Laura J Chavez1, Katharine Bradley2, Nathan Tefft3, Chuan-Fen Liu4, Paul Hebert4, Beth Devine5. 1. Health Services Research & Development, Seattle Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108, United States; Department of Health Services, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, United States. Electronic address: ljchavez@u.washington.edu. 2. Health Services Research & Development, Seattle Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108, United States; Center of Excellence in Substance Abuse Treatment and Education, Veterans Affairs Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108, United States; Department of Health Services, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, United States; Department of Medicine, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, United States; Group Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, WA 98101, United States. 3. Bates College, 2 Andrews Rd, Lewiston, ME 04240, United States. 4. Health Services Research & Development, Seattle Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108, United States; Department of Health Services, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, United States. 5. Department of Health Services, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, United States; Department of Pharmacy, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, United States.
Abstract
BACKGROUND: Little is known about the cost-utility of population-based alcohol interventions. One barrier to research has been the lack of preference weights needed to calculate Quality Adjusted Life Years (QALYs). Preference weights can be estimated from measures of health-related quality of life (HRQOL). The objective of this study was to describe preference weights for the full spectrum of alcohol use. METHODS: This cross-sectional study included participants in both the National Health Interview Survey (NHIS; 1999-2002) and the Medical Expenditure Panel Survey (MEPS; 2000-2003). The AUDIT-C alcohol screen was derived from NHIS with scores categorized into 6 groups (0,1-3, 4-5, 6-7, 8-9, 10-12 points), ranging from nondrinking (0) to very severe unhealthy alcohol use (10-12). AUDIT-C scores were mapped to EQ-5D and SF-6D preference weights using the linked datasets and analyses adjusted for demographics. RESULTS: Among 17,440 participants, mean EQ-5D and SF-6D preference weights were 0.82 (95% CI 0.82-0.83) and 0.79 (95% CI 0.79-0.80), respectively. Adjusted EQ-5D preference weights for nondrinking (0.80; 95% CI 0.79-0.81) and moderate unhealthy drinking (0.85; 95% CI 0.84-0.86) were significantly different from low-risk drinking (0.83; 95% CI 0.83-0.84), but no other differences were significant. Results for the SF-6D were similar. CONCLUSIONS: This study provides EQ-5D and SF-6D preference weights for various alcohol use categories in a representative U.S. adult sample. However, neither measure suggested meaningful differences in HRQOL based on AUDIT-C categories. Self-reported alcohol consumption may not be associated with preference weights or generic instruments may not capture alcohol-related differences in HRQOL.
BACKGROUND: Little is known about the cost-utility of population-based alcohol interventions. One barrier to research has been the lack of preference weights needed to calculate Quality Adjusted Life Years (QALYs). Preference weights can be estimated from measures of health-related quality of life (HRQOL). The objective of this study was to describe preference weights for the full spectrum of alcohol use. METHODS: This cross-sectional study included participants in both the National Health Interview Survey (NHIS; 1999-2002) and the Medical Expenditure Panel Survey (MEPS; 2000-2003). The AUDIT-C alcohol screen was derived from NHIS with scores categorized into 6 groups (0,1-3, 4-5, 6-7, 8-9, 10-12 points), ranging from nondrinking (0) to very severe unhealthy alcohol use (10-12). AUDIT-C scores were mapped to EQ-5D and SF-6D preference weights using the linked datasets and analyses adjusted for demographics. RESULTS: Among 17,440 participants, mean EQ-5D and SF-6D preference weights were 0.82 (95% CI 0.82-0.83) and 0.79 (95% CI 0.79-0.80), respectively. Adjusted EQ-5D preference weights for nondrinking (0.80; 95% CI 0.79-0.81) and moderate unhealthy drinking (0.85; 95% CI 0.84-0.86) were significantly different from low-risk drinking (0.83; 95% CI 0.83-0.84), but no other differences were significant. Results for the SF-6D were similar. CONCLUSIONS: This study provides EQ-5D and SF-6D preference weights for various alcohol use categories in a representative U.S. adult sample. However, neither measure suggested meaningful differences in HRQOL based on AUDIT-C categories. Self-reported alcohol consumption may not be associated with preference weights or generic instruments may not capture alcohol-related differences in HRQOL.
Authors: Laura J Chavez; Chuan-Fen Liu; Nathan Tefft; Paul L Hebert; Beth Devine; Katharine A Bradley Journal: J Behav Health Serv Res Date: 2017-10 Impact factor: 1.505
Authors: Vanessa A Palzes; Constance Weisner; Felicia W Chi; Andrea H Kline-Simon; Derek D Satre; Matthew E Hirschtritt; Murtuza Ghadiali; Stacy Sterling Journal: JMIR Med Inform Date: 2020-07-22