期刊
MEDICAL CARE
卷 43, 期 7, 页码 736-749出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/01.mlr.0000172050.67085.4f
关键词
health-related quality of life; cost-utility analysis; utility; chronic disease
Background: The variability in preferences used in quality-adjusted life-years 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 catalog of off-the-shelf' preference weights associated with conditions that can be used by health researchers without the burden of collecting primary data. Objective: The current research responds to the call by developing a nationally representative catalog of preference-based scores for chronic conditions and associated sociodemographic characteristics. Methods: The authors report the EQ-5D(index) scores of chronic conditions and associated sociodemographic characteristics in the nationally representative Medical Expenditure Panel Survey (MEPS). Chronic conditions were coded using quality priority conditions (QPC) and clinical classification categories (CCC). OLS, Tobit, and censored least absolute deviations (CLAD) regression models were used to provide condition estimates adjusted for age, comorbidity, gender, race, ethnicity, income, and education. Results: Unadjusted and adjusted EQ-5D(index) scores for each QPC and CCC code are presented. EQ-5D(index) 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(index) scores were higher for individuals with higher education and income levels. Conclusion: The preference-based chronic condition scores reported in this research are nationally representative and may be useful to researchers to calculate quality-adjusted life-years for cost-effectiveness analyses and population-based burden of illness studies without the difficulty of primary data collection. Further research is necessary to validate these scores in condition-specific studies.
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