title: Dollars may not buy as many QALYs as we think: a problem with defining quality-of-life adjustments.
abstract: The scale of health state quality that should be used to compute quality-adjusted life years (QALYs) ranges from 0 (death) to 1.0 (excellent health); this is called the "Q" scale. But many cost-utility analyses (CUAs) in the literature use the upper anchor of the scale to denote only the absence of the particular health condition under investigation, and weight the disease state proportional to this endpoint; these are called "q" scales. Computations using q-scale health-state weights ignore the fact that the average patient is still subject to chronic and acute conditions comorbid with the condition being analyzed; the absence of a particular condition is not in general the same as excellent health, i.e., the Q scale is longer than a q scale. CUAs based on q scales yield "qALYs." Incremental $/qALY ratios are generally lower than $/QALY ratios; in the example presented, $/qALY must be inflated by about 15% to yield $/QALY. Other CUAs correctly weight disease states using the Q scale, but erroneously assign a quality weight of 1.0 to absence of the disease in the CUA computations. The results of such analyses are called "NP-QALYs," as the correction factor to compute QALYs is not a simple proportional adjustment. The authors suggest that analysis doing cost-utility analyses without access to primary data from treated patients use average age-specific health-related quality-of-life weights from population-based studies to represent the state of not having a particular disease. Consumers of CUAs should closely examine the nature of the QALYs in any published analyses before making decisions based on their results.
authors.affiliation: Department of Preventive Medicine, University of Wisconsin-Madison, USA. dfryback@facstaff.wisc.edu
authors.forename: D G
authors.initials: DG
authors.lastname: Fryback
authors.forename: W F
authors.initials: WF
authors.lastname: Lawrence
mesh_terms: D000328:Adult; D000368:Aged; D015897:Comorbidity; D003362:Cost-Benefit Analysis; D003658:Decision Making, Computer-Assisted; D003661:Decision Support Techniques; D005260:Female; D015276:Health Care Rationing / Q000191:economics*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011785:Quality Assurance, Health Care / Q000191:economics*; D019057:Quality-Adjusted Life Years; D016019:Survival Analysis