STATUS: Completed Project
Given that a great deal of effort has been put toward improving health care system processes and prevention efforts in the population, policymakers are looking at other stressors on the health care system, including the community context in which health services are provided. There is growing demand for a data platform that integrates information about social determinants of health (SDOH), health service utilization, and systems of care. For decades, researchers have emphasized the importance of SDOH and have developed many different conceptual models to explain the inter-relationship between individual, family, and societal factors on the health of an individual. Research has demonstrated that for many SDOH factors, small-area data (data at the community or sub-county level) may be necessary to conduct meaningful analyses; for other SDOH factors, data at other geographic levels are more meaningful. Considerable evidence exists about the relationship between inequities in SDOH factors and poor health outcomes (e.g., mortality, acute and chronic disease, disability). Some studies have shown a relationship between SDOH factors and health care utilization, and studies are just beginning to emerge on the effectiveness of health care interventions that integrate patient and community SDOH information on patient and community health outcomes. Health care systems are already moving toward obtaining critical SDOH data to improve care coordination and the quality of health care services for vulnerable populations.
Researchers expend substantial resources linking multiple datasets to create data files suitable for analyses of SDOH data because the current datasets lack standardized metrics or estimates at the small-area level. Those databases are often derived to look at the health of an individual or community, including information about the neighborhood and built environment, health and health care, social and community context, education, and economic stability. However, these databases are limited in their ability to examine SDOH in small geographic areas. There is no complete source of longitudinal information with uniformly formatted, community-level data on SDOH readily available for health services research.
PROJECT PURPOSE & GOALS
The goal of this project was to develop a consolidated set of national standardized databases on valid and reliable SDOH factors at the small-area and other geographic levels, building on existing databases developed by federal agencies (e.g., AHRQ, Health Resources and Services Administration [HRSA], Centers for Disease Control and Prevention [CDC], Assistant Secretary for Planning and Evaluation [ASPE], and National Institutes of Health [NIH]).
- Conduct an environmental scan to identify a comprehensive set of U.S. Department of Health and Human Services (HHS) and other federal datasets with existing or analyzable small-area level and other geographic level data on SDOH variables.
- Design and create a publicly available data platform for a valid and reliable standardized set of SDOH data sources at various geographic areas to include, but not limited to, U.S. census blocks, zip codes, counties, primary care service areas, market areas, and states.
- Coordinate and expand the SDOH data collection efforts across HHS.
- Use the new data platform to conduct a minimum of two patient-centered outcomes research (PCOR) studies.
- Disseminate the SDOH data platform to end-users across the federal government, PCOR researchers, and health services researchers.
- Establish a sustainability and growth plan for the SDOH data platform.
PROJECT ACHIEVEMENTS AND HIGHLIGHTS
- The project team identified existing SDOH-related data sources through an SDOH environmental scan.
- The project developed standardized SDOH Database files that are easy to use and facilitate research on SDOH, aligning with the HHS priority to address and improve health equity.
- The team developed a national longitudinal database of valid and reliable SDOH factors at small-area geographic levels including county, zip code, and U.S. census tract to provide accessible community-level SDOH data.
- The project developed a data visualization with county-level data to demonstrate the capability to conduct direct analysis with the SDOH Database.
PUBLICATIONS, PRESENTATIONS, AND OTHER PUBLICALLY AVAILABLE RESOURCES
- The SDOH Database includes data at the county and census tract levels for the years 2009-2020 and zip code-level data from 2011-2020 across five key SDOH domains: social context, economic context, education, physical infrastructure, and health care context. The Database is available here: https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html.
- A data visualization with county-level data on poverty and access to the internet is available here: https://www.ahrq.gov/sdoh/data-analytics/sdoh-tech-poverty.html.
- A sortable Excel file summarizing findings from the environmental scan of existing SDOH-related data sources and variables is available here: https://www.ahrq.gov/sites/default/files/wysiwyg/sdoh/sdoh-environmental-scan.xlsx.
- Tools for practice improvement to help health care organizations address SDOH are available here: https://www.ahrq.gov/sdoh/practice-improvement.html#resources.
- The project published a summary of the SDOH data visualization, “Internet Access as a Social Determinant of Health,” in the July 2021 edition of the Journal of the American Medical Association. The summary is available here: https://jamanetwork.com/journals/jama/article-abstract/2782319.
- The project team reviewed updated SDOH Database contents, topics covered in the SDOH Database, geographic and temporal coverage, user documentation, and methodological considerations during a July 2022 webinar. The webinar slides are available here: https://www.ahrq.gov/sites/default/files/wysiwyg/sdoh/SDOH-overview-presentation.pdf.
- The project was discussed during the March 2022 Digital Health Symposium: Advancing Broadband Connectivity as a Social Determinant of Health.
Below is a list of ASPE-funded PCORTF projects that are related to this project
Data Capacity for Patient-Centered Outcomes Research through Creation of an Electronic Care Plan for People with Multiple Chronic Conditions – Led by AHRQ and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), this project will build data capacity to conduct pragmatic patient-centered outcomes research by developing an interoperable electronic care (eCare) plan to facilitate aggregation and sharing of critical patient-centered data across home-, community-, clinic-, and research-based settings by extracting data from electronic health records (EHRs) and exchanging that data across settings. The eCare Plan will be an overarching, longitudinal blueprint of the prioritized health concerns, goals, interventions, and status of an individual patient across all care settings where—and all health care team members through whom—the patient receives care. The pilot eCare Plan application developed for this project will be designed for use with patients who have chronic kidney disease (CKD), cardiovascular disease (CVD), diabetes, and/or pain with opioid use disorder (OUD).
Data Capacity for Patient-Centered Outcomes Research through Creation of an Electronic Care Plan for People with Multiple Chronic Conditions 2.0: Development of the Patient-Facing Application – The eCare Plan 2.0 project continues the work of AHRQ and NIDDK’s 2019 OS-PCORTF eCare Plan 1.0 project, which is developing an open-source clinician-facing eCare Plan application. The application and corresponding implementation guide support research efforts and clinical care delivery for individuals living with multiple chronic conditions. This project will develop an analogous patient-facing eCare Plan application capable of coordination with the clinician-facing application to facilitate the collection and the exchange of vital data on patients living with CKD, CVD, diabetes, and/or chronic pain with or without OUD. By working in concert with the clinician-facing eCare Plan application, the patient tool will enrich current understanding of the complex care requirements and outcomes of high-need patients.