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.***Microdata: YesLevel of Analysis: Local - Tract, CountyVariables Present: Separate DocumentFile Layout: .xslxCodebook: Yes Methods: YesWeights (with appropriate documentation): YesPublications: NoAggregate Data: Yes

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_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_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.

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.

These datasets contain data from the AHRQ Social Determinants of Health (SDOH) Database (https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html), processed to facilitate machine learning/multivariate analyses focusing on the healthcare context of counties. The datasets derive from the AHRQ 2019 and 2018 county-level SDOH files. Three sets of files are provided. The first "Raw" set has the source SDOH data with a few core pre-processing steps applied. The second, “Full” set has variables characterizing the health and healthcare context of counties (rather than outcomes), with further processing steps applied to facilitate multivariate and machine learning analytics (e.g. handling of missing data, normalizing, standardizing). The third set, labeled “Reduced”, incorporates those same data processing steps but in addition has had a further data reduction step applied in which groups of highly intercorrelated variables were removed and replaced with corresponding principal component scores, one for each group. These files would be useful for investigators interested in characterizing and comparing the broad SDOH context of US counties.

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.

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 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.

Social Determinants of Health Data Platforms Market Outlook

According to our latest research, the global Social Determinants of Health (SDOH) Data Platforms market size reached USD 3.2 billion in 2024. The market is expected to grow at a robust CAGR of 18.7% during the forecast period, reaching a projected value of USD 15.1 billion by 2033. This significant growth is primarily driven by the increasing recognition of how non-clinical factors—such as economic stability, education, neighborhood, and social context—profoundly impact health outcomes and healthcare costs worldwide.

One of the most compelling growth factors for the Social Determinants of Health Data Platforms market is the intensifying focus on value-based care and population health management among healthcare stakeholders. As healthcare systems globally transition from traditional fee-for-service models to value-based care, there is a growing need to incorporate SDOH data into clinical workflows, risk stratification, and care coordination. Payers, providers, and government agencies are investing in platforms that aggregate, analyze, and operationalize diverse data sources, including demographic, socioeconomic, and behavioral factors. This integration enables healthcare organizations to identify at-risk populations, personalize interventions, and ultimately reduce costly health disparities, fueling substantial market demand.

Another pivotal driver is the expanding regulatory and policy support for addressing social determinants in healthcare delivery. Government agencies, especially in North America and Europe, are enacting mandates and incentives to encourage the collection and utilization of SDOH data. For instance, the Centers for Medicare & Medicaid Services (CMS) in the United States has introduced new requirements and payment models that reward the integration of social risk factors into patient assessments and care planning. Similarly, the World Health Organization (WHO) and other international bodies are emphasizing the importance of SDOH in achieving equitable health outcomes. These regulatory tailwinds are prompting healthcare organizations to adopt advanced SDOH data platforms, further accelerating market growth.

Technological advancements in data analytics, artificial intelligence, and interoperability are also propelling the Social Determinants of Health Data Platforms market forward. Modern SDOH data platforms leverage machine learning algorithms and predictive analytics to derive actionable insights from vast, complex datasets. Enhanced interoperability standards, such as FHIR (Fast Healthcare Interoperability Resources), are making it easier to integrate SDOH data with electronic health records (EHRs) and other health IT systems. These innovations are not only improving the accuracy and timeliness of SDOH data capture but also enabling real-time decision support for clinicians and care managers. As a result, healthcare organizations are increasingly deploying sophisticated SDOH data platforms to gain a competitive edge and improve patient outcomes.

From a regional perspective, North America currently dominates the Social Determinants of Health Data Platforms market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The United States, in particular, is at the forefront due to its advanced healthcare IT infrastructure, proactive regulatory environment, and substantial investments in population health initiatives. However, the Asia Pacific region is expected to register the fastest CAGR during the forecast period, driven by rising healthcare digitization, growing awareness of health disparities, and supportive government policies. Europe is also witnessing steady growth, bolstered by cross-border health data initiatives and strong public health systems. Latin America and the Middle East & Africa are gradually emerging as promising markets as healthcare modernization efforts gain momentum.

