DHS COMPARATIVE

REPORTS 42

LEVELS AND TRENDS IN MATERNAL 

AND CHILD HEALTH DISPARITIES 

BY WEALTH AND REGION IN ELEVEN 

COUNTRIES WITH DHS SURVEYS 

August 2016

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

Shireen Assaf and Thomas Pullum.

DHS Comparative Reports No. 42 

Levels and Trends in Maternal and Child Health 

Disparities by Wealth and Region 

in Eleven Countries with DHS Surveys 

Shireen Assaf 

Thomas Pullum 

ICF International 

Rockville, Maryland, USA 

August 2016 

Corresponding author: Shireen Assaf, International Health and Development, ICF International, 530 

Gaither Road, Suite 500, Rockville, MD 20850, USA; telephone: +1 301–407–6500; fax: +1 301–407–

6501; email: Shireen.Assaf@icfi.com 
Acknowledgment: The authors are grateful for the comments provided by Saifuddin Ahmed on a draft of 

this report.

Editor: Bryant Robey 

Document Production: Natalie La Roche 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AIDOAA-C-13-00095). The views expressed are 

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

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

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

DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. Phone: +1 

301-407-6500; Fax: +1 301-407-6501; Email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Assaf, Shireen, and Thomas Pullum. 2016. Levels and Trends in Maternal and Child Health Disparities by 

Wealth and Region in Eleven Countries with DHS Surveys. DHS Comparative Reports No. 42. Rockville, 

Maryland, USA: ICF International. 
iii 

Contents 

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

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

Preface ......................................................................................................................................................... xi 

Abstract ..................................................................................................................................................... xiii 

Executive Summary .................................................................................................................................... xv 

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

1.1. Background ...................................................................................................................................... 1 

1.2. Previous Research ............................................................................................................................ 2 

2. Data and Methods ................................................................................................................................ 3 

2.1. Data .................................................................................................................................................. 3 

2.2. Measures .......................................................................................................................................... 3 

2.3. Analysis ........................................................................................................................................... 5 

3. Results................................................................................................................................................... 7 

3.1. Four or More ANC Visits ................................................................................................................ 7 

3.2. Modern Contraceptive Rate (mCPR) ............................................................................................. 15 

3.3. Delivery by an SBA ....................................................................................................................... 22 

3.4. Delivery in a Health Facility (DHF) .............................................................................................. 27 

3.5. Three Doses of DPT Vaccine (DPT3) ........................................................................................... 34 

3.6. Care Seeking for ARI ..................................................................................................................... 41 

3.7. Care Seeking for Fever .................................................................................................................. 48 

3.8. Care Seeking for Diarrhea .............................................................................................................. 55 

3.9. Exclusive Breastfeeding (EBF) ...................................................................................................... 63 

3.10. Stunting .......................................................................................................................................... 70 

3.11. Wasting ........................................................................................................................................... 77 

3.12. Overall Summary ........................................................................................................................... 83 

4. Discussion and Conclusion ................................................................................................................. 89 

References ................................................................................................................................................... 91 

v 

Tables 

Table 1. DHS surveys included in the analysis ....................................................................................... 3 

Table 2. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the ANC indicator ................................................................................................. 11 

Table 3. Summary table for the ANC indicator showing the top three countries with the following 

measures for the most recent survey ........................................................................................ 14

Table 4. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the mCPR indicator ............................................................................................... 19 

Table 5. Summary table for the mCPR indicator showing the top three countries with the following 

measures for the most recent survey ........................................................................................ 21

Table 6. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the SBA indicator ................................................................................................. 24 

Table 7. Summary table for the SBA indicator showing the top three countries with the following 

measures for the most recent survey. ....................................................................................... 27

Table 8. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the DHF indicator ................................................................................................. 30 

Table 9. Summary table for the DHF indicator showing the top three countries with the following 

measures for the most recent survey ........................................................................................ 33

Table 10. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the DPT3 indicator ................................................................................................ 37 

Table 11. Summary table for the DPT3 indicator showing the top three countries with the following 

measures for the most recent survey ........................................................................................ 40

Table 12. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for care seeking for ARI symptoms ............................................................................ 45 

Table 13. Summary table for the ARI indicator showing the top three countries with the following 

measure for the most recent survey ......................................................................................... 47

Table 14. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for care seeking for fever symptoms .......................................................................... 51 

Table 15. Summary table for the fever indicator showing the top three countries with the following 

measures for the most recent survey ........................................................................................ 54

Table 16. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for care seeking for diarrhea ....................................................................................... 59 

Table 17. Summary table for the diarrhea indicator showing the top three countries with the 

following measure for the most recent survey ......................................................................... 62 

Table 18. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the exclusive breastfeeding indicator .................................................................... 67 

Table 19. Summary table for the EBF indicator showing the top three countries with the following 

measures for the most recent survey ........................................................................................ 69

Table 20. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the stunting indicator ............................................................................................ 73 

Table 21. Summary table for the stunting indicator showing the top three countries with the 

following measures for the most recent survey ....................................................................... 76 

Table 22. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the wasting indicator ............................................................................................. 80 

Table 23. Summary table for the wasting indicator showing the top three countries with the 

following measures for the most recent survey ....................................................................... 83 

Table 24. Maternal health indicators summary for the most recent surveys ........................................... 83 
vi 

Table 25. Child health indicators summary for the most recent surveys ................................................. 84 

Table 26. Child nutrition indicators summary for the most recent surveys ............................................. 84 

Table 27. Summary of changes in the concentration index for all 11 indicators. Significant 

improvement in equality between the last two surveys for each country appears in green 

and significant deterioration appears in red. No color indicates no significant change 

between surveys. ...................................................................................................................... 88 

Appendix 1. Maternal health indicators overall prevalence, difference index and coefficient index .......... 94 

Appendix 2. Child health indicators overall prevalence, difference index and coefficient index ................ 95 

Appendix 3. Child nutrition indicators overall prevalence, difference index and coefficient index ............ 96 

Appendix 4. Maternal health indicators with the national, lowest and highest regional estimates .............. 97 

Appendix 5. Child health indicators with the national, lowest and highest regional estimates .................... 98 

Appendix 6. Child nutrition indicators with the national, lowest and highest regional estimates ................ 99 
vii 

Figures 

Figure 1. Percentage of women age 15-49 who have attended at least four ANC visits for their most 

recent pregnancy ........................................................................................................................ 7

Figure 2. Percentage of women age 15-49 who have attended at least four ANC visits for their most 

recent pregnancy by wealth quintiles q1-q5 .............................................................................. 8

Figure 3. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of women age 15-49 who attended at least four ANC visits for their most 

recent pregnancy ........................................................................................................................ 9

Figure 4. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of attending 

at least four ANC visits with the wealth quintile as a categorical predictor ............................ 10

Figure 5. Percentage of women age 15-49 who have attended four or more ANC visits with the 

national estimates and lowest and highest region estimates .................................................... 12

Figure 6. Regional map for the most recent survey of women age 15-49 who have attended at 

least four ANC visits for their most recent pregnancy ............................................................ 13

Figure 7. Percentage of women age 15-49 in a union currently using a modern contraceptive 

method ..................................................................................................................................... 15

Figure 8. Percentage of women age 15-49 in a union currently using a modern contraceptive 

method by wealth quintiles q1-q5............................................................................................ 16

Figure 9. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of women age 15-49 in a union currently using a modern contraceptive 

method ..................................................................................................................................... 17

Figure 10. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of using a modern 

contraceptive method with the wealth quintile as a categorical predictor. .............................. 18

Figure 11. Percentage of women age 15-49 in a union currently using a modern contraceptive 

method with the national estimates and lowest and highest region estimates ......................... 19

Figure 12. Regional map for the most recent survey of women age 15-49 in a union currently using 

a modern contraceptive method ............................................................................................... 20

Figure 13. Percentage of women age 15-49 who had their most recent birth assisted by an SBA ........... 22

Figure 14. Percentage of women age 15-49 who had their most recent birth assisted by an SBA 

by wealth quintiles q1-q5 ........................................................................................................ 23

Figure 15. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of women age 15-49 who had their most recent birth assisted by an SBA ........... 23

Figure 16. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of assisted by 

SBA with the wealth quintile as a categorical predictor. ......................................................... 24

Figure 17. Percentage of women age 15-49 who had their most recent birth assisted by an SBA 

with the national estimates and lowest and highest region estimates ...................................... 25

Figure 18. Regional map for the most recent survey of women age 15-49 who had their most recent 

birth assisted by an SBA .......................................................................................................... 26

Figure 19. Percentage of women age 15-49 who delivered their most recent birth in a health facility .... 28

Figure 20. Percentage of women age 15-49 who delivered their most recent birth in a health facility 

by wealth quintiles q1-q5 ........................................................................................................ 28

Figure 21. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of women age 15-49 who delivered their most recent birth in a health facility .... 29
viii 

Figure 22. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of delivery in a 

health facility with the wealth quintile as a categorical predictor. .......................................... 30

Figure 23. Percentage of women age 15-49 who delivered their most recent birth in a health facility 

with the national estimates and lowest and highest region estimates ...................................... 31

Figure 24. Regional map for the most recent survey of women age 15-49 who delivered their most 

recent birth in a health facility ................................................................................................. 32

Figure 25. Percentage of children age 12-23 months who completed three doses of the DPT vaccine .... 34

Figure 26. Percentage of children age 12-23 months who completed three doses of the DPT vaccine 

by wealth quintiles q1-q5 ........................................................................................................ 35

Figure 27. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of children age 12-23 months who completed three doses of the DPT vaccine .... 36

Figure 28. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of receiving three 

doses of DPT vaccine with the wealth quintile as a categorical predictor. .............................. 37

Figure 29. Percentage of children age 12-23 months who completed three doses of the DPT vaccine 

with the national estimates and lowest and highest region estimates ...................................... 38

Figure 30. Regional map for the most recent survey of children age 12-23 months who completed 

three doses of the DPT vaccine ............................................................................................... 39

Figure 31. Percentage of children under age 5 with care seeking for ARI symptoms .............................. 41

Figure 32. Percentage of children under age 5 with care seeking for ARI symptoms by wealth 

quintiles q1-q5 ......................................................................................................................... 42

Figure 33. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of children under age 5 with care seeking for ARI symptoms .............................. 43

Figure 34. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of care seeking for 

ARI symptoms with the wealth quintile as a categorical predictor. ........................................ 44

Figure 35. Percentage of children under age 5 with care seeking for ARI symptoms with the national 

estimates and lowest and highest region estimates .................................................................. 46

Figure 36. Percentage of children under age 5 with care seeking for fever symptoms ............................. 48

Figure 37. Percentage of children under age 5 with care seeking for fever symptoms by wealth 

quintiles q1-q5 ......................................................................................................................... 49

Figure 38. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of children under age 5 with care seeking for fever symptoms ............................. 50

Figure 39. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of care seeking for 

fever symptoms with the wealth quintile as a categorical predictor. ....................................... 51

Figure 40. Percentage of children under age 5 with care seeking for fever symptoms with the national 

estimates and lowest and highest region estimates .................................................................. 52

Figure 41. Regional map for the most recent survey of children under age 5 with care seeking for 

fever symptoms ....................................................................................................................... 53

Figure 42. Percentage of children under age 5 with care seeking for diarrhea ......................................... 55

Figure 43. Percentage of children under age 5 with care seeking for diarrhea by wealth quintiles 

q1-q5 ........................................................................................................................................ 56

Figure 44. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of children under age 5 with care seeking for diarrhea ......................................... 57
ix 

Figure 45. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of care seeking 

for diarrhea with the wealth quintile as a categorical predictor. .............................................. 58

Figure 46. Percentage of children under age 5 with care seeking for diarrhea with the national 

estimates and lowest and highest region estimates .................................................................. 60

Figure 47. Regional map for the most recent survey of children under age 5 with care seeking for 

diarrhea .................................................................................................................................... 61

Figure 48. Percentage of youngest children under age 6 months who were exclusively breastfed .......... 63

Figure 49. Percentage of youngest children under age 6 months who were exclusively breastfed 

by wealth quintiles q1-q5 ........................................................................................................ 64

Figure 50. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of youngest children under age 6 months who were exclusively breastfed ........... 65

Figure 51. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of being 

exclusively breastfed with the wealth quintile as a categorical predictor. ............................... 66

Figure 52. Percentage of youngest children under age 6 months who were exclusively breastfed 

with the national estimates and lowest and highest region estimates ...................................... 67

Figure 53. Regional map for the most recent survey of youngest children under age 6 months who 

were exclusively breastfed ....................................................................................................... 68

Figure 54. Percentage of de facto children under age 5 who were stunted ............................................... 70

Figure 55. Percentage of de facto children under age 5 who were stunted by wealth quintiles q1-q5 ..... 71

Figure 56. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of de facto children under age 5 who were stunted ............................................... 72

Figure 57. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of stunting with 

the wealth quintile as a categorical predictor. .......................................................................... 73

Figure 58. Percentage of de facto children under age 5 who were stunted with the national estimates 

and lowest and highest region estimates .................................................................................. 74

Figure 59. Regional map for the most recent survey of de facto children under age 5 who were 

stunted ..................................................................................................................................... 75

Figure 60. Percentage of de facto children under age 5 who were wasted ............................................... 77

Figure 61. Percentage of de facto children under age 5 who were wasted by wealth quintiles q1-q5 ...... 78

Figure 62. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus 

the percentage of de facto children under age 5 who were wasted .......................................... 79

Figure 63. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of wasting with 

the wealth quintile as a categorical predictor. .......................................................................... 80

Figure 64. Percentage of de facto children under age 5 who were wasted with the national estimates 

and lowest and highest region estimates .................................................................................. 81

Figure 65. Regional map for the most recent survey of de facto children under age 5 who were 

wasted ...................................................................................................................................... 82

Figure 66. Wealth inequality score ........................................................................................................... 85

Figure 67. Regional inequality score ........................................................................................................ 86

xi 

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 

xiii 

Abstract 

This report describes recent levels and trends in 11 maternal and child health (MCH) indicators in 11 

countries, using the two most recent DHS surveys from each country. The emphasis is on within-country 

disparities by wealth quintile and region and how they may have changed. Six measures and a map are used 

to describe each indicator in each country. One measure is the overall prevalence of the indicator; four 

measures describe disparities by wealth; and one measure quantifies regional disparities. Maps show the 

prevalence of the indicator by region in the most recent survey. Wealth and regional inequality scores 

summarize the measures in the most recent survey. Nigeria, Mali, Haiti, and Pakistan have the highest 

wealth inequality scores for almost all indicators. Nigeria, Mali, and Pakistan also have high regional 

inequality scores. Inequality has declined in some countries for some certain indicators, but several other 

countries have consistently high levels of inequality and little improvement. According to the concentration 

index for wealth, Mali had significant deterioration for 7 indicators, followed Nigeria, with deterioration 

for 4 indicators. Most of the significant improvements were in Ghana, Indonesia, and Liberia, where 6 

indicators improved significantly. Despite some limitations, this report may help in planning and focusing 

interventions to improve both the level and equality of maternal and child health. For each country, health 

care interventions could be prioritized by specific indicators, wealth quintiles, or regions identified as 

having the most need. 

KEYWORDS: Maternal health, child health, wealth inequalities, regional inequalities, concentration index 

xv 

Executive Summary 

This report describes recent levels and trends in 11 maternal and child health (MCH) indicators in 11 

countries, using the two most recent DHS surveys in each country. It has particular emphasis on withincountry disparities by wealth quintile and region and how they may have changed between surveys. The 

indicators examined include: 1) having four or more visits for antenatal care (ANC); 2) the contraceptive 

prevalence rate for modern methods (mCPR); 3) delivery by a skilled birth attendant (SBA); 4) delivery in 

a health facility (DHF); 5) completing three doses of DPT vaccine (DPT3); 6) care seeking for symptoms 

of acute respiratory infection (ARI); 7) care seeking for fever; 8) care seeking for diarrhea; 9) exclusive 

breastfeeding; 10) stunting; and 11) wasting. The countries included in the analysis are the Democratic 

Republic of Congo (DRC), Ghana, Haiti, Indonesia, Kenya, Liberia, Mali, Nigeria, Pakistan, Senegal, and 

Zambia. 

To assess the wealth and regional disparities for each MCH indicator, the study used six measures and one 

map: 1) overall prevalence of the indicator; 2) prevalence of the indicator for each wealth quintile; 3) the 

difference between the highest and lowest quintiles (q5-q1) compared with the overall level of the indicator; 

4) coefficients from the logit regression of the indicator on the highest and lowest wealth quintiles, including 

95% confidence intervals; 5) concentration indices, including one-sided tests between surveys to identify 

statistically significant improvements; 6) the lowest and highest regional estimates compared with the 

national estimate; 7) country maps for each indicator that show the prevalence of the indicator by region in 

the most recent survey. 

We created wealth and regional inequality scores to summarize the measures in the most recent survey, 

with higher scores indicating greater inequality. Nigeria, Mali, Haiti, and Pakistan had the highest wealth 

inequality scores for almost all of the indicators. The only notable exceptions to this pattern are that Haiti 

had a lower wealth inequality score for the nutrition indicators (stunting and wasting) and Pakistan had a 

lower score for the maternal indicators (ANC, mCPR, SBA, and DHF). Nigeria, Mali, and Pakistan also 

had high regional inequality scores. Pakistan had the highest regional inequality score for the maternal and 

child indicators. Mali had a high inequality score for the maternal indicators. Nigeria, the DRC, and Senegal 

had high regional inequality scores for the child nutrition indicators (DPT3, ARI, and care seeking for fever 

and diarrhea). 

Some countries showed improvements in certain indicators, but several others had consistently high levels 

of inequality and showed little improvement between surveys. According to the concentration index for 

wealth, which includes all five quintiles, not just the highest and lowest, significant deterioration was found 

for 7 of the 11 indicators for Mali, followed by deterioration for 4 indicators for Nigeria. Most of the 

significant improvements were found for Ghana, Indonesia, and Liberia, where 6 of the 11 indicators 

improved according to the concentration index. In addition, Ghana had a concentration index that was not 

significantly different from zero for 7 out of the 11 indicators, suggesting that by the time of the most recent 

survey Ghana had achieved equality for most of the indicators examined. Ghana was followed by Senegal 

and Zambia, which had achieved equality for 5 of the 11 indicators. 

