DHS COMPARATIVE

REPORTS 37

THE PREVALENCE OF HOUSEHOLD 

RISK FACTORS FOR CHILDREN AGE 

0-17, ESTIMATED FOR 2000-2015 

USING DHS AND MICS SURVEYS

SEPTEMBER 2015

This publication was produced for review by the United States Agency for International Development (USAID). The report was prepared by

Thomas Pullum of ICF International.

DHS Comparative Reports No. 37 

The Prevalence of Household Risk Factors for 

Children Age 0-17, Estimated for 2000-2015 

Using DHS and MICS Surveys 

Thomas Pullum 

ICF International 

Rockville, Maryland, USA 

September 2015 

Corresponding author: Thomas Pullum, International Health and Development, ICF International, 530 

Gaither Road, Suite 500, Rockville, Maryland, USA; phone: 1-301-572-0220; fax: 1-301-407-6501; 

email: Tom.Pullum@icfi.com 
Acknowledgments: The need for this report emerged at the 2014 annual meeting of the Monitoring and 

Evaluation (M&E) Working Group of the Inter-Agency Task Team (IATT) for Children and AIDS 

(CABA). It represents a collaboration between The DHS Program and UNICEF New York. Priscilla Idele 

and Chiho Suzuki of UNICEF made important conceptual contributions and reviewed the report. Shireen 

Assaf, Joy Fishel, and Rebecca Winter of the DHS staff also provided helpful comments. 

Editor: Diane Stoy 

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 (#AID-OAA-C-13-00095). The views expressed are 

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

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

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

The DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 

301-407-6500; fax: 301-407-6501; email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Pullum, Thomas. 2015. The Prevalence of Household Risk Factors for Children Age 0-17, Estimated for 

2000-2015 Using DHS and MICS Surveys. DHS Comparative Reports No. 37. Rockville, Maryland, USA: 

ICF International. 
iii 

Contents 

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

Figures ......................................................................................................................................................... v 

Preface ....................................................................................................................................................... vii 

Abstract ........................................................................................................................................................ ix 

Executive Summary ..................................................................................................................................... xi 

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

2. Data and Methods ............................................................................................................................ 5 

2.1 Data ..................................................................................................................................... 5 

2.2 Methods ............................................................................................................................ 13 

3. Descriptive Results ........................................................................................................................ 17 

3.1 Overview of the Prevalence of Vulnerability across All Surveys ..................................... 17 

3.2 The Prevalence of Vulnerabilities in Each Survey ........................................................... 23 

3.3 The Number of Vulnerable Children in the Year of Each Survey .................................... 26 

4. Conclusions .................................................................................................................................... 31 

References ................................................................................................................................................... 35 

Appendix A. Construction of Indicators ..................................................................................................... 37 

Appendix B. Population Estimates of Children Age 0-17 in 2000, 2005, 2010, and 2015 ........................ 39 

Appendix C. Country Pages ........................................................................................................................ 41 

Index to Country Pages ............................................................................................................................. 113 
iv 
v 

Tables 

Table 1. The number of DHS and MICS surveys available for each country included in this 

report ................................................................................................................................... 8 

Table 2. The number of DHS and MICS surveys available for calendar years 2000 to 2014 .......... 9 

Table 3. For each survey included in this report, the unweighted and weighted numbers 

of children age 0-17 and the estimated population age 0-17 in the year of the survey 

(July 1, in thousands). Population estimates from the U.N. Population Division, 

2015 Revision ..................................................................................................................... 9 

Table 4. For each country included in this report, the estimated prevalence of HIV among 

adults age 15-49 (a percentage) and the number of children age 0-14 who were 

“living with HIV” in 2010. Estimates from UNAIDS, 2015 ............................................ 12 

Table 5. The observed percentages of children age 0-17 with each component and combination 

of vulnerability, in each survey ......................................................................................... 23 

Table 6. The estimated numbers of children age 0-17 with each component and combination 

of vulnerability, in the year of the survey (July 1, in thousands) ...................................... 27 

Figures 

Figure 1. Distribution of vulnerability component 1 (children who have lost one or both parents) 

across all 70 countries ....................................................................................................... 17 

Figure 2. Distribution of vulnerability component 2 (children who are not living with either 

parent) across all 70 countries ........................................................................................... 18 

Figure 3. Distribution of vulnerability component 3 (children who are living in a household in 

which all of the adults have no education) across all 70 countries ................................... 19 

Figure 4. Distribution of vulnerability component 4 (children who are living in a household 

ranked in the bottom two wealth quintiles) across all 70 countries .................................. 19 

Figure 5. Distribution of vulnerability combination 1 (both components 1 and 4) across all 70 

countries ............................................................................................................................ 20 

Figure 6. Distribution of vulnerability combination 2 (both components 2 and 4) across all 70 

countries ............................................................................................................................ 20 

Figure 7. Distribution of vulnerability combination 3 (both components 3 and 4) across all 70 

countries ............................................................................................................................ 21 

Figure 8. Distribution of vulnerability combination 4 (components 1 or 2 or 3, and component 4) 

across all 70 countries ....................................................................................................... 21 

vii 

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 provide policymakers and program managers in low- and 

middle-income countries with easily accessible data on levels and trends for a wide range of health and 

demographic indicators. DHS Comparative Reports provide such information, usually for a large number 

of countries in each report. These reports are largely descriptive, without multivariate methods, but when 

possible they include confidence intervals and/or statistical tests. 

The topics in the DHS Comparative Reports series are selected by The DHS Program in consultation with 

the U.S. Agency for International Development. 

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

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program 

ix 

Abstract 

Children whose parents and households have certain characteristics are known to be more vulnerable to 

negative child outcomes. Four components of vulnerability have been identified by UNICEF: (1) the child 

has lost one or both parents; (2) the child is not living with either parent; (3) the child lives in a household 

in which no adult has any formal education; and (4) the child’s household is in the bottom two wealth 

quintiles. Children with the greatest vulnerability have components (1) and/or (2) and/or (3), and component 

(4). This report uses 80 surveys conducted by The Demographic and Health Surveys Program (DHS) and 

55 Multiple Indicator Cluster Surveys (MICS), between 2000 and 2014 in 70 different countries, to estimate 

the prevalence of the components and combinations of vulnerability. These prevalences are combined with 

estimated numbers of children age 0-17, from the U.N. Population Division, to estimate the numbers of 

vulnerable children in the years of the surveys and in 2000, 2005, 2010, and 2015, except when those years 

are more than five years away from the survey dates. The countries with the highest combined prevalence 

are South Sudan, Burkina Faso, Niger, Mali, Guinea, Benin, Sierra Leone, Chad, Afghanistan, and Senegal. 

In these countries, 27% to 36% of children age 0-17 are in the combination of greatest vulnerability. 

Afghanistan has the highest combined prevalence outside of sub-Saharan Africa. These prevalences and 

population estimates may be used to assess the need for assistance and the coverage of programs. 

xi 

Executive Summary 

The planning and evaluation of programs to alleviate child deprivation in low- and middle-income countries 

require estimates of the level of need for assistance. Such estimates can focus on either the levels of 

deprivation—that is, the prevalence of negative child outcomes—or the levels of risk—the prevalence of 

types of disadvantages that often lead to negative child outcomes. A previous DHS comparative report 

(#32) examined several dimensions of deprivation for children age 0-17 in sub- Saharan Africa. The present 

comparative report describes the vulnerabilities or risk factors of children age 0-17 in a broader set of 70 

countries. 

Recent studies by UNICEF have provided firm evidence that child deprivations are disproportionately 

concentrated among children in four categories of risk that can be described with both DHS and MICS 

surveys. For example, children who have lost a parent or are not living with a parent tend to have worse 

outcomes than children whose parents are alive and living with the child. Thus, the first component of risk 

or vulnerability is present if a child has lost one or both parents. The second component is present if the 

child is not living with a parent. These two components are related because a child who has lost both parents 

cannot be living with a parent, and a child who has lost one parent is less likely to be living with a parent. 

The third component of risk is related to the educational level of adults in the household. If no adult in the 

household has any formal schooling, the children in that household tend to have worse outcomes. This 

source of vulnerability is not restricted to the parents of the child, because if it were, it would not be defined 

for children not living with a parent. 

The fourth component of risk is present if the child’s household is in the bottom two wealth quintiles. The 

DHS and MICS calculation of a continuous score for household wealth is based on many components. The 

list differs from one country to another, but includes such indicators as source of water, type of sanitation, 

having electricity, type of construction materials, and possession of vehicles, household articles, and 

livestock. It is well established that children in the poorer quintiles are more likely to have worse outcomes 

than children in the higher quintiles. Since about 40% of children in every country will be in the bottom 

two wealth quintiles, this component of risk is inherently relative rather than absolute. 

This report provides estimates of the percentage of children 0-17 who have each of the four components as 

well as four combinations of risk, and estimates of the actual numbers of children in as many countries and 

time points as possible with DHS and MICS surveys conducted between 2000 and 2014. For that purpose, 

we combine 80 DHS surveys and 55 MICS surveys from 70 countries. The estimates are provided for the 

year of each survey. By using a process of smoothing and extrapolation, estimates are also provided for the 

years 2000, 2005, 2010, and 2015, except when more than five years of extrapolation would be required. 

The population numbers are based on the U.N. Population Division’s annual estimates of the number of 

children age 0-17 in each country. For reference purposes, the report includes the UNAIDS estimates of 

HIV prevalence in 2010 for all the countries, but no attempt is made to relate HIV prevalence to the 

components of vulnerability. 

The report includes a description of variations in the prevalence of child vulnerabilities in the 70 countries. 

The distributions of the prevalences are skewed, with very high values in a relatively small number of 

surveys and countries. The percentage of children who have lost a parent ranges from 3% to 28%, and there 

are six countries with a percentage greater than 15%: Lesotho, Zimbabwe, Swaziland, Rwanda, Burundi, 

and Namibia. The percentage of children who do not have a parent in their household ranges from virtually 

none to 37%, but is greater than 23% in only six countries: Namibia, Lesotho, Swaziland, Zimbabwe, Sierra 

Leone, and Liberia. In both distributions, these countries are conspicuously higher than the rest, and are 

essentially outliers. There is partial, but not complete, overlap in the two groups of six countries because of 

the definitional overlap in the first two risk factors. 
xii 

These two risk factors are strongly related to the child’s age. At birth, virtually all children have two 

surviving parents and are living with both or at least with the mother. Whatever the percentage is for all 

children age 0-17, it will be much higher for the older children in this age range. 

The distribution of the prevalence of living in a household in which all of the adults have no education is 

much broader. It ranges from none to 69%. The highest values occur in Niger, Burkina Faso, South Sudan, 

Mali, Chad, Afghanistan, Guinea, Benin, and Sierra Leone. Among the countries included in this report, 

Afghanistan has the lowest level of adult education outside of sub-Saharan Africa. 

UNICEF suggests that the best synthesis of these four components of vulnerability consists of children who 

have lost one or both parents, and/or are not living with a parent, and/or are in a household with no 

education adults, and are in the bottom two wealth quintiles. The prevalence of this synthesis ranges from 

3% to 36%. It is highest in the countries which are highest on the respective components, but is most 

strongly related to the third component, education, because that component has the broadest distribution 

across the 70 countries. The prevalence is highest, 27% or more of all children age 0-17, in the following 

ten countries: South Sudan, Burkina Faso, Niger, Mali, Guinea, Benin, Sierra Leone, Chad, Afghanistan, 

and Senegal. 

In some countries with multiple surveys, the prevalences may appear to have increased or declined, but we 

do not make explicit inferences about whether any apparent trends are statistically significant. In nearly all 

countries, the estimated numbers of children with the various vulnerabilities have increased, primarily 

because of growth in the population sizes estimated by the U.N. Population Division (UNPD). It is hoped 

that the estimated prevalences and population numbers will be useful for establishing the magnitude of 

child vulnerability or disadvantage faced by children in low- and middle-income countries. 
1 

1. Introduction 

The DHS surveys are best known for data collected on two sub-populations: women age 15-49 and children 

age 0-4. Most of the data, indicators, and analyses focus on these two groups. However, the design of DHS 

surveys, particularly because of the collection of data about the households in which those women and 

children can be identified, allow for a wider range of analyses. This report focuses on the sub-population 

of children age 0-17 who can also be identified within households, and factors that affect their well-being. 

An earlier DHS report (Kanamori and Pullum 2013) described levels and trends in many indicators of child 

deprivation in sub-Saharan Africa. The study population for that report was also children age 0-17. Five 

types of potential deprivation were included: nutrition, health, water and sanitation, shelter, and education. 

The data on two types of deprivation—water and sanitation, and shelter—come from the household survey 

and refer to all household members. The nutrition indicators come from the height and weight 

measurements in the household survey that apply to children age 0-4 and girls age 15-17. Data on schooling 

come from the household survey and apply to children who have reached the minimum age for school 

attendance. The health indicators, which apply to children age 0-4 whose mothers are in the same 

household, are obtained from the interview with the mother. Very few indicators of child deprivation, or 

child-level outcomes more generally, can be obtained in comparable form throughout the age range 0-17. 

This report shifts attention to potential risk factors for child deprivation. This focus is based on the premise, 

strongly supported by evidence, that the survival of parents, coresidence with parents, the education of the 

adults in the household, and the relative well-being of the household as a unit are important determinants 

of child-level outcomes. We will report the percentages and numbers of children age 0-17 with these risk 

factors and combinations of the risk factors, in many countries and for the interval 2000 to 2015, to inform 

programs that focus on mitigating the risk of negative child outcomes. 

There is a long history of research into how parental and household characteristics may influence child 

outcomes. The particular stream of such research into which this report may be placed is identified with the 

label “orphans and vulnerable children” or “OVC”. This label has been used since the 1990s with reference 

to children, mostly in sub-Saharan Africa, who lost one or both parents because of AIDS. The initial 

application of the label “OVC” to children orphaned by AIDS has expanded because of the difficulty in 

determining the cause of death of a parent and because the implications of a parental death are similar 

regardless of the cause of death. The welfare of children who have been orphaned or have other householdlevel disadvantages is of great international concern. Programs to assist these children have faced 

substantial difficulty in assessing the numbers of children with these disadvantages. The primary objective 

of this report is to provide such estimates for a large number of countries. 

This is not the place to review the literature on how orphanhood, household structure, and other household 

characteristics may be related to child outcomes. We also do not discuss the tools that identify children at 

risk or the mechanisms to assist those children, their families, or communities (for example, see Nyberg et 

al. 2012). Rather, we will identify the two most important antecedents for this report. The first is an analysis 

by Akwara and colleagues at UNICEF (Akwara et al. 2010) which established that (1) living in a household 

with chronically ill adults is not a consistent marker of child vulnerability, and (2) orphanhood is not a 

necessary or sufficient determinant of vulnerability or risk. Briefly stated, the impact of orphanhood 

depends on the type of household in which a child lives. The second antecedent is a report by UNICEF 

(2014) that recommended a new definition of child vulnerability in the context of HIV/AIDS, as well as 

more broadly, based on further analysis of household survey data from 11 countries. The new definition of 

child vulnerability focuses on four variables that form the foundation for the present analysis: orphanhood 

status, coresidence with biological parents, the education level of adults in the household, and household 

wealth. 
2 

The UNICEF (2014) report described four types of vulnerability that will be described as components of 

risk, and four groups that will be referred to as combinations of risk: 

Component 1: Children who have lost one or both parents 

Component 2: Children who are not living with either parent 

Component 3: Children living in a household in which all adults have no education 

Component 4: Children living in a household ranked in the bottom two wealth quintiles 

Combination 11

: Children with risk components 1 and 4 

Combination 2: Children with risk components 2 and 4 

Combination 3: Children with risk components 3 and 4 

Combination 4: Children with risk components 1 or 2 or 3 and with risk component 4 

All four components are binary. The first component of risk is based on parental survival and identifies 

children whose biological mother and/or father has died. This includes single orphans (one parent has died) 

as well as double orphans (both parents have died). The second component identifies children who are not 

living with either the biological mother or the biological father. The first two components are associated. 

Double orphans, for example, will have both components 1 and 2. Single orphans, with only one surviving 

parent, have component 1 and do not necessarily have component 2, although they have a greater chance 

of component 2. 

It is well-established that parental education, particularly maternal education, is related to child welfare. 

The third component identifies whether the child lives in a household in which no adult, whatever the 

relationship to the child, has any formal education at all. The component is not limited to the parents, 

because if it were, it could only be assessed for children whose parents are alive and living in the same 

household. It also is not limited to the household head. 

The fourth component identifies children in the bottom two wealth quintiles. The DHS wealth quintiles are 

constructed on the basis of household assets, usually including source of water, type of sanitation, 

household construction, and possessions. A continuous wealth score is constructed as the first component 

of a principal components analysis (PCA), which is then partitioned into five quintiles so that the weighted 

number of individuals in the household sample, regardless of age, will be the same in each quintile. The 

wealth quintiles are inherently relative rather than absolute. One reason for including the bottom two 

quintiles, and not just the bottom quintile, is that the bottom quintile usually consists of largely of rural 

households. 

Although 40% of the weighted population in the household survey will always be in the bottom two 

quintiles, the percentage of children (of any age, but here specifically age 0-17) in the bottom two quintiles 

will typically exceed 40%, simply because poorer households tend to have more children than households 

that are better off. 

All four combinations of risk include the wealth component. The first three combinations are simply the 

first three components, in succession, together with the wealth component. The percentage of children with 

combinations 1, 2, and 3 will always be less than the corresponding percentages with components 1, 2, and 

3, respectively. The fourth combination is always larger than any of the first three combinations, because it 

 

1

 In the UNICEF report, combinations 1 and 2 are in the reverse order of what is given here. We re-order them into 

the same sequence as components 1 and 2. 
3 

includes children who have any of the first three components of risk—and the fourth component. The fourth 

combination is most comprehensive of all combinations. The percentages of children in the first three 

components, and all four combinations, have a theoretical lower limit of 0%. 

Table 8 in the 2014 UNICEF (2014) report used 8 DHS surveys and 3 MICS surveys to estimate the 

percentages of children in the four components and the four combinations at the time of each survey. The 

DHS surveys were Cambodia 2005, Haiti 2005-06, Rwanda 2005, Swaziland 2006-07, Uganda 2006, 

Tanzania 2007-08, Zambia 2007, and Zimbabwe 2005-06. The three MICS surveys were Central African 

Republic 2006, Malawi 2006, and Sierra Leone 2005. Table 8 applied these percentages to the estimated 

population of children age 0-17 in 2010 in each country, available from the UNPD, to estimate the actual 

number of children with each of the components and risk combinations on July 1, 2010. The 2012 Revision2

was used in the 2014 UNICEF report because it was the most recent revision available at the time. This 

report uses the estimates from the 2015 Revision. The dates of the 11 surveys varied but the percentages 

from the surveys were all applied to a fixed reference date for the population numbers (July 1, 2010) that 

was within five years of the surveys. 

This report will provide essentially the same types of estimated percentages and populations, but for a 

greatly expanded list of countries and surveys. We will provide the estimated percentages at the dates of 

the surveys and the estimated populations on July 1 of the calendar year in which the mean date of interview 

occurred. In addition, this report will combine multiple surveys from each country to provide estimated 

percentages and populations for each country for July 1 of 2000, 2005, 2010, and 2015, except when the 

interval between those dates and the dates of the available surveys is greater than five years and is therefore 

judged to be too great to produce reliable estimates. 

As in Table 8 of the 2014 UNICEF report, we will include UNAIDS estimates of HIV prevalence in 

2010, for all countries. However, there will be no analysis of the relationship between HIV prevalence 

and the level of vulnerability. 

The estimates of the numbers of children age 0-17 with specific types of vulnerabilities are intended to 

help the programming and monitoring of programs for vulnerable children. The population estimates can 

serve as denominators for estimates of program coverage, and with care they can be projected into the 

future for program planning. The information will contribute to global level advocacy dialogues, as well 

as to country-level program and policy needs. 

This report is innovative because it includes large numbers of MICS and DHS surveys. It is the first DHS 

report to use a significant amount of MICS data. It is also innovative among DHS reports in the 

incorporation of population estimates from the UNPD. Finally, the procedure to produce estimates for the 

calendar years 2000, 2005, 2010, and 2015, synthesizing DHS and MICS surveys from various dates, is 

new in DHS reports. 

 

2

 The U.N. Population Division periodically issues population estimates and projections in a series of reports called 

“World Population Prospects.” “The 2015 Revision” simply identifies the round of estimates and projections issued 

in July 2015. 

5 

2. Data and Methods 

2.1 Data 

This report will present information on 70 different countries, with an average of nearly two surveys for 

each country. The report integrates four major sources of data, all of which are publicly available through 

different websites. 

First, the UNPD is the source of estimated population numbers. An Excel spreadsheet3

 was downloaded 

from the UNPD website (http://esa.un.org/wpp/). It provides the estimated population of every country in 

the world, in five-year intervals from 1950 to 2015, in broad age groups for both sexes combined. One of 

the broad age groups is ages 0-17. The estimates refer to July 1 of the calendar year. The estimates come 

from the 2015 Revision of World Population Prospects. Only the estimates for reference years 2000, 2005, 

2010, and 2015 were extracted. These numbers are given in a table in Appendix B. Estimates for July 1 of 

the years between 2000 and 2015 other than 2005 and 2010 (e.g. 2001, 2002, 2003, and 2004) were obtained 

by interpolation (linear on a log scale) between pairs of pairs of reference years (e.g. 2000 and 2005). 

Second, UNAIDS is the source of HIV prevalence estimates for adults age 15-49. The numbers were drawn 

from a spreadsheet available at the UNAIDS website (http://www.aidsinfo.unaids.org). The DHS surveys 

that include HIV testing are a major contributor to the UNAIDS database, although we emphasize that the 

prevalence estimates come from UNAIDS and not directly from DHS surveys. Only estimates for 2010 are 

provided.4

 We include the point estimate of adult prevalence (men and women combined, ages 15-49), a 

low estimate and a high estimate (for an uncertainty interval that is not necessarily interpretable as a 95% 

confidence interval), and the estimated number of children (ages 0-14) “living with HIV”. For UNAIDS 

purposes, the 15th birthday, rather than the 18th birthday, is the boundary between childhood and adulthood. 

