DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2016 No. 126

Grace A. K. Ettyang

Caroline J. Sawe

August 2016

This document was produced for review by the United States Agency for

International Development.

Factors Associated with Stunting in Children

under Age 2 in the Cambodia and

Kenya 2014 Demographic and Health Surveys

Factors Associated with Stunting in Children 

under Age 2 in the Cambodia and 

Kenya 2014 Demographic and Health Surveys 

Grace A. K. Ettyang1

Caroline J. Sawe1

ICF International 

Rockville, Maryland, USA 

August 2016 

1

 Department of Human Nutrition, Moi University 

Corresponding author: Grace Ettyang, Department of Human Nutrition, Moi University; 

gaettyang@gmail.com 
Acknowledgments 

The authors are very grateful to ICF International for accepting their application for DHS 2016 

program and United States Agency for International Development (USAID) for the funding. We 

acknowledge our able training facilitators Wenjuan Wang and Shireen Assaf, for their 

unconditional support and to the former DHS fellows; Damian Jeremia Damian, Simona Simona, 

and Elizabeth Nansubuga for their co-facilitation during the trainings. Our appreciation also goes 

to the 2016 DHS fellows’ teams from Lesotho, Nigeria, Malawi, and Zambia for their moral and 

professional support. We also wish to thank all the reviewers for the useful comments on drafts 

of this paper. Finally, we appreciate the opportunity given to us by the Department of Human 

Nutrition, School of Public Health, College of Health Science, Moi University, Kenya for allowing 

us to participate in the 2016 DHS Fellows Program. 

Editor: Bryant Robey 

Document Production: Natalie La Roche 

The DHS Working Papers series is a prepublication series of papers reporting on research in 

progress that is based on Demographic and Health Surveys (DHS) data. This research is carried 

out with support provided by the United States Agency for International Development (USAID) 

through The DHS Program (#AIDOAA-C-13-00095). The views expressed are those of the 

authors and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and 

evaluate population, health, and nutrition programs. For additional information about The DHS 

Program, contact DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 

20850, USA. Phone: +1 301-407-6500; Fax: +1 301-407-6501; Email: reports@dhsprogram.com; 

Internet: www.dhsprogram.com. 

Recommended citation: 

Ettyang, Grace A. K. and Caroline J. Sawe. 2016. Factors Associated with Stunting in Children 

under Age 2 in the Cambodia and Kenya 2014 Demographic and Health Surveys. DHS Working 

Papers No. 126. Rockville, Maryland, USA: ICF International.
iii 

Abstract 

Background: This study examined the relationships between child, maternal, household, and 

gender inequality characteristics and child stunting in Kenya and Cambodia. Globally, an 

estimated 171 million children are stunted, including 167 million in low- and middle-income 

countries, with especially high prevalence levels in Africa and Asia. Child stunting reflects chronic 

undernutrition, which often begins before birth and is almost irreversible after the second year 

of life. 

Methods: The study analyzed data from the 2014 Demographic and Health Surveys (DHS 

surveys) in Kenya and Cambodia for children under age 2. Bivariate and logistic regression 

analyses were performed to find associations between the variables and child stunting. 

Results: The prevalence of stunting among children under age 2 in Kenya was 22%, and in 

Cambodia, 25%. Child’s age, perceived birth size, family wealth status, and region of residence 

were significantly associated with stunting. In both countries children from the richest households 

had 0.4 times lower odds of being stunted compared with those from the poorest households. In 

Kenya alone, female children had 0.6 times lower odds of being stunted compared with male 

children. In Cambodia alone, children from rural areas had 0.6 times lower odds of being stunted 

compared with those from urban areas, while children whose mothers were underweight had 1.7 

times higher odds of being stunted than children whose mothers were not underweight. In both 

countries, there was general lack of a strong and significant relationship between gender inequality 

and child stunting. 

Conclusions: Children’s characteristics were more important in predicting stunting than factors 

related to mothers, households, or gender. More extensive analysis of the DHS data should be done 

to include other aspects of gender inequality, such as decisions on choice and preparation of food 

and purchase of household goods. 

Keywords: stunting, Kenya, Cambodia, gender inequality 

1 

1. Introduction 

1.1. Background 

Globally, an estimated 171 million children are stunted, including 167 million children in low- and 

middle-income countries (Black et al. 2013; de Onis, Blössner, and Borghi 2012). Stunting is 

defined as having a height-for-age Z-score (HAZ) that is more than two standard deviations below 

the age–sex median for a well-nourished reference population. Stunting has remained widespread 

in low- and middle-income countries (Hoddinott et al. 2013), with the majority of stunted children 

living in Asia and sub-Saharan Africa (de Onis et al. 2012). The factors underlying this high 

prevalence of malnutrition include low birth weight, maternal health problems, poor child care 

related to dynamics within the family, and inequality between men and women (Ramalingaswami, 

Jonsson, and Rohde 1997) 

Globally, the percentage of children under age 5 who are stunted has decreased, from 40% in 1990 

to 28% in 2010, with an anticipated further drop to 22% (142 million) by 2020 (de Onis et al. 

2012). In Africa, however, prevalence of stunting among children under age 5 was 36% compared 

with 27% in Asia, estimated in 2011. It is projected that by 2020, Asia and Africa will have almost 

similar numbers of stunted children (68 million and 60 million, respectively). These levels are 

much higher than the number of children stunted in Latin America, at 7 million in 2010 (de Onis, 

Blössner, and Borghi 2012). 

