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      Biochemical Urine Testing of Medication Adherence and Its Association With Clinical Markers in an Outpatient Population of Type 2 Diabetes Patients: Analysis in the DIAbetes and LifEstyle Cohort Twente (DIALECT)

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          Abstract

          OBJECTIVE

          To assess adherence to the three main drug classes in real-world patients with type 2 diabetes using biochemical urine testing, and to determine the association of nonadherence with baseline demographics, treatment targets, and complications.

          RESEARCH DESIGN AND METHODS

          Analyses were performed of baseline data on 457 patients in the DIAbetes and LifEstyle Cohort Twente (DIALECT) study. Adherence to oral antidiabetics (OADs), antihypertensives, and statins was determined by analyzing baseline urine samples using liquid chromatography–tandem mass spectrometry. Primary outcomes were microvascular and macrovascular complications and treatment targets of LDL cholesterol, HbA1c, and blood pressure. These were assessed cross-sectionally at baseline.

          RESULTS

          Overall, 89.3% of patients were identified as adherent. Adherence rates to OADs, antihypertensives, and statins were 95.7%, 92.0%, and 95.5%, respectively. The prevalence of microvascular (81.6% vs. 66.2%; P = 0.029) and macrovascular complications (55.1% vs. 37.0%; P = 0.014) was significantly higher in nonadherent patients. The percentage of patients who reached an LDL cholesterol target of ≤2.5 mmol/L was lower (67.4% vs. 81.1%; P = 0.029) in nonadherent patients. Binary logistic regression indicated that higher BMI, current smoking, elevated serum LDL cholesterol, high HbA1c, presence of diabetic kidney disease, and presence of macrovascular disease were associated with nonadherence.

          CONCLUSIONS

          Although medication adherence of real-world type 2 diabetes patients managed in specialist care was relatively high, the prevalence of microvascular and macrovascular complications was significantly higher in nonadherent patients, and treatment targets were reached less frequently. This emphasizes the importance of objective detection and tailored interventions to improve adherence.

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          Most cited references18

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Medication Adherence Measures: An Overview

            WHO reported that adherence among patients with chronic diseases averages only 50% in developed countries. This is recognized as a significant public health issue, since medication nonadherence leads to poor health outcomes and increased healthcare costs. Improving medication adherence is, therefore, crucial and revealed on many studies, suggesting interventions can improve medication adherence. One significant aspect of the strategies to improve medication adherence is to understand its magnitude. However, there is a lack of general guidance for researchers and healthcare professionals to choose the appropriate tools that can explore the extent of medication adherence and the reasons behind this problem in order to orchestrate subsequent interventions. This paper reviews both subjective and objective medication adherence measures, including direct measures, those involving secondary database analysis, electronic medication packaging (EMP) devices, pill count, and clinician assessments and self-report. Subjective measures generally provide explanations for patient's nonadherence whereas objective measures contribute to a more precise record of patient's medication-taking behavior. While choosing a suitable approach, researchers and healthcare professionals should balance the reliability and practicality, especially cost effectiveness, for their purpose. Meanwhile, because a perfect measure does not exist, a multimeasure approach seems to be the best solution currently.
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              Is Open Access

              Poor medication adherence in type 2 diabetes: recognizing the scope of the problem and its key contributors

              At least 45% of patients with type 2 diabetes (T2D) fail to achieve adequate glycemic control (HbA1c <7%). One of the major contributing factors is poor medication adherence. Poor medication adherence in T2D is well documented to be very common and is associated with inadequate glycemic control; increased morbidity and mortality; and increased costs of outpatient care, emergency room visits, hospitalization, and managing complications of diabetes. Poor medication adherence is linked to key nonpatient factors (eg, lack of integrated care in many health care systems and clinical inertia among health care professionals), patient demographic factors (eg, young age, low education level, and low income level), critical patient beliefs about their medications (eg, perceived treatment inefficacy), and perceived patient burden regarding obtaining and taking their medications (eg, treatment complexity, out-of-pocket costs, and hypoglycemia). Specific barriers to medication adherence in T2D, especially those that are potentially modifiable, need to be more clearly identified; strategies that target poor adherence should focus on reducing medication burden and addressing negative medication beliefs of patients. Solutions to these problems would require behavioral innovations as well as new methods and modes of drug delivery.
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                Author and article information

                Contributors
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                Journal
                Diabetes Care
                American Diabetes Association
                0149-5992
                1935-5548
                June 01 2021
                April 23 2021
                June 01 2021
                April 23 2021
                : 44
                : 6
                : 1419-1425
                Affiliations
                [1 ]Department of Internal Medicine/Nephrology, Ziekenhuis Groep Twente, Almelo, the Netherlands
                [2 ]Leicester Diabetes Centre, Leicester General Hospital, Leicester, U.K.
                [3 ]Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, U.K.
                [4 ]Department of Chemical Pathology and Metabolic Diseases, University Hospitals of Leicester NHS Trust, Leicester, U.K.
                [5 ]Department of Cardiovascular Sciences, University of Leicester, Leicester, U.K.
                [6 ]Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
                [7 ]Medication Adherence Expertise Center of the Northern Netherlands, Groningen, the Netherlands
                [8 ]Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
                [9 ]Biomedical Signals and Systems, University of Twente, Enschede, the Netherlands
                Article
                10.2337/dc20-2533
                33893164
                edb86811-878d-45d7-8ce5-065403959856
                © 2021

                https://www.diabetesjournals.org/content/license

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