Medication Adherence and Associated Factors in Patients With Type 2 Diabetes: A Structural Equation Model

Huang, Jing; Ding, Shenglan; Xiong, Shuyuan; Liu, Zhiping

doi:10.3389/fpubh.2021.730845

Cited by 35 publications

(26 citation statements)

References 56 publications

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“…Previous systematic reviews have reported different risk factors for non-adherence due to differences in the definition of target diseases and therapeutic medications. 12 , 16 , 18 , 25 Age, sex, dementia, and presence of a roommate have been reported as risk factors for non-adherence. 12 , 18 , 21 In our study, we newly analyzed the number of medications to be discontinued before surgery and the number of days between the pharmacist interview and surgery as risk factors for non-adherence.…”

Section: Discussionmentioning

confidence: 99%

Risk Factors for Non-Adherence to Medications That Affect Surgery: A Retrospective Study in Japan

Akamine¹,

Nagasaki²,

Tomizawa³

et al. 2022

PPA

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Data on risk factors for non-adherence to doctors' and pharmacists' instructions to discontinue medications prior to surgery are lacking. This study aimed to identify characteristics and risk factors for such non-adherent patients. Patients and Methods: Data (including patient age, sex, prescription medications, comorbidities, presence of roommate at home, and number of days between receiving instruction and surgery) of 887 patients who used medications affecting surgery at a university hospital from April 2017 to March 2020 were retrospectively evaluated. The primary endpoint was to investigate the rate of non-adherence and to explore independent risk factors for non-adherence (with age categorized as ≥65 [versus <65] years). Secondary endpoints included analysis of limited number of departments subgroup and a sensitivity analysis (with age categorized as ≥75 [versus <75] years) to confirm the robustness of the primary endpoint results. Independent risk factors for non-adherence were identified using logistic regression analysis. Results:The non-adherence rate was 11.4% (n=101/887), median age (interquartile range) at admission was 73 (70-79) years, and proportion of male patients was 81.2% (n=82). The main analysis adjusted for age ≥65 (versus <65) years showed age as a risk factor for increased nonadherence (adjusted odds ratio: 2.1, 95% confidence interval: 1.09-4.05; p=0.027). However, analyses adjusted for departments (other than urology, gynecology, and breast surgery, with a large sex bias in hospitalized patients) and for age ≥75 (versus <75) years showed no such risk. Conclusion: Age ≥65 years was associated with a higher risk of non-adherence to medications that should be discontinued before surgery. It is important for doctors and pharmacists to ensure that patients at high risk for non-adherence are aware of the importance of adherence. Our findings may help identify patients at high risk for non-adherence to such medications.

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Section: Discussionmentioning

confidence: 99%

Risk Factors for Non-Adherence to Medications That Affect Surgery: A Retrospective Study in Japan

Akamine¹,

Nagasaki²,

Tomizawa³

et al. 2022

PPA

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“…Although approximately 4% of overall global deaths of T2DM occur, more than 80% of mortality occurs in the low-and middle-income countries (LMICs) [7,8]. Additionally, the management of this chronic metabolic disease incurs remarkable a burden due to its diverse complications and outcomes largely attributed to non-adherence to anti-diabetic medications that remain a concern [9,10]. The World Health Organization (WHO) projected that by 2040, 8.5% of the world's population (more than 642 million patients with diabetes) will have T2DM in comparison to 4.7% in 1980s [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…More, high cost of antidiabetic drugs, long duration of taking medications, side effects of anti-diabetic drugs such as fatigue, nausea, vomiting, psychological problems and itching, socio-cultural issues, substance use, health literacy, belief, availability and affordability of insulin, and non-persistence are factors of non-adherence [7,39,40]. Previous studies identified factors of non-adherence such as poorer treatment outcomes, low demographic characteristics (like young age, lower education and income), progression of disease symptoms and complications [10,28]. Optimal glucose control can be achieved through strict compliance to medications, diet, and lifestyle modifications, which in turn minimize long-term complications [41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Non-adherence to medication and associated factors among type 2 diabetes patients at Clinique Medicale Fraternite, Rwanda: a cross-sectional study

Murwanashyaka

Ndagijimana

Biracyaza³

et al. 2022

BMC Endocr Disord

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Background Type 2 Diabetes Miletus (T2DM) is a public health burdens that alarmingly increases and leads to morbidity and mortality over the last decades globally. Its management is multifaceted and adherence to diabetic medications plays great roles in life of T2DM patients. But epidemiology on adherence and its associated factors remain unknown in Rwanda. Therefore, this study determined the extent of non-adherence and its predictors among T2DM patients seeking healthcare services at the Clinique Medicale la Fraternite. Methods A cross-sectional study among 200 adults’ patients with T2DM receiving care in the Medicale la Fraternite clinic was investigated. Bivariate and multivariate logistic regression models were performed based on odds ratio employed to examine associated predictors of non-adherence. The cut-off value for all statistical significances tests were considered at p < 0.05 with 95% for the confidence intervals. Results Overall, more than a half of T2DM patients (53.5%) had poor medication adherence. Being females [OR = 2.1, 95%CI(1.13–3.71), p = 0.002], consuming anti-diabetic drugs for 4–10 years [OR = 2.18, 95%CI(1.09–4.34), p = 0.027], experiencing poor communication with healthcare providers [OR = 2.4; 95%CI (1.36–4.25), p = 0.003] and being perceived as burden of the family [OR = 5.8; 95%CI(1.3–25.7), p < 0.021] had higher odds of non-adherence to anti-diabetic medications. Those with poor HbA1C [OR = 4.26; 95%CI(1.7–10.67), p = 0.002] had 4.26 times higher odds to be non-adherent compared to those with good HbA1C. Respondents with primary [OR = 3.56; 95%CI (1.12–11.28), p = 0.031] and secondary education [OR = 2.96; 95%CI (1.11–7.87), p = 0.03] were more likely to be non-adherent than those with informal education respectively. Those with normal BMI [OR = 5.17; 95%CI(1.63–16.37), p = 0.005] and those with overweight or obese [OR = 3.6; 95%CI (1.04–9.1), p < 0.02] had higher odds of being non-adherent than those with underweight. Conclusion Sex, glycaemia, communication with healthcare providers, education and gycosylated hemoglobin were the major predictors of non-adherence. Interventions for tackling this problem through bringing together efforts to stem this epidemic and controlling predictors of non-adherence are urgently recommended.