The higher prevalence of child stunting in Africa and Asia is a public health problem that has often 

gone unrecognized. Child stunting reflects a failure to receive adequate nutrition over a long period 

of time and may be affected by intrauterine growth retardation, poor feeding practices, and frequent 

exposure to infections (Long and Mulatu 2013). When stunting spans generations, it results in 

grave consequences that include poor quality of life, morbidity, and mortality (Black et al. 2013; 

Shroff et al. 2009). 

The 2014 Demographic and Health Surveys (DHS surveys) for Kenya and Cambodia showed that 

the prevalence of stunting among children under age 2 was 22% and 25% respectively. The 

prevalence of stunting in children under age 5 in Kenya and Cambodia was higher, at 32% and 

26% respectively (National Institute of Statistics et al. 2015; KDHS 2014). A focus on stunting 

prevalence in the under-5 age group as a whole masks the early onset of stunting among children; 

the key “window of opportunity” for reducing stunting is the first 1,000days of life, from 

conception until age 2 (Victora et al. 2010). The greatest potential for reducing child stunting is 

intervention under age 2. The benefit is greatest if nutrition and health interventions focus on the 

first 1,000 days (Ruel and Alderman 2013). So far, however, the nutritional interventions made 

during this crucial time have had a modest impact on child stunting and growth (Bhutta et al. 2013). 
2 

A number of studies have identified risk factors for stunting in this crucial period (Semba et al. 

2016). Researchers have found that poverty, poor health and nutrition, and social factors are 

associated with risks to child growth. These factors have prevented over 200 million children in 

developing countries from attaining their full potential (Walker et al. 2007). A 2006 UNICEF 

report found a link between children’s nutritional status and women’s decision-making power. In 

developing countries, where mostly women are denied a voice in household decisions, they are 

most likely to be undernourished themselves and less likely to have access to resources that can 

be directed toward children’s nutrition (UNICEF 2006). Therefore, women should be given a 

chance to participate in household decisions, which might also have a positive impact on the 

nutritional status of their children (Ramalingaswami, Jonsson, and Rohde 1997). There is a need 

to investigate maternal and gender factors that could be associated with child stunting. 

International goals to reduce stunting (Alkire and Samman 2014; de Onis et al. 2013) cannot be 

achieved if factors specific to stunting in children under age 2 are ignored. The World Health 

Assembly aims to reduce the level of child stunting in the world by 40% by 2025 (World Health 

Organization 2012). Because sub-Saharan Africa has the highest share of child stunting in the 

world, followed by Asia, there is a need to investigate the factors associated with the high 

prevalence of child stunting in these regions. 

Reduction in child stunting is a key indicator for achievement of Sustainable Development Goal 

Number 2 (Murray 2015) and is considered a good indicator of chronic malnutrition that is 

associated with inadequate nutrition and infectious disease (Richard et al. 2012). If malnutrition is 

addressed before age 24 months, accelerated growth can occur and the original height trajectory 

can be achieved (Van IJzendoorn, Bakermans-Kranenburg, and Juffer 2007). 

This study therefore investigated the maternal, household, and gender factors that are associated 

with stunting among children under age 2 in Kenya and Cambodia, using data from the 2014 DHS 

survey in each country. 

 
3 

Conceptual Framework 

1.2. Research Questions 

1. Are factors associated with stunting in children under age 2 different for Kenya compared 

with Cambodia? 

2. To what extent are gender inequalities in Kenya and Cambodia a significant factor for 

stunting in children under age 2? 

Maternal and household

characteristics

Age at first birth 

Mother’s nutritional status 

Wealth index 

Place of residence 

Region of residence 

Women empowerment 

characteristics 

Ownership of assets 

Decision on mother’s health 

Decision on large household purchases 

Mother’s employment status 

Mother’s education status 

Father’s education status Child’s 

characteristics 

Age 

Sex 

Perceived birth 

size 

Vaccination status 

Birth interval

S

T

U

N

T

I 

N

G

5 

2. Data and Methods 

The study used data from the 2014 Kenya and Cambodia DHS surveys. The 2014 Kenya DHS 

contained both a long and short questionnaire. For this analysis, the data for Kenya were all from 

the long questionnaire, which included many variables used in the analysis (namely the gender 

inequality variables, father’s education level, perceived birth size, and mother’s nutritional status). 

DHS data are cross-sectional in nature, and data about children are obtained from their mothers. 

Our study specifically examined stunting among children under age 2 and its association with 

child, maternal, household, and gender factors. The sample size was limited to children under age 

2 with valid anthropometric measurements and whose parents were married and or declared that 

they were in a union. Information that is collected only from women in union in the surveys is 

required to construct gender variables. In addition, when constructing the mother’s nutritional 

status variable, pregnant women and women with a child under age 2 months were coded as 

missing. This created 325 missing cases for this variable in Kenya and 202 in Cambodia. Figure 1 

shows the procedure for sample size determination. 

