Further, significant predictors associated with the gaps in adherence trajectory included increasing age, and comorbidities. Significant predictors associated with the rapid decline trajectory included male sex, comorbidities, and increased CMS risk score. Significant predictors associated with all lower adherence trajectories included 90 days refill, >2 number of other medications, ≥1 hospitalizations, and prevalent users. Predictors included various socio-demographic and clinical patient characteristics.Ī total of 22,774 patients were included in the analysis and categorized into 4 distinct adherence trajectories: rapid decline (12.6%) adherent (58.5%) gaps in adherence (12.2%), and gradual decline (16.6%). Further, a multinomial logistic regression was conducted to determine predictors of each identified adherence trajectory. The monthly PDC was added to a logistic group-based trajectory model to provide distinct patterns of adherence. PDC was used to measure monthly patient adherence during the one-year follow-up period. Patients with an ACEI/ARBs prescription were identified between July 2017 and December 2017 using a Medicare Advantage dataset. The objective of this study was to evaluate adherence using GBTMs among patients prescribed ACEI/ARBs and identify predictors associated with each adherence trajectory. Unlike traditional single estimates of proportion of days covered (PDC), group-based trajectory modeling (GBTM) can graphically display the dynamic nature of adherence. However, these medications are associated with suboptimal adherence leading to inadequately controlled blood pressure. Commonly prescribed medications among patients with comorbid diabetes mellitus and hypertension include ARBs and ACEIs.
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