DHS COMPARATIVE

REPORTS 38

LEVELS AND TRENDS IN CARE 

SEEKING FOR CHILDHOOD ILLNESS 

IN USAID MCH PRIORITY COUNTRIES

SEPTEMBER 2015

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

Rebecca Winter, Wenjuan Wang, Lia Florey, and Thomas Pullum of ICF International.

DHS Comparative Reports No. 38 

Levels and Trends in Care Seeking for Childhood Illness in 

USAID MCH Priority Countries 

Rebecca Winter 

Wenjuan Wang 

Lia Florey 

Thomas Pullum 

ICF International 

Rockville, Maryland, USA 

September 2015 

Corresponding author: Rebecca Winter, International Health and Development, ICF International, 530 

Gaither Road, Suite 500, Rockville, Maryland, 20850, USA; phone: 301-572-0541; fax: 301-407-6501; 

email: Rebecca.Winter@icfi.com 
Acknowledgment: The authors are grateful for input and feedback from Debra Prosnitz, Fred Arnold, the 

Maternal and Child Health group in the Office of Health, Infectious Diseases, and Nutrition at USAID, 

Roland T. Suomie, Vicki MacDonald, Eric Sarriot, Sarah Staveteig, Lindsay Mallick, Clara Burgert, and 

Matt Pagan. 

Editors: Bryant Robey and Diane Stoy 

Document Production: Natalie La Roche 

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

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

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

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

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

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

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

Recommended citation: 

Winter, Rebecca, Wenjuan Wang, Lia Florey, and Thomas Pullum. 2015. Levels and Trends in Care 

Seeking for Childhood Illness in USAID MCH Priority Countries. DHS Comparative Reports No. 38. 

Rockville, Maryland, USA: ICF International. 
iii 

Contents 

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

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

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

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

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

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

1.1. Rationale for this Study ............................................................................................................. 1

1.2. Background ................................................................................................................................ 1

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

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

2.2. Analytic Strategy ....................................................................................................................... 9

2.3. Definitions of Indicators ............................................................................................................ 9

2.4. Limitations ............................................................................................................................... 12

3. Results ................................................................................................................................................... 13

3.1. Current Care Seeking in USAID MCH Priority Countries ...................................................... 13

3.2. Trends in Care Seeking ............................................................................................................ 21

3.3. Equity in Recent Trends in Care Seeking ................................................................................ 26

3.4. Country Case Studies: Have Child Health Interventions Helped? .......................................... 38

3.5. Appropriateness of Care .......................................................................................................... 44

4. Discussion and Conclusions .................................................................................................................. 53

4.1. Levels of Care Seeking ............................................................................................................ 53

4.2. Recent Trends in Care Seeking ............................................................................................... 55

4.3. Appropriateness of Care .......................................................................................................... 56

References ........................................................................................................................................................... 59

Appendix ............................................................................................................................................................. 63
iii 
v 

Tables 

Table 1. USAID priority countries for maternal and child health and DHS survey availability ................. 5 

Table 2. Percentage of children under age 5 who were reported to have fever, symptoms of ARI, 

or diarrhea in the two weeks preceding the interview, USAID MCH priority countries .............. 7 

Table 3. Percent distribution of combinations of illness symptoms among children with recent fever, 

symptoms of ARI, or diarrhea, USAID MCH priority countries .................................................. 8 

Table 4. Care seeking for diarrhea for POUZN and non-POUZN districts in Nepal, 2006 and 

2011 Nepal DHS ......................................................................................................................... 40 

Table 5. Odds ratios for care seeking for children with recent diarrhea in POUZN Project Districts 

vs. non-project districts after adjusting for maternal, child, and household characteristics, 

Nepal 2006 and 2011 DHS ......................................................................................................... 40 

Table 6. Care seeking for recent fever within EQUIP and non-EQUIP counties, Liberia 2007 DHS 

and 2011 MIS ............................................................................................................................. 43 

Table 7. Odds ratios for care seeking for children with recent fever in EQUIP project districts vs. 

non-project districts after adjusting for maternal, child, and household characteristics, 

Liberia 2007 DHS and 2011 MIS ............................................................................................... 44 

Table 8a. Among children under age 5 with recent fever, the percentage who had a finger or heel 

stick for malaria testing, by source of care, USAID MCH priority countries ............................. 45 

Table 8b. Among children under age 5 with recent fever, the percentage who received 

Artemisinin-based Combination Therapy (ACT) among children who received any 

antimalarial, by source of care, USAID MCH priority countries ............................................... 46 

Table 8c. Among children under age 5 with recent fever, the percentage who received first-line 

antimalarial treatment among children who received any antimalarial, by source of care, 

USAID MCH priority countries .................................................................................................. 47 

Table 9a. Among children under age 5 with recent diarrhea for whom care was sought, the percentage 

who received ORS, by source of care, USAID MCH priority countries .................................... 49 

Table 9b. Among children under age 5 with recent diarrhea for whom care was sought, the percentage 

who received ORS and zinc, by source of care, USAID MCH priority countries ...................... 50 

Table 9c. Among children under age 5 with recent non-bloody diarrhea for whom care was sought, 

the percentage who received antibiotics, by source of care, USAID MCH priority countries ... 51 

Table A1. Sources of care for fever by place of residence, USAID MCH priority countries ...................... 64 

Table A2. Sources of care for symptoms of ARI by place of residence, USAID MCH priority 

 countries ..................................................................................................................................... 67 

Table A3. Sources of care for diarrhea by place of residence, USAID MCH priority countries ................. 70 

Table A4. Sources of care for fever by household wealth, USAID MCH priority countries ...................... 73 

Table A5. Sources of care for symptoms of ARI by household wealth, USAID MCH priority 

countries ...................................................................................................................................... 78 

Table A6. Sources of care for diarrhea by household wealth, USAID MCH priority countries .................. 83 

Table A7. Use of public, private, pharmacy, and other sources of care for fever, symptoms of ARI, 

and diarrhea by place of residence, with confidence intervals, USAID MCH priority countries88 

Table A8. Use of public, private, pharmacy, and other sources of care for fever, symptoms of ARI, 

and diarrhea by household wealth, with confidence intervals, USAID MCH priority countries 90 

Table A9. Trend in coverage of care seeking among rural children and urban children and rural-urban 

gap in care seeking equity, USAID MCH priority countries ...................................................... 94 

Table A10. Trend in coverage of care seeking among children in poorest and wealthiest quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH priority 

countries ...................................................................................................................................... 96 

Table A11. Background characteristics among children with recent diarrhea by intervention coverage 

area, Nepal 2006 and 2011 DHS ................................................................................................. 97 

Table A12. Background characteristics among children with recent fever by intervention coverage 

area, Liberia 2007 DHS and 2011 MIS....................................................................................... 98

vii 

Figures 

Figure 1. USAID priority countries for maternal and child health ........................................................ 2

Figure 2. Percentage of children who received care from any source for recent illness, 

USAID MCH priority countries ........................................................................................... 13

Figure 3. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent fever, USAID MCH priority countries ....... 14

Figure 4. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent symptoms of ARI, USAID MCH priority 

countries ............................................................................................................................... 15

Figure 5. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent diarrhea, USAID MCH priority 

countries ............................................................................................................................... 15

Figure 6. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent fever by place of residence, 

USAID MCH priority countries ........................................................................................... 16

Figure 7. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent symptoms of ARI by place of residence, 

USAID MCH priority countries ........................................................................................... 17

Figure 8. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent diarrhea by place of residence, 

USAID MCH priority countries ........................................................................................... 17

Figure 9. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent fever by wealth quintile, USAID MCH 

priority countries .................................................................................................................. 18

Figure 10. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent symptoms of ARI by wealth quintile, 

USAID MCH priority countries ........................................................................................... 18

Figure 11. Percentage of children who received care from public, private, pharmacy, 

and other sources among children with recent diarrhea by wealth quintile, 

USAID MCH priority countries ........................................................................................... 19

Figure 12. Percentage of children who received care from any source among children 

with recent fever, by the co-occurrence of other illness symptoms, 

USAID MCH priority countries ........................................................................................... 19

Figure 13. Percentage of children who received care from any source among children 

with recent symptoms of ARI, by the co-occurrence of other illness symptoms, 

USAID MCH priority countries ........................................................................................... 20

Figure 14. Percentage of children who received care from any source among children 

with recent diarrhea, by the co-occurrence of other illness symptoms, USAID 

MCH priority countries ........................................................................................................ 20

Figure 15. Trend in coverage of care seeking from any source among children under 

age 5 with fever in the two weeks preceding the survey, USAID MCH priority 

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

Figure 16. Trend in coverage of care seeking from public sources among children under 

age 5 with fever in the two weeks preceding the survey, USAID MCH priority 

countries ............................................................................................................................... 22

Figure 17. Trend in coverage of care seeking from private sources among children under 

age 5 with fever in the two weeks preceding the survey, USAID MCH priority 

countries ............................................................................................................................... 22
viii 

Figure 18. Trend in coverage of care seeking from any source among children under 

age 5 with symptoms of ARI in the two weeks preceding the survey, USAID 

MCH priority countries ........................................................................................................ 23

Figure 19. Trend in coverage of care seeking from public sources among children under 

age 5 with symptoms of ARI in the two weeks preceding the survey, USAID 

MCH priority countries ........................................................................................................ 23

Figure 20. Trend in coverage of care seeking from private sources among children under 

age 5 with symptoms of ARI in the two weeks preceding the survey, USAID 

MCH priority countries ........................................................................................................ 24

Figure 21. Trend in coverage of care seeking from any source among children under 

age 5 with diarrhea in the two weeks preceding the survey, USAID MCH 

priority countries .................................................................................................................. 24

Figure 22. Trend in coverage of care seeking from public sources among children under 

age 5 with diarrhea in the two weeks preceding the survey, USAID MCH 

priority countries .................................................................................................................. 25

Figure 23. Trend in coverage of care seeking from private sources among children under 

age 5 with diarrhea in the two weeks preceding the survey, USAID MCH 

priority countries .................................................................................................................. 25

Figure 24. Trend in coverage of public care seeking for fever among children in rural 

households and rural-urban gap in care seeking equity, USAID MCH 

priority countries .................................................................................................................. 27

Figure 25. Trend in coverage of private care seeking for fever among children in rural 

households and rural-urban gap in care seeking equity, USAID Asian MCH priority 

countries and Haiti ............................................................................................................... 27

Figure 26. Trend in coverage of private care seeking for fever among children in rural 

households and rural-urban gap in care seeking equity, African USAID MCH 

priority countries .................................................................................................................. 28

Figure 27. Trend in coverage of public care seeking for symptoms of ARI among children in 

rural households and rural-urban gap in care seeking equity, USAID MCH priority 

countries ............................................................................................................................... 29

Figure 28. Trend in coverage of private care seeking for symptoms of ARI among children in 

rural households and rural-urban gap in care seeking equity, USAID Asian MCH 

priority countries and Haiti .................................................................................................. 29

Figure 29. Trend in coverage of private care seeking for symptoms of ARI among children in 

rural households and rural-urban gap in care seeking equity, USAID African MCH 

priority countries .................................................................................................................. 30

Figure 30. Trend in coverage of public care seeking for diarrhea among children in rural 

households and rural-urban gap in care seeking equity, USAID MCH priority 

countries ............................................................................................................................... 31

Figure 31. Trend in coverage of private care seeking for diarrhea among children in rural 

households and rural-urban gap in care seeking equity, Asian USAID MCH priority 

countries and Haiti ............................................................................................................... 31

Figure 32. Trend in coverage of private care seeking for diarrhea among children in rural 

households and rural-urban gap in care seeking equity, African USAID MCH 

priority countries .................................................................................................................. 32

Figure 33. Trend in coverage of public care seeking for fever among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID 

MCH priority countries ........................................................................................................ 33

Figure 34. Trend in coverage of private care seeking for fever among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, Asian 

USAID MCH priority countries and Haiti ........................................................................... 33
ix 

Figure 35. Trend in coverage of private care seeking for fever among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, African USAID 

MCH priority countries ........................................................................................................ 34

Figure 36. Trend in coverage of public care seeking for symptoms of ARI among children in 

poorest-quintile households and poorest-wealthiest quintile gap in care seeking equity, 

USAID MCH priority countries ........................................................................................... 35

Figure 37. Trend in coverage of private care seeking for symptoms of ARI among children in 

poorest-quintile households and poorest-wealthiest quintile gap in care seeking equity, 

USAID MCH priority countries ........................................................................................... 35

Figure 38. Trend in coverage of public care seeking for diarrhea among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH 

priority countries .................................................................................................................. 36

Figure 39. Trend in coverage of private care seeking for diarrhea among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, Asian 

USAID MCH priority countries and Haiti ........................................................................... 37

Figure 40. Trend in coverage of private care seeking for diarrhea among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, African 

USAID MCH priority countries ........................................................................................... 37

Figure 41. Percentage of children with recent diarrhea for whom care was sought from a private 

source, Nepal 2006 and 2011 DHS ...................................................................................... 39

Figure 42. Percentage of children with recent diarrhea for whom care was sought from a public 

source, Nepal 2006 and 2011 DHS ...................................................................................... 39

Figure 43. Percentage of children with recent diarrhea for whom care was sought from any source, 

Nepal 2006 and 2011 DHS .................................................................................................. 39

Figure 44. Percentage of children with recent diarrhea who were treated with ORS, Nepal 2006 

and 2011 DHS ...................................................................................................................... 39

Figure 45. Percentage of children with recent diarrhea who were given zinc, Nepal 2006 and 

2011 DHS ............................................................................................................................. 39

Figure 46. Percentage of children with recent fever for whom care was sought from a public 

source, Liberia 2007 DHS and 2011 MIS ............................................................................ 42

Figure 47. Percentage of children with recent fever for whom care was sought from a private 

source, Liberia 2007 DHS and 2011 MIS ............................................................................ 42

Figure 48. Percentage of children with recent fever for whom care was sought from any source, 

Liberia 2007 DHS and 2011 MIS ........................................................................................ 43

Figure A1. Comparison of care seeking from public versus private facilities for fever, 

USAID MCH priority countries ........................................................................................... 63

Figure A2. Comparison of care seeking from public versus private facilities for symptoms of ARI, 

USAID MCH priority countries ........................................................................................... 63

Figure A3. Comparison of care seeking from public versus private facilities for diarrhea, 

USAID MCH priority countries ........................................................................................... 63

Figure A4. Trend in coverage of any care seeking for fever among children in rural households 

and rural-urban gap in care seeking equity, USAID MCH priority countries...................... 99

Figure A5. Trend in coverage of any care seeking for symptoms of ARI among children in rural 

households and rural-urban equity gap in care seeking ....................................................... 99

Figure A6. Trend in coverage of any care seeking for diarrhea among children in rural households 

and rural-urban gap in care seeking equity, USAID MCH priority countries.................... 100

Figure A7. Trend in coverage of any care seeking for fever among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH 

priority countries ................................................................................................................ 100
x 

Figure A8. Trend in coverage of any care seeking for symptoms of ARI among children in poorestquintile households and poorest-wealthiest quintile gap in care seeking equity, 

USAID MCH priority countries ......................................................................................... 101

Figure A9. Trend in coverage of any care seeking for diarrhea among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH 

priority countries ................................................................................................................ 101
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 study uses Demographic and Health Survey (DHS) data to examine patterns and trends in care 

seeking for children who have experienced recent fever, symptoms of acute respiratory infection (ARI), 

or diarrhea in USAID maternal and child health (MCH) priority countries. Overall, current levels of care 

seeking among children with recent illness vary widely across the 21 priority countries, from nearly 90 

percent in the Indonesia 2012 survey to 33 percent in Ethiopia 2011. Results show that care seeking from 

public sources is predominant in African MCH priority countries while care seeking from private sources 

is predominant in Asian MCH priority countries. The majority of countries did not show significant 

improvements between the two most recent DHS surveys in the level of care seeking for fever or 

symptoms of ARI, although care seeking for diarrhea showed more widespread improvement. The use of 

appropriate antimalarial treatment varied widely across MCH priority countries. Despite the widespread 

increase in care seeking for diarrhea, there has not been a parallel increase in coverage of appropriate 

treatment for diarrhea. In conclusion, our results show that the percentage of children with diarrhea, fever, 

and symptoms of ARI who reach the health system remains low in many high childhood mortality 

settings. Findings would be most useful when interpreted at the country-level, with national childhood 

illness management goals in mind. 

KEY WORDS: child health, care seeking for childhood illness, pneumonia, diarrhea, malaria 

xv 

Executive Summary 

This study, a follow-up to Hodgins, Pullum, and Dougherty (2013), examines patterns in care seeking for 

children who have experienced recent fever, symptoms of acute respiratory infection (ARI), or diarrhea in 

USAID maternal and child health (MCH) priority countries for which Demographic and Health Surveys 

(DHS) data are available since 2000. The results are categorized into five sections. First, we describe the 

current landscape of care seeking for children’s recent illness. Second, we present trends in care seeking 

between the two most recent surveys in the 20 countries. Third, we focus specifically on the equity of 

these recent trends. Fourth, we present two case studies as examples, in which we use DHS data to look 

for ecological evidence of intervention impact on care seeking patterns in two countries, Liberia and 

Nepal. Finally, we examine the appropriateness of the care that was received for fever and diarrhea. 

Overall, current levels of care seeking among children with recent illness vary widely across the priority 

countries, from nearly 90 percent in the Indonesia 2012 survey to 33 percent in Ethiopia 2011. In three 

countries—Ethiopia, Madagascar, and Haiti—less than half of children under age 5 with recent illness 

received care from any source. For all three illnesses, care seeking from public sources is predominant in 

African MCH priority countries while care seeking from private sources is predominant in Asian MCH 

priority countries. 

The majority of countries did not show significant improvements between the two most recent DHS 

surveys in the level of care seeking for fever or for symptoms of ARI (from any source, private sources, 

or public sources). Care seeking for diarrhea, however, showed more widespread improvement; 13 of 20 

countries showed a statistically significant increase in care seeking from any source for diarrhea, and no 

country showed a decrease in coverage. 

The study identified distinct patterns in recent trends in care seeking from public versus private sources. 

For all three illnesses, the use of public sources of care either remained the same or increased between 

surveys; there were no decreases. The trend in use of private sources of care was more mixed. For fever 

and diarrhea, some countries, primarily in Asia, experienced an increase in the level of care seeking from 

private sources while others, all in sub-Saharan Africa, experienced a decrease in the level of care seeking 

from private sources. 

There is general agreement that to have the greatest impact on child survival and wellness, interventions 

around childhood illness should focus on the poorest and most vulnerable children. Nevertheless, we 

found only limited evidence that increases in care seeking were concentrated in these vulnerable children. 

We examined trends specifically among children in poorest-wealth quintile households and among 

children in rural households, and found that the patterns within these populations roughly followed the 

national patterns. In most cases, the change in coverage of care seeking between surveys was not 

significantly different between children in the poorest and the wealthiest households, or between rural and 

urban children. 

Using case studies in Liberia and Nepal, we attempted to identify ecological evidence of the impact of 

programs by examining temporal trends in care seeking separately for areas covered and not covered by 

two USAID health initiatives. In Liberia our results show that the increase in care seeking from public 

facilities between 2007 and 2011 was significantly greater in Liberian counties covered by the PMIfunded EQUIP project compared with the increase in areas with no EQUIP presence, after adjusting for 

socio-demographic characteristics. However, overall levels of care seeking from any source did not 

improve in EQUIP project districts between 2007 and 2011. Instead, there appears to have been a shift 

from the use of private providers to public providers. In Nepal, despite countrywide improvements in 

levels of care seeking for diarrhea between the 2006 and 2011 DHS surveys, we did not find evidence that 

increases in districts covered by the POUZN project were greater than those in non-project districts. There 
xvi 

are several possible explanations for this null finding, such as the time lapse between the end of the 

program and the 2011 survey, the possibility that using district as a proxy for program exposure may be 

inappropriate, and the fact that certain aspects of the program’s communication campaigns were broadcast 

nationally, which could potentially dilute the differential between primary project districts and the noncovered districts. 

Levels and trends in coverage of care seeking are only meaningful if the sources of care consistently 

provide children and caregivers with appropriate diagnoses, advice, and treatment. Despite the 

widespread increase in coverage of care seeking for diarrhea, there has not been a parallel increase in 

coverage of appropriate treatment. Treatment with oral rehydration solution (ORS) increased significantly 

in just four of the 20 countries, coverage of treatment with both ORS and zinc remains under 5 percent in 

16 of the 18 countries with data available, and no country showed an improvement (reduction) in 

inappropriate use of antibiotics to treat non-bloody diarrhea. 

Appropriateness of antimalarial treatment—in terms of the percentage of children who had a finger or 

heel stick among those with recent fever for whom care was sought, and the percentage who received 

artemisinin-based combination therapy (ACT) or other first-line treatment among those who received any 

antimalarial—varied widely across MCH priority countries. The results indicate varied levels of access to 

essential medications but do not provide reasons for successes or failures in the provision of appropriate 

antimalarial treatment. 

We know that in order to save lives, children need to receive ACT for malaria, antibiotics for pneumonia, 

and ORS and zinc for diarrhea. Yet coverage of these proven interventions remains far too low. In order 

to receive the correct treatment, children first must receive appropriate diagnosis, care, and counseling 

from a qualified provider. Our results show that the percentage of children with diarrhea, fever, and 

symptoms of ARI who reach the health system remains low in many high childhood mortality settings. 

The results would be most useful when interpreted within the landscape of child health interventions and 

programming, and with national childhood illness management goals in mind. 
1 

1. Introduction 

1.1. Rationale for this Study 

Malaria, diarrhea, and pneumonia are the leading causes of death among children under age 5. Together, 

as of 2013, these three conditions account for about 40 percent of under-five deaths in sub-Saharan 

Africa, about 25 percent of under-five deaths in Southern Asia, and nearly one-third of under-five deaths 

globally (UNICEF et al. 2014). Scale-up of both preventive interventions and case management 

interventions for improved care and treatment of illness are needed to reduce the burden of child illness 

and death from these conditions. In order to develop appropriate potentially life-saving case management 

programs, it is essential to understand where caregivers are taking their sick children. This study, a 

follow-up to Hodgins, Pullum, and Dougherty (2013), examines patterns in care seeking for children who 

have experienced recent fever, symptoms of acute respiratory infection (ARI)1

, or diarrhea. 

Chapter 1 provides background information on the burden of malaria, pneumonia, and diarrhea, and on 

international standards for case management. Chapter 2 describes the data and the methodology of the 

study, defines all variables, and presents study limitations. Chapter 3 has five sections that describe 

results. First, we describe the current landscape of care seeking for children’s fever, symptoms of ARI, 

and diarrhea in the 21 USAID maternal and child health (MCH) priority countries for which 

Demographic and Health Surveys (DHS) data are available since 2000. Second, among the 20 countries 

with data available from at least two surveys since 2000, we present trends in care seeking between the 

two most recent surveys. Third, we focus specifically on the equity of these recent trends. Fourth, as 

examples, we present two case studies in which we use DHS data to find ecological evidence of 

intervention impact on care seeking patterns in two countries, Nepal and Liberia. Finally, we examine the 

appropriateness of the care that was received for fever and diarrhea. Chapter 4 provides interpretation of 

key findings, overall conclusions, and policy implications. 

1.2. Background 

Millennium Development Goal 4 (MDG 4) established the target of a two-thirds reduction in under-five 

mortality between 1990 and 2015. Although this goal will not be met, impressive gains have been made. 

Between 2000 and 2013, under-five mortality declined from 77 to 46 deaths per 1,000 live births, with the 

largest reductions found in pneumonia, diarrhea and measles deaths (Liu et al. 2015). The reductions in 

pneumonia and diarrhea deaths alone accounted for nearly 40 percent of the total observed reduction in 

under-five mortality. During this period, under-five deaths caused by diarrhea, pneumonia, and malaria 

declined at annual rates of 6.5 percent, 5 percent, and 4.5 percent, respectively (Liu et al. 2015). 

Despite these gains, malaria, diarrhea, and pneumonia remain leading causes of death among children 

under age 5. In 2013, pneumonia was responsible for an estimated 935,000 deaths to children under age 5, 

diarrhea for an estimated 578,000 deaths, and malaria for an estimated 456,000 deaths (Liu et al. 2015). 

 

1 Note on terminology. In the report we use the term “pneumonia” carefully and sparingly: only in the context of discussing the 

global burden of this childhood ailment based on findings from other studies and when discussing illness symptoms or standard 

treatment recommendations. Using DHS data we cannot assess whether a child had recent pneumonia. Instead, we know whether 

he or she had symptoms of acute respiratory infection (ARI), which are not specific to pneumonia. Similarly, we do not know 

whether a child had recent malaria – instead we know whether the child had fever. All discussion of study results uses the terms 

fever and symptoms of ARI. Section 2.1 provides a more detailed discussion of the limitations of these non-specific proxy 

measures. 
2 

1.2.1. Equity and the geography of burden 

The vast majority of under-five deaths occur in low- and middle-income countries, with the highest 

numbers in South Asia and sub-Saharan Africa. Nearly 90 percent of all child deaths from pneumonia and 

diarrhea occur in sub-Saharan Africa and South Asia (WHO and UNICEF 2013). More generally, nearly 

half of all under-five deaths worldwide occur in just four countries: India (22 percent), Nigeria (13 

percent), Pakistan (6 percent), and the Democratic Republic of Congo (6 percent) (UNICEF 2013). Based 

on the distribution of the global burden of maternal and child death, USAID selected 24 MCH priority 

countries—displayed in Figure 1—as the focus of programmatic efforts designed to scale up high-impact 

interventions and strengthen health systems (USAID 2013). These 24 priority countries are the focus of 

the current study. 

Figure 1. USAID priority countries for maternal and child health 

Beyond the global concentration of child deaths in a few low- and middle-income countries, inequities 

exist within these high-burden countries. The children who become sick with diarrhea and pneumonia are 

often those who are poor and malnourished, live in remote areas, and lack access to simple life-saving 

interventions (UNICEF 2012). Highlighting the economic inequity in intervention coverage and its 

impact on under-five mortality, the United Nations Children’s Fund (UNICEF) estimated that, within the 

75 countries with the highest burden of child death, more than two million child deaths related to 

pneumonia and diarrhea could be averted between 2012 and 2015 if national coverage of key pneumonia 

and diarrhea interventions were raised to levels found in the wealthiest 20 percent of households in each 

country. Scale-up to the levels of intervention coverage in the wealthiest 20 percent of households could 

reduce under-five deaths from pneumonia by 30 percent, and under-five deaths from diarrhea by as much 

as 60 percent (UNICEF 2012). Our report examines levels of inequity in patterns of care seeking in 

USAID MCH priority countries and recent trends in care seeking, specifically among children in rural 

areas and the poorest households. 
3 

1.2.2. Management of childhood illness 

Health ministries, international partners, and aid organizations have utilized numerous approaches in their 

attempts to reach all children—with specific efforts that target the poor, remote, and hard-to-reach—and 

with expanded access to preventive and curative care for childhood illnesses. One key approach, the 

Integrated Management of Childhood Illnesses (IMCI), was developed by WHO (World Health 

Organization) and UNICEF in the mid-1990s. IMCI has been implemented widely across low- and 

middle-income countries. Given that 70 percent of child deaths are caused by five conditions—

pneumonia, diarrhea, malaria, measles, and malnutrition—and that these often occur in combination and 

could be prevented with many of the same measures, IMCI uses an integrated approach to the prevention 

and treatment of childhood illness that works at the family, community, and health systems levels 

(UNICEF 2012). IMCI guidelines for assessing, classifying, treating, counseling, and preventing the most 

common childhood illnesses focus on the child rather than on any single illness. 

Another strategy supported by UNICEF and WHO to bring appropriate care to hard-to-reach children 

with malaria, pneumonia, and diarrhea is the use of community health workers. In most high-mortality 

countries, facility-based health services alone cannot provide adequate access to timely treatment. The 

aim of integrated community case management (iCCM) is to train, supply, and supervise the community 

health workers who will identify, diagnose, and treat sick children. The iCCM strategy is supported by 

research that estimates that community case management of diarrhea, pneumonia, and malaria with oral 

rehydration solution (ORS) and zinc, antibiotics, and artemisinin-based combination therapy (ACT), 

respectively, can reduce mortality attributed to these three diseases by 50 percent (WHO and UNICEF 

2012). 

Promotion of the private sector is a third strategy for increasing the reach of health systems. The USAID 

Strengthening Health Outcomes through the Private Sector (SHOPS) program, for example, works with 

nongovernmental organizations (NGOs) and for-profit facilities to increase availability, improve quality, 

and expand coverage of essential health products and services in the private health sector (Abt. Associates 

n.d.). Support for the private health sector through the SHOPS program is expected to work in tandem 

with the expansion of public sector health services to serve those with the greatest need (USAID 2009). 

The rationale suggests that “increasing private sector involvement to serve those who can pay for private 

health services, drugs and health products will free up public sector resources to better serve low-income 

populations” (USAID 2009). 

1.2.3. Public versus private sources of care 

Since the 1990s, international partners and aid organizations have encouraged low-income countries to 

expand their private health sectors. Public sectors in developing countries face a number of challenges, 

such as government decentralization, economic hardships, humanitarian crises, political instability and 

corruption, and an exodus of educated health care workers. Within the health care system, additional 

challenges include poor infrastructure, insufficient funding, inconsistent supplies of pharmaceuticals and 

commodities, and weak health information and referral systems (White and Levin 2006). A World Bank 

study (2008) estimated that in select countries where the environment is favorable to private sector 

participation, the private sector could potentially provide 45-70 percent of the needed increase in health 

care capacity (International Finance Corporation World Bank Group 2008). In theory, the private sector 

has the potential to provide higher quality care, a broader range of care, and more flexible hours and 

payment options (International Finance Corporation World Bank Group 2008). Despite these potential 

benefits, growth in the private sector raises concerns about the potential impact on equity, affordability, 

regulation, and the quality of service provision (Hanson et al. 2008; Lagomarsino, Nachuk, and Kundra 

2009). This report examines patterns in the use of public and private sources of care to treat children’s 
4 

illness and to the extent possible, the appropriateness of treatment provided by public versus private 

sources of care (see Section 2.3 for a discussion of indicators that assess the appropriateness of treatment). 

1.2.4. Symptoms of illness and recommended treatments 

The scale-up of both preventive and case management interventions are needed to reduce the burden of 

child death from malaria, pneumonia, and diarrhea. This study, however, focuses primarily on patterns in 

care seeking for sick children. International treatment recommendations for sick children with suspected 

malaria, suspected pneumonia, and diarrhea are summarized below, along with key symptoms of the 

illnesses. 

Malaria. Early symptoms of malaria are nonspecific and include headache, lassitude, fatigue, abdominal 

discomfort, and muscle/joint aches. These are usually followed by fever, chills, perspiration, anorexia, 

vomiting, and worsening malaise. Young children may also experience lethargy, poor feeding, and cough. 

If treated promptly with effective antimalarials at an early stage with no evidence of vital organ 

dysfunction, a child can typically expect a rapid, full recovery. Disease progression to severe malaria 

(which can take days or hours) usually includes coma, metabolic acidosis, severe anemia, hypoglycemia, 

acute renal failure, and/or acute pulmonary edema (WHO, 2015). 

According to the latest WHO recommendations, all cases of suspected malaria should have a 

parasitological test, with either microscopy or a rapid diagnostic test (RDT) to confirm the diagnosis 

(WHO 2015). Treatment of malaria depends on the Plasmodium species and disease severity. For 

uncomplicated, i.e., non-severe, P. falciparum malaria, children should be treated with ACT for three 

days. For uncomplicated P. vivax, P. ovale, P. malariae, or P. knowlesi malaria in areas with chloroquinesusceptible infections, children may be treated with either ACT or chloroquine. In areas with chloroquineresistant infections, children should be treated with ACT. For severe malaria, children should first be 

given intravenous or intramuscular artesunate for at least 24 hours, after which they should be treated 

with three days of ACT when they can tolerate oral medication. Some cases may require pre-referral 

rectal administration of a single dose of artesunate. Dosages depend on the child’s weight and age (WHO 

2015). 

Pneumonia. In low-income settings, chest radiology, blood tests, and sputum samples are often 

unavailable to confirm a pneumonia diagnosis and identify the disease-causing pathogen. As a result, 

pneumonia is classified and treated based on symptoms and physical examinations, according to WHO 

and UNICEF IMCI guidelines (UNICEF 2012). Pneumonia symptoms include coughing and fast/difficult 

breathing. The 2014 IMCI guidelines outline two paths to treatment which depend on the severity of 

symptoms (WHO 2014). Children with a cough or difficult breathing, as well as stridor (a high-pitched 

wheezing sound caused by disrupted airflow) or any general danger sign, are classified as having severe 

or very severe pneumonia. These children should be given a first dose of an appropriate antibiotic and 

referred urgently to a hospital. Children with a cough or difficult breathing, and either chest indrawing or 

rapid breathing for their age and no signs of severe pneumonia, are diagnosed with pneumonia. These 

children should be treated with oral amoxicillin for five days, and given home care. IMCI provides 

specific guidance on home care, which should include more frequent, longer periods of breastfeeding and 

increased fluid intake. Young infants must be breastfed and kept warm at all times. The dosage of 

amoxicillin depends on the child’s age and weight, and the presence of chest indrawing. Injectable 

antibiotics are given only in cases of severe pneumonia (WHO 2014). 

Diarrhea. WHO and UNICEF recommend the provision of low-osmolarity ORS with zinc 

supplementation for 10-14 days to treat diarrhea in children under age 5 (WHO and UNICEF 2004). 

Continued feeding and increased breastfeeding are recommended during this time, in which unnecessary 

antibiotics should be avoided. Antibiotics are only appropriate in the presence of bloody diarrhea or 

shigellosis (WHO and UNICEF 2004). 
5 

2. Data and Methods 

2.1. Data 

This study uses data from DHS surveys in 21 of the 24 USAID MCH priority countries. These are 

nationally representative, population-based household surveys that monitor demographic trends, 

reproductive health behaviors, attitudes, outcomes, and socio-demographic characteristics of women and 

men of reproductive age. The data are collected with face-to-face household interviews. All surveys 

include information on care seeking for children under age 5 with reported fever, diarrhea, or symptoms 

of acute respiratory infection (ARI) in the two weeks preceding the interview. Each survey includes a 

standard core questionnaire that enables comparisons across countries and over time. 

Table 1 lists the USAID MCH priority countries and DHS survey availability. The study uses the two 

most recent DHS surveys. For 20 countries—Bangladesh, DR Congo, Ethiopia, Ghana, Haiti, Indonesia, 

Kenya, Liberia, Madagascar, Malawi, Mali2

, Mozambique, Nepal, Nigeria, Pakistan, Rwanda, Senegal, 

Tanzania, Uganda, and Zambia—two DHS surveys are available since 2000. For India, just one survey is 

available since 2000. 

Table 1. USAID priority countries for maternal and child health and DHS survey availability

Country and DHS country code Survey 1 Survey 2 

Afghanistan (AF) n/a n/a 

Bangladesh (BD) 2007 DHS 2011 DHS 

DR Congo (CD) 2007 DHS 2013-14 DHS 

Ethiopia (ET) 2005 DHS 2011 DHS 

Ghana (GH) 2003 DHS 2008 DHS 

Haiti (HT) 2005-6 DHS 2012 DHS 

India (IA) n/a 2005-6 DHS 

Indonesia (ID) 2007 DHS 2012 DHS 

Kenya (KE) 2003 DHS 2008-9 DHS 

Liberia (LB) 2007 DHS 2013 DHS 

Madagascar (MD) 2003-4 DHS 2008-9 DHS 

Malawi (MW) 2004 DHS 2010 DHS 

Mali (ML) 2006 DHS 2012-13 DHS 

Mozambique (MZ) 2003 DHS 2011 DHS 

Nepal (NP) 2006 DHS 2011 DHS 

Nigeria (NG) 2008 DHS 2013 DHS 

Pakistan (PK) 2006-7 DHS 2012-13 DHS 

Rwanda (RW) 2005 DHS 2010 DHS 

Senegal (SN) 2005 DHS 2010-11 DHS 

South Sudan (SS) n/a n/a 

Tanzania (TZ) 2004-5 DHS 2010 DHS 

Uganda (UG) 2006 DHS 2011 DHS 

Yemen (YE) n/a n/a 

Zambia (ZM) 2007 DHS 2013-14 DHS 

Note: We have no data for Afghanistan, South Sudan, or Yemen as of June 2014. Recode 

files are not available for the Afghanistan 2010 Mortality Survey. n/a= data not available 

 

2 Note on Mali surveys: The 2006 survey included all eight regions of Mali and Bamako (CPS/DNSI and Macro International, 

2007), while for reasons of security the 2012-13 survey excluded the three northern regions of Gao, Kidal, and Timbuktu, and did 

not fully represent the region of Mopti (CPS et al. 2014). A strong case could be made that comparison of the two surveys should 

omit Gao, Kidal, Timbuktu, and Mopti from the 2006 survey and should omit Mopti from the 2012-13 survey to make the two 

surveys comparable. However, to avoid discarding data that have been collected and to present estimates that are as close to 

nationally representative as possible, this report does not omit any regions from either survey. Readers are warned that the 

differences between the estimates from the 2006 and 2012-13 surveys are potentially biased estimates of the differences that 

would have been found with complete coverage in both surveys. A separate analysis of trends in malaria prevention and care 

seeking for children’s fever found that the direction and significance of the trend between surveys remained the same after 

removing the four northern regions to create comparable populations. These results are not shown. 
6 

The study population was restricted to the 143,961 children under age 5 with reported illness—fever, 

symptoms of ARI, or diarrhea—in the two weeks preceding the mother’s interview. While identifying the 

population of children with recent malaria, pneumonia, and diarrhea is ideal, the DHS is limited in its 

ability to diagnose these illnesses, particularly malaria and pneumonia. We do not know if a child had 

malaria in the two weeks preceding the survey; instead, we know if the mother reports that the child had a 

fever during this period. The percentage of fever cases due to malaria varies across country and across 

endemicity zones within a country, and can be highly dependent on the season of fieldwork.

We also do not know from the DHS whether a child had pneumonia. Instead, we know if the child had a 

reported cough and chest-related rapid/difficult breathing. These symptoms are not specific to pneumonia 

and could identify a number of childhood illnesses such as the common cold or bronchitis. For this 

reason, the collection of symptoms is referred to as ARI. The indicator is based on the following standard 

DHS questions: Respondents are asked whether the child has had an illness with a cough at any time in 

the last two weeks. If the response is yes, the respondents are asked “When [name of child] had an illness 

with a cough, did he/she breathe faster than usual with short, rapid breaths or have difficulty breathing?” 

For most surveys conducted after 2003, respondents are then asked “Was the fast or difficult breathing 

due to a problem in the chest or to a blocked or runny nose?” For consistency, the analysis of trends in 

care seeking for symptoms of ARI was restricted to surveys with all three questions. In the study, children 

are classified as having had symptoms of ARI if they were reported to have had a cough with difficulty 

breathing or short, rapid breaths, and if the child’s difficulty breathing was due to a problem in the chest 

or to a problem in the chest and blocked/runny nose. The need for caution in interpreting the ARI 

estimates is underscored by a recent prospective study in Pakistan and Bangladesh (Hazir et al. 2013). 

This study found that the specificity (the true negative rate) of the symptoms of ARI questions used by 

DHS and the UNICEF MICS surveys is well below the levels required for the proxy measure to provide 

accurate estimates of the prevalence of pneumonia; thus, estimates should not be used for that purpose. 

Finally, while we know if a child had diarrhea in the two weeks preceding the survey and if the diarrhea 

was bloody, we have no further information about the severity or duration of illness or the cause of the 

diarrhea. According to the 2013 Global Burden of Disease Study, rotavirus was the main cause of 

diarrhea in children younger than age 5, and the most common cause of diarrhea deaths for children under 

age 5 in 2013, followed by cholera, cryptosporidium, and shigellosis (Liu et al. 2015). 

Table 2 lists the prevalence of the three illnesses among children under age 5 in each survey. The 

prevalence of fever ranged from 9 percent among children under age 5 in the Madagascar 2008-9 survey 

and the Mali 2012-13 survey to about 40 percent in Kenya 2003 and Uganda in 2006 and 2011. The 

prevalence of symptoms of ARI ranged from 2 percent in Mali 2012-13, Mozambique 2011, and Nigeria 

2013, to 19 percent of children under age 5 in Malawi 2004. The prevalence of diarrhea in the two weeks 

preceding the survey ranged from 5 percent in the Bangladesh 2011 survey to 26 percent in Uganda 2006. 

The analysis of care seeking is restricted to these children with recent illness symptoms. 
7 

Table 2. Percentage of children under age 5 who were reported to have fever, 

symptoms of ARI, or diarrhea in the two weeks preceding the interview, USAID 

MCH priority countries 

Fever 

% 

Symptoms of 

ARI 

% 

Diarrhea 

(any) 

% 

Diarrhea 

(bloody) 

% 

N 

children under 

Country/survey year age 5 

Bangladesh 2007 38.2 4.8 9.8 n/a 5,719 

Bangladesh 2011 36.5 5.8 4.6 0.6 8,395 

Congo DR 20071 30.8 15.4 16.4 n/a 8,009 

Congo DR 2013-14 29.5 6.7 16.8 3.0 17,017 

Ethiopia 20051 18.7 12.6 18.0 6.2 10,109 

Ethiopia 2011 17.1 7.0 13.4 3.3 11,042 

Ghana 20031 21.3 10.0 15.2 n/a 3,340 

Ghana 2008 19.9 5.5 19.8 3.1 2,731 

Haiti 2005-6 27.9 10.3 23.7 5.0 5,322 

Haiti 2012 27.1 14.4 20.8 2.0 6,410 

India 2005-6 14.9 5.8 9.0 0.9 52,868 

Indonesia 20071 31.6 11.2 13.7 n/a 15,925 

Indonesia 2012 31.0 5.1 14.3 0.1 16,380 

Kenya 20031 40.6 18.4 16.0 n/a 5,560 

Kenya 2008-9 23.7 7.6 16.6 2.6 5,481 

Liberia 2007 30.7 8.6 19.8 4.9 5,132 

Liberia 2013 28.6 6.5 22.0 3.9 6,047 

Madagascar 2003-41 20.6 8.6 9.8 n/a 5,841 

Madagascar 2008-9 9.3 2.9 8.3 0.9 11,976 

Mali 20061 17.9 5.6 13.3 n/a 12,523 

Mali 2012-13 8.6 1.6 8.6 2.0 9,655 

Malawi 20041 37.1 18.8 22.3 n/a 9,777 

Malawi 2010 34.5 6.8 17.5 2.4 18,013 

Mozambique 20031 26.7 9.8 14.1 2.8 9,400 

Mozambique 2011 13.4 1.5 11.1 1.5 10,835 

Nepal 2006 16.9 5.3 11.9 2.0 5,252 

Nepal 2011 18.7 4.6 13.8 1.6 5,140 

Nigeria 2008 15.9 2.8 10.1 2.0 24,975 

Nigeria 2013 12.5 2.0 10.2 2.1 28,950 

Pakistan 2006-7 30.7 14.1 12.6 3.1 8,367 

Pakistan 2012-13 37.6 15.9 22.5 2.3 11,040 

Rwanda 20051 26.2 17.1 14.1 n/a 7,797 

Rwanda 2010 15.8 3.7 13.2 2.0 8,605 

Senegal 20051 29.8 13.2 22.3 n/a 9,709 

Senegal 2010-11 22.6 5.4 20.6 2.0 10,893 

Tanzania 2004-51 24.4 8.1 12.6 n/a 7,976 

Tanzania 2010 22.9 4.3 14.5 1.9 7,667 

Uganda 2006 40.9 14.5 25.8 5.6 7,664 

Uganda 2011 40.4 14.8 23.4 4.2 7,535 

Zambia 2007 17.8 5.2 15.5 2.0 5,861 

Zambia 2013-14 21.0 3.7 16.1 2.5 12,634 

1 For 12 surveys, “symptoms of ARI” is defined by a less specific definition (having a cough with rapid 

breathing, rather than having a cough with rapid breathing that is chest-related), because these surveys did

not collect information on whether the child's rapid breathing was chest-related. n/a= data not available 

For the most part, this study examines care seeking separately by illness; that is, we examine care seeking 

in three populations: all children with recent fever, all children with recent symptoms of ARI, and all 

children with recent diarrhea. However, children may have experienced combinations of these illnesses. 

Table 3 presents the percent distribution of illness symptoms among all children with reported illness in 

the two weeks preceding the interview. In all surveys the largest percentage of children with recent illness 

experienced only fever in the two weeks preceding the interview, without coinciding symptoms of ARI or 

diarrhea. This percentage ranged from 19 percent in the Ethiopia 2005 survey to 75 percent in Bangladesh 

2011. Between 1 percent (Bangladesh 2007) and 13 percent (Haiti 2012) of children with recent illness 

experienced only symptoms of ARI, and between 5 percent (Bangladesh 2011) and 39 percent (Mali 

2012-13) experienced only diarrhea. 
8 

In all surveys except Bangladesh 2011, at least 10 percent of children with recent illness were reported to 

have experienced both fever and diarrhea. The reported co-occurrence of diarrhea and symptoms of ARI 

is much less common; in all surveys, 4 percent of children or less experienced both diarrhea and 

symptoms of ARI. Between 2 percent (Mozambique 2011) and 23 percent (Pakistan 2006-7) experienced 

fever along with symptoms of ARI. 

Finally, a small percentage of children experienced all three illnesses or illness symptoms in the two 

weeks preceding the survey. This ranged from 1 percent in Bangladesh 2007 and 2011 and in Mali 2012-

13 to 14 percent in Ethiopia 2005. 

Table 3. Percent distribution of combinations of illness symptoms among children with recent 

fever, symptoms of ARI, or diarrhea, USAID MCH priority countries 

Country/survey year 

Fever 

only 

% 

Symptoms 

of ARI only 

% 

Diarrhea 

only 

% 

Fever 

and ARI 

% 

Fever 

and 

diarrhea 

% 

Diarrhea 

and ARI 

% 

All three 

% 

Total 

% 

N 

children 

with recent 

illness 

Bangladesh 2007 67.6 1.4 9.9 8.3 11.3 0.1 1.4 100.0 2,467 

Bangladesh 2011 74.8 2.3 4.9 11.2 5.5 0.1 1.1 100.0 3,306 

Congo DR 20071 35.2 7.9 14.7 17.0 12.6 2.5 10.0 100.0 3,299 

Congo DR 2013-14 46.5 5.0 19.4 6.5 17.4 1.8 3.3 100.0 6,802 

Ethiopia 20051 19.1 9.8 26.0 13.1 14.4 4.0 13.6 100.0 3,133 

Ethiopia 2011 30.9 7.4 24.6 9.7 17.4 1.9 8.2 100.0 2,848 

Ghana 20031 32.2 9.4 24.2 12.3 13.3 2.2 6.3 100.0 1,107 

Ghana 2008 31.7 4.7 35.2 5.3 17.0 1.6 4.5 100.0 930 

Haiti 2005-6 28.9 6.8 25.8 8.9 21.3 2.2 6.1 100.0 2,279 

Haiti 2012 30.0 12.5 23.0 11.0 14.8 4.0 4.8 100.0 2,866 

India 2005-6 38.9 6.6 24.8 14.0 10.2 1.6 3.9 100.0 11,717 

Indonesia 20071 45.0 6.6 11.3 13.5 15.1 1.5 7.0 100.0 6,245 

Indonesia 2012 53.5 3.1 17.0 6.8 16.5 0.5 2.6 100.0 6,404 

Kenya 20031 40.6 7.1 9.4 20.0 12.8 1.7 8.4 100.0 2,755 

Kenya 2008-9 37.5 3.6 25.7 10.3 14.5 1.1 7.4 100.0 1,868 

Liberia 2007 40.8 2.9 21.7 8.6 16.8 1.3 7.8 100.0 2,129 

Liberia 2013 36.5 3.9 24.6 5.9 22.9 1.7 4.6 100.0 2,474 

Madagascar 2003-41 41.2 7.7 16.3 15.6 11.0 1.0 7.2 100.0 1,602 

Madagascar 2008-9 33.9 5.6 34.8 8.3 13.2 1.0 3.1 100.0 1,906 

Mali 20061 37.6 5.6 27.0 8.1 14.7 1.7 5.3 100.0 3,409 

Mali 2012-13 37.8 4.7 39.3 2.9 12.6 1.5 1.3 100.0 1,523 

Malawi 20041 33.9 10.9 13.1 11.8 16.3 3.7 10.3 100.0 5,020 

Malawi 2010 48.6 2.8 16.2 7.9 19.5 1.0 4.0 100.0 7,763 

Mozambique 20031 42.6 8.5 16.0 10.1 14.5 2.2 5.9 100.0 3,427 

Mozambique 2011 42.2 2.3 32.9 2.4 17.7 0.6 2.0 100.0 2,268 

Nepal 2006 38.7 3.2 29.1 11.5 11.5 1.2 4.8 100.0 1,339 

Nepal 2011 38.2 3.2 28.5 8.5 16.5 0.7 4.3 100.0 1,421 

Nigeria 2008 46.6 2.4 25.6 5.7 15.4 0.9 3.5 100.0 5,577 

Nigeria 2013 41.5 2.4 32.4 3.6 16.1 0.9 3.0 100.0 5,654 

Pakistan 2006-7 39.6 5.7 14.8 22.5 9.8 1.2 6.4 100.0 3,283 

Pakistan 2012-13 32.5 4.1 17.2 17.4 17.9 1.8 9.1 100.0 5,399 

Rwanda 20051 28.2 11.4 14.7 21.9 10.8 2.6 10.4 100.0 2,871 

Rwanda 2010 36.6 4.9 30.7 5.6 17.6 1.1 3.4 100.0 2,142 

Senegal 20051 29.9 7.0 21.7 11.7 18.1 2.8 8.8 100.0 4,217 

Senegal 2010-11 30.5 3.1 30.3 6.8 23.6 1.2 4.5 100.0 3,769 

Tanzania 2004-51 45.1 2.4 24.5 6.8 16.7 0.8 3.7 100.0 2,428 

Tanzania 2010 42.9 6.2 15.6 11.1 15.8 0.9 7.5 100.0 2,522 

Uganda 2006 37.1 5.5 16.4 9.7 19.7 2.2 9.4 100.0 4,134 

Uganda 2011 38.1 7.4 13.7 10.1 20.1 2.4 8.1 100.0 3,979 

Zambia 2007 34.2 6.1 30.1 5.8 17.7 2.0 4.2 100.0 1,688 

Zambia 2013-14 41.3 4.0 28.0 3.8 18.9 1.4 2.5 100.0 3,991 

1 For 12 surveys, “symptoms of ARI” is defined using a less specific definition (having a cough with rapid breathing, rather than 

having a cough with rapid breathing that is chest-related), because these surveys did not collect information on whether the child's 

rapid breathing was chest-related. 
9 

2.2. Analytic Strategy 

The analysis is primarily descriptive. First, we describe the current landscape of care seeking in MCH 

priority countries, and the differentials by household wealth and place of residence. The confidence 

intervals around point estimates are adjusted for the DHS sample design. 

Second, we examine temporal trends in levels3

 of care seeking from public sources of care, private 

sources of care, and any source of care between the two most recent DHS surveys. Logit regression tested 

if the levels of care seeking changed significantly between surveys. 

Third, we examine trends over time in equity (between children in rural versus urban households, and 

between children in the poorest versus wealthiest quintiles of household wealth). Trends in care seeking 

from public sources and from private sources are presented for rural children and for children in the 

poorest households in Figures 24-40. In these figures, bubble size represents the percentage point 

difference in care seeking coverage between the disadvantaged (rural children and children in the poorest 

households) and advantaged groups (urban and children in the wealthiest households). 

To test whether the differential in care seeking between children in rural versus urban households 

changed significantly between surveys, logit regression models were run on the two combined surveys 

with an indicator for survey year, urban/rural residence, and an interaction term between the two. A 

significant interaction term indicates that the change in care seeking over time differed significantly 

between children in rural versus urban households. Similarly, logit regression models tested whether the 

trend in care seeking over time was different between children in the poorest and wealthiest quintiles, 

with children in the middle three wealth quintiles removed. 

Fourth, in two MCH priority countries (Liberia and Nepal) where subnational USAID-funded 

interventions were implemented to promote appropriate care seeking for childhood illness, logit 

regression examined whether improvements in care seeking behavior between surveys were significantly 

greater in intervention catchment areas than in non-intervention areas. 

Finally, to assess the appropriateness of care that children received during the study period, we compared 

indicators of appropriate care (described in detail below) for children in study countries who received care 

from private sources, public sources, and from any source. All analyses were conducted with STATA 13. 

STATA svy commands generated robust standard error estimates that incorporated the DHS complex 

sample design. 

2.3. Definitions of Indicators 

2.3.1. Place of treatment 

Indicators for place of treatment are based on two standard DHS interview questions asked of female 

respondents who reported that their child under age 5 had an illness in the two weeks preceding the 

survey. These women were asked “Did you seek advice or treatment for the illness from any source?” 

and, if yes, “Where did you seek advice or treatment?” The women were then asked the question 

“Anywhere else?” to identify all sources of care. These questions were asked one time for women who 

reported that their child had either fever or cough, and separately for respondents who reported their child 

had diarrhea. 

 

3 Here and throughout the report, the term level is used interchangeably with the term coverage to refer to the percentage of 

children with recent illness for whom care was sought. 
10 

The majority of tables and figures in this report categorize care seeking into four groups: public sources 

of care, private sources of care, pharmacy only, and any other source. Some tables and figures also use a 

summary indicator to identify children who received any care outside the home for a recent illness. The 

place of treatment indicators are defined below: 

Public sources of care include a public hospital, health center, health post, mobile clinic, 

community health worker, or any other public sector source. 

Private sources of care include a private hospital, doctor, nurse, mobile clinic, private 

community health worker, religious or NGO-run facilities, or other private sector source. 

Pharmacy only indicates children for whom care was sought only from a pharmacy. 

Other sources include a market or informal shop, traditional healer or other non-allopathic 

sources, friend, relative, or any other source of care sought outside the home. 

Any care includes all sources of care included in the four summary groups defined above. 

With the exception of pharmacies, care for a child that is sought from multiple sources (i.e., from both a 

public and private facility) contributes to both categories. However, in some settings, pharmacies are used 

as a primary source of advice and treatment rather than a place for filling a prescription based on advice 

from another category of provider. To differentiate between pharmacy as a primary source of care and as 

a secondary source of medication, the analyses restrict this category to children for whom care was sought 

only from a pharmacy. 

Annex tables provide more detailed information about where children were taken for care for each illness. 

These tables provide the percentage of children who were taken to a public hospital, public peripheral 

health facility (health center, health post, mobile clinic, or other public sector care), private clinic or 

clinician (private hospital, doctor, nurse, mobile clinic, or other private sector care), public community 

health worker, private community health worker, religious or NGO facility, pharmacy, market, informal 

market/shop, non-allopathic source of care (traditional healer), or any other source outside the home 

(friend, relative, other) disaggregated by place of residence (Annex Tables A1-A3), and by household 

wealth (Annex Tables A4-A6). Unless otherwise specified, the child contributes to multiple categories if 

care was sought from multiple sources. 

2.3.2. Background characteristics 

Place of residence. This variable identifies whether the household in which the child’s mother was 

interviewed is in an urban or rural location. The DHS uses the prevailing definitions of urban and rural 

residence in each country. Children in urban locations are generally expected to have better access to care 

and reduced exposure to some infections. However, the benefits of an urban residence depend on the 

economic resources of the household and community. Children in urban slums, for example, are 

particularly vulnerable to childhood illness because of overcrowding, unhygienic surroundings, poverty, 

and the absence of basic health infrastructure (Fernandez, Mondkar, and Mathai 2003; Mutisya et al. 

2010). 

Household wealth quintile. The standard DHS wealth index uses household-level data on assets, 

services, and amenities to rank households according to their level of wealth. The survey population in 

each country was divided into fifths from poorest to wealthiest, based on the distribution of wealth index 

scores. Caregivers in poorer households are expected to face more barriers to accessing care for their sick 

children, who are expected to be more exposed and vulnerable to some infections (UNICEF 2012).
11 

2.3.3. Indicators of appropriate care 

Fever 

The percentage of children who had a finger or heel stick for malaria testing, among children under 

age 5 with fever in the last two weeks. In DHS surveys conducted since roughly 2008, respondents who 

indicated that a child had fever in the two preceding weeks were asked: “At any time during the illness, 

did the child have blood taken from his/her finger or heel for testing?” We examined the percentage of 

children who were tested, among children with recent fever for whom care was sought from a public 

source, private source, and any source. This standard Roll Back Malaria Partnership (RBM) indicator is a 

proxy measure of the extent to which children with fever obtain a parasitological diagnosis when they 

present at a health facility. Since most malaria endemic countries now have policies that require universal 

diagnostic testing for malaria before treatment, this is an important measure of the appropriateness of care 

(MEASURE Evaluation et al.2013).

The percentage of children who received ACT, among children under age 5 with fever in the last 

two weeks who received any antimalarial drugs. For each child who had a fever or cough in the two 

weeks preceding the survey, DHS respondents are asked: “At any time during the illness, did the child 

take any drugs for the illness?” and, if yes, “What drugs did the child take?” Respondents are asked the 

question “Any other drugs?” to identify all drugs given to the child. This indicator is calculated separately 

among children with recent fever for whom care was sought from any public source, private source, and 

any source. This standard RBM indicator measures the extent to which ACT (where ACT is the 

recommended first-line treatment for uncomplicated malaria) is being used instead of another antimalarial 

option. This assumes that an appropriate diagnosis was made. The indicator is a measure of the 

appropriateness of treatment (MEASURE Evaluation et al. 2013). Ideally, in surveys conducted since the 

inception of policies that recommend ACT as first-line antimalarial treatment and in which Plasmodium 

vivax infections are uncommon, ACT should represent almost all antimalarial treatment. 

The percentage of children who received first-line antimalarial therapy, among children under age 

5 with fever in the last two weeks who received any antimalarial drugs. Much like the previous 

indicator, this outcome is intended to assess the appropriateness of treatment that follows a correct 

diagnosis of malaria. Given that recommended treatments for malaria have changed over the course of the 

study period, two indicators are needed for this outcome. To include surveys from the earlier part of the 

study period when ACT may not have been a treatment option, the prevailing first-line antimalarial is 

included. Ideally, all children who are given an antimalarial should receive a first-line antimalarial 

treatment. 

Diarrhea 

The percentage of children who were given ORS, among children under age 5 with diarrhea in the 

last two weeks. For each child who had diarrhea in the two weeks preceding the survey, DHS 

respondents are asked: “Was he/she given any of the following to drink at any time since he/she started 

having the diarrhea: (a) A fluid made from a special packet called [local name for ORS packet]? (b) A 

pre-packaged ORS liquid [in countries that have this type of ORS] (c) A government-recommended 

homemade fluid?” and “What (else) was given to treat the diarrhea?” Interviewers are required to probe to 

record all treatments given to the child. ORS has been the primary treatment for children’s diarrhea since 

the 1980s and is known to reduce diarrhea mortality (WHO 2005). This standard indicator, which 

measures the extent to which ORS is being used to treat diarrhea, is a measure of the appropriateness of 

treatment. Ideally, all children with diarrhea should be treated with ORS. 
12 

The percentage of children who received ORS with zinc, among children under age 5 with diarrhea 

in the last two weeks. Since 2004, WHO and UNICEF have recommended the use of zinc 

supplementation for 10-14 days along with an updated ORS formula with reduced levels of glucose and 

salt to treat children’s diarrhea (WHO 2005). This indicator is based on the same open-ended question 

described above: “What (else) was given to treat the diarrhea?” The indicator, which measures the extent 

to which ORS and zinc are being used to treat diarrhea, is thus a measure of the appropriateness of 

treatment. Ideally, all children with diarrhea should be treated with both ORS and zinc. 

The percentage of children who received antibiotics, among children under age 5 with non-bloody 

diarrhea in the last two weeks. Unlike the indicators above, which describe appropriate care, this 

indicator measures inappropriate care, in which antibiotics are given to children with non-bloody 

diarrhea. The indicator is based on the same DHS question about what drugs the child was given, but is 

restricted to children with non-bloody diarrhea.

2.4. Limitations 

The study has several limitations. The study relies heavily on the respondent’s recall of the care her 

children received for recent illnesses. Such information is subject to recall bias, reporting bias, and 

misclassification. For example, a respondent may not accurately recall the type of medication her child 

was given for fever, or she may misclassify the type of facility to which her child was brought (e.g. 

incorrectly identify a private religious or non-profit clinic as a public clinic because it does not charge 

fees). She may not know where the child was taken if another person took the child to the facility. A 

respondent whose child had severe disease, co-morbidities, or unsuccessful treatment may also 

differentially recall and report on her care seeking behavior. Given that the period of recall is only two 

weeks, we expect most types of bias to be minimal. 

As mentioned, we are interested in care seeking for malaria, pneumonia, and diarrhea but are limited in 

our ability to identify children with clinical or parasitological diagnoses. Instead, we study patterns of 

care seeking among all children with recent fever, symptoms of ARI, and diarrhea. A large number of 

children with symptoms of ARI do not have pneumonia, and some children with fever do not have 

malaria. Thus, some children in our study may not have needed the care or medical treatment we are 

examining, or would like to examine. Such discrepancies are particularly problematic for children with 

symptoms of ARI. While antibiotics are the recommended treatment for pneumonia, antibiotic treatment 

is not necessary for children with some other acute respiratory infections such as the common cold. Since 

we cannot distinguish children with recent pneumonia from children with other acute respiratory 

infections, we cannot evaluate the appropriateness of the treatment provided for these children. 

In order to examine the appropriateness of care for fever and diarrhea, we assume that the child obtained 

drugs or treatment from the source of care identified. However, this may not have been the case. When 

children sought care from both public and private sources, we attribute the drug treatment given to the 

child to both sources, since we cannot identify the true source. This bias will dilute any genuine 

differences in the appropriateness of care received from public versus private sources. 

Finally, the scope of the analysis is limited by the sample size. While we would like to examine patterns 

in appropriateness of care by children’s background characteristics such as wealth, we could not do so 

because of the small number of children in the sample who had the symptoms and who sought care from 

the various providers. 
13 

3. Results 

3.1. Current Care Seeking in USAID MCH Priority Countries 

In this section we examine the most recent data on care seeking available for each MCH priority country. 

Figure 2 shows the percentage of children who received care from any source outside the home, among 

all children under age 5 who experienced any illness symptom (i.e., fever, symptoms of ARI, or diarrhea) 

in the two weeks preceding the interview. In Asian MCH priority countries, care seeking from any source 

ranged from 69 percent in Nepal to 87 percent in Indonesia while in African MCH priority countries, care 

seeking ranged from 33 percent in Ethiopia to 84 percent in Uganda. In Madagascar, Ethiopia, and Haiti, 

care outside the home was sought for less than half of children under age 5 with recent illness. 

Figure 2. Percentage of children who received care from any source for recent illness, USAID MCH 

priority countries 

Note: The figure presents the prevalence of care seeking from any source among children with reported fever, diarrhea, or symptoms of ARI in the two 

weeks preceding the survey. Here and in all subsequent figures, LAC refers to Latin America and the Caribbean. 

Figures 3, 4, and 5 highlight the percentage of children who received care from any public source, private 

source, a pharmacy only, and from any other source for children with recent fever, recent symptoms of 

ARI, and recent diarrhea. Since children can contribute to multiple categories if care was sought from 

multiple sources, the sum of the percentages across the four groupings in most cases yields a slightly 

larger number than the total percentage of children for whom care was sought. 

Care seeking from public sources is consistently more prevalent in African MCH priority countries than 

in Asian MCH priority countries (see Figures 3-5). For fever, for example, care seeking from public 

sources in Africa ranges from 17 percent of children with recent fever in Ethiopia to 68 percent of 

children with recent fever in Zambia, while in Asian MCH priority countries care seeking from public 

sources ranges from 9 percent in Pakistan and Bangladesh to 25 percent in Indonesia (see Figure 3). 

In contrast, care seeking from private sources is consistently more prevalent in Asian MCH priority 

countries than in African MCH priority countries. In Asia, care seeking from private sources ranges from 

24 percent of children with recent fever in Nepal to 71 percent in Pakistan, while in African MCH priority 

countries the prevalence of care seeking from private sources is well under 20 percent in all countries 

87.4

81.3

75.0 75.3

69.0

83.7

77.8 74.7 74.5 73.6 73.3

67.6 63.3 62.6 61.0

54.6 54.4 51.9

44.2

32.7

47.7

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14 

with the exception of Uganda (48 percent). Use of the private sector is less than 5 percent in Zambia, 

Mozambique, Mali, and Rwanda. Appendix Figures 1-3 highlight the public-private differential. 

The use of pharmacies as the sole source of care for children’s illness ranges widely across MCH priority 

countries. In nine countries, a pharmacy was the only source of advice or treatment for a recent fever for 

less than 5 percent of children, compared with 10 percent to 20 percent in six countries, 28 percent in 

Nepal, and 35 percent in Nigeria. In these cases, the pharmacy was the source of advice or treatment, 

although this does not necessarily mean that the child was taken to the pharmacy for treatment. The 

caregiver could have visited the pharmacy, described the child’s symptoms, and received medication. 

Other sources of care include a market/informal shop, traditional healer or other non-allopathic sources, 

friends, relatives, and other sources. In three countries—Indonesia, Liberia, and Mali—more than 10 

percent of children with recent fever were taken to one of these sources of care. 

The patterns of care seeking for children’s illnesses are quite similar across the three illnesses (see Figures 

3-5). For all three, Uganda is the only African country in which care seeking from private facilities is 

more prevalent than from public facilities. Nigeria is noteworthy for its widespread use of pharmacies as 

the single source of care for children’s illness, and Mali for its use of other sources of care. 

Figure 3. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent fever, USAID MCH priority countries 

Note: Public sources include: public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private 

sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Since children for whom care was sought from multiple sources contribute to each 

source’s total, the total percentage may exceed the percentage of children who received any care. 

25

9 9 13 19

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51 52

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15 

Figure 4. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent symptoms of ARI, USAID MCH priority countries 

Note: Public sources include: public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private 

sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Children for whom care was sought from multiple sources contribute to each source’s 

total, so the total percentage may exceed the percentage of children who received any care. 

Figure 5. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent diarrhea, USAID MCH priority countries 

Note: Public sources include: public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private 

sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Children for whom care was sought from multiple sources contribute to each source’s 

total, so the total percentage may exceed the percentage of children who received any care. 

3.1.1. Place of residence and care seeking 

Figures 6, 7, and 8 present patterns in care seeking for fever, symptoms of ARI, and diarrhea, 

respectively, disaggregated by place of residence. There is strikingly little variation in the patterns of care 

seeking between children in urban and rural households. 

In the majority of countries there is little difference in the level of care seeking from public sources for 

fever between urban and rural households (see Figure 6). In three countries—Mozambique, Senegal, and 

Mali—the level of care seeking from public sources for fever is higher for children in urban households 

27

9 12 12

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16 

compared with rural households, and the confidence intervals between estimates do not overlap4 (see 

Appendix Table A7 for the confidence intervals). In contrast, the level of care seeking from public 

sources in DR Congo is higher among children in rural households compared with urban households, with 

non-overlapping 95 percent confidence intervals. 

There is a more consistent differential between children in urban and rural households in the level of care 

seeking from private sources. In 13 countries—more than half of the MCH priority countries—the level 

of care seeking for fever from private sources is higher in urban areas than in rural areas, with nonoverlapping confidence intervals. 

The percentage of children with recent fever for whom care was sought only from a pharmacy is similar 

in urban and rural households. However, the level of care seeking from a pharmacy in Indonesia, 

Senegal, and Mali is higher among children in urban areas than in rural areas, with non-overlapping 

confidence intervals. 

Levels of care seeking for fever from other sources are approximately the same for children in urban and 

rural households. However, in Indonesia, Nigeria, Mozambique, and Madagascar, care seeking for fever 

from other sources is higher among children in rural households, with non-overlapping confidence 

intervals. 

Figure 6. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent fever by place of residence, USAID MCH priority countries 

Note: Public sources include: public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private 

sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Since children for whom care was sought from multiple sources contribute to each 

source’s total, the total percentage may exceed the percentage of children who received any care. Please refer to Table 1 for a listing of DHS country 

codes and corresponding country names. 

The general patterns of care seeking described for children with recent fever also apply to care seeking for 

symptoms of ARI and diarrhea, as depicted in Figures 7 and 8. The confidence intervals around estimates 

are available in Appendix Table A7. 

 

4 If estimates for different sub-populations have non-overlapping confidence intervals, they are necessarily significantly different. 

If they have overlapping confidence intervals, it is not necessarily true that they are not significantly different. In some cases 

where the confidence intervals between two point estimates overlap slightly, a statistical test is required to identify a significant 

difference between the two estimates. 

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Pharmacy Only

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Public
17 

Figure 7. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent symptoms of ARI by place of residence, USAID MCH priority 

countries 

Note: Public sources include: public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private 

sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy Only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Since children for whom care was sought from multiple sources contribute to each 

source’s total, the total percentage may exceed the percentage of children who received any care. Please refer to Table 1 for a listing of DHS country 

codes and corresponding country names. Care seeking estimates for urban areas in the Ghana 2008 DHS and the Mali 2012-13 DHS are based on 

25-49 unweighted cases and should be interpreted with caution. 

Figure 8. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent diarrhea by place of residence, USAID MCH priority countries 

Note: Public sources include: public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private 

sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Since children for whom care was sought from multiple sources contribute to each 

source’s total, the total percentage may exceed the percentage of children who received any care. Please refer to Table 1 for a listing of DHS country 

codes and corresponding country names. 

3.1.2. Household wealth and care seeking 

Interesting patterns emerge when we examine care seeking by household wealth quintile. Figure 9 shows 

patterns in care seeking for fever across levels of household wealth, while Figures 10 and 11 show these 

patterns for symptoms of ARI and diarrhea, respectively. The patterns of care seeking for symptoms of 

ARI and diarrhea across levels of household wealth are very similar to those described for fever. 

Figure 9 shows that in nearly all MCH priority countries, the use of private sources of care for fever 

increases incrementally with increasing household wealth. The association between wealth and the use of 

public sources of care is more varied. In Indonesia, Nepal, Uganda, and Liberia, for example, the use of 

public facilities is negatively associated with household wealth. In these countries, as the level of wealth 

0

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2010

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2008-9

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2011

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Asia Africa LAC

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Pharmacy only

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18 

increases, care seeking from public sources decreases. In other countries such as Mozambique, Senegal, 

Mali, Rwanda, and Ethiopia, care seeking from public sources for fever increases with increasing 

household wealth. In Pakistan, Bangladesh, and Tanzania, care seeking from public sources is similar 

across all wealth quintiles. 

In most countries, there is no clear association between household wealth and the use of a pharmacy as 

the only source of care for fever. However, in DR Congo, pharmacy use increases with increasing 

household wealth. Finally, there is a negative association between household wealth and the use of other 

sources of care in Indonesia, Liberia, Nigeria, and Mali. The use of other sources of care is highest among 

children in the poorest households. Appendix Table A8 shows the coverage estimates included in Figures 

9, 10, and 11, with confidence intervals. 

Figure 9. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent fever by wealth quintile, USAID MCH priority countries 

Note: Public sources include public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private sources 

include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Since children for whom care was sought from multiple sources contribute to each 

source’s total, the total percentage may exceed the percentage of children who received any care. Please refer to Table 1 for a listing of DHS country 

codes and corresponding country names. 