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“…with mediating improvements in diabetes clinical outcomes in patient-only interventions. 40,41 However, in this trial, improvements in cardiac risk score and individual physiological outcomes were similar in both arms.…”

Section: Discussionmentioning

confidence: 67%

Effectiveness of a Health Coaching Intervention for Patient-Family Dyads to Improve Outcomes Among Adults With Diabetes

et al. 2022

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ImportanceMore than 75% of US adults with diabetes do not meet treatment goals. More effective support from family and friends (“supporters”) may improve diabetes management and outcomes.ObjectiveTo determine if the Caring Others Increasing Engagement in Patient Aligned Care Teams (CO-IMPACT) intervention improves patient activation, diabetes management, and outcomes compared with standard care.Design, Setting, and ParticipantsThis randomized clinical trial was conducted from November 2016 to August 2019 among participants recruited from 2 Veterans Health Administration primary care sites. All patient participants were adults aged 30 to 70 years with diabetes who had hemoglobin A1c (HbA1c) levels greater than 8% of total hemoglobin (to convert to proportion of total hemoglobin, multiply by 0.01) or systolic blood pressure (SBP) higher than 150 mm Hg; each participating patient had an adult supporter. Of 1119 recruited, 239 patient-supporter dyads were enrolled between November 2016 and May 2018, randomized 1:1 to receive the CO-IMPACT intervention or standard care, and followed up for 12 to 15 months. Investigators and analysts were blinded to group assignment.InterventionsPatient-supporter dyads received a health coaching session focused on dyadic information sharing and positive support techniques, then 12 months of biweekly automated monitoring telephone calls to prompt dyadic actions to meet diabetes goals, coaching calls to help dyads prepare for primary care visits, and after-visit summaries. Standard-care dyads received general diabetes education materials only.Main Outcomes and MeasuresIntent-to-treat analyses were conducted according to baseline dyad assignment. Primary prespecified outcomes were 12-month changes in Patient Activation Measure–13 (PAM-13) and UK Prospective Diabetes Study (UKPDS) 5-year diabetes-specific cardiac event risk scores. Secondary outcomes included 12-month changes in HbA1c levels, SBP, diabetes self-management behaviors, diabetes distress, diabetes management self-efficacy, and satisfaction with health system support for the involvement of family supporters. Changes in outcome measures between baseline and 12 months were analyzed using linear regression models.ResultsA total of 239 dyads enrolled; among patient participants, the mean (SD) age was 60 (8.9) years, and 231 (96.7%) were male. The mean (SD) baseline HbA1c level was 8.5% (1.6%) and SBP was 140.2 mm Hg (18.4 mm Hg). A total of 168 patients (70.3%) lived with their enrolled supporter; 229 patients (95.8%) had complete 12-month outcome data. In intention-to-treat analyses vs standard care, CO-IMPACT patients had greater 12-month improvements in PAM-13 scores (intervention effect, 2.60 points; 95% CI, 0.02-5.18 points; P = .048) but nonsignificant differences in UKPDS 5-year cardiac risk (intervention effect, 1.01 points; 95% CI, −0.74 to 2.77 points; P = .26). Patients in the CO-IMPACT arm also had greater 12-month improvements in healthy eating (intervention effect, 0.71 d/wk; 95% CI, 0.20-1.22 d/wk; P = .007), diabetes self-efficacy (intervention effect, 0.40 points; 95% CI, 0.09-0.71 points; P = .01), and satisfaction with health system support for the family supporter participants’ involvement (intervention effect, 0.28 points; 95% CI, 0.07-0.49 points; P = .009); however, the 2 arms had similar improvements in HbA1c levels and in other measures.Conclusions and RelevanceIn this randomized clinical trial, the CO-IMPACT intervention successfully engaged patient-supporter dyads and led to improved patient activation and self-efficacy. Physiological outcomes improved similarly in both arms. More intensive direct coaching of supporters, or targeting patients with less preexisting support or fewer diabetes management resources, may have greater impact.Trial RegistrationClinicalTrials.gov Identifier: NCT02328326

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Medication Adherence and Associated Factors in Patients With Type 2 Diabetes: A Structural Equation Model

Cited by 35 publications

References 56 publications

Risk Factors for Non-Adherence to Medications That Affect Surgery: A Retrospective Study in Japan

Risk Factors for Non-Adherence to Medications That Affect Surgery: A Retrospective Study in Japan

Non-adherence to medication and associated factors among type 2 diabetes patients at Clinique Medicale Fraternite, Rwanda: a cross-sectional study

Effectiveness of a Health Coaching Intervention for Patient-Family Dyads to Improve Outcomes Among Adults With Diabetes

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