metadata: authors: 0: author: Joseph,George
1: author: Gething,Peter William
2: author: Bhatt,Samir
3: author: Ayling,Sophie Charlotte Emi
count: Tanzania
docna: 0: docna: Understanding the Geographical Distribution
            of Stunting in Tanzania : A Geospatial Analysis of the
            2015-16 Demographic and Health Survey
docty: Policy Research Working Paper
subtopic: Reproductive Health,Early Child and Children's Health,Health Care Services Industry,Hydrology,Inequality,Nutrition
teratopic: Industry,Water Resources,Health, Nutrition and Population,Poverty Reduction
abstracts: cdata!: Tanzania is home to the third highest
            population of stunted children in Sub-Saharan Africa, with
            about 2.7 million children under the age of five failing to
            reach their full potential of growth attainment compared
            with the reference population as per the World Health
            Organization standards. Several studies have shown that
            stunted growth during childhood entraps the future of
            children in a vicious circle of recurrent diseases, reduced
            human development, and lower earnings, thus increasing their
            likelihood of being poor when they grow up. To reduce
            stunting, the Government of Tanzania and development
            partners are introducing a convergence of multisectoral
            interventions adapted to local needs. However, the existing
            stunting data are representative only at higher
            administrative levels, thus making it difficult to implement
            these efforts. The paper uses the 2016 geo-referenced
            Demographic and Health Survey in conjunction with relevant
            spatially gridded covariate data, such as nighttime lights,
            water and sanitation access, vegetation index, travel time,
            and so on. Geospatial techniques, such as model-based
            statistics and Bayesian inference implemented using the INLA
            algorithm, along with appropriate model validation exercises
            are employed to develop high-resolution maps of stunting in
            Tanzania at 1×1-kilometer spatial resolution. The maps show
            that areas of consistently high stunting rates tend to be
            more common in rural parts of the country, especially
            throughout the western and southwestern border areas. There
            is high prevalence of low stunting in the urban areas around
            Dar es Salaam, Arusha, and Dodoma, as well as in the south
            of Lake Victoria.
geo_reg_key: 517191
repnme: repnme: Understanding the Geographical Distribution
            of Stunting in Tanzania : A Geospatial Analysis of the
            2015-16 Demographic and Health Survey
display_title: Understanding the Geographical
            Distribution of Stunting in Tanzania : A Geospatial Analysis
            of the 2015-16 Demographic and Health Survey
listing_relative_url: /research/2019/01/30736663/understanding-geographical-distribution-stunting-tanzania-geospatial-analysis-2015-16-demographic-health-survey
topicv3: Water m1326522 137,Poverty m1326687 695,Nutrition m1326779 644,Stunting m1327504 1445,Health m1504661 3480
publishtoextweb_dt: 2019-01-03T00:00:00Z
historic_topic: Industry,Water Resources,Health, Nutrition and Population,Poverty Reduction
no_of_pages: 18
author_keys: 0: author_key: 912994
1: author_key: 1310590
2: author_key: 1404724
model_extractions: WPS8688 Policy Research Working Paper 8688 Understanding the Geographical Distribution of Stunting in Tanzania A Geospatial Analysis of the 2015–16 Demographic and Health Survey _George Joseph Peter W. Gething Samir Bhatt Sophie C.E. Ayling_ Water Global Practice January 2019
Policy Research Working Paper 8688

# **Abstract**

Tanzania is home to the third highest population of stunted children in Sub-Saharan Africa, with about 2.7 million children under the age of five failing to reach their full potential of growth attainment compared with the reference population as per the World Health Organization standards. Several studies have shown that stunted growth during childhood entraps the future of children in a vicious circle of recurrent diseases, reduced human development, and lower earnings, thus increasing their likelihood of being poor when they grow up. To reduce stunting, the Government of Tanzania and development partners are introducing a convergence of multisectoral interventions adapted to local needs. However, the existing stunting data are representative only at higher administrative levels, thus making it difficult to implement these efforts. The paper uses the 2016 geo-referenced Demographic and Health Survey in conjunction with relevant spatially gridded covariate data, such as nighttime lights, water and sanitation access, vegetation index, travel time, and so on. Geospatial techniques, such as model-based statistics and Bayesian inference implemented using the INLA algorithm, along with appropriate model validation exercises are employed to develop high-resolution maps of stunting in Tanzania at 1×1-kilometer spatial resolution. The maps show that areas of consistently high stunting rates tend to be more common in rural parts of the country, especially throughout the western and southwestern border areas. There is high prevalence of low stunting in the urban areas around Dar es Salaam, Arusha, and Dodoma, as well as in the south of Lake Victoria.

