ArticlePharmacy (Basel, Switzerland)2025
Investigating the Time-Varying Nature of Medication Adherence Predictors: An Experimental Approach Using Andersen's Behavioral Model of Health Services Use.
Article in Pharmacy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Examining the association between medication adherence trajectories and disease-specific clinical outcomes: An analysis focusing on the drug classes included in Part D quality bonus payments.Journal of managed care & specialty pharmacy · 2025Article
- Clinical Implementation of the Hill-Bone Compliance to High Blood Pressure Therapy Scale: Antihypertensive Adherence Assessment in a Romanian Cardiology Outpatient Center.Patient preference and adherence · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Medication adherence is a crucial factor for managing chronic conditions, especially in aging adults. Previous studies have identified predictors of medication adherence. However, current methods fail to capture the time-varying nature of how risk factors can influence adherence behavior. This objective of this study was to implement multitrajectory group-based models to compare a time-varying to a time-fixed approach to identifying non-adherence risk factors. The study population comprised 11,068 Medicare beneficiaries aged 65 and older taking select medications for hypertension, high blood cholesterol, and oral diabetes medications, between 2008 and 2016. Time-fixed predictors (e.g., sex, education) were examined using generalized multinomial logistic regression, while time-varying predictors were explored through multitrajectory group-based modeling. Several predisposing, enabling, and need characteristics were identified as risk factors for following at least one non-adherence trajectory. Time-varying predictors displayed an alternative representation of those risk factors, especially depression symptoms. This study highlights the dynamic nature of medication adherence predictors and the utility of multitrajectory modeling. Findings suggest that targeted interventions can be developed by addressing the key time-varying factors affecting adherence.
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