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
- 439 Downloads
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).
- 1.Institute of Medicine. (2012). Living well with chronic illness: A call for public health action. Washington, DC: The National Academies Press.Google Scholar
- 4.Field, M. J., & Gold, M. R. (Eds.). (1998). Summarizing population health: Directions for the development and application of population metrics. Washington, DC: Institute of Medicine, National Academy Press.Google Scholar
- 5.Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (Eds.). (1996). Cost-effectiveness in health and medicine. New York: Oxford University Press.Google Scholar
- 6.Brown, D. S., Jia, H., Zack, M. M., Thompson, W. W., Haddix, A. C., & Kaplan, R. M. (2013). Using health-related quality of life and quality-adjusted life expectancy for effective public health surveillance and prevention. Expert Review of Pharmacoeconomics and Outcomes Research, 13(4), 425–427.CrossRefPubMedGoogle Scholar
- 8.Murray, C. J., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., et al. (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: A systematic analysis for the global burden of disease study 2010. Lancet, 380(9859), 2197–2223.CrossRefPubMedGoogle Scholar
- 10.Chiang, C. L. (1984). Statistical inference regarding life table functions. In C. L. Chiang (Ed.), The life table and its applications (pp. 153–167). Malabar, FL: Robert E. Krieger Publishers.Google Scholar
- 11.National Research Council. (2010). Accounting for health and health care: Approaches to measuring the sources and costs of their improvement. Panel to Advance a research program on the Design of National Health Accounts, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.Google Scholar
- 23.Heron, M. (2015). Deaths: Leading causes for 2011. National Vital Statistics Reports, 64(7), 1–96.Google Scholar
- 25.Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2005–2006. http://wwwn.cdc.gov/nchs/nhanes/search/nhanes05_06.aspx. Accessed 20 July 2015.
- 26.Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). NCHS Data Linked to Mortality Files. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2011. http://www.cdc.gov/nchs/data_access/data_linkage/mortality.htm. Accessed 20 July 2015.
- 27.Centers for Disease Control and Prevention. (2000). Measuring healthy days: Population assessment of health-related quality of life. U.S. Department of Health and Human Services. Centers for Disease Control and Prevention. National Center for Chronic Disease Prevention and Health Promotion. Division of Adult and Community Health. http://www.cdc.gov/hrqol/pdfs/mhd.pdf. Accessed 20 July 2015.
- 34.American Cancer Society. Cancer Facts & Figures 2015. Atlanta: American Cancer Society; 2015. http://www.cancer.org/acs/groups/content/@editorial/documents/document/acspc-044552.pdf. Accessed on 20 July 2015.
- 37.Vos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., et al. (2012). Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: A systematic analysis for the global burden of disease study 2010. Lancet, 380(9859), 2163–2196.CrossRefPubMedGoogle Scholar
- 38.Vogeli, C., Shields, A. E., Lee, T. A., Gibson, T. B., Marder, W. D., Weiss, K. B., et al. (2007). Multiple chronic conditions: Prevalence, health consequences, and implications for quality, care management, and costs. Journal of General Internal Medicine, 22(Suppl 3), 391–395.CrossRefPubMedPubMedCentralGoogle Scholar