CHRR County Health Rankings and Roadmaps - onetomapanalytics/Meta_Data GitHub Wiki

CHR&R - County Health Rankings and Roadmaps

General description

  1. Database primary purpose - Provide data, evidence, guidance, and examples to raise awareness of the multiple factors that affect health and assist community leaders in enhancing community strength to improve health equity.
  2. Overall data type - Social Determinant of Health (SDoH)
  3. Dataset type - Longitudinal
  4. Data source - Registry/cohort
  5. Data level - County level
  6. Geographic location of the data collection sites - United States
  7. Sponsor, manager, or home institution - University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation (RWJF)
  8. Date range - 2010 - 2021
  9. Geolocation data - State and county FIPS code, 5-digit FIPS code, state abbreviation, and county name
  10. Dates - release year
  11. Longitudinal tracking - Track county
  12. Clinical areas of interest - all
  13. Variables that are uniquely present in this dataset - The CHR&R are unique in their ability to assess the health of nearly every county in all 50 states, and they are accompanied by guidance, tools, and resources designed to accelerate community learning and action. CHR&R is renowned for effectively translating and communicating complex data and evidence-based policy into understandable models, reports, and products that deepen the understanding of what makes communities healthy and inspire and support improvement efforts.
  14. Other - Guidance and tools for policies, programs, and health improvement strategies at Policies & Programs menu

Applicable methods

  1. Association analysis, such as univariate and multivariate regression models (1), linear regression models (2), logistic regression model (3, 4), negative binomial regression (5), random effects regression models (6), Poisson regression model (7)
  2. Exploratory analysis (8, 9)
  3. Inferential tests (10)
  4. Time-series (11, 12)
  5. Spatial regression models (13)
  6. Machine learning (14, 15, 16)

High-impact designs

  • Evaluate the association between health factors and health outcomes and describe the performance of the CHR&R model factor weightings by state (17)

  • Compare different methods of estimating the effect of health care on health outcomes (18)

  • Assess the association between health care satisfaction and community-level health outcomes (19)

  • Estimate the relative contributions of health behaviors, clinical care, social and economic factors, and the physical environment to health outcomes (20)

  • Examine the association between environmental quality measures and health outcomes, and test whether a revised environmental quality measure could improve the model (21)

  • Measure mortality and smoking rates in a rural community over decades before, during, and after prevention program reductions (22)

  • Evaluate the barriers to access to screening programs (23)

  • Enrichment of the dataset by linking the CHR&R with another dataset using geolocation data, such as VA administrative data (24); the Area Health Resources Files (AHRF) (25, 26, 27); New York state Community Health Indicator Reports (28); U.S. Census Bureau (29, 30, 31); National Center for Health Statistics (32); Behavioral Risk Factor Surveillance System (32, 33); Medicaid analytic eXtract files (26); American Hospital Association (AHA) annual survey (34); Hospital Inpatient Prospective Payment System (IPPS) data (34); CMS Hospital Compare (34); National Center for Health Statistics, Health Resources and Services Administration, and Centers for Disease Control and Prevention (35); National Association of County and City Health Officials (36); Medicare Current Beneficiary Survey (37); Food Environment Atlas Data File (27); Internal Revenue Service (38)

Data dictionary

To access the data dictionary, click here

Variable categories

  1. Length of life (e.g., premature death, infant and child mortality, life expectancy)
  2. Quality of life (e.g., low birth weight, poor physical or mental health, physical or mental distress, diabetes prevalence, HIV prevalence)
  3. Health behaviors (e.g., rates of alcohol and drug use, diet and exercise, sexual activity, tobacco use)
  4. Clinical care (e.g., access to and quality of health care)
  5. Social and economic factors (e.g., education, employment, income, family and social support, community safety)
  6. Physical environment [e.g., air and water quality, housing (severe problems, homeownership, cost burden, broadband access), and transit (driving alone, traffic volume)]

Linkage to other datasets

  • Linkages can be established for any dataset that might have geolocation data (i.e., state and county FIPS code, 5-digit FIPS code, state abbreviation, and county name)