1265-P: The Paradox of Care Delivery for Type 1 Diabetes in Primary Care Settings: A Call for Interventions to Reduce Health Disparities

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1265-P ◽  
Author(s):  
ASHBY F. WALKER ◽  
NICOLAS CUTTRISS ◽  
MICHAEL J. HALLER ◽  
KATARINA YABUT ◽  
CLAUDIA ANEZ-ZABALA ◽  
...  
Keyword(s):  
Primary Care ◽  
Type 1 Diabetes ◽  
Health Disparities ◽  
Care Delivery ◽  
Primary Care Settings

Identifying and managing eating disorders in type 1 diabetes mellitus: An all-Wales study

2021 ◽  
pp. 135910452110095
Author(s):  
Jacinta O A Tan ◽  
Imogen Spector-Hill
Keyword(s):  
Diabetes Mellitus ◽  
Primary Care ◽  
Eating Disorders ◽  
Type 1 Diabetes ◽  
Eating Disorder ◽  
Type 1 Diabetes Mellitus ◽  
Healthcare Professionals ◽  
Health And Wellbeing ◽  
Level Of Knowledge

Background: Co-morbid diabetes and eating disorders have a particularly high mortality, significant in numbers and highly dangerous in terms of impact on health and wellbeing. However, not much is known about the level of awareness, knowledge and confidence amongst healthcare professionals regarding co-morbid Type 1 Diabetes Mellitus (T1DM) and eating disorders. Aim: To understand the level of knowledge and confidence amongst healthcare professionals in Wales regarding co-morbid T1DM and eating disorder presentations, identification and treatment. Results: We conducted a survey of 102 Welsh clinicians in primary care, diabetes services and eating disorder services. 60.8% expressed low confidence in identification of co-morbid T1DM and eating disorders. Respondents reported fewer cases seen than would be expected. There was poor understanding of co-morbid T1DM and eating disorders: 44.6% identified weight loss as a main symptom, 78.4% used no screening instruments, and 80.3% consulted no relevant guidance. The respondents expressed an awareness of their lack of knowledge and the majority expressed willingness to accept training and education. Conclusion: We suggest that priority must be given to education and training of all healthcare professionals in primary care, diabetes services and mental health services who may see patients with co-morbid T1DM and eating disorders.

scholarly journals Evaluating the implementation of a primary care intervention to reduce prescriptions of benzodiazepines (BENZORED IV)

2021 ◽  
Author(s):  
Isabel Socias ◽  
Alfonso Leiva ◽  
Haizea Pombo-Ramos ◽  
Ferran Bejarano ◽  
Ermengol Sempere-Verdú ◽  
...  
Keyword(s):  
Primary Care ◽  
Traffic Accidents ◽  
Implementation Strategies ◽  
Developed Countries ◽  
Successful Implementation ◽  
Implementation Climate ◽  
Primary Care Settings ◽  
The Individual

Abstract Background: General practitioners (GPs) in developed countries widely prescribe benzodiazepines (BZDs) for their anxiolytic, hypnotic, and muscle-relaxant effects. Treatment duration, however, is rarely limited and this results in a significant number of chronic users. Long-term BZD use is associated with cognitive impairment, falls with hip fractures, traffic accidents, and increased mortality. The BENZORED IV trial was a hybrid type 1 trial conducted to evaluate the effectiveness and implementation of an intervention to reduce BZD prescription in primary care. The purpose of this qualitative study was to analyze facilitator and barriers to implement the intervention to primary care settings.Methods: Focus group meetings with GPs from the intervention arm of the BENZORED IV trial were held at primary healthcare centers in the three districts. For sampling purposes, the GPs were classified as high or low implementers according to the success of the intervention measured at 12 months. The Consolidated Framework for Implementation Research (CFIR) was used to conduct the meetings and to code, rate and analyze the dataResults: Three of the 41 CFIR constructs strongly distinguished between high and low implementers: The complexity in the intervention, the individual Stage of Change and the key stakeholder’s engagement. Seven constructs weakly discriminated between the two groups: the adaptability in the intervention, the external policy and incentives, the implementation climate, the relative priority, the self-efficacy and formally appointed implementation leader engaging. Fourteen constructs did not discriminate between the two groups, six had insufficient data for evaluation, and eleven had no data for evaluation.Conclusion: We identified constructs that could explain the variation in the implementation of the intervention, this information is relevant to design successful implementation strategies to implement the intervention.

