GHUCCTS Programs & Resources

The Environmental Determinants of Health (EDH) initiative uses geospatial analysis and advanced computing to support projects that investigate how neighborhood context and physical environmental exposures influence health outcomes. The initiative focuses on quantifying and modeling geographic risk factors—such as air quality, noise, weather extremes, and built environment characteristics—across the U.S. to inform policy, prevention, and health service planning.

This work is a collaboration between the Washington, DC-based GHUCCTS institutions and Oak Ridge National Laboratory (ORNL) in Tennessee, supported by the Department of Energy. The partnership combines local health expertise with ORNL’s advanced computing and geospatial modeling capabilities. The DC Department of Health (DC DOH) also contributes data and regional insight.

A central achievement of this effort is the development of the Multi-Exposure Environmental Index (MEEI)—a composite measure that captures multiple environmental exposures known to influence health, aggregated at the U.S. Census Tract level. This index quantifies cumulative physical exposures—such as air pollution, noise, heat, green space access, and proximity to toxic sites—that may interact to elevate health risk.
📄 Peluso et al., Health & Place (2024): https://doi.org/10.1016/j.healthplace.2024.103303

Additional modeling projects across the GHUCCTS partnership include:

• Maternal and Neonatal Health in Washington, D.C.: Geospatial analysis of birth outcomes—such as preterm birth and small-for-gestational-age deliveries—linked to neighborhood-level conditions including access to care, environmental exposures, and local infrastructure.
• Chronic Disease Trajectories During COVID-19: Modeling shifts in healthcare utilization among patients with chronic conditions before and during the COVID-19 pandemic, with a focus on how geographic context influenced access to care and continuity of treatment.
• Healthcare Access and Stress in HIV-Positive Women: Investigating how local environmental and structural factors affect care engagement, mental health, and health outcomes in participants from the Multi-Site HIV-Positive Women’s Cohort Study (MWCCS).
• Obesity, Food Access, and Surgical Outcomes: Analyzing associations between obesity-related surgical outcomes (e.g., bariatric surgery), neighborhood food insecurity, and area-level deprivation measures in the D.C. region.
• UrbanPop: A spatial microsimulation model that generates realistic, georeferenced synthetic populations to support health and planning analyses while preserving individual privacy.
• Environmental and Spatial Data Infrastructure: Ongoing efforts to curate, harmonize, and maintain high-resolution environmental and contextual spatial data—supporting reproducible, scalable modeling across multiple public health research domains.

Co-Director: Anuj Kapadia, PhD

Anuj J. Kapadia is a Distinguished R&D Scientist and Section Head for Advanced Computing in Health Sciences at Oak Ridge National Laboratory (ORNL). He is also an Adjunct Professor of Radiology, Physics, and Medical Physics at Duke University, with a Ph.D. in Biomedical Engineering from Duke. His work focuses on artificial intelligence (AI), machine learning (ML), and simulation and modeling in health applications. With over 20 years of experience, Dr. Kapadia has expertise in neutron and X-ray scattering, Monte Carlo simulation development, and data analytics for security and medical applications. He has received significant recognition for his contributions to the field, including funding from prominent organizations like the DOE, DOD, NIH, and others. Dr. Kapadia is a fellow of AAPM and a senior member of IEEE and SPIE professional societies.

Co-Director: Thomas A. Mellman, MD
Professor of Psychiatry
Director of the Center for Clinical and Translational Research and Stress/Sleep Studies Program at Howard University College of Medicine.

Dr. Mellman is Professor of Psychiatry and Director of the Center for Clinical and Translational Research and Stress/Sleep Studies Program at Howard University College of Medicine. He is the principal investigator representing Howard for the Georgetown Howard Universities Center for Clinical and Translational Science supported by a Clinical Translational Science Award from NIH. 

He received training at the NIMH Division of Intramural Research Programs and has previously held faculty appointments and achieved the rank of Professor at the University of Miami and Dartmouth. Dr. Mellman has had continuous funding as PI on federal research grants since 1991 including a VA Merit award, and R01, R21, R34 and K24 awards from NIMH, NHLBI, NIMHD, and the DOD. His primary research interests have been the role of sleep in posttraumatic stress disorder (PTSD) and the role of sleep in the effects of stress on physical and emotional health. He has a consistent track record of mentoring junior investigators and interdisciplinary collaboration. He recently finished service as a member of the NIH study section for Mechanisms of Emotion Stress and Health, was previously a member of NIMH IRGs for Violence and Traumatic Stress and Interventions, and has served on several review committees for the NIH Roadmap and Department of Defense research programs. Dr. Mellman was a member of the original ISTSS committee for developing treatment guidelines for PTSD, APA committee for text revision of the DSM-IV, and the Institute of Medicine Committee for review of the evidence regarding the treatment of PTSD. He received the International Society for Traumatic Stress Studies distinguished mentorship award for 2016.