An analysis of the association between HIV prevalence and child vulnerability is beyond the scope of this 

report. 

The third source of data is 85 DHS surveys.5

 Since the late 1990s, almost all DHS surveys have included a 

question about the mother’s survival; if she is alive, there is a question about whether she is in the same 

household as the child. There are parallel questions about the father. These questions are part of the 

household survey and are asked about all children, up to age 14 or 15 in the earliest surveys and up to age 

17 in the more recent surveys. The information is provided by the “household respondent”, i.e. an adult, 

typically the household head or spouse of the household head, and not by the child. If the parent is said to 

reside in the household, the line number on the household roster is provided. If a parent has died, the surveys 

do not include information about the date or cause of death. 

The main report on every DHS survey includes detailed information drawn from the responses to these four 

binary questions. There are nine possible combinations of responses about parental survival and 

coresidence. The main report typically provides the distribution across these categories within a chapter 

entitled “Orphans and Vulnerable Children.” The distribution normally includes categories of age and sex 

of the child, type of place of residence, region within the country, and wealth quintile. The reports also 

include two columns that are exactly equivalent to components 1 and 2: the percentage of children with one 

or both parents who have died and the percentage of children not living with either parent. 

 

3

 WPP2015_POP_F08_1_TOTAL_POPULATION_BY_BROAD_AGE_GROUP_BOTH_SEXES.XLS 

4

 2010 is the reference year that is closest to the median year of the data. 5

 The label “DHS survey” includes AIDS Indicator Surveys and Malaria Indicator Surveys if they contained the 

necessary information. 
6 

In order to match the numbers in the DHS reports on coresidence with parents, a subtle check on residency 

is necessary. The household respondent may say that a child’s parent lives in the household and provide a 

line number, but it is possible that the adult on that line does not satisfy a de jure requirement for residency. 

In this situation, the residency requirement takes precedence,6

 and it will be concluded that the parent does 

not live with the child. Appendix A provides the specific Stata code for these calculations, and also the 

process for handling “missing” codes. 

Highest level of education is included in all surveys with a standard DHS variable that typically has 

categories “None (or preschool)”, “Primary”, “Secondary”, and “Higher”. An indicator for any schooling 

is set to 0 for the household, and is then replaced with 1 if anyone in the household (a) is age 18 or above, 

and (b) has a level of education other than “None (or preschool)”, and (c) satisfies the de jure residency 

requirement. If the household remains with a 0 on this indicator, then all children age 0-17 in the household 

will have the third component of vulnerability. 

The wealth quintile is coded onto the record of every person in the household and is the same for every 

member of a household. Quintiles 1 and 2 comprise the lowest two quintiles. 

The early surveys in which the orphanhood and coresidency questions only extended to age 14 or 15 will 

not be included in this report.7

 It would be possible to extrapolate to age 17 by leveraging information in 

those surveys that do include children up to age 17. However, this kind of extrapolation would be necessary 

for components 1 and 2 and combinations 1, 2, and 4, with separate adjustments for each, and any 

adjustment would introduce some uncertainty. Readers familiar with DHS data are likely to be surprised 

that some countries have omitted the orphanhood or coresidency questions entirely, or only recently 

included those questions up to age 17. Such countries are omitted or may be included for only one survey. 

Any omissions of a survey or a country from this report are due solely to unavailability of the required data. 

The fourth and final data source includes 50 MICS household surveys with the same information described 

above. Permission to use the MICS surveys was readily obtained from UNICEF (http://mics.unicef.org/). 

Identification of the relevant variables was complicated by less standardization in variable names across 

surveys, although there was a high level of comparability with DHS coding and no surveys had to be 

discarded for reasons other than the age limitation. Coordination between the DHS and MICS survey 

platforms was helpful and certainly contributed to the very high level of agreement between the DHS and 

MICS results from the same country. Because DHS and MICS do not entirely overlap in the countries 

where they work, the use of both types of surveys has substantially broadened the scope of this report 

beyond what would have been possible with either platform alone. Since the MICS surveys always have a 

de jure criterion for household residency, it was not necessary to check that criterion in the construction of 

codes for coresidency with parents or for the education of adults in the household. 

The countries represented in the report are listed in Table 1. The columns of the table describe how many 

DHS and/or MICS surveys are included for each country. Tanzania had five surveys, the largest number, 

and several countries had four—Ghana, Nigeria, Sierra Leone, Uganda, and Zimbabwe. In Table 1, as 

elsewhere in this report, the countries are listed in alphabetical order and are not grouped by region. A 

majority of the countries are in sub-Saharan Africa. 

 

6

 DHS surveys normally use a de facto definition of residence, which is operationalized with hv103=1, i.e. the person 

“slept here last night.” However, in the present context it is required that the child and the parent have de jure residence, 

which is operationalized with hv102=1, i.e. the person “usually sleeps here,” to be consistent with the MICS and 

UNICEF estimates. 

7

 A few surveys appear to include orphanhood and coresidency only up to age 14 or 15, but have country-specific 

variables that extend to age 17. All of the surveys that appear to have the lower cutoff were checked for a possible 

country-specific variable with a higher cutoff, and if it could be found, they were retained. 
7 

The fieldwork for many surveys extends across two calendar years. For each survey, the mean date of 

interview was calculated; we only identify the calendar year in which the mean date was located, rather 

than the two calendar years in which the fieldwork may have been conducted. In the calculation of the mean 

date, the year and month (not the day) of interview were used.8

 Dates in the surveys of Ethiopia and Nepal 

were converted to the Gregorian calendar. 

The distribution of the surveys across calendar years 2000 through 2014 is described in Table 2. Two DHS 

surveys from 2000 are included, but no surveys from 2001 or 2002. Most of the MICS surveys (42 out of 

55) are concentrated in 2005-06 and 2010-11, corresponding with MICS rounds 3 and 4, respectively. 

Several earlier MICS surveys, in round 2, included the required information but only extended to age 14 or 

15 for the questions on parental survival and coresidence. Several DHS surveys prior to 2004 are also 

omitted because of that limitation. The median survey year is 2009. 

Table 3 lists all the surveys with the year and whether the survey was conducted by DHS or MICS. The 

table provides the number of children in the sampled households who were recorded to be age 0-17 at the 

date of interview. Both the unweighted and weighted numbers are provided. The weighted numbers are 

adjusted for the sample design. This involved taking account of the usual pattern of over-sampling smaller 

strata and under-sampling larger strata, and adjusting further for cluster-level non-response. The 

unweighted and weighted numbers are always very close, but we provide both numbers because all 

estimates given in this report are weighted. The total number of children age 0-17 in all the surveys is over 

four million. 

The final column of Table 3 shows the estimated number of children age 0-17 in the country on July 1 of 

the year of the survey, in thousands. These estimates are taken from the UNPD spreadsheet. The estimates 

are rounded to the nearest thousand. The mean dates of the surveys and the dates of the population estimates 

are slightly asynchronous, but not by more than half a year. All population estimates in this report are set 

at July 1 of the calendar year. 

There is enormous variation in the populations of the various countries, far more variation than in the sample 

sizes, although countries with smaller populations tend to have smaller samples. The population number of 

children age 0-17 ranges from 81,000 in Sao Tome and Principe in 2008 to 446,799,000 in India in 2006. 

The number in the samples ranges from 5,830 in the Ukraine 2005 MICS survey to 198,294 in the India 

2006 DHS survey. 

Table 4 lists the countries with indicators from UNAIDS of the prevalence of HIV in 2010. This year was 

selected because it is close to the median year of the surveys (2009) and is one of the four reference years 

(2000, 2005, 2010, and 2015). The first column provides the estimated prevalence of HIV in the population 

of adults age 15-49, men and women combined and expressed as a percentage. Adult HIV prevalence is 

estimated to have been greater than 10% (an arbitrary cutoff) in 2010 in Lesotho, Malawi, Mozambique, 

Namibia, Swaziland, Zambia, and Zimbabwe. The second and third columns show low and high estimates, 

respectively, as described earlier. The last column is the estimated number of children who “living with 

AIDS,” that is, are HIV positive. This number is not given in thousands but is usually rounded to the nearest 

thousand. The number in the last column is greater than 100,000 (an arbitrary cutoff) in Ethiopia, 

Mozambique, Nigeria, Uganda, Zambia, and Zimbabwe. The entries “NA”, for “Not Available” apply to 

countries with “...” in the UNAIDS spreadsheet as well as countries that are included in this report but are 

not included in the UNAIDS spreadsheet: Comoros, Iraq, Jordan, Sao Tome and Principe, and Ukraine. 

 

8

 To avoid a half-month of error in the calculation of the mean date of interview, it was taken into account that the 

numbers normally assigned to years and months are ordinal, and must be translated to a continuous scale of time. 
8 

Table 1. The number of DHS and MICS surveys available for each country included in this report 

Country DHS MICS Total Country DHS MICS Total 

Afghanistan 0 1 1 Lesotho 2 0 2 

Azerbaijan 1 0 1 Liberia 2 0 2 

Bangladesh 0 1 1 Madagascar 1 0 1 

Belize 0 2 2 Malawi 2 1 3 

Benin 2 0 2 Maldives 1 0 1 

Bolivia 2 0 2 Mali 1 0 1 

Burkina Faso 1 1 2 Mauritania 0 2 2 

Burundi 1 1 2 Moldova 1 1 2 

Cambodia 2 0 2 Mozambique 0 1 1 

Cameroon 2 1 3 Namibia 2 0 2 

Central African Rep 0 2 2 Nepal 2 0 2 

Chad 1 1 2 Niger 1 0 1 

Colombia 3 0 3 Nigeria 2 2 4 

Comoros 1 0 1 Pakistan 1 0 1 

Congo 3 0 3 Rwanda 2 0 2 

Costa Rica 0 1 1 Sao Tome and Principe 1 0 1 

Côte d'Ivoire 2 1 3 Senegal 2 0 2 

Democratic Republic of the Congo 2 1 3 Sierra Leone 2 2 4 

Egypt 3 0 3 Somalia 0 1 1 

Ethiopia 2 0 2 South Sudan 0 1 1 

Gabon 1 0 1 Sudan 0 1 1 

Gambia 0 1 1 Swaziland 1 1 2 

Georgia 0 1 1 Tanzania 5 0 5 

Ghana 2 2 4 Thailand 0 1 1 

Guinea 1 0 1 Timor-Leste 1 0 1 

Guinea-Bissau 0 1 1 Togo 1 2 3 

Guyana 2 0 2 Tunisia 0 1 1 

Haiti 2 0 2 Uganda 4 0 4 

Honduras 2 0 2 Ukraine 0 2 2 

India 1 0 1 Uzbekistan 0 1 1 

Iraq 0 1 1 Viet Nam 1 2 3 

Jamaica 0 1 1 Yemen 0 1 1 

Jordan 3 0 3 Zambia 2 0 2 

Kazakhstan 0 2 2 Zimbabwe 2 2 4 

Kyrgyz Republic 1 1 2 Total 85 50 135
9 

Table 2. The number of DHS and MICS surveys available for calendar years 2000 to 2014 

Survey year DHS MICS Total 

2000 2 0 2 

2003 2 0 2 

2004 6 0 6 

2005 11 5 16 

2006 8 18 26 

2007 6 2 8 

2008 6 1 7 

2009 7 1 8 

2010 8 10 18 

2011 7 9 16 

2012 13 3 16 

2013 7 0 7 

2014 2 1 3 

Total 85 50 135 

Table 3. For each survey included in this report, the unweighted and weighted numbers of children 

age 0-17 and the estimated population age 0-17 in the year of the survey (July 1, in thousands). 

Population estimates from the U.N. Population Division, 2015 Revision 

Country Year Survey type

Unweighted 

sample size

Weighted 

sample size 

Population 

age 0-17 

Afghanistan 2011 MICS 54,214 54,270 15,465 

Azerbaijan 2006 DHS 9,717 9,420 2,760 

Bangladesh 2006 MICS 127,250 127,359 58,567 

Belize 2006 MICS 3,404 3,370 130 

Belize 2011 MICS 7,321 7,196 137 

Benin 2006 DHS 48,734 48,084 4,291 

Benin 2012 DHS 48,453 47,071 4,973 

Bolivia 2003 DHS 37,856 37,961 3,851 

Bolivia 2008 DHS 34,185 34,338 4,005 

Burkina Faso 2006 MICS 20,517 20,422 7,364 

Burkina Faso 2010 DHS 44,221 44,517 8,289 

Burundi 2005 MICS 22,799 23,000 4,197 

Burundi 2010 DHS 22,270 22,099 4,803 

Cambodia 2005 DHS 33,463 31,832 5,901 

Cambodia 2010 DHS 30,789 29,910 5,827 

Cameroon 2004 DHS 25,004 25,274 9,086 

Cameroon 2006 MICS 21,303 21,495 9,492 

Cameroon 2011 DHS 35,828 36,081 10,552 

Central African Republic 2006 MICS 27,391 27,274 1,992 

Central African Republic 2010 MICS 28,884 28,774 2,097 

Chad 2004 DHS 16,158 16,315 5,395 

Chad 2010 MICS 51,243 51,123 6,600 

Colombia 2000 DHS 17,615 17,169 15,267 

Colombia 2005 DHS 58,732 55,438 15,078 

Colombia 2010 DHS 72,545 66,428 14,670 

Comoros 2012 DHS 11,384 11,425 347 

Congo 2005 DHS 14,526 14,849 1,694 

Congo 2009 DHS 14,374 14,156 1,905 

Congo 2011 DHS 25,265 24,329 2,018 

Costa Rica 2011 MICS 7,377 6,578 1,352 

Côte d'Ivoire 2005 DHS 11,423 11,491 9,159 

Côte d'Ivoire 2006 MICS 28,658 28,103 9,345 

Côte d'Ivoire 2012 DHS 24,805 23,979 10,542 

(Continued)

 
10 

Table 3. − Continued

Country Year Survey type

Unweighted 

sample size

Weighted 

sample size 

Population 

age 0-17 

Democratic Republic of the Congo 2007 DHS 25,219 25,860 31,796 

Democratic Republic of the Congo 2010 MICS 33,257 33,287 34,978 

Democratic Republic of the Congo 2013 DHS 53,324 54,349 38,272 

Egypt 2005 DHS 45,155 43,371 30,021 

Egypt 2008 DHS 36,033 34,359 30,600 

Egypt 2014 DHS 46,699 46,299 34,233 

Ethiopia 2005 DHS 34,180 37,002 40,446 

Ethiopia 2011 DHS 38,876 40,513 45,738 

Gabon 2012 DHS 19,084 18,108 709 

Gambia 2006 MICS 23,379 23,289 784 

Georgia 2005 MICS 10,323 10,244 1,058 

Ghana 2003 DHS 13,163 12,481 9,633 

Ghana 2006 MICS 12,742 12,457 10,236 

Ghana 2008 DHS 21,775 20,537 10,661 

Ghana 2011 MICS 27,466 25,889 11,339 

Guinea 2012 DHS 23,653 23,620 5,768 

Guinea-Bissau 2006 MICS 20,677 20,618 734 

Guyana 2005 DHS 4,373 4,324 312 

Guyana 2009 DHS 9,529 8,351 307 

Haiti 2006 DHS 22,280 21,685 4,178 

Haiti 2012 DHS 26,152 25,122 4,266 

Honduras 2006 DHS 45,211 42,856 3,209 

Honduras 2012 DHS 43,131 40,762 3,174 

India 2006 DHS 198,294 209,752 446,799 

Iraq 2011 MICS 117,912 113,261 15,373 

Jamaica 2011 MICS 6,665 6,530 892 

Jordan 2007 DHS 36,050 33,511 2,519 

Jordan 2009 DHS 31,822 29,783 2,699 

Jordan 2012 DHS 34,716 32,240 2,935 

Kazakhstan 2006 MICS 17,072 16,405 4,777 

Kazakhstan 2010 MICS 16,107 16,253 4,798 

Kyrgyz Republic 2005 MICS 10,015 9,800 1,951 

Kyrgyz Republic 2012 DHS 13,873 13,085 2,062 

Lao PDR 2006 MICS 16,263 15,960 2,763 

Lao PDR 2011 MICS 43,411 42,277 2,796 

Lesotho 2004 DHS 16,514 15,961 904 

Lesotho 2009 DHS 16,675 15,804 905 

Liberia 2007 DHS 17,928 17,724 1,754 

Liberia 2013 DHS 24,862 23,729 2,103 

Madagascar 2009 DHS 44,897 44,643 10,337 

Malawi 2004 DHS 32,134 31,981 6,676 

Malawi 2006 MICS 71,425 71,087 7,044 

Malawi 2010 DHS 64,806 63,345 7,849 

Maldives 2009 DHS 15,913 15,493 120 

Mali 2012 DHS 31,936 32,244 8,663 

Mauritania 2007 MICS 30,474 30,441 1,592 

Mauritania 2011 MICS 30,613 30,454 1,740 

Moldova 2005 DHS 7,810 7,926 1,016 

Moldova 2012 MICS 6,363 6,527 827 

Mozambique 2008 MICS 34,728 35,136 12,017 

Namibia 2007 DHS 18,858 18,933 965 

Namibia 2013 DHS 17,903 18,392 1,034 

Nepal 2006 DHS 19,935 19,806 11,802 

Nepal 2011 DHS 20,808 20,839 11,704 

Niger 2012 DHS 37,972 38,486 9,972 

(Continued)

 
11 

Table 3. − Continued

Country Year Survey type

Unweighted 

sample size

Weighted 

sample size 

Population 

age 0-17 

Nigeria 2007 MICS 63,187 62,228 73,824 

Nigeria 2008 DHS 78,662 74,788 75,870 

Nigeria 2011 MICS 76,606 75,474 82,352 

Nigeria 2013 DHS 89,859 90,438 86,974 

Pakistan 2012 DHS 42,278 40,022 74,766 

Rwanda 2005 DHS 24,938 24,867 4,512 

Rwanda 2010 DHS 28,295 28,433 4,994 

Sao Tome and Principe 2008 DHS 6,660 6,762 82 

Senegal 2011 DHS 39,535 36,941 6,730 

Senegal 2013 DHS 21,034 18,882 7,150 

Sierra Leone 2005 MICS 21,022 21,029 2,565 

Sierra Leone 2008 DHS 21,408 22,280 2,756 

Sierra Leone 2010 MICS 31,674 31,741 2,890 

Sierra Leone 2013 DHS 38,843 38,417 3,053 

Somalia 2006 MICS 18,620 18,619 4,695 

South Sudan 2010 MICS 31,931 31,956 5,036 

Sudan 2010 MICS 42,212 42,458 17,563 

Swaziland 2006 DHS 10,910 11,488 553 

Swaziland 2010 MICS 9,696 9,858 556 

Tanzania 2004 DHS 16,903 16,854 19,648 

Tanzania 2004 DHS 25,022 24,621 19,648 

Tanzania 2007 DHS 23,085 22,457 21,479 

Tanzania 2010 DHS 25,652 25,179 23,554 

Tanzania 2012 DHS 27,657 26,998 25,100 

Thailand 2006 MICS 38,954 38,271 17,316 

Timor-Leste 2009 DHS 34,611 34,411 516 

Togo 2006 MICS 16,063 15,582 2,866 

Togo 2010 MICS 15,944 15,416 3,155 

Togo 2014 DHS 23,633 21,929 3,470 

Tunisia 2012 MICS 11,209 11,025 3,065 

Uganda 2000 DHS 20,732 21,757 13,330 

Uganda 2006 DHS 25,771 25,706 16,290 

Uganda 2011 DHS 30,625 30,708 19,075 

Uganda 2011 DHS 25,279 25,583 19,075 

Ukraine 2005 MICS 5,830 4,264 8,790 

Ukraine 2012 MICS 8,635 5,893 7,894 

Uzbekistan 2006 MICS 19,906 20,063 10,154 

Viet Nam 2005 DHS 9,033 9,408 28,295 

Viet Nam 2006 MICS 12,736 12,089 27,850 

Viet Nam 2010 MICS 14,183 13,852 26,140 

Yemen 2006 MICS 13,637 13,624 11,113 

Zambia 2007 DHS 19,156 19,130 6,831 

Zambia 2013 DHS 45,029 44,861 8,074 

Zimbabwe 2005 DHS 21,218 20,908 6,455 

Zimbabwe 2009 MICS 26,677 26,208 6,738 

Zimbabwe 2010 DHS 20,029 19,713 6,811 

Zimbabwe 2014 MICS 32,704 32,894 7,360 
12 

Table 4. For each country included in this report, the estimated prevalence of HIV among adults age 

15-49 (a percentage) and the number of children age 0-14 who were “living with HIV” in 2010. 

