1 University of Maryland School of Pharmacy, Baltimore, Maryland.
2 University of Maryland School of Medicine, Baltimore, Maryland.
J Manag Care Spec Pharm. 2016 Sep;22(9):1019-27. doi: 10.18553/jmcp.2016.22.9.1019.
Noninsulin antihyperglycemic agents (NAAs) are the mainstay of treatment for type 2 diabetes, yet persistence in NAA use is suboptimal in many diabetes patients. Most of the research on NAA discontinuance has focused on sociodemographic characteristics and general health status, but such factors are inherently limited in explaining dynamic events such as discontinuance.
To assess the relative importance of static and proximal dynamic factors in explaining long-term NAA discontinuance among Medicare beneficiaries with diabetes.
Two sets of probability models were estimated to predict NAA discontinuance as a function of static variables (age, sex, race, original reason for Medicare entitlement, low-income subsidy and dual Medicare/Medicaid eligibility status, and disease burden) and 21 dynamic factors capturing month-by-month changes in drug use, health status, and use of medical services leading up to discontinuance (defined as month 0) and the previous 4 months (designated months -1 to -4) among 71,619 patients with diabetes enrolled in Medicare Part D plans in 2006-2008.
Static variables explained just 1.2% of the variance in probability of NAA discontinuance compared with 14% for all variables combined. Key time-related predictors of NAA discontinuance included discontinuation with angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs) and statins, hypoglycemia, NAA usage gaps, insulin use, and discharge from hospitals and skilled nursing facilities (SNFs). The strongest significant predictors (P < 0.05) of NAA discontinuance were discontinuation with statins and ACEIs/ARBs in month 0 (predicted probabilities of 37% and 34%, respectively). Other variables that significantly increased the probability of NAA discontinuance by 10% or more were hypoglycemia in month 0 (14%) and month -1 (17%), discontinuance with ACEIs/ARBs in months -1 (15%) and -2 (10%), discontinuance with statins in month -1 (13%), and insulin use in month 0 (12%). Experiencing a previous gap in NAA therapy was associated with higher likelihood of discontinuance if the gap occurred in month -2 (10%) or month -4 (6%), but a gap in therapy in month -1 actually reduced the likelihood of discontinuance by 13%. Discharge from a hospital or SNF was consistently associated with higher probabilities of NAA discontinuance ranging between 4% and 10%, with higher probabilities occurring closer to month 0.
A cascade of dynamic changes preceding discontinuance with NAA therapy among Medicare Part D enrollees with diabetes was observed between 2006 and 2008. Understanding that lack of persistence in drug use is a dynamic rather than a static phenomenon opens up new avenues for investigating and ultimately improving adherence behavior in the elderly.
This study was funded by Merck & Co. Huang and Raipathak are employees of Merck & Co. Brandt reports consultancy and speaker fees from Catapult, Omnicare, RAND, HRSA, CMS, and AGS Beers Criteria. Loh is currently employed at Touro College of Pharmacy. All other authors have no relevant potential conflicts of interest to disclose. Study concept and design were primarily contributed by Stuart, Quinn, and Brandt, along with Shen, Roberto, Hendrick, Huang, and Rajpathak. Shen, Loh, Hendrick, and Kim collected the data, and data interpretation was performed primarily by Stuart, Shen, and Roberto, assisted by Quinn, Brandt, Hendrick, Huang, and Rajpathak. The manuscript was written primarily by Stuart, with assistance from the other authors, and revised by Huang, Rajpathak, and Stuart, with assistance from the other authors.
非胰岛素类抗高血糖药物(NAAs)是治疗 2 型糖尿病的主要药物,但许多糖尿病患者对 NAA 的持续使用并不理想。大多数关于 NAA 停药的研究都集中在社会人口特征和一般健康状况上,但这些因素在解释停药等动态事件方面存在固有局限性。
评估静态和近端动态因素在解释 2006-2008 年医疗保险受益的糖尿病患者长期 NAA 停药的相对重要性。
为了预测 NAA 停药,我们估计了两组概率模型,将静态变量(年龄、性别、种族、医疗保险资格的原始原因、低收入补贴和双重医疗保险/医疗补助资格状况以及疾病负担)和 21 个动态因素结合起来,这些因素在 71619 名 2006-2008 年参加医疗保险 D 计划的糖尿病患者中,每月变化的药物使用、健康状况以及导致停药的医疗服务使用(定义为 0 月)和前 4 个月(指定为-1 至-4 月)。
静态变量仅解释了 NAA 停药概率的 1.2%,而所有变量组合解释了 14%。NAA 停药的主要时间相关预测因素包括与血管紧张素转换酶抑制剂/血管紧张素受体阻滞剂(ACEIs/ARBs)和他汀类药物、低血糖、NAA 使用间隙、胰岛素使用以及从医院和熟练护理设施(SNFs)出院相关的停药。NAA 停药的最强显著预测因素(P<0.05)是 0 月时与他汀类药物和 ACEIs/ARBs 的停药(分别为 37%和 34%)。其他使 NAA 停药概率增加 10%或以上的变量包括 0 月时的低血糖(14%)和-1 月(17%)、-1 月(15%)和-2 月(10%)时与 ACEIs/ARBs 的停药、-1 月时与他汀类药物的停药(13%)和 0 月时的胰岛素使用(12%)。如果 NAA 治疗的间隙发生在-2 月(10%)或-4 月(6%),则经历过先前的 NAA 治疗间隙与停药的可能性更高,但-1 月的治疗间隙实际上使停药的可能性降低了 13%。从医院或 SNF 出院与 NAA 停药的可能性增加 4%至 10%之间存在一致的关联,并且更接近 0 月时的可能性更高。
在 2006 年至 2008 年期间,观察到医疗保险 D 计划中患有糖尿病的患者中,NAA 治疗停药前存在一系列动态变化。了解药物使用的缺乏持久性是一个动态而不是静态的现象,为研究和最终改善老年人的依从性行为开辟了新的途径。
这项研究由默克公司资助。Huang 和 Raipathak 是默克公司的员工。Brandt 报告了与 Catapult、Omnicare、RAND、HRSA、CMS 和 AGS Beers 标准有关的咨询和演讲费。Loh 目前在图罗学院药学工作。所有其他作者均无相关潜在利益冲突披露。研究概念和设计主要由 Stuart、Quinn 和 Brandt 提出,Shen、Roberto、Hendrick、Huang 和 Rajpathak 提供协助。Shen、Loh、Hendrick 和 Kim 收集了数据,Stuart、Shen 和 Roberto 主要进行了数据解释,Quinn、Brandt、Hendrick、Huang 和 Rajpathak 提供了协助。手稿主要由 Stuart 撰写,其他作者提供了协助,并由 Huang、Rajpathak 和 Stuart 进行了修订,其他作者也提供了协助。