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 and Lopez 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. We use the same method to estimate the all-age SAF (ASAF) for both sexes for 69 countries. In this talk, we present a strong and cross-nationally consistent pattern of the evolution of the ASAF 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
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.