title: Health Condition Impacts in a Nationally Representative Cross-Sectional Survey Vary Substantially by Preference-Based Health Index.
abstract: Many cost-utility analyses rely on generic utility measures for estimates of disease impact. Commonly used generic preference-based indexes may generate different absolute estimates of disease burden despite sharing anchors of dead at 0 and full health at 1.0.
  
  
  
  We compare the impact of 16 prevalent chronic health conditions using 6 utility-based indexes of health and a visual analog scale.
  
  
  
  Data were from the National Health Measurement Study (NHMS), a cross-sectional telephone survey of 3844 adults aged 35 to 89 years in the United States.
  
  
  
  The NHMS included the EuroQol-5D-3L, Health and Activities Limitation Index (HALex), Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), preference-based scoring for the SF-36v2 (SF-6D), Quality of Well-Being Scale, and visual analog scale. Respondents self-reported 16 chronic conditions. Survey-weighted regression analyses for each index with all health conditions, age, and sex were used to estimate health condition impact estimates in terms of quality-adjusted life years (QALYs) lost over 10 years. All analyses were stratified by ages 35 to 69 and 70 to 89 years.
  
  
  
  There were significant differences between the indexes for estimates of the absolute impact of most conditions. On average, condition impacts were the smallest with the SF-6D and EQ-5D-3L and the largest with the HALex and HUI3. Likewise, the estimated loss of QALYs varied across indexes. Condition impact estimates for EQ-5D-3L, HUI2, HUI3, and SF-6D generally had strong Spearman correlations across conditions (i.e., >0.69).
  
  
  
  This analysis uses cross-sectional data and lacks health condition severity information.
  
  
  
  Health condition impact estimates vary substantially across the indexes. These results imply that it is difficult to standardize results across cost-utility analyses that use different utility measures.
authors: Department of Internal Medicine, University of Pittsburgh, Pittsburgh, PA (JH)
authors: Janel
authors: J
authors: Hanmer
authors: Partnership for Health Analytic Research, LLC, Beverly Hills, CA (DC)
authors: Dasha
authors: D
authors: Cherepanov
authors: Population Health Sciences, University of Wisconsin-Madison, Madison, WI (MP, DF)
authors: Mari
authors: M
authors: Palta
authors: UCLA Department of Health Services, University of California, Los Angeles, CA (RMK)
authors: Robert M
authors: RM
authors: Kaplan
authors: Population Health Sciences, University of Wisconsin-Madison, Madison, WI (MP, DF)|Department of Economics, McMaster University, Hamilton, ON, Canada (DF)
authors: David
authors: D
authors: Feeny
authors: Health Utilities Incorporated, Dundas, ON (DGF)
authors: Dennis G
authors: DG
authors: Fryback
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002908:Chronic Disease / Q000191:economics* / Q000523:psychology; D003430:Cross-Sectional Studies; D005260:Female; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D011594:Psychometrics; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D012044:Regression Analysis; D012720:Severity of Illness Index; D012737:Sex Factors; D011795:Surveys and Questionnaires / Q000592:standards*; D014481:United States / Q000453:epidemiology
keywords: cost utility analysis; quality of life; utility inconsistencies; utility measurement