Estimates from UNAIDS, 2015 

Country 

Adult HIV 

Prevalence 

Low 

Estimate 

High 

Estimate 

No. children age 0-14 

living with HIV 

Afghanistan <0.1 <0.1 <0.1 <500 

Azerbaijan 0.1 <0.1 0.2 <100 

Bangladesh <0.1 <0.1 <0.1 <1000 

Belize 1.3 1.2 1.4 <200 

Benin 1.2 1.1 1.4 7,900 

Bolivia 0.3 0.2 0.4 <1000 

Burkina Faso 1.1 0.9 1.2 17,000 

Burundi 1.6 1.4 1.8 18,000 

Cambodia 0.8 0.5 1.5 7,000 

Cameroon 5.1 4.8 5.3 60,000 

Central African Republic 5.2 4.7 5.7 19,000 

Chad 3.0 2.5 3.6 29,000 

Colombia 0.4 0.3 0.5 3,500 

Comoros NA NA NA NA 

Congo 3.2 2.9 3.5 12,000 

Costa Rica 0.2 0.2 0.3 <200 

Côte d'Ivoire 4.0 3.6 4.3 47,000 

Democratic Republic of Congo 1.2 1.1 1.3 62,000 

Egypt <0.1 <0.1 <0.1 <200 

Ethiopia 1.4 1.2 1.6 150,000 

Gabon 4.7 4.2 5.4 4,500 

Gambia 2.0 1.6 2.5 2,100 

Georgia 0.2 0.1 0.2 NA 

Ghana 1.7 1.3 2.1 25,000 

Guinea 1.6 1.4 1.8 NA 

Guinea-Bissau 3.9 3.5 4.3 4,700 

Guyana 1.5 1.1 2.2 <500 

Haiti 2.1 2.0 2.1 12,000 

Honduras 0.5 0.5 0.6 2,700 

India NA NA NA NA 

Iraq NA NA NA NA 

Jamaica 1.8 1.6 2.3 <1000 

Jordan NA NA NA NA 

Kazakhstan 0.1 0.1 0.2 NA 

Kyrgyz Republic 0.2 0.2 0.3 <500 

Lao 0.3 0.2 0.3 <1000 

Lesotho 23.3 22.1 24.6 22,000 

Liberia 1.5 1.3 1.7 4,400 

Madagascar 0.4 0.3 0.4 5,000 

Malawi 11.7 10.9 12.4 150,000 

Maldives NA NA NA NA 

Mali 1.4 1.2 1.6 17,000 

Mauritania 0.8 0.7 1.0 1,700 

Moldova 0.5 0.5 0.6 <200 

Mozambique 11.2 9.9 12.9 170,000 

Namibia 14.3 13.5 15.3 17,000 

Nepal 0.3 0.2 0.3 1,700 

Niger 0.7 0.6 0.8 NA 

Nigeria 3.5 3.2 3.8 360,000 

Pakistan <0.1 <0.1 <0.1 1,000 

Rwanda 3.1 2.9 3.4 31,000 

Sao Tome and Principe NA NA NA NA 

Senegal 0.7 0.6 0.8 3,600 

Sierra Leone 1.6 1.4 1.8 4,500 

(Continued)
13 

Table 4. − Continued

Country 

Adult HIV 

Prevalence 

Low 

Estimate 

High 

Estimate 

No. children age 0-14 

living with HIV 

Somalia 0.6 0.5 0.8 5,100 

South Sudan 2.8 2.0 3.9 16,000 

Sudan 0.2 0.2 0.3 3,500 

Swaziland 27.4 26.6 28.2 19,000 

Tanzania 6.1 5.5 6.7 NA 

Thailand 1.3 1.1 1.4 8,300 

Timor-Leste NA NA NA NA 

Togo 2.8 2.3 3.4 14,000 

Tunisia <0.1 <0.1 <0.1 NA 

Uganda 6.9 6.6 7.3 170,000 

Ukraine NA NA NA NA 

Uzbekistan 0.2 0.2 0.3 NA 

Viet Nam 0.4 0.4 0.5 3,500 

Yemen <0.1 <0.1 <0.1 <500 

Zambia 13.0 12.4 13.7 110,000 

Zimbabwe 18.0 17.2 18.7 170,000 

NA: Not Available 

2.2 Methods 

The construction of the eight binary indicators for each child age 0-17—the four components and four 

combinations of vulnerability—was described above. Chapter 3 will present the distributions of these types 

of child vulnerability, across surveys, with the percentages from each survey and the estimated numbers of 

children with each component and combination of vulnerability, on July 1 of the year of the survey. The 

numbers of children are obtained by simply multiplying the estimated population in the last column of 

Table 3 by the estimated proportion with the respective types of vulnerability. The observed prevalences 

(synonymous with “percentages”) and implied population numbers in the years of the surveys will be 

provided in Chapter 3 in Tables 5 and 6. 

The usefulness of these purely descriptive estimates in Chapter 3 is limited by irregularities in the dates of 

the surveys as well as by differences9

 in estimates coming from successive surveys in the same country. 

Comparisons across countries, or trends within countries, are difficult to perceive from Tables 5 and 6 in 

this report. In the UNICEF (2014) report, variation in the timing of the surveys was counteracted by 

selecting a specific reference year, 2010. As described earlier, the estimated prevalences from the surveys 

were applied to the UNPD estimates of the numbers of children aged 0-17 on July 1 of the reference year. 

The approach in this report is similar, but includes four reference years, rather than one, and allows for 

reconciliation of multiple surveys from the same country by constructing fitted values on lines. 

Synthesizing the surveys and providing estimates for reference years requires a combination of smoothing 

and extrapolation that depends on the number of surveys available for each country. The objective of this 

procedure is to obtain estimated prevalences and population numbers at the midpoint of reference years 

2000, 2005, 2010 and 2015. Such numbers can facilitate comparisons across countries which had surveys 

scattered across different years. This also simplifies extrapolation by users of this report, if desired, into the 

future—although any extrapolation requires caution. 

 

9

 Such differences may arise from sampling error or non-sampling error or genuine changes in prevalence. 
14 

The results of this procedure will be presented in the form of a separate figure and table for each country. 

Because of the large number of countries, these results are included in Appendix C. We now describe the 

methodology for producing these figures and tables. 

With only one survey, the prevalence from that survey will be extended forward and backward, unchanged, 

to the nearest reference years. For example, if the mean date of interview for that single survey, on a 

continuous time scale, is 2006.32, then the prevalences of the components and combinations of vulnerability 

will be extended forward to 2010.5 and backward to 2005.5. The estimated prevalences will be the same at 

both dates, but the population age 0-17 will be different in 2010 from what it was in 2005 and the numbers 

of vulnerable children will be different. Thus, the prevalences from the survey, specifically with the date 

2006.32, will be applied to the population at date 2006.5 in Chapter 3, Table 6, and to the population at 

2005.5 and 2010.5 in Appendix C. The associated graphs of the prevalences in Appendix C will have a 

horizontal straight line. 

It is certainly possible that the underlying prevalence was not constant within the five-year interval between 

two reference years such as 2005 and 2010. However, with just one data point, there is no basis for applying 

a slope to the line connecting the two reference years. The procedure followed here is equivalent to that 

used in the UNICEF (2014) report, in which prevalences from surveys conducted in 2005-2008 were 

applied to the reference year 2010. Users are certainly free to ignore the extensions to the reference years 

if they are uncomfortable with the horizontal extrapolation. 

If the country had two surveys with the necessary information, the two data points (for each component and 

combination of vulnerability) will be connected with a line that passes through those two points and is then 

extrapolated to the reference year before and nearest to the first survey and to the reference year after and 

nearest to the second survey. The line going through the two data points is a logistic curve and not a straight 

line. The logistic function is preferred for a binary outcome and has the highly desirable property that it is 

constrained to be between 0 and 1 (or 0% and 100%). The curvature is not evident between the data points, 

but appears in the extrapolation. If the country had more than two surveys with the necessary information, 

the only modification is that the line will not go through the data points exactly but will be the “best fitting” 

logistic. All graphs include the upper and lower 95% confidence bands. 

We now describe the methods in more detail. The mean dates of interview and the prevalences are 

calculated with individual-level files for each of the 135 surveys, using sampling weights. The estimated 

prevalences can be calculated simply as weighted means of the binary (0/1) outcomes. However, in order 

to develop a better sense of their statistical stability, the prevalences were actually calculated with logit 

regressions adjusted for clustering as well as for sample weights. In a logit regression with no covariates, 

the constant term (or intercept) can be exponentiated to obtain the odds, and the odds can then be converted 

to a proportion.10 This “fitted” proportion will agree exactly with the proportion that could be calculated 

directly as the weighted mean of a binary (0/1) outcome. The advantage to using a logit model in this 

context is that Stata will produce two estimated variances that can be compared. One is the estimated 

variance (the square of the standard error) for the constant term, adjusted for the weights and clusters. The 

other is the estimated variance without these adjustments, i.e., for a simple random sample (srs) of the same 

size. These two estimated variances will be referred to as V1 and V0, respectively. V0, which is not adjusted 

for the survey design, will always be substantially smaller than V1. The weights, and in particular the cluster 

design of DHS and MICS surveys, will reduce the efficiency of all estimates and will produce more 

uncertainty, larger standard errors, and larger variances. This loss of efficiency can be restated in terms of 

a reduction of the “effective” sample size. The design effect (DEFT) for a survey is given by the ratio 

V1/V0, and the effective sample size is obtained by dividing the nominal sample size by the DEFT. The 

 

10 The proportion is the odds divided by 1+odds. 
15 

standard DHS main reports provide the adjusted standard errors and DEFTs for many indicators in their 

Appendix B. The DEFT in the present analysis is different for each outcome. 

Clustering reduces the efficiency of a sample because cases in the same cluster or enumeration area tend to 

be more similar to one another than cases in different clusters. That is, adding another case in the same 

cluster does not increase the information in the data as much as adding another case outside that cluster. 

This redundancy or repetitiveness of information reduces the “effective” sample size relative to a simple 

random sample, and increases the DEFT. 

Our strategy to calculate the DEFT is somewhat different from that used in the preparation of the main DHS 

reports. The software used in DHS data processing is not user-friendly, but it is more comprehensive 

because it includes an adjustment for the stratification in the sample. The calculation described here ignores 

the potential effect of stratification, and is likely to over-estimate the DEFT. All DHS samples are stratified, 

usually by combinations of urban-rural residence and region. Stratification generally increases the 

efficiency of a sample, and counteracts the effect of clustering. With the “svyset” and “svy” commands, 

Stata adjusts for stratification, as well as weighting and clustering. However, we omitted that adjustment 

because of the large number of surveys, uncertainty about the specification of the stratification code in DHS 

and (especially) MICS surveys, and the simple fact that the procedure often failed because the variables 

used here are so highly clustered. Within a survey, typically with several hundred clusters, it can easily 

happen that all children in some clusters have the specified characteristic or no children have the 

characteristic11. In such clusters there is no within-cluster variation. The cluster option works satisfactorily 

by itself, but not when it is combined with the stratification option. As a result, our estimates of the DEFT 

tend to be somewhat too high and the confidence bands are somewhat too wide. Such a bias is preferable 

to providing confidence intervals that are too narrow and imply an exaggerated accuracy of the estimates. 

The results described above for each survey, with the sample size, the summary date of interview, the eight 

prevalences, and the DEFT associated with each of these prevalences, were summarized on a single record 

for each survey. In a second step, the surveys for each country were grouped together, and the estimates for 

the reference years were generated. In these eight logit regressions, the frequency for each data point is the 

effective sample size, i.e., the nominal sample size divided by the DEFT. When there are more than two 

surveys for a country, the line of best fit takes into account the differences between surveys in the effective 

sample size, and fits more closely the point estimates from surveys with a larger effective sample size. 

The results for each country are presented in Appendix C, with each country on a separate page. Each page 

includes a table with all the estimated prevalences and populations in 2000, 2005, 2010, and 2015, except 

for reference years that were more than five years away from any data points. The page for a specific 

country also includes a figure that visually presents the fitted lines for vulnerability components 1, 2, and 

3, and vulnerability combination 4. 

The fitted lines are accompanied by 95% confidence bands, as described above. The confidence bands 

convey a visual sense of the uncertainty in the estimates, and are not provided numerically in the tables. If 

there is a single survey, the confidence interval for that point estimate is simply extended to the nearest 

reference years before and after that survey. If there are two or more surveys, the extrapolated estimates 

tend to have wider confidence bands, depending both on the extent of the extrapolation (in time) and the 

effective sample sizes. 

The uncertainty associated with the prevalence estimates cannot easily be translated into uncertainty 

associated with the population estimates. All the numbers from the UNPD are estimates with their own 

 

11 The computational difficulties can be equally serious if the number of children in a cluster with a specified outcome 

is close to none or close to all. 
16 

unspecified degrees of uncertainty. It would be virtually impossible to specify the uncertainty of the 

population estimates that would incorporate both the uncertainty in the prevalence estimates and the 

uncertainty in the UNPD numbers. 

There is a high degree of consistency between estimates from successive surveys in the same country. 

Differences between successive estimates could be the result of sampling error, other measurement error, 

or genuine change in the underlying population. This report will not attempt to make inferences about 

whether differences or apparent trends are statistically significant. Any appearance of trends, particularly 

when extrapolated, should be examined with care; interpretations should take into account the visual 

uncertainty intervals. 

For two specific countries, Burundi and Kyrgyz Republic, preliminary evidence of a trend between two 

surveys was judged to be spurious. The Burundi 2005 MICS survey provides an estimate of the prevalence 

of vulnerability component 1 that is substantially higher than the 2010 DHS survey, while estimates for 

components 2 and 3, as well as combination 4, are substantially lower. These discrepancies could not be 

traced to any apparent coding errors. Rather than suppress or ignore that survey, it was decided to include 

the estimates from the earlier survey in every way except in the regression lines and the table on the country 

page for Burundi. Similarly, for Kyrgyz Republic, the 2005 MICS survey produced estimates of 

vulnerability component 2 and combination 4 that were substantially lower than the estimates from the 

2012 DHS survey. This difference could not be explained by careful comparison of the data files. The 

earlier survey was then detached from the regression lines and estimates in the table on the country page 

for Kyrgyz Republic. 

In both countries, the earlier survey was detached. The later survey was retained and treated as a single 

point within a five-year interval. The detached surveys were MICS and the retained surveys were DHS, but 

the criterion was the relative timing, and not the source of the surveys. Again, we repeat that no DHS or 

MICS surveys that met the sole criterion of including the parental survival and coresidence information up 

to age 17 were dropped. Users are certainly free to modify the decision described above for Burundi and 

Kyrgyz Republic, and construct lines that include both surveys. 
17 

3. Descriptive Results 

3.1 Overview of the Prevalence of Vulnerability across All Surveys 

An overview of the four components and four combinations of vulnerability will now be presented, based 

on the values of the indicators in the most recent survey in each of the 70 countries. As described earlier, 

the number of surveys varies across countries. It would be possible to examine the distributions across all 

surveys, but the interpretation is easier if we select one survey per country—the most recent survey—and 

use countries rather than surveys as units. There is less variation across countries in the date of the most 

recent survey than in the dates of all the surveys. Of the 70 most recent surveys, none was in the interval 

2000-2004; 18 were in the interval 2005-2009; the remaining 52 were in the interval 2010-2014. 42 surveys 

were in the three-year interval 2010-2012. The breakdown between DHS and MICS is almost exactly the 

same is for all surveys: 42 were DHS and 28 were MICS.

All eight distributions will be shown visually with histograms. In all histograms in this section, the 

horizontal axis is the prevalence, i.e., the percentage of children age 0-17 with the characteristic. The 

vertical axis is the number of surveys with the specified prevalence. The horizontal and vertical scales, and 

the width of the bars, differ from one histogram to another because the distributions are so different.

Figure 1 shows the distribution of the first component, the loss of one or both parents. The most common 

values are 4% and 5%.12 A total of 42 countries, 60% of the total, are in a narrow range from 4% to 8%. 

All except 4 countries are in a range from 2% to 13%. In descending order, the 4 countries in which more 

than 13% of children have experienced the loss of a parent are Lesotho (27.7%), Swaziland (24.3%), 

Zimbabwe (18.5%), and South Sudan (17.4%). Lesotho, Swaziland, and Zimbabwe are the three countries 

with highest adult HIV prevalence in Table 4 (23.3%, 27.4%, and 18.0%, respectively). Beyond other 

considerations, a high prevalence of orphanhood will result in high prevalences of component 2 and 

combinations 1, 2, and 4.

Figure 1. Distribution of vulnerability component 1 (children who have lost one or both parents) 

across all 70 countries 

 

12 “5%”, for example, should be interpreted here as 5.00% to 5.99%. 

0 5 10 15

Number of Surveys

0 5 10 15 20 25 30

Prevalence of Vulnerability Component 1 (%)
18 

Figure 2. Distribution of vulnerability component 2 (children who are not living with either parent) 

across all 70 countries 

Figure 2 shows much higher dispersion in the prevalence of the second component, which is not living with 

either parent. In some surveys, virtually no children age 0-17 are living without at least one parent. It is also 

not unusual for the prevalence to be as high as 20%, although the prevalence drops abruptly after that. Only 

6 countries have a prevalence of 24% or more. These are Namibia (37.9%), Lesotho (37.1%), Swaziland 

(33.3%), Zimbabwe (26.6%), Liberia (25.2%), and Sierra Leone (24.7%). Table 4 shows that the adult 

prevalence of HIV is high in Namibia (14.3%), but is low in Liberia and Sierra Leone (1.5% and 1.6%, 

respectively). 

If we could follow a birth cohort of children from their birth until they reach age 17, it would be observed 

that the percentage of children who are orphans and/or not living with a parent is close to 0 at birth and 

increases steadily with age. The same principle applies to components 1 and 2 and combinations 1 and 2. 

That is, whatever prevalence is observed for the full age range 0-17, the value for the younger ages will 

lower and the value for the older ages, say 12-17, will be much higher.

Components 1 and 2 are strongly associated, by definition, but component 3, living in a household in which 

all the adults have no formal education, does not have a built-in association with those components. The 

distribution of the percentage of children with this characteristic is shown in Figure 3. The most common 

values are 0% and 1%, although the distribution is skewed to the right with a tail reaching almost 70% of 

children. Using an arbitrary threshold of 40%, the countries with the highest prevalence are Niger (69.6%), 

Burkina Faso (66.8%), South Sudan (64.4%), Mali (60.5%), Chad (52.8%), Afghanistan (49.5%), Guinea 

(48.3%), Benin (45.1%), and Sierra Leone (42.5%). Other countries with a prevalence above 30% are 

Ethiopia (38.7%), Côte d'Ivoire (35.8%), Senegal (35.4%), Gambia (32.0%), and Burundi (31.3%). 

Afghanistan is the only country outside of sub-Saharan Africa to appear among the highest values on the 

first three vulnerability components. 

0 5 10

Number of Surveys

0 5 10 15 20 25 30 35 40

Prevalence of Vulnerability Component 2 (%)
19 

Figure 3. Distribution of vulnerability component 3 (children who are living in a household in which 

all of the adults have no education) across all 70 countries 

Figure 4. Distribution of vulnerability component 4 (children who are living in a household ranked 

in the bottom two wealth quintiles) across all 70 countries 

The fourth component refers to children in the bottom two quintiles of the wealth index. As mentioned 

earlier, although it could be expected that 40% of children would be in the bottom two wealth quintiles, 

there is considerable variation in a range from 38.0% in Moldova to 48.5% in Costa Rica, shown in Figure 

4. Most surveys exceed the 40% level because poorer parents tend to have higher fertility, and poorer 

households usually contain more children. 

It would be possible to adjust the fourth component in two ways. For example, the 40% threshold could be 

calculated on the basis of the continuous wealth scale, for all children age 0-17. This would identify exactly 

the lowest 40% of children. An alternative would be to use an absolute scale of wealth.

The first three combinations of vulnerability describe children who have one of the first three components 

and are in the bottom two wealth quintiles. If all the children who lost a parent were also in the bottom two 

wealth quintiles, the prevalence of combination 1 would be the same as the prevalence of component 1. 

Otherwise, the prevalence of combination 1 will be less than the prevalence of component 1. Similarly for 

0 5 10 15

Number of Surveys

0 10 20 30 40 50 60 70

Prevalence of Vulnerability Component 3 (%)

0 5 10

Number of Surveys

35 40 45 50

Prevalence of Vulnerability Component 4 (%)
20 

combinations and components 2 and 3. We expect the distributions of combinations 1, 2, and 3 to be similar 

to the distributions of components 1, 2, and 3, respectively, but more compressed.

The distribution of combination 1, shown in Figure 5, shows this type of similarity and compression when 

compared with Figure 1. For example, the highest 4 countries in Figure 1 had at least 13% of children who 

had lost one or both parents. The same countries are the highest 4 in Figure 5, in which they have at least 

7% who had lost one or both parents and were in the bottom two wealth quintiles. The countries are Lesotho 

(12.2%), Swaziland (12.0%), Zimbabwe (8.9%), and South Sudan (7.5%). The only other countries with a 

prevalence of combination 1 above 5% are Namibia (6.8%), Burundi (6.4%), Rwanda (6.4%), Malawi 

(5.7%), and Uganda (5.6%). The remaining 61 countries have a prevalence below 5%. 

Figure 5. Distribution of vulnerability combination 1 (both components 1 and 4) across all 70 

countries 

Figure 6. Distribution of vulnerability combination 2 (both components 2 and 4) across all 70 

countries 

The distribution of combination 2 is shown in Figure 6. Component 2 (in Figure 2) had 6 countries with a 

prevalence of at least 24%, i.e., 24% of children living without either parent. The highest values in Figure 

6 have a prevalence of at least 10%, i.e., 10% of children living without either parent and being in the 

0 5 10 15

Number of Surveys

0 5 10 15

Prevalence of Vulnerability Combination 1 (%)

0 5 10 15

Number of Surveys

0 5 10 15 20

Prevalence of Vulnerability Combination 2 (%)
21 

bottom two wealth quintiles. There are five such countries: Namibia (19.3%), Lesotho (17.0%), Swaziland 

(16.8%), Zimbabwe (11.9%), and Thailand (10.9%). The highest 4 countries on component 4 are exactly 

the same, and in the same sequence, as the highest 4 countries on combination 2.

Combination 3, like component 3, has a skewed distribution with a long tail to the right. In Figure 3, the 

percentage of children living in a household in which no adult had any formal education reached a 

maximum of approximately 70%, but in Figure 7 the maximum is half of that value, 35%. The same 

countries that were high on component 3 are also high on combination 3, but on a compressed scale and 

with some minor re-arrangements. The 12 countries with a prevalence of at least 20% are South Sudan 

(35.2%), Burkina Faso (34.8%), Niger (34.5%), Mali (31.8%), Guinea (28.7%), Benin (28.7%), 

Afghanistan (27.2%), Chad (27.0%), Sierra Leone (24.4%), Nigeria (23.5%), Senegal (23.4%), and 

Ethiopia (22.0%). The only country outside of sub-Saharan Africa with a prevalence of 20% or more is 

Afghanistan. 

Figure 7. Distribution of vulnerability combination 3 (both components 3 and 4) across all 70 

countries 

Figure 8. Distribution of vulnerability combination 4 (components 1 or 2 or 3, and component 4) 

across all 70 countries 

0 5 10 15

Number of Surveys

0 5 10 15 20 25 30 35

Prevalence of Vulnerability Combination 3 (%)

0 1 2 3 4 5

Number of Surveys

0 5 10 15 20 25 30 35 40

Prevalence of Vulnerability Combination 4 (%)
22 

Combination 4 is a summary of all four risk factors or vulnerabilities. It identifies children who have any

of the first three components and the fourth component. That is, the combination identifies children who 

have lost one or both parents, and/or are not living with either parent, and/or are living in a household in 

which all adults have no education, and are living in a household ranked in the bottom two wealth quintiles. 

