Using geographical analysis to identify child health inequality in sub-Saharan Africa

PloS One
Jennifer YourkavitchStephen Delgado

Abstract

One challenge to achieving Millennium Development Goals was inequitable access to quality health services. In order to achieve the Sustainable Development Goals, interventions need to reach underserved populations. Analyzing health indicators in small geographic units aids the identification of hotspots where coverage lags behind neighboring areas. The purpose of these analyses is to identify areas of low coverage or high need in order to inform effective resource allocation to reduce child health inequity between and within countries. Using data from The Demographic and Health Survey Program surveys conducted in 27 selected African countries between 2010 and 2014, we computed estimates for six child health indicators for subnational regions. We calculated Global Moran's I statistics and used Local Indicator of Spatial Association analysis to produce a spatial layer showing spatial associations. We created maps to visualize sub-national autocorrelation and spatial clusters. The Global Moran's I statistic was positive for each indicator (range: 0.41 to 0.68), and statistically significant (p <0.05), suggesting spatial autocorrelation across national borders, and highlighting the need to examine health indicators both across coun...Continue Reading

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Citations

Aug 21, 2019·International Journal of Environmental Research and Public Health·Chenjing FanWensheng Miao
Jul 25, 2019·Nature Medicine·Natalia V BhattacharjeeSimon I Hay
May 2, 2020·International Journal of Environmental Research and Public Health·Samuel MandaRobert Bergquist
Jan 20, 2021·PloS One·Benjamin-Samuel Schlüter, Bruno Masquelier
Jun 22, 2021·AAS Open Research·Moses AloboUNKNOWN African Academy of Sciences Maternal and Neonatal Health Working Group for Africa
Dec 4, 2021·Scientific Reports·Anaïs LadoyStéphane Joost

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Software Mentioned

LISA
Spatial Autocorrelation (
Local Indicator Spatial Association ( LISA ) Moran
GeoDa
ArcGIS
Stata
Spatial Statistics Tools

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