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Quantifying the effects of using detailed spatial demographic data on health metrics: a systematic analysis for the AfriPop, AsiaPop, and AmeriPop projects

Quantifying the effects of using detailed spatial demographic data on health metrics: a systematic analysis for the AfriPop, AsiaPop, and AmeriPop projects
Quantifying the effects of using detailed spatial demographic data on health metrics: a systematic analysis for the AfriPop, AsiaPop, and AmeriPop projects
Background: the Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress towards their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, prompting the development of sophisticated cartographic techniques for mapping and modelling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition are lacking, prompting many health metric studies to overlook the substantial demographic variations that exist subnationally and to merely apply national-level adjustments.

Methods: here, we describe the development of high-resolution age and sex structured spatial population datasets for Africa, Asia, and Latin America in 2000—15, built from millions of measurements mapped to more than 200 000 subnational units, and originating from censuses, census microdata, and household surveys.

Findings: we analysed the substantial variations seen within countries, by settlement type, and across the continents for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and found that substantial differences in various MDG-related health and development indicators can result through using only national-level statistics compared with accounting for subnational variation.

Interpretation: progress towards meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on health metrics. Further details and data are available through the project websites: www.afripop.org, www.asiapop.org, and www.ameripop.org.

Funding: AJT acknowledges funding support from the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health, and is also supported by grants from NIH/NIAID (U19AI089674) and the Bill & Melinda Gates Foundation (#49446 and #1032350)
0140-6736
S142
Tatem, Andrew J.
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Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Stevens, Forrest R.
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Patel, Nirav N.
ffe57351-45aa-4f43-a329-fd8f6cdf24db
Jia, Peng
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Pandey, Aman
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Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Patel, Nirav N.
ffe57351-45aa-4f43-a329-fd8f6cdf24db
Jia, Peng
dbe85528-4e0b-47c5-aa2e-189cda38d131
Pandey, Aman
0d626001-9a02-414a-97e7-38d430233926
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856

Tatem, Andrew J., Gaughan, Andrea E., Stevens, Forrest R., Patel, Nirav N., Jia, Peng, Pandey, Aman and Linard, Catherine (2013) Quantifying the effects of using detailed spatial demographic data on health metrics: a systematic analysis for the AfriPop, AsiaPop, and AmeriPop projects. The Lancet, 381, S142. (doi:10.1016/S0140-6736(13)61396-3).

Record type: Article

Abstract

Background: the Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress towards their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, prompting the development of sophisticated cartographic techniques for mapping and modelling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition are lacking, prompting many health metric studies to overlook the substantial demographic variations that exist subnationally and to merely apply national-level adjustments.

Methods: here, we describe the development of high-resolution age and sex structured spatial population datasets for Africa, Asia, and Latin America in 2000—15, built from millions of measurements mapped to more than 200 000 subnational units, and originating from censuses, census microdata, and household surveys.

Findings: we analysed the substantial variations seen within countries, by settlement type, and across the continents for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and found that substantial differences in various MDG-related health and development indicators can result through using only national-level statistics compared with accounting for subnational variation.

Interpretation: progress towards meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on health metrics. Further details and data are available through the project websites: www.afripop.org, www.asiapop.org, and www.ameripop.org.

Funding: AJT acknowledges funding support from the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health, and is also supported by grants from NIH/NIAID (U19AI089674) and the Bill & Melinda Gates Foundation (#49446 and #1032350)

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More information

Published date: 2013
Organisations: Global Env Change & Earth Observation, WorldPop, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 353891
URI: https://eprints.soton.ac.uk/id/eprint/353891
ISSN: 0140-6736
PURE UUID: ee7daf55-1eb0-468b-87ea-6d940b6f4228
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 24 Jun 2013 10:29
Last modified: 06 Oct 2018 00:31

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Contributors

Author: Andrew J. Tatem ORCID iD
Author: Andrea E. Gaughan
Author: Forrest R. Stevens
Author: Nirav N. Patel
Author: Peng Jia
Author: Aman Pandey
Author: Catherine Linard

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