DHS SPATIAL ANALYSIS

REPORTS 12

GEOGRAPHIC VARIATION IN KEY 

INDICATORS OF MATERNAL AND 

CHILD HEALTH ACROSS 27 COUNTRIES 

IN SUB-SAHARAN AFRICA

SEPTEMBER 2015

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

Clara R. Burgert-Brucker, Jennifer Yourkavitch, Shireen Assaf, and Stephen Delgado of ICF International, Rockville, MD, USA.

DHS Spatial Analysis Reports No. 12

Geographic Variation in Key Indicators of 

Maternal and Child Health 

Across 27 Countries in Sub-Saharan Africa 

Clara R. Burgert-Brucker 

Jennifer Yourkavitch 

Shireen Assaf 

Stephen Delgado 

ICF International 

Rockville, Maryland, USA 

 

September 2015 

Corresponding author: Clara R. Burgert-Brucker, International Health and Development, ICF 

International, 530 Gaither Road, Suite 500, Rockville, Maryland, USA; phone: 301-572-0446; 

email: clara.burgert@icfi.com 
Acknowledgment: The authors would like to acknowledge Joy Fishel, Erica Nybro, Matt Pagan, and 

Tom Pullum for their assistance and guidance in developing this report, Maria Muniz and Kimberly 

Peven for their review of this report. 

Editor: Bryant Robey 

Document Production: Natalie La Roche 

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

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

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

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

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

DHS Program, ICF 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: 

Burgert-Brucker, Clara R., Jennifer Yourkavitch, Shireen Assaf, and Stephen Delgado. 2015. Geographic 

Variation in Key Indicators of Maternal and Child Health Across 27 Countries in Sub-Saharan Africa.

DHS Spatial Analysis Reports No. 12. Rockville, Maryland, USA: ICF International.
iii 

Contents 

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

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

Abbreviations .............................................................................................................................................. vii

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

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

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

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

1.1 Spatial Implications for Maternal and Child Health ........................................................... 2

1.2 Spatial Distribution of Maternal and Child Health Inequity ............................................... 4

1.3 Analytical Rationale for DHS Spatial Analysis Report 12 ................................................. 5

2 Methods ................................................................................................................................................ 9

2.1 Data ..................................................................................................................................... 9

2.2 Analysis Methods ............................................................................................................. 11

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

3.1 Map Interpretation ............................................................................................................ 14

3.2 Indicator-Level Results ..................................................................................................... 14

4 Discussion .......................................................................................................................................... 35

4.1 Future Directions to Equitably Improve Maternal and Child Health ................................ 37

5 Conclusion .......................................................................................................................................... 39

References ................................................................................................................................................... 41

Appendix A ................................................................................................................................................. 49

Appendix B ................................................................................................................................................. 51
iv 
v 

Tables 

Table 1. Maternal and child health indicators in this analysis .......................................................... 6

Table 2. Country survey year, type, and number of survey regions included in analysis ................. 8

Table A.1. Skilled birth attendant (SBA) definitions by country ....................................................... 49

Table B.1. Country estimate and 95% confidence interval for key indictors ..................................... 51

Figures 

Figure 1. Counties included in study by survey year ......................................................................... 9

Figure 2. Illustration of queen contiguity ......................................................................................... 10

Figure 3. Illustration of positive, negative, and no spatial autocorrelation ...................................... 11

Figure 4. Summary of key indicators by country ............................................................................. 13

Figure 5. Contraceptive prevalence (CPR), any modern method country-level estimate and 

regional comparison .......................................................................................................... 16

Figure 6. Contraceptive prevalence (CPR), any modern method LISA and standard error maps ... 17

Figure 7. Antenatal care, four or more visits (ANC 4+) country-level estimate and regional 

comparison ........................................................................................................................ 18

Figure 8. Antenatal care, four or more visits (ANC 4+) LISA and standard error maps ................. 19

Figure 9. Skilled birth attendance (SBA) country-level estimate and regional comparison ............ 20

Figure 10. Skilled birth attendance (SBA) LISA and standard error maps ........................................ 21

Figure 11. Exclusive breastfeeding (EBF) country-level estimate and regional comparison ............ 22

Figure 12. Exclusive breastfeeding (EBF) LISA and standard error maps ........................................ 23

Figure 13. Measles vaccination country-level estimate and regional comparison ............................. 24

Figure 14. Measles vaccination: LISA and standard error maps ........................................................ 25

Figure 15. DPT3 vaccine coverage country-level estimate and regional comparison ....................... 26

Figure 16. DPT3 vaccine coverage: LISA and standard error maps .................................................. 27

Figure 17. Care seeking behavior country-level estimate and regional comparison .......................... 28

Figure 18. Care seeking behavior: LISA and standard error maps .................................................... 29

Figure 19. Stunting prevalence country-level estimate and regional comparison .............................. 30

Figure 20. Stunting prevalence LISA and standard error maps ......................................................... 31

Figure 21. Under-five mortality rate country-level estimate and regional comparison ..................... 32

Figure 22. Under-five mortality rate LISA and 95% confidence interval width maps ...................... 33

vii 

Abbreviations 

ANC Antenatal care 

CHW Community health workers 

CPR Contraceptive prevalence 

DHS Demographic and Health Survey 

DPT Diphtheria, pertussis, and tetanus 

DRC Democratic Republic of the Congo 

EBF Exclusive breastfeeding 

ESDA Exploratory spatial data analysis 

GIS Geographic Information System 

H4+ An inter-agency mechanism aimed at harmonizing and accelerating actions to improve 

maternal and newborn health, comprising UNICEF, WHO, The World Bank, and UNFPA. 

HIV Human immunodeficiency virus 

MCH Maternal and child health 

MDG Millennium Development Goals 

LISA Local indicator of spatial association 

SBA Skilled birth attendance 

USAID United States Agency for International Development 

WHO World Health Organization 

ix 

Preface 

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

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

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

The DHS Spatial Analysis Reports supplement the other series of DHS reports to meet the increasing 

interest in a spatial perspective on demographic and health data. The principal objectives of all DHS 

report series are to provide information for policy formulation at the international level and to examine 

individual country results in an international context. 

The topics in the DHS Spatial Analysis Reports are selected by The DHS Program in consultation with 

the U.S. Agency for International Development. A range of methodologies are used, including 

geostatistical and multivariate statistical techniques. 

It is hoped that the DHS Spatial Analysis Reports series will be useful to researchers, policymakers, and 

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

used to enhance the quality and analysis of survey data. 

Sunita Kishor 

Director, The DHS Program 

xi 

Abstract 

The Millennium Development Goals expire in 2015. While progress has been made in many areas, it 

appears that the maternal, newborn and child health goals (MDG 4 and 5) will not be universally 

achieved. There was early recognition that it could be possible to achieve the health goals while 

increasing health inequity, because most of the gains might go to the better-off rather than to the very 

poor. Concerns about health inequity indicate the need for analysis of health indicators in small 

geographic units, to aid in the identification of hotspots where coverage of key interventions lags behind 

neighboring areas. This report analyzes the spatial distribution of nine key maternal and child health 

indicators across 27 countries in sub-Saharan Africa. We created maps with 255 polygons representing 

each survey region from 27 DHS surveys, joined to indicator estimates. These maps show sub-regionallevel autocorrelation for each indicator as well as spatial clusters, and we characterized those clusters in 

terms of the relationships among neighboring sub-regions. Patterns of substantial differences among 

contiguous subareas are apparent for different indicators, with some intra-country differences greater than 

20 percentage points for all indicators examined. Our analysis shows some cross-border association with 

groups of high-high or low-low clustering present for all indicators. This analysis facilitates the 

identification of hotspots of low coverage or high need and can be used to allocate resources effectively to 

reduce health inequities between and within countries. 

xiii 

Executive Summary 

In 2000, the United Nations established the Millennium Development Goals (MDGs), in which all 191 

Member States agreed to try to achieve eight development goals by 2015 (United Nations 2000). While 

progress has been made in many areas, it seems certain that the maternal, newborn, and child health goals 

(MDGs 4 and 5) will not be universally achieved (Ebener et al. 2015; UNICEF 2015). Shortly after the 

MDGs were adopted, some recognized that it could be possible to achieve the health goals while 

increasing health inequity, because most of the gains might go to the better-off rather than to the very 

poor (Gwatkin 2002). Addressing inequities in maternal and child health (MCH) is seen as a critical 

strategy to improve health and survival overall (Victora et al. 2003). Geographic inequity in maternal and 

newborn health outcomes is found at regional, national, and subnational levels, and the location of health 

services is a key determinant of access to care (Ebener et al. 2015). 

Concerns about health inequity indicate the need for analysis of health indicators in small geographic 

units to aid the identification of hotspots where coverage of key interventions lags behind neighboring 

areas. Geospatial analysis facilitates the identification of those areas, aiding precise allocation of 

resources and interventions where they are most needed and ultimately leading to effective health 

programming (Burgert 2014; Rosero-Bixby 2004). 

This report focuses on 27 “Countdown to 2015” priority countries in sub-Saharan Africa: Benin, Burkina 

Faso, Burundi, Cameroon, Republic of Congo, Côte d’Ivoire, Democratic Republic of Congo (DRC), 

Ethiopia, Gabon, The Gambia, Ghana, Guinea, Kenya, Liberia, Malawi, Mali, Mozambique, Niger, 

Nigeria, Rwanda, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. Data from the 

most recent DHS surveys conducted in these countries from 2008 to 2013 were used to compute nine key 

indicators (ICF International 2008-2014a): contraceptive prevalence rate (CPR) (any modern method), 

antenatal care with 4+ visits (ANC), skilled birth attendance (SBA), exclusive breastfeeding (EBF), 

measles vaccination, DPT3 vaccine coverage, care seeking behavior, stunting prevalence, and under-five 

mortality rate. 

DHS survey region borders were obtained from the Spatial Data Repository (ICF International 2008-

2014b). Each country has between 3 and 26 regions, with a mean number of regions of 9.4 and median of 

10. Individual country shapefiles were merged into a single feature class in ArcGIS 10.2. The final feature

class had 255 polygons representing each survey region from the 27 DHS surveys. Indicator estimates 

were joined to the polygon dataset from the files created in Stata. The weight matrix defines as spatial 

neighbors any given areas that are touching either a border (edge) or a corner. All regions had at least one 

neighbor. 

We produced maps that show sub-regional-level exploratory spatial data analysis (ESDA) results for each 

indicator as well as spatial clusters, and characterized those clusters in terms of the relationships among 

neighboring sub-regions, i.e., high-high values, high-low values, low-high values, and low-low values. 

The first measure used in this study is global Moran’s I, which gives an indication of the overall spatial 

autocorrelation of a dataset. The second measure is a local indicator of spatial association (LISA) measure 

of local Moran’s I, which indicates the “presence or absence of significant spatial clusters or outliers for 

each location” in a dataset (GeoDa 2015a). In addition to visualizing sub-regional indicator values, the 
xiv 

main purpose of this analysis is to look at the value of an area compared with the values of its neighboring 

areas. The hotspots and coldspots (high-high and low-low clusters respectively) tend to be grouped 

together in several area units, while the high-low and low-high clusters, which indicate “outliers” 

compared with their neighbors, are usually a single area unit. An interactive version of each map is 

available at http://spatialdata.dhsprogram.com/SAR12/ to allow for further exploration and visualization 

of the results. 

The differences in indicator coverage are striking, both at the national level between countries and at the 

subnational level within a country. We observed patterns of substantial differences among contiguous 

subareas for different indicators, with some intra-country differences greater than 20 percentage points for 

all indicators examined. Examples of extreme within-country differences are ANC, SBA, and DPT3 in 

Ethiopia (greater than 70 percentage points); at the other extreme, Malawi has within-country differences 

for CPR and ANC of less than 7 percentage points. We observed statistically significant variation 

between countries for SBA, EBF, DPT3, and stunting. Our analysis shows some cross-border association 

with groups of high-high or low-low clustering present for all indicators. More analysis could be done to 

examine all the factors among subnational regions that lead to the large differences in indicator coverage 

seen in most countries. 

To our knowledge, this is the first study that has applied geospatial analysis to a comprehensive set of 

MCH indicators within and among countries. The spatial autocorrelation analysis presented in this report 

augments the body of established research that identifies and explores spatial implications for maternal 

and child health with a new visualization of subnational population averages of key MCH indicators. This 

analysis facilitates the identification of hotspots of low coverage or high need and can be used to allocate 

resources effectively to reduce health inequities between and within countries. 

