Using Previous Medication Adherence to Predict Future Adherence


Kumamaru H, Lee MP, Choudhry NK, Dong Y-H, Krumme AA, Khan N, Brill G, Kohsaka S, Miyata H, Schneeweiss S, et al. Using Previous Medication Adherence to Predict Future Adherence. J Manag Care Spec Pharm. 2018;24 (11) :1146-1155.

Date Published:

2018 Nov


BACKGROUND: Medication nonadherence is a major public health problem. Identification of patients who are likely to be and not be adherent can guide targeted interventions and improve the design of comparative-effectiveness studies. OBJECTIVE: To evaluate multiple measures of patient previous medication adherence in light of predicting future statin adherence in a large U.S. administrative claims database. METHODS: We identified a cohort of patients newly initiating statins and measured their previous adherence to other chronic preventive medications during a 365-day baseline period, using metrics such as proportion of days covered (PDC), lack of second fills, and number of dispensations. We measured adherence to statins during the year after initiation, defining high adherence as PDC ≥ 80%. We built logistic regression models from different combinations of baseline variables and previous adherence measures to predict high adherence in a random 50% sample and tested their discrimination using concordance statistics (c-statistics) in the other 50%. We also assessed the association between previous adherence and subsequent statin high adherence by fitting a modified Poisson model from all relevant covariates plus previous mean PDC categorized as < 25%, 25%-79%, and ≥ 80%. RESULTS: Among 89,490 statin initiators identified, a prediction model including only demographic variables had a c-statistic of 0.578 (95% CI = 0.573-0.584). A model combining information on patient comorbidities, health care services utilization, and medication use resulted in a c-statistic of 0.665 (95% CI = 0.659-0.670). Models with each of the previous medication adherence measures as the only explanatory variable yielded c-statistics ranging between 0.533 (95% CI = 0.529-0.537) for lack of second fill and 0.666 (95% CI = 0.661-0.671) for maximum PDC. Adding mean PDC to the combined model yielded a c-statistic of 0.695 (95% CI = 0.690-0.700). Given a sensitivity of 75%, the predictor improved the specificity from 47.7% to 53.6%. Patients with previous mean PDC < 25% were half as likely to show high adherence to statins compared with those with previous mean PDC ≥ 80% (risk ratio = 0.49, 95% CI = 0.46-0.50). CONCLUSIONS: Including measures of previous medication adherence yields better prediction of future statin adherence than usual baseline clinical measures that are typically used in claims-based studies. DISCLOSURES: This study was funded by the Patient-Centered Outcomes Research Institute (ME-1309-06274). Kumamaru, Kohsaka, and Miyata are affiliated with the Department of Healthcare Quality Assessment at the University of Tokyo, which is a social collaboration department supported by National Clinical Database. The department was formerly supported by endowments from Johnson & Johnson K.K., Nipro, Teijin Pharma, Kaketsuken K.K., St. Jude Medical Japan, Novartis Pharma K.K., Taiho Pharmaceutical, W. L. Gore & Associates, Olympus Corporation, and Chugai Pharmaceutical. Gagne has received grants from Novartis Pharmaceuticals and Eli Lilly and Company to the Brigham and Women's Hospital for unrelated work. He is a consultant to Aetion, a software company, and to Optum. Choudhry has received grants from the National Heart, Lung, and Blood Institute, PhRMA Foundation, Merck, Sanofi, AstraZeneca, CVS, and MediSafe. Schneeweiss is consultant to WHISCON and Aetion, a software manufacturer of which he also owns equity. He is principal investigator of investigator-initiated grants to the Brigham and Women's Hospital from Bayer, Genentech, and Boehringer Ingelheim unrelated to the topic of this study. He does not receive personal fees from biopharmaceutical companies. No potential conflict of interest was reported by the other authors.