DHS SPATIAL ANALYSIS

REPORTS 13

GEOSPATIAL MODELING OF CHILD 

MORTALITY ACROSS 27 COUNTRIES 

IN SUB-SAHARAN AFRICA

AUGUST 2016

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

by Carla Pezzulo, Tomas Bird, Edson C. Utazi, Alessandro Sorichetta, Andrew J. Tatem, Jennifer Yourkavitch, and Clara R. Burgert-Brucker.

DHS Spatial Analysis Reports No. 13 

Geospatial Modeling of Child Mortality across 27 Countries 

in Sub-Saharan Africa 

Carla Pezzulo1,2

Tomas Bird1,2

Edson C. Utazi3,1

Alessandro Sorichetta1,2

Andrew J. Tatem1,2

Jennifer Yourkavitch4 

Clara R. Burgert-Brucker4

ICF International 

Rockville, Maryland, USA 

August 2016 

1

 WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK 

2

 Flowminder Foundation, Stockholm, Sweden 

3

 Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK 

4

 ICF International, Rockville, MD 

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

International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: +1-301-572-0446; fax: 

+1-301-407-6501; email: clara.burgert@icfi.com 
Acknowledgment: The authors would like to acknowledge Tom Pullum and Trevor Croft for their 

assistance and guidance in developing this report, and Shea Rutstein, Danzhen You, and Jon Pedersen for 

their review of this report. 

Editor: Diane Stoy 

Document Production: Natalie La Roche 

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

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

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

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

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

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: 

Carla Pezzulo, Tomas Bird, Edson C. Utazi, Alessandro Sorichetta, Andrew J. Tatem, Jennifer Yourkavitch, 

and Clara R. Burgert-Brucker. 2016. Geospatial Modeling of Child Mortality across 27 Countries in SubSaharan Africa. DHS Spatial Analysis Reports No. 13. 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. Background ................................................................................................................................ 1 

1.2. Rationale for DHS Spatial Analysis Report 13 ......................................................................... 7 

2. Data .............................................................................................................................................................. 9

2.1. Study Area and Data Description .............................................................................................. 9 

2.2. Data Preparation ...................................................................................................................... 15 

3. Analysis Methods ...................................................................................................................................... 27

3.1. Overview of Analysis Plan ...................................................................................................... 27 

3.2. Covariate and Model Selection ................................................................................................ 27 

3.3. Spatial Analysis Using a Conditional Autoregressive (CAR) Model ...................................... 29 

4. Results ....................................................................................................................................................... 31

4.1. Results from Covariate and Model Selection .......................................................................... 31 

4.2. Results from Spatial Analysis using a Conditional Autoregressive (CAR) Model ................. 34 

5. Discussion .................................................................................................................................................. 43

6. Policy Implications ................................................................................................................................... 47

7. Summary and Conclusions ...................................................................................................................... 49

References .......................................................................................................................................................... 51

Appendix A ........................................................................................................................................................ 61

A.1 United Nations Geographical Region Composition for the 27 Sub-Saharan African 

Countries included in Our Analysis ......................................................................................... 61

A.2. Geospatial Datasets ................................................................................................................. 62 

Appendix B – Detailed Methods and Results ................................................................................................. 87

B.1. Conditional Autoregressive (CAR) Model .............................................................................. 87 

v 

Tables 

Table 1. DHS indicators tested in analysis and their relevance to child mortality .................................. 4 

Table 2. Spatially relevant factors tested in the analysis and their relevance to child mortality ............. 6 

Table 3. Countries, number of DHS sub-national areas in each country, and year of the DHS 

surveys used in the framework of this study ........................................................................... 11 

Table 4. Full range of geospatial datasets and derived covariates tested for modeling child mortality 

in the study area ....................................................................................................................... 24 

Table 5. Summary of single covariate models. Coefficients are presented as both unstandardized 

(i.e., native units) or standardized. ........................................................................................... 31 

Table 6. Top interaction models ............................................................................................................ 34 

Table 7. Posterior estimates of the parameters of the spatial model ...................................................... 35 

Table A.1. List of countries in analysis by United Nations Regions ......................................................... 61 

Table B.1. Top 10 models resulting from the model selection process ..................................................... 88 

Table B.2. Posterior means of the parameters of the spatial model without region-level interactions ..... 88 

Figures 

Figure 1. Conceptual model describing prevention and treatment interventions for, as well as distal 

factors affecting, the main causes of death for children under age 5 in sub-Saharan Africa, 

as identified by Liu et al. (2012). ............................................................................................... 2 

Figure 2. Focus countries and year of the DHS survey used for this study .............................................. 9 

Figure 3a. Percentage of literate women (age 15-49) in the 225 DHS sub-national areas ....................... 16 

Figure 3b. Percentage of births with a birth interval less than 36 months between birth and conception 

in the 255 DHS sub-national areas .......................................................................................... 16

Figure 3c. Percentage of children born in last 5 years with access to a health facility in the 225 DHS 

sub-national areas (access defined as delivery in a health facility) ......................................... 17 

Figure 3d. Percentage of stunting children age (12-59 months) in the 255 DHS sub-national areas ........ 17 

Figure 3e. Percentage of live children age 12-23 months who received the measles vaccination at any 

time prior to the survey in the 255 DHS sub-national areas .................................................... 18 

Figure 3f. Percentage of population practicing open defecation in the 225 DHS sub-national areas ...... 18 

Figure 4a. Population density in each DHS sub-national area .................................................................. 20 

Figure 4b. Satellite-based night-time lights’ intensity spatially averaged within each DHS 

sub-national area ...................................................................................................................... 20 

Figure 4c. Number of dominant ethnic groups, indicating ethnic variability, in each DHS 

sub-national area ...................................................................................................................... 21 

Figure 4d. Spatially averaged economic activity estimated using the grid-based equivalent of the 

purchasing parity gross domestic product, within each DHS sub-national area ...................... 21 

Figure 4e. Annual mean temperature spatially averaged within each DHS sub-national area ................. 22 

Figure 4f. Population-weighted mean Plasmodium falciparum prevalence in each DHS sub-national 

area .......................................................................................................................................... 22 

Figure 5. United Nations geoscheme regions used in the analysis ......................................................... 23 

Figure 6. Flow diagram of analysis methods adopted in the study ......................................................... 27 

Figure 7a. Plots of selected DHS variables against child mortality rates. The blue lines are linear 

regression lines while the red lines are nonparametric loess fits to the data. .......................... 32 

Figure 7b. Plots of selected geospatial covariates against child mortality rates. The blue lines are 

linear regression lines while the red lines are nonparametric loess fits to the data. ................ 33 
vi 

Figure 8a. Observed mortality rates and 95% confidence interval width for the 255 DHS 

sub-national areas in sub-Saharan Africa ................................................................................ 38 

Figure 8b. Predicted mortality rates for the 255 DHS sub-national areas in sub-Saharan Africa ............. 38 

Figure 8c. Predicted standard deviations of the child mortality rates for the 255 DHS sub-national 

areas in sub-Saharan Africa ..................................................................................................... 39 

Figure 8d. Observed minus predicted child mortality rates for the 255 DHS sub-national areas in 

sub-Saharan Africa .................................................................................................................. 39 

Figure 9. A plot of observed vs. predicted child mortality rates ............................................................. 40 

Figure 10. Classification of child mortality regions .................................................................................. 41 

Figure A.1. Population distribution ............................................................................................................ 62 

Figure A.2a. MODIS-based spatial distribution of built-up areas ................................................................ 63 

Figure A.2b. GHSL-based spatial distribution of built-up areas .................................................................. 64 

Figure A.3. Spatial pattern of VIIRS-based night-time lights’ intensity .................................................... 65 

Figure A.4a. Travel-time to major centers with more than 50,000 inhabitants ............................................ 66 

Figure A.4b. Travel-time to major centers with more than 100,000 inhabitants .......................................... 67 

Figure A.4c. Travel-time to major centers with more than 500,000 inhabitants .......................................... 67 

Figure A.5a. Spatial distribution of dominant ethnic groups ........................................................................ 68 

Figure A.5b. Legend of Figure A.5a ............................................................................................................. 69 

Figure A.6. Location of conflict events and associated fatalities between 2010 and 2013 ......................... 70 

Figure A.7. Spatial distribution of the gross cell product representing the regional equivalent of gross 

domestic product ..................................................................................................................... 71 

Figure A.8a. Sheep density ........................................................................................................................... 72 

Figure A.8b. Cattle density ........................................................................................................................... 73 

Figure A.8c. Pig density ............................................................................................................................... 73 

Figure A.8d. Goat density ............................................................................................................................. 74 

Figure A.9a. 30-year average annual temperature ........................................................................................ 75 

Figure A.9b. 30-year average annual rainfall ............................................................................................... 75 

Figure A.10. 14-year average MODIS-based daytime Land Surface Temperature ...................................... 76 

Figure A.11. 50 year-based Aridity Index .................................................................................................... 77 

Figure A.12. Spatial distribution of the growing season length .................................................................... 78 

Figure A.13. Relative frequencies of drought occurrences and their spatial distribution ............................. 79 

Figure A.14. 14-year average MODIS-based Enhanced Vegetation Index .................................................. 80 

Figure A.15a. Topographic elevation ............................................................................................................. 81 

Figure A.15b. Topographic slope ................................................................................................................... 82 

Figure A.15c. Topographic roughness calculated using a neighborhood window of 3x3 grid cells .............. 82 

Figure A.15d. Topographic roughness calculated using neighborhood window of 5x5 grid cells ................. 83 

Figure A.15e. Topographic roughness calculated using neighborhood window of 11x11 grid cells ............. 83 

Figure A.16. MERIS-based landcover classification .................................................................................... 84 

Figure A.17. 15-year average Plasmodium falciparum prevalence distribution ........................................... 85 
vii 

Abbreviations 

4q1 Child mortality rate (calculated as the number of deaths among children between the age 

of 1 and 4 in the five-year period preceding the survey, which is expressed per 1,000 

children surviving to age 12 months) 

ACLED Armed Conflict Location & Event Data Project 

AIC Akaike information criterion 

CAFR Central Africa Region 

CAR Conditional autoregressive model 

CGIAR Consortium of International Agricultural Research Centers 

CIESIN Center for International Earth Science Information Network 

CPR Contraceptive Prevalence Rate 

DHS Demographic and Health Surveys 

DPT Diphtheria, pertussis, and tetanus 

DRC Democratic Republic of the Congo 

EAFR Eastern Africa Region 

EBF Exclusive breastfeeding 

ESA European Space Agency 

EVI Enhanced Vegetation Index 

FAO Food and Agriculture Organization 

GIS Geographic Information System 

GLW2 Gridded Livestock of the World, version 2.0 

GREG Geo-referencing of ethnic groups 

LST Land Surface Temperature 

MCMC Markov chain Monte Carlo methods 

MDG Millennium Development Goals 

MODIS Moderate Resolution Imaging Spectroradiometer 

NPP National Polar-orbiting Partnership 

NOAA National Oceanic and Atmospheric Administration 

SAR12 DHS Spatial Analysis Reports 12 

SDG Sustainable Development Goal 

UN United Nations 

UNDP United Nations Development Programme 

UNFPA United Nations Population Fund 

UNICEF United Nations Children’s Fund 

USAID United States Agency for International Development 

WAFR Western Africa Region 

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 

Preventable mortality of children has been targeted as one of the UN’s Sustainable Development Goals for 

the 2015-30 period. Global decreases in child mortality (4q1) have been seen, although sub-Saharan Africa 

remains an area of concern, with child mortality rates remaining high relative to global averages or even 

increasing in some cases. Furthermore, the spatial distribution of child mortality in sub-Saharan Africa is 

highly heterogeneous. Thus, research that identifies primary risk factors and protective measures in the 

geographic context of sub-Saharan Africa is needed. In this study, household survey data collected by The 

Demographic and Health Surveys (DHS) Program aggregated at DHS sub-national area scale are used to 

evaluate the spatial distribution of child mortality (age 1 to 4) across 27 sub-Saharan Africa countries in 

relation to a number of demographic and health indicators collected in the DHS surveys. In addition, this 

report controls for spatial variation in potential environmental drivers of child mortality by modeling it 

against a suite of geospatial datasets. These datasets vary across the study area in an autoregressive spatial 

model that accounts for the spatial autocorrelation present in the data. 

This study shows that socio-demographic factors such as birth interval, stunting, access to health facilities 

and literacy, along with geospatial factors such as prevalence of Plasmodium falciparum malaria, variety 

of ethnic groups, mean temperature, and intensity of lights at night can explain up to 60% of the variance 

in child mortality across 255 DHS sub-national areas in the 27 countries. Additionally, three regions - 

Western, Central, and Eastern Africa - have markedly different mortality rates. By identifying the relative 

importance of policy-relevant socio-demographic and environmental factors, this study highlights priorities 

for research and programs targeting child mortality over the next decade. 

xiii 

Executive Summary 

In 2015, the United Nations established the Sustainable Development Goals (SDGs), which followed the 

eight Millennium Development Goals (MDGs). The aim of the third SDG is ending preventable deaths of 

newborns and children under age 5, with all countries focused on reducing neonatal mortality at least as 

low as 12 per 1,000 live births and under-five mortality at least as low as 25 per 1,000 live births (United 

Nations General Assembly 2015). Sub-Saharan Africa carries about half of the burden of the world’s underfive deaths (United Nations 2015). 

The third SDG recognizes the importance of addressing different aspects of child and maternal health in 

order to decrease mortality rates. This will involve reducing risk factors and increasing protections (United 

Nations General Assembly 2015). Part of the complexity in understanding child mortality across such a 

large region as sub-Saharan Africa lies in the heterogeneity across the region in mortality rates and 

associated risk factors (Balk et al. 2004). In addition to variability in socio-economic drivers, sub-national 

inequalities in health (Arku et al. 2016; Colson et al. 2015; Roberts et al. 2015; Tatem et al. 2012; Wollum 

et al. 2015), geographic differences in environmental conditions, and spatially varying factors can also 

affect under-five mortality (Balk et al. 2003; Balk et al. 2004). 

This report builds on the work reported in The Demographic and Health Surveys (DHS) Spatial Analysis 

Report 12 (SAR 12, Burgert-Brucker et al. 2015) that explained spatial patterns in child mortality rates 

(4q1 - defined as the number of deaths among children between age 1 and 4 in the five-year period 

preceding the survey per 1,000 children surviving to age 12 months1

). In SAR 12, descriptive geospatial 

analyses identified spatial clustering in nine maternal and child health indicators within and among 27 

countries at the DHS sub-national area level in sub-Saharan Africa. This report adds geospatial factors to 

the usual demographic and socio-economic determinants of child mortality in order to (i) identify the 

geospatial, demographic, and socio-economic covariates (or combinations of them) that might best explain 

the observed patterns of child mortality at the DHS sub-national area level, (ii) explain patterns in 

geographic regions, and (iii) account for spatial clustering of the data identified in SAR 12 by using spatially 

explicit models. 

Plausible factors that may affect or be related to child mortality have been assembled and summarized at 

the DHS sub-national area level. Polygons that represent DHS sub-national areas were obtained from the 

DHS Spatial Data Repository (ICF International 2008-2015b). A suite of determinants of child mortality 

were calculated within each of these areas based on the DHS data including socio-economic (housing and 

literacy), health (vaccination coverage), and demographic (birth interval) factors. Additional geographic 

information was extracted from a set of geospatial data including indicators such as night-time light 

intensity, travel time to major population centers, and malaria prevalence. These geospatial covariates were 

then aggregated within each DHS sub-national area. Covariate and model selection processes were first 

performed to explore which individual (and combinations of) DHS and geographic covariates could explain 

the variation in 4q1 in a linear model setting. An autoregressive spatial model was then applied to account 

for the spatial autocorrelation present in the data and to test the associations among demographic, socioeconomic, geospatial factors, and child mortality. 