scholarly journals Presentation to primary care during the prodrome of type 1 diabetes in childhood: A case‐control study using record data linkage

2019 ◽  
Vol 20 (3) ◽  
pp. 330-338
Author(s):  
Julia Townson ◽  
Rebecca Cannings‐John ◽  
Nick Francis ◽  
Dan Thayer ◽  
John W. Gregory
Keyword(s):  
Primary Care ◽  
Type 1 Diabetes ◽  
Case Control Study ◽  
Data Linkage ◽  
Case Control ◽  
Diabetes In Childhood ◽  
Record Data ◽  
Control Study

scholarly journals Validation of a type 1 diabetes algorithm using electronic medical records and administrative healthcare data to study the population incidence and prevalence of type 1 diabetes in Ontario, Canada

2020 ◽  
Vol 8 (1) ◽  
pp. e001224
Author(s):  
Alanna Weisman ◽  
Karen Tu ◽  
Jacqueline Young ◽  
Matthew Kumar ◽  
Peter C Austin ◽  
...  
Keyword(s):  
Primary Care ◽  
Type 1 Diabetes ◽  
Administrative Data ◽  
Predictive Value ◽  
Incidence Rates ◽  
Diabetes Clinic ◽  
Healthcare Data ◽  
Minimum Estimate ◽  
Maximum Estimate

IntroductionWe aimed to develop algorithms distinguishing type 1 diabetes (T1D) from type 2 diabetes in adults ≥18 years old using primary care electronic medical record (EMRPC) and administrative healthcare data from Ontario, Canada, and to estimate T1D prevalence and incidence.Research design and methodsThe reference population was a random sample of patients with diabetes in EMRPC whose charts were manually abstracted (n=5402). Algorithms were developed using classification trees, random forests, and rule-based methods, using electronic medical record (EMR) data, administrative data, or both. Algorithm performance was assessed in EMRPC. Administrative data algorithms were additionally evaluated using a diabetes clinic registry with endocrinologist-assigned diabetes type (n=29 371). Three algorithms were applied to the Ontario population to evaluate the minimum, moderate and maximum estimates of T1D prevalence and incidence rates between 2010 and 2017, and trends were analyzed using negative binomial regressions.ResultsOf 5402 individuals with diabetes in EMRPC, 195 had T1D. Sensitivity, specificity, positive predictive value and negative predictive value for the best performing algorithms were 80.6% (75.9–87.2), 99.8% (99.7–100), 94.9% (92.3–98.7), and 99.3% (99.1–99.5) for EMR, 51.3% (44.0–58.5), 99.5% (99.3–99.7), 79.4% (71.2–86.1), and 98.2% (97.8–98.5) for administrative data, and 87.2% (81.7–91.5), 99.9% (99.7–100), 96.6% (92.7–98.7) and 99.5% (99.3–99.7) for combined EMR and administrative data. Administrative data algorithms had similar sensitivity and specificity in the diabetes clinic registry. Of 11 499 711 adults in Ontario in 2017, there were 24 789 (0.22%, minimum estimate) to 102 140 (0.89%, maximum estimate) with T1D. Between 2010 and 2017, the age-standardized and sex-standardized prevalence rates per 1000 person-years increased (minimum estimate 1.7 to 2.56, maximum estimate 7.48 to 9.86, p<0.0001). In contrast, incidence rates decreased (minimum estimate 0.1 to 0.04, maximum estimate 0.47 to 0.09, p<0.0001).ConclusionsPrimary care EMR and administrative data algorithms performed well in identifying T1D and demonstrated increasing T1D prevalence in Ontario. These algorithms may permit the development of large, population-based cohort studies of T1D.

