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Datasets | Analytic Tools

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������Ƶhas extensive data resources that can be used today by researchers to study the relationships between health, SDOH, and healthcare.

The Healthcare Cost and Utilization Project (HCUP)

  The includes the largest collection of longitudinal hospital care data in the United States, with all-payer, encounter-level information beginning in 1988. , a free, online query tool, provides access to health statistics and information on hospital inpatient and emergency department utilization. National and State-level datasets, available for purchase, include inpatient, pediatric, and emergency department discharge data, as well as ambulatory surgery and readmission databases.

SDOH variables that can be studied in HCUP databases include: race, ethnicity, median household income for patient's ZIP Code, population density (i.e., urban-rural continuum), homelessness (for some States), and expected payer. In addition, the HCUP data can be used in conjunction with other data sources using geographic indicators, including county and ZIP Code of patient or of hospital where care was received, depending on the HCUP dataset.

Find information on .

  The Medical Expenditure Panel Survey (MEPS)

  The , which began in 1996, is a set of large-scale surveys of families and individuals, their medical providers (doctors, hospitals, pharmacies, etc.), and employers across the United States. MEPS collects data on the specific health services that Americans use, how frequently they use them, the cost of these services, and how they are paid for, as well as data on the cost, scope, and breadth of health insurance held by and available to U.S. workers.

Individual-level SDOH variables that can be studied in the MEPS household survey datasets include:

• Demographics (e.g., age, sex, race, ethnicity, nativity/time in the U.S., language).
• Socioeconomics (household income, education, employment, food insecurity).
• Attitudes and values (risk tolerance, attitudes about insurance and doctors, past experiences with health care providers).
• Community (e.g., urban-rural location of residence, point of care).
• Access to care (e.g., public and private insurance coverage and eligibility, usual source of care, unmet need).

Download .

In addition, all sample households are geocoded and can be linked to a variety of geographic and/or spatial data (e.g., county or tract characteristics, spatial environmental data, distance to providers or facilities). To do so, analysts must apply for permission to use these data in the ������ƵSecure Data Center or an affiliated Census Research Data Center.

To enhance the ability to study SDOH, ������Ƶis fielding a self-administered questionnaire (SAQ) that will ask adult respondents additional questions related to food insecurity, housing security, transportation, financial strain, general well-being, social connectedness, physical safety, and adverse childhood experiences.

Selected Finding from these datasets

������Ƶpublishes Statistical Briefs using data from HCUP and MEPS. Below are some Stat Briefs published in recent years that relate to SDOH.

• ;  Infographic ().
• . This 2016 brief describes how two States have analyzed race and ethnicity data and targeted interventions to specific geographic locations. 
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������Ƶcollects data on SDOH and produces reports, chartbooks, and brief data analyses.

National Healthcare Quality and Disparities Reports (QDR)

The National Healthcare Quality and Disparities Report (QDR) presents trends for measures related to access to care, affordable care, care coordination, effective treatment, healthy living, patient safety, and person-centered care using national data. The report presents, in chart form, the latest available findings on quality of and access to healthcare, as well as disparities related to race and ethnicity, income, and other SDOH.

NHQDR Chartbooks related to SDOH include:

• Rural Health Care (updated October 2017) (PDF, 2.5 MB).
• Care Affordability (updated August 2016) (PDF, 1.53 MB).
• Access (updated May 2016) (PDF, 3 MB).
• Health Care for Blacks (February 2016) (PDF, 2.96 MB).
• Hispanic Health Care (September 2015) (PDF, 4.4 MB).

You can conduct your own SDOH analyses of QDR data using the free online . The available disparity categories are race, ethnicity, education, income, geographic location, gender, and health insurance status. Disparities data may be limited for some measures.

You can also view the data by State using the tool. The available disparity categories in the State Snapshots tool are race, ethnicity, and income.

Datasets and Analytic Tools That Can Power SDOH Research

You can conduct your own SDOH analyses using AHRQ’s free online query tools.

• . The available SDOH categories are community-level income, urbanicity/rurality, expected payer, and community-level statistics. In addition, you can get quick statistics on  to examine topics by community-level income, urbanicity/rurality, and expected payer.
• . The available SDOH categories available on the MEPS Data Tools are age, sex, race, ethnicity, marital status, employment status, income, insurance coverage, self-rated health and mental health, and geographic region of residence.

������ƵVisualization Resources of Community-Level Social Determinants of Health Challenge

The ������ƵVisualization Resources of Community-Level Social Determinants of Health Challenge seeks tools that support visualizing data clusters to enhance the research and analysis of community-level health services. Challenge participants were charged with developing visualization tools to augment the insights drawn from the analysis of medical expenditure and healthcare utilization data at the community level. Tools must use publicly available and free SDOH data from Federal, State, or locally available data sources. In February, ������Ƶawarded Mathematica the grand prize of the Agency’s Visualization Resources of Community Health-Level Social Determinants of Health Challenge for its “Community Connector” data visualization tool. The grand prize comes with a $50,000 award. Salish Research Group was named the runner up and will receive $35,000, and Rensselaer Polytechnic Institute was named third-place finisher and will receive $15,000. For more details, read the ������ƵViews blog and press release.