Estimating and Forecasting the Smoking-Attributable Mortality Fraction for Both Genders Jointly in Over 60 Countries

TL;DR

This paper develops a Bayesian hierarchical model to estimate and forecast the smoking-attributable mortality fraction across over 60 countries for both genders, providing insights into smoking epidemics and mortality projections.

Contribution

It introduces a novel Bayesian hierarchical approach to jointly estimate and forecast smoking-attributable mortality in multiple countries and genders, improving predictive accuracy and uncertainty quantification.

Findings

Strong, consistent patterns of SAF evolution across countries

Model provides accurate out-of-sample forecasts

Forecast intervals are well calibrated

Abstract

Smoking is one of the preventable threats to human health and is a major risk factor for lung cancer, upper aero-digestive cancer, and chronic obstructive pulmonary disease. Estimating and forecasting the smoking attributable fraction (SAF) of mortality can yield insights into smoking epidemics and also provide a basis for more accurate mortality and life expectancy projection. Peto et al. (1992) proposed a method to estimate the SAF using the lung cancer mortality rate as an indicator of exposure to smoking in the population of interest. Here we use the same method to estimate the all-age SAF (ASAF) for both genders for over 60 countries. We document a strong and cross-nationally consistent pattern of the evolution of the SAF over time. We use this as the basis for a new Bayesian hierarchical model to project future male and female ASAF from over 60 countries simultaneously. This gives forecasts as well as predictive distributions that can be used to find uncertainty intervals for any quantities of interest. We assess the model using out-of-sample predictive validation, and find that it provides good forecasts and well calibrated forecast intervals.