DHS METHODOLOGICAL 

REPORTS 16

An Assessment of the Quality

of DHS Anthropometric Da

2005-2014

SEPTEM

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Shireen Assaf, Monica T. Kothari, and Thomas Pullum of ICF International.

DHS Methodological Reports No. 16 

An Assessment of the Quality of DHS 

Anthropometric Data, 2005-2014 

Shireen Assaf 

Monica T. Kothari 

Thomas Pullum 

ICF International 

Rockville, Maryland, USA 

September 2015 

Corresponding author: Monica Kothari, International Health and Development Division, ICF International, 

530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: +1 301-572-0950; fax: +1 301-407-6501; 

email: Monica.Kothari@icfi.com 
Acknowledgment: 

The authors are grateful for the comments provided by Justine Kavle and Fred Arnold on an earlier version 

of the report. Special thanks go to Sidney Moore for editing the text and to Natalie La Roche for formatting 

the report. Finally, we would like to acknowledge all DHS survey managers and others who are responsible 

for the collection and processing of the data used to produce this analysis. 

Editor: Sidney Moore 

Document Production: Natalie La Roche 

This study was 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: 

Assaf, Shireen, Monica T. Kothari, and Thomas Pullum. 2015. An Assessment of the Quality of DHS 

Anthropometric Data, 2005-2014. DHS Methodological Reports No. 16. Rockville, Maryland, USA: ICF 

International. 
iii 

Contents 

Tables ......................................................................................................................................................... v 

Figures ....................................................................................................................................................... vii 

Preface ........................................................................................................................................................ ix 

Abstract ........................................................................................................................................................ xi 

Executive Summary ................................................................................................................................... xiii 

1. Introduction ...................................................................................................................................... 1 

1.1 Growth Reference Standards and Flags .............................................................................. 2 

1.2 Anthropometry Data Quality Measures .............................................................................. 3 

2. Methods and Data ............................................................................................................................ 7 

2.1 Data ..................................................................................................................................... 7 

2.2 Methods Used for Assessing Data Quality in This Analysis .............................................. 8 

3. Results ............................................................................................................................................ 11 

3.1 Anthropometric Estimates ................................................................................................ 11 

3.2 Z-score SDs ....................................................................................................................... 18 

3.3 Heaping of Height, Weight, and Age ................................................................................ 26 

3.4 DHS Flags ......................................................................................................................... 30 

3.5 Summarizing the Data Quality Indicators ......................................................................... 32 

3.6 Bivariate SD Results ......................................................................................................... 34 

3.7 Regression Results ............................................................................................................ 38 

4. Discussion ............................................................................................................................................... 43 

5. Conclusion and Recommendations ......................................................................................................... 45 

References ................................................................................................................................................... 47 

Appendix A: Standard deviations of height-for-age z-scores (HAZ) ......................................................... 49 

Appendix B: Standard deviations of weight-for-height z-scores (WHZ) ................................................... 51 

Appendix C: Heaping indices for height and weight measurements in 52 DHS surveys ........................... 53 

Appendix D: Myers’ heaping Index of age in 52 DHS surveys ................................................................. 55 

Appendix E: WHO flags and height out of plausible limits in 52 DHS surveys ........................................ 57 

Appendix F: Mean and standard deviations of the z-scores by background variables in 

7 DHS countries ..................................................................................................................... 59 

Appendix G: HAZ linear regressions for 52 DHS surveys ........................................................................ 61 

Appendix H: WAZ linear regressions for 52 DHS surveys ........................................................................ 63 

Appendix I: WHZ linear regressions for 52 DHS surveys ......................................................................... 65

v 

Tables 

Table 1. Anthropometric indicators of children age 0-59 months using WHO flags, DHS surveys 

2005-2014 ................................................................................................................................ 11 

Table 2. Countries with the highest values on the 10 measures of z-score standard deviations, 

height, weight and age, Myers’ index, and flagged cases, ranked from highest to lowest 

values, DHS surveys 2005-2014 ............................................................................................. 33 

Table 3. Countries with the lowest values on the 10 measures of z-score standard deviations, 

height, weight and age, Myers’ index, and flagged cases, ranked from lowest to highest 

values, DHS surveys 2005-2014 ............................................................................................. 33 

Table 4. Summary of HAZ linear regressions for 52 DHS surveys 2005-2014 .................................... 38 

Table 5. Summary of WAZ linear regression for 52 DHS surveys 2005-2014..................................... 39 

Table 6. Summary of WHZ linear regression for 52 DHS surveys 2005-2014..................................... 39 

Table 7. R-squared values of the HAZ regressions before and after including the cluster variable 

with the adjusted HAZ SD, 52 DHS surveys 2005-2014 ........................................................ 41 

Table A. Standard deviations of height-for-age z-scores (HAZ) for measured children under 

two years of age and measured children two years of age and over in 52 DHS surveys 

(2005-2014) ............................................................................................................................. 49 

Table B. Standard deviations of weight-for-height z-scores (WHZ) for measured children under 

two years of age and measured children two years of age and over in 52 DHS surveys 

(2005-2014) ............................................................................................................................. 51 

Table C. Heaping indices for height and weight measurements in 52 DHS surveys (2005-2014) ........ 53 

Table D. Myers’ heaping Index of age in 52 DHS surveys (2005-2014) ............................................... 55 

Table E. WHO flags and height out of plausible limits in 52 DHS surveys (2005-2014) ..................... 57 

Table F. Mean and standard deviations of the z-scores by background variables in 7 DHS countries 

(2005-2014) ............................................................................................................................. 59 

Table G. HAZ linear regressions for 52 DHS surveys (2005-2014) ...................................................... 61 

Table H. WAZ linear regressions for 52 DHS surveys (2005-2014) ..................................................... 63 

Table I. WHZ linear regressions for 52 DHS surveys (2005-2014) ..................................................... 65 

vii 

Figures 

Figure 1. Percentage of children age 0-59 months who are stunted, DHS surveys 2005-2014 .............. 13 

Figure 2. Percentage of children age 0-59 months who are underweight, DHS surveys 2005-2014 ..... 14 

Figure 3. Percentage of children age 0-59 months who are wasted, DHS surveys 2005-2014 .............. 15 

Figure 4. Percentage of children age 0-59 months who are overweight, DHS surveys 2005-2014 ....... 17 

Figure 5. Standard deviations of height-for-age (HAZ) z-scores, DHS surveys 2005-2014 .................. 19 

Figure 6. Standard deviations of weight-for-age (WAZ) z-scores, DHS surveys 2005-2014 ................ 21 

Figure 7. Standard deviations of weight-for-height (WHZ) z-scores, DHS surveys 2005-2014 ............ 22 

Figure 8. Standard deviations of height-for-age z-scores (HAZ) for children under 2 years and 

children 2 years and over, DHS surveys 2005-2014 ............................................................... 24 

Figure 9. Standard deviations of weight-for-height z-scores (WHZ) for children under 2 years and 

children 2 years and over, DHS surveys 2005-2014 ............................................................... 25 

Figure 10. Myers’ Index for height and weight for children 0-59 months, DHS surveys 2005-2014 ...... 27 

Figure 11. Myers’ Index for age for children age 0-59 months, DHS surveys 2005-2014 ...................... 29 

Figure 12. Percentage of children 0-59 months with WHO flags and flags for height out of plausible 

limits, DHS surveys 2005-2014 .............................................................................................. 31

Figure 13. Standard deviations of height-for-age (HAZ) by background variables, DHS surveys 

2005-2014 ................................................................................................................................ 35 

Figure 14. Standard deviations of weight-for-age (WAZ) by background variables, DHS surveys 

2005-2014 ................................................................................................................................ 36 

Figure 15. Standard deviations of weight-for-height (WHZ) by background variables, DHS surveys 

2005-2014 ................................................................................................................................ 37 

Figure 16. Standard deviations of height-for-age (HAZ) z-scores before and after adjusting for cluster 

heterogeneity with percent reduction, 52 DHS surveys 2005-2014 ........................................ 42 

ix 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to continually assess and improve the methodology and 

procedures used to carry out national-level surveys as well as to offer additional tools for analysis. 

Improvements in methods used will enhance the accuracy and depth of information collected by The DHS 

Program and relied on by policymakers and program managers in low- and middle-income countries. 

While data quality is a main topic of the DHS Methodological Reports series, the reports also examine 

issues of sampling, questionnaire comparability, survey procedures, and methodological approaches. The 

topics explored in this series are selected by The DHS Program in consultation with the U.S. Agency for 

International Development. 

It is hoped that the DHS Methodological Reports will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries, and will be used to 

enhance the quality and analysis of survey data. 

Sunita Kishor 

Director, The DHS Program

xi 

Abstract 

This methodological report examines the quality of anthropometric data from 52 DHS surveys conducted 

between 2005 and 2014. The analysis includes height, weight, and age measurements of children under five 

years of age as well as three nutritional status indices—height-for-age (HAZ), weight-for-age (WAZ), and 

weight-for-height (WHZ)—that follow WHO guidelines. The data quality indicators used to investigate the 

measurements include: standard deviation of z-scores; heaping of measures of height, weight, and age; and 

the percentage of extreme cases flagged during data processing. In addition, linear regressions of the zscores were conducted to examine the amount of heterogeneity in z-scores that can be explained by 

covariates, including cluster-level variation. The findings identified surveys that have outperformed others 

in terms of anthropometric data quality along with surveys that have been deficient in data quality. Based 

on the results, recommendations were made that will improve the quality of anthropometric data in future 

surveys. 

KEY WORDS: Anthropometry, stunting, wasting, underweight, nutritional status, z-score, Demographic 

and Health Surveys, data quality. 

xiii 

Executive Summary 

Providing reliable estimates of anthropometric indicators such as the prevalence of stunting, wasting, and 

underweight among children is important for monitoring global progress toward the goals of eradicating 

hunger, reducing health inequalities, and assessing the progress of short- and long-term nutrition and health 

interventions. The collection of anthropometric data has been a key component of the Demographic and 

Health Surveys (DHS) since 1986. To date, DHS has collected height and weight data for more than three 

million children and adults in 238 surveys in 77 countries. The DHS Program regularly conducts further 

analyses of the quality of data from these surveys to identify areas for future improvement. The current 

methodological report examines the quality of anthropometric data from the most recent survey conducted 

in 52 DHS countries between 2005 and 2014. Data quality was assessed using multiple indicators that are 

already included in DHS field check tables plus new indicators suggested in the literature. These indicators 

include the standard deviations of the WHO z-scores known as HAZ (height-for-age), WAZ (weight-forage), and WHZ (weight-for-height); heaping of height, weight, and age; and flagged cases identified 

through data processing and WHO limits regarding extreme values. Based on the results, recommendations 

were made of ways to improve the quality of anthropometric data in future DHS surveys. 

The 52 countries whose data were analyzed vary substantially in terms of their anthropometric indicators 

but also in terms of the data quality indicators examined. Some countries appeared to perform poorly on 

several of the data quality indicators, particularly Albania and Benin, while other countries such as 

Colombia, Honduras, and Peru were identified as having high quality data. Of particular concern is the 

higher variability in the standard deviations for children at younger ages, which may be due to the difficulty 

of measuring very young children lying down in contrast to measuring older children standing up. This 

observation draws attention to the need for more focused effort in training on length measurements of 

children under two years of age in future DHS surveys. 

Linear regressions for each of the z-scores showed similarities in relationships with several covariates. In 

most surveys, the z-scores had positive significant relationships with perceived size at birth (average and 

large versus small size at birth as the reference category) and mother’s weight according to her BMI (normal 

and above versus thin as the reference category). The percentage of variation explained by such variables 

was usually less than 10%. 

A separate regression for the HAZ z-score was estimated for each of the 52 countries by adding the cluster 

as a fixed-effect categorical covariate, in addition to the other covariates in the first regression. The results 

showed that the R-squared value increased substantially for most countries after the addition of the cluster 
xiv 

variable. This increase indicates a high level of heterogeneity across or between the clusters. The finding is 

likely due to variations in heterogeneity from one cluster to another, but it could also be due to variations 

in the quality of measurements taken by different fieldwork teams. However, the cluster variable did not 

explain a large portion of the variability in the HAZ z-scores for all surveys. In some surveys, a high 

standard deviation of z-scores is more likely due to either within-cluster heterogeneity or measurement 

error versus between cluster variability. 

The inclusion of the following information when presenting anthropometry data has been recommended by 

WHO: general characteristics of the population; sample size; measurement methods; method of determining 

age; percentage of excluded data; prevalence based on fixed cutoff; confidence intervals of the prevalence 

estimates; mean z-scores with 95% confidence intervals; standard deviations of z-scores; and frequency 

distribution plots against the reference distribution. In DHS surveys all the indicators are already included 

in the main survey reports except SDs of the z-scores and frequency distribution plots. There remains a 

need for well-defined and internationally accepted criteria to assess anthropometry data quality. It should 

also be recognized that direct information on the quality of anthropometric measurements is only a subset 

of the information that is needed to assess the overall quality of a population-based survey. 

Recommendations to improve the quality of DHS anthropometric data include more training on 

measurement of children, especially younger children, and identifying new types of equipment to accurately 

measure the height/length of children, including digital and lightweight measuring boards. 
1 

1. Introduction 

In 2012, the World Health Organization (WHO) member states endorsed six World Health Assembly global 

targets for improving maternal, infant, and young child nutrition by 2025 (World Health Organization 

2014a). These targets include three anthropometric indicators of nutritional status: stunting, wasting, and 

overweight. The recently developed Sustainable Development Goals (SDG) also reinforced the Millennium 

Development Goals (MDG) of ending hunger and improving food security and nutrition. About this time, 

WHO released a new action-oriented slogan, “what is measured gets done” (World Health Organization 

2014b), and it became apparent that providing reliable estimates of anthropometric indicators such as the 

prevalence of stunting, wasting, and underweight among children is important for monitoring global 

progress toward the goals of eradicating hunger, reducing health inequalities, and assessing the progress of 

short- and long-term nutrition and health interventions. 

As highlighted by the first Global Nutrition Report released in 2014, “nutrition is central to sustainable 

development” (International Food Policy Research Institute 2014, p3). It is important to monitor the healthy 

growth of children, especially in the first years of life. Undernourished children are not only at risk of death 

and disease but also are unable to reach their full cognitive potential. Anthropometric indicators can also 

be used as proxy measures of child health inequalities and economic development (International Food 

Policy Research Institute 2014). 

Between 1990 and 2015 one of the nutrition targets set by MDG 1 was to reduce by half the proportion of 

people who suffer from hunger (United Nations 2014). One of the indicators used for this target is the 

prevalence of underweight among children under five. This target has almost been met, but in 2012 there 

were still an estimated 99 million children under five years who were underweight (United Nations 2014). 

The recently developed Sustainable Development Goals (SDG) recommend changing the undernutrition 

indicators to stunting and wasting in children under five years of age (Schmidt-Traub et al. 2015). 

The term “anthropometry” is derived from the Greek words “anthropos” and “metron,” meaning “human 

measurement.” Anthropometric data collection has been a key component of Demographic and Health 

Surveys since 1986. To date, DHS has collected height and weight data for more than 3 million children 

and adults in 238 surveys in 77 countries. In DHS surveys, anthropometric data are collected by measuring 

the height and weight of children under the age of five who stayed in the household the night before the 

survey. In early surveys the measurements of children were limited to the children of interviewed mothers, 

but since 1997 this limitation has been removed. To ensure the production of high quality anthropometric 

data, DHS provides interviewers with extensive training on how to obtain and record height and weight 
2 

measurements as well as the birthdate, including day of birth. Other efforts to achieve high data quality 

include field check tables, multiple layers of supervision, and field visits as part of the standard DHS 

protocol. The DHS program has also begun using Computer Assisted Personal Interviews (CAPI) in several 

countries, enabling real-time data quality assessment. 

DHS regularly conducts further analyses of the quality of data to identify areas for future improvements. In 

2008 an assessment of the data quality of health and nutrition indicators was conducted by Pullum (2008) 

for surveys implemented between 1993 and 2003. That methodological report included anthropometry data 

quality assessment indicators, mainly investigating missing or flagged values of height/length and weight; 

it did not include a detailed examination of the z-scores. 

The current methodological report examines the quality of anthropometric data from 52 countries that had 

a recent DHS survey (between 2005 and 2014). Data quality will be assessed using multiple indicators 

already included in the DHS field check tables and new indicators suggested in the literature (Crowe et al. 

2014; Mei and Grummer-Strawn 2007; World Health Organization 1995). Based on the results, 

recommendations will be made to further improve anthropometric data quality in the surveys. 

1.1 Growth Reference Standards and Flags 

To obtain anthropometric indicators on stunting, underweight, wasting, and overweight of children from 

height/length, weight and/or age, WHO growth reference standards are used to compute three nutritional 

scores described as z-scores. These z-scores are the HAZ (height-for-age), WAZ (weight-for-age), and 

WHZ (weight-for-height). Low values on these scales (below standard cutoffs) identify stunting, 

underweight, and wasting, respectively. 

Heuristically, each z-score is calculated by comparing the child’s height/length or weight with the median 

value in the reference population. The difference is divided by the standard deviation of the reference 

population (WHO Multicentre Growth Reference Study Group 2006) as shown in the formula below. The 

actual computation of z-scores is substantially more complicated however, and requires the use of reference 

lists of coefficients. 

Z-score = Individual value of the child – median value of children in the reference population 

 Standard deviation of the reference population 

From 1997 to 2006, the reference population used by DHS for calculating z-scores was the International 

Growth Reference developed by the National Center for Health Statistics (NCHS) in 1977. In 2006, WHO 

developed a new standard using a diverse geographic sample of children to replace the NCHS 1977 
3 

reference population. The purpose of the WHO 2006 standard was to describe a normal child’s growth 

under ideal child-rearing and environmental conditions (WHO Multicentre Growth Reference Study Group 

2006). Comparisons of these two reference populations using longitudinal data revealed that the WHO 2006 

Standard provided higher stunting and overweight estimates for all ages and higher underweight and 

wasting estimates for children in infancy. It provided a better tool for monitoring the rapid and changing 

rate of growth in early infancy (de Onis et al. 2006). The WHO 2006 Standard was adopted internationally 

and has been used by DHS since 2007. In order to allow for comparability with previous years, z-scores 

according to the WHO 2006 standard have been calculated for all previous surveys and are available on the 

DHS website. 

After obtaining the z-scores according to the reference population, data sets are cleaned by flagging cases 

with z-scores beyond specified lower or upper cutoffs and excluding them from the computation of 

prevalence of stunting, etc. The purpose of flagging is to eliminate extreme values that are most probably 

due to measurement errors or data-entry errors. Alternative cutoff values could be used for flagging the 

data. The most commonly used flags were specified as part of the WHO (2006) growth standards. Other 

flags include the SMART flags and the WHO 1995 flexible criteria (the same as the NCHS cleaning criteria) 

(Crowe et al. 2014). These flags are summarized in the table below. 

