DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2017 No. 131

Chhorvann Chhea

Por Ir

Heng Sopheab

July 2017

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

International Development.

Low Birth Weight of Institutional Births in Cambodia:

Analysis of the 2010 and 2014 Demographic and Health 

Surveys 

Low Birth Weight of Institutional Births in Cambodia: 

Analysis of the 2010 and 2014 Demographic and Health 

Surveys 

Chhorvann Chhea1

Por Ir1

Heng Sopheab1

ICF 

Rockville, Maryland, USA 

July 2017 

1 National Institute of Public Health, Phnom Penh, Cambodia 

Corresponding author: Chhorvann Chhea, National Institute of Public Health, Phnom Penh, Cambodia 
Acknowledgments 

The authors wish to thank USAID through ICF for giving us an opportunity to take part in the 

2017 DHS Fellows Program. Our special thanks go to Wenjuan Wang and Shireen Assaf for their 

invaluable contribution to the preparation of the research paper. We are grateful to the cofacilitators, Bupe B. Bwalya and Mulenga C. Mulenga, for their support. We further wish to extend 

our gratitude to Dr. Gheda Temsah, who reviewed our paper and provided helpful comments. 

Finally, we would like to thank the Cambodian Ministry of Health for allowing us to participate in 

this 2017 DHS Fellows Program. 

Editor: Bryant Robey 

Document Production: Joan Wardell 

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 (#AID-OAA-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, 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: 

Chhea, Chhorvann, Por Ir, and Heng Sopheab. 2017. Low Birth Weight of Institutional Births in 

Cambodia: Analysis of the 2010 and 2014 Demographic and Health Surveys. DHS Working Paper 

No. 131. Rockville, Maryland, USA: ICF. 
iii 

Abstract 

Low birth weight (LBW), an important risk factor for early childhood mortality and morbidity, is 

a major public health concern in developing countries. In Cambodia, the prevalence of LBW 

remains at levels above the average East Asia and the Pacific. Using data from two Cambodia 

Demographic and Health Surveys in 2010 and 2014, this study examined the prevalence of LBW 

across provinces in Cambodia, tracked changes over time, and identified factors associated with 

LBW. Analyses were restricted to the youngest singleton babies born at health facilities. 

The results show that the prevalence of LBW remained stable between 2010 and 2014, at around 

7% of all institutional births, but with significant variation across provinces. Factors significantly 

associated with LBW included mother’s education, number of antenatal care visits, and child’s 

birth order. Babies born to mothers with no education were more likely to have LBW compared 

with those whose mothers had secondary or higher education. Babies born to mothers who had 

fewer than four antenatal care visits during the pregnancy were more likely to have LBW compared 

with those whose mothers had at least four antenatal care visits. First-born babies were at greater 

risk of LBW than second-born babies. 

The results point to key subpopulations at greater risk and to regions where LBW is particularly 

prevalent. Programs should target the provinces where prevalence of LBW remains high. Illiterate 

women, especially those pregnant for the first time, should be a program priority. The current 

national program policy, which recommends that pregnant women have at least four antenatal care 

visits during pregnancy, should be further reinforced and implemented. Program design should 

consider ways to communicate the importance of women making the recommended number of 

antenatal care visits, especially those with no formal education. 

1 

1. Introduction 

Low birth weight (LBW) has been defined as a birth weight of less than 2,500 grams (or 5.5 pounds), 

irrespective of gestational age (UNICEF and WHO 2004). Birth weight is the first weight of a newborn 

measured after birth, and preferably should be measured within the first hour of life, before significant 

postnatal weight loss has occurred. LBW can be subdivided into very low (less than 1500 grams) and 

extremely low (less than 1000 grams) categories. Its prevalence is often expressed as a percentage of live 

births in a given time period (UNICEF and WHO 2004). 

LBW is a strong risk factor for neonatal and early childhood mortality and morbidity (March of Dimes 

PMNCH and WHO 2012). It is estimated that LBW babies are 20 times more likely to die in the first year 

than babies with normal weight (WHO and UNICEF 2004). In India, very low birth weight contributes to 

nearly 30% of early neonatal deaths (Basu, Rathore, and Bhatia 2008). Recent studies show that LBW 

also increases the risk for noncommunicable diseases, such as diabetes and cardiovascular disease, later 

in life (Risnes et al. 2011). Recognizing the short- and long-term implications of LBW on wellbeing, the 

global health community has set the reduction of LBW by 30% as one of the six global nutritional targets 

for 2025 (WHO 2014a). 

LBW continues to be a major public health problem worldwide, particularly in developing countries. 

Globally in 2013, nearly 22 million newborns (16% of all newborns) had LBW (UNICEF 2013). 

Prevalence of LBW varies considerably across regions and within countries. However, it was estimated 

that 97% of LBW occurs in low- and middle-income countries (UNICEF and WHO 2004), and especially 

among the most vulnerable populations, including the poor in remote areas. Among regions, prevalence 

of LBW is highest in South Asia, at 28%, followed by Sub-Saharan Africa, 13%, Latin America and the 

Caribbean, 9%, and 6% in East Asia and the Pacific (Kim and Saada 2013; UNICEF and WHO 2004; 

WHO 2014a). 

Within East Asia and the Pacific, Cambodia is a lower-middle-income country, with an estimated 

population of nearly 16 million in 2016 (Ministry of Health Cambodia 2017). The prevalence of LBW in 

Cambodia is higher than the average in East Asia and the Pacific as a whole. According to the World 

Bank, the prevalence of LBW in Cambodia in 2000 was 11%. Prevalence declined to around 8% of all 

live births in 2005 (NIS, Directorate General for Health, and Macro 2006) and has remained at about the 

same level, based on the Cambodia Demographic and Health Surveys (CDHS) in 2010 (NIS, Directorate 

General for Health, and Macro 2011) and 2014 (NIS, Directorate General for Health, and Macro 2015). 

The CDHS 2014 shows great variation in LBW by place of residence; LBW is higher in rural areas (8%) 

compared with urban areas (6%), and varies across the country’s regions, from 5% in Battambang/Pailin 

provinces to over 12% in Siem Reap province (NIS, Directorate General for Health, and Macro 2015). 

