DHS SPATIAL ANALYSIS

REPORTS 16

A PRIMER ON THE DEMOGRAPHIC AND 

HEALTH SURVEYS PROGRAM SPATIAL 

COVARIATE DATA AND THEIR 

APPLICATIONS

July 2018

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

by Trinadh Dontamsetti, Shireen Assaf, Jennifer Yourkavitch, and Benjamin Mayala.

DHS Spatial Analysis Reports No. 16

A Primer on The Demographic and Health Surveys Program 

Spatial Covariate Data and Their Applications

Trinadh Dontamsetti

Shireen Assaf

Jennifer Yourkavitch

Benjamin Mayala

ICF

Rockville, Maryland, USA

July 2018

Corresponding author: Trinadh Dontamsetti, International Health and Development, ICF, 530 Gaither 

Road, Suite 500, Rockville, MD 20850, USA; phone: 301-572-0562; email: trinadh.dontamsetti@icf.com
Acknowledgments: The authors wish to thank Clara Burgert-Brucker and Trevor Croft for their comments 

on an earlier version of the report. The authors also wish to thank Tom Pullum, Lindsay Mallick, Rukundo 

Benedict, and Tom Fish for their assistance and invaluable guidance in developing this report.

Editor: Bryant Robey

Document Production: Joan Wardell

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. Additional information about The Demographic and Health 

Surveys Program can be obtained from ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850 USA; 

telephone: +1 301-407-6500, fax: +1 301-407-6501, email: info@DHSprogram.com, internet: 

www.DHSprogram.com.

Recommended citation:

Dontamsetti, Trinadh, Shireen Assaf, Jennifer Yourkavitch, and Benjamin Mayala. 2018. A Primer on The 

Demographic and Health Surveys Program Spatial Covariate Data and Their Applications. DHS Spatial 

Analysis Reports No. 16. Rockville, Maryland, USA: ICF.
iii

CONTENTS

TABLES........................................................................................................................................................ v

FIGURES ...................................................................................................................................................... v

PREFACE ................................................................................................................................................... vii

ABSTRACT.................................................................................................................................................. ix

ACRONYMS AND ABBREVIATIONS ........................................................................................................ xi

1 BACKGROUND OF THE REPORT ................................................................................................ 1

1.1 DHS Spatial Covariates ...................................................................................................... 1

1.1.1 Curation of spatial covariates................................................................................. 2

1.1.2 Extraction of spatial covariates .............................................................................. 4

2 DATA AND METHODS ................................................................................................................... 7

2.1 Study Area and Description ................................................................................................ 7

2.2 Outcomes............................................................................................................................ 8

2.3 Sociodemographic and Spatial Covariates......................................................................... 9

2.4 Analytical Methods ............................................................................................................ 10

3 CASE STUDIES............................................................................................................................. 13

3.1 Anemia in Children Age 6-59 Months ............................................................................... 13

3.2 Basic Vaccination Coverage of Children Age 12-23 Months............................................ 13

4 RESULTS ...................................................................................................................................... 15

4.1 Results from Correlation Matrix ........................................................................................ 15

4.2 Results of Logit Regression–Case Study 1: Anemia in Children Age 6-59 Months ......... 16

4.3 Results of Logit Regression–Case Study 2: Full Vaccination Coverage for Children 

Age 12-23 Months............................................................................................................. 19

5 DISCUSSION................................................................................................................................. 21

5.1 Evaluation of Spatial Covariates ....................................................................................... 21

5.3 Suggestions for Further Study .......................................................................................... 22

APPENDICES............................................................................................................................................. 23

APPENDIX A DOCUMENTATION FOR INCLUDED COVARIATES....................................................... 25

APPENDIX B SUBSET OF RWANDA 2014-15 DHS SPATIAL COVARIATE DATASET ...................... 31

APPENDIX C ANEMIA MODEL SELECTION–FULL MODEL................................................................. 33

APPENDIX D VACCINATION MODEL SELECTION–FULL MODEL...................................................... 35

REFERENCES............................................................................................................................................ 37

v

TABLES

Table 1 Year one spatial covariates.................................................................................... 3

Table 2 Characteristics of DHS surveys ............................................................................. 8

Table 3 Anemia model selection....................................................................................... 17

Table 4 Vaccination model selection ................................................................................ 19

Appendix B Subset of Rwanda 2014-15 DHS spatial covariate dataset................................. 31

Appendix Table C Anemia model selection–Full model for each survey .......................................... 33

Appendix Table D Vaccination model selection–Full model for each survey.................................... 35

FIGURES

Figure 1 Spatial covariate rasters ........................................................................................ 4

Figure 2 Buffered raster extraction methodology................................................................. 5

Figure 3 Map of study countries........................................................................................... 7

Figure 4 Percentage of children age 6-59 months with anemia and percentage 

of children age 12-23 months who have all basic vaccinations............................. 8

Figure 5 Analytical approach.............................................................................................. 11

Figure 6 Correlation matrix covariates using Rwanda survey data ................................... 16

vii

PREFACE

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

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

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

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 this series are selected by The Demographic and Health Surveys 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

ix

ABSTRACT

The Demographic and Health Surveys Program geospatial team has prepared a set of geospatial covariates 

datasets that provide researchers and analysts with powerful demographic, environmental, and geophysical 

variables to include in their analysis of DHS survey data. Often, individuals attempt to manually source and 

link these variables to DHS data, but are unfamiliar with Geographic Information Systems (GIS) data 

sources or lack the necessary skills to correctly extract and perform this link. The DHS Program has 

therefore prepared these files with an intuitive method of linking them to DHS survey data, allowing 

individuals of any statistical or geospatial analytical skill to incorporate geospatial data into their work. The 

DHS Program will continually update newly released surveys with the current suite of covariates and will 

seek to develop and release new covariates annually. This report examines methods of preparing the data 

for fitting a regression model using these covariates. Through two case studies, we examine the modelbuilding process required to study the effects of spatial factors on the prevalence of anemia in children and 

the receipt of all basic vaccinations for children.

KEY WORDS: anemia, vaccination, spatial variables, geographic information system, model building

xi

ACRONYMS AND ABBREVIATIONS

AIC Akaike’s information criterion

AIS AIDS Indicator Survey

BCG Bacille Calmette-Guerin

DHS Demographic and Health Survey

DPT Diphtheria-Pertussis-Tetanus

EA enumeration area

EVI enhanced vegetation index

GIS geographic information system

GPS global positioning system

GSL growing season length

MAP Malaria Atlas Project

MIS Malaria Indicator Survey

SAR Spatial Analysis Report

SES socioeconomic status

1

1 BACKGROUND OF THE REPORT

The Demographic and Health Surveys (DHS) Program provides a unified set of survey and geospatial data 

to conduct analysis and inform policy. In recent years, however, it became apparent that many users have 

attempted to or expressed interest in linking DHS data to spatial data. Since September 2017, the DHS 

Program has been providing a set of geospatial covariates in addition to survey cluster Global Positioning 

System (GPS) data collected during each survey. These covariate datasets are available for download in 

two places: the DHS Program website (https://dhsprogram.com/data/available-datasets.cfm) and the Spatial 

Data Repository (https://spatialdata.dhsprogram.com/covariates/). The covariate datasets are produced 

using both DHS GPS data and publicly available external datasets through a standardized extraction 

method. While datasets including these covariates are available elsewhere online, linking these data to DHS 

survey data is often problematic. The DHS geospatial team sought to simplify the process of conducting 

geospatial analyses for experienced data users and provide a user-friendly introduction to these analyses for 

inexperienced users by preparing and releasing the covariate datasets. Following their release, a DHS blog 

was written (https://blog.dhsprogram.com/spatial-covariates/) announcing availability of the datasets and 

offering potential users information on the covariates, but further guidance on the practical usage of these 

data was necessary.

