Group-Based Trajectory Modeling to Identify Patterns of Adherence and Its Predictors Among Older Adults on Angiotensin-Converting Enzyme Inhibitors (ACEIs)/Angiotensin Receptor Blockers (ARBs)

Rutugandha Paranjpe,1 Michael L Johnson,1 Ekere J Essien,1 Jamie C Barner,2 Omar Serna,3 Esteban Gallardo,3 Zahra Majd,1 Marc L Fleming,4 Nancy Ordonez,1 Marcia M Holstad,5 Susan M Abughosh1 1Pharmaceutical Health Outcomes and Policy, University of Houston, Houston, TX, USA; 2Health Outcomes Division, The University of Texas at Austin, Austin, TX, USA; 3CareAllies, Houston, TX, USA; 4Department of Pharmacotherapy, University of North Texas Health Science Center, Fort Worth, TX, USA; 5Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USACorrespondence: Susan M AbughoshDepartment of Pharmaceutical Health Outcomes and Policy, University of Houston, College of Pharmacy, 4849 Calhoun Road, Houston, TX 77204-5047, USATel +1 832-842-8395Fax +1 832-842-8383Email smabughosh@uh.eduPurpose: 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.Keywords: adherence, trajectory modeling, angiotensin converting enzyme inhibitors, angiotensin receptor blockers, predictors