Abstract

An integral part of delivering high-quality healthcare is understanding the social determinants of health (SDOH) of patients and of communities in which healthcare is provided. SDOH are defined by the World Health Organization as the conditions in which people are born, grow, live, work, and age.

SDOH, although experienced by individuals, exist at the community level. Healthcare systems that learn about the communities in which their patients live can adapt their services to meet the communities’ specific needs. This, in turn, can help patients and community members overcome obstacles to achieving and maintaining good health.

Usage

Additional information about SDOH is available on the Agency for Healthcare Research and Quality (AHRQ) SDOH website (https://www.ahrq.gov/sdoh/about.html).

AHRQ's database on Social Determinants of Health (SDOH) was created under a project funded by the Patient Centered Outcomes Research (PCOR) Trust Fund. The purpose of this project is to create easy to use, easily linkable SDOH-focused data to use in PCOR research, inform approaches to address emerging health issues, and ultimately contribute to improved health outcomes.The database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis.Variables in the files correspond to five key SDOH domains: 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). The files can be linked to other data by geography (county, ZIP Code, and census tract). The database includes data files and codebooks by year at three levels of geography, as well as a documentation file.The data contained in the SDOH database are drawn from multiple sources and variables may have differing availability, patterns of missing, and methodological considerations across sources, geographies, and years. Users should refer to the data source documentation and codebooks, as well as the original data sources, to help identify these patterns

The table SDOH_ZCTA_2018 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 33120 rows across 166 variables.

This archived SDOH Database (beta version) is available for reference. The most recent version of the SDOH Database replaces the beta version and is available on the main page. To ensure consistency in variable names and construction, analyses should not combine data from the beta version and the updated database.Download DataThe SDOH Data Source Documentation (PDF, 1.5 MB) file contains information for researchers about the structure and contents of the database and descriptions of each data source used to populate the database.The Variable Codebook (XLSX, 494 KB) Excel file provides descriptive statistics for each SDOH variable by year.

The table county_2009 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 3225 rows across 201 variables.

The table SDOH_ZCTA_2015 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 33120 rows across 162 variables.

The table county_2017 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 3224 rows across 308 variables.

The table county_2018 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 3224 rows across 238 variables.

The table SDOH_ZCTA_2014 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 33120 rows across 162 variables.

These datasets contain summarizing clusters and dimensions of place-based social determinant of health measures for the United States from AHRQ's Social Determinants of Health Database (https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html), along with the underlying SDOH data. Summary clusters and dimensions are available for both counties and Zip codes. The measures are taken from the 2019 and 2018 AHRQ SDOH datasets. Underlying SDOH measures are in the domains of social context, economic context, education, physical infrastructure, and healthcare context. The summary dimensions and cluster memberships for counties and Zip codes were generated using principal components analysis and hierarchical cluster analysis techniques to provide simple high-level representations of the SDOH context for counties and Zip codes.

The table SDOH_ZCTA_2017 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 33120 rows across 166 variables.

The table SDOH_ZCTA_2011 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 33120 rows across 133 variables.

Brand performance data collected from AI search platforms for the query "social determinants of health data sources".

The table county_2011 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 3225 rows across 256 variables.

BackgroundDespite the incentives and provisions created for hospitals by the US Affordable Care Act related to value-based payment and community health needs assessments, concerns remain regarding the adequacy and distribution of hospital efforts to address SDOH. This scoping review of the peer-reviewed literature identifies the key characteristics of hospital/health system initiatives to address SDOH in the US, to gain insight into the progress and gaps.MethodsPRISMA-ScR criteria were used to inform a scoping review of the literature. The article search was guided by an integrated framework of Healthy People SDOH domains and industry recommended SDOH types for hospitals. Three academic databases were searched for eligible articles from 1 January 2018 to 30 June 2023. Database searches yielded 3,027 articles, of which 70 peer-reviewed articles met the eligibility criteria for the review.ResultsMost articles (73%) were published during or after 2020 and 37% were based in Northeast US. More initiatives were undertaken by academic health centers (34%) compared to safety-net facilities (16%). Most (79%) were research initiatives, including clinical trials (40%). Only 34% of all initiatives used the EHR to collect SDOH data. Most initiatives (73%) addressed two or more types of SDOH, e.g., food and housing. A majority (74%) were downstream initiatives to address individual health-related social needs (HRSNs). Only 9% were upstream efforts to address community-level structural SDOH, e.g., housing investments. Most initiatives (74%) involved hot spotting to target HRSNs of high-risk patients, while 26% relied on screening and referral. Most initiatives (60%) relied on internal capacity vs. community partnerships (4%). Health disparities received limited attention (11%). Challenges included implementation issues and limited evidence on the systemic impact and cost savings from interventions.ConclusionHospital/health system initiatives have predominantly taken the form of downstream initiatives to address HRSNs through hot-spotting or screening-and-referral. The emphasis on clinical trials coupled with lower use of EHR to collect SDOH data, limits transferability to safety-net facilities. Policymakers must create incentives for hospitals to invest in integrating SDOH data into EHR systems and harnessing community partnerships to address SDOH. Future research is needed on the systemic impact of hospital initiatives to address SDOH.

