title: Predicting the EuroQol Group's EQ-5D index from CDC's "Healthy Days" in a US sample.
abstract: Obtaining reliable preference-based scores from the widely used Healthy Days measures would enable calculation of quality-adjusted life years (QALYs) and cost-utility analyses in many US community populations and over time. Previous studies translating the Healthy Days to the EQ-5D, a preference-based measure, relied on an indirect method because of a lack of population-based survey data that asked both sets of questions of the same respondents.
  
  
  
  Data from the 2005-2006 National Health Measurement Study (NHMS; n = 3844 adults 35 years old or older) were used to develop regression-based models to estimate EQ-5D index scores from self-reported age, self-rated general health, and numbers of unhealthy days.
  
  
  
  The models explained up to 52% of the variance in the EQ-5D. Estimated EQ-5D scores matched well to the observed EQ-5D scores in mean scores overall and by age, gender, race/ethnicity, income, education, body mass index, smoking, and disease categories. The average absolute differences were 0.005 to 0.006 on a health utility scale. After estimating mean EQ-5D index scores overall and for various subgroups in a large representative US sample of Healthy Days respondents, the authors found that these mean scores also closely matched the corresponding mean scores of EQ-5D respondents obtained from another large US representative sample with an average absolute difference of 0.013 points.
  
  
  
  This study yielded a mapping algorithm to estimate EQ-5D index scores from the Healthy Days measures for populations of adults 35 years old and older. Such analysis confirms it is feasible to estimate mean EQ-5D index scores with acceptable validity for use in calculating QALYs and cost-utility analyses based on the overall model fit and relatively small differences between the observed and the estimated mean scores.
authors.affiliation: School of Nursing and Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, (HJ)
authors.forename: Haomiao
authors.initials: H
authors.lastname: Jia
authors.affiliation: Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia (MMZ, DGM)
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Zack
authors.affiliation: Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia (MMZ, DGM)
authors.forename: David G
authors.initials: DG
authors.lastname: Moriarty
authors.affiliation: Department of Population Health Sciences, University of Wisconsin, Madison (DGF)
authors.forename: Dennis G
authors.initials: DG
authors.lastname: Fryback
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002487:Centers for Disease Control and Prevention, U.S.; D005260:Female; D006304:Health Status; D054624:Health Status Disparities; D006305:Health Status Indicators; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D011594:Psychometrics; D011788:Quality of Life / Q000523:psychology*; D019057:Quality-Adjusted Life Years; D012044:Regression Analysis; D057566:Self Report; D011795:Surveys and Questionnaires; D014481:United States