As in many other countries, LBW in Cambodia is a significant cause of neonatal deaths, which has 

remained at a high ratio of 18 deaths per 1,000 live births (NIS, Directorate General for Health, and Macro 

2015). According to the CDHS 2005 (NIS, Directorate General for Health, and Macro 2006), 25% of 
2 

neonatal deaths were among infants with low birth weight. According to the latest WHO data published 

in May 2014 (WHO 2014b), LBW contributed to 2,227 deaths or 3.1% of the total number of deaths in 

Cambodia. These statistics suggest that LBW is an important public health problem in Cambodia, and 

further reduction of LBW prevalence should be a national policy priority. 

LBW is associated with a number of risk factors. These include racial/ethnic origin, maternal height, prepregnancy weight, paternal weight and height, maternal birth weight, parity, history of prior LBW, 

gestational weight gain and caloric intake, general morbidity and illnesses, malaria, cigarette smoking, 

alcohol consumption, tobacco chewing, level of household wealth, and history of LBW and prematurity 

(Kramer 1987; Mahumud, Sultana, and Sarker 2017; De Bernabe et al. 2004). Other risk factors are 

ethnicity and national origin (Fuentes-Afflick and Hessol 1997), mother’s BMI, mother’s age and 

education (Coutinho et al. 2009; Kader and Perera 2014; Mahumud, Sultana, and Sarker 2017), mother’s 

marital status (Valero De Bernabe et al. 2004), and number of antenatal care visits during pregnancy 

(Coutinho et al. 2009; Fuentes-Afflick and Hessol 1997; Kader and Perera 2014; Mahumud, Sultana, and 

Sarker 2017). Mother’s level of hemoglobin, past and current illnesses, as well as baby’s birth interval, 

are also associated with LBW (Coutinho et al. 2009; Valero De Bernabe et al. 2004). The relative 

importance of these factors, however, is time-bound and country-specific. 

Reducing the prevalence of LBW in Cambodia requires a better understanding of its geographic 

distribution and associated risk factors to help identify subpopulations at greater risk and priority 

geographic regions. To the authors’ knowledge, no in-depth study of the factors associated with LBW in 

Cambodia has been conducted to date. This study addresses this knowledge gap based on CDHS data from 

2010 and 2014. The objectives of this study are twofold: first, to describe the prevalence of LBW across 

CDHS 2010 and 2014 by province using GIS mapping; and second, to explore key individual and 

household-level factors associated with LBW. Better understanding of such factors may help develop 

policies and programs with more effective strategies and interventions to reduce prevalence of LBW, 

thereby contributing to the reduction of neonatal mortality in Cambodia. 
3 

2. Conceptual Framework 

Figure 1 presents the conceptual framework for the individual and household-level factors associated with 

low birth weight. 

Figure 1. Conceptual framework for factors associated with low birth weight 

Factors related to mother’s characteristics include age, marital status, educational attainment, and current 

employment. A number of studies show that mother’s age (at the time of giving birth) is associated with 

LBW (Mahumud, Sultana, and Sarker 2017; Nobile et al. 2007; Rajashree, Prashanth, and Revathy 2015; 

Sutan et al. 2014). A recent analysis of DHS data in 10 developing countries (Mahumud, Sultana, and 

Sarker 2017) found that older mothers (age 35-49) had significantly higher risk of delivering LBW babies 

compared with younger mothers, while other studies have found a higher prevalence of LBW among 

babies born to younger mothers (Rajashree, Prashanth, and Revathy 2015; Sutan et al. 2014). A number 

of studies also have found that marital status is a risk factor for LBW, with single mothers more likely 

than married mothers to have LBW babies (Valero De Bernabe et al. 2004). Mothers with lower 

educational attainment are generally at a higher risk of having a baby with LBW compared with mothers 

with more education (Coutinho et al. 2009; Ghaemmaghami et al. 2013; Kader and Perera 2014; Khatun 

and Rahman 2008; Mahumud, Sultana, and Sarker 2017; Muthayya 2009; Rajashree, Prashanth, and 
4 

Revathy 2015). Mahumud et al. (2017) and Rajashree et al. (2015) found that illiterate mothers (no formal 

education) had approximately 1.5 times more risk of delivering LBW babies compared with those with a 

secondary or higher education. Current employment of the mother is considered a potential factor 

associated with LBW and has been investigated in a number of studies, but without finding any significant 

association. 

Factors related to mother’s health-related behaviors include: whether the mother smokes cigarettes, 

whether the mother perceived any problems in accessing care, number of antenatal care (ANC) visits 

during pregnancy, and whether the mother received ANC with nutritional counseling. Literature shows 

that smoking is an important risk factor of LBW (Coutinho et al. 2009; Kramer 1987; Zheng et al. 2016). 

A study by Zheng and colleagues among 92,641 mothers in Okinawa showed that maternal smoking was 

significantly associated with LBW in all age groups, while Coutinho and colleagues found that smoking 

beyond the fourth month of pregnancy was a risk factor for LBW, irrespective of the number of cigarettes 

smoked per day. Mothers’ perceived problems in accessing services could affect their ability to receive 

necessary health care during pregnancy, and thus could increase the risk of LBW. A number of studies 

show that the number of ANC visits is also associated with LBW (Coutinho et al. 2009; Feresu, Harlow, 

and Woelk 2015; Fuentes-Afflick and Hessol 1997; Kader and Perera 2014; Mahumud, Sultana, and 

Sarker 2017). Kader and Perera found attending fewer than four ANC visits to be a maternal risk factor 

for LBW, whereas the other studies did not specify a threshold number of ANC visits, but only an 

inadequate number of ANC visits. Quality of ANC is also included in some studies as a potential risk 

factor of LBW (Pinzon-Rondon et al. 2015). We consider ANC with nutritional counseling as a proxy 

indicator of good-quality ANC. In this study we include health insurance coverage as a predictor of LBW. 

