Health Research Institute of the Balearic Islands (IdISBa), Hospital Universitari Son Espases, Palma, Spain.
Primary Care Research Unit of Mallorca, Balearic Islands Health Service, Palma, Spain.
Eur J Gen Pract. 2023 Dec;29(1):2268838. doi: 10.1080/13814788.2023.2268838. Epub 2023 Oct 24.
A better understanding of patient non-adherence to type 2 diabetes medication is needed to design effective interventions to address this issue.
(1) To estimate the prevalence of non-adherence to diabetes medication; (2) to examine its impact on glycemic control and insulin initiation; (3) to develop and validate a prediction model of non-adherence.
We conducted a longitudinal cohort study based on data from electronic health records. We included adult patients registered within the Health Service of the Balearic Islands (Spain) starting a new prescription of a non-insulin glucose-lowering drug between January 2016 and December 2018. We calculated non-adherence at 12 months follow-up, defined as medication possession ratio (MPR) ≤ 80%. We fitted multivariable regression models to examine the association between non-adherence and glycemic control and insulin initiation and identified predictors of non-adherence.
Of 18,119 patients identified, after 12 months follow-up, 5,740 (31.68%) were non-adherent. Compared with non-adherent, adherent patients presented lower HbA1c levels (mean difference = -0.32%; 95%CI = -0.38%; -0.27%) and were less likely to initiate insulin (aOR = 0.77; 95%CI = 0.63; 0.94). A predictive model explained 22.3% of the variation and presented a satisfactory performance (AUC = 0.721; Brier score = 0.177). The most important predictors of non-adherence were: non-Spanish nationality, currently working, low adherence to previous drugs, taking biguanides, smoker and absence of hypertension.
Around one-third of the patients do not adhere to their non-insulin glucose-lowering drugs. More research is needed to optimise the performance of the predicting model before considering its implementation in routine clinical practice.
为了设计有效的干预措施来解决这个问题，需要更好地了解 2 型糖尿病患者的不依从药物治疗情况。
（1）估计不依从糖尿病药物治疗的发生率；（2）研究其对血糖控制和胰岛素起始的影响；（3）建立和验证不依从的预测模型。
我们基于电子健康记录中的数据进行了一项纵向队列研究。我们纳入了 2016 年 1 月至 2018 年 12 月期间在巴利阿里群岛卫生服务处新开始服用非胰岛素类降糖药物的成年患者。我们在 12 个月的随访时计算不依从率，定义为药物利用率（MPR）≤80%。我们拟合了多变量回归模型来研究不依从与血糖控制和胰岛素起始之间的关系，并确定了不依从的预测因素。
在确定的 18119 名患者中，经过 12 个月的随访，有 5740 名（31.68%）患者不依从。与不依从的患者相比，依从的患者的糖化血红蛋白（HbA1c）水平较低（平均差值为-0.32%；95%置信区间为-0.38%；-0.27%），且更不可能起始胰岛素治疗（优势比[OR] = 0.77；95%置信区间[CI] = 0.63；0.94）。一个预测模型解释了 22.3%的变异，并表现出良好的性能（AUC = 0.721；Brier 评分 = 0.177）。不依从的最重要预测因素包括：非西班牙国籍、正在工作、对以前药物的依从性低、服用二甲双胍、吸烟者和无高血压。
大约三分之一的患者不依从他们的非胰岛素类降糖药物。在考虑将预测模型用于常规临床实践之前，需要进行更多的研究来优化其性能。
