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Medication Adherence Trajectories: A Systematic Literature Review
Mai Alhazami1, Vasco M Pontinha2, Julie A Patterson2
1Kuwait University, Jabriya.
Journal of Managed Care & Specialty Pharmacy
|August 29, 2020
Summary
Group-based trajectory modeling (GBTM) reveals distinct medication adherence patterns over time. Socioeconomic status and disease characteristics predict patient adherence trajectories, aiding targeted interventions.
Area of Science:
- Pharmacoeconomics and Health Outcomes Research
- Biostatistics and Epidemiology
Background:
- Traditional adherence metrics like PDC and MPR inadequately capture long-term patient adherence dynamics.
- Group-based trajectory modeling (GBTM) offers a novel approach to visualize and categorize long-term medication adherence behavior.
Purpose of the Study:
- To systematically review and compare medication nonadherence trajectories from existing literature.
- To identify consistent trends in adherence patterns and patient/disease characteristics influencing these trajectories.
Main Methods:
- A systematic literature review was performed using PubMed and CINAHL databases in April 2020.
- MeSH terms and keywords were combined to identify relevant studies, which were screened based on predefined inclusion/exclusion criteria.
- Included studies were evaluated for variables relevant to group-based trajectory models.
Main Results:
- Twenty-one articles were reviewed, generally identifying 4 to 6 distinct adherence trajectory groups.
- Common trajectories included consistent high adherence, declining adherence, consistent nonadherence, and initial nonadherence followed by improvement.
- Socioeconomic status, disease characteristics, and therapy initiation were frequently associated with specific trajectory group membership.
Conclusions:
- Adherence trajectories and their predictors appear consistent across various disease states.
- GBTM effectively illustrates dynamic, longitudinal medication adherence patterns, supporting the development of targeted adherence interventions.
- The findings have implications for value-based payment models and patient management strategies.
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