Although existing studies on LBW have not included health insurance, some studies show that health 

insurance coverage can reduce financial barriers to accessing health care (Jacobs et al. 2008; Xu et al. 

2007), thus facilitating women’s access to necessary care during pregnancy. 

Mother’s own health is another factor expected to influence LBW. Measures of maternal health include 

whether the mother is anemic and mother’s body mass index (BMI). Two studies assessed the association 

of mother’s hemoglobin level with LBW (Mahumud, Sultana, and Sarker 2017; Rajashree, Prashanth, and 

Revathy 2015), but only one study (Rajashree, Prashanth, and Revathy 2015) found that having 

hemoglobin levels below 11g/dl at the time of delivery is a risk factor for LBW. BMI is an indicator of 

mother’s nutritional status. Babies born to mothers with low BMI (≤18.5 kg/m2) are at greater risk of 

having LBW compared with those born to mothers with normal weight (Coutinho et al. 2009; Kader and 

Perera 2014; Mahumud, Sultana, and Sarker 2017). 

Factors associated with the baby’s own characteristics are birth order, birth interval, and sex. A study of 

858 mothers found that first-born and fourth-born births had lower birth weights compared with secondand third-born births, at all maternal ages (Ghaemmaghami et al. 2013). Baby’s birth interval (≤2years or 

≥3 years) is also associated with LBW (Coutinho et al. 2009; Rajashree, Prashanth, and Revathy 2015). 

Female babies are more likely to be born with LBW compared with males (Kader and Perera 2014; 

Muchemi, Echoka, and Makokha 2015). 
5 

Factors related to household characteristics include the household’s socioeconomic status, place of 

residence (urban-rural), and geographic area (province and region). An asset-based household wealth 

index, a proxy indicator of family socioeconomic status, is correlated with LBW; mothers in poorer 

households have greater risk of having LBW babies than those in richer households (Ghaemmaghami et 

al. 2013; Mahumud, Sultana, and Sarker 2017). The prevalence of LBW is higher among babies born in 

rural areas compared with urban areas (Gebremedhin et al. 2015; Kayode et al. 2014). LBW prevalence 

also differs by region within Cambodia (province or group of provinces). 

7 

3. Data and Methods 

3.1 Data 

This study used data for children from the Cambodia Demographic Health Survey (CDHS) 2010 and 

CDHS 2014 to map the distribution of low birth weight by province, and used CDHS 2014 to conduct 

multivariate analyses to explore the factors associated with LBW among babies born in the 5 years 

preceding the survey. 

The CDHS (NIS, Directorate General for Health, and ICF International 2011, 2015) is a population-based 

nationally representative survey in which participants were selected using a two-stage stratified cluster 

sampling design. In the first stage, a defined number of enumeration areas (EAs or clusters) stratified by 

urban-rural category were selected with probability proportional to EA size. In the second stage, a defined 

number of households (generally 25-30) were randomly selected from the listed households in each EA 

with simple random selection. Eligible women age 15-49 in the selected households were invited for 

interviews, and data about the women and their children born in the 5 years preceding the survey were 

collected. 

Figure 2 illustrates the derivation of the analytical samples for this study. In total, there were 5,929 live 

births in the 5 years preceding the 2010 CDHS and 6,584 live births in the 5 years preceding the 2014 

CDHS (panel 1). We limited the analysis to last births because the CDHS collected data on ANC only for 

women’s last pregnancies. Due to potential inaccuracy of birth weight taken at home, we restricted our 

analysis to live births that occurred at health facilities, which represent 53% and 82% of all live births in 

the 2010 and 2014 surveys, respectively. This process resulted in a final sample size of 3,522 children 

from the 2010 CDHS and 4,991 children from the 2014 CDHS (panel 2). Multivariate logistic regression 

was further restricted to singleton babies, because multiple births are strongly associated with premature 

birth and LBW. Multiple births accounted for only 1.2% of all last live births that occurred at health 

facilities. Therefore, the sample for multivariate logistic regression included the 4,932 youngest, singleton 

children born at health facilities in the CDHS 2014 (panel 3). 
8 

Figure 2. Sample selection diagram 

3.2 Outcome Variable 

The outcome variable of interest is the prevalence of low birth weight among the youngest, singleton 

children born at a health facility. Based on the definition of LBW (weight at birth of less than 2,500 

grams), children were classified into 2 groups: non-LBW (birth weight ≥2,500g, coded 0) or LBW (birth 

weight <2,500g, coded 1). 

3.3 Explanatory Variables 

As described in the conceptual framework, the study included the following explanatory variables: 

household and mother’s characteristics and behaviors, access to health care during pregnancy, maternal 

health status, and baby’s characteristics. 

Maternal age at the time of giving birth was divided into three categories: <18 years; 18-34 years; and 35-

49 years. Marital status was classified as currently married/in union or formerly married. Level of 

education was defined as: no schooling; primary school; and secondary and higher. Current employment 

status was classified into: no job or not working; agricultural jobs or self-employed; professional or 

technical or sales jobs; and other types of jobs, which included manual labor and unskilled jobs. Whether 

the mother smoked cigarettes was recoded as yes (coded 1) or no (coded 0). Mothers’ perceived problems 

in accessing health care is a dichotomous variable, coded 0 if the mother reported no perceived barrier 

and coded 1 if the mother reported 1 or more barriers (distance, money, and waiting time). Number of 

ANC visits was recoded as a binary variable: mothers who made fewer than four visits were coded 0, and 

those who made four or more visits were coded 1. Whether the mother received ANC with nutritional 

counseling and health insurance coverage were both recoded as either yes or no. Mother’s anemia was 
9 

defined by her hemoglobin level: severe (0-69g/dl); moderate (70-99g/dl); mild (100-119g/dl); and not 

anemic (>120g/dl). Because of few observations, the severe anemia category was grouped together with 

moderate anemia. Mother’s BMI was calculated as weight-to-height ratio (kg/m2), and further categorized 

as: underweight (<18.5 kg/m2

); normal or healthy weight (18.5-24.9 kg/m2); and overweight (≥24.9 

kg/m2

). 