Despite some limitations, this report may help in planning and focusing interventions to improve both the 

level and equality of maternal and child health. For each country, health care interventions could be 

prioritized by specific indicators, wealth quintiles, or regions identified as having the most need. 

1 

1. Introduction 

1.1. Background 

This report describes recent levels and trends in major indicators of maternal and child health (MCH) in 11 

countries, using the two most recent surveys in each country conducted as part of The Demographic and 

Health Surveys (DHS) Program. It has particular emphasis on within-country disparities and how they may 

have changed between surveys. 

All countries, including the 11 in this report, give high priority to achieving and sustaining high levels of 

maternal and child health. Ambitious global targets were specified within the framework of the Millennium 

Development Goals (MDGs) for the period 1990-2015, and are being developed for the period 2015-2030 

as part of the Sustainable Development Goals (SDGs). National Ministries of Health and related 

government offices, international agencies such as WHO and UNICEF, donors such as USAID, and NGOs 

have sought to expand access to services, sponsored new interventions, and developed action plans with 

specific indicators and targets for planning. 

A primary mandate of The DHS Program is to monitor maternal and child health with repeated surveys in 

low- and middle-income countries. A strength of the DHS household surveys is in monitoring the use of 

services such as delivery in a health facility or care seeking for childhood illness. This report will include 

four indicators for mothers and seven for children. The indicators for the mother—which have strong 

implications for the health of children as well—are making four or more antenatal care visits; use of modern 

contraception; delivery by a skilled birth attendant; and delivery in a health facility. Three of these 

indicators pertain to the most recent childbirth in the five years before the survey. Contraceptive use is an 

indicator of reproductive health but is relevant here because it enables the avoidance of births that carry 

high risks for the mother and the child. The seven indicators for children include immunizations 

(specifically the third DPT immunization, which usually signals that the child has had all recommended 

immunizations); care seeking for symptoms of acute respiratory infection (ARI) or for fever or diarrhea in 

the past two weeks; whether a child under age 6 months meets the criteria for exclusive breastfeeding; and 

whether the child is stunted (short for age) or wasted (low weight for height). 

Cross-sectional household surveys are not well suited for assessing the impact of interventions on the 

survival of mothers and children, and for that reason this report is mainly limited to the use of services and 

to health behaviors (process indicators). The exception is the inclusion of direct indicators of the nutritional 

status of children, stunting, and wasting. The DHS Program also conducts Service Provision Assessment 

(SPA) surveys, which provide information about the coverage and quality of services from the perspective 

of facilities. Analyses of SPA data on maternal and child health can supplement this report. Together, 

household surveys and SPA surveys can provide a complete picture of access, quality, and use of services. 

In addition to overall improvement of maternal and child health, governments and agencies have a 

secondary goal of reducing disparities or inequalities. Typically, in every country some women and children 

are more likely than others to use services and interventions. Differences in use of health care services tend 

to be due to variations in knowledge and access rather than need or preferences, and to vary by 

socioeconomic status and location. The ideal pattern of change would be to observe improvements in 

indicators for all subpopulations in a country—here, improvements for all five wealth quintiles and in all 

regions. The amount of improvement would be greater as a percentage for subpopulations with lower initial 

levels of the indicator, and thus disparities would diminish and all subpopulations would converge toward 

the same level. This report includes several measures of disparity and convergence, as well as a synthesis 

of these measures. 
2 

Wealth quintiles and regions are not the only possible dimensions of inequality. Place of residence (urban 

and rural) and population density, for example, may influence access to services, but usually these factors 

are also associated statistically with relative wealth and/or region of residence. For that reason, as well as 

to keep this report of a manageable size, no covariates other than wealth quintiles and regions are included. 

1.2. Previous Research 

Reflecting the ambitious goal of eliminating global disparities, several studies have assessed equity in MCH 

indicators between and within countries. These studies vary somewhat in their selection of countries, data 

sources, outcomes, covariates, and measures. Rather than reviewing the empirical findings of these studies, 

the main emphasis in this brief review will be on their discussions of measurement. 

No single measure of equality can capture all disparities. Comparisons between countries can be difficult, 

especially when there is great variation in the overall level of an indicator. Boerma et al. (2008) used data 

from 54 countries between 1990 and 2006 to compute a coverage index that combined eight MCH 

indicators. The index was then computed for each wealth quintile to identify gaps in coverage. The authors 

found large disparities in coverage by wealth, especially in South Asia and in eastern and southern African 

countries. A combined index is useful for assessing overall disparities in MCH coverage but, in order to 

plan targeted interventions, must be supplemented with information on the progress for each country and 

each indicator separately. 

Barros et al. (2012) conducted an equity analysis of MCH in 54 countries, using several equality measures 

and surveys between 2000 and 2008. In addition to the coverage index, two indicators of absolute

inequality—the difference between the top and bottom quintile and the slope—and two indicators of 

relative inequality—the ratio of the top to the bottom quintile and the concentration index—were also 

computed. The authors discussed the limitation of the coverage index in potentially masking different 

patterns of coverage and inequality for separate indicators. They also discussed the difficulty in combining 

different equality measures, since absolute and relative measures may lead to conflicting findings in certain 

situations. Their discussion of inequality mainly focused on one measure: the concentration index. 

Houweling et al. (2007) showed that both absolute and relative measures can be useful for monitoring 

inequality, but only if the overall level of the outcome is taken into account. They also advocated the odds 

ratio of the richest category to the poorest category as preferable to a difference for measuring inequality. 

Hosseinpoor et al. (2016) used 11 absolute and relative measures to assess regional inequalities in four 

countries. The results showed that there were many similarities in the measures. The authors recommended 

using pairwise measures rather than complex measures because they lead to similar conclusions and are 

easier to convey to general audiences. 

To summarize the methodology of these earlier studies, they generally recommend using more than one 

equality measure, as no single measure can be identified as the best measure, and especially when trends 

are being assessed, but they also recommend avoiding measures that are duplicative and distinguishing 

between absolute and relative measures (Barros and Victora 2013; Barros et al. 2012; Hosseinpoor et al. 

2016; Houweling et al. 2007; Mackenbach and Kunst 1997). 

 
3 

2. Data and Methods 

2.1. Data 

The analysis presented here includes data from 11 high-priority USAID MCH countries with recent DHS 

surveys, focusing on the two most recent surveys in each country so that inferences can be made about 

trends as well as levels. Table 1 lists these countries and surveys along with the country codes and DHS 

phase numbers that appear in the graphics of this report. 

Table 1. DHS surveys included in the analysis 

Country Country code

Survey 1 

DHS phase: year 

Survey 2 

DHS phase: year 

Democratic Republic of the Congo (DRC) CD 5: 2007 6: 2013-14 

Ghana GH 5: 2008 7: 2014 

Haiti HT 5: 2005-6 6: 2012 

Indonesia ID 5: 2007 6: 2012 

Kenya KE 5: 2008-9 7: 2014 

Liberia LB 5: 2007 6: 2013 

Mali ML 5: 2006 6: 2012-13 

Nigeria NG 5: 2008 6: 2013 

Pakistan PK 5: 2006-7 6: 2012-13 

Senegal SN 6: 2012-13 7: 2014 

Zambia ZM 5: 2007 6: 2014 

The 2012-13 survey conducted in Mali coincided with a security crisis that made the regions of Timbuktu 

(or Tombouctou), Gao, Kidal, and part of Mopti inaccessible. The survey covered the remaining five 

regions and the capital, Bamako, all located in the south of the country. Since the excluded regions 

represented less than 10% of the total sample, they were retained in the 2006 survey. 

Almost all of the surveys have a gap of 5-7 years between the successive surveys in Table 1. The exception 

is Senegal which has a one year gap. The Senegal surveys in Table 1 are part of the Senegal Continuous 

survey project that began in 2012 and is expected to end in 2017 which performs a new DHS survey in each 

year or round of the project. 

2.2. Measures 

The report examines 11 indicators related to MCH outcomes. The indicators are listed and defined below. 

Four or more visits for antenatal care (ANC): 

The proportion of women age 15-49 who have attended at least four antenatal care visits for their most 

recent pregnancy in the five years before the survey. 

Contraceptive prevalence rate for modern methods (mCPR): 

The proportion of women age 15-49 who are currently in a union and are using a modern contraceptive 

method. Modern contraceptive methods include pills, IUD, injections, implants, diaphragm, female and 

male condoms, female and male sterilization, foam or jelly, and lactational amenorrhea method (LAM). 

The mCPR may also include other modern contraceptive methods that are country-specific or less common 

but were reported by the respondent and identified in the datasets as modern methods. 
4 

Delivery by a skilled birth attendant (SBA): 

The proportion of women age 15-49 for whom the most recent birth in the five years before the survey was 

delivered by a skilled birth attendant. If more than one person assisted the delivery, the most qualified 

person is described. The definition of a skilled birth attendant is country-specific but commonly refers to a 

doctor, nurse, midwife, or auxiliary midwife. As an example of a country-specific variation, in Indonesia 

SBA also includes village midwife, which accounts for the highest proportion of assisted delivery in 

Indonesia. In other countries a village midwife may not be considered an SBA. 

Delivery in a health facility (DHF): 

The proportion of women age 15-49 for whom the most recent birth in the five years before the survey was 

delivered in a health facility. This indicator distinguishes between home deliveries and facility deliveries. 

Health facilities could be government, private, NGO, or another type such as a maternity clinic. 

Completing three doses of DPT vaccine (DPT3): 

The proportion of children age 12-23 months who have received the third dose of the DPT vaccine. The 

DPT3 immunization is selected for the indicator because children who receive this vaccine generally have 

received all of the other recommended immunizations. 

Care seeking for symptoms of ARI: 

The proportion of children under age 5 who had symptoms of ARI (Acute Respiratory Infection, possibly 

pneumonia) in the two weeks before the survey and for whom advice or treatment was sought from a health 

facility or provider. Symptoms of ARI are “short, rapid breaths” that are “due to a problem in the chest.” 

These symptoms are not equivalent to a medical diagnosis. For all care seeking indicators, the analysis 

excludes treatment sought from pharmacies, shops, or traditional healers. 

Care seeking for fever: 

The proportion of children under age 5 who had symptoms of fever in the two weeks before the survey and 

for whom advice or treatment was sought from a health facility or provider. 

Care seeking for diarrhea: 

The proportion of children under age 5 who had diarrhea in the two weeks before the survey and for whom 

advice or treatment was sought from a health facility or provider. 

Exclusive breastfeeding: 

The proportion of children under age 6 months who are being breastfed and have not had any water, other 

liquids, or solids in the day or night before the survey. Limited to children who are living with the mother. 

Stunting: 

Proportion of de facto1

 children under age 5 who have a height-for-age z-score that is less than two standard 

deviations below the median of the WHO 2007 reference population. Both Indonesian DHS surveys and 

the Pakistan 2006-7 survey did not include height and weight measurements for children; therefore stunting 

and wasting could not be computed for these three surveys. 

 

1

 Slept in the household the previous night before the survey 
5 

Wasting: 

Proportion of de facto children under age 5 who have a weight-for-height z-score that is less than two 

standard deviations below the median of the WHO 2007 reference population. 

2.3. Analysis 

2.3.1. Wealth inequality measures 

The DHS quintiles were used to describe inequalities by wealth for the MCH indicators. The DHS computes 

a continuous wealth index for each survey, based on the presence or absence of a large number of potential 

household assets. Cut points are then calculated, such that an equal number of weighted de jure2

 individuals 

in the household sample are in each of five quintiles. The wealth quintiles are labeled as follows: poorest 

(q1), poor (q2), middle (q3), rich (q4), and richest (q5). 

Three indices were computed to describe inequities by wealth in the selected MCH indicators. These indices 

include: 

1. The difference in prevalence for each indicator between the richest (or highest) wealth quintile 

(q5) and poorest (or lowest) wealth quintile (q1). 

2. An index computed from the logit regression of each indicator on the wealth quintiles, as a 

categorical variable, using the coefficient for the difference between the richest category and the 

poorest category. These are unadjusted coefficients since the objective is to describe the level of 

inequality by wealth and not to provide an explanation of the process with a model. Coefficient 

estimates were used instead of odds ratios to have more comparable scales from one indicator to 

another. 

3. the concentration index, which describes the extent to which the beneficiaries of MCH tend not to be 

distributed uniformly across the wealth quintiles 

The concentration index is a relative measure of inequality with values that range from -1 to 1 (Barros et 

al. 2012; World Health Organization 2013), conveying both the magnitude and the direction of inequity. A 

value of zero represents perfect equality, that is, identical prevalence of an outcome in all five quintiles. 

Positive values indicate that the outcome is more concentrated in wealthier quintiles; negative values 

indicate that it is more concentrated in the poorer. For indicators that are bounded between zero and one, 

the amount of dispersion is related to the overall mean. That is, if the overall prevalence is near 0% or 

100%, the amount of variation across categories such as wealth quintiles or regions is necessarily low, but 

if the overall prevalence is near 50%, the amount of potential variation across categories can be very large. 

Corrections to the concentration index that take into account the mean of the outcome were first proposed 

by Wagstaff (2005) and more recently by Erreygers (2009). A correction is important because it allows for 

comparisons between countries with very different levels of the outcomes. Our analysis uses the Erreygers 

(2009) correction (previously published in a department paper in 2006), following other researchers who 

have studied health inequalities (Binnendijk, Koren, and Dror 2012; Monteiro et al. 2010; Van de Poel et 

al. 2007; Van de Poel et al. 2008; Van Malderen et al. 2013). The calculations are performed in Stata 14 

with the conindex command (O'Donnell et al. 2016). In addition, a one-sided test was performed to 

determine whether the concentration index significantly improved from the first survey to the second. For 

indicators that are usually concentrated among the richer households, such as all the MCH indicators in this 

report apart from stunting and wasting, the concentration index is expected to be positive. For those 

 

2

 Usual resident 
6 

indicators, improved equity will be implied if a positive concentration index in the first survey becomes 

less positive, and closer to zero, in the second survey. For stunting and wasting, the concentration index is 

expected to be negative since the outcome is concentrated among the poorer households; equity is improved 

if the concentration index becomes less negative, and closer to zero, from one survey to the next. 

Plots on a graph will visually describe the inequalities of the outcome and the level in each wealth quintile. 

To produce a plot for the difference index, the adjustment for the mean of the outcome was used as 

suggested by Houweling et al. (2007). The adjustment improves comparability among countries. Plots were 

produced that show the difference index (percentage of indicator in richest minus percentage of indicator 

in the poorest) versus the prevalence of the outcome. Finally, coefficient plots were produced for each 

outcome with upper and lower 95% confidence bounds. Together, these four types of plots highlight the 

wealth inequalities between countries and also show whether there have been improvements by comparing 

between two consecutive surveys from the same country. 

2.3.2. Mapping regional inequalities 

Two figures were produced to describe disparities among regions for each health indicator. The first figure 

shows the national estimate for each indicator along with the estimates for the lowest and highest regions. 

This format was repeated for all the surveys in order to assess the inter-country inequalities and whether 

they decreased from one survey to the next, as well as the intra-country inequalities and the range of the 

indicators across regions. Appendices 4-6 identify the names of the lowest and highest regions for each 

indicator and survey. The second figure consists of prevalence maps for the most recent surveys for each 

country. The maps are produced for each indicator except care seeking for ARI symptoms due to the small 

sample sizes within each region. They show the prevalence of the indicator by region, which makes it 

possible to visually assess regional disparities between and within countries. 

2.3.3. Assessing inequalities 

To assess the level of inequality for each MCH indicator, we used six measures and a map: 1) Overall 

percentage or prevalence of the indicator; 2) a dot plot, or equiplot3

, of the prevalence of the indicator for 

each wealth quintile; 3) a plot of the difference (q5-q1) versus the overall level for the indicator with a line 

connecting the two surveys from the same country; 4) a coefficient plot with 95% confidence intervals; 5) 

concentration indices with one-sided tests between surveys to identify statistically significant 

improvements; 6) an equiplot of the lowest and highest regional estimates compared with the national 

estimate; and 7) a map of the most recent survey showing the prevalence of the indicator by region. The 

last section of the results is a summary to describe the overall worst-performing and best-performing 

countries. All analyses use sample weights, and the concentration index is adjusted for the cluster design 

of the samples. The logit regression estimates are also adjusted for the stratification of the samples. 

 

3

 http://www.equidade.org/equiplot.php 
7 

3. Results 

For each MCH indicator, presentation of the results begins with summarizing the wealth inequalities as 

measured by the overall level of the indicator, the spread by wealth quintiles, the difference index, the 

coefficient index, and the concentration index. This summary is followed by a discussion of regional 

disparities as shown by the gaps between the regions with the lowest and highest values on the indicator as 

well as by maps of the level of the indicator by region for the most recent survey. 

3.1. Four or More ANC Visits 

Figure 1 shows the percentage of women age 15-49 who have attended four or more ANC visits for their 

most recent pregnancy, with confidence bands. The highest percentages were found in Ghana, Indonesia, 

and Liberia, with levels close to 80% or more for the most recent survey. Close to half of the women in the 

Democratic Republic of the Congo (DRC), Nigeria, and Senegal had attended four or more ANC visits. In 

Mali and Pakistan the level was approximately 40% or less. Almost all countries except Zambia improved 

in this indicator, with the greatest increases found in Haiti and Liberia. There was a small and nonsignificant increase for the DRC and Senegal. The intervals between the two consecutive surveys are not 

the same for all countries (Table 1). 

Figure 1. Percentage of women age 15-49 who have attended at least four ANC visits for their most 

recent pregnancy 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
8 

In Figure 2 we see that in every survey the percentage of women with four or more ANC visits was greater 

in higher wealth quintiles. The amount of spread across wealth quintiles identifies wealth-related disparities. 

The greatest disparities were found in Nigeria and Pakistan and the lowest in Zambia. Comparing the two 

Zambian surveys we can see that the disparities slightly increased from the first survey to the second, but 

even at the second survey there was less inequality than in any other country. 

Figure 2. Percentage of women age 15-49 who have attended at least four ANC visits for their most 

recent pregnancy by wealth quintiles q1-q5 
9 

Figure 3 shows the difference in the percentage of the ANC indicator for the richest wealth quintile (q5) 

minus the poorest (q1) wealth quintile plotted versus the overall level or prevalence of the ANC indicator. 