 

1

 Child mortality rates (age 1 to 4) includes deaths reported at age 12-59 months and excludes infant deaths (deaths 

occurring between age 0 and 11 months). 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) and further 

described in Pullum et al. 2013. 
xiv 

The results show how a large degree (60%) of the regional variation in child mortality can be explained by 

the socio-demographic and environmental indicators in the analysis, the interactions among them, and the 

patterns in child mortality. Socio-demographic factors such as birth interval, stunting, access to health 

facilities, and literacy, as well as geospatial factors such as the prevalence of Plasmodium falciparum 

malaria, ethnic group variety, mean annual temperature, night-time lights, and economic activity explain a 

considerable portion of the variance in child mortality across 255 DHS sub-national areas in the 27 

countries. 

Interestingly, the role of interaction terms suggests that risk factors can be attenuated by protective factors 

and vice versa. For example, increased literacy reduces the effect of a too-short birth interval on child 

mortality. Conversely, an increase in literacy is associated with an increase in the effect of access to a health 

facility on child mortality. The results indicate that literacy rate is a strong predictor of women’s 

reproductive and health behavior, influences access to health facilities, and has an overarching role as a 

determinant of child mortality. This suggests the need for integrated, multi-dimensional approaches to 

planning and implementing interventions that are designed to reduce child mortality (age 1 to 4) in subSaharan Africa. 

After confirming patterns of positive spatial autocorrelation in child mortality rates across the 255 DHS 

sub-national areas, the results also show differences in child mortality rates across the Western, Central, 

and Eastern Africa regions, with the highest child mortality rates in Central and Western Africa and the 

lowest in Eastern Africa. 
1 

1. Introduction 

1.1. Background 

1.1.1. Policy relevance and socio-demographic determinants of child mortality 

In 2015, the United Nations established the Sustainable Development Goals (SDGs), following the eight 

Millennium Development Goals (MDGs). The third SDG is to end preventable deaths of newborns and 

children under age 5, with all countries aiming to reduce neonatal mortality at least as low as 12 per 1,000 

live births and under-five mortality at least as low as 25 per 1,000 live births (United Nations General 

Assembly 2015). In Africa, progress has been made in many areas to improve survival rates of children 

(UNICEF et al. 2015; WHO 2016a) through the reduction of diseases and improved access to health 

facilities and quality health care for mothers (WHO 2016a). In sub-Saharan Africa, under-five mortality 

rates have fallen from 179 deaths per 1,000 live births in 1990 to 86 deaths per 1,000 live births in 2015 

(United Nations 2015). Despite this substantial improvement, sub-Saharan Africa carries about half of the 

burden of the world’s under-five deaths - 3 million in 2015. 

The SDG 3 recognizes the importance of addressing different aspects of child and maternal health to further 

decrease mortality rates. This would involve reducing risk factors such as the prevalence of tropical diseases 

to increasing protections such as access to high quality maternal health care (United Nations General 

Assembly 2015). About half of under-five child deaths are due to diseases that are preventable or treatable 

through simple, affordable interventions (WHO 2016a). A causal pie by Liu et al. (2012) identified the 

causes of child mortality in sub-Saharan Africa. To conceptualize the relationships among potential 

covariates related to child mortality for the current study, protective factors (behaviors or conditions that 

can reduce the risk of mortality through prevention or treatment) were overlaid onto the causal pie and 

identified distal factors related to child mortality, including mothers’ education and the spatial environment. 

Figure 1 presents a conceptual model that illustrates the conditions driving child mortality in sub-Saharan 

Africa. 

Prevalent causes of death in children are preventable diseases such as pneumonia, diarrhea, and malaria 

(Figure 1) (UNICEF 2015; WHO 2015b). Risk factors that increase mortality from these diseases include 

nutrition-related factors such as stunting (Bhutta et al. 2008; Black et al. 2008; Bryce et al. 2005). Lack of 

adequate sanitation and hygiene has been identified as one of the major causes of diarrheal deaths (Anand 

and Roy 2016; Fink, Günther, and Hill 2011; WHO 2009), which represent 11% of child deaths in subSaharan Africa (Liu et al. 2012, and Figure 1). 
2 

Figure 1. Conceptual model describing prevention and treatment interventions for, as well as distal 

factors affecting, the main causes of death for children under age 5 in sub-Saharan Africa, as 

identified by Liu et al. (2012). 

Conversely, strengthening the health system and infrastructure to provide high quality services (Lavy et al. 

1996; WHO 2016a) and improving access to health facilities (Karra, Fink, and Canning 2016) and 

interventions (Eisele et al. 2012) will help reduce deaths. Maternal education also plays a crucial contextual 

role as a protective factor against the mortality of children born in rural areas and poor households, or for a 

mother with no access to basic education (WHO 2016a). Caldwell (1979) suggested that mothers with 

higher levels of education have improved health knowledge and greater control over health choices for their 

children, which enhance child survival. Moreover, a mother’s education is a protective factor that enables 

the prevention and management of childhood diseases (UNFPA 2007). Table 1 lists the demographic and 

socio-economic factors relevant to child mortality that were tested in this analysis. 
3 

1.1.2. Spatial heterogeneity in child mortality and driving factors 

Part of the complexity in understanding child mortality across a large region such as sub-Saharan Africa 

lies in the heterogeneity across the area in terms of mortality rates and factors that contribute to those rates 

(Balk et al. 2004). Overall, countries in Western and Central Africa have the highest burden of under-five 

deaths. Perhaps unsurprisingly, as compared to Eastern and Southern Africa, these regions also have the 

slowest rates of improvement in many of the key drivers of mortality such as stunting and the lowest rates 

of insecticide-treated bed net use (UNICEF 2015). Inequalities in health have also been detected at subnational levels in a wide range of literature (Arku et al. 2016; Colson et al. 2015; Roberts et al. 2015; Tatem 

et al. 2012; Wollum et al. 2015) that stresses the need for analysis of health indicators in small geographic 

units to aid in the identification of areas of greatest need and a more targeted, efficient allocation of 

resources (Arku et al. 2016; Burgert-Brucker et al. 2015; DHS Spatial Interpolation Working Group 2014; 

Rosero-Bixby 2004). 

Geographic differences in environmental conditions can also add to the variability present in socioeconomic drivers. Several studies have explored environmental effects on under-five mortality and have 

highlighted how geography affects health inequalities (Balk et al. 2003; Balk et al. 2004). Balk et al. (2003) 

analyzed the role of environmental factors that are explicitly spatial, together with usual proximate and 

socio-economic determinants, in predicting infant and child mortality in West Africa. Some spatial factors 

explored in their study (proximity to urban areas and population density) explain a good deal of the countryspecific variation in mortality. Among other findings, Balk et al. detected a correlation between arid and 

semi-arid areas and higher risk of under-five deaths. 

Spatially varying factors can have an indirect effect on under-five mortality. As described in Balk et al. 

(2003), population density was highly correlated with transmission of diseases (Root 1997) while 

overcrowding and slum conditions have negative effects on both infant and child survival (Defo 1994; 

Gupta and Baghel 1999; Woods 2003). However, areas with high population density are usually urban 

where access to improved water and sanitation is higher; this reduces exposure to vector borne and other 

diseases (Hay et al. 2005; Qi et al. 2012; Rutstein 2000; Tatem et al. 2013) and makes sharing of information 

and resources easier. Children residing in urban areas have, on average, higher access to water and 

sanitation and better nutritional status than those in rural areas; therefore, these children have better chances 

of survival (Balk et al. 2003, 2005). 

Urbanization has been historically linked to development. Urban populations, excluding those living in 

slums (Vlahov et al. 2007), generally have better access to healthcare and better quality care services, 

including those for mothers and children. This facilitates access to vaccination and other treatments that 

could prevent risk of child mortality (Dye 2008; Rutherford, Mulholland, and Hill 2010). Urban areas are 

generally more economically prosperous than rural areas, and this results in improved living conditions 

such as better housing that reduce the risk of contracting infectious diseases (Hay et al. 2005), and a higher 

likelihood of affording healthcare and treatment and reducing the risk of child deaths (Rutherford, 

Mulholland, and Hill 2010). Better access to schools, higher levels of education, and increased parental 

employment rates are also strongly associated with reduced child mortality (Gakidou et al. 2010). 

Climatic and environmental factors such as precipitation, vegetation density, and length of growing season 

are strongly associated with the prevalence of vector-borne diseases such as malaria because these factors 

are related to favorable habitats for disease vectors (Tottrup et al. 2009). Such factors can be related to 

overall health through increased malnutrition (Curtis and Hossein 1998; de Sherbinin 2011; Jankowska et 

al. 2012). Vegetation density is associated with intensity of poverty in West Africa (Sedda et al. 2015). 

Spatial variations in rainfall and temperature are associated with increasing of cases of diarrhea (Balk et al. 

2003; Bandyopadhyay, Kanji, and Wang 2012), the spread of infectious diseases in Mali (Findley et al. 
4 

2002), and child mortality in Burkina Faso, especially in the context of specific agro-climatic regions (Dos 

Santos and Henry 2008). 

Temperature and other climatic factors have been widely used in malaria prediction modeling efforts 

(Gething et al. 2011; Weiss et al. 2014) to predict seasonality of malaria incidence (Cairns et al. 2012) and 

dengue risk (Brady et al. 2014). Environmental data have also been used for mapping the distribution of 

infectious diseases and malaria (Hay et al. 2006), as discussed in Kraemer et al. (2016) and Clements et al. 

(2013). Moreover, climate and environment have an impact on agricultural production, which in turn affects 

household livelihoods (Balk et al. 2003; Mabhaudhi, Chibarabada, and Modi 2016). Table 2 further 

describes some of the most relevant spatial environmental factors that influence child mortality that were 

tested in this analysis. 

Table 1. DHS indicators tested in analysis and their relevance to child mortality 

Indicator Significance Status in sub-Saharan Africa

Child Mortality rate2 

(4q1) 

Outcome indicator Under-five mortality rate sub-Saharan Africa 

was 86 per 1,000 live births in 2015. The region 

carries half of the burden of the world’s underfive deaths—3 million in 2015. These rates are 

expected to rise in the future (United Nations 

2015). 

Female Literacy Education of women helps determine child 

survival via increased health knowledge, greater 

control over health choices for children, and 

influencing prevention and proactive 

management of childhood diseases (Caldwell 

1979; Hobcraft 1993; UNFPA 2007). 

Sub-Saharan Africa still has the lowest literacy 

rates and widest gender gap in literacy rates in 

the world (UNESCO UIS 2015). 

Contraceptive 

prevalence, (CPR), 

any modern 

method 

Family planning can prevent closely spaced and 

ill-timed pregnancies and births, which 

contribute to some of the world’s highest infant 

mortality rates. Chances of survival and 

improvement of health status of children can be 

prevented by the use of modern contraception 

(Cleland et al. 2012; Rutstein 2011; WHO 

2015a). 

Contraceptive use among women aged 15 to 49 

doubled between 1990 and 2015, from 13% to 

28% (United Nations 2015). 

Birth interval Birth intervals shorter than 36 months have 

been shown to increase the risk of mortality, 

under-nutrition, and pregnancy morbidity 

(Gribble, Murray, and Menotti 2009; Rutstein 

2008, 2011). Family planning brings large 

potential health and survival benefits for 

children, mainly as a result of wider intervals 

between births (Cleland et al. 2012; Cleland et 

al. 2006). 

Trends in birth intervals for 46 countries showed 

that mothers would prefer a median birth interval 

of 41.5 months - over 9 months longer than their 

actual median interval of 32.5 months in the five 

years preceding the surveys. (Rutstein 2011) 

Continued

 

2

 Child mortality rates (age 1 to 4) includes deaths reported as age 12– 59 months. Therefore, infant deaths (before 

age 1) are not included in the metric used in this report. 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) and further 

described in Pullum et al. 2013. 
5 

Table 1—Continued 

Indicator Significance Status in Sub-Saharan Africa

Access to a health 

facility 

In the context of child mortality (excluding infant 

mortality), delivery in a health facility is used as 

proximate indication of access to health facilities 

and affordability (Karra, Fink, and Canning 

2016), service availability and presence of 

health infrastructures (WHO 2016a) and 

perceived quality of healthcare (Thaddeus and 

Maine 1994). This broader definition of access 

to health facilities also captures the idea of care 

seeking behavior. 

About a quarter of births take place without the 

assistance of a skilled birth attendant. In 2015 

alone, this translated into more than 35 million 

unattended births in South Asia and subSaharan Africa (UNICEF 2016). 

Exclusive 

breastfeeding 

(EBF) 

Exclusive breastfeeding reduces risks of 

ARI/pneumonia, diarrheal disease and 

malnutrition (Black et al. 2008; Jones et al. 

2003; WHO Collaborative Study Team 2000), 

which are leading causes of child mortality (Liu 

et al. 2012). Exclusive breastfeeding is also 

associated with increased spacing between 

pregnancies. 

Prevalence of EBF in West and Central Africa is 

estimated at 28% in 2010, and 47% in Eastern 

and Southern Africa (Cai, Wardlaw, and Brown 

2012). 

Stunting prevalence 

in children 

Undernutrition is the underlying cause of 3.1 

million child deaths in 2011 (Black et al. 2011). 

Stunting prevalence in 2014 was 32% in Africa 

(UNICEF, WHO, and The World Bank 2015). 

Measles vaccination 

coverage3

Measles is still a leading cause of death for 

children under age five and can lead to serious 

complications, including severe diarrhea and 

pneumonia (Burgert-Brucker et al. 2015; WHO 

2015b). Immunization coverage of at least 90% 

is required for herd immunity (Luna, Monga, and 

Morgan 2014). 

“In 2014, about 85% of the world’s children 

received one dose of measles vaccine by their 

first birthday through routine health services – 

up from 73% in 2000” (WHO 2015b). 

Diphtheria-tetanuspertussis (DTP3) 

immunization 

coverage 

Diphtheria, pertussis, and tetanus (DPT) are 

vaccine-preventable diseases that cause 

substantial global disease burden among 

children under age five (Burgert-Brucker et al. 

2015). 

“As of 2014, DPT3 immunization coverage 

among one-year-olds across Africa was 75%, 

the lowest among all WHO regions” (WHO 

2014). 

Coverage of 

improved water 

sources 

The majority of diarrhea deaths are attributed to 

unimproved water supply or unimproved 

sanitation, making it among the most important 

cause of under-five deaths (Anand and Roy 

2016; Fink, Günther, and Hill 2011; WHO 2009).

In sub-Saharan Africa, the coverage of 

improved water sources is about 68% with 

widespread disparities existing between 

countries and across regions (WHO 2016b). 

Sanitation practices Open defecation practice facilitates the 

transmission of diarrheal diseases – one of the 

leading causes of mortality in children under-five 

in sub-Saharan Africa (Galan, Kim, and Graham 

2013). 

In sub-Saharan Africa, it is estimated that 215 

million people continue to engage in open 

defecation (United Nations 2015). 

 

3

 Two doses of measles vaccination are recommended. 
6 

Table 2. Spatially relevant factors tested in the analysis and their relevance to child mortality 

Spatial environmental factors 

relevant to child mortality Significance 

Population density As described in Balk et al. (2003), population density is highly correlated with the 

transmission of diseases (Root 1997) where, for example, overcrowding has 

negative effects on both infant and child mortality (Defo 1994). Slum areas, where 

maternal services are usually inadequate, have higher mortality rates (Gupta and 

Baghel 1999). 

