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- W2028753709 abstract "The metric of disability-adjusted life years (DALYs) has become the global standard of measuring burden of disease. DALYs are comprised of years of life lost due to premature mortality and years of healthy life lost due to living with disability. In order to calculate the second part of the DALY equation, disease specific disability weights have to be established, i.e. measures for the decline of health associated with these disease states, which vary between zero for perfect health and one for death. Although these disability weights are key for estimating DALYs, there have not been many comprehensive studies with empirical determinations of them. This article describes a systematic review on the state of the art with respect to empirically determining disability weights. Based on this review, a multi-method approach is outlined, which has also been implemented in a US study to measure burden of disease. This approach involves the use of psychometric methodology as well as economic trade-off methods for determining the value of health states. It is conceptualized as a disaggregated approach, where the disability weight of any health state can be calculated if the attributes of this health state are known. The US study received the collaboration of experts from more than 20 institutes of the National Institutes of Health and of the Centers for Disease Control and Prevention. First results will be available by the end of this year." @default.
- W2028753709 created "2016-06-24" @default.
- W2028753709 creator A5014789737 @default.
- W2028753709 creator A5061128992 @default.
- W2028753709 date "2010-02-28" @default.
- W2028753709 modified "2023-10-17" @default.
- W2028753709 title "Valuation of health states in the US study to establish disability weights: lessons from the literature" @default.
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- W2028753709 doi "https://doi.org/10.1002/mpr.300" @default.
- W2028753709 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3306052" @default.
- W2028753709 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20191661" @default.
- W2028753709 hasPublicationYear "2010" @default.
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