The DHS Program

Geographic Variation in Key Indicators of Maternal and Child Health Across 27 Countries in Sub-Saharan Africa (English) 

Document Type

Spatial Analysis Reports

Publication Topic(s)

Child Health and Development, Geographic Information, Maternal Health

Language

English

Recommended Citation

Burgert-Brucker, Clara R., Jennifer Yourkavitch, Shireen Assaf, and Stephen Delgado. 2015. Geographic Variation in Key Indicators of Maternal and Child Health Across 27 Countries in Sub-Saharan Africa. DHS Spatial Analysis Reports No. 12. Rockville, Maryland, USA: ICF International.

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Publication Date

September 2015

Publication ID

SAR12

Abstract:

While progress has been made in many areas of the Millennium Development Goals (MDGs), which expire in 2015, it appears that the maternal, newborn and child health goals (MDG 4 and 5) will not be universally achieved. There was early recognition that it could be possible to achieve the health goals while increasing health inequity, because most of the gains might go to the better-off rather than to the very poor. Concerns about health inequity indicate the need for analysis of health indicators in smaller geographic units, to aid in the identification of hotspots where coverage of key interventions lags behind neighboring areas. This report analyzes the spatial distribution of nine key maternal and child health indicators across 27 countries in sub-Saharan Africa. We created maps with 255 polygons representing each survey region from 27 Demographic and Health Surveys (DHS), joined to indicator estimates. These maps show sub-regional-level autocorrelation for each indicator as well as spatial clusters, and we characterized those clusters in terms of the relationships among neighboring sub-regions. Patterns of substantial differences among contiguous subareas are apparent for different indicators, with some intra-country differences greater than 20 percentage points for all indicators examined. Our analysis shows some cross-border association with groups of high-high or low-low clustering present for all indicators. This analysis facilitates the identification of hotspots of low coverage or high need and can be used to allocate resources effectively to reduce health inequities between and within countries.