Kathleen A McGinnis1, Janet P Tate2, Emily C Williams3, Melissa Skanderson4, Kendall J Bryant5, Adam J Gordon6, Kevin L Kraemer7, Stephen A Maisto8, Steven Crystal9, David A Fiellin10, Amy C Justice10. 1. Veterans Aging Cohort Study Coordinating Center, VA CT Healthcare System, West Haven, CT, United States. Electronic address: kathleen.mcginnis3@va.gov. 2. Veterans Aging Cohort Study Coordinating Center, VA CT Healthcare System, West Haven, CT, United States; Division of General Internal Medicine, Yale University School of Medicine, New Haven, CT, United States. 3. Yale University School of Public Health, New Haven, CT, United States; VA Health Services Research & Developments, Denver-Seattle Center for Innovation for Veteran-Centered and Value-Driven Care, Seattle, WA, United States. 4. Veterans Aging Cohort Study Coordinating Center, VA CT Healthcare System, West Haven, CT, United States. 5. Department of Health Services, University of Washington, Seattle, WA, United States. 6. National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States; Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States. 7. National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States. 8. Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, United States. 9. Department of Psychology, Syracuse University, Syracuse, NY, United States. 10. Veterans Aging Cohort Study Coordinating Center, VA CT Healthcare System, West Haven, CT, United States; Division of General Internal Medicine, Yale University School of Medicine, New Haven, CT, United States; Health Services Research, Rutgers University, New Brunswick, NJ, United States.
Abstract
BACKGROUND: Using electronic medical record (EMR) data for clinical decisions, quality improvement, and research is common. While unhealthy alcohol use is particularly risky among HIV infected individuals (HIV+), the validity of EMR data for identifying unhealthy alcohol use among HIV+ is unclear. Among HIV+ and uninfected, we: (1) assess agreement of EMR and research AUDIT-C at validated cutoffs for unhealthy alcohol use; (2) explore EMR cutoffs that maximize agreement; and (3) assess subpopulation variation in agreement. METHODS: Using data from the Veterans Aging Cohort Study (VACS), EMR AUDIT-C cutoffs of 2+, 3+, and 4+ for men (2+ and 3+ for women) were compared to research AUDIT-C 4+ for men (3+ for women). Agreement was compared by demographics, HIV, hepatitis C infection, and alcohol related diagnosis. RESULTS: Among 1082 HIV+ and 1160 uninfected men, 14% and 22% had an EMR and research AUDIT-C 4+, respectively. Among 32 HIV+ and 115 uninfected women, 9% and 14% had an EMR and research AUDIT-C 3+. For men, EMR agreement with the research AUDIT-C 4+ was highest at a cutoff of 3+ (kappa=0.49). For women, EMR agreement with AUDIT-C 3+ was highest at a cutoff of 2+ (kappa=0.46). Moderate agreement was consistent across subgroups. CONCLUSIONS: EMR AUDIT-C underestimates unhealthy alcohol use compared to research AUDIT-C in both HIV+ and uninfected individuals. Methods for improving quality of clinical screening may be in need of investigation. Researchers and clinicians may consider alternative EMR cutoffs that maximize agreement given limitations of clinical screening. Published by Elsevier Ireland Ltd.
BACKGROUND: Using electronic medical record (EMR) data for clinical decisions, quality improvement, and research is common. While unhealthy alcohol use is particularly risky among HIV infected individuals (HIV+), the validity of EMR data for identifying unhealthy alcohol use among HIV+ is unclear. Among HIV+ and uninfected, we: (1) assess agreement of EMR and research AUDIT-C at validated cutoffs for unhealthy alcohol use; (2) explore EMR cutoffs that maximize agreement; and (3) assess subpopulation variation in agreement. METHODS: Using data from the Veterans Aging Cohort Study (VACS), EMR AUDIT-C cutoffs of 2+, 3+, and 4+ for men (2+ and 3+ for women) were compared to research AUDIT-C 4+ for men (3+ for women). Agreement was compared by demographics, HIV, hepatitis C infection, and alcohol related diagnosis. RESULTS: Among 1082 HIV+ and 1160 uninfected men, 14% and 22% had an EMR and research AUDIT-C 4+, respectively. Among 32 HIV+ and 115 uninfected women, 9% and 14% had an EMR and research AUDIT-C 3+. For men, EMR agreement with the research AUDIT-C 4+ was highest at a cutoff of 3+ (kappa=0.49). For women, EMR agreement with AUDIT-C 3+ was highest at a cutoff of 2+ (kappa=0.46). Moderate agreement was consistent across subgroups. CONCLUSIONS: EMR AUDIT-C underestimates unhealthy alcohol use compared to research AUDIT-C in both HIV+ and uninfected individuals. Methods for improving quality of clinical screening may be in need of investigation. Researchers and clinicians may consider alternative EMR cutoffs that maximize agreement given limitations of clinical screening. Published by Elsevier Ireland Ltd.
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