Open Access
March 2021 Accounting for smoking in forecasting mortality and life expectancy
Yicheng Li, Adrian E. Raftery
Author Affiliations +
Ann. Appl. Stat. 15(1): 437-459 (March 2021). DOI: 10.1214/20-AOAS1381


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.


Download Citation

Yicheng Li. Adrian E. Raftery. "Accounting for smoking in forecasting mortality and life expectancy." Ann. Appl. Stat. 15 (1) 437 - 459, March 2021.


Received: 1 November 2019; Revised: 1 July 2020; Published: March 2021
First available in Project Euclid: 18 March 2021

Digital Object Identifier: 10.1214/20-AOAS1381

Keywords: Bayesian hierarchical model , life expectancy , probabilislic projection , Smoking attributable fraction

Rights: Copyright © 2021 Institute of Mathematical Statistics

Vol.15 • No. 1 • March 2021
Back to Top