 9.5 37.7 19.9 39.3 3,352
Divorced/separated/
widowed 69.9 36.1 41.1 22.6 59.7 47.6 24.1 670
Number of living children
0 48.8 24.0 18.2 8.5 39.9 21.9 39.1 203
1-2 55.9 19.2 21.6 10.7 42.1 24.2 35.0 1,657
3-4 50.3 24.9 20.6 12.8 42.9 25.4 37.4 1,238
5+ 51.0 26.0 19.6 12.7 38.3 24.4 38.6 925
Employment
Employed for cash 55.2 24.8 22.7 13.6 44.4 27.3 34.2 2,427
Employed not for cash 51.5 22.0 17.0 11.0 41.0 21.0 35.0 665
Not employed 47.1 18.1 17.9 7.3 33.8 19.8 44.7 929
Education
No education 40.7 23.7 15.4 9.4 33.2 19.1 50.3 375
Primary incomplete 54.5 27.9 21.9 13.6 42.0 27.5 35.9 1,150
Primary complete 56.3 21.3 21.3 12.0 42.1 24.4 32.9 1,120
Secondary+ 51.6 19.5 20.5 10.5 42.4 23.5 37.0 1,378
Wealth quintile
Lowest 50.4 26.8 20.9 10.7 39.4 24.4 39.0 707
Second 51.2 24.6 20.2 13.5 40.2 24.5 37.5 781
Middle 55.3 25.3 21.5 13.4 43.7 26.1 32.9 760
Fourth 53.5 20.5 21.7 10.9 41.3 25.5 37.1 894
Highest 52.9 18.1 19.1 10.2 42.0 22.1 37.3 880
Woman afraid of
husband/partner
Most of the time afraid 72.1 47.7 42.3 32.7 65.1 53.4 16.3 495
Sometimes afraid 64.1 31.7 29.6 17.6 50.8 34.9 26.6 949
Never afraid 44.9 14.7 13.1 5.5 33.3 15.1 44.3 2,571
Total 52.7 22.8 20.6 11.7 41.4 24.5 36.8 4,023
Note: Husband/partner refers to the current husband/partner for currently married women and the most recent husband/partner for divorced, separated or
widowed women. Total includes one woman for whom information on religion is missing, two women for whom information on employment is missing, and
seven women for whom information on fear of husband/partner is missing. An asterisk denotes a figure based on fewer than 25 unweighted cases that has
been suppressed.
304 • Domestic Violence
Table 16.8.2 Marital control exercised by wives
Percentage of ever-married men age 15-49 whose wives/partners have ever demonstrated specific types of controlling behaviours, by background
characteristics, Kenya 2014
Percentage of men whose wife/partner:
Is jealous or Frequently Does not Insists on Displays 3 or
angry if he accuses him permit him to Tries to limit knowing more of the Displays none Number of
Background talks to other of being meet his male his contact where he is at specific of the specific ever-married
characteristic women unfaithful friends with his family all times behaviours behaviours men
Age
15-19 * * * * * * * 9
20-24 66.4 39.1 15.7 2.7 54.9 32.9 23.0 168
25-29 57.2 26.7 12.3 4.8 43.0 20.2 29.6 534
30-39 57.9 31.3 10.3 5.7 42.6 21.5 31.0 1,115
40-49 56.6 29.4 10.8 6.5 40.8 21.2 32.8 799
Religion
Roman Catholic 60.2 32.8 11.1 6.4 44.3 23.2 28.8 592
Protestant/other Christian 56.9 29.3 11.7 5.8 43.4 21.7 30.8 1,750
Muslim 62.0 29.8 5.9 3.7 38.1 19.4 32.9 157
No religion 53.4 32.9 12.5 1.6 35.3 21.2 38.2 115
Other * * * * * * * 10
Residence
Urban 59.3 29.8 12.0 6.6 43.4 21.5 29.0 1,192
Rural 56.5 30.5 10.6 4.7 42.5 22.1 32.4 1,432
Region
Coast 73.2 35.1 13.0 3.5 63.6 30.3 18.0 244
North Eastern 40.1 16.5 0.0 0.0 15.5 4.9 54.8 42
Eastern 59.3 34.2 11.4 7.7 55.8 26.3 25.4 371
Central 55.5 24.8 7.0 4.5 52.9 19.7 26.4 337
Rift Valley 54.0 29.0 7.6 3.1 27.5 16.4 39.6 655
Western 51.5 27.0 11.8 7.8 25.3 16.7 40.1 253
Nyanza 56.5 35.5 19.8 8.6 53.3 29.1 24.6 345
Nairobi 61.9 28.8 13.0 6.5 39.9 21.8 30.3 377
Marital status
Married or living together 56.5 28.1 10.3 4.7 41.6 20.0 31.9 2,408
Divorced/separated/
widowed 71.4 53.0 21.2 15.7 57.3 42.1 19.0 216
Number of living children
0 61.9 25.8 12.6 3.9 53.5 23.2 27.6 206
1-2 58.8 28.5 11.0 5.5 45.1 21.5 29.1 1,083
3-4 55.1 30.4 11.0 5.3 39.9 21.7 33.3 800
5+ 58.1 35.1 11.5 6.9 38.6 22.0 32.1 535
Employment
Employed for cash 58.0 30.2 11.3 5.7 43.2 22.0 30.6 2,398
Employed not for cash 54.2 28.8 10.4 3.7 38.6 18.5 33.9 213
Not employed (74.0) (51.4) (10.2) (11.4) (58.2) (37.2) (20.3) 13
Education
No education 53.5 34.9 3.3 2.0 28.9 14.4 38.0 101
Primary incomplete 57.2 34.7 11.3 6.0 38.6 22.8 32.7 603
Primary complete 55.8 29.3 12.2 6.7 43.0 22.1 32.5 770
Secondary+ 59.8 28.1 11.2 4.9 46.3 21.8 28.2 1,150
Wealth quintile
Lowest 58.3 36.3 11.3 5.9 35.1 20.6 32.4 375
Second 55.0 32.7 14.0 7.6 39.7 23.3 33.6 470
Middle 60.1 31.0 11.4 3.9 47.5 24.0 27.5 496
Fourth 54.9 26.5 9.5 4.8 42.9 19.8 32.7 638
Highest 60.5 27.8 10.7 6.1 46.1 21.7 28.7 645
Man afraid of wife/partner
Most of the time afraid (66.2) (48.0) (29.8) (11.8) (65.5) (45.9) (25.4) 35
Sometimes afraid 79.0 59.2 32.8 19.5 61.7 49.9 11.5 168
Never afraid 56.2 27.9 9.5 4.5 41.4 19.6 32.2 2,411
Total 15-49 57.8 30.2 11.2 5.6 42.9 21.8 30.9 2,624
50-54 52.1 28.2 8.0 6.1 41.4 22.3 36.8 265
Total 15-54 57.2 30.0 10.9 5.6 42.7 21.9 31.4 2,890
Note: Wife/partner refers to the current wife/partner for currently married men and the most recent wife/partner for divorced, separated or widowed men. Total
includes one man for whom information on religion is missing, two men for whom information on employment is missing, and nine men for whom information
on fear of wife/partner is missing. Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25
unweighted cases that has been suppressed.
Domestic Violence • 305
The main controlling behaviours women experienced from their husbands were jealousy or anger if
they talked to other men (53 percent) and insisting on knowing where they are at all times (41 percent). The
next most common behaviours were husbands frequently accusing them of being unfaithful (23 percent) and
not permitting them to meet female friends (21 percent).
Twenty-five percent of ever-married women say that their husbands display three or more of these
controlling behaviours. Notably, among women who report being afraid of their husbands most of the time,
the proportion who report that their husbands display three or more of these controlling behaviours is twice
as high (53 percent) as the average for all ever-married women. About half of formerly married women also
report that their former husband displayed three or more of these behaviours (48 percent). Women who report
having no religion (31 percent), women living in the Coast and Nairobi regions (32-33 percent), women
employed for cash (27 percent), and women with primary incomplete education (28 percent) are also more
likely than most other women to have husbands who display three or more controlling behaviours.
Table 16.8.2 shows that, similar to women, the main controlling behaviours men experience from
their wives were jealousy or anger if they talked to other women (58 percent) and insisting on knowing
where they are at all times (43 percent). Thirty percent of men reported that their wives frequently accuse
them of being unfaithful, and 11 percent said that their wives do not permit them to meet male friends.
Twenty-two percent of ever-married men say that their wives display three or more of these
controlling behaviours. As with women, men who are afraid of their wives are much more likely to report
controlling behaviours by their wives. Further, men reporting that their wives display three or more
controlling behaviours is relatively high among men age 20-24 (33 percent), Christian men (22-23 percent),
men living in the Coast and in Nyanza (29-30 percent), and formerly married men (42 percent). The
percentage of men whose wives display at least three controlling behaviours is lowest among those with no
education compared to those with some education (14 percent compared with 22-23 percent) and varies
inconsistently by wealth.
16.10 FORMS OF SPOUSAL VIOLENCE
Different types of violence are not mutually exclusive, and people may report multiple forms of
violence. Research suggests that physical violence in intimate relationships is often accompanied by
psychological abuse and, in one-third to more than one-half of cases, by sexual abuse (Krug et al., 2002).
Tables 16.9.1 and 16.9.2 show the percentage of ever-married women and ever-married men age 15-49,
respectively, who have experienced various forms of violence by their spouse over the course of the marriage
and in the 12 months preceding the survey. Note that respondents who are currently married reported on
violence by their current spouse, and respondents who are widowed, divorced, or separated reported on
violence by their most recent spouse.
Table 16.9.1 shows that 37 percent of ever-married women reported ever experiencing physical
violence committed by their current or most recent husband or partner, 13 percent reported sexual violence,
and 32 percent reported emotional violence. About 4 in 10 ever-married women (39 percent) have
experienced physical and/or sexual violence, and slightly less than half (47 percent) have experienced at
least one of the three forms of spousal violence.
Figure 6.1 shows the percentage of ever-married women reporting specific acts of physical or sexual
violence perpetrated by their current or most recent husband. The most common form of spousal physical or
sexual violence reported by ever-married women is being slapped (31 percent) followed by being pushed,
shaken, or having something thrown at them. About equal percentages of women report experiencing being
punched with his fist or something that could hurt her and being kicked dragged or beaten up (13-14 percent).
The most common form of spousal sexual violence is being physically forced to have sex against her will.
306 • Domestic Violence
Figure 16.1 Percentage of ever-married women age 15-49 who have experienced
specific types of spousal physical and sexual violence by the current or most recent
husband/partner
Pushed her, shook her, or threw something at 20
her 13
Slapped her 31
18
Twisted her arm or pulled her hair 10
6
Punched her with his fist or with something that 13
could hurt her 8
Kicked her, dragged her, or beat her up 14
9
4 Ever
Tried to choke her or burn her on purpose
3 In the past 12 months
Threatened to attack her or attacked her with a 6
gun or other weapon 3
Physically forced her to have sexual 12
intercourse with him when she did not want to 9
Physically forced her to perform any other 4
sexual acts she did not want to 3
Forced her with threats or in any other way to 6
perform sexual acts she did not want to 4
Percentage
KDHS 2014
Specific acts of emotional violence are also quite common. Twenty-six percent of women reported
that their husbands have insulted them or made them feel bad about themselves and 18 percent reported that
their husband said or did something to humiliate them in front of others.
Twenty-three percent of ever-married women reported experiencing spousal physical violence in
the past 12 months, with 7 percent having experienced it often during the period. Ten percent reported having
experienced spousal sexual violence in the past 12 months (3 percent often). Additionally, 24 percent of
women reported spousal emotional violence in the past 12 months (9 percent often). Overall, one-third (33
percent) of ever-married women experienced at least one of the three forms of violence by their current or
most recent husband or partner in the past year.
Ever-married women who had been married more than once were also asked about physical or
sexual violence by their earlier husband(s) or partner(s). Different forms of violence perpetrated by all
husbands/partners is provided in the lower panel of Table 16.9.1. Overall, 38 percent of ever-married women
have experienced physical violence by any husband ever and 14 percent have experienced sexual violence
and 41 percent have experienced physical or sexual violence.
Domestic Violence • 307
Table 16.9.1 Forms of spousal violence: Women
Percentage of ever-married women age 15-49 who have experienced various forms of violence ever or in the 12
months preceding the survey, committed by their husband/partner, Kenya 2014
In the past 12 months1
Often or
Type of violence Ever Often Sometimes sometimes
SPOUSAL VIOLENCE COMMITTED BY CURRENT OR MOST RECENT HUSBAND/PARTNER
Physical violence
Any physical violence 36.9 6.7 15.9 22.6
Pushed her, shook her, or threw
something at her 19.6 3.8 9.3 13.0
Slapped her 31.3 4.5 13.2 17.7
Twisted her arm or pulled her hair 9.5 2.2 4.0 6.2
Punched her with his fist or with
something that could hurt her 13.1 2.8 4.7 7.5
Kicked her, dragged her, or beat
her up 14.3 3.2 5.5 8.7
Tried to choke her or burn her on
purpose 3.8 0.7 1.9 2.5
Threatened her or attacked her
with a knife, gun, or other
weapon 5.8 1.1 2.3 3.4
Sexual violence
Any sexual violence 13.3 3.4 6.4 9.8
Physically forced her to have
sexual intercourse with him
when she did not want to 11.8 2.8 6.0 8.8
Physically forced her to perform
any other sexual acts she did not
want to 4.4 1.4 2.0 3.4
Forced her with threats or in any
other way to perform sexual acts
she did not want to 5.8 1.5 2.7 4.2
Emotional violence
Any emotional violence 32.4 9.4 14.4 23.8
Said or did something to humiliate
her in front of others 17.9 4.9 8.2 13.1
Threatened to hurt or harm her or
someone she cared about 14.7 3.7 6.3 10.0
Insulted her or made her feel bad
about herself 25.7 7.4 11.9 19.2
Any form of physical and/or sexual
violence 39.4 7.9 17.5 25.4
Any form of emotional and/or physical
and/or sexual violence 47.1 12.2 20.5 32.7
SPOUSAL VIOLENCE COMMITTED BY ANY HUSBAND/PARTNER
Physical violence 38.4 na na 22.7
Sexual violence 14.0 na na 9.8
Physical and/or sexual violence 40.7 na na 25.5
Number of ever-married women 4,023 4,023 4,023 4,023
1
For widows, estimates of spousal violence by the current or most recent spouse in the past 12 months are not
known; hence widows are excluded from the estimate of spousal violence by the current or most recent spouse
in the past 12 months. However, widows are included in the estimate of spousal violence committed by any
husband/partner in the past 12 months.
na = Not applicable
Table 16.9.2 shows that, among ever-married men, 7 percent reported ever experiencing physical
violence by their current or most recent wife or partner, 4 percent reported sexual violence, and 21 percent
reported emotional violence. About 1 in 10 men (9 percent) have ever experienced physical and/or sexual
violence, and about 1 in 4 (24 percent) have experienced at least one of the three forms of spousal violence
by their current or most recent wife or partner.
308 • Domestic Violence
Table 16.9.2 Forms of spousal violence: Men
Percentage of ever-married men age 15-49 who have experienced various forms of violence ever or in the 12
months preceding the survey, committed by their wife/partner, Kenya 2014
In the past 12 months
Often or
Type of violence Ever Often Sometimes sometimes
SPOUSAL VIOLENCE COMMITTED BY CURRENT OR MOST RECENT WIFE/PARTNER
Physical violence
Any physical violence 7.1 0.6 4.4 5.0
Pushed him, shook him, or threw
something at him 5.0 0.4 3.1 3.5
Slapped him 2.4 0.3 1.4 1.7
Twisted his arm or pulled his hair 1.4 0.1 1.0 1.1
Punched him with her fist or with
something that could hurt him 2.0 0.2 1.0 1.2
Kicked him, dragged him, or beat
him up 1.0 0.1 0.7 0.8
Tried to choke him or burn him on
purpose 0.7 0.0 0.5 0.6
Threatened him or attacked him
with a knife, gun, or other
weapon 2.8 0.3 1.6 1.9
Sexual violence
Any sexual violence 4.0 0.8 2.4 3.2
Physically forced him to have
sexual intercourse with her when
he did not want to 3.4 0.6 2.1 2.7
Physically forced him to perform
any other sexual acts he did not
want to 1.1 0.2 0.7 1.0
Forced him with threats or in any
other way to perform sexual acts
he did not want to 1.3 0.2 0.9 1.1
Emotional violence
Any emotional violence 20.9 3.6 11.7 15.3
Said or did something to humiliate
him in front of others 13.2 1.9 7.5 9.5
Threatened to hurt or harm him or
someone he cared about 6.5 1.1 3.3 4.3
Insulted him or made him feel bad
about himself 15.4 1.9 9.0 10.9
Any form of physical and/or sexual
violence 9.4 1.3 5.8 7.1
Any form of emotional and/or physical
and/or sexual violence 24.1 4.2 14.0 18.2
SPOUSAL VIOLENCE COMMITTED BY ANY WIFE/PARTNER
Physical violence 8.6 na na 5.0
Sexual violence 4.4 na na 3.3
Physical and/or sexual violence 11.1 na na 7.2
Number of ever-married men 2,624 2,624 2,624 2,624
1
For widowers, estimates of spousal violence by the current or most recent spouse in the past 12 months are
not known; hence widowers are excluded from the estimate of spousal violence by the current or most recent
spouse in the past 12 months. However, widowers are included in the estimate of spousal violence committed
by any wife/partner in the past 12 months.
na = Not applicable
Fifteen percent of ever-married men reported that their current or most recent spouse or partner ever
insulted them or made them feel bad about themselves, and 13 percent reported that their wife or partner
ever said or did something to humiliate them in front of others.
Five percent of ever-married men reported experiencing spousal physical violence in the past 12
months, with 1 percent having experienced it often. Three percent reported having experienced spousal
sexual violence in the past 12 months (1 percent often). In addition, 15 percent of men reported emotional
violence in the past 12 months (4 percent often). Overall, 18 percent of ever-married men have experienced
at least one of the three forms of spousal violence by their current or most recent wife or partner in the past
year.
About 1 in 10 men (11 percent) reported having ever experienced physical and/or sexual violence
by any current or former wife or partner.
Domestic Violence • 309
16.11 SPOUSAL VIOLENCE BY BACKGROUND CHARACTERISTICS
Tables 16.10.1 and 16.10.2 show the percentage of ever-married women and men age 15-49,
respectively, who have experienced spousal emotional, physical, or sexual violence by selected background
characteristics.
Table 16.10.1 Spousal violence by background characteristics: Women
Percentage of ever-married women age 15-49 who have ever experienced emotional, physical or sexual violence committed by their
husband/partner, by background characteristics, Kenya 2014
Physical Number of
and sexual Physical or ever-
Background Emotional Physical Sexual Physical and Physical or sexual or married
characteristic violence violence violence and sexual emotional sexual emotional women
Age
15-19 18.7 20.3 6.6 2.6 2.6 24.4 30.7 111
20-24 27.5 33.0 12.2 8.4 7.2 36.8 41.5 647
25-29 32.7 36.7 14.3 11.0 8.5 40.0 48.7 1,002
30-39 33.7 38.2 13.7 11.7 8.9 40.2 48.5 1,395
40-49 35.2 40.1 13.0 11.8 9.6 41.4 49.5 867
Religion
Roman Catholic 31.0 39.9 13.3 11.8 7.9 41.4 48.3 783
Protestant/other Christian 34.6 38.0 14.2 11.3 9.3 41.0 49.1 2,853
Muslim 11.7 17.4 4.9 4.2 2.9 18.1 22.7 271
No religion 30.9 38.1 8.8 6.8 5.4 40.1 46.0 100
Other * * * * * * * 15
Residence
Urban 32.3 33.6 14.0 11.1 8.9 36.6 46.4 1,588
Rural 32.4 39.1 12.8 10.6 8.2 41.3 47.6 2,435
Region
Coast 22.2 25.9 8.2 5.4 4.2 28.7 35.2 439
North Eastern 4.7 10.1 0.4 0.4 0.4 10.1 12.2 97
Eastern 32.0 39.6 12.3 10.2 8.1 41.6 48.3 585
Central 27.6 31.3 7.9 5.3 2.9 33.9 42.5 518
Rift Valley 28.2 31.1 9.3 7.8 5.9 32.7 41.4 983
Western 45.5 49.2 23.8 19.5 16.6 53.4 59.5 433
Nyanza 40.9 47.7 18.5 16.0 12.8 50.2 56.9 553
Nairobi 40.8 44.7 21.1 17.6 14.5 48.2 59.6 414
Marital status
Married or living together 28.8 33.7 11.5 9.0 6.9 36.2 43.4 3,352
Divorced/separated/widowed 50.4 53.1 22.3 19.7 16.5 55.6 65.7 670
Number of living children
0 21.9 17.1 7.7 3.1 2.4 21.7 31.9 203
1-2 28.8 31.4 12.4 9.2 7.2 34.6 43.3 1,657
3-4 36.5 42.5 14.6 12.8 10.5 44.3 51.1 1,238
5+ 35.3 43.6 14.3 12.5 9.5 45.4 52.0 925
Employment
Employed for cash 36.3 39.9 15.1 12.3 9.7 42.7 51.2 2,427
Employed not for cash 35.7 42.0 15.0 11.6 9.6 45.4 52.8 665
Not employed 19.6 25.4 7.1 6.1 4.4 26.4 32.3 929
Education
No education 22.7 33.0 9.2 7.9 7.3 34.3 37.6 375
Primary incomplete 38.5 46.3 15.9 14.1 10.8 48.1 54.3 1,150
Primary complete 31.7 37.6 15.8 12.8 9.4 40.6 48.3 1,120
Secondary+ 30.4 29.7 10.2 7.2 6.1 32.6 42.8 1,378
Wealth quintile
Lowest 30.7 37.1 13.6 10.5 9.0 40.2 46.0 707
Second 35.6 44.4 13.7 11.5 8.4 46.6 52.7 781
Middle 36.8 40.9 15.9 13.8 11.2 43.0 50.3 760
Fourth 34.4 35.5 12.5 10.2 8.0 37.8 47.7 894
Highest 24.9 28.1 11.1 8.3 6.4 30.9 39.7 880
Total 15-49 32.4 36.9 13.3 10.8 8.5 39.4 47.1 4,023
Note: Husband/partner refers to the current husband/partner for currently married women and the most recent husband/partner for divorced,
separated or widowed women. Total includes one woman for whom information on religion is missing and two women for whom information
on employment is missing. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
Forty-seven percent of ever-married women have experienced at least one form of spousal violence
(emotional, physical, or sexual) and 9 percent have experienced all three forms of violence.
The percentage of women who have experienced at least one form of spousal violence generally
increases with age. It is higher among Christian women (48-49 percent) and lower among Muslim women
(23 percent). By region, women in Western and Nairobi report the highest levels of violence (60 percent
each). Those who are divorced, separated, or widowed (66 percent) more often report violence than any other
310 • Domestic Violence
group of women. Women with no education are less likely to experience any form of violence (38 percent)
than women in the other educational categories, even women who have completed secondary education (43
percent). Experience of at least one form of violence peaks among women in the second wealth quintile (53
percent) and is lower in other wealth quintiles.
Table 16.10.2 shows that 24 percent of ever-married men have experienced at least one form of
spousal violence (emotional, physical, or sexual), and just 1 percent have experienced all three forms of
violence. Notably, most of the spousal violence men report is in the form of emotional violence. Only 9
percent of ever-married men report spousal physical or sexual violence compared with 39 percent of ever-
married women. Muslim men, men in the North Eastern region, and men with no education are less likely
than other subgroups of men to report at least one form of spousal violence.
Table 16.10.2 Spousal violence by background characteristics: Men
Percentage of ever-married men age 15-49 who have ever experienced emotional, physical or sexual violence committed by their wife/partner,
by background characteristics, Kenya 2014
Physical Number of
and sexual Physical or ever-
Background Emotional Physical Sexual Physical and Physical or sexual or married
characteristic violence violence violence and sexual emotional sexual emotional men
Age
15-19 * * * * * * * 9
20-24 18.3 10.6 4.8 1.5 1.1 14.0 25.3 168
25-29 23.0 7.3 6.2 2.3 2.0 11.1 26.0 534
30-39 21.9 5.9 3.7 1.8 1.6 7.8 25.0 1,115
40-49 18.7 7.6 2.6 1.0 0.7 9.3 21.4 799
Religion
Roman Catholic 20.5 8.2 6.4 2.7 2.4 11.9 24.7 592
Protestant/ other Christian 21.7 7.2 3.6 1.4 1.1 9.3 24.7 1,750
Muslim 13.5 3.9 0.2 0.0 0.0 4.1 16.7 157
No religion 23.6 4.5 2.4 1.9 1.9 5.0 24.5 115
Other * * * * * * * 10
Residence
Urban 19.9 7.1 4.4 2.1 1.9 9.4 23.4 1,192
Rural 21.8 7.1 3.6 1.2 0.9 9.4 24.7 1,432
Region
Coast 22.2 3.6 1.8 0.8 0.6 4.6 23.3 244
North Eastern 5.9 3.2 0.0 0.0 0.0 3.2 9.2 42
Eastern 13.6 7.0 3.4 1.2 0.8 9.2 18.0 371
Central 19.9 5.3 3.1 1.1 0.9 7.3 20.4 337
Rift Valley 19.7 6.5 3.6 1.1 0.9 9.0 23.1 655
Western 20.3 10.4 4.3 2.0 1.8 12.6 26.1 253
Nyanza 28.7 7.5 6.0 2.7 2.3 10.8 31.6 345
Nairobi 25.4 9.8 5.6 2.9 2.9 12.6 29.5 377
Marital status
Married or living together 18.3 5.5 3.3 1.3 1.1 7.6 21.1 2,408
Divorced/separated/widowed 50.3 24.6 10.7 5.5 4.8 29.8 58.0 216
Number of living children
0 15.1 11.1 2.4 1.0 0.6 12.4 20.0 206
1-2 21.8 7.2 4.4 1.7 1.5 9.8 25.4 1,083
3-4 20.3 5.5 3.6 1.1 0.8 7.9 23.3 800
5+ 22.2 7.8 4.3 2.4 2.3 9.7 24.5 535
Employment
Employed for cash 21.0 7.1 4.1 1.7 1.5 9.4 24.3 2,398
Employed not for cash 19.6 5.9 2.7 0.8 0.8 7.9 21.7 213
Not employed (27.4) (32.1) (0.0) (0.0) (0.0) (32.1) (32.1) 13
Education
No education 11.9 2.5 0.8 0.0 0.0 3.4 13.0 101
Primary incomplete 22.4 7.5 4.4 1.1 0.9 10.8 26.6 603
Primary complete 19.8 7.0 4.4 1.8 1.4 9.7 22.8 770
Secondary+ 21.7 7.3 3.7 1.9 1.8 9.1 24.7 1,150
Wealth quintile
Lowest 23.6 7.2 3.7 0.9 0.9 10.0 26.1 375
Second 22.3 7.9 4.8 1.7 1.4 11.0 26.6 470
Middle 21.8 9.4 3.9 2.1 1.6 11.3 25.7 496
Fourth 21.1 6.0 3.2 1.2 0.9 8.0 23.8 638
Highest 17.5 5.7 4.2 2.1 2.0 7.8 20.4 645
Total 15-49 20.9 7.1 4.0 1.6 1.4 9.4 24.1 2,624
50-54 14.0 6.4 2.1 1.8 1.8 6.7 16.2 265
Total 15-54 20.3 7.0 3.8 1.6 1.4 9.2 23.4 2,890
Note: Wife/partner refers to the current wife/partner for currently married men and the most recent wife/partner for divorced, separated or
widowed men. Total includes one man for whom information on religion is missing and two men for whom information on employment is
missing. Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted
cases that has been suppressed.
Domestic Violence • 311
16.12 VIOLENCE BY SPOUSAL CHARACTERISTICS AND WOMEN’S EMPOWERMENT
INDICATORS
Tables 16.11.1 and 16.11.2 present information on ever-married women and men age 15-49,
respectively, who have experienced emotional, physical, or sexual violence committed by their spouse
according to spousal characteristics and empowerment indicators.
Table 16.11.1 Spousal violence by husband’s characteristics and empowerment indicators
Percentage of ever-married women age15-49 who have ever experienced emotional, physical or sexual violence committed by their
husband/partner, by husband’s characteristics and empowerment indicators, Kenya 2014
Physical Number of
and sexual Physical or ever-
Background Emotional Physical Sexual Physical and Physical or sexual or married
characteristic violence violence violence and sexual emotional sexual emotional women
Husband’s/partner’s
education
No education 21.7 36.0 8.6 7.3 5.9 37.3 40.7 297
Primary 34.7 41.6 14.5 12.1 9.6 44.0 51.1 1,879
Secondary 32.4 35.8 14.8 12.2 9.6 38.4 46.3 1,316
More than secondary 29.6 23.4 7.6 4.6 3.4 26.4 38.9 483
Husband’s/partner’s
alcohol consumption
Does not drink 25.7 28.0 9.6 7.3 5.5 30.3 38.4 2,541
Drinks/never gets drunk * * * * * * * 13
Gets drunk sometimes 33.5 42.1 13.8 10.8 8.2 45.0 52.8 912
Gets drunk very often 61.0 69.5 29.7 27.3 22.9 71.8 78.1 551
Spousal education
difference
Husband better educated 32.8 37.8 13.6 11.4 8.8 39.9 47.6 1,877
Wife better educated 35.7 41.1 13.5 10.4 8.6 44.2 52.1 909
Both equally educated 31.7 33.4 14.0 11.3 8.9 36.2 44.8 974
Neither educated 17.1 29.8 6.2 5.9 5.4 30.1 33.5 205
Spousal age difference1
Wife older 28.5 27.1 12.4 8.6 8.6 30.9 37.8 98
Wife is same age 26.6 30.5 9.6 7.0 5.7 33.0 42.7 110
Wife’s 1-4 years younger 27.5 32.4 10.9 8.3 5.8 35.0 42.8 1,105
Wife’s 5-9 years younger 28.8 35.6 11.1 9.1 6.9 37.6 44.8 1,200
Wife’s 10+ years younger 30.0 33.4 12.9 10.1 8.5 36.3 42.7 814
Number of marital control
behaviours displayed by
husband/partner2
0 11.7 18.4 4.3 3.2 1.1 19.5 23.7 1,479
1-2 32.2 38.6 10.5 8.4 7.2 40.7 49.5 1,558
3-4 58.3 56.1 26.9 21.0 17.2 61.9 74.9 754
5 81.2 81.5 44.5 41.4 36.1 84.6 90.8 231
Number of decisions in
which women
participate3
0 35.3 36.8 20.4 17.7 14.5 39.5 46.3 78
1-2 29.3 39.1 11.1 7.6 5.7 42.5 47.5 696
3-4 28.4 32.2 11.3 9.1 7.0 34.3 42.2 2,578
Number of reasons for
which wife-beating is
justified4
0 29.0 32.4 11.7 9.7 7.7 34.3 41.9 2,283
1-2 36.3 39.6 14.1 11.0 9.0 42.7 51.4 995
3-4 37.3 44.8 15.5 12.6 9.6 47.7 55.8 588
5 38.0 57.1 22.1 17.7 12.5 61.5 63.8 156
Woman’s father beat
mother
Yes 39.1 46.1 18.8 15.9 12.5 49.0 57.1 1,509
No 28.2 30.6 9.7 7.8 6.1 32.5 40.4 2,262
Don’t know/missing 28.8 38.7 12.3 7.2 6.0 43.8 48.0 252
Woman afraid of
husband/partner
Most of the time afraid 65.3 79.1 38.1 36.8 31.0 80.5 82.5 495
Sometimes afraid 44.9 53.8 19.9 16.3 12.2 57.4 65.3 949
Never afraid 21.4 22.5 6.1 3.8 2.8 24.8 33.6 2,571
Total 15-49 32.4 36.9 13.3 10.8 8.5 39.4 47.1 4,023
Note: Husband/partner refers to the current husband/partner for currently married women and the most recent husband/partner for divorced,
separated or widowed women. Total includes 54 women for whom information about husband’s/partner’s education is missing or unknown,
five missing information on husband’s/partner’s alcohol consumption, 66 missing spousal education difference 26 missing spousal age
difference, and seven missing fear of husband/partner. Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes
a figure based on fewer than 25 unweighted cases that has been suppressed.
1
Includes only currently married women.
2
According to the wife’s report. See Table 16.8.1 for list of behaviours.
3
According to the wife’s report. Includes only currently married women. See Table 15.6.1 for list of decisions.
4
According to the wife’s report. See Table 15.7.1 for list of reasons.
312 • Domestic Violence
Table 16.11.2 Spousal violence by wife’s characteristics and empowerment indicators
Percentage of ever-married men age15-49 who have ever experienced emotional, physical or sexual violence committed by their
wife/partner, by husband’s characteristics and empowerment indicators, Kenya 2014
Physical Number of
and sexual Physical or ever-
Background Emotional Physical Sexual Physical and Physical or sexual or married
characteristic violence violence violence and sexual emotional sexual emotional men
Wife’s/partner’s alcohol
consumption
Does not drink 20.0 6.5 3.8 1.5 1.3 8.7 23.1 2,514
Drinks/never gets drunk * * * * * * * 11
Gets drunk sometimes * * * * * * * 77
Gets drunk very often * * * * * * * 17
Spousal age difference1
Husband older 18.2 5.5 2.9 1.0 0.8 7.5 21.1 2,111
Husband is same age 15.1 5.6 3.3 3.0 3.0 5.8 16.9 79
Husband’s 1-4 years
younger 12.4 4.5 8.6 4.1 4.1 9.0 13.4 76
Husband’s 5-9 years
younger * * * * * * * 14
Husband’s 10+ years
younger * * * * * * * 4
Number of marital control
behaviours displayed by
wife/partner2
0 5.8 2.8 0.3 0.0 0.0 3.1 8.0 810
1-2 18.8 5.0 3.1 0.7 0.6 7.3 22.2 1,242
3-4 43.8 15.0 9.2 4.2 3.9 20.0 48.1 495
5 67.0 34.6 22.4 15.9 12.9 41.1 71.6 77
Number of decisions in
which he participates3
0 13.8 7.7 2.2 0.0 0.0 9.8 20.9 67
1-2 18.4 5.4 3.4 1.3 1.1 7.5 21.1 2,341
Number of reasons for
which wife-beating is
justified4
0 18.8 5.0 3.0 1.4 1.2 6.6 20.9 1,673
1-2 23.9 9.7 5.2 1.8 1.4 13.1 28.3 628
3-4 26.0 13.6 7.2 2.9 2.4 17.9 33.2 275
5 25.8 9.2 2.5 1.2 1.2 10.4 30.8 48
Man’s father beat mother
Yes 24.0 9.4 5.4 2.3 2.0 12.5 28.3 1,253
No 18.3 5.0 2.5 0.9 0.7 6.6 20.4 1,204
Don’t know/missing 17.2 4.9 3.4 1.7 1.7 6.5 19.1 167
Man afraid of wife/partner
Most of the time afraid (43.5) (28.1) (19.9) (10.7) (9.7) (37.2) (44.5) 35
Sometimes afraid 58.9 33.7 14.5 7.2 6.0 41.0 70.1 168
Never afraid 18.0 5.0 3.0 1.1 1.0 6.8 20.7 2,411
Total 15-49 20.9 7.1 4.0 1.6 1.4 9.4 24.1 2,624
50-54 14.0 6.4 2.1 1.8 1.8 6.7 16.2 265
Total 15-54 20.3 7.0 3.8 1.6 1.4 9.2 23.4 2,890
Note: Wife/partner refers to the current wife/partner for currently married men and the most recent wife/partner for divorced, separated or
widowed men. Total includes five men for whom information about wife’s/partner’s alcohol consumption is missing or unknown, one missing
spousal education difference, and nine missing fear of husband/partner. Figures in parentheses are based on 25-49 unweighted cases. An
asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
1
Includes only currently married men who have only one wife.
2
According to the husband’s report. See Table 16.8.2 for list of behaviours.
3
According to the husband’s report. Includes only currently married men. See Table 15.6.2 for list of decisions.
4
According to the husband’s report. See Table 15.7.2 for list of reasons.
Table 16.11.1 shows that, among ever-married women, spousal violence is higher among those
whose husband has only a primary education (51 percent) and those who are better educated than their
husband (52 percent). Although women whose husbands get drunk very often are much more likely (78
percent) to experience at least one form of spousal violence than women whose husbands do not drink, it is
notable that even among the latter women, 38 percent report experiencing at least one form of violence.
Spousal violence increases with the number of controlling behaviours displayed by the husband.
Among women whose husbands exhibit five types of controlling behaviours, more than 9 in 10 (91 percent)
have experienced one or more forms of violence. In contrast, among women whose husbands display none
of the five controlling behaviours, about one in four (24 percent) have experienced any form
Domestic Violence • 313
of spousal violence. Women’s experience of vio- Table 16.12.1 Physical or sexual violence in the past 12 months by
lence tends to decrease as the number of decisions in any husband/partner: Women
which they participate increases. Women’s expe- Percentage of ever-married women who have experienced physical or
sexual violence by any husband/partner in the past 12 months, by
rience of violence increases with the number of background characteristics, Kenya 2014
reasons they agree with for which wife beating is Percentage of women
justified. Women whose father beat their mother are who have experienced
physical or sexual
much more likely to experience any type of violence violence in the past
Background 12 months from any Number of ever-
by their husband than women whose father did not characteristic husband/partner married women
beat their mother (57 percent versus 40 percent). Age
Finally, women who are never afraid of their 15-19 23.0 111
20-24 28.0 647
husband/partner are much less likely to experience 25-29 28.1 1,002
spousal violence than women who are afraid of him 30-39
40-49
25.3
21.3
1,395
867
most of the time (34 percent versus 83 percent). Religion
Roman Catholic 27.2 783
Table 16.11.2 shows similar patterns in Protestant/other Christian
Muslim
26.3
10.3
2,853
271
spousal violence against ever-married men according No religion 33.4 100
Other * 15
to wives’ characteristics. In many cases, the numbers
Residence
are too small to reach meaningful conclusions. Urban 25.1 1,588
However, similar to women, spousal violence against Rural 25.8 2,435
Region
men increases with the number of controlling Coast 19.2 439
behaviours displayed by the wife, from 8 percent North Eastern 5.8 97
Eastern 25.1 585
among men whose wives/partners display none of Central 20.5 518
the controlling behaviours to 72 percent among those Rift Valley
Western
20.1
36.6
983
433
whose wives/partners exhibit all five types of Nyanza 33.5 553
Nairobi 34.5 414
controlling behaviours. Men’s experience of spousal
Marital status
violence generally increases as the number of rea- Married or living together 25.6 3,352
sons they agree with for which wife beating is justi- Divorced/separated/
widowed 25.0 670
fied increases. Men whose father did not beat their Employment
mother are less likely to experience any type of Employed for cash 27.3 2,427
Employed not for cash 29.4 665
spousal violence than men whose father beat their Not employed 17.9 929
mother (20 percent versus 28 percent). As expected, Number of living children
0 17.3 203
men who are never afraid of their wife/partner are 1-2 23.8 1,657
less likely to experience spousal violence than other 3-4 27.0 1,238
5+ 28.5 925
men. Education
No education 22.5 375
16.13 RECENT SPOUSAL VIOLENCE Primary incomplete
Primary complete
30.9
26.3
1,150
1,120
Secondary+ 21.2 1,378
Tables 16.12.1 and 16.12.2 show the per- Wealth quintile
centage of ever-married women and men age 15-49, Lowest
Second
27.2
29.5
707
781
respectively, who have experienced physical or Middle 28.3 760
Fourth 22.3 894
sexual violence by any spouse/partner in the past 12 Highest 21.5 880
months, by background characteristics. For women Woman afraid of
husband/partner
and men who have been married more than once, Most of the time afraid 50.4 495
these tables include violence by any previous Sometimes afraid 39.8 949
Never afraid 15.5 2,571
spouse/partner.
Total 15-49 25.5 4,023
Overall, 26 percent of women experienced Note: Any husband/partner includes all current, most recent and former
physical or sexual violence by any husband or part- husbands/partners. Total includes one woman for whom information
on religion is missing, two women for whom information on
ner in the past 12 months. The percentage of women employment is missing, and seven women for whom information on
fear of husband/partner is missing. An asterisk denotes a figure based
who have experienced recent physical or sexual on fewer than 25 unweighted cases that has been suppressed.
violence by any spouse or partner is higher among
those age 20-29 (28 percent), than among younger or older women. Muslim women (10 percent) are much
less likely than Christian (26-27 percent) women or women with no religion (33 percent) to report any recent
spousal violence. By region, violence is most common among women living in Western region (37 percent)
and least common among women in the North Eastern region (6 percent). The percentage experiencing
recent violence increases with number of living children.
314 • Domestic Violence
Women with an incomplete primary educa- Table 16.12.2 Physical or sexual violence in the past 12 months by
tion (31 percent), women in the bottom three wealth any wife/partner: Men
quintiles (27-30 percent), and women who are afraid Percentage of ever-married men who have experienced physical or
sexual violence by any wife/partner in the past 12 months, by
of their husband/partner most of the time (50 background characteristics, Kenya 2014
percent) are more likely than most other women to Percentage of men
have experienced physical or sexual violence by any who have experienced
physical or sexual
husband or partner in the past 12 months. violence in the past
Background 12 months from any Number of ever-
characteristic wife/partner married men
Among ever-married men, 7 percent expe-
Age
rienced physical or sexual violence in the past 12 15-19 * 9
months by any wife or partner. Men age 20-24 (12 20-24 12.3 168
25-29 9.5 534
percent), men living in Nairobi (11 percent), previ- 30-39 5.8 1,115
ously married men (16 percent), and men with no 40-49 6.3 799
Religion
living children (10 percent) are more likely to have Roman Catholic 9.2 592
experienced recent physical or sexual violence by Protestant/other Christian 7.1 1,750
Muslim 3.9 157
any spouse or partner. Similarly, men with an in- No religion 3.1 115
complete primary education (8 percent), those in the Other * 10
bottom three wealth quintiles (8 percent), and those Residence
Urban 7.7 1,192
who are afraid of their wife/partner sometimes (31 Rural 6.8 1,432
percent) are more likely than other men to have Region
Coast 3.5 244
experienced physical or sexual violence from any North Eastern 3.2 42
wife or partner in the past 12 months. Eastern 7.0 371
Central 4.2 337
Rift Valley 6.5 655
16.14 ONSET OF SPOUSAL VIOLENCE Western
Nyanza
8.6
9.2
253
345
Nairobi 11.3 377
To obtain information on the onset of mari- Marital status
tal violence, the 2014 KDHS asked ever-married Married or living together 6.4 2,408
Divorced/separated/
women and men who reported spousal violence widowed 15.9 216
when in their marriage the violence first occurred. Employment
Tables 16.13.1 and 16.13.2 show the timing of the Employed for cash
Employed not for cash
7.2
6.5
2,398
213
first experience of violence by marital duration Not employed (27.1) 13
among currently married women and men age 15-49 Number of living children
0 10.2 206
who have been married only once, respectively. 1-2 7.4 1,083
3-4 5.8 800
5+ 7.6 535
Table 16.13.1 shows that 65 percent of cur-
Education
rently married women married only once have never No education 2.0 101
experienced spousal physical or sexual violence by Primary incomplete 8.1 603
Primary complete 6.9 770
their current husband, 11 percent experienced Secondary+ 7.4 1,150
violence in the first two years of marriage, 24 percent Wealth quintile
experienced it in the first five years, and 30 percent Lowest
Second
8.0
8.2
375
470
experienced it within the first 10 years of marriage. Middle 8.4 496
Fourth 5.8 638
Among women who have experienced spousal Highest 6.5 645
physical or sexual violence, about one-third first Man afraid of wife/partner
experienced it in the first two years of marriage. Most of the time afraid (29.0) 35
Sometimes afraid 31.2 168
Never afraid 5.2 2,411
Among currently married men who have Total 15-49 7.2 2,624
been married only once, more than 9 in 10 (93 per- 50-54 4.1 265
cent) have not experienced spousal physical or Total 15-54 6.9 2,890
sexual violence by their current wife, 2 percent
Note: Any wife/partner includes all current, most recent and former
experienced violence in the first two years of mar- wives/partners. Total includes one man for whom information on
riage, 5 percent experienced it in the first five years, religion is missing, two men for whom information on employment is
missing, and nine men for whom information on fear of wife/partner is
and 6 percent experienced it within the first 10 years missing. Figures in parentheses are based on 25-49 unweighted cases.
An asterisk denotes a figure based on fewer than 25 unweighted cases
of marriage (Table 16.13.2). that has been suppressed.
Domestic Violence • 315
Table 16.13.1 Experience of spousal violence by duration of marriage: Women
Among currently married women age 15-49 who have been married only once, the percentage who first
experienced physical or sexual violence committed by their current husband/partner by specific exact years since
marriage according to marital duration, Kenya 2014
Percentage who Number of
Percentage who first experienced spousal have not currently married
physical or sexual violence by exact marital experienced women who have
duration: spousal sexual or been married only
Duration of marriage 2 years 5 years 10 years physical violence once
Years since marriage
<2 na na na 78.4 310
2-4 15.5 na na 71.5 440
5-9 10.5 29.7 na 63.2 676
10+ 7.3 20.3 29.4 61.0 1,686
Total 10.6 23.6 30.0 64.7 3,112
na = Not applicable
Table 16.13.2 Experience of spousal violence by duration of marriage: Men
Among currently married men age 15-49 who have been married only once, the percentage who first experienced
physical or sexual violence committed by their current wife/partner by specific exact years since marriage
according to marital duration, Kenya 2014
Percentage who Number of
Percentage who first experienced spousal have not currently married
physical or sexual violence by exact marital experienced men who have
duration: spousal sexual or been married only
Duration of marriage 2 years 5 years 10 years physical violence once
Years since marriage
<2 na na na 92.4 212
2-4 4.4 na na 93.5 392
5-9 0.6 4.2 na 93.9 491
10+ 1.2 3.8 5.1 93.1 879
Total 2.3 4.7 5.8 93.3 1,974
na = Not applicable
16.15 PHYSICAL CONSEQUENCES OF SPOUSAL VIOLENCE
In the 2014 KDHS, ever-married women and men who reported spousal violence were asked
whether they had sustained some form of injury as a result of physical or sexual violence inflicted by their
spouse. Tables 16.14.1 and 16.14.2 show the percentage of ever-married women and men age 15-49 who
have experienced spousal violence by the types of injuries they suffered, according to the type of violence
reported and whether they experienced the violence ever and in the last 12 months.
About one-third of women (32 percent) who reported ever having experienced spousal physical or
sexual violence suffered cuts, bruises, or aches; 18 percent had eye injuries, sprains, dislocations, or burns;
and 10 percent had deep wounds, broken bones, broken teeth, or other serious injuries (Table 16.14.1).
Overall, 39 percent of women who had ever experienced spousal physical or sexual violence suffered one or
more of these injuries. The prevalence of all forms of injury is slightly higher among women who
experienced violence in the past 12 months.
Table 16.14.2 shows that, among men who reported ever having experienced spousal physical or
sexual violence, about one in five (21 percent) suffered cuts, bruises, or aches; 9 percent had eye injuries,
sprains, dislocations, or burns; and 5 percent had deep wounds, broken bones, broken teeth, or other serious
injuries. Twenty-four percent of men who had ever experienced spousal physical or sexual violence suffered
one or more of these injuries. Similar percentages of men who had experienced violence in the past 12
months suffered each of the above injuries.
316 • Domestic Violence
Table 16.14.1 Injuries to women due to spousal violence: Women
Percentage of ever-married women age 15-49 who have experienced specific types of spousal violence by types of injuries
resulting from the violence, according to the type of violence and whether they experienced the violence ever and in the 12
months preceding the survey, Kenya 2014
Number of ever-
Deep wounds, married women
Eye injuries, broken bones, who have ever
sprains, broken teeth, or experienced any
Cuts, bruises, or dislocations, or any other Any of these physical or
Type of violence aches burns serious injury injuries sexual violence
Experienced physical violence1
Ever2 33.5 19.4 10.1 40.3 1,485
In the past 12 months 38.0 23.3 12.4 45.7 908
Experienced sexual violence
Ever2 40.3 25.6 12.9 47.6 534
In the past 12 months 42.6 27.1 13.3 49.3 395
Experienced physical or sexual
violence1
Ever2 32.0 18.3 9.5 38.5 1,585
In the past 12 months 35.7 21.4 11.1 42.9 1,022
Note: Husband/partner refers to the current husband/partner for currently married women and the most recent husband/partner
for divorced, separated or widowed women.
1
Excludes women who reported violence only in response to a direct question on violence during pregnancy
2
Includes in the past 12 months
Table 16.14.2 Injuries to men due to spousal violence: Men
Percentage of ever-married men age 15-49 who have experienced specific types of spousal violence by types of injuries
resulting from the violence, according to the type of violence and whether they experienced the violence ever and in the 12
months preceding the survey, Kenya 2014
Number of ever-
Deep wounds, married men
Eye injuries, broken bones, who have ever
sprains, broken teeth, or experienced any
Cuts, bruises, or dislocations, or any other Any of these physical or
Type of violence aches burns serious injury injuries sexual violence
Experienced physical violence
Ever1 25.4 10.0 5.0 29.9 186
In the past 12 months 28.5 11.1 6.2 33.5 132
Experienced sexual violence
Ever1 18.5 8.5 5.8 20.8 104
In the past 12 months 19.4 10.4 7.1 22.2 84
Experienced physical or sexual
violence
Ever1 20.7 8.8 5.0 24.1 247
In the past 12 months 22.2 9.7 6.1 25.7 187
Note: Wife/partner refers to the current wife/partner for currently married men and the most recent wife/partner for divorced,
separated or widowed men.
1
Includes in the past 12 months
16.16 VIOLENCE BY WOMEN AND MEN AGAINST THEIR SPOUSE
In cases of domestic violence, either person (husband or wife) can be the perpetrator of violence. In
the 2014 KDHS, ever-married women and men were asked about instances when they were the instigator of
spousal violence. Specifically, all eligible ever-married respondents were asked whether they had ever
initiated physical violence against their spouse when he or she was not already hitting or beating the
respondent.
Tables 16.15.1 and 16.15.2 show the percentage of ever-married women and men age 15-49,
respectively, who reported initiating physical violence against their spouses ever and in the 12 months prior
to the survey, by background characteristics.
Domestic Violence • 317
Table 16.15.1 Women’s violence against their spouse by background characteristics
Percentage of ever-married women age 15-49 who have committed physical violence against
their current or most recent husband/partner when he was not already beating or physically
hurting her, ever and in the past 12 months, according to women’s own experience of spousal
violence and background characteristics, Kenya 2014
Percentage who have committed
physical violence against their
husband/partner
Background In the past 12 Number of ever-
characteristic Ever1 months married women
Woman’s experience of
spousal physical violence
Ever1 9.0 5.9 1,485
In the past 12 months 10.7 8.6 908
Never 0.8 0.6 2,537
Age
15-19 3.6 3.6 111
20-24 4.6 3.6 647
25-29 3.7 3.0 1,002
30-39 4.0 2.1 1,395
40-49 3.1 1.9 867
Religion
Roman Catholic 5.9 3.6 783
Protestant/other Christian 3.5 2.3 2,853
Muslim 2.2 2.0 271
No religion 2.7 2.7 100
Other * * 15
Residence
Urban 5.1 3.0 1,588
Rural 3.0 2.3 2,435
Region
Coast 3.0 2.8 439
North Eastern 0.2 0.2 97
Eastern 2.0 1.2 585
Central 3.9 1.8 518
Rift Valley 2.5 1.6 983
Western 4.3 3.6 433
Nyanza 4.6 3.8 553
Nairobi 9.6 5.2 414
Marital status
Married or living together 3.2 2.6 3,352
Divorced/separated/
widowed 7.0 2.4 670
Employment
Employed for cash 5.0 3.0 2,427
Employed not for cash 2.2 1.8 665
Not employed 1.9 1.8 929
Number of living children
0 5.6 4.7 203
1-2 4.1 2.7 1,657
3-4 2.8 1.4 1,238
5+ 4.3 3.4 925
Education
No education 3.5 3.4 375
Primary incomplete 4.2 3.5 1,150
Primary complete 3.9 2.3 1,120
Secondary+ 3.5 1.8 1,378
Wealth quintile
Lowest 3.1 2.5 707
Second 4.4 3.3 781
Middle 3.8 2.5 760
Fourth 4.1 2.7 894
Highest 3.5 1.9 880
Total 3.8 2.6 4,023
Note: Husband/partner refers to the current husband/partner for currently married women
and the most recent husband/partner for divorced, separated or widowed women. Total
includes one woman for whom information on religion is missing and two women for whom
information on employment is missing. An asterisk denotes a figure based on fewer than 25
unweighted cases that has been suppressed.
1
Includes in the past 12 months
318 • Domestic Violence
Table 16.15.2 Men’s violence against their spouse by background characteristics
Percentage of ever-married men age 15-49 who have committed physical violence against
their current or most recent wife/partner when she was not already beating or physically
hurting him, ever and in the past 12 months, according to men’s own experience of spousal
violence and background characteristics, Kenya 2014
Percentage who have committed
physical violence against their
wife/partner
Background In the past 12 Number of ever-
characteristic Ever1 months married men
Man’s experience of
spousal physical violence
Ever1 69.2 35.3 186
In the past 12 months 69.1 45.5 132
Never 34.4 11.9 2,438
Age
15-19 * * 9
20-24 38.3 17.9 168
25-29 33.8 16.7 534
30-39 34.3 13.7 1,115
40-49 42.1 10.4 799
Religion
Roman Catholic 39.0 13.4 592
Protestant/other Christian 37.3 13.8 1,750
Muslim 25.3 12.4 157
No religion 35.1 12.9 115
Other * * 10
Residence
Urban 36.4 13.6 1,192
Rural 37.2 13.6 1,432
Region
Coast 28.6 9.1 244
North Eastern 4.3 1.1 42
Eastern 38.6 11.2 371
Central 34.4 10.1 337
Rift Valley 26.6 10.9 655
Western 37.2 14.2 253
Nyanza 59.1 24.5 345
Nairobi 43.3 17.6 377
Marital status
Married or living together 35.9 13.7 2,408
Divorced/separated/
widowed 47.7 11.7 216
Employment
Employed for cash 37.3 13.4 2,398
Employed not for cash 30.7 13.4 213
Not employed (50.8) (38.7) 13
Number of living children
0 23.1 10.5 206
1-2 30.9 12.1 1,083
3-4 40.0 14.9 800
5+ 49.5 15.9 535
Education
No education 30.7 16.9 101
Primary incomplete 43.0 13.0 603
Primary complete 38.9 15.9 770
Secondary+ 32.7 12.0 1,150
Wealth quintile
Lowest 41.3 14.7 375
Second 44.5 14.3 470
Middle 38.1 17.4 496
Fourth 35.0 11.5 638
Highest 29.4 11.5 645
Total 15-49 36.8 13.6 2,624
50-54 44.7 5.2 265
Total 15-54 37.6 12.8 2,890
Note: Wife/partner refers to the current wife/partner for currently married men and the most
recent wife/partner for divorced, separated or widowed men. Total includes one man for
whom information on religion is missing and two men for whom information on employment
is missing. Figures in parentheses are based on 25-49 unweighted cases. An asterisk
denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
1
Includes in the past 12 months
Domestic Violence • 319
Overall, 4 percent of ever-married women reported that they had initiated physical violence against
their husbands, and 3 percent had done so in the past 12 months. Women who have been physically abused
by their husband ever and in the past 12 months are more likely to have initiated spousal physical abuse (9
percent and 11 percent, respectively) than women who have never been abused (1 percent). Women’s use of
violence against their husbands does not vary notably by other background characteristics.
Table 16.15.2 shows that 37 percent of ever-married men age 15-49 reported having initiated
physical violence against their wives, and 14 percent had done so in the past 12 months. Men who have been
physically abused by their spouse ever and in the past 12 months are about twice as likely (69 percent each)
as those who have never been abused (34 percent) to initiate physical violence against their wives. Men age
40-49 (42 percent), Christian men (37-39 percent), men in Nyanza (59 percent), previously married men (48
percent), men with five or more living children (50 percent), men with an incomplete primary education (43
percent), and men in the second wealth quintile (45 percent) are most likely to have initiated physical
violence against their wife or partner. There are similar variations by background characteristics in the
percentage of ever-married men who reported that they had initiated physical violence against their wives in
the past 12 months.
16.17 VIOLENCE AGAINST SPOUSES BY SPOUSAL CHARACTERISTICS AND WOMEN’S
EMPOWERMENT INDICATORS
Tables 16.16.1 and 16.16.2 present information on ever-married women and men age 15-49,
respectively, who have committed physical violence against their spouse ever and in the past 12 months,
according to spousal characteristics and empowerment indicators.
Table 16.16.1 shows that women whose husband gets drunk very often (10 percent), women whose
husband displays five controlling behaviours (13 percent), and women who are afraid of their
husband/partner most of the time (9 percent) are most likely to have committed physical violence against
their spouse. Similar variations by background characteristics are observed in women’s physical violence
against their spouse in the past 12 months.
Table 16.16.2 shows that men whose wife gets drunk sometimes (52 percent) are most likely to have
committed violence against their spouse. The percentage of men who have committed violence against their
spouse increases steadily as the number of controlling behaviours displayed by the wife increases. Twenty-
four percent of men whose wife displays none of the six controlling behaviours have initiated physical
violence against their spouse, as compared with 60 percent of men whose wife exhibits five controlling
behaviours. The percentage of men who initiate physical violence against their spouse is lowest among those
who do not agree with any of the reasons that justify wife beating. Men whose father did not beat their
mother are much less likely to commit physical violence against their spouse than men whose father beat
their mother (30 percent versus 44 percent). Similar to women, men who are afraid of their wife/partner
sometimes (58 percent) are much more likely than men who are not afraid to commit physical violence
against their spouse.
320 • Domestic Violence
Table 16.16.1 Women’s violence against their spouse by spouse’s characteristics and
empowerment indicators
Percentage of ever-married women age 15-49 who have committed physical violence against their
current or most recent husband/partner when he was not already beating or physically hurting her,
ever and in the past 12 months, according their husband’s characteristics, Kenya 2014
Percentage who have committed
physical violence against their
husband/partner
Background In the past 12 Number of ever-
characteristic Ever1 months married women
Husband’s/partner’s education
No education 2.2 2.1 297
Primary 3.3 2.7 1,879
Secondary 5.1 2.9 1,316
More than secondary 3.3 1.5 483
Husband’s/partner’s alcohol
consumption
Does not drink 2.4 1.9 2,541
Drinks/never gets drunk * * 13
Gets drunk sometimes 3.9 2.6 912
Gets drunk very often 10.1 5.8 551
Spousal education difference
Husband better educated 4.2 2.8 1,877
Wife better educated 4.2 2.5 909
Both equally educated 3.2 2.5 974
Neither educated 1.2 1.2 205
Spousal age difference2
Wife older 3.4 3.4 98
Wife is same age 2.0 2.0 110
Wife’s 1-4 years younger 2.8 2.1 1,105
Wife’s 5-9 years younger 4.2 3.4 1,200
Wife’s 10+ years younger 2.5 2.1 814
Number of marital control
behaviours displayed by
husband/partner3
0 1.1 0.6 1,479
1-2 3.3 2.4 1,558
3-4 7.4 4.5 754
5 13.0 9.5 231
Number of decisions in which
women participate4
0 3.5 3.5 78
1-2 4.2 3.8 696
3-4 2.9 2.2 2,578
Number of reasons for which
wife-beating is justified5
0 4.0 2.5 2,283
1-2 3.3 2.3 995
3-4 4.0 3.4 588
5 3.0 2.0 156
Woman’s father beat mother
Yes 4.6 3.2 1,509
No 3.2 2.1 2,262
Don’t know/missing 4.2 3.1 252
Woman afraid of
husband/partner
Most of the time afraid 8.5 4.9 495
Sometimes afraid 4.9 3.8 949
Never afraid 2.5 1.7 2,571
Total 3.8 2.6 4,023
Note: Husband/partner refers to the current husband/partner for currently married women and the
most recent husband/partner for divorced, separated or widowed women. Total includes 54 women
for whom information about husband’s/partner’s education is missing or unknown, five missing
information on husband’s/partner’s alcohol consumption, 66 missing spousal education difference
26 missing spousal age difference, and seven missing fear of husband/partner. Figures in
parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer
than 25 unweighted cases that has been suppressed.
1
Includes in the past 12 months
2
Includes only currently married women.
3
According to the wife’s report. See Table 16.8.1 for list of behaviours.
4
According to the wife’s report. Includes only currently married women. See Table 15.6.1 for list of
decisions.
5
According to the wife’s report. See Table 15.7.1 for list of reasons.
Domestic Violence • 321
Table 16.16.2 Men’s violence against their spouse by spouse’s characteristics and
empowerment indicators
Percentage of ever-married men age 15-49 who have committed physical violence against their
current or most recent wife/partner when she was not already beating or physically hurting him,
ever and in the past 12 months, according their husband’s characteristics, Kenya 2014
Percentage who have committed
physical violence against their
wife/partner
Background In the past 12 Number of ever-
characteristic Ever1 months married men
Wife’s/partner’s alcohol
consumption
Does not drink 36.4 13.5 2,514
Drinks/never gets drunk * * 11
Gets drunk sometimes 51.5 16.8 77
Gets drunk very often (52.5) (24.7) 17
Spousal age difference2
Husband older 35.3 13.1 2,111
Husband is same age 29.1 16.4 79
Husband’s 1-4 years younger 38.5 20.4 76
Husband’s 5-9 years younger * * 14
Husband’s 10+ years younger * * 4
Number of marital control
behaviours displayed by
wife/partner3
0 24.0 6.9 810
1-2 36.8 11.8 1,242
3-4 54.2 27.2 495
5 60.4 24.4 77
Number of decisions in which he
participates4
0 42.4 25.5 67
1-2 35.7 13.4 2,341
Number of reasons for which
wife-beating is justified5
0 31.1 9.0 1,673
1-2 44.0 18.4 628
3-4 53.5 26.5 275
5 48.0 35.0 48
Man’s father beat mother
Yes 43.5 16.0 1,253
No 30.2 11.1 1,204
Don’t know/missing 34.6 13.4 167
Man afraid of wife/partner
Most of the time afraid (52.1) (23.7) 35
Sometimes afraid 58.3 25.1 168
Never afraid 35.3 12.7 2,411
Total 15-49 36.8 13.6 2,624
50-54 44.7 5.2 265
Total 15-54 37.6 12.8 2,890
Note: Wife/partner refers to the current wife/partner for currently married men and the most recent
wife/partner for divorced, separated or widowed men. Total includes five men for whom information
about wife’s/partner’s alcohol consumption is missing or unknown, one missing spousal education
difference, and nine missing fear of husband/partner. Figures in parentheses are based on 25-49
unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has
been suppressed.
1
Includes in the past 12 months
2
Includes only currently married men who have only one wife.
3
According to the husband’s report. See Table 16.8.2 for list of behaviours.
4
According to the husband’s report. Includes only currently married men. See Table 15.6.2 for list
of decisions.
5
According to the husband’s report. See Table 15.7.2 for list of reasons.
16.18 HELP-SEEKING BEHAVIOUR BY WOMEN AND MEN WHO EXPERIENCE VIOLENCE
Tables 16.17.1 and 16.17.2 show the percent distribution of women and men age 15-49 who have
ever experienced physical or sexual violence committed by anyone, respectively, according to whether they
ever sought help to stop the violence and, among those who did not seek help, whether or not they told
anyone about the violence.
322 • Domestic Violence
Table 16.17.1 Help seeking to stop violence: Women
Percent distribution of women age 15-49 who have ever experienced physical or sexual violence by their help-seeking behaviour by type
of violence and background characteristics, Kenya 2014
Number of
women who
have ever
Never sought Never sought experienced any
Background Sought help to help but told help, never told Missing/don’t physical or
characteristic stop violence someone anyone know Total sexual violence
Type of violence
experienced
Physical only 40.0 12.3 42.4 5.3 100.0 1,884
Sexual only 33.9 10.3 53.1 2.6 100.0 148
Physical and sexual 58.6 8.4 32.3 0.7 100.0 649
Age
15-19 32.6 10.8 50.4 6.2 100.0 350
20-24 40.0 13.7 39.6 6.7 100.0 499
25-29 44.7 11.6 40.9 2.8 100.0 585
30-39 48.7 11.7 36.7 2.9 100.0 750
40-49 49.3 7.8 39.7 3.2 100.0 496
Religion
Roman Catholic 42.9 13.3 38.8 5.0 100.0 534
Protestant/other Christian 44.3 10.9 41.3 3.5 100.0 1,988
Muslim 39.0 11.7 41.1 8.2 100.0 102
No religion 65.5 4.4 24.5 5.6 100.0 51
Other * * * * 100.0 5
Residence
Urban 41.6 13.7 40.9 3.8 100.0 1,051
Rural 45.9 9.7 40.2 4.2 100.0 1,630
Region
Coast 36.7 14.6 43.1 5.7 100.0 231
North Eastern 18.5 6.7 56.7 18.1 100.0 18
Eastern 53.7 8.6 34.0 3.6 100.0 400
Central 52.7 8.7 36.8 1.7 100.0 285
Rift Valley 38.8 9.8 47.4 4.0 100.0 573
Western 51.6 9.8 35.4 3.1 100.0 362
Nyanza 37.9 8.6 45.9 7.6 100.0 467
Nairobi 42.2 21.8 35.1 1.0 100.0 344
Marital status
Never married 34.2 15.5 43.4 6.9 100.0 561
Married or living together 43.0 10.3 42.9 3.8 100.0 1,663
Divorced/separated/
widowed 60.7 9.6 28.0 1.7 100.0 456
Number of living children
0 33.5 14.9 44.1 7.5 100.0 513
1-2 43.8 10.7 42.4 3.1 100.0 945
3-4 47.2 10.5 38.5 3.7 100.0 717
5+ 51.5 9.5 36.1 3.0 100.0 505
Employment
Employed for cash 47.3 12.0 37.6 3.1 100.0 1,611
Employed not for cash 48.4 8.9 39.9 2.8 100.0 411
Not employed 33.9 10.8 48.0 7.3 100.0 657
Education
No education 34.3 12.2 43.9 9.6 100.0 159
Primary incomplete 47.9 11.4 37.6 3.1 100.0 838
Primary complete 49.1 9.2 37.9 3.8 100.0 686
Secondary+ 39.3 12.4 44.2 4.2 100.0 997
Wealth quintile
Lowest 39.1 12.9 43.1 4.8 100.0 411
Second 47.5 8.6 39.5 4.4 100.0 574
Middle 46.9 9.8 37.3 6.0 100.0 571
Fourth 47.9 9.2 41.2 1.6 100.0 592
Highest 37.5 16.6 42.1 3.8 100.0 532
Total 44.2 11.2 40.5 4.1 100.0 2,680
Note: Total includes one woman for whom information on religion is missing and two women for whom information on employment is
missing. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
Domestic Violence • 323
Table 16.17.2 Help seeking to stop violence: Men
Percent distribution of men age 15-49 who have ever experienced physical or sexual violence by their help-seeking behaviour by type of
violence and background characteristics, Kenya 2014
Number of
women who
have ever
Never sought Never sought experienced any
Background Sought help to help but told help, never told Missing/don’t physical or
characteristic stop violence someone anyone know Total sexual violence
Type of violence
experienced
Physical only 27.0 20.2 45.8 7.0 100.0 1,856
Sexual only 6.7 15.3 61.0 17.0 100.0 71
Physical and sexual 38.2 19.6 40.8 1.4 100.0 204
Age
15-19 19.9 24.8 50.1 5.2 100.0 408
20-24 22.7 19.8 48.0 9.6 100.0 395
25-29 29.7 22.3 40.5 7.5 100.0 378
30-39 29.9 18.4 45.2 6.4 100.0 572
40-49 34.5 14.9 45.0 5.6 100.0 378
Religion
Roman Catholic 30.7 21.0 42.6 5.8 100.0 490
Protestant/other Christian 27.4 19.8 46.4 6.4 100.0 1,442
Muslim 16.4 13.8 60.4 9.4 100.0 118
No religion 28.0 23.7 28.8 19.5 100.0 73
Other * * * * 100.0 7
Residence
Urban 24.4 21.3 46.1 8.2 100.0 816
Rural 29.3 19.1 45.6 5.9 100.0 1,316
Region
Coast 21.1 17.5 56.7 4.7 100.0 206
North Eastern 6.5 5.7 73.3 14.4 100.0 27
Eastern 26.5 15.3 54.0 4.2 100.0 335
Central 33.6 25.1 38.1 3.2 100.0 250
Rift Valley 27.5 22.7 41.8 7.9 100.0 501
Western 24.6 12.2 47.3 15.9 100.0 279
Nyanza 39.5 25.1 33.2 2.2 100.0 330
Nairobi 14.7 21.0 55.2 9.1 100.0 204
Marital status
Never married 21.1 21.4 50.0 7.5 100.0 893
Married or living together 30.8 18.4 44.0 6.8 100.0 1,105
Divorced/separated/
widowed 41.7 23.4 32.3 2.6 100.0 133
Number of living children
0 22.0 22.1 48.3 7.5 100.0 917
1-2 26.4 19.8 46.7 7.0 100.0 563
3-4 35.7 17.8 41.5 5.1 100.0 385
5+ 36.4 15.7 41.4 6.5 100.0 266
Employment
Employed for cash 29.5 19.7 44.2 6.6 100.0 1,562
Employed not for cash 27.9 19.4 45.6 7.1 100.0 237
Not employed 17.3 21.5 53.5 7.6 100.0 330
Education
No education 23.1 15.8 56.4 4.7 100.0 45
Primary incomplete 27.8 18.3 46.5 7.5 100.0 565
Primary complete 30.5 21.0 43.0 5.5 100.0 522
Secondary+ 25.8 20.5 46.4 7.2 100.0 998
Wealth quintile
Lowest 30.9 20.4 41.7 7.0 100.0 289
Second 31.0 21.1 42.4 5.5 100.0 439
Middle 29.1 15.6 46.7 8.5 100.0 447
Fourth 26.2 19.6 48.1 6.1 100.0 543
Highest 21.0 23.7 48.2 7.1 100.0 413
Total 15-49 27.4 20.0 45.8 6.8 100.0 2,131
50-54 37.1 15.3 42.7 4.9 100.0 124
Total 15-54 28.0 19.7 45.6 6.7 100.0 2,255
Note: Total includes one man for whom information on religion is missing and two men for whom information on employment is missing. An
asterisk denotes a figure based on fewer than 25 unweighted cases that has been suppressed.
324 • Domestic Violence
Overall, more than 4 in 10 women (44 percent) who have experienced any type of physical or sexual
violence from anyone sought help from any source to stop the violence. A similar proportion (41 percent)
never sought help and never told anyone, and 11 percent never sought help but told someone.
Women who have experienced both physical and sexual violence (59 percent), women age 30-49
(49 percent), women who report having no religion (66 percent), women in rural areas (46 percent), and
women in Eastern region (54 percent) are more likely than other women to seek help to stop violence. A
much higher proportion of divorced, separated, or widowed women (61 percent) than never-married women
(34 percent) and currently married women (43 percent) have ever sought help. Help seeking increases with
number of living children, from 34 percent among women with no living children to 52 percent among those
with five or more children. Unemployed women (34 percent), those with no education (34 percent), and
those in the highest wealth quintile (38 percent) are less likely than other women to seek help from any
source to stop the violence.
Among ever-married men who have experienced any type of physical or sexual violence from
anyone, 27 percent sought help from any source to stop the violence. Forty-six percent never sought help
and never told anyone, and 20 percent never sought help but told someone. The observed patterns in help
seeking among men by background characteristics are similar to those among women.
Tables 16.18.1 and 16.18.2 show the percentage of women and men age 15-49, respectively, who
experienced physical or sexual violence and sought help by the sources from which help was sought. The
most common sources of help among women are their own family (65 percent), their husband’s or partner’s
family (31 percent), others (12 percent), neighbours (9 percent), and friends (8 percent). Among men, the
most common sources are their own family (49 percent), the police (19 percent), others (17 percent), friends
(14 percent), and doctors/medical personnel (13 percent).
Table 16.18.1 Sources for help to stop the violence: Women
Percentage of women age 15-49 who have experienced physical or sexual violence and sought help
by sources from which they sought help, according to the type of violence that women reported, Kenya
2014
Type of violence experienced
Physical and
Person Physical only Sexual only sexual Total
Own family 66.4 (76.2) 59.3 64.6
Husband/partner’s family 31.4 (14.9) 31.8 30.8
Husband/partner 0.8 (0.0) 2.5 1.3
Boyfriend 0.2 (0.0) 0.0 0.1
Friend 6.2 (4.0) 12.2 8.0
Neighbour 8.7 (2.4) 9.1 8.6
Religious leader 3.5 (3.0) 3.3 3.4
Doctor/medical personnel 1.0 (7.8) 6.1 2.9
Police 6.2 (0.0) 9.5 7.0
Lawyer 0.4 (0.0) 0.7 0.5
Social work organisation 1.0 (0.0) 3.4 1.7
Other 11.7 (14.6) 11.4 11.7
Number of women who have
experienced violence and
sought help 755 50 380 1,185
Note: Women can report more than one source from which they sought help. Figures in parentheses
are based on 25-49 unweighted cases.
Domestic Violence • 325
Table 16.18.2 Sources for help to stop the violence: Men
Percentage of men age 15-49 who have experienced physical or sexual violence and sought help by
sources from which they sought help, according to the type of violence that women reported, Kenya
2014
Type of violence experienced
Physical and
Person Physical only Sexual only sexual Total
Own family 46.2 * 62.7 48.6
Wife’s/partner’s family 4.2 * 13.6 5.4
Current/former wife/partner 1.1 * 1.5 1.2
Current/former girlfriend 0.4 * 0.0 0.4
Friend 12.3 * 22.1 13.9
Neighbour 6.0 * 6.8 6.1
Religious leader 2.2 * 1.8 2.2
Doctor/medical personnel 14.5 * 6.3 13.3
Police 20.5 * 8.2 18.7
Lawyer 0.2 * 0.0 0.2
Social service organisation 0.7 * 0.0 0.6
Other 17.5 * 11.5 16.6
Number of men who have
experienced violence and
sought help 502 5 78 585
Note: Men can report more than one source from which they sought help. An asterisk denotes a figure
based on fewer than 25 unweighted cases that has been suppressed.
326 • Domestic Violence
ADULT AND MATERNAL MORTALITY 17
Alfred Agwanda, John Anampiu, Andrew A. Imbwaga
Key Findings
• Fourteen percent of women and 18 percent of men are likely to die
between exact ages 15 and 50.
• Maternal deaths account for 14 percent of all deaths to women age 15-
49.
• The maternal mortality ratio was 362 maternal deaths per 100,000 live
births for the seven-year period preceding the survey.
• When comparing the estimate of an MMR of 362 with the MMR estimated
in the previous KDHS (2008-09 KDHS estimate of 520 maternal deaths
per 100,000 live births), the differential is not large enough to conclude
whether or not there has been any change over time between the two
surveys.
A
dult and maternal mortality rates are key indicators of the health status of a population. They are
also national development indicators. Moreover, levels and trends in overall adult mortality have
important implications for health and social programmes in Kenya in their own right, especially
with regard to the potential impact of the AIDS epidemic, other infectious diseases, and noncommunicable
diseases. Estimation of these mortality rates requires comprehensive and accurate reporting of adult deaths.
This chapter includes results based on sibling history data collected in Section 11 of the 2014 KDHS
Woman’s Questionnaire.
In addition to adult mortality rates for five-year age groups, this chapter includes a summary
measure (35q15) that represents the probability of dying between exact ages 15 and 50. To allow for
assessment of trends in adult mortality probabilities, 35q15 values were also calculated from the 2003 and
2008-09 KDHS survey data.
17.1 DATA AND ASSESSMENT OF QUALITY
To obtain a sibling history, the 2014 KDHS first asked each female respondent to list all children
born to her biological mother, starting with the firstborn. The survey then asked the respondent whether each
of these siblings was still alive. For living siblings, the questionnaire asked the current age of each sibling.
For deceased siblings, the age at death and the number of years since death were recorded. When a
respondent could not provide precise information on age at death or years since death, approximate but
quantitative answers were accepted. For sisters who died at age 12 or older, the KDHS asked three questions
to determine whether the death was maternal: ‘Was [NAME OF SISTER] pregnant when she died?’ and, if
the response was negative, ‘Did she die during childbirth?’ and, if negative again, ‘Did she die within two
months after the end of a pregnancy or childbirth?’
Table C.8 in Appendix C shows that, in the 2014 KDHS, a total of 84,613 siblings were recorded
in the sibling histories. The survival status was not reported for 39 siblings. Among surviving siblings,
current age was not reported for 305 siblings (0.4 percent). Ninety-eight percent of deceased siblings had
both age at death (AD) and years since death (YSD) reported. In less than 1 percent of cases, both age at
death and years since death were missing. The sex ratio of the enumerated siblings (the ratio of brothers to
sisters multiplied by 100) is 100.2 (Table C.9), which is slightly lower than the expected value of 102-106.
Indicators of data quality for the 2014 KDHS show some improvement compared with the 2008-09 KDHS.
Adult and Maternal Mortality • 327
17.2 ESTIMATES OF ADULT MORTALITY
One way to assess the quality of data used to estimate maternal mortality is to evaluate the
plausibility and stability of overall adult mortality estimates over a given time period. If the estimated rates
of overall adult mortality are implausible, rates based on a subset of deaths—maternal mortality in
particular—are likely to be unreliable.
The direct estimation of adult mortality uses the reported Table 17.1 Adult mortality rates
ages at death and years since death of the respondents’ brothers and Direct estimates of female and male mortality
sisters. Mortality rates are calculated by dividing the number of rates for the seven years preceding the survey, by
five-year age groups, Kenya 2014
deaths in each age group of women and men by the total person-
Exposure Mortality
years of exposure to the risk of dying in that age group during a Age Deaths years rates1
specified period prior to the survey. To have a sufficiently large FEMALE
number of adult deaths to generate a robust estimate, the rates are 15-19
20-24
54
85
32,170
40,528
1.67
2.10
calculated for the seven-year period preceding the survey. It should 25-29 105 39,466 2.66
30-34 158 33,397 4.73
be noted that age-specific mortality rates obtained in this manner are 35-39 174 25,719 6.78
40-44 119 17,486 6.83
subject to considerable sampling variation. 45-49 55 10,965 5.00
15-49 750 199,731 3.72a
The confidence intervals for adult mortality rates are MALE
presented in Appendix Table B.13. When calculating confidence 15-19 66 32,206 2.05
intervals (CIs), we assume the sample from which the estimate is 20-24 25-29
93
141
39,368
38,953
2.36
3.62
drawn follows a normal distribution. When comparing point 30-34 175 33,478 5.23
35-39 179 25,185 7.11
estimates between two surveys and calculating CIs to determine 40-44 165 16,990 9.71
whether or not the difference is statistically significant, we assume 45-49 110 10,622 10.39
4.78a
the sample from which each estimate was calculated follows a 15-49 929 196,802
normal distribution and that the two samples are independent. Thus 1a Expressed per 1,000 population
Age-adjusted rate
when comparing mortality estimates, for example, we can refer to
the CI when gauging whether the difference between two surveys estimates within sex and within an age
group, but we cannot gauge statistical significance of differences across sex within the same survey, or across
age groups within the same survey, because the assumption of independence within the survey is violated.
Table 17.1 shows age-specific mortality rates for women and men age 15-49 for the seven-year
period preceding the survey. The age-specific rates generally show the expected pattern of increasing
mortality with increasing age. Overall, the estimated level of adult mortality is slightly higher among men
(4.78 deaths per 1,000 population) than among women (3.72 deaths per 1,000 population). The overall rate
for ages 15-49 were standardised by the age distribution of the survey respondents to remove the effect of
truncation bias (the upper boundary for eligibility for women interviewed in the KDHS was 49 years and 54
years for men).
Table 17.2 shows trends in the summary measure of the risk of Table 17.2 Adult mortality probabilities
dying between exact ages 15 and 50 (35q15). According to data from the The probability of dying between the ages of
2014 KDHS, 14 percent of women and 18 percent of men are likely to 15 and 50 for women and men for the seven
years preceding the survey, Kenya 2014
die between age 15 and age 50 if the prevailing mortality probabilities Female Male
continue to apply. Estimates of 35q15 calculated from the 2003, 2008-09, Survey 35q15
1
35q15
1
and 2014 KDHS surveys show that the probability of dying sometime 2014 KDHS 138 183
2008-09 KDHS 214 231
between exact ages 15 and 50 has been declining. Confidence intervals 2003 KDHS 235 240
for the 35q15 estimates from all three surveys can be found in Appendix 1 The probability of dying between exact ages
Table B.13. Note that the surveys did not detect a significant change 15 and 50, expressed per 1,000 person-
years of exposure
between the 2003 and 2008-09 surveys; the 2014 survey has detected a
fall in the probability of dying between age 15 and 50 has occurred over the previous seven years among
men and women.
328 • Adult and Maternal Mortality
17.3 ESTIMATES OF MATERNAL MORTALITY
Two procedures involving sisterhood data (sibling history data) are generally used to estimate
maternal mortality in developing countries; these procedures employ an indirect variant (Graham et al.,
1989) and a direct estimation method (Rutenberg et al., 1991). In this report, the direct estimation procedure
is applied. Age-specific mortality rates are calculated by dividing the number of maternal deaths by woman-
years of exposure. As was done in the calculation of adult mortality rates, the overall rate for women age 15-
49 is standardised by the age distribution of the survey respondents.
Table 17.3 presents direct estimates of maternal mortality for the seven-year period preceding the
survey. Maternal deaths represent about 14 percent of all deaths among women age 15-49. The percentage
of female deaths that are maternal varies by age from about 5 percent among women age 45-49 to 27 percent
among women age 25-29.
Table 17.3 Maternal mortality
Direct estimates of maternal mortality rates for the seven years preceding the survey, by five-year age
groups, Kenya 2014
Percentage of
female deaths Maternal
Age that are maternal Maternal deaths Exposure years mortality rate1
15-19 6.8 4 32,170 0.11
20-24 21.8 19 40,528 0.46
25-29 27.4 29 39,466 0.73
30-34 13.7 22 33,397 0.65
35-39 12.8 22 25,719 0.87
40-44 7.3 9 17,486 0.50
45-49 4.5 2 10,965 0.22
15-49 14.1 106 199,731 0.51
2 a
General fertility rate (GFR) 142
Maternal mortality ratio (MMR)3 362 CI: (254, 471)
Lifetime risk of maternal death4 0.015
CI: Confidence interval
1
Expressed per 1,000 woman-years of exposure
2
Expressed per 1,000 woman age 15-49
3
Expressed per 100,000 live births; calculated as the age-adjusted maternal mortality rate times 100
divided by the age-adjusted general fertility rate
4
Calculated as 1-(1-MMR)TFR where TFR represents the total fertility rate for the seven years preceding
the survey
a
Age-adjusted rate
The data indicate that the rate of mortality associated with pregnancy and childbearing is 0.51
maternal deaths per 1,000 woman-years of exposure. The estimated age-specific maternal mortality rates
display a generally plausible pattern, being higher at the peak childbearing ages (20s and 30s) than in the
younger and older age groups. By five-year age groups, the maternal mortality rate is highest among women
age 35-39 (0.87), followed by those age 25-29 (0.73).
The maternal mortality rate can be converted to a maternal mortality ratio (expressed as deaths per
100,000 live births) by dividing the maternal mortality rate by the general fertility rate (GFR) of 142 that
prevailed during the same time period and multiplying the result by 100,000. This procedure produces a
maternal mortality ratio (MMR) of 362 deaths per 100,000 live births during the seven-year period preceding
the survey. In other words, for every 1,000 live births in Kenya in the seven years preceding the 2014 KDHS,
approximately four women died during pregnancy, during childbirth, or within two months of childbirth.
The lifetime risk of maternal death (0.015) indicates that approximately 2 percent of women, or about 1 in
67, will have a maternal death (i.e., they will die during pregnancy, during childbirth, or within two months
of childbirth).
In order to compare maternal mortality estimates for the 2014 KDHS with previous estimates, the
maternal mortality ratio for the seven years prior to the survey was re-calculated with data from the 2003
KDHS and the 2008-09 KDHS. As shown in Figure 17.1, the confidence intervals surrounding the 2003
Adult and Maternal Mortality • 329
point estimate are completely encompassed by the confidence interval surrounding the 2008-09 estimate,
concluding that maternal mortality did not change significantly in the period 1996-2009. However, the
confidence intervals surrounding the estimates around the 2008-09 (CI: 343, 696) and 2014 (CI:254, 471)
estimates overlap, such that the estimate of 362 deaths per 1,000 population (2014 KDHS) is encompassed
within the 2008-09 confidence interval, but the 2008-09 estimate (520 deaths per 1,000 population) is not
encompassed within the 2014 confidence interval. Thus we conclude that the survey has not detected a
change between the two surveys. However, there could be a change that was too small to be detected by the
survey. Even with their large sample sizes, DHS surveys are able to detect only very large changes in the
maternal mortality ratio.
Figure 17.1 Maternal mortality ratio (MMR) with confidence intervals for the
seven years preceding the KDHS
Maternal deaths
per 100,000 live
births
800
700 696
600 614
500 506 520
471
400 398
343 362
300
254
200
Seven years preceding the Seven years preceding the Seven years preceding the
2003 Kenya DHS (1996-2003) 2008-09 Kenya DHS 2014 Kenya DHS (2007-2014)
(2002-2009)
330 • Adult and Maternal Mortality
FEMALE GENITAL CUTTING 18
Rosemary Uside Kongani, Robert C.B. Buluma, Lawrence Ikamari
Key Findings
• Twenty-one percent of women age 15-49 have been circumcised.
• There is some evidence of a trend over time to circumcise girls at
younger ages. Twenty-eight percent of circumcised women age 20-24
were circumcised at age 5-9, as compared with 17 percent of
circumcised women age 45-49.
• With respect to type of circumcision, 2 percent of circumcised women
age 15-49 had cutting with no flesh removed, 87 percent had cutting with
flesh removed, and 9 percent had their genital area sewn closed after
cutting (a procedure known as infibulation).
• Girls age 0-14 are more likely to be circumcised if their mother is
circumcised. Likewise, girls age 0-14 are more likely to be infibulated if
their mother is also infibulated.
• Eight percent of girls age 0-14 have had their genital area sewn closed.
• Eleven percent or less of women and men believe that the practice of
female genital cutting is required by their community or their religion or
that the practice should continue.
F emale genital cutting (FGC)—also called female circumcision and female genital mutilation—
involves cutting some part of the clitoris or labia for non-therapeutic reasons, usually as part of a rite
of passage into adolescence. It is practiced by some ethnic groups in Kenya as well as in other East
African countries and is motivated by beliefs about what is considered proper sexual behaviour for women
and what is necessary to prepare them for marriage (WHO, 2014b). However, the practice is widely
acknowledged as a violation of children and women’s rights, and it has the potential to cause serious
medical complications. In 2011, Kenya passed a law—the Prohibition of Female Genital Mutilation Act
2011—that banned female genital mutilation nationwide. Under this law, it is illegal to practice FGC in
Kenya or to take someone abroad for FGC.
The 2014 KDHS collected information about FGC in Kenya from women and men age 15-49. The
topics covered included knowledge of female circumcision and attitudes towards the practice of
circumcision. This is the first KDHS to collect information from men. Female respondents who had ever
heard of female circumcision were asked additional questions, including whether they were circumcised
and, if so, their age at circumcision, the type of circumcision, and the person who performed the procedure.
They were also asked questions regarding the circumcision status of their daughters up to age 14.
18.1 KNOWLEDGE OF FEMALE CIRCUMCISION
Table 18.1 shows the percentage of women and men age 15-49 who have heard of female
circumcision by background characteristics. Knowledge of FGC is virtually universal. Ninety-seven
percent of women and 98 percent of men have heard of FGC. Similarly, in the 2008-09 KDHS, 96 percent
of women had heard of FGC.
