p-ISSN 2086-6380 Jurnal Ilmu Kesehatan Masyarakat, Nov2022, 13(3):385-398

e-ISSN 2548-7949 DOI: https://doi.org/10.26553/jikm.2022.13.2.385-398

Available online at http://ejournal.fkm.unsri.ac.id/index.php/jikm

THE CORRELATION BETWEEN FOOD INSECURITY LEVEL AND STUNTING

IN INDONESIA

Siti Masitoh 1,2, Sudarto Ronoatmodjo1, Siti Nurokhmah3

1
Department of Epidemiology, Faculty of Public Health, Universitas Indonesia, Kampus UI Depok, Jawa
Barat, Indonesia
2
National Research and Innovation Agency, Gedung B.J. Habibie, Jl. M.H. Thamrin No. 8, Jakarta Pusat,
Indonesia
3
Nutrition Science Study Program, Faculty of Health Sciences, Universitas Muhammadiyah Surakarta. Jl.
Ahmad Yani, Pabelan, Kartasura, Surakarta, Indonesia

ABSTRACT
Indonesia is among the five countries with the highest burden of stunting. Food insecurity reflecting the
availability of food in the household is one of the indirect causes of undernutrition. This study examines the
relationship between food insecurity and stunting in Indonesia. We used data from the 2021 Indonesian
Nutrition Status Survey (SSGI) with a sample size of 82,777 under-five children selected using multistage
random sampling. The relationship between food insecurity and stunting was calculated using cox regression
to obtain crude and adjusted prevalence ratios (PR) considering strata and weights. Children from
households with moderate food insecurity had a (PR unadj) 1.24 (1,18 – 1,31) times higher risk of stunting,
and the risk rose in households with severe food insecurity (PR unadj) 1.39 (1,27 – 1,53). Subgroup analysis
based on regional categories showed that the association between food insecurity and stunting was only
significant in rural areas. Children in rural regions who experienced moderate food insecurity had a 1.09
(95% CI 1.02-1.16) times greater risk of stunting and an increased risk of 1.15 times (95% CI 1.03 – 1.28) in
households with severe food insecurity. Interventions are needed to prevent stunting by improving household
food security, particularly in rural areas. Further research is needed with better study designs to prove a
causal relationship between food insecurity and stunting.

Keywords: food insecurity, stunting, children under five years

ABSTRAK
Prevalensi stunting di Indonesia masih cukup tinggi bahkan masuk dalam lima besar kasus stunting di dunia.
Kerawanan pangan menjadi cerminan dari ketersediaan pangan dalam rumah tangga merupakan salah satu
penyebab tidak langsung dari permasalahan gizi. Tujuan dari studi ini untuk melihat hubungan tingkat
kerawanan pangan dengan kejadian stunting di Indonesia. Studi ini menggunakan data Survei Status Gizi
Indonesia (SSGI) tahun 2021 dengan besar sampel 82.777 responden yang dipilih dengan cara multistage
random samping. Hubungan antara kerawanan pangan dengan stunting dihitung menggunakan rasio
prevalens baik crude maupun adjusted dengan menggunakan cox regression dengan tingkat kepercayaan
95% dan menggunakan complex sample untuk mempertimbangkan strata dan bobot. Hasil studi
menunjukkan balita dari keluarga rawan pangan sedang memiliki risiko (PR unadj) 1,24 (1,18 – 1,31)kali
lebih tinggi untuk mengalami stunting dan meningkat risikonya pada keluarga rawan pangan berat (PR unadj)
1,39 (1,27 – 1,53). Namun, jika kita lakukan analisis subgroup berdasarkan kategori wilayah ternyata
hubungan kerawanan pangan terhadap stunting hanya terlihat signifikan di wilayah perdesaan. Di wilayah
perdesaan, balita dari keluarga rawan pangan sedang memiliki risiko (PR adj)1,09 (95% CI 1,022 – 1,160)
kali lebih tinggi untuk mengalami stunting dan meningkat risikonya 1,15 kali (95% CI 1,035 – 1,277) pada
keluarga rawan pangan berat. Dapat disimpukan bahwa kerawanan pangan berhubungan dengan stunting
terutama pada wilayah perdesaan. Dibutuhkan penelitian lebih lanjut dengan desain studi yang lebih baik
untuk membuktikan hubungan kausal antara kerawanan pangan dengan stunting.
Kata kunci : kerawanan pangan, stunting, balita, status gizi