The distribution of the prevalence of this summary combination, across all 70 countries, is shown in Figure 

8. The distribution is very wide, with prevalences that range from a minimum of 3% of all children age 0-

17 to a maximum of 36%. The countries with the highest prevalences are mainly the countries with the 

highest levels of component 3. In Figure 8 there are 10 countries with a prevalence of combination 4 of 

27% or more: South Sudan (36.9%), Burkina Faso (35.6%), Niger (35.2%), Mali (32.5%), Guinea (31.2%), 

Benin (30.8%), Sierra Leone (28.7%), Chad (28.4%), Afghanistan (28.0%), and Senegal (27.4%). 

We will briefly describe the statistical associations among the risk factors, continuing with the most recent 

survey from each of the 70 countries. Correlations will be calculated, giving equal weight to each country—

that is, not taking into account the sizes of the surveys or countries. To simplify the terminology, the four 

components will be labelled C1, C2, C3, and C4, respectively, and the four combinations or groupings will 

be labelled G1, G2, G3, and G4, respectively. The letter “r” will indicate a correlation. For example, the 

correlation between component 1 and component 2 will be denoted as r(C1,C2). 

There are six possible bivariate correlations among the four components of vulnerability. Two of the 

calculated correlations would be considered numerically large. The largest is r(C1,C2)=0.74. This 

relationship is expected to be strong by definition, because children who have lost both parents cannot be 

living with either parent, and children who have lost one parent will have an increased risk of not living 

with either parent, when compared with children whose parents are still living. The association is 

fundamentally at the level of individual children, although it would also be expected that a country with 

many orphans will also have many children living without a parent in the household. Nevertheless, within 

each country and across countries, there is variation in the degree to which children with living parents will 

actually be living with those parents. If we calculate the square of r(C1,C2) and multiply by 100, we find 

that about 55% of the variation in C2 is statistically explained by variation in C1, or vice versa.13 The 

remaining 45% of the variation in coresidence is due to other factors, which vary from country to country. 

The next largest correlation among the four components is r(C3,C4)=-0.31. We would not expect negative 

correlations among the indicators of vulnerability. It is counter-intuitive that education and wealth would 

be negatively correlated. In this case, the negative correlation is clearly spurious. It is not calculated with 

child-level data within countries, but at an ecological or aggregated level. Component 4, as described 

earlier, would be expected to be equally likely in all countries, with a prevalence of 40%. If it were exactly 

40% in every country, i.e., a constant, then all correlations with component 4 would be zero. Dispersion in 

this component from one country to another is not due to variations in poverty but to variations in the 

number of children per household. The negative correlation can largely be traced to 12 countries with very 

high levels of component 3, but somewhat below average levels of component 4. These are the 12 countries 

in which the prevalence of component 3 is 35% or more. In a sequence that begins with the highest level of 

component 3, these are Niger, Burkina Faso, South Sudan, Mali, Chad, Afghanistan, Guinea, Benin, Sierra 

Leone, Ethiopia, Côte d'Ivoire, and Senegal. Most of these countries are geographically contiguous; only 

one, Afghanistan, is outside of sub-Saharan Africa. In all of these countries, more than 40% of children are 

in the bottom two wealth quintiles, but the excess above 40%, which is due mainly to higher fertility in 

poorer households, is not as great as in most of the other countries. If these 12 countries are excluded, 

r(C3,C4) drops to -0.08. The sign remains negative but the magnitude is close to zero. 

The remaining four correlations among the vulnerability components range from -0.05 to +0.07, with 

corresponding percentages of explained variation less than 1%. The associations between the first three 

 

13 Here and elsewhere, calculations are based on more decimal places than are shown. 
23 

components and the corresponding combinations, r(C1,G1), r(C2,G2), and r(C3,G3), are very high (0.98, 

0.96, and 0.98, respectively) because the variation in the three combinations is driven primarily by the 

variation in the corresponding components. These high correlations are certainly expected because of the 

definitions of the first three components and the first three combinations. 

The prevalence of combination 4, the synthesis of the four components, is found with regression to have a 

simple empirical relationship to the prevalence of components 1, 2, 3, and 4: it is almost exactly equal to a 

weighted average of the prevalences of the four components. The weights for the four components are 0.17, 

0.31, 0.45, and 0.07, respectively. These weights add to 1.00 and the R2

 value for the model is 0.99.14 This 

regression, with 70 data points—one for the most recent survey from each country—shows that the biggest 

driver of combination 4 is component 3, the prevalence of children living in households with no educated 

adults, with a weight of 0.45. Component 2 is next most important, with weight 0.31; component 1 is next 

most important, with weight 0.17; and component 4 is least important, with weight 0.07. 

This regression simply describes an empirical pattern of association among indicators of risk, with no direct 

implications for child outcomes or for the underlying concept of “vulnerability”. Nevertheless, at an 

aggregate level, it is striking that the prevalence of combination 4, which was constructed for each country 

with individual-level information, can be reproduced almost perfectly and additively from the prevalences 

of components 1, 2, 3, and 4. Moreover, the sum of the weights for components 1 and 2 (0.48) is almost 

exactly the same as the weight for component 3 (0.45). In terms of specifying the synthesized indicator, 

this suggests an almost equal importance of (a) the survival and coresidence status of parents and (b) the 

formal education of adults in the household. More informative analysis of the associations among 

vulnerabilities would be conducted with individual children, within specific countries, as the units of 

analysis. 

3.2 The Prevalence of Vulnerabilities in Each Survey 

Table 5 lists the observed percentages of children age 0-17 in each survey with each of the four components 

or four combinations of vulnerability. For most countries, these numbers are remarkably consistent across 

successive surveys. In Appendix C, the percentages for components 1, 2, and 3, and combination 4, are 

represented by dots or points. The dots are blue for DHS surveys and red for MICS surveys. The prevalences 

in the Mali 2012 DHS survey are not actually national, because that survey omitted parts of the country for 

security reasons. The report on that survey provides a description of its coverage. 

Few countries show large changes in the prevalence of the vulnerability indicators. The decline in 

orphanhood in Rwanda is likely the result of orphans from the genocide of 1994 having grown over time 

to be above age 18, and with the recovery and stability of the country fewer orphans emerged. In countries 

with high HIV prevalence, a decline in orphanhood may be due to the increased use of antiretroviral (ARV) 

drugs, or a decline in the incidence of new HIV cases. Some countries show a decline in component 3 

because long-term increases in educational levels, in particular the expansion of primary education, have 

led to better-educated adults. 

Any apparent changes should be confirmed with country-specific investigation. Over a period of five years, 

say, the prevalence of components 1, 2, or 3 would not be expected to change dramatically. Most 

(approximately 70% to 75%) of the children under 18 in 2005, for example, were also under 18 in 2010. 

 

14 These weights are the coefficients in a linear regression of the prevalence of combination 4 on the prevalences of 

components 1, 2, 3, and 4, with no intercept. There is no mathematical reason why the coefficients should add to 1.00. 

Without rounding, they add to .9982. When this model is applied to subsets of the 70 countries, the sum of the 

coefficients can be greater or smaller than 1. The sum of 1.00 for the full dataset is helpful for the interpretation but is 

not necessary. 
24 

One consequence of this demographic continuity is inertia in the prevalence of risk factors. Children who 

were orphans in 2005 will also be orphans in 2010. If their household contained no educated adults in 2005, 

the same will almost certainly be true in 2010. At a national level, changes in the prevalence of the risk 

factors is gradual. 

Table 5. The observed percentages of children age 0-17 with each component and combination of 

vulnerability, in each survey 

 Survey 

type Year 

Component Combination 

Country 1 2 3 4 1 2 3 4 

Afghanistan MICS 2011 4.7 1.7 49.5 41.0 2.1 0.8 27.2 28.0

Azerbaijan DHS 2006 3.6 2.1 0.1 43.5 1.4 1.0 0.1 2.2 

Bangladesh MICS 2006 5.9 5.5 24.9 44.0 2.6 1.9 17.7 19.9

Belize MICS 2006 5.2 6.6 2.6 44.3 1.9 2.4 2.2 5.8 

Belize MICS 2011 3.9 6.9 2.5 45.4 1.7 2.9 2.3 6.1 

Benin DHS 2006 7.1 13.1 45.6 41.6 3.0 4.1 28.0 29.9

Benin DHS 2012 6.1 14.6 45.1 42.6 2.6 5.4 28.7 30.8

Bolivia DHS 2003 5.3 7.3 2.8 44.5 2.8 3.0 2.1 6.3 

Bolivia DHS 2008 4.5 9.2 3.7 44.4 2.3 3.7 2.6 6.8 

Burkina Faso MICS 2006 7.4 10.5 62.5 41.4 2.9 2.6 33.9 35.1

Burkina Faso DHS 2010 5.6 9.4 66.8 41.8 2.1 3.1 34.8 35.6

Burundi MICS 2005 19.4 8.6 15.7 40.1 8.8 3.4 7.7 14.9

Burundi DHS 2010 13.3 9.5 31.3 40.2 6.4 3.7 18.5 21.8

Cambodia DHS 2005 8.8 7.8 7.9 43.8 4.3 2.7 5.9 10.6

Cambodia DHS 2010 6.9 9.0 6.9 44.9 3.8 3.4 5.3 10.2

Cameroon DHS 2004 10.3 18.3 17.3 43.1 3.9 5.8 13.3 18.4

Cameroon MICS 2006 10.6 16.5 17.2 43.2 4.4 5.8 13.7 19.0

Cameroon DHS 2011 9.7 18.7 15.1 43.7 3.8 6.7 12.4 18.5

Central African Republic MICS 2006 12.1 16.3 16.4 39.3 4.4 5.7 10.9 15.8

Central African Republic MICS 2010 12.8 14.7 14.2 40.2 4.7 5.0 9.1 14.0

Chad DHS 2004 8.1 11.9 55.2 41.4 3.1 4.5 31.2 32.8

Chad MICS 2010 8.0 9.2 52.8 40.6 3.4 3.1 27.0 28.4

Colombia DHS 2000 6.3 10.1 2.0 45.3 3.0 4.9 1.7 8.5 

Colombia DHS 2005 6.5 10.3 1.8 45.7 3.1 5.4 1.6 8.6 

Colombia DHS 2010 5.4 9.2 1.3 46.6 2.7 5.3 1.1 7.9 

Comoros DHS 2012 5.1 17.0 23.2 45.6 2.1 6.6 16.7 21.7

Congo DHS 2005 9.3 16.7 2.4 43.0 3.8 6.2 1.8 9.2 

Congo DHS 2009 6.9 14.6 2.2 42.2 2.7 5.6 1.9 8.1 

Congo DHS 2011 6.7 15.3 2.8 43.1 3.1 5.8 2.2 8.8 

Costa Rica MICS 2011 3.0 4.2 0.7 48.5 1.5 2.4 0.6 4.0 

Côte d'Ivoire DHS 2005 8.1 19.4 34.1 41.8 3.2 6.2 18.7 22.8

Côte d'Ivoire MICS 2006 8.5 20.7 31.7 42.9 3.3 6.8 19.8 24.5

Côte d'Ivoire DHS 2012 8.7 21.7 35.8 41.5 3.2 8.3 18.1 23.1

Democratic Republic of the Congo DHS 2007 9.3 13.6 7.1 39.5 4.0 4.5 4.4 10.1

Democratic Republic of the Congo MICS 2010 10.4 12.1 6.5 40.5 4.4 4.2 4.1 9.6 

Democratic Republic of the Congo DHS 2013 8.9 13.9 6.4 41.0 4.1 5.2 4.5 10.5

Egypt DHS 2005 5.0 1.2 10.3 43.7 2.5 0.7 7.9 10.0

Egypt DHS 2008 4.7 1.1 9.9 43.5 2.6 0.6 8.0 10.4

Egypt DHS 2014 3.6 0.9 7.9 41.6 1.7 0.4 5.5 7.1 

Ethiopia DHS 2005 11.3 10.4 48.6 41.3 4.8 3.6 27.6 29.4

Ethiopia DHS 2011 9.3 10.9 38.7 42.0 4.0 3.5 22.0 24.4

Gabon DHS 2012 6.8 19.8 2.3 41.6 2.6 8.9 1.2 10.8

Gambia MICS 2006 8.7 15.9 32.0 43.0 3.6 5.7 18.5 23.5

Georgia MICS 2005 4.9 2.9 0.1 39.0 2.1 1.0 0.1 2.8 

Ghana DHS 2003 7.7 17.7 24.8 43.8 3.4 6.0 17.2 22.0

Ghana MICS 2006 8.0 14.3 21.2 43.7 3.2 4.3 16.6 20.6

Ghana DHS 2008 7.6 17.9 19.8 44.1 3.3 6.5 15.3 20.3

Ghana MICS 2011 7.7 17.1 17.9 44.5 4.1 6.6 14.2 20.1

Guinea DHS 2012 9.3 18.3 48.3 41.5 3.5 5.9 28.7 31.2

(Continued)
25 

Table 5. − Continued

 Survey 

type Year 

Component Combination 

Country 1 2 3 4 1 2 3 4 

Guinea-Bissau MICS 2006 11.5 18.9 26.9 40.9 4.4 7.4 17.3 23.4

Guyana DHS 2005 7.4 11.5 1.1 47.1 3.7 4.4 1.0 7.5 

Guyana DHS 2009 6.8 12.8 0.5 46.0 3.0 5.3 0.5 7.3 

Haiti DHS 2006 11.4 20.2 20.6 43.5 4.5 6.8 15.4 21.0

Haiti DHS 2012 12.2 20.6 13.9 44.9 4.9 8.5 10.7 18.4

Honduras DHS 2006 6.1 13.2 3.7 44.5 2.6 4.7 2.9 8.4 

Honduras DHS 2012 6.4 13.2 2.3 44.8 3.0 5.2 1.9 8.3 

India DHS 2006 4.9 4.3 25.2 46.1 2.7 1.8 19.6 21.6

Iraq MICS 2011 5.2 1.8 4.1 44.8 2.2 0.7 3.6 5.8 

Jamaica MICS 2011 5.2 12.7 0.1 45.7 2.5 4.8 0.1 6.6 

Jordan DHS 2007 2.7 0.5 0.5 42.6 1.4 0.3 0.5 2.0 

Jordan DHS 2009 3.4 1.0 0.5 43.9 1.5 0.5 0.5 2.2 

Jordan DHS 2012 2.9 0.8 0.5 43.4 1.8 0.5 0.4 2.3 

Kazakhstan MICS 2006 6.8 3.7 0.2 47.1 3.3 1.8 0.1 4.7 

Kazakhstan MICS 2010 5.1 3.3 0.2 46.7 2.6 1.7 0.1 4.1 

Kyrgyz Republic MICS 2005 5.5 5.4 0.1 43.6 2.1 2.3 0.1 4.2 

Kyrgyz Republic DHS 2012 4.0 12.8 0.2 43.1 1.3 6.8 0.1 7.6 

Lao PDR MICS 2006 6.6 3.9 12.1 45.5 3.1 1.2 10.0 12.5

Lao PDR MICS 2011 5.3 6.3 10.2 47.3 2.9 1.9 8.9 12.0

Lesotho DHS 2004 28.1 33.3 5.5 41.5 12.5 14.2 3.5 21.1

Lesotho DHS 2009 27.7 37.1 5.4 43.7 12.2 17.0 3.4 22.0

Liberia DHS 2007 7.2 22.0 21.3 39.9 3.1 6.5 13.7 18.2

Liberia DHS 2013 7.2 25.2 17.8 40.4 2.8 8.0 11.9 17.9

Madagascar DHS 2009 7.3 14.6 12.7 43.3 3.6 5.7 10.2 15.5

Malawi DHS 2004 14.9 20.1 14.8 40.7 6.7 8.6 10.2 17.4

Malawi MICS 2006 12.5 18.1 13.9 41.0 5.1 7.0 8.6 14.8

Malawi DHS 2010 12.6 18.8 10.3 41.5 5.7 7.4 7.5 14.7

Maldives DHS 2009 3.0 6.2 7.9 44.5 1.7 1.8 5.7 8.5 

Mali DHS 2012 4.5 9.2 60.5 40.7 1.8 3.3 31.8 32.5

Mauritania MICS 2007 7.7 10.1 16.1 44.0 3.6 4.6 11.7 16.6

Mauritania MICS 2011 7.2 9.4 13.8 44.9 3.0 4.7 10.1 15.2

Moldova DHS 2005 4.4 8.8 0.6 41.1 2.3 3.5 0.1 5.3 

Moldova MICS 2012 4.6 10.6 1.4 38.0 2.6 5.0 0.6 7.3 

Mozambique MICS 2008 12.3 14.6 14.4 41.5 4.8 4.8 9.1 14.4

Namibia DHS 2007 17.4 36.0 7.1 46.7 9.9 19.3 5.2 25.4

Namibia DHS 2013 13.6 37.9 5.5 46.1 6.8 19.3 4.5 23.5

Nepal DHS 2006 5.1 7.4 30.5 44.1 2.6 2.2 19.2 21.1

Nepal DHS 2011 4.9 7.5 27.4 45.4 2.6 2.4 19.0 21.2

Niger DHS 2012 5.2 10.2 69.6 41.1 2.3 4.4 34.5 35.2

Nigeria MICS 2007 6.3 7.4 34.6 42.1 2.0 2.0 24.3 25.9

Nigeria DHS 2008 6.2 11.3 27.4 43.4 2.3 4.2 21.2 24.1

Nigeria MICS 2011 6.7 8.8 21.3 42.9 2.4 3.1 18.0 20.9

Nigeria DHS 2013 5.7 10.2 29.1 43.7 1.9 3.6 23.5 25.8

Pakistan DHS 2012 4.6 2.2 23.3 44.5 2.3 1.0 18.0 19.7

Rwanda DHS 2005 20.5 14.0 15.5 40.6 9.1 4.4 8.2 15.5

Rwanda DHS 2010 13.4 13.2 10.6 41.9 6.4 5.3 7.5 14.2

Sao Tome and Principe DHS 2008 4.4 17.5 5.2 39.9 1.6 5.7 3.3 8.3 

Senegal DHS 2011 7.2 15.2 39.5 44.7 3.2 6.2 26.7 30.3

Senegal DHS 2013 7.0 14.9 35.4 44.4 3.1 6.0 23.4 27.4

Sierra Leone MICS 2005 11.3 20.3 51.4 40.2 4.4 6.6 28.6 30.9

Sierra Leone DHS 2008 11.4 25.6 48.8 40.3 4.4 8.8 27.5 31.0

Sierra Leone MICS 2010 13.0 22.4 41.4 40.4 4.8 7.2 25.3 28.9

Sierra Leone DHS 2013 10.4 24.4 42.5 41.3 3.7 8.4 24.4 28.7

Somalia MICS 2006 9.7 8.5 25.2 40.4 3.5 2.8 14.3 16.9

South Sudan MICS 2010 17.4 13.2 64.4 41.5 7.5 4.7 35.2 36.9

Sudan MICS 2010 5.7 4.1 22.6 44.4 2.8 2.2 17.8 20.0

(Continued)
26 

Table 5. − Continued

 Survey 

type Year 

Component Combination 

Country 1 2 3 4 1 2 3 4 

Swaziland DHS 2006 23.3 34.2 6.6 44.8 11.4 16.3 4.7 22.6

Swaziland MICS 2010 24.3 33.3 6.5 45.7 12.0 16.8 5.5 23.6

Tanzania DHS 2004 10.8 16.2 10.2 41.2 3.6 5.7 7.0 12.5

Tanzania DHS 2004 9.9 15.6 11.4 41.8 4.0 5.1 8.2 13.6

Tanzania DHS 2007 10.8 16.7 11.2 41.6 4.8 6.0 7.8 13.9

Tanzania DHS 2010 9.9 16.8 10.1 42.5 4.3 6.0 8.0 14.2

Tanzania DHS 2012 9.4 16.7 8.7 42.5 4.0 6.1 6.8 13.4

Thailand MICS 2006 4.8 19.3 2.2 43.9 2.3 10.9 1.4 12.9

Timor-Leste DHS 2009 7.2 9.2 18.9 40.9 3.5 3.1 13.0 16.0

Togo MICS 2006 9.9 16.7 23.9 43.5 3.8 5.3 16.6 21.1

Togo MICS 2010 9.6 14.7 22.4 44.4 3.9 4.6 15.8 19.7

Togo DHS 2014 8.8 16.2 21.4 45.2 3.5 5.2 14.5 19.2

Tunisia MICS 2012 2.8 0.9 3.6 41.8 1.4 0.3 3.1 4.4 

Uganda DHS 2000 14.2 18.9 11.0 39.7 4.9 6.2 7.1 13.1

Uganda DHS 2006 14.9 19.9 9.6 40.9 6.4 6.7 6.5 13.5

Uganda DHS 2011 11.8 20.4 9.3 41.1 5.2 7.2 6.0 13.0

Uganda DHS 2011 11.5 19.0 8.9 42.0 5.6 7.0 6.2 13.6

Ukraine MICS 2005 5.3 1.0 0.1 44.7 2.7 0.1 0.1 2.9 

Ukraine MICS 2012 4.8 2.7 0.1 38.9 2.2 1.3 0.1 3.1 

Uzbekistan MICS 2006 4.1 1.9 0.1 44.0 1.8 0.7 0.1 2.1 

Viet Nam DHS 2005 4.2 3.9 3.8 45.2 2.5 1.6 3.6 6.8 

Viet Nam MICS 2006 3.8 2.8 2.5 38.5 1.7 0.8 2.3 4.5 

Viet Nam MICS 2010 3.9 5.3 2.5 43.4 2.3 2.6 2.3 6.4 

Yemen MICS 2006 5.2 1.7 17.2 41.8 2.4 0.9 12.9 14.8

Zambia DHS 2007 14.9 19.2 6.0 42.7 4.9 6.9 4.5 11.6

Zambia DHS 2013 11.3 17.3 4.1 42.7 4.7 5.9 3.4 10.5

Zimbabwe DHS 2005 23.9 29.0 4.8 44.9 10.9 12.9 3.4 18.0

Zimbabwe MICS 2009 25.2 26.1 3.1 44.4 11.4 11.0 2.1 16.5

Zimbabwe DHS 2010 21.3 29.2 3.3 44.9 9.9 12.9 2.4 17.0

Zimbabwe MICS 2014 18.5 26.6 3.2 44.8 8.9 11.9 2.3 16.1

3.3 The Number of Vulnerable Children in the Year of Each Survey 

Table 6 lists the estimated numbers of children age 0-17 with each type of vulnerability on July 1 of the 

year of each survey—the year that includes the mean date of interview. The numbers, in thousands of 

children, are calculated simply by multiplying the UNPD estimated population of children age 0-17 by the 

proportion of children who have the specified type of vulnerability, as given (in percentages) in Table 5. 