Birth order was classified as: first child; second and third child; and fourth+ child. Birth interval was 

defined by the number of years following the first-born child, and children were then classified in 4 groups: 

first child; <2 years; 2-3 years; and >3 years. The sex of the child was recorded as boy or girl. 

Household wealth status was measured based on the ownership of durable assets, using principal 

component analysis. The constructed wealth index values were then equally split into quintiles: lowest; 

second; middle; fourth; and highest. Place of residence was classified as rural or urban. Since the number 

of CDHS domains (provinces) is too many for meaningful analysis (19), we regrouped them for regression 

analysis into four geographical regions, as administratively defined in Cambodia: Northwest; Northeast; 

Central; and South. 

3.4 Statistical Analysis 

Key socioeconomic and demographic characteristics of the sample in CDHS 2014—youngest children 

born in health facilities in the past 5 years preceding the survey and their mothers—were described and 

expressed in frequency and percentage. 

The prevalence of low birth weight in Cambodia and its distribution across the 19 domains (provinces) 

was estimated, using the 2010 and 2014 DHS surveys. The results were used to construct maps illustrating 

provincial variation in the prevalence of LBW and trends over time, using QGIS software V 2.18.7 (GIS 

Development Team). Also, significant difference was assessed between year 2010 and 2014, using Chisquare test; and between provincial LBW in CDHS 2010 and CDHS 2014, using SVY: logit command. 

The study used bivariate analyses to assess the association between prevalence of LBW and household 

characteristics, mother’s characteristics and behaviors, access to health care during pregnancy, maternal 

health status, and baby’s characteristics. Logistic regression was used to predict the independent factors 

associated with LBW. 

The analyses were performed in STATA version 14. Stata SVY command was used to account for the DHS 

complex sample design. All covariates significantly associated with LBW at the p value ≤ 0.20 in 

univariate logistic regression were included in the multivariate regression. Also, interested covariates were 

included in the multivariate regression regardless of their significant levels. Multicollinearity among the 

following variables was assessed: geographical regions, residence, household wealth index, mother’s age 

at the time of giving birth, anemia, BMI, and child’s birth order. No serious collinearity between variables 

was detected (the highest correlation coefficient was <0.6). In the final model, nine covariates were kept. 

These covariates were mother’s age at the time of giving birth; education level; reported number of ANC 
10 

visits; anemia level; BMI level; child’s sex; residence (urban-rural); geographical regions; and wealth 

index. 

The CDHS data are publicly accessible and were made available to us upon request to The DHS Program.
11 

4. Results 

4.1 Description of the Sample 

Table 1 describes the socioeconomic and demographic characteristics of the 4,991 youngest children born 

in health facilities in the 5 years preceding the survey, and the characteristics of their mothers. Eightythree percent of the children were born in rural areas. Most of the children were singleton births and only 

1.2% were twins. Among the sample, 1.5% died after birth. Their average weight at birth was 3,100 grams. 

Nearly 90% of the mothers were age 18-34 at the time of childbirth, and 95% were currently married or 

in union. Eleven percent of the mothers had no schooling, 51% had a primary education, and 38% had a 

secondary or higher education. Only 1% of the mothers smoked cigarettes. The great majority of the 

mothers (81%) attended at least four ANC visits during pregnancy, 15% 2-3 visits, 2% 1 visit, and 1% no 

ANC visit. Just over half (56%) of the mothers were not anemic, whereas 39% had mild anemia, and 6% 

moderate anemia, respectively. Only 0.1% of them had severe anemia. 

 
12 

Table 1. Socioeconomic and demographic characteristics of the final study 

samples, CDHS 2014 

N = 4,991 

Variables Frequency % 

Mother’s characteristics and behaviors 

Age at the time of giving birth

<18 114 2.2 

18-34 4,454 89.3 

35-49 423 8.5 

Marital status 

Currently married/in union 4,741 95.0 

Formerly married 250 5.0 

Education 

No schooling 556 11.1 

Primary school 2,522 50.5 

Secondary school/higher 1,913 38.3 

Current employment status 

No job/not working 1,256 25.2 

Agricultural jobs and self-employed 1,546 31.0 

Professional, technical and sales jobs 2,128 42.6 

Others (manual labor and unskilled jobs) 60 1.2 

Smoking cigarettes 

No 4,935 98.9 

Yes 55 1.1 

Perceived problems in accessing health 

service 

No barrier 1,558 31.2 

≥ 1 barrier (distance/money/waiting time) 3,433 68.8 

Number of ANC visits 

No ANC visit 66 1.3 

1 visit 107 2.1 

2-3 visits 747 15.0 

≥ 4 visits 4,059 81.3 

Don’t know/missing 12 0.2 

ANC with nutrition counseling

No 567 11.4 

Yes 4,358 87.3 

No information 66 1.3 

Health insurance coverage 

No 4,117 82.5 

Yes 874 17.5 

Anemia* (N = 3,277) 

Severe 4 0.1 

Moderate 182 5.6 

Mild 1,265 38.6 

No anemia 1,826 55.7 

Body Mass Index* (N = 3,017) 

Underweight 326 10.8 

Normal weight 2,161 71.6 

Overweight 437 14.5 

Obese 93 3.1 

Continued... 

 
13 

Table 1—Continued

N = 4,991 

Variables Frequency % 

Children’s characteristics 

Birth status 

Single birth 4,932 98.8 

Twin birth 59 1.2 

Birth order

1st child 1,918 38.4 

2nd or 3rd child 2,386 47.8 

4th child or higher 687 13.8 

Birth interval* (N = 4,973) 

1st child 1,918 38.6 

<2 years 357 7.2 

2-3 years 712 14.3 

>3 years 1,986 39.9 

Sex 

Boy 2,544 51.0 

Girl 2,447 49.0 

Status after birth 

Not alive 76 1.5 

Alive 4,915 98.5 

Average birth weight (SD) 3,100 grams (SD = 9.3)

Range 700 – 6000 grams 

Household characteristics 

Place of residence 

Urban 836 16.8 

Rural 4,155 83.2 

Wealth index 

Lowest 936 18.8 

Second 955 19.1 

Middle 989 19.8 

Fourth 968 19.4 

Highest 1,144 22.9 

* The total sample in some variables were less than the total final study sample (N = 4991). 

