A flexible Bayesian model for estimating subnational mortality

Monica Alexander, University of California, Berkeley
Emilio Zagheni, University of Washington, Seattle
Magali Barbieri, Institut National d'Études Démographiques (INED) and University of California, Berkeley

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. Preliminary testing shows reasonable estimates and uncertainty levels when the model was applied to both simulated data which mimic US counties, and on real data for French departments. The estimates produced by the model have direct applications to the study of subregional health patterns and disparities.

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Presented in Session 86: Modelling mortality