Adherence trajectories of adjuvant endocrine therapy in the five years after its initiation among women with non-metastatic breast cancer: a cohort study using administrative databases

Breast Cancer Research and Treatment(2020)

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摘要
Purpose Despite the benefits of adjuvant endocrine therapy (AET) for reducing recurrence and mortality risks after hormone-sensitive breast cancer, AET adherence is sub-optimal for a high proportion of women. However, little is known about long-term patterns of AET adherence over the minimally recommended 5 years. Our objectives were to: (1) identify 5-year AET adherence trajectory groups; (2) describe trajectory groups according to adherence measures traditionally used (i.e., Proportion of Days Covered); and (3) explore factors associated with trajectories. Methods We conducted a 5-year cohort study using data from a French national study that included AET dispensing data. Women diagnosed with first non-metastatic breast cancer and having at least 1 AET dispensing in the 12 months after diagnosis were included. Group-based trajectory modeling was used to identify adherence trajectory groups by clustering similar patterns of monthly AET dispensing. Multinomial logistic regressions were used to identify factors associated with trajectories. Results Among 674 women, five AET adherence trajectory groups were identified: (1) quick decline and stop (5.2% of women); (2) moderate decline and stop (6.4%); (3) slow decline (17.2%); (4) high adherence (30.0%); and (5) maintenance of very high adherence (41.2%). Mean 5-year Proportion of Days Covered varied from 10 to 97% according to trajectories. Women who did not receive chemotherapy or a personalized care plan were more likely to belong to trajectories where AET adherence declined and stopped. Conclusion Our results provide information on the diversity of longitudinal AET adherence patterns, the timing of decline and discontinuation and associated factors that could inform healthcare professionals.
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关键词
Breast cancer, Adjuvant endocrine therapy, Medication adherence, Group-based trajectory modeling
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