Improving the accuracy of medication adherence measures using linked prescription and dispensation data: findings from the ESOSVAL cohort of patients treated with osteoporosis drugs

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Abstract

Objective: We compare estimates of proportion of days covered (PDC) based on dispensation-only data versus linked prescription and dispensation information, and we analyse their differences in a real-world cohort of patients with osteoporosis. Methods: Prospective cohort study. We compared four alternative measures of PDC, using dispensation-only data: a) with a fixed assessment interval; b) censoring the assessment interval at the moment of the last refill; and using linked prescription and dispensation data: c) considering a minimum prescription gap of three months to interpret interruption by the physician; and d) considering any prescription gap. Results: The mean PDC at 12 months for new users was 63.1% using dispensation-only data and a fixed interval, 86.0% using dispensation-only data and a last-refill interval, 81% using linked dispensation and prescription data and censoring any period without prescription, and 78.3% when using linked prescription and dispensation data and censoring periods of at least 3 months. For experienced users, the figures were 80.0%, 88.9%, 83% and 81%, respectively. Overall, dispensation-based measures presented issues of patient misclassification. Conclusions: Linked prescription and dispensation data allows for more precise PDC estimates than dispensation-only data, as both primary non-adherence and early non-adherence periods, and fully non-adherent patients, are all identified and accounted for.

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García-Sempere, A., Hurtado, I., Sanfélix-Genovés, J., Rodríguez-Bernal, C., Peiró, S., & Sanfélix-Gimeno, G. (2019). Improving the accuracy of medication adherence measures using linked prescription and dispensation data: findings from the ESOSVAL cohort of patients treated with osteoporosis drugs. Current Medical Research and Opinion, 35(9), 1535–1544. https://doi.org/10.1080/03007995.2019.1601944

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