Accordingly, this report will provide the information that users need to understand the geospatial covariate 

datasets and to begin using them for their own analyses. This document does not provide a comprehensive 

review of the development of these datasets, which is described in a PDF file included with every covariate 

dataset. (Appendix A provides examples of these files for the covariates used in this report.) Rather, this 

report presents guidance in the form of practical applications of analysis including geospatial covariates.

This Spatial Analysis Report (SAR) is intended for all users conducting statistical analysis of DHS survey 

data, irrespective of their skills or knowledge of geospatial analysis. The authors hope that all users find

this a useful primer for incorporating geospatial covariates in analyses of DHS survey data, especially those 

who have not previously attempted to conduct such analysis due to unfamiliarity with Geographic 

Information Systems. The authors also hope this document will give these users confidence in their ability 

to bring a spatial dimension to their work. This report includes two main sections: a description of the 

covariate data—including the efforts to determine an appropriate set of covariates, extraction methods, and 

the creation of the dataset—and two case studies using two health outcomes as examples of including 

geospatial covariates in analysis.

1.1 DHS Spatial Covariates

The DHS spatial covariates came from various data sources and were not consistently or easily linked to 

DHS data. Further, many users were unfamiliar with the GIS skills required to perform these linkages and 

were discouraged from further incorporating spatial elements in their work. To address these issues, the 

DHS Program prepared a standardized set of publicly available geospatial covariates that include many 

commonly used variables. These covariates can be linked easily to DHS survey data without the need for 

GIS software, allowing researchers who may lack GIS experience to include a spatial component in their 

analysis.
2

1.1.1 Curation of spatial covariates

The DHS Program conducted interviews with geospatial experts to obtain guidance for curating the list of 

covariates. Seventeen individuals and groups provided feedback that focused on:

 What they thought DHS should include as possible covariates,

 What specific datasets they recommend for various topics (such as for rainfall or temperature),

 What specific concerns they had regarding use of these data.

The DHS Program also disseminated a short survey to all DHS dataset users who had downloaded any type 

of data from the DHS Program website between April 2016 and March 2017, seeking feedback on their 

topic areas of interest, their interest in using geospatial data in their work, and their current level of 

experience with geospatial data. Finally, the DHS Program conducted a literature review of publications 

between 2001 and 2016 that used DHS data. As a result of this work, 192 geospatial covariates were 

identified and divided into eight categories: agriculture, climate, environment, health condition, infrastructure, physical earth, political, and population.

The DHS Program identified geospatial covariates to be prepared by combining the results of the literature 

review with the guidance provided by the respondents of the user survey and the expert interviews to select 

external datasets based on four key criteria:

 Datasets must have either global or regional extent, such as coverage for all of Africa or Asia. As 

such, data organized by a single country or not available for the majority of countries were not 

considered.

 Datasets must be publicly available.

 Datasets must have a well-documented acquisition or creation process along with detailed 

metadata of both inputs and processing procedures.

 Datasets must have data available for relevant timeframe(s), determined during the preparation 

phase.

These selection criteria helped the DHS Program to curate the list of covariates to be prepared, resulting in 

a set of covariates that are highly standardized. A total of 22 covariates were prepared from 15 dataset 

sources. More information regarding each covariate’s specific data source, unit, timeframe, data 

description, and full citation can be found in the data definition file included with every covariate dataset 

download, and reproduced in Appendix A. A full list of covariates prepared during the first year of this 

activity can be found in Table 1.
3

Table 1 Year one spatial covariates

Topic Covariate Timeframe Units Data Source

Agriculture Drought Episodes Single Dataset Count of Episodes, 1980-2000 SEDAC

Agriculture Growing Season Length§ Single Dataset Mode of 16 Categories Representing a 

Range of the Number of Days Within the 

Period of Temperatures Above 5C When 

Moisture Conditions Are Considered 

Adequate

IIASA/FAO

Environment Aridity Single Dataset Unitless Index. Higher Aridity Index (AI) 

Reflect Greater Humidity. Lower AI 

Reflects Greater Aridity.

CGIAR

Environment Enhanced Vegetation 

Index§

1985-2015 in 5-yr Increments Unitless Index. Higher Scores Indicate 

Higher Vegetation Vigor/Photosynthetic 

Activity

AVHRR (1985-

1995), MODIS 

(2000-2015)

Environment Potential 

Evapotranspiration

Single Dataset Monthly Estimate of Pet (Mm/Month) 

Averaged Over the 1950-2000 Period

CGIAR

Environment Proximity to Coast/

Large Lakes§

N/A Meters GSHHG

Environment Proximity to Protected 

Areas

N/A Meters Protected 

Planet

Environment Rainfall§ 1985-2015 in 5-yr Increments Mm/Year CHIRPS

Environment Slope Single Dataset Meters Above Sea Level SRTM

Environment Temperature 

(Average Monthly)

January - December Averages 

for 1970-2000

Degrees Celsius WorldClim

Health ITN Net Coverage 2000-2015 in 5-yr Increments Percent of People Who Slept under an 

Insecticide-Treated Net (ITN) on Any 

Given Night

MAP

Health Malaria 2000-2015 in 5-yr Increments Plasmodium falciparum Malaria Cases Per 

Person Per Year Observed

MAP

Infrastructure Nightlights§ 2015 Annual Composite Average Cloud-Free Radiance Values VIIRS (DNB)

Infrastructure Proximity to National 

Border

2014 Meters State Dept. 

LSIB

Infrastructure Travel Times Year 2000 Estimate Estimated Travel Time (Minutes) to the 

Nearest City of 50,000 or More People in 

Year 2000

FOROBS

Infrastructure Urbanization - Global 

Human Footprint

1995-2004 (Overall) Unitless Index. Higher Values Indicate 

Higher Human Influence.

SEDAC

Infrastructure Urbanization - GHS 

Settlement Grid

1990, 2000, 2015 Mode of Categories: 1 = “Rural Cells” or 

Base (BAS); 2 = “Urban Clusters” or LowDensity Clusters (LDC); 3 = “Urban 

Centers” or High-Density Clusters (HDC)

GHSL-SMOD 

(EC JRC)

Infrastructure Urbanization - GHS 

Build-Up Grid

1990, 2000, 2015 Built-Up Presence Index, Range 0-1. 

Higher Value Is More Confidence that It Is 

Built Up

GHSL 

(Landsat) (EC 

JRC)

Population UN Adjusted 

Population Count

2000-2015 in 5-yr Increments 

(Adjusted)

Number of People GPW v4

Population UN Adjusted 

Population Density

2000-2015 in 5-yr Increments 

(Adjusted)

Number of People/Km2 GPW v4

Population Count§ 2000-2015 in 5-yr Increments (UN 

Adjusted)

Number of People WorldPop

Population Density 2000-2015 in 5-yr Increments Number of People/Km2 WorldPop

Note: § indicates the covariates that were used in this report.
4

Figure 1 Spatial covariate rasters

 

For illustrative purposes, Figure 1 shows the GPS data points for the Tanzania 2015-16 DHS overlaid atop 

the rainfall covariate raster dataset (left), and the Uganda 2016 DHS points atop the Enhanced Vegetation 

Index (EVI) covariate raster (right). The GPS data are used to extract the covariate data from the source 

dataset, as explained in the following section.