This report summarizes a body of research to establish the significance of geography in analyzing health 

indicator coverage differences. It also illustrates the role of geography as a health determinant in 

analyzing coverage inequity, and how geospatial analysis can be used to identify areas of low coverage or 

high mortality or stunting relative to neighboring areas within and between countries. Cross-border 

patterns of maternal and child health service coverage are uniquely identified by spatial autocorrelation 

analysis, which can point to the need for further research to reveal underlying reasons for low coverage 

and to aid efficient targeting of resources. The global community has coordinated multilateral regional 

responses to malaria, HIV, Ebola Virus Disease, and other outbreaks and health conditions; similar efforts 

could be considered to address persistently low coverage of maternal and child health indicators. 
1 

1 Introduction 

In 2000, the United Nations established the Millennium Development Goals (MDGs), in which all 191 

Member States agreed to try to achieve eight development goals by 2015 (United Nations 2000). As the 

deadline for achieving the MDGs nears, however, it is unlikely that the maternal, newborn and child 

health goals (MDG 4 and 5) will be universally achieved (Ebener et al. 2015), although progress has been 

made in many areas (UNICEF 2015). In 2013, 289,000 women died due to conditions related to 

pregnancy or childbirth (WHO 2014). While under-five child mortality is decreasing, neonatal deaths 

have not decreased as quickly and are now 44 percent of all under-five deaths (UNICEF 2014). The 

leading causes of under-five deaths are preterm birth complications, pneumonia, birth complications, 

diarrhea, and malaria, with nearly half of deaths attributable to undernutrition (UNICEF 2014). Estimates 

suggest that most of the 7.6 million deaths of children under age 5 in 2010 could have been averted by 

increased coverage of proven interventions, such as exclusive breastfeeding and skilled birth attendance 

(Chopra et al. 2012). 

Shortly after the MDGs were adopted, some recognized that it could be possible to achieve the health 

goals while increasing health inequity, because most of the gains might go to the better-off rather than to 

the poor (Gwatkin 2002). Addressing inequities in maternal and child health (MCH) is seen as a critical 

strategy to improve health and survival overall (Victora et al. 2003). The World Health Organization 

(WHO) defines health equity as “the absence of avoidable or remediable health differences among groups 

of people, whether those groups are defined socially, economically, demographically, or geographically” 

(WHO 2015a). Geographic inequity in maternal and newborn health outcomes is found at regional, 

national, and subnational levels, and the location of health services is a key determinant of access to care 

(Ebener et al. 2015). There are concerns about growing inequity among socioeconomic groups within the 

boundaries of countries or sub-regions that seem to be generally “doing well” in reducing preventable 

morbidity and mortality (Bhutta and Reddy 2012). While progress has been made over the past decade, 

significant gaps remain, and new tools or methods are needed to accelerate progress. 

Given the spatial dimension of health inequities, it is appropriate to analyze health indicators 

geographically and to make use of the approaches afforded by Geographic Information Systems (GIS) 

and geospatial analysis (Johnson et al. 2015). Geospatial analysis has been used to map the urban poor’s 

access to health services (Adams et al. 2015) and has shown that geographic proximity to health services 

is an important determinant of child survival in sub-Saharan Africa (Becher et al. 2004; Kadobera et al. 

2012; Kazembe et al. 2006; Schoeps et al. 2011). Geo-analytic techniques uncovered residual spatial 

patterning of infant mortality showing higher concentration (“hotspots”) in certain regions of Nepal (Chin 

et al. 2011). Concerns about health inequity indicate the need for analysis of health indicators in small 

geographic units to aid the identification of hotspots where coverage of key interventions lags behind 

neighboring areas. Geospatial analysis facilitates the identification of those areas, aiding precise 

allocation of resources and interventions where they are most needed and ultimately leading to effective 

health programming (Burgert 2014; Rosero-Bixby 2004). 
2 

1.1. Spatial Implications for Maternal and Child Health 

There has been increasing recognition that achieving the MDGs requires understanding and integrating 

the spatial dimension (Chirwa 2014). Qayum et al. identified settlement, forest cover, water bodies, 

rainfall, relative humidity, and temperature as geographic features that can be useful when analyzed 

together with epidemiological and socioeconomic factors to identify disease or service coverage hotspots 

(Qayum et al. 2015). Mapping these features helps to identify the spatial distribution of infectious disease 

and also shows geographic barriers to access to health care and influences on health-related behavior. In 

addition, because geographic barriers can lead to geographic isolation, they may be associated with 

delayed expansion of health programs (Victora et al. 2006) and thus be a cause of health inequity at the 

subnational level. 

Climate change is expected to have its worst effects on the poorest people in low- and middle-income 

countries (Smith and Ezzati 2005). Geospatial and geo-climatic features affect food production and 

consumption patterns, with clear effects on nutritional status and health in different populations. While 

many population groups have adapted to seasonal weather patterns and their effects on food availability, 

prolonged unfavorable weather patterns that restrict access to and availability of food for children, 

adolescents, and pregnant women can have lasting ill effects on their health, since stunting begins in utero 

and has a multigenerational effect (Prendergast and Humphrey 2014). Hookworm, malaria, and other 

infections common to tropical climates and impacted by geographic features can also affect nutritional 

status and contribute to stunting. 

Tanahashi proposed a framework that bridges geographic analysis and health service coverage and use, 

defining four domains of context-specific service provision: availability, acceptability, accessibility, and 

quality (Tanahashi 1978). The framework has been adopted by several multi-country initiatives, including 

H4+, USAID, and WHO (Ebener et al. 2015). It facilitates the identification of geography-related 

bottlenecks to the use of health services, such as distance from a health facility (Peters et al. 2008). Since 

there is evidence that actual and theoretical use of services based on distance needed to travel are highly 

correlated (Noor et al. 2003), the demonstrated inverse relationship between distance from services and 

use of services indicates an important barrier to access (Hjortsberg 2003) that can be assumed even when 

data on the use of services are not available. Peters cites several factors associated with geographic 

accessibility of health services: good roads to transport people and supplies; communication services 

(subject to weather conditions in remote areas); and more money for supervision and other travel-related 

expenses for remote health centers (Peters et al. 2008). 

Considering geographic barriers to access, facility-based services pose an obvious challenge to coverage 

of key MCH indicators. Barros and colleagues cited SBA and four or more ANC visits as the 

interventions with the most inequitable coverage in 54 countries (Barros et al. 2012). Gitimu found that 

living within five kilometers of a health facility was one determinant of SBA use in Kenya (Gitimu et al. 

2015), while Mayhew found that living within 60 minutes travel time was one determinant of SBA use in 

Afghanistan (Mayhew et al. 2008). In addition, numerous studies show that distance from the nearest 

health facility is a significant barrier to seeking care for child illness, both because of the direct cost of 

travel and the indirect cost of time lost during travel (Bennett et al. 2015; Bruce et al. 2014; Feikin et al. 

2009; Kassile et al. 2014). 
3 

Further, an indicator such as care seeking depends on a “supply and demand” interplay that can be 

attenuated by geographic access (Peters et al. 2008). Care seeking for child illness depends upon both 

knowledge and behavior. Caregivers need to recognize illness danger signs in children and decide to seek 

care within a short timeframe (“demand”), and health care services must be available, acceptable, and of 

high quality (“supply”), in addition to being an accessible distance or travel time, and cost (Adedini et al. 

2014). Providing good-quality care in communities can mitigate the barrier of geographic access 

(UNICEF 2012) but may require other investments from health systems, such as maintaining education 

and motivation for caregivers, and training and supervising community-based service providers to ensure 

the quality of services—all of which can be difficult to provide to remote areas. 

Not all maternal and child health indicators rely on facility-based provision of services; spatial analysis of 

these indicators draws upon the spatial distribution of natural and cultural features, considering “the 

context of place” (Callaghan 2014). This broader concept of geography extends beyond physical and 

political boundaries to incorporate social determinants of health (Callaghan 2014). Indicators of health 

behavior in homes and communities, such as exclusive breastfeeding, benefit from spatial analysis that 

can identify where support is needed. Suboptimal infant feeding behaviors affect mothers’ and children’s 

health (WHO 2011a) and can affect stunting and nutritional status. Since breastfeeding outcomes are 

better when women are supported in their breastfeeding efforts (Britton et al. 2007), the challenge for 

health service providers is to ensure that breastfeeding support is not limited to providers at health 

facilities but extends into communities through community health workers (CHW) and trained peer 

support groups, to help provide support to all those who need it, where they need it. 

Community-based interventions should be prioritized as a way to improve coverage and increase equity 

(Carrera et al. 2012). Several kinds of health services have been provided in communities to overcome 

geography-related access barriers and to increase coverage. The Reaching Every District campaign 

adopted by WHO, UNICEF, and other partners seeks to improve immunization coverage in high-need 

areas. A key component to improving immunization coverage is to maintain regular outreach services 

(WHO 2005). In addition, this approach encourages countries and districts within countries to use 

coverage data to analyze the distribution of unimmunized children and to prioritize areas with poor access 

and low levels of use (Vandelaer et al. 2008). Community-based distribution of contraceptives has also 

contributed to increasing their use in different settings, including rural communities and isolated urban 

neighborhoods (Prata et al. 2005). Community-based interventions challenge health systems to make 

trained staff and supplies available where people live. Increasing reliance on CHWs to deliver highquality health services that can overcome geographic barriers to care requires government support for this 

cadre, whose work has gained recognition as a critical component of ensuring population health (Bhutta et 

al. 2010). 

The spatial autocorrelation analysis presented in this report augments the body of established research that 

identifies and explores spatial implications for maternal and child health with a new visualization of 

subnational population averages of key MCH indicators. This analysis facilitates the identification of 

hotspots of low coverage or high need and can be used to allocate resources effectively to reduce health 

inequities between and within countries. 
4 

1.2. Spatial Distribution of Maternal and Child Health Inequity 

Place of residence is a key dimension of health inequity (Gwatkin 2007a). Geographic variations in 

coverage of maternal and child health indicators can reveal inequities between and within countries. 

Identifying areas of need is the first step in discovering and addressing causes of inequity, whether 

political, environmental, cultural, or socioeconomic. In Uganda, for example, a study found that poor 

geographic access to health care overlapped with poverty, in that the two regions with the worst access to 

health care were also the regions where large segments of the population (63 percent and 46 percent) 

lived below the poverty line (Kiwanuka et al. 2008). Distance was a barrier to obtaining health care for 20 

percent of the poorest in Uganda compared with only 9 percent of the richest (Odaga 2004). There is 

some evidence that improving accessibility to health care in low- and middle-income countries can reduce 

socioeconomic gaps in care (Peters et al. 2008). 

WHO reports in The State of Inequality: “Overall, inequalities were to the detriment of women, infants 

and children in disadvantaged population subgroups; that is, the poorest, the least educated and those 

residing in rural areas had lower health intervention coverage and worse health outcomes than the more 

advantaged” (WHO 2015c) p. 1). Research by Victora et al. found an inequitable distribution of child 

survival interventions within countries; the authors recommended equity analyses based on geographical 

deployment of interventions to assess whether these interventions reach people most in need (Victora et 

al. 2006). Gwatkin et al. found large disparities in health conditions and health service use overlapping 

with geographic health disparities among economic quintiles within 56 countries (Gwatkin et al. 2007b). 

In addition, there is evidence that where fertility is higher among poor people there will be a higher-thanaverage proportion of “newborns at risk for infant mortality,” among other adverse health outcomes 

within poor groups (Gwatkin et al. 2007b). 

An example from the Democratic Republic of Congo shows that malnutrition is spatially structured—

higher in rural areas than in urban centers—and overlaps with political and economic factors: 

Malnutrition is high in provinces where mining is the main industry, where there is conflict, and where 

food is produced, likely due to the economic benefit of selling food versus consuming it (Kandala et al. 

2011). These findings showing the spatial distribution of malnutrition and its underlying relationship to 

poverty have implications for programs designed to improve nutrition status. 

Reducing health inequity requires policies tailored to geographic areas. Targeting health interventions to 

high-need populations can be a cost-effective approach to reducing child mortality and can reduce 

inequities in coverage between the most and least deprived geographic areas (Carrera et al. 2012), but the 

strategy requires first knowing where to find high-need populations. Spatial autocorrelation analyses like 

the analysis presented in this report can identify those areas. Some researchers have recommended the 

inclusion of geo-referenced information in all surveys (Wirth et al. 2006), to facilitate the identification of 

such areas. Monitoring progress across geographic and socioeconomic indicators between and within 

countries can determine if programs and policies are benefiting the poorest people (Tugwell et al. 2007). 
5 

1.3. Analytical Rationale for DHS Spatial Analysis Report 12 

Maternal and child health are key priorities for the global health community, including the United States 

Agency for International Development (USAID), WHO, and the United Nations. In addition to the 

MDGs, in 2011 the UN Commission on Information and Accountability for Women’s and Children’s 

Health (the Commission) issued a report, “Keeping Promises, Measuring Results,” which identified 11 

core indicators to monitor, review, and act on the health of women and children across the continuum of 

care (WHO 2011b). In 2013 USAID issued its “Global Health Strategic Framework,” which establishes 

the agency’s core global health priorities for 2012-2016. These include saving mothers’ lives, improving 

child survival, and enhancing family planning and reproductive health (USAID 2013). These global 

strategic priorities and indicators underpin our analysis. 

Sub-Saharan Africa is a compelling region to examine because of the high need and low coverage of 

several of the selected indicators and uneven progress on addressing the problem. Our timeframe, 

determined by using the most recent survey in the last seven years among the countries studied, provides 

an important check on health equity by examining coverage differences among sub-regions during the 

second half of the MDG monitoring period. 

