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Small area forecasts of cause‐specific mortality: application of a Bayesian hierarchical model to US vital registration data
Author(s) -
Foreman Kyle J.,
Li Guangquan,
Best Nicky,
Ezzati Majid
Publication year - 2017
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12157
Subject(s) - bayesian probability , statistics , econometrics , hierarchical database model , bayesian hierarchical modeling , mortality rate , computer science , bayesian inference , data mining , mathematics , demography , sociology
Summary 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.