Evaluating Comprehensive Smoke Free Policy at the Community Level Using Systems Modeling
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Introduction: Advocating for comprehensive smoke-free policies is a priority for many community-based organizations. However, it is difficult to appeal to law makers and gain community support without being able to estimate the population-level health outcomes that could result from new policy. Hypothesis: We expect that by using agent based modeling, we can estimate the effects of comprehensive smoke-free policy in a way that is useful for community-level decision making. Methods: Using an agent-based model, we were able to estimate the potential effects of comprehensive smoke-free policies both Arlington and Mesquite, Texas. The model simulates the intervention effects based on established effect sizes from published literature. The simulation was conducted in 2017 and was used to determine the effects of policy change on reducing CVD risk over a 10- and 20-year periods. Results: In Arlington, Texas, the smoke-free policy was estimated to decrease population rates for myocardial infarction from 4.9% to 4.7%, stroke from 3.0% to 2.7%, and diabetes from 20.3% to 20.2% over 10 years. The effect of the comprehensive smoke free policy was more pronounced over a 20-year period, with all effects being statistically significant (p<.001). In Mesquite, Texas, the policy did not have a significant impact on the proportion of population experiencing stroke in 10 years (p=.107). However, in 20 years, the policy was shown to decrease the rate of diabetes from 29.5% to 28.4%, myocardial infarction from 8.5% to 7.9%, and stroke from 3.3% to 3.0%, all of which are statistically significant (p<.001). Conclusions: This data is helpful in understanding the potential community-level impact of policy on cardiovascular health over time. Population-level health interventions are powerful tools, however, the length of time that they take to show an impact limits uptake. By using systems modeling, we can estimate the potential long-term impacts of policies and garner intervention support in a timelier manner. In conclusion, this tool can be used to build support for smoke-free policies or other CVD interventions in a variety of communities.