Urbanicity and night-time 

lights 

Urban areas have lower risk of contracting infectious diseases (Hay et al. 2005) and 

are usually associated with lower mortality rates than rural areas. By contrast, very 

sparsely populated areas may have inadequate infrastructure to support prenatal 

and delivery services (Balk et al. 2003). Balk et al. (2005) found strong relationships 

among urbanicity, population density, and child malnutrition. Moreover, urban 

residence is overall associated with development, with urban areas being more 

economically prosperous than rural areas, and providing better access to health 

care services (Rutherford, Mulholland, and Hill 2010), vaccination coverage, and 

schools (Gakidou et al. 2010). Intensity of lights at night is often considered as a 

proxy of population agglomerations and urbanicity, and can therefore capture 

urban-rural differences as well as different levels of wealth and access (Xie et al. 

2015). 

Travel time to major 

population centers 

Entwisle et al. (1997) found significant effects of travel time on contraceptive choice 

and accessibility to towns. Populations living in remote and isolated areas are 

typically poorer (Tatem et al. 2014) and have lower access to population centers, 

resources, and vaccination coverage (Metcalf et al. 2015). Pozzi, Robinson, and 

Nelson (2010) found that distance and travel time to population centers in the Horn 

of Africa are highly correlated with wealth indices: “Welfare decreases rapidly as 

access to population centers gets worse” (Pozzi, Robinson, and Nelson 2010). 

Ethnicity A study conducted in 11 sub-Saharan African countries showed large disparities in 

early child survival chances among ethnic groups in a wide range of African 

countries (Brockerhoff and Hewett 2000). Other studies (Platas 2010) have found 

associations between ethnic group variety, provision of health services, and health 

outcomes; these show that greater ethnic diversity is associated with poorer health 

outcomes, including higher infant and child mortality and lower public health 

expenditure. 

Conflicts Conflict areas are usually deprived and can lack basic access to clean water, 

sanitation, and adequate food. Moreover, some association has been found 

between conflict zones and malaria prevalence (Sedda, Qi, and Tatem 2015). 

Economic activity The global distribution of per-capita gross domestic product is correlated with 

malaria and poverty; malaria-endemic countries also have lower rates of economic 

growth (Gallup and Sachs 2001; Sachs and Malaney 2002). The development of 

health systems and poverty reduction are major drivers for the reduction of health 

inequalities (Marmot 2005; Wagstaff 2002); on the other hand, the distribution of 

mortality aids in understanding inequalities (Sen 1998). 

Livestock density Ownership of livestock is usually associated with rural residency, where households 

depend on agriculture for part of their livelihood (FAO 2011). Livestock is 

recognized as an indispensable asset of the poor that contributes to their nutrition 

and health (Randolph et al. 2007). Ownership of livestock is associated with lower 

prevalence of child stunting in three countries in Eastern Africa (Mosites et al. 

2015). In a more recent work conducted in Western Kenya, Mosites et al. (2016) did 

not find associations between ownership and child growth, although they saw some 

correlation between presence of livestock diseases and lower growth rates in some 

groups. 

Temperature Bandyopadhyay, Kanji, and Wang (2012) found that raised temperatures raise 

diarrheal prevalence in dry seasons in sub-Saharan Africa (in Balk et al. 2003). 

Continued 
7 

Table 2—Continued 

Spatial environmental factors 

relevant to child mortality Significance 

Rainfall Findley et al. (2002) detected effects of rainfall on the incidence of infectious 

diseases (in Balk et al. 2004); Adams (1994) and colleagues (Sauerborn, Adams, 

and Hien 1996) linked climate anomalies in rainfall-dependent agricultural 

communities with health and nutrition of household members (in Balk et al. 2004). 

Balk et al. (2003) observed that variation in average daily rainfall affected the 

survival of children age 1-4, and interpreted this as a link between agricultural 

production and children dietary needs. Together with raised temperatures, rainfall 

shortage raised diarrheal prevalence in dry seasons (Bandyopadhyay, Kanji, and 

Wang 2012). Dos Santos and Henry (2008) also found a relationship between 

rainfall and child mortality in Burkina Faso, especially in specific agro-climatic 

regions. 

Aridity / Growing season 

length 

Curtis and Hossein (1998) found that aridity and length of growing season affect 

child malnutrition. Balk et al. (2003) also found that children living in areas with the 

shortest growing seasons, classified as arid and semiarid, had a higher risk of 

death. 

Drought de Sherbinin (2011) found that, among other geographic and socio-economic 

variables, drought prevalence was significantly correlated with malnutrition in subSaharan Africa. 

Vegetation density Vegetation indices such as the Enhanced Vegetation Index (EVI) are considered 

proxies of vector habitat; disease transmission and physical environment are 

strongly correlated with disease environments in determining the intensity of 

exposure to infectious diseases or providing favorable habitats for disease vectors 

(Tottrup et al. 2009). Tottrup et al. (2009) also found that districts with dense 

vegetation, high rainfall, and low elevation experienced the lowest reductions in 

child mortality. Finally, vegetation was associated with intensity of poverty in West 

Africa (Sedda et al. 2015). 

Topography Topographic spatial data (elevation, slope, and roughness) are usually used as 

proxies for natural barriers to geographical access to city centers, markets, and 

health care (Ombok et al. 2010), and for different exposures to infectious diseases, 

given the spatial variability of malaria prevalence (Gemperli et al. 2004). Webb 

(1998) found an association between topographic conditions and child stunting, 

while a similar association between elevation and underweight rates was reported 

by Balk et al. (2005). However, de Sherbinin (2011) found no association between 

elevation and malnutrition. 

Land cover Land cover serves as proxy for vector habitats and ecological factors (Balk et al. 

2003; Mutuku et al. 2009) 

Malaria prevalence Mosley and Chen (1984) described a child survival conceptual framework that 

highlighted the relationship between environmental contamination and disease 

transmission (exposure to malaria). Balk et al. (2003) found correlations between 

high transmission of malaria and child mortality. Snow et al. (1999) used climatic 

data and probability models to estimate populations exposed to the risk of infection 

of malaria in Africa. Gemperli et al. (2004) found a spatial pattern of infant mortality 

related to the foci of malaria transmission in Mali. 

1.2. Rationale for DHS Spatial Analysis Report 13 

This report builds upon the work reported in Demographic and Health Surveys (DHS) Spatial Analysis 

Report 12 (SAR 12) (Burgert-Brucker et al. 2015), in which descriptive geospatial analyses were applied 

to nine maternal and child health indicators within and among countries, using DHS data for 27 countries 

in sub-Saharan Africa. Burgert-Brucker et al. (2015) were able to (i) explore sub-national geographic 

variability of nine maternal and child health indicators across the 27 countries; (ii) identify areas of high or 
8 

low coverage through comparison with neighboring areas by using sub-national area level autocorrelation 

for each indicator as well as spatial clusters; (iii) establish the significance of geography in analyzing 

differences in the coverage of health indicators. This work set the stage for the current analysis of 

differences in coverage among contiguous political sub-national areas. 

While SAR 12 identified spatial clustering for some of the DHS indicators across 27 sub-Saharan DHS subnational areas, this report aims to add spatial environmental factors to the usual demographic and socioeconomic determinants of child mortality. The report also aims to further identify the spatial or 

demographic and socio-economic covariates (or combinations of covariates) that might best explain 

observed patterns of child mortality at DHS sub-national area level, particularly those that may have 

implications for future interventions and policy relevance. Finally, this report will also account for spatial 

clustering of the data identified in SAR 12 using spatially explicit models. More specifically, this report 

aims to understand the extent to which patterns of child mortality can be explained by combining the effects 

of geospatial covariates with the usual metrics of socio-economic and demographic determinants of child 

mortality by: 

1. Accounting for geospatial factors that might be associated with patterns in child mortality on their 

own; 

2. Understanding how socio-economic, environmental, and geographic variables interact to produce 

observed patterns in child mortality; 

3. Defining patterns in child mortality that are likely due to factors that transcend country boundaries; 

4. Explaining patterns in terms of geographic regions. 

These findings will be used to make policy recommendations that address the combinations of factors 

identified within the study. 

In this report, child mortality rates are calculated as the number of deaths among children between age 1 

and 4 in the five-year period preceding the survey per 1,000 children surviving to age 12 months4

. This 

includes deaths reported at age 12–59 months. Therefore, neonatal deaths and other deaths before age 1 are 

not included in this metric. In general, the literature shows that factors associated with child survivorship 

after the first year of life are different from those associated with infant survivorship (Balk et al. 2003). 

Since this analysis only considers children between ages 1 and 4, only factors associated with the probability 

of dying between the age 1 and 4 are considered. Among those factors, as also discussed in (Balk et al. 

2003), spatial environmental factors are expected to act more strongly on child mortality than infant 

mortality. 

 

4

 Child mortality rates (age 1 to 4) include deaths reported as age 12– 59 months. Therefore, infant deaths (before 

age 1) are not included in the metric used in this report. 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) 

and further described in Pullum et al. 2013. 
9 

2. Data 

2.1. Study Area and Data Description 

This report focuses on 27 countries located in sub-Saharan Africa (Figure 2): 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 2. Focus countries and year of the DHS survey used for this study 

These countries were selected as the focus countries for the SAR 12 report because they had recent DHS 

survey data (from after 2010) and were contiguous in space. This report uses the same countries but includes 

updated data when available (Ghana and Kenya; Figure 2). In addition, these 27 countries have high policy 

relevance both in the framework of the new Agenda for Sustainable Development Goals (United Nations 
10 

General Assembly 2015) and the Strategic Plan 2016-2020 of the Partnership for Maternal, Newborn & 

Child Health (PMNCH 2015). Investigating further aspects of child mortality in these 27 countries would 

contribute to tracking progress of countries in the achievement of child mortality related goals. 

Polygons representing DHS sub-national areas were obtained from the DHS Spatial Data Repository (ICF 

International 2008-2015b). Each country contains between 3 and 26 sub-national areas, with a mean number 

of areas of 9.4 and median of 10 for a total of 255 sub-national areas across the 27 countries (Table 3; 

Burgert-Brucker et al. 2015). The DHS sub-national areas correspond to administrative level-one areas 

(provinces) or a combination of such areas within each survey country. 

Determinants of child mortality also include environmental and physical (climate, land cover, and 

accessibility to cities), socio-economic (housing and poverty), health (vaccination coverage) and 

demographic (birth interval) factors. Thus, this analysis is based on DHS survey data and additional 

information extracted from a variety of spatial data sources. In this report, a number of plausible factors 

that may affect or be related to child mortality have been assembled and summarized at the DHS subnational area level. 

Data from the most recent DHS surveys conducted in these 27 countries between 2010 and 2014 were used 

to compute the key child mortality rate indicator (Pullum et al. 2013; Rutstein and Rojas 2006) and a set of 

other indicators known in the literature to be associated with child mortality (ICF International 2008-2015a, 

Table 1): contraceptive prevalence (any modern method), access to a health facility (delivery in a health 

facility is used as a proxy for access to health facilities), female literacy, exclusive breastfeeding (EBF), 

measles vaccination, DPT-3 immunization coverage, coverage of improved water sources, sanitation 

practices, stunting prevalence in children, and birth interval. All DHS surveys used in this report are 

representative at national and the DHS sub-national area level. Sampling weights were applied to calculate 

appropriate estimates for each indicator at the DHS sub-national area level with Stata 14 software. 

As with the DHS indicator estimates, geospatial datasets that represent factors associated with or having an 

impact on child mortality rates in sub-Saharan Africa (Table 2) were used to extract and summarize 

covariates at the DHS sub-national area level. A full list of the geospatial datasets assembled in this study’s 

framework and the derived geospatial covariates tested in the analysis appear in Table 4. 

 
11 

Table 3. Countries, number of DHS sub-national areas in each country, and year 

of the DHS surveys used in the framework of this study 

Country 

DHS sub-national 

areas DHS Survey Year 

Benin 12 2012 

Burkina Faso 13 2010 

Burundi 5 2010 

Cameroon 12 2011 

Congo 12 2012 

Côte d’Ivoire 11 2012 

The Democratic Republic of the Congo 11 2014 

Ethiopia 11 2011 

Gabon 10 2012 

The Gambia 8 2013 

Ghana 10 2014 

Guinea 8 2012 

Kenya 8 2014 

Liberia 5 2013 

Malawi 3 2010 

Mali 6 2013 

Mozambique 11 2011 

Niger 8 2012 

Nigeria 6 2013 

Rwanda 5 2010 

Senegal 14 2011 

Sierra Leone 4 2013 

United Republic of Tanzania 26 2010 

Togo 6 2013-14 

Uganda 10 2011 

Zambia 10 2013 

Zimbabwe 10 2011 

2.1.1. DHS indicators 

Definitions of the DHS indicators used in the analysis are described briefly below. Section 2.2.1 describes 

how these indicators were summarized at the DHS sub-national area level for use as covariates in the 

analysis. All covariates were estimated using the proper DHS sample weights for each survey. 

Child mortality rate (4q1) 

Number of deaths among children between age 1 and 4 in the five-year period preceding the survey per 

1,000 children surviving to age 12 months5

. This rate includes deaths reported at age 12–59 months and 

excludes infant deaths (age 0 to 11 months). In this analysis, rates were calculated by DHS sub-national 

areas and were used as the response variable (outcome). 

 

5

 Child mortality rates (age 1 to 4) includes deaths reported as age 12–59 months. Therefore, infant deaths (before age 

1) are not included in the metric used in this report. 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) and further 

described in Pullum et al. 2013. 
12 

Female literacy 

Percentage of women age 15-49 who attended primary schooling and can read a whole or part of a sample 

sentence. A respondent who attended secondary education or higher is coded as literate, as well as 

respondents who could read a whole sentence. 

Contraceptive prevalence (CPR), any modern method 

Percentage of all women age 15-49 who report current use of a modern contraceptive method.6

Preceding Birth interval 

Percentage of births with a birth interval less than 36 months between the preceding birth and conception 

of the index child. 

Access to a health facility 

Percentage of children born in the last 5 years who were delivered in a health facility. In the context of child 

mortality (age 1 to 4), this variable is used as a proxy for (and in this work referred interchangeably with) 

access to a health facility. 

Exclusive breastfeeding (EBF) 

Percentage of last-born infants under age 6 months who are living with the mother, breastfeeding, and have 

not had any water, liquids, or solids in the day or night before the interview. 

Stunting prevalence in children 

Stunting is defined as those children whose height is two standard deviations (SD) or more below the 

median height for children of the same sex and age in an internationally standard index, defined in the WHO 

Child Growth Standards that were adopted in 2006. 

Measles vaccination coverage 

Percentage of live children age 12-23 months who received the measles vaccination at any time before the 

survey. 

Diphtheria-tetanus-pertussis (DTP3) immunization coverage 

Percentage of live children age 12-23 months who received three doses of DPT vaccine at any time before 

the survey. 

Coverage of improved water sources 

Percentage of de jure population whose main source of drinking water is a household connection (piped), 

public standpipe, borehole, protected dug well or spring, rainwater collection, or bottled water.

 

6

 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.
13 

Sanitation practices 

Percentage of de jure population that use fields, bushes, forests, open bodies of water, or other open spaces 

rather than using the toilet to defecate (United Nations 2016). 

2.1.2. Geospatial datasets 

Geospatial datasets that represent factors potentially correlated with child mortality in the study area (Table 

2) were obtained from multiple existing sources or produced ad hoc for this study (topographic slope and 

roughness). The datasets are different in format (grid or polygons), type (categorical or continuous), 

accuracy, survey scale, spatial resolution, and temporal and spatial coverage. 

