Evaluating community-driven cardiovascular health policy changes in the United States using agent-based modeling

Area of Science:

• Public Health
• Epidemiology
• Health Policy

Background:

• Comprehensive smoke-free policies are crucial for population-level cardiovascular disease (CVD) prevention.
• Evaluating the long-term impact of these policies presents significant challenges.
• Agent-based modeling (ABM) offers a potential solution for assessing policy outcomes.

Purpose of the Study:

• To estimate the long-term effects of comprehensive smoke-free policies on myocardial infarction (MI), stroke, and diabetes.
• To utilize an agent-based model to simulate policy impacts in two Texas communities: Arlington and Mesquite.
• To demonstrate the utility of ABM as an evaluation strategy for public health interventions.

Main Methods:

• An agent-based model was developed to simulate the implementation of comprehensive smoke-free policies.
• The model projected the prevalence of MI, stroke, and diabetes over 10 and 20 years post-policy adoption.
• Data from two communities, Arlington and Mesquite, Texas, were used for the simulation.

Main Results:

• In Arlington, a 0.5% decrease in the population with MI, stroke, and diabetes was observed over 20 years.
• In Mesquite, significant reductions were noted: 1.1% for diabetes, 0.6% for MI, and 0.3% for stroke after 20 years.
• All observed reductions were statistically significant (p < 0.001).

Conclusions:

• Comprehensive smoke-free policies yield measurable long-term reductions in cardiovascular disease and diabetes.
• Agent-based modeling is a valuable tool for predicting the long-term health outcomes of public health policies.
• ABM can support evidence-based decision-making and garner support for policy implementation.