Health data and assessment department; Survey, data-science and assessment division, Institut National du Cancer (INCa), Boulogne Billancourt, France.
Aix Marseille Univ, Inserm, IRD, SESSTIM, Equipe Labellisée Ligue Contre le Cancer, Marseille, France.
Clin Breast Cancer. 2021 Aug;21(4):e415-e426. doi: 10.1016/j.clbc.2021.01.007. Epub 2021 Jan 13.
Adjuvant endocrine therapy (AET) improves long-term survival of breast cancer patients, yet many women are nonadherent or discontinue this treatment. In this study we aimed to describe AET adherence trajectories over 5 years after treatment initiation and to identify factors associated with these trajectories, in a nationwide French cohort of breast cancer survivors.
Every woman diagnosed with a first nonmetastatic breast cancer in 2011 in France who initiated AET in the 12 months after surgery was included from the French cancer cohort. We identified all reimbursements for AET from national health administrative data sets and modeled AET adherence trajectories over 5 years, using group-based trajectory modeling on the basis of the monthly proportion of days covered by AET. Associated factors were identified using multinomial logistic regressions.
We included 33,260 women. A 6-trajectory model was selected: 1, immediate discontinuation (6.6%); 2, continuous suboptimal adherence (4.3%); 3, progressive nonadherence then discontinuation (6.3%); 4, early nonadherence then discontinuation (5.7%); 5, continuous optimal adherence (68.8%); and 6, late nonadherence then discontinuation (8.3%). The main factors associated with nonadherence trajectories were extreme age (younger than 50 and older than 70 years) and switching AET.
Approximately 70% of women had optimal adherence over all 5 years. The original nationwide approach enabled us to identify the "continuous suboptimal adherence trajectory" never previously described.
辅助内分泌治疗(AET)可改善乳腺癌患者的长期生存,但许多女性不遵守或停止这种治疗。在这项研究中,我们旨在描述起始治疗后 5 年内 AET 的依从轨迹,并在法国全国乳腺癌幸存者队列中确定与这些轨迹相关的因素。
自法国癌症队列中,纳入 2011 年在法国首次诊断出非转移性乳腺癌且在手术后 12 个月内开始 AET 的每位女性。我们从国家健康行政数据集识别所有 AET 报销情况,并使用基于 AET 覆盖天数的每月比例的基于分组的轨迹建模来模拟 5 年内 AET 依从轨迹。使用多项逻辑回归确定相关因素。
我们纳入了 33260 名女性。选择了 6 个轨迹模型:1,立即停药(6.6%);2,持续的亚最佳依从(4.3%);3,逐渐不依从然后停药(6.3%);4,早期不依从然后停药(5.7%);5,持续最佳依从(68.8%);6,晚期不依从然后停药(8.3%)。与不依从轨迹相关的主要因素是极端年龄(小于 50 岁和大于 70 岁)和 AET 转换。
大约 70%的女性在所有 5 年内具有最佳的依从性。原始的全国性方法使我们能够识别以前从未描述过的“持续的亚最佳依从轨迹”。