Flags used in cleaning anthropometric data prior to computing malnutrition indicators

Cleaning method 

Flagged cases 

HAZ WAZ WHZ Reference mean 

WHO (2006) growth standards <-6 or >6 <-6 or >5 <-5 or >-5 Growth standards reference population 

SMART flags <-3 or >3 <-3 or >3 <-3 or >3 Survey sample 

WHO 2005 Flexible criteria <-4 or >3 <-4 or >4 <-4 or >4 Survey sample 

Source: (Crowe et al. 2014)

A study by Crowe et al. (2014) on data from 21 DHS countries compared the effect of using these different 

flags on the estimated prevalence of stunting and other nutrition indicators. The findings showed that 

SMART flags are the least inclusive, resulting in the lowest reported malnutrition prevalence. The WHO 

2006 flags are the most inclusive, resulting in the highest reported prevalence (Crowe et al. 2014). DHS 

has used the WHO 2006 flags since it started using the WHO 2006 reference population to compute the zscores. 

1.2 Anthropometry Data Quality Measures 

The quality of anthropometric measurements cannot be captured with just a single indicator. One data 

quality assessment tool is the standard deviation (SD) of anthropometric z-scores (Mei and GrummerStrawn 2007). Mei and Grummer-Strawn (2007) showed that the SDs of the z-scores computed for 51 DHS 
4 

surveys were relatively stable and did not vary with the z-score means; i.e., the means appeared to be 

independent of the SDs (Mei and Grummer-Strawn 2007). This finding indicates that the SD can be used 

to measure data quality in various countries and settings. However, using the SD alone may be misleading 

because it is a measure of both heterogeneity in the population (with respect to factors that affect nutrition) 

and data quality. Unlike the populations of countries in which DHS surveys are implemented, the reference 

population used to compute the z-score is very homogeneous (again, with respect to factors that affect 

nutrition) while other populations are expected to be much more heterogeneous (WHO Multicentre Growth 

Reference Study Group 2006). The WHO technical report on the use and interpretation of anthropometry 

recommends using several indicators and tools for anthropometric data quality assessment, including 

general characteristics of the population, sample size, survey design, measurement methods, method of 

determining age, proportion of missing data due to likely error and the exclusion criteria (i.e. flags), as well 

as the mean and SD of the z-scores (World Health Organization 1995). In addition, the WHO technical 

report suggests that if the HAZ distribution is found to decrease with increasing age, one may reasonably 

assume that the measurements of infant length are of poor quality (World Health Organization 1995). 

Heaping1

 and digit preference regarding height and weight measures can also be used to measure data 

quality (Siegel, Swanson, and Shryock 2004). Age is another key factor in the assessment of nutritional 

status of children. Computing the correct age of the child can be very challenging. If the exact birth date of 

the child is not known, an event calendar is required to estimate the date of birth as accurately as possible. 

Computing heaping of age in months can help assess the quality of the data on children’s age. 

Some researchers have created scoring options by using multiple indicators to assign quality labels to data. 

For example, ENA software used by SMART surveys conducts plausibility checks on anthropometry data 

to assess the quality of the data (Jaysaekaran 2012; Standardized Monitoring and Assessment of Relief and 

Transitions (SMART) 2015). Similarly, an unpublished study from Harvard included an index to assess the 

quality of anthropometry data (Corsi and Subramanian 2014). 

1.3 Other Sources of Anthropometric Data 

A decade ago DHS was the only source of national-level anthropometry data for developing countries. 

Currently, in addition to DHS, anthropometric data are also available from the National Nutrition Surveys 

(NNS), UNICEF Multiple Indicator Cluster Surveys (MICS), and the World Bank Living Standards 

Measurement Study (LSMS). NNS usually employ the Standardized Monitoring and Assessment of Relief 

and Transitions (SMART) methodology in their data collection and analysis, designed for obtaining 

 

1

 Heaping is a departure from a uniform distribution across final digits that indicates a preference for one or more 

digits, such as 0 and 5, in the reporting of height and weight, over others. 
5 

anthropometric estimates in emergency settings (Standardized Monitoring and Assessment of Relief and 

Transitions (SMART) 2006). As in DHS, NNS and MICS measure the height and weight of children under 

five years of age to assess nutritional status. The methodologies and data collection procedures are similar 

but differ in some respects (Hancioglu and Arnold 2013). For instance, nutritional status is reported for de 

jure (usual residents) children in MICS and de facto (slept in household last night) children in DHS. The 

overall length of interviewer training is typically longer in DHS (4 weeks) than in MICS (3 weeks), and the 

typical duration of fieldwork is 1-2 months longer for DHS surveys than for MICS surveys. 

SMART surveys were initially designed to collect anthropometric measures rapidly and in emergency 

settings, but the SMART methodology is now being used in national surveys and non-emergency settings 

in some countries; an example is the Tanzania 2014 NNS (Tanzania Food and Nutrition Centre 2014). The 

SMART methodology uses ENA software to enter data which highlights cases that have extreme z-scores, 

based on the WHO criteria (Jaysaekaran 2012; Standardized Monitoring and Assessment of Relief and 

Transitions (SMART) 2006). While SMART surveys focus on obtaining nutrition and anthropometric data 

on the child (plus mortality data), DHS and MICS surveys are much broader in scope and include other 

data on population and health as well as background information on the household and the parents of the 

child. Also, some of the data collection procedures differ. For example, in DHS and MICS surveys the 

child’s age is computed from the interview date and the birth date. In contrast, NNS surveys do not usually 

include the date of birth of the child, instead using an estimated age in months. In another example, 

following WHO guidelines, whether the child is measured lying down (length) or standing up (height) is 

determined in DHS by the child’s age. Children under 24 months of age are measured lying down and older 

children are measured standing up. In the SMART methodology this is determined by the child’s height; 

children under 87 cm are measured lying down (Jaysaekaran 2012). 

7 

2. Methods and Data

2.1 Data 

Data from 52 countries that have completed a recent (2005-2014) DHS survey were used for analysis. These 

countries are listed in Table 1 in the results section along with their sample sizes and year of the survey 

implementation. In each survey, height/length and weight measurements of all children in the household 

under age five years (60 months) are taken. Height/length measurements are typically carried out using an 

Infant/Child/Adult Shorr Board while weight is measured using a SECA digital scale. In DHS, measurers 

are trained to measure the length of children under 24 months lying down and the height of children 24 

months or older standing up. The measurer records whether the child was measured lying down or standing 

up. During data processing, if a child below age 24 months was measured standing up, 0.7 cm is added to 

the height; if a child age 24 months or older was measured lying down, 0.7 cm is subtracted from the length. 

This is a standard adjustment made by MICS and SMART, as well as DHS. Z-scores are computed based 

on these measurements using the WHO 2006 standards. 

DHS interviewers are trained to measure height/length and weight according to the internationally 

recommended standard protocol (ICF international 2012). At least three days of training on anthropometric 

measurements is provided, which includes a standardization exercise (repeated measurements of the same 

child) for the measurers and the equipment. Special emphasis is given to the assessment of age. During the 

fieldwork, team supervisors and editors are trained to pay attention to the out-of-range height and weight 

values and are instructed to provide feedback to the measurers in the field if they identify issues with the 

anthropometry data. Extensive field monitoring is also carried out by staff from the central survey office 

and DHS staff and consultants. Field check tables are run periodically for paper-based surveys, and can be 

run in near real-time for CAPI surveys. The field check tables are used to assess the quality of the data, and 

feedback to the teams is provided promptly if any issues are identified, but interviewers and supervisors are 

not provided with any feedback as to whether the z-scores themselves are out of range. We now consider 

selected indicators that are used to assess anthropometry data quality; these indicators are part of the 

standard DHS field check tables. 
8 

2.2 Methods Used for Assessing Data Quality in This Analysis 

2.2.1 Standard deviations of z-scores 

In addition to presenting a description of the estimates for stunting, underweight, wasting and overweight 

from the 52 DHS countries, the mean and standard deviation (SD) of each z-score were examined. 

Additionally, the SD of the z-scores for children under age two years and two years and over were compared 

to examine the effect of measuring children standing up versus lying down. All measures and estimates 

were obtained for de facto children using sampling weights. 

2.2.2 Height/length, weight and age heaping and flagged cases 

Data quality checks include examining the heaping for height, weight, and the age of children in months, 

and the percentage of flagged cases in the data. Three indices were used to examine heaping for height and 

weight: the percentage of observations with final digit 0 or 5 minus the expected percentage (a difference); 

the ratio of observed cases with final digit 0 or 5 to the expected number (a ratio); and Myers’ Blended 

Index, which detects any pattern of digit preference, not just a preference for terminal digits 0 or 5. Myers’ 

Index was also calculated for the age in months of the child. 

Myers’ Blended Index is virtually identical to Whipple’s Index and the Index of Dissimilarity as a measure 

of how much an observed distribution across terminal digits 0,..., 9 differs from a uniform distribution in 

which each digit would be equally likely. It will identify disproportionate use of 0 or 5 or even numbers, 

etc. The adjective “Blended,” which is often omitted, describes a minor adjustment to compensate for the 

possible impact that genuine non-uniformity in the full (multi-digit) distribution of age, etc., could have on 

the terminal digit. These indices can be interpreted as the percentage of observations that would have to be 

shifted from over-reported to under-reported digits in order to achieve a uniform distribution. The ideal 

value of the index is 0. 

Two main flags are applied to DHS data. The first refers to values that fall outside the WHO limits described 

above for each z-score. A high percentage of flagged cases can indicate measurement error, especially in 

conjunction with other data quality measures. The second flag in DHS data is assigned to recorded height 

values falling outside of plausible limits, which are specified to be 45-110 cm for children measured lying 

down, and 65-120 cm for children measured standing up. 

 
9 

2.2.3 Regressions of z-scores 

In addition to the data quality checks above, linear regressions were performed for the 52 DHS countries 

using the stratified sample design for each country and each z-score index. The strata were assumed to be 

combinations of locality (urban/rural) and region for each country. The regression models were fitted using 

the z-scores as the outcomes (separate regressions for HAZ, WAZ and WHZ) and several independent 

variables found to be associated with the anthropometric status of children in the literature (Adair and 

Guilkey 1997; Mamabolo et al. 2005; Mamiro et al. 2005; Sereebutra et al. 2006; Willey et al. 2009). These 

independent variables, all of which are categorical, include locality (urban/rural), wealth index (lowest, 

second, middle, fourth, highest), mother’s level of education (none or primary, secondary and higher), 

father’s level of education (none or primary, secondary and higher), mother’s work status (currently 

working or not), mother’s body mass index (categorized as thin, normal, overweight, or obese), mother’s 

age when she gave birth to the child (categorized as under 18 years, 18-34 years, 35 years and over), child’s 

birth order (1, 2, 3, 4, or more), child’s sex, and mother’s perceived size of the child at birth (small, average, 

large). Because of the high correlations observed between locality and the wealth index, a variable was 

constructed to combine these two variables to create a joint locality-by-wealth variable with four categories 

(rural poor, rural non-poor, urban poor, and urban non-poor). The distributions of wealth varied 

substantially by locality; therefore, to create this variable, respondents from the middle wealth quintile were 

combined with the lowest two wealth quintiles for urban locality to produce the urban poor category, and 

for the rural locality they were combined with the highest two wealth quintiles to produce the rural nonpoor category. For these regressions, children age 0-59 months of interviewed mothers were selected. The 

samples differ slightly from those used to illustrate the data quality measures discussed previously, which 

included all children age 0-59 in the household. The results of the regressions for the 52 countries for each 

z-score are presented in the Appendices G, H, and I. Summary tables of these regressions are included in 

the Results section (Section 3) below. In addition, bivariate analyses of the HAZ, WAZ, and WHZ SDs for 

each category of the covariates used in the regressions were examined. 

A second regression model was fitted for the HAZ z-score for each country with the cluster id code added 

as a categorical covariate in the model. The purpose was to examine the level of heterogeneity across 

clusters of the HAZ z-scores. For this model the locality variable was removed because each cluster is either 

entirely rural or entirely urban. The wealth index was included to replace the locality-by-wealth variable in 

the first model. This second model shows the extent to which variation in the HAZ z-score can be explained 

by heterogeneity of the children across or between the clusters. 

11 

3. Results 

3.1 Anthropometric Estimates 

Table 1 summarizes the anthropometric estimates of stunting, underweight, wasting, and overweight as 

well as the means and SDs of the HAZ, WAZ and WHZ for the 52 countries in the analysis. The estimates 

and sample sizes vary substantially across the countries. Sample sizes of measured de facto children with 

valid z-scores range from 1,289 in Albania to 46,655 in India. The stunting, underweight, wasting, and 

overweight estimates also differ greatly, as can be seen more clearly in Figures 1-4 for each of these 

estimates. 

Table 1. Anthropometric indicators of children age 0-59 months using WHO flags, DHS surveys 2005-2014

 Survey Weighted Stunted HAZ 

Underweight WAZ Wasted WHZ Overweight 

Country year N % mean SD % mean SD % mean SD %

Albania 2008-2009 1289 19.3% -0.40 2.02 5.2% 0.15 1.31 9.1% 0.58 1.86 21.7% 

Armenia 2010 1333 19.3% -0.74 1.64 4.7% 0.05 1.12 4.0% 0.67 1.47 15.3% 

Azerbaijan 2006 1979 25.1% -1.05 1.65 7.7% -0.41 1.09 6.8% 0.30 1.53 12.9% 

Bangladesh 2011 7861 41.3% -1.68 1.41 36.4% -1.61 1.15 15.6% -0.94 1.20 1.5% 

Benin 2011-2012 8079 44.6% -1.61 2.33 21.3% -0.92 1.49 16.0% 0.03 2.02 17.9% 

Bolivia 2008 8422 27.1% -1.24 1.31 4.3% -0.27 1.04 1.4% 0.62 1.08 8.5% 

Burkina Faso 2010 6994 34.6% -1.40 1.60 25.7% -1.27 1.20 15.5% -0.67 1.38 2.4% 

Burundi 2010-2011 3590 57.7% -2.20 1.38 28.8% -1.42 1.10 5.8% -0.21 1.16 2.7% 

Cambodia 2010-2011 3975 39.9% -1.66 1.38 28.3% -1.44 1.05 10.9% -0.70 1.13 1.6% 

Cameroon 2011 5860 32.5% -1.26 1.71 14.6% -0.63 1.31 5.6% 0.13 1.31 6.2% 

Colombia 2009-2010 15702 13.2% -0.83 1.12 3.4% -0.24 1.00 0.9% 0.32 1.00 4.8% 

Comoros 2012 2762 30.1% -1.16 1.91 15.3% -0.75 1.33 11.1% -0.13 1.60 9.3% 

Congo Brazzaville 2011-2012 4591 24.4% -1.02 1.48 11.6% -0.72 1.11 5.9% -0.20 1.19 3.3% 

Congo Democratic 

Republic 2013-2014 9030 42.7% -1.60 1.84 22.6% -1.09 1.28 7.9% -0.21 1.32 4.1% 

Côte d’Ivoire 2011-2012 3581 29.8% -1.23 1.60 14.9% -0.83 1.16 7.5% -0.18 1.24 3.0% 

Dominican Republic 2013 3619 6.9% -0.30 1.24 3.8% 0.03 1.12 2.0% 0.27 1.18 7.3% 

Egypt 2014 13601 21.4% -0.57 2.02 5.5% -0.08 1.20 8.4% 0.38 1.66 14.9% 

Ethiopia 2011 10883 44.4% -1.69 1.69 28.7% -1.33 1.24 9.7% -0.51 1.20 1.7% 

Gabon 2012 3856 16.5% -0.70 1.48 6.0% -0.24 1.16 3.3% 0.22 1.24 7.4% 

Gambia 2013 3372 24.5% -1.01 1.55 16.2% -0.99 1.12 11.5% -0.60 1.29 2.7% 

Ghana 2008 2525 28.0% -1.08 1.65 13.9% -0.79 1.20 8.5% -0.24 1.35 5.3% 

Guinea 2012 3531 31.2% -1.12 1.82 18.0% -0.87 1.30 9.6% -0.31 1.36 3.6% 

Guyana 2009 1522 18.2% -0.85 1.44 10.5% -0.50 1.21 5.3% -0.03 1.31 6.2% 

Haiti 2012 4529 21.9% -0.97 1.43 11.4% -0.64 1.18 5.1% -0.12 1.19 3.6% 

Honduras 2011-2012 10167 22.6% -1.11 1.22 7.0% -0.42 1.11 1.4% 0.31 1.05 5.1% 

India 2005-2006 46655 48.0% -1.86 1.66 42.5% -1.78 1.23 19.8% -1.02 1.29 1.5% 

Jordan 2012 5851 7.7% -0.40 1.18 3.0% -0.10 1.01 2.4% 0.17 1.08 4.4% 

Kenya 2008-2009 5470 35.3% -1.41 1.59 16.1% -0.86 1.19 6.7% -0.09 1.29 4.7% 

Kyrgyz Republic 2012 4337 17.7% -0.80 1.45 3.4% -0.14 1.02 2.7% 0.44 1.21 8.5% 

Lesotho 2009-2010 2086 39.2% -1.54 1.54 13.2% -0.72 1.19 3.8% 0.24 1.28 7.2% 

Liberia 2013 3520 31.6% -1.23 1.66 15.0% -0.84 1.21 6.0% -0.17 1.21 2.9% 

Malawi 2010 4849 47.1% -1.78 1.61 12.8% -0.81 1.13 4.0% 0.30 1.29 8.3% 

Maldives 2009 2513 18.9% -0.93 1.43 17.3% -0.84 1.28 10.6% -0.45 1.41 5.9% 

(Continued...)
12 

Table 1. − Continued

 Survey Weighted Stunted HAZ 

Underweight WAZ Wasted WHZ Overweight 

Country year N % mean SD % mean SD % mean SD %

Mali 2012-2013 4857 38.3% -1.46 1.87 25.5% -1.23 1.31 12.7% -0.55 1.36 2.3% 

Mozambique 2011 10313 42.6% -1.68 1.65 14.9% -0.86 1.17 5.9% 0.17 1.37 7.4% 

Namibia 2013 2281 23.7% -1.09 1.42 13.3% -0.78 1.14 6.2% -0.21 1.23 3.4% 

Nepal 2011 2485 40.5% -1.67 1.40 28.8% -1.42 1.11 10.9% -0.65 1.13 1.4% 

Niger 2012 5481 43.9% -1.73 1.68 36.4% -1.60 1.27 18.0% -0.86 1.38 2.4% 

Nigeria 2013 26190 36.8% -1.38 2.01 28.7% -1.26 1.42 18.0% -0.66 1.58 4.0% 

Pakistan 2012-2013 3466 44.8% -1.79 1.72 30.0% -1.40 1.26 10.8% -0.51 1.29 3.2% 

Peru 2012 9168 18.1% -1.04 1.08 3.4% -0.20 1.07 0.6% 0.55 1.01 7.1% 

Rwanda 2010-2011 4356 44.2% -1.76 1.40 11.4% -0.77 1.07 2.8% 0.35 1.16 6.7% 

São Tomé and 

Príncipe 2008-2009 1544 29.3% -1.20 1.67 13.1% -0.70 1.21 10.5% 0.01 1.66 10.5% 

Senegal 2012-2013 5829 18.7% -0.91 1.35 15.7% -0.92 1.11 8.8% -0.60 1.11 1.4% 

Sierra Leone 2013 5094 37.9% -1.39 1.93 16.4% -0.82 1.36 9.3% -0.01 1.51 7.5% 

Swaziland 2006-2007 2940 28.9% -1.25 1.49 5.4% -0.29 1.13 2.5% 0.59 1.23 10.8% 

Tajikistan 2012 5080 26.2% -1.14 1.59 12.1% -0.80 1.16 9.9% -0.21 1.45 5.9% 

Tanzania 2009-2010 7491 42.0% -1.70 1.42 15.8% -0.95 1.12 4.8% 0.03 1.22 5.0% 

Timor-Leste 2009-2010 8171 58.1% -2.16 1.83 44.7% -1.79 1.23 18.6% -0.78 1.55 4.7% 

Uganda 2011 2350 33.4% -1.41 1.57 13.8% -0.82 1.15 4.7% -0.02 1.17 3.4% 

Zambia 2007 5602 45.4% -1.69 1.72 14.6% -0.83 1.14 5.2% 0.21 1.36 7.9% 

Zimbabwe 2010-2011 5260 32.0% -1.37 1.41 9.7% -0.66 1.08 3.0% 0.17 1.16 5.5% 

Figure 1 shows that the highest percentage of stunted children was found in Timor-Leste and Burundi, both 

nearly 60%. The lowest percentage of stunted children was found in the Dominican Republic and Jordan, 

7% to 8%. The ranking of countries differs according to the anthropometric measure examined. For 

instance, Burundi had one of the highest levels of stunting but ranked #7 for underweight and #32 for 

wasting, as shown in Figures 2 and 3, respectively. Such variation can be expected because the different 

measures capture different aspects of malnutrition: stunting measures chronic malnutrition, wasting 

measures acute malnutrition, and underweight measures overall malnutrition. Four of the six countries with 

the highest prevalence of underweight children are in South Asia; in contrast, four of the six countries with 

the lowest prevalence of underweight children are in Latin America and the Caribbean. The countries with 

the highest percentages of wasted children were India, Timor-Leste, Niger, and Nigeria, all between 18% 

and 20%. The percentage of wasted children was lowest in Peru and Columbia, both below 1%. 

