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HIV/AIDS in Ethiopia: Health View

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JAS0010.1177/0021909615570957Journal of Asian and African StudiesSusuman

Original Article
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Journal of Asian and African Studies
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HIV/AIDS in Ethiopia: Health © The Author(s) 2015
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DOI: 10.1177/0021909615570957
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A Sathiya Susuman
University of the Western Cape, South Africa

Abstract
The aim of the research is to examine the HIV risk factors affecting poor health in Ethiopia by epidemiological
perspectives. The Demographic and Health Survey 2011 and other secondary data were used. Ethiopian
population growth has slowed dramatically or stopped due to HIV and AIDS. A logistic regression and
correlation between HIV positive towards AIDS, high risk factors leading to exposure to HIV infection was
adopted with selected variables. The study confirmed that the high level of HIV positivity and poor health
was highly affected by socio-economic and demographic factors.

Keywords
Demographic, socio-economic, HIV/AIDS, epidemic, vulnerable diseases

Introduction
The HIV/AIDS epidemic is no longer merely a health problem but one of the greatest development
challenges the world has ever faced (UNAIDS, 2010; UNICEF, 2002; WHO, 2014). Still, the dis-
ease continues to spread with no cure yet in sight. The epidemic is rightly said to be associated with
poverty, but the reality is much more complex. In many sub-Saharan African countries, Ethiopia in
particular, HIV/AIDS remains one of the key challenges for the country’s overall development.
The national HIV prevalence in adults is at 1.9%; 14,695 people are HIV positive (Ethiopia
Demographic and Health Survey, 2011). The country faces a mixed epidemic, where prevalence is
low among the general population but high among sub-populations and certain geographic areas.
The primary mode of HIV transmission in Ethiopia is heterosexual contact (Central Statistical
Authority and ORC Macro, 2001). Young women are more vulnerable to infection than young
men, and urban women are three times more likely to be infected than urban men are (Central
Statistical Authority and ORC Macro, 2001; Ethiopia Demographic and Health Survey, 2005).
From the evidence, the government of Ethiopia has proven committed in its response to the epi-
demic. Over the last three decades, the government has established numerous entities to coordinate
efforts to deal with the epidemic (Ministry of Finance and Economic Development (MOFED)
Ethiopia, 2010). These include a national HIV/AIDS Task Force within the Ministry of Health, an

Corresponding author:
A Sathiya Susuman, Dept. of Statistics and Population Studies, University of the Western Cape, Cape Town, 7530, South
Africa.
Email: sappunni@uwc.ac.za

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2 Journal of Asian and African Studies 