A systematic review of 621 articles about using geospatial analysis in health research found that studies 

focusing on particular health problems are less common than studies focusing on disease surveillance, 

access to care, or environmental risk analysis (Nykiforuk and Flaman 2011). A rapid review by Ebener et 

al. found 33 articles focused on maternal and neonatal health, with a noticeable increase since 2010 

(Ebener et al. 2015; Nykiforuk and Flaman 2011). DHS Spatial Analysis Report 12 augments this body of 

research by exposing the subnational geographic variability of nine maternal and child health indicators 

across 27 countries in sub-Saharan Africa and identifying areas of high or low coverage through 

comparison with neighboring areas, a methodology that is also known as spatial autocorrelation analysis. 

Identifying subnational geographic areas of high need can inform research on why MCH indicators lag in 

certain areas, in order to direct resources effectively where they are needed most. Table 1 explains the 

indicators included in this analysis. 

This report summarizes a body of research to establish the significance of geography in analyzing 

differences in coverage of health indicators by explaining the geographic factors that influence maternal 

and child health, setting the stage for our analysis of differences in coverage among contiguous political 

sub-regions. It also illustrates the role of geography as a health determinant in analyzing coverage 

inequity, and how geospatial analysis can be used to identify areas of low coverage or high mortality or 

stunting relative to neighboring areas within and between countries. Cross-border patterns of MCH 

service coverage are uniquely identified by spatial autocorrelation analysis, which can point to the need 

for further research to reveal underlying reasons for low coverage and to aid efficient targeting of 

resources. For example, cultural and language connections across country borders may be associated with 

similar health behaviors (Amin et al. 2002) or illness patterns, and consequently may drive bi-national 

hotspots, implying a need for coordinated efforts to address them. In addition, infectious diseases do not 

conform to political boundaries. The global community has coordinated multilateral regional responses to 

malaria, HIV, Ebola Virus Disease, and other outbreaks and health conditions; similar efforts could be 

considered to address persistently low coverage of maternal and child health indicators. 
6 

Table 1. Maternal and child health indicators in this analysis 

Indicator Definition Significance Status 

Maternal health indicators 

Contraceptive 

prevalence 

(CPR), any 

modern method 

Proportion of women age 15-49 

who report current use of a modern 

contraceptive method1

Contraceptive use reduces the risk of 

maternal mortality and increases child 

survival (Rutstein 2008; Singh et al. 2010). 

From 1990 to 2012, in sub-Saharan Africa the 

CPR among women married or in union 

increased from 13 to 26 percent (United 

Nations 2014). 

Antenatal care 

(ANC) 4+ visits 

Proportion of women age 15-49 

who made four or more antenatal 

care visits during their last 

pregnancy within the last five years 

ANC prevents or identifies and treats 

conditions that could threaten the health of 

mother and fetus or newborn (Banta 2003). 

ANC may be positively correlated with SBA 

in some countries (Mishra and Retherford 

2006). 

From 1990 to 2012, in sub-Saharan Africa the 

proportion of ANC attendance at four or more 

visits increased from 48 to 50 percent (United 

Nations 2014). 

Skilled birth 

attendance (SBA) 

Proportion of most-recent live 

births during the past five years 

that were attended by skilled health 

personnel2 

SBA reduces risk of maternal and newborn 

morbidity and mortality (Carlough and 

McCall 2005). 

From 1990 to 2012, in sub-Saharan Africa the 

proportion of deliveries attended by skilled 

health personnel increased from 40 to 53 

percent (United Nations 2014). 

Child health indicators 

Exclusive 

breastfeeding 

(EBF) 

Proportion of last-born infants 

under age 6 months who are living 

with the mother and breastfeeding 

and have not had any water, 

liquids, or solids in the day or night 

preceding the interview 

EBF is an effective intervention requiring 

few system inputs (Roberts et al. 2013). 

Suboptimal breastfeeding practices 

increase childhood risks of morbidity and 

mortality (Lim et al. 2012). 

Prevalence of EBF in West and Central Africa 

increased from 12 percent in 1995 to 28 

percent in 2010, and from 35 percent to 47 

percent in Eastern and Southern Africa (Cai et 

al. 2012). 

Measles 

vaccination 

Proportion of children age 12-23 

months who received the measles 

vaccination at any time prior to the 

survey 

Although it is preventable with 

immunization, measles is still a leading 

cause of death for children under age 5. In 

addition, serious complications in children 

under age 5 can lead to severe diarrhea 

and pneumonia. Measles is highly 

contagious, and infected pregnant women 

risk severe complications (WHO 2015b). 

Herd immunity from measles requires 

immunization coverage of at least 90 

percent (Luna et al. 2014). 

Coverage with one dose of vaccine among 

one-year-olds (two doses are recommended) 

increased in the WHO Africa Region from 53 

percent in 2000 to 74 percent in 2013 (WHO 

Regional Office for Africa 2015). 

DPT3 vaccine 

coverage 

Proportion of children age 12-23 

months who received three doses 

of DPT vaccine at any time prior to 

the survey 

Diphtheria, pertussis, and tetanus (DPT) 

are vaccine-preventable diseases that 

cause substantial global disease burden 

among children under age 5. 

As of 2013, DPT3 immunization coverage 

among one-year-olds across Africa was 75 

percent, the lowest among all WHO regions 

(WHO 2013). 

Care seeking 

behavior 

Proportion of children age 0-59 

months who had cough, diarrhea, 

or fever in the last two weeks and 

sought treatment 

Prompt diagnosis and appropriate 

management of diarrhea, malaria, and 

pneumonia is crucial for reducing underfive morbidity and mortality (Bennett et al.

2015). 

Only 28 percent of children with fever were 

taken for treatment to a public health facility 

(Gething et al. 2010). Only 43 percent of 

children with symptoms of acute respiratory 

infection in low-income countries were taken to 

a health care provider (Das et al. 2013). It is 

likely that many children are not taken for 

treatment at all, making surveillance and 

disease management difficult in these 

countries. Coverage of oral rehydration salts 

(ORS), used in the treatment of diarrhea, was 

35 percent globally between 2008 and 2012 

(Bennett et al. 2015). 

(Continued) 
7 

Table 2. − Continued 

Indicator Definition Significance Status

Child health indicators 

Stunting 

prevalence 

Proportion of de facto children age 

0-59 months whose height-for-age 

z-score is less than 2 standard 

deviations below the median on the 

WHO 2006 international reference 

standard 

Stunting is a sign of undernutrition that 

occurred during pregnancy and the first two 

years of life. It increases lifetime risks of 

impaired health and affects educational 

and economic performance (Dewey and 

Begum 2011). Stunting is also the best 

proxy measure for child health inequalities 

(Wamani et al. 2007). 

Stunting prevalence ranges from 12 percent to 

30 percent among 16 sub-Saharan African 

countries (Wamani et al. 2007). 

Under-five child 

mortality rate3

Number of deaths among children 

under age 5 in the five-year period 

preceding the survey per 1000 live 

births3

Impact indicator From 1990 to 2012, in sub-Saharan Africa the 

under-five mortality rate declined from 177 to 

98 deaths per 1,000 live births, a decrease of 

nearly 45 percent. However, this is still the 

highest regional mortality rate in the world for 

children under age 5. In 2012, 3.2 million 

children under age 5 in sub-Saharan Africa 

died, accounting for nearly half of global underfive deaths (United Nations 2014). 

1

Modern contraceptive methods include female sterilization, male sterilization, the contraceptive pill (oral contraceptives), intrauterine contraceptive 

device (IUD), injectables (Depo-Provera), implants (Norplant), female condom, male condom, diaphragm, contraceptive foam and contraceptive 

jelly, lactational amenorrhea method (LAM), country-specific modern methods and other modern contraceptive methods mentioned by the 

respondent (including cervical cap, contraceptive sponge, and others). Abortion, menstrual regulation, and withdrawal are NOT considered modern 

contraceptive methods. 

2

The definition of “skilled health personnel” or “skilled birth attendant” can vary by country but commonly includes doctors, nurses, midwives, and 

auxiliary midwives. See Appendix A for country-specific definitions of skilled birth attendant (SBA). 3

The DHS Program uses a synthetic cohort life table approach to directly estimate the under-five mortality rate. Details may be found in the Guide to 

DHS Statistics, pp. 90-95 (http://www.dhsprogram.com/publications/publication-DHSG1-DHS-Questionnaires-and-Manuals.cfm). 

1.3.1. Analysis objectives 

This report uses the most recent data from DHS surveys conducted in the selected 27 countries, almost all 

of which occurred in the last five years. The units of analysis are DHS regions, which correspond to 

administrative level-one areas (e.g. provinces) or a combination of such areas within each survey country 

(Table 2). The data are statistically representative estimates of each indicator of interest for the population 

within each DHS region at the time of each survey. 

Broadly, the report uses exploratory spatial data analysis (ESDA) methods to visualize and measure the 

spatial association between and among regions that are spatially contiguous. Specifically, we will: 

1. Visualize the spatial patterns of MCH indicators across 27 countries and their sub-regions in subSaharan Africa. This analysis provides a quantitative assessment of each indicator both within

and across national boundaries.

2. Identify areas with statistically significant clustering of high values (hotspots) or low values

(coldspots), as well as spatial outliers, for the selected indicators. This analysis provides a

statistical intra- and inter-country assessment of relatively high and low performing areas with

respect to geographically proximal areas.
8 

Table 3. Country survey year, type, and number of survey regions included in analysis 

Country Survey Year Survey Type 

Number of Survey 

Regions 

Benin 2012 DHS 12 

Burkina Faso 2010 DHS 13 

Burundi 2010 DHS 5 

Cameroon 2011 DHS 12 

Congo 2012 DHS 12 

Cote d’Ivoire 2012 DHS 11 

Democratic Republic of 

the Congo (DRC) 2014 DHS 11 

Ethiopia 2011 DHS 11 

Gabon 2012 DHS 10 

Gambia 2013 DHS 8 

Ghana 2008 DHS 10 

Guinea 2012 DHS 8 

Kenya 2008-09 DHS 8 

Liberia 2013 DHS 5 

Malawi 2010 DHS 3 

Mali 2013 DHS 6 

Mozambique 2011 DHS 11 

Niger 2012 DHS 8 

Nigeria 2013 DHS 6 

Rwanda 2010 DHS 5 

Senegal 2011 DHS 14 

Sierra Leone 2013 DHS 4 

Tanzania 2010 DHS 26 

Togo 2013-14 DHS 6 

Uganda 2011 DHS 10 

Zambia 2013 DHS 10 

Zimbabwe 2011 DHS 10 
9 

2. Methods

2.1. Data 

This report focuses on the following 27 countries in sub-Saharan Africa: Benin, Burkina Faso, Burundi, 

Cameroon, Republic of Congo, Côte d’Ivoire, Democratic Republic of Congo (DRC), Ethiopia, Gabon, 

The Gambia, Ghana, Guinea, Kenya, Liberia, Malawi, Mali, Mozambique, Niger, Nigeria, Rwanda, 

Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe (Figure 1). All of these are 

considered high-burden priority countries for the Countdown to 2015 (WHO and UNICEF 2012). 

Figure 1. Counties included in study by survey year 
10 

2.1.1 Survey data 

Data from the most recent DHS surveys conducted in these 27 countries between 2008 and 2013 were 

used to compute the nine key indicators described in Table 1 (ICF International 2008-2014a). These 

indicators cover three maternal health indicators—contraceptive prevalence (CPR) (any modern method), 

four or more ANC visits (ANC 4+), and skilled birth attendance (SBA)—and six child health indicators—

exclusive breastfeeding (EBF), measles vaccination, DPT3 vaccine coverage, care seeking behavior, 

stunting prevalence, and under-five mortality rate. All of these indicators have a standardized definition in 

the DHS, as Table 1 shows, except for SBA, which is a country-specific variable. In 23 out of the 27 

countries in this analysis, the “SBA” designation is restricted to a professional cadre, generally including 

a doctor, nurse, midwife, or trained birth attendant, in line with the generally accepted definition of skilled 

attendance; however, four countries include other categories of attendants as SBAs. Therefore, comparing 

this indicator across countries is challenging, and the results should be interpreted cautiously. Appendix A 

lists the country-specific definitions of SBA. 

The key indicators were constructed according to the definitions shown in Table 1 and indicator estimates 

calculated for the country as a whole as well as for each DHS sample region. For SBA, individuals with 

missing responses were excluded from the denominator. Sampling weights and the stratified sample 

design were used to obtain the estimates and their confidence intervals and standard errors using Stata 

software version 13.0. The standard error or 95% confidence interval width are displayed in the results to 

provide context for evaluating the precision of the estimates. The micro-level datasets were used instead 

of data directly from the Spatial Data Repository linked to the STATCompiler database because standard 

error and confidence intervals are not available for all these indicators in the STATCompiler database 

2.1.2 Spatial Data 

DHS survey region borders were obtained from the 

Spatial Data Repository (ICF International 2008-2014b). 