Women (95 percent) and men (94 percent) in the youngest age group (15-19) are least likely to
have heard of FGC, as are women (79 percent) and men (94 percent) who do not identify with any religion.
Women in the Mijikenda/Swahili ethnic group (83 percent) and women in the Coast region (90 percent)
are less likely than other women to have heard of FGC. Among both women and men, knowledge of FGC
generally increases with increasing education and wealth.
Female Genital Cutting • 331
Table 18.1 Knowledge of female circumcision
Percentage of women and men 15-49 who have heard of female circumcision, according to
background characteristics, Kenya 2014
Women Men
Have heard of Have heard of
Background female Number of female Number of
characteristic circumcision women circumcision men
Age
15-19 95.0 2,717 93.7 2,540
20-24 97.2 2,691 98.1 2,125
25-29 97.0 2,932 98.9 2,104
30-34 97.7 2,162 98.5 1,785
35-39 96.9 1,780 99.0 1,483
40-44 96.8 1,292 99.2 1,224
45-49 97.5 1,052 98.4 800
Religion
Roman Catholic 97.5 2,920 97.9 2,583
Protestant/other Christian 97.0 10,497 97.7 8,141
Muslim 97.5 916 98.0 784
No religion 78.7 244 94.2 492
Other (93.0) 48 99.6 59
Ethnic group
Embu 99.4 147 98.2 118
Kalenjin 99.5 1,785 99.2 1,467
Kamba 96.3 1,649 98.2 1,521
Kikuyu 99.0 3,136 98.3 2,523
Kisii 99.9 863 99.8 712
Luhya 97.4 2,301 97.5 1,927
Luo 91.9 1,560 93.1 1,311
Maasai 100.0 280 100.0 220
Meru 99.5 826 98.9 717
Mijikenda/Swahili 83.1 767 93.4 623
Somali 99.6 354 99.9 260
Taita/Taveta 99.6 139 99.5 134
Turkana 94.1 189 97.0 106
Samburu 100.0 68 (100.0) 12
Other 94.7 558 98.0 399
Residence
Urban 97.7 5,929 98.2 5,300
Rural 96.2 8,696 97.2 6,762
Region
Coast 90.1 1,421 96.2 1,260
North Eastern 99.7 299 99.9 227
Eastern 97.6 2,066 98.0 1,825
Central 98.7 1,905 98.3 1,564
Rift Valley 99.1 3,714 99.1 3,050
Western 95.9 1,571 95.8 1,164
Nyanza 93.5 1,908 94.8 1,405
Nairobi 98.2 1,742 98.3 1,568
Education
No education 92.3 1,015 96.8 345
Primary incomplete 94.7 3,793 94.7 3,071
Primary complete 97.1 3,543 97.9 2,734
Secondary+ 98.6 6,274 99.0 5,913
Wealth quintile
Lowest 92.9 2,236 95.4 1,691
Second 95.8 2,590 97.2 2,145
Middle 96.5 2,859 97.1 2,370
Fourth 98.1 3,113 97.6 2,959
Highest 99.0 3,827 99.7 2,897
Total 15-49 96.8 14,625 97.6 12,063
50-54 na na 99.2 756
Total 15-54 na na 97.7 12,819
Note: Totals include three women and three men for whom information on religion is
missing and seven women and 16 men for whom information on ethnic group is missing.
Figures in parentheses are based on 25-49 unweighted cases.
na = Not applicable
332 • Female Genital Cutting
18.2 PREVALENCE OF AND AGE AT CIRCUMCISION
Table 18.2 presents the percentage of women age 15-49 who are circumcised and the percent
distribution of circumcised women by type of circumcision, according to background characteristics. In the
2014 KDHS, 21 percent of women reported being circumcised, as compared with 27 percent in 2008-09
and 32 percent in 2003. The majority of circumcised women (87 percent) had a cut with flesh removed, 9
percent reported that their genital area had been sewn closed, 2 percent were cut with no flesh removed,
and 2 percent did not know or did not have a response recorded.
Table 18.2 Prevalence of female circumcision
Percentage of women 15-49 circumcised, and percent distribution of circumcised women by type of circumcision according to background characteristics,
Kenya 2014
Percentage of Type of circumcision Number of
Background women Number of Cut, no flesh Cut, flesh Don’t know/ circumcised
characteristic circumcised women removed removed Sewn closed missing Total women
Age
15-19 11.4 2,717 1.4 85.8 9.8 2.9 100.0 309
20-24 14.7 2,691 1.5 88.0 8.8 1.7 100.0 395
25-29 18.0 2,932 1.3 86.4 10.8 1.6 100.0 527
30-34 22.9 2,162 2.2 86.2 8.7 2.9 100.0 495
35-39 27.8 1,780 1.5 88.1 9.0 1.4 100.0 496
40-44 32.1 1,292 1.3 87.4 9.9 1.5 100.0 415
45-49 40.9 1,052 1.6 88.4 8.2 1.7 100.0 430
Religion
Roman Catholic 21.5 2,920 1.7 91.4 5.5 1.4 100.0 628
Protestant/other Christian 17.9 10,497 1.6 90.4 5.7 2.3 100.0 1,879
Muslim 51.1 916 1.2 67.1 30.1 1.7 100.0 468
No religion 32.9 244 0.7 95.1 4.2 0.0 100.0 80
Other (19.7) 48 * * * * 100.0 9
Ethnic group
Embu 30.7 147 6.7 88.4 3.2 1.7 100.0 45
Kalenjin 27.9 1,785 3.6 90.3 5.2 0.9 100.0 498
Kamba 10.7 1,649 0.7 75.4 22.0 2.0 100.0 176
Kikuyu 14.6 3,136 1.5 87.7 8.4 2.4 100.0 459
Kisii 84.4 863 1.1 96.5 1.6 0.8 100.0 728
Luhya 0.4 2,301 * * * * 100.0 10
Luo 0.2 1,560 * * * * 100.0 3
Maasai 77.9 280 0.7 92.0 4.5 2.8 100.0 218
Meru 30.7 826 0.2 97.7 2.0 0.0 100.0 253
Mijikenda/Swahili 2.4 767 (0.0) (93.7) (5.1) (1.2) 100.0 18
Somali 93.6 354 1.4 64.6 32.3 1.6 100.0 331
Taita/Taveta 22.3 139 4.4 56.7 8.5 30.4 100.0 31
Turkana 1.7 189 * * * * 100.0 3
Samburu 86.0 68 0.4 95.0 4.2 0.4 100.0 58
Other 41.4 558 0.9 79.6 17.0 2.4 100.0 231
Residence
Urban 13.8 5,929 2.8 84.1 10.1 3.0 100.0 815
Rural 25.9 8,696 1.1 88.3 9.1 1.5 100.0 2,251
Region
Coast 10.2 1,421 3.9 61.2 26.2 8.7 100.0 145
North Eastern 97.5 299 1.1 66.1 31.3 1.5 100.0 292
Eastern 26.4 2,066 0.3 90.2 9.4 0.1 100.0 545
Central 16.5 1,905 0.9 86.9 11.6 0.6 100.0 314
Rift Valley 26.9 3,714 2.4 90.8 5.1 1.6 100.0 999
Western 0.8 1,571 * * * * 100.0 13
Nyanza 32.4 1,908 0.3 97.0 1.7 1.0 100.0 618
Nairobi 8.0 1,742 (5.1) (78.8) (5.2) (10.9) 100.0 140
Total 21.0 14,625 1.6 87.2 9.3 1.9 100.0 3,066
Note: Among all women, total includes three women for whom information on religion is missing and seven women for whom information on ethnic group is
missing. Among circumcised women, total includes two women for whom information on religion is missing and three women for whom information on ethnic
group is missing. Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that
has been suppressed.
The proportion of circumcised women increases with age. Muslim women (51 percent) are more
likely to have been circumcised than women from other religious groups. However, even among
Protestants/other Christians, the religious group with the lowest prevalence, 18 percent of women have
been circumcised. Muslim women are more likely to have had the type of circumcision in which their
genital area is sewn closed (30 percent) than women from all other religious groups (4-6 percent).
Female Genital Cutting • 333
The proportion of women who are circumcised varies by ethnic group (Figure 18.1), with the
majority of women in the Somali (94 percent), Samburu (86 percent), Kisii (84 percent), and Maasai (78
percent) groups being circumcised. In contrast, 2 percent or less of women in the Luo, Luhya, Turkana,
and Mijikenda/Swahili ethnic groups are circumcised. Rural women (26 percent) are more likely than
urban women (14 percent) to be circumcised. There are large regional variations; the proportion of
circumcised women ranges from 1 percent in Western to 98 percent in North Eastern.
Figure 18.1 Percentage of women age 15-49 circumcised by ethnic group
Percent
94
84 86
78
41
31 28 31
22
15
11
<1 <1 2 2
KDHS 2014
Table 18.3 shows the percent distribution of circumcised women age 15-49 by age at
circumcision, according to background characteristics. It should be noted that when women are
circumcised at a young age, their recollection of exactly how old they were at the time may be imperfect.
A small proportion of women (2 percent) were circumcised when they were less than age 5. Twenty-seven
percent were circumcised when they were age 5-9, 43 percent were circumcised when they were age 10-
14, and 27 percent were circumcised at age 15 or older.
There is some evidence of a trend over time to circumcise girls at younger ages. Forty-six percent
of circumcised women age 15-19 were circumcised at age 5-9, as compared with 17 percent of circumcised
women age 45-49. While there is overlap in these categories, Muslim women are much more likely to be
circumcised at age 5-9 (65 percent) than women from other religious groups, as are Somali women (73
percent. Urban women are more likely to be circumcised at age 5-9 (34 percent) than rural women (24
percent). About 78 percent of women in urban areas are circumcised by age 14, compared with 69 percent
of those in rural areas. Women in the Coast region were most likely to have been circumcised when they
were less than age 5 (22 percent).
334 • Female Genital Cutting
Table 18.3 Age at circumcision
Percent distribution of circumcised women age 15-49 by age at circumcision according to background characteristics,
Kenya 2014
Age at circumcision Number of
Background Don’t know/ circumcised
1
characteristic <5 5-9 10-14 15+ missing Total women
Age
15-19 2.8 46.0 42.8 7.1 1.3 100.0 309
20-24 1.1 27.9 44.2 24.1 2.6 100.0 395
25-29 2.3 33.4 41.5 21.6 1.2 100.0 527
30-34 1.7 28.0 41.8 26.2 2.3 100.0 495
35-39 2.3 21.9 44.2 30.9 0.7 100.0 496
40-44 3.3 16.3 37.2 41.5 1.8 100.0 415
45-49 2.5 16.7 46.5 32.4 1.9 100.0 430
Religion
Roman Catholic 1.8 25.6 43.0 27.8 1.7 100.0 628
Protestant/other Christian 1.4 18.1 46.6 32.3 1.6 100.0 1,879
Muslim 7.0 65.0 24.3 1.6 2.1 100.0 468
No religion 0.0 5.4 51.7 42.9 0.0 100.0 80
Other * * * * * 100.0 9
Ethnic group
Embu 0.0 9.6 77.9 10.9 1.7 100.0 45
Kalenjin 0.0 1.5 27.2 70.9 0.4 100.0 498
Kamba 5.0 47.7 41.4 2.5 3.4 100.0 176
Kikuyu 0.9 2.1 54.0 42.8 0.1 100.0 459
Kisii 0.4 48.6 47.1 1.3 2.6 100.0 728
Luhya * * * * * 100.0 10
Luo * * * * * 100.0 3
Maasai 0.8 10.7 59.5 26.9 2.0 100.0 218
Meru 0.4 5.0 44.0 50.4 0.2 100.0 253
Mijikenda/Swahili 27.8 35.2 33.4 2.4 1.2 100.0 18
Somali 5.2 72.7 18.9 0.2 2.9 100.0 331
Taita/Taveta 61.3 22.0 11.1 4.9 0.7 100.0 31
Turkana * * * * * 100.0 3
Samburu 0.0 2.6 57.9 38.9 0.6 100.0 58
Other 4.2 25.6 50.9 18.6 0.6 100.0 231
Residence
Urban 2.8 33.7 41.9 20.0 1.7 100.0 815
Rural 2.1 24.0 42.8 29.4 1.7 100.0 2,251
Region
Coast 21.7 43.9 28.6 4.1 1.7 100.0 145
North Eastern 5.1 76.3 16.1 0.0 2.4 100.0 292
Eastern 1.6 22.1 45.6 29.4 1.3 100.0 545
Central 1.3 6.3 58.7 33.6 0.2 100.0 314
Rift Valley 0.5 7.6 41.9 49.0 1.0 100.0 999
Western * * * * * 100.0 13
Nyanza 0.4 42.7 49.9 4.4 2.5 100.0 618
Nairobi (1.9) (30.8) (37.5) (24.8) (5.0) 100.0 140
Total 2.3 26.6 42.6 26.9 1.7 100.0 3,066
Note: Total includes two women for whom information on religion is missing and three women for whom information on
ethnic group is missing. Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes a figure based
on fewer than 25 unweighted cases that has been suppressed.
1
Includes women who reported they were circumcised during infancy but did not provide a specific age.
Table 18.4 shows the percent distribution of girls age 0-14 by age at circumcision and the
percentage of girls circumcised according to current age. Three percent of girls age 0-14 are circumcised;
the likelihood of being circumcised increases with age, and 7 percent of girls age 10-14 are circumcised.
Table 18.4 Prevalence of circumcision and age at circumcision: Girls 0-14
Percent distribution of girls age 0-14 by age at circumcision, and percentage of girls circumcised according to current age, Kenya
2014
Age at circumcision
Percentage
Background not Number of Percentage
characteristic <1 1-4 5-9 10-14 circumcised Total girls circumcised
Current age of girls
0-4 0.0 0.0 na na 99.8 100.0 4,426 0.2
5-9 0.0 0.3 1.9 na 97.8 100.0 4,453 2.2
10-14 0.0 0.2 5.0 1.7 93.1 100.0 3,508 6.9
Total 0.0 0.2 2.1 0.5 97.2 100.0 12,388 2.8
Note: The circumcision status of girls is reported by their mothers. Totals may not add up to 100 percent because girls with missing
information are not shown separately.
na = Not applicable due to censoring.
Female Genital Cutting • 335
A comparison of age at circumcision between girls age 0-14 and women age 15-49 is shown in
Figure 18.2. A slightly higher proportion of women than girls were circumcised at each particular age. For
example, girls currently age 14 are less likely to be circumcised at that age than women who are currently
age 15-49.
Figure 18.2 Percentage of women age 15-49 and girls age 0-14 circumcised by age
Percent
20
15
10
5
Girls age 0-14
Women age 15-49
0
<1 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Age (in years)
Note: Data on age at circumcision for women who were circumcised before age 5 is not available in
single years of age. Data for girls is based on current status. The figure shows the percentage of girls
currently in each age group who have been circumcised (according to mother's report). Data for women
age 15-49 is based on recall and shows the percentage who were circumcised by specific exact ages. KDHS 2014
Table 18.5 shows the percentage of girls age 0-14 who are circumcised according to age and
mother’s background characteristics. Twenty percent of girls with Muslim mothers are circumcised.
Thirty-six percent of girls whose mothers are Somali and 16 percent whose mothers are Kisii are
circumcised, as compared with 6 percent or less among all other ethnic groups. Forty percent of girls in
North Eastern are circumcised.
336 • Female Genital Cutting
Table 18.5 Circumcision of girls age 0-14 by mother’s background
characteristics
Percentage of girls age 0-14 who are circumcised, according to age and
mother’s background characteristics, Kenya 2014
Background Current age of girls
characteristic 0-4 5-9 10-14 All 0-14
Religion
Roman Catholic 0.0 1.2 5.9 2.1
Protestant/other Christian 0.3 0.5 3.5 1.3
Muslim 0.7 20.3 45.0 19.8
No religion 0.0 0.4 0.0 0.1
Ethnic group
Embu (0.0) (3.8) (0.0) 1.2
Kalenjin 0.5 0.3 0.7 0.5
Kamba 0.0 0.4 0.9 0.4
Kisii 0.1 5.4 46.9 16.1
Luhya 0.6 0.0 0.1 0.2
Luo 0.0 0.1 0.0 0.0
Maasai 0.4 2.4 7.6 2.8
Meru 0.0 1.9 0.0 0.7
Mijikenda/Swahili 0.0 0.1 0.1 0.1
Somali 1.5 40.0 76.4 36.0
Taita/Taveta 0.0 1.6 2.2 1.3
Turkana 0.0 0.0 0.2 0.0
Samburu 0.0 1.0 12.8 3.8
Other 0.0 3.4 14.1 5.5
Residence
Urban 0.2 1.5 5.5 2.0
Rural 0.3 2.6 7.5 3.2
Region
Coast 0.0 1.2 4.4 1.6
North Eastern 1.6 44.8 82.2 40.2
Eastern 0.0 1.7 3.1 1.6
Central 0.0 0.0 0.5 0.2
Rift Valley 0.4 0.4 2.0 0.8
Western 0.3 0.0 0.2 0.1
Nyanza 0.3 2.1 16.9 5.9
Nairobi 0.0 0.0 2.1 0.5
Mother’s education
No education 0.5 13.2 28.1 13.0
Primary incomplete 0.0 0.8 4.1 1.5
Primary complete 0.3 0.6 3.0 1.2
Secondary+ 0.3 0.7 4.1 1.5
Mother’s circumcision
status
Circumcised 0.3 7.5 20.8 9.6
Not circumcised 0.2 0.1 0.1 0.1
Wealth quintile
Lowest 0.5 5.9 14.5 6.2
Second 0.4 1.2 5.9 2.3
Middle 0.2 1.3 6.2 2.4
Fourth 0.1 0.9 4.6 1.7
Highest 0.0 1.0 1.4 0.7
Total 0.2 2.2 6.9 2.8
Note: The circumcision status of girls is reported by their mothers. Figures in
parentheses are based on 25-49 unweighted cases.
18.3 ASPECTS OF CIRCUMCISION AMONG CIRCUMCISED GIRLS AND WOMEN
Infibulation is the excision of exterior genitalia and the sewing shut of the opening to the vagina. It
is the type of FGC most likely to result in severe, long-term medical complications. Table 18.6 presents the
percent distribution of girls age 0-14 who are circumcised by whether or not they are infibulated, according
to their mother’s background characteristics.
Eight percent of circumcised girls are infibulated. Eighty-six percent of circumcised girls are not
infibulated, and infibulation status is unknown for 6 percent of circumcised girls. Infibulation is most
common among girls whose mothers are Muslim (13 percent), have no education (11 percent), or are
themselves infibulated (25 percent).
Female Genital Cutting • 337
Table 18.6 Infibulation among circumcised girls age 0-14
Percent distribution of girls age 0-14 who are circumcised by whether or not they are infibulated, according
to mother’s background characteristics, Kenya 2014
Infibulation status
Background Not sewn Don’t know/
characteristic Sewn closed closed missing Total Number
Religion
Roman Catholic 1.1 90.7 8.2 100.0 50
Protestant/other Christian 2.5 85.0 12.5 100.0 111
Muslim 12.7 86.0 1.3 100.0 189
No religion * * * 100.0 1
Ethnic group
Embu * * * 100.0 1
Kalenjin * * * 100.0 8
Kamba * * * 100.0 5
Kisii 2.4 92.5 5.1 100.0 117
Luhya * * * 100.0 5
Luo * * * 100.0 1
Maasai * * * 100.0 11
Meru * * * 100.0 4
Mijikenda/Swahili * * * 100.0 1
Somali 11.3 87.9 0.8 100.0 163
Taita/Taveta * * * 100.0 1
Turkana * * * 100.0 0
Samburu * * * 100.0 3
Other 17.8 78.2 4.0 100.0 32
Residence
Urban 7.2 87.4 5.4 100.0 80
Rural 8.0 86.0 6.0 100.0 271
Region
Coast 45.3 50.3 4.4 100.0 21
North Eastern 9.0 90.2 0.8 100.0 157
Eastern 2.6 92.9 4.5 100.0 27
Central * * * 100.0 2
Rift Valley (8.7) (62.9) (28.4) 100.0 29
Western * * * 100.0 2
Nyanza 0.6 93.0 6.4 100.0 109
Nairobi * * * 100.0 5
Mother’s education
No education 10.5 88.9 0.6 100.0 200
Primary incomplete 4.3 87.6 8.1 100.0 63
Primary complete 7.5 75.7 16.8 100.0 39
Secondary+ 1.6 82.9 15.6 100.0 50
Mother’s circumcision
status
Infibulated 24.7 73.9 1.4 100.0 70
Circumcised, not
infibulated 3.7 93.7 2.7 100.0 269
Not circumcised * * * 100.0 13
Wealth quintile
Lowest 10.2 87.1 2.8 100.0 179
Second 3.9 84.9 11.2 100.0 59
Middle 7.0 83.4 9.7 100.0 60
Fourth 2.3 94.3 3.4 100.0 38
Highest (12.2) (75.0) (12.7) 100.0 15
Total 7.8 86.3 5.9 100.0 352
Note: The circumcision status of girls is reported by their mothers. Figures in parentheses are based on 25-
49 unweighted cases. An asterisk denotes a figure based on fewer than 25 unweighted cases that has been
suppressed.
Communities that practice FGC have people who specialise in performing the procedure,
including traditional circumcisers, traditional birth attendants, and medical professionals. Table 18.7 shows
the percent distribution of circumcised girls age 0-14 and women age 15-49 according to the person
performing the circumcision and the type of circumcision. Seventy-three percent of girls and 81 percent of
women were circumcised by a traditional circumciser. Younger girls (age 5-9) are more likely than older
girls (age 10-14) to have been circumcised by a traditional circumciser (85 percent versus 70 percent).
Among women age 15-49, there has been an increase in the proportion circumcised by a traditional
circumciser since the 2008-09 KDHS (75 percent).
338 • Female Genital Cutting
Table 18.7 Aspects of circumcision among circumcised girls age 0-14 and women age 15-49
Percent distribution of circumcised girls age 0-14 by current age and women age 15-49, according
to person performing the circumcision and type of circumcision, Kenya 2014
Background Current age of girls Girls age Women age
characteristic 0-4 5-9 10-14 0-14 15-49
Person who performed the
circumcision
Traditional agent * 86.8 72.0 74.9 83.3
Traditional circumciser * 84.7 69.9 72.9 80.5
Traditional birth attendant * 2.1 2.0 2.0 2.5
Other traditional agent * 0.0 0.0 0.0 0.4
Medical professional * 8.7 24.8 19.7 14.8
Doctor * 1.9 4.2 3.5 6.4
Nurse/midwife * 6.8 20.6 16.2 8.3
Other health professional * 0.0 0.0 0.0 0.1
Don’t know/missing * 4.4 3.2 5.4 1.9
Total 100.0 100.0 100.0 100.0 100.0
Type of circumcision
Sewn closed * 8.2 8.0 7.8 9.3
Not sewn closed * 86.5 88.6 86.3 89.2
Don’t know/missing * 5.3 3.5 5.9 1.5
Total 100.0 100.0 100.0 100.0 100.0
Number 11 99 242 352 3,066
Note: The circumcision status of girls is reported by their mothers. An asterisk denotes a figure
based on fewer than 25 unweighted cases that has been suppressed.
18.4 RELIGIOUS AND COMMUNITY ATTITUDES TOWARDS FGC
Table 18.8 shows the percent distribution of women and men age 15-49 who have heard of female
circumcision according to their opinion on whether or not their religion requires female circumcision, by
background characteristics. Five percent of women and 6 percent of men believe that circumcision is
required by their religion. Circumcised women are more likely (18 percent) than those who have not been
circumcised to say that circumcision is required by their religion (1 percent). Muslim women (44 percent)
and men (36 percent) are more likely to say that circumcision is required by their religion than members of
other religious groups.
Respondents’ opinions about whether circumcision is required by their religion vary according to
ethnic group; the majority of Somali women (82 percent) and men (83 percent) believe that circumcision is
required by their religion. Residents of North Eastern are most likely to report that circumcision is required
by their religion (89 percent of women and 87 percent of men). Women (36 percent) and men (37 percent)
with no education are more likely to report that circumcision is required by their religion than women and
men with any education. Women (15 percent) and men (14 percent) in the lowest wealth quintile are most
likely to believe that circumcision is required by their religion.
Female Genital Cutting • 339
Table 18.8 Opinions of women and men about whether circumcision is required by religion
Percent distribution of women and men age 15-49 who have heard of female circumcision by opinion on whether their religion requires female circumcision,
according to background characteristics, Kenya 2014
Women Men
Background Required by Not required Don’t know/ Number of Required by Not required Don’t know/ Number of
characteristic religion by religion missing Total women religion by religion missing Total men
Female circumcision
status
Circumcised 18.2 79.0 2.8 100.0 3,066 na na na na na
Not circumcised 0.7 98.1 1.2 100.0 11,087 na na na na na
Age
15-19 5.4 93.6 1.0 100.0 2,582 5.4 92.1 2.5 100.0 2,381
20-24 4.4 93.6 2.0 100.0 2,616 4.8 91.2 4.0 100.0 2,086
25-29 3.9 94.5 1.6 100.0 2,846 5.0 89.6 5.4 100.0 2,081
30-34 4.6 94.1 1.3 100.0 2,113 4.8 91.1 4.1 100.0 1,757
35-39 4.5 93.9 1.6 100.0 1,726 7.0 91.1 2.0 100.0 1,468
40-44 4.6 94.0 1.5 100.0 1,250 7.3 89.7 2.9 100.0 1,214
45-49 3.9 94.0 2.1 100.0 1,026 5.1 90.3 4.6 100.0 788
Religion
Roman Catholic 2.5 96.9 0.6 100.0 2,847 5.6 93.2 1.2 100.0 2,530
Protestant/other Christian 1.6 97.5 0.9 100.0 10,181 2.6 96.3 1.1 100.0 7,952
Muslim 43.7 54.1 2.2 100.0 893 36.1 62.8 1.1 100.0 768
No religion 4.4 51.0 44.6 100.0 192 2.0 33.2 64.8 100.0 464
Other (6.7) (82.7) (10.7) 100.0 45 25.8 69.7 4.4 100.0 59
Ethnic group
Embu 3.9 95.4 0.6 100.0 146 8.2 90.0 1.8 100.0 116
Kalenjin 0.7 97.1 2.1 100.0 1,775 1.6 92.2 6.2 100.0 1,455
Kamba 1.2 97.5 1.2 100.0 1,587 2.0 95.1 2.9 100.0 1,494
Kikuyu 1.2 98.1 0.7 100.0 3,105 4.6 89.7 5.6 100.0 2,479
Kisii 3.4 94.3 2.3 100.0 863 9.8 87.6 2.7 100.0 710
Luhya 0.9 98.0 1.1 100.0 2,241 0.7 97.2 2.1 100.0 1,878
Luo 0.9 98.2 0.8 100.0 1,434 2.0 96.4 1.6 100.0 1,220
Maasai 15.1 81.0 3.9 100.0 280 14.8 79.3 6.0 100.0 220
Meru 1.4 97.7 0.9 100.0 822 2.8 95.9 1.3 100.0 708
Mijikenda/Swahili 1.2 92.4 6.4 100.0 638 0.9 93.9 5.3 100.0 582
Somali 82.3 17.1 0.5 100.0 352 83.4 16.4 0.3 100.0 259
Taita/Taveta 1.8 97.9 0.3 100.0 138 4.2 94.1 1.7 100.0 133
Turkana 0.0 95.8 4.2 100.0 178 3.7 91.7 4.5 100.0 103
Samburu 30.7 64.8 4.5 100.0 68 (67.7) (4.7) (27.6) 100.0 12
Other 23.5 74.4 2.1 100.0 529 19.2 78.4 2.4 100.0 391
Residence
Urban 3.5 95.6 0.9 100.0 5,792 5.2 91.4 3.4 100.0 5,205
Rural 5.2 92.8 2.0 100.0 8,367 5.8 90.4 3.8 100.0 6,571
Region
Coast 4.9 91.2 3.9 100.0 1,280 4.1 93.1 2.8 100.0 1,212
North Eastern 89.3 10.1 0.6 100.0 298 86.8 13.0 0.2 100.0 226
Eastern 4.9 93.9 1.2 100.0 2,017 5.1 91.6 3.3 100.0 1,788
Central 1.7 97.5 0.8 100.0 1,880 7.0 91.1 1.9 100.0 1,537
Rift Valley 2.8 95.0 2.2 100.0 3,682 2.8 90.1 7.1 100.0 3,023
Western 1.2 97.7 1.2 100.0 1,506 0.3 99.1 0.5 100.0 1,115
Nyanza 2.9 96.0 1.1 100.0 1,785 5.6 93.2 1.2 100.0 1,333
Nairobi 0.2 99.0 0.7 100.0 1,711 2.9 92.7 4.4 100.0 1,541
Education
No education 35.5 60.0 4.5 100.0 937 37.4 55.9 6.8 100.0 334
Primary incomplete 3.9 93.6 2.6 100.0 3,593 6.4 88.5 5.1 100.0 2,907
Primary complete 2.6 96.1 1.2 100.0 3,439 5.0 90.6 4.4 100.0 2,678
Secondary+ 1.2 98.1 0.7 100.0 6,189 3.5 94.1 2.4 100.0 5,857
Wealth quintile
Lowest 15.4 80.5 4.1 100.0 2,077 13.9 80.1 6.0 100.0 1,613
Second 3.1 94.7 2.2 100.0 2,480 4.8 90.8 4.4 100.0 2,085
Middle 2.3 96.5 1.2 100.0 2,759 3.8 93.0 3.2 100.0 2,302
Fourth 3.0 96.1 0.9 100.0 3,056 4.1 93.2 2.7 100.0 2,887
Highest 2.3 97.2 0.5 100.0 3,788 4.2 92.8 3.1 100.0 2,889
Total 15-49 4.5 93.9 1.5 100.0 14,159 5.5 90.8 3.6 100.0 11,776
50-54 na na na na na 7.6 88.5 3.8 100.0 750
Total 15-54 na na na na na 5.7 90.7 3.6 100.0 12,526
Note: Figures in parentheses are based on 25-49 unweighted cases.
na = Not applicable
Table 18.9 shows the percent distribution of women and men age 15-49 who have heard of female
circumcision according to their opinion on whether their community requires female circumcision, by
background characteristics. Eight percent of women and 11 percent of men believe that circumcision is
required by their community. The patterns seen above for opinions related to religion are repeated,
although circumcised women are more likely to say that circumcision is required by their community (30
percent) than by their religion (18 percent). Also, men with no education are more likely to say that
340 • Female Genital Cutting
circumcision is required by their community (50 percent) than by their religion (37 percent). Both women
(23 percent) and men (22 percent) in the lowest wealth quintile are more likely to say that circumcision is
required by their community than by their religion (15 percent and 14 percent, respectively).
Table 18.9 Opinions of women and men about whether circumcision is required by the community
Percent distribution of women and men age 15-49 who have heard of female circumcision by opinion on whether their community requires female circumcision,
according to background characteristics, Kenya 2014
Women Men
Required by Not required Required by Not required
Background the by the Don’t know/ Number of the by the Don’t know/ Number of
characteristic community community missing Total women community community missing Total men
Female circumcision
status
Circumcised 29.9 69.8 0.3 100.0 3,066 na na na na na
Not circumcised 1.8 97.8 0.4 100.0 11,087 na na na na na
Age
15-19 7.7 92.0 0.4 100.0 2,582 9.4 89.9 0.8 100.0 2,381
20-24 8.0 91.6 0.4 100.0 2,616 9.2 90.2 0.6 100.0 2,086
25-29 6.9 92.6 0.6 100.0 2,846 10.6 88.6 0.8 100.0 2,081
30-34 8.8 90.8 0.3 100.0 2,113 11.1 88.6 0.3 100.0 1,757
35-39 8.4 91.1 0.6 100.0 1,726 12.5 87.1 0.3 100.0 1,468
40-44 8.6 91.2 0.2 100.0 1,250 11.8 87.7 0.4 100.0 1,214
45-49 7.3 92.0 0.6 100.0 1,026 12.0 87.4 0.7 100.0 788
Religion
Roman Catholic 5.7 93.8 0.4 100.0 2,847 10.3 89.4 0.3 100.0 2,530
Protestant/other Christian 5.1 94.5 0.4 100.0 10,181 7.5 91.8 0.7 100.0 7,952
Muslim 43.3 55.9 0.7 100.0 893 38.5 61.3 0.2 100.0 768
No religion 17.7 81.6 0.7 100.0 192 17.9 80.9 1.2 100.0 464
Other (28.5) (71.5) (0.0) 100.0 45 25.5 74.5 0.0 100.0 59
Ethnic group
Embu 8.5 91.5 0.0 100.0 146 24.3 75.7 0.0 100.0 116
Kalenjin 4.4 95.4 0.2 100.0 1,775 8.4 91.2 0.4 100.0 1,455
Kamba 3.0 96.6 0.4 100.0 1,587 4.1 95.3 0.6 100.0 1,494
Kikuyu 3.1 96.6 0.3 100.0 3,105 9.1 89.4 1.5 100.0 2,479
Kisii 19.0 80.8 0.2 100.0 863 23.5 75.8 0.7 100.0 710
Luhya 1.0 98.2 0.8 100.0 2,241 1.6 98.3 0.1 100.0 1,878
Luo 1.9 97.6 0.5 100.0 1,434 4.0 95.7 0.3 100.0 1,220
Maasai 37.4 62.4 0.2 100.0 280 40.0 58.7 1.3 100.0 220
Meru 6.1 93.5 0.4 100.0 822 10.6 89.0 0.4 100.0 708
Mijikenda/Swahili 1.3 98.2 0.5 100.0 638 1.4 98.6 0.0 100.0 582
Somali 82.7 16.9 0.4 100.0 352 87.0 13.0 0.0 100.0 259
Taita/Taveta 4.1 95.4 0.5 100.0 138 9.9 89.8 0.3 100.0 133
Turkana 1.3 97.5 1.2 100.0 178 5.6 94.4 0.0 100.0 103
Samburu 72.0 27.4 0.5 100.0 68 (95.3) (4.7) (0.0) 100.0 12
Other 30.2 68.8 1.0 100.0 529 34.7 65.0 0.3 100.0 391
Residence
Urban 6.3 93.4 0.3 100.0 5,792 9.6 89.9 0.5 100.0 5,205
Rural 9.0 90.4 0.5 100.0 8,367 11.4 87.9 0.7 100.0 6,571
Region
Coast 6.3 93.2 0.4 100.0 1,280 6.8 93.2 0.0 100.0 1,212
North Eastern 90.4 9.2 0.4 100.0 298 90.4 9.6 0.0 100.0 226
Eastern 8.3 91.4 0.3 100.0 2,017 10.9 88.5 0.6 100.0 1,788
Central 3.6 96.0 0.4 100.0 1,880 10.8 88.3 0.8 100.0 1,537
Rift Valley 7.8 91.9 0.3 100.0 3,682 10.8 88.1 1.0 100.0 3,023
Western 2.1 96.8 1.0 100.0 1,506 1.5 98.5 0.0 100.0 1,115
Nyanza 9.1 90.4 0.6 100.0 1,785 11.8 87.8 0.4 100.0 1,333
Nairobi 3.1 96.7 0.2 100.0 1,711 6.6 92.8 0.6 100.0 1,541
Education
No education 44.8 54.4 0.7 100.0 937 50.1 49.1 0.8 100.0 334
Primary incomplete 7.8 91.5 0.7 100.0 3,593 12.3 87.0 0.8 100.0 2,907
Primary complete 5.0 94.7 0.3 100.0 3,439 9.8 89.6 0.6 100.0 2,678
Secondary+ 3.9 95.8 0.3 100.0 6,189 7.9 91.6 0.5 100.0 5,857
Wealth quintile
Lowest 22.6 76.7 0.8 100.0 2,077 21.8 77.7 0.6 100.0 1,613
Second 7.1 92.1 0.8 100.0 2,480 10.2 89.3 0.5 100.0 2,085
Middle 4.6 95.0 0.4 100.0 2,759 9.2 89.8 1.1 100.0 2,302
Fourth 5.6 94.1 0.2 100.0 3,056 8.4 91.3 0.3 100.0 2,887
Highest 4.6 95.2 0.2 100.0 3,788 8.0 91.4 0.6 100.0 2,889
Total 15-49 7.9 91.7 0.4 100.0 14,159 10.6 88.8 0.6 100.0 11,776
50-54 na na na na na 15.6 84.1 0.3 100.0 750
Total 15-54 na na na na na 10.9 88.5 0.6 100.0 12,526
Note: Figures in parentheses are based on 25-49 unweighted cases.
na = Not applicable
Female Genital Cutting • 341
18.5 SUPPORT FOR THE CONTINUATION OF FGC
Table 18.10 shows the percent distribution of women and men age 15-49 who have heard of
circumcision according to their opinion on whether the practice should be continued, by background
characteristics. Six percent of women and 9 percent of men believe that circumcision should continue.
Among women, there has been some change since 2008-09 in the percentage who believe that the practice
should continue (9 percent). The patterns seen above for respondents’ opinions on whether female
circumcision is required by their religion or their community are repeated. There has been a slight decrease
since 2008-09 in the proportion of circumcised women who believe that the practice should continue (from
29 percent in 2008-09 to 23 percent in 2014); the proportion has also decreased among women living in
Nyanza (from 17 percent to 9 percent) and Nairobi (from 6 percent to 2 percent). However, there has been
a slight increase since 2008-09 in the proportion of women with no education who believe that the practice
should continue (from 34 percent to 40 percent).
342 • Female Genital Cutting
Table 18.10 Opinions of women and men about whether the practice of circumcision should continue
Percent distribution of women and men age 15-49 who have heard of female circumcision by opinion on whether the practice of circumcision should be continued, by
background characteristics, Kenya 2014
Women Men
Don’t know/ Don’t know/
Background Not missing/ Number of Not missing/ Number of
characteristic Continued continued depends Total women Continued continued depends Total men
Female circumcision
status
Circumcised 23.0 75.1 1.9 100.0 3,066 na na na na na
Not circumcised 1.6 97.3 1.1 100.0 11,087 na na na na na
Age
15-19 6.2 92.8 1.0 100.0 2,582 7.6 91.3 1.1 100.0 2,381
20-24 5.7 93.2 1.1 100.0 2,616 7.7 91.0 1.3 100.0 2,086
25-29 5.9 93.1 1.0 100.0 2,846 8.6 89.0 2.4 100.0 2,081
30-34 6.5 91.6 1.9 100.0 2,113 10.3 87.4 2.3 100.0 1,757
35-39 6.9 91.3 1.7 100.0 1,726 11.8 86.7 1.5 100.0 1,468
40-44 6.5 92.2 1.4 100.0 1,250 10.6 86.1 3.4 100.0 1,214
45-49 6.3 92.3 1.4 100.0 1,026 11.2 86.3 2.5 100.0 788
Religion
Roman Catholic 4.3 94.3 1.4 100.0 2,847 9.4 89.1 1.5 100.0 2,530
Protestant/other Christian 3.6 95.2 1.3 100.0 10,181 6.2 91.8 2.0 100.0 7,952
Muslim 40.8 58.1 1.1 100.0 893 33.6 64.5 1.9 100.0 768
No religion 13.2 82.5 4.3 100.0 192 17.1 79.8 3.0 100.0 464
Other (6.3) (92.3) (1.4) 100.0 45 32.9 64.3 2.8 100.0 59
Ethnic group
Embu 4.6 95.4 0.0 100.0 146 12.2 87.2 0.6 100.0 116
Kalenjin 1.9 97.0 1.1 100.0 1,775 5.1 93.2 1.7 100.0 1,455
Kamba 2.2 97.1 0.7 100.0 1,587 6.9 91.5 1.6 100.0 1,494
Kikuyu 2.0 97.0 1.0 100.0 3,105 9.9 88.3 1.8 100.0 2,479
Kisii 11.1 86.8 2.1 100.0 863 17.2 79.9 2.9 100.0 710
Luhya 1.4 97.4 1.2 100.0 2,241 1.9 96.7 1.5 100.0 1,878
Luo 5.3 92.6 2.1 100.0 1,434 6.5 89.4 4.1 100.0 1,220
Maasai 22.6 76.2 1.1 100.0 280 21.3 77.0 1.7 100.0 220
Meru 3.1 96.1 0.9 100.0 822 7.8 91.4 0.7 100.0 708
Mijikenda/Swahili 0.9 97.0 2.2 100.0 638 1.6 97.0 1.4 100.0 582
Somali 81.2 17.2 1.6 100.0 352 79.8 20.1 0.1 100.0 259
Taita/Taveta 4.4 92.6 3.0 100.0 138 9.4 88.2 2.3 100.0 133
Turkana 1.5 96.7 1.9 100.0 178 1.5 97.7 0.8 100.0 103
Samburu 50.3 43.1 6.6 100.0 68 (30.7) (39.7) (29.6) 100.0 12
Other 22.3 76.6 1.2 100.0 529 20.3 77.2 2.5 100.0 391
Residence
Urban 4.6 94.5 0.9 100.0 5,792 8.5 89.4 2.1 100.0 5,205
Rural 7.3 91.1 1.6 100.0 8,367 9.9 88.3 1.8 100.0 6,571
Region
Coast 4.8 93.7 1.5 100.0 1,280 5.8 93.4 0.8 100.0 1,212
North Eastern 89.3 9.8 0.9 100.0 298 83.5 16.4 0.1 100.0 226
Eastern 5.6 93.7 0.6 100.0 2,017 10.4 88.2 1.4 100.0 1,788
Central 2.6 96.6 0.8 100.0 1,880 10.4 88.2 1.3 100.0 1,537
Rift Valley 4.7 94.0 1.3 100.0 3,682 7.2 91.0 1.9 100.0 3,023
Western 1.9 96.2 1.9 100.0 1,506 0.7 97.9 1.5 100.0 1,115
Nyanza 8.7 88.9 2.4 100.0 1,785 11.7 84.8 3.5 100.0 1,333
Nairobi 1.9 97.2 0.8 100.0 1,711 6.9 89.8 3.2 100.0 1,541
Education
No education 39.9 57.6 2.5 100.0 937 42.8 55.0 2.3 100.0 334
Primary incomplete 6.7 91.2 2.1 100.0 3,593 12.3 85.9 1.8 100.0 2,907
Primary complete 3.8 95.1 1.1 100.0 3,439 10.2 87.4 2.4 100.0 2,678
Secondary+ 2.2 97.0 0.8 100.0 6,189 5.4 92.8 1.7 100.0 5,857
Wealth quintile
Lowest 18.8 78.7 2.4 100.0 2,077 18.8 79.6 1.6 100.0 1,613
Second 6.4 91.3 2.3 100.0 2,480 9.0 89.7 1.4 100.0 2,085
Middle 3.6 95.7 0.8 100.0 2,759 8.2 89.3 2.4 100.0 2,302
Fourth 3.5 95.6 0.9 100.0 3,056 7.5 90.5 2.0 100.0 2,887
Highest 3.3 95.9 0.7 100.0 3,788 6.8 91.2 2.0 100.0 2,889
Total 15-49 6.2 92.5 1.3 100.0 14,159 9.3 88.8 1.9 100.0 11,776
50-54 na na na na na 10.3 86.2 3.5 100.0 750
Total 15-54 na na na na na 9.3 88.6 2.0 100.0 12,526
Note: Figures in parentheses are based on 25-49 unweighted cases.
na = Not applicable
Female Genital Cutting • 343
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SAMPLE IMPLEMENTATION Appendix A
Table A.1 Enumeration Areas and households
Distribution of the enumeration areas and average size of households in the sampling frame by region and
residence
Number of EAs Number of households
County Urban Rural Total Urban Rural Total
Coast
Mombasa 3,079 na 3,079 268,700 na 268,700
Kwale 296 969 1,265 29,088 92,959 122,047
Kilifi 884 1,459 2,343 71,974 127,790 199,764
Tana River 94 532 626 7,879 39,535 47,414
Lamu 54 211 265 4,793 17,391 22,184
Taita Taveta 180 791 971 13,877 57,213 71,090
North Eastern
Garissa 339 522 861 24,864 73,726 98,590
Wajir 163 652 815 14,163 74,411 88,574
Mandera 254 784 1,038 22,629 102,868 125,497
Eastern
Marsabit 156 497 653 11,922 45,019 56,941
Isiolo 171 226 397 14,618 16,708 31,326
Meru 373 2,823 3,196 33,631 285,985 319,616
Tharaka-Nithi 253 849 1,102 22,103 66,700 88,803
Embu 244 1,052 1,296 25,062 106,621 131,683
Kitui 523 3,064 3,587 33,779 171,712 205,491
Machakos 1,634 1,418 3,052 149,580 114,920 264,500
Makueni 335 2,009 2,344 26,331 160,147 186,478
Central
Nyandarua 268 990 1,258 30,409 113,470 143,879
Nyeri 533 1,544 2,077 54,290 147,413 201,703
Kirinyaga 296 1,105 1,401 27,359 126,861 154,220
Murang’a 414 2,103 2,517 39,252 216,444 255,696
Kiambu 3,425 1,521 4,946 309,981 159,263 469,244
Rift Valley
Turkana 285 1,235 1,520 19,364 103,827 123,191
West Pokot 121 1,286 1,407 8,893 84,884 93,777
Samburu 104 438 542 8,948 38,406 47,354
Trans-Nzoia 430 1,181 1,611 40,284 129,833 170,117
Uasin Gishu 1,016 1,096 2,112 96,901 105,390 202,291
Elgeyo Marakwet 152 955 1,107 11,448 66,107 77,555
Nandi 235 1,542 1,777 23,970 130,103 154,073
Baringo 216 1,754 1,970 16,322 94,327 110,649
Laikipia 280 743 1,023 24,708 78,406 103,114
Nakuru 2,438 2,212 4,650 211,254 198,582 409,836
Narok 199 1,653 1,852 16,446 152,774 169,220
Kajiado 1,021 934 1,955 86,771 86,693 173,464
Kericho 605 892 1,497 50,770 76,811 127,581
Bomet 239 1,780 2,019 21,102 153,812 174,914
Western
Kakamega 620 2,723 3,343 57,578 298,101 355,679
Vihiga 415 857 1,272 39,224 84,123 123,347
Bungoma 514 2,046 2,560 47,698 223,126 270,824
Busia 247 1,486 1,733 20,851 133,201 154,052
Nyanza
Siaya 280 1,903 2,183 22,397 176,637 199,034
Kisumu 1,272 1,135 2,407 123,811 102,908 226,719
Homa Bay 350 1,934 2,284 31,943 174,312 206,255
Migori 767 1,369 2,136 66,286 113,925 180,211
Kisii 561 2,027 2,588 52,795 192,234 245,029
Nyamira 186 1,105 1,291 17,654 113,385 131,039
Nairobi 10,323 na 10,323 985,016 na 985,016
KENYA 36,844 59,407 96,251 3,338,718 5,429,063 8,767,781
Source: 2009 Kenya Population and Housing Census
Note: Nairobi county and Mombasa county have only urban areas.
na = Not applicable
Appendix A • 351
Table A.2 Population
Distribution of the population in the sampling frame by county and residence, Kenya 2014
Percent of
Population in Frame total
County Urban Rural Total population Urban
Coast
Mombasa 939,370 na 939,370 2.4 100.0
Kwale 117,676 532,255 649,931 1.7 18.0
Kilifi 284,254 825,481 1,109,735 2.9 26.0
Tana River 36,065 204,010 240,075 0.6 15.0
Lamu 20,238 81,301 101,539 0.3 20.0
Taita Taveta 49,565 235,092 284,657 0.7 17.0
North Eastern
Garissa 141,390 481,670 623,060 1.6 23.0
Wajir 91,300 570,641 661,941 1.7 14.0
Mandera 179,202 846,554 1,025,756 2.7 17.0
Eastern
Marsabit 64,615 226,551 291,166 0.8 22.0
Isiolo 62,924 80,370 143,294 0.4 44.0
Meru 106,856 1,249,445 1,356,301 3.5 8.0
Tharaka-Nithi 80,791 284,539 365,330 0.9 22.0
Embu 83,399 432,813 516,212 1.3 16.0
Kitui 139,493 873,216 1,012,709 2.6 14.0
Machakos 568,759 529,825 1,098,584 2.8 52.0
Makueni 103,192 781,335 884,527 2.3 12.0
Central
Nyandarua 115,051 481,217 596,268 1.5 19.0
Nyeri 169,342 524,216 693,558 1.8 24.0
Kirinyaga 82,427 445,627 528,054 1.4 16.0
Murang’a 133,794 808,787 942,581 2.4 14.0
Kiambu 1,010,991 612,291 1,623,282 4.2 62.0
Rift Valley
Turkana 102,958 752,441 855,399 2.2 12.0
West Pokot 41,805 470,885 512,690 1.3 8.0
Samburu 38,265 185,682 223,947 0.6 17.0
Trans-Nzoia 162,623 656,134 818,757 2.1 20.0
Uasin Gishu 347,574 546,605 894,179 2.3 39.0
Elgeyo Marakwet 52,376 317,622 369,998 1.0 14.0
Nandi 103,559 649,406 752,965 2.0 14.0
Baringo 64,278 491,283 555,561 1.4 12.0
Laikipia 80,929 318,298 399,227 1.0 20.0
Nakuru 727,345 875,980 1,603,325 4.2 45.0
Narok 57,673 793,247 850,920 2.2 7.0
Kajiado 283,164 404,148 687,312 1.8 41.0
Kericho 228,318 362,372 590,690 1.5 39.0
Bomet 101,829 790,006 891,835 2.3 11.0
Western
Kakamega 235,567 1,425,084 1,660,651 4.3 14.0
Vihiga 174,105 380,517 554,622 1.4 31.0
Bungoma 214,220 1,160,843 1,375,063 3.6 16.0
Busia 85,082 658,000 743,082 1.9 11.0
Nyanza
Siaya 90,840 751,464 842,304 2.2 11.0
Kisumu 504,322 464,587 968,909 2.5 52.0
Homa Bay 137,156 826,638 963,794 2.5 14.0
Migori 309,832 607,338 917,170 2.4 34.0
Kisii 230,789 921,493 1,152,282 3.0 20.0
Nyamira 77,328 520,924 598,252 1.5 13.0
Nairobi 3,138,369 na 3,138,369 8.1 100.0
KENYA 12,171,000 26,438,233 38,609,233 100.0 na
Source: 2009 Kenya Population and Housing Census
Note: Nairobi county and Mombasa county have only urban areas.
na = Not applicable
352 • Appendix A
Table A.3 Sample allocation of clusters and households
Sample allocation of clusters and households by region according to residence, Kenya 2014
Allocation of clusters Allocation of households
County Urban Rural Total Urban Rural Total
Coast
Mombasa 36 na 36 900 na 900
Kwale 11 21 32 275 525 800
Kilifi 14 20 34 350 500 850
Tana River 9 23 32 225 575 800
Lamu 14 18 32 350 450 800
Taita Taveta 10 22 32 250 550 800
North Eastern
Garissa 12 22 34 300 550 850
Wajir 12 22 34 300 550 850
Mandera 10 24 34 250 600 850
Eastern
Marsabit 10 22 32 250 550 800
Isiolo 14 18 32 350 450 800
Meru 9 28 37 225 700 925
Tharaka-Nithi 10 22 32 250 550 800
Embu 10 22 32 250 550 800
Kitui 10 24 34 250 600 850
Machakos 19 17 36 475 425 900
Makueni 9 25 34 225 625 850
Central
Nyandarua 11 22 33 275 550 825
Nyeri 13 22 35 325 550 875
Kirinyaga 10 23 33 250 575 825
Murang’a 10 26 36 250 650 900
Kiambu 23 16 39 575 400 975
Rift Valley
Turkana 9 23 32 225 575 800
West Pokot 8 24 32 200 600 800
Samburu 9 23 32 225 575 800
Trans-Nzoia 12 22 34 300 550 850
Uasin Gishu 17 18 35 425 450 875
Elgeyo Marakwet 9 23 32 225 575 800
Nandi 9 24 33 225 600 825
Baringo 9 23 32 225 575 800
Laikipia 10 22 32 250 550 800
Nakuru 19 19 38 475 475 950
Narok 8 26 34 200 650 850
Kajiado 17 17 34 425 425 850
Kericho 14 18 32 350 450 800
Bomet 8 25 33 200 625 825
Western
Kakamega 11 27 38 275 675 950
Vihiga 13 19 32 325 475 800
Bungoma 11 25 36 275 625 900
Busia 9 24 33 225 600 825
Nyanza
Siaya 9 25 34 225 625 850
Kisumu 18 17 35 450 425 875
Homa Bay 10 24 34 250 600 850
Migori 15 20 35 375 500 875
Kisii 12 24 36 300 600 900
Nyamira 9 24 33 225 600 825
Nairobi 56 na 56 1,400 na 1,400
KENYA 617 995 1,612 15,425 24,875 40,300
Note: Nairobi county and Mombasa county have only urban areas.
na = Not applicable
Appendix A • 353
Table A.4 Sample allocation of completed interviews with women and men
Sample allocation of expected number of completed interview with women and men by county, according to
residence, Kenya 2014
Women 15-49 Men 15-54
County Urban Rural Total Urban Rural Total
Coast
Mombasa 716 na 716 303 na 303
Kwale 219 460 679 92 177 269
Kilifi 278 439 717 118 168 286
Tana River 179 503 682 76 193 269
Lamu 278 394 672 118 152 270
Taita Taveta 200 482 682 84 185 269
North Eastern
Garissa 239 482 721 101 185 286
Wajir 239 482 721 101 185 286
Mandera 200 526 726 84 202 286
Eastern
Marsabit 200 482 682 84 185 269
Isiolo 278 394 672 118 152 270
Meru 179 613 792 76 236 312
Tharaka-Nithi 200 482 682 84 185 269
Embu 200 482 682 84 185 269
Kitui 200 526 726 84 202 286
Machakos 379 373 752 160 143 303
Makueni 179 548 727 76 210 286
Central
Nyandarua 219 482 701 92 185 277
Nyeri 259 482 741 109 185 294
Kirinyaga 200 503 703 84 193 277
Murang’a 200 570 770 84 219 303
Kiambu 457 351 808 193 135 328
Rift Valley
Turkana 179 503 682 76 193 269
West Pokot 159 526 685 67 202 269
Samburu 179 503 682 76 193 269
Trans-Nzoia 239 482 721 101 185 286
Uasin Gishu 338 394 732 143 152 295
Elgeyo Marakwet 179 503 682 76 193 269
Nandi 179 526 705 76 202 278
Baringo 179 503 682 76 193 269
Laikipia 200 482 682 84 185 269
Nakuru 379 416 795 160 160 320
Narok 159 570 729 67 219 286
Kajiado 338 373 711 143 143 286
Kericho 278 394 672 118 152 270
Bomet 159 548 707 67 210 277
Western
Kakamega 219 592 811 92 227 319
Vihiga 259 416 675 109 160 269
Bungoma 219 548 767 92 210 302
Busia 179 526 705 76 202 278
Nyanza
Siaya 179 548 727 76 210 286
Kisumu 359 373 732 152 143 295
Homa Bay 200 526 726 84 202 286
Migori 298 439 737 126 168 294
Kisii 239 526 765 101 202 303
Nyamira 179 526 705 76 202 278
Nairobi 1,114 na 1,114 472 na 472
KENYA 12,286 21,799 34,085 5,191 8,370 13,561
Note: Nairobi county and Mombasa county have only urban areas.
na = Not applicable
354 • Appendix A
Table A.5 Sample implementation: Women
Percent distribution of households and eligible women by results of the household and individual interviews, and household, eligible women and overall women
response rates, according to urban-rural residence and region (unweighted), Kenya 2014
Residence Region
North
Result Urban Rural Coast Eastern Eastern Central Rift Valley Western Nyanza Nairobi Total
Selected households
Completed (C) 90.2 92.8 91.0 83.5 93.7 91.9 92.4 92.7 92.8 88.6 91.8
Household present but no
competent respondent at home
(HP) 1.3 0.4 1.0 0.5 0.3 0.6 0.7 0.3 1.0 3.1 0.7
Postponed (P) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Refused (R) 0.3 0.1 0.1 0.2 0.1 0.1 0.2 0.0 0.1 1.0 0.2
Dwelling not found (DNF) 0.1 0.1 0.0 0.3 0.1 0.1 0.0 0.0 0.0 0.0 0.1
Household absent (HA) 2.8 3.0 3.7 8.1 2.7 2.5 2.1 3.1 2.9 1.3 2.9
Dwelling vacant/address not a
dwelling (DV) 4.2 2.4 3.0 3.6 2.2 4.0 3.3 2.9 2.3 5.4 3.1
Dwelling destroyed (DD) 0.5 0.9 0.7 3.2 0.4 0.5 0.9 0.5 0.4 0.2 0.8
Other (O) 0.5 0.4 0.4 0.6 0.5 0.4 0.4 0.5 0.4 0.5 0.4
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of sampled households 15,419 24,260 4,920 2,224 6,682 4,399 11,406 3,475 5,173 1,400 39,679
Household response rate (HRR)1 98.1 99.5 98.7 98.8 99.4 99.1 99.1 99.7 98.8 95.6 99.0
Eligible women
Completed (EWC) 95.5 97.3 96.4 95.2 97.8 95.7 96.5 98.0 97.2 91.1 96.6
Not at home (EWNH) 2.9 1.6 2.1 2.6 1.2 3.1 2.3 1.0 1.8 5.5 2.1
Postponed (EWP) 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Refused (EWR) 0.5 0.1 0.2 0.7 0.2 0.2 0.3 0.0 0.1 1.3 0.3
Partly completed (EWPC) 0.3 0.1 0.2 0.1 0.1 0.3 0.1 0.2 0.1 0.9 0.2
Incapacitated (EWI) 0.4 0.7 0.7 0.6 0.4 0.7 0.6 0.6 0.7 0.1 0.6
Other (EWO) 0.4 0.2 0.3 0.7 0.2 0.0 0.3 0.2 0.1 1.1 0.3
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of women 12,157 20,015 4,047 1,748 5,364 3,254 9,389 2,898 4,376 1,096 32,172
Eligible women response rate
(EWRR)2 95.5 97.3 96.4 95.2 97.8 95.7 96.5 98.0 97.2 91.1 96.6
Overall women response rate
(ORR)3 93.8 96.7 95.2 94.1 97.2 94.8 95.6 97.7 96.0 87.1 95.6
1
Using the number of households falling into specific response categories, the household response rate (HRR) is calculated as:
100 * C
——————————
C + HP + P + R + DNF
2
The eligible women response rate (EWRR) is equivalent to the percentage of interviews completed (EWC).
3
The overall women response rate (OWRR) is calculated as: OWRR = HRR * EWRR/100
Appendix A • 355
Table A.6 Sample implementation: Men
Percent distribution of households and eligible men by results of the household and individual interviews, and household, eligible men and overall men response
rates, according to urban-rural residence and region (unweighted), Kenya 2014
Residence Region
North
Result Urban Rural Coast Eastern Eastern Central Rift Valley Western Nyanza Nairobi Total
Selected households
Completed (C) 89.9 92.5 90.0 83.9 93.1 91.3 92.2 92.9 93.2 86.2 91.5
Household present but no
competent respondent at home
(HP) 1.4 0.5 1.0 0.7 0.4 0.7 0.8 0.2 1.0 4.0 0.8
Postponed (P) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Refused (R) 0.4 0.1 0.2 0.1 0.2 0.1 0.2 0.0 0.1 1.5 0.2
Dwelling not found (DNF) 0.1 0.1 0.0 0.4 0.2 0.1 0.1 0.0 0.0 0.0 0.1
Household absent (HA) 2.9 3.0 4.2 8.2 2.8 2.8 1.9 3.1 2.7 1.3 3.0
Dwelling vacant/address not a
dwelling (DV) 4.4 2.5 3.3 3.5 2.5 3.9 3.5 2.8 2.3 6.3 3.2
Dwelling destroyed (DD) 0.4 1.0 0.9 3.0 0.4 0.5 0.9 0.5 0.4 0.1 0.8
Other (O) 0.5 0.4 0.4 0.4 0.5 0.6 0.4 0.5 0.3 0.6 0.4
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of sampled households 7,394 11,636 2,359 1,066 3,211 2,111 5,469 1,665 2,477 672 19,030
Household response rate (HRR)1 97.9 99.3 98.7 98.7 99.2 99.0 98.8 99.7 98.8 94.0 98.8
Eligible men
Completed (EMC) 86.6 92.5 86.3 85.6 94.2 89.2 90.4 93.0 92.5 76.7 90.2
Not at home (EMNH) 10.4 5.3 11.3 10.2 3.5 9.0 6.9 4.7 5.4 19.3 7.4
Postponed (EMP) 0.1 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Refused (EMR) 0.8 0.4 0.5 1.5 0.5 0.5 0.6 0.2 0.6 1.0 0.6
Partly completed (EMPC) 0.4 0.1 0.3 0.0 0.1 0.4 0.2 0.1 0.3 0.8 0.2
Incapacitated (EMI) 0.7 1.1 1.2 1.1 0.9 0.8 0.9 1.1 0.8 0.0 0.9
Other (EMO) 1.1 0.5 0.2 1.5 0.7 0.1 0.9 1.0 0.4 2.2 0.7
Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Number of men 5,676 8,541 1,851 729 2,443 1,536 4,063 1,309 1,783 503 14,217
Eligible men response rate
(EMRR)2 86.6 92.5 86.3 85.6 94.2 89.2 90.4 93.0 92.5 76.7 90.2
Overall men response rate (ORR)3 84.7 91.9 85.2 84.5 93.4 88.3 89.3 92.7 91.4 72.1 89.1
1
Using the number of households falling into specific response categories, the household response rate (HRR) is calculated as:
100 * C
——————————
C + HP + P + R + DNF
2
The eligible men response rate (EMRR) is equivalent to the percentage of interviews completed (EMC).
3
The overall men response rate (OMRR) is calculated as: OMRR = HRR * EMRR/100
356 • Appendix A
ESTIMATES OF SAMPLING ERRORS Appendix B
T
he estimates from a sample survey are affected by two types of errors: non-sampling errors and
sampling errors. Non-sampling errors are the results of mistakes made in implementing data
collection and data processing, such as failure to locate and interview the correct household,
misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry
errors. Although numerous efforts were made during the implementation of the 2014 Kenya Demographic
and Health Survey (2014 KDHS) to minimise this type of error, non-sampling errors are impossible to
avoid and difficult to evaluate statistically.
Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents
selected in the 2014 KDHS is only one of many samples that could have been selected from the same
population, using the same design and expected size. Each of these samples would yield results that differ
somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability
between all possible samples. Although the degree of variability is not known exactly, it can be estimated
from the survey results.
Sampling error is usually measured in terms of the standard error for a particular statistic (mean,
percentage, etc.), which is the square root of the variance. The standard error can be used to calculate
confidence intervals within which the true value for the population can reasonably be assumed to fall. For
example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a
range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of
identical size and design.
If the sample of respondents had been selected as a simple random sample, it would have been
possible to use straightforward formulas for calculating sampling errors. However, the 2014 KDHS sample
is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex
formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF Macro.
These programs use the Taylor linearisation method of variance estimation for survey estimates that are
means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of
more complex statistics such as fertility and mortality rates.
The Taylor linearisation method treats any percentage or average as a ratio estimate, r = y/x,
where y represents the total sample value for variable y, and x represents the total number of cases in the
group or subgroup under consideration. The variance of r is computed using the formula given below, with
the standard error being the square root of the variance:
1− f H mh mh 2 zh2
2
SE ( r ) = var ( r ) = 2
x
m − 1
zhi −
m
h =1 h i =1 h
in which
z hi = y hi − rxhi , and z h = y h − rxh
where h represents the stratum which varies from 1 to H,
mh is the total number of clusters selected in the hth stratum,
yhi is the sum of the weighted values of variable y in the ith cluster in the hth stratum,
xhi is the sum of the weighted number of cases in the ith cluster in the hth stratum, and
f is the overall sampling fraction, which is so small that it is ignored.
Appendix B • 357
The Jackknife repeated replication method derives estimates of complex rates from each of several
replications of the parent sample, and calculates standard errors for these estimates using simple formulae.
Each replication considers all but one cluster in the calculation of the estimates. Pseudo-independent
replications are thus created. In the 2014 KDHS, there were 1,594 non-empty clusters. Hence, 1,594
replications were created. The variance of a rate r is calculated as follows:
k
1
SE 2 (r ) = var (r ) =
k ( k − 1) i =1
(ri − r ) 2
in which
ri = kr − ( k − 1) r( i )
where r is the estimate computed from the full sample of 1,594 clusters,
r(i) is the estimate computed from the reduced sample of 1,593 clusters (ith cluster excluded),
and
k is the total number of clusters.
In addition to the standard error, the design effect (DEFT) for each estimate is also calculated. The
design effect is defined as the ratio between the standard error using the given sample design and the
standard error that would result if a simple random sample had been used. A DEFT value of 1.0 indicates
that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates
the increase in the sampling error due to the use of a more complex and less statistically efficient design.
Relative standard errors and confidence limits for the estimates are also calculated.
Sampling errors for the 2014 KDHS are calculated for selected variables considered to be of
primary interest. The results are presented in this appendix for the country as a whole, for urban and rural
areas separately, and for each of the eight regions. For each variable, the type of statistic (mean,
proportion, or rate) and the base population are given in Table B.1. Tables B.2 through B.12 present the
value of the statistic (R), its standard error (SE), the number of un-weighted (N) and weighted (WN) cases,
the design effect (DEFT), the relative standard error (SE/R), and the 95 percent confidence limits (R±2SE),
for each variable. The sampling errors for mortality rates are presented for the five year period preceding
the survey for the whole country and for the ten year period preceding the survey by residence and region.
The DEFT is considered undefined when the standard error considering a simple random sample is zero
(when the estimate is close to 0 or 1). In the case of the total fertility rate, the number of un-weighted cases
is not relevant, as there is no known un-weighted value for woman-years of exposure to childbearing.
The confidence interval (e.g., as calculated for children ever born to women age 40-49) can be
interpreted as follows: the overall average from the national sample is 5.036 and its standard error is 0.057.
Therefore, to obtain the 95 percent confidence limits, one adds and subtracts twice the standard error to the
sample estimate, i.e., 5.036±2×0.057. There is a high probability (95 percent) that the true average number
of children ever born to all women aged 40 to 49 is between 4.923 and 5.149.
For the total sample, the value of the DEFT, averaged over all variables, is 1.568. This means that,
due to multi-stage clustering of the sample, the average standard error is increased by a factor of 1.568
over that in an equivalent simple random sample.
358 • Appendix B
Table B.1 List of selected variables for sampling errors, Kenya 2014
Variable Estimate Base population
WOMEN
Urban residence Proportion All women 15-49
Literacy Proportion All women 15-49
No education Proportion All women 15-49
Secondary or higher education Proportion All women 15-49
Never married/in union Proportion All women 15-49
Currently married/in union Proportion All women 15-49
Married before age 20 Proportion All women 20-49
Had sexual intercourse before age 18 Proportion All women 20-49
Currently pregnant Proportion All women 15-49
Children ever born Mean All women 15-49
Children surviving Mean All women 15-49
Children ever born to women age 40-49 Mean All women 40-49
Know any contraceptive method Proportion Currently married women 15-49
Know any modern contraceptive method Proportion Currently married women 15-49
Currently using any method Proportion Currently married women 15-49
Currently using a modern method Proportion Currently married women 15-49
Currently using a traditional method Proportion Currently married women 15-49
Currently using pill Proportion Currently married women 15-49
Currently using IUD Proportion Currently married women 15-49
Currently using male condoms Proportion Currently married women 15-49
Currently using injectables Proportion Currently married women 15-49
Currently using female sterilisation Proportion Currently married women 15-49
Currently using implant Proportion Currently married women 15-49
Currently using rhythm Proportion Currently married women 15-49
Currently using withdrawal Proportion Currently married women 15-49
Used public sector source for family planning Proportion Current users of modern method
Want no more children Proportion Currently married women 15-49
Want to delay next birth at least 2 years Proportion Currently married women 15-49
Ideal number of children Mean All women 15-49
Mothers received antenatal care for last birth Proportion Women with a live birth in last five years
Mothers protected against tetanus for last birth Proportion Women with a live birth in last five years
Births with skilled attendant at delivery Proportion Births occurring 1-59 months before survey
Delivery in a health facility Proportion Births occurring 1-59 months before survey
Had diarrhoea in the past 2 weeks Proportion Children under 5
Treated with ORS Proportion Children under 5 with diarrhoea in past 2 weeks
Sought medical treatment Proportion Children under 5 with diarrhoea in past 2 weeks
Vaccination card seen Proportion Children 12-23 months
Received BCG vaccination Proportion Children 12-23 months
Received DPT vaccination (3 doses) Proportion Children 12-23 months
Received polio vaccination (3 doses) Proportion Children 12-23 months
Received measles vaccination Proportion Children 12-23 months
Fully vaccinated Proportion Children 12-23 months
Vitamin A supplementation in last 6 months Proportion Children 6-59 months
Owns at least one insecticide treated net (ITN) Proportion Households
Child slept under ITN last night Proportion Children under 5 in household
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) Proportion Women 15-49 with birth in last 2 years
Child has fever in last two weeks Proportion Children under 5 in women’s birth history
Child took antimalarial Proportion Children under 5 in women’s birth history
Height-for-age (-2SD) Proportion Children under 5 who are measured
Weight-for-height (-2SD) Proportion Children under 5 who are measured
Weight-for-age (-2SD) Proportion Children under 5 who are measured
Body Mass Index (BMI) <18.5 Proportion All women 15-49 who were measured
Had 2+ sexual partners in past 12 months Proportion All women 15-49
Condom use at last sex Proportion Women 15-49 with 2+ partners in past 12 months
Abstinence among youth (never had sex) Proportion Never-married women 15-24
Sexually active in past 12 months among never-married youth Proportion Never-married women 15-24
Had an HIV test and received results in past 12 months Proportion All women 15-49
Accepting attitudes towards people with HIV Proportion All women who have heard of HIV/AIDS
Ever experienced any physical violence since age 15 by anyone Proportion All women 15-49
Ever experienced any sexual violence by anyone Proportion All women 15-49
Ever experienced physical/sexual violence by any husband/partner Proportion Ever-married women 15-49
Physical/sexual violence in the past 12 months by any husband/partner Proportion Ever-married women 15-49
Total fertility rate (3 years) Rate Women-years of exposure to childbearing
Neonatal mortality rate¹ Rate Children exposed to the risk of mortality
Post-neonatal mortality rate¹ Rate Children exposed to the risk of mortality
Infant mortality rate¹ Rate Children exposed to the risk of mortality
Child mortality rate¹ Rate Children exposed to the risk of mortality
Under-five mortality rate¹ Rate Children exposed to the risk of mortality
Continued…
Appendix B • 359
Table B.1—Continued
Variable Estimate Base population
MEN
Urban residence Proportion All men 15-49
Literacy Proportion All men 15-49
No education Proportion All men 15-49
Secondary or higher education Proportion All men 15-49
Never married/in union Proportion All men 15-49
Currently married/in union Proportion All men 15-49
Had sexual intercourse before age 18 Proportion All men 20-49
Know any contraceptive method Proportion Currently married men 15-49
Know a modern method Proportion Currently married men 15-49
Want no more children Proportion Currently married men 15-49
Want to delay next birth at least 2 years Proportion Currently married men 15-49
Ideal number of children Mean All men 15-49
Had 2+ sexual partners in past 12 months Proportion All men 15-49
Condom use at last sex Proportion Men 15-49 with 2+ partners in past 12 months
Abstinence among youth (never had sex) Proportion Never-married men 15-24
Sexually active in past 12 months among never-married youth Proportion Never-married men 15-24
Paid for sexual intercourse in past 12 months Proportion All men 15-49
Had an HIV test and received results in past 12 months Proportion All men 15-49
Accepting attitudes towards people with HIV Proportion All men who have heard of HIV/AIDS
Ever experienced any physical violence since age 15 by anyone Proportion All men 15-49
Ever experienced any sexual violence by anyone Proportion All men 15-49
Ever experienced physical/sexual violence by any wife/partner Proportion Ever-married men 15-49
Physical/sexual violence in the past 12 months by any wife/partner Proportion Ever-married men 15-49
1
The mortality rates are calculated for 5 years and 10 years before the survey for the national sample and regional samples, respectively.