 
13 

0%

10%20%30%40%50%60%

Timor-Leste

Burundi

India

Malawi

Zambia

Pakistan

Benin

Ethiopia

Rwanda

Niger

Congo Democratic Republic

Mozambique

Tanzania

Bangladesh

Nepal

Cambodia

Lesotho

Mali

Sierra Leone

Nigeria

Kenya

Burkina Faso

Uganda

Cameroon

Zimbabwe

Liberia

Guinea

Comoros

Côte d’Ivoire

São Tomé and Príncipe

Swaziland

Ghana

Bolivia

Tajikistan

Azerbaijan

Gambia

Congo Brazzaville

Namibia

Honduras

Haiti

Egypt

Armenia

Albania

Maldives

Figure 1. Percentage of children age 0-59 months who are stunted, DHS surveys 2005-2014 
14 

0%

10%20%30%40%50%

Timor-Leste

India

Niger

Bangladesh

Pakistan

Nepal

Burundi

Nigeria

Ethiopia

Cambodia

Burkina Faso

Mali

Congo Democratic...

Benin

Guinea

Maldives

Sierra Leone

Gambia

Kenya

Tanzania

Senegal

Comoros

Liberia

Mozambique

Côte d’Ivoire

Cameroon

Zambia

Ghana

Uganda

Namibia

Lesotho

São Tomé and Príncipe

Malawi

Tajikistan

Congo Brazzaville

Rwanda

Haiti

Guyana

Zimbabwe

Azerbaijan

Honduras

Gabon

Egypt

Swaziland

Figure 2. Percentage of children age 0-59 months who are underweight, DHS surveys 2005-2014 
15 

0%5%

10%15%20%25%

India

Timor-Leste

Niger

Nigeria

Benin

Bangladesh

Burkina Faso

Mali

Gambia

Comoros

Cambodia

Nepal

Pakistan

Maldives

São Tomé and Príncipe

Tajikistan

Ethiopia

Guinea

Sierra Leone

Albania

Senegal

Ghana

Egypt

Congo Democratic Republic

Côte d’Ivoire

Azerbaijan

Kenya

Namibia

Liberia

Congo Brazzaville

Mozambique

Burundi

Cameroon

Guyana

Zambia

Haiti

Tanzania

Uganda

Armenia

Malawi

Lesotho

Gabon

Zimbabwe

Rwanda

Figure 3. Percentage of children age 0-59 months who are wasted, DHS surveys 2005-2014 
16 

The prevalence of overweight children can be high even when there is evidence of insufficient nutrition in 

the population of children. For instance, Benin was found to have the second highest percentage of 

overweight children (18% in Figure 4) and also one of the highest percentages of stunting (45% in Figure 

1). This seeming contradiction may be due to the heterogeneity of the population, but it may also indicate 

poor data quality. 

 
17 

 0%5% 10% 15% 20% 25%

Albania

Benin

Armenia

Egypt

Azerbaijan

Swaziland

Sao Tome and Principe

Comoros

Kyrgyz Republic

Bolivia

Malawi

Zambia

Sierra Leone

Mozambique

Gabon

Dominican Republic

Lesotho

Peru

Rwanda

Guyana

Cameroon

Maldives

Tajikistan

Zimbabwe

Ghana

Honduras

Tanzania

Colombia

Timor-Leste

Kenya

Jordan

Congo Democratic Republic

Nigeria

Guinea

Haiti

Uganda

Namibia

Congo Brazzaville

Pakistan

Cote d'Ivoire

Liberia

Gambia

Burundi

Niger

Burkina Faso

Mali

Figure 4. Percentage of children age 0-59 months who are overweight, DHS surveys 2005-2014 
18 

3.2 Z-score SDs 

Figures 5-7 show the SDs of the HAZ, WAZ, and WHZ z-scores. Benin, Albania, Egypt, and Nigeria were 

found to have HAZ SDs that were at or above 2. These high SDs can indicate a data quality problem, 

although population heterogeneity may be another possible explanation. Many other data quality measures 

need to be assessed in addition to the SD of the z-score. A total of 22 of the 52 countries analyzed had SDs 

that were below 1.5, with the lowest SDs in Peru and Colombia (SDs near 1.1). 

 
19 

0.00.51.01.52.02.5

Benin

Albania

Egypt

Nigeria

Sierra Leone

Comoros

Mali

Congo Democratic Republic

Timor-Leste

Guinea

Pakistan

Zambia

Cameroon

Ethiopia

Niger

São Tomé and Príncipe

Liberia

India

Ghana

Azerbaijan

Mozambique

Armenia

Malawi

Burkina Faso

Côte d’Ivoire

Kenya

Tajikistan

Uganda

Gambia

Lesotho

Swaziland

Congo Brazzaville

Gabon

Kyrgyz Republic

Guyana

Maldives

Haiti

Namibia

Tanzania

Zimbabwe

Bangladesh

Nepal

Rwanda

Burundi

Cambodia

Sl

Figure 5. Standard deviations of height-for-age (HAZ) z-scores, DHS surveys 2005-2014 
20 

Overall, the WAZ SDs were smaller than the HAZ SDs. For example, Benin had the highest WAZ SD 

(1.49) but the HAZ SD (2.33) was much higher. Most of the countries (58%) had WAZ SDs below 1.3. 

Figure 7 shows that the WHZ SDs were also lower than the HAZ SDs; however, the WHZ SD for Benin 

was above 2. The average standard deviations of the z-scores for the 52 countries were highest for the HAZ 

(1.58), second highest for the WHZ (1.31), and lowest for the WAZ (1.18) 

 
21 

0.00.51.01.52.02.5

Benin

Nigeria

Sierra Leone

Comoros

Cameroon

Albania

Mali

Guinea

Maldives

Congo Democratic Republic

Niger

Pakistan

Ethiopia

Timor-Leste

India

Liberia

São Tomé and Príncipe

Guyana

Egypt

Burkina Faso

Ghana

Kenya

Lesotho

Haiti

Mozambique

Côte d’Ivoire

Gabon

Tajikistan

Uganda

Bangladesh

Namibia

Zambia

Swaziland

Malawi

Tanzania

Gambia

Dominican Republic

Armenia

Honduras

Senegal

Congo Brazzaville

Nepal

Burundi

Azerbaijan

Zimbabwe

Figure 6. Standard deviations of weight-for-age (WAZ) z-scores, DHS surveys 2005-2014 
22 

0.00.51.01.52.02.5

Benin

Albania

Egypt

São Tomé and Príncipe

Comoros

Nigeria

Timor-Leste

Azerbaijan

Sierra Leone

Armenia

Tajikistan

Maldives

Burkina Faso

Niger

Mozambique

Guinea

Mali

Zambia

Ghana

Congo Democratic Republic

Cameroon

Guyana

Pakistan

Malawi

Gambia

India

Kenya

Lesotho

Gabon

Côte d’Ivoire

Swaziland

Namibia

Tanzania

Liberia

Kyrgyz Republic

Bangladesh

Ethiopia

Haiti

Congo Brazzaville

Dominican Republic

Uganda

Burundi

Zimbabwe

Rwanda

Nepal

Cambodia

Senegal

Blii

Figure 7. Standard deviations of weight-for-height (WHZ) z-scores, DHS surveys 2005-2014 
23 

As described earlier, children under two years of age were supposed to have their height (length) measured 

lying down, while children two years of age or older were supposed to be measured standing up. A further 

examination of the HAZ and WHZ z-score SDs (both include height) was performed to compare the SDs 

for children under two years of age and those age two to four years. Figures 8 and 9 show that the SDs of 

the HAZ and WHZ z-scores are always higher for children under two years of age except for the WHZ in 

Armenia. For the HAZ SDs, the largest differences were found for Timor-Leste, São Tomé and Príncipe, 

Benin, Swaziland, Zambia, Albania, and Lesotho, all of which had a difference of approximately 0.5 

between the SDs of the two age categories (see Appendix A). Armenia, Peru, Honduras, Azerbaijan, Niger, 

and Senegal all had differences of approximately 0.0-0.1 between the two age categories. The largest 

differences for the WHZ SDs were found for Mozambique, Lesotho, São Tomé and Príncipe, Malawi, 

Tanzania, Gambia, and Timor-Leste, all of which had a difference of approximately 0.4 between the two 

age categories (see Appendix B). Eleven countries had a difference of about 0.0-0.1 between the WHZ SDs 

of the two age groups: Armenia, Benin, Maldives, Colombia, Honduras, Peru, Nigeria, Congo Brazzaville, 

Egypt, Azerbaijan, and the Dominican Republic. On average, the SDs of the HAZ were 0.29 lower for 

children age two years and over than for children under two years of age. The average difference in the SDs 

of the WHZ was slightly lower (0.24). 
24 

0.00.51.01.52.02.5

Benin

Albania

Egypt

Timor-Leste

Nigeria

Comoros

Sierra Leone

Mali

São Tomé and Príncipe

Zambia

Guinea

Congo Democratic Republic

Ethiopia

Cameroon

Malawi

Mozambique

Pakistan

Ghana

Tajikistan

India

Lesotho

Liberia

Kenya

Swaziland

Burkina Faso

Gambia

Uganda

Niger

Côte d’Ivoire

Armenia

Congo Brazzaville

Azerbaijan

Kyrgyz Republic

Maldives

Gabon

Namibia

Rwanda

Zimbabwe

Tanzania

Guyana

Haiti

Bangladesh

Cambodia

Burundi

Nepal

Bolivia

Senegal

DiiRbli

HAZ SD < 2 years HAZ SD >= 2 years

Figure 8. Standard deviations of height-for-age z-scores (HAZ) for children under 2 years and children 2 years and over, DH
25 

0.00.51.01.52.02.5

Benin

Albania

São Tomé and Príncipe

Timor-Leste

Egypt

Comoros

Sierra Leone

Nigeria

Azerbaijan

Mozambique

Tajikistan

Niger

Guinea

Ghana

Zambia

Malawi

Lesotho

Gambia

Burkina Faso

Cameroon

Mali

Armenia

Congo Democratic Republic

India

Maldives

Pakistan

Kenya

Swaziland

Tanzania

Guyana

Côte d’Ivoire

Bangladesh

Gabon

Kyrgyz Republic

Namibia

Rwanda

Ethiopia

Liberia

Haiti

Zimbabwe

Burundi

Uganda

Dominican Republic

Nepal

WHZ SD < 2 years WHZ SD >= 2 years

Figure 9. Standard deviations of weight-for-height z-scores (WHZ) for children under 2 years and children 2 years and over, DH
26 

3.3 Heaping of Height, Weight, and Age 

In DHS, weight is measured by a digital scale and recorded to the nearest 0.1 kg while height/length is 

measured using a wooden measuring board (whether lying down or standing up) and is recorded to the 

nearest 0.1 cm (or 1 mm). Figure 10 shows the indicator of heaping, Myers’ Index, for all the countries. 

This index should ideally be zero if there were no heaping at all of height and weight measurements. Figure 

10 shows that the Myers’ index was almost always much higher for height than for weight; the single 

exception being Guinea, where the index value is the same for height and weight. This was also the case 

for the other measures of heaping (excess and ratio), as shown in Appendix C, although the differences 

were smaller for the ratios. São Tomé and Príncipe had the largest amount of heaping for both height and 

weight, but this was due to removal of the right-most digit during the course of rounding in data processing, 

and should not be interpreted as heaping during data collection. Most of the countries had a Myers’ Index 

for height in the double digits with only seven countries—Guinea, Peru, Bangladesh, Ethiopia, Cameroon, 

Swaziland, and Colombia—with Myers’ index for height below 10. The Myers’ index for weight was 

greater than 10 for only four countries— São Tomé and Príncipe, Benin, Armenia and Sierra Leone. For 

the 51 counties other than São Tomé and Príncipe, the average Myers’ Index was 17.8 for height and 4.6 

percent for weight. This means that there would be no heaping for height if 17.8% of the observations were 

shifted to over-reported or under-reported digits and no heaping for weight if 4.6% of observations were 

shifted. 

 
27 

 0102030405060708090

São Tomé and Príncipe

Benin

Armenia

Namibia

Maldives

Malawi

Niger

Burundi

Tajikistan

Zambia

Guyana

Sierra Leone

Comoros

Timor-Leste

Mozambique

Rwanda

Tanzania

Cambodia

Egypt

Côte d’Ivoire

Nigeria

Kyrgyz Republic

Bolivia

Pakistan

Gambia

Burkina Faso

India

Mali

Jordan

Gabon

Kenya

Senegal

Lesotho

Liberia

Ghana

Uganda

Haiti

Albania

Azerbaijan

Congo Brazzaville

Congo Democratic Republic

Dominican Republic

Zimbabwe

Honduras

Height Weight

Figure 10. Myers’ Index for height and weight for children 0-59 months, DHS surveys 2005-2014 
28 

The age of the child is determined in the DHS from the date of interview and the date of birth of each child. 

As shown in Figure 11 and in Appendix D, the Myers’ Index for age was highest in Niger, Guinea and 

Mali, all of which were above 10, and was lowest in Zambia at 2.2. Myers’ Index for age identifies a 

departure from a uniform distribution across months 0-11 within the separate years of age (0, 1, 2, 3, and 

4). A high value on the index may indicate that in some cases the birthdate was not actually known by the 

respondent, but was recorded by the interviewer on the basis of a stated age such as “2 years”, “2 1⁄2 years”, 

etc. In some cases the birthdate is genuinely not known, and a more detailed response would not necessarily 

be more accurate. 

 
29 

 024681012

Niger

Guinea

Mali

Sierra Leone

Dominican Republic

Albania

Comoros

Egypt

Nepal

Ghana

São Tomé and Príncipe

Timor-Leste

Kenya

Malawi

Maldives

Rwanda

Congo Brazzaville

Gabon

Liberia

Lesotho

Guyana

Congo Democratic Republic

Jordan

Benin

Bangladesh

Armenia

Cambodia

Côte d’Ivoire

Colombia

Peru

Haiti

Azerbaijan

India

Tanzania

Bolivia

Honduras

Cameroon

Swaziland

Zimbabwe

Nigeria

Pakistan

Kyrgyz Republic

Uganda

Namibia

Figure 11. Myers’ Index for age for children age 0-59 months, DHS surveys 2005-2014 
30 

3.4 DHS Flags 

Figure 12 presents two types of flags: WHO flagged cases and cases with height out of plausible limits. 

WHO flags refers to values that fall outside the WHO limits, i.e., HAZ <-6 or >6, WAZ <-6 or >5 and WHZ 

<-6 or >5. The second flag in DHS data is assigned to height/length values which are specified to be 45-

110 cm for children measured lying down, and 65-120 cm for children measured standing up. These two 

types of extreme values are most probably due to data measurement errors or data entry errors. As shown 

in Figure 12 and Appendix E, Benin and Albania had the highest percentage of flagged cases (15% and 

11%, respectively). The lowest percentage of flagged cases was found for Peru, Colombia, Honduras, 

Jordan, and Nepal, all of which were below 1%. The average percentage of WHO flagged cases for the 52 

countries was 3.6%. For the second type of flag, the highest percentages were found for Namibia (10%), 

Malawi (6%), and Swaziland (6%). For this indicator, 35 countries had a value of 1% or less, and the 

average percentage for the 52 countries was 1.4%. 