1.1.2 Extraction of spatial covariates

These geospatial covariates were grouped into two categories—neighborhood calculations and distance 

calculations—based on the nature of the covariate and the corresponding extraction method it necessitated.

For neighborhood calculations, the covariate data are extracted based on a raster extraction method 

specified in Spatial Analysis Report 8 (Perez-Heydrich et al. 2013) and further discussed in other literature 

(Warren et al. 2016). Briefly, a circular buffer with a 10 kilometer radius is drawn around rural points, while 

a 2 kilometer buffer is drawn around urban points. All raster cells whose centroids fall within the buffer 

surrounding a point are used in the extraction calculation, while raster cells whose centroids do not fall 

within the buffer are excluded. The appropriate calculation (mode, mean, etc.) for each individual covariate 

is performed using the Zonal Statistics as Table tool through ArcPy. This calculates the chosen metric for 

the covariate for each cluster in the input DHS dataset, and these results are exported to a table. Figure 2 

gives an example of this methodology.
5

Figure 2 Buffered raster extraction methodology

For distance calculations, vector data are used rather than rasters. Using the Near tool in ArcMap, which 

calculates the geodesic distance between each DHS point and the nearest boundary of a selected polygon, 

proximity covariates were prepared. These included distance of the DHS cluster to the nearest national 

border, to coasts and large lakes, and to protected areas. These results are appended to the same table within 

which the results of the neighborhood calculation are exported for each point. An example of the exported 

data can be found in Appendix B, which shows the data of the chosen spatial covariates for the first 30 

clusters of the Rwanda 2014-15 DHS survey.

7

2 DATA AND METHODS

2.1 Study Area and Description

The DHS Program is a leader in collecting and providing survey data on core development indicators. Most 

surveys now provide geocoded data for individual survey clusters (enumeration areas (EAs)). GPS 

coordinates for DHS survey clusters provide a method of linking local-scale data to survey outputs for 

analysis of demographic and health status indicators.

This analysis uses data from six surveys in four neighboring East African countries—Burundi 2017 DHS, 

Rwanda 2014-15 DHS, Uganda 2016 DHS, Tanzania 2008 THMIS (a combined AIS/MIS survey), 2010 

DHS, and 2015 DHS—to explore the process of building a regression model using the spatial covariate 

datasets. These models are then used to study the impact of geospatial factors on the prevalence of anemia 

in children age 6-59 months and receipt of all eight basic vaccinations for children age 12-23 months. Any 

anemia in children is defined by hemoglobin levels of <11.0 g/dL. Receipt of all eight basic vaccinations is 

considered to include bacille Calmette-Guérin (BCG), three doses of Diphtheria-Pertussis-Tetanus (DPT) 

or a DPT-containing vaccination such as a Pentavalent vaccine, three doses of oral polio vaccine, and one 

dose of a measles-containing vaccine.

Figure 3 Map of study countries

Figure 3 shows the four countries with DHS survey data used for this analysis.
8

Table 2 Characteristics of DHS surveys

DHS Survey

Number of 

Households 

Interviewed

GPS 

Clusters 

Collected

Burundi 2016 DHS 16,620 554

Rwanda 2014 DHS 12,793 492

Uganda 2016 DHS 20,880 696

Tanzania 2015-16 DHS 13,376 608

Tanzania 2010 DHS 10,300 475

Tanzania 2007-08 AIS 9,144 475

Table 2 describes characteristics of the six DHS surveys involved in this analysis, specifically the number 

of households interviewed during fieldwork for each survey and the corresponding number of survey 

clusters for which GPS data were collected.

2.2 Outcomes

The outcomes for the analysis in this report are split into two case studies: the first concerns any anemia in 

children age 6-59 months, and the second, receipt of all basic vaccinations for children age 12-23 months. 

As Figure 4 shows, the burden of anemia in children is high across the countries of interest. Burundi, 

Uganda, and Tanzania all have prevalence over 50%, while about a third of children in Rwanda have any 

anemia. Burundi and Rwanda fare best in vaccination coverage: both countries have greater than 80% 

coverage. However, both Uganda and Tanzania are below 80%. Section 3 provides the background on these 

two case studies.

Figure 4 Percentage of children age 6-59 months with anemia and percentage of children age 12-23 

months who have all basic vaccinations

0 20 40 60 80 100

Burundi 2016 DHS

Rwanda 2014 DHS

Uganda 2016 DHS

Tanzania 2015-16 DHS

Tanzania 2010 DHS

Tanzania 2007-08 AIS

Basic Vaccination

Any Anemia
9

2.3 Sociodemographic and Spatial Covariates

A review of the literature on anemia prevalence and vaccination coverage revealed two things about these 

studies: (1) most focused on the nonspatial factors affecting anemia and vaccination, and (2) those that did 

venture into spatial analysis of anemia or vaccine coverage either stratified their data by urban or rural 

settings (Kuziga, Adoke, and Wanyenze 2017; Canavan et al. 2014), or assessed the spatial relationships of 

the survey data by evaluating spatial autocorrelation (Ejigu, Wencheko, and Berhane 2018). However, we 

did not find widespread use of spatial covariates such as rainfall, vegetation, or population estimates 

throughout the literature, which we believe indicates a lack of accessibility to these data and provides further 

evidence of the need for such data to be readily available and easily accessed. This gave us the opportunity 

to explore the impact that these environmental and geophysical factors potentially have on both the 

prevalence of anemia and the receipt of basic vaccinations.

Rainfall creates conditions that allow sufficient surface water for mosquito breeding, and is often studied 

for its effects on transmission of malaria (Arab, Jackson, and Kongoli 2014; Sewe et al. 2015). However, 

research on direct interactions between rainfall and anemia is limited. One study did take droughts into 

consideration in its analysis, but primarily in response to conditions in the field during data collection for a 

malaria prevention trial (Gari et al. 2017). Given the association between rainfall and malaria transmission, 

along with rainfall’s role in promoting vegetation growth, we included the DHS Program’s rainfall 

covariate, defined as the average rainfall of the cells whose centroid falls within a radius of 10 km (for rural 

points) or 2 km (for urban points).

Similarly to rainfall, vegetation cover linked to population data has been identified as a predictor for 

malarial transmission. Studies have shown the risk of dense vegetation cover near households as a facilitator 

of malaria transmission (Ricotta et al. 2014). For instance, farmers clearing vegetation to gain more 

productive land can often inadvertently lead to the creation of breeding sites for mosquitoes (Walsh, 

Molyneux, and Birley 1993). Increased agricultural development and irrigation, such as rice farming, can 

increase the transmission of malaria due to creation of large stagnant pools of water that provide breeding 

habitats favorable to Anopheles gambiae and, in Tanzania, Anopheles arabiensis larvae (Janko et al. 2018; 

Mboera et al. 2010; Ernst et al. 2009). Given their indirect associations with anemia through malaria 

transmission and the relative paucity of literature examining them in the context of anemia, we felt it 

important to include the Enhanced Vegetation Index covariate along with the Growing Season Length 

(GSL) covariate as part of our analysis. Malaria was not used as a covariate in the anemia models. This was 

because the malaria covariate is produced by the Malaria Atlas Project (MAP), which uses some of the 

spatial covariates in their predictive models (for instance, nighttime lights). These spatial covariates are also 

used in our models and therefore would have produced some bias. Definitions for these covariates can be 

found in Appendix A.

