Abstract

Previous research assessing medication adherence with P2Y12 inhibitors has shown good adherence rates, ranging from 78% to 92%. Studies that used administrative claims data defined adherence using an arbitrary cut point of ≥ 80% medication possession ratio (MPR) or proportion of days covered (PDC). While this method is used frequently, it does not allow the researcher to observe how each factor impacts adherence along the entire distribution. The objective of the study was to use conditional quantile regression (CQR) and unconditional quantile regression (UQR) to assess heterogenous effects of adherence to P2Y12 inhibitors and covariates of interest and compare these results to those from a traditional logistic regression framework. This study used the commercial claims and encounters databases from IBM® MarketScan® from 2010 to 2017. We included patients who had an incident percutaneous coronary intervention, used a drug-eluting stent, and filled an incident prescription for a P2Y12 inhibitor. Adherence was measured for 185days using PDC. Adherence to branded clopidogrel, generic clopidogrel, branded prasugrel, and branded ticagrelor was assessed, along with factors that could impact adherence, using logistic regression, CQR, and UQR. We found that while adherence to the antiplatelets was generally high, prasugrel and ticagrelor had significantly lower PDC compared to branded clopidogrel, especially around the 30th percentile. Across all quantiles in both the CRQ and UQR frameworks, comorbidities such as diabetes and depression and living in the southern region had significant negative effects on adherence, although the relative impact differed across quantiles. Using CQR and UQR allowed for heterogenous assessment of covariates along the adherence distribution, which is not possible with the traditional logistic regression method. The UQR framework revealed patients who initiate prasugrel or ticagrelor generally have lower adherence compared to those treated with branded clopidogrel, especially around the 30th quantile. Using these methods in other types of data sets, such as electronic health records, could help strengthen our results to develop policies to improve antiplatelet adherence in a targeted population.

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