 
31 

 0%2%4%6%8% 10% 12% 14% 16%

Benin

Albania

Comoros

Sierra Leone

Nigeria

São Tomé and Príncipe

Egypt

Pakistan

Azerbaijan

Timor-Leste

Mali

Armenia

Guyana

Gambia

Tajikistan

Niger

Malawi

Ghana

Zambia

India

Congo Democratic Republic

Guinea

Mozambique

Kenya

Gabon

Burkina Faso

Senegal

Liberia

Lesotho

Swaziland

Côte d’Ivoire

Namibia

Cameroon

Cambodia

Bangladesh

Maldives

Ethiopia

Zimbabwe

Tanzania

Kyrgyz Republic

Dominican Republic

Uganda

Burundi

Bolivia

WHO flags height out of plausible limits

Figure 12. Percentage of children 0-59 months with WHO flags and flags for height out of plausible limits, DHS surveys 2005-201
32 

3.5 Summarizing the Data Quality Indicators 

The mapping of countries was done to identify the countries that performed best and worst on the 10 

selected anthropometry data quality indicators used in this analysis. Table 2 and Table 3 present the 

countries with the highest values (indicating possible data quality issues) and those with the lowest values 

(indicating the best data quality) on the 10 data quality indicators discussed in this report. The data quality 

indicators used were the following: 1) HAZ SD, 2) WAZ SD, 3) WHZ SD, 4) the difference between HAZ 

SD under 2 years and HAZ SD 2 years and over, 5) the difference between WHZ SD under 2 years and 

WHZ SD 2 years and over, 6) Myers’ Index for height, 7) Myers’ Index for weight, 8) Myers’ Index for 

age, 9) WHO flags (values outside the WHO boundaries), and 10) height out of plausible limits. As seen in 

Tables 2 and 3, there are a few countries that appear repeatedly with high values on the 10 data quality 

indicators. Those countries that appear in the tables five or more times are indicated in color. In Table 2, 

Albania and Benin appear in the table eight out of 10 times; Sierra Leone and Comoros appear seven out 

of 10 times; São Tomé and Príncipe appears six times; and Egypt and Timor-Leste appear five times. These 

findings indicate that the data quality in these countries is not as good as the data quality in the other 

countries. 
33 

Table 2. Countries with the highest values on the 10 measures of z-score standard deviations, height, weight 

and age, Myers’ index, and flagged cases, ranked from highest to lowest values, DHS surveys 2005-2014 

HAZ SD WAZ SD WHZ SD 

HAZ SD < 2 

years - HAZ 

SD ≥ 2 years 

WHZ SD < 2 

years - WHZ 

SD ≥ 2 years 

Height 

Myers’ 

index 

Weight 

Myers’ 

index 

Age 

Myers’ 

index 

WHO 

flags 

Height out 

of plausible 

limits 

Benin Benin Benin Timor-Leste Mozambique 

São Tomé 

and 

Príncipe 

São Tomé 

and 

Príncipe 

Niger Benin Namibia 

Albania Nigeria Albania São Tomé and 

Príncipe Lesotho Benin Benin Guinea Albania Malawi 

Egypt Sierra 

Leone Egypt Benin São Tomé and Príncipe Armenia Armenia Mali Comoros Swaziland 

Nigeria Comoros São Tomé 

and Príncipe Swaziland Malawi Namibia Sierra Leone 

Sierra 

Leone 

Sierra 

Leone Sierra Leone

Sierra 

Leone Cameroon Comoros Zambia Tanzania Maldives Egypt Dominican 

Republic Nigeria Gambia 

Comoros Albania Nigeria Albania Gambia Malawi Comoros Albania 

São Tomé 

and 

Príncipe 

Benin 

Mali Mali Timor-Leste Lesotho Timor-Leste Niger Azerbaijan Comoros Egypt Peru 

Congo 

Democratic 

Republic 

Guinea Azerbaijan Kyrgyz 

Republic Ghana Burundi Albania Egypt Pakistan Nepal 

Timor-Leste Maldives Sierra Leone Tajikistan Guinea Tajikistan Bolivia Nepal Azerbaijan Guyana 

Guinea 

Congo 

Democratic 

Republic 

Armenia Comoros Côte d’Ivoire Zambia Kyrgyz 

Republic Ghana Timor-Leste Albania 

Note: Countries that appear five or more times in the table are highlighted in different colors. 

Some countries also appear repeatedly in Table 3, which lists the countries that had the lowest values on 

the 10 data quality indicators. Colombia appears in the table nine out of 10 times, Honduras and Peru appear 

in the table eight out of 10 times, and Bolivia appears five out of 10 times. These countries appear to have 

better quality anthropometric data than the other countries in the analysis. 

Table 3. Countries with the lowest values on the 10 measures of z-score standard deviations, height, weight 

and age, Myers’ index, and flagged cases, ranked from lowest to highest values, DHS surveys 2005-2014 

HAZ SD WAZ SD WHZ SD 

HAZ SD < 2 

years - HAZ 

SD ≥ 2 years 

WHZ SD < 2 

years - WHZ 

SD ≥ 2 years 

Height 

Myers’ 

index 

Weight 

Myers’ 

index 

Age 

Myers’ 

index 

WHO 

flags 

Height out 

of plausible 

limits 

Peru Colombia Colombia Armenia Armenia Guinea Zimbabwe Zambia Peru Azerbaijan 

Colombia Jordan Peru Peru Benin Peru Honduras Tajikistan Colombia Colombia 

Jordan Kyrgyz 

Republic Honduras Honduras Maldives Bangladesh 

Dominican 

Republic Senegal Honduras Congo Brazzaville 

Honduras Bolivia Jordan Azerbaijan Colombia Ethiopia Cameroon Burkina 

Faso Jordan Honduras 

Dominican 

Republic Cambodia Bolivia Niger Honduras Cameroon Colombia Burundi Nepal Kyrgyz 

Republic 

Bolivia Rwanda Senegal Senegal Peru Swaziland Banglades

h 

Mozambiq

ue Rwanda Bolivia 

Senegal Peru Cambodia Bangladesh Nigeria Colombia Peru Ethiopia Haiti Rwanda 

Cambodia Zimbabwe Nepal Haiti Congo 

Brazzaville Nepal Tanzania Gambia Congo Brazzaville Cambodia 

Burundi Azerbaijan Rwanda Colombia Egypt Honduras Kenya Namibia Bolivia Dominican 

Republic 

Rwanda Burundi Zimbabwe Pakistan Azerbaijan Zimbabwe Guinea Uganda Burundi Haiti 

Note: Countries that appear five or more times in the table are highlighted in different colors. 
34 

Comparing the two tables, we note that Benin appears 8 times in Table 2 but also once in Table 3 (for the 

WHZ SD difference between the two age categories). Similarly, Peru appears once in Table 2 (for the 

height-out-of-plausible-limits indicator) while it appears 8 times in Table 3, indicating it is a country with 

very good anthropometric data quality. This finding highlights the importance of using more than one 

indicator to assess data quality. Relying on just one indicator is not sufficient for drawing conclusions about 

the quality of anthropometric data from a DHS survey. 

3.6 Bivariate SD Results 

The HAZ, WAZ, and WHZ SDs for each category of the covariates used in the regression analysis for 

Benin, Albania, Sierra Leone, India, Rwanda, Columbia and Peru are presented in Figures 13-15 below and 

in Appendix F. These countries were selected because of their performance in the data quality indicators 

examined previously (from low to high performance). As can be seen in the figures, there are large 

variations in the SDs by age, especially for Benin, Albania and Sierra Leone. A higher SD at lower ages 

could indicate measurement issues for younger children. Variations in the SDs by the remaining categories 

were not as large. Exceptions were found in Albania, which had large variations in the SDs of the WAZ by 

mother’s BMI and in the SDs of the WHZ by mother’s age at child’s birth. 

 
35 

 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 

Sex Birth order Perceived size at birth Locality Wealth index Mother's education Father's education Mother's work status Mother

BMI 

Age

Figure 13. Standard deviations of height-for-age (HAZ) by background variables, DHS surveys 2005-2014 
36 

 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 Age Sex Birth order Perceived size at birth Locality Wealth index Mother's education Father's education Mother's work status Mother

BMI 

Figure 14. Standard deviations of weight-for-age (WAZ) by background variables, DHS surveys 2005-2014 
37 

 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 Age Sex Birth order Perceived size at birth Locality Wealth index Mother's education Father's education Mother's work status Mother's BMI 

Figure 15. Standard deviations of weight-for-height (WHZ) by background variables, DHS surveys 2005-2014 
38 

3.7 Regression Results 

Tables 4-6 summarize the regression results of the HAZ, WAZ, and WHZ for all 52 countries. Linear 

regressions were performed with the z-score as the outcome variable and selected covariates found to be 

associated with anthropometric measures in the literature (see Appendices G-I for the regression results for 

each country). The summaries show the number of countries that are in agreement in terms of the 

significance and sign of the coefficients. The summaries indicate that most countries have positive and 

significant associations between the z-scores and the perceived size of the child at birth (average and large 

versus small size at birth as the reference category). This was also the case for associations with the mother’s 

BMI (normal and above versus thin as the reference category). For these covariates, the statistically 

significant coefficients were always positive. For the HAZ and WAZ regressions, many countries also had 

positive significant coefficients for the categories urban non-poor and rural non-poor versus rural poor as 

the reference category; only one country had a negative significant coefficient. This pattern was not as 

consistent in the WHZ regression, and in fact a few countries had a negative significant coefficient for these 

categories. Most of the coefficients, when significant, were in agreement, i.e., if significant they had the 

same sign in almost all the countries. Exceptions were mainly found for the coefficients of the work status 

category (i.e., mother working); however, this coefficient was found to be significant in only a few 

countries. Most countries did not have significant coefficients for the urban poor category, for mother’s age 

at birth 35 years and over, and for the second and third birth order categories. 

Table 4. Summary of HAZ linear regressions for 52 DHS surveys 2005-2014 

 

Within significant 

coefficients 

Covariates Category Significant 

Positive 

coefficient 

Negative 

coefficient 

Not 

significant Total 

Locality by wealth 

(Ref. rural poor) 

rural non-poor 28 27 1 24 52 

urban poor 12 12 0 40 52 

urban non-poor 37 37 0 15 52 

Education father 

(Ref. none/primary) secondary+ 22 22 0 30 52 

Education mother 

(Ref. none/primary) secondary+ 28 28 0 24 52 

Mother’s work status 

(Ref. not working) working 9 2 7 43 52 

Mother’s BMI 

(Ref. thin) 

normal 15 15 0 36 51 

overweight/obese 31 31 0 20 51 

Mother’s age at birth 

(Ref. 18-34 years ) 

less than 18 years 24 0 24 28 52 

35 years and above 7 6 1 45 52 

Child’s birth order 

2 10 0 10 42 52 

3 14 2 12 38 52 

4 or more 20 1 19 32 52 

Sex 

(Ref. male) female 31 31 0 21 52 

Size at birth 

(Ref. small) 

average 37 37 0 14 51 

large 46 46 0 5 51 

Note: Armenia and Senegal did not have the mother’s weight variable and Columbia did not have the size at birth variable in the

regressions. 
39 

Table 5. Summary of WAZ linear regression for 52 DHS surveys 2005-2014 

 

Within significant 

coefficients 

Covariates Category Significant 

Positive 

coefficient 

Negative 

coefficient 

Not 

significant Total 

Locality by wealth 

(Ref. rural poor) 

rural non-poor 28 27 1 24 52 

urban poor 8 8 0 44 52 

urban non-poor 36 36 0 16 52 

Education father 

(Ref. none/primary) secondary+ 17 17 0 35 52 

Education mother 

(Ref. none/primary) secondary+ 29 29 0 23 52 

Mother’s work status 

(Ref. not working) working 4 2 2 48 52 

Mother’s BMI 

(Ref. thin) 

normal 42 42 0 9 51 

overweight/obese 47 47 0 4 51 

Mother’s age at birth 

(Ref. 18-34 years ) 

less than 18 years 21 0 21 31 52 

35 years and above 4 3 1 48 52 

Child’s birth order 

2 12 0 12 40 52 

3 16 1 15 36 52 

4 or more 23 0 23 29 52 

Sex 

(Ref. male) female 26 25 1 26 52 

Size at birth 

(Ref. small) 

average 46 46 0 5 51 

large 49 49 0 2 51 

Note: Armenia and Senegal did not have the mother’s weight variable and Columbia did not have the size at birth variable in the

regressions. 

Table 6. Summary of WHZ linear regression for 52 DHS surveys 2005-2014 

 

Within significant 

coefficients 

Covariates Category Significant 

Positive 

coefficient 

Negative 

coefficient 

Not 

significant Total 

Locality by wealth 

(Ref. rural poor) 

rural non-poor 10 7 3 42 52 

urban poor 4 2 2 48 52 

urban non-poor 11 10 1 41 52 

Education father 

(Ref. none/primary) secondary+ 3 3 0 49 52 

Education mother 

(Ref. none/primary) secondary+ 14 13 1 38 52 

Mother’s work status 

(Ref. not working) working 10 6 4 42 52 

Mother’s BMI 

(Ref. thin) 

normal 41 41 0 10 51 

overweight/obese 45 45 0 6 51 

Mother’s age at birth 

(Ref. 18-34 years ) 

less than 18 years 7 2 5 45 52 

35 years and above 3 1 2 49 52 

Child’s birth order 

2 11 2 9 41 52 

3 13 0 13 39 52 

4 or more 20 0 20 32 52 

Sex 

(Ref. male) female 10 10 0 42 52 

Size at birth 

(Ref. small) 

average 33 33 0 18 51 

large 45 45 0 6 51 

Note: Armenia and Senegal did not have the mother’s weight variable and Columbia did not have the size at birth variable in the

regressions. 

 
40 

In addition to the regressions above, another regression model was estimated for each of the 52 countries 

for the HAZ z-score by adding the cluster as a fixed effect categorical covariate in addition to the other 

covariates in the first regression. In this model, the locality by wealth variable was removed and replaced 

with only the wealth index, because clusters are either all urban or all rural. The resulting R-squared values 

of these regressions can then be used to observe how much of the SD in the HAZ z-score can be explained 

by the heterogeneity of children between clusters. Table 7 shows that the R-squared value increased 

substantially for most countries after the addition of the cluster variable. For Namibia, for example, the Rsquared value was 0.09 before adding the cluster variable and 0.61 after adding it. This adjusts the SD by a 

factor of 0.62 or the square root of one minus the R-squared value (ඥ(1 − ܴଶ)). The adjusted HAZ SD for 

Namibia becomes 0.90 compared with the HAZ SD of 1.44 before taking into account the cluster 

heterogeneity; this translates into a 38% reduction in the SD, implying that much of the variability was 

explained by the cluster characteristics. For the countries with the highest HAZ SD, such as Benin, Albania, 

Egypt and Nigeria (all with HAZ SDs above 2), the adjustment factor (ඥ(1 − ܴଶ)) was between 0.8 and 

0.9. For Albania and Egypt the percent reduction in the SD after removing the cluster heterogeneity was 

between 21% and 25% and for Benin and Nigeria the percent reduction in the SD was approximately 12%, 

indicating that the high SDs for these countries is not due to heterogeneity across clusters but rather to either 

within-cluster heterogeneity or data quality issues. Table 7 and Figure 16 show that after Namibia, the 

second and third highest percent reduction in the SDs were found in Peru and Colombia (28% and 27%, 

respectively) indicating that in these countries there is also a high level of heterogeneity between clusters 

in terms of height-for-age for children under five. The lowest percent reductions in the SDs were found in 

Senegal, Ethiopia, and Zimbabwe, all of which were below 8%. 
41 

Table 7. R-squared values of the HAZ regressions before and after including the cluster variable with the 

adjusted HAZ SD, 52 DHS surveys 2005-2014 

Country HAZ SD R2a R2b Sqrt (1-R2b)

Adjusted 

HAZ SD 

% reduction 

in SD 

Albania 2.08 0.05 0.43 0.75 1.57 24.50 

Armenia 1.68 0.03 0.31 0.83 1.39 16.93 

Azerbaijan 1.62 0.06 0.40 0.77 1.25 22.54 

Bangladesh 1.41 0.08 0.20 0.89 1.26 10.56 

Benin 2.34 0.01 0.23 0.88 2.05 12.25 

Bolivia 1.31 0.18 0.37 0.79 1.04 20.63 

Burkina Faso 1.59 0.04 0.19 0.90 1.43 10.00 

Burundi 1.42 0.10 0.27 0.85 1.22 14.56 

Cambodia 1.41 0.07 0.28 0.85 1.20 15.15 

Cameroon 1.67 0.09 0.25 0.87 1.45 13.40 

Colombia 1.15 0.05 0.47 0.73 0.84 27.20 

Comoros 1.90 0.05 0.21 0.89 1.69 11.12 

Congo Brazzaville 1.50 0.07 0.23 0.88 1.31 12.25 

Congo Democratic Republic 1.84 0.06 0.18 0.91 1.66 9.45 

Côte d’Ivoire 1.55 0.06 0.27 0.85 1.32 14.56 

Dominican Republic 1.21 0.06 0.27 0.85 1.04 14.56 

Egypt 2.01 0.01 0.38 0.79 1.58 21.26 

Ethiopia 1.76 0.04 0.15 0.92 1.62 7.80 

Gabon 1.53 0.11 0.30 0.84 1.28 16.33 

Gambia 1.54 0.08 0.23 0.88 1.35 12.25 

Ghana 1.64 0.06 0.34 0.81 1.33 18.76 

Guinea 1.81 0.04 0.23 0.88 1.58 12.25 

Guyana 1.47 0.16 0.46 0.73 1.08 26.52 

Haiti 1.42 0.07 0.24 0.87 1.24 12.82 

Honduras 1.23 0.22 0.40 0.77 0.96 22.54 

India 1.67 0.07 0.21 0.89 1.48 11.12 

Jordan 1.20 0.07 0.29 0.84 1.01 15.74 

Kenya 1.66 0.05 0.17 0.91 1.51 8.90 

Kyrgyz Republic 1.44 0.04 0.19 0.90 1.30 10.00 

Lesotho 1.55 0.05 0.34 0.81 1.26 18.76 

Liberia 1.62 0.08 0.25 0.87 1.40 13.40 

Malawi 1.58 0.05 0.27 0.85 1.35 14.56 

Maldives 1.41 0.06 0.25 0.87 1.22 13.40 

Mali 1.88 0.05 0.23 0.88 1.65 12.25 

Mozambique 1.61 0.05 0.20 0.89 1.44 10.56 

Namibia 1.44 0.09 0.61 0.62 0.90 37.55 

Nepal 1.37 0.10 0.27 0.85 1.17 14.56 

Niger 1.67 0.03 0.23 0.88 1.47 12.25 

Nigeria 2.00 0.09 0.23 0.88 1.75 12.25 

Pakistan 1.90 0.11 0.36 0.80 1.52 20.00 

Peru 1.08 0.28 0.49 0.71 0.77 28.59 

Rwanda 1.40 0.10 0.29 0.84 1.18 15.74 

São Tomé and Príncipe 1.76 0.09 0.23 0.88 1.54 12.25 

Senegal 1.37 0.05 0.14 0.93 1.27 7.26 

Sierra Leone 1.97 0.02 0.25 0.87 1.71 13.40 

Swaziland 1.43 0.06 0.30 0.84 1.19 16.33 

Tajikistan 1.60 0.03 0.17 0.91 1.46 8.90 

Tanzania 1.44 0.06 0.20 0.89 1.29 10.56 

Timor-Leste 1.88 0.02 0.19 0.90 1.69 10.00 

Uganda 1.54 0.07 0.35 0.81 1.24 19.38 

Zambia 1.74 0.03 0.16 0.92 1.59 8.35 

Zimbabwe 1.43 0.03 0.15 0.92 1.32 7.80 

R2a = R-squared values for the regression omitting the cluster variable 

R2b = R-squared values for the regression including the cluster variable 
42 

 

0.0 0.5 1.0 1.5 2.0 2.5

Benin

Albania

Egypt

Nigeria

Sierra Leone

Comoros

Pakistan

Mali

Timor-Leste

Congo Democratic...

