Past and Future Burden of Inflammatory Bowel Diseases Based on Modeling of Population-Based Data
Bibliographic record
Abstract
BACKGROUND & AIMS: Inflammatory bowel diseases (IBDs) exist worldwide, with high prevalence in North America. IBD is complex and costly, and its increasing prevalence places a greater stress on health care systems. We aimed to determine the past current, and future prevalences of IBD in Canada. METHODS: We performed a retrospective cohort study using population-based health administrative data from Alberta (2002-2015), British Columbia (1997-2014), Manitoba (1990-2013), Nova Scotia (1996-2009), Ontario (1999-2014), Quebec (2001-2008), and Saskatchewan (1998-2016). Autoregressive integrated moving average regression was applied, and prevalence, with 95% prediction intervals (PIs), was forecasted to 2030. Average annual percentage change, with 95% confidence intervals, was assessed with log binomial regression. RESULTS: In 2018, the prevalence of IBD in Canada was estimated at 725 per 100,000 (95% PI 716-735) and annual average percent change was estimated at 2.86% (95% confidence interval 2.80%-2.92%). The prevalence in 2030 was forecasted to be 981 per 100,000 (95% PI 963-999): 159 per 100,000 (95% PI 133-185) in children, 1118 per 100,000 (95% PI 1069-1168) in adults, and 1370 per 100,000 (95% PI 1312-1429) in the elderly. In 2018, 267,983 Canadians (95% PI 264,579-271,387) were estimated to be living with IBD, which was forecasted to increase to 402,853 (95% PI 395,466-410,240) by 2030. CONCLUSION: Forecasting prevalence will allow health policy makers to develop policy that is necessary to address the challenges faced by health systems in providing high-quality and cost-effective care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".