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

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa
Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa
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.
Hierink, Fleur
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Boo, Gianluca
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Macharia, Peter
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Ouma, Paul
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Timoner, Pablo
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Levy, Marc
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Tschirhart, Kevin
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Leyk, Stefan
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Oliphant, Nicholas
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Tatem, Andrew
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Ray, Nicolas
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Hierink, Fleur
809752f5-fd2e-40b5-a2dc-40f5df552298
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
Macharia, Peter
9e88c6b8-5c52-40a6-889d-4849cba446fd
Ouma, Paul
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Timoner, Pablo
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Levy, Marc
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Tschirhart, Kevin
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Leyk, Stefan
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Oliphant, Nicholas
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Tatem, Andrew
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Ray, Nicolas
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Hierink, Fleur, Boo, Gianluca, Macharia, Peter, Ouma, Paul, Timoner, Pablo, Levy, Marc, Tschirhart, Kevin, Leyk, Stefan, Oliphant, Nicholas, Tatem, Andrew and Ray, Nicolas (2022) Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa. (doi:10.21203/rs.3.rs-1514074/v1).

Record type: Other

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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Published date: 28 April 2022

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Local EPrints ID: 457402
URI: http://eprints.soton.ac.uk/id/eprint/457402
PURE UUID: 2232a035-9169-478f-b21c-dd4b29d1e49e
ORCID for Gianluca Boo: ORCID iD orcid.org/0000-0002-4078-8221
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 07 Jun 2022 16:36
Last modified: 17 Mar 2024 03:29

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Contributors

Author: Fleur Hierink
Author: Gianluca Boo ORCID iD
Author: Peter Macharia
Author: Paul Ouma
Author: Pablo Timoner
Author: Marc Levy
Author: Kevin Tschirhart
Author: Stefan Leyk
Author: Nicholas Oliphant
Author: Andrew Tatem ORCID iD
Author: Nicolas Ray

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