The two surveys from each country were connected by a line to show the trend. A red dot at one end of the 

line identifies the first survey and an orange dot at the other end identifies the second survey. Ideally, we 

would see a decrease in the difference between wealth quintiles and an increase in the overall prevalence 

from one survey to the next (that is, a line with the orange dot below and to the right of the red dot). Ghana, 

Indonesia, and Haiti, as well as Liberia to a slight degree, exhibit this pattern, with a decreasing differences 

and increasing percentages from one survey to the next. The prevalence of ANC visits improved in Nigeria, 

Pakistan, Mali, and Liberia but with an increase in the difference, implying that the improvements were 

concentrated among women with greater wealth. Zambia was the only country to have both a decrease in 

the prevalence and an increase in the difference. Despite the move to less equality for the ANC indicator, 

Figure 3 shows that, as in Figure 2, Zambia has the least disparity of all 11 countries. 

Figure 3. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of women age 15-49 who attended at least four ANC visits for their most recent 

pregnancy 
10 

Figure 4 provides a third perspective on trends with the ANC indicator. It graphically presents, for each 

survey in each country, the logit regression coefficient for the richest wealth quintile, with the poorest 

quintile as the reference category. This coefficient is the log of an odds ratio. It takes the value zero (shown 

in the figure with a vertical line) if the odds ratio is one, that is, if both quintiles have the same prevalence. 

In contrast with Figures 2 and 3, Figure 4 focuses exclusively on equality of ANC visits by wealth quintile, 

separately from prevalence, and it includes confidence intervals, which were not provided in Figures 2 and 

3. Ideally, we would see coefficients close to zero, with confidence intervals overlapping the zero reference 

line. This would indicate that there is no significant difference between the extreme wealth categories in 

terms of the outcome of interest. Figure 4 shows that only the Zambia 2007 survey (ZM5) had a nonsignificant coefficient for the richest wealth category compared with the poorest. The coefficient was small 

and close to zero. However, in the most recent Zambian survey the coefficient increased and became 

significant, indicating a significant increase in inequality between the extreme wealth quintiles. One or both 

surveys in Ghana, Indonesia, Nigeria, and Pakistan had coefficients that were near three or greater than 

three (a coefficient that translates to an odds ratio of about 20 or even more). 

Figure 4. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of attending at least four 

ANC visits with the wealth quintile as a categorical predictor 
11 

Finally, for the ANC indicator, Table 2 provides the concentration indices for each survey. The 

concentration index summarizes the disparities across all wealth quintiles, not just the first and the fifth, 

and changes in the index can be tested for statistical significance. Table 2 includes a one-sided p-value from 

a test of whether, in the population, the concentration index decreased between the two consecutive surveys. 

Ideally, the concentration index should be close to zero and the difference of the concentration index in 

survey 2 minus survey 1 should be negative with a large magnitude. Since these concentration indices were 

corrected for binary outcomes that also take into account the mean of the outcome (Erreygers 2009; 

O'Donnell et al. 2016), comparisons can be made across countries. All the concentration indices in the table 

are positive, indicating that the ANC indicator was concentrated in the richer households. Table 2 shows 

that Nigeria had the highest concentration index, indicating the highest inequality, and Zambia had the 

lowest index. Significant improvements were only found in Ghana, Haiti, and Indonesia, with a significant 

reduction in the concentration index in the most recent survey. The concentration index significantly 

increased in the DRC, Mali, Pakistan, and Zambia. None of the concentration indices in the most recent 

surveys were significantly different from zero, indicating that inequalities remain for these countries. 

Table 2. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the ANC indicator 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased

p-value 

CD 0.122 (0.03) 5441 0.202 (0.02) 11288 0.079 0.974* 

GH 0.247 (0.02) 2147 0.177 (0.02) 4294 -0.069 0.020

HT 0.365 (0.02) 4237 0.288 (0.02) 5414 -0.077 0.009

ID 0.270 (0.01) 15334 0.195 (0.01) 15260 -0.075 <0.001

KE 0.233 (0.02) 4082 0.250 (0.01) 14945 0.017 0.750 

LB 0.209 (0.03) 3996 0.172 (0.02) 5348 -0.037 0.174 

ML 0.283 (0.02) 9018 0.363 (0.02) 6723 0.080 0.995* 

NG 0.532 (0.01) 17882 0.559 (0.01) 20192 0.027 0.902 

PK 0.411 (0.02) 5697 0.485 (0.02) 7461 0.073 0.998* 

SN 0.322 (0.03) 4470 0.287 (0.02) 4484 -0.035 0.163 

ZM 0.003 (0.02) 4148 0.074 (0.02) 9344 0.071 0.996 

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 

The formats of Figures 2-4 and Table 2 are repeated for each indicator in this report for the analysis of 

inequality by wealth quintiles. The analysis in inequality by regions is summarized differently, in a dotplot 

and a set of maps. Figures 5 and 6, respectively, describe inequality in ANC by region. 
12 

Figure 5 shows the regional spread in the ANC indicator by comparing the lowest and highest regional 

estimates with the national estimate. This figure is analogous to Figure 2, except that each survey in Figure 

2 had five dots on a line, for the five wealth quintiles; Figure 5 has three dots on a line. Zambia, Haiti, 

Ghana, and Liberia showed relatively small differences between the highest and lowest regions (differences 

of less than 25 percentage points). The highest differences were found in the most recent surveys of Pakistan 

and Nigeria (differences of more than 60 percentage points). The difference between the lowest and highest 

regions increased the most from one survey to the next in Indonesia and Pakistan and increased to a lesser 

degree in Kenya and Mali. 

Figure 5. Percentage of women age 15-49 who have attended four or more ANC visits with the 

national estimates and lowest and highest region estimates 
13 

Figure 6 provides maps of the regional estimates for the most recent survey in each country. Prevalence is 

highest in regions colored purple and lowest in regions colored red. Regional disparities are large in several 

countries, especially in Nigeria. Zambia and Haiti have the fewest regional disparities, with almost all the 

regions in Zambia between 51% and 60%, and in Haiti between 71% and 80%. 

Figure 6. Regional map for the most recent survey of women age 15-49 who have attended at least 

four ANC visits for their most recent pregnancy 
14 

Summary: 

In order to summarize the levels and disparities in the ANC indicator according to wealth and region, Table 

3 uses a different format that identifies the three most extreme countries using five criteria. These criteria, 

shown in the columns, are the lowest prevalence, the largest difference between the fifth and first quintiles, 

the largest coefficient from the logit regression on the fifth and first quintiles, the highest concentration 

index by wealth quintiles, and the highest difference between the highest and lowest region for the ANC 

indicator. Thus the first criterion refers to the overall level (shown in Figure 1), the next three criteria refer 

to disparities by wealth (shown in Figures 2-4), and the final criterion refers to disparity by region (described 

in Figures 5 and 6). Pakistan is the only country that is in the bottom three for all five criteria. Nigeria is in 

the bottom three for all four of the criteria that measure inequality. Mali is in the bottom three for three 

criteria. Ghana is in the bottom three for one of the measures of wealth disparity (the logit regression 

coefficient). Indonesia is in the bottom three for regional disparity. The DRC is in the bottom three for low 

overall prevalence but not for any of the measures of disparity. The remaining five countries in this analysis 

are not in the bottom three for any of these criteria. 

Table 3. Summary table for the ANC indicator showing the top three countries with the 

following measures for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 
15 

3.2. Modern Contraceptive Rate (mCPR) 

We now turn to the second indicator, the use of modern contraception. The mCPR is the percentage of 

women age 15-49 in a union who are currently using a modern contraceptive method. Figure 7 shows that 

the mCPR ranged from less than 10% in the DRC, Mali, and Nigeria to approximately 50% in Indonesia in 

both surveys and Kenya in the most recent survey. Significant increases in prevalence occurred between 

surveys in most countries except the DRC, Indonesia, and Nigeria. 

Figure 7. Percentage of women age 15-49 in a union currently using a modern contraceptive method 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
16 

Figure 8 shows that the largest spread across wealth quintiles is in Kenya and Zambia. The gaps between 

the wealth quintiles decreased for some countries, particularly Ghana, Haiti, and Liberia. The mCPR often 

increases monotonically with wealth, but not always. Exceptions to this pattern were found in one or both 

surveys in Ghana, Haiti, Indonesia, Kenya, Liberia, Senegal, and Zambia. However, the departures from 

this pattern were mostly small and may not be statistically significant (tests of significance were not 

performed). 

Figure 8. Percentage of women age 15-49 in a union currently using a modern contraceptive method 

by wealth quintiles q1-q5 
17 

Figure 9 shows that modern contraceptive prevalence increased in almost all countries (the second survey 

is to the right of the first survey) and in almost all countries the difference between richest and poorest 

wealth categories was reduced (the second survey is lower than the first survey). The greatest improvements 

were found for Ghana and Haiti, where the difference in the most recent survey was close to zero or moved 

slightly in the opposite direction (that is, the mCPR was higher in the poorest wealth quintile than in the 

richest). The disparity increased for Zambia, Mali, Nigeria, and the DRC. In Zambia and Mali the overall 

level of the mCPR increased significantly but the disparity also increased, whereas in Nigeria and the DRC 

neither the levels nor the disparities changed in the desired direction. 

Figure 9. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of women age 15-49 in a union currently using a modern contraceptive method 
18 

Figure 10 shows the logit regression coefficient (the log of the odds ratio) for the use of modern 

contraception and wealth quintile. We see that for the most recent surveys in Ghana and Haiti, the 

coefficient for the richest wealth category was small and not significant, indicating that the difference 

between the extreme wealth quintiles was not statistically significant. The most recent survey for Indonesia 

had a very small coefficient but it was still significant. Larger disparities were found in the DRC, Mali, and 

Nigeria, with a great increase in the coefficient for Nigeria. Most surveys showed an improvement between 

the two surveys as their coefficients became smaller. 

Figure 10. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of using a modern 

contraceptive method with the wealth quintile as a categorical predictor. 
19 

Table 4 gives the concentration index for every survey for use of modern contraception. All the indices are 

positive, indicating that richer households use modern contraception more than poorer households. In the 

most recent Ghana, Haiti, and Indonesia surveys, however, the concentration index was not significantly 

different from zero (p-value not shown in table), indicating that these countries have reached equality in 

mCPR according to this measure. There were significant improvements in the concentration index in 

Indonesia, Kenya, and Pakistan. In Mali, Nigeria, and Zambia the concentration index significantly 

increased, implying more disparity and less equality. 

Table 4. Concentration index (CI) estimates with tests of difference between two surveys for each

country for the mCPR indicator 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD 0.082 (0.01) 7520 0.105 (0.01) 14282 0.024 0.949 

GH 0.072 (0.02) 3370 -0.028 (0.02) 6355 -0.100 <0.001

HT 0.096 (0.02) 7358 -0.021 (0.02) 9041 -0.117 <0.001

ID 0.046 (0.01) 32895 0.012 (0.01) 34865 -0.035 0.016

KE 0.230 (0.02) 5904 0.155 (0.01) 22504 -0.075 0.001

LB 0.114 (0.01) 5186 0.076 (0.02) 6834 -0.038 0.086 

ML 0.096 (0.01) 12838 0.150 (0.01) 8943 0.055 1.000* 

NG 0.156 (0.01) 25364 0.173 (0.01) 29128 0.018 0.963* 

PK 0.154 (0.01) 10023 0.104 (0.02) 13558 -0.050 0.006

SN 0.173 (0.02) 6163 0.145 (0.02) 6218 -0.028 0.183 

ZM 0.136 (0.02) 5205 0.182 (0.01) 11658 0.047 0.961* 

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 

Figure 11 shows that Indonesia and Kenya have the greatest regional disparities between the lowest and 

highest regions in both of their surveys. Haiti showed an improvement in the lowest and highest regions. 

Improvements were only found for the lowest region in Zambia and for the highest region in Pakistan. 

Figure 11. Percentage of women age 15-49 in a union currently using a modern contraceptive 

method with the national estimates and lowest and highest region estimates 
20 

The map of the recent surveys in Figure 12 shows relatively little regional disparity for many countries, 

especially the DRC, where in almost all regions the mCPR was below 10%. Of course, a uniformly low 

level is not a desirable form of equality. As Figure 7 indicated, the level of mCPR was higher in Indonesia 

and Kenya than in the other countries, but the maps indicate some regional disparities within these two 

countries. In some regions mCPR is below 20%, while in other regions the range is 61-70%. 

Figure 12. Regional map for the most recent survey of women age 15-49 in a union currently using 

a modern contraceptive method 
21 

Summary: 

Table 5 provides the summary of equality-related measures for the mCPR indicator. Nigeria is one of the 

three most disparate countries for all five measures except regional difference. Kenya shows relatively high 

inequality on three of the measures. 

Table 5. Summary table for the mCPR indicator showing the top three countries with the 

following measures for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 
22 

3.3. Delivery by an SBA 

Figures 13 shows the overall level of assistance by a skilled birth attendant (SBA) for the last birth. In most 

countries more than half of women had their most recent delivery assisted by an SBA. The highest levels 

are in the DRC, Ghana, Indonesia, and Zambia. In these four countries the level was approximately 70% or 

more, for both surveys or the most recent one. Significant increases in the level of SBA assistance were 

found for all the countries except the DRC, Nigeria, and Senegal. The lowest levels were found in Haiti and 

Nigeria, with a level of 40% for the most recent surveys. All countries except Nigeria had an increase in 

SBA coverage between the two most recent surveys. 

Figure 13. Percentage of women age 15-49 who had their most recent birth assisted by an SBA 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
23 

Figure 14 indicates large gaps in the prevalence of SBA assistance by wealth quintiles in almost all the 

countries. Nigeria exhibited the largest spread across the wealth quintiles with a range of 80 percentage 

points between the poorest and richest quintiles, but most countries had a gap of at least 50 percentage 

points. Most countries showed some reduction of these gaps. Figure 15, which focuses on the lowest and 

highest quintiles, shows this pattern clearly. Ghana greatly reduced the difference between the extreme 

wealth quintiles (by 23 percentage points) while increasing the overall level of the indicator. The same trend 

was observed in Zambia, Indonesia, and Senegal and to a lesser degree in Liberia, Pakistan, and the DRC. 

Haiti and Mali showed an increase in prevalence but also an increase in the difference by wealth. 

Figure 14. Percentage of women age 15-49 who had their most recent birth assisted by an SBA by 

wealth quintiles q1-q5 

Figure 15. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of women age 15-49 who had their most recent birth assisted by an SBA 
24 

Figure 16 shows that all the countries had large and significant logit regression coefficients with small and 

non-significant improvements between surveys. In Kenya, Mali, and Nigeria the coefficients increased 

between surveys, indicating that the inequalities between the extreme wealth groups grew larger. 

Figure 16. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of assisted by SBA with the 

wealth quintile as a categorical predictor. 

Table 6, which includes all wealth quintiles, not just the lowest and highest, shows relatively high 

concentration indices, with the highest indices found in the most recent Nigeria survey. Significant 

decreases in the concentration index, implying movement toward more equality, were found for Ghana, 

Indonesia, Liberia, Senegal, and Zambia. Haiti and Mali had significant increases in the concentration 

index, implying movement toward more inequality. None of the concentration indices in the most recent 

surveys were significantly different from zero, indicating that inequalities in delivery by an SBA persist in 

all these countries. 

Table 6. Concentration index (CI) estimates with tests of difference between two surveys for each

country for the SBA indicator 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD 0.292 (0.03) 5483 0.256 (0.02) 11293 -0.036 0.143 

GH 0.552 (0.02) 2147 0.410 (0.03) 4294 -0.141 <0.001

HT 0.476 (0.02) 4237 0.534 (0.02) 5414 0.058 0.976*

ID 0.393 (0.02) 15334 0.279 (0.01) 15262 -0.113 <0.001

KE 0.472 (0.03) 4082 0.476 (0.01) 14949 0.005 0.556

LB 0.451 (0.04) 3996 0.346 (0.03) 5348 -0.105 0.024

ML 0.380 (0.03) 9036 0.472 (0.02) 6723 0.092 0.995*

NG 0.634 (0.01) 18028 0.652 (0.01) 20192 0.018 0.838

PK 0.462 (0.02) 5724 0.428 (0.03) 7461 -0.034 0.147

SN 0.520 (0.03) 4470 0.450 (0.03) 4484 0.071 0.048

ZM 0.530 (0.02) 4148 0.377 (0.02) 9353 -0.153 <0.001

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction.
25 

Figure 17 shows large gaps in SBA coverage between the lowest and highest regions for most countries. 

The regional disparities increased significantly in Pakistan, from a gap of approximately 23 percentage 

points between the lowest and highest regions in the first survey to a gap of 69 percentage points in the 

second survey. In contrast, Liberia greatly reduced its regional disparities between the two surveys. 

Figure 17. Percentage of women age 15-49 who had their most recent birth assisted by an SBA with 

the national estimates and lowest and highest region estimates 
26 

The maps in Figure 18 show many regional disparities in all the countries. In Pakistan for instance, SBA 

coverage ranged from 0-10% in one region to 61-70% in a neighboring region. Indonesia had relatively low 

regional disparities for almost all regions except Papua, where SBA prevalence was much lower than in the 

other regions. 

Figure 18. Regional map for the most recent survey of women age 15-49 who had their most recent 

birth assisted by an SBA 
27 

Summary: 

In the summary shown in Table 7, Nigeria consistently appears among the three countries with greatest 

inequality for all five measures. Haiti appears among the most extreme three countries for all measures 

other than regional difference. Nigeria and Haiti were among the most extreme three countries for the three 

wealth inequality measures. With the exception of Nigeria, the regional inequality measure was not 

consistent with the wealth inequality measures. 