The literature also helped us choose typical sociodemographic controls for the analysis of both our case 

studies. In sub-Saharan Africa, for example, sex of child has been studied extensively in this context (Ncogo 

et al. 2017; Ngesa and Mwambi 2014). In addition, child’s age, categorized as 5-29 months and as 30-59 

months, was included as a control. In our analysis, age category was not included in the vaccination model, 

which is only relevant for children age 12-23 months. Caretaker’s education level is also consistently 

included as a control variable (Simbauranga et al. 2015; Ismael, Olivier, and Barthelemy 2007). In our 

model, education was categorized as none, primary, and secondary or above. Because studies often measure 
10

outcomes by socioeconomic status (SES) (Foote et al. 2013), our analysis used the DHS Program’s standard 

household wealth index variable (categorized into quintles: lowest, second, middle, fourth, and highest) as 

a covariate.

Other geospatial covariates, such as nightlights, UN population count, and proximity to coast/large lakes, 

were selected for inclusion in our analysis specifically because of the relative lack of literature that explores 

the impact of these factors on either anemia prevalence or vaccination coverage. We were unable to find 

any such research. We believe this report is an opportunity to conduct an exploratory analysis to examine 

the potential links between these covariates and the two outcomes.

2.4 Analytical Methods

This analysis uses Stata 14 software to conduct a simple logit regression model of anemia in children age 

6-59 months and basic vaccination for children age 12-23 months. This is accomplished by using DHS 

survey data for each of the six surveys included, along with each survey’s corresponding geospatial 

covariate data. For each survey, correlation matrices were constructed for the covariates to be included in 

the model in order to identify potential confounding as a result of highly correlated independent variables. 

A covariate was determined to be too highly correlated to another if the absolute value of the correlation 

coefficient was greater than or equal to 0.7. These matrices can be found in Section 4.1 of this report.

A regression model was fit for each spatial covariate separately for each outcome along with the control 

variables. After examining the significance of the geospatial covariate, a final model was fit for each survey 

and each outcome that included the controls and all the significant geospatial covariates. The significant 

geospatial variables were only added together in the model if they were not highly correlated. In addition 

to examining the significance of the covariates, we computed the Akaike Information Criterion (AIC) for 

the models and compared it with the AIC of the base model, which includes only the control variables. A 

substantial reduction in the AIC from the base model would indicate that is important to include the 

geospatial covariate in the model. Selecting the geospatial covariate to include in the final model based on 

its significance is generally in agreement with selecting it based on the reduction in the AIC value. If two 

covariates that are highly correlated are both significant, then the covariate that provides a model with a 

lower AIC value is selected for inclusion. AIC were computed considering the sampling weight but not the 

stratification design of the survey. This was beccause the STATA command used to produce the AIC (estat 

ic) does not support the use of survey design commands. For the logit regression models, the survey 

stratification and sampling weights were accounted for. Figure 5 presents a summary of our approach.
11

Figure 5 Analytical approach

Flow diagram showing the steps used for modeling the relationship 

between anemia/vaccination and the underlying factors

In addition to the comparison of the logit regression model-building procedures for the two outcomes across 

all surveys, one country was selected to demonstrate the model-building procedure for the two outcomes 

across time. Tanzania was selected because it has three surveys since 2000 with GPS data (2007-08 THMIS, 

2010 DHS, and 2015-16 DHS). This analysis was performed to demonstrate the possible change in the 

inclusion of the geospatial covariates for the same country across time. As there could be a lag between the 

measure of the covariates and their impacts on survey respondents, each covariate was selected for the most 

recent year preceding the survey for which data were available.

13

3 CASE STUDIES

3.1 Anemia in Children Age 6-59 Months

Anemia is a condition characterized by low hemoglobin concentration in the blood, and it has significant 

health, social, and economic consequences. It is associated with poor birth outcomes, including low birth 

weight, prematurity, and perinatal and maternal mortality (Balarajan et al. 2011). It is also associated with 

poor cognitive function and motor development in children (WHO 2017b). In addition, the collective 

burden of anemia has been estimated to account for decreased economic growth and millions of years lived 

with disability in 2010 alone (WHO 2017b). Although the prevalence of anemia decreased between 1990 

and 2010, it is estimated to affect one-third of the world’s population, including 800 million women and 

children (WHO 2017b).

Causes of anemia are generally classified in terms of nutrient deficiencies, infections and disease, and 

genetic disorders. Of the main three causes of anemia globally—iron deficiency, hemoglobinopathies, and 

malaria—iron deficiency contributes to 42% of anemia cases among children under age 5 (WHO 2017b). 

Children under age 5 have the largest burden of anemia globally because of their rapid growth and high 

iron requirements, especially in the first two years of life (WHO 2017b). Poor iron status can be transferred 

from mother to child (WHO 2017b; Khan, Awan, and Misu 2016), and typical complementary foods are

low in iron (WHO 2017b).

While in adults there is an increased risk of anemia for women of reproductive age, male children are more 

likely to be anemic than female children (Legason et al. 2017; Ngesa and Mwambi 2014). Infant iron stores 

are lower in males than in females during the first year of life (WHO 2017b). Gendered feeding practices 

may also contribute to differences in anemia between boys and girls. Urban-rural location, household wealth

status, and maternal education are also associated with children’s anemia status (WHO 2017b).

There is also regional variation in anemia prevalence, depending in part on geospatial factors such as 

malaria prevalence and coverage of insecticide-treated bed nets (ITNs). In Tanzania, malaria is associated 

with severe anemia in children (Simbauranga et al. 2015). Regional variation signals the importance of 

investigating the contribution of geospatial covariates to the burden of anemia. The purpose of this case 

study is to illustrate how geospatial covariates can be used with demographic covariates to analyze the

contribution to the burden of anemia in children under age 5.

3.2 Basic Vaccination Coverage of Children Age 12-23 Months

Immunization is one of the most successful, equitable, and cost-effective interventions, preventing 2–3 

million deaths each year (WHO 2017a), and basic vaccination coverage is a measure of both child health 

and health system performance. An estimated 19.5 million infants are still missing basic vaccine coverage 

(WHO 2018); in 2016, one in every 10 infants around the world did not receive any vaccinations (WHO 

2017a). In addition to measles vaccine coverage, the addition of new vaccines (pneumococcal and rotavirus) 

to the list of recommended routine child immunizations holds promise for reducing the prevalence of child 

deaths due to pneumonia and diarrheal disease, respectively (Liu et al. 2015). The World Health 

Organization (WHO) and partners developed the Global Vaccine Action Plan to guide efforts to ensure 

equitable access to vaccines, but progress toward its 2020 targets is not on track (WHO 2018).
14

DHS Spatial Analysis Report 12 (Burgert-Brucker et al. 2015) identified uneven distribution of 

immunization coverage between and within 27 countries in sub-Saharan Africa. This varying immunization 

coverage within countries has led to recommendations to address regional variation with different strategies 

for delivery of information and services (Lakew, Bekele, and Biadgilign 2015).
15

4 RESULTS

4.1 Results from Correlation Matrix

Before running the final logit models for each outcome, we examined the results of the correlation testing 

for the independent variables, which helped us to determine, between a pair of highly correlated, statistically 

significant covariates, which variables to select for inclusion in the models. In the Tanzania 2008 survey, 

both the proximity and elevation covariates were statistically significant per the model-building exercise 

(proximity: p < .001, elevation: p < .001), but were highly correlated per the correlation matrix (correlation 

coefficient of 0.7967). Reviewing the relative change to AIC attributed to each covariate (proximity: -32.5 

relative to base model; elevation: -266.7 relative to base model), we decided to include the elevation

covariate in our final logit regression model rather than the proximity covariate, because it contributed a 

larger change in the model AIC.

