1 Center for Health System Improvement, Department of Medicine-General Internal Medicine, University of Tennessee Health Science Center, Memphis.
2 Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis.
J Manag Care Spec Pharm. 2018 Mar;24(3):198-207. doi: 10.18553/jmcp.2018.24.3.198.
Nonadherence to essential chronic medications has been identified as a potential driver of high health care costs in superutilizers of inpatient services. Few studies, however, have documented the levels of nonadherence and factors associated with nonadherence in this high-cost, vulnerable population.
To examine the factors associated with nonadherence to essential chronic medications, with special emphasis on mental illness and use of opioid medications.
This study was a retrospective panel analysis of 2-year baseline data for Medicare Part D beneficiaries eligible for the SafeMed care transitions program in Memphis, Tennessee, from February 2013 to December 2014. The 2-year baseline data for each patient were divided into four, 6-month patient periods. The study included Medicare superutilizers (defined as patients with ≥ 3 hospitalizations or ≥ 2 hospitalizations with ≥ 2 emergency visits in 6 months) with continuous Part D coverage who had filled at least 1 drug class used to treat hypertension, diabetes mellitus, congestive heart failure, coronary artery disease, or chronic lung disease. The outcome included medication nonadherence assessed using proportion of days covered (PDC), with PDC < 80% defined as nonadherent, and the main exposure variables included mental illness (defined as a diagnosis of depression or anxiety or ≥ 1 anxiolytic or antidepressant fill) and opioid medication fills assessed in each 6-month period. Pooled observations from the four 6-month periods were used for multivariable analyses using the patient periods as the unit of analysis. A random effects model with robust standard errors and a binary distribution were used to examine associations between independent variables (time invariant and time variant factors) and medication nonadherence. The model included lagged effects of time variant factors measured in each period.
Overall nonadherence to essential chronic medications ranged from 39.3% to 58.4%, with the highest for chronic lung disease medications (49.1%-64.4%). Factors associated with nonadherence included ≥ 4 opioid medication fills in the previous 6-month period (adjusted odds ratio [OR] = 1.90, 95% CI = 1.32-2.73); age 22-44 and 45-64 years vs. ≥ 65 years (OR = 3.57, 95% CI = 2.07-6.16, and OR = 2.07, 95% CI = 1.49-2.88); and a higher number of unique prescribers (OR = 1.10, 95% CI = 1.04-1.17). Factors protecting against nonadherence included higher number of unique medications filled (OR = 0.95, 95% CI = 0.92-0.98) and ≥ 1 physician office visit in the previous 6-month period (OR = 0.66, 95% CI = 0.46-0.94).
This study demonstrated that high levels of opioid medication use are significantly associated with essential chronic disease medication nonadherence among superutilizers. Other risk factors for nonadherence were aged < 65 years, low-income status, and a higher number of unique prescribers. Factors protecting against nonadherence were physician office visits and filling higher number of medications. Medication management interventions targeting superutilizers should focus on supporting chronic disease medication adherence.
This project was supported by Funding Opportunity Number 1C1CMS331067-01-00 from the Centers for Medicare & Medicaid Services, Center for Medicare and Medicaid Innovation. Support was also provided by the Pharmaceutical Research and Manufacturers of America Foundation. The content of this study is solely the responsibility of the authors. The authors declare no relevant conflicts of interest or financial relationships. Study concept and design were contributed by Surbhi, Bailey, and Graetz. Surbhi and Wan collected the data, and data interpretation was performed primarily by Surbhi, along with Graetz, Bailey, and Gatwood. The manuscript was primarily written by Surbhi, with assistance from Bailey and Graetz, and revised by Bailey, Graetz, Gatwood, and Surbhi. This study was presented as a poster at the Academy Health Annual Research Meeting in Boston, Massachusetts, on June 26-28, 2016.
非坚持使用基本慢性病药物已被确定为导致高医疗费用的潜在因素之一，尤其是在住院服务的超级用户中。然而，很少有研究记录在这一高成本、弱势人群中，药物不依从的程度以及与药物不依从相关的因素。
研究与基本慢性病药物不依从相关的因素，特别强调精神疾病和阿片类药物的使用。
本研究是对 2013 年 2 月至 2014 年 12 月期间田纳西州孟菲斯市安全医疗过渡计划的医疗保险 D 部分合格患者的 2 年基线数据进行的回顾性面板分析。每位患者的 2 年基线数据分为四个，每 6 个月为一个患者期。该研究包括医疗保险超级用户（定义为在 6 个月内住院≥3 次或住院≥2 次且≥2 次急诊就诊），并连续覆盖部分 D，至少有 1 种用于治疗高血压、糖尿病、充血性心力衰竭、冠状动脉疾病或慢性肺部疾病的药物类别。结果包括使用比例天数（PDC）评估的药物不依从，PDC<80%定义为不依从，主要暴露变量包括精神疾病（定义为抑郁或焦虑的诊断或≥1种抗焦虑或抗抑郁药物的处方）和每个 6 个月期间评估的阿片类药物的使用情况。使用患者期作为分析单位，对四个 6 个月期的汇总观察值进行多变量分析。使用具有稳健标准误差和二项分布的随机效应模型，检查独立变量（时不变和时变因素）与药物不依从之间的关联。该模型包括每个时期测量的时变因素的滞后效应。
基本慢性病药物的整体不依从率为 39.3%至 58.4%，慢性肺部疾病药物的不依从率最高（49.1%-64.4%）。与不依从相关的因素包括在前 6 个月内有≥4 次阿片类药物的使用（调整后的优势比[OR] = 1.90，95%置信区间[CI] = 1.32-2.73）；年龄 22-44 岁和 45-64 岁与≥65 岁（OR = 3.57，95%CI = 2.07-6.16，OR = 2.07，95%CI = 1.49-2.88）；以及更多的独特处方者（OR = 1.10，95%CI = 1.04-1.17）。预防不依从的因素包括更多独特药物的使用（OR = 0.95，95%CI = 0.92-0.98）和在前 6 个月内有≥1 次医生就诊（OR = 0.66，95%CI = 0.46-0.94）。
本研究表明，阿片类药物的大量使用与超级用户基本慢性病药物不依从显著相关。不依从的其他风险因素包括年龄<65 岁、低收入和更多独特的处方者。预防不依从的因素包括医生就诊和使用更多的药物。针对超级用户的药物管理干预措施应侧重于支持慢性病药物的依从性。
本项目得到了医疗保险和医疗补助服务中心、医疗保险和医疗补助创新中心的 1C1CMS331067-01-00 拨款机会的支持。研究还得到了制药研究和制造商协会基金会的支持。本研究的内容完全由作者负责。作者声明没有相关的利益冲突或财务关系。研究概念和设计由 Surbhi、Bailey 和 Graetz 做出贡献。Surbhi 和 Wan 收集了数据，Surbhi 与 Graetz、Bailey 和 Gatwood 一起对数据进行了解释。手稿主要由 Surbhi 撰写，Bailey 和 Graetz 提供了帮助，Surbhi、Bailey、Graetz 和 Gatwood 对其进行了修订。该研究于 2016 年 6 月 26 日至 28 日在马萨诸塞州波士顿举行的学术健康年度研究会议上以海报形式展示。