Correspondence Address: Siti Masitoh, Badan Riset dan Inovasi Nasional, Gedung B.J. Habibie, Jl. M.H. Thamrin No. 8, Jakarta
Pusat, Indonesia E-mail: siti.masitoh1726@gmail.com

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Received : December 26, 2022 Accepted : January 20, 2023 Published: January 27, 2023

Introduction
Stunting still becomes a health problem in the world, and it is one of the most significant
obstacles to human development, affecting around 162 million children globally. 1 Global Health
Observatory data showed that around 150 million children under five were stunted, or around
21.9%. In line with the global public health priority agenda, it is hoped that the prevalence of
stunting will decrease in 2030 to 17.5%.2 The problem of stunting is experienced by most poor
children and developing countries like Indonesia. 3 The prevalence of stunting in Indonesia is still
quite high, even in the top five cases of stunting in the world. Indonesia's position is only better
than India, China, Nigeria, and Pakistan.4 The stunting rate in Indonesia continues to decline from
37.8% in 2013 to 31% in 2018, and the latest data for 2021 shows the stunting rate has fallen to
24.4%.5,6 Even though in Indonesia there has been a decrease in the prevalence of stunting by
13.4% from 2013 to 2021, stunting is still one of the focuses of the problem because this figure is
still below the WHO stunting case standard of 20 percent 7 and Indonesia is targeting to reduce the
stunting rate to 14% by 2024.8
Stunting during childhood can result in negative health effects throughout life, including
high morbidity and mortality, such as life-threatening complications during childbirth, increased
infant mortality, decreased cognitive performance and development, increased risk of infection,
poor psychomotor development, a decline in school performance, poor intellectual intelligence
(IQ), the emergence of chronic diseases, decreased productive capacity as adults, and can affect the
loss of economic growth and social development of the country. 9 The magnitude of the impact of
stunting is one of the reasons why stunting must be addressed immediately both in Indonesia and at
the global level.
Several factors cause children to become malnourished, including stunting, inappropriate
feeding practices and behavior, and inadequate intake of micronutrients. 10 Other influential factor
11 12
in developing countries includes low maternal education , household structure , household
economic shocks, inadequate ANC, low birth weight, short birth spacing, living in rural areas, and
poor access to health services. Some of these factors are the root cause of poverty. Malnutrition is
the result of various factors, most of which are caused by unfavorable socio-economic conditions,
such as difficulty obtaining food, food insecurity, high unemployment, which determines unstable
income for the breadwinner of the family, limited access to education and health services, or
diseases that caused by unsanitary conditions. 13 At the household level, food insecurity is
associated with low socioeconomic status, inadequate food intake, and poor nutritional status. Low
household socioeconomic status has characteristics such as low monthly income, low per capita

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income, low education level, large household size, unemployment among adults, single female
head of household, and contracted residence status. 14,15 ,16
Data from Central Statistics Agency (BPS) showed that the prevalence of people with
moderate or severe food insecurity based on the experience of food insecurity scale (FIES)
continues to decline from 5.42% in 2019 to 4.79% in 2021. 17 At the household level, food
insecurity is closely related to low socioeconomic status, inadequate food intake, poverty, and
nutritional status.14 The results of a study in Surabaya show that in children aged 2-5 years, food
insecurity is closely related to the risk of stunting at mild, moderate, and severe levels. Children
from families with mild food insecurity are at risk of 1,687, moderate 1,562, and severe 2,005
times to experience stunting.18 According to Tiwari et al., 2014, family food insecurity was
strongly associated with stunting at both the light and severe food insecurity levels in children aged
0 to 59 months and 0 to 23 months. This showed that household food insecurity was significantly
related to stunting in preschool children.19
Studies on the relationship between food insecurity and stunting still show inconsistent
results, although most show a positive relationship. Studies in Indonesia are utilizing large survey
data where national representation on this matter has not been carried out much, so this study is
important to do. This study aims to determine the relationship between the level of food insecurity
and stunting in children aged 0-59 months, representing results at the national level.

