Using Group-Based Trajectory Modeling (GBTM) to Characterize the Association of Past ACEI/ARBS Adherence Trajectories with Subsequent Statin Adherence Trajectories Among New Statin Users

Author(s)

Majd Z1, Mohan A2, Serna O3, Abughosh S1
1College of Pharmacy, University of Houston, Houston, TX, USA, 2College of Pharmacy, University of Houston, HOUSTON, TX, USA, 3CareAllies, Houston, TX, USA

OBJECTIVES: Statins are among the most widely prescribed medications in the US. Despite well-documented benefits, statin adherence remains suboptimal. This study aims to examine the association between adherence patterns to newly initiated statins and previous adherence patterns of angiotensin-converting enzyme inhibitors (ACEI)/angiotensin receptor blockers (ARB) using GBTM.

METHODS: This retrospective cohort study was conducted among statin initiators using administrative claims data. Patients were included if they had continuous enrollment and at least two prescriptions for ACEI/ARBs 1 year prior to statin initiation (index date). GBTM was used to characterize adherence patterns for statins during the 12 months following the index date as well as for the ACEI/ARBs during the 12 months before the index date. Monthly adherence was calculated using proportion of days covered (PDC), which were modeled as a longitudinal response in a logistic group-based trajectory model to provide distinct adherence patterns. A multinomial logistic regression was conducted to examine the association between baseline ACEI/ARB adherence trajectories with the outcome of statin trajectories (adherent trajectory as the reference group), controlling for demographic and clinical characteristics. Other predictors of statin trajectories were also evaluated.

RESULTS: 1,078 patients were included in the analysis and categorized into 4 distinct statin adherence trajectories: adherent (40.8%); gradual decline (37.4%); gaps in adherence (13.9%), and rapid decline (7.9%). Patients were further categorized into 4 groups based on their past ACEI/ARBs trajectories: adherent (43%); gaps in adherence (29%); late nonadherence (15.2%), and gradual decline (12.8%). In the regression model, patients in the gaps in adherence or gradual decline groups were significantly more likely to follow similar trajectories for statin use as compared to the adherent trajectory.

CONCLUSIONS: Previous adherence trajectories of ACEI/ARBs may predict future adherence patterns for newly initiated statins. Knowledge of past medication-taking behavior could provide valuable information to guide the development of tailored interventions to improve adherence.

Conference/Value in Health Info

2021-05, ISPOR 2021, Montreal, Canada

Value in Health, Volume 24, Issue 5, S1 (May 2021)

Code

PDB38

Topic

Methodological & Statistical Research, Patient-Centered Research, Real World Data & Information Systems

Topic Subcategory

Adherence, Persistence, & Compliance, Health & Insurance Records Systems

Disease

Cardiovascular Disorders, Diabetes/Endocrine/Metabolic Disorders

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