Hospitalization Rates for Inflammatory Bowel Disease Are Decreasing Over Time: A Population-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Recent advances in the management of inflammatory bowel disease (IBD) striving for new treatment targets may have decreased rates of hospitalization for flares. We compared all-cause, IBD-related, and non-IBD-related hospitalizations while accounting for the rising prevalence of IBD. METHODS: Population-based, administrative health care databases identified all individuals living with IBD in Alberta between fiscal year 2002 and 2018. Hospitalization rates (all-cause, IBD-related, and non-IBD-related) were calculated using the prevalent Alberta IBD population. Hospitalizations were stratified by disease type, age, sex, and metropolitan status. Data were age and sex standardized to the 2019 Canadian population. Log-linear models calculated Average Annual Percentage Change (AAPC) in hospitalization rates with associated 95% confidence intervals (CIs). RESULTS: From 2002-2003 to 2018-2019, all-cause hospitalization rates decreased from 36.57 to 16.72 per 100 IBD patients (AAPC, -4.18%; 95% CI, -4.69 to -3.66). Inflammatory bowel disease-related hospitalization rate decreased from 26.44 to 9.24 per 100 IBD patients (AAPC, -5.54%; 95% CI, -6.19 to -4.88). Non-IBD-related hospitalization rate decreased from 10.13 to 7.48 per 100 IBD patients (AAPC, -1.82%; 95% CI, -2.14 to -1.49). Those over 80 years old had the greatest all-cause and non-IBD-related hospitalization rates. Temporal trends showing decreasing hospitalization rates were observed across age, sex, IBD type, and metropolitan status. CONCLUSIONS: Hospitalization rates are decreasing for all-cause, IBD-related, and non-IBD-related hospitalizations. Over the past 20 years, the care of IBD has transitioned from hospital-based care to ambulatory-centric IBD management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".