Similarly, the Tanzania 2010 and Tanzania 2015-16 surveys exhibited a high degree of correlation between 

elevation and proximity (correlation coefficient 0.8199 and 0.8005 respectively), while both covariates 

were shown to be statistically significant during the model building (both p < .001 in each survey). A review 

of the relative change to model AIC (Tanzania 2010: elevation: -102.8 relative to the base model, proximity: 

-32.7 relative to the base model; Tanzania 2015-16: elevation: -47.9 relative to the base model, proximity:

-18.9 relative to the base model) helped us to determine that, as with Tanzania 2008, elevation would be 

included in the base model instead of proximity for both surveys due to causing a larger drop in model AIC.

Figure 6 shows an example of the correlation matrix for the covariates used in the analysis for the Rwanda 

survey. In the case of Rwanda, a correlation coefficient of 0.7463 was observed between the elevation 

(altitude) and rainfall (2010) covariates, indicating a high degree of correlation. However, the modelbuilding exercise revealed that rainfall was not a statistically significant covariate, and as such would not 

be included in the final logit regression model, regardless. We therefore included elevation in the final 

model.
16

Figure 6 Correlation matrix covariates using Rwanda survey data

No other covariate pairs in any other survey exhibited high degrees of correlation that needed special 

attention during the logit model-building activity. As the same set of demographic and geospatial covariates 

was used for both case studies, the same pairs of highly correlated covariates were examined when 

analyzing basic vaccination among children age 12-23 months. In the vaccination analysis, no geospatial 

covariates were significant in the Rwanda 2014-15 DHS or any of the three Tanzania surveys, thereby 

rendering moot the review of relative change to model AIC. However, Burundi 2017 DHS and Uganda 

2016 DHS had covariates that were significant but not strongly correlated, and so no decision had to be 

made regarding selecting the covariates for inclusion in the final models.

4.2 Results of Logit Regression – Case Study 1: Anemia in Children Age 

6-59 Months

Table 3 summarizes the results of the model selection for the anemia outcome. The top half of the table 

summarizes the significance and AIC values for the models that include each geospatial covariate with the 

control variables. The bottom half of the table indicates the final model selected for each survey. The 

coefficients for the full models are summarized in Appendix C. The significance of the geospatial covariates 

varied by survey. However, elevation was significant for all surveys, and rainfall was significant for three 

of the four countries in the most recent surveys.
17

Table 3 Anemia model selection

Burundi Rwanda Uganda Tanzania 2015 Tanzania 201

Model coef

1 p-value AIC coef1 p-value AIC coef1 p-value AIC coef1 p-value AIC coef1 p-value 

BM2 6984.6 3944.5 4954.3 9558.2 

BM + ‘EVI’ -0.0002 0.102 6981.4 0.0001 0.172 3944.0 -0.0002 0.075 4949.0 0.0002 0.017 9548.2 -0.0002 0.002 

BM + ‘Growing’ -0.0759 0.237 6983.7 -0.1436 <0.001 3928.9 -0.0140 0.694 4956.0 0.0842 0.002 9539.2 -0.0536 0.064 

BM + 

‘Nightlights’ 0.0316 0.733 6986.3 -0.0297 0.143 3944.3 0.0344 0.079 4952.9 0.0365 0.002 9548.5 0.0413 0.006 

BM + ‘Proximity’ -0.0000007 0.662 6986.0 0.000003 0.345 3945.4 -0.0000006 0.661 4955.9 0.0000009 <0.001 9539.2 0.000001 <0.001 

BM + ‘Rain’ -0.0009 <0.001 6939.9 -0.0005 0.086 3941.9 0.0007 <0.001 4930.6 0.0006 <0.001 9511.9 0.00005 0.772 

BM + ‘UN_Pop’ -0.0001 0.858 6986.5 -0.0000003 0.666 3946.2 0.00003 0.930 4956.3 0.0000009 0.006 9554.0 0.0000001 0.864 

BM + ‘Elevation’ -0.0004 0.001 6937.9 -0.0004 0.010 3935.3 -0.0010 <0.001 4911.3 -0.0004 <0.001 9510.3 -0.0006 <0.001 

Full models

3

BM + ‘Elevation’

+ ‘Rain’ - - 6904.4 - - - - - - - - - - - 

BM + ‘Elevation’

+ Growing’ - - - - - 3929.4 - - - - - - - - 

BM + ‘Elevation’

+ ‘Rain’ - - - - - - - - 4898.8 - - - - - 

BM + All 

Covariates -

‘Proximity’

4

- - - - - - - - - - - 9483.0 - - 

BM + ‘EVI’ + 

‘Nightlights’ + 

‘Elevation’

4

- - - - - - - - - - - - - - 

BM + ‘EVI’ + 

‘Growing’ + 

‘Elevation’ + 

‘Rain’

4

- - - - - - - - - - - - - - 

Notes: 1 - Coefficient for the added geospatial covariate; 2 - BM=Base model. Includes the sociodemographic variables of wealth index, child’s age, child’s sex, and 

the full model for each survey are found in Appendix B; 4 - Proximity omitted due to high correlation with Elevation covariate.
18

For Burundi, the rainfall and elevation covariates were significant and showed a substantial reduction in 

the AIC compared with the base model. This indicates that these variables provide a significant explanation 

to the model and should be included. Therefore, the final model for Burundi included both these covariates,

which reduced the AIC further—from 6,984.6 for the base model to 6,904.4 in the final model. Two 

geospatial covariates were also significant in the Rwanda survey: growing season length and elevation. The 

final model that included these two covariates reduced the AIC from 3,944.5 in the base model to 3,929.4

in the final model. For Uganda, rainfall and elevation were the only two statistically significant covariates. 

The inclusion of these two covariates reduced the AIC from 4,954.3 in the base model to 4,898.8 in the 

final model.

The model selection procedure was also used for the three surveys in Tanzania. For the Tanzania 2015-16 

DHS, all the geospatial covariates were significant. However, because proximity was omitted due to strong 

correlation with elevation, the final model for the 2015 Tanzania survey included all geospatial covariates 

except proximity. This reduced the AIC from 9,558.2 to 9,483.0, which indicates an improvement in the 

model due to the addition of the geospatial covariates. In the Tanzania 2010 DHS survey, only EVI, 

nightlights, proximity, and elevation were significant. In the Tanzania 2008 DHS survey, EVI, growing 

season length, proximity, rainfall, and elevation were all significant. Again, for the 2010 and 2008 surveys,

elevation was highly correlated with proximity, and therefore proximity was excluded from the models. 

Across all three Tanzania surveys, the enhanced vegetation index, proximity, and elevation covariates were 

significant for the anemia outcome.
19

4.3 Results of Logit Regression – Case Study 2: Full Vaccination Coverage for Childre

Months

Table 4 Vaccination model selection

Burundi Rwanda Uganda Tanzania 2015 

Model coef

1 p-value AIC coef1 p-value AIC coef1 p-value AIC coef1 p-value AIC c

BM2 2234.8 797.3 3946.6 2298.4 

BM + ‘EVI’ -0.0001 0.528 2236.1 -0.0003 0.269 797.0 0.0001 0.098 3944.2 -0.00004 0.740 2300.3 0.

