A179 A CANADA-WIDE STUDY OF TRENDS IN HOSPITALIZATION RATES FOR INFLAMMATORY BOWEL DISEASE
Bibliographic record
Abstract
Abstract Background Hospitalizations among individuals with inflammatory bowel disease (IBD) place a strain on healthcare resources. The decline in hospitalization rates during the era of anti-TNF therapies remains debated in the literature. Aims To examine temporal trends in hospitalization rates among individuals with in IBD across Canada. Methods We used population-based administrative healthcare data (2002–2014) from seven Canadian provinces (AB, BC, MB, NS, QC, ON, SK) to identify hospitalizations in prevalent IBD cases. Hospitalizations were categorized as: 1. all-cause, any hospitalization of an IBD patient; 2. IBD-related, admission for IBD or symptoms/comorbidities associated with IBD (eg. venous thromboembolism). We calculated hospitalization rates per 100 IBD persons with 95% confidence intervals (CIs) using IBD prevalence data. Hospitalization rates were forecast from 2015–2025, with 95% prediction intervals (PIs), using auto regressive integrated moving average models on log transformed data. We calculated average annual percentage change (AAPC) using Poisson models with quadratic equations applied for non-linear trends. We stratified by IBD subtype (CD, UC), age (<18, 18–64, 65+), and sex (female, male). We calculated AAPCs for counts to assess the actual number of hospitalizations. Results From 2002–2014, hospitalizations rates decreased for both all-cause and IBD-related admissions for IBD patients, and across age, sex, and IBD type (Table 1). In 2025, we forecast hospitalization rates to be 15.82 (95%CI:14.17,17.66) per 100 for all-cause and 7.87 (95%CI:6.16,9.90) per 100 for IBD-related. Hospitalization rates are falling, but AAPCs for hospitalization counts significantly increased for all-cause (2.65%; 95%CI: 2.42,2.89) and IBD-related (1.52%; 95%CI: 1.29,1.76). The disparity between decreasing rates and increasing counts is due to the faster rise in the AAPC of IBD prevalence (denominator) compared to hospital counts (numerator). Conclusions During the anti-TNF era (2002–2014), hospitalization rates for IBD steadily declined across Canada and are projected to continue decreasing through 2025. Despite this decline, the actual number of hospitalizations is increasing, likely driven by the rising prevalence of IBD. ¥ Includes IBD-Unclassified *Non-linear Funding Agencies CIHR
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".