All geospatial datasets in this study are briefly described below and summarized in Appendix A and Table 

4, which also provides source links for obtaining additional information about the methodology and data 

used to produce them. (See Section 2.2.2 below for a description of how the geospatial covariates in the 

analysis were extracted from these datasets and summarized at the DHS sub-national area level.) 

Population density 

The WorldPop (www.worldpop.org) 2010 UN adjusted People Per Pixel raster datasets (Linard et al. 2012; 

Stevens et al. 2015), which are publicly available for all countries in this study and have a resolution of 

approximately 100 m at the equator, were used to represent the population distribution within the study area 

(Figure A.1 in Appendix A). 

Urbanicity 

The publicly available 2000/2001 “MODIS 500m Global Urban Extent” raster dataset (Schneider, Friedl, 

and Potere 2009, 2010), with a resolution of approximately 500 m near the equator, was used to represent 

the extent of built-up and settled areas within the study area (Figure A.2a in Appendix A). 

The European Commission Joint Research Centre “Global Human Settlement Layer” raster dataset 

(courtesy of M. Pesaresi), with a resolution of 38 m (Figure A.2b in Appendix A), provided an alternative 

representation of the extent of built-up areas within the study area. 

Night-time lights 

The publicly available global NOAA Suomi National Polar-orbiting Partnership (NPP) composite nighttime light raster dataset (Elvidge et al. 2013) for April 2012, with a spatial resolution of approximately 500 

m near the equator, was used to measure the night light intensity in the study area (Figure A.3 in 

Appendix A). 

Travel time to major population centers 

The publicly available global European Union Joint Research Centre “travel time to major cities” raster 

datasets (Uchida and Nelson 2008), representing the time required to travel between two grid cells with a 

spatial resolution of approximately 1km near the equator, were used to estimate the accessibility, via all 

transport methods, to settlements with more than 50,000, 100,000, and 500,000 inhabitants located within 

the study area (Figure A.4a, A.4b, A.4c in Appendix A). 

Ethnicity 

The 2010 global “Geo-Referencing of Ethnic Groups (GREG)” vector dataset (Weidmann, Rød, and 

Cederman 2010), was used to map the spatial distribution of the various ethnic groups within the study area 

(Figure A.5 in Appendix A). 
14 

Conflicts 

The publicly available “Armed Conflict Location & Event Data Project (ACLED)” database 

(www.acleddata.com), with disaggregated conflict and protest data that included event locations and 

associated fatalities, was used to map the level of political instability and violence within the study area 

(Figure A.6 in Appendix A). 

Economic activity 

The global “Geographically-based Economic (G-Econ)” raster dataset (Nordhaus and Chen 2009), 

representing the regional equivalent of the gross domestic product at a spatial resolution of approximately 

5 km near the equator, was used to depict the level of economic activity in 2005 within the study area 

(Figure A.7 in Appendix A). 

Livestock density 

The publicly available FAO “Gridded Livestock of the World, v2.0 (GLW2)” (Robinson et al. 2014), with 

a spatial resolution of approximately 1 km near the equator, were used to represent the cattle, goat, pig, and 

sheep density within the study area (Figure A.8a, A.8b, A.8c, A8d in Appendix A). 

Temperature and rainfall 

The publicly available WorldClim - Global Climate Data “Annual Mean Temperature (BIO1)” and “Annual 

Total Precipitation (BIO12)” raster datasets (Hijmans et al. 2005), both with a spatial resolution of 

approximately 1km at the equator, were used to represent the current conditions in climate conditions within 

the study area (Figure A.9a and A.9b in Appendix A). 

MODIS-based daytime Land Surface Temperature (LST) monthly composite raster datasets (MOD11C3), 

with a spatial resolution of approximately 5.6 km near the equator, were also used to represent the current 

annual average temperature in the study area (Figure A.10 in Appendix A). 

Aridity 

The publicly available CGIAR Global Aridity Index raster dataset (Zomer et al. 2008), with a spatial 

resolution of approximately 1km near the equator, was used to represent the current aridity conditions in 

the study area (Figure A.11 in Appendix A). 

Growing season length 

The publicly available FAO “Global length of growing periods” raster dataset (dataset lgp_pl71available 

at http://www.fao.org/geonetwork/srv/en/main.home), with a resolution of approximately 50 km near the 

equator, was used to characterize the agricultural setting within the study area (Figure A.12 in Appendix A). 

Drought 

The CIESIN Global Drought Hazard Frequency and Distribution, v1 raster dataset (Dilley et al. 2005), with 

a spatial resolution of about 5 km near the equator, was used to estimate the relative frequencies of drought 

occurrences within the study area (Figure A.13 in Appendix A). 

Vegetation density 

MODIS-based Enhanced Vegetation Index (EVI) monthly composite raster datasets (MOD13C2), with a 

spatial resolution of approximately 5.6 km near the equator, were used to represent the vegetation conditions 

in the study area (Figure A.14 in Appendix A). 
15 

Topography 

The publicly available Global 30 Arc-Second Elevation raster dataset (GTOPO30; 

http://webgis.wr.usgs.gov/globalgis/gtopo30/gtopo30.htm), with a spatial resolution of approximately 1 km 

near the equator, was used to represent the topographic characteristics of the study area (Figure A.15a, 

A.15b, A.15c, A.15d, A.15e in Appendix A). 

Land cover 

The publicly available ESA 2009 GlobCover raster dataset (Bontemps et al. 2011), with a resolution of 

approximately 300 m near the equator, was used to represent the land cover within the study area (Figure 

A.16 in Appendix A). 

Malaria prevalence 

The publicly available Malaria Atlas Project “PfPR2-10 in Africa 2000-2015” raster datasets, with a 

resolution of approximately 5 km near the equator, were used to depict the Plasmodium falciparum

infection prevalence in children age 2-10 within the study area (Figure A.17 in Appendix A). 

2.2. Data Preparation 

All DHS indicators and geospatial datasets that represented the factors in this study were summarized at 

the DHS sub-national area level with the methodology described below. 

2.2.1. DHS covariates aggregation 

The units of analysis of this work are DHS sub-national areas, which correspond to administrative levelone areas (provinces) or a combination of such areas within each survey country. The data are statistically 

representative estimates of each indicator of interest for the population within each DHS sub-national areas 

at the time of each survey. Sampling weights were applied to calculate appropriate estimates for each 

indicator at the sub-national area level. 

In addition to the child mortality rate indicator, which represents a rate expressed as number of deaths per 

1,000 children surviving to age 12 months by DHS sub-national area, all other DHS indicators described 

above were calculated and used as percentages by DHS sub-national area. 

Figures 3(a-f) below shows the spatial distribution at the DHS sub-national area level for the six most 

relevant DHS indicators selected for use in the final statistical model (birth interval, sanitation, female 

literacy, access to a health facility (delivery in a health facility used as a proxy for access to health facilities), 

measles vaccination, and stunting). In Figures 3(a-f), the scale ranges vary for each indicator. A common 

color scheme is used for all indicators in these figures, with relatively better conditions shown in green and 

worse conditions in red. For example, reddish colors are associated with higher values of stunting 

prevalence in children and with lower values of female literacy. 
16 

Figure 3a. Percentage of literate women (age 15-49) in the 225 DHS sub-national areas 

Figure 3b. Percentage of births with a birth interval less than 36 months between birth and 

conception in the 255 DHS sub-national areas
17 

Figure 3c. Percentage of children born in last 5 years with access to a health facility in the 225 DHS 

sub-national areas (access defined as delivery in a health facility) 

Figure 3d. Percentage of stunting children age (12-59 months) in the 255 DHS sub-national areas 
18 

Figure 3e. Percentage of live children age 12-23 months who received the measles vaccination at 

any time prior to the survey in the 255 DHS sub-national areas 

Figure 3f. Percentage of population practicing open defecation in the 225 DHS sub-national areas 
19 

2.2.2. Geospatial covariates preparation 

In order to extract covariates from the geospatial datasets described in subsection 2.1.2 and to summarize 

these covariates at the DHS sub-national area level (at the same spatial detail of the DHS covariates), the 

DHS sub-national areas (ICF International, 2008-2015b), were used to calculate GIS-based zonal statistics 

and append the results to the corresponding aggregate DHS covariates. 

The WorldPop 2010 UN adjusted People Per Pixel datasets were mosaicked and used to calculate the 

population density of each DHS polygon by dividing the total population in each polygon by the surface 

area of the corresponding polygon. 

The mosaicked WorldPop dataset was also combined with the temporally averaged “PfPR2-10 in Africa 

2000-2015” datasets to calculate the population-weighted mean malaria prevalence value for each DHS 

polygon. This involved multiplying the two raster datasets and dividing the total number of infected people 

within each polygon (calculated with the output raster generated by the multiplication) by the total 

population in the same polygon (calculated with the mosaicked WorldPop dataset). 

The “MODIS 500m Global Urban Extent” and “Global Human Settlement Layer” were used to calculate 

two different estimates of the percentage of urban area in each DHS polygon. These estimates were 

subsequently combined with the mosaicked WorldPop to produce two estimates of the percentage of urban 

population living within each polygon. 

The georeferenced event locations and the associated fatalities were extracted from the ACLED database 

and were used to calculate the total number of events and fatalities in each DHS polygon. The total number 

of events and fatalities was then divided by the area and total population of the corresponding polygon, 

respectively. 

The “Annual Total Precipitation (BIO12)” was used to calculate the annual rainfall rate, in each DHS 

polygon, defined as the average volume of water in the form of rain (mm) that falls per unit of area (~km2

) 

and per unit of time (year). This involved calculating the total amount of annual rain falling in each DHS 

polygon and dividing this total by the area of each polygon. 

Geospatial covariate values to be used in the statistical analysis were summarized for each DHS polygon 

in two different ways according to the geospatial dataset type from which they were extracted (Table 4). 

For continuous covariates (night-time lights, travel time to major population centers, economic activity, 

livestock density, temperature, aridity, drought, EVI, topographic elevation, slope, and roughness), the 

mean (referred as spatial average in Figure 4b, 4d, 4e, and in Table 4) and median values were calculated 

and extracted. In particular, for each DHS polygon, (i) the mean was calculated by averaging the values of 

all grid cells located within it, while (ii) the median was calculated by identifying the value dividing into 

two equal parts the ordered values of the grid cell located within the polygon. For categorical covariates 

(landcover and a 30 arc seconds rasterized version of the GREG vector dataset), the modal value (the value 

that appears most often) and the variety of the values (the number of unique values) were considered. 

In order to calculate and extract the mean and median values from the “global length of growing periods” 

dataset, the dataset was reclassified into a continuous dataset by replacing, for each grid cell, the day interval 

(representing the growing season length) with its corresponding mean value. Figure 4(a-f) below shows the 

spatial distribution at the DHS sub-national area level for the most relevant geospatial covariates used in 

the analysis. Finally, because policy actions can often be implemented across large regions and patterns in 

4q1 appear to vary within distinct areas across sub-Saharan Africa (UNICEF 2015), three broad regions 

(East, Central, and West) were defined, as described by the United Nations geoscheme (Figure 5 and Table 

A.1 in Appendix A). 
20 

Figure 4a. Population density in each DHS sub-national area

Figure 4b. Satellite-based night-time lights’ intensity spatially averaged within each DHS subnational area 
21 

Figure 4c. Number of dominant ethnic groups, indicating ethnic variability, in each DHS subnational area 

Figure 4d. Spatially averaged economic activity estimated using the grid-based equivalent of the 

purchasing parity gross domestic product, within each DHS sub-national area
22 

Figure 4e. Annual mean temperature spatially averaged within each DHS sub-national area 

Figure 4f. Population-weighted mean Plasmodium falciparum prevalence in each DHS sub-national 

area 
23 

Figure 5. United Nations geoscheme regions used in the analysis 
24

Table 4. Full range of geospatial datasets and derived covariates tested for modeling child mortality in the study area Geospatial dataset 

Temporal 

coverage Type Format

Native 

resolution Source 

Derived 

geospatial 

covariate 

Covariate 

temporal 

coverage

Covariate 

type 

Covariate 

Spatial 

detail 

Population distribution 

(WorldPop People Per Pixel) 

2010 Continuous Raster 3 arc seconds WorldPop Project http://www.worldpop.org.uk/data/data_ sources/ Population density 2010 Continuous DHS sub- national area 

Urbanicity 

(MODIS 500m Global Urban Extent) 

2000/ 

2001 

Categorical 

(binary) Raster 15 arc seconds SAGE https://nelson.wisc.edu/sage/data-and- models/schneider.php 

Percentage of 

urban area 

2000/ 

2001 Continuous

DHS subnational 

area 

Percentage of 

WorldPop 

urban 

population 

2000/ 

2001-

2010 

Continuous

DHS subnational 

area 

Urbanicity 

(Global Human Settlement Layer) 

2014 Categorical (binary) Raster 38m EC-JRC (personal communication) http://ghslsys.jrc.ec.europa.eu/ 

Proportion of 

urban area 2014 Continuous

DHS subnational 

area 

Proportion of 

WorldPop 

urban 

population 

2010-

2014 Continuous

DHS subnational 

area 

Night-time lights 

(Suomi NPP VIIRS) 2012 Continuous Raster 15 arc seconds NOAA http://ngdc.noaa.gov/eog/viirs/download_ monthly.html Spatially averaged night-lights’ intensity 2012 Continuous DHS sub- national area 

Travel time to major 

population centers with more than 50/100/500k inhabitants 

2000 Continuous Raster 30 arc seconds EC-JRC http://forobs.jrc.ec.europa.eu/products/ gam/ 

Spatially 

averaged 

travel time to 

populated 

places 

2000 Continuous

DHS subnational 

area 

Ethnicity 

(Geo-referencing of Ethnic Groups - GREG) 

2010 Categorical Vector Polygon locations ETH International Conflict Research http://www.icr.ethz.ch/data/other/greg 

Modal ethnic 

group

2010 Categorical

DHS subnational 

area

Ethnic group 

variety 2010 Continuous

DHS subnational 

area

Conflicts 

(Armed Conflict Location & Event Data - ACLED) 

1997-

2016 

Continuous/

categorical Table Point locations ACLED Project http://www.acleddata.com/data/acled- versions-1-5-data-1997-2014/ 

Conflict density 2010- 2013 Continuous DHS sub- national 

area 

Fatalities due 

to conflicts (as 

a proportion of 

the total 

population)

2010-

2013 Continuous

DHS subnational 

area

Continued 
25

Table 4—Continued Geospatial dataset 

Temporal 

coverage Type Format

Native 

resolution Source 

Derived 

geospatial 

covariate 

Covariate 

temporal 

coverage

Covariate 

type 

Covariate 

Spatial 

detail 

Economic activity 

(Geographically-based Economic Data - G-Econ) 

2005 Continuous Raster 2.5 arc minutes Yale University http://gecon.yale.edu/ 

Spatially 

averaged 

gross cell 

product 

2005 Continuous

DHS subnational 

area 

Livestock density 

(Gridded Livestock of the World, v2.0) 

2006 Continuous Raster 30 arc seconds Livestock Geo-Wiki http://livestock.geo-wiki.org/ Sheep/Pig/ Cattle/Goat density 2006 Continuous DHS sub- national area 

Temperature 

(WorldClim BIO1) 

Current 

(~1960-

1990) 

Continuous Raster 30 arc seconds WorldClim http://www.worldclim.org/current 

Spatially 

averaged 

annual mean 

temperature 

Current

(~1960-

1990) 

