Suchergebnisse

IntroductionType 1 diabetes mellitus (T1DM) is an autoimmune condition characterised by hyperglycaemia, caused by the destruction of insulin producing β-cells in the pancreas. Previous epidemiological population level studies of T1DM and its complications have typically used recorded T1DM diagnoses to determine diabetes status and define cohorts.
Objectives and ApproachThe objective was to identify all persons with T1DM in Wales from Primary (~70\% population coverage) and Secondary Care (100% coverage) data held in the Secure Anonymised Information Linkage (SAIL) databank. People with a coded T1DM diagnosis (using Read codes in Primary Care data and International Classification of Disease (ICD10) codes in Secondary Care data), plus either insulin prescribed shortly after diagnosis or a hospital admission for diabetic ketoacidosis were identified as having T1DM. A sub-group of this SAIL e-cohort were validated using a register of persons diagnosed with T1DM in Wales under 15 years old (Brecon cohort).
Results18,285 people had a T1DM diagnosis and 10,539 had more T1DM than type 2 diabetes mellitus (T2DM) diagnoses. 6,375 persons were identified with T1DM in Primary Care data using our criteria, with a median diagnosis age of 19.2 years (interquartile range 11.0, 35.5). 47.5\% were diagnosed under 18 years of age. 39.6% of people with a T1DM diagnosis did not have T1DM using our criteria. False positive and negative rates of 4.8% and 4.5% respectively were achieved by comparing persons in the SAIL e-cohort against the Brecon cohort. Clinician estimated false positive and negative rates were 1.4% and 3.9% respectively. The prevalence of T1DM in Wales in 2016 was 0.37% or 11,049 people.
Conclusion/ImplicationsOur criteria for identifying people with T1DM was more reliable than using diagnosis codes alone, allowing for a more accurate, efficient and reproducible means of identifying individuals with T1DM for researchers utilising the SAIL databank, and other national health repositories.

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.

The Coalition for Clinical Research—Self-Monitoring of Blood Glucose Scientific Board, a group of nine academic clinicians and scientists from the United States and Europe, convened in San Francisco, California, on June 11–12, 2008, to discuss the appropriate uses of self-monitoring of blood glucose (SMBG) and the measures necessary to accurately assess the potential benefit of this practice in noninsulin-treated type 2 diabetes mellitus (T2DM). Thirteen consultants from the United States, Europe, and Canada from academia, practice, and government also participated and contributed based on their fields of expertise. These experts represent a range of disciplines that include adult endocrinology, pediatric endocrinology, health education, mathematics, statistics, psychology, nutrition, exercise physiology, and nursing. This coalition was organized by Diabetes Technology Management, Inc. Among the participants, there was consensus that: protocols assessing the performance of SMBG in noninsulin treated T2DM must provide the SMBG intervention subjects with blood glucose (BG) goals and instructions on how to respond to BG data in randomized controlled trials (RCTs);intervention subjects in clinical trials of SMBG-driven interventions must aggressively titrate their therapeutic responses or lifestyle changes in response to hyperglycemia;control subjects in clinical trials of SMBG must be isolated from SMBG-driven interventions and not be contaminated by physician experience with study subjects receiving a SMBG intervention;the best endpoints to measure in a clinical trial of SMBG in T2DM include delta Hemoglobin A1c levels, hyperglycemic events, hypoglycemic events, time to titrate noninsulin therapy to a maximum necessary dosage, and quality of life indices;either individual randomization or cluster randomization may be appropriate methods for separating control subjects from SMBG intervention subjects, provided that precautions are taken to avoid bias and that the sample size is adequate;treatment algorithms for ...