Each country has between 3 and 26 regions, with a mean 

number of regions of 9.4 and median of 10. Table 2 

summarizes the number of regions per country. Individual 

country shapefiles were merged into a single feature class 

in ArcGIS 10.2. Topographic polygon overlap errors were 

cleaned using ArcGIS, but some small gap slivers were 

left in the dataset. The final feature class had 255 

polygons representing each survey region from the 27 

DHS surveys. Indicator estimates were joined to the 

polygon dataset from the files created in Stata. A spatial 

weight matrix was constructed in ArcGIS using the 

Spatial Statistics Tools and a “Contiguity Edges Corners” (i.e. Queen) conceptualization of spatial 

relationships and row standardization. The ArcGIS approach to creating the spatial weight matrix is more 

forgiving of topology issues then the method used in GeoDa software. This weight matrix defines as 

spatial neighbors any given areas that are touching either a border (edge) or a corner. All regions had at 

least one neighbor. Figure 2 illustrates in green all the first order neighbors for location X (yellow 

square), these are all contiguity edge corners, or a queen contiguity. 

Figure 2. Illustration of queen contiguity 
11 

2.2. Analysis Methods 

This report uses exploratory spatial data analysis (ESDA) techniques to measure the spatial 

autocorrelation between and among regions that are spatially contiguous. We used two ESDA techniques 

for this analysis. The first technique is a simple visual inspection of the indicator values displayed on a 

map in five categories. The category definitions differ between the maps and are defined in each map 

legend. Several maps have equal interval categories, such as 0-20 percent, 21-40 percent, 41-60 percent, 

61-80 percent, and 81-100 percent. This technique has the advantage of being more easily interpretable by 

non-geospatial specialist but, unlike some other techniques used for displaying continuous data (e.g. jenks 

natural breaks), may place data that is close in value in different bins instead of breaking the data into 

groups more closely resembling the actual trends in the dataset. 

The second technique uses two measures of spatial autocorrelation—that is, the degree to which one area 

is similar to or different from its neighboring areas. The Moran’s I statistic measures clustering in a 

dataset: Values closer to zero indicate no or random spatial clustering, positive values indicate spatial 

clustering where neighbors tend to have similar values, and negative values indicate that neighbors tend to 

have different values. Figure 3 illustrates an example of positive, negative, and no spatial autocorrelation, 

where each grid square could represent a different subnational region. 

Figure 3. Illustration of positive, negative, and no spatial autocorrelation 

The first measure used in this study is global Moran’s I, which gives an indication of the overall spatial 

autocorrelation of a dataset. The second measure is a local indicator of spatial association (LISA) measure 

of local Moran’s I, which indicates the “presence or absence of significant spatial clusters or outliers for 

each location” in a dataset (GeoDa 2015a). The Global Moran’s I and LISA analyses were conducted in 

the software package GeoDa with 99,999 permutations and a pseudo p-value for cluster of <0.05 

specified (GeoDa 2015a). GeoDa uses a numerical, data-driven approach to test for statistical significance 

that does not assume that the underlying distribution of the data is normal. However, this means that the 

p-value measured is dependent on the number of permutations chosen; in this analysis we chose the 

maximum number of permutations allowed, which is the most conservative approach. The term “pseudo 

p-value” refers to its dependence on the number of permutations, rather than its independent selection. 

Due to the random number selection that is part of this analysis process, the results would be slightly 

different each time the analysis is run, and thus the results are not exactly replicable. 
12 

The LISA analysis results are output in three ways: as a scatter plot; a pseudo p-value spatial layer; and a 

spatial layer that shows up to five types of spatial association and outliers, as follows (GeoDa 2015b): 

• not significant: Areas that are not significant at a pseudo p-value level of 0.05. 

• high-high: High values surrounded by high values. (Note: these values are not “high” in the 

sense that their absolute value is necessarily high; rather, they are the high values from this 

dataset.) 

• low-low: Low values surrounded by low values. (Note: these values are not “low” in the sense 

that their absolute value is necessarily low; rather, they are the low values from this dataset.) 

• low-high: Low values surrounded by high values. 

• high-low: High values surrounded by low values 

The three outputs from the LISA analysis show the same results in different ways and with slightly 

different purposes, and thus we have chosen to show only the spatial association layer in this report, 

which is most relevant for our study objective. 
13 

3. Results

Countries vary widely for all the indicators studied. Figure 4 illustrates the nine key indicators by country, 

depicting when a country is in the top (green) or bottom (orange) quintile for that indicator, among the 

counties studied. For most of the indicators high values are “good” while low values are “bad,” except for 

stunting prevalence and under-five mortality rate, where the opposite is true. Thus, high values for 

stunting and under-five mortality are highlighted in the bottom quintile, while low values for stunting and 

child mortality are highlighted in the top quintile. The country point estimates and 95% confidence 

intervals appear in Appendix B. 

There is some difference between the relative ranking of the key indicators for maternal health and those 

for children’s health. Some countries were most often in the bottom 20 percent of the countries for child 

health, including Ethiopia, Guinea, Niger, and Nigeria. Others were more likely to be in the top 20 

percent for child health indicators, including Burundi, Ghana, Malawi, The Gambia, and Rwanda. For 

maternal health, the lowest 20 percent often included Ethiopia, Guinea, and Niger, while the highest 20 

percent often included Congo and Gabon. Ethiopia is in the bottom quintile for both maternal and child 

health indicators. Burundi, Malawi, and Rwanda are all relatively high performers for several child health 

indicators but are also all in the worst quintile for child stunting prevalence. 

Figure 4. Summary of key indicators by country Benin DHS 2011-2012 Burkina Faso DHS 2010 Burundi DHS 2010 Cameroon DHS 2011 Congo DHS 2011-2012 Cote d’Ivoire DHS 2011- DRC DHS 2013-2014 Ethiopia DHS 2011 Gabon DHS 2012 Gambia DHS 2013

Ghana DHS 2008Guinea DHS 2012Kenya DHS 2008-2009Liberia DHS 2013Malawi DHS 2010Mali DHS 2012-2013Mozambique DHS 2011Niger DHS 2012Nigeria DHS 2013Rwanda DHS 2010Senegal DHS 2010-2011Sierra Leone DHS 2013Tanzania DHS 2010Togo DHS 1998Uganda DHS 2011Zambia DHS 2013-2014Zimbabwe DHS 2010-2011

CPR, any modern method 9 14 11 16 22 14 8 19 24 7 14 7 28 21 33 10 12 11 11 25 9 21 24 17 21 33 41

ANC 4+ 58 34 33 62 79 44 48 19 78 78 78 57 47 78 46 41 51 33 51 35 50 76 43 57 48 56 65

SBA 85 73 63 68 95 63 81 12 92 59 77 43 48 65 74 61 60 33 40 72 87 74 55 62 61 69 67

EBF 34 25 69 20 21 12 48 52 6 47 63 21 32 55 72 34 41 23 17 85 39 32 53 58 62 73 32

Care seeking behavior 70 87 94 71 75 65 72 56 74 88 90 62 85 74 93 72 82 69 42 95 82 79 85 74 76 85 79

Measles vaccination 62 90 95 68 56 64 61 37 22 88 89 50 86 71 93 63 76 68 38 97 83 78 88 83 72 86 73

DTP3 vaccine coverage 55 61 57 55 59 61 56 29 61 66 64 53 59 73 70 50 60 61 73 47 48 73 76 55 78 71 40

Stunting prevalence 45 35 58 33 24 30 43 44 17 25 28 31 35 32 47 38 43 44 37 44 27 38 42 28 33 40 32

Under-five mortality rate 70 128 96 122 68 108 104 88 65 54 80 123 74 94 112 95 97 127 128 76 72 156 81 88 90 75 84

*For stunting prevalence and under-five mortality high-values are worse, while low values are better

Orange squares indicate countries that are in the worst five countries for that indicator among studied countries

Green squares indicate countries that are in the best five countries for that indicator among studied countries
14 

3.1. Map Interpretation 

Regional-level ESDA results for each indicator are shown in Figures 6, 8, 10, 12, 14, 16, 18, 20, and 22. 

Each figure contains two maps, one larger and one smaller. 

The larger map shows indicator values ranging from low values (in yellow) to high (in blue). The 

univariate LISA output overlays the indicator-level data: 

• The high-high spatial clusters (red dot marks) are the hotspots and low-low spatial clusters 

(orange dot marks) are the coldspots. They represent positive spatial autocorrelation or locations 

surrounded by neighbors with similar values. 

• The high-low (purple hatch marks) and low-high (pink hatch marks) locations are spatial outliers; 

which represent negative spatial autocorrelation or locations surrounded by neighbors with 

dissimilar values. 

• Locations that do not display any hatch marks are those areas where there is no spatial 

autocorrelation or significant clustering detected among neighboring regions. 

The smaller map shows the standard error or width of the 95% confidence interval (under-five mortality 

rate only) for the indicator value, ranging from low (light yellow) to high (red). The standard error maps 

use the same five categories for the data display, which means that some maps do not display data in each 

category. 

The main purpose of this analysis is to look at the value of an area compared with the values of its 

neighboring areas, rather than the absolute values of areas. The hotspots and coldspots (high-high and 

low-low clusters respectively) tend to be grouped together in several area units, while the high-low and 

low-high clusters, which indicate outliers compared with their neighbors, are usually a single area unit. 

This analysis highlights the relationship of indicator values between and among neighboring areas. As 

mentioned previously, for all indicators except stunting and under-five mortality, areas of high-high are 

“good” and areas of low-low are not good; for stunting and under-five mortality it is the opposite. Not all 

types of spatial associations exist for every indicator. The spatial clustering patterns may at times not 

seem to be completely matched with the underlying data layer showing the indicator values. This is due to 

the categorization that is necessary in displaying continuous measures on a one-dimensional map; thus 

values that might be close in an absolute sense are in different categories depending on how the data were 

categorized. 

An interactive version of each map is available at http://spatialdata.dhsprogram.com/SAR12/ to allow for 

further exploration and visualization of the results. 

3.2. Indicator-Level Results 

Overall, the indicator-level results show as wide a distribution of values of the indicators at the 

subnational level as was observed at the national level (Figure 4, although the national estimates mask 

large subnational variation in some cases. The lowest subnational region value and highest subnational 

region value for each country, along with the national estimate, are presented for each indicator 
15 

respectively in Figures 5, 7, 9, 11, 13, 15, 17, 19, and 21. These figures illustrate the country-level 

geographic differences in indicator values. 

Global Moran’s I statistics vary across the nine indicators studied. Although the global Moran’s I may be 

statistically non-significant, local areas of spatial clustering (i.e., statistically significant local Moran’s I) 

may exist at among the countries of sub-Saharan Africa and at subnational levels, highlighting the need to 

evaluate local as well as global statistics—that is, aggregate statistics for the geographic areas included in 

the study. A statistically significant positive global Moran’s I suggests spatial autocorrelation across 

national borders, highlighting the need to examine across countries and not just within them. A 

statistically significant negative global Moran’s I indicates non-random geographical contrasts within the 

region, highlighting the need to identify the scale and location of these differences. Across the 27 

countries, in the maps (Figures 6, 8, 10, 12, 14, 16, 18, 20, and 22) some areas are more consistently in 

one of the LISA cluster categories, including some areas of the West African Sahel and East Africa. 

Across indicators, the high-high and low-low clusters often cross national borders, while the outliers often 

have at least one neighboring area in a different country. The overall standard errors for the estimates are 

within an acceptable range for all the indicators studied, even those for rare events such as mortality. 
16 

3.2.1. Maternal health indicators 

Contraceptive prevalence (CPR), any modern method 

Use of any modern method of contraception is generally low (< 50 percent) throughout and across the 

countries in this analysis (Figure 5). Most countries have inter-country differences of greater than 10 

percentage points, with the exception of Malawi with a difference of 3 percentage points between the 

highest and lowest regions. In contrast, Kenya has a difference of 40 percentage points between its lowest 

and highest regions. 

The global Moran’s I statistic indicates no significant spatial clustering at the regional level (Global 

Moran’s I: -0.049, p-value: 0.152). The LISA map shows areas of high-high spatial association in the East 

and Southern Africa while low-low spatial clustering is focused across West and in Central Africa (Figure 

6). These spatial clusters cover many area units and several countries. A few areas of low-high outliers in 

Ethiopia and Mozambique are contiguous to the high-high cluster stretching through eastern and Southern 

Africa. The standard errors for the prevalence estimates are uniformly low. 

Figure 5. Contraceptive prevalence (CPR), any modern method country-level estimate and 

regional comparison 
17 

Figure 6. Contraceptive prevalence (CPR), any modern method LISA and standard error maps 
18 

Antenatal care, four or more visits (ANC 4+) 

ANC 4+ has striking coverage variation among countries in this analysis, as well as large coverage 

differences within countries. Coverage ranges from 7 percent in the lowest region of Ethiopia to 90 

percent in the highest region of Ghana (Figure 7). Some countries, including Ethiopia, have very high 

intra-country differences (79 percentage points), while others, such as Malawi and Sierra Leone, have less 

than 7 percentage points between their highest and lowest regions. 

