Estimating Under Five Mortality in Space and Time in a Developing World Context

TL;DR

This paper introduces new Bayesian hierarchical models to estimate under-5 mortality rates across regions and years in Kenya, accounting for survey design and HIV bias, revealing significant spatial and temporal variability.

Contribution

The paper develops innovative models for spatial-temporal U5MR estimation that incorporate survey design and HIV bias adjustments, applied to Kenyan data from 1980-2014.

Findings

Sharp decline in U5MR from 1980 to 2014

Significant subnational variability in mortality rates

Potential covariates include climate and malaria prevalence

Abstract

Accurate estimates of the under-5 mortality rate (U5MR) in a developing world context are a key barometer of the health of a nation. This paper describes new models to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is, wishing to estimate U5MR across regions and years, and to investigate the association between the U5MR and spatially-varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980 - 2014 using data from demographic health surveys (DHS). We use a binomial likelihood with fixed effects for the urban/rural stratification to account for the complex survey design. We carry out smoothing using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in U5MR in the period 1980 - 2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. Temperature, precipitation and a measure of malaria infection prevalence were candidates for inclusion in the covariate model.