Most countries have increasing populations of children age 0-17 within the interval from 2000 to 2015. 

(This generalization does not apply to all countries; for example, the population age 0-17 in Bangladesh is 

believed to have decreased in recent years, and the UNPD estimates a temporary decline from 2005 to 2010 

in Timor-Leste because of political conflict and resulting displacement.) Thus, even if the prevalence of a 

type of vulnerability does not change, the number of children with that type of vulnerability will tend to 

increase. The population increase can be substantial. For example, an annual increase of 3% in the base 

population would produce an increase of about 16% in five years, 34% in ten years, or 56% in 15 years. In 

the calculation of numbers of children with each type of vulnerability, the population base was not adjusted 

for incomplete coverage of any surveys, such as occurred in the Mali 2012 DHS survey. 

The prevalences in Table 5 and the population estimates in Table 6 are supplemented with smoothed and 

extrapolated estimates in Appendix C for reference years 2000, 2005, 2010, and 2015, but estimates more 

than five years beyond the range of the observations are suppressed. 
27 

Table 6. The estimated numbers of children age 0-17 with each component and combination of 

vulnerability, in the year of the survey (July 1, in thousands) 

 Survey 

type Year 

Component Combination 

Country 1 2 3 4 1 2 3 4 

Afghanistan MICS 2011 725 258 7,653 6,345 327 121 4,212 4,334 

Azerbaijan DHS 2006 98 58 3 1,201 40 28 3 62 

Bangladesh MICS 2006 3,433 3,249 14,595 25,751 1,550 1,120 10,350 11,673 

Belize MICS 2006 7 9 3 58 2 3 3 8 

Belize MICS 2011 5 9 3 62 2 4 3 8 

Benin DHS 2006 306 561 1,958 1,786 130 176 1,201 1,281 

Benin DHS 2012 302 726 2,244 2,121 131 271 1,425 1,532 

Bolivia DHS 2003 205 279 110 1,714 107 114 79 244 

Bolivia DHS 2008 180 367 147 1,778 90 150 106 270 

Burkina Faso MICS 2006 544 771 4,601 3,049 213 191 2,496 2,587 

Burkina Faso DHS 2010 463 782 5,538 3,464 177 258 2,882 2,947 

Burundi MICS 2005 815 361 658 1,682 370 142 322 624 

Burundi DHS 2010 640 457 1,502 1,933 309 176 890 1,046 

Cambodia DHS 2005 518 462 468 2,587 252 160 351 626 

Cambodia DHS 2010 405 522 404 2,617 221 200 309 591 

Cameroon DHS 2004 938 1,662 1,569 3,914 351 529 1,207 1,676 

Cameroon MICS 2006 1,005 1,567 1,635 4,104 418 552 1,303 1,808 

Cameroon DHS 2011 1,019 1,973 1,593 4,608 406 704 1,305 1,947 

Central African Republic MICS 2006 240 325 326 783 87 113 218 315 

Central African Republic MICS 2010 268 308 298 842 98 105 191 294 

Chad DHS 2004 440 644 2,976 2,235 166 243 1,686 1,769 

Chad MICS 2010 531 604 3,483 2,681 227 203 1,781 1,875 

Colombia DHS 2000 968 1,539 305 6,918 459 755 263 1,292 

Colombia DHS 2005 986 1,559 273 6,886 473 814 237 1,289 

Colombia DHS 2010 799 1,349 191 6,830 403 779 166 1,153 

Comoros DHS 2012 18 59 81 158 7 23 58 75 

Congo DHS 2005 157 282 40 729 65 105 30 155 

Congo DHS 2009 132 279 41 804 51 106 36 155 

Congo DHS 2011 136 309 57 871 62 117 44 177 

Costa Rica MICS 2011 41 57 10 656 21 32 8 55 

Côte d'Ivoire DHS 2005 746 1,775 3,125 3,825 295 565 1,710 2,091 

Côte d'Ivoire MICS 2006 798 1,937 2,964 4,006 306 638 1,851 2,288 

Côte d'Ivoire DHS 2012 918 2,284 3,771 4,373 339 872 1,911 2,437 

Democratic Republic of the Congo DHS 2007 2,970 4,339 2,243 12,564 1,286 1,434 1,393 3,196 

Democratic Republic of the Congo MICS 2010 3,629 4,219 2,276 14,157 1,547 1,481 1,437 3,372 

Democratic Republic of the Congo DHS 2013 3,410 5,320 2,454 15,698 1,563 1,988 1,721 4,025 

Egypt DHS 2005 1,512 351 3,097 13,120 755 196 2,363 3,013 

Egypt DHS 2008 1,450 342 3,041 13,304 789 198 2,458 3,191 

Egypt DHS 2014 1,243 323 2,702 14,243 566 132 1,897 2,426 

Ethiopia DHS 2005 4,580 4,225 19,673 16,716 1,939 1,442 11,161 11,891 

Ethiopia DHS 2011 4,251 4,979 17,691 19,211 1,828 1,615 10,082 11,172 

Gabon DHS 2012 48 141 16 295 19 63 8 77 

Gambia MICS 2006 68 125 251 337 28 45 145 184 

Georgia MICS 2005 52 30 1 413 22 11 1 30 

Ghana DHS 2003 743 1,705 2,387 4,217 326 582 1,657 2,121 

Ghana MICS 2006 817 1,461 2,172 4,478 332 445 1,699 2,107 

Ghana DHS 2008 807 1,904 2,113 4,703 348 693 1,627 2,162 

Ghana MICS 2011 871 1,936 2,031 5,050 470 749 1,605 2,277 

Guinea DHS 2012 536 1,054 2,784 2,395 202 338 1,654 1,800 

Guinea-Bissau MICS 2006 84 139 198 300 12 14 3 24 

Guyana DHS 2005 23 36 3 147 9 16 1 22 

Guyana DHS 2009 21 39 1 141 187 284 642 875 

Haiti DHS 2006 474 844 860 1,816 209 363 457 784 

Haiti DHS 2012 519 878 591 1,915 12 14 3 24 

Honduras DHS 2006 194 424 120 1,429 82 150 92 269 

Honduras DHS 2012 205 420 73 1,423 94 164 59 264 

India DHS 2006 21,874 19,349 112,640 205,851 11,969 8,138 87,395 96,355 

Iraq MICS 2011 796 269 635 6,886 343 100 559 896 

Jamaica MICS 2011 47 113 1 408 23 43 1 59 

(Continued)
28 

Table 6. − Continued

 Survey 

type Year 

Component Combination 

Country 1 2 3 4 1 2 3 4 

Jordan DHS 2007 68 14 13 1,072 36 7 13 51 

Jordan DHS 2009 93 26 15 1,184 41 13 13 60 

Jordan DHS 2012 86 22 14 1,274 52 13 12 68 

Kazakhstan MICS 2006 323 178 10 2,250 160 87 6 226 

Kazakhstan MICS 2010 243 161 9 2,239 124 83 5 196 

Kyrgyz Republic MICS 2005 108 106 2 850 41 45 2 82 

Kyrgyz Republic DHS 2012 82 263 4 888 26 141 2 157 

Lao PDR MICS 2006 182 109 333 1,256 86 33 277 346 

Lao PDR MICS 2011 149 177 286 1,321 82 54 248 336 

Lesotho DHS 2004 254 301 50 376 113 129 32 191 

Lesotho DHS 2009 251 336 49 396 110 154 31 199 

Liberia DHS 2007 126 385 373 699 54 115 241 319 

Liberia DHS 2013 152 530 375 851 59 169 250 376 

Madagascar DHS 2009 756 1,513 1,313 4,477 372 591 1,058 1,606 

Malawi DHS 2004 992 1,339 989 2,715 446 576 679 1,160 

Malawi MICS 2006 880 1,273 982 2,886 360 494 603 1,042 

Malawi DHS 2010 990 1,474 806 3,258 448 583 586 1,155 

Maldives DHS 2009 4 7 9 53 2 2 7 10 

Mali DHS 2012 388 799 5,242 3,527 152 287 2,752 2,819 

Mauritania MICS 2007 123 161 256 700 58 73 186 264 

Mauritania MICS 2011 126 163 239 780 52 82 175 264 

Moldova DHS 2005 45 90 6 418 23 36 1 54 

Moldova MICS 2012 38 87 12 314 21 41 5 61 

Mozambique MICS 2008 1,478 1,752 1,735 4,986 576 574 1,090 1,733 

Namibia DHS 2007 168 347 68 450 96 186 50 245 

Namibia DHS 2013 141 391 57 477 70 199 46 243 

Nepal DHS 2006 605 876 3,604 5,199 305 262 2,269 2,486 

Nepal DHS 2011 577 875 3,209 5,315 309 278 2,222 2,485 

Niger DHS 2012 518 1,012 6,939 4,097 232 440 3,440 3507 

Nigeria MICS 2007 4,638 5,440 25,570 31,093 1,488 1,493 17,957 19,139 

Nigeria DHS 2008 4,674 8,584 20,769 32,916 1,768 3,219 16073 18,291 

Nigeria MICS 2011 5,513 7,287 17,539 35,326 2,000 2,570 14828 17,230 

Nigeria DHS 2013 4,919 8,895 25,326 38,003 1,644 3,136 20402 22,421 

Pakistan DHS 2012 3,462 1,657 17,414 33,268 1,715 745 13438 14,726 

Rwanda DHS 2005 926 632 697 1,834 410 198 371 701 

Rwanda DHS 2010 670 662 529 2,092 321 265 374 709 

Sao Tome and Principe DHS 2008 4 14 4 33 1 5 3 7 

Senegal DHS 2011 482 1,021 2,661 3,008 216 419 1,797 2,042 

Senegal DHS 2013 501 1,063 2,531 3,174 222 432 1,674 1,960 

Sierra Leone MICS 2005 290 520 1,319 1,031 112 170 734 792 

Sierra Leone DHS 2008 315 706 1,345 1,110 122 241 758 855 

Sierra Leone MICS 2010 375 647 1,196 1,168 137 209 732 834 

Sierra Leone DHS 2013 316 743 1,297 1,261 114 258 743 876 

Somalia MICS 2006 455 398 1,183 1,897 164 130 671 794 

South Sudan MICS 2010 874 666 3,241 2,088 378 234 1,774 1,859 

Sudan MICS 2010 993 728 3,964 7,798 498 390 3,119 3,510 

Swaziland DHS 2006 129 189 36 248 63 90 26 125 

Swaziland MICS 2010 135 185 36 254 67 94 30 131 

Tanzania DHS 2004 2,122 3,192 2,009 8,100 709 1,110 1,377 2,454 

Tanzania DHS 2004 1,949 3,064 2,234 8,209 784 1,010 1,609 2,665 

Tanzania DHS 2007 2,324 3,583 2,413 8,934 1,035 1,281 1,675 2,995 

Tanzania DHS 2010 2,342 3,960 2,375 10,008 1,011 1,409 1,873 3,335 

Tanzania DHS 2012 2,363 4,184 2,187 10,666 1,008 1,522 1,717 3,352 

Thailand MICS 2006 831 3,347 376 7,594 399 1,889 251 2,238 

Timor-Leste DHS 2009 37 48 97 211 18 16 67 83 

Togo MICS 2006 284 480 684 1,248 109 153 476 605 

Togo MICS 2010 304 463 706 1,401 122 145 499 621 

Togo DHS 2014 305 563 743 1,567 122 180 505 668 

Tunisia MICS 2012 87 28 111 1,282 42 9 96 134 

(Continued)
29 

Table 6. − Continued

 Survey 

type Year 

Component Combination 

Country 1 2 3 4 1 2 3 4 

Uganda DHS 2000 1,891 2,520 1,463 5,287 657 828 953 1740 

Uganda DHS 2006 2,432 3,238 1,570 6,656 1,035 1,096 1,059 2,196 

Uganda DHS 2011 2,243 3,887 1,775 7,847 991 1,370 1,145 2,482 

Uganda DHS 2011 2,197 3,619 1,693 8,008 1,066 1,330 1,186 2,590 

Ukraine MICS 2005 469 85 12 3,933 242 11 9 251 

Ukraine MICS 2012 383 214 8 3,068 176 100 8 248 

Uzbekistan MICS 2006 414 189 10 4,463 182 69 10 210 

Viet Nam DHS 2005 1,190 1,109 1,069 12,785 716 455 1,005 1,910 

Viet Nam MICS 2006 1,058 787 702 10,726 480 229 645 1,253 

Viet Nam MICS 2010 1,016 1,379 657 11,348 607 671 602 1,673 

Yemen MICS 2006 577 190 1,917 4,645 270 100 1,430 1,641 

Zambia DHS 2007 1,017 1,314 408 2,915 336 470 311 794 

Zambia DHS 2013 914 1,394 334 3,446 383 477 271 847 

Zimbabwe DHS 2005 1,542 1,872 308 2,897 701 835 217 1,165 

Zimbabwe MICS 2009 1,701 1,760 206 2,992 768 740 141 1,109 

Zimbabwe DHS 2010 1,449 1,991 226 3,055 675 881 165 1,155 

Zimbabwe MICS 2014 1,365 1,955 239 3,297 654 876 168 1,182 

31 

4. Conclusions 

This report relies heavily on earlier work by Priscilla Idele and other researchers, primarily at UNICEF, 

that was described in the introduction. In their framework, child vulnerability is defined in terms of 

household-level risk factors, more specifically the survivorship of the parents, coresidence with the parents, 

educational level of adults in the household, and relative poverty. On the basis of the earlier empirical work, 

it is believed that children with these risk factors, alone or in combination, have increased probabilities of 

negative outcomes such as malnutrition, lack of schooling, and other childhood deprivations with 

potentially lifelong consequences. 

The objective of this report has been to provide national estimates of the prevalence of these risk factors, 

and the numbers of children who have them, for use by programs intended to mitigate the risks of negative 

outcomes. 

There has been a transition away from alternative indicators of child vulnerability that have been included 

in many DHS and MICS surveys and that focus on illness, potentially due to HIV infections. An earlier 

definition proposed by UNICEF (2005, p. 17) had the following components for use in household surveys: 

“A child made vulnerable by HIV/AIDS is below the age of 18 and: 

i) has lost one or both parents, or 

ii) has a chronically ill parent (regardless of whether the parent lives in the same household as the child), 

or 

iii) lives in a household where in the past 12 months at least one adult died and was sick for 3 of the 12 

months before he/she died, or 

iv) lives in a household where at least one adult was seriously ill for at least 3 months in the past 12 

months.” 

When vulnerability is defined in such narrow and transitory terms, it is difficult to measure and to link to 

child outcomes. Illnesses and deaths in the child’s household are not restricted to the effects of HIV/AIDS. 

Such conditions also exclude most of the children in developing countries who are demonstrably at high 

risk of negative outcomes for other reasons. The new indicators are better suited for global monitoring and 

multi-country analysis. Individual countries may of course select or define indicators that are most 

appropriate for their own circumstances, available data, and programs. 

Our application of the UNICEF (2014) indicators of child vulnerability has maximized international 

coverage and relevance to programs that counteract high risk. The report includes 70 countries—all the 

countries that had a DHS or MICS survey with the required information. The main limitation is that surveys 

in which the questions on parental survival or coresidence only extended to ages 14 or 15 were discarded, 

with little significant impact, because almost all the countries that had an early survey with a lower age 

limit also had at least one subsequent survey that extended to age 17. 

The most comprehensive indicator of vulnerability in the new framework is combination 4: children who 

have lost one or both parents, and/or are not living with either parent, and/or are living in a household in 

which all adults have no education, and are living in a household ranked in the bottom two wealth quintiles. 

The prevalence of this synthesis ranges from 3% to 36%. It is highest in the countries which are highest on 

the respective components, but is most strongly related to the third component, education, because that 
32 

component has the broadest distribution across the 70 countries. That is, the absence of any adult with 

formal education in the household, component 3, has the greatest influence on the main indicator. The 

prevalence is highest, 27% or more of all children age 0-17, in the following ten countries: South Sudan, 

Burkina Faso, Niger, Mali, Guinea, Benin, Sierra Leone, Chad, Afghanistan, and Senegal. Afghanistan has 

the highest prevalence outside of sub-Saharan Africa. South Sudan is the only other country on this list that 

is outside of a geographically contiguous band from Senegal to Chad. 

By including all relevant DHS and MICS surveys, the number of countries that could be included is 

substantially larger than it would have been with just with one survey program or the other. An examination 

of Table 1 shows that 32 countries had only a DHS survey (one or more); 22 countries had only a MICS 

survey (one or more), and 16 countries had both DHS and MICS surveys. Moreover, the use of both DHS 

and MICS surveys has increased the number of data points within those 16 countries with both types of 

surveys. The harmonization of questions and categories made it possible to blend the estimates within those 

countries. 

The relevance to programs has been achieved by estimating actual numbers of children at risk, not just the 

percentages or prevalences. For this purpose, we used the base populations of children age 0-17 estimated 

by another UN agency, the Population Division. Within DHS publications, the extrapolation from 

prevalences to population numbers is rare. It is done here to provide denominators for program planning or 

coverage estimates. 

The smoothing and extrapolation of prevalence to provide estimates for a set of reference years—2000, 

2005, 2010, 2015—is innovative, at least within DHS reports. The strategy included a simple adjustment 

for the DEFT that allows for the use of aggregated data, rather than individual-level data. This kind of data 

reduction is desirable when working with large files—in this case, a total of more than four million children 

age 0-17 are included in the 135 surveys. Refinement of this adjustment is possible in future analyses. 

An important limitation of the approach described here is that it is restricted to children within the household 

population. The most vulnerable children in most countries are likely to be living outside of households and 

are not identified with DHS and MICS surveys, which are household-based. For example, many children 

in the upper end of the 0-17 age interval who are exploited for their labor are outside the household 

population—even though such exploitation can certainly also take place within households. Children who 

are displaced by disasters or conflict can be of any age and, even when accompanied by their parents or 

other adults, are at high risk of negative outcomes but are outside the sampling frame of household surveys. 

Two principal directions for potential modifications may be suggested. First, the bottom two wealth 

quintiles as calculated by DHS and MICS may not be optimal for international comparisons. The wealth 

quintiles describe variation in household assets within a country. It is possible that an absolute measure of 

wealth would be a better indicator of risk. DHS has developed strategies to move in the direction of absolute 

measures of wealth while retaining the underlying logic of the continuous version of the wealth index (see 

Rutstein and Staveteig 2014 and Staveteig and Mallick 2014). Second, an ongoing research objective is the 

refinement of the risk factors and the clarification of their impact on outcomes that are often age-specific. 

The synthesis report (UNICEF 2014) examined the relationship of the risk factors to several child health 

and development outcomes, spanning a child’s life cycle; that analysis formed the basis for redefining 

“vulnerable children,” in a way that the current study builds on. Country-specific analyses for selected 

countries have been completed using the most recent survey data, and UNICEF is in the process of turning 

those analyses into summary reports and policy briefs. 

Research that links household structure with child outcomes is challenging, particularly when restricted to 

the past and current information in DHS and MICS surveys. We do not know, for example, how long the 

child has been orphaned or living separately from parents. We have little knowledge of whether there may 
33 

be another adult in the household, even if not a parent, who is a dedicated substitute for a parent. We do not 

know whether the parents or other adults in the household, even if not formally educated themselves, may 

place a high value on education for the child. Such information is not available in these surveys and would 

be difficult to incorporate in future surveys. Some other information has been only partially exploited. For 

example, the current framework could be broadened to take into account how many other children are in 

the household and how they are related to the adults and the reference child. More use could be made of 

the relationship to the household head and characteristics of the household head. 

Although the framework applied here is compatible with more nuanced research into risk factors and 

outcomes, there is an advantage to its simplicity. The DHS and MICS data, no matter how cleverly they are 

refined, will inevitably lead to classifying some children as being at high risk, when in fact they do not have 

measurable negative outcomes, and to classifying other children as not being at high risk, when in fact they 

do have negative outcomes, such as not attending school. The estimates provided here are best taken at face 

value, as the percentages and numbers of children in 70 countries who have specific disadvantages that are 

likely to lead to deprivations with long-term effects. 

35 

References 

Akwara, P. A., et al. 2010. “Who is the Vulnerable Child: Using Survey Data to Identify Children at Risk 

in the Era of HIV and AIDS.” AIDS Care, vol. 22, no. 9, 2010, pp. 1066–1085. 

Kanamori, M. J. and T. Pullum. 2013. Indicators of Child Deprivation in sub-Saharan Africa: Levels and 

Trends from the Demographic and Health Surveys. DHS Comparative Reports No. 32. Calverton, 

Maryland, USA: ICF International. Available at http://dhsprogram.com/pubs/pdf/CR32/CR32.pdf. 

Nyberg, B. J., et al. 2012. “Saving Lives for a Lifetime: Supporting Orphans and Vulnerable Youth 

Impacted by HIV/AIDS.” Journal of Acquired Immune Deficiency Syndromes, vol. 60, no. 3, pp. S127–

S135 

Rutstein, S. O. and S. Staveteig. 2014. Making the Demographic and Health Surveys Wealth Index 

Comparable. DHS Methodological Reports No. 9. Rockville, Maryland, USA: ICF International. Available 

at http://dhsprogram.com/pubs/pdf/MR9/MR9.pdf. 