There is non-significant positive spatial autocorrelation seen at the regional level (Global Moran’s I: 0.06, 

p-value: 0.067). ANC 4+ has high-high spatial association along the coastline of West and Central Africa, 

and low-low association in both West and eastern Africa inlands (Figure 8). A couple of “belts” of lowlow coverage stretch across the Sahel and into the Sahara, and along the horn of Eastern Africa. A few 

spatial outliers of low-high are present around the high-high clusters in West and Central Africa. There is 

one area of high-low association, which is contiguous to a low-low cluster in the Great Lakes Region. The 

standard errors for the coverage estimates are nearly uniformly low. 

Figure 7. Antenatal care, four or more visits (ANC 4+) country-level estimate and regional 

comparison 
19 

Figure 8. Antenatal care, four or more visits (ANC 4+) LISA and standard error maps 
20 

Skilled birth attendance (SBA) 

Like ANC 4+, there are large intra-country and inter-country differences in SBA coverage, from 8 percent 

in Ethiopia to nearly 100 percent in Congo (Brazzaville) (Figure 9). All countries studied have at least one 

region with SBA coverage higher than 75 percent, while 22 countries have at least one region with 

coverage less than 60 percent. 

There is significant positive spatial autocorrelation seen at the regional level (Global Moran’s I: 0.125, pvalue: 0.002). A belt of moderately high SBA coverage stretches across Central Africa, with a cluster of 

high-high associations in the western areas of Central Africa, with a few pockets in western Africa 

(Figure 10). There are large areas of low-low association clustered in western and eastern Africa. There 

are a few high-low pockets scattered in western and eastern Africa and contiguous to large areas of lowlow clustering. It is important to keep in mind that SBA definitions vary among countries, including 

several of countries with higher SBA coverage with more inclusive definitions, which influences some of 

the observed differences. There is some variation among the standard errors for these coverage estimates, 

with generally low standard errors in West and Central Africa and an area of moderately high variability 

in East Africa. 

Figure 9. Skilled birth attendance (SBA) country-level estimate and regional comparison 
21 

Figure 10. Skilled birth attendance (SBA) LISA and standard error maps 
22 

3.2.2. Child health indicators 

Exclusive breastfeeding (EBF) 

EBF prevalence ranges from 6 to 85 percent among the countries studied, but these national estimates 

hide substantial subnational regional variation (Figure 11). The majority of countries (22 of 27) have 

intra-country coverage differences of more than 20 percentage points. 

Significant positive spatial autocorrelation exists at the regional level (Moran’s I: 0.100, p-value: 0.011). 

Figure 12 shows that there are cross-border similarities apparent between countries, with subnational 

regions that are adjoining at national borders often being in the same categories. There are high-high 

clusters in eastern Africa, with a smaller cluster in West Africa, mainly in Ghana. Areas of low-low 

spatial clustering are mostly in West and Central Africa. There are low-high outliers surrounding the 

high-high clustering in eastern Africa, while pockets of high-low clustering appear in a few areas 

contiguous to low-low clusters in West and Central Africa. The standard error map signals that some 

areas in western Africa and Tanzania have higher standard errors than other areas. 

Figure 11. Exclusive breastfeeding (EBF) country-level estimate and regional comparison 

 
23 

Figure 12. Exclusive breastfeeding (EBF) LISA and standard error maps 
24 

Measles vaccination 

With a few exceptions, measles vaccination coverage is generally high among the countries studied 

(Figure 13). The subnational variation remains high in some countries (up to 60 percent points), but in 

most countries, as coverage increases at the national level, the subnational difference decreases. 

No significant spatial autocorrelation was found at the regional level (Global Moran’s I: 0.014, p-value: 

0.325). However, the LISA clustering for coverage of measles vaccination has two large areas of highhigh clustering: one in East Africa and one in the center part of West Africa, with smaller areas 

encompassing Rwanda and Burundi in east-central Africa and The Gambia in West Africa (Figure 14). 

Low-low clusters appear in West and Central Africa, as well as much of Ethiopia. There are also a few 

high-low outliers, including areas in Benin, Cameroon, Ethiopia, and Gabon, while there is only a single 

low-high outlier in the southwestern corner of Uganda. Map 2 shows a few select areas of higher standard 

error. 

Figure 13. Measles vaccination country-level estimate and regional comparison 

 
25 

Figure 14. Measles vaccination: LISA and standard error maps 
26 

DPT3 vaccine coverage 

DPT3 vaccine coverage is generally high among the countries studied, with a few exceptions where 

coverage is less than 60 percent (Figure 15). Subnational variation, however, remains high, and a majority 

of countries have a difference of more than 20 percentage points between their lowest and highest 

regions. 

Significant negative spatial autocorrelation exist within the whole DPT3 dataset (Global Moran’s I: -

0.089, p-value: 0.021). The LISA clustering looks similar to that on the Measles map, although the lowlow areas comprise a smaller number of units, while the high-high clusters contain more areas and are 

more contiguous, with a long stretch in eastern Africa and two smaller clusters in western Africa (Figure 

16). Low-low areas are concentrated in West and Central Africa, as well as Ethiopia. There are just three 

areas of high-low outliers: two in Ethiopia and one in Niger. There are no low-high areas visible. Map 2 

indicates some areas with relatively high standard error. 

Figure 15. DPT3 vaccine coverage country-level estimate and regional comparison 

 
27 

Figure 16. DPT3 vaccine coverage: LISA and standard error maps 
28 

Care seeking behavior 

The care seeking behavior indicator shows a moderately high prevalence across the countries studied, 

with most countries at 50-80 percent (Figure 17). Subnational variation is above 20 percentage points in 

most countries, with a few exceptions. 

The global measure of spatial autocorrelation for the care seeking behavior dataset was non-significant 

(Global Moran’s I: 0.016, p-value: 0.318). Figure 18 shows that at the local level there are three main 

areas of high-high spatial association: a stretch in eastern Africa, a cluster in western-central Africa and a 

cluster in western Africa including parts of Liberia and Sierra Leone. Low-low associations appear 

mainly in West Africa, Ethiopia, Zimbabwe, and a small section of Central Africa. Low-high outliers are 

grouped around the high-high areas of East Africa, while a single high-low area is located in Cameroon. 

Standard errors are consistently low, with a few pockets of higher ranges in western and eastern Africa. 

Figure 17. Care seeking behavior country-level estimate and regional comparison 

 
29 

Figure 18. Care seeking behavior: LISA and standard error maps 
30 

Stunting prevalence 

Stunting prevalence is high in most areas studied (> 20 percent), with some subnational regions below 20 

percent. Intra-country variation is high in most countries; Nigeria has regional values ranging from 16 to 

55 percent and DRC has regional values ranging from 17 to 53 percent (Figure 19). 

Significant negative spatial autocorrelation exist within the whole DPT3 dataset (Global Moran’s I: -

0.090, p-value: 0.021). In the LISA analysis, high-high areas are in Central and East Africa, as well as 

parts of Niger and Nigeria, and northern Ethiopia (Figure 20). Pockets of low-low clustering appear along 

the Central and West African coastline. A single low-high area appears in Tanzania, while a few high-low 

clusters include areas in Ghana and Togo, DRC, Congo, and Senegal. Standard errors are generally 

uniformly low; low-high and high-low areas seem to be higher in prevalence of stunting compared with 

other areas. 

Figure 19. Stunting prevalence country-level estimate and regional comparison 

 
31 

Figure 20. Stunting prevalence LISA and standard error maps 
32 

Under-five mortality rate 

The under-five mortality rate is moderately high across the countries studied, with a few areas that are 

very high. Subnational variation is generally high between the lowest and highest regions in each country, 

except for Malawi (Figure 21). 

There is no spatial clustering seen at the regional level for this indicator (Global Moran I: 0.067, p-value: 

0.055). Figure 22 shows high-high clustering along the West African countries of Niger, Nigeria, Burkina 

Faso, Mali, Guinea, and Liberia. A few low-low clusters appear in parts of Benin, Gabon, Senegal, 

Kenya, Tanzania, Zambia, and Zimbabwe. Three small low-high outliers are scattered along the Sahel 

high-high cluster, while two high-low areas appear in Gabon and Zimbabwe. Precision is more variable 

for these estimates than for those on the other maps. There are large widths in confidence interval 

throughout the areas shown, with particularly high variability in pockets of north-central, western coastal, 

and eastern Africa. 

Figure 21. Under-five mortality rate country-level estimate and regional comparison 
33 

Figure 22. Under-five mortality rate LISA and 95% confidence interval width maps 

35 

4. Discussion

To our knowledge, this is the first study to apply geospatial analysis to a comprehensive set of MCH 

indicators within and among countries. No previous studies using DHS data have focused on the crossborder relationship that may exist in maternal and child health behaviors or service coverage, as well as 

intra-country variability. A few studies have looked at the spatial autocorrelation present for single or 

multiple indicators within a single country or within several countries—for example, water and sanitation 

indicators (Pullan et al. 2014)—but these have not compared indicators among countries. Although this 

analysis was not designed to examine the underlying reasons for differences in indicator coverage among 

contiguous sub-regions, in this section we use maternal and child health information to discuss the spatial 

distributions we observed and to indicate areas for further research. 

The differences in indicator coverage are striking, both at the national level between countries and at the 

subnational level within a country. Intra-country variability is clearly visible for all indicators across all 

the countries included in the study, with a few exceptions. The observed difference between the highest 

region and lowest region in a country, often over 20 percentage points, reinforces the recognition that 

advancements in MDG health indicators might be achieved while increasing health coverage inequity 

(Gwatkin 2002). More analysis could be done to examine all the factors among subnational regions that 

lead to the large differences in indicator coverage seen in most countries. 

In our analysis, all indicators have some cross-border association with groups of high-high or low-low 

clustering present. It is not surprising to see these cross-border bands, given the cultural influences that 

may exist for many of the MCH indicators studied, especially exclusive breastfeeding, stunting, modern 

contraceptive use, and antenatal care, and the fact that many ethnic groups reside in geographic areas that 

cross-political boundaries. The political boundaries of most countries in Africa were drawn during the 

colonial period with little regard for traditional boundaries and thus often divided ethnic groups between 

two or more countries. 

Some indicators have larger multi-country clusters. In particular, estimates of prevalence of exclusive 

breastfeeding and stunting seem to group together in large areas, such that it is possible to identify bands 

of similar values that stretch across different countries. Exclusive breastfeeding has a band of low 

prevalence across West Africa, interrupted by bands of very low prevalence in the same region; a band of 

moderate prevalence stretching across central to eastern Africa; and bands ranging from very low to 

moderate prevalence of exclusive breastfeeding stretching north-south through Ethiopia and Kenya. 

Stunting, too, depends in part on culturally influenced consumption and feeding practices, and we see a 

great swath of high rates of stunting in north-central Africa as well as Central Africa stretching to East 

Africa. Since stunting begins in utero, it also reflects mothers’ health. Conditions where women have little 

autonomy, are deprived of human rights related to health and well-being, and are forced to marry young 

affect maternal health, and thus affect the health of children and societies generally. These conditions 

point to the need for systemic changes that would affect stunting as well as other health issues. For 

investments focused on health, researching cultural practices related to maternal health and infant feeding, 

implementing culturally appropriate interventions, and then sharing knowledge, tools, and lessons learned 

with other culturally similar areas could achieve broader positive impact than an investment in health with 

a limited, country-specific geographic focus. This type of regional investment may be more cost-effective 

over the long-term. 
36 

Other indicators with cross-border clustering of high-high or low-low values may be influenced less by 

culture than by other factors, such as access to goods and services, or wealth. Antenatal care use is higher 

in coastal areas, in general. Care seeking is also higher in coastal areas, with some exceptions of high 

coverage for inland areas in eastern Africa. Coastal areas may be more geographically accessible and 

wealthier due to tourism and fishing. Wealth, both at the area level and individual level, may increase the 

accessibility of facility-based services, because the costs of transportation and of time away from 

productive work are not as prohibitive as in poorer areas. 

Care-seeking coverage may show improvements in subsequent surveys due to the adoption of policies for 

community-case management of childhood illness by many countries in sub-Saharan Africa (Rasanathan 

et al. 2014). Under this policy, community health workers are trained to diagnose and treat pneumonia, 

diarrhea, and malaria in communities, thereby greatly reducing geographic barriers to care seeking. 

Modern contraceptive use, measles vaccination, and DPT3 coverage are generally lower in more remote 

areas of West and Central Africa, while the band of high-high in East and Southern Africa is likely related 

to factors other than simply access such as culture or government policies. In some countries community 

health workers are able to distribute contraceptives at the community level. Vaccination campaigns can 

provide community-level interventions, although they are more often facility-based. However, as the type 

of available vaccinations increases (pneumonia, rotavirus, etc.) and delivery mechanisms and cold-chain 

storage needs evolve, community health workers may also be able to provide more vaccinations at the 

community level. 

Environmental factors have been previously associated with the band of high under-five mortality in the 

West African Sahel region (Balk et al. 2004). The sporadic areas of low under-five mortality seem to be 

more coastal geographically, or in the case of the two areas in Southern Africa, bordering areas where 

there were no data and thus may simply be an artifact of the data more than a true low cluster. 

