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

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.