title: Does self-reported health bias the measurement of health inequalities in U.S. adults? Evidence using anchoring vignettes from the Health and Retirement Study.
abstract: OBJECTIVE
  Measurement of health inequalities based on self-reports may be biased if individuals use response scales in systematically different ways. We use anchoring vignettes to test and adjust for reporting differences by education, race/ethnicity, and gender in self-reported health in 6 domains (pain, sleep, mobility, memory, shortness of breath, and depression).
  
  
  METHODS
  Using data from the 2006 U.S. Health and Retirement Study (HRS) and the 2007 Disability Vignette Survey, we estimated generalized ordered probit models of the respondent's rating of each vignette character's health problem, allowing cut-points to vary by age, gender, education, and race/ethnicity. We then used one-step hierarchical ordered probit (HOPIT) models to jointly estimate the respondent's cut-points from the vignettes and the severity of the respondent's own health problems based on these vignette cut-points.
  
  
  RESULTS
  We found strong evidence of reporting differences by age, gender, education, and race/ethnicity, with the magnitude depending on the specific health domain. Overall, traditional models not accounting for reporting differences underestimated the magnitude of health inequalities by education and race/ethnicity.
  
  
  CONCLUSIONS
  These results suggest caution in relying on self-reported health measures to quantify and explain health disparities by socioeconomic status and race/ethnicity/ethnicity in the United States. The findings support expansion of the use of anchoring vignettes to properly account for reporting differences in self-reports of health.
authors.affiliation: School of Public Health, Hunter College, City University of New York (CUNY), NY 10010, USA. jdowd@hunter.cuny.edu
authors.forename: Jennifer Beam
authors.initials: JB
authors.lastname: Dowd
authors.forename: Megan
authors.initials: M
authors.lastname: Todd
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001294:Attitude to Health; D015982:Bias; D044383:Black People / Q000523:psychology*; D004185:Disability Evaluation; D004522:Educational Status; D005260:Female; D015577:Geriatric Assessment / Q000706:statistics & numerical data; D006305:Health Status Indicators; D006306:Health Surveys; D006630:Hispanic or Latino / Q000523:psychology*; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D012959:Socioeconomic Factors; D014481:United States; D044465:White People / Q000523:psychology*; D001741:Black or African American