The distribution of the sample by geographic domain (province or group of provinces) in CDHS 2010 and 

CDHS 2014 is presented in Appendix 1. In CDHS 2010, Phnom Penh (14%), Kampong Cham (11%), and 

Kandal (11%) accounted for the three largest samples, while Odar Meanchey, Kratie, Preah Vihear/Stung 

Treng, and Modulkiri/Rattanakiri were least represented, at only about 1% each, among the 19 domains. 

This distribution changed slightly in CDHS 2014. Phnom Penh (10%) and Kampong Cham (14%) were 

the two largest samples, while Kandal fell to 7% of the total sample in CDHS 2014. Odar Meanchey, 

Kratie, Preah Vihear/Stung Treng, and Modulkiri/Rattanakiri remained the 4 smallest samples. 

4.2 Descriptive Analysis of LBW Prevalence in 2010 and 2014 by Province 

Overall, the prevalence of low birth weight among the sample (last births at health facilities) remained 

stable between 2010 and 2014, at about 7%. However, the variation by province varied across the two 

surveys. 
14 

Figure 3 shows the distribution of LBW prevalence in CDHS 2010 and CDHS 2014 by province, with the 

darker color indicating higher prevalence of LBW (Appendix 2 presents the data on LBW prevalence for 

the 19 domains). In 2010, the highest LBW prevalence was in Takeo province (12%) and Svay Rieng 

(10%), and the lowest was in Odar Meanchey (2.5%) and Sihanouk Ville/Koh Kong (3.5%). This 

distribution changed between 2010 and 2014. In 2014, the highest prevalence of LBW was in Siem Reap 

(11%) and Kampong Chhnang (10%), an increase compared with 2010 (7% for Siem Reap and 8% for 

Kampong Chhnang). However, the increase in both provinces was not statistically significant. LBW 

prevalence in Svay Rieng and Takeo declined slightly in 2014, to 9% and 10%, respectively, but not 

statistically significant. 

Figure 3. Distribution of LBW prevalence in CDHS 2010 and 2014 by province 

4.3 Bivariate Analysis of LBW Prevalence in 2014 by Key Explanatory Variables 

Table 2 presents the prevalence of LBW by key explanatory variables, including household and mother’s 

characteristics and behaviors, access to health care during pregnancy, health insurance coverage, maternal 

health status, and child’s characteristics. 

Prevalence of LBW among children born to mothers with no schooling, at 9%, was higher than among 

those born to mothers with a primary education or a secondary or higher education, at 6% each, although 

the difference was not statistically significant. The prevalence of LBW did not vary by mother’s age, 

marital status, current employment status, smoking status, perceptions of problems in accessing health 

service, and health insurance coverage. Yet, LBW prevalence among children born to mothers attending 

less than four ANC visits during pregnancy (10%) was significantly higher than for those born to mothers 

attending four or more ANC visits (6%). The prevalence of LBW was lower among babies born to mothers 
15 

who received nutritional counseling during the ANC visits (6%) compared with those who did not (9%). 

There was no significant difference in the prevalence of LBW by mother’s anemia level or BMI. 

First-born babies had a higher LBW prevalence (9%) than second- and third-born babies (5%) and fourthborn or higher (7%). There were no differences in the prevalence of LBW between male and female 

babies. 

Prevalence of LBW was substantially higher in rural areas (7%) than urban areas (4%) but did not differ 

much by region: Northwest (6%), Northeast (8%), Central (6%), and South (7%). The prevalence of LBW 

among children born to mothers in the lowest and second household wealth quintiles (each about 8%) was 

higher than for those in the middle quintile (6%), the fourth quintile (6%), and highest quintile (5%). This 

difference was only marginally statistically significant (p-value = 0.069).

Table 2. Prevalence of LBW among singleton babies born in health facilities by key 

explanatory variables, CDHS 2014 

Variables 

CDHS 2014 (N = 4,932) 

Number 

 of births 

% with 

LBW 95% CI P value 

Mother’s characteristics and behaviors

Age at the time of giving birth 

<18 114 9.0 [5.0, 15.7] 0.142 

18-34 4,399 6.2 [5.3, 7.2] 

35-49 419 8.7 [5.8, 12.8] 

Marital status 

Currently married/in union 4,685 6.4 [5.5, 7.3] 0.447 

Formerly married 247 7.9 [4.6, 13.2] 

Education 

No schooling 544 9.3 [6.5, 13.1] 0.073 

Primary school 2,489 5.9 [4.8, 7.2] 

Secondary school/higher 1,899 6.4 [5.2, 7.8] 

Current employment status 

Not working 1,233 6.6 [5.1, 8.6] 0.493 

Agricultural jobs and self employed 1,526 7.3 [5.8, 9.0] 

Professional, technical and sales jobs 2,113 5.8 [4.6, 7.3] 

Others (manual labor and unskilled jobs) 60 5.3 [1.5, 16.9] 

Smoking cigarettes 

No 4,880 6.4 [5.6, 7.4] 0.937 

Yes 51 6.1 [1.9, 17.9] 

Perceived problem in accessing health 

service 

No barrier 1,548 6.4 [5.1, 8.1] 0.989 

≥ 1 barrier (distance/money/waiting time) 3,384 6.4 [5.5, 7.5] 

Number of ANC visits during pregnancy

<4 visits 917 9.9 [7.4, 13.1] < 0.001 

≥4 visits 4,016 5.6 [4.8, 6.6] 

ANC with nutritional counseling 

No 559 8.9 [5.9, 13.1] 0.045 

Yes 4,308 6.0 [5.2, 7.0] 

No information 66 12.5 [5.8, 24.9] 

Continued...
16 

Table 2—Continued

CDHS 2014 (N = 4,932) 