Guinea

Ethiopia

São Tomé and Príncipe

Zambia

Armenia

Cameroon

Niger

India

Kenya

Ghana

Azerbaijan

Liberia

Mozambique

Tajikistan

Burkina Faso

Malawi

Côte d’Ivoire

Lesotho

Gambia

Uganda

Gabon

Congo Brazzaville

Guyana

Kyrgyz Republic

Tanzania

Namibia

Zimbabwe

Swaziland

Burundi

Haiti

Maldives

Cambodia

Bangladesh

Rwanda

Nepal

Senegal

Bolivia

Honduras

Dominican Republic

Jordan

Standard deviations

% reduction in SDHAZ SDadjusted HAZ SD

Figure 16. Standard deviations of height-for-age (HAZ) z-scores before and after adjusting for cluster heterogeneity with pe52 DHS surveys 2005-2014 
43 

4. Discussion 

Because it is difficult to obtain accurate measurements of the height/length and weight of young children 

in the field, all surveys are subject to measurement errors. To minimize anthropometric measurement errors 

the DHS Program has implemented procedures to reduce the occurrence of these errors and strengthen and 

improve the data quality control systems used in DHS surveys. The current study analyzes estimates of 10 

indicators of data quality that have been applied to anthropometric data from 52 DHS surveys conducted 

between 2005 and 2014. 

The prevalence of undernutrition varies substantially across the 52 countries included in the report, which 

highlights the complexity of measuring nutritional status. The surveys differ widely according to the 10 

data quality indicators used in this analysis. Several countries were identified (Columbia, Peru and 

Honduras) that consistently performed well across the data quality indicators, while other countries were 

identified (Benin and Albania) that consistently performed poorly. Although Benin has been included in 

this analysis, the nutrition data in the Benin DHS final report were suppressed because the country 

implementing organization decided that the data quality issues were too serious. 

It is generally expected that the standard deviations of the weight-for-height z-scores (WHZ) and the 

weight-for-age (WAZ) will be smaller than the standard deviations of the height-for-age (HAZ) z-scores 

because the latter is associated with measurement error in age and height that tend to have higher error than 

measurements of weight. In this study, the HAZ showed the greatest dispersion, with an SD ranging from 

1.08 to 2.33 compared to the standard deviations of the other two scores. The wide dispersion is partly due 

to heterogeneity but also reflects measurement error caused by the difficulty in measuring height/length of 

children under five years. Determining how much of the dispersion can be attributed to heterogeneity and 

how much to measurement error is an ongoing data quality issue. The only way to estimate the technical 

error of measurement (TEM) would be to measure children twice in the field. However, given the structure 

of DHS surveys, initiating a duplicate measurement procedure—particularly one involving independent 

measurements—would be difficult and impractical. The most that could reasonably be implemented would 

be re-measurement of a subset of children, but that would not give an accurate measure of the TEM for the 

entire survey. 

It is also expected that all or almost all of the individual z-score values should fall within the WHO limits. 

WHO has suggested that the quality of anthropometry data is questionable if more than 1% of values fall 

outside these limits (World Health Organization 1995). In this analysis, 12 of the 52 countries had 5 percent 

or more of cases flagged according to the WHO limits. These cases were flagged because of extreme values 

on any combination of age, weight, and length/height. It is difficult to identify the reason the values are 
44 

being flagged—that is, which of the three z-scores is/are out of range. In future analyses it would be useful 

to distinguish the source of the flag. 

It is expected that the SDs of sub-populations will be more homogeneous than the SDs of the whole 

population. To test this we ran a few additional tables on selected countries to investigate mean z-scores 

and SDs of various subgroups. Seven countries identified as good, average, and poor in data quality were 

part of this investigation. Figures 13-15 and Appendix F indicate that countries that were labeled as having 

poor data quality (Albania and Benin) had SDs that fluctuated by age much more than countries with good 

data quality (Columbia and Peru). Further, except for age (and two other covariates for Albania only), no 

other background variables included in the analysis had a major impact on SD variation. 

In the countries with poor and average data quality, the SDs of HAZ are largest for younger children and 

become tighter as the age of children increases. As suggested earlier, this pattern may be due to the difficulty 

of measuring the youngest children, who are lying down. Figures 8-9 and Appendix A support the inference 

that measurement of length of children is an issue in some DHS surveys. When z-scores are disaggregated 

for children under two years of age and children two years of age and over, a clear distinction in the SDs is 

seen across many countries. An alternative explanation for this pattern could be that a one-month error in 

the estimated age of a child will have a larger impact on the z-score for a 7-month-old child than for a child 

who is, say, 37 months of age. 

In the quest to further identify the source of variations in z-scores, the cluster id code was introduced in a 

regression model in addition to selected background characteristics. It is evident from Figure 16 (Table 7) 

that for many countries a high percentage of the variability in scores is between or across clusters. The 

cluster variation is likely due primarily to heterogeneity of the clusters. However, different interviewing 

teams work in different clusters; so, cluster variation can, conceivably, also be due to variations in the 

quality of the measurements taken by the different teams. Even in Peru, identified as a country with very 

good quality data, a large portion of the variation was explained by the cluster variable, implying 

heterogeneity across clusters. 

It is possible to simulate the effect of random error and/or bias in the measurements of age, height, and 

weight, and to estimate their impact on the means and SDs of the z-scores and on the estimates of the 

prevalence of stunting, underweight, overweight, and wasting. Obviously, random unbiased measurement 

error will tend to increase the SDs and the estimates of stunting, etc. Errors in the measurement of age will 

affect the HAZ and WAZ but not the WHZ; errors in the measurement of height will affect the HAZ and 

WHZ but not the WAZ; and errors in the measurement of weight will affect the WAZ and WHZ. We have 

carried out simulations of various potential patterns of measurement error but have not included them in 

this report. 
45 

5. Conclusion and Recommendations 

The anthropometry data presented in the majority of DHS surveys appears to be of good quality. However, 

a substantial minority of the surveys have levels of flagged cases or dispersion that suggest measurement 

error. It has been suggested by some that surveys with high-quality anthropometric measurements should 

have HAZ, WAZ, and WHZ measurements that are normally distributed with a standard deviation of one

at all levels of aggregation. However, most of the countries that conduct DHS surveys include subpopulations that are known to be malnourished, in which case genuine variability in nutritional status 

indicators may be confounded with measurement error. From the results presented in this report it is difficult 

to accept an assumption that the (true) z-scores in a population are normally distributed with a SD close to 

1.0. Certainly, for higher levels of aggregation, moving from clusters to regions to an entire country, the 

level of heterogeneity in terms of factors that affect nutrition will tend to increase, with a tendency for the 

SDs to increase. Measurement error will also tend to increase the SDs, leading to potential over-estimates 

of the prevalence of malnutrition. The risk of over-estimating the level of malnutrition must be weighed 

against the risk of under-estimating the level of malnutrition, which could result from over-editing the data. 

As in most decision-making situations, there are two complementary types of potential errors, false 

positives and false negatives. The consequences of these two types of error are very different. 

We have identified several DHS surveys with clear symptoms of measurement error. The Benin survey is 

the most egregious example and serves as a validation of the 10 selected indicators of misreporting. It must 

be emphasized that although this survey was included in our analysis, the nutrition data from the survey 

have not been released. Benin is the only country in which the anthropometric estimates on height/length 

and weight were not published in the final survey report because of data quality concerns. In the case of the 

Benin survey, the decision to suppress the nutrition results was made by the implementing agency in Benin, 

not by DHS or USAID. The policy of DHS is to make the data, including the computer files, publicly 

available so that users worldwide can carry out their own analyses, including adjustments, if they so choose. 

The Benin nutrition data are included in the publicly available DHS data sets. 

The WHO 1995 expert committee report recommended the inclusion of the following information when 

presenting anthropometry data: general characteristics of the population; sample size; measurement 

methods; method of determining age; percentage of excluded data; prevalence based on fixed cutoff; 

confidence intervals of the prevalence estimates; mean z-scores with 95% confidence intervals; SD of 

z-scores; and frequency distribution plots against the reference distribution. In DHS surveys all the 

indicators are included in the main survey reports except SDs of the z-scores and frequency distribution 
46 

plots. It should be recognized that this information is only a subset of the information that is needed to 

assess the overall quality of the data collected in a population-based survey. It is equally important to 

examine such factors as the quality of the sample design, whether a complete mapping and household listing 

operation is conducted, whether households are selected independently and not by the interviewing team, 

the implementation of a robust field monitoring plan at multiple levels, the extent and quality of secondary 

editing of the data, and the public availability of all the survey data and all the survey documentation. 

Well-defined and internationally accepted criteria to assess anthropometry data quality are needed. A 

monitoring and evaluation reference group (MERG) for nutrition would be useful for developing criteria to 

assess all the platforms collecting population-level anthropometry data. 

Regarding training in future surveys, an area where more emphasis and practice is desirable is the 

measurement of length in children under two years of age. DHS will continue to explore the possibility of 

using new types of equipment to measure the height/length of children, including digital and lightweight 

measuring boards. We also recommend that additional variables be created in the DHS recode data files to 

flag weight height/length and the three z-scores separately. 
47 

References 

Adair, L.S., and D.K. Guilkey. 1997. "Age-specific Determinants of Stunting in Filipino Children." The 

Journal of Nutrition 127(2):314-320. 

Corsi, D., and S. Subramanian. 2014. Report on the Quality of Anthropometric Data on Children’s Height 

and Weight in 100 Health and Nutrition Surveys in the West Central Africa Region. United 

Nations Children’s Fund. New York (forthcoming). 

Crowe, S., A. Seal, C. Grijalva-Eternod, and M. Kerac. 2014. "Effect of Nutrition Survey ‘Cleaning 

Criteria’ on Estimates of Malnutrition Prevalence and Disease Burden: Secondary Data 

Analysis." PeerJ 2:doi: 10.7717/peerj.380. 

de Onis, M., A.W. Onyango, E. Borghi, C. Garza, and H. Yang. 2006. "Comparison of the World Health 

Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO 

International Growth Reference: Implications for Child Health Programmes." Public Health 

Nutrition 9(7):942-947. 

Hancioglu, A., and F. Arnold. 2013. "Measuring Coverage in MNCH: Tracking Progress in Health for 

Women and Children using DHS and MICS Household Surveys." PLOS Medicine 10(5):107-114. 

ICF international. 2012. Measure DHS Biomarker Field Manual. ICF International. Calverton, Maryland, 

U.S.A. 

International Food Policy Research Institute. 2014. Global Nutrition Report 2014: Actions and 

Accountability to Accelerate the World’s Progress on Nutrition. Washington D.C. 

Jaysaekaran, D. 2012. Emergency Nutrition Assessment for Standardized Monitoring and Assessment of 

Relief and Transitions (ENA for SMART): Software User Manual ENA 2011. Available at 

http://smartmethodology.org/survey-planning-tools/smart-emergency-nutrition-assessment 

Mamabolo, R.L., M. Alberts, N.P. Steyn, H.A. Delemarre-van de Waal, and N.S. Levitt. 2005. 

"Prevalence and Determinants of Stunting and Overweight in 3-year-old Black South African 

Children Residing in the Central Region of Limpopo Province, South Africa." Public Health 

Nutrition 8(5):501-508. 

Mamiro, P.S., P. Kolsteren, D. Roberfroid, S. Tatala, A.S. Opsomer, and J.H. Van Camp. 2005. "Feeding 

Practices and Factors Contributing to Wasting, Stunting, and Iron-deficiency Anaemia Among 3-

23-month Old Children in Kilosa District, Rural Tanzania." Journal of Health, Population and 

Nutrition 23(3):222-230. 

Mei, Z., and L.M. Grummer-Strawn. 2007. "Standard Deviation of Anthropometric Z-scores as a Data 

Quality Assessment Tool Using the 2006 WHO Growth Standards: A Cross Country Analysis." 

Bulletin of the World Health Organization 85(6):441-448. 

Pullum, T.W. 2008. An Assessment of the Quality of Data on Health and Nutrition in the DHS Surveys, 

1993-2003. Methodological Reports No. 6. Calverton, Maryland, USA: Macro International Inc. 

Schmidt-Traub, G., E. Karoubi, J. Espey, and Sustainable Development Solutions Network. 2015. 

Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a 

Data Revolution for the SDGS. Sustainable Development Solutions Network. Available at 

http://unsdsn.org/wp-content/uploads/2015/03/150320-SDSN-Indicator-Report.pdf 

Sereebutra, P., N. Solomons, M.H. Aliyu, and P.E. Jolly. 2006. "Sociodemographic and Environmental 

Predictors of Childhood Stunting in Rural Guatemala." Nutrition Research 26(2):65-70. 

Siegel, J.S., D.A. Swanson, and H.S. Shryock. 2004. The Methods and Materials of Demography. 

Bingley, UK: Emerald Group. 

Standardized Monitoring and Assessment of Relief and Transitions (SMART). 2006. "Measuring 

Mortality, Nutritional Status, and Food Security in Crisis Situations: SMART Methodology." 

Version 1, April 2006. Available at http://pdf.usaid.gov/pdf_docs/Pnadi428.pdf 

Standardized Monitoring and Assessment of Relief and Transitions (SMART). 2015. ENA Software for 

SMART. Available at http://smartmethodology.org/survey-planning-tools/smart-emergencynutrition-assessment 
48 

Tanzania Food and Nutrition Centre. 2014. Tanzania National Nutrition Survey 2014. Dar-Es-Salaam, 

Tanzania: The United Republic of Tanzania, Ministry of Health and Social Welfare. 

United Nations. 2014. The Millennium Development Goals Report 2014: We Can End Poverty 2015. New 

York. Available at 

http://www.un.org/millenniumgoals/2014%20MDG%20report/MDG%202014%20English%20w

eb.pdf. 

Willey, B.A., N. Cameron, S.A. Norris, J.M. Pettifor, and P.L. Griffiths. 2009. "Socio-economic 

Predictors of Stunting in Preschool Children: A Population-based Study From Johannesburg and 

Soweto." SAMJ: South African Medical Journal 99(6):450-456. 

WHO Multicentre Growth Reference Study Group. 2006. WHO Child Growth Standards: Length/Heightfor-Age, Weight-for-Age, Weight-for-Length, Weight-for-Height and Body Mass Index-for-Age. 

Geneva, Switzerland: World Health Organization. 

World Health Organization. 1995. Physical Status: The Use of and Interpretation of Anthropometry, 

Report of a WHO Expert Committee. Geneva, Switzerland: WHO. 

World Health Organization. 2014a. Global Nutrition Targets 2025: Policy Brief Series. Available at 

http://apps.who.int/iris/bitstream/10665/149018/1/WHO_NMH_NHD_14.2_eng.pdf 

World Health Organization. 2014b. What is Measured Gets Done. Available at 

http://www.who.int/nutrition/globaltarget_indicators_flyer_en.pdf?ua=1 
49 

Appendix A 

Table A. Standard deviations of height-for-age z-scores (HAZ) for measured children under two years of age 

and measured children two years of age and over in 52 DHS surveys (2005-2014) 

 HAZ SD < 2 years HAZ SD ≥ 2 years 

Country mean SD mean SD difference 

Albania -0.3 2.3 -0.4 1.9 0.5 

Armenia -0.6 1.7 -0.9 1.6 0.0 

Azerbaijan -0.6 1.6 -1.4 1.5 0.1 

Bangladesh -1.4 1.5 -1.8 1.3 0.2 

Benin -1.1 2.6 -2.0 2.1 0.5 

Bolivia -0.9 1.4 -1.5 1.2 0.3 

Burkina Faso -0.9 1.7 -1.7 1.4 0.3 

Burundi -1.9 1.5 -2.4 1.3 0.2 

Cambodia -1.2 1.5 -2.0 1.2 0.3 

Cameroon -0.8 1.8 -1.6 1.5 0.3 

Colombia -0.7 1.2 -0.9 1.0 0.2 

Comoros -0.9 2.1 -1.3 1.7 0.4 

Congo Brazzaville -0.8 1.6 -1.2 1.3 0.3 

Congo Democratic Republic -1.0 1.9 -2.0 1.7 0.2 

Côte d’Ivoire -0.8 1.7 -1.5 1.5 0.2 

Dominican Republic -0.3 1.4 -0.3 1.2 0.2 

Egypt -0.4 2.2 -0.7 1.9 0.4 

Ethiopia -1.0 1.8 -2.1 1.5 0.4 

Gabon -0.6 1.6 -0.8 1.4 0.2 

Gambia -0.7 1.7 -1.3 1.4 0.4 

Ghana -0.5 1.8 -1.4 1.4 0.4 

Guinea -0.5 1.9 -1.6 1.6 0.3 

Guyana -0.8 1.5 -0.9 1.4 0.2 

Haiti -0.6 1.5 -1.2 1.3 0.2 

Honduras -0.8 1.2 -1.3 1.2 0.1 

India -1.5 1.8 -2.1 1.5 0.2 

Jordan -0.2 1.3 -0.5 1.1 0.3 

Kenya -1.2 1.8 -1.6 1.5 0.3 

Kyrgyz Republic -0.4 1.6 -1.1 1.2 0.4 

Lesotho -1.2 1.8 -1.8 1.3 0.5 

Liberia -0.7 1.8 -1.6 1.5 0.3 

Malawi -1.5 1.8 -2.0 1.4 0.4 

Maldives -0.9 1.6 -0.9 1.3 0.3 

Mali -0.9 2.0 -1.8 1.7 0.3 

Mozambique -1.4 1.8 -1.9 1.5 0.3 

Namibia -0.6 1.6 -1.4 1.2 0.3 

Nepal -1.2 1.5 -2.0 1.3 0.2 

Niger -1.3 1.7 -2.0 1.6 0.1 

Nigeria -0.9 2.2 -1.7 1.8 0.3 

Pakistan -1.4 1.8 -2.0 1.6 0.2 

Peru -1.0 1.1 -1.1 1.1 0.0 

Rwanda -1.4 1.6 -2.0 1.2 0.3 

São Tomé and Príncipe -1.4 2.0 -1.1 1.5 0.5 

Senegal -0.7 1.4 -1.1 1.3 0.1 

Sierra Leone -1.0 2.1 -1.6 1.8 0.3 

Swaziland -0.9 1.7 -1.5 1.3 0.5 

Tajikistan -0.8 1.8 -1.4 1.4 0.4 

Tanzania -1.4 1.6 -1.9 1.3 0.3 

Timor-Leste -1.7 2.2 -2.4 1.5 0.6 

Uganda -1.1 1.7 -1.7 1.4 0.3 

Zambia -1.3 1.9 -1.9 1.5 0.5 

Zimbabwe -1.0 1.6 -1.7 1.2 0.3 

51 

Appendix B 

Table B. Standard deviations of weight-for-height z-scores (WHZ) for measured children under two years of 

age and measured children two years of age and over in 52 DHS surveys (2005-2014) 

