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Fine resolution mapping of population age-structures for health and development applications

Fine resolution mapping of population age-structures for health and development applications
Fine resolution mapping of population age-structures for health and development applications
The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.
demography, geo-statistics, mapping
20150073-[11pp]
Alegana, V.A.
42ca5362-7e95-4d8e-8add-08bef6d57636
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Pezzulo, C.
876a5393-ffbd-479a-9edf-f72a59ca2cb5
Sorichetta, A.
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Weiss, D.
7970ae1f-feb6-4bed-b65c-8bd8489dacd4
Bird, T.
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Erbach-Schoenberg, E.
03b68803-54c5-412c-8def-87299b688577
Tatem, A. J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Alegana, V.A.
42ca5362-7e95-4d8e-8add-08bef6d57636
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Pezzulo, C.
876a5393-ffbd-479a-9edf-f72a59ca2cb5
Sorichetta, A.
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Weiss, D.
7970ae1f-feb6-4bed-b65c-8bd8489dacd4
Bird, T.
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Erbach-Schoenberg, E.
03b68803-54c5-412c-8def-87299b688577
Tatem, A. J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Alegana, V.A., Atkinson, P.M., Pezzulo, C., Sorichetta, A., Weiss, D., Bird, T., Erbach-Schoenberg, E. and Tatem, A. J. (2015) Fine resolution mapping of population age-structures for health and development applications. Journal of the Royal Society Interface, 12 (105), 20150073-[11pp]. (doi:10.1098/rsif.2015.0073). (PMID:25788540)

Record type: Article

Abstract

The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.

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Accepted/In Press date: 25 February 2015
e-pub ahead of print date: 18 March 2015
Published date: April 2015
Keywords: demography, geo-statistics, mapping
Organisations: Global Env Change & Earth Observation, WorldPop, Geography & Environment, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 377083
URI: https://eprints.soton.ac.uk/id/eprint/377083
PURE UUID: bed09d17-1b3b-48a3-a40a-abfb9dbb592a
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for A. Sorichetta: ORCID iD orcid.org/0000-0002-3576-5826
ORCID for A. J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 19 May 2015 10:46
Last modified: 20 Jul 2019 01:20

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Contributors

Author: V.A. Alegana
Author: P.M. Atkinson ORCID iD
Author: C. Pezzulo
Author: A. Sorichetta ORCID iD
Author: D. Weiss
Author: T. Bird
Author: E. Erbach-Schoenberg
Author: A. J. Tatem ORCID iD

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