Care delivery and outcomes among Belgian children and adolescents with type 1 diabetes

2012 ◽  
Vol 171 (11) ◽  
pp. 1679-1685 ◽  
Author(s):  
K. Doggen ◽  
N. Debacker ◽  
D. Beckers ◽  
K. Casteels ◽  
M. Coeckelberghs ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Children And Adolescents ◽  
Care Delivery

Problem solving in type 1 diabetes for primary care nurses–part 2

2019 ◽  
Vol 2019 (12) ◽  
pp. 25-28
Author(s):  
David Morris
Keyword(s):  
Primary Care ◽  
Type 1 Diabetes ◽  
Problem Solving ◽  
Primary Care Nurses

In the second part of this series, David Morris answers more questions around treating patients with T1DM

Addressing health disparities in type 1 diabetes through peer mentorship

2019 ◽  
Vol 21 (1) ◽  
pp. 120-127
Author(s):  
Ashby F. Walker ◽  
Michael J. Haller ◽  
Matthew J. Gurka ◽  
Heather L. Morris ◽  
Brittany Bruggeman ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Health Disparities ◽  
Peer Mentorship

scholarly journals Monitoring Thyroid Function Tests in Patients With Type 1 Diabetes Mellitus: Adherence to Recommended Guidelines and Comparison of Practice Patterns in a Health Care System

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A852-A852
Author(s):  
Ling Cui ◽  
Madeleine Walsh ◽  
Sai Deepika Potluri ◽  
Nawar Suleman ◽  
Pamela Schroeder
Keyword(s):  
Diabetes Mellitus ◽  
Primary Care ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Thyroid Function ◽  
Primary Care Physicians ◽  
Practice Patterns ◽  
Care Physician ◽  
Screening Rate

Abstract Approximately 1.7 million people in the U.S. have type 1 diabetes mellitus. Autoimmune thyroid disease occurs in 17 to 30% of patients with type 1 diabetes. The American Diabetes Association recommends that thyroid function be assessed at diagnosis of type 1 diabetes and repeated every 1 to 2 years thereafter or sooner if clinically indicated. With Centricity, our former electronic medical records (EMR) system, an EMR aid automatically imported key diabetes metrics including the TSH test. Our new EMR system, MedConnect, does not have an EMR aid that imports these metrics. We hypothesized that the screening rate for thyroid dysfunction in type 1 diabetic patients would be higher with the previous EMR system than with the new EMR system. We also hypothesized that the screening rate in patients followed by an endocrinologist would be higher than in those followed by a primary care physician. Methods: We compared practice patterns with Centricity (from June 1, 2013, to May 30, 2016) versus MedConnect (from January 1, 2017, to December 31, 2019) in both primary care and endocrinology clinics. A total of 502 patients (271 Centricity, 231 MedConnect) were identified by chart review with age ≥18 years and ICD 9/10 codes for type 1 diabetes mellitus in outpatient clinics in our multicenter system. Results: Baseline TSH was done in 253 of 271 (93.4%) Centricity patients and in 181 of 231 (78.4%) MedConnect patients. The odds of baseline TSH with Centricity was 3.88 times higher compared to MedConnect (OR = 3.88, 95% CI=2.19,6.88). Of the 214 patients with normal baseline TSH, 135 (63.1%) had repeat TSH done in 1-2 years; and of the 136 MedConnect patients with normal baseline TSH, 86 (63.2%) had repeat TSH done in 1-2 years. Of 434 patients with baseline TSH, 81 (18.6%) had abnormal TSH. Of these patients, 67 (82.7%) had hypothyroidism, 1 (1.2%) had hyperthyroidism, 8 (9.8%) had subclinical hypothyroidism, and 5 (6.1%) had subclinical hyperthyroidism. Of the total 502 patients, 380 (75.7%) were followed by an endocrinologist and 122 (24.3%) were followed by a primary care provider. Among patients followed by an endocrinologist, 348 (91.6%) had a baseline TSH result. Only 86 of 122 (70.5 %) patients followed by a primary care physician had a baseline TSH. Endocrinologists had 4.6 times higher odds of TSH screening at baseline compared with primary care physicians (p &lt;0.0001). Conclusion: Thyroid function was not assessed at baseline in all patients with type 1 diabetes mellitus and was not followed at the recommended intervals per the guidelines. Higher screening rates were seen with an EMR aid. Endocrinologists screened significantly more patients than primary care physicians. Education of providers regarding the guidelines is needed, and addition of an EMR aid may help to improve detecting thyroid dysfunction in patients with type 1 diabetes mellitus.

Sign in / Sign up

Export Citation Format

Share Document