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 gender differences in mortality. As Bongaarts (Popul. Dev. Rev. 32 (2006) 605–628) and Janssen (Genus 74 (2018) 21) 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 genders and for 69 countries/regions with good data on smoking-related mortality. The main idea is to convert the forecast of the nonsmoking 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 nonsmoking 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.
The authors thank John Bongaarts for helpful discussions and the Associate Editor and two anonymous referees for very helpful comments that greatly improved the manuscript.
Supported by NIH Grants R01 HD054511 and R01 HD070936, and the Center for Advanced Research in the Behavioral Sciences at Stanford University.
"Accounting for smoking in forecasting mortality and life expectancy." Ann. Appl. Stat. 15 (1) 437 - 459, March 2021. https://doi.org/10.1214/20-AOAS1381