scholarly journals Modeling the economic impact of medication adherence in type 2 diabetes: a theoretical approach

2010 ◽  
pp. 283 ◽  
Author(s):  
David Cobden
Keyword(s):  
Type 2 Diabetes ◽  
Medication Adherence ◽  
Economic Impact ◽  
Theoretical Approach

815-P: Medication Adherence by Pill Count Using Medical Reconciliation in Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 815-P
Author(s):  
MEGUMI SHIOMI ◽  
YOICHI TANAKA ◽  
MOMOKA KUROBUCHI ◽  
TESSHU TAKADA ◽  
KATSUYA OTORI
Keyword(s):  
Type 2 Diabetes ◽  
Medication Adherence ◽  
Pill Count

scholarly journals Economic Impact of Hypoglycemia Among Type 2 Diabetes Mellitus in Taiwan

2018 ◽  
Vol 21 ◽  
pp. S37-S38
Author(s):  
EC Tan ◽  
M Yang ◽  
Y Wu ◽  
C Chen
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Economic Impact

scholarly journals Optimal cut-off points for adherence measure among patients with type 2 diabetes in primary care clinics: a retrospective analysis

2021 ◽  
Vol 12 ◽  
pp. 204062232199026
Author(s):  
Ming Tsuey Lim ◽  
Norazida Ab Rahman ◽  
Xin Rou Teh ◽  
Chee Lee Chan ◽  
Shantini Thevendran ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Medication Adherence ◽  
Poor Adherence ◽  
Adherence Measure ◽  
Primary Care Clinics ◽  
Assessment Period ◽  
Hba1c Target ◽  
Optimal Adherence

Background: Medication adherence measures are often dichotomized to classify patients into those with good or poor adherence using a cut-off value ⩾80%, but this cut-off may not be universal across diseases or medication classes. This study aimed to examine the cut-off value that optimally distinguish good and poor adherence by using the medication possession ratio (MPR) and proportion of days covered (PDC) as adherence measures and glycated hemoglobin (HbA1c) as outcome measure among type 2 diabetes mellitus (T2DM) patients. Method: We used pharmacy dispensing data of 1461 eligible T2DM patients from public primary care clinics in Malaysia treated with oral antidiabetic drugs between January 2018 and May 2019. Adherence rates were calculated during the period preceding the HbA1c measurement. Adherence cut-off values for the following conditions were compared: adherence measure (MPR versus PDC), assessment period (90-day versus 180-day), and HbA1c target (⩽7.0% versus ⩽8.0%). Results: The optimal adherence cut-offs for MPR and PDC in predicting HbA1c ⩽7.0% ranged between 86.1% and 98.3% across the two assessment periods. In predicting HbA1c ⩽8.0%, the optimal adherence cut-offs ranged from 86.1% to 92.8%. The cut-off value was notably higher with PDC as the adherence measure, shorter assessment period, and a stricter HbA1c target (⩽7.0%) as outcome. Conclusion: We found that optimal adherence cut-off appeared to be slightly higher than the conventional value of 80%. The adherence thresholds may vary depending on the length of assessment period and outcome definition but a reasonably wise cut-off to distinguish good versus poor medication adherence to be clinically meaningful should be at 90%.

scholarly journals Patient-Centered Care, Diabetes Empowerment, and Type 2 Diabetes Medication Adherence Among American Indian Patients

2017 ◽  
Vol 35 (5) ◽  
pp. 281-285 ◽  
Author(s):  
Nathan L. Ratner ◽  
Emily B. Davis ◽  
Laura L. Lhotka ◽  
Stephanie M. Wille ◽  
Melissa L. Walls
Keyword(s):  
Type 2 Diabetes ◽  
Medication Adherence ◽  
American Indian ◽  
Patient Centered Care ◽  
Patient Centered ◽  
Diabetes Medication ◽  
Indian Patients ◽  
Centered Care ◽  
Diabetes Empowerment

Predictors of medication adherence in patients with type 2 diabetes mellitus

2013 ◽  
Vol 29 (10) ◽  
pp. 1275-1286 ◽  
Author(s):  
Suellen M. Curkendall ◽  
Nina Thomas ◽  
Kelly F. Bell ◽  
Paul L. Juneau ◽  
Audrey J. Weiss
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Medication Adherence

scholarly journals Disparities in the use of a mHealth medication adherence promotion intervention for low-income adults with type 2 diabetes

2015 ◽  
Vol 23 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Lyndsay A Nelson ◽  
Shelagh A Mulvaney ◽  
Tebeb Gebretsadik ◽  
Yun-Xian Ho ◽  
Kevin B Johnson ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Depressive Symptoms ◽  
Medication Adherence ◽  
Health Literacy ◽  
Low Income ◽  
Mhealth Intervention ◽  
Lower Health ◽  
The Relationship ◽  
Lower Health Literacy

Abstract Objective Mobile health (mHealth) interventions may improve diabetes outcomes, but require engagement. Little is known about what factors impede engagement, so the authors examined the relationship between patient factors and engagement in an mHealth medication adherence promotion intervention for low-income adults with type 2 diabetes (T2DM). Materials and Methods Eighty patients with T2DM participated in a 3-month mHealth intervention called MEssaging for Diabetes that leveraged a mobile communications platform. Participants received daily text messages addressing and assessing medication adherence, and weekly interactive automated calls with adherence feedback and questions for problem solving. Longitudinal repeated measures analyses assessed the relationship between participants’ baseline characteristics and the probability of engaging with texts and calls. Results On average, participants responded to 84.0% of texts and participated in 57.1% of calls. Compared to Whites, non-Whites had a 63% decreased relative odds (adjusted odds ratio [AOR] = 0.37, 95% confidence interval [CI], 0.19-0.73) of participating in calls. In addition, lower health literacy was associated with a decreased odds of participating in calls (AOR = 0.67, 95% CI, 0.46-0.99, P = .04), whereas older age ( Pnonlinear = .01) and more depressive symptoms (AOR = 0.62, 95% CI, 0.38-1.02, P = .059) trended toward a decreased odds of responding to texts. Conclusions Racial/ethnic minorities, older adults, and persons with lower health literacy or more depressive symptoms appeared to be the least engaged in a mHealth intervention. To facilitate equitable intervention impact, future research should identify and address factors interfering with mHealth engagement.

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