Coverage of skilled birth attendance is difficult to compare between countries due to national variations in 

the definition. The finding of high coverage of skilled birth attendance in otherwise poorly covered areas 

such as DRC or Congo is suspect. In Congo this may be due in part to the inclusion of auxiliary midwife, 

trained birth attendant, and community health worker in the definition of skilled birth attendant. 

Geographic areas that have high coverage but are surrounded by areas of low coverage and vice versa are 

of particular interest for all indicators examined. In particular, for indicators where high values indicate 

“good” outcomes, high areas of coverage surrounded by low areas indicate the need for further study to 

understand what environmental, cultural, or political barriers make these areas different from their 

neighbors. The same is true for low areas of stunting or under-five mortality surrounded by high areas. 

Qualitative research into the “positive deviance” of those outliers may illuminate factors that could be 

extended to or replicated in neighboring areas. Low areas of coverage surrounded by high areas also point 

to the need for further investigation to uncover perhaps untapped resources or structural barriers that 

separate those residents’ coverage status from that of their neighbors. In several cases small but densely 

populated urban areas are very different from the rest of the mostly rural country—for example in 

Ethiopia, where Addis Ababa and Dire Dawa have good values while the rest of the country has quite 

poor values for the majority of indicators studied. 
37 

Clearly, a study of this kind at the DHS survey region level misses subtleties within the large geographic 

regions. As household surveys such as the DHS start to have data available at lower geographic levels 

(administrative 2, district), which in many countries are also the administrative boundaries often used for 

funding decisions, this type of analysis could prove useful both within individual countries and across the 

whole continent. Africa is particularly suited for this type of analysis due to its large size and relative 

geographic continuity. Another region, for example Asia, would be more difficult or nearly impossible to 

analyze in the same way due to the large expanse of coastlines, with low spatial contiguity along with 

many island nations. Some more geographically contained sub-regions in Asia would likely be 

appropriate for an analysis of this type. 

There are several limitations in this study. First, the analysis could be biased by the modifiable areal unit 

problem. This occurs when the areas used in an analysis are “artificial” in their size and the same data 

could be aggregated into different-sized units, which would result in a different outcome to the analysis. 

The varied size of the subnational areas across countries could influence the results toward areas with 

larger size or areas that aggregate larger-sized population. Second, the analysis outcome may be different 

if they included areas not included in the present analysis, due to not having recent DHS data available. 

Areas that are on the outer edge of the areas that are being analyzed have only some of their neighbors 

included in the analysis and thus it is not known if those excluded neighbors might make that area a 

hotspot or a coldspot, for example. As more data become available, or if a mixture of other sources of 

data was used, the geographic coverage of the analysis could be expanded in the future. Finally, 

limitations remain in the timing of surveys, as the estimate for some indicators can be sensitive to specific 

shocks (drought, epidemics, etc.) or programmatic interventions (mass coverage campaigns, changes in 

national policy on skilled birth attendance, etc.). In this study all surveys were within a six-year period, 

and all neighboring countries have indicator values that were calculated within two or three years of each 

other. This reduces the influence of shocks or interventions on differences in indicators. Although the 

politics and intervention approaches of each country differ, we did not seek here to analyze the underlying 

political or socio-cultural reasons why sub-regional values differ, which would require more intensive 

ESDA, including some multilevel modeling with spatial regression techniques. 

4.1. Future Directions to Equitably Improve Maternal and Child Health 

Program implementers and health advocates have deliberately pursued equity in strategic ways (Luna et 

al. 2014). To respond to growing disparities in many countries, governments of the United States, India, 

and Ethiopia, in partnership with UNICEF, launched the Child Survival Call to Action, calling on 

countries around the world to “refocus their health systems on scaling up access to high quality services 

for populations suffering from a disproportionate burden of disease, especially rural, poor, and 

marginalized populations” and to implement better mechanisms to monitor progress in the most 

disadvantaged areas (UNICEF 2012, p.4). GIS is a powerful tool to aid focused monitoring of activities 

because it reveals geographic variation in coverage (Burgert 2014). 

Decentralizing health care funding is one way to prioritize disadvantaged areas. Decentralization 

empowers local levels to use health resources as they choose, and assumes that targeting and monitoring 

will take place within a focused geographic area. However, analyses that simultaneously stratify by 

indicator and urban versus rural residence show that allocation to a region does not necessarily reach the 

most needy. For example, SBA coverage in the Greater Accra region of Ghana is 73 percent, which seems 
38 

to indicate that resources should be focused on improving SBA coverage in areas of greater need. 

However, a closer look through sub-regional analysis indicates that urban SBA coverage in Greater Accra 

is 86 percent, but rural coverage is only 23 percent (Wirth et al. 2008). To advocate for this type of 

multifactorial analysis, Wirth, et al. acknowledges that poverty has many dimensions, so analysis of 

wealth should be complemented by analysis of ethnicity, geography, and education. In addition, particular 

attention should be paid to people in conflict regions, who may not be represented in surveys (Wirth et al. 

2008). 

Chopra identified promising strategies to improve coverage of effective interventions, including using 

community health workers, which requires both increasing their numbers and “task shifting” by enabling 

them to provide communities with services that are typically provided in health facilities (Luna et al. 

2014). Harnessing the power of geospatial analysis to identify hotspots of low coverage and high need 

can help to determine where community health workers and other health programs can have the most 

impact and to deploy resources accordingly. To realize this potential of geospatial analysis, Ebener et al. 

(2015) calls for standardizing geospatial information in the health sector; coordinating support to maternal 

and newborn health programs to strengthen geospatial analysis capacity; integrating capacity building for 

geospatial analysis as part of strengthening health information systems; and systematically considering 

geospatial analysis for implementing and monitoring maternal and newborn programs. Investments in 

increasing the availability of geographic data through geo-referencing facility registries and providers 

would also facilitate geospatial analysis from the lowest levels. Since some funding mechanisms are now 

requesting descriptions of the “geographic dimension” of disease with the location of hotspots of 

transmission along with maps to visualize disease burden and identify areas of high need (Global Fund 

2014), Ebener’s (2015) call for investment in geographic information systems and the capacity to use and 

maintain them is timely and important. 
39 

5. Conclusion

It is likely that the MDGs focused on maternal, newborn, and child health will not be universally met, 

even though these are key priorities for the global health community. Geographic barriers to optimal 

health include distance to health services as well as climatic, social, and economic characteristics that 

cluster geographically, leading to a spatial distribution of persistent health inequities. This report used 

Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial autocorrelation of nine key 

maternal and child health indicator values between and among regions in 27 sub-Saharan African 

countries. This type of geospatial analysis can pinpoint areas of need and facilitate effective investments 

and program targeting to redress health inequities. We observed patterns of substantial differences among 

contiguous subareas for different indicators, with some intra-country differences greater than 20 

percentage points for all indicators examined. Examples of extreme within-country differences are ANC, 

SBA, and DPT3 in Ethiopia (more than 70 percentage points); at the other extreme, Malawi has withincountry differences for CPR and ANC of less than 7 percentage points. We observed statistically 

significant variation between countries for SBA, EBF, DPT3, and stunting. This analysis was not 

designed to identify underlying reasons for differences nor specific interventions to increase coverage, but 

those are the next logical steps. 

This report illustrates a preliminary analysis of the possibility and potential for using these spatial analysis 

techniques across multiple DHS survey countries. Future studies may also investigate the relationship 

between indicators (e.g. antenatal care and skilled birth attendance; exclusive breastfeeding and stunting; 

stunting and female autonomy, etc.) and how the indicator estimate relates to the underlying population at 

risk or population burden. Additionally, further bivariate LISA or spatial regression analyses 

incorporating information on environment, wealth, education, population density, or government policies, 

among others, would deepen the understanding of the interplay between health determinants and 

geography. 

41 

References 

Adams, A.M., R. Islam, and T. Ahmed. 2015. “Who Serves the Urban Poor? A Geospatial and 

Descriptive Analysis of Health Services in Slum Settlements in Dhaka, Bangladesh.” Health Policy Plan

30(Suppl 1):i32-i45. 

Adedini, S.A., C. Odimegwu, O. Bamiwuye, O. Fadeyibi, and N. De Wet. 2014. “Barriers to Accessing 

Health Care in Nigeria: Implications for Child Survival.” Global Health Action

7:doi:10.3402/gha.v7.23499. 

Amin, S.B., A.M. Basu, and R. Stephenson 2002. “Spatial Variation in Contraceptive Use in Bangladesh: 

Looking Beyond the Borders.” Demography 39(2):251-267. 

Balk, D., T. Pullum, A. Storeygard, F. Greenwell, and Neuman. 2004. “A Spatial Analysis of Childhood 

Mortality in West Africa.” Population, Space and Place 10(3):175-216. 

Banta, D. 2003. What is the Efficacy/Effectiveness of Antenatal Care and the Financial and 

Organizational Implications? Health Evidence Network. Copenhagen: WHO Regional Office for Europe. 

Available at http://www.euro.who.int/Document/E82996.pdf. 

Barros, A.J.D., C. Ronsmans, H. Axelson, E. Loaiza, A.D. Bertoldi, G.V.A. França, J. Bryce, J.T. 

Boerma, and C.G. Victora. 2012. “Equity in Maternal, Newborn, and Child Health Interventions in 

Countdown to 2015: A Retrospective Review of Survey Data from 54 Countries.” The Lancet

379(9822):1225-1233. 

Becher, H., O. Müller, A. Jahn, A. Gbangou, G. Kynast-Wolf, and B. Kouyate. 2004. “Risk Factors of 

Infant and Child Mortality in Rural Burkina Faso.” Bulletin of the World Health Organization 82:265-

273. 

Bennett, A.E., T. Eisele, J. Keating, and J. Yukich. 2015. Global Trends in Care Seeking and Access to 

Diagnosis and Treatment of Childhood Illnesses. DHS Working Papers. Rockville, MD: ICFI. 

Bhutta, Z.A., Z.S. Lassi, G. Pariyo, and L. Huicho. 2010. Global Experience of Community Health 

Workers for Delivery of Health-Related Millennium Development Goals: A Systematic Review, Country 

Case Studies, and Recommendations for Integration into National Health Systems. Geneva: Global 

Health Workforce Alliance and WHO. 

Bhutta, Z.R., and K.S Reddy. 2012. “Achieving Equity in Global Health: So Near and Yet so Far.” JAMA

307(19):2035-2037. 

Britton, C.M., F.M. McCormick, M.J. Renfrew, A. Wade, and S.E. King. 2007. “Support for 

Breastfeeding Mothers.” Cochrane Database of Systematic Reviews Issue 1: Art. No. CD001141. doi: 

10.1002/14651858.CD001141.pub3. 
42 

Bruce, N.P., D. Pope, B. Arana, C. Shiels, C. Romero, R. Klein, and D. Stanistreet. 2014. “Determinants 

of Care Seeking for Children with Pneumonia and Diarrhea in Guatemala: Implications for Intervention 

Strategies.” American Journal of Public Health 104(4):647-657. 

Burgert, C. 2014. Spatial Interpolation with Demographic and Health Survey Data: Key Considerations. 

DHS Spatial Analysis Reports. Rockville, MD: ICFI. 

Cai, X., W. Tessa, and D.W. Brown. 2012. “Global Trends in Exclusive Breastfeeding.” International 

Breastfeeding Journal 7(12):1-5. 

Callaghan, W.M. 2014. “Geographic Variation of Reproductive Health Indicators and Outcomes in the 

United States: Place Matters.” Amerucab Journal of Obstetrics and Gynecology 211(3):278-284. 

Carlough, M., and M. McCall. 2005. “Skilled Birth Attendance: What Does it Mean and How Can it Be 

Measured? A Clinical Skills Assessment of Maternal and Child Health Workers in Nepal.” International 

Journal of Gynaecology and Obstetrics 89(2):200-208. 

Carrera, C., A. Azrack, G. Begkoyian, J. Pfaffmann, E. Ribaira, T. O’Connell, P. Doughty, K.M. Aung, 

L. Prieto, K. Rasanathan, A. Sharkey, M. Chopra, and R. Knippenberg. 2012. “The Comparative Costeffectiveness of an Equity-focused Approach to Child Survival, Health, and Nutrition: A Modelling 

Approach.” The Lancet 380(9850):1341-1351. 

Chin, B., L. Montana, and X. Basagana. 2011. “Spatial Modeling of Geographic Inequalities in Infant and 

Child Mortality Across Nepal.” Health Place 17(4):929-936. 

Chirwa, T.F., J.N. Mantempa, F.L. Kinziunga, J.D. Kandala, and N. Kandala. 2014. “An Exploratory 

Spatial Analysis of Geographical Inequalities of Birth Intervals Among Young Women in the Democratic 

Republic of Congo (DRC): A Cross-sectional Study.” BMC Pregnancy and Childbirth 14 

(271):doi:10.1186/1471-2393-14-271. 

Chopra, M., A. Sharkey, N. Dalmiya, D. Anthony, and N. Binkin. 2012. “Strategies to Improve Health 

Coverage and Narrow the Equity Gap in Child Survival, Health, and Nutrition.” The Lancet

380(9850):1331-1340. 

