Mid-Career Transition -- Mapping Paths to Success in Achieving Community-Level He

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DESCRIPTION (provided by applicant): This career-development award will fund the investigator's transition from executive leadership to intellectual leadership in the field of healh equity research. For over twenty years, the investigator has contributed to building the Morehouse School of Medicine, one of four historically-black medical schools in the nation and recently ranked #1 in the nation for achieving a social mission. He also led development of the National Center for Primary Care, a research, training, and resource center focused on achieving health equity. This mid-career award will allow him to transition from executive leadership to focus exclusively on research that seeks to build models of success toward achieving health equity in communities across the U.S. The proposed research will provide a roadmap to success in achieving more optimal and equitable asthma outcomes for all children, with broader application to a wide range of chronic disease outcome disparities across all age groups. In the proposed progression of research, we plan to build on our experience with Medicaid data by adding more sophisticated computer simulation models of health disparities at the community level, and also to add more sophisticated datasets, including clinical data from electronic health records and social determinant data with geospatial data links. We propose to first study the causes of local-area variation in asthma outcome disparities among millions of low-income, Medicaid enrolled children and youth across 29 states. These states hold over 80% of the entire U.S. Medicaid population, and over 90% of African American and Hispanic / Latino enrollees. Specifically, we will further refine our Monte Carlo simulations and Markov modeling to assess the impact of different treatment strategies filtered through real-world patient adherence. We will use multi-level modeling and geospatial analyses to quantify the effect of patient, provider, hospital, and community factors contributing to racial-ethnic and local-area variation in outcomes. We will engage in structural equation modeling to quantify conceptual models that include latent variables such as "poverty" or "quality of care". Finally, we will exploe agent- based modeling (overlaid on community-level geospatial topography) to simulate the complex dynamics of health disparities as they evolve in space over time. We will also refine these models using clinical data from electronic health records, and assess the applicability of these models to other chronic diseases and to overall health outcome disparities. Our ultimate goal is to save lives and decrease suffering by developing computer models that reveal community- specific intervention points most likely to reduce health disparities in each community.