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Accounting for smoking in forecasting mortality and life expectancy
- Source :
- Ann Appl Stat
- Publication Year :
- 2021
- Publisher :
- Institute of Mathematical Statistics, 2021.
-
Abstract
- Smoking is one of the main risk factors that has affected human mortality and life expectancy over the past century. Smoking accounts for a large part of the nonlinearities in the growth of life expectancy and of the geographic and sex differences in mortality. As Bongaarts (2006) and Janssen (2018) suggested, accounting for smoking could improve the quality of mortality forecasts due to the predictable nature of the smoking epidemic. We propose a new Bayesian hierarchical model to forecast life expectancy at birth for both sexes and for 69 countries with good data on smoking-related mortality. The main idea is to convert the forecast of the non-smoking life expectancy at birth (i.e., life expectancy at birth removing the smoking effect) into life expectancy forecast through the use of the age-specific smoking attributable fraction (ASSAF). We introduce a new age-cohort model for the ASSAF and a Bayesian hierarchical model for non-smoking life expectancy at birth. The forecast performance of the proposed method is evaluated by out-of-sample validation compared with four other commonly used methods for life expectancy forecasting. Improvements in forecast accuracy and model calibration based on the new method are observed.
- Subjects :
- FOS: Computer and information sciences
Statistics and Probability
business.industry
Calibration (statistics)
media_common.quotation_subject
05 social sciences
Accounting
Statistics - Applications
01 natural sciences
Article
3. Good health
010104 statistics & probability
Modeling and Simulation
0502 economics and business
Attributable risk
Life expectancy
Bayesian hierarchical modeling
Applications (stat.AP)
Quality (business)
050207 economics
0101 mathematics
Statistics, Probability and Uncertainty
business
Psychology
media_common
Subjects
Details
- ISSN :
- 19326157
- Volume :
- 15
- Database :
- OpenAIRE
- Journal :
- The Annals of Applied Statistics
- Accession number :
- edsair.doi.dedup.....112803c6bfe9c8909c33f242fd59beed
- Full Text :
- https://doi.org/10.1214/20-aoas1381