HIV/AIDS Prevention and Control Office (represented at federal, regional, district and kebele
levels) responsible for the leadership and coordination of the country’s multi-sectoral response,
and implementation of national strategic plans to enhance the country’s response (Ministry of
Finance and Economic Development (MOFED) Ethiopia, 2010). By the end of 2012, the global
estimate of people living with HIV/AIDS had reached 45 million, while more than 23 million peo-
ple had died of AIDS since the emergence of the disease barely two decades ago (Bicego, 1997).
While the pandemic is global, its prevalence and impact are overwhelming in the developing
regions of the world, especially in Africa (Garenne et al., 1994; Ryder et al., 1991). Estimates sug-
gest a 17.2% rise in global prevalence of the disease between 2000 and 2010, and a corresponding
estimated 20% rise in sub-Saharan Africa during the same period. There is a growing body of lit-
erature on the demographic impact of HIV/AIDS on various populations, especially in Africa
(Allen et al., 1993; Central Statistical Authority and ORC Macro, 2011). Obtainable studies have
investigated the mortality implication of the pandemic, its impact on fertility, and its effect on
population growth and age structure (Caldwel, 1997; Gregson et al., 1997; Ministry of Finance and
Economic Development (MOFED) Ethiopia, 2010).
In Ethiopia, several studies have been conducted, focusing on the reproductive health issues of
adolescents. Their findings show that in the country, sexual practice among unmarried young peo-
ple starts at early ages, and the prevalence of sexually transmitted diseases (STDs) like HIV and
AIDS is relatively high, most of them leading to death. For example, HIV and AIDS accounted for
an estimated 34% of all deaths in people between the ages of 15 and 24 years who lived in the
country, and 66% of all deaths of those age 15 to 49 years who resided in urban settings such as
Addis Ababa (Central Statistical Authority and ORC Macro, 2011).
The studies also showed that adults aged 25 to 49 years and those aged 15 to 24 years are the
centre of the HIV and AIDS pandemic (Ministry of Health, 2000). In addition, numerous studies
have shown that early sexual activity has a profound influence on a young person’s current and
future health, most directly through exposure to unprotected sexual practices resulting in unin-
tended pregnancy and childbearing (Central Statistical Authority and ORC Macro, 2011 ).
Deaths were also due to induced abortion in hazardous circumstances and to STDs, including
HIV leading to AIDS. Factors believed to have contributed to the spread of HIV/AIDS include
poverty, illiteracy, the low status of women, ignorance and denial, all of which foster risky sex-
ual behaviour (Daly, 2000). This seems to apply to the Ethiopian situation, where despite the
high level of awareness of HIV/AIDS (96% among men and 85% among women), only 42% of
men and 35% of the women interviewed in the 2011 Ethiopia Demographic and Health Survey
knew at least one way to avoid the disease (Central Statistical Authority and ORC Macro, 2011).
Until awareness leads to attitudinal and behavioural changes, the prevalence rate might not sta-
bilize (Dowell et al., 1994).
The Demographic and Health Survey (DHS) 2011 reported that, the overall adult HIV preva-
lence in Ethiopia has remained low. The HIV prevalence among adults aged 15 to 49 years in the
2011 Ethiopia DHS (EDHS) was 1.5%, while it was 1.4% in the 2005 EDHS (Ethiopia Demographic
and Health Survey, 2005, 2011). Many findings show that the pandemic has varied impact that
touches on the social, economic and demographic aspects of lives of the individuals and of society
at large, as recognized by policy makers (Central Statistical Agency, 2007; Federal Democratic
Republic of Ethiopia, 1998; Ministry of Finance and Economic Development (MOFED) Ethiopia,
2010; Statistical Abstract of Ethiopia, 2008). However, an understanding of the magnitude, nature
and consequences of the demographic, social and economic impacts would help to generate greater
commitment and adoption of more aggressive strategies for effective prevention of the spread of
the virus and care for those living with the disease. The number of AIDS-related orphans was
nearly 2.8 million by 2010 (Ethiopia Demographic and Health Survey, 2011). Obviously, this

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Susuman 3

aggravates social problems. Most of the orphans in school would drop out without the help of rela-
tives, due to the problems of fees, clothing, books and other related costs of education. In the
absence of a social welfare system, many grandparents, who themselves need care, would of neces-
sity care for both themselves and their orphaned grandchildren. The number of households headed
by adolescents would also increase. The number of street children would tend to rise, while the
problem of child labour would worsen as the orphans struggle for survival.
Besides these issues, large declines in expectation of life at birth would invariably result in
a ‘young’ population in the long run, which has an inherent, adverse consequence on national
development. Under a purely demographic process, a young population resulting from a high
rate of natural increase constitutes an obstacle to a country’s socio-economic development
(Ministry of Finance and Economic Development (MOFED) Ethiopia, 2010). A young popula-
tion resulting from the demographic impact of HIV/AIDS would impede development not only
due to the high rate of natural increase but also as a result of the loss of a substantial proportion
of the productive members of the population, which would compound social and economic
problems, especially at the household level (Argeseanu, 2004; Carpenter et al., 1997; Fontanet,
1998; Kidane, 1994; UNAIDS, 2003). By its nature, the demographic impact either generates
or exacerbates the social and economic consequences of the disease. The fact that mostly peo-
ple in their prime productive and reproductive ages (15–49) are affected by the pandemic has a
demographic impact with both economic and social consequences. A serious HIV/AIDS pan-
demic can result in the loss of a substantial proportion of the skilled labour force. This suggests
that unless the adult infection rate is reduced appreciably, AIDS deaths are likely to reduce the
Ethiopian labour force substantially by the close of this decade. Yet, most of those who would
die of AIDS would leave behind widows and orphans (who themselves may eventually die of
the disease), thus adding to the social problems. As of 2001, about one million Ethiopian chil-
dren had lost their parents to AIDS since the emergence of the pandemic (Carpenter et al., 1997;
Fontanet, 1998; UNAIDS, 2003).
The latest report, DHS 2011, mentioned that for women aged 15–49 years, the HIV prevalence
is 1.9%, and among men aged 15–49 and 15–59, HIV prevalence is 1.0%. For women, HIV preva-
lence increases with age, to a peak of 3.7% at age 30–34. For men, HIV prevalence increases from
0.0% at age 15–19, to 3.0% at age 35–39 and drops thereafter. Overall, HIV prevalence is higher
for women than for men in most age groups (Ethiopia Demographic and Health Survey, 2011).
Available evidence indicates that the HIV/AIDS epidemic has now spread to every region of the
country and that the prevalence rate has been increasing rapidly, especially in Gambela, where it is
6.5%, and in Addis Ababa, where it is 5.2% (Ethiopia Demographic and Health Survey, 2011). For
purposes of this paper, the researchers focused mainly on Addis Ababa, the capital city of Ethiopia.
We wanted to understand why the HIV prevalence is so high in Addis Ababa and whether HIV
prevalence does correlate with the wellbeing of those who are underprivileged in Addis Ababa,
Ethiopia.
People in rural areas migrated to Addis Ababa for various reasons, but mainly for employment,
education and treatment purposes. Interestingly, Addis Ababa achieved below replacement level
fertility (1.2 children per woman) (Ethiopia Demographic and Health Survey, 2011). Because of
this, the present study intends to contribute to facilitating appreciation of the magnitude, nature and
possible consequences of the demographic and socio-economic impact of HIV/AIDS in Addis
Ababa, in particular, and in Ethiopia in general. Such an understanding is necessary to generate an
aggressive response that can effectively stabilize and possibly reverse the rising trend of the spread
of the disease and consequently mitigate its devastating impact on the population. Therefore, the
specific aim of the research is to examine the HIV risk factors affecting poor health in Addis Ababa
by epidemiological perspectives.

