About the Tobacco Control Policy tool
The TCP (Tobacco Control Policy) tool allows users to interactively examine the effects of different tobacco control policies using a policy simulation model.
The model draws upon existing research on the effects of tobacco policies on smoking behavior to simulate population health outcomes. The results compare smoking and mortality outcomes under a baseline scenario ('status quo') with those under your specified policy scenario. For the baseline 'status quo' scenario, the model assumes that current rates for smoking initiation and cessation remain constant for future birth cohorts. This policy model is an extension of the CISNET Smoking History Generator (SHG).
The Smoking History Generator (SHG) policy module
All CISNET lung cancer models are based on inputs from the Smoking History Generator (SHG), which simulates detailed individual-level life and smoking histories: birth, and probabilities of smoking initiation, smoking cessation, and death. The SHG relies on a variety of data sources, including the National Health Interview Survey, to determine the probability that a never smoker becomes a smoker and that a smoker quits per year. These probabilities are specific to an individual’s gender, age, and year of birth. Mortality rates and the relative risks of death by smoking status are then used to generate the probability of death based on age, gender, birth cohort, and smoking status. From these individual simulations, the SHG is able to model smoking prevalence and deaths in the US population. More information about the Smoking History Generator and its applications1-3 can be found on the CISNET Publication Support and Modeling Resources website.
The SHG policy module extends the SHG to modify each individual’s response to different tobacco control interventions by modifying the probability of smoking initiation and the probability of quitting. The modifiers used for the policy module are the policy’s effects on that individual. These modifiers account for different policy effects by age and for the tendency for the impact of a policy to decline over time. Note that the model only simulates the impact of a policy on smoking behavior, and does not consider the health benefits of reduced secondhand smoke exposure.
Policy scenarios simulate the potential impact of interventions at the individual level, and this information is aggregated to evaluate the effect of a specific policy on adult smoking prevalence, the number of life-years gained, and the number of deaths avoided. To date, the SHG policy module has been developed for four types of tobacco control policies: smoke-free air laws, increasing cigarette taxes, raising the minimum age of legal access to tobacaco (MLA), and increasing the level of tobacco control program expenditures.
To make the TCP tool useful to decision-makers at the state level, model results have been scaled to approximate 1) the size of each state’s population according to population estimates from the Census Bureau, and 2) smoking rates within each state based on prevalence estimates from the Behavioral Risk Factor Surveillance System.
- Holford TR, Meza R, McKay LA, Clarke L, Racine B, Meza R, Land S, Jeon J, Feuer EJ. Patterns of birth cohort-specific smoking histories, 1965-2009. Am J Prev Med 2014;46(2):e31-7. [PubMed]
- Moolgavkar SH, Holford TR, Levy DT, Kong CY, Foy M, Clarke L, Jeon J, Hazelton WD, Meza R, Schultz F, McCarthy W, Boer R, Gorlova O, Gazelle GS, Kimmel M, McMahon PM, de Koning HJ, Feuer EJ. Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975-2000. J Natl Cancer Inst2012 Apr 4;104(7):541-8. Epub 2012 Mar 14. [PubMed]
- Jeon J, Meza R, Krapcho M, Clarke LD, Byrne J, Levy DT. Actual and counterfactual smoking prevalence rates in the U.S. population via microsimulation. Risk Anal. 2012;32:S51–S68. [PubMed]