Methods
This research analyzed the secondary data from the 2021 Indonesian Nutrition Status Survey
(SSGI). The SSGI of 2021 collected information on nutritional status and other indicators of
children's health. The sampling method was carried out by multistage random sampling. SSGI 2021
was carried out in two stages; the first was to estimate the provincial level, and the second was to
estimate the district level. This research only used the first stage of SSGI 2021, where the
questionnaire included complete information. The first phase of the SSGI 2021 sample consisted of
95,911 children aged 0-59 months in 9,500 census blocks covering 34 provinces. The sample in
this study were toddlers aged 0-59 months. The inclusion criteria in the study were toddlers aged 0-
59 months with complete data, while the exclusion criteria were toddlers with physical disabilities.
All samples that met the exclusion and inclusion criteria in the dataset were taken as the sample,
and the total was 82,777 respondents.
The dependent variable in this study was stunting, where a child was said to be stunted if his
height was below -2 SD from the WHO standard. The calculation of the z-score was carried out by
using the WHO anthro application. The main independent variable in this study was food
insecurity. Food insecurity was defined as the limited or uncertain availability of sufficiently
nutritious and safe food or the limited or uncertain ability to obtain acceptable food in a socially

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acceptable manner.20 Measuring food insecurity used the FIES instrument developed by the World
Food and Agriculture Organization (FAO) through the Voice of Hunger Project and has been used
by BPS in the National Socioeconomic Survey (Susenas) to measure the level of food insecurity in
Indonesia since 2017, and this scale had been validated. 21 The Food Insecurity Experience Scale
(FIES) measured the percentage of individuals in the national population who have experienced or
experienced moderate or severe food insecurity at least once in the last 12 months. The FIES
instrument consisted of 8 questions which were arranged sequentially and described the increasing
level of food insecurity based on the experiences of the respondents, which included: 1) worries
that they will not have enough food to eat due to lack of money or other resources, 2) have never
been able to eat healthy food and nutritious food due to lack of money or other resources, 3) has
eaten very little food due to lack of money or other resources, 4) has skipped one meal on a
particular day due to lack of money or other resources, 5) has eaten less than it should due to lack
of money or other resources, 6) have run out of food due to lack of money or other resources, 7)
have been hungry but have not eaten due to lack of money or other resources, and 8) have not eaten
all day due to lack of money or other resources.
Respondents will answer "yes" (code 1) or "no" (code 0) to 8 questions. Then the total score
was made, ranging from 0 to 8. The level of food insecurity was divided into 3 categories, namely
1) not experiencing food insecurity if it has a score of 1-3, 2) moderate food insecurity if it has a
total score of 4-6, and 3) severe food insecurity (severe) if it has a total score of 7-8. This was
based on the assumption that the FIES question sequence was designed to capture increasing levels
of food insecurity where questions 1-3 lead to mild food insecurity (mild), questions 4-6 lead to
moderate food insecurity, and questions 7-8 lead to in severe food insecurity.22 The relationship
between the independent and dependent variables would be controlled with covariate variables,
including the area of residence, mother's education, father's education, mother's employment status,
family planning participation, number of household members, wealth quintile, low birth weight,
and history of infectious diseases/morbidity.
The analysis was carried out in stages starting from univariate, bivariate, and multivariate.
Univariate analysis was conducted to determine the characteristics of each variable. Bivariate and
multivariate analyzes were performed using the Cox Regression test with a 95% confidence level to
obtain the Prevalence Ratio (PR) value, both crude and adjusted with the covariate variables. Data
analysis was carried out using a complex sample, considering strata, sample units, and weights
because this data is survey data, and sampling was not random. This research has received ethical
approval from the Health Research and Development Agency, Ministry of Health, with number
LB.02.01/2/KE.677/2021.