360 • Appendix B
Table B.2 Sampling errors: Total sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.408 0.008 31,079 31,079 2.816 0.019 0.393 0.424
Literacy 0.878 0.004 31,079 31,079 1.934 0.004 0.871 0.885
No education 0.070 0.003 31,079 31,079 2.022 0.042 0.064 0.076
Secondary or higher education 0.427 0.007 31,079 31,079 2.452 0.016 0.413 0.441
Never married/in union 0.289 0.004 31,079 31,079 1.676 0.015 0.281 0.298
Currently married/in union 0.597 0.005 31,079 31,079 1.641 0.008 0.588 0.606
Married before age 20 0.463 0.006 25,001 25,259 1.841 0.013 0.452 0.475
Had sexual intercourse before age 18 0.495 0.007 25,001 25,259 2.058 0.013 0.482 0.508
Currently pregnant 0.063 0.002 31,079 31,079 1.357 0.030 0.059 0.066
Children ever born 2.482 0.024 31,079 31,079 1.726 0.009 2.434 2.529
Children surviving 2.294 0.022 31,079 31,079 1.733 0.009 2.251 2.337
Children ever born to women age 40-49 5.036 0.057 5,337 5,142 1.583 0.011 4.923 5.149
Know any contraceptive method 0.987 0.001 19,036 18,549 1.385 0.001 0.985 0.990
Know a modern method 0.987 0.001 19,036 18,549 1.460 0.001 0.984 0.989
Currently using any method 0.580 0.006 19,036 18,549 1.567 0.010 0.568 0.591
Currently using a modern method 0.532 0.006 19,036 18,549 1.563 0.011 0.521 0.544
Currently using a traditional method 0.048 0.002 19,036 18,549 1.358 0.044 0.043 0.052
Currently using pill 0.080 0.003 19,036 18,549 1.652 0.041 0.073 0.086
Currently using IUD 0.034 0.002 19,036 18,549 1.769 0.068 0.030 0.039
Currently using male condoms 0.022 0.002 19,036 18,549 1.439 0.070 0.019 0.025
Currently using injectables 0.264 0.005 19,036 18,549 1.502 0.018 0.254 0.274
Currently using female sterilisation 0.032 0.002 19,036 18,549 1.400 0.056 0.028 0.035
Currently using implant 0.099 0.003 19,036 18,549 1.478 0.032 0.092 0.105
Currently using rhythm 0.038 0.002 19,036 18,549 1.326 0.049 0.034 0.041
Currently using withdrawal 0.007 0.001 19,036 18,549 1.459 0.130 0.005 0.008
Used public sector source for family planning 0.599 0.008 10,990 12,131 1.785 0.014 0.583 0.616
Want no more children 0.502 0.007 9,016 8,710 1.398 0.015 0.487 0.517
Want to delay next birth at least 2 years 0.319 0.007 9,016 8,710 1.416 0.022 0.305 0.333
Ideal number of children 3.605 0.024 14,246 14,311 1.482 0.007 3.557 3.654
Mothers received antenatal care for last birth 0.955 0.002 14,949 14,442 1.363 0.002 0.950 0.960
Mothers protected against tetanus for last birth 0.756 0.007 7,176 6,876 1.373 0.009 0.742 0.770
Births with skilled attendant at delivery 0.618 0.008 20,964 19,564 1.843 0.012 0.603 0.634
Delivery in a health facility 0.612 0.008 20,964 19,564 1.850 0.012 0.597 0.627
Had diarrhoea in the last 2 weeks 0.152 0.004 20,093 18,702 1.513 0.027 0.144 0.160
Treated with ORS 0.538 0.013 2,953 2,844 1.321 0.024 0.512 0.564
Sought medical treatment for diarrhoea 0.576 0.013 2,953 2,844 1.375 0.023 0.550 0.603
Vaccination card seen 0.747 0.011 4,052 3,777 1.482 0.014 0.726 0.768
Received BCG vaccination 0.967 0.004 4,052 3,777 1.340 0.004 0.959 0.974
Received DPT vaccination (3 doses) 0.899 0.007 4,052 3,777 1.384 0.008 0.885 0.913
Received polio vaccination (3 doses) 0.899 0.007 4,052 3,777 1.343 0.007 0.885 0.912
Received measles vaccination 0.871 0.007 4,052 3,777 1.218 0.008 0.857 0.884
Fully vaccinated 0.792 0.009 4,052 3,777 1.282 0.011 0.775 0.809
Vitamin A supplementation in last 6 months 0.987 0.001 18,256 17,008 1.360 0.001 0.985 0.990
Owns at least one insecticide treated net (ITN) 0.589 0.006 36,430 36,430 2.194 0.010 0.578 0.601
Child slept under ITN last night 0.543 0.007 21,445 19,798 1.722 0.014 0.528 0.557
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.169 0.007 7,925 7,357 1.543 0.040 0.155 0.182
Child has fever in last two weeks 0.244 0.005 20,093 18,702 1.542 0.022 0.233 0.254
Child took antimalarial 0.270 0.009 4,764 4,562 1.308 0.035 0.251 0.288
Height-for-age (-2SD) 0.260 0.005 20,524 18,986 1.459 0.019 0.250 0.270
Weight-for-height (-2SD) 0.040 0.002 20,524 18,986 1.527 0.056 0.036 0.045
Weight-for-age (-2SD) 0.110 0.004 20,524 18,986 1.535 0.034 0.102 0.117
Body Mass Index (BMI) <18.5 0.089 0.003 13,215 13,143 1.261 0.035 0.083 0.095
Had 2+ sexual partners in past 12 months 0.014 0.002 14,741 14,625 1.629 0.112 0.011 0.017
Condom use at last sex 0.401 0.064 182 205 1.746 0.159 0.273 0.529
Abstinence among youth (never had sex) 0.587 0.013 3,369 3,434 1.586 0.023 0.560 0.614
Sexually active in past 12 months among youth 0.263 0.012 3,369 3,434 1.527 0.044 0.240 0.286
Had an HIV test and received results in past 12 months 0.528 0.005 31,079 31,079 1.690 0.009 0.519 0.538
Accepting attitudes towards people with HIV 0.261 0.006 14,661 14,587 1.668 0.023 0.249 0.273
Ever experienced any physical violence since age 15 by anyone 0.448 0.010 5,657 5,657 1.548 0.023 0.427 0.468
Ever experienced any sexual violence by anyone 0.141 0.007 5,657 5,657 1.451 0.048 0.127 0.154
Ever experienced any physical/sexual violence by any husband/partner 0.407 0.011 4,519 4,023 1.494 0.027 0.386 0.429
Physical/sexual violence in the last 12 months by any husband/partner 0.255 0.010 4,519 4,023 1.524 0.039 0.235 0.275
Total fertility rate (3 years) 3.905 0.066 87,077 87,611 1.653 0.017 3.772 4.037
Neonatal mortality rate (0-4 years) 22.301 1.577 21,138 19,760 1.406 0.071 19.147 25.455
Post-neonatal mortality rate (0-4 years) 16.408 1.186 21,156 19,720 1.269 0.072 14.036 18.779
Infant mortality rate (0-4 years) 38.709 1.981 21,161 19,785 1.347 0.051 34.747 42.671
Child mortality rate (last 0-4 years) 14.197 1.186 21,187 19,821 1.278 0.084 11.825 16.569
Under-five mortality rate (last 0-4 years) 52.356 2.214 21,271 19,890 1.302 0.042 47.929 56.784
Continued…
Appendix B • 361
Table B.2—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.439 0.010 12,014 12,063 2.257 0.023 0.419 0.460
Literacy 0.924 0.004 12,014 12,063 1.466 0.004 0.917 0.931
No education 0.029 0.002 12,014 12,063 1.283 0.068 0.025 0.033
Secondary or higher education 0.490 0.009 12,014 12,063 1.913 0.018 0.473 0.508
Never married/in union 0.444 0.008 12,014 12,063 1.743 0.018 0.428 0.459
Currently married/in union 0.505 0.008 12,014 12,063 1.690 0.015 0.490 0.521
Had sexual intercourse before age 18 0.563 0.008 9,203 9,522 1.466 0.013 0.548 0.579
Know any contraceptive method 0.997 0.001 5,989 6,095 1.149 0.001 0.996 0.999
Know any modern contraceptive method 0.997 0.001 5,989 6,095 1.102 0.001 0.995 0.998
Want no more children 0.420 0.009 5,989 6,095 1.473 0.022 0.402 0.439
Want to delay next birth at least 2 years 0.368 0.009 5,989 6,095 1.478 0.025 0.350 0.386
Ideal number of children 3.894 0.035 11,704 11,825 1.413 0.009 3.824 3.965
Had 2+ sexual partners in past 12 months 0.127 0.004 12,014 12,063 1.442 0.035 0.118 0.136
Condom use at last sex 0.444 0.019 1,386 1,531 1.450 0.044 0.406 0.483
Abstinence among youth (never had sex) 0.417 0.012 4,332 4,214 1.582 0.028 0.393 0.441
Sexually active in past 12 months among youth 0.415 0.012 4,332 4,214 1.626 0.029 0.391 0.439
Had an HIV test and received results in past 12 months 0.457 0.007 12,014 12,063 1.516 0.015 0.443 0.471
Accepting attitudes towards people with HIV 0.437 0.008 11,985 12,039 1.660 0.017 0.422 0.452
Ever experienced any physical violence since age 15 by anyone 0.439 0.011 4,689 4,694 1.525 0.025 0.417 0.461
Ever experienced any sexual violence by anyone 0.059 0.004 4,689 4,694 1.305 0.076 0.050 0.067
Ever experienced any physical/sexual violence by any wife/partner 0.111 0.008 3,000 2,624 1.360 0.070 0.096 0.127
Physical/sexual violence in the last 12 months by any wife/partner 0.072 0.007 3,000 2,624 1.420 0.093 0.059 0.085
362 • Appendix B
Table B.3 Sampling errors: Urban sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 1.000 0.000 11,614 12,690 na 0.000 1.000 1.000
Literacy 0.936 0.004 11,614 12,690 1.573 0.004 0.929 0.943
No education 0.036 0.003 11,614 12,690 1.710 0.082 0.030 0.042
Secondary or higher education 0.582 0.012 11,614 12,690 2.571 0.020 0.558 0.605
Never married/in union 0.301 0.008 11,614 12,690 1.949 0.028 0.285 0.318
Currently married/in union 0.574 0.009 11,614 12,690 1.869 0.015 0.557 0.591
Married before age 20 0.362 0.009 9,667 10,831 1.916 0.026 0.343 0.380
Had sexual intercourse before age 18 0.393 0.011 9,667 10,831 2.280 0.029 0.371 0.416
Currently pregnant 0.060 0.003 11,614 12,690 1.522 0.056 0.054 0.067
Children ever born 1.882 0.036 11,614 12,690 2.038 0.019 1.810 1.953
Children surviving 1.755 0.033 11,614 12,690 2.074 0.019 1.689 1.821
Children ever born to women age 40-49 3.903 0.081 1,706 1,629 1.502 0.021 3.740 4.066
Know any contraceptive method 0.996 0.001 6,806 7,285 0.917 0.001 0.994 0.997
Know a modern method 0.995 0.001 6,806 7,285 0.910 0.001 0.994 0.997
Currently using any method 0.618 0.009 6,806 7,285 1.554 0.015 0.600 0.636
Currently using a modern method 0.569 0.009 6,806 7,285 1.575 0.017 0.550 0.588
Currently using a traditional method 0.049 0.004 6,806 7,285 1.380 0.073 0.042 0.057
Currently using pill 0.107 0.007 6,806 7,285 1.764 0.062 0.094 0.120
Currently using IUD 0.047 0.005 6,806 7,285 1.806 0.099 0.037 0.056
Currently using male condoms 0.026 0.003 6,806 7,285 1.453 0.107 0.021 0.032
Currently using injectables 0.247 0.008 6,806 7,285 1.490 0.032 0.231 0.262
Currently using female sterilisation 0.021 0.002 6,806 7,285 1.442 0.121 0.016 0.026
Currently using implant 0.120 0.006 6,806 7,285 1.414 0.047 0.108 0.131
Currently using rhythm 0.038 0.003 6,806 7,285 1.380 0.084 0.032 0.045
Currently using withdrawal 0.007 0.001 6,806 7,285 1.350 0.201 0.004 0.009
Used public sector source for family planning 0.470 0.013 4,451 5,248 1.778 0.028 0.443 0.496
Want no more children 0.427 0.013 3,259 3,445 1.493 0.030 0.401 0.453
Want to delay next birth at least 2 years 0.363 0.013 3,259 3,445 1.535 0.036 0.337 0.389
Ideal number of children 3.239 0.034 5,328 5,818 1.503 0.011 3.171 3.307
Mothers received antenatal care for last birth 0.978 0.003 5,164 5,561 1.386 0.003 0.972 0.984
Mothers protected against tetanus for last birth 0.764 0.012 2,515 2,677 1.464 0.016 0.740 0.789
Births with skilled attendant at delivery 0.824 0.009 6,828 7,024 1.613 0.011 0.806 0.841
Delivery in a health facility 0.820 0.009 6,828 7,024 1.636 0.011 0.802 0.838
Had diarrhoea in the last 2 weeks 0.143 0.008 6,532 6,677 1.704 0.055 0.127 0.159
Treated with ORS 0.575 0.023 941 957 1.298 0.039 0.529 0.620
Sought medical treatment for diarrhoea 0.567 0.022 941 957 1.281 0.039 0.523 0.611
Vaccination card seen 0.672 0.022 1,261 1,330 1.605 0.032 0.628 0.715
Received BCG vaccination 0.977 0.006 1,261 1,330 1.498 0.007 0.964 0.990
Received DPT vaccination (3 doses) 0.912 0.013 1,261 1,330 1.512 0.014 0.886 0.937
Received polio vaccination (3 doses) 0.913 0.011 1,261 1,330 1.376 0.012 0.891 0.935
Received measles vaccination 0.917 0.010 1,261 1,330 1.303 0.011 0.897 0.938
Fully vaccinated 0.829 0.014 1,261 1,330 1.330 0.017 0.800 0.858
Vitamin A supplementation in last 6 months 0.985 0.002 5,942 6,104 1.339 0.002 0.980 0.989
Owns at least one insecticide treated net (ITN) 0.561 0.010 13,914 15,290 2.344 0.018 0.542 0.581
Child slept under ITN last night 0.593 0.014 6,739 6,563 1.855 0.023 0.565 0.620
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.141 0.009 2,550 2,618 1.308 0.065 0.123 0.160
Child has fever in last two weeks 0.217 0.010 6,532 6,677 1.724 0.044 0.198 0.236
Child took antimalarial 0.204 0.014 1,490 1,447 1.160 0.067 0.177 0.231
Height-for-age (-2SD) 0.198 0.009 6,382 6,206 1.669 0.047 0.179 0.217
Weight-for-height (-2SD) 0.034 0.003 6,382 6,206 1.255 0.090 0.028 0.040
Weight-for-age (-2SD) 0.070 0.005 6,382 6,206 1.395 0.072 0.060 0.080
Body Mass Index (BMI) <18.5 0.055 0.004 4,887 5,246 1.284 0.077 0.046 0.063
Had 2+ sexual partners in past 12 months 0.021 0.003 5,472 5,929 1.786 0.167 0.014 0.027
Condom use at last sex 0.474 0.096 91 122 1.806 0.203 0.281 0.666
Abstinence among youth (never had sex) 0.506 0.025 1,164 1,261 1.683 0.049 0.457 0.556
Sexually active in past 12 months among youth 0.351 0.024 1,164 1,261 1.717 0.068 0.303 0.399
Had an HIV test and received results in past 12 months 0.578 0.009 11,614 12,690 1.902 0.015 0.560 0.595
Accepting attitudes towards people with HIV 0.302 0.011 5,455 5,921 1.800 0.037 0.280 0.324
Ever experienced any physical violence since age 15 by anyone 0.439 0.018 2,088 2,251 1.697 0.042 0.402 0.476
Ever experienced any sexual violence by anyone 0.152 0.012 2,088 2,251 1.569 0.081 0.127 0.177
Ever experienced any physical/sexual violence by any husband/partner 0.377 0.020 1,644 1,588 1.686 0.053 0.337 0.418
Physical/sexual violence in the last 12 months by any husband/partner 0.251 0.018 1,644 1,588 1.698 0.072 0.214 0.287
Total fertility rate (3 years) 3.074 0.085 33,169 36,603 1.736 0.028 2.903 3.244
Neonatal mortality rate (last 0-9 years) 26.287 2.607 13,438 13,285 1.656 0.099 21.074 31.501
Post-neonatal mortality rate (last 0-9 years) 16.349 1.504 13,464 13,279 1.330 0.092 13.341 19.356
Infant mortality rate (last 0-9 years) 42.636 2.925 13,445 13,293 1.516 0.069 36.785 48.487
Child mortality rate (last 0-9 years) 14.959 1.773 13,231 12,975 1.375 0.119 11.414 18.505
Under-five mortality rate (last 0-9 years) 56.958 3.276 13,479 13,327 1.430 0.058 50.405 63.510
Continued…
Appendix B • 363
Table B.3—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 1.000 0.000 4,648 5,300 na 0.000 1.000 1.000
Literacy 0.967 0.004 4,648 5,300 1.467 0.004 0.960 0.975
No education 0.012 0.002 4,648 5,300 1.127 0.151 0.008 0.015
Secondary or higher education 0.626 0.015 4,648 5,300 2.058 0.023 0.597 0.655
Never married/in union 0.408 0.015 4,648 5,300 2.047 0.036 0.378 0.437
Currently married/in union 0.546 0.014 4,648 5,300 1.947 0.026 0.518 0.574
Had sexual intercourse before age 18 0.550 0.013 3,834 4,591 1.614 0.024 0.525 0.576
Know any contraceptive method 0.998 0.001 2,440 2,894 1.791 0.001 0.995 1.001
Know any modern contraceptive method 0.998 0.001 2,440 2,894 1.791 0.001 0.995 1.001
Want no more children 0.376 0.015 2,440 2,894 1.557 0.041 0.345 0.407
Want to delay next birth at least 2 years 0.389 0.016 2,440 2,894 1.579 0.040 0.358 0.420
Ideal number of children 3.666 0.054 4,540 5,211 1.507 0.015 3.557 3.775
Had 2+ sexual partners in past 12 months 0.144 0.007 4,648 5,300 1.442 0.052 0.129 0.158
Condom use at last sex 0.466 0.032 597 761 1.559 0.068 0.403 0.530
Abstinence among youth (never had sex) 0.333 0.022 1,467 1,545 1.747 0.065 0.290 0.376
Sexually active in past 12 months among youth 0.504 0.024 1,467 1,545 1.814 0.047 0.457 0.552
Had an HIV test and received results in past 12 months 0.513 0.012 4,648 5,300 1.626 0.023 0.489 0.537
Accepting attitudes towards people with HIV 0.443 0.013 4,644 5,298 1.750 0.029 0.417 0.468
Ever experienced any physical violence since age 15 by anyone 0.393 0.019 1,853 1,981 1.688 0.049 0.355 0.431
Ever experienced any sexual violence by anyone 0.064 0.007 1,853 1,981 1.314 0.117 0.049 0.078
Ever experienced any physical/sexual violence by any wife/partner 0.112 0.013 1,239 1,192 1.458 0.117 0.085 0.138
Physical/sexual violence in the last 12 months by any wife/partner 0.077 0.012 1,239 1,192 1.555 0.153 0.053 0.100
364 • Appendix B
Table B.4 Sampling errors: Rural sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.000 0.000 19,465 18,389 na na 0.000 0.000
Literacy 0.838 0.005 19,465 18,389 2.043 0.006 0.827 0.849
No education 0.093 0.004 19,465 18,389 2.134 0.048 0.085 0.102
Secondary or higher education 0.321 0.007 19,465 18,389 1.971 0.021 0.307 0.334
Never married/in union 0.281 0.004 19,465 18,389 1.378 0.016 0.272 0.290
Currently married/in union 0.613 0.005 19,465 18,389 1.387 0.008 0.603 0.622
Married before age 20 0.539 0.007 15,334 14,428 1.616 0.012 0.526 0.552
Had sexual intercourse before age 18 0.571 0.006 15,334 14,428 1.564 0.011 0.559 0.584
Currently pregnant 0.064 0.002 19,465 18,389 1.212 0.033 0.060 0.068
Children ever born 2.896 0.026 19,465 18,389 1.374 0.009 2.844 2.947
Children surviving 2.666 0.023 19,465 18,389 1.368 0.009 2.619 2.713
Children ever born to women age 40-49 5.561 0.067 3,631 3,513 1.552 0.012 5.427 5.695
Know any contraceptive method 0.982 0.002 12,230 11,265 1.482 0.002 0.979 0.986
Know a modern method 0.981 0.002 12,230 11,265 1.567 0.002 0.977 0.985
Currently using any method 0.555 0.007 12,230 11,265 1.556 0.013 0.541 0.569
Currently using a modern method 0.509 0.007 12,230 11,265 1.531 0.014 0.495 0.522
Currently using a traditional method 0.046 0.003 12,230 11,265 1.331 0.055 0.041 0.051
Currently using pill 0.062 0.003 12,230 11,265 1.426 0.050 0.056 0.068
Currently using IUD 0.026 0.002 12,230 11,265 1.660 0.091 0.022 0.031
Currently using male condoms 0.019 0.002 12,230 11,265 1.403 0.091 0.016 0.023
Currently using injectables 0.275 0.006 12,230 11,265 1.502 0.022 0.263 0.287
Currently using female sterilisation 0.039 0.002 12,230 11,265 1.385 0.062 0.034 0.044
Currently using implant 0.086 0.004 12,230 11,265 1.477 0.044 0.078 0.093
Currently using rhythm 0.037 0.002 12,230 11,265 1.275 0.059 0.033 0.042
Currently using withdrawal 0.007 0.001 12,230 11,265 1.530 0.171 0.004 0.009
Used public sector source for family planning 0.698 0.010 6,539 6,883 1.708 0.014 0.678 0.717
Want no more children 0.551 0.009 5,757 5,265 1.330 0.016 0.534 0.568
Want to delay next birth at least 2 years 0.291 0.008 5,757 5,265 1.323 0.027 0.275 0.306
Ideal number of children 3.856 0.032 8,918 8,493 1.445 0.008 3.793 3.920
Mothers received antenatal care for last birth 0.940 0.003 9,785 8,881 1.376 0.004 0.934 0.947
Mothers protected against tetanus for last birth 0.751 0.008 4,661 4,199 1.304 0.011 0.734 0.768
Births with skilled attendant at delivery 0.504 0.010 14,136 12,540 1.904 0.019 0.484 0.523
Delivery in a health facility 0.495 0.010 14,136 12,540 1.906 0.019 0.476 0.514
Had diarrhoea in the last 2 weeks 0.157 0.005 13,561 12,025 1.383 0.030 0.148 0.166
Treated with ORS 0.519 0.016 2,012 1,886 1.331 0.030 0.488 0.551
Sought medical treatment for diarrhoea 0.581 0.017 2,012 1,886 1.431 0.029 0.548 0.614
Vaccination card seen 0.787 0.011 2,791 2,447 1.340 0.014 0.766 0.809
Received BCG vaccination 0.961 0.005 2,791 2,447 1.293 0.005 0.951 0.971
Received DPT vaccination (3 doses) 0.892 0.008 2,791 2,447 1.314 0.009 0.876 0.908
Received polio vaccination (3 doses) 0.890 0.008 2,791 2,447 1.335 0.009 0.874 0.907
Received measles vaccination 0.846 0.009 2,791 2,447 1.204 0.010 0.828 0.863
Fully vaccinated 0.772 0.010 2,791 2,447 1.263 0.014 0.752 0.793
Vitamin A supplementation in last 6 months 0.988 0.001 12,314 10,904 1.373 0.002 0.985 0.991
Owns at least one insecticide treated net (ITN) 0.610 0.007 22,516 21,140 2.040 0.011 0.596 0.623
Child slept under ITN last night 0.518 0.009 14,706 13,236 1.668 0.017 0.501 0.535
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.184 0.009 5,375 4,739 1.625 0.048 0.166 0.201
Child has fever in last two weeks 0.259 0.006 13,561 12,025 1.441 0.024 0.247 0.271
Child took antimalarial 0.300 0.012 3,274 3,114 1.377 0.040 0.276 0.324
Height-for-age (-2SD) 0.291 0.006 14,142 12,780 1.375 0.020 0.279 0.302
Weight-for-height (-2SD) 0.044 0.003 14,142 12,780 1.641 0.069 0.038 0.050
Weight-for-age (-2SD) 0.129 0.005 14,142 12,780 1.573 0.037 0.119 0.139
Body Mass Index (BMI) <18.5 0.112 0.004 8,328 7,897 1.265 0.039 0.103 0.120
Had 2+ sexual partners in past 12 months 0.010 0.001 9,269 8,696 1.182 0.124 0.007 0.012
Condom use at last sex 0.296 0.062 91 84 1.280 0.209 0.173 0.420
Abstinence among youth (never had sex) 0.634 0.015 2,205 2,172 1.500 0.024 0.604 0.665
Sexually active in past 12 months among youth 0.212 0.012 2,205 2,172 1.324 0.054 0.189 0.235
Had an HIV test and received results in past 12 months 0.494 0.005 19,465 18,389 1.494 0.011 0.483 0.505
Accepting attitudes towards people with HIV 0.233 0.007 9,206 8,666 1.515 0.029 0.219 0.246
Ever experienced any physical violence since age 15 by anyone 0.453 0.012 3,569 3,406 1.424 0.026 0.430 0.477
Ever experienced any sexual violence by anyone 0.133 0.008 3,569 3,406 1.337 0.057 0.118 0.149
Ever experienced any physical/sexual violence by any husband/partner 0.427 0.012 2,875 2,435 1.347 0.029 0.402 0.452
Physical/sexual violence in the last 12 months by any husband/partner 0.258 0.011 2,875 2,435 1.380 0.044 0.236 0.281
Total fertility rate (3 years) 4.545 0.078 53,908 51,007 1.527 0.017 4.389 4.701
Neonatal mortality rate (last 0-9 years) 21.245 1.139 29,318 26,059 1.233 0.054 18.967 23.523
Post-neonatal mortality rate (last 0-9 years) 18.462 1.173 29,422 26,145 1.322 0.064 16.116 20.808
Infant mortality rate (last 0-9 years) 39.707 1.698 29,343 26,084 1.313 0.043 36.311 43.103
Child mortality rate (last 0-9 years) 16.467 1.134 29,414 26,123 1.248 0.069 14.200 18.734
Under-five mortality rate (last 0-9 years) 55.520 2.103 29,444 26,170 1.281 0.038 51.315 59.725
Continued…
Appendix B • 365
Table B.4—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.000 0.000 7,366 6,762 na na 0.000 0.000
Literacy 0.890 0.005 7,366 6,762 1.459 0.006 0.880 0.901
No education 0.042 0.003 7,366 6,762 1.351 0.075 0.036 0.048
Secondary or higher education 0.384 0.009 7,366 6,762 1.598 0.024 0.366 0.402
Never married/in union 0.472 0.008 7,366 6,762 1.390 0.017 0.455 0.488
Currently married/in union 0.473 0.008 7,366 6,762 1.395 0.017 0.457 0.490
Had sexual intercourse before age 18 0.575 0.008 5,369 4,931 1.229 0.014 0.559 0.592
Know any contraceptive method 0.996 0.001 3,549 3,201 0.666 0.001 0.995 0.997
Know any modern contraceptive method 0.995 0.001 3,549 3,201 0.702 0.001 0.993 0.997
Want no more children 0.461 0.011 3,549 3,201 1.318 0.024 0.439 0.483
Want to delay next birth at least 2 years 0.349 0.010 3,549 3,201 1.296 0.030 0.329 0.370
Ideal number of children 4.075 0.045 7,164 6,614 1.323 0.011 3.985 4.164
Had 2+ sexual partners in past 12 months 0.114 0.005 7,366 6,762 1.381 0.045 0.104 0.124
Condom use at last sex 0.423 0.022 789 771 1.258 0.052 0.378 0.467
Abstinence among youth (never had sex) 0.465 0.013 2,865 2,669 1.351 0.027 0.440 0.491
Sexually active in past 12 months among youth 0.363 0.012 2,865 2,669 1.332 0.033 0.339 0.387
Had an HIV test and received results in past 12 months 0.413 0.008 7,366 6,762 1.314 0.018 0.398 0.428
Accepting attitudes towards people with HIV 0.432 0.009 7,341 6,741 1.550 0.021 0.414 0.450
Ever experienced any physical violence since age 15 by anyone 0.473 0.013 2,836 2,713 1.380 0.027 0.447 0.498
Ever experienced any sexual violence by anyone 0.055 0.005 2,836 2,713 1.284 0.100 0.044 0.066
Ever experienced any physical/sexual violence by any wife/partner 0.111 0.009 1,761 1,432 1.244 0.084 0.092 0.130
Physical/sexual violence in the last 12 months by any wife/partner 0.068 0.007 1,761 1,432 1.236 0.109 0.053 0.083
366 • Appendix B
Table B.5 Sampling errors: Coast sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.490 0.022 3,902 3,076 2.718 0.044 0.446 0.533
Literacy 0.802 0.013 3,902 3,076 2.062 0.016 0.775 0.828
No education 0.163 0.012 3,902 3,076 2.067 0.075 0.139 0.188
Secondary or higher education 0.315 0.014 3,902 3,076 1.907 0.045 0.287 0.344
Never married/in union 0.280 0.010 3,902 3,076 1.358 0.035 0.260 0.299
Currently married/in union 0.592 0.010 3,902 3,076 1.313 0.017 0.571 0.613
Married before age 20 0.506 0.016 3,095 2,472 1.767 0.031 0.475 0.538
Had sexual intercourse before age 18 0.467 0.013 3,095 2,472 1.417 0.027 0.442 0.493
Currently pregnant 0.066 0.005 3,902 3,076 1.339 0.081 0.055 0.076
Children ever born 2.540 0.054 3,902 3,076 1.297 0.021 2.431 2.649
Children surviving 2.338 0.048 3,902 3,076 1.252 0.020 2.243 2.433
Children ever born to women age 40-49 5.516 0.172 639 484 1.524 0.031 5.172 5.860
Know any contraceptive method 0.994 0.002 2,364 1,821 1.298 0.002 0.989 0.998
Know a modern method 0.993 0.002 2,364 1,821 1.293 0.002 0.989 0.998
Currently using any method 0.439 0.017 2,364 1,821 1.638 0.038 0.406 0.473
Currently using a modern method 0.383 0.017 2,364 1,821 1.731 0.045 0.348 0.417
Currently using a traditional method 0.056 0.007 2,364 1,821 1.416 0.119 0.043 0.070
Currently using pill 0.047 0.006 2,364 1,821 1.396 0.129 0.035 0.060
Currently using IUD 0.022 0.004 2,364 1,821 1.401 0.193 0.013 0.030
Currently using male condoms 0.015 0.003 2,364 1,821 1.299 0.217 0.009 0.022
Currently using injectables 0.187 0.012 2,364 1,821 1.440 0.062 0.164 0.210
Currently using female sterilisation 0.016 0.004 2,364 1,821 1.671 0.266 0.008 0.025
Currently using implant 0.094 0.010 2,364 1,821 1.680 0.107 0.074 0.114
Currently using rhythm 0.042 0.005 2,364 1,821 1.292 0.128 0.031 0.052
Currently using withdrawal 0.014 0.004 2,364 1,821 1.527 0.261 0.007 0.022
Used public sector source for family planning 0.662 0.024 1,088 882 1.665 0.036 0.614 0.710
Want no more children 0.371 0.019 1,120 850 1.297 0.051 0.333 0.408
Want to delay next birth at least 2 years 0.339 0.019 1,120 850 1.349 0.056 0.301 0.377
Ideal number of children 4.197 0.082 1,647 1,336 1.571 0.020 4.034 4.361
Mothers received antenatal care for last birth 0.975 0.005 1,857 1,471 1.340 0.005 0.966 0.985
Mothers protected against tetanus for last birth 0.837 0.016 873 698 1.292 0.019 0.805 0.869
Births with skilled attendant at delivery 0.582 0.024 2,650 2,023 2.055 0.041 0.535 0.630
Delivery in a health facility 0.577 0.024 2,650 2,023 2.046 0.041 0.529 0.625
Had diarrhoea in the last 2 weeks 0.176 0.014 2,531 1,936 1.750 0.080 0.148 0.205
Treated with ORS 0.631 0.030 460 341 1.212 0.047 0.571 0.690
Sought medical treatment for diarrhoea 0.647 0.027 460 341 1.131 0.042 0.592 0.701
Vaccination card seen 0.786 0.027 517 391 1.467 0.035 0.731 0.840
Received BCG vaccination 0.971 0.013 517 391 1.687 0.013 0.946 0.997
Received DPT vaccination (3 doses) 0.919 0.016 517 391 1.275 0.017 0.887 0.950
Received polio vaccination (3 doses) 0.920 0.017 517 391 1.421 0.019 0.885 0.955
Received measles vaccination 0.866 0.020 517 391 1.303 0.023 0.826 0.906
Fully vaccinated 0.805 0.024 517 391 1.312 0.029 0.757 0.852
Vitamin A supplementation in last 6 months 0.974 0.006 2,247 1,711 1.609 0.006 0.963 0.986
Owns at least one insecticide treated net (ITN) 0.693 0.013 4,476 3,569 1.890 0.019 0.666 0.719
Child slept under ITN last night 0.656 0.017 2,652 2,006 1.431 0.025 0.622 0.689
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.525 0.025 1,036 793 1.589 0.048 0.475 0.576
Child has fever in last two weeks 0.272 0.017 2,531 1,936 1.780 0.061 0.238 0.305
Child took antimalarial 0.119 0.019 672 526 1.465 0.163 0.080 0.158
Height-for-age (-2SD) 0.308 0.015 2,539 1,926 1.517 0.050 0.277 0.338
Weight-for-height (-2SD) 0.045 0.006 2,539 1,926 1.279 0.129 0.033 0.056
Weight-for-age (-2SD) 0.136 0.011 2,539 1,926 1.437 0.080 0.114 0.157
Body Mass Index (BMI) <18.5 0.110 0.009 1,644 1,262 1.213 0.086 0.091 0.129
Had 2+ sexual partners in past 12 months 0.011 0.003 1,841 1,421 1.182 0.267 0.005 0.016
Condom use at last sex 0.269 0.125 25 15 1.353 0.463 0.020 0.518
Abstinence among youth (never had sex) 0.656 0.036 421 327 1.567 0.055 0.584 0.729
Sexually active in past 12 months among youth 0.236 0.034 421 327 1.617 0.142 0.169 0.304
Had an HIV test and received results in past 12 months 0.534 0.015 3,902 3,076 1.932 0.029 0.503 0.565
Accepting attitudes towards people with HIV 0.161 0.010 1,840 1,420 1.206 0.064 0.140 0.182
Ever experienced any physical violence since age 15 by anyone 0.394 0.023 684 568 1.212 0.058 0.349 0.440
Ever experienced any sexual violence by anyone 0.083 0.012 684 568 1.126 0.143 0.059 0.107
Ever experienced any physical/sexual violence by any husband/partner 0.302 0.025 556 439 1.289 0.083 0.252 0.352
Physical/sexual violence in the last 12 months by any husband/partner 0.192 0.023 556 439 1.355 0.118 0.147 0.238
Total fertility rate (3 years) 4.302 0.225 10,909 8,647 1.929 0.052 3.852 4.753
Neonatal mortality rate (last 0-9 years) 25.117 2.535 5,383 4,055 1.051 0.101 20.047 30.187
Post-neonatal mortality rate (last 0-9 years) 18.704 2.568 5,376 4,037 1.240 0.137 13.568 23.839
Infant mortality rate (last 0-9 years) 43.821 3.415 5,386 4,056 1.056 0.078 36.991 50.650
Child mortality rate (last 0-9 years) 14.035 1.941 5,281 3,940 1.128 0.138 10.152 17.917
Under-five mortality rate (last 0-9 years) 57.240 3.827 5,401 4,065 1.087 0.067 49.586 64.894
Continued…
Appendix B • 367
Table B.5—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.568 0.026 1,505 1,260 2.070 0.047 0.515 0.621
Literacy 0.939 0.009 1,505 1,260 1.426 0.009 0.922 0.957
No education 0.042 0.007 1,505 1,260 1.440 0.177 0.027 0.057
Secondary or higher education 0.439 0.020 1,505 1,260 1.558 0.045 0.399 0.479
Never married/in union 0.458 0.019 1,505 1,260 1.505 0.042 0.420 0.497
Currently married/in union 0.489 0.019 1,505 1,260 1.488 0.039 0.451 0.528
Had sexual intercourse before age 18 0.480 0.018 1,190 1,005 1.235 0.037 0.444 0.516
Know any contraceptive method 1.000 0.000 754 617 na 0.000 1.000 1.000
Know any modern contraceptive method 0.998 0.001 754 617 0.803 0.001 0.996 1.001
Want no more children 0.362 0.027 754 617 1.551 0.075 0.308 0.417
Want to delay next birth at least 2 years 0.409 0.026 754 617 1.450 0.064 0.357 0.461
Ideal number of children 3.962 0.095 1,404 1,204 1.583 0.024 3.772 4.152
Had 2+ sexual partners in past 12 months 0.119 0.012 1,505 1,260 1.485 0.104 0.094 0.144
Condom use at last sex 0.320 0.049 157 150 1.305 0.153 0.222 0.417
Abstinence among youth (never had sex) 0.465 0.038 507 437 1.723 0.082 0.389 0.542
Sexually active in past 12 months among youth 0.369 0.036 507 437 1.666 0.097 0.298 0.441
Had an HIV test and received results in past 12 months 0.405 0.020 1,505 1,260 1.596 0.050 0.364 0.445
Accepting attitudes towards people with HIV 0.438 0.024 1,498 1,256 1.877 0.055 0.390 0.486
Ever experienced any physical violence since age 15 by anyone 0.419 0.032 580 481 1.581 0.078 0.354 0.484
Ever experienced any sexual violence by anyone 0.036 0.009 580 481 1.151 0.249 0.018 0.053
Ever experienced any physical/sexual violence by any wife/partner 0.072 0.016 365 244 1.165 0.219 0.041 0.104
Physical/sexual violence in the last 12 months by any wife/partner 0.035 0.011 365 244 1.098 0.301 0.014 0.057
368 • Appendix B
Table B.6 Sampling errors: North Eastern sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.370 0.037 1,664 648 3.100 0.100 0.296 0.443
Literacy 0.239 0.023 1,664 648 2.200 0.096 0.193 0.285
No education 0.749 0.023 1,664 648 2.129 0.030 0.704 0.795
Secondary or higher education 0.103 0.015 1,664 648 2.014 0.146 0.073 0.133
Never married/in union 0.239 0.019 1,664 648 1.770 0.077 0.202 0.276
Currently married/in union 0.695 0.018 1,664 648 1.611 0.026 0.659 0.732
Married before age 20 0.631 0.018 1,306 505 1.343 0.028 0.595 0.667
Had sexual intercourse before age 18 0.416 0.019 1,306 505 1.387 0.046 0.378 0.454
Currently pregnant 0.120 0.010 1,664 648 1.211 0.080 0.101 0.139
Children ever born 3.526 0.097 1,664 648 1.253 0.028 3.332 3.720
Children surviving 3.300 0.097 1,664 648 1.348 0.029 3.105 3.494
Children ever born to women age 40-49 7.090 0.266 231 90 1.651 0.038 6.557 7.622
Know any contraceptive method 0.708 0.021 1,144 451 1.530 0.029 0.667 0.749
Know a modern method 0.688 0.023 1,144 451 1.680 0.033 0.642 0.734
Currently using any method 0.034 0.007 1,144 451 1.322 0.210 0.019 0.048
Currently using a modern method 0.034 0.007 1,144 451 1.322 0.210 0.019 0.048
Currently using a traditional method 0.000 0.000 1,144 451 na na 0.000 0.000
Currently using pill 0.006 0.002 1,144 451 1.050 0.406 0.001 0.011
Currently using IUD 0.001 0.001 1,144 451 1.016 1.011 0.000 0.003
Currently using male condoms 0.001 0.001 1,144 451 0.712 0.719 0.000 0.002
Currently using injectables 0.019 0.005 1,144 451 1.123 0.240 0.010 0.028
Currently using female sterilisation 0.000 0.000 1,144 451 na na 0.000 0.000
Currently using implant 0.006 0.003 1,144 451 1.156 0.440 0.001 0.011
Currently using rhythm 0.000 0.000 1,144 451 na na 0.000 0.000
Currently using withdrawal 0.000 0.000 1,144 451 na na 0.000 0.000
Used public sector source for family planning 0.724 0.065 42 16 0.934 0.090 0.594 0.854
Want no more children 0.061 0.015 538 209 1.427 0.241 0.032 0.091
Want to delay next birth at least 2 years 0.295 0.031 538 209 1.563 0.104 0.233 0.357
Ideal number of children 9.260 0.228 700 270 1.749 0.025 8.804 9.716
Mothers received antenatal care for last birth 0.665 0.028 925 372 1.859 0.043 0.609 0.722
Mothers protected against tetanus for last birth 0.599 0.046 453 178 2.002 0.077 0.507 0.691
Births with skilled attendant at delivery 0.324 0.034 1,594 650 2.221 0.105 0.256 0.392
Delivery in a health facility 0.292 0.030 1,594 650 2.027 0.104 0.232 0.353
Had diarrhoea in the last 2 weeks 0.078 0.011 1,538 625 1.592 0.146 0.055 0.100
Treated with ORS 0.553 0.047 116 49 0.976 0.085 0.459 0.647
Sought medical treatment for diarrhoea 0.442 0.093 116 49 1.943 0.211 0.256 0.629
Vaccination card seen 0.511 0.037 295 121 1.295 0.072 0.437 0.584
Received BCG vaccination 0.834 0.025 295 121 1.161 0.029 0.785 0.883
Received DPT vaccination (3 doses) 0.774 0.040 295 121 1.668 0.051 0.695 0.853
Received polio vaccination (3 doses) 0.746 0.035 295 121 1.431 0.048 0.675 0.817
Received measles vaccination 0.698 0.040 295 121 1.541 0.058 0.618 0.779
Fully vaccinated 0.556 0.038 295 121 1.339 0.068 0.481 0.632
Vitamin A supplementation in last 6 months 0.987 0.004 1,401 577 1.315 0.004 0.978 0.995
Owns at least one insecticide treated net (ITN) 0.488 0.028 1,857 724 2.378 0.057 0.433 0.544
Child slept under ITN last night 0.402 0.033 1,637 664 2.043 0.081 0.337 0.467
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.021 0.008 570 228 1.262 0.355 0.006 0.036
Child has fever in last two weeks 0.087 0.010 1,538 625 1.261 0.112 0.067 0.106
Child took antimalarial 0.073 0.024 146 54 1.000 0.330 0.025 0.121
Height-for-age (-2SD) 0.247 0.020 1,485 604 1.752 0.082 0.206 0.287
Weight-for-height (-2SD) 0.133 0.011 1,485 604 1.201 0.081 0.112 0.154
Weight-for-age (-2SD) 0.190 0.014 1,485 604 1.301 0.074 0.162 0.218
Body Mass Index (BMI) <18.5 0.287 0.023 624 239 1.269 0.081 0.241 0.334
Had 2+ sexual partners in past 12 months 0.000 0.000 779 299 na na 0.000 0.000
Condom use at last sex 0.000 0.000 0 0 na na 0.000 0.000
Abstinence among youth (never had sex) 0.977 0.013 170 64 1.115 0.013 0.951 1.003
Sexually active in past 12 months among youth 0.010 0.008 170 64 1.032 0.780 0.000 0.026
Had an HIV test and received results in past 12 months 0.201 0.014 1,664 648 1.469 0.072 0.172 0.230
Accepting attitudes towards people with HIV 0.028 0.008 742 284 1.359 0.296 0.011 0.044
Ever experienced any physical violence since age 15 by anyone 0.150 0.026 313 118 1.273 0.172 0.099 0.202
Ever experienced any sexual violence by anyone 0.006 0.004 313 118 0.996 0.717 0.000 0.015
Ever experienced any physical/sexual violence by any husband/partner 0.121 0.024 265 97 1.198 0.199 0.073 0.169
Physical/sexual violence in the last 12 months by any husband/partner 0.058 0.016 265 97 1.101 0.272 0.027 0.090
Total fertility rate (3 years) 6.417 0.311 4,611 1,798 1.581 0.048 5.795 7.038
Neonatal mortality rate (last 0-9 years) 24.115 4.109 3,361 1,353 1.413 0.170 15.898 32.332
Post-neonatal mortality rate (last 0-9 years) 12.616 2.263 3,392 1,366 1.091 0.179 8.090 17.142
Infant mortality rate (last 0-9 years) 36.731 5.293 3,363 1,354 1.486 0.144 26.144 47.318
Child mortality rate (last 0-9 years) 7.621 1.863 3,441 1,384 1.162 0.244 3.894 11.347
Under-five mortality rate (last 0-9 years) 44.072 5.618 3,372 1,357 1.395 0.127 32.836 55.307
Continued…
Appendix B • 369
Table B.6—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.430 0.046 591 227 2.243 0.107 0.338 0.521
Literacy 0.672 0.027 591 227 1.391 0.040 0.618 0.726
No education 0.369 0.027 591 227 1.374 0.074 0.314 0.424
Secondary or higher education 0.284 0.028 591 227 1.487 0.097 0.229 0.339
Never married/in union 0.516 0.032 591 227 1.529 0.061 0.453 0.579
Currently married/in union 0.454 0.028 591 227 1.357 0.061 0.398 0.510
Had sexual intercourse before age 18 0.118 0.019 398 153 1.150 0.158 0.081 0.155
Know any contraceptive method 0.892 0.020 263 103 1.065 0.023 0.851 0.933
Know any modern contraceptive method 0.892 0.020 263 103 1.065 0.023 0.851 0.933
Want no more children 0.035 0.014 263 103 1.269 0.413 0.006 0.064
Want to delay next birth at least 2 years 0.386 0.050 263 103 1.645 0.129 0.287 0.485
Ideal number of children 12.915 0.406 547 209 1.503 0.031 12.103 13.727
Had 2+ sexual partners in past 12 months 0.059 0.015 591 227 1.573 0.260 0.028 0.089
Condom use at last sex 0.065 0.048 23 13 0.923 0.743 0.000 0.162
Abstinence among youth (never had sex) 0.860 0.040 272 100 1.906 0.047 0.780 0.941
Sexually active in past 12 months among youth 0.026 0.012 272 100 1.221 0.456 0.002 0.049
Had an HIV test and received results in past 12 months 0.228 0.024 591 227 1.365 0.104 0.180 0.275
Accepting attitudes towards people with HIV 0.310 0.032 590 225 1.683 0.104 0.246 0.374
Ever experienced any physical violence since age 15 by anyone 0.320 0.046 210 83 1.436 0.145 0.227 0.413
Ever experienced any sexual violence by anyone 0.000 0.000 210 83 na na 0.000 0.000
Ever experienced any physical/sexual violence by any wife/partner 0.032 0.031 124 42 1.893 0.942 0.000 0.094
Physical/sexual violence in the last 12 months by any wife/partner 0.032 0.031 124 42 1.893 0.942 0.000 0.094
370 • Appendix B
Table B.7 Sampling errors: Eastern sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.257 0.016 5,247 4,375 2.662 0.063 0.225 0.289
Literacy 0.894 0.007 5,247 4,375 1.742 0.008 0.880 0.909
No education 0.048 0.005 5,247 4,375 1.842 0.114 0.037 0.058
Secondary or higher education 0.357 0.014 5,247 4,375 2.056 0.038 0.329 0.384
Never married/in union 0.286 0.008 5,247 4,375 1.237 0.027 0.270 0.301
Currently married/in union 0.610 0.009 5,247 4,375 1.386 0.015 0.591 0.628
Married before age 20 0.429 0.011 4,215 3,526 1.409 0.025 0.407 0.450
Had sexual intercourse before age 18 0.501 0.014 4,215 3,526 1.805 0.028 0.473 0.528
Currently pregnant 0.046 0.004 5,247 4,375 1.526 0.096 0.037 0.055
Children ever born 2.426 0.045 5,247 4,375 1.460 0.018 2.337 2.516
Children surviving 2.278 0.041 5,247 4,375 1.437 0.018 2.196 2.360
Children ever born to women age 40-49 4.691 0.119 981 855 1.639 0.025 4.453 4.929
Know any contraceptive method 0.997 0.001 3,197 2,667 0.735 0.001 0.996 0.999
Know a modern method 0.997 0.001 3,197 2,667 0.766 0.001 0.996 0.998
Currently using any method 0.704 0.013 3,197 2,667 1.619 0.019 0.678 0.730
Currently using a modern method 0.639 0.014 3,197 2,667 1.620 0.022 0.611 0.666
Currently using a traditional method 0.065 0.006 3,197 2,667 1.314 0.088 0.054 0.077
Currently using pill 0.089 0.008 3,197 2,667 1.515 0.086 0.073 0.104
Currently using IUD 0.029 0.005 3,197 2,667 1.643 0.167 0.020 0.039
Currently using male condoms 0.015 0.004 3,197 2,667 1.665 0.238 0.008 0.022
Currently using injectables 0.379 0.013 3,197 2,667 1.475 0.033 0.354 0.404
Currently using female sterilisation 0.048 0.006 3,197 2,667 1.466 0.115 0.037 0.060
Currently using implant 0.078 0.007 3,197 2,667 1.485 0.090 0.064 0.092
Currently using rhythm 0.056 0.005 3,197 2,667 1.332 0.096 0.046 0.067
Currently using withdrawal 0.005 0.002 3,197 2,667 1.595 0.389 0.001 0.009
Used public sector source for family planning 0.620 0.018 2,102 2,050 1.711 0.029 0.584 0.656
Want no more children 0.603 0.019 1,514 1,268 1.477 0.031 0.566 0.640
Want to delay next birth at least 2 years 0.251 0.017 1,514 1,268 1.510 0.067 0.218 0.285
Ideal number of children 3.079 0.036 2,450 2,046 1.190 0.012 3.007 3.150
Mothers received antenatal care for last birth 0.972 0.004 2,299 1,834 1.157 0.004 0.964 0.980
Mothers protected against tetanus for last birth 0.791 0.017 1,115 891 1.342 0.021 0.758 0.825
Births with skilled attendant at delivery 0.633 0.020 3,015 2,321 1.886 0.031 0.593 0.673
Delivery in a health facility 0.627 0.020 3,015 2,321 1.873 0.032 0.587 0.666
Had diarrhoea in the last 2 weeks 0.143 0.010 2,906 2,235 1.345 0.067 0.124 0.162
Treated with ORS 0.472 0.037 381 320 1.351 0.078 0.398 0.545
Sought medical treatment for diarrhoea 0.574 0.035 381 320 1.314 0.061 0.504 0.644
Vaccination card seen 0.853 0.020 585 431 1.285 0.024 0.812 0.894
Received BCG vaccination 0.987 0.005 585 431 1.049 0.005 0.977 0.998
Received DPT vaccination (3 doses) 0.936 0.018 585 431 1.647 0.019 0.901 0.972
Received polio vaccination (3 doses) 0.913 0.020 585 431 1.567 0.021 0.874 0.952
Received measles vaccination 0.921 0.017 585 431 1.382 0.018 0.888 0.954
Fully vaccinated 0.855 0.026 585 431 1.636 0.030 0.803 0.906
Vitamin A supplementation in last 6 months 0.993 0.002 2,656 2,051 1.451 0.002 0.988 0.998
Owns at least one insecticide treated net (ITN) 0.563 0.014 6,261 5,262 2.253 0.025 0.535 0.591
Child slept under ITN last night 0.531 0.020 3,138 2,464 1.874 0.038 0.491 0.571
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.098 0.012 1,144 872 1.247 0.118 0.075 0.121
Child has fever in last two weeks 0.182 0.012 2,906 2,235 1.491 0.063 0.159 0.205
Child took antimalarial 0.181 0.025 509 406 1.423 0.139 0.131 0.231
Height-for-age (-2SD) 0.301 0.013 3,058 2,409 1.482 0.044 0.274 0.328
Weight-for-height (-2SD) 0.044 0.005 3,058 2,409 1.267 0.113 0.034 0.054
Weight-for-age (-2SD) 0.122 0.010 3,058 2,409 1.501 0.081 0.102 0.141
Body Mass Index (BMI) <18.5 0.098 0.008 2,299 1,918 1.229 0.078 0.083 0.113
Had 2+ sexual partners in past 12 months 0.010 0.002 2,495 2,066 1.233 0.247 0.005 0.015
Condom use at last sex 0.418 0.110 32 20 1.228 0.262 0.199 0.638
Abstinence among youth (never had sex) 0.683 0.035 580 480 1.828 0.052 0.612 0.754
Sexually active in past 12 months among youth 0.188 0.023 580 480 1.415 0.122 0.142 0.234
Had an HIV test and received results in past 12 months 0.510 0.012 5,247 4,375 1.775 0.024 0.486 0.535
Accepting attitudes towards people with HIV 0.188 0.012 2,478 2,062 1.531 0.064 0.164 0.212
Ever experienced any physical violence since age 15 by anyone 0.488 0.023 962 792 1.404 0.046 0.443 0.533
Ever experienced any sexual violence by anyone 0.122 0.015 962 792 1.451 0.126 0.091 0.152
Ever experienced any physical/sexual violence by any husband/partner 0.423 0.027 765 585 1.499 0.063 0.369 0.477
Physical/sexual violence in the last 12 months by any husband/partner 0.251 0.022 765 585 1.428 0.089 0.206 0.296
Total fertility rate (3 years) 3.404 0.111 14,658 12,239 1.354 0.033 3.182 3.626
Neonatal mortality rate (last 0-9 years) 23.788 2.795 6,456 5,046 1.301 0.117 18.199 29.377
Post-neonatal mortality rate (last 0-9 years) 11.995 2.099 6,460 5,050 1.471 0.175 7.797 16.194
Infant mortality rate (last 0-9 years) 35.783 3.298 6,461 5,050 1.248 0.092 29.187 42.380
Child mortality rate (last 0-9 years) 9.268 1.502 6,462 5,067 1.186 0.162 6.265 12.271
Under-five mortality rate (last 0-9 years) 44.720 3.844 6,471 5,060 1.266 0.086 37.033 52.407
Continued…
Appendix B • 371
Table B.7—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.296 0.033 2,144 1,825 3.382 0.113 0.229 0.362
Literacy 0.916 0.011 2,144 1,825 1.798 0.012 0.895 0.938
No education 0.030 0.006 2,144 1,825 1.631 0.199 0.018 0.042
Secondary or higher education 0.389 0.021 2,144 1,825 2.035 0.055 0.347 0.432
Never married/in union 0.472 0.015 2,144 1,825 1.419 0.032 0.441 0.502
Currently married/in union 0.458 0.016 2,144 1,825 1.516 0.036 0.425 0.490
Had sexual intercourse before age 18 0.649 0.017 1,612 1,405 1.433 0.026 0.615 0.683
Know any contraceptive method 1.000 0.000 957 835 na 0.000 1.000 1.000
Know any modern contraceptive method 1.000 0.000 957 835 na 0.000 1.000 1.000
Want no more children 0.477 0.022 957 835 1.378 0.047 0.432 0.522
Want to delay next birth at least 2 years 0.348 0.027 957 835 1.739 0.077 0.294 0.402
Ideal number of children 3.339 0.045 2,128 1,805 1.302 0.013 3.250 3.429
Had 2+ sexual partners in past 12 months 0.125 0.011 2,144 1,825 1.524 0.087 0.103 0.147
Condom use at last sex 0.503 0.046 233 229 1.398 0.091 0.411 0.595
Abstinence among youth (never had sex) 0.424 0.021 806 663 1.225 0.050 0.381 0.467
Sexually active in past 12 months among youth 0.368 0.020 806 663 1.201 0.055 0.327 0.409
Had an HIV test and received results in past 12 months 0.398 0.017 2,144 1,825 1.598 0.043 0.364 0.431
Accepting attitudes towards people with HIV 0.352 0.022 2,138 1,820 2.128 0.062 0.308 0.397
Ever experienced any physical violence since age 15 by anyone 0.416 0.025 798 773 1.417 0.059 0.367 0.466
Ever experienced any sexual violence by anyone 0.049 0.009 798 773 1.194 0.187 0.030 0.067
Ever experienced any physical/sexual violence by any wife/partner 0.098 0.018 467 371 1.277 0.180 0.063 0.133
Physical/sexual violence in the last 12 months by any wife/partner 0.070 0.017 467 371 1.407 0.237 0.037 0.104
372 • Appendix B
Table B.8 Sampling errors: Central sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.440 0.022 3,114 3,994 2.487 0.050 0.396 0.484
Literacy 0.949 0.005 3,114 3,994 1.188 0.005 0.940 0.958
No education 0.009 0.002 3,114 3,994 1.094 0.212 0.005 0.012
Secondary or higher education 0.547 0.017 3,114 3,994 1.868 0.030 0.514 0.581
Never married/in union 0.295 0.014 3,114 3,994 1.693 0.047 0.267 0.323
Currently married/in union 0.582 0.015 3,114 3,994 1.712 0.026 0.551 0.612
Married before age 20 0.347 0.014 2,632 3,394 1.533 0.041 0.319 0.376
Had sexual intercourse before age 18 0.352 0.014 2,632 3,394 1.542 0.041 0.323 0.380
Currently pregnant 0.048 0.004 3,114 3,994 1.087 0.086 0.040 0.057
Children ever born 2.031 0.062 3,114 3,994 1.889 0.030 1.908 2.154
Children surviving 1.921 0.057 3,114 3,994 1.882 0.030 1.806 2.036
Children ever born to women age 40-49 3.717 0.098 716 837 1.398 0.026 3.520 3.914
Know any contraceptive method 0.999 0.001 1,852 2,323 1.082 0.001 0.997 1.001
Know a modern method 0.999 0.001 1,852 2,323 1.082 0.001 0.997 1.001
Currently using any method 0.728 0.013 1,852 2,323 1.211 0.017 0.703 0.753
Currently using a modern method 0.669 0.012 1,852 2,323 1.079 0.018 0.646 0.693
Currently using a traditional method 0.059 0.008 1,852 2,323 1.380 0.129 0.044 0.074
Currently using pill 0.195 0.014 1,852 2,323 1.475 0.070 0.168 0.223
Currently using IUD 0.090 0.010 1,852 2,323 1.500 0.111 0.070 0.110
Currently using male condoms 0.024 0.005 1,852 2,323 1.398 0.209 0.014 0.034
Currently using injectables 0.216 0.013 1,852 2,323 1.347 0.060 0.191 0.242
Currently using female sterilisation 0.035 0.005 1,852 2,323 1.150 0.141 0.025 0.045
Currently using implant 0.107 0.009 1,852 2,323 1.289 0.086 0.089 0.126
Currently using rhythm 0.049 0.007 1,852 2,323 1.338 0.136 0.036 0.063
Currently using withdrawal 0.007 0.002 1,852 2,323 1.243 0.357 0.002 0.011
Used public sector source for family planning 0.524 0.022 1,483 1,873 1.664 0.041 0.480 0.567
Want no more children 0.555 0.021 903 1,113 1.261 0.038 0.514 0.597
Want to delay next birth at least 2 years 0.288 0.022 903 1,113 1.428 0.075 0.245 0.331
Ideal number of children 3.152 0.044 1,499 1,887 1.362 0.014 3.063 3.240
Mothers received antenatal care for last birth 0.973 0.006 1,197 1,528 1.195 0.006 0.962 0.984
Mothers protected against tetanus for last birth 0.797 0.020 562 715 1.192 0.025 0.757 0.837
Births with skilled attendant at delivery 0.897 0.010 1,420 1,796 1.158 0.011 0.877 0.917
Delivery in a health facility 0.902 0.009 1,420 1,796 1.114 0.010 0.883 0.921
Had diarrhoea in the last 2 weeks 0.104 0.013 1,356 1,725 1.602 0.128 0.078 0.131
Treated with ORS 0.506 0.056 137 180 1.313 0.111 0.394 0.619
Sought medical treatment for diarrhoea 0.632 0.051 137 180 1.236 0.080 0.530 0.733
Vaccination card seen 0.761 0.030 291 363 1.174 0.039 0.702 0.820
Received BCG vaccination 0.996 0.003 291 363 0.761 0.003 0.990 1.002
Received DPT vaccination (3 doses) 0.955 0.012 291 363 0.998 0.013 0.930 0.979
Received polio vaccination (3 doses) 0.960 0.012 291 363 1.090 0.013 0.935 0.985
Received measles vaccination 0.972 0.009 291 363 0.916 0.009 0.954 0.989
Fully vaccinated 0.933 0.015 291 363 0.990 0.016 0.904 0.962
Vitamin A supplementation in last 6 months 0.996 0.002 1,247 1,579 1.368 0.002 0.991 1.001
Owns at least one insecticide treated net (ITN) 0.380 0.017 4,041 5,012 2.174 0.044 0.347 0.413
Child slept under ITN last night 0.431 0.025 1,458 1,792 1.713 0.058 0.381 0.480
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.045 0.012 526 682 1.372 0.271 0.021 0.069
Child has fever in last two weeks 0.179 0.017 1,356 1,725 1.586 0.097 0.144 0.213
Child took antimalarial 0.048 0.016 234 308 1.167 0.337 0.015 0.080
Height-for-age (-2SD) 0.184 0.014 1,401 1,694 1.256 0.073 0.157 0.211
Weight-for-height (-2SD) 0.023 0.004 1,401 1,694 1.089 0.196 0.014 0.032
Weight-for-age (-2SD) 0.053 0.007 1,401 1,694 1.128 0.135 0.039 0.068
Body Mass Index (BMI) <18.5 0.062 0.007 1,376 1,697 1.099 0.117 0.048 0.077
Had 2+ sexual partners in past 12 months 0.013 0.003 1,511 1,905 1.213 0.277 0.006 0.020
Condom use at last sex 0.270 0.136 18 24 1.242 0.504 0.000 0.541
Abstinence among youth (never had sex) 0.635 0.042 300 396 1.494 0.066 0.551 0.718
Sexually active in past 12 months among youth 0.240 0.031 300 396 1.253 0.129 0.178 0.302
Had an HIV test and received results in past 12 months 0.530 0.013 3,114 3,994 1.430 0.024 0.504 0.555
Accepting attitudes towards people with HIV 0.298 0.017 1,510 1,904 1.419 0.056 0.265 0.332
Ever experienced any physical violence since age 15 by anyone 0.353 0.031 595 736 1.582 0.088 0.290 0.415
Ever experienced any sexual violence by anyone 0.098 0.016 595 736 1.308 0.163 0.066 0.130
Ever experienced any physical/sexual violence by any husband/partner 0.355 0.029 458 518 1.295 0.082 0.297 0.413
Physical/sexual violence in the last 12 months by any husband/partner 0.205 0.026 458 518 1.385 0.128 0.152 0.257
Total fertility rate (3 years) 2.805 0.134 8,890 11,459 1.435 0.048 2.536 3.073
Neonatal mortality rate (last 0-9 years) 24.431 3.652 2,885 3,625 1.260 0.149 17.126 31.736
Post-neonatal mortality rate (last 0-9 years) 13.609 2.311 2,894 3,632 1.070 0.170 8.986 18.232
Infant mortality rate (last 0-9 years) 38.040 4.704 2,885 3,625 1.297 0.124 28.633 47.448
Child mortality rate (last 0-9 years) 4.266 1.181 2,928 3,644 0.948 0.277 1.904 6.629
Under-five mortality rate (last 0-9 years) 42.144 4.862 2,890 3,629 1.274 0.115 32.421 51.868
Continued…
Appendix B • 373
Table B.8—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.429 0.027 1,248 1,564 1.901 0.062 0.376 0.482
Literacy 0.958 0.007 1,248 1,564 1.210 0.007 0.944 0.971
No education 0.003 0.002 1,248 1,564 1.117 0.575 0.000 0.006
Secondary or higher education 0.584 0.020 1,248 1,564 1.418 0.034 0.545 0.624
Never married/in union 0.440 0.024 1,248 1,564 1.687 0.054 0.393 0.488
Currently married/in union 0.494 0.023 1,248 1,564 1.594 0.046 0.449 0.539
Had sexual intercourse before age 18 0.461 0.016 994 1,273 1.024 0.035 0.428 0.493
Know any contraceptive method 0.994 0.006 627 773 1.848 0.006 0.983 1.005
Know any modern contraceptive method 0.994 0.006 627 773 1.848 0.006 0.983 1.005
Want no more children 0.432 0.027 627 773 1.388 0.064 0.377 0.487
Want to delay next birth at least 2 years 0.352 0.030 627 773 1.585 0.086 0.291 0.413
Ideal number of children 3.411 0.059 1,230 1,545 1.469 0.017 3.294 3.528
Had 2+ sexual partners in past 12 months 0.055 0.008 1,248 1,564 1.273 0.149 0.039 0.072
Condom use at last sex 0.437 0.082 86 86 1.508 0.187 0.274 0.600
Abstinence among youth (never had sex) 0.456 0.037 414 517 1.517 0.082 0.382 0.531
Sexually active in past 12 months among youth 0.396 0.032 414 517 1.332 0.081 0.332 0.460
Had an HIV test and received results in past 12 months 0.401 0.018 1,248 1,564 1.287 0.045 0.366 0.437
Accepting attitudes towards people with HIV 0.525 0.020 1,245 1,560 1.428 0.039 0.485 0.565
Ever experienced any physical violence since age 15 by anyone 0.441 0.029 512 566 1.297 0.065 0.384 0.498
Ever experienced any sexual violence by anyone 0.029 0.009 512 566 1.269 0.324 0.010 0.048
Ever experienced any physical/sexual violence by any wife/partner 0.091 0.022 338 337 1.412 0.244 0.047 0.135
Physical/sexual violence in the last 12 months by any wife/partner 0.042 0.016 338 337 1.463 0.381 0.010 0.074
374 • Appendix B
Table B.9 Sampling errors: Rift Valley sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.320 0.013 9,059 7,953 2.732 0.042 0.293 0.347
Literacy 0.845 0.009 9,059 7,953 2.339 0.011 0.827 0.862
No education 0.092 0.007 9,059 7,953 2.425 0.080 0.078 0.107
Secondary or higher education 0.403 0.012 9,059 7,953 2.357 0.030 0.379 0.428
Never married/in union 0.295 0.008 9,059 7,953 1.580 0.026 0.280 0.310
Currently married/in union 0.591 0.008 9,059 7,953 1.460 0.013 0.575 0.606
Married before age 20 0.483 0.011 7,361 6,461 1.934 0.023 0.460 0.505
Had sexual intercourse before age 18 0.531 0.011 7,361 6,461 1.838 0.020 0.510 0.552
Currently pregnant 0.070 0.003 9,059 7,953 1.241 0.047 0.064 0.077
Children ever born 2.659 0.046 9,059 7,953 1.747 0.017 2.567 2.751
Children surviving 2.509 0.043 9,059 7,953 1.734 0.017 2.424 2.595
Children ever born to women age 40-49 5.535 0.106 1,475 1,307 1.559 0.019 5.322 5.747
Know any contraceptive method 0.985 0.003 5,509 4,696 1.759 0.003 0.979 0.991
Know a modern method 0.984 0.003 5,509 4,696 1.822 0.003 0.978 0.990
Currently using any method 0.528 0.010 5,509 4,696 1.490 0.019 0.508 0.548
Currently using a modern method 0.468 0.010 5,509 4,696 1.517 0.022 0.448 0.488
Currently using a traditional method 0.060 0.004 5,509 4,696 1.369 0.073 0.051 0.068
Currently using pill 0.055 0.005 5,509 4,696 1.501 0.084 0.046 0.065
Currently using IUD 0.029 0.005 5,509 4,696 1.999 0.155 0.020 0.039
Currently using male condoms 0.019 0.003 5,509 4,696 1.373 0.132 0.014 0.024
Currently using injectables 0.268 0.008 5,509 4,696 1.372 0.031 0.251 0.284
Currently using female sterilisation 0.022 0.002 5,509 4,696 1.250 0.112 0.017 0.027
Currently using implant 0.072 0.005 5,509 4,696 1.525 0.074 0.061 0.083
Currently using rhythm 0.047 0.004 5,509 4,696 1.233 0.075 0.040 0.054
Currently using withdrawal 0.010 0.002 5,509 4,696 1.541 0.207 0.006 0.014
Used public sector source for family planning 0.609 0.015 2,931 2,760 1.685 0.025 0.579 0.640
Want no more children 0.481 0.013 2,586 2,171 1.273 0.026 0.456 0.506
Want to delay next birth at least 2 years 0.365 0.013 2,586 2,171 1.356 0.035 0.339 0.391
Ideal number of children 3.822 0.050 4,170 3,659 1.605 0.013 3.721 3.923
Mothers received antenatal care for last birth 0.939 0.005 4,760 4,002 1.368 0.005 0.929 0.948
Mothers protected against tetanus for last birth 0.743 0.011 2,305 1,899 1.222 0.015 0.721 0.766
Births with skilled attendant at delivery 0.513 0.015 6,850 5,677 2.003 0.029 0.483 0.542
Delivery in a health facility 0.502 0.015 6,850 5,677 1.996 0.029 0.473 0.531
Had diarrhoea in the last 2 weeks 0.132 0.006 6,618 5,457 1.252 0.042 0.121 0.143
Treated with ORS 0.530 0.022 881 718 1.241 0.042 0.485 0.575
Sought medical treatment for diarrhoea 0.589 0.022 881 718 1.253 0.038 0.544 0.633
Vaccination card seen 0.773 0.018 1,314 1,083 1.530 0.023 0.737 0.809
Received BCG vaccination 0.967 0.006 1,314 1,083 1.168 0.006 0.955 0.979
Received DPT vaccination (3 doses) 0.879 0.012 1,314 1,083 1.305 0.014 0.855 0.903
Received polio vaccination (3 doses) 0.867 0.013 1,314 1,083 1.376 0.015 0.841 0.834
Received measles vaccination 0.831 0.012 1,314 1,083 1.133 0.015 0.807 0.856
Fully vaccinated 0.739 0.016 1,314 1,083 1.241 0.021 0.708 0.770
Vitamin A supplementation in last 6 months 0.988 0.002 5,995 4,956 1.412 0.002 0.984 0.993
Owns at least one insecticide treated net (ITN) 0.557 0.010 10,534 9,249 2.157 0.019 0.536 0.578
Child slept under ITN last night 0.430 0.014 6,918 5,713 1.799 0.032 0.403 0.458
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.069 0.007 2,641 2,167 1.428 0.104 0.055 0.084
Child has fever in last two weeks 0.209 0.008 6,618 5,457 1.426 0.039 0.192 0.225
Child took antimalarial 0.133 0.012 1,389 1,139 1.240 0.093 0.108 0.157
Height-for-age (-2SD) 0.298 0.008 6,608 5,466 1.322 0.027 0.282 0.314
Weight-for-height (-2SD) 0.057 0.006 6,608 5,466 1.989 0.106 0.045 0.069
Weight-for-age (-2SD) 0.153 0.009 6,608 5,466 1.786 0.056 0.136 0.170
Body Mass Index (BMI) <18.5 0.118 0.007 3,786 3,349 1.398 0.062 0.103 0.132
Had 2+ sexual partners in past 12 months 0.008 0.001 4,254 3,714 1.039 0.173 0.005 0.011
Condom use at last sex 0.414 0.077 49 31 1.078 0.185 0.260 0.568
Abstinence among youth (never had sex) 0.538 0.027 935 889 1.650 0.050 0.484 0.592
Sexually active in past 12 months among youth 0.296 0.022 935 889 1.503 0.076 0.252 0.341
Had an HIV test and received results in past 12 months 0.518 0.008 9,059 7,953 1.493 0.015 0.502 0.533
Accepting attitudes towards people with HIV 0.298 0.012 4,234 3,701 1.735 0.041 0.273 0.322
Ever experienced any physical violence since age 15 by anyone 0.378 0.019 1,626 1,435 1.589 0.051 0.340 0.416
Ever experienced any sexual violence by anyone 0.105 0.011 1,626 1,435 1.503 0.109 0.082 0.128
Ever experienced any physical/sexual violence by any husband/partner 0.338 0.019 1,300 983 1.441 0.056 0.300 0.375
Physical/sexual violence in the last 12 months by any husband/partner 0.201 0.015 1,300 983 1.319 0.073 0.172 0.230
Total fertility rate (3 years) 4.539 0.129 25,476 22,450 1.691 0.028 4.282 4.796
Neonatal mortality rate (last 0-9 years) 19.598 1.614 13,546 11,169 1.235 0.082 16.370 22.826
Post-neonatal mortality rate (last 0-9 years) 14.097 1.446 13,573 11,193 1.335 0.103 11.204 16.989
Infant mortality rate (last 0-9 years) 33.695 2.311 13,551 11,176 1.334 0.069 29.073 38.317
Child mortality rate (last 0-9 years) 11.576 1.561 13,341 10,969 1.451 0.135 8.454 14.699
Under-five mortality rate (last 0-9 years) 44.881 2.536 13,577 11,194 1.245 0.057 39.809 49.954
Continued…
Appendix B • 375
Table B.9—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.338 0.017 3,484 3,050 2.065 0.049 0.305 0.371
Literacy 0.901 0.007 3,484 3,050 1.420 0.008 0.887 0.916
No education 0.043 0.005 3,484 3,050 1.462 0.117 0.033 0.053
Secondary or higher education 0.457 0.016 3,484 3,050 1.896 0.035 0.425 0.489
Never married/in union 0.453 0.012 3,484 3,050 1.432 0.027 0.429 0.477
Currently married/in union 0.499 0.012 3,484 3,050 1.395 0.024 0.476 0.523
Had sexual intercourse before age 18 0.628 0.011 2,750 2,412 1.204 0.018 0.606 0.650
Know any contraceptive method 0.999 0.001 1,796 1,523 0.756 0.001 0.997 1.000
Know any modern contraceptive method 0.997 0.001 1,796 1,523 0.818 0.001 0.995 0.999
Want no more children 0.423 0.015 1,796 1,523 1.297 0.036 0.392 0.453
Want to delay next birth at least 2 years 0.375 0.014 1,796 1,523 1.230 0.037 0.347 0.404
Ideal number of children 4.017 0.067 3,435 3,014 1.317 0.017 3.883 4.152
Had 2+ sexual partners in past 12 months 0.115 0.008 3,484 3,050 1.427 0.067 0.100 0.131
Condom use at last sex 0.512 0.033 414 351 1.337 0.064 0.447 0.578
Abstinence among youth (never had sex) 0.386 0.019 1,189 1,058 1.350 0.049 0.348 0.425
Sexually active in past 12 months among youth 0.453 0.021 1,189 1,058 1.455 0.046 0.411 0.495
Had an HIV test and received results in past 12 months 0.471 0.013 3,484 3,050 1.485 0.027 0.446 0.496
Accepting attitudes towards people with HIV 0.469 0.013 3,476 3,044 1.492 0.027 0.444 0.495
Ever experienced any physical violence since age 15 by anyone 0.397 0.018 1,360 1,201 1.335 0.045 0.361 0.432
Ever experienced any sexual violence by anyone 0.047 0.008 1,360 1,201 1.450 0.178 0.030 0.063
Ever experienced any physical/sexual violence by any wife/partner 0.095 0.011 888 655 1.140 0.118 0.073 0.118
Physical/sexual violence in the last 12 months by any wife/partner 0.065 0.010 888 655 1.168 0.149 0.046 0.084
376 • Appendix B
Table B.10 Sampling errors: Western sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.167 0.014 2,840 3,225 1.975 0.083 0.139 0.195
Literacy 0.901 0.009 2,840 3,225 1.535 0.010 0.884 0.918
No education 0.028 0.004 2,840 3,225 1.275 0.140 0.020 0.036
Secondary or higher education 0.368 0.014 2,840 3,225 1.507 0.037 0.341 0.395
Never married/in union 0.303 0.010 2,840 3,225 1.201 0.034 0.283 0.324
Currently married/in union 0.604 0.011 2,840 3,225 1.174 0.018 0.583 0.626
Married before age 20 0.551 0.016 2,164 2,436 1.520 0.030 0.518 0.583
Had sexual intercourse before age 18 0.615 0.014 2,164 2,436 1.360 0.023 0.586 0.643
Currently pregnant 0.067 0.005 2,840 3,225 1.099 0.077 0.057 0.077
Children ever born 2.890 0.059 2,840 3,225 1.145 0.021 2.771 3.009
Children surviving 2.601 0.052 2,840 3,225 1.131 0.020 2.496 2.706
Children ever born to women age 40-49 6.053 0.161 495 556 1.339 0.027 5.732 6.375
Know any contraceptive method 1.000 0.000 1,735 1,950 0.915 0.000 0.999 1.000
Know a modern method 1.000 0.000 1,735 1,950 0.915 0.000 0.999 1.000
Currently using any method 0.586 0.016 1,735 1,950 1.363 0.028 0.554 0.619
Currently using a modern method 0.569 0.016 1,735 1,950 1.337 0.028 0.538 0.601
Currently using a traditional method 0.017 0.003 1,735 1,950 1.113 0.204 0.010 0.024
Currently using pill 0.046 0.006 1,735 1,950 1.218 0.133 0.034 0.058
Currently using IUD 0.013 0.003 1,735 1,950 1.002 0.210 0.008 0.018
Currently using male condoms 0.025 0.004 1,735 1,950 1.111 0.168 0.016 0.033
Currently using injectables 0.275 0.014 1,735 1,950 1.333 0.052 0.247 0.304
Currently using female sterilisation 0.059 0.007 1,735 1,950 1.292 0.124 0.044 0.073
Currently using implant 0.152 0.013 1,735 1,950 1.539 0.087 0.125 0.178
Currently using rhythm 0.011 0.003 1,735 1,950 1.018 0.229 0.006 0.016
Currently using withdrawal 0.003 0.002 1,735 1,950 1.359 0.607 0.000 0.006
Used public sector source for family planning 0.739 0.021 1,182 1,320 1.629 0.028 0.697 0.780
Want no more children 0.564 0.020 819 929 1.153 0.035 0.524 0.604
Want to delay next birth at least 2 years 0.303 0.017 819 929 1.060 0.056 0.269 0.337
Ideal number of children 3.700 0.048 1,366 1,544 1.017 0.013 3.604 3.797
Mothers received antenatal care for last birth 0.972 0.006 1,398 1,590 1.306 0.006 0.960 0.983
Mothers protected against tetanus for last birth 0.669 0.028 688 790 1.600 0.042 0.613 0.726
Births with skilled attendant at delivery 0.478 0.019 1,977 2,255 1.487 0.039 0.441 0.515
Delivery in a health facility 0.470 0.019 1,977 2,255 1.524 0.041 0.432 0.508
Had diarrhoea in the last 2 weeks 0.201 0.013 1,889 2,166 1.390 0.065 0.175 0.227
Treated with ORS 0.456 0.035 395 436 1.294 0.078 0.385 0.527
Sought medical treatment for diarrhoea 0.473 0.037 395 436 1.369 0.079 0.399 0.548
Vaccination card seen 0.746 0.024 364 419 1.072 0.032 0.697 0.794
Received BCG vaccination 0.959 0.016 364 419 1.600 0.017 0.926 0.992
Received DPT vaccination (3 doses) 0.902 0.020 364 419 1.289 0.022 0.863 0.942
Received polio vaccination (3 doses) 0.910 0.018 364 419 1.207 0.020 0.874 0.946
Received measles vaccination 0.857 0.024 364 419 1.319 0.028 0.810 0.905
Fully vaccinated 0.813 0.025 364 419 1.228 0.030 0.763 0.863
Vitamin A supplementation in last 6 months 0.985 0.003 1,729 1,967 1.076 0.004 0.978 0.992
Owns at least one insecticide treated net (ITN) 0.819 0.009 3,220 3,604 1.337 0.011 0.800 0.837
Child slept under ITN last night 0.690 0.013 2,176 2,526 1.155 0.019 0.663 0.717
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.384 0.024 719 827 1.367 0.064 0.335 0.433
Child has fever in last two weeks 0.361 0.016 1,889 2,166 1.382 0.046 0.328 0.394
Child took antimalarial 0.518 0.023 731 782 1.103 0.044 0.473 0.564
Height-for-age (-2SD) 0.252 0.014 2,124 2,476 1.397 0.056 0.224 0.281
Weight-for-height (-2SD) 0.019 0.003 2,124 2,476 1.103 0.170 0.013 0.026
Weight-for-age (-2SD) 0.090 0.008 2,124 2,476 1.249 0.090 0.074 0.107
Body Mass Index (BMI) <18.5 0.086 0.010 1,261 1,431 1.249 0.115 0.066 0.105
Had 2+ sexual partners in past 12 months 0.009 0.003 1,386 1,571 1.140 0.316 0.003 0.015
Condom use at last sex 0.216 0.128 12 15 1.032 0.595 0.000 0.473
Abstinence among youth (never had sex) 0.632 0.034 396 456 1.388 0.053 0.564 0.699
Sexually active in past 12 months among youth 0.205 0.025 396 456 1.220 0.121 0.155 0.254
Had an HIV test and received results in past 12 months 0.454 0.010 2,840 3,225 1.112 0.023 0.433 0.475
Accepting attitudes towards people with HIV 0.245 0.014 1,384 1,568 1.201 0.057 0.217 0.273
Ever experienced any physical violence since age 15 by anyone 0.533 0.026 530 640 1.185 0.048 0.482 0.585
Ever experienced any sexual violence by anyone 0.219 0.022 530 640 1.230 0.101 0.175 0.263
Ever experienced any physical/sexual violence by any husband/partner 0.556 0.029 404 433 1.176 0.052 0.498 0.615
Physical/sexual violence in the last 12 months by any husband/partner 0.366 0.031 404 433 1.297 0.085 0.304 0.428
Total fertility rate (3 years) 4.668 0.143 7,824 8,834 1.178 0.031 4.382 4.954
Neonatal mortality rate (last 0-9 years) 19.300 3.238 3,991 4,603 1.409 0.168 12.823 25.776
Post-neonatal mortality rate (last 0-9 years) 20.747 3.088 4,014 4,625 1.313 0.149 14.571 26.924
Infant mortality rate (last 0-9 years) 40.047 5.270 3,996 4,610 1.576 0.132 29.506 50.588
Child mortality rate (last 0-9 years) 24.594 2.893 4,004 4,608 1.120 0.118 18.808 30.380
Under-five mortality rate (last 0-9 years) 63.656 6.019 4,022 4,634 1.362 0.095 51.619 75.693
Continued…
Appendix B • 377
Table B.10—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.198 0.014 1,130 1,164 1.183 0.071 0.170 0.226
Literacy 0.869 0.014 1,130 1,164 1.378 0.016 0.841 0.896
No education 0.009 0.003 1,130 1,164 1.093 0.340 0.003 0.015
Secondary or higher education 0.388 0.020 1,130 1,164 1.381 0.052 0.348 0.429
Never married/in union 0.462 0.021 1,130 1,164 1.410 0.045 0.420 0.504
Currently married/in union 0.482 0.022 1,130 1,164 1.495 0.046 0.437 0.526
Had sexual intercourse before age 18 0.605 0.022 792 825 1.251 0.036 0.561 0.648
Know any contraceptive method 1.000 0.000 524 561 na 0.000 1.000 1.000
Know any modern contraceptive method 1.000 0.000 524 561 na 0.000 1.000 1.000
Want no more children 0.460 0.026 524 561 1.206 0.057 0.408 0.513
Want to delay next birth at least 2 years 0.388 0.024 524 561 1.126 0.062 0.340 0.436
Ideal number of children 3.901 0.072 1,128 1,161 1.303 0.018 3.757 4.045
Had 2+ sexual partners in past 12 months 0.125 0.013 1,130 1,164 1.281 0.101 0.100 0.150
Condom use at last sex 0.272 0.039 135 145 1.008 0.142 0.195 0.350
Abstinence among youth (never had sex) 0.509 0.028 475 483 1.225 0.055 0.453 0.566
Sexually active in past 12 months among youth 0.287 0.024 475 483 1.164 0.084 0.238 0.335
Had an HIV test and received results in past 12 months 0.398 0.019 1,130 1,164 1.272 0.047 0.361 0.435
Accepting attitudes towards people with HIV 0.437 0.020 1,129 1,162 1.326 0.045 0.398 0.476
Ever experienced any physical violence since age 15 by anyone 0.609 0.034 428 445 1.419 0.055 0.542 0.676
Ever experienced any sexual violence by anyone 0.072 0.015 428 445 1.201 0.209 0.042 0.102
Ever experienced any physical/sexual violence by any wife/partner 0.155 0.025 272 253 1.159 0.165 0.104 0.206
Physical/sexual violence in the last 12 months by any wife/partner 0.086 0.017 272 253 1.009 0.200 0.051 0.120
378 • Appendix B
Table B.11 Sampling errors: Nyanza sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 0.300 0.017 4,254 4,038 2.485 0.058 0.265 0.335
Literacy 0.917 0.006 4,254 4,038 1.345 0.006 0.906 0.929
No education 0.014 0.002 4,254 4,038 1.191 0.153 0.010 0.018
Secondary or higher education 0.399 0.014 4,254 4,038 1.922 0.036 0.370 0.427
Never married/in union 0.269 0.010 4,254 4,038 1.452 0.037 0.249 0.289
Currently married/in union 0.625 0.010 4,254 4,038 1.290 0.015 0.606 0.644
Married before age 20 0.612 0.015 3,354 3,164 1.777 0.024 0.582 0.642
Had sexual intercourse before age 18 0.696 0.014 3,354 3,164 1.733 0.020 0.668 0.723
Currently pregnant 0.059 0.005 4,254 4,038 1.284 0.078 0.050 0.069
Children ever born 2.954 0.059 4,254 4,038 1.467 0.020 2.836 3.073
Children surviving 2.594 0.051 4,254 4,038 1.479 0.019 2.493 2.696
Children ever born to women age 40-49 5.807 0.121 696 643 1.248 0.021 5.564 6.049
Know any contraceptive method 0.999 0.001 2,679 2,525 0.991 0.001 0.998 1.000
Know a modern method 0.999 0.001 2,679 2,525 0.991 0.001 0.998 1.000
Currently using any method 0.564 0.012 2,679 2,525 1.252 0.021 0.540 0.588
Currently using a modern method 0.539 0.011 2,679 2,525 1.174 0.021 0.516 0.562
Currently using a traditional method 0.025 0.003 2,679 2,525 1.005 0.121 0.019 0.031
Currently using pill 0.034 0.004 2,679 2,525 1.088 0.111 0.027 0.042
Currently using IUD 0.020 0.003 2,679 2,525 1.193 0.161 0.014 0.026
Currently using male condoms 0.029 0.004 2,679 2,525 1.296 0.144 0.021 0.038
Currently using injectables 0.293 0.012 2,679 2,525 1.386 0.042 0.269 0.318
Currently using female sterilisation 0.036 0.005 2,679 2,525 1.459 0.146 0.025 0.046
Currently using implant 0.124 0.008 2,679 2,525 1.210 0.062 0.108 0.139
Currently using rhythm 0.020 0.003 2,679 2,525 1.051 0.141 0.015 0.026
Currently using withdrawal 0.003 0.001 2,679 2,525 1.000 0.339 0.001 0.005
Used public sector source for family planning 0.696 0.016 1,745 1,654 1.423 0.023 0.665 0.727
Want no more children 0.575 0.018 1,283 1,203 1.308 0.031 0.539 0.611
Want to delay next birth at least 2 years 0.277 0.015 1,283 1,203 1.181 0.053 0.247 0.306
Ideal number of children 3.431 0.036 1,963 1,856 1.139 0.011 3.358 3.503
Mothers received antenatal care for last birth 0.966 0.005 2,085 1,988 1.228 0.005 0.956 0.976
Mothers protected against tetanus for last birth 0.701 0.017 980 934 1.191 0.025 0.666 0.735
Births with skilled attendant at delivery 0.650 0.018 2,926 2,790 1.772 0.028 0.614 0.687
Delivery in a health facility 0.648 0.019 2,926 2,790 1.835 0.029 0.610 0.685
Had diarrhoea in the last 2 weeks 0.189 0.009 2,757 2,638 1.155 0.048 0.171 0.208
Treated with ORS 0.553 0.029 504 500 1.245 0.052 0.495 0.610
Sought medical treatment for diarrhoea 0.597 0.032 504 500 1.419 0.054 0.532 0.661
Vaccination card seen 0.724 0.024 580 552 1.272 0.033 0.677 0.771
Received BCG vaccination 0.956 0.010 580 552 1.239 0.011 0.935 0.977
Received DPT vaccination (3 doses) 0.897 0.018 580 552 1.374 0.020 0.862 0.932
Received polio vaccination (3 doses) 0.906 0.016 580 552 1.325 0.018 0.873 0.938
Received measles vaccination 0.853 0.018 580 552 1.244 0.021 0.816 0.889
Fully vaccinated 0.769 0.021 580 552 1.196 0.027 0.727 0.811
Vitamin A supplementation in last 6 months 0.985 0.003 2,528 2,413 1.184 0.003 0.979 0.991
Owns at least one insecticide treated net (ITN) 0.812 0.009 4,801 4,559 1.510 0.010 0.795 0.829
Child slept under ITN last night 0.692 0.015 2,992 2,894 1.411 0.021 0.663 0.721
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.218 0.017 1,089 1,035 1.359 0.078 0.184 0.251
Child has fever in last two weeks 0.374 0.013 2,757 2,638 1.258 0.034 0.349 0.400
Child took antimalarial 0.487 0.021 989 987 1.226 0.044 0.444 0.530
Height-for-age (-2SD) 0.227 0.010 2,860 2,769 1.215 0.044 0.207 0.247
Weight-for-height (-2SD) 0.020 0.003 2,860 2,769 1.087 0.140 0.015 0.026
Weight-for-age (-2SD) 0.074 0.006 2,860 2,769 1.233 0.083 0.062 0.087
Body Mass Index (BMI) <18.5 0.063 0.006 1,825 1,729 1.132 0.103 0.050 0.076
Had 2+ sexual partners in past 12 months 0.014 0.003 2,015 1,908 1.040 0.194 0.009 0.020
Condom use at last sex 0.396 0.105 28 27 1.114 0.266 0.185 0.607
Abstinence among youth (never had sex) 0.536 0.027 472 469 1.167 0.050 0.482 0.590
Sexually active in past 12 months among youth 0.275 0.025 472 469 1.193 0.089 0.226 0.324
Had an HIV test and received results in past 12 months 0.604 0.010 4,254 4,038 1.336 0.017 0.584 0.624
Accepting attitudes towards people with HIV 0.260 0.014 2,013 1,906 1.456 0.055 0.232 0.289
Ever experienced any physical violence since age 15 by anyone 0.571 0.025 781 756 1.412 0.044 0.521 0.621
Ever experienced any sexual violence by anyone 0.220 0.018 781 756 1.194 0.080 0.185 0.256
Ever experienced any physical/sexual violence by any husband/partner 0.519 0.023 647 553 1.163 0.044 0.474 0.565
Physical/sexual violence in the last 12 months by any husband/partner 0.335 0.024 647 553 1.285 0.071 0.287 0.382
Total fertility rate (3 years) 4.294 0.143 11,794 11,177 1.321 0.033 4.008 4.580
Neonatal mortality rate (last 0-9 years) 18.841 2.189 6,191 5,876 1.169 0.116 14.463 23.219
Post-neonatal mortality rate (last 0-9 years) 31.244 3.079 6,237 5,917 1.284 0.099 25.086 37.403
Infant mortality rate (last 0-9 years) 50.086 3.484 6,202 5,886 1.142 0.070 43.117 57.054
Child mortality rate (last 0-9 years) 33.238 3.223 6,277 5,967 1.196 0.097 26.792 39.684
Under-five mortality rate (last 0-9 years) 81.659 4.957 6,243 5,926 1.175 0.061 71.744 91.573
Continued…
Appendix B • 379
Table B.11—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 0.318 0.021 1,542 1,405 1.753 0.065 0.276 0.359
Literacy 0.950 0.007 1,542 1,405 1.318 0.008 0.936 0.965
No education 0.005 0.002 1,542 1,405 1.123 0.418 0.001 0.009
Secondary or higher education 0.486 0.019 1,542 1,405 1.526 0.040 0.447 0.524
Never married/in union 0.425 0.016 1,542 1,405 1.255 0.037 0.394 0.457
Currently married/in union 0.546 0.017 1,542 1,405 1.308 0.030 0.513 0.579
Had sexual intercourse before age 18 0.598 0.017 1,129 1,018 1.177 0.029 0.564 0.632
Know any contraceptive method 1.000 0.000 850 767 na 0.000 1.000 1.000
Know any modern contraceptive method 1.000 0.000 850 767 na 0.000 1.000 1.000
Want no more children 0.469 0.023 850 767 1.351 0.049 0.423 0.515
Want to delay next birth at least 2 years 0.325 0.021 850 767 1.275 0.063 0.284 0.366
Ideal number of children 3.876 0.088 1,469 1,343 1.318 0.023 3.700 4.052
Had 2+ sexual partners in past 12 months 0.184 0.011 1,542 1,405 1.108 0.060 0.162 0.205
Condom use at last sex 0.476 0.036 269 258 1.176 0.075 0.404 0.548
Abstinence among youth (never had sex) 0.397 0.022 572 536 1.095 0.056 0.352 0.442
Sexually active in past 12 months among youth 0.442 0.022 572 536 1.073 0.050 0.398 0.487
Had an HIV test and received results in past 12 months 0.562 0.016 1,542 1,405 1.239 0.028 0.531 0.594
Accepting attitudes towards people with HIV 0.460 0.017 1,539 1,403 1.302 0.036 0.427 0.493
Ever experienced any physical violence since age 15 by anyone 0.563 0.027 637 568 1.360 0.048 0.509 0.617
Ever experienced any sexual violence by anyone 0.134 0.015 637 568 1.099 0.111 0.104 0.163
Ever experienced any physical/sexual violence by any wife/partner 0.139 0.021 426 345 1.235 0.149 0.098 0.181
Physical/sexual violence in the last 12 months by any wife/partner 0.092 0.016 426 345 1.147 0.175 0.060 0.124
380 • Appendix B
Table B.12 Sampling errors: Nairobi sample, Kenya DHS 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Urban residence 1.000 0.000 999 3,770 na 0.000 1.000 1.000
Literacy 0.965 0.006 999 3,770 1.109 0.007 0.952 0.978
No education 0.017 0.005 999 3,770 1.293 0.314 0.006 0.027
Secondary or higher education 0.661 0.029 999 3,770 1.920 0.044 0.603 0.718
Never married/in union 0.303 0.021 999 3,770 1.431 0.069 0.262 0.345
Currently married/in union 0.562 0.022 999 3,770 1.398 0.039 0.518 0.606
Married before age 20 0.315 0.022 874 3,302 1.390 0.069 0.271 0.358
Had sexual intercourse before age 18 0.318 0.028 874 3,302 1.801 0.089 0.261 0.374
Currently pregnant 0.068 0.009 999 3,770 1.080 0.127 0.050 0.085
Children ever born 1.566 0.077 999 3,770 1.581 0.049 1.411 1.721
Children surviving 1.462 0.073 999 3,770 1.610 0.050 1.315 1.609
Children ever born to women age 40-49 3.051 0.203 104 370 1.124 0.067 2.644 3.458
Know any contraceptive method 0.998 0.002 556 2,117 0.804 0.002 0.994 1.001
Know a modern method 0.998 0.002 556 2,117 0.804 0.002 0.994 1.001
Currently using any method 0.626 0.023 556 2,117 1.143 0.038 0.579 0.673
Currently using a modern method 0.583 0.025 556 2,117 1.197 0.043 0.533 0.633
Currently using a traditional method 0.044 0.007 556 2,117 0.862 0.171 0.029 0.059
Currently using pill 0.125 0.016 556 2,117 1.126 0.126 0.094 0.157
Currently using IUD 0.045 0.011 556 2,117 1.202 0.234 0.024 0.067
Currently using male condoms 0.033 0.007 556 2,117 0.920 0.211 0.019 0.047
Currently using injectables 0.236 0.020 556 2,117 1.096 0.084 0.197 0.276
Currently using female sterilisation 0.020 0.006 556 2,117 1.003 0.301 0.008 0.031
Currently using implant 0.121 0.013 556 2,117 0.903 0.103 0.096 0.146
Currently using rhythm 0.032 0.007 556 2,117 0.959 0.225 0.017 0.046
Currently using withdrawal 0.003 0.003 556 2,117 1.190 0.994 0.000 0.008
Used public sector source for family planning 0.390 0.031 417 1,575 1.302 0.080 0.328 0.452
Want no more children 0.416 0.033 253 968 1.057 0.079 0.350 0.481
Want to delay next birth at least 2 years 0.400 0.033 253 968 1.061 0.082 0.334 0.465
Ideal number of children 3.024 0.070 451 1,712 1.153 0.023 2.883 3.164
Mothers received antenatal care for last birth 0.976 0.008 428 1,657 1.029 0.008 0.961 0.991
Mothers protected against tetanus for last birth 0.830 0.030 200 771 1.132 0.036 0.770 0.889
Births with skilled attendant at delivery 0.891 0.022 532 2,051 1.355 0.025 0.847 0.935
Delivery in a health facility 0.887 0.023 532 2,051 1.390 0.026 0.841 0.932
Had diarrhoea in the last 2 weeks 0.156 0.021 498 1,920 1.266 0.137 0.113 0.199
Treated with ORS 0.634 0.047 79 300 0.834 0.075 0.539 0.729
Sought medical treatment for diarrhoea 0.574 0.051 79 300 0.872 0.088 0.473 0.675
Vaccination card seen 0.617 0.057 106 417 1.187 0.092 0.504 0.731
Received BCG vaccination 0.976 0.017 106 417 1.184 0.018 0.941 1.010
Received DPT vaccination (3 doses) 0.880 0.033 106 417 0.987 0.038 0.813 0.946
Received polio vaccination (3 doses) 0.913 0.027 106 417 0.987 0.029 0.860 0.966
Received measles vaccination 0.925 0.023 106 417 0.920 0.025 0.879 0.971
Fully vaccinated 0.812 0.035 106 417 0.888 0.043 0.743 0.882
Vitamin A supplementation in last 6 months 0.987 0.005 453 1,754 1.013 0.005 0.976 0.998
Owns at least one insecticide treated net (ITN) 0.444 0.026 1,240 4,451 1.841 0.059 0.392 0.496
Child slept under ITN last night 0.506 0.037 474 1,738 1.420 0.073 0.432 0.579
Received 2+ doses of SP/Fansidar during antenatal visit (IPTp) 0.013 0.007 200 753 0.880 0.549 0.000 0.027
Child has fever in last two weeks 0.187 0.024 498 1,920 1.370 0.130 0.138 0.236
Child took antimalarial 0.106 0.033 94 359 1.032 0.309 0.040 0.171
Height-for-age (-2SD) 0.172 0.025 449 1,643 1.335 0.144 0.123 0.222
Weight-for-height (-2SD) 0.025 0.007 449 1,643 0.951 0.278 0.011 0.039
Weight-for-age (-2SD) 0.038 0.011 449 1,643 1.142 0.280 0.017 0.059
Body Mass Index (BMI) <18.5 0.028 0.007 400 1,517 0.911 0.270 0.013 0.042
Had 2+ sexual partners in past 12 months 0.042 0.010 460 1,742 1.098 0.244 0.022 0.063
Condom use at last sex 0.500 0.151 18 73 1.228 0.302 0.198 0.802
Abstinence among youth (never had sex) 0.404 0.055 95 353 1.090 0.137 0.294 0.515
Sexually active in past 12 months among youth 0.436 0.063 95 353 1.226 0.144 0.311 0.562
Had an HIV test and received results in past 12 months 0.604 0.022 999 3,770 1.434 0.037 0.559 0.648
Accepting attitudes towards people with HIV 0.363 0.027 460 1,742 1.215 0.075 0.308 0.417
Ever experienced any physical violence since age 15 by anyone 0.539 0.048 166 611 1.229 0.089 0.443 0.634
Ever experienced any sexual violence by anyone 0.200 0.035 166 611 1.111 0.173 0.131 0.269
Ever experienced any physical/sexual violence by any husband/partner 0.490 0.058 124 414 1.277 0.118 0.375 0.606
Physical/sexual violence in the last 12 months by any husband/partner 0.345 0.053 124 414 1.230 0.153 0.239 0.450
Total fertility rate (3 years) 2.703 0.196 2,916 11,007 1.284 0.073 2.310 3.096
Neonatal mortality rate (last 0-9 years) 39.125 7.813 943 3,617 1.158 0.200 23.500 54.750
Post-neonatal mortality rate (last 0-9 years) 16.124 3.948 940 3,605 0.991 0.245 8.227 24.021
Infant mortality rate (last 0-9 years) 55.249 8.594 944 3,620 1.123 0.156 38.062 72.437
Child mortality rate (last 0-9 years) 17.262 5.241 911 3,518 1.068 0.304 6.781 27.743
Under-five mortality rate (last 0-9 years) 71.557 9.671 947 3,630 1.093 0.135 52.216 90.899
Continued…
Appendix B • 381
Table B.12—Continued
Variable R SE N WN DEFT SE/R R-2SE R+2SE
MEN
Urban residence 1.000 0.000 370 1,568 na 0.000 1.000 1.000
Literacy 0.986 0.009 370 1,568 1.491 0.009 0.968 1.004
No education 0.000 0.000 370 1,568 na na 0.000 0.000
Secondary or higher education 0.729 0.035 370 1,568 1.528 0.049 0.658 0.800
Never married/in union 0.375 0.040 370 1,568 1.582 0.106 0.295 0.455
Currently married/in union 0.584 0.038 370 1,568 1.469 0.065 0.509 0.660
Had sexual intercourse before age 18 0.520 0.032 338 1,431 1.182 0.062 0.456 0.585
Know any contraceptive method 1.000 0.000 218 916 na 0.000 1.000 1.000
Know any modern contraceptive method 1.000 0.000 218 916 na 0.000 1.000 1.000
Want no more children 0.373 0.036 218 916 1.110 0.098 0.300 0.446
Want to delay next birth at least 2 years 0.382 0.034 218 916 1.017 0.088 0.315 0.449
Ideal number of children 3.525 0.129 363 1,543 1.292 0.037 3.266 3.784
Had 2+ sexual partners in past 12 months 0.190 0.019 370 1,568 0.918 0.099 0.153 0.228
Condom use at last sex 0.457 0.066 69 299 1.084 0.144 0.326 0.588
Abstinence among youth (never had sex) 0.198 0.048 97 419 1.178 0.243 0.102 0.293
Sexually active in past 12 months among youth 0.671 0.053 97 419 1.101 0.079 0.565 0.776
Had an HIV test and received results in past 12 months 0.579 0.027 370 1,568 1.052 0.047 0.525 0.633
Accepting attitudes towards people with HIV 0.381 0.026 370 1,568 1.017 0.067 0.330 0.433
Ever experienced any physical violence since age 15 by anyone 0.335 0.051 164 577 1.374 0.152 0.234 0.437
Ever experienced any sexual violence by anyone 0.069 0.020 164 577 1.004 0.289 0.029 0.109
Ever experienced any physical/sexual violence by any wife/partner 0.150 0.034 120 377 1.047 0.229 0.081 0.218
Physical/sexual violence in the last 12 months by any wife/partner 0.113 0.033 120 377 1.122 0.289 0.048 0.178
382 • Appendix B
Table B.13 Sampling errors for adult and maternal mortality rates, Kenya 2014
Variable R SE N WN DEFT SE/R R-2SE R+2SE
WOMEN
Adult mortality rates
15-19 1.674 0.384 32,908 32,170 1.684 0.230 0.906 2.443
20-24 2.101 0.293 40,719 40,528 1.250 0.140 1.514 2.687
25-29 2.656 0.364 39,877 39,466 1.407 0.137 1.929 3.383
30-34 4.725 0.507 33,643 33,397 1.338 0.107 3.712 5.739
35-39 6.781 0.721 25,589 25,719 1.389 0.106 5.340 8.222
40-44 6.825 0.843 17,384 17,486 1.348 0.124 5.139 8.511
45-49 4.997 0.761 11,083 10,965 1.129 0.152 3.475 6.518
15-49 (age-adjusted) 3.716 0.196 201,204 199,731 1.377 0.053 3.324 4.108
Adult mortality probabilities
35q15 2014 138 7 201,204 199,731 1.835 0.052 124 153
35q15 2008-09 214 17 111,333 117,126 2.268 0.078 181 248
35q15 2003 235 12 115,690 115,971 1.382 0.053 210 259
Maternal mortality rates
15-19 0.114 0.072 32,908 32,170 1.212 0.634 0.000 0.258
20-24 0.458 0.146 40,719 40,528 1.376 0.319 0.166 0.751
25-29 0.728 0.227 39,877 39,466 1.674 0.312 0.273 1.183
30-34 0.646 0.195 33,643 33,397 1.401 0.302 0.256 1.035
35-39 0.865 0.255 25,589 25,719 1.391 0.294 0.356 1.375
40-44 0.500 0.178 17,384 17,486 1.053 0.356 0.144 0.857
45-49 0.223 0.118 11,083 10,965 0.829 0.529 0.000 0.460
15-49 (age-adjusted) 0.512 0.076 201,204 199,731 1.443 0.149 0.360 0.665
Maternal mortality ratio (MMR) 2014 362 54 201,204 199,731 1.443 0.150 254 471
Maternal mortality ratio (MMR) 2008-09 520 88 111,333 117,126 1.717 0.170 343 696
Maternal mortality ratio (MMR) 2003 506 54 115,690 115,971 1.07 0.107 398 614
MEN
Adult mortality rates
15-19 2.046 0.344 33,203 32,206 1.353 0.168 1.357 2.734
20-24 2.364 0.297 39,985 39,368 1.194 0.126 1.769 2.959
25-29 3.619 0.401 39,079 38,953 1.277 0.111 2.816 4.422
30-34 5.225 0.536 33,578 33,478 1.331 0.103 4.152 6.298
35-39 7.112 0.698 25,383 25,185 1.274 0.098 5.716 8.509
40-44 9.714 0.977 17,411 16,990 1.256 0.101 7.761 11.667
45-49 10.389 1.307 10,972 10,622 1.309 0.126 7.775 13.002
15-49 (age-adjusted) 4.784 0.242 199,611 196,802 1.295 0.050 4.301 5.267
Adult mortality probabilities
35q15 2014 183 9 199,611 196,802 1.648 0.050 165 202
35q15 2008-09 231 16 112,322 116,604 1.849 0.069 199 263
35q15 2003 240 13 116,586 116,446 1.428 0.053 214 266
Appendix B • 383
DATA QUALITY Appendix C
Table C.1 Household age distribution
Single-year age distribution of the de facto household population by sex (weighted), Kenya 2014
Female Male Female Male
Age Number Percent Number Percent Age Number Percent Number Percent
0 1,906 2.7 1,910 2.8 38 811 1.2 712 1.1
1 1,852 2.6 1,984 2.9 39 616 0.9 557 0.8
2 1,959 2.8 2,040 3.0 40 823 1.2 950 1.4
3 2,051 2.9 2,157 3.2 41 594 0.8 457 0.7
4 1,994 2.8 2,005 3.0 42 600 0.9 719 1.1
5 1,972 2.8 1,962 2.9 43 416 0.6 396 0.6
6 2,214 3.1 2,264 3.4 44 516 0.7 401 0.6
7 2,205 3.1 2,153 3.2 45 651 0.9 656 1.0
8 2,179 3.1 2,155 3.2 46 469 0.7 407 0.6
9 2,001 2.8 2,043 3.0 47 397 0.6 325 0.5
10 2,150 3.1 2,156 3.2 48 409 0.6 319 0.5
11 1,821 2.6 1,725 2.6 49 346 0.5 364 0.5
12 1,983 2.8 2,037 3.0 50 468 0.7 574 0.9
13 1,729 2.5 1,694 2.5 51 519 0.7 364 0.5
14 1,693 2.4 1,829 2.7 52 575 0.8 386 0.6
15 1,286 1.8 1,375 2.0 53 373 0.5 300 0.4
16 1,270 1.8 1,383 2.1 54 465 0.7 374 0.6
17 1,125 1.6 1,250 1.9 55 454 0.6 319 0.5
18 1,298 1.8 1,317 2.0 56 413 0.6 426 0.6
19 1,139 1.6 1,018 1.5 57 257 0.4 287 0.4
20 1,289 1.8 1,159 1.7 58 311 0.4 250 0.4
21 1,069 1.5 960 1.4 59 226 0.3 211 0.3
22 1,388 2.0 1,086 1.6 60 444 0.6 469 0.7
23 1,037 1.5 938 1.4 61 192 0.3 178 0.3
24 1,244 1.8 990 1.5 62 294 0.4 296 0.4
25 1,356 1.9 1,105 1.6 63 161 0.2 151 0.2
26 1,305 1.9 1,060 1.6 64 251 0.4 192 0.3
27 1,234 1.8 1,119 1.7 65 315 0.4 304 0.5
28 1,322 1.9 1,233 1.8 66 205 0.3 185 0.3
29 1,075 1.5 819 1.2 67 136 0.2 150 0.2
30 1,365 1.9 1,363 2.0 68 167 0.2 146 0.2
31 799 1.1 752 1.1 69 164 0.2 123 0.2
32 999 1.4 1,046 1.6 70+ 1,991 2.8 1,514 2.2
33 691 1.0 594 0.9 Don’t know/
34 844 1.2 834 1.2 missing 5 0.0 11 0.0
35 995 1.4 1,065 1.6
36 794 1.1 804 1.2 Total 70,341 100.0 67,439 100.0
37 671 1.0 585 0.9
Note: The de facto population includes all residents and nonresidents who stayed in the household the night before the interview.
