ArticlePatient preference and adherence2020
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).
Article in Patient preference and adherence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- A Motivational Interviewing Intervention to Improve Adherence to ACEIs/ARBs among Nonadherent Older Adults with Comorbid Hypertension and Diabetes.Drugs & aging · 2023Trial
- Group-Based Trajectory Modeling to Identify Patterns and Predictors of Adherence to Oral Endocrine Therapies in Underserved Population of Greater Houston Area.Patient preference and adherence · 2025Article
- Adherence to prescription proton pump inhibitor therapy amongst individuals diagnosed with Barrett's esophagus.Pharmacoepidemiology and drug safety · 2024Article
- Identifying temporal patterns of adherence to antidepressants, bisphosphonates and statins, and associated patient factors.SSM - population health · 2022Article
- Patient-Reported Barriers to Adherence Among ACEI/ARB Users from a Motivational Interviewing Telephonic Intervention.Patient preference and adherence · 2022Article
- Modeling Patterns of Medication Adherence in Primary Open-Angle Glaucoma.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
purposeCommonly 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
methodsPatients 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.
resultsA 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.
conclusionIdentifying various patient characteristics associated with non-adherent trajectories can guide the development of tailored interventions to enhance adherence to ACEI/ARBs.
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