Figure 1. Analysis of sample derivation 

Kenya Cambodia 

 

All children under age 5 

18,986 4,893 

Children under age 2 selected with valid anthropometric measurements 

8,157 2,006 

Children whose mothers completed full questionnaires (Kenya) and were in 

union/married for both countries 

3229 1,824 

Final sample size after applying weights 

2961 1,856 
6 

2.1. Dependent Variable 

Stunting: The 2014 Kenya and Cambodia DHS measured stunting by height-for-age according to 

WHO recommendations (WHO 2006). To increase data quality and minimize errors, the field staff 

were thoroughly trained and a pilot study was done prior to the main survey. Height-for-age, Zscores were tabulated. A Z-score less than -2 standard deviations (SD) for height-for-age indicated 

stunting based on the WHO/National Center for Health Statistics reference (WHO 2006). A binary 

variable was created to define stunting, “not stunted” (Z-score greater than -2 SD), and “stunted” 

(Z- score less than and equal to -2 SD). 

2.2. Independent Variables 

2.2.1. Child characteristics 

The child variables included children’s age in months (0-24 months), sex, perceived birth size by 

the mother (small, average and large), and the birth interval between the current child of interest 

and the previous birth, counting twins as one and giving an interval of zero for first births. 

Vaccination status was used to measure whether the child had received all the necessary DPT, 

measles, polio, and BCG vaccinations at the time of the survey. The perceived size of the child at 

birth was used instead of the actual size at birth, as there were a large number of missing responses 

from mothers who were asked the exact weight of their child at birth. 

2.2.2. Maternal and household characteristics 

The maternal variables included mother’s age at first birth, mother’s nutritional status, which was 

based on body mass index (BMI), defined as weight in kilograms divided by the height squared in 

meters (W/H2). Women who were pregnant and women with a child under age 2 months were 

excluded from the BMI calculation. The BMI was then used to identify mothers as either 

underweight (BMI less than 18.5) or not. 

The household variables included wealth index, place of residence (urban-rural), and region. 

2.2.3. Gender inequality characteristics 

Decisions on the mother’s healthcare: In the survey, women were asked if they make decisions 

about their own health alone or jointly with their husbands or partners. Women’s responses that 

they made decisions alone or jointly with their husbands or partners were categorized as yes, while 

responses that the husbands, partners, or others made the decisions were categorized as no. 

Ownership of assets: Women were asked if they owned any land or a house. If they owned property 

alone or jointly with their husbands or partners, we categorized this as yes, but if their husbands 

or partners owned such assets alone, we categorized it as no. 
7 

Mother’s work status: Women were asked if they ever worked in the last 12 months. Those who 

reported not working were categorized as no, and those worked in the past 12 months or were 

currently working or on leave from work were categorized as yes. 

Education level: This category, for both women and men, was defined as the highest education 

level attended: no education, primary, secondary, and higher. The categories of secondary and 

higher were combined to create a category of secondary and above. 

2.3. Statistical Analysis 

Statistical analyses were carried out using STATA 14. The datasets for households and women 

were used and the “sample weight” variable was used to adjust for disproportionate sampling and 

nonresponse. Two-stage sampling design was used, where clusters were selected first and then the 

households to be interviewed were chosen. Univariate analysis was performed to describe all the 

variables (Table 1). Bivariate analysis with Chi-square statistics was performed to test the 

independence of distribution between the independent variables and the dependent variable (Table 

2), and a final logistic regression was done to assess the net associations of all variables with 

stunting. Three models were generated—(1) a model with child characteristics only; (2) a model 

with child, maternal, and household characteristics; and (3) a model with the all of the 

characteristics, including gender inequality variables (Table 3). The associations between the 

outcome and main explanatory variables were considered to be statistically significant when the 

p-value <0.05. 

9 

3. Results 

The final sample size for Kenya was 2,961 women and for Cambodia 1,856 women who were 

interviewed in the 2014 DHS, had children under age 2, and were in a union at the time of the 

survey. Table 1 presents the descriptive statistics of the child, maternal, household, and gender 

inequality variables. The prevalence of stunting among children under age 2 was 22% in Kenya 

and 25% in Cambodia. In Kenya, 50% of the children were male, with a majority age 18-24 months 

(28%), compared with 52% of males in Cambodia, with 29% age 18-24 months. According to their 

mothers’ perceptions, 57% of children in Kenya and 55% in Cambodia were born with average 

size. 

An estimated 62% of Kenyan mothers and 69% of Cambodian mothers reported giving birth to 

their first child at age 18-24. The great majority of mothers were not underweight (Kenya 90% and 

Cambodia 86%). In both countries, almost a quarter of children were from households in the 

poorest wealth category. In addition, 64% of Kenyan children and 86% of Cambodian children 

lived in rural areas. 

In both countries most women responded in the DHS that they had a say in decisions on their own 

health care, at 76% in Kenya and 91% in Cambodia. In Cambodia, 92% of women were involved 

in the purchase of large household items compared with 66% in Kenya. Concerning ownership of 

property, in Kenya 60% of women reported owning a house and 53% owned land, while in 

Cambodia 63% owned a house and 55% owned land. Both countries had similar percentages for 

women who were working, at 64%, and the level of education attained was also similar, with 

primary highest at 54%. Forty percent of the fathers in Kenya and 46% in Cambodia had secondary 

and above as the highest level of education. 