Staveteig, S. and L. Mallick. 2014. Intertemporal Comparisons of Poverty and Wealth with DHS Data: A 

Harmonized Asset Index Approach. DHS Methodological Reports No. 15. Rockville, Maryland, USA: ICF 

International. Available at http://dhsprogram.com/pubs/pdf/MR15/MR15.pdf. 

UNICEF. 2005. Guide to the Monitoring and Evaluation of the National Response for Children Orphaned 

and Made Vulnerable by HIV/AIDS. Available at http://hivdata.dhsprogram.com/guides/ovcguide.pdf 

UNICEF. 2014. Measuring the Determinants of Child Vulnerability. Available at 

http://data.unicef.org/corecode/uploads/document6/uploaded_pdfs/corecode/Measuring-theDeterminants-of-Childhood-Vulnerability_Final-Report-5_8-LR-_172.pdf 

37 

Appendix A. Construction of Indicators 

Some specific coding instructions are provided because the relevant variables have some missing values 

and there can be ambiguity about how they are interpreted. The following Stata instructions are specific to 

DHS files or MICS files. 

In DHS files, hv111 is survival of the mother (1=yes, 0=no) and hv113 is survival of the father. hv112 is 

line mother of the mother, if in the household, and hv114 is line number of the father, if in the household. 

In a step not shown here, hv112 and hv114 are recoded to the value 98 if the parent is in the household but 

is not a de jure resident. 

gen one_or_both_parents_dead=0 

replace one_or_both_parents_dead=1 if hv111==0 | hv113==0 

gen with_neither_parent=0 

replace with_neither_parent=1 if (hv112==0 | hv112>=98) & (hv114==0 | 

hv114>=98)

In MICS files, the variables corresponding with hv111-hv114 have different names in different surveys. 

For convenience, after the variables were identified, they were renamed hv111-hv114, but the values were 

not recoded. The MICS files are always limited to de jure residents so it was not necessary to construct 

codes 98 for hv112 and hv114. The renamed hv111 and hv113 MICS variables are consistently coded with 

1=yes and 2=no. In order to match the numbers in MICS reports, some missing value codes need to be 

introduced. 

gen one_or_both_parents_dead=0 

replace one_or_both_parents_dead=1 if hv111==2 | hv113==2 

replace one_or_both_parents_dead=. if (hv111==1 & hv113>2) | (hv111>2 & 

hv113==1) | (hv111>2 & hv113>2) 

gen mo_in_hh=0 

replace mo_in_hh=1 if hv112>0 & hv112<. 

replace mo_in_hh=. if hv111>2 & hv112==. 

gen fa_in_hh=0 

replace fa_in_hh=1 if hv114>0 & hv114<. 

replace fa_in_hh=. if hv113>2 & hv114==. 

gen with_neither_parent=0 

replace with_neither_parent=1 if mo_in_hh==0 & fa_in_hh==0 

The matching criteria for these calculations were the percentages of children with one or both parents dead, 

or living with neither parent, in DHS and MICS main reports, on STATcompiler, or in table 8 of the 

UNICEF report. It is difficult to match all numbers exactly, but it is believed that any discrepancies are 

negligible, rarely as much as one percentage point, and can be traced to rounding error. 

All calculations for this report were done with Stata 13. 

39 

Appendix B. Population Estimates of Children Age 0-17 in 2000, 

2005, 2010, and 2015 

Table B.1. For each country included in this report, the estimated population age 0-17 in 2000, 2005, 

2010, and 2015 (July 1, in thousands). Estimates from the U.N. Population Division, 2015 Revision 

 Year 

Country 2000 2005 2010 2015

Afghanistan 10,865 13,232 15,167 16,716 

Azerbaijan 3,024 2,802 2,600 2,538 

Bangladesh 57,622 58,656 58,213 57,168 

Belize 118 129 136 140 

Benin 3,610 4,181 4,760 5,312 

Bolivia 3,744 3,923 4,060 4,138 

Burkina Faso 6,234 7,150 8,289 9,475 

Burundi 3,804 4,197 4,803 5,685 

Cambodia 6,133 5,901 5,827 5,850 

Cameroon 8,308 9,292 10,334 11,472 

Central African Rep 1,817 1,967 2,097 2,242 

Chad 4,619 5,609 6,600 7,671 

Colombia 15,267 15,078 14,670 14,144 

Comoros 280 305 334 368 

Congo 1,501 1,694 1,962 2,259 

Costa Rica 1,441 1,411 1,363 1,308 

Côte d'Ivoire 8,236 9,159 10,129 11,193 

Democratic Republic of the Congo 25,482 29,837 34,978 40,639 

Egypt 29,602 30,021 30,993 35,095 

Ethiopia 35,400 40,446 45,085 48,448 

Gabon 581 631 684 748 

Gambia 647 758 895 1,051 

Georgia 1,268 1,058 906 832 

Ghana 9,067 10,030 11,104 12,330 

Guinea 4,457 4,888 5,499 6,196 

Guinea-Bissau 665 721 787 871 

Guyana 306 312 306 279 

Haiti 4,069 4,160 4,248 4,294 

Honduras 3,084 3,207 3,218 3,109 

India 432,541 445,384 452,501 451,990 

Iraq 11,738 13,157 14,933 17,270 

Jamaica 1,000 974 911 821 

Jordan 2,218 2,352 2,794 3,160 

Kazakhstan 5,009 4,771 4,798 5,356 

Kyrgyz Republic 2,057 1,951 1,994 2,170 

Lao PDR 2,677 2,755 2,791 2,813 

Lesotho 900 905 905 921 

Liberia 1,445 1,627 1,963 2,203 

Madagascar 8,120 9,409 10,583 11,776 

Malawi 6,002 6,856 7,849 8,949 

Maldives 137 127 118 119 

Mali 5,892 6,855 8,132 9,526 

Mauritania 1,342 1,521 1,704 1,890 

Moldova 1,230 1,016 862 777 

Mozambique 9,413 10,977 12,764 14,589 

Namibia 891 947 992 1,063 

Nepal 11,283 11,802 11,803 11,316 

Niger 6,052 7,440 9,158 11,332 

Nigeria 61,723 69,896 80,134 91,855 

(Continued) 
40 

Table B.1. − Continued

 Year 

Country 2000 2005 2010 2015

Pakistan 66,057 69,344 72,822 77,779 

Rwanda 4,248 4,512 4,994 5,532 

Sao Tome and Principe 70 78 85 94 

Senegal 5,142 5,759 6,530 7,596 

Sierra Leone 2,070 2,565 2,890 3,166 

Somalia 3,942 4,577 5,201 5,787 

South Sudan 3,418 4,122 5,036 6,028 

Sudan 14,140 15,887 17,563 18,954 

Swaziland 557 552 556 569 

Tanzania 17,593 20,198 23,554 27,611 

Thailand 18,312 17,761 15,647 14,656 

Timor-Leste 482 545 509 581 

Togo 2,506 2,798 3,155 3,553 

Tunisia 3,500 3,209 3,033 3,115 

Uganda 13,330 15,776 18,518 21,473 

Ukraine 10,534 8,790 7,892 7,897 

Uzbekistan 10,729 10,231 9,849 10,081 

Viet Nam 30,553 28,295 26,140 25,578 

Yemen 9,918 10,944 11,813 12,629 

Zambia 5,660 6,459 7,429 8,535 

Zimbabwe 6,326 6,455 6,811 7,504 
41 

Appendix C. Country Pages 

Afghanistan ...................................................... 43 

Azerbaijan ........................................................ 44 

Bangladesh ....................................................... 45 

Belize ............................................................... 46 

Benin ................................................................ 47 

Bolivia .............................................................. 48 

Burkina Faso .................................................... 49 

Burundi ........................................................... 50 

Cambodia ......................................................... 51 

Cameroon ......................................................... 52 

Central African Republic ................................. 53 

Chad ................................................................. 54 

Colombia .......................................................... 55 

Comoros ........................................................... 56 

Congo ............................................................... 57 

Costa Rica ........................................................ 58 

Côte d’Ivoire .................................................... 59 

Democratic Republic of the Congo .................. 60 

Egypt ................................................................ 61 

Ethiopia ............................................................ 62 

Gabon ............................................................... 63 

Gambia ............................................................. 64 

Georgia ............................................................. 65 

Ghana ............................................................... 66 

Guinea .............................................................. 67 

Guinea-Bissau .................................................. 68 

Guyana ............................................................. 69 

Haiti ................................................................. 70 

Honduras .......................................................... 71 

India ................................................................. 72 

Iraq ................................................................... 73 

Jamaica ............................................................. 74 

Jordan ............................................................... 75 

Kazakhstan ....................................................... 76 

Kyrgyz Republic .............................................. 77 

Lao ................................................................... 78 

Lesotho ............................................................ 79 

Liberia ............................................................ 80 

Madagascar ...................................................... 81 

Malawi ............................................................ 82 

Maldives .......................................................... 83 

Mali ................................................................. 84 

Mauritania ........................................................ 85 

Moldova ........................................................... 86 

Mozambique .................................................... 87 

Namibia ........................................................... 88 

Nepal ............................................................... 89 

Niger ............................................................... 90 

Nigeria ............................................................ 91 

Pakistan ............................................................ 92 

Rwanda ............................................................ 93 

Sao Tome and Principe .................................... 94 

Senegal ............................................................ 95 

Sierra Leone ..................................................... 96 

Somalia ............................................................ 97 

South Sudan ..................................................... 98 

Sudan ............................................................... 99 

Swaziland....................................................... 100 

Tanzania......................................................... 101 

Thailand ......................................................... 102 

Timor-Leste ................................................... 103 

Togo .............................................................. 104 

Tunisia .......................................................... 105 

Uganda .......................................................... 106 

Ukraine .......................................................... 107 

Uzbekistan ..................................................... 108 

Viet Nam........................................................ 109 

Yemen .......................................................... 110 

Zambia .......................................................... 111 

Zimbabwe ...................................................... 112
42 

This appendix gives the vulnerability indicators and population estimates for each country as of July 1 of 

reference years 2000, 2005, 2010, and 2015, except when those dates are more than five years before or 

after the dates of the surveys, defined as the mean date of interview. The estimated percentages of children 

age 0-17 who have the component or combination of vulnerability are obtained by smoothing and 

extrapolating the available survey data, as described in the report. All countries that had a DHS or MICS 

survey in the interval from 2000 to 2014 with the parental survival and coresidence information up to age 

17 are included. For convenience, the four components and combinations of vulnerability are listed again: 

Component 1: Children who have lost one or both parents 

Component 2: Children who are not living with either parent 

Component 3: Children living in a household in which all adults have no education 

Component 4: Children living in a household ranked in the bottom two wealth quintiles 

Combination 1: Children with risk components 1 and 4 

Combination 2: Children with risk components 2 and 4 

Combination 3: Children with risk components 3 and 4 

Combination 4: Children with risk components 1 or 2 or 3 and with risk component 4 

When there was only one survey, the same prevalence estimates are given for the reference years 

immediately before and after. For example, if there was only one survey, and it was in 2006, then the 

prevalences from that survey are given in table 5 of the report and the population numbers are given in table 

6 of the report, both for 2006, but in this appendix the prevalences and population numbers are given for 

2005 and 2010. Users who have reason to believe that the underlying prevalences changed between 2005 

and 2010 are free to ignore the extrapolations to 2005 and 2010. 

The estimated percentages for components 1, 2, 3, and combination 4 are shown graphically. The graphs 

include approximate 95% confidence bands. The estimated percentages for all components and 

combinations are given numerically in a table. Estimated numbers of children, in thousands, are obtained 

by applying the percentages to the estimated numbers of children age 0-17, at the specified reference dates, 

from the U.N. Population Division World Population Prospects, 2015 Revision. Those population numbers 

are given in Appendix B. 

 
43 

Figure AF. Observed and fitted percentages of children age 0-17 in Afghanistan with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table AF. Fitted percentages and estimated populations of children age 0-17 in Afghanistan with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 4.7 1.7 49.5 41.0 2.1 0.8 27.2 28.0 

2010 Population 711 253 7,506 6,223 321 119 4,131 4,250

2015 Percentage 4.7 1.7 49.5 41.0 2.1 0.8 27.2 28.0 

2015 Population 783 279 8,272 6,859 354 131 4,552 4,684

 

Afghanistan

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Afghanistan

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Afghanistan

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Afghanistan

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
44 

Figure AZ. Observed and fitted percentages of children age 0-17 in Azerbaijan with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table AZ. Fitted percentages and estimated populations of children age 0-17 in Azerbaijan with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 3.6 2.1 0.1 43.5 1.4 1.0 0.1 2.2 

2005 Population 100 58 3 1219 40 28 3 63 

2010 Percentage 3.6 2.1 0.1 43.5 1.4 1.0 0.1 2.2 

2010 Population 93 54 3 1131 37 26 3 58 

 

Azerbaijan

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Azerbaijan

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Azerbaijan

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Azerbaijan

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
45 

Figure BD. Observed and fitted percentages of children age 0-17 in Bangladesh with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table BD. Fitted percentages and estimated populations of children age 0-17 in Bangladesh with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 5.9 5.5 24.9 44.0 2.6 1.9 17.7 19.9 

2005 Population 3,438 3,254 14,617 25,790 1,552 1,122 10,365 11,691

2010 Percentage 5.9 5.5 24.9 44.0 2.6 1.9 17.7 19.9 

2010 Population 3,412 3,229 14,506 25,596 1,541 1,113 10,287 11,602

Bangladesh

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Bangladesh

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Bangladesh

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Bangladesh

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
46 

Figure BZ. Observed and fitted percentages of children age 0-17 in Belize with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table BZ. Fitted percentages and estimated populations of children age 0-17 in Belize with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 5.4 6.6 2.6 44.1 1.9 2.4 2.2 5.8 

2005 Population 7 9 3 57 2 3 3 7 

2010 Percentage 4.1 6.8 2.5 45.2 1.7 2.8 2.3 6.1 

2010 Population 6 9 3 61 2 4 3 8 

2015 Percentage 3.1 7.1 2.5 46.3 1.6 3.3 2.4 6.3 

2015 Population 4 10 3 65 2 5 3 9 

Belize

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Belize

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Belize

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Belize

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
47 

Figure BJ. Observed and fitted percentages of children age 0-17 in Benin with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table BJ. Fitted percentages and estimated populations of children age 0-17 in Benin with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 7.4 12.7 45.8 41.4 3.1 3.8 27.8 29.6 

2005 Population 309 532 1,913 1,731 130 161 1,164 1,239 

2010 Percentage 6.4 14.1 45.3 42.3 2.7 5.0 28.5 30.5 

2010 Population 303 673 2,155 2,015 130 239 1,355 1,453 

2015 Percentage 5.5 15.7 44.8 43.3 2.4 6.5 29.1 31.4 

2015 Population 292 832 2,380 2,299 128 346 1,545 1,668 

 

Benin

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Benin

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Benin

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Benin

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
48 

Figure BO. Observed and fitted percentages of children age 0-17 in Bolivia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table BO. Fitted percentages and estimated populations of children age 0-17 in Bolivia with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 6.0 6.1 2.3 44.6 3.3 2.5 1.7 6.1 

2000 Population 226 227 88 1,670 122 93 64 227 

2005 Percentage 5.0 7.9 3.1 44.5 2.6 3.2 2.3 6.5 

2005 Population 196 311 123 1,744 101 127 88 255 

2010 Percentage 4.1 10.3 4.2 44.3 2.0 4.2 3.0 7.0 

2010 Population 167 418 169 1,800 82 170 121 283 

Bolivia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Bolivia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Bolivia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Bolivia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
49 

Figure BF. Observed and fitted percentages of children age 0-17 in Burkina Faso with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table BF. Fitted percentages and estimated populations of children age 0-17 in Burkina Faso with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 7.8 10.7 61.6 41.3 3.1 2.5 33.7 35.1 

2005 Population 557 764 4,407 2,955 219 179 2,412 2,506 

2010 Percentage 5.7 9.5 66.6 41.8 2.2 3.1 34.7 35.5 

2010 Population 470 787 5,519 3,462 180 255 2,878 2,945 

2015 Percentage 4.1 8.4 71.2 42.2 1.5 3.8 35.7 36.0 

2015 Population 389 798 6,744 3,999 145 359 3,384 3,412 

Burkina Faso

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Burkina Faso

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Burkina Faso

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Burkina Faso

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
50 

Figure BU. Observed and fitted percentages of children age 0-17 in Burundi with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys. The 

MICS 2005 and DHS 2010 surveys are inconsistent and are not pooled. 

Table BU. Fitted percentages and estimated populations of children age 0-17 in Burundi with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. The MICS 2005 survey has inconsistent values for components 

1 and 2. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 13.3 9.5 31.3 40.2 6.4 3.7 18.5 21.8 

2010 Population 640 457 1,502 1,933 309 176 890 1,046 

2015 Percentage 13.3 9.5 31.3 40.2 6.4 3.7 18.5 21.8 

2015 Population 757 541 1,778 2,288 366 209 1,054 1,238 

Burundi

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Burundi

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Burundi

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Burundi

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
51 

Figure KH. Observed and fitted percentages of children age 0-17 in Cambodia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table KH. Observed and fitted percentages of children age 0-17 in Cambodia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 9.0 7.7 8.0 43.7 4.3 2.6 6.0 10.7 

2005 Population 529 456 473 2,582 254 156 354 629 

2010 Percentage 7.0 8.9 7.0 44.8 3.8 3.4 5.3 10.2 

2010 Population 410 518 407 2,613 222 197 311 593 

2015 Percentage 5.5 10.2 6.1 45.9 3.4 4.3 4.8 9.7 

2015 Population 322 596 356 2,688 197 252 278 569 

Cambodia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Cambodia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Cambodia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Cambodia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
52 

Figure CM. Observed and fitted percentages of children age 0-17 in Cameroon with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table CM. Fitted percentages and estimated populations of children age 0-17 in Cameroon with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 10.9 16.9 19.0 42.7 4.2 5.3 14.2 18.8 

2000 Population 909 1,407 1,575 3,551 345 437 1,184 1,560

2005 Percentage 10.4 17.6 17.1 43.2 4.0 5.9 13.4 18.7 

2005 Population 963 1,632 1,591 4,011 375 545 1,245 1,734

2010 Percentage 9.8 18.2 15.4 43.6 3.9 6.5 12.6 18.5 

2010 Population 1,014 1,883 1,595 4,505 405 674 1,300 1,916

2015 Percentage 9.3 18.9 13.9 44.0 3.8 7.2 11.8 18.4 

2015 Population 1,065 2,167 1,593 5,050 437 832 1,356 2,114

Cameroon

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Cameroon

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Cameroon

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Cameroon

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
53 

Figure CF. Observed and fitted percentages of children age 0-17 in Central African Republic with 

vulnerability components 1-3 and combination 4, estimated from all available DHS and/or MICS 

surveys 

Table CF. Fitted percentages and estimated populations of children age 0-17 in Central African 

Republic with the four specified components and the four specified combinations of vulnerability, 

estimated from available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 11.9 16.8 17.0 39.1 4.3 5.9 11.5 16.3 

2005 Population 233 331 334 769 84 116 227 321 

2010 Percentage 12.8 14.7 14.3 40.1 4.7 5.0 9.2 14.1 

2010 Population 268 309 299 841 98 105 192 296 

2015 Percentage 13.7 12.9 11.9 41.2 5.1 4.2 7.2 12.1 

2015 Population 308 289 268 924 114 95 162 272 

Central African Rep

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Central African Rep

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Central African Rep

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Central African Rep

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
54 

Figure TD. Observed and fitted percentages of children age 0-17 in Chad with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table TD. Fitted percentages and estimated populations of children age 0-17 in Chad with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 8.2 14.6 57.0 42.0 2.8 6.0 34.8 36.4 

2000 Population 380 673 2,633 1,942 130 279 1,606 1,681 

2005 Percentage 8.1 11.5 54.8 41.3 3.1 4.3 30.6 32.2 

2005 Population 456 646 3,076 2,317 175 240 1,718 1,804 

2010 Percentage 8.0 9.0 52.7 40.6 3.5 3.0 26.8 28.2 

2010 Population 531 596 3,475 2,678 228 199 1,769 1,862 

Chad

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Chad

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Chad

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Chad

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
55 

Figure CO. Observed and fitted percentages of children age 0-17 in Colombia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table CO. Fitted percentages and estimated populations of children age 0-17 in Colombia with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 6.8 10.5 2.1 45.1 3.2 5.1 1.8 8.7 

2000 Population 1,035 1,608 321 6,892 490 785 277 1,333

2005 Percentage 6.1 9.9 1.7 45.8 3.0 5.3 1.4 8.3 

2005 Population 927 1,494 252 6,910 452 792 218 1,255

2010 Percentage 5.6 9.3 1.3 46.5 2.8 5.4 1.2 7.9 

2010 Population 818 1,368 195 6,825 410 787 169 1,163

 

Colombia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%)

2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Colombia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%)

2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Colombia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%)

2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Colombia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%)

2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
56 

Figure KM. Observed and fitted percentages of children age 0-17 in Comoros with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table KM. Fitted percentages and estimated populations of children age 0-17 in Comoros with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 5.1 17.0 23.2 45.6 2.1 6.6 16.7 21.7 

2010 Population 17 57 77 152 7 22 56 72 

2015 Percentage 5.1 17.0 23.2 45.6 2.1 6.6 16.7 21.7 

2015 Population 19 63 85 168 8 24 61 80 

Comoros

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Comoros

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Comoros

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Comoros

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
57 

Figure CG. Observed and fitted percentages of children age 0-17 in Congo with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table CG. Fitted percentages and estimated populations of children age 0-17 in Congo with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 9.2 16.1 2.3 42.8 3.6 6.0 1.7 8.9 

2005 Population 156 272 38 724 61 102 30 152 

2010 Percentage 7.0 15.2 2.6 42.9 3.0 5.8 2.1 8.7 

2010 Population 137 298 51 841 59 113 41 170 

2015 Percentage 5.3 14.3 3.0 43.0 2.5 5.5 2.4 8.4 

2015 Population 119 323 68 971 57 125 55 189 

Congo

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Congo

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Congo

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Congo

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
58 

Figure CR. Observed and fitted percentages of children age 0-17 in Costa Rica with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table CR. Fitted percentages and estimated populations of children age 0-17 in Costa Rica with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 3.0 4.2 0.7 48.5 1.5 2.4 0.6 4.0 