Variables 

Number 

 of births 

% with 

LBW 95% CI P value 

Health insurance coverage 

No 4,079 6.1 [5.3, 7.1] 0.171 

Yes 853 7.9 [5.7, 10.9] 

Anemia (N = 3,243) 

Severe/moderate 185 7.5 [3.8, 14.4] 0.864 

Mild 1,249 6.8 [5.0, 9.1] 

No anemia 1,809 6.2 [5.0, 7.7] 

Body mass index (N = 2,984) 

Underweight 323 7.3 [4.5, 11.8] 0.159 

Normal weight 2,140 6.8 [5.5, 8.3] 

Overweight 428 5.1 [2.9, 8.8] 

Obese 93 1.1 [0.3, 3.9] 

Child’s characteristics Birth order 

1st child 1,918 8.5 [7.1, 10.2] < 0.001 

2nd or 3rd child 2,357 4.7 [3.6, 6.0] 

4th child or higher 657 6.7 [4.6, 9.7] 

Birth interval 

1st child 1,918 8.5 [7.1, 10.2] < 0.001 

<2 years 352 7.1 [4.2, 11.7] 

2-3 years 701 6.0 [4.0, 8.9] 

>3 years 1,961 4.4 [3.5, 5.6] 

Sex 

Boy 2,523 6.1 [5.1, 7.2] 0.348 

Girl 2,409 6.8 [5.6, 8.2] 

Household characteristics Place of residence 

Urban 828 4.4 [3.0, 6.5] 0.037 

Rural 4,104 6.8 [5.9, 7.9] 

Geographical regions* 

Northwest 1,243 5.9 [4.31, 7.9] 0.602 

Northeast 296 8.1 [6.3, 10.7] 

Central 1,573 6.2 [4.8, 8.0] 

South 1,820 6.7 [5.4, 8.3] 

Wealth index 

Lowest 915 8.1 [6.0, 10.9] 0.069 

Second 943 7.8 [5.9, 10.1] 

Middle 981 6.4 [4.8, 8.6] 

Fourth 955 5.7 [4.2, 7.8] 

Highest 1,138 4.6 [3.2, 6.6] 

*Northwest: Banteay Meanchey, Pursat, Siem Reap, Odor Meanchey, Battambang/Pailin 

Northeast: Kratie, Preah Vihear/Stung Treng, Modulkiri/Rattanakiri 

Central: Kampong Chhnang, Kampong Thom, Kampong Cham, Kandal, Phnom Penh

South: Takeo, Kampot/Kep, Koh Kong/Sihanouk Ville 

4.4 Results from logistic regression analysis 

Table 3 summarizes the results of the logistic regression of factors associated with low birth weight among 

singleton children born in health facilities, based on the 2014 CDHS. For comparison purposes, the 

unadjusted odds ratios and 95% CI are also presented (the first two columns of Table 3). The unadjusted 
17 

odds ratios indicate that the following factors were significantly associated with LBW: mother’s education 

level, number of ANC visits during pregnancy, place of residence, household wealth index, birth order, 

and birth interval. Mothers with no schooling had higher odds of having babies with LBW compared with 

those with a secondary or higher education (OR=1.7, 95% CI: 1.1 -2.4). Mothers who attended fewer than 

four ANC visits during their last pregnancy had 1.8 times higher odds of LBW compared with women 

having four or more ANC visits (OR=1.8, 95% CI: 1.3–2.6). Children born to mothers in rural areas had 

1.6 times higher odds of LBW than those born to urban mothers (OR=1.6, 95% CI: 1.0 – 2.5). Children 

in the highest household quintile had lower odds of LBW compared with those in the lowest quintile 

(OR=0.5, 95% CI: 0.3 – 0.9). First-born children and children born within 1 year from a prior birth were 

each about twice as likely to have LBW. Mother’s marital status, current employment, smoking status, 

perception of barriers to health care access, health insurance coverage, and receipt of nutritional 

counseling were not correlated with LBW and therefore were excluded from the multivariate analysis. 

Other factors were also not significantly associated with LBW, such as geographical region, mother’s age 

at the time of giving birth, BMI, and anemia, but these variables were still included in the multivariate 

model. 

In the multivariate logistic model (the last two columns of Table 3), factors significantly and independently 

associated with LBW included mother’s education level, number of ANC visits, and birth order of the 

children. Again, mother’s education was a strong predictor of LBW among singleton children born at 

health facilities. Children born to mothers with no education were more likely to have LBW compared 

with those whose mother had a secondary or higher education (AOR=1.6, 95% CI: 1.0-2.6), after 

controlling other factors. Mothers reported attending fewer than four ANC visits during their last 

pregnancy remained an independent predictor of having babies with LBW (AOR=2.0, 95% CI: 1.5–2.8) 

compared with mothers attending four or more ANC visits, after controlling for other factors that influence 

birth weight. Finally, being a first-born child was still strongly associated with LBW (AOR=1.4, 95% CI: 

1.0 – 2.0) compared with being the second child. The effects of rural-urban residence and household 

wealth index were no longer statistically significant after controlling for other covariates in the model. 

 
18 

Table 3. Factors associated with LBW among singleton babies born in health facilities, results 