Figure 10. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent symptoms of ARI by wealth quintile, USAID MCH priority 

countries 

Note: Public sources include: public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private 

sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy Only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Since children for whom care was sought from multiple sources contribute to each 

source’s total, the total percentage may exceed the percentage of children who received any care. Care seeking estimates based on fewer than 25 

unweighted cases are not shown. For the following countries, estimates are based on 25-49 unweighted cases and should be interpreted with caution: 

Ghana 2008 (quintiles 2,4), Liberia 2013 (quintiles 4,5), Mali 2012-13 (quintiles 1-5), Mozambique 2011 (quintiles 1,2,4,5), Nepal 2011 (quintiles 2-5), 

Nigeria 2013 (quintile 5), Rwanda 2010 (quintile 4). Please refer to Table 1 for a listing of DHS country codes and corresponding country names. 

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19 

Figure 11. Percentage of children who received care from public, private, pharmacy, and other 

sources among children with recent diarrhea by wealth quintile, USAID MCH priority countries 

Note: Public sources include public hospital, health center, health post, mobile clinic, community health worker, and other public sector; Private sources 

include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; 

Pharmacy only highlights children for whom care was sought only from a pharmacy; Other sources include: market/informal shop, traditional healer or 

other non-allopathic sources, friend, relative, or other sources. Since children for whom care was sought from multiple sources contribute to each 

source’s total, the total percentage may exceed the percentage of children who received any care. Care seeking estimates for children in the wealthiest 

quintile in Ghana 2008 are based on 25-49 unweighted cases and should be interpreted with caution. Please refer to Table 1 for a listing of DHS 

country codes and corresponding country names. 

3.1.3. Co-occurrence of symptoms and care seeking 

Figure 12 shows the prevalence of any care seeking for fever, by co-occurrence with other symptoms. In 

four of the five Asian countries—Indonesia, Pakistan, Bangladesh, and India—levels of care seeking are 

higher for children who had both fever and symptoms of ARI than for children who had only fever, with 

non-overlapping confidence intervals between estimates. In most African countries a similar pattern is 

found, but it is weaker. The confidence intervals overlap in all but four of 14 African countries, and in 

Haiti. Having diarrhea in addition to fever did not increase the likelihood of care seeking. In five of the 21 

countries studied—Pakistan, Malawi, Nigeria, Kenya, and Rwanda—care seeking was higher for children 

with all three illness symptoms than for children who had only fever, with no overlap in the confidence 

intervals. 

Figure 12. Percentage of children who received care from any source among children with recent 

fever, by the co-occurrence of other illness symptoms, USAID MCH priority countries 

Note: Care seeking estimates for children with all three illnesses in Bangladesh 2011 and Ghana 2008, and for children with fever and symptoms of 

ARI only in Mali 2012-13 are based on 25-49 unweighted cases and should be interpreted with caution. Care seeking estimates for children with all 

three illnesses in Mali 2012-13 are based on fewer than 25 cases and are not shown. Please refer to Table 1 for a listing of DHS country codes and 

corresponding country names. 

0

10

20

30

40

50

60

70

80

90

100

110

12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345 12345

ID

2012

PK

2012-13

BD

2011

IA

2005-6

NP

2011

UG

2011

LB

2013

MW

2010

TZ

2010

NG

2013

ZM

2013-14

GH

2008

KE

2008-9

MZ

2011

CD

2013-14

SN

2010-11

ML

2012-13

RW

2010

MD

2008-9

ET

2011

HT

2012

Asia Africa LAC

Other

Pharmacy only

Private

Public

0

10

20

30

40

50

60

70

80

90

100

ID

2012

PK

2012-13

BD

2011

IA

2005-6

NP

2011

UG

2011

LB

2013

MW

2010

TZ

2010

NG

2013

ZM

2013-14

GH

2008

KE

2008-9

MZ

2011

CD

2013-14

SN

2010-11

ML

2012-13

RW

2010

MD

2008-9

ET

2011

HT

2012

Asia Africa LAC

Fever only Fever and symtoms of ARI only Fever and diarrhea only All three
20 

Figure 13 shows the prevalence of any care seeking for symptoms of ARI, by co-occurrence with other 

symptoms. Compared with children who experienced only symptoms of ARI, children who experienced 

both ARI symptoms and fever were more likely to have received care in 11 of the 21 countries with no 

overlap in the confidence intervals. Having diarrhea in addition to symptoms of ARI did not increase the 

likelihood of care seeking, and the combination of all three illnesses did not increase the likelihood of 

care seeking beyond the likelihood among children with symptoms of ARI and fever. 

Figure 13. Percentage of children who received care from any source among children with recent 

symptoms of ARI, by the co-occurrence of other illness symptoms, USAID MCH priority countries 

Note: Care seeking estimates for children with all three illnesses in Bangladesh 2011 and Ghana 2008, and for children with symptoms of ARI and 

fever only in Mali 2012-13 are based on 25-49 unweighted cases and should be interpreted with caution. Care seeking estimates for children with all 

three illnesses in Mali 2012-13 are based on fewer than 25 cases and are not shown. Please refer to Table 1 for a listing of DHS country codes and 

corresponding country names. 

Figure 14 shows the prevalence of any care seeking for diarrhea, by co-occurrence with other symptoms. 

Compared with children who experienced only diarrhea, children who experienced both diarrhea and 

fever were more likely to have received any care, with non-overlapping confidence intervals in 11 of the 

21 countries. Having symptoms of ARI in addition to diarrhea did not increase the likelihood of care 

seeking, and the combination of all three illnesses did not increase the likelihood of care seeking beyond 

the likelihood among children with diarrhea and fever. 

Figure 14. Percentage of children who received care from any source among children with recent 

diarrhea, by the co-occurrence of other illness symptoms, USAID MCH priority countries 

Note: Care seeking estimates for children with all three illnesses in Bangladesh 2011 and Ghana 2008 are based on 25-49 unweighted cases and 

should be interpreted with caution. Care seeking estimates for children with all three illnesses in Mali 2012-13 are based on fewer than 25 cases and 

are not shown. Please refer to Table 1 for a listing of DHS country codes and corresponding country names. 

0

10

20

30

40

50

60

70

80

90

100

ID

2012

PK

2012-13

BD

2011

IA

2005-6

NP

2011

UG

2011

LB

2013

MW

2010

TZ

2010

NG

2013

ZM

2013-14

GH

2008

KE

2008-9

MZ

2011

CD

2013-14

SN

2010-11

ML

2012-13

RW

2010

MD

2008-9

ET

2011

HT

2012

Asia Africa LAC

Symptoms of ARI only Symptoms of ARI and fever only Symptoms of ARI and diarrhea only All three

0

10

20

30

40

50

60

70

80

90

100

ID

2012

PK

2012-13

BD

2011

IA

2005-6

NP

2011

UG

2011

LB

2013

MW

2010

TZ

2010

NG

2013

ZM

2013-14

GH

2008

KE

2008-9

MZ

2011

CD

2013-14

SN

2010-11

ML

2012-13

RW

2010

MD

2008-9

ET

2011

HT

2012

Asia Africa LAC

Diarrhea only Diarrhea and fever only Diarrhea and symtoms of ARI only All three
21 

3.2. Trends in Care Seeking 

3.2.1. National trends in care seeking for fever 

In this section we examine trends in care seeking among children under age 5 between the most recent 

two DHS surveys in the 20 MCH priority countries with two recent surveys available. Figures 15, 16, and 

17 present trends in care seeking for fever from any source, public sources, and private sources. Logit 

regression models were run to determine whether the change in coverage between surveys was 

statistically significant. In the figures, a solid line indicates a significant change between surveys, while a 

dotted line indicates no change. Detailed regression results are not shown. 

Figure 15 presents the trend in coverage of care seeking from any source for recent fever. In 11 of the 20 

countries examined, there was no change in the level of care seeking from any source between the two 

most recent surveys. In four countries—Ethiopia, Nepal, Nigeria, and Zambia—there was a significant 

increase in coverage, while in five countries—Kenya, Mali, Malawi, Rwanda, and Tanzania—there was a 

significant decrease. 

Figure 15. Trend in coverage of care seeking from any source among children under age 5 with 

fever in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). Please refer 

to Table 1 for a listing of DHS country codes and corresponding country names. 

Figure 16 presents the trend between surveys in coverage of care seeking from public sources for fever. In 

14 of the 20 countries there was no change in this level. In six African countries—Kenya, Malawi, 

Mozambique, Rwanda, Uganda, and Zambia—there was a significant increase in coverage. No significant 

decreases in care seeking from public sources for fever were observed in any country. 

BD

CD

ET

GH

HT

ID

KE

LB

MD

ML

MW

MZ

NG

NP

PK

RW

SN

TZ UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year
22 

Figure 16. Trend in coverage of care seeking from public sources among children under age 5 

with fever in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). Please refer 

to Table 1 for a listing of DHS country codes and corresponding country names. 

Figure 17 shows the trend in coverage of care seeking from private sources for fever. In seven African 

countries there was a significant decrease in care seeking from private sources for fever. In six 

countries—Ethiopia, Malawi, and four Asian countries—there was a significant increase in care seeking 

from private sources for fever. 

Figure 17. Trend in coverage of care seeking from private sources among children under age 5 

with fever in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). Please refer 

to Table 1 for a listing of DHS country codes and corresponding country names. 

3.2.2. National trends in care seeking for symptoms of ARI 

For symptoms of ARI, we examine the trend in care seeking in the eight countries with complete 

information on symptoms of ARI in both DHS surveys. In three of the countries—Nigeria, Nepal, and 

Haiti—care seeking from any source for symptoms of ARI increased significantly (see Figure 18). No 

significant decreases in care seeking for symptoms of ARI were observed in any country. 

BD

CD

ET

GH

HT

ID

KE

LB

MD ML

MW

MZ

NP NG

PK

RW

SN

TZ

UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year

BD

CD

ET

GH

HT

ID

KE LB

MD

ML

MW

MZ NG

NP

PK

RW

SN TZ

UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year
23 

Figure 18. Trend in coverage of care seeking from any source among children under age 5 with 

symptoms of ARI in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). The figure is 

restricted to USAID MCH priority countries with complete information on symptoms 

of ARI in two surveys. Please refer to Table 1 for a listing of DHS country codes and 

corresponding country names. 

In seven of the eight countries there was no change between surveys in the coverage of care seeking from 

public sources for symptoms of ARI (see Figure 19). In Liberia, the level of care seeking from public 

sources for symptoms of ARI decreased significantly. 

Figure 19. Trend in coverage of care seeking from public sources among children under age 5 

with symptoms of ARI in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). The figure is 

restricted to USAID MCH priority countries with complete information on symptoms 

of ARI. Please refer to Table 1 for a listing of DHS country codes and corresponding 

country names. 

In two of the eight countries—Uganda and Bangladesh—there was a significant increase in care seeking 

from private sources for symptoms of ARI (see Figure 20). There was no change in the remaining six 

countries. 

BD

HT

LB

NG NP

PK

UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year

BD HT

LB

NG

NP

PK

UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year
24 

Figure 20. Trend in coverage of care seeking from private sources among children under age 5 

with symptoms of ARI in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). The figure is 

restricted to USAID MCH priority countries with complete information on symptoms 

of ARI. Please refer to Table 1 for a listing of DHS country codes and corresponding 

country names. 

3.2.3. National trends in care seeking for diarrhea 

As Figure 21 shows, there have been substantial improvements in recent years in the coverage of care 

seeking for diarrhea, with statistically significant increases in care seeking from any source in 13 of the 20 

countries studied. No significant decreases in care seeking for diarrhea were observed in any country. 

Figure 21. Trend in coverage of care seeking from any source among children under age 5 with 

diarrhea in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). Please refer 

to Table 1 for a listing of DHS country codes and corresponding country names. 

BD

HT

LB

NG

NP

PK

UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year

BD

CD

ET

GH

HT

ID

KE

LB

MD

ML

MW MZ

NG

NP

PK

RW

SN

TZ

UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year
25 

Figure 22 highlights the trend in care seeking from public sources for children’s diarrhea. Coverage 

increased significantly in nine of 20 countries, all in Africa. There were no significant decreases in 

coverage in any country. 

Figure 22. Trend in coverage of care seeking from public sources among children under age 5 

with diarrhea in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). Please refer to 

Table 1 for a listing of DHS country codes and corresponding country names. 

Figure 23 shows the trend in care seeking from private sources for children’s diarrhea. In seven of 20 

countries—DR Congo, Ethiopia, Malawi and all four Asian countries—there was a statistically 

significant increase in coverage. Bangladesh showed the greatest increase in coverage, from 11 percent in 

2007 to 41 percent in 2011. There were significant decreases in the level of care seeking from private 

sources in Liberia, Nigeria, Tanzania, and Zambia. In the other nine countries, there was no significant 

change in the level of care seeking from private sources for children’s diarrhea. 

Figure 23. Trend in coverage of care seeking from private sources among children under age 5 

with diarrhea in the two weeks preceding the survey, USAID MCH priority countries 

Note: A solid line indicates a significant change between surveys, while a dotted line 

indicates no significant change (detailed regression results not shown). Please refer to 

Table 1 for a listing of DHS country codes and corresponding country names. 

BD

CD

ET

GH

HT

KE ID

LB

MD ML

MW

MZ

NG

NP

PK

RW

SN

TZ

UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year

BD

GH ET CD

HT

ID

KE

LB

MD

ML

MW

MZ

NG NP

PK

SNRW

TZ

UG

ZM

0 20 40 60 80 100

Percentage of children for whom care was sought

2002 2004 2006 2008 2010 2012 2014 Survey Year
26 

3.3. Equity in Recent Trends in Care Seeking 

There is a consensus that to have the greatest impact on child survival and wellness, interventions for 

childhood illness should focus on the poorest, most vulnerable children (UNICEF 2012). In recent years, 

many countries, health ministries, and child health programs have focused on reaching these vulnerable 

groups with strategies such as IMCI, iCCM, and private-public partnerships. Policymakers could benefit 

from knowing the extent to which these efforts have raised the level of care seeking in vulnerable 

populations, and narrowed the equity gap between children in rural compared with urban areas, and 

between children in the poorest households compared with the wealthiest. 

To address these questions, we examine recent trends in care seeking specifically among children in rural 

households and among children in households in the poorest wealth quintile. Figures 24-32 depict trends 

in care seeking from public and private sources among rural children. A solid line indicates a significant 

change in care seeking coverage between surveys, while a dotted line indicates no change. The bubble 

size represents the percentage point difference in care seeking between rural and urban children as an 

indicator of equity. Larger bubble size indicates greater inequity between rural and urban children. 

Bubbles in a solid color identify a rural disadvantage, while clear bubbles identify an advantage among 

rural children. An “E” identifies countries with a significant change in inequity between surveys. 

Similarly, Figures 33-40 present trend lines for care seeking from public and private sources among the 

children in the poorest households compared with children in households in the wealthiest quintile. 

Appendix Figures A4-A9 present trends in care seeking from any source among rural children and among 

children in households in the poorest quintile. 

3.3.1. Trends in care seeking among children in rural households 

Fever 

Similar to the national trend, in 13 of 20 countries there was no change in the percentage of rural children 

for whom care was sought from a public source between surveys (see Appendix Table A9 for detailed 

results). Figure 24 highlights the trend in the seven countries with a significant change in either coverage 

or equity between surveys. In those seven countries, all in Africa, care seeking from public sources 

increased between surveys for rural children. 

As indicated by the bubble color and size, in all surveys except Uganda 2012 and Liberia 2013, coverage 

of care seeking was lower among rural than urban children (see Figure 24, where an increasing 

differential is indicated by increasing colored bubble size). In Malawi and Liberia, the rural-urban equity 

gap narrowed significantly between surveys (identified by an “E” in Figure 24). This indicated that the 

increase in care seeking from public sources was more concentrated among rural children. In Liberia, 

coverage in rural areas appears to have actually surpassed coverage in urban areas. 
27 

Figure 24. Trend in coverage of public care seeking for fever among children in rural households 

and rural-urban gap in care seeking equity, USAID MCH priority countries 

Since the patterns in care seeking from private sources are quite different in Asia and Africa, the trends 

are presented separately in Figures 25 and 26, respectively. In all four Asian countries with two recent 

surveys available, there was a significant increase in care seeking from private sources for fever (see 

Figure 25). In Bangladesh, the rural-urban equity gap narrowed significantly due to a dramatic increase in 

care seeking from private sources among children in rural households. 

Figure 25. Trend in coverage of private care seeking for fever among children in rural households 

and rural-urban gap in care seeking equity, USAID Asian MCH priority countries and Haiti 

Kenya

Liberia

Mozambique

Rwanda

Uganda

Malawi

Zambia

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a public source for fever

Survey Year

E

E

Bangladesh

Indonesia

Nepal

Pakistan

0

10

20

30

40

50

60

70

80

90

100

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Percentage of children for whom care was sought from a private source for fever

Survey Year

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys
28 

As Figure 26 shows, the pattern is very different in the 15 African MCH priority countries. Eight 

countries showed a significant decline in coverage of care seeking from private sources for fever among 

children in rural households. Two countries, Malawi and Ethiopia, showed a significant increase in 

coverage among rural children. In the remaining five countries, there was no change between the two 

surveys. 

The rural-urban equity gap in care seeking from private sources for fever did not narrow in any African 

country. In Zambia, Liberia, and Uganda, the gap widened significantly. In Zambia and Liberia, the 

decline in coverage of care seeking from private sources was more concentrated among rural children. In 

Uganda, by contrast, the increase in use of private sources of care for fever was more concentrated among 

urban children. 

Figure 26. Trend in coverage of private care seeking for fever among children in rural households and 

rural-urban gap in care seeking equity, African USAID MCH priority countries 

Note: Due to low levels of private care seeking in African countries, the figure ranges from 0 to 50 percent coverage, rather than 0 to 100 percent coverage. 

Symptoms of ARI 

When we examine care seeking from public sources for symptoms of ARI among children in rural 

households, we find no evidence of change in coverage between the two surveys in any of the eight 

countries with complete information to identify children with symptoms of ARI. In Bangladesh and 

Liberia, however, there was a significant reduction in the rural-urban equity gap (see Figure 27). In both 

countries, unfortunately, this was due to a significant decline in care seeking from public sources among 

urban children, along with no change in coverage among rural children (see Appendix Table A9 for 

detailed results). 

DR Congo

Ethiopia

Kenya

Liberia

Nigeria

Rwanda

Tanzania Zambia

Senegal

Malawi

Uganda

-10

10

30

50

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a private source for fever

Survey Year

E

E

E

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys
29 

Figure 27. Trend in coverage of public care seeking for symptoms of ARI among children in rural 

households and rural-urban gap in care seeking equity, USAID MCH priority countries 

We also examine care seeking from private sources for symptoms of ARI among children in rural 

households. In both Nepal and Bangladesh, the percentage of rural children for whom care was sought 

from a private provider increased significantly, although there were no significant changes in the ruralurban equity gap (see Figure 28). 

Figure 28. Trend in coverage of private care seeking for symptoms of ARI among children in rural 

households and rural-urban gap in care seeking equity, USAID Asian MCH priority countries and 

Haiti 

Three of the six African MCH priority countries showed a significant decline in the percentage of rural 

children for whom care was sought from a private provider for symptoms of ARI (see Figure 29). In all 

three countries, the decline in coverage was disproportionately concentrated among rural children, thus 

leading to an even greater rural disadvantage. 

Liberia

Bangladesh

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a public source for symptoms of ARI

Survey Year

E

E

Bangladesh

Nepal

0

10

20

30

40

50

60

70

80

90

100

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Percentage of children for whom care was sought from a private source for 

symptoms of ARI

Survey Year

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys
30 

Figure 29. Trend in coverage of private care seeking for symptoms of ARI among children in rural 

households and rural-urban gap in care seeking equity, USAID African MCH priority countries 

Note: Due to low levels of private care seeking in African countries, the figure ranges from 0 to 50 percent coverage, rather than 0 to 100 percent 

coverage. 

Diarrhea 

The trend in coverage of care seeking from public sources for diarrhea among children in rural 

households closely parallels the overall national trends, with significant increases in coverage in 10 of the 

20 countries with data (see Figure 30). In seven of these countries, according to the most recent survey, 

care seeking from public sources was higher among rural households than urban households. In three 

countries—Ghana, DR Congo, and Liberia—there was a significant increase in the rural-urban equity gap 

between surveys. This favored rural areas where the increase in coverage was disproportionately 

concentrated. 

Liberia

Nigeria

Zambia

-10

10

30

50

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a private source for symptoms of ARI

Survey Year

E E

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys
31 

Figure 30. Trend in coverage of public care seeking for diarrhea among children in rural 

households and rural-urban gap in care seeking equity, USAID MCH priority countries 

In four Asian study countries, there was a significant increase between surveys in the percentage of rural 

children for whom care was sought from a private provider for diarrhea (see Figure 31). In Indonesia, 

there was a significant change in the rural-urban equity gap, but favoring rural areas where the increase in 

coverage was disproportionately concentrated. 

Figure 31. Trend in coverage of private care seeking for diarrhea among children in rural households 

and rural-urban gap in care seeking equity, Asian USAID MCH priority countries and Haiti 

Liberia

DR Congo

Ghana

Kenya

Mali

Mozambique

Rwanda

Senegal

Malawi

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Survey Year

E

E

E

Percentage of children for whom care was sought from a public source for diarrhea

Bangladesh

Indonesia

Nepal

Pakistan

0

10

20

30

40

50

60

70

80

90

100

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Survey Year

E

Percentage of children for whom care was sought from a 

private source for diarrhea

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys
32 

In the majority of African countries, there was no significant change between surveys in the percentage of 

rural children for whom care was sought from a private provider for diarrhea. In Liberia, Zambia, and 

Tanzania, the percentage decreased while in Malawi and Ethiopia the percentage increased (see 

Figure 32). 

In three African countries—Liberia, Zambia, and DR Congo—there was a significant change in the ruralurban equity gap between surveys with an increase in the rural disadvantage. In Liberia and Zambia, a 

decline in use of private sources of care was concentrated among rural children, while in DR Congo an 

increase in use of private sources of care was concentrated among urban children. 

Figure 32. Trend in coverage of private care seeking for diarrhea among children in rural households and 

rural-urban gap in care seeking equity, African USAID MCH priority countries 

Note: Due to low levels of private care seeking in African countries, the figure ranges from 0 to 50 percent coverage, rather than 0 to 100 percent coverage. 

3.3.2. Trends in care seeking among children in households in the poorest wealth quintile 

Fever 

In 13 countries, there was no change between surveys in the level of care seeking for fever from public 

sources among children in the poorest wealth quintile (see Appendix A10 for detailed results). Figure 33 

presents the trend in the percentage of poorest-quintile children for whom care for fever was sought from 

any source for the seven countries with a significant change in coverage or equity. All seven countries 

showed a significant increase between surveys in care seeking from public sources among children in the 

poorest wealth quintile. 

The level of equity in coverage of care seeking between children in the poorest and wealthiest wealth 

quintiles changed significantly in two countries, Liberia and Indonesia. In both, the increase in care 

seeking was disproportionately concentrated in poorest-quintile children (see Appendix Table A10). 

According to the most recent survey, coverage of care seeking from public sources in both countries was 

higher among the children in the poorest households than among children in the wealthiest households. 

Ethiopia

Liberia

Nigeria

Tanzania

Zambia

Malawi

DR Congo

-10

10

30

50

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a private source 

for diarrhea

Survey Year

E

E

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

U/R gap between surveys

- - - Indicates no significant change in the 

level of care seeking among rural children 

between surveys 

 Indicates a significant change in the 

level of care seeking among children in the 

poorest quintile between surveys
33 

Figure 33. Trend in coverage of public care seeking for fever among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH priority 

countries 

In two of the four Asian countries studied—Bangladesh and Indonesia—the percentage of poorestquintile children for whom care was sought from a private provider increased significantly between 

surveys. The wealth equity gap in care seeking decreased significantly in Bangladesh (see Figure 34). 

Figure 34. Trend in coverage of private care seeking for fever among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, Asian USAID MCH priority 

countries and Haiti 

Kenya

Liberia

Mozambique

Rwanda

Malawi

Zambia

Indonesia

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a public source for 

fever

Survey Year

E

E

Bangladesh

Indonesia

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a private source for fever

Survey Year

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 
34 

In contrast, in six of the seven African countries with a significant change between surveys, there was a 

decline in the percentage of poorest-quintile children for whom care was sought from a private provider 

for fever (see Figure 35). Only in Malawi was there an increase in the percentage of poorest-quintile 

children for whom care for fever was sought from a private provider. 

In three countries—Senegal, Liberia, and Zambia—there was a significant increase in the disadvantage 

among poor children between surveys. This was driven by decreases in the level of care seeking from 

private sources for fever among poorest-quintile children. 

Figure 35. Trend in coverage of private care seeking for fever among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, African USAID MCH priority 

countries 

Note: Due to low levels of private care seeking in African countries, the figure ranges from 0 to 50 percent coverage, rather than 0 to 100 percent coverage. 

Symptoms of ARI 

Among children in the poorest wealth quintile in the eight countries with complete information to identify 

children with symptoms of ARI, care seeking from public sources for symptoms of ARI increased 

significantly in Nigeria and Haiti but did not change in the other six countries (see Figure 36). 

In Liberia, there was a significant change in the wealth equity gap, with higher levels of care seeking from 

public sources among poorest-quintile children in the most recent survey (see Figure 36). This shift was 

driven by a significant decline in care seeking from public sources among children in the wealthiest 

quintile (see Appendix Table A10). 

Kenya

Liberia

Tanzania Nigeria

Malawi

Senegal

Zambia

-10

10

30

50

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a private source for fever

Survey Year

E

E

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 
35 

Figure 36. Trend in coverage of public care seeking for symptoms of ARI among children in 

poorest-quintile households and poorest-wealthiest quintile gap in care seeking equity, USAID 

MCH priority countries 

Note: For the following countries, care seeking estimates for children in the wealthiest quintile are based on 25-49 unweighted cases and should be 

interpreted with caution: Liberia 2007, Liberia 2013, Nigeria 2008, Nigeria 2013, Haiti 2005-6. 

Among children in the poorest wealth quintile, the level of care seeking from private sources changed 

significantly in two of the eight countries with complete information to identify children with symptoms 

of ARI (see Figure 37). In Bangladesh, the percentage of poorest-quintile children for whom care was 

sought from a private provider increased significantly between surveys, from 13 percent to 42 percent. In 

contrast, in Zambia the coverage of care seeking from private sources for symptoms of ARI dropped 

significantly between surveys for children in the poorest quintile, from 13 percent to 2 percent. In 

Zambia, the decrease in coverage of care seeking from private sources among poorest-quintile children 

coincided with a significant increase in the wealth equity gap. 

Figure 37. Trend in coverage of private care seeking for symptoms of ARI among children in 

poorest-quintile households and poorest-wealthiest quintile gap in care seeking equity, USAID 

MCH priority countries 

Note: For the following countries, care seeking estimates for children in the wealthiest quintile are based on 25-49 unweighted cases and should be 

interpreted with caution: Bangladesh 2007, Zambia 2007. 

Liberia

Nigeria

Haiti

-20

0

20

40

60

80

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a public 

source for symptoms of ARI

Survey Year

E

Bangladesh

Zambia

-20

0

20

40

60

80

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a private 

source for symptoms of ARI

Survey Year

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 
36 

Diarrhea 

Among children in the poorest wealth quintile, there was a significant increase in coverage of care 

seeking for diarrhea from public sources in 10 of the 20 countries (see Figure 38). In seven of those 

countries, according to the most recent survey, the level of care seeking from public sources was higher in 

poorest-quintile households than in wealthiest-quintile households. 

In Madagascar, Liberia, and DR Congo, the change in wealth equity in care seeking from public sources 

between surveys was statistically significant. In Liberia and DR Congo, the change resulted in higher 

coverage among the children in the poorest households relative to the wealthiest households; in 

Madagascar, the disadvantage among the children from the poorest households grew, with the increase in 

coverage of care seeking from public sources disproportionately concentrated in children from the 

wealthiest households. 

Figure 38. Trend in coverage of public care seeking for diarrhea among children in poorestquintile households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH 

priority countries 

Note: For Ghana 2008, care seeking estimates for children in the wealthiest quintile are based on 25-49 unweighted cases and should be interpreted 

with caution. 

In three Asian MCH priority countries, there was a significant increase between surveys in the percentage 

of poorest-quintile children for whom care was sought from a private provider for diarrhea (see 

Figure 39). 

In Indonesia and Nepal, there was a significant change in wealth equity between surveys. In Indonesia, 

there was a significant narrowing of the wealth equity gap along with significant improvement in 

coverage among children in the poorest households; in Nepal, there was a significant increase in the 

wealth equity gap, despite a small but significant increase in use of private sources of care among children 

in poorest-quintile households. 

DR Congo

Kenya

Ghana

Madagascar Liberia

Mozambique

Rwanda

Malawi

Senegal

Indonesia

Bangladesh

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a public source for 

diarrhea

Survey Year

E E

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 
37 

Figure 39. Trend in coverage of private care seeking for diarrhea among children in poorestquintile households and poorest-wealthiest quintile gap in care seeking equity, Asian USAID MCH 

priority countries and Haiti 

In most African MCH priority countries (11 of 15), there was no significant change between surveys in 

the percentage of poorest-quintile children for whom care was sought from a private provider for diarrhea. 

In both Malawi and Ethiopia, the percentage increased while in Tanzania and Nigeria, the percentage 

decreased (see Figure 40). 

In two African countries, Mali and DR Congo, there was a significant change in wealth equity between 

surveys. In Mali, the level of wealth equity improved significantly, while in DR Congo the advantage 

among the wealthiest-quintile children grew. 

Figure 40. Trend in coverage of private care seeking for diarrhea among children in poorestquintile households and poorest-wealthiest quintile gap in care seeking equity, African USAID 

MCH priority countries 

Note: Due to low levels of private care seeking in African countries, the figure ranges from 0 to 50 percent coverage, rather than 0 to 100 percent 

coverage. 

 

Bangladesh

Indonesia

Nepal

-20

0

20

40

60

80

100

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Percentage of children for whom care was sought from a private 

source for diarrhea

Survey Year

E

E

DR Congo

Mali

Nigeria Tanzania

Ethiopia

Malawi

-10

0

10

20

30

40

50

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom care was sought from a private 

source for diarrhea

Survey Year

E

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 
38 

3.4. Country Case Studies: Have Child Health Interventions Helped? 

To what extent can we attribute the observed trends in care seeking to intervention programs? Have 

community-based and other child health programs had a measureable impact on care seeking behavior? 

Since we cannot directly address these questions with cross-sectional data, we analyzed DHS data for 

ecological evidence of programmatic influence on care seeking. In this section we present two examples 

of subnational programs implemented during the study period. With DHS data, we identified the 

geographic areas covered and not covered by these programs, and compared changes in care seeking over 

time. 

3.4.1. Diarrhea treatment in Nepal 

In 2004, WHO and UNICEF revised their guidelines for the treatment of diarrhea in children under age 5 

to include the use of zinc supplementation for 10-14 days, along with an updated ORS with reduced levels 

of glucose and salt (WHO 2005). Between 2005 and 2010, USAID funded the Social Marketing Plus for 

Diarrheal Disease Control: Point of Use Water Disinfection and Zinc Treatment (POUZN) project, which 

was implemented by Abt Associates in partnership with Population Services International (PSI). A central 

goal of this project was mobilizing the private sector to expand the use of zinc with ORS in the treatment 

of diarrhea in children. The POUZN project in Nepal began in January 2007 and was expanded to 30 of 

75 districts by the project’s end in September 2008 (Wang and MacDonald 2009). 

The POUZN project identified and partnered with local manufacturers to ensure the availability of goodquality, affordable zinc tablets and to facilitate their distribution through private-sector channels. The 

POUZN project in Nepal used training materials, developed in partnership with the Ministry of Health 

and PSI, to train nearly 6,000 licensed private care providers across the 30 project districts and more than 

2,000 public-sector care providers and volunteers in the three Kathmandu Valley districts. Training 

providers is believed to have an important impact on caregivers’ behavior and children’s treatment, since 

providers are often relied upon for treatment advice. The program also used multiple communication 

channels such as a national television campaign, which began in 2008, and a radio mass-media campaign, 

which began in 2007, to increase awareness of zinc as the correct treatment for diarrhea and to promote 

the use of zinc and ORS. More details of the project can be found in Wang and MacDonald (2009). 

We analyzed the 2006 and 2011 Nepal DHS surveys to evaluate how trends in care seeking for children’s 

diarrhea in districts covered by the POUZN project compared with trends in care seeking in non-project 

districts. More specifically, we examined five indicators of care seeking: whether care was sought from 

any provider, any public provider, or any private provider, and whether the child received zinc or ORS. 

We compared absolute changes during the period in care seeking behavior between project districts and 

non-project districts (difference in differences). We then fitted logit regression models to test if the 

changes between the two groups were different after controlling for maternal, child, and household 

characteristics. 

In Nepal, care seeking for diarrhea from any source increased significantly from 51 percent to 62 percent 

between the 2006 and 2011 surveys. There was also a significant increase in care seeking from any 

private source, from 6 percent to 15 percent. When we disaggregated the data to compare the trend in 

districts covered by the POUZN project with the trend in the remaining districts of Nepal, we found that 

the trends between 2006 and 2011 appeared similar across the two groups for all five indicators of interest 

(see Figures 41- 45). Both groups showed significant increases in the coverage of care seeking from 

private sources and no change in the coverage of care seeking from public sources (see Figures 41 and 

42). The increase in the use of ORS was not statistically significant in POUZN districts, but was 

significant in non-POUZN districts. Zinc use increased similarly and significantly in both groups. 
39 

Figure 41. Percentage of children with recent 

diarrhea for whom care was sought from a 

private source, Nepal 2006 and 2011 DHS 

Note: A Solid line indicates a statistically significant change in coverage 

between surveys; a dotted line indicates no change. 

Figure 42. Percentage of children with recent 

diarrhea for whom care was sought from a 

public source, Nepal 2006 and 2011 DHS 

Note: A Solid line indicates a statistically significant change in coverage 

between surveys; a dotted line indicates no change. 

Figure 43. Percentage of children with recent 

diarrhea for whom care was sought from any 

source, Nepal 2006 and 2011 DHS 

Note: A Solid line indicates a statistically significant change in coverage 

between surveys; a dotted line indicates no change. 

Figure 44. Percentage of children with recent 

diarrhea who were treated with ORS, Nepal 2006 

and 2011 DHS 

Note: A Solid line indicates a statistically significant change in coverage 

between surveys; a dotted line indicates no change. Due to low levels of 

ORS use, the figure ranges from 0 to 50 percent coverage, rather than 0 

to 100 percent coverage. 

Figure 45. Percentage of children with recent 

diarrhea who were given zinc, Nepal 2006 and 

2011 DHS 

Note: A Solid line indicates a statistically significant change in coverage 

between surveys; a dotted line indicates no change. Due to low levels of 

zinc use, the figure ranges from 0 to 50 percent coverage, rather than 0 to 

100 percent coverage. In the 2006 survey there was no use of zinc in 

POUZN districts. In order to test the significance of the change in zinc 

coverage between surveys in POUZN districts, we replaced the zeroes 

with an arbitrary small number, 0.001. This way we were able to perform 

a conservative statistical test. 

0

10

20

30

40

50

60

70

80

90

100

2006 2011

POUZN non-POUZN

0

10

20

30

40

50

60

70

80

90

100

2006 2011

POUZN non-POUZN

0

10

20

30

40

50

60

70

80

90

100

2006 2011

POUZN non-POUZN

0

5

10

15

20

25

30

35

40

45

50

2006 2011

POUZN non-POUZN

0

5

10

15

20

25

30

35

40

45

50

2006 2011

POUZN non-POUZN
40 

For the five indicators, we found no evidence that the improvement in coverage was greater in districts 

covered by POUZN than in non-project districts (see Table 4). 

Table 4. Care seeking for diarrhea for POUZN and non-POUZN districts in Nepal, 2006 and 2011 

Nepal DHS

Non-POUZN Districts POUZN Districts 

Difference in the 

differences 

2006 DHS 

N=305 

2011 DHS 

N=318 Change 

2006 DHS 

N=328 

2011 DHS 

N=383 Change 

 % % % % % % % Sig. 

Any public care sought 21.8 25.3 3.5 19.6 22.9 3.3 -0.2 

Any private care sought 3.3 9.6 6.3 9.4 19.4 10.0 3.7 

Any care sought 47.0 57.9 10.9 54.1 65.6 11.5 0.6 

Child was given ORS 25.5 37.2 11.7 32.9 40.6 7.7 -4.0 

Child was given zinc 0.8 6.6 5.8 0.0 5.8 5.8 0.0 

Note: * indicates p<.05; ** indicates p<.01; *** indicates p<.001.

As shown in Table 5, after adjusting for background characteristics of the child, household, and mother, 

we found no evidence that the improvement in care seeking for diarrhea was greater in project districts. 

Table 5. Odds ratios for care seeking for children with recent diarrhea in POUZN Project 

Districts vs. non-project districts after adjusting for maternal, child, and household 

characteristics, Nepal 2006 and 2011 DHS 

Outcome variable Unadjusted OR 95% CI Adjusted OR 95% CI 

Any public care sought 1.012 0.462 - 2.217 0.937 0.431 - 2.036 

Any private care sought 0.737 0.242 - 2.238 1.203 0.381 - 3.797 

Any care sought 1.045 0.529 - 2.063 1.274 0.642 - 2.528 

Child was given ORS 0.806 0.413 - 1.572 0.824 0.417 - 1.628 

Child was given zinc n/a n/a 

Observations 1,338 1,338 

Note: * indicates p<.05; ** indicates p<.01; *** indicates p<.001. The adjusted model controls for place of residence, household 

wealth quintile, maternal age, maternal education, the mother’s exposure to media , child's age, and the sex of child, as well as 

main effects for survey year and intervention coverage. Since at the time of the 2006 survey there was no zinc use in POUZN 

districts, the logit regression did not produce a coefficient that could be tested for significance. Using methods not shown, we 

were able to perform an approximate test of significance, and found no evidence that the increase in zinc use was greater in 

POUZN districts. Instead, POUZN districts had a lower increase in zinc use compared with non-POUZN districts. The difference 

was small but statistically significant. 

Despite countrywide improvements in levels of care seeking, the two most recent DHS did not provide 

evidence that increases in project districts were greater than those in non-project districts. There are 

several possible explanations for this null finding. The first is related to the timing of the endline survey. 

Fieldwork in 2011 occurred several years after the end of the POUZN project in September 2008. 

According to a 2008 household survey conducted by the POUZN project, which was representative of 26 

of the 30 POUZN districts, 15 percent of children with diarrhea in the past two weeks were treated with 

zinc (Wang and MacDonald 2009). According to the 2011 DHS, just 6 percent of children with diarrhea 

in the past two weeks in project districts were treated with zinc. It is possible that program effects on zinc 

use and diarrhea treatment peaked during and immediately after the project, but waned before the 2011 

DHS. In addition, the discrepancy in zinc coverage between the 2008 POUZN survey and the 2011 DHS 

could be the result of differences in survey methodologies. In the POUZN project questionnaire, a visual 

aid was used to assist with the correct identification and classification of diarrhea treatments, while the 

DHS questionnaire asks the respondent to list all diarrhea treatments given to the child but provides no 

visual aids or direct questions about zinc. If the 2011 DHS survey underestimated zinc use in Nepal, this 

could dampen the observed intervention effect (MacDonald 2015). In addition, it is possible that using 

district as a proxy for program coverage was not appropriate. Many of the behavioral change components 

of POUZN relied on media channels, although only 34 percent of caregivers of children with recent 
41 

diarrhea interviewed in the 2011 DHS reported watching television at least once a week, 33 percent 

reported listening to the radio at least once a week, and 52 percent reported either watching television or 

listening to the radio at least once a week. It is possible that the program had a measurable impact on 

caregivers who were exposed to the program messaging but that this group represented only a small 

percentage of the DHS sample. The fact that the POUZN television programs and some radio programs 

were broadcast on national stations could have potentially diluted the differential between the 30 primary 

project districts and the remaining non-covered districts. In addition, POUZN was one of many child 

health initiatives underway between 2006 and 2011 in various regions of Nepal. The POUZN project and 

other programs operating across other districts could have each had a measurable impact, but these effects 

are difficult to isolate. 

3.4.2. Malaria communities program in Liberia 

Through the Malaria Communities Program (MCP), the President’s Malaria Initiative (PMI) awarded 

small grants to local organizations in 12 countries to implement 20 projects5

 on malaria prevention and 

treatment (USAID). One such organization, EQUIP Liberia, was awarded an MCP grant to work in two 

Liberian counties, Nimba and Sinoe. In these counties, the EQUIP project piloted integrated community 

case management (iCCM), trained more than 200 general Community Health Volunteers (gCHVs) on 

iCCM, and used behavior change communication with radio drama programs and household visits to 

share messages about malaria prevention and control, generate demand, and increase care seeking 

(Prosnitz 2015). In addition to increasing the demand for care seeking, EQUIP was committed to 

ensuring that care for childhood illnesses was accessible, available, and affordable. The EQUIP’s health 

system support was heavily focused on the public sector. In 2008, EQUIP supported 15 Ministry of 

Health (MOH)/public clinics in the project counties. From 2009 to 2012, EQUIP supported 21 

MOH/public health facilities and two private health facilities, and ensured that these facilities were fully 

functional with the required staff, medical services, and drug supplies (Suomie 2015). 

Since the EQUIP MCP project commenced in 2008 and ended in 2011, the 2007 Liberia DHS survey and 

2011 Liberia Malaria Indicator Survey (LMIS)6

 were well-timed to provide baseline and endline 

information on coverage of care seeking for children’s fever. With these data we evaluated how trends in 

care seeking in counties covered by the EQUIP project compared with trends in non-project counties. In 

particular, we examined the percentage of children for whom care was sought outside the home for 

fever—from a public provider, private provider, and any provider. 

As Figures 46-48 show, care seeking patterns in non-EQUIP counties closely mirrored the overall 

national patterns, while EQUIP counties deviated from this national pattern. The level of care seeking 

from public sources appeared to increase in EQUIP counties (marginally significantly, p=0.06). The level 

of care seeking from public sources decreased between 2007 and 2011 in non-EQUIP areas and in Liberia 

as a whole (statistically-significant in non-EQUIP areas only) (see Figure 46). 

 

5 Note on the process used to select EQUIP Liberia as an example case study: To select an example case study from among 

the 20 PMI-funded local MCP projects, we first eliminated 13 projects that did not have both (1) geographically identifiable 

project areas, and (2) DHS survey data available as of April 2015 that were appropriately timed to provide baseline and endline

data on coverage of care seeking. The remaining seven projects included one in Ethiopia, two in Liberia, one in Senegal, and 

three in Uganda. Three of these seven projects were eliminated because the number of fever cases in intervention areas was not 

sufficient to conduct the analysis. In two of the three projects—one in Uganda and one in Liberia—we found no evidence of an 

association between program coverage and the rate of change in care seeking for fever. This case study highlights the third 

remaining project for which findings suggest potential impact—EQUIP Liberia. 6

 While the report relies primarily on the two most recent Liberia DHS surveys (2007 and 2013), the case study uses the 2007 

DHS and the 2011 MIS since the case study is specific to malaria care seeking, and 2011 is better timed to provide endline 

coverage information. 
42 

Figure 46. Percentage of children with recent fever for whom care was sought from a public 

source, Liberia 2007 DHS and 2011 MIS 

Note: A Solid line indicates a statistically significant change in coverage 

between surveys; a dotted line indicates no change. 

The level of care seeking from private sources, in contrast, decreased in EQUIP-counties but increased in 

non-EQUIP counties, with non-EQUIP areas again closely mirroring the national trend (see Figure 47). 

Figure 47. Percentage of children with recent fever for whom care was sought from a private 

source, Liberia 2007 DHS and 2011 MIS 

Note: A Solid line indicates a statistically significant change in coverage between 

surveys; a dotted line indicates no change. 

Despite changes in the public-private mix of care seeking, the overall level of any care seeking for fever 

did not change between 2007 and 2011 in the counties covered by EQUIP, non-program counties, or 

Liberia as a whole (see Figure 48). 

0

10

20

30

40

50

60

70

80

90

100

2007 DHS 2011 MIS

Non-EQUIP EQUIP Total

0

10

20

30

40

50

60

70

80

90

100

2007 DHS 2011 MIS

Non-EQUIP EQUIP Total
43 

Figure 48. Percentage of children with recent fever for whom care was sought from any source, 

Liberia 2007 DHS and 2011 MIS 

Note: A Solid line indicates a statistically significant change in coverage between 

surveys; a dotted line indicates no change. 

Table 6 summarizes the trends in care seeking coverage in project and non-project areas between 2007 

and 2011. As shown, the difference in the percentage point change between project and non-project areas 

was statistically significant for care seeking from both public and private sources. 

Table 6. Care seeking for recent fever within EQUIP and non-EQUIP counties, Liberia 2007 DHS and 

2011 MIS 

Non-EQUIP EQUIP Difference in 

2007 LDHS 2011 LMIS Change 2007 LDHS 2011 LMIS Change the differences Sig. 

% % % % 

Any public care sought 39.5 30.3 -9.2 21.4 43.1 21.7 30.9 ** 

Any private care sought 17.8 25.1 7.3 37.1 14.2 -22.9 -30.2 ** 

Any care sought 74.6 77.7 3.1 82.7 74.7 -8.0 -11.1 

Note: * indicates p<.05; ** indicates p<.01; *** indicates p<.001.

The observed coverage trends could have been affected by differing socio-demographic characteristics of 

children and caregivers in project and non-project areas, as well as by changes in these characteristics 

between the two surveys. In EQUIP project areas, for example, caregivers had more education according 

to the 2011 survey compared with the 2007 survey, and the percentage of children in households in the 

poorest wealth quintile in EQUIP project areas was lower in 2011 than in 2007 (see Appendix A12). We 

developed three logit regression models to assess the odds that care was sought from any public facility, 

private facility, and any source outside the home. We controlled for these and other socio-demographic 

characteristics of the mother, child, and household. 

Results show that the increase in care seeking from public facilities between 2007 and 2011 in Liberia 

was significantly greater in EQUIP project areas compared with the increase in areas with no EQUIP

presence, after adjusting for socio-demographic characteristics (the adjusted OR for the additional 

increase associated with project counties: OR=3.6, p<0.05, see Table 7). Conversely, the decline in care 

seeking from private facilities in EQUIP project areas was significantly more rapid than the decline in 

areas with no EQUIP presence (the adjusted OR for additional decline associated with project counties: 

OR=0.14, p<0.001). Overall, the slight decline in care seeking from any source in EQUIP project areas 

was significantly different from the slight increase in areas with no EQUIP presence (the adjusted OR for 

additional decline associated with project counties: OR=0.39, p<0.05). 

0

10

20

30

40

50

60

70

80

90

100

2007 DHS 2011 MIS

Non-EQUIP EQUIP Total
44 

Table 7. Odds ratios for care seeking for children with recent fever in EQUIP project districts 

vs. non-project districts after adjusting for maternal, child, and household characteristics, 

Liberia 2007 DHS and 2011 MIS 

Outcome variable Unadjusted OR 95% CI Adjusted OR 95% CI 

Any public care sought 4.16 ** 1.43 - 12.15 3.55 * 1.27 - 10.14 

Any private care sought 0.18 ** 0.06 - 0.58 0.14 *** 0.04 - 0.44 

Any care sought 0.52 0.26 - 1.03 0.39 * 0.19 - 0.81 

Observations 3,290 3,290 

Note: * indicates p<.05; ** indicates p<.01; *** indicates p<.001. The adjusted model controls for place of residence, household 

wealth quintile, maternal age, maternal education, child's age, and the sex of child, as well as main effects for survey year and 

intervention coverage. 

Given the content of EQUIP’s MCP program in two counties of Liberia, we would expect the program to 

have a positive effect on levels of care seeking. In particular, since EQUIP activities were heavily focused 

on the public sector, we would expect the program to raise levels of care seeking from public sources—

and this is what we found. While levels of care seeking from any source did not improve in EQUIP

project districts between 2007 and 2011, there appears to have been a shift from the use of private 

providers to the use of public providers. The EQUIP program coverage was significantly associated with 

increased levels of care seeking from public sources between surveys. This shift is plausible, given that 

the gCHVs and other community health volunteers were trained to create awareness and to refer mothers 

and caregivers to public facilities where services were available and free of charge, in contrast to private 

facilities, which charged fees (Suomie 2015). While the observed patterns could be explained by external 

factors such as urbanization, health system expansion, or other programs implemented during the same 

period, the results provide some evidence that EQUIP’s community-based malaria control program had a 

positive impact on care seeking from public sources in two counties in Liberia. 

3.5. Appropriateness of Care 

In this section we investigate the appropriateness of the care received by children when they were taken to 

a health provider for care. 

3.5.1. Appropriateness of care for fever 

Standard malaria case management protocols include universal testing of fever cases before treatment 

with recommended first-line antimalarials, which are typically ACT. Several indicators are necessary to 

measure implementation of these policies. First, the percentage of children under age 5 with fever who 

receive appropriate malaria diagnostic tests is a measure of the extent to which universal diagnosis is 

being implemented. Second, the proportion of children whose treatment correctly follows diagnostic test 

results (the percentage with positive malaria tests who receive appropriate antimalarial treatment, and the 

inverse, the percentage of children with negative malaria diagnostic test results who do not receive 

antimalarials) is necessary for monitoring the implementation of policies. 

Due to the limitation of household surveys, these indicators cannot be measured directly with DHS data. 

Instead, three proxy measures are used. First, the percentage of children who had blood taken from a 

finger or heel among children under age 5 with fever in the last two weeks is used as a proxy for 

diagnostic testing for malaria, with the assumption that blood taken from young children with fever will 

be used for this purpose. The second household survey indicator measures the percentage of children 

under age 5 with recent fever who received ACT or first-line antimalarials among all children who 

received any antimalarials. This indicator assumes that a malaria diagnosis was made correctly and 

measures the extent to which treatment is occurring according to policy. Assuming that all of the reported 

fever cases that are reported and diagnosed as malaria are uncomplicated cases, this indicator would 
45 

ideally be 100 percent if treatment policies were being properly implemented. A similar, third indicator is 

necessary when using data from older surveys, i.e., before ACT was recommended or widely available, or 

in countries in which ACT is not the recommended first-line antimalarial treatment. In this case, the firstline antimalarial medication is used instead of ACT in the indicator. These household survey indicators do 

not permit monitoring and evaluation of all factors in the case management setting. It is not possible with 

current DHS/MIS data to assess the extent to which treatment is implemented according to a diagnostic 

result, since diagnostic results are not measured or recorded in the surveys. 

The percentage of children with recent fever who had a finger or heel stick varied widely across the 12 

MCH priority countries endemic for malaria and with data available (see Table 8a). In Pakistan, only 4 

percent of children with recent fever were reported to have had a finger or heel stick. This proportion did 

not vary significantly depending on source of care, whether public, private, or any care. Among the 11 

MCH priority countries in sub-Saharan Africa with data for this indicator, the percentage of children with 

recent fever for whom a finger/heel stick was reported among those who received care from any source 

ranged from 13 percent in Nigeria 2013 to 61 percent in Zambia 2013. Among children who received care 

from public sources, the range was from 17 percent in Tanzania 2010 to 66 percent in Liberia 2013. 

Among children who received care at private facilities, the percentage reporting finger/heel sticks ranged 

from 17 percent in Senegal 2010-11 to 79 percent in Rwanda 2010. It should be noted that sample sizes 

are quite small for some of the estimates of private care. In most surveys, the percentage of children with 

recent fever receiving finger/heel sticks is lower among those receiving care from any source compared 

with those who sought care from public facilities or private sources. This is likely because other sources 

of care included in the any care estimates, e.g., markets, shops, and traditional healers, are unlikely to 

have diagnostic capacity and are therefore unlikely to take blood samples from children. 

In Uganda 2011, finger/heel sticks were more commonly done in children seeking care in public facilities 

than in private facilities. In contrast, in Malawi 2010, Tanzania 2010, Nigeria 2013, and Rwanda 2010, 

children seeking care at private facilities were more likely to have finger/heel sticks than those attending 

public facilities. This comparison could not be assessed in Mozambique 2011 or Mali 2012-13 due to the 

small number of children with fever seeking care at private facilities. 

Table 8a. Among children under age 5 with recent fever, the percentage who had a finger or heel 

stick for malaria testing, by source of care, USAID MCH priority countries 

Public Private Any source 

% LB UB N % LB UB N % LB UB N 

Asia 

Pakistan 2012-13 4.4 2.4 8.1 370 4.6 3.6 5.7 2,940 4.4 3.5 5.4 3,404

Africa 

Uganda 2011 40.2 35.2 45.4 1,092 25.5 22.2 29.1 1,453 29.6 26.7 32.8 2,583

Liberia 2013 65.5 60.7 70.0 709 65.1 54.9 74.2 288 50.7 46.1 55.3 1,368

Malawi 2010 19.9 17.9 21.9 3,149 38.7 33.1 44.6 930 21.8 19.7 24.0 4,524

Tanzania 2010 17.0 13.5 21.1 921 45.7 32.6 59.3 119 16.6 13.3 20.4 1,382

Nigeria 2013 21.5 18.1 25.3 935 33.3 25.9 41.6 207 13.1 11.4 15.0 2,685

Zambia 2013-14 64.1 60.8 67.2 1,797 67.0 53.3 78.3 118 61.1 58.0 64.2 2,009

Mozambique 2011 46.3 42.1 50.6 850 * 6 43.8 39.2 48.5 912

DR Congo 2013-14 37.8 33.4 42.5 1,429 36.1 29.2 43.7 576 28.3 25.5 31.1 2,943

Senegal 2010-11 18.3 14.7 22.7 960 16.9 8.8 29.9 117 15.5 12.6 19.0 1,321

Mali 2012-13 26.9 20.9 33.9 214 * 21 17.7 13.9 22.2 407

Rwanda 2010 42.0 37.6 46.6 553 (78.7) 62.9 88.9 29 38.2 34.3 42.3 688

Note: This table includes all surveys with information regarding whether the child had a finger or heel stick. Figures in parentheses 

are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has 

been suppressed. LB and UB refer to the lower and upper bounds of the 95% confidence interval. 
46 

Table 8b shows the percentage of children who received ACT when care was sought for fever and an 

antimalarial was taken for treatment. Again, this is a proxy for appropriate treatment that assumes a 

diagnosis was properly done. Data to calculate this indicator were available in 24 surveys from 16 MCH 

priority countries. A large range in appropriate treatment was evident across these surveys. Among 

children seeking care from any source, less than 10 percent in six surveys received appropriate 

antimalarial treatment. In contrast, seven surveys showed appropriate antimalarial treatment in over half 

of children with fever, and in three surveys (Rwanda 2010, Zambia 2013-14, and Malawi 2010) coverage 

of appropriate antimalarial treatment was over 80 percent. 

Appropriate antimalarial treatment with ACT was more commonly reported among children for whom 

care was sought from public facilities than private sources in Uganda (both the 2006 and 2011 surveys), 

Malawi 2010, and Tanzania 2010. 

Table 8b. Among children under age 5 with recent fever, the percentage who received Artemisininbased Combination Therapy (ACT) among children who received any antimalarial, by source of 

care, USAID MCH priority countries 

Public Private Any source 

% LB UB N % LB UB N % LB UB N 

Asia 

Indonesia 2012 (34.9) 14.0 63.9 16 (42.5) 22.8 65.0 18 33.8 19.2 52.3 39 

Pakistan 2012-13 (32.3) 13.9 58.5 13 18.2 11.0 28.7 116 18.8 11.7 28.7 130 

Africa 

Uganda 2006 10.1 7.3 13.9 714 2.7 1.7 4.4 999 5.2 3.8 7.1 1,804 

Uganda 2011 76.5 72.2 80.3 855 61.4 55.3 67.1 971 67.9 63.8 71.7 1,816 

Liberia 2007 18.3 13.1 24.9 425 10.8 6.3 17.9 263 15.0 11.3 19.6 856 

Liberia 2013 42.3 35.6 49.2 471 45.4 33.0 58.5 203 43.7 37.7 49.9 882 

Malawi 2010 89.7 87.8 91.4 1,942 68.9 62.7 74.4 564 84.9 82.7 86.9 2,511 

Tanzania 2010 74.3 69.6 78.5 660 33.7 21.8 48.2 92 63.9 58.1 69.2 931 

Nigeria 2008 8.8 6.3 12.2 508 8.1 3.9 15.8 177 7.5 5.8 9.6 1,094 

Nigeria 2013 20.6 16.5 25.4 464 15.7 9.2 25.5 105 19.1 16.3 22.3 1,060 

Zambia 2007 30.7 24.1 38.3 317 (21.1) 10.4 38.1 41 29.2 23.0 36.2 375 

Zambia 2013-14 91.4 88.7 93.5 886 87.1 76.0 93.5 72 91.2 88.7 93.2 975 

Ghana 2008 55.0 44.1 65.5 120 (51.5) 33.7 68.9 38 54.0 45.3 62.5 206 

Kenya 2008-9 39.4 31.1 48.5 190 (46.9) 27.4 67.4 41 38.0 30.5 46.1 254 

Mozambique 2011 62.9 54.8 70.3 374 * 0 62.1 54.3 69.3 389 

DR Congo 2007 3.2 1.4 7.0 280 2.5 1.1 5.6 208 2.4 1.3 4.3 607 

DR Congo 2013-14 28.3 22.0 35.5 723 14.4 8.6 23.1 251 22.2 17.9 27.2 1,177 

Senegal 2010-11 39.9 27.8 53.3 154 * 5 39.8 28.8 51.8 189 

Mali 2006 0.8 0.2 3.3 347 (4.0) 0.7 21.1 39 0.8 0.2 2.5 536 

Mali 2012-13 21.1 13.8 31.0 104 * 12 21.5 15.0 29.7 145 

Rwanda 2010 96.8 91.7 98.8 136 * 5 96.3 91.4 98.4 143 

Madagascar 2008-9 8.5 3.3 20.0 84 (1.1) 0.2 8.2 32 5.6 2.3 13.3 134 

Ethiopia 2005 0.0 75 (0.1) 0.0 0.9 32 0.0 0.0 0.2 120 

Ethiopia 2011 36.7 17.1 61.9 25 (14.3) 2.1 57.0 11 32.0 17.3 51.4 39 

Note: This table includes all surveys with information regarding whether the child was treated with ACTs. Figures in parentheses are 

based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been 

suppressed. LB and UB refer to the lower and upper bounds of the 95% confidence interval. 

Table 8c shows the percentage of children who received first-line antimalarials when care was sought for 

fever and an antimalarial was taken for treatment. When interpreting trends in this indicator, it is 

important to note that the recommended first-line antimalarial changed in most countries over the time 

period included in this study. In some countries the recommended antimalarial was not yet widely 

available at the time of the survey. Data to calculate this indicator were available from 37 surveys from 18 

countries. Among children with recent fever for whom care was sought outside the home, a large range in 

treatment with first-line antimalarials was observed. In five surveys—Uganda 2006, Nigeria 2008, DR 

Congo 2007, Madagascar 2008-9, and Ethiopia 2005—less than 10 percent of children who received 

antimalarials were treated with recommended first-line medications. More than 90 percent first-line 
47 

treatment was observed in Zambia 2013-14, Ghana 2003, Rwanda 2010, Madagascar 2004, and Haiti 

2012. 

In Uganda 2006 and 2011, Malawi 2005 and 2010, and Tanzania 2004-5 and 2010, appropriate 

antimalarial treatment was more commonly reported among children for whom care was sought from 

public facilities compared to private sources. 

Table 8c. Among children under age 5 with recent fever, the percentage who received first-line 

antimalarial treatment among children who received any antimalarial, by source of care, USAID 

MCH priority countries 

Public Private Any Source 

% LB UB N % LB UB N % LB UB N 

Asia 

Indonesia 2007 (92.1) 78.0 97.5 19 * 7 93.8 84.5 97.7 37 

Indonesia 2012 (34.9) 14.0 63.9 16 (42.5) 22.8 65.0 18 33.8 19.2 52.3 39 

Pakistan 2006-7 8.0 1.9 28.7 12 17.2 9.6 29.0 69 15.7 9.0 26.1 82 

Pakistan 2012-13 32.3 13.9 58.5 13 18.2 11 28.7 116 18.8 11.7 28.7 130 

India 2005-6 40.2 28.1 53.5 122 28.9 22.1 36.8 449 29.2 23.1 36.1 615 

Africa 

Uganda 2006 10.1 7.3 13.9 714 2.7 1.7 4.4 999 5.2 3.8 7.1 1,804 

Uganda 2011 76.5 72.2 80.3 855 61.4 55.3 67.1 971 67.9 63.8 71.7 1,816 

Liberia 2007 18.3 13.1 24.9 425 10.8 6.3 17.9 263 15.0 11.3 19.6 856 

Liberia 2013 42.3 35.6 49.2 471 45.4 33.0 58.5 203 43.7 37.7 49.9 882 

Malawi 2005 84.5 80.8 87.5 710 71.2 62.2 78.7 131 81.5 78.3 84.4 946 

Malawi 2010 89.7 87.8 91.4 1,942 68.9 62.7 74.4 564 84.9 82.7 86.9 2,511 

Tanzania 2004-5 49.2 44.5 54.0 687 28.6 21.4 37.1 177 41.5 37.4 45.6 1,077 

Tanzania 2010 74.3 69.6 78.5 660 33.7 21.8 48.2 92 63.9 58.1 69.2 931 

Nigeria 2008 8.8 6.3 12.2 508 8.1 3.9 15.8 177 7.5 5.8 9.6 1,094 

Nigeria 2013 20.6 16.5 25.4 464 15.7 9.2 25.5 105 19.1 16.3 22.3 1,060 

Zambia 2007 30.7 24.1 38.3 317 (21.1) 10.4 38.1 41 29.2 23.0 36.2 375 

Zambia 2013-14 91.4 88.7 93.5 886 87.1 76.0 93.5 72 91.2 88.7 93.2 975 

Ghana 2003 94.6 90.7 96.9 217 (88.9) 70.3 96.5 26 93.2 89.5 95.7 350 

Ghana 2008 55 44.1 65.5 120 (51.5) 33.7 68.9 38 54.0 45.3 62.5 206 

Kenya 2003 44.9 37.1 52.9 267 40.5 31.8 49.8 142 42.4 36.8 48.2 512 

Kenya 2008-9 39.4 31.1 48.5 190 (46.9) 27.4 67.4 41 38.0 30.5 46.1 254 

Mozambique 2004 100.0 52 * 10 100.0 153 

Mozambique 2011 62.9 54.8 70.3 374 * 0 62.1 54.3 69.3 389 

DR Congo 2007 3.2 1.4 7.0 280 2.5 1.1 5.6 208 2.4 1.3 4.3 607 

DR Congo 2013-14 28.3 22 35.5 723 14.4 8.6 23.1 251 22.2 17.9 27.2 1,177 

Senegal 2005 56.2 50.5 61.7 503 62.2 43.6 77.8 112 59.1 53.1 64.9 664 

Senegal 2010-11 39.9 27.8 53.3 154 * 5 39.8 28.8 51.8 189 

Mali 2006 64.1 57.1 70.6 347 53.9 35.4 71.4 39 65.1 59.1 70.6 536 

Mali 2012-13 21.1 13.8 31.0 104 34.4 10.8 69.5 12 21.5 15.0 29.7 145 

Rwanda 2005 46.4 37.6 55.5 142 48.3 20.0 77.7 19 47.9 40.3 55.7 238 

Rwanda 2010 96.8 91.7 98.8 136 * 5 96.3 91.4 98.4 143 

Madagascar 2004 98.6 95.2 99.6 164 93.6 84.1 97.6 66 97.4 94.6 98.8 252 

Madagascar 2008-9 8.5 3.3 20.0 84 1.1 0.2 8.2 32 5.6 2.3 13.3 134 

Ethiopia 2005 0.0 75 (0.1) 0.0 0.9 32 0.0 0.0 0.2 120 

Ethiopia 2011 36.7 17.1 61.9 25 (14.3) 2.1 57 11 32.0 17.3 51.4 39 

LAC

Haiti 2005-6 * 18 (80.0) 61.6 90.9 42 76.4 62.8 86.1 60 

Haiti 2012 * 9 (91.2) 75.0 97.3 24 (92.3) 80.5 97.2 34 

Note: This table includes all surveys with information on whether the child was treated with a first-line antimalarial drug, based on 

the country's national malaria policy at the time of the survey. Figures in parentheses are based on 25-49 unweighted cases. An

asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. LB and UB refer to the lower 

and upper bounds of the 95% confidence interval. 

 
48 

3.5.2. Appropriateness of care for diarrhea 

Oral rehydration solution (ORS) has been a key component of treatment for children’s diarrhea. Given the 

proven effectiveness of using ORS with zinc supplementation, the new WHO and UNICEF guidelines for 

diarrhea treatment recommend the use of a new ORS formula, along with zinc supplementation for 10-14 

days, for the treatment of diarrhea among children under age 5 (WHO 2005). The guidelines also specify 

that antibiotics should be given only to children with bloody diarrhea. In this section, we examine the use 

of ORS and the use of both ORS and zinc, as well as the inappropriate use of antibiotics among children 

for whom care was sought for diarrhea. 

Table 9a presents the percentage of children who received ORS when care was sought for diarrhea, by the 

source of care. In Asia, Bangladesh had the highest use of ORS—in the 2011 survey, 80 percent of 

children for whom any care was sought outside the home for diarrhea received ORS. The coverage in 

other Asian countries, in the most recent survey in each country, ranged from 45 percent to 55 percent, 

except in India, where the most recent survey in 2005-06 showed that one-third of children received ORS. 

Large variations in ORS coverage exist in Africa, from 37 percent in Malawi 2010 to 83 percent in Kenya 

2008-09 and Zambia 2013. In 11 of 14 priority countries in Africa, ORS coverage was 50 percent or 

higher. The exceptions are Madagascar 2008-09 at 37 percent, Senegal 2010-11 at 38 percent, and Nigeria 

2013 at 41 percent. Fairly high levels of use of ORS were reported in Kenya 2008-09, Malawi 2010, and 

Zambia 2013, at 80 percent or higher. Receipt of ORS significantly increased between the two most 

recent surveys in only four countries (Kenya, Mali, Nigeria, and Rwanda), from 17 percentage points in 

Kenya to 26 percentage points in Rwanda. While most countries observed an increasing trend in the use 

of ORS, Tanzania experienced a considerable decrease, from 72 percent in 2004-05 to 58 percent in 2010. 

In Haiti, about two-thirds of children with diarrhea for whom any care was sought received ORS. 
49 

Table 9a. Among children under age 5 with recent diarrhea for whom care was sought, the 

percentage who received ORS, by source of care, USAID MCH priority countries 

Pubic Private Any source 

% LB UB N % LB UB N % LB UB N 

Asia 

Indonesia 2007 62.9 56.2 69.2 443 46.7 41.1 52.4 725 42.2 38.4 46.1 1,614

Indonesia 2012 60.4 54.6 65.9 553 48.8 44.3 53.3 1,036 46.4 43.1 49.7 1,875

Pakistan 2006-7 65.7 55.3 74.8 92 53.1 47.7 58.4 615 52.5 47.9 57.0 759

Pakistan 2012-13 52.5 43.0 61.9 238 46.0 41.7 50.4 1,637 45.8 41.9 49.7 1,897

Bangladesh 2007 85.8 68.8 94.3 55 83.9 71.2 91.7 60 83.1 77.8 87.3 403

Bangladesh 2011 (84.1) 68.9 92.6 43 80.0 71.6 86.5 160 80.3 74.2 85.3 295

India 2005-6 44.8 39.9 49.8 667 33.8 31.1 36.6 2,181 32.6 30.3 34.9 3,285

Nepal 2006 60.9 51.5 69.6 129 (49.5) 30.9 68.2 40 44.7 37.5 52.0 316

Nepal 2011 64.7 54.5 73.8 171 57.8 43.8 70.6 106 54.7 48.2 61.0 441

Africa 

Uganda 2006 64.9 60.0 69.5 605 44.2 39.9 48.5 838 48.7 45.4 52.0 1,547

Uganda 2011 67.2 62.3 71.8 558 44.9 40.2 49.8 762 52.4 48.4 56.4 1,353

Liberia 2007 72.7 65.6 78.9 299 73.5 63.2 81.7 207 62.5 56.9 67.9 794

Liberia 2013 80.2 75.6 84.0 472 67.3 57.1 76.2 159 69.8 65.9 73.4 977

Malawi 205 87.3 84.3 89.8 629 87.1 80.1 91.9 165 77.6 74.6 80.4 1,252

Malawi 2010 83.6 81.3 85.7 1,623 79.2 73.3 84.0 384 80.0 77.6 82.2 2,257

Tanzania 2004-5 84.6 79.7 88.4 393 67.5 52.8 79.5 79 72.3 67.5 76.6 619

Tanzania 2010 63.3 63.3 63.3 491 (59.9) 38.8 77.8 49 57.5 52.3 62.5 708

Nigeria 2008 51.9 46.6 57.1 595 41.0 33.6 48.8 219 33.8 30.6 37.1 1,630

Nigeria 2013 61.6 57.1 65.9 715 57.2 46.2 67.5 132 41.2 38.0 44.4 2,104

Zambia 2007 85.6 81.6 88.8 480 88.4 79.1 93.9 62 79.7 75.3 83.4 595

Zambia 2013-14 85.1 82.7 87.2 1,268 83.1 70.5 91.6 69 82.6 80.1 84.9 1,394

Ghana 2003 67.5 57.9 75.8 112 * n/a n/a 18 51.8 45.1 58.4 255

Ghana 2008 69.9 62.0 76.8 193 (69.0) 48.7 83.9 32 56.9 51.6 62.1 344

Kenya 2003 65.5 56.2 73.8 179 51.0 40.4 61.5 85 53.3 46.8 59.7 334

Kenya 2008-9 88.2 83.2 91.9 351 80.1 66.8 89.0 95 83.3 79.0 86.9 547

Mozambique 2004 77.9 73.2 82.0 636 * n/a n/a 16 70.6 65.7 75.1 738

Mozambique 2011 77.3 72.9 81.3 693 * n/a n/a 12 71.8 67.0 76.2 770

DR Congo 2007 54.7 46.1 63.1 368 23.5 13.7 37.3 75 47.9 40.7 55.2 497

DR Congo 2013-14 64.0 58.5 69.2 834 57.1 49.3 64.5 289 52.4 48.4 56.4 1,659

Senegal 2005 49.1 43.4 54.8 377 (54.2) 38.7 68.9 57 30.7 26.4 35.3 734

Senegal 2010-11 47.2 42.1 52.3 718 (37.9) 22.2 56.6 64 38.2 34.1 42.4 7,071

Mali 2006 49.7 39.5 59.9 271 (52.4) 31.6 72.5 27 30.2 23.7 37.7 509

Mali 2012-13 69.1 61.2 76.0 216 (64.3) 43.7 80.8 21 50.2 44.0 56.4 446

Rwanda 2005 43.5 34.8 52.5 133 * n/a n/a 23 24.1 19.2 29.7 323

Rwanda 2010 65.0 60.0 69.8 403 * n/a n/a 19 51.0 46.3 55.6 564

Madagascar 2004 32.5 22.4 44.6 119 19.4 9.4 35.9 63 23.7 16.8 32.3 218

Madagascar 2009 44.3 36.9 52.0 259 35.9 24.1 49.8 92 37.1 30.6 44.2 404

Ethiopia 2005 65.5 58.8 71.7 342 37.2 21.3 56.5 63 57.4 51.1 63.4 467

Ethiopia 2011 62.5 55.0 69.4 357 60.6 47.7 72.1 131 57.9 51.6 63.9 527

LAC 

Haiti 2005-6 78.5 70.0 85.1 164 73.7 65.5 80.5 224 69.2 63.4 74.5 505 

Haiti 2012 81.7 74.5 87.2 209 73.6 66.8 79.4 276 72.4 67.7 76.8 583 

Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 

unweighted cases and has been suppressed. LB and UB refer to the lower and upper bounds of the 95% confidence interval. n/a= 

data not available. 

