Trends in Epidemiology of Pediatric Inflammatory Bowel Disease in Canada: Distributed Network Analysis of Multiple Population-Based Provincial Health Administrative Databases
Bibliographic record
Abstract
OBJECTIVES: The incidence of pediatric-onset inflammatory bowel disease (IBD) is increasing worldwide. We used population-based health administrative data to determine national Canadian IBD incidence, prevalence, and trends over time of childhood-onset IBD. METHODS: We identified children <16 years (y) diagnosed with IBD 1999-2010 from health administrative data in five provinces (Alberta, Manitoba, Nova Scotia, Ontario, Quebec), comprising 79.2% of the Canadian population. Standardized incidence and prevalence were calculated per 100,000 children. Annual percentage change (APC) in incidence and prevalence were determined using Poisson regression analysis. Provincial estimates were meta-analyzed using random-effects models to produce national estimates. RESULTS: 5,214 incident cases were diagnosed during the study period (3,462 Crohn's disease, 1,382 ulcerative colitis, 279 type unclassifiable). The incidence in Canada was 9.68 (95% CI 9.11 to 10.25) per 100,000 children. Incidence was similar amongst most provinces, but higher in Nova Scotia. APC in incidence did not significantly change over the study period in the overall cohort (+2.06%, 95% CI -0.64% to +4.76%). However, incidence significantly increased in children aged 0-5y (+7.19%, 95% +2.82% to +11.56%). Prevalence at the end of the study period in Canada was 38.25 (95% CI 35.78 to 40.73) per 100,000 children. Prevalence increased significantly over time, APC +4.56% (95% CI +3.71% to +5.42%). CONCLUSIONS: Canada has amongst the highest incidence of childhood-onset IBD in the world. Prevalence significantly increased over time. Incidence was not statistically changed with the exception of a rapid increase in incidence in the youngest group of children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.022 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".