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

Foreman, Kyle, Li, Guangquan, Best, Nicky and Ezzati, Majid (2017) Small area forecasts of cause-specific mortality: application of a Bayesian hierarchical model to US vital registration data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66 (1). pp. 121-139. ISSN 0035-9254

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Official URL: http://dx.doi.org/10.1111/rssc.12157

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.

Item Type: Article
Uncontrolled Keywords: Bayesian hierarchical models; Cause-specific mortality; Forecasting methods; Population health; Spatiotemporal modelling
Subjects: B900 Others in Subjects allied to Medicine
G100 Mathematics
G300 Statistics
Department: Faculties > Engineering and Environment > Mathematics and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 09 Jun 2016 13:50
Last Modified: 31 May 2017 14:49
URI: http://nrl.northumbria.ac.uk/id/eprint/27085

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