Appendix C • 385
Table C.2.1 Age distribution of eligible and interviewed women
De facto household population of women age 10-54 and interviewed women
age 15-49; and percent distribution and percentage of eligible women who
were interviewed (weighted), by five-year age groups, Kenya 2014
Household
population of Percentage of
women age Interviewed women age 15-49 eligible women
Age group 10-54 Number Percentage interviewed
10-14 9,376 na na na
15-19 6,118 5,776 18.6 94.4
20-24 6,027 5,757 18.6 95.5
25-29 6,293 6,073 19.6 96.5
30-34 4,699 4,529 14.6 96.4
35-39 3,888 3,769 12.2 96.9
40-44 2,950 2,878 9.3 97.6
45-49 2,272 2,202 7.1 96.9
50-54 2,400 na na na
15-49 32,247 30,984 100.0 96.1
Note: The de facto population includes all residents and nonresidents who
stayed in the household the night before the interview. Weights for both
household population of women and interviewed women are household
weights. Age is based on the Household Questionnaire.
na = Not applicable
Table C.2.2 Age distribution of eligible and interviewed men
De facto household population of men age 10-64 and interviewed men age
15-59; and percent distribution and percentage of eligible men who were
interviewed (weighted), by five-year age groups, Kenya 2014
Household
population of Percentage of
men age Interviewed men age 15-54 eligible men
Age group 10-59 Number Percentage interviewed
10-14 4,600 na na na
15-19 2,849 2,628 20.1 92.2
20-24 2,330 2,122 16.2 91.1
25-29 2,452 2,184 16.7 89.1
30-34 2,123 1,829 14.0 86.2
35-39 1,688 1,457 11.1 86.3
40-44 1,423 1,253 9.6 88.0
45-49 953 845 6.5 88.7
50-54 864 771 5.9 89.2
55-59 720 na na na
60-64 630 na na na
15-59 15,402 13,089 100.0 85.0
Note: The de facto population includes all residents and nonresidents who
stayed in the household the night before the interview. Weights for both
household population of men and interviewed men are household weights.
Age is based on the Household Questionnaire.
na = Not applicable
386 • Appendix C
Table C.3 Completeness of reporting
Percentage of observations missing information for selected demographic and health questions (weighted),
Kenya 2014
Percentage with
information
Subject missing Number of cases
Birth date Births in the 15 years preceding the survey
Month only 0.90 55,412
Month and year 0.04 55,412
Age at death Deceased children born in the 15 years 0.00 3,353
preceding the survey
Age/date at first union1 Ever married women age 15-49 0.26 22,082
Ever married men age 15-54 0.26 7,457
Respondent’s education All women age 15-49 0.00 31,079
All men age 15-54 0.00 12,819
Diarrhoea in past 2 weeks Living children 0-59 months 0.99 18,702
Anthropometry of children Living children age 0-59 months (from the
Height Household Questionnaire) 2.47 19,790
Weight 2.25 19,790
Height or weight 2.51 19,790
Anthropometry of women Women age 15-49 (from the Household
Height Questionnaire) 5.67 15,260
Weight 5.50 15,260
Height or weight 5.68 15,260
1
Both year and age missing
Table C.4 Births by calendar years
Number of births, percentage with complete birth date, sex ratio at birth, and calendar year ratio by calendar year, according to living, dead, and total children
(weighted), Kenya 2014
Percentage with complete
Number of births birth date1 Sex ratio at birth2 Calendar year ratio3
Calendar year Living Dead Total Living Dead Total Living Dead Total Living Dead Total
2014 2,111 68 2,179 100.0 100.0 100.0 99.8 134.6 100.7 na na na
2013 3,904 159 4,062 99.9 100.0 99.9 103.7 98.1 103.5 na na na
2012 3,678 172 3,849 99.8 98.5 99.8 103.0 100.9 102.9 94.9 98.9 95.1
2011 3,846 189 4,035 99.8 99.2 99.8 109.5 126.5 110.2 101.8 96.4 101.5
2010 3,879 220 4,099 99.9 97.9 99.8 98.7 119.3 99.7 106.5 117.6 107.0
2009 3,439 186 3,625 99.6 98.9 99.6 102.9 106.5 103.1 85.8 78.4 85.4
2008 4,140 253 4,393 99.2 92.2 98.8 98.0 133.6 99.8 113.4 118.0 113.7
2007 3,861 243 4,104 98.9 97.7 98.8 94.9 107.4 95.6 98.3 95.0 98.1
2006 3,715 259 3,974 99.0 92.8 98.6 96.8 87.1 96.1 103.0 109.3 103.4
2005 3,352 231 3,583 98.8 97.3 98.7 108.4 141.7 110.2 92.7 89.8 92.5
0-4 17,417 807 18,224 99.9 98.9 99.8 103.2 113.4 103.6 na na na
5-9 18,506 1,172 19,679 99.1 95.5 98.9 99.8 113.3 100.5 na na na
10-14 15,168 1,286 16,454 98.7 94.6 98.4 97.8 119.0 99.3 na na na
15-19 10,402 1,161 11,563 98.6 94.2 98.2 97.4 123.5 99.8 na na na
20+ 9,809 1,396 11,206 98.5 94.6 98.0 96.9 122.3 99.7 na na na
All 71,303 5,822 77,125 99.1 95.3 98.8 99.4 118.7 100.8 na na na
na = Not applicable
1
Both year and month of birth given
2
(Bm/Bf)x100, where Bm and Bf are the numbers of male and female births, respectively
3
[2Bx/(Bx-1+Bx+1)]x100, where Bx is the number of births in calendar year x
Appendix C • 387
Table C.5 Reporting of age at death in days
Distribution of reported deaths under age 1 month by age at death in days and the
percentage of neonatal deaths reported to occur at ages 0-6 days, for five-year periods of
birth preceding the survey (weighted), Kenya 2014
Number of years preceding the survey
Age at death (days) 0-4 5-9 10-14 15-19 Total 0-19
<1 127 168 165 135 596
1 77 89 95 51 312
2 43 47 29 17 135
3 34 35 30 12 111
4 6 10 11 3 30
5 9 12 4 3 28
6 8 2 5 4 18
7 60 51 35 35 180
8 1 5 2 0 8
9 5 1 4 3 13
10 2 4 0 1 8
12 1 6 3 1 11
13 1 0 4 0 5
14 34 18 18 9 79
15 1 0 1 0 2
16 0 0 0 0 1
17 0 0 1 0 1
18 1 0 0 0 1
20 0 2 1 2 5
21 9 7 7 8 31
22 0 0 2 0 2
23 2 1 0 0 3
25 2 0 2 3 6
28 3 1 0 0 3
Total 0-30 427 460 416 286 1,590
Percentage early neonatal1 71.2 79.1 81.1 78.4 77.4
1
≤6 days / ≤30 days
Table C.6 Reporting of age at death in months
Distribution of reported deaths under age 2 by age at death in months and the
percentage of infant deaths reported to occur at age less than 1 month, for five-year
periods of birth preceding the survey, Kenya 2014
Number of years preceding the survey
Age at death (months) 0-4 5-9 10-14 15-19 Total 0-19
a
<1 427 460 416 286 1,590
1 42 42 39 35 158
2 44 44 49 36 174
3 49 46 47 34 176
4 14 29 46 36 126
5 33 32 28 22 115
6 18 38 53 40 148
7 18 31 21 25 96
8 16 30 53 47 147
9 41 37 33 42 153
10 10 11 21 8 51
11 8 15 17 7 46
12 37 49 93 77 256
13 4 9 11 5 30
14 6 6 13 8 33
15 2 5 11 14 31
16 2 6 7 7 22
17 11 4 3 4 22
18 5 15 20 16 55
19 5 1 5 3 14
20 4 5 8 1 18
21 1 1 0 1 4
22 5 2 2 0 8
23 0 0 2 1 4
24+ 1 1 1 0 3
1 Year 7 3 0 6 16
Total 0-11 721 815 824 619 2,978
Percentage neonatal1 59.3 56.5 50.5 46.2 53.4
a
Includes deaths under one month reported in days
1
Under one month / under one year
388 • Appendix C
Table C.7 Nutritional status of children based on the NCHS/CDC/WHO International Reference Population
Percentage of children under five years classified as malnourished according to three anthropometric indices of nutritional status: height-for-age, weight-for-height, and
weight-for-age, by background characteristics, based on the NCHS/CDC/WHO International Reference Population, Kenya 2014
Height-for-age1 Weight-for-height Weight-for-age
Percent- Percent-
Percent- Percent- Mean Percent- Percent- age Mean Percent- Percent- age Mean
Background age below age below Z-score age below age below above Z-score age below age below above Z-score Number of
characteristic -3 SD -2 SD2 (SD) -3 SD -2 SD2 +2 SD (SD) -3 SD -2 SD2 +2 SD (SD) children
Age in months
<6 0.4 3.7 -0.1 0.2 0.8 12.4 0.7 0.1 0.8 10.0 0.5 1,569
6-8 1.8 8.9 -0.5 0.3 4.4 6.3 0.1 0.6 7.2 3.2 -0.3 937
9-11 2.3 14.4 -0.7 0.3 5.7 2.9 -0.2 2.8 13.7 2.3 -0.8 960
12-17 6.6 23.5 -1.1 0.9 6.7 2.7 -0.4 4.0 22.5 1.4 -1.1 1,996
18-23 8.9 31.5 -1.4 0.8 7.0 2.5 -0.4 3.2 18.2 1.9 -1.0 1,786
24-35 5.4 21.1 -1.0 0.2 3.2 0.7 -0.4 3.1 17.6 0.8 -1.0 3,934
36-47 6.4 22.7 -1.1 0.3 3.0 1.3 -0.3 1.9 15.0 0.9 -0.9 4,024
48-59 6.8 21.6 -1.1 0.1 3.0 1.0 -0.3 1.8 14.6 0.9 -0.9 3,776
Sex
Male 6.3 22.5 -1.1 0.4 4.0 2.7 -0.2 2.7 15.8 1.7 -0.9 9,661
Female 4.8 18.2 -0.9 0.4 3.7 2.6 -0.2 1.9 14.0 2.2 -0.7 9,320
Birth interval in months3
First birth4 4.0 16.4 -0.8 0.3 3.3 3.6 -0.1 1.5 12.4 2.1 -0.6 4,257
<24 8.3 25.7 -1.2 0.3 4.3 1.9 -0.3 3.3 18.1 1.8 -1.0 2,237
24-47 6.3 23.6 -1.1 0.7 4.4 2.3 -0.3 2.7 17.8 1.6 -0.9 6,396
48+ 3.4 16.0 -0.8 0.2 3.3 3.1 -0.1 1.7 11.1 2.6 -0.6 4,271
Size at birth3
Very small 14.2 34.9 -1.5 1.3 7.0 0.5 -0.6 5.2 32.8 0.1 -1.4 255
Small 8.3 28.2 -1.3 0.0 5.7 4.2 -0.4 3.7 22.6 0.6 -1.1 950
Average or larger 4.4 18.1 -0.9 0.3 3.0 3.0 -0.1 1.4 13.0 1.9 -0.7 6,919
Missing 9.5 27.2 -1.3 1.3 10.1 1.2 -0.6 8.0 22.2 0.0 -1.2 109
Mother’s interview status
Interviewed 5.3 20.2 -1.0 0.4 3.9 2.8 -0.2 2.3 14.8 2.0 -0.8 17,161
Not interviewed but in
household 5.2 14.8 -1.2 0.1 3.6 1.1 -0.8 1.8 12.3 2.9 -1.1 391
Not interviewed and not in
the household5 9.2 24.2 -1.0 0.0 3.5 1.4 -0.4 3.1 17.1 1.1 -0.9 1,429
Mother’s nutritional status6
Thin (BMI <18.5) 6.7 24.8 -1.3 0.5 8.9 1.7 -0.8 5.1 29.4 0.1 -1.4 644
Normal (BMI 18.5-24.9) 5.6 21.9 -1.1 0.3 3.3 2.4 -0.2 2.1 15.7 1.3 -0.9 4,499
Overweight/ obese (BMI ≥25) 3.7 13.8 -0.7 0.2 2.4 4.4 0.0 1.0 8.0 2.8 -0.5 2,141
Residence
Urban 4.1 14.6 -0.7 0.3 3.3 3.6 -0.1 1.4 10.5 3.0 -0.6 6,209
Rural 6.3 23.2 -1.1 0.4 4.1 2.2 -0.3 2.8 17.1 1.5 -0.9 12,773
Region
Coast 7.0 24.8 -1.2 0.3 3.9 2.3 -0.3 2.2 18.4 1.5 -1.0 1,926
North Eastern 8.4 20.3 -0.8 1.6 11.4 1.5 -0.8 4.5 25.2 1.3 -1.1 600
Eastern 5.3 24.2 -1.2 0.5 4.1 2.6 -0.3 2.3 16.9 1.2 -1.0 2,400
Central 3.2 13.0 -0.8 0.0 2.2 4.1 -0.0 1.1 8.4 3.5 -0.5 1,691
Rift Valley 6.4 23.6 -1.1 0.5 5.4 2.7 -0.4 3.6 20.1 1.7 -1.0 5,464
Western 5.8 20.2 -1.0 0.1 1.9 2.0 -0.1 1.3 12.2 2.0 -0.7 2,482
Nyanza 5.6 17.9 -0.8 0.2 2.0 2.8 -0.0 1.6 9.6 2.7 -0.6 2,778
Nairobi 2.8 10.8 -0.6 0.3 3.0 2.6 -0.1 1.2 7.3 2.2 -0.4 1,641
Mother’s education7
No education 7.6 24.8 -1.1 1.2 9.2 1.4 -0.7 4.5 26.8 1.1 -1.2 2,106
Primary incomplete 6.3 23.2 -1.1 0.4 3.4 2.0 -0.2 2.4 15.8 1.4 -0.9 9,901
Primary complete 2.9 14.2 -0.8 0.1 2.9 4.0 -0.1 1.3 9.7 2.8 -0.6 4,096
Secondary+ 2.1 8.3 -0.4 0.1 1.9 6.1 0.2 0.8 4.6 5.5 -0.2 1,450
Wealth quintile
Lowest 8.6 29.5 -1.3 0.8 6.9 1.2 -0.5 4.5 25.5 0.7 -1.2 4,610
Second 6.6 24.1 -1.2 0.2 2.8 2.2 -0.2 2.7 16.3 1.2 -0.9 4,092
Middle 4.9 19.3 -1.0 0.2 3.4 2.6 -0.2 1.8 12.2 2.0 -0.8 3,536
Fourth 4.0 15.7 -0.8 0.4 2.7 3.4 -0.1 1.2 10.2 2.7 -0.6 3,294
Highest 2.5 9.3 -0.5 0.1 2.4 4.4 -0.0 0.6 6.6 3.9 -0.3 3,450
Total 5.6 20.4 -1.0 0.4 3.8 2.6 -0.2 2.3 14.9 2.0 -0.8 18,982
Note: Table is based on children who slept in the household the night before the interview. Each of the indices is expressed in standard deviation units (SD) from the
median of the NCHS/CDC/WHO International Reference Population. Table is based on children with valid dates of birth (month and year) and valid measurement of
both height and weight.
1
Recumbent length is measured for children under age 2, or in the few cases when the age of the child is unknown and the child is less than 85cm; standing height is
measured for all other children to be consistent with Table 11.1.1
2
Includes children who are below -3 standard deviations (SD) from the International Reference Population median
3
Excludes children whose mothers were not interviewed
4
First-born twins (triplets, etc.) are counted as first births because they do not have a previous birth interval.
5
Includes children whose mothers are deceased
6
Excludes children whose mothers were not interviewed, children whose mothers were not weighed and measured, and children whose mothers are pregnant or gave
birth within the preceding 2 months. Mother’s nutritional status in terms of BMI (Body Mass Index) is presented in Table 11.10.1
7
For women who are not interviewed, information is taken from the Household Questionnaire. Excludes children whose mothers are not listed in the Household
Questionnaire.
Appendix C • 389
Table C.8 Completeness of information on siblings
Completeness of data on survival status of sisters and brothers reported by interviewed women, age of living siblings and
age at death (AD) and years since death (YSD) of dead siblings (unweighted), Kenya 2014
Sisters Brothers All siblings
Number Percent Number Percent Number Percent
All siblings 42,006 100.0 42,607 100.0 84,613 100.0
Living 37,414 89.1 37,292 87.5 74,706 88.3
Dead 4,577 10.9 5,291 12.4 9,868 11.7
Survival status unknown 15 0.0 24 0.1 39 0.0
Living siblings 37,414 100.0 37,292 100.0 74,706 100.0
Age reported 37,253 99.6 37,148 99.6 74,401 99.6
Age missing 161 0.4 144 0.4 305 0.4
Dead siblings 4,577 100.0 5,291 100.0 9,868 100.0
AD and YSD reported 4,509 98.5 5,174 97.8 9,683 98.1
Missing only AD 29 0.6 45 0.9 74 0.7
Missing only YSD 16 0.3 25 0.5 41 0.4
Missing AD and YSD 23 0.5 47 0.9 70 0.7
Table C.9 Sibship size and sex ratio of siblings
Mean sibship size and sex ratio of siblings at birth,
Kenya 2014
Sex ratio of
Mean sibship siblings at
Age of respondents size1 birth2
15-19 5.8 98.8
20-24 6.1 95.7
25-29 6.5 101.4
30-34 6.9 102.9
35-39 7.3 103.3
40-44 7.7 98.5
45-49 7.8 101.4
Total 6.7 100.2
1
Includes the respondent
2
Excludes the respondent
390 • Appendix C
PERSONS INVOLVED IN THE 2014 KENYA
DEMOGRAPHIC AND HEALTH SURVEY Appendix D
TECHNICAL AND ADMINISTRATIVE SURVEY COORDINATORS
Zachary Mwangi Survey Director
Macdonald G. Obudho Deputy Survey Director
Andrew A. Imbwaga Technical Coordinator
Michael Musyoka Assistant Technical Coordinator
Margaret Nyakang’o Regional Coordinator
Cleophas Kiio Regional Coordinator
Torome Saitoti Regional Coordinator
James Gatungu Regional Coordinator
Collins Omondi Regional Coordinator
Henry Osoro Trainer/Coordinator
James Munguti Trainer/Coordinator/Logistics
A.A. Awes Trainer/Coordinator
Rosemary Kongani Trainer/Coordinator
Robert Buluma Trainer/Coordinator
Godfrey Otieno Trainer/Coordinator
John Mburu Trainer/Coordinator
S.M.T. Wambua Trainer/Coordinator
David Alunga Trainer/Coordinator
Mutua Kakinyi Trainer/Coordinator
Shem Onsare Trainer/Coordinator
Ruth Muia Trainer/Coordinator
Andolo Miheso Trainer/Coordinator
Christine Mbuli Trainer/Coordinator
Lucy Gathigi Trainer/Coordinator
Rosemary Bowen Trainer/Coordinator
John Wanyungu Trainer/Coordinator
STEERING COMMITTEE (SC)
Zachary Mwangi Director General, Kenya National Bureau of Statistics (KNBS)
Macdonald G. Obudho Director Population & Social Statistics, KNBS
Cleophas Kiio Director Information Communication & Technology, KNBS
Margaret Nyakang’o Director, Finance, KNBS
Andrew Imbwaga Technical Coordinator, KNBS
Michael Musyoka KNBS
Stephen Psiwa KNBS
Padma Shetty USAID-Kenya
Washington Omwomo USAID-Kenya
Andrea Kim US Centers for Disease Control
George Kichamu National Council for Population and Development (NCPD)
Josephine Kibaru Mbae NCPD
Yvonne Forsen World Food Programme (WFP)
Annah Wamae Ministry of Health (MOH)
Caroline Wangeci Dale UK Department for International Development (DfID)
Rigah Mwakio Gender Division
Appendix D • 391
Yasuyo Kawamura Japan International Corporation Agency (JICA)
Solomon Mpoke KEMRI
Nduku Kilonzo National Aids Control Council (NACC)
Martin Sirengo National AIDS/STI Control Programme (NASCOP)
Gurumurthy Rangaiyan UNAIDS Country Coordinator
Bouri Jean Victor Sanhoudi United Nations Population Fund (UNFPA)
Marcel K. Rudasingwa UNICEF Kenya Country Office
Joanne Bosworth UNICEF Kenya Country Office
Charles Muitherero World Health Organization (WHO)
Julia Fimpel GDC Sector Coordinator Health
Ramana N.V. Gandham World Bank
Gerald Macharia Clinton Health Access Initiative (CHAI)
Chris Wanyoike Micronutrients Initiative (MI)
TECHNICAL WORKING COMMITTEE (TWC)
Macdonald Obudho KNBS
Andrew A. Imbwaga KNBS
Michael Musyoka KNBS
Samuel Ogola KNBS
James Munguti KNBS
Henry Osoro KNBS
Rosemary Kongani KNBS
Abdikkadir A. Awes KNBS
Robert Buluma KNBS
Godfrey Otieno KNBS
John Bore KNBS
Mutua Kakinyi KNBS
Alfred Agwanda University of Nairobi
Ben Obonyo Jarabi University of Nairobi
Ezekiel Ngure UNFPA
Washington Omwomo USAID
Patrick Mureithi NACC
Joshua Gitonga NACC
Rosemary Kihoto CHAI
Louise Mwirigi UNICEF
Paul Mpuga UNICEF
Elijah Mbiti MI
Yvonne Forsen WFP
Andolo Miheso MOH
Ann Njeru MOH
Ruth Muia MOH
Rebecca Kiptui MOH
Alfred Maina MOH
Claire Nyambati IOM
John Wanyungu NASCOP
James Muttunga KEMRI
392 • Appendix D
SURVEY TEAMS
Baringo Garissa
Paulo Chemjor Supervisor Ali Noor Hussein Supervisor
Hellen Tanui Editor Halima Ibrahim Abdi Editor
Joseph C Cheruiyot Interviewer Muhiyadin Hassan Mohamed Interviewer
Chemutia S Keter Interviewer Adey Gange Adan Interviewer
Monica Aengwo Interviewer Khadija Hajir Abdikadir Interviewer
Emmy Jerotich Chemjor Interviewer Dahabo Ali Ibarim Interviewer
Bomet Homa Bay
George Ondanje Supervisor Mary Atemo Supervisor
Caroline Koima Editor Sally Ogutu Editor
Korir Evans Interviewer Mark Opanga Interviewer
Joyce Chepkoech Interviewer Immaculate Mallowa Interviewer
Tabitha Chepkemoi Interviewer Agutu Lydia Akinyi Interviewer
Janeth Cherotich Interviewer Valerie A. Achieng Interviewer
Bungoma Isiolo
Peter Ochieng Supervisor Jim Kirimi Supervisor
Lydia Nafula Editor Dirre Dika Bilala Editor
Caleb Mwangani Interviewer Adan Mohamud Adan Interviewer
Stella Mollen Nabwire Interviewer Leripe Stephen Sadikey Interviewer
Kimkung C. Caroline Interviewer Maria Lele Apeyan Interviewer
Mildred Khalili Milimu Interviewer Rawia Abdi Galma Interviewer
Busia Kajiado
Maurice Aringo Supervisor Zakary Ochola Supervisor
Mary Makokha Magero Editor Elizabeth N. Nganga Editor
Donutus Ndubi Interviewer William Kisaakua Sipai Interviewer
Linet Ambani Interviewer Deborah Mooke Interviewer
Imelda M. Lumula Interviewer Grace Resistor Kilonka Interviewer
Susan Emachar Interviewer Lillian Wangui Muta Interviewer
Elgeyo Marakwet Kakamega
Grace Onyango Supervisor Francis Nyongesa Supervisor
Abigael J. Rutto Editor Beuter Obura Editor
Bowen Chelimo Simion Interviewer Antony Aluda Interviewer
Philaries J. Keino Interviewer Caroline Osundwa Interviewer
Phylis J. Kimaiyo Interviewer Maryline A. Mikoli Interviewer
Chirchir J. Faith Interviewer Angela Aseyo Omega Interviewer
Embu Kericho
Elizabeth Maina Mukami Supervisor Lilian Onono Supervisor
Everlyne W. Njeru Editor Rose Chepkorir Langat Editor
Kevin N. Micheni Interviewer Ronald Kirui Interviewer
Caroline Karimi Ndwiga Interviewer Emma Chepkurui Maswai Interviewer
Christine Muthui Interviewer Cynthia Chepkurui Sang Interviewer
Pauline Mumbi Njuki Interviewer Janet Chelagat Belyon Interviewer
Appendix D • 393
Kiambu Kwale
Susan Kagema Supervisor Francis Ruwa Kilumi Supervisor
Caroline W. Muturi Editor Salma Nchayu Kadingo Editor
Dennis Mburu Muchuchu Interviewer Jushua Kalama James Interviewer
Irene Wanjiru Interviewer Mwanaisha M. Abdalla Interviewer
Judy Wanjiru Ngethe Interviewer Correy N. Kioko Interviewer
Sophia Wangari Interviewer Fatuma Mbwana Mattussy Interviewer
Kilifi Laikipia
Lucy M. Mulwa Supervisor Leah Wambugu Supervisor
Myles Fondo Editor Irene Wanjiru Mwihaki Editor
Patrick K. Ngumbao Interviewer Martin Mwangi Ndichu Interviewer
Alice Kaingu Interviewer Mary Ann Wanja Kariuki Interviewer
Magdalene Sidi Ngala Interviewer Evalyne Ntipas Kamoiro Interviewer
Harriet Charo Interviewer Esther Kinyanjui Interviewer
Kirinyaga Lamu
David Ngesa Supervisor George Murithi Magara Supervisor
Zippora Nyambura Editor Asia Mohamed Mahmoud Shambe Editor
Joseph Miano Interviewer Mwandeje Beja Jackson Interviewer
Catherine W. Wanyoro Interviewer Jacinta Mnyazi Mwanzia Interviewer
Constance Njeri Ngengi Interviewer Grace Njeri Kibathi Interviewer
Winrose Wanjiku Kimani Interviewer Maryam Mohamed Hussein Interviewer
Kisii Machakos
Aloys Otenyo Supervisor Job Mose Nyandwaki Supervisor
Lilian G. Basweti Editor Damiana S. Kwame Editor
Justus N. Ochoi Interviewer Kelvin Mulwa Makoma Interviewer
Rhoda Nyakundi Interviewer Lydia Mutinda Interviewer
Peris Kwamboka Ogoti Interviewer Dorren Ndungwa Muia Interviewer
Janet Kemunto Obwoge Interviewer Norah Munyiva Mutua Interviewer
Kisumu Makueni
Maurice Owino Omwomo Supervisor Milka Mwangangi Supervisor
Roseline Oloo Editor Alice Kanini Mutunga Editor
Cecil Onyango Ogembo Interviewer Morris M. Ndunda Interviewer
Winne A. Okoyo Interviewer Monicah Kavuli Wambua Interviewer
Maryanne Anyango Interviewer Jacinta Maasai Interviewer
Anne A. Owino Interviewer Joyce Katunge Interviewer
Kitui Mandera
Pius Nganga Supervisor Robert Muthembwa Supervisor
Catherine Kusola Editor Habiba Mohamed Osman Editor
Joseph Kimanzi Nzungu Interviewer Dakane Shaaban Birik Interviewer
Bernice Tabitha Interviewer Ali Habiba Birkan Interviewer
Michelle Syokau Mbwika Interviewer Fardowsa Abdulrashid Mohamed Interviewer
Fredinah Nguta Nzengo Interviewer Suleka Ali Osman Interviewer
394 • Appendix D
Marsabit Nairobi 2
Edson Nganga Supervisor Ezan Sande Supervisor
Sabdio Galgalo Editor Consolata W. Muthembwa Editor
Salad Diba Interviewer Branice Namenge Uluma Interviewer
Halaku Dima Jaldesa Interviewer Michael Maina Nyakango Interviewer
Damaris Narokwe Interviewer Munyao Caroline Nduku Interviewer
Zamzam Hassan Interviewer Rosemary A. Obilo Interviewer
Meru Nakuru
Ellah Koome Supervisor Willie Konde Supervisor
Gladys Githinji Editor Hannah Wangechi Komu Editor
Paul Muthiora Rukunga Interviewer Ben Mwangi Interviewer
Susan Mwende Gitari Interviewer Rhoda Jemaech Kimattah Interviewer
Florence Nyokabi Interviewer Purity Chepkemoi Sang Interviewer
Gacii Kioga Interviewer Irene Kanana Mwebia Interviewer
Migori Narok
Rennice Bunde Supervisor John Asudi Supervisor
Florence A. Odhiambo Editor Letura Naserian Editor
Lucas Abich Otieno Interviewer Sanoi S. Simai Interviewer
Lydia A. Ogira Interviewer Joan Nenkokuai Lekiyieyo Interviewer
Mary Robi Meteti Interviewer Silole Ann Sorimpan Interviewer
Billie Achieng Obura Interviewer Cherono Judy Sigei Interviewer
Mombasa Nyandarua
Stanslaus M. Nzuki Supervisor Jairus Kuria Supervisor
Mwanajuma Mohamed Kondo Editor Catherine Wamuyu Mwangi Editor
David Kanini Interviewer Oscar Maina Interviewer
Zipporah Hiuko Gachemi Interviewer Joan Mumbi Gichuki Interviewer
Khadija Omar Hamisi Interviewer Milka Wangui Njihia Interviewer
Salma Hassan Mahamoud Interviewer Agatha Wanjiku Mbugua Interviewer
Muranga Nyeri
Peter Wanjohi Supervisor Peter K. Kamau Supervisor
Prisca W. Mwangi Editor Eunice Gathigia Maingi Editor
Simon Muchiri Interviewer Marvin Ndegwa Irungu Interviewer
Grace Wanjiru Gakuo Interviewer Rose W. Wanjohi Interviewer
Christine Mweru Maina Interviewer Judith Wanjiru Muchiri Interviewer
Anastacia Irungu Interviewer Catherine Wanjiku Wahome Interviewer
Nairobi 1 Nandi
George Okuku Supervisor Milcah Leting Supervisor
Sidney Kamadi Kongani Editor Milcah Jepkemboi Sawe Editor
Paul Onyango Interviewer Leonard Kiprop Melly Interviewer
Priscilla M. Ndayara Interviewer Eucabeth Jepleting Interviewer
Linda Katunge Kiamba Interviewer Judith Chepkogei Rotich Interviewer
Anne Chepkuriot Interviewer Antonet Jelagat Interviewer
Appendix D • 395
Nyamira Trans Nzoia
George M. Ogendi Supervisor Benard Obasi Supervisor
Roseline Bisieri Nyaberi Editor Faith Lundu Luvai Editor
Brian Mochama Interviewer Johnson O. Oola Interviewer
Risper Moraa Interviewer Phanice Chumba Interviewer
Selina K. Ombogo Interviewer Laura Musati Interviewer
Jedidah Oirere Bochaberi Interviewer Anne Sudi Mung’Ou Interviewer
Samburu Turkana
Joseph K. M’Ekebu Supervisor William Nyongo Supervisor
George L. Nang’Iro Editor Jamila Bisbas Editor
Tom Leparachau Interviewer Muya Fredrick Lokol Interviewer
Naisula S. Lepanyo Interviewer Alimlim Teresa Interviewer
Diana M. Lenotoisoni Interviewer Dorcus Loote Lokuruka Interviewer
Lucia V. Lonyait Interviewer Priscilla Akwanga Elim Interviewer
Siaya Uasin Gishu
Joshua Silingi Supervisor Patrick A. Ndivo Supervisor
Austine O. Dieto Interviewer Christine Milimo Editor
Michelle Atieno Okinda Editor Benjamin Kimutai Birgen Interviewer
Christine V.A. Onyango Interviewer Sylvia C. Kerich Interviewer
Jedidah Atieno Okeyo Interviewer Hildah Jerotich Rutto Interviewer
Faith Atieno Nyaura Interviewer Nancy Chepkurui Ruttoh Interviewer
Taita Taveta Vihiga
Gilbert Mwadenje Supervisor Rose Malova Supervisor
Kongo Scholastica Kingi Editor Ethel Mwachi Editor
Jackson Mnyamwenzi Interviewer Fanuel Odari Interviewer
Jemimah Wanjala Mwalugha Interviewer Mary Kanaga Interviewer
Joyce Kayemba Mwangangi Interviewer Jackline Madiga Interviewer
Margaret Wachia Wokini Interviewer Juliet Munala Interviewer
Tana River Wajir
James Opoti Mkuzi Supervisor Joel Nzivo Supervisor
Leila Nuru Shungi Editor Habon Mohamed Sheikh Editor
Tobias Guyo Galogalo Interviewer Abdi Abdillahi Aden Interviewer
Hilda Haluva Deye Interviewer Saadia Noor Bulle Interviewer
Mwanamisi N. Mohmaed Interviewer Rehema Hodan Sanbur Interviewer
Jamila Sadia Shune Interviewer Shides Mohamed Hilowle Interviewer
Tharaka Nithi West Pokot
Jephew Kathuru Supervisor Isaiah Leting Supervisor
Dis K. Gituyu Editor Sharon Cherono Editor
David Mwenda Interviewer Lotunale Elijah Interviewer
Maryrose Nyakio Kithinji Interviewer Elizabeth C. Merikou Interviewer
Leah Bonface Kiragu Interviewer Rofina Chepkiach Interviewer
Martha Njagi Interviewer Brenda C. Pkemoi Interviewer
396 • Appendix D
DATA ENTRY CLERKS
Annet Ayuma Nelson Nyausi Rahab Wambui Mburu
Bradley Kipkemboi Pascal Wanyama Paul Onyango Opondo
Celine Atieno Racheal J. Kibira Irene Nzisa Owayo
Doris Ogumo Ronald Akaliche Philip Kioko Mwinzi
Dynesius Nyangau Samuel Leiyan Irene Gachuiri
Florence Kanyambu Tabitha Nyaguthii Peter Chesang Kulei
Hibaq Abdullahi Aden Emmanuel Ayuya Daisy Kiptuya Komen
Jane Mbulwa Masila Fanuel M. Wanjira David Kibet Sirma
Levi Mwale Ifedha Kevin Omondi Owuor Vidy Wairimu Muchoki
Lilian Patrick Michael Kioko Mutuva Ann Wango Mwaniki
Matayo Mwenesi Nancy Chemtai Kitelo Veronica Nzuilla Kyalo
Mildred Ojiambo Francis Kiarie Mary Wanjiru Kaigu
Molly Anyango Ochieng Esther M. Kasolia Jackline Chemtai Bet
DATA ENTRY SUPERVISORS
Eunice Maina
Domatila Kivuvo
Redempta Muyuma
Anselim Okwoyo
DATA PROCESSING MANAGERS
Mutua Kakinyi
Paul Waweru
QUALITY ASSURANCE
Alice Omondi Hilda G. Akhonya Ruth Tamaro Nyaundi
Anthony Njeru Janet Akoya Salat Osman Bare
Antony O. Anyango Kennedy Kimani Gathura Salya Mboto Mwatsahu
Carolyne Muthonjo Margaret Nyamwok Sharon Adisa
Cynthia Waswa Mary Machinji Sospeter Kibii
Daniel Mayiani Naroki Mary Mbuvi Stephanie Nerima
David Kigen Mathew Mburu Wato Galgalo Elma
Dorcas A. Angolo Mike Kukat William Mbusya
Francis Oreno Miyara Nancy Nkatha Kithinjii
Geofry Waiganjo Purity Nyamai
Appendix D • 397
RESERVE/DATA PROCESSING
Annet Ayuma Jane Mbulwa Sila Nelson Nyausi
Bradley Kipkemboi Levi Mwale Ifedha Pascal Wanyama
Celine Atieno Lilian Patrick Racheal J. Kibira
Doris Ogumo Matayo Mwenesi Ronald Akaliche
Dynesius Nyangau Mildred Ojiambo Samuel Leiyan
Hibaq Abdullahi Aden Molly Anyango Ochieng Tabitha Nyaguthii
ICF INTERNATIONAL, INC.
Sara K. Head Survey Manager
Ann Way Regional Coordinator
Ladys Ortiz Parra Data Processing Specialist
Ruilin Ren Sampling Statistician
Joy Fishel Senior Technical Specialist
Trevor Croft Technical Director
Luis Sevilla Survey Manager
Jean de Dieu Bizimana Survey Manager
Mehari Belachew Research Associate
Julia Fleuret Survey Manager
Zhuzhi Moore Survey Manager (Report Reviewer)
Kia Reinis Survey Manager (Report Reviewer)
Michelle Gamber Survey Manager (Report Reviewer)
Christopher Gramer Production Specialist/Cover Design
Nancy Johnson Editor
Greg Edmondson Editor
398 • Appendix D
QUESTIONNAIRES Appendix E
OVERVIEW OF DATA COLLECTED IN FULL AND SHORT QUESTIONNAIRES
Household Questionnaire
Full Short
Composition (e.g., headship, size, age, sex, education) • •
Characteristics (e.g., source of water, type of sanitation facilities; exposure to
• •
second-hand smoke inside the home)
Wealth index • •
Household ownership and use of mosquito nets • •
Household ownership of dwelling, land •
Household receipt of social assistance •
Nutritional status of women age 15-49 years1 •
Nutritional status of children under age five years • •
Woman’s Questionnaire
Full Short
Individual characteristics (e.g., age, sex, education, marital status, media exposure) • •
Fertility and reproductive history • •
Knowledge and use of family planning methods • •
Fertility preferences •
Antenatal and delivery care • •
Breastfeeding •
Vaccinations and childhood illnesses • •
Infant and child feeding practices •
Childhood mortality • •
Marriage and sexual activity • •
Woman’s work and husband’s background characteristics •
Awareness and behaviour about HIV and other sexually transmitted infections • •
Adult and maternal mortality •
Domestic violence •
Female circumcision •
Fistula •
1 Women’s nutritional status, calculated from anthropometry measurements, is an exception to the 2014 KDHS recommendations
for estimation of indicators at the county level. Although anthropometry data were not collected from women in the one-half of
households administered the short questionnaire, there are sufficient cases from the one-half of households administered the full
questionnaire to calculate county level estimates of women's nutritional status.
Appendix E • 399
1 LONG VERSION QUESTIONNAIRE SERIAL NUMBER
2014 KENYA DEMOGRAPHIC AND HEALTH SURVEY
HOUSEHOLD QUESTIONNAIRE - LONG VERSION
CONFIDENTIAL
REPUBLIC OF KENYA
IDENTIFICATION
COUNTY
DISTRICT
LOCATION/TOWN
SUBLOCATION
NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NAME OF HOUSEHOLD HEAD
IS HOUSEHOLD SELECTED FOR WOMEN'S SECTION 14? (YES=1; NO=2) . . . . . . . . . . . . . . . . . .
INTERVIEWER VISITS
1 2 3 FINAL VISIT
DATE DAY
MONTH
YEAR
INTERVIEWER'S NAME INT. NUMBER
RESULT* RESULT
NEXT VISIT: DATE
TOTAL NUMBER
TIME OF VISITS
*RESULT CODES: LINE NO. OF
1 COMPLETED RESPONDENT TO
2 NO HOUSEHOLD MEMBER AT HOME / NO COMPETENT HOUSHOLD
RESPONDENT AT HOME AT TIME OF VISIT QUESTIONNAIRE
3 ENTIRE HOUSEHOLD ABSENT FOR EXTENDED PERIOD OF TIME
4 POSTPONED TOTAL PERSONS
5 REFUSED IN HOUSEHOLD
6 DWELLING VACANT OR ADDRESS NOT A DWELLING
7 DWELLING DESTROYED TOTAL ELIGIBLE
8 DWELLING NOT FOUND WOMEN
9 OTHER
TOTAL ELIGIBLE
(SPECIFY) MEN
LANGUAGE OF TRANSLATOR USED
QUESTIONNAIRE** 1 7 LANGUAGE OF
INTERVIEW:
NATIVE LANGUAGE
OF RESPONDENT: (YES = 1, NO = 2)
LANGUAGE OF
QUESTIONNAIRE: English
**LANGUAGE 01 BORANA 05 KIKUYU 09 LUO 13 POKOT 17 ENGLISH
CODES: 02 EMBU 06 KISII 10 MAASAI 14 SOMALI 18 OTHER
03 KALENJIN 07 LUHYA 11 MERU 15 SWAHILI
04 KAMBA 08 MARAGOLI 12 MIJIKENDA 16 TURKANA
SUPERVISOR FIELD EDITOR OFFICE EDITOR KEYED BY
NAME NAME
HH-1
Appendix E • 401
THIS PAGE IS INTENTIONALLY BLANK
HH-2
402 • Appendix E
INTRODUCTION AND CONSENT
Hello. My name is _______________________________________. I am working with the Kenya National Bureau
of Statistics. We are conducting a survey about health all over Kenya. The information we collect will help the
government to plan health services. Your household was selected for the survey. I would like to ask you some
questions about your household. The questions usually take about 30 to 60 minutes. All of the answers you give will
be confidential and will not be shared with anyone other than members of our survey team. You don't have to be in
the survey, but we hope you will agree to answer the questions since your views are important. If I ask you any
question you don't want to answer, just let me know and I will go on to the next question or you can stop the
interview at any time. In case you need more information about the survey, you may contact the person listed on
this card.
GIVE CARD WITH CONTACT INFORMATION
Do you have any questions?
May I begin the interview now?
SIGNATURE OF INTERVIEWER: DATE:
RESPONDENT AGREES TO BE INTERVIEWED . . . 1 RESPONDENT DOES NOT AGREE TO BE INTERVIEWED . . . 2 END
HH-3
Appendix E • 403
HOUSEHOLD SCHEDULE
IF AGE 15 IF AGE 0-17 YEARS IF A
OR OLDER
LINE USUAL RESIDENTS AND RELATIONSHIP SEX RESIDENCE AGE MARITAL ELIGIBILITY SURVIVORSHIP AND
NO. VISITORS TO HEAD OF STATUS RESIDENCE OF
HOUSEHOLD BIOLOGICAL PARENTS BIOLO
1 2 3 4 5 6 7 8 9 10 11 12 13
Please give me the names What is the Is Does Did How old is What is (NAME)'s CIRCLE CIRCLE CIRCLE Is (NAME)'s Does
of the persons who usually relationship of (NAME) (NAME) (NAME) (NAME)? current marital LINE LINE LINE natural (NAME)'s
live in your household and (NAME) to the male or usually stay here status? NUMBER NUMBER NUMBER mother natural
guests of the household who head of the female? live here? last OF ALL OF ALL OF ALL alive? mother
stayed here last night, household? night? WOMEN MEN CHILDREN usually live in
starting with the head of the this
AGE AGE AGE 0-5
household. household or
SEE CODES IF 95 15-49 15-54
was she a
guest last
BELOW. OR MORE,
night?
RECORD
AFTER LISTING THE '95'. IF YES: What
NAMES AND RECORDING is her name?
THE RELATIONSHIP
AND SEX FOR EACH 1 = MARRIED
PERSON, ASK OR LIVING
QUESTIONS 2A-2C TOGETHER RECORD
TO BE SURE THAT THE 2 = DIVORCED/ MOTHER'S
LISTING IS COMPLETE. SEPARATED LINE
3 = WIDOWED NUMBER.
THEN ASK APPROPRIATE 4 = NEVER- IF NO,
QUESTIONS IN COLUMNS MARRIED/LIVED RECORD
5-20A FOR EACH PERSON. TOGETHER '00'.
M F Y N Y N IN YEARS Y N DK
01 1 2 1 2 1 2 01 01 01 1 2 8
GO TO 14
02 1 2 1 2 1 2 02 02 02 1 2 8
GO TO 14
03 1 2 1 2 1 2 03 03 03 1 2 8
GO TO 14
04 1 2 1 2 1 2 04 04 04 1 2 8
GO TO 14
05 1 2 1 2 1 2 05 05 05 1 2 8
GO TO 14
06 1 2 1 2 1 2 06 06 06 1 2 8
GO TO 14
07 1 2 1 2 1 2 07 07 07 1 2 8
GO TO 14
08 1 2 1 2 1 2 08 08 08 1 2 8
GO TO 14
09 1 2 1 2 1 2 09 09 09 1 2 8
GO TO 14
10 1 2 1 2 1 2 10 10 10 1 2 8
GO TO 14
2A) Just to make sure that I have a complete CODES FOR Q. 3: RELATIONSHIP TO HEAD OF HOUSEHOLD
listing: are there any other persons such as
small children or infants that we have not
YES ADD TO 01 = HEAD 08 = BROTHER OR SISTER
listed?
TABLE NO 02 = WIFE OR HUSBAND 09 = OTHER RELATIVE
2B) Are there any other people who may not be 03 = SON OR DAUGHTER 10 = ADOPTED/FOSTER/
members of your family, such as domestic 04 = SON-IN-LAW OR STEPCHILD
servants, lodgers, or friends who usually live
YES ADD TO DAUGHTER-IN-LAW 11 = NOT RELATED
here?
TABLE NO 05 = GRANDCHILD 98 = DON'T KNOW
2C) Are there any guests or temporary visitors 06 = PARENT
staying here, or anyone else who stayed
ADD TO 07 = PARENT-IN-LAW
here last night, who have not been listed?
YES TABLE NO
404 • Appendix E HH-4
IF AGE 0-17 YEARS IF AGE 3 YEARS IF AGE 3-24 YEARS IF AGE 0-4 YEARS
OR OLDER
LINE SURVIVORSHIP AND EVER ATTENDED CURRENT/RECENT BIRTH
NO. RESIDENCE OF SCHOOL SCHOOL ATTENDANCE REGISTRATION
BIOLOGICAL PARENTS
14 15 16 17 18 19 19A 19B 20 20A
Is Does Has What is the highest Did During the 2014 Did During the 2013 Has (NAME) ever Why was
(NAME)'s (NAME)'s (NAME) level of school (NAME) school year, what (NAME) school year, what been registered with (NAME) never
natural natural father ever (NAME) has attend level and grade attend level and grade did the civil authority? registered?
father usually live in attended attended? school at [is/was] (NAME) school at (NAME) attend?
alive? this school? any time attending? any time IF YES: With a birth
household or What is the highest during the during the certificate?
was he a grade (NAME) 2014 2013
1=TOO FAR
guest last completed at that school school
night? level? year? year?
SEE CODES SEE CODES
IF YES: What BELOW. BELOW. 2=NO MONEY
is his name?
1 = YES, REGISTERED 3=NOT AWARE
WITH BIRTH
CERTIFICATE 4=NOT
SEE CODES NECESSARY
RECORD BELOW. 2 = YES, REGISTERED
FATHER'S WITHOUT BIRTH 5=NOMADIC
LINE CERTIFICATE LIFE, DIFFICULT
NUMBER. TERRAIN,
IF NO, 8 = DON'T KNOW INSECURITY
RECORD
'00'. 3 = NOT REGISTERED 8=OTHER
Y N DK Y N LEVEL GRADE Y N LEVEL GRADE Y N LEVEL GRADE Y1 Y2 DK NO
01 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
02 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
03 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
04 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
05 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
06 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
07 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
08 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
09 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
10 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
CODES FOR Qs. 17, 19, AND 19B: EDUCATION
LEVEL GRADE
0 = PRE-PRIMARY 00 = LESS THAN 1 YEAR COMPLETED
1 = PRIMARY (USE '00' FOR Q. 17 ONLY.
2 = POST-PRIMARY, VOCATIONAL THIS CODE IS NOT ALLOWED
3 = SECONDARY/'A' LEVEL FOR Q. 19 OR 19B)
4 = COLLEGE (MIDDLE LEVEL) 98 = DON'T KNOW
5 = UNIVERSITY
8 = DON'T KNOW
HH-5 Appendix E • 405
IF AGE 15 IF AGE 0-17 YEARS IF A
OR OLDER
LINE USUAL RESIDENTS AND RELATIONSHIP SEX RESIDENCE AGE MARITAL ELIGIBILITY SURVIVORSHIP AND
NO. VISITORS TO HEAD OF STATUS RESIDENCE OF
HOUSEHOLD BIOLOGICAL PARENTS BIOLO
1 2 3 4 5 6 7 8 9 10 11 12 13
Please give me the names What is the Is Does Did How old is What is (NAME)'s CIRCLE CIRCLE CIRCLE Is (NAME)'s Does
of the persons who usually relationship of (NAME) (NAME) (NAME) (NAME)? current marital LINE LINE LINE natural (NAME)'s
live in your household and (NAME) to the male or usually stay here status? NUMBER NUMBER NUMBER mother natural
guests of the household who head of the female? live here? last OF ALL OF ALL OF ALL alive? mother
stayed here last night, household? night? WOMEN MEN CHILDREN usually live in
starting with the head of the this
AGE AGE AGE 0-5
household. household or
SEE CODES IF 95 15-49 15-54
was she a
guest last
BELOW. OR MORE,
night?
RECORD
AFTER LISTING THE '95'. IF YES: What
NAMES AND RECORDING is her name?
THE RELATIONSHIP
AND SEX FOR EACH 1 = MARRIED
PERSON, ASK OR LIVING
QUESTIONS 2A-2C TOGETHER RECORD
TO BE SURE THAT THE 2 = DIVORCED/ MOTHER'S
LISTING IS COMPLETE. SEPARATED LINE
3 = WIDOWED NUMBER.
THEN ASK APPROPRIATE 4 = NEVER- IF NO,
QUESTIONS IN COLUMNS MARRIED/LIVED RECORD
5-20A FOR EACH PERSON. TOGETHER '00'.
M F Y N Y N IN YEARS Y N DK
11 1 2 1 2 1 2 11 11 11 1 2 8
GO TO 14
12 1 2 1 2 1 2 12 12 12 1 2 8
GO TO 14
13 1 2 1 2 1 2 13 13 13 1 2 8
GO TO 14
14 1 2 1 2 1 2 14 14 14 1 2 8
GO TO 14
15 1 2 1 2 1 2 15 15 15 1 2 8
GO TO 14
16 1 2 1 2 1 2 16 16 16 1 2 8
GO TO 14
17 1 2 1 2 1 2 17 17 17 1 2 8
GO TO 14
18 1 2 1 2 1 2 18 18 18 1 2 8
GO TO 14
19 1 2 1 2 1 2 19 19 19 1 2 8
GO TO 14
20 1 2 1 2 1 2 20 20 20 1 2 8
GO TO 14
TICK HERE IF CONTINUATION SHEET USED CODES FOR Q. 3: RELATIONSHIP TO HEAD OF HOUSEHOLD
2A) Just to make sure that I have a complete 01 = HEAD 08 = BROTHER OR SISTER
listing: are there any other persons such as 02 = WIFE OR HUSBAND 09 = OTHER RELATIVE
small children or infants that we have not
YES ADD TO 03 = SON OR DAUGHTER 10 = ADOPTED/FOSTER/
listed?
TABLE NO 04 = SON-IN-LAW OR STEPCHILD
2B) Are there any other people who may not be DAUGHTER-IN-LAW 11 = NOT RELATED
members of your family, such as domestic 05 = GRANDCHILD 98 = DON'T KNOW
servants, lodgers, or friends who usually live
YES ADD TO 06 = PARENT
here?
TABLE NO 07 = PARENT-IN-LAW
2C) Are there any guests or temporary visitors
staying here, or anyone else who stayed
ADD TO
here last night, who have not been listed?
YES TABLE NO
406 • Appendix E HH-6
IF AGE 0-17 YEARS IF AGE 3 YEARS IF AGE 3-24 YEARS IF AGE 0-4 YEARS
OR OLDER
LINE SURVIVORSHIP AND EVER ATTENDED CURRENT/RECENT BIRTH
NO. RESIDENCE OF SCHOOL SCHOOL ATTENDANCE REGISTRATION
BIOLOGICAL PARENTS
14 15 16 17 18 19 19A 19B 20 20A
Is Does Has What is the highest Did During the 2014 Did During the 2013 Has (NAME) ever Why was
(NAME)'s (NAME)'s (NAME) level of school (NAME) school year, what (NAME) school year, what been registered with (NAME) never
natural natural father ever (NAME) has attend level and grade attend level and grade did the civil authority? registered?
father usually live in attended attended? school at [is/was] (NAME) school at (NAME) attend?
alive? this school? any time attending? any time IF YES: With a birth
household or What is the highest during the during the certificate?
was he a grade (NAME) 2014 2013
1=TOO FAR
guest last completed at that school school
night? level? year? year?
SEE CODES SEE CODES
IF YES: What BELOW. BELOW. 2=NO MONEY
is his name?
1 = YES, REGISTERED 3=NOT AWARE
WITH BIRTH
CERTIFICATE 4=NOT
SEE CODES NECESSARY
RECORD BELOW. 2 = YES, REGISTERED
FATHER'S WITHOUT BIRTH 5=NOMADIC
LINE CERTIFICATE LIFE, DIFFICULT
NUMBER. TERRAIN,
IF NO, 8 = DON'T KNOW INSECURITY
RECORD
'00'. 3 = NOT REGISTERED 8=OTHER
Y N DK Y N LEVEL GRADE Y N LEVEL GRADE Y N LEVEL GRADE Y1 Y2 DK NO
11 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
12 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
13 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
14 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
15 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
16 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
17 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
18 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
19 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
20 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
CODES FOR Qs. 17, 19, AND 19B: EDUCATION
LEVEL GRADE
0 = PRE-PRIMARY 00 = LESS THAN 1 YEAR COMPLETED
1 = PRIMARY (USE '00' FOR Q. 17 ONLY.
2 = POST-PRIMARY, VOCATIONAL THIS CODE IS NOT ALLOWED
3 = SECONDARY/'A' LEVEL FOR Q. 19 OR 19B)
4 = COLLEGE (MIDDLE LEVEL) 98 = DON'T KNOW
5 = UNIVERSITY
8 = DON'T KNOW
HH-7 Appendix E • 407
TABLE FOR SELECTION OF WOMEN FOR SECTION 14: DOMESTIC VIOLENCE QUESTIONS
101A ONLY ONE INDIVIDUAL (ONE WOMAN OR ONE MAN) SHOULD BE SELECTED FOR DOMESTIC VIOLENCE QUESTIONS
CHECK COVER PAGE: YES NO
HOUSEHOLD SELECTED FOR WOMEN'S SECTION 14?
GO TO 101B
USE THE TABLE BELOW TO
SELECT ONE WOMAN FROM THIS
HH TO BE INTERVIEWED WITH THE
DV QUESTIONS
HOW TO USE THE TABLE FOR SELECTION OF A RESPONDENT
LAST DIGIT OF QUESTIONANIRE SERIAL NUMBER TOTAL NUMBER OF ELIGIBLE WOMEN (COL 9)
(GO TO THIS ROW NUMBER) (GO TO THIS COLUMN NUMBER)
IF ZERO GO TO 102
LOOK AT THE LAST DIGIT OF THE HOUSEHOLD QUESTIONNAIRE SERIAL NUMBER ON THE COVER PAGE. THIS IS
THE ROW NUMBER YOU SHOULD GO TO. CHECK THE TOTAL NUMBER OF ELIGIBLE WOMEN (COLUMN 9) IN THE
HOUSEHOLD SCHEDULE. THIS IS THE COLUMN NUMBER YOU SHOULD GO TO. FOLLOW THE SELECTED ROW AND
COLUMN TO THE CELL WHERE THEY MEET AND CIRCLE THE NUMBER IN THE CELL. THIS IS THE NUMBER OF THE
WOMAN SELECTED FOR THE DOMESTIC VIOLENCE QUESTIONS FROM THE LIST OF ELIGIBLE WOMEN IN COLUMN
9 OF THE HOUSEHOLD SCHEDULE. WRITE THE NAME AND LINE NUMBER OF THE SELECTED WOMAN IN THE
SPACE BELOW THE TABLE.
EXAMPLE: THE HOUSEHOLD QUESTIONNAIRE SERIAL NUMBER IS ‘716’ AND THE HOUSEHOLD SCHEDULE COLUMN
9 SHOWS THAT THERE ARE THREE ELIGIBLE WOMEN AGE 15-49 IN THE HOUSEHOLD (LINE NUMBERS 02, 04, AND
05). SINCE THE LAST DIGIT OF THE HOUSEHOLD SERIAL NUMBER IS '6' GO TO ROW '6' AND SINCE THERE ARE
THREE ELIGIBLE WOMEN IN THE HOUSEHOLD, GO TO COLUMN '3’. FOLLOW THE ROW AND COLUMN AND FIND
THE NUMBER IN THE CELL WHERE THEY MEET (‘2') AND CIRCLE THE NUMBER. NOW GO TO THE HOUSEHOLD
SCHEDULE AND FIND THE SECOND WOMAN WHO IS ELIGIBLE FOR THE WOMAN'S INTERVIEW (LINE NUMBER '04'
IN THIS EXAMPLE). WRITE HER NAME AND LINE NUMBER IN THE SPACE BELOW THE TABLE.
LAST DIGIT OF THE TOTAL NUMBER OF ELIGIBLE WOMEN AGE 15-49 IN HOUSEHOLD SCHEDULE COLUMN 9
HOUSEHOLD
QUESTIONNAIRE
1 2 3 4 5 6 7 8
SERIAL NUMBER
0 1 2 2 4 3 6 5 4
1 1 1 3 1 4 1 6 5
2 1 2 1 2 5 2 7 6
3 1 1 2 3 1 3 1 7
4 1 2 3 4 2 4 2 8
5 1 1 1 1 3 5 3 1
6 1 2 2 2 4 6 4 2
7 1 1 3 3 5 1 5 3
8 1 2 1 4 1 2 6 4
9 1 1 2 1 2 3 7 5
NAME OF SELECTED WOMAN: HH LINE NUMBER OF SELECTED WOMAN:
GO TO 102
408 • Appendix E HH-8
TABLE FOR SELECTION OF MEN FOR SECTION 10: DOMESTIC VIOLENCE QUESTIONS
101B ONLY ONE INDIVIDUAL (ONE WOMAN OR ONE MAN) SHOULD BE SELECTED FOR DOMESTIC VIOLENCE QUESTIONS
CHECK COVER PAGE: NO YES
HOUSEHOLD SELECTED FOR WOMEN'S SECTION 14?
GO TO 102
USE THE TABLE BELOW TO
SELECT ONE MAN FROM THIS HH
TO BE INTERVIEWED WITH THE DV
QUESTIONS
HOW TO USE THE TABLE FOR SELECTION OF A RESPONDENT
LAST DIGIT OF QUESTIONNAIRE SERIAL NUMBER TOTAL NUMBER OF ELIGIBLE MEN (COL 10)
(GO TO THIS ROW NUMBER) (GO TO THIS COLUMN NUMBER)
IF ZERO GO TO 102
LOOK AT THE LAST DIGIT OF THE HOUSEHOLD QUESTIONNAIRE SERIAL NUMBER ON THE COVER PAGE. THIS IS
THE ROW NUMBER YOU SHOULD GO TO. CHECK THE TOTAL NUMBER OF ELIGIBLE MEN (COLUMN 10) IN THE
HOUSEHOLD SCHEDULE. THIS IS THE COLUMN NUMBER YOU SHOULD GO TO. FOLLOW THE SELECTED ROW AND
COLUMN TO THE CELL WHERE THEY MEET AND CIRCLE THE NUMBER IN THE CELL. THIS IS THE NUMBER OF THE
MAN SELECTED FOR THE DOMESTIC VIOLENCE QUESTIONS FROM THE LIST OF ELIGIBLE MEN IN COLUMN 10 OF
THE HOUSEHOLD SCHEDULE. WRITE THE NAME AND LINE NUMBER OF THE SELECTED MAN IN THE SPACE
BELOW THE TABLE.
EXAMPLE: THE HOUSEHOLD QUESTIONNAIRE SERIAL NUMBER IS ‘716’ AND THE HOUSEHOLD SCHEDULE COLUMN
10 SHOWS THAT THERE ARE THREE ELIGIBLE MEN AGE 15-54 IN THE HOUSEHOLD (LINE NUMBERS 02, 04, AND
05). SINCE THE LAST DIGIT OF THE HOUSEHOLD SERIAL NUMBER IS '6' GO TO ROW '6' AND SINCE THERE ARE
THREE ELIGIBLE MEN IN THE HOUSEHOLD, GO TO COLUMN '3’. FOLLOW THE ROW AND COLUMN AND FIND THE
NUMBER IN THE CELL WHERE THEY MEET (‘2') AND CIRCLE THE NUMBER. NOW GO TO THE HOUSEHOLD
SCHEDULE AND FIND THE SECOND MAN WHO IS ELIGIBLE FOR THE MAN'S INTERVIEW (LINE NUMBER '04' IN THIS
EXAMPLE). WRITE HIS NAME AND LINE NUMBER IN THE SPACE BELOW THE TABLE.
LAST DIGIT OF THE TOTAL NUMBER OF ELIGIBLE MEN AGE 15-54 IN HOUSEHOLD SCHEDULE COLUMN 10
HOUSEHOLD
QUESTIONNAIRE
1 2 3 4 5 6 7 8
SERIAL NUMBER
0 1 2 2 4 3 6 5 4
1 1 1 3 1 4 1 6 5
2 1 2 1 2 5 2 7 6
3 1 1 2 3 1 3 1 7
4 1 2 3 4 2 4 2 8
5 1 1 1 1 3 5 3 1
6 1 2 2 2 4 6 4 2
7 1 1 3 3 5 1 5 3
8 1 2 1 4 1 2 6 4
9 1 1 2 1 2 3 7 5
NAME OF SELECTED MAN: HH LINE NUMBER OF SELECTED MAN:
HH-9 Appendix E • 409
HOUSEHOLD CHARACTERISTICS
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
102 What is the main source of drinking water for members of your PIPED WATER
household? PIPED INTO DWELLING . . . . . . . . . . . . 11
PIPED TO YARD/PLOT . . . . . . . . . . . . . . 12 105
PUBLIC TAP/STANDPIPE . . . . . . . . . . . . 13
TUBE WELL OR BOREHOLE . . . . . . . . . . . . 21
DUG WELL
PROTECTED WELL . . . . . . . . . . . . . . . . 31
UNPROTECTED WELL . . . . . . . . . . . . . . 32
WATER FROM SPRING
PROTECTED SPRING . . . . . . . . . . . . . . 41
UNPROTECTED SPRING . . . . . . . . . . . . 42
RAINWATER . . . . . . . . . . . . . . . . . . . . . . . . 51 105
TANKER TRUCK . . . . . . . . . . . . . . . . . . . . 61
CART WITH SMALL TANK . . . . . . . . . . . . 71
SURFACE WATER (RIVER/DAM/
LAKE/POND/STREAM/CANAL/
IRRIGATION CHANNEL) . . . . . . . . . . . . 81
BOTTLED WATER . . . . . . . . . . . . . . . . . . . . 91
OTHER 96
(SPECIFY)
103 Where is that water source located? IN OWN DWELLING . . . . . . . . . . . . . . . . . . 1
IN OWN YARD/PLOT ................ 2 105
ELSEWHERE ...................... 3
104 How long does it take to go there, get water, and come back?
MINUTES . . . . . . . . . . . . . . 1
IF 995 OR MORE, ENTER '995'. DON'T KNOW .................... 998
104A Who usually goes to this source to fetch the water for your ADULT WOMAN . . . . . . . . . . . . . . . . . . . . . . 1
household? ADULT MAN . . . . . . . . . . . . . . . . . . . . . . . . 2
FEMALE CHILD
UNDER 15 YEARS OLD . . . . . . . . . . . . . . 3
MALE CHILD
UNDER 15 YEARS OLD . . . . . . . . . . . . . . 4
OTHER 6
(SPECIFY)
105 Do you do anything to the water to make it safer to drink? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW ...................... 8 107
106 What do you usually do to make the water safer to drink? BOIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A
ADD BLEACH/CHLORINE ............ B
Anything else? STRAIN THROUGH A CLOTH ......... C
USE WATER FILTER (CERAMIC/
SAND/COMPOSITE/ETC.) . . . . . . . . . . . . D
RECORD ALL MENTIONED. SOLAR DISINFECTION . . . . . . . . . . . . . . . . E
LET IT STAND AND SETTLE . . . . . . . . . . . . F
COVER THE WATER CONTAINER . . . . . . . G
OTHER X
(SPECIFY)
DON'T KNOW ...................... Z
HH-10
410 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
107 What kind of toilet facility do members of your household FLUSH OR POUR FLUSH TOILET
usually use? FLUSH TO PIPED SEWER
SYSTEM . . . . . . . . . . . . . . . . . . . . . . 11
FLUSH TO SEPTIC TANK . . . . . . . . . . . . 12
FLUSH TO PIT LATRINE . . . . . . . . . . . . 13
FLUSH TO SOMEWHERE ELSE . . . . . . . 14
FLUSH, DON'T KNOW WHERE . . . . . . . 15
PIT LATRINE
VENTILATED IMPROVED
PIT LATRINE . . . . . . . . . . . . . . . . . . . . 21
PIT LATRINE WITH SLAB . . . . . . . . . . . . 22
PIT LATRINE WITHOUT SLAB/
OPEN PIT . . . . . . . . . . . . . . . . . . . . . . 23
COMPOSTING TOILET . . . . . . . . . . . . . . . . 31
BUCKET TOILET . . . . . . . . . . . . . . . . . . . . 41
HANGING TOILET/HANGING
LATRINE . . . . . . . . . . . . . . . . . . . . . . . . . . 51
NO FACILITY/BUSH/FIELD . . . . . . . . . . . . 61 110
OTHER 96
(SPECIFY)
108 Do you share this toilet facility with other households? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 110
109 How many households use this toilet facility? NO. OF HOUSEHOLDS
IF LESS THAN 10 . . . . . . . . .
0
10 OR MORE HOUSEHOLDS ....... 95
DON'T KNOW .................... 98
110 Does your household have:
YES NO
a) Electricity? a) ELECTRICITY . . . . . . . . . . . . . . 1 2
b) A radio? b) RADIO . . . . . . . . . . . . . . . . . . . . 1 2
c) A television? c) TELEVISION . . . . . . . . . . . . . . 1 2
d) A mobile telephone? d) MOBILE TELEPHONE . . . . . . . 1 2
e) A non-mobile telephone? e) NON-MOBILE TELEPHONE . . . 1 2
f) A refrigerator? f) REFRIGERATOR . . . . . . . . . . . . 1 2
g) A solar panel? g) SOLAR PANEL . . . . . . . . . . . . 1 2
h) A table? h) TABLE . . . . . . . . . . . . . . . . . . . . 1 2
i) A chair? i) CHAIR . . . . . . . . . . . . . . . . . . . . 1 2
j) A sofa? j) SOFA . . . . . . . . . . . . . . . . . . . . 1 2
k) A bed? k) BED . . . . . . . . . . . . . . . . . . . . . . 1 2
l) A cupboard? l) CUPBOARD . . . . . . . . . . . . . . . . 1 2
m) A clock? m) CLOCK . . . . . . . . . . . . . . . . . . 1 2
n) A microwave oven? n) MICROWAVE OVEN . . . . . . . 1 2
o) A DVD player? o) DVD PLAYER . . . . . . . . . . . . . . 1 2
p) A cassette or CD player? p) CASSETTE/CD PLAYER . . . . . 1 2
110A Does this household receive a cash transfer or any social YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
assistance from the government? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 111
HH-11
Appendix E • 411
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
110B For what reason does the household receive a cash transfer or ORPHANED CHILDREN 18 YEARS
social assistance? OR YOUNGER . . . . . . . . . . . . . . . . . . A
ELDERLY PERSON . . . . . . . . . . . . . . . . . . . . B
Any other reason? PERSON WITH SEVERE DISABILITY . . . . . C
URBAN FOOD SUBSIDY .............. D
RECORD ALL MENTIONED FOOD AID FOR PERSONS IN ARID
AND SEMI-ARID LANDS . . . . . . . . . . . . . . E
HEALTH VOUCHER . . . . . . . . . . . . . . . . . . . . F
FOOD/CASH FOR WORK . . . . . . . . . . . . . . G
SCHOOL FEEDING . . . . . . . . . . . . . . . . . . . . H
HUNGER SAFETY NET PROGRAMME. . . . . I
OTHER X
(SPECIFY)
111 What type of fuel does your household mainly use for cooking? ELECTRICITY . . . . . . . . . . . . . . . . . . . . . . . . 01
LPG\NATURAL GAS . . . . . . . . . . . . . . . . . . 02
BIOGAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 04
PARAFIN/KEROSENE . . . . . . . . . . . . . . . . . . 05
COAL, LIGNITE . . . . . . . . . . . . . . . . . . . . . . 06
CHARCOAL . . . . . . . . . . . . . . . . . . . . . . . . 07
WOOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 08
STRAW/SHRUBS/GRASS . . . . . . . . . . . . . . 09
AGRICULTURAL CROP . . . . . . . . . . . . . . . . 10
ANIMAL DUNG . . . . . . . . . . . . . . . . . . . . . . 11
NO FOOD COOKED
IN HOUSEHOLD . . . . . . . . . . . . . . . . . . . . 95 114
OTHER 96
(SPECIFY)
112 Is the cooking usually done in the house, in a separate IN THE HOUSE . . . . . . . . . . . . . . . . . . . . . . 1
building, or outdoors? IN A SEPARATE BUILDING ............ 2
OUTDOORS . . . . . . . . . . . . . . . . . . . . . . . . 3
114
OTHER 6
(SPECIFY)
113 Do you have a separate room which is used as a kitchen? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
114 MAIN MATERIAL OF THE FLOOR. NATURAL FLOOR
EARTH/SAND .................... 11
RECORD OBSERVATION. DUNG . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
RUDIMENTARY FLOOR
WOOD PLANKS .................. 21
PALM/BAMBOO .................. 22
FINISHED FLOOR
PARQUET OR POLISHED
WOOD . . . . . . . . . . . . . . . . . . . . . . . . 31
VINYL OR ASPHALT STRIPS ....... 32
CERAMIC TILES .................. 33
CEMENT ........................ 34
CARPET . . . . . . . . . . . . . . . . . . . . . . . . . . 35
OTHER 96
(SPECIFY)
HH-12
412 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
115 MAIN MATERIAL OF THE ROOF. NATURAL ROOFING
NO ROOF . . . . . . . . . . . . . . . . . . . . . . . . 11
RECORD OBSERVATION. THATCH/GRASS/MAKUTI . . . . . . . . . . . . 12
DUNG/MUD/SOD . . . . . . . . . . . . . . . . . . . . 13
RUDIMENTARY ROOFING
IRON SHEETS . . . . . . . . . . . . . . . . . . . . . . 21
TIN CANS . . . . . . . . . . . . . . . . . . . . . . . . 22
FINISHED ROOFING
ASBETOS SHEET . . . . . . . . . . . . . . . . . . 31
CONCRETE . . . . . . . . . . . . . . . . . . . . . . . . 32
TILES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
OTHER 96
(SPECIFY)
116 MAIN MATERIAL OF THE EXTERNAL WALLS. NATURAL WALLS
NO WALLS . . . . . . . . . . . . . . . . . . . . . . . . 11
RECORD OBSERVATION. CANE/PALM/TRUNKS . . . . . . . . . . . . . . 12
DUNG/MUD/SOD . . . . . . . . . . . . . . . . . . . . 13
RUDIMENTARY WALLS
BAMBOO WITH MUD . . . . . . . . . . . . . . . . 21
STONE WITH MUD . . . . . . . . . . . . . . . . 22
UNCOVERED ADOBE . . . . . . . . . . . . . . 23
PLYWOOD . . . . . . . . . . . . . . . . . . . . . . 24
CARDBOARD . . . . . . . . . . . . . . . . . . . . 25
REUSED WOOD . . . . . . . . . . . . . . . . . . 26
IRON SHEETS . . . . . . . . . . . . . . . . . . . . . . 27
FINISHED WALLS
CEMENT . . . . . . . . . . . . . . . . . . . . . . . . 31
STONE WITH LIME/CEMENT . . . . . . . . . 32
BRICKS . . . . . . . . . . . . . . . . . . . . . . . . . . 33
CEMENT BLOCKS . . . . . . . . . . . . . . . . . . 34
COVERED ADOBE . . . . . . . . . . . . . . . . 35
WOOD PLANKS/SHINGLES . . . . . . . . . 36
OTHER 96
(SPECIFY)
117 How many rooms in this household are used for sleeping?
ROOMS . . . . . . . . . . . . . . . . . . . . . .
118 Does any member of this household own:
YES NO
a) A watch? a) WATCH . . . . . . . . . . . . . . . . 1 2
b) A bicycle? b) BICYCLE . . . . . . . . . . . . . . . . 1 2
c) A motorcycle or motor scooter? c) MOTORCYCLE/SCOOTER . 1 2
d) An animal-drawn cart? d) ANIMAL-DRAWN CART . . . 1 2
e) A car or truck? e) CAR/TRUCK . . . . . . . . . . . . 1 2
f) A boat with a motor? f) BOAT WITH MOTOR . . . . . 1 2
118A Does your household own this structure (house, flat, shack), OWNS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
do you pay rent, or do you live here without paying rent? PAYS RENT/LEASE . . . . . . . . . . . . . . . . . . 2
NO RENT W. CONSENT OF OWNER . . . . . 3
NO RENT, SQUATTING . . . . . . . . . . . . . . . . 4
118B Does your household own the land on which the structure OWNS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
(house, flat, shack) sits? PAYS RENT/LEASE . . . . . . . . . . . . . . . . . . 2
NO RENT W. CONSENT OF OWNER . . . . . 3
NO RENT, SQUATTING . . . . . . . . . . . . . . . . 4
119 Does any member of this household own any agricultural land? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 121
HH-13
Appendix E • 413
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
120 How many acres or hectares of agricultural land do members
of this household own? ACRES ....... 1 .
ACRES / HECTARES: IF 995 OR MORE, HECTARES . . . . . 2 .
RECORD '995.0' IN APPROPRIATE BOX.
PLOT SIZE
PLOT SIZE (SQ FT): IF 99995 OR MORE, (SQ FT) 3 .
RECORD '99995.0' IN APPROPRIATE BOX.
DON'T KNOW . . . . . . . . . . . . . . . . . . 9999998
121 Does this household own any livestock, herds, other farm YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
animals, or poultry? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 123
122 How many of the following animals does this household own?
IF NONE, ENTER '00'.
IF 95 OR MORE, ENTER '95'.
IF UNKNOWN, ENTER '98'.
a) Local cattle (indigenous)? a) LOCAL CATTLE . . . . . . . . . . . . . .
b) Exotic/grade cattle? b) EXOTIC/GRADE CATTLE . . . . . . .
c) Horses, donkeys, or camels? c) HORSES/DONKEYS/CAMELS . . .
d) Goats? d) GOATS ....................
e) Sheep? e) SHEEP ....................
f) Chickens? f) CHICKENS . . . . . . . . . . . . . . . . . .
123 Does any member of this household have a bank account? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW ...................... 8
123A In the past 7 days were there days when your household did YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
not have enough food or money to buy food? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 123C
123B How many days did your household have to: NUMBER
OF
DAYS
a) Rely on less preferred, less expensive food? a) LESS PREFERRED FOOD . . . . . . . . .
b) Rely on borrowed food from friends or relatives? b) RELY ON BORROWED FOOD . . . . . . .
c) Reduce the number of meals eaten per day? c) REDUCE NUMBER OF MEALS. . . . . . .
d) Reduce the portion size of meals? d) REDUCE SIZE OF MEALS . . . . . . . . .
e) Reduce the quantities eaten by adults in order for small e) REDUCE QUANTITIES FOR ADULTS .
children to eat?
123C How often does anyone smoke inside your house? DAILY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Would you say daily, weekly, monthly, less than monthly, or WEEKLY . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
never? MONTHLY . . . . . . . . . . . . . . . . . . . . . . . . . . 3
LESS THAN MONTHLY . . . . . . . . . . . . . . . . 4
NEVER . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
124 At any time in the past 12 months, has anyone come into your YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
dwelling to spray the interior walls against mosquitoes? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW ...................... 8 126
124A How many months ago did someone spray your dwelling MONTHS AGO ................
against mosquitos?
HH-14
414 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
125 Who sprayed the dwelling? GOVERNMENT WORKER/PROGRAM . . . . . A
PRIVATE COMPANY ................ B
NONGOVERNMENTAL
ORGANIZATION (NGO) . . . . . . . . . . . . . . C
OTHER X
(SPECIFY)
DON'T KNOW ...................... Z
126 Does your household have any mosquito nets that can be YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
used while sleeping? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 137
127 How many mosquito nets does your household have?
NUMBER OF NETS . . . . . . . . . . . . . . . . . .
IF 7 OR MORE NETS, RECORD '7'.
HH-15
Appendix E • 415
NET #1 NET #2 NET #3
128 ASK THE RESPONDENT TO
SHOW YOU ALL THE NETS IN
THE HOUSEHOLD
IF MORE THAN 3 NETS, USE OBSERVED ..... 1 OBSERVED ..... 1 OBSERVED ..... 1
ADDITIONAL QUESTIONNAIRE(S). NOT OBSERVED . . . 2 NOT OBSERVED . . . 2 NOT OBSERVED . . . 2
129 How many months ago did your MONTHS MONTHS MONTHS
household get the mosquito net? AGO . . . AGO . . . AGO . . .
IF LESS THAN ONE MONTH AGO, MORE THAN 36 MORE THAN 36 MORE THAN 36
RECORD '00'. MONTHS AGO . . . 95 MONTHS AGO . . . 95 MONTHS AGO . . . 95
NOT SURE . . . . . . . 98 NOT SURE . . . . . . . 98 NOT SURE . . . . . . . 98
130 OBSERVE OR ASK THE BRAND/ LONG-LASTING NET LONG-LASTING NET LONG-LASTING NET
TYPE OF MOSQUITO NET. OLYSET (SUPA- OLYSET (SUPA- OLYSET (SUPA-
NET EXTRA) 11 NET EXTRA) 11 NET EXTRA) 11
PERMANET (SUPA- PERMANET (SUPA- PERMANET (SUPA-
NET EXTRA) 12 NET EXTRA) 12 NET EXTRA) 12
NETPROTECT . . . 13 NETPROTECT . . . 13 NETPROTECT . . . 13
IF BRAND IS UNKNOWN AND OTHER/ OTHER/ OTHER/
YOU CANNOT OBSERVE THE DK BRAND . . . 16 DK BRAND . . . 16 DK BRAND . . . 16
NET, SHOW PICTURES OF (SKIP TO 134) (SKIP TO 134) (SKIP TO 134)
TYPICAL NET TYPES/BRANDS
TO RESPONDENT. 'CONVENTIONAL' NET 'CONVENTIONAL' NET 'CONVENTIONAL' NET
BRAND
KINGA NET
C . . . 21 BRAND
KINGA NET
C . . . 21 BRAND
KINGA NET
C . . . 21
BRAND
SUPANET
D . . . . . 22 BRAND
SUPANET
D . . . . . 22 BRAND
SUPANET
D . . . . . 22
UNBRANDED UNBRANDED UNBRANDED
RURAL NET . . . 23 RURAL NET . . . 23 RURAL NET . . . 23
OTHER/ OTHER/ OTHER/
DK BRAND . . . 26 DK BRAND . . . 26 DK BRAND . . . 26
(SKIP TO 132) (SKIP TO 132) (SKIP TO 132)
OTHER BRAND . . . 96 OTHER BRAND . . . 96 OTHER BRAND . . . 96
DK BRAND . . . . . . . 98 DK BRAND . . . . . . . 98 DK BRAND . . . . . . . 98
131 When you got the net, was it already YES ............ 1 YES ............ 1 YES ............ 1
treated with an insecticide to kill or NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
repel mosquitoes? NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8
132 Since you got the net, was it ever YES ............ 1 YES ............ 1 YES ............ 1
soaked or dipped in a liquid to kill or NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
repel mosquitoes? (SKIP TO 134) (SKIP TO 134) (SKIP TO 134)
NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8
133 How many months ago was the net MONTHS MONTHS MONTHS
last soaked or dipped? AGO . . . AGO . . . AGO . . .
MORE THAN 24 MORE THAN 24 MORE THAN 24
MONTHS AGO . . . 95 MONTHS AGO . . . 95 MONTHS AGO . . . 95
IF LESS THAN ONE MONTH AGO,
RECORD '00'. NOT SURE . . . . . . . 98 NOT SURE . . . . . . . 98 NOT SURE . . . . . . . 98
133A The last time the net was treated,
was a liquid from a packet like this YES ............ 1 YES ............ 1 YES ............ 1
added to the treatment solution? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8
SHOW SACHET FOR K-O TAB 1-2-
3 BINDING AGENT.
133B The last time the net was treated, YES ............ 1 YES ............ 1 YES ............ 1
was it treated as part of a net NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
retreatment campaign? NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8
HH-16
416 • Appendix E
NET #1 NET #2 NET #3
134 Did anyone sleep under this YES ............ 1 YES ............ 1 YES ............ 1
mosquito net last night? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 136) (SKIP TO 136) (SKIP TO 136)
NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8
135 Who slept under this mosquito net
last night?
NAME NAME NAME
RECORD THE PERSON'S NAME
AND LINE NUMBER FROM THE LINE LINE LINE
HOUSEHOLD SCHEDULE. NO. ..... NO. ..... NO. .....
NAME NAME NAME
LINE LINE LINE
NO. ..... NO. ..... NO. .....
NAME NAME NAME
LINE LINE LINE
NO. ..... NO. ..... NO. .....
NAME NAME NAME
LINE LINE LINE
NO. ..... NO. ..... NO. .....
136 GO BACK TO 128 FOR GO BACK TO 128 FOR GO TO 128 IN FIRST
NEXT NET; OR, IF NO NEXT NET; OR, IF NO COLUMN OF A NEW
MORE NETS, MORE NETS, QUESTIONNAIRE;
GO TO 137. GO TO 137. OR, IF NO MORE
NETS, GO TO 137.
