Cardiovascular disease (CVD) is a major public health problem, particularly for patients and neighborhoods with low socioeconomic status. Growing evidence has encouraged health systems to explore the use of community health workers (CHWs) to improve screening and management of CVD and cardiometabolic risks (CMR) among patients and communities with disparate disease burden. Yet broad systems change has been limited by a lack of outcome and cost data of sufficient scope and duration. We propose using innovative systems-science methods to model and analyze the effectiveness and cost of individually-targeted and neighborhood-targeted CHW interventions to reduce CVD disparities. The results will provide sought-after guidance for health systems implementation of optimally targeted programs to improve population health disparities. Public health in general will benefit significantly from the creation and validation of a systems-based framework for targeting communities experiencing CVD disparities with coordinated clinic- and community-based CHW programs. By merging spatial hotspot analysis with established microsimulation, this study's innovative methodology will provide novel and actionable knowledge based on simulated large- scale implementation of CHW interventions currently supported by small-scale trial data, yielding a replicable means for health systems and policymakers to identify, model, and improve health disparities in the future. The project has five stages. Stage 1: Data collection, geocoding, and spatial aggregation of de-identified data; Stage 2: identification of geographic hotspots of CVD and spatial statistical analysis to determine factors of neighborhood risk; Stage 3: adaptation of our established microsimulation model to the disease, demographic, and social characteristics of hotspots and to the parameters of CHW interventions; Stage 4: microsimulation modeling of clinic-based CHW interventions targeted to patients with individual and neighborhood CVD risks and community-based CHW programs targeted to neighborhoods according to disparate burdens of CMR and CVD; Stage 5: comparison of the disease outcomes, geographic clustering of CVD, cost-effectiveness, and sustainability for CHW interventions for CMR and CVD along 2-, 10-, and 20-year time frames. We will collect, de-identify, and aggregate service data from the 2 safety-net health care systems serving most patients and high-risk communities in a Midwestern metropolitan area. That data will provide a granular view of the neighborhood distribution of CVD and CMR illness burden across the metropolitan area to allow for robust spatial analysis and microsimulation modeling. Microsimulation analyses informed by empiric data will allow us to compare the effects of targeted clinic- and community-based CHW programs on disparities within communities across the metropolitan area. By melding spatial hotspot analysis with microsimulation in an innovative systems-science approach, this study will provide useful insights into the impact, cost-effectiveness, and sustainability of programs to reduce CVD disparities that are currently supported only by pilot data.
This project will provide evidence for health systems change to address community health by (1) using geographic hotspot analysis to identify communities with disparate cardiovascular disease (CVD) burden and neighborhood risk, then (2) populating a detailed microsimulation model with empirical data from those populations, in order to (3) compare the impact of individual and neighborhood targeting of community health worker efforts on CVD outcomes and disparities, cost-effectiveness and financial sustainability over short-, intermediate-, and long-term horizons.