 
10 

Table 1. Descriptive characteristics of the sample in Kenya and Cambodia, 2014 DHS

 KENYA CAMBODIA 

Variable % CI N % CI N 

Nutritional status 

Not stunted 77.9 [75.8,80.0] 2,308 75.5 [73.0,77.9] 1,402

Stunted 22.1 [20.0,24.2] 653 24.5 [22.1,27.0] 454

Age of child

Under 6 months 25.5 [23.4,27.6] 772 28.3 [25.8,30.9] 552 

6-8 months 8.7 [7.4,10.1] 263 8.1 [6.6,10.0] 159 

9-11 months 12.4 [11.0,13.9] 374 11.4 [9.7,13.4] 223 

12-17 months 26.0 [23.8,28.4] 789 23.1 [20.8,25.6] 451 

18-24 months 27.5 [25.5,29.5] 833 29.1 [26.7,31.6] 567 

Child’s birth size 

Small 16.2 [14.5,18.0] 485 10.8 [9.0,12.9] 209 

Average 57.4 [54.9,59.8] 1,722 54.8 [51.7,57.8] 1,062 

Large 26.5 [24.4,28.6] 794 34.4 [31.5,37.4] 667 

Birth interval 

<24 32.5 [30.4,34.7] 985 8.4 [6.9,10.1] 164 

24-47 40.0 [37.7,42.5] 1,214 24.4 [22.0,27.0] 477 

47+ 27.5 [25.4,29.7] 833 67.2 [64.3,69.9] 1,311 

Received all required vaccinations (DPT, polio, BCG, and measles) 

Yes 58.4 [56.1,60.8] 1,771 56.4 [53.4,59.4] 1,101 

No 41.6 [39.2,43.9] 1,260 43.6 [40.6,46.6] 851 

Sex of child 

Male 50.5 [48.1,52.9] 1,531 52.4 [49.2,55.5] 1,022 

Female 49.5 [47.1,51.9] 1,500 47.6 [44.5,50.8] 930 

Mother’s nutritional status 

Not underweight 90.9 [89.5,92.1] 2,459 85.9 [83.6,87.9] 1,503 

Underweight 9.1 [7.9,10.5] 246 14.1 [12.1,16.4] 247 

Mother’s age at 1st birth 

<18 29.4 [27.3,31.6] 891 8.4 [7.0,10.1] 164 

18-24 61.5 [59.2,63.8] 1,865 68.9 [66.3,71.5] 1,346 

25+ 9.1 [7.8,10.6] 275 22.6 [20.2,25.2] 442 

Wealth index 

Poorest 25.5 [23.1,28.1] 773 24.7 [21.3,28.3] 481 

Poorer 19.8 [18.0,21.8] 601 19.6 [17.3,22.2] 383 

Middle 16.8 [15.2,18.6] 511 20.1 [17.7,22.8] 393 

Richer 17.1 [15.1,19.3] 517 17.6 [15.2,20.3] 344 

Richest 20.8 [18.2,23.5] 629 18.0 [15.6,20.6] 351 

Region, Kenya 

Coast 10.3 [8.7,12.2] 313

North Eastern 3.8 [3.1,4.8] 116 

Eastern 12.1 [10.6,13.8] 368

Central 9.2 [7.8,10.9] 279 

Rift Valley 29.2 [27.0,31.6] 886

Western 11.9 [10.0,14.1] 360 

Nyanza 13.3 [11.8,14.9] 403

Nairobi 10.1 [8.3,12.3] 307 

(Continued...)

 
11 

Table 1—Continued

 KENYA CAMBODIA 

Variable % CI N % CI N 

Region, Cambodia 

Banteay mean chey 3.8 [2.8,5.0] 74 

Kampong cham 12.8 [10.6,15.5] 250 

Kampong chhnang 3.8 [3.2,4.5] 74 

Kampong speu 6.8 [5.1,8.9] 132 

Kampong thom 4.8 [4.0,5.9] 95 

Kandal 6.8 [5.4,8.5] 132 

Kratie 3.9 [2.6,5.7] 76 

Phnom Penh 8.4 [6.8,10.3] 164 

Prey Veng 6.9 [5.6,8.3] 134 

Pursat 4.4 [3.3,5.9] 86 

Siem Reap 6.8 [5.4,8.5] 133 

Svay Rieng 3.8 [3.1,4.7] 75 

Takeo 5.4 [4.4,6.6] 106 

Otdar Mean Chey 2.0 [1.6,2.4] 38 

Battambang & Pailin 7.7 [5.3,11.2] 151 

Kampot & Kep 4.3 [3.5,5.1] 83 

Preah Sihanouk & Kaoh Kong 2.1 [1.7,2.6] 40 

Preah Vihear & Steung Treng 3.0 [2.3,4.0] 59 

Mondol Kiri & Rattanak Kiri 2.6 [2.0,3.3] 51 

Type of place of residence 

Urban 36.0 [33.4,38.6] 1,090 13.9 [12.2,15.7] 271 

Rural 64.0 [61.4,66.6] 1,941 86.1 [84.3,87.8] 1,680 

Mother decides on own healthcare (alone or jointly) 

No 24.8 [22.8,27.0] 752 9.4 [7.8,11.4] 184 

Yes 75.2 [73.0,77.2] 2,276 90.6 [88.6,92.2] 1,767 

Mother decides on household large purchases (alone or jointly) 

