A Flexible Bayesian Model for Estimating Subnational Mortality

TL;DR

This paper introduces a Bayesian hierarchical model that accurately estimates subnational mortality rates by pooling data across regions and smoothing over time, addressing challenges posed by small populations and high variability.

Contribution

The paper presents a novel Bayesian model that leverages age pattern characteristics and principal components to improve subnational mortality estimation.

Findings

Reasonable estimates and uncertainty levels in simulated US county data

Effective application to real French department data

Enhanced understanding of health disparities at subregional levels

Abstract

Reliable mortality estimates at the subnational level are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations, where the stochastic variation in death counts is relatively high, and so the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when the model is applied to both simulated data which mimic US counties, and real data for French departments. The estimates produced by the model have direct applications to the study of subregional health patterns and disparities.