Forecasting the Incidence and Prevalence of Inflammatory Bowel Disease: A Canadian Nationwide Analysis
Bibliographic record
Abstract
INTRODUCTION: Canada has a high burden of inflammatory bowel disease (IBD). Historical trends of IBD incidence and prevalence were analyzed to forecast the Canadian burden over the next decade. METHODS: Population-based surveillance cohorts in 8 provinces derived from health administrative data assessed the national incidence (2007-2014) and prevalence (2002-2014) of IBD. Autoregressive integrated moving average models were used to forecast incidence and prevalence, stratified by age, with 95% prediction intervals (PI), to 2035. The average annual percentage change (AAPC) with 95% confidence interval (CI) was calculated for the forecasted incidence and prevalence. RESULTS: The national incidence of IBD is estimated to be 29.9 per 100,000 (95% PI 28.3-31.5) in 2023. With a stable AAPC of 0.36% (95% CI -0.05 to 0.72), the incidence of IBD is forecasted to be 31.2 per 100,000 (95% PI 28.1-34.3) in 2035. The incidence in pediatric patients (younger than 18 years) is increasing (AAPC 1.27%; 95% CI 0.82-1.67), but it is stable in adults (AAPC 0.26%; 95% CI -0.42 to 0.82). The prevalence of IBD in Canada was 843 per 100,000 (95% PI 716-735) in 2023 and is expected to steadily climb (AAPC 2.43%; 95% CI 2.32-2.54) to 1,098 per 100,000 (95% PI 1,068-1,127) by 2035. The highest prevalence is in seniors with IBD (1,174 per 100,000 in 2023; AAPC 2.78%; 95% CI 2.75-2.81). DISCUSSION: Over the next decade, the Canadian health care systems will contend with the juxtaposition of rising incidence of pediatric IBD and a rising prevalence of overall IBD driven by the aging population.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".