The integration of Social Determinants of Health Analytics AI is becoming increasingly vital in the healthcare industry. By leveraging artificial intelligence, healthcare providers can analyze vast amounts of SDOH data to uncover patterns and insights that were previously unattainable. AI-driven analytics enable the identification of at-risk populations more accurately and efficiently

The table SDOH_ZCTA_2012 is part of the dataset Social Determinants of Health Database (SDOH), available at https://redivis.com/datasets/js6v-91cgjnnm6. It contains 33120 rows across 142 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.

Social Determinants of Health Market Outlook

According to our latest research, the global Social Determinants of Health (SDOH) market size reached USD 7.2 billion in 2024, reflecting robust momentum driven by the integration of advanced analytics and digital health solutions across healthcare ecosystems. The market is anticipated to expand at a CAGR of 22.8% from 2025 to 2033, with the total market size expected to reach USD 59.6 billion by 2033. This accelerated growth is primarily fueled by the increasing recognition of the critical impact that social, economic, and environmental factors have on health outcomes, as well as the growing adoption of value-based care models globally. As per our latest research, the demand for holistic patient care and the need to address health disparities are the main catalysts propelling the SDOH market forward.

The surge in the Social Determinants of Health market is fundamentally driven by the global shift towards preventive healthcare and population health management. Healthcare organizations are increasingly recognizing that clinical care alone accounts for only a fraction of overall health outcomes, with social determinants such as housing, education, employment, and food security playing a pivotal role. This realization is prompting investments in SDOH data collection, analytics, and intervention programs that enable healthcare providers and payers to identify at-risk populations, design targeted interventions, and ultimately improve health equity. The proliferation of electronic health records (EHRs) and interoperable data platforms is further facilitating the integration of SDOH insights into clinical workflows, enhancing the ability to deliver personalized and effective care.

Another major growth driver for the SDOH market is the transition to value-based care and risk-based reimbursement models. Governments and private payers worldwide are incentivizing healthcare organizations to focus on outcomes rather than volume, which necessitates a comprehensive understanding of the social and environmental factors influencing patient health. As a result, there is a growing demand for advanced analytics, machine learning, and artificial intelligence solutions that can process and interpret large volumes of SDOH data. These technologies are enabling stakeholders to stratify risk, predict adverse health events, and allocate resources more efficiently, thereby reducing costs and improving quality of care. The increasing availability of real-time data from wearable devices, mobile applications, and community sources is also expanding the scope and effectiveness of SDOH initiatives.

Furthermore, regulatory mandates and policy initiatives are playing a crucial role in accelerating the adoption of SDOH solutions. In the United States, for instance, the Centers for Medicare & Medicaid Services (CMS) and other agencies have introduced guidelines and incentive programs that require healthcare organizations to screen for and address social determinants as part of routine care. Similar efforts are being observed in Europe and Asia Pacific, where governments are prioritizing health equity and social inclusion in their public health agendas. These policies are not only driving demand for SDOH data analytics and intervention platforms but are also fostering collaboration between healthcare providers, payers, community organizations, and technology vendors, thereby creating a vibrant and dynamic market landscape.

From a regional perspective, North America continues to dominate the SDOH market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced healthcare infrastructure, strong regulatory support, and early adoption of health IT solutions. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid urbanization, rising healthcare expenditures, and increasing awareness of social health disparities. Europe also presents significant opportunities, especially with the implementation of digital health strategies and cross-sector collaborations aimed at addressing the root causes of health inequities. Latin America and the Middle East & Africa are gradually catching up, supported by government-led health reforms and international investments in healthcare infrastructure.

Evaluate treatment and outcomes by social determinants of health (SDoH) in multiple myeloma (MM), which are important for improving care and outcomes. This was a retrospective study of real-world patients enrolled in a US insurance claims database (MM diagnosis, July 2018–December 2022) with linkage to a SDoH database, supplemented with mortality, provider affiliation (academic/community), and socioeconomically disadvantaged area databases. Treatment and outcomes were evaluated across SDoH domains: race/ethnicity, education level, transportation access, food insecurity, risky health behaviors, living in disadvantaged areas, healthcare needs, and ease of healthcare-systems engagement. The study included 4768 patients (2295 and 2731 with care-setting and treatment data); median follow-up, 584 days. Patients treated in academic versus community settings were less likely to be food insecure and live in disadvantaged areas and had lower healthcare needs. Stem cell transplant was more common in White versus non-White patients, those with low versus high food insecurity and healthcare needs, and high versus low ease of healthcare-systems engagement. In multivariable analysis, high versus low disadvantaged areas (HR = 1.75) and medium versus low food insecurity (HR = 1.80) were associated with shorter overall survival. These findings indicate a need for improved access to care in the broader MM population.