In comparisons of public and private providers, there were no significant differences in the provision of 

ORS in any of the priority countries except Indonesia 2012 and Uganda 2011. Both surveys showed that a 

significantly higher percentage of children with diarrhea were given ORS in the public sector than in the 

private sector. 

Table 9b shows the percentage of children who received both ORS and zinc when care was sought for 

diarrhea. In 18 older surveys, data were not available for this indicator because these surveys did not 

collect information on the use of zinc. Globally, receipt of both zinc and ORS remained at very low 

levels. Among 18 MCH priority countries with data available, coverage was below 5 percent in 16 of the 

countries, and many had coverage below 1 percent. Notably, in Bangladesh a considerable percentage of 
50 

children for whom care was sought outside the home received both ORS and zinc, at 28 percent in the 

2007 DHS survey and 40 percent in the 2011 survey. Nepal is the other country that had higher use of 

both ORS and zinc than other MCH priority countries. According to the 2011 DHS survey, 8 percent of 

children for whom care was sought in Nepal received both ORS and zinc. 

Table 9b. Among children under age 5 with recent diarrhea for whom care was sought, 

the percentage who received ORS and zinc, by source of care, USAID MCH priority 

countries 

Pubic Private Any source 

% LB UB N % LB UB N % LB UB N 

Asia 

Indonesia 2012 1.5 0.5 4.1 553 0.3 0.1 0.7 1,036 0.6 0.3 1.3 1,875

Pakistan 2012-13 4.6 1.1 17.7 238 1.0 0.4 2.7 1,637 1.0 0.4 2.4 1,897

Bangladesh 2007 29.7 16.9 46.6 55 39.9 28.2 52.8 60 27.6 22.1 33.8 403

Bangladesh 2011 (41.5) 27.2 57.3 43 43.1 34.6 52.0 160 39.7 33.5 46.3 295

India 2005-6 0.6 0.2 2.0 667 0.3 0.1 0.8 2,181 0.3 0.1 0.7 3,285

Nepal 2011 10.2 6.1 16.6 171 13.1 5.6 27.6 106 7.8 5.0 11.9 441

Africa 

Uganda 2006 0.3 0.0 2.1 605 0.6 0.2 1.9 838 0.5 0.2 1.2 1,547

Uganda 2011 2.1 1.1 4.1 558 1.3 0.7 2.5 762 1.5 0.9 2.5 1,353

Liberia 2007 0.0 n/a n/a 299 1.4 0.3 6.1 207 0.5 0.2 1.8 794

Liberia 2013 3.0 1.6 5.7 472 6.0 2.3 14.8 159 3.5 2.1 5.6 977

Malawi 2010 0.2 0.1 0.5 1,623 0.0 n/a n/a 384 0.2 0.1 0.5 2,257

Tanzania 2010 5.3 3.1 9.1 491 (3.2) 0.4 19.7 49 4.4 2.7 7.2 708

Nigeria 2008 0.7 0.3 1.8 595 0.2 0.0 1.5 219 0.4 0.2 0.9 1,630

Nigeria 2013 4.2 2.4 7.2 715 2.5 0.7 8.2 132 1.8 1.1 2.9 2,104

Ghana 2008 1.7 0.5 5.7 193 (5.1) 0.7 28.8 32 1.5 0.5 4.1 344

Kenya 2008-9 0.1 0.0 0.8 351 1.2 0.2 8.5 95 0.3 0.1 1.3 547

Congo DR 2013-14 4.2 2.7 6.5 834 2.4 0.8 6.7 289 2.8 1.9 4.1 1,659

Senegal 2010-11 0.2 0.1 0.7 718 (1.1) 0.1 7.5 64 0.2 0.1 0.6 1,071

Mali 2012-13 2.9 1.3 6.4 216 (0.0) n/a n/a 21 2.0 1.0 3.9 446

Madagascar 2008-9 4.0 1.6 9.5 259 0.0 n/a n/a 92 2.5 1.0 6.1 404

Ethiopia 2005 0.5 0.1 3.3 342 0.0 n/a n/a 63 0.4 0.0 2.4 467

Ethiopia 2011 0.2 0.0 1.0 357 0.4 0.1 2.9 131 0.1 0.0 0.6 527

LAC

Haiti 2012 0.0 n/a n/a 209 0.5 0.1 2.1 276 0.2 0.1 1.0 583

Note: This table includes all surveys with complete information on whether the child was given ORS and or zinc for their 

diarrhea. Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on 

fewer than 25 unweighted cases and has been suppressed. LB and UB refer to the lower and upper bounds of the 95% 

confidence interval. LB and UB refer to the lower and upper bounds of the 95% confidence interval. 

n/a=data not available 

Antibiotics should only be given to children with bloody diarrhea. We examined the use of antibiotics 

among children with non-bloody diarrhea to assess the extent to which inappropriate care was provided. 

The older surveys in 11 countries and both surveys in Bangladesh did not ask mothers about use of 

antibiotics for their children with diarrhea. Overall, a considerable proportion of children were given 

antibiotics although they did not have bloody diarrhea. Such inappropriate care was most commonly 

reported in Tanzania, where almost two-thirds of children for whom care was sought from any source for 

non-bloody diarrhea were given antibiotics, based on the Tanzania 2012-13 DHS survey. In a few other 

countries such as Pakistan, Nigeria, Ghana, Mozambique, Senegal, and DR Congo, over one-third of 

children were given such inappropriate treatment. In countries with data available from both surveys, the 

change between surveys in the use of antibiotics for children with non-bloody diarrhea was only 

significant in Zambia, where it increased from 25 percent in 2007 to 37 percent in 2013. The prevalence 

of inappropriate use of antibiotics was similar between private and public providers. 
51 

Table 9c. Among children under age 5 with recent non-bloody diarrhea for whom care 

was sought, the percentage who received antibiotics, by source of care, USAID MCH 

priority countries 

Pubic Private Any source 

% LB UB N % LB UB N % LB UB N 

Asia 

Indonesia 2012 15.5 11.6 20.3 546 18.2 15.1 21.8 1,025 15.0 12.9 17.4 1,857

Pakistan 2012-13 42.6 33.4 52.3 208 40.2 35.6 44.9 1,444 40.2 36.0 44.5 1,670

India 2005-6 21.5 17.6 26.1 598 22.3 19.4 25.4 1,959 20.8 18.4 23.5 2,933

Nepal 2006 11.8 6.2 21.4 99 (19.2) 7.4 41.4 30 14.3 9.4 21.0 247

Nepal 2011 14.6 8.5 23.8 144 23.1 13.2 37.2 86 18.9 14.2 24.7 369

Africa 

Uganda 2006 29.6 24.6 35.1 448 36.9 31.9 42.2 647 32.4 28.6 36.5 1,181

Uganda 2011 34.8 28.5 41.6 457 40.5 35.5 45.6 610 38.1 34.1 42.3 1,097

Liberia 2007 17.9 12.9 24.4 216 16.0 9.2 26.4 156 13.2 10.0 17.4 591

Liberia 2013 16.1 10.8 23.4 353 24.3 16.0 35.2 134 15.0 11.5 19.4 779

Malawi 2010 24.9 22.1 27.9 1,387 34.1 25.8 43.5 340 24.5 22.0 27.3 1,936

Tanzania 2010 66.0 60.0 71.5 416 (66.1) 48.0 80.4 42 65.3 60.3 70.0 594

Nigeria 2008 51.4 45.8 56.8 471 48.6 40.0 57.3 167 43.5 39.8 47.3 1,275

Nigeria 2013 51.1 45.9 56.3 578 61.4 49.4 72.2 99 42.7 39.5 45.9 1,708

Zambia 2007 25.8 21.4 30.8 417 30.9 18.8 46.3 54 24.9 21.0 29.3 509

Zambia 2013-14 37.5 33.8 41.3 1,041 38.6 24.8 54.5 61 37.2 33.8 40.7 1,149

Ghana 2008 40.3 31.8 49.4 151 * n/a n/a 23 41.6 35.5 47.9 277

Kenya 2008-9 25.2 19.1 32.4 269 21.3 11.0 37.0 83 21.1 16.4 26.7 440

Mozambique 2011 42.2 37.0 47.6 586 * n/a n/a 12 39.4 34.6 44.4 652

DR Congo 2007 44.3 35.6 53.4 368 6.8 2.0 20.1 75 40.7 32.9 49.0 497

DR Congo 2013-14 33.7 27.2 40.9 649 41.5 33.2 50.3 229 36.1 32.5 39.9 1,315

Senegal 2010-11 45.6 39.5 51.9 628 (35.6) 20.9 53.5 56 37.4 33.0 42.0 933

Mali 2012-13 31.6 23.8 40.7 155 * n/a n/a 17 19.4 14.9 24.9 335

Rwanda 2010 20.4 16.0 25.6 317 * n/a n/a 15.0 16.7 13.3 20.7 448

Madagascar 2009 35.2 28.5 42.6 222 32.1 20.5 46.5 79 32.0 26.4 38.1 347

Ethiopia 2005 30.1 21.0 41.1 201 (24.7) 11.3 45.7 39 29.1 21.7 37.9 272

Ethiopia 2011 22.6 16.2 30.6 260 26.9 15.7 41.9 104 24.4 18.6 31.3 396

LAC

Haiti 2005-6 30.2 20.5 42.1 122 19.4 12.5 29.0 170 22.4 16.7 29.4 391

Haiti 2012 18.2 12.1 26.4 186 17.9 12.5 24.8 250 16.1 12.6 20.5 522

Note: This table includes all surveys with complete information on whether the child's diarrhea was bloody, and whether 

the child was given antibiotics for their diarrhea. Figures in parentheses are based on 25-49 unweighted cases. An 

asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. LB and UB refer 

to the lower and upper bounds of the 95% confidence interval. 

n/a= data not available 

53 

4. Discussion and Conclusions 

This follow-up study to Hodgins, Pullum, and Dougherty (2013) examined levels and trends in care 

seeking for children who have experienced recent fever, symptoms of ARI, or diarrhea across MCH 

priority countries. Key findings and interpretation of current levels of care seeking, recent trends in care 

seeking and equity of care seeking, and appropriateness of care are summarized below. 

4.1. Levels of Care Seeking 

Overall, current levels of care seeking among children with recent illness vary widely across the 21 

priority countries, from nearly 90 percent in the Indonesia 2012 survey to 33 percent in Ethiopia 2011. In 

three countries—Ethiopia, Madagascar, and Haiti—less than half of children under age 5 with recent 

illness received care from any source. 

Consistent with Hodgins, Pullum, and Dougherty (2013), our results show that care seeking from public 

sources is predominant in African MCH priority countries while care seeking from private sources is 

predominant in Asian MCH priority countries. This pattern is similar across the three types of illness. The 

regional differential is likely due in part to differences in history, development trajectories, and economy. 

In South Asia, global recessions in the 1970s and 1980s constrained government spending in health care, 

and fostered the growth of the private sector (Baru 2003). Private sector growth in South Asia, 

particularly in the 1970s and 1980s, was driven largely by multilateral donor organizations, which were 

influenced by the World Bank and IMF’s structural adjustment policies that encouraged privatization, and 

by the pharmaceutical industry (Baru 2003). The observed difference in use of the private health sector in 

Asia and Africa could also be related to the relatively larger size of the middle class in Asian MCH 

priority countries compared with African MCH priority countries. The middle class typically supplies 

both the private sector workforce and the clientele, which enable private health sector growth (Baru 

2003). 

A few countries are worth highlighting for their mix of care sources. Uganda is the only sub-Saharan 

African country in our study with a heavy reliance on private sources of care to treat children’s illness. 

Use of private sources of care in Uganda is highest among children in wealthiest-quintile households, 

although the private sector is an important source of care among children in all wealth quintiles and in 

both urban and rural areas. In Uganda’s public health sector, mismanagement of funds, lack of resources 

and commodities, low salaries for healthcare workers, poorly maintained infrastructure, and overall poor 

service delivery have driven the need for and growth of private health care (Soderlund, Mendoza-Arana, 

and Goudge 2003). In a study of three rural districts in Uganda, Konde-Lule and colleagues found that 

private providers far outnumbered public providers, and that a majority of individuals in the community 

chose to see private providers when seeking care (Konde-Lule et al. 2010). Proximity to the source of 

care was the most common reason for choosing a private provider, while the perceived skill of the 

provider and cost were other important factors. It is important to note that while public services are 

officially free, many health workers in Ugandan public facilities charge informal fees (Konde-Lule et al. 

2010). 

Nigeria is unique among MCH priority countries for its high reliance on pharmacies as the sole source of 

care for children’s fever, symptoms of ARI, and diarrhea. Care was sought from a pharmacy for over onethird of children with fever and for nearly one-third of children with symptoms of ARI and diarrhea (35 

percent, 32 percent, and 32 percent, respectively). In Nigeria, according to current Ministry of Health 

policy, for-profit drug retailers and patent and proprietary medicine vendors are eligible to become 

community-based resource persons who can be trained and certified to deliver iCCM. However, current 

regulations also prohibit these vendors from selling antibiotics. A recent census of patent and proprietary 
54 

medicine vendors conducted in two Nigerian states (Kogi and Kwara) found that levels of knowledge of 

the most effective treatment for malaria, pneumonia, and diarrhea among vendors are low (Treleaven et 

al. 2015). More than two-thirds of vendors correctly named the most effective treatment for malaria, less 

than one-third named the most effective treatment for diarrhea, and virtually no vendors named the most 

effective treatment for pneumonia (Treleaven et al. 2015). Given the importance of for-profit drug 

retailers, patent medicine vendors, and propriety medicine vendors as sources of care for fever, symptoms 

of ARI, and diarrhea, additional training on treating childhood illnesses could have a substantial impact 

on child survival and health in Nigeria and in other settings where reliance on pharmacies and shops is 

high. 

Mali’s use of other sources of care for childhood illnesses is noteworthy. As Appendix Tables A1-6 

illustrate, the most common other sources of care in Mali are markets or informal shops and nonallopathic sources. The importance of informal shops and traditional healers in Mali is well documented 

(Ellis et al. 2007). In a qualitative study of household management of diarrhea, Ellis et al. (2007) 

conducted in-depth, semi-structured interviews with parents, community health workers, and traditional 

healers in the Sikasso region. Ellis et al. found that treatment of an episode of diarrhea typically began in 

the home with traditional medicines and/or antibiotics, which could be purchased from vendors in the 

village or market. If initial treatments did not help the child, parents would often seek care from a 

community health worker, community health center, or traditional healer. Traditional healers were often 

consulted for their knowledge of preparing more powerful or complicated traditional medicines, the 

ingredients for which could often be collected around the village or purchased inexpensively at the 

market. 

Despite major emphasis in recent years on community case management and care provision by 

community health workers, only a small percentage of care—3 percent or less for each illness in all 

countries except Rwanda and Indonesia—is being sought from this source, according to the most recent 

DHS data (see Appendix Tables A1-6 for detailed results). This finding is consistent with other recent 

studies that report low use of community health workers (Geldsetzer et al. 2014; Hodgins, Pullum, and 

Dougherty 2013). In a systematic literature review of studies on care seeking across low- and middleincome countries, Geldsetzer and colleagues (2014) report that a median of only 1.3 percent of caregivers 

use community health workers for malaria, 4.2 percent for pneumonia, and 5.4 percent for diarrhea. 

Caregivers have identified the perceived low status of community health workers within the community 

and a lack of supplies and medications as the reasons families do not use their services (Geldsetzer et al. 

2014). While community case management continues to be adopted, rolled out and scaled up as a major 

child survival strategy, particularly in Africa, this finding suggests that to have widespread impact, 

program efforts may need to strengthen linkages between community health workers, communities, and 

health facilities, and consider new or additional ways to reach the most remote children. 

When interpreting these findings, it is also important to recall that integrated community case 

management is an extension of the health system service delivery strategy. It is also a foray into treatment 

of community-based and sometimes community-owned health promotion strategies. It fundamentally 

remains a medium-term strategy for reaching the hardest-to-reach children, strengthening community 

health, and providing a two-way linkage of treatment and referral with the rest of the health system, while 

strengthening the larger health system and more equitably extending access to services. The iCCM 

algorithms require referrals to health facilities for newborns7

 and complicated cases. A child may still die 

when a referral is not completed. Integrated community case management is essential to save lives of the 

hardest-to-reach children, but is only part of the continuum of care. Overall health system strengthening 

should remain a priority as iCCM is strengthened and expanded as the lowest level of care. 

 

7 Some countries, such as Malawi, are piloting integration of newborn care packages in iCCM. 
55 

4.2. Recent Trends in Care Seeking 

The majority of countries studied did not show significant improvements between the two most recent 

DHS surveys in the level of care seeking for fever or for symptoms of ARI from any source, private 

sources, or public sources. Care seeking for diarrhea, however, showed more widespread improvement; 

13 of 20 countries showed a statistically significant increase in care seeking from any source for diarrhea, 

while no country showed a decrease in coverage. These findings are consistent with Bennett and 

colleagues (2015), who reported a significant increase in care seeking for diarrhea between 2000 and 

2013 in Western Africa, Eastern Africa, Southeast Asia, and Western Asia in an analysis of 248 DHS 

surveys. The researchers found significant (p<.05) regional increases in care seeking for fever only in 

Western Africa, and no significant regional increases in care seeking for symptoms of ARI. 

The greater gains in care seeking for children’s diarrhea could be driven in part by the fact that levels of 

care seeking were initially lower for diarrhea than the other childhood illnesses. Other studies have found 

that lower baseline levels of child health intervention coverage were associated with greater annual 

improvements (Boschi-Pinto, Bahl, and Martines 2009). However, the greater gains in care seeking for 

diarrhea could also be driven in part by the initiation and scale-up of programs that introduced zinc into 

diarrhea case management during the study period. These programs (such as SHOPS in Ghana, Nigeria, 

Uganda, and Kenya, and POUZN in Nepal) were often accompanied by behavioral change campaigns 

focused on the care and treatment for children’s diarrhea. Our Nepal case study did not find ecological 

evidence of an effect of POUZN on care seeking in project districts, but we offered several explanations 

that could account for our inability to detect a true program effect (see Section 3.3.1). 

The lack of increase in care seeking for fever over the study period could have several explanations. Fever 

in children is a symptom of illness known to instill fear in caregivers and prompt action (Sakai et al. 

2012). Thus, constant rates of care seeking for fever are not surprising. Because the decision to seek care 

is based on perception of risk, it is also possible that care seeking for fever would be seen as less urgent 

because malaria prevalence has declined drastically in some of the MCH priority countries included in 

this analysis. If care seeking for fever expanded to previously marginalized populations, overall increases 

might not occur because care seeking declined among non-marginalized groups. Patterns of care seeking 

for fever also depend on the source of care. Focusing on care seeking from any source can obscure 

important shifts in use of public versus private sources of care, as in the Liberia case study. Similarly, 

sources of care could include pharmacies, markets, shops, and traditional healers. All sources of care may 

not provide equal quality of care or have accessibility to the necessary resources for appropriate case 

management. Thus, overall increases in care seeking for fever do not necessarily translate into improved 

access to appropriate care. 

The study identified distinct patterns in recent trends between the two survey periods in care seeking from 

public versus private sources. For all three illnesses, the use of public sources of care either remained the 

same or increased between the two periods; there were no decreases. The trend in use of private sources 

of care was mixed. For fever and diarrhea, some countries, primarily in Asia, experienced an increase in 

the level of care seeking from private sources, while others, all in sub-Saharan Africa, experienced a 

decrease in the level of care seeking from private sources. Given the focus on IMCI, iCCM, and 

community-based programs in recent years, and the reality that public sources of care are often free, it 

may not be surprising that the expansion of care seeking in African countries has been concentrated in the 

public sector. Ideally, growth in these programs would lead to an overall increase in care seeking rather 

than a transfer of care seeking from private to public sources, as observed in the Liberia case study. 

Additional research should identify factors that contribute to decreases in use of the private sector in 

African MCH priority countries and investigate reasons why, in the study countries within Africa, 

increased care seeking has been predominantly in the public sector. 
56 

There is wide agreement that in order to make gains in child survival, child health programs must reach 

children who are hardest to reach, i.e., those who are poor and live in remote areas. With this in mind, we 

examined trends within two vulnerable populations: children in poorest-wealth quintile households and 

children in rural households. The patterns within these populations roughly followed the national patterns. 

In most cases, the change in coverage of care seeking between surveys was not significantly different 

between children in the poorest and the wealthiest households, or between rural and urban children. Thus, 

despite general agreement that child health programs should focus specifically on these vulnerable 

children in order to make gains in child survival, we found only limited evidence that increases in care 

seeking were concentrated in these vulnerable populations. Noteworthy exceptions include the rapid 

expansion of care seeking from private sources for children’s fever in Bangladesh, which was 

disproportionately concentrated in poorest-quintile households, and which narrowed the equity gap; and 

the disproportionate concentration of the expansion of care seeking from private sources for diarrhea 

among poorest-quintile households in Indonesia. 

With case studies in Nepal and Liberia, we attempted to identify ecological evidence of the impact of 

programs by examining temporal trends in care seeking separately for areas covered and not covered by 

two USAID health initiatives. In Liberia our results show that the increase in care seeking from public 

facilities between 2007 and 2011 was significantly greater in Liberian counties covered by the PMIfunded EQUIP project compared with the increase in areas with no EQUIP presence, after adjusting for 

socio-demographic characteristics. However, overall levels of care seeking from any source did not 

improve in EQUIP project districts between 2007 and 2011; rather, there appears to have been a shift 

from the use of private providers to the use of public providers. In Nepal, despite countrywide 

improvements in levels of care seeking for diarrhea between the 2006 and 2011 DHS surveys, we did not 

find evidence that increases in districts covered by the POUZN project were greater than those in nonproject districts. We discussed potential reasons for this null finding in the case study. 

4.3. Appropriateness of Care 

Levels and trends in coverage of care seeking are meaningful only if the sources of care consistently 

provide children and caregivers with appropriate diagnoses, advice, and treatment. Despite the 

widespread increase in coverage of care seeking for diarrhea, there has not been a parallel increase in 

coverage of appropriate treatment. Treatment with ORS increased significantly in just four of the 20 

countries, coverage of treatment with both ORS and zinc remains under 5 percent in 16 of the 18 

countries with data available, and no country showed an improvement (reduction) in inappropriate use of 

antibiotics to treat non-bloody diarrhea. Other studies have also observed the limited improvement in 

diarrhea case management over time (Boschi-Pinto, Bahl, and Martines 2009; Geldsetzer et al. 2014; 

UNICEF and WHO 2009). Geldsetzer and colleagues (2014) report that studies on trends in the use of 

oral rehydration therapy showed a constant level of ORS use over time, with a median of 31 percent 

before 1990, 35 percent from 1990 to 1999, and 36 percent from 2000 to 2010. Similarly, Boschi-Pinto 

and colleagues (2009) found that 17 of 29 African and Asian countries showed no significant annual 

improvement in ORS use for diarrhea between roughly 1990 and 2006 (Boschi-Pinto, Bahl, and Martines 

2009). 

Major barriers to the provision of zinc include its scarcity in health facilities and in some countries, the 

absence of national policies that support the use of zinc for routine diarrhea case management (Gill et al. 

2013). For example, while 75 percent of patent and proprietary medicine vendors (PPMVs) in two 

Nigerian states (Kogi and Kwara) were stocked with ORS, according to a recent census, less than 3 

percent were stocked with zinc (Treleaven et al. 2015). Other barriers to ORS and zinc treatment include 

counterproductive incentives for health workers to offer antibiotics over zinc or ORS for children’s 

diarrhea; inefficiencies in procurement, production, and distribution of ORS and zinc; lack of government 

policies or incentives for private sector investment in supply chain management systems; lack of support 
57 

for locally produced zinc or oral rehydration solution; and low demand for these treatments (Gill et al. 

2013). 

Turning to indicators of appropriate care for fever, we find that in most surveys the percentage of children 

with recent fever receiving finger/heel sticks is lower among those receiving care from any source than 

those seeking care from public or private sources. This is likely because the other sources of care included 

in the estimates, e.g., markets, shops, and traditional healers, are unlikely to have diagnostic capacity and 

therefore are unlikely to take blood samples from children. The range in coverage of finger/heel sticks 

among children with fever in the MCH priority countries with available data is not surprising because 

universal test and treatment policies have been adopted and implemented at different rates across these 

countries. Access to commodities necessary for implementation of these policies such as microscopy or 

RDT is not equal across countries. In Uganda, the one African MCH priority country with high levels of 

care seeking from private sources for childhood illness, children with fever seeking care at private 

facilities were significantly less likely to receive a finger/heel stick than children for whom care was 

sought at public facilities. The inverse was true in several other African countries. Public facilities had 

higher rates of providing finger/heel sticks than did private facilities. Interpretation of these results is 

challenging without additional information. It is possible that the necessary diagnostic capacity is lacking 

in public facilities in these countries. 

Appropriateness of antimalarial treatment, either with ACT in more recent surveys or with the first-line 

antimalarial in older surveys, also revealed a large range of values across MCH priority countries. These 

results indicate varied levels of access to essential medications but do not provide reasons for successes or 

failures in provision of appropriate antimalarial treatment. Surveys in several countries showed very high 

use of appropriate antimalarials among those who received any antimalarial—85 percent in Malawi 

(2010), 91 percentin Zambia (2013-14), and 96 percent in Rwanda (2010). These countries could be 

studied in order to identify the predictors of appropriate care. Are ACTs more available and affordable in 

these countries than elsewhere? Are behavior change and communications (BCC) programs superior? Are 

drug supplies better regulated? 

In three countries (Malawi, Uganda, and Tanzania), appropriate treatment with ACT or other first-line 

antimalarials was significantly higher when care was sought from public than private sources. Many 

malaria-endemic countries have implemented subsidies to encourage appropriate use of ACT, which may 

only be available at public health facilities. Uganda and Tanzania were both a focus of the Affordable 

Medicines Facility-Malaria Program implemented from 2010-12. This program aimed to improve access 

to ACT by increasing availability, particularly through private outlets, and decreasing the price of ACT 

through subsidies. In these two countries, public sources of care seem to be linked to more appropriate 

use of antimalarials when compared with private sources. 

Another important consideration when interpreting these results is that the national scale is not always the 

most relevant level at which to assess malaria indicators. In some settings, risk of malaria transmission 

varies substantially throughout the country or by season, and this range in risk is difficult to account for 

with household survey data. Patterns of care seeking for fever may be influenced by different factors in 

settings that have high malaria transmission risk than in those with lower risk, where fevers are likely to 

have causes other than malaria. 

We know that in order to save lives, children need to receive ACT for malaria, antibiotics for pneumonia, 

and ORS and zinc for diarrhea. Yet coverage of these proven interventions remains far too low. In order 

to receive the correct treatment, children first must receive appropriate diagnosis, care, and counseling 

from a qualified provider. Our results show that the percentage of children with diarrhea, fever, and 

symptoms of ARI who reach the health system remains low in many high-mortality settings. Our results 

also provide information about where caregivers are taking their sick children. At the country level, we 
58 

hope these results will inform strategies for improving case management for childhood illness and 

directing resources and attention towards preferred sources of care. The results would most useful when 

interpreted within the landscape of child health interventions and programming, and with national 

childhood illness management goals in mind. Complementary studies with other data sources such as 

health management information system (HMIS) data, health facility data such as Service Provision 

Assessments (SPA), and qualitative research are needed to fill in critical gaps in knowledge about the 

health system, household context, and caregivers’ motivations for seeking care and selecting particular 

sources of care. 
59 

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63 

Appendix 

Figure A1. Comparison of care 

seeking from public versus private 

facilities for fever, USAID MCH 

priority countries 

Figure A2. Comparison of care 

seeking from public versus private 

facilities for symptoms of ARI, 

USAID MCH priority countries 

Figure A3. Comparison of care 

seeking from public versus private 

facilities for diarrhea, USAID MCH 

priority countries 

0 50 100

Haiti 2012

Ethiopia 2011

Rwanda 2010

Rwanda 2010

Mali 2012-13

Senegal 2010-11

Congo DR...

Mozambique...

Kenya 2008-9

Ghana 2008

Zambia 2013-14

Nigeria 2013

Tanzania 2010

Malawi 2010

Liberia 2013

Uganda 2011

Nepal 2011

India 2005-6

Bangladesh 2011

Pakistan 2012-13

Indonesia 2012

Public

Private

0 50 100

Haiti 2012

Ethiopia 2011

Rwanda 2010

Rwanda 2010

Mali 2012-13

Senegal 2010-11

Congo DR 2013-14

Mozambique 2011

Kenya 2008-9

Ghana 2008

Zambia 2013-14

Nigeria 2013

Tanzania 2010

Malawi 2010

Liberia 2013

Uganda 2011

Nepal 2011

India 2005-6

Bangladesh 2011

Pakistan 2012-13

Indonesia 2012

Public

Private

0 50 100

Haiti 2012

Ethiopia 2011

Rwanda 2010

Rwanda 2010

Mali 2012-13

Senegal 2010-11

Congo DR 2013-14

Mozambique 2011

Kenya 2008-9

Ghana 2008

Zambia 2013-14

Nigeria 2013

Tanzania 2010

Malawi 2010

Liberia 2013

Uganda 2011

Nepal 2011

India 2005-6

Bangladesh 2011

Pakistan 2012-13

Indonesia 2012

Public

Private
64 

Table A1. Sources of care for fever by place of residence, USAID MCH priority countries 

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non- allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Bangladesh 2007 Urban 434 4.1 7.9 24.0 0.0 0.0 1.3 24.9 0.0 0.0 9.9 5.5 23.9 0.0 12.0 25.2 23.9 15.3 74.8 Bangladesh 2007 Rural 1751 1.5 6.8 12.1 0.0 0.0 0.5 23.1 1.0 0.0 23.6 4.9 22.8 1.0 8.3 12.6 22.8 29.4 71.4 

Bangladesh 2007 All 2185 2.0 7.0 14.5 0.0 0.0 0.7 23.4 0.8 0.0 20.9 5.0 23.0 0.8 9.0 15.1 23.0 26.6 72.0 Bangladesh 2011 Urban 595 3.3 7.7 44.5 0.0 0.3 0.5 19.8 0.0 0.0 2.7 0.5 18.5 0.0 10.9 45.3 18.5 3.1 76.3 Bangladesh 2011 Rural 2469 1.5 6.9 42.6 0.0 0.1 0.5 21.1 0.0 0.0 4.5 0.5 19.1 0.0 8.3 43.1 19.1 5.1 74.3 Bangladesh 2011 All 3064 1.9 7.0 42.9 0.0 0.2 0.5 20.8 0.0 0.0 4.2 0.5 19.0 0.0 8.8 43.5 19.0 4.7 74.7 Congo DR 2007 Urban 950 7.4 14.7 23.2 0.0 3.8 0.0 19.5 0.7 0.0 1.3 2.4 17.6 0.7 21.4 27.0 17.6 4.5 67.8 Congo DR 2007 Rural 1519 2.7 24.8 7.1 0.0 9.9 0.0 9.9 0.9 0.0 4.1 2.2 9.2 0.9 27.3 16.9 9.2 7.1 57.6 Congo DR 2007 All 2469 4.5 20.9 13.3 0.0 7.6 0.0 13.6 0.9 0.0 3.0 2.3 12.4 0.8 25.0 20.8 12.4 6.1 61.5 Congo DR 2013-14 Urban 1579 3.9 16.5 17.9 0.0 0.0 0.0 21.1 0.0 0.1 1.2 2.1 19.2 0.0 20.0 17.9 19.2 3.4 59.2 Congo DR 2013-14 Rural 3434 1.9 30.5 8.6 0.1 0.0 0.0 14.5 0.3 0.5 3.4 0.9 13.4 0.3 32.4 8.6 13.4 5.1 58.5 Congo DR 2013-14 All 5013 2.6 26.1 11.5 0.1 0.0 0.0 16.6 0.2 0.4 2.7 1.3 15.2 0.2 28.5 11.5 15.2 4.6 58.7 Ethiopia 2005 Urban 121 10.6 19.1 13.2 0.6 0.0 2.5 3.3 0.0 0.3 0.0 0.1 0.8 0.0 30.3 15.7 0.8 0.3 46.4 Ethiopia 2005 Rural 1765 3.9 8.7 2.2 0.4 0.0 0.5 1.5 0.0 0.3 0.4 0.0 1.0 0.0 13.1 2.7 1.0 0.7 17.2 Ethiopia 2005 All 1886 4.4 9.4 2.9 0.4 0.0 0.6 1.6 0.0 0.3 0.4 0.0 1.0 0.0 14.2 3.5 1.0 0.7 19.1 Ethiopia 2011 Urban 226 4.3 19.1 16.0 0.0 0.0 1.0 3.8 0.0 0.0 0.0 0.0 3.4 0.0 22.2 16.4 3.4 0.0 41.2 Ethiopia 2011 Rural 1659 0.7 15.6 6.2 0.0 0.0 0.7 0.9 0.7 0.0 0.5 0.0 0.6 0.7 16.0 6.7 0.6 1.2 24.2 Ethiopia 2011 All 1885 1.1 16.1 7.4 0.0 0.0 0.7 1.2 0.6 0.0 0.4 0.0 0.9 0.6 16.8 7.9 0.9 1.1 26.2 Ghana 2003 Urban 250 34.4 10.4 10.9 0.6 0.0 0.0 17.7 0.5 0.0 0.5 1.0 16.2 0.0 45.4 10.9 16.2 2.0 73.6 Ghana 2003 Rural 460 20.8 17.0 4.9 0.1 0.0 0.0 26.5 0.5 0.0 1.5 4.0 25.6 0.5 36.9 4.9 25.6 6.0 72.6 Ghana 2003 All 710 25.6 14.7 7.0 0.3 0.0 0.0 23.4 0.5 0.0 1.2 3.0 22.3 0.3 39.9 7.0 22.3 4.6 73.0 Ghana 2008 Urban 197 33.8 8.1 18.5 0.0 0.0 0.0 21.4 0.0 1.5 0.4 0.5 19.6 0.0 41.6 18.5 19.6 2.3 81.5 Ghana 2008 Rural 347 16.1 24.5 4.9 0.0 0.0 0.3 10.5 1.4 2.9 0.3 3.4 10.0 1.4 40.5 5.2 10.0 8.1 63.7 Ghana 2008 All 544 22.5 18.6 9.8 0.0 0.0 0.2 14.5 0.9 2.4 0.4 2.3 13.5 0.9 40.9 10.0 13.5 6.0 70.1 Haiti 2005-6 Urban 466 7.7 9.6 18.4 0.0 0.0 5.1 3.9 2.1 1.9 2.5 5.9 3.9 2.1 17.3 23.4 3.9 10.4 49.8 Haiti 2005-6 Rural 1020 1.1 11.9 13.8 0.0 0.0 8.6 1.2 1.5 0.4 2.4 3.2 1.1 1.4 12.9 22.4 1.1 6.8 41.4 Haiti 2005-6 All 1486 3.1 11.2 15.3 0.0 0.0 7.5 2.1 1.7 0.9 2.4 4.1 2.0 1.6 14.3 22.7 2.0 7.9 44.0 Haiti 2012 Urban 611 8.9 6.7 23.7 0.0 0.2 8.5 2.0 0.7 0.6 0.1 3.9 1.2 0.6 15.4 31.7 1.2 5.3 51.5 Haiti 2012 Rural 1124 3.9 10.1 15.0 0.0 0.3 9.9 0.4 1.5 1.7 0.9 5.8 0.2 1.5 13.9 25.1 0.2 8.7 45.8 Haiti 2012 All 1735 5.6 8.9 18.0 0.0 0.3 9.4 0.9 1.2 1.3 0.6 5.1 0.5 1.1 14.4 27.4 0.5 7.5 47.8 India 2005-6 Urban 1918 8.1 4.5 65.3 0.0 0.0 0.3 3.2 0.5 0.0 3.1 1.2 2.8 0.5 12.5 65.7 2.8 4.6 83.0 India 2005-6 Rural 5934 3.8 9.8 53.0 0.0 0.0 0.4 6.4 1.0 0.0 3.4 1.8 6.0 0.9 13.5 53.4 6.0 6.1 76.6 India 2005-6 All 7852 4.9 8.5 56.0 0.0 0.0 0.3 5.6 0.9 0.0 3.3 1.6 5.2 0.8 13.2 56.4 5.2 5.7 78.1 Indonesia 2007 Urban 1937 2.5 25.0 41.3 0.0 5.7 1.0 12.1 9.6 0.0 0.4 1.0 11.0 8.5 27.1 47.9 11.0 11.1 92.8 Indonesia 2007 Rural 3096 1.8 23.5 26.3 0.0 15.1 1.4 7.6 15.5 0.0 2.1 2.6 7.1 14.7 25.0 42.3 7.1 19.6 89.6 Indonesia 2007 All 5033 2.1 24.1 32.1 0.0 11.5 1.2 9.4 13.2 0.0 1.5 2.0 8.6 12.3 25.8 44.5 8.6 16.3 90.8 Indonesia 2012 Urban 2400 2.2 25.7 46.3 0.0 6.0 0.0 15.0 3.7 0.0 1.2 1.8 12.4 3.0 27.7 52.1 12.4 6.7 90.4 Indonesia 2012 Rural 2686 1.5 21.8 33.5 0.0 20.0 0.0 7.7 9.9 0.0 2.7 2.0 6.9 8.6 23.0 52.3 6.9 14.3 88.8 Indonesia 2012 All 5086 1.8 23.6 39.6 0.0 13.4 0.0 11.1 7.0 0.0 2.0 1.9 9.5 5.9 25.2 52.2 9.5 10.7 89.6 Kenya 2003 Urban 423 13.3 15.9 21.2 0.0 0.2 3.3 17.1 5.1 0.0 0.7 1.3 15.5 4.9 29.2 24.7 15.5 7.1 75.2 Kenya 2003 Rural 1833 5.1 24.3 13.2 0.0 0.7 2.8 14.2 7.8 0.0 0.9 1.5 13.0 7.4 29.0 16.6 13.0 10.3 66.9 Kenya 2003 All 2255 6.6 22.7 14.7 0.0 0.6 2.9 14.7 7.3 0.0 0.9 1.5 13.5 6.9 29.0 18.1 13.5 9.7 68.4 Kenya 2008-9 Urban 223 23.8 17.7 9.0 0.0 0.1 3.6 8.4 2.5 0.0 0.3 0.0 8.4 2.4 40.6 12.2 8.4 2.8 63.0 Kenya 2008-9 Rural 1079 9.7 29.4 7.6 0.0 0.1 2.0 7.7 6.7 0.0 0.2 0.8 7.7 5.9 38.7 9.8 7.7 7.6 62.2 Kenya 2008-9 All 1302 12.1 27.4 7.9 0.0 0.1 2.3 7.8 6.0 0.0 0.2 0.7 7.8 5.3 39.0 10.2 7.8 6.8 62.3 Liberia 2007 Urban 450 16.6 29.5 22.7 0.0 0.0 0.0 14.2 2.4 0.0 2.0 2.3 10.4 2.3 46.0 22.7 10.4 6.3 80.9 Liberia 2007 Rural 1127 5.7 26.2 21.2 0.0 0.0 0.0 7.9 7.1 0.0 8.8 6.8 7.4 6.8 31.8 21.2 7.4 20.6 74.4 

(Continued...)

64 
65 

Table A1. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non- allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Liberia 2007 All 1577 8.8 27.1 21.6 0.0 0.0 0.0 9.7 5.7 0.0 6.9 5.5 8.3 5.5 35.8 21.6 8.3 16.5 76.3 Liberia 2013 Urban 793 14.3 22.4 25.3 0.0 0.0 0.0 16.5 2.1 2.3 0.9 0.1 15.2 2.1 36.6 25.3 15.2 5.4 82.0 

Liberia 2013 Rural 935 9.1 35.5 9.4 0.2 0.0 0.0 9.0 2.3 11.7 2.3 0.7 7.9 2.2 44.8 9.4 7.9 16.7 76.8 Liberia 2013 All 1728 11.5 29.5 16.7 0.1 0.0 0.0 12.5 2.2 7.4 1.7 0.4 11.3 2.2 41.0 16.7 11.3 11.5 79.2 Madagascar 2003-4 Urban 214 12.3 17.9 29.6 0.0 0.0 0.0 1.0 1.6 0.0 0.3 0.3 1.0 1.6 30.3 29.6 1.0 2.3 61.8 Madagascar 2003-4 Rural 986 1.1 24.4 8.3 0.0 0.0 0.0 0.9 2.9 0.0 1.5 1.2 0.9 2.9 25.5 8.3 0.9 5.6 40.0 Madagascar 2003-4 All 1201 3.1 23.2 12.1 0.0 0.0 0.0 0.9 2.7 0.0 1.3 1.1 0.9 2.7 26.3 12.1 0.9 5.0 43.9 Madagascar 2008-9 Urban 164 7.7 15.9 39.3 0.0 0.4 0.0 1.4 0.3 0.0 0.0 0.0 1.1 0.3 23.6 39.7 1.1 0.3 63.3 Madagascar 2008-9 Rural 952 2.5 27.8 8.2 0.0 0.9 0.0 0.5 4.2 0.0 1.6 0.1 0.5 4.1 30.3 9.1 0.5 5.8 44.7 Madagascar 2008-9 All 1116 3.3 26.0 12.7 0.0 0.8 0.0 0.6 3.7 0.0 1.4 0.0 0.6 3.5 29.3 13.6 0.6 5.0 47.4 Malawi 2010 Urban 786 29.6 23.5 12.6 0.0 0.0 4.5 0.0 3.3 0.0 0.0 0.7 0.0 3.1 53.1 17.0 0.0 4.1 72.3 Malawi 2010 Rural 5428 10.9 38.6 4.8 1.9 0.1 9.9 0.0 8.3 0.0 0.6 0.9 0.0 7.7 50.3 14.7 0.0 9.8 72.9 Malawi 20104 All 6214 13.3 36.7 5.8 1.6 0.1 9.2 0.0 7.7 0.0 0.5 0.9 0.0 7.1 50.7 15.0 0.0 9.0 72.8 Mali 2006 Urban 558 4.1 36.6 4.8 0.0 1.3 0.0 11.3 0.0 2.6 12.4 0.9 10.6 0.0 40.5 6.1 10.6 15.8 71.9 Mali 2006 Rural 1680 0.6 24.9 0.6 0.0 2.2 0.0 0.9 2.4 8.5 12.4 2.2 0.7 2.4 25.5 2.8 0.7 24.5 52.6 Mali 2006 All 2238 1.5 27.8 1.7 0.0 2.0 0.0 3.5 1.8 7.0 12.4 1.9 3.2 1.8 29.2 3.6 3.2 22.3 57.4 Mali 2012-13 Urban 154 2.4 39.8 7.1 0.0 0.3 0.0 10.7 0.0 6.3 5.4 1.8 9.8 0.0 41.5 7.4 9.8 12.5 70.8 Mali 2012-13 Rural 676 0.0 22.2 0.5 0.0 1.0 0.0 2.4 0.9 8.3 9.5 2.6 2.4 0.9 22.2 1.5 2.4 19.5 44.2 Mali 2012-13 All 830 0.5 25.4 1.7 0.0 0.9 0.0 3.9 0.8 8.0 8.7 2.5 3.7 0.8 25.7 2.6 3.7 18.2 49.1 Mozambique 2003 Urban 731 12.7 51.2 1.6 0.0 0.0 0.3 1.4 0.0 0.8 1.4 2.5 1.3 0.0 63.3 1.9 1.3 4.7 70.8 Mozambique 2003 Rural 1778 2.8 41.9 0.9 0.3 0.0 1.1 0.2 0.0 2.7 3.3 3.4 0.2 0.0 45.0 2.0 0.2 9.2 56.0 Mozambique 2003 All 2509 5.7 44.6 1.1 0.2 0.0 0.9 0.6 0.0 2.1 2.7 3.1 0.5 0.0 50.3 2.0 0.5 7.9 60.3 Mozambique 2011 Urban 388 69.9 0.2 1.5 1.2 0.0 0.0 2.0 0.0 0.0 1.0 0.5 0.8 0.0 71.1 1.5 0.8 1.4 74.0 Mozambique 2011 Rural 1069 49.1 1.1 0.0 4.0 0.0 0.0 0.2 0.0 2.0 3.0 0.7 0.1 0.0 53.7 0.0 0.1 5.6 58.4 Mozambique 2011 All 1457 54.6 0.8 0.4 3.3 0.0 0.0 0.7 0.0 1.4 2.5 0.6 0.3 0.0 58.4 0.4 0.3 4.5 62.6 Nepal 2006 Urban 145 15.8 4.1 20.3 0.1 0.0 1.3 27.3 0.2 0.0 2.6 0.2 23.1 0.2 19.8 21.6 23.1 3.0 66.0 Nepal 2006 Rural 745 3.9 18.1 7.7 3.2 0.0 1.5 26.0 0.6 0.0 1.4 0.0 24.5 0.4 23.7 9.2 24.5 2.0 58.1 Nepal 2006 All 890 5.9 15.8 9.8 2.7 0.0 1.5 26.2 0.5 0.0 1.6 0.0 24.3 0.4 23.1 11.2 24.3 2.1 59.4 Nepal 2011 Urban 91 12.8 5.7 36.0 0.5 0.0 0.6 27.3 0.0 0.0 0.5 1.4 24.5 0.0 19.0 36.6 24.5 1.9 81.4 Nepal 2011 Rural 869 2.1 14.9 21.7 2.1 0.0 0.6 29.9 0.1 0.0 0.7 1.1 28.7 0.1 18.9 22.3 28.7 2.0 70.5 Nepal 2011 All 960 3.2 14.1 23.0 1.9 0.0 0.6 29.6 0.1 0.0 0.7 1.1 28.3 0.1 18.9 23.6 28.3 2.0 71.5 Nigeria 2008 Urban 987 18.6 9.7 12.1 0.8 0.6 0.0 30.0 2.8 0.0 0.9 0.9 28.8 2.8 28.6 12.6 28.8 4.6 73.9 Nigeria 2008 Rural 2981 9.4 15.6 7.8 0.6 0.9 0.0 27.7 6.5 0.0 1.3 0.9 26.6 6.2 25.3 8.6 26.6 8.7 68.1 Nigeria 2008 All 3968 11.7 14.1 8.8 0.6 0.8 0.0 28.3 5.6 0.0 1.2 0.9 27.1 5.4 26.2 9.6 27.1 7.7 69.5 Nigeria 2013 Urban 1262 16.2 10.8 9.0 0.7 0.0 0.0 37.1 0.0 0.4 1.9 1.0 35.5 0.0 27.5 9.0 35.5 3.2 74.2 Nigeria 2013 Rural 2370 9.7 14.7 3.9 0.4 0.0 0.0 35.9 3.6 1.6 4.6 0.5 35.4 3.6 24.8 3.9 35.4 10.1 73.8 Nigeria 2013 All 3632 12.0 13.3 5.7 0.5 0.0 0.0 36.4 2.4 1.2 3.7 0.6 35.4 2.4 25.7 5.7 35.4 7.7 73.9 Pakistan 2006-7 Urban 791 7.7 0.7 70.0 0.7 0.0 0.0 0.5 1.3 0.0 3.6 0.5 0.5 1.3 9.1 70.0 0.5 5.1 83.3 Pakistan 2006-7 Rural 1777 8.7 1.9 64.9 0.3 0.0 0.0 0.8 2.7 0.0 2.0 0.2 0.7 2.7 10.7 64.9 0.7 4.8 80.2 Pakistan 2006-7 All 2569 8.4 1.5 66.5 0.4 0.0 0.0 0.7 2.3 0.0 2.5 0.3 0.7 2.2 10.2 66.5 0.7 4.9 81.2 Pakistan 2012-13 Urban 1198 9.8 0.6 72.8 0.0 0.0 0.0 0.6 1.4 0.0 0.2 0.4 0.6 1.4 10.4 72.8 0.6 1.9 85.0 Pakistan 2012-13 Rural 2954 6.9 1.2 70.0 0.2 0.0 0.0 0.5 1.7 0.0 0.9 0.3 0.4 1.6 8.3 70.0 0.4 2.9 80.7 Pakistan 2012-13 All 4153 7.7 1.0 70.8 0.2 0.0 0.0 0.5 1.6 0.0 0.7 0.3 0.5 1.6 8.9 70.8 0.5 2.6 82.0 Rwanda 2005 Urban 289 7.8 23.5 13.8 0.2 0.0 0.0 18.9 1.2 0.0 4.9 9.4 18.9 0.9 31.5 13.8 18.9 14.7 77.2 Rwanda 2005 Rural 1757 1.5 21.5 2.2 0.7 0.0 0.0 15.7 1.9 0.0 6.5 9.5 15.5 1.9 23.6 2.2 15.5 17.6 58.1 

(Continued...)

65 
66 

Table A1. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non- allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Rwanda 2005 All 2046 2.4 21.7 3.8 0.6 0.0 0.0 16.1 1.8 0.0 6.3 9.5 16.0 1.8 24.7 3.8 16.0 17.2 60.8 Rwanda 2010 Urban 172 2.6 27.9 12.7 13.5 0.0 0.0 8.1 0.0 0.0 0.9 1.2 7.3 0.0 44.0 12.7 7.3 2.1 65.3 

Rwanda 2010 Rural 1183 1.5 24.9 0.5 15.9 0.0 0.1 2.8 0.0 0.0 1.4 4.0 2.6 0.0 40.3 0.6 2.6 5.3 48.7 Rwanda 2010 All 1355 1.6 25.3 2.1 15.6 0.0 0.1 3.4 0.0 0.0 1.3 3.6 3.2 0.0 40.8 2.1 3.2 4.9 50.8 Senegal 2005 Urban 1097 5.3 33.9 13.2 0.8 0.0 2.9 10.6 2.5 0.0 1.9 0.1 9.8 1.9 39.4 16.2 9.8 4.2 67.3 Senegal 2005 Rural 1794 1.0 31.1 7.1 0.4 0.3 0.9 2.0 6.8 0.0 2.7 0.3 2.0 6.7 32.5 8.3 2.0 9.9 51.0 Senegal 2005 All 2891 2.7 32.2 9.4 0.6 0.2 1.7 5.3 5.2 0.0 2.4 0.2 5.0 4.8 35.2 11.3 5.0 7.7 57.2 Senegal 2010-11 Urban 1211 12.1 30.4 6.6 1.1 0.5 0.0 8.9 0.2 0.4 1.3 3.1 8.3 0.2 43.6 7.1 8.3 5.0 62.1 Senegal 2010-11 Rural 1252 3.0 30.6 1.3 0.9 1.1 0.0 2.8 1.7 0.5 3.1 1.5 2.7 1.7 34.5 2.5 2.7 6.5 45.5 Senegal 2010-11 All 2463 7.5 30.5 3.9 1.0 0.8 0.0 5.8 1.0 0.4 2.2 2.3 5.5 1.0 39.0 4.7 5.5 5.8 53.7 Tanzania 2004-5 Urban 351 22.5 33.8 18.3 0.0 0.0 4.6 15.1 0.0 0.0 0.0 0.0 15.1 0.0 55.9 23.0 15.1 0.0 92.8 Tanzania 2004-5 Rural 1598 4.7 42.8 5.7 0.2 0.0 5.4 23.7 0.0 0.0 0.0 2.1 22.2 0.0 47.4 11.0 22.2 2.1 82.0 Tanzania 2004-5 All 1949 7.9 41.2 8.0 0.1 0.0 5.2 22.1 0.0 0.0 0.0 1.7 20.9 0.0 48.9 13.2 20.9 1.7 83.9 Tanzania 2010 Urban 454 18.6 32.0 16.6 0.0 0.0 0.0 14.6 0.0 0.0 0.0 1.5 12.2 0.0 50.2 16.6 12.2 1.5 79.8 Tanzania 2010 Rural 1300 2.4 51.6 3.1 0.0 0.0 0.3 23.0 0.0 0.0 0.0 0.7 21.4 0.0 53.3 3.4 21.4 0.7 78.4 Tanzania 2010 All 1754 6.6 46.6 6.6 0.0 0.0 0.2 20.8 0.0 0.0 0.0 0.9 19.0 0.0 52.5 6.8 19.0 0.9 78.8 Uganda 2006 Urban 218 21.3 10.7 44.5 2.5 0.0 0.0 8.6 0.0 0.0 0.0 0.0 8.3 0.0 34.5 44.5 8.3 0.0 85.1 Uganda 2006 Rural 2919 4.5 23.7 45.0 2.9 2.0 0.0 6.8 1.4 0.0 0.7 0.8 6.3 1.2 30.7 46.8 6.3 3.0 83.0 Uganda 2006 All 3138 5.6 22.8 44.9 2.9 1.9 0.0 6.9 1.3 0.0 0.7 0.8 6.4 1.1 30.9 46.6 6.4 2.8 83.2 Uganda 2011 Urban 330 12.1 16.0 62.2 0.2 1.7 0.0 3.5 0.9 0.2 0.0 1.8 3.3 0.9 28.3 63.6 3.3 2.9 92.7 Uganda 2011 Rural 2712 4.8 31.5 45.1 0.9 0.7 0.0 1.6 2.8 0.1 0.3 0.7 1.3 2.5 36.8 45.8 1.3 4.0 84.0 Uganda 2011 All 3042 5.6 29.8 47.0 0.8 0.8 0.0 1.8 2.6 0.1 0.3 0.8 1.5 2.3 35.9 47.8 1.5 3.9 84.9 Zambia 2007 Urban 276 8.9 54.4 1.7 0.0 0.4 0.6 3.1 4.4 0.0 0.0 0.3 1.3 4.0 63.1 2.7 1.3 4.6 71.1 Zambia 2007 Rural 768 3.0 49.9 1.0 0.0 1.9 6.8 1.4 5.4 0.0 1.6 1.5 1.3 4.9 52.5 9.7 1.3 8.5 70.6 Zambia 2007 All 1044 4.6 51.1 1.2 0.0 1.5 5.2 1.9 5.1 0.0 1.1 1.2 1.3 4.7 55.3 7.8 1.3 7.5 70.7 Zambia 2013 Urban 802 8.6 62.1 5.0 0.4 0.0 1.1 1.5 2.2 0.3 0.0 0.4 1.3 1.9 70.6 6.1 1.3 2.9 79.4 Zambia 2013 Rural 1853 2.9 59.9 0.1 4.0 0.3 3.3 0.7 3.0 0.0 1.0 0.6 0.5 2.4 66.5 3.7 0.5 4.5 74.0 Zambia 2013 All 2655 4.6 60.5 1.6 2.9 0.2 2.6 0.9 2.7 0.1 0.7 0.5 0.7 2.2 67.7 4.4 0.7 4.0 75.7 Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Children for whom care was sought from multiple sources contribute to each source's total, so 

the total percentage may exceed the percentage of children who received any care. 

1 The following Disaggregated Sources of Care categories include multiple standard response options: Public Peripheral Health Facility (HF) includes: health center, health post, mobile clinic, or other public sources of care; Private Clinic or Clinician includes private 

hospital, doctor, nurse, mobile clinic, other private sources; and Other includes any other source of care sought outside the home, including a friend, relative, or other. 2 The Public summary category combines the Public Hospital, Public Peripheral HF, and Public CHW disaggregated sources of care categories; the Private summary category combines the Private Clinic or Clinician, Private CHW, and Religious or NGO Facility 

disaggregated sources of care categories (note that Private does not include pharmacy); Pharmacy Only includes children for whom care was sought only from a pharmacy; and the Other summary category combines the Shop, Market/Informal Shop, NonAllopathic, and Other disaggregated sources of care categories. 

3 Any Care includes children who received care from any source outside the home (including all disaggregated source of care categories). 4 Questions on care seeking for children's fever in the 2004 Malawi DHS were non-standard and results are not shown in this disaggregated table. Respondents in the 2004 survey were asked what things were done in response to their child’s fever. Responses included: gave medicine from home, gave medicine without prescription, taken to government health center, private health center, mission health center, consulted a traditional healer, consulted a CHW, provided tepid sponging, herbs at home, other, or nothing. 

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67 

Table A2. Sources of care for symptoms of ARI by place of residence, USAID MCH priority countries 

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non- allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Bangladesh 2007 Urban 40 8.6 17.5 28.8 0.0 0.0 1.8 20.6 0.0 0.0 7.1 3.2 20.6 0.0 26.1 30.6 20.6 10.3 85.9 Bangladesh 2007 Rural 237 1.0 8.3 25.7 0.0 0.0 0.0 30.7 0.0 0.0 22.8 4.0 29.7 0.0 9.1 25.7 29.7 26.7 87.6 

Bangladesh 2007 All 277 2.1 9.6 26.1 0.0 0.0 0.3 29.3 0.0 0.0 20.5 3.9 28.4 0.0 11.5 26.4 28.4 24.4 87.3 Bangladesh 2011 Urban 89 4.7 6.8 57.1 0.0 0.9 0.4 21.0 0.0 0.0 3.3 0.7 21.0 0.0 11.5 58.4 21.0 4.0 91.4 Bangladesh 2011 Rural 397 3.3 8.8 44.8 0.0 0.0 0.3 22.4 0.0 0.0 5.6 1.0 19.9 0.0 12.1 44.8 19.9 6.6 79.9 Bangladesh 2011 All 486 3.6 8.4 47.0 0.0 0.2 0.3 22.2 0.0 0.0 5.1 1.0 20.1 0.0 12.0 47.3 20.1 6.1 82.0 Congo DR 2007 Urban 435 9.0 14.6 20.7 0.0 2.8 0.0 27.2 0.1 0.0 2.5 1.4 25.0 0.1 23.4 23.5 25.0 4.0 72.6 Congo DR 2007 Rural 801 2.6 23.6 6.9 0.0 8.4 0.0 11.4 1.3 0.0 4.9 1.9 10.3 1.3 26.0 15.1 10.3 7.9 55.3 Congo DR 2007 All 1237 4.8 20.4 11.8 0.0 6.4 0.0 17.0 0.9 0.0 4.0 1.7 15.5 0.9 25.1 18.1 15.5 6.5 61.4 Congo DR 2013-14 Urban 317 4.6 17.0 17.2 0.0 0.0 0.0 22.7 0.0 0.0 2.1 1.4 19.9 0.0 21.6 17.2 19.9 3.5 61.4 Congo DR 2013-14 Rural 816 1.3 35.1 6.8 0.1 0.0 0.0 9.6 0.3 0.5 5.9 0.3 9.3 0.3 36.2 6.8 9.3 7.0 58.5 Congo DR 2013-14 All 1133 2.2 30.0 9.7 0.1 0.0 0.0 13.3 0.3 0.3 4.9 0.6 12.3 0.3 32.1 9.7 12.3 6.0 59.3 Ethiopia 2005 Urban 64 7.6 21.9 13.1 0.0 0.0 3.6 0.5 0.0 1.2 0.0 0.1 0.4 0.0 29.5 16.7 0.4 1.3 47.3 Ethiopia 2005 Rural 1205 3.7 10.8 1.9 0.3 0.0 0.9 1.8 0.1 0.0 0.3 0.0 1.4 0.1 14.8 2.7 1.4 0.4 18.9 Ethiopia 2005 All 1269 3.9 11.4 2.4 0.2 0.0 1.0 1.8 0.1 0.1 0.3 0.0 1.3 0.1 15.5 3.4 1.3 0.4 20.3 Ethiopia 2011 Urban 69 5.7 15.3 28.1 0.0 0.0 1.7 3.7 0.1 0.0 0.0 0.0 3.3 0.1 18.9 28.1 3.3 0.1 50.2 Ethiopia 2011 Rural 703 1.0 19.3 5.2 0.0 0.0 0.9 1.6 0.6 0.0 0.3 0.0 1.4 0.6 19.9 5.6 1.4 0.9 27.2 Ethiopia 2011 All 773 1.4 18.9 7.2 0.0 0.0 0.9 1.8 0.5 0.0 0.2 0.0 1.5 0.5 19.8 7.6 1.5 0.8 29.3 Ghana 2003 Urban 99 35.9 12.5 5.5 0.0 0.0 0.0 22.8 1.3 0.0 0.0 1.3 22.8 0.0 48.5 5.5 22.8 2.6 77.2 Ghana 2003 Rural 236 22.3 13.3 5.9 0.0 0.0 0.0 25.9 0.0 0.0 1.7 2.1 24.6 0.0 34.2 5.9 24.6 3.8 67.9 Ghana 2003 All 335 26.3 13.1 5.8 0.0 0.0 0.0 25.0 0.4 0.0 1.2 1.9 24.1 0.0 38.4 5.8 24.1 3.5 70.7 Ghana 2008 Urban 53 (35.6) (3.2) (15.4) (0.0) (0.0) (0.0) (10.7) (0.0) (0.0) (0.0) (2.8) (10.1) (0.0) (37.7) (15.4) (10.1) (2.8) (62.6) 

Ghana 2008 Rural 97 14.1 31.7 4.3 0.0 0.0 0.0 10.6 1.1 3.2 1.2 1.7 9.6 1.1 45.4 4.3 9.6 7.2 66.4 

Ghana 2008 All 150 21.7 21.7 8.2 0.0 0.0 0.0 10.6 0.7 2.0 0.8 2.1 9.8 0.7 42.7 8.2 9.8 5.6 65.1 Haiti 2005-6 Urban 126 2.9 10.4 16.6 0.0 0.0 4.7 1.2 0.0 1.4 2.8 2.5 1.2 0.0 13.2 21.0 1.2 6.7 39.5 Haiti 2005-6 Rural 420 1.6 7.9 12.6 0.0 0.0 8.9 0.2 1.6 0.6 3.0 4.8 0.2 1.6 9.6 21.5 0.2 8.8 37.4 Haiti 2005-6 All 546 1.9 8.5 13.5 0.0 0.0 7.9 0.4 1.2 0.8 3.0 4.3 0.4 1.2 10.4 21.4 0.4 8.3 37.9 Haiti 2012 Urban 338 6.0 5.4 26.2 0.0 0.9 7.7 2.0 1.8 1.4 0.0 5.3 1.7 1.2 10.9 34.5 1.7 7.7 51.9 Haiti 2012 Rural 586 4.6 9.9 13.2 0.0 0.5 8.3 0.9 1.0 1.3 0.7 5.8 0.9 0.7 14.5 22.0 0.9 7.8 43.0 Haiti 2012 All 924 5.1 8.3 18.0 0.0 0.6 8.1 1.3 1.3 1.3 0.4 5.6 1.2 0.9 13.2 26.6 1.2 7.7 46.3 India 2005-6 Urban 691 9.0 4.8 64.9 0.0 0.0 0.1 4.0 0.0 0.0 3.3 0.7 3.3 0.0 13.4 65.0 3.3 3.7 81.6 India 2005-6 Rural 2367 3.5 8.5 53.8 0.0 0.0 0.2 5.8 1.2 0.0 3.2 1.6 5.2 1.0 12.0 54.0 5.2 5.9 73.7 India 2005-6 All 3058 4.8 7.7 56.3 0.0 0.0 0.2 5.4 0.9 0.0 3.2 1.4 4.8 0.8 12.3 56.5 4.8 5.4 75.5 Indonesia 2007 Urban 686 3.4 22.1 44.5 0.0 4.9 0.6 14.9 8.3 0.0 0.4 1.0 14.2 7.1 25.1 49.9 14.2 9.7 94.0 Indonesia 2007 Rural 1103 2.1 22.6 25.7 0.0 15.8 1.8 8.6 15.8 0.0 2.2 3.3 8.2 15.1 24.6 42.5 8.2 20.2 90.5 Indonesia 2007 All 1789 2.6 22.4 32.9 0.0 11.7 1.4 11.0 12.9 0.0 1.5 2.4 10.5 12.1 24.8 45.4 10.5 16.2 91.9 Indonesia 2012 Urban 366 1.3 28.3 46.0 0.0 7.6 0.0 13.1 3.1 0.0 1.9 4.0 11.1 2.4 29.7 53.4 11.1 9.0 90.6 Indonesia 2012 Rural 467 3.2 22.4 36.2 0.0 18.7 0.0 6.6 9.1 0.0 3.3 2.7 4.9 7.7 25.2 54.7 4.9 13.7 90.6 Indonesia 2012 All 833 2.3 25.0 40.5 0.0 13.8 0.0 9.5 6.5 0.0 2.7 3.2 7.6 5.4 27.2 54.1 7.6 11.6 90.6 Kenya 2003 Urban 174 17.1 20.0 22.0 0.0 0.5 4.4 19.1 3.6 0.0 1.8 1.6 15.8 3.2 37.1 26.8 15.8 6.9 83.4 Kenya 2003 Rural 849 6.9 24.2 12.3 0.0 0.8 3.4 14.1 6.5 0.0 1.8 0.5 12.7 6.3 30.6 16.5 12.7 8.7 67.0 Kenya 2003 All 1023 8.7 23.5 13.9 0.0 0.7 3.6 14.9 6.0 0.0 1.8 0.7 13.2 5.7 31.7 18.2 13.2 8.4 69.8 Kenya 2008-9 Urban 71 19.8 29.8 12.1 0.0 0.0 9.7 13.5 0.3 0.0 0.0 0.0 13.2 0.0 47.3 20.0 13.2 0.3 78.7 Kenya 2008-9 Rural 346 11.6 33.3 9.1 0.0 0.0 2.0 8.7 5.3 0.0 0.8 1.0 8.7 4.8 44.1 11.1 8.7 6.7 68.7 Kenya 2008-9 All 416 13.0 32.7 9.6 0.0 0.0 3.3 9.5 4.4 0.0 0.7 0.8 9.5 4.0 44.6 12.6 9.5 5.7 70.4 Liberia 2007 Urban 104 14.8 42.7 16.4 0.0 0.0 0.0 8.9 0.8 0.0 0.1 0.8 8.5 0.8 57.6 16.4 8.5 1.7 83.1 Liberia 2007 Rural 336 8.0 33.8 17.9 0.0 0.0 0.0 10.3 5.0 0.0 3.5 3.9 9.9 4.6 41.8 17.9 9.9 11.7 78.4 Liberia 2007 All 440 9.6 35.9 17.6 0.0 0.0 0.0 9.9 4.0 0.0 2.7 3.2 9.6 3.7 45.5 17.6 9.6 9.3 79.5 Liberia 2013 Urban 164 11.4 14.0 23.9 0.9 0.0 0.0 24.9 1.6 2.4 0.0 0.0 24.1 0.8 25.5 23.9 24.1 4.0 76.6 Liberia 2013 Rural 232 8.4 34.3 9.6 0.0 0.0 0.0 5.8 2.5 11.0 1.2 0.4 5.7 2.3 42.7 9.6 5.7 15.2 71.0 Liberia 2013 All 396 9.6 25.9 15.5 0.4 0.0 0.0 13.7 2.2 7.4 0.7 0.2 13.3 1.7 35.6 15.5 13.3 10.5 73.3 Madagascar 2003-4 Urban 96 15.3 22.7 25.4 0.0 0.0 0.0 0.5 1.0 0.0 0.0 0.4 0.5 1.0 38.0 25.4 0.5 1.4 63.7 Madagascar 2003-4 Rural 409 3.4 30.2 10.3 0.0 0.0 0.0 2.1 2.5 0.0 1.9 1.6 2.1 2.5 33.7 10.3 2.1 5.9 52.0 Madagascar 2003-4 All 505 5.7 28.8 13.2 0.0 0.0 0.0 1.8 2.2 0.0 1.6 1.4 1.8 2.2 34.5 13.2 1.8 5.1 54.2 

 (Continued...)