Continuous

DHS subnational 

area 

Temperature 

(MODIS Land Surface Temperature - MOD11C3) 

2001-2015 Continuous Raster 30 arc seconds NASA https://lpdaac.usgs.gov/dataset_discover y/modis/modis_products_table/mod11a1 Spatially averaged LST 2001- 2015 Continuous DHS sub- national area 

Rainfall 

(WorldClim BIO12) 

Current 

(~1960-

1990) 

Continuous Raster 30 arc seconds WorldClim http://www.worldclim.org/current 

Spatially 

averaged 

annual rainfall 

rate 

Current

(~1960-

1990) 

Continuous

DHS subnational 

area 

Aridity 1950-2000 Continuous Raster 30 arc seconds CGIAR-CSI http://www.cgiar-csi.org/data/global- aridity-and-pet-database Spatially averaged Aridity Index 1950- 2000 Continuous DHS sub- national area 

Growing season length 2000 Categorical Raster 0.5 degree FAO http://www.fao.org/geonetwork/srv/en/ main.home Spatially averaged growing season length 2000 Continuous DHS sub- national area 

Drought 

(Global Drought Hazard Frequency and Distribution, v1) 

1980-2000 Continuous Raster 2.5 arc minutes

CIESIN 

http://sedac.ciesin.columbia.edu/data/set

/ndh-drought-hazard-frequencydistribution 

Spatially 

averaged 

relativefrequency of 

drought 

occurrence 

1980-

2000 Continuous

DHS subnational 

area 

Vegetation density 

(MODIS Enhanced Vegetation Index - MOD13C2) 

2001-2015 Continuous Raster 30 arc seconds NASA https://lpdaac.usgs.gov/dataset_discover y/modis/modis_products_table/mod13a3 Spatially averaged EVI 2001- 2015 Continuous DHS sub- national area 

Continued 
26

Table 4—ContinuedGeospatial dataset 

Temporal 

coverage Type Format

Native 

resolution Source 

Derived 

geospatial 

covariate 

Covariate 

temporal 

coverage

Covariate 

type 

Covariate 

Spatial 

detail 

Topographic elevation 

(GTOPO) 2000 Continuous Raster 30 arc seconds USGS https://lta.cr.usgs.gov/GTOPO30 

Spatially 

averaged 

topographic 

elevation 

2000 Continuous

DHS subnational 

area 

Topographic slope 

(GTOPO-derived) 2000 Continuous Raster 30 arc seconds - 

Spatially 

averaged 

topographic 

slope 

2000 Continuous

DHS subnational 

area 

Topographic roughness 

(GTOPO-derived using different window sizes of 3x3, 5x5, and 11x11 grid cells) 

2000 Continuous Raster 30 arc seconds - 

Spatially 

averaged 

topographic 

roughness 

2000 Continuous

DHS subnational 

area 

Land cover 

(MERIS-based GlobCover) 

2009 Categorical Raster 10 arc seconds ESA http://due.esrin.esa.int/page_globcover. php 

Modal 

landcover 2009 Categorical

DHS subnational 

area 

Landcover 

variety 2009 Continuous

DHS subnational 

area 

Malaria prevalence 

(PfPR2-10 in Africa 2000-2015) 

2000-2015 Continuous Raster 2.5 arc minutes Malaria Atlas Project http://www.map.ox.ac.uk/ Population- weighted mean Pf prevalence 2000- 2015 Continuous DHS sub- national area 
27 

3. Analysis Methods 

3.1. Overview of Analysis Plan 

This section describes the spatial and non-spatial analysis methods used in the study. In general, the analysis 

involved three main steps. The first step, data processing, has been described in the previous section. The 

next step involved covariate and model selection in which individual (and combinations of) DHS and 

geographic covariates that were capable of explaining variation in 4q1 in a linear model setting were 

explored. This step included an exhaustive model selection process. Finally, a spatial model along with the 

top linear model identified in the model selection process was fitted. These can be visualized in the flow 

diagram in Figure 6 and are described in detail in the following sections. 

3.2. Covariate and Model Selection 

The exploratory analysis methods included 1) selection of the most relevant set of covariates, 2) selection 

of top interaction terms and 3) the model selection procedure that resulted in the best combination of 

covariate terms for use in the final analysis. 

Figure 6. Flow diagram of analysis methods adopted in the study 
28 

3.2.1. Covariate selection 

To select the best set from the 10 DHS and over 30 geospatial covariates, both statistical and policy 

relevance considerations were explored. First, the relationships between each covariate and 4q1 was 

visualized with scatter plot diagrams to ensure assumptions of linearity were met. The latter was 

implemented by fitting nonparametric smooth curves (specifically, locally weighted regression (loess) 

curves; Cleveland and Devlin 1988) to the points in the scatter plots, as well as simple linear regressions 

for comparison. 

To identify potential issues arising from collinearity between covariates, all covariate pairs were compared 

for the correlation between them. The pairs with a correlation of greater than 0.5 (in absolute value) were 

identified for potential exclusion. As an additional step to address the problem of collinearity between the 

covariates, the variance inflation factor (VIF) of each variable was calculated. The VIF measures the 

increase in the variance of an estimated regression coefficient due to (multi) collinearity. For this analysis, 

collinearity was considered high for covariates with a VIF greater than 4 - a value that represents a twofold increase in the standard error of a regression coefficient as a result of collinearity. When choosing 

between collinear variables after the VIF analysis, the decision was based on the variables with the highest 

R2

 statistic when compared to 4q1, as well as the policy relevance of each variable. In a few cases, collinear 

covariates with moderate VIFs and high policy relevance were retained. 

3.2.2. Single covariate models, selection of interaction terms and best non-spatial model 

To determine an optimal subset of covariates for modeling 4q1, single covariate Gaussian regression models 

were fitted to the screened set of covariates and the AIC (Akaike information criteria), R2

 and predictive R2

(PR2

) statistics were calculated. The AIC is a measure of the relative quality of fit of a statistical model; the 

smaller the AIC, the better the fit. The predictive R2

 statistic was calculated with Monte Carlo crossvalidation to determine the performance of the models for the out-of-sample prediction. The out-of-sample 

prediction, with a randomly selected 80% of the data, was used for model training while the remaining 20% 

was used for validation. This process was repeated 10 times for 10 different splits of the data and measures 

of model fit averaged across these iterations. All the fitted models were then ranked based on the R2

 statistic. 

All covariates whose R2

 statistics were less than 5% were eliminated after this ranking. 

To identify potentially important interactions between covariates, models that included interactions 

between pairs of covariates were explored and measures of model fit recorded. To reduce the candidate set 

of interaction terms from the selection process and to maintain interpretability of the resulting models, 

particular emphasis was placed on interactions with potential policy relevance. By default, interaction 

models include terms for each of the covariates in the interaction, as well as a term for the interaction. To 

ensure that the interaction significantly improved the fit of the model, the interaction model was also 

compared with a model that included only the two main effects. Only interaction models that resulted in a 

greater than 2 improvement in AIC score were retained. All interaction models with an R2

 of less than 0.25 

were excluded. In addition, we investigated potential differences in the effects of each covariate on 4q1 

between East, Central, and Western Africa by testing models that included an interaction with region. 

With the set of interaction terms identified (excluding selected region-level interactions), an exhaustive 

model selection procedure was used to test all possible combinations of multiple (first and second order) 

terms in a model. Inclusion of additional covariates was stopped when there was no discernible 

improvement to model fit with the inclusion of greater numbers of covariates. To guard against overfitting, 

the difference between PR2

 and R2 scores was monitored; models in which PR2 was lower than R2 by greater 

than 5% were eliminated. Models were assessed by AIC, R2

 and PR2 and ranked by R2; the model with the 

lowest AIC score was retained. 
29 

3.3. Spatial Analysis Using a Conditional Autoregressive (CAR) Model 

In a non-spatial modeling framework, the observed data are assumed to be identically and independently 

distributed. However, data collected over space such as the 4q1 typically exhibit spatial autocorrelation. 

Although a proportion of this spatial autocorrelation may be accounted for by the inclusion of spatiallyvarying covariates in standard linear regression models, it is often the case that spatial dependence remains 

in the residuals that result from fitting such models. To test for spatial autocorrelation in the residuals, 

Moran’s I permutation test was used (Banerjee, Carlin, and Gelfand 2015, p.75). The Moran’s I statistic is 

similar to a correlation coefficient. Positive values indicate that neighboring observations tend to be more 

alike whereas negative values indicate that nearby values tend to be dissimilar. A value of zero indicates a 

random spatial pattern. Spatial random effects were modeled with a conditional autoregressive (CAR; e.g., 

Leroux, Lei, and Breslow (2000) fitted in a Bayesian framework using the CARBayes software package in 

R; Lee 2013). A full description and implementation details of the model are provided in Appendix B. 

31 

4. Results 

4.1. Results from Covariate and Model Selection 

4.1.1. Covariate selection 

The plots of each covariate against 4q1, together with the loess fits, showed that the relationships could be 

reasonably modeled as linear (see Figures 7a and 7b). Testing for highly correlated covariates restricted the 

list to 29 covariates for the analysis (Table 5). 

4.1.2. Single covariate models, interaction terms and best non-spatial model 

Table 5 shows the rankings of the 29 retained covariates in terms of R2

. The unstandardized and 

standardized (centered and scaled by the standard deviation of the covariate) coefficients of the covariates 

are also reported. Note that standardized coefficients are only included to allow comparison between the 

relative magnitudes of the effects of the covariates on 4q1. This comparison is untenable using the 

unstandardized coefficients because units/scales on which the covariates were measured were different. For 

example, the unstandardized coefficient of malaria prevalence, measured in proportions, is estimated at 

55.02. This coefficient will, however, change to 0.55 when this covariate is expressed in percentages to 

make it comparable with the DHS covariates. The R2

 statistics show that some of the covariates offer little 

or no explanatory power for modeling 4q1. Using the criterion of an R2

 value of at least 5%, the first 12 

covariates were selected as the best subset to be tested for inclusion in the final model. This subset includes 

6 DHS and 6 geospatial covariates. Figures 7a and 7b show the relationships between these covariates 

and 4q1. 

Table 5. Summary of single covariate models. Coefficients are presented as both unstandardized (i.e. native 

units) or standardized. 

Rank Covariate 

Unstandardized 

coefficient 

Standardized 

coefficient AIC R2 PR2

1 Literacy -0.42 -10.74 2235 0.24 0.24

2 Malaria prevalence 55.02 10.44 2240 0.23 0.23

3 Birth interval 1.04 9.46 2252 0.18 0.20

4 Access to a health facility -0.36 -8.09 2268 0.14 0.13

5 Sanitation 0.31 7.99 2269 0.13 0.11

6 Measles vaccination -0.56 -7.93 2269 0.13 0.13

7 Stunting 0.62 6.82 2279 0.10 0.08

8 Ethnic group variety 2.33 6.53 2281 0.09 0.10

9 Mean temperature 0.22 6.27 2283 0.08 0.08

10 Population density 0.00 -5.33 2289 0.06 0.06

11 Economic activity -8.19 -5.14 2290 0.05 0.05

*12 Night-time lights -2.20 -4.75 2292 0.05 0.05

13 DPT3 immunization -0.21 -4.50 2294 0.04 0.04

14 Breastfeeding -0.20 -4.27 2295 0.04 0.04

15 Conflict density -8.64 -3.15 2299 0.02 0.02

16 Modal ethnic group -0.01 -2.89 2300 0.02 0.01

17 Landcover variety -0.90 -2.79 2300 0.02 0.01

18 Cattle density -0.05 -2.53 2301 0.01 0.01

19 Water sources -0.10 -2.47 2301 0.01 0.01

20 Growing season length 0.02 2.39 2302 0.01 0.02

21 Roughness -3.97×106 -2.35 2302 0.01 0.02

22 Pig density -0.16 -1.92 2303 0.01 0.01

23 Travel time 0.00 -1.19 2304 0.00 0.00

24 Rainfall 0.00 0.96 2304 0.00 0.00

25 Vegetation -7.41 -0.75 2304 0.00 0.00

26 Modal landcover -0.01 -0.55 2304 0.00 0.00

27 Urban population 0.00 -0.48 2304 0.00 0.00

28 Conflict fatalities -189.91 -0.13 2305 0.00 0.00

29 Drought -0.04 -0.11 2305 0.00 0.00

*The top 12 covariates have R2 values greater than 5%.
32 

Figure 7a. Plots of selected DHS variables against child mortality rates. The blue lines are linear 

regression lines while the red lines are nonparametric loess fits to the data. 

Measles vaccination Delivery in a health facility Literacy

Stunting Sanitation Birth interval

0

25

50

75

100

125

0

25

50

75

100

125

20 40 60 80 100 25 50 75 100 0 25 50 75 100

20 40 60 0 25 50 75 20 30 40 50 60

Child_mortality_rate

Access to a health facility

Child mortality rate 
33 

Figure 7b. Plots of selected geospatial covariates against child mortality rates. The blue lines are 

linear regression lines while the red lines are nonparametric loess fits to the data. 

Relationships between the DHS variables and child mortality are reasonably linear (Figure 7a). Similarly, 

the linearity assumption for the geospatial covariates is also justifiable in most cases (Figure 7b). Population 

density and night-time lights exhibit substantial skewness. Nevertheless, residual diagnostics showed that 

this was not a problem in the analysis. 

Table 6 shows the results of selection of interaction terms. Note that not all interactions were included in 

the model selection; only interactions that were relevant for policy and had R2

 values greater than 25% were 

retained. Most top interactions included either female literacy or birth interval in combination with other 

top DHS and geographic variables. Top interactions with region also included stunting and access to a 

health facility. When considering pairs of explanatory factors, the interaction models show significant 

improvement over the single covariate models. The best single covariate model explains only about 24% 

Malaria prevalence Economic activity Temperature

Ethnic groups variety Population density Night-time lights

0

40

80

120

0

40

80

120

0.0 0.2 0.4 0.6 01234 200 250

5 10 15 20 0 2500 5000 7500 10000 0 5 10 15

Child mortality rate Child_mortality_rate
34 

of the variation in child mortality; the best interaction model accounts for as much as 40% of this variation. 

This clearly shows the importance of interactions between covariates when modeling child mortality. 

Table 6. Top interaction models. A ‘:’ indicates the interaction between covariates. 

Interactions between covariates Region-level interactions

Model AIC R2 Model AIC R2

Malaria prevalence: Birth interval 2176.98 0.40 Birth interval: Region 2213.76 0.32 

Literacy: Ethnic group variety 2204.61 0.34 Access to a health facility: Region 2215.08 0.32 

Literacy: Birth interval 2205.36 0.33 Literacy: Region 2218.45 0.31 

Literacy: Access to a health facility 2224.45 0.28 Stunting: Region 2240.61 0.25 

Birth interval: Temperature 2228.78 0.27 

Literacy: Sanitation 2230.91 0.26 

Literacy: Night-time lights 2232.99 0.26 

Literacy: Economic activity 2233.00 0.26 

Birth interval: Ethnic group variety 2235.14 0.25 

In the non-spatial model selection phase, top models included up to 8 of the 12 covariate terms. Models 

with greater numbers of covariates exhibited only marginal improvements in AIC scores and increasing 

evidence of overfitting as shown by the large differences in R2

 and PR2 values. Top models had adjusted 

R2

 values of up to 0.54, which indicated good explanatory power. These models also had PR2 values of 

≈0.53 that indicated no overfitting of the models to the data. A list of the top 10 models as ranked by the 

AIC is presented in Table B.1 (Appendix B). 

