• Rocío Zamanillo-Campos, María Zaforteza Dezcallar, Maria Asunción Boronat Moreiro, Alfonso Leiva Rus, Joana Ripoll Amengual, Jadwiga Konieczna, Maria Antonia Fiol-deRoque, and Ignacio Ricci-Cabello.
• Health Research Institute of the Balearic Islands (IdISBa), Hospital Universitari Son Espases, Palma, Spain.
• Eur J Gen Pract. 2023 Dec 1; 29 (1): 22688382268838.

BackgroundA better understanding of patient non-adherence to type 2 diabetes medication is needed to design effective interventions to address this issue.Objectives(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.MethodsWe 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.ResultsOf 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.ConclusionAround 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.

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