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4 Journal of Asian and African Studies 

3.5

Per cent HIV posive

2.5

2
Female
1.5
Male
1

0.5

0
15-19 20-24 25-29 30-34 35-39 40-44 45-49

Figure 1.  HIV prevalence in women and men aged 15–49 years in Ethiopia, 2011. Source: Ethiopia
Demographic and Health Survey 2011.

Data
The DHS 2011 data sampled nearly 16,000 households in Ethiopia. There are different data files
such as births, household, individuals, children, males, household members, and couples’ records.
Of the total number of respondents used for the study, 46.9% (4105) were male and 53.6% (4740)
were female in Ethiopia. Respondents were asked questions on background characteristics includ-
ing age, education and media exposure, birth history, child mortality, knowledge and use of family
planning methods, fertility preferences, antenatal care, delivery and postnatal care, breastfeeding
and infant feeding practices, vaccinations, childhood illnesses, marriage and sexual activities,
women’s occupations, husband’s background characteristics, awareness and behaviours regarding
AIDS and other sexually transmitted infections, and adult mortality.

Methods
The fundamental needs for the study variables are identification of any household with a member(s)
living with HIV/AIDS or whose member(s) had died of AIDS. Such information is not easy to
obtain in a society where HIV/AIDS victims are stigmatized and systematic registration of death is
lacking. At the household level, a background questionnaire was used to collect demographic,
social and economic data as well as the main wealth index and expenditures of the household in the
last 12 months before the survey. This information was collected from households with HIV/
AIDS-related deaths (or morbidity) and those with non-HIV/AIDS-related deaths (or morbidity).
Common symptoms included: coughing and shortness of breath, difficult or painful swallowing,
mental symptoms such as confusion and forgetfulness, severe and persistent diarrhoea, fever,
abdominal cramps, vomiting, weight loss, extreme fatigue, severe headaches, etc. The present
study specifically focused on two dependent variables: (1) HIV positive and (2) symptoms of (a
selected number of) diseases, such as severe fatigue, pneumonia, chronic coughing, high fever and
night sweats, difficulty in breathing, and other symptoms of illness. In the latest HIV prevalence in
women and men aged 15–49 years in Ethiopia, 2011 showed in the Figure 1.

