|
Activity Number:
|
158
- Statistical Demography
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
|
|
Sponsor:
|
Social Statistics Section
|
|
Abstract #309772
|
|
|
Title:
|
ACCOUNTING for SMOKING in FORECASTING MORTALITY and LIFE EXPECTANCY
|
|
Author(s):
|
Yicheng Li* and Adrian Raftery
|
|
Companies:
|
University of Washington and University of Washington
|
|
Keywords:
|
Smoking attributable fraction;
Bayesian hierarchical model;
Life expectancy;
Probability Forecast
|
|
Abstract:
|
Smoking is one of the main risk factors that has aff ected 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 di fferences 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 genders and for over 60 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 eff ect) into life expectancy forecast through the use of the age-speci c 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. Improvements in forecast accuracy and model calibration based on the new method are observed by out-of-sample validation compared with four other commonly used methods for life expectancy forecasting.
|
Authors who are presenting talks have a * after their name.
