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

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.

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Self-Reported Diagnosis of Type 1 and Type 2 Diabetes and Lifestyle Change Among Uninsured Primary Care Patients

Health Services Research and Managerial Epidemiology ◽

10.1177/2333392816689528 ◽

2017 ◽

Vol 4 ◽

pp. 233339281668952

Author(s):

Shannon Weaver ◽

Jeanie Ashby ◽

Akiko Kamimura

Keyword(s):

Physical Activity ◽

Type 2 Diabetes ◽

Primary Care ◽

Type 1 Diabetes ◽

Lifestyle Change ◽

Free Clinic ◽

Diabetes Clinic ◽

Primary Care Patients

Introduction: The purpose of this study is to examine self-reported diagnosis of type 1 and type 2 diabetes and lifestyle change among uninsured primary care patients utilizing a free clinic. Methods: Free clinic patients participated in a self-administered survey in May and June 2016. Patients with the following self-reported diagnoses were analyzed: type 2 diabetes only (n = 84), and type 1 diabetes only or both (n = 43). Results: Participants who reported having type 2 diabetes only and/or were patients of the diabetes clinic were less likely to have modified diet and/or physical activity to manage diabetes compared to those with type 1 diabetes and/or those who were not patients of the diabetes clinic. Participants with hypertension were more likely to have changed diet and/or physical activity compared to those without hypertension. Conclusion: Uninsured primary care patients may not know whether they have type 1 or type 2 diabetes. This is problematic as type 1 and type 2 diabetes require different prevention and self-management strategies. Future studies should examine the impact of misunderstanding the 2 types of diabetes on health behaviors and outcomes and explore the context of the misunderstanding.

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A retrospective epidemiological study of Type 1 Diabetes Mellitus in Wales, UK between 2008 and 2018

International Journal for Population Data Science ◽

10.23889/ijpds.v6i1.1387 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

James Rafferty ◽

Jeffery W Stephens ◽

Mark D Atkinson ◽

Stephen D Luzio ◽

Ashley Akbari ◽

...

Keyword(s):

Type 1 Diabetes ◽

Epidemiological Study ◽

Routine Data ◽

Incidence Rates ◽

Socioeconomic Deprivation ◽

Data Repositories ◽

Targeted Interventions ◽

Healthcare Data ◽

Deprived Areas

IntroductionStudies of prevalence and the demographic profile of type 1 diabetes are challenging because of the relative rarity of the condition, however, these outcomes can be determined using routine healthcare data repositories. Understanding the epidemiology of type 1 diabetes allows for targeted interventions and care of this life-affecting condition. ObjectivesTo describe the prevalence, incidence and demographics of persons with type 1 diabetes diagnosed in Wales, UK, using the Secure Anonymised Information Linkage (SAIL) Databank. MethodsData derived from primary and secondary care throughout Wales available in the SAIL Databank were used to identify people with type 1 diabetes to determine the prevalence and incidence of type 1 diabetes over a 10 year period (2008–18) and describe the demographic and clinical characteristics of this population by age, socioeconomic deprivation and settlement type. The seasonal variation in incidence rates was also examined. ResultsThe prevalence of type 1 diabetes in 2018 was 0.32% in the whole population, being greater in men compared to women (0.35% vs 0.28% respectively); highest in those aged 15-29 years (0.52%) and living in the most socioeconomically deprived areas (0.38%). The incidence of type 1 diabetes over 10 years was 14.0 cases/100,000 people/year for the whole population of Wales. It was highest in children aged 0-14 years (33.6 cases/100,000 people/year) and areas of high socioeconomic deprivation (16.8 cases/100,000 people/year) and least in those aged 45-60 years (6.5 cases/100,000 people/year) and in areas of low socioeconomic deprivation (11.63 cases/100,000 people/year). A seasonal trend in the diagnoses of type 1 diabetes was observed with higher incidence in winter months. ConclusionThis nation-wide retrospective epidemiological study using routine data revealed that the incidence of type 1 diabetes in Wales was greatest in those aged 0-14 years with a higher incidence and prevalence in the most deprived areas. These findings illustrate the need for health-related policies targeted at high deprivation areas to include type 1 diabetes in their remit.

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

Diabetes ◽

10.2337/db19-1265-p ◽

2019 ◽

Vol 68 (Supplement 1) ◽

pp. 1265-P ◽

Cited By ~ 1

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

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

Clinical Child Psychology and Psychiatry ◽

10.1177/13591045211009591 ◽

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.

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Ultrasound evaluation of the fetal fat tissue, heart, liver and umbilical cord measurements in pregnancies complicated by gestational and type 1 diabetes mellitus: potential application in the fetal birth-weight estimation and prediction of the fetal macrosomia

Diabetology & Metabolic Syndrome ◽

10.1186/s13098-021-00634-7 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Paweł Jan Stanirowski ◽

