Abstract
BACKGROUND AND OBJECTIVE: Adherence to antifibrotic medications has been evaluated in a few studies using annual proportion of days covered (PDC), a common adherence metric. However, PDC alone cannot identify and distinguish between different patterns of adherence over time, which can be accomplished using group-based trajectory models (GBTM) of monthly PDC. The objective is to assess nintedanib adherence trajectories using GBTM and identify characteristics of patients within each trajectory group. METHODS: Individuals with idiopathic pulmonary fibrosis (IPF) who initiated nintedanib during 10/1/2014-12/31/2018 were identified in 100% Medicare claims and enrollment data. The sample consisted of community-dwelling older adults (≥ 66 years) with continuous coverage in Medicare Parts A, B and D for one year before (baseline) and after (follow-up) initiating nintedanib. A series of GBTMs of adherence was estimated to identify the best-fitting specification. Patients were then grouped based on their estimated adherence trajectories. Associations between baseline patient characteristics, including demographics, comorbidities, and health care use, and group membership probabilities were quantified as odds ratios using fractional multinomial logit modeling. RESULTS: Among the 1,798 patients initiating nintedanib, mean age was 75.4 years, 61.1% were male, and 91.1% were non-Hispanic white. The best-fitting GBTM had five adherence trajectory groups: high adherence (43.1%), moderate adherence (11.9%), high-then-poor adherence (10.4%), delayed-poor adherence (13.2%), and early-poor adherence (21.5%). The principal factors associated with higher odds of being in at least one of the poor-adherence groups were older age, female sex, race and ethnicity other than non-Hispanic white, and number of medications during baseline. CONCLUSIONS: GBTM identified distinct patterns of nintedanib adherence for the IPF patient cohort. Identifying adherence trajectory groups and understanding the characteristics of their members provide more actionable information to personalize interventions than conventional metrics of medication adherence.