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Adherence to antidiabetic treatment among patients managed in primary care centres in Spain: the INTENSE study

  • Author Footnotes
    1 0000-0003-3768-8112
    Bogdan Vlacho
    Correspondence
    Correspondence to: USR Barcelona, DIAP J Gol, Sardenya 375 Entresol, Barcelona 08025, Spain.
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    1 0000-0003-3768-8112
    Affiliations
    DAP-Cat group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain

    Institut de Recerca Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
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  • Flora López Simarro
    Affiliations
    DAP-Cat group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain

    Primary Health Care Center Martorell, Gerència Territorial Metropolitana Sud, Institut Català de la Salut, Martorell, Spain
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  • Manel Mata-Cases
    Affiliations
    DAP-Cat group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain

    Primary Health Care Center La Mina, Gerència d’Àmbit d'Atenció Primària Barcelona Ciutat, Institut Català de la Salut, Sant Adrià de Besòs, Spain

    CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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  • Sonia Miravet
    Affiliations
    DAP-Cat group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain

    Primary Health Care Center Martorell, Gerència Territorial Metropolitana Sud, Institut Català de la Salut, Martorell, Spain
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  • José Escribano-Serrano
    Affiliations
    Unidad Gestión Clínica San Roque, Área de Gestión Sanitaria Campo de Gibraltar Este, Instituto de Investigación e Innovación Biomédica de Cádiz, Spain
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  • David Asensio
    Affiliations
    Medical & Health Public Affairs Department, Almirall, S.A., Sant Feliu de Llobregat, Spain
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  • Xavi Cortes
    Affiliations
    Medical & Health Public Affairs Department, Almirall, S.A., Sant Feliu de Llobregat, Spain
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  • Author Footnotes
    2 0000-0002-5175-1555
    Josep Franch-Nadal
    Correspondence
    Correspondence to: DAP-Cat group, Unitat de Suport a la Recerca Barcelona Ciutat, Carrer Sardenya 375, Entresuelo, Barcelona 08025, Spain.
    Footnotes
    2 0000-0002-5175-1555
    Affiliations
    DAP-Cat group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain

    CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain

    Primary Health Care Center Raval Sud, Gerència d’Àmbit d'Atenció Primària Barcelona Ciutat, Institut Català de la Salut, Barcelona, Spain
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  • Author Footnotes
    1 0000-0003-3768-8112
    2 0000-0002-5175-1555
Open AccessPublished:November 02, 2022DOI:https://doi.org/10.1016/j.pcd.2022.10.004

      Highlights

      • 49.5% of the participants were poorly adherent (PDC<80%) to NIADs.
      • The lowest mean adherence was observed among participants using GLP1-RA.
      • High HbA1c, the use of GLP1-RA or SGLT-2i was associated with poor adherence.
      • Higher ARMS-e score was observed in the poor adherence group.

      Abstract

      Aims

      To determine the degree and factors related to non-insulin antidiabetic drug (NIAD) adherence in people with type 2 diabetes mellitus (DM2) treated in primary carecentres in Spain. Methods: We did a cross-sectional study. During the study visit, variables related todifferent clinical characteristics, Adherence to Refills and Medications Scale Spanishversion (ARMS-e) and usage of NIAD were collected. We estimated the adherence toNIADs using the proportion of days covered (PDC) equation. Results: In total, 515 participants were included in the study. The mean PDC ratio was70.6 ( ± 28.9), and 50.5% (260) were classified as good adherent (PDC ≥80). Good adherence was highest among users of metformin (67.3%) and lowest among the participants using thiazolidinedione (0.8%). The score for ARMS-e was higher in the poor adherence group. In the multivariable analysis, HbA1c and the use of GLP1-RA or SGLT-2i were negatively associated with good adherence (odds ratio [OR]: 0.67, 95% confidence interval [CI]: 0.54, 0.82, OR: 0.20, 95%CI: 0.08, 0.46; OR: 0.56, 95%CI: 0.35, 0.89, respectively). Conclusions: Adherence to NIADs observed in our study is far from optimal. HbA1c and ARMS-e items could be used as adherence indicators to encourage treatment changes to improve T2DM control.

      Keywords

      1. Introduction

      Type 2 diabetes mellitus (T2DM) is a prevalent chronic disease caused by a progressive loss of insulin secretion from β cells, where the abnormal and prolonged elevation of blood glucose increases the risk of microvascular complications [

      Fundación redGDPS, Guía de diabetes tipo 2 para clínicos: Recomendaciones de la redGDPS, 2018. Available at: 〈https://www.redgdps.org/gestor/upload/colecciones/Guia〉 DM2_web.pdf.

      ]. In Spain, the prevalence of T2DM is estimated to be around 10.5% in adults 20–79 years, according to the IDF Diabetes Atlas 2019 [

      International Diabetes Federation. IDF Diabetes Atlas, 9th edn. Brussels, Belgium: 2019. Available at: 〈https://www.diabetesatlas.org〉.