No 34.0 [31.4,36.7] 1,029 8.5 [7.0,10.3] 167 

Yes 66.0 [63.3,68.6] 1,998 91.5 [89.7,93.0] 1,784 

Mother owns house (alone or jointly) 

No 39.9 [37.0,42.7] 1,206 37.3 [34.4,40.3] 728 

Yes 60.1 [57.3,63.0] 1,820 62.7 [59.7,65.6] 1,224 

Mother owns land (alone or jointly) 

No 46.6 [44.0,49.3] 1,411 45.4 [42.2,48.6] 886 

Yes 53.4 [50.7,56.0] 1,616 54.6 [51.4,57.8] 1,066 

Mother worked in the past 12 months 

No 36.5 [34.0,39.0] 1,106 35 [31.6,38.5] 683 

Yes 63.5 [61.0,66.0] 1,925 65 [61.5,68.4] 1,269 

Mother’s education 

No education 13.3 [11.6,15.4] 405 12.6 [10.6,14.8] 245 

Primary 54.0 [51.1,56.8] 1,635 53.9 [50.9,56.9] 1,053 

Secondary + 32.7 [30.0,35.5] 991 33.5 [30.6,36.5] 653 

Father’s education 

No education 9.8 [8.4,11.5] 296 9.8 [7.9,12.2] 191 

Primary 50.6 [48.0,53.1] 1,524 44.1 [41.2,47.1] 856 

Secondary + 39.6 [37.1,42.2] 1,194 46.1 [43.0,49.2] 894 
12 

Table 2 shows the results of bivariate analysis on the association between background 

characteristics of the sample and child stunting. In both Kenya and Cambodia the factors associated 

with stunting were child’s age, perceived birth size, vaccination status, place of residence, father’s 

highest education attained, and wealth index. The following factors were significant in Kenya 

alone: child’s birth interval, sex of the child, mother’s age, and mother’s highest education 

attained. In Cambodia alone significant factors included region and mother’s ownership of a house. 

The associations between the variables and stunting were considered to be statistically significant 

when P <0.05. 

Table 2. Percentage of children under age 2 who were stunted by child, maternal, household, 

and gender variables in Kenya and Cambodia, 2014 DHS 

Variable 

KENYA CAMBODIA 

Stunted Stunted 

% CI p-value % CI p-value 

Age of child 

Under 6 months 9.9 [7.7,12.7] <0.001 15.2 [11.4,20.0] <0.001 

6-8 months 11.6 [7.7,17.1] 15.3 [9.0,24.7] 

9-11 months 16.2 [12.1,21.2] 18.0 [12.5,25.2] 

12-17 months 28.8 [24.6,33.2] 29.4 [24.1,35.2] 

18-24 months 32.6 [28.5,36.9] 33.9 [29.0,39.2] 

Child’s birth size 

Small 34.3 [28.5,40.5] <0.001 45.0 [36.5,53.8] <0.001 

Average 20.8 [18.4,23.5] 24.6 [21.4,28.1] 

Large 17.2 [13.9,21.0] 17.7 [14.3,21.6] 

Birth interval 

<24 24.2 [20.8,27.9] 0.006 28.5 [20.9,37.6] 0.149 

24-47 23.9 [20.9,27.1] 27.8 [23.1,33.1] 

47+ 16.9 [13.6,20.8] 22.8 [19.7,26.1] 

Received all required vaccinations (DPT, polio, BCG, and measles)

Yes 19.7 [17.3,22.3] 0.002 22.0 [18.8,25.5] 0.024 

No 25.4 [22.4,28.7] 27.6 [24.2,31.2] 

Sex of child 

Male 25.0 [22.1,28.2] <0.001 26.5 [23.3,29.9] 0.057 

Female 19.1 [16.7,21.7] 22.2 [19.1,25.7] 

Nutritional status 

Not underweight 21.7 [19.5,24.1] 0.509 23.1 [20.4,26.1] <0.001 

Underweight 23.6 [18.8,29.2] 37.0 [29.6,45.0] 

Mothers age at 1st birth 

<18 27.3 [23.7,31.4] 0.001 22.2 [15.0,31.4] 0.407 

18-24 20.4 [18.0,23.1] 25.6 [22.8,28.6] 

25+ 15.9 [11.1,22.2] 21.9 [17.2,27.3] 

Wealth index 

Poorest 29.2 [25.8,32.7] <0.001 35.5 [30.3,41.2] <0.001 

Poorer 24.6 [20.6,29.2] 26.7 [21.8,32.3] 

Middle 17.8 [14.3,22.0] 23.5 [18.4,29.5] 

Richer 21.4 [16.8,27.0] 18.7 [14.0,24.6] 

Richest 15.0 [10.2,21.5] 13.8 [9.9,18.9] 

(Continued...)