Table 7. Summary table for the SBA indicator showing the top three countries with the 

following measures for the most recent survey. 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 

3.4. Delivery in a Health Facility (DHF) 

More than half of women age 15-49 had their most recent delivery in a health facility for the most recent 

surveys except for Haiti and Nigeria (Figure 19). In the DRC, Ghana, Senegal, and Zambia the level was 

above 70%. DHF prevalence significantly increased between surveys in all countries except Nigeria and 

Senegal. 
28 

Figure 19. Percentage of women age 15-49 who delivered their most recent birth in a health facility 

As Figure 20 shows, similar to the SBA results in Figure 14, large gaps (almost all over 50 percentage 

points) were found between the wealth quintiles for the DHF indicator. Figure 21, however, shows that 

these gaps tended to diminish between surveys. As with the SBA difference plot, the differences between 

the extreme quintiles (q5-q1) decreased along with increasing prevalence in Ghana, Zambia, Indonesia, 

Liberia, Senegal, the DRC, and (very slightly) in Pakistan and Kenya (Figure 21 lists the countries in 

decreasing order of the difference between the two surveys). Overall prevalence of delivery in a health 

facility increased in Haiti and Mali, but the inequality by wealth also increased. 

Figure 20. Percentage of women age 15-49 who delivered their most recent birth in a health facility 

by wealth quintiles q1-q5 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
29 

Figure 21. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of women age 15-49 who delivered their most recent birth in a health facility 
30 

The coefficient plot in Figure 22 shows large and significant logit regression coefficients for DHF, similar 

to those for SBA, with increasing coefficients in Kenya, Mali, and Nigeria between surveys. 

Figure 22. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of delivery in a health facility 

with the wealth quintile as a categorical predictor. 

In Table 8 the concentration indices are over 0.4 for almost all the surveys, indicating a large level of 

inequality with the indicator concentrated by wealth. In the DRC, Ghana, Indonesia, Liberia, and Zambia 

the concentration index significantly decreased, showing movement toward more equality. In contrast, in 

Haiti and Mali the concentration index significantly increased, moving toward more inequality. None of 

the concentration indices in the most recent surveys were significantly different from zero, indicating that 

inequalities in health facility delivery persist in all these countries. 

Table 8. Concentration index (CI) estimates with tests of difference between two surveys for each

country for the DHF indicator 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD 0.332 (0.03) 5483 0.264 (0.02) 11293 -0.068 0.033

GH 0.537 (0.02) 2147 0.413 (0.03) 4294 -0.124 <0.001

HT 0.454 (0.02) 4237 0.513 (0.02) 5414 0.059 0.979*

ID 0.542 (0.02) 15334 0.437 (0.01) 15262 -0.105 <0.001

KE 0.485 (0.03) 4082 0.483 (0.01) 14949 -0.002 0.473

LB 0.406 (0.04) 3996 0.306 (0.04) 5348 -0.100 0.036

ML 0.393 (0.03) 9036 0.516 (0.02) 6723 0.123 1.00* 

NG 0.584 (0.01) 18028 0.599 (0.01) 20192 0.015 0.787 

PK 0.461 (0.02) 5724 0.433 (0.03) 7461 -0.028 0.184

SN 0.461 (0.03) 4470 0.399 (0.03) 4484 -0.062 0.066 

ZM 0.516 (0.02) 4148 0.336 (0.02) 9353 -0.180 <0.001

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 
31 

Figure 23 shows very large gaps in DHF between the lowest and highest regions for most countries, as 

Figure 17 showed for the SBA regional disparities. Also as with the SBA indicator, regional disparities in 

the DHF indicator increased substantially between surveys in Pakistan but decreased in Liberia. 

Figure 23. Percentage of women age 15-49 who delivered their most recent birth in a health facility 

with the national estimates and lowest and highest region estimates 
32 

The DHF map in Figure 24 appears similar to the SBA map in Figure 18, with the exception of Indonesia. 

This difference between the DHF and SBA indicators in Indonesia is due to the classification of a village 

midwife as an SBA in Indonesia, but village midwives do not usually perform deliveries in a health facility. 

Figure 24. Regional map for the most recent survey of women age 15-49 who delivered their most 

recent birth in a health facility 
33 

Summary: 

As Table 9 shows, Haiti, Mali, and Nigeria are consistently the three most extreme countries according to 

the five measures, and especially according to the wealth inequality measures. The only other countries 

identified as extreme in Table 9 are Indonesia, in terms of extreme regional disparity, and Pakistan, in terms 

of both extreme regional disparity and low overall prevalence of DHF. 

Table 9. Summary table for the DHF indicator showing the top three countries with the 

following measures for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 
34 

3.5. Three Doses of DPT Vaccine (DPT3) 

Figure 25 shows the level of completion of three doses of DPT vaccine among children age 12-23 months. 

Ghana, Kenya, Senegal, and Zambia reached a level of about 90% for this indicator in their most recent 

surveys. The lowest levels were found in Nigeria, at approximately 40% coverage, followed by the DRC 

and Haiti, both at about 60% for the most recent survey. Liberia showed the greatest improvement, from 

DPT3 coverage of 50% in the first survey to 70% in the most recent survey. 

Figure 25. Percentage of children age 12-23 months who completed three doses of the DPT vaccine 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
35 

Figure 26 shows the large spread across wealth quintiles in Nigeria and Pakistan. Ghana had the lowest 

spread for the most recent survey, followed by Senegal, Kenya, and Zambia. Liberia showed the most 

improvement in reducing the gaps between the wealth quintiles, but in Mali the gaps increased from the 

first survey to the second. 

Figure 26. Percentage of children age 12-23 months who completed three doses of the DPT vaccine 

by wealth quintiles q1-q5 
36 

Figure 27 shows a clustering of Ghana, Kenya, Senegal, and Zambia in the lower right corner of the plot. 

These countries have the smallest differences between the richest and poorest wealth quintiles and the 

highest levels of DPT3. Nigeria stands out as the country with the highest wealth difference and the lowest 

prevalence of DPT3. Improvements were found for the DRC, Liberia, Haiti, and Indonesia, where 

differences by wealth decreased and prevalence of DPT3 increased from one survey to the next. For 

Pakistan and Mali, however, the differences increased. 

Figure 27. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of children age 12-23 months who completed three doses of the DPT vaccine 
37 

In Figure 28 the logit regression coefficient plot shows that only Ghana, Haiti, and Senegal had nonsignificant coefficients for the comparison of the richest wealth category with the poorest. Nigeria had the 

largest coefficients in both surveys, indicating the highest level of inequality for DPT3. The coefficients 

increased in Mali and Pakistan but the increase was not significant. 

Figure 28. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of receiving three doses of 

DPT vaccine with the wealth quintile as a categorical predictor. 

Table 10 shows that Liberia was the only country with a significant decrease in the concentration index. 

Nigeria and Pakistan had the highest concentration indices for DPT3, in both their first and second surveys. 

The concentration index increased significantly in Nigeria and Mali. The concentration index for Ghana, 

Haiti, and Senegal in the most recent survey were not significantly different from zero, indicating that 

equality was reached for the DPT3 indicator according to this measure. 

Table 10. Concentration index (CI) estimates with tests of difference between two surveys for each

country for the DPT3 indicator 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD 0.336 (0.05) 1632 0.276 (0.03) 3443 -0.059 0.141 

GH 0.056 (0.03) 569 0.035 (0.03) 1128 -0.021 0.312

HT 0.156 (0.05) 1186 0.081 (0.05) 1370 -0.075 0.129

ID 0.285 (0.03) 3487 0.246 (0.02) 3502 -0.039 0.135

KE 0.092 (0.04) 1119 0.079 (0.02) 4052 -0.013 0.375

LB 0.348 (0.05) 996 0.168 (0.04) 1433 -0.181 0.004

ML 0.090 (0.03) 2562 0.232 (0.03) 1844 0.142 0.999* 

NG 0.524 (0.02) 5022 0.593 (0.02) 5834 0.069 0.996* 

PK 0.357 (0.03) 1541 0.416 (0.04) 2039 0.059 0.872

SN 0.118 (0.03) 1329 0.058 (0.03) 1333 -0.060 0.082 

ZM 0.116 (0.03) 1266 0.124 (0.02) 2580 0.009 0.600 

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 
38 

The regional disparities shown in Figure 29 indicate that Indonesia and Nigeria had the largest spread in 

DPT3 estimates between the highest and lowest regions. Ghana, Kenya, and Zambia had the lowest regional 

disparities in the most recent surveys. Liberia showed the greatest improvement in decreasing the regional 

gap, followed by the DRC, Mali, Kenya, and Zambia. The widening regional disparity in Pakistan was due 

to a large increase in the level of DPT3 for the highest region. 

Figure 29. Percentage of children age 12-23 months who completed three doses of the DPT vaccine 

with the national estimates and lowest and highest region estimates 

Consistent with Figure 29, Figure 30 shows that several countries have achieved high national levels of 

DPT3 and low regional disparities. Most of the countries with lower national levels of DPT3 show 

substantial regional disparities. In Nigeria a large regional spread was found, from 81% in the South East 

region to 14% in the North West region. In Indonesia a large regional spread was due mainly to the low 

levels found in Papua region. 
39 

Figure 30. Regional map for the most recent survey of children age 12-23 months who completed 

three doses of the DPT vaccine 
40 

Summary: 

In Table 11, Nigeria, Pakistan, and the DRC consistently appear as the most extreme countries according 

to the five measures, and especially according to the wealth inequality measures. High regional inequality 

corresponds with high wealth inequality in Nigeria and Pakistan but not the DRC. The only other countries 

highlighted in Table 11 are Haiti, with low coverage of DPT3, and Indonesia, with high regional inequality 

because of low coverage in Papua. 

Table 11. Summary table for the DPT3 indicator showing the top three countries with the 

following measures for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 
41 

3.6. Care Seeking for ARI 

Treatment for the symptoms of Acute Respiratory Infection (ARI) in the two weeks before the survey was 

sought for more than half of children in Ghana, Indonesia, Kenya, Liberia, Pakistan, and Zambia (Figure 

31). Nigeria and Mali had the lowest level of care seeking for ARI symptoms, at approximately 30% in the 

most recent survey. Although some countries showed improvements for this indicator, the improvements 

were not significant. As mentioned earlier, the number of children included in the denominator for this 

indicator is relatively small, and as a result the confidence intervals are relatively wide. 

Figure 31. Percentage of children under age 5 with care seeking for ARI symptoms 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
42 

Figure 32 shows that the level of care seeking for ARI does not always increase with increasing household 

wealth. In Kenya and Zambia the spread across the wealth quintiles decreased as prevalence increased. The 

spread also decreased in Ghana, Liberia, and Senegal but this was due to a reduction in the level of care 

seeking for the richest wealth category—an unfavorable pattern. Ideally, a reduction in the gaps between 

the wealth quintiles should occur from an increase in prevalence of care seeking among the lowest wealth 

groups rather than from a decrease among the wealthiest. 

Figure 32. Percentage of children under age 5 with care seeking for ARI symptoms by wealth 

quintiles q1-q5 
43 

In Figure 33, only Indonesia exhibited the desired trend of decreasing difference by wealth and increasing 

prevalence of care seeking for ARI between the two surveys. Figure 32, above, however, showed that the 

difference between the first and fourth quintile in the most recent Indonesia survey was almost as large as 

the difference between the first and fifth quintile in the previous survey. In Ghana and Liberia there was a 

large decrease in the difference accompanied by an increase in overall prevalence. In Senegal the difference 

became negative in the most recent survey due to a decrease in care seeking for the richest wealth category, 

as noted above. This was also true for Zambia in the first survey, but in the most recent survey the level of 

care seeking for ARI symptoms increased for the richest wealth category. 

Figure 33. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of children under age 5 with care seeking for ARI symptoms 
44 

Figure 34 shows that the logit regression coefficient for the richest wealth quintile compared with the 

poorest quintile is small and not significantly different from zero in the recent surveys of the DRC, Ghana, 

Indonesia, Liberia, Senegal, and Zambia, implying equality between the richest and poorest wealth 

categories. The wide confidence intervals found for Ghana and Senegal are due to the small sample sizes 

of children with ARI symptoms, as Table 12 shows. In Haiti and Kenya the coefficient was significant in 

the most recent survey but was not significant in the previous survey. 

Figure 34. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of care seeking for ARI 

symptoms with the wealth quintile as a categorical predictor. 
45 

Table 12, which takes into account all five wealth quintiles, not just the highest and lowest, shows that only 

Liberia and Senegal significantly reduced the concentration index for care seeking for ARI symptoms. The 

concentration index significantly changed in Liberia, Senegal, and Zambia to a value that is not significantly 

different from zero. The concentration index for the most recent survey was also not statistically different 

from zero in the DRC, Ghana, and Kenya. 

Table 12. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for care seeking for ARI symptoms 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD 0.062 (0.03) 1224 0.044 (0.06) 1075 -0.018 0.392 

GH 0.214 (0.11) 154 0.13 (0.11) 189 -0.084 0.291

HT 0.171 (0.07) 457 0.243 (0.04) 1008 0.072 0.825

ID 0.206 (0.04) 2175 0.134 (0.05) 975 -0.072 0.127

KE 0.025 (0.10) 432 0.05 (0.03) 1722 0.024 0.593

LB 0.213 (0.06) 503 -0.035 (0.07) 499 -0.248 0.005

ML 0.237 (0.05) 699 0.197 (0.09) 158 -0.040 0.349 

NG 0.272 (0.05) 724 0.194 (0.05) 608 -0.078 0.141 

PK 0.214 (0.04) 1167 0.182 (0.05) 1610 -0.033 0.297

SN 0.276 (0.10) 218 -0.100 (0.11) 133 -0.376 0.006

ZM -0.084 (0.07) 298 0.092 (0.06) 455 0.176 0.994* 

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 
46 

Figure 35 describes the regional spread of care seeking for ARI. In Senegal the range is due to the presence 

of some regions with no cases of care seeking for ARI and other regions in which all children with 

symptoms are reported to have been taken for treatment, as shown in Appendix 5. This is a problem of 

sample size because some regions had very few cases of children under age 5 with ARI symptoms in the 

two weeks before the survey. The greatest improvement in regional inequality was found in Haiti due to an 

increase in prevalence of care seeking for ARI in the lowest region. In Liberia the gap decreased 

significantly, but this was due to a decline in the prevalence in the highest region. There was an increase in 

the regional gap in Pakistan due to a decline in the prevalence in the lowest region—which was not the 

same region in both surveys (see Appendix 5). Due to the small sample sizes of children with ARI 

symptoms within each region, no map was produced for the ARI indicator. 

Figure 35. Percentage of children under age 5 with care seeking for ARI symptoms with the national 

estimates and lowest and highest region estimates 
47 

Summary: 

As Table 13 shows, Haiti and Nigeria consistently appeared among the three most extreme countries 

according to the measures identified in the table. Mali is among the most extreme three countries for three 

of the five measures. The wealth and regional disparity measures did not correspond. 

Table 13. Summary table for the ARI indicator showing the top three countries with the 

following measure for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 

Note: While Senegal did have the largest regional difference as shown in Figure 35, this was mainly a 

sample size problem and was not considered a true estimate. 
48 

3.7. Care Seeking for Fever 

As with care seeking for ARI symptoms, the prevalence of care seeking for fever in the two weeks before 

the survey was above 50% for Ghana, Indonesia, Kenya, Liberia, Pakistan, and Zambia (Figure 36). The 

only countries in which the prevalence significantly increased between surveys were Kenya and Zambia. It 

declined significantly in Nigeria, from above 50% to approximately 30%. 

Figure 36. Percentage of children under age 5 with care seeking for fever symptoms 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
49 

Figure 37 shows reductions in the spread across the wealth quintiles in the DRC, Ghana, Indonesia, Liberia, 

and Zambia. Only in Zambia and Indonesia was this reduction accompanied by an increase in prevalence 

of care seeking for fever for all the quintile groups. In Kenya the prevalence increased in all quintiles but 

the spread remained approximately the same. In Haiti and Mali the wealth gaps increased and in Nigeria 

the gaps diminished but with a significant decline in overall prevalence. 

Figure 37. Percentage of children under age 5 with care seeking for fever symptoms by wealth 

quintiles q1-q5 
50 

Figure 38 shows that primarily Indonesia, followed by Liberia and Senegal, exhibited the desired trend of 

a reduction in the difference between wealth quintiles with an increase in prevalence of care seeking. The 

difference decreased significantly for Ghana and the DRC but overall prevalence decreased slightly, rather 

than improving. The difference between the highest and lowest wealth quintiles increased in Mali, Haiti, 

Kenya, Zambia, and Pakistan. 

Figure 38. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of children under age 5 with care seeking for fever symptoms 
51 

In Figure 39 the logit regression coefficients for the most recent surveys in the DRC, Ghana, Liberia, 

Senegal, and Zambia are not significantly different from zero, indicating that there is no significant 

inequality between the richest and poorest wealth categories in care seeking for fever. The coefficients 

increased for Haiti, Kenya, and Mali but the increase was not significant. 

Figure 39. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of care seeking for fever 

symptoms with the wealth quintile as a categorical predictor. 

Table 14 shows that the concentration index for care seeking for fever symptoms decreased significantly 

toward more equality across all five wealth quintiles in the DRC, Ghana, Indonesia, and Nigeria. Haiti 

exhibited a significant increase in the concentration index, making it the country with the highest 

concentration index for the most recent survey. In the DRC, Ghana, Senegal, and Zambia the concentration 

index was not significantly different from zero for the most recent survey. 

Table 14. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for care seeking for fever symptoms 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD 0.120 (0.04) 2556 0.007 (0.03) 5234 -0.113 0.006

GH 0.280 (0.05) 551 0.016 (0.06) 824 -0.264 <0.001

HT 0.117 (0.05) 1430 0.215 (0.03) 1882 0.099 0.961*

ID 0.203 (0.02) 5802 0.098 (0.02) 5405 -0.104 <0.001

KE -0.007 (0.05) 1385 0.051 (0.02) 4764 0.058 0.865

LB 0.192 (0.05) 1673 0.095 (0.04) 2203 -0.097 0.065 

ML 0.176 (0.03) 2094 0.208 (0.04) 809 0.032 0.731 

NG 0.265 (0.02) 3965 0.124 (0.03) 3691 -0.141 <0.001

PK 0.181 (0.03) 2495 0.188 (0.03) 3930 0.007 0.563

SN 0.113 (0.04) 1146 0.073 (0.07) 761 -0.039 0.322 

ZM 0.047 (0.04) 1034 0.048 (0.03) 2745 0.001 0.505 

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 
52 

As Figure 40 shows, the largest regional spreads for the most recent survey were found for Ghana, Mali, 

and Pakistan. Regional gaps decreased in most countries, but only Kenya exhibited a decrease in the gap 

accompanied by an increase in prevalence of care seeking in both the lowest and the highest regions. Liberia 

had the greatest reduction in regional gaps but this was due to the combination of an increase in the 

prevalence of care seeking for fever symptoms in the lowest region and a decrease in the highest region. 

