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Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

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  • Fleur Hierink
  • Gianluca Boo
  • Peter Macharia
  • Paul Ouma
  • Pablo Timoner
  • Marc Levy
  • Kevin Tschirhart
  • Stefan Leyk
  • Nicholas Oliphant
  • Andrew Tatem
  • Nicolas Ray
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Article number117
<mark>Journal publication date</mark>16/09/2022
<mark>Journal</mark>communications medicine
Volume2
Number of pages13
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Background
Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels.

Methods
Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min).

Results
Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels.

Conclusions
The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.