Identification of Longitudinal Trajectories of Medication Non-Adherence and Their Time-Varying Predictors in Aging Patients in the United States

Speaker(s)

Pontinha V1, Farris K2, Patterson J3, Holdford DA1
1Virginia Commonwealth University School of Pharmacy, Richmond, VA, USA, 2University of Michigan, Ann Arbor, MI, USA, 3Virginia Commonwealth University, Richmond, VA, USA

BACKGROUND: Medication adherence is a major obstacle to improving health outcomes in chronic diseases. This study aimed to identify trajectories of adherence to oral medications and investigate the association with predisposing, enabling, and need predictors, according to Andersen’s Behavior Model of health services use (ABM) theoretical framework.

OBJECTIVES: N/A

METHODS: This study used administrative claims data linked to participants from the Health Retirement Study between 2008-2016. GBTM elicited the number and shape of adherence trajectories, (n=11,068). Medication adherence was estimated using monthly PDC.Time-fixed and time-varying predictors were examined using logistic regression and multi-GBTM, respectively.

RESULTS: GBTM were estimated for the sample population taking hypertension medications (n=7,272), statins (n=8,221), and diabetes medications (n=3,214). The hypertension model found three trajectories: near-perfect adherence (47.5%), slow (33%), and rapid decline (19.5%) trajectories. The statins model found 5 trajectories: near-perfect adherence (35.5%), slow decline (17.1%), low then increase adherence (23.6%), moderate decline (12.6%), and rapid decline (11.2%). The diabetes medications model displayed 6 trajectories: near-perfect adherence (24.2%), slow decline (16.9%), high then increase adherence (25.1%), low then increase (13.8%), moderate decline (10.7%), and rapid decline (9.3%). Multi-GBTM identified time-varying predictors of medication adherence trajectories, with disparate trends by drug class. Those pertaining to need characteristics were generally found to occur in parallel with decreases in adherence, or the opposite.

CONCLUSIONS: This study showed how changes in the context of the patient can be associated with inflexions of individual medication adherence trajectories. This study implemented a method to identify the context in which patients pursue non-adherent behaviors, but further research should be able to use richer and more contextual data pertaining to the enabling and need dimensions of the ABM to inform initiatives to improve medication adherence and associated outcomes

Code

MSR13

Topic

Clinical Outcomes, Methodological & Statistical Research

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics, Performance-based Outcomes

Disease

Cardiovascular Disorders (including MI, Stroke, Circulatory), Diabetes/Endocrine/Metabolic Disorders (including obesity)