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
137 Please show me where members of your household most often OBSERVED ........................ 1
wash their hands. NOT OBSERVED,
NOT IN DWELLING/YARD/PLOT . . . . . . . 2
NOT OBSERVED,
139A
NO PERMISSION TO SEE . . . . . . . . . . . . 3
NOT OBSERVED, OTHER REASON . . . . . 4
138 OBSERVATION ONLY:
WATER IS AVAILABLE .............. 1
OBSERVE PRESENCE OF WATER AT THE WATER IS NOT AVAILABLE . . . . . . . . . . . . 2
PLACE FOR HANDWASHING.
139 OBSERVATION ONLY: SOAP OR DETERGENT
(BAR, LIQUID, POWDER, PASTE) ... A
OBSERVE PRESENCE OF SOAP, DETERGENT, OR ASH, MUD, SAND . . . . . . . . . . . . . . . . . . . . B
OTHER CLEANSING AGENT. NONE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C
139A Do members of your household wash their hands with soap? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 139C
139B When do they wash their hands? AFTER TOILET . . . . . . . . . . . . . . . . . . . . . . A
BEFORE COOKING . . . . . . . . . . . . . . . . . . . . B
Any other time? BEFORE EATING . . . . . . . . . . . . . . . . . . . . . . C
AFTER CLEANING BABY'S BACKSIDE . . . D
RECORD ALL MENTIONED BEFORE FEEDING BABY . . . . . . . . . . . . . . . E
OTHER X
(SPECIFY)
HH-17
Appendix E • 417
HOUSEHOLD FOOD CONSUMPTION
139C A Now, I would like to talk to you about the food consumed in your B What was the main source
household during the past 7 days. How many days during the past NUMBER of the (NAME OF FOOD
7 days, did members of your household consume the following OF DAYS ITEM)?
food items, prepared or eaten at home? EATEN IN
PAST 7 SEE SOURCE CODES
DAYS BELOW
a) Cereals and grains such as rice, pasta, bread, sorghum, millet, or ONE OR MORE
maize? ZERO
b) Roots and tubers such as potato, yam, cassava, normal sweet ONE OR MORE
potatoes, taro, cooking banana/plantain or other tubers? ZERO
c) Pulses/nuts such as beans, cowpeas, peanuts, lentils, soy, pigeon ONE OR MORE
peas, or other nuts? ZERO
d) Orange vegetables such as carrots, red peppers, pumpkin, orange ONE OR MORE
sweet potato? ZERO
e) Green leafy vegetables such as sukumu wiki, spinach, broccoli, ONE OR MORE
amaranth, cassava leaves, or other dark green leaves? ZERO
f) Other vegetables such as onion, tomatoes, cucumber, radishes, ONE OR MORE
green beans, peas, lettuce? ZERO
g) Orange fruits such as mango, paw paw, tree tomato? ONE OR MORE
ZERO
h) Other fruits such as banana, apple, lemon? ONE OR MORE
ZERO
i) Meat such as goat, beef, chicken, pork? (meat in large quantities ONE OR MORE
and not as a condiment) ZERO
j) Liver, kidney, heart, or other organ meats? ONE OR MORE
ZERO
k) Fish or shellfish such as dried fish, canned tuna, or other seafood? ONE OR MORE
(seafood in large quantities and not as a condiment) ZERO
l) Eggs? ONE OR MORE
ZERO
m) Milk and other dairy products such as yogurt or cheese? ONE OR MORE
ZERO
n) Oil, fat, and butter? ONE OR MORE
ZERO
o) Sugar or sweet things such as honey, jam, cakes, candy, biscuits, ONE OR MORE
pastries, sugary drinks? ZERO
p) Condiments and spices such as tea, coffee, cocoa, salt, garlic, ONE OR MORE
spices, yeast, baking powder, tomato sauce, meat or fish in very ZERO
small quantities as condiments?
(GO TO 140)
CODES FOR Q. 139CB SOURCE OF FOOD
21 = OWN PRODUCTION (CROPS, ANIMAL) 24 = PURCHASED 27 = GIFT FROM FAMILY, FRIENDS
22 = FISHING, HUNTING, GATHERING 25 = BEGGED 28 = FOOD AID FROM CIVIL SOCIETY, NGO, GOVERNMENT
23 = LOANED, BORROWED 26 = EXCHANGED FOR LABOR 96 = OTHER
140 ASK RESPONDENT FOR A TEASPOONFUL OF IODINE PRESENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
COOKING SALT. NO IODINE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
NO SALT IN HOUSEHOLD ...................... 3
TEST SALT FOR IODINE. SALT NOT TESTED 6
(SPECIFY REASON)
HH-18
418 • Appendix E
WEIGHT AND HEIGHT MEASUREMENT FOR CHILDREN AGE 0-5
201 CHECK COLUMN 11 IN HOUSEHOLD SCHEDULE. RECORD THE LINE NUMBER AND NAME FOR ALL ELIGIBLE CHILDREN 0-5 YEARS
IN QUESTION 202. IF MORE THAN SIX CHILDREN, USE ADDITIONAL QUESTIONNAIRE(S).
CHILD 1 CHILD 2 CHILD 3
202 LINE NUMBER FROM COLUMN 11 LINE LINE LINE
NUMBER . . . . . NUMBER . . . . . NUMBER . . . . .
NAME FROM COLUMN 2 NAME NAME NAME
203 IF MOTHER INTERVIEWED, COPY
MONTH AND YEAR OF BIRTH DAY . . . . . . . . . DAY . . . . . . . . . DAY . . . . . . . . .
FROM BIRTH HISTORY AND ASK
DAY; IF MOTHER NOT MONTH ..... MONTH ..... MONTH .....
INTERVIEWED, ASK:
What is (NAME)'s birth date? YEAR YEAR YEAR
204 CHECK 203: YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1
CHILD BORN IN JANUARY NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2
2009 OR LATER? (GO TO 203 FOR NEXT (GO TO 203 FOR NEXT (GO TO 203 FOR NEXT
CHILD OR, IF NO CHILD OR, IF NO CHILD OR, IF NO
MORE CHILDREN, MORE CHILDREN, MORE CHILDREN,
GO TO 214) GO TO 214) GO TO 214)
205 WEIGHT IN KILOGRAMS
KG. . KG. . KG. .
NOT PRESENT . . . 9994 NOT PRESENT . . . 9994 NOT PRESENT . . . 9994
REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995
OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996
206 HEIGHT IN CENTIMETERS
CM. . CM. . CM. .
NOT PRESENT . . . 9994 NOT PRESENT . . . 9994 NOT PRESENT . . . 9994
REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995
OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996
207 MEASURED LYING DOWN OR LYING DOWN . . . . . . . 1 LYING DOWN . . . . . . . 1 LYING DOWN . . . . . . . 1
STANDING UP? STANDING UP . . . . . . . 2 STANDING UP . . . . . . . 2 STANDING UP . . . . . . . 2
NOT MEASURED . . . 3 NOT MEASURED . . . 3 NOT MEASURED . . . 3
213 GO BACK TO 203 IN NEXT COLUMN OF THIS QUESTIONNAIRE OR IN THE FIRST COLUMN OF THE NEXT PAGE; IF NO MORE
CHILDREN, GO TO 214.
HH-19 Appendix E • 419
CHILD 4 CHILD 5 CHILD 6
202 LINE NUMBER FROM COLUMN 11 LINE LINE LINE
NUMBER . . . . . NUMBER . . . . . NUMBER . . . . .
NAME FROM COLUMN 2 NAME NAME NAME
203 IF MOTHER INTERVIEWED, COPY
MONTH AND YEAR OF BIRTH DAY ....... DAY ....... DAY .......
FROM BIRTH HISTORY AND ASK
DAY; IF MOTHER NOT MONTH ..... MONTH ..... MONTH .....
INTERVIEWED, ASK:
What is (NAME)'s birth date? YEAR YEAR YEAR
204 CHECK 203: YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1
CHILD BORN IN JANUARY NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2
2009 OR LATER? (GO TO 203 FOR NEXT (GO TO 203 FOR NEXT (GO TO 203 IN FIRST
CHILD OR, IF NO CHILD OR, IF NO COLUMN OF NEW
MORE CHILDREN, MORE CHILDREN, QUESTIONNAIRE; OR,
GO TO 214) GO TO 214) IF NO MORE CHILDREN,
GO TO 214)
205 WEIGHT IN KILOGRAMS
KG. . KG. . KG. .
NOT PRESENT. . . . . 9994 NOT PRESENT. . . . . 9994 NOT PRESENT. . . . . 9994
REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995
OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996
206 HEIGHT IN CENTIMETERS
CM. . CM. . CM. .
NOT PRESENT . . . 9994 NOT PRESENT . . . 9994 NOT PRESENT . . . 9994
REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995
OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996
207 MEASURED LYING DOWN OR LYING DOWN . . . . . . . 1 LYING DOWN . . . . . . . 1 LYING DOWN . . . . . . . 1
STANDING UP? STANDING UP . . . . . . . 2 STANDING UP . . . . . . . 2 STANDING UP . . . . . . . 2
NOT MEASURED . . . 3 NOT MEASURED . . . 3 NOT MEASURED . . . 3
213 GO BACK TO 203 IN NEXT COLUMN OF THIS QUESTIONNAIRE OR IN THE FIRST COLUMN OF AN ADDITIONAL QUESTIONNAIRE;
IF NO MORE CHILDREN, GO TO 214.
HH-20
420 • Appendix E
WEIGHT AND HEIGHT MEASUREMENT FOR WOMEN AGE 15-49
214 CHECK COLUMN 9 IN HOUSEHOLD SCHEDULE. RECORD THE LINE NUMBER AND NAME FOR ALL ELIGIBLE WOMEN IN 215.
IF THERE ARE MORE THAN THREE WOMEN, USE ADDITIONAL QUESTIONNAIRE(S).
WOMAN 1 WOMAN 2 WOMAN 3
215 LINE NUMBER LINE LINE LINE
FROM COLUMN 9 NUMBER ............ NUMBER ............ NUMBER ............
NAME FROM
COLUMN 2 NAME NAME NAME
216 WEIGHT
IN KILOGRAMS KG. . KG. . KG. .
NOT PRESENT . . . . . . . . . 99994 NOT PRESENT . . . . . . . . . 99994 NOT PRESENT . . . . . . . . . 99994
REFUSED . . . . . . . . . . . . . . 99995 REFUSED . . . . . . . . . . . . . . 99995 REFUSED . . . . . . . . . . . . . . 99995
OTHER . . . . . . . . . . . . . . . . 99996 OTHER . . . . . . . . . . . . . . . . 99996 OTHER . . . . . . . . . . . . . . . . 99996
217 HEIGHT
IN CENTIMETERS CM. . CM. . CM. .
NOT PRESENT . . . . . . . . . . . . 9994 NOT PRESENT . . . . . . . . . . . . 9994 NOT PRESENT . . . . . . . . . . . . 9994
REFUSED . . . . . . . . . . . . . . . . 9995 REFUSED . . . . . . . . . . . . . . . . 9995 REFUSED . . . . . . . . . . . . . . . . 9995
OTHER . . . . . . . . . . . . . . . . . . 9996 OTHER . . . . . . . . . . . . . . . . . . 9996 OTHER . . . . . . . . . . . . . . . . . . 9996
242 GO BACK TO 216 IN NEXT COLUMN OF THIS QUESTIONNAIRE OR IN THE FIRST COLUMN OF AN ADDITIONAL QUESTIONNAIRE; IF NO MORE
WOMEN, END INTERVIEW
HH-21
Appendix E • 421
422 • Appendix E
2 SHORT VERSION QUESTIONNAIRE SERIAL NUMBER
2014 KENYA DEMOGRAPHIC AND HEALTH SURVEY
HOUSEHOLD QUESTIONNAIRE - SHORT VERSION
CONFIDENTIAL
REPUBLIC OF KENYA
IDENTIFICATION
COUNTY
DISTRICT
LOCATION/TOWN
SUBLOCATION
NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NAME OF HOUSEHOLD HEAD
INTERVIEWER VISITS
1 2 3 FINAL VISIT
DATE DAY
MONTH
YEAR
INTERVIEWER'S NAME INT. NUMBER
RESULT* RESULT
NEXT VISIT: DATE
TOTAL NUMBER
TIME OF VISITS
*RESULT CODES: LINE NO. OF
1 COMPLETED RESPONDENT TO
2 NO HOUSEHOLD MEMBER AT HOME / NO COMPETENT HOUSHOLD
RESPONDENT AT HOME AT TIME OF VISIT QUESTIONNAIRE
3 ENTIRE HOUSEHOLD ABSENT FOR EXTENDED PERIOD OF TIME
4 POSTPONED TOTAL PERSONS
5 REFUSED IN HOUSEHOLD
6 DWELLING VACANT OR ADDRESS NOT A DWELLING
7 DWELLING DESTROYED TOTAL ELIGIBLE
8 DWELLING NOT FOUND WOMEN
9 OTHER
TOTAL ELIGIBLE
(SPECIFY) MEN
LANGUAGE OF TRANSLATOR USED
QUESTIONNAIRE** 1 7 LANGUAGE OF
INTERVIEW:
NATIVE LANGUAGE
OF RESPONDENT: (YES = 1, NO = 2)
LANGUAGE OF
QUESTIONNAIRE: English
**LANGUAGE 01 BORANA 05 KIKUYU 09 LUO 13 POKOT 17 ENGLISH
CODES: 02 EMBU 06 KISII 10 MAASAI 14 SOMALI 18 OTHER
03 KALENJIN 07 LUHYA 11 MERU 15 SWAHILI
04 KAMBA 08 MARAGOLI 12 MIJIKENDA 16 TURKANA
SUPERVISOR FIELD EDITOR OFFICE EDITOR KEYED BY
NAME NAME
HH-1
Appendix E • 423
THIS PAGE IS INTENTIONALLY BLANK
HH-2
424 • Appendix E
INTRODUCTION AND CONSENT
Hello. My name is _______________________________________. I am working with the Kenya National Bureau
of Statistics. We are conducting a survey about health all over Kenya. The information we collect will help the
government to plan health services. Your household was selected for the survey. I would like to ask you some
questions about your household. The questions usually take about 30 to 60 minutes. All of the answers you give will
be confidential and will not be shared with anyone other than members of our survey team. You don't have to be in
the survey, but we hope you will agree to answer the questions since your views are important. If I ask you any
question you don't want to answer, just let me know and I will go on to the next question or you can stop the
interview at any time. In case you need more information about the survey, you may contact the person listed on
this card.
GIVE CARD WITH CONTACT INFORMATION
Do you have any questions?
May I begin the interview now?
SIGNATURE OF INTERVIEWER: DATE:
RESPONDENT AGREES TO BE INTERVIEWED . . . 1 RESPONDENT DOES NOT AGREE TO BE INTERVIEWED . . . 2 END
HH-3
Appendix E • 425
HOUSEHOLD SCHEDULE
IF AGE 15 IF AGE 0-17 YEARS
OR OLDER
LINE USUAL RESIDENTS AND RELATIONSHIP SEX RESIDENCE AGE MARITAL ELIGIBILITY SURVIVORSHIP AND
NO. VISITORS TO HEAD OF STATUS RESIDENCE OF
HOUSEHOLD BIOLOGICAL PARENTS
1 2 3 4 5 6 7 8 9 10 11 12 13
Please give me the names What is the Is Does Did How old is What is (NAME)'s CIRCLE CIRCLE Is (NAME)'s Does
of the persons who usually relationship of (NAME) (NAME) (NAME) (NAME)? current marital LINE LINE natural (NAME)'s
live in your household and (NAME) to the male or usually stay here status? NUMBER NUMBER mother natural mother
guests of the household who head of the female? live here? last OF ALL OF ALL alive? usually live in
stayed here last night, household? night? WOMEN CHILDREN this household
starting with the head of the or was she a
AGE AGE 0-5
household. guest last
SEE CODES IF 95 15-49
night?
BELOW. OR MORE,
RECORD
IF YES: What
AFTER LISTING THE '95'. is her name?
NAMES AND RECORDING
THE RELATIONSHIP
AND SEX FOR EACH 1 = MARRIED
PERSON, ASK OR LIVING
QUESTIONS 2A-2C TOGETHER RECORD
TO BE SURE THAT THE 2 = DIVORCED/ MOTHER'S
LISTING IS COMPLETE. SEPARATED LINE
3 = WIDOWED NUMBER.
THEN ASK APPROPRIATE 4 = NEVER- IF NO,
QUESTIONS IN COLUMNS MARRIED/LIVED RECORD
5-20A FOR EACH PERSON. TOGETHER '00'.
M F Y N Y N IN YEARS Y N DK
01 1 2 1 2 1 2 01 01 1 2 8
GO TO 14
02 1 2 1 2 1 2 02 02 1 2 8
GO TO 14
03 1 2 1 2 1 2 03 03 1 2 8
GO TO 14
04 1 2 1 2 1 2 04 04 1 2 8
GO TO 14
05 1 2 1 2 1 2 05 05 1 2 8
GO TO 14
06 1 2 1 2 1 2 06 06 1 2 8
GO TO 14
07 1 2 1 2 1 2 07 07 1 2 8
GO TO 14
08 1 2 1 2 1 2 08 08 1 2 8
GO TO 14
09 1 2 1 2 1 2 09 09 1 2 8
GO TO 14
10 1 2 1 2 1 2 10 10 1 2 8
GO TO 14
2A) Just to make sure that I have a complete CODES FOR Q. 3: RELATIONSHIP TO HEAD OF HOUSEHOLD
listing: are there any other persons such as
small children or infants that we have not
YES ADD TO 01 = HEAD 08 = BROTHER OR SISTER
listed?
TABLE NO 02 = WIFE OR HUSBAND 09 = OTHER RELATIVE
2B) Are there any other people who may not be 03 = SON OR DAUGHTER 10 = ADOPTED/FOSTER/
members of your family, such as domestic 04 = SON-IN-LAW OR STEPCHILD
servants, lodgers, or friends who usually live
YES ADD TO DAUGHTER-IN-LAW 11 = NOT RELATED
here?
TABLE NO 05 = GRANDCHILD 98 = DON'T KNOW
2C) Are there any guests or temporary visitors 06 = PARENT
staying here, or anyone else who stayed
ADD TO 07 = PARENT-IN-LAW
here last night, who have not been listed?
YES TABLE NO
426 • Appendix E HH-4
IF AGE 0-17 YEARS IF AGE 3 YEARS IF AGE 3-24 YEARS IF AGE 0-4 YEARS
OR OLDER
LINE SURVIVORSHIP AND EVER ATTENDED CURRENT/RECENT BIRTH
NO. RESIDENCE OF SCHOOL SCHOOL ATTENDANCE REGISTRATION
BIOLOGICAL PARENTS
14 15 16 17 18 19 19A 19B 20 20A
Is Does Has What is the highest Did During the 2014 Did During the 2013 Has (NAME) ever Why was
(NAME)'s (NAME)'s (NAME) level of school (NAME) school year, what (NAME) school year, what been registered with (NAME) never
natural natural father ever (NAME) has attend level and grade attend level and grade did the civil authority? registered?
father usually live in attended attended? school at [is/was] (NAME) school at (NAME) attend?
alive? this school? any time attending? any time IF YES: With a birth
household or What is the highest during the during the certificate?
was he a grade (NAME) 2014 2013
1=TOO FAR
guest last completed at that school school
night? level? year? year?
2=NO MONEY
IF YES: What SEE CODES SEE CODES
is his name? BELOW. BELOW. 1 = YES, REGISTERED 3=NOT AWARE
WITH BIRTH
CERTIFICATE 4=NOT
SEE CODES NECESSARY
RECORD BELOW. 2 = YES, REGISTERED
FATHER'S WITHOUT BIRTH 5=NOMADIC
LINE CERTIFICATE LIFE, DIFFICULT
NUMBER. TERRAIN,
IF NO, 8 = DON'T KNOW INSECURITY
RECORD
'00'. 3 = NOT REGISTERED 8=OTHER
Y N DK Y N LEVEL GRADE Y N LEVEL GRADE Y N LEVEL GRADE Y1 Y2 DK NO
01 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
02 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
03 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
04 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
05 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
06 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
07 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
08 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
09 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
10 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
CODES FOR Qs. 17, 19, AND 19B: EDUCATION
LEVEL GRADE
0 = PRE-PRIMARY 00 = LESS THAN 1 YEAR COMPLETED
1 = PRIMARY (USE '00' FOR Q. 17 ONLY.
2 = POST-PRIMARY, VOCATIONAL THIS CODE IS NOT ALLOWED
3 = SECONDARY/'A' LEVEL FOR Q. 19 OR 19B)
4 = COLLEGE (MIDDLE LEVEL) 98 = DON'T KNOW
5 = UNIVERSITY
8 = DON'T KNOW
HH-5 Appendix E • 427
IF AGE 15 IF AGE 0-17 YEARS
OR OLDER
LINE USUAL RESIDENTS AND RELATIONSHIP SEX RESIDENCE AGE MARITAL ELIGIBILITY SURVIVORSHIP AND
NO. VISITORS TO HEAD OF STATUS RESIDENCE OF
HOUSEHOLD BIOLOGICAL PARENTS
1 2 3 4 5 6 7 8 9 10 11 12 13
Please give me the names What is the Is Does Did How old is What is (NAME)'s CIRCLE CIRCLE Is (NAME)'s Does
of the persons who usually relationship of (NAME) (NAME) (NAME) (NAME)? current marital LINE LINE natural (NAME)'s
live in your household and (NAME) to the male or usually stay here status? NUMBER NUMBER mother natural mother
guests of the household who head of the female? live here? last OF ALL OF ALL alive? usually live in
stayed here last night, household? night? WOMEN CHILDREN this household
starting with the head of the or was she a
AGE AGE 0-5
household. guest last
SEE CODES IF 95 15-49
night?
BELOW. OR MORE,
RECORD
IF YES: What
AFTER LISTING THE '95'. is her name?
NAMES AND RECORDING
THE RELATIONSHIP
AND SEX FOR EACH 1 = MARRIED
PERSON, ASK OR LIVING
QUESTIONS 2A-2C TOGETHER RECORD
TO BE SURE THAT THE 2 = DIVORCED/ MOTHER'S
LISTING IS COMPLETE. SEPARATED LINE
3 = WIDOWED NUMBER.
THEN ASK APPROPRIATE 4 = NEVER- IF NO,
QUESTIONS IN COLUMNS MARRIED/LIVED RECORD
5-20A FOR EACH PERSON. TOGETHER '00'.
M F Y N Y N IN YEARS Y N DK
11 1 2 1 2 1 2 11 11 1 2 8
GO TO 14
12 1 2 1 2 1 2 12 12 1 2 8
GO TO 14
13 1 2 1 2 1 2 13 13 1 2 8
GO TO 14
14 1 2 1 2 1 2 14 14 1 2 8
GO TO 14
15 1 2 1 2 1 2 15 15 1 2 8
GO TO 14
16 1 2 1 2 1 2 16 16 1 2 8
GO TO 14
17 1 2 1 2 1 2 17 17 1 2 8
GO TO 14
18 1 2 1 2 1 2 18 18 1 2 8
GO TO 14
19 1 2 1 2 1 2 19 19 1 2 8
GO TO 14
20 1 2 1 2 1 2 20 20 1 2 8
GO TO 14
TICK HERE IF CONTINUATION SHEET USED CODES FOR Q. 3: RELATIONSHIP TO HEAD OF HOUSEHOLD
2A) Just to make sure that I have a complete 01 = HEAD 08 = BROTHER OR SISTER
listing: are there any other persons such as 02 = WIFE OR HUSBAND 09 = OTHER RELATIVE
small children or infants that we have not
YES ADD TO 03 = SON OR DAUGHTER 10 = ADOPTED/FOSTER/
listed?
TABLE NO 04 = SON-IN-LAW OR STEPCHILD
2B) Are there any other people who may not be DAUGHTER-IN-LAW 11 = NOT RELATED
members of your family, such as domestic 05 = GRANDCHILD 98 = DON'T KNOW
servants, lodgers, or friends who usually live
YES ADD TO 06 = PARENT
here?
TABLE NO 07 = PARENT-IN-LAW
2C) Are there any guests or temporary visitors
staying here, or anyone else who stayed
ADD TO
here last night, who have not been listed?
YES TABLE NO
428 • Appendix E HH-6
IF AGE 0-17 YEARS IF AGE 3 YEARS IF AGE 3-24 YEARS IF AGE 0-4 YEARS
OR OLDER
LINE SURVIVORSHIP AND EVER ATTENDED CURRENT/RECENT BIRTH
NO. RESIDENCE OF SCHOOL SCHOOL ATTENDANCE REGISTRATION
BIOLOGICAL PARENTS
14 15 16 17 18 19 19A 19B 20 20A
Is Does Has What is the highest Did During the 2014 Did During the 2013 Has (NAME) ever Why was
(NAME)'s (NAME)'s (NAME) level of school (NAME) school year, what (NAME) school year, what been registered with (NAME) never
natural natural father ever (NAME) has attend level and grade attend level and grade did the civil authority? registered?
father usually live in attended attended? school at [is/was] (NAME) school at (NAME) attend?
alive? this school? any time attending? any time IF YES: With a birth
household or What is the highest during the during the certificate?
was he a grade (NAME) 2014 2013
1=TOO FAR
guest last completed at that school school
night? level? year? year?
2=NO MONEY
IF YES: What SEE CODES SEE CODES
is his name? BELOW. BELOW. 1 = YES, REGISTERED 3=NOT AWARE
WITH BIRTH
CERTIFICATE 4=NOT
SEE CODES NECESSARY
RECORD BELOW. 2 = YES, REGISTERED
FATHER'S WITHOUT BIRTH 5=NOMADIC
LINE CERTIFICATE LIFE, DIFFICULT
NUMBER. TERRAIN,
IF NO, 8 = DON'T KNOW INSECURITY
RECORD
'00'. 3 = NOT REGISTERED 8=OTHER
Y N DK Y N LEVEL GRADE Y N LEVEL GRADE Y N LEVEL GRADE Y1 Y2 DK NO
11 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
12 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
13 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
14 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
15 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
16 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
17 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
18 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
19 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
20 1 2 8 1 2 1 2 1 2 1 2 8 3
TO
GO TO 16 GO TO 20 GO TO 19A GO TO 20 TO NEXT LINE 20A
CODES FOR Qs. 17, 19, AND 19B: EDUCATION
LEVEL GRADE
0 = PRE-PRIMARY 00 = LESS THAN 1 YEAR COMPLETED
1 = PRIMARY (USE '00' FOR Q. 17 ONLY.
2 = POST-PRIMARY, VOCATIONAL THIS CODE IS NOT ALLOWED
3 = SECONDARY/'A' LEVEL FOR Q. 19 OR 19B)
4 = COLLEGE (MIDDLE LEVEL) 98 = DON'T KNOW
5 = UNIVERSITY
8 = DON'T KNOW
HH-7 Appendix E • 429
HOUSEHOLD CHARACTERISTICS
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
102 What is the main source of drinking water for members of your PIPED WATER
household? PIPED INTO DWELLING ............ 11
PIPED TO YARD/PLOT . . . . . . . . . . . . . . 12 105
PUBLIC TAP/STANDPIPE . . . . . . . . . . . . 13
TUBE WELL OR BOREHOLE . . . . . . . . . . . . 21
DUG WELL
PROTECTED WELL . . . . . . . . . . . . . . . . 31
UNPROTECTED WELL . . . . . . . . . . . . . . 32
WATER FROM SPRING
PROTECTED SPRING .............. 41
UNPROTECTED SPRING . . . . . . . . . . . . 42
RAINWATER . . . . . . . . . . . . . . . . . . . . . . . . 51 105
TANKER TRUCK .................... 61
CART WITH SMALL TANK ............ 71
SURFACE WATER (RIVER/DAM/
LAKE/POND/STREAM/CANAL/
IRRIGATION CHANNEL) ............ 81
BOTTLED WATER . . . . . . . . . . . . . . . . . . . . 91
OTHER 96
(SPECIFY)
103 Where is that water source located? IN OWN DWELLING . . . . . . . . . . . . . . . . . . 1
IN OWN YARD/PLOT ................ 2 105
ELSEWHERE ...................... 3
104 How long does it take to go there, get water, and come back?
MINUTES ................
IF 995 OR MORE, ENTER '995'. DON'T KNOW .................... 998
105 Do you do anything to the water to make it safer to drink? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW ...................... 8 107
106 What do you usually do to make the water safer to drink? BOIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A
ADD BLEACH/CHLORINE ............ B
Anything else? STRAIN THROUGH A CLOTH .......... C
USE WATER FILTER (CERAMIC/
SAND/COMPOSITE/ETC.) . . . . . . . . . . . . D
RECORD ALL MENTIONED. SOLAR DISINFECTION . . . . . . . . . . . . . . . . E
LET IT STAND AND SETTLE . . . . . . . . . . . . F
COVER THE WATER CONTAINER . . . . . . . G
OTHER X
(SPECIFY)
DON'T KNOW ...................... Z
107 What kind of toilet facility do members of your household FLUSH OR POUR FLUSH TOILET
usually use? FLUSH TO PIPED SEWER
SYSTEM ...................... 11
FLUSH TO SEPTIC TANK . . . . . . . . . . . . 12
FLUSH TO PIT LATRINE ............ 13
FLUSH TO SOMEWHERE ELSE . . . . . . . 14
FLUSH, DON'T KNOW WHERE . . . . . . . 15
PIT LATRINE
VENTILATED IMPROVED
PIT LATRINE . . . . . . . . . . . . . . . . . . . . 21
PIT LATRINE WITH SLAB . . . . . . . . . . . . 22
PIT LATRINE WITHOUT SLAB/
OPEN PIT . . . . . . . . . . . . . . . . . . . . . . 23
COMPOSTING TOILET . . . . . . . . . . . . . . . . 31
BUCKET TOILET .................... 41
HANGING TOILET/HANGING
LATRINE . . . . . . . . . . . . . . . . . . . . . . . . . . 51
NO FACILITY/BUSH/FIELD ............ 61 110
OTHER 96
(SPECIFY)
HH-8
430 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
108 Do you share this toilet facility with other households? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 110
109 How many households use this toilet facility? NO. OF HOUSEHOLDS
IF LESS THAN 10 . . . . . . . . . .
0
10 OR MORE HOUSEHOLDS ....... 95
DON'T KNOW .................... 98
110 Does your household have:
YES NO
a) Electricity? a) ELECTRICITY . . . . . . . . . . . . . . 1 2
b) A radio? b) RADIO . . . . . . . . . . . . . . . . . . . . 1 2
c) A television? c) TELEVISION . . . . . . . . . . . . . . 1 2
d) A mobile telephone? d) MOBILE TELEPHONE . . . . . . . 1 2
e) A non-mobile telephone? e) NON-MOBILE TELEPHONE . . . 1 2
f) A refrigerator? f) REFRIGERATOR . . . . . . . . . . . . 1 2
g) A solar panel? g) SOLAR PANEL . . . . . . . . . . . . 1 2
h) A table? h) TABLE . . . . . . . . . . . . . . . . . . . . 1 2
i) A chair? i) CHAIR . . . . . . . . . . . . . . . . . . . . 1 2
j) A sofa? j) SOFA . . . . . . . . . . . . . . . . . . . . 1 2
k) A bed? k) BED . . . . . . . . . . . . . . . . . . . . . . 1 2
l) A cupboard? l) CUPBOARD . . . . . . . . . . . . . . . . 1 2
m) A clock? m) CLOCK . . . . . . . . . . . . . . . . . . 1 2
n) A microwave oven? n) MICROWAVE OVEN . . . . . . . 1 2
o) A DVD player? o) DVD PLAYER . . . . . . . . . . . . . . 1 2
p) A cassette or CD player? p) CASSETTE/CD PLAYER . . . . . 1 2
111 What type of fuel does your household mainly use for cooking? ELECTRICITY . . . . . . . . . . . . . . . . . . . . . . . . 01
LPG\NATURAL GAS . . . . . . . . . . . . . . . . . . 02
BIOGAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 04
PARAFIN/KEROSENE . . . . . . . . . . . . . . . . . . 05
COAL, LIGNITE . . . . . . . . . . . . . . . . . . . . . . 06
CHARCOAL . . . . . . . . . . . . . . . . . . . . . . . . 07
WOOD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 08
STRAW/SHRUBS/GRASS . . . . . . . . . . . . . . 09
AGRICULTURAL CROP . . . . . . . . . . . . . . . . 10
ANIMAL DUNG . . . . . . . . . . . . . . . . . . . . . . 11
NO FOOD COOKED
IN HOUSEHOLD . . . . . . . . . . . . . . . . . . . . 95 114
OTHER 96
(SPECIFY)
112 Is the cooking usually done in the house, in a separate IN THE HOUSE . . . . . . . . . . . . . . . . . . . . . . 1
building, or outdoors? IN A SEPARATE BUILDING ............ 2
OUTDOORS . . . . . . . . . . . . . . . . . . . . . . . . 3
114
OTHER 6
(SPECIFY)
113 Do you have a separate room which is used as a kitchen? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
114 MAIN MATERIAL OF THE FLOOR. NATURAL FLOOR
EARTH/SAND . . . . . . . . . . . . . . . . . . . . 11
RECORD OBSERVATION. DUNG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
RUDIMENTARY FLOOR
WOOD PLANKS . . . . . . . . . . . . . . . . . . 21
PALM/BAMBOO . . . . . . . . . . . . . . . . . . 22
FINISHED FLOOR
PARQUET OR POLISHED
WOOD . . . . . . . . . . . . . . . . . . . . . . . . 31
VINYL OR ASPHALT STRIPS . . . . . . . 32
CERAMIC TILES . . . . . . . . . . . . . . . . . . 33
CEMENT . . . . . . . . . . . . . . . . . . . . . . . . 34
CARPET . . . . . . . . . . . . . . . . . . . . . . . . . . 35
OTHER 96
(SPECIFY)
HH-9
Appendix E • 431
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
115 MAIN MATERIAL OF THE ROOF. NATURAL ROOFING
NO ROOF . . . . . . . . . . . . . . . . . . . . . . . . 11
RECORD OBSERVATION. THATCH/GRASS/MAKUTI . . . . . . . . . . . . 12
DUNG/MUD/SOD . . . . . . . . . . . . . . . . . . . . 13
RUDIMENTARY ROOFING
IRON SHEETS . . . . . . . . . . . . . . . . . . . . . . 21
TIN CANS . . . . . . . . . . . . . . . . . . . . . . . . 22
FINISHED ROOFING
ASBETOS SHEET . . . . . . . . . . . . . . . . . . 31
CONCRETE . . . . . . . . . . . . . . . . . . . . . . . . 32
TILES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
OTHER 96
(SPECIFY)
116 MAIN MATERIAL OF THE EXTERNAL WALLS. NATURAL WALLS
NO WALLS . . . . . . . . . . . . . . . . . . . . . . . . 11
RECORD OBSERVATION. CANE/PALM/TRUNKS . . . . . . . . . . . . . . 12
DUNG/MUD/SOD . . . . . . . . . . . . . . . . . . . . 13
RUDIMENTARY WALLS
BAMBOO WITH MUD . . . . . . . . . . . . . . . . 21
STONE WITH MUD . . . . . . . . . . . . . . . . 22
UNCOVERED ADOBE . . . . . . . . . . . . . . 23
PLYWOOD . . . . . . . . . . . . . . . . . . . . . . 24
CARDBOARD . . . . . . . . . . . . . . . . . . . . 25
REUSED WOOD . . . . . . . . . . . . . . . . . . 26
IRON SHEETS . . . . . . . . . . . . . . . . . . . . . . 27
FINISHED WALLS
CEMENT . . . . . . . . . . . . . . . . . . . . . . . . 31
STONE WITH LIME/CEMENT . . . . . . . . . . 32
BRICKS . . . . . . . . . . . . . . . . . . . . . . . . . . 33
CEMENT BLOCKS . . . . . . . . . . . . . . . . . . 34
COVERED ADOBE . . . . . . . . . . . . . . . . 35
WOOD PLANKS/SHINGLES . . . . . . . . . . 36
OTHER 96
(SPECIFY)
117 How many rooms in this household are used for sleeping?
ROOMS . . . . . . . . . . . . . . . . . . . . . .
118 Does any member of this household own:
YES NO
a) A watch? a) WATCH . . . . . . . . . . . . . . . . 1 2
b) A bicycle? b) BICYCLE . . . . . . . . . . . . . . . . 1 2
c) A motorcycle or motor scooter? c) MOTORCYCLE/SCOOTER . 1 2
d) An animal-drawn cart? d) ANIMAL-DRAWN CART . . . 1 2
e) A car or truck? e) CAR/TRUCK . . . . . . . . . . . . 1 2
f) A boat with a motor? f) BOAT WITH MOTOR . . . . . 1 2
119 Does any member of this household own any agricultural land? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 121
HH-10
432 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
120 How many acres or hectares of agricultural land do members
of this household own? ACRES ....... 1 .
ACRES / HECTARES: IF 995 OR MORE, HECTARES . . . . . 2 .
RECORD '995.0' IN APPROPRIATE BOX.
PLOT SIZE
PLOT SIZE (SQ FT): IF 99995 OR MORE, (SQ FT) 3 .
RECORD '99995.0' IN APPROPRIATE BOX.
DON'T KNOW . . . . . . . . . . . . . . . . . . 9999998
121 Does this household own any livestock, herds, other farm YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
animals, or poultry? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 123
122 How many of the following animals does this household own?
IF NONE, ENTER '00'.
IF 95 OR MORE, ENTER '95'.
IF UNKNOWN, ENTER '98'.
a) Local cattle (indigenous)? a) LOCAL CATTLE . . . . . . . . . . . . . .
b) Exotic/grade cattle? b) EXOTIC/GRADE CATTLE . . . . . . .
c) Horses, donkeys, or camels? c) HORSES/DONKEYS/CAMELS . . .
d) Goats? d) GOATS ....................
e) Sheep? e) SHEEP ....................
f) Chickens? f) CHICKENS . . . . . . . . . . . . . . . . . .
123 Does any member of this household have a bank account? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW ...................... 8
124 At any time in the past 12 months, has anyone come into your YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
dwelling to spray the interior walls against mosquitoes? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW ...................... 8
126 Does your household have any mosquito nets that can be YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
used while sleeping? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 140
127 How many mosquito nets does your household have?
NUMBER OF NETS . . . . . . . . . . . . . . . . . .
IF 7 OR MORE NETS, RECORD '7'.
HH-11
Appendix E • 433
NET #1 NET #2 NET #3
128 ASK THE RESPONDENT TO
SHOW YOU ALL THE NETS IN
THE HOUSEHOLD
IF MORE THAN 3 NETS, USE OBSERVED ..... 1 OBSERVED ..... 1 OBSERVED ..... 1
ADDITIONAL QUESTIONNAIRE(S). NOT OBSERVED . . . 2 NOT OBSERVED . . . 2 NOT OBSERVED . . . 2
129 How many months ago did your MONTHS MONTHS MONTHS
household get the mosquito net? AGO . . . AGO . . . AGO . . .
IF LESS THAN ONE MONTH AGO, MORE THAN 36 MORE THAN 36 MORE THAN 36
RECORD '00'. MONTHS AGO . . . 95 MONTHS AGO . . . 95 MONTHS AGO . . . 95
NOT SURE . . . . . . . 98 NOT SURE . . . . . . . 98 NOT SURE . . . . . . . 98
130 OBSERVE OR ASK THE BRAND/ LONG-LASTING NET LONG-LASTING NET LONG-LASTING NET
TYPE OF MOSQUITO NET. OLYSET (SUPA- OLYSET (SUPA- OLYSET (SUPA-
NET EXTRA) 11 NET EXTRA) 11 NET EXTRA) 11
PERMANET (SUPA- PERMANET (SUPA- PERMANET (SUPA-
NET EXTRA) 12 NET EXTRA) 12 NET EXTRA) 12
NETPROTECT . . . 13 NETPROTECT . . . 13 NETPROTECT . . . 13
IF BRAND IS UNKNOWN AND OTHER/ OTHER/ OTHER/
YOU CANNOT OBSERVE THE DK BRAND . . . 16 DK BRAND . . . 16 DK BRAND . . . 16
NET, SHOW PICTURES OF (SKIP TO 134) (SKIP TO 134) (SKIP TO 134)
TYPICAL NET TYPES/BRANDS
TO RESPONDENT. 'CONVENTIONAL' NET 'CONVENTIONAL' NET 'CONVENTIONAL' NET
KINGA NET . . . 21 KINGA NET . . . 21 KINGA NET . . . 21
SUPANET . . . . . 22 SUPANET . . . . . 22 SUPANET . . . . . 22
UNBRANDED UNBRANDED UNBRANDED
RURAL NET . . . 23 RURAL NET . . . 23 RURAL NET . . . 23
OTHER/ OTHER/ OTHER/
DK BRAND . . . 26 DK BRAND . . . 26 DK BRAND . . . 26
(SKIP TO 132) (SKIP TO 132) (SKIP TO 132)
OTHER BRAND . . . 96 OTHER BRAND . . . 96 OTHER BRAND . . . 96
DK BRAND . . . . . . . 98 DK BRAND . . . . . . . 98 DK BRAND . . . . . . . 98
131 When you got the net, was it already YES ............ 1 YES ............ 1 YES ............ 1
treated with an insecticide to kill or NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
repel mosquitoes? NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8
132 Since you got the net, was it ever YES ............ 1 YES ............ 1 YES ............ 1
soaked or dipped in a liquid to kill or NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
repel mosquitoes? (SKIP TO 134) (SKIP TO 134) (SKIP TO 134)
NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8
133 How many months ago was the net MONTHS MONTHS MONTHS
last soaked or dipped? AGO . . . AGO . . . AGO . . .
MORE THAN 24 MORE THAN 24 MORE THAN 24
MONTHS AGO . . . 95 MONTHS AGO . . . 95 MONTHS AGO . . . 95
IF LESS THAN ONE MONTH AGO,
RECORD '00'. NOT SURE . . . . . . . 98 NOT SURE . . . . . . . 98 NOT SURE . . . . . . . 98
134 Did anyone sleep under this YES ............ 1 YES ............ 1 YES ............ 1
mosquito net last night? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 136) (SKIP TO 136) (SKIP TO 136)
NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8 NOT SURE . . . . . . . 8
HH-12
434 • Appendix E
NET #1 NET #2 NET #3
135 Who slept under this mosquito net
last night?
NAME NAME NAME
RECORD THE PERSON'S NAME
AND LINE NUMBER FROM THE LINE LINE LINE
HOUSEHOLD SCHEDULE. NO. ..... NO. ..... NO. .....
NAME NAME NAME
LINE LINE LINE
NO. ..... NO. ..... NO. .....
NAME NAME NAME
LINE LINE LINE
NO. ..... NO. ..... NO. .....
NAME NAME NAME
LINE LINE LINE
NO. ..... NO. ..... NO. .....
136 GO BACK TO 128 FOR GO BACK TO 128 FOR GO TO 128 IN FIRST
NEXT NET; OR, IF NO NEXT NET; OR, IF NO COLUMN OF A NEW
MORE NETS, MORE NETS, QUESTIONNAIRE;
GO TO 140. GO TO 140. OR, IF NO MORE
NETS, GO TO 140.
140 ASK RESPONDENT FOR A TEASPOONFUL OF IODINE PRESENT. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
COOKING SALT. NO IODINE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
NO SALT IN HOUSEHOLD . . . . . . . . . . . . . . . . . . . . 3
TEST SALT FOR IODINE.
SALT NOT TESTED 6
(SPECIFY REASON)
HH-13
Appendix E • 435
WEIGHT AND HEIGHT MEASUREMENT FOR CHILDREN AGE 0-5
201 CHECK COLUMN 11 IN HOUSEHOLD SCHEDULE. RECORD THE LINE NUMBER AND NAME FOR ALL ELIGIBLE CHILDREN 0-5 YEARS
IN QUESTION 202. IF MORE THAN SIX CHILDREN, USE ADDITIONAL QUESTIONNAIRE(S).
CHILD 1 CHILD 2 CHILD 3
202 LINE NUMBER FROM COLUMN 11 LINE LINE LINE
NUMBER . . . . . NUMBER . . . . . NUMBER . . . . .
NAME FROM COLUMN 2 NAME NAME NAME
203 IF MOTHER INTERVIEWED, COPY
MONTH AND YEAR OF BIRTH DAY . . . . . . . . . DAY . . . . . . . . . DAY . . . . . . . . .
FROM BIRTH HISTORY AND ASK
DAY; IF MOTHER NOT MONTH ..... MONTH ..... MONTH .....
INTERVIEWED, ASK:
What is (NAME)'s birth date? YEAR YEAR YEAR
204 CHECK 203: YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1
CHILD BORN IN JANUARY NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2
2009 OR LATER? (GO TO 203 FOR NEXT (GO TO 203 FOR NEXT (GO TO 203 FOR NEXT
CHILD OR, IF NO CHILD OR, IF NO CHILD OR, IF NO
MORE CHILDREN, MORE CHILDREN, MORE CHILDREN,
GO TO 214) GO TO 214) GO TO 214)
205 WEIGHT IN KILOGRAMS
KG. . KG. . KG. .
NOT PRESENT . . . 9994 NOT PRESENT . . . 9994 NOT PRESENT . . . 9994
REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995
OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996
206 HEIGHT IN CENTIMETERS
CM. . CM. . CM. .
NOT PRESENT . . . 9994 NOT PRESENT . . . 9994 NOT PRESENT . . . 9994
REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995
OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996
207 MEASURED LYING DOWN OR LYING DOWN . . . . . . . 1 LYING DOWN . . . . . . . 1 LYING DOWN . . . . . . . 1
STANDING UP? STANDING UP . . . . . . . 2 STANDING UP . . . . . . . 2 STANDING UP . . . . . . . 2
NOT MEASURED . . . 3 NOT MEASURED . . . 3 NOT MEASURED . . . 3
213 GO BACK TO 203 IN NEXT COLUMN OF THIS QUESTIONNAIRE OR IN THE FIRST COLUMN OF THE NEXT PAGE; IF NO MORE
CHILDREN, END INTERVIEW.
436 • Appendix E HH-14
CHILD 4 CHILD 5 CHILD 6
202 LINE NUMBER FROM COLUMN 11 LINE LINE LINE
NUMBER . . . . . NUMBER . . . . . NUMBER . . . . .
NAME FROM COLUMN 2 NAME NAME NAME
203 IF MOTHER INTERVIEWED, COPY
MONTH AND YEAR OF BIRTH DAY ....... DAY ....... DAY .......
FROM BIRTH HISTORY AND ASK
DAY; IF MOTHER NOT MONTH ..... MONTH ..... MONTH .....
INTERVIEWED, ASK:
What is (NAME)'s birth date? YEAR YEAR YEAR
204 CHECK 203: YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . 1
CHILD BORN IN JANUARY NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . 2
2009 OR LATER? (GO TO 203 FOR NEXT (GO TO 203 FOR NEXT (GO TO 203 IN FIRST
CHILD OR, IF NO CHILD OR, IF NO COLUMN OF NEW
MORE CHILDREN, MORE CHILDREN, QUESTIONNAIRE; OR,
GO TO 214) GO TO 214) IF NO MORE CHILDREN,
GO TO 214)
205 WEIGHT IN KILOGRAMS
KG. . KG. . KG. .
NOT PRESENT. . . . . 9994 NOT PRESENT. . . . . 9994 NOT PRESENT. . . . . 9994
REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995
OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996
206 HEIGHT IN CENTIMETERS
CM. . CM. . CM. .
NOT PRESENT . . . 9994 NOT PRESENT . . . 9994 NOT PRESENT . . . 9994
REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995 REFUSED . . . . . . . 9995
OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996 OTHER . . . . . . . . . 9996
207 MEASURED LYING DOWN OR LYING DOWN . . . . . . . 1 LYING DOWN . . . . . . . 1 LYING DOWN . . . . . . . 1
STANDING UP? STANDING UP . . . . . . . 2 STANDING UP . . . . . . . 2 STANDING UP . . . . . . . 2
NOT MEASURED . . . 3 NOT MEASURED . . . 3 NOT MEASURED . . . 3
213 GO BACK TO 203 IN NEXT COLUMN OF THIS QUESTIONNAIRE OR IN THE FIRST COLUMN OF AN ADDITIONAL QUESTIONNAIRE;
IF NO MORE CHILDREN, END INTERVIEW.
HH-15
Appendix E • 437
438 • Appendix E
1 LONG VERSION
2014 KENYA DEMOGRAPHIC AND HEALTH SURVEY
WOMAN'S QUESTIONNAIRE - LONG VERSION
CONFIDENTIAL
REPUBLIC OF KENYA
IDENTIFICATION
COUNTY
DISTRICT
LOCATION/TOWN
SUBLOCATION
NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NAME OF HOUSEHOLD HEAD
NAME AND LINE NUMBER OF WOMAN
YES . . . . . . . . . . . . . . . . . . 1
CHECK 101A IN HOUSEHOLD QUESTIONNAIRE: IS WOMAN SELECTED FOR SECTION 14? NO . . . . . . . . . . . . . . . . . . 2
INTERVIEWER VISITS
1 2 3 FINAL VISIT
DATE DAY
MONTH
YEAR
INTERVIEWER'S
NAME INT. NUMBER
RESULT* RESULT
NEXT VISIT: DATE
TOTAL NUMBER
TIME OF VISITS
*RESULT CODES:
1 COMPLETED 4 REFUSED
2 NOT AT HOME 5 PARTLY COMPLETED 7 OTHER
3 POSTPONED 6 INCAPACITATED (SPECIFY)
**LANGUAGE OF
QUESTIONNAIRE: 1 7 LANGUAGE OF
INTERVIEW:
NATIVE LANGUAGE
OF RESPONDENT:
TRANSLATOR USED
(YES = 1, NO = 2)
LANGUAGE OF
QUESTIONNAIRE: English
**LANGUAGE 01 BORANA 05 KIKUYU 09 LUO 13 POKOT 17 ENGLISH
CODES: 02 EMBU 06 KISII 10 MAASAI 14 SOMALI 18 OTHER
03 KALENJIN 07 LUHYA 11 MERU 15 SWAHILI
04 KAMBA 08 MARAGOLI 12 MIJIKENDA 16 TURKANA
SUPERVISOR FIELD EDITOR OFFICE KEYED BY
EDITOR
NAME NAME
W-1
Appendix E • 439
SECTION 1. RESPONDENT'S BACKGROUND
INTRODUCTION AND CONSENT
INFORMED CONSENT
Hello. My name is _______________________________________. I am working with the Kenya National Bureau of Statistics. We are
conducting a survey about health all over Kenya. The information we collect will help the government to plan health services. Your
household was selected for the survey. The questions usually take about 30 to 60 minutes. All of the answers you give will be confidential
and will not be shared with anyone other than members of our survey team. You don't have to be in the survey, but we hope you will agree
to answer the questions since your views are important. If I ask you any question you don't want to answer, just let me know and I will go on
to the next question or you can stop the interview at any time.
In case you need more information about the survey, you may contact the person listed on the card that has already been given to your
household.
Do you have any questions? May I begin the interview now?
SIGNATURE OF INTERVIEWER: DATE:
RESPONDENT AGREES TO BE INTERVIEWED . . . . . 1 RESPONDENT DOES NOT AGREE TO BE INTERVIEWED . . . 2 END
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
101 RECORD THE TIME.
HOUR . . . . . . . . . . . . . . . . . . . .
MINUTES . . . . . . . . . . . . . . . . . .
101A First I would like to ask some questions about you and your NAIROBI/ MOMBASA/ KISUMU . . . . . 1
household. For most of the time until you were 12 years old, did you TOWN . . . . . . . . . . . . . . . . . . . . . . . . . . 2
live in Nairobi, Mombasa, Kisumu, in a town, in the countryside, or COUNTRYSIDE . . . . . . . . . . . . . . . . . . 3
outside of Kenya? OUTSIDE KENYA . . . . . . . . . . . . . . . . 4
101B How long have you been living continuously in (NAME OF YEARS
CURRENT PLACE OF RESIDENCE)?
ALWAYS . . . . . . . . . . . . . . . . . . . . . . 95
IF LESS THAN ONE YEAR, RECORD '00' YEARS VISITOR . . . . . . . . . . . . . . . . . . . . . . . . 96 101D
101C Just before you moved here, did you live in Nairobi, Mombasa, NAIROBI/ MOMBASA/ KISUMU . . . . . 1
Kisumu, in a town, in the countryside, or outside of Kenya? TOWN . . . . . . . . . . . . . . . . . . . . . . . . . . 2
COUNTRYSIDE . . . . . . . . . . . . . . . . . . 3
OUTSIDE OF KENYA . . . . . . . . . . . . . . 4
101D What is your nationality? KENYAN . . . . . . . . . . . . . . . . . . . . . . . . 01 102
TANZANIAN . . . . . . . . . . . . . . . . . . . . 02
UGANDAN . . . . . . . . . . . . . . . . . . . . . . 03
SOMALI . . . . . . . . . . . . . . . . . . . . . . . . 04
ETHIOPIAN . . . . . . . . . . . . . . . . . . . . . . 05
SUDANESE . . . . . . . . . . . . . . . . . . . . . . 06
OTHER 96
(SPECIFY)
101E What was the main reason for moving to Kenya? JOIN FAMILY LIVING IN KENYA . . . . . 01
MARRIAGE . . . . . . . . . . . . . . . . . . . . . . 02
WORK . . . . . . . . . . . . . . . . . . . . . . . . . . 03
SCHOOL . . . . . . . . . . . . . . . . . . . . . . . . 04
ESCAPE INSECURITY/WAR . . . . . . . 05
ESCAPE ENVIRONMENTAL DISASTER
(E.G. FLOOD, DROUGHT, ETC.) . . . 06
OTHER 96
(SPECIFY)
W-2
440 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
102 In what month and year were you born?
MONTH ..................
DON'T KNOW MONTH . . . . . . . . . . . . 98
YEAR . . . . . . . . . . . .
DON'T KNOW YEAR . . . . . . . . . . . . 9998
103 How old were you at your last birthday?
AGE IN COMPLETED YEARS
COMPARE AND CORRECT 102 AND/OR 103 IF INCONSISTENT.
104 Have you ever attended school? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 108
105 What is the highest level of school you attended: primary, PRIMARY . . . . . . . . . . . . . . . . . . . . . . 1
vocational, secondary, or higher? POST-PRIMARY/VOCATIONAL . . . . . 2
SECONDARY/ 'A' LEVEL ......... 3
COLLEGE (MIDDLE LEVEL) . . . . . . . 4
UNIVERSITY . . . . . . . . . . . . . . . . . . . . 5
106 What is the highest (standard/form/year) you completed at that
level? STANDARD/FORM/YEAR ...
IF COMPLETED LESS THAN ONE YEAR AT THAT LEVEL,
RECORD '00'.
107 CHECK 105:
PRIMARY, SECONDARY
POST-PRIMARY/ OR HIGHER 110
VOCATIONAL
108 Now I would like you to read this sentence to me. CANNOT READ AT ALL . . . . . . . . . . . . 1
ABLE TO READ ONLY PARTS OF
SHOW CARD TO RESPONDENT. SENTENCE . . . . . . . . . . . . . . . . . . . . 2
ABLE TO READ WHOLE SENTENCE 3
IF RESPONDENT CANNOT READ WHOLE SENTENCE, PROBE: NO CARD WITH REQUIRED
Can you read any part of the sentence to me? LANGUAGE 4
(SPECIFY LANGUAGE)
BLIND/VISUALLY IMPAIRED . . . . . . . 5
109 CHECK 108:
CODE '2', '3' CODE '1' OR '5'
OR '4' CIRCLED 111
CIRCLED
110 Do you read a newspaper or magazine at least once a week, less AT LEAST ONCE A WEEK . . . . . . . . . 1
than once a week or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
111 Do you listen to the radio at least once a week, less than once a AT LEAST ONCE A WEEK . . . . . . . . . 1
week or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
112 Do you watch television at least once a week, less than once a AT LEAST ONCE A WEEK . . . . . . . . . 1
week or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
W-3
Appendix E • 441
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
113 What is your religion? ROMAN CATHOLIC .............. 1
PROTESTANT/ OTHER CHRISTIAN . 2
MUSLIM . . . . . . . . . . . . . . . . . . . . . . . . 3
NO RELIGION . . . . . . . . . . . . . . . . . . . . 4
OTHER 6
(SPECIFY)
114 What is your ethnic group / tribe? EMBU . . . . . . . . . . . . . . . . . . . . . . . . . . 01
KALENJIN . . . . . . . . . . . . . . . . . . . . . . 02
KAMBA . . . . . . . . . . . . . . . . . . . . . . . . 03
KIKUYU . . . . . . . . . . . . . . . . . . . . . . . . 04
KISII . . . . . . . . . . . . . . . . . . . . . . . . . . . . 05
LUHYA . . . . . . . . . . . . . . . . . . . . . . . . 06
LUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 07
MAASAI . . . . . . . . . . . . . . . . . . . . . . . . 08
MERU . . . . . . . . . . . . . . . . . . . . . . . . . . 09
MIJIKENDA/ SWAHILI . . . . . . . . . . . . . . 10
SOMALI . . . . . . . . . . . . . . . . . . . . . . . . 11
TAITA/ TAVETA . . . . . . . . . . . . . . . . . . 12
OTHER 96
(SPECIFY)
115 In the last 12 months, how many times have you been away from
home for one or more nights? NUMBER OF TIMES .......
NONE . . . . . . . . . . . . . . . . . . . . . . . . . . 00 201
116 In the last 12 months, have you been away from home for more YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
than one month at a time? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
W-4
442 • Appendix E
SECTION 2. REPRODUCTION
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
201 Now I would like to ask about all the births you have had during your YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
life. Have you ever given birth? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 206
202 Do you have any sons or daughters to whom you have given birth YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
who are now living with you? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 204
203 How many sons live with you?
SONS AT HOME . . . . . . . . . . . .
And how many daughters live with you?
DAUGHTERS AT HOME . . . . .
IF NONE, RECORD '00'.
204 Do you have any sons or daughters to whom you have given birth YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
who are alive but do not live with you? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 206
205 How many sons are alive but do not live with you?
SONS ELSEWHERE .......
And how many daughters are alive but do not live with you?
DAUGHTERS ELSEWHERE
IF NONE, RECORD '00'.
206 Have you ever given birth to a boy or girl who was born alive but
later died?
YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
IF NO, PROBE: Any baby who cried or showed signs of life but did NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 208
not survive?
207 How many boys have died?
BOYS DEAD ..............
And how many girls have died?
GIRLS DEAD ..............
IF NONE, RECORD '00'.
208 SUM ANSWERS TO 203, 205, AND 207, AND ENTER TOTAL.
IF NONE, RECORD '00'. TOTAL BIRTHS ............
CHECK 208:
209 Just to make sure that I have this right: you have had in TOTAL
_____ births during your life. Is that correct?
PROBE AND
YES NO CORRECT
201-208 AS
NECESSARY.
210 CHECK 208:
ONE OR MORE NO BIRTHS
BIRTHS 226
W-5
Appendix E • 443
211 Now I would like to record the names of all your births, whether still alive or not, starting with the first one you had.
RECORD NAMES OF ALL THE BIRTHS IN 212. RECORD TWINS AND TRIPLETS ON SEPARATE ROWS.
(IF THERE ARE MORE THAN 12 BIRTHS, USE AN ADDITIONAL QUESTIONNAIRE, STARTING WITH THE SECOND ROW).
212 213 214 215 216 217 218 219 220 221
IF ALIVE: IF ALIVE: IF ALIVE: IF DEAD:
What name Is Were any In what month and Is How old was Is (NAME) RECORD How old was (NAME) Were there
was given to (NAME) of these year was (NAME) (NAME) (NAME) at living with HOUSE- when he/she died? any other live
your a boy or births born? still his/her last you? births between
HOLD LINE
(first/next) a girl? twins? alive? birthday? IF '1 YR', PROBE: How (NAME OF
NUMBER OF
baby? PROBE: What is many months old was PREVIOUS
his/her birthday? CHILD (NAME)? BIRTH) and
(RECORD '00' (NAME),
IF CHILD NOT including any
children who
RECORD RECORD LISTED IN
died after
NAME. AGE IN HOUSE- RECORD DAYS IF
birth?
COM- HOLD). LESS THAN 1
BIRTH PLETED MONTH; MONTHS IF
HISTORY YEARS. LESS THAN TWO
NUMBER YEARS; OR YEARS.
01 MONTH AGE IN HOUSEHOLD DAYS . . . . 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER
YEAR MONTHS 2
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2
YEARS . . .3
220 (NEXT BIRTH)
02 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
03 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
04 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
05 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
06 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
07 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
W-6
444 • Appendix E
212 213 214 215 216 217 218 219 220 221
IF ALIVE: IF ALIVE: IF ALIVE: IF DEAD:
What name Is Were any In what month and Is How old was Is (NAME) RECORD How old was (NAME) Were there
was given to (NAME) of these year was (NAME) (NAME) (NAME) at living with HOUSE- when he/she died? any other live
your a boy or births born? still his/her last you? births between
HOLD LINE
(first/next) a girl? twins? alive? birthday? IF '1 YR', PROBE: How (NAME OF
NUMBER OF
baby? PROBE: What is many months old was PREVIOUS
his/her birthday? CHILD (NAME)? BIRTH) and
(RECORD '00' (NAME),
IF CHILD NOT including any
children who
RECORD LISTED IN
died after
NAME. RECORD HOUSE- RECORD DAYS IF
birth?
AGE IN HOLD). LESS THAN 1
BIRTH COM- MONTH; MONTHS IF
HISTORY PLETED LESS THAN TWO
NUMBER YEARS. YEARS; OR YEARS.
08 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
09 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
10 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
11 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
12 MONTH AGE IN HOUSEHOLD DAYS . . . . 1 YES ... 1
BOY 1 SING 1 YES . . .1. YEARS YES . . . .1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . .2 NO . . . . .2 NO . . . . . 2
YEARS . . .3 NEXT
220 (GO TO 221) BIRTH
222 Have you had any live births since the birth of (NAME OF LAST YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ADD
BIRTH)? BIRTH
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
223 COMPARE 208 WITH NUMBER OF BIRTHS IN HISTORY ABOVE AND MARK:
NUMBERS NUMBERS ARE
ARE SAME DIFFERENT (PROBE AND RECONCILE)
224 CHECK 215:
NUMBER OF BIRTHS IN 2009 OR LATER
ENTER THE NUMBER OF BIRTHS IN 2009 OR LATER.
NONE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 226
W-7
Appendix E • 445
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
225 FOR EACH BIRTH SINCE JANUARY 2009, ENTER 'B' IN THE MONTH OF BIRTH IN THE
CALENDAR. WRITE THE NAME OF THE CHILD TO THE LEFT OF THE 'B' CODE. FOR EACH BIRTH,
C ASK THE NUMBER OF MONTHS THE PREGNANCY LASTED AND RECORD 'P' IN EACH OF THE
PRECEDING MONTHS ACCORDING TO THE DURATION OF PREGNANCY. (NOTE: THE NUMBER
OF 'P's MUST BE ONE LESS THAN THE NUMBER OF MONTHS THAT THE PREGNANCY LASTED.)
226 Are you pregnant now? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
UNSURE . . . . . . . . . . . . . . . . . . . . . . . . 8 230
227 How many months pregnant are you?
MONTHS . . . . . . . . . . . . . . . . . .
RECORD NUMBER OF COMPLETED MONTHS.
ENTER 'P's IN THE CALENDAR, BEGINNING WITH
C THE MONTH OF INTERVIEW AND FOR THE TOTAL
NUMBER OF COMPLETED MONTHS.
228 When you got pregnant, did you want to get pregnant at that time? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 230
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
229 Did you want to have a baby later on or did you not want any (more) LATER . . . . . . . . . . . . . . . . . . . . . . . . . . 1
children? NO MORE . . . . . . . . . . . . . . . . . . . . . . 2
230 Have you ever had a pregnancy that miscarried, was aborted, or YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
ended in a stillbirth? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 238
231 When did the last such pregnancy end?
MONTH ..................
YEAR . . . . . . . . . . . .
232 CHECK 231:
LAST PREGNANCY LAST PREGNANCY
ENDED IN ENDED BEFORE 238
JAN. 2009 OR LATER JAN. 2009
233 How many months pregnant were you when the last such
pregnancy ended? MONTHS . . . . . . . . . . . . . . . . . .
RECORD NUMBER OF COMPLETED MONTHS. ENTER
C T' IN THE CALENDAR IN THE MONTH THAT THE
PREGNANCY TERMINATED AND 'P' FOR THE
REMAINING NUMBER OF COMPLETED MONTHS.
234 Since January 2009, have you had any other pregnancies that did YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
not result in a live birth? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 236
235 ASK THE DATE AND THE DURATION OF PREGNANCY FOR EACH EARLIER NON-LIVE BIRTH PREGNANCY
BACK TO JANUARY 2009.
C ENTER 'T' IN THE CALENDAR IN THE MONTH THAT EACH PREGNANCY TERMINATED AND 'P'
FOR THE REMAINING NUMBER OF COMPLETED MONTHS.
236 Did you have any miscarriages, abortions or stillbirths that ended YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
before 2009? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 238
237 When did the last such pregnancy that terminated before 2009 end?
MONTH ..................
YEAR . . . . . . . . . . . .
W-8
446 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
238 When did your last menstrual period start?
DAYS AGO . . . . . . . . . . . . 1
WEEKS AGO . . . . . . . . . 2
MONTHS AGO . . . . . . . . . 3
(DATE, IF GIVEN) YEARS AGO ......... 4
IN MENOPAUSE/
HAS HAD HYSTERECTOMY ... 994
BEFORE LAST BIRTH . . . . . . . . . . . . 995
NEVER MENSTRUATED . . . . . . . . . 996
239 From one menstrual period to the next, are there certain days when YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
a woman is more likely to become pregnant? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 301
240 Is this time just before her period begins, during her period, right JUST BEFORE HER PERIOD
after her period has ended, or halfway between two periods? BEGINS . . . . . . . . . . . . . . . . . . . . . . 1
DURING HER PERIOD . . . . . . . . . . . . 2
RIGHT AFTER HER
PERIOD HAS ENDED . . . . . . . . . . . . 3
HALFWAY BETWEEN
TWO PERIODS . . . . . . . . . . . . . . . . 4
OTHER 6
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
W-9
Appendix E • 447
SECTION 3. CONTRACEPTION
301 Now I would like to talk about family planning - the various ways or methods that a couple can use to delay or avoid a pregnancy.
Have you ever heard of (METHOD)?
01 Female Sterilization. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have an operation to avoid having any more NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
children.
02 Male Sterilization. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can have an operation to avoid having any more NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
children.
03 IUD. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have a loop or coil placed inside them by a NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
doctor or a nurse.
04 Injectables. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have an injection by a health provider that NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
stops them from becoming pregnant for one or more months.
05 Implants. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have one or more small rods placed in their NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
upper arm by a doctor or nurse which can prevent pregnancy for
one or more years.
06 Pill. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can take a pill every day to avoid becoming NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
pregnant.
07 Male Condom. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can put a rubber sheath on their penis before sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse.
08 Female Condom. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can place a sheath in their vagina before sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse.
09 Lactational Amenorrhea Method (LAM). YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
10 Rhythm Method. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: To avoid pregnancy, women do not have sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse on the days of the month they think they can get
pregnant.
11 Withdrawal. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can be careful and pull out before climax. NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
12 Emergency Contraception. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: As an emergency measure, within three days after they NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
have unprotected sexual intercourse, women can take special pills
to prevent pregnancy.
13 Have you heard of any other ways or methods that women or men YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
can use to avoid pregnancy?
(SPECIFY)
(SPECIFY)
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
W-10
448 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
302 CHECK 226:
NOT PREGNANT PREGNANT
OR UNSURE 311
303 Are you currently doing something or using any method to delay or YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
avoid getting pregnant? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 311
304 Which method are you using? FEMALE STERILIZATION . . . . . . . . . A
MALE STERILIZATION . . . . . . . . . . . . B 307
CIRCLE ALL MENTIONED. IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . C
INJECTABLES .................. D
IF MORE THAN ONE METHOD MENTIONED, FOLLOW SKIP IMPLANTS . . . . . . . . . . . . . . . . . . . . . . E
307A
INSTRUCTION FOR HIGHEST METHOD IN LIST. PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . . F
MALE CONDOM . . . . . . . . . . . . . . . . . . G
FEMALE CONDOM .............. H
LACTATIONAL AMEN. METHOD . . . . . K
RHYTHM METHOD . . . . . . . . . . . . . . . . L
WITHDRAWAL . . . . . . . . . . . . . . . . . . M 308A
OTHER MODERN METHOD . . . . . . . X
OTHER TRADITIONAL METHOD ... Y
307 In what facility did the sterilization take place? PUBLIC SECTOR
GOVT. HOSPITAL . . . . . . . . . . . . . . 11
PROBE TO IDENTIFY THE TYPE OF SOURCE. GOVT. HEALTH CENTER . . . . . . . 12
GOVT. DISPENSARY . . . . . . . . . . . . 13
OTHER PUBLIC
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR 16
SECTOR, WRITE THE NAME OF THE PLACE. (SPECIFY)
PRIVATE MEDICAL SECTOR
(NAME OF PLACE) FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . 21
308
FAMILY OPTIONS/FHOK CLINIC . . . 22
PRIVATE HOSPITAL/CLINIC . . . . . 23
NURSING/ MATERNITY HOME . . . 24
MOBILE CLINIC . . . . . . . . . . . . . . . . 25
OTHER PRIVATE MEDICAL
SECTOR 26
(SPECIFY)
OTHER 96
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 98
307A The last time you obtained (HIGHEST METHOD ON LIST IN 304), COST . . . . . . .
how much did you pay in total, including the cost of the method and
any consultation you may have had. 308A
FREE . . . . . . . . . . . . . . . . . . . . . . 99995
DON'T KNOW . . . . . . . . . . . . . . . . 99998
308 In what month and year was the sterilization performed?
308A Since what month and year have you been using (CURRENT MONTH ..................
METHOD) without stopping?
YEAR . . . . . . . . . . . .
PROBE: For how long have you been using (CURRENT METHOD)
now without stopping?
W-11
Appendix E • 449
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
309 CHECK 308/308A, 215 AND 231:
ANY BIRTH OR PREGNANCY TERMINATION AFTER MONTH AND YES NO
YEAR OF START OF USE OF CONTRACEPTION IN 308/308A?
GO BACK TO 308/308A, PROBE AND RECORD MONTH AND YEAR AT START OF CONTINUOUS
USE OF CURRENT METHOD (MUST BE AFTER LAST BIRTH OR PREGNANCY TERMINATION).
310 CHECK 308/308A:
YEAR IS 2009 OR LATER YEAR IS 2008 OR EARLIER
ENTER CODE FOR METHOD USED IN MONTH ENTER CODE FOR METHOD USED IN MONTH OF
C OF INTERVIEW IN THE CALENDAR
AND IN EACH MONTH BACK TO THE DATE
STARTED USING.
C INTERVIEW IN THE CALENDAR AND
EACH MONTH BACK TO JANUARY 2009
THEN SKIP TO 322
311 I would like to ask you some questions about the times you or your partner may have used a method to avoid
getting pregnant during the last few years.
USE CALENDAR TO PROBE FOR EARLIER PERIODS OF USE AND NONUSE, STARTING WITH MOST
RECENT USE, BACK TO JANUARY 2009.
USE NAMES OF CHILDREN, DATES OF BIRTH, AND PERIODS OF PREGNANCY AS REFERENCE POINTS.
C IN COLUMN 1, ENTER METHOD USE CODE OR '0' FOR NONUSE IN EACH BLANK MONTH.
ILLUSTRATIVE QUESTIONS:
a) When was the last time you used a method? Which method was that?
b) When did you start using that method? How long after the birth of (NAME)?
c) How long did you use the method then?
IN COLUMN 2, ENTER CODES FOR DISCONTINUATION NEXT TO THE LAST MONTH OF USE.
NUMBER OF CODES IN COLUMN 2 MUST BE SAME AS NUMBER OF INTERRUPTIONS OF
METHOD USE IN COLUMN 1.
ASK WHY SHE STOPPED USING THE METHOD. IF A PREGNANCY FOLLOWED, ASK
WHETHER SHE BECAME PREGNANT UNINTENTIONALLY WHILE USING THE METHOD OR
DELIBERATELY STOPPED TO GET PREGNANT.
ILLUSTRATIVE QUESTIONS:
d) Why did you stop using the (METHOD)? Did you become pregnant while using (METHOD), or did
you stop to get pregnant, or did you stop for some other reason?
e) IF DELIBERATELY STOPPED TO BECOME PREGNANT, ASK:
How many months did it take you to get pregnant after you stopped using (METHOD)?
AND ENTER ‘0’ IN EACH SUCH MONTH IN COLUMN 1.
312 CHECK THE CALENDAR FOR USE OF ANY CONTRACEPTIVE METHOD IN ANY MONTH
NO METHOD USED ANY METHOD USED
314
313 Have you ever used anything or tried in any way to delay or avoid YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
getting pregnant? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 324
W-12
450 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
314 CHECK 304: NO CODE CIRCLED . . . . . . . . . . . . . . 00 324
FEMALE STERILIZATION . . . . . . . . . 01 317A
CIRCLE METHOD CODE: MALE STERILIZATION . . . . . . . . . . . . 02 326
IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 03
IF MORE THAN ONE METHOD CODE CIRCLED IN 304, INJECTABLES . . . . . . . . . . . . . . . . . . . . 04
CIRCLE CODE FOR HIGHEST METHOD IN LIST. IMPLANTS . . . . . . . . . . . . . . . . . . . . . . 05
PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06
MALE CONDOM . . . . . . . . . . . . . . . . . . 07
FEMALE CONDOM . . . . . . . . . . . . . . . . 08
LACTATIONAL AMEN. METHOD . . . . . 11
RHYTHM METHOD . . . . . . . . . . . . . . . . 12 315A
WITHDRAWAL . . . . . . . . . . . . . . . . . . 13
OTHER MODERN METHOD . . . . . . . 95 326
OTHER TRADITIONAL METHOD . . . . . 96
315 You first started using (CURRENT METHOD) in (DATE FROM PUBLIC SECTOR
308/308A). Where did you get it at that time? GOVT. HOSPITAL . . . . . . . . . . . . . . 11
GOVT. HEALTH CENTER . . . . . . . 12
GOVT. DISPENSARY . . . . . . . . . . . . 13
OTHER PUBLIC
SECTOR 16
(SPECIFY)
315A Where did you learn how to use the rhythm/lactational amenorrhea PRIVATE MEDICAL SECTOR
method? PRIVATE HOSPITAL/CLINIC . . . . . 21
PHARMACY/CHEMIST . . . . . . . . . . . . 22
NURSING/MATERNITY HOME . . . . . 23
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . 24
FAMILY OPTIONS/FHOK CLINIC . . . 25
OTHER PRIVATE MEDICAL
SECTOR 26
PROBE TO IDENTIFY THE TYPE OF SOURCE. (SPECIFY)
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR, OTHER SOURCE
WRITE THE NAME OF THE PLACE. SHOP . . . . . . . . . . . . . . . . . . . . . . . . 31
MOBILE CLINIC . . . . . . . . . . . . . . . . 32
COMMUNITY-BASED DISTRIBUTOR 33
COMMUNITY HEALTH WORKER/
(NAME OF PLACE) CHW . . . . . . . . . . . . . . . . . . . . . . 34
FRIEND/RELATIVE . . . . . . . . . . . . . . 35
OTHER 96
(SPECIFY)
316 CHECK 304: IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 03
INJECTABLES . . . . . . . . . . . . . . . . . . . . 04
CIRCLE METHOD CODE: IMPLANTS . . . . . . . . . . . . . . . . . . . . . . 05
PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06
IF MORE THAN ONE METHOD CODE CIRCLED IN 304, MALE CONDOM . . . . . . . . . . . . . . . . . . 07 323
CIRCLE CODE FOR HIGHEST METHOD IN LIST. FEMALE CONDOM . . . . . . . . . . . . . . . . 08 320
LACTATIONAL AMEN. METHOD . . . . . 11 326
RHYTHM METHOD . . . . . . . . . . . . . . . . 12 326
317 At that time, were you told about side effects or problems you might
have with the method? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 319
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
317A When you got sterilized, were you told about side effects or
problems you might have with the method?
318 Were you ever told by a health or family planning worker about side YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
effects or problems you might have with the method? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 320
W-13
Appendix E • 451
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
319 Were you told what to do if you experienced side effects or YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
problems? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
320 CHECK 317:
CODE '1' CODE '1'
CIRCLED NOT
CIRCLED
a) At that time, were you told b) When you obtained
about other methods of (CURRENT METHOD FROM
family planning that you 314) from (SOURCE OF
could use? METHOD FROM 307 OR YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 322
315), were you told about
other methods of family
planning that you could use?
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
321 Were you ever told by a health or family planning worker about other YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
methods of family planning that you could use? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
322 CHECK 304: FEMALE STERILIZATION . . . . . . . . . 01
MALE STERILIZATION . . . . . . . . . . . . 02 326
CIRCLE METHOD CODE: IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 03
INJECTABLES . . . . . . . . . . . . . . . . . . . . 04
IF MORE THAN ONE METHOD CODE CIRCLED IN 304, IMPLANTS . . . . . . . . . . . . . . . . . . . . . . 05
CIRCLE CODE FOR HIGHEST METHOD IN LIST. PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06
MALE CONDOM . . . . . . . . . . . . . . . . . . 07
FEMALE CONDOM . . . . . . . . . . . . . . . . 08
LACTATIONAL AMEN. METHOD . . . . . 11
RHYTHM METHOD . . . . . . . . . . . . . . . . 12
WITHDRAWAL . . . . . . . . . . . . . . . . . . 13 326
OTHER MODERN METHOD . . . . . . . 95
OTHER TRADITIONAL METHOD . . . 96
323 Where did you obtain (CURRENT METHOD) the last time? PUBLIC SECTOR
GOVT. HOSPITAL . . . . . . . . . . . . . . 11
PROBE TO IDENTIFY THE TYPE OF SOURCE. GOVT. HEALTH CENTER . . . . . . . 12
GOVT. DISPENSARY . . . . . . . . . . . . 13
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE OTHER PUBLIC
SECTOR, WRITE THE NAME OF THE PLACE. SECTOR 16
(SPECIFY)
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . 21
PHARMACY/CHEMIST . . . . . . . . . . . . 22
NURSING/MATERNITY HOME . . . . . 23
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . 24
(NAME OF PLACE) FAMILY OPTIONS/FHOK CLINIC . . . 25 326
OTHER PRIVATE MEDICAL
SECTOR 26
(SPECIFY)
OTHER SOURCE
SHOP . . . . . . . . . . . . . . . . . . . . . . . . 31
MOBILE CLINIC . . . . . . . . . . . . . . . . 32
COMMUNITY-BASED DISTRIBUTOR 33
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . . . 34
FRIEND/RELATIVE . . . . . . . . . . . . . . 35
OTHER 96
(SPECIFY)
324 Do you know of a place where you can obtain a method of family YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
planning? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 326
W-14
452 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
325 Where is that? PUBLIC SECTOR
GOVT. HOSPITAL . . . . . . . . . . . . . . A
Any other place? GOVT. HEALTH CENTER . . . . . . . B
GOVT. DISPENSARY . . . . . . . . . . . . C
OTHER PUBLIC
PROBE TO IDENTIFY EACH TYPE OF SOURCE. SECTOR D
(SPECIFY)
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE
SECTOR, WRITE THE NAME OF THE PLACE. PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . E
PHARMACY/CHEMIST . . . . . . . . . . . . F
NURSING/MATERNITY HOME . . . . . G
(NAME OF PLACE(S)) FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . H
FAMILY OPTIONS/FHOK CLINIC . . . I
OTHER PRIVATE MEDICAL
SECTOR J
(SPECIFY)
OTHER SOURCE
SHOP . . . . . . . . . . . . . . . . . . . . . . . . K
MOBILE CLINIC . . . . . . . . . . . . . . . . L
COMMUNITY-BASED DISTRIBUTOR M
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . . . N
FRIEND/RELATIVE . . . . . . . . . . . . . . O
OTHER X
(SPECIFY)
326 In the last 12 months, were you visited by a fieldworker who talked YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
to you about family planning? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
327 In the last 12 months, have you visited a health facility for care for YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
yourself (or your children)? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 401
328 Did any staff member at the health facility speak to you about family YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
planning methods? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
W-15
Appendix E • 453
SECTION 4. PREGNANCY AND POSTNATAL CARE
401 CHECK 224:
ONE OR MORE NO 556
BIRTHS BIRTHS
IN 2009 IN 2009
OR LATER OR LATER
CHECK 215: ENTER IN THE TABLE THE BIRTH HISTORY NUMBER, NAME, AND SURVIVAL STATUS OF EACH BIRTH
IN 2009 OR LATER. ASK THE QUESTIONS ABOUT ALL OF THESE BIRTHS. BEGIN WITH THE LAST BIRTH.
(IF THERE ARE MORE THAN 3 BIRTHS, USE LAST 2 COLUMNS OF ADDITIONAL QUESTIONNAIRES).
402 Now I would like to ask some questions about your children born in the last five years. (We will talk about each separately.)
403 BIRTH HISTORY NUMBER LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
FROM 212 IN BIRTH HISTORY BIRTH BIRTH BIRTH
HISTORY HISTORY HISTORY
NUMBER NUMBER NUMBER
404 FROM 212 AND 216 NAME NAME NAME
LIVING DEAD LIVING DEAD LIVING DEAD
405 When you got pregnant with YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
(NAME), did you want to get (SKIP TO 408) (SKIP TO 430) (SKIP TO 430)
pregnant at that time? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
406 Did you want to have a baby later LATER . . . . . . . . . . . . 1 LATER . . . . . . . . . . . . 1 LATER . . . . . . . . . 1
on, or did you not want any (more) NO MORE . . . . . . . 2 NO MORE . . . . . . . 2 NO MORE . . . . . . . 2
children? (SKIP TO 408) (SKIP TO 430) (SKIP TO 430)
407 How much longer did you want to
wait? MONTHS . .1 MONTHS . .1 MONTHS . .1
YEARS ..2 YEARS ..2 YEARS ..2
DON'T KNOW . . . 998 DON'T KNOW . . . 998 DON'T KNOW . . . 998
408 Did you see anyone for antenatal YES . . . . . . . . . . . . . . 1
care for this pregnancy? (SKIP TO 409)
NO . . . . . . . . . . . . . . 2
408A What are the reasons for not DISTANCE . . . . . . . A
receiving antenatal care for this COST . . . . . . . . . . . . B
pregnancy? TOO MUCH WORK . C
HUSBAND
REFUSED . . . . . D
RELIGIOUS
RECORD ALL MENTIONED REASONS . . . E
OTHER X
(SPECIFY)
(SKIP TO 415)
409 Whom did you see? HEALTH PERSONNEL
DOCTOR . . . . . . . A
Anyone else? NURSE/MIDWIFE . B
OTHER PERSON
COMMUNITY HEALTH
PROBE TO IDENTIFY EACH TYPE WORKER . . . C
OF PERSON AND RECORD ALL TRADITIONAL BIRTH
MENTIONED. ATTENDANT . D
OTHER X
(SPECIFY)
W-16
454 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
410 Where did you receive antenatal HOME
care for this pregnancy? YOUR HOME . . . A
OTHER HOME . . . B
Anywhere else?
PUBLIC SECTOR
GOVT. HOSPITAL . C
GOVT. HEALTH
CENTER . . . . . D
PROBE TO IDENTIFY EACH TYPE GOVT.
OF SOURCE. DISPENSARY . E
OTHER PUBLIC
IF UNABLE TO DETERMINE SECTOR
IF PUBLIC OR PRIVATE F
SECTOR, WRITE THE NAME (SPECIFY)
OF THE PLACE. PRIVATE MED. SECTOR
PVT. HOSPITAL/
CLINIC . . . . . . . G
(NAME OF PLACE(S)) FAITH-BASED,
CHURCH, HOSP.
/ CLINIC . . . . . H
NURSING / MATERNITY
HOME . . . . . . . I
OTHER PRIVATE
MED. SECTOR
J
(SPECIFY)
OTHER X
(SPECIFY)
411 How many months pregnant were
you when you first received MONTHS . . .
antenatal care for this pregnancy?
DON'T KNOW . . . . . 98
412 How many times did you receive NUMBER
antenatal care during this OF TIMES .
pregnancy?
DON'T KNOW . . . . . 98
413 As part of your antenatal care
during this pregnancy, were any of
the following done at least once:
YES NO
a) Was your blood pressure a) BP ....... 1 2
measured?
b) Did you give a urine sample? b) URINE . . . . . 1 2
c) Did you give a blood sample? c) BLOOD ... 1 2
d) Were you weighed? d) WEIGHT . . . 1 2
e) Was your height measured? e) HEIGHT . . . 1 2
413A Were you given any information or YES . . . . . . . . . . . . . . 1
counselled about breastfeeding? NO . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8
413B Were you given any information or YES . . . . . . . . . . . . . . 1
counselled about iron tablets, iron NO . . . . . . . . . . . . . . 2
syrup, or iron and folic acid DON'T KNOW . . . . . 8
supplementation?
W-17
Appendix E • 455
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
414 During (any of) your antenatal care YES . . . . . . . . . . . . . . 1
visit(s), were you told about things NO . . . . . . . . . . . . . . 2
to look out for that might suggest DON'T KNOW . . . . . 8
problems with the pregnancy?
414A During any of your antenatal care YES . . . . . . . . . . . . . . 1
visits, were you asked about your NO . . . . . . . . . . . . . . 2
family planning needs after DON'T KNOW . . . . . 8
delivery?
415 During this pregnancy, were you YES . . . . . . . . . . . . . . 1
given an injection in the arm to
prevent the baby from getting NO . . . . . . . . . . . . . . 2
tetanus, that is, convulsions after (SKIP TO 418)
birth? DON'T KNOW . . . . . 8
416 During this pregnancy, how many
times did you get a tetanus TIMES . . . . . . . . .
injection?
DON'T KNOW . . . . . 8
417 CHECK 416: 2 OR MORE OTHER
TIMES
(SKIP TO 421)
418 At any time before this pregnancy, YES . . . . . . . . . . . . . . 1
did you receive any tetanus NO . . . . . . . . . . . . . . 2
injections? (SKIP TO 421)
DON'T KNOW ... 8
419 Before this pregnancy, how many
times did you receive a tetanus TIMES . . . . . . . . .
injection?
IF 7 OR MORE TIMES, DON'T KNOW . . . . . 8
RECORD '7'.
420 How many years ago did you YEARS
receive the last tetanus injection AGO . . . . .
before this pregnancy?
421 During this pregnancy, were you YES . . . . . . . . . . . . . . 1
given or did you buy any iron
tablets, iron syrup, or iron and folic NO . . . . . . . . . . . . . . 2
acid supplements? (SKIP TO 423)
DON'T KNOW . . . . . 8
SHOW TABLETS/SYRUP.
422 During the whole pregnancy, for
how many days did you take the DAYS .
tablets, syrup, or supplement?
DON'T KNOW . . . 998
IF ANSWER IS NOT NUMERIC,
PROBE FOR APPROXIMATE
NUMBER OF DAYS.
W-18
456 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
423 During this pregnancy, did you take YES . . . . . . . . . . . . . . 1
any drug for intestinal worms? NO . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8
424 During this pregnancy, did you take YES . . . . . . . . . . . . . . 1
any drugs to keep you from getting
malaria? NO . . . . . . . . . . . . . . 2
(SKIP TO 430)
DON'T KNOW . . . . . 8
425 What drugs did you take? SP/FANSIDAR . . . . . A
CHLOROQUINE . . . B
RECORD ALL MENTIONED.
IF TYPE OF DRUG IS NOT OTHER X
DETERMINED, SHOW TYPICAL (SPECIFY)
ANTIMALARIAL DRUGS TO DON'T KNOW . . . . . Z
RESPONDENT.
426 CHECK 425: CODE 'A' CODE
CIRCLED A' NOT
SP/FANSIDAR TAKEN FOR CIRCLED
MALARIA PREVENTION.
(SKIP TO 430)
427 How many times did you take
(SP/Fansidar) during this TIMES . . . . .
pregnancy?