      ]. T2DM and associated complications can be successfully managed and prevented, especially if they are detected early and treated promptly [
      • Khunti K.
      • Seidu S.
      Therapeutic inertia and the legacy of dysglycemia on themicrovascular and macrovascular complications of diabetes.
      ]. For the majority of therapeutic guidelines, proper lifestyle changes (self-management education, nutrition, routine physical activity, smoking cessation counselling when needed, and psychosocial care) in combination with the pharmacologic therapy are essential for achieving sufficient treatment goals [

      Fundación redGDPS, Guía de diabetes tipo 2 para clínicos: Recomendaciones de la redGDPS, 2018. Available at: 〈https://www.redgdps.org/gestor/upload/colecciones/Guia〉 DM2_web.pdf.

      ,
      • Association A.D.
      Pharmacologic approaches to glycemic treatment: standards of medical care in diabetes-2020.
      ,
      • Davies M.J.
      • D’Alessio D.A.
      • Fradkin J.
      • Kernan W.N.
      • Mathieu C.
      • Mingrone G.
      • Rossing P.
      • Tsapas A.
      • Wexler D.J.
      • Buse J.B.
      Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD).
      ,
      • Gomez-Peralta F.
      • Escalada San Martín F.J.
      • Menéndez Torre E.
      • Mata Cases M.
      • Ferrer García J.C.
      • Ezkurra Loiola P.
      • Ávila Lachica L.
      • Fornos Pérez J.A.
      • Artola Menéndez S.
      • Álvarez-Guisasola F.
      • Rica Echevarría I.
      • Girbés Borrás J.
      Recomendaciones de la Sociedad Española de Diabetes (SED) para el tratamiento farmacológico de la hiperglucemia en la diabetes tipo 2: Actualización 2018.
      ]. Currently, there are more than 30 different oral antidiabetic agents commercialised in Spain, with different mechanisms of action, safety profiles, and costs. Despite this, the extent of glycaemic control is far from optimal. A large population study from Catalonia showed that good glycaemic control (HbA1C<7%) was achieved in only 55.4% of people with T2DM [
      • Mata-Cases M.
      • Franch-Nadal J.
      • Real J.
      • et al.
      Evaluation of clinical and antidiabetic treatment characteristics of different sub-groups of patients with type 2 diabetes: data from a Mediterranean population database.
      ]. The reasons for this are multiple and complex, driven both by healthcare professionals and patients. For some primary care practitioners, the clinical decision process is sometimes difficult due to the lack of knowledge in a fast-changing scientific landscape or the lack of resources available to care for the patients [
      • Seidu S.
      • Cos X.
      • Brunton S.
      • Harris S.B.
      • Jansson S.P.O.
      • Mata-Cases M.
      • Neijens A.M.J.
      • Topsever P.
      • Khunti K.
      A disease state approach to the pharmacological management of Type 2 diabetes in primary care: a position statement by Primary Care Diabetes Europe.
      ]. The other reason for the poor glycaemic control is the lack of adherence to the antidiabetic treatment by patients with T2DM.
      Adherence is defined as "the extent to which a patient acts following the prescribed interval and dosing regimen" [
      • Cramer J.A.
      • Roy A.
      • Burrell A.
      • Fairchild C.J.
      • Fuldeore M.J.
      • Ollendorf D.A.
      • et al.
      Medication compliance and persistence: terminology and definitions.
      ]. The therapeutic adherence could be based on: patient self-report, clinician perception, pill counts, pharmacologic tracers, electronic measurement devices, or pharmacy claims data in the case of large population databases [
      • Sikka R.
      • Xia F.
      • Aubert R.E.
      Estimating medication persistency using administrative claims data.
      ]. The degree of therapeutic adherence can usually be expressed by the Medication Possession Ratio (MPR) and the Proportion of Days Covered (PDC). The MPR counts the ratio of days’ supply dispensed, divided by the number of days before the patient discontinues the drug [
      • Sikka R.
      • Xia F.
      • Aubert R.E.
      Estimating medication persistency using administrative claims data.
      ]. In comparison, The PDC measures the numbers of days’ supply covered by a prescription refill over a specific period (e.g. 1 year) divided by the total number of days in that period (e.g 365 days if the period is a year) [
      • Curkendall S.M.
      • Thomas N.
      • Bell K.F.
      • Juneau P.L.
      • Weiss A.J.
      Predictors of medication adherence in patients with type 2 diabetes mellitus.
      ]. Both equations usually result in ratios less than 1.0, where good adherence is defined if the person has values for these ratios equal or greater than 0.8 (80%) [
      • Sikka R.
      • Xia F.
      • Aubert R.E.
      Estimating medication persistency using administrative claims data.
      ].
      Evidence from a real-world data (RWD) study has shown that adherence to oral antidiabetic drugs is very low and a lack of adherence is common, generally below 50% in the first year and even lower in the second year of follow-up [
      • Farr A.M.
      • Sheehan J.J.
      • Curkendall S.M.
      • Smith D.M.
      • Johnston S.S.
      • Kalsekar I.
      Retrospective analysis of long-term adherence to and persistence with DPP-4 inhibitors in U.S. adults with type 2 diabetes mellitus.
      ]. Many factors can influence adherence to oral antidiabetic treatment, including comprehension of the treatment regimen and its benefits, emotional well-being, regimen complexity (multiple agents), medication costs and adverse events [
      • van Bruggen R.
      • Gorter K.
      • Stolk R.P.
      • et al.
      Refill adherence and polypharmacyamong patients with type 2 diabetes in general practice.
      ,
      • Rubin R.R.
      Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus.
      ]. In addition, a cohort study from Quebec reported that while 78% of the subjects were considered adherent to antidiabetic treatment (MPR≥80%) overall, people with increased age (aged 54 years or above) and those living in rural regions were associated with better adherence [
      • Guénette L.
      • Moisan J.
      • Breton M.C.
      • Sirois C.
      • Grégoire J.P.
      Difficulty adhering to antidiabetic treatment: factors associated with persistence and compliance.
      ]. Another observational RWD study from Sweden with 171,220 T2DM patients reported that good adherence (MPR>80%) was observed in 89.8% of the patients; adherence was generally not influenced by age, sex indication or specialisation of the prescriber but good adherence occurred slightly more among older adults and women [
      • Haupt D.
      • Weitoft G.R.
      • Nilsson J.L.G.
      Refill adherence to oral antihyperglycaemic drugs in Sweden.
      ]. In contrast, a RWD study from the U.S., with 1,482,593 patients, reported that good adherence (PDC≥0.8) was only observed in 45.1% of T2DM subjects, female sex, as well as younger age, study drug, presence of microvascular and serious disease (cancer or HIV) being associated with poor adherence [
      • Curkendall S.M.
      • Thomas N.
      • Bell K.F.
      • Juneau P.L.
      • Weiss A.J.
      Predictors of medication adherence in patients with type 2 diabetes mellitus.
      ]. A meta-analysis of 27 observational studies showed that adherence among T2DM persons was highly variable (38.5–93.1%), mainly due to the differences in the populations studied, the medication used, and type of adherence measurements in the studies; depression and healthcare costs were consistently associated with a lack of adherence across multiple studies but the authors also highlighted the importance of further research into factors that influence adherence in order to help optimise diabetes control [
      • Krass I.
      • Schieback P.
      • Dhippayom T.
      Adherence to diabetes medication: a systematic review.
      ]. The goal of our study was to determine the extent of adherence to non-insulin antidiabetic drugs (NIAD) in people with DM2 treated in primary care centres in Spain and the factors related to adherence.

