Juno Cho1, Leslie M Niziol1, Paul P Lee1, Michele Heisler2, Ken Resnicow3, David C Musch1, Paula Anne Newman-Casey4. 1. Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan. 2. Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan. 3. School of Public Health, University of Michigan, Ann Arbor, Michigan. 4. Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan. Electronic address: panewman@umich.edu.
Abstract
PURPOSE: To assess the accuracy of 5 subjective self-assessment tools (3 adherence measures and 2 psychometric scales) and pharmacy refill data in predicting objective electronically monitored nonadherence. DESIGN: Prospective cohort study. PARTICIPANTS: Glaucoma patients (> 40 years old, >1 medication with poor self-reported adherence) were recruited from University of Michigan Kellogg Eye Center for a study assessing the impact of a personalized glaucoma coaching program on medication adherence. METHODS: Participants completed an initial assessment including 5 self-assessment tools and a 3-month period of electronic monitoring of glaucoma medication adherence (AdhereTech); pharmacy refill data were obtained. Electronically monitored adherence was calculated monthly as the percentage of doses taken on time. The median of these adherence rates was designated as baseline adherence. Patients with adherence of ≤80% by electronic monitoring were considered nonadherent. Self-assessment tools were scored, and pharmacy refill data were summarized as the proportion of days covered. Correlation between measures of adherence was estimated with Pearson and Spearman correlation coefficients. Receiver operating characteristic curves, including estimation of area under the curve (AUC), sensitivity, specificity, and accuracy were used to compare measures of adherence with respect to predicting electronically monitored nonadherence. MAIN OUTCOME MEASURES: Accuracy of self-reported and pharmacy refill data adherence measures in predicting electronically monitored nonadherence. RESULTS: Ninety-five patients completed 3 months of electronic monitoring with a median monthly adherence of 74 (± 21%); 53 patients (56%) were nonadherent. Pharmacy refill data were not correlated significantly with electronically monitored medication adherence (r = 0.12; P = 0.2). Of all the measures, a single-item adherence question ("Over the past month, what percentage of your drops do you think you took correctly?") showed the largest correlation with median electronically monitored adherence (r = 0.47; P < 0.0001), largest AUC for predicting nonadherence (AUC = 0.76 [95% confidence interval [CI], 0.66-0.87]), best accuracy (71% [95% CI, 61%-82%]), and good sensitivity (84% [95% CI, 73%-96%]). CONCLUSIONS: The single-item question was the most accurate in predicting electronically monitored nonadherence among participants with poor self-reported adherence. In clinical practice, where alternatives are too resource intensive, this free single-item screening question can help to identify glaucoma patients at risk of poor medication adherence with reasonable accuracy.
PURPOSE: To assess the accuracy of 5 subjective self-assessment tools (3 adherence measures and 2 psychometric scales) and pharmacy refill data in predicting objective electronically monitored nonadherence. DESIGN: Prospective cohort study. PARTICIPANTS: Glaucoma patients (> 40 years old, >1 medication with poor self-reported adherence) were recruited from University of Michigan Kellogg Eye Center for a study assessing the impact of a personalized glaucoma coaching program on medication adherence. METHODS: Participants completed an initial assessment including 5 self-assessment tools and a 3-month period of electronic monitoring of glaucoma medication adherence (AdhereTech); pharmacy refill data were obtained. Electronically monitored adherence was calculated monthly as the percentage of doses taken on time. The median of these adherence rates was designated as baseline adherence. Patients with adherence of ≤80% by electronic monitoring were considered nonadherent. Self-assessment tools were scored, and pharmacy refill data were summarized as the proportion of days covered. Correlation between measures of adherence was estimated with Pearson and Spearman correlation coefficients. Receiver operating characteristic curves, including estimation of area under the curve (AUC), sensitivity, specificity, and accuracy were used to compare measures of adherence with respect to predicting electronically monitored nonadherence. MAIN OUTCOME MEASURES: Accuracy of self-reported and pharmacy refill data adherence measures in predicting electronically monitored nonadherence. RESULTS: Ninety-five patients completed 3 months of electronic monitoring with a median monthly adherence of 74 (± 21%); 53 patients (56%) were nonadherent. Pharmacy refill data were not correlated significantly with electronically monitored medication adherence (r = 0.12; P = 0.2). Of all the measures, a single-item adherence question ("Over the past month, what percentage of your drops do you think you took correctly?") showed the largest correlation with median electronically monitored adherence (r = 0.47; P < 0.0001), largest AUC for predicting nonadherence (AUC = 0.76 [95% confidence interval [CI], 0.66-0.87]), best accuracy (71% [95% CI, 61%-82%]), and good sensitivity (84% [95% CI, 73%-96%]). CONCLUSIONS: The single-item question was the most accurate in predicting electronically monitored nonadherence among participants with poor self-reported adherence. In clinical practice, where alternatives are too resource intensive, this free single-item screening question can help to identify glaucoma patients at risk of poor medication adherence with reasonable accuracy.
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