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J R Soc Interface. 2015 Apr 6;12(105). pii: 20150073. doi: 10.1098/rsif.2015.0073.

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

Author information

1
Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield Southampton, UK vaalg10@soton.ac.uk.
2
Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield Southampton, UK.
3
Department of Zoology, University of Oxford, Oxford, UK.
4
Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield Southampton, UK Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Flowminder Foundation, Stockholm, Sweden.

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 Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings.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.

KEYWORDS:

demography; geo-statistics; mapping

PMID:
25788540
PMCID:
PMC4387535
DOI:
10.1098/rsif.2015.0073
[Indexed for MEDLINE]
Free PMC Article

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