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_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.

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 first Social Drivers of Health (SDoH) dataset contains percentages of preventable hospitalizations (i.e., discharges) by Race/Ethnicity, preferred language spoken, expected payer, percent of employment, percent of home ownership, percent of park access and percent of access to basic kitchen facilities by the stated year. Preventable hospitalizations rates were created by dividing the number of patients who are 18 years and older and were admitted to a hospital for at least one of the preventable hospitalization diagnoses (see list below) by the total number of hospitalizations. List of preventable hospitalization diagnoses: diabetes with short-term complications, diabetes with long-term complications, uncontrolled diabetes without complications, diabetes with lower-extremity amputation, chronic obstructive pulmonary disease, asthma, hypertension, heart failure, angina without a cardiac procedure, dehydration, bacterial pneumonia, or urinary tract infection were counted as a preventable hospitalization. These conditions correspond with the conditions used in the Agency for Healthcare Research and Quality’s (AHRQ), Prevention Quality Indicator - Overall Composite Measure (PQI #90). The SDoH "overtime" dataset contains percentages of preventable hospitalizations (i.e., discharges) by Race/Ethnicity, preferred language spoken and expected payer overtime in the stated year range.

This dataset contains ZCTA-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

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 dataset contains place-level (incorporated and census-designated places) social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

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.

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 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.

This data package contains information on the conditions in the environment in which people are born, live, learn, work, play, worship; it also has age information that affects a wide range of health, functioning, and quality-of-life outcomes and risks. Conditions (e.g., social, economic, and physical) in these various environments and settings (e.g., school, church, workplace, and neighborhood) have been referred to as place.

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.

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

This dataset contains census tract-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning.

To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

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 included dataset contains 10,000 synthetic Veteran patient records generated by Synthea. The scope of the data includes over 500 clinical concepts across 90 disease modules, as well as additional social determinants of health (SDoH) data elements that are not traditionally tracked in electronic health records. Each synthetic patient conceptually represents one Veteran in the existing US population; each Veteran has a name, sociodemographic profile, a series of documented clinical encounters and diagnoses, as well as associated cost and payer data. To learn more about Synthea, please visit the Synthea wiki at https://github.com/synthetichealth/synthea/wiki. To find a description of how this dataset is organized by data type, please visit the Synthea CSV File Data Dictionary at https://github.com/synthetichealth/synthea/wiki/CSV-File-Data-Dictionary.The included dataset contains 10,000 synthetic Veteran patient records generated by Synthea. The scope of the data includes over 500 clinical concepts across 90 disease modules, as well as additional social determinants of health (SDoH) data elements that are not traditionally tracked in electronic health records. Each synthetic patient conceptually represents one Veteran in the existing US population; each Veteran has a name, sociodemographic profile, a series of documented clinical encounters and diagnoses, as well as associated cost and payer data. To learn more about Synthea, please visit the Synthea wiki at https://github.com/synthetichealth/synthea/wiki. To find a description of how this dataset is organized by data type, please visit the Synthea CSV File Data Dictionary at https://github.com/synthetichealth/synthea/wiki/CSV-File-Data-Dictionary.

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.

This dataset contains county-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning.

To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

[1] Status is determined using the baseline, final, and target value. The statuses used in Healthy People 2020 were:

1 - Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target. (The percentage of targeted change achieved was equal to or greater than 100%.); (ii) The baseline and most recent values were equal to or exceeded the target. (The percentage of targeted change achieved was not assessed.)

2 - Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change.

3 - Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point.

4 - Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline.

5 - Baseline only—The objective only had one data point, so progress toward target attainment could not be assessed. Note that if additional data points did not meet the criteria for statistical reliability, data quality, or confidentiality, the objective was categorized as baseline only.

6 - Informational—A target was not set for this objective, so progress toward target attainment could not be assessed.

[2] The final value is generally based on data available on the Healthy People 2020 website as of January 2020. For objectives that are continuing into Healthy People 2030, more recent data are available on the Healthy People 2030 website: https://health.gov/healthypeople.

[3] For objectives that moved toward their targets, movement toward the target was measured as the percentage of targeted change achieved (unless the target was already met or exceeded at baseline):

Percentage of targeted change achieved = (Final value - Baseline value) / (HP2020 target - Baseline value) * 100

[4] For objectives that were not improving, did not meet or exceed their targets, and did not move towards their targets, movement away from the baseline was measured as the magnitude of the percent change from baseline:

Magnitude of percent change from baseline = |Final value - Baseline value| / Baseline value * 100

[5] Statistical significance was tested when the objective had a target, at least two data points (of unequal value), and available standard errors of the data. A normal distribution was assumed. All available digits were used to test statistical significance. Statistical significance of the percentage of targeted change achieved or the magnitude of the percentage change from baseline was assessed at the 0.05 level using a normal one-sided test.

[6] For more information on the Healthy People 2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf.

NOTE: Measurable objectives have baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 database.