This presentation is part of: Oral Concurrent Session A - Patient and Physician Behavior/Preferences 2
A NATIONAL CATALOGUE OF PREFERENCE WEIGHTS FOR CHRONIC CONDITIONS
Patrick Sullivan, PhD, Pharmaceutical Outcomes Research Program, University of Colorado School of Pharmacy, Department of Clinical Pharmacy, Denver, CO and William F. Lawrence, Agency for Healthcare Research and Quality, Center for Outcomes and Evidence, Rockville, MD.
Purpose: The variability in preferences used in QALY estimation jeopardizes the comparability of cost-effectiveness analyses and has led the Panel on Cost-Effectiveness in Health and Medicine (the PCEHM) to call for a catalogue of “off the shelf” preference weights associated with conditions that can be used by health researchers without the burden of collecting primary data. Methods: The current research responds to the call by developing a nationally representative catalogue of preference weights for chronic conditions and associated sociodemographic characteristics. The authors report the EQ-5D scores associated with chronic conditions and associated sociodemographic characteristics in the nationally representative Medical Expenditure Panel Survey (MEPS). Chronic conditions were coded using “Quality Priority Conditions” (QPC), Clinical Classification Categories (CCC) and the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9). In order to provide accurate age-adjusted estimates in the face of the ceiling effect exhibited by preference measures, OLS, Tobit and censored least absolute deviations (CLAD) regression models were employed and compared. Results: As with many health status measures in population health surveys, it is clear from these results that the EQ-5D is not normally distributed, exhibits a significant ceiling effect and the mean underestimates the population EQ-5D score. In addition, errors from OLS estimation exhibit significant heteroskedasticity. Given these factors, the CLAD estimates (or median) appear to be a more appropriate measure of central tendency of EQ-5D scores. Unadjusted and age-adjusted EQ-5D scores as well as the age, gender, race, ethnicity, poverty status and education level distribution associated with each QPC, CCC and ICD-9 code are presented. EQ-5D scores for older age categories were lower than younger categories, female scores were lower than males, certain racial groups had lower scores than others, and EQ-5D scores were higher for individuals with higher education and income levels. Conclusion: Use of the mean may not be the most appropriate measure of central tendency when estimating population EQ-5D scores. The chronic condition scores reported in this research are nationally representative and may be useful to researchers as preference weights that can be used to calculate QALYs for cost-effectiveness analyses without the burden of primary data collection.