BM + ‘Growing’ -0.3103 0.005 2223.8 -0.1094 0.270 797.7 -0.1060 0.002 3936.2 -0.0477 0.375 2298.9 0.

BM + ‘Nightlights’ -0.1372 0.438 2235.7 -0.0157 0.753 799.2 -0.0454 0.011 3942.1 0.0658 0.066 2294.4 0.

BM + ‘Proximity’ 0.000001 0.583 2236.4 -0.000006 0.504 798.7 0.000005 0.001 3932.1 0.0000006 0.312 2298.4 0.0

BM + ‘Rain’ -0.0006 0.083 2231.9 0.0008 0.272 797.0 -0.0002 0.186 3946.2 0.0001 0.586 2300.0 0.

BM + ‘UN_Pop’ 0.0015 0.092 2231.6 0.000002 0.272 797.3 0.0006 0.078 3945.0 0.000002 0.053 2294.4 0.

BM + ‘Elevation’ 0.0006 0.014 2218.3 -0.0003 0.544 798.6 0.0007 <0.001 3932.9 -0.0001 0.517 2299.6 -0.

Full models

3

BM + ‘Growing’ + 

‘Elevation’ - - 2199.8 - - - - - - - - - 

BM + ‘Growing’ + 

‘Nightlights’ + 

‘Elevation’ -

‘Proximity’ - - - - - - - - 3911.2 - - - 

Notes: 1 - Coefficient for the added geospatial covariate; 2 – BM=Base model. Includes the sociodemographic variables of wealth index, child’s age, 

of mother; 3 - Coefficients for the full model for each survey are found in Appendix C.
20

Table 4 summarizes the results of the model selection for the vaccination outcome. The coefficients for the 

full models are summarized in Appendix D. Very little significance in the geospatial covariates was found 

for the vaccination outcome. The significant geospatial covariates were only found in Burundi and Uganda. 

In both these countries, growing season length (GSL) was significant. For the Burundi 2017 DHS survey,

elevation was also significant, and the inclusion of this covariate with the GSL covariate reduced the AIC 

from 2,234.8 in the base model to 2,199.8 in the final model. For Uganda 2016 DHS, the final model 

included GSL, nightlights, proximity, and elevation (as mentioned before, proximity was dropped due to 

high correlation with the elevation covariate). This covariate selection reduced the model AIC from 3,946.6

to 3,911.2.

With respect to Tanzania, in the 2015 and 2010 surveys no geospatial covariate was significant. Vaccination 

was not measured in the 2008 survey, and therefore the models could not be fit for the vaccination outcome 

for this survey.
21

5 DISCUSSION

5.1 Evaluation of Spatial Covariates

A discussion of the specific impacts of the chosen covariates on the prevalence of anemia or receipt of all 

eight basic vaccinations is beyond the scope of this paper. The spatial covariates chosen for this analysis—

EVI, GSL, nightlights, proximity to lake/coast, rainfall, UN population count, and elevation—showed 

mixed results in their use. Several of these covariates were significantly associated with the anemia 

outcome, as this is an outcome that we know to be more susceptible to environmental and geophysical 

forces. Elevation was the covariate most frequently found significant in our analysis (significant in all six 

surveys), followed by rainfall (four surveys), and EVI, growing season length, and proximity (three surveys 

each). Except for the Rwanda 2014-15 DHS, in all other surveys the elevation contributed to the largest 

decrease in the model AIC compared with the AIC of the base model, with rainfall contributing the secondlargest decrease, on average. While elevation appeared to be an important environmental variable in 

predicting the anemia outcome, we believe it is still worth further investigating EVI, GSL, and rainfall, as 

these covariates are all closely tied to adequate conditions for both the production and availability of 

agriculture and the propagation and spread of malaria, a known risk factor for the development of anemia 

(Menon and Yoon 2015; Foote et al. 2013).

These same covariates fared much more poorly when used to analyze receipt of all eight basic vaccinations, 

a coverage indicator. Only the Burundi 2016-17 DHS and the Uganda 2016 DHS showed any statistically 

significant spatial covariates (GSL and elevation in Burundi; GSL, nightlights, elevation, and proximity to 

lake/coast in Uganda). This comes as no real surprise: access to basic vaccinations would be unlikely to be 

affected by environmental factors, such as rainfall, except in the case of catastrophic extremes—for 

example, roads entirely cut off by flooding due to heavy rains. However, the significance of the relationship 

between GSL and, separately, elevation in both Burundi and Uganda to receipt of vaccination, warrants 

further, country-specific investigation. While length of growing season is associated with prevalence of 

vector-borne diseases (Tottrup et al. 2009), and rainfall and temperature during the growing season can 

increase the prevalence of malnutrition (Hagos et al. 2014), growing season could also be associated with 

immunization coverage. For instance, caregivers could be busy working in fields during longer growing 

seasons and might not have time to take children to well-child visits where they would get vaccinations. 

Conversely, shorter growing seasons are associated with increased poverty, resulting in less access to 

transportation to health services; thus, children’s health can be affected.

The results of the temporal analysis of both case studies in Tanzania showed the spatial covariates were 

better suited for a model with the anemia outcome than the vaccination outcome. Whereas no spatial 

covariates were significantly associated with receipt of all eight basic vaccinations in the Tanzania 2010 or 

2015-16 DHS surveys (vaccination data were not collected as part of the Tanzania 2008 THMIS), most 

covariates were found significantly associated with anemia in all three surveys. Of note is the Tanzania 

2015-16 survey, wherein all the covariates were significant in the analysis of any anemia in children, with 

elevation and rainfall once again contributing the greatest decreases to the model AIC compared with the 

AIC of the base model (-47.9 and -46.3, respectively).
22

5.3 Suggestions for Further Study

This study attempted to demonstrate the feasibility of the DHS Program’s spatial covariate datasets for 

inclusion in regression modeling, and as such it focused primarily on the model-building process and the 

quality of the subsequent regression model itself. However, there were several limitations to our approach. 

Primarily, we were unable to include the malaria covariate in our analysis because it was developed using 

DHS survey data as well as some of the geospatial variables, such as nighttime lights, which would 

invariably have led to a circular analysis. This was a disappointment, as ample literature has implicated 

concomitant malaria infection as a risk factor for development of anemia, and as several covariates included 

in this study—such as rainfall and EVI, both factors associated with the proliferation of Anopheles gambiae

mosquitoes—could potentially be serving as corollaries for malaria. Including a spatially linked malaria 

covariate could potentially help clear up these interactions.

As noted in Section 5.1, the chosen covariates did not model well for a coverage indicator such as receipt 

of all eight basic vaccinations. Accordingly, the development and use of covariates better suited for 

coverage indicators is required. The travel times covariate, which measures the amount of time required to 

reach a settlement of >50,000 people, along with the population density and urbanicity covariates, may 

have been better fits for modeling vaccination coverage. Further, the creation of covariates measuring the 

cluster’s proximity to nearest health facility could potentially help define access to health care for 

individuals, a direct predictor of their access to vaccinations. However, this depends on the existence of 

master facility datasets—of which we are not currently aware—that can be used for the creation of this 

dataset. This report was mainly used as a demonstration of the procedures that could be used to include 

geospatial variables in the analysis of DHS data, and was not intended for finding the best model for the 

case studies. Future studies on vaccination coverage using geospatial variables may want to consider travel 

times as another potential covariate to include in their analyses.