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Results
The total sample in this study was 82,777 respondents from 34 provinces in Indonesia, and
this data could represent the national level. Table 1 showed the characteristics of the respondents
where more than half of the respondents lived in urban areas (57.7%), most of the mothers and
fathers had secondary education, those who had graduated from junior high or high school (61.2%
and 60.9%), as many as 67 .7% of mothers did not work, most did not use family planning (86.3%),
more than half of the respondents came from families with <5 household members, when it was
viewed from the wealth quintile, they were spread evenly from quintile 1 to 5. The main focus of
the independent variable in this study was food insecurity, where 84.8% came from families with
food insecure status, 11.9% came from families with moderate food insecurity status, and 3.3%
came from families with severe food insecurity status. If it was seen from the characteristics of the
toddlers, most of them were born not LBW (84.8%), and there was no history of infectious diseases
(87.4%).
Factors related to stunting from the bivariate results were food insecurity, area of residence,
mother's education, father's education, mother's employment status, family planning participation
status, number of household members, ownership index quintile, low birth weight status, and
history of infectious diseases. Parity and received antenatal care (Table 1). Toddlers from
moderately food-insecure families had a 1.24 times higher risk of experiencing stunting, and an
increased risk in severely food-insecure families (PR unadj 1.39), toddlers living in rural areas were
1.26 times more at risk of experiencing stunting than those in urban areas, the lower level of
education of mothers and fathers the higher the risk of having children with stunting, children with
mothers who did not work have a 1.15 times higher risk compared to working mothers, children of
mothers who have never used family planning have a risk of 0.80 times more children from
families with ≥5 household members have a 1.05 times higher risk of stunting, the lower the
wealth/poor quintile have a higher risk of stunting, children born with LBW have a 1.861 times
higher risk experience stunting and children who had a history of infectious diseases have a 1.2
times higher risk of experiencing stunting. However, these results have not been controlled for by
other variables, so a multivariate analysis was necessary.

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Table 1. The Characteristics of Respondents and Bivariate Analysis of Factors Associated

with Stunting

Stunting Normal Total Simple cox regression

Variable
n (%) n (%) N (%) PR unadj (96% CI) p-value
Food Insecurity
Mild 16.022 (22,8%) 54.199 (77,2%) 70.222 (84,8%) reference
Moderate 2.788 (28,4%) 7.043 (71,6%) 9.831 (11,9%) 1,24 <0,001
(1,18 – 1,31)
Severe 864 (31,7%) 1.860 (68,3%) 2724 (3,3% 1,39 <0,001
(1,27 – 1,53)
Residential area
Urban 10.247 (21,4%) 37.540 (78,6%) 47.787 (57,7%) reference
Rural 9.427 (26,9%) 25.563 (73,1%) 34.990 (42,3%) 1,26 <0,001
(1,20 – 1,31)
Mother’s Education
High 1.558 (14,7%) 9.052 (85,3%) 10.610 (12,8%) reference
Low 6.506 (30,2%) 15.036 (69,8%) 21.542 (26,0%) 2,06 <0,001
(1,91 – 2,22)
Moderate 11.609 (22,9%) 39.016 (77,1%) 50.625 (61,2%) 1,56 <0,001
(1,45- 1,68)
Father’s Education
High 1.271 (15,1%) 7.135 (84,9%) 8.406 (10,1%) reference
Low 7.130 (29,7%) 16.861 (70,3%) 23.991 (29,0%) 1,97 <0,001
(1,82 – 2,13)
Moderate 11.273 (22,4%) 39.106 (77,6%) 50.380 (60,9%) 1,480 <0,001
(1,82 – 2,13)
Mother's employment
status
Working 5.745 (21,5%) 20.964 (78,5%) 26.709 (32,3%) reference
Not working 13.929 (24,8%) 42.139 (75,2%) 56.068 (67,7%) 1,15 <0,001
(1,11 – 1,21)
KB membership
status
Using KB 17.466 (24,5%) 53.968 (75,5%) 71.434 (86,3%) reference
Not using KB 2.208 (19,5%) 9.135 (80,5%) 11.343 (13,7%) 0,80 <0,001
(0,75 – 0,85)
Total number of
family
< 5 people 10.093 (23,3%) 33.468 (76,8%) 43.561 (52,6%) reference
>= 5 people 9.581 (24,4%) 29.635 (75,6%) 39.216 (47,4%) 1,05 <0,10
(1,01 – 1,10)
Ownership index
quintile
Quintile 5 2.266 (14,4%) 13.516 (85,6%) 15.782 (19,1%) reference
Quintile 1 4.882 (33,7%) 9.611 (66,3%) 14.493 (17,5%) 2,35 <0,001
(2,20 – 2,51)
Quintile 2 5.028 (26,7%) 13.796 (73,3%) 18.824 (22,7%) 1,860 <0,001
(1,74 – 1,99)
Quintile 3 4.056 (24,0%) 12.872 (76,0%) 16.928 (20,4%) 1,669 <0,001
(1,56 – 1,79)
Quintile 4 3.442 (20,5%) 13.308 (79,5%) 16.750 (20,2%) 1,431 <0,001
(1,33 – 1,54)
Low Birth Weight
(LBW)
Not LBW 17.415 (22,5%) 59.970 (77,5%) 77.385 (93,5%) reference
LBW 2.259 (41,9%) 3.133 (58,1%) 5.392 (6,5%) 1,861 <0,001
(1,76 – 1,967)
History of infectious
disease
Not having 16.763 (23,2%) 55.567 (76,8%) 72.330 (87,4%) reference
Having 2.911 (27,9%) 7.536 (72,1%) 10.447 (12,6%) 1,20 <0,001
(1,14 – 1,27)
Total 19.674 (23,8%) 63.103 (76,2%) 82.777 (100%)