The second largest reduction was in Haiti, mainly due to increased prevalence in the lowest region. 

Figure 40. Percentage of children under age 5 with care seeking for fever symptoms with the 

national estimates and lowest and highest region estimates 
53 

Figure 41 shows large regional disparities in care seeking for fever symptoms for Ghana and Pakistan. The 

high regional spread shown above in Figure 40 for Malawi was due to the high level of care seeking in the 

capital, Bamako, compared with the other regions. Kenya and Liberia appear to have the lowest level of 

regional disparities, with almost all their regions having the same level of care seeking for fever. 

Figure 41. Regional map for the most recent survey of children under age 5 with care seeking for 

fever symptoms 
54 

Summary: 

In Table 15, Haiti, Mali, and Pakistan are consistently the three countries with the most extreme values of 

the wealth inequality measures. Pakistan and Mali are also among the three countries with the highest 

regional differences. Mali, Nigeria, and CDR are the three countries with lowest prevalence of care seeking 

for fever symptoms in the most recent survey. 

Table 15. Summary table for the fever indicator showing the top three countries with the 

following measures for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 
55 

3.8. Care Seeking for Diarrhea 

Figure 42 shows that treatment for diarrhea in the two weeks before the most recent survey was sought for 

more than half of the children in Indonesia, Kenya, Pakistan, and Zambia. Nigeria, Mali, and Senegal had 

the lowest prevalence of care seeking for diarrhea, at approximately 30% in the most recent survey. The 

prevalence significantly increased in Indonesia, Kenya, and Mali and significantly decreased in Ghana and 

Nigeria. 

Figure 42. Percentage of children under age 5 with care seeking for diarrhea 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
56 

Figure 43 shows that the spread across the wealth quintiles decreased most in Indonesia and Kenya. Zambia 

also exhibited a relatively small spread that persisted from one survey to the next. Pakistan, Nigeria, and 

Haiti had the largest gaps by wealth in the most recent survey. The prevalence of care seeking increased 

between the surveys in all the wealth quintiles in Pakistan, however, while in Haiti the increase was only 

in the fourth and fifth wealth quintiles. In Nigeria there was a decrease in the prevalence in all wealth 

quintiles. 

Figure 43. Percentage of children under age 5 with care seeking for diarrhea by wealth quintiles 

q1-q5 
57 

In Figure 44 we see that, although the difference in prevalence of care seeking for diarrhea between the 

highest and lowest quintiles decreased in Nigeria, Senegal, Liberia, and Ghana, this reduction of inequality 

was not accompanied by an increase in prevalence. For the most recent surveys in Ghana, the DRC, Kenya, 

and Zambia, the differences were negative (that is, the level of care seeking was higher in the poorest 

quintile than in the richest quintile) but close to zero. Only Indonesia exhibited the desired trend of a 

reduction in the difference in care seeking between the two extreme wealth quintiles (in absolute value) 

with an increase in the overall prevalence of care seeking for diarrhea. 

Figure 44. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of children under age 5 with care seeking for diarrhea 
58 

Figure 45 shows that the most recent surveys in the DRC, Ghana, Indonesia, Kenya, Liberia, Senegal, and 

Zambia all exhibit the desired pattern of equality, with a logit regression coefficient that is small and not 

significantly different from zero. In Haiti the coefficient was not significant in the earlier survey but 

increased and became significant in the later survey. Mali, Nigeria, and Pakistan had the highest 

coefficients, which are significantly different from zero, indicating a significant difference between the 

richest and poorest wealth groups in care seeking for diarrhea. 

Figure 45. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of care seeking for diarrhea 

with the wealth quintile as a categorical predictor. 
59 

As Table 16 shows, the concentration index, our most comprehensive measure of inequality by wealth, 

moved significantly toward more equality for care seeking for diarrhea in Ghana, Indonesia, Liberia, and 

Nigeria. In addition, the concentration indices were not significantly different from zero in the most recent 

surveys of the DRC, Ghana, Indonesia, Kenya, Liberia, Senegal, and Zambia (p-values are not shown in 

Table 16, but the test statistic is the ratio of the coefficient to its standard error). The concentration index 

increased for the DRC, Haiti, Mali, and Pakistan but the increase was not statistically significant. 

Table 16. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for care seeking for diarrhea 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD -0.018 (0.05) 1287 -0.042 (0.03) 2818 -0.024 0.338 

GH 0.101 (0.06) 553 -0.111 (0.07) 671 -0.212 0.008

HT 0.068 (0.04) 1217 0.154 (0.04) 1415 0.086 0.935

ID 0.224 (0.04) 2536 0.000 (0.03) 2505 -0.224 <0.001

KE 0.041 (0.05) 946 -0.049 (0.03) 2953 -0.089 0.076

LB 0.157 (0.05) 1072 0.038 (0.05) 1675 -0.119 0.038

ML 0.103 (0.03) 1450 0.136 (0.04) 844 0.033 0.742 

NG 0.237 (0.03) 2645 0.160 (0.03) 2968 -0.077 0.025

PK 0.155 (0.03) 1877 0.166 (0.04) 2298 0.012 0.591

SN 0.119 (0.05) 972 0.026 (0.07) 1272 0.092 0.152 

ZM -0.031 (0.05) 909 -0.003 (0.03) 2045 0.028 0.679 

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 
60 

The largest regional disparities in the most recent surveys were found for the DRC, Ghana, and Pakistan 

(Figure 46). In the DRC, regional disparities increased between the surveys, but prevalence of care seeking 

for diarrhea in the lowest and highest regions increased as well. The regional gaps also increased for 

Pakistan but mainly due to a decrease in the prevalence for the lowest region. The largest reductions in 

regional disparities were found for Indonesia followed by Nigeria, Liberia, Senegal, Kenya, Zambia, and 

Haiti. For all of these countries other than Nigeria the decrease in the regional gap was partially due to an 

increase in the prevalence of care seeking for diarrhea for the lowest region, a desirable pattern. In Nigeria, 

however, the prevalence for both the lowest and highest regions decreased, an undesirable pattern. 

Figure 46. Percentage of children under age 5 with care seeking for diarrhea with the national 

estimates and lowest and highest region estimates 
61 

As with the maps presented earlier for care seeking for fever, the maps in Figure 47 for care seeking for 

diarrhea show the largest regional disparities in Ghana and Pakistan. Most regions of the DRC had a level 

of 31-40%, but one region reached 71-80%. 

Figure 47. Regional map for the most recent survey of children under age 5 with care seeking for 

diarrhea 
62 

Summary: 

In Table 17, Nigeria and Pakistan consistently appear among the three most extreme countries in the five 

measures of wealth inequality. Pakistan is also one of the three countries with the most extreme regional 

differences, along with Ghana and the DRC. 

Table 17. Summary table for the diarrhea indicator showing the top three countries with 

the following measure for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 
63 

3.9. Exclusive Breastfeeding (EBF) 

The level of exclusive breastfeeding was over 50% only for the most recent surveys in Ghana, Kenya, 

Liberia, and Zambia. It was below 20% for the two Nigeria surveys. Significant improvements in 

prevalence of exclusive breastfeeding were found for the DRC, Indonesia, Kenya, Liberia, Nigeria, and 

Zambia. The confidence intervals are relatively wide because the denominator is limited to children who 

are alive and living with the mother and are under age 6 months. 

Figure 48. Percentage of youngest children under age 6 months who were exclusively breastfed 

0

10

20

30

40

50

60

70

80

90

100

CD5 CD6 GH5 GH7 HT5 HT6 ID5 ID6 KE5 KE7 LB5 LB6 ML5ML6 NG5NG6 PK5 PK6 SN6 SN7 ZM5ZM6
64 

In Figure 49 we see that the spread across the wealth quintiles increased most in Ghana, Liberia, and 

Nigeria. While in Ghana and Nigeria this resulted in a decrease in the prevalence of exclusive breastfeeding 

for some wealth quintiles, in Liberia the prevalence increased for all wealth quintiles. The figure also shows 

that the prevalence does not always increase with increasing wealth. In fact, the highest wealth quintiles 

often have the lowest or second lowest prevalence of exclusive breastfeeding, because many women prefer 

to use powdered milk, and women who are better off can more easily afford it. 

Figures 50 and 51 also show many negative differences. Liberia and Ghana exhibit the largest negative 

difference in the most recent survey, with the level of exclusive breastfeeding highest in the poorest wealth 

category. The difference moved toward zero for Haiti, Indonesia, Mali, Pakistan, Senegal, and Zambia (see 

Appendix 3). However, as noted in Figure 50, this shift did not always translate to a narrower gap between 

the wealth quintiles. For instance, while in Senegal the difference between the fifth and first wealth quintiles 

decreased between surveys, the gap between the first and fourth quintiles remained large, as Figure 49 

shows. 

Figure 49. Percentage of youngest children under age 6 months who were exclusively breastfed by 

wealth quintiles q1-q5 
65 

Figure 50. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of youngest children under age 6 months who were exclusively breastfed 
66 

In Figure 51 shows small and non-significant logit regression coefficients for almost all countries in the 

most recent survey. The exceptions were Ghana, Liberia, and Nigeria. In Ghana and Liberia the coefficient 

was non-significant in the earlier survey but then moved below zero and became significantly negative in 

the most recent survey. This indicates that in these two countries children in the richest wealth quintile were 

less likely than children in the poorest quintile to be exclusively breastfed. In Nigeria the coefficient was 

positive and relatively large, and it increased between the surveys. It was the only country to show such a 

pattern. 

Figure 51. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of being exclusively 

breastfed with the wealth quintile as a categorical predictor. 
67 

Table 18 shows relatively low concentration indices for EBF, especially for the most recent surveys. The 

concentration indices were not statistically different from zero for any countries other than Liberia, Kenya, 

and Nigeria. Thus according to this measure most countries have reached equality in EBF. In Nigeria the 

concentration index significantly increased toward a larger level of inequality. 

Table 18. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the exclusive breastfeeding indicator 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD -0.016 (0.06) 907 -0.037 (0.04) 1934 -0.021 0.379 

GH 0.037 (0.07) 317 -0.107 (0.06) 606 -0.144 0.052

HT -0.123 (0.06) 598 -0.062 (0.06) 726 0.061 0.763

ID -0.159 (0.04) 1799 -0.014 (0.04) 1686 0.144 0.997*

KE -0.105 (0.06) 587 0.134 (0.05) 856 0.240 0.999

LB -0.018 (0.06) 504 -0.168 (0.06) 717 -0.150 0.040

ML -0.084 (0.04) 1420 0.037 (0.04) 999 0.122 0.985* 

NG 0.130 (0.02) 2886 0.245 (0.02) 2928 0.115 1.00* 

PK -0.082 (0.04) 947 -0.002 (0.04) 1075 0.080 0.907

SN 0.073 (0.05) 671 0.006 (0.06) 611 -0.067 0.195 

ZM 0.140 (0.05) 618 0.008 (0.04) 1189 -0.131 0.013

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 

Figure 52 shows that the largest regional disparity for the most recent survey was found for Ghana, followed 

by Senegal, Indonesia, the DRC, and Pakistan. The largest increases in the regional gaps were found for 

Pakistan followed by Nigeria and Ghana. Mali had the largest decrease in the regional gap but this was due 

to a decrease in the prevalence of exclusive breastfeeding for the highest region. The regional gap also 

decreased in Kenya, with increases in prevalence for the lowest and highest regions. 

Figure 52. Percentage of youngest children under age 6 months who were exclusively breastfed 

with the national estimates and lowest and highest region estimates 
68 

Figure 53 shows that most countries appear to have regional disparities for the EBF indicator. High and 

low levels of exclusive breastfeeding were found in Ghana, Senegal, Indonesia, the DRC, and Pakistan, 

with some regions within the same country reaching over 60% and others remaining below 20%. 

Figure 53. Regional map for the most recent survey of youngest children under age 6 months who 

were exclusively breastfed 

 
69 

Summary: 

In Table 19, Kenya, Mali, and Nigeria consistently appear as the three most extreme countries according to 

the wealth inequality measures. Mali and Nigeria also had the lowest prevalence of exclusive breastfeeding. 

The regional disparity measure did not correspond with the wealth disparity measures. Three other 

countries—Ghana, Indonesia, and Senegal—have the greatest regional disparities, with no overlap in the 

measures. 

Table 19. Summary table for the EBF indicator showing the top three countries with the 

following measures for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 
70 

3.10. Stunting 

The desired trends for stunting and wasting are in the opposite direction compared with the MCH indicators 

previously discussed. This is because stunting and wasting are negative outcomes; we would like their 

prevalence to move toward lower levels, rather than higher levels. Figure 54 shows that the lowest levels 

of stunting were found in Senegal, Ghana, and Haiti. The highest levels were found in DRC, Pakistan, and 

Zambia, where they reached approximately 40-45%. Significant declines in the prevalence of stunting were 

observed in Ghana, Haiti, Kenya, Liberia, Nigeria, and Zambia. Pakistan did not measure height and weight 

in the next-to-last survey, and neither of the surveys in Indonesia included those measurements. Therefore 

these surveys are not included in the analysis of stunting and wasting. 

Figure 54. Percentage of de facto children under age 5 who were stunted 

0

5

10

15

20

25

30

35

40

45

50

CD5 CD6 GH5 GH7 HT5 HT6 KE5 KE7 LB5 LB6 ML5 ML6 NG5 NG6 PK6 SN6 SN7 ZM5 ZM6
71 

Figure 55 shows that in most countries stunting tends to increase as wealth decreases. Pakistan, Nigeria, 

and the DRC had the largest spread across the wealth quintiles. The gaps between the wealth quintiles also 

widened for Nigeria and DRC from one survey to the next. The greatest decrease in the gaps was in Haiti 

due to a decrease in stunting among children in the first, second, and third wealth quintiles. A decrease in 

the gaps was also observed in Ghana and Liberia. 

Figure 55. Percentage of de facto children under age 5 who were stunted by wealth quintiles q1-q5 
72 

The differences shown in Figure 56 are all negative, consistent with stunting being more prevalent in the 

poorer wealth groups. Unlike the previous indicators, for stunting and wasting indicators, we would ideally 

see an increase in the difference (that is, becoming less negative, diminishing in magnitude) and a decrease 

in the prevalence from one survey to the next (that is, a point for the second survey that is above and to the 

left of the point for the first survey). For the most recent survey, the largest differences were found in 

Pakistan, Nigeria, and DRC. No changes could be plotted for Pakistan but the difference is reported with 

an orange dot for the most recent Pakistan survey. In that survey Pakistan had the highest prevalence of 

stunting among all the most recent surveys. The ideal trend (with the second dot above and to the left of the 

first dot) was observed for Haiti, Ghana, and Liberia. For Nigeria, Kenya, the DRC, and Zambia there was 

a decrease in the prevalence of stunting, but the magnitude of the difference between the highest and lowest 

quintiles increased. 

Figure 56. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of de facto children under age 5 who were stunted 
73 

In Figure 57 all of the logit regression coefficients are negative and significantly different from zero. 

Pakistan, Nigeria, and Haiti had the lowest coefficients (most negative and largest). No improvements were 

observed between the surveys in terms of coefficients moving closer to zero or becoming non-significant. 

Figure 57. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of stunting with the wealth 

quintile as a categorical predictor. 

In Table 20 all of the concentration indices for wealth are negative, since the outcome is more concentrated 

in the poorer households. Only Haiti had a significant movement in the concentration index toward a less 

negative value. In the DRC and Nigeria the concentration index significantly moved toward a more negative 

value and more inequality. None of the concentration indices in the most recent surveys were significantly 

different from zero, indicating that according to this measure inequalities in stunting persist in all these 

countries. 

Table 20. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the stunting indicator 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD -0.106 (0.03) 3597 -0.174 (0.02) 8884 -0.068 0.958* 

GH -0.161 (0.02) 2640 -0.136 (0.02) 3034 0.025 0.200

HT -0.254 (0.02) 2930 -0.176 (0.02) 4694 0.078 0.004

KE -0.161 (0.02) 5563 -0.173 (0.01) 20524 -0.013 0.722

LB -0.131 (0.02) 5200 -0.106 (0.03) 3817 0.025 0.217 

ML -0.157 (0.02) 11567 -0.185 (0.02) 4803 -0.027 0.862 

NG -0.225 (0.01) 20633 -0.291 (0.01) 26306 -0.066 1.00* 

PK NA NA -0.295 (0.02) 3134 NA NA

SN -0.124 (0.02) 6456 -0.159 (0.02) 6697 -0.035 0.915 

ZM -0.103 (0.02) 5600 -0.125 (0.01) 12408 -0.022 0.796 

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 
74 

As Figure 58 shows, the greatest regional disparities were found in Pakistan, Nigeria, and the DRC. 

Regional gaps between the highest and lowest regions decreased the most for Haiti, followed by Liberia 

and Zambia. These countries also had the smallest differences between the highest and lowest regions in 

the most recent survey. Improvements in regional inequality were mainly due to a decrease in the prevalence 

of stunting in the highest region. 

Figure 58. Percentage of de facto children under age 5 who were stunted with the national estimates 

and lowest and highest region estimates 
75 

Figure 59 shows large regional disparities in Pakistan, with levels of stunting reaching 82% in Balochistan 

compared with 22% in Islamabad, the capital city (see Appendix 6). Large regional disparities were also 

found in Nigeria and the DRC. Relatively small disparities were found in Haiti, Liberia, and Zambia. Haiti 

had levels between 11% and 30% for all regions. In Liberia regions had levels between 21% and 40%, and 

in Zambia they were between 31% and 50%. 

Figure 59. Regional map for the most recent survey of de facto children under age 5 who were 

stunted 

 
76 

Summary: 

In Table 21, Nigeria and Pakistan consistently appear among the three most extreme countries according to 

the five measures. However, Nigeria is not among the three countries with the highest overall prevalence 

of stunting. CDR is among the most extreme three countries in the overall prevalence of stunting, the 

difference between the highest and lowest wealth quintiles, and regional differences. 