of the univariate and multivariate logistic regressions, CDHS 2014 

Variables 

Total, N = 4,932 Total, N = 3,941 

OR 95% CI AORa 95% CI 

Mother’s age of the time of giving birth

<18 1.0 - 1.0 - 

18-34 1.0 0.5 - 2.2 0.7 0.3 - 1.7 

35-49 0.7 0.4 - 1.1 1.4 0.8 - 2.4 

Mother’s marital status 

Currently married/in union 1.0 - - - 

Formerly married 1.3 0.7 - 2.3 - - 

Mother’s level of education 

No schooling 1.7** 1.1 - 2.4 1.6* 1.0 - 2.6 

Primary school 1.0 0.7 - 1.3 0.9 0.6 - 1.3 

Secondary school/higher 1.0 - 1.0 - 

Mother’s current employment 

No job 1.3 0.3 - 4.5 - - 

Agricultural jobs and self-employed 1.4 0.4 - 5.1 - - 

Others (manual labor and other jobs) 1.1 0.3 - 4.0 - - 

Professional, technical and sales jobs 1.0 - - - 

Mother smoking cigarettes 

No 1.0 - - - 

Yes 1.1 0.3 - 3.5 - - 

Perceived problem in accessing health services

No barrier 1.0 - - - 

≥ 1 barrier (distance/money/waiting) 1.0 0.7 - 1.3 - - 

Number of ANC visits during pregnancy

< 4 visits 1.8** 1.3 - 2.6 2.0*** 1.5 - 2.8 

≥ 4 visits 1.0 - 1.0 - 

ANC with nutritional counseling 

Yes 1.0 - - - 

No 1.5 1.0 - 2.4 - - 

No information 2.2 1.0 - 5.2 - - 

Health insurance coverage 

No 1.3 0.9 - 1.9 - - 

Yes 1.0 - - - 

Mother’s anemia 

Severe/moderate 1.2 0.6 - 2.6 1.2 0.7 - 2.1 

Mild 1.1 0.7 - 1.6 1.0 0.7 - 1.4 

No anemia 1.0 - 1.0 - 

Mother’s Body Mass Index 

<18.5 (underweight) 1.7 0.9 - 3.5 1.5 0.8 - 2.5 

18.5-24.9 (normal weight) 1.6 0.9 - 2.8 1.2 0.8 - 1.8 

≥ 25 (Overweight) 1.0 - 1.0 - 

Continued...

 
19 

Table 3—Continued

Variables 

Total, N = 4,932 Total, N = 3,941 

OR 95% CI AOR 95% CI 

Child’s birth order

1st child 1.9*** 1.4 - 2.6 1.4* 1.0 - 2.0 

4th child or higher 1.5 0.9 - 2.4 1.2 0.7 - 2.0 

2nd or 3rd child 1.0 - 1.0 - 

Child’s birth interval 

1st child 2.0*** 1.5 - 2.8 - - 

<2 years 1.7 1.0 - 2.9 - - 

2-3 years 1.4 0.8 - 2.2 - - 

>3 years 1.0 - - - 

Child’s sex 

Boy 1.0 - 1.0 - 

Girl 1.1 0.9 - 1.5 1.2 0.9 - 1.7 

Residence 

Urban 1.0 - 1.0 - 

Rural 1.6* 1.0 - 2.5 1.2 0.6 - 2.2 

Geographical region1

Northwest 1.0 - 1.0 - 

Northeast 1.4 0.9 - 2.2 0.7 0.4 - 1.2 

Central 1.1 0.7 - 1.6 1.0 0.6 - 1.6 

South 1.2 0.8 - 1.7 1.0 0.6 - 1.6 

Wealth index 

Lowest 1.0 - 1.0 - 

Second 1.0 0.6 - 1.5 0.8 0.5 - 1.3 

Middle 0.8 0.5 - 1.2 0.8 0.5 - 1.2 

Fourth 0.7 0.4 - 1.1 0.9 0.5 - 1.4 

Highest 0.5* 0.3 - 0.9 0.6 0.3 - 1.1 

*** p<0.001, ** p<0.01, * p<0.0; AOR = Adjusted Odds Ratio 1Northwest: Banteay Meanchey, Pursat, Siem Reap, Odor Meanchey, Battambang/Pailin 

Northeast: Kratie, Preah Vihear/Stung Treng, Modulkiri/Rattanakiri 

Central: Kampong Chhnang, Kampong Thom, Kampong Cham, Kandal, Phnom Penh

South: Takeo, Kampot/Kep, Koh Kong/Sihanouk Ville

21 

5. Discussion 

This study described provincial variations in the prevalence of low birth weight in Cambodia and changes 

in prevalence over time, based on the DHS surveys conducted in 2010 and 2014. The study also explored 

key individual and household-level factors associated with LBW among singleton live births that occurred 

in health facilities, using data from CDHS 2014. The prevalence of LBW resulting from our analysis 

remained stable at around 7% of all singleton live births in health facilities in both CDHS 2010 and CDHS 

2014. This is consistent with the findings presented in the two survey reports, which included all live 

births regardless of place of birth and number of babies (NIS, Directorate General for Health, and Macro 

2011, 2015). This figure is much lower than the global average of 15%, but slightly higher than the average 

of 6% in the East Asia and Pacific region (UNICEF and WHO 2004; WHO 2014a). The distribution of 

LBW prevalence by province across the two survey rounds showed great variation, with extraordinarily 

high rates in some provinces, in particular Siem Reap and Kampong Chhnang, where prevalence increased 

considerably between surveys (although the increases were not statistically significant). 

Results from multivariate logistic regression showed that mother’s educational level, number of ANC 

visits during the last pregnancy, and birth order were significantly and independently associated with LBW 

in Cambodia, after controlling for individual and household-level factors that influence birth weight. 

These findings are consistent with other studies that document the importance of mother’s educational 

level and number of ANC visits. The findingthat mothers with no schooling (illiterate mothers) were 1.6 

times more likely to have a LBW baby compared with those with a secondary or higher education is 

similar to findings of three studies in other countries that illiteracy of mothers is a risk factor for LBW 

(Kader and Perera 2014; Mahumud, Sultana, and Sarker 2017; Rajashree, Prashanth, and Revathy 2015). 

The study’s finding that mothers who attended fewer than four ANC visits during their last pregnancy 

were twice as likely to have babies with LBW, compared with mothers who attended four or more ANC 

visits, is also in line with other studies elsewhere, and supports the current national policy of Cambodia, 

which recommends at least four ANC visits during pregnancy (Ministry of Health Cambodia, PMNCH, 

WHO, World Bank, and AHPSR 2014). 

This finding that the first child is more likely to have LBW compared with the second or third child has 

not been seen in other studies, except for a study showing that first and fourth children tend to have lower 

birth weight than second- and third-born children (Ghaemmaghami et al. 2013). 