This paper is a product of the Water Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at goseph@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Understanding the Geographical Distribution of Stunting in Tanzania: A Geospatial Analysis of the 2015–16 Demographic and Health Survey**<sup>**1**</sup>

# **George Joseph**<sup>**a**</sup> **, Peter W. Gething**<sup>**i§**</sup> **,  Samir Bhatt**<sup>**iii**</sup> **, Sophie C.E. Ayling**<sup>**ii**</sup>

i. Water Global Practice Group, World Bank Group, 1818 H Street NW, Washington DC, 20433, USA.

ii. Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK.

iii. Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK.

§ Corresponding author JEL: O55; I14; C31 Key Words: Stunting; Geo‐statistics; Tanzania; Africa, Health Policy

> 1 This work was made possible by the Swedish International Development Cooperation Agency and benefited from funding from the Government of Japan through the Japan Trust Fund for Scaling Up Nutrition.
# **Understanding the Geographical Distribution of Stunting in Tanzania: A Geospatial Analysis of the 2015–16 Demographic and Health Survey**

# **Background**

Stunting, defined as being more than two standard deviations below the World Health Organization (WHO) Child Growth Standards median,<sup>1</sup> arises from a range of factors including low breastfeeding rates and malnutrition among mothers, inadequate health care or poor access to safe water and sanitation leading to a range of acute or subclinical infections in early life. Globally, around 155 million children are estimated to be stunted, and international targets have been established to reduce global stunting by 40% by 2025<sup>2,3</sup> as one component of achieving the SDG goal 2.2 of eliminating all forms of malnutrition by 2030.<sup>4</sup> However, the current average annual rate of reduction in stunting of 2.3% falls short of the 4% required if this goal is to be met.<sup>5</sup> Recent World Bank estimates suggest that the per capita income penalty incurred among contemporary workforces as a result of failure to eliminate stunting in their childhood amounts, on average across all countries, to around 7% of gross domestic product (GDP) per capita. In Sub‐Saharan Africa and South Asia, these figures rise to around 9% to 10%.<sup>6</sup> Several studies have shown that stunted growth during childhood entraps the future of children in a vicious circle of recurrent diseases, reduced human development<sup>7,8,9</sup> and lower earnings.<sup>10,11</sup> This, in turn, can increase the likelihood of being poor and even lead to the transmission of poverty to the next generation.<sup>12,13</sup> Like many low‐income countries, stunting is widespread in Tanzania. An estimated 2.7 million children in Tanzania are stunted, making the country home to the third highest population of stunted children in Sub‐Saharan Africa, after Ethiopia and the Democratic Republic of Congo. Although multisectoral efforts to reduce stunting rates have yielded some progress (with prevalence among children under 5 declining from an estimated 42% in 2010<sup>14</sup> to 34% in 2016<sup>15</sup> ), there is a renewed call to accelerate this progress and further reduce stunting nationwide through a host of nutrition‐specific and nutrition‐ sensitive interventions, particularly focusing on the first 1,000 days of life.<sup>16,17</sup> These multisectoral interventions—spanning the health, education, social protection, water supply, and sanitation sectors—are intended to converge at the local level for maximum effectiveness.<sup>18</sup> As part of these efforts, there is an increasing recognition of the need to understand the geographic heterogeneity of stunting across the country at a granular level. This granularity supports strategies that are optimally 2
targeted to populations in greatest need and tailored to local conditions, thereby promoting the efficiency and impact of program efforts.

The emergence of standardized cross‐sectional household surveys such as the Demographic and Health Surveys (DHS) Program,<sup>19</sup> which now routinely include georeferencing (i.e., latitude and longitude coordinates), provides a new opportunity to understand subnational geographic variations in a wide range of socioeconomic, health and demographic indicators, including stunting. A recent DHS survey has been undertaken in Tanzania<sup>15</sup> and, like nearly all others, is configured to provide representative estimates of stunting rates at the first subnational unit level (regions in Tanzania), but not below. However, geospatial methods have been established that can use the georeferenced survey data alongside relevant spatially gridded covariate data to reliably estimate DHS indicators at much finer levels of spatial disaggregation.<sup>20,21</sup> The use of georeferenced survey data with geospatial models to generate interpolated surfaces is perhaps most established in the field of infectious diseases.<sup>22,23,24,25,26</sup> In recent years, these methods have also been developed and applied to a broader set of social, demographic, and health contexts. This includes the use of both DHS and World Bank Living Standards Measurement Survey data, among other sources, to create high‐resolution poverty maps for a range of Sub‐Saharan African countries.<sup>27,28,29,30</sup> Most recently, the method has been applied to mapping water and sanitation indicators in Nigeria based on the 2015 Nigeria Water and Sanitation Survey.<sup>31</sup> This study describes the use of such an approach to generate a new high‐resolution map of stunting prevalence in Tanzania.

