title: Using health state utility values from the general population to approximate baselines in decision analytic models when condition-specific data are not available.
abstract: BACKGROUND
  Decision analytic models in health care require baseline health-related quality of life data to accurately assess the benefits of interventions. The use of inappropriate baselines such as assuming the value of perfect health (EQ-5D = 1) for not having a condition may overestimate the benefits of some treatment and thus distort policy decisions informed by cost per quality adjusted life years thresholds.
  
  
  OBJECTIVE
  The primary objective was to determine if data from the general population are appropriate for baseline health state utility values (HSUVs) when condition specific data are not available.
  
  
  METHODS
  Data from four consecutive Health Surveys for England were pooled. Self-reported health status and EQ-5D data were extracted and used to generate mean HSUVs for cohorts with or without prevalent health conditions. These were compared with mean HSUVs from all respondents irrespective of health status.
  
  
  RESULTS
  More than 45% of respondents (n = 41,174) reported at least one condition and almost 20% reported at least two. Our results suggest that data from the general population could be used to approximate baseline HSUVs in some analyses, but not all. In particular, HSUVs from the general population would not be an appropriate baseline for cohorts who have just one condition. In these instances, if condition specific data are not available, data from respondents who report they do not have any prevalent health condition may be more appropriate. Exploratory analyses suggest the decrement on health-related quality of life may not be constant across ages for all conditions and these relationships may be condition specific. Additional research is required to validate our findings.
authors.affiliation: The University of Sheffield, Sheffield, UK. r.m.ara@sheffield.ac.uk
authors.forename: Roberta
authors.initials: R
authors.lastname: Ara
authors.forename: John E
authors.initials: JE
authors.lastname: Brazier
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D003661:Decision Support Techniques; D004739:England / Q000453:epidemiology; D005260:Female; D006305:Health Status Indicators; D006306:Health Surveys / Q000379:methods* / Q000592:standards*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance / Q000379:methods*