Das, J.K., Z.S. Lassi, R.A. Salam, and Z.A. Bhutta. 2013. “Effect of Community Based Interventions on 

Childhood Diarrhea and Pneumonia: Uptake of Treatement Modalities and Impact on Mortality.” BMC 

Public Health 13(Suppl 3):S29. 

Dewey, K.G., and K. Begum. 2011. “Long-term Consequences of Stunting in Early Life.” Maternal and 

Child Nutrition 7(Suppl 3):5-18. 

Ebener, S., M. Guerra-Arias, J. Campbell, A.J. Tatem, A.C. Moran, F. Amoako Johnson, H. Fogstad, K. 

Stenberg, S. Neal, P. Bailey, R. Porter, and Z. Matthews. 2015. “The Geography of Maternal and 

Newborn Health: The State of the Art.” International Journal of Health Geographics 14(19): doi 

10.1186/s12942-015-0012-x. 
43 

Feikin, D.R., L.M. Nguyen, K. Adazu, M. Ombok, A. Audi, L. Slutsker, and K.A. Lindblade. 2009. “The 

Impact of Distance of Residence from a Peripheral Health Faciltiy on Pediatric Health Utilisation in Rural 

Western Kenya.” Tropical Medicine and International Health 14(1):54-61. 

GeoDa. 2015a. Glossary of Key Terms. Arizona State University GeoDa Center for Geospatial Analysis 

and Computation. Available at https://geodacenter.asu.edu/node/390. 

GeoDa. 2015b. Local Spatial Autocorrelation. Arizona State University GeoDa Center for Geospatial 

Analysis and Computation. Available at https://geodacenter.asu.edu/node/393. 

Gething, P.W., V.C. Kirui, V.A. Alegana, E.A. Okiro, A.M. Noor, and R.W. Snow. 2010. “Estimating the 

Number of Paediatric Fevers Associated with Malaria Infection Presenting to Africa’s Public Health 

Sector in 2007.” PloS Medicine 7(7):doi:10.1371/journal.pmed.1000301. 

Gitimu, A.H., C. Herr, H. Oruko, E. Karijo, R. Gichuki, P. Ofware, A. Lakati, and J. Nyagero. 2015. 

“Determinants of Use of Skilled Birth Attendant at Delivery in Makueni, Kenya: A Cross Sectional 

Study.” BMC Pregnancy and Childbirth 15(9):doi:10.1186/s12884-015-0442-2. 

Global Fund. 2014. Standard Concept Note Instructions: Investing for Impact Against HIV Tuberculosis 

and Malaria. The Global Fund to Fight AIDS, Tuberculosis and Malaria. Available at 

www.theglobalfund.org 

Gwatkin, D.R. 2002. Who Would Gain Most from Efforts to Reach the Millennium Development Goals 

for Health? HNP Discussion Paper. Washington, DC: The World Bank. 

Gwatkin, D.R. 2007a. “10 Best Resources on... Health Equity.” Health Policy Plan 22(5):348-51. 

Gwatkin, D.R., S. Rustein, K. Johnson, E. Suliman, A. Wagstaff, and A. Amouzou. 2007b. Socioeconomic Differences in Health, Nutrition, and Population within Developing Countries. Washington, 

D.C.: The World Bank. 

Hjortsberg, C. 2003. “Why Do the Sick Not Utilise Health Care? The Case of Zambia.” Health 

Economics 12(9):755-770. 

ICF International. 2008-2014a. Demographic and Health Surveys (Various) [Datasets]. Rockville, 

Maryland: ICF International [Distributor]. 

ICF International. 2008-2014b. Spatial Data Repository, The DHS Program (Various) [Datasets]. 

Rockville, Maryland: ICF International [Distributor]. Available from spatialdata.dhsprogram.com. 

[Accessed 8 March 2015] 

Johnson, F.A., F. Frempong-Ainguah, Z. Matthews, A.L.P. Harfoot, P. Nyarko, A. Baschieri, P.W. 

Gething, J. Falkingham, and P.M. Atkinson. 2015. “Evaluating the Impact of the Community-based 

Health Planning and Services Initiative on Uptake of Skilled Birth Care in Ghana.” PLOS One

10(3):doi:10.1371/journal.pone.0120556 
44 

Kadobera, D., B. Sartorius, H. Masanja, A. Mathew, and P. Waiswa. 2012. “The Effect of Distance to 

Formal Health Facility on Childhood Mortality in Rural Tanzania, 2005-2007.” Global Health Action

5:doi:10.3402/gha.v5i0.19099. 

Kandala, N.B., T.P. Madungu, J.B. Emina, K.P. Nzita, and F.P. Cappuccio. 2011. “Malnutrition Among 

Children Under the Age of Five in the Democratic Republic of Congo (DRC): Does Geographic Location 

Matter?” BMC Public Health 11(261):doi:10.1186/1471-2458-11-261. 

Kassile, T.L., R. Lokina, P. Mujinja, and B. Mmbando. 2014. “Determinants of Delay in Care Seeking 

Among Children Under Five With Fever in Dodoma Region, Central Tanzania: A Cross-Sectional 

Study.” Malaria Journal 13(348):1-10. 

Kazembe, L.N., I. Kleinschmidt, and B.L. Sharp. 2006. “Patterns of Malaria-related Hospital Admissions 

and Mortality Among Malawian Children: An Example of Spatial Modelling of Hospital Register Data.” 

Malaria Journal 5(93):doi:10.1186/1475-2875-5-93. 

Kiwanuka, S.N., E.K. Ekirapa, S. Peterson, O. Okui, M.H. Rahman, D. Peters, and G.W. Pariyo. 2008. 

“Access to and Utilisation of Health Services for the Poor in Uganda: A Systematic Review of Available 

Evidence.” Transactions of the Royal Society of Tropical Medicine and Hygiene 102(11):1067-1074. 

Lim, S.S., V. Theo, A.D. Flaxman, G. Danaei, K. Shibuya, H. Adair-Rohani, et al. 2012. “A Comparative 

Risk Assessment of Burden of Disease and Injury Attributable to 67 Risk Factors and Risk Factor 

Clusters in 21 Regions, 1990-2010: A Systematic Analysis for the Global Burden of Disease Study 

2010.” Lancet 380(9859):2224-2260. 

Luna, J.W., T. Monga, and L. Morgan. 2014. Equity Matters - Lessons from MCHIP and CSHGP in 

Measuring and Improving Equity. Maternal Child Health Integrated Program. 

Mayhew, M., P. Hansen, D. Peters, A. Edward, L. Singh, V. Dwivedi, A. Mashkoor, and G. Burnham. 

2008. “Determinants of Skilled Birth Attendant Utilization in Afghanistan: A Cross-Sectional Study.” 

American Journal of Public Health 98(10):1849-1856. 

Mishra, V. and R.D. Retherford. 2006. The Effect of Antenatal Care on Professional Assistance at 

Delivery in Rural India. DHS Working Papers. Calverton, MD: ORC Macro. 

Noor, A.M., D. Zurovac, S.I. Hay, S.A. Ochola, and R.W. Snow. 2003. “Defining Equity in Physical 

Access to Clinical Services Using Geographical Information Systems as Part of Malaria Planning and 

Monitoring in Kenya.” Tropical Medicine and International Health 8(10):917-933. 

Nykiforuk, C.I., and L.M. Flaman. 2011. “Geographic Information Systems (GIS) for Health Promotion 

and Public Health: A Review.” Health Promotion and Practice 12(1):63-73. 

Odaga, J. 2004. “Health Inequality in Uganda - the Role of Financial and Non-financial Barriers.” Health 

Policy and Development 2(3):192-208. 
45 

Peters, D.H., A. Garg, G. Bloom, D.G. Walker, W.R. Brieger, and M.H. Rahman. 2008. “Poverty and 

Access to Health Care in Developing Countries.” Annals of the New York Academy of Sciences 1136:161-

71. 

Prata, N., F. Vahidnia, M. Potts, and I. Dries-Daffner. 2005. “Revisiting Community-based Distribution 

Programs: Are They Still Needed?” Contraception 72(6):402-407. 

Prendergast, A.J., and J.H. Humphrey. 2014. “The Stunting Syndrome in Developing Countries.” 

Paediatrics and International Child Health 34(4):250-265. 

Pullan, R.L., M.C. Freeman, P.W. Gething, and S.J. Brooker. 2014. “Geographical Inequalities in Use of 

Improved Drinking Water Supply and Sanitation Across sub-Saharan Africa: Mapping and Spatial 

Analysis of Cross-sectional Survey Data.” PLoS Medicine 11(4):e1001626. 

Qayum, A., R. Arya, P. Kumar, and A.M. Lynn. 2015. “Socio-economic, Epidemiological and 

Geographic Features Based on GIS-integrated Mapping to Identify Malarial Hotspots.” Malaria Journal

14(192):doi:10.1186/s12936-015-0685-4. 

Rasanathan, K., M. Muniz, S. Bakshi, M. Kumar, A. Solano, W. Kariuki, A. George, M. Sylla, R. Nefdt, 

M. Young, and T. Diaz. 2014. “Community Case Management of Childhood Illness in sub-Saharan 

Africa - Findings From a Cross-sectional Survey on Policy and Implementation.” Journal of Global 

Health 4(2):doi:10.7189/jogh.04.020401. 

Roberts, T.J., E. Carnahan, and E. Gakidou. 2013. “Can Breastfeeding Promote Child Health Equity? A 

Comprehensive Analysis of Breastfeeding Patterns Across the Developing World and What We Can 

Learn From Them.” BMC Medicine 11(254):doi:10.1186/1741-7015-11-254. 

Rosero-Bixby, L. 2004. “Spatial Access to Health Care in Costa Rica and its Equity: A GIS-Based 

Study.” Social Science & Medicine 58(7):1271-1284. 

Rutstein, S.O. 2008. Further Evidence of the Effects of Preceding Birth Intervals on Neonatal, Infant, and 

Under-five-years Mortality and Nutritional Status in Developing Countries: Evidence From the 

Demographic and Health Surveys. DHS Working Papers Series. ICF Macro. 

Schoeps, A., S. Gabrysch, L. Niamba, A. Sie, and H. Becher. 2011. “The Effect of Distance to Healthcare Facilities on Childhood Mortality in Rual Burkina Faso.” American Journal of Epidemiology

173(5):492-498. 

Singh, S., G. Sedgh, and R. Hussain. 2010. “Unintended Pregnancy: Worldwide Levels, Trends, and 

Outcomes.” Studies in Family Planning 41(4):241-250. 

Smith, K.E., and M. Ezzati. 2005. “How Environmental Health Risks Change with Development.” 

Annual Review of Environmental Resources 30:291-333. 

Tanahashi, T. 1978. “Health Service Coverage and its Evaluation.” Bulletin of the World Health 

Organization 56(2):295-304. 
46 

Tugwell, P., V. Robinson, and E. Morris. 2007. Mapping Global Health Inequalities: Challenges and 

Opportunities. Mapping Global Inequalities. Center for Global, International and Regional Studies. UC 

Santa Cruz: Available at http://escholarship.org/uc/item/2f11d67c 

UNICEF. 2012. Chid Survival Call to Action: Ending Preventable Child Deaths. UNICEF. Available at 

http://5thbday.usaid.gov/pages/responsesub/roadmap.pdf 

UNICEF. 2014. Levels and Trends in Child Mortality. UNICEF. 

UNICEF. 2015. Progress for Children: Beyond Averages: Learning from the MDGs. New York, USA: 

UNICEF. 

United Nations. 2000. United Nations Millennium Declaration. 

United Nations. 2014. The Millennium Development Goals Report. United Nations. 

USAID. 2013. Global Health Strategic Framework. Available at http://www.usaid.gov/sites/ 

default/files/documents/1864/gh_framework2012.pdf. 

Vandelaer, J., J. Bilous, and D. Nshimirimana. 2008. “Reaching Every District (Red) Approach: A Way 

to Improve Immunization Performance.” Bulletin of the World Health Organization 86(3):161-240. 

Victora, C.G., A. Wagstaff, J.A. Schellenberg, D. Gwatkin, M. Claeson, and J.-P. Habicht. 2003. 

“Applying an Equity Lens to Child Health and Mortality: More of the Same is Not Enough.” The Lancet

362(9379):233-241. 

Victora, C.G., L. Huicho, J.J. Amaral, J. Armstrong-Schellenberg, F. Manzi, E. Mason, and R. 

Scherpbier. 2006. “Are Health Interventions Implemented Where They Are Most needed? District Uptake 

of the Integrated Management of Childhood Illness Strategy in Brazil, Peru and the United Republic of 

Tanzania.” Bulletin of the World Health Organization 84:792-801. 

Wamani, H., A.N. Astrom, S. Peterson, J.K. Tumwine, and T. Tylleskar. 2007. “Boys Are More Stunted 

Than Girls in sub-Saharan Africa: A Meta-Analysis of 16 Demographic and Health Surveys.” BMC 

Pediatrics 7(17):doi:10.1186/1471-2431-7-17 . 