# **Methods**

# _Data_

Data on stunting were obtained from the Tanzania DHS 2015‐16.<sup>15</sup> This consisted of a two‐stage cluster randomized survey design and a final sample of 9,846 children under the age of five for whom anthropometric and age data were obtained and stunting rates for those children computed.

Geospatial covariates (spatial gridded data for which values are known at all 1×1km pixels across Tanzania) were also included in the model, as they may partially explain the observed spatial variation in stunting rates and thus allow more accurate predictions at locations without DHS survey data. An initial set was identified based on previous performance in predicting poverty, water and sanitation indicators and other related metrics in Sub‐Saharan African countries.<sup>32,33</sup> These included satellite‐ derived measures of vegetation density (Enhanced Vegetation Index, EVI), temperature (Land Surface Temperature, LST), land wetness (the Tasselled Cap Wetness index, TCW), and land brightness 3
(Tasselled Cap Brightness index, TCB), as measured by NASA’s MODIS system <u>(http://modis.gsfc.nasa.gov/); aridity, as measured by the CGIAR project (http://csi.cgiar.org/Aridity/);</u> brightness of night‐time lights measured by the DMSP OLS project (https://ngdc.noaa.gov/eog/); and a delineation of urban versus rural areas constructed by the Nelson Institute (https://nelson.wisc.edu/sage/data‐and‐models/schneider.php). A final covariate was constructed based on physical access to improved water sources. This covariate was created for the current study by, first, using a ‘friction surface’ that estimates the time required to traverse each 1×1km pixel across Tanzania.<sup>34</sup> This varied according to the type of landcover, topography, and the layout of the road and wider transport network across the country. The friction surface was then used in a least‐cost path algorithm to estimate the likely journey time from the center of each 1×1km pixel to the nearest functioning water point (such as a well, bore hole, or pump). The latitude and longitude, as well as level of functionality, of every such water point in Tanzania were recorded as part of the Water Point Mapping project undertaken by The Ministry of Water and Irrigation, Government of Tanzania.<sup>35</sup>

# _Model‐based Geostatistics framework_

The predictive approach used in this study to generate a fine‐scale map of stunting prevalence across Tanzania was based on a body of statistical theory known as model‐based geostatistics (MBG)<sup>36,37</sup> . MBG models are a class of generalized linear mixed model, with an approximation of a multivariate Normal random field (i.e., a Gaussian process) used as a spatially autocorrelated random effect term. The proportion of children who were recorded as being stunted , _Y_  _xi_ , at each location in Tanzania was modeled as a transformation of a spatially structured field superimposed with additional random variation . <mark>The count of individuals stunted from the total sample of in each survey cluster was modeled as a conditionally independent binomial variate given the unobserved underlying</mark> _Y_  _xi_ value. The spatial component was represented by a stationary Gaussian process with mean and covariance . <mark>The unstructured component was represented as Gaussian with zero mean and variance . Both the inference and prediction stages were coded using the INLA framework,</mark><sup>38</sup> <mark>primarily in the R programming language.</mark><sup>39</sup> The mean component was modeled as a linear function of the _n_ geospatial covariates described above,<sup>,wherewas a vector consisting of a constant and the</sup> covariates indexed by spatial location , and was a corresponding vector of regression coefficients. Each covariate was converted to _z_ ‐scores before analysis. Covariance between spatial locations was modeled using a Matern covariance function: 4
Where _d_ ( _xi_ ; _xj_ ) is the geographical separation between two points;  , , are parameters of the covariance function defining, respectively, its amplitude, degree of differentiability, and scale;<sup>_K_</sup> is the modified Bessel function of the second kind of order , and is the gamma function.

<mark>Bayesian inference was implemented using the INLA algorithm to generate approximations of the</mark> marginal posterior distributions of the stunting prevalence _Y_  _xi_ at each location on a regular 1×1 km spatial grid across Tanzania <mark>and of the unobserved parameters of the mean, covariance function and Gaussian random noise component. At each location, the posterior distribution was summarized using the posterior mean as a point estimate and the width of the posterior 95% credible intervals (CIs) as a metric of uncertainty, and maps were generated of each of these metrics in ArcGIS 10.4.</mark> While 1×1km resolution maps provide the most fine‐grained picture of variation in stunting across the country, it is also useful to summarize these patterns at higher levels of aggregation corresponding to administrative unit levels at which program planning, implementation and decision making are carried out. Aggregated stunting prevalence and the count of stunted children was therefore calculated at regional (1<sup>st</sup> sub‐national administrative level) and district (2<sup>nd</sup> ) levels, using an underlying gridded population surface to provide pixel‐level population denominator values.<sup>40</sup> The predictive performance of the model was assessed via out‐of‐sample validation. A four‐fold hold‐ out procedure was implemented whereby 25% of the data points were randomly withdrawn from the data set, the model run in full using the remaining 75% of data, and the predicted values at the locations of the hold‐out data compared to their observed values. This was repeated four times without replacement such that every data point was held out once across the four validation runs. Standard validation statistics were computed as measures of model precision (mean absolute error) and bias (mean error).

