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。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)改善心血管疾病和心脏代谢风险的筛查和管理 (CMR)在疾病负担不同的患者和社区之间。然而，广泛的制度变革已经发生 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 针对个人和社区的CHW干预措施，以减少心血管疾病的差异。结果将会是 为卫生系统实施最有针对性的计划提供受欢迎的指导，以改善 人口健康差距。一般而言，公共卫生将从创建和验证 一个以系统为基础的框架，旨在通过协调的诊所- 和以社区为基础的CHW项目。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，基于模拟的大型 目前由小规模试验数据支持的CHW干预措施的规模实施，产生了可复制的 卫生系统和政策制定者识别、模拟和改善未来健康差距的手段。 该项目分为五个阶段。阶段1：数据收集、地理编码和未识别数据的空间聚合； 阶段2：确定心血管疾病的地理热点和空间统计分析，以确定影响心血管疾病的因素 社区风险；阶段3：使我们建立的微观模拟模型适应疾病、人口统计学、 和热点的社会特征以及CHW干预的参数；阶段4：微观模拟 针对个体和邻里心血管风险患者的基于临床的CHW干预模型 以及以社区为基础的CHW计划，根据CMR和 心血管疾病；阶段5：比较疾病结果、心血管疾病的地理聚集性、成本效益和 CHW对CMR和CVD的干预措施在2年、10年和20年的时间框架内的可持续性。 我们将从服务最多的两个安全网医疗保健系统收集、识别和汇总服务数据 患者和中西部大都市地区的高危社区。这些数据将提供有关 CVD和CMR疾病负担在整个大都市地区的邻里分布，以允许稳健 空间分析和微观模拟建模。由经验数据提供的微观模拟分析将使我们能够 比较有针对性的诊所和基于社区的CHW项目对 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，在 创新的系统科学方法，这项研究将对影响、成本效益、 以及减少心血管疾病差距的方案的可持续性，目前只有试点数据支持这些方案。

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.