Selected independent variables

The odds ratio for the logistics regression model of demographic and socio-economic implications of
HIV/AIDS in the study area was adopted. There are nine variables included in the analysis: sex,

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Susuman 5

literacy (for those aged five and older), education (for those aged five and older) work status, source
of income, marital status, natural mother alive, natural father alive, and occurrence of illness (these
variables are used as categorical variables). The correlates between HIV positive toward AIDS and
high-risk factors leading to exposure to HIV infection with background variables were tested. This
prompted further analysis of background variables and the analysis of variance was performed to
isolate the effects of individual background variables on the HIV positive toward AIDS variable and
high-risk factors leading to exposure to HIV infection by logistics regression analysis. The quantita-
tive weighted data were analysed using SPSS 22.0 version for Windows. Quantitative data analysis
is simple and clearer to give a more holistic view of the rate at which respondents suffered diseases
and identification of the demographic and socio-economic correlates on HIV/AIDS in the study area.

Results
The findings of the study confirm the high level of HIV infection and that people of poor health
were highly affected (when looking at the background characteristics). The sex composition of
respondents showed that there were fewer young male respondents, at 46.9% (4105), than female
respondents, at 53.6% (4740). The age composition demonstrated a higher number of respondents
in the younger age groups (15–24 years), at about 26.5% for both sexes. Age at first marriage was
29.61 years, which was late compared with the national average at first marriage, which was 18
years. Regardless of the sex differences, 34.3% of the respondents married between the ages of 20
and 24 years and 23.3% married at the age of 15–19 years; half (57.6%) of the respondents married
at the age of between 15 and 24 years.
A higher incidence of illiteracy was found in females (14.4%) than in males (7.6%), although the
level of illiteracy was low for both sexes. In addition, more females were found to have primary
(46.4%) and secondary education (35.3%) than their male counterparts, which was 44.5% for primary
and 33.6% for secondary education. At the tertiary levels, the percentage of males (22%) exceeded that
of females (18.3%). Regarding work status and occupation, more males indicated they were working
(51.9%) than not working (47.0%) compared with females, of whom 33.7% were working and 48.5%
were not working. There was only a slight difference in the primary economic activity (farmer/animal
husbandry) between males (3.6%) and females (3.9%), with results for females higher than results for
males. Nearly 30% of the respondents were identified as HIV positive, while the majority of the
respondents were negative (66.5%). Most of the HIV positive respondents were distributed evenly
across the different age groups (see Table 1). A high proportion of 20–24 years age group, 1047 out of
3052 cases, reported HIV positive. Symptoms of illness collected from the respondents for the last 12
months showed that 15.1% of male respondents experienced severe fatigue, 15.0% lacked appetite,
13.0% had high fevers and night sweats and 10.1% experienced rapid weight loss. Similarly, 16.0% of
females endured severe fatigue, 15.3% lacked appetite, 13.3% had high fevers and night sweats and
10% observed rapid weight loss. For both males and females, the results showed a similar pattern for
dominant symptoms of illness. The three least-reported symptoms of illness, exhibited by smaller
proportions of respondents showing symptoms of illness, were irritating white patches in the mouth,
gum and tongue (1.8% for males and 1.9% for females); skin rashes on the chest/abdomen/back (2.0%
for males and 2.1% for females); and swollen lymph glands in the armpit, groin or neck (1.9% for
males and 2.2% for females). Generally, the three most important symptoms of illness were severe
fatigue, high fever plus night sweats, and rapid weight loss (Table 2).

Regression analysis
Two odds ratio models were used to estimate the consequences of socio-economic and demographic
factors related to HIV/AIDS and occurrence of diseases. These variables are provided as dichotomous

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6 Journal of Asian and African Studies 

Table 1.  Percentage distribution of respondents by background characteristics in Ethiopia, 2011.