Alina Peluso, PhD

Dr. Alina Peluso is a research scientist at ORNL specializing in biostatistics, with expertise in applying geospatial analysis and statistical modeling to assess how environmental and contextual factors affect health outcomes. She collaborates with Georgetown University, MedStar Health, DC Health, and the University of North Carolina at Chapel Hill on multidisciplinary initiatives. Her work focuses on developing statistical models that quantify complex environmental influences at the community level, providing data-driven insights to support public health policy, prevention efforts, and healthcare planning.

Additional team members:

• Kevin Sparks (Scientist, EDH datasets lead)
• Hilda Klasky (Scientist, Datasets and documentation)
• Joshua Grant (Scientist, Vizualization lead)
• Joe Tuccillo (Scientist, Synthetic populations)

Social and environmental factors significantly influence health outcomes by shaping the conditions in which individuals live, work, and receive care. This module integrates research on a wide range of social determinants—such as housing quality, employment status, income, education, and access to services—with environmental exposures including air quality, noise, heat, and proximity to pollutants.

The primary goal of the Environmental Determinants of Health component is to enhance the understanding of how these social and environmental factors, individually and in combination, contribute to health risks across communities. By developing and applying advanced statistical and geospatial models, this initiative quantifies cumulative exposures and contextual influences at fine geographic scales, such as census tracts and neighborhoods.

This integrated approach supports research that generates actionable insights for public health policy, prevention programs, and healthcare system planning. It facilitates the identification of geographic risk factors that influence health outcomes, enabling more targeted and effective interventions.

In addition to research activities, this module prioritizes training and capacity-building for investigators and research teams, promoting methodological rigor and fostering interdisciplinary approaches. It also emphasizes robust data collection and community engagement to ensure findings are relevant and actionable for diverse populations.

Selected Research and Resources:

• Environmental determinants of health: Measuring multiple physical environmental exposures at the United States census tract level
  Peluso et al. (2024) developed the Multi-Exposure Environmental Index (MEEI), a composite measure that quantifies multiple environmental exposures—such as air pollution, noise, heat, green space access, and proximity to toxic sites—aggregated at the U.S. Census Tract level. This open-access study offers a robust framework for assessing cumulative environmental risks and their impact on health outcomes nationwide.
  Read the full article: https://www.sciencedirect.com/science/article/pii/S135382922400131X?via%3Dihub
• UrbanPop: A spatial microsimulation framework for exploring demographic influences on human dynamics
  Tuccillo et al. (2023) describe a spatial microsimulation model that generates synthetic, georeferenced populations to support detailed health and planning analyses while preserving privacy. This framework aids in understanding demographic and spatial factors that influence population health dynamics.
  Read the full article: https://www.sciencedirect.com/science/article/pii/S0143622822002156

• High-Performance Computing (HPC) Resources: Access to diverse HPC architectures from ORNL for analyzing and modeling health data, including large publicly available datasets such as The Cancer Genome Atlas (TCGA), clinicaltrials.gov, Semantic MEDLINE, and others.
  
  
• Geospatial Analysis Tools: Development and implementation of tools like the Environmental Deprivation Index and Urban Pop to model social determinants of health (SDOH) in urban environments.
  
  
• Collaborative Data Integration: Integration of live birth data, social determinants of health (violent crime rates, access to transportation, etc.), and health data (DC Ward data) for predictive modeling and analysis across partner institutions (Howard University, Georgetown University, and others).
  
  
• Health Risk Predictive Modeling: Utilization of geospatial and SDOH data to model and predict health risks, including the impact of social conditions on chronic disease trajectories, sleep health, and maternal and neonatal health outcomes.
  
  
• Project-Specific Analytics and Support: Support for research projects like evaluating relationships between neighborhood stress indicators and health outcomes (e.g., sleep health), and modeling healthcare trajectories during the COVID-19 pandemic using hospitalization and appointment data.
  
  
• Collaborative Research & Partnerships: Collaborative opportunities with partners like Oak Ridge National Laboratory (ORNL), the DC Department of Health, and other institutions to enhance research on social and environmental health determinants.
  
  
• Researcher Education and Workforce Development: Educational initiatives to improve the expertise of researchers in study design, data analysis, and methodology, with a focus on addressing health disparities and developing innovative approaches to SDOH and EDH.
  
  
• Data Collection and Engagement Strategies: Establishment of data collection frameworks and community engagement strategies to support ongoing research and workforce development focused on social and environmental determinants of health.