Digital Twin Neighborhoods for Research on Geographic Patterns in Mid-Life Health Trajectories

Increasingly, it is recognized that the implementation of high-value therapies, screening tools, and preventive services has not fully benefited the U.S. population, contributing to variation in health outcomes. 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 strong influence of social and neighborhood-level indicators, relative to traditional clinical measures, in shaping individual health outcomes. Thus, our overall objective is to equip local organizations, health systems, and decision-makers with evidence from place-based research to inform, prioritize, evaluate, and implement effective health interventions. The cornerstone innovation of our work is the development of Digital Twin Neighborhoods, which will dramatically expand access to data and algorithms for analyzing geographic patterns in health and related factors. 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 geographic variation in mid-life health trajectories; and iii) examine the generalizability and scalability of the DTN model for studying mid-life health and aging. 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 geographic patterns and informing strategic population health policy decisions. Thus, the results of this work will i) provide a framework for mechanistic understanding life expectancy, multi-morbidity, and the onset and management of chronic disease and ii) support researchers and institutions in improving population health through evidence-informed strategies tailored to local contexts.