Bayesian Forecasting of Mortality Rates for Small Areas Using Spatiotemporal Models.
Demography
; 61(2): 439-462, 2024 Apr 01.
Article
en En
| MEDLINE
| ID: mdl-38482996
ABSTRACT
Estimation and prediction of subnational mortality rates for small areas are essential planning tools for studying health inequalities. Standard methods do not perform well when data are noisy, a typical behavior of subnational datasets. Thus, reliable estimates are difficult to obtain. I present a Bayesian hierarchical model framework for prediction of mortality rates at a small or subnational level. By combining ideas from demography and epidemiology, the classical mortality modeling framework is extended to include an additional spatial component capturing regional heterogeneity. Information is pooled across neighboring regions and smoothed over time and age. To make predictions more robust and address the issue of model selection, a Bayesian version of stacking is considered using leave-future-out validation. I apply this method to forecast mortality rates for 96 regions in Bavaria, Germany, disaggregated by age and sex. Uncertainty surrounding the forecasts is provided in terms of prediction intervals. Using posterior predictive checks, I show that the models capture the essential features and are suitable to forecast the data at hand. On held-out data, my predictions outperform those of standard models lacking a regional component.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Teorema de Bayes
Límite:
Humans
País/Región como asunto:
Europa
Idioma:
En
Revista:
Demography
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania
Pais de publicación:
Estados Unidos