  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.
