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

用于研究中年地区健康不平等的数字孪生社区

• 批准号: 10583781
• 负责人: JARROD DALTON
• 金额: $ 61.55万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10583781
• 关键词: Address; Adult; Age; Algorithms; Alzheimer' s Disease; Antihypertensive Agents; Automobile Driving; Biological; Biometry; Blood Pressure; COVID-19; Cardiovascular Diseases; Cardiovascular system; Chronic Disease; Clinic; Clinical; Cloud Computing; Communities; Community Healthcare; County; Data; Data Analyses; Data Science; Death Certificates; Development; Diabetes Mellitus; Disease; Disparity; Electronic Health Record; Environment; Equity; Event; Funding; Geography; Health; Health Policy; Health Promotion; Health system; Healthcare; Homelessness; Improve Access; Incidence; Individual; Inequality; Infant Care; Inpatients; Institution; Intervention; Investigation; Life Expectancy; Lipids; Literature; Malignant neoplasm of lung; Measures; Medical; Mental Depression; Mental Health Services; Mental disorders; Methods; Modeling; Neighborhoods; Ohio; Outcome; Outpatients; Pharmaceutical Preparations; Politics; Preventive service; Primary Care; Privacy; Procedures; Racial Segregation; Research; Research Design; Research Personnel; Risk Factors; Screening procedure; Series; Smoking; Socioeconomic Status; Sociology; Translating; United States National Institutes of Health; Vulnerable Populations; Work; Writing; bench to bedside; cardiovascular risk factor; career; clinical risk; cohort; community engagement; digital; digital twin; efficacy evaluation; empowerment; ethnic minority; experience; health data; health disparity; health inequalities; improved; innovation; low socioeconomic status; member; middle age; multiple chronic conditions; open data; population based; population health; preference; privacy preservation; racial minority population; response; sex; social; social disparities; social influence

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.

数字孪生邻居用于中年基于地点的健康不平等研究 人们越来越认识到，高价值的治疗、筛查工具和预防服务已经创造或 少数种族和少数族裔背景的人、低收入群体的人 社会经济地位（SEP）和其他弱势群体。该项目旨在为以下方面制定新的课程： 了解基于地方的人口健康战略。越来越多的关于健康和位置的文献 证明了社会和邻里指标与传统临床指标相比的巨大影响力， in driving驱动individual个人health健康outcomes结果.因此，我们的总体目标是增强社区成员的能力， 组织，地方卫生系统和人口健康和政治领导人使用来自地方的证据- 基于研究的信息，优先考虑，评估和实施健康促进战略， 差距。我们工作的基石创新是数字孪生社区的发展， 将极大地扩大对数据和算法的访问，以了解基于位置的健康和社会 不等式数字孪生社区（DTN）是真实的社区的数字复制品，包括生物， 云计算环境中的社会和地理数据和算法。在这个项目中，我们将（i） 建立以社区和隐私为中心的程序，以建设数字孪生社区， 纳入EHR数据; ii）评估DTN方法理解地方机制的有效性- 在多种健康状况和地理区域的中年健康不平等;以及iii）检查 研究基于地点的中年健康不平等现象的DTN方法的普遍性和可扩展性。的 开发的开放科学DTN平台将使建模能力和隐私相结合 保留多部门举措的特点，旨在评估地方卫生不平等现象， 为战略性人口健康政策决定提供信息。因此，这项工作的结果将i）提供一个框架 对临床、社会和环境力量造成生活差异的机械理解 预期，多种发病率，以及慢性疾病的发病和管理，ii）催化研究人员 以及地方和国家的社区和保健机构，以提高公平性， 他们所服务的社区。