A stratified age-period-cohort model for spatial heterogeneity in all-cause mortality

TL;DR

This paper introduces a Bayesian hierarchical stratified age-period-cohort model to analyze spatial heterogeneity in mortality rates, addressing identifiability issues and enabling direct comparison across groups.

Contribution

It develops a novel stratified APC model with matrix-normal priors for better assessment of heterogeneity in mortality data.

Findings

Model effectively captures spatial heterogeneity in mortality.

Bayesian hierarchical approach improves comparison across regions.

Application to EU data demonstrates model's practical utility.

Abstract

A common goal in modeling demographic rates is to compare two or more groups. For ex- ample comparing mortality rates between men and women or between geographic regions may reveal health inequalities. A popular class of models for all-cause mortality as well as incidence of specific diseases like cancer is the age-period-cohort (APC) model. Extending this model to the multivariate setting is not straightforward because the univariate APC model suffers from well-known identifiability problems. Often APC models are fit separately for each strata, and then comparisons are made post hoc. This paper introduces a stratified APC model to directly assess the sources of heterogeneity in mortality rates using a Bayesian hierarchical model with matrix-normal priors that share information on linear and nonlinear aspects of the APC effects across strata. Computing, model selection, and prior specification are addressed and the model is then applied to all-cause mortality data from the European Union.