While a study in several developing countries found that rural residence and poverty are risk factors for 

LBW (Mahumud, Sultana, and Sarker 2017), the present study found that these factors, although 

significantly associated with LBW in univariate analysis, were no longer associated with LBW after 

adjusting for other covariates in the multivariate logistic regression. 

23 

6. Limitations 

This study has three major limitations. First, it was limited to explanatory variables for which data were 

collected and reported in CDHS 2010 and CDHS 2014. Data on many potential correlates of low birth 

weight or risk factors were not available, such as past history of premature births or LBW, and maternal 

morbidity and illnesses during pregnancy, including chronic noncommunicable diseases such as 

hypertension and diabetes. 

Second, the available data were measured at the time of survey, not during pregnancy or at the time of 

delivery. While many of these variables, such as household wealth status and residence, are unlikely to 

have changed, others such as mother’s employment status, marital status, BMI, and anemia may differ 

between the time of pregnancy or delivery and the time of survey. We estimated maternal age at the time 

of giving birth by deducting the child’s age from the mother’s age. For BMI, the analysis did not include 

women who were pregnant at the time of survey but who had a live birth in the 5 years preceding the 

survey. 

Third, the analysis was restricted to children born at a health facility. It excluded home births because 

babies born at home may not have been weighed or appropriately weighed. This might bias the results, 

particularly since children not weighed or not appropriately weighed at birth are more likely to be born to 

less educated or poorer mothers who may be less aware of the importance of maternal health care or lack 

the financial resources to afford the minimum recommended four ANC visits, and therefore more likely 

to have LBW children. 

25 

7. Conclusion and Policy Implications 

Despite some limitations, the findings suggest that reducing the prevalence of low birth weight in 

Cambodia is possible, but challenging. Interventions to reduce LBW should target provinces where 

prevalence remains relatively high, or has increased in recent years, such as Siem Reap and Kampong 

Chhnang. Such targeting should be guided by further in-depth studies of the factors related to LBW and 

maternal health care. 

Furthermore, the study shows that mother’s educational level, number of ANC visits during the last 

pregnancy, and child’s birth order are significantly associated with LBW or risk factors of LBW. These 

results generally confirm the findings of research in other countries. The findings that illiterate mothers 

have a higher risk of having LBW babies and that first-born children are at greater risk than subsequent 

births suggest that women with no formal education, especially those who are pregnant for the first time, 

should be a priority for programs to reduce the incidence of LBW. The findings strongly support and 

reconfirm the current national program policy in Cambodia that recommends pregnant women to have at 

least four ANC visits during pregnancy. This policy should be further reinforced and implemented, with 

consideration for finding effective ways to reach illiterate women with information and support for 

antenatal care.

27 

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30 

Appendix 1. Sample distribution by domain (province or group of provinces) in CDHS 

2010 and 2014 

Variables 

CDHS 2010 (N = 3,566) CDHS 2014 (N = 4,991) 

Frequency % Frequency % 

Provinces 

1 Banteay Meanchey 134 3.8 197 4.0 

2 Kampong Cham 379 10.6 680 13.6 

3 Kampong Chhnang 159 4.5 200 4.0 

4 Kampong Speu 196 5.5 329 6.6 

5 Kampong Thom 131 3.7 206 4.1 

6 Kandal 408 11.4 343 6.9 

7 Kratie 46 1.3 104 2.1 

8 Phnom Penh 504 14.1 509 10.2 

9 Prey Veng 230 6.4 372 7.5 

10 Pursat 104 2.9 193 3.9 

11 Siem Reap 322 9.0 356 7.1 

12 Svay Rieng 110 3.1 181 3.6 

13 Takeo 280 7.9 294 5.9 

14 Odar Meanchey 51 1.4 103 2.1 

15 Battambong/Pailin 225 6.3 413 8.3 

16 Kampot/Kep 118 3.3 190 3.8 

17 Sihanoukville/Koh Kong 86 2.4 127 2.5 

18 Preah Vihear/Stung Treng 41 1.1 102 2.0 

19 Mondulkiri/Rattanakiri 40 1.1 93 1.9 

Appendix 2. Prevalence of low birth weight in the 19 domains in CDHS 2010 and 2014 

Province 

CDHS 2010 (N = 3,566) CDHS 2014 (N = 4,991) 

% of LBW [95% CI] % of LBW [95% CI] 

Banteay Meanchey 5.9 [3.2,10.6] 5.6 [3.4,9.1] 

Kampong Cham 8.5 [4.9,14.4] 5.9 [3.5,9.9] 

Kampong Chhnang 7.9 [4.7,13.0] 10.1 [6.8,14.9] 

Kampong Speu 3.6 [1.4,9.0] 6.5 [3.7,11.3] 

Kampong Thom 8.9 [4.8,15.9] 7.4 [4.5,12.1] 

Kandal 5.1 [2.6,9.8] 5.5 [3.0,10.1] 

Kratie 8.9 [4.1,18.4] 7.7 [4.6,12.6] 

Phnom Penh 5.1 [3.3,7.9] 4.9 [2.7,8.7] 

Prey Veng 5.9 [3.1,11.0] 9.2 [5.8,14.4] 

Pursat 6.5 [3.6,11.2] 2.2 [0.8,5.8] 

Siem Reap 6.9 [4.1,11.5] 11.1 [7.0,17.1] 

Svay Rieng 10.1 [5.0,19.4] 9.9 [7.1,13.7] 

Takeo 11.9 [6.6,20.4] 8.6 [5.7,12.9] 

Odar Meanchey 2.5 [1.1,5.8] 8.0 [5.4,11.8] 

Battambong/Pailin 7.7 [4.5,12.9] 4.7 [2.6,8.3] 

Kampot/Kep 6.4 [2.9,13.3] 6.4 [3.6,11.1] 

Sihanoukville/Koh Kong 3.5 [1.6,7.6] 6.4 [3.7,10.9] 

Preah Vihear/Stung Treng 6.6 [2.4,16.5] 6.9 [3.9,11.9] 

Mondulkiri/Rattanakiri 8.0 [4.8,13.0] 9.9 [6.6,14.6]