428 CHECK 409: CODE 'A', OTHER
OR 'B'
ANTENATAL CARE FROM CIRCLED
HEALTH PERSONNEL
DURING THIS PREGNANCY
(SKIP TO 430)
429 Did you get the (SP/Fansidar) ANTENATAL VISIT . 1
during any antenatal care visit, ANOTHER FACILITY
during another visit to a health VISIT . . . . . . . . . 2
facility or from another source? OTHER SOURCE . . . 6
430 When (NAME) was born, was VERY LARGE . . . . . 1 VERY LARGE . . . . . 1 VERY LARGE . . . . . 1
he/she very large, larger than LARGER THAN LARGER THAN LARGER THAN
average, average, smaller than AVERAGE . . . . . 2 AVERAGE . . . . . 2 AVERAGE . . . . . 2
average, or very small? AVERAGE . . . . . . . 3 AVERAGE . . . . . . . 3 AVERAGE . . . . . . . 3
SMALLER THAN SMALLER THAN SMALLER THAN
AVERAGE . . . . . 4 AVERAGE . . . . . 4 AVERAGE . . . . . 4
VERY SMALL . . . . . 5 VERY SMALL . . . . . 5 VERY SMALL . . . . . 5
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
431 Was (NAME) weighed at birth? YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 432A) (SKIP TO 432A) (SKIP TO 432A)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
W-19
Appendix E • 457
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
432 How much did (NAME) weigh? KG FROM CARD KG FROM CARD KG FROM CARD
1 . 1 . 1 .
RECORD WEIGHT IN
KILOGRAMS FROM MOTHER
AND CHILD HEALTH BOOKLET, KG FROM RECALL KG FROM RECALL KG FROM RECALL
OR FROM CHILD HEALTH CARD,
IF AVAILABLE. 2 . 2 . 2 .
DON'T KNOW . 99998 DON'T KNOW . 99998 DON'T KNOW . 99998
(SKIP TO 433) (SKIP TO 433) (SKIP TO 433)
432A Was (NAME) weighed within two YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
weeks after birth?
NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
433 Who assisted with the delivery of HEALTH PERSONNEL HEALTH PERSONNEL HEALTH PERSONNEL
(NAME)? DOCTOR ..... A DOCTOR ..... A DOCTOR ..... A
NURSE/MIDWIFE . B NURSE/MIDWIFE . B NURSE/MIDWIFE . B
Anyone else? (SKIP TO 434) (SKIP TO 434) (SKIP TO 434)
OTHER PERSON OTHER PERSON OTHER PERSON
COMMUNITY HLTH COMMUNITY HLTH COMMUNITY HLTH
PROBE FOR THE TYPE(S) OF WORKER . . . C WORKER . . . C WORKER . . . D
PERSON(S) AND RECORD ALL (SKIP TO 434) (SKIP TO 434) (SKIP TO 434)
MENTIONED.
TRADITIONAL BIRTH TRADITIONAL BIRTH TRADITIONAL BIRTH
ATTENDANT . . D ATTENDANT . . D ATTENDANT . . D
RELATIVE/FRIEND . E RELATIVE/FRIEND . E RELATIVE/FRIEND . E
IF RESPONDENT SAYS NO ONE OTHER OTHER OTHER
ASSISTED, PROBE TO X X X
DETERMINE WHETHER ANY (SPECIFY) (SPECIFY) (SPECIFY)
ADULTS WERE PRESENT AT NO ONE ASSISTED Y NO ONE ASSISTED Y NO ONE ASSISTED Y
THE DELIVERY. (SKIP TO 434) (SKIP TO 434) (SKIP TO 434)
433A What are the reasons you preferred DISTANCE . . . . . . . A
a (Traditional Birth BETTER CARE THAN
Attendant/relative) in the birth of FACILITY . . . . . . . B
(NAME)? RELIGIOUS
REASONS . . . . . C
HUSBAND
PREFERENCE . . . D
RECORD ALL MENTIONED
OTHER X
(SPECIFY)
W-20
458 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
434 Where did you give birth to HOME HOME HOME
(NAME)? YOUR HOME . . . 11 YOUR HOME . . . 11 YOUR HOME . . . 11
(SKIP TO 438) (SKIP TO 448) (SKIP TO 448)
PROBE TO IDENTIFY THE TYPE OTHER HOME . . . 12 OTHER HOME . . . 12 OTHER HOME . . . 12
OF SOURCE.
PUBLIC SECTOR PUBLIC SECTOR PUBLIC SECTOR
IF UNABLE TO DETERMINE GOVT. HOSPITAL . 21 GOVT. HOSPITAL . 21 GOVT. HOSPITAL . 21
IF PUBLIC OR PRIVATE GOVT. HEALTH GOVT. HEALTH GOVT. HEALTH
SECTOR, WRITE THE CENTER . . . . . 22 CENTER . . . . . 22 CENTER . . . . . 22
NAME OF THE PLACE. GOVT. GOVT. GOVT.
DISPENSARY . 23 DISPENSARY . 23 DISPENSARY . 23
OTHER PUBLIC OTHER PUBLIC OTHER PUBLIC
(NAME OF PLACE) SECTOR SECTOR SECTOR
26 26 26
(SPECIFY) (SPECIFY) (SPECIFY)
PRIVATE MED. SECTOR PRIVATE MED. SECTOR PRIVATE MED. SECTOR
MISSION HOSPITAL/ MISSION HOSPITAL/ MISSION HOSPITAL/
CLINIC . . . . . . . 31 CLINIC . . . . . . . 31 CLINIC . . . . . . . 31
PVT. HOSPITAL/ PVT. HOSPITAL/ PVT. HOSPITAL/
CLINIC . . . . . . . 32 CLINIC . . . . . . . 32 CLINIC . . . . . . . 32
NURSING/MATERNITY NURSING/MATERNITY NURSING/MATERNITY
HOME . . . . . . . 33 HOME . . . . . . . 33 HOME . . . . . . . 33
OTHER PRIVATE OTHER PRIVATE OTHER PRIVATE
MED. SECTOR MED. SECTOR MED. SECTOR
36 36 36
(SPECIFY) (SPECIFY) (SPECIFY)
OTHER 96 OTHER 96 OTHER 96
(SPECIFY) (SPECIFY) (SPECIFY)
(SKIP TO 438) (SKIP TO 448) (SKIP TO 448)
434A How long after (NAME) was
delivered did you stay there? HOURS . 1
DAYS . 2
IF LESS THAN ONE DAY,
RECORD HOURS. WEEKS . 3
IF LESS THAN ONE WEEK,
RECORD DAYS. DON'T KNOW . . . 998
435 Was (NAME) delivered by YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
caesarean, that is, did they cut your
belly open to take the baby out? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
436 I would like to talk to you about YES . . . . . . . . . . . . . . 1
checks on your health after delivery, (SKIP TO 439)
for example, someone asking you NO . . . . . . . . . . . . . . 2
questions about your health or
examining you. Did anyone check
on your health while you were still in
the facility?
437 Did anyone check on your health YES . . . . . . . . . . . . . . 1
after you left the facility? (SKIP TO 439)
NO . . . . . . . . . . . . . . 2
(SKIP TO 442)
W-21
Appendix E • 459
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
438 I would like to talk to you about YES . . . . . . . . . . . . . . 1
checks on your health after delivery, NO . . . . . . . . . . . . . . 2
for example, someone asking you (SKIP TO 442)
questions about your health or
examining you. Did anyone check
on your health after you gave birth
to (NAME)?
439 Who checked on your health at that HEALTH PERSONNEL
time? DOCTOR . . . . . . . 11
NURSE/MIDWIFE . 12
OTHER PERSON
COMMUNITY HLTH
WORKER . . . 21
PROBE FOR MOST QUALIFIED TRADITIONAL BIRTH
PERSON. ATTENDANT . 22
OTHER 96
(SPECIFY)
440 How long after delivery did the first
check take place? HOURS . 1
DAYS . 2
IF LESS THAN ONE DAY,
RECORD HOURS. WEEKS . 3
IF LESS THAN ONE WEEK,
RECORD DAYS. DON'T KNOW . . . 998
440A Did the person who checked your
health after you gave birth discuss YES . . . . . . . . . . . . . . 1
with you about family planning? NO . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8
442 In the two months after (NAME) was
born, did any health care provider or YES . . . . . . . . . . . . . . 1
a traditional birth attendant check NO . . . . . . . . . . . . . . 2
on his/her health? (SKIP TO 446)
DON'T KNOW . . . . . 8
443 How many hours, days or weeks HRS AFTER
after the birth of (NAME) did the first BIRTH . . 1
check take place? DAYS AFTER
BIRTH . . 2
WKS AFTER
IF LESS THAN ONE DAY, BIRTH . . 3
RECORD HOURS.
IF LESS THAN ONE WEEK, DON'T KNOW . . . 998
RECORD DAYS.
444 Who checked on (NAME)'s health HEALTH PERSONNEL
at that time? DOCTOR . . . . . . . 11
NURSE/MIDWIFE . 12
OTHER PERSON
PROBE FOR MOST QUALIFIED COMMUNITY HLTH
PERSON. WORKER . . . 21
TRADITIONAL BIRTH
ATTENDANT . 22
OTHER 96
(SPECIFY)
W-22
460 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
445 Where did this first check of HOME
(NAME) take place? YOUR HOME . . . 11
OTHER HOME . . . 12
PROBE TO IDENTIFY THE TYPE PUBLIC SECTOR
OF SOURCE AND CIRCLE THE GOVT. HOSPITAL . 21
APPROPRIATE CODE. GOVT. HEALTH
CENTER . . . . . 22
IF UNABLE TO DETERMINE GOVT.
IF PUBLIC OR PRIVATE DISPENSARY . 23
SECTOR, WRITE THE OTHER PUBLIC
NAME OF THE PLACE. 26
(SPECIFY)
(NAME OF PLACE) PRIVATE MED. SECTOR
MISSION HOSPITAL /
CLINIC . . . . . . . 31
PVT. HOSPITAL/
CLINIC . . . . . . . 32
NURSING/MATERNITY
HOME . . . . . . . 33
OTHER PRIVATE
MED. 36
(SPECIFY)
OTHER 96
(SPECIFY)
446 In the first two months after delivery,
did you receive a vitamin A dose YES . . . . . . . . . . . . . . 1
like (this/any of these)?
NO . . . . . . . . . . . . . . 2
SHOW COMMON TYPES OF DON'T KNOW . . . . . 8
AMPULES/CAPSULES/SYRUPS.
447 Has your menstrual period returned YES . . . . . . . . . . . . . . 1
since the birth of (NAME)? (SKIP TO 449)
NO . . . . . . . . . . . . . . 2
(SKIP TO 450)
448 Did your period return between the YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
birth of (NAME) and your next NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
pregnancy? (SKIP TO 452) (SKIP TO 452)
449 For how many months after the birth
of (NAME) did you not have a MONTHS . . . MONTHS . . . MONTHS . . .
period?
DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98
450 CHECK 226: NOT PREGNANT
PREG- OR
IS RESPONDENT PREGNANT? NANT UNSURE
(SKIP TO 452)
451 Have you had sexual intercourse YES . . . . . . . . . . . . . . 1
since the birth of (NAME)? NO . . . . . . . . . . . . . . 2
(SKIP TO 453)
452 For how many months after the birth
of (NAME) did you not have sexual MONTHS . . . MONTHS . . . MONTHS . . .
intercourse?
DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98
W-23
Appendix E • 461
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
453 Did you ever breastfeed (NAME)? YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 459B) (SKIP TO 459B) (SKIP TO 459B)
455 How long after birth did you first put
(NAME) to the breast?
IMMEDIATELY . . . 000
IF LESS THAN 1 HOUR,
RECORD ‘00' HOURS.
IF LESS THAN 24 HOURS, HOURS . 1
RECORD HOURS.
OTHERWISE, RECORD DAYS. DAYS . 2
456 In the first three days after delivery, YES . . . . . . . . . . . . . . 1
was (NAME) given anything to drink NO . . . . . . . . . . . . . . 2
other than breast milk? (SKIP TO 458)
457 What was (NAME) given to drink? MILK (OTHER THAN
BREAST MILK ) . A
Anything else? PLAIN WATER . . . B
SUGAR OR GLU-
COSE WATER . . . C
RECORD ALL LIQUIDS GRIPE WATER . . . D
MENTIONED. SUGAR-SALT-WATER
SOLUTION . . . . . E
FRUIT JUICE . . . . . F
INFANT FORMULA . G
TEA/INFUSIONS . . . H
COFFEE . . . . . . . . . I
HONEY . . . . . . . . . J
OTHER X
(SPECIFY)
457A What are the reasons (NAME) was NOT ENOUCH
given drinks other than breast milk? BREAST MILK . . . A
BABY CRIED
Anything else? TOO MUCH . . . . . B
CULTURAL
REASONS . . . . . C
WORK-RELATED
RECORD ALL MENTIONED OBLIGATIONS . . . D
WEATHER TOO
HOT . . . . . . . . . . . . E
FIRST MILK NOT GOOD
FOR BABIES . . . F
OTHER X
(SPECIFY)
458 CHECK 404: LIVING DEAD
IS CHILD LIVING?
( SKIP TO 459A)
459 Are you still breastfeeding (NAME)? YES . . . . . . . . . . . . . . 1
(SKIP TO 459C)
NO . . . . . . . . . . . . . . 2
459A For how many months did you MONTHS . . . MONTHS . . . MONTHS . . .
breastfeed (NAME)?
DON'T KNOW . . . 98 DON'T KNOW . . . 98 DON'T KNOW . . . 98
W-24
462 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
459B CHECK 404: LIVING DEAD LIVING DEAD LIVING DEAD
IS CHILD LIVING?
(GO BACK TO (GO BACK TO (GO BACK TO
405 IN NEXT 405 IN NEXT 405 IN NEXT-TO-LAST
COLUMN; OR, COLUMN; OR, COLUMN OF NEW
IF NO MORE IF NO MORE QUESTIONNAIRE;
BIRTHS, GO BIRTHS, GO OR, IF NO MORE
TO 501) TO 501) BIRTHS, GO
(SKIP TO 460) TO 501)
459C Was (NAME) breastfed yesterday YES . . . . . . . . . . . . . . 1
during the day or at night? NO . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8
460 Did (NAME) drink anything from a YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
bottle with a nipple yesterday or last NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
night? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
461 GO BACK TO 405 IN GO BACK TO 405 IN GO BACK TO 405 IN
NEXT COLUMN; OR, IF NEXT COLUMN; OR, IF NEXT-TO-LAST
NO MORE BIRTHS, GO NO MORE BIRTHS, GO COLUMN OF NEW
TO 501. TO 501. QUESTIONNAIRE; OR,
IF NO MORE BIRTHS,
GO TO 501.
W-25
Appendix E • 463
SECTION 5. CHILD IMMUNIZATION, HEALTH AND NUTRITION
501 ENTER IN THE TABLE THE BIRTH HISTORY NUMBER, NAME, AND SURVIVAL STATUS OF EACH BIRTH IN 2009 OR LATER.
ASK THE QUESTIONS ABOUT ALL OF THESE BIRTHS. BEGIN WITH THE LAST BIRTH.
(IF THERE ARE MORE THAN 3 BIRTHS, USE LAST 2 COLUMNS OF ADDITIONAL QUESTIONNAIRES).
502 LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
BIRTH HISTORY
NUMBER FROM 212 BIRTH HISTORY BIRTH HISTORY BIRTH HISTORY
IN BIRTH HISTORY NUMBER . . . . . . . . NUMBER . . . . . . . . NUMBER . . . . . . .
503 FROM 212 NAME NAME NAME
AND 216
LIVING DEAD LIVING DEAD LIVING DEAD
(GO TO 503 (GO TO 503 (GO TO 503 IN NEXT-
IN NEXT COLUMN IN NEXT COLUMN TO-LAST COLUMN OF
OR, IF NO MORE OR, IF NO MORE NEW Q'NNAIRE, OR IF NO
BIRTHS, GO TO 553) BIRTHS, GO TO 553) MORE BIRTHS, GO TO 553)
504 Do you have a card /
child health book where YES, SEEN . . . . . . . . . . . . 1 YES, SEEN . . . . . . . . . . . . 1 YES, SEEN . . . . . . . . . . . 1
(NAME)'s vaccinations (SKIP TO 506) (SKIP TO 506) (SKIP TO 506)
are written down? YES, NOT SEEN . . . . . . . . 2 YES, NOT SEEN . . . . . . . . 2 YES, NOT SEEN . . . . . . . 2
IF YES: May I see it (SKIP TO 509) (SKIP TO 509) (SKIP TO 509)
please? NO CARD . . . . . . . . . . . . . 3 NO CARD . . . . . . . . . . . . . 3 NO CARD . . . . . . . . . . . . 3
505 Did you ever have a YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . 1
vaccination card or child (SKIP TO 509) (SKIP TO 509) (SKIP TO 509)
health book for (NAME)? NO . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . 2
506 (1) COPY DATES FROM THE CARD.
(2) WRITE ‘44' IN ‘DAY' COLUMN IF CARD SHOWS THAT A DOSE WAS GIVEN, BUT NO DATE IS RECORDED.
(3) IF MORE THAN TWO VITAMIN 'A' DOSES, RECORD DATES FOR MOST RECENT AND SECOND MOST RECENT DOSES.
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
DAY MONTH YEAR DAY MONTH YEAR DAY MONTH YEAR
BCG (AT BIRTH) BCG BCG
POLIO 0 (POLIO
P0 P0
GIVEN AT BIRTH)
OPV 1 P1 P1
OPV 2 P2 P2
OPV 3 P3 P3
DPT, HEPATITIS,
D1 D1
HIB, 1st DOSE
DPT, HEPATITIS,
D2 D2
HIB, 2nd DOSE
DPT, HEPATITIS,
D3 D3
HIB, 3rd DOSE
PNEUMOCOCCAL
PN1 PN1
VACCINE 1
PNEUMOCOCCAL
PN2 PN2
VACCINE 2
PNEUMOCOCCAL
PN3 PN3
VACCINE 3
ROTA VIRUS
R1 R1
VACCINE 1
ROTA VIRUS
R2 R2
VACCINE 2
MEASLES MEA MEA
YELLOW FEVER YF YF
VITAMIN A
VITA1 VITA1
(MOST RECENT)
VITAMIN A (2nd
VITA2 VITA2
MOST RECENT)
AL/MEBENDAZOLE
A/M A/M
(MOST RECENT)
507 CHECK 506: BCG TO YELLOW OTHER BCG TO YELLOW OTHER BCG TO YELLOW OTHER
FEVER ALL RECORDED FEVER ALL RECORDED FEVER ALL RECORDED
(GO TO 511) (GO TO 511) (GO TO 511)
W-26
464 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
508 Has (NAME) had any vaccinations YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
that are not recorded on this card, (PROBE FOR (PROBE FOR (PROBE FOR
including vaccinations given in a VACCINATIONS AND VACCINATIONS AND VACCINATIONS AND
national immunization day WRITE ‘66' IN THE WRITE ‘66' IN THE WRITE ‘66' IN THE
campaign? CORRESPONDING CORRESPONDING CORRESPONDING
DAY COLUMN IN 506) DAY COLUMN IN 506) DAY COLUMN IN 506)
RECORD 'YES' ONLY IF THE (SKIP TO 511) (SKIP TO 511) (SKIP TO 511)
RESPONDENT MENTIONS
AT LEAST ONE OF THE NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
VACCINATIONS IN 506 THAT (SKIP TO 511) (SKIP TO 511) (SKIP TO 511)
ARE NOT RECORDED AS DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
HAVING BEEN GIVEN.
509 Did (NAME) ever have any
vaccinations to prevent him/her
from getting diseases, including YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
vaccinations received in a national NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
immunization day campaign? (SKIP TO 511) (SKIP TO 511) (SKIP TO 511)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
510 Please tell me if (NAME) had any of
the following vaccinations:
510A A BCG vaccination against YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
tuberculosis, that is, an injection in NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
the arm or shoulder that usually DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
causes a scar?
510B Polio vaccine, that is, drops in the YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
mouth? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 510E) (SKIP TO 510E) (SKIP TO 510E)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
510C Was the first polio vaccine given in FIRST 2 WEEKS . . . 1 FIRST 2 WEEKS . . . 1 FIRST 2 WEEKS . . . 1
the first two weeks after birth or LATER . . . . . . . . . . . . 2 LATER . . . . . . . . . . . . 2 LATER . . . . . . . . . . . . 2
later?
510D How many times was the polio NUMBER NUMBER NUMBER
vaccine given? OF TIMES ..... OF TIMES ..... OF TIMES .....
510E A Pentavalent vaccination, that is, YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
an injection given in the left outer NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
thigh, sometimes at the same time (SKIP TO 510F1) (SKIP TO 510F1) (SKIP TO 510F1)
as polio drops? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
510F How many times was the NUMBER NUMBER NUMBER
Pentavalent vaccination given? OF TIMES ..... OF TIMES ..... OF TIMES .....
510F1 A Pneumococcal vaccination, that YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
is, an injection given in the right NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
outer thigh, sometimes at the same (SKIP TO 510F3) (SKIP TO 510F3) (SKIP TO 510F3)
time as polio drops or the DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
Pentavalent vaccination?
510F2 How many times was the NUMBER NUMBER NUMBER
Pneumococcal vaccination given? OF TIMES ..... OF TIMES ..... OF TIMES .....
W-27
Appendix E • 465
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
510F3 A Rota virus vaccination given YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
orally? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 510G) (SKIP TO 510G) (SKIP TO 510G)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
510F4 How many times was the Rota virus NUMBER NUMBER NUMBER
vaccination given? OF TIMES ..... OF TIMES ..... OF TIMES .....
510G A measles injection - that is, a shot YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
in the arm at the age of 9 months or NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
older - to prevent him/her from DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
getting measles?
510H A yellow fever injection - that is, a YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
shot in the arm or shoulder at the NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
age of 9 months or older - to DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
prevent him/her from getting yellow
fever?
511 Within the last six months, was YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
(NAME) given a vitamin A dose like NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(this/any of these)? (SKIP TO 512) (SKIP TO 512) (SKIP TO 512)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
SHOW COMMON TYPES OF
AMPULES/CAPSULES/SYRUPS.
511A How many times was Vitamin A NUMBER NUMBER NUMBER
given in the last six months? OF TIMES ..... OF TIMES ..... OF TIMES .....
512 In the last seven days, was (NAME) YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
given iron pills, sprinkles with iron, NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
or iron syrup like (this/any of these)? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
SHOW COMMON TYPES OF
PILLS/SPRINKLES/
SYRUPS.
513 Was (NAME) given any drug for YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
intestinal worms in the last six NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
months? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
514 Has (NAME) had diarrhoea in the YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
last 2 weeks? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 525) (SKIP TO 525) (SKIP TO 525)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
515 Was there any blood in the stools? YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
W-28
466 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
516 Now I would like to know how much
(NAME) was given to drink during
the diarrhoea (including breast
milk).
Was he/she given less than usual to
drink, about the same amount, or
MUCH LESS . . . . . 1 MUCH LESS . . . . . 1 MUCH LESS . . . . . 1
more than usual to drink?
SOMEWHAT LESS . 2 SOMEWHAT LESS . 2 SOMEWHAT LESS . 2
ABOUT THE SAME . 3 ABOUT THE SAME . 3 ABOUT THE SAME . 3
IF LESS, PROBE: Was he/she
given much less than usual to drink MORE . . . . . . . . . . . . 4 MORE . . . . . . . . . . . . 4 MORE . . . . . . . . . . . . 4
or somewhat less? NOTHING TO DRINK . 5 NOTHING TO DRINK . 5 NOTHING TO DRINK . 5
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
517 When (NAME) had diarrhoea, was
he/she given less than usual to eat, MUCH LESS . . . . . 1 MUCH LESS . . . . . 1 MUCH LESS . . . . . 1
about the same amount, more than SOMEWHAT LESS . 2 SOMEWHAT LESS . 2 SOMEWHAT LESS . 2
usual, or nothing to eat? ABOUT THE SAME . 3 ABOUT THE SAME . 3 ABOUT THE SAME . 3
MORE . . . . . . . . . . . . 4 MORE . . . . . . . . . . . . 4 MORE . . . . . . . . . . . . 4
IF LESS, PROBE: Was he/she STOPPED FOOD . . . 5 STOPPED FOOD . . . 5 STOPPED FOOD . . . 5
given much less than usual to eat or
NEVER GAVE FOOD . 6 NEVER GAVE FOOD . 6 NEVER GAVE FOOD . 6
somewhat less?
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
518 Did you seek advice or treatment for YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
the diarrhoea from any source? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 521B) (SKIP TO 521B) (SKIP TO 521B)
519 Where did you seek advice or PUBLIC SECTOR PUBLIC SECTOR PUBLIC SECTOR
treatment? GOVT HOSPITAL A GOVT HOSPITAL A GOVT HOSPITAL A
GOVT HEALTH GOVT HEALTH GOVT HEALTH
Anywhere else? CENTER . . . . . B CENTER . . . . . B CENTER . . . . . B
GOVT GOVT GOVT
PROBE TO IDENTIFY EACH DISPENSARY . C DISPENSARY . C DISPENSARY . C
TYPE OF SOURCE. OTHER PUBLIC OTHER PUBLIC OTHER PUBLIC
SECTOR SECTOR SECTOR
IF UNABLE TO DETERMINE D D D
IF PUBLIC OR PRIVATE (SPECIFY) (SPECIFY) (SPECIFY)
SECTOR, WRITE THE NAME
OF THE PLACE. PRIVATE MEDICAL PRIVATE MEDICAL PRIVATE MEDICAL
SECTOR SECTOR SECTOR
PVT. HOSPITAL/ PVT. HOSPITAL/ PVT. HOSPITAL/
(NAME OF PLACE(S)) CLINIC . . . . . . . E CLINIC . . . . . . . E CLINIC . . . . . . . E
PHARMACY . . . F PHARMACY . . . F PHARMACY . . . F
MISSION HOSP./ MISSION HOSP./ MISSION HOSP./
CLINIC . . . . . . G CLINIC . . . . . . G CLINIC . . . . . . G
OTHER PRIVATE OTHER PRIVATE OTHER PRIVATE
SECTOR SECTOR SECTOR
H H H
(SPECIFY) (SPECIFY) (SPECIFY)
OTHER SOURCE OTHER SOURCE OTHER SOURCE
MOBILE CLINIC . I MOBILE CLINIC . I MOBILE CLINIC . I
COMMUNITY COMMUNITY COMMUNITY
HLTH WORKER J HLTH WORKER J HLTH WORKER J
SHOP . . . . . . . . . K SHOP . . . . . . . . . K SHOP . . . . . . . . . K
TRADITIONAL TRADITIONAL TRADITIONAL
PRACTITIONER L PRACTITIONER L PRACTITIONER L
RELATIVE/FRIEND M RELATIVE/FRIEND M RELATIVE/FRIEND M
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
W-29
Appendix E • 467
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
520 CHECK 519: TWO OR ONLY TWO OR ONLY TWO OR ONLY
MORE ONE MORE ONE MORE ONE
CODES CODE CODES CODE CODES CODE
CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED
(SKIP TO 521A) (SKIP TO 521A) (SKIP TO 521A)
521 Where did you first seek advice or
treatment?
FIRST PLACE . . . FIRST PLACE . . . FIRST PLACE . . .
USE LETTER CODE FROM 519.
521A How many days after the diarrhoea
began did you first seek advice or
treatment for (NAME)? DAYS . . . . . DAYS . . . . . DAYS . . . . .
IF SAME DAY, RECORD '00' SKIP TO 521C SKIP TO 521C SKIP TO 521C
521B Why did you not seek advice or EPISODE WAS NOT EPISODE WAS NOT EPISODE WAS NOT
treatment? SERIOUS . . . . . . . A SERIOUS . . . . . . . A SERIOUS . . . . . . . A
TOO FAR/NO TOO FAR/NO TOO FAR/NO
TRANSPORT . . . B TRANSPORT . . . B TRANSPORT . . . B
TOO EXPENSIVE . . . C TOO EXPENSIVE . . . C TOO EXPENSIVE . . . C
RECORD ALL MENTIONED BELIEVE HOME BELIEVE HOME BELIEVE HOME
REMEDIES ARE REMEDIES ARE REMEDIES ARE
EFFECTIVE . . . . . D EFFECTIVE . . . . . D EFFECTIVE . . . . . D
NO REASON . . . . . E NO REASON . . . . . E NO REASON . . . . . E
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
521C Does (NAME) still have diarrhoea? YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
522 Was he/she given any of the
following to drink at any time since
he/she started having the diarrhoea:
YES NO DK YES NO DK YES NO DK
a) A fluid made from a special a) FLUID FROM a) FLUID FROM a) FLUID FROM
packet called ORS? ORS PKT 1 2 8 ORS PKT 1 2 8 ORS PKT 1 2 8
b) A home-made sugar-salt b) SUGAR- 1 2 8 b) SUGAR- 1 2 8 b) SUGAR- 1 2 8
solution? SALT SOL. SALT SOL. SALT SOL.
c) Other home-made liquid such as
porridge, soup, yoghurt, coconut c) HOMEMADE c) HOMEMADE c) HOMEMADE
water, fresh fruit juice, tea, milk, FLUID . . . 1 2 8 FLUID . . . 1 2 8 FLUID . . . 1 2 8
or rice water?
523 Was anything (else) given to treat YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
the diarrhoea? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 525) (SKIP TO 525) (SKIP TO 525)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
W-30
468 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
524 What (else) was given to treat the PILL OR SYRUP PILL OR SYRUP PILL OR SYRUP
diarrhoea? ANTIBIOTIC . . . . . A ANTIBIOTIC . . . . . A ANTIBIOTIC . . . . . A
ANTIMOTILITY . . . B ANTIMOTILITY . . . B ANTIMOTILITY . . . B
Anything else? ZINC TABLET . . . C ZINC TABLET . . . C ZINC TABLET . . . C
OTHER (NOT ANTI- OTHER (NOT ANTI- OTHER (NOT ANTI-
RECORD ALL TREATMENTS BIOTIC, ANTI- BIOTIC, ANTI- BIOTIC, ANTI-
GIVEN. MOTILITY, OR MOTILITY, OR MOTILITY, OR
ZINC TABLET) . D ZINC TABLET) . D ZINC TABLET) . D
UNKNOWN PILL UNKNOWN PILL UNKNOWN PILL
OR SYRUP . . . E OR SYRUP . . . E OR SYRUP . . . E
INJECTION INJECTION INJECTION
ANTIBIOTIC . . . . . F ANTIBIOTIC . . . . . F ANTIBIOTIC . . . . . F
NON-ANTIBIOTIC . G NON-ANTIBIOTIC . G NON-ANTIBIOTIC . G
UNKNOWN UNKNOWN UNKNOWN
INJECTION . . . H INJECTION . . . H INJECTION . . . H
(IV) INTRAVENOUS (IV) INTRAVENOUS (IV) INTRAVENOUS
FLUID . . . . . . . I FLUID . . . . . . . I FLUID . . . . . . . I
HOME REMEDY/ HOME REMEDY/ HOME REMEDY/
HERBAL MED- HERBAL MED- HERBAL MED-
ICINE . . . . . . . . . J ICINE . . . . . . . . . J ICINE . . . . . . . . . J
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
524A CHECK 524 CODE "C" CODE "C" CODE "C" CODE "C" CODE "C" CODE "C"
CIRCLED NOT CIRCLED NOT CIRCLED NOT
GIVEN ZINC TABLETS? CIRCLED CIRCLED CIRCLED
(SKIP TO 525) (SKIP TO 525) (SKIP TO 525)
524B How many days was (NAME) given
zinc tablets? DAYS . . . . . DAYS . . . . . DAYS . . . . .
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
525 Has (NAME) been ill with a fever at YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
any time in the last 2 weeks? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 527) (SKIP TO 527) (SKIP TO 527)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
526 At any time during the illness, did YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
(NAME) have blood taken from NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
his/her finger or heel for testing? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
527 Has (NAME) had an illness with a YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
cough at any time in the last 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
weeks? (SKIP TO 530) (SKIP TO 530) (SKIP TO 530)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
528 When (NAME) had an illness with a YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
cough, did he/she breathe faster NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
than usual with short, rapid breaths (SKIP TO 531) (SKIP TO 531) (SKIP TO 531)
or have difficulty breathing? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
529 Was the fast or difficult breathing CHEST ONLY ... 1 CHEST ONLY ... 1 CHEST ONLY ... 1
due to a problem in the chest or to a NOSE ONLY ..... 2 NOSE ONLY ..... 2 NOSE ONLY ..... 2
blocked or runny nose? BOTH . . . . . . . . . . . . 3 BOTH . . . . . . . . . . . . 3 BOTH . . . . . . . . . . . . 3
OTHER 6 OTHER 6 OTHER 6
(SPECIFY) (SPECIFY) (SPECIFY)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
(SKIP TO 531) (SKIP TO 531) (SKIP TO 531)
W-31
Appendix E • 469
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
530 CHECK 525: YES NO OR DK YES NO OR DK YES NO OR DK
HAD FEVER?
(GO BACK TO (GO BACK TO (GO TO 503
503 IN NEXT 503 IN NEXT IN NEXT-TO-LAST
COLUMN; OR, COLUMN; OR, COLUMN OF NEW
IF NO MORE IF NO MORE QUESTIONNAIRE; OR,
BIRTHS, GO BIRTHS, GO IF NO MORE BIRTHS,
TO 553) TO 553) GO TO 553)
531 Now I would like to know how much
(NAME) was given to drink
(including breast milk) during the
illness with a (fever/cough). Was
he/she given less than usual to
drink, about the same amount, or MUCH LESS . . . . . 1 MUCH LESS . . . . . 1 MUCH LESS . . . . . 1
more than usual to drink?
SOMEWHAT LESS . 2 SOMEWHAT LESS . 2 SOMEWHAT LESS . 2
ABOUT THE SAME . 3 ABOUT THE SAME . 3 ABOUT THE SAME . 3
IF LESS, PROBE: Was he/she
MORE . . . . . . . . . . . . 4 MORE . . . . . . . . . . . . 4 MORE . . . . . . . . . . . . 4
given much less than usual to drink
or somewhat less? NOTHING TO DRINK . 5 NOTHING TO DRINK . 5 NOTHING TO DRINK . 5
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
532 When (NAME) had a (fever/cough),
was he/she given less than usual to
eat, about the same amount, more MUCH LESS . . . . . 1 MUCH LESS . . . . . 1 MUCH LESS . . . . . 1
than usual, or nothing to eat? SOMEWHAT LESS . 2 SOMEWHAT LESS . 2 SOMEWHAT LESS . 2
ABOUT THE SAME . 3 ABOUT THE SAME . 3 ABOUT THE SAME . 3
IF LESS, PROBE: Was he/she MORE . . . . . . . . . . . . 4 MORE . . . . . . . . . . . . 4 MORE . . . . . . . . . . . . 4
given much less than usual to eat or
STOPPED FOOD . . . 5 STOPPED FOOD . . . 5 STOPPED FOOD . . . 5
somewhat less?
NEVER GAVE FOOD . 6 NEVER GAVE FOOD . 6 NEVER GAVE FOOD . 6
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
533 Did you seek advice or treatment for YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
the illness from any source? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 537) (SKIP TO 537) (SKIP TO 537)
W-32
470 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
534 Where did you seek advice or PUBLIC SECTOR PUBLIC SECTOR PUBLIC SECTOR
treatment? GOVT HOSPITAL A GOVT HOSPITAL A GOVT HOSPITAL A
GOVT HEALTH GOVT HEALTH GOVT HEALTH
Anywhere else? CENTER . . . . . B CENTER . . . . . B CENTER . . . . . B
GOVT GOVT GOVT
PROBE TO IDENTIFY EACH DISPENSARY . C DISPENSARY . C DISPENSARY . C
TYPE OF SOURCE. OTHER PUBLIC OTHER PUBLIC OTHER PUBLIC
SECTOR SECTOR SECTOR
IF UNABLE TO DETERMINE D D D
IF PUBLIC OR PRIVATE (SPECIFY) (SPECIFY) (SPECIFY)
SECTOR, WRITE THE NAME
OF THE PLACE. PRIVATE MEDICAL PRIVATE MEDICAL PRIVATE MEDICAL
SECTOR SECTOR SECTOR
PVT. HOSPITAL/ PVT. HOSPITAL/ PVT. HOSPITAL/
CLINIC . . . . . . . E CLINIC . . . . . . . E CLINIC . . . . . . . E
(NAME OF PLACE(S)) PHARMACY . . . F PHARMACY . . . F PHARMACY . . . F
MISSION HOSP./ MISSION HOSP./ MISSION HOSP./
CLINIC . . . . . . G CLINIC . . . . . . G CLINIC . . . . . . G
OTHER PRIVATE OTHER PRIVATE OTHER PRIVATE
SECTOR SECTOR SECTOR
H H H
(SPECIFY) (SPECIFY) (SPECIFY)
OTHER SOURCE OTHER SOURCE OTHER SOURCE
MOBILE CLINIC . I MOBILE CLINIC . I MOBILE CLINIC . I
COMMUNITY COMMUNITY COMMUNITY
HLTH WORKER J HLTH WORKER J HLTH WORKER J
SHOP . . . . . . . . . K SHOP . . . . . . . . . K SHOP . . . . . . . . . K
TRADITIONAL TRADITIONAL TRADITIONAL
PRACTITIONER L PRACTITIONER L PRACTITIONER L
RELATIVE/FRIEND M RELATIVE/FRIEND M RELATIVE/FRIEND M
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
535 CHECK 534: TWO OR ONLY TWO OR ONLY TWO OR ONLY
MORE ONE MORE ONE MORE ONE
CODES CODE CODES CODE CODES CODE
CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED
(SKIP TO 536A) (SKIP TO 536A) (SKIP TO 536A)
536 Where did you first seek advice or
treatment?
FIRST PLACE . . . FIRST PLACE . . . FIRST PLACE . . .
USE LETTER CODE FROM 534.
536A How many days after the illness did
you first seek advice or treatment
for (NAME)? DAYS . . . . . DAYS . . . . . DAYS . . . . .
IF THE SAME DAY, RECORD '00'
536B Is (NAME) still sick with a FEVER ONLY . . . . . 1 FEVER ONLY . . . . . 1 FEVER ONLY . . . . . 1
(fever/cough)? COUGH ONLY . . . . . 2 COUGH ONLY . . . . . 2 COUGH ONLY . . . . . 2
BOTH FEVER AND BOTH FEVER AND BOTH FEVER AND
COUGH . . . . . . . 3 COUGH . . . . . . . 3 COUGH . . . . . . . 3
NO, NEITHER . . . . . 4 NO, NEITHER . . . . . 4 NO, NEITHER . . . . . 4
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
W-33
Appendix E • 471
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
537 At any time during the illness, did YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
(NAME) take any drugs for the NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
illness?
(GO TO 551A) (GO TO 551A) (GO TO 551A)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
538 What drugs did (NAME) take? ANTIMALARIAL DRUGS ANTIMALARIAL DRUGS ANTIMALARIAL DRUGS
SP/FANSIDAR . . . A SP/FANSIDAR . . . A SP/FANSIDAR . . . A
Any other drugs? CHLOROQUINE . B CHLOROQUINE . B CHLOROQUINE . B
AMODIAQUINE . C AMODIAQUINE . C AMODIAQUINE . C
QUININE . . . . . . . D QUININE . . . . . . . D QUININE . . . . . . . D
RECORD ALL MENTIONED. AL/COARTEM . . . E AL/COARTEM . . . E AL/COARTEM . . . E
OTHER ANTI- OTHER ANTI- OTHER ANTI-
MALARIAL MALARIAL MALARIAL
F F F
(SPECIFY) (SPECIFY) (SPECIFY)
ANTIBIOTIC DRUGS ANTIBIOTIC DRUGS ANTIBIOTIC DRUGS
PILL/SYRUP . . . G PILL/SYRUP . . . G PILL/SYRUP . . . G
INJECTION ... H INJECTION ... H INJECTION ... H
OTHER DRUGS OTHER DRUGS OTHER DRUGS
ASPIRIN . . . . . . . I ASPIRIN . . . . . . . I ASPIRIN . . . . . . . I
ACETAMINOPHEN/ ACETAMINOPHEN/ ACETAMINOPHEN/
PARACETAMOL J PARACETAMOL J PARACETAMOL J
IBUPROFEN . . . K IBUPROFEN . . . K IBUPROFEN . . . K
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
DON'T KNOW . . . . . Z DON'T KNOW . . . . . Z DON'T KNOW . . . . . Z
539 CHECK 538: YES NO YES NO YES NO
ANY CODE A-G CIRCLED?
(GO TO 551A) (GO TO 551A) (GO TO 551A)
539A Did you already have (NAME OF ANTIMALARIAL DRUGS ANTIMALARIAL DRUGS ANTIMALARIAL DRUGS
DRUG FROM 538) at home when SP/FANSIDAR . . . A SP/FANSIDAR . . . A SP/FANSIDAR . . . A
the child became ill? CHLOROQUINE . B CHLOROQUINE . B CHLOROQUINE . B
AMODIAQUINE . C AMODIAQUINE . C AMODIAQUINE . C
QUININE . . . . . . . D QUININE . . . . . . . D QUININE . . . . . . . D
ASK SEPARATELY FOR EACH AL/COARTEM . . . E AL/COARTEM . . . E AL/COARTEM . . . E
OF THE DRUGS 'A' THROUGH 'G' OTHER ANTI- OTHER ANTI- OTHER ANTI-
THAT THE CHILD IS RECORDED MALARIAL MALARIAL MALARIAL
AS HAVING TAKEN IN 538 ... F ... F ... F
(SPECIFY) (SPECIFY) (SPECIFY)
IF YES FOR ANY DRUG, CIRCLE
CODE FOR THAT DRUG ANTIBIOTIC DRUGS ANTIBIOTIC DRUGS ANTIBIOTIC DRUGS
IF NO FOR ALL DRUGS, PILL/SYRUP . . . G PILL/SYRUP . . . G PILL/SYRUP . . . G
CIRCLE 'Y'
NO DRUG AT HOME Y NO DRUG AT HOME Y NO DRUG AT HOME Y
540 CHECK 538: CODE 'A' CODE 'A' CODE 'A' CODE 'A' CODE 'A' CODE 'A'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
SP/FANSIDAR ('A') GIVEN CIRCLED CIRCLED CIRCLED
(SKIP TO 542) (SKIP TO 542) (SKIP TO 542)
W-34
472 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
541 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take (SP/Fansidar)? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
541A For how many days did (NAME)
take the (SP/Fansidar)? DAYS . . . . . . . . . DAYS . . . . . . . . . DAYS . . . . . . . . .
IF 7 DAYS OR MORE, WRITE 7. DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
542 CHECK 538: CODE 'B' CODE 'B' CODE 'B' CODE 'B' CODE 'B' CODE 'B'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
CHLOROQUINE ('B') GIVEN CIRCLED CIRCLED CIRCLED
(SKIP TO 544) (SKIP TO 544) (SKIP TO 544)
543 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take chloroquine? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
543A For how many days did (NAME)
take the chloroquine? DAYS . . . . . . . . . DAYS . . . . . . . . . DAYS . . . . . . . . .
IF 7 DAYS OR MORE, WRITE 7. DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
544 CHECK 538: CODE 'C' CODE 'C' CODE 'C' CODE 'C' CODE 'C' CODE 'C'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
AMODIAQUINE ('C') GIVEN CIRCLED CIRCLED CIRCLED
(SKIP TO 546) (SKIP TO 546) (SKIP TO 546)
545 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take amodiaquine? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
545A For how many days did (NAME)
take the amodiaquine? DAYS . . . . . . . . . DAYS . . . . . . . . . DAYS . . . . . . . . .
IF 7 DAYS OR MORE, WRITE 7. DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
546 CHECK 538: CODE 'D' CODE 'D' CODE 'D' CODE 'D' CODE 'D' CODE 'D'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
QUININE ('D') GIVEN CIRCLED CIRCLED CIRCLED
(SKIP TO 548) (SKIP TO 548) (SKIP TO 548)
W-35
Appendix E • 473
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
547 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take quinine? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
547A For how many days did (NAME)
take the quinine? DAYS . . . . . . . . . DAYS . . . . . . . . . DAYS . . . . . . . . .
IF 7 DAYS OR MORE, WRITE 7. DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
548 CHECK 538: CODE 'E' CODE 'E' CODE 'E' CODE 'E' CODE 'E' CODE 'E'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
ARTEMISININ+LUMEFANTRINE CIRCLED CIRCLED CIRCLED
(AL/COARTEM) ('E') GIVEN
(SKIP TO 550) (SKIP TO 550) (SKIP TO 550)
549 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take AL/Coartem? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
549A For how many days did (NAME)
take AL/Coartem? DAYS . . . . . . . . . DAYS . . . . . . . . . DAYS . . . . . . . . .
IF 7 DAYS OR MORE, WRITE 7. DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
550 CHECK 538: CODE 'F' CODE 'F' CODE 'F' CODE 'F' CODE 'F' CODE 'F'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
OTHER ANTIMALARIAL ('F') CIRCLED CIRCLED CIRCLED
GIVEN
(GO TO 551A) (GO TO 551A) (GO TO 551A)
551 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take (OTHER NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
ANTIMALARIAL)? TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
W-36
474 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
551A CHECK 525: YES NO OR DK YES NO OR DK YES NO OR DK
HAD FEVER?
(GO TO 552) (GO TO 552) (GO TO 552)
551B Was anything else done about YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
(NAME'S) fever? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 552) (SKIP TO 552) (SKIP TO 552)
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
551C What was done about (NAME'S) CONSULTED CONSULTED CONSULTED
fever? TRAD'L HEALER . A TRAD'L HEALER . A TRAD'L HEALER . A
GAVE WARM GAVE WARM GAVE WARM
SPONGING . . . . . B SPONGING . . . . . B SPONGING . . . . . B
GAVE HERBS . . . . . C GAVE HERBS . . . . . C GAVE HERBS . . . . . C
OTHER . . . . . . . . . X OTHER . . . . . . . . . X OTHER . . . . . . . . . X
552 GO BACK TO 503 IN GO BACK TO 503 IN GO TO 503 IN
NEXT COLUMN; OR, IF NEXT COLUMN; OR, IF NEXT-TO-LAST
NO MORE BIRTHS, GO NO MORE BIRTHS, GO COLUMN OF NEW
TO 553. TO 553. QUESTIONNAIRE; OR,
IF NO MORE BIRTHS,
GO TO 553.
W-37
Appendix E • 475
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
553 CHECK 215 AND 218, ALL ROWS:
NUMBER OF CHILDREN BORN IN 2009 OR LATER LIVING WITH THE RESPONDENT
ONE OR MORE NONE 556
RECORD NAME OF YOUNGEST CHILD LIVING
WITH HER AND CONTINUE WITH 554
(NAME)
554 The last time (NAME FROM 553) passed stools, what was done to CHILD USED TOILET OR LATRINE . . . 01
dispose of the stools? PUT/RINSED
INTO TOILET OR LATRINE . . . . . . . 02
PUT/RINSED
INTO DRAIN OR DITCH . . . . . . . . . 03
THROWN INTO GARBAGE . . . . . . . . . 04
BURIED . . . . . . . . . . . . . . . . . . . . . . . . 05
LEFT IN THE OPEN . . . . . . . . . . . . . . . . 06
OTHER 96
(SPECIFY)
554A When a child is ill, what signs of illness would tell you that he or she NOT ABLE TO DRINK/BREASTFEED . A
should be taken to a health facility or health worker? FEVER, SHIVERING . . . . . . . . . . . . . . B
REPEATED VOMITING . . . . . . . . . . . . C
DIARRHOEA . . . . . . . . . . . . . . . . . . . . D
BLOOD IN STOOLS .............. E
FAST BREATHING . . . . . . . . . . . . . . . . F
RECORD ALL MENTIONED CONVULSIONS . . . . . . . . . . . . . . . . . . G
WEAKNESS . . . . . . . . . . . . . . . . . . . . H
GETTING SICKER . . . . . . . . . . . . . . . . I
OTHER X
(SPECIFY)
555 CHECK 522(a), ALL COLUMNS:
NO CHILD ANY CHILD
RECEIVED FLUID RECEIVED FLUID 556B
FROM ORS PACKET FROM ORS PACKET
556 Have you ever heard of a special product called ORS you can get YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
for the treatment of diarrhoea? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 556B
556A Where did you get this information? HEALTH WORKERS IN A PUBLIC
HOSPITAL . . . . . . . . . . . . . . . . . . . . A
HEALTH WORKERS IN A PRIVATE
HOSPITAL . . . . . . . . . . . . . . . . . . . . B
MINISTRY OF HEALTH THROUGH
RADIO, TV, POSTERS . . . . . . . . . . . . C
RECORD ALL MENTIONED COMMUNITY HEALTH WORKER/CHW D
FRIENDS OR RELATIVES . . . . . . . . . E
OTHER X
(SPECIFY)
556B CHECK 524 ALL COLUMNS:
524 ALL COLUMNS BLANK, OR CODE "C"
CODE "C" NOT CIRCLED CIRCLED 557
ZINC TABLETS NOT GIVEN ANY CHILD
RECEIVED ZINC TABLETS
556C Have you ever heard of zinc tablets which you can get for the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
treatment of diarrhoea? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 557
W-38
476 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
556D Where did you get this information? HEALTH WORKERS IN A PUBLIC
HOSPITAL . . . . . . . . . . . . . . . . . . . . A
HEALTH WORKERS IN A PRIVATE
HOSPITAL . . . . . . . . . . . . . . . . . . . . B
MINISTRY OF HEALTH THROUGH
RADIO, TV, POSTERS . . . . . . . . . . . . C
RECORD ALL MENTIONED COMMUNITY HEALTH WORKER/CHW D
FRIENDS OR RELATIVES . . . . . . . . . E
OTHER X
(SPECIFY)
557 CHECK 215 AND 218, ALL ROWS:
NUMBER OF CHILDREN BORN IN 2012 OR LATER LIVING WITH THE RESPONDENT
ONE OR MORE NONE 601
RECORD NAME OF YOUNGEST CHILD LIVING
WITH HER AND CONTINUE WITH 558
(NAME)
W-39
Appendix E • 477
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
558 Now I would like to ask you about liquids or foods that (NAME FROM 557) had yesterday during the day or at night. I
am interested in whether your child had the item I mention even if it was combined with other foods.
Did (NAME FROM 557) (drink/eat):
YES NO DK
a) Plain water? a) 1 2 8
b) Juice or juice drinks? b) 1 2 8
c) Clear broth? c) 1 2 8
d) Milk such as tinned, powdered, or fresh animal milk? d) 1 2 8
IF YES: How many times did (NAME) drink milk?
NUMBER OF TIMES
IF 7 OR MORE TIMES, RECORD ‘7'. DRANK MILK
e) Infant formula? e) 1 2 8
IF YES: How many times did (NAME) drink infant formula? NUMBER OF TIMES
IF 7 OR MORE TIMES, RECORD ‘7'. DRANK FORMULA
f) Any other liquids? f) 1 2 8
g) Yogurt? g) 1 2 8
IF YES: How many times did (NAME) eat yogurt? NUMBER OF TIMES
IF 7 OR MORE TIMES, RECORD ‘7'. ATE YOGURT
h) Any fortified baby food like Cerelac? h) 1 2 8
i) Maize, rice, wheat, porridge, sorghum, bread, or other foods made from grains? i) 1 2 8
j) Pumpkin, carrots, squash or yellow sweet potatoes that are yellow or orange inside? j) 1 2 8
k) Irish potatoes, yams, cassava, white sweet potatoes, or any other foods made from k) 1 2 8
roots?
l) Sukumu wiki or any dark green, leafy vegetables? l) 1 2 8
m) Ripe mangoes, pawpaw, guava? m) 1 2 8
n) Any other fruits or vegetables? n) 1 2 8
o) Liver, kidney, heart or other organ meats? o) 1 2 8
p) Any meat, such as beef, pork, lamb, goat, chicken, or duck? p) 1 2 8
q) Eggs? q) 1 2 8
r) Fresh or dried fish or shellfish? r) 1 2 8
s) Any foods made from beans, peas, lentils, or nuts? s) 1 2 8
t) Cheese or other food made from milk? t) 1 2 8
u) Any other solid, semi-solid, or soft food? u) 1 2 8
559 CHECK 558 (CATEGORIES "g" THROUGH "u"):
NOT A SINGLE AT LEAST ONE
"YES" "YES" 561
W-40
478 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
560 Did (NAME) eat any solid, semi-solid, or soft foods yesterday during YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
the day or at night? (GO BACK TO 558 TO RECORD
FOOD EATEN YESTERDAY)
IF ‘YES’ PROBE: What kind of solid, semi-solid or soft foods did
(NAME) eat? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 601
561 How many times did (NAME FROM 557) eat solid, semi-solid, or NUMBER OF
soft foods yesterday during the day or at night? TIMES . . . . . . . . . . . . . . . . . . . . . . . .
IF 7 OR MORE TIMES, RECORD ‘7'. DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
W-41
Appendix E • 479
SECTION 6. MARRIAGE AND SEXUAL ACTIVITY
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
601 Are you currently married or living together with a man as if YES, CURRENTLY MARRIED . . . . . . . 1
married? YES, LIVING WITH A MAN . . . . . . . . . 2 604
NO, NOT IN UNION . . . . . . . . . . . . . . . . 3
602 Have you ever been married or lived together with a man as if YES, FORMERLY MARRIED . . . . . . . 1
married? YES, LIVED WITH A MAN . . . . . . . . . 2
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 612
603 What is your marital status now: are you widowed, divorced, or WIDOWED . . . . . . . . . . . . . . . . . . . . . . 1
separated? DIVORCED . . . . . . . . . . . . . . . . . . . . . . 2 609
SEPARATED . . . . . . . . . . . . . . . . . . . . 3
604 Is your (husband/partner) living with you now or is he staying LIVING WITH HER . . . . . . . . . . . . . . . . 1
elsewhere? STAYING ELSEWHERE . . . . . . . . . . . . 2
605 RECORD THE HUSBAND'S/PARTNER'S NAME AND LINE NAME
NUMBER FROM THE HOUSEHOLD QUESTIONNAIRE.
IF HE IS NOT LISTED IN THE HOUSEHOLD, RECORD '00'.
LINE NO. . . . . . . . . . . . . . . . . . .
606 Does your (husband/partner) have other wives or does he live with YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
other women as if married? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 609
607 Including yourself, in total, how many wives or live-in partners does TOTAL NUMBER OF WIVES
he have? AND LIVE-IN PARTNERS . . . . .
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 98
608 Are you the first, second, … wife?
RANK . . . . . . . . . . . . . . . . . . . .
609 Have you been married or lived with a man only once or more than ONLY ONCE . . . . . . . . . . . . . . . . . . . . 1
once? MORE THAN ONCE . . . . . . . . . . . . . . . . 2
610 CHECK 609:
MARRIED/ MARRIED/
LIVED WITH A MAN LIVED WITH A MAN MONTH ..................
ONLY ONCE MORE THAN ONCE
a) In what month and year did b) Now I would like to ask about DON'T KNOW MONTH . . . . . . . . . . . . . . 98
you start living with your your first (husband/partner). In
(husband/partner)? what month and year did you
start living with him? YEAR . . . . . . . . . . . . 611A
DON'T KNOW YEAR . . . . . . . . . . . . 9998
611 How old were you when you first started living with him?
AGE ....................
611A When you got married or lived with a man, was it your choice or was OWN CHOICE . . . . . . . . . . . . . . . . . . . . 1
it arranged? ARRANGED . . . . . . . . . . . . . . . . . . . . 2
611B When you first got married or lived with a man, was the man older OLDER . . . . . . . . . . . . . . . . . . . . . . . . 1
than you, younger than you, or the same age as you? YOUNGER . . . . . . . . . . . . . . . . . . . . . . 2
ABOUT THE SAME AGE . . . . . . . . . . . . 3
DON'T KNOW/DON'T REMEMBER . . . 8
612 CHECK FOR THE PRESENCE OF OTHERS. BEFORE CONTINUING, MAKE EVERY EFFORT TO ENSURE PRIVACY.
613 Now I would like to ask some questions about sexual activity in NEVER HAD SEXUAL
order to gain a better understanding of some important life issues. INTERCOURSE . . . . . . . . . . . . . . . .00 628
How old were you when you had sexual intercourse for the very first
time? AGE IN YEARS ............
FIRST TIME WHEN STARTED
LIVING WITH (FIRST)
HUSBAND/PARTNER . . . . . . . . . . . . 95
W-42
480 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
613A CHECK 103:
AGE 15-24 AGE 25-49 614
613B The first time you had sexual intercourse, was a condom used? YES ........................ 1
NO ...................... 2
DON'T KNOW/DON'T REMEMBER . . . 8
613C How old was the person you first had sexual intercourse with? AGE OF PARTNER
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 98
614 Now I would like to ask you some questions about your recent sexual activity. Let me assure you again that your answers are
completely confidential and will not be told to anyone. If we should come to any question that you don't want to answer, just let me
know and we will go to the next question.
615 When was the last time you had sexual intercourse?
DAYS AGO . . . . . . . . . . . . 1
IF LESS THAN 12 MONTHS, ANSWER MUST BE RECORDED
IN DAYS, WEEKS OR MONTHS. WEEKS AGO . . . . . . . . . 2
IF 12 MONTHS (ONE YEAR) OR MORE, ANSWER MUST BE
RECORDED IN YEARS. MONTHS AGO ....... 3
YEARS AGO ......... 4 627
W-43
Appendix E • 481
LAST SECOND-TO-LAST THIRD-TO-LAST
SEXUAL PARTNER SEXUAL PARTNER SEXUAL PARTNER
616 When was the last time you had DAYS DAYS
sexual intercourse with this person? AGO . 1 AGO . 1
WEEKS WEEKS
AGO . 2 AGO . 2
MONTHS MONTHS
AGO . 3 AGO . 3
617 The last time you had sexual YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
intercourse (with this second/third NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
person), was a condom used? (SKIP TO 619) (SKIP TO 619) (SKIP TO 619)
617A What is the main reason you used a PREVENT STD/HIV . 1 PREVENT STD/HIV . 1 PREVENT STD/HIV . 1
condom on that occasion? AVOID PREGNANCY 2 AVOID PREGNANCY 2 AVOID PREGNANCY 2
BOTH PREVENT STD/HIV BOTH PREVENT STD/HIV BOTH PREVENT STD/HIV
AND PREGNANCY 3 AND PREGNANCY 3 AND PREGNANCY 3
DID NOT TRUST PARTNER DID NOT TRUST PARTNER DID NOT TRUST PARTNER
/HE MAY HAVE OTHER /HE MAY HAVE OTHER /HE MAY HAVE OTHER
PARTNERS . . . . . 4 PARTNERS . . . . . 4 PARTNERS . . . . . 4
PARTNER WANTED PARTNER WANTED PARTNER WANTED
TO USE . . . . . . . 5 TO USE . . . . . . . 5 TO USE . . . . . . . 5
OTHER 6 OTHER 6 OTHER 6
(SPECIFY) (SPECIFY) (SPECIFY)
618 Was a condom used every time you YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
had sexual intercourse with this NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
person in the last 12 months?
619 What was your relationship to this HUSBAND . . . . . . . 1 HUSBAND . . . . . . . 1 HUSBAND . . . . . . . 1
person with whom you had sexual LIVE-IN PARTNER . 2 LIVE-IN PARTNER . 2 LIVE-IN PARTNER . 2
intercourse? BOYFRIEND NOT BOYFRIEND NOT BOYFRIEND NOT
LIVING WITH LIVING WITH LIVING WITH
IF BOYFRIEND: Were you living RESPONDENT . . . 3 RESPONDENT . . . 3 RESPONDENT . . . 3
together as if married? CASUAL CASUAL CASUAL
ACQUAINTANCE . 4 ACQUAINTANCE . 4 ACQUAINTANCE . 4
IF YES, CIRCLE '2'. CLIENT/PROSTITUTE .5 CLIENT/PROSTITUTE .5 CLIENT/PROSTITUTE .5
IF NO, CIRCLE '3'. OTHER 6 OTHER 6 OTHER 6
(SPECIFY) (SPECIFY) (SPECIFY)
(SKIP TO 622) (SKIP TO 622) (SKIP TO 622)
620 CHECK 609: MARRIED MARRIED MARRIED MARRIED MARRIED MARRIED
ONLY MORE ONLY MORE ONLY MORE
ONCE THAN ONCE THAN ONCE THAN
ONCE ONCE ONCE
(SKIP (SKIP (SKIP
TO 622) TO 622) TO 622)
621 CHECK 613: FIRST TIME WHEN FIRST TIME WHEN FIRST TIME WHEN
STARTED LIVING STARTED LIVING STARTED LIVING
FIRST TIME WHEN STARTED WITH FIRST WITH FIRST WITH FIRST
LIVING WITH FIRST HUSBAND HUSBAND OTHER HUSBAND OTHER HUSBAND OTHER
(CODE 95)
(SKIP TO 623) (SKIP TO 623) (SKIP TO 623)
622 How long ago did you first have DAYS DAYS DAYS
sexual intercourse with this AGO . 1 AGO . 1 AGO . 1
(second/third) person? WEEKS WEEKS WEEKS
AGO . 2 AGO . 2 AGO . 2
MONTHS MONTHS MONTHS
AGO . 3 AGO . 3 AGO . 3
YEARS YEARS YEARS
AGO . 4 AGO . 4 AGO . 4
W-44
482 • Appendix E
LAST SECOND-TO-LAST THIRD-TO-LAST
SEXUAL PARTNER SEXUAL PARTNER SEXUAL PARTNER
623 How many times during the last 12 NUMBER OF NUMBER OF NUMBER OF
months did you have sexual TIMES . . . . . TIMES . . . . . TIMES . . . . .
intercourse with this person?
IF NON-NUMERIC ANSWER,
PROBE TO GET AN ESTIMATE.
IF NUMBER OF TIMES IS 95 OR
MORE, WRITE '95'.
624 How old is this person? AGE OF AGE OF AGE OF
PARTNER . PARTNER . PARTNER .
DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98
625 Apart from (this person/these two YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
people), have you had sexual (GO BACK TO 616 (GO BACK TO 616
intercourse with any other person in IN NEXT COLUMN) IN NEXT COLUMN)
the last 12 months? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 626A) (SKIP TO 626A)
626 In total, with how many different NUMBER OF
people have you had sexual PARTNERS
intercourse in the last 12 months? LAST 12
MONTHS . . .
IF NON-NUMERIC ANSWER,
PROBE TO GET AN ESTIMATE. DON'T KNOW . . . 98
IF NUMBER OF PARTNERS IS
95 OR MORE, WRITE '95'.
W-45
Appendix E • 483
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
626A In the last 12 months, have you ever given or received money, gifts, YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
or favors in return for sex? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
627 In total, with how many different people have you had sexual NUMBER OF PARTNERS
intercourse in your lifetime? IN LIFETIME . . . . . . . . . . . . . .
IF NON-NUMERIC ANSWER, PROBE TO GET AN ESTIMATE. DON'T KNOW . . . . . . . . . . . . . . . . . . 98
IF NUMBER OF PARTNERS IS 95 OR MORE,
WRITE '95'.
628 PRESENCE OF OTHERS DURING THIS SECTION YES NO
CHILDREN <10 . . . . . . . . . . . . 1 2
MALE ADULTS . . . . . . . . . . . . 1 2
FEMALE ADULTS . . . . . . . . . 1 2
629 Do you know of a place where a person can get male condoms? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 632
630 Where is that? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . A
Any other place? GOVT. HEALTH CENTER . . . . . . . B
GOVT. DISPENSARY . . . . . . . . . . . . C
PROBE TO IDENTIFY EACH TYPE OF SOURCE. OTHER PUBLIC
SECTOR D
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE (SPECIFY)
SECTOR, WRITE THE NAME OF THE PLACE.
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . E
(NAME OF PLACE(S)) PHARMACY/CHEMIST . . . . . . . . . . . . F
NURSING/MATERNITY HOME . . . . . G
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . H
FAMILY OPTIONS/FHOK CLINIC . . . I
OTHER PRIVATE MEDICAL
SECTOR J
(SPECIFY)
OTHER SOURCE
SHOP . . . . . . . . . . . . . . . . . . . . . . . . K
MOBILE CLINIC . . . . . . . . . . . . . . . . L
COMMUNITY-BASED DISTRIBUTOR M
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . . . N
FRIEND/RELATIVE . . . . . . . . . . . . . . O
DISPENSER . . . . . . . . . . . . . . . . . . . . P
OTHER X
(SPECIFY)
631 If you wanted to, could you yourself get a male condom? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW/UNSURE . . . . . . . . . . . . 8
632 Do you know of a place where a person can get female condoms? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 701
W-46
484 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
633 Where is that? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . A
Any other place? GOVT. HEALTH CENTER . . . . . . . B
GOVT. DISPENSARY . . . . . . . . . . . . C
PROBE TO IDENTIFY EACH TYPE OF SOURCE. OTHER PUBLIC
SECTOR D
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE (SPECIFY)
SECTOR, WRITE THE NAME OF THE PLACE.
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . E
(NAME OF PLACE(S)) PHARMACY/CHEMIST . . . . . . . . . . . . F
NURSING/MATERNITY HOME . . . . . G
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . H
FAMILY OPTIONS/FHOK CLINIC . . . I
OTHER PRIVATE MEDICAL
SECTOR J
(SPECIFY)
OTHER SOURCE
SHOP . . . . . . . . . . . . . . . . . . . . . . . . K
MOBILE CLINIC . . . . . . . . . . . . . . . . L
COMMUNITY-BASED DISTRIBUTOR M
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . . . N
FRIEND/RELATIVE . . . . . . . . . . . . . . O
OTHER X
(SPECIFY)
634 If you wanted to, could you yourself get a female condom? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW/UNSURE . . . . . . . . . . . . 8
W-47
Appendix E • 485
SECTION 7. FERTILITY PREFERENCES
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
701 CHECK 304:
NEITHER HE OR SHE
STERILIZED STERILIZED 712
702 CHECK 226:
NOT PREGNANT
PREGNANT OR UNSURE 704
703 Now I have some questions about the future. After the child you are HAVE ANOTHER CHILD . . . . . . . . . . . . 1 705
expecting now, would you like to have another child, or would you NO MORE . . . . . . . . . . . . . . . . . . . . . . 2
prefer not to have any more children? UNDECIDED/DON'T KNOW . . . . . . . . . 8 711
704 Now I have some questions about the future. Would you like to have HAVE (A/ANOTHER) CHILD . . . . . . . 1
(a/another) child, or would you prefer not to have any (more) NO MORE/NONE . . . . . . . . . . . . . . . . . . 2 707
children? SAYS SHE CAN'T GET PREGNANT . 3 712
UNDECIDED/DON'T KNOW . . . . . . . . . 8 710
705 CHECK 226:
MONTHS . . . . . . . . . . . . . . 1
NOT PREGNANT PREGNANT
OR UNSURE YEARS .............. 2
a) How long would you like to b) After the birth of the child you are
wait from now before the expecting now, how long would SOON/NOW . . . . . . . . . . . . . . . . . . 993 710
birth of (a/another) child? you like to wait before the birth of SAYS SHE CAN'T GET PREGNANT . 994 712
another child? AFTER MARRIAGE . . . . . . . . . . . . . . 995
OTHER 996 710
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . 998
706 CHECK 226:
NOT PREGNANT PREGNANT
OR UNSURE 711
707 CHECK 303: USING A CONTRACEPTIVE METHOD?
NOT CURRENTLY
CURRENTLY USING 712
USING
708 CHECK 705:
NOT 24 OR MORE MONTHS 00-23 MONTHS
ASKED OR 02 OR MORE YEARS OR 00-01 YEAR 711
W-48
486 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
709 CHECK 704: NOT MARRIED .................. A
WANTS TO HAVE WANTS NO MORE/ FERTILITY-RELATED REASONS
A/ANOTHER CHILD NONE NOT HAVING SEX . . . . . . . . . . . . . . B
INFREQUENT SEX . . . . . . . . . . . . . . C
MENOPAUSAL/HYSTERECTOMY . D
a) You have said that you do b) You have said that you do not CAN'T GET PREGNANT . . . . . . . . . E
not want (a/another) child want any (more) children. NOT MENSTRUATED SINCE
soon. LAST BIRTH . . . . . . . . . . . . . . . . F
Can you tell me why you are not BREASTFEEDING . . . . . . . . . . . . . . G
Can you tell me why you are using a method to prevent UP TO GOD/FATALISTIC . . . . . . . . . H
not using a method to pregnancy?
prevent pregnancy?
OPPOSITION TO USE
Any other reason?
RESPONDENT OPPOSED . . . . . . . I
Any other reason?
HUSBAND/PARTNER OPPOSED . . . J
OTHERS OPPOSED . . . . . . . . . . . . K
RELIGIOUS PROHIBITION . . . . . . . L
LACK OF KNOWLEDGE
KNOWS NO METHOD . . . . . . . . . . . . M
RECORD ALL REASONS MENTIONED. KNOWS NO SOURCE . . . . . . . . . . . . N
METHOD-RELATED REASONS
SIDE EFFECTS/HEALTH
CONCERNS . . . . . . . . . . . . . . . . . . O
LACK OF ACCESS/TOO FAR . . . . . P
COSTS TOO MUCH . . . . . . . . . . . . Q
PREFERRED METHOD
NOT AVAILABLE . . . . . . . . . . . . . . R
NO METHOD AVAILABLE . . . . . . . S
INCONVENIENT TO USE . . . . . . . T
INTERFERES WITH BODY'S
NORMAL PROCESSES . . . . . . . U
OTHER X
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . . Z
710 CHECK 303: USING A CONTRACEPTIVE METHOD?
NOT NO, YES,
ASKED NOT CURRENTLY USING CURRENTLY USING 712
711 Do you think you will use a contraceptive method to delay or avoid YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
pregnancy at any time in the future? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 711B
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 712
711A What contraceptive method would you prefer to use? FEMALE STERILIZATION . . . . . . . . . 01
MALE STERILIZATION . . . . . . . . . . . . 02
IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 03
INJECTABLES .................. 04
IMPLANTS . . . . . . . . . . . . . . . . . . . . . . 05
PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . . 06
CONDOM . . . . . . . . . . . . . . . . . . . . . . 07
FEMALE CONDOM .............. 08 712
LACTATIONAL AMEN. METHOD . . . . . 09
RHYTHM METHOD . . . . . . . . . . . . . . . . 10
WITHDRAWAL . . . . . . . . . . . . . . . . . . 11
OTHER 96
(SPECIFY)
UNSURE . . . . . . . . . . . . . . . . . . . . . . . . 98
W-49
Appendix E • 487
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
711B What is the main reason that you think you will not use a NOT MARRIED . . . . . . . . . . . . . . . . . . 11
contraceptive method at any time in the future?
FERTILITY-RELATED REASONS
INFREQUENT SEX/NO SEX . . . . . . . 12
MENOPAUSAL/HYSTERECTOMY . 13
SUBFECUND/INFECUND . . . . . . . . . 14
WANTS AS MANY CHILDREN AS
POSSIBLE . . . . . . . . . . . . . . . . . . 15
OPPOSITION TO USE
RESPONDENT OPPOSED . . . . . . . 16
HUSBAND/PARTNER OPPOSED . . . 17
OTHERS OPPOSED . . . . . . . . . . . . 18
RELIGIOUS PROHIBITION . . . . . . . 19
LACK OF KNOWLEDGE
KNOWS NO METHOD . . . . . . . . . . . . 20
KNOWS NO SOURCE . . . . . . . . . . . . 21
METHOD-RELATED REASONS
HEALTH CONCERNS . . . . . . . . . . . . 22
FEAR OF SIDE EFFECTS . . . . . . . 23
LACK OF ACCESS/TOO FAR ... 24
COSTS TOO MUCH . . . . . . . . . . . . 25
INCONVENIENT TO USE . . . . . . . . . 26
INTERFERES WITH BODY'S
NORMAL PROCESSES . . . . . . . 27
OTHER 96
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 98
712 CHECK 216:
HAS LIVING CHILDREN NO LIVING CHILDREN
NONE . . . . . . . . . . . . . . . . . . . . . . . . . . 00 714
a) If you could go back to the b) If you could choose exactly the
time you did not have any number of children to have in NUMBER . . . . . . . . . . . . . . . . . .
children and could choose your whole life, how many would
exactly the number of that be?
children to have in your OTHER 96 714
whole life, how many would (SPECIFY)
that be?
PROBE FOR A NUMERIC RESPONSE.
713 How many of these children would you like to be boys, how many BOYS GIRLS EITHER
would you like to be girls and for how many would it not matter if it’s
a boy or a girl? NUMBER
OTHER 96
(SPECIFY)
714 In the last few months have you: YES NO
a) Heard about family planning on the radio? a) RADIO . . . . . . . . . . . . . . . . . . . . 1 2
b) Seen anything about family planning on the television? b) TELEVISION .............. 1 2
c) Read about family planning in a newspaper or magazine? c) NEWSPAPER OR MAGAZINE . 1 2
W-50
488 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
715A In the last 12 months have you: YES NO
a) Heard family planning at public forums, such as Barazas or public a) PUBLIC FORUMS ......... 1 2
gatherings?
b) Seen family planning informational material, such as posters, b) INFORMATIONAL MATERIAL . 1 2
brochures, or stickers?
c) Been visited by a health worker or health professional to discuss c) VISITED BY HEALTH WRKER . 1 2
family planning issues?
d) Received family planning messages through social media d) SOCIAL MEDIA ............ 1 2
platforms, such as Facebook or twitter?
e) Received family planning messages through a mobile phone via e) MOBILE PHONE . . . . . . . . . . . . 1 2
text or email?
f) Heard political / religious / community leaders talk favorably f) COMMUNITY LEADERS . . . . . 1 2
about family planning?
716 CHECK 601:
YES, YES, NO,
CURRENTLY LIVING NOT IN 801
MARRIED WITH A MAN UNION
716A Now I want to ask you about your husband's / partner's views on APPROVES . . . . . . . . . . . . . . . . . . . . . . 1
family planning. Do you think that your husband / partner approves DISAPPROVES . . . . . . . . . . . . . . . . . . 2
or disapproves of couples using a method to avoid pregnancy? DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
716B How often have you talked to your husband / partner about family NEVER . . . . . . . . . . . . . . . . . . . . . . . . 1
planning in the past year? ONCE OR TWICE. . . . . . . . . . . . . . . . . . 2
MORE OFTEN . . . . . . . . . . . . . . . . . . . . 3
717 CHECK 303: USING A CONTRACEPTIVE METHOD?
NOT
CURRENTLY CURRENTLY
USING USING 720
OR NOT ASKED
717A CHECK 304: CURRENT CONTRACEPTIVE METHOD USED
CODE B, G, OR M
OTHER CODE CIRCLED 718
717B Does your husband / partner know you are using a method of family YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
planning? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
718 Would you say that using contraception is mainly your decision, MAINLY RESPONDENT ......... 1
mainly your (husband's/partner's) decision, or did you both decide MAINLY HUSBAND/PARTNER . . . . . 2
together? JOINT DECISION . . . . . . . . . . . . . . . . 3
OTHER 6
(SPECIFY)
719 CHECK 304:
NEITHER HE OR SHE
STERILIZED STERILIZED 801
720 Does your (husband/partner) want the same number of children that SAME NUMBER . . . . . . . . . . . . . . . . . . 1
you want, or does he want more or fewer than you want? MORE CHILDREN . . . . . . . . . . . . . . . . 2
FEWER CHILDREN . . . . . . . . . . . . . . . . 3
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
W-51
Appendix E • 489
SECTION 8. HUSBAND'S BACKGROUND AND WOMAN'S WORK
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
801 CHECK 601 AND 602:
CURRENTLY FORMERLY 803
MARRIED/ MARRIED/ NEVER MARRIED
LIVING WITH LIVED WITH AND NEVER 807
A MAN A MAN LIVED WITH A MAN
802 How old was your (husband/partner) on his last birthday?
AGE IN COMPLETED YEARS .
803 Did your (last) (husband/partner) ever attend school? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 806
804 What was the highest level of school he attended: primary, PRIMARY . . . . . . . . . . . . . . . . . . . . . . 1
vocational, secondary, or higher? POST-PRIMARY/VOCATIONAL . . . . . 2
SECONDARY/ 'A' LEVEL ......... 3
COLLEGE (MIDDLE LEVEL) . . . . . . . 4
UNIVERSITY . . . . . . . . . . . . . . . . . . . . 5
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 806
805 What was the highest (standard/form/year) he completed at that
level? STANDARD/FORM/YEAR ...
IF COMPLETED LESS THAN ONE YEAR AT THAT LEVEL,
RECORD '00'. DON'T KNOW . . . . . . . . . . . . . . . . . . 98
806 CHECK 801:
CURRENTLY MARRIED/ FORMERLY MARRIED/
LIVING WITH A MAN LIVED WITH A MAN
a) What is your (husband's/ b) What was your (last) (husband's/
partner's) occupation? That partner's) occupation? That is,
is, what kind of work does what kind of work does he mainly
he mainly do? do?
807 Aside from your own housework, have you done any work in the last YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 811
seven days? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
808 As you know, some women take up jobs for which they are paid in
cash or kind. Others sell things, have a small business or work on
the family farm or in the family business. In the last seven days, YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 811
have you done any of these things or any other work? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
809 Although you did not work in the last seven days, do you have any
job or business from which you were absent for leave, illness, YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 811
vacation, maternity leave, or any other such reason? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
810 Have you done any work in the last 12 months? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 815
811 What is your occupation, that is, what kind of work do you mainly
do?
811A CHECK 811:
WORKS IN DOES NOT WORK
AGRICULTURE IN AGRICULTURE 812
W-52
490 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
811B Do you work mainly on your own land or on family land, or do you OWN LAND . . . . . . . . . . . . . . . . . . . . . . 1
work on land that you rent from someone else, or do you work on FAMILY LAND . . . . . . . . . . . . . . . . . . . . 2
someone else's land? RENTED LAND . . . . . . . . . . . . . . . . . . 3
SOMEONE ELSE'S LAND . . . . . . . . . 4
OTHER 6
(SPECIFY)
812 Do you do this work for a member of your family, for someone else, FOR FAMILY MEMBER . . . . . . . . . . . . 1
or are you self-employed? FOR SOMEONE ELSE . . . . . . . . . . . . 2
SELF-EMPLOYED .............. 3
813 Do you usually work throughout the year, or do you work seasonally, THROUGHOUT THE YEAR . . . . . . . . . 1
or only once in a while? SEASONALLY/PART OF THE YEAR . 2
ONCE IN A WHILE . . . . . . . . . . . . . . . . 3
814 Are you paid in cash or kind for this work or are you not paid at all? CASH ONLY . . . . . . . . . . . . . . . . . . . . 1
CASH AND KIND . . . . . . . . . . . . . . . . . . 2
IN KIND ONLY . . . . . . . . . . . . . . . . . . . . 3
NOT PAID . . . . . . . . . . . . . . . . . . . . . . 4
815 CHECK 601:
CURRENTLY
MARRIED/LIVING NOT IN UNION
WITH A MAN 823
816 CHECK 814:
CODE 1 OR 2
CIRCLED OTHER 819
817 Who usually decides how the money you earn will be used: you, RESPONDENT . . . . . . . . . . . . . . . . . . 1
your (husband/partner), or you and your (husband/partner) jointly? HUSBAND/PARTNER . . . . . . . . . . . . . . 2
RESPONDENT AND
HUSBAND/PARTNER JOINTLY . . . 3
OTHER 6
(SPECIFY)
818 Would you say that the money that you earn is more than what your MORE THAN HIM . . . . . . . . . . . . . . . . . . 1
(husband/partner) earns, less than what he earns, or about the LESS THAN HIM . . . . . . . . . . . . . . . . . . 2
same? ABOUT THE SAME . . . . . . . . . . . . . . . . 3
HUSBAND/PARTNER HAS
NO EARNINGS . . . . . . . . . . . . . . . . 4 820
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
819 Who usually decides how your (husband's/partner's) earnings will be RESPONDENT . . . . . . . . . . . . . . . . . . 1
used: you, your (husband/partner), or you and your HUSBAND/PARTNER . . . . . . . . . . . . . . 2
(husband/partner) jointly? RESPONDENT AND
HUSBAND/PARTNER JOINTLY . . . 3
HUSBAND/PARTNER HAS
NO EARNINGS . . . . . . . . . . . . . . . . 4
OTHER 6
(SPECIFY)
820 Who usually makes decisions about health care for yourself: you, RESPONDENT . . . . . . . . . . . . . . . . . . 1
your (husband/partner), you and your (husband/partner) jointly, or HUSBAND/PARTNER . . . . . . . . . . . . . . 2
someone else? RESPONDENT AND
HUSBAND/PARTNER JOINTLY . . . 3
SOMEONE ELSE . . . . . . . . . . . . . . . . . . 4
OTHER . . . . . . . . . . . . . . . . . . . . . . . . 6
821 Who usually makes decisions about making major household RESPONDENT . . . . . . . . . . . . . . . . . . 1
purchases? HUSBAND/PARTNER . . . . . . . . . . . . . . 2
RESPONDENT AND
HUSBAND/PARTNER JOINTLY . . . 3
SOMEONE ELSE . . . . . . . . . . . . . . . . . . 4
OTHER . . . . . . . . . . . . . . . . . . . . . . . . 6
W-53
Appendix E • 491
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
822 Who usually makes decisions about visits to your family or RESPONDENT . . . . . . . . . . . . . . . . . . 1
relatives? HUSBAND/PARTNER . . . . . . . . . . . . . . 2
RESPONDENT AND
HUSBAND/PARTNER JOINTLY . . . 3
SOMEONE ELSE . . . . . . . . . . . . . . . . . . 4
OTHER . . . . . . . . . . . . . . . . . . . . . . . . 6
822A Who usually makes decisions about what food should be cooked RESPONDENT . . . . . . . . . . . . . . . . . . 1
each day? HUSBAND/PARTNER . . . . . . . . . . . . . . 2
RESPONDENT AND
HUSBAND/PARTNER JOINTLY . . . 3
SOMEONE ELSE . . . . . . . . . . . . . . . . . . 4
OTHER . . . . . . . . . . . . . . . . . . . . . . . . 6
823 Do you own this or any other house either alone or jointly with ALONE ONLY . . . . . . . . . . . . . . . . . . . . 1
someone else? JOINTLY ONLY . . . . . . . . . . . . . . . . . . 2
BOTH ALONE AND JOINTLY . . . . . . . 3
DOES NOT OWN . . . . . . . . . . . . . . . . 4
824 Do you own any land either alone or jointly with someone else? ALONE ONLY . . . . . . . . . . . . . . . . . . . . 1
JOINTLY ONLY . . . . . . . . . . . . . . . . . . 2
BOTH ALONE AND JOINTLY . . . . . . . 3
DOES NOT OWN . . . . . . . . . . . . . . . . 4
825 PRESENCE OF OTHERS AT THIS POINT (PRESENT AND PRES./ PRES./ NOT
LISTENING, PRESENT BUT NOT LISTENING, OR NOT LISTEN. NOT PRES.
PRESENT) LISTEN.
CHILDREN < 10 . . . . . 1 2 3
HUSBAND . . . . . . . . . 1 2 3
OTHER MALES . . . . . 1 2 3
OTHER FEMALES . . . 1 2 3
826 In your opinion, is a husband justified in hitting or beating his wife in
the following situations: YES NO DK
a) If she goes out without telling him? a) GOES OUT . . . . . . . . . 1 2 8
b) If she neglects the children? b) NEGL. CHILDREN . . . 1 2 8
c) If she argues with him? c) ARGUES . . . . . . . . . . . . 1 2 8
d) If she refuses to have sex with him? d) REFUSES SEX . . . . . 1 2 8
e) If she burns the food? e) BURNS FOOD . . . . . . . 1 2 8
W-54
492 • Appendix E
SECTION 9. HIV/AIDS
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
901 Now I would like to talk about something else. Have you ever heard YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
of an illness called AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 937
902 Can people reduce their chance of getting the AIDS virus by having YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
just one uninfected sex partner who has no other sex partners? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
903 Can people get the AIDS virus from mosquito bites? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
904 Can people reduce their chance of getting the AIDS virus by using a YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
condom every time they have sex? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
904A Can people reduce their chance of getting the AIDS virus by not YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
having sexual intercourse at all? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
905 Can people get the AIDS virus by sharing food with a person who YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
has AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
906 Can people get the AIDS virus because of witchcraft or other YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
supernatural means? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
907 Is it possible for a healthy-looking person to have the AIDS virus? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
907A Do you know someone personally who has the virus that causes YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
AIDS or someone who died of AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
908 Can the virus that causes AIDS be transmitted from a mother to her
baby: YES NO DK
a) During pregnancy? a) DURING PREGNANCY. 1 2 8
b) During delivery? b) DURING DELIVERY . . . 1 2 8
c) By breastfeeding? c) BREASTFEEDING . . . 1 2 8
909 CHECK 908:
AT LEAST OTHER
ONE 'YES' 911
910 Are there any special drugs that a doctor or a nurse can give to a YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
woman infected with the AIDS virus to reduce the risk of NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
transmission to the baby? DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
911 CHECK 208 AND 215: NO BIRTHS
926
LAST BIRTH SINCE LAST BIRTH BEFORE
JANUARY 2012 JANUARY 2012 926
912 CHECK 408 FOR LAST BIRTH:
HAD NO
ANTENATAL ANTENATAL
CARE CARE 920
913
CHECK FOR PRESENCE OF OTHERS. BEFORE CONTINUING, MAKE EVERY EFFORT TO ENSURE PRIVACY.
W-55
Appendix E • 493
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
914 During any of the antenatal visits for your last birth were you given
any information about:
YES NO DK
a) Babies getting the AIDS virus from their mother? a) AIDS FROM MOTHER . 1 2 8
b) Things that you can do to prevent getting the AIDS virus? b) THINGS TO DO ...... 1 2 8
c) Getting tested for the AIDS virus? c) TESTED FOR AIDS . . . 1 2 8
915 Were you offered a test for the AIDS virus as part of your antenatal YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
care? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
916 I don't want to know the results, but were you tested for the AIDS YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
virus as part of your antenatal care? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 920
917 Where was the test done? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . . 11
PROBE TO IDENTIFY THE TYPE OF SOURCE. GOVT. HEALTH CENTER\CLINIC . 12
GOVERNMENT DISPENSARY . . . . 13
OTHER PUBLIC
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR, SECTOR 18
WRITE THE NAME OF THE PLACE. (SPECIFY)
PRIVATE MEDICAL SECTOR
(NAME OF PLACE) PRIVATE HOSPITAL/CLINIC . . . . . . 21
MISSIONARY/CHURCH HOSP./
CLINIC . . . . . . . . . . . . . . . . . . . . . . .22
FAMILY OPTIONS/FHOK CLINIC . . 23
VCT CENTRE . . . . . . . . . . . . . . . . . . .24
NURSING/MATERNITY HOMES . . 25
BLOOD TRANSFUSION SERVICES 26
OTHER PRIVATE
MEDICAL SECTOR
27
(SPECIFY)
OTHER SOURCE
HOME . . . . . . . . . . . . . . . . . . . . . . . . . 31
CORRECTIONAL FACILITY . . . . . . . . 32
OTHER 96
(SPECIFY)
918 I don't want to know the results, but did you get the results of the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
test? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 924
919 All women are supposed to receive counseling after being tested. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
After you were tested, did you receive counseling? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 924
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
920 CHECK 434 FOR LAST BIRTH:
ANY CODE OTHER 926
21-36 CIRCLED
921 Between the time you went for delivery but before the baby was YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
born, were you offered a test for the AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
922 I don't want to know the results, but were you tested for the AIDS YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
virus at that time? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 926
923 I don't want to know the results, but did you get the results of the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
test? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
924 Have you been tested for the AIDS virus since that time you were YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 927
tested during your pregnancy? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
925 How many months ago was your most recent HIV test?
MONTHS AGO ............ 931A
TWO OR MORE YEARS . . . . . . . . . . 95
W-56
494 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
926 I don't want to know the results, but have you ever been tested to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
see if you have the AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 930
927 How many months ago was your most recent HIV test?
MONTHS AGO ............
