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Demography

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10-10-2017

A Flexible Bayesian Model for Estimating Subnational Mortality

Authors: Monica Alexander, Emilio Zagheni, Magali Barbieri

Published in: Demography | Issue 6/2017

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Abstract

Reliable subnational mortality estimates 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 among which the stochastic variation in death counts is relatively high, and thus 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 are smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when it is applied both to simulated data that mimic U.S. counties and to real data for French départements. The model estimates have direct applications to the study of subregional health patterns and disparities.

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Throughout this article, we refer to the Y _px as principal components for simplicity, even though technically Y _px is the pth vector of principal component loadings.

Data on age-specific mortality rates are available through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) tool (https://wonder.cdc.gov/).

Personal communication to Magali Barbieri by the Division des statistiques régionales, locales et urbaines, INSEE (May 28, 2013). We chose to use French départements data because at the time of writing, data for all U.S. counties were not readily available.

See shiny.demog.berkeley.edu/monicah/French/.

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Title: A Flexible Bayesian Model for Estimating Subnational Mortality
Authors: Monica Alexander
Emilio Zagheni
Magali Barbieri
Publication date: 10-10-2017
Publisher: Springer US
Published in: Demography / Issue 6/2017
Print ISSN: 0070-3370
Electronic ISSN: 1533-7790
DOI: https://doi.org/10.1007/s13524-017-0618-7

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