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

• Published in: Journal of public health policy Vol. 43; no. 1; pp. 40 - 53
• Main Authors: Garney, Whitney R., Panjwani, Sonya, Garcia, Kristen, Szucs, Leigh E., Primm, Kristin, McLeroy, Kenneth, Li, Yan
• Format: Journal Article
• Language: English
• Published: London Palgrave Macmillan UK 01.03.2022; Palgrave Macmillan
• Subjects: Agent-based models; Bans; Cardiovascular disease; Cardiovascular diseases; Cerebral infarction; Chronic illnesses; Community; Diabetes; Diabetes mellitus; Disease control; Disease prevention; Equality and Human Rights; Health care policy; Health Policy; Heart attacks; Heart diseases; Humans; Intervention; Long term; Long-term effects; Medical Sociology; Mesquite; Mortality; Myocardial infarction; No-smoking policies; Original Article; Patient Protection & Affordable Care Act 2010-US; Policy making; Population; Population policy; Public Health; Simulation; Smoke; Smoke-Free Policy; Smoking; Social Justice; Social Policy; Social Sciences; Sociology; Statistical analysis; Stroke; Systems Analysis; Tobacco; United States - epidemiology; United States; Evaluation; Tobacco; Health policy; Chronic disease; Heart disease
• ISSN: 0197-5897; 1745-655X
• DOI: 10.1057/s41271-021-00332-8

Comprehensive smoke-free policy is a strategy to prevent cardiovascular disease (CVD) at a population-level; however, evaluating their long-term outcomes is difficult. This study used an agent-based model to estimate long-term impacts of a comprehensive smoke-free policy, as it was implemented in two communities, Arlington and Mesquite, Texas. The model predicted the percentage of myocardial infarction (MI), stroke, and diabetes in the population 10 and 20 years following policy adoption. In Arlington, the percentage of the population with these conditions each decreased by approximately 0.5% over 20 years; in Mesquite, the percentage of the population with diabetes, myocardial infarction (MI), and stroke decreased by 1.1%, 0.6%, and 0.3%, respectively, after 20 years. The results were statistically significant ( p  < 0.001). As an evaluation strategy, agent-based modeling can help researchers and practitioners estimate the potential long-term effects of policies and garner intervention support for implementation.