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The results of the multivariate analysis (Table 2) showed that if subgroups were carried out
based on the area of residence, in rural areas, children from families with moderate food insecurity
were at risk of 1.09 (95% CI 1.022 – 1.160) times higher for stunting, and the risk increases by 1.15
(95% CI 1.035 - 1.277) times in families with severe food insecurity (Adj PR 0.136; 95% CI: 0.09-
0.20) after controlling for variables of mother's education, number of household members,
ownership index quintile, low birth weight and history of infection. However, the results were
different in urban areas, food insecurity status was not statistically related to the risk of stunting.

Table 2. Multivariate Analysis of the Relationship between Food Insecurity and Stunting by
Region Category

Variable Rural Urban

PR adj (95% CI) p-value PR adj (95% CI) p-value
Food Insecurity
Mild food insecurity (ref) reference reference
Moderate food insecurity 1,09 (1,02 – 1,16) 0,008 0,99 (0,91-1,07) 0,763
Severe food insecurity 1,15 (1,04 – 1,28) 0,009 1,04 (0,90-1,20) 0,594
Mother’s Education
High reference reference
Low 1,34 (1,22 – 1,47) <0,001 1,43 (1,26-163) <0,001
Moderate 1,22 (1,12- 1,34) <0,001 1,20 (1,08-1,34) <0,001
Mother's employment status
Working - reference
Not working - 1,09 (1,01-1,16) 0,018
KB membership status
Using KB - 0,89 (0,81-0,97) 0,009
Not using KB - reference
Total number of family
< 5 people reference reference
>= 5 people 1,05 (1,00 – 1,09) 0,046 1,08 (1,01-1,15) 0,017
Ownership index quintile
Quintile 5 (ref) reference reference
Quintile 1 1,62 (1,46 – 1,80) <0,001 2,02 (1,80-2,28) <0,001
Quintile 2 1,35 (1,23 – 1,49) <0,001 1,68 (1,51-1,87) <0,001
Quintile 3 1,31 (1,87 – 1,46) <0,001 1,50 (1,35-166) <0,001
Quintile 4 1,19 (1,07 – 1,32) <0,001 1,33 (1,21-1,47) <0,001
Low Birth Weight
Not Low Birth Weight reference reference
Low Birth Weight 1,78 (1,69 – 1,90) <0,001 1,78 (1,64-1,94) <0,001
History of infectious disease
Not having reference reference
Having 1,15 (1,07 – 1,22) <0,001 1,09 (1,00-1,20) 0,047

Discussion
The prevalence of households experiencing food insecurity from this study was 11.9% with a
moderate level of food insecurity and 3.3% of households with a severe level of food insecurity.
This figure is higher than the results of the 2017 Susenas where 8.5% of households experienced
food insecurity, and 1.12% of households experienced severe food insecurity. 23 The prevalence of
food-insecure households in Indonesia was still relatively low when compared to world data, where

