Small area forecasts of cause-specific mortality: application of a Bayesian hierarchical model to US vital registration data

Kyle Foreman, Guangquan Li, Nicky Best, Majid Ezzati

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Mortality forecasts are typically limited in that they pertain only to national death rates, predict only all-cause mortality or do not capture and utilize the correlation between diseases. We present a novel Bayesian hierarchical model that jointly forecasts cause-specific death rates for geographic subunits. We examine its effectiveness by applying it to US vital statistics data for 1979–2011 and produce forecasts to 2024. Not only does the model generate coherent forecasts for mutually exclusive causes of death, but also it has lower out-of-sample error than alternative commonly used models for forecasting mortality.
Original languageEnglish
Pages (from-to)121-139
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume66
Issue number1
Early online date20 May 2016
DOIs
Publication statusPublished - Jan 2017

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