Variables Male % Female % Total %

Sex 4105 46.9 4740 53.6 8845 100
Marital status  
  Currently married 1011 26.6 1000 23.1 2011 24.7
 Divorced 60 1.6 136 3.1 196 2.4
 Separated 68 1.8 88 2.0 156 1.9
  Widowed 192 5.1 497 11.5 689 8.5
  Never married 2467 65.0 2609 60.3 5076 62.5
Total 3798 100.1 4330 100 8128 100
Age at first marriage  
  < 14 years 8 0.6 89 5.2 97 3.2
 15–19 115 8.6 595 4.6 710 23.3
 20–24 433 32.5 614 35.7 1047 34.3
 25–29 381 28.6 215 12.5 596 19.5
  30–34 168 12.6 46 2.7 214 7.0
 35+ 226 17.0 162 9.4 388 12.7
Total 1331 100 1721 100 3052 100
Literacy status  
 Literate 3529 88.3 3764 81.9 7293 84.9
  Illiterate 304 7.6 662 14.4 966 11.2
  Not stated 162 4.1 171 3.7 333 3.9
Total 3995 100 4597 100 8592 100
Educational level  
 Primary 1659 44.5 1475 46.4 3134 45.4
 Secondary 1253 33.6 1122 35.3 2375 34.4
 Tertiary 818 21.9 582 18.3 1400 20.3
Total 3730 100 3179 100 6909 100
Work status  
 Working 1971 51.9 1458 33.7 3429 42.2
  Home makers 41 1.1 770 17.8 811 10.0
  Not working 1786 47.0 2102 48.5 3888 47.8
Total 3798 100 4330 100 8128 100
Occupation  
  Farmer/animal husbandry 71 3.6 57 3.9 128 3.7
  Merchant /trader 411 20.9 372 25.5 783 22.8
 Teacher 167 8.5 170 11.7 337 9.8
  Civil servant 555 28.2 418 28.7 973 28.4
 Driver 174 8.8 35 2.4 209 6.1
  Daily labourer 203 10.3 119 8.2 322 9.4
 Other 390 19.8 287 19.7 677 19.7
Total 1971 100 1458 100 3429 100

variables. The outcome of the logistics regression scrutiny on the implications of HIV/AIDS variables
regressed on socio-economic and demographic variables is specified in Table 3. Nine independent
background variables were observed to be absolutely significant with HIV-positive respondents in
model I. That left two independent variables, which are education (if the person was aged five or older)

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Susuman 7

Table 2.  Percentage distribution of respondents who have had illness at least for a month in the last 12
months by symptoms of illness, Addis Ababa, 2011.

Symptoms of illness Male Female Total

Number % Number % Number %

Sever fatigue 318 15.1 493 16.0 811 15.7
Pneumonia 127 6.0 130 4.2 257 5.0
Chronic coughing 187 8.9 240 7.8 427 8.3
High fever and night sweats 274 13.0 410 13.3 684 13.2
Difficulty in breathing 123 5.9 177 5.8 300 5.8
Irritating white patches in the 37 1.8 58 1.9 95 1.8
mouth, gum and tongue
Skin rashes on the chest abdomen/ 42 2.0 66 2.1 108 2.1
back
Swollen lymph glands in the 39 1.9 68 2.2 107 2.1
armpit, groin or neck
Lack of appetite 315 15.0 470 15.3 785 15.2
Vomiting 201 9.6 280 9.1 481 9.3
Rapid weight loss 213 10.1 304 9.9 517 10.0
Diarrhoea with bloody stool 70 3.3 106 3.5 176 3.4
Pain or burning when urinating 74 3.5 129 4.2 203 3.9
Memory loss and confusion 81 3.9 141 4.6 222 4.3
Communicable, maternal, perinatal 60%
and nutritional conditionsa 9%
Cardiovascular
Totala 2101 43.6 3072 56.4 5173 100.0

Sources: World Health Organization, Non-communicable Diseases (NCD) Country Profile, 2014; Central Statistical Agency
and ORC Macro, Demographic and Health Survey 2011.
aPercentage of population living in urban areas: 17.0%; population proportion between ages 30 and 70 years: 26.4%; total

deaths: 691,000; non-communicable diseases (NCDs) are estimated to account for 30% of total deaths.

and natural father alive; these were shown not to be significantly related to being HIV positive. All
these factors were used as explanatory variables in the equation to determine the comparative influ-
ence of each factor on the implications of HIV/AIDS in the two models. For marital status of respond-
ents, those who were identified by the ‘other’ category showed vastly significant (p<0.001) results
when compared with the currently married group. At the same time, occurrence of illness among those
who were identified by the ‘other’ category showed noteworthy effects on HIV-positive categories.
Those who reported having their natural mother alive (within the ‘other’ category) were less
likely (odds ratio 0.80; significant, p<0.05) to have HIV/AIDS and occurrence of diseases, com-
pared with the reference category. The total number of 8845 cases at –2 log likelihood values
showed 10,711.34 with the effect of all background explanatory variables affecting the HIV-
positive variable. Overall, the HIV-positive effect plays a key role in the selected independent
background variables. The effect of the odds ratio for the logistic model of demographic and socio-
economic implications of HIV/AIDS in the study area is clearly noticeable.
In model II, two variables – persons aged five years or older exhibiting literacy, and education
of persons aged five years or older – show highly significant effects on dependent variables. The
odds ratios provided an exact picture of the present study; both of the models clearly show the
effect and positive significant effect with the dependent variables. Also visible was the effect of the

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8 Journal of Asian and African Studies 

Table 3.  Odds ratios for the logistic model of demographic and socio-economic implications of HIV/AIDS
in Ethiopia, 2011.

