DOI
https://doi.org/10.25772/W0DH-F169
Defense Date
2018
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Pharmaceutical Sciences
First Advisor
David Holdford
Abstract
Introduction: Warfarin is the most commonly prescribed drug for stroke prevention among Atrial Fibrillation (AF) patients, especially in older adult populations, but medication nonadherence reduces its effectiveness in clinical practice. Group Based Trajectory Models (GBTM) have been used to identify distinct patterns of adherence behavior related to various medications and understand the patient characteristics associated with each trajectory. The objectives of the study were: 1) Describe trajectories of warfarin adherence among Medicare AF patients, 2) Assess impact of adherence trajectories on AF-related hospitalization, 3) Estimate the AF-related direct costs for each adherence trajectory group.
Methods: We identified elderly AF patients initiating warfarin treatment during 2008-2010 using data from a random sample of Medicare beneficiaries. The study’s first aim is to classify patients into different trajectory groups based on their monthly adherence patterns using a Group-Based Trajectory Model (GBTM). A multinomial regression model was used to assess associations between baseline characteristics and adherence trajectories. The second aim is to evaluate the association between adherence trajectories and time to first hospitalization related to stroke or bleeding event. Hospitalization events due to bleeding or stroke were identified using corresponding ICD-9 codes, and a Cox proportional hazard model was performed. The third aim of the study is to calculate AF-related direct medical costs associated with each trajectory group. SASv9.4 was used for analysis.
Results: Among 3,246 beneficiaries who met inclusion criteria, six adherence trajectories were identified: 1) rapid-decline non-adherence group (11.5%), 2) moderate non-adherence group (24%), 3) rapid-decline then increasing adherence group (6.8%), 4) moderate-decline non-adherence group (8.2%), 5) slow-decline non-adherence group (24.3%), and 6) perfect adherence group (25.3%). Even though no statistical significances were found in the hazard of hospitalization among the adherence groups, there were higher odds of hospitalization among the lower adherence groups compared to perfect adherence group. Outpatient and monitoring costs were significantly higher in the lower adherence trajectories compared to perfect adherence group.
Conclusion:The GBTM is considered an innovative methodological approach that can be applied to longitudinal medication adherence data and account for the dynamic nature of adherence behavior in a better way than traditional adherence measures.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-6-2018