 
13 

Table 2—Continued 

Variable 

KENYA CAMBODIA 

Stunted Stunted 

% CI p-value % CI p-value 

Type of place of residence 

Urban 18.2 [14.7,22.3] 0.014 18.8 [14.8,23.7] 0.02 

Rural 24.2 [21.8,26.8] 25.4 [22.7,28.2] 

Mother decides on own health care (alone or jointly) 

No 22.7 [19.1,26.7] 0.7 20.9 [14.6,28.9] 0.324 

Yes 21.9 [19.6,24.3] 24.8 [22.4,27.5] 

Mother decides on household large purchases (alone or jointly)

No 21.2 [18.0,24.8] 0.565 18.9 [12.8,27.1] 0.144 

Yes 22.5 [19.9,25.3] 25.0 [22.5,27.6] 

Mother owns house (alone or jointly) 

No 20.4 [16.8,24.5] 0.223 20.6 [17.2,24.4] 0.011 

Yes 23.2 [21.0,25.7] 26.8 [23.8,30.1] 

Mother owns land (alone or jointly) 

No 21.7 [18.4,25.3] 0.2574 22.5 [18.9,26.4] 0.16 

Yes 22.4 [20.0,25.0] 26.1 [23.0,29.6] 

Mother worked in the past 12 months 

No 23.5 [20.1,27.3] 0.29 23.1 [19.5,27.1] 0.365 

Yes 21.2 [18.9,23.8] 25.2 [22.4,28.2] 

Mother’s education 

No education 26.0 [21.3,31.2] 0.005 29.1 [22.1,37.4] 0.064 

Primary 24.1 [21.5,27.0] 25.8 [22.5,29.4] 

Secondary + 17.2 [13.6,21.4] 20.6 [17.1,24.5] 

Father’s education 

No education 26.0 [20.6,32.2] 0.001 34.0 [25.2,44.0] 0.048 

Primary 24.9 [22.2,27.9] 24.1 [20.4,28.3] 

Secondary + 17.5 [14.3,21.2] 22.3 [19.2,25.8] 

*** p<0.001, ** p<0.01, * p<0.05 

Table 3 summarizes the results of the three logistic regression models to assess the net association 

of all variables with stunting. As mentioned, the first model included child characteristics only, 

the second included child, maternal, and household characteristics, and the third contained all of 

the characteristics, including gender inequality variables. Before running the regression, the 

correlations between the independent variables used in the analysis were checked, and none of the 

variables were highly correlated. 

In the first model, the child characteristics that were significantly associated with stunting for both 

countries were age, sex, and perceived birth size, and for Kenya alone, birth interval. In Kenya, 

children age 18-24 months had 5.6 times higher odds of being stunted compared with children 

under age 6 months [OR= 5.6; CI 3.6,8.7], and in Cambodia, 3.8 times higher odds [OR 3.8; 

CI 2.3,6.2]. Children with average perceived birth size had 0.5 and 0.4 times lower odds, for Kenya 

and Cambodia respectively, of being stunted compared with children whose mothers perceived 
14 

them to be small at birth [OR = 0.5; CI 0.3, 0.6 for Kenya, and OR = 0.4; CI 0.3, 0.6 for Cambodia]. 

Female children had lower odds of being stunted compared with male children [OR = 0.6; CI 0.5, 

0.8 for Kenya and OR = 0.8; CI 0.6, 1.0 for Cambodia]. In Kenya, children whose birth was 

followed by a subsequent birth more than 47 months later had 0.6 times lower odds of being stunted 

compared with children with a subsequent birth interval of less than 24 months. 

In the second model, the variables for child, maternal, and household characteristics that were 

significantly associated with stunting in both countries included child’s age, perceived birth size, 

wealth index, and region of residence. For the child characteristics that remained significant in the 

second model, the odds ratios did not change substantially from the first model. In the second 

model child’s sex was no longer significant in Cambodia, and child’s birth interval was not 

significant in either country. In Kenya alone, child’s sex was significant for predicting stunting, 

and in Cambodia alone the significant variables were mother’s nutritional status and child’s place 

of residence. In both countries, children in the richest households had 0.4 times lower odds of 

being stunted compared with children in the poorest households [Kenya OR 0.4; CI 0.2-0.7 and 

Cambodia OR 0.4; CI 0.2-0.8]. In Cambodia alone, children in rural areas had 0.6 times lower 

odds of being stunted compared with children in urban areas [OR 0.6; CI 0.4-0.9], while children 

whose mothers were underweight had 1.7 times higher odds of being stunted compared with 

children whose mothers were not underweight [OR 1.7; CI 1.1-2.7] 

In the third model, which included all child, maternal, household, and gender characteristics, none 

of the gender inequality variables were significantly associated with child stunting, for both 

countries. Child’s age, perceived birth size, household wealth status, and region of residence 

showed some significance, with stunting in both countries, while child’s sex was significant in 

Kenya alone, and mother’s nutritional status and child’s place of residence were significant in 

Cambodia alone. 

 
15 

Table 3. Results of logistic regressions of child stunting on child, maternal, household, and gender 

variables in Kenya and Cambodia, 2014 DHS 

MODEL 1 MODEL 2 MODEL 3 

 KENYA CAMBODIA KENYA CAMBODIA KENYA CAMBODIA 

Characteristics OR CI OR CI OR CI OR CI OR CI OR CI 

Age of child 

Under 6 months 1.0 1.0 1.0 1.0 1.0 1.0 

6-8 months 1.1 0.6 - 1.9 1.0 0.5 - 1.9 1.2 0.7 - 2.0 0.9 0.5 - 1.6 1.1 0.6 - 2.0 0.8 0.5 - 1.5

9-11 months 2.0** 1.3 - 3.2 1.4 0.8 - 2.5 2.1** 1.3 - 3.4 1.2 0.6 - 2.2 2.1** 1.3 - 3.5 1.2 0.6 - 2.2