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it was predicted that around 29.5% of the world's population was experiencing moderate or severe
food insecurity.24 Nonetheless, this almost double difference was a crucial figure and requires
more serious attention because of the high rates of food insecurity in households with children
under five that need to be addressed, especially in households with infants aged <2 years who were
still in the golden age.
The prevalence of stunting in this study is 23.8% (Table 1), where the results were slightly
different by 0.6% from the SSGI 2021 results, which may be due to this study only using SSGI data
for phase 1 only.6 The Ministry of Health was guided by WHO provisions; if the prevalence of
stunting was more than 20 percent, then it can be considered a chronic public health problem. This
meant that nationally the problem of stunting in Indonesia was classified as chronic. 25 The results
of the study showed that toddlers from moderate food insecure families had a 1.24 times higher risk
of experiencing stunting, and the risk increased in severely food insecure families (PR unadj 1.39).
These results were in line with previous research in the urban area of Surabaya, Indonesia, that
food insecurity was positively related not only to stunting but also to obesity/overweight in
mothers.18. The results of a study in Malaysia showed that children from food insecure households
were 2.15 times more likely to experience underweight and 3 times more likely to experience
stunting. 15 Food insecure households were vulnerable to having malnourished children due to the
inability to meet quality and varied food, which was proven to contribute significantly to
malnutrition, especially micronutrient deficiencies. 26 Research in Nepal27, and Iran 28 also reported
similar results to this study. Food insecurity related to delays in the introduction of complementary
foods, a tendency to consume more foods high in starch but low in protein; consuming less
nutritious foods such as vitamin A, iron, and zinc; lower intake of animal protein, higher intake of
snack foods, and a lower intake of fruits and vegetables. 29,30
If we did a subgroup analysis based on the rural and urban areas, it would turn out that the
relationship between food insecurity and stunting was only significant in rural areas. This result is
in line with previous studies in Indonesia, which estimated the prevalence of stunting in villages to
be 55% higher compared to 34.9% in cities with an AOR of 1.55. 31, and also the results of a study
in Nepal 19. One possible cause was poverty, where based on BPS data for 2021, the percentage of
poor people in rural areas was higher (12.53%) than in urban areas (7.6%). 32 This was also the case
in Malaysia, where malnourished children were more common in rural areas with a high poverty
index.14 Analysis of IDHS data in Nepal from 2011-2017 shows that families with the lowest
wealth quintile had a greater risk of being stunted. 33 In poor families, low income and limited
resources lead to an inability to buy food, and then it became the main cause of food insecurity and
family nutrition.34 Another study by Mkhawani et al. suggested that a higher prevalence of stunting
in rural areas compared to urban areas is associated with greater sensitivity to changes in food
prices. Families in rural areas were more sensitive to rising food prices because they allocated two-

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fifths of their budget for basic needs. Along with rising food prices, the purchasing power of
families in rural areas has decreased, making it increasingly difficult to meet their essential
nutritional needs.35 Another factor that also influences nutritional status is the low level of
36
education, where the percentage of people with low education in Indonesia was more in rural
areas than urban areas.
In addition, adequate access to health services was important for rural and urban
communities because health services were one of the factors associated with stunting. 37 But
unfortunately, in Indonesia, there was a gap in access to health services between rural and urban
areas. People living in urban areas had access to better health services and were supported by other
related infrastructure, such as roads which reduced travel time to health care facilities, while access
to health services in rural areas was limited. As many as 6.3% of sub-districts in Indonesia did not
have access to Puskesmas, and as many as 4.2% of Puskesmas in rural areas did not have doctors
on duty at health facilities and also infrastructure facilities which were more limited in rural areas.38
The advantages of this study were using a relatively large sample that came from a large
sample calculation with a high-power test, the sample was selected by multistage random sampling,
and the results could be generalized to the national level. This study also used food insecurity
instruments which were validated and have been used by BPS in the National Socioeconomic
Survey (Susenas) to measure the level of food insecurity since 2017, and interviews were
conducted by trained interviewers so as to reduce the bias. However, this study also had several
limitations. Where the design used was cross-sectional, so it could not draw causal conclusions
between food insecurity and stunting, so further research was needed with a better study design.

Conclusion
The results of the bivariate analysis showed that food insecurity was related to stunting in
children under five, and the risk was increasing in households with a severe level of food
insecurity. However, the results of the multivariate analysis showed that the relationship between
food insecurity and stunting only looked significantly different in rural areas. Therefore, efforts to
tackle stunting needed to be focused on households with food insecurity, especially in rural areas.
Further research was needed with better study designs, such as cohort studies, to support and prove
a causal relationship between food insecurity and stunting.

Acknowledgment
The authors thank the Health Policy and Development Agency (BKPK) for allowing the use
of this data for further analysis.

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Funding
There is no sponsorship/funding for this article.

Conflict of Interest
The author declared that there was no conflict of interest in writing this article.

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