The table SDOH_ZCTA_2016 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 33120 rows across 162 variables.

This database has been created following the completion of two scoping reviews that examined the production of migrant health reseach in Ireland covering the period 2000 - 2023 (Villarroel N, Hannigan A, Severoni S, Puthoopparambil S, MacFarlane A. Migrant health research in the Republic of Ireland: a scoping review. BMC Public Health. 2019 Mar 20;19(1):324. doi: 10.1186/s12889-019-6651-2 and Cronin A, Hannigan A, Ibrahim N, Seidler Y, Owoeye BO, Gasmalla W, Moyles T, MacFarlane A. An updated scoping review of migrant health research in Ireland. BMC Public Health. 2024 May 28;24(1):1425. doi: 10.1186/s12889-024-18920-0).This database lists all 142 studies included in this work and which were analysed using the 9 strategic areas identified in the World Health Organisation Strategy and Action Plan (SaAP) for Refugee and Migrant Health 2016–2023 (World Health Organization. EUR/RC66/8 Strategy and action plan for refugee and migrant health in the WHO European Region. 2016).The WHO SaAP lists 9 strategic priority areas (SA's) to improve refugee and migrant health;SA1: Collaborative action on migrant health issuesSA2: Advocacy for the right to health of refugees and migrantsSA3: Addressing the social determinants of healthSA4: Achieving public health preparednessSA5: Strengthening health systemsSA6: Communicable diseasesSA7: Noncommunicable diseasesSA8: Health screening and assessmentSA9: Improving health information and communicationFor further discussion on the framework analysis, please refer to the studies themselves.The creation of this database was a recommendation of the second scoping review, which is in line with Goal 4 of Ireland’s current National Intercultural Health Strategy (NIHS) (HSE. Second National Intercultural Health Strategy 2018–2023 [Internet]. 2018. https://www.hse.ie/eng/about/who/primarycare/socialinclusion/intercultural-health/intercultural-health-strategy.pdf). NIHS emphasises the need for the development of an accessible and comprehensive evidence base to guide policymaking and provide detailed analyses of health trends and needs within migrant communities. Such evidence supports public health education and offers critical oversight of migrant health research in Ireland. This database serves as a valuable resource for refugees, migrants, researchers, policymakers, NGOs, and community stakeholders. It provides easy access to the body of evidence, when drafting informed submissions to government bodies regarding service gaps and assists health service planners in developing evidence-based policy responses tailored to migrant populations.This database has undergone two rounds of user testing, engaging participants from diverse sectors, including academia, research governance, migrant-focused NGOs, policymakers, and postgraduate researchers. As a result, this iteration reflects their extensive feedback, ensuring its relevance, usability, and alignment with stakeholder needs.

This is a non-interventional retrospective administrative claims database study using existing data from the Healthcare Integrated Research Database (HIRD®), for the period of January 1, 2016 – July 28, 2023. This study will describe the treatment patterns and social determinants of health (SDOH) for newly diagnosed ulcerative colitis (UC) patients, and how treatment and SDOH vary by geographic level.

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Race and Ethnicity measure in this release is an indicator of income inequity which is measured using the index of concentration at the extremes (ICE). ICE is a measure of social polarization within a particular geographic unit. It shows whether people or households in a geographic unit are concentrated in privileged or deprived extremes. The privileged group in this study is the number of households with a householder identifying as White alone, not Hispanic or Latino, with an income equal to or greater than $100,000. The deprived group in this study is the number of households with a householder identifying as a different race/ethnic group (e.g., Black alone, Asian alone, Hispanic or Latino), with an income equal to or less than $25,000. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Gender measures in this release include the state-level poverty ratio, which compares the proportion of females living in poverty to the proportion of males living in poverty in a given state in a given year. To work with the IPUMS CDOH data, researchers will need to first merge the NCHAT data to DS1 (MATCH ID and State FIPS Data). This merged file can then be linked to the IPUMS CDOH datafile (DS2) using the STATEFIPS variable.