Hotspotting Cardiometabolic Disparities for Simulated Advances in Population Care

项目摘要 心血管疾病（CVD）是一个主要的公共卫生问题，特别是对患者和社区 社会经济地位低下。越来越多的证据鼓励卫生系统探索使用 社区卫生工作者（CHW）改善CVD和心脏代谢风险的筛查和管理 (CMR)在不同疾病负担的患者和社区中。然而，广泛的系统变革一直是 由于缺乏足够范围和持续时间的成果和成本数据而受到限制。 我们建议使用创新的系统科学方法来建模和分析的有效性和成本， 针对个人和社区的CHW干预措施，以减少CVD差异。结果将 为卫生系统实施最佳目标计划提供广受欢迎的指导，以改善 人口健康差距。一般公共卫生将大大受益于建立和验证 一个以系统为基础的框架，以协调的诊所为目标， 和基于社区的CHW方案。通过将空间热点分析与已建立的微观模拟相结合， 这项研究的创新方法将提供新的和可操作的知识的基础上模拟大， 大规模实施CHW干预措施，目前得到小规模试验数据的支持， 卫生系统和政策制定者在未来识别、建模和改善健康差距的手段。 该项目分为五个阶段。第一阶段：数据收集、地理编码和去识别数据的空间汇总; 第二阶段：识别心血管疾病的地理热点并进行空间统计分析以确定心血管疾病的影响因素 邻里风险;第3阶段：我们建立的微观模拟模型适应疾病，人口统计， 和社会特点的热点和参数的CHW干预;第4阶段：微观模拟 针对个体和社区CVD风险患者的基于诊所的CHW干预建模 和基于社区的CHW计划，根据CMR的不同负担， CVD;第5阶段：比较疾病结局、CVD的地理聚集性、成本效益和 在沿着2年、10年和20年的时间框架内，CHW对CMR和CVD干预的可持续性。 我们将收集、去识别和汇总来自两个安全网医疗保健系统的服务数据， 病人和高风险社区在中西部大都市地区。这些数据将提供一个粒度视图， CVD和CMR疾病负担在整个大都市地区的社区分布， 空间分析和微观模拟建模。根据经验数据进行微观模拟分析， 比较有针对性的诊所和社区为基础的CHW计划对内部差异的影响， 大都会地区的社区。通过将空间热点分析与微观模拟相结合， 创新的系统科学方法，这项研究将提供有益的见解的影响，成本效益， 以及减少心血管疾病差异的方案的可持续性，目前只有试点数据支持。

Project Summary 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.