# **Results**

# _Model validation_

Figure 1 shows a scatterplot plotting values predicted in the out‐of‐sample validation exercise ( _x_ ‐axis) against the corresponding observed values that were held out ( _y_ ‐axis). Points close to the 1:1 line were 5
therefore predicted more accurately than those further away. The plot reveals no systematic tendency for over or under prediction at any level of observed prevalence. The validation statistics (Table 1) further indicate a relatively well performing model, with reasonable correspondence between predicted and observed values (correlation 63%), precision (mean absolute error 11%) and accuracy (mean square error 2%). The latter two metrics are expressed in the same units as the predictions – i.e. prevalence on a scale of 0‐100%.

# _Pixel‐level predictions of stunting prevalence_

Figure 2 shows the predicted map of stunting prevalence in children under five years old, constructed using the mean of the predicted posterior distribution within each 1×1km grid cell. Many areas of the country are within the yellow part of the color scale, which is similar to the national mean stunting prevalence reported in the DHS survey of 34% [DHS, 2016]. Areas of notably lower stunting prevalence (shown in the greener shades) include the predominately urban areas around Dar es Salaam, Arusha and Dodoma, as well as a broader region to the south of Lake Victoria centered around the city of Tabora. Areas of consistently high stunting rates (red shades) tended to be more common in the more rural parts of the country, especially throughout the western and southwestern border areas.

Figure 3 shows how the uncertainty of the stunting prevalence predictions varies geographically. The uncertainty metric used is the width of the posterior 95% credible interval for each grid cell. Cells where prevalence could be predicted with high certainty had narrow posterior distributions around the point estimate, and thus the range of possible prevalence values between the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles of the distribution was small, and vice‐versa. Areas predicted with relatively low prevalence (green in Figure 2) tended to also have smaller uncertainty, represented by the blue shades in Figure 3, while areas of higher stunting tended to be more uncertain, shown in the purple and pink shades in Figure 3. Even in these regions, however, the 95% credible interval tended to be no wider than 10%, meaning the model could predict stunting prevalence with 95% probability to within a range of 10 percentage points.

_Aggregated estimates of stunting_ Figure 4 shows estimates of stunting prevalence and the number of stunted children aggregated to the Tanzanian 1<sup>st</sup> (Regions) and 2<sup>nd</sup> (Districts) administrative levels. While the pixel‐level map shown in Figure 2 provides the most granular, fine detailed, prediction of the landscape of stunting across the country, the aggregated prevalence maps (top row) provide a summary of this spatial pattern at different levels that may more directly inform programmatic planning. Both the District and Region maps highlight how high‐prevalence areas are concentrated towards the southwest, with the lowest 6
levels in the central and northeastern parts of the country. The maps of estimated numbers of stunted children (bottom row) show rather different patterns to the prevalence maps. This is because these maps combine both relative frequency of stunting with the absolute number of children living in different Districts and Regions. This means that many parts of Tanzania with high prevalence, particularly in the southwest, do not feature prominently because they are very sparsely populated, while other regions of only intermediate prevalence nevertheless contain large numbers of stunted children because of higher population density.

# **Discussion**

Given the importance of reducing stunting through convergent multisectoral approaches, there is a growing need to generate high‐resolution predictions of stunting at the local level. This study has demonstrated the use of cross‐sectional, georeferenced, household survey data in a geospatial modeling framework to generate highly granular predictions of the landscape of stunting in Tanzanian children. The map reveals the considerable spatial heterogeneity in stunting across Tanzania, in particular the stark urban‐rural divide, with cities having notably lower average levels of stunting relative to even nearby rural areas. Similarly, areas proximal to major transport routes across the country tended to have lower average levels of stunting than the most remote rural areas. A rigorous model performance assessment has demonstrated that the map is predicted with reasonably high accuracy at the level of individual pixels, and an accompanying map of uncertainty indicates those places where predictions could be made with more or less certainty.

Aggregation of this pixel‐level map to administrative units provides a simple summary of prevalence rates within subsections of the country that may correspond to the units at which operational planning to counter childhood stunting is carried out and programs implemented. This aggregation also allows the straightforward estimation of the number of children stunted within each unit and, taken together, the two measures (prevalence and number stunted) provide distinct and complimentary information for decision making.

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