      2. Materials and methods

      We performed a cross-sectional observational study on a national level with 341 primary care physicians, members of the Primary care Diabetes Study Group Network Foundation (Fundación RedGDPS) between July 2019 and February 2020. The study consisted of a single visit, which concurred with one of the regular medical visits of the patients for control of their T2DM. Participants were selected using a consecutive sampling approach as a patient arrived at the clinic, based on availability and willingness to take part in the study. Before inclusion in the study, the medical physician had to explain all of the study procedures to the potential participants. Only those subjects who agreed to participate and signed a written informed consent were included. Due to the study's observational nature, all of the study procedures were within routine clinical practice. The anonymised data of interest were collected on an electronic case report form (eCRF) from the electronic medical history of the study participants. The Adherence to Refill and Medication Scale Spanish validated version (ARMS-e) [
      • González-Bueno J.
      • Calvo-Cidoncha E.
      • Sevilla-Sánchez D.
      • Espaulella-Panicot J.
      • Codina-Jané C.
      • Santos-Ramos B.
      Spanish translation and cross-cultural adaptation of the ARMS-scale for measuring medication adherence in polypathological patients.
      ] was completed by the participants and introduced into the eCRF by the study physicians. The ARMS-e is a reliable and validated scale to assess adherence in chronic disease with items related to the administration of the drugs and items related to the dispensation of the drugs [
      • Capoccia K.
      • Odegard P.S.
      • Letassy N.
      Medication adherence with diabetes medication: a systematic review of the literature.
      ]. The overall adherence scores range from 12 to 48, with higher scores representing poorer medication adherence [
      • Capoccia K.
      • Odegard P.S.
      • Letassy N.
      Medication adherence with diabetes medication: a systematic review of the literature.
      ].
      Participants were included if they were 30 years or older, with a diagnosis of T2DM (according to the American Diabetes Association 2017 guidelines) for more than a year and on treatment with NIADs only. All participants with other types of diabetes (Type 1, MODY, LADA, gestational or secondary) were excluded.
      During the study visit, variables related to sociodemographic characteristics (age, sex, and toxic habits), comorbidities (hypertension, hyperlipidaemia, macrovascular and microvascular complications), concomitant treatment (oral antidiabetic and other drugs), NIAD-related adverse reactions, and clinical and laboratory parameters related to T2DM were collected.
      For the estimation of the extent of adherence to NIADs, variables such as treatment indication, dose and posology, treatment start and end date, number of NIAD packages prescribed, and number of packages dispensed from the pharmacy in the last twelve months were collected. For each participant, we calculated PDC as the number of days in the period "covered" by the NIADs divided by the number of days in the period [
      • Curkendall S.M.
      • Thomas N.
      • Bell K.F.
      • Juneau P.L.
      • Weiss A.J.
      Predictors of medication adherence in patients with type 2 diabetes mellitus.
      ]. For our analysis, we defined that number of days in the period to be of 365 days. We considered participants with good adherence if PDC ratios ≥ 0.8 (≥ 80%). The participants with PDC values of 0 or 1.1 were censored from the analysis due to the possibility of errors or recent medication changes. The quantitative variables were described with mean, standard deviation (S.D.), median, 25th percentile-Q1 and 75th percentile-Q3). Qualitative variables were described by absolute and relative frequencies. To compare two qualitative variables, contingency tables were made, and Chi-square or Fisher tests were used, depending on the distribution of the sample. To compare two quantitative variables, mean comparisons were made, and the T-test or Mann-Whitney tests were used, depending on the distribution of the sample. Logistic regression analysis was performed to study the possible relationship between a binary dependent variable (PDC ratios <0.8) and several independent variables (quantitative or qualitative). The hypothesis tests that were carried out were bilateral in all cases with a significance level of 0.05. IBM SPSS Statistics V22.0 software was used for the data analysis.