12-17 months 4.6*** 3.0 - 7.2 2.7*** 1.7 - 4.4 4.5*** 2.7 - 7.6 2.3** 1.3 - 3.8 4.6*** 2.7 - 7.7 2.2** 1.3 - 3.8

18-24 months 5.6*** 3.6 - 8.7 3.8*** 2.3 - 6.2 5.2*** 3.2 - 8.5 3.2*** 1.9 - 5.3 5.3*** 3.2 - 8.8 3.1*** 1.8 - 5.3

Child birth size

Small 1.0 1.0 1.0 1.0 1.0 1.0 

Average 0.5*** 0.3 - 0.6 0.4*** 0.3 - 0.6 0.4*** 0.3 - 0.6 0.4*** 0.2 - 0.6 0.4*** 0.3 - 0.6 0.4*** 0.2 - 0.6

Large 0.3*** 0.2 - 0.5 0.2*** 0.2 - 0.4 0.3*** 0.2 - 0.5 0.3*** 0.2 - 0.5 0.3*** 0.2 - 0.5 0.3*** 0.2 - 0.5

Birth interval 

<24 1.0 1.0 1.0 1.0 1.0 1.0 

24-47 0.8 0.6 - 1.2 1.2 0.7 - 2.0 0.9 0.6 - 1.3 1.1 0.6 - 2.0 0.9 0.6 - 1.3 1.1 0.6 - 1.9

47+ 0.6* 0.4 - 0.9 0.8 0.5 - 1.3 0.7 0.5 - 1.1 0.8 0.5 - 1.4 0.8 0.5 - 1.2 0.8 0.5 - 1.4

Received all required vaccinations (DPT, polio, BCG, and measles) 

No 1.0 1.0 1.0 1.0 1.0 1.0 

Yes 0.8 0.6 - 1.1 0.7* 0.5 - 1.0 0.8 0.6 - 1.2 0.8 0.6 - 1.2 0.8 0.6 - 1.2 0.8 0.6 - 1.2

Sex of child

Male 1.0 1.0 1.0 1.0 1.0 1.0 

Female 0.6*** 0.5 - 0.8 0.8* 0.6 - 1.0 0.6*** 0.5 - 0.8 0.8 0.6 - 1.0 0.6*** 0.5 - 0.8 0.8 0.6 - 1.0

Mother’s nutritional status 

Not underweight 1.0 1.0 1.0 1.0 

Underweight 0.8 0.6 - 1.1 1.7* 1.1 - 2.7 0.8 0.6 - 1.1 1.8* 1.1 - 2.8

Mothers age at 1st birth

 25 years + 1.0 1.0 1.0 1.0 

 18 – 24 years 1.2 0.7 - 2.0 1.1 0.8 - 1.6 1.2 0.7 - 2.0 1.0 0.7 - 1.5

< 18 years 1.6 0.9 - 2.7 0.8 0.4 - 1.5 1.5 0.8 - 2.6 0.8 0.4 - 1.5

Wealth index

Poorest 1.0 1.0 1.0 1.0 

Poorer 0.8 0.6 - 1.1 0.7 0.5 - 1.1 0.8 0.6 - 1.1 0.7 0.5 - 1.1

Middle 0.6** 0.4 - 0.8 0.6 0.4 - 1.0 0.6** 0.4 - 0.9 0.6* 0.4 - 0.9

Richer 0.7 0.4 - 1.0 0.5* 0.3 - 0.9 0.7 0.4 - 1.1 0.5** 0.3 - 0.8

Richest 0.4** 0.2 - 0.7 0.4** 0.2 - 0.8 0.4** 0.2 - 0.8 0.3** 0.2 - 0.7

Type of place of residence

Urban 1.0 1.0 1.0 1.0 

Rural 1.3 0.9 - 1.8 0.6* 0.4 - 0.9 1.3 1.0 - 1.9 0.6* 0.4 - 1.0

Mother decides on own health care (alone or jointly)

No 1.0 1.0 

Yes 1.0 0.7 - 1.4 0.9 0.5 - 1.4

Mother decides on household large purchases (alone or jointly)

No 1.0 1.0 

Yes 1.3 0.9 - 1.8 1.4 0.8 - 2.6

Mother owns house (alone or jointly)

No 1.0 1.0 

Yes 1.1 0.8 - 1.6 1.1 0.7 - 1.8

(Continued...)
16 

Table 3—Continued 

MODEL 1 MODEL 2 MODEL 3 

 KENYA CAMBODIA KENYA CAMBODIA KENYA CAMBODIA 

Characteristics OR CI OR CI OR CI OR CI OR CI OR CI 

Mother owns land (alone or jointly) 

No 1.0 1.0 

Yes 0.8 0.6 - 1.1 1.1 0.7 - 1.8

Mother worked in the past 12 months

No 1.0 1.0 

Yes 0.8 0.6 - 1.1 0.9 0.6 - 1.3

Mother’s education 

No education 1.0 1.0 

Primary 1.0 0.6 - 1.6 1.1 0.7 - 1.7

Secondary + 0.9 0.5 - 1.6 1.1 0.6 - 1.9

Father’s education

No education 1.0 1.0 

Primary 1.3 0.7 - 2.2 0.9 0.6 - 1.6

Secondary + 1.1 0.6 - 2.0 1.3 0.7 - 2.3

*** p<0.001, ** p<0.01, * p<0.05 

Notes: Model II and Model III control for region of residence. 
17 

4. Discussion 

The first 1,000 days of an infant’s life, from conception until age 2, are a crucial period in which 

inadequate nutrition and bouts of infection can result in stunting that is then largely irreversible 

(Save the Children 2012) . Long-term effects of stunting include diminished cognitive and physical 

development, reduced productive capacity, and poor health. 