67 
68 

Table A2. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non- allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Madagascar 2008-9 Urban 52 7.5 17.6 35.2 0.0 1.3 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 25.2 36.5 1.0 0.0 60.9 Madagascar 2008-9 Rural 292 1.9 28.7 10.0 0.0 0.8 0.0 0.8 5.7 0.0 1.8 0.2 0.8 5.3 30.1 10.8 0.8 7.7 47.2 

Madagascar 2008-9 All 345 2.8 27.0 13.9 0.0 0.9 0.0 0.9 4.8 0.0 1.5 0.1 0.9 4.5 29.4 14.7 0.9 6.5 49.3 Malawi 2004 Urban 152 25.9 14.7 2.0 0.0 0.0 4.2 0.4 16.8 0.0 2.0 0.5 0.4 16.8 40.6 6.2 0.4 19.3 66.5 Malawi 2004 Rural 1688 5.1 22.3 3.0 0.6 0.1 4.9 0.0 30.3 0.0 3.2 1.0 0.0 29.8 27.9 8.0 0.0 34.0 69.1 Malawi 2004 All 1840 6.8 21.6 2.9 0.5 0.1 4.8 0.0 29.2 0.0 3.1 0.9 0.0 28.7 29.0 7.8 0.0 32.8 68.9 Malawi 2010 Urban 168 33.9 20.5 10.8 0.0 0.0 3.0 0.0 2.0 0.0 0.0 0.0 0.0 0.6 54.4 13.8 0.0 2.0 67.6 Malawi 2010 Rural 1053 14.1 40.2 8.1 1.6 0.1 10.5 0.1 6.1 0.0 1.0 0.8 0.0 5.5 54.5 18.2 0.0 8.0 77.5 Malawi 2010 All 1221 16.8 37.5 8.5 1.4 0.1 9.5 0.0 5.5 0.0 0.9 0.7 0.0 4.8 54.5 17.6 0.0 7.2 76.1 Mali 2006 Urban 188 4.7 38.6 7.2 0.0 0.8 0.0 10.9 0.0 2.5 5.5 1.0 9.6 0.0 43.0 8.0 9.6 9.0 69.1 Mali 2006 Rural 518 0.8 31.6 0.7 0.0 0.4 0.0 0.6 1.3 8.1 11.3 2.0 0.4 1.3 32.4 1.2 0.4 22.1 55.5 Mali 2006 All 706 1.9 33.4 2.4 0.0 0.5 0.0 3.4 1.0 6.6 9.8 1.7 2.9 1.0 35.2 3.0 2.9 18.6 59.1 Mali 2012-13 Urban 20 (3.9) (27.9) (10.5) (0.0) (0.0) (0.0) (13.0) (0.0) (0.0) (9.2) (3.3) (13.0) (0.0) (27.9) (10.5) (13.0) (9.2) (60.7) 

Mali 2012-13 Rural 138 0.0 24.6 0.4 0.0 0.0 0.0 3.7 1.1 18.6 9.5 3.8 3.2 1.1 24.6 0.4 3.2 30.3 56.9 

Mali 2012-13 All 158 0.5 25.0 1.7 0.0 0.0 0.0 4.8 0.9 16.2 9.4 3.8 4.5 0.9 25.0 1.7 4.5 27.6 57.4 Mozambique 2003 Urban 335 17.3 41.3 1.8 0.0 0.0 0.2 1.7 0.0 0.6 2.5 1.7 1.6 0.0 58.0 2.0 1.6 4.8 65.9 Mozambique 2003 Rural 583 4.2 47.7 0.5 0.5 0.0 1.3 0.3 0.0 3.0 2.7 2.6 0.3 0.0 52.3 1.8 0.3 8.3 62.3 Mozambique 2003 All 919 9.0 45.4 1.0 0.3 0.0 0.9 0.8 0.0 2.1 2.6 2.3 0.8 0.0 54.4 1.8 0.8 7.0 63.6 Mozambique 2011 Urban 50 64.4 0.0 0.0 1.8 0.0 0.0 1.4 0.0 0.0 1.8 2.2 1.4 0.0 64.4 0.0 1.4 3.9 68.0 Mozambique 2011 Rural 115 44.0 0.4 0.0 3.0 0.0 0.0 0.0 0.0 0.4 4.0 0.8 0.0 0.0 47.0 0.0 0.0 4.8 49.6 Mozambique 2011 All 166 50.2 0.3 0.0 2.6 0.0 0.0 0.4 0.0 0.3 3.3 1.2 0.4 0.0 52.3 0.0 0.4 4.5 55.1 Nepal 2006 Urban 33 10.9 5.0 37.9 0.0 0.0 1.3 20.6 0.3 0.0 0.0 0.0 17.9 0.3 15.8 39.3 17.9 0.3 71.9 Nepal 2006 Rural 244 7.9 22.1 11.3 1.9 0.0 3.3 25.6 0.6 0.0 1.5 0.0 22.9 0.0 28.2 14.5 22.9 2.0 65.2 Nepal 2006 All 277 8.3 20.1 14.4 1.7 0.0 3.1 25.0 0.5 0.0 1.3 0.0 22.3 0.0 26.7 17.4 22.3 1.8 66.0 Nepal 2011 Urban 24 28.2 4.5 35.5 0.9 0.0 0.0 26.2 0.0 0.0 0.0 0.0 20.3 0.0 33.5 35.5 20.3 0.0 89.3 Nepal 2011 Rural 215 2.3 20.1 23.6 2.6 0.0 1.4 30.9 0.0 0.0 1.7 1.3 28.1 0.0 24.1 25.1 28.1 3.0 76.7 Nepal 2011 All 238 4.9 18.6 24.8 2.4 0.0 1.3 30.4 0.0 0.0 1.5 1.2 27.3 0.0 25.1 26.1 27.3 2.7 78.0 Nigeria 2008 Urban 172 18.0 6.4 9.1 0.0 0.0 0.0 38.8 3.9 0.0 2.8 0.5 37.7 3.9 24.4 9.1 37.7 7.1 76.8 Nigeria 2008 Rural 519 8.6 13.3 8.2 1.2 0.4 0.0 27.2 8.7 0.0 1.9 0.1 26.3 7.9 22.8 8.6 26.3 10.6 66.2 Nigeria 2008 All 690 10.9 11.6 8.5 0.9 0.3 0.0 30.1 7.5 0.0 2.1 0.2 29.2 6.9 23.2 8.7 29.2 9.8 68.8 Nigeria 2013 Urban 154 24.2 10.9 12.6 0.0 0.0 0.0 33.8 0.0 0.0 2.0 1.7 31.9 0.0 35.1 12.6 31.9 3.7 79.5 Nigeria 2013 Rural 411 10.8 15.8 3.2 0.2 0.0 0.0 32.5 6.2 3.2 5.8 0.5 31.9 6.0 26.7 3.2 31.9 14.9 75.8 Nigeria 2013 All 565 14.5 14.4 5.8 0.1 0.0 0.0 32.8 4.5 2.3 4.8 0.8 31.9 4.3 29.0 5.8 31.9 11.8 76.8 Pakistan 2006-7 Urban 323 8.0 1.2 76.3 0.7 0.0 0.0 0.0 1.2 0.0 3.4 0.3 0.0 1.2 9.9 76.3 0.0 4.9 88.5 Pakistan 2006-7 Rural 854 8.0 1.7 67.4 0.0 0.0 0.0 0.5 2.2 0.0 2.1 0.2 0.5 2.2 9.7 67.4 0.5 4.5 81.3 Pakistan 2006-7 All 1178 8.0 1.5 69.9 0.2 0.0 0.0 0.4 1.9 0.0 2.5 0.2 0.4 1.9 9.7 69.9 0.4 4.6 83.3 Pakistan 2012-13 Urban 478 9.3 0.0 77.6 0.0 0.0 0.0 0.3 0.1 0.0 0.0 0.1 0.3 0.1 9.4 77.6 0.3 0.2 86.7 Pakistan 2012-13 Rural 1273 8.1 1.0 71.4 0.4 0.0 0.0 0.8 1.7 0.0 0.2 0.1 0.6 1.7 9.4 71.4 0.6 2.0 82.2 Pakistan 2012-13 All 1751 8.4 0.7 73.1 0.3 0.0 0.0 0.7 1.3 0.0 0.1 0.1 0.5 1.2 9.4 73.1 0.5 1.5 83.5 Rwanda 2005 Urban 211 5.4 20.8 14.6 0.5 0.0 0.0 18.5 1.2 0.0 3.7 13.2 18.3 0.9 26.7 14.6 18.3 17.5 75.5 Rwanda 2005 Rural 1121 1.9 21.2 1.9 0.5 0.0 0.0 15.6 2.6 0.0 8.2 11.8 15.5 2.6 23.6 1.9 15.5 22.3 61.9 Rwanda 2005 All 1332 2.5 21.2 3.9 0.5 0.0 0.0 16.1 2.4 0.0 7.5 12.0 15.9 2.3 24.1 3.9 15.9 21.5 64.1 Rwanda 2010 Urban 54 3.2 41.8 21.0 9.8 0.0 0.0 5.8 0.0 0.0 0.0 1.9 5.8 0.0 54.8 21.0 5.8 1.9 83.5 Rwanda 2010 Rural 269 1.1 33.9 0.0 13.6 0.0 0.0 3.2 0.0 0.0 1.2 8.7 3.2 0.0 45.1 0.0 3.2 9.5 57.8 Rwanda 2010 All 322 1.5 35.2 3.5 13.0 0.0 0.0 3.6 0.0 0.0 1.0 7.5 3.6 0.0 46.7 3.5 3.6 8.2 62.1 Senegal 2005 Urban 589 5.7 33.9 15.2 1.9 0.0 2.6 9.8 2.2 0.0 2.6 0.1 7.7 1.7 41.1 17.8 7.7 4.9 68.7 Senegal 2005 Rural 690 1.9 36.5 5.2 0.5 0.5 1.2 3.6 3.8 0.0 1.9 1.1 3.5 3.8 38.9 6.9 3.5 6.7 54.6 Senegal 2005 All 1279 3.6 35.3 9.8 1.2 0.3 1.8 6.4 3.0 0.0 2.2 0.6 5.5 2.8 39.9 11.9 5.5 5.9 61.1 Senegal 2010-11 Urban 310 13.5 36.9 8.5 2.1 0.3 0.0 7.6 0.0 0.0 0.8 0.5 7.2 0.0 51.7 8.9 7.2 1.3 69.0 Senegal 2010-11 Rural 278 3.0 31.9 1.1 0.9 1.3 0.0 2.0 1.5 0.8 6.1 3.0 2.0 1.5 35.8 2.3 2.0 10.9 49.8 Senegal 2010-11 All 589 8.5 34.5 5.0 1.6 0.8 0.0 5.0 0.7 0.4 3.3 1.7 4.7 0.7 44.2 5.8 4.7 5.8 59.9 Tanzania 2004-5 Urban 108 24.7 31.3 13.4 0.0 0.0 4.6 14.4 0.0 0.0 0.0 0.3 13.6 0.0 56.0 18.1 13.6 0.3 86.5 Tanzania 2004-5 Rural 540 5.2 46.8 4.7 0.0 0.0 6.3 23.3 0.0 0.0 0.0 2.3 22.4 0.0 51.4 10.8 22.4 2.3 86.1 Tanzania 2004-5 All 648 8.4 44.2 6.1 0.0 0.0 6.0 21.8 0.0 0.0 0.0 2.0 21.0 0.0 52.1 12.0 21.0 2.0 86.2 

 (Continued...)

68 
69 

Table A2. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non- allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Tanzania 2010 Urban 85 24.6 46.5 11.3 0.0 0.0 0.0 10.0 0.0 0.0 0.0 2.5 4.3 0.0 70.0 11.3 4.3 2.5 85.1 Tanzania 2010 Rural 247 6.1 51.4 4.3 0.0 0.0 0.0 17.8 0.0 0.0 0.0 1.4 16.2 0.0 56.5 4.3 16.2 1.4 77.9 

Tanzania 2010 All 332 10.8 50.1 6.1 0.0 0.0 0.0 15.8 0.0 0.0 0.0 1.6 13.1 0.0 60.0 6.1 13.1 1.6 79.7 Uganda 2006 Urban 93 18.3 5.6 45.1 1.5 0.0 0.0 9.8 0.0 0.0 0.2 0.0 9.0 0.0 25.4 45.1 9.0 0.2 77.5 Uganda 2006 Rural 1016 5.3 26.7 42.0 1.9 1.6 0.0 8.1 1.4 0.0 1.2 1.2 7.1 1.0 33.6 43.3 7.1 3.8 83.4 Uganda 2006 All 1109 6.3 24.9 42.2 1.9 1.5 0.0 8.3 1.2 0.0 1.1 1.1 7.2 0.9 32.9 43.4 7.2 3.5 82.9 Uganda 2011 Urban 141 12.5 16.3 54.6 0.0 3.0 0.0 3.7 3.4 0.0 0.3 3.4 3.3 3.4 28.3 57.0 3.3 6.8 89.9 Uganda 2011 Rural 977 4.2 28.6 49.2 0.7 0.7 0.0 1.5 1.5 0.1 0.6 1.0 1.5 1.1 32.9 49.9 1.5 3.2 82.4 Uganda 2011 All 1118 5.2 27.0 49.9 0.6 1.0 0.0 1.7 1.8 0.1 0.6 1.3 1.7 1.4 32.3 50.8 1.7 3.7 83.4 Zambia 2007 Urban 95 9.6 57.1 0.7 0.0 0.0 0.0 7.7 2.1 0.0 0.4 0.0 3.3 1.9 66.2 0.7 3.3 2.5 72.5 Zambia 2007 Rural 209 5.4 54.3 1.4 0.0 1.1 8.0 2.1 2.5 0.0 3.4 0.9 1.9 1.9 58.6 10.5 1.9 6.9 74.9 Zambia 2007 All 304 6.7 55.1 1.2 0.0 0.8 5.5 3.9 2.4 0.0 2.5 0.6 2.3 1.9 61.0 7.5 2.3 5.5 74.2 Zambia 2013 Urban 136 10.6 64.3 4.7 0.0 0.0 2.5 0.9 4.0 1.7 0.0 0.0 0.9 4.0 74.3 7.2 0.9 5.7 84.8 Zambia 2013 Rural 333 3.1 59.1 0.3 2.2 0.2 1.7 0.3 1.4 0.5 0.8 1.1 0.0 1.4 63.7 2.3 0.0 3.7 69.3 Zambia 2013 All 469 5.3 60.6 1.6 1.6 0.2 2.0 0.4 2.1 0.8 0.6 0.8 0.2 2.1 66.8 3.7 0.2 4.3 73.8 Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Children for whom care was sought from multiple sources contribute to each source's total, so the total percentage may exceed the percentage of children who received any care. 

1 The following Disaggregated Sources of Care categories include multiple standard response options: Public Peripheral Health Facility (HF) includes: health center, health post, mobile clinic, or other public sector care; Private Clinic or Clinician includes private 

hospital, doctor, nurse, mobile clinic, other private sector; and Other includes any other source of care sought outside the home, including a friend, relative, or other. 2 The Public summary category combines the Public Hospital, Public Peripheral HF, and Public CHW disaggregated sources of care categories; the Private summary category combines the Private Clinic or Clinician, Private CHW, and Religious or NGO Facility 

disaggregated sources of care categories (note that Private does not include pharmacy); Pharmacy Only includes children for whom care was sought only from a pharmacy; and the Other summary category combines the Shop, Market/Informal Shop, NonAllopathic, and Other disaggregated sources of care categories. 3 Any Care includes children who received care from any source outside the home (including all disaggregated source of care categories). 

69 
70 

Table A3. Sources of care for diarrhea by place of residence, USAID MCH priority countries 

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non-allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Bangladesh 2007 Urban 122 3.5 7.7 16.4 0.0 0.0 2.6 25.2 0.0 0.0 16.7 3.0 24.3 0.0 11.3 19.0 24.3 19.7 70.4 Bangladesh 2007 Rural 437 0.9 8.5 7.9 0.0 0.0 0.6 28.1 0.4 0.0 25.1 3.4 27.7 0.3 9.4 8.4 27.7 28.6 72.5 

Bangladesh 2007 All 559 1.5 8.3 9.7 0.0 0.0 1.0 27.5 0.3 0.0 23.3 3.3 26.9 0.2 9.8 10.7 26.9 26.7 72.0 Bangladesh 2011 Urban 70 6.9 7.6 44.5 0.0 0.0 1.4 14.9 0.0 0.0 1.1 0.0 14.6 0.0 14.5 45.9 14.6 1.1 74.9 Bangladesh 2011 Rural 318 1.5 8.8 40.0 0.0 0.0 0.3 25.1 0.0 0.0 3.6 0.4 23.7 0.0 10.3 40.4 23.7 4.0 76.4 Bangladesh 2011 All 388 2.5 8.6 40.9 0.0 0.0 0.5 23.3 0.0 0.0 3.1 0.3 22.0 0.0 11.1 41.4 22.0 3.4 76.1 Congo DR 2007 Urban 499 3.1 21.7 0.3 8.6 3.5 0.1 3.2 0.0 0.0 0.0 3.1 2.0 0.0 32.3 3.8 2.0 3.1 38.6 Congo DR 2007 Rural 814 1.5 19.7 0.0 5.8 6.8 0.1 4.3 0.0 0.0 0.0 3.0 4.0 0.0 25.4 6.8 4.0 3.0 37.3 Congo DR 2007 All 1313 2.1 20.5 0.1 6.9 5.5 0.1 3.9 0.0 0.0 0.0 3.0 3.2 0.0 28.0 5.7 3.2 3.0 37.8 Congo DR 2013-14 Urban 971 5.2 13.3 13.9 0.1 0.0 0.0 21.0 0.0 0.3 1.3 3.0 18.5 0.0 18.3 13.9 18.5 4.5 54.4 Congo DR 2013-14 Rural 1881 3.3 32.0 8.2 0.3 0.0 0.0 13.5 0.5 0.6 3.6 2.2 12.1 0.5 34.9 8.2 12.1 6.9 60.1 Congo DR 2013-14 All 2852 3.9 25.6 10.1 0.3 0.0 0.0 16.1 0.3 0.5 2.8 2.5 14.3 0.3 29.2 10.1 14.3 6.1 58.2 Ethiopia 2005 Urban 91 8.5 20.3 5.1 0.0 0.0 2.0 3.6 0.0 0.4 0.0 0.0 3.4 0.0 28.8 7.1 3.4 0.4 38.7 Ethiopia 2005 Rural 1727 4.5 14.0 2.6 0.1 0.0 0.7 2.0 0.8 0.7 0.5 0.2 1.3 0.8 18.3 3.3 1.3 2.2 25.0 Ethiopia 2005 All 1819 4.7 14.3 2.7 0.1 0.0 0.8 2.1 0.7 0.7 0.5 0.2 1.4 0.7 18.8 3.5 1.4 2.1 25.7 Ethiopia 2011 Urban 158 2.1 34.1 22.6 0.0 0.0 0.6 3.6 0.9 0.0 0.0 0.0 3.6 0.9 35.4 23.1 3.6 0.9 58.0 Ethiopia 2011 Rural 1326 0.5 22.4 6.3 0.0 0.4 0.4 1.8 1.1 0.0 0.7 0.4 1.7 1.1 22.7 7.1 1.7 2.1 32.8 Ethiopia 2011 All 1483 0.7 23.6 8.0 0.0 0.4 0.4 2.0 1.1 0.0 0.6 0.3 1.9 1.1 24.1 8.8 1.9 2.0 35.5 Ghana 2003 Urban 152 21.6 6.5 7.4 0.0 0.0 0.0 18.3 0.0 0.0 0.5 1.7 17.7 0.0 28.1 7.4 17.7 2.2 54.9 Ghana 2003 Rural 357 10.3 8.4 1.9 0.6 0.4 0.0 21.0 0.2 4.2 1.1 0.8 20.9 0.2 19.3 1.9 20.9 6.2 48.0 Ghana 2003 All 509 13.7 7.8 3.6 0.4 0.2 0.0 20.2 0.1 2.9 0.9 1.0 19.9 0.1 21.9 3.6 19.9 5.0 50.1 Ghana 2008 Urban 181 22.0 7.7 8.1 0.0 0.9 0.0 25.7 0.0 2.1 0.0 1.6 25.2 0.0 29.4 8.1 25.2 3.6 66.3 Ghana 2008 Rural 361 16.5 21.9 4.9 0.3 0.0 0.0 12.4 2.6 3.5 0.9 1.0 11.9 2.3 38.6 4.9 11.9 8.0 62.0 Ghana 2008 All 542 18.3 17.2 5.9 0.2 0.3 0.0 16.9 1.7 3.0 0.6 1.2 16.3 1.5 35.6 5.9 16.3 6.5 63.4 Haiti 2005-6 Urban 407 6.2 11.6 8.8 0.0 0.0 5.9 7.7 2.0 0.0 1.1 5.8 7.1 2.0 17.8 14.6 7.1 8.8 46.6 Haiti 2005-6 Rural 856 1.2 9.5 12.4 0.0 0.0 6.9 0.9 2.1 1.1 0.7 3.5 0.9 1.9 10.7 19.2 0.9 7.0 36.8 Haiti 2005-6 All 1263 2.8 10.2 11.2 0.0 0.0 6.6 3.1 2.1 0.7 0.9 4.2 2.9 1.9 13.0 17.7 2.9 7.6 40.0 Haiti 2012 Urban 509 11.4 6.1 16.1 0.0 0.4 6.8 0.8 0.7 0.4 0.0 8.3 0.8 0.6 17.5 22.6 0.8 8.8 46.7 Haiti 2012 Rural 825 2.8 11.8 9.5 0.0 0.4 9.9 0.1 1.1 0.8 0.5 7.7 0.1 0.9 14.6 19.5 0.1 9.5 41.9 Haiti 2012 All 1334 6.1 9.6 12.0 0.0 0.4 8.7 0.4 1.0 0.7 0.3 7.9 0.4 0.8 15.7 20.7 0.4 9.2 43.7 India 2005-6 Urban 1215 9.4 4.0 51.6 0.0 0.0 0.2 7.4 1.2 0.0 2.2 2.1 7.2 1.2 13.4 51.6 7.2 5.0 73.8 India 2005-6 Rural 3540 3.9 10.6 43.8 0.0 0.0 0.2 6.1 2.0 0.0 2.3 2.2 5.6 1.9 14.2 43.9 5.6 6.3 67.5 India 2005-6 All 4755 5.3 8.9 45.8 0.0 0.0 0.2 6.4 1.8 0.0 2.2 2.2 6.0 1.7 14.0 45.9 6.0 5.9 69.1 Indonesia 2007 Urban 799 1.2 18.2 33.6 0.0 3.9 0.1 9.6 9.9 0.8 0.0 4.6 8.8 9.6 19.2 37.5 8.8 15.3 77.2 Indonesia 2007 Rural 1381 1.6 18.8 18.4 0.0 12.1 0.9 4.9 12.0 2.2 0.0 6.0 4.3 11.3 20.3 30.8 4.3 19.8 72.3 Indonesia 2007 All 2180 1.4 18.6 24.0 0.0 9.1 0.6 6.6 11.3 1.7 0.0 5.5 5.9 10.7 19.9 33.3 5.9 18.1 74.1 Indonesia 2012 Urban 1078 2.5 21.6 37.1 0.0 5.8 0.0 12.7 4.4 0.0 0.0 3.8 11.0 3.7 24.0 42.6 11.0 8.2 80.1 Indonesia 2012 Rural 1263 1.2 22.4 27.8 0.0 18.7 0.0 5.2 7.0 0.0 0.0 5.9 4.4 6.3 23.3 45.7 4.4 12.8 80.1 Indonesia 2012 All 2341 1.8 22.0 32.1 0.0 12.8 0.0 8.6 5.8 0.0 0.0 5.0 7.4 5.1 23.6 44.3 7.4 10.7 80.1 Kenya 2003 Urban 180 6.1 10.5 12.8 0.0 0.4 0.9 4.0 1.1 0.0 3.0 2.0 3.8 1.1 16.6 14.2 3.8 6.1 39.5 Kenya 2003 Rural 708 3.7 17.6 4.3 0.0 0.6 3.6 2.9 1.8 0.0 1.4 3.1 2.9 1.8 21.1 8.4 2.9 5.7 37.1 Kenya 2003 All 888 4.2 16.1 6.0 0.0 0.6 3.1 3.1 1.7 0.0 1.7 2.9 3.1 1.7 20.2 9.5 3.1 5.8 37.6 Kenya 2008-9 Urban 169 17.9 14.5 13.4 0.0 0.0 2.6 8.6 3.8 0.0 0.2 0.0 7.1 3.8 32.4 16.1 7.1 4.0 59.3 Kenya 2008-9 Rural 740 7.7 32.5 6.7 0.0 0.7 1.8 4.7 3.4 0.0 3.3 2.0 4.4 2.8 40.1 9.1 4.4 8.8 60.3 Kenya 2008-9 All 909 9.6 29.2 7.9 0.0 0.5 2.0 5.4 3.5 0.0 2.7 1.7 4.9 3.0 38.6 10.4 4.9 7.9 60.1 Liberia 2007 Urban 293 10.7 28.8 14.9 0.0 0.0 0.0 20.5 3.8 0.0 3.3 4.5 15.9 3.8 39.2 14.9 15.9 11.6 77.0 Liberia 2007 Rural 721 4.2 22.1 22.6 0.0 0.0 0.0 9.7 8.2 0.0 15.8 10.2 9.6 8.0 25.6 22.6 9.6 29.2 78.8 Liberia 2007 All 1014 6.1 24.1 20.4 0.0 0.0 0.0 12.8 6.9 0.0 12.2 8.6 11.4 6.8 29.5 20.4 11.4 24.1 78.3 Liberia 2013 Urban 607 11.8 19.3 17.5 0.0 0.0 0.0 18.9 2.5 5.0 1.1 0.1 17.2 2.5 31.1 17.5 17.2 8.7 73.5 Liberia 2013 Rural 724 8.2 30.9 7.3 0.3 0.0 0.0 7.4 4.1 11.7 6.6 0.2 6.4 4.0 39.1 7.3 6.4 22.1 73.3 Liberia 2013 All 1330 9.8 25.6 12.0 0.2 0.0 0.0 12.6 3.3 8.7 4.1 0.2 11.3 3.3 35.4 12.0 11.3 16.0 73.4 Madagascar 2003-4 Urban 95 7.6 12.9 20.9 0.0 0.0 0.0 0.0 0.0 0.0 0.4 1.2 0.0 0.0 20.5 20.9 0.0 1.6 42.7 Madagascar 2003-4 Rural 475 1.2 19.9 9.1 0.0 0.0 0.0 2.0 0.9 0.0 1.8 2.6 2.0 0.9 21.1 9.1 2.0 5.3 37.3 Madagascar 2003-4 All 570 2.2 18.7 11.1 0.0 0.0 0.0 1.7 0.7 0.0 1.6 2.4 1.7 0.7 21.0 11.1 1.7 4.7 38.2 

 (Continued...)

70 
71 

Table A3. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non-allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Madagascar 2008-9 Urban 151 3.6 24.0 23.5 0.0 0.0 0.0 3.2 0.4 0.0 0.8 5.0 1.2 0.4 27.6 23.5 1.2 6.2 57.1 Madagascar 2008-9 Rural 841 2.0 24.0 6.2 0.0 0.5 0.0 0.4 1.9 0.1 1.9 1.9 0.3 1.9 25.9 6.7 0.3 5.5 37.7 

Madagascar 2008-9 All 993 2.2 24.0 8.8 0.0 0.5 0.0 0.8 1.6 0.1 1.7 2.4 0.4 1.6 26.1 9.3 0.4 5.6 40.7 Malawi 2004 Urban 234 17.9 15.3 4.6 0.0 0.0 0.9 0.0 10.8 0.0 0.0 0.0 0.0 10.8 33.2 5.5 0.0 10.8 49.5 Malawi 2004 Rural 1943 4.8 21.1 2.3 2.6 0.5 5.1 0.0 17.4 0.0 4.9 1.4 0.0 17.0 28.4 7.8 0.0 23.1 58.4 Malawi 2004 All 2177 6.2 20.4 2.5 2.3 0.4 4.6 0.0 16.7 0.0 4.4 1.3 0.0 16.3 28.9 7.6 0.0 21.7 57.5 Malawi 2010 Urban 467 24.5 25.1 3.4 0.0 0.0 4.1 0.0 5.3 0.0 0.7 2.5 0.0 4.6 49.6 7.6 0.0 8.6 63.0 Malawi 2010 Rural 2691 9.4 39.7 4.5 3.6 0.3 8.5 0.0 7.0 0.0 2.1 1.5 0.0 6.7 51.7 13.0 0.0 10.6 72.9 Malawi 2010 All 3158 11.6 37.5 4.3 3.0 0.3 7.9 0.0 6.8 0.0 1.9 1.7 0.0 6.3 51.4 12.2 0.0 10.3 71.5 Mali 2006 Urban 325 3.4 25.1 5.0 0.0 0.0 0.0 0.0 0.9 0.7 7.6 1.8 0.0 0.9 27.7 5.0 0.0 10.9 42.3 Mali 2006 Rural 1334 0.1 13.5 0.8 0.0 0.0 0.0 0.0 2.2 0.5 7.9 3.6 0.0 2.2 13.5 0.8 0.0 13.7 27.9 Mali 2006 All 1660 0.7 15.8 1.6 0.0 0.0 0.0 0.0 1.9 0.6 7.8 3.2 0.0 1.9 16.3 1.6 0.0 13.2 30.7 Mali 2012-13 Urban 171 4.9 26.7 4.8 0.0 0.0 0.0 7.2 0.3 7.2 11.7 2.7 7.2 0.3 31.6 4.8 7.2 20.8 62.5 Mali 2012-13 Rural 661 0.0 24.5 0.3 0.0 1.6 0.0 2.9 1.1 11.1 11.2 2.4 2.5 1.1 24.5 1.9 2.5 24.1 51.4 Mali 2012-13 All 832 1.0 25.0 1.2 0.0 1.3 0.0 3.8 1.0 10.3 11.3 2.4 3.5 1.0 26.0 2.5 3.5 23.4 53.7 Mozambique 2003 Urban 440 14.6 38.0 0.6 0.0 0.0 0.0 1.4 0.0 0.3 1.3 1.6 1.4 0.0 52.5 0.6 1.4 3.2 57.5 Mozambique 2003 Rural 887 4.3 41.1 1.2 0.6 0.0 0.2 0.0 0.0 1.8 5.0 1.3 0.0 0.0 45.6 1.5 0.0 8.1 54.7 Mozambique 2003 All 1328 7.7 40.1 1.0 0.4 0.0 0.2 0.5 0.0 1.3 3.8 1.4 0.5 0.0 47.9 1.2 0.5 6.5 55.6 Mozambique 2011 Urban 366 64.7 0.2 1.1 0.9 0.0 0.0 1.9 0.0 0.2 2.4 0.6 1.2 0.0 65.8 1.1 1.2 3.2 71.2 Mozambique 2011 Rural 839 50.3 0.4 0.9 3.2 0.0 0.0 0.3 0.0 1.1 5.8 0.4 0.1 0.0 53.9 0.9 0.1 7.3 60.7 Mozambique 2011 All 1205 54.7 0.4 1.0 2.5 0.0 0.0 0.8 0.0 0.9 4.8 0.4 0.4 0.0 57.5 1.0 0.4 6.1 63.9 Nepal 2006 Urban 75 9.2 4.0 9.3 0.3 0.0 6.1 23.4 0.0 0.0 5.5 0.0 21.6 0.0 13.2 15.4 21.6 5.5 54.2 Nepal 2006 Rural 548 3.8 16.2 4.2 4.1 0.0 0.9 24.7 0.0 0.0 1.5 0.0 22.0 0.0 21.7 5.1 22.0 1.5 50.1 Nepal 2006 All 623 4.4 14.7 4.9 3.6 0.0 1.5 24.6 0.0 0.0 2.0 0.0 21.9 0.0 20.7 6.4 21.9 2.0 50.6 Nepal 2011 Urban 65 10.8 3.8 28.5 0.7 0.0 0.0 26.0 0.0 0.0 0.7 0.0 24.9 0.0 14.9 28.5 24.9 0.7 68.8 Nepal 2011 Rural 646 2.1 19.9 13.4 2.9 0.0 0.2 22.4 0.5 0.0 0.5 1.2 21.8 0.5 24.9 13.5 21.8 2.3 61.4 Nepal 2011 All 711 2.9 18.4 14.8 2.7 0.0 0.1 22.7 0.5 0.0 0.6 1.1 22.1 0.5 24.0 14.9 22.1 2.1 62.1 Nigeria 2008 Urban 608 17.2 10.5 11.9 0.7 0.6 0.0 23.8 4.7 0.0 0.9 1.2 23.0 4.5 28.2 12.5 23.0 6.9 69.6 Nigeria 2008 Rural 1922 8.3 13.3 5.9 0.6 1.5 0.0 21.0 10.4 0.0 2.0 1.8 20.3 10.0 22.1 7.4 20.3 14.0 62.8 Nigeria 2008 All 2530 10.4 12.6 7.4 0.6 1.3 0.0 21.7 9.0 0.0 1.7 1.6 21.0 8.7 23.5 8.6 21.0 12.3 64.4 Nigeria 2013 Urban 958 15.5 12.0 7.5 0.4 0.0 0.0 33.0 0.2 0.1 2.4 1.0 31.8 0.2 27.8 7.5 31.8 3.7 70.3 Nigeria 2013 Rural 2008 8.0 14.2 3.0 0.2 0.0 0.0 32.7 4.2 2.5 8.0 0.9 32.1 4.2 22.3 3.0 32.1 15.3 71.2 Nigeria 2013 All 2966 10.4 13.5 4.4 0.3 0.0 0.0 32.8 2.9 1.7 6.2 0.9 32.0 2.9 24.1 4.4 32.0 11.5 70.9 Pakistan 2006-7 Urban 325 7.1 0.9 65.6 0.0 0.0 0.0 1.1 1.2 0.0 7.3 0.1 1.1 0.9 7.9 65.6 1.1 8.7 78.3 Pakistan 2006-7 Rural 732 6.7 1.9 54.9 0.5 0.0 0.0 0.0 3.2 0.0 2.7 0.3 0.0 3.2 9.0 54.9 0.0 6.0 68.9 Pakistan 2006-7 All 1058 6.8 1.6 58.2 0.4 0.0 0.0 0.3 2.6 0.0 4.1 0.3 0.3 2.5 8.7 58.2 0.3 6.8 71.8 Pakistan 2012-13 Urban 719 7.2 0.4 70.3 0.2 0.0 0.0 0.6 0.7 0.0 0.9 0.0 0.6 0.7 7.6 70.3 0.6 1.7 79.3 Pakistan 2012-13 Rural 1764 8.1 1.9 64.1 0.4 0.0 0.0 0.8 0.9 0.0 0.2 0.2 0.6 0.8 10.4 64.1 0.6 1.3 75.2 Pakistan 2012-13 All 2482 7.8 1.4 65.9 0.3 0.0 0.0 0.7 0.8 0.0 0.4 0.1 0.6 0.8 9.6 65.9 0.6 1.4 76.4 Rwanda 2005 Urban 145 2.4 8.7 5.2 0.0 0.0 0.0 9.6 0.5 0.0 3.1 4.7 9.6 0.5 11.1 5.2 9.6 8.2 33.7 Rwanda 2005 Rural 958 0.9 10.8 1.6 0.5 0.0 0.0 6.5 0.6 0.0 4.4 4.0 6.4 0.6 12.2 1.6 6.4 9.0 28.7 Rwanda 2005 All 1103 1.1 10.5 2.0 0.4 0.0 0.0 6.9 0.6 0.0 4.2 4.1 6.8 0.6 12.1 2.0 6.8 8.9 29.3 Rwanda 2010 Urban 140 0.8 24.0 4.9 6.1 0.0 0.0 7.9 0.0 0.0 4.2 3.0 7.9 0.0 28.6 4.9 7.9 7.2 48.2 Rwanda 2010 Rural 992 1.0 23.4 1.2 13.9 0.0 0.0 3.7 0.1 0.0 2.5 7.6 3.3 0.1 36.6 1.2 3.3 10.0 50.0 Rwanda 2010 All 1132 1.0 23.5 1.7 12.9 0.0 0.0 4.2 0.1 0.0 2.7 7.0 3.8 0.1 35.6 1.7 3.8 9.6 49.8 Senegal 2005 Urban 801 1.8 14.7 3.9 0.6 0.0 1.2 2.0 3.7 0.0 0.7 9.6 1.9 3.3 16.9 5.1 1.9 13.3 36.1 Senegal 2005 Rural 1367 0.9 16.6 0.3 0.2 0.0 0.9 0.2 4.8 0.0 1.4 8.4 0.2 4.8 17.7 1.2 0.2 14.1 32.6 Senegal 2005 All 2168 1.2 15.9 1.6 0.4 0.0 1.0 0.9 4.4 0.0 1.1 8.8 0.8 4.2 17.4 2.6 0.8 13.8 33.9 Senegal 2010-11 Urban 978 10.1 21.2 3.8 0.1 0.6 0.0 5.9 1.2 1.3 3.2 3.5 5.4 1.2 30.7 4.4 5.4 8.6 48.2 Senegal 2010-11 Rural 1268 3.0 29.5 1.1 0.5 0.6 0.0 1.0 3.1 2.2 4.5 2.9 1.0 3.1 32.9 1.7 1.0 12.4 47.2 Senegal 2010-11 All 2246 6.1 25.9 2.3 0.3 0.6 0.0 3.2 2.3 1.8 3.9 3.1 2.9 2.3 32.0 2.9 2.9 10.7 47.7 

 (Continued...)

71 
72 

Table A3. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Place of 

residence N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private 

ReligiousNGO Phar- macy Shop Market/ Informal Shop Non-allo- pathic Other Phar- macy Only Shop Only Public Private Phar- macy Only Other Any Care3 

Tanzania 2004-5 Urban 156 16.4 18.0 9.2 0.0 0.0 3.1 12.9 0.0 0.0 0.0 0.0 11.4 0.0 34.3 12.2 11.4 0.0 57.9 Tanzania 2004-5 Rural 848 3.4 36.3 3.8 0.4 0.0 3.3 14.3 0.0 0.0 0.0 2.1 13.4 0.0 40.1 7.1 13.4 2.1 62.3 

Tanzania 2004-5 All 1004 5.4 33.5 4.6 0.3 0.0 3.2 14.1 0.0 0.0 0.0 1.7 13.1 0.0 39.2 7.9 13.1 1.7 61.6 Tanzania 2010 Urban 276 10.3 29.7 12.3 0.0 0.0 0.0 14.0 0.0 0.0 0.0 0.2 12.3 0.0 39.1 12.3 12.3 0.2 63.5 Tanzania 2010 Rural 833 1.9 44.3 1.8 0.0 0.0 0.0 15.5 0.0 0.0 0.0 2.0 14.8 0.0 46.0 1.8 14.8 2.0 63.9 Tanzania 2010 All 1109 4.0 40.6 4.4 0.0 0.0 0.0 15.1 0.0 0.0 0.0 1.6 14.2 0.0 44.3 4.4 14.2 1.6 63.8 Uganda 2006 Urban 172 16.8 11.9 41.0 0.0 0.0 0.0 5.4 0.0 0.0 2.7 0.6 5.4 0.0 28.7 41.0 5.4 3.3 75.8 Uganda 2006 Rural 1802 4.5 25.9 41.9 0.7 0.7 0.0 4.5 0.7 0.0 4.1 0.8 4.3 0.7 30.9 42.6 4.3 5.6 78.6 Uganda 2006 All 1974 5.6 24.7 41.9 0.7 0.7 0.0 4.5 0.7 0.0 4.0 0.8 4.4 0.7 30.7 42.4 4.4 5.4 78.4 Uganda 2011 Urban 237 10.9 14.1 46.5 0.1 1.7 0.0 3.4 0.6 0.0 0.1 0.4 2.9 0.6 25.1 48.2 2.9 1.2 74.3 Uganda 2011 Rural 1528 4.4 28.0 41.7 0.7 0.7 0.0 1.3 1.7 0.1 1.0 1.1 1.0 1.5 32.6 42.3 1.0 3.9 77.0 Uganda 2011 All 1766 5.2 26.1 42.3 0.7 0.8 0.0 1.6 1.6 0.1 0.9 1.0 1.3 1.4 31.6 43.1 1.3 3.6 76.6 Zambia 2007 Urban 291 6.8 46.9 3.3 0.0 0.1 0.0 2.4 1.7 0.0 0.3 0.6 2.1 1.7 53.7 3.4 2.1 2.6 59.7 Zambia 2007 Rural 619 1.5 50.8 1.2 0.0 2.1 5.1 0.5 2.1 0.0 6.5 2.4 0.1 2.1 52.3 8.4 0.1 10.3 68.0 Zambia 2007 All 911 3.2 49.5 1.8 0.0 1.5 3.5 1.1 1.9 0.0 4.5 1.8 0.7 1.9 52.7 6.8 0.7 7.8 65.3 Zambia 2013-14 Urban 772 6.9 53.0 3.9 0.1 0.0 0.7 1.1 1.5 0.0 0.2 0.3 1.1 0.9 59.8 4.6 1.1 2.0 66.5 Zambia 2013-14 Rural 1258 2.6 59.3 0.3 2.4 0.4 1.9 0.4 1.3 0.0 2.8 1.4 0.4 0.9 64.0 2.7 0.4 5.4 70.0 Zambia 2013-14 All 2030 4.2 56.9 1.7 1.5 0.3 1.5 0.7 1.4 0.0 1.8 1.0 0.7 0.9 62.4 3.4 0.7 4.1 68.7 Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Children for whom care was sought from multiple sources contribute to each source's total, so the total percentage may exceed the percentage of children who received any care. 

1 The following Disaggregated Sources of Care categories include multiple standard response options: Public Peripheral Health Facility (HF) includes: health center, health post, mobile clinic, or other public sector care; Private Clinic or Clinician includes private hospital, 

doctor, nurse, mobile clinic, other private sector; and Other includes any other source of care sought outside the home, including a friend, relative, or other. 

2 The Public summary category combines the Public Hospital, Public Peripheral HF, and Public CHW disaggregated sources of care categories; the Private summary category combines the Private Clinic or Clinician, Private CHW, and Religious or NGO Facility 

disaggregated sources of care categories (note that Private does not include pharmacy); Pharmacy Only includes children for whom care was sought only from a pharmacy; and the Other summary category combines the Shop, Market/Informal Shop, Non-Allopathic, and 

Other disaggregated sources of care categories. 3 Any Care includes children who received care from any source outside the home (including all disaggregated source of care categories). 

72 
73 

Table A4. Sources of care for fever by household wealth, USAID MCH priority countries 

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Bangladesh 2007 Poorest 498 1.0 5.8 6.2 0.0 0.0 0.6 23.3 0.7 0.0 20.8 6.8 23.0 0.7 6.8 6.8 23.0 28.2 63.5 Bangladesh 2007 Poorer 495 1.2 6.0 11.4 0.0 0.0 0.5 21.1 0.9 0.0 23.9 4.0 20.6 0.9 7.1 11.9 20.6 28.8 66.3 

Bangladesh 2007 Middle 420 1.3 9.0 12.6 0.0 0.0 0.0 21.5 0.6 0.0 27.5 5.6 21.1 0.6 10.2 12.6 21.1 33.2 75.8 Bangladesh 2007 Wealthier 419 3.8 7.8 19.1 0.0 0.0 0.8 26.2 1.2 0.0 18.2 4.5 25.8 1.2 11.7 19.9 25.8 23.9 78.5 Bangladesh 2007 Wealthiest 353 3.1 7.1 27.1 0.0 0.0 1.6 26.0 0.6 0.0 12.1 3.9 25.5 0.6 10.2 28.7 25.5 16.5 79.9 Bangladesh 2007 All 2185 2.0 7.0 14.5 0.0 0.0 0.7 23.4 0.8 0.0 20.9 5.0 23.0 0.8 9.0 15.1 23.0 26.6 72.0 Bangladesh 2011 Poorest 800 1.0 7.9 40.0 0.0 0.4 0.2 20.2 0.0 0.0 4.8 0.3 17.8 0.0 9.0 40.4 17.8 5.1 70.3 Bangladesh 2011 Poorer 618 1.9 6.3 41.1 0.0 0.0 1.0 25.0 0.0 0.0 4.4 0.0 22.8 0.0 8.1 42.0 22.8 4.4 76.4 Bangladesh 2011 Middle 617 1.7 9.1 41.9 0.0 0.0 0.4 20.7 0.0 0.0 4.5 1.1 19.5 0.0 10.6 42.3 19.5 5.6 76.4 Bangladesh 2011 Wealthier 600 2.4 5.6 45.7 0.0 0.1 0.3 19.2 0.0 0.0 3.8 0.9 17.5 0.0 8.0 46.1 17.5 4.7 75.0 Bangladesh 2011 Wealthiest 429 2.8 5.5 48.4 0.0 0.4 0.7 18.3 0.0 0.0 2.8 0.1 17.3 0.0 8.3 49.5 17.3 3.0 77.6 Bangladesh 2011 All 3064 1.9 7.0 42.9 0.0 0.2 0.5 20.8 0.0 0.0 4.2 0.5 19.0 0.0 8.8 43.5 19.0 4.7 74.7 Congo DR 2007 Poorest 566 0.9 24.7 6.9 0.0 6.7 0.0 10.9 1.7 0.0 6.1 2.1 10.2 1.5 25.2 13.4 10.2 9.6 54.5 Congo DR 2007 Poorer 499 2.1 28.0 7.2 0.0 10.0 0.0 10.0 1.6 0.0 2.2 1.5 8.8 1.6 30.0 17.1 8.8 5.2 58.1 Congo DR 2007 Middle 528 4.1 22.8 7.7 0.0 9.5 0.0 11.0 0.4 0.0 3.3 1.9 10.2 0.4 26.5 17.2 10.2 5.6 57.3 Congo DR 2007 Wealthier 539 7.8 13.2 14.5 0.0 7.4 0.0 22.3 0.1 0.0 1.1 3.8 20.6 0.1 20.9 22.0 20.6 5.0 66.2 Congo DR 2007 Wealthiest 337 9.2 13.2 39.9 0.0 2.4 0.0 13.7 0.4 0.0 1.9 1.7 12.1 0.4 21.6 42.3 12.1 3.9 77.5 Congo DR 2007 All 2469 4.5 20.9 13.3 0.0 7.6 0.0 13.6 0.9 0.0 3.0 2.3 12.4 0.8 25.0 20.8 12.4 6.1 61.5 Congo DR 2013-14 Poorest 1120 1.3 27.2 9.1 0.2 0.0 0.0 11.5 0.6 0.7 4.4 1.1 11.0 0.6 28.5 9.1 11.0 6.8 54.3 Congo DR 2013-14 Poorer 1055 2.0 33.3 9.6 0.2 0.0 0.0 13.7 0.2 0.6 2.4 0.6 12.7 0.2 35.1 9.6 12.7 3.7 59.8 Congo DR 2013-14 Middle 1024 1.7 26.9 9.0 0.0 0.0 0.0 17.4 0.0 0.3 3.7 1.4 15.9 0.0 28.5 9.0 15.9 5.4 57.5 Congo DR 2013-14 Wealthier 1072 4.4 24.6 10.8 0.0 0.0 0.0 19.3 0.1 0.2 1.8 1.4 17.1 0.1 28.5 10.8 17.1 3.5 59.1 Congo DR 2013-14 Wealthiest 741 3.9 15.2 22.4 0.0 0.0 0.0 23.3 0.0 0.1 0.4 2.2 21.7 0.0 19.1 22.4 21.7 2.7 64.9 Congo DR 2013-14 All 5013 2.6 26.1 11.5 0.1 0.0 0.0 16.6 0.2 0.4 2.7 1.3 15.2 0.2 28.5 11.5 15.2 4.6 58.7 Ethiopia 2005 Poorest 422 3.1 6.3 1.1 0.0 0.0 0.3 0.0 0.0 0.0 0.2 0.0 0.0 0.0 9.3 1.5 0.0 0.2 11.1 Ethiopia 2005 Poorer 413 4.4 7.0 1.1 1.7 0.0 0.0 0.9 0.0 0.8 0.6 0.0 0.3 0.0 13.1 1.1 0.3 1.4 15.9 Ethiopia 2005 Middle 436 2.2 9.2 3.6 0.0 0.0 1.8 2.6 0.0 0.0 0.4 0.0 1.8 0.0 11.3 5.4 1.8 0.4 18.5 Ethiopia 2005 Wealthier 357 4.4 9.0 2.9 0.0 0.0 0.0 1.3 0.2 0.4 0.0 0.0 0.8 0.2 13.5 3.0 0.8 0.6 17.9 Ethiopia 2005 Wealthiest 258 10.1 19.2 7.5 0.3 0.0 1.2 4.1 0.0 0.1 0.6 0.0 2.4 0.0 29.6 8.6 2.4 0.8 40.2 Ethiopia 2005 All 1886 4.4 9.4 2.9 0.4 0.0 0.6 1.6 0.0 0.3 0.4 0.0 1.0 0.0 14.2 3.5 1.0 0.7 19.1 Ethiopia 2011 Poorest 459 0.6 11.9 4.0 0.0 0.0 0.0 0.5 0.8 0.0 0.1 0.0 0.5 0.8 12.6 4.0 0.5 0.9 17.4 Ethiopia 2011 Poorer 401 0.5 16.3 4.6 0.0 0.0 0.0 0.1 0.7 0.0 0.9 0.0 0.1 0.7 16.3 4.7 0.1 1.7 22.7 Ethiopia 2011 Middle 410 0.2 15.7 6.1 0.0 0.1 1.2 1.1 1.3 0.0 0.4 0.0 0.4 1.3 15.9 7.3 0.4 1.7 25.2 Ethiopia 2011 Wealthier 353 1.6 17.1 10.5 0.0 0.0 1.3 2.1 0.0 0.0 0.5 0.0 1.4 0.0 18.0 11.0 1.4 0.5 30.1 Ethiopia 2011 Wealthiest 262 3.8 22.0 15.5 0.0 0.0 1.3 3.5 0.0 0.0 0.2 0.0 3.1 0.0 24.7 16.3 3.1 0.3 43.5 Ethiopia 2011 All 1885 1.1 16.1 7.4 0.0 0.0 0.7 1.2 0.6 0.0 0.4 0.0 0.9 0.6 16.8 7.9 0.9 1.1 26.2 Ghana 2003 Poorest 186 17.4 22.3 2.7 0.0 0.0 0.0 23.3 0.5 0.0 2.0 8.5 22.9 0.5 38.6 2.7 22.9 10.9 73.9 Ghana 2003 Poorer 141 18.9 9.5 5.5 0.5 0.0 0.0 33.7 0.9 0.0 0.6 1.7 32.2 0.9 28.0 5.5 32.2 3.3 68.9 Ghana 2003 Middle 144 20.9 15.3 6.4 0.0 0.0 0.0 24.7 0.0 0.0 1.8 0.0 23.9 0.0 36.1 6.4 23.9 1.8 68.3 Ghana 2003 Wealthier 133 26.8 18.3 10.0 1.0 0.0 0.0 17.7 1.0 0.0 0.8 1.9 16.6 0.0 45.0 10.0 16.6 3.7 72.8 Ghana 2003 Wealthiest 106 53.5 2.9 13.7 0.0 0.0 0.0 15.5 0.0 0.0 0.0 0.4 12.8 0.0 56.4 13.7 12.8 0.4 83.3 Ghana 2003 All 710 25.6 14.7 7.0 0.3 0.0 0.0 23.4 0.5 0.0 1.2 3.0 22.3 0.3 39.9 7.0 22.3 4.6 73.0 Ghana 2008 Poorest 136 12.7 24.7 2.3 0.0 0.0 0.9 9.4 3.5 5.3 0.9 3.1 8.7 3.5 37.4 3.2 8.7 12.7 62.0 Ghana 2008 Poorer 136 18.5 20.2 6.8 0.0 0.0 0.0 13.0 0.0 0.9 0.0 1.7 13.0 0.0 38.4 6.8 13.0 2.6 60.8 Ghana 2008 Middle 111 20.5 21.9 4.3 0.0 0.0 0.0 22.1 0.2 3.3 0.0 4.0 19.6 0.2 41.9 4.3 19.6 7.5 72.7 Ghana 2008 Wealthier 104 27.7 13.8 19.6 0.0 0.0 0.0 17.5 0.0 0.9 0.8 1.6 16.7 0.0 41.5 19.6 16.7 3.2 80.6 Ghana 2008 Wealthiest 56 50.3 1.8 27.8 0.0 0.0 0.0 9.8 0.0 0.0 0.0 0.0 8.3 0.0 52.0 27.8 8.3 0.0 88.2 Ghana 2008 All 544 22.5 18.6 9.8 0.0 0.0 0.2 14.5 0.9 2.4 0.4 2.3 13.5 0.9 40.9 10.0 13.5 6.0 70.1 Haiti 2005-6 Poorest 376 0.5 9.4 11.3 0.0 0.0 7.3 0.2 0.8 0.9 2.9 1.5 0.2 0.8 9.9 18.6 0.2 5.9 34.1 Haiti 2005-6 Poorer 321 1.5 8.0 14.1 0.0 0.0 9.1 1.5 1.9 0.4 2.2 4.6 1.5 1.9 9.5 23.2 1.5 8.4 40.9 Haiti 2005-6 Middle 300 4.8 13.5 12.8 0.0 0.0 12.8 2.1 1.4 1.3 1.6 1.9 1.6 0.9 18.3 25.6 1.6 5.4 47.0 Haiti 2005-6 Wealthier 306 4.6 18.9 16.4 0.0 0.0 2.0 2.1 3.2 1.6 3.4 6.0 2.1 3.2 23.5 18.5 2.1 11.6 50.4 Haiti 2005-6 Wealthiest 182 6.2 3.5 27.6 0.0 0.0 5.2 6.6 1.2 0.0 1.5 8.4 6.6 1.2 9.8 32.6 6.6 9.0 54.3 Haiti 2005-6 All 1486 3.1 11.2 15.3 0.0 0.0 7.5 2.1 1.7 0.9 2.4 4.1 2.0 1.6 14.3 22.7 2.0 7.9 44.0 

 (Continued...)

73 
74 

Table A4. − Continued

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Haiti 2012 Poorest 376 1.5 11.2 8.9 0.0 0.1 7.5 0.0 1.2 2.4 0.6 5.3 0.0 1.2 12.8 16.5 0.0 8.8 36.0 Haiti 2012 Poorer 389 2.2 6.4 12.4 0.0 0.5 12.8 0.0 0.9 0.3 1.1 5.4 0.0 0.9 8.6 25.7 0.0 7.1 39.9 

Haiti 2012 Middle 366 5.4 10.7 20.1 0.0 0.2 10.0 2.1 3.1 2.7 0.2 4.7 1.1 2.8 16.1 30.0 1.1 8.0 54.1 Haiti 2012 Wealthier 372 10.3 8.1 25.6 0.0 0.5 7.2 1.7 0.6 0.6 1.0 6.2 0.8 0.4 18.3 32.4 0.8 8.4 55.6 Haiti 2012 Wealthiest 231 11.0 7.7 27.1 0.0 0.0 9.1 0.8 0.0 0.3 0.0 3.3 0.8 0.0 18.2 36.2 0.8 3.6 57.8 Haiti 2012 All 1735 5.6 8.9 18.0 0.0 0.3 9.4 0.9 1.2 1.3 0.6 5.1 0.5 1.1 14.4 27.4 0.5 7.5 47.8 India 2005-6 Poorest 1904 2.6 9.9 48.8 0.0 0.0 0.2 5.7 0.7 0.0 4.2 2.0 5.5 0.7 12.4 49.0 5.5 6.7 71.2 India 2005-6 Poorer 1861 4.1 9.5 51.3 0.0 0.0 0.3 6.7 1.8 0.0 3.7 2.3 6.0 1.6 13.6 51.6 6.0 7.7 76.2 India 2005-6 Middle 1569 6.4 10.5 54.5 0.0 0.0 0.2 5.7 1.0 0.0 3.0 1.1 5.3 1.0 16.7 54.7 5.3 5.2 79.9 India 2005-6 Wealthier 1445 6.2 6.7 62.5 0.0 0.0 0.8 5.6 0.2 0.0 1.8 1.3 5.2 0.2 12.6 63.3 5.2 3.3 81.2 India 2005-6 Wealthiest 1074 6.1 3.7 70.6 0.0 0.0 0.3 3.6 0.3 0.0 3.4 0.9 3.0 0.3 9.8 70.9 3.0 4.6 86.9 India 2005-6 All 7852 4.9 8.5 56.0 0.0 0.0 0.3 5.6 0.9 0.0 3.3 1.6 5.2 0.8 13.2 56.4 5.2 5.7 78.1 Indonesia 2007 Poorest 1264 1.9 23.7 13.4 0.0 13.6 2.3 5.6 20.4 0.0 3.3 3.9 5.0 19.6 25.6 29.2 5.0 26.3 82.5 Indonesia 2007 Poorer 1035 1.5 28.0 26.1 0.0 14.6 1.1 9.9 15.9 0.0 1.5 1.7 9.4 15.0 29.3 40.9 9.4 19.0 92.5 Indonesia 2007 Middle 1102 1.5 25.8 36.4 0.0 11.5 0.6 8.9 12.6 0.0 0.9 1.8 7.8 11.3 27.2 48.0 7.8 15.3 93.9 Indonesia 2007 Wealthier 927 2.4 25.9 38.7 0.0 12.4 0.7 11.3 7.4 0.0 0.4 0.7 10.4 6.8 27.6 51.5 10.4 8.4 94.1 Indonesia 2007 Wealthiest 705 3.6 13.8 58.9 0.0 2.2 1.1 13.4 5.1 0.0 0.3 0.9 12.8 4.2 16.7 62.1 12.8 6.3 94.0 Indonesia 2007 All 5033 2.1 24.1 32.1 0.0 11.5 1.2 9.4 13.2 0.0 1.5 2.0 8.6 12.3 25.8 44.5 8.6 16.3 90.8 Indonesia 2012 Poorest 1150 1.7 32.3 19.2 0.0 16.0 0.0 6.4 14.1 0.0 3.4 2.8 5.4 12.8 33.7 35.0 5.4 20.1 85.8 Indonesia 2012 Poorer 1057 1.7 27.5 34.0 0.0 16.0 0.0 10.6 7.0 0.0 2.4 1.5 9.6 5.5 29.2 49.1 9.6 10.9 90.7 Indonesia 2012 Middle 1023 2.1 20.0 42.6 0.0 13.8 0.0 12.2 6.9 0.0 2.2 2.0 10.9 6.3 22.0 55.7 10.9 10.4 90.0 Indonesia 2012 Wealthier 1033 1.8 22.3 46.0 0.0 12.4 0.0 13.9 2.8 0.0 0.7 2.3 11.4 1.7 23.8 57.6 11.4 5.8 90.0 Indonesia 2012 Wealthiest 824 1.8 12.6 63.3 0.0 7.0 0.0 13.6 2.3 0.0 0.7 0.5 11.1 1.8 14.3 69.1 11.1 3.6 92.3 Indonesia 2012 All 5086 1.8 23.6 39.6 0.0 13.4 0.0 11.1 7.0 0.0 2.0 1.9 9.5 5.9 25.2 52.2 9.5 10.7 89.6 Kenya 2003 Poorest 504 4.8 22.8 10.5 0.0 0.5 4.1 16.6 7.6 0.0 2.1 0.3 14.8 6.5 27.3 15.0 14.8 9.9 64.9 Kenya 2003 Poorer 505 3.7 24.6 12.7 0.0 1.0 2.1 14.3 8.9 0.0 1.1 1.9 13.9 8.4 27.9 15.5 13.9 11.9 66.1 Kenya 2003 Middle 442 8.8 22.9 12.4 0.0 0.4 3.4 14.3 7.8 0.0 0.3 1.9 13.0 7.8 31.0 15.9 13.0 10.0 68.5 Kenya 2003 Wealthier 393 6.7 27.6 13.7 0.0 1.1 1.2 13.7 6.9 0.0 0.0 2.6 12.1 6.9 34.1 16.0 12.1 9.5 70.6 Kenya 2003 Wealthiest 412 10.1 15.6 25.9 0.0 0.2 3.5 14.3 4.8 0.0 0.7 1.0 13.2 4.6 25.7 29.6 13.2 6.5 73.4 Kenya 2003 All 2255 6.6 22.7 14.7 0.0 0.6 2.9 14.7 7.3 0.0 0.9 1.5 13.5 6.9 29.0 18.1 13.5 9.7 68.4 Kenya 2008-9 Poorest 353 10.3 36.4 5.3 0.0 0.0 0.9 3.7 9.2 0.0 0.3 0.9 3.6 7.4 46.1 6.2 3.6 10.5 64.1 Kenya 2008-9 Poorer 228 6.9 28.3 6.2 0.0 0.5 1.7 8.9 7.0 0.0 0.1 2.4 8.9 6.6 34.8 8.4 8.9 9.0 58.5 Kenya 2008-9 Middle 252 10.2 29.6 8.9 0.0 0.2 1.9 12.3 3.7 0.0 0.3 0.0 12.3 3.3 39.8 11.0 12.3 4.0 66.4 Kenya 2008-9 Wealthier 259 16.1 16.8 9.6 0.0 0.0 4.1 9.3 5.7 0.0 0.2 0.0 9.3 5.7 32.4 13.8 9.3 5.9 60.9 Kenya 2008-9 Wealthiest 210 18.3 21.5 10.5 0.0 0.2 3.3 6.3 2.3 0.0 0.0 0.0 6.3 2.2 39.1 13.4 6.3 2.3 60.1 Kenya 2008-9 All 1302 12.1 27.4 7.9 0.0 0.1 2.3 7.8 6.0 0.0 0.2 0.7 7.8 5.3 39.0 10.2 7.8 6.8 62.3 Liberia 2007 Poorest 309 3.3 17.5 21.5 0.0 0.0 0.0 4.5 4.0 0.0 9.7 10.7 4.1 3.3 20.7 21.5 4.1 20.6 63.2 Liberia 2007 Poorer 393 5.7 26.4 18.5 0.0 0.0 0.0 8.9 6.1 0.0 13.1 7.6 8.9 6.0 32.1 18.5 8.9 24.4 73.8 Liberia 2007 Middle 336 11.4 32.4 22.7 0.0 0.0 0.0 8.4 8.4 0.0 5.2 4.0 6.3 8.2 43.8 22.7 6.3 16.8 82.7 Liberia 2007 Wealthier 339 12.9 29.7 22.7 0.0 0.0 0.0 11.5 5.3 0.0 0.9 1.7 10.5 5.2 42.1 22.7 10.5 7.9 80.6 Liberia 2007 Wealthiest 200 12.3 30.3 24.4 0.0 0.0 0.0 18.3 4.0 0.0 3.2 2.5 12.9 4.0 42.7 24.4 12.9 8.7 83.2 Liberia 2007 All 1577 8.8 27.1 21.6 0.0 0.0 0.0 9.7 5.7 0.0 6.9 5.5 8.3 5.5 35.8 21.6 8.3 16.5 76.3 Liberia 2013 Poorest 460 9.9 33.1 7.5 0.2 0.0 0.0 7.8 2.0 13.1 3.3 0.4 7.4 1.9 43.2 7.5 7.4 18.5 73.7 Liberia 2013 Poorer 397 10.5 39.7 7.5 0.2 0.0 0.0 8.5 3.3 8.6 2.1 1.1 7.2 3.3 50.3 7.5 7.2 14.8 78.1 Liberia 2013 Middle 345 12.0 36.8 12.3 0.0 0.0 0.0 9.8 4.4 7.7 0.5 0.3 7.7 4.4 48.8 12.3 7.7 12.4 80.3 Liberia 2013 Wealthier 306 11.1 19.5 30.6 0.0 0.0 0.0 23.1 0.5 2.3 1.1 0.0 21.6 0.5 30.6 30.6 21.6 3.9 86.7 Liberia 2013 Wealthiest 220 16.2 6.1 39.9 0.0 0.0 0.0 18.7 0.0 0.0 0.0 0.1 17.9 0.0 22.3 39.9 17.9 0.1 80.2 Liberia 2013 All 1728 11.5 29.5 16.7 0.1 0.0 0.0 12.5 2.2 7.4 1.7 0.4 11.3 2.2 41.0 16.7 11.3 11.5 79.2 Madagascar 2003-4 Poorest 394 0.9 23.5 7.7 0.0 0.0 0.0 0.0 6.0 0.0 1.5 0.3 0.0 6.0 24.4 7.7 0.0 7.6 39.6 Madagascar 2003-4 Poorer 221 0.6 28.4 3.6 0.0 0.0 0.0 0.3 2.5 0.0 2.8 1.5 0.3 2.5 29.0 3.6 0.3 6.8 38.2 Madagascar 2003-4 Middle 249 1.5 22.8 6.8 0.0 0.0 0.0 2.5 0.8 0.0 1.1 2.2 2.5 0.8 24.3 6.8 2.5 4.2 37.8 Madagascar 2003-4 Wealthier 171 7.1 22.4 20.2 0.0 0.0 0.0 2.4 0.3 0.0 0.0 1.6 2.4 0.3 29.5 20.2 2.4 1.9 53.8 Madagascar 2003-4 Wealthiest 166 10.0 17.0 33.4 0.0 0.0 0.0 0.3 0.3 0.0 0.2 0.2 0.3 0.3 27.0 33.4 0.3 0.8 60.5 Madagascar 2003-4 All 1201 3.1 23.2 12.1 0.0 0.0 0.0 0.9 2.7 0.0 1.3 1.1 0.9 2.7 26.3 12.1 0.9 5.0 43.9 

 (Continued...)

74 
75 

Table A4. − Continued

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Madagascar 2008-9 Poorest 279 0.9 27.2 5.4 0.0 1.0 0.0 0.6 5.2 0.0 2.0 0.2 0.6 4.9 28.1 6.4 0.6 7.4 42.0 Madagascar 2008-9 Poorer 249 1.8 26.9 3.6 0.0 0.9 0.0 0.6 5.7 0.0 1.7 0.0 0.6 5.7 28.7 4.5 0.6 7.1 40.6 

Madagascar 2008-9 Middle 198 2.8 26.8 7.0 0.0 1.7 0.0 0.9 4.9 0.0 2.8 0.0 0.9 4.4 29.6 8.7 0.9 7.7 43.5 Madagascar 2008-9 Wealthier 198 3.8 32.2 12.5 0.0 0.0 0.0 0.4 1.2 0.0 0.0 0.0 0.4 1.2 36.0 12.5 0.4 1.2 49.9 Madagascar 2008-9 Wealthiest 192 8.4 16.1 41.4 0.0 0.3 0.0 0.6 0.0 0.0 0.0 0.0 0.4 0.0 24.6 41.8 0.4 0.0 65.5 Madagascar 2008-9 All 1116 3.3 26.0 12.7 0.0 0.8 0.0 0.6 3.7 0.0 1.4 0.0 0.6 3.5 29.3 13.6 0.6 5.0 47.4 Malawi 2010 Poorest 1397 8.5 37.5 3.7 2.0 0.1 11.4 0.0 8.2 0.0 0.1 0.5 0.0 7.4 46.8 14.5 0.0 8.9 68.6 Malawi 2010 Poorer 1357 11.2 38.9 3.5 1.6 0.1 7.1 0.0 9.1 0.0 0.7 0.9 0.0 8.6 51.0 10.6 0.0 10.6 70.8 Malawi 2010 Middle 1469 12.9 43.1 4.8 2.2 0.3 7.8 0.1 7.1 0.0 1.2 0.7 0.1 6.6 56.7 12.7 0.1 9.0 76.3 Malawi 2010 Wealthier 1127 16.5 31.1 6.5 1.7 0.0 9.1 0.0 9.0 0.0 0.2 1.4 0.0 8.1 49.0 15.6 0.0 10.6 73.0 Malawi 2010 Wealthiest 865 20.5 28.1 13.7 0.1 0.2 11.6 0.0 3.9 0.0 0.0 1.1 0.0 3.7 48.4 25.5 0.0 5.0 76.4 Malawi 20104 All 6214 13.3 36.7 5.8 1.6 0.1 9.2 0.0 7.7 0.0 0.5 0.9 0.0 7.1 50.7 15.0 0.0 9.0 72.8 Mali 2006 Poorest 450 0.0 20.7 1.3 0.0 1.4 0.0 0.7 4.0 12.9 11.4 1.9 0.7 4.0 20.7 2.7 0.7 28.8 52.6 Mali 2006 Poorer 492 1.5 26.9 0.8 0.0 1.4 0.0 0.7 2.2 6.5 13.2 3.1 0.5 2.2 28.3 2.2 0.5 23.9 54.5 Mali 2006 Middle 472 0.7 24.3 0.2 0.0 2.7 0.0 0.8 2.0 6.6 14.9 2.2 0.7 2.0 25.0 2.9 0.7 25.4 52.4 Mali 2006 Wealthier 490 1.3 30.4 1.1 0.0 2.5 0.0 6.1 0.2 5.6 12.9 1.2 5.9 0.2 31.5 3.6 5.9 19.1 58.7 Mali 2006 Wealthiest 334 4.7 40.2 6.3 0.0 1.8 0.0 11.2 0.0 2.5 8.4 0.9 10.1 0.0 44.8 8.1 10.1 11.5 73.2 Mali 2006 All 2238 1.5 27.8 1.7 0.0 2.0 0.0 3.5 1.8 7.0 12.4 1.9 3.2 1.8 29.2 3.6 3.2 22.3 57.4 Mali 2012-13 Poorest 182 0.0 17.2 0.0 0.0 0.0 0.0 1.1 0.8 8.4 13.2 4.0 1.1 0.8 17.2 0.0 1.1 24.8 41.7 Mali 2012-13 Poorer 174 0.0 25.7 0.0 0.0 2.4 0.0 0.3 1.5 9.3 7.7 1.7 0.3 1.5 25.7 2.4 0.3 17.8 45.7 Mali 2012-13 Middle 163 0.0 17.4 1.3 0.0 1.6 0.0 3.3 0.8 7.5 7.6 2.5 3.3 0.8 17.4 2.9 3.3 17.0 38.8 Mali 2012-13 Wealthier 192 0.4 26.8 2.2 0.0 0.0 0.0 4.4 0.5 9.2 10.0 2.7 4.4 0.5 27.2 2.2 4.4 20.0 52.4 Mali 2012-13 Wealthiest 119 2.5 46.4 6.5 0.0 0.4 0.0 13.4 0.0 4.1 3.0 0.9 12.2 0.0 47.9 6.9 12.2 7.5 74.1 Mali 2012-13 All 830 0.5 25.4 1.7 0.0 0.9 0.0 3.9 0.8 8.0 8.7 2.5 3.7 0.8 25.7 2.6 3.7 18.2 49.1 Mozambique 2003 Poorest 629 4.0 35.9 1.1 0.4 0.0 1.5 0.3 0.0 2.3 4.2 3.9 0.3 0.0 40.2 2.6 0.3 10.4 53.0 Mozambique 2003 Poorer 488 2.1 40.0 0.0 0.7 0.0 1.6 0.0 0.0 2.6 3.2 3.3 0.0 0.0 42.7 1.6 0.0 9.2 53.3 Mozambique 2003 Middle 559 5.2 43.5 1.0 0.0 0.0 0.4 0.1 0.0 3.3 3.6 2.1 0.1 0.0 48.7 1.3 0.1 8.6 58.0 Mozambique 2003 Wealthier 462 4.9 57.7 0.7 0.0 0.0 0.2 0.5 0.0 1.4 1.2 3.8 0.5 0.0 62.2 1.0 0.5 6.5 69.5 Mozambique 2003 Wealthiest 371 14.8 50.8 3.1 0.0 0.0 0.5 2.5 0.0 0.4 0.2 2.1 2.4 0.0 65.0 3.6 2.4 2.7 73.7 Mozambique 2003 All 2509 5.7 44.6 1.1 0.2 0.0 0.9 0.6 0.0 2.1 2.7 3.1 0.5 0.0 50.3 2.0 0.5 7.9 60.3 Mozambique 2011 Poorest 361 46.9 0.9 0.0 6.4 0.0 0.0 0.1 0.0 2.0 4.2 0.2 0.0 0.0 53.8 0.0 0.0 6.1 57.6 Mozambique 2011 Poorer 339 42.3 0.4 0.0 4.1 0.0 0.0 0.0 0.0 0.9 3.0 0.3 0.0 0.0 46.6 0.0 0.0 4.3 49.5 Mozambique 2011 Middle 285 58.3 1.2 0.0 2.2 0.0 0.0 0.2 0.0 2.9 2.0 0.7 0.0 0.0 61.2 0.0 0.0 5.6 66.5 Mozambique 2011 Wealthier 274 68.7 1.6 0.0 1.2 0.0 0.0 2.3 0.0 0.6 1.6 1.6 0.8 0.0 70.7 0.0 0.8 3.9 75.2 Mozambique 2011 Wealthiest 198 65.1 0.0 2.9 0.7 0.0 0.0 1.4 0.0 0.4 0.5 0.3 1.1 0.0 65.8 2.9 1.1 1.2 71.1 Mozambique 2011 All 1457 54.6 0.8 0.4 3.3 0.0 0.0 0.7 0.0 1.4 2.5 0.6 0.3 0.0 58.4 0.4 0.3 4.5 62.6 Nepal 2006 Poorest 215 3.0 17.1 2.6 0.5 0.0 0.6 17.4 0.0 0.0 1.8 0.0 16.4 0.0 20.6 3.2 16.4 1.8 41.9 Nepal 2006 Poorer 176 1.8 17.6 4.8 4.6 0.0 3.8 26.4 1.0 0.0 1.4 0.0 24.6 0.2 23.3 8.6 24.6 2.3 57.3 Nepal 2006 Middle 174 3.2 16.9 7.4 5.3 0.0 0.0 30.6 1.1 0.0 0.8 0.0 27.8 1.1 23.9 7.4 27.8 1.9 60.3 Nepal 2006 Wealthier 158 8.7 21.8 12.0 2.2 0.0 1.6 32.8 0.0 0.0 2.4 0.0 31.4 0.0 30.1 13.6 31.4 2.4 73.7 Nepal 2006 Wealthiest 169 13.7 5.5 24.4 1.5 0.0 1.7 26.5 0.5 0.0 1.7 0.1 23.5 0.5 18.7 26.1 23.5 2.3 69.4 Nepal 2006 All 890 5.9 15.8 9.8 2.7 0.0 1.5 26.2 0.5 0.0 1.6 0.0 24.3 0.4 23.1 11.2 24.3 2.1 59.4 Nepal 2011 Poorest 177 1.1 19.1 6.9 3.5 0.0 0.3 20.2 0.0 0.0 1.1 0.5 19.3 0.0 23.2 7.2 19.3 1.5 49.5 Nepal 2011 Poorer 217 2.7 15.2 17.9 2.9 0.0 0.2 30.2 0.0 0.0 1.3 1.9 29.5 0.0 20.7 18.1 29.5 3.2 69.7 Nepal 2011 Middle 223 2.3 18.0 22.4 1.8 0.0 1.3 28.7 0.5 0.0 0.9 0.7 28.2 0.5 21.2 23.7 28.2 2.1 74.5 Nepal 2011 Wealthier 213 6.3 10.4 33.8 0.0 0.0 0.2 38.8 0.0 0.0 0.0 1.3 35.4 0.0 16.7 34.0 35.4 1.3 86.8 Nepal 2011 Wealthiest 131 3.2 4.7 36.9 1.8 0.0 1.3 28.0 0.0 0.0 0.0 1.3 27.1 0.0 9.6 38.2 27.1 1.3 74.4 Nepal 2011 All 960 3.2 14.1 23.0 1.9 0.0 0.6 29.6 0.1 0.0 0.7 1.1 28.3 0.1 18.9 23.6 28.3 2.0 71.5 Nigeria 2008 Poorest 1001 7.5 12.0 5.9 1.0 0.9 0.0 23.5 10.7 0.0 1.0 1.2 22.9 10.6 20.3 6.9 22.9 12.9 62.1 Nigeria 2008 Poorer 953 9.9 16.0 6.5 0.3 0.8 0.0 25.8 7.0 0.0 2.0 0.7 25.1 6.8 26.0 7.3 25.1 9.6 66.9 Nigeria 2008 Middle 765 10.2 18.0 5.9 0.5 1.1 0.0 32.8 3.6 0.0 1.7 0.7 31.3 3.2 28.4 7.0 31.3 5.9 71.3 Nigeria 2008 Wealthier 674 15.8 13.9 11.5 0.3 0.8 0.0 34.0 2.4 0.0 0.5 1.5 32.1 2.3 29.9 12.3 32.1 4.5 77.7 

 (Continued...)