      3. Results

      Overall, 1205 subjects with T2DM were screened between July 2019 and February 2020. From those, 515 participants from 190 primary healthcare centres were included in the study. The mean PDC ratio was 70.6 ( ± 28.9), about 50.5% (260) were classified as good adherent (PDC ≥ 80%) and 49.5% (255) as poorly adherent (PDC<80%). Supplementary Fig. S1 shows the flowchart and total mean PDC of the participants in the study.

      3.1 Participants characteristics

      The participants’ characteristics according to adherence (good and poor) are presented in Table 1. Over half were male in both groups, and the mean age was 65.7 ( ± 10.6) to 64.6 ( ± 9.9) years in the two groups, respectively. Participants classified as poor adherents had a poorer comorbidity profile for almost all variables except stroke and diabetic nephropathy, although without statistical significance. Minor statistical differences were observed for systolic and diastolic blood pressure, higher in the poor adherence group. We observed statistically significant differences between the two groups for mean HbA1c, triglycerides and total cholesterol, which were higher among the participants with poor adherence. No differences between groups were observed for concomitant treatments.
      Table 1Characteristics of the participants.
      Good adherence

      N = 260
      Poor adherence

      N = 255
      p-value
      Age, mean (SD), years65.7 ( ± 10.6)64.6 ( ± 9.9)0.24
      Age, median (Q1-Q3), years66.2 (58.7–73.7)64.7 (57.6–72.0)0.24
      Sex, n (%), male156 (60.0)144 (56.5)0.42
      Alcohol consumption+, n (%)65 (25.0)62 (24.3)0.86
      Current Smoking, n (%)40 (15.4)42 (16.5)0.74
      Comorbidities, n, (%)
      Hypertension184 (70.8)185 (72.5)0.65
      Hyperlipidaemia179 (68.8)182 (71.4)0.53
      Ischemic heart disease23 (8.8)30 (11.8)0.28
      Stroke12 (4.6)6 (2.4)0.16
      Peripheral artery disease15 (5.8)18 (7.1)0.55
      Heart failure12 (4.6)18 (7.1)0.24
      Chronic kidney disease24 (9.2)26 (10.2)0.71
      Diabetic retinopathy14 (5.4)14 (5.5)0.95
      Diabetic neuropathy8 (3.1)8 (3.1)0.97
      Mental illness9 (3.5)14 (5.5)0.27
      Clinical variables,mean, (SD)
      BMI29.5(4.8)30.2(5.6)0.11
      Systolic blood pressure130.4 (12.3)132.4 (11.8)0.03
      Diastolic blood pressure77.7(8.5)79.0(8.2)0.03
      Laboratory parameters, mean, (SD)
      HbA1c (%)7.0 ( ± 0.9)7.3 ( ± 1.0)< 0.001
      Triglycerides (mg/dL)149.5 (71.7)179.8 (199.8)0.07
      Cholesterol total (mg/dL)182.6 (41.4)189.4 (40.9)0.05
      Cholesterol LDL (mg/dL)103.5 (33.9)106.0 (35.7)0.42
      Concomitant treatments, n, (%)
      Antihypertensive drugs183 (70.4)177 (69.4)0.810
      Lipid-lowering drugs184 (70.8)177 (69.4)0.737
      Antiplatelet drugs61 (23.5)62(24.3)0.821
      Anticoagulant drugs15 (5.8)19 (7.5)0.442
      NIAD drugs, n, (%)
      Metformin175 (67.3)158 (62.2)0.27
      SU15 (5.8)19 (6.6)0.44
      Glinides11(4.2)9 (3.5)0.69
      TZDs2 (0.8)2 (0.8)> 0.99
      DPP-4i142 (54.6)141 (55.3)0.88
      SGLT-2i40 (15.4)64 (25.2)0.006
      GLP1-RA8 (3.1)33 (13.0)< 0.001
      Questionnaires, mean, (SD)
      ARMS-e total17.0 (3.1)17.6 (3.4)0.02
      ARMS-e Items A10.6 (2.4)11.0 (2.6)0.05
      ARMS-e Items B6.4 (1.3)6.6 (1.4)0.08
      BMI: Body mass index; HbA1c: glycosylated haemoglobin; DPP-4i: Dipeptidyl peptidase 4 (DPP-4) inhibitors; GLP1-RA: glucagon-like peptide-1 receptors agonists; Items A: related to the administration of the drugs; Items B: related to the dispensation of the drugs; NIAD: non-insulin antidiabetic drugs; SGLT-2i: Sodium-glucose co-transporter 2 (SGLT2) inhibitors; SU: sulphonylureas; SD: standard deviation; TZDs: Thiazolidinediones; + : moderate and high-risk alcohol consumption;
      The majority of the participants were on treatment with metformin. The percentage of participants with good adherence was higher among users on metformin (67.3%), and in second place among participants on DPP-4i (54.6%), the lowest percentage was observed among participants using thiazolidinedione (0.8%). Thus, statistically significant differences favouring poor adherence were observed between participants on DPP-4i or SGLT-2i.
      Regarding the scale for adherence, a statistically significant difference for total mean scores (overall and items related to the administration of the drugs) was observed between groups, and it was higher in the poor adherence group (Table 1). There was no statistical difference between groups for the items related to the dispensation of the drug. The results of the items of the ARMS-e questionnaire are presented in Supplementary Table S1 and Fig. 1.
      Fig. 1
      Fig. 1ARMS-e items distribution. Item1: How often do you miss scheduled appointments?; Item 2: How often do you decide not to take your medicine? Item 3: How often do you forget to get prescriptions filled? Item 4: How often do you run out of medicine? Item 5: How often do you skip a dose of your medicine before you go to the doctor? Item 6: How often do you miss taking you medicine when you feel better? Item 7: How often do you miss taking your medicine when you feel sick? Item 8: How often do you miss taking your medicine when you are careless? Item 9: How often do you change the dose of your medicines to suit your needs (like when you take more or less pill than you are supposed to)? Item 10: How often do you forget to take your medicine when you are supposed to take it more than once a day? Item11: How often do you put off refilling your medicines because they cost too much money? Item 12: How often do you plan ahead and refill your medicines before they run out? Items 1, 2, 5, 6, 7, 8, 9 and 10 are related with the administration of the drugs; Items 3,4,11 and 12 related with the dispensation of the drugs.