TWO OR MORE YEARS . . . . . . . . . . 95
928 I don't want to know the results, but did you get the results of the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
test? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
929 Where was the test done? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . . 11
PROBE TO IDENTIFY THE TYPE OF SOURCE. GOVT. HEALTH CENTER\CLINIC . 12
GOVERNMENT DISPENSARY . . . . 13
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR, OTHER PUBLIC
WRITE THE NAME OF THE PLACE. SECTOR 18
(SPECIFY)
(NAME OF PLACE) PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC/ . . . . 21
MISSIONARY/CHURCH HOSP./
CLINIC . . . . . . . . . . . . . . . . 22
FAMILY OPTIONS/FHOK CLINIC . . 23 931A
VCT CENTRE . . . . . . . . . . . . . . 24
NURSING/MATERNITY HOMES . . 25
BLOOD TRANSFUSION SERVICES . 26
OTHER PRIVATE
MEDICAL SECTOR
27
(SPECIFY)
OTHER SOURCE
HOME . . . . . . . . . . . . . . . . . . . . . . . . . 31
CORRECTIONAL FACILITY . . . . . . . . 32
OTHER 96
(SPECIFY)
930 Do you know of a place where people can go to get tested for the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 931A
931 Where is that? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . . A
Any other place? GOVT. HEALTH CENTER\CLINIC . B
GOVERNMENT DISPENSARY . . . . C
PROBE TO IDENTIFY EACH TYPE OF SOURCE. OTHER PUBLIC
SECTOR D
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE (SPECIFY)
SECTOR, WRITE THE NAME OF THE PLACE.
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC/ . . . . . . E
MISSIONARY/CHURCH HOSP./
(NAME OF PLACE(S)) CLINIC . . . . . . . . . . . . . . . . . . . . . F
FAMILY OPTIONS/FHOK CLINIC . . G
VCT CENTRE . . . . . . . . . . . . . . . . . . . H
NURSING/MATERNITY HOMES . . I
BLOOD TRANSFUSION SERVICES . J
OTHER PRIVATE
MEDICAL SECTOR
K
(SPECIFY)
OTHER X
(SPECIFY)
931A CHECK 601:
CURRENTLY
MARRIED/LIVING NOT IN UNION
WITH A MAN 932
W-57
Appendix E • 495
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
931B Have you ever talked with your (husband / partner) about ways to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
prevent getting the virus that causes AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
932 Would you buy fresh vegetables from a shopkeeper or vendor if you YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
knew that this person had the AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
933 If a member of your family got infected with the AIDS virus, would YES, REMAIN A SECRET . . . . . . . . . . 1
you want it to remain a secret or not? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DK/NOT SURE/DEPENDS . . . . . . . . . . 8
934 If a member of your family became sick with AIDS, would you be YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
willing to care for her or him in your own household? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DK/NOT SURE/DEPENDS . . . . . . . . . . 8
935 In your opinion, if a female teacher has the AIDS virus but is not SHOULD BE ALLOWED . . . . . . . . . . . . 1
sick, should she be allowed to continue teaching in the school? SHOULD NOT BE ALLOWED . . . . . . . . 2
DK/NOT SURE/DEPENDS . . . . . . . . . . 8
936 Should children age 12-14 be taught about using a condom to avoid YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
getting AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DK/NOT SURE/DEPENDS . . . . . . . . . . 8
937 CHECK 901:
HEARD ABOUT NOT HEARD
AIDS ABOUT AIDS
a) Apart from AIDS, have you b) Have you heard about
heard about other infections infections that can be
that can be transmitted transmitted through sexual YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
through sexual contact? contact? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 938
937A If a man has a sexually transmitted disease, what symptoms might ABDOMINAL PAIN . . . . . . . . . . . . . . . . A
he have? GENITAL DISCHARGE/DRIPPING . . . B
FOUL SMELL/DISCHARGE . . . . . . . . . . C
BURNING PAIN ON URINATION . . . . . . D
Any others? REDNESS/INFLAMATION IN GENITAL
AREA . . . . . . . . . . . . . . . . . . . . . . . . . E
SWELLING IN GENITAL AREA . . . . . . F
RECORD ALL MENTIONED GENITAL SORES/ULCERS . . . . . . . . . . G
GENITAL WARTS . . . . . . . . . . . . . . . . H
GENITAL ITCHING . . . . . . . . . . . . . . . . I
BLOOD IN URINE . . . . . . . . . . . . . . . . . . . J
LOSS OF WEIGHT . . . . . . . . . . . . . . . . K
IMPOTENCE/NO ERECTION . . . . . . . . L
OTHER W
(SPECIFY)
OTHER X
(SPECIFY)
NO SYMPTOMS . . . . . . . . . . . . . . . . . . . Y
DOES NOT KNOW . . . . . . . . . . . . . . . . Z
W-58
496 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
937B If a woman has a sexually transmitted disease, what symptoms ABDOMINAL PAIN . . . . . . . . . . . . . . . . A
might she have? GENITAL DISCHARGE/DRIPPING . . . B
FOUL SMELL/DISCHARGE . . . . . . . . . . C
BURNING PAIN ON URINATION . . . . . . D
Any others? REDNESS/INFLAMATION IN GENITAL
AREA . . . . . . . . . . . . . . . . . . . . . . . . . E
SWELLING IN GENITAL AREA . . . . . . F
RECORD ALL MENTIONED GENITAL SORES/ULCERS . . . . . . . . . . G
GENITAL WARTS . . . . . . . . . . . . . . . . H
GENITAL ITCHING . . . . . . . . . . . . . . . . I
BLOOD IN URINE . . . . . . . . . . . . . . . . J
LOSS OF WEIGHT . . . . . . . . . . . . . . . . K
HARD TO GET PREGNANT . . . . . . . . . . L
OTHER W
(SPECIFY)
OTHER X
(SPECIFY)
NO SYMPTOMS . . . . . . . . . . . . . . . . . . . Y
DOES NOT KNOW . . . . . . . . . . . . . . . . Z
938 CHECK 613:
HAS HAD SEXUAL NEVER HAD SEXUAL
INTERCOURSE INTERCOURSE 946
939 CHECK 937: HEARD ABOUT OTHER SEXUALLY TRANSMITTED INFECTIONS?
YES NO 941
940 Now I would like to ask you some questions about your health in the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
last 12 months. During the last 12 months, have you had a disease NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
which you got through sexual contact? DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
941 Sometimes women experience a bad-smelling abnormal genital YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
discharge. During the last 12 months, have you had a bad-smelling NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
abnormal genital discharge? DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
942 Sometimes women have a genital sore or ulcer. During the last 12 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
months, have you had a genital sore or ulcer? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
943 CHECK 940, 941, AND 942:
HAS HAD AN HAS NOT HAD AN
INFECTION INFECTION OR 946
(ANY 'YES') DOES NOT KNOW
944 The last time you had (PROBLEM FROM 940/941/942), did you YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
seek any kind of advice or treatment? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 945A
W-59
Appendix E • 497
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
945 Where did you go? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . . A
Any other place? GOVT. HEALTH CENTRE/CLINIC . . . B
GOVT. DISPENSARY . . . . . . . . . . . . C
PROBE TO IDENTIFY EACH TYPE OF SOURCE. OTHER PUBLIC
SECTOR D
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE (SPECIFY)
SECTOR, WRITE THE NAME OF THE PLACE.
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC/
(NAME OF PLACE(S)) PRIVATE DOCTOR . . . . . . . . . . . . E
MISSIONARY/CHURCH HOSP/
CLINIC . . . . . . . . . . . . . . . . . . . . . F
FAMILY OPTIONS/FHOK CLINIC . . . G
VCT CENTRE . . . . . . . . . . . . . . . . . . . H
NURSING/MATERNITY HOMES . . . I
BLOOD TRANSFUSION SERVICES . J
OTHER PRIVATE MEDICAL . . . . . . . .
SECTOR K
(SPECIFY)
OTHER SOURCE
SHOP/PHARMACY . . . . . . . . . . . . . . L
TRADITIONAL HEALER . . . . . . . . . . M
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . . . . N
FRIENDS/RELATIVES . . . . . . . . . . . . O
OTHER X
(SPECIFY)
945A When you had (PROBLEM(S) FROM 940/941/942), did you inform YES, INFORMED ALL PARTNERS . . . 1
the persons with whom you were having sex? INFORMED SOME, NOT ALL . . . . . . . . 2
NO, INFORMED NONE . . . . . . . . . . . . . . 3
DID NOT HAVE A PARTNER . . . . . . . . 4 946
945B When you had (PROBLEM(S) FROM 940/941/942), did you do YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
anything to avoid infecting your sexual partner(s)? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 946
945C What did you do to avoid infecting your partner(s)? Did you: YES NO
a) Use medicine? a) USE MEDICINE . . . . . . . . . . . . 1 2
b) Stop sex? b) STOP HAVING SEX . . . . . . . . . . 1 2
c) Use a condom when having sex? c) USE CONDOM . . . . . . . . . . . . . . 1 2
946 If a wife knows her husband has a disease that she can get during YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
sexual intercourse, is she justified in asking that they use a condom NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
when they have sex? DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
947 Is a wife justified in refusing to have sex with her husband when she YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
knows he has sex with women other than his wives? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . . 8
948 CHECK 601:
CURRENTLY MARRIED/
LIVING WITH A MAN NOT IN UNION 1001
949 Can you say no to your (husband/partner) if you do not want to have YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
sexual intercourse? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DEPENDS/NOT SURE . . . . . . . . . . . . . . 8
950 Could you ask your (husband/partner) to use a condom if you YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
wanted him to? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DEPENDS/NOT SURE . . . . . . . . . . . . . . 8
W-60
498 • Appendix E
SECTION 10. OTHER HEALTH ISSUES
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1001 Now I would like to ask you some other questions relating to health
matters. Have you had an injection for any reason in the last 12
months? NUMBER OF INJECTIONS . . .
IF YES: How many injections have you had?
IF NUMBER OF INJECTIONS IS 90 OR MORE, NONE . . . . . . . . . . . . . . . . . . . . . . . . . . 00 1003A
OR DAILY FOR 3 MONTHS OR MORE, RECORD '90'.
IF NON-NUMERIC ANSWER, PROBE TO GET AN ESTIMATE.
1002 Among these injections, how many were administered by a doctor, a
nurse, a pharmacist, a dentist, or any other health worker? NUMBER OF INJECTIONS . . .
IF NUMBER OF INJECTIONS IS 90 OR MORE,
OR DAILY FOR 3 MONTHS OR MORE, RECORD '90'. NONE . . . . . . . . . . . . . . . . . . . . . . . . . . 00 1003A
IF NON-NUMERIC ANSWER, PROBE TO GET AN ESTIMATE.
1003 The last time you got an injection from a health worker, did he/she YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
take the syringe and needle from a new, unopened package? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
1003A Have you ever been told by a doctor or health worker that you have YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
raised blood pressure or hypertension? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1003B Have you ever been told by a doctor or health worker that you have YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
raised blood sugar or diabetes? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1003C In the past 12 months, have you been involved in a road traffic YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
accident as a driver, passenger, pedestrian, or cyclist? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1003D In the past 12 months, were you injured accidentally, not related to a YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
traffic accident? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1003F
1003E How did the injury happen? FALL . . . . . . . . . . . . . . . . . . . . . . . . . . A
BURN . . . . . . . . . . . . . . . . . . . . . . . . . . B
POISONING . . . . . . . . . . . . . . . . . . . . . . C
RECORD ALL MENTIONED CUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . D
NEAR-DROWNING . . . . . . . . . . . . . . . . E
ANIMAL BITE . . . . . . . . . . . . . . . . . . . . F
SHOOTING . . . . . . . . . . . . . . . . . . . . . . G
OTHER X
(SPECIFY)
1003F Have you ever heard of an illness called tuberculosis or TB? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1004
1003G How does tuberculosis spread from one person to another? THROUGH THE AIR WHEN
COUGHING OR SNEEZING . . . . . . . A
PROBE: Any other ways? THROUGH SHARING UTENSILS . . . . . B
THROUGH TOUCHING A PERSON
WITH TB . . . . . . . . . . . . . . . . . . . . . . C
THROUGH FOOD . . . . . . . . . . . . . . . . D
RECORD ALL MENTIONED THROUGH SEXUAL CONTACT . . . . . E
THROUGH MOSQUITO BITES . . . . . . . F
OTHER X
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . . Z
1004 Do you currently smoke cigarettes? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1006
1005 In the last 24 hours, how many cigarettes did you smoke? NUMBER OF
CIGARETTES . . . . . . . . . . . . . .
W-61
Appendix E • 499
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1006 Do you currently smoke or use any (other) type of tobacco? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1007A
1007 What (other) type of tobacco do you currently smoke or use? PIPE . . . . . . . . . . . . . . . . . . . . . . . . . . A
CHEWING TOBACCO . . . . . . . . . . . . . . B
RECORD ALL MENTIONED. SNUFF . . . . . . . . . . . . . . . . . . . . . . . . C
WATER PIPE / SHISHA . . . . . . . . . . . . D
OTHER X
(SPECIFY)
1007A Do you drink alcohol? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1007C
1007B During the last two weeks, on how many days did you have at least NUMBER OF
one alcoholic drink? DAYS . . . . . . . . . . . . . . . . . . . .
1007C Are you involved in exercise that causes an increase in your heart
rate for at least 10 minutes continuously…: YES NO
a) At work? a) AT WORK ................ 1 2
b) During other physical activities? b) OTHER PHYSICAL ACTIVITIES 1 2
1008 Many different factors can prevent women from getting medical
advice or treatment for themselves. When you are sick and want to BIG NOT A BIG
get medical advice or treatment, is each of the following a big PROB- PROB-
problem or not: LEM LEM
a) Getting permission to go to the doctor? a) PERMISSION TO GO . 1 2
b) Getting money needed for advice or treatment? b) GETTING MONEY ... 1 2
c) The distance to the health facility? c) DISTANCE . . . . . . . . . 1 2
d) Not wanting to go alone? d) GO ALONE . . . . . . . . . 1 2
1008A Now I would like to ask you about women's health. Have you ever YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
heard of cervical cancer? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1008D
1008B Have you ever had a test or exam to see if you had cervical cancer? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1008D
1008C What type of exam did you have to see if you have cervical cancer? PAP SMEAR . . . . . . . . . . . . . . . . . . . . A
VISUAL INSPECTION
(WITH ACETIC ACID (VIA)/
LUGOL'S IODINE (VILI)) . . . . . . . . . B
DON'T KNOW / NOT SURE . . . . . . . . . X
1008D Have you ever examined your breasts to detect or check for breast YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
cancer? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1008E Has a doctor or other health professional examined your breasts to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
detect or check for breast cancer? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
W-62
500 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1009 Are you covered by any health insurance? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1101
1010 What type of health insurance are you covered by? MUTUAL HEALTH ORGANIZATION/
COMMUNITY-BASED HEALTH
RECORD ALL MENTIONED. INSURANCE . . . . . . . . . . . . . . . . . . A
HEALTH INSURANCE THROUGH
EMPLOYER . . . . . . . . . . . . . . . . . . . . B
NATIONAL HEATLH INSURANCE
SCHEME . . . . . . . . . . . . . . . . . . . . . . C
PRIVATELY PURCHASED
COMMERCIAL HEALTH INSURANCE .D
PRE-PAYMENT SCHEME. . . . . . . . . . . . E
OTHER X
(SPECIFY)
W-63
Appendix E • 501
SECTION 11. MATERNAL MORTALITY
NO. CODING CATEGORIES SKIP
1101 Now I would like to ask you some questions about your brothers NUMBER OF BIRTHS TO
and sisters, that is, all of the children born to your natural mother, NATURAL MOTHER . . . . . . . .
including those who are living with you, those living elsewhere and
those who have died. How many children did your mother give
birth to, including you?
1102 CHECK 1101:
TWO OR MORE BIRTHS ONLY ONE BIRTH
(RESPONDENT ONLY) 1201
1103 How many births did your mother have before you were born? NUMBER OF
PRECEDING BIRTHS . . . . . . . . .
1104 What was the (1) (2) (3) (4) (5) (6)
name given to
your oldest (next
oldest) brother or
sister?
1105 Is (NAME) male MALE 1 MALE 1 MALE 1 MALE 1 MALE 1 MALE 1
or female? FEMALE 2 FEMALE 2 FEMALE 2 FEMALE 2 FEMALE 2 FEMALE 2
1106 Is (NAME) still YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1
alive? NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2
GO TO 1108 GO TO 1108 GO TO 1108 GO TO 1108 GO TO 1108 GO TO 1108
DK . . . 8 DK . . . 8 DK . . . 8 DK . . . 8 DK . . . 8 DK . . . 8
GO TO (2) GO TO (3) GO TO (4) GO TO (5) GO TO (6) GO TO (7)
1107 How old is
(NAME)?
GO TO (2) GO TO (3) GO TO (4) GO TO (5) GO TO (6) GO TO (7)
1108 How many years
ago did (NAME)
die?
1109 How old was
(NAME) when
he/she died? IF MALE OR IF MALE OR IF MALE OR IF MALE OR IF MALE OR IF MALE OR
DIED BEFORE DIED BEFORE DIED BEFORE DIED BEFORE DIED BEFORE DIED BEFORE
12 YEARS 12 YEARS 12 YEARS 12 YEARS 12 YEARS 12 YEARS
OF AGE OF AGE OF AGE OF AGE OF AGE OF AGE
GO TO (2) GO TO (3) GO TO (4) GO TO (5) GO TO (6) GO TO (7)
1110 Was (NAME) YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1
pregnant when GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113
she died? NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2
1111 Did (NAME) die YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1
during childbirth? GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113
NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2
1112 Did (NAME) die YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1
within two NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2
months after the
end of a
pregnancy or
childbirth?
1113 How many live
born children did
(NAME) give birth
to during her
lifetime?
IF NO MORE BROTHERS OR SISTERS, GO TO 1201.
W-64
502 • Appendix E
1104 What was the (7) (8) (9) (10) (11) (12)
name given to
your oldest (next
oldest) brother or
sister?
1105 Is (NAME) male MALE 1 MALE 1 MALE 1 MALE 1 MALE 1 MALE 1
or female? FEMALE 2 FEMALE 2 FEMALE 2 FEMALE 2 FEMALE 2 FEMALE 2
1106 Is (NAME) still YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1
alive? NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2
GO TO 1108 GO TO 1108 GO TO 1108 GO TO 1108 GO TO 1108 GO TO 1108
DK . . . 8 DK . . . 8 DK . . . 8 DK . . . 8 DK . . . 8 DK . . . 8
GO TO (8) GO TO (9) GO TO (10) GO TO (11) GO TO (12) GO TO (13)
1107 How old is
(NAME)?
GO TO (8) GO TO (9) GO TO (10) GO TO (11) GO TO (12) GO TO (13)
1108 How many years
ago did (NAME)
die?
1109 How old was
(NAME) when
he/she died? IF MALE OR IF MALE OR IF MALE OR IF MALE OR IF MALE OR IF MALE OR
DIED BEFORE DIED BEFORE DIED BEFORE DIED BEFORE DIED BEFORE DIED BEFORE
12 YEARS 12 YEARS 12 YEARS 12 YEARS 12 YEARS 12 YEARS
OF AGE OF AGE OF AGE OF AGE OF AGE OF AGE
GO TO (8) GO TO (9) GO TO (10) GO TO (11) GO TO (12) GO TO (13)
1110 Was (NAME) YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1
pregnant when GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113
she died? NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2
1111 Did (NAME) die YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1
during childbirth? GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113 GO TO 1113
NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2
1112 Did (NAME) die YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1 YES . . . 1
within two NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2 NO . . . 2
months after the
end of a
pregnancy or
childbirth?
1113 How many live
born children did
(NAME) give birth
to during her
lifetime?
IF NO MORE BROTHERS OR SISTERS, GO TO NEXT SECTION.
W-65
Appendix E • 503
SECTION 12: FISTULA
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1201 Sometimes a woman can have a problem of constant leakage of
urine or stool from her vagina during the day and night. This
problem usually occurs after a difficult childbirth, but may also occur
after a sexual assault or after pelvic surgery.
Have you ever experienced a constant leakage of urine or stool from YES .......................... 1 1203
your vagina during the day and night?
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1202 Have you ever heard of this problem? YES .......................... 1
1301
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1203 Did this problem start after you delivered a baby or had a stillbirth? AFTER DELIVERED BABY . . . . . . . . . 1
AFTER HAD STILLBIRTH . . . . . . . . . . . . 2
NEITHER . . . . . . . . . . . . . . . . . . . . . . . . 3 1205
1204 Did this problem start after a normal labor and delivery, or after a NORMAL LABOR/DELIVERY ..... 1
1206
very difficult labor and delivery? VERY DIFFICULT LABOR/DELIVERY . 2
1205 What do you think caused this problem? SEXUAL ASSAULT .............. 1
PELVIC SURGERY .............. 2
OTHER 6
(SPECIFY)
DON'T KNOW .................. 8 1207
1206 How many days after (CAUSE OF PROBLEM FROM 1203 OR NUMBER OF DAYS AFTER
1205) did the leakage start? DELIVERY/OTHER EVENT
(ENTER 90 IF 90 DAYS OR MORE)
1207 Have you sought treatment for this condition? YES .......................... 1 1209
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1208 Why have you not sought treatment? DO NOT KNOW CAN BE FIXED . . . . . A
DO NOT KNOW WHERE TO GO . . . . . B
PROBE AND RECORD ALL MENTIONED. TOO EXPENSIVE . . . . . . . . . . . . . . . . C
TOO FAR . . . . . . . . . . . . . . . . . . . . . . D
POOR QUALITY OF CARE ....... E
1301
COULD NOT GET PERMISSION .... F
EMBARRASSMENT .............. G
PROBLEM DISAPPEARED . . . . . . . . H
OTHER X
(SPECIFY)
1209 From whom did you last seek treatment? HEALTH PROFESSIONAL
DOCTOR .................... 1
NURSE/MIDWIFE . . . . . . . . . . . . . . 2
OTHER PERSON
COMMUNITY/VILLAGE
HEALTH WORKER ......... 3
OTHER 6
(SPECIFY)
1210 Did you have an operation to fix the problem? YES .......................... 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1211 Did the treatment stop the leakage completely? YES, STOPPED COMPLETELY . . . . . 1
NOT STOPPED BUT REDUCED . . . . . 2
IF NO: Did the treatment reduce the leakage? NOT STOPPED AT ALL ......... 3
DID NOT RECEIVE TREATMENT . . . . . 4
W-66
504 • Appendix E
SECTION 13: FEMALE GENITAL CUTTING
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1301 Have you ever heard of female circumcision? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1303
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1302 In some countries, there is a practice in which a girl may have part YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
of her genitals cut. Have you ever heard about this practice? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1401
1303 Have you yourself ever been circumcised? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1309
1304 Now I would like to ask you what was done to you at that time. Was YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1306
any flesh removed from the genital area? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . 8
1305 Was the genital area just nicked without removing any flesh? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . 8
1306 Was your genital area sewn closed? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . 8
1307 How old were you when you were circumcised?
AGE IN COMPLETED YEARS
IF THE RESPONDENT DOES NOT KNOW THE EXACT AGE, AS A BABY/DURING INFANCY . . . . . 95
PROBE TO GET AN ESTIMATE. DON'T KNOW . . . . . . . . . . . . . . . . . . . 98
1308 Who performed the circumcision? TRADITIONAL
TRAD. CIRCUMCISER . . . . . . . . . . . 11
TRAD. BIRTH ATTENDANT . . . . . . . 12
OTHER TRAD. 16
(SPECIFY)
HEALTH PROFESSIONAL
DOCTOR . . . . . . . . . . . . . . . . . . . . . . 21
NURSE/MIDWIFE . . . . . . . . . . . . . . . 22
OTHER HEALTH
PROFESSIONAL 26
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . 98
1309 CHECK 213, 215 AND 216:
HAS ONE OR MORE HAS NO LIVING
LIVING DAUGHTERS DAUGHTERS 1315A
BORN IN 1999 BORN IN 1999
OR LATER OR LATER
W-67
Appendix E • 505
CHECK 213, 215 AND 216: ENTER IN THE TABLE THE BIRTH HISTORY NUMBER AND NAME OF EACH LIVING DAUGHTER
BORN IN 1999 OR LATER. ASK THE QUESTIONS ABOUT ALL OF THESE DAUGHTERS. BEGIN WITH
THE YOUNGEST DAUGHTER. (IF THERE ARE MORE THAN 3 DAUGHTERS, USE ADDITIONAL QUESTIONNAIRES).
READ TO RESPONDENT
Now I would like to ask you some questions about your (daughter/daughters).
1310 BIRTH HISTORY NUMBER YOUNGEST LIVING NEXT-TO-YOUNGEST SECOND-TO-YOUNGEST
AND NAME DAUGHTER LIVING DAUGHTER LIVING DAUGHTER
OF EACH LIVING DAUGHTER BIRTH BIRTH BIRTH
BORN IN 1999 OR LATER HISTORY HISTORY HISTORY
NUMBER NUMBER NUMBER
NAME NAME NAME
1311 Is (NAME OF DAUGHTER) YES ............ 1 YES ............ 1 YES ............ 1
circumcised? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(GO TO 1311 (GO TO 1311 (GO TO 1311
IN NEXT COLUMN; IN NEXT COLUMN; IN FIRST COLUMN
OR IF NO MORE OR IF NO MORE OF NEW
DAUGHTERS, DAUGHTERS, QUESTIONNAIRE; OR IF
GO TO 1315A) GO TO 1315A) NO MORE DAUGHTERS,
GO TO 1315A)
1312 How old was (NAME OF AGE IN AGE IN AGE IN
DAUGHTER) when she was COMPLETED COMPLETED COMPLETED
circumcised? YEARS . . . YEARS . . . YEARS . . .
IF THE RESPONDENT DOES NOT DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98
KNOW THE AGE, PROBE TO
GET AN ESTIMATE.
1313 Was her genital area sewn closed? YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
1314 Who performed the circumcision? TRADITIONAL TRADITIONAL TRADITIONAL
TRADITIONAL TRADITIONAL TRADITIONAL
CIRCUMCISER 11 CIRCUMCISER 11 CIRCUMCISER 11
TRAD. BIRTH TRAD. BIRTH TRAD. BIRTH
ATTENDANT 12 ATTENDANT 12 ATTENDANT 12
OTHER TRAD. OTHER TRAD. OTHER TRAD.
16 16 16
(SPECIFY) (SPECIFY) (SPECIFY)
HEALTH PROFESSIONAL HEALTH PROFESSIONAL HEALTH PROFESSIONAL
DOCTOR . . . . . . . 21 DOCTOR . . . . . . . 21 DOCTOR . . . . . 21
NURSE/MIDWIFE 22 NURSE/MIDWIFE 22 NURSE/MIDWIFE 22
OTHER HEALTH OTHER HEALTH OTHER HEALTH
PROFESSIONAL PROFESSIONAL PROFESSIONAL
26 26 26
(SPECIFY) (SPECIFY) (SPECIFY)
DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98
1315 GO BACK TO 1311 IN GO BACK TO 1311 IN GO TO 1311 IN
NEXT COLUMN; OR, IF NEXT COLUMN; OR, IF FIRST COLUMN OF NEW
NO MORE DAUGHTERS, NO MORE DAUGHTERS, QUESTIONNAIRE; OR IF
GO TO 1315A. GO TO 1315A. NO MORE DAUGHTERS,
GO TO 1315A.
1315A Do you believe that this practice is required by your community? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
1316 Do you believe that this practice is required by your religion? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
NO RELIGION .................. 3
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
1317 Do you think that female circumcision should be continued, or CONTINUED . . . . . . . . . . . . . . . . . . . . 1
should it be stopped? STOPPED . . . . . . . . . . . . . . . . . . . . . . 2
DEPENDS . . . . . . . . . . . . . . . . . . . . . . 3
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
W-68
506 • Appendix E
SECTION 14: DOMESTIC VIOLENCE
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1401 CHECK COVER PAGE: IS WOMAN SELECTED FOR SECTION 14?
WOMAN SELECTED WOMAN
FOR THIS SECTION NOT SELECTED 1433
1401A CHECK FOR PRESENCE OF OTHERS:
DO NOT CONTINUE UNTIL PRIVACY IS ENSURED.
PRIVACY PRIVACY
OBTAINED . . . . . . . 1 NOT POSSIBLE . . . . . . . 2 1432
READ TO THE RESPONDENT
Now I would like to ask you questions about some other important aspects of a woman's life. You may find some of
these questions very personal. However, your answers are crucial for helping to understand the condition of women in
Kenya. Let me assure you that your answers are completely confidential and will not be told to anyone and no one else
in your household will know that you were asked these questions.
1402 CHECK 601 AND 602:
FORMERLY
CURRENTLY MARRIED/ NEVER MARRIED/
MARRIED/ LIVED WITH A MAN NEVER LIVED WITH
LIVING (READ IN PAST TENSE A MAN
WITH A MAN AND USE 'LAST' WITH 1416
HUSBAND/PARTNER')
1403 First, I am going to ask you about some situations which happen to
some women. Please tell me if these apply to your relationship with
your (last) (husband/partner)?
YES NO DK
a) He (is/was) jealous or angry if you (talk/talked) to other men? a) JEALOUS ....... 1 2 8
b) He frequently (accuses/accused) you of being unfaithful? b) ACCUSES ....... 1 2 8
c) He (does/did) not permit you to meet your female friends? c) NOT MEET FRIENDS . 1 2 8
d) He (tries/tried) to limit your contact with your family? d) NO FAMILY . . . . . . . 1 2 8
e) He (insists/insisted) on knowing where you (are/were) at all e) WHERE YOU ARE . 1 2 8
times?
1404 Now I need to ask some more questions about your relationship
with your (last) (husband/partner).
A Did your (last) (husband/partner) ever: B How often did this happen during the last 12
months: often, only sometimes, or not at
all?
SOME- NOT IN LAST
EVER OFTEN TIMES 12 MONTHS
a) say or do something to humiliate you in front of a) YES 1 1 2 3
others? NO 2
b) threaten to hurt or harm you or someone you b) YES 1 1 2 3
care about? NO 2
c) insult you or make you feel bad about yourself? c) YES 1 1 2 3
NO 2
W-69
Appendix E • 507
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1405 A Did your (last) (husband/partner) ever do any of B How often did this happen during the last 12
the following things to you: months: often, only sometimes, or not at
all?
SOME- NOT IN LAST
EVER OFTEN TIMES 12 MONTHS
a) push you, shake you, or throw something at you? a) YES 1 1 2 3
NO 2
b) slap you? b) YES 1 1 2 3
NO 2
c) twist your arm or pull your hair? c) YES 1 1 2 3
NO 2
d) punch you with his fist or with something that d) YES 1 1 2 3
could hurt you? NO 2
e) kick you, drag you, or beat you up? e) YES 1 1 2 3
NO 2
f) try to choke you or burn you on purpose? f) YES 1 1 2 3
NO 2
g) threaten or attack you with a knife, gun, or other g) YES 1 1 2 3
weapon? NO 2
h) physically force you to have sexual intercourse h) YES 1 1 2 3
with him when you did not want to? NO 2
i) physically force you to perform any other sexual i) YES 1 1 2 3
acts you did not want to? NO 2
j) force you with threats or in any other way to j) YES 1 1 2 3
perform sexual acts you did not want to? NO 2
1406 CHECK 1405A (a-j):
AT LEAST ONE NOT A SINGLE
'YES' 'YES' 1409
1407 How long after you first (got married/started living together) with
your (last) (husband/partner) did (this/any of these things) first NUMBER OF YEARS . . . . . . .
happen?
BEFORE MARRIAGE/BEFORE
IF LESS THAN ONE YEAR, RECORD '00'. LIVING TOGETHER . . . . . . . . . . . . . . 95
1408 Did the following ever happen as a result of what your (last)
(husband/partner) did to you:
a) You had cuts, bruises, or aches? a) YES . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . 2
b) You had eye injuries, sprains, dislocations, or burns? b) YES . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . 2
c) You had deep wounds, broken bones, broken teeth, or any other c) YES . . . . . . . . . . . . . . . . . . . . . . . . . . 1
serious injury? NO . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1409 Have you ever hit, slapped, kicked, or done anything else to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
physically hurt your (last) (husband/partner) at times when he was NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1411
not already beating or physically hurting you?
W-70
508 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1410 In the last 12 months, how often have you done this to your (last) OFTEN . . . . . . . . . . . . . . . . . . . . . . . . 1
(husband/partner): often, only sometimes, or not at all? SOMETIMES . . . . . . . . . . . . . . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
1411 Does (did) your (last) (husband/partner) drink alcohol? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1413
1412 How often does (did) he get drunk: often, only sometimes, or never? OFTEN . . . . . . . . . . . . . . . . . . . . . . . . 1
SOMETIMES . . . . . . . . . . . . . . . . . . . . 2
NEVER . . . . . . . . . . . . . . . . . . . . . . . . 3
1413 Are (Were) you afraid of your (last) (husband/partner): most of the MOST OF THE TIME AFRAID . . . . . . . 1
time, sometimes, or never? SOMETIMES AFRAID . . . . . . . . . . . . . . 2
NEVER AFRAID . . . . . . . . . . . . . . . . . . 3
1414 CHECK 609:
MARRIED MORE MARRIED ONLY
THAN ONCE ONCE 1416
1415 A. So far we have been talking about the behavior of your B. How long ago did this last happen?
(current/last) (husband/partner). Now I want to ask you about the
behavior of any previous (husband/partner).
0 - 11 12+ DON'T
EVER MONTHS MONTHS REMEMBER
AGO AGO
a) Did any previous (husband/partner) ever hit, slap, a) YES 1 1 2 3
kick, or do anything else to hurt you physically? NO 2
b) Did any previous (husband/partner) physically b) YES 1 1 2 3
force you to have intercourse or perform any NO 2
other sexual acts against your will?
1416 CHECK 601 AND 602:
EVER MARRIED/EVER NEVER MARRIED/NEVER
LIVED WITH A MAN LIVED WITH A MAN
a) From the time you were 15 b) From the time you were 15 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
years old has anyone other years old has anyone hit you, NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
than (your/any) slapped you, kicked you, or REFUSED TO ANSWER/
(husband/partner) hit you, done anything else to hurt you NO ANSWER . . . . . . . . . . . . . . . . . . 3 1419
slapped you, kicked you, or physically?
done anything else to hurt you
physically?
1417 Who has hurt you in this way? MOTHER/STEP-MOTHER . . . . . . . . . A
FATHER/STEP-FATHER . . . . . . . . . . . . B
Anyone else? SISTER/BROTHER . . . . . . . . . . . . . . . . C
DAUGHTER/SON . . . . . . . . . . . . . . . . D
OTHER RELATIVE . . . . . . . . . . . . . . . . E
CURRENT BOYFRIEND . . . . . . . . . . . . F
RECORD ALL MENTIONED. FORMER BOYFRIEND . . . . . . . . . . . . . . G
MOTHER-IN-LAW . . . . . . . . . . . . . . . . H
FATHER-IN-LAW ................ I
OTHER IN-LAW . . . . . . . . . . . . . . . . . . J
TEACHER . . . . . . . . . . . . . . . . . . . . . . K
EMPLOYER/SOMEONE AT WORK . . . L
POLICE/SOLDIER . . . . . . . . . . . . . . . . M
OTHER X
(SPECIFY)
W-71
Appendix E • 509
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1418 In the last 12 months, how often has (this person/have these OFTEN . . . . . . . . . . . . . . . . . . . . . . . . 1
persons) physically hurt you: often, only sometimes, or not at all? SOMETIMES . . . . . . . . . . . . . . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
1419 CHECK 201, 226, AND 230:
EVER BEEN NEVER BEEN
PREGNANT PREGNANT 1422
(YES ON 201
OR 226 OR 230)
1420 Has any one ever hit, slapped, kicked, or done anything else to hurt YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
you physically while you were pregnant? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1422
1421 Who has done any of these things to physically hurt you while you CURRENT HUSBAND/PARTNER . . . . . A
were pregnant? MOTHER/STEP-MOTHER . . . . . . . . . B
FATHER/STEP-FATHER . . . . . . . . . . . . C
Anyone else? SISTER/BROTHER . . . . . . . . . . . . . . . . D
DAUGHTER/SON . . . . . . . . . . . . . . . . E
OTHER RELATIVE . . . . . . . . . . . . . . . . F
FORMER HUSBAND/PARTNER . . . . . G
RECORD ALL MENTIONED. CURRENT BOYFRIEND . . . . . . . . . . . . H
FORMER BOYFRIEND . . . . . . . . . . . . . . I
MOTHER-IN-LAW . . . . . . . . . . . . . . . . J
FATHER-IN-LAW ................ K
OTHER IN-LAW . . . . . . . . . . . . . . . . . . L
TEACHER . . . . . . . . . . . . . . . . . . . . . . M
EMPLOYER/SOMEONE AT WORK . . . N
POLICE/SOLDIER . . . . . . . . . . . . . . . . O
OTHER X
(SPECIFY)
1422 CHECK 601 AND 602:
EVER MARRIED/EVER NEVER MARRIED/NEVER
LIVED WITH A MAN LIVED WITH A MAN 1422B
1422A Now I want to ask you about things that may have been done to you
by someone other than (your/any) (husband/partner).
YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1423
At any time in your life, as a child or as an adult, has anyone ever NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
forced you in any way to have sexual intercourse or perform any REFUSED TO ANSWER/
other sexual acts when you did not want to? NO ANSWER . . . . . . . . . . . . . . . . . . 3 1424A
1422B At any time in your life, as a child or as an adult, has anyone ever YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
forced you in any way to have sexual intercourse or perform any NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
other sexual acts when you did not want to? REFUSED TO ANSWER/
NO ANSWER . . . . . . . . . . . . . . . . . . 3 1426
1423 Who was the person who was forcing you the very first time this CURRENT HUSBAND/PARTNER . . . . . 01
happened? FORMER HUSBAND/PARTNER . . . . . 02
CURRENT/FORMER BOYFRIEND . . . 03
FATHER/STEP-FATHER . . . . . . . . . . . . 04
BROTHER/STEP-BROTHER . . . . . . . 05
OTHER RELATIVE . . . . . . . . . . . . . . . . 06
IN-LAW . . . . . . . . . . . . . . . . . . . . . . . . 07
OWN FRIEND/ACQUAINTANCE . . . . . 08
FAMILY FRIEND . . . . . . . . . . . . . . . . . . 09
TEACHER . . . . . . . . . . . . . . . . . . . . . . 10
EMPLOYER/SOMEONE AT WORK . . . 11
POLICE/SOLDIER . . . . . . . . . . . . . . . . 12
PRIEST/RELIGIOUS LEADER . . . . . . . 13
STRANGER . . . . . . . . . . . . . . . . . . . . 14
OTHER 96
(SPECIFY)
W-72
510 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1424 CHECK 601 AND 602:
EVER MARRIED/EVER NEVER MARRIED/NEVER
LIVED WITH A MAN LIVED WITH A MAN
a) In the last 12 months, has b) In the last 12 months has
anyone other than (your/any) anyone physically forced you
(husband/partner) physically to have sexual intercourse YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
forced you to have sexual when you did not want to? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1425
intercourse when you did not
want to?
1424A CHECK 1405A(h-j) and 1415A(b)
AT LEAST ONE NOT A
'YES' SINGLE 'YES' 1426
1425 CHECK 601 AND 602:
EVER MARRIED/EVER NEVER MARRIED/NEVER
LIVED WITH A MAN LIVED WITH A MAN
a) How old were you the first time b) How old were you the first time
you were forced to have you were forced to have
sexual intercourse or perform sexual intercourse or perform AGE IN COMPLETED YEARS .
any other sexual acts by any other sexual acts?
anyone, including (your/any) DON'T KNOW . . . . . . . . . . . . . . . . . . . . 98
husband/partner?
1426 CHECK 1405A (a-j), 1415A (a,b), 1416, 1420, 1422A, AND 1422B:
AT LEAST ONE NOT A SINGLE
'YES' 'YES' 1430
1427 Thinking about what you yourself have experienced among the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
different things we have been talking about, have you ever tried to NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1429
seek help?
1428 From whom have you sought help? OWN FAMILY . . . . . . . . . . . . . . . . . . . . A
HUSBAND'S/PARTNER'S FAMILY . . . B
Anyone else? CURRENT/FORMER
HUSBAND/PARTNER . . . . . . . . . . . . C
RECORD ALL MENTIONED. CURRENT/FORMER BOYFRIEND . . . D
FRIEND . . . . . . . . . . . . . . . . . . . . . . . . E
NEIGHBOR . . . . . . . . . . . . . . . . . . . . . . F
RELIGIOUS LEADER . . . . . . . . . . . . . . G 1430
DOCTOR/MEDICAL PERSONNEL . . . H
POLICE . . . . . . . . . . . . . . . . . . . . . . . . I
LAWYER ...................... J
SOCIAL SERVICE ORGANIZATION . K
OTHER X
(SPECIFY)
1429 Have you ever told any one about this? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1430 As far as you know, did your father ever beat your mother? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
W-73
Appendix E • 511
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
THANK THE RESPONDENT FOR HER COOPERATION AND REASSURE HER ABOUT THE CONFIDENTIALITY OF HER
ANSWERS. FILL OUT THE QUESTIONS BELOW WITH REFERENCE TO THE DOMESTIC VIOLENCE MODULE ONLY.
1431 DID YOU HAVE TO INTERRUPT THE YES YES, MORE
INTERVIEW BECAUSE SOME ADULT WAS ONCE THAN ONCE NO
TRYING TO LISTEN, OR CAME INTO THE HUSBAND . . . . . . . . . . . . 1 2 3
ROOM, OR INTERFERED IN ANY OTHER OTHER MALE ADULT . . . 1 2 3
WAY? FEMALE ADULT . . . . . . . 1 2 3
1432 INTERVIEWER'S COMMENTS / EXPLANATION FOR NOT COMPLETING THE DOMESTIC VIOLENCE MODULE
1433 RECORD THE TIME.
HOUR . . . . . . . . . . . . . . . . . . . .
MINUTES . . . . . . . . . . . . . . . . . .
W-74
512 • Appendix E
INTERVIEWER'S OBSERVATIONS
TO BE FILLED IN AFTER COMPLETING INTERVIEW
COMMENTS ABOUT RESPONDENT:
COMMENTS ON SPECIFIC QUESTIONS:
ANY OTHER COMMENTS:
SUPERVISOR'S OBSERVATIONS
NAME OF SUPERVISOR: DATE:
EDITOR'S OBSERVATIONS
NAME OF EDITOR: DATE:
W-75
Appendix E • 513
INSTRUCTIONS: 1 2
ONLY ONE CODE SHOULD APPEAR IN ANY BOX. 12 DEC 01
COLUMN 1 REQUIRES A CODE IN EVERY MONTH. 11 NOV 02
10 OCT 03
INFORMATION TO BE CODED FOR EACH COLUMN 2 09 SEP 04 2
0 08 AUG 05 0
COLUMN 1: BIRTHS, PREGNANCIES, CONTRACEPTIVE USE 07 JUL 06
1 1
B BIRTHS 06 JUN 07
4 4
P PREGNANCIES 05 MAY 08
T TERMINATIONS 04 APR 09
03 MAR 10
0 NO METHOD 02 FEB 11
1 FEMALE STERILIZATION 01 JAN 12
2 MALE STERILIZATION
3 IUD 12 DEC 13
4 INJECTABLES 11 NOV 14
5 IMPLANTS 10 OCT 15
6 PILL 2 09 SEP 16 2
7 CONDOM 08 AUG 17
0 0
8 FEMALE CONDOM 07 JUL 18
1 1
K LACTATIONAL AMENORRHEA METHOD 06 JUN 19
3 3
L RHYTHM METHOD 05 MAY 20
M WITHDRAWAL 04 APR 21
X OTHER MODERN METHOD 03 MAR 22
Y OTHER TRADITIONAL METHOD 02 FEB 23
01 JAN 24
COLUMN 2: DISCONTINUATION OF CONTRACEPTIVE USE
0 INFREQUENT SEX/HUSBAND AWAY 12 DEC 25
1 BECAME PREGNANT WHILE USING 11 NOV 26
2 WANTED TO BECOME PREGNANT 10 OCT 27
3 HUSBAND/PARTNER DISAPPROVED 2 09 SEP 28 2
4 WANTED MORE EFFECTIVE METHOD 08 AUG 29
0 0
5 SIDE EFFECTS/HEALTH CONCERNS 07 JUL 30
1 1
6 LACK OF ACCESS/TOO FAR 06 JUN 31
2 2
7 COSTS TOO MUCH 05 MAY 32
8 INCONVENIENT TO USE 04 APR 33
F UP TO GOD/FATALISTIC 03 MAR 34
A DIFFICULT TO GET PREGNANT/MENOPAUSAL 02 FEB 35
D MARITAL DISSOLUTION/SEPARATION 01 JAN 36
X OTHER
(SPECIFY) 12 DEC 37
Z DON'T KNOW 11 NOV 38
10 OCT 39
2 09 SEP 40 2
0 08 AUG 41 0
07 JUL 42
1 1
06 JUN 43
1 1
05 MAY 44
04 APR 45
03 MAR 46
02 FEB 47
01 JAN 48
12 DEC 49
11 NOV 50
10 OCT 51
2 09 SEP 52 2
0 08 AUG 53 0
07 JUL 54
1 1
06 JUN 55
0 0
05 MAY 56
04 APR 57
03 MAR 58
02 FEB 59
01 JAN 60
12 DEC 61
11 NOV 62
10 OCT 63
2 09 SEP 64 2
0 08 AUG 65 0
07 JUL 66
0 0
06 JUN 67
9 9
05 MAY 68
04 APR 69
03 MAR 70
02 FEB 71
01 JAN 72
W-76
514 • Appendix E
2 SHORT
2014 KENYA DEMOGRAPHIC AND HEALTH SURVEY
WOMAN'S QUESTIONNAIRE - SHORT VERSION
CONFIDENTIAL
REPUBLIC OF KENYA
IDENTIFICATION
COUNTY
DISTRICT
LOCATION/TOWN
SUBLOCATION
NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NAME OF HOUSEHOLD HEAD
NAME AND LINE NUMBER OF WOMAN
INTERVIEWER VISITS
1 2 3 FINAL VISIT
DATE DAY
MONTH
YEAR
INTERVIEWER'S
NAME INT. NUMBER
RESULT* RESULT
NEXT VISIT: DATE
TOTAL NUMBER
TIME OF VISITS
*RESULT CODES:
1 COMPLETED 4 REFUSED
2 NOT AT HOME 5 PARTLY COMPLETED 7 OTHER
3 POSTPONED 6 INCAPACITATED (SPECIFY)
**LANGUAGE OF LANGUAGE OF NATIVE LANGUAGE TRANSLATOR USED
QUESTIONNAIRE: 17 INTERVIEW: OF RESPONDENT: (YES = 1, NO = 2)
LANGUAGE OF
QUESTIONNAIRE: English
**LANGUAGE 01 BORANA 05 KIKUYU 09 LUO 13 POKOT 17 ENGLISH
CODES: 02 EMBU 06 KISII 10 MAASAI 14 SOMALI 18 OTHER
03 KALENJIN 07 LUHYA 11 MERU 15 SWAHILI
04 KAMBA 08 MARAGOLI 12 MIJIKENDA 16 TURKANA
SUPERVISOR FIELD EDITOR OFFICE KEYED BY
EDITOR
NAME NAME
W-1
Appendix E • 515
SECTION 1. RESPONDENT'S BACKGROUND
INTRODUCTION AND CONSENT
INFORMED CONSENT
Hello. My name is _______________________________________. I am working with the Kenya National Bureau of Statistics. We are
conducting a survey about health all over Kenya. The information we collect will help the government to plan health services. Your
household was selected for the survey. The questions usually take about 30 to 60 minutes. All of the answers you give will be confidential
and will not be shared with anyone other than members of our survey team. You don't have to be in the survey, but we hope you will agree
to answer the questions since your views are important. If I ask you any question you don't want to answer, just let me know and I will go on
to the next question or you can stop the interview at any time.
In case you need more information about the survey, you may contact the person listed on the card that has already been given to your
household.
Do you have any questions? May I begin the interview now?
SIGNATURE OF INTERVIEWER: DATE:
RESPONDENT AGREES TO BE INTERVIEWED . . . . . 1 RESPONDENT DOES NOT AGREE TO BE INTERVIEWED . . . 2 END
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
101 RECORD THE TIME.
HOUR . . . . . . . . . . . . . . . . . . . .
MINUTES . . . . . . . . . . . . . . . . . .
102 In what month and year were you born?
MONTH ..................
DON'T KNOW MONTH . . . . . . . . . . . . 98
YEAR . . . . . . . . . . . .
DON'T KNOW YEAR . . . . . . . . . . . . 9998
103 How old were you at your last birthday?
AGE IN COMPLETED YEARS
COMPARE AND CORRECT 102 AND/OR 103 IF INCONSISTENT.
104 Have you ever attended school? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 108
105 What is the highest level of school you attended: primary, PRIMARY . . . . . . . . . . . . . . . . . . . . . . 1
vocational, secondary, or higher? POST-PRIMARY/VOCATIONAL . . . . . 2
SECONDARY/ 'A' LEVEL ......... 3
COLLEGE (MIDDLE LEVEL) . . . . . . . 4
UNIVERSITY . . . . . . . . . . . . . . . . . . . . 5
106 What is the highest (standard/form/year) you completed at that
level? STANDARD/FORM/YEAR ...
IF COMPLETED LESS THAN ONE YEAR AT THAT LEVEL,
RECORD '00'.
107 CHECK 105:
PRIMARY, SECONDARY
POST-PRIMARY/ OR HIGHER 110
VOCATIONAL
W-2
516 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
108 Now I would like you to read this sentence to me. CANNOT READ AT ALL . . . . . . . . . . . . 1
ABLE TO READ ONLY PARTS OF
SHOW CARD TO RESPONDENT. SENTENCE . . . . . . . . . . . . . . . . . . . . 2
ABLE TO READ WHOLE SENTENCE 3
IF RESPONDENT CANNOT READ WHOLE SENTENCE, PROBE: NO CARD WITH REQUIRED
Can you read any part of the sentence to me? LANGUAGE 4
(SPECIFY LANGUAGE)
BLIND/VISUALLY IMPAIRED. . . . . . . . . 5
109 CHECK 108:
CODE '2', '3' CODE '1' OR '5'
OR '4' CIRCLED 111
CIRCLED
110 Do you read a newspaper or magazine at least once a week, less AT LEAST ONCE A WEEK . . . . . . . . . 1
than once a week or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
111 Do you listen to the radio at least once a week, less than once a AT LEAST ONCE A WEEK . . . . . . . . . 1
week or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
112 Do you watch television at least once a week, less than once a AT LEAST ONCE A WEEK . . . . . . . . . 1
week or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
113 What is your religion? ROMAN CATHOLIC .............. 1
PROTESTANT/ OTHER CHRISTIAN . 2
MUSLIM . . . . . . . . . . . . . . . . . . . . . . . . 3
NO RELIGION . . . . . . . . . . . . . . . . . . . . 4
OTHER 6
(SPECIFY)
114 What is your ethnic group / tribe? EMBU . . . . . . . . . . . . . . . . . . . . . . . . . . 01
KALENJIN . . . . . . . . . . . . . . . . . . . . . . 02
KAMBA . . . . . . . . . . . . . . . . . . . . . . . . 03
KIKUYU . . . . . . . . . . . . . . . . . . . . . . . . 04
KISII . . . . . . . . . . . . . . . . . . . . . . . . . . . . 05
LUHYA . . . . . . . . . . . . . . . . . . . . . . . . 06
LUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 07
MAASAI . . . . . . . . . . . . . . . . . . . . . . . . 08
MERU . . . . . . . . . . . . . . . . . . . . . . . . . . 09
MIJIKENDA/ SWAHILI . . . . . . . . . . . . . . 10
SOMALI . . . . . . . . . . . . . . . . . . . . . . . . 11
TAITA/ TAVETA . . . . . . . . . . . . . . . . . . 12
OTHER 96
(SPECIFY)
W-3
Appendix E • 517
SECTION 2. REPRODUCTION
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
201 Now I would like to ask about all the births you have had during your YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
life. Have you ever given birth? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 206
202 Do you have any sons or daughters to whom you have given birth YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
who are now living with you? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 204
203 How many sons live with you?
SONS AT HOME . . . . . . . . . . . .
And how many daughters live with you?
DAUGHTERS AT HOME . . . . .
IF NONE, RECORD '00'.
204 Do you have any sons or daughters to whom you have given birth YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
who are alive but do not live with you? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 206
205 How many sons are alive but do not live with you?
SONS ELSEWHERE .......
And how many daughters are alive but do not live with you?
DAUGHTERS ELSEWHERE
IF NONE, RECORD '00'.
206 Have you ever given birth to a boy or girl who was born alive but
later died?
YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
IF NO, PROBE: Any baby who cried or showed signs of life but did NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 208
not survive?
207 How many boys have died?
BOYS DEAD ..............
And how many girls have died?
GIRLS DEAD ..............
IF NONE, RECORD '00'.
208 SUM ANSWERS TO 203, 205, AND 207, AND ENTER TOTAL.
IF NONE, RECORD '00'. TOTAL BIRTHS ............
CHECK 208:
209 Just to make sure that I have this right: you have had in TOTAL
_____ births during your life. Is that correct?
PROBE AND
YES NO CORRECT
201-208 AS
NECESSARY.
210 CHECK 208:
ONE OR MORE NO BIRTHS
BIRTHS 226
W-4
518 • Appendix E
211 Now I would like to record the names of all your births, whether still alive or not, starting with the first one you had.
RECORD NAMES OF ALL THE BIRTHS IN 212. RECORD TWINS AND TRIPLETS ON SEPARATE ROWS.
(IF THERE ARE MORE THAN 12 BIRTHS, USE AN ADDITIONAL QUESTIONNAIRE, STARTING WITH THE SECOND ROW).
212 213 214 215 216 217 218 219 220 221
IF ALIVE: IF ALIVE: IF ALIVE: IF DEAD:
What name Is Were any In what month and Is How old was Is (NAME) RECORD How old was (NAME) Were there
was given to (NAME) of these year was (NAME) (NAME) (NAME) at living with HOUSE- when he/she died? any other live
your a boy or births born? still his/her last you? births between
HOLD LINE
(first/next) a girl? twins? alive? birthday? IF '1 YR', PROBE: How (NAME OF
NUMBER OF
baby? PROBE: What is many months old was PREVIOUS
his/her birthday? CHILD (NAME)? BIRTH) and
(RECORD '00' (NAME),
IF CHILD NOT including any
children who
RECORD RECORD LISTED IN
died after
NAME. AGE IN HOUSE- RECORD DAYS IF
birth?
COM- HOLD). LESS THAN 1
BIRTH PLETED MONTH; MONTHS IF
HISTORY YEARS. LESS THAN TWO
NUMBER YEARS; OR YEARS.
01 MONTH AGE IN HOUSEHOLD DAYS . . . 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER
YEAR MONTHS 2
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2
YEARS . . 3
220 (NEXT BIRTH)
02 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
03 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
04 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
05 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
06 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
07 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
W-5
Appendix E • 519
212 213 214 215 216 217 218 219 220 221
IF ALIVE: IF ALIVE: IF ALIVE: IF DEAD:
What name Is Were any In what month and Is How old was Is (NAME) RECORD How old was (NAME) Were there
was given to (NAME) of these year was (NAME) (NAME) (NAME) at living with HOUSE- when he/she died? any other live
your a boy or births born? still his/her last you? births between
HOLD LINE
(first/next) a girl? twins? alive? birthday? IF '1 YR', PROBE: How (NAME OF
NUMBER OF
baby? PROBE: What is many months old was PREVIOUS
his/her birthday? CHILD (NAME)? BIRTH) and
(RECORD '00' (NAME),
IF CHILD NOT including any
children who
RECORD LISTED IN
died after
NAME. RECORD HOUSE- RECORD DAYS IF
birth?
AGE IN HOLD). LESS THAN 1
BIRTH COM- MONTH; MONTHS IF
HISTORY PLETED LESS THAN TWO
NUMBER YEARS. YEARS; OR YEARS.
08 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
09 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
10 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
11 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
12 MONTH AGE IN HOUSEHOLD DAYS . . . 1 YES ... 1
BOY 1 SING 1 YES . . 1 YEARS YES . . . 1 LINE NUMBER ADD
YEAR MONTHS 2 BIRTH
GIRL 2 MULT 2 NO . . . 2 NO . . . . 2 NO . . . . . 2
YEARS . . 3 NEXT
220 (GO TO 221) BIRTH
222 Have you had any live births since the birth of (NAME OF LAST YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 ADD
BIRTH)? BIRTH
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
223 COMPARE 208 WITH NUMBER OF BIRTHS IN HISTORY ABOVE AND MARK:
NUMBERS NUMBERS ARE
ARE SAME DIFFERENT (PROBE AND RECONCILE)
224 CHECK 215:
NUMBER OF BIRTHS IN 2009 OR LATER
ENTER THE NUMBER OF BIRTHS IN 2009 OR LATER.
NONE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0
226 Are you pregnant now? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
UNSURE . . . . . . . . . . . . . . . . . . . . . . . . . 8
W-6
520 • Appendix E
SECTION 3. CONTRACEPTION
301 Now I would like to talk about family planning - the various ways or methods that a couple can use to delay or avoid a pregnancy.
Have you ever heard of (METHOD)?
01 Female Sterilization. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have an operation to avoid having any more NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
children.
02 Male Sterilization. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can have an operation to avoid having any more NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
children.
03 IUD. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have a loop or coil placed inside them by a NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
doctor or a nurse.
04 Injectables. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have an injection by a health provider that NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
stops them from becoming pregnant for one or more months.
05 Implants. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have one or more small rods placed in their NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
upper arm by a doctor or nurse which can prevent pregnancy for
one or more years.
06 Pill. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can take a pill every day to avoid becoming NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
pregnant.
07 Male Condom. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can put a rubber sheath on their penis before sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse.
08 Female Condom. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can place a sheath in their vagina before sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse.
09 Lactational Amenorrhea Method (LAM). YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
10 Rhythm Method. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: To avoid pregnancy, women do not have sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse on the days of the month they think they can get
pregnant.
11 Withdrawal. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can be careful and pull out before climax. NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
12 Emergency Contraception. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: As an emergency measure, within three days after they NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
have unprotected sexual intercourse, women can take special pills
to prevent pregnancy.
13 Have you heard of any other ways or methods that women or men YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
can use to avoid pregnancy?
(SPECIFY)
(SPECIFY)
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
W-7
Appendix E • 521
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
302 CHECK 226:
NOT PREGNANT PREGNANT
OR UNSURE 401
303 Are you currently doing something or using any method to delay or YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
avoid getting pregnant? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 313
304 Which method are you using? FEMALE STERILIZATION . . . . . . . . . A
MALE STERILIZATION . . . . . . . . . . . . B 307
CIRCLE ALL MENTIONED. IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . C
INJECTABLES .................. D
IF MORE THAN ONE METHOD MENTIONED, FOLLOW SKIP IMPLANTS . . . . . . . . . . . . . . . . . . . . . . E
323
INSTRUCTION FOR HIGHEST METHOD IN LIST. PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . . F
MALE CONDOM . . . . . . . . . . . . . . . . . . G
FEMALE CONDOM .............. H
LACTATIONAL AMEN. METHOD . . . . . K
315A
RHYTHM METHOD . . . . . . . . . . . . . . . . L
WITHDRAWAL . . . . . . . . . . . . . . . . . . M
OTHER MODERN METHOD . . . . . . . X 401
OTHER TRADITIONAL METHOD ... Y
307 In what facility did the sterilization take place? PUBLIC SECTOR
GOVT. HOSPITAL . . . . . . . . . . . . . . 11
PROBE TO IDENTIFY THE TYPE OF SOURCE. GOVT. HEALTH CENTER . . . . . . . 12
GOVT. DISPENSARY . . . . . . . . . . . . 13
OTHER PUBLIC
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR 16
SECTOR, WRITE THE NAME OF THE PLACE. (SPECIFY)
PRIVATE MEDICAL SECTOR
(NAME OF PLACE) FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . 21
401
FAMILY OPTIONS/FHOK CLINIC . . . 22
PRIVATE HOSPITAL/CLINIC . . . . . 23
NURSING/ MATERNITY HOME. . . . . 24
MOBILE CLINIC . . . . . . . . . . . . . . . . 25
OTHER PRIVATE MEDICAL
SECTOR 26
(SPECIFY)
OTHER 96
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 98
313 Have you ever used anything or tried in any way to delay or avoid YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
getting pregnant? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 401
W-8
522 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
PUBLIC SECTOR
315A Where did you learn how to use the rhythm/lactational amenorrhea GOVT. HOSPITAL . . . . . . . . . . . . . . 11
method? GOVT. HEALTH CENTER . . . . . . . 12
GOVT. DISPENSARY . . . . . . . . . . . . 13
OTHER PUBLIC
SECTOR 16
(SPECIFY)
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . 21
PHARMACY/CHEMIST . . . . . . . . . . . . 22
NURSING/MATERNITY HOME . . . . . 23
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . 24
FAMILY OPTIONS/FHOK CLINIC . . . 25
401
OTHER PRIVATE MEDICAL
SECTOR 26
PROBE TO IDENTIFY THE TYPE OF SOURCE. (SPECIFY)
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR, OTHER SOURCE
WRITE THE NAME OF THE PLACE. SHOP . . . . . . . . . . . . . . . . . . . . . . . . 31
MOBILE CLINIC . . . . . . . . . . . . . . . . 32
COMMUNITY-BASED DISTRIBUTOR 33
COMMUNITY HEALTH WORKER/
(NAME OF PLACE) CHW . . . . . . . . . . . . . . . . . . . . . . 34
FRIEND/RELATIVE . . . . . . . . . . . . . . 35
OTHER 96
(SPECIFY)
323 Where did you obtain (CURRENT METHOD) the last time? PUBLIC SECTOR
GOVT. HOSPITAL . . . . . . . . . . . . . . 11
PROBE TO IDENTIFY THE TYPE OF SOURCE. GOVT. HEALTH CENTER . . . . . . . 12
GOVT. DISPENSARY . . . . . . . . . . . . 13
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE OTHER PUBLIC
SECTOR, WRITE THE NAME OF THE PLACE. SECTOR 16
(SPECIFY)
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . 21
PHARMACY/CHEMIST . . . . . . . . . . . . 22
NURSING/MATERNITY HOME . . . . . 23
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . 24
(NAME OF PLACE) FAMILY OPTIONS/FHOK CLINIC . . . 25
401
OTHER PRIVATE MEDICAL
SECTOR 26
(SPECIFY)
OTHER SOURCE
SHOP . . . . . . . . . . . . . . . . . . . . . . . . 31
MOBILE CLINIC . . . . . . . . . . . . . . . . 32
COMMUNITY-BASED DISTRIBUTOR 33
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . . . 34
FRIEND/RELATIVE . . . . . . . . . . . . . . 35
OTHER 96
(SPECIFY)
W-9
Appendix E • 523
SECTION 4. PREGNANCY AND POSTNATAL CARE
401 CHECK 224:
ONE OR MORE NO 601
BIRTHS BIRTHS
IN 2009 IN 2009
OR LATER OR LATER
CHECK 215: ENTER IN THE TABLE THE BIRTH HISTORY NUMBER, NAME, AND SURVIVAL STATUS OF EACH BIRTH
IN 2009 OR LATER. ASK THE QUESTIONS ABOUT ALL OF THESE BIRTHS. BEGIN WITH THE LAST BIRTH.
(IF THERE ARE MORE THAN 3 BIRTHS, USE LAST 2 COLUMNS OF ADDITIONAL QUESTIONNAIRES).
402 Now I would like to ask some questions about your children born in the last five years. (We will talk about each separately.)
403 BIRTH HISTORY NUMBER LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
FROM 212 IN BIRTH HISTORY BIRTH BIRTH BIRTH
HISTORY HISTORY HISTORY
NUMBER NUMBER NUMBER
404 FROM 212 AND 216 NAME NAME NAME
LIVING DEAD LIVING DEAD LIVING DEAD
408 Did you see anyone for antenatal YES . . . . . . . . . . . . . . 1
care for this pregnancy? NO . . . . . . . . . . . . . . 2
(SKIP TO 424)
409 Whom did you see? HEALTH PERSONNEL
DOCTOR . . . . . . . A
Anyone else? NURSE/MIDWIFE . B
OTHER PERSON
PROBE TO IDENTIFY EACH TYPE COMMUNITY HEALTH
OF PERSON AND RECORD ALL WORKER . . . C
MENTIONED. TRADITIONAL BIRTH
ATTENDANT . D
OTHER X
(SPECIFY)
411 How many months pregnant were
you when you first received MONTHS . . .
antenatal care for this pregnancy?
DON'T KNOW . . . . . 98
412 How many times did you receive NUMBER
antenatal care during this OF TIMES .
pregnancy?
DON'T KNOW . . . . . 98
424 During this pregnancy, did you take YES . . . . . . . . . . . . . . 1
any drugs to keep you from getting
malaria? NO . . . . . . . . . . . . . . 2
(SKIP TO 433)
DON'T KNOW . . . . . 8
425 What drugs did you take? SP/FANSIDAR . . . . . A
CHLOROQUINE . . . B
RECORD ALL MENTIONED.
IF TYPE OF DRUG IS NOT OTHER X
DETERMINED, SHOW TYPICAL (SPECIFY)
ANTIMALARIAL DRUGS TO DON'T KNOW . . . . . Z
RESPONDENT.
426 CHECK 425: CODE 'A' CODE
CIRCLED A' NOT
SP/FANSIDAR TAKEN FOR CIRCLED
MALARIA PREVENTION.
(SKIP TO 433)
427 How many times did you take
(SP/Fansidar) during this TIMES . . . . .
pregnancy?
W-10
524 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME ________________ NAME ________________ NAME ________________
428 CHECK 409: CODE 'A', OTHER
OR 'B'
ANTENATAL CARE FROM CIRCLED
HEALTH PERSONNEL
DURING THIS PREGNANCY
(SKIP TO 433)
429 Did you get the (SP/Fansidar) ANTENATAL VISIT . 1
during any antenatal care visit, ANOTHER FACILITY
during another visit to a health VISIT . . . . . . . . . . 2
facility or from another source? OTHER SOURCE . . . 6
433 Who assisted with the delivery of HEALTH PERSONNEL HEALTH PERSONNEL HEALTH PERSONNEL
(NAME)? DOCTOR ..... A DOCTOR ..... A DOCTOR ..... A
NURSE/MIDWIFE . B NURSE/MIDWIFE . B NURSE/MIDWIFE B
Anyone else? OTHER PERSON OTHER PERSON OTHER PERSON
COMMUNITY HLTH COMMUNITY HLTH COMMUNITY HLTH
PROBE FOR THE TYPE(S) OF WORKER . . . C WORKER . . . C WORKER . . . C
PERSON(S) AND RECORD ALL TRADITIONAL BIRTH TRADITIONAL BIRTH TRADITIONAL BIRTH
MENTIONED. ATTENDANT . . D ATTENDANT . . D ATTENDANT . . D
RELATIVE/FRIEND E RELATIVE/FRIEND E RELATIVE/FRIEND E
IF RESPONDENT SAYS NO ONE OTHER OTHER OTHER
ASSISTED, PROBE TO X X X
DETERMINE WHETHER ANY (SPECIFY) (SPECIFY) (SPECIFY)
ADULTS WERE PRESENT AT NO ONE ASSISTED Y NO ONE ASSISTED Y NO ONE ASSISTED Y
THE DELIVERY.
434 Where did you give birth to HOME HOME HOME
(NAME)? YOUR HOME . . . 11 YOUR HOME . . . 11 YOUR HOME . . . 11
(SKIP TO 461) (SKIP TO 461) (SKIP TO 461)
PROBE TO IDENTIFY THE TYPE OTHER HOME . . . 12 OTHER HOME . . . 12 OTHER HOME . . . 12
OF SOURCE.
PUBLIC SECTOR PUBLIC SECTOR PUBLIC SECTOR
IF UNABLE TO DETERMINE GOVT. HOSPITAL . 21 GOVT. HOSPITAL . 21 GOVT. HOSPITAL . 21
IF PUBLIC OR PRIVATE GOVT. HEALTH GOVT. HEALTH GOVT. HEALTH
SECTOR, WRITE THE CENTER . . . . . 22 CENTER . . . . . 22 CENTER . . . . . 22
NAME OF THE PLACE. GOVT. GOVT. GOVT.
DISPENSARY . 23 DISPENSARY . 23 DISPENSARY . 23
OTHER PUBLIC OTHER PUBLIC OTHER PUBLIC
(NAME OF PLACE) SECTOR SECTOR SECTOR
26 26 26
(SPECIFY) (SPECIFY) (SPECIFY)
PRIVATE MED. SECTOR PRIVATE MED. SECTOR PRIVATE MED. SECTOR
MISSION HOSPITAL/ MISSION HOSPITAL/ MISSION HOSPITAL/
CLINIC . . . . . . . 31 CLINIC . . . . . . . 31 CLINIC . . . . . . . 31
PVT. HOSPITAL/ PVT. HOSPITAL/ PVT. HOSPITAL/
CLINIC . . . . . . . 32 CLINIC . . . . . . . 32 CLINIC . . . . . . . 32
NURSING/MATERNITY NURSING/MATERNITY NURSING/MATERNITY
HOME . . . . . . . 33 HOME . . . . . . . 33 HOME . . . . . . . 33
OTHER PRIVATE OTHER PRIVATE OTHER PRIVATE
MED. SECTOR MED. SECTOR MED. SECTOR
36 36 36
(SPECIFY) (SPECIFY) (SPECIFY)
OTHER 96 OTHER 96 OTHER 96
(SPECIFY) (SPECIFY) (SPECIFY)
(SKIP TO 461) (SKIP TO 461) (SKIP TO 461)
435 Was (NAME) delivered by YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
caesarean, that is, did they cut your
belly open to take the baby out? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
461 GO BACK TO 433 IN GO BACK TO 433 IN GO BACK TO 433 IN
NEXT COLUMN; OR, IF NEXT COLUMN; OR, IF NEXT-TO-LAST
NO MORE BIRTHS, GO NO MORE BIRTHS, GO COLUMN OF NEW
TO 501. TO 501. QUESTIONNAIRE; OR,
IF NO MORE BIRTHS,
GO TO 501.
W-11
Appendix E • 525
SECTION 5. CHILD IMMUNIZATION, HEALTH AND NUTRITION
501 ENTER IN THE TABLE THE BIRTH HISTORY NUMBER, NAME, AND SURVIVAL STATUS OF EACH BIRTH IN 2009 OR LATER.
ASK THE QUESTIONS ABOUT ALL OF THESE BIRTHS. BEGIN WITH THE LAST BIRTH.
(IF THERE ARE MORE THAN 3 BIRTHS, USE LAST 2 COLUMNS OF ADDITIONAL QUESTIONNAIRES).
502 LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
BIRTH HISTORY
NUMBER FROM 212 BIRTH HISTORY BIRTH HISTORY BIRTH HISTORY
IN BIRTH HISTORY NUMBER . . . . . . . . NUMBER . . . . . . . . NUMBER . . . . . . .
503 FROM 212 NAME NAME NAME
AND 216
LIVING DEAD LIVING DEAD LIVING DEAD
(GO TO 503 (GO TO 503 (GO TO 503 IN NEXT-
IN NEXT COLUMN IN NEXT COLUMN TO-LAST COLUMN OF
OR, IF NO MORE OR, IF NO MORE NEW Q'NNAIRE, OR IF NO
BIRTHS, GO TO 601) BIRTHS, GO TO 601) MORE BIRTHS, GO TO 601)
504 Do you have a card /
child health book where YES, SEEN . . . . . . . . . . . . 1 YES, SEEN . . . . . . . . . . . . 1 YES, SEEN . . . . . . . . . . . 1
(NAME)'s vaccinations (SKIP TO 506) (SKIP TO 506) (SKIP TO 506)
are written down? YES, NOT SEEN . . . . . . . . 2 YES, NOT SEEN . . . . . . . . 2 YES, NOT SEEN . . . . . . . 2
IF YES: May I see it (SKIP TO 509) (SKIP TO 509) (SKIP TO 509)
please? NO CARD . . . . . . . . . . . . . 3 NO CARD . . . . . . . . . . . . . 3 NO CARD . . . . . . . . . . . . 3
505 Did you ever have a YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . . . . . 1
vaccination card or child (SKIP TO 509) (SKIP TO 509) (SKIP TO 509)
health book for (NAME)? NO . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . . . . . 2
506 (1) COPY DATES FROM THE CARD.
(2) WRITE ‘44' IN ‘DAY' COLUMN IF CARD SHOWS THAT A DOSE WAS GIVEN, BUT NO DATE IS RECORDED.
(3) IF MORE THAN TWO VITAMIN 'A' DOSES, RECORD DATES FOR MOST RECENT AND SECOND MOST RECENT DOSES.
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
DAY MONTH YEAR DAY MONTH YEAR DAY MONTH YEAR
BCG (AT BIRTH) BCG BCG
POLIO 0 (POLIO
P0 P0
GIVEN AT BIRTH)
OPV 1 P1 P1
OPV 2 P2 P2
OPV 3 P3 P3
DPT, HEPATITIS,
D1 D1
HIB, 1st DOSE
DPT, HEPATITIS,
D2 D2
HIB, 2nd DOSE
DPT, HEPATITIS,
D3 D3
HIB, 3rd DOSE
PNEUMOCOCCAL
PN1 PN1
VACCINE 1
PNEUMOCOCCAL
PN2 PN2
VACCINE 2
PNEUMOCOCCAL
PN3 PN3
VACCINE 3
ROTA VIRUS
R1 R1
VACCINE 1
ROTA VIRUS
R2 R2
VACCINE 2
MEASLES MEA MEA
YELLOW FEVER YF YF
VITAMIN A
VITA1 VITA1
(MOST RECENT)
VITAMIN A (2nd
VITA2 VITA2
MOST RECENT)
AL/MEBENDAZOLE
A/M A/M
(MOST RECENT)
507 CHECK 506: BCG TO YELLOW OTHER BCG TO YELLOW OTHER BCG TO YELLOW OTHER
FEVER ALL RECORDED FEVER ALL RECORDED FEVER ALL RECORDED
(GO TO 511) (GO TO 511) (GO TO 511)
W-12
526 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
508 Has (NAME) had any vaccinations YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
that are not recorded on this card, (PROBE FOR (PROBE FOR (PROBE FOR
including vaccinations given in a VACCINATIONS AND VACCINATIONS AND VACCINATIONS AND
national immunization day WRITE ‘66' IN THE WRITE ‘66' IN THE WRITE ‘66' IN THE
campaign? CORRESPONDING CORRESPONDING CORRESPONDING
DAY COLUMN IN 506) DAY COLUMN IN 506) DAY COLUMN IN 506)
RECORD 'YES' ONLY IF THE (SKIP TO 511) (SKIP TO 511) (SKIP TO 511)
RESPONDENT MENTIONS
AT LEAST ONE OF THE NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
VACCINATIONS IN 506 THAT (SKIP TO 511) (SKIP TO 511) (SKIP TO 511)
ARE NOT RECORDED AS DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
HAVING BEEN GIVEN.
509 Did (NAME) ever have any
vaccinations to prevent him/her
from getting diseases, including YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
vaccinations received in a national NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
immunization day campaign? (SKIP TO 511) (SKIP TO 511) (SKIP TO 511)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
510 Please tell me if (NAME) had any of
the following vaccinations:
510A A BCG vaccination against YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
tuberculosis, that is, an injection in NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
the arm or shoulder that usually DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
causes a scar?
510B Polio vaccine, that is, drops in the YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
mouth? NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
(SKIP TO 510E) (SKIP TO 510E) (SKIP TO 510E)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
510C Was the first polio vaccine given in FIRST 2 WEEKS . . . 1 FIRST 2 WEEKS . . . 1 FIRST 2 WEEKS . . . 1
the first two weeks after birth or LATER . . . . . . . . . . . 2 LATER . . . . . . . . . . . 2 LATER . . . . . . . . . . . 2
later?
510D How many times was the polio NUMBER NUMBER NUMBER
vaccine given? OF TIMES ..... OF TIMES ..... OF TIMES .....
510E A Pentavalent vaccination, that is, YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
an injection given in the left outer NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
thigh, sometimes at the same time (SKIP TO 510F1) (SKIP TO 510F1) (SKIP TO 510F1)
as polio drops? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
510F How many times was the NUMBER NUMBER NUMBER
Pentavalent vaccination given? OF TIMES ..... OF TIMES ..... OF TIMES .....
510F1 A Pneumococcal vaccination, that YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
is, an injection given in the right NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
outer thigh, sometimes at the same (SKIP TO 510F3) (SKIP TO 510F3) (SKIP TO 510F3)
time as polio drops or the DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
Pentavalent vaccination?
510F2 How many times was the NUMBER NUMBER NUMBER
Pneumococcal vaccination given? OF TIMES ..... OF TIMES ..... OF TIMES .....
510F3 A Rota virus vaccination given YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
orally? NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
(SKIP TO 510G) (SKIP TO 510G) (SKIP TO 510G)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
W-13
Appendix E • 527
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
510F4 How many times was the Rota virus NUMBER NUMBER NUMBER
vaccination given? OF TIMES ..... OF TIMES ..... OF TIMES .....
510G A measles injection - that is, a shot YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
in the arm at the age of 9 months or NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
older - to prevent him/her from DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
getting measles?
510H A yellow fever injection - that is, a YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
shot in the arm or shoulder at the NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
age of 9 months or older - to DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
prevent him/her from getting yellow
fever?
511 Within the last six months, was YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
(NAME) given a vitamin A dose like NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
(this/any of these)? (SKIP TO 514) (SKIP TO 514) (SKIP TO 514)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
SHOW COMMON TYPES OF
AMPULES/CAPSULES/SYRUPS.
511A How many times was Vitamin A NUMBER NUMBER NUMBER
given in the last six months? OF TIMES ..... OF TIMES ..... OF TIMES .....
514 Has (NAME) had diarrhoea in the YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
last 2 weeks? NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
(SKIP TO 525) (SKIP TO 525) (SKIP TO 525)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
515 Was there any blood in the stools? YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
516 Now I would like to know how much
(NAME) was given to drink during
the diarrhoea (including breast
milk).
Was he/she given less than usual
to drink, about the same amount, or
MUCH LESS . . . . . 1 MUCH LESS . . . . . 1 MUCH LESS . . . . . 1
more than usual to drink?
SOMEWHAT LESS . 2 SOMEWHAT LESS . 2 SOMEWHAT LESS . 2
IF LESS, PROBE: Was he/she ABOUT THE SAME . 3 ABOUT THE SAME . 3 ABOUT THE SAME . 3
given much less than usual to drink MORE . . . . . . . . . . . 4 MORE . . . . . . . . . . . 4 MORE . . . . . . . . . . . 4
or somewhat less? NOTHING TO DRINK . 5 NOTHING TO DRINK . 5 NOTHING TO DRINK . 5
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
517 When (NAME) had diarrhoea, was
he/she given less than usual to eat, MUCH LESS . . . . . 1 MUCH LESS . . . . . 1 MUCH LESS . . . . . 1
about the same amount, more than SOMEWHAT LESS . 2 SOMEWHAT LESS . 2 SOMEWHAT LESS . 2
usual, or nothing to eat? ABOUT THE SAME . 3 ABOUT THE SAME . 3 ABOUT THE SAME . 3
MORE . . . . . . . . . . . 4 MORE . . . . . . . . . . . 4 MORE . . . . . . . . . . . 4
IF LESS, PROBE: Was he/she STOPPED FOOD . . . 5 STOPPED FOOD . . . 5 STOPPED FOOD . . . 5
given much less than usual to eat
NEVER GAVE FOOD . 6 NEVER GAVE FOOD . 6 NEVER GAVE FOOD . 6
or somewhat less?
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
518 Did you seek advice or treatment YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
for the diarrhoea from any source? NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
(SKIP TO 522) (SKIP TO 522) (SKIP TO 522)
W-14
528 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
519 Where did you seek advice or PUBLIC SECTOR PUBLIC SECTOR PUBLIC SECTOR
treatment? GOVT HOSPITAL A GOVT HOSPITAL A GOVT HOSPITAL A
GOVT HEALTH GOVT HEALTH GOVT HEALTH
Anywhere else? CENTER . . . . . B CENTER . . . . . B CENTER . . . . . B
GOVT GOVT GOVT
PROBE TO IDENTIFY EACH DISPENSARY . C DISPENSARY . C DISPENSARY . C
TYPE OF SOURCE. OTHER PUBLIC OTHER PUBLIC OTHER PUBLIC
SECTOR SECTOR SECTOR
IF UNABLE TO DETERMINE D D D
IF PUBLIC OR PRIVATE (SPECIFY) (SPECIFY) (SPECIFY)
SECTOR, WRITE THE NAME
OF THE PLACE. PRIVATE MEDICAL PRIVATE MEDICAL PRIVATE MEDICAL
SECTOR SECTOR SECTOR
PVT. HOSPITAL/ PVT. HOSPITAL/ PVT. HOSPITAL/
(NAME OF PLACE(S)) CLINIC . . . . . . . E CLINIC . . . . . . . E CLINIC . . . . . . . E
PHARMACY . . . F PHARMACY . . . F PHARMACY . . . F
MISSION HOSP./ MISSION HOSP./ MISSION HOSP./
CLINIC . . . . . . G CLINIC . . . . . . G CLINIC . . . . . . G
OTHER PRIVATE OTHER PRIVATE OTHER PRIVATE
SECTOR SECTOR SECTOR
H H H
(SPECIFY) (SPECIFY) (SPECIFY)
OTHER SOURCE OTHER SOURCE OTHER SOURCE
MOBILE CLINIC . I MOBILE CLINIC . I MOBILE CLINIC . I
COMMUNITY COMMUNITY COMMUNITY
HLTH WORKER J HLTH WORKER J HLTH WORKER J
SHOP . . . . . . . . . K SHOP . . . . . . . . . K SHOP . . . . . . . . . K
TRADITIONAL TRADITIONAL TRADITIONAL
PRACTITIONER L PRACTITIONER L PRACTITIONER L
RELATIVE/FRIEND M RELATIVE/FRIEND M RELATIVE/FRIEND M
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
520 CHECK 519: TWO OR ONLY TWO OR ONLY TWO OR ONLY
MORE ONE MORE ONE MORE ONE
CODES CODE CODES CODE CODES CODE
CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED
(SKIP TO 522) (SKIP TO 522) (SKIP TO 522)
521 Where did you first seek advice or
treatment?
FIRST PLACE . . . FIRST PLACE . . . FIRST PLACE . . .
USE LETTER CODE FROM 519.
522 Was he/she given any of the
following to drink at any time since
he/she started having the
diarrhoea: YES NO DK YES NO DK YES NO DK
a) A fluid made from a special a) FLUID FROM a) FLUID FROM a) FLUID FROM
packet called ORS? ORS PKT 1 2 8 ORS PKT 1 2 8 ORS PKT 1 2 8
b) A home-made sugar-salt b) SUGAR- 1 2 8 b) SUGAR- 1 2 8 b) SUGAR- 1 2 8
solution? SALT SOL. SALT SOL. SALT SOL.
c) Other home-made liquid such as
porridge, soup, yoghurt, coconut c) HOMEMADE c) HOMEMADE c) HOMEMADE
water, fresh fruit juice, tea, milk, FLUID . . . 1 2 8 FLUID . . . 1 2 8 FLUID . . . 1 2 8
or rice water?
W-15
Appendix E • 529
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
523 Was anything (else) given to treat YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
the diarrhoea? NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
(SKIP TO 525) (SKIP TO 525) (SKIP TO 525)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
524 What (else) was given to treat the PILL OR SYRUP PILL OR SYRUP PILL OR SYRUP
diarrhoea? ANTIBIOTIC . . . . . A ANTIBIOTIC . . . . . A ANTIBIOTIC . . . . . A
ANTIMOTILITY . . . B ANTIMOTILITY . . . B ANTIMOTILITY . . . B
Anything else? ZINC TABLET . . . C ZINC TABLET . . . C ZINC TABLET . . . C
OTHER (NOT ANTI- OTHER (NOT ANTI- OTHER (NOT ANTI-
RECORD ALL TREATMENTS BIOTIC, ANTI- BIOTIC, ANTI- BIOTIC, ANTI-
GIVEN. MOTILITY, OR MOTILITY, OR MOTILITY, OR
ZINC TABLET) . D ZINC TABLET) . D ZINC TABLET) . D
UNKNOWN PILL UNKNOWN PILL UNKNOWN PILL
OR SYRUP . . . E OR SYRUP . . . E OR SYRUP . . . E
INJECTION INJECTION INJECTION
ANTIBIOTIC . . . . . F ANTIBIOTIC . . . . . F ANTIBIOTIC . . . . . F
NON-ANTIBIOTIC . G NON-ANTIBIOTIC . G NON-ANTIBIOTIC . G
UNKNOWN UNKNOWN UNKNOWN
INJECTION . . . H INJECTION . . . H INJECTION . . . H
(IV) INTRAVENOUS (IV) INTRAVENOUS (IV) INTRAVENOUS
FLUID . . . . . . . I FLUID . . . . . . . I FLUID . . . . . . . I
HOME REMEDY/ HOME REMEDY/ HOME REMEDY/
HERBAL MED- HERBAL MED- HERBAL MED-
ICINE . . . . . . . . . J ICINE . . . . . . . . . J ICINE . . . . . . . . . J
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
525 Has (NAME) been ill with a fever at YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
any time in the last 2 weeks? NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
(SKIP TO 527) (SKIP TO 527) (SKIP TO 527)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
526 At any time during the illness, did YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
(NAME) have blood taken from NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
his/her finger or heel for testing? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
527 Has (NAME) had an illness with a YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
cough at any time in the last 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
weeks? (SKIP TO 530) (SKIP TO 530) (SKIP TO 530)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
528 When (NAME) had an illness with a YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
cough, did he/she breathe faster NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
than usual with short, rapid breaths (SKIP TO 531) (SKIP TO 531) (SKIP TO 531)
or have difficulty breathing? DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
529 Was the fast or difficult breathing CHEST ONLY ... 1 CHEST ONLY ... 1 CHEST ONLY ... 1
due to a problem in the chest or to NOSE ONLY ..... 2 NOSE ONLY ..... 2 NOSE ONLY ..... 2
a blocked or runny nose? BOTH . . . . . . . . . . . 3 BOTH . . . . . . . . . . . 3 BOTH . . . . . . . . . . . 3
OTHER 6 OTHER 6 OTHER 6
(SPECIFY) (SPECIFY) (SPECIFY)
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
(SKIP TO 531) (SKIP TO 531) (SKIP TO 531)
W-16
530 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
530 CHECK 525: YES NO OR DK YES NO OR DK YES NO OR DK
HAD FEVER?
(GO BACK TO (GO BACK TO (GO TO 503
503 IN NEXT 503 IN NEXT IN NEXT-TO-LAST
COLUMN; OR, COLUMN; OR, COLUMN OF NEW
IF NO MORE IF NO MORE QUESTIONNAIRE; OR,
BIRTHS, GO BIRTHS, GO IF NO MORE BIRTHS,
TO 601) TO 601) TO 601)
531 Now I would like to know how much
(NAME) was given to drink
(including breast milk) during the
illness with a (fever/cough). Was
he/she given less than usual to
drink, about the same amount, or MUCH LESS . . . . . 1 MUCH LESS . . . . . 1 MUCH LESS . . . . . 1
more than usual to drink?