75 
76 

Table A4. − Continued

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Nigeria 2008 Wealthiest 575 18.9 9.7 18.4 1.0 0.0 0.0 28.0 0.5 0.0 0.5 0.4 26.5 0.5 29.2 18.4 26.5 1.4 74.8 Nigeria 2008 All 3968 11.7 14.1 8.8 0.6 0.8 0.0 28.3 5.6 0.0 1.2 0.9 27.1 5.4 26.2 9.6 27.1 7.7 69.5 

Nigeria 2013 Poorest 899 6.4 15.6 2.8 0.3 0.0 0.0 30.9 6.3 2.5 6.8 0.3 30.4 6.3 22.3 2.8 30.4 15.3 70.2 Nigeria 2013 Poorer 837 10.8 13.4 4.2 0.7 0.0 0.0 36.6 3.1 1.4 4.5 0.8 35.7 3.1 24.9 4.2 35.7 9.8 74.5 Nigeria 2013 Middle 756 13.3 12.8 3.6 0.6 0.0 0.0 44.2 0.3 0.3 2.4 0.2 43.1 0.3 26.3 3.6 43.1 3.1 75.9 Nigeria 2013 Wealthier 614 18.3 10.3 6.3 0.1 0.0 0.0 39.4 0.2 0.7 2.4 0.6 38.5 0.2 28.7 6.3 38.5 3.6 76.7 Nigeria 2013 Wealthiest 526 14.3 13.5 15.3 0.8 0.0 0.0 30.4 0.0 0.5 0.3 1.7 28.7 0.0 28.6 15.3 28.7 2.5 73.2 Nigeria 2013 All 3632 12.0 13.3 5.7 0.5 0.0 0.0 36.4 2.4 1.2 3.7 0.6 35.4 2.4 25.7 5.7 35.4 7.7 73.9 Pakistan 2006-7 Poorest 573 7.0 2.7 59.4 0.3 0.0 0.0 0.0 4.8 0.0 1.3 0.2 0.0 4.8 10.0 59.4 0.0 6.3 75.0 Pakistan 2006-7 Poorer 503 9.2 0.9 62.7 0.3 0.0 0.0 1.0 1.7 0.0 1.4 0.1 1.0 1.7 10.3 62.7 1.0 3.1 76.2 Pakistan 2006-7 Middle 484 9.4 2.4 67.0 1.1 0.0 0.0 0.7 1.6 0.0 4.2 0.3 0.7 1.6 12.2 67.0 0.7 6.1 83.7 Pakistan 2006-7 Wealthier 519 10.0 1.1 71.6 0.5 0.0 0.0 1.1 1.2 0.0 1.7 0.4 0.8 1.2 11.6 71.6 0.8 3.3 86.5 Pakistan 2006-7 Wealthiest 490 6.5 0.4 72.5 0.0 0.0 0.0 0.9 1.6 0.0 4.1 0.4 0.9 1.6 6.9 72.5 0.9 5.7 85.2 Pakistan 2006-7 All 2569 8.4 1.5 66.5 0.4 0.0 0.0 0.7 2.3 0.0 2.5 0.3 0.7 2.2 10.2 66.5 0.7 4.9 81.2 Pakistan 2012-13 Poorest 901 7.3 1.4 67.3 0.0 0.0 0.0 0.0 1.0 0.0 0.4 0.8 0.0 1.0 8.6 67.3 0.0 2.2 77.7 Pakistan 2012-13 Poorer 891 7.3 1.6 67.0 0.5 0.0 0.0 0.3 2.2 0.0 1.8 0.0 0.3 2.2 9.3 67.0 0.3 4.0 79.1 Pakistan 2012-13 Middle 850 9.1 0.9 70.6 0.3 0.0 0.0 1.4 1.8 0.0 0.9 0.0 1.1 1.5 10.3 70.6 1.1 2.7 84.0 Pakistan 2012-13 Wealthier 848 6.9 0.7 74.8 0.0 0.0 0.0 0.3 1.8 0.0 0.0 0.1 0.3 1.8 7.6 74.8 0.3 1.9 83.7 Pakistan 2012-13 Wealthiest 663 8.2 0.4 75.8 0.0 0.0 0.0 0.7 1.3 0.0 0.2 0.5 0.7 1.3 8.6 75.8 0.7 2.0 86.9 Pakistan 2012-13 All 4153 7.7 1.0 70.8 0.2 0.0 0.0 0.5 1.6 0.0 0.7 0.3 0.5 1.6 8.9 70.8 0.5 2.6 82.0 Rwanda 2005 Poorest 448 0.9 20.0 1.5 0.6 0.0 0.0 13.4 2.2 0.0 7.0 10.6 13.4 2.2 21.5 1.5 13.4 19.6 54.8 Rwanda 2005 Poorer 398 1.8 22.6 1.2 0.0 0.0 0.0 16.5 2.4 0.0 5.1 9.8 15.8 2.4 24.4 1.2 15.8 16.8 57.9 Rwanda 2005 Middle 418 2.0 17.8 4.7 1.8 0.0 0.0 13.7 2.1 0.0 7.6 11.9 13.7 2.1 21.4 4.7 13.7 21.0 60.2 Rwanda 2005 Wealthier 420 1.8 21.6 1.1 0.4 0.0 0.0 17.7 1.6 0.0 7.1 6.9 17.7 1.6 23.8 1.1 17.7 15.3 57.0 Rwanda 2005 Wealthiest 361 6.2 27.7 12.0 0.1 0.0 0.0 20.2 0.6 0.0 4.3 7.8 19.9 0.3 34.0 12.0 19.9 12.4 76.4 Rwanda 2005 All 2046 2.4 21.7 3.8 0.6 0.0 0.0 16.1 1.8 0.0 6.3 9.5 16.0 1.8 24.7 3.8 16.0 17.2 60.8 Rwanda 2010 Poorest 355 1.5 19.5 0.0 15.6 0.0 0.0 1.4 0.0 0.0 1.5 3.7 1.4 0.0 35.1 0.0 1.4 5.2 41.7 Rwanda 2010 Poorer 313 1.3 20.1 0.0 17.3 0.0 0.0 3.1 0.0 0.0 1.2 3.9 2.8 0.0 35.9 0.0 2.8 5.1 43.4 Rwanda 2010 Middle 264 0.5 27.4 0.0 16.4 0.0 0.3 4.3 0.0 0.0 1.6 3.1 3.9 0.0 42.3 0.3 3.9 4.7 50.9 Rwanda 2010 Wealthier 190 1.0 32.1 1.2 17.5 0.0 0.0 2.5 0.0 0.0 2.4 6.4 2.5 0.0 49.1 1.2 2.5 8.8 61.6 Rwanda 2010 Wealthiest 234 4.1 33.1 11.0 11.1 0.0 0.0 6.6 0.0 0.0 0.0 1.4 6.1 0.0 47.5 11.0 6.1 1.4 65.4 Rwanda 2010 All 1355 1.6 25.3 2.1 15.6 0.0 0.1 3.4 0.0 0.0 1.3 3.6 3.2 0.0 40.8 2.1 3.2 4.9 50.8 Senegal 2005 Poorest 647 0.7 28.7 7.4 0.0 0.0 0.5 0.9 7.9 0.0 2.5 0.3 0.9 7.8 29.4 7.9 0.9 10.7 46.9 Senegal 2005 Poorer 659 1.3 28.8 6.5 0.9 0.5 1.5 1.0 7.0 0.0 2.7 0.2 1.0 6.7 31.1 8.5 1.0 10.0 49.1 Senegal 2005 Middle 558 1.7 33.1 8.1 0.0 0.0 1.6 3.3 5.5 0.0 2.6 0.3 3.3 5.1 34.7 9.7 3.3 8.5 54.9 Senegal 2005 Wealthier 564 3.6 39.3 12.7 0.2 0.3 4.1 8.8 2.1 0.0 2.3 0.0 8.1 1.7 42.7 17.1 8.1 4.5 69.2 Senegal 2005 Wealthiest 462 7.5 32.1 13.9 1.9 0.0 0.7 15.6 2.2 0.0 1.5 0.2 14.6 1.6 40.4 14.6 14.6 3.3 71.4 Senegal 2005 All 2891 2.7 32.2 9.4 0.6 0.2 1.7 5.3 5.2 0.0 2.4 0.2 5.0 4.8 35.2 11.3 5.0 7.7 57.2 Senegal 2010-11 Poorest 490 1.0 26.6 1.1 0.9 0.4 0.0 1.0 2.2 0.4 3.7 2.7 1.0 2.2 28.6 1.5 1.0 8.6 39.0 Senegal 2010-11 Poorer 390 2.3 35.2 0.5 1.0 0.9 0.0 2.0 1.8 1.2 3.7 0.6 2.0 1.8 38.1 1.4 2.0 7.4 47.9 Senegal 2010-11 Middle 430 7.3 37.7 0.2 1.6 0.3 0.0 6.0 1.0 0.0 1.5 0.7 4.8 1.0 46.6 0.5 4.8 3.1 54.0 Senegal 2010-11 Wealthier 626 11.0 25.0 6.4 0.7 0.3 0.0 9.4 0.1 0.7 0.2 3.9 8.9 0.1 36.8 6.7 8.9 4.8 56.6 Senegal 2010-11 Wealthiest 528 13.5 31.4 9.1 0.9 2.2 0.0 8.6 0.2 0.0 2.5 2.5 8.6 0.2 45.7 11.4 8.6 5.2 67.7 Senegal 2010-11 All 2463 7.5 30.5 3.9 1.0 0.8 0.0 5.8 1.0 0.4 2.2 2.3 5.5 1.0 39.0 4.7 5.5 5.8 53.7 Tanzania 2004-5 Poorest 467 3.9 42.7 3.5 0.6 0.0 3.3 22.0 0.0 0.0 0.0 2.4 21.4 0.0 46.9 6.6 21.4 2.4 76.8 Tanzania 2004-5 Poorer 428 5.3 49.0 5.0 0.0 0.0 3.7 21.5 0.0 0.0 0.0 2.7 19.8 0.0 53.9 8.7 19.8 2.7 84.6 Tanzania 2004-5 Middle 396 4.2 40.7 6.0 0.0 0.0 6.2 28.5 0.0 0.0 0.0 0.7 26.3 0.0 44.9 12.2 26.3 0.7 83.7 Tanzania 2004-5 Wealthier 377 10.7 36.1 7.5 0.0 0.0 8.3 21.9 0.0 0.0 0.0 2.0 21.5 0.0 46.2 15.7 21.5 2.0 84.7 Tanzania 2004-5 Wealthiest 281 20.1 34.1 23.7 0.0 0.0 5.2 14.6 0.0 0.0 0.0 0.0 13.4 0.0 54.2 28.9 13.4 0.0 93.9 Tanzania 2004-5 All 1949 7.9 41.2 8.0 0.1 0.0 5.2 22.1 0.0 0.0 0.0 1.7 20.9 0.0 48.9 13.2 20.9 1.7 83.9 Tanzania 2010 Poorest 373 3.1 47.0 1.7 0.0 0.0 0.2 22.1 0.0 0.0 0.0 0.7 20.5 0.0 49.5 1.9 20.5 0.7 72.5 Tanzania 2010 Poorer 351 4.4 51.2 3.7 0.0 0.0 0.0 22.3 0.0 0.0 0.0 0.1 21.6 0.0 54.5 3.7 21.6 0.1 79.8 

 (Continued...)

76 
77 

Table A4. − Continued

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Tanzania 2010 Middle 360 2.1 50.4 2.9 0.0 0.0 0.8 25.8 0.0 0.0 0.0 1.4 22.3 0.0 52.5 3.7 22.3 1.4 79.5 Tanzania 2010 Wealthier 404 7.7 46.8 7.7 0.0 0.0 0.0 23.1 0.0 0.0 0.0 0.9 20.9 0.0 53.5 7.7 20.9 0.9 82.4 

Tanzania 2010 Wealthiest 267 18.8 34.2 20.4 0.0 0.0 0.0 6.8 0.0 0.0 0.0 1.7 6.1 0.0 52.6 20.4 6.1 1.7 79.7 Tanzania 2010 All 1754 6.6 46.6 6.6 0.0 0.0 0.2 20.8 0.0 0.0 0.0 0.9 19.0 0.0 52.5 6.8 19.0 0.9 78.8 Uganda 2006 Poorest 823 3.3 34.1 39.7 3.2 2.5 0.0 6.1 1.0 0.0 0.5 0.2 5.5 0.8 40.3 41.7 5.5 1.8 84.9 Uganda 2006 Poorer 769 4.1 24.0 40.6 3.5 3.3 0.0 6.6 1.6 0.0 0.3 0.7 6.2 1.2 30.7 43.8 6.2 2.6 80.0 Uganda 2006 Middle 569 5.3 19.7 44.9 3.6 1.1 0.0 7.9 1.5 0.0 1.3 1.2 7.5 1.5 28.5 46.0 7.5 4.0 82.8 Uganda 2006 Wealthier 570 6.8 17.1 49.1 1.7 1.3 0.0 6.8 2.0 0.0 0.8 1.3 6.4 1.8 25.4 50.1 6.4 4.1 81.6 Uganda 2006 Wealthiest 406 12.3 9.9 58.0 1.4 0.0 0.0 7.9 0.4 0.0 0.5 0.7 7.3 0.4 23.5 58.0 7.3 1.6 88.4 Uganda 2006 All 3138 5.6 22.8 44.9 2.9 1.9 0.0 6.9 1.3 0.0 0.7 0.8 6.4 1.1 30.9 46.6 6.4 2.8 83.2 Uganda 2011 Poorest 832 4.7 39.2 37.9 1.3 0.9 0.0 0.4 3.1 0.3 0.1 0.7 0.4 3.1 44.3 38.8 0.4 4.2 83.2 Uganda 2011 Poorer 679 4.0 32.8 45.3 0.5 0.2 0.0 0.3 3.3 0.0 0.6 0.8 0.2 2.6 37.0 45.5 0.2 4.7 83.2 Uganda 2011 Middle 556 7.8 31.5 43.7 1.1 1.5 0.0 2.9 1.0 0.0 0.3 1.0 2.0 0.3 40.2 45.2 2.0 2.3 85.6 Uganda 2011 Wealthier 542 4.9 23.9 51.2 0.2 0.5 0.0 2.9 3.6 0.0 0.2 0.3 2.7 3.5 28.9 51.7 2.7 4.2 84.1 Uganda 2011 Wealthiest 432 8.1 12.4 66.1 0.9 1.2 0.0 3.8 1.4 0.2 0.2 1.7 3.7 1.4 21.2 67.0 3.7 3.4 90.9 Uganda 2011 All 3042 5.6 29.8 47.0 0.8 0.8 0.0 1.8 2.6 0.1 0.3 0.8 1.5 2.3 35.9 47.8 1.5 3.9 84.9 Zambia 2007 Poorest 240 3.8 47.9 0.0 0.0 1.0 8.5 1.0 7.2 0.0 2.9 0.6 1.0 6.4 51.2 9.5 1.0 10.8 70.5 Zambia 2007 Poorer 251 3.7 50.3 0.0 0.0 2.6 6.7 1.5 4.2 0.0 0.8 1.9 1.0 3.8 53.1 9.2 1.0 6.8 69.5 Zambia 2007 Middle 237 4.0 47.9 1.0 0.0 2.5 5.1 2.1 4.7 0.0 1.3 1.9 2.1 4.2 51.6 8.6 2.1 7.8 68.6 Zambia 2007 Wealthier 212 5.2 60.7 2.0 0.0 0.3 1.5 3.4 6.0 0.0 0.0 0.9 1.5 5.7 65.9 3.7 1.5 6.9 77.1 Zambia 2007 Wealthiest 105 8.6 48.2 5.4 0.0 0.0 1.7 1.2 1.9 0.0 0.0 0.0 0.3 1.9 56.8 7.1 0.3 1.9 66.0 Zambia 2007 All 1044 4.6 51.1 1.2 0.0 1.5 5.2 1.9 5.1 0.0 1.1 1.2 1.3 4.7 55.3 7.8 1.3 7.5 70.7 Zambia 2013-14 Poorest 687 1.7 58.8 0.0 4.1 0.3 3.8 0.2 2.4 0.0 1.1 1.3 0.0 1.6 64.5 4.1 0.0 4.8 72.1 Zambia 2013-14 Poorer 684 4.4 61.4 0.0 4.4 0.5 2.5 0.6 3.1 0.0 0.6 0.4 0.4 2.7 69.6 3.1 0.4 3.9 76.1 Zambia 2013-14 Middle 544 6.5 57.4 1.0 2.9 0.0 3.4 1.0 3.6 0.0 1.0 0.0 1.0 3.1 66.6 4.4 1.0 4.6 75.5 Zambia 2013-14 Wealthier 409 6.0 66.5 1.0 0.7 0.0 1.8 2.2 3.0 0.6 0.3 0.0 2.0 2.5 73.1 2.9 2.0 3.8 79.8 Zambia 2013-14 Wealthiest 331 6.1 60.1 9.9 0.0 0.0 0.1 1.7 0.8 0.0 0.0 0.9 1.1 0.8 65.4 10.0 1.1 1.7 77.2 Zambia 2013-14 All 2655 4.6 60.5 1.6 2.9 0.2 2.6 0.9 2.7 0.1 0.7 0.5 0.7 2.2 67.7 4.4 0.7 4.0 75.7 Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Children for whom care was sought from multiple sources contribute to each source's total, so 

the total percentage may exceed the percentage of children who received any care. 

1 The following Disaggregated Sources of Care categories include multiple standard response options: Public Peripheral Health Facility (HF) includes: health center, health post, mobile clinic, or other public sector care; Private Clinic or Clinician includes private 

hospital, doctor, nurse, mobile clinic, other private sector; and Other includes any other source of care sought outside the home, including a friend, relative, or other. 2 The Public summary category combines the Public Hospital, Public Peripheral HF, and Public CHW disaggregated sources of care categories; the Private summary category combines the Private Clinic or Clinician, Private CHW, and Religious or NGO Facility disaggregated sources of care categories (note that Private does not include pharmacy); Pharmacy Only includes children for whom care was sought only from a pharmacy; and the Other summary category combines the Shop, Market/Informal Shop, Non-Allopathic, 

and Other disaggregated sources of care categories. 3 Any Care includes children who received care from any source outside the home (including all disaggregated source of care categories). 

4 Questions on care seeking for children's fever in the 2004 Malawi DHS were non-standard and results are not shown in this disaggregated table. Respondents in the 2004 survey were asked what things were done in response to their child’s fever. Responses 

included: gave medicine from home, gave medicine without prescription, taken to government health center, private health center, mission health center, consulted a traditional healer, consulted a CHW, provided tepid sponging, herbs at home, other, or nothing. 

77 
78 

Table A5. Sources of care for symptoms of ARI by household wealth, USAID MCH priority countries 

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Bangladesh 2007 Poorest 83 0.8 11.0 13.4 0.0 0.0 0.0 29.5 0.0 0.0 22.3 8.5 29.5 0.0 11.8 13.4 29.5 30.9 82.5 Bangladesh 2007 Poorer 71 1.9 4.4 22.8 0.0 0.0 0.0 37.8 0.0 0.0 27.9 3.4 35.5 0.0 5.6 22.8 35.5 31.4 89.7 

Bangladesh 2007 Middle 44 1.5 16.1 25.5 0.0 0.0 0.0 19.7 0.0 0.0 29.7 2.7 18.2 0.0 17.6 25.5 18.2 32.4 90.9 Bangladesh 2007 Wealthier 52 3.3 5.2 40.1 0.0 0.0 0.0 31.7 0.0 0.0 8.7 0.0 31.7 0.0 8.6 40.1 31.7 8.7 87.5 Bangladesh 2007 Wealthiest 26 (5.1) (17.1) (48.4) (0.0) (0.0) (2.7) (16.4) (0.0) (0.0) (2.5) (0.0) (16.4) (0.0) (22.2) (51.1) (16.4) (2.5) (89.7) 

Bangladesh 2007 All 277 2.1 9.6 26.1 0.0 0.0 0.3 29.3 0.0 0.0 20.5 3.9 28.4 0.0 11.5 26.4 28.4 24.4 87.3 

Bangladesh 2011 Poorest 143 1.4 8.2 41.8 0.0 0.6 0.0 24.3 0.0 0.0 6.3 0.0 21.9 0.0 9.5 42.4 21.9 6.3 77.2 Bangladesh 2011 Poorer 92 5.3 11.9 40.1 0.0 0.0 0.0 27.0 0.0 0.0 10.3 0.0 25.2 0.0 17.2 40.1 25.2 10.3 87.5 Bangladesh 2011 Middle 97 2.0 5.0 48.8 0.0 0.0 1.0 16.7 0.0 0.0 3.0 4.3 14.8 0.0 7.0 48.8 14.8 7.3 72.8 Bangladesh 2011 Wealthier 77 4.5 12.3 52.1 0.0 0.0 0.0 18.2 0.0 0.0 4.0 0.0 14.2 0.0 16.8 52.1 14.2 4.0 84.9 Bangladesh 2011 Wealthiest 76 6.5 5.0 57.9 0.0 0.0 0.5 23.3 0.0 0.0 0.7 0.8 23.3 0.0 11.5 58.4 23.3 1.5 93.0 Bangladesh 2011 All 486 3.6 8.4 47.0 0.0 0.2 0.3 22.2 0.0 0.0 5.1 1.0 20.1 0.0 12.0 47.3 20.1 6.1 82.0 Congo DR 2007 Poorest 277 2.9 22.9 6.9 0.0 8.7 0.0 14.7 1.0 0.0 5.1 1.1 13.5 1.0 25.9 15.1 13.5 6.6 57.7 Congo DR 2007 Poorer 270 3.5 28.7 6.2 0.0 6.3 0.0 11.7 0.6 0.0 2.1 1.6 10.3 0.6 31.8 12.5 10.3 4.3 56.5 Congo DR 2007 Middle 268 3.7 24.4 3.5 0.0 5.4 0.0 12.0 0.5 0.0 6.1 1.5 10.9 0.5 28.1 8.9 10.9 8.1 51.1 Congo DR 2007 Wealthier 282 7.1 12.3 16.6 0.0 7.9 0.0 25.2 1.7 0.0 2.8 2.9 22.7 1.7 19.5 24.5 22.7 7.3 69.7 Congo DR 2007 Wealthiest 139 8.7 8.5 38.4 0.0 0.9 0.0 24.5 0.5 0.0 4.5 1.3 23.6 0.5 16.4 39.3 23.6 6.3 81.7 Congo DR 2007 All 1237 4.8 20.4 11.8 0.0 6.4 0.0 17.0 0.9 0.0 4.0 1.7 15.5 0.9 25.1 18.1 15.5 6.5 61.4 Congo DR 2013-14 Poorest 266 0.7 30.9 5.5 0.3 0.0 0.0 8.2 0.8 0.3 6.0 0.2 7.3 0.8 31.9 5.5 7.3 7.3 50.4 Congo DR 2013-14 Poorer 255 1.6 32.4 6.6 0.0 0.0 0.0 10.3 0.3 0.8 8.3 0.0 10.0 0.3 33.5 6.6 10.0 9.2 58.6 Congo DR 2013-14 Middle 254 1.8 36.4 11.3 0.0 0.0 0.0 11.6 0.0 0.0 4.2 0.0 11.6 0.0 38.0 11.3 11.6 4.2 64.4 Congo DR 2013-14 Wealthier 214 5.2 27.3 6.7 0.2 0.0 0.0 14.0 0.0 0.4 2.1 2.8 12.9 0.0 32.6 6.7 12.9 5.3 56.9 Congo DR 2013-14 Wealthiest 146 2.3 17.0 24.6 0.0 0.0 0.0 29.9 0.0 0.0 1.9 0.3 25.8 0.0 19.3 24.6 25.8 2.2 71.4 Congo DR 2013-14 All 1133 2.2 30.0 9.7 0.1 0.0 0.0 13.3 0.3 0.3 4.9 0.6 12.3 0.3 32.1 9.7 12.3 6.0 59.3 Ethiopia 2005 Poorest 272 5.2 11.3 0.9 0.0 0.0 1.2 0.1 0.0 0.0 0.0 0.0 0.1 0.0 16.5 2.1 0.1 0.0 18.6 Ethiopia 2005 Poorer 241 4.4 5.6 1.0 1.3 0.0 0.0 0.8 0.0 0.2 0.0 0.1 0.8 0.0 11.3 1.0 0.8 0.3 13.4 Ethiopia 2005 Middle 323 2.4 13.8 3.1 0.0 0.0 1.4 3.1 0.0 0.0 0.5 0.0 2.0 0.0 16.2 4.5 2.0 0.5 22.7 Ethiopia 2005 Wealthier 265 3.6 8.9 0.7 0.0 0.0 0.0 2.7 0.3 0.0 0.0 0.0 1.9 0.3 12.4 0.8 1.9 0.3 15.4 Ethiopia 2005 Wealthiest 167 4.3 19.3 8.3 0.0 0.0 2.8 1.8 0.0 0.5 1.0 0.1 1.7 0.0 23.6 11.1 1.7 1.5 36.3 Ethiopia 2005 All 1269 3.9 11.4 2.4 0.2 0.0 1.0 1.8 0.1 0.1 0.3 0.0 1.3 0.1 15.5 3.4 1.3 0.4 20.3 Ethiopia 2011 Poorest 188 0.0 12.4 3.1 0.0 0.0 0.0 0.4 0.8 0.0 0.1 0.0 0.4 0.8 12.5 3.1 0.4 0.8 16.7 Ethiopia 2011 Poorer 148 0.0 19.7 5.6 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.0 1.6 0.0 19.7 5.6 1.6 0.0 26.9 Ethiopia 2011 Middle 197 0.1 15.8 6.1 0.0 0.1 0.2 0.0 1.3 0.0 0.9 0.0 0.0 1.3 15.9 6.2 0.0 2.2 24.3 Ethiopia 2011 Wealthier 173 4.0 25.0 6.6 0.0 0.0 2.5 4.4 0.0 0.0 0.0 0.1 3.5 0.0 27.3 7.5 3.5 0.1 36.8 Ethiopia 2011 Wealthiest 67 5.8 29.0 27.4 0.0 0.0 3.5 4.2 0.1 0.0 0.0 0.0 3.7 0.1 32.7 29.1 3.7 0.1 65.5 Ethiopia 2011 All 773 1.4 18.9 7.2 0.0 0.0 0.9 1.8 0.5 0.0 0.2 0.0 1.5 0.5 19.8 7.6 1.5 0.8 29.3 Ghana 2003 Poorest 98 13.8 13.7 4.1 0.0 0.0 0.0 23.2 0.0 0.0 2.0 3.4 22.7 0.0 27.0 4.1 22.7 5.4 58.9 Ghana 2003 Poorer 73 22.1 10.9 8.6 0.0 0.0 0.0 30.7 0.0 0.0 0.0 2.3 27.7 0.0 31.4 8.6 27.7 2.3 70.0 Ghana 2003 Middle 68 33.0 11.3 2.9 0.0 0.0 0.0 22.6 0.0 0.0 1.3 0.0 22.6 0.0 44.3 2.9 22.6 1.3 71.1 Ghana 2003 Wealthier 59 35.2 16.1 3.8 0.0 0.0 0.0 30.2 2.2 0.0 1.9 0.0 29.5 0.0 48.7 3.8 29.5 4.1 80.3 Ghana 2003 Wealthiest 37 (41.6) (14.1) (13.3) (0.0) (0.0) (0.0) (14.7) (0.0) (0.0) (0.0) (3.6) (14.7) (0.0) (55.6) (13.3) (14.7) (3.6) (87.2) 

Ghana 2003 All 335 26.3 13.1 5.8 0.0 0.0 0.0 25.0 0.4 0.0 1.2 1.9 24.1 0.0 38.4 5.8 24.1 3.5 70.7 

Ghana 2008 Poorest 43 8.0 26.4 2.1 0.0 0.0 0.0 10.2 2.5 5.3 2.8 0.0 8.0 2.5 34.4 2.1 8.0 10.6 55.1 Ghana 2008 Poorer 34 (16.0) (22.1) (12.4) (0.0) (0.0) (0.0) (18.9) (0.0) (0.0) (0.0) (0.0) (17.9) (0.0) (36.9) (12.4) (17.9) (0.0) (66.2) 

Ghana 2008 Middle 23 * * * * * * * * * * * * * * * * * * 

Ghana 2008 Wealthier 39.0 (40.4) (15.3) (7.5) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (4.1) (0.0) (0.0) (55.7) (7.5) (0.0) (4.1) (67.4) 

Ghana 2008 Wealthiest 11 * * * * * * * * * * * * * * * * * * 

Ghana 2008 All 150 21.7 21.7 8.2 0.0 0.0 0.0 10.6 0.7 2.0 0.8 2.1 9.8 0.7 42.7 8.2 9.8 5.6 65.1 

Haiti 2005-6 Poorest 158 0.6 4.3 11.3 0.0 0.0 4.6 0.5 1.2 0.0 3.8 1.3 0.5 1.2 4.9 16.0 0.5 6.2 26.3 Haiti 2005-6 Poorer 137 3.8 5.6 8.1 0.0 0.0 11.3 0.0 2.6 1.7 1.1 9.0 0.0 2.6 9.4 19.5 0.0 13.3 36.1 Haiti 2005-6 Middle 115 1.2 10.7 19.3 0.0 0.0 15.1 0.9 0.0 0.0 2.1 1.3 0.9 0.0 11.9 34.5 0.9 3.4 49.8 Haiti 2005-6 Wealthier 93 0.8 19.3 14.2 0.0 0.0 0.5 0.4 1.2 1.9 6.8 3.7 0.4 1.2 20.0 14.7 0.4 10.0 43.2 Haiti 2005-6 Wealthiest 43 (5.1) (4.2) (21.7) (0.0) (0.0) (6.1) (0.0) (0.0) (0.0) (0.0) (9.5) (0.0) (0.0) (9.4) (26.9) (0.0) (9.5) (43.2) 

Haiti 2005-6 All 546 1.9 8.5 13.5 0.0 0.0 7.9 0.4 1.2 0.8 3.0 4.3 0.4 1.2 10.4 21.4 0.4 8.3 37.9 

 (Continued...)

78 
79 

Table A5. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Haiti 2012 Poorest 197 2.6 10.9 5.7 0.0 0.4 5.5 0.0 1.1 2.1 0.0 5.7 0.0 0.8 13.4 11.6 0.0 8.7 32.8 Haiti 2012 Poorer 182 5.5 7.0 9.1 0.0 1.1 10.8 0.0 1.4 0.4 0.2 5.9 0.0 1.1 12.5 21.0 0.0 6.6 38.1 

Haiti 2012 Middle 219 5.4 6.6 18.5 0.0 0.0 9.3 1.2 1.4 1.5 0.0 8.1 1.2 1.2 11.2 27.8 1.2 9.2 48.1 Haiti 2012 Wealthier 200 6.9 9.4 31.1 0.0 1.0 5.5 2.7 1.0 0.0 1.8 0.7 2.2 1.0 16.2 37.3 2.2 3.5 56.0 Haiti 2012 Wealthiest 125 5.1 7.1 28.0 0.0 0.8 10.2 3.0 1.6 3.3 0.0 8.7 3.0 0.0 12.2 39.0 3.0 12.0 60.4 Haiti 2012 All 924 5.1 8.3 18.0 0.0 0.6 8.1 1.3 1.3 1.3 0.4 5.6 1.2 0.9 13.2 26.6 1.2 7.7 46.3 India 2005-6 Poorest 785 3.2 8.0 49.7 0.0 0.0 0.2 4.1 1.2 0.0 4.2 1.5 3.6 1.2 11.1 49.8 3.6 6.9 66.8 India 2005-6 Poorer 805 3.2 7.2 55.9 0.0 0.0 0.0 5.8 1.9 0.0 2.8 2.7 4.9 1.5 10.4 55.9 4.9 7.2 75.1 India 2005-6 Middle 650 6.9 11.6 52.7 0.0 0.0 0.2 6.5 0.4 0.0 2.7 0.6 6.0 0.3 18.3 52.9 6.0 3.8 77.0 India 2005-6 Wealthier 495 5.6 5.7 64.5 0.0 0.0 0.5 7.2 0.0 0.0 2.9 0.6 6.4 0.0 11.0 65.0 6.4 3.2 82.9 India 2005-6 Wealthiest 323 6.9 3.2 68.1 0.0 0.0 0.1 2.8 0.0 0.0 3.4 0.7 2.6 0.0 9.6 68.2 2.6 4.1 83.2 India 2005-6 All 3058 4.8 7.7 56.3 0.0 0.0 0.2 5.4 0.9 0.0 3.2 1.4 4.8 0.8 12.3 56.5 4.8 5.4 75.5 Indonesia 2007 Poorest 505 2.5 23.5 15.7 0.0 14.6 2.1 6.2 17.6 0.0 3.3 5.4 5.9 16.2 25.9 32.0 5.9 23.9 83.4 Indonesia 2007 Poorer 389 1.9 25.6 23.5 0.0 14.2 2.1 12.8 18.5 0.0 2.1 1.6 12.3 18.3 27.6 38.6 12.3 22.3 94.6 Indonesia 2007 Middle 337 2.2 23.2 42.8 0.0 11.1 0.6 7.6 13.6 0.0 0.3 2.3 7.0 12.1 25.1 54.1 7.0 16.2 95.5 Indonesia 2007 Wealthier 302 3.3 20.8 43.1 0.0 11.9 0.2 17.1 6.8 0.0 0.2 0.2 16.6 6.3 23.7 54.4 16.6 7.2 96.3 Indonesia 2007 Wealthiest 255 3.9 16.0 56.1 0.0 2.4 1.3 15.0 1.7 0.0 0.1 0.5 14.3 1.0 19.6 59.8 14.3 2.4 94.4 Indonesia 2007 All 1789 2.6 22.4 32.9 0.0 11.7 1.4 11.0 12.9 0.0 1.5 2.4 10.5 12.1 24.8 45.4 10.5 16.2 91.9 Indonesia 2012 Poorest 236 1.6 31.0 18.2 0.0 14.8 0.0 6.4 14.6 0.0 3.2 2.7 6.0 11.5 32.0 32.6 6.0 20.4 81.0 Indonesia 2012 Poorer 190 4.5 26.6 43.7 0.0 18.1 0.0 9.6 3.8 0.0 2.6 2.0 5.1 3.6 31.0 61.7 5.1 8.4 94.2 Indonesia 2012 Middle 158 1.6 29.8 46.4 0.0 11.9 0.0 11.1 5.3 0.0 1.8 5.0 9.2 4.4 31.4 58.3 9.2 8.1 94.4 Indonesia 2012 Wealthier 137 2.1 20.1 52.7 0.0 16.1 0.0 7.8 2.8 0.0 0.9 3.8 6.7 2.5 22.2 68.2 6.7 7.6 96.4 Indonesia 2012 Wealthiest 112 1.6 9.0 59.4 0.0 4.5 0.0 15.3 0.2 0.0 5.1 3.3 14.3 0.2 10.6 63.6 14.3 8.5 92.3 Indonesia 2012 All 833 2.3 25.0 40.5 0.0 13.8 0.0 9.5 6.5 0.0 2.7 3.2 7.6 5.4 27.2 54.1 7.6 11.6 90.6 Kenya 2003 Poorest 242 7.4 24.5 6.8 0.0 0.3 6.1 13.7 5.6 0.0 3.6 0.0 11.8 5.6 31.7 13.2 11.8 9.2 64.7 Kenya 2003 Poorer 265 3.7 24.0 13.1 0.0 0.0 2.1 17.2 8.2 0.0 2.0 0.9 16.6 7.3 27.0 15.1 16.6 10.7 67.4 Kenya 2003 Middle 184 10.6 21.2 12.2 0.0 0.9 3.2 13.8 6.4 0.0 0.7 0.0 12.6 6.4 31.0 16.3 12.6 7.1 65.2 Kenya 2003 Wealthier 168 12.2 26.1 15.8 0.0 2.5 0.7 11.8 6.0 0.0 0.0 2.6 8.8 6.0 38.4 19.1 8.8 8.7 74.9 Kenya 2003 Wealthiest 165 12.7 20.8 25.7 0.0 0.5 5.8 17.6 2.9 0.0 1.8 0.0 14.8 2.5 33.6 31.9 14.8 4.7 81.1 Kenya 2003 All 1023 8.7 23.5 13.9 0.0 0.7 3.6 14.9 6.0 0.0 1.8 0.7 13.2 5.7 31.7 18.2 13.2 8.4 69.8 Kenya 2008-9 Poorest 151 15.6 35.5 5.6 0.0 0.0 0.9 5.8 8.0 0.0 0.0 1.2 5.8 7.2 50.0 6.5 5.8 9.2 70.8 Kenya 2008-9 Poorer 78 8.8 31.1 10.8 0.0 0.0 2.9 13.7 6.5 0.0 2.7 2.0 13.7 6.1 39.9 13.7 13.7 9.8 70.8 Kenya 2008-9 Middle 64 11.2 42.3 7.5 0.0 0.0 1.6 6.3 0.0 0.0 1.2 0.0 6.3 0.0 53.5 9.0 6.3 1.2 68.9 Kenya 2008-9 Wealthier 69 15.0 25.4 10.5 0.0 0.0 3.4 19.9 1.1 0.0 0.0 0.0 19.9 1.1 38.2 13.8 19.9 1.1 73.0 Kenya 2008-9 Wealthiest 54 11.3 24.8 20.3 0.0 0.0 12.5 4.4 1.0 0.0 0.0 0.0 4.0 0.5 34.3 30.5 4.0 1.0 67.1 Kenya 2008-9 All 416 13.0 32.7 9.6 0.0 0.0 3.3 9.5 4.4 0.0 0.7 0.8 9.5 4.0 44.6 12.6 9.5 5.7 70.4 Liberia 2007 Poorest 88 5.7 34.6 18.2 0.0 0.0 0.0 8.8 5.4 0.0 2.4 5.9 8.5 5.2 40.3 18.2 8.5 12.4 79.2 Liberia 2007 Poorer 123 7.5 23.8 15.7 0.0 0.0 0.0 7.5 3.7 0.0 5.2 6.1 7.5 3.1 31.3 15.7 7.5 13.8 65.7 Liberia 2007 Middle 85 14.6 43.4 15.5 0.0 0.0 0.0 8.1 7.2 0.0 1.2 0.0 7.7 6.4 58.0 15.5 7.7 8.3 86.3 Liberia 2007 Wealthier 88 13.0 35.2 14.2 0.0 0.0 0.0 21.4 1.9 0.0 2.8 0.0 20.4 1.9 48.2 14.2 20.4 4.7 83.4 Liberia 2007 Wealthiest 56 (7.4) (54.5) (29.1) (0.0) (0.0) (0.0) (1.6) (1.1) (0.0) (0.0) (2.1) (1.6) (1.1) (61.9) (29.1) (1.6) (3.2) (93.9) 

Liberia 2007 All 440 9.6 35.9 17.6 0.0 0.0 0.0 9.9 4.0 0.0 2.7 3.2 9.6 3.7 45.5 17.6 9.6 9.3 79.5 

Liberia 2013 Poorest 115 9.1 32.2 7.1 1.2 0.0 0.0 3.8 1.6 9.2 1.4 0.8 3.6 0.0 42.5 7.1 3.6 13.1 61.2 Liberia 2013 Poorer 89 11.4 38.2 6.7 0.2 0.0 0.0 8.9 3.9 14.2 1.3 0.0 7.5 3.9 49.8 6.7 7.5 19.4 82.6 Liberia 2013 Middle 66 19.4 26.3 14.0 0.0 0.0 0.0 1.9 4.0 4.9 0.0 0.0 1.9 4.0 45.7 14.0 1.9 8.9 70.6 Liberia 2013 Wealthier 64 (5.1) (14.2) (25.0) (0.0) (0.0) (0.0) (23.2) (0.8) (4.6) (0.0) (0.0) (23.2) (0.8) (19.3) (25.0) (23.2) (5.4) (72.9) 

Liberia 2013 Wealthiest 61 (2.2) (7.9) (36.1) (0.0) (0.0) (0.0) (42.2) (0.0) (0.0) (0.0) (0.0) (42.2) (0.0) (7.9) (36.1) (42.2) (0.0) (86.2) Liberia 2013 All 396 9.6 25.9 15.5 0.4 0.0 0.0 13.7 2.2 7.4 0.7 0.2 13.3 1.7 35.6 15.5 13.3 10.5 73.3 

Madagascar 2003-4 Poorest 157 4.9 37.5 8.1 0.0 0.0 0.0 0.4 3.6 0.0 2.2 0.0 0.4 3.6 42.4 8.1 0.4 5.4 55.9 Madagascar 2003-4 Poorer 104 1.2 30.9 11.5 0.0 0.0 0.0 0.0 5.2 0.0 1.8 0.0 0.0 5.2 32.1 11.5 0.0 7.0 50.5 Madagascar 2003-4 Middle 113 1.6 22.9 11.2 0.0 0.0 0.0 3.5 0.0 0.0 2.3 3.8 3.5 0.0 24.5 11.2 3.5 6.1 45.2 Madagascar 2003-4 Wealthier 64 7.9 23.7 16.7 0.0 0.0 0.0 6.3 0.0 0.0 0.0 4.2 6.3 0.0 31.6 16.7 6.3 4.2 58.8 Madagascar 2003-4 Wealthiest 67 19.4 19.9 27.7 0.0 0.0 0.0 0.7 0.2 0.0 0.0 0.1 0.7 0.2 39.2 27.7 0.7 0.3 66.7 Madagascar 2003-4 All 505 5.7 28.8 13.2 0.0 0.0 0.0 1.8 2.2 0.0 1.6 1.4 1.8 2.2 34.5 13.2 1.8 5.1 54.2 

 (Continued...)

79 
80 

Table A5. −Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Madagascar 2008-9 Poorest 85 0.0 29.5 4.1 0.0 0.8 0.0 1.9 6.7 0.0 3.0 0.6 1.9 6.7 29.5 4.9 1.9 10.2 45.4 Madagascar 2008-9 Poorer 86 3.5 20.4 5.2 0.0 0.7 0.0 0.0 6.5 0.0 1.7 0.0 0.0 6.5 24.0 5.9 0.0 8.3 37.4 

Madagascar 2008-9 Middle 56 3.5 29.4 12.0 0.0 1.9 0.0 2.5 7.1 0.0 2.3 0.0 2.5 5.3 30.3 13.9 2.5 9.5 48.5 Madagascar 2008-9 Wealthier 61 2.9 30.7 19.1 0.0 0.0 0.0 0.0 2.1 0.0 0.0 0.0 0.0 2.1 33.6 19.1 0.0 2.1 53.7 Madagascar 2008-9 Wealthiest 57 5.0 26.8 37.9 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31.9 39.1 0.0 0.0 69.3 Madagascar 2008-9 All 345 2.8 27.0 13.9 0.0 0.9 0.0 0.9 4.8 0.0 1.5 0.1 0.9 4.5 29.4 14.7 0.9 6.5 49.3 Malawi 2004 Poorest 373 3.4 18.6 2.6 1.2 0.3 3.9 0.0 32.4 0.0 3.9 2.1 0.0 32.4 23.2 6.8 0.0 37.4 66.9 Malawi 2004 Poorer 431 4.7 22.2 2.9 0.1 0.1 4.2 0.0 29.6 0.0 2.1 0.7 0.0 28.8 27.0 7.2 0.0 32.0 65.5 Malawi 2004 Middle 516 5.5 23.9 2.9 0.3 0.0 4.7 0.0 31.6 0.0 4.3 0.9 0.0 31.0 29.6 7.6 0.0 36.5 72.7 Malawi 2004 Wealthier 337 10.5 22.5 1.8 0.6 0.0 6.0 0.2 25.4 0.0 2.7 0.0 0.2 24.9 33.7 7.8 0.2 28.1 69.3 Malawi 2004 Wealthiest 183 15.6 18.4 5.6 0.9 0.0 6.3 0.0 21.8 0.0 1.2 0.7 0.0 21.8 34.9 11.8 0.0 23.7 69.3 Malawi 2004 All 1840 6.8 21.6 2.9 0.5 0.1 4.8 0.0 29.2 0.0 3.1 0.9 0.0 28.7 29.0 7.8 0.0 32.8 68.9 Malawi 2010 Poorest 258 11.0 32.6 7.4 1.8 0.0 14.1 0.0 8.1 0.0 0.4 0.5 0.0 7.6 43.3 19.6 0.0 9.0 70.1 Malawi 2010 Poorer 254 12.4 42.3 6.8 1.4 0.0 6.6 0.0 6.2 0.0 0.8 1.5 0.0 5.9 54.7 13.4 0.0 8.6 74.3 Malawi 2010 Middle 289 18.4 44.7 6.5 1.6 0.3 7.8 0.0 3.7 0.0 2.7 0.3 0.0 3.3 63.2 14.6 0.0 6.7 81.3 Malawi 2010 Wealthier 230 20.7 34.9 10.3 1.6 0.0 6.7 0.0 6.8 0.0 0.0 1.3 0.0 4.9 56.7 17.0 0.0 8.1 77.7 Malawi 2010 Wealthiest 189 23.6 29.6 12.9 0.0 0.0 12.9 0.3 2.4 0.0 0.0 0.0 0.0 1.8 53.2 25.8 0.0 2.4 76.9 Malawi 2010 All 1221 16.8 37.5 8.5 1.4 0.1 9.5 0.0 5.5 0.0 0.9 0.7 0.0 4.8 54.5 17.6 0.0 7.2 76.1 Mali 2006 Poorest 139 0.0 25.8 2.0 0.0 0.0 0.0 0.7 2.0 12.3 12.0 1.7 0.7 2.0 25.8 2.0 0.7 27.7 56.2 Mali 2006 Poorer 167 2.3 29.3 0.0 0.0 0.0 0.0 0.5 1.5 10.2 9.4 2.8 0.5 1.5 31.6 0.0 0.5 23.1 55.2 Mali 2006 Middle 136 0.3 26.7 0.7 0.0 2.1 0.0 0.3 0.9 4.5 13.3 1.2 0.3 0.9 27.0 2.8 0.3 19.9 50.1 Mali 2006 Wealthier 132 0.9 40.4 1.6 0.0 0.5 0.0 2.7 0.3 1.8 11.1 2.0 1.9 0.3 41.2 2.1 1.9 14.0 56.9 Mali 2006 Wealthiest 132 5.9 46.6 8.6 0.0 0.3 0.0 13.7 0.0 3.0 2.8 0.8 11.8 0.0 52.0 8.9 11.8 6.6 78.7 Mali 2006 All 706 1.9 33.4 2.4 0.0 0.5 0.0 3.4 1.0 6.6 9.8 1.7 2.9 1.0 35.2 3.0 2.9 18.6 59.1 Mali 2012-13 Poorest 51 (0.0) (18.4) (0.0) (0.0) (0.0) (0.0) (0.0) (2.9) (23.4) (14.2) (3.8) (0.0) (2.9) (18.4) (0.0) (0.0) (39.9) (54.5) 

Mali 2012-13 Poorer 29 (0.0) (20.3) (0.0) (0.0) (0.0) (0.0) (4.7) (0.0) (20.7) (8.5) (4.3) (4.7) (0.0) (20.3) (0.0) (4.7) (28.5) (53.5) Mali 2012-13 Middle 28 (0.0) (23.3) (0.0) (0.0) (0.0) (0.0) (6.9) (0.0) (12.7) (2.1) (0.0) (6.9) (0.0) (23.3) (0.0) (6.9) (14.8) (44.0) Mali 2012-13 Wealthier 29 (2.6) (35.1) (5.9) (0.0) (0.0) (0.0) (6.0) (0.0) (9.2) (9.3) (7.2) (4.0) (0.0) (35.1) (5.9) (4.0) (25.7) (70.7) Mali 2012-13 Wealthiest 20 (0.0) (36.1) (4.3) (0.0) (0.0) (0.0) (12.7) (0.0) (7.0) (9.0) (3.2) (12.7) (0.0) (36.1) (4.3) (12.7) (16.0) (69.0) Mali 2012-13 All 158 0.5 25.0 1.7 0.0 0.0 0.0 4.8 0.9 16.2 9.4 3.8 4.5 0.9 25.0 1.7 4.5 27.6 57.4 

Mozambique 2003 Poorest 194 5.8 34.1 0.9 1.4 0.0 3.4 0.8 0.0 3.8 2.2 2.6 0.8 0.0 41.2 4.3 0.8 8.6 54.4 Mozambique 2003 Poorer 200 3.9 50.1 0.0 0.0 0.0 0.4 0.0 0.0 1.1 4.2 2.6 0.0 0.0 53.8 0.4 0.0 7.9 61.7 Mozambique 2003 Middle 152 7.5 45.7 0.0 0.0 0.0 0.0 0.0 0.0 5.8 3.0 0.1 0.0 0.0 53.2 0.0 0.0 8.9 62.1 Mozambique 2003 Wealthier 159 6.6 58.5 0.9 0.0 0.0 0.3 0.0 0.0 0.7 3.0 4.8 0.0 0.0 65.0 1.2 0.0 8.5 73.6 Mozambique 2003 Wealthiest 214 19.5 41.4 2.7 0.0 0.0 0.0 2.7 0.0 0.0 1.0 1.4 2.5 0.0 59.8 2.7 2.5 2.4 67.4 Mozambique 2003 All 919 9.0 45.4 1.0 0.3 0.0 0.9 0.8 0.0 2.1 2.6 2.3 0.8 0.0 54.4 1.8 0.8 7.0 63.6 Mozambique 2011 Poorest 38 (44.1) (1.3) (0.0) (2.4) (0.0) (0.0) (0.0) (0.0) (1.3) (3.6) (0.0) (0.0) (0.0) (44.1) (0.0) (0.0) (3.6) (44.1) 

Mozambique 2011 Poorer 34 (39.4) (0.0) (0.0) (10.3) (0.0) (0.0) (0.0) (0.0) (0.0) (11.2) (2.6) (0.0) (0.0) (49.8) (0.0) (0.0) (13.8) (57.3) Mozambique 2011 Middle 27 * * * * * * * * * * * * * * * * * * 

Mozambique 2011 Wealthier 33 (51.4) (0.0) (0.0) (0.0) (0.0) (0.0) (2.1) (0.0) (0.0) (1.1) (3.3) (2.1) (0.0) (51.4) (0.0) (2.1) (4.4) (57.9) 

Mozambique 2011 Wealthiest 34 (66.8) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (66.8) (0.0) (0.0) (0.0) (66.8) Mozambique 2011 All 166 50.2 0.3 0.0 2.6 0.0 0.0 0.4 0.0 0.3 3.3 1.2 0.4 0.0 52.3 0.0 0.4 4.5 55.1 

Nepal 2006 Poorest 73 3.9 22.2 7.7 0.8 0.0 1.7 16.6 0.0 0.0 1.4 0.0 15.8 0.0 26.9 9.3 15.8 1.4 53.2 Nepal 2006 Poorer 54 3.6 19.5 2.0 5.1 0.0 9.4 33.5 2.7 0.0 2.6 0.0 28.0 0.2 24.9 11.4 28.0 5.3 66.2 Nepal 2006 Middle 52 7.2 29.9 8.9 0.0 0.0 0.7 26.7 0.0 0.0 2.2 0.0 23.4 0.0 33.8 9.6 23.4 2.2 66.9 Nepal 2006 Wealthier 56 16.9 20.0 25.3 0.0 0.0 0.7 31.1 0.0 0.0 0.0 0.0 27.6 0.0 29.2 26.0 27.6 0.0 77.5 Nepal 2006 Wealthiest 40 (11.8) (4.4) (35.0) (3.1) (0.0) (3.4) (18.0) (0.0) (0.0) (0.0) (0.0) (17.5) (0.0) (16.2) (38.4) (17.5) (0.0) (71.5) 

Nepal 2006 All 277 8.3 20.1 14.4 1.7 0.0 3.1 25.0 0.5 0.0 1.3 0.0 22.3 0.0 26.7 17.4 22.3 1.8 66.0 

Nepal 2011 Poorest 62 1.6 25.2 5.2 3.9 0.0 0.9 23.5 0.0 0.0 0.0 0.0 20.8 0.0 30.7 6.1 20.8 0.0 55.7 Nepal 2011 Poorer 53 (5.2) (15.5) (26.5) (0.0) (0.0) (0.0) (34.7) (0.0) (0.0) (4.8) (3.0) (34.7) (0.0) (20.7) (26.5) (34.7) (7.8) (82.8) 

Nepal 2011 Middle 58 (2.8) (21.1) (24.0) (5.8) (0.0) (4.3) (27.6) (0.0) (0.0) (2.0) (0.0) (25.4) (0.0) (26.4) (28.3) (25.4) (2.0) (79.4) Nepal 2011 Wealthier 50 (3.9) (15.9) (41.8) (0.0) (0.0) (0.0) (41.1) (0.0) (0.0) (0.0) (2.3) (34.2) (0.0) (19.8) (41.8) (34.2) (2.3) (95.7) Nepal 2011 Wealthiest 16 (27.7) (1.6) (45.7) (0.0) (0.0) (0.0) (20.2) (0.0) (0.0) (0.0) (0.0) (12.7) (0.0) (29.3) (45.7) (12.7) (0.0) (87.6) Nepal 2011 All 238 4.9 18.6 24.8 2.4 0.0 1.3 30.4 0.0 0.0 1.5 1.2 27.3 0.0 25.1 26.1 27.3 2.7 78.0 

 (Continued...)

80 
81 

Table A5. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Nigeria 2008 Poorest 216 4.2 8.9 7.3 2.1 0.6 0.0 26.8 14.1 0.0 0.2 0.0 26.1 14.1 15.2 7.9 26.1 14.3 63.1 Nigeria 2008 Poorer 200 10.5 15.2 5.7 0.4 0.2 0.0 26.1 7.5 0.0 4.7 0.2 25.1 7.3 25.4 5.9 25.1 12.5 66.7 

Nigeria 2008 Middle 118 5.0 16.9 11.7 0.6 0.0 0.0 39.0 4.7 0.0 0.5 0.7 38.2 1.9 22.6 11.7 38.2 5.9 74.4 Nigeria 2008 Wealthier 97 22.8 9.8 7.6 0.0 0.0 0.0 34.8 0.5 0.0 2.3 0.0 33.8 0.5 32.6 7.6 33.8 2.8 74.4 Nigeria 2008 Wealthiest 59 (29.1) (1.6) (17.2) (0.0) (0.0) (0.0) (30.1) (0.0) (0.0) (2.8) (0.0) (28.5) (0.0) (30.8) (17.2) (28.5) (2.8) (76.5) 

Nigeria 2008 All 690 10.9 11.6 8.5 0.9 0.3 0.0 30.1 7.5 0.0 2.1 0.2 29.2 6.9 23.2 8.7 29.2 9.8 68.8 

Nigeria 2013 Poorest 140 8.9 16.4 1.6 0.0 0.0 0.0 31.8 11.8 5.5 9.7 1.4 30.2 11.8 25.3 1.6 30.2 26.0 81.3 Nigeria 2013 Poorer 189 11.5 11.3 4.6 0.3 0.0 0.0 32.2 3.9 1.5 6.1 0.0 32.2 3.9 23.1 4.6 32.2 11.4 71.4 Nigeria 2013 Middle 114 22.6 15.2 2.9 0.0 0.0 0.0 35.8 1.3 0.0 0.6 0.0 35.8 0.6 37.8 2.9 35.8 2.0 77.4 Nigeria 2013 Wealthier 72 13.7 13.8 9.4 0.0 0.0 0.0 42.0 0.0 3.8 0.0 0.0 38.0 0.0 27.6 9.4 38.0 3.8 77.8 Nigeria 2013 Wealthiest 50 (24.0) (19.7) (23.5) (0.0) (0.0) (0.0) (18.0) (0.0) (0.0) (2.4) (5.3) (18.0) (0.0) (43.7) (23.5) (18.0) 7.7) (81.7) 

Nigeria 2013 All 565 14.5 14.4 5.8 0.1 0.0 0.0 32.8 4.5 2.3 4.8 0.8 31.9 4.3 29.0 5.8 31.9 11.8 76.8 

Pakistan 2006-7 Poorest 281 6.5 2.3 58.7 0.0 0.0 0.0 0.0 3.1 0.0 0.0 0.2 0.0 3.0 8.6 58.7 0.0 3.3 70.5 Pakistan 2006-7 Poorer 250 8.0 0.8 66.6 0.0 0.0 0.0 0.9 1.5 0.0 1.6 0.0 0.9 1.5 8.7 66.6 0.9 3.2 78.5 Pakistan 2006-7 Middle 217 11.2 1.1 71.1 1.1 0.0 0.0 0.0 1.6 0.0 4.5 0.9 0.0 1.6 13.4 71.1 0.0 6.9 89.0 Pakistan 2006-7 Wealthier 237 10.4 1.8 74.0 0.0 0.0 0.0 0.0 2.1 0.0 2.7 0.0 0.0 2.1 12.2 74.0 0.0 4.9 89.7 Pakistan 2006-7 Wealthiest 192 3.9 1.6 84.1 0.0 0.0 0.0 1.1 0.9 0.0 4.5 0.0 1.1 0.9 5.5 84.1 1.1 5.4 93.8 Pakistan 2006-7 All 1178 8.0 1.5 69.9 0.2 0.0 0.0 0.4 1.9 0.0 2.5 0.2 0.4 1.9 9.7 69.9 0.4 4.6 83.3 Pakistan 2012-13 Poorest 345 10.8 0.6 69.2 0.0 0.0 0.0 0.0 0.6 0.0 0.3 0.5 0.0 0.6 11.4 69.2 0.0 1.4 81.0 Pakistan 2012-13 Poorer 417 7.1 1.6 69.0 1.1 0.0 0.0 0.3 3.0 0.0 0.0 0.0 0.3 3.0 9.5 69.0 0.3 3.1 80.1 Pakistan 2012-13 Middle 385 9.3 0.6 72.3 0.0 0.0 0.0 1.8 1.7 0.0 0.3 0.0 1.3 1.7 9.9 72.3 1.3 2.0 84.6 Pakistan 2012-13 Wealthier 356 9.7 0.5 75.7 0.0 0.0 0.0 0.7 0.0 0.0 0.0 0.0 0.7 0.0 10.2 75.7 0.7 0.0 85.7 Pakistan 2012-13 Wealthiest 248 4.1 0.0 83.0 0.0 0.0 0.0 0.3 0.1 0.0 0.0 0.0 0.3 0.1 4.1 83.0 0.3 0.1 87.5 Pakistan 2012-13 All 1751 8.4 0.7 73.1 0.3 0.0 0.0 0.7 1.3 0.0 0.1 0.1 0.5 1.2 9.4 73.1 0.5 1.5 83.5 Rwanda 2005 Poorest 291 1.8 19.7 1.2 0.5 0.0 0.0 15.3 2.0 0.0 7.2 13.1 15.1 2.0 21.9 1.2 15.1 21.6 58.1 Rwanda 2005 Poorer 261 2.4 23.0 1.5 0.0 0.0 0.0 10.6 4.4 0.0 7.8 13.6 10.6 4.4 25.5 1.5 10.6 25.1 61.6 Rwanda 2005 Middle 275 3.3 16.4 3.6 1.3 0.0 0.0 16.8 2.4 0.0 10.1 14.1 16.8 2.4 20.9 3.6 16.8 26.7 67.0 Rwanda 2005 Wealthier 255 0.8 20.9 1.0 0.2 0.0 0.0 18.3 1.7 0.0 8.9 10.4 18.2 1.7 21.9 1.0 18.2 20.7 60.6 Rwanda 2005 Wealthiest 249 4.1 26.5 13.1 0.2 0.0 0.0 19.5 1.5 0.0 3.3 8.3 19.1 1.1 30.9 13.1 19.1 12.8 74.0 Rwanda 2005 All 1332 2.5 21.2 3.9 0.5 0.0 0.0 16.1 2.4 0.0 7.5 12.0 15.9 2.3 24.1 3.9 15.9 21.5 64.1 Rwanda 2010 Poorest 101 1.0 29.8 0.0 12.4 0.0 0.0 0.9 0.0 0.0 2.0 8.9 0.9 0.0 40.3 0.0 0.9 10.0 51.2 Rwanda 2010 Poorer 64 3.3 36.3 0.0 15.2 0.0 0.0 4.9 0.0 0.0 0.0 7.7 4.9 0.0 49.6 0.0 4.9 7.7 62.3 Rwanda 2010 Middle 54 0.0 36.4 0.0 16.6 0.0 0.0 4.1 0.0 0.0 2.2 11.1 4.1 0.0 48.7 0.0 4.1 13.3 66.1 Rwanda 2010 Wealthier 50 (1.9) (35.3) (0.0) (9.4) (0.0) (0.0) (2.5) (0.0) (0.0) (0.0) (6.5) (2.5) (0.0) (46.6) (0.0) (2.5) (6.5) (55.6) 

Rwanda 2010 Wealthiest 53 1.4 42.9 21.1 11.1 0.0 0.0 7.8 0.0 0.0 0.0 2.1 7.8 0.0 53.7 21.1 7.8 2.1 84.7 

Rwanda 2010 All 322 1.5 35.2 3.5 13.0 0.0 0.0 3.6 0.0 0.0 1.0 7.5 3.6 0.0 46.7 3.5 3.6 8.2 62.1 Senegal 2005 Poorest 223 1.1 31.8 8.4 0.0 0.4 0.3 1.4 2.9 0.0 0.7 0.8 1.4 2.9 32.9 9.1 1.4 4.5 46.6 Senegal 2005 Poorer 239 1.3 34.0 4.6 1.5 0.6 0.5 1.6 6.4 0.0 3.0 1.8 1.3 6.4 36.8 5.8 1.3 11.2 52.6 Senegal 2005 Middle 276 2.6 34.9 4.7 0.1 0.0 3.5 5.3 3.0 0.0 1.7 0.7 5.3 3.0 37.7 8.2 5.3 5.3 55.9 Senegal 2005 Wealthier 303 3.7 43.5 12.6 2.5 0.5 3.4 10.3 1.7 0.0 3.0 0.0 7.9 0.9 49.0 16.4 7.9 4.7 72.7 Senegal 2005 Wealthiest 239 9.4 29.9 18.6 1.5 0.0 0.8 12.5 1.4 0.0 2.5 0.0 10.5 1.4 40.8 19.3 10.5 3.9 74.3 Senegal 2005 All 1279 3.6 35.3 9.8 1.2 0.3 1.8 6.4 3.0 0.0 2.2 0.6 5.5 2.8 39.9 11.9 5.5 5.9 61.1 Senegal 2010-11 Poorest 115 1.3 29.3 0.6 0.6 0.2 0.0 1.6 0.7 0.6 8.9 3.6 1.6 0.7 31.2 0.9 1.6 13.0 45.1 Senegal 2010-11 Poorer 74 2.0 33.6 1.7 1.0 0.0 0.0 5.5 2.5 2.3 2.8 3.8 5.5 2.5 36.7 1.7 5.5 11.5 55.4 Senegal 2010-11 Middle 105 12.8 41.0 0.4 1.6 0.0 0.0 4.3 1.5 0.0 3.4 0.8 3.0 1.5 55.4 0.4 3.0 5.0 62.3 Senegal 2010-11 Wealthier 156 13.4 27.6 7.2 1.0 0.0 0.0 6.4 0.0 0.0 0.7 1.3 6.4 0.0 40.3 7.2 6.4 2.0 55.9 Senegal 2010-11 Wealthiest 140 9.3 42.0 11.4 3.3 3.0 0.0 6.3 0.0 0.0 1.9 0.0 6.3 0.0 54.6 14.4 6.3 1.9 77.1 Senegal 2010-11 All 589 8.5 34.5 5.0 1.6 0.8 0.0 5.0 0.7 0.4 3.3 1.7 4.7 0.7 44.2 5.8 4.7 5.8 59.9 Tanzania 2004-5 Poorest 162 3.6 42.7 4.0 0.0 0.0 6.4 19.8 0.0 0.0 0.0 2.4 19.2 0.0 45.6 9.8 19.2 2.4 76.3 Tanzania 2004-5 Poorer 124 6.9 54.0 0.0 0.0 0.0 3.9 27.5 0.0 0.0 0.0 0.5 26.6 0.0 60.1 3.9 26.6 0.5 91.1 Tanzania 2004-5 Middle 140 4.5 51.4 6.2 0.0 0.0 8.9 19.2 0.0 0.0 0.0 1.3 19.2 0.0 55.9 15.1 19.2 1.3 91.4 Tanzania 2004-5 Wealthier 130 10.0 40.5 5.6 0.0 0.0 5.7 23.6 0.0 0.0 0.0 3.3 22.8 0.0 49.7 11.3 22.8 3.3 85.6 Tanzania 2004-5 Wealthiest 91 22.6 28.0 19.0 0.0 0.0 4.0 19.1 0.0 0.0 0.0 2.2 16.6 0.0 50.6 23.0 16.6 2.2 89.9 Tanzania 2004-5 All 648 8.4 44.2 6.1 0.0 0.0 6.0 21.8 0.0 0.0 0.0 2.0 21.0 0.0 52.1 12.0 21.0 2.0 86.2 

 (Continued...)

81 
82 

Table A5. − Continued

Disaggregated Sources of Care1 

Summary Categories used throughout 

Report2 

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3 

Tanzania 2010 Poorest 61 0.2 46.9 3.0 0.0 0.0 0.0 20.0 0.0 0.0 0.0 2.3 18.3 0.0 46.9 3.0 18.3 2.3 70.6 Tanzania 2010 Poorer 53 12.0 65.3 2.8 0.0 0.0 0.0 5.6 0.0 0.0 0.0 0.0 5.6 0.0 72.9 2.8 5.6 0.0 80.3 

Tanzania 2010 Middle 85 2.1 42.2 4.6 0.0 0.0 0.0 26.8 0.0 0.0 0.0 2.3 23.2 0.0 44.3 4.6 23.2 2.3 74.2 Tanzania 2010 Wealthier 76 12.9 54.2 7.0 0.0 0.0 0.0 17.7 0.0 0.0 0.0 2.8 11.7 0.0 65.9 7.0 11.7 2.8 84.7 Tanzania 2010 Wealthiest 58 31.1 46.0 13.4 0.0 0.0 0.0 1.9 0.0 0.0 0.0 0.0 1.3 0.0 77.1 13.4 1.3 0.0 90.6 Tanzania 2010 All 332 10.8 50.1 6.1 0.0 0.0 0.0 15.8 0.0 0.0 0.0 1.6 13.1 0.0 60.0 6.1 13.1 1.6 79.7 Uganda 2006 Poorest 319 3.9 38.6 37.7 2.2 2.1 0.0 5.5 0.6 0.0 0.6 0.4 4.4 0.2 44.5 39.2 4.4 1.6 85.1 Uganda 2006 Poorer 289 6.7 21.3 37.9 2.9 2.8 0.0 9.9 1.4 0.0 1.0 1.2 8.8 1.4 30.3 40.6 8.8 3.5 79.5 Uganda 2006 Middle 212 5.7 24.6 38.7 2.1 0.3 0.0 8.7 1.7 0.0 1.5 3.0 8.2 1.0 32.5 39.0 8.2 6.2 82.5 Uganda 2006 Wealthier 177 10.1 17.3 47.2 0.8 0.9 0.0 9.8 2.4 0.0 2.3 0.7 8.9 1.9 27.8 47.2 8.9 5.4 84.2 Uganda 2006 Wealthiest 111 7.7 7.5 65.4 0.0 0.0 0.0 9.0 0.0 0.0 0.1 0.0 6.6 0.0 15.2 65.4 6.6 0.1 84.3 Uganda 2006 All 1109 6.3 24.9 42.2 1.9 1.5 0.0 8.3 1.2 0.0 1.1 1.1 7.2 0.9 32.9 43.4 7.2 3.5 82.9 Uganda 2011 Poorest 336 5.4 32.8 43.7 0.9 0.7 0.0 0.2 0.4 0.3 1.0 0.7 0.2 0.4 38.1 44.3 0.2 2.5 80.2 Uganda 2011 Poorer 263 3.8 30.6 49.4 0.0 0.0 0.0 2.0 2.5 0.0 0.4 0.5 2.0 1.5 33.7 49.4 2.0 3.4 82.9 Uganda 2011 Middle 190 6.2 23.2 51.0 1.2 1.4 0.0 1.6 1.8 0.0 0.0 2.4 1.6 1.0 30.2 52.4 1.6 4.2 83.1 Uganda 2011 Wealthier 161 2.2 30.0 51.0 0.0 0.7 0.0 3.6 2.3 0.0 0.9 0.0 3.6 2.3 32.3 51.7 3.6 3.1 83.9 Uganda 2011 Wealthiest 170 8.9 11.6 60.8 0.8 3.2 0.0 2.7 2.8 0.0 0.3 3.5 2.4 2.8 21.3 63.5 2.4 6.2 90.1 Uganda 2011 All 1118 5.2 27.0 49.9 0.6 1.0 0.0 1.7 1.8 0.1 0.6 1.3 1.7 1.4 32.3 50.8 1.7 3.7 83.4 Zambia 2007 Poorest 64 4.9 59.9 2.4 0.0 1.4 9.3 0.0 3.5 0.0 9.5 1.1 0.0 1.4 64.8 13.1 0.0 14.0 86.2 Zambia 2007 Poorer 86 5.4 52.0 0.0 0.0 1.7 7.6 1.8 1.8 0.0 0.0 0.8 1.8 1.8 54.9 9.3 1.8 2.6 67.7 Zambia 2007 Middle 54 10.2 49.2 0.5 0.0 0.0 5.1 6.7 3.4 0.0 2.8 0.0 5.7 2.9 58.6 5.6 5.7 6.2 74.6 Zambia 2007 Wealthier 53 7.3 67.4 1.8 0.0 0.0 2.9 6.3 3.4 0.0 0.0 1.1 1.6 3.4 74.8 4.8 1.6 4.4 83.4 Zambia 2007 Wealthiest 49 (6.6) (47.7) (1.8) (0.0) (0.0) (0.0) (6.8) (0.0) (0.0) (0.0) (0.0) (3.2) (0.0) (54.3) (1.8) (3.2) (0.0) (59.3) 

Zambia 2007 All 304 6.7 55.1 1.2 0.0 0.8 5.5 3.9 2.4 0.0 2.5 0.6 2.3 1.9 61.0 7.5 2.3 5.5 74.2 

Zambia 2013-14 Poorest 115 1.0 61.1 0.0 3.0 0.6 1.2 0.0 1.5 1.4 1.6 2.2 0.0 1.5 65.1 1.9 0.0 6.7 73.6 Zambia 2013-14 Poorer 119 5.3 58.5 0.3 2.2 0.0 2.2 0.1 2.4 0.0 0.7 0.9 0.1 2.4 64.2 2.6 0.1 4.0 69.7 Zambia 2013-14 Middle 96 6.8 59.6 0.0 0.5 0.0 2.4 0.0 2.7 0.0 0.0 0.0 0.0 2.7 65.3 2.4 0.0 2.7 70.1 Zambia 2013-14 Wealthier 80 4.8 64.9 4.1 1.0 0.0 2.9 1.0 0.4 2.9 0.0 0.0 0.0 0.4 70.7 7.0 0.0 3.3 75.7 Zambia 2013-14 Wealthiest 59 12.0 60.1 6.6 0.0 0.0 0.8 1.8 4.4 0.0 0.0 0.0 1.8 4.4 72.1 7.4 1.8 4.4 85.8 Zambia 2013-14 All 469 5.3 60.6 1.6 1.6 0.2 2.0 0.4 2.1 0.8 0.6 0.8 0.2 2.1 66.8 3.7 0.2 4.3 73.8 Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Children for whom care was sought from multiple source contribute to each source's total,so the total percentage may exceed the percentage of children who received any care. 