Selected independent variables Weighted odds ratios

Model I Model II
Sex  
 Malea 1.00 1.00
 Female 0.79* 0.95
Persons aged five years or older, literacy  
 Yesa 1.00 1.00
  No/DK (others) 0.91** 0.04***
Persons aged five years or older, education  
 Yesa 1.00 1.00
  No/DK (others) 0.20 0.00***
Work status  
 Workinga 1.00 1.00
  Not working 0.15* 0.81
Source of Income  
  Husband incomea 1.00 1.00
  Others 0.20** 0.96*
Marital Status  
  Currently marrieda 1.00 1.00
 Other 0.00*** 0.00**
Natural mother alive  
 Yesa 1.00 1.00
 Others 0.80* 0.80*
Natural father alive  
  Yesa 1.00 1.00
 Other 0.36 0.41*
Did ever have illness  
  Yesa 1.00 NA
 Other 0.00*** NA
–2 Log – likelihood 10,711.34 9653.46
Number of cases 8845 8845

Dependent variables: (model I) HIV positive and (model II) symptoms of diseases.
aReference category.

NA: not applicable.

*p <0 .05
**p<0.01
***p<0.001

correlation between the variable of HIV positive toward AIDS high-risk factors leading to expo-
sure to HIV infection, which demonstrates an additional proof of the study.
The correlation analysis perhaps suggested that the HIV positive toward AIDS and high-risk
factors leading to HIV infection were more significantly correlated with the background variables
than HIV positive towards AIDS and illness high-risk factors such as severe fatigue, pneumonia,
chronic cough, high fever, difficulty breathing and other problems. The results of the multiple clas-
sification analysis of the dependent variables, namely, HIV positive and symptoms of diseases, are

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Susuman 9

Table 4.  Correlation between HIV positive towards AIDS, high risk factors leading to exposure to HIV
infection with background variables, Ethiopia 2011.

Variables HIV High risk factors leading to HIV infection

positive
Severe Pneumonia Chronic High Difficult Other
fatigue coughing fever breathing diseases
Sex –0.09 –0.03 0.04 –0.00 –0.04 –0.32 –0.23
Aged 5 years or 0.02 0.04 0.18* –0.12 –0.02 –0.24 –0.00
older, literacy
Aged five years or –0.54** –0.33* –0.29* –0.54** –0.16 –0.21 –0.32
older, education
Work status 0.30** 0.30* 0.16 0.42* 0.12 0.25** 0.26
Source of income 0.33* 0.22* 0.10 0.36 0.17 0.15* 0.24
Marital status 0.39* 0.31* 0.42 0.43* 0.99 0.02 0.57
Natural mother alive 0.05 0.97** 0.78 0.88 0.55 0.20 –0.32**
Natural father alive 0.04 0.85 0.77 0.88 0.23 0.37 –0.33

*p<0.01
**p<0.001

presented in Table 4. A more convenient way to interpret the main effect is to have a logistic regres-
sion model. Work status of the respondents has shown to be highly significant with HIV positive
(at 0.30) and source of income is 0.33 times as likely to be a consequence of being HIV positive.
At the same time, the marital status of the respondents showed a significant level, at 0.39 times the
effect, and the probability value is p<0.01. Education of persons aged five years or older had a
negative effect on HIV positive, which is –0.54 times, with the possibility value of p<0.001. High-
risk factors leading to HIV infection during the course of some of the diseases include severe
fatigue, pneumonia, chronic coughing, high fever, difficult breathing and other diseases. Severe
fatigue disease is almost a significant outcome on several background variables, but education of
those persons aged five years or older produced a negatively significant result with severe fatigue
disease.
The circumstance of the mother being alive is decidedly significant with severe fatigue, but
three variables – sex, literacy in persons aged five years or older, and circumstance of the father
being alive – have not shown any significant effect on severe fatigue. Other diseases, such as pneu-
monia, showed significance on only two factors: literacy of persons aged five years or older had a
significant effect (at 0.18) and education of persons aged five years had a negative significant
effect (at –0.29) on pneumonia. While considering the effect of the predictor variable (literacy of
persons aged five years) on the HIV-positive variable, it was observed that the unadjusted means
for the categories ‘no’ and ‘other’ were 0.91 and 0.04, respectively. This study showed that those
who were working exhibited more knowledge of AIDS compared with those in the non-working
category.
Surprisingly, the non-working respondents exhibited more knowledge of AIDS than those
respondents who worked in the primary sector. Although AIDS is considered one of the most seri-
ous public health challenges facing the country, the majority of women have never heard of the
disease. Moreover, many of those who have heard of the disease do not know how to avoid AIDS.
For more than a decade, HIV and AIDS prevalence levels have been on the rise in Addis Ababa.
Prevention programmes need to continue to strengthen their educational outreach efforts, espe-
cially among less-educated women who have little or no exposure to the mass media.

