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

V A Alegana,E Erbach-Schoenberg,D Weiss,C Pezzulo,A J Tatem,T Bird,P M Atkinson,A Sorichetta

doi:10.1098/rsif.2015.0073

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 Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings.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

Highlights

Age is an important demographic variable that affects disease burden estimates [1] and mortality [2]
The production of health metrics [5,6] and spatial models of processes influenced by demographics [7,8] are increasingly reliant on spatial data on population age-structures
Despite an improvement in gross domestic product [24], the majority of the population still live on less than US$1.25 per day and child mortality indicators are still short of the millennium development goals (MDGs) targets with under-five mortality at 128 per 1000 live births (MDG target is 64 per 1000 live births) and infant mortality at 69 per 1000 live births [25]

Summary

Introduction

Age is an important demographic variable that affects disease burden estimates [1] and mortality [2]. Defining the extent of public health need for specific age-groups and its distribution in space and time are critical to support interventions to combat disease burden, and plan and manage resources effectively This includes interventions such as vaccination [3], insecticide-treated bednets (ITNs) for malaria as well as the delivery of healthcare to underserved populations [4]. The production of health metrics [5,6] and spatial models of processes influenced by demographics [7,8] are increasingly reliant on spatial data on population age-structures To support such efforts, quantitative information on the numbers or proportions of age-groups of interest in space and time is needed because these can vary significantly within and across countries.

Methods

Results

Conclusion