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

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 Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings.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

Figure 1.

(a) The distribution of cluster-level data from the national representative household surveys (the DHS, MIS and LSMS-AIS) and (b) the associated covariance function from SPDE (black dots) for the data (n = 1624) with superimposed theoretical Matérn model (red line) showing only slight deviation beyond 550 km (or 5°). The x-axis shows the distance in degrees latitude and longitude, whereas the y-axis shows the covariance with scaling parameter log(κ) = −0.47(−1.07 − −0.46) (confidence interval) and smoothing parameter log(τ) = 2.85(2.42–2.85). The model calculated nominal range of influence on the x-axis was approximately 535 km. (Online version in colour.)

Figure 2.

Validation plots showing. (a) Scatter plot of the association between the observed against predictions of the 10% subset data (n = 1624) and (b) semi-variogram plot (y-axis semi-variance and x-axis distance in degrees) and associated envelopes (minimum and maximum range expected by chance in the absence of spatial autocorrelation) of the standardized residuals. The semi-variogram is a measure of autocorrelation with distance. (Online version in colour.)

Figure 3.

(a) Mean predicted percentage of population under the age of 5 years based on model-based geostatistics (b) map of differences (high and low) between the upper and lower limit of predictions (i.e. the 95% Bayesian credible intervals). (Online version in colour.)

Figure 4.

Plot of the estimated percentage of children under the age of 5 years in Nigeria (y-axis) by state (x-axis) from the three different estimates namely: the model-based geostatistics (MBG) approach (red rectangles with Bayesian prediction intervals), the projected census estimates (black circles) and a single UN estimate value for the whole of Nigeria (triangles). The plot has been ordered by the census estimates. (Online version in colour.)

Figure 5.

Comparison of the number of children not vaccinated (y-axis) by state (x-axis) from the three different estimates namely: the model-based geostatistics (MBG) approach (red rectangles with Bayesian prediction intervals), the projected census estimates (black circles) and UN estimates for the whole of Nigeria (triangles). (Online version in colour.)

Figure 6.

Comparison of the number of children not using an ITN (y-axis) by state (x-axis) from the three different estimates namely: the model-based geostatistics (MBG) approach (red rectangles with Bayesian prediction intervals), the projected census estimates (black circles) and UN estimates for the whole of Nigeria (triangles). (Online version in colour.)