Table 21. Summary table for the stunting indicator showing the top three countries with 

the following measures for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 NA NA NA NA NA

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 

Note: NA indicates that the indicator is not available for the survey 
77 

3.11. Wasting 

Figure 60 presents the levels of wasting for all the surveys except Indonesia and the 2006-2007 Pakistan, 

survey, which did not collect measurements of height and weight. Nigeria, Mali, and Pakistan had the 

highest levels of wasting, all over 10%, and in Nigeria there was a significant increase to approximately 

18% in the most recent survey. There was a significant decrease in wasting in Ghana, Haiti, Kenya, and 

Senegal. 

Figure 60. Percentage of de facto children under age 5 who were wasted 

0

5

10

15

20

25

CD5 CD6 GH5 GH7 HT5 HT6 KE5 KE7 LB5 LB6 ML5 ML6 NG5 NG6 PK6 SN6 SN7 ZM5 ZM6
78 

As Figure 61 shows, the largest spread across the wealth quintiles was found for Pakistan and Nigeria. 

While in Nigeria the gaps between the wealth quintiles narrowed between the first and second surveys, this 

improvement was accompanied by an increase in wasting. The smallest gaps in the level of wasting were 

found in the second survey in Zambia. Haiti showed the greatest improvement, with a decrease in the gaps 

by wealth as well as a decrease in wasting in all quintiles. 

Figure 61. Percentage of de facto children under age 5 who were wasted by wealth quintiles q1-q5 
79 

Figure 62 shows that the largest differences between the first and fifth wealth quintiles were found in 

Nigeria and Pakistan. As with stunting, the ideal trend we would like to see in Figure 62 would be a line 

with the orange dot above and to the left of the red dot. Kenya clearly exhibits this trend, followed to a 

lesser extent by Ghana and Haiti. The magnitude of the difference between quintiles also decreased in 

Nigeria, but this improvement was accompanied by an increase in the prevalence of wasting. The DRC, 

Mali, and Liberia showed clear increases in the magnitude of the difference, along with a decrease in the 

prevalence of wasting. 

Figure 62. The difference between the richest (q5) and the poorest (q1) wealth quintiles versus the 

percentage of de facto children under age 5 who were wasted 
80 

In Figure 63 the logit regression coefficients were not significant for the most recent surveys of Ghana, 

Haiti, Liberia, Mali, and Zambia. Kenya, Senegal, and Pakistan had the coefficients that were most negative 

(highest in magnitude). In the DRC the coefficient was significantly negative in the recent survey but was 

not significant in the previous survey, indicating a move to greater inequality in wasting among children. 

Figure 63. The coefficient for the richest wealth quintile with the poorest wealth quintile as the 

reference category. Coefficients were produced from a logit regression of wasting with the wealth 

quintile as a categorical predictor. 

As with the concentration indices for stunting, the concentration indices for wasting shown in Table 22 are 

all negative. The concentration indices for Ghana, Haiti, Liberia, and Zambia were not significantly 

different from zero, indicating that these countries reached equality according to this measure. The largest 

concentration index for the most recent surveys, in terms of magnitude, was found for Pakistan, Nigeria, 

and Mali. Nigeria was the only country that significantly improved its concentration index, while for Mali 

it had significantly moved toward a more negative value and more inequality. 

Table 22. Concentration index (CI) estimates with tests of difference between two surveys for each 

country for the wasting indicator 

Country CI (se) survey 1 survey 1 N CI (se) survey 2 survey 2 N 

survey 2- 

survey 1 

CI decreased 

p-value 

CD -0.016 (0.02) 3597 -0.033 (0.01) 8884 -0.017 0.811 

GH -0.030 (0.01) 2640 -0.004 (0.02) 3034 0.026 0.088

HT -0.019 (0.02) 2930 -0.006 (0.01) 4694 0.014 0.256

KE -0.056 (0.01) 5563 -0.035 (0.01) 20524 0.020 0.045

LB -0.005 (0.01) 5200 -0.008 (0.01) 3817 -0.003 0.575 

ML -0.013 (0.01) 11567 -0.045 (0.02) 4803 -0.032 0.955* 

NG -0.095 (0.01) 20633 -0.061 (0.01) 26306 0.035 0.011

PK NA NA -0.070 (0.02) 3134 NA NA

SN -0.044 (0.01) 6456 -0.041 (0.01) 6697 0.003 0.437 

ZM -0.019 (0.01) 5600 -0.006 (0.01) 12408 0.013 0.114 

Notes: the p-value is for a one-sided test and the concentration index includes the Erreygers (2009) correction. 

* Indicates that the p-value was significant in the opposite direction. 
81 

Figure 64 shows that the highest regional disparities in the most recent survey were in the DRC and Nigeria. 

Regional gaps also increased the most for the DRC, mainly due to an increase in the prevalence of wasting 

in the highest region. Mali and Haiti showed the greatest level of improvement, with a reduction of the 

regional gap due to a large decline in the level of wasting in the highest region. In Haiti there was also a 

substantial decrease in the prevalence of wasting in the lowest region. 

Figure 64. Percentage of de facto children under age 5 who were wasted with the national estimates 

and lowest and highest region estimates 
82 

The scale in Figure 65 for the wasting indicator maps is different from the scale in the maps for previous 

indicators. This is because the wasting indicator reaches a maximum of approximately 30% among these 

countries. The scale was reduced accordingly. As Figure 65 shows, the highest regional disparities were 

found in DRC and Nigeria, where wasting levels reached over 21% in certain regions. Almost all the regions 

in Kenya and Haiti had wasting levels below 7%. 

Figure 65. Regional map for the most recent survey of de facto children under age 5 who were 

wasted 
83 

Summary: 

Table 23 summarizes the indicators of wasting. Nigeria and Pakistan appear among the three most extreme 

countries for four of the five measures. Senegal is among the most extreme three countries for three 

indicators. 

Table 23. Summary table for the wasting indicator showing the top three countries with 

the following measures for the most recent survey 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 NA NA NA NA NA

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 

Note: NA indicates that the indicator is not available for the survey 

3.12. Overall Summary 

Tables 24-26 below provide an overall summary of the most extreme countries according to the five 

measures and all 11 indicators combined. The indicators are divided into three groups. Table 24 summaries 

the maternal health indicators of ANC, mCPR, SBA, and DHF. Table 25 gives the summary for the child 

health indicators of DPT3 and the three indicators of care seeking, for ARI, fever, and diarrhea. Table 26 

provides the summary for the child nutrition indicators of EBF, stunting and wasting. Within the tables, 

each indicator is assigned a different color. That color appears in the columns of the tables for the three 

countries with the most extreme values of the indicator and the measure. The overall position of each 

country is reflected by the number of times its row is highlighted with a color. 

Table 24. Maternal health indicators summary for the most recent surveys 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 

ANC mCPR SBA DHF 

 
84 

Table 25. Child health indicators summary for the most recent surveys 

 

Lowest 

prevalence 

Largest 

difference 

Highest 

coefficient 

Highest 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 

DPT3 ARI Fever Diarrhea 

 

Table 26. Child nutrition indicators summary for the most recent surveys 

 

Lowest/highest 

prevalence 

Largest magnitude 

difference 

Highest magnitude 

coefficient 

Highest magnitude 

concentration 

index 

Highest regional 

difference 

CD6 

GH7 

HT6 

ID6 NA NA NA NA NA NA NA NA NA NA 

KE7 

LB6 

ML6 

NG6 

PK6 

SN7 

ZM6 

Note: the prevalence measure is taken as the lowest prevalence for the EBF indicator and the highest prevalence for 

stunting and wasting. Stunting and wasting indicators were not available (NA) for Indonesia. 

EBF Stunt Wast 

 

To summarize the information in Tables 24-26, we constructed a wealth inequality score and a regional 

inequality score (the higher the score the higher the inequality). The wealth inequality score adds up the 

number of times each country appeared in the tables for the three wealth related measures (difference, logit 

regression coefficient, and concentration index), and divides the sum by its maximum possible value—the 

number of indicators and the number of wealth equality measures. Therefore, for the maternal and child 

indicators the count was divided by 12 (four indicators and three measures) and for the child nutrition 

indicators the count was divided by nine (three indicators and three measures). The normalized total was 

then multiplied by 100, to be interpretable as the percentage of the maximum possible value. Figure 66 

presents the resulting percentages. 
85 

As Figure 66 shows, Nigeria, Mali, Haiti, and Pakistan have the highest scores for inequality by wealth. 

These countries are repeated the most often for the indicators in Tables 24-26. Each country is similar on 

the three sets of indicators, except that Haiti had a lower score for the nutrition indicators and Pakistan had 

a lower score for the maternal health indicators. At the other extreme, Indonesia and Liberia had a zero 

score for all the indicators, suggesting low levels of inequality by wealth for these indicators and measures. 

As these scores are based on a tally of the three highest levels of inequality for each indicator and measure, 

we cannot jump to the conclusion that Indonesia and Liberia actually have the lowest levels of inequality, 

but there is good consistency across indicators and measures. The specific values of indicators provided in 

the tables could be used to develop other syntheses and ranking procedures. 

The other five countries—Kenya, Ghana, Zambia, CDR, and Senegal—have intermediate levels of wealth 

inequality, more often with the maternal health indicators or nutrition indicators, and least often with the 

child health indicators. 

Figure 66. Wealth inequality score 

0

10

20

30

40

50

60

70

80

90

100

NG6 ML6 HT6 PK6 KE7 GH7 ZM6 CD6 SN7 ID6 LB6

Maternal indicators Child indicators Nutrition indicators
86 

The regional inequality scores presented in Figure 67 are based only on the measure of the regional 

difference between the highest and lowest region. The score was produced from the number of times each 

country appears in Tables 24-26 for this measure, divided by the number of indicators represented in the 

table and multiplied by 100 to be interpretable as the percentage of the maximum possible value. Pakistan 

has the highest regional inequality score for the maternal and child indicators. Mali has a high inequality 

score for the maternal indicators. Nigeria, DRC, and Senegal have high regional inequality scores for the 

child nutrition indicators. While Indonesia has a score of zero according to the wealth inequality score, it 

has a regional inequality score of 50% for the maternal and child indicators. As the maps have shown, this 

was mainly due to the Papua region, which showed large differences in the indicators compared with the 

other regions of Indonesia. 

Haiti, Liberia, and Zambia have minimal evidence of regional inequality, with a score of zero for all three 

sets of indicators. Kenya has only one appearance among the top three countries for regional inequality, 

and that is for the use of modern contraception, or mCPR, which is particularly low in the North Eastern 

region. 

Figure 67. Regional inequality score 

 

0

10

20

30

40

50

60

70

80

90

100

PK6 ML6 ID6 NG6 KE7 GH7 CD6 SN7 HT6 LB6 ZM6

Maternal indicators Child indicators Nutrition indicators
87 

Table 27 provides a summary of the concentration index for all the indicators given earlier in Tables 2, 4, 

6, 8, 10, 12, 14, 16, 18, 20, and 22. A green bar indicates a significant improvement in the concentration 

index between the two surveys for each country, and a red bar indicates a significant deterioration in the 

concentration index or a move toward less equality. A null symbol (Ø) indicates that the concentration index 

for the most recent survey was not significantly different from zero, implying that equality was reached for 

that indicator and country. 

Table 27 shows that overall the countries have improved in equality, with many reaching equality in their 

most recent survey particularly for DPT3, the three indicators of care seeking, and exclusive breastfeeding. 

Ghana, Indonesia, and Liberia showed significant improvements in 6 of the 11 indicators, and Ghana 

reached equality in the most recent survey for 7 indicators. Senegal and Zambia reached equality for five 

indicators, mainly the child health indicators. In Senegal, two indicators showed significant improvement 

in equality despite only having one year between the successive surveys in the analysis. 

Some countries, however, have moved in the direction of greater disparities. In Mali the concentration index 

increased for seven indicators. Mali was followed by Nigeria, with a move toward greater inequality for 4 

of the 11 indicators. While Zambia moved to greater inequality for care seeking for ARI symptoms and 

Mali for the EBF indicator, the concentration index for the most recent survey was still not significantly 

different from zero, implying equality in Zambia and Mali for ARI and EBF respectively. For these two 

countries and indicators, the concentration was not significantly different from zero in the earlier survey as 

well. Therefore, while there appears to be a deterioration when comparing the two surveys, in fact equality 

of the indicators across wealth quintiles was maintained. 

No countries reached equality for the maternal health indicators of ANC, SBA, and DHF. Only Ghana, 

Haiti, and Indonesia reached equality for the mCPR indicator. None of the countries reached equality for 

the stunting indicator. In contrast, eight countries reached equality for the EBF indicator, followed by seven 

countries for the indicator of care seeking for diarrhea. 
88

Table 27. Summary of changes in the concentration index for all 11 indicators. Significant improvement in equality between the last two surveys for each country appears in green and significant deterioration appears in red. No color indicates no significant change between surveys. 

ANC mCPR SBA DHF DPT3 ARI Fever Diarrhea EBF Stunt Wast 

Total 

Green 

Total 

Red 

Reached 

equality

DRC Ø Ø Ø Ø 2 2 4 Ghana Ø Ø Ø Ø Ø Ø Ø 6 0 7 Haiti Ø Ø Ø Ø 3 3 4 Indonesia Ø Ø Ø NA NA 6 0 3 Kenya Ø Ø 1 0 2 Liberia Ø Ø Ø 6 0 2 Mali Ø 0 7 1 Nigeria 3 4 0 Pakistan Ø NA NA 1 1 1 Senegal Ø Ø Ø Ø Ø 2 0 5 Zambia Ø Ø Ø Ø Ø 3 2 5 Reached equality 

by indicator 0 3 0 0 3 6 4 7 8 0 4 

Note: Ø Indicates a concentration index for the most recent survey was not significantly different from zero, implying equality has been reached. 
89 

4. Discussion and Conclusion 

This report provides an overview of the wealth and regional disparities for 11 maternal and child health 

(MCH) indicators in 11 countries. Several measures and methods were used to examine these disparities. 

Some were descriptive and covered only the lowest and highest wealth quintiles or the lowest and highest 

regions. Those approaches were included because they are easier to understand and communicate. Other 

approaches were more complex, used more information, and included confidence intervals or tests of 

significance. The analysis of regional disparities was mainly descriptive. 

It is challenging to find a comprehensive measure to summarize the overall disparity in MCH for these 11 

countries. The measures have limitations. Perhaps most important, a measure may indicate an apparent 

reduction of inequality but this reduction may not actually be an improvement. For instance, the gap 

between the lowest and highest wealth quintiles may have decreased between two surveys because of a 

decline in the prevalence of the indicator for the highest wealth index. That is, the gap was reduced because 

prevalence declined. This spurious evidence of improvement is a limitation with measures that use only the 

lowest and highest wealth quintiles, but it can also occur in more disguised form with a measure such as 

the concentration index that uses all the wealth quintiles. 

Another limitation is that after a country has reached a high level of overall prevalence, there is less room 

for improvements, in either prevalence or equality, compared with a country with very low coverage. For 

instance, the DRC did not have a significant increase in the prevalence of delivery with assistance by an 

SBA, but it had one of the highest coverage levels for this indicator compared with other countries, reaching 

81% in the most recent survey. 

Despite these challenges, the summaries have highlighted countries that appear to be performing worse 

than other countries that have made great improvements. Mali, Nigeria, and Pakistan stand out as having 

difficulties in achieving equality by wealth for most of the MCH indicators, while Ghana, Indonesia, and 

Liberia show great improvements. Mali and Pakistan also ranked highest in regional inequality. 

Indonesia was one of the best performing countries in terms of wealth inequalities, but had difficulties with 

achieving regional equality—surely due in part to the widely dispersed geographic nature of Indonesia. The 

summary of the concentration indices, which includes all the wealth quintiles and adjusts for the overall 

prevalence, shows several significant improvements in equality, and many countries have reached equality 

in the most recent survey, especially in the child health indicators (see Table 27). 

Ghana, Senegal, and Zambia have reached equality for the highest number of indicators, according to the 

concentration index for the most recent survey. Comparisons of the concentration indices between the two 

surveys showed that Mali significantly moved toward greater inequality for 7 of the 11 indicators. It is 

important to note that the most recent Mali survey was conducted during a security crisis that made three 

regions and part of one region virtually inaccessible. The effect of this crisis can be seen in the performance 

of Mali for most of the MCH indicators examined. It is possible that the estimates of inequality would have 

been even greater if the regions omitted from the second survey could have been included. 

The summary of the concentration indices (Table 27) provides a great deal of information on the wealth 

inequalities. However, regional disparities must also be taken into account before reaching broader 

conclusions about inequality in these countries. For instance, according to Table 27, wealth equality was 

reached for eight countries for the EBF indicator and for seven countries for the indicator of care seeking 

for diarrhea. However, regional disparities in care seeking for diarrhea (shown in Figures 46 and 47) and in 

EBF (shown in Figures 52 and 53) still remain in several of those countries. It is clear that equality across 

wealth quintiles does not necessarily imply equality across regions. 
90 

Finally, no single measure in this report can tell the full story of a country’s status for a particular indicator. 