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10 Journal of Asian and African Studies 

Discussion
As per the study findings, younger age groups were more affected by HIV during sickness. Older
people more often died of diseases due to being HIV positive. This study strongly recommends that
the demographic and socio-economic implications of HIV/AIDS studies are considered at this
juncture. Further research is required to establish how HIV and socio-economic status are related
to poor health or illness in Addis Ababa. As HIV continues to spread – with no existing vaccine or
cure – prevention remains the key strategy for curbing the epidemic. The most common mode of
HIV transmission is sexual contact; thus, HIV prevention is closely linked to men’s and women’s
sexual behaviour and reproductive health. Effective prevention programmes include interventions
that promote abstaining from sex, delaying the onset of sexual activity, staying with one mutually
faithful partner, limiting the number of sexual partners, consistently and correctly using condoms
and counselling and testing for HIV. The most effective mix of these interventions depends on the
characteristics of the groups infected with HIV. Effective programmes also consider the social,
economic and cultural factors that influence people’s behaviour. Preventing HIV transmission
from mothers to their infants is also key to saving lives (Gupta, 2000; Argeseanu, 2004; Kloos
et al., 2007; Kidane, 1994; Statistical Abstract of Ethiopia. 2010). Women who are HIV positive
need contraceptive choices and counselling to help them decide whether to have a pregnancy.
Helping HIV-infected women avoid unintended pregnancies could prevent many HIV-positive
births. Increasing contraceptive use to prevent such pregnancies appears to be at least as cost-
effective as providing antiretroviral drug therapy during delivery and to new-borns of HIV-infected
mothers. Key challenges for the future include controlling further spread of the epidemic in infants
and young adults; treating and supporting the millions of people living with HIV; and mitigating
the impacts of the epidemic in poor countries like Ethiopia. To meet these challenges, the interna-
tional community, governments and civil society need to act. It is clear that much more needs to be
done, especially in resource-poor countries.

Conclusions
Many children are dying, while many more are experiencing the scars that AIDS can leave on their
lives – almost all of which is avoidable. The state of medical treatment is such that, in a developed
country, a woman living with HIV can now be almost certain that her child will not be infected and
yet there are still delays in making the appropriate tests and drugs available around the world. If
infected with HIV, children can be effectively treated, and, given this treatment, can lead longer,
healthier lives—yet they continue to die, because the treatment is not available in many countries.
However, developing countries like Ethiopia need not only the drugs to treat children, but also
special training for staff and funding to enable treatment and on-going care. This study will be
immensely useful for the policy makers and health care service providers, who already possess the
tools to save children from needless suffering, but the tools are not reaching most of those who
need them. Therefore, to understand in greater detail how HIV prevalence correlates to poor health
in terms of the epidemic, further in-depth analysis on existing data and other data sources is
strongly recommended.

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12 Journal of Asian and African Studies 

Author biography
A Sathiya Susuman is currently working as a full time faculty in the Department of Statistics and Population
Studies, Faculty of Natural Sciences, University of the Western Cape, Cape Town, South Africa. His research
efforts are concentrated in the field of Population components such as Fertility, Mortality and Migration.
Keeping abreast of his research aspirations in the field of technical demography, he focuses mainly on the
‘Demography of Reproductive Health’, with attention to a life course analysis of health risks, interventions
and outcomes. He worked on several research projects in India, Ethiopia, South Africa and West African
countries. He has published several articles in reputed international journals. He is a life member of several
Population Associations.

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