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. 2011 Jan-Feb;31(1):174-85.
doi: 10.1177/0272989X10364845. Epub 2010 Apr 7.

Predicting the EuroQol Group's EQ-5D index from CDC's "Healthy Days" in a US sample

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Predicting the EuroQol Group's EQ-5D index from CDC's "Healthy Days" in a US sample

Haomiao Jia et al. Med Decis Making. 2011 Jan-Feb.

Abstract

Background: Obtaining reliable preference-based scores from the widely used Healthy Days measures would enable calculation of quality-adjusted life years (QALYs) and cost-utility analyses in many US community populations and over time. Previous studies translating the Healthy Days to the EQ-5D, a preference-based measure, relied on an indirect method because of a lack of population-based survey data that asked both sets of questions of the same respondents.

Method: Data from the 2005-2006 National Health Measurement Study (NHMS; n = 3844 adults 35 years old or older) were used to develop regression-based models to estimate EQ-5D index scores from self-reported age, self-rated general health, and numbers of unhealthy days.

Results: The models explained up to 52% of the variance in the EQ-5D. Estimated EQ-5D scores matched well to the observed EQ-5D scores in mean scores overall and by age, gender, race/ethnicity, income, education, body mass index, smoking, and disease categories. The average absolute differences were 0.005 to 0.006 on a health utility scale. After estimating mean EQ-5D index scores overall and for various subgroups in a large representative US sample of Healthy Days respondents, the authors found that these mean scores also closely matched the corresponding mean scores of EQ-5D respondents obtained from another large US representative sample with an average absolute difference of 0.013 points.

Conclusions: This study yielded a mapping algorithm to estimate EQ-5D index scores from the Healthy Days measures for populations of adults 35 years old and older. Such analysis confirms it is feasible to estimate mean EQ-5D index scores with acceptable validity for use in calculating QALYs and cost-utility analyses based on the overall model fit and relatively small differences between the observed and the estimated mean scores.

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