For planning purposes, the measures should be examined together, by indicator and by country, and ideally 

should be supplemented by other covariates in order to have a more complete view of the pattern of 

inequality in health by wealth status and geography, and to better identify social strata and regions where 

interventions are most needed. Further analyses of this type, using new information as it becomes available, 

can be useful for tracking progress and identifying possible deteriorations related to for disparities within 

countries. 
91 

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93 

Appendices 
94

Appendix 1. Maternal health indicators overall prevalence, difference index and coefficient index

Four or more ANC visits Modern CPR use for women in a union Last birth assisted by SBA Last birth delivered in health facility 

Survey % [C.I.] %q5-%q1 q5 coeff. [C.I.] % [C.I.] %q5-%q1 q5 coeff. [C.I.] % [C.I.] %q5-%q1 q5 coeff. [C.I.] % [C.I.] %q5-%q1 q5 coeff. [C.I.] CD5 47.1 [43.5,50.7] 21.3 0.9 [0.6,0.2] 5.9 [4.9,7.1] 11.2 1.8 [1.2,2.4] 76.0 [72.1,79.6] 36.2 3.3 [2.8,3.9] 72.3 [68.0,76.3] 40.3 3.2 [2.7,3.7] CD6 48.0 [45.7,50.3] 29.8 1.2 [1.0,0.3] 8.0 [7.1,9.1] 13.4 1.8 [1.4,2.1] 81.1 [78.4,83.5] 30.8 3.1 [2.7,3.6] 80.6 [78.0,83.0] 32.2 3.3 [2.8,3.7] GH5 78.2 [75.7,80.4] 30.9 2.2 [1.6,0.6] 15.6 [14.2,17.2] 9.3 0.7 [0.3,1.1] 61.6 [58.5,64.6] 70.7 4.1 [3.4,4.8] 60.2 [57.0,63.2] 68.6 3.6 [3.1,4.2] 

GH7 87.3 [85.3,89.0] 21.7 2.7 [1.9,0.7] 21.7 [20.0,23.4] -1.6 -0.1 [-0.4,0.2] 76.0 [73.4,78.4] 47.7 3.6 [3.0,4.2] 75.2 [72.6,77.7] 48.1 3.4 [2.9,4.0] 

HT5 53.8 [51.0,56.6] 49.0 2.2 [1.9,0.6] 22.8 [21.3,24.3] 12.8 0.8 [0.5,1.1] 29.0 [26.2,32.0] 62.6 3.4 [3.0,3.8] 27.4 [24.6,30.4] 60.5 3.3 [2.9,3.7] 

HT6 67.3 [64.8,69.6] 37.2 2.0 [1.6,0.5] 28.6 [27.0,30.2] -4.2 -0.2 [-0.4,0.0] 41.1 [38.5,43.8] 69.7 3.5 [3.2,3.9] 39.5 [36.9,42.2] 67.9 3.5 [3.2,3.8] 

ID5 81.5 [80.1,82.9] 35.3 2.8 [2.5,0.7] 54.4 [53.3,55.5] 8.0 0.3 [0.2,0.4] 74.2 [72.2,76.1] 50.1 3.3 [2.9,3.6] 48.0 [45.7,50.2] 69.4 3.4 [3.2,3.7] 

ID6 87.8 [86.9,88.6] 26.2 2.5 [2.2,0.7] 54.6 [53.6,55.5] 3.2 0.1 [0.0,0.2] 84.3 [83.0,85.6] 37.5 3.0 [2.6,3.5] 64.4 [62.6,66.2] 57.3 2.8 [2.6,3.1] 

KE5 47.1 [44.8,49.5] 27.4 1.1 [0.9,0.3] 37.1 [35.1,39.1] 29.9 1.5 [1.2,1.8] 48.0 [44.4,51.5] 59.9 2.8 [2.3,3.2] 46.8 [43.3,50.3] 62.1 2.9 [2.4,3.4] 

KE7 57.6 [56.3,58.9] 31.0 1.3 [1.2,0.4] 49.9 [48.8,51.1] 25.1 1.1 [0.9,1.2] 66.6 [65.1,68.0] 58.4 3.2 [3.0,3.5] 65.9 [64.4,67.4] 59.4 3.3 [3.0,3.5] 

LB5 66.0 [61.7,70.0] 23.5 1.1 [0.7,0.4] 10.7 [9.3,12.3] 12.8 1.6 [1.1,2.0] 51.2 [46.3,56.1] 53.9 2.5 [1.9,3.0] 41.6 [37.2,46.2] 50.6 2.2 [1.7,2.8] 

LB6 78.1 [76.0,80.1] 20.5 1.2 [0.7,0.4] 19.5 [17.2,22.0] 7.7 0.5 [0.1,1.0] 65.3 [61.9,68.5] 44.8 2.4 [1.7,3.2] 60.3 [56.7,63.8] 38.7 1.8 [1.1,2.4] 

ML5 35.5 [33.2,37.8] 41.1 1.8 [1.5,0.5] 6.8 [6.0,7.6] 13.2 1.9 [1.6,2.3] 51.4 [48.2,54.6] 50.8 2.5 [2.0,3.0] 47.5 [44.1,50.8] 53.2 2.5 [2.0,2.9] 

ML6 41.2 [38.8,43.7] 46.8 2.0 [1.8,0.6] 9.9 [9.0,11.0] 19.6 2.2 [1.8,2.5] 61.3 [58.1,64.4] 58.8 3.5 [3.2,3.8] 57.8 [54.4,61.1] 64.9 3.7 [3.4,4.0] 

NG5 45.2 [43.6,46.9] 65.5 3.1 [2.9,0.8] 9.6 [9.0,10.3] 19.5 2.4 [2.1,2.6] 40.6 [38.9,42.3] 77.7 4.2 [3.9,4.5] 36.5 [34.8,38.1] 72.5 3.9 [3.6,4.1] 

NG6 51.1 [49.0,53.2] 67.6 3.3 [3.1,0.9] 9.8 [9.0,10.6] 21.8 3.5 [3.1,3.9] 40.0 [37.9,42.0] 80.9 4.6 [4.4,4.9] 37.4 [35.4,39.4] 75.2 4.2 [3.9,4.5] 

PK5 28.5 [26.8,30.4] 54.0 2.8 [2.4,0.8] 20.7 [19.7,21.8] 18.7 1.2 [1.0,1.4] 41.7 [39.6,43.7] 59.8 2.8 [2.5,3.0] 37.0 [35.0,39.1] 60.4 2.9 [2.6,3.1] 

PK6 36.6 [34.1,39.1] 64.5 3.1 [2.9,0.8] 24.9 [23.6,26.2] 12.7 0.7 [0.4,1.0] 55.2 [51.7,58.7] 53.6 2.6 [2.2,3.0] 51.6 [48.3,54.9] 54.9 2.6 [2.2,2.9] 

SN6 46.5 [44.0,49.0] 42.0 1.8 [1.5,0.5] 15.4 [13.7,17.2] 21.7 1.8 [1.4,2.3] 52.7 [48.9,56.5] 65.8 3.2 [2.7,3.7] 73.9 [70.1,77.5] 58.4 3.6 [3.0,4.2] 

SN7 48.1 [45.5,50.7] 40.6 1.7 [1.5,0.4] 19.4 [17.5,21.5] 15.6 1.1 [0.8,1.4] 60.6 [56.5,64.6] 52.3 2.4 [1.8,3.1] 78.8 [75.5,81.9] 47.9 3.5 [2.7,4.2] 

ZM5 60.3 [58.4,62.2] 2.3 0.1 [-0.2,0.1] 30.1 [28.7,31.6] 15.0 0.7 [0.5,0.9] 49.1 [46.4,51.8] 65.4 3.5 [3.1,4.0] 50.5 [47.8,53.3] 63.6 3.5 [3.0,3.9] 

ZM6 55.5 [54.0,57.0] 13.8 0.6 [0.4,0.1] 41.2 [39.9,42.5] 24.8 1.1 [0.9,1.2] 68.6 [66.9,70.3] 45.8 3.0 [2.7,3.3] 71.8 [70.1,73.5] 41.8 3.0 [2.6,3.4] 
95

Appendix 2. Child health indicators overall prevalence, difference index and coefficient index

Three doses of DPT Treatment of ARI symptoms Treatment of fever symptoms Treatment of diarrhea 

Survey % [C.I.] %q5-%q1 

q5 coeff. 

[C.I.] % [C.I.] %q5-%q1 

q5 coeff. 

[C.I.] % [C.I.] %q5-%q1 

q5 coeff. 

[C.I.] % [C.I.] %q5-%q1 

q5 coeff. 

[C.I.] 

CD5 45.0 [40.0,50.2] 45.3 2.0 [1.4,2.5] 41.9 [37.8,46.1] 14.6 0.6 [0.1,1.1] 45.1 [41.4,48.8] 25.6 1.0 [0.7,1.4] 32.6 [27.2,38.6] -0.5 0.0 [-0.7,0.6] CD6 60.5 [57.2,63.8] 35.0 1.7 [1.2,2.1] 41.6 [35.6,47.9] 6.3 0.3 [-0.5,1.1] 39.9 [37.3,42.4] 4.1 0.2 [-0.1,0.5] 39.0 [36.0,42.0] -4.4 -0.2 [-0.6,0.2] GH5 88.8 [85.3,91.5] 5.3 0.6 [-0.5,1.8] 60.4 [50.7,69.4] 42.5 2.1 [-0.2,4.4] 64.0 [58.7,69.0] 38.9 2.0 [1.0,3.0] 57.1 [51.9,62.1] 17.1 0.7 [-0.1,1.5] 

GH7 88.5 [85.9,90.7] 4.6 0.5 [-0.5,1.5] 52.6 [42.3,62.7] 2.1 0.1 [-1.4,1.6] 55.9 [51.1,60.7] -1.1 0.0 [-0.7,0.6] 44.9 [39.1,50.7] -8.3 -0.3 [-1.1,0.5] 

HT5 53.0 [48.2,57.7] 26.4 1.1 [0.5,1.7] 33.3 [26.7,40.6] 16.7 0.8 [-0.2,1.8] 36.0 [32.5,39.6] 12.7 0.6 [0.0,1.1] 30.7 [26.7,34.9] 10.9 0.5 [-0.1,1.2] 

HT6 62.5 [58.6,66.3] 13.2 0.6 [0.0,1.2] 37.9 [34.0,42.1] 28.4 1.3 [0.8,1.8] 40.1 [37.0,43.3] 26.4 1.1 [0.8,1.5] 33.9 [30.6,37.3] 21.9 0.9 [0.5,1.4] 

ID5 66.7 [64.0,69.3] 36.5 1.7 [1.3,2.1] 66.4 [62.5,70.1] 23.4 1.1 [0.5,1.6] 66.8 [64.6,69.0] 24.1 1.1 [0.7,1.4] 51.0 [47.7,54.4] 26.6 1.1 [0.6,1.6] 

ID6 72.0 [69.7,74.2] 32.9 1.7 [1.3,2.0] 75.3 [71.3,78.9] 10.3 0.5 [-0.2,1.2] 73.5 [71.6,75.3] 13.5 0.7 [0.4,1.0] 64.6 [61.7,67.5] -1.4 -0.1 [-0.5,0.4] 

KE5 86.4 [83.2,89.1] 12.3 0.9 [0.2,1.7] 55.9 [47.9,63.6] 6.1 0.3 [-1.0,1.5] 48.6 [44.8,52.4] -0.5 0.0 [-0.5,0.5] 48.6 [43.4,53.9] -2.3 -0.1 [-0.8,0.6] 

KE7 89.9 [88.4,91.2] 9.4 0.9 [0.4,1.5] 65.7 [62.6,68.7] 10.9 0.5 [0.0,1.0] 62.5 [60.4,64.6] 9.5 0.4 [0.1,0.7] 57.6 [55.0,60.3] -5.2 -0.2 [-0.6,0.2] 

LB5 50.3 [44.7,55.8] 42.3 1.8 [1.1,2.5] 62.2 [56.1,68.0] 30.5 1.7 [0.7,2.8] 56.7 [52.6,60.8] 22.4 0.9 [0.3,1.5] 49.3 [43.5,55.0] 22.4 0.9 [0.3,1.5] 

LB6 71.4 [67.4,75.1] 21.5 1.0 [0.4,1.6] 50.7 [44.1,57.3] -4.3 -0.2 [-1,0.7] 57.5 [53.6,61.4] 11.6 0.5 [-0.1,1.1] 46.8 [42.7,51.0] 6.0 0.2 [-0.5,1.0] 

ML5 67.6 [64.2,70.8] 12.3 0.6 [0.1,1.1] 38.1 [33.4,42.9] 32.6 1.4 [0.8,1.9] 32.8 [29.8,36.0] 29.4 1.3 [0.9,1.7] 17.8 [14.9,21.0] 16.9 1.0 [0.4,1.6] 

ML6 63.1 [59.8,66.3] 30.0 1.3 [0.9,1.7] 26.7 [19.1,35.9] 21.9 1.1 [-0.1,2.3] 28.3 [24.1,33.0] 37.5 1.8 [1.1,2.4] 28.3 [24.9,31.9] 19.1 1.0 [0.4,1.6] 

NG5 35.4 [33.3,37.6] 67.8 3.6 [3.2,3.9] 45.4 [40.5,50.4] 34.4 1.4 [0.7,2.1] 54.1 [51.9,56.4] 29.9 1.2 [1.0,1.5] 42.2 [39.6,44.8] 30.7 1.3 [0.9,1.7] 

NG6 38.2 [35.9,40.6] 72.5 3.9 [3.6,4.3] 34.5 [29.8,39.5] 36.9 1.6 [0.7,2.5] 31.5 [29.1,33.9] 18.4 0.8 [0.5,1.2] 28.9 [26.4,31.6] 19.3 1.0 [0.6,1.4] 

PK5 58.5 [55.1,61.8] 43.1 1.9 [1.5,2.3] 69.3 [65.7,72.6] 27.7 1.5 [0.9,2.1] 65.8 [63.0,68.4] 19.6 0.9 [0.5,1.3] 54.5 [51.4,57.6] 20.1 0.8 [0.4,1.2] 

PK6 65.2 [60.9,69.2] 58.1 2.8 [2.3,3.4] 64.4 [60.0,68.6] 22.3 1.1 [0.3,1.8] 64.8 [61.7,67.7] 22.6 1.1 [0.7,1.5] 61.0 [57.3,64.6] 22.8 1.0 [0.6,1.5] 

SN6 88.5 [86.1,90.5] 15.1 1.5 [0.5,2.5] 53.0 [43.2,62.6] 27.2 1.1 [0,2.2] 43.5 [39.4,47.7] 13.7 0.6 [0.1,1.0] 38.8 [33.8,44.0] 16.0 0.7 [0.1,1.3] 

SN7 89.3 [86.4,91.7] 5.9 0.6 [-0.7,1.9] 42.2 [31.8,53.3] -19.6 -0.8 [-2.2,0.5] 45.8 [39.8,51.9] 12.2 0.5 [-0.4,1.3] 32.8 [28.2,37.9] 11.4 0.5 [-0.4,1.4] 

ZM5 79.6 [76.3,82.6] 16.2 1.5 [0.7,2.2] 68.2 [61.1,74.6] -21.8 -1 [-2,0] 62.8 [58.9,66.7] 3.2 0.1 [-0.5,0.8] 58.8 [54.5,62.9] -3.6 -0.2 [-0.9,0.5] 

ZM6 85.8 [83.8,87.7] 15.8 1.6 [1.0,2.3] 69.7 [64.0,74.8] 12.6 0.7 [-0.3,1.6] 71.9 [69.5,74.2] 6.6 0.3 [-0.1,0.7] 65.6 [62.9,68.2] -1.8 -0.1 [-0.5,0.3] 
96

Appendix 3. Child nutrition indicators overall prevalence, difference index and coefficient index 

Exclusive breastfeeding Stunting Wasting 

Survey % [C.I.] %q5-%q1 q5 coeff. [C.I.] % [C.I.] %q5-%q1 q5 coeff. [C.I.] % [C.I.] %q5-%q1 q5 coeff. [C.I.] CD5 36.1 [31.1,41.4] -10.2 -0.5 [-1.3,0.3] 45.5 [42.7,48.4] -20.8 -0.9 [-1.3,-0.5] 10 [8.2,12.2] -1.6 -0.2 [-0.7,0.4] CD6 47.8 [44.3,51.4] -9.4 -0.4 [-0.8,0.1] 42.7 [40.9,44.6] -26.7 -1.2 [-1.4,-1.0] 7.9 [7.0,8.9] -4.3 -0.8 [-1.1,-0.4] GH5 63.1 [57.2,68.6] 2.5 0.1 [-0.8,1.0] 28 [25.8,30.2] -20.7 -1.2 [-1.6,-0.8] 8.5 [7.5,9.6] -2.9 -0.4 [-0.9,0.1] 

GH7 52.3 [47.2,57.3] -19.4 -0.8 [-1.4,-0.2] 18.8 [17,20.6] -16.3 -1.3 [-1.8,-0.7] 4.7 [3.5,6.1] -1.8 -0.4 [-1.1,0.3] 

HT5 40.7 [34.9,46.7] -13.0 -0.6 [-1.3,0.2] 29.4 [26.9,32] -33.0 -2.1 [-2.6,-1.5] 10.2 [8.3,12.4] -3.7 -0.5 [-1.2,0.2] 

HT6 39.7 [35.2,44.4] -5.1 -0.2 [-0.9,0.5] 21.9 [20,23.8] -24.4 -1.8 [-2.3,-1.4] 5.1 [4.4,5.9] -2.4 -0.6 [-1.2,0.0] 

ID5 32.5 [28.9,36.3] -21.7 -1.1 [-1.6,-0.6] NA NA NA NA NA NA 

ID6 41.5 [38.0,45.0] 2.8 0.1 [-0.3,0.6] NA NA NA NA NA NA 

KE5 31.9 [26.7,37.7] -13.3 -0.6 [-1.3,0.1] 35.3 [33.2,37.4] -19.9 -0.9 [-1.2,-0.6] 6.7 [5.8,7.8] -7.5 -1.2 [-1.6,-0.7] 

KE7 61.4 [57.2,65.5] 13.9 0.6 [0.0,1.2] 26 [25,27.1] -22.1 -1.3 [-1.4,-1.1] 4.0 [3.6,4.5] -4.8 -1.1 [-1.5,-0.7] 

LB5 29.1 [23.8,35.0] -1.7 -0.1 [-1.1,1.0] 39.4 [37.5,41.4] -18.2 -0.8 [-1.1,-0.6] 7.5 [6.4,8.6] 0.6 0.1 [-0.4,0.6] 

LB6 55.2 [49.7,60.7] -23.6 -1.0 [-1.8,-0.1] 31.6 [29.3,34] -15.4 -0.8 [-1.2,-0.4] 6.0 [5.0,7.3] -2.2 -0.5 [-1.2,0.3] 

ML5 37.8 [34.4,41.3] -10.9 -0.5 [-0.9,0.0] 37.7 [36.2,39.2] -22.0 -1.0 [-1.2,-0.8] 15.2 [14.3,16.2] -2.3 -0.2 [-0.4,0.0] 