Finally, because its scope was limited to an exercise in building a logit regression model, this report does 

not delve into the specific associations between the covariates and the outcomes. It is our hope that the 

report demonstrates that using these covariates in such a fashion is a feasible analytical method that can be 

used by other researchers, who will be able to probe these associations further.
23

APPENDICES

25

APPENDIX A DOCUMENTATION FOR INCLUDED 

COVARIATES

Column Name: Growing Season Length

Derived Data Set: Length of Available Growing Period (16 classes)

Derived Data Set Cell Size: 5 Arc Minute (~0.0833333 decimal degrees; ~10 km)

Derived Data Set License: None (All Rights Reserved)

Year: Based on data collected between 1961 and 1991

Units: Individual classes between 1 and 16. The values are listed below.

1: 0 days

2: 1 - 29 days

3: 30 - 59 days

4: 60 - 89 days

5: 90 - 119 days

6: 120 - 149 days

7: 150 - 179 days

8: 180 - 209 days

9: 210 - 239 days

10: 240 - 269 days

11: 270 - 299 days

12: 300 - 329 days

13: 330 - 364 days

14: < 365 days

15: 365 days

16: > 365 days

Description:

It is impossible to deep link to the dataset. Searching for “growing season” and limiting results to “World”

datasets should bring “Length of Available Growing Period (16 classes).”

Length of available growing period refers to the number of days within the period of temperatures above 

5° C when moisture conditions are considered adequate. Under rain-fed conditions, the beginning of the 

growing period is linked to the start of the rainy season. The growing period for most crops continues 

beyond the rainy season and, to a greater or lesser extent, crops mature on moisture stored in the soil profile.

The mode of the growing season length indices of the cells whose centroid falls within a radius of 10 km 

(for rural points) or 2 km (for urban points).

Citation:

Food and Agriculture Organization. 2007. “Length of Available Growing Period (16 classes).” Accessed 

August 21, 2017. http://www.fao.org/geonetwork/srv/en/main.home.
26

Column Name: Enhanced Vegetation Index YEAR

Derived Data Set: Vegetation Index and Phenology (VIP) Phenology EVI-2 Yearly Global 0.05Deg CMG 

V004

Derived Data Set Cell Size: 0.05 decimal degrees (~5 km)

Derived Data Set License: Public Domain

Year: 1985, 1990, 1995, 2000, 2005, 2010, or 2015

Units: Vegetation index value between 0 (least vegetation) and 10000 (most vegetation)

Description:

The enhanced vegetation index was calculated by measuring the density of green leaves in the near-infrared 

and visible bands.

The average enhanced vegetation index of the cells whose centroid falls within a radius of 10 km (for rural 

points) or 2 km (for urban points).

Citation:

Kamel Didan. 2016. “NASA MEaSUREs Vegetation Index and Phenology (VIP) Phenology EVI2 Yearly 

Global 0.05Deg CMG.” Accessed August 21, 2017. 

https://doi.org/10.5067/measures/vip/vipphen_evi2.004.
27

Column Name: Proximity to Water

Derived Data Set: GSHHG (Global Self-consistent, Hierarchical, High-resolution Geography Database)

Derived Data Set License: GNU Lesser General Public License

Year: 2017

Units: Meters

Description:

Straight-line distance to the nearest major water body. Based on the World Vector Shorelines, CIA World 

Data Bank II, and Atlas of the Cryosphere.

Citation:

Wessel, Paul, and Walter Smith. 1996. “A Global Self-consistent, Hierarchical, High-resolution Shoreline 

Database” Journal of Geophysical Research 101:8741-8743. https://doi.org/10.1029/96JB00104.

Wessel, Paul, and Walter Smith. 2017. “A Global Self-consistent, Hierarchical, High-resolution 

Geography Database Version 2.3.7.” Accessed August 21, 2017. 

http://www.soest.hawaii.edu/pwessel/gshhg/.
28

Column Name: Rainfall YEAR

Derived Data Set: Climate Hazards Group InfraRed Precipitation with Station data 2.0

Derived Data Set Cell Size: 0.05 decimal degrees (~5 km)

Derived Data Set License: Public Domain

Year: 1985, 1990, 1995, 2000, 2005, 2010, 2015

Units: Millimeters per year

Description:

The average rainfall of the cells whose centroid falls within a radius of 10 km (for rural points) or 2 km (for 

urban points).

Citation:

Climate Hazards Group. 2017. “Climate Hazards Group InfraRed Precipitation with Station Data 2.0.” Accessed 

August 21, 2017. http://chg.geog.ucsb.edu/data/chirps/index.html.

Funk, Chris, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory 

Husak, James Rowland, Laura Harrison, Andrew Hoell and Joel Michaelsen. 2015. “The Climate Hazards 

Infrared Precipitation with Stations—a New Environmental Record for Monitoring Extremes.” Scientific Data

2. http://doi.org/10.1038/sdata.2015.66.
29

Column Name: Nightlights Composite

Derived Data Set: Version 1 VIIRS Day/Night Band Nighttime Lights

Derived Data Set Cell Size: 15 Arc Second (~0.00416667 decimal degrees; ~500 m)

Derived Data Set License: Public Domain

Year: 2015

Units: Composite cloud-free radiance values

Description:

The average radiance of the cells whose centroid falls within a radius of 10 km (for rural points) or 2 km 

(for urban points).

Citation:

Mills, Stephen, Stephanie Weiss, and Calvin Liang. 2013. “VIIRS Day/night Band (DNB) Stray Light 

Characterization and Correction.” Proceedings of SPIE 8866. http://dx.doi.org/10.1117/12.2023107.

National Centers for Environmental Information. 2015. “2015 VIIRS Nighttime Lights Annual 

Composite.” Accessed August 21, 2017. https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html.
30

Column Name: UN Population Count YEAR

Derived Data Set: UN-Adjusted Population Count, v4 (2000, 2005, 2010, 2015, 2020)

Derived Data Set Cell Size: 30 arc seconds (~0.00833333 decimal degrees; ~1 km)

Derived Data Set License: Non-Standard Noncommercial License

Year: 2000, 2005, 2010, 2015

Units: Number of people

Description:

The average number of people in the cells whose centroid falls within a radius of 10 km (for rural points) 

or 2 km (for urban points).

