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

Abstract

Access to health care is imperative to health equity and well-being. Geographic access to health care can be modelled by combining different spatial datasets, among others, on 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 explore the effect of six among the most popular gridded population datasets on coverage statistics at different administrative levels. We found differences in accessibility coverage of more than 70% at the sub-national level, based on a one-hour travel time threshold. Differences are significant in large and sparsely populated administrative units, dramatically shaping patterns of health care accessibility at the national and sub-national level. The results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed in policy-making.

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Identifiers

ORCID for Gianluca Boo: orcid.org/0000-0002-4078-8221

ORCID for Andrew Tatem: orcid.org/0000-0002-7270-941X

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