Agata Majewska ◽

Michał Lipa ◽

Dorota Bomba-Opoń ◽

Mirosław Wielgoś

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Birth Weight ◽

Soft Tissue ◽

Type 1 Diabetes Mellitus ◽

Umbilical Cord ◽

Predictive Value ◽

Fetal Macrosomia ◽

Fetal Birth Weight

Abstract Background The aim of the study was to evaluate the ultrasound-derived measurements of the fetal soft-tissue, heart, liver and umbilical cord in pregnancies complicated by gestational (GDM) and type 1 diabetes mellitus (T1DM), and further to assess their applicability in the estimation of the fetal birth-weight and prediction of fetal macrosomia. Methods Measurements were obtained from diet-controlled GDM (GDMG1) (n = 40), insulin-controlled GDM (GDMG2) (n = 40), T1DM (n = 24) and healthy control (n = 40) patients. The following parameters were selected for analysis: fetal sub-scapular fat mass (SSFM), abdominal fat mass (AFM), mid-thigh fat/lean mass (MTFM/MTLM) and inter-ventricular septum (IVS) thicknesses, heart and thorax circumference and area (HeC/HeA; ThC/ThA), liver length (LL), umbilical cord/vein/arteries circumference and area (UmC/UmA; UvC/UvA; UaC/UaA) together with total umbilical vessels (UveA) and Wharton's jelly area (WjA). Regression models were created in order to assess the contribution of selected parameters to fetal birth-weight (FBW) and risk of fetal macrosomia. Results Measurements of the fetal SSFM, AFM, MTFM, MTFM/MTLM ratio, HeC, HeA, IVS, LL, UmC, UmA, UaC, UaA, UveA and WjA were significantly increased among patients with GDMG2/T1DM as compared to GDMG1 and/or control groups (p < .05). The regression analysis revealed that maternal height as well as fetal biparietal diameter, abdominal circumference (AC), AFM and LL measurements were independent predictors of the FBW (p < .05). In addition, increase in the fetal AFM, AC and femur length (FL) was associated with a significant risk of fetal macrosomia occurrence (p < .05). The equation developed for the FBW estimation [FBW(g) = − 2254,942 + 17,204 * FL (mm) + 105,531 * AC (cm) + 131,347 * AFM (mm)] provided significantly lower mean absolute percent error than standard formula in the sub-group of women with T1DM (5.7% vs 9.4%, p < .05). Moreover, new equation including AC, FL and AFM parameters yielded sensitivity of 93.8%, specificity 77.7%, positive predictive value 54.5% and negative predictive value of 97.8% in the prediction of fetal macrosomia. Conclusions Ultrasound measurements of the fetal soft tissue, heart, liver and umbilical cord are significantly increased among women with GDM treated with insulin and T1DM. In addition to standard biometric measurements, parameters, such as AFM, may find application in the management of diabetes-complicated pregnancies.

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NKT Cells and Type-1 Diabetes and the "Hygiene Hypothesis" to Explain the Rising Incidence Rates

Diabetes Technology & Therapeutics ◽

10.1089/152091502760098465 ◽

2002 ◽

Vol 4 (3) ◽

pp. 323-333 ◽

Cited By ~ 25

Author(s):

Anjli Kukreja ◽

Noel K. Maclaren

Keyword(s):

Type 1 Diabetes ◽

Hygiene Hypothesis ◽

Nkt Cells ◽

Incidence Rates ◽

Rising Incidence

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P29 Age- and gender-specific differences in incidence rates of type 1 diabetes in Antwerp, Belgium (1989–1997)

Diabetes Research and Clinical Practice ◽

10.1016/s0168-8227(99)90051-7 ◽

1999 ◽

Vol 44 ◽

pp. S20

Author(s):

I. Weets ◽

H. Dorchy ◽

M. Coeckelberghs ◽

I. De Leeuw ◽

M. Du Caju ◽

...

Keyword(s):

Type 1 Diabetes ◽

Incidence Rates ◽

Age And Gender ◽

Gender Specific Differences ◽

And Gender ◽

Gender Specific

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Genetics of type 1 diabetes mellitus

Oxford Textbook of Endocrinology and Diabetes ◽

10.1093/med/9780199235292.003.1316 ◽

2011 ◽

pp. 1719-1723

Author(s):

David A. Savage ◽

Stephen C. Bain

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Familial Aggregation ◽

Cell Mass ◽

Monozygotic Twins ◽

Incidence Rates ◽

Distinct Pattern ◽

Dependent Diabetes Mellitus ◽

Sibling Recurrence Risk

Type 1 diabetes, previously known as insulin-dependent diabetes mellitus, is a common chronic T-cell-mediated disease in which there is selective autoimmune destruction of the insulin-producing β‎ cells of the pancreas. Although the mechanisms underlying this process are not fully understood, type 1 diabetes occurs as a result of complex interactions between multiple genes (reviewed in references 1–3) and environmental influences, which may both promote and protect against disease. Type 1 diabetes clusters in some families, but with no distinct pattern of inheritance. The concordance rates in monozygotic twins for type 1 diabetes can reach 50%, compared to 6% for dizygotic twins. The sibling recurrence risk ratio (λ‎s) (risk to siblings ÷ risk to general population) value for type 1 diabetes is 15 (6.0 ÷ 0.4 or 6% ÷ 0.4%), and twin studies suggest that 80% to 85% of familial aggregation is accounted for by genes. Type 1 diabetes has been noted to coexist with other autoimmune diseases—notably, Graves’ disease and coeliac disease—in certain families, implying the involvement of common autoimmune pathways. Improved understanding of the so-called ‘allelic architecture’ (the identity of disease-associated gene variants, their frequencies, and size of the risk conferred by each variant) and biological pathways involved in type 1 diabetes is expected to facilitate the identification of new therapeutic targets for the development of new treatments. DNA biomarkers could also assist risk prediction at a population level. This is clinically relevant since individuals can survive with only 20% intact β‎-cell mass, and the time to reach this level of destruction can be considerably delayed in some individuals, offering a window of opportunity for intervention therapy. Furthermore, clinical trials should be improved by only focusing on those patients at highest risk of developing type 1 diabetes. Early prediction, improved treatments, and, ultimately, prevention of type 1 diabetes are major goals because incidence rates are increasing. A recent study by the EURODIAB Study Group, involving 20 population-based registries across 17 European countries, has assessed incidence trends in children diagnosed with type 1 diabetes under the age of 15 between 1989 and 2003: an overall increase of 3.9% per year was reported, and, in the under 5 age group, an increase of 5.4% per year was observed (4).

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