 WHZ SD < 2 years WHZ SD ≥ 2 years 

Country mean SD mean SD difference 

Albania 0.5 2.0 0.6 1.8 0.2 

Armenia 0.6 1.5 0.7 1.5 0.0 

Azerbaijan 0.0 1.6 0.5 1.4 0.1 

Bangladesh -0.8 1.4 -1.1 1.1 0.3 

Benin -0.1 2.0 0.1 2.0 0.0 

Bolivia 0.5 1.2 0.7 1.0 0.2 

Burkina Faso -1.1 1.5 -0.4 1.2 0.2 

Burundi -0.4 1.3 -0.1 1.0 0.3 

Cambodia -0.7 1.2 -0.7 1.0 0.2 

Cameroon 0.0 1.5 0.3 1.2 0.3 

Colombia 0.3 1.0 0.3 1.0 0.1 

Comoros -0.2 1.7 -0.1 1.5 0.2 

Congo Brazzaville -0.3 1.3 -0.1 1.1 0.1 

Congo Democratic Republic -0.3 1.4 -0.1 1.2 0.2 

Côte d’Ivoire -0.4 1.4 0.0 1.1 0.3 

Dominican Republic 0.4 1.3 0.2 1.1 0.1 

Egypt 0.3 1.7 0.4 1.6 0.1 

Ethiopia -0.6 1.3 -0.4 1.1 0.2 

Gabon 0.3 1.4 0.2 1.2 0.2 

Gambia -0.6 1.5 -0.6 1.1 0.4 

Ghana -0.6 1.5 0.0 1.2 0.3 

Guinea -0.6 1.5 -0.1 1.2 0.3 

Guyana 0.0 1.4 0.0 1.2 0.2 

Haiti -0.2 1.3 -0.1 1.1 0.3 

Honduras 0.3 1.1 0.3 1.0 0.1 

India -1.1 1.4 -1.0 1.2 0.3 

Jordan 0.3 1.2 0.1 1.0 0.2 

Kenya 0.0 1.4 -0.2 1.2 0.3 

Kyrgyz Republic 0.5 1.3 0.4 1.1 0.2 

Lesotho 0.2 1.5 0.2 1.1 0.4 

Liberia -0.5 1.3 0.0 1.1 0.3 

Malawi 0.2 1.5 0.3 1.1 0.4 

Maldives -0.3 1.4 -0.6 1.4 0.1 

Mali -0.8 1.5 -0.4 1.3 0.2 

Mozambique 0.0 1.6 0.3 1.2 0.4 

Namibia -0.5 1.3 0.0 1.1 0.2 

Nepal -0.8 1.3 -0.6 1.0 0.2 

Niger -1.0 1.6 -0.8 1.2 0.3 

Nigeria -0.9 1.6 -0.5 1.5 0.1 

Pakistan -0.7 1.4 -0.4 1.2 0.3 

Peru 0.6 1.1 0.5 1.0 0.1 

Rwanda 0.2 1.3 0.4 1.0 0.3 

São Tomé and Príncipe 0.3 1.9 -0.2 1.5 0.4 

Senegal -0.4 1.2 -0.7 1.0 0.2 

Sierra Leone -0.1 1.7 0.1 1.4 0.2 

Swaziland 0.6 1.4 0.6 1.1 0.3 

Tajikistan -0.4 1.6 0.0 1.3 0.3 

Tanzania 0.0 1.4 0.0 1.1 0.4 

Timor-Leste -0.6 1.8 -0.9 1.4 0.4 

Uganda -0.2 1.3 0.1 1.0 0.3 

Zambia 0.1 1.5 0.3 1.2 0.3 

Zimbabwe 0.1 1.3 0.2 1.0 0.3 

53 

Appendix C 

Table C. Heaping indices for height and weight measurements in 52 DHS surveys (2005-2014) 

 Height Weight 

Country Myers’ Index excess ratio Myers’ Index excess ratio 

Albania 13.7 4.9 1.3 7.1 -0.2 1.0 

Armenia 36.0 35.5 2.8 16.4 14.8 1.7 

Azerbaijan 13.6 5.8 1.3 7.2 -0.8 1.0 

Bangladesh 7.6 1.1 1.1 2.0 0.0 1.0 

Benin 48.5 48.5 3.4 17.4 16.6 1.8 

Bolivia 16.9 16.1 1.8 7.0 5.8 1.3 

Burkina Faso 15.9 13.2 1.7 3.1 0.2 1.0 

Burundi 27.9 27.6 2.4 6.6 3.9 1.2 

Cambodia 20.0 17.3 1.9 3.6 2.3 1.1 

Cameroon 8.4 -0.7 1.0 1.7 -0.9 1.0 

Colombia 9.6 8.0 1.4 2.0 0.9 1.0 

Comoros 23.4 20.2 2.0 8.7 1.1 1.1 

Congo Brazzaville 12.2 7.9 1.4 2.8 0.8 1.0 

Congo Democratic Republic 12.1 7.2 1.4 4.8 1.1 1.1 

Côte d’Ivoire 18.7 17.7 1.9 3.7 1.7 1.1 

Dominican Republic 11.8 7.1 1.4 1.7 -0.1 1.0 

Egypt 19.7 6.8 1.3 9.1 3.1 1.2 

Ethiopia 8.2 4.3 1.2 3.4 1.3 1.1 

Gabon 14.6 12.5 1.6 4.0 1.1 1.1 

Gambia 15.9 12.9 1.9 5.5 1.5 1.1 

Ghana 13.8 12.0 1.6 2.9 -0.4 1.0 

Guinea 2.3 0.1 1.0 2.3 0.1 1.0 

Guyana 24.3 24.3 2.2 6.8 3.7 1.2 

Haiti 13.7 10.1 1.5 3.3 0.8 1.0 

Honduras 10.1 8.0 1.4 1.6 -0.6 1.0 

India 15.6 12.2 1.6 2.4 1.1 1.1 

Jordan 15.1 15.1 1.8 4.1 0.7 1.0 

Kenya 14.3 14.3 1.7 2.2 0.2 1.0 

Kyrgyz Republic 17.5 15.8 1.8 6.8 5.6 1.3 

Lesotho 14.0 12.3 1.6 2.3 1.9 1.1 

Liberia 14.0 6.3 1.3 3.9 2.6 1.1 

Malawi 31.8 31.8 2.6 4.5 4.0 1.2 

Maldives 31.9 31.9 2.6 3.1 2.0 1.1 

Mali 15.6 13.6 1.7 4.5 4.2 1.2 

Mozambique 22.2 21.2 2.1 3.5 0.8 1.0 

Namibia 33.6 33.5 2.7 5.7 3.0 1.2 

Nepal 10.0 7.7 1.4 2.6 -0.3 1.0 

Niger 28.5 28.1 2.4 5.0 2.3 1.1 

Nigeria 18.1 13.2 1.7 4.6 0.6 1.0 

Pakistan 16.3 11.2 1.6 3.3 1.6 1.1 

Peru 4.4 0.3 1.0 2.1 -0.5 1.0 

Rwanda 20.7 19.6 2.0 3.0 0.4 1.0 

São Tomé and Príncipe 78.7 74.0 4.7 73.5 72.7 4.6 

Senegal 14.2 10.2 1.5 3.0 1.2 1.1 

Sierra Leone 24.1 21.7 2.1 11.4 5.1 1.3 

Swaziland 8.6 4.9 1.2 2.9 0.4 1.0 

Tajikistan 25.8 24.6 2.2 6.1 3.7 1.2 

Tanzania 20.3 19.7 2.0 2.1 1.5 1.1 

Timor-Leste 22.6 18.9 1.9 5.3 2.9 1.1 

Uganda 13.7 12.7 1.6 2.7 0.6 1.0 

Zambia 25.6 24.8 2.2 3.1 1.7 1.1 

Zimbabwe 11.6 9.2 1.5 1.2 0.9 1.0 

55 

Appendix D 

Table D. Myers’ heaping Index of age in 52 DHS surveys (2005-2014) 

Country Myers’ Index 

Albania 7.6 

Armenia 5.2 

Azerbaijan 4.8 

Bangladesh 5.3 

Benin 5.3 

Bolivia 4.6 

Burkina Faso 3.0 

Burundi 3.2 

Cambodia 5.1 

Cameroon 4.5 

Colombia 5.1 

Comoros 7.4 

Congo Brazzaville 6.0 

Congo Democratic Republic 5.5 

Côte d’Ivoire 5.1 

Dominican Republic 8.3 

Egypt 7.3 

Ethiopia 3.6 

Gabon 6.0 

Gambia 3.7 

Ghana 7.0 

Guinea 11.7 

Guyana 5.5 

Haiti 4.8 

Honduras 4.6 

India 4.7 

Jordan 5.5 

Kenya 6.6 

Kyrgyz Republic 4.2 

Lesotho 5.6 

Liberia 5.7 

Malawi 6.5 

Maldives 6.3 

Mali 10.4 

Mozambique 3.3 

Namibia 3.7 

Nepal 7.1 

Niger 12.0 

Nigeria 4.4 

Pakistan 4.3 

Peru 5.0 

Rwanda 6.2 

São Tomé and Príncipe 7.0 

Senegal 3.0 

Sierra Leone 9.0 

Swaziland 4.5 

Tajikistan 2.8 

Tanzania 4.7 

Timor-Leste 7.0 

Uganda 4.0 

Zambia 2.2 

Zimbabwe 4.4 

57 

Appendix E 

Table E. WHO flags and height out of plausible limits in 52 DHS surveys (2005-2014) 

Country Flagged cases Height out of plausible limits 

Albania 11.4% 2.7% 

Armenia 5.0% 0.6% 

Azerbaijan 6.2% 0.0% 

Bangladesh 2.1% 0.5% 

Benin 14.6% 3.1% 

Bolivia 1.0% 0.1% 

Burkina Faso 2.5% 0.5% 

Burundi 1.0% 0.2% 

Cambodia 2.2% 0.2% 

Cameroon 2.2% 0.3% 

Colombia 0.3% 0.0% 

Comoros 9.0% 2.4% 

Congo Brazzaville 1.0% 0.0% 

Congo Democratic Republic 3.5% 0.5% 

Côte d’Ivoire 2.3% 0.4% 

Dominican Republic 1.3% 0.2% 

Egypt 7.5% 1.8% 

Ethiopia 1.9% 0.6% 

Gabon 2.5% 0.7% 

Gambia 4.6% 3.3% 

Ghana 3.9% 2.1% 

Guinea 3.3% 0.8% 

Guyana 4.9% 2.7% 

Haiti 1.0% 0.2% 

Honduras 0.4% 0.0% 

India 3.6% 0.8% 

Jordan 0.7% 0.3% 

Kenya 2.6% 1.0% 

Kyrgyz Republic 1.3% 0.1% 

Lesotho 2.3% 1.1% 

Liberia 2.4% 0.5% 

Malawi 3.9% 6.4% 

Maldives 2.1% 0.5% 

Mali 5.1% 1.8% 

Mozambique 3.1% 0.6% 

Namibia 2.3% 10.0% 

Nepal 0.9% 2.8% 

Niger 4.4% 0.8% 

Nigeria 8.0% 1.4% 

Pakistan 6.7% 2.2% 

Peru 0.2% 3.0% 

Rwanda 1.0% 0.1% 

São Tomé and Príncipe 7.9% 1.4% 

Senegal 2.4% 0.5% 

Sierra Leone 8.1% 4.0% 

Swaziland 2.3% 6.3% 

Tajikistan 4.5% 0.6% 

Tanzania 1.7% 0.4% 

Timor-Leste 6.1% 0.8% 

Uganda 1.3% 0.4% 

Zambia 3.9% 0.8% 

Zimbabwe 1.8% 0.5% 

Average 3.6% 1.4% 

Appendix F 

Table F. Mean and standard deviations of the z-scores by background variables in 7 DHS countries (2005-2014) 

Benin Albania Sierra Leone India Rwanda 

HAZ WAZ WHZ HAZ WAZ WHZ HAZ WAZ WHZ HAZ WAZ WHZ HAZ WAZ WHZ HAZ 

Variable Category mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD Age of child in months 

0-11 -0.60 2.51 -0.58 1.57 -0.13 2.13 -0.47 2.45 -0.25 1.44 0.25 2.31 -0.69 1.99 -0.57 1.55 -0.04 1.79 -0.90 1.74 -1.47 1.33 -1.16 1.58 -0.81 1.55 -0.43 1.23 0.21 1.50 -0.53 1.21 12-23 -1.38 2.53 -0.77 1.52 -0.09 1.90 -0.26 2.22 0.37 1.41 0.68 1.69 -1.25 2.16 -0.81 1.47 -0.26 1.54 -2.02 1.62 -1.78 1.27 -1.07 1.33 -1.88 1.40 -0.77 1.12 0.20 1.18 -0.93 1.17 24-35 -1.81 2.34 -0.93 1.56 0.06 1.96 -0.40 2.16 0.17 1.21 0.53 1.74 -1.67 2.10 -0.82 1.42 0.13 1.44 -2.16 1.61 -1.87 1.23 -0.96 1.19 -2.05 1.32 -0.80 1.03 0.45 1.06 -0.95 1.10 36-47 -1.89 2.03 -1.05 1.40 0.08 1.94 -0.46 1.89 0.21 1.23 0.71 1.81 -1.63 1.69 -0.88 1.26 0.10 1.37 -2.14 1.53 -1.89 1.16 -0.93 1.18 -2.03 1.13 -0.81 0.88 0.54 1.02 -0.87 1.04 48-59 -2.10 1.86 -1.16 1.32 0.20 2.12 -0.44 1.57 0.15 1.24 0.63 1.78 -1.64 1.52 -0.94 1.08 0.10 1.37 -2.02 1.45 -1.87 1.11 -0.98 1.14 -1.93 1.15 -0.99 0.96 0.30 1.01 -0.84 1.02 

Sex of child male -1.67 2.32 -1.00 1.49 -0.06 2.03 -0.26 2.00 0.12 1.34 0.42 1.88 -1.41 1.97 -0.85 1.40 -0.06 1.55 -1.88 1.65 -1.76 1.23 -1.03 1.30 -1.86 1.39 -0.82 1.06 0.34 1.19 -0.85 1.14 

female -1.45 2.33 -0.79 1.49 0.11 1.99 -0.56 2.03 0.18 1.27 0.74 1.81 -1.33 1.94 -0.75 1.36 0.06 1.49 -1.85 1.66 -1.81 1.23 -1.01 1.27 -1.65 1.39 -0.71 1.06 0.35 1.15 -0.79 1.10 

Birth order of child 

1 -1.57 2.32 -0.82 1.49 0.14 1.99 -0.18 1.98 0.20 1.38 0.46 1.81 -1.24 2.00 -0.71 1.43 0.04 1.52 -1.64 1.58 -1.57 1.22 -0.91 1.30 -1.55 1.38 -0.59 0.98 0.42 1.17 -0.71 1.12 2 -1.51 2.34 -0.88 1.50 0.02 1.99 -0.52 2.13 0.25 1.24 0.83 1.84 -1.45 1.89 -0.82 1.32 0.04 1.51 -1.75 1.60 -1.68 1.21 -0.97 1.30 -1.69 1.36 -0.68 1.07 0.40 1.16 -0.81 1.08 3 -1.48 2.40 -0.83 1.53 0.06 2.05 -0.47 1.91 0.10 1.21 0.55 1.79 -1.24 1.98 -0.80 1.38 -0.10 1.58 -1.96 1.66 -1.87 1.18 -1.06 1.28 -1.84 1.41 -0.84 1.05 0.31 1.18 -0.87 1.12 4+ -1.62 2.29 -0.98 1.46 -0.05 2.01 -0.62 1.99 -0.15 1.36 0.30 2.04 -1.43 1.94 -0.83 1.38 0.01 1.50 -2.19 1.74 -2.08 1.22 -1.17 1.26 -1.88 1.39 -0.88 1.09 0.29 1.17 -1.10 1.15 

Perceived size of child at birth 

small -1.67 2.27 -0.92 1.46 0.08 2.07 -0.51 2.26 -0.03 1.16 0.48 1.79 -1.63 1.93 -1.11 1.44 -0.22 1.53 -2.05 1.65 -2.06 1.20 -1.25 1.30 -2.06 1.36 -1.14 1.05 0.09 1.16 na na average -1.59 2.37 -0.91 1.52 0.03 2.02 -0.51 1.96 0.12 1.28 0.60 1.82 -1.39 1.99 -0.76 1.37 0.08 1.49 -1.83 1.66 -1.73 1.21 -0.97 1.28 -1.74 1.40 -0.81 1.04 0.26 1.14 na na large -1.38 2.20 -0.76 1.39 0.06 1.97 0.06 2.09 0.37 1.43 0.51 2.02 -1.26 1.92 -0.74 1.34 0.00 1.53 -1.77 1.65 -1.66 1.24 -0.93 1.27 -1.67 1.38 -0.60 1.04 0.49 1.17 na na 

Locality urban -1.46 2.32 -0.81 1.49 0.07 1.99 -0.36 2.07 0.16 1.27 0.54 1.90 -0.99 2.02 -0.57 1.41 0.03 1.52 -1.56 1.66 -1.48 1.25 -0.85 1.35 -1.14 1.50 -0.43 1.09 0.30 1.25 -0.75 1.11 

rural -1.62 2.33 -0.95 1.49 -0.01 2.02 -0.44 1.98 0.15 1.32 0.61 1.82 -1.48 1.92 -0.87 1.36 -0.01 1.52 -1.97 1.64 -1.88 1.20 -1.07 1.26 -1.84 1.36 -0.81 1.05 0.35 1.16 -1.00 1.13 

Wealth 

lowest -1.76 2.41 -1.08 1.49 -0.08 2.03 -0.89 2.07 -0.02 1.41 0.78 1.85 -1.51 2.09 -0.82 1.42 0.10 1.53 -2.29 1.69 -2.18 1.20 -1.23 1.29 -2.05 1.38 -0.93 1.09 0.36 1.20 -1.10 1.17 

second -1.72 2.30 -1.01 1.53 -0.01 2.10 -0.33 1.89 0.14 1.14 0.46 1.77 -1.55 1.84 -0.99 1.28 -0.12 1.50 -2.07 1.64 -1.99 1.18 -1.13 1.25 -1.96 1.35 -0.93 1.03 0.28 1.22 -0.83 1.08 middle -1.49 2.42 -0.84 1.52 0.05 2.03 -0.31 2.04 0.24 1.31 0.62 1.85 -1.41 1.86 -0.80 1.40 0.02 1.53 -1.89 1.60 -1.78 1.16 -1.00 1.27 -1.84 1.30 -0.78 1.05 0.39 1.12 -0.77 1.10 fourth -1.51 2.28 -0.90 1.45 -0.02 1.95 -0.47 1.89 0.10 1.24 0.53 1.82 -1.18 1.93 -0.71 1.43 -0.02 1.54 -1.61 1.55 -1.54 1.15 -0.89 1.27 -1.67 1.29 -0.72 1.00 0.34 1.10 -0.68 1.08 highest -1.33 2.22 -0.66 1.42 0.17 1.93 0.03 2.10 0.31 1.37 0.48 1.98 -0.94 2.01 -0.53 1.32 0.05 1.47 -1.10 1.50 -1.08 1.16 -0.67 1.30 -1.08 1.47 -0.35 1.04 0.37 1.21 -0.52 1.07 

Education of mother 

none/primary -1.58 2.35 -0.93 1.49 0.00 2.02 -0.52 2.02 0.09 1.27 0.58 1.84 -1.41 1.96 -0.83 1.37 0.00 1.51 -2.12 1.66 -2.01 1.20 -1.12 1.28 -1.83 1.35 -0.81 1.05 0.35 1.16 -1.08 1.12 secondary+ -1.44 2.21 -0.70 1.48 0.20 1.97 -0.20 2.01 0.27 1.36 0.58 1.87 -1.15 1.92 -0.65 1.40 0.03 1.55 -1.43 1.55 -1.39 1.18 -0.83 1.28 -0.94 1.57 -0.29 1.08 0.35 1.24 -0.73 1.10 

