AHRF Area Health Resources File - onetomapanalytics/Meta_Data GitHub Wiki

AHRF - Area Health Resources File

General description

  1. Database primary purpose - Combines information from different sources on health care professions, health facilities, population characteristics, economics, health professions training, hospital utilization, hospital expenditures, and environment at the county, state, and national levels. The AHRF data are designed to be used by planners, policymakers, researchers, and others interested in the nation’s healthcare delivery system and factors that may impact health status and healthcare in the United States.
  2. Overall data type - Health facilities, hospital expenditures, demographics, SDOH, economics, environment
  3. Dataset type - Cross-sectional
  4. Data source - Over 50 data sources, including American medical and hospital associations (e.g., American Hospital Association, American Dental Association, American Osteopathic Association, American Medical Association), census, Center for Medicare and Medicaid Services (e.g., CMS Enroll Dashboard, CMS Marketplace, CMS State County Penetration), the U.S. Dept of Veterans Affairs, Environmental Protection Agency, among others
  5. Data level - Hospital level
  6. Geographic location of the data collection sites - United States
  7. Sponsor, manager, or home institution - Bureau of Health Workforce, Health Resources and Services Administration
  8. Date range - 2018 - 2020 for both county- and national-levels
  9. Geolocation data - State, county, FIPS state and county codes, census region and division, federal region
  10. Dates - Year of the data
  11. Financial variables - facilities reporting expenses, payment rates, and fee for service
  12. Clinical areas of interest - all
  13. Number of records - Approximately a 5% sample of the U.S. population.
  14. Variables that are uniquely present in this dataset - This dataset provides current as well as historical data for more than 6,000 variables for each of the nation’s counties, as well as state and national data.
  15. Database caveats and limitations - Data are provided from different sources using different approaches, and thus it provides slightly different values.

Applicable methods

  1. Association analysis, such as Generalized Estimating Equation (GEE) (1, 2)), multiple linear regression (3), multivariable logistic regression models (4, 5), Cox regression (6), hierarchical linear regression (7)
  2. Causal analysis, such as difference-in-difference (8, 9)
  3. Descriptive analysis (10)
  4. Machine learning (11)
  5. Propensity scores (12)
  6. Time-to-event (13)

High-impact designs

  • Enrichment of the dataset by linking the AHRF with another dataset using FIPS codes, such as the Medicare claims (1, 11), Medicaid Analytic Extract (8), VA health facility information from the Department of Veterans Affairs (10), Health and Retirement Study (HRS) (14), US Census Bureau (15), and a combination of several datasets (16)

Data dictionary

To access the data dictionaries, click here for county level and here for state and national level.

Variable categories

  1. Demographics (e.g., population estimate, census population, gender, race, ethnicity, age, foreign-born, urban/rural, veterans)
  2. Population rates (e.g., rates by age, sex, and race/ethnicity; birth, infant mortality, infant death, AIDS death, infectious diseases, diseases rates, disability)
  3. Income variables (e.g., per capita personal income, household, poverty status)
  4. Area resources [e.g., low education, low employment, high poverty, population loss, Health Professional Shortage Area (HPSA) codes (e.g., primary care, dentists, mental health)]
  5. Area classification (e.g., metropolitan division; rural-urban; economic-, farming-, mining-, manufacturing-dependent)
  6. Health professions (physicians, dentists, nurses, psychologists, etc.) characteristics (e.g., sex, age, specialty, professional activity, office/hospital practice, residents, teaching, researchers)
  7. Health facilities (e.g., total number, short or long-term, psychiatric, rehabilitation, children)
  8. Utilization (e.g., inpatient days per facility type, outpatient visits, emergency department visits, number of surgeries)
  9. Environment (e.g., total area, water area, land area, population density, toxic waste sites, air quality measures)

Linkage to other datasets

  • Linkages can be established for any dataset that might have geographic descriptors, such as FIPS codes.