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.

Social Determinants of Health Analytics Market Outlook

According to our latest research, the global Social Determinants of Health (SDOH) Analytics market size reached USD 3.21 billion in 2024, demonstrating robust growth driven by the rising emphasis on value-based care and population health management. The market is expected to expand at a CAGR of 22.7% from 2025 to 2033, reaching an estimated USD 24.38 billion by 2033. This growth is primarily fueled by the increasing integration of social, economic, and environmental data into healthcare analytics, as well as the growing recognition of the impact of social determinants on health outcomes and healthcare costs.

One of the primary growth factors for the Social Determinants of Health Analytics market is the global shift toward value-based care and population health management. Healthcare systems and payers are increasingly leveraging SDOH analytics to identify at-risk populations, reduce healthcare disparities, and improve health outcomes by addressing non-clinical factors such as housing, education, income, and access to nutritious food. The integration of SDOH data into clinical workflows and electronic health records (EHRs) allows for more comprehensive patient profiles, enabling healthcare providers to develop targeted interventions and preventive care strategies. Additionally, the growing adoption of interoperable health IT solutions and advanced analytics platforms is enhancing the ability of organizations to aggregate and analyze large volumes of SDOH data efficiently and effectively.

Another significant growth driver is the rising investment in digital health infrastructure and the proliferation of big data technologies in healthcare. Governments, private organizations, and non-profit entities are increasingly funding initiatives aimed at capturing, standardizing, and analyzing SDOH data to address health inequities and support public health objectives. The widespread deployment of cloud-based analytics solutions is facilitating real-time access to SDOH insights, enabling stakeholders to make informed decisions at both the individual and population levels. Moreover, advances in artificial intelligence (AI) and machine learning are further enhancing the predictive capabilities of SDOH analytics, allowing for more accurate risk stratification and resource allocation.

The regulatory landscape is also playing a crucial role in shaping the growth trajectory of the SDOH analytics market. Regulatory bodies in major markets such as the United States and Europe are mandating the inclusion of SDOH data in quality reporting and reimbursement frameworks, incentivizing healthcare organizations to invest in robust analytics solutions. Furthermore, the increasing focus on health equity and social justice is driving the adoption of SDOH analytics among government agencies, payers, and community organizations. These regulatory and policy developments are expected to sustain the momentum of market growth over the forecast period.

Regionally, North America holds the dominant share of the SDOH analytics market, largely due to the advanced healthcare IT infrastructure, supportive regulatory environment, and high adoption rates of value-based care models. The United States, in particular, is at the forefront of integrating SDOH data into healthcare delivery, supported by significant investments from both public and private sectors. Europe is also witnessing substantial growth, driven by national initiatives to address health disparities and improve population health outcomes. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by increasing healthcare digitization and government efforts to enhance access to care. Latin America and the Middle East & Africa are gradually adopting SDOH analytics, albeit at a slower pace, due to infrastructural and economic challenges.

Component Analysis

The SDOH analytics market by component is segmented into software and services, with both segments playing critical roles in the overall ecosystem. The software segment encompasses analytics platforms, data integration tools, dashboards, and reporting solutions that enable healthcare organizations to collect, process, and visualize SDOH data. These software solutions are increasingly leveraging advanced technologies such as AI, machine learning, and natural language processing to extract actionable insights from complex and diverse data sources. The demand for user-fr

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.

MIMIC-SDOH is a database resulting from the integration of the MIMIC-IV clinical database with Social Determinants of Health (SDOH) databases.

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.

This project presents an annotated dataset derived from MIMIC-III and MIMIC-IV discharge summaries, focusing on key Social Determinants of Health (SDoH) factors—social support, occupation, and substance use—and their association with adverse pregnancy outcomes. Leveraging a combination of manual annotation and advanced Natural Language Processing (NLP) techniques, we developed and validated multiple models (rule-based, Word2Vec, and Clinical BERT) to automate the extraction of these features. The resulting de-identified dataset, along with our code scripts for data preprocessing, model development, and validation, is made publicly available through this PhysioNet project.