Social Determinants of Health (SDOH) and Quality of Life II
The session recording will be archived on the site until June 25th, 2023
This session was streamed but not recorded
Date: 3/26/2023
Time: 4:30 PM - 5:50 PM
Room: Centennial Ballroom C, Hyatt Regency, Third Floor
Type: Paper,
Theme:
Curated Track:
Sponsor Group(s):
Geographic Information Science and Systems Specialty Group, Health and Medical Geography Specialty Group, Spatial Analysis and Modeling Specialty Group
Organizer(s):
Chang Zhao NORC at the University of Chicago
Chair(s):
Yanjia Cao The University of Hong Kong
Description:
Social determinants of health (SDOH) are non-medical factors that influence health outcomes. SDOH corresponds to five key domains, including social context (e.g., age, race/ethnicity, veteran status), economic context (e.g., income, unemployment rate), education, physical infrastructure (e.g., housing, crime, transportation), and healthcare context (e.g., health insurance).
Both policymakers and healthcare systems have begun to recognize the importance of addressing SDOH, for example, Healthy People 2030, an initiative from the U.S. Department of Health and Human Services, embraces a holistic view of health and well-being by explicitly highlighting the importance of SDOH as key factors in improving health and reducing health disparities. Research into the contributions of contextual, community-level factors moves beyond a focus on individual-level risk factors and enables public health professionals to address underlying structural causes of health disparities. A focus on underlying SDOH aspects not only promotes the development of strategies to achieve multiple health objectives in a socially equitable way, but also facilitate the engagement of actionable community-level efforts to improve health outcomes, such as establishing farmers markets in food deserts, safe green space, or affordable housing.
Studies have used spatial analysis, spatial statistics, social sensing, and other geospatial techniques to examine the intersection of SDOH and disease dynamics. Advanced data collection technologies and computational methods are being leveraged to gain novel SDOH insights. While many studies extract SDOH from existing administrative data, the application of artificial intelligence (AI)-based approaches, such as machine learning and deep learning, can augment the retrieval of SDOH hidden in unstructured data sources (e.g., text in clinical notes, satellite, and street view images). Combining multiple data sources also allows for the study of new aspects of SDOH by linking data less commonly used in health research (e.g., access to healthy foods, neighborhood safety) to health outcome data.
This session will cover a variety of ways in which SDOH are extracted, assessed, and utilized in health research. It will present research (perspectives, methods, case studies) that highlight the innovative and effective incorporation of SDOH in studying spatial and temporal heterogeneity of disease patterns and advancing health equity and outcomes.
Topics of Interest
The topics we welcome include, but are not limited to:
• Investigate health disparities and inequities resulting from geographic inequities including but not limited to epidemics, environmental exposure, ecosystem services, climate change, urban development, transportation, food insecurity, and migration.
• Linking SDOH and disease inequality, such as obesity, diabetes, heart disease, respiratory disease, diarrheal disease, HIV, Tuberculosis, mental health disorder, substance use disorder, and cancer.
• Use of spatial statistics, spatial epidemiology, location tracking, and other geospatial methods and technologies to understand health outcomes and their relationships to SDOH risk factors.
• Enrich SDOH insights by using multiple, disparate data sources (e.g., Electronic Health Records (EHRs), Census data, surveys, administrative data, crowd-sourced data, i.e., images, texts, videos, sounds).
• Develop and/or apply analytic and Artificial Intelligence (AI)-based methods (e.g., machine learning, deep learning) to create novel SDOH measures and assess SDOH.
• Create novel sources of data, platforms and tools to improve access to SDOH data.
This session is a part of the Geospatial Health Research Symposium, which is organized by the Health and Medical Geography Specialty Group. This yearly symposium aims to bring together national and international scholars, practitioners, and policy makers from different specialties, institutions, sectors, and continents to share ideas, findings, methodologies, and technologies, and to establish and strengthen personal connections, communication channels, and research collaborations. For more information about the symposium, please feel free to contact Paul Delamater at pld@email.unc.edu.
Presentations (if applicable) and Session Agenda:
Lukas Marek |
Inequities in life course air pollution exposure and health |
Noemie Letellier |
How do environmental characteristics jointly contribute to cardiometabolic health? A quantile g-computation mixture analysis |
Hanxue Wei |
Tree canopy and cardiovascular health disparities—Implications for Environmental Planning |
Michelle Kondo |
A Greening Theory of Change on How Neighborhood Greening Impacts Adolescent Health Disparities |
Non-Presenting Participants
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Social Determinants of Health (SDOH) and Quality of Life II
Description
Type: Paper,
Date: 3/26/2023
Time: 4:30 PM - 5:50 PM
Room: Centennial Ballroom C, Hyatt Regency, Third Floor
Contact the Primary Organizer
Chang Zhao NORC at the University of Chicago
changzhao2011@gmail.com