Citation:

Center for International Earth Science Information Network – Columbia University. 2016. “Gridded 

Population of the World, Version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN 

WPP Country Totals.” http://dx.doi.org/10.7927/H4SF2T42.
31

APPENDIX B SUBSET OF RWANDA 2014-15 DHS SPATIAL COVARIATE 

Appendix B Subset of Rwanda 2014-15 DHS spatial covariate dataset

DHSID

GPS_

Dataset

DHS

CC

DHS

YEAR

DHS 

cluster

Survey

ID

Enhanced_

Vegetation_

Index_2010

Enhanced_

Vegetation_

Index_2015

Growing_

Season_

Length

Nightlights_

Composite

Proximity_

to_Water

Rainfall_

2010

Rainf

201

RW201400000001 RWGE72FL RW 2014 1 RW2015DHS 3491.6 3437.9 11 0.0717 35146.9 1268.1 1145

RW201400000002 RWGE72FL RW 2014 2 RW2015DHS 3641.1 3450.4 11 0.0053 26115.0 1039.6 862

RW201400000003 RWGE72FL RW 2014 3 RW2015DHS 3797.4 3789.6 13 0.0061 29001.5 1430.1 1232

RW201400000004 RWGE72FL RW 2014 4 RW2015DHS 3507.2 3416.6 11 0.0004 10328.9 1108.4 895

RW201400000005 RWGE72FL RW 2014 5 RW2015DHS 3230.8 3003.1 11 0.0263 15599.0 985.3 745

RW201400000006 RWGE72FL RW 2014 6 RW2015DHS 3053.0 3137.0 11 0.1765 21380.9 1097.0 864

RW201400000007 RWGE72FL RW 2014 7 RW2015DHS 3855.9 3809.6 13 0.0361 17969.6 1323.6 1084

RW201400000008 RWGE72FL RW 2014 8 RW2015DHS 3818.3 3656.5 11 0.0357 41680.6 1178.4 990

RW201400000009 RWGE72FL RW 2014 9 RW2015DHS 3053.0 3137.0 11 5.3833 22203.4 1067.0 830

RW201400000010 RWGE72FL RW 2014 10 RW2015DHS 3779.0 3682.0 11 1.2922 11274.4 1051.0 856

RW201400000011 RWGE72FL RW 2014 11 RW2015DHS 3551.1 3407.6 11 0.1689 17244.3 1037.4 842

RW201400000012 RWGE72FL RW 2014 12 RW2015DHS 3555.8 3527.9 12 0.1722 29085.7 1154.7 998

RW201400000013 RWGE72FL RW 2014 13 RW2015DHS 3833.7 3852.4 13 0.0020 19472.5 1386.2 1316

RW201400000014 RWGE72FL RW 2014 14 RW2015DHS 3711.6 3620.3 12 0.0189 25255.9 1307.6 1150

RW201400000015 RWGE72FL RW 2014 15 RW2015DHS 3577.0 3518.0 13 0.1327 34118.0 1384.0 1201

RW201400000016 RWGE72FL RW 2014 16 RW2015DHS 2768.7 2693.7 10 0.0017 2015.5 1028.5 833

RW201400000017 RWGE72FL RW 2014 17 RW2015DHS 3688.6 3677.3 12 0.0108 28709.7 1115.0 919

RW201400000018 RWGE72FL RW 2014 18 RW2015DHS 3284.0 3315.0 14 0.0956 24186.5 1576.0 1407

RW201400000019 RWGE72FL RW 2014 19 RW2015DHS 3819.1 3879.3 11 0.0061 8071.9 1124.2 927

RW201400000020 RWGE72FL RW 2014 20 RW2015DHS 3685.0 3590.9 12 0.0948 34415.9 1213.3 1112

RW201400000021 RWGE72FL RW 2014 21 RW2015DHS 3724.0 3730.1 13 0.0018 17324.7 1329.9 1224

RW201400000022 RWGE72FL RW 2014 22 RW2015DHS 3703.5 3710.0 12 0.2033 20059.4 1167.2 1007

RW201400000023 RWGE72FL RW 2014 23 RW2015DHS 3321.8 3364.8 11 3.1595 25420.4 1107.7 861

RW201400000024 RWGE72FL RW 2014 24 RW2015DHS 3830.7 3832.9 14 0.1668 10506.8 1389.0 1216

RW201400000025 RWGE72FL RW 2014 25 RW2015DHS 3245.0 3192.0 14 0.0362 4828.9 1372.0 1131

RW201400000026 RWGE72FL RW 2014 26 RW2015DHS 3472.3 3370.3 12 0.0026 22541.1 947.2 822

RW201400000027 RWGE72FL RW 2014 27 RW2015DHS 2451.4 2423.0 14 0.5506 2670.3 1262.6 1120

RW201400000028 RWGE72FL RW 2014 28 RW2015DHS 3835.2 3806.9 13 0.0155 15307.4 1480.4 1278

RW201400000029 RWGE72FL RW 2014 29 RW2015DHS 2513.0 2598.0 11 21.8008 30236.8 1084.0 872

RW201400000030 RWGE72FL RW 2014 30 RW2015DHS 3673.6 3550.2 11 0.0137 28770.6 974.9 793

33

APPENDIX C ANEMIA MODEL SELECTION – FULL MODEL

Appendix Table C Anemia model selection – Full model for each survey

Variable

Burundi 

2016 DHS

Rwanda 

2014 DHS

Uganda 

2016 DHS

Tanzania 

2015 DHS

Tanzania 2010 

DHS

Tanzania 

2008 DHS

Geospatial variables

Enhanced Vegetation Index 2015 -0.00002

Growing Season Length -0.1157* 0.0546 0.1734***

Nightlights Composite 0.0124 -0.01509 -0.0080

Proximity to Water 0.0000001 0.000000009 0.0000004

Rainfall 2015 -0.0008*** 0.0005** 0.0004*

UN Population Count 2015 -0.00000006

Cluster Altitude in Meters -0.0003* -0.0002 -0.0009*** -0.0003* -0.00071*** -0.0006***

 

Enhanced Vegetation Index 2010 -0.00032***

 

Enhanced Vegetation Index 2005 0.00002

Rainfall 2005 0.0005

UN Population Count 2005 -0.000003***

 

Control variables

Wealth index

Lowest (Ref.)

Second -0.0350 -0.0465 -0.3981** -0.1528 0.0803 -0.1196

Middle -0.3360** -0.1707 -0.6552*** -0.2231* -0.0974 -0.2426

Fourth -0.5033*** -0.4301** -0.6386*** -0.5544*** -0.1669 -0.2135

Highest -0.7472*** -0.7003*** -0.7845*** -0.7479*** -0.2793 -0.2307

Sex of child

Male (Ref.)

Female -0.1644** -0.0216 -0.1114 -0.1419** -0.3008*** -0.1841**

Age of Child

5-29 months (Ref.)

30-59 months -0.4550*** -1.1257*** -1.0797*** -1.1861*** -0.9462*** -1.0317***

Mother’s education

None (Ref.)

Primary -0.1838** -0.1802 -0.4117** -0.3588*** -0.1085 -0.1860*

Secondary or more -0.4604*** 0.0115 -0.4359* -0.3957** -0.1153 0.2732

Observations 5,273 3,144 3,872 7,850 5,906 5,437

*p<0.05, **p<0.01,***p<0.001

35

APPENDIX D VACCINATION MODEL SELECTION – FULL 

MODEL

Appendix Table D Vaccination model selection – Full model for each survey

Variable

Burundi 

2016 DHS

Uganda 

2016 DHS

Geospatial variables

Enhanced Vegetation Index 2015

Growing Season Length -0.3930*** -0.0917*

Nightlights Composite -0.0272

Proximity to Water 0.000003

Rainfall 2015

UN Population Count 2015

Cluster Altitude in Meters 0.0008*** 0.0008***

Enhanced Vegetation Index 2010

Rainfall 2010

UN Population Count 2010

Control variables

Wealth index

Lowest (Ref.)

Second 0.3511* 0.0037

Middle 0.2955 0.0235

Fourth 0.422 -0.0059

Highest 0.3203 0.0906

Sex of child

Male (Ref.)

Female 0.1282 -0.0331

Mother’s education

None (Ref.)

Primary 0.2633* 0.0114

Secondary or more 0.2053 0.3229

Observations 2,585 2,922

*p<0.05, **p<0.01,***p<0.001

37

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