Impact of nine chronic conditions for US adults aged 65 years and older: an application of a hybrid estimator of quality-adjusted life years throughout remainder of lifetime
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To estimate quality-adjusted life years (QALY) loss due to each of the following nine chronic conditions—depression, diabetes mellitus, hypertension, heart disease, stroke, emphysema, asthma, arthritis, and cancer.
We ascertained respondents’ health-related quality of life scores and mortality status from the 2005 to 2008 National Health and Nutrition Examination Survey (NHANES) with mortality follow-up data through December 31, 2011. We included respondents aged 65 years and older (n = 2380). A hybrid estimator was used to calculate QALY from two parts: QALY during the follow-up period and QALY beyond the follow-up period. We calculated QALY by each of the nine chronic conditions.
For persons aged 65 and older, QALY throughout the reminder of lifetime was 12.3 years. After adjusting for age- and sex-related differences, depression had an associated 8.2 years of QALY loss; diabetes, 5.6 years; hypertension, 2.5 years; heart disease, 5.4 years; stroke, 6.4 years; emphysema, 8.0 years; asthma, 4.8 years; arthritis, 0.3 years; and cancer, 2.5 years. Compared to persons without any chronic conditions, persons with one condition had an associated 4.7 years of QALY loss; persons with two conditions, 7.9 years; and persons with three or more conditions, 10.8 years.
This study presents a QALY estimator for respondents in the NHANES-Linked Mortality File and demonstrates the utility of this method to other follow-up data. Continued application of our method would enable the burden of disease to be compared for a range of health conditions and risk factors in the ongoing effort to improve population health.
KeywordsQuality-adjusted life year (QALY) Health-related quality of life (HRQOL) Chronic conditions Burden of disease
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this paper.
This analysis used de-identified data produced by federal agencies in the public domain. Data were downloaded from the Centers for Disease Control and Prevention Website (ftp://ftp.cdc.gov/pub).
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