For example, there is a positive feedback loop between smoking behavior and nicotine dependence: smoking behavior leads to the development of nicotine dependence symptoms and nicotine dependence in turn increases and perpetuates later smoking behavior. A further challenge in existing policy research is the limited ability to account for feedback in complex systems. Thus, confounding is a pervasive challenge in accurately evaluating anti-smoking policies. Specifically, it is rare that an appropriate control group is available for a given policy and even in this case, the true effects of the policy itself are difficult to tease apart from other influences of the outcome, such as larger economic trends.
Ī prominent challenge in evaluating the effectiveness of these policies is the uncertainty in their true causal effect on smoking rates, which stems from the limitations of observational data. A related issue is retailer compliance enforcement efforts aimed at retailers are successful at reducing sales to minors, but this does not necessarily translate into reduced smoking. Another common policy is legislating age restrictions on purchasing cigarettes, though the evidence is mixed as to whether age restrictions in isolation are actually effective in decreasing smoking prevalence. Successful strategies for reducing smoking rates include excise taxes on cigarettes and comprehensive tobacco control programs (which include media campaigns, cessation resources, and interventions). This is a critical period during which to intervene to reduce the smoking-related burden in the general adult population. However, there is still progress to be made, particularly among adolescents since the majority of smokers initiate in their adolescent years. Various anti-smoking policies and programs have been effective at reducing smoking rates. More generally, system dynamics offers an important complement to conventional analysis of observational data. System dynamics modeling suggests that increasing cigarette excise taxes are particularly effective at reducing adolescent smoking rates. The effects of each policy were additive. Other policies reduced smoking by a much lesser degree (7.0 and 3.2% for comprehensive tobacco control program funding and retailer compliance, respectively). The policy model showed that increasing taxes to $2.20 per pack starting in 2015 was the most effective of the three policies, producing a 32.6% reduction in youth smoking rate by 2032. The explanatory model successfully replicated historical trends in adolescent smoking behavior in North Dakota from 1992 to 2014. Three different policies were simulated: 1) an increase in cigarette excise taxes 2) increased funding for CDC-recommended comprehensive tobacco control programs and 3) enforcement of increased retailer compliance with age restrictions on cigarette sales.
MethodsĪn explanatory model was built to replicate historical trends in the youth smoking rate. The current study utilizes system dynamics to model the determinants of youth smoking and simulate effects of anti-smoking policies in the context of North Dakota, a state with one of the lowest cigarette tax rates in the USA.
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