2010 Population 41 57 10 662 21 33 8 55 

2015 Percentage 3.0 4.2 0.7 48.5 1.5 2.4 0.6 4.0 

2015 Population 39 55 10 635 20 31 8 53 

 

Costa Rica

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Costa Rica

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Costa Rica

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Costa Rica

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
59 

Figure CI. Observed and fitted percentages of children age 0-17 in Côte d’Ivoire with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table CI. Fitted percentages and estimated populations of children age 0-17 in Côte d’Ivoire with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 8.3 20.0 32.0 42.7 3.3 6.4 19.6 24.1 

2005 Population 761 1,829 2,932 3,907 299 585 1,799 2,205

2010 Percentage 8.6 21.3 34.7 41.8 3.2 7.8 18.5 23.4 

2010 Population 873 2,156 3,515 4,237 327 789 1,879 2,373

2015 Percentage 8.9 22.7 37.5 41.0 3.2 9.5 17.5 22.8 

2015 Population 1,002 2,536 4,197 4,589 358 1,060 1,959 2,552

Côte d'Ivoire

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Côte d'Ivoire

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Côte d'Ivoire

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Côte d'Ivoire

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
60 

Figure DR. Observed and fitted percentages of children age 0-17 in Democratic Republic of the 

Congo with vulnerability components 1-3 and combination 4, estimated from all available DHS 

and/or MICS surveys 

Table DR. Fitted percentages and estimated populations of children age 0-17 in Democratic 

Republic of the Congo with the four specified components and the four specified combinations of 

vulnerability, estimated from available DHS and/or MICS surveys. Population estimates are in 

thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 10.0 12.3 7.1 39.3 4.3 4.0 4.2 9.6 

2005 Population 2,990 3,656 2,117 11,716 1,279 1,186 1,241 2,855

2010 Percentage 9.5 13.0 6.7 40.3 4.2 4.6 4.3 10.1 

2010 Population 3,311 4,560 2,328 14,107 1,472 1,610 1,513 3,519

2015 Percentage 8.9 13.9 6.2 41.4 4.1 5.3 4.5 10.6 

2015 Population 3,632 5,632 2,536 16,827 1,680 2,163 1,827 4,298

Democratic Republic of the Congo

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Democratic Republic of the Congo

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Democratic Republic of the Congo

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Democratic Republic of the Congo

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
61 

Figure EG. Observed and fitted percentages of children age 0-17 in Egypt with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table EG. Fitted percentages and estimated populations of children age 0-17 in Egypt with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 6.1 1.3 12.2 45.1 3.5 0.9 10.1 13.0 

2000 Population 1,817 393 3,616 13,342 1,021 274 2,996 3,853

2005 Percentage 5.1 1.2 10.5 43.8 2.7 0.7 8.3 10.6 

2005 Population 1,533 353 3,152 13,162 806 206 2,485 3,195

2010 Percentage 4.2 1.0 9.0 42.6 2.1 0.5 6.7 8.7 

2010 Population 1,314 323 2,790 13,210 647 158 2,091 2,683

2015 Percentage 3.5 0.9 7.7 41.4 1.6 0.4 5.5 7.0 

2015 Population 1,233 324 2,702 14,533 568 132 1,923 2,461

Egypt

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Egypt

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Egypt

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Egypt

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
62 

Figure ET. Observed and fitted percentages of children age 0-17 in Ethiopia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table ET. Fitted percentages and estimated populations of children age 0-17 in Ethiopia with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4

2000 Percentage 13.4 10.1 57.4 40.7 5.6 3.6 33.1 34.2 

2000 Population 4,739 3,568 20,318 14,424 1,984 1,272 11,700 12,092

2005 Percentage 11.3 10.5 48.6 41.3 4.8 3.6 27.5 29.4 

2005 Population 4,573 4,227 19,640 16,718 1,936 1,442 11,141 11,874

2010 Percentage 9.5 10.8 39.8 41.9 4.1 3.5 22.6 25.0 

2010 Population 4,289 4,884 17,948 18,901 1,840 1,594 10,210 11,260

2015 Percentage 8.0 11.2 31.7 42.5 3.5 3.5 18.4 21.1 

2015 Population 3,866 5,441 15,342 20,599 1,685 1,698 8,913 10,199

Ethiopia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ethiopia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ethiopia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ethiopia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
63 

Figure GA. Observed and fitted percentages of children age 0-17 in Gabon with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table GA. Fitted percentages and estimated populations of children age 0-17 in Gabon with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 6.8 19.8 2.3 41.6 2.6 8.9 1.2 10.8 

2010 Population 47 136 16 284 18 61 8 74 

2015 Percentage 6.8 19.8 2.3 41.6 2.6 8.9 1.2 10.8 

2015 Population 51 148 17 311 20 66 9 81 

Gabon

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Gabon

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Gabon

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Gabon

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
64 

Figure GM. Observed and fitted percentages of children age 0-17 in Gambia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table GM. Fitted percentages and estimated populations of children age 0-17 in Gambia with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 8.7 15.9 32.0 43.0 3.6 5.7 18.5 23.5 

2005 Population 66 121 243 326 27 43 140 178 

2010 Percentage 8.7 15.9 32.0 43.0 3.6 5.7 18.5 23.5 

2010 Population 78 142 286 385 32 51 166 210 

 

Gambia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Gambia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Gambia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Gambia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
65 

Figure GE. Observed and fitted percentages of children age 0-17 in Georgia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table GE. Fitted percentages and estimated populations of children age 0-17 in Georgia with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 4.9 2.9 0.1 39.0 2.1 1.0 0.1 2.8 

2005 Population 52 30 1 413 22 11 1 30 

2010 Percentage 4.9 2.9 0.1 39.0 2.1 1.0 0.1 2.8 

2010 Population 45 26 1 353 19 9 1 25 

 

Georgia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Georgia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Georgia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Georgia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
66 

Figure GH. Observed and fitted percentages of children age 0-17 in Ghana with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table GH. Fitted percentages and estimated populations of children age 0-17 in Ghana with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 7.8 16.6 27.4 43.3 2.9 4.8 18.8 22.3 

2000 Population 708 1,505 2,482 3,925 261 439 1,709 2,025

2005 Percentage 7.7 16.8 22.7 43.8 3.3 5.5 16.7 21.2 

2005 Population 777 1,686 2,281 4,395 330 554 1,671 2,129

2010 Percentage 7.7 17.0 18.7 44.3 3.8 6.3 14.7 20.2 

2010 Population 853 1,890 2,076 4,924 418 699 1,629 2,239

2015 Percentage 7.6 17.2 15.2 44.9 4.3 7.2 12.9 19.1 

2015 Population 939 2,126 1,877 5,533 530 884 1,589 2,360

Ghana

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ghana

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ghana

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ghana

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
67 

Figure GN. Observed and fitted percentages of children age 0-17 in Guinea with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table GN. Fitted percentages and estimated populations of children age 0-17 in Guinea with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 9.3 18.3 48.3 41.5 3.5 5.9 28.7 31.2 

2010 Population 511 1,005 2,654 2,284 192 323 1,577 1,716

2015 Percentage 9.3 18.3 48.3 41.5 3.5 5.9 28.7 31.2 

2015 Population 576 1,132 2,991 2,573 217 364 1,777 1,933

 

Guinea

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guinea

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guinea

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guinea

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
68 

Figure GW. Observed and fitted percentages of children age 0-17 in Guinea-Bissau with 

vulnerability components 1-3 and combination 4, estimated from all available DHS and/or MICS 

surveys 

Table GW. Fitted percentages and estimated populations of children age 0-17 in Guinea-Bissau with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 11.5 18.9 26.9 40.9 4.4 7.4 17.3 23.4 

2005 Population 83 136 194 295 31 53 125 169 

2010 Percentage 11.5 18.9 26.9 40.9 4.4 7.4 17.3 23.4 

2010 Population 90 149 212 322 34 58 137 184 

Guinea-Bissau

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guinea-Bissau

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guinea-Bissau

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guinea-Bissau

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
69 

Figure GY. Observed and fitted percentages of children age 0-17 in Guyana with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table GY. Fitted percentages and estimated populations of children age 0-17 in Guyana with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 7.4 11.5 1.1 47.2 3.8 4.4 1.0 7.5 

2005 Population 23 36 3 147 12 14 3 24 

2010 Percentage 6.6 13.2 0.4 45.7 2.8 5.6 0.4 7.2 

2010 Population 20 40 1 140 8 17 1 22 

 

Guyana

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guyana

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guyana

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Guyana

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
70 

Figure HT. Observed and fitted percentages of children age 0-17 in Haiti with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table HT. Fitted percentages and estimated populations of children age 0-17 in Haiti with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 11.3 20.2 21.3 43.3 4.4 6.7 15.9 21.2 

2005 Population 469 839 886 1,803 185 277 661 882 

2010 Percentage 11.9 20.5 15.6 44.5 4.8 8.0 11.9 19.1 

2010 Population 507 870 662 1,889 203 339 506 811 

2015 Percentage 12.6 20.8 11.2 45.6 5.1 9.5 8.8 17.1 

2015 Population 542 892 480 1,959 220 409 379 736 

 

Haiti

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Haiti

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Haiti

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Haiti

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
71 

Figure HN. Observed and fitted percentages of children age 0-17 in Honduras with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table HN. Fitted percentages and estimated populations of children age 0-17 in Honduras with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 6.0 13.2 3.9 44.5 2.5 4.6 3.0 8.4 

2005 Population 193 423 126 1,427 81 148 97 269 

2010 Percentage 6.3 13.2 2.6 44.8 2.9 5.0 2.1 8.3 

2010 Population 204 425 84 1,440 92 162 67 268 

2015 Percentage 6.7 13.2 1.7 45.0 3.2 5.5 1.5 8.3 

2015 Population 208 411 54 1,400 100 169 45 258 

Honduras

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Honduras

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Honduras

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Honduras

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
72 

Figure IA. Observed and fitted percentages of children age 0-17 in India with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table IA. Fitted percentages and estimated populations of children age 0-17 in India with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4

2005 Percentage 4.9 4.3 25.2 46.1 2.7 1.8 19.6 21.6 

2005 Population 21,805 19,288 112,284 205,199 11,931 8,112 87,118 96,050

2010 Percentage 4.9 4.3 25.2 46.1 2.7 1.8 19.6 21.6 

2010 Population 22,153 19,596 114,078 208,478 12,122 8,242 88,510 97,584

 

India

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

India

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

India

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

India

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
73 

Figure IQ. Observed and fitted percentages of children age 0-17 in Iraq with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table IQ. Fitted percentages and estimated populations of children age 0-17 in Iraq with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 5.2 1.8 4.1 44.8 2.2 0.7 3.6 5.8 

2010 Population 774 261 617 6,689 333 97 543 870 

2015 Percentage 5.2 1.8 4.1 44.8 2.2 0.7 3.6 5.8 

2015 Population 895 302 713 7,736 386 113 628 1,007 

 

Iraq

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Iraq

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Iraq

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Iraq

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
74 

Figure JM. Observed and fitted percentages of children age 0-17 in Jamaica with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table JM. Fitted percentages and estimated populations of children age 0-17 in Jamaica with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 5.2 12.7 0.1 45.7 2.5 4.8 0.1 6.6 

2010 Population 48 115 1 416 23 44 1 60 

2015 Percentage 5.2 12.7 0.1 45.7 2.5 4.8 0.1 6.6 

2015 Population 43 104 1 375 21 40 1 54 

 

Jamaica

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Jamaica

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Jamaica

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Jamaica

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
75 

Figure JO. Observed and fitted percentages of children age 0-17 in Jordan with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table JO. Fitted percentages and estimated populations of children age 0-17 in Jordan with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 2.8 0.6 0.6 42.6 1.3 0.2 0.6 1.9 

2005 Population 65 13 13 1,001 30 5 13 46 

2010 Percentage 3.0 0.7 0.5 43.3 1.6 0.4 0.5 2.2 

2010 Population 84 20 14 1,210 44 11 13 62 

2015 Percentage 3.3 1.0 0.5 44.1 2.0 0.6 0.4 2.5 

2015 Population 103 30 15 1,393 62 20 11 80 

 

Jordan

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Jordan

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Jordan

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Jordan

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
76 

Figure KK. Observed and fitted percentages of children age 0-17 in Kazakhstan with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

KK. Fitted percentages and estimated populations of children age 0-17 in Kazakhstan with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 7.0 3.8 0.2 47.2 3.5 1.8 0.1 4.8 

2005 Population 334 181 10 2,250 165 87 6 230 

2010 Percentage 5.2 3.4 0.2 46.7 2.6 1.7 0.1 4.1 

2010 Population 250 162 9 2,241 127 84 5 198 

2015 Percentage 3.8 3.0 0.2 46.2 2.0 1.7 0.1 3.5 

2015 Population 206 161 10 2,477 108 89 5 189 

 

Kazakhstan

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Kazakhstan

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Kazakhstan

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Kazakhstan

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
77 

Figure KY. Observed and fitted percentages of children age 0-17 in Kyrgyz Republic with 

vulnerability components 1-3 and combination 4, estimated from all available DHS and/or MICS 

surveys. The MICS 2005 and DHS 2012 surveys are inconsistent and are not pooled. 

Table KY. Fitted percentages and estimated populations of children age 0-17 in Kyrgyz Republic 

with the four specified components and the four specified combinations of vulnerability, estimated 

from available DHS and/or MICS surveys. The MICS 2005 survey has an inconsistent value for 

component 2. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 4.0 12.8 0.2 43.1 1.3 6.8 0.1 7.6 

2010 Population 80 254 4 858 25 136 2 152 

2015 Percentage 4.0 12.8 0.2 43.1 1.3 6.8 0.1 7.6 

2015 Population 87 277 4 935 28 148 2 165 

Kyrgyz Republic

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Kyrgyz Republic

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Kyrgyz Republic

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Kyrgyz Republic

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
78 

Figure LA. Observed and fitted percentages of children age 0-17 in Lao with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table LA. Fitted percentages and estimated populations of children age 0-17 in Lao with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 6.8 3.7 12.3 45.2 3.2 1.1 10.2 12.6 

2005 Population 187 102 340 1,246 87 31 281 347 

2010 Percentage 5.6 5.6 10.7 46.8 3.0 1.7 9.2 12.1 

2010 Population 157 157 298 1,306 83 48 255 339 

2015 Percentage 4.6 8.5 9.2 48.4 2.8 2.6 8.2 11.7 

2015 Population 131 238 259 1,361 80 74 231 329 

 

Lao PDR

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Lao PDR

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Lao PDR

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Lao PDR

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
79 

Figure LS. Observed and fitted percentages of children age 0-17 in Lesotho with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table LS. Fitted percentages and estimated populations of children age 0-17 in Lesotho with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 28.4 30.1 5.6 39.7 12.8 12.2 3.6 20.3 

2000 Population 255 271 50 357 115 110 33 183 

2005 Percentage 28.0 33.8 5.5 41.8 12.5 14.6 3.5 21.2 

2005 Population 254 306 50 379 113 132 32 192 

2010 Percentage 27.7 37.6 5.4 44.0 12.2 17.3 3.4 22.1 

2010 Population 251 341 49 398 110 157 31 200 

Lesotho

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Lesotho

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Lesotho

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Lesotho

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
80 

Figure LB. Observed and fitted percentages of children age 0-17 in Liberia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table LB. Fitted percentages and estimated populations of children age 0-17 in Liberia with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 7.2 21.2 22.2 39.7 3.1 6.2 14.2 18.3 

2005 Population 117 344 362 646 51 101 232 297 

2010 Percentage 7.2 23.7 19.3 40.2 2.9 7.3 12.7 18.0 

2010 Population 142 465 380 789 57 144 250 353 

2015 Percentage 7.3 26.4 16.8 40.6 2.8 8.6 11.3 17.8 

2015 Population 160 582 369 895 61 190 249 391 

 

Liberia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Liberia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Liberia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Liberia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
81 

Figure MD. Observed and fitted percentages of children age 0-17 in Madagascar with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table MD. Fitted percentages and estimated populations of children age 0-17 in Madagascar with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 7.3 14.6 12.7 43.3 3.6 5.7 10.2 15.5 

2005 Population 688 1,377 1,195 4,075 338 538 963 1,462 

2010 Percentage 7.3 14.6 12.7 43.3 3.6 5.7 10.2 15.5 

2010 Population 774 1,549 1,344 4,583 381 605 1,083 1,644 

 

Madagascar

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Madagascar

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Madagascar

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Madagascar

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
82 

Figure MW. Observed and fitted percentages of children age 0-17 in Malawi with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table MW. Fitted percentages and estimated populations of children age 0-17 in Malawi with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 15.4 19.4 20.4 40.1 6.1 8.4 11.9 17.6 

2000 Population 926 1,163 1,225 2,404 369 507 716 1,057 

2005 Percentage 13.8 18.9 14.7 40.8 5.8 7.8 9.4 16.0 

2005 Population 949 1,299 1,011 2,796 399 536 645 1,095 

2010 Percentage 12.4 18.5 10.4 41.5 5.5 7.2 7.4 14.5 

2010 Population 974 1,454 819 3,258 433 567 579 1,135 

2015 Percentage 11.1 18.1 7.3 42.2 5.2 6.7 5.7 13.1 

2015 Population 993 1,620 652 3,779 468 597 515 1,169 

Malaw i

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Malaw i

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Malaw i

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Malaw i

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
83 

Figure MV. Observed and fitted percentages of children age 0-17 in Maldives with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table MV. Fitted percentages and estimated populations of children age 0-17 in Maldives with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 3.0 6.2 7.9 44.5 1.7 1.8 5.7 8.5 

2005 Population 4 8 10 57 2 2 7 11 

2010 Percentage 3.0 6.2 7.9 44.5 1.7 1.8 5.7 8.5 

2010 Population 3 7 9 53 2 2 7 10 

 

Maldives

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Maldives

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Maldives

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Maldives

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
84 

Figure ML. Observed and fitted percentages of children age 0-17 in Mali with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table ML. Fitted percentages and estimated populations of children age 0-17 in Mali with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 4.5 9.2 60.5 40.7 1.8 3.3 31.8 32.5 

2010 Population 364 750 4,920 3,311 143 270 2,583 2,646 

2015 Percentage 4.5 9.2 60.5 40.7 1.8 3.3 31.8 32.5 

2015 Population 426 879 5,764 3,878 167 316 3,026 3,100 

 

Mali

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mali

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mali

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mali

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
85 

Figure MR. Observed and fitted percentages of children age 0-17 in Mauritania with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table MR. Fitted percentages and estimated populations of children age 0-17 in Mauritania with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 8.0 10.5 17.4 43.5 4.0 4.5 12.6 17.4 

2005 Population 122 160 265 662 61 69 192 265 

2010 Percentage 7.4 9.6 14.4 44.6 3.1 4.7 10.5 15.5 

2010 Population 125 163 245 760 54 80 179 265 

2015 Percentage 6.7 8.7 11.8 45.7 2.4 4.9 8.7 13.8 

2015 Population 127 164 222 864 46 92 164 262 

 

Mauritania

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mauritania

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mauritania

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mauritania

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
86 

Figure MB. Observed and fitted percentages of children age 0-17 in Moldova with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table MB. Fitted percentages and estimated populations of children age 0-17 in Moldova with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 4.4 8.8 0.6 41.1 2.3 3.5 0.1 5.3 

2005 Population 45 89 6 418 23 36 1 54 

2010 Percentage 4.5 10.0 1.1 38.8 2.5 4.6 0.4 6.7 

2010 Population 39 87 10 335 22 39 3 58 

2015 Percentage 4.6 11.5 2.0 36.6 2.7 5.9 1.2 8.5 

2015 Population 36 89 16 284 21 46 9 66 

Moldova

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Moldova

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Moldova

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Moldova

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
87 

Figure MZ. Observed and fitted percentages of children age 0-17 in Mozambique with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table MZ. Fitted percentages and estimated populations of children age 0-17 in Mozambique with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 12.3 14.6 14.4 41.5 4.8 4.8 9.1 14.4 

2005 Population 1,350 1,600 1,585 4,554 526 524 996 1,583

2010 Percentage 12.3 14.6 14.4 41.5 4.8 4.8 9.1 14.4 

2010 Population 1,569 1,861 1,843 5,296 611 610 1,158 1,841

 

Mozambique

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mozambique

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mozambique

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Mozambique

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
88 

Figure NM. Observed and fitted percentages of children age 0-17 in Namibia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table NM. Fitted percentages and estimated populations of children age 0-17 in Namibia with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 18.4 35.6 7.5 46.8 10.9 19.3 5.4 25.8 

2005 Population 174 337 71 443 103 183 51 245 

2010 Percentage 15.3 37.0 6.2 46.4 8.1 19.3 4.8 24.4 

2010 Population 152 367 62 460 80 191 48 241 

2015 Percentage 12.7 38.4 5.1 46.0 6.0 19.3 4.3 22.9 

2015 Population 135 408 55 489 64 205 46 244 

Namibia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Namibia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Namibia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Namibia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
89 

Figure NP. Observed and fitted percentages of children age 0-17 in Nepal with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table NP. Fitted percentages and estimated populations of children age 0-17 in Nepal with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 5.2 7.4 31.1 43.8 2.6 2.2 19.3 21.0 

2005 Population 609 875 3,668 5,173 303 259 2,273 2,483 

2010 Percentage 5.0 7.5 27.9 45.2 2.6 2.3 19.0 21.2 

2010 Population 586 881 3,290 5,336 310 277 2,245 2,503 

2015 Percentage 4.8 7.5 24.9 46.6 2.7 2.5 18.8 21.4 

2015 Population 540 851 2,815 5,273 304 284 2,126 2,418 

Nepal

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Nepal

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Nepal

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Nepal

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
90 

Figure NI. Observed and fitted percentages of children age 0-17 in Niger with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table NI. Fitted percentages and estimated populations of children age 0-17 in Niger with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 5.2 10.2 69.6 41.1 2.3 4.4 34.5 35.2 

2010 Population 476 930 6,372 3,763 213 404 3,159 3,220

2015 Percentage 5.2 10.2 69.6 41.1 2.3 4.4 34.5 35.2 

2015 Population 589 1,150 7,886 4,656 263 500 3,909 3,985

 