1 The following Disaggregated Sources of Care categories include multiple standard response options: Public Peripheral Health Facility (HF) includes: health center, health post, mobile clinic, or other public sector care; Private Clinic or Clinician includes private hospital, doctor, nurse, mobile clinic, other private sector; and Other includes any other source of care sought outside the home, including a friend, relative, or other. 2 The public summary category combines the Public Hospital, Public Peripheral HF, and Public CHW disaggregated sources of care categories; the private summary category combines the Private Clinic or Clinician, Private CHW, and Religious or NGO Facility disaggregated sources of care categories (note that Private does not include pharmacy); Pharmacy Only includes children for whom care was sought only from a pharmacy; and the Other summary category combines the Shop, Market/Informal Shop, NonAllopathic, and Other disaggregated sources of care categories. 

3 Any Care includes children who received care from any source outside the home (including all disaggregated source of care categories). 

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83 

Table A6. Sources of care for diarrhea by household wealth, USAID MCH priority countries 

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3

Bangladesh 2007 Poorest 130 0.0 4.3 6.4 0.0 0.0 0.0 35.5 0.5 0.0 17.7 6.3 35.5 0.5 4.3 6.4 35.5 24.5 70.4 Bangladesh 2007 Poorer 119 1.0 10.0 4.5 0.0 0.0 1.1 29.4 0.0 0.0 27.9 2.7 29.4 0.0 11.1 5.6 29.4 30.1 73.4 

Bangladesh 2007 Middle 123 0.4 7.5 11.3 0.0 0.0 1.6 28.0 1.0 0.0 29.7 1.3 27.1 0.4 7.9 12.9 27.1 31.9 76.9 Bangladesh 2007 Wealthier 104 2.4 13.1 10.8 0.0 0.0 1.2 17.2 0.0 0.0 27.0 2.9 16.2 0.0 15.5 12.1 16.2 29.8 71.9 Bangladesh 2007 Wealthiest 83 4.7 7.4 18.7 0.0 0.0 1.3 24.1 0.0 0.0 11.4 2.8 23.3 0.0 12.1 20.0 23.3 13.5 65.4 Bangladesh 2007 All 559 1.5 8.3 9.7 0.0 0.0 1.0 27.5 0.3 0.0 23.3 3.3 26.9 0.2 9.8 10.7 26.9 26.7 72.0 Bangladesh 2011 Poorest 108 1.5 11.6 35.9 0.0 0.0 0.0 24.4 0.0 0.0 3.2 0.0 22.4 0.0 13.1 35.9 22.4 3.2 73.4 Bangladesh 2011 Poorer 75 1.1 7.3 36.0 0.0 0.0 0.0 39.3 0.0 0.0 3.0 0.0 36.2 0.0 8.4 36.0 36.2 3.0 80.6 Bangladesh 2011 Middle 97 1.2 11.1 39.6 0.0 0.0 0.0 23.4 0.0 0.0 4.6 0.0 23.4 0.0 12.3 39.6 23.4 4.6 77.6 Bangladesh 2011 Wealthier 49 7.2 1.6 47.2 0.0 0.0 0.7 8.6 0.0 0.0 3.8 0.0 8.1 0.0 8.9 47.9 8.1 3.8 68.7 Bangladesh 2011 Wealthiest 59 4.3 6.1 53.1 0.0 0.0 2.9 12.7 0.0 0.0 0.0 2.2 12.7 0.0 10.4 55.9 12.7 2.2 79.1 Bangladesh 2011 All 388 2.5 8.6 40.9 0.0 0.0 0.5 23.3 0.0 0.0 3.1 0.3 22.0 0.0 11.1 41.4 22.0 3.4 76.1 Congo DR 2007 Poorest 276 0.2 18.6 0.0 7.7 12.4 0.2 2.7 0.0 0.0 0.0 4.5 2.4 0.0 26.2 12.5 2.4 4.5 41.8 Congo DR 2007 Poorer 277 1.8 21.7 0.0 3.4 4.6 0.0 3.8 0.0 0.0 0.0 3.0 3.4 0.0 26.1 4.6 3.4 3.0 35.9 Congo DR 2007 Middle 273 0.8 22.1 0.0 8.1 5.8 0.0 6.9 0.0 0.0 0.0 2.3 5.5 0.0 26.8 5.8 5.5 2.3 39.5 Congo DR 2007 Wealthier 300 3.2 17.5 0.1 8.1 2.9 0.1 3.6 0.0 0.0 0.0 4.0 2.7 0.0 27.9 3.1 2.7 4.0 33.7 Congo DR 2007 Wealthiest 187 5.8 23.7 0.6 7.4 0.5 0.0 1.7 0.0 0.0 0.0 0.5 1.7 0.0 35.5 1.1 1.7 0.5 38.9 Congo DR 2007 All 1313 2.1 20.5 0.1 6.9 5.5 0.1 3.9 0.0 0.0 0.0 3.0 3.2 0.0 28.0 5.7 3.2 3.0 37.8 Congo DR 2013-14 Poorest 631 1.6 26.3 8.9 0.6 0.0 0.0 12.7 0.4 1.1 5.5 1.2 11.9 0.4 28.4 8.9 11.9 8.2 55.9 Congo DR 2013-14 Poorer 581 4.0 31.4 10.7 0.3 0.0 0.0 9.2 1.0 0.6 3.5 3.1 7.6 1.0 34.0 10.7 7.6 8.2 58.4 Congo DR 2013-14 Middle 500 2.6 34.1 6.0 0.2 0.0 0.0 17.2 0.3 0.6 2.1 2.4 16.3 0.3 35.9 6.0 16.3 5.3 62.6 Congo DR 2013-14 Wealthier 621 5.4 24.4 10.1 0.0 0.0 0.0 22.3 0.0 0.2 2.3 2.3 19.6 0.0 29.7 10.1 19.6 4.5 61.8 Congo DR 2013-14 Wealthiest 519 6.1 11.7 15.0 0.0 0.0 0.0 19.2 0.0 0.0 0.1 3.5 16.3 0.0 17.8 15.0 16.3 3.6 52.0 Congo DR 2013-14 All 2852 3.9 25.6 10.1 0.3 0.0 0.0 16.1 0.3 0.5 2.8 2.5 14.3 0.3 29.2 10.1 14.3 6.1 58.2 Ethiopia 2005 Poorest 395 4.5 8.4 0.6 0.5 0.0 0.4 1.6 1.4 0.8 0.3 0.5 1.6 1.4 13.3 1.0 1.6 2.9 18.8 Ethiopia 2005 Poorer 428 3.7 9.6 3.4 0.0 0.0 0.5 0.2 0.6 1.2 1.2 0.0 0.2 0.6 13.2 3.9 0.2 2.9 20.3 Ethiopia 2005 Middle 436 5.9 17.0 3.0 0.0 0.0 1.4 4.4 0.4 0.3 0.5 0.4 2.2 0.4 22.8 4.3 2.2 1.6 30.6 Ethiopia 2005 Wealthier 339 4.1 15.2 2.3 0.0 0.0 0.9 2.0 0.2 0.5 0.0 0.0 2.0 0.2 18.7 3.2 2.0 0.7 24.5 Ethiopia 2005 Wealthiest 221 5.5 27.1 4.9 0.0 0.0 0.9 2.4 1.3 0.7 0.2 0.0 0.9 1.3 31.8 5.8 0.9 2.1 40.2 Ethiopia 2005 All 1819 4.7 14.3 2.7 0.1 0.0 0.8 2.1 0.7 0.7 0.5 0.2 1.4 0.7 18.8 3.5 1.4 2.1 25.7 Ethiopia 2011 Poorest 372 0.1 17.8 5.5 0.0 0.0 0.0 0.9 0.9 0.0 0.3 0.0 0.5 0.8 17.8 5.6 0.5 1.2 24.0 Ethiopia 2011 Poorer 301 1.0 22.4 2.6 0.0 0.0 0.0 2.5 0.9 0.0 0.0 0.7 2.5 0.9 22.8 2.6 2.5 1.7 29.5 Ethiopia 2011 Middle 298 0.1 25.9 8.9 0.0 0.1 1.1 0.7 2.9 0.0 1.4 0.0 0.7 2.9 26.0 10.1 0.7 4.3 39.9 Ethiopia 2011 Wealthier 323 0.2 22.0 9.5 0.0 1.7 0.1 1.2 0.2 0.0 0.9 0.9 1.0 0.2 22.2 11.3 1.0 2.0 35.9 Ethiopia 2011 Wealthiest 190 2.7 36.4 17.7 0.0 0.0 1.1 6.9 0.3 0.0 0.3 0.0 6.9 0.3 38.5 18.7 6.9 0.6 60.2 Ethiopia 2011 All 1483 0.7 23.6 8.0 0.0 0.4 0.4 2.0 1.1 0.0 0.6 0.3 1.9 1.1 24.1 8.8 1.9 2.0 35.5 Ghana 2003 Poorest 170 9.9 10.0 0.6 0.5 0.0 0.0 16.4 0.0 7.3 1.7 1.2 16.4 0.0 20.4 0.6 16.4 10.2 47.1 Ghana 2003 Poorer 105 14.0 5.7 4.0 0.0 1.2 0.0 22.5 0.6 1.5 0.9 0.7 22.1 0.6 19.8 4.0 22.1 3.7 49.3 Ghana 2003 Middle 91 10.2 10.9 2.0 1.2 0.0 0.0 25.9 0.0 1.0 0.0 1.9 25.9 0.0 22.3 2.0 25.9 2.9 53.2 Ghana 2003 Wealthier 87 17.1 6.8 3.4 0.0 0.0 0.0 20.9 0.0 0.0 0.9 0.0 20.9 0.0 23.9 3.4 20.9 0.9 48.2 Ghana 2003 Wealthiest 55 25.1 1.8 14.6 0.0 0.0 0.0 16.9 0.0 0.0 0.0 1.4 15.4 0.0 26.9 14.6 15.4 1.4 58.4 Ghana 2003 All 509 13.7 7.8 3.6 0.4 0.2 0.0 20.2 0.1 2.9 0.9 1.0 19.9 0.1 21.9 3.6 19.9 5.0 50.1 Ghana 2008 Poorest 176 15.4 24.2 0.9 0.6 0.0 0.0 10.6 4.5 3.9 1.2 0.0 9.4 4.5 40.2 0.9 9.4 9.6 60.2 Ghana 2008 Poorer 131 12.1 20.7 6.5 0.0 0.0 0.0 19.9 1.0 5.3 0.8 2.0 19.9 0.2 32.5 6.5 19.9 9.0 66.3 Ghana 2008 Middle 109 22.1 14.8 4.9 0.0 0.0 0.0 23.5 0.0 2.1 0.2 1.0 22.9 0.0 36.6 4.9 22.9 3.3 65.2 Ghana 2008 Wealthier 86 25.0 4.7 13.2 0.0 1.9 0.0 14.9 0.0 0.2 0.0 3.3 14.9 0.0 29.7 13.2 14.9 3.6 61.4 Ghana 2008 Wealthiest 40 (27.0) (7.9) (13.1) (0.0) (0.0) (0.0) (20.9) (0.0) (0.0) (0.0) (0.0) (19.7) (0.0) (34.9) (13.1) (19.7) (0.0) (67.7)

Ghana 2008 All 542 18.3 17.2 5.9 0.2 0.3 0.0 16.9 1.7 3.0 0.6 1.2 16.3 1.5 35.6 5.9 16.3 6.5 63.4 

Haiti 2005-6 Poorest 329 0.8 5.5 11.5 0.0 0.0 7.8 0.6 1.7 2.4 0.7 4.3 0.6 1.7 6.3 19.3 0.6 8.2 33.7 Haiti 2005-6 Poorer 266 0.0 9.8 13.5 0.0 0.0 7.0 0.3 2.3 0.0 0.9 5.0 0.3 2.2 9.8 20.1 0.3 8.2 36.8 Haiti 2005-6 Middle 264 5.7 12.1 11.6 0.0 0.0 5.6 4.5 3.0 0.5 1.5 1.9 4.2 2.5 17.8 17.2 4.2 6.5 44.2 Haiti 2005-6 Wealthier 264 4.5 13.7 6.1 0.0 0.0 6.1 6.7 1.6 0.0 0.8 6.4 6.1 1.6 18.2 12.2 6.1 8.9 44.4 Haiti 2005-6 Wealthiest 140 4.1 11.6 14.9 0.0 0.0 5.7 5.1 1.6 0.0 0.0 2.7 5.1 1.6 15.8 20.6 5.1 4.3 44.5 Haiti 2005-6 All 1263 2.8 10.2 11.2 0.0 0.0 6.6 3.1 2.1 0.7 0.9 4.2 2.9 1.9 13.0 17.7 2.9 7.6 40.0 

 (Continued...)

83 
84 

Table A6. − Continued

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3

Haiti 2012 Poorest 264 3.2 12.2 5.7 0.0 0.1 9.3 0.0 0.4 1.2 1.2 4.7 0.0 0.4 15.4 14.9 0.0 7.3 36.7 Haiti 2012 Poorer 317 0.8 10.4 7.1 0.0 0.9 9.6 0.0 1.6 0.1 0.3 5.7 0.0 0.9 11.2 17.5 0.0 7.7 34.9 

Haiti 2012 Middle 311 5.9 8.6 12.8 0.0 0.6 8.2 1.3 1.1 1.1 0.0 9.1 1.3 1.1 14.5 21.2 1.3 9.8 45.5 Haiti 2012 Wealthier 286 10.3 9.0 16.5 0.0 0.0 8.2 0.0 0.9 0.0 0.0 13.5 0.0 0.7 19.3 24.1 0.0 13.8 51.8 Haiti 2012 Wealthiest 156 14.2 6.9 23.1 0.0 0.0 7.7 0.6 0.6 1.3 0.0 5.3 0.6 0.6 21.1 29.6 0.6 5.9 55.3 Haiti 2012 All 1334 6.1 9.6 12.0 0.0 0.4 8.7 0.4 1.0 0.7 0.3 7.9 0.4 0.8 15.7 20.7 0.4 9.2 43.7 India 2005-6 Poorest 1166 3.6 12.3 35.4 0.0 0.0 0.0 6.5 1.4 0.0 1.7 3.1 5.9 1.4 15.7 35.5 5.9 5.7 60.0 India 2005-6 Poorer 1046 4.0 9.4 41.5 0.0 0.0 0.3 6.5 2.6 0.0 3.1 2.4 5.7 2.5 13.2 41.8 5.7 7.8 66.9 India 2005-6 Middle 978 7.2 8.6 46.0 0.0 0.0 0.0 7.2 1.6 0.0 2.2 1.6 7.0 1.4 15.6 46.0 7.0 5.2 70.2 India 2005-6 Wealthier 915 5.7 8.1 51.0 0.0 0.0 0.4 6.6 2.1 0.0 2.4 1.9 6.4 1.8 13.6 51.0 6.4 6.0 73.8 India 2005-6 Wealthiest 649 7.0 3.7 63.6 0.0 0.0 0.2 4.7 1.3 0.0 1.7 1.4 4.7 1.3 10.6 63.6 4.7 4.4 80.6 India 2005-6 All 4755 5.3 8.9 45.8 0.0 0.0 0.2 6.4 1.8 0.0 2.2 2.2 6.0 1.7 14.0 45.9 6.0 5.9 69.1 Indonesia 2007 Poorest 642 1.6 18.3 7.9 0.0 10.9 1.0 3.3 13.7 2.9 0.0 7.2 3.0 13.4 19.8 19.8 3.0 23.2 63.8 Indonesia 2007 Poorer 454 1.1 20.4 17.0 0.0 9.5 1.4 7.7 14.6 2.4 0.0 7.3 6.7 14.4 21.4 27.3 6.7 24.1 77.1 Indonesia 2007 Middle 393 1.6 21.2 28.3 0.0 11.8 0.2 3.7 12.4 0.4 0.0 3.5 3.2 10.0 22.6 40.1 3.2 16.3 78.3 Indonesia 2007 Wealthier 399 0.7 21.7 33.1 0.0 7.2 0.1 10.6 4.5 1.1 0.0 4.1 9.5 4.3 22.3 39.7 9.5 9.8 75.9 Indonesia 2007 Wealthiest 291 2.4 8.5 52.2 0.0 3.2 0.0 11.0 8.3 0.3 0.0 3.3 10.2 8.3 10.6 54.5 10.2 11.7 83.6 Indonesia 2007 All 2180 1.4 18.6 24.0 0.0 9.1 0.6 6.6 11.3 1.7 0.0 5.5 5.9 10.7 19.9 33.3 5.9 18.1 74.1 Indonesia 2012 Poorest 598 1.7 32.8 17.1 0.0 14.7 0.0 4.8 9.0 0.0 0.0 6.3 3.7 7.7 34.2 31.6 3.7 15.0 78.6 Indonesia 2012 Poorer 491 1.7 24.1 29.9 0.0 14.0 0.0 6.3 5.3 0.0 0.0 6.0 4.8 4.9 25.7 43.4 4.8 11.2 79.4 Indonesia 2012 Middle 479 1.8 22.2 34.4 0.0 14.9 0.0 9.9 6.7 0.0 0.0 5.5 8.7 5.6 23.9 48.8 8.7 12.2 83.4 Indonesia 2012 Wealthier 447 2.7 10.8 43.1 0.0 10.6 0.0 9.4 3.6 0.0 0.0 3.7 8.4 3.2 13.3 53.2 8.4 7.3 78.8 Indonesia 2012 Wealthiest 325 0.7 14.1 44.4 0.0 7.2 0.0 16.5 2.5 0.0 0.0 1.8 15.2 2.5 14.8 50.1 15.2 4.3 80.8 Indonesia 2012 All 2341 1.8 22.0 32.1 0.0 12.8 0.0 8.6 5.8 0.0 0.0 5.0 7.4 5.1 23.6 44.3 7.4 10.7 80.1 Kenya 2003 Poorest 245 4.1 18.1 2.3 0.0 1.6 3.6 4.1 2.4 0.0 2.9 4.9 3.9 2.4 22.2 7.6 3.9 8.8 40.8 Kenya 2003 Poorer 202 1.3 18.2 4.7 0.0 0.0 1.9 1.0 1.7 0.0 0.0 3.4 1.0 1.7 19.0 6.0 1.0 5.1 29.8 Kenya 2003 Middle 158 6.0 13.7 2.9 0.0 0.3 2.3 3.8 1.3 0.0 2.1 2.1 3.8 1.3 19.8 5.5 3.8 5.4 34.4 Kenya 2003 Wealthier 124 8.8 15.2 4.6 0.0 0.0 4.2 3.2 1.2 0.0 1.4 1.9 3.2 1.2 24.0 8.7 3.2 4.0 39.9 Kenya 2003 Wealthiest 159 2.4 13.6 17.6 0.0 0.5 3.7 3.6 1.3 0.0 2.2 0.6 3.6 1.3 16.0 21.8 3.6 4.1 44.0 Kenya 2003 All 888 4.2 16.1 6.0 0.0 0.6 3.1 3.1 1.7 0.0 1.7 2.9 3.1 1.7 20.2 9.5 3.1 5.8 37.6 Kenya 2008-9 Poorest 265 6.8 36.1 5.6 0.0 0.0 0.5 4.8 5.0 0.0 3.9 2.4 4.2 3.7 42.7 6.1 4.2 11.2 61.8 Kenya 2008-9 Poorer 175 5.0 27.4 3.4 0.0 0.6 1.9 4.7 4.8 0.0 4.0 4.4 4.7 4.8 32.2 6.0 4.7 13.1 53.8 Kenya 2008-9 Middle 153 11.4 33.5 8.9 0.0 2.5 2.0 4.4 1.1 0.0 3.8 0.7 4.4 0.8 44.9 13.4 4.4 5.5 65.4 Kenya 2008-9 Wealthier 188 15.3 28.9 6.9 0.0 0.0 3.0 8.3 1.5 0.0 0.7 0.0 6.9 1.1 44.0 9.9 6.9 2.3 62.7 Kenya 2008-9 Wealthiest 128 11.2 12.4 19.4 0.0 0.0 3.5 4.6 4.6 0.0 0.3 0.0 4.2 4.6 23.6 22.9 4.2 4.8 55.3 Kenya 2008-9 All 909 9.6 29.2 7.9 0.0 0.5 2.0 5.4 3.5 0.0 2.7 1.7 4.9 3.0 38.6 10.4 4.9 7.9 60.1 Liberia 2007 Poorest 218 2.4 21.5 13.5 0.0 0.0 0.0 11.9 5.9 0.0 10.6 10.2 11.5 5.8 23.9 13.5 11.5 26.7 69.1 Liberia 2007 Poorer 237 5.9 21.5 21.7 0.0 0.0 0.0 12.4 7.0 0.0 22.6 15.0 11.7 7.0 27.3 21.7 11.7 33.0 83.9 Liberia 2007 Middle 233 5.3 20.9 24.1 0.0 0.0 0.0 10.3 11.2 0.0 13.7 8.0 9.9 10.7 26.2 24.1 9.9 30.1 79.8 Liberia 2007 Wealthier 208 12.3 27.3 20.7 0.0 0.0 0.0 14.3 3.9 0.0 4.8 2.9 10.8 3.9 36.6 20.7 10.8 10.3 76.4 Liberia 2007 Wealthiest 119 3.9 34.4 22.5 0.0 0.0 0.0 18.0 5.8 0.0 4.5 4.2 14.8 5.8 38.4 22.5 14.8 14.5 84.1 Liberia 2007 All 1014 6.1 24.1 20.4 0.0 0.0 0.0 12.8 6.9 0.0 12.2 8.6 11.4 6.8 29.5 20.4 11.4 24.1 78.3 Liberia 2013 Poorest 370 9.8 27.1 6.5 0.0 0.0 0.0 6.0 3.2 12.1 9.7 0.2 5.1 3.1 36.9 6.5 5.1 24.8 71.7 Liberia 2013 Poorer 307 9.1 32.6 5.9 0.2 0.0 0.0 8.0 5.6 10.7 3.5 0.4 7.4 5.6 41.1 5.9 7.4 19.6 73.3 Liberia 2013 Middle 246 11.8 33.5 9.7 0.7 0.0 0.0 14.2 3.6 8.6 1.1 0.1 12.2 3.6 46.0 9.7 12.2 13.4 78.8 Liberia 2013 Wealthier 251 6.8 16.2 21.5 0.0 0.0 0.0 19.4 2.6 4.1 2.0 0.0 18.7 2.6 23.1 21.5 18.7 8.6 71.0 Liberia 2013 Wealthiest 157 13.2 10.8 25.3 0.0 0.0 0.0 23.8 0.0 3.7 0.0 0.0 20.3 0.0 24.0 25.3 20.3 3.7 73.2 Liberia 2013 All 1330 9.8 25.6 12.0 0.2 0.0 0.0 12.6 3.3 8.7 4.1 0.2 11.3 3.3 35.4 12.0 11.3 16.0 73.4 Madagascar 2003-4 Poorest 228 3.2 21.0 6.6 0.0 0.0 0.0 0.0 1.8 0.0 2.9 3.8 0.0 1.8 24.2 6.6 0.0 8.6 39.1 Madagascar 2003-4 Poorer 104 0.9 21.1 7.4 0.0 0.0 0.0 2.9 0.0 0.0 2.1 0.0 2.9 0.0 22.0 7.4 2.9 2.1 34.4 Madagascar 2003-4 Middle 98 1.1 18.3 9.9 0.0 0.0 0.0 3.9 0.0 0.0 0.0 4.7 3.9 0.0 19.5 9.9 3.9 4.7 38.0 Madagascar 2003-4 Wealthier 76 1.9 18.7 16.8 0.0 0.0 0.0 3.6 0.0 0.0 0.0 0.0 3.6 0.0 20.6 16.8 3.6 0.0 40.9 Madagascar 2003-4 Wealthiest 64 2.9 7.5 27.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 10.4 27.9 0.0 0.5 38.4 Madagascar 2003-4 All 570 2.2 18.7 11.1 0.0 0.0 0.0 1.7 0.7 0.0 1.6 2.4 1.7 0.7 21.0 11.1 1.7 4.7 38.2 

 (Continued...)

84 
85 

Table A6. − Continued

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3

Madagascar 2008-9 Poorest 241 2.1 21.7 2.9 0.0 0.3 0.0 0.5 3.7 0.5 3.0 1.7 0.5 3.7 23.7 3.2 0.5 8.0 35.3 Madagascar 2008-9 Poorer 235 0.8 27.0 5.9 0.0 0.0 0.0 0.2 2.8 0.0 1.8 1.7 0.2 2.8 27.8 5.9 0.2 6.2 39.5 

Madagascar 2008-9 Middle 177 3.2 18.8 3.1 0.0 2.2 0.0 0.6 0.5 0.0 1.8 0.9 0.6 0.5 21.5 5.3 0.6 3.2 29.9 Madagascar 2008-9 Wealthier 167 2.9 25.3 10.5 0.0 0.0 0.0 0.6 0.0 0.0 1.4 2.6 0.1 0.0 28.3 10.5 0.1 4.1 42.5 Madagascar 2008-9 Wealthiest 172 2.7 27.2 25.4 0.0 0.0 0.0 2.4 0.0 0.0 0.0 5.3 0.6 0.0 29.9 25.4 0.6 5.3 59.3 Madagascar 2008-9 All 993 2.2 24.0 8.8 0.0 0.5 0.0 0.8 1.6 0.1 1.7 2.4 0.4 1.6 26.1 9.3 0.4 5.6 40.7 Malawi 2004 Poorest 498 4.8 21.4 0.6 2.7 0.9 4.3 0.0 15.0 0.0 7.0 2.0 0.0 14.9 28.8 5.7 0.0 23.1 56.6 Malawi 2004 Poorer 519 5.7 22.6 2.8 3.2 0.7 5.7 0.0 15.9 0.0 5.4 0.9 0.0 15.2 31.6 9.1 0.0 21.8 61.4 Malawi 2004 Middle 495 2.9 21.9 2.2 2.0 0.0 2.8 0.0 19.2 0.0 4.9 2.2 0.0 18.9 26.8 5.0 0.0 25.0 56.2 Malawi 2004 Wealthier 375 9.7 16.8 2.7 1.8 0.1 4.7 0.0 20.9 0.0 1.9 0.7 0.0 20.7 28.3 7.6 0.0 23.3 59.0 Malawi 2004 Wealthiest 289 10.5 17.3 5.9 1.1 0.4 6.2 0.2 11.2 0.0 0.4 0.0 0.2 10.8 28.5 12.5 0.2 11.6 52.4 Malawi 2004 All 2177 6.2 20.4 2.5 2.3 0.4 4.6 0.0 16.7 0.0 4.4 1.3 0.0 16.3 28.9 7.6 0.0 21.7 57.5 Malawi 2010 Poorest 717 8.8 39.9 2.6 5.5 0.5 9.4 0.0 5.6 0.0 2.7 0.9 0.0 4.9 53.1 11.8 0.0 9.1 71.4 Malawi 2010 Poorer 682 8.7 42.0 4.5 4.1 0.3 6.9 0.0 8.0 0.0 1.2 1.7 0.0 7.7 53.7 11.3 0.0 10.9 73.1 Malawi 2010 Middle 709 10.7 38.8 3.5 1.9 0.4 7.1 0.0 7.2 0.0 2.4 1.6 0.0 6.6 50.6 10.8 0.0 10.9 70.4 Malawi 2010 Wealthier 538 15.6 33.6 4.8 2.0 0.0 10.0 0.0 7.0 0.0 2.8 2.4 0.0 6.7 50.6 14.6 0.0 12.3 76.0 Malawi 2010 Wealthiest 512 16.7 30.5 7.2 0.9 0.1 5.9 0.0 6.1 0.0 0.1 1.9 0.0 5.8 47.7 13.1 0.0 8.1 66.1 Malawi 2010 All 3158 11.6 37.5 4.3 3.0 0.3 7.9 0.0 6.8 0.0 1.9 1.7 0.0 6.3 51.4 12.2 0.0 10.3 71.5 Mali 2006 Poorest 337 0.0 12.7 1.3 0.0 0.0 0.0 0.0 2.5 1.0 6.0 7.6 0.0 2.5 12.7 1.3 0.0 16.5 30.1 Mali 2006 Poorer 396 0.0 11.9 0.9 0.0 0.0 0.0 0.0 2.8 0.1 9.5 2.6 0.0 2.8 11.9 0.9 0.0 14.1 26.8 Mali 2006 Middle 388 0.1 15.8 0.5 0.0 0.0 0.0 0.0 1.2 0.5 8.5 1.3 0.0 1.2 15.9 0.5 0.0 11.5 27.8 Mali 2006 Wealthier 356 0.4 18.6 0.7 0.0 0.0 0.0 0.0 1.5 0.8 7.9 3.1 0.0 1.5 19.0 0.7 0.0 13.3 32.7 Mali 2006 Wealthiest 182 5.5 24.3 8.3 0.0 0.0 0.0 0.0 1.4 0.3 5.8 0.7 0.0 1.4 28.2 8.3 0.0 8.3 42.3 Mali 2006 All 1660 0.7 15.8 1.6 0.0 0.0 0.0 0.0 1.9 0.6 7.8 3.2 0.0 1.9 16.3 1.6 0.0 13.2 30.7 Mali 2012-13 Poorest 144 0.0 14.0 0.0 0.0 3.2 0.0 1.8 0.9 12.6 11.3 2.6 1.8 0.9 14.0 3.2 1.8 25.7 42.5 Mali 2012-13 Poorer 145 0.0 28.2 0.0 0.0 1.4 0.0 2.2 3.1 13.5 13.5 3.0 2.2 3.1 28.2 1.4 2.2 28.6 58.0 Mali 2012-13 Middle 195 0.0 20.2 0.5 0.0 1.8 0.0 3.3 0.1 8.9 13.5 2.0 3.3 0.1 20.2 2.3 3.3 23.7 48.3 Mali 2012-13 Wealthier 182 1.8 31.5 1.9 0.0 0.3 0.0 5.9 0.8 9.6 6.8 3.4 4.7 0.8 33.4 2.2 4.7 19.8 58.7 Mali 2012-13 Wealthiest 166 3.0 30.0 3.5 0.0 0.0 0.0 5.0 0.3 8.1 11.8 1.4 5.0 0.3 33.1 3.5 5.0 20.6 60.3 Mali 2012-13 All 832 1.0 25.0 1.2 0.0 1.3 0.0 3.8 1.0 10.3 11.3 2.4 3.5 1.0 26.0 2.5 3.5 23.4 53.7 Mozambique 2003 Poorest 369 4.9 35.7 1.0 0.0 0.0 0.1 0.0 0.0 3.1 5.4 0.9 0.0 0.0 40.6 1.0 0.0 9.5 51.1 Mozambique 2003 Poorer 232 4.5 38.2 1.1 1.4 0.0 0.4 0.0 0.0 1.7 7.1 0.5 0.0 0.0 43.9 1.4 0.0 9.4 53.3 Mozambique 2003 Middle 286 9.2 49.6 1.4 0.7 0.0 0.0 0.0 0.0 0.1 4.2 1.7 0.0 0.0 58.7 1.4 0.0 6.0 66.0 Mozambique 2003 Wealthier 212 4.7 47.2 0.0 0.0 0.0 0.4 0.3 0.0 0.0 0.8 0.9 0.3 0.0 52.0 0.4 0.3 1.7 53.9 Mozambique 2003 Wealthiest 229 16.3 30.4 1.6 0.0 0.0 0.0 2.4 0.0 0.4 0.0 3.3 2.4 0.0 46.5 1.6 2.4 3.7 53.8 Mozambique 2003 All 1328 7.7 40.1 1.0 0.4 0.0 0.2 0.5 0.0 1.3 3.8 1.4 0.5 0.0 47.9 1.2 0.5 6.5 55.6 Mozambique 2011 Poorest 301 48.9 0.0 1.1 4.7 0.0 0.0 0.2 0.0 1.3 5.8 0.0 0.0 0.0 53.5 1.1 0.0 7.1 60.2 Mozambique 2011 Poorer 234 47.1 0.2 0.6 2.2 0.0 0.0 0.9 0.0 1.3 5.4 0.5 0.0 0.0 49.5 0.6 0.0 7.2 55.5 Mozambique 2011 Middle 252 56.8 1.1 1.1 1.4 0.0 0.0 0.0 0.0 0.0 6.4 0.0 0.0 0.0 59.3 1.1 0.0 6.4 65.4 Mozambique 2011 Wealthier 239 61.0 0.4 1.4 1.5 0.0 0.0 0.8 0.0 0.1 3.3 0.4 0.5 0.0 62.9 1.4 0.5 3.8 68.5 Mozambique 2011 Wealthiest 180 62.9 0.0 0.3 2.0 0.0 0.0 2.8 0.0 1.8 2.0 1.7 2.3 0.0 64.8 0.3 2.3 5.5 72.6 Mozambique 2011 All 1205 54.7 0.4 1.0 2.5 0.0 0.0 0.8 0.0 0.9 4.8 0.4 0.4 0.0 57.5 1.0 0.4 6.1 63.9 Nepal 2006 Poorest 177 1.9 18.2 0.0 1.2 0.0 0.1 10.0 0.0 0.0 0.6 0.0 7.9 0.0 21.4 0.1 7.9 0.6 29.9 Nepal 2006 Poorer 131 2.0 18.3 3.0 8.6 0.0 1.6 28.4 0.0 0.0 2.1 0.0 25.4 0.0 22.2 4.7 25.4 2.1 54.3 Nepal 2006 Middle 113 2.6 18.8 6.9 2.0 0.0 0.0 33.0 0.0 0.0 0.5 0.0 30.1 0.0 23.0 6.9 30.1 0.5 60.0 Nepal 2006 Wealthier 108 9.1 10.0 5.5 4.6 0.0 1.9 30.0 0.0 0.0 3.9 0.0 27.6 0.0 21.4 7.5 27.6 3.9 60.4 Nepal 2006 Wealthiest 94 9.4 3.7 13.3 1.9 0.0 5.4 30.3 0.0 0.0 4.4 0.0 27.0 0.0 13.7 18.7 27.0 4.4 62.2 Nepal 2006 All 623 4.4 14.7 4.9 3.6 0.0 1.5 24.6 0.0 0.0 2.0 0.0 21.9 0.0 20.7 6.4 21.9 2.0 50.6 Nepal 2011 Poorest 167 3.5 22.3 4.4 2.9 0.0 0.4 19.1 0.5 0.0 0.6 0.0 17.9 0.5 28.6 4.8 17.9 1.1 51.5 Nepal 2011 Poorer 162 1.6 22.2 12.2 4.1 0.0 0.3 19.8 0.0 0.0 0.3 1.9 19.8 0.0 27.8 12.5 19.8 2.1 60.7 Nepal 2011 Middle 181 2.8 22.0 12.4 1.8 0.0 0.0 23.4 1.4 0.0 0.8 0.0 23.0 1.4 26.6 12.4 23.0 2.2 63.5 Nepal 2011 Wealthier 116 3.3 12.2 28.7 2.0 0.0 0.0 27.8 0.0 0.0 1.0 4.2 27.8 0.0 17.5 28.7 27.8 5.2 77.0 Nepal 2011 Wealthiest 86 4.0 4.6 25.9 2.9 0.0 0.0 27.1 0.0 0.0 0.0 0.0 25.0 0.0 11.2 25.9 25.0 0.0 62.1 Nepal 2011 All 711 2.9 18.4 14.8 2.7 0.0 0.1 22.7 0.5 0.0 0.6 1.1 22.1 0.5 24.0 14.9 22.1 2.1 62.1 

 (Continued...)

85 
86 

Table A6. − Continued

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3

Nigeria 2008 Poorest 811 6.0 10.1 5.5 0.8 0.9 0.0 20.3 13.8 0.0 1.2 1.6 19.4 13.5 16.9 6.3 19.4 16.5 58.4 Nigeria 2008 Poorer 717 7.9 12.7 6.0 0.1 2.1 0.0 22.9 10.6 0.0 2.3 1.9 22.8 10.3 20.5 8.1 22.8 14.6 65.0 

Nigeria 2008 Middle 441 12.5 17.8 6.5 0.8 1.0 0.0 25.6 5.9 0.0 2.9 1.4 24.3 5.1 30.9 7.5 24.3 10.3 70.8 Nigeria 2008 Wealthier 348 15.8 14.2 8.4 1.0 1.0 0.0 22.4 3.9 0.0 1.4 1.2 21.4 3.9 30.8 9.4 21.4 6.4 67.4 Nigeria 2008 Wealthiest 213 22.6 8.8 19.7 0.4 0.8 0.0 13.5 0.6 0.0 0.0 1.9 13.1 0.6 31.4 20.4 13.1 2.5 67.4 Nigeria 2008 All 2530 10.4 12.6 7.4 0.6 1.3 0.0 21.7 9.0 0.0 1.7 1.6 21.0 8.7 23.5 8.6 21.0 12.3 64.4 Nigeria 2013 Poorest 832 4.8 10.9 2.4 0.2 0.0 0.0 29.2 5.9 3.9 10.3 1.0 29.1 5.9 15.8 2.4 29.1 20.6 67.1 Nigeria 2013 Poorer 774 10.7 13.9 2.7 0.4 0.0 0.0 34.3 3.8 1.1 7.3 0.6 33.7 3.8 24.9 2.7 33.7 12.6 72.1 Nigeria 2013 Middle 549 12.6 15.0 3.7 0.3 0.0 0.0 42.1 1.0 0.7 4.7 0.4 40.2 1.0 27.9 3.7 40.2 6.8 78.0 Nigeria 2013 Wealthier 450 14.5 15.5 7.1 0.2 0.0 0.0 29.9 0.0 1.3 2.5 1.4 29.2 0.0 30.3 7.1 29.2 5.3 70.2 Nigeria 2013 Wealthiest 362 14.2 13.6 10.8 0.4 0.0 0.0 27.1 0.2 0.2 1.2 1.8 25.9 0.2 28.1 10.8 25.9 3.4 67.4 Nigeria 2013 All 2966 10.4 13.5 4.4 0.3 0.0 0.0 32.8 2.9 1.7 6.2 0.9 32.0 2.9 24.1 4.4 32.0 11.5 70.9 Pakistan 2006-7 Poorest 239 3.6 3.9 55.2 0.0 0.0 0.0 0.0 4.8 0.0 0.7 0.7 0.0 4.8 7.5 55.2 0.0 5.5 66.7 Pakistan 2006-7 Poorer 234 10.3 0.5 52.3 0.4 0.0 0.0 1.2 2.4 0.0 1.8 0.0 1.2 2.4 11.2 52.3 1.2 4.2 68.5 Pakistan 2006-7 Middle 211 10.8 0.9 55.6 0.8 0.0 0.0 0.0 2.6 0.0 4.6 0.4 0.0 2.1 11.7 55.6 0.0 7.6 73.4 Pakistan 2006-7 Wealthier 175 4.9 1.4 58.8 0.3 0.0 0.0 0.5 1.2 0.0 3.6 0.2 0.5 1.2 6.5 58.8 0.5 5.0 70.0 Pakistan 2006-7 Wealthiest 199 4.2 1.0 70.9 0.5 0.0 0.0 0.0 1.3 0.0 10.8 0.0 0.0 1.3 5.7 70.9 0.0 12.1 81.7 Pakistan 2006-7 All 1058 6.8 1.6 58.2 0.4 0.0 0.0 0.3 2.6 0.0 4.1 0.3 0.3 2.5 8.7 58.2 0.3 6.8 71.8 Pakistan 2012-13 Poorest 586 9.3 2.6 61.3 0.6 0.0 0.0 0.0 0.5 0.0 0.3 0.0 0.0 0.2 12.5 61.3 0.0 0.8 72.9 Pakistan 2012-13 Poorer 559 7.2 1.8 63.5 0.0 0.0 0.0 0.4 1.2 0.0 0.0 0.1 0.0 1.2 9.0 63.5 0.0 1.3 72.8 Pakistan 2012-13 Middle 514 8.6 1.7 64.2 0.7 0.0 0.0 1.7 1.1 0.0 0.4 0.5 1.7 1.1 11.0 64.2 1.7 2.1 78.7 Pakistan 2012-13 Wealthier 516 6.6 0.2 70.6 0.2 0.0 0.0 0.9 0.9 0.0 0.7 0.0 0.9 0.9 7.0 70.6 0.9 1.6 78.9 Pakistan 2012-13 Wealthiest 308 6.8 0.2 74.1 0.0 0.0 0.0 0.7 0.3 0.0 0.9 0.0 0.7 0.3 7.0 74.1 0.7 1.2 81.7 Pakistan 2012-13 All 2482 7.8 1.4 65.9 0.3 0.0 0.0 0.7 0.8 0.0 0.4 0.1 0.6 0.8 9.6 65.9 0.6 1.4 76.4 Rwanda 2005 Poorest 257 1.3 10.4 0.6 0.9 0.0 0.0 4.5 0.0 0.0 4.7 3.9 4.5 0.0 12.6 0.6 4.5 8.6 26.4 Rwanda 2005 Poorer 227 0.6 10.9 0.0 0.0 0.0 0.0 6.8 0.4 0.0 3.6 5.2 6.8 0.4 11.5 0.0 6.8 9.1 26.7 Rwanda 2005 Middle 220 1.3 9.3 2.6 0.4 0.0 0.0 7.0 1.7 0.0 4.1 4.7 7.0 1.7 10.9 2.6 7.0 10.5 31.0 Rwanda 2005 Wealthier 243 1.0 10.4 3.3 0.7 0.0 0.0 6.2 0.7 0.0 3.8 4.5 6.2 0.7 12.0 3.3 6.2 9.0 29.3 Rwanda 2005 Wealthiest 155 1.6 12.1 4.6 0.0 0.0 0.0 12.1 0.0 0.0 5.4 1.1 11.1 0.0 13.7 4.6 11.1 6.5 35.6 Rwanda 2005 All 1103 1.1 10.5 2.0 0.4 0.0 0.0 6.9 0.6 0.0 4.2 4.1 6.8 0.6 12.1 2.0 6.8 8.9 29.3 Rwanda 2010 Poorest 321 1.0 17.0 0.0 11.4 0.0 0.0 3.7 0.2 0.0 2.4 9.1 3.4 0.2 26.5 0.0 3.4 11.7 40.7 Rwanda 2010 Poorer 251 0.8 23.7 0.5 15.8 0.0 0.0 2.6 0.0 0.0 1.9 6.0 2.2 0.0 37.8 0.5 2.2 7.9 47.3 Rwanda 2010 Middle 208 0.0 27.4 0.0 14.1 0.0 0.0 3.0 0.0 0.0 2.3 9.2 2.4 0.0 40.5 0.0 2.4 11.5 53.5 Rwanda 2010 Wealthier 188 1.1 20.2 2.8 15.4 0.0 0.0 3.0 0.0 0.0 5.4 6.8 3.0 0.0 36.2 2.8 3.0 11.1 52.1 Rwanda 2010 Wealthiest 165 2.5 34.6 7.7 7.0 0.0 0.0 10.8 0.0 0.0 2.1 2.1 10.0 0.0 43.1 7.7 10.0 4.2 63.9 Rwanda 2010 All 1132 1.0 23.5 1.7 12.9 0.0 0.0 4.2 0.1 0.0 2.7 7.0 3.8 0.1 35.6 1.7 3.8 9.6 49.8 Senegal 2005 Poorest 524 0.0 16.9 0.0 0.0 0.0 1.1 0.3 4.6 0.0 1.5 9.8 0.3 4.6 16.9 1.1 0.3 15.2 32.8 Senegal 2005 Poorer 489 1.2 13.9 0.2 0.7 0.0 1.2 0.1 6.7 0.0 1.8 8.7 0.1 6.7 15.8 1.4 0.1 16.8 33.3 Senegal 2005 Middle 436 1.2 16.3 0.3 0.0 0.0 0.0 0.4 4.8 0.0 0.5 9.5 0.2 4.8 17.5 0.3 0.2 13.8 31.6 Senegal 2005 Wealthier 360 0.7 18.7 5.4 1.2 0.0 1.8 2.0 2.5 0.0 0.7 7.5 1.9 2.5 20.6 7.2 1.9 10.7 38.8 Senegal 2005 Wealthiest 360 3.6 13.7 3.8 0.1 0.0 1.1 2.3 2.4 0.0 1.0 8.1 2.3 1.6 16.9 4.9 2.3 10.7 34.0 Senegal 2005 All 2168 1.2 15.9 1.6 0.4 0.0 1.0 0.9 4.4 0.0 1.1 8.8 0.8 4.2 17.4 2.6 0.8 13.8 33.9 Senegal 2010-11 Poorest 501 1.2 27.7 0.1 1.1 0.6 0.0 0.7 3.3 2.7 5.6 3.2 0.7 3.3 30.0 0.8 0.7 14.7 45.0 Senegal 2010-11 Poorer 437 2.4 31.2 0.8 0.3 0.7 0.0 1.3 3.5 1.8 4.6 1.3 1.3 3.5 33.9 1.4 1.3 10.7 46.8 Senegal 2010-11 Middle 411 5.8 29.1 2.2 0.0 0.0 0.0 2.6 2.2 1.9 3.5 4.2 2.2 2.2 34.8 2.2 2.2 10.7 49.3 Senegal 2010-11 Wealthier 548 9.7 18.6 2.9 0.1 0.5 0.0 5.6 0.5 0.8 3.0 4.7 5.5 0.5 28.4 3.4 5.5 9.1 45.7 Senegal 2010-11 Wealthiest 349 12.4 24.3 6.2 0.0 1.3 0.0 6.0 2.1 1.9 2.5 1.8 5.0 2.1 34.7 7.5 5.0 7.5 53.7 Senegal 2010-11 All 2246 6.1 25.9 2.3 0.3 0.6 0.0 3.2 2.3 1.8 3.9 3.1 2.9 2.3 32.0 2.9 2.9 10.7 47.7 Tanzania 2004-5 Poorest 241 4.4 36.3 2.6 0.0 0.0 2.0 15.9 0.0 0.0 0.0 2.8 15.5 0.0 40.7 4.6 15.5 2.8 63.1 Tanzania 2004-5 Poorer 236 6.5 40.9 1.0 0.5 0.0 4.0 11.6 0.0 0.0 0.0 3.5 9.8 0.0 47.8 5.0 9.8 3.5 65.7 Tanzania 2004-5 Middle 204 2.6 34.3 6.8 1.0 0.0 3.1 17.5 0.0 0.0 0.0 0.7 16.9 0.0 37.9 9.9 16.9 0.7 65.4 Tanzania 2004-5 Wealthier 206 4.9 26.3 2.5 0.0 0.0 3.4 13.9 0.0 0.0 0.0 0.4 12.8 0.0 31.2 5.9 12.8 0.4 50.2 Tanzania 2004-5 Wealthiest 118 10.9 23.8 16.1 0.0 0.0 4.1 10.0 0.0 0.0 0.0 0.2 8.7 0.0 34.7 20.2 8.7 0.2 63.8 Tanzania 2004-5 All 1004 5.4 33.5 4.6 0.3 0.0 3.2 14.1 0.0 0.0 0.0 1.7 13.1 0.0 39.2 7.9 13.1 1.7 61.6 

 (Continued...)

86 
87 

Table A6. − Continued

Disaggregated Sources of Care1 Summary Categories used throughout Report2

Survey 

Wealth 

Quintile N 

Public 

Hospital 

Public 

Periph 

HF 

Private 

Clinic(ian)

CHWPublic CHW- Private Religious- NGO Pharmacy Shop 

Market/ 

Informal 

Shop 

Nonallopathic Other Pharmacy Only Shop Only Public Private Pharmacy Only Other Any Care3

Tanzania 2010 Poorest 238 1.7 47.8 0.6 0.0 0.0 0.0 13.1 0.0 0.0 0.0 1.9 12.5 0.0 48.9 0.6 12.5 1.9 63.9 Tanzania 2010 Poorer 232 1.3 47.7 2.8 0.0 0.0 0.0 15.7 0.0 0.0 0.0 1.1 15.7 0.0 49.0 2.8 15.7 1.1 67.4 

Tanzania 2010 Middle 243 3.6 34.8 1.4 0.0 0.0 0.0 21.4 0.0 0.0 0.0 2.7 20.9 0.0 38.4 1.4 20.9 2.7 62.4 Tanzania 2010 Wealthier 226 5.0 36.6 7.0 0.0 0.0 0.0 16.7 0.0 0.0 0.0 1.5 13.0 0.0 41.3 7.0 13.0 1.5 62.7 Tanzania 2010 Wealthiest 170 9.9 34.7 13.0 0.0 0.0 0.0 6.2 0.0 0.0 0.0 0.2 6.2 0.0 43.5 13.0 6.2 0.2 62.2 Tanzania 2010 All 1109 4.0 40.6 4.4 0.0 0.0 0.0 15.1 0.0 0.0 0.0 1.6 14.2 0.0 44.3 4.4 14.2 1.6 63.8 Uganda 2006 Poorest 573 3.9 38.0 37.3 1.2 1.3 0.0 3.4 0.6 0.0 2.9 1.0 3.3 0.6 42.3 38.5 3.3 4.5 84.2 Uganda 2006 Poorer 469 3.9 28.4 38.4 0.6 0.8 0.0 5.9 0.3 0.0 2.2 0.4 5.8 0.3 32.6 39.2 5.8 2.9 76.8 Uganda 2006 Middle 362 3.9 20.6 38.8 0.4 0.6 0.0 3.5 0.2 0.0 9.5 0.4 3.0 0.2 24.9 39.2 3.0 10.1 71.7 Uganda 2006 Wealthier 344 10.9 11.7 48.1 0.2 0.0 0.0 5.1 1.7 0.0 3.9 1.3 5.1 1.7 22.8 48.1 5.1 6.9 77.3 Uganda 2006 Wealthiest 226 7.8 9.7 55.9 0.7 0.0 0.0 5.3 1.0 0.0 1.4 1.0 5.3 1.0 18.2 55.9 5.3 3.4 79.2 Uganda 2006 All 1974 5.6 24.7 41.9 0.7 0.7 0.0 4.5 0.7 0.0 4.0 0.8 4.4 0.7 30.7 42.4 4.4 5.4 78.4 Uganda 2011 Poorest 481 3.9 35.8 34.9 1.0 1.0 0.0 0.3 0.9 0.2 1.4 0.6 0.3 0.9 40.3 35.9 0.3 3.2 76.0 Uganda 2011 Poorer 402 4.3 24.9 44.5 0.0 0.4 0.0 2.4 2.6 0.0 1.1 2.0 1.7 2.5 29.2 44.9 1.7 5.8 79.8 Uganda 2011 Middle 329 5.3 27.7 43.8 1.2 0.4 0.0 1.7 1.8 0.0 0.6 0.6 1.4 1.1 34.1 44.1 1.4 3.0 77.4 Uganda 2011 Wealthier 274 7.4 23.5 45.1 0.8 0.5 0.0 2.0 1.5 0.0 1.2 1.2 1.7 1.5 29.9 45.6 1.7 3.9 78.3 Uganda 2011 Wealthiest 279 6.7 11.8 47.6 0.1 1.9 0.0 2.1 1.0 0.0 0.0 0.4 1.9 1.0 18.6 49.5 1.9 1.3 70.3 Uganda 2011 All 1766 5.2 26.1 42.3 0.7 0.8 0.0 1.6 1.6 0.1 0.9 1.0 1.3 1.4 31.6 43.1 1.3 3.6 76.6 Zambia 2007 Poorest 200 1.7 53.4 0.0 0.0 1.2 4.9 0.8 0.0 0.0 11.2 4.9 0.0 0.0 55.1 6.1 0.0 15.3 69.9 Zambia 2007 Poorer 220 1.2 51.0 0.3 0.0 2.2 7.2 0.0 2.6 0.0 4.6 0.5 0.0 2.6 52.2 9.8 0.0 7.2 67.6 Zambia 2007 Middle 168 3.3 45.2 1.0 0.0 3.6 2.8 0.0 3.9 0.0 5.3 1.4 0.0 3.9 48.5 7.3 0.0 9.5 64.1 Zambia 2007 Wealthier 196 4.3 50.5 3.0 0.0 0.0 0.7 3.5 2.5 0.0 0.0 0.3 2.6 2.5 54.8 3.7 2.6 2.8 62.5 Zambia 2007 Wealthiest 127 7.3 45.1 6.8 0.0 0.0 0.0 1.3 0.5 0.0 0.0 2.1 1.3 0.5 52.2 6.8 1.3 2.5 60.1 Zambia 2007 All 911 3.2 49.5 1.8 0.0 1.5 3.5 1.1 1.9 0.0 4.5 1.8 0.7 1.9 52.7 6.8 0.7 7.8 65.3 Zambia 2013-14 Poorest 453 2.4 54.0 0.6 4.5 0.7 1.6 0.2 0.9 0.0 3.3 1.5 0.2 0.7 60.7 2.9 0.2 5.7 67.0 Zambia 2013-14 Poorer 445 1.7 62.4 0.0 1.9 0.6 1.5 0.6 1.3 0.0 3.2 1.6 0.6 1.0 65.6 2.1 0.6 6.1 70.6 Zambia 2013-14 Middle 413 6.5 58.2 1.2 0.2 0.0 2.4 0.4 1.8 0.0 1.2 0.9 0.4 1.2 64.8 3.5 0.4 3.9 70.9 Zambia 2013-14 Wealthier 428 4.9 58.6 2.1 0.2 0.0 1.1 1.5 1.7 0.0 0.3 0.3 1.5 1.2 63.6 3.1 1.5 2.3 70.1 Zambia 2013-14 Wealthiest 291 6.5 49.0 6.0 0.0 0.0 0.4 0.6 1.2 0.1 0.0 0.2 0.6 0.5 55.3 6.4 0.6 1.5 62.9 Zambia 2013-14 All 2030 4.2 56.9 1.7 1.5 0.3 1.5 0.7 1.4 0.0 1.8 1.0 0.7 0.9 62.4 3.4 0.7 4.1 68.7 Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Children for whom care was sought from multiple sources contribute to each source's total, 

so the total percentage may exceed the percentage of children who received any care. 

1 The following Disaggregated Sources of Care categories include multiple standard response options: Public Peripheral Health Facility (HF) includes: health center, health post, mobile clinic, or other public sector care; Private Clinic or Clinician includes private 

hospital, doctor, nurse, mobile clinic, other private sector; and Other includes any other source of care sought outside the home, including a friend, relative, or other. 2 The public summary category combines the Public Hospital, Public Peripheral HF, and Public CHW disaggregated sources of care categories; the private summary category combines the Private Clinic or Clinician, Private CHW, and Religious or NGO Facility 

disaggregated sources of care categories (note that Private does not include pharmacy); Pharmacy Only includes children for whom care was sought only from a pharmacy; and the Other summary category combines the Shop, Market/Informal Shop, NonAllopathic, and Other disaggregated sources of care categories. 3 Any Care includes children who received care from any source outside the home (including all disaggregated source of care categories). 

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88 

Table A7. Use of public, private, pharmacy, and other sources of care for fever, symptoms of ARI, and diarrhea by place of residence, with confidence intervals, USAID MCH priority countries 

Fever Symptoms of ARI Diarrhea 

Public Private Pharmacy Only Other Public Private Pharmacy Only Other Public Private Pharmacy Only Other 

Country/Survey 

Place of 

Residence % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB 

Bangladesh 2007 Urban 12.0 8.9 15.9 25.2 21.4 29.5 23.9 20.3 27.9 15.3 12.4 18.9 26.1 13.6 44.2 30.6 19.7 44.0 20.6 10.2 37.1 10.3 3.6 26.0 11.3 6.8 18.2 19.0 13.9 25.3 24.3 16.9 33.6 19.7 13.1 28.5 Bangladesh 2007 Rural 8.3 6.3 10.8 12.6 10.6 15.0 22.8 19.8 26.2 29.4 26.2 32.7 9.1 5.5 14.6 25.7 18.8 34.0 29.7 23.4 36.9 26.7 20.6 33.9 9.4 6.2 13.9 8.4 5.9 11.9 27.7 21.5 34.9 28.6 23.3 34.7 Bangladesh 2011 Urban 10.9 8.6 13.7 45.3 40.5 50.2 18.5 15.2 22.2 3.1 2.0 4.9 11.5 6.3 20.0 58.4 48.1 68.1 21.0 13.5 31.1 4.0 1.1 12.7 14.5 7.9 25.0 45.9 32.8 59.6 14.6 8.4 24.2 1.1 0.2 4.8 Bangladesh 2011 Rural 8.3 7.0 9.9 43.1 40.1 46.1 19.1 17.3 21.1 5.1 4.0 6.4 12.1 8.9 16.1 44.8 38.1 51.6 19.9 15.0 25.8 6.6 4.1 10.6 10.3 7.1 14.9 40.4 34.5 46.5 23.7 18.4 29.9 4.0 1.9 8.0 Congo DR 2007 Urban 21.4 16.9 26.8 27.0 22.7 31.9 17.6 13.4 22.8 4.5 3.1 6.5 23.4 18.3 29.3 23.5 17.6 30.7 25.0 18.3 33.1 4.0 2.3 7.1 32.3 25.3 40.1 3.8 2.0 7.1 2.0 0.9 4.2 3.1 1.4 6.8 Congo DR 2007 Rural 27.3 21.9 33.4 16.9 11.8 23.5 9.2 6.8 12.3 7.1 5.1 9.7 26.0 18.9 34.7 15.1 11.3 19.9 10.3 7.4 14.2 7.9 5.2 11.8 25.4 18.7 33.6 6.8 4.2 10.9 4.0 1.7 8.9 3.0 1.5 5.7 Congo DR 2013-14 Urban 20.0 17.0 23.5 17.9 14.9 21.3 19.2 15.4 23.7 3.4 2.4 4.8 21.6 16.2 28.3 17.2 11.6 24.8 19.9 11.1 33.2 3.5 1.4 8.1 18.3 15.1 22.1 13.9 10.9 17.5 18.5 14.7 23.1 4.5 2.8 7.2 Congo DR 2013-14 Rural 32.4 29.0 36.1 8.6 6.5 11.3 13.4 10.2 17.5 5.1 3.9 6.7 36.2 28.3 44.9 6.8 4.0 11.3 9.3 6.4 13.4 7.0 4.5 10.6 34.9 31.1 38.8 8.2 6.4 10.5 12.1 8.6 16.6 6.9 5.3 8.9 Ethiopia 2005 Urban 30.3 19.3 44.2 15.7 9.0 26.0 0.8 0.3 1.9 0.3 0.1 1.7 29.5 15.3 49.2 16.7 6.5 36.7 0.4 0.1 2.1 1.3 0.2 7.9 28.8 21.4 37.6 7.1 3.5 14.0 3.4 0.7 14.3 0.4 0.0 2.6 Ethiopia 2005 Rural 13.1 10.6 16.0 2.7 1.5 4.8 1.0 0.6 1.8 0.7 0.3 1.7 14.8 11.6 18.7 2.7 1.4 5.4 1.4 0.7 2.8 0.4 0.1 1.6 18.3 15.5 21.5 3.3 1.9 5.5 1.3 0.7 2.3 2.2 1.4 3.3 Ethiopia 2011 Urban 22.2 15.2 31.4 16.4 10.9 23.9 3.4 1.4 7.9 0.0 0.0 0.1 18.9 9.6 34.0 28.1 11.4 54.2 3.3 1.0 10.1 0.1 0.0 0.5 35.4 26.3 45.8 23.1 14.6 34.6 3.6 1.8 7.4 0.9 0.2 3.6 Ethiopia 2011 Rural 16.0 13.5 18.9 6.7 4.7 9.4 0.6 0.2 1.4 1.2 0.7 2.3 19.9 15.5 25.2 5.6 3.7 8.5 1.4 0.5 3.5 0.9 0.3 2.6 22.7 19.2 26.7 7.1 4.8 10.4 1.7 0.9 3.3 2.1 1.3 3.6 Ghana 2003 Urban 45.4 37.3 53.7 10.9 6.8 17.1 16.2 11.8 21.8 2.0 0.7 5.3 48.5 37.4 59.7 5.5 2.3 12.6 22.8 13.8 35.5 2.6 0.7 10.1 28.1 19.9 38.0 7.4 3.6 14.7 17.7 11.8 25.8 2.2 0.6 7.6 Ghana 2003 Rural 36.9 31.7 42.3 4.9 3.1 7.6 25.6 21.0 30.7 6.0 4.0 8.9 34.2 27.7 41.4 5.9 3.2 10.7 24.6 18.3 32.2 3.8 1.9 7.4 19.3 14.6 25.0 1.9 0.9 4.1 20.9 16.4 26.2 6.2 4.1 9.1 Ghana 2008 Urban 41.6 34.0 49.6 18.5 13.2 25.2 19.6 13.5 27.5 2.3 1.0 5.4 (37.7) 21.7 56.9 (15.4) 7.1 30.3 (10.1) 3.8 24.0 (2.8) 0.4 19.3 29.4 22.8 37.1 8.1 4.6 13.8 25.2 18.0 34.0 3.6 1.5 8.6 Ghana 2008 Rural 40.5 34.0 47.2 5.2 3.1 8.6 10.0 6.5 15.0 8.1 5.3 12.1 45.4 34.4 56.9 4.3 0.9 18.3 9.6 4.2 20.4 7.2 3.2 15.1 38.6 32.7 45.0 4.9 2.8 8.4 11.9 8.1 17.0 8.0 5.4 11.7 Haiti 2005-6 Urban 17.3 12.8 22.9 23.4 18.9 28.7 3.9 2.3 6.5 10.4 7.0 15.0 13.2 8.0 21.0 21.0 13.4 31.3 1.2 0.2 5.4 6.7 3.1 14.0 17.8 13.2 23.5 14.6 10.2 20.5 7.1 4.3 11.5 8.8 5.6 13.7 Haiti 2005-6 Rural 12.9 10.2 16.3 22.4 17.9 27.5 1.1 0.4 2.8 6.8 4.8 9.6 9.6 5.9 15.1 21.5 14.4 30.9 0.2 0.0 0.8 8.8 5.7 13.4 10.7 6.8 16.4 19.2 13.9 25.8 0.9 0.4 2.2 7.0 4.7 10.1 Haiti 2012 Urban 15.4 12.1 19.3 31.7 26.0 37.9 1.2 0.5 3.0 5.3 3.5 7.8 10.9 7.6 15.3 34.5 28.8 40.7 1.7 0.5 4.9 7.7 5.0 11.7 17.5 14.0 21.6 22.6 18.0 27.9 0.8 0.2 2.8 8.8 6.2 12.3 Haiti 2012 Rural 13.9 11.0 17.5 25.1 21.8 28.8 0.2 0.0 0.6 8.7 6.6 11.2 14.5 10.8 19.2 22.0 17.8 26.9 0.9 0.2 3.5 7.8 5.5 10.8 14.6 11.5 18.3 19.5 16.1 23.4 0.1 0.0 0.7 9.5 7.0 12.8 India 2005-6 Urban 12.5 10.6 14.6 65.7 62.9 68.4 2.8 2.0 3.9 4.6 3.5 6.0 13.4 10.7 16.7 65.0 60.3 69.4 3.3 1.9 5.6 3.7 2.0 6.8 13.4 11.1 16.0 51.6 47.8 55.4 7.2 5.5 9.5 5.0 3.5 7.1 India 2005-6 Rural 13.5 12.2 14.8 53.4 51.3 55.5 6.0 4.9 7.2 6.1 5.1 7.2 12.0 10.1 14.2 54.0 51.0 57.0 5.2 3.9 6.9 5.9 4.6 7.6 14.2 12.7 16.0 43.9 41.4 46.4 5.6 4.6 6.8 6.3 5.0 7.8 Indonesia 2007 Urban 27.1 24.1 30.5 47.9 43.9 51.9 11.0 8.9 13.6 11.1 8.8 13.8 25.1 20.1 31.0 49.9 42.9 56.9 14.2 10.1 19.6 9.7 6.7 13.9 19.2 14.8 24.5 37.5 32.5 42.8 8.8 6.2 12.3 15.3 11.7 19.7 Indonesia 2007 Rural 25.0 22.5 27.7 42.3 39.3 45.4 7.1 5.9 8.6 19.6 17.5 21.9 24.6 21.3 28.3 42.5 37.7 47.5 8.2 6.0 11.0 20.2 17.0 23.9 20.3 17.0 23.9 30.8 27.4 34.5 4.3 3.0 6.2 19.8 16.8 23.2 Indonesia 2012 Urban 27.7 25.0 30.6 52.1 48.7 55.5 12.4 10.5 14.6 6.7 5.3 8.3 29.7 22.9 37.5 53.4 45.9 60.7 11.1 7.3 16.4 9.0 5.5 14.5 24.0 20.4 28.1 42.6 38.2 47.1 11.0 8.3 14.4 8.2 6.1 10.9 Indonesia 2012 Rural 23.0 20.9 25.4 52.3 49.5 55.0 6.9 5.6 8.6 14.3 12.3 16.4 25.2 20.6 30.4 54.7 49.2 60.2 4.9 3.2 7.7 13.7 10.4 17.7 23.3 19.9 27.0 45.7 41.7 49.8 4.4 3.1 6.1 12.8 10.7 15.3 Kenya 2003 Urban 29.2 25.1 33.6 24.7 20.7 29.2 15.5 11.5 20.6 7.1 4.5 11.0 37.1 30.8 43.9 26.8 21.6 32.7 15.8 10.1 23.8 6.9 3.9 12.0 16.6 12.2 22.0 14.2 10.1 19.5 3.8 1.8 8.1 6.1 3.3 11.1 Kenya 2003 Rural 29.0 25.2 33.1 16.6 14.0 19.6 13.0 10.9 15.5 10.3 8.6 12.3 30.6 26.4 35.2 16.5 13.2 20.3 12.7 10.1 15.7 8.7 6.6 11.4 21.1 17.4 25.3 8.4 5.4 12.8 2.9 1.8 4.7 5.7 3.8 8.5 Kenya 2008-9 Urban 40.6 29.4 52.8 12.2 6.9 20.7 8.4 3.9 16.9 2.8 1.3 6.1 47.3 30.4 64.8 20.0 8.1 41.6 13.2 3.5 39.0 0.3 0.0 2.5 32.4 24.2 41.9 16.1 5.9 37.1 7.1 2.9 16.1 4.0 0.9 16.1 Kenya 2008-9 Rural 38.7 34.4 43.1 9.8 7.5 12.5 7.7 5.2 11.2 7.6 5.3 10.7 44.1 36.8 51.7 11.1 7.5 16.2 8.7 4.5 16.2 6.7 3.5 12.6 40.1 34.9 45.4 9.1 6.7 12.4 4.4 3.0 6.4 8.8 6.2 12.3 Liberia 2007 Urban 46.0 38.8 53.3 22.7 17.3 29.1 10.4 7.2 14.6 6.3 4.0 9.7 57.6 42.2 71.6 16.4 8.5 29.2 8.5 4.0 17.0 1.7 0.5 5.9 39.2 31.3 47.6 14.9 10.8 20.2 15.9 12.0 20.7 11.6 7.7 17.2 Liberia 2007 Rural 31.8 26.5 37.6 21.2 16.4 26.9 7.4 5.6 9.8 20.6 16.8 25.0 41.8 35.2 48.6 17.9 13.3 23.7 9.9 6.5 15.0 11.7 8.0 16.7 25.6 20.1 32.1 22.6 17.0 29.4 9.6 5.9 15.3 29.2 23.0 36.4 Liberia 2013 Urban 36.6 29.0 44.9 25.3 19.4 32.2 15.2 10.9 20.7 5.4 3.1 9.2 25.5 15.6 38.9 23.9 15.4 35.1 24.1 13.3 39.7 4.0 1.7 9.3 31.1 23.1 40.5 17.5 11.8 25.1 17.2 13.3 21.9 8.7 5.8 12.9 Liberia 2013 Rural 44.8 38.7 51.0 9.4 7.0 12.6 7.9 5.6 11.2 16.7 12.9 21.2 42.7 34.6 51.2 9.6 6.2 14.6 5.7 3.4 9.4 15.2 10.0 22.4 39.1 33.4 45.0 7.3 4.9 10.8 6.4 4.5 9.0 22.1 17.5 27.5 Madagascar 2003-4 Urban 30.3 23.8 37.6 29.6 24.5 35.2 1.0 0.4 2.6 2.3 0.8 6.2 38.0 29.3 47.5 25.4 19.0 33.1 0.5 0.1 3.6 1.4 0.4 4.9 20.5 14.4 28.4 20.9 15.3 28.0 0.0 1.6 0.6 4.0 Madagascar 2003-4 Rural 25.5 19.9 32.0 8.3 5.3 12.7 0.9 0.2 3.8 5.6 3.4 9.1 33.7 25.7 42.7 10.3 5.9 17.3 2.1 0.6 7.4 5.9 3.1 11.1 21.1 15.5 28.0 9.1 5.3 15.0 2.0 0.8 5.2 5.3 2.5 10.8 Madagascar 2008-9 Urban 23.6 16.9 31.9 39.7 32.8 47.0 1.1 0.3 4.6 0.3 0.0 2.3 25.2 16.9 35.7 36.5 24.2 51.0 1.0 0.1 7.3 0.0 27.6 20.4 36.2 23.5 18.5 29.5 1.2 0.4 3.6 6.2 3.0 12.2 Madagascar 2008-9 Rural 30.3 26.2 34.7 9.1 6.7 12.1 0.5 0.2 1.2 5.8 4.3 7.9 30.1 24.0 37.0 10.8 7.0 16.4 0.8 0.3 2.5 7.7 4.5 12.8 25.9 22.4 29.6 6.7 4.8 9.4 0.3 0.1 0.7 5.5 3.8 8.0 Mali 2006 Urban 40.5 34.9 46.3 6.1 3.5 10.4 10.6 6.3 17.2 15.8 11.2 21.7 43.0 35.6 50.7 8.0 4.1 14.9 9.6 5.3 16.7 9.0 5.3 14.8 27.7 20.4 36.4 5.0 2.8 8.8 0.0 0.0 0.2 10.9 6.9 16.8 Mali 2006 Rural 25.5 22.0 29.4 2.8 1.9 4.1 0.7 0.4 1.4 24.5 21.1 28.3 32.4 26.9 38.3 1.2 0.5 2.9 0.4 0.1 1.4 22.1 17.0 28.3 13.5 10.9 16.7 0.8 0.4 1.6 0.0 13.7 11.3 16.5 Mali 2012-13 Urban 41.5 34.5 48.9 7.4 4.2 12.8 9.8 5.7 16.3 12.5 7.4 20.2 (27.9) 14.6 46.9 (10.5) 2.6 34.3 (13.0) 3.0 41.6 (9.2) 1.8 36.1 31.6 25.0 39.1 4.8 2.7 8.5 7.2 4.2 12.2 20.8 15.3 27.7 Mali 2012-13 Rural 22.2 17.7 27.4 1.5 0.7 3.1 2.4 1.3 4.3 19.5 15.6 24.0 24.6 16.6 34.9 0.4 0.0 2.7 3.2 1.1 9.3 30.3 21.1 41.4 24.5 20.8 28.7 1.9 1.1 3.4 2.5 1.3 5.0 24.1 20.3 28.3 Malawi 2004 Urban 52.5 45.2 59.7 4.5 2.1 9.6 23.4 16.4 32.3 1.0 0.2 4.1 40.6 32.9 48.8 6.2 2.9 13.0 0.4 0.0 2.9 19.3 12.0 29.7 33.2 25.3 42.2 5.5 2.3 12.6 0.0 10.8 7.4 15.5 Malawi 2004 Rural 33.3 30.6 36.0 6.6 5.5 8.0 37.0 34.4 39.6 1.3 1.0 1.9 27.9 24.9 31.2 8.0 6.5 9.8 0.0 34.0 31.1 37.1 28.4 25.5 31.4 7.8 6.2 9.8 0.0 0.0 0.2 23.1 20.7 25.6 Malawi 2010 Urban 53.1 47.2 59.0 17.0 12.2 23.3 0.0 4.1 2.3 7.0 54.4 37.5 70.3 13.8 6.8 25.9 0.0 2.0 0.5 7.4 49.6 43.1 56.1 7.6 3.5 15.8 0.0 8.6 5.4 13.4 Malawi 2010 Rural 50.3 47.9 52.8 14.7 12.9 16.6 0.0 0.0 0.2 9.8 8.6 11.1 54.5 50.4 58.5 18.2 15.0 21.9 0.0 8.0 6.1 10.4 51.7 48.9 54.5 13.0 11.0 15.2 0.0 10.6 9.0 12.4 Mozambique 2003 Urban 63.3 58.9 67.6 1.9 0.9 4.0 1.3 0.7 2.7 4.7 3.1 7.0 58.0 50.4 65.2 2.0 1.0 3.9 1.6 0.7 3.5 4.8 2.5 9.1 52.5 43.8 61.0 0.6 0.1 2.8 1.4 0.6 3.4 3.2 1.2 7.9 Mozambique 2003 Rural 45.0 40.8 49.2 2.0 1.1 3.8 0.2 0.1 0.7 9.2 7.4 11.4 52.3 46.1 58.5 1.8 0.9 3.6 0.3 0.0 1.9 8.3 5.3 12.8 45.6 40.3 51.0 1.5 0.6 3.7 0.0 8.1 5.5 11.8 Mozambique 2011 Urban 71.1 65.5 76.1 1.5 0.7 3.0 0.8 0.2 2.7 1.4 0.6 3.2 64.4 50.2 76.5 0.0 1.4 0.2 10.0 3.9 0.9 15.6 65.8 58.9 72.0 1.1 0.4 3.3 1.2 0.4 3.3 3.2 1.5 6.5 Mozambique 2011 Rural 53.7 49.0 58.5 0.0 0.1 0.0 0.6 5.6 3.3 9.3 47.0 35.5 58.8 0.0 0.0 4.8 1.7 12.6 53.9 48.3 59.4 0.9 0.3 2.4 0.1 0.0 0.8 7.3 4.6 11.4 Nigeria 2008 Urban 28.6 24.7 32.9 12.6 9.9 15.9 28.8 24.8 33.2 4.6 3.0 6.9 24.4 18.0 32.1 9.1 4.9 16.3 37.7 27.8 48.7 7.1 3.4 14.4 28.2 23.3 33.6 12.5 9.6 16.3 23.0 18.6 27.9 6.9 4.6 10.1 Nigeria 2008 Rural 25.3 23.0 27.9 8.6 7.3 10.1 26.6 24.2 29.1 8.7 7.4 10.2 22.8 18.1 28.3 8.6 5.7 12.7 26.3 21.9 31.3 10.6 7.8 14.4 22.1 19.5 24.8 7.4 5.9 9.2 20.3 18.0 22.8 14.0 11.8 16.6 Nigeria 2013 Urban 27.5 24.1 31.3 9.0 6.9 11.8 35.5 31.6 39.7 3.2 2.0 5.1 35.1 25.5 46.1 12.6 8.0 19.5 31.9 23.3 41.8 3.7 1.2 10.5 27.8 24.1 32.0 7.5 5.5 10.2 31.8 27.1 36.8 3.7 2.3 5.9 

 (Continued...)