ML6 34.2 [30.5,38.1] 3.5 0.2 [-0.3,0.7] 38.3 [36.2,40.4] -25.2 -1.2 [-1.4,-0.9] 12.7 [11.1,14.4] -4.4 -0.4 [-0.8,0.0] 

NG5 13.1 [11.5,14.8] 14.5 1.3 [0.8,1.8] 40.6 [39.5,41.7] -27.9 -1.2 [-1.4,-1.1] 13.9 [13.1,14.8] -11.2 -0.9 [-1.1,-0.7] 

NG6 17.4 [15.5,19.5] 33.4 2.4 [2.0,2.9] 36.8 [35.6,38] -35.7 -1.7 [-1.8,-1.5] 18.0 [16.9,19.1] -8.0 -0.6 [-0.7,-0.4] 

PK5 37.3 [33.6,41.1] -9.8 -0.4 [-1.0,0.1] NA NA NA NA NA NA 

PK6 37.8 [33.7,42.0] 3.0 0.1 [-0.4,0.7] 44.8 [42,47.6] -38.6 -1.7 [-2.0,-1.3] 10.8 [9,12.9] -9.1 -0.9 [-1.4,-0.3] 

SN6 37.4 [33.2,41.9] 13.7 0.6 [0.0,1.1] 18.7 [17.3,20.1] -14.5 -1.0 [-1.4,-0.6] 8.8 [7.9,9.9] -4.8 -0.6 [-1.1,-0.2] 

SN7 32.4 [27.3,38.1] -4.8 -0.3 [-1.0,0.5] 18.7 [17.2,20.4] -20.3 -1.5 [-1.9,-1.0] 5.9 [5.0,6.9] -4.9 -1.0 [-1.8,-0.2] 

ZM5 60.9 [56.8,64.8] 11.3 0.5 [-0.1,1.0] 45.4 [43.5,47.3] -14.9 -0.6 [-0.9,-0.3] 5.2 [4.6,5.9] -2.4 -0.5 [-1.0,0.0] 

ZM6 72.5 [69.5,75.4] -3.6 -0.2 [-0.7,0.4] 40.1 [38.9,41.3] -18.9 -0.8 [-1.0,-0.6] 6.0 [5.5,6.6] -1.1 -0.2 [-0.5,0.2] Note: NA indicates that the indicator is not available for the survey 
97

Appendix 4. Maternal health indicators with the national, lowest and highest regional estimates 

Four or more ANC visits Modern CPR use for women in a union Last birth assisted by SBA Last birth delivered in health facility 

survey 

Low 

region 

Low region 

name 

National 

estimate 

High 

region 

High region 

name 

Low 

region

Low region 

name 

National 

estimate

High 

region

High region 

name 

Low 

region

Low region 

name 

National 

estimate

High 

region

High region 

name 

Low 

region

Low region 

name 

National 

estimate

High 

region

High region 

name 

CD5 22.6 Sud-kivu 47.1 69.0 Kinshasa 2.0 Kasa 

occident 

5.9 12.9 Kinshasa 53.0 Equateur 76.0 97.3 Kinshasa 38.8 Equateur 72.3 97.1 Kinshasa 

CD6 35.3 Sud-kivu 48.0 73.9 Kinshasa 3.6 Katanga 8.0 18.0 Kinshasa 63.1 Katanga 81.1 97.6 Kinshasa 62.6 Katanga 80.6 98.5 KinshasaGH5 66.9 Upper east 78.2 89.5 Greater 

Accra 

5.4 Northern 15.6 20.3 Greater 

Accra 

32.3 Northern 61.6 85.6 Greater 

Accra 

31.9 Northern 60.2 85.5 Greater 

Accra 

GH7 73.0 Northern 87.3 93.5 Ashanti 10.7 Northern 21.7 28.6 Central 39.5 Northern 76.0 93.2 Greater 

Accra 

38.8 Northern 75.2 93.0 Greater 

Accra 

HT5 37.3 Sud-est 53.8 62.0 Nord-est 12.1 Sud-est 22.8 27.3 Artibonite 14.4 Sud-est 29.0 41.0 Aire 

metropolitaine/ restequest 

12.6 Sud-est 27.4 38.9 Aire 

metropolitaine/ restequest 

HT6 59.9 Grand-anse 67.3 79.3 Nord-est 24.6 Sud-est 28.6 37.3 Centre 19.4 Grand-anse 41.1 52.3 Camps 19.2 Grand-anse 39.5 50.7 Camps

ID5 49.8 Papua barat 81.5 97.6 Dki jakarta 24.6 Papua 54.4 67.1 Bengkulu 35.8 Maluku 74.2 97.4 Dki jakarta 8.9 Southeast 

sulawesi 

48.0 91.8 Bali

ID6 34.0 Papua 87.8 97.2 Yogyakarta 17.7 Papua 54.6 63.6 Lampung 43.1 Papua 84.3 98.8 Jakarta 19.0 West 

sulawesi 

64.4 98.2 Bali

KE5 32.0 North 

eastern 

47.1 66.8 Nairobi 3.1 North 

eastern 

37.1 58.3 Central 29.6 Western 48.0 89.6 Nairobi 18.4 Northeastern 46.8 90.3 Nairobi

KE7 36.9 North 

eastern 

57.6 73.1 Nairobi 3.3 North 

eastern 

49.9 60.7 Central 34.6 North 

eastern 

66.6 91.0 Nairobi 30.6 North 

eastern 

65.9 90.9 Central

LB5 51.3 South 

eastern A 

66.0 75.9 Monrovia 5.3 South 

eastern b 

10.7 17.3 Monrovia 34.6 South 

eastern b 

51.2 85.8 Monrovia 25.2 North 

western 

41.6 72.4 Monrovia

LB6 59.8 South 

Eastern B 

78.1 81.5 South 

central 

13.2 North central 19.5 22.6 South 

central 

54.0 North 

western 

65.3 74.6 South 

central 

50.3 North 

western 

60.3 68.5 South 

central 

ML5 18.9 Tombouctou 35.5 68.0 Bamako 2.0 Mopti 6.8 16.2 Bamako 19.1 Kidal 51.4 93.3 Bamako 19.5 Kidal 47.5 91.1 Bamako

ML6 20.4 Mopti 41.2 71.4 Bamako 2.8 Mopti 9.9 22.2 Bamako 32.2 Mopti 61.3 95.7 Bamako 28.7 Mopti 57.8 95.1 Bamako

NG5 20.8 North west 45.2 81.4 South west 2.5 North west 9.6 20.5 South west 11.0 North west 40.6 81.5 South east 9.3 North west 36.5 73.8 South east

NG6 30.4 North west 51.1 86.9 South west 2.7 North east 9.8 24.6 South west 13.9 North west 40.0 83.6 South west 12.8 North west 37.4 78.5 South east

PK5 7.4 Balochistan 28.5 36.5 Sindh 12.9 Balochistan 20.7 21.8 Punjab 25.0 Balochistan 41.7 47.5 Sindh 18.6 Balochistan 37.0 44.9 Sindh

PK6 12.2 Balochistan 36.6 82.1 Islamabad 

(ict) 

15.9 Balochistan 24.9 42.4 Islamabad 

(ict) 

20.3 Balochistan 55.2 89.2 Islamabad 

(ict) 

18.1 Balochistan 51.6 88.3 Islamabad 

(ict) 

SN6 28.6 Kolda 46.5 61.9 Dakar 4.4 Kedougou 15.4 28.6 Dakar 24.6 Kaffrine 52.7 87.0 Dakar 37.6 Tambacounda 73.9 94.9 Dakar

SN7 28.2 Sedhiou 48.1 57.9 Ziguinchor 6.6 Diourbel 19.4 31.4 Dakar 34.6 Kedougou 60.6 84.3 Dakar 44.2 Kedougou 78.8 97.2 Dakar

ZM5 54.4 Lusaka 60.3 68.0 Luapula 13.5 Luapula 30.1 45.9 Eastern 29.4 Northern 49.1 80.1 Lusaka 31.5 Northern 50.5 80.7 Lusaka

ZM6 50.6 Western 55.5 62.9 Copperbelt 29.4 Western 41.2 49.1 Lusaka 48.6 Northern 68.6 90.3 Lusaka 51.6 Northern 71.8 91.1 Lusaka
98

Appendix 5. Child health indicators with the national, lowest and highest regional estimates 

Three doses of DPT Treatment of ARI symptoms Treatment of fever symptoms Treatment of diarrhea 

Survey 

Low 

region 

Low region 

name 

National 

estimate 

High 

region 

High region 

name 

Low 

region

Low region 

name 

National 

estimate

High 

region

High region 

name 

Low 

region

Low region 

name 

National 

estimate

High 

region

High region 

name 

Low 

region

Low region 

name 

National 

estimate

High 

region

High region 

name 

CD5 17.4 Maniema 45.0 83.4 Bas-congo 30.3 Kasa 

oriental 

41.9 61.3 Kinshasa 35.4 Kasa 

oriental 

45.1 67.3 Kinshasa 19.1 Kasa 

oriental 

32.6 50.2 Katanga 

CD6 42.6 Equateur 60.5 87.0 Nord-kivu 29.9 Nord-kivu 41.6 55.7 Kasaioriental 31.9 Orientale 39.9 48.7 Kinshasa 32.4 Kasai- oriental 39.0 70.3 ManiemaGH5 75.1 Northern 88.8 96.0 Western 31.6 Volta 60.4 78.4 Upper east 44.8 Volta 64.0 83.6 Upper east 33.1 Volta 57.1 77.3 Upper east

GH7 80.7 Northern 88.5 96.7 Upper west 36.7 Northern 52.6 76.9 Western 39.3 Greater 

Accra 

55.9 79.8 Upper east 27.5 Ashanti 44.9 75.9 Western

HT5 38.6 Nord-quest 53.0 76.9 Nord-est 7.1 Nippes 33.3 45.2 Nord-quest 13.0 Nippes 36.0 47.9 Artibonite 16.9 Sud 30.7 47.0 Nord-quest

HT6 48.1 Nord-quest 62.5 86.1 Nippes 22.6 Nord-est 37.9 44.9 Aire 

metropolitaine/ restequest 

30.2 Sud-est 40.1 46.0 Aire 

metropolitaine/ restequest 

24.0 Artibonite 33.9 44.1 Nord-est

ID5 33.7 Di aceh 66.7 97.0 Di 

yogyakarta 

39.2 Maluku 66.4 86.3 South 

sulawesi 

43.2 Maluku 66.8 83.5 Dki jakarta 21.8 Maluku 51.0 84.3 Bali

ID6 35.3 Papua 72.0 96.4 Yogyakarta 53.7 Papua 75.3 100.0 West papua 52.0 South 

kalimantan 

73.5 84.1 Bali 45.3 Yogyakarta 64.6 81.6 Bengkulu

KE5 57.1 Northeastern 86.4 92.9 Rift valley 45.2 Central 55.9 89.9 Nairobi 33.3 Western 48.6 56.5 Coast 31.4 Western 48.6 67.6 Northeaster

n 

KE7 77.4 North 

eastern 

89.9 95.5 Central 35.4 North 

eastern 

65.7 71.0 Nyanza 49.5 North 

eastern 

62.5 68.7 Eastern 44.2 North 

eastern 

57.6 64.7 Coast

LB5 19.1 South 

eastern B 

50.3 75.1 Monrovia 38.4 South 

eastern A 

62.2 83.7 Monrovia 41.4 South 

eastern a 

56.7 69.0 Monrovia 34.0 South 

eastern A 

49.3 63.9 South 

eastern b 

LB6 52.0 South 

eastern b 

71.4 81.5 North 

western 

47.8 South 

central 

50.7 55.5 South 

eastern a 

53.7 North central 57.5 60.6 South 

central 

45.3 North central 46.8 55.6 South 

eastern b 

ML5 24.8 Kidal 67.6 79.6 Bamako 26.0 Koulikoro 38.1 52.6 Bamako 0.0 Kidal 32.8 45.8 Bamako 0.0 Kidal 17.8 31.0 Bamako

ML6 43.6 Mopti 63.1 74.1 Bamako 13.2 Mopti 26.7 39.7 Bamako 20.5 Koulikoro 28.3 55.2 Bamako 21.3 Mopti 28.3 34.0 Sikasso

NG5 9.1 North west 35.4 66.9 South east 30.7 North east 45.4 63.7 South east 42.6 North east 54.1 71.9 South east 35.9 North east 42.2 75.2 South east

NG6 13.9 North west 38.2 80.7 South east 28.5 North central 34.5 57.6 South west 23.9 South east 31.5 48.6 South west 24.4 North east 28.9 42.0 North 

central 

PK5 46.7 Balochistan 58.5 64.5 Punjab 49.8 Nwfp 69.3 78.0 Sindh 49.1 Balochistan 65.8 75.0 Sindh 40.1 Nwfp 54.5 66.2 Sindh

PK6 27.1 Balochistan 65.2 91.2 Islamabad 

(ict) 

29.3 Khyber 

pakhtunkhwa 

64.4 81.6 Sindh 26.5 Khyber 

pakhtunkhwa 

64.8 77.6 Sindh 23.0 Khyber 

pakhtunkhwa 

61.0 73.0 Sindh

SN6 66.6 Tambacounda 88.5 97.7 Thies 0.0 Matam 53.0 100.0 Kedougou 33.2 Thies 43.5 63.2 Kedougou 17.6 Thies 38.8 60.4 Louga

SN7 64.9 Kedougou 89.3 97.5 Louga 0.0 Saint-louis 42.2 100.0 Ziguinchor 24.9 Diourbel 45.8 59.0 Ziguinchor 23.2 Thies 32.8 47.3 Ziguinchor

ZM5 60.5 Northwester

n 

79.6 91.9 Lusaka 49.3 Luapula 68.2 87.8 Southern 52.2 Northern 62.8 80.9 Southern 48.6 Luapula 58.8 74.9 Eastern

ZM6 79.2 Luapula 85.8 94.4 Copperbelt 57.4 Luapula 69.7 79.2 North 61.9 Central 71.9 86.0 North 57.9 Central 65.6 73.6 Eastern
99

Appendix 6. Child nutrition indicators with the national, lowest and highest regional estimates 

Exclusive breastfeeding Stunting Wasting 

Survey Low region 

Low region 

name 

National 

estimate High region

High region 

name Low region 

Low region 

name 

National 

estimate High region 

High region 

name Low region 

Low region 

name 

National 

estimate High region

High region 

name 

CD5 23.4 Kasa 

occident 

36.1 72.2 Nord-kivu 23.4 Kinshasa 45.5 55.5 Sud-kivu 6.6 Nord-kivu 10.0 14.6 Kasa oriental

CD6 27.3 Kinshasa 47.8 77.1 Maniema 17.3 Kinshasa 42.7 53.0 Sud-kivu 3.5 Kinshasa 7.9 22.7 ManiemaGH5 45.1 Ashanti 63.1 83.3 Volta 14.2 Greater 

Accra 

28.0 37.9 Eastern 5.2 Volta 8.5 13.9 Upper west

GH7 27.7 Western 52.3 83.3 Upper west 10.4 Greater 

Accra 

18.8 33.1 Northern 2.5 Volta 4.7 9.4 Upper east

HT5 14.5 Sud-est 40.7 55.5 Centre 20.9 Aire 

metropolitaine/ restequest 

29.4 44.6 Centre 7.1 Sud-est 10.2 20.5 Artibonite

HT6 21.7 Artibonite 39.7 56.5 Nord-est 16.4 Aire 

metropolitaine/ restequest 

21.9 28.8 Sud-est 3.2 Sud-est 5.1 7.5 Nord-quest

ID5 13.0 Kep bangka 

belitung 

32.5 62.5 Sulawesi 

barat 

Na Na Na Na Na Na Na Na Na Na

ID6 13.7 Bangka 

belitung 

41.5 65.6 West nusa 

tenggara 

Na Na Na Na Na Na Na Na Na Na

KE5 5.5 Northeastern 31.9 55.4 Eastern 28.5 Nairobi 35.3 41.9 Eastern 2.3 Western 6.7 19.5 Northeastern

KE7 43.1 North eastern 61.4 75.9 Nairobi 17.2 Nairobi 26.0 30.8 Coast 1.9 Western 4.0 13.3 North eastern

LB5 8.3 South 

eastern a 

29.1 37.7 North central 30.1 Monrovia 39.4 45.2 South 

eastern b 

3.7 North 

western 

7.5 9.8 Monrovia

LB6 42.2 South 

eastern a 

55.2 65.8 North 

western 

29.0 North 

western 

31.6 34.5 North central 4.2 South 

eastern b 

6.0 7.1 South 

eastern a 

ML5 26.5 Tombouctou 37.8 74.9 Kidal 23.2 Bamako 37.7 45.2 Sikasso 12.7 Mopti 15.2 27.2 Kidal

ML6 30.3 Sikasso 34.2 39.2 Koulikoro 21.1 Bamako 38.3 46.5 Mopti 11.1 Koulikoro 12.7 14.7 Mopti

NG5 4.0 North east 13.1 22.0 South west 21.7 South east 40.6 52.6 North west 7.5 South south 13.9 22.2 North east

NG6 4.3 North west 17.4 42.1 South west 16.0 South east 36.8 54.8 North west 10.0 South west 18.0 27.1 North west

PK5 32.5 Punjab 37.3 55.2 Nwfp Na Na Na Na Na Na Na Na Na Na

PK6 23.1 Balochistan 37.8 66.3 Gilgit 

baltistan 

22.2 Islamabad 

(ict) 

44.8 81.9 Balochistan 8.1 Gilgit 

baltistan 

10.8 13.6 Sindh

SN6 18.3 Kaolack 37.4 63.5 Ziguinchor 13.8 Thies 18.7 33.1 Kaffrine 4.1 Thies 8.8 17.2 Matam

SN7 12.2 Kaolack 32.4 64.5 Kedougou 12.2 Dakar 18.7 36.1 Kolda 1.3 Dakar 5.9 15.2 Matam

ZM5 44.1 Luapula 60.9 80.9 Central 36.2 Southern 45.4 56.3 Luapula 2.3 Copperbelt 5.2 10.6 Western

ZM6 61.7 North 72.5 90.9 Southern 35.7 Lusaka 40.1 48.5 Northern 3.7 Northern 6.0 13.1 LuapulaNote: NA indicates that the indicator is not available for the survey