WHO. 2005. Global Immunization Vision and Strategy. Available at http://www.who.int/ 

immunization/givs/en/ 

WHO. 2011a. Exclusive Breastfeeding for Six Months Best for Babies Everywhere. World Health 

Organization. Geneva 

WHO. 2011b. Keeping Promises, Measuring Results. World Health Organization. Geneva. 

WHO. 2013. DPT3 Data by WHO Region. Edited by Global Health Observatory Data Repository. 

WHO. 2014. Trends in Maternal Mortality: 1990 to 2013. World Health Organization. Geneva. 
47 

WHO. 2015a. Equity. World Health Organization. Available at http://www.who.int/healthsystems/ 

topics/equity/en/. 

WHO. 2015b. Measles Fact sheet. Available at http://www.who.int/mediacentre/factsheets/fs286/en/. 

WHO. 2015c. State of Inequality: Reproductive, Maternal, Newborn and Child Health. WHO. 

WHO and UNICEF. 2012. Countdown to 2015: The 2012 report. Available at http://www. 

countdown2015mnch.org/reports-and-articles/previous-reports/2012-report. 

WHO Regional Office for Africa. 2015. Measles-containing Vaccine (MCV) Immunization Coverage 

Among 1-year-olds. Available at http://www.aho.afro.who.int/en/data-statistics/measles-containingvaccine-mcv-immunization-coverage-among-1-year-olds. 

Wirth, M.E., D. Balk, E. Delamonica, A. Storeygard, E. Sacks, and A. Minujin. 2006. “Setting the Stage 

for Equity-sensitive Monitoring of the Maternal and Child Health Millennium Development Goals.” 

Bulletin of the World Health Organization 84:519-528. 

Wirth, M., E. Sacks, E. Delamonica, A. Storeygard, A. Minujin, and D. Balk. 2008. “Delivering on the 

MDGs?: Equity and Maternal Health in Ghana, Ethiopia and Kenya.” East African Journal Public Health

5(3):133-141. 

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Appendix A 

Table A.1. Skilled birth attendant (SBA) definitions by country 

Country Definition 

Benin doctor, nurse, midwife, auxiliary nurse, assistant midwife. 

Burkina Faso doctor, nurse, midwife, matron or professional birth attendant, auxiliary professional 

birth attendant. 

Burundi doctor, nurse, midwife 

Cameroon doctor, nurse, midwife, auxiliary midwife 

Congo doctor, nurse, midwife, auxiliary midwife, trained birth attendant, community health 

worker 

Cote d’Ivoire doctor, nurse, midwife, auxiliary midwife 

Democratic Republic of Congo doctor, nurse, birth attendant 

Ethiopia doctor, nurse, midwife 

Gabon doctor, nurse, midwife, auxiliary nurse, matron 

Gambia doctor, nurse, midwife 

Ghana doctor, nurse, midwife, auxiliary midwife, community health officer 

Guinea doctor, nurse, midwife, auxiliary nurse, midwife 

Kenya doctor, nurse, midwife 

Liberia doctor, nurse, midwife, physician’s assistant 

Malawi doctor, clinical officer, nurse, midwife 

Mali doctor, nurse, midwife, matron, other health professional 

Mozambique doctor, nurse, midwife, auxiliary nurse/midwife 

Niger doctor, nurse, midwife 

Nigeria doctor, nurse, midwife, auxiliary nurse/midwife 

Rwanda doctor, nurse, medical assistant, midwife 

Senegal doctor, nurse, midwife 

Sierra Leone doctor, nurse, midwife, MCH Aide 

Tanzania doctor/AMO, clinical officer, assistant clinical officer, nurse/midwife, MCH aide 

Togo doctor, medical assistant, nurse, midwife, auxiliary midwife 

Uganda doctor, nurse/midwife, medical assistant/clinical officer 

Zambia doctor, clinical officer, nurse/midwife 

Zimbabwe doctor, nurse-midwife, nurse 

Appendix B 

Table B.1. Country estimate and 95% confidence interval for key indictors 

CPR, any 

modern method ANC 4+ SBA EBF 

Measles 

vaccination 

DTP3 vaccine 

coverage 

Care seeking 

behavior 

Stunting 

prevalence 

Under-five 

mortality rate 

Benin DHS 

2011-2012 9.0 [8.3,9.6] 58.2 [56.6,59.8] 85.4 [83.6,87.0] 33.9 [31.0,37.0] 70.0 [67.6,72.4] 62.2 [59.5,64.9] 55.3 [52.3,58.2] 44.6 [43.2,46.1] 70.1 [65,75]

Burkina Faso DHS 

2010 14.3 [13.4,15.2] 33.7 [32.2,35.2] 72.9 [70.4,75.2] 24.8 [22.1,27.8] 87.3 [85.6,88.9] 89.5 [87.7,91.1] 60.8 [58.5,63.0] 34.6 [33.1,36.1] 128.4 [122,135]

Burundi DHS 2010 11.0 [10.2,11.9] 33.4 [31.5,35.4] 63.1 [60.8,65.4] 69.4 [64.6,73.8] 94.3 [92.8,95.5] 95.4 [93.9,96.5] 57.1 [54.9,59.3] 57.7 [55.7,59.7] 96.1 [88,104]Cameroon DHS 

2011 16.1 [15.3,17.1] 62.2 [60.2,64.2] 67.5 [65.1,69.8] 20.2 [17.6,23.1] 70.6 [67.8,73.2] 68.4 [65.2,71.3] 55.3 [52.7,58.0] 32.5 [31.0,34.0] 121.8 [114,129]

Congo DHS 

2011-2012 22.3 [21.0,23.7] 78.9 [77.0,80.7] 94.6 [93.6,95.5] 20.5 [17.0,24.5] 74.9 [71.1,78.4] 55.9 [52.3,59.4] 59.0 [55.8,62.1] 24.4 [22.4,26.4] 67.8 [59,76]

Cote d’Ivoire DHS 

2011-2012 13.9 [12.8,15.1] 44.2 [41.6,46.7] 62.5 [59.3,65.7] 12.2 [9.6,15.3] 64.5 [59.8,68.9] 63.8 [59.6,67.8] 60.8 [57.7,63.9] 29.8 [27.7,31.9] 108.0 [99,117]

DRC DHS 

2013-2014 8.1 [7.3,9.1] 48.0 [45.8,50.2] 81.2 [78.7,83.5] 47.8 [44.3,51.3] 71.6 [69.1,74.0] 60.5 [57.2,63.8] 55.9 [52.9,58.8] 42.7 [40.9,44.5] 104.2 [98,110]

Ethiopia DHS 2011 18.7 [17.3,20.2] 19.1 [17.3,21.0] 11.8 [10.3,13.4] 52.0 [47.3,56.7] 55.7 [51.5,59.8] 36.5 [32.8,40.3] 28.5 [25.6,31.6] 44.4 [42.7,46.2] 88.0 [80,96]Gabon DHS 2012 24.0 [22.0,26.0] 77.6 [75.5,79.6] 92.4 [91.0,93.6] 6.0 [3.7,9.7] 74.3 [70.6,77.8] 22.2 [18.3,26.6] 61.3 [57.4,65.1] 16.5 [14.3,18.9] 64.6 [54,75]Gambia DHS 2013 6.5 [5.7,7.5] 77.6 [75.8,79.4] 59.2 [56.0,62.3] 46.8 [41.8,51.8] 87.8 [83.7,91.1] 87.7 [85.2,89.8] 66.3 [63.1,69.4] 24.5 [22.4,26.7] 53.6 [46,61]Ghana DHS 2008 13.5 [12.4,14.7] 78.2 [75.7,80.4] 76.7 [74.0,79.2] 63.1 [57.2,68.6] 90.2 [87.0,92.6] 88.8 [85.3,91.5] 63.9 [59.7,67.9] 28.0 [25.8,30.2] 80.0 [69,91]Guinea DHS 2012 7.0 [6.1,8.0] 56.6 [53.7,59.4] 43.4 [39.8,47.1] 20.6 [16.5,25.4] 61.8 [57.7,65.6] 49.8 [45.4,54.1] 52.8 [48.8,56.7] 31.2 [29.2,33.2] 122.9 [114,132]Kenya DHS 

2008-2009 28.0 [26.5,29.5] 47.1 [44.8,49.5] 48.0 [44.5,51.5] 31.9 [26.7,37.7] 85.0 [81.8,87.7] 86.4 [83.2,89.1] 59.2 [55.6,62.6] 35.3 [33.2,37.4] 73.6 [64,83]

Liberia DHS 2013 20.5 [18.6,22.6] 78.1 [76.1,80.0] 65.3 [62.2,68.3] 55.2 [49.6,60.7] 74.2 [70.6,77.5] 71.4 [67.5,75.1] 73.0 [70.4,75.5] 31.6 [29.3,34.0] 93.8 [84,103]Malawi DHS 2010 32.6 [31.7,33.6] 45.5 [44.2,46.7] 74.1 [72.5,75.6] 71.5 [68.1,74.6] 93.0 [91.8,94.0] 93.0 [91.7,94.1] 70.2 [68.7,71.7] 47.1 [45.2,49.0] 112.1 [106,118]Mali DHS 2012-2013 9.6 [8.7,10.6] 41.2 [38.8,43.7] 61.3 [58.1,64.4] 34.2 [30.5,38.1] 71.7 [68.7,74.5] 63.1 [59.8,66.2] 49.6 [46.0,53.2] 38.3 [36.3,40.4] 95.1 [88,102]Mozambique DHS 

2011 12.1 [11.2,13.0] 50.6 [48.7,52.6] 60.1 [57.3,62.8] 41.1 [37.1,45.3] 81.5 [78.8,83.8] 76.2 [73.2,78.9] 59.7 [56.5,62.8] 42.6 [41.0,44.2] 96.9 [90,104]

Niger DHS 2012 11.0 [10.0,12.1] 32.8 [30.9,34.7] 33.0 [30.5,35.6] 23.4 [19.9,27.2] 68.7 [65.8,71.4] 68.1 [64.5,71.4] 60.6 [57.4,63.7] 43.9 [41.9,45.9] 127.2 [120,134]Nigeria DHS 2013 11.1 [10.5,11.9] 51.1 [49.1,53.1] 40.2 [38.2,42.1] 17.4 [15.6,19.4] 42.1 [39.8,44.4] 38.2 [36.0,40.5] 73.1 [71.2,74.8] 36.8 [35.6,38.0] 128.0 [123,133]Rwanda DHS 2010 25.2 [24.4,26.1] 35.4 [33.9,37.0] 72.2 [70.6,73.8] 84.9 [81.9,87.5] 95.0 [93.7,96.1] 96.8 [95.6,97.7] 47.4 [45.0,49.9] 44.2 [42.5,46.0] 75.7 [70,81]Senegal DHS 

2010-2011 8.9 [8.2,9.6] 50.0 [48.1,51.9] 86.6 [84.7,88.2] 38.9 [35.6,42.3] 82.1 [79.8,84.2] 82.6 [80.0,84.8] 47.9 [45.5,50.3] 26.5 [24.5,28.7] 71.6 [66,77]

Sierra Leone DHS 

2013 20.9 [19.4,22.5] 76.0 [73.7,78.2] 74.1 [71.9,76.2] 32.1 [28.1,36.3] 78.6 [75.8,81.1] 77.9 [75.0,80.5] 72.6 [69.7,75.3] 37.9 [35.9,39.8] 155.8 [148,164]

Tanzania DHS 2010 23.6 [22.3,25.0] 42.8 [40.8,44.8] 54.5 [51.6,57.4] 52.6 [48.5,56.7] 84.5 [81.8,86.9] 88.0 [85.2,90.4] 76.4 [73.9,78.8] 42.0 [40.4,43.6] 81.1 [74,88]Togo DHS 1998 16.7 [15.6,17.9] 57.2 [55.0,59.3] 61.7 [58.4,64.8] 58.3 [53.5,63.0] 74.3 [70.4,77.7] 82.8 [79.4,85.8] 55.1 [50.4,59.7] 27.5 [25.7,29.4] 88.3 [81,96]Uganda DHS 2011 20.7 [19.5,21.9] 47.6 [45.5,49.7] 60.6 [57.8,63.3] 62.4 [58.4,66.3] 75.8 [72.7,78.6] 71.5 [68.1,74.6] 78.1 [76.1,80.0] 33.4 [30.9,35.9] 90.0 [82,98]Zambia DHS 

2013-2014 32.5 [31.4,33.5] 55.5 [54.0,57.0] 68.6 [66.9,70.3] 72.5 [69.5,75.4] 84.9 [82.9,86.7] 85.8 [83.8,87.7] 70.5 [68.6,72.4] 40.1 [38.9,41.3] 74.6 [69,80]

Zimbabwe DHS 

2010-2011 40.5 [39.2,41.8] 64.8 [62.7,66.8] 67.4 [64.6,70.0] 31.6 [27.6,35.8] 79.1 [75.8,82.1] 72.9 [68.9,76.6] 40.4 [37.2,43.6] 32.0 [30.6,33.4] 84.1 [74,94]

51