88 
89 

Table A7. − Continued

Fever Symptoms of ARI Diarrhea 

Public Private Pharmacy Only Other Public Private Pharmacy Only Other Public Private Pharmacy Only Other 

Country/Survey 

Place of 

Residence % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB 

Nigeria 2013 Rural 24.8 22.2 27.5 3.9 3.0 5.2 35.4 32.1 38.8 10.1 8.2 12.3 26.7 22.0 32.1 3.2 1.8 5.6 31.9 26.8 37.6 14.9 10.3 21.0 22.3 19.7 25.2 3.0 2.2 4.0 32.1 29.1 35.2 15.3 13.2 17.6 Nepal 2006 Urban 19.8 13.2 28.7 21.6 14.8 30.4 23.1 16.5 31.4 3.0 1.2 7.5 15.8 7.3 31.1 39.3 20.9 61.2 17.9 9.4 31.4 0.3 0.0 2.0 13.2 8.0 21.0 15.4 8.6 26.1 21.6 12.0 35.6 5.5 1.1 23.6 Nepal 2006 Rural 23.7 19.2 28.9 9.2 6.6 12.8 24.5 20.3 29.1 2.0 1.0 3.8 28.2 21.0 36.7 14.5 9.7 21.3 22.9 16.3 31.1 2.0 0.8 4.9 21.7 16.8 27.6 5.1 3.3 8.0 22.0 16.7 28.2 1.5 0.6 4.0 Nepal 2011 Urban 19.0 12.9 27.1 36.6 29.4 44.5 24.5 18.1 32.4 1.9 0.7 4.8 33.5 20.1 50.3 35.5 21.9 52.0 20.3 12.0 32.1 0.0 14.9 9.4 22.8 28.5 20.4 38.4 24.9 16.3 36.2 0.7 0.1 5.0 Nepal 2011 Rural 18.9 15.8 22.4 22.3 18.3 26.9 28.7 24.4 33.4 2.0 1.1 3.6 24.1 17.6 32.2 25.1 17.7 34.2 28.1 19.8 38.1 3.0 1.1 8.0 24.9 20.2 30.3 13.5 9.8 18.4 21.8 18.0 26.1 2.3 1.2 4.5 Pakistan 2006-7 Urban 9.1 6.8 12.0 70.0 64.9 74.7 0.5 0.2 1.3 5.1 3.2 8.0 9.9 6.6 14.6 76.3 70.0 81.7 0.0 4.9 2.4 9.8 7.9 5.1 12.1 65.6 58.8 71.8 1.1 0.4 3.0 8.7 4.2 16.9 Pakistan 2006-7 Rural 10.7 8.8 12.9 64.9 61.8 67.9 0.7 0.3 1.5 4.8 3.6 6.5 9.7 7.5 12.3 67.4 63.5 71.1 0.5 0.1 1.8 4.5 2.8 7.2 9.0 6.7 11.9 54.9 50.3 59.5 0.0 6.0 4.1 8.5 Pakistan 2012-13 Urban 10.4 8.2 13.2 72.8 68.1 77.0 0.6 0.2 1.6 1.9 1.0 3.7 9.4 7.0 12.5 77.6 72.7 81.9 0.3 0.1 1.1 0.2 0.0 0.7 7.6 5.3 10.8 70.3 65.3 74.9 0.6 0.2 1.8 1.7 0.7 3.7 Pakistan 2012-13 Rural 8.3 6.7 10.2 70.0 67.1 72.8 0.4 0.2 0.9 2.9 1.8 4.7 9.4 7.3 11.9 71.4 67.0 75.4 0.6 0.2 1.6 2.0 1.0 3.7 10.4 8.4 12.9 64.1 60.4 67.8 0.6 0.2 1.8 1.3 0.7 2.4 Rwanda 2005 Urban 31.5 26.1 37.4 13.8 9.4 19.8 18.9 14.9 23.7 14.7 11.1 19.1 26.7 21.9 32.2 14.6 10.2 20.4 18.3 13.3 24.5 17.5 12.7 23.6 11.1 7.2 16.6 5.2 2.5 10.5 9.6 6.2 14.6 8.2 5.2 12.7 Rwanda 2005 Rural 23.6 21.2 26.2 2.2 1.4 3.4 15.5 13.3 17.9 17.6 15.5 20.0 23.6 20.7 26.7 1.9 1.2 3.1 15.5 13.0 18.4 22.3 19.5 25.3 12.2 10.1 14.8 1.6 0.9 2.9 6.4 4.9 8.2 9.0 7.1 11.3 Rwanda 2010 Urban 44.0 36.9 51.4 12.7 6.8 22.7 7.3 4.2 12.6 2.1 0.7 6.2 54.8 41.5 67.5 21.0 11.1 36.0 5.8 1.7 17.5 1.9 0.2 14.3 28.6 22.2 35.9 4.9 2.6 8.9 7.9 4.5 13.4 7.2 3.7 13.6 Rwanda 2010 Rural 40.3 37.2 43.5 0.6 0.3 1.4 2.6 1.7 3.9 5.3 4.2 6.9 45.1 38.6 51.9 0.0 3.2 1.5 6.7 9.5 6.3 14.0 36.6 33.4 39.9 1.2 0.6 2.5 3.3 2.3 4.7 10.0 8.0 12.3 Senegal 2005 Urban 39.4 32.8 46.5 16.2 9.8 25.5 9.8 7.2 13.2 4.2 2.5 6.9 41.1 34.2 48.4 17.8 10.3 29.0 7.7 4.7 12.5 4.9 2.3 10.1 16.9 12.1 23.1 5.1 2.8 9.2 1.9 0.8 4.4 13.3 8.5 20.2 Senegal 2005 Rural 32.5 29.4 35.8 8.3 6.6 10.3 2.0 1.3 3.1 9.9 8.0 12.0 38.9 33.5 44.6 6.9 4.8 10.0 3.5 2.1 5.7 6.7 4.7 9.4 17.7 15.2 20.5 1.2 0.7 2.1 0.2 0.1 0.6 14.1 11.9 16.6 Senegal 2010-11 Urban 43.6 37.5 49.9 7.1 4.5 11.0 8.3 5.4 12.4 5.0 3.3 7.5 51.7 39.1 64.1 8.9 4.2 17.9 7.2 3.8 12.9 1.3 0.3 5.3 30.7 26.2 35.6 4.4 2.7 7.2 5.4 3.5 8.2 8.6 5.5 13.1 Senegal 2010-11 Rural 34.5 31.5 37.6 2.5 1.4 4.3 2.7 1.6 4.5 6.5 5.1 8.3 35.8 29.9 42.2 2.3 0.8 6.9 2.0 0.9 4.4 10.9 8.0 14.7 32.9 30.0 36.0 1.7 1.0 2.7 1.0 0.6 1.9 12.4 10.3 14.9 Tanzania 2004-5 Urban 55.9 47.0 64.5 23.0 16.7 30.6 15.1 11.4 19.7 0.0 56.0 42.2 68.9 18.1 10.3 29.9 13.6 7.3 24.0 0.3 0.0 2.2 34.3 23.2 47.5 12.2 7.0 20.6 11.4 6.4 19.5 0.0 Tanzania 2004-5 Rural 47.4 43.3 51.5 11.0 9.1 13.3 22.2 19.1 25.6 2.1 1.4 3.1 51.4 45.2 57.5 10.8 7.9 14.5 22.4 18.1 27.4 2.3 1.2 4.3 40.1 35.5 44.7 7.1 5.0 10.0 13.4 10.2 17.5 2.1 1.2 3.4 Tanzania 2010 Urban 50.2 40.9 59.5 16.6 12.2 22.1 12.2 6.0 23.2 1.5 0.5 4.5 70.0 53.9 82.3 11.3 5.1 23.5 4.3 1.1 15.5 2.5 0.3 17.6 39.1 31.0 47.8 12.3 7.5 19.5 12.3 6.8 21.4 0.2 0.0 0.7 Tanzania 2010 Rural 53.3 48.7 57.8 3.4 2.1 5.4 21.4 17.4 26.0 0.7 0.3 1.7 56.5 47.8 64.8 4.3 2.1 8.4 16.2 9.9 25.3 1.4 0.3 5.4 46.0 41.0 51.0 1.8 0.8 3.8 14.8 11.6 18.6 2.0 1.1 3.7 Uganda 2006 Urban 34.5 24.7 45.9 44.5 35.2 54.3 8.3 4.1 16.0 0.0 25.4 13.8 42.1 45.1 27.6 63.9 9.0 3.4 22.1 0.2 0.0 1.4 28.7 19.7 39.8 41.0 33.3 49.1 5.4 2.6 10.9 3.3 1.7 6.6 Uganda 2006 Rural 30.7 27.6 33.9 46.8 43.6 50.0 6.3 5.1 7.7 3.0 2.2 4.0 33.6 28.8 38.7 43.3 38.5 48.2 7.1 5.3 9.4 3.8 2.5 5.5 30.9 27.8 34.1 42.6 39.1 46.1 4.3 3.4 5.4 5.6 4.3 7.4 Uganda 2011 Urban 28.3 22.0 35.5 63.6 57.8 69.0 3.3 1.7 6.3 2.9 1.4 5.9 28.3 20.0 38.4 57.0 45.5 67.8 3.3 1.4 7.4 6.8 1.9 21.4 25.1 18.3 33.4 48.2 41.7 54.9 2.9 1.3 6.2 1.2 0.5 2.9 Uganda 2011 Rural 36.8 32.7 41.2 45.8 41.6 50.2 1.3 0.8 2.3 4.0 3.0 5.2 32.9 29.0 37.1 49.9 45.1 54.8 1.5 0.7 3.1 3.2 2.0 5.1 32.6 28.7 36.8 42.3 38.5 46.3 1.0 0.6 1.9 3.9 3.0 5.1 Zambia 2007 Urban 63.1 56.5 69.2 2.7 1.2 5.8 1.3 0.4 4.1 4.6 2.4 8.9 66.2 52.9 77.4 0.7 0.2 3.0 3.3 1.1 9.3 2.5 0.5 11.5 53.7 46.0 61.2 3.4 1.6 6.9 2.1 0.6 6.8 2.6 0.9 7.1 Zambia 2007 Rural 52.5 47.2 57.6 9.7 6.4 14.4 1.3 0.5 3.0 8.5 6.4 11.2 58.6 49.7 67.0 10.5 5.8 18.2 1.9 0.4 7.6 6.9 3.8 12.2 52.3 46.3 58.2 8.4 5.7 12.2 0.1 0.0 0.7 10.3 7.5 13.8 Zambia 2013-14 Urban 70.6 66.2 74.6 6.1 3.9 9.4 1.3 0.6 2.9 2.9 1.6 5.1 74.3 64.2 82.3 7.2 3.1 15.9 0.9 0.2 3.3 5.7 1.7 17.2 59.8 55.5 64.0 4.6 3.0 7.0 1.1 0.4 2.8 2.0 1.1 3.5 Zambia 2013-14 Rural 66.5 63.3 69.5 3.7 2.5 5.5 0.5 0.2 1.1 4.5 3.4 5.9 63.7 56.6 70.2 2.3 1.2 4.3 0.0 3.7 2.0 6.9 64.0 60.4 67.5 2.7 1.7 4.1 0.4 0.2 1.0 5.4 4.0 7.2 Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Public sources include public hospital, health center, health post, mobile clinic, community health worker, and other public sector; private sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; pharmacy only highlights children for whom care was sought only from a pharmacy; other sources 

include: market/informal shop, traditional healer or other non-allopathic sources, friend, relative, or other sources. Children for whom care was sought from multiple sources contribute to each source's total, so the total percentage may exceed the percentage of children who 

received any care. LB and UB refer to the lower and upper bounds of the 95% confidence interval. 

89 
90 

Table A8. Use of public, private, pharmacy, and other sources of care for fever, symptoms of ARI, and diarrhea by household wealth, with confidence intervals, USAID MCH priority countries 

Fever Symptoms of ARI Diarrhea 

Public Private Pharmacy Only Other Any Care Public Private Pharmacy Only Other Any Care Public Private Pharmacy Only Other Any Care 

Country/Survey 

Household 

Wealth 

Quintile % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB 

Bangladesh 2007 Poorest 6.8 4.1 10.9 6.8 4.4 10.3 23.0 18.2 28.6 28.2 23.9 32.9 63.5 57.5 69.1 11.8 5.4 24.0 13.4 7.3 23.4 29.5 19.0 42.8 30.9 20.0 44.4 82.5 72.8 89.3 4.3 1.6 11.1 6.4 2.9 13.9 35.5 24.6 48.2 24.5 16.3 35.1 70.4 61.9 77.7 

Bangladesh 2007 Poorer 7.1 4.5 10.9 11.9 9.0 15.7 20.6 16.4 25.6 28.8 24.1 34.0 66.3 60.8 71.4 5.6 2.0 14.8 22.8 12.1 38.8 35.5 23.1 50.3 31.4 20.0 45.6 89.7 76.2 96.0 11.1 5.9 19.7 5.6 2.6 11.7 29.4 20.4 40.3 30.1 21.1 41.0 73.4 61.7 82.5 

Bangladesh 2007 Middle 10.2 7.0 14.7 12.6 8.9 17.4 21.1 16.8 26.3 33.2 27.7 39.1 75.8 70.7 80.4 17.6 8.0 34.3 25.5 12.0 46.2 18.2 9.5 32.1 32.4 17.8 51.5 90.9 78.6 96.4 7.9 3.9 15.7 12.9 8.0 20.0 27.1 17.5 39.3 31.9 23.8 41.4 76.9 66.7 84.8 Bangladesh 2007 Wealthier 11.7 8.2 16.5 19.9 16.0 24.6 25.8 19.7 33.0 23.9 18.0 31.0 78.5 73.6 82.7 8.6 2.9 22.6 40.1 25.6 56.6 31.7 17.3 50.8 8.7 2.5 26.0 87.5 70.1 95.4 15.5 8.7 26.2 12.1 6.5 21.3 16.2 7.7 30.9 29.8 19.4 42.9 71.9 58.8 82.0 Bangladesh 2007 Wealthiest 10.2 7.3 14.0 28.7 23.6 34.4 25.5 21.4 30.1 16.5 12.4 21.7 79.9 75.3 83.7 (22.2) 7.6 50.0 (51.1) 28.7 73.1 (16.4) 6.3 36.4 (2.5) 0.3 17.7 (89.7) 69.1 97.2 12.1 5.6 24.0 20.0 13.9 27.9 23.3 14.5 35.2 13.5 7.3 23.5 65.4 50.5 77.7 

Bangladesh 2011 Poorest 9.0 6.7 11.9 40.4 35.2 45.9 17.8 14.7 21.3 5.1 3.6 7.2 70.3 65.3 74.9 9.5 5.4 16.2 42.4 33.3 52.0 21.9 14.9 31.0 6.3 3.0 12.9 77.2 68.1 84.3 13.1 7.2 22.8 35.9 25.5 47.8 22.4 13.2 35.3 3.2 0.7 14.2 73.4 62.0 82.4 

Bangladesh 2011 Poorer 8.1 6.1 10.9 42.0 37.4 46.8 22.8 19.2 26.9 4.4 3.0 6.6 76.4 72.0 80.3 17.2 11.0 26.0 40.1 29.4 51.8 25.2 16.1 37.0 10.3 5.0 19.7 87.5 78.5 93.0 8.4 3.9 17.4 36.0 25.1 48.4 36.2 25.3 48.7 3.0 0.4 19.8 80.6 68.4 88.8 

Bangladesh 2011 Middle 10.6 7.9 14.1 42.3 37.7 47.0 19.5 16.0 23.4 5.6 3.8 8.2 76.4 72.1 80.2 7.0 3.1 15.2 48.8 37.2 60.6 14.8 7.7 26.4 7.3 2.9 16.8 72.8 58.5 83.5 12.3 6.7 21.6 39.6 27.4 53.2 23.4 13.8 36.9 4.6 1.5 12.9 77.6 65.7 86.2 Bangladesh 2011 Wealthier 8.0 5.8 10.8 46.1 40.5 51.8 17.5 14.0 21.6 4.7 2.9 7.5 75.0 69.9 79.5 16.8 8.8 29.9 52.1 38.2 65.8 14.2 6.8 27.2 4.0 0.9 16.7 84.9 72.4 92.4 8.9 2.7 25.6 47.9 34.4 61.7 8.1 2.8 20.9 3.8 0.8 16.8 68.7 54.6 79.9 Bangladesh 2011 Wealthiest 8.3 5.8 11.8 49.5 44.0 54.9 17.3 13.3 22.2 3.0 1.6 5.3 77.6 73.1 81.6 11.5 5.3 23.2 58.4 43.2 72.1 23.3 12.0 40.3 1.5 0.4 6.2 93.0 81.9 97.5 10.4 4.0 24.7 55.9 41.1 69.8 12.7 6.6 23.1 2.2 0.3 15.0 79.1 65.3 88.4 

Congo DR 2007 Poorest 25.2 19.9 31.5 13.4 9.9 17.9 10.2 6.0 16.7 9.6 6.2 14.6 54.5 46.9 61.9 25.9 19.6 33.3 15.1 10.7 21.0 13.5 7.2 23.9 6.6 2.8 14.8 57.7 46.0 68.6 26.2 18.3 36.2 12.5 7.6 19.9 2.4 1.2 5.0 4.5 2.2 9.1 41.8 33.0 51.2 

Congo DR 2007 Poorer 30.0 21.5 40.2 17.1 12.2 23.5 8.8 5.4 13.9 5.2 3.2 8.3 58.1 51.4 64.5 31.8 22.0 43.4 12.5 8.3 18.3 10.3 6.0 17.0 4.3 2.2 8.1 56.5 47.5 65.0 26.1 17.8 36.5 4.6 2.0 10.2 3.4 1.1 9.6 3.0 1.2 7.6 35.9 26.6 46.5 

Congo DR 2007 Middle 26.5 20.4 33.6 17.2 10.3 27.4 10.2 6.8 15.1 5.6 3.1 10.0 57.3 49.1 65.1 28.1 19.1 39.2 8.9 5.4 14.3 10.9 6.4 17.9 8.1 4.0 15.6 51.1 40.5 61.6 26.8 16.0 41.2 5.8 2.2 14.4 5.5 1.7 16.2 2.3 0.8 6.4 39.5 29.1 50.9 

Congo DR 2007 Wealthier 20.9 14.5 29.2 22.0 16.7 28.3 20.6 15.3 27.1 5.0 2.6 9.3 66.2 59.8 72.2 19.5 11.5 31.1 24.5 17.2 33.7 22.7 14.8 33.2 7.3 4.0 13.0 69.7 60.7 77.4 27.9 20.8 36.3 3.1 1.4 6.7 2.7 0.9 8.1 4.0 1.5 10.0 33.7 27.4 40.5 

Congo DR 2007 Wealthiest 21.6 16.1 28.4 42.3 35.2 49.7 12.1 8.5 16.9 3.9 2.2 6.9 77.5 71.6 82.5 16.4 10.2 25.2 39.3 31.9 47.3 23.6 14.3 36.3 6.3 3.0 12.6 81.7 73.7 87.6 35.5 24.6 48.3 1.1 0.3 4.4 1.7 0.6 5.0 0.5 0.1 2.5 38.9 27.5 51.6 

Congo DR 2013-14 Poorest 28.5 24.7 32.7 9.1 6.1 13.4 11.0 8.7 13.8 6.8 5.0 9.3 54.3 49.4 59.1 31.9 22.2 43.3 5.5 2.4 11.8 7.3 3.7 14.0 7.3 3.9 13.3 50.4 39.6 61.3 28.4 23.3 34.2 8.9 6.6 12.0 11.9 8.8 15.8 8.2 6.1 10.9 55.9 50.1 61.7 Congo DR 2013-14 Poorer 35.1 29.6 41.0 9.6 7.1 12.7 12.7 9.7 16.5 3.7 2.6 5.3 59.8 54.8 64.6 33.5 25.4 42.7 6.6 3.1 13.3 10.0 4.9 19.4 9.2 3.8 20.7 58.6 47.7 68.6 34.0 28.2 40.4 10.7 6.8 16.4 7.6 4.3 12.9 8.2 5.4 12.3 58.4 50.9 65.6 

Congo DR 2013-14 Middle 28.5 23.6 34.0 9.0 6.5 12.2 15.9 11.1 22.3 5.4 3.5 8.4 57.5 52.2 62.7 38.0 28.0 49.1 11.3 6.1 20.0 11.6 7.3 17.8 4.2 1.8 9.3 64.4 51.9 75.1 35.9 30.6 41.6 6.0 3.9 8.9 16.3 11.9 21.8 5.3 3.6 7.9 62.6 56.8 68.1 

Congo DR 2013-14 Wealthier 28.5 23.8 33.8 10.8 7.8 14.7 17.1 12.6 22.8 3.5 2.2 5.5 59.1 51.8 66.0 32.6 22.9 44.1 6.7 3.7 11.8 12.9 6.6 23.8 5.3 2.4 11.5 56.9 43.9 69.0 29.7 23.5 36.7 10.1 6.9 14.5 19.6 14.0 26.7 4.5 2.7 7.6 61.8 53.6 69.4 

Congo DR 2013-14 Wealthiest 19.1 13.7 26.2 22.4 17.7 27.8 21.7 15.9 28.8 2.7 1.3 5.6 64.9 58.2 71.1 19.3 11.8 30.1 24.6 13.8 39.9 25.8 9.7 53.0 2.2 0.4 10.2 71.4 48.5 86.8 17.8 13.6 23.0 15.0 11.0 20.2 16.3 11.1 23.3 3.6 1.5 8.3 52.0 44.3 59.7 

Ethiopia 2005 Poorest 9.3 5.9 14.6 1.5 0.5 4.4 0.0 0.0 0.3 0.2 0.0 1.3 11.1 7.2 16.7 16.5 10.5 24.9 2.1 0.7 6.5 0.1 0.0 0.5 0.0 18.6 12.4 27.1 13.3 8.1 21.1 1.0 0.3 3.8 1.6 0.5 5.2 2.9 1.3 6.5 18.8 12.8 26.7 

Ethiopia 2005 Poorer 13.1 8.8 19.1 1.1 0.3 4.4 0.3 0.0 2.0 1.4 0.4 4.6 15.9 11.1 22.3 11.3 5.6 21.6 1.0 0.1 6.9 0.8 0.2 3.6 0.3 0.1 1.6 13.4 7.3 23.4 13.2 9.4 18.3 3.9 1.6 9.2 0.2 0.1 0.9 2.9 1.3 6.5 20.3 15.4 26.3 

Ethiopia 2005 Middle 11.3 7.5 16.7 5.4 2.2 12.6 1.8 0.8 4.3 0.4 0.1 2.6 18.5 13.4 24.9 16.2 10.7 23.7 4.5 1.3 14.7 2.0 0.7 5.4 0.5 0.1 3.5 22.7 16.0 31.1 22.8 16.5 30.7 4.3 1.9 9.6 2.2 0.9 5.4 1.6 0.7 4.0 30.6 24.0 38.1 

Ethiopia 2005 Wealthier 13.5 9.0 19.7 3.0 1.3 6.6 0.8 0.1 4.2 0.6 0.2 2.7 17.9 12.8 24.4 12.4 8.1 18.5 0.8 0.1 4.7 1.9 0.5 6.6 0.3 0.0 2.1 15.4 10.3 22.5 18.7 13.4 25.4 3.2 1.3 7.5 2.0 0.8 5.1 0.7 0.2 2.9 24.5 18.5 31.7 Ethiopia 2005 Wealthiest 29.6 21.3 39.5 8.6 5.2 14.0 2.4 1.0 5.6 0.8 0.2 3.9 40.2 30.6 50.6 23.6 15.4 34.5 11.1 5.3 21.9 1.7 0.4 6.6 1.5 0.3 6.0 36.3 25.0 49.4 31.8 25.1 39.4 5.8 3.0 10.8 0.9 0.3 3.4 2.1 0.6 7.6 40.2 32.8 48.2 

Ethiopia 2011 Poorest 12.6 8.8 17.7 4.0 2.2 7.3 0.5 0.2 1.2 0.9 0.3 2.5 17.4 12.9 22.9 12.5 7.4 20.1 3.1 1.3 7.2 0.4 0.1 2.0 0.8 0.1 5.0 16.7 10.5 25.7 17.8 12.9 24.0 5.6 2.7 11.2 0.5 0.2 1.4 1.2 0.3 5.1 24.0 18.7 30.1 Ethiopia 2011 Poorer 16.3 11.8 22.1 4.7 2.4 9.0 0.1 0.0 0.2 1.7 0.4 6.2 22.7 17.5 28.8 19.7 11.7 31.3 5.6 2.0 14.6 1.6 0.3 9.4 0.0 26.9 17.4 39.1 22.8 16.4 30.9 2.6 0.9 7.2 2.5 1.0 6.3 1.7 0.5 5.2 29.5 22.4 37.9 

Ethiopia 2011 Middle 15.9 11.6 21.4 7.3 4.2 12.3 0.4 0.1 1.8 1.7 0.6 5.2 25.2 19.6 31.8 15.9 10.0 24.3 6.2 2.7 13.8 0.0 2.2 0.5 9.0 24.3 15.9 35.2 26.0 19.7 33.6 10.1 5.7 17.4 0.7 0.2 2.9 4.3 2.0 9.0 39.9 31.9 48.5 

Ethiopia 2011 Wealthier 18.0 11.9 26.1 11.0 6.8 17.3 1.4 0.4 5.2 0.5 0.1 3.1 30.1 23.2 37.9 27.3 16.5 41.8 7.5 3.3 16.0 3.5 1.0 11.7 0.1 0.0 0.4 36.8 25.1 50.2 22.2 16.4 29.4 11.3 6.4 19.0 1.0 0.3 4.0 2.0 0.6 6.5 35.9 27.5 45.1 

Ethiopia 2011 Wealthiest 24.7 17.7 33.5 16.3 11.3 23.0 3.1 1.3 7.1 0.3 0.0 1.6 43.5 34.9 52.4 32.7 19.5 49.4 29.1 15.0 48.9 3.7 1.3 10.3 0.1 0.0 0.5 65.5 49.0 79.0 38.5 30.2 47.6 18.7 12.1 27.8 6.9 3.4 13.6 0.6 0.2 2.5 60.2 48.5 70.7 

Ghana 2003 Poorest 38.6 31.3 46.5 2.7 0.9 8.2 22.9 17.2 29.8 10.9 6.6 17.5 73.9 67.2 79.7 27.0 19.2 36.5 4.1 1.4 11.2 22.7 15.3 32.4 5.4 2.0 13.6 58.9 48.5 68.5 20.4 14.0 28.7 0.6 0.2 2.4 16.4 10.6 24.3 10.2 6.6 15.4 47.1 40.2 54.0 Ghana 2003 Poorer 28.0 20.8 36.4 5.5 2.2 12.7 32.2 24.2 41.4 3.3 1.4 7.4 68.9 59.4 77.0 31.4 21.2 43.7 8.6 2.9 22.9 27.7 16.4 42.9 2.3 0.5 9.3 70.0 55.9 81.2 19.8 12.5 29.8 4.0 1.6 9.8 22.1 15.0 31.3 3.7 1.6 8.3 49.3 38.1 60.6 

Ghana 2003 Middle 36.1 27.8 45.4 6.4 3.4 11.8 23.9 16.3 33.8 1.8 0.4 7.1 68.3 57.6 77.3 44.3 31.5 57.8 2.9 0.6 13.5 22.6 13.0 36.3 1.3 0.2 9.5 71.1 55.6 82.8 22.3 14.1 33.4 2.0 0.4 8.7 25.9 16.1 39.0 2.9 0.6 12.3 53.2 41.6 64.5 Ghana 2003 Wealthier 45.0 33.7 56.8 10.0 5.0 19.1 16.6 9.4 27.7 3.7 1.3 9.9 72.8 62.6 81.1 48.7 33.2 64.4 3.8 0.9 15.1 29.5 14.8 50.2 4.1 0.9 16.1 80.3 66.1 89.5 23.9 15.7 34.7 3.4 1.0 10.3 20.9 13.1 31.7 0.9 0.1 6.6 48.2 38.6 57.9 Ghana 2003 Wealthiest 56.4 42.7 69.3 13.7 7.4 23.9 12.8 7.0 22.4 0.4 0.0 2.7 83.3 74.1 89.7 (55.6) 35.8 73.8 (13.3) 5.5 28.8 (14.7) 5.1 35.7 (3.6) 0.4 24.4 (87.2) 70.8 95.0 26.9 13.8 46.0 14.6 6.6 29.4 15.4 8.1 27.4 1.4 0.2 10.7 58.4 42.3 72.9 

Ghana 2008 Poorest 37.4 27.7 48.3 3.2 1.2 8.0 8.7 4.3 16.7 12.7 7.6 20.5 62.0 51.2 71.8 34.4 21.3 50.4 2.1 0.3 14.7 8.0 1.7 30.5 10.6 3.7 26.7 55.1 36.2 72.5 40.2 31.2 49.9 0.9 0.2 3.8 9.4 4.7 18.1 9.6 5.9 15.3 60.2 50.6 69.1 

Ghana 2008 Poorer 38.4 29.1 48.6 6.8 2.9 15.2 13.0 7.2 22.2 2.6 0.8 8.0 60.8 49.3 71.2 (36.9) 21.7 55.2 (12.4) 2.3 45.6 (17.9) 7.7 36.4 (0.0) (66.2) 45.4 82.2 32.5 24.5 41.7 6.5 2.8 14.3 19.9 10.7 34.0 9.0 4.8 16.4 66.3 55.4 75.7 

Ghana 2008 Middle 41.9 32.0 52.5 4.3 1.5 11.7 19.6 12.5 29.2 7.5 3.9 13.8 72.7 61.8 81.5 * * * * * * * * * * * * * * * 36.6 27.0 47.4 4.9 1.9 11.9 22.9 14.6 34.1 3.3 1.1 9.7 65.2 53.1 75.7 Ghana 2008 Wealthier 41.5 30.0 54.1 19.6 12.1 30.3 16.7 10.2 26.1 3.2 0.9 10.3 80.6 69.0 88.6 (55.7) 32.7 76.5 (7.5) 2.2 22.5 (0.0) (4.1) 0.4 30.1 (67.4) 43.8 84.5 29.7 19.6 42.3 13.2 6.9 23.9 14.9 8.3 25.4 3.6 0.9 12.8 61.4 48.5 72.9 

Ghana 2008 Wealthiest 52.0 35.6 68.1 27.8 16.2 43.5 8.3 2.9 21.4 0.0 88.2 73.8 95.2 * * * * * * * * * * * * * * * (34.9) 21.8 50.8 (13.1) 5.1 29.8 (19.7) 9.0 37.8 (0.0) (67.7) 49.6 81.7 

Haiti 2005-6 Poorest 9.9 6.8 14.2 18.6 13.3 25.3 0.2 0.0 0.9 5.9 3.6 9.6 34.1 28.1 40.6 4.9 1.9 11.9 16.0 8.4 28.3 0.5 0.1 2.2 6.2 3.3 11.5 26.3 18.0 36.6 6.3 3.8 10.2 19.3 11.9 29.7 0.6 0.1 2.2 0.0 5.4 12.3 33.7 25.7 42.7 

Haiti 2005-6 Poorer 9.5 6.2 14.4 23.2 17.4 30.3 1.5 0.5 4.7 8.4 5.4 13.0 40.9 35.2 46.9 9.4 4.9 17.5 19.5 9.8 35.2 0.0 13.3 7.6 22.2 36.1 26.3 47.3 9.8 6.1 15.4 20.1 13.9 28.2 0.3 0.0 2.1 8.2 4.8 13.6 36.8 30.0 44.1 

Haiti 2005-6 Middle 18.3 12.5 26.0 25.6 17.8 35.4 1.6 0.5 4.7 5.4 2.6 10.7 47.0 38.9 55.2 11.9 6.8 20.1 34.5 21.3 50.6 0.9 0.1 6.8 3.4 1.3 9.0 49.8 37.7 62.0 17.8 11.4 26.6 17.2 11.7 24.8 4.2 1.6 10.7 6.5 3.5 11.9 44.2 35.4 53.4 Haiti 2005-6 Wealthier 23.5 16.4 32.3 18.5 13.3 25.1 2.1 0.9 5.0 11.6 7.7 17.1 50.4 41.3 59.6 20.0 10.1 35.9 14.7 7.3 27.4 0.4 0.1 3.1 10.0 3.5 25.1 43.2 30.6 56.7 18.2 11.6 27.3 12.2 7.6 19.1 6.1 3.3 10.9 8.9 5.4 14.2 44.4 35.6 53.6 Haiti 2005-6 Wealthiest 9.8 5.7 16.2 32.6 23.2 43.6 6.6 2.5 16.7 9.0 3.3 22.4 54.3 45.4 62.9 (9.4) 3.7 22.0 (26.9) 14.2 45.0 (0.0) (9.5) 1.5 41.2 (43.2) 24.3 64.3 15.8 7.3 30.7 20.6 12.2 32.7 5.1 1.8 13.3 4.3 1.6 11.5 44.5 31.5 58.3 

Haiti 2012 Poorest 12.8 9.7 16.7 16.5 12.5 21.3 0.0 8.8 6.0 12.6 36.0 30.5 41.8 13.4 9.1 19.4 11.6 7.5 17.6 0.0 8.7 5.2 14.1 32.8 25.5 41.0 15.4 10.8 21.6 14.9 11.1 19.6 0.0 7.3 4.8 11.1 36.7 30.1 43.8 Haiti 2012 Poorer 8.6 5.0 14.4 25.7 20.5 31.8 0.0 7.1 4.7 10.8 39.9 34.0 46.0 12.5 6.5 22.8 21.0 14.1 30.2 0.0 6.6 3.8 11.3 38.1 28.4 48.8 11.2 7.6 16.4 17.5 13.2 23.0 0.0 7.7 4.0 14.3 34.9 28.4 42.1 

Haiti 2012 Middle 16.1 11.2 22.4 30.0 23.7 37.1 1.1 0.3 3.9 8.0 5.0 12.5 54.1 47.4 60.6 11.2 7.4 16.6 27.8 21.5 35.1 1.2 0.2 5.3 9.2 5.8 14.3 48.1 40.8 55.5 14.5 10.2 20.1 21.2 16.6 26.7 1.3 0.4 4.5 9.8 6.6 14.2 45.5 39.8 51.3 Haiti 2012 Wealthier 18.3 13.0 25.2 32.4 25.7 40.0 0.8 0.2 3.1 8.4 5.1 13.5 55.6 48.6 62.5 16.2 9.7 25.8 37.3 27.9 47.8 2.2 0.4 10.1 3.5 1.1 10.3 56.0 46.2 65.4 19.3 13.7 26.6 24.1 17.3 32.6 0.0 13.8 8.7 21.2 51.8 42.4 61.0 Haiti 2012 Wealthiest 18.2 11.9 26.7 36.2 28.2 45.0 0.8 0.2 3.1 3.6 1.6 7.9 57.8 50.3 65.0 12.2 6.4 22.0 39.0 30.2 48.6 3.0 0.7 12.1 12.0 6.2 22.1 60.4 50.2 69.8 21.1 14.1 30.3 29.6 21.1 39.8 0.6 0.1 4.1 5.9 2.5 13.4 55.3 46.5 63.9 

India 2005-6 Poorest 12.4 10.1 15.0 49.0 45.4 52.5 5.5 3.6 8.2 6.7 5.0 8.8 71.2 68.2 74.1 11.1 7.8 15.6 49.8 45.0 54.7 3.6 2.1 6.2 6.9 4.7 10.0 66.8 62.1 71.2 15.7 12.8 19.0 35.5 31.3 39.9 5.9 4.2 8.2 5.7 4.1 7.8 60.0 55.7 64.3 India 2005-6 Poorer 13.6 11.5 16.0 51.6 48.0 55.2 6.0 4.6 7.9 7.7 5.7 10.3 76.2 73.3 78.9 10.4 7.8 13.7 55.9 51.0 60.7 4.9 3.1 7.4 7.2 4.7 10.8 75.1 70.6 79.2 13.2 10.6 16.2 41.8 37.6 46.1 5.7 4.0 8.1 7.8 5.3 11.3 66.9 62.8 70.7 

India 2005-6 Middle 16.7 14.4 19.3 54.7 51.3 58.1 5.3 3.9 7.2 5.2 3.8 7.0 79.9 76.9 82.6 18.3 14.6 22.7 52.9 47.4 58.3 6.0 3.8 9.2 3.8 2.1 6.7 77.0 72.2 81.2 15.6 13.1 18.6 46.0 41.5 50.5 7.0 5.0 9.6 5.2 3.6 7.5 70.2 65.6 74.4 India 2005-6 Wealthier 12.6 10.6 15.0 63.3 59.7 66.7 5.2 3.6 7.6 3.3 2.3 4.6 81.2 78.2 84.0 11.0 8.3 14.6 65.0 58.4 71.0 6.4 3.5 11.4 3.2 1.7 5.7 82.9 76.8 87.6 13.6 11.1 16.6 51.0 46.8 55.3 6.4 4.5 9.1 6.0 4.1 8.7 73.8 69.4 77.8 India 2005-6 Wealthiest 9.8 7.9 12.0 70.9 67.5 74.1 3.0 2.0 4.7 4.6 3.3 6.5 86.9 84.1 89.2 9.6 6.9 13.3 68.2 61.8 74.0 2.6 1.1 6.2 4.1 2.2 7.4 83.2 77.1 87.8 10.6 8.1 13.7 63.6 58.6 68.3 4.7 3.0 7.3 4.4 2.8 6.9 80.6 76.3 84.3 

Indonesia 2007 Poorest 25.6 22.2 29.3 29.2 25.8 32.9 5.0 3.9 6.4 26.3 23.2 29.6 82.5 79.0 85.6 25.9 21.3 30.9 32.0 26.4 38.1 5.9 4.1 8.5 23.9 19.4 29.2 83.4 76.6 88.5 19.8 15.9 24.4 19.8 16.2 24.0 3.0 2.1 4.4 23.2 18.5 28.7 63.8 58.7 68.7 

Indonesia 2007 Poorer 29.3 25.0 34.0 40.9 36.5 45.5 9.4 6.9 12.5 19.0 15.6 23.0 92.5 90.1 94.4 27.6 21.6 34.5 38.6 31.7 46.0 12.3 7.7 19.0 22.3 15.8 30.6 94.6 92.4 96.2 21.4 16.2 27.6 27.3 22.3 33.0 6.7 4.1 10.7 24.1 19.0 30.1 77.1 70.2 82.8 

Indonesia 2007 Middle 27.2 23.3 31.4 48.0 42.7 53.4 7.8 5.6 10.8 15.3 12.1 19.3 93.9 91.4 95.7 25.1 19.2 32.2 54.1 44.9 63.1 7.0 3.8 12.3 16.2 10.6 23.9 95.5 90.2 98.0 22.6 16.8 29.6 40.1 32.6 48.0 3.2 1.4 6.9 16.3 11.4 22.7 78.3 70.8 84.3 

Indonesia 2007 Wealthier 27.6 23.4 32.3 51.5 45.8 57.2 10.4 7.4 14.5 8.4 6.1 11.5 94.1 91.4 96.0 23.7 17.5 31.3 54.4 44.8 63.7 16.6 10.3 25.6 7.2 4.0 12.5 96.3 90.7 98.6 22.3 15.9 30.4 39.7 31.9 48.1 9.5 5.6 15.5 9.8 5.6 16.4 75.9 67.8 82.4 

Indonesia 2007 Wealthiest 16.7 12.7 21.7 62.1 55.5 68.4 12.8 9.2 17.6 6.3 3.8 10.1 94.0 90.3 96.4 19.6 12.2 29.9 59.8 50.1 68.7 14.3 7.9 24.5 2.4 1.2 4.6 94.4 88.4 97.4 10.6 6.1 17.8 54.5 43.2 65.3 10.2 5.5 18.0 11.7 7.4 18.0 83.6 74.2 90.1 

Indonesia 2012 Poorest 33.7 30.5 37.0 35.0 31.5 38.6 5.4 4.0 7.2 20.1 17.3 23.3 85.8 83.2 88.1 32.0 25.3 39.4 32.6 26.3 39.6 6.0 3.3 10.8 20.4 15.1 26.9 81.0 73.5 86.8 34.2 29.3 39.5 31.6 26.8 36.7 3.7 2.5 5.5 15.0 12.2 18.2 78.6 74.7 82.1 Indonesia 2012 Poorer 29.2 25.4 33.2 49.1 44.9 53.2 9.6 7.2 12.7 10.9 8.5 13.9 90.7 87.6 93.0 31.0 22.9 40.4 61.7 54.6 68.4 5.1 3.1 8.5 8.4 4.9 14.0 94.2 86.8 97.6 25.7 19.9 32.5 43.4 37.8 49.1 4.8 2.9 7.8 11.2 8.2 15.3 79.4 74.2 83.8 

(Continued...)

90 
91 

Table A8. − Continued

Fever Symptoms of ARI Diarrhea 

Public Private Pharmacy Only Other Any Care Public Private Pharmacy Only Other Any Care Public Private Pharmacy Only Other Any Care 

Country/Survey 

Household 

Wealth 

Quintile % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB 

Indonesia 2012 Middle 22.0 18.6 25.7 55.7 51.1 60.2 10.9 8.3 14.2 10.4 7.8 13.8 90.0 87.1 92.4 31.4 21.2 43.8 58.3 45.6 70.0 9.2 4.9 16.5 8.1 3.5 17.5 94.4 88.2 97.4 23.9 18.6 30.2 48.8 42.1 55.5 8.7 5.9 12.7 12.2 8.3 17.8 83.4 77.7 87.8 

Indonesia 2012 Wealthier 23.8 20.2 27.8 57.6 53.0 62.1 11.4 8.7 14.7 5.8 4.1 8.2 90.0 86.6 92.6 22.2 14.2 32.9 68.2 57.8 77.1 6.7 2.6 16.2 7.6 3.3 16.3 96.4 92.6 98.3 13.3 9.2 18.9 53.2 45.9 60.4 8.4 5.1 13.4 7.3 4.4 11.9 78.8 73.0 83.6 

Indonesia 2012 Wealthiest 14.3 11.3 18.1 69.1 63.6 74.1 11.1 7.8 15.4 3.6 1.9 6.7 92.3 89.1 94.5 10.6 6.0 18.2 63.6 50.2 75.2 14.3 6.7 27.7 8.5 2.4 26.0 92.3 78.0 97.6 14.8 9.4 22.5 50.1 41.3 58.9 15.2 9.0 24.3 4.3 1.7 10.1 80.8 73.2 86.7 

Kenya 2003 Poorest 27.3 21.4 34.0 15.0 10.8 20.5 14.8 10.9 19.9 9.9 7.3 13.4 64.9 58.7 70.5 31.7 24.6 39.9 13.2 8.1 20.7 11.8 7.4 18.4 9.2 5.4 15.0 64.7 56.1 72.4 22.2 16.0 29.8 7.6 3.5 15.7 3.9 1.9 7.7 8.8 5.0 15.0 40.8 33.8 48.2 Kenya 2003 Poorer 27.9 22.7 33.7 15.5 11.9 19.9 13.9 10.2 18.5 11.9 8.5 16.3 66.1 60.3 71.4 27.0 20.3 34.9 15.1 10.1 22.1 16.6 11.4 23.5 10.7 6.6 17.0 67.4 60.1 73.9 19.0 13.1 26.8 6.0 3.0 11.5 1.0 0.2 5.5 5.1 2.3 10.7 29.8 21.9 39.1 

Kenya 2003 Middle 31.0 25.0 37.7 15.9 12.0 20.9 13.0 9.6 17.4 10.0 6.9 14.2 68.5 61.9 74.5 31.0 22.3 41.3 16.3 10.9 23.5 12.6 8.1 19.1 7.1 3.4 14.0 65.2 55.8 73.6 19.8 12.7 29.4 5.5 2.8 10.5 3.8 1.8 7.7 5.4 2.7 10.5 34.4 26.1 43.8 Kenya 2003 Wealthier 34.1 26.9 42.1 16.0 11.6 21.6 12.1 8.9 16.3 9.5 6.5 13.8 70.6 64.1 76.3 38.4 28.6 49.2 19.1 12.5 28.0 8.8 5.1 15.0 8.7 5.1 14.3 74.9 65.8 82.3 24.0 16.3 33.8 8.7 4.3 17.0 3.2 1.3 7.6 4.0 1.6 9.6 39.9 29.9 50.8 Kenya 2003 Wealthiest 25.7 21.5 30.5 29.6 23.6 36.4 13.2 9.4 18.3 6.5 3.9 10.6 73.4 68.4 77.9 33.6 26.6 41.3 31.9 25.0 39.8 14.8 8.6 24.2 4.7 2.3 9.4 81.1 72.6 87.3 16.0 10.8 23.1 21.8 14.2 31.9 3.6 1.5 8.4 4.1 1.6 10.0 44.0 34.6 53.9 

Kenya 2008-9 Poorest 46.1 39.3 53.0 6.2 3.2 11.6 3.6 2.0 6.4 10.5 6.1 17.5 64.1 57.4 70.2 50.0 37.8 62.2 6.5 2.4 16.5 5.8 2.8 11.8 9.2 3.6 21.8 70.8 60.0 79.6 42.7 35.2 50.5 6.1 3.1 11.4 4.2 2.3 7.7 11.2 7.2 17.1 61.8 54.8 68.3 

Kenya 2008-9 Poorer 34.8 25.8 45.0 8.4 4.8 14.1 8.9 3.2 22.6 9.0 4.8 16.4 58.5 50.2 66.4 39.9 25.9 55.7 13.7 7.2 24.5 13.7 3.5 41.2 9.8 4.1 21.4 70.8 53.8 83.4 32.2 23.0 43.0 6.0 2.4 13.9 4.7 2.1 10.2 13.1 6.8 24.0 53.8 42.8 64.4 

Kenya 2008-9 Middle 39.8 32.6 47.4 11.0 6.4 18.3 12.3 7.3 20.0 4.0 2.0 8.2 66.4 60.0 72.3 53.5 36.4 69.8 9.0 3.2 22.8 6.3 1.2 26.7 1.2 0.2 8.3 68.9 50.7 82.7 44.9 34.0 56.4 13.4 7.2 23.6 4.4 2.0 9.5 5.5 2.5 11.7 65.4 54.0 75.3 Kenya 2008-9 Wealthier 32.4 24.3 41.6 13.8 8.6 21.3 9.3 4.7 17.6 5.9 2.9 11.6 60.9 51.2 69.9 38.2 23.8 55.1 13.8 6.4 27.5 19.9 6.7 46.0 1.1 0.1 8.0 73.0 56.2 85.1 44.0 33.8 54.8 9.9 4.3 21.1 6.9 2.9 15.8 2.3 0.8 6.2 62.7 51.7 72.6 Kenya 2008-9 Wealthiest 39.1 28.7 50.6 13.4 7.8 22.1 6.3 3.2 11.8 2.3 0.9 5.7 60.1 47.7 71.3 34.3 19.6 52.9 30.5 14.3 53.7 4.0 1.1 13.1 1.0 0.2 4.4 67.1 36.9 87.7 23.6 14.6 35.8 22.9 9.4 45.8 4.2 1.4 11.6 4.8 0.7 25.8 55.3 39.5 70.2 

Liberia 2007 Poorest 20.7 14.5 28.8 21.5 13.5 32.4 4.1 2.1 8.0 20.6 14.3 28.8 63.2 55.0 70.7 40.3 28.4 53.5 18.2 8.7 34.1 8.5 3.1 21.3 12.4 7.0 21.1 79.2 66.2 88.1 23.9 16.0 34.1 13.5 8.8 20.3 11.5 4.8 25.1 26.7 17.9 37.7 69.1 60.6 76.5 Liberia 2007 Poorer 32.1 25.6 39.4 18.5 12.8 26.0 8.9 6.0 13.0 24.4 18.2 32.0 73.8 66.8 79.8 31.3 21.0 43.9 15.7 8.8 26.6 7.5 3.1 17.1 13.8 7.0 25.5 65.7 53.6 76.1 27.3 20.0 36.1 21.7 13.6 32.7 11.7 6.8 19.3 33.0 22.8 45.0 83.9 76.7 89.2 

Liberia 2007 Middle 43.8 36.4 51.6 22.7 15.9 31.3 6.3 3.6 10.8 16.8 11.6 23.6 82.7 75.5 88.1 58.0 45.7 69.4 15.5 8.4 26.9 7.7 2.1 24.8 8.3 3.7 17.5 86.3 72.1 93.9 26.2 18.7 35.3 24.1 15.7 35.1 9.9 5.5 17.2 30.1 22.7 38.7 79.8 71.0 86.5 Liberia 2007 Wealthier 42.1 33.2 51.4 22.7 17.0 29.7 10.5 7.0 15.4 7.9 5.1 12.2 80.6 75.1 85.1 48.2 33.8 62.9 14.2 7.4 25.6 20.4 11.4 33.8 4.7 1.2 16.5 83.4 70.9 91.2 36.6 28.3 45.8 20.7 15.6 26.9 10.8 6.9 16.5 10.3 6.2 16.8 76.4 68.3 82.9 

Liberia 2007 Wealthiest 42.7 32.9 53.1 24.4 17.3 33.2 12.9 8.7 18.7 8.7 4.6 15.7 83.2 74.9 89.2 (61.9) 39.5 80.1 (29.1) 13.9 51.1 (1.6) 0.2 13.3 (3.2) 0.7 14.3 (93.9) 78.4 98.5 38.4 26.3 52.0 22.5 14.9 32.5 14.8 9.0 23.1 14.5 8.0 24.6 84.1 74.7 90.5 

Liberia 2013 Poorest 43.2 37.6 48.9 7.5 5.0 11.2 7.4 4.6 11.7 18.5 14.5 23.4 73.7 69.7 77.3 42.5 33.7 51.7 7.1 3.2 15.0 3.6 1.3 9.3 13.1 8.2 20.2 61.2 50.9 70.5 36.9 31.1 43.2 6.5 3.4 12.1 5.1 3.1 8.4 24.8 19.8 30.6 71.7 66.7 76.1 Liberia 2013 Poorer 50.3 42.8 57.7 7.5 5.1 10.9 7.2 4.8 10.5 14.8 9.2 23.0 78.1 73.1 82.4 49.8 38.1 61.5 6.7 3.1 13.8 7.5 3.2 16.5 19.4 10.3 33.5 82.6 73.8 88.9 41.1 33.8 48.9 5.9 3.4 10.2 7.4 4.4 12.2 19.6 13.9 27.0 73.3 66.6 79.1 

Liberia 2013 Middle 48.8 41.9 55.8 12.3 8.3 17.8 7.7 4.9 11.7 12.4 8.0 18.7 80.3 74.3 85.2 45.7 28.6 63.9 14.0 5.1 32.9 1.9 0.6 6.5 8.9 3.8 19.5 70.6 51.4 84.5 46.0 38.6 53.7 9.7 5.4 16.9 12.2 8.0 18.1 13.4 8.8 19.8 78.8 73.1 83.6 Liberia 2013 Wealthier 30.6 21.2 41.9 30.6 22.6 40.0 21.6 14.1 31.7 3.9 1.9 8.1 86.7 81.4 90.7 (19.3) 8.0 39.5 (25.0) 10.3 49.1 (23.2) 6.5 56.8 (5.4) 1.7 15.8 (72.9) 48.9 88.3 23.1 13.8 36.0 21.5 14.9 29.9 18.7 12.1 27.7 8.6 4.7 15.4 71.0 60.7 79.5 

Liberia 2013 Wealthiest 22.3 11.2 39.4 39.9 25.7 56.1 17.9 10.2 29.7 0.1 0.0 0.6 80.2 65.3 89.7 (7.9) 2.2 24.6 (36.1) 18.8 57.8 (42.2) 20.0 68.1 (0.0) (86.2) 66.6 95.2 24.0 11.6 43.2 25.3 10.8 48.4 20.3 11.3 33.6 3.7 0.9 14.5 73.2 52.7 87.0 

Madagascar 2003-4 Poorest 24.4 17.2 33.5 7.7 4.0 14.3 0.0 7.6 3.5 15.9 39.6 30.5 49.5 42.4 31.2 54.4 8.1 4.1 15.3 0.4 0.1 3.4 5.4 1.3 19.9 55.9 43.8 67.4 24.2 16.3 34.3 6.6 2.6 15.6 0.0 8.6 4.0 17.4 39.1 24.8 55.5 Madagascar 2003-4 Poorer 29.0 20.0 39.9 3.6 1.5 8.5 0.3 0.0 2.2 6.8 3.1 14.3 38.2 29.0 48.3 32.1 19.2 48.4 11.5 3.8 29.9 0.0 7.0 2.6 17.5 50.5 35.6 65.4 22.0 12.7 35.4 7.4 2.1 23.1 2.9 0.4 19.2 2.1 0.4 10.5 34.4 20.7 51.3 

Madagascar 2003-4 Middle 24.3 15.5 35.9 6.8 3.5 12.9 2.5 0.5 12.4 4.2 1.9 9.0 37.8 29.2 47.2 24.5 13.2 40.9 11.2 4.7 24.1 3.5 0.5 20.8 6.1 1.6 19.9 45.2 30.6 60.7 19.5 8.9 37.4 9.9 3.3 26.3 3.9 0.9 15.3 4.7 1.3 16.1 38.0 22.3 56.8 Madagascar 2003-4 Wealthier 29.5 20.1 41.2 20.2 11.4 33.4 2.4 0.3 14.7 1.9 0.4 8.6 53.8 39.3 67.7 31.6 18.7 48.0 16.7 4.5 45.6 6.3 0.8 35.3 4.2 0.8 19.2 58.8 43.2 72.8 20.6 10.5 36.3 16.8 6.4 37.2 3.6 0.6 18.0 0.0 40.9 23.7 60.7 

Madagascar 2003-4 Wealthiest 27.0 19.0 36.9 33.4 25.7 42.1 0.3 0.0 2.1 0.8 0.3 2.2 60.5 50.0 70.0 39.2 23.8 57.2 27.7 16.0 43.6 0.7 0.1 5.2 0.3 0.0 1.3 66.7 53.5 77.7 10.4 6.1 17.3 27.9 15.4 45.3 0.0 0.5 0.1 3.0 38.4 23.5 55.9 

Madagascar 2008-9 Poorest 28.1 20.8 36.8 6.4 3.8 10.8 0.6 0.2 2.0 7.4 4.6 11.7 42.0 34.9 49.5 29.5 18.7 43.2 4.9 1.9 11.8 1.9 0.4 7.6 10.2 4.6 20.9 45.4 33.1 58.3 23.7 18.2 30.3 3.2 1.6 6.3 0.5 0.1 1.9 8.0 4.8 13.0 35.3 28.4 42.8 Madagascar 2008-9 Poorer 28.7 22.1 36.3 4.5 2.7 7.7 0.6 0.1 2.3 7.1 3.9 12.4 40.6 33.0 48.6 24.0 14.9 36.1 5.9 2.1 15.3 0.0 8.3 3.2 19.7 37.4 25.4 51.3 27.8 21.7 34.9 5.9 3.2 10.7 0.2 0.0 1.5 6.2 3.6 10.5 39.5 32.0 47.5 

Madagascar 2008-9 Middle 29.6 21.7 38.9 8.7 4.9 15.0 0.9 0.1 5.9 7.7 4.3 13.5 43.5 35.0 52.4 30.3 19.4 44.1 13.9 5.8 29.9 2.5 0.6 10.5 9.5 3.2 25.0 48.5 32.3 64.9 21.5 15.2 29.5 5.3 2.6 10.7 0.6 0.2 2.6 3.2 1.5 6.8 29.9 21.8 39.4 

Madagascar 2008-9 Wealthier 36.0 28.0 44.8 12.5 8.1 18.7 0.4 0.1 2.9 1.2 0.4 3.3 49.9 41.9 57.8 33.6 23.0 46.3 19.1 10.4 32.6 0.0 2.1 0.3 15.5 53.7 41.0 65.9 28.3 20.9 36.9 10.5 6.3 17.1 0.1 0.0 0.8 4.1 1.3 11.9 42.5 33.7 51.8 Madagascar 2008-9 Wealthiest 24.6 17.2 33.8 41.8 33.5 50.6 0.4 0.1 3.0 0.0 65.5 55.8 74.1 31.9 19.4 47.6 39.1 23.9 56.7 0.0 69.3 54.4 81.0 29.9 21.2 40.4 25.4 19.0 33.1 0.6 0.1 3.0 5.3 1.5 17.1 59.3 49.1 68.7 

Mali 2006 Poorest 20.7 15.8 26.6 2.7 1.4 5.1 0.7 0.2 2.3 28.8 23.2 35.2 52.6 46.1 59.0 25.8 17.7 36.1 2.0 0.5 7.9 0.7 0.1 5.1 27.7 17.6 40.8 56.2 45.3 66.5 12.7 9.2 17.3 1.3 0.4 3.6 0.0 16.5 11.7 22.9 30.1 24.4 36.4 

Mali 2006 Poorer 28.3 23.0 34.4 2.2 1.1 4.4 0.5 0.1 1.9 23.9 18.9 29.8 54.5 47.7 61.2 31.6 22.5 42.4 0.0 0.5 0.1 3.3 23.1 16.0 32.3 55.2 44.5 65.5 11.9 8.3 16.7 0.9 0.3 3.1 0.0 14.1 10.3 19.0 26.8 21.5 32.9 

Mali 2006 Middle 25.0 19.7 31.1 2.9 1.5 5.6 0.7 0.2 1.7 25.4 20.2 31.4 52.4 45.7 59.1 27.0 18.9 37.1 2.8 1.0 7.8 0.3 0.0 2.2 19.9 13.0 29.4 50.1 40.8 59.3 15.9 11.3 21.8 0.5 0.1 2.1 0.0 11.5 7.8 16.6 27.8 22.5 33.9 Mali 2006 Wealthier 31.5 26.5 37.0 3.6 1.7 7.4 5.9 1.9 16.6 19.1 14.3 25.1 58.7 47.7 69.0 41.2 29.3 54.3 2.1 0.6 7.4 1.9 0.5 7.0 14.0 8.8 21.7 56.9 44.8 68.2 19.0 12.4 28.1 0.7 0.2 2.8 0.0 0.0 0.2 13.3 8.8 19.5 32.7 26.3 39.8 Mali 2006 Wealthiest 44.8 38.1 51.8 8.1 5.3 12.3 10.1 6.3 15.9 11.5 7.8 16.7 73.2 66.2 79.2 52.0 44.0 59.8 8.9 4.2 18.1 11.8 6.4 20.7 6.6 2.8 14.5 78.7 68.2 86.4 28.2 19.1 39.7 8.3 4.8 13.9 0.0 8.3 4.6 14.4 42.3 31.9 53.4 

Mali 2012-13 Poorest 17.2 10.4 27.2 0.0 1.1 0.2 5.1 24.8 18.6 32.1 41.7 31.7 52.4 (18.4) 8.3 36.0 (0.0) (0.0) (39.9) 23.5 58.9 (54.5) 34.7 73.0 14.0 8.1 23.2 3.2 1.2 8.0 1.8 0.4 7.5 25.7 17.7 35.6 42.5 33.1 52.4 Mali 2012-13 Poorer 25.7 18.3 34.8 2.4 0.7 7.9 0.3 0.0 2.2 17.8 11.7 26.1 45.7 36.2 55.6 (20.3) 6.8 47.2 (0.0) (4.7) 0.7 26.3 (28.5) 14.1 49.3 (53.5) 33.8 72.2 28.2 21.2 36.5 1.4 0.4 4.9 2.2 0.6 8.2 28.6 21.3 37.2 58.0 47.6 67.7 

Mali 2012-13 Middle 17.4 11.4 25.7 2.9 1.1 7.5 3.3 1.1 9.7 17.0 10.4 26.4 38.8 29.8 48.5 (23.3) 9.3 47.3 (0.0) (6.9) 0.8 40.2 (14.8) 5.1 36.1 (44.0) 23.5 66.7 20.2 14.6 27.3 2.3 0.8 6.1 3.3 0.7 14.6 23.7 18.2 30.3 48.3 39.9 56.8 

Mali 2012-13 Wealthier 27.2 19.5 36.5 2.2 0.8 6.2 4.4 1.8 10.2 20.0 13.3 28.9 52.4 41.8 62.8 (35.1) 14.4 63.6 (5.9) 1.2 24.5 (4.0) 0.5 26.6 (25.7) 10.1 51.5 (70.7) 40.4 89.5 33.4 26.3 41.3 2.2 0.8 5.9 4.7 2.2 9.6 19.8 13.4 28.3 58.7 50.0 66.8 Mali 2012-13 Wealthiest 47.9 39.7 56.3 6.9 3.5 13.0 12.2 7.4 19.7 7.5 4.1 13.1 74.1 65.4 81.2 (36.1) 18.0 59.1 (4.3) 0.9 18.7 (12.7) 3.1 39.7 (16.0) 4.6 43.0 (69.0) 50.7 82.8 33.1 25.8 41.3 3.5 1.8 6.8 5.0 2.6 9.2 20.6 15.1 27.4 60.3 51.7 68.4 

Malawi 2004 Poorest 31.0 26.8 35.5 5.4 3.5 8.3 38.5 34.0 43.2 1.3 0.7 2.3 75.0 71.0 78.6 23.2 18.2 29.1 6.8 4.4 10.4 0.0 37.4 31.0 44.4 66.9 59.4 73.6 28.8 24.1 34.0 5.7 3.7 8.8 0.0 23.1 19.1 27.7 56.6 50.6 62.3 Malawi 2004 Poorer 34.6 30.0 39.6 6.7 4.9 9.1 38.2 33.9 42.7 0.8 0.4 1.9 79.6 75.6 83.0 27.0 21.9 32.9 7.2 4.7 10.9 0.0 32.0 26.9 37.6 65.5 59.5 71.0 31.6 25.9 37.8 9.1 6.2 13.0 0.0 21.8 18.0 26.2 61.4 56.4 66.1 

Malawi 2004 Middle 33.8 29.9 38.1 5.7 4.1 7.9 37.7 33.6 41.9 2.2 1.2 4.0 77.2 73.5 80.6 29.6 24.5 35.3 7.6 5.2 11.0 0.0 36.5 31.0 42.4 72.7 67.4 77.4 26.8 22.2 32.0 5.0 2.9 8.5 0.0 25.0 20.8 29.7 56.2 50.4 61.8 Malawi 2004 Wealthier 38.8 34.2 43.6 6.4 4.5 9.0 31.6 27.7 35.8 1.5 0.8 2.9 77.2 73.0 80.9 33.7 28.1 39.7 7.8 4.9 12.2 0.2 0.0 1.2 28.1 22.8 34.1 69.3 62.6 75.2 28.3 23.1 34.2 7.6 4.9 11.5 0.0 23.3 18.8 28.6 59.0 53.3 64.5 Malawi 2004 Wealthiest 41.6 35.5 47.9 8.8 5.5 13.9 27.4 21.6 34.2 0.4 0.1 1.4 77.9 72.6 82.4 34.9 26.5 44.4 11.8 7.3 18.7 0.0 23.7 17.1 32.0 69.3 57.2 79.2 28.5 22.0 35.9 12.5 8.1 18.7 0.2 0.0 1.4 11.6 7.8 16.9 52.4 44.0 60.7 

Malawi 2010 Poorest 46.8 43.0 50.7 14.5 11.8 17.7 0.0 8.9 7.0 11.2 68.6 65.2 71.8 43.3 35.9 51.0 19.6 13.6 27.5 0.0 9.0 5.3 14.7 70.1 62.1 77.0 53.1 48.4 57.9 11.8 8.9 15.5 0.0 9.1 6.6 12.4 71.4 67.0 75.5 

Malawi 2010 Poorer 51.0 47.0 54.9 10.6 8.4 13.4 0.0 10.6 8.7 12.9 70.8 67.4 74.0 54.7 47.3 62.0 13.4 8.8 19.9 0.0 8.6 5.7 12.8 74.3 67.1 80.4 53.7 48.5 58.9 11.3 8.5 14.9 0.0 10.9 8.4 14.1 73.1 68.3 77.5 

Malawi 2010 Middle 56.7 52.7 60.6 12.7 10.7 15.1 0.1 0.0 0.8 9.0 7.2 11.2 76.3 73.3 79.2 63.2 55.0 70.7 14.6 10.2 20.4 0.0 6.7 3.9 11.2 81.3 73.3 87.4 50.6 45.5 55.8 10.8 8.3 13.9 0.0 10.9 8.4 14.1 70.4 65.6 74.8 Malawi 2010 Wealthier 49.0 44.8 53.1 15.6 12.6 19.1 0.0 10.6 8.3 13.3 73.0 69.6 76.2 56.7 45.9 67.0 17.0 10.7 26.0 0.0 8.1 4.5 14.2 77.7 67.5 85.4 50.6 44.1 57.0 14.6 10.2 20.6 0.0 12.3 8.3 17.7 76.0 71.1 80.2 

Malawi 2010 Wealthiest 48.4 42.5 54.4 25.5 20.2 31.6 0.0 5.0 3.2 7.7 76.4 71.9 80.4 53.2 41.9 64.2 25.8 16.5 38.0 0.0 2.4 0.9 6.7 76.9 67.3 84.3 47.7 41.8 53.8 13.1 8.2 20.5 0.0 8.1 5.6 11.7 66.1 59.1 72.4 

Mozambique 2003 Poorest 40.2 34.2 46.6 2.6 1.4 4.8 0.3 0.0 1.8 10.4 7.4 14.5 53.0 47.6 58.4 41.2 31.2 52.0 4.3 1.9 9.6 0.8 0.1 5.7 8.6 3.4 20.1 54.4 43.2 65.2 40.6 33.8 47.9 1.0 0.2 4.6 0.0 9.5 5.1 16.8 51.1 44.1 58.0 

Mozambique 2003 Poorer 42.7 36.6 49.1 1.6 0.3 8.7 0.0 9.2 6.2 13.3 53.3 46.9 59.7 53.8 43.3 64.0 0.4 0.1 1.8 0.0 7.9 3.6 16.6 61.7 52.6 70.0 43.9 35.1 53.1 1.4 0.5 3.8 0.0 9.4 5.2 16.3 53.3 44.0 62.5 

Mozambique 2003 Middle 48.7 42.7 54.7 1.3 0.5 3.8 0.1 0.0 1.0 8.6 5.9 12.2 58.0 51.9 63.9 53.2 40.2 65.7 0.0 0.0 8.9 3.8 19.2 62.1 48.7 73.8 58.7 50.6 66.3 1.4 0.3 5.9 0.0 6.0 3.1 11.3 66.0 58.0 73.2 Mozambique 2003 Wealthier 62.2 55.9 68.1 1.0 0.4 2.2 0.5 0.1 2.0 6.5 4.3 9.7 69.5 63.6 74.8 65.0 55.9 73.1 1.2 0.3 4.2 0.0 8.5 4.6 15.1 73.6 65.7 80.3 52.0 40.7 63.0 0.4 0.1 2.9 0.3 0.0 2.0 1.7 0.7 4.3 53.9 42.5 65.0 

Mozambique 2003 Wealthiest 65.0 60.0 69.7 3.6 1.6 7.7 2.4 1.1 5.0 2.7 1.4 5.1 73.7 69.3 77.7 59.8 53.0 66.3 2.7 1.3 5.6 2.5 1.1 5.5 2.4 1.1 5.1 67.4 60.5 73.6 46.5 37.7 55.5 1.6 0.5 5.4 2.4 0.9 6.2 3.7 1.0 12.9 53.8 45.1 62.2 

Mozambique 2011 Poorest 53.8 46.8 60.6 0.0 0.0 6.1 2.7 13.3 57.6 49.9 64.9 (44.1) 25.3 64.8 (0.0) (0.0) (3.6) 0.8 15.9 (44.1) 25.3 64.8 53.5 44.1 62.7 1.1 0.3 4.6 0.0 7.1 3.7 13.0 60.2 50.9 68.9 Mozambique 2011 Poorer 46.6 39.2 54.1 0.0 0.0 4.3 2.2 8.0 49.5 42.0 57.0 (49.8) 27.8 71.9 (0.0) (0.0) (13.8) 8.3 22.0 (57.3) 36.4 75.9 49.5 40.2 58.9 0.6 0.1 4.3 0.0 7.2 3.6 13.7 55.5 46.9 63.8 

Mozambique 2011 Middle 61.2 54.4 67.6 0.0 0.0 5.6 2.5 11.8 66.5 60.5 72.0 * * * * * * * * * * * * * * * 59.3 50.7 67.4 1.1 0.2 7.6 0.0 6.4 2.9 13.4 65.4 57.6 72.5 

Mozambique 2011 Wealthier 70.7 64.3 76.3 0.0 0.8 0.2 2.4 3.9 1.7 8.5 75.2 69.3 80.2 (51.4) 34.2 68.3 (0.0) (2.1) 0.2 16.5 (4.4) 0.8 20.1 (57.9) 39.8 74.2 62.9 55.1 70.1 1.4 0.4 4.6 0.5 0.1 3.3 3.8 1.7 8.0 68.5 60.0 76.0 

Mozambique 2011 Wealthiest 65.8 57.6 73.3 2.9 1.4 5.8 1.1 0.2 5.2 1.2 0.4 3.8 71.1 63.1 78.0 (66.8) 49.6 80.4 (0.0) (0.0) (0.0) (66.8) 49.6 80.4 64.8 56.1 72.7 0.3 0.0 2.1 2.3 0.8 6.4 5.5 2.9 10.0 72.6 64.8 79.3 

 (Continued...)