4.2. Results from Spatial Analysis using a Conditional Autoregressive (CAR) Model 

Testing for spatial autocorrelation in the 4q1 data using Moran’s I yielded a statistic of 0.1164 (p<0.005); 

this indicated the presence of significant positive spatial autocorrelation in the residuals. Fitting of a spatial 

random effect with the top selected model yielded an adjusted R2

 of 0.60; this showed some improvement 

over the non-spatial model. Spatial models without region-level interactions were also fitted to provide 

confirmatory tests of significance for the marginal effects of the covariates with region-level interactions 

(see Table B.2 in Appendix B for the results of this model). Table 7 reports the posterior means, standard 

deviations and quantiles of the covariate effects (ࢼ(, the variance parameters (ߪଶ, ߬ଶ), and the correlation 

parameter (ߩ (of the spatial model. 

 
35 

Table 7. Posterior estimates of the parameters of the spatial model 

Parameter Mean 

Standard 

Deviation Median 95% Credible Interval 

(Intercept) 27.0289 2.8163 27.2437 (21.1830, 32.0304) 

Region(CAFR) 17.6602 4.9860 17.5071 (8.8964, 27.6040) 

Region(WAFR) 7.2262 4.6206 7.0430 (-0.6524, 16.3675) 

Stunting 0.4594 0.1935 0.4623 (0.0646, 0.8045)

Malaria prevalence 49.9618a 7.5869 49.9206 (35.8431, 64.4336) 

Access to a health facility -0.1821 0.1030 -0.1733 (-0.3990, 0.0049) 

Birth interval 0.5418 0.2423 0.5480 (0.0787, 0.9899) 

Literacy 0.1298 0.1073 0.1145 (-0.0670, 0.3620) 

Temperature 0.0214 0.0641 0.0228 (-0.1094, 0.1596) 

Night-time lights 0.0678 1.2112 -0.0215 (-2.1017, 2.5609) 

Ethnic groups’ variety -0.7261 0.3939 -0.7016 (-1.5366, 0.0667) 

Literacy: Birth interval -0.0159 0.0076 -0.0154 (-0.0307, -0.0011) 

Literacy: Access to a health facility 0.0063 0.0032 0.0064 (0.0002, 0.0129) 

Birth interval: Malaria prevalence 1.6979 0.8352 1.7029 (-0.1293, 3.4090) 

Birth interval: Temperature -0.0069 0.0065 -0.0072 (-0.0186, 0.0046) 

Literacy: Night-time lights -0.0222 0.0337 -0.0201 (-0.0931, 0.0405) 

Birth interval: Ethnic groups’ 

variety 

0.0790 0.0478 0.0740 (-0.0040, 0.1813) 

Birth interval: Region(CAFR) 0.0918 0.5292 0.0535 (-0.9604, 1.1593) 

Birth interval: Region(WAFR) 0.7182 0.4311 0.7471 (-0.1750, 1.5533) 

Access to a health facility: 

Region(CAFR) 

-0.3455 0.2330 -0.3549 (-0.7955, 0.1560) 

Access to a health facility: 

Region(WAFR) 

0.0584 0.1616 0.0502 (-0.2476, 0.3994) 

Literacy: Region(CAFR) -0.3164 0.2771 -0.3177 (-0.8563, 0.2214) 

Literacy: Region(WAFR) -0.2182 0.1527 -0.2186 (-0.5356, 0.0873) 

Stunting: Region(CAFR) -0.3964 0.4566 -0.4265 (-1.3164, 0.4256) 

Stunting: Region(WAFR) -0.3345 0.2585 -0.3226 (-0.8072, 0.1601) 

 (262.5637 0.0014,) 204.9695 104.8733 144.8332 ૛࣌

 (404.6400 0.0005,) 0.0231 155.2425 117.1275 ૛࣎

 (0.8736 0.0390,) 0.2495 0.2407 0.3339 ࣋

a Note that malaria prevalence is measured in proportions. This estimate is equal to 0.4996 (i.e., 49.9618 

divided by 100) when malaria prevalence is transformed to percentage. 

A number of conclusions can be drawn from these results. First, the 95% credible intervals of both 

parameters of the spatial random effects (߬ଶ, ߩ (do not include zero; this affirmed the existence of substantial 

(positive) spatial autocorrelation in child mortality across the DHS sub-national areas. General patterns in 

the model coefficients (the parameter estimates) can be interpreted as: 

1. Child mortality rates vary significantly across the three regions in the study. 

2. Stunting, malaria prevalence, access to a health facility, and too short birth interval have significant 

marginal effects on child mortality (conditional on the average values of other covariates in each 

case). The marginal effects of other covariates were not significant. 

3. The interactions between female literacy, birth interval, and access to a health facility had a 

significant effect on child mortality (conditional on the average values of other covariates in each 

case). Other inter-covariate interactions were not significant. 

4. None of the region-level variations in the effects of the tested covariates (birth interval, access to a 

health facility, literacy, and stunting) on child mortality were significant. 
36 

A more detailed interpretation of these patterns is described below. All coefficients are interpreted in the 

context of holding all other covariates at their mean values. 

Intercept, CAFR, WAFR: The intercept (set to EAFR in this model) has an estimated value of 27.03. This 

value is the average predicted mortality rate per 1,000 children surviving to age 12 months for EAFR. 

Similarly, 27.03 + 17.67 = 44.69 and 27.03 + 7.26 = 34.3 are the respective average predicted mortality 

rates per 1,000 children surviving to age 12 months for CAFR and WAFR. The 95% credible intervals of 

these coefficients show that the average mortality rate for the reference region (EAFR) is significant. There 

is a significant difference between the average predicted mortality rates for EAFR and CAFR, but not 

between EAFR and WAFR. This implies that regional variation in mortality is significant. Finally, the 

average predicted mortality rate is highest in CAFR and lowest in EAFR. 

Stunting: Estimates of regression coefficients for stunting, stunting:CAFR, and stunting:WAFR are 0.46, 

-0.40 and -0.33 respectively. Thus, a unit increase in the percentage of stunting prevalence in EAFR will 

lead to an increase of 0.46 in child mortality. Similarly, for CAFR and WAFR, these are 0.46 - 0.40 = 0.06 

and 0.46 – 0.34 = 0.12, respectively. These region-level variations in the effect of stunting are not 

significant. Table B.2 (Appendix B) shows that for all the DHS sub-national areas, a unit increase in stunting 

will lead to a significant increase of 0.33 in child mortality. 

Malaria Prevalence (Plasmodium falciparum): In the fitted model, there exists an interaction between 

malaria prevalence and birth interval. The effect of this interaction is estimated at 1.70. Thus, a unit increase 

in too short birth intervals will increase the effect of malaria prevalence (in percentage) on child mortality 

by 0.02 (≈ 0.17 divided by 100) and vice versa. This interaction is not significant. A percentage increase 

in the prevalence of Plasmodium falciparum infection will result in an additional 0.5 deaths in children age 

1 – 5. The 95% credible interval shows that this marginal effect is significant. 

Birth Interval: The interaction between short birth interval and malaria prevalence has been explained. 

Birth interval also interacts with literacy, temperature, and ethnic group variety. The estimates of these 

interaction effects are -0.02, -0.01, and 0.08, respectively. Hence, a unit increase in ethnic group variety 

will increase the effect of too short birth intervals on child mortality by 0.08, whereas increasing mean 

temperature and female literacy will decrease its effect by 0.01 and 0.02, respectively. However, only the 

interaction between birth interval and female literacy is significant in the model. These conclusions are 

relevant for the reverse cases when the roles of these covariates are interchanged. For example, a unit 

increase in too short birth intervals will also increase the effect of ethnic group variety on child mortality 

by 0.08. 

The model also includes interactions between region and birth interval. The estimates of the interaction 

effects are 0.54 for EAFR, 0.63 (= 0.54 + 0.09) for CAFR and 1.26 (= 0.54 + 0.72) for WAFR. Thus, a unit 

increase in too short birth intervals will increase child mortality by 0.54, 0.63, and 1.26 in EAFR, CAFR 

and WAFR respectively. However, these regional variations in the effect of birth interval are not significant. 

Table B.2 (Appendix B) shows that a unit increase in too short birth intervals will significantly increase 

child mortality by 0.78. 

Literacy: This covariate interacts with birth interval as previously explained. The covariate also interacts 

with access to a health facility and night-time lights. The respective estimates of the effects of these 

interactions are 0.01 and -0.02. Thus, a unit increase in night-time lights will reduce the effect of female 

literacy on child mortality by 0.02. On the other hand, an increase in access to a health facility will increase 

the effect of female literacy on child mortality by 0.01. The reverse cases of these relationships are also 

true. While other interaction effects are significant, the interaction between literacy rate and night-time 

lights is not significant. 
37 

The regional effects of this variable have also been estimated. These include 0.13 for EAFR, -0.19 (= 0.13 

- 0.32) for CAFR and -0.09 (= 0.13 - 0.22) for WAFR. A unit increase in female literacy corresponds with 

a reduction of child mortality by 0.19 in CAFR and 0.09 in WAFR. In EAFR, there is a positive (but nonsignificant) relationship between female literacy and child mortality, which may be due to the attenuating 

effect of other covariates. Table B.2 (Appendix B) shows that the overall effect of female literacy is not 

significant. 

Access to a Health Facility (delivery in a health facility used as a proxy for access to a health facility): 

This covariate interacts with literacy rate. The model also includes its interaction with region. The estimates 

of these regional effects are -0.18, -0.53 (= -0.18 - 0.35), and -0.12 (= -0.18 + 0.06) for EAFR, CAFR, and 

WAFR regions respectively. Thus, conditional on the average values of all other covariates, a unit increase 

in access to a health facility will reduce child mortality by 0.18 in EAFR, 0.53 in CAFR, and 0.12 in WAFR. 

The regional differences in the effect of access to a health facility are not significant. Table B.2 (Appendix 

B) shows that conditional on the average values of other covariates, a unit increase in access to a health 

facility corresponds to a reduction of child mortality by 0.16. 

Temperature, Night-time Lights, and Ethnic Group Variety: Unit increases in mean temperature and 

night-time lights are associated with non-significant increases of 0.02 and 0.07 in child mortality, 

respectively. Similarly, a unit increase in ethnic groups’ variety will lead to a non-significant reduction of 

child mortality by 0.73. There also exist non-significant interactions between temperature and birth interval, 

night-time lights and literacy, and ethnic group variety and birth interval as explained previously. 

4.2.1. Model predictions of child mortality rates 

Figures 8a displays the observed child mortality rates and the width of the 95% confidence interval for the 

indicator value; Figure 8b displays the predicted (or fitted) child mortality rates. These predicted rates 

represent smoothed values of the observed rates that have been adjusted for covariate effects as well as 

spatial autocorrelation. The maps of the observed and predicted rates both show a concentration of high 

mortality regions mostly in Central and Western Africa. The maps also show some evidence of clustering 

in the spatial distribution of child mortality with most neighboring DHS sub-national areas that exhibit 

similar mortality rates. Standard deviations around these predicted regional rates (Figure 8c) suggest that 

there should be more variability within some regions than in others. 
38 

Figure 8a. Observed mortality rates and 95% confidence interval width for the 255 DHS sub-national 

areas in sub-Saharan Africa 

Figure 8b. Predicted mortality rates for the 255 DHS sub-national areas in sub-Saharan Africa 
39 

Figure 8c. Predicted standard deviations of the child mortality rates for the 255 DHS sub-national 

areas in sub-Saharan Africa 

Figure 8d. Observed minus predicted child mortality rates for the 255 DHS sub-national areas in 

sub-Saharan Africa 
40 

The three highest predicted mortality rates occurred in a DHS sub-national area in Burkina Faso (Western 

Africa) and two sub-national areas in Cameroon (Central Africa). The three lowest predicted mortality rates 

occurred in two sub-national areas in Kenya (Eastern Africa) and a sub-national area in Ethiopia (Eastern 

Africa). The standard deviations of the predicted values show high uncertainty in few DHS sub-national 

areas but generally moderate to low uncertainty in others; this indicates a good fit to the data. In most DHS 

sub-national areas, differences between observed and expected 4q1 are relatively low (Figure 8d), with one 

exception in northern Tanzania. 

Finally, Figure 9 is a scatter plot of the strong correlation between predicted mortality rates against the 

observed rates. This affirms the linear relationships between child mortality and the covariates used in the 

model. The figure reveals that high mortality rates are concentrated in Western and Central Africa as 

compared to Eastern Africa, where mortality values appear to be lower. 

Figure 9. A plot of observed vs. predicted child mortality rates 

4.2.2. Classification of mortality regions 

The average predicted child mortality rate for all 255 DHS sub-national areas was 37.55 deaths per 1,000 

children surviving to age 12 months (37.58 deaths per 1,000 for the observed rates). However, there was a 

good deal of variation around this average, with some neighboring areas exhibiting strongly different 

differences. In this section, the DHS survey regions were classified as low, about average, and high 

mortality regions based on their predicted mortality rates. Low mortality regions were defined as regions 

whose 95% prediction intervals fell below the average predicted rate. High mortality regions were regions 

whose 95% prediction intervals were above the average predicted rate. Similarly, the 95% prediction 

intervals of the regions classified as about average included the average predicted rate. 

Figure 10 maps the child mortality classes. The figure shows that high mortality regions are primarily 

concentrated in Western and Central Africa, while most low and about average regions are located in the 

Eastern region. The DHS sub-national areas are shown in yellow – significantly lower than average 

predicted mortality rate; red – significantly higher than average predicted mortality rate; and orange – not 

significantly different from average predicted mortality rate. 

0 20 40 60 80 100 120

20 40 60 80 100

Observed child mortality

Predicted child mortality

EAFR

CAFR

WAFR
41 

Figure 10. Classification of child mortality regions 

43 

5. Discussion 

The DHS Spatial Analysis Report 12 (SAR12) analyzed the spatial distribution of key maternal and child 

health indicators for 255 DHS sub-national areas across 27 countries in sub-Saharan Africa. The report 

found spatial autocorrelation and cross-border relationships in the indicators. The presence of spatial 

autocorrelation in the indicators implies that neighboring DHS sub-national areas are more likely to share 

similar patterns than areas that are geographically distant. The present report builds on SAR 12 by exploring 

the spatial autocorrelation observed in child mortality rates to understand the determinants of its spatial 

distribution. Moreover, this report accounts for the effect of spatial environmental factors along with the 

usual demographic and socio-economic determinants on child mortality rates. This choice is supported by 

previous studies such as Balk et al. (2003, 2004) in which spatial environmental factors were identified as 

acting more strongly on child mortality than on infant mortality. 

Child mortality rates in this study were defined as the number of deaths among children between the age of 

1 and 4 in the five-year period preceding the survey per 1,000 children surviving to age 12 months7

. This 

includes deaths reported at age 12– 59 months and excludes infant deaths (deaths occurring between age 0 

and 11 months). To the authors’ knowledge, this is one of the first studies to explore spatial patterns in child 

mortality rates across such a large region and accounting for both the effect of demographic and socioeconomic determinants and spatially explicit environmental factors. Importantly, the protective and risk 

factors used in the models to explain variation in child mortality have direct relevance for both policy and 

interventions. 

The results of this report show how a large degree (60%) of regional variation in child mortality can be 

explained by the socio-demographic and environmental indicators included in the analysis, the interactions 

between them, and the patterns in child mortality. While the results of this report do not necessarily help 

better predict child mortality rates, they do provide greater clarity about which of these factors are the most 

important drivers across the region. 

