1 Division of Pharmacoepidemiology and Pharmacoeconomics and Center for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
2 Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
J Manag Care Spec Pharm. 2018 May;24(5):469-477. doi: 10.18553/jmcp.2018.24.5.469.
Efforts at predicting long-term adherence to medications have been focused on patients filling typical month-long supplies of medication. However, prediction remains difficult for patients filling longer initial supplies, a practice that is becoming increasingly common as a method to enhance medication adherence.
To (a) extend methods involving short-term filling behaviors and (b) develop novel variables to predict adherence in a cohort of patients receiving longer initial prescriptions.
In this retrospective cohort study, we used claims from a large national insurer to identify patients initiating a 90-day supply of oral medications for diabetes, hypertension, and hyperlipidemia (i.e., statins). Patients were included in the cohort if they had continuous database enrollment in the 180 days before and 365 days after medication initiation. Adherence was measured in the subsequent 12 months using the proportion of days covered metric. In total, 125 demographic, clinical, and medication characteristics at baseline and in the first 30-120 days after initiation were used to predict adherence using logistic regression models. We used 10-fold cross-validation to assess predictive accuracy by discrimination (c-statistic) measures.
In total, 32,249 patients met the inclusion criteria, including 14,930 patients initiating statins, 12,887 patients initiating antihypertensives, and 4,432 patients initiating oral hypoglycemics. Prediction using only baseline variables was relatively poor (cross-validated c-statistic = 0.644). Including indicators of acute clinical conditions, health resource utilization, and short-term medication filling in the first 120 days greatly improved predictive ability (0.823). A model that incorporated all baseline characteristics and predictors within the first 120 days after medication initiation more accurately predicted future adherence (0.832). The best performing model that included all 125 baseline and postbaseline characteristics had strong predictive ability (0.837), suggesting the utility of measuring these novel postbaseline variables in this population.
We demonstrate that long-term, 12-month adherence in patients filling longer supplies of medication can be strongly predicted using a combination of clinical, health resource utilization, and medication filling characteristics before and after treatment initiation.
This work was supported by an unrestricted grant from CVS Health to Brigham and Women's Hospital. Shrank and Matlin were employees and shareholders at CVS Health at the time of this study; they report no financial interests in products or services that are related to this subject. Spettell is an employee of, and shareholder in, Aetna. This research was previously presented at the 2016 Annual Conference of the International Society for Pharmacoepidemiology; August 25-28, 2016; Dublin, Ireland.
预测长期用药依从性的努力一直集中在患者填写典型的一个月用药量上。然而，对于初始用药量较长的患者，预测仍然很困难，这种做法作为提高用药依从性的一种方法越来越普遍。
（a）扩展涉及短期用药行为的方法；（b）开发新的变量，以预测接受更长初始处方的患者的依从性。
在这项回顾性队列研究中，我们使用来自一家大型全国保险公司的理赔数据，确定了开始服用 90 天剂量口服药物治疗糖尿病、高血压和高血脂（即他汀类药物）的患者。如果患者在药物起始前 180 天和起始后 365 天内连续在数据库中注册，则将其纳入队列。在接下来的 12 个月内，使用覆盖天数比例来衡量依从性。共有 125 个基线和起始后 30-120 天内的人口统计学、临床和药物特征，用于使用逻辑回归模型预测依从性。我们使用 10 折交叉验证通过判别（c 统计量）测量来评估预测准确性。
共有 32249 名患者符合纳入标准，其中包括 14930 名开始服用他汀类药物的患者、12887 名开始服用抗高血压药物的患者和 4432 名开始服用口服降糖药的患者。仅使用基线变量进行预测的效果相对较差（交叉验证 c 统计量=0.644）。在最初的 120 天内纳入急性临床状况、卫生资源利用和短期药物使用的指标，大大提高了预测能力（0.823）。在药物起始后 120 天内纳入所有基线特征和预测因子的模型能更准确地预测未来的依从性（0.832）。纳入治疗开始前后所有 125 个基线和基线后特征的最佳表现模型具有很强的预测能力（0.837），表明在该人群中测量这些新的基线后特征具有实用性。
我们证明，通过治疗开始前后的临床、卫生资源利用和药物使用特征的组合，可以很好地预测服用更长时间药物的患者的 12 个月长期依从性。
这项工作得到了 CVS Health 对布莱根妇女医院的一项无限制赠款的支持。Shrank 和 Matlin 在研究期间是 CVS Health 的员工和股东；他们没有报告与本主题相关的产品或服务的财务利益。Spettell 是 Aetna 的员工和股东。这项研究曾在 2016 年国际药物流行病学学会年会上提出；2016 年 8 月 25-28 日；爱尔兰都柏林。