      3.2 Adherence to the treatment

      The results related to the adherence among different NIAD classes are presented in Table 2. The highest mean adherence was observed among the participants using sulphonylureas (75.0 ± 28.4), while the lowest mean adherence was observed among participants using GLP1-RA (8.2 ± 4.4).
      Table 2Adherence to the non-insulin antidiabetic treatment.
      Mean PDC (S.D.)Median PDC

      [IQR: 25th;75th]
      Total

      n
      Any NIAD70.6 (28.9)81.1 [49.3; 92.1]515
      Metformin73.5 (28.6)82.2 [68.5; 95.9]451
      SU75.0 (28.4)82.2 [49.3; 98.6]19
      Glinides64.6 (31.7)69.9 [32.9; 98.6]14
      TZDs59.5 (47.9)61.4 [13.4; 103.6]4
      DPP-4i70.7 (30.4)92.1 [46.0; 93.0]312
      SGLT-2i68.7 (32.2)82.2 [49.3; 98.6]97
      GLP1-RA8.2 (4.4)9.3 [5.5; 12.1]27
      PDC: Proportion of days covered; DPP-4i: Dipeptidyl peptidase 4 (DPP-4) inhibitors; SGLT-2i: Sodium-glucose co-transporter 2 (SGLT2) inhibitors; GLP1-RA: Glucagon-like peptide-1 (GLP-1) analogues; TZDs: Thiazolidinediones; SU: sulphonylureas; SD: standard deviation; IQR: interquartile range; NIAD: non-insulin antidiabetic drugs
      Using logistic regression analysis, we found statistically significant association regarding the use of GLP1-RA, SGLT-2i and glycaemic control (HbA1c values). The use of GLP1-RA and SGLT-2i was positively associated with poor adherence (OR: 4.94, 95%CI: 2.17, 11.22; OR: 1.82, 95%CI: 1.15, 2.89, respectively), and the same positive association was observed for HbA1c values (OR: 1.49, 95%CI: 1.23, 1.83), whereby the possibility of good adherence decreased as the HbA1c values increased. The results of multiple logistic regressions are presented in Fig. 2 and Supplementary Tables S2.1 and S2.2.
      Fig. 2
      Fig. 2Factors related to the poor adherence (PDC<80%). Adjusted Odds Ratios (AOR) for the poor adherence (PDC<80%) patient age, sex, clinical variables and adverse events; B) AOR for the poor adherence (PDC<80%) patient age, sex and antidiabetic drug use.