SOMEWHAT LESS . 2 SOMEWHAT LESS . 2 SOMEWHAT LESS . 2
ABOUT THE SAME . 3 ABOUT THE SAME . 3 ABOUT THE SAME . 3
IF LESS, PROBE: Was he/she
given much less than usual to drink MORE . . . . . . . . . . . 4 MORE . . . . . . . . . . . 4 MORE . . . . . . . . . . . 4
or somewhat less? NOTHING TO DRINK . 5 NOTHING TO DRINK . 5 NOTHING TO DRINK . 5
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
532 When (NAME) had a (fever/cough),
was he/she given less than usual to
eat, about the same amount, more MUCH LESS . . . . . 1 MUCH LESS . . . . . 1 MUCH LESS . . . . . 1
than usual, or nothing to eat? SOMEWHAT LESS . 2 SOMEWHAT LESS . 2 SOMEWHAT LESS . 2
ABOUT THE SAME . 3 ABOUT THE SAME . 3 ABOUT THE SAME . 3
IF LESS, PROBE: Was he/she MORE . . . . . . . . . . . 4 MORE . . . . . . . . . . . 4 MORE . . . . . . . . . . . 4
given much less than usual to eat
STOPPED FOOD . . . 5 STOPPED FOOD . . . 5 STOPPED FOOD . . . 5
or somewhat less?
NEVER GAVE FOOD . 6 NEVER GAVE FOOD . 6 NEVER GAVE FOOD . 6
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8
533 Did you seek advice or treatment YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
for the illness from any source? NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
(SKIP TO 537) (SKIP TO 537) (SKIP TO 537)
W-17
Appendix E • 531
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
534 Where did you seek advice or PUBLIC SECTOR PUBLIC SECTOR PUBLIC SECTOR
treatment? GOVT HOSPITAL A GOVT HOSPITAL A GOVT HOSPITAL A
GOVT HEALTH GOVT HEALTH GOVT HEALTH
Anywhere else? CENTER . . . . . B CENTER . . . . . B CENTER . . . . . B
GOVT GOVT GOVT
PROBE TO IDENTIFY EACH DISPENSARY . C DISPENSARY . C DISPENSARY . C
TYPE OF SOURCE. OTHER PUBLIC OTHER PUBLIC OTHER PUBLIC
SECTOR SECTOR SECTOR
IF UNABLE TO DETERMINE D D D
IF PUBLIC OR PRIVATE (SPECIFY) (SPECIFY) (SPECIFY)
SECTOR, WRITE THE NAME
OF THE PLACE. PRIVATE MEDICAL PRIVATE MEDICAL PRIVATE MEDICAL
SECTOR SECTOR SECTOR
PVT. HOSPITAL/ PVT. HOSPITAL/ PVT. HOSPITAL/
CLINIC . . . . . . . E CLINIC . . . . . . . E CLINIC . . . . . . . E
(NAME OF PLACE(S)) PHARMACY . . . F PHARMACY . . . F PHARMACY . . . F
MISSION HOSP./ MISSION HOSP./ MISSION HOSP./
CLINIC . . . . . . G CLINIC . . . . . . G CLINIC . . . . . . G
OTHER PRIVATE OTHER PRIVATE OTHER PRIVATE
SECTOR SECTOR SECTOR
H H H
(SPECIFY) (SPECIFY) (SPECIFY)
OTHER SOURCE OTHER SOURCE OTHER SOURCE
MOBILE CLINIC . I MOBILE CLINIC . I MOBILE CLINIC . I
COMMUNITY COMMUNITY COMMUNITY
HLTH WORKER J HLTH WORKER J HLTH WORKER J
SHOP . . . . . . . . . K SHOP . . . . . . . . . K SHOP . . . . . . . . . K
TRADITIONAL TRADITIONAL TRADITIONAL
PRACTITIONER L PRACTITIONER L PRACTITIONER L
RELATIVE/FRIEND M RELATIVE/FRIEND M RELATIVE/FRIEND M
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
535 CHECK 534: TWO OR ONLY TWO OR ONLY TWO OR ONLY
MORE ONE MORE ONE MORE ONE
CODES CODE CODES CODE CODES CODE
CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED CIRCLED
(SKIP TO 537) (SKIP TO 537) (SKIP TO 537)
536 Where did you first seek advice or
treatment?
FIRST PLACE . . . FIRST PLACE . . . FIRST PLACE . . .
USE LETTER CODE FROM 534.
537 At any time during the illness, did YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . 1
(NAME) take any drugs for the NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . 2
illness? (GO BACK TO 503 (GO BACK TO 503 (GO TO 503 IN
IN NEXT COLUMN; IN NEXT COLUMN; NEXT-TO-LAST
OR, IF NO MORE OR, IF NO MORE COLUMN OF NEW
BIRTHS, GO TO 601) BIRTHS, GO TO 601) QUESTIONNAIRE;
DON'T KNOW . . . . . 8 DON'T KNOW . . . . . 8 OR, IF NO MORE
BIRTHS, GO TO 601)
DON'T KNOW . . . . . 8
W-18
532 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
538 What drugs did (NAME) take? ANTIMALARIAL DRUGS ANTIMALARIAL DRUGS ANTIMALARIAL DRUGS
SP/FANSIDAR . . . A SP/FANSIDAR . . . A SP/FANSIDAR . . . A
Any other drugs? CHLOROQUINE . B CHLOROQUINE . B CHLOROQUINE . B
AMODIAQUINE . C AMODIAQUINE . C AMODIAQUINE . C
QUININE . . . . . . . D QUININE . . . . . . . D QUININE . . . . . . . D
RECORD ALL MENTIONED. AL/COARTEM . . . E AL/COARTEM . . . E AL/COARTEM . . . E
OTHER ANTI- OTHER ANTI- OTHER ANTI-
MALARIAL MALARIAL MALARIAL
... F ... F ... F
(SPECIFY) (SPECIFY) (SPECIFY)
ANTIBIOTIC DRUGS ANTIBIOTIC DRUGS ANTIBIOTIC DRUGS
PILL/SYRUP . . . G PILL/SYRUP . . . G PILL/SYRUP . . . G
INJECTION ... H INJECTION ... H INJECTION ... H
OTHER DRUGS OTHER DRUGS OTHER DRUGS
ASPIRIN . . . . . . . I ASPIRIN . . . . . . . I ASPIRIN . . . . . . . I
ACETAMINOPHEN/ ACETAMINOPHEN/ ACETAMINOPHEN/
PARACETAMOL J PARACETAMOL J PARACETAMOL J
IBUPROFEN . . . K IBUPROFEN . . . K IBUPROFEN . . . K
OTHER X OTHER X OTHER X
(SPECIFY) (SPECIFY) (SPECIFY)
DON'T KNOW . . . . . Z DON'T KNOW . . . . . Z DON'T KNOW . . . . . Z
539 CHECK 538: YES NO YES NO YES NO
ANY CODE A-F CIRCLED?
(GO BACK TO (GO BACK TO (GO TO 503 IN
503 IN NEXT 503 IN NEXT NEXT-TO-LAST
COLUMN; OR, COLUMN; OR, COLUMN OF NEW
IF NO MORE IF NO MORE QUESTIONNAIRE;
BIRTHS, GO BIRTHS, GO OR, IF NO MORE
TO 601) TO 601) BIRTHS, GO TO 601)
540 CHECK 538: CODE 'A' CODE 'A' CODE 'A' CODE 'A' CODE 'A' CODE 'A'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
SP/FANSIDAR ('A') GIVEN CIRCLED CIRCLED CIRCLED
(SKIP TO 542) (SKIP TO 542) (SKIP TO 542)
541 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take (SP/Fansidar)? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
542 CHECK 538: CODE 'B' CODE 'B' CODE 'B' CODE 'B' CODE 'B' CODE 'B'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
CHLOROQUINE ('B') GIVEN CIRCLED CIRCLED CIRCLED
(SKIP TO 544) (SKIP TO 544) (SKIP TO 544)
W-19
Appendix E • 533
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
543 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take chloroquine? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
544 CHECK 538: CODE 'C' CODE 'C' CODE 'C' CODE 'C' CODE 'C' CODE 'C'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
AMODIAQUINE ('C') GIVEN CIRCLED CIRCLED CIRCLED
(SKIP TO 546) (SKIP TO 546) (SKIP TO 546)
545 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take amodiaquine? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
546 CHECK 538: CODE 'D' CODE 'D' CODE 'D' CODE 'D' CODE 'D' CODE 'D'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
QUININE ('D') GIVEN CIRCLED CIRCLED CIRCLED
(SKIP TO 548) (SKIP TO 548) (SKIP TO 548)
547 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take quinine? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
548 CHECK 538: CODE 'E' CODE 'E' CODE 'E' CODE 'E' CODE 'E' CODE 'E'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
ARTEMISININ+LUMEFANTRINE CIRCLED CIRCLED CIRCLED
(AL/COARTEM) ('E') GIVEN
(SKIP TO 550) (SKIP TO 550) (SKIP TO 550)
549 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take AL/Coartem? NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
W-20
534 • Appendix E
LAST BIRTH NEXT-TO-LAST BIRTH SECOND-FROM-LAST BIRTH
NO. QUESTIONS AND FILTERS NAME NAME NAME
550 CHECK 538: CODE 'F' CODE 'F' CODE 'F' CODE 'F' CODE 'F' CODE 'F'
CIRCLED NOT CIRCLED NOT CIRCLED NOT
OTHER ANTIMALARIAL ('F') CIRCLED CIRCLED CIRCLED
GIVEN
(GO BACK TO (GO BACK TO (GO TO 503 IN
503 IN NEXT 503 IN NEXT NEXT-TO-LAST
COLUMN; OR, COLUMN; OR, COLUMN OF NEW
IF NO MORE IF NO MORE QUESTIONNAIRE;
BIRTHS, GO BIRTHS, GO OR, IF NO MORE
TO 601) TO 601) BIRTHS, GO TO 601)
551 How long after the fever started did SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0 SAME DAY . . . . . . . 0
(NAME) first take (OTHER NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1 NEXT DAY . . . . . . . 1
ANTIMALARIAL)? TWO DAYS AFTER TWO DAYS AFTER TWO DAYS AFTER
FEVER . . . . . . . 2 FEVER . . . . . . . 2 FEVER . . . . . . . 2
THREE DAYS AFTER THREE DAYS AFTER THREE DAYS AFTER
FEVER . . . . . . . 3 FEVER . . . . . . . 3 FEVER . . . . . . . 3
FOUR OR MORE DAYS FOUR OR MORE DAYS FOUR OR MORE DAYS
AFTER FEVER . . . 4 AFTER FEVER . . . 4 AFTER FEVER . . . 4
DON'T KNOW ... 8 DON'T KNOW ... 8 DON'T KNOW ... 8
552 GO BACK TO 503 IN GO BACK TO 503 IN GO TO 503 IN
NEXT COLUMN; OR, IF NEXT COLUMN; OR, IF NEXT-TO-LAST
NO MORE BIRTHS, GO NO MORE BIRTHS, GO COLUMN OF NEW
TO 601. TO 601. QUESTIONNAIRE; OR,
IF NO MORE BIRTHS,
GO TO 601.
W-21
Appendix E • 535
SECTION 6. MARRIAGE AND SEXUAL ACTIVITY
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
601 Are you currently married or living together with a man as if YES, CURRENTLY MARRIED . . . . . . . 1
married? YES, LIVING WITH A MAN . . . . . . . . . 2 605
NO, NOT IN UNION . . . . . . . . . . . . . . . . 3
602 Have you ever been married or lived together with a man as if YES, FORMERLY MARRIED . . . . . . . 1
married? YES, LIVED WITH A MAN . . . . . . . . . 2
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 612
603 What is your marital status now: are you widowed, divorced, or WIDOWED . . . . . . . . . . . . . . . . . . . . . . 1
separated? DIVORCED . . . . . . . . . . . . . . . . . . . . . . 2 609
SEPARATED . . . . . . . . . . . . . . . . . . . . 3
605 RECORD THE HUSBAND'S/PARTNER'S NAME AND LINE NAME
NUMBER FROM THE HOUSEHOLD QUESTIONNAIRE.
IF HE IS NOT LISTED IN THE HOUSEHOLD, RECORD '00'.
LINE NO. . . . . . . . . . . . . . . . . . .
609 Have you been married or lived with a man only once or more than ONLY ONCE . . . . . . . . . . . . . . . . . . . . 1
once? MORE THAN ONCE . . . . . . . . . . . . . . . . 2
610 CHECK 609:
MARRIED/ MARRIED/
LIVED WITH A MAN LIVED WITH A MAN MONTH ..................
ONLY ONCE MORE THAN ONCE
a) In what month and year did b) Now I would like to ask about DON'T KNOW MONTH . . . . . . . . . . . . . . 98
you start living with your your first (husband/partner). In
(husband/partner)? what month and year did you
start living with him? YEAR . . . . . . . . . . . . 612
DON'T KNOW YEAR . . . . . . . . . . . . 9998
611 How old were you when you first started living with him?
AGE ....................
612 CHECK FOR THE PRESENCE OF OTHERS. BEFORE CONTINUING, MAKE EVERY EFFORT TO ENSURE PRIVACY.
613 Now I would like to ask some questions about sexual activity in NEVER HAD SEXUAL
order to gain a better understanding of some important life issues. INTERCOURSE . . . . . . . . . . . . . . . .00
How old were you when you had sexual intercourse for the very first
time? AGE IN YEARS ............
FIRST TIME WHEN STARTED
LIVING WITH (FIRST)
HUSBAND/PARTNER . . . . . . . . . . . . 95
W-22
536 • Appendix E
SECTION 9. HIV/AIDS
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
901 Now I would like to talk about something else. Have you ever heard YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
of an illness called AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1433
902 Can people reduce their chance of getting the AIDS virus by having YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
just one uninfected sex partner who has no other sex partners? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
903 Can people get the AIDS virus from mosquito bites? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
904 Can people reduce their chance of getting the AIDS virus by using a YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
condom every time they have sex? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
905 Can people get the AIDS virus by sharing food with a person who YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
has AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
906 Can people get the AIDS virus because of witchcraft or other YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
supernatural means? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
907 Is it possible for a healthy-looking person to have the AIDS virus? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
908 Can the virus that causes AIDS be transmitted from a mother to her
baby: YES NO DK
a) During pregnancy? a) DURING PREG. . . . . . 1 2 8
b) During delivery? b) DURING DELIVERY . . . 1 2 8
c) By breastfeeding? c) BREASTFEEDING . . . 1 2 8
911 CHECK 208 AND 215: NO BIRTHS
926
LAST BIRTH SINCE LAST BIRTH BEFORE
JANUARY 2012 JANUARY 2012 926
912 CHECK 408 FOR LAST BIRTH:
HAD NO
ANTENATAL ANTENATAL
CARE CARE 920
913
CHECK FOR PRESENCE OF OTHERS. BEFORE CONTINUING, MAKE EVERY EFFORT TO ENSURE PRIVACY.
914 During any of the antenatal visits for your last birth were you given
any information about:
YES NO DK
a) Babies getting the AIDS virus from their mother? a) AIDS FROM MOTHER . 1 2 8
b) Things that you can do to prevent getting the AIDS virus? b) THINGS TO DO ..... 1 2 8
c) Getting tested for the AIDS virus? c) TESTED FOR AIDS . . . 1 2 8
915 Were you offered a test for the AIDS virus as part of your antenatal YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
care? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
916 I don't want to know the results, but were you tested for the AIDS YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
virus as part of your antenatal care? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 920
W-23
Appendix E • 537
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
917 Where was the test done? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . . 11
PROBE TO IDENTIFY THE TYPE OF SOURCE. GOVT. HEALTH CENTER\CLINIC . 12
GOVERNMENT DISPENSARY . . . . 13
OTHER PUBLIC
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR, SECTOR 18
WRITE THE NAME OF THE PLACE. (SPECIFY)
PRIVATE MEDICAL SECTOR
(NAME OF PLACE) PRIVATE HOSPITAL/CLINIC . . . . . 21
MISSIONARY/CHURCH HOSP./
CLINIC . . . . . . . . . . . . . . . . . . . . . . .22
FAMILY OPTIONS/FHOK CLINIC . . 23
VCT CENTRE . . . . . . . . . . . . . . . . . . 24
NURSING/MATERNITY HOMES . . 25
BLOOD TRANSFUSION SERVICES . 26
OTHER PRIVATE
MEDICAL SECTOR
27
(SPECIFY)
OTHER SOURCE
HOME . . . . . . . . . . . . . . . . . . . . . . . . . 31
CORRECTIONAL FACILITY . . . . . . . . 32
OTHER 96
(SPECIFY)
918 I don't want to know the results, but did you get the results of the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
test? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 924
919 All women are supposed to receive counseling after being tested. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
After you were tested, did you receive counseling? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 924
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
920 CHECK 434 FOR LAST BIRTH:
ANY CODE OTHER 926
21-36 CIRCLED
921 Between the time you went for delivery but before the baby was YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
born, were you offered a test for the AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
922 I don't want to know the results, but were you tested for the AIDS YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
virus at that time? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 926
923 I don't want to know the results, but did you get the results of the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
test? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
924 Have you been tested for the AIDS virus since that time you were YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 927
tested during your pregnancy? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
925 How many months ago was your most recent HIV test?
MONTHS AGO ............ 1433
TWO OR MORE YEARS . . . . . . . . . . 95
926 I don't want to know the results, but have you ever been tested to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
see if you have the AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1433
927 How many months ago was your most recent HIV test?
MONTHS AGO ............
TWO OR MORE YEARS . . . . . . . . . . 95
928 I don't want to know the results, but did you get the results of the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
test? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1433 RECORD THE TIME.
HOUR . . . . . . . . . . . . . . . . . . . .
MINUTES . . . . . . . . . . . . . . . . . .
W-24
538 • Appendix E
INTERVIEWER'S OBSERVATIONS
TO BE FILLED IN AFTER COMPLETING INTERVIEW
COMMENTS ABOUT RESPONDENT:
COMMENTS ON SPECIFIC QUESTIONS:
ANY OTHER COMMENTS:
SUPERVISOR'S OBSERVATIONS
NAME OF SUPERVISOR: DATE:
EDITOR'S OBSERVATIONS
NAME OF EDITOR: DATE:
W-25
Appendix E • 539
540 • Appendix E
2014 KENYA DEMOGRAPHIC AND HEALTH SURVEY
MAN'S QUESTIONNAIRE
CONFIDENTIAL
REPUBLIC OF KENYA
IDENTIFICATION
COUNTY
DISTRICT
LOCATION/TOWN
SUBLOCATION
NASSEP CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
KDHS CLUSTER NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
HOUSEHOLD NUMBER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NAME OF HOUSEHOLD HEAD
NAME AND LINE NUMBER OF MAN
YES . . . . . . . . . . . . . . . . . . 1
CHECK 101B IN HOUSEHOLD QUESTIONNAIRE: IS MAN SELECTED FOR SECTION 10? NO . . . . . . . . . . . . . . . . . . 2
INTERVIEWER VISITS
1 2 3 FINAL VISIT
DATE DAY
MONTH
YEAR
INTERVIEWER'S
NAME INT. NUMBER
RESULT* RESULT
NEXT VISIT: DATE
TOTAL NUMBER
TIME OF VISITS
*RESULT CODES:
1 COMPLETED 4 REFUSED
2 NOT AT HOME 5 PARTLY COMPLETED 7 OTHER
3 POSTPONED 6 INCAPACITATED (SPECIFY)
LANGUAGE OF TRANSLATOR USED
QUESTIONNAIRE** 1 7 LANGUAGE OF
INTERVIEW:
NATIVE LANGUAGE
OF RESPONDENT: (YES = 1, NO = 2)
LANGUAGE OF
QUESTIONNAIRE: English
**LANGUAGE 01 BORANA 05 KIKUYU 09 LUO 13 POKOT 17 ENGLISH
CODES: 02 EMBU 06 KISII 10 MAASAI 14 SOMALI 18 OTHER
03 KALENJIN 07 LUHYA 11 MERU 15 SWAHILI
04 KAMBA 08 MARAGOLI 12 MIJIKENDA 16 TURKANA
SUPERVISOR FIELD EDITOR OFFICE KEYED BY
EDITOR
NAME NAME
M-1
Appendix E • 541
SECTION 1. RESPONDENT'S BACKGROUND
INTRODUCTION AND CONSENT
INFORMED CONSENT
Hello. My name is _______________________________________. I am working with the Kenya National Bureau of Statistics. We are
conducting a survey about health all over Kenya. The information we collect will help the government to plan health services. Your
household was selected for the survey. The questions usually take about 20 minutes. All of the answers you give will be confidential and will
not be shared with anyone other than members of our survey team. You don't have to be in the survey, but we hope you will agree to answer
the questions since your views are important. If I ask you any question you don't want to answer, just let me know and I will go on to the next
question or you can stop the interview at any time.
In case you need more information about the survey, you may contact the person listed on the card that has already been given to your
household.
Do you have any questions?
May I begin the interview now?
SIGNATURE OF INTERVIEWER: DATE:
RESPONDENT AGREES TO BE INTERVIEWED . . . . . 1 RESPONDENT DOES NOT AGREE TO BE INTERVIEWED . . . 2 END
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
101 RECORD THE TIME.
HOUR . . . . . . . . . . . . . . . . . . . .
MINUTES . . . . . . . . . . . . . . . . .
101A First I would like to ask some questions about you and your NAIROBI/ MOMBASA/ KISUMU . . . . . 1
household. For most of the time until you were 12 years old, did you OTHER CITY/ TOWN . . . . . . . . . . . . . . 2
live in Nairobi, Mombasa, Kisumu, in a town, in the countryside, or COUNTRYSIDE . . . . . . . . . . . . . . . . . . 3
outside of Kenya? OUTSIDE KENYA . . . . . . . . . . . . . . . . 4
101B How long have you been living continuously in (NAME OF YEARS
CURRENT PLACE OF RESIDENCE)?
ALWAYS . . . . . . . . . . . . . . . . . . . . . . 95
IF LESS THAN ONE YEAR, RECORD '00' YEARS VISITOR . . . . . . . . . . . . . . . . . . . . . . . . 96 101D
101C Just before you moved here, did you live in Nairobi, Mombasa, NAIROBI/ MOMBASA/ KISUMU . . . . . 1
Kisumu, in a town, in the countryside, or outside of Kenya? TOWN . . . . . . . . . . . . . . . . . . . . . . . . . . 2
COUNTRYSIDE . . . . . . . . . . . . . . . . . . 3
OUTSIDE OF KENYA . . . . . . . . . . . . . . 4
101D What is your nationality? KENYAN . . . . . . . . . . . . . . . . . . . . . . . . 01 102
TANZANIAN . . . . . . . . . . . . . . . . . . . . 02
UGANDAN . . . . . . . . . . . . . . . . . . . . . . 03
SOMALI . . . . . . . . . . . . . . . . . . . . . . . . 04
ETHIOPIAN . . . . . . . . . . . . . . . . . . . . . . 05
SUDANESE . . . . . . . . . . . . . . . . . . . . . . 06
OTHER 96
(SPECIFY)
101E What was the main reason for moving to Kenya? JOIN FAMILY LIVING IN KENYA . . . . . 01
MARRIAGE . . . . . . . . . . . . . . . . . . . . . . 02
WORK . . . . . . . . . . . . . . . . . . . . . . . . . . 03
SCHOOL . . . . . . . . . . . . . . . . . . . . . . . . 04
ESCAPE INSECURITY/WAR . . . . . . . 05
ESCAPE ENVIRONMENTAL DISASTER
(E.G. FLOOD, DROUGHT, ETC.) . . . 06
OTHER 96
(SPECIFY)
M-2
542 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
102 In what month and year were you born?
MONTH ..................
DON'T KNOW MONTH . . . . . . . . . . . . 98
YEAR . . . . . . . . . . . .
DON'T KNOW YEAR . . . . . . . . . . . . 9998
103 How old were you at your last birthday?
AGE IN COMPLETED YEARS
COMPARE AND CORRECT 102 AND/OR 103 IF INCONSISTENT.
104 Have you ever attended school? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 108
105 What is the highest level of school you attended: primary, PRIMARY . . . . . . . . . . . . . . . . . . . . . . 1
vocational, secondary, or higher? POST-PRIMARY/VOCATIONAL . . . . . 2
SECONDARY/ 'A' LEVEL ......... 3
COLLEGE (MIDDLE LEVEL) . . . . . . . 4
UNIVERSITY . . . . . . . . . . . . . . . . . . . . 5
106 What is the highest (standard/form/year) you completed at that
level? STANDARD/FORM/YEAR ...
IF COMPLETED LESS THAN ONE YEAR AT THAT LEVEL,
RECORD '00'.
107 CHECK 105:
PRIMARY SECONDARY
POST-PRIMARY/ OR HIGHER 110
VOCATIONAL
108 Now I would like you to read this sentence to me. CANNOT READ AT ALL . . . . . . . . . . . . 1
ABLE TO READ ONLY PARTS OF
SHOW CARD TO RESPONDENT. SENTENCE . . . . . . . . . . . . . . . . . . . . 2
ABLE TO READ WHOLE SENTENCE . 3
IF RESPONDENT CANNOT READ WHOLE SENTENCE, PROBE: NO CARD WITH REQUIRED
Can you read any part of the sentence to me? LANGUAGE 4
(SPECIFY LANGUAGE)
BLIND/VISUALLY IMPAIRED . . . . . . . 5
109 CHECK 108:
CODE '2', '3' CODE '1' OR '5'
OR '4' CIRCLED 111
CIRCLED
110 Do you read a newspaper or magazine, at least once a week, less AT LEAST ONCE A WEEK . . . . . . . . . 1
than once a week, or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
111 Do you listen to the radio, at least once a week, less than once a AT LEAST ONCE A WEEK . . . . . . . . . 1
week, or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
112 Do you watch television, at least once a week, less than once a AT LEAST ONCE A WEEK . . . . . . . . . 1
week, or not at all? LESS THAN ONCE A WEEK . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
M-3
Appendix E • 543
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
113 What is your religion? ROMAN CATHOLIC .............. 1
PROTESTANT/ OTHER CHRISTIAN . 2
MUSLIM . . . . . . . . . . . . . . . . . . . . . . . . 3
NO RELIGION . . . . . . . . . . . . . . . . . . . . 4
OTHER 6
(SPECIFY)
114 What is your ethnic group / tribe? EMBU . . . . . . . . . . . . . . . . . . . . . . . . . . 01
KALENJIN . . . . . . . . . . . . . . . . . . . . . . 02
KAMBA . . . . . . . . . . . . . . . . . . . . . . . . 03
KIKUYU . . . . . . . . . . . . . . . . . . . . . . . . 04
KISII . . . . . . . . . . . . . . . . . . . . . . . . . . . . 05
LUHYA . . . . . . . . . . . . . . . . . . . . . . . . 06
LUO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 07
MAASAI . . . . . . . . . . . . . . . . . . . . . . . . 08
MERU . . . . . . . . . . . . . . . . . . . . . . . . . . 09
MIJIKENDA/ SWAHILI . . . . . . . . . . . . . . 10
SOMALI . . . . . . . . . . . . . . . . . . . . . . . . 11
TAITA/ TAVETA . . . . . . . . . . . . . . . . . . 12
OTHER 96
(SPECIFY)
115 In the last 12 months, how many times have you been away from
home for one or more nights? NUMBER OF TIMES .......
NONE . . . . . . . . . . . . . . . . . . . . . . . . . . 00 201
116 In the last 12 months, have you been away from home for more YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
than one month at a time? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
M-4
544 • Appendix E
SECTION 2. REPRODUCTION
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
201 Now I would like to ask about any children you have had during your
life. I am interested in all of the children that are biologically yours, YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
even if they are not legally yours or do not have your last name. NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 206
Have you ever fathered any children with any woman?
202 Do you have any sons or daughters that you have fathered who are YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
now living with you? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 204
203 How many sons live with you?
SONS AT HOME . . . . . . . . . . . .
And how many daughters live with you?
DAUGHTERS AT HOME . . . . .
IF NONE, RECORD ‘00'.
204 Do you have any sons or daughters that you have fathered who are YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
alive but do not live with you? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 206
205 How many sons are alive but do not live with you?
SONS ELSEWHERE .......
And how many daughters are alive but do not live with you?
DAUGHTERS ELSEWHERE . . .
IF NONE, RECORD ‘00'.
206 Have you ever fathered a son or a daughter who was born alive but
later died?
YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
IF NO, PROBE: Any baby who cried or showed signs of life but did NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
not survive? DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 208
207 How many boys have died?
BOYS DEAD ..............
And how many girls have died?
GIRLS DEAD ..............
IF NONE, RECORD ‘00'.
208 SUM ANSWERS TO 203, 205, AND 207, AND ENTER TOTAL.
TOTAL CHILDREN . . . . . . . . .
IF NONE, RECORD ‘00'.
209 CHECK 208:
HAS HAD HAS HAD 212
MORE THAN ONLY
ONE CHILD ONE CHILD HAS NOT HAD
ANY CHILDREN 301
210 Did all of the children you have fathered have the same biological YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 212
mother? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
211 In all, how many women have you fathered children with?
NUMBER OF WOMEN . . . . . . .
212 How old were you when your (first) child was born?
AGE IN YEARS ............
213 CHECK 203 AND 205:
AT LEAST ONE NO LIVING
301
LIVING CHILD CHILDREN
214 How old is your (youngest) child?
AGE IN YEARS ............
M-5
Appendix E • 545
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
215 CHECK 214:
(YOUNGEST) CHILD OTHER
301
IS AGE 0-2 YEARS
216 What is the name of your (youngest) child?
WRITE NAME OF (YOUNGEST) CHILD
(NAME OF (YOUNGEST) CHILD)
217 When (NAME)'s mother was pregnant with (NAME), did she have YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
any antenatal check-ups? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW .................. 8 219
218 Were you ever present during any of those antenatal check-ups? PRESENT . . . . . . . . . . . . . . . . . . . . . . 1
NOT PRESENT . . . . . . . . . . . . . . . . . . 2
219 Was (NAME) born in a hospital or health facility? HOSPITAL/HEALTH FACILITY . . . . . 1
OTHER . . . . . . . . . . . . . . . . . . . . . . . . 2
220 When a child has diarrhoea, how much should he or she be given to MORE THAN USUAL . . . . . . . . . . . . . . 1
drink: more than usual, about the same as usual, less than usual, or ABOUT THE SAME .............. 2
nothing to drink at all? LESS THAN USUAL . . . . . . . . . . . . . . 3
NOTHING TO DRINK . . . . . . . . . . . . . . 4
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
M-6
546 • Appendix E
SECTION 3. CONTRACEPTION
301 Now I would like to talk about family planning - the various ways or methods that a couple can use to delay or avoid a pregnancy.
Have you ever heard of (METHOD)?
01 Female Sterilization. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have an operation to avoid having any more NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
children.
02 Male Sterilization. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can have an operation to avoid having any more NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
children.
03 IUD. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have a loop or coil placed inside them by a NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
doctor or a nurse.
04 Injectables. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have an injection by a health provider that NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
stops them from becoming pregnant for one or more months.
05 Implants. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can have one or more small rods placed in their NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
upper arm by a doctor or nurse which can prevent pregnancy for
one or more years.
06 Pill. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can take a pill every day to avoid becoming NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
pregnant.
07 Male Condom. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can put a rubber sheath on their penis before sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse.
08 Female Condom. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Women can place a sheath in their vagina before sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse.
09 Lactational Amenorrhea Method (LAM). YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
10 Rhythm Method. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: To avoid pregnancy, women do not have sexual NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
intercourse on the days of the month they think they can get
pregnant.
11 Withdrawal. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: Men can be careful and pull out before climax. NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
12 Emergency Contraception. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
PROBE: As an emergency measure, within three days after they NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
have unprotected sexual intercourse, women can take special pills
to prevent pregnancy.
13 Have you heard of any other ways or methods that women or men YES .......................... 1
can use to avoid pregnancy?
(SPECIFY)
(SPECIFY)
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
M-7
Appendix E • 547
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
302 In the last few months have you: YES NO
a) Heard about family planning on the radio? a) RADIO . . . . . . . . . . . . . . . . . . . . 1 2
b) Seen anything about family planning on the television? b) TELEVISION .............. 1 2
c) Read about family planning in a newspaper or magazine? c) NEWSPAPER OR MAGAZINE . 1 2
303 In the last few months, have you discussed family planning with a YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
health worker or health professional? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
304 Now I would like to ask you about a woman's risk of pregnancy.
YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
From one menstrual period to the next, are there certain days when NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
a woman is more likely to become pregnant when she has sexual DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 306
relations?
305 Is this time just before her period begins, during her period, right JUST BEFORE HER
after her period has ended, or halfway between two periods? PERIOD BEGINS .............. 1
DURING HER PERIOD . . . . . . . . . . . . 2
RIGHT AFTER HER
PERIOD HAS ENDED . . . . . . . . . . . . 3
HALFWAY BETWEEN
TWO PERIODS .............. 4
OTHER 6
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
306 I will now read you some statements about contraception. Please
tell me if you agree or disagree with each one. DIS-
AGREE AGREE DK
a) Contraception is a woman’s business and a man should not have a) CONTRACEPTION
to worry about it. WOMAN'S BUSINESS 1 2 8
b) Women who use contraception may become promiscuous. b) WOMEN MAY BECOME
PROMISCUOUS 1 2 8
307 CHECK 301 (07): KNOWS MALE CONDOM
YES NO 311
308 Do you know of a place where a person can get male condoms? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 311
M-8
548 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
309 Where is that? PUBLIC SECTOR
GOVT. HOSPITAL . . . . . . . . . . . . . . A
Any other place? GOVT. HEALTH CENTER . . . . . . . B
GOVT. DISPENSARY . . . . . . . . . . . . C
PROBE TO IDENTIFY EACH TYPE OF SOURCE. OTHER PUBLIC
SECTOR D
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR, (SPECIFY)
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . E
PHARMACY/CHEMIST . . . . . . . . . F
NURSING/MATERNITY HOME . . . . . G
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . H
FAMILY OPTIONS/FHOK CLINIC . . . I
OTHER PRIVATE MEDICAL
SECTOR J
WRITE THE NAME OF THE PLACE. (SPECIFY)
OTHER SOURCE
SHOP . . . . . . . . . . . . . . . . . . . . . . . . K
MOBILE CLINIC . . . . . . . . . . . . . . . . L
(NAME OF PLACE(S)) COMMUNITY-BASED DISTRIBUTOR M
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . N
FRIEND/RELATIVE . . . . . . . . . . . . . . O
DISPENSER . . . . . . . . . . . . . . . . . . . . P
OTHER X
(SPECIFY)
310 If you wanted to, could you yourself get a male condom? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
311 CHECK 301 (08): KNOWS FEMALE CONDOM
YES NO 401
312 Do you know of a place where a person can get female condoms? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 401
M-9
Appendix E • 549
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
313 Where is that? PUBLIC SECTOR
GOVT. HOSPITAL . . . . . . . . . . . . . . A
Any other place? GOVT. HEALTH CENTER . . . . . . . B
GOVT. DISPENSARY . . . . . . . . . . . . C
PROBE TO IDENTIFY EACH TYPE OF SOURCE. OTHER PUBLIC
SECTOR D
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE (SPECIFY)
SECTOR, WRITE THE NAME OF THE PLACE.
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . E
PHARMACY/CHEMIST . . . . . . . . . . . . F
(NAME OF PLACE(S)) NURSING/MATERNITY HOME . . . . . G
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . H
FAMILY OPTIONS/FHOK CLINIC
CLINIC . . . . . . . . . . . . . . . . . . . . . . I
OTHER PRIVATE MEDICAL
SECTOR J
(SPECIFY)
OTHER SOURCE
SHOP . . . . . . . . . . . . . . . . . . . . . . . . K
MOBILE CLINIC . . . . . . . . . . . . . . . . L
COMMUNITY-BASED DISTRIBUTOR M
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . N
FRIEND/RELATIVE . . . . . . . . . . . . . . O
OTHER X
(SPECIFY)
314 If you wanted to, could you yourself get a female condom? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
M-10
550 • Appendix E
SECTION 4. MARRIAGE AND SEXUAL ACTIVITY
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
401 Are you currently married or living together with a woman as if YES, CURRENTLY MARRIED . . . . . . . 1
married? YES, LIVING WITH A WOMAN . . . . . 2 404
NO, NOT IN UNION . . . . . . . . . . . . . . . . 3
402 Have you ever been married or lived together with a woman as if YES, FORMERLY MARRIED . . . . . . . 1
married? YES, LIVED WITH A WOMAN . . . . . . . 2
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 413
403 What is your marital status now: are you widowed, divorced, or WIDOWED . . . . . . . . . . . . . . . . . . . . . . 1
separated? DIVORCED . . . . . . . . . . . . . . . . . . . . . . 2 410
SEPARATED . . . . . . . . . . . . . . . . . . . . 3
404 Is your (wife/partner) living with you now or is she staying LIVING WITH HIM . . . . . . . . . . . . . . . . 1
elsewhere? STAYING ELSEWHERE . . . . . . . . . . . . 2
405 Do you have other wives or do you live with other women as if YES (MORE THAN ONE) . . . . . . . . . . . . 1
married? NO (ONLY ONE) . . . . . . . . . . . . . . . . . . 2 407
406 Altogether, how many wives or live-in partners do you have? TOTAL NUMBER OF WIVES
AND LIVE-IN PARTNERS . . .
407 CHECK 405: 408
How old was
ONE WIFE/ MORE THAN (NAME) on her
PARTNER ONE WIFE/ last birthday?
PARTNER
a) Please tell me the name of b) Please tell me the name of
(your wife/the woman you are each of your wives or each
living with as if married). woman you are living with as if
married. LINE
NAME NUMBER AGE
RECORD THE NAME AND THE LINE NUMBER FROM
THE HOUSEHOLD QUESTIONNAIRE FOR EACH WIFE
AND LIVE-IN PARTNER.
IF A WOMAN IS NOT LISTED IN THE HOUSEHOLD,
RECORD '00'.
ASK 408 FOR EACH PERSON.
409 CHECK 407:
MORE THAN
ONE WIFE/ ONE WIFE/
PARTNER PARTNER 411A
410 Have you been married or lived with a woman only once or more ONLY ONCE . . . . . . . . . . . . . . . . . . . . 1
than once? MORE THAN ONCE . . . . . . . . . . . . . . 2 411A
M-11
Appendix E • 551
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
411 In what month and year did you start living with your (wife/partner)?
MONTH ..................
411A Now I would like to ask about your first (wife/partner). In what month
and year did you start living with her? DON'T KNOW MONTH . . . . . . . . . . . . 98
413
YEAR . . . . . . . . . . . .
DON'T KNOW YEAR . . . . . . . . . . . . 9998
412 How old were you when you first started living with her?
AGE ....................
413 CHECK FOR THE PRESENCE OF OTHERS.
BEFORE CONTINUING, MAKE EVERY EFFORT TO ENSURE PRIVACY.
414 Now I would like to ask some questions about sexual activity in NEVER HAD SEXUAL
order to gain a better understanding of some important life issues. INTERCOURSE . . . . . . . . . . . . . . . .00 501
How old were you when you had sexual intercourse for the very first
time? AGE IN YEARS ............
FIRST TIME WHEN STARTED
LIVING WITH (FIRST)
WIFE/PARTNER . . . . . . . . . . . . . . . . 95
414A CHECK 103:
AGE 15-24 AGE 25-54 415
414B The first time you had sexual intercourse, was a condom used? YES ........................ 1
NO ...................... 2
DON'T KNOW/DON'T REMEMBER . . . 8
414C How old was the person you first had sexual intercourse with? AGE OF PARTNER
DON'T KNOW . . . 98
415 Now I would like to ask you some questions about your recent sexual activity. Let me assure you again that your answers are
completely confidential and will not be told to anyone. If we should come to any question that you don't want to answer, just let me
know and we will go to the next question.
416 When was the last time you had sexual intercourse?
DAYS AGO . . . . . . . . . . . . 1
IF LESS THAN 12 MONTHS, ANSWER MUST BE RECORDED WEEKS AGO . . . . . . . . . 2
IN DAYS, WEEKS OR MONTHS.
IF 12 MONTHS (ONE YEAR) OR MORE, ANSWER MUST BE MONTHS AGO ....... 3
RECORDED IN YEARS.
YEARS AGO ......... 4 430
M-12
552 • Appendix E
LAST SECOND-TO-LAST THIRD-TO-LAST
SEXUAL PARTNER SEXUAL PARTNER SEXUAL PARTNER
417 When was the last time you had DAYS DAYS
sexual intercourse with this person? AGO 1 AGO 1
WEEKS WEEKS
AGO 2 AGO 2
MONTHS MONTHS
AGO 3 AGO 3
418 The last time you had sexual YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
intercourse (with this second/third NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
person), was a condom used? (SKIP TO 420) (SKIP TO 420) (SKIP TO 420)
418A What is the main reason you used a PREVENT STD/HIV . 1 PREVENT STD/HIV . 1 PREVENT STD/HIV . 1
condom on that occasion? AVOID PREGNANCY 2 AVOID PREGNANCY 2 AVOID PREGNANCY 2
BOTH PREVENT STD/HIV BOTH PREVENT STD/HIV BOTH PREVENT STD/HIV
AND PREGNANCY 3 AND PREGNANCY 3 AND PREGNANCY 3
DID NOT TRUST PARTNER DID NOT TRUST PARTNER DID NOT TRUST PARTNER
/SHE MAY HAVE OTHER /SHE MAY HAVE OTHER /SHE MAY HAVE OTHER
PARTNERS . . . . . 4 PARTNERS . . . . . 4 PARTNERS . . . . . 4
PARTNER WANTED PARTNER WANTED PARTNER WANTED
TO USE . . . . . . . 5 TO USE . . . . . . . 5 TO USE . . . . . . . 5
OTHER 6 OTHER 6 OTHER 6
(SPECIFY) (SPECIFY) (SPECIFY)
419 Was a condom used every time you YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
had sexual intercourse with this NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
person in the last 12 months?
420 What was your relationship to this WIFE . . . . . . . . . . . . 1 WIFE . . . . . . . . . . . . 1 WIFE . . . . . . . . . . . . 1
person with whom you had sexual LIVE-IN PARTNER . 2 LIVE-IN PARTNER . 2 LIVE-IN PARTNER . 2
intercourse? GIRLFRIEND NOT GIRLFRIEND NOT GIRLFRIEND NOT
LIVING WITH LIVING WITH LIVING WITH
IF GIRLFRIEND: Were you living RESPONDENT . . . 3 RESPONDENT . . . 3 RESPONDENT . . . 3
together as if married? CASUAL CASUAL CASUAL
ACQUAINTANCE . . 4 ACQUAINTANCE . . 4 ACQUAINTANCE . . 4
CLIENT/PROSTITUTE .5 CLIENT/PROSTITUTE .5 CLIENT/PROSTITUTE .5
OTHER 6 OTHER 6 OTHER 6
IF YES, CIRCLE '2'. (SPECIFY) (SPECIFY) (SPECIFY)
IF NO, CIRCLE '3'. (SKIP TO 423) (SKIP TO 423) (SKIP TO 423)
421 CHECK 410: MARRIED MARRIED MARRIED MARRIED MARRIED MARRIED
ONLY MORE ONLY MORE ONLY MORE
ONCE THAN ONCE THAN ONCE THAN
ONCE ONCE ONCE
OR BLANK OR BLANK OR BLANK
(SKIP (SKIP (SKIP
TO 423) TO 423) TO 423)
422 CHECK 414: FIRST TIME FIRST TIME FIRST TIME
WHEN STARTED WHEN STARTED WHEN STARTED
FIRST TIME WHEN STARTED LIVING WITH LIVING WITH LIVING WITH
LIVING WITH FIRST WIFE FIRST WIFE OTHER FIRST WIFE OTHER FIRST WIFE OTHER
(CODE 95)
(SKIP TO 424) (SKIP TO 424) (SKIP TO 424)
423 How long ago did you first have DAYS DAYS DAYS
sexual intercourse with this AGO . 1 AGO . 1 AGO . 1
(second/third) person? WEEKS WEEKS WEEKS
AGO . 2 AGO . 2 AGO . 2
MONTHS MONTHS MONTHS
AGO . 3 AGO . 3 AGO . 3
YEARS YEARS YEARS
AGO . 4 AGO . 4 AGO . 4
M-13
Appendix E • 553
LAST SECOND-TO-LAST THIRD-TO-LAST
SEXUAL PARTNER SEXUAL PARTNER SEXUAL PARTNER
424 How many times during the last 12 NUMBER OF NUMBER OF NUMBER OF
months did you have sexual TIMES . . . . . TIMES . . . . . TIMES . . . . .
intercourse with this person?
IF NON-NUMERIC ANSWER,
PROBE TO GET AN ESTIMATE.
IF NUMBER OF TIMES IS
95 OR MORE, WRITE '95'.
425 How old is this person? AGE OF AGE OF AGE OF
PARTNER . PARTNER . PARTNER .
DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98 DON'T KNOW . . . . . 98
426 Apart from (this person/these two YES . . . . . . . . . . . . . . 1 YES . . . . . . . . . . . . . . 1
people), have you had sexual (GO BACK TO 417 (GO BACK TO 417
intercourse with any other person in IN NEXT COLUMN) IN NEXT COLUMN)
the last 12 months? NO . . . . . . . . . . . . . . 2 NO . . . . . . . . . . . . . . 2
(SKIP TO 428) (SKIP TO 428)
427 In total, with how many different NUMBER OF
people have you had sexual PARTNERS
intercourse in the last 12 months? LAST 12
MONTHS . . .
IF NON-NUMERIC ANSWER,
PROBE TO GET AN ESTIMATE. DON'T KNOW . . . 98
IF NUMBER OF PARTNERS IS
95 OR MORE, WRITE '95'.
M-14
554 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
428 CHECK 420 (ALL COLUMNS):
AT LEAST ONE PARTNER NO PARTNERS
IS PROSTITUTE ARE PROSTITUTES 430
429 CHECK 420 AND 418 (ALL COLUMNS):
CONDOM USED WITH 433
EVERY PROSTITUTE
OTHER
434
430 In the last 12 months, did you pay anyone in exchange for having YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 432
sexual intercourse? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
431 Have you ever paid anyone in exchange for having sexual YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
intercourse? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 434
432 The last time you paid someone in exchange for having sexual YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
intercourse, was a condom used? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 434
433 Was a condom used during sexual intercourse every time you paid YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
someone in exchange for having sexual intercourse in the last 12 NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
months? DON'T KNOW .................. 8
434 In total, with how many different people have you had sexual NUMBER OF PARTNERS
intercourse in your lifetime? IN LIFETIME . . . . . . . . . . . . . .
IF NON-NUMERIC ANSWER, PROBE TO GET AN ESTIMATE. DON'T KNOW . . . . . . . . . . . . . . . . . . . . 98
IF NUMBER OF PARTNERS IS 95 OR MORE,
WRITE '95'.
435 CHECK 418, MOST RECENT PARTNER (FIRST COLUMN):
NOT
ASKED 438
CONDOM NO CONDOM
USED USED 438
M-15
Appendix E • 555
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
437 From where did you obtain the condom the last time? PUBLIC SECTOR
GOVT. HOSPITAL . . . . . . . . . . . . . . 11
GOVT. HEALTH CENTER . . . . . . . 12
PROBE TO IDENTIFY TYPE OF SOURCE. GOVT. DISPENSARY . . . . . . . . . . . . 13
OTHER PUBLIC
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR 16
SECTOR, WRITE THE NAME OF THE PLACE. (SPECIFY)
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC . . . . . 21
(NAME OF PLACE) PHARMACY/CHEMIST . . . . . . . . . . . . 22
NURSING/MATERNITY HOME . . . . . 23
FAITH-BASED, CHURCH, MISSION
HOSPITAL / CLINIC . . . . . . . . . . . . 24
FAMILY OPTIONS/FHOK CLINIC . . . 25
OTHER PRIVATE MEDICAL
SECTOR 26
(SPECIFY)
OTHER SOURCE
SHOP . . . . . . . . . . . . . . . . . . . . . . . . 31
MOBILE CLINIC . . . . . . . . . . . . . . . . 32
COMMUNITY-BASED DISTRIBUTOR 33
COMMUNITY HEALTH WORKER/
CHW . . . . . . . . . . . . . . . . . . . . 34
FRIEND/RELATIVE . . . . . . . . . . . . . . 35
OTHER 96
(SPECIFY)
438 The last time you had sex did you or your partner use any method YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
(other than a condom) to avoid or prevent a pregnancy? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW .................. 8 501
439 What method did you or your partner use? FEMALE STERILIZATION . . . . . . . . . A
MALE STERILIZATION . . . . . . . . . . . . B
PROBE: Did you or your partner use any other method to prevent IUD . . . . . . . . . . . . . . . . . . . . . . . . . . . . C
pregnancy? INJECTABLES .................. D
IMPLANTS . . . . . . . . . . . . . . . . . . . . . . E
PILL . . . . . . . . . . . . . . . . . . . . . . . . . . . . F
FEMALE CONDOM .............. G
RECORD ALL MENTIONED. LAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . J
RHYTHM METHOD . . . . . . . . . . . . . . . . K
WITHDRAWAL . . . . . . . . . . . . . . . . . . L
OTHER MODERN METHOD . . . . . . . X
OTHER TRADITIONAL METHOD . . . . . Y
M-16
556 • Appendix E
SECTION 5. FERTILITY PREFERENCES
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
501 CHECK 401:
CURRENTLY MARRIED OR NOT CURRENTLY MARRIED
LIVING WITH A PARTNER AND 509
NOT LIVING WITH A PARTNER
502 CHECK 439:
MAN NOT STERILIZED MAN
OR 439 IS BLANK STERILIZED 509
503 (Is your (wife/partner)/Are any of your (wives/partners)) currently YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
pregnant? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 505
504 Now I have some questions about the future. After the HAVE ANOTHER CHILD ......... 1 506
(child/children) you and your (wife(wives)/partner(s)) are expecting NO MORE . . . . . . . . . . . . . . . . . . . . . . 2
now, would you like to have another child, or would you prefer not UNDECIDED/DON'T KNOW ....... 8 509
have any more children?
505 Now I have some questions about the future. Would you like to have HAVE (A/ANOTHER) CHILD . . . . . . . 1
(a/another) child, or would you prefer not to have any (more) NO MORE/NONE ................ 2
children? SAYS COUPLE
CAN’T GET PREGNANT . . . . . . . . . 3
509
WIFE (WIVES)/PARTNER(S)
STERILIZED . . . . . . . . . . . . . . . . . . . . 4
UNDECIDED/DON'T KNOW ....... 8
506 CHECK 407:
ONE WIFE/ MORE THAN
PARTNER ONE WIFE/ 508
PARTNER
507 CHECK 503:
WIFE/PARTNER WIFE/PARTNER MONTHS . . . . . . . . . . . . . . 1
NOT PREGNANT PREGNANT
OR DON'T KNOW YEARS .............. 2
509
a) How long would you like to b) After the birth of the child you are SOON/NOW . . . . . . . . . . . . . . . . . . 993
wait from now before the expecting now, how long would COUPLE INFECUND . . . . . . . . . . . . 994
birth of (a/another) child? you like to wait before the birth of
another child? OTHER 996
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . 998
508 How long would you like to wait from now before the birth of
(a/another) child? MONTHS . . . . . . . . . . . . . . 1
YEARS .............. 2
SOON/NOW . . . . . . . . . . . . . . . . . . 993
HE/ALL HIS WIVES/PARTNERS
ARE INFECUND . . . . . . . . . . . . . . 994
OTHER 996
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . 998
M-17
Appendix E • 557
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
509 CHECK 203 AND 205:
HAS LIVING CHILDREN NO LIVING CHILDREN
NONE . . . . . . . . . . . . . . . . . . . . . . . . . . 00 601
a) If you could go back to the b) If you could choose exactly the
time you did not have any number of children to have in NUMBER . . . . . . . . . . . . . . . . . .
children and could choose your whole life, how many would
exactly the number of that be?
children to have in your OTHER 96 601
whole life, how many would (SPECIFY)
that be?
PROBE FOR A NUMERIC RESPONSE.
510 How many of these children would you like to be boys, how many BOYS GIRLS EITHER
would you like to be girls and for how many would it not matter if it’s
a boy or a girl? NUMBER
OTHER 96
(SPECIFY)
M-18
558 • Appendix E
SECTION 6. EMPLOYMENT AND GENDER ROLES
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
601 Have you done any work in the last seven days? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 604
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
602 Although you did not work in the last seven days, do you have any
job or business from which you were absent for leave, illness, YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 604
vacation, or any other such reason? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
603 Have you done any work in the last 12 months? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 607
604 What is your occupation, that is, what kind of work do you mainly
do?
604A CHECK 604:
WORKS IN DOES NOT WORK
AGRICULTURE IN AGRICULTURE 605
604B Do you work mainly on your own land or on family land, or do you OWN LAND . . . . . . . . . . . . . . . . . . . . . . 1
work on land that you rent from someone else, or do you work on FAMILY LAND . . . . . . . . . . . . . . . . . . . . 2
someone else's land? RENTED LAND . . . . . . . . . . . . . . . . . . 3
SOMEONE ELSE'S LAND . . . . . . . . . 4
OTHER 6
(SPECIFY)
605 Do you usually work throughout the year, or do you work seasonally, THROUGHOUT THE YEAR . . . . . . . . . 1
or only once in a while? SEASONALLY/PART OF THE YEAR . 2
ONCE IN A WHILE . . . . . . . . . . . . . . . . 3
606 Are you paid in cash or kind for this work or are you not paid at all? CASH ONLY . . . . . . . . . . . . . . . . . . . . 1
CASH AND KIND . . . . . . . . . . . . . . . . . . 2
IN KIND ONLY . . . . . . . . . . . . . . . . . . . . 3
NOT PAID . . . . . . . . . . . . . . . . . . . . . . 4
607 CHECK 401:
CURRENTLY MARRIED OR NOT CURRENTLY MARRIED
LIVING WITH A PARTNER AND 612
NOT LIVING WITH A PARTNER
608 CHECK 606:
CODE 1 OR 2 OTHER 610
CIRCLED
609 Who usually decides how the money you earn will be used: you, RESPONDENT . . . . . . . . . . . . . . . . . . 1
your (wife/partner), or you and your (wife/partner) jointly? WIFE/PARTNER . . . . . . . . . . . . . . . . . . 2
RESPONDENT AND WIFE/
PARTNER JOINTLY . . . . . . . . . . . . . . 3
OTHER 6
(SPECIFY)
610 Who usually makes decisions about health care for yourself: you, RESPONDENT . . . . . . . . . . . . . . . . . . 1
your (wife/partner), you and your (wife/partner) jointly, or someone WIFE/PARTNER . . . . . . . . . . . . . . . . . . 2
else? RESPONDENT AND WIFE/
PARTNER JOINTLY . . . . . . . . . . . . . . 3
SOMEONE ELSE . . . . . . . . . . . . . . . . 4
OTHER 6
(SPECIFY)
M-19
Appendix E • 559
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
611 Who usually makes decisions about making major household RESPONDENT . . . . . . . . . . . . . . . . . . 1
purchases? WIFE/PARTNER . . . . . . . . . . . . . . . . . . 2
RESPONDENT AND WIFE/
PARTNER JOINTLY . . . . . . . . . . . . . . 3
SOMEONE ELSE . . . . . . . . . . . . . . . . 4
OTHER 6
(SPECIFY)
612 Do you own this or any other house either alone or jointly with ALONE ONLY . . . . . . . . . . . . . . . . . . . . 1
someone else? JOINTLY ONLY . . . . . . . . . . . . . . . . . . 2
BOTH ALONE AND JOINTLY ..... 3
DOES NOT OWN . . . . . . . . . . . . . . . . 4
613 Do you own any land either alone or jointly with someone else? ALONE ONLY . . . . . . . . . . . . . . . . . . . . 1
JOINTLY ONLY . . . . . . . . . . . . . . . . . . 2
BOTH ALONE AND JOINTLY ..... 3
DOES NOT OWN . . . . . . . . . . . . . . . . 4
614 In your opinion, is a husband justified in hitting or beating his wife in
the following situations: YES NO DK
a) If she goes out without telling him? a) GOES OUT . . . . . . . . . 1 2 8
b) If she neglects the children? b) NEGL. CHILDREN . . . 1 2 8
c) If she argues with him? c) ARGUES . . . . . . . . . . . . 1 2 8
d) If she refuses to have sex with him? d) REFUSES SEX . . . . . 1 2 8
e) If she burns the food? e) BURNS FOOD . . . . . . . 1 2 8
M-20
560 • Appendix E
SECTION 7. HIV/AIDS
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
701 Now I would like to talk about something else. Have you ever heard YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
of an illness called AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 723
702 Can people reduce their chance of getting the AIDS virus by having YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
just one uninfected sex partner who has no other sex partners? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
703 Can people get the AIDS virus from mosquito bites? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
704 Can people reduce their chance of getting the AIDS virus by using a YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
condom every time they have sex? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
705 Can people get the AIDS virus by sharing food with a person who YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
has AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
705A Can people reduce their chance of getting the AIDS virus by not YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
having sexual intercourse at all? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
706 Can people get the AIDS virus because of witchcraft or other YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
supernatural means? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
707 Is it possible for a healthy-looking person to have the AIDS virus? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
707A Do you know someone personally who has the virus that causes YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
AIDS or someone who has died of AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
708 Can the virus that causes AIDS be transmitted from a mother to her
baby: YES NO DK
a) During pregnancy? a) DURING PREG. . . . . . 1 2 8
b) During delivery? b) DURING DELIVERY . . . 1 2 8
c) By breastfeeding? c) BREASTFEEDING . . . 1 2 8
709 CHECK 708:
AT LEAST OTHER
ONE 'YES' 711
710 Are there any special drugs that a doctor or a nurse can give to a YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
woman infected with the AIDS virus to reduce the risk of NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
transmission to the baby? DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
711 CHECK FOR PRESENCE OF OTHERS. BEFORE CONTINUING, MAKE EVERY EFFORT TO ENSURE PRIVACY.
712 I don't want to know the results, but have you ever been tested to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
see if you have the AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 716
713 How many months ago was your most recent HIV test?
MONTHS AGO ............
TWO OR MORE YEARS . . . . . . . . . 95
714 I don't want to know the results, but did you get the results of the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
test? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
M-21
Appendix E • 561
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
715 Where was the test done? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . 11
PROBE TO IDENTIFY THE TYPE OF SOURCE. GOVT. HEALTH CENTER\CLINIC . . . 12
GOVERNMENT DISPENSARY . . . . 13
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR, OTHER PUBLIC
WRITE THE NAME OF THE PLACE. SECTOR 18
(SPECIFY)
PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC/ . . . . . 21
(NAME OF PLACE) MISSIONARY/CHURCH HOSP./
CLINIC . . . . . . . . . . . . . . . . . . . . 22
FAMILY OPTIONS/FHOK CLINIC . . 23 717A
VCT CENTRE . . . . . . . . . . . . . . . . . . 24
NURSING/MATERNITY HOMES . . 25
BLOOD TRANSFUSION SERVICES 26
OTHER PRIVATE
MEDICAL SECTOR
27
(SPECIFY)
OTHER SOURCE
HOME . . . . . . . . . . . . . . . . . . . . . . . . 31
CORRECTIONAL FACILITY . . . . . . . 32
OTHER 96
(SPECIFY)
716 Do you know of a place where people can go to get tested for the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 717A
717 Where is that? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . A
Any other place? GOVT. HEALTH CENTER\CLINIC . . . B
GOVERNMENT DISPENSARY . . . . C
OTHER PUBLIC
PROBE TO IDENTIFY EACH TYPE OF SOURCE. SECTOR D
(SPECIFY)
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR,
WRITE THE NAME OF THE PLACE. PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC/ . . . . . E
MISSIONARY/CHURCH HOSP./
(NAME OF PLACE) CLINIC . . . . . . . . . . . . . . . . . . . . F
FAMILY OPTIONS/FHOK CLINIC . . G
VCT CENTRE . . . . . . . . . . . . . . . . . . H
NURSING/MATERNITY HOMES . . I
BLOOD TRANSFUSION SERVICES . J
OTHER PRIVATE
MEDICAL SECTOR
K
(SPECIFY)
OTHER X
(SPECIFY)
717A CHECK 401:
CURRENTLY MARRIED OR NOT CURRENTLY MARRIED
LIVING WITH A PARTNER AND 718
NOT LIVING WITH A PARTNER
717B Have you ever talked with your wife / partner about ways to prevent YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
getting the virus that causes AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
718 Would you buy fresh vegetables from a shopkeeper or vendor if you YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
knew that this person had the AIDS virus? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
M-22
562 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
719 If a member of your family got infected with the AIDS virus, would YES, REMAIN A SECRET . . . . . . . . . 1
you want it to remain a secret or not? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DK/NOT SURE/DEPENDS . . . . . . . . . 8
720 If a member of your family became sick with AIDS, would you be YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
willing to care for her or him in your own household? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DK/NOT SURE/DEPENDS . . . . . . . . . 8
721 In your opinion, if a female teacher has the AIDS virus but is not SHOULD BE ALLOWED . . . . . . . . . . . . 1
sick, should she be allowed to continue teaching in the school? SHOULD NOT BE ALLOWED . . . . . . . 2
DK/NOT SURE/DEPENDS . . . . . . . . . 8
722 Should children age 12-14 be taught about using a condom to avoid YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
getting AIDS? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DK/NOT SURE/DEPENDS . . . . . . . . . 8
723 CHECK 701:
HEARD ABOUT NOT HEARD
AIDS ABOUT AIDS
a) Apart from AIDS, have you b) Have you heard about
heard about other infections infections that can be
that can be transmitted transmitted through sexual YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
through sexual contact? contact? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 724
723A If a man has a sexually transmitted disease, what symptoms might ABDOMINAL PAIN . . . . . . . . . . . . . . . . A
he have? GENITAL DISCHARGE/DRIPPING . . . B
FOUL SMELL/DISCHARGE . . . . . . . . . C
Any others? BURNING PAIN ON URINATION . . . . . D
REDNESS/INFLAMATION IN GENITAL
AREA . . . . . . . . . . . . . . . . . . . . . . . . E
RECORD ALL MENTIONED SWELLING IN GENITAL AREA . . . . . F
GENITAL SORES/ULCERS . . . . . . . . . G
GENITAL WARTS . . . . . . . . . . . . . . . . H
GENITAL ITCHING . . . . . . . . . . . . . . . . I
BLOOD IN URINE . . . . . . . . . . . . . . . . . . J
LOSS OF WEIGHT . . . . . . . . . . . . . . . . K
IMPOTENCE/NO ERECTION . . . . . . . L
OTHER W
(SPECIFY)
OTHER X
(SPECIFY)
NO SYMPTOMS . . . . . . . . . . . . . . . . . . Y
DOES NOT KNOW . . . . . . . . . . . . . . . . Z
723B If a woman has a sexually transmitted disease, what symptoms ABDOMINAL PAIN ................ A
might she have? GENITAL DISCHARGE/DRIPPING . . . B
FOUL SMELL/DISCHARGE . . . . . . . . . C
Any others? BURNING PAIN ON URINATION . . . . . D
REDNESS/INFLAMATION IN GENITAL
AREA . . . . . . . . . . . . . . . . . . . . . . . . E
RECORD ALL MENTIONED SWELLING IN GENITAL AREA . . . . . F
GENITAL SORES/ULCERS . . . . . . . . . G
GENITAL WARTS . . . . . . . . . . . . . . . . H
GENITAL ITCHING . . . . . . . . . . . . . . . . I
BLOOD IN URINE . . . . . . . . . . . . . . . . J
LOSS OF WEIGHT . . . . . . . . . . . . . . . . K
HARD TO GET PREGNANT . . . . . . . . . L
OTHER W
(SPECIFY)
OTHER X
(SPECIFY)
NO SYMPTOMS . . . . . . . . . . . . . . . . . . Y
DOES NOT KNOW . . . . . . . . . . . . . . . . Z
M-23
Appendix E • 563
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
724 CHECK 414:
HAS HAD SEXUAL HAS NOT HAD SEXUAL
INTERCOURSE INTERCOURSE 732
725 CHECK 723: HEARD ABOUT OTHER SEXUALLY TRANSMITTED INFECTIONS?
YES NO 727
726 Now I would like to ask you some questions about your health in the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
last 12 months. During the last 12 months, have you had a disease NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
which you got through sexual contact? DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
727 Sometimes men experience an abnormal discharge from their penis. YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
During the last 12 months, have you had an abnormal discharge NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
from your penis? DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
728 Sometimes men have a sore or ulcer near their penis. During the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
last 12 months, have you had a sore or ulcer near your penis? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
729 CHECK 726, 727, AND 728:
HAS HAD AN HAS NOT HAD AN
INFECTION INFECTION OR 732
(ANY 'YES') DOES NOT KNOW
730 The last time you had (PROBLEM FROM 726/727/728), did you YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
seek any kind of advice or treatment? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 731A
731 Where did you go? PUBLIC SECTOR
GOVERNMENT HOSPITAL . . . . . . . A
Any other place? GOVT. HEALTH CENTRE/CLINIC . B
GOVT. DISPENSARY . . . . . . . . . . . . C
OTHER PUBLIC
PROBE TO IDENTIFY EACH TYPE OF SOURCE. SECTOR D
(SPECIFY)
IF UNABLE TO DETERMINE IF PUBLIC OR PRIVATE SECTOR,
WRITE THE NAME OF THE PLACE. PRIVATE MEDICAL SECTOR
PRIVATE HOSPITAL/CLINIC/
PRIVATE DOCTOR . . . . . . . . . . . . E
(NAME OF PLACE(S)) MISSIONARY/CHURCH HOSP/CLINIC F
FAMILY OPTIONS/FHOK CLINIC . . . G
VCT CENTRE . . . . . . . . . . . . . . . . . . H
NURSING/MATERNITY HOMES . . . I
BLOOD TRANSFUSION SERVICES . J
OTHER PRIVATE MEDICAL . . . . . . .
SECTOR K
(SPECIFY)
OTHER SOURCE
SHOP/PHARMACY . . . . . . . . . . . . . . M
TRADITIONAL HEALER . . . . . . . . . N
FRIENDS/RELATIVES . . . . . . . . . . . . O
OTHER X
(SPECIFY)
731A When you had (PROBLEM(S) FROM 726/727/728), did you inform YES, INFORMED ALL PARTNERS . . . 1
the persons with whom you were having sex? INFORMED SOME, NOT ALL . . . . . . . 2
NO, INFORMED NONE . . . . . . . . . . . . . . 3
DID NOT HAVE A PARTNER . . . . . . . 4 732
731B When you had (PROBLEM(S) FROM 726/727/728), did you do YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
anything to avoid infecting your sexual partner(s)? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 732
M-24
564 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
731C What did you do to avoid infecting your partner(s)? Did you: YES NO
a) Use medicine? a) USE MEDICINE . . . . . . . . . . . . 1 2
b) Stop sex? b) STOP HAVING SEX . . . . . . . . . 1 2
c) Use a condom when having sex? c) USE CONDOM . . . . . . . . . . . . . . 1 2
732 If a wife knows her husband has a disease that she can get during YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
sexual intercourse, is she justified in asking that they use a condom NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
when they have sex? DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
733 Is a wife justified in refusing to have sex with her husband when she YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
knows her husband has sex with women other than his wives? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
M-25
Appendix E • 565
SECTION 8. OTHER HEALTH ISSUES
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
801 Some men are circumcised, that is, the foreskin is completely YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
removed from the penis. Are you circumcised? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8 805
802 How old were you when you got circumcised? AGE IN
COMPLETED YEARS . . . . . . . .
DURING CHILDHOOD (<5 YEARS) . 95
DON'T KNOW . . . . . . . . . . . . . . . . . . 98
803 Who did the circumcision? TRADITIONAL PRACTITIONER/
FAMILY/FRIEND .............. 1
HEALTH WORKER/PROFESSIONAL 2
OTHER . . . . . . . . . . . . . . . . . . . . . . . . . 3
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
804 Where was it done? HEALTH FACILITY . . . . . . . . . . . . . . . . 1
HOME OF A HEALTH WORKER/
PROFESSIONAL . . . . . . . . . . . . . . . . 2
CIRCUMCISION DONE AT HOME . . . 3
RITUAL SITE . . . . . . . . . . . . . . . . . . . . 4
OTHER HOME/PLACE ............ 5
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
805 Now I would like to ask you some other questions relating to health
matters. Have you had an injection for any reason in the last 12
months? NUMBER OF INJECTIONS . . .
IF YES: How many injections have you had?
IF NUMBER OF INJECTIONS IS 90 OR MORE, NONE . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 807A
OR DAILY FOR 3 MONTHS OR MORE, RECORD '90'.
IF NON-NUMERIC ANSWER, PROBE TO GET AN ESTIMATE.
806 Among these injections, how many were administered by a doctor, a
nurse, a pharmacist, a dentist, or any other health worker? NUMBER OF INJECTIONS . . .
IF NUMBER OF INJECTIONS IS 90 OR MORE,
OR DAILY FOR 3 MONTHS OR MORE, RECORD '90'. NONE . . . . . . . . . . . . . . . . . . . . . . . . . . . 00 807A
IF NON-NUMERIC ANSWER, PROBE TO GET AN ESTIMATE.
807 The last time you got an injection from a health worker, did he/she YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
take the syringe and needle from a new, unopened package? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
807A Have you ever been told by a doctor or health worker that you have YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
raised blood pressure or hypertension?
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
807B Have you ever been told by a doctor or health worker that you have YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
raised blood sugar or diabetes? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
807C In the past 12 months, have you been involved in a road traffic YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
accident as a driver, passenger, pedestrian, or cyclist? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
807D In the past 12 months, were you injured accidentally, not related to a YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
traffic accident? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 807F
M-26
566 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
807E How did the injury happen? FALL . . . . . . . . . . . . . . . . . . . . . . . . . . . A
BURN . . . . . . . . . . . . . . . . . . . . . . . . . . . B
RECORD ALL MENTIONED POISONING . . . . . . . . . . . . . . . . . . . . . . . C
CUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D
NEAR-DROWNING . . . . . . . . . . . . . . . . E
ANIMAL BITE . . . . . . . . . . . . . . . . . . . . F
SHOOTING . . . . . . . . . . . . . . . . . . . . . . . G
OTHER X
(SPECIFY)
807F Have you ever heard of an illness called tuberculosis or TB? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 808
807G How does tuberculosis spread from one person to another? THROUGH THE AIR WHEN
COUGHING OR SNEEZING . . . . . . . . A
PROBE: Any other ways? THROUGH SHARING UTENSILS . . . . . B
THROUGH TOUCHING A PERSON
WITH TB . . . . . . . . . . . . . . . . . . . . . . . C
THROUGH FOOD . . . . . . . . . . . . . . . . D
RECORD ALL MENTIONED THROUGH SEXUAL CONTACT . . . . . E
THROUGH MOSQUITO BITES . . . . . . . . F
OTHER X
(SPECIFY)
DON'T KNOW . . . . . . . . . . . . . . . . . . . . Z
808 Do you currently smoke cigarettes? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 810
809 In the last 24 hours, how many cigarettes did you smoke? NUMBER OF
CIGARETTES . . . . . . . . . . . . . .
810 Do you currently smoke or use any (other) type of tobacco? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 811A
811 What (other) type of tobacco do you currently smoke or use? PIPE . . . . . . . . . . . . . . . . . . . . . . . . . . . A
CHEWING TOBACCO . . . . . . . . . . . . . . B
SNUFF . . . . . . . . . . . . . . . . . . . . . . . . . C
RECORD ALL MENTIONED. WATER PIPE / SHISHA . . . . . . . . . . . . D
OTHER X
(SPECIFY)
811A Do you drink alcohol? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 811C
811B During the last two weeks, on how many days did you have at least NUMBER OF
one alcoholic drink? DAYS . . . . . . . . . . . . . . . . . . . .
811C Are you involved in exercise that causes an increase in your heart
rate for at least 10 minutes continuously? YES NO
a) At work? a) AT WORK ................ 1 2
b) During other physical activities? b) OTHER PHYSICAL ACTIVITIES 1 2
811D Now I would like to ask you about men's health. Have you ever YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
heard of prostate cancer? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 811I
811E Has a doctor or health care professional ever examined you to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
detect or test for prostate cancer? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 811I
811F Did this prostate exam happen within the last 5 years? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
811G Did the doctor or health care professional who examined you tell you YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
that you have a problem with your prostate? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 811I
M-27
Appendix E • 567
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
811H Were you treated or referred for treatment for the prostate problem? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
811I Sometimes a woman can have a problem of constant leakage of YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
urine or stool from her vagina during the day and night. This problem NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
usually occurs after a difficult childbirth, but may also occur after a
sexual assault or after pelvic surgery.
Have you ever heard of this problem?
812 Are you covered by any health insurance? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 901
813 What type of health insurance are you covered by? MUTUAL HEALTH ORGANIZATION/
COMMUNITY-BASED HEALTH
INSURANCE .................. A
RECORD ALL MENTIONED. HEALTH INSURANCE THROUGH
EMPLOYER . . . . . . . . . . . . . . . . . . . . B
NATIONAL HEATLH INSURANCE
SCHEME . . . . . . . . . . . . . . . . . . . . . . . C
PRIVATELY PURCHASED
COMMERCIAL HEALTH INSURANCE . D
PRE-PAYMENT SCHEME . . . . . . . . . . . . E
OTHER X
(SPECIFY)
M-28
568 • Appendix E
SECTION 9. FEMALE GENITAL CUTTING
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
901 Have you ever heard of female circumcision? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 902A
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
902 In some countries, there is a practice in which a girl may have part YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
of her genitals cut. Have you ever heard about this practice? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1001
902A Do you believe that female circumcision is required by your YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
community? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
903 Do you believe that female circumcision is required by your religion? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
NO RELIGION .................. 3
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
904 Do you think that female circumcision should be continued, or CONTINUED . . . . . . . . . . . . . . . . . . . . 1
should it be stopped? STOPPED . . . . . . . . . . . . . . . . . . . . . . 2
DEPENDS . . . . . . . . . . . . . . . . . . . . . . 3
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
M-29
Appendix E • 569
SECTION 10: DOMESTIC VIOLENCE
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1001 CHECK COVER PAGE: IS MAN SELECTED FOR SECTION 10?
MAN SELECTED MAN NOT SELECTED OR
FOR THIS SECTION HH QUESTION 101B IS BLANK 1033
1001A CHECK FOR PRESENCE OF OTHERS:
DO NOT CONTINUE UNTIL PRIVACY IS ENSURED.
PRIVACY PRIVACY
OBTAINED . . . . . . . 1 NOT POSSIBLE . . . . . . . 2 1032
READ TO THE RESPONDENT
Now I would like to ask you questions about some other important aspects of a man's life. You may find some of these
questions very personal. However, your answers are crucial for helping to understand the condition of men in Kenya.
Let me assure you that your answers are completely confidential and will not be told to anyone and no one else in your
household will know that you were asked these questions.
1002 CHECK 401 AND 402:
FORMERLY
CURRENTLY MARRIED/ NEVER MARRIED/
MARRIED/ LIVED WITH A WOMAN NEVER LIVED WITH
LIVING (READ IN PAST TENSE A WOMAN
WITH A WOMAN AND USE 'LAST' WITH 1016
WIFE/PARTNER')
1003 First, I am going to ask you about some situations which happen to
some men. Please tell me if these apply to your relationship with
your (last) (wife/partner)?
YES NO DK
a) She (is/was) jealous or angry if you (talk/talked) to other women? a) JEALOUS ....... 1 2 8
b) She frequently (accuses/accused) you of being unfaithful? b) ACCUSES ....... 1 2 8
c) She (does/did) not permit you to meet your male friends? c) NOT MEET FRIENDS . 1 2 8
d) She (tries/tried) to limit your contact with your family? d) NO FAMILY . . . . . . . 1 2 8
e) She (insists/insisted) on knowing where you (are/were) at all e) WHERE YOU ARE . 1 2 8
times?
1004 Now I need to ask some more questions about your relationship
with your (last) (wife/partner).
A Did your (last) (wife/partner) ever: B How often did this happen during the last 12
months: often, only sometimes, or not at
all?
SOME- NOT IN LAST
EVER OFTEN TIMES 12 MONTHS
a) Say or do something to humiliate you in front of a) YES 1 1 2 3
others? NO 2
b) Threaten to hurt or harm you or someone you b) YES 1 1 2 3
care about? NO 2
c) Insult you or make you feel bad about yourself? c) YES 1 1 2 3
NO 2
M-30
570 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1005 A Did your (last) (wife/partner) ever do any of the B How often did this happen during the last 12
following things to you: months: often, only sometimes, or not at
all?
SOME- NOT IN LAST
EVER OFTEN TIMES 12 MONTHS
a) Push you, shake you, or throw something at you? a) YES 1 1 2 3
NO 2
b) Slap you? b) YES 1 1 2 3
NO 2
c) Twist your arm or pull your hair? c) YES 1 1 2 3
NO 2
d) Punch you with her fist or with something that d) YES 1 1 2 3
could hurt you? NO 2
e) Kick you, drag you, or beat you up? e) YES 1 1 2 3
NO 2
f) Try to choke you or burn you on purpose? f) YES 1 1 2 3
NO 2
g) Threaten or attack you with a knife, gun, or other g) YES 1 1 2 3
weapon? NO 2
h) Physically force you to have sexual intercourse h) YES 1 1 2 3
with her when you did not want to? NO 2
i) Physically force you to perform any other sexual i) YES 1 1 2 3
acts you did not want to? NO 2
j) Force you with threats or in any other way to j) YES 1 1 2 3
perform sexual acts you did not want to? NO 2
1006 CHECK 1005A (a-j):
AT LEAST ONE NOT A SINGLE
'YES' 'YES' 1009
1007 How long after you first (got married/started living together) with
your (last) (wife/partner) did (this/any of these things) first happen? NUMBER OF YEARS . . . . . . .
BEFORE MARRIAGE/BEFORE
IF LESS THAN ONE YEAR, RECORD '00'. LIVING TOGETHER . . . . . . . . . . . . . . 95
1008 Did the following ever happen as a result of what your (last)
(wife/partner) did to you:
a) You had cuts, bruises, or aches? a) YES . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . 2
b) You had eye injuries, sprains, dislocations, or burns? b) YES . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . 2
c) You had deep wounds, broken bones, broken teeth, or any other c) YES . . . . . . . . . . . . . . . . . . . . . . . . . . 1
serious injury? NO . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1009 Have you ever hit, slapped, kicked, or done anything else to YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
physically hurt your (last) (wife/partner) at times when she was not NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1011
already beating or physically hurting you?
M-31
Appendix E • 571
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1010 In the last 12 months, how often have you done this to your (last) OFTEN . . . . . . . . . . . . . . . . . . . . . . . . 1
(wife/partner): often, only sometimes, or not at all? SOMETIMES . . . . . . . . . . . . . . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
1011 Does (did) your (last) (wife/partner) drink alcohol? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1013
1012 How often does (did) she get drunk: often, only sometimes, or OFTEN . . . . . . . . . . . . . . . . . . . . . . . . 1
never? SOMETIMES . . . . . . . . . . . . . . . . . . . . 2
NEVER . . . . . . . . . . . . . . . . . . . . . . . . 3
1013 Are (Were) you afraid of your (last) (wife/partner): most of the time, MOST OF THE TIME AFRAID . . . . . . . 1
sometimes, or never? SOMETIMES AFRAID . . . . . . . . . . . . . . 2
NEVER AFRAID . . . . . . . . . . . . . . . . . . 3
1014 CHECK 410:
MARRIED MORE MARRIED ONLY
THAN ONCE ONCE 1016
OR 410 IS BLANK
1015 A So far we have been talking about the behavior of your B How long ago did this last happen?
(current/last) (wife/partner). Now I want to ask you about the
behavior of any previous (wife/partner).
0 - 11 12+ DON'T
EVER MONTHS MONTHS REMEMBER
AGO AGO
a) Did any previous (wife/partner) ever hit, slap, a) YES 1 1 2 3
kick, or do anything else to hurt you physically? NO 2
b) Did any previous (wife/partner) physically force b) YES 1 1 2 3
you to have intercourse or perform any other NO 2
sexual acts against your will?
1016 CHECK 401 AND 402:
EVER MARRIED/EVER NEVER MARRIED/NEVER
LIVED WITH A WOMAN LIVED WITH A WOMAN
a) From the time you were 15 years b) From the time you were 15 YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
old has anyone other than years old has anyone hit you, NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
(your/any) (wife/partner) hit you, slapped you, kicked you, or REFUSED TO ANSWER/
slapped you, kicked you, or done done anything else to hurt you NO ANSWER . . . . . . . . . . . . . . . . . . 3 1022
anything else to hurt you physically?
physically?
1017 Who has hurt you in this way? MOTHER/STEP-MOTHER . . . . . . . . . A
FATHER/STEP-FATHER . . . . . . . . . . . . B
Anyone else? SISTER/BROTHER . . . . . . . . . . . . . . . . C
DAUGHTER/SON . . . . . . . . . . . . . . . . D
OTHER RELATIVE . . . . . . . . . . . . . . . . E
CURRENT GIRLFRIEND . . . . . . . . . . . . F
RECORD ALL MENTIONED. FORMER GIRLFRIEND . . . . . . . . . . . . G
MOTHER-IN-LAW . . . . . . . . . . . . . . . . H
FATHER-IN-LAW ................ I
OTHER IN-LAW . . . . . . . . . . . . . . . . . . J
TEACHER . . . . . . . . . . . . . . . . . . . . . . K
EMPLOYER/SOMEONE AT WORK . . . L
POLICE/SOLDIER . . . . . . . . . . . . . . . . M
OTHER X
(SPECIFY)
1018 In the last 12 months, how often has (this person/have these OFTEN . . . . . . . . . . . . . . . . . . . . . . . . 1
persons) physically hurt you: often, only sometimes, or not at all? SOMETIMES . . . . . . . . . . . . . . . . . . . . 2
NOT AT ALL . . . . . . . . . . . . . . . . . . . . 3
M-32
572 • Appendix E
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1022 CHECK 401 AND 402:
EVER MARRIED/EVER NEVER MARRIED/NEVER 1022B
LIVED WITH A WOMAN LIVED WITH A WOMAN
1022A Now I want to ask you about things that may have been done to you
by someone other than (your/any) (wife/partner).
YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1023
At any time in your life, as a child or as an adult, has anyone ever NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
forced you in any way to have sexual intercourse or perform any REFUSED TO ANSWER/
other sexual acts when you did not want to? NO ANSWER . . . . . . . . . . . . . . . . . . 3 1024A
1022B At any time in your life, as a child or as an adult, has anyone ever YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
forced you in any way to have sexual intercourse or perform any NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
other sexual acts when you did not want to? REFUSED TO ANSWER/
NO ANSWER . . . . . . . . . . . . . . . . . . 3 1026
1023 Who was the person who was forcing you the very first time this CURRENT WIFE/PARTNER . . . . . . . . . 01
happened? FORMER WIFE/PARTNER . . . . . . . . . 02
CURRENT/FORMER GIRLFRIEND . . . 03
FATHER/STEP-FATHER . . . . . . . . . . . . 04
BROTHER/STEP-BROTHER . . . . . . . 05
OTHER RELATIVE . . . . . . . . . . . . . . . . 06
IN-LAW . . . . . . . . . . . . . . . . . . . . . . . . 07
OWN FRIEND/ACQUAINTANCE . . . . . 08
FAMILY FRIEND . . . . . . . . . . . . . . . . . . 09
TEACHER . . . . . . . . . . . . . . . . . . . . . . 10
EMPLOYER/SOMEONE AT WORK . . . 11
POLICE/SOLDIER . . . . . . . . . . . . . . . . 12
PRIEST/RELIGIOUS LEADER . . . . . . . 13
STRANGER . . . . . . . . . . . . . . . . . . . . 14
OTHER 96
(SPECIFY)
1024 CHECK 401 AND 402:
EVER MARRIED/EVER NEVER MARRIED/NEVER
LIVED WITH A WOMAN LIVED WITH A WOMAN
a) In the last 12 months, has b) In the last 12 months has
anyone other than (your/any) anyone physically forced you
(wife/partner) physically forced to have sexual intercourse YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
you to have sexual intercourse when you did not want to? NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1025
when you did not want to?
1024A CHECK 1005A (h-j) and 1015A(b)
AT LEAST ONE NOT A
'YES' SINGLE 'YES' 1026
1025 CHECK 401 AND 402:
EVER MARRIED/EVER NEVER MARRIED/NEVER
LIVED WITH A WOMAN LIVED WITH A WOMAN
a) How old were you the first time b) How old were you the first time
you were forced to have sexual you were forced to have
intercourse or perform any other sexual intercourse or perform AGE IN COMPLETED YEARS .
sexual acts by anyone, including any other sexual acts?
(your/any) wife/partner? DON'T KNOW . . . . . . . . . . . . . . . . . . . . 98
M-33
Appendix E • 573
NO. QUESTIONS AND FILTERS CODING CATEGORIES SKIP
1026 CHECK 1005A (a-j), 1015A (a,b), 1016, 1022A, AND 1022B:
AT LEAST ONE NOT A SINGLE
'YES' 'YES' 1030
1027 Thinking about what you yourself have experienced among the YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
different things we have been talking about, have you ever tried to NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1029
seek help?
1028 From whom have you sought help? OWN FAMILY . . . . . . . . . . . . . . . . . . . . A
WIFE'S/PARTNER'S FAMILY . . . . . . . B
Anyone else? CURRENT/FORMER
WIFE/PARTNER . . . . . . . . . . . . . . . . C
RECORD ALL MENTIONED. CURRENT/FORMER GIRLFRIEND . . . D
FRIEND . . . . . . . . . . . . . . . . . . . . . . . . E
NEIGHBOR . . . . . . . . . . . . . . . . . . . . . . F
RELIGIOUS LEADER . . . . . . . . . . . . . . G 1030
DOCTOR/MEDICAL PERSONNEL . . . H
POLICE . . . . . . . . . . . . . . . . . . . . . . . . I
LAWYER ...................... J
SOCIAL SERVICE ORGANIZATION . K
OTHER X
(SPECIFY)
1029 Have you ever told any one about this? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1030 As far as you know, did your father ever beat your mother? YES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
NO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
DON'T KNOW . . . . . . . . . . . . . . . . . . . . 8
THANK THE RESPONDENT FOR HIS COOPERATION AND REASSURE HIM ABOUT THE CONFIDENTIALITY OF HIS
ANSWERS. FILL OUT THE QUESTIONS BELOW WITH REFERENCE TO THE DOMESTIC VIOLENCE MODULE ONLY.
1031 DID YOU HAVE TO INTERRUPT THE YES YES, MORE
INTERVIEW BECAUSE SOME ADULT WAS ONCE THAN ONCE NO
TRYING TO LISTEN, OR CAME INTO THE WIFE ............ 1 2 3
ROOM, OR INTERFERED IN ANY OTHER OTHER FEMALE ADULT . 1 2 3
WAY? MALE ADULT . . . . . . . . . 1 2 3
1032 INTERVIEWER'S COMMENTS / EXPLANATION FOR NOT COMPLETING THE DOMESTIC VIOLENCE MODULE
1033 RECORD THE TIME.
HOUR . . . . . . . . . . . . . . . . . . . .
MINUTES . . . . . . . . . . . . . . . . . .
M-34
574 • Appendix E
INTERVIEWER'S OBSERVATIONS
TO BE FILLED IN AFTER COMPLETING INTERVIEW
COMMENTS ABOUT RESPONDENT:
COMMENTS ON SPECIFIC QUESTIONS:
ANY OTHER COMMENTS:
SUPERVISOR'S OBSERVATIONS
NAME OF SUPERVISOR: DATE:
EDITOR'S OBSERVATIONS
NAME OF EDITOR: DATE:
M-35
Appendix E • 575
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