Niger

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Niger

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Niger

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Niger

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
91 

Figure NG. Observed and fitted percentages of children age 0-17 in Nigeria with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table NG. Fitted percentages and estimated populations of children age 0-17 in Nigeria with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4

2005 Percentage 6.5 9.3 31.9 42.2 2.2 2.6 22.3 24.5 

2005 Population 4,543 6,502 22,272 29,520 1,562 1,801 15,601 17,131

2010 Percentage 6.1 9.9 27.7 43.1 2.2 3.3 21.7 24.2 

2010 Population 4,925 7,943 22,184 34,557 1,730 2,617 17,371 19,363

2015 Percentage 5.8 10.6 23.9 44.0 2.1 4.1 21.0 23.8 

2015 Population 5,338 9,698 21,917 40,433 1,915 3,795 19,333 21,881

Nigeria

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Nigeria

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Nigeria

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Nigeria

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
92 

Figure PK. Observed and fitted percentages of children age 0-17 in Pakistan with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table PK. Fitted percentages and estimated populations of children age 0-17 in Pakistan with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 4.6 2.2 23.3 44.5 2.3 1.0 18.0 19.7 

2010 Population 3,372 1,614 16,962 32,403 1,671 726 13,088 14,343

2015 Percentage 4.6 2.2 23.3 44.5 2.3 1.0 18.0 19.7 

2015 Population 3,602 1,724 18,116 34,609 1,784 775 13,979 15,320

 

Pakistan

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Pakistan

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Pakistan

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Pakistan

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
93 

Figure RW. Observed and fitted percentages of children age 0-17 in Rwanda with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table RW. Fitted percentages and estimated populations of children age 0-17 in Rwanda with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 28.7 14.7 21.0 39.6 12.2 3.7 8.9 16.8 

2000 Population 1,218 625 893 1,682 516 157 380 714 

2005 Percentage 20.3 14.0 15.3 40.7 9.0 4.4 8.2 15.5 

2005 Population 917 631 691 1,835 406 199 371 700 

2010 Percentage 13.9 13.3 10.9 41.8 6.6 5.2 7.5 14.3 

2010 Population 695 665 547 2,087 330 261 377 714 

2015 Percentage 9.3 12.7 7.7 42.9 4.8 6.2 6.9 13.2 

2015 Population 515 700 426 2,373 267 343 383 729 

Rw anda

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Rw anda

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Rw anda

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Rw anda

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
94 

Figure ST. Observed and fitted percentages of children age 0-17 in Sao Tome and Principe with 

vulnerability components 1-3 and combination 4, estimated from all available DHS and/or MICS 

surveys 

Table ST. Fitted percentages and estimated populations of children age 0-17 in Sao Tome and 

Principe with the four specified components and the four specified combinations of vulnerability, 

estimated from available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 4.4 17.5 5.2 39.9 1.6 5.7 3.3 8.3 

2005 Population 3 14 4 31 1 4 3 6 

2010 Percentage 4.4 17.5 5.2 39.9 1.6 5.7 3.3 8.3 

2010 Population 4 15 4 34 1 5 3 7 

Sao Tome and Principe

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sao Tome and Principe

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sao Tome and Principe

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sao Tome and Principe

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
95 

Figure SN. Observed and fitted percentages of children age 0-17 in Senegal with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table SN. Fitted percentages and estimated populations of children age 0-17 in Senegal with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 7.2 15.3 40.7 44.8 3.2 6.3 27.6 31.2 

2010 Population 470 996 2,656 2,924 212 410 1,804 2,034 

2015 Percentage 6.8 14.5 30.9 44.1 3.0 5.8 20.0 24.3 

2015 Population 519 1,103 2,347 3,346 228 444 1,519 1,844 

 

Senegal

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Senegal

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Senegal

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Senegal

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
96 

Figure SL. Observed and fitted percentages of children age 0-17 in Sierra Leone with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table SL. Fitted percentages and estimated populations of children age 0-17 in Sierra Leone with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 11.8 21.3 51.1 39.9 4.6 6.9 28.8 31.2 

2005 Population 303 546 1,310 1,025 118 177 740 800 

2010 Percentage 11.5 23.2 44.9 40.7 4.2 7.7 25.9 29.5 

2010 Population 332 670 1,297 1,175 122 224 749 854 

2015 Percentage 11.1 25.2 38.8 41.4 3.9 8.7 23.2 27.9 

2015 Population 353 797 1,229 1,311 123 276 735 884 

 

Sierra Leone

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sierra Leone

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sierra Leone

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sierra Leone

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
97 

Figure SO. Observed and fitted percentages of children age 0-17 in Somalia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table SO. Fitted percentages and estimated populations of children age 0-17 in Somalia with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 9.7 8.5 25.2 40.4 3.5 2.8 14.3 16.9 

2005 Population 444 388 1,153 1,849 159 127 654 774 

2010 Percentage 9.7 8.5 25.2 40.4 3.5 2.8 14.3 16.9 

2010 Population 504 440 1,310 2,101 181 144 744 880 

 

Somalia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Somalia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Somalia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Somalia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
98 

Figure SS. Observed and fitted percentages of children age 0-17 in South Sudan with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table SS. Fitted percentages and estimated populations of children age 0-17 in South Sudan with 

the four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 17.4 13.2 64.4 41.5 7.5 4.7 35.2 36.9 

2005 Population 715 545 2,652 1,709 309 192 1,452 1,521 

2010 Percentage 17.4 13.2 64.4 41.5 7.5 4.7 35.2 36.9 

2010 Population 874 666 3,241 2,088 378 234 1,774 1,859 

 

South Sudan

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

South Sudan

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

South Sudan

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

South Sudan

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
99 

Figure SD. Observed and fitted percentages of children age 0-17 in Sudan with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table SD. Fitted percentages and estimated populations of children age 0-17 in Sudan with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 5.7 4.1 22.6 44.4 2.8 2.2 17.8 20.0 

2005 Population 898 659 3,586 7,054 450 353 2,821 3,175 

2010 Percentage 5.7 4.1 22.6 44.4 2.8 2.2 17.8 20.0 

2010 Population 993 728 3,964 7,798 498 390 3,119 3,510 

 

Sudan

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sudan

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sudan

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sudan

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
100 

Figure SZ. Observed and fitted percentages of children age 0-17 in Swaziland with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table SZ. Fitted percentages and estimated populations of children age 0-17 in Swaziland with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 22.9 34.4 6.6 44.5 11.3 16.1 4.4 22.3 

2005 Population 127 190 36 246 62 89 25 123 

2010 Percentage 24.2 33.4 6.5 45.7 12.0 16.8 5.4 23.5 

2010 Population 134 186 36 254 67 93 30 131 

2015 Percentage 25.5 32.4 6.5 46.9 12.8 17.6 6.6 24.8 

2015 Population 145 184 37 267 73 100 38 141 

 

Sw aziland

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sw aziland

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sw aziland

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Sw aziland

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
101 

Figure TZ. Observed and fitted percentages of children age 0-17 in Tanzania with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table TZ. Fitted percentages and estimated populations of children age 0-17 in Tanzania with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 11.0 15.6 12.2 40.9 3.9 5.1 8.1 13.0 

2000 Population 1928 2741 2139 7178 676 893 1414 2282 

2005 Percentage 10.4 16.1 10.9 41.6 4.0 5.5 7.7 13.4 

2005 Population 2078 3218 2171 8297 806 1100 1543 2665 

2010 Percentage 9.9 16.7 9.7 42.3 4.2 6.0 7.4 13.7 

2010 Population 2280 3848 2241 9767 980 1381 1714 3170 

2015 Percentage 9.4 17.2 8.6 43.0 4.5 6.5 7.1 14.1 

2015 Population 2502 4600 2310 11495 1190 1732 1904 3770 

Tanzania

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Tanzania

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Tanzania

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Tanzania

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
102 

Figure TH. Observed and fitted percentages of children age 0-17 in Thailand with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table TH. Fitted percentages and estimated populations of children age 0-17 in Thailand with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 4.8 19.3 2.2 43.9 2.3 10.9 1.4 12.9 

2005 Population 852 3,433 386 7,789 409 1,937 257 2,295 

2010 Percentage 4.8 19.3 2.2 43.9 2.3 10.9 1.4 12.9 

2010 Population 751 3,025 340 6,862 360 1,706 227 2,022 

 

Thailand

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Thailand

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Thailand

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Thailand

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
103 

Figure TL. Observed and fitted percentages of children age 0-17 in Timor-Leste with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table TL. Fitted percentages and estimated populations of children age 0-17 in Timor-Leste with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 7.2 9.2 18.9 40.9 3.5 3.1 13 16

2005 Population 39 50 103 223 19 17 71 87

2010 Percentage 7.2 9.2 18.9 40.9 3.5 3.1 13 16

2010 Population 37 47 96 208 18 16 66 82

Timor-Leste

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Timor-Leste

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Timor-Leste

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Timor-Leste

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
104 

Figure TG. Observed and fitted percentages of children age 0-17 in Togo with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table TG. Fitted percentages and estimated populations of children age 0-17 in Togo with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 10.2 16.1 24.2 43.3 3.9 5.1 17.0 21.2 

2005 Population 286 451 676 1,212 110 142 476 594 

2010 Percentage 9.4 15.8 22.5 44.4 3.7 5.0 15.6 20.0 

2010 Population 297 498 710 1,401 118 158 492 631 

2015 Percentage 8.7 15.5 20.9 45.4 3.5 5.0 14.3 18.8 

2015 Population 308 550 744 1,615 125 176 508 668 

 

Togo

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Togo

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Togo

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Togo

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
105 

Figure TN. Observed and fitted percentages of children age 0-17 in Tunisia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table TN. Fitted percentages and estimated populations of children age 0-17 in Tunisia with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2010 Percentage 2.8 0.9 3.6 41.8 1.4 0.3 3.1 4.4 

2010 Population 86 27 110 1,269 42 9 95 133 

2015 Percentage 2.8 0.9 3.6 41.8 1.4 0.3 3.1 4.4 

2015 Population 88 28 113 1,303 43 9 98 136 

 

Tunisia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Tunisia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Tunisia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Tunisia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
106 

Figure UG. Observed and fitted percentages of children age 0-17 in Uganda with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table UG. Fitted percentages and estimated populations of children age 0-17 in Uganda with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2000 Percentage 15.2 19.2 10.9 39.7 5.3 6.2 7.2 13.2 

2000 Population 2,031 2,557 1,458 5,287 700 827 955 1,755

2005 Percentage 13.7 19.5 10.0 40.5 5.4 6.6 6.7 13.2 

2005 Population 2,167 3,070 1,584 6,392 848 1,041 1,050 2,089

2010 Percentage 12.4 19.7 9.2 41.4 5.5 7.0 6.2 13.3 

2010 Population 2,288 3,657 1,706 7,664 1,018 1,299 1,144 2,465

2015 Percentage 11.1 20.0 8.4 42.3 5.6 7.5 5.7 13.4 

2015 Population 2,384 4,302 1,814 9,073 1,208 1,601 1,232 2,874

Uganda

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Uganda

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Uganda

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Uganda

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
107 

Figure UA. Observed and fitted percentages of children age 0-17 in Ukraine with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table UA. Fitted percentages and estimated populations of children age 0-17 in Ukraine with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 5.4 0.9 0.1 45.1 2.8 0.1 0.1 2.8 

2005 Population 471 80 13 3,963 244 10 9 250 

2010 Percentage 5.0 1.9 0.1 40.8 2.4 0.6 0.1 3.0 

2010 Population 395 152 9 3,221 189 47 8 240 

2015 Percentage 4.7 4.0 0.1 36.7 2.1 3.0 0.1 3.3 

2015 Population 369 317 7 2,896 162 235 8 258 

 

Ukraine

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ukraine

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ukraine

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Ukraine

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
108 

Figure UZ. Observed and fitted percentages of children age 0-17 in Uzbekistan with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table UZ. Fitted percentages and estimated populations of children age 0-17 in Uzbekistan with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 4.1 1.9 0.1 44.0 1.8 0.7 0.1 2.1 

2005 Population 417 190 10 4,497 184 70 10 212 

2010 Percentage 4.1 1.9 0.1 44.0 1.8 0.7 0.1 2.1 

2010 Population 402 183 10 4,329 177 67 10 204 

 

Uzbekistan

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Uzbekistan

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Uzbekistan

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Uzbekistan

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
109 

Figure VN. Observed and fitted percentages of children age 0-17 in Viet Nam with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table VN. Fitted percentages and estimated populations of children age 0-17 in Viet Nam with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 4.0 2.9 3.2 41.4 2.0 0.9 3.0 5.4 

2005 Population 1,125 816 914 11,705 566 261 853 1,533

2010 Percentage 3.9 4.9 2.5 42.8 2.2 2.3 2.3 6.2 

2010 Population 1,012 1,291 653 11,182 585 595 597 1,624

2015 Percentage 3.8 8.3 1.9 44.2 2.5 5.5 1.7 7.1 

2015 Population 965 2,130 494 11,305 641 1,405 442 1,820

 

Viet Nam

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Viet Nam

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Viet Nam

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Viet Nam

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
110 

Figure YE. Observed and fitted percentages of children age 0-17 in Yemen with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table YE. Fitted percentages and estimated populations of children age 0-17 in Yemen with the four 

specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 5.2 1.7 17.2 41.8 2.4 0.9 12.9 14.8 

2005 Population 568 187 1,888 4,575 266 99 1,408 1,616 

2010 Percentage 5.2 1.7 17.2 41.8 2.4 0.9 12.9 14.8 

2010 Population 613 202 2,037 4,937 287 106 1,520 1,744 

 

Yemen

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Yemen

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Yemen

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Yemen

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
111 

Figure ZM. Observed and fitted percentages of children age 0-17 in Zambia with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table ZM. Fitted percentages and estimated populations of children age 0-17 in Zambia with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 16.2 19.9 6.7 42.7 5.0 7.2 5.0 12.0 

2005 Population 1,043 1,284 431 2,756 322 465 322 775 

2010 Percentage 13.1 18.3 5.0 42.7 4.8 6.4 3.9 11.1 

2010 Population 974 1,359 374 3,170 360 476 293 823 

2015 Percentage 10.6 16.8 3.8 42.7 4.7 5.7 3.1 10.2 

2015 Population 902 1,435 322 3,643 402 486 266 873 

 

Zambia

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Zambia

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Zambia

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Zambia

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
112 

Figure ZW. Observed and fitted percentages of children age 0-17 in Zimbabwe with vulnerability 

components 1-3 and combination 4, estimated from all available DHS and/or MICS surveys 

Table ZW. Fitted percentages and estimated populations of children age 0-17 in Zimbabwe with the 

four specified components and the four specified combinations of vulnerability, estimated from 

available DHS and/or MICS surveys. Population estimates are in thousands. 

 Component Combination 

Reference year 1 2 3 4 1 2 3 4 

2005 Percentage 26.0 28.5 4.2 44.7 11.6 12.4 3.0 17.8 

2005 Population 1,675 1,838 268 2,885 746 803 192 1,151 

2010 Percentage 21.8 27.4 3.4 44.7 10.1 12.1 2.4 16.8 

2010 Population 1,487 1,869 235 3,046 686 824 167 1,142 

2015 Percentage 18.2 26.4 2.9 44.7 8.8 11.8 2.0 15.7 

2015 Population 1,366 1,984 214 3,357 658 882 151 1,181 

Zimbabw e

0 5 10 15 20 25 30 35 Prevalence of Component 1 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Zimbabw e

0 5 10 15 20 25 30 35 40 45 Prevalence of Component 2 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Zimbabw e

0 10 20 30 40 50 60 70 Prevalence of Component 3 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence

Zimbabw e

0 5 10 15 20 25 30 35 40 45 Prevalence of Combination 4 (%) 2000.5 2005.5 2010.5 2015.5 Year

DHS surv ey MICS surv ey

Lower c.i. boundary Upper c.i. boundary

Estimated prev alence
113 

Index to Country Pages 

Afghanistan 

Figure AF ......................................................... 43 

Table AF ........................................................... 43 

Azerbaijan 

Figure AZ ......................................................... 44 

Table AZ .......................................................... 44 

Bangladesh 

Figure BD ......................................................... 45 

Table BD .......................................................... 45 

Belize 

Figure BZ ......................................................... 46 

Table BZ ........................................................... 46 

Benin 

Figure BJ .......................................................... 47 

Table BJ ............................................................ 47 

Bolivia 

Figure BO ......................................................... 48 

Table BO .......................................................... 48 

Burkina Faso 

Figure BF .......................................................... 49 

Table BF ........................................................... 49 

Burundi 

Figure BU ......................................................... 50 

Table BU .......................................................... 50 

Cambodia 

Figure KH ......................................................... 51 

Table KH .......................................................... 51 

Cameroon 

Figure CM ........................................................ 52 

Table CM .......................................................... 52 

Central African Republic 

Figure CF .......................................................... 53 

Table CF ........................................................... 53 

Chad 

Figure TD ......................................................... 54 

Table TD .......................................................... 54 

Colombia 

Figure CO ......................................................... 55 

Table CO .......................................................... 55 

Comoros 

Figure KM ........................................................ 56 

Table KM ......................................................... 56 

Congo 

Figure CG ......................................................... 57 

Table CG .......................................................... 57 

Costa Rica 

Figure CR ......................................................... 58 

Table CR .......................................................... 58 

Côte d’Ivoire 

Figure CI .......................................................... 59 

Table CI ............................................................ 59 

Democratic Republic of the Congo 

Figure DR ......................................................... 60 

Table DR .......................................................... 60 

Egypt 

Figure EG ......................................................... 61 

Table EG .......................................................... 61 

Ethiopia 

Figure ET .......................................................... 62 

Table ET ........................................................... 62 

Gabon 

Figure GA ......................................................... 63 

Table GA .......................................................... 63 

Gambia 

Figure GM ........................................................ 64 

Table GM ......................................................... 64 

Georgia 

Figure GE ......................................................... 65 

Table GE .......................................................... 65 

Ghana 

Figure GH ......................................................... 66 

Table GH .......................................................... 66 

Guinea 

Figure GN ......................................................... 67 

Table GN .......................................................... 67 

Guinea-Bissau 

Figure GW ........................................................ 68 

Table GW ......................................................... 68 

Guyana 

Figure GY ......................................................... 69 

Table GY .......................................................... 69 

Haiti 

Figure HT ......................................................... 70 

Table HT .......................................................... 70 

Honduras 

Figure HN ......................................................... 71 

Table HN .......................................................... 71 

India 

Figure IA .......................................................... 72 

Table IA ............................................................ 72 

Iraq 

Figure IQ .......................................................... 73 

Table IQ ............................................................ 73 

Jamaica 

Figure JM ......................................................... 74 

Table JM ........................................................... 74 

Jordan 

Figure JO .......................................................... 75 

Table JO ........................................................... 75 

Kazakhstan 

Figure KK ......................................................... 76 

Table KK .......................................................... 76 
114 

Kyrgyz Republic 

Figure KY ......................................................... 77 

Table KY .......................................................... 77 

Lao 

Figure LA ......................................................... 78 

Table LA .......................................................... 78 

Lesotho 

Figure LS .......................................................... 79 

Table LS ........................................................... 79 

Liberia 

Figure LB ......................................................... 80 

Table LB ........................................................... 80 

Madagascar 

Figure MD ........................................................ 81 

Table MD ......................................................... 81 

Malawi 

Figure MW ....................................................... 82 

Table MW ........................................................ 82 

Maldives 

Figure MV ........................................................ 83 

Table MV. ........................................................ 83 

Mali 

Figure ML ........................................................ 84 

Table ML .......................................................... 84 

Mauritania 

Figure MR ........................................................ 85 

Table MR .......................................................... 85 

Moldova 

Figure MB ........................................................ 86 

Table MB .......................................................... 86 

Mozambique 

Figure MZ ........................................................ 87 

Table MZ .......................................................... 87 

Namibia 

Figure NM ........................................................ 88 

Table NM ......................................................... 88 

Nepal 

Figure NP ......................................................... 89 

Table NP ........................................................... 89 

Niger 

Figure NI .......................................................... 90 

Table NI ............................................................ 90 

Nigeria 

Figure NG ......................................................... 91 

Table NG .......................................................... 91 

Pakistan 

Figure PK ......................................................... 92 

Table PK ........................................................... 92 

Rwanda 

Figure RW ........................................................ 93 

Table RW. ........................................................ 93 

Sao Tome and Principe 

Figure ST .......................................................... 94 

Table ST ........................................................... 94 

Senegal 

Figure SN. ........................................................ 95 

Table SN ........................................................... 95 

Sierra Leone 

Figure SL .......................................................... 96 

Table SL ........................................................... 96 

Somalia 

Figure SO. ....................................................... 97 

Table SO ........................................................... 97 

South Sudan 

Figure SS. ......................................................... 98 

Table SS ........................................................... 98 

Sudan 

Figure SD ......................................................... 99 

Table SD ........................................................... 99 

Swaziland 

Figure SZ ........................................................ 100 

Table SZ ......................................................... 100 

Tanzania 

Figure TZ ........................................................ 101 

Table TZ ......................................................... 101 

Thailand 

Figure TH ....................................................... 102 

Table TH ........................................................ 102 

Timor-Leste 

Figure TL ........................................................ 103 

Table TL ......................................................... 103 

Togo 

Figure TG ....................................................... 104 

Table TG ........................................................ 104 

Tunisia 

Figure TN ....................................................... 105 

Table TN ........................................................ 105 

Uganda 

Figure UG ....................................................... 106 

Table UG ........................................................ 106 

Ukraine 

Figure UA ....................................................... 107 

Table UA ........................................................ 107 

Uzbekistan 

Figure UZ ....................................................... 108 

Table UZ ........................................................ 108 

Viet Nam 

Figure VN ....................................................... 109 

Table VN ........................................................ 109 

Yemen 

Figure YE ....................................................... 110 

Table YE ........................................................ 110 

Zambia 

Figure ZM ...................................................... 111 

Table ZM ........................................................ 111 

Zimbabwe 

Figure ZW ...................................................... 112 

Table ZW ........................................................ 112