Education of partner 

none/primary -1.64 2.37 -0.95 1.52 0.01 2.03 -0.59 2.01 0.08 1.29 0.62 1.81 -1.42 1.93 -0.82 1.39 0.00 1.52 -2.16 1.68 -2.04 1.21 -1.14 1.29 -1.84 1.35 -0.82 1.05 0.35 1.16 -1.01 1.14 secondary+ -1.42 2.20 -0.76 1.42 0.10 1.93 -0.19 2.01 0.24 1.31 0.53 1.91 -1.30 1.96 -0.72 1.30 0.07 1.53 -1.64 1.60 -1.58 1.20 -0.92 1.28 -1.18 1.52 -0.41 1.08 0.36 1.19 -0.72 1.09 

Mother’s working status 

not working -1.68 2.46 -0.96 1.56 0.04 2.01 -0.48 2.02 0.12 1.32 0.59 1.87 -1.21 2.00 -0.72 1.50 0.00 1.64 -1.79 1.66 -1.71 1.23 -0.98 1.31 -1.69 1.42 -0.75 1.12 0.32 1.21 -0.84 1.11 working -1.51 2.28 -0.87 1.46 0.01 2.01 -0.13 2.00 0.27 1.26 0.54 1.80 -1.42 1.94 -0.82 1.34 0.00 1.48 -2.05 1.63 -1.95 1.20 -1.11 1.22 -1.78 1.38 -0.77 1.04 0.35 1.16 -0.80 1.13 

Mother’s BMI 

thin -1.96 2.22 -1.25 1.48 -0.19 1.90 -1.35 2.14 -0.13 0.96 0.97 1.86 -1.62 1.86 -1.11 1.36 -0.24 1.53 -2.07 1.61 -2.06 1.16 -1.26 1.23 -1.94 1.31 -1.24 1.06 -0.20 1.25 -0.96 1.09 normal -1.61 2.35 -0.96 1.50 -0.02 2.01 -0.40 2.03 0.11 1.36 0.50 1.86 -1.36 1.98 -0.79 1.37 0.01 1.47 -1.75 1.66 -1.64 1.21 -0.90 1.28 -1.83 1.38 -0.84 1.03 0.30 1.15 -0.87 1.10 overweight/ obese -1.36 2.30 -0.69 1.43 0.17 2.02 -0.41 1.98 0.18 1.21 0.65 1.85 -1.29 1.88 -0.66 1.31 0.14 1.62 -1.26 1.53 -1.06 1.18 -0.50 1.24 -1.39 1.43 -0.38 1.08 0.60 1.17 -0.75 1.12 

Age of mother at child’s birth 

<18 -2.03 2.15 -1.15 1.54 0.03 2.11 -0.75 1.92 -0.06 1.17 0.47 1.19 -1.65 1.87 -1.02 1.37 -0.08 1.52 -2.18 1.56 -1.98 1.16 -1.02 1.30 -2.14 1.48 -0.81 0.98 0.59 1.02 -0.92 1.14 18-34 -1.52 2.34 -0.89 1.48 0.00 2.01 -0.39 2.04 0.18 1.30 0.60 1.82 -1.33 1.97 -0.78 1.37 0.00 1.53 -1.83 1.66 -1.75 1.23 -1.01 1.29 -1.73 1.39 -0.74 1.05 0.37 1.17 -0.82 1.11 35+ -1.68 2.32 -0.90 1.55 0.13 2.00 -0.55 1.86 -0.07 1.32 0.36 2.22 -1.42 1.93 -0.82 1.42 0.03 1.47 -2.07 1.77 -2.01 1.31 -1.17 1.28 -1.84 1.38 -0.89 1.10 0.25 1.19 -0.78 1.15 

Total -1.56 2.33 -0.90 1.49 0.02 2.01 -0.41 2.02 0.15 1.30 0.58 1.85 -1.37 1.95 -0.80 1.38 0.00 1.52 -1.87 1.66 -1.78 1.23 -1.02 1.29 -1.76 1.39 -0.77 1.06 0.35 1.17 -0.82 1.12 na = Information not available 

Appendix G 

Table G. HAZ linear regressions for 52 DHS surveys (2005-2014) 

Variable Category Albania Armenia Azerbaijan Bangladesh Benin Bolivia 

Burkina 

Faso Burundi Cambodia Cameroon Colombia Comoros 

Congo 

Brazzaville 

Congo 

Democratic 

Republic Côte d’

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coeffic

Locality by wealth (ref. rural poor) 

rural nonpoor 0.27 -0.28 0.20 0.20*** 0.06 0.34*** 0.04 0.22*** 0.21** 0.42*** 0.01 0.24 0.24* 0.05 0.2urban poor -0.12 0.06 0.07 -0.07 0.07 0.30*** 0.19 -0.28 -0.02 0.13 0.05 0.34 0.21* -0.12 0.5urban nonpoor -0.05 0.06 0.53*** 0.33*** 0.11 0.69*** 0.35*** 0.74*** 0.42*** 0.48*** 0.18*** 0.32 0.48*** 0.38*** 0.5

Education of partner (ref. none/primary) secondary+ 0.32 -0.22 0.04 0.25*** 0.12 0.16*** 0.17 0.06 0.26*** 0.19** 0.10** -0.06 0.06 0.30*** -0.0

Education of mother (ref. none/primary) secondary+ 0.11 0.08 0.77 0.18*** 0.01 0.28*** 0.49*** 0.67*** 0.03 0.28*** 0.18*** 0.46** 0.26* 0.26** 0.2

Mother’s working status 

(ref. not working) working 0.31 -0.01 -0.03 -0.00 0.18* -0.13*** 0.16** -0.04 -0.22*** -0.15* -0.01 0.23 -0.18 -0.29*** -0.0Mother’s BMI (ref. thin) 

normal 0.91 na -0.35 0.21*** 0.37* 0.11 0.33*** 0.16 0.10 0.26 0.11 0.39 0.27* -0.15 0.2overweight/ obese 0.89 na -0.22 0.46*** 0.59*** 0.22 0.44*** 0.39** 0.08 0.49** 0.28*** 0.65 0.37* 0.14 0.3

Age of mother at child’s 

birth (ref. 18-34) 

<18 -0.27 -0.32 0.05 -0.23*** -0.41* -0.05 -0.33** 0.06 -0.04 -0.43*** -0.23*** -0.68* -0.15 -0.36* -0.435+ -0.12 -0.12 -0.21 -0.03 -0.19 0.07 0.24*** 0.07 0.06 0.06 0.14** -0.17 0.19 0.20 -0.0

Birth order of child (ref. 1) 

2 -0.44* -0.43*** 0.07 -0.08 0.03 -0.15** 0.04 -0.10 0.03 -0.10 -0.15*** -0.03 0.04 -0.01 -0.13 -0.35* -0.26 0.07 -0.14** 0.00 -0.21*** 0.10 -0.23* -0.13 -0.20* -0.16*** -0.08 0.05 -0.06 -0.24+ -0.54* -0.61 -0.17 -0.19** -0.12 -0.31*** -0.04 -0.21* -0.23* -0.17 -0.34*** -0.08 0.03 -0.07 -0.1

Sex of child (ref. male) female -0.18 0.17 0.24* -0.01 0.26*** 0.11** 0.18*** 0.28*** 0.03 0.12 0.06* 0.25* -0.04 0.25*** 0.1

Perceived size of child at birth (ref. small) 

average 0.06 0.51** 0.56*** 0.31*** 0.02 0.29*** 0.24** 0.40*** 0.32** 0.09 na 0.03 0.30* 0.15 0.1large 0.60 0.64** 0.58*** 0.43*** 0.21 0.75*** 0.39*** 0.53*** 0.52*** 0.38*** na -0.23 0.48*** 0.42*** 0.3

Observations 1,211 1,225 1,429 7,027 5,814 6,406 5,573 2,736 3,210 3,880 12,757 1,862 3,270 6,064 2,21R-squared 0.05 0.03 0.06 0.08 0.01 0.18 0.04 0.10 0.07 0.09 0.05 0.05 0.07 0.06 0.0

61 
Table G. − Continued

Variable Category Gabon Gambia Ghana Guinea Guyana Guinea Guyana Haiti Honduras India Jordan Kenya 

Kyrgyz 

Republic Lesotho Liberia M

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coe

Locality by wealth (ref. rural poor) 

rural nonpoor 0.38 0.02 0.11 0.01 0.59*** 0.01 0.59*** 0.29** 0.52*** 0.25*** 0.27*** 0.13 0.13 0.14 0.17 0urban poor 0.30** 0.35* 0.01 0.16 0.51** 0.16 0.51** -0.11 0.28*** 0.04 0.11 0.47 0.07 0.18 -0.14 -0urban nonpoor 0.73*** 0.37*** 0.32* 0.58*** 0.31 0.58*** 0.31 0.34*** 0.54*** 0.36*** 0.49*** 0.21 0.14 0.16 0.16 0

Education of partner (ref. none/primary) secondary+ 0.01 0.16 0.05 0.09 -0.00 0.09 -0.00 0.06 0.16*** 0.10*** 0.19* 0.28** -0.96 0.26* 0.09 0

Education of mother (ref. none/primary) secondary+ 0.20* 0.39** 0.09 0.47* 0.40* 0.47* 0.40* 0.20* 0.27*** 0.32*** 0.16 0.14 -0.13 0.07 0.28* 0

Mother’s working status 

(ref. not working) working 0.02 -0.22** -0.01 0.13 0.20 0.13 0.20 -0.12 -0.05 -0.08** 0.11 -0.01 0.03 -0.00 -0.18 -0Mother’s BMI (ref. thin) 

normal 0.18 0.03 -0.10 0.30* 0.19 0.30* 0.19 0.33* 0.12 0.21*** 0.27 0.20* 0.15 0.33 0.56* 0Overweight/ obese 0.44* 0.07 0.38* 0.36* 0.52** 0.36* 0.52** 0.45** 0.30** 0.39*** 0.29* 0.38*** 0.16 0.55* 0.90*** 0

Age of mother at child’s 

birth (ref. 18-34) 

<18 -0.36 0.14 -0.84** -0.18 -0.29 -0.18 -0.29 -0.46** -0.18*** -0.36*** -0.13 -0.13 0.19 0.25 -0.49* 035+ 0.36* -0.09 0.07 0.11 0.21 0.11 0.21 -0.02 0.03 0.06 0.05 0.05 0.20 -0.01 0.07 -0

Birth order of child (ref. 1) 

2 -0.00 0.23 0.05 -0.03 0.03 -0.03 0.03 -0.21** -0.15*** -0.13*** -0.00 -0.04 -0.11 -0.08 -0.16 03 -0.03 0.34* -0.17 0.05 0.08 0.05 0.08 -0.20* -0.21*** -0.23*** -0.27** -0.00 -0.08 0.04 -0.35 04+ -0.33* 0.32* 0.08 0.05 -0.25 0.05 -0.25 -0.27** -0.34*** -0.34*** -0.12 -0.17 -0.33*** -0.07 -0.27 0

Sex of child (ref. male) female 0.24* 0.18* 0.12 0.19* 0.05 0.19* 0.05 0.08 0.18*** 0.04 0.05 0.11 0.16** 0.37*** 0.31** 0

Perceived size of child at birth (ref. small) 

average 0.40** 0.13 0.28* 0.24 0.34* 0.24 0.34* 0.11 0.47*** 0.17*** 0.46*** 0.37*** 0.51*** 0.40*** 0.05 0large 0.57*** 0.50*** 0.41*** 0.34* 0.63*** 0.34* 0.63*** 0.28*** 0.84*** 0.21*** 0.60*** 0.55*** 0.84*** 0.56*** 0.47** 0

Observations 1,900 2,491 1,910 2,519 1,054 2,519 1,054 3,324 8,598 35,933 5,443 4,127 3,519 1,482 2,371 3R-squared 0.11 0.08 0.06 0.04 0.16 0.04 0.16 0.07 0.22 0.07 0.07 0.05 0.04 0.05 0.08 0

Variable Category Namibia Nepal Niger Nigeria Pakistan Peru Rwanda 

São Tomé 

and Príncipe Senegal Sierra Leone Swaziland Tajikistan Tanzania Timor-Le

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficie

Locality by wealth (ref. rural poor) 

rural non-poor -0.12 0.37*** 0.06 0.50*** 0.49*** 0.28* 0.29*** 0.56** 0.34*** 0.18 0.24* 0.16* 0.14* 0.04 urban poor -0.15 0.49* -0.85 0.44*** 0.25 0.33*** 0.28 -0.18 0.29** 0.44** 0.43* 0.15 -0.08 -0.08 urban nonpoor 0.30 0.61*** 0.28** 0.56*** 0.35** 0.76*** 0.80*** 0.31 0.33** 0.43** 0.44** 0.41*** 0.39*** 0.40*

Education of partner (ref. none/primary) secondary+ 0.32* 0.08 0.27* 0.20*** 0.21* 0.14*** 0.31*** 0.20 0.14 -0.02 0.14 0.18 0.30** 0.07 

Education of mother (ref. none/primary) secondary+ -0.05 0.25* 0.37** 0.48*** 0.56*** 0.29*** 0.37*** -0.01 0.23* 0.14 0.18 0.16 0.41*** 0.07 

Mother’s working status 

(ref. not working) working -0.07 0.02 -0.06 0.03 0.07 -0.02 0.00 0.07 -0.05 -0.10 0.12 -0.10 -0.08 -0.06 Mother’s BMI (ref. thin) 

normal 0.25 0.10 0.20 0.24*** 0.26 0.02 0.16 0.20 na 0.22 0.18 -0.04 0.25** 0.29*overweight/ obese 0.41 0.35* 0.43*** 0.46*** 0.44** 0.15 0.42*** 0.37 na 0.15 0.27 0.03 0.44*** 0.30*

Age of mother at child’s birth (ref. 18-34) 

<18 -0.62** -0.47*** -0.34* -0.46*** -0.36 -0.13* -1.06*** 0.01 -0.19 -0.56** 0.33 -0.48 -0.09 -0.05 35+ -0.04 0.02 0.03 0.11* 0.35* 0.19*** 0.10 0.21 0.11 -0.04 -0.03 0.07 -0.13* 0.01 

Birth order of child ( ref. 1) 

2 -0.07 -0.12 -0.09 -0.00 -0.05 -0.11** -0.13 -0.23 -0.20* -0.23 0.24 -0.06 -0.03 -0.06 3 -0.04 -0.10 -0.01 -0.11 -0.15 -0.17*** -0.24** -0.28 -0.02 -0.02 0.36* -0.04 0.04 0.00 4+ -0.23 -0.41*** -0.02 -0.21*** -0.07 -0.40*** -0.31*** -0.37 -0.18* -0.18 0.22 -0.19* 0.04 -0.07 

Sex of child (ref. male) female -0.02 0.11 0.21*** 0.21*** 0.18* 0.04 0.25*** 0.07 0.15** 0.09 0.22* -0.02 0.19*** 0.18*

Perceived size of child at birth (ref. small) 

average 0.35** 0.24** 0.06 0.04 0.37** 0.36*** 0.25*** 0.62* 0.40*** 0.24* 0.47*** 0.29*** 0.35*** 0.01 large 0.53*** 0.38** 0.23* 0.32*** 0.70*** 0.67*** 0.40*** 1.32*** 0.32*** 0.33** 0.67*** 0.50*** 0.57*** 0.10 

Observations 760 2,130 3,736 19,471 2,522 8,074 3,353 1,170 4,410 3,234 1,162 3,626 5,341 6,445 R-squared 0.09 0.10 0.03 0.09 0.11 0.28 0.10 0.09 0.05 0.02 0.06 0.03 0.06 0.02 *** p<0.001, ** p<0.01, * p<0.05 na = Information not available 

62 
Appendix H 

Table H. WAZ linear regressions for 52 DHS surveys (2005-2014) 

Variable Category Albania Armenia Azerbaijan Bangladesh Benin Bolivia 

Burkina 

Faso Burundi Cambodia Cameroon Colombia Comoros 

Congo 

Brazzaville 

Congo 

Democratic 

Republic 

Côt

d’Ivo

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coeffic

Locality by wealth (ref. rural poor) 

rural nonpoor 0.10 0.04 0.11 0.16*** 0.10 0.24*** 0.07 0.23*** 0.06 0.51*** 0.10 0.23* 0.06 0.07 0.1urban poor -0.06 0.21 0.10 0.05 -0.01 0.17*** 0.10 -0.26 -0.08 0.27*** 0.00 0.13 0.16 -0.05 0.1urban nonpoor -0.07 0.13 0.50*** 0.33*** 0.09 0.42*** 0.20** 0.40*** 0.34*** 0.49*** 0.14*** 0.17 0.21** 0.39*** 0.3

Education of partner (ref. none/primary) secondary+ 0.14 -0.30 0.12 0.13*** 0.05 0.16*** 0.03 0.03 0.18*** 0.15** 0.05 0.02 0.09 0.12* 0.0

Education of mother (ref. none/primary) secondary+ 0.10 0.42 0.24 0.24*** 0.06 0.08* 0.42*** 0.44*** 0.08 0.29*** 0.16*** 0.26** 0.18* 0.13* 0.1

Mother’s working status 

(ref. not working) working 0.17 -0.02 0.12 0.00 0.08 -0.03 0.09* 0.02 -0.10* 0.02 0.04 -0.04 -0.11 -0.08 0.0Mother’s BMI (Ref. thin) 

normal 0.20 na -0.06 0.29*** 0.30** 0.36** 0.44*** 0.39*** 0.24*** 0.58*** 0.38*** 0.45** 0.41*** 0.23** 0.5overweight/ obese 0.28 na 0.04 0.61*** 0.52*** 0.60*** 0.75*** 0.81*** 0.38*** 1.03*** 0.63*** 0.63*** 0.61*** 0.58*** 0.8

Age of mother at child’s 

birth (ref. 18-34) 

<18 -0.15 -0.72 0.01 -0.23*** -0.32* -0.07 -0.16 -0.02 -0.04 -0.31*** -0.17*** -0.40 -0.04 -0.24* -0.135+ -0.14 -0.22 -0.08 -0.07 0.01 -0.01 0.09 0.00 -0.06 0.01 0.10** -0.22* 0.11 0.03 -0.0

Birth order of child (ref. 1) 

2 0.01 -0.05 -0.16* -0.11** -0.11 -0.13*** -0.12 -0.06 0.00 -0.15* -0.16*** -0.04 -0.10 0.04 -0.03 -0.08 0.02 -0.11 -0.15** -0.11 -0.20*** -0.02 -0.14 -0.04 -0.17* -0.21*** -0.07 -0.05 0.06 -0.14+ -0.36* -0.21 -0.36** -0.20** -0.25*** -0.26*** -0.09 -0.16* -0.09 -0.24*** -0.39*** -0.04 -0.19 -0.08 -0.0

Sex of child (ref. male) female 0.10 -0.03 0.11 -0.06* 0.23*** 0.12*** 0.10** 0.17*** -0.01 0.08 0.00 0.13 -0.04 0.14** 0.0

