A longitudinal cohort study describing childhood type 1 diabetes incidence and prevalence rates in British Columbia, Canada over 27 years (1997–2023)
Bibliographic record
Abstract
AIMS: Our study described incidence and prevalence trends of type 1 diabetes in children and youth under 20 years of age from 1997 to 2023 in the Canadian province of British Columbia (BC) and assessed for a 4-, 5-, or 6-year cyclicity or increase in incidence during the COVID-19 pandemic. METHODS: Using linked population-level databases and a validated case-finding and diabetes differentiating algorithm, we identified children with type 1 diabetes diagnosed between 1997 and 2023. Data sources included hospital admissions, outpatient physician visits, and dispensed prescriptions. Population denominators were based on annual age- and sex-stratified population estimates. We calculated incidence per 100,000 and prevalence per 100 cases, using JoinPoint regression to identify trend changes. Cyclical patterns were tested using sine and cosine terms. RESULTS: From 1997 to 2023, the incidence of paediatric type 1 diabetes increased markedly until 2008 and then declined very gradually, with similar patterns across age and sex. Incidence was higher in boys, highest among children aged 5 to 14 years, and lowest in those aged 15 to 19 years. After 2012, prevalent cases stabilized in those under 10 and decreased in those over 10 years old. There was no evidence of cyclical trends or changes in incidence or prevalence during or after the COVID-19 pandemic. CONCLUSIONS/INTERPRETATION: We report a stabilization of type 1 diabetes incidence and prevalence, along with the absence of pandemic-related increases. These trends may in part reflect demographic changes in British Columbia's paediatric population, including a growing proportion of children from immigrant backgrounds with historically lower type 1 diabetes risk. Unfortunately, our data sources do not include ethnicity, limiting our ability to explore these patterns directly. This study will support the optimization of resource allocation and inform healthcare improvement and long-term management of childhood type 1 diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".