      3.3 Adverse reactions and treatment discontinuation

      Among our study participants, gastrointestinal adverse reactions were the most frequently reported (11.8% overall). However, no differences were observed between the groups. Second most frequent were the urogenital adverse reactions, which were more frequently reported among participants with good adherence (3.9% vs 1.5%), although no statistically significant differences were observed. Finally, hypoglycaemia was only reported in six patients, the majority in the group with poor adherence. The adverse reaction distributions are presented in Fig. 3 and Supplementary Table S3.
      Fig. 3
      Fig. 3Adverse reactions and treatment discontinuation among the groups. a) Adverse reactions reported during the study; b) Reasons for discontinuation of the NIAD treatment.
      Fig. 3 and Supplementary Table S4 show the reasons for discontinuation of NIADs. In total, 54 treatment discontinuations were registered. The principal reason for discontinuation was intensification with other antidiabetic drugs (42.6%), which was statistically significantly higher in the poor adherence group (51.2% vs 9.1%).

      4. Discussion

      Adherence to antidiabetic treatment is a key indicator of T2DM control, which remains an important issue for patients and healthcare providers [
      • Capoccia K.
      • Odegard P.S.
      • Letassy N.
      Medication adherence with diabetes medication: a systematic review of the literature.
      ]. However, there is a general lack of knowledge regarding adherence to diabetes treatment with NIADs, compounded by heterogeneity in adherence measurement between studies and different findings according to different populations (e.g different demographics, clinical characteristics, and drug availability across countries) [
      • Rubin R.R.
      Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus.
      ]. Achieving good adherence to antidiabetic treatment is crucial for reaching the treatment goals [
      • Krass I.
      • Schieback P.
      • Dhippayom T.
      Adherence to diabetes medication: a systematic review.
      ]. Moreover, data from one recent meta-analysis suggests that good adherence to antidiabetic treatment is associated with a lower hospitalisation rate and all-cause mortality among persons with T2DM [
      • Khunti K.
      • Seidu S.
      • Kunutsor S.
      • Davies M.
      Association between adherence to pharmacotherapy and outcomes in type 2 diabetes: a meta-analysis.
      ]. Here we have performed a large cross-sectional study involving 515 participants from 190 primary healthcare centres from Spain.
      We have found that only half of the participants with T2DM (50.5%) had good adherence (PDC≥80%) to NIADs, a finding which shows less than optimal adherence. However, the average adherence (PDC) in our study was higher compared with the Swiss RWE retrospective cohort study with 10,430 patients where authors only measured adherence to oral antidiabetic drugs (70.6 ± 28.9 vs 67.8 ± 26.5, respectively) [
      • Huber C.A.
      • Reich O.
      Medication adherence in patients with diabetes mellitus: Does physician drug dispensing enhance quality of care? evidence from a large health claims database in Switzerland.
      ]. Comparing with the previously published systemic reviews, the overall adherence in our study is in line with the previously reported ranges (57–85% adherence rates) [
      • González-Bueno J.
      • Calvo-Cidoncha E.
      • Sevilla-Sánchez D.
      • Espaulella-Panicot J.
      • Codina-Jané C.
      • Santos-Ramos B.
      Spanish translation and cross-cultural adaptation of the ARMS-scale for measuring medication adherence in polypathological patients.
      ,
      • Capoccia K.
      • Odegard P.S.
      • Letassy N.
      Medication adherence with diabetes medication: a systematic review of the literature.
      ]. However, comparing the medians for PDC, we observed higher values compared with the Canadian study with 5982 individuals where adherence was measured only to oral antidiabetic drugs (median PDC, [Q1-Q2] 81.1[49.3; 92.1] vs 65.6 [24.7–95.1], respectively) [
      • Zongo A.
      • Grégoire J.P.
      • Moisan J.
      • Guénette L.
      Measuring adherence to oral antidiabetic multi-drug treatment: comparative validity of prescription claims-based adherence measures against hospitalisation.
      ].
      There is also high variability among the studies regarding the percentage of subjects achieving good adherence. For example, the percentage of subjects achieving good adherence in our study was lower (50.5% vs 60–79.1%, respectively) than results from a systemic review of 34 retrospective studies from six countries, considering different methodologies [
      • Capoccia K.
      • Odegard P.S.
      • Letassy N.
      Medication adherence with diabetes medication: a systematic review of the literature.
      ]. However, this percentage of good adherent subjects was higher than Swiss and US RWE studies (50.5% vs 41.8% and 45.1%, respectively) [
      • Huber C.A.
      • Reich O.
      Medication adherence in patients with diabetes mellitus: Does physician drug dispensing enhance quality of care? evidence from a large health claims database in Switzerland.
      ,
      • Curkendall S.M.
      • Thomas N.
      • Bell K.F.
      • Juneau P.L.
      • Weiss A.J.
      Predictors of medication adherence in patients with type 2 diabetes mellitus.
      ]. The reasons for the variability of antidiabetic treatment adherence among the studies are various. There are country-specific factors such as differences in the health care systems structure, health insurance systems, disease prevalence and comorbidities, and factors related to the cultural differences in patient attitudes or beliefs toward medication taking [
      • Menditto E.
      • Cahir C.
      • Aza-Pascual-Salcedo M.
      • Bruzzese D.
      • Poblador-Plou B.
      • Malo S.
      • Costa E.
      • González Rubio F.
      • Gimeno-Miguel A.
      • Orlando V.
      • Kardas P.
      • Prados-Torres A.
      Adherence to chronic medication in older populations: application of a common protocol among three European cohorts.
      ].
      The majority of our participants were male in both groups, with mean age ranging from 64.6 ( ± 9.9) to 65.7 ( ± 10.6) years. This is in line with a population-based study from Spain where T2DM patients were also predominantly male and slightly older (mean age 70.3 ± 12.1 years) [
      • Mata-Cases M.
      • Franch-Nadal J.
      • Real J.
      • et al.
      Evaluation of clinical and antidiabetic treatment characteristics of different sub-groups of patients with type 2 diabetes: data from a Mediterranean population database.
      ]. Regarding the clinical characteristics, although no statistically significant differences were observed between the two groups, the participants in the poor adherence group were younger, with more comorbidities and higher BMI. We observed only statistically significant differences for glycaemic control (mean HbA1c) which was worst among the persons with poor adherence. These results are also in line with a previously published study from Spain [
      • López-Simarro F.
      • Brotons C.
      • Moral I.
      • Aguado-Jodar A.
      • Cols-Sagarra C.
      • Miravet-Jiménez S.
      Concordance between two methods in measuring treatment adherence in patients with type 2 diabetes.
      ]. Compared with the RWE US study with 117,702 subjects, the baseline characteristics of the subjects in the non-adherent group were opposite to ours; the non-adherent patients had fewer macro-vascular and micro-vascular complications [
      • Curkendall S.M.
      • Thomas N.
