Institute for Health Research, Kaiser Permanente Colorado, Denver, CO 80237, USA.
J Gen Intern Med. 2012 Jan;27(1):57-64. doi: 10.1007/s11606-011-1829-z. Epub 2011 Aug 31.
Information comparing characteristics of patients who do and do not pick up their prescriptions is sparse, in part because adherence measured using pharmacy claims databases does not include information on patients who never pick up their first prescription, that is, patients with primary non-adherence. Electronic health record medication order entry enhances the potential to identify patients with primary non-adherence, and in organizations with medication order entry and pharmacy information systems, orders can be linked to dispensings to identify primarily non-adherent patients.
This study aims to use database information from an integrated system to compare patient, prescriber, and payment characteristics of patients with primary non-adherence and patients with ongoing dispensings of newly initiated medications for hypertension, diabetes, and/or hyperlipidemia.
This is a retrospective observational cohort study. PARTICIPANTS (OR PATIENTS OR SUBJECTS): Participants of this study include patients with a newly initiated order for an antihypertensive, antidiabetic, and/or antihyperlipidemic within an 18-month period.
Proportion of patients with primary non-adherence overall and by therapeutic class subgroup. Multivariable logistic regression modeling was used to investigate characteristics associated with primary non-adherence relative to ongoing dispensings.
The proportion of primarily non-adherent patients varied by therapeutic class, including 7% of patients ordered an antihypertensive, 11% ordered an antidiabetic, 13% ordered an antihyperlipidemic, and 5% ordered medications from more than one of these therapeutic classes within the study period. Characteristics of patients with primary non-adherence varied across therapeutic classes, but these characteristics had poor ability to explain or predict primary non-adherence (models c-statistics = 0.61-0.63).
Primary non-adherence varies by therapeutic class. Healthcare delivery systems should pursue linking medication orders with dispensings to identify primarily non-adherent patients. We encourage conduct of research to determine interventions successful at decreasing primary non-adherence, as characteristics available from databases provide little assistance in predicting primary non-adherence.
比较患者是否取走处方的特征的信息很少，部分原因是使用药房理赔数据库测量的依从性不包括从未取走第一份处方的患者（即原发性不依从的患者）的信息。电子健康记录药物医嘱录入增强了识别原发性不依从患者的潜力，在具有药物医嘱录入和药房信息系统的组织中，可以将医嘱与配药相链接，以识别原发性不依从的患者。
本研究旨在使用集成系统的数据库信息比较原发性不依从患者和持续配药的新启动高血压、糖尿病和/或高血脂药物的患者的患者、医生和支付特征。
这是一项回顾性观察队列研究。
参与者（或患者或受试者）：本研究的参与者包括在 18 个月内新启动抗高血压、抗糖尿病和/或抗高血脂药物医嘱的患者。
总体和按治疗类别亚组的原发性不依从患者的比例。使用多变量逻辑回归模型来研究与持续配药相比与原发性不依从相关的特征。
原发性不依从患者的比例因治疗类别而异，包括 7%的患者开了抗高血压药物，11%的患者开了抗糖尿病药物，13%的患者开了抗高血脂药物，5%的患者在研究期间开了一种以上这些治疗类别的药物。原发性不依从患者的特征因治疗类别而异，但这些特征在解释或预测原发性不依从方面的能力较差（模型 C 统计量=0.61-0.63）。
原发性不依从的情况因治疗类别而异。医疗保健提供系统应寻求将药物医嘱与配药相链接，以识别原发性不依从的患者。我们鼓励开展研究，以确定减少原发性不依从的干预措施，因为数据库中的特征在预测原发性不依从方面几乎没有帮助。
