Digital Twin Neighborhoods for Research on Place-Based Health Inequalities in Mid-Life

数字孪生社区在中年健康不平等研究中的应用 人们越来越认识到，高价值疗法、筛查工具和预防性服务创造了或 种族和少数族裔背景的人所经历的不平等加剧，即低收入者 社会经济地位(SEP)和其他弱势群体。该项目旨在为以下项目开辟新的道路 了解以地点为基础的人口健康战略。越来越多的关于健康和地方的文献已经 展示了社会和邻里指标相对于传统临床措施的超大影响 在推动个人健康成果方面的作用。因此，我们的总体目标是赋予社区成员和 组织、当地卫生系统和人口健康以及政治领导人使用来自地方的证据- 以研究为基础，告知、优先排序、评估和实施接近健康的健康促进战略 差距。我们工作的基石创新是数字孪生社区的发展， 将极大地扩展对数据和算法的访问，以了解基于位置的健康和社交 不平等。数字孪生社区(DTN)是真实社区的数字复制品，包括生物、 云计算环境中的社交和地理数据和算法。在这个项目中，我们将i) 建立以社区和隐私为重点的程序，以建设数字孪生社区 纳入EHR数据；ii)评估DTN方法在理解地点--机制方面的有效性 基于多种健康状况和地域的中年健康不平等；以及iii)检查 基于地点的中年健康不平等研究的DTN方法的概括性和可扩展性。这个 开发的开放科学DTN平台将使建模能力和隐私相结合 保留可供多部门倡议使用的特点，这些倡议旨在评估地方卫生不平等和 为战略性人口健康政策决策提供信息。因此，这项工作的结果将：i)提供一个框架 对导致生活差异的临床、社会和环境因素的机械性理解 预期、多种发病率、慢性病的发病和管理以及ii)催化研究人员 以及地方和国家的社区和保健机构，以改善公平并满足以下需求 他们所服务的社区。

Digital Twin Neighborhoods for Research on Place-Based Health Inequalities in Mid-Life Increasingly, it is recognized that high-value therapies, screening tools and preventive services have created or increased inequalities experienced by persons from racial and ethnic minority backgrounds, those of low socioeconomic position (SEP) and other vulnerable groups. This project aims to chart a new course for understanding place-based population health strategies. A growing literature on health and place has demonstrated the outsize influence of social and neighborhood indicators versus traditional clinical measures in driving individual health outcomes. Thus, our overall objective is to empower community members and organizations, local health systems and population health and political leaders to use evidence from place- based research to inform, prioritize, evaluate and implement health-promoting strategies that close health disparities. The cornerstone innovation of our work is the development of Digital Twin Neighborhoods which will dramatically expand access to data and algorithms for understanding placed-based health and social inequalities. Digital Twin Neighborhoods (DTNs) are digital replicas of real communities, including biological, social and geographic data and algorithms in a cloud computing environment. In this project, we will i) establish community- and privacy-focused procedures for constructing Digital Twin Neighborhoods which incorporate EHR data; ii) evaluate the efficacy of a DTN approach to understanding mechanisms of place- based health inequities in mid-life across multiple health conditions and geographies; and iii) examine the generalizability and scalability of the DTN approach for studying place-based mid-life health inequalities. The developed open science DTN platform will make the combination of modeling capabilities and privacy preserving features available to multi-sector initiatives that are aimed at evaluating local health inequalities and informing strategic population health policy decisions. Thus, the results of this work will i) provide a framework for mechanistic understanding of clinical, social and environmental forces producing disparities in life expectancy, multi-morbidity, and the onset and management of chronic disease and ii) catalyze researchers and community and health care institutions both locally and nationally to improve equity and meet the needs of the communities they serve.