Adaptive Gaussian Markov Random Fields for Child Mortality Estimation

TL;DR

This paper introduces an adaptive Gaussian Markov random field model for estimating child mortality rates, accounting for shocks during crises, and demonstrates its effectiveness through simulations and application to Rwanda's data from 1985-2019.

Contribution

The paper presents a novel adaptive GMRF smoothing model that incorporates knowledge of mortality shocks, improving estimates during crises compared to traditional models.

Findings

Improved U5MR estimates during shocks in simulations.

Effective application to Rwanda's mortality data from 1985-2019.

Model captures mortality shocks during civil war and genocide.

Abstract

The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks in mortality relative to their neighbors, which can lead to oversmoothing of U5MR estimates. In this paper, we develop a spatial and temporal smoothing approach based on Gaussian Markov random field models which incorporate knowledge of these expected shocks in mortality. We demonstrate the potential for these models to improve upon alternatives not incorporating knowledge of expected shocks in a simulation study. We apply these models to estimate U5MR in Rwanda at the national level from 1985-2019, a time period which includes the Rwandan civil war and genocide.