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

描述（由申请人提供）：这个职业发展奖将资助调查员在健康公平研究领域从行政领导过渡到知识领导。二十多年来，研究人员为建立莫尔豪斯医学院做出了贡献，莫尔豪斯医学院是全国四所历史上的黑人医学院之一，最近在全国排名第一，以实现社会使命。他还领导了国家初级保健中心的发展，这是一个专注于实现健康公平的研究，培训和资源中心。这个职业生涯中期的奖项将使他能够从行政领导过渡到专注于研究，旨在建立成功的模式，实现健康公平的社区在整个美国拟议的研究将提供一个路线图，以成功实现更优化和公平的哮喘结果为所有儿童，更广泛的应用范围广泛的慢性疾病的结果差异在所有年龄组。在拟议的研究进展中，我们计划通过在社区一级添加更复杂的健康差异计算机模拟模型，并添加更复杂的数据集，包括来自电子健康记录的临床数据和具有地理空间数据链接的社会决定因素数据，来建立我们的医疗补助数据经验。我们建议首先研究29个州数百万低收入，医疗补助登记的儿童和青少年之间哮喘结果差异的局部地区差异的原因。这些州拥有超过80%的美国医疗补助人口，以及超过90%的非洲裔美国人和西班牙裔/拉丁裔注册者。具体来说，我们将进一步完善我们的蒙特卡罗模拟和马尔可夫模型，以评估通过真实世界患者依从性过滤的不同治疗策略的影响。我们将使用多层次建模和地理空间分析来量化患者，提供者，医院和社区因素对结果中种族-民族和局部区域变化的影响。我们将参与结构方程模型，以量化概念模型，包括潜在的变量，如“贫困”或“护理质量”。最后，我们将探索基于代理的建模（覆盖在社区级别的地理空间地形），以模拟健康差异的复杂动态，因为它们在空间上随着时间的推移而演变。我们还将使用来自电子健康记录的临床数据来完善这些模型，并评估这些模型对其他慢性疾病和整体健康结果差异的适用性。我们的最终目标是通过开发计算机模型，揭示最有可能减少每个社区健康差距的社区特定干预点，从而挽救生命并减少痛苦。

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.