This analysis further investigated and confirmed patterns of positive spatial autocorrelation in child 

mortality rates across the 255 DHS sub-national areas. On a larger scale, this spatial autocorrelation 

manifests as the presence of significant differences in child mortality rates across Western, Central, and 

Eastern Africa regions, with the highest child mortality rates in Central and Western Africa and the lowest 

in Eastern Africa (see Figure 8b). The results from the spatial regression model also highlight that several 

socio-economic and environmental factors have significant effects on child mortality in the 255 DHS subnational areas. These effects, however, do not vary significantly across Western, Central, and Eastern 

regions. Therefore, the application of interventions to reduce child mortality may have similar effects on 

child mortality in the three regions. 

Single covariate models used in this study are useful for identifying factors that can explain a meaningful 

portion of the variation in child mortality. These include female literacy, prevalence of malaria, birth 

interval, access to a health facility, sanitation practices, measles vaccination, and stunting. All these 

covariates had R2

 values greater than 10%. This agrees with other studies about the significance of these 

factors (Table 5; Balk et al. 2004). The ranking of these covariates using the R2

 statistic already makes a 

strong case for which protective and risk factors should take priority in addressing child mortality. Indeed, 

 

7 Child mortality rates (age 1 to 4) includes deaths reported as age 12– 59 months. Therefore, infant deaths (before 

age 1) are not included in the metric used in this report. 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) and further 

described in Pullum et al. 2013.
44 

many of these factors have been highlighted as priorities in the 2015 SDGs (United Nations General 

Assembly 2015). However, the effects of single covariates are not necessarily as useful as when other 

explanatory factors are considered. 

The interpretation of the coefficients of the spatial model provides a perspective on the relationship between 

each factor and child mortality, while also accounting for all other factors present in the model. This 

provides a fuller picture of the geographic variation in child mortality. When interpreting marginal effects, 

it is important to remember that each coefficient represents the marginal increase in child mortality in 

response to a unit increase in the factor of interest when all other covariates are kept at their average values. 

Thus, for all 255 DHS sub-national areas, stunting showed a statistically significant and positive effect on 

child mortality. For each increase in a percentage point of stunting, there is an increase of 0.33 deaths per 

1,000 children surviving to age 12 months. There were no regional differences observed in this effect. 

Malaria prevalence is also a significant factor correlated with child mortality in all the DHS sub-national 

areas analyzed. A unit increase in the percentage of the population with Plasmodium falciparum parasites 

in their blood (measured in the 2-10 age group) corresponds with an increase of 0.5 deaths per 1,000 

children surviving to age 12 months. Similarly, a unit increase in the percentage of births with a birth 

interval defined as too short is related to a 0.78 increase in child mortality. The marginal effect of access to 

a health facility is also statistically significant; a unit increase in the percentage of women who accessed a 

health facility is associated with a reduction of 0.16 deaths per 1,000 children surviving to age 12 months. 

Interestingly, interactions between pairs of covariates explained a large amount of the variation in child 

mortality. This perspective has not been well reported. The presence of interaction terms in the model 

indicates that risk factors can be attenuated by protective factors and vice versa. For example, although the 

marginal effect of literacy rates on child mortality is not significant, its combination (interactions) with 

other covariates such as a short birth interval and access to a health facility is statistically significant. 

Increased literacy reduces the effect of a too short birth interval on child mortality. Conversely, an increase 

in literacy is associated with an increase in the effect of access to a health facility on child mortality. 

Therefore, interventions to increase literacy rates will reduce the risk effect of a too short birth interval on 

child mortality. On the contrary, improving literacy increases the protective effect of accessing a health 

facility on child mortality. 

In contrast with other work that assessed the demographic and socio-economic determinants of child 

mortality across the 255 DHS sub-national areas in sub-Saharan Africa, this study controlled for spatial 

environmental factors. Among the covariates tested in this study, mean temperature, night-time lights and 

ethnic group variety showed a significant correlation with child mortality rates in the single covariates 

model. While none of these terms or their interactions were significant in the final model, their inclusion 

helped with the overall predictive power. These factors have strong geographic variation, and can be viewed 

as covariates that help explain the spatial variability in child mortality. 

There are several limitations to the present study that should be taken into account when interpreting the 

total results. First, although the sample size (N=255) was sufficient to highlight patterns across sub-Saharan 

Africa, regional differences in the effects of the covariates were not significant in the final model. This may 

be a consequence of the predictive power of the model given the data. That is, larger sample sizes or more 

focused studies might help clarify some factors at the regional level; this could further highlight priorities 

for interventions. One simple solution to this problem would be exploring the use of smaller aggregation 

areas for the analysis. The DHS data in this analysis included tens of thousands survey clusters aggregated 

at the DHS sub-national area level. This increases the support and therefore reliability of each data point, 

although it reduces the total number of available data points. In addition, aggregation at DHS sub-national 

level is likely to obscure some of the finer-scale variation in each factor. Thus, the relationship between 

covariates and child mortality might be clearer if it were analyzed with a finer geographical scale. 
45 

Similarly, aggregating geospatial covariates within the DHS sub-national area level may obscure the effects 

of these factors by not representing the local conditions that are experienced at the cluster level. For 

example, a region that is mostly arid and uninhabited but has a small area of high vegetation cover and a 

densely populated area would be represented as arid in this analysis, whereas the conditions experienced in 

the inhabited areas would be different. Moreover, another limitation is that some of the geospatial datasets 

used in this study were available only for specific time points up to 15 years prior to the temporal coverage 

of the DHS surveys. Indeed, although it is relatively safe to assume that time-invariant datasets (topography) 

did not change over time, temporally explicit datasets (economic activity) may have significantly changed 

over time; consequently, their use may have produced results that could be improved if more recent datasets 

were available. 

Furthermore, it is important to note that all geospatial datasets in this study have a degree of uncertainty 

that will be propagated into the analysis. Each DHS variable has sampling error associated with each 

estimate. Additionally, some surveys included in this analysis may be have less reliable child mortality 

estimates as assessed by UN IGME model (www.childmortality.org). An alternative approach for analysis 

would involve working at the DHS survey primary sampling unit scale, for which GIS coordinates (latitude 

and longitude) are usually available (but not for all the 27 countries included in this work). Child mortality 

aggregated at finer scales would then be used as a binary outcome (detected or not detected) and modeled 

in a logistic regression. This approach would have the disadvantage of not being easily relatable to the 

numbers of deaths per 1,000, but it would present a view of overall risk with potentially greater resolution 

than the aggregated approaches. Further investigations are needed to understand the degree of error and 

uncertainty that arises when calculating a rare event such as child mortality at fine spatial scale. The 

modeling approach used in this study allowed us to use the maximum amount of available data despite the 

limited size of the DHS sub-national areas. Further analyses might investigate alternative approaches to 

overcome these limitations. 

Despite this work showing that a great amount of variation in child mortality (60%) can be explained by a 

relatively small set of factors, other factors that were not included in the model may contribute in capturing 

some of the unexplained variance. For example, a direct measure of access to a health facility that includes 

the number and type of medical staff for a given area, which indicates the degree of supply of health 

services, was not available for the 27 countries; a proximate variable was used instead to account for access 

to health facilities. Moreover, none of the variables tested in this study provide a direct measure of HIV 

prevalence. The set of geospatial covariates tested in this work covered several different aspects that 

influence child mortality; however, future work could focus on testing additional geospatial covariates, 

such as rates of air pollution. Household wealth in this work was captured to an extent by using two 

household level covariates: coverage of improved water sources and sanitation practices. Future areas for 

investigation may include the use of a comparable wealth index that is standardized across countries and 

areas. Finally, the association of some variables with child mortality, such as ethnic group variety , might 

be a subject for future research. 

It is remarkable that such strong trends across such broad regions were detected in spite of the above 

limitations. The emergence of these trends highlights the power of standardized surveys that are deployed 

across broad regions and cover such a wide range of topics. This broad perspective allows for greater 

understanding than that achieved by looking within a smaller region. It is important that policy-makers 

around the world utilize and support broad-scale data collection initiatives. Broad trends can also help to 

focus research on particular drivers within localized areas, and to understand finer-scale variability in the 

drivers and protective factors for child mortality. In addition, integrating various data sources could greatly 

enhance this type of analysis with survey data, data collected from study areas such as surveillance sites, 

civil and vital registration systems, and routine health system data. 
46 

The importance of geographic factors in predicting child mortality presents an interesting opportunity in 

the context of preventing child mortality. The availability of geographic data at high spatial resolutions, 

aided by the continuous evolution of geospatial analyses, makes it possible to use these data to predict in 

areas where DHS data are not available and or at higher spatial resolution in the current study. Such 

approaches have been successfully deployed, for example, in mapping children for the polio vaccination 

program in Nigeria (Alegana et al. 2015) and modeling malaria prevalence (Bhatt et al. 2015), both of 

which facilitate the improved targeting of interventions. 
47 

6. Policy Implications 

The findings of this study have important implications for both program planning and implementation of 

programs designed to reduce child mortality (age 1 to 4) in sub-Saharan Africa. The results show that child 

mortality is best explained by a multitude of interrelated factors. This suggests that there is a role for 

integrated, multi-dimensional approaches to planning and implementing interventions. This is particularly 

true where linkages between environmental, demographic, and socio-economic factors are found. 

Relationships between individual and environmental factors should be further considered and addressed in 

order to strengthen policy and program effectiveness. Analysis of the interaction terms in this study further 

highlights the importance of acting not just on single factors that may affect child mortality. Instead, it is 

important to coordinate actions in which different factors are considered simultaneously for possibly 

magnifying or dampening the effects on each other. 

One particular interaction worth highlighting is female literacy with birth interval. In this study, female 

literacy significantly influenced the magnitude of the impact of birth interval and access to health care 

facilities on child mortality. Therefore, a program designed to reduce child mortality by increasing birth 

interval and improving access to health care could magnify the effects of these interventions with the 

addition of an intervention that increases female literacy. Alternatively, if program funding is restricted to 

health interventions, knowing the level of female literacy in the program area would provide valuable 

information for program planning such as setting targets and creating a delivery strategy. To date, many 

programs and interventions are targeted at individual determinants of child mortality because of operational 

constraints or the areas of expertise available within NGOs or government agencies. Partnerships between 

agencies should therefore be considered for enhanced program impact. 

This report also highlights a number of broad geographic patterns that could inform policy and programs. 

There were significant differences in the distribution of child mortality between Western, Central, and 

Eastern Africa. Generally, there are higher levels of child mortality in Central and Western Africa as 

compared to Eastern Africa. This pattern corresponds to elevated risk factors in Central and Western Africa 

such as lower availability of improved sanitation and higher malaria prevalence than in Eastern Africa. 

Central and Western regions also suffer from lower prevalence of protective factors such as literacy rates 

and access to health facilities, as compared to Eastern Africa. Although disaggregated analyses could 

provide more specific information, there may be reasons to consider cross-border and regional interventions 

along with country-level policies that reduce child mortality through specific interventions. 

Finally, it should be noted that the spatial, seasonal distribution of malaria prevalence is strongly related to 

climate conditions. This is especially true where the disease is not effectively controlled (Gething et al. 

2010; Grover-Kopec et al. 2006). Thus, considering malaria prevalence and mean annual temperature 

among the most relevant geospatial factors that affect child mortality suggests the potential importance of 

accounting for climate change in future analyses. 

49 

7. Summary and Conclusions 

Sustainable Development Goal 3 will ensure healthy lives and promote well-being for all people at all ages. 

This goal aims to end preventable deaths of newborns and children under age 5 by 2030, with a benchmark 

for neonatal mortality of less than 12 per 1,000 live births and under-five mortality at least as low as 25 per 

1,000 live births. 

With this context in mind, this report provided insight into the drivers of child mortality in the highly 

heterogeneous landscape of sub-Saharan Africa, a region that carries about half of the burden of the world’s 

under-five deaths. To do so, this report explored how the importance of policy-relevant risk and protective 

factors and their interactions affect child mortality in sub-Saharan Africa. This report built on the work 

completed in SAR 12 by combining the effects of spatial-environmental factors with socio-economic and 

demographic determinants of child mortality. The goal was a better understanding of the drivers of spatial 

heterogeneity in child mortality and identification of areas for policy interventions. 

Data for 27 sub-Saharan Africa countries aggregated at DHS sub-national area scale were assembled to 

observe the spatial distribution of child mortality across the 27 countries and Western, Central, and Eastern 

Africa regions. An autoregressive spatial model was applied to account for the spatial autocorrelation 

present in the data and to test the association between a number of demographic, socio-economic, and 

spatial-environmental factors on child mortality. 

The study detected strong differences in child mortality across Western, Central, and Eastern Africa regions. 

The study found that socio-demographic factors (birth interval, stunting, access to health facilities, and 

literacy), along with geospatial factors (prevalence of Plasmodium falciparum malaria, ethnic group variety, 

mean annual temperature, intensity of lights at night, and economic activity) explain a considerable portion 

of the variance in child mortality across 255 DHS sub-national areas in the 27 countries. 

The role of interaction terms explored in this study suggests that literacy rate is a strong driver of women’s 

reproductive and health behavior, influences access to health facilities, and has an overarching place as a 

determinant of child mortality. The results highlight the importance of accounting for spatial heterogeneity 

in addressing child mortality in multiple countries in sub-Saharan Africa, designing policy interventions 

that reduce inequalities, and exploring some of the observed trends at finer geographic scales. 

51 

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61 

Appendix A 

A.1 United Nations Geographical Region Composition for the 27 Sub-Saharan African 

Countries included in Our Analysis 

Table A.1. List of Countries in analysis by United Nations Regions 

WESTERN AFRICA REGION 

COUNTRIES (WAFR) 

EASTERN AFRICA REGION 

COUNTRIES (EAFR) 

CENTRAL AFRICA REGION 

COUNTRIES (CAFR) 

Benin 

Burkina-Faso 

Côte d’Ivoire 

Gambia 

Ghana 

Guinea 

Liberia 

Mali 

Niger 

Nigeria 

Senegal 

Sierra Leone 

Togo 

Burundi 

Ethiopia 

Kenya 

Malawi 

Mozambique 

Rwanda 

Tanzania 

Uganda 

Zambia 

Zimbabwe 

Cameroon 

Congo 

Democratic Republic of Congo 

(DRC) 

Gabon 

Source: (United Nations Statistics Division 2013) 
62 

A.2. Geospatial Datasets 

This section of the Appendix briefly describes how the geospatial datasets in this study were produced. The 

section contains the figures, referenced in Section 2.2.2, which show the spatial distribution, over the study 

area, of all spatial factors relevant to child mortality that were considered in the study. 

Population distribution 

WorldPop 2010 UN adjusted People Per Pixel raster datasets (www.worldpop.org) are produced using 

ancillary data to asymmetrically disaggregate administrative, unit-based population counts to a regular grid 

of fixed spatial resolution. National totals for 2010 were adjusted to match United Nations Population 

Division estimates (UNDP 2014). 

Figure A.1. Population distribution 
63 

Urbanicity (MODIS) 

The “MODIS 500m Global Urban Extent” dataset (https://nelson.wisc.edu/sage/data-andmodels/schneider.php) was produced with Collection 5 (C5) MODIS 500-m resolution data and a 

supervised decision tree classification algorithm to identify urban areas by using region-specific parameters 

(https://nelson.wisc.edu/sage/data-and-models/schneider-readme.php). 

Figure A.2a. MODIS-based spatial distribution of built-up areas 
64 

Urbanicity (GHSL) 

The “Global Human Settlement Layer” dataset (http://ghslsys.jrc.ec.europa.eu/) was produced by the 

European Union Joint Research Centre. This dataset used learning and classification techniques to process 

high and very high resolution images that identify areas that are densely built. Low-resolution thematic 

layers served as a reference (Pesaresi et al. 2013). 

