Smoking remains the leading cause of preventable death in the USA but can be reduced through policy interventions. Computational models of smoking can provide estimates of the projected impact of tobacco
control policies and can be used to inform public health decision making. We outline a protocol for simulating the
effects of tobacco policies on population health outcomes.
Methods and analysis
We extend the Smoking History Generator (SHG), a microsimulation model based on data from the National Health Interview Surveys, to evaluate the effects of tobacco control policies on projections of smoking prevalence and mortality in the USA. The SHG simulates individual life trajectories including smoking initiation, cessation and mortality. We illustrate the application of the SHG policy module for four types of tobacco control policies at the national and state levels: smoke-free air laws, cigarette taxes, increasing tobacco control programme expenditures and raising the minimum age of legal access to tobacco. Smoking initiation and cessation rates are modified by age, birth cohort, gender and years since policy implementation. Initiation and cessation rate modifiers are adjusted for differences across age groups and the level of existing policy coverage. Smoking prevalence, the number of population deaths avoided, and life-years gained are calculated for each policy scenario at the national and state levels. The model only considers direct individual benefits through reduced smoking and does not consider benefits through reduced exposure to secondhand smoke.