Using agent-based modeling to compare strategies that can reduce rural-urban disparities in cardiovascular disease

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Project Summary Rural-urban disparities in mortality attributable to cardiovascular disease (CVD) have widened in the United States during the past several decades. The complex interplay of preventive health care delivery and community-level behavioral and contextual factors contribute to the differences in cardiovascular health between rural and urban residents. Recently, systems science and simulation modeling have played an important role in the evaluation, selection, and implementation of evidence-based interventions. However, existing models do not account for either the specific characteristics of populations living in rural communities, or the health care services and other contextual factors of these communities. We propose to use agent-based modeling -- a systems science approach that incorporates data from various sources and simulates real-world clinical or community settings -- to help disentangle these complexities, elucidate causal pathways, and identify potentially effective interventions in rural communities. Our long-term goal is to find effective clinical and public health solutions to reduce rural-urban disparities in cardiovascular health among rural communities in Georgia and New York. Taking an integrated preventive health care and community perspective, we will accomplish our specific aims using an agent-based model of community-based CVD prevention and test the effectiveness of the following interventions at the rural county level. First, we will estimate the health impact of improving health care delivery and access using home-based telemonitoring programs and expanding insurance coverage, focusing on three major CVD risk factors: hypertension, diabetes and high cholesterol (Aim 1). Second, we will estimate the health impact of public health interventions, including improving the food environment, community-based health promotion, and increasing tobacco taxes for reducing four important lifestyle factors related with CVD: obesity, unhealthy diet, physical inactivity, and smoking (Aim 2). In Aim 3, we will use the CVD Policy Model, a well- validated US population-based CVD epidemiology simulation model and translate projected beneficial effects on the seven risk factors and lifestyles tested in Aim 1 and 2 into downstream impact on CVD events. This will allow us to assess the potential of these individual or combined interventions on rural-urban disparities in the incidence and mortality of CVD and direct medical costs at the state level. The proposed research is innovative because we develop a policy simulation model to inform decision-making for health care and public health management of CVD in rural counties, integrating clinical with community systems to find the most effective evidence-based intervention.