Figure A.2b. GHSL-based spatial distribution of built-up areas 
65 

Night-time lights 

The NOAA Suomi National Polar-orbiting Partnership (NPP) composite night-time light dataset is 

produced by averaging monthly radiance data, acquired by the Visible Infrared Imaging Radiometer Suite 

(VIIRS) Day/Night Band (DNB). The data are filtered to exclude data affected by stray light, lightning, 

lunar illumination, and cloud-cover (http://ngdc.noaa.gov/eog/viirs/download_monthly.html). 

Figure A.3. Spatial pattern of VIIRS-based night-time lights’ intensity 

 
66 

Travel-times to major population centers 

“Travel time to major cities” datasets (http://forobs.jrc.ec.europa.eu/products/gam/download.php) were 

produced by the European Union Joint Research Centre. The datasets used a cost-distance algorithm to 

compute the travel-time between two grid cells, expressed as a function of the cost required to travel across 

them (http://forobs.jrc.ec.europa.eu/products/gam/description.php). 

Figure A.4a. Travel-time to major centers with more than 50,000 inhabitants 
67 

Figure A.4b. Travel-time to major centers with more than 100,000 inhabitants 

Figure A.4c. Travel-time to major centers with more than 500,000 inhabitants 
68 

Ethnicity 

The “Geo-Referencing of Ethnic Groups (GREG)” dataset (http://www.icr.ethz.ch/data/other/greg) was 

created with spatial GIS techniques and data from the Soviet Narodov Mira atlas (Bruk and Apenčenko 

1964) to represent group territories as vector polygons. For the purpose of this study, values for the 

Tanzanian islands of Zanzibar and Pemba (two of the DHS sub-national areas within the study area that are 

not available in the GREG dataset) were set to the values of the closest group polygon in the mainland. 

Figure A.5a. Spatial distribution of dominant ethnic groups 

 
69 

Figure A.5b. Legend of Figure A.5a 
70 

Conflicts 

For Africa, the “Armed Conflict Location & Event Data Project (ACLED)” database 

(http://www.acleddata.com/data/) covers the period from 1997 to the present. The database is constantly 

updated with data from multiple sources that include local media, national reports, humanitarian agencies, 

and research publications (http://www.acleddata.com/methodology/). Only data from 2010 to 2013 were 

used in this study. 

Figure A.6. Location of conflict events and associated fatalities between 2010 and 2013 
71 

Economic activity 

The global “Geographically-based Economic (G-Econ)” dataset (http://gecon.yale.edu/data-anddocumentation-g-econ-project) was produced with different country-based methodologies for estimating 

per capita gross cell product and calculating the gross cell product, with the latter representing the gridbased equivalent of the purchasing parity of gross domestic product. 

Figure A.7. Spatial distribution of the gross cell product representing the regional equivalent of 

gross domestic product 
72 

Livestock 

The FAO Gridded Livestock of the World, v2.0 (GLW2) datasets (http://livestock.geo-wiki.org/) were 

obtained by disaggregating sub-national livestock figures from ancillary environmental and demographic 

datasets. 

Figure A.8a. Sheep density 
73 

Figure A.8b. Cattle density 

Figure A.8c. Pig density 
74 

Figure A.8d. Goat density 

 
75 

Temperature and rainfall 

WorldClim datasets (http://www.worldclim.org/current) were generated by modeling 30-years (1960-1990) 

of average monthly climate data from ground-based weather stations, as a function of latitude, longitude, 

and elevation (http://www.worldclim.org/methods1). 

Figure A.9a. 30-year average annual temperature 

Figure A.9b. 30-year average annual rainfall 
76 

Temperature (MODIS) 

The MODIS-based daytime Land Surface Temperature (LST) monthly composite datasets 

(https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11c3) are derived from daily 

5.6km LST datasets (MOD11C1) by averaging values of clear-sky LST values during a one-month period. 

In this study, all LST monthly composite datasets that covered the whole study area, from June 2001 to 

June 2015, were mosaicked and then averaged across time. 

Figure A.10. 14-year average MODIS-based daytime Land Surface Temperature 
77 

Aridity 

The CGIAR Global Aridity Index raster dataset (http://www.cgiar-csi.org/data/global-aridity-and-petdatabase), which was produced with WorldClim Global Climate Data (Hijmans et al. 2005), is used to 

quantify precipitation availability over atmospheric water demand (Middleton and Thomas 1997). The 

index is defined as the ratio of mean annual precipitation to the mean annual potential evapo-transpiration; 

thus, the values increase from more arid to more humid conditions (http://www.cgiar-csi.org/wpcontent/uploads/2012/11/Global-Aridity-and-Global-PET-Methodology.pdf). 

Figure A.11. 50 year-based Aridity Index 
78 

Growing season length 

The FAO “Global length of growing periods” dataset (http://www.fao.org/geonetwork/ 

srv/en/main.home) represents, for each grid cell, the number of days expressed as intervals during the year 

when both moisture availability and temperature are conducive to crop growth. This was produced by using 

the Agro-ecological Zones (AEZ) methodology for land productivity assessments 

(http://www.fao.org/fileadmin/user_upload/gaez/docs/GAEZ_Model_Documentation.pdf). 

Figure A.12. Spatial distribution of the growing season length 

 
79 

Drought 

The CIESIN Global Drought Hazard Frequency and Distribution, v1 dataset 

(http://sedac.ciesin.columbia.edu/data/set/ndh-drought-hazard-frequency-distribution/data-download) was 

created using the International Research Institute for Climate Prediction’s (IRI) Weighted Anomaly of 

Standardized Precipitation (WASP; (Lyon and Barnston 2005) and average monthly precipitation data from 

1980 through 2000 to calculate the number of drought events that occurred in each grid cell during the 

considered time period. According to the number of drought occurrences, grid cells are reclassified into 10 

classes, with each one containing the same number of grid cells 

(http://sedac.ciesin.columbia.edu/data/set/ndh-drought-hazard-frequency-distribution). 

Figure A.13. Relative frequencies of drought occurrences and their spatial distribution 
80 

Vegetation 

The MODIS Enhanced Vegetation Index datasets (https://lpdaac.usgs.gov/dataset_discovery/modis/ 

modis_products_table/mod13c2), that represent a measure of photosynthetic activity ranging from 0 (no 

vegetation) to 1 (complete vegetation), are cloud-free spatial composites of 16-day 1 km EVI datasets 

(MOD13A2). In this study, all EVI monthly composite datasets that covered the whole study area, from 

June 2001 to June 2015, were mosaicked and then averaged across time. 

Figure A.14. 14-year average MODIS-based Enhanced Vegetation Index 
81 

Topography 

The GTOPO30 (https://lta.cr.usgs.gov/GTOPO30) is a digital elevation model (DEM) that was produced 

using multiple vector and raster sources of topographic information (https://lta.cr.usgs.gov/ 

sites/default/files/gt30src.gif). In this study, it was used to derive a slope raster dataset and three roughness 

raster datasets for the study area, all of which have the same spatial resolution of the input GTOPO30 DEM. 

Since a raster-based roughness dataset expresses the amount of elevation difference between each grid cell 

and the considered surrounding area in the input DEM (grid cell values are a function of the size of the 

specific surrounding area selected to calculate the roughness), three different roughness datasets were 

produced by using three different neighborhood window sizes (3x3, 5x5, and 11x11 grid cells). 

Figure A.15a. Topographic elevation 
82 

Figure A.15b. Topographic slope 

Figure A.15c. Topographic roughness calculated using a neighborhood window of 3x3 grid cells 
83 

Figure A.15d. Topographic roughness calculated using neighborhood window of 5x5 grid cells 

Figure A.15e. Topographic roughness calculated using neighborhood window of 11x11 grid cells 
84 

Landcover 

The ESA 2009 GlobCover dataset (http://due.esrin.esa.int/page_globcover.php) was produced using 

Envisat MERIS fine resolution data and an automatic pre-processing and classification chain to identify 22 

classes compatible with the UN Land Cover Classification System (LCCS). 

Figure A.16. MERIS-based landcover classification 
85 

Malaria prevalence 

The Malaria Atlas Project “PfPR2-10 in Africa 2000-2015” datasets (http://www.map.ox.ac.uk/) were 

produced using malaria prevalence survey data (1995-2014), along with temporally explicit intervention 

coverage, environmental and socio-demographic covariates, as input to a spatiotemporal Bayesian 

geostatistical model to estimate PfPR trends (Bhatt et al. 2015). In this study, all “PfPR2-10 in Africa 2000-

2015” datasets were averaged across time. 

Figure A.17. 15-year average Plasmodium falciparum prevalence distribution 

87 

Appendix B – Detailed Methods and Results 

In this section of the appendix, we describe the complete spatial model discussed in Section 3.3. All the 

additional figures and tables referenced in Sections 3 and 4 are also presented. 

B.1. Conditional Autoregressive (CAR) Model 

The study region is partitioned into ݊ = 255 areal units, otherwise known as the DHS regions. These 

regions are denoted by झ = {࣭ଵ, ... , 	࣭{௡. Let ܇) = Yଵ,...,Y௡) denote the vector of child mortality rates 

associated with each region. Also, each region, ࣭௜, is linked with a vector of covariates (including an 

intercept term and p covariates) which we denote by ܠ௜

் = (1, x௜ଵ,...,x௜௣). Considering a Gaussian 

distribution for the mortality rates, the spatial linear model is given by 

Y௜|ߤ~௜ܰ)y௜|ߤ௜, ఙଶ), for ݅ = 1, ... , ݊, 

௜ܠ = ௜ߤ 

 (1) .௜߶+ࢼ்

In model (1), ࢼ௣)ାଵ)×ଵ denotes the regression coefficients, ఙଶ is a scale parameter for the mortality rates 

and ߶௜ is a spatial random effect for region, ࣭௜. 

The random effects ࣘ߶) = ଵ,...,߶௡) model residual spatial autocorrelation in ܇. In models of the type 

given in (1), these random effects ࣘ are typically characterized using a conditional autoregressive (CAR) 

model. A number of global CAR models have been proposed in the statistical literature for this purpose. 

These include the intrinsic CAR and Besag-York-Mollie (BYM) models (Besag, York, and Mollié 1991) 

and their alternatives (Leroux, Lei, and Breslow 2000; Stern and Cressie 1999). However, based on a recent 

review study by (Lee 2013), we adopted the CAR model by Leroux et al. for our analysis. This CAR prior 

is given by 

௡ ௝߶௝௜ݓ ∑ ߩ ቆܰ௜ ∼ିࣘ|௜߶

௝ୀଵ

௡ ߩ+1− ௝௜ݓ ∑ ߩ

௝ୀଵ

, ߬ଶ

௡ ߩ+1− ௝௜ݓ ∑ ߩ

௝ୀଵ ቇ	,	

where ߩ is a spatial autocorrelation parameter and ߬ଶ is a variance parameter. The ݊×݊ matrix of the ݓ‘௝௜s 

(ܹ) characterizes the neighborhood structure of the regions; this induces spatial autocorrelation in ࣘ. A 

binary specification is often used for ܹ where ݓ = ௝௜1 if regions (࣭௜, ࣭ (௝share a common border and is 

zero if they do not. 

The model was estimated in a Bayesian framework that naturally takes care of uncertainty in predictions 

and parameter estimation. To complete our model specification, we placed the following priors on the 

parameters: ߚ	~	௝ܰ)0,1000)	(݆ = 0, ... , ݌(, ߪଶ	~	Uniform(0, 1000), ߩ	~	Uniform(0, 1) and 

߬ଶ	~	Uniform	(0, 1000).
88 

Table B.1. Top 10 models resulting from the model selection process. Models are ranked according 

to R2. 

Model AIC R2 PR2

stunting + malaria prevalence*birth interval + literacy*birth interval + literacy*access to a health 

facility + birth interval*temperature + literacy*night-time lights + birth interval*ethnic groups 

variety 

2132 0.54 0.53

stunting + temperature + malaria prevalence*birth interval + literacy*birth interval + 

literacy*access to a health facility + literacy*night-time lights + birth interval*ethnic groups variety 

2130 0.54 0.53

stunting + night-time lights + malaria prevalence*birth interval + literacy * birth interval + literacy 

* access to a health facility + birth interval *temperature + birth interval*ethnic groups variety 

2130 0.54 0.53

stunting + malaria prevalence*birth interval + literacy*birth interval + literacy * access to a health 

facility + birth interval * temperature + literacy*economic activity + birth interval * ethnic groups 

variety 

2132 0.54 0.53

stunting + temperature + night-time lights +malaria prevalence* birth interval + literacy* birth 

interval + literacy * access to a health facility + birth interval* ethnic groups variety 

2128 0.54 0.53

stunting + temperature + malaria prevalence*birth interval + literacy* birth interval + literacy* 

access to a health facility + literacy *economic activity + birth interval * ethnic groups variety 

2130 0.54 0.53

stunting + economic activity + malaria prevalence* birth interval + literacy* birth interval + 

literacy* access to a health facility + birth interval* temperature + birth interval * ethnic groups 

variety 

2130 0.54 0.53

stunting + malaria prevalence*birth interval + literacy * access to a health facility + birth 

interval*temperature + literacy *sanitation + literacy*economic activity + birth interval*ethnic 

groups variety 

2134 0.54 0.53

stunting + temperature + economic activity + malaria prevalence* birth interval + literacy* birth 

interval + literacy * access to a health facility + birth interval * ethnic groups variety 

2128 0.54 0.53

stunting + population density + malaria prevalence * birth interval + literacy* birth interval + 

literacy* access to a health facility + birth interval*temperature + birth interval * ethnic groups 

variety 

2130 0.54 0.53

Table B.2. Posterior means of the parameters of the spatial model without region-level interactions 

Parameter Mean SD Median 95% Credible interval 

(Intercept) 33.4298 1.1676 33.4633 (31.1412, 35.7095) 

stunting 0.3254 0.1201 0.3233 (0.0915, 0.5600) 

Malaria prevalence 50.6905 7.5511 51.0639 (35.3227, 65.1751) 

Birth interval 0.7751 0.1722 0.7664 (0.4497, 1.1245) 

Literacy 0.0146 0.0863 0.0103 (-0.1439, 0.1952) 

Access to a health facility -0.1614 0.0660 -0.1601 (-0.2905, -0.0287) 

Temperature 0.1103 0.0576 0.1080 (-0.0006, 0.2278) 

Night-time lights -0.2923 1.2151 -0.3366 (-2.6548, 2.0590) 

Ethnic groups variety -0.5791 0.4347 -0.5878 (-1.3887, 0.2862) 

Malaria prevalence: Birth interval 2.0500 0.8843 2.0393 (0.2837, 3.7821) 

Birth interval: Literacy -0.0162 0.0078 -0.0163 (-0.0310, -0.0008) 

Literacy: Access to a health facility 0.0049 0.0025 0.0050 (0.0000, 0.0097) 

Birth interval: Temperature -0.0008 0.0051 -0.0007 (-0.0107, 0.0094) 

Literacy: Night-time lights -0.0174 0.0342 -0.0181 (-0.0811, 0.0537) 

Birth interval: Ethnic group variety 0.0960 0.0491 0.0954 (0.0028, 0.1934) 

 (278.2934 0.0218,) 221.3824 90.8631 179.5640 ૛࣌

 (468.1414 0.0004,) 0.0107 161.5442 102.9706 ૛࣎

 (0.8792 0.0413,) 0.3627 0.2289 0.3974 ࣋