Neighborhoods and health equity are the throughline of everything this lab does — from redlining maps drawn in the 1930s to the digital twins we build today.
Poorer Americans are now expected to live roughly 10 fewer years than wealthier Americans, and cardiovascular disease alone accounts for a huge share of that gap. Traditional risk models built from clinical data alone routinely underestimate risk for people living in disadvantaged neighborhoods — because they leave out the place a person actually lives.
Digital Twin Neighborhoods closes that gap. Working with co-Principal Investigator Jarrod Dalton, PhD (Cleveland Clinic), Dr. Perzynski's team combines de-identified electronic health record data with detailed social determinants of health data to construct privacy-protecting, statistically faithful digital replicas of real neighborhoods. Health systems, community organizations, and policymakers can use these digital twins to simulate what would actually happen to health outcomes — before committing real money or displacing real families — if a neighborhood gained a park, a clinic, cooling infrastructure, or a housing investment.
The project is explicitly community-grounded: its methods were proven through structured community conversations and Community Engagement Studios that let residents shape how their own neighborhoods are modeled and represented.
Selected coverage: Cleveland Clinic Newsroom · Lerner Research Institute · Crain's Cleveland Business · Community conversations paper (PMC)
Restoring Health Equity and Resilience to Cleveland Through Vacant Land Improvements
Office of Minority Health · $2.4M · Site Co-PI
NAVIGATE — Neighborhood Assessment Via Inclusive Gathering and Timely Evaluation
MetroHealth PHERI Pilot · Co-Investigator
Cleveland Healthy Home Data Collaborative
Kresge, RWJF, de Beaumont & W.K. Kellogg Foundations · Site PI, Housing.Health software
The foundational finding: patients from disadvantaged neighborhoods had major cardiovascular events at more than twice the rate predicted by standard clinical risk tools — the direct ancestor of Digital Twin Neighborhoods.
Mapping how neighborhood disadvantage tracks with uncontrolled hypertension across an entire health system's patient population.
A quantitative reckoning with 1930s redlining maps and their measurable, present-day health consequences.
A methodological backbone for the lab's neighborhood measurement work, built in part on the open-source sociome R package.
Walking interviews with residents that ground quantitative neighborhood models in lived community priorities.
Extending the digital twin approach to dementia risk and care — one of several biosocial applications of the neighborhood model.
Alongside its population-level neighborhood modeling, the lab runs biosocial studies that connect social and neighborhood exposures directly to biological measurement — genetics, epigenetics, stress physiology, and clinical biomarkers.
Examining how chronic stress drives epigenetic change and cancer risk in underserved populations (MetroHealth Cancer Institute / PHERI Pilot Award).
Investigating tumor genetics (including WAVE3 expression) alongside race/ethnicity and neighborhood socioeconomic factors (Breast Cancer Research and Treatment, 2023).
Exploiting residual pediatric blood samples to examine sociomedical risk and resilience — linking early-life social exposure to biological markers of fast and slow aging.
A transdisciplinary demonstration project connecting social/neighborhood exposure to brain injury outcomes and Alzheimer's risk (CWRU Provost's Breaking Boundaries Grant, PI Perzynski).