Jointly Estimating Subnational Mortality for Multiple Populations

TL;DR

This paper introduces a Bayesian hierarchical model using principal components to jointly estimate subnational mortality rates across multiple populations, effectively capturing correlations and structural differences, especially in small populations.

Contribution

It extends principal component-based Bayesian methods to model correlations across subpopulations, improving mortality estimation in small or divided populations.

Findings

Model successfully extracts meaningful mortality patterns.

Ancillary correlation parameters reveal convergence/divergence trends.

Approach performs well on US county-level data.

Abstract

Understanding patterns in mortality across subpopulations is essential for local health policy decision making. One of the key challenges of subnational mortality rate estimation is the presence of small populations and zero or near zero death counts. When studying differences between subpopulations, this challenge is compounded as the small populations are further divided along socioeconomic or demographic lines. In this paper, we build on principal component-based Bayesian hierarchical approaches for subnational mortality rate estimation to model correlations across subpopulations. The principal components identify structural differences between subpopulations, and coefficient and error models track the correlations between subpopulations over time. We illustrate the use of the model in a simulation study as well as on county-level sex-specific US mortality data. We find that results from the model are reasonable and that it successfully extracts meaningful patterns in US sex-specific mortality. Additionally, we show that ancillary correlation parameters are a useful tool for studying the convergence and divergence of mortality patterns over time.