In Kenya and Cambodia the prevalence of stunting among children under age 2 was 22% and 25% 

respectively, and stunting became even more prevalent among children age 18-24 months, at 33% 

in Kenya and 34% in Cambodia. This rising risk of stunting with age suggests that for young 

children the window of opportunity for reversing the negative effects of stunting might be lost 

forever. After 6 months of exclusive breastfeeding, children should be introduced to 

complementary feeds even as they continue being fed with breast milk. In poor settings, however, 

the quality of these complimentary feeds may be compromised. Because young children have 

increased nutritional needs due to their rapid growth, inadequate nutrition might negatively affect 

their chances for growth. It is therefore important that expectant mothers get vital nutrients, babies 

be breastfed exclusively for 6 months, and adequate solid foods be introduced at the right time. 

Throughout childhood, adequate health care and good hygiene and sanitation are vital. 

In both Kenya and Cambodia, child’s age, perceived birth size, wealth index, and region of 

residence were significantly associated with stunting. In Cambodia alone, mother’s nutritional 

status was also significantly associated with stunting. This finding was in tandem with a study in 

Cambodia in 2013 that found age, wealth, and maternal nutritional status to be associated with 

child stunting (Ikeda, Irie, and Shibuya 2013). 

Our study agrees with a study by Miller and Rodgers that found no association between either 

child vaccination or perceived birth size with child stunting (Miller and Rodgers 2009), but it 

contradicts a study in Nigeria that found child vaccination was associated with child stunting 

(Adekanmbi, Kayode, and Uthman 2013). With vaccination, there is usually a buildup of the 

immune system, which has a positive effect on the nutritional status of children. This study also 

contradicts a study by Black and colleagues that had shown that 20% of stunted children in lowand middle-income countries had small size at birth (Black et al. 2013). 

The study found higher risk for stunting among males compared with females in Kenya than in 

Cambodia. Studies have shown that males are more likely to become stunted in their first year of 

life, and females are likely to become stunted in their second year (Adair and Guilkey 1997). Male 

children may have early exposure to early inappropriate weaning practices and complimentary 

feedings especially in low- and middle-income countries and in African culture. 
18 

Stunting is generally an indicator of chronic under-nutrition. Studies have shown that stunting is 

more common among children who live in poor households (Ikeda, Irie, and Shibuya 2013). Our 

study also found that children from the poorest families were more likely to be stunted. This could 

be because they lack the resources for obtaining health-care services and for adequate nutrition. 

Gender, as it relates to women’s empowerment, may be a key factor responsible for influencing 

children’s nutritional status (Shroff et al. 2009). Ownership of assets by women has increasingly 

been put forth as a way to empower women, increase their productivity, and improve their welfare. 

A study by Bhutta and colleagues (2013) found that women who owned land were significantly 

more likely to have the final say in household decisions, which is a measure of empowerment. 

Similarly, children of mothers who own land were significantly less likely to be severely 

underweight (Bhutta et al. 2013). A study in Indonesia and Bangladesh concluded that both 

maternal and paternal education were strong determinants of child stunting (Semba et al. 2008). 

These findings do not agree with our findings of no association between gender characteristics and 

child stunting in Kenya and Cambodia. This difference may be related to the way data are 

structured and collected and the relatively longer pathway from gender inequality to stunting in 

comparison with more direct factors, such as children’s characteristics. Current survey data on 

women’s empowerment are limited in scope and focus only on married couples. Most data are 

collected from only one household member, and thus the DHS may not capture intra-household 

gender inequalities. 

Conclusions 

Nutrition is key to children’s survival, growth, and development. Well-nourished children are 

healthier than undernourished children, they can grow and develop to their full potential, and they 

perform better in school and as adults. Despite many interventions in improving child stunting, 

about a quarter of children under age 2 in Kenya (22%) and Cambodia (25%) are stunted. In both 

countries stunting appears to be strongly associated with child’s age, perceived birth size, 

household wealth index, and region of residence. In our study, factors related to women’s 

empowerment were not associated with stunting, but additional studies could further examine other 

data on women’s empowerment in the DHS to explore whether they show an association with 

child stunting. 

Policy Recommendation

Since stunting is a key measure obtained in DHS surveys, we suggest that its usefulness as a 

measure of achievement of the UN Sustainable Development Goals would be enhanced if future 

survey instruments could incorporate an easy linkage of stunting to more comprehensive data on 

gender. Just as The DHS Program has the Service Provision Assessment (SPA) and Geographic 
19 

Information Systems (GIS) tools, there may be a need for developing an Assessment of Gender 

Inequality tool (AGI). The Women’s Empowerment in Agricultural Index (WEAI) has such an 

instrument. There is also a need for governments to introduce nutrition interventions that target 

children under age 2 to combat stunting and malnutrition among this key group. This would be of 

great importance in achieving the Sustainable Development Goals. 

21 

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