Case-finding for cognitive impairment among people with Type 2 diabetes in primary care using the Test Your Memory and Self-Administered Gerocognitive Examination questionnaires: the Cog-ID study

2015 ◽  
Vol 33 (6) ◽  
pp. 812-819 ◽  
Author(s):  
P. S. Koekkoek ◽  
J. Janssen ◽  
M. Kooistra ◽  
J. M. Biesbroek ◽  
O. Groeneveld ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Cognitive Impairment ◽  
Case Finding

Abstract # P-1: Use of Type 2 Diabetes Screening Guidelines in Primary Care: Concordance Between Physician Survey and Medical Records

2016 ◽  
Vol 22 ◽  
pp. 14
Author(s):  
Michelle Mocarski ◽  
Sandhya Mehta ◽  
Karin Gillespie ◽  
Tami Wisniewski ◽  
K.M. Venkat Narayan ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Medical Records ◽  
Physician Survey ◽  
Diabetes Screening ◽  
Screening Guidelines

scholarly journals Risk factors for developing dementia in type 2 diabetes mellitus patients with mild cognitive impairment

2019 ◽  
Vol Volume 15 ◽  
pp. 167-175 ◽  
Author(s):  
Oana Albai ◽  
Mirela Frandes ◽  
Romulus Timar ◽  
Deiana Roman ◽  
Bogdan Timar
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment

scholarly journals Effectiveness of two procedures for deploying a facilitated collaborative modeling implementation strategy—the PVS-PREDIAPS strategy—to optimize type 2 diabetes prevention in primary care: the PREDIAPS cluster randomized hybrid type II implementation trial

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Alvaro Sanchez ◽  
◽  
Susana Pablo ◽  
Arturo Garcia-Alvarez ◽  
Silvia Dominguez ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Diabetes Prevention ◽  
Implementation Strategy ◽  
Type Ii ◽  
Collaborative Process ◽  
Type 2 Diabetes Prevention ◽  
Cluster Randomized ◽  
Implementation Trial

Abstract Background The most efficient procedures to engage and guide healthcare professionals in collaborative processes that seek to optimize practice are unknown. The PREDIAPS project aims to assess the effectiveness and feasibility of different procedures to perform a facilitated interprofessional collaborative process to optimize type 2 diabetes prevention in routine primary care. Methods A type II hybrid cluster randomized implementation trial was conducted in nine primary care centers of the Basque Health Service. All centers received training on effective healthy lifestyle promotion. Headed by a local leader and an external facilitator, centers conducted a collaborative structured process—the PVS-PREDIAPS implementation strategy—to adapt the intervention and its implementation to their specific context. The centers were randomly allocated to one of two groups: one group applied the implementation strategy globally, promoting the cooperation of all health professionals from the beginning, and the other performed it sequentially, centered first on nurses, who later sought the pragmatic cooperation of physicians. The following patients were eligible for inclusion: all those aged ≥ 30 years old with at least one known cardiovascular risk factor and an impaired fasting glucose level (≥ 110-125 mg/dl) but without diabetes who attended centers during the study period. The main outcome measures concerned changes in type 2 diabetes prevention practice indicators after 12 months. Results After 12 months, 3273 eligible patients at risk of type 2 diabetes had attended their family physician at least once, and of these, 490 (15%) have been addressed by assessing their healthy lifestyles in both comparison groups. The proportion of at-risk patients receiving a personalized prescription of lifestyle change was slightly higher (8.6%; range 13.5-5.9% vs 6.8%; range 7.2-5.8%) and 2.3 times more likely (95% CI for adjusted hazard ratio, 1.38-3.94) in the sequential than in the global centers, after 8 months of the intervention program implementation period. The probability of meeting the recommended levels of physical activity and fruit and vegetable intake were four- and threefold higher after the prescription of lifestyle change than only assessment and provision of advice. The procedure of engagement in and execution of the implementation strategy does not modify the effect of prescribing healthy habits (p interaction component of intervention by group, p > 0.05). Discussion Our results show that the PVS-PREDIAPS implementation strategy manages to integrate interventions with proven efficacy in the prevention of type 2 diabetes in clinical practice in primary care. Further, they suggest that implementation outcomes were somewhat better with a sequential facilitated collaborative process focused on enhancing the autonomy and responsibility of nurses who subsequently seek a pragmatic cooperation of GPs. Trial registration Clinicaltrials.gov identifier: NCT03254979. Registered 16 August 2017—retrospectively registered.

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 Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

2021 ◽  
Vol 10 (1) ◽  
pp. e001087
Author(s):  
Tarek F Radwan ◽  
Yvette Agyako ◽  
Alireza Ettefaghian ◽  
Tahira Kamran ◽  
Omar Din ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Education Programme ◽  
Educational Programme ◽  
Data Driven ◽  
Treatment Targets ◽  
Care Processes ◽  
Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

Effect of Professional CGM (pCGM) on Glucose Management in Type 2 Diabetes Patients in Primary Care

2021 ◽  
pp. 193229682199872
Author(s):  
Gregg D. Simonson ◽  
Richard M. Bergenstal ◽  
Mary L. Johnson ◽  
Janet L. Davidson ◽  
Thomas W. Martens
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Glucose Monitoring ◽  
Care Model ◽  
Time In Range ◽  
Glucose Management ◽  
The Impact ◽  
Primary Care Management

Background: Little data exists regarding the impact of continuous glucose monitoring (CGM) in the primary care management of type 2 diabetes (T2D). We initiated a quality improvement (QI) project in a large healthcare system to determine the effect of professional CGM (pCGM) on glucose management. We evaluated both an MD and RN/Certified Diabetes Care and Education Specialist (CDCES) Care Model. Methods: Participants with T2D for >1 yr., A1C ≥7.0% to <11.0%, managed with any T2D regimen and willing to use pCGM were included. Baseline A1C was collected and participants wore a pCGM (Libre Pro) for up to 2 weeks, followed by a visit with an MD or RN/CDCES to review CGM data including Ambulatory Glucose Profile (AGP) Report. Shared-decision making was used to modify lifestyle and medications. Clinic follow-up in 3 to 6 months included an A1C and, in a subset, a repeat pCGM. Results: Sixty-eight participants average age 61.6 years, average duration of T2D 15 years, mean A1C 8.8%, were identified. Pre to post pCGM lowered A1C from 8.8% ± 1.2% to 8.2% ± 1.3% (n=68, P=0.006). The time in range (TIR) and time in hyperglycemia improved along with more hypoglycemia in the subset of 37 participants who wore a second pCGM. Glycemic improvement was due to lifestyle counseling (68% of participants) and intensification of therapy (65% of participants), rather than addition of medications. Conclusions: Using pCGM in primary care, with an MD or RN/CDCES Care Model, is effective at lowering A1C, increasing TIR and reducing time in hyperglycemia without necessarily requiring additional medications.

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