91 
92 

Table A8. − Continued

Fever Symptoms of ARI Diarrhea 

Public Private Pharmacy Only Other Any Care Public Private Pharmacy Only Other Any Care Public Private Pharmacy Only Other Any Care 

Country/Survey 

Household 

Wealth 

Quintile % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB 

Nigeria 2008 Poorest 20.3 17.2 23.8 6.9 5.1 9.1 22.9 19.3 26.9 12.9 10.4 15.9 62.1 58.3 65.8 15.2 10.6 21.3 7.9 4.6 13.3 26.1 19.7 33.9 14.3 9.4 21.3 63.1 54.5 71.0 16.9 14.3 20.0 6.3 4.5 8.7 19.4 16.4 22.9 16.5 13.1 20.6 58.4 54.0 62.7 

Nigeria 2008 Poorer 26.0 22.3 30.2 7.3 5.6 9.6 25.1 21.4 29.2 9.6 7.6 12.1 66.9 62.5 71.1 25.4 18.5 34.0 5.9 2.6 12.9 25.1 17.9 34.0 12.5 7.7 19.6 66.7 56.3 75.7 20.5 17.0 24.6 8.1 5.8 11.3 22.8 18.8 27.3 14.6 11.4 18.5 65.0 59.8 69.8 

Nigeria 2008 Middle 28.4 24.8 32.3 7.0 5.1 9.6 31.3 27.2 35.8 5.9 4.1 8.4 71.3 67.3 74.9 22.6 15.7 31.4 11.7 6.3 20.5 38.2 28.1 49.5 5.9 2.8 11.8 74.4 63.6 82.9 30.9 26.2 36.1 7.5 5.3 10.4 24.3 20.0 29.2 10.3 7.5 13.9 70.8 65.5 75.6 

Nigeria 2008 Wealthier 29.9 25.5 34.6 12.3 9.5 15.9 32.1 27.7 36.7 4.5 2.9 6.9 77.7 73.7 81.3 32.6 23.7 43.0 7.6 3.3 16.4 33.8 21.6 48.5 2.8 0.8 9.5 74.4 61.3 84.2 30.8 24.6 37.8 9.4 6.4 13.6 21.4 17.0 26.6 6.4 3.8 10.7 67.4 60.6 73.4 

Nigeria 2008 Wealthiest 29.2 24.4 34.5 18.4 14.6 23.0 26.5 22.0 31.7 1.4 0.6 3.2 74.8 70.2 78.9 (30.8) 18.1 47.2 (17.2) 8.0 33.2 (28.5) 16.6 44.3 (2.8) 0.4 19.1 (76.5) 61.5 86.9 31.4 24.3 39.7 20.4 13.8 29.2 13.1 8.1 20.3 2.5 0.7 8.0 67.4 58.8 75.0 

Nigeria 2013 Poorest 22.3 18.5 26.7 2.8 1.7 4.7 30.4 25.3 36.2 15.3 11.8 19.6 70.2 65.5 74.6 25.3 18.7 33.2 1.6 0.2 10.5 30.2 21.4 40.8 26.0 16.1 39.2 81.3 73.9 87.0 15.8 12.8 19.4 2.4 1.3 4.3 29.1 24.8 33.9 20.6 17.4 24.2 67.1 62.2 71.6 Nigeria 2013 Poorer 24.9 21.6 28.5 4.2 2.4 7.2 35.7 31.5 40.2 9.8 7.5 12.8 74.5 70.5 78.1 23.1 16.8 31.1 4.6 2.2 9.4 32.2 25.2 40.1 11.4 6.7 18.9 71.4 60.9 80.0 24.9 21.1 29.2 2.7 1.6 4.5 33.7 29.8 37.8 12.6 9.9 16.0 72.1 67.9 75.9 

Nigeria 2013 Middle 26.3 22.0 31.2 3.6 2.3 5.5 43.1 38.1 48.3 3.1 1.8 5.3 75.9 71.5 79.8 37.8 29.0 47.6 2.9 1.2 6.8 35.8 27.3 45.3 2.0 0.7 5.6 77.4 66.6 85.5 27.9 23.0 33.4 3.7 2.4 5.5 40.2 34.5 46.1 6.8 4.5 10.1 78.0 72.6 82.6 Nigeria 2013 Wealthier 28.7 24.2 33.6 6.3 4.5 9.0 38.5 33.5 43.8 3.6 1.9 6.7 76.7 72.3 80.6 27.6 18.0 39.8 9.4 4.4 18.9 38.0 24.1 54.1 3.8 0.9 14.6 77.8 58.1 89.9 30.3 24.4 37.0 7.1 4.3 11.4 29.2 23.8 35.3 5.3 3.0 9.1 70.2 64.5 75.4 

Nigeria 2013 Wealthiest 28.6 23.4 34.4 15.3 11.2 20.7 28.7 23.0 35.3 2.5 1.1 5.8 73.2 67.1 78.5 (43.7) 21.5 68.7 (23.5) 11.9 40.9 (18.0) 7.8 36.3 (7.7) 1.6 30.4 (81.7) 61.3 92.6 28.1 21.8 35.3 10.8 7.4 15.6 25.9 20.9 31.7 3.4 1.5 7.5 67.4 60.8 73.5 

Nepal 2006 Poorest 20.6 14.9 27.9 3.2 1.2 8.2 16.4 11.0 23.8 1.8 0.6 5.1 41.9 34.5 49.8 26.9 15.7 42.1 9.3 3.4 23.1 15.8 7.2 31.3 1.4 0.2 9.2 53.2 40.2 65.7 21.4 14.1 30.9 0.1 0.0 0.9 7.9 4.9 12.5 0.6 0.1 4.2 29.9 21.9 39.3 Nepal 2006 Poorer 23.3 14.4 35.4 8.6 4.0 17.3 24.6 15.7 36.2 2.3 1.0 5.5 57.3 49.3 64.9 24.9 16.2 36.3 11.4 2.7 37.2 28.0 15.7 44.8 5.3 1.6 16.3 66.2 53.6 76.9 22.2 12.5 36.2 4.7 1.6 13.2 25.4 17.4 35.5 2.1 0.7 6.4 54.3 42.2 65.9 

Nepal 2006 Middle 23.9 16.3 33.5 7.4 3.3 15.5 27.8 20.8 36.1 1.9 0.6 6.1 60.3 50.4 69.4 33.8 18.4 53.8 9.6 2.9 27.5 23.4 13.0 38.7 2.2 0.3 15.6 66.9 49.7 80.6 23.0 14.4 34.6 6.9 3.0 15.1 30.1 18.5 44.9 0.5 0.1 3.4 60.0 47.6 71.1 Nepal 2006 Wealthier 30.1 21.5 40.3 13.6 7.9 22.5 31.4 23.0 41.3 2.4 0.3 15.3 73.7 63.6 81.7 29.2 16.9 45.6 26.0 13.8 43.4 27.6 13.9 47.6 0.0 77.5 61.7 88.1 21.4 13.7 32.0 7.5 3.6 14.9 27.6 20.0 36.8 3.9 0.7 19.1 60.4 51.7 68.5 Nepal 2006 Wealthiest 18.7 12.0 27.9 26.1 19.4 34.1 23.5 16.5 32.3 2.3 0.8 6.8 69.4 60.6 77.0 (16.2) 7.4 31.7 (38.4) 21.6 58.6 (17.5) 7.5 35.6 (0.0) (71.5) 53.2 84.7 13.7 7.7 23.1 18.7 11.1 29.7 27.0 15.5 42.6 4.4 0.9 19.0 62.2 49.2 73.7 

Nepal 2011 Poorest 23.2 17.4 30.2 7.2 4.0 12.7 19.3 12.8 28.0 1.5 0.3 7.0 49.5 41.6 57.4 30.7 17.7 47.8 6.1 2.5 14.3 20.8 8.8 41.6 0.0 55.7 39.1 71.2 28.6 20.6 38.3 4.8 2.2 9.8 17.9 11.8 26.3 1.1 0.3 3.6 51.5 40.9 61.8 Nepal 2011 Poorer 20.7 15.1 27.8 18.1 11.3 27.5 29.5 21.7 38.9 3.2 1.1 8.8 69.7 58.5 78.9 (20.7) 9.0 40.8 (26.5) 11.9 49.3 (34.7) 20.2 52.7 (7.8) 1.7 29.9 (82.8) 66.6 92.0 27.8 19.3 38.4 12.5 6.4 22.8 19.8 11.9 31.1 2.1 0.5 8.4 60.7 49.4 70.9 

Nepal 2011 Middle 21.2 14.9 29.3 23.7 17.6 31.1 28.2 20.3 37.6 2.1 0.9 5.1 74.5 66.0 81.4 (26.4) 15.0 42.2 (28.3) 12.8 51.5 (25.4) 12.0 46.2 (2.0) 0.2 14.8 (79.4) 59.4 91.1 26.6 19.0 35.9 12.4 7.4 19.8 23.0 16.3 31.3 2.2 0.5 8.5 63.5 52.8 73.0 

Nepal 2011 Wealthier 16.7 12.0 22.7 34.0 27.0 41.7 35.4 29.3 42.1 1.3 0.3 6.6 86.8 79.6 91.7 (19.8) 8.7 38.9 (41.8) 29.9 54.7 (34.2) 20.2 51.6 (2.3) 0.3 15.6 (95.7) 86.8 98.7 17.5 10.5 27.7 28.7 19.1 40.8 27.8 18.8 39.0 5.2 1.6 16.0 77.0 63.2 86.7 

Nepal 2011 Wealthiest 9.6 5.1 17.3 38.2 28.6 48.9 27.1 17.4 39.5 1.3 0.3 5.8 74.4 64.0 82.6 (29.3) 11.7 56.5 (45.7) 19.8 74.2 (12.7) 4.7 30.0 (0.0) (87.6) 47.7 98.2 11.2 5.6 21.4 25.9 17.2 37.0 25.0 14.9 38.8 0.0 62.1 48.9 73.7 

Pakistan 2006-7 Poorest 10.0 7.3 13.5 59.4 54.2 64.4 0.0 6.3 4.0 9.9 75.0 69.7 79.6 8.6 5.4 13.4 58.7 50.6 66.4 0.0 3.3 1.3 8.2 70.5 62.1 77.7 7.5 4.3 12.8 55.2 48.4 61.9 0.0 5.5 2.7 10.8 66.7 58.9 73.7 Pakistan 2006-7 Poorer 10.3 7.4 14.2 62.7 57.4 67.8 1.0 0.3 3.1 3.1 1.9 5.1 76.2 71.4 80.5 8.7 5.3 14.0 66.6 59.7 72.9 0.9 0.2 4.6 3.2 1.6 6.2 78.5 72.1 83.9 11.2 7.2 17.0 52.3 44.1 60.4 1.2 0.4 3.5 4.2 2.1 8.5 68.5 60.1 75.8 

Pakistan 2006-7 Middle 12.2 8.7 17.0 67.0 61.0 72.5 0.7 0.2 2.8 6.1 3.6 10.0 83.7 78.8 87.7 13.4 8.1 21.3 71.1 63.2 77.8 0.0 6.9 3.2 14.1 89.0 82.7 93.2 11.7 7.3 18.2 55.6 46.9 63.9 0.0 7.6 4.0 14.1 73.4 64.8 80.6 

Pakistan 2006-7 Wealthier 11.6 8.6 15.5 71.6 66.4 76.3 0.8 0.3 2.2 3.3 1.9 5.7 86.5 82.2 89.9 12.2 8.3 17.6 74.0 66.6 80.2 0.0 4.9 2.4 9.4 89.7 84.3 93.4 6.5 3.6 11.7 58.8 50.1 67.0 0.5 0.1 3.6 5.0 2.4 9.9 70.0 61.4 77.3 

Pakistan 2006-7 Wealthiest 6.9 4.5 10.4 72.5 65.3 78.7 0.9 0.2 3.4 5.7 3.1 10.3 85.2 79.0 89.8 5.5 2.5 11.8 84.1 75.9 89.8 1.1 0.1 7.5 5.4 2.1 13.3 93.8 87.2 97.1 5.7 2.9 10.9 70.9 61.2 79.0 0.0 12.1 5.4 25.1 81.7 74.1 87.4 

Pakistan 2012-13 Poorest 8.6 5.9 12.4 67.3 60.7 73.2 0.0 2.2 1.1 4.5 77.7 72.3 82.4 11.4 7.3 17.4 69.2 60.3 76.8 0.0 1.4 0.6 3.3 81.0 73.8 86.6 12.5 8.6 17.8 61.3 53.9 68.3 0.0 0.8 0.3 2.1 72.9 66.2 78.8 Pakistan 2012-13 Poorer 9.3 6.7 12.8 67.0 61.9 71.7 0.3 0.0 1.6 4.0 1.4 10.8 79.1 74.3 83.3 9.5 6.2 14.4 69.0 62.0 75.2 0.3 0.0 1.8 3.1 1.3 7.0 80.1 73.7 85.2 9.0 6.4 12.6 63.5 58.3 68.5 0.0 0.0 0.1 1.3 0.4 4.0 72.8 67.6 77.5 

Pakistan 2012-13 Middle 10.3 7.7 13.6 70.6 65.7 75.1 1.1 0.5 2.7 2.7 1.4 5.3 84.0 80.3 87.0 9.9 6.7 14.5 72.3 64.6 78.9 1.3 0.4 4.2 2.0 0.5 8.1 84.6 78.4 89.3 11.0 7.6 15.7 64.2 58.1 69.9 1.7 0.5 5.7 2.1 0.9 4.8 78.7 73.3 83.2 

Pakistan 2012-13 Wealthier 7.6 5.5 10.4 74.8 69.8 79.2 0.3 0.0 2.0 1.9 0.9 4.0 83.7 79.8 86.9 10.2 6.9 14.9 75.7 69.7 80.8 0.7 0.1 4.6 0.0 85.7 80.1 89.9 7.0 4.5 10.7 70.6 65.7 75.0 0.9 0.3 3.1 1.6 0.6 4.4 78.9 74.3 82.9 

Pakistan 2012-13 Wealthiest 8.6 5.9 12.4 75.8 71.0 80.1 0.7 0.3 1.7 2.0 0.9 4.5 86.9 82.9 90.1 4.1 1.9 8.5 83.0 75.6 88.5 0.3 0.1 1.3 0.1 0.0 0.9 87.5 80.2 92.3 7.0 4.0 12.0 74.1 66.4 80.5 0.7 0.2 2.6 1.2 0.4 3.7 81.7 75.0 86.9 

Rwanda 2005 Poorest 21.5 17.3 26.4 1.5 0.7 3.2 13.4 9.6 18.3 19.6 15.6 24.3 54.8 49.3 60.1 21.9 17.3 27.4 1.2 0.5 3.0 15.1 10.6 21.1 21.6 16.6 27.7 58.1 51.3 64.7 12.6 8.7 18.0 0.6 0.2 2.6 4.5 2.5 8.0 8.6 5.3 13.7 26.4 20.3 33.7 Rwanda 2005 Poorer 24.4 19.6 29.9 1.2 0.5 3.0 15.8 12.1 20.4 16.8 13.0 21.5 57.9 51.9 63.6 25.5 19.6 32.4 1.5 0.5 4.1 10.6 7.1 15.6 25.1 19.6 31.5 61.6 54.1 68.7 11.5 7.1 18.2 0.0 6.8 3.8 11.9 9.1 5.8 14.0 26.7 20.3 34.3 

Rwanda 2005 Middle 21.4 17.2 26.3 4.7 2.8 7.7 13.7 10.2 18.2 21.0 16.8 26.0 60.2 54.8 65.3 20.9 15.8 27.0 3.6 1.9 6.8 16.8 12.1 23.0 26.7 20.9 33.3 67.0 59.9 73.4 10.9 7.1 16.4 2.6 1.0 6.5 7.0 4.1 11.6 10.5 6.8 15.8 31.0 24.7 38.0 Rwanda 2005 Wealthier 23.8 19.5 28.7 1.1 0.4 3.1 17.7 13.5 22.8 15.3 11.5 20.0 57.0 51.1 62.8 21.9 16.6 28.2 1.0 0.3 3.0 18.2 13.1 24.6 20.7 15.7 26.7 60.6 53.0 67.6 12.0 8.0 17.7 3.3 1.4 7.8 6.2 3.7 10.2 9.0 5.4 14.6 29.3 23.1 36.3 

Rwanda 2005 Wealthiest 34.0 28.5 40.0 12.0 8.5 16.7 19.9 15.9 24.7 12.4 9.2 16.4 76.4 71.1 81.1 30.9 25.1 37.3 13.1 9.1 18.6 19.1 14.6 24.6 12.8 8.9 18.2 74.0 68.1 79.1 13.7 9.3 19.9 4.6 2.2 9.2 11.1 7.1 16.8 6.5 3.1 13.2 35.6 28.1 44.0 

Rwanda 2010 Poorest 35.1 29.9 40.7 0.0 1.4 0.6 3.4 5.2 3.2 8.3 41.7 36.1 47.6 40.3 29.0 52.7 0.0 0.9 0.1 6.6 10.0 4.9 19.3 51.2 38.8 63.3 26.5 21.6 32.0 0.0 3.4 1.8 6.5 11.7 8.5 16.0 40.7 35.2 46.5 Rwanda 2010 Poorer 35.9 30.3 41.8 0.0 2.8 1.4 5.7 5.1 3.0 8.5 43.4 37.7 49.3 49.6 37.3 62.0 0.0 4.9 1.2 18.4 7.7 2.4 22.1 62.3 49.6 73.4 37.8 32.2 43.7 0.5 0.1 3.2 2.2 0.9 5.2 7.9 5.0 12.1 47.3 41.3 53.4 

Rwanda 2010 Middle 42.3 35.9 48.9 0.3 0.0 2.3 3.9 2.1 7.2 4.7 2.6 8.5 50.9 44.6 57.3 48.7 36.5 61.1 0.0 4.1 0.9 15.9 13.3 6.0 26.9 66.1 52.9 77.2 40.5 33.6 47.8 0.0 2.4 1.0 5.9 11.5 7.5 17.1 53.5 46.2 60.6 

Rwanda 2010 Wealthier 49.1 41.4 56.8 1.2 0.3 4.5 2.5 0.9 6.7 8.8 5.3 14.4 61.6 54.3 68.4 (46.6) 30.9 63.0 (0.0) (2.5) 0.3 17.9 (6.5) 1.9 20.0 (55.6) 39.2 70.7 36.2 29.2 43.9 2.8 1.2 6.4 3.0 1.2 7.0 11.1 6.8 17.6 52.1 44.0 60.2 

Rwanda 2010 Wealthiest 47.5 40.7 54.4 11.0 6.4 18.2 6.1 3.5 10.3 1.4 0.4 4.7 65.4 58.3 71.9 53.7 39.1 67.7 21.1 11.7 35.0 7.8 2.6 21.3 2.1 0.2 15.8 84.7 70.1 92.9 43.1 35.3 51.3 7.7 4.4 13.2 10.0 5.9 16.4 4.2 1.9 8.8 63.9 55.8 71.3 

Senegal 2005 Poorest 29.4 25.1 34.0 7.9 5.6 11.1 0.9 0.3 2.2 10.7 8.0 14.2 46.9 41.9 51.9 32.9 26.2 40.3 9.1 5.3 15.0 1.4 0.4 4.2 4.5 2.3 8.7 46.6 39.4 53.9 16.9 13.3 21.4 1.1 0.4 3.1 0.3 0.1 1.2 15.2 11.8 19.3 32.8 28.5 37.3 Senegal 2005 Poorer 31.1 27.0 35.6 8.5 6.2 11.6 1.0 0.5 1.9 10.0 7.4 13.4 49.1 44.4 53.7 36.8 29.6 44.7 5.8 2.9 11.0 1.3 0.5 3.2 11.2 7.3 16.8 52.6 44.8 60.3 15.8 12.4 19.9 1.4 0.6 3.4 0.1 0.0 0.8 16.8 13.5 20.7 33.3 28.7 38.2 

Senegal 2005 Middle 34.7 29.5 40.4 9.7 5.6 16.4 3.3 1.8 6.0 8.5 5.9 12.1 54.9 50.3 59.5 37.7 30.4 45.5 8.2 4.5 14.4 5.3 2.5 10.8 5.3 3.0 9.2 55.9 50.5 61.1 17.5 13.4 22.5 0.3 0.1 1.1 0.2 0.0 1.7 13.8 9.4 19.8 31.6 26.4 37.3 

Senegal 2005 Wealthier 42.7 35.1 50.6 17.1 10.8 26.0 8.1 5.1 12.6 4.5 2.4 8.1 69.2 61.6 75.8 49.0 40.0 58.0 16.4 9.4 27.1 7.9 4.3 14.2 4.7 1.8 11.8 72.7 62.2 81.2 20.6 13.9 29.4 7.2 3.9 12.9 1.9 0.6 5.4 10.7 5.4 19.9 38.8 30.6 47.7 

Senegal 2005 Wealthiest 40.4 30.2 51.6 14.6 6.8 28.6 14.6 9.5 21.7 3.3 1.6 6.5 71.4 61.6 79.5 40.8 29.6 52.9 19.3 7.6 41.0 10.5 5.0 20.7 3.9 0.9 14.9 74.3 62.9 83.1 16.9 10.6 25.8 4.9 2.3 10.2 2.3 0.8 6.0 10.7 4.0 25.7 34.0 23.8 46.0 

Senegal 2010-11 Poorest 28.6 24.6 32.9 1.5 0.7 2.9 1.0 0.4 2.3 8.6 6.3 11.6 39.0 35.0 43.1 31.2 23.1 40.7 0.9 0.2 3.9 1.6 0.5 4.9 13.0 8.3 19.8 45.1 35.6 55.0 30.0 26.4 33.9 0.8 0.4 1.6 0.7 0.3 1.5 14.7 11.7 18.3 45.0 40.6 49.5 Senegal 2010-11 Poorer 38.1 33.0 43.4 1.4 0.6 2.9 2.0 0.9 4.3 7.4 5.2 10.4 47.9 42.4 53.6 36.7 26.8 47.8 1.7 0.2 11.1 5.5 2.0 14.3 11.5 5.4 23.0 55.4 43.5 66.6 33.9 29.7 38.4 1.4 0.6 3.4 1.3 0.3 4.6 10.7 8.1 14.2 46.8 42.0 51.6 

Senegal 2010-11 Middle 46.6 40.2 53.2 0.5 0.1 1.9 4.8 2.8 8.2 3.1 1.7 5.7 54.0 47.7 60.2 55.4 43.9 66.4 0.4 0.1 3.3 3.0 1.2 7.0 5.0 2.2 11.0 62.3 50.8 72.6 34.8 29.7 40.3 2.2 0.8 6.2 2.2 1.1 4.4 10.7 7.6 15.0 49.3 44.0 54.7 Senegal 2010-11 Wealthier 36.8 30.0 44.1 6.7 3.9 11.3 8.9 5.2 14.9 4.8 2.7 8.5 56.6 49.8 63.2 40.3 25.7 56.9 7.2 2.0 22.6 6.4 2.0 19.0 2.0 0.7 5.9 55.9 43.5 67.7 28.4 21.7 36.1 3.4 1.8 6.5 5.5 3.3 9.0 9.1 5.4 15.0 45.7 38.9 52.7 

Senegal 2010-11 Wealthiest 45.7 35.8 56.1 11.4 6.6 19.0 8.6 4.5 15.8 5.2 2.5 10.5 67.7 58.5 75.7 54.6 30.1 77.0 14.4 5.5 32.7 6.3 1.4 24.2 1.9 0.2 14.1 77.1 59.6 88.5 34.7 28.2 41.7 7.5 3.8 14.3 5.0 2.1 11.3 7.5 3.7 14.5 53.7 46.5 60.8 

Tanzania 2004-5 Poorest 46.9 40.2 53.8 6.6 4.3 9.8 21.4 16.4 27.4 2.4 1.2 4.8 76.8 71.3 81.5 45.6 35.7 55.8 9.8 5.1 18.1 19.2 12.5 28.2 2.4 0.8 7.5 76.3 68.7 82.6 40.7 34.3 47.4 4.6 2.2 9.2 15.5 10.5 22.2 2.8 1.1 7.1 63.1 56.6 69.1 

Tanzania 2004-5 Poorer 53.9 47.1 60.5 8.7 6.0 12.5 19.8 15.5 25.0 2.7 1.4 5.5 84.6 79.8 88.4 60.1 49.6 69.8 3.9 1.5 9.9 26.6 18.9 36.0 0.5 0.1 3.3 91.1 83.6 95.3 47.8 39.3 56.5 5.0 2.8 8.7 9.8 5.9 15.8 3.5 2.0 6.0 65.7 58.0 72.8 

Tanzania 2004-5 Middle 44.9 38.3 51.6 12.2 8.9 16.5 26.3 20.3 33.4 0.7 0.3 2.1 83.7 78.3 88.0 55.9 45.1 66.2 15.1 9.8 22.5 19.2 12.4 28.4 1.3 0.3 5.1 91.4 84.2 95.5 37.9 29.9 46.7 9.9 4.6 20.0 16.9 11.0 25.2 0.7 0.1 4.7 65.4 56.0 73.8 Tanzania 2004-5 Wealthier 46.2 39.5 53.0 15.7 11.3 21.5 21.5 17.0 26.9 2.0 1.0 4.1 84.7 78.9 89.2 49.7 38.9 60.6 11.3 5.6 21.5 22.8 13.9 35.1 3.3 1.3 8.4 85.6 77.8 90.9 31.2 24.1 39.2 5.9 3.3 10.6 12.8 7.8 20.3 0.4 0.1 2.7 50.2 42.4 58.0 Tanzania 2004-5 Wealthiest 54.2 44.6 63.5 28.9 21.5 37.7 13.4 9.1 19.2 0.0 0.0 0.3 93.9 87.3 97.2 50.6 37.5 63.6 23.0 14.3 34.9 16.6 9.0 28.6 2.2 0.3 14.0 89.9 65.5 97.7 34.7 21.8 50.4 20.2 11.9 32.0 8.7 3.6 19.5 0.2 0.0 1.7 63.8 52.2 73.9 

Tanzania 2010 Poorest 49.5 41.6 57.4 1.9 0.7 5.0 20.5 15.9 25.9 0.7 0.2 2.8 72.5 65.7 78.4 46.9 32.8 61.6 3.0 0.7 12.6 18.3 9.9 31.4 2.3 0.3 15.6 70.6 55.8 82.1 48.9 40.6 57.3 0.6 0.1 3.0 12.5 8.0 18.9 1.9 0.4 8.2 63.9 56.1 71.0 

Tanzania 2010 Poorer 54.5 47.2 61.6 3.7 1.6 8.7 21.6 16.3 28.0 0.1 0.0 0.8 79.8 73.7 84.7 72.9 53.2 86.5 2.8 0.6 11.8 5.6 1.6 17.7 0.0 80.3 60.3 91.6 49.0 39.8 58.3 2.8 1.1 6.8 15.7 10.6 22.6 1.1 0.3 4.2 67.4 58.7 75.1 

Tanzania 2010 Middle 52.5 45.3 59.5 3.7 1.6 8.4 22.3 16.2 30.0 1.4 0.4 4.2 79.5 72.8 85.0 44.3 32.2 57.1 4.6 1.4 13.9 23.2 11.6 41.2 2.3 0.3 15.3 74.2 59.3 85.0 38.4 30.6 46.9 1.4 0.2 9.3 20.9 14.2 29.8 2.7 1.0 7.3 62.4 54.1 70.0 

Tanzania 2010 Wealthier 53.5 44.0 62.8 7.7 4.5 12.6 20.9 12.1 33.7 0.9 0.2 3.2 82.4 75.3 87.8 65.9 44.3 82.5 7.0 2.1 21.2 11.7 2.4 41.8 2.8 0.4 18.1 84.7 70.6 92.7 41.3 32.1 51.1 7.0 3.8 12.6 13.0 7.7 21.1 1.5 0.4 5.4 62.7 53.3 71.3 

Tanzania 2010 Wealthiest 52.6 44.6 60.3 20.4 14.8 27.4 6.1 3.5 10.6 1.7 0.5 6.3 79.7 71.5 86.0 77.1 61.2 87.8 13.4 6.2 26.6 1.3 0.2 9.7 0.0 90.6 74.2 97.0 43.5 34.0 53.6 13.0 7.4 21.7 6.2 2.6 14.1 0.2 0.0 1.1 62.2 51.7 71.7 

Uganda 2006 Poorest 40.3 34.6 46.2 41.7 36.2 47.5 5.5 3.6 8.2 1.8 1.0 3.2 84.9 81.8 87.6 44.5 37.2 52.1 39.2 32.7 46.1 4.4 2.7 7.3 1.6 0.6 4.1 85.1 79.3 89.5 42.3 36.9 48.0 38.5 32.8 44.4 3.3 2.0 5.4 4.5 2.9 6.9 84.2 80.5 87.4 Uganda 2006 Poorer 30.7 26.0 35.8 43.8 38.8 48.8 6.2 4.4 8.6 2.6 1.6 4.2 80.0 75.9 83.5 30.3 23.6 38.0 40.6 33.0 48.7 8.8 5.6 13.6 3.5 1.8 6.9 79.5 72.4 85.1 32.6 27.1 38.7 39.2 33.3 45.5 5.8 4.0 8.4 2.9 1.5 5.5 76.8 71.1 81.6 

Uganda 2006 Middle 28.5 22.8 35.1 46.0 39.8 52.3 7.5 5.3 10.4 4.0 2.2 7.2 82.8 78.1 86.7 32.5 19.9 48.2 39.0 28.6 50.5 8.2 4.6 14.1 6.2 3.0 12.4 82.5 75.2 88.0 24.9 19.6 31.0 39.2 33.0 45.8 3.0 1.7 5.3 10.1 6.1 16.2 71.7 65.7 76.9 

 (Continued...)

92 
93 

Table A8. − Continued

Fever Symptoms of ARI Diarrhea 

Public Private Pharmacy Only Other Any Care Public Private Pharmacy Only Other Any Care Public Private Pharmacy Only Other Any Care 

Country/Survey 

Household 

Wealth 

Quintile % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB % LB UB 

Uganda 2006 Wealthier 25.4 21.0 30.4 50.1 44.6 55.7 6.4 4.3 9.4 4.1 2.4 6.9 81.6 77.7 85.0 27.8 20.2 36.9 47.2 37.7 56.9 8.9 5.6 13.9 5.4 2.6 10.9 84.2 76.0 90.0 22.8 17.5 29.2 48.1 41.1 55.1 5.1 3.2 8.0 6.9 4.4 10.7 77.3 71.8 82.1 

Uganda 2006 Wealthiest 23.5 17.8 30.3 58.0 51.4 64.3 7.3 4.4 11.8 1.6 0.6 4.2 88.4 83.8 91.8 15.2 8.6 25.3 65.4 52.5 76.3 6.6 2.1 18.8 0.1 0.0 1.1 84.3 75.6 90.2 18.2 12.3 26.0 55.9 49.0 62.5 5.3 2.9 9.6 3.4 1.5 7.5 79.2 71.0 85.6 

Uganda 2011 Poorest 44.3 37.3 51.6 38.8 32.2 45.9 0.4 0.1 1.3 4.2 2.6 6.8 83.2 78.6 87.0 38.1 31.2 45.6 44.3 35.3 53.6 0.2 0.0 1.7 2.5 1.2 5.3 80.2 72.9 86.0 40.3 32.8 48.3 35.9 29.4 42.9 0.3 0.1 1.3 3.2 1.8 5.4 76.0 70.3 81.0 

Uganda 2011 Poorer 37.0 31.3 43.0 45.5 39.1 52.0 0.2 0.0 1.3 4.7 2.9 7.6 83.2 79.1 86.6 33.7 27.4 40.5 49.4 42.0 56.8 2.0 0.6 6.3 3.4 1.6 7.1 82.9 75.5 88.5 29.2 23.5 35.6 44.9 38.8 51.2 1.7 0.7 4.2 5.8 3.6 9.1 79.8 74.3 84.4 

Uganda 2011 Middle 40.2 34.4 46.3 45.2 39.1 51.5 2.0 0.8 4.7 2.3 1.1 4.8 85.6 81.4 89.0 30.2 22.5 39.3 52.4 43.3 61.4 1.6 0.4 6.5 4.2 1.5 11.2 83.1 74.5 89.2 34.1 26.8 42.3 44.1 37.3 51.1 1.4 0.5 3.8 3.0 1.4 6.2 77.4 70.7 83.0 Uganda 2011 Wealthier 28.9 23.2 35.5 51.7 45.2 58.1 2.7 1.2 6.3 4.2 2.4 7.1 84.1 79.8 87.6 32.3 23.2 42.8 51.7 40.5 62.8 3.6 1.0 12.6 3.1 1.1 8.5 83.9 76.4 89.4 29.9 23.7 37.0 45.6 38.5 52.9 1.7 0.6 4.5 3.9 1.9 7.7 78.3 72.0 83.5 Uganda 2011 Wealthiest 21.2 16.4 26.9 67.0 60.9 72.5 3.7 2.1 6.6 3.4 1.8 6.7 90.9 87.2 93.6 21.3 13.5 31.9 63.5 52.6 73.1 2.4 1.0 5.5 6.2 2.3 15.7 90.1 83.4 94.2 18.6 13.8 24.7 49.5 42.2 56.7 1.9 0.6 5.7 1.3 0.5 3.7 70.3 62.0 77.5 

Zambia 2007 Poorest 51.2 42.8 59.5 9.5 5.2 16.9 1.0 0.3 3.1 10.8 6.7 16.9 70.5 62.7 77.3 64.8 50.6 76.8 13.1 6.5 24.5 0.0 14.0 6.0 29.5 86.2 75.9 92.5 55.1 44.7 65.1 6.1 3.0 12.1 0.0 15.3 10.0 22.8 69.9 60.1 78.2 

Zambia 2007 Poorer 53.1 45.2 60.8 9.2 5.2 16.0 1.0 0.2 4.3 6.8 3.8 11.8 69.5 62.0 76.1 54.9 41.3 67.7 9.3 3.4 23.1 1.8 0.2 13.1 2.6 0.7 9.6 67.7 54.5 78.6 52.2 42.8 61.5 9.8 5.7 16.4 0.0 7.2 3.7 13.8 67.6 59.7 74.6 

Zambia 2007 Middle 51.6 44.3 58.8 8.6 5.3 13.6 2.1 0.7 6.5 7.8 4.5 13.2 68.6 61.4 75.1 58.6 43.8 72.1 5.6 1.4 19.9 5.7 1.1 24.0 6.2 2.1 16.9 74.6 59.7 85.3 48.5 39.7 57.4 7.3 3.8 13.5 0.0 9.5 5.5 15.8 64.1 56.9 70.7 

Zambia 2007 Wealthier 65.9 58.3 72.7 3.7 1.7 8.1 1.5 0.4 5.4 6.9 3.8 12.3 77.1 68.7 83.8 74.8 57.8 86.5 4.8 1.2 17.2 1.6 0.4 6.0 4.4 0.7 22.4 83.4 71.6 90.9 54.8 47.3 62.2 3.7 1.9 7.1 2.6 0.6 10.1 2.8 1.1 6.6 62.5 55.9 68.7 

Zambia 2007 Wealthiest 56.8 43.0 69.6 7.1 3.5 14.0 0.3 0.0 2.0 1.9 0.6 5.9 66.0 53.2 76.9 (54.3) 34.6 72.7 (1.8) 0.2 13.6 (3.2) 0.4 20.5 (0.0) (59.3) 35.9 79.1 52.2 37.6 66.4 6.8 3.1 14.2 1.3 0.5 3.7 2.5 0.7 9.4 60.1 45.7 72.9 

Zambia 2013 Poorest 64.5 59.7 69.1 4.1 2.5 6.5 0.0 4.8 3.1 7.2 72.1 67.6 76.2 65.1 53.5 75.1 1.9 0.6 5.6 0.0 6.7 2.8 14.8 73.6 62.8 82.2 60.7 54.8 66.3 2.9 1.6 5.3 0.2 0.0 1.3 5.7 3.8 8.4 67.0 61.5 72.1 

Zambia 2013 Poorer 69.6 65.4 73.5 3.1 1.8 5.1 0.4 0.1 1.4 3.9 2.6 5.8 76.1 72.4 79.4 64.2 53.1 73.9 2.6 1.0 6.5 0.1 0.0 0.7 4.0 1.5 10.4 69.7 58.5 79.0 65.6 60.6 70.2 2.1 1.0 4.1 0.6 0.2 2.0 6.1 3.7 9.8 70.6 65.7 75.1 

Zambia 2013 Middle 66.6 61.0 71.8 4.4 2.3 8.3 1.0 0.3 2.9 4.6 2.7 7.8 75.5 70.3 80.1 65.3 51.8 76.8 2.4 0.6 8.2 0.0 2.7 0.4 14.7 70.1 56.9 80.6 64.8 59.2 70.0 3.5 1.8 6.8 0.4 0.1 1.8 3.9 2.3 6.7 70.9 65.6 75.8 Zambia 2013-14 Wealthier 73.1 67.1 78.4 2.9 1.5 5.5 2.0 0.7 5.2 3.8 1.9 7.5 79.8 74.5 84.2 70.7 57.6 81.1 7.0 2.1 20.5 0.0 3.3 0.6 15.1 75.7 63.5 84.8 63.6 57.5 69.2 3.1 1.7 5.7 1.5 0.5 4.9 2.3 1.2 4.7 70.1 64.0 75.6 Zambia 2013-14 Wealthiest 65.4 56.9 73.1 10.0 5.7 16.9 1.1 0.3 3.7 1.7 0.5 5.5 77.2 70.1 83.1 72.1 53.9 85.1 7.4 2.0 24.5 1.8 0.4 8.1 4.4 0.8 21.7 85.8 70.5 93.8 55.3 47.7 62.6 6.4 3.6 11.3 0.6 0.2 1.8 1.5 0.4 4.8 62.9 55.4 69.9 

Figures in parentheses are based on 25-49 unweighted cases. An asterisk indicates that a figure is based on fewer than 25 unweighted cases and has been suppressed. Public sources include public hospital, health center, health post, mobile clinic, community health worker, and other public sector; private sources include: private hospital, doctor, nurse, mobile clinic, private community health worker, religious or NGO-run facilities, and other private sector; pharmacy only highlights children for whom care was sought only from a pharmacy; other sources include: market/informal shop, traditional healer or other non-allopathic sources, friend, relative, or other sources. Children for whom care was sought from multiple sources contribute to each source's total, so the total percentage may exceed the percentage of children who received any care. LB and UB refer to the lower and upper bounds of the 95% confidence interval. 

93 
94 

Table A9. Trend in coverage of care seeking among rural children and urban children and rural-urban gap in care seeking equity, USAID MCH priority countries 

Fever Symptoms of ARI Diarrhea 

Care seeking from any 

source 

Care seeking from 

public sources 

Care seeking from 

private sources 

Care seeking from any 

source 

Care seeking from public 

sources 

Care seeking from 

private sources 

Care seeking from any 

source 

Care seeking from public 

sources 

Care seeking from 

private sources 

Country 

Survey 

Year Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 

Bangladesh 2007 71.4 74.8 3.4 8.3 12.0 3.7 12.6 25.3 12.7 87.6 85.9 -1.7 9.1 26.1 17.0 25.7 30.6 4.9 72.5 70.4 -2.1 9.4 11.3 1.9 8.4 19.0 10.5 

2011 74.3 76.3 2.0 8.4 10.9 2.5 43.1 45.3 2.2 *** 79.9 91.4 11.5 12.1 11.5 -0.6 * 44.8 58.4 13.7 76.4 74.9 -1.5 10.3 14.5 4.1 40.4 45.9 5.6

*** *** * *** ** *** **

DR Congo 2007 57.6 67.8 10.2 27.3 21.5 -5.8 16.9 27.0 10.1 37.3 38.6 1.3 25.4 32.3 6.8 6.8 3.8 -3.0 

2013-14 58.5 59.2 0.8 32.4 20.0 -12.4 8.6 17.9 9.3 60.1 54.4 -5.7 34.9 18.3 -16.5 *** 8.2 13.9 5.7 * 

 * ** ** *** ** * *** *** 

Ethiopia 2005 17.3 46.4 29.1 13.1 30.3 17.2 2.7 15.7 13.0 25.0 38.7 13.7 18.3 28.8 10.5 3.3 7.1 3.8

2011 24.2 41.2 17.0 16.1 22.2 6.2 6.7 16.4 9.7 32.9 58.0 25.2 22.7 35.4 12.7 7.1 23.1 16.0 

  **  ** * **

Ghana 2003 72.6 73.6 1.0 36.9 45.4 8.5 4.9 11.0 6.1 48.0 54.9 6.9 19.3 28.1 8.8 2.0 7.4 5.5 

2008 63.7 81.5 17.8 * 40.5 41.6 1.1 5.2 18.5 13.3 62.0 66.3 4.3 38.6 29.4 -9.2 * 4.9 8.1 3.2 

* ** *** 

Haiti 2005-6 41.4 49.8 8.5 12.9 17.3 4.4 22.4 23.4 1.1 37.5 39.5 2.1 9.6 13.2 3.6 21.5 21.0 -0.6 36.8 46.6 9.8 10.7 17.8 7.1 19.2 14.6 -4.6

2012 45.8 51.5 5.8 13.9 15.4 1.4 25.1 31.7 6.5 43.0 51.9 8.9 14.5 10.9 -3.6 22.0 34.5 12.5 41.9 46.7 4.8 14.6 17.5 3.0 19.5 22.6 3.1

* * ** *

Indonesia 2007 89.6 92.8 3.2 25.0 27.2 2.2 42.3 47.9 5.6 72.3 77.2 4.9 20.3 19.2 -1.1 30.9 37.5 6.6 

2012 88.8 90.4 1.6 23.1 27.7 4.7 52.3 52.1 -0.2 80.1 80.1 0.0 23.3 24.0 0.8 45.7 42.6 -3.1 * 

 * *** ** *** *** 

Kenya 2003 66.9 75.2 8.3 29.0 29.2 0.1 16.6 24.7 8.1 37.1 39.5 2.4 21.1 16.6 -4.5 8.4 14.2 5.8

2008-9 62.2 63.0 0.8 38.7 40.6 1.9 9.8 12.2 2.5 60.3 59.3 -1.0 40.1 32.4 -7.7 9.2 16.1 6.9

* ** *** * *** ** *** **

Liberia 2007 74.4 80.9 6.5 31.8 46.0 14.2 ** 21.2 22.7 1.5 78.4 83.1 4.7 41.8 57.6 15.8 17.9 16.4 -1.5 78.8 77.0 -1.8 25.6 39.2 13.6 22.6 14.9 -7.7

2013 76.8 82.0 5.2 44.8 36.6 -8.2 9.4 25.3 15.9 ** 71.0 76.6 5.6 42.7 25.5 -17.2 ** 9.6 23.9 14.2 * 73.3 73.5 0.2 39.1 31.1 -7.9 ** 7.4 17.5 10.2 *** 

 ** *** * * ** *** 

Madagascar 2003-4 40.0 61.8 21.8 25.5 30.3 4.8 8.3 29.6 21.3 37.3 42.7 5.4 21.1 20.5 -0.6 9.1 21.0 11.9 

2008-9 44.7 63.3 18.6 30.3 23.6 -6.7 9.1 39.7 30.6 37.7 57.1 19.4 25.9 27.6 1.8 6.7 23.5 16.8 

* *

Mali 2007 52.6 71.9 19.3 25.5 40.5 14.9 2.8 6.1 3.3 27.9 42.3 14.4 13.5 27.7 14.2 0.8 5.0 4.2 

2013 44.2 70.8 26.7 22.2 41.5 19.3 1.5 7.4 5.9 51.4 62.5 11.1 24.5 31.6 7.1 1.9 4.8 2.9 

* *** *** *** 

Malawi 2004 77.0 80.9 4.0 33.3 52.5 19.3 ** 6.6 4.5 -2.1 58.5 49.5 -8.9 28.4 33.2 4.9 7.8 5.5 -2.3

2010 72.9 72.3 -0.6 50.3 53.2 2.8 14.7 17.1 2.4 72.9 63.0 -10.0 51.7 49.6 -2.1 13.0 7.6 -5.4

** * *** *** ** *** * *** ** ***

Mozambique 2003 56.0 70.8 14.8 45.0 63.3 18.4 2.0 1.9 -0.2 54.7 57.5 2.8 45.6 52.5 6.9 1.5 0.6 -0.8 

2011 58.4 74.0 15.6 53.8 71.1 17.3 0.0 1.5 1.5 60.7 71.2 10.4 53.9 65.8 11.9 0.9 1.2 0.3 

 ** * * ** 

Nigeria 2008 68.1 73.9 5.8 25.4 28.6 3.3 8.6 12.6 4.0 66.2 76.8 10.6 22.8 24.4 1.6 8.6 9.1 0.6 62.8 69.6 6.9 22.1 28.2 6.1 7.4 12.6 5.2

2013 73.8 74.2 0.4 24.8 27.5 2.8 3.9 9.0 5.1 75.8 79.5 3.7 26.8 35.1 8.3 3.2 12.7 9.5 * 71.2 70.3 -1.0 * 22.3 27.9 5.5 3.0 7.5 4.5

** *** * ** *** *** *

Nepal 2006 58.1 66.0 8.0 23.7 19.8 -3.9 9.2 21.6 12.4 65.2 71.9 6.7 28.2 15.9 -12.3 14.5 39.3 24.7 50.2 54.2 4.0 21.7 13.2 -8.5 5.1 15.4 10.3 

2011 70.5 81.5 11.0 18.9 19.0 0.2 22.3 36.6 14.3 76.7 89.3 12.6 24.1 33.5 9.4 25.1 35.5 10.4 61.4 68.9 7.5 24.9 14.9 -10.0 13.5 28.5 15.0 

*** * *** * * * * *** 

Pakistan 2006-7 80.2 83.3 3.1 10.7 9.1 -1.6 64.9 70.0 5.1 81.3 88.5 7.1 9.7 9.9 0.2 67.4 76.3 8.9 68.9 78.3 9.3 9.0 8.0 -1.0 54.9 65.6 10.7 

2012-13 80.8 85.0 4.2 8.3 10.4 2.2 70.0 72.8 2.8 82.2 86.7 4.5 9.4 9.4 0.0 71.4 77.6 6.2 75.2 79.3 4.1 10.4 7.6 -2.8 64.2 70.3 6.2

* * **

Rwanda 2005 58.1 77.2 19.1 23.6 31.5 7.9 2.2 13.8 11.6 28.7 33.7 5.1 12.3 11.1 -1.2 1.6 5.2 3.6 

2010 48.7 65.3 16.7 40.3 44.0 3.7 0.6 12.7 12.1 50.0 48.2 -1.9 36.6 28.6 -8.0 1.2 4.9 3.7 

*** ** ***   ** * ** *** *** 

Senegal 2005 51.0 67.3 16.3 32.6 39.5 6.9 8.3 16.2 7.9 32.6 36.1 3.5 17.7 16.9 -0.8 1.2 5.1 3.9

2010-11 45.5 62.1 16.7 34.5 43.6 9.1 2.5 7.1 4.6 47.2 48.2 1.0 32.9 30.7 -2.2 1.7 4.4 2.7

* *** * *** ** *** ***

Tanzania 2004-5 82.0 92.8 10.8 47.4 55.9 8.5 11.0 23.0 11.9 62.3 58.0 -4.3 40.1 34.3 -5.7 7.1 12.2 5.2 

2010 78.4 79.9 1.4 53.3 50.2 -3.1 3.4 16.6 13.2 63.9 63.5 -0.4 46.0 39.1 -6.9 1.8 12.3 10.5 

 ** *** * * ** 

(Continued...)

94 
95 

Table A9. − Continued

Fever Symptoms of ARI Diarrhea 

Care seeking from any 

source 

Care seeking from 

public sources 

Care seeking from 

private sources 

Care seeking from any 

source 

Care seeking from public 

sources 

Care seeking from 

private sources 

Care seeking from any 

source 

Care seeking from public 

sources 

Care seeking from 

private sources 

Country 

Survey 

Year Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 Rural Urban Diff.1 Sig.2 

Uganda 2006 83.0 85.1 2.1 30.7 34.5 3.9 46.8 44.6 -2.2 83.4 77.5 -5.9 * 33.6 25.4 -8.2 43.3 45.1 1.8 78.6 75.8 -2.8 30.9 28.7 -2.2 42.6 41.0 -1.6 

2011 84.0 92.7 8.7 * 36.8 28.3 -8.6 45.9 63.6 17.7 ** 82.4 89.9 7.4 32.9 28.3 -4.6 50.0 57.0 7.1 77.0 74.3 -2.7 32.6 25.1 -7.5 42.4 48.3 5.9

* * *** *

Zambia 2007 70.6 71.1 0.5 52.5 63.1 10.6 9.7 2.7 -7.0 74.9 72.5 -2.4 * 58.6 66.2 7.7 10.5 0.7 -9.8 68.0 59.7 -8.2 52.3 53.7 1.4 8.4 3.4 -5.1 

2013-14 74.0 79.4 5.4 66.5 70.6 4.1 3.7 6.1 2.4 *** 69.3 76.2 6.9 63.7 74.3 10.6 2.3 7.2 4.9 *** 70.0 66.5 -3.4 64.0 59.9 -4.2 2.7 4.6 2.0 ** 

 * *** ** *** ** *** *** 

For symptoms of ARI, the table is restricted to USAID MCH priority countries with complete information on symptoms of ARI.1 Percentage point difference between care seeking coverage among rural and urban children.2 To test whether the equity gap in care seeking between children in rural and urban households changed significantly between surveys, logistic regression models were run on the two combined surveys with an indicator for survey year and urban/rural residence, and an interaction term between these two indicators. A significant interaction term indicates that the change in care seeking differed significantly between children in urban versus rural households. 

95 
96 

Table A10. Trend in coverage of care seeking among children in poorest and wealthiest quintile households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH priority countries 

Fever Symptoms of ARI Diarrhea 

Care seeking from any 

source 

Care seeking from public 

sources 

Care seeking from private 

sources 

Care seeking from any 

source 

Care seeking from public 

sources 

Care seeking from private 

sources 

Care seeking from any 

source 

Care seeking from public 

sources 

Care seeking from private 

sources 

Country 

Survey 

Year 

Poorest Wealth -iest Diff.1 Sig.2 Poor- est Wealth -iest Diff.1 Sig.2 Poor- est Wealth -iest Diff.1 Sig.2 Poor- est Wealth -iest Diff.1 Sig.2 Poor- est Wealth -iest Diff.1 Sig.2 Poor- est Wealth -iest Diff.1 Sig.2 Poor- est Wealth -iest Diff.1 Sig.2 Poor- est Wealth -iest Diff.1 Sig.2 Poor- est Wealth -iest Diff.1 Sig.2 

Bangladesh 2007 63.5 79.9 16.3 6.8 10.2 3.4 6.8 28.7 21.9 82.5 (89.7) 7.2 11.8 (22.2) 10.4 13.4 (51.1) 37.7 70.4 65.4 -5.0 4.3 12.1 7.8 6.5 20.0 13.5

2011 70.3 77.6 7.3 9.0 8.3 -0.6 40.4 49.5 9.0 *** 77.2 93.0 15.9 9.6 11.5 2.0 42.4 58.4 16.0 73.4 79.1 5.7 13.1 10.4 -2.7 35.9 56.0 20.1

*** *** *** * *** ***

DR Congo 2007 54.5 77.5 23.0 25.2 21.6 -3.6 13.4 42.3 28.9 38.9 38.9 26.2 35.5 9.3 12.5 1.1 -11.4 

2013-14 54.3 65.0 10.7 * 28.5 19.2 -9.4 9.1 22.4 13.3 55.9 52.0 -3.9 28.4 17.8 -10.6 * 8.9 15.0 6.1 *** 

 ** *** * ** *** 

Ethiopia 2005 11.1 40.2 29.1 9.3 29.6 20.2 1.5 8.6 7.2 18.8 40.2 21.4 13.3 31.8 18.5 1.0 5.8 4.8

2011 17.4 43.5 26.1 12.6 24.8 12.2 4.0 16.3 12.3 24.0 60.2 36.2 17.8 38.5 20.8 5.6 18.7 13.2

* ** * **

Ghana 2003 73.9 83.3 9.4 38.6 56.4 17.8 2.7 13.7 10.9 47.1 58.4 11.3 20.4 26.9 6.6 0.6 14.6 14.0 

2008 62.0 88.2 26.2 37.4 52.0 14.6 3.2 27.8 24.7 60.2 (67.7) 7.5 40.2 (34.9) -5.3 0.9 (13.1) 12.2 

* * ** 

Haiti 2005-6 34.1 54.3 20.3 9.9 9.8 -0.1 18.6 32.6 14.0 26.3 (43.2) 16.9 4.9 (9.4) 4.5 16.0 (26.9) 10.9 33.7 44.5 10.8 6.3 15.8 9.5 19.3 20.6 1.4

2012 36.0 57.9 21.9 12.8 18.2 5.4 16.5 36.2 19.7 32.8 60.4 27.6 13.4 12.2 -1.2 11.6 39.0 27.4 36.7 55.3 18.7 15.4 21.1 5.7 14.9 29.6 14.8

* **

Indonesia 2007 82.5 -82.5 25.6 16.7 -8.9 29.2 62.1 32.9 63.9 83.6 19.8 19.8 10.6 -9.2 19.8 54.5 34.7 

2012 85.8 92.3 6.4 33.7 14.3 -19.4 * 35.0 69.1 34.1 78.6 80.8 2.2 * 34.2 14.8 -19.4 31.6 50.1 18.6 * 

 ** * *** *** *** 

Kenya 2003 64.9 73.4 8.6 27.3 25.7 -1.6 15.0 29.6 14.6 40.8 44.0 3.2 22.2 16.0 -6.2 7.6 21.8 14.2

2008-9 64.1 60.1 -4.0 46.1 39.1 -6.9 6.2 13.4 7.2 61.8 55.3 -6.5 42.7 23.6 -19.1 6.1 22.9 16.8

* *** * * ** *** ***

Liberia 2007 63.2 83.2 20.1 20.7 42.7 21.9 21.5 24.4 2.9 79.2 (93.9) 14.6 40.3 (61.9) 21.5 18.2 (29.1) 11.0 69.1 84.2 15.0 23.9 38.4 14.4 13.5 22.5 9.0 

2013 73.7 80.2 6.5 43.2 22.3 -20.9 *** 7.5 39.9 32.4 *** 61.2 (86.2) 25.0 42.5 (7.9) -34.6 *** 7.1 (36.1) 28.9 71.7 73.2 1.6 36.9 24.0 -13.0 * 6.5 25.3 18.8 

* *** * *** * *** * 

Madagascar 2003-4 39.7 60.5 20.8 24.5 27.0 2.6 7.7 33.4 25.6 39.1 38.4 -0.7 24.2 10.5 -13.8 6.6 27.9 21.4

2008-9 42.0 65.5 23.5 28.1 24.6 -3.6 6.4 41.8 35.4 35.3 59.3 24.0 23.7 29.9 6.2 ** 3.2 25.4 22.2

* ***

Mali 2007 52.6 73.2 20.6 20.7 44.9 24.1 2.7 8.1 5.5 30.1 42.3 12.2 12.7 28.2 15.5 1.3 8.3 7.0 

2013 41.7 74.1 32.4 17.2 47.9 30.7 6.9 6.9 42.5 60.3 17.8 14.0 33.1 19.1 3.2 3.5 0.3 * 

** * 

Malawi 2004 75.0 77.9 2.9 31.0 41.6 10.6 5.4 8.8 3.4 56.6 52.4 -4.2 28.8 28.5 -0.4 5.7 12.5 6.8

2010 68.6 76.4 7.8 46.8 48.4 1.6 14.5 25.5 11.0 71.4 66.1 -5.4 53.1 47.8 -5.4 11.8 13.1 1.3

* *** *** *** *** * *** *** **

Mozambique 2003 53.0 73.7 20.7 40.2 65.0 24.8 2.6 3.6 1.0 51.1 53.8 2.7 40.6 46.5 5.9 1.0 1.6 0.6 

2011 57.6 71.1 13.6 53.8 65.8 12.1 2.9 2.9 60.3 72.6 12.4 53.5 64.8 11.3 1.1 0.3 -0.9 

 ** ** * ** 

Nigeria 2008 62.1 74.8 12.7 20.3 29.2 8.9 6.9 18.4 11.6 63.1 76.5 13.4 15.2 30.8 15.6 7.9 17.3 9.3 58.4 67.4 9.0 16.9 31.4 14.5 6.3 20.5 14.1

2013 70.3 73.2 3.0 22.3 28.6 6.2 2.8 15.3 12.5 81.3 81.7 0.4 25.3 43.7 18.4 1.6 23.5 21.9 67.1 67.4 0.4 15.8 28.1 12.3 2.4 10.8 8.4

  **  * ** ** *

Nepal 2006 42.0 69.4 27.5 20.6 18.7 -1.9 3.2 26.1 22.9 53.2 (71.5) 18.3 26.9 (16.2) -10.7 9.3 (38.4) 29.1 29.9 62.2 32.4 21.4 13.7 -7.7 0.1 18.7 18.6 

2011 49.5 74.4 24.9 23.2 9.6 -13.6 7.3 38.3 31.0 55.7 (87.6) 31.9 30.7 (29.3) -1.4 6.1 (45.7) 39.6 51.5 62.1 10.6 28.6 11.3 -17.4 4.8 25.9 21.1 ** 

** *** 

Pakistan 2006-7 75.0 85.2 10.2 10.0 6.9 -3.1 59.4 72.6 13.1 70.5 93.8 23.3 8.6 5.5 -3.1 58.7 84.1 25.4 66.7 81.7 14.9 7.5 5.7 -1.8 55.2 70.9 15.7

2012-13 77.7 86.9 9.2 8.6 8.6 0.0 67.3 75.8 8.6 81.0 87.5 6.4 * 11.4 4.1 -7.4 69.2 83.0 13.8 73.0 81.7 8.7 12.5 7.0 -5.5 61.3 74.1 12.8

*

Rwanda 2005 54.8 76.4 21.7 21.5 34.0 12.6 1.5 12.0 10.5 26.4 35.6 9.2 12.6 13.7 1.1 0.7 4.6 3.9 

2010 41.7 65.4 23.7 35.1 47.5 12.4 11.0 11.0 40.7 63.9 23.2 26.5 43.1 16.7 7.7 7.7 

** * ***   ** * *** *** 

Senegal 2005 46.9 71.4 24.5 29.4 40.5 11.1 7.9 14.6 6.7 32.8 34.0 1.2 17.0 16.9 -0.1 1.1 5.0 3.9

2010-11 39.0 67.7 28.8 28.6 45.7 17.2 1.5 11.4 9.9 * 45.1 53.8 8.7 30.0 34.7 4.7 0.8 7.5 6.8

* *** ** *** **

Tanzania 2004-5 76.8 93.9 17.1 46.9 54.2 7.3 6.6 29.0 22.4 63.1 63.8 0.7 40.7 34.7 -6.0 4.6 20.2 15.6 

2010 72.5 79.7 7.2 49.5 52.6 3.1 1.9 20.4 18.5 63.9 62.2 -1.7 48.9 43.5 -5.4 0.6 13.0 12.4 

 ** * * 

Uganda 2006 84.9 88.4 3.4 40.3 23.5 -16.8 41.7 58.0 16.2 85.1 84.3 -0.8 44.5 15.2 -29.4 39.2 65.4 26.2 84.2 79.2 -5.0 42.4 18.2 -24.2 38.5 55.9 17.4

2011 83.3 90.9 7.7 44.3 21.2 -23.1 38.8 67.0 28.2 80.2 90.1 9.8 38.1 21.3 -16.9 44.3 63.5 19.2 76.0 70.3 -5.7 40.3 18.6 -21.7 35.9 49.5 13.6

0.0 * **

Zambia 2007 70.5 66.0 -4.5 51.2 56.8 5.6 9.5 7.1 -2.4 86.2 (59.3) -26.9 ** 64.8 (54.3) -10.5 13.1 (1.8) -11.3 * 69.9 60.1 -9.9 55.1 52.2 -2.9 6.1 6.8 0.7 

2013-14 72.1 77.2 5.1 64.5 65.5 0.9 4.1 10.0 5.9 * 73.6 85.8 12.2 65.1 72.1 7.0 1.9 7.5 5.6 67.0 62.9 -4.1 60.7 55.3 -5.4 2.9 6.4 3.5 

 ** * * ** 

Figures in parentheses are based on 25-49 unweighted cases. For symptoms of ARI, the table is restricted to USAID MCH priority countries with complete information on symptoms of ARI. 

1 Percentage point difference between care seeking coverage between children in the poorest and wealthiest wealth quintiles.2 To test whether the equity gap in care seeking between children in the poorest and wealthiest quintile households changed significantly between surveys, logistic regression models were run on the two combined surveys with an indicator for survey year and wealth quintile (with children in the middle three quintiles removed), 

and an interaction term between these two indicators. A significant interaction term indicates that the change in care seeking differed significantly between children in the poorest and wealthiest households. 

96 
97 

Table A11. Background characteristics among children with recent diarrhea by intervention 

coverage area, Nepal 2006 and 2011 DHS 

2006 NDHS 2011 NDHS 

NonPOUZN 

Districts 

POUZN 

Districts Total N

NonPOUZN 

Districts

POUZN 

Districts Total N % % % % % %

Any private care sought 

No 96.7 90.6 93.6 584 90.4 80.6 85.1 605

Yes 3.3 9.4 6.4 40 9.6 19.4 14.9 106

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

Any public care sought 

No 78.2 80.4 79.3 495 74.7 77.1 76.0 540

Yes 21.8 19.6 20.7 129 25.3 22.9 24.0 171

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

Any care sought 

No 53.0 45.9 49.4 308 42.1 34.4 37.9 270

Yes 47.0 54.1 50.6 316 57.9 65.6 62.1 441

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

Child was given ORS 

No 74.5 67.1 70.7 441 62.8 59.4 61.0 433

Yes 25.5 32.9 29.3 183 37.2 40.6 39.0 278

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

Child was given zinc 

No 99.2 100.0 99.6 620 93.4 94.2 93.8 663

Yes 0.8 0.0 0.4 3 6.6 5.8 6.2 44

Total 100.0 100.0 100.0 622 100.0 100.0 100.0 707

Child was given ORS and zinc 

No 100.0 100.0 100.0 623 95.2 95.1 95.2 673

Yes 0.0 0.0 0.0 0 4.8 4.9 4.8 34

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 707

Place of residence 

Urban 5.2 18.6 12.0 75 6.3 11.5 9.1 65

Rural 94.8 81.4 88.0 548 93.7 88.5 90.9 646

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

Household wealth quintile 

Poorest 43.6 13.9 28.4 177 31.6 16.6 23.5 167

Poorer 23.1 19.1 21.0 131 26.0 19.9 22.7 162

Middle 16.6 19.6 18.1 113 21.9 28.4 25.4 181

Wealthier 12.0 22.4 17.3 108 14.2 18.0 16.2 116

Wealthiest 4.8 25.0 15.1 94 6.3 17.1 12.1 86

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

Maternal age 

19 yrs or younger 7.0 11.8 9.5 59 8.1 8.3 8.2 58

20-34 78.3 78.5 78.4 489 80.1 82.6 81.4 579

35-49 14.6 9.6 12.1 75 11.9 9.2 10.4 74

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

Mother's education level 

None 65.9 52.2 58.9 367 47.2 50.2 48.8 347

Primary 15.5 19.8 17.7 110 25.6 15.6 20.2 144

secondary or higher 18.6 28.0 23.4 146 27.2 34.3 31.0 220

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

Mother's media exposure 

Listens to the radio or watches 

television less than once per week, 

if at all 40.3 33.3 36.7 229 48.1 47.6 47.8 340 

Listens to the radio or watches 

television at least once per week 59.7 66.7 63.3 395 51.9 52.4 52.2 371

100.0 100.0 100.0 623 100 100 100 711

Child's age 

Less than 1 year 23.1 29.5 26.4 164 25.7 26.7 26.3 187

1-2 years 32.6 29.2 30.9 193 36.5 31.0 33.5 239

2-3 years 18.3 20.7 19.5 121 20.0 20.4 20.2 144

3-4 years 15.0 11.2 13.1 81 10.5 14.6 12.7 90

4-5 years 11.0 9.4 10.2 64 7.3 7.3 7.3 52

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711 

Sex of child 

Female 48.2 42.1 45.1 281 39.2 44.6 42.1 299

Male 51.8 57.9 54.9 342 60.8 55.4 57.9 412

Total 100.0 100.0 100.0 623 100.0 100.0 100.0 711

 
98 

Table A12. Background characteristics among children with recent fever by intervention coverage 

area, Liberia 2007 DHS and 2011 MIS 

2007 LDHS 2011 LMIS 

Non-EQUIP 

Counties 

EQUIP 

Counties Total 

Non-EQUIP 

Counties 

EQUIP 

Counties Total 

% % % N % % % N 

Any public care sought 

No 60.5 78.6 64.2 1,012 69.7 56.9 67.4 955 

Yes 39.5 21.4 35.8 565 30.3 43.1 32.6 461 

Total 100.0 100.0 100.0 1,577 100.0 100.0 100.0 1416 

Any private care sought 

No 82.2 62.9 78.4 1,236 74.9 85.8 76.8 1088 

Yes 17.8 37.1 21.6 341 25.1 14.2 23.2 328 

Total 100.0 100.0 100.0 1,577 100.0 100.0 100.0 1416 

Any care sought 

No 25.4 17.3 23.7 374 22.3 25.3 22.8 323 

Yes 74.6 82.7 76.3 1,203 77.7 74.7 77.2 1093 

Total 100.0 100.0 100.0 1,577 100.0 100.0 100.0 1416 

Place of Residence 

Urban 34.5 4.8 28.6 450 44.4 26.0 41.1 583 

Rural 65.5 95.2 71.4 1,127 55.6 74.0 58.9 833 

Total 100.0 100.0 100.0 1,577 100.0 100.0 100.0 1,416 

Wealth Index 

Poorest 18.8 22.9 19.6 309 28.9 7.1 25.1 355 

Poorer 22.7 34.0 24.9 393 23.3 38.0 25.8 366 

Middle 19.9 26.8 21.3 336 15.1 42.4 19.8 281 

Wealthier 23.8 12.3 21.5 339 19.2 11.0 17.7 251 

Wealthiest 14.9 3.9 12.7 200 13.6 1.4 11.5 162 

Total 100.0 100.0 100.0 1,577 100.0 100.0 100.0 1,416 

Maternal Age 

19 years or younger 7.0 9.7 7.6 119 7.2 3.9 6.6 93 

20-34 years 66.3 62.0 65.5 1,033 68.5 72.4 69.2 980 

35-49 years 26.6 28.3 27.0 425 24.3 23.7 24.2 343 

Total 100.0 100.0 100.0 1,577 100.0 100.0 100.0 1,416 

Maternal Education 

None 47.5 38.2 45.6 719 48.8 33.0 46.1 652 

Primary 33.8 46.7 36.4 574 30.3 37.2 31.5 446 

secondary or higher 18.6 15.1 17.9 282 20.8 29.9 22.4 317 

Total 100.0 100.0 100.0 1,575 100.0 100.0 100.0 1416 

Child's Age 

Less than 1 year 21.6 17.2 20.8 327 20.7 14.9 19.6 278 

1-2 years 23.4 17.0 22.1 349 24.8 23.0 24.5 347 

2-3 years 23.0 27.3 23.9 376 21.3 21.2 21.2 301 

3-4 years 17.7 22.7 18.7 295 16.6 18.9 17.0 241 

4-5 years 14.3 15.9 14.6 230 16.6 22.0 17.6 249 

Total 100.0 100.0 100.0 1,577 100.0 100.0 100.0 1,416 

Sex of Child 

Female 49.0 46.2 48.4 763 47.0 49.8 47.5 672 

Male 51.0 53.8 51.6 814 53.0 50.2 52.5 744 

Total 100.0 100.0 100.0 1,577 100.0 100.0 100.0 1,416 

 
99 

Figure A4. Trend in coverage of any care seeking for fever among children in rural households 

and rural-urban gap in care seeking equity, USAID MCH priority countries 

Figure A5. Trend in coverage of any care seeking for symptoms of ARI among children in rural 

households and rural-urban equity gap in care seeking 

 

Ethiopia

Ghana

Mali

Nigeria

Rwanda

Senegal

Uganda

Malawi

Nepal

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom any care was sought for fever

Survey Year

E

E

Note: Countries are included only if (1) a significant change in care seeking coverage between surveys was observed in rural houseohlds or (2) a significant change in 

urban-rural equity in care seeking coverage was observed between surveys.

Bangladesh

Nigeria Nepal

Uganda

Zambia

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom any care was sought for symptoms of ARI

Survey Year

E

E

Note: Countries are included only if (1) a significant change in care seeking coverage between surveys was observed in rural houseohlds , or

(2) a significant change in urban-rural equity in care seeking coverage was observed between surveys. 

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys
100 

Figure A6. Trend in coverage of any care seeking for diarrhea among children in rural households 

and rural-urban gap in care seeking equity, USAID MCH priority countries 

Figure A7. Trend in coverage of any care seeking for fever among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH priority 

countries 

DR Congo

Ethiopia

Ghana

Indonesia

Kenya

Mali

Nigeria

Nepal

Pakistan

Rwanda

Senegal

Malawi

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom any care was sought for diarrhea

Survey Year

E

DR Congo

Ghana

Liberia Nigeria

Rwanda

Senegal

Malawi

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom any care was sought for fever

Survey Year

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in rural and 

urban households

Solid bubble - Indicates higher coverage 

among children in urban households

Clear bubble - Indicates higher coverage 

among children in rural households

E - Indicates a significant change in the 

R/U gap between surveys

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

rural children between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 
101 

Figure A8. Trend in coverage of any care seeking for symptoms of ARI among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH priority countries 

For the following countries, care seeking estimates for children in the wealthiest quintile are based on 25-49 unweighted cases and should be 

interpreted with caution: Liberia 2007, Liberia 2013, Nigeria 2008, Nigeria 2013, Zambia 2007. 

Figure A9. Trend in coverage of any care seeking for diarrhea among children in poorest-quintile 

households and poorest-wealthiest quintile gap in care seeking equity, USAID MCH priority countries 

For Ghana 2008, care seeking estimates for children in the wealthiest quintile are based on 25-49 unweighted cases and should be interpreted with 

caution. 

Liberia

Nigeria

Pakistan

Zambia

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom any care was sought for 

symptomes of ARI

Survey Year

E

E

DR Congo

Ghana

Kenya

Mali

Malawi

Nigeria

Rwanda

Senegal

Uganda

Indonesia

0

10

20

30

40

50

60

70

80

90

100

2002 2004 2006 2008 2010 2012 2014 2016

Percentage of children for whom any care was sought for diarrhea

Survey Year

E

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys 

Legend

Bubble size - Represents absolute 

percentage point difference in care 

seeking between children in the poorest 

and wealthiest quintile households 

Solid bubble - Indicates higher coverage 

among children in the wealthiest-quintile 

households 

Clear bubble - Indicates higher coverage 

among children in the poorest-quintile 

households 

E - Indicates a significant change in the 

wealth gap between surveys 

- - - (dotted line) Indicates no significant 

change in the level of care seeking among 

children in the poorest wealth quintile 

between surveys 

 (solid line) Indicates a significant 

change in the level of care seeking among 

children in the poorest quintile between 

surveys