![[ Visit Client Website ]](images/banner.gif)
Schedule:
Thursday, June 2, 2016
Pacific N/O (Hyatt Regency San Francisco)
* noted as presenting author
Introduction: Legislation restricting sales and advertising of cigarettes have been successful to some degree in reducing smoking rates in the US. However, there remains significant progress to be made. For example, North Dakota recently rejected bills that would increase the excise tax from 44¢/pack (the third lowest in the country) to $2/pack. Some research suggests that price increases are one of the most effective ways to reduce youth smoking rates; however, these studies are subject to the limitations and uncertainties of observational data analysis. System dynamics modeling has the potential to overcome these limitations through simulations.
Methods: A system dynamics model was developed representing adolescent smoking and its system of hypothesized causes, including parental smoking, nicotine dependence, social pressures, and advertising. The system was calibrated to represent North Dakota, using publicly available survey data. Once the simulation model replicated historical behavior of youth smoking rates, three policies were simulated: 1) increasing excise taxes; 2) informational campaigns; and 3) increasing compliance of existing laws.
Results: All three policies were effective at reducing the youth smoking rate based on simulations. Increasing excise taxes produced the largest decline in simulated youth smoking rates, followed by enforcing existing laws, followed by informational campaigns. A cost-effectiveness analysis that weighed the cumulative healthcare costs against the implementation costs suggested that the excise tax increase is the most cost-effective strategy.
Conclusions: In a system dynamics simulation framework tailored to model North Dakota, increasing the excise taxes on cigarettes was found to be the most effective policy for reducing the youth smoking rate. System dynamics modeling can offer valuable insights: simulations allow rapid, inexpensive, and ethical testing of causal hypotheses within a complex system.