Perceived size of child at birth (ref. small) 

average 0.19 0.50** 0.46*** 0.39*** 0.01 0.29*** 0.28*** 0.32*** 0.27*** 0.21** na 0.30** 0.11 0.19* 0.2large 0.41* 0.67*** 0.51*** 0.60*** 0.14 0.69*** 0.56*** 0.55*** 0.53*** 0.71*** na 0.15 0.42*** 0.50*** 0.5

Observations 1,211 1,224 1,429 7,027 5,814 6,406 5,573 2,736 3,210 3,880 12,756 1,862 3,270 6,064 2,21R-squared 0.03 0.05 0.10 0.14 0.02 0.16 0.08 0.11 0.09 0.25 0.06 0.06 0.09 0.09 0.0

63 
Table H. − Continued

Variable Category Gabon Gambia Ghana Guinea Guyana Haiti Honduras India Jordan Kenya 

Kyrgyz 

Republic Lesotho Liberia 

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficien

Locality by wealth (ref. rural poor) 

rural non-poor 0.22 -0.12 0.06 0.04 0.31* 0.16* 0.32*** 0.25*** 0.18** 0.13* 0.09 0.15* 0.09 urban poor 0.08 0.24* -0.07 0.13 0.17 -0.14 0.21*** 0.07 -0.01 0.19 0.16 0.16 -0.09 urban non-poor 0.40*** 0.20* 0.26* 0.31*** 0.09 0.16* 0.38*** 0.34*** 0.24*** 0.20* 0.06 0.16 0.00 

Education of partner (ref. none/primary) secondary+ 0.01 0.05 0.06 0.11 0.05 0.03 0.13*** 0.07*** 0.17* 0.26*** -0.01 0.17 0.09 

Education of mother (ref. none/primary) secondary+ 0.23** 0.35*** 0.08 0.28 0.19 0.17* 0.23*** 0.25*** -0.04 0.21*** -0.16 0.14 0.15 

Mother’s working status 

(ref. not working) working -0.09 -0.08 0.01 0.16 0.15 -0.05 -0.01 -0.05** 0.16* -0.02 0.02 0.00 -0.10 Mother’s BMI (Ref. thin) 

normal 0.35* 0.22* 0.12 0.43*** 0.36* 0.60*** 0.37*** 0.33*** 0.43** 0.48*** 0.28** 0.30 0.59*** overweight/ obese 0.75*** 0.40*** 0.49*** 0.55*** 0.81*** 0.78*** 0.62*** 0.64*** 0.61*** 0.76*** 0.42*** 0.59*** 0.93*** 

Age of mother at child’s birth (ref. 18-34) 

<18 -0.23 -0.00 -0.33 -0.07 -0.28 -0.26* -0.12* -0.21*** -0.20 -0.21* 0.07 0.05 -0.22 35+ 0.11 -0.05 0.07 0.04 0.19 -0.02 -0.03 -0.00 0.03 0.09 0.01 -0.07 0.02 

Birth order of child (ref. 1) 

2 0.01 0.14 -0.04 0.04 -0.05 -0.12 -0.14*** -0.12*** -0.10 0.01 -0.13* -0.01 -0.08 3 -0.15 0.21* -0.11 0.03 0.06 -0.24** -0.19*** -0.20*** -0.24*** -0.09 -0.18** 0.03 -0.23 4+ -0.29* 0.10 -0.06 0.04 -0.23 -0.20* -0.29*** -0.31*** -0.19* -0.19* -0.21*** -0.14 -0.15 

Sex of child (ref. male) female 0.08 0.10 0.09 0.04 0.12 0.04 0.13*** -0.03 0.01 0.10* 0.03 0.25*** 0.18** 

Perceived size of child at birth (ref. small) 

average 0.26** 0.30*** 0.25* 0.40*** 0.53*** 0.28*** 0.43*** 0.27*** 0.45*** 0.45*** 0.30*** 0.62*** 0.19* large 0.54*** 0.51*** 0.56*** 0.58*** 0.91*** 0.56*** 0.79*** 0.32*** 0.68*** 0.72*** 0.65*** 0.92*** 0.67*** 

Observations 1,900 2,491 1,910 2,519 1,054 3,324 8,598 35,933 5,443 4,127 3,519 1,482 2,371 R-squared 0.14 0.10 0.09 0.07 0.20 0.11 0.20 0.13 0.09 0.15 0.05 0.11 0.12 

Variable Category Namibia Nepal Niger Nigeria Pakistan Peru Rwanda 

São Tomé and 

Príncipe Senegal Sierra Leone Swaziland Tajikistan Tanzania 

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficien

Locality by wealth (ref. rural poor) 

rural non-poor -0.06 0.19* 0.10 0.33*** 0.38*** 0.15 0.20*** 0.02 0.26*** 0.08 0.19* 0.20** 0.14** urban poor 0.02 0.12 -0.08 0.19* 0.09 0.20*** 0.02 -0.16 0.21* 0.27* 0.42 0.10 -0.19 urban non-poor 0.49** 0.33*** 0.19* 0.19** 0.19 0.66*** 0.40*** 0.07 0.32*** 0.21* 0.31** 0.21** 0.27**

Education of partner (ref. none/primary) secondary+ 0.08 0.01 0.15 0.18*** 0.18* 0.08* 0.19** 0.04 0.04 -0.01 0.10 0.37* 0.14 

Education of mother (ref. none/primary) secondary+ 0.18 0.27** 0.20 0.31*** 0.37*** 0.19*** 0.20** 0.02 0.28*** 0.22** 0.14 0.25* 0.22* 

Mother’s working status 

(ref. not working) working -0.03 0.02 0.02 -0.01 0.03 -0.04 0.06 0.09 -0.02 -0.01 0.06 0.01 0.12* Mother’s BMI (Ref. thin) 

normal 0.29 0.34*** 0.45*** 0.32*** 0.34** 0.22 0.43*** 0.41* na 0.31** -0.34 0.25** 0.40**overweight/ obese 0.56** 0.73*** 0.64*** 0.64*** 0.67*** 0.51* 0.78*** 0.56** na 0.35** -0.11 0.39*** 0.65**

Age of mother at child’s birth (ref. 18-34) 

<18 -0.26 -0.20 -0.37*** -0.25*** -0.16 0.01 -0.39* -0.40 -0.22* -0.42** -0.26 -0.15 0.08 35+ -0.18 0.02 0.04 0.11** 0.16 0.12** 0.02 -0.21 0.12 -0.01 -0.03 0.00 -0.07 

Birth order of child (ref. 1) 

2 -0.20 -0.14* -0.09 0.05 -0.12 -0.10* -0.08 -0.11 -0.07 -0.17 -0.10 0.01 -0.04 3 -0.04 -0.22* -0.12 -0.03 -0.26* -0.18*** -0.26*** -0.07 -0.00 -0.15 0.05 -0.06 0.05 4+ -0.19 -0.43*** -0.20* -0.19*** -0.09 -0.33*** -0.28*** -0.16 -0.08 -0.17 -0.17 -0.16* -0.00 

Sex of child (ref. male) female 0.04 0.09 0.13** 0.15*** 0.11* -0.01 0.14*** 0.21* 0.10* 0.11* 0.10 -0.03 0.11**

Perceived size of child at birth (ref. small) 

average 0.48*** 0.34*** 0.19*** 0.13** 0.35*** 0.37*** 0.28*** 0.65** 0.38*** 0.34*** 0.35** 0.37*** 0.45**large 0.62*** 0.61*** 0.45*** 0.44*** 0.62*** 0.67*** 0.53*** 1.18*** 0.48*** 0.34*** 0.62*** 0.56*** 0.67**

Observations 760 2,130 3,736 19,471 2,522 8,074 3,353 1,170 4,410 3,234 1,162 3,626 5,341 R-squared 0.18 0.16 0.06 0.11 0.15 0.22 0.11 0.09 0.07 0.03 0.09 0.05 0.07 *** p<0.001, ** p<0.01, * p<0.05 na = Information not available 

64 
Appendix I 

Table I. WHZ linear regressions for 52 DHS surveys (2005-2014) 

Variable Category Albania Armenia Azerbaijan 

Bangladesh Benin Bolivia Burkina Faso Burundi Cambodia Cameroon Colombia Comoros Congo Brazzaville 

Congo 

Democratic 

Republic 

Cô

d’Ivo

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coeffi

Locality by wealth (ref. rural poor) 

rural nonpoor -0.08 0.34* 0.04 0.05 0.09 0.07 0.06 0.14* -0.10 0.35*** 0.16 0.14 -0.12 0.05 0.1urban poor -0.03 0.25 0.08 0.11 -0.08 -0.01 -0.05 -0.09 -0.07 0.23** -0.05 -0.05 0.03 0.01 -0.2urban nonpoor -0.08 0.14 0.30 0.19** 0.03 0.05 -0.02 -0.07 0.11 0.27*** 0.04 -0.01 -0.10 0.22** -0.0

Education of partner (ref. none/primary) secondary+ -0.04 -0.25 0.15 -0.02 -0.04 0.08 -0.09 -0.02 0.06 0.06 -0.01 0.07 0.06 -0.08 0.0

Education of mother (ref. none/primary) secondary+ 0.05 0.54* -0.30 0.18*** 0.08 -0.10** 0.23* 0.07 0.10 0.19** 0.08** -0.03 0.05 -0.02 -0.0

Mother’s working status 

(ref. not working) working -0.01 -0.02 0.16 -0.00 -0.06 0.07 -0.01 0.06 0.01 0.13* 0.06** -0.26* -0.02 0.13 0.0Mother’s BMI (Ref. thin) 

normal -0.50 na 0.21 0.25*** 0.18 0.47*** 0.35*** 0.43*** 0.27*** 0.59*** 0.44*** 0.35 0.36*** 0.50*** 0.6overweight/ obese -0.33 na 0.25 0.52*** 0.32* 0.74*** 0.73*** 0.79*** 0.49*** 1.05*** 0.68*** 0.42 0.56*** 0.75*** 0.9

Age of mother at child’s 

birth (ref. 18-34) 

<18 -0.05 -0.67 -0.02 -0.12* -0.15 -0.06 0.05 -0.13 0.02 -0.12 -0.06 -0.07 0.05 -0.05 0.135+ -0.09 -0.23 0.10 -0.09 0.16 -0.06 -0.06 -0.06 -0.15* -0.05 0.04 -0.18 0.00 -0.11 -0.0

Birth order of child (ref. 1) 

2 0.40* 0.28* -0.31** -0.09* -0.16 -0.08 -0.19* -0.01 -0.01 -0.16* -0.11*** -0.04 -0.18* 0.09 0.03 0.19 0.24 -0.20 -0.09 -0.16 -0.13* -0.09 -0.03 0.06 -0.09 -0.17*** -0.04 -0.13 0.15 -0.14+ -0.09 0.22 -0.41* -0.11 -0.27** -0.12* -0.08 -0.06 0.08 -0.21* -0.28*** 0.04 -0.29** -0.04 0.0

Sex of child (ref. male) female 0.29* -0.18 -0.05 -0.00 0.17** 0.08** 0.05 0.07 0.01 0.05 -0.04 0.03 0.00 0.04 0.0

Perceived size of child at birth (ref. small) 

average 0.12 0.27 0.21 0.29*** 0.01 0.18*** 0.21** 0.13 0.12 0.22** na 0.38** -0.09 0.11 0.1large 0.00 0.44 0.25 0.49*** 0.02 0.38*** 0.47*** 0.33*** 0.31** 0.68*** na 0.35** 0.19 0.33*** 0.3

Observations 1,211 1,224 1,429 7,027 5,814 6,406 5,573 2,736 3,210 3,880 12,750 1,862 3,270 6,064 2,21R-squared 0.02 0.04 0.03 0.06 0.01 0.04 0.03 0.04 0.04 0.17 0.04 0.02 0.04 0.05 0.0

65 
Table I. − Continued

Variable Category Gabon Gambia Ghana Guinea Guyana Haiti Honduras India Jordan Kenya 

Kyrgyz 

Republic Lesotho Liberia Malawi 

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient 

Locality by wealth (ref. rural poor) 

rural non-poor 0.03 -0.20** -0.04 0.03 -0.02 -0.03 0.02 0.14*** 0.03 0.08 0.02 0.11 -0.03 -0.01 urban poor -0.16 0.05 -0.16 0.02 -0.20 -0.11 0.05 0.07 -0.12* -0.13 0.14 0.02 -0.02 0.25* urban nonpoor -0.03 -0.04 0.06 -0.03 -0.12 -0.06 0.09* 0.16*** -0.05 0.12 -0.00 0.07 -0.17 0.15 

Education of partner (ref. none/primary) secondary+ 0.02 -0.06 0.04 0.09 0.11 0.01 0.06 0.01 0.10 0.11 0.64* 0.00 0.06 -0.05 

Education of mother (ref. none/primary) secondary+ 0.17 0.19 0.05 0.03 -0.11 0.07 0.10* 0.09*** -0.18 0.20** -0.35 0.15 -0.01 0.13 

Mother’s working status 

(ref. not working) working -0.16* 0.05 0.01 0.13 0.04 0.01 0.04 -0.03 0.14* -0.02 -0.02 -0.03 0.01 0.07 

Mother’s BMI (Ref. thin) 

normal 0.33* 0.27* 0.24 0.36*** 0.37* 0.59*** 0.42*** 0.32*** 0.43* 0.54*** 0.29* 0.20 0.38** 0.29* overweight/ obese 0.68*** 0.51*** 0.41* 0.50*** 0.75*** 0.75*** 0.64*** 0.61*** 0.68** 0.79*** 0.49*** 0.45 0.61*** 0.42** 

Age of mother at child’s birth (ref. 18-34) 

<18 -0.04 -0.12 0.28 0.05 -0.15 0.04 -0.01 0.03 -0.17 -0.18 0.06 -0.16 0.07 -0.23 35+ -0.11 0.03 0.05 -0.03 0.10 -0.04 -0.07 -0.05 0.00 0.09 -0.14 -0.10 -0.02 0.09 

Birth order of child (ref. 1) 

2 0.03 -0.02 -0.12 0.10 -0.09 0.02 -0.09* -0.06** -0.15* 0.03 -0.09 0.06 -0.03 -0.13 3 -0.17 0.01 -0.01 0.03 0.02 -0.17 -0.11* -0.08** -0.14 -0.11 -0.19** 0.01 -0.11 -0.11 4+ -0.13 -0.17 -0.15 0.03 -0.13 -0.04 -0.14** -0.15*** -0.19** -0.13 -0.05 -0.13 -0.04 -0.14 

Sex of child (ref. male) female -0.06 0.04 0.05 -0.07 0.13 0.00 0.05 0.04* -0.03 0.08 -0.08 0.08 0.04 -0.05 

Perceived size of child at birth (ref. small) 

average 0.07 0.32** 0.13 0.36*** 0.50*** 0.27*** 0.19*** 0.24*** 0.26*** 0.35*** 0.02 0.48*** 0.21* 0.21** large 0.32** 0.33*** 0.48*** 0.54*** 0.82*** 0.54*** 0.42*** 0.28*** 0.48*** 0.58*** 0.24* 0.79*** 0.55*** 0.26** 

Observations 1,900 2,491 1,910 2,519 1,054 3,324 8,598 35,933 5,443 4,127 3,519 1,482 2,371 3,810 R-squared 0.06 0.04 0.04 0.03 0.10 0.07 0.06 0.05 0.05 0.09 0.02 0.06 0.05 0.02 Variable Category Namibia Nepal Niger Nigeria Pakistan Peru Rwanda 

São Tomé 

and Príncipe Senegal Sierra Leone Swaziland Tajikistan Tanzania Timor-Leste 

coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient coefficient 

Locality by wealth (ref. rural poor) 

rural non-poor 0.01 -0.07 0.09 0.05 0.10 -0.01 0.04 -0.43* 0.07 -0.03 0.10 0.13 0.06 -0.18** urban poor 0.19 -0.28* 0.62 -0.10 -0.08 0.01 -0.22 -0.07 0.06 0.03 0.31 -0.00 -0.16 -0.09 urban nonpoor 0.46** -0.08 0.01 -0.21** -0.07 0.32*** -0.12 -0.14 0.16** -0.05 0.10 -0.04 0.05 -0.02 

Education of partner (ref. none/primary) secondary+ -0.13 -0.07 -0.00 0.11** 0.08 -0.00 0.01 -0.07 -0.04 0.03 0.04 0.41 -0.04 0.09 

Education of mother (ref. none/primary) secondary+ 0.27* 0.20* -0.02 0.04 0.04 0.00 -0.02 -0.01 0.23** 0.15 0.03 0.22 -0.04 0.00 

Mother’s working status 

(ref. not working) working 0.01 -0.04 0.07 -0.04 -0.04 -0.05 0.08 0.01 -0.01 0.06 -0.01 0.08 0.21*** -0.22*** 

Mother’s BMI (Ref. thin) 

normal 0.21 0.41*** 0.52*** 0.27*** 0.30** 0.32 0.48*** 0.50** na 0.26* -0.63 0.42*** 0.39*** 0.28*** overweight/ obese 0.49** 0.77*** 0.59*** 0.53*** 0.65*** 0.64*** 0.78*** 0.57* na 0.37* -0.37 0.57*** 0.60*** 0.48*** 

Age of mother at child’s birth (ref. 18-34) 

<18 0.20 0.14 -0.24* 0.02 0.12 0.11 0.40** -0.59 -0.15 -0.15 -0.58** 0.26 0.21* -0.28 35+ -0.20 0.06 0.05 0.09* -0.05 0.01 -0.05 -0.49** 0.09 0.03 -0.02 -0.02 0.01 -0.01 

Birth order of child (ref. 1) 

2 -0.23 -0.12 -0.04 0.09 -0.08 -0.07 0.00 0.01 0.06 -0.07 -0.33* 0.07 -0.03 0.12 3 0.00 -0.24* -0.16 0.05 -0.22* -0.14** -0.14* 0.12 -0.00 -0.21 -0.20 -0.04 0.02 0.08 4+ -0.11 -0.29** -0.28** -0.11* -0.09 -0.16** -0.12* 0.11 0.02 -0.11 -0.39** -0.10 -0.04 0.06 

Sex of child (ref. male) female 0.12 0.09 0.08 0.10*** 0.09 -0.05 0.01 0.28* 0.06 0.14* -0.02 -0.00 0.01 0.10* 

Perceived size of child at birth (ref. small) 

average 0.39** 0.28*** 0.19*** 0.14** 0.14 0.23*** 0.17** 0.25 0.21*** 0.31** 0.11 0.27** 0.32*** 0.05 large 0.43** 0.52*** 0.42*** 0.35*** 0.26* 0.39*** 0.40*** 0.49 0.41*** 0.23* 0.32** 0.38*** 0.44*** 0.22** 

Observations 760 2,130 3,736 19,471 2,522 8,074 3,353 1,170 4,410 3,234 1,162 3,626 5,341 6,445 R-squared 0.11 0.09 0.04 0.02 0.04 0.08 0.04 0.04 0.03 0.01 0.04 0.03 0.03 0.02 *** p<0.001, ** p<0.01, * p<0.05 na = Information not available 

66