Increasing incidence of paediatric inflammatory bowel disease in Ontario, Canada: evidence from health administrative data
Bibliographic record
Abstract
OBJECTIVE: Health administrative databases can be used to track chronic diseases. The aim of this study was to validate a case ascertainment definition of paediatric-onset inflammatory bowel disease (IBD) using administrative data and describe its epidemiology in Ontario, Canada. METHODS: A population-based clinical database of patients with IBD aged <15 years was used to define cases, and patient information was linked to health administrative data to compare the accuracy of various patterns of healthcare use. The most accurate algorithm was validated with chart data of children aged <18 years from 12 medical practices. Administrative data from the period 1991-2008 were used to describe the incidence and prevalence of IBD in Ontario children. Changes in incidence were tested using Poisson regression. RESULTS: Accurate identification of children with IBD required four physician contacts or two hospitalisations (with International Classification of Disease (ICD) codes for IBD) within 3 years if they underwent colonoscopy and seven contacts or three hospitalisations within 3 years in those without colonoscopy (children <12 years old, sensitivity 90.5%, specificity >99.9%; children <15 years old, sensitivity 89.6%, specificity >99.9%; children <18 years old, sensitivity 91.1%, specificity 99.5%). Age- and sex-standardised prevalence per 100 000 population of paediatric IBD has increased from 42.1 (in 1994) to 56.3 (in 2005). Incidence per 100 000 has increased from 9.5 (in 1994) to 11.4 (in 2005). Statistically significant increases in incidence were noted in 0-4 year olds (5.0%/year, p = 0.03) and 5-9 year olds (7.6%/year, p<0.0001), but not in 10-14 or 15-17 year olds. CONCLUSION: Ontario has one of the highest rates of childhood-onset IBD in the world, and there is an accelerated increase in incidence in younger children.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".