      • Bell K.F.
      • Juneau P.L.
      • Weiss A.J.
      Predictors of medication adherence in patients with type 2 diabetes mellitus.
      ]. The participants with poor adherence in our study were on average 12.7 years older than those from the U.S. study, which could account for the differences between the diabetes complications between the two studies.
      Factors related to adherence are generally classified into three categories: patient-related, medical or treatment-related, and healthcare-related factors [
      • Krass I.
      • Schieback P.
      • Dhippayom T.
      Adherence to diabetes medication: a systematic review.
      ]. We only investigated the first two in this study. As patient-related factors, mental illnesses were more frequent in the non-adherent group; however, no statistical differences were observed between the groups. The clinical relationship between diabetes and mental illnesses and their treatments is well recognised; nevertheless, the impact of mental illnesses on T2DM remains unclear [
      • Simarro F.L.
      • Serrano J.E.
      Adherencia terapéutica. Revisión de la literatura.
      ]. According to a systemic review, the presence of schizophrenia and its treatment is associated with good adherence to the treatment of T2DM [
      • Gorczynski P.
      • Patel H.
      • Ganguli R.
      Adherence to diabetes medication in individuals with schizophrenia: a systematic review of rates and determinants of adherence.
      ], though in another study, depression among patients with diabetes was related to poorer adherence to treatment [
      • Lunghi C.
      • Zongo A.
      • Moisan J.
      • Grégoire J.-P.
      • Guénette L.
      The impact of incident depression on medication adherence in patients with type 2 diabetes.
      ]. We found statistically significant differences between HbA1c values and poor adherence (PDC<80%). This aligns with the findings of a review article that included nine studies that used MPR to measure adherence to oral antidiabetic drugs [
      • Doggrell S.A.
      • Warot S.
      The association between the measurement of adherence to anti-diabetes medicine and the HbA1c.
      ]. Hyperglycaemia and long-term complications are related to poor adherence to antidiabetic drugs and could have relevant clinical consequences for patients with T2DM [
      • Guerci B.
      • Chanan N.
      • Kaur S.
      • Jasso-Mosqueda J.G.
      • Lew E.
      Lack of treatment persistence and treatment non-adherence as barriers to glycaemic control in patients with type 2 diabetes.
      ]. Indeed, the subjects in our poor adherence group had the worst mean values of HbA1c and a higher presence of microvascular or macrovascular complications than the good adherence group. It was previously reported that inadequate medication adherence was found to have a higher risk for poor glycaemic control among the T2DM persons [
      • Feldman B.S.
      • Cohen-Stavi C.J.
      • Leibowitz M.
      • Hoshen M.B.
      • Singer S.R.
      • Bitterman H.
      • Lieberman N.
      • Balicer R.D.
      Defining the role of medication adherence in poor glycemic control among a general adult population with diabetes.
      ].
      For medical and treatment-related factors, we evaluated the use of each NIAD pharmacological group. From all of the groups of NIAD we only observed that GLP-1RA and SGLT-2i drugs were positively associated with poor adherence. In our study, the mean PDC for the GLP1- RAs was 8.2%, and the majority of the users were in the poor adherent group. So far, we know from previously published RWE studies that the adherence rates (PDC≥80%) vary significantly, depending on the patient population or specific GLP-1RA drugs [
      • Guerci B.
      • Charbonnel B.
      • Gourdy P.
      • Hadjadj S.
      • Hanaire H.
      • Marre M.
      • et al.
      Efficacy and adherence of glucagon-like peptide-1 receptor agonist treatment in patients with type 2 diabetes mellitus in real-life settings.
      ]. Moreover, it has previously been reported that GLP-1RAs have high discontinuation rates (due to local or general adverse effects), although there is also a difference in adherence rates within the class (i.e. between different GLP-1RAs) [
      • Giugliano D.
      • Maiorino M.I.
      • Bellastella G.
      • Esposito K.
      Clinical inertia, reverse clinical inertia, and medication non-adherence in type 2 diabetes.
      ]. Adverse reactions are another possible reason for non-adherence to antidiabetic treatment [
      • Rubin R.R.
      Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus.
      ]. However, in our study, we have a low number of adverse reactions, mainly reported as gastrointestinal or urogenital, but without significant differences between the two groups. It is common that patients rarely address adverse reactions in-office visits; less than 25% of patients mentioned them to their healthcare providers [
      • Rubin R.R.
      Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus.
      ]. Regarding the ARMS-e, higher score for the items was observed for the poor adherent group. The ARMS-e is a reliable and validated 12 items scale to assess adherence in chronic disease populations where higher scores represent worse medication adherence [
      • González-Bueno J.
      • Calvo-Cidoncha E.
      • Sevilla-Sánchez D.
      • Espaulella-Panicot J.
      • Codina-Jané C.
      • Santos-Ramos B.
      Spanish translation and cross-cultural adaptation of the ARMS-scale for measuring medication adherence in polypathological patients.
      ,
      • Capoccia K.
      • Odegard P.S.
      • Letassy N.
      Medication adherence with diabetes medication: a systematic review of the literature.
      ,
      • Kripalani S.
      • Risser J.
      • Gatti M.E.
      • Jacobson T.A.
      Development and evaluation of the adherence to refills and medications scale (ARMS) among low-literacy patients with chronic disease.
      ].
      This was a pragmatic observational study where the subjects were invited to participate as part of their routine clinical care for the control of T2DM. Therefore, there are some inherent limitations related to this study design. The main limitation was the lack of data; from 1205 participants initially screened, only 515 had sufficient information to respond to the objectives related to adherence and to calculate the PDC. Another important limitation is that therapeutic adherence was measured by indirect methods (drug dispensation data and self-reported method), which does not ensure that the participant took the drugs properly. Adherence measured by PDC reflects the proportion of days covered by treatment. Among the persons classified as good adherent (PDC≥80%), there would probably be persons who just took the antidiabetic medication from the pharmacies but did not properly take it. The different number of participants in each treatment group precludes reaching conclusions on the adherence to different NIADs. Participants were selected using a consecutive sampling approach as a patient arrived at the clinic, based on availability and willingness to participate in the study. Despite those limitations, the strengths of our study are multiple. Our results are recent and give a cross-sectional perspective of the adherence to NIAD treatment among primary care T2DM subjects in Spain. Over the years, antidiabetic treatment options have increased, and treatment complexity is an important factor for adherence. We used well-validated tools to calculate adherence; such as PDC, which is commonly used to calculate adherence, complemented with ARMS-e items, which help us to analyse a range of possible factors for poor adherence.

