https://orcid.org/0000-0001-8157-8290
Abstract
Purpose
Commonly prescribed medications among patients with comorbid diabetes mellitus and hypertension include ARBs and ACEIs. However, these medications are associated with suboptimal adherence leading to inadequately controlled blood pressure. Unlike traditional single estimates of proportion of days covered (PDC), group-based trajectory modeling (GBTM) can graphically display the dynamic nature of adherence. The objective of this study was to evaluate adherence using GBTMs among patients prescribed ACEI/ARBs and identify predictors associated with each adherence trajectory.
Patients and Methods
Patients with an ACEI/ARBs prescription were identified between July 2017 and December 2017 using a Medicare Advantage dataset. PDC was used to measure monthly patient adherence during the one-year follow-up period. The monthly PDC was added to a logistic group-based trajectory model to provide distinct patterns of adherence. Further, a multinomial logistic regression was conducted to determine predictors of each identified adherence trajectory. Predictors included various socio-demographic and clinical patient characteristics.
Results
A 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%). Significant predictors associated with all lower adherence trajectories included 90 days refill, >2 number of other medications, ≥1 hospitalizations, and prevalent users. Significant predictors associated with the rapid decline trajectory included male sex, comorbidities, and increased CMS risk score. Further, significant predictors associated with the gaps in adherence trajectory included increasing age, and comorbidities. Lastly, significant predictors associated with the gradual decline trajectory included increasing age, no health plan subsidy, comorbidities, and increasing CMS risk score.
Conclusion
Identifying various patient characteristics associated with non-adherent trajectories can guide the development of tailored interventions to enhance adherence to ACEI/ARBs.
Ethics
The data accessed comply with relevant data protection and privacy regulations.
Disclosure
Dr. Abughosh reports grants from NIH related to the work under consideration for publication and grants from NIH/NHLBI, during the conduct of the study. Dr. Abughosh also reports grants from Regeneron-Sanofi, BMS-Pfizer, Valeant Pharmaceuticals, Regeneron-Sanofi, and BMS-Pfizer, outside the submitted work. Dr. Barner reports grants from NIH and personal fees from University of Houston, during the conduct of the study. Dr. Fleming reports grants from National Heart, Lung, and Blood Institute and Regeneron during the conduct of the study. Dr. Fleming also reports grants from Sanofi, Regeneron-Sanofi, Texas Health and Human Services Commission, outside the submitted work. Dr. Gallardo is an employee of CareAllies, a subsidiary of Cigna, outside of the submitted work. The authors report no other potential conflicts of interest for this work.