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Estimating and Forecasting the Smoking-Attributable Mortality Fraction for Both Sexes Jointly in 69 Countries

Yicheng Li and Adrian Elmes Raftery

February 2019 CSSS Working Paper #160

Abstract

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 sexes for 69 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 69 countries simultaneously. This gives forecasts as well as predictive distributions that can be used to fi nd uncertainty intervals for any quantities of interest. We assess the model using out-of-sample predictive validation, and fi nd that it provides good forecasts and well calibrated forecast intervals.

Keywords: Smoking attributable fraction, Peto-Lopez method, Bayesian hierarchical model, double logistic curve, probabilistic projection