      5. Conclusions

      Adherence to NIADs observed in our study is far from optimal. Approximately half of our non-adherent T2DM participants should improve NIAD treatment adherence to achieve proper glycaemic control. Adherence indicators such as poorly controlled HbA1c and ARMS-e items should be considered to help inform clinicians regarding changes to treatment strategies to achieve better control of the disease. There is a need for specially designed intervention studies to improve personalised strategies to achieve better control of diabetes.

      Funding

      This research was funded by RedGDPS Foundation and Almirall , S.A., grant number 19–008 .

      CRediT authorship contribution statement

      Conceptualization. B.V., F.L-S, M.M-C, S.M., J.E. and J.F-N.; Methodology. B.V., F.L-S and J.F-N; Writing B.V., J.F-N—original draft preparation. B.V.; review and editing. B.V., F.L-S, M.M-C, S.M., J.E., D.A., X.C., and J.F-N; Supervision. F.L-S, X.C. and J.F-N.; Funding acquisition. J.F-N. All authors have read and agreed to the published version of the manuscript.

      Declaration of interests

      The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: M.M-C has received advisory and or speaking fees from Astra-Zeneca, Bayer, Boehringer Ingelheim, GSK, Lilly, MSD, Novartis, Novo Nordisk, and Sanofi; he has received research grants to the institution from Astra- Zeneca, GSK, Lilly, MSD, Novartis, Novo Nordisk, and Sanofi. J.F-N has received advisory and or speaking fees from Astra-Zeneca, Ascensia, Boehringer Ingelheim, GSK, Lilly, MSD, Novartis, Novo Nordisk, and Sanofi; he has received research grants to the institution from Astra-Zeneca, GSK, Lilly, MSD, Novartis, Novo Nordisk, Sanofi, and Boehringer. D.A. and X.C. are full-time employees of Almirall, S.A. F.L-S has received advisory and or speaking fees from Astra-Zeneca, Boehringer Ingelheim, Lilly, MSD, Novo Nordisk, and Sanofi. B.V., S.M. and J.E. declare no conflict of interest.

      Acknowledgements

      We want to thank CRO Dynamic, a company of Evidenze (Marisa Fernández, Dolores Pérez, Pedro Hernández and Cristina Romera López) for project administration, software, validation, formal analysis and data curation. We would like to tank all of the primary healthcare investigators from the network of RedGDPS Foundation.

      Appendix A. Supplementary material

      .

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