Pontinha Vasco M, Patterson Julie A, Dixon Dave L, Carroll Norman V, Mays D'Arcy, Farris Karen B, Holdford David A
Department of Pharmacotherapy and Outcomes Science, VCU School of Pharmacy, Richmond, VA 23298, USA.
Center for Pharmacy Practice Innovation, VCU School of Pharmacy, Richmond, VA 23298, USA.
Pharmacy (Basel). 2025 Apr 9;13(2):53. doi: 10.3390/pharmacy13020053.
Medication adherence is a crucial factor for managing chronic conditions, especially in aging adults. Previous studies have identified predictors of medication adherence. However, current methods fail to capture the time-varying nature of how risk factors can influence adherence behavior. This objective of this study was to implement multitrajectory group-based models to compare a time-varying to a time-fixed approach to identifying non-adherence risk factors. The study population comprised 11,068 Medicare beneficiaries aged 65 and older taking select medications for hypertension, high blood cholesterol, and oral diabetes medications, between 2008 and 2016. Time-fixed predictors (e.g., sex, education) were examined using generalized multinomial logistic regression, while time-varying predictors were explored through multitrajectory group-based modeling. Several predisposing, enabling, and need characteristics were identified as risk factors for following at least one non-adherence trajectory. Time-varying predictors displayed an alternative representation of those risk factors, especially depression symptoms. This study highlights the dynamic nature of medication adherence predictors and the utility of multitrajectory modeling. Findings suggest that targeted interventions can be developed by addressing the key time-varying factors affecting adherence.
药物依从性是管理慢性病的关键因素，在老年人中尤为如此。先前的研究已经确定了药物依从性的预测因素。然而，目前的方法未能捕捉到风险因素如何影响依从行为的随时间变化的特性。本研究的目的是实施基于多轨迹组的模型，以比较识别不依从风险因素的随时间变化方法和固定时间方法。研究人群包括2008年至2016年间11,068名年龄在65岁及以上的医疗保险受益人，他们正在服用治疗高血压、高血胆固醇和口服糖尿病药物的特定药物。使用广义多项逻辑回归分析固定时间预测因素（如性别、教育程度），同时通过基于多轨迹组的建模探索随时间变化的预测因素。确定了几个易患、促成和需求特征作为遵循至少一条不依从轨迹的风险因素。随时间变化的预测因素显示出这些风险因素的另一种表现形式，尤其是抑郁症状。本研究强调了药物依从性预测因素的动态特性以及多轨迹建模的实用性。研究结果表明，可以通过解决影响依从性的关键随时间变化因素来制定有针对性的干预措施。
