The hospitalization burden of inflammatory bowel disease in China: a nationwide study from 2013 to 2018
Bibliographic record
Abstract
Background: The past decade has witnessed a dramatic increase in the number of patients with inflammatory bowel disease (IBD) in China. The nationwide burden of hospitalization remains unclear, however. We aimed to address this gap by conducting analysis using a nationwide database. Methods: Population-based hospitalization rates from 2013 to 2018 were calculated by extrapolating the number of patients in the database to the national level. Surgical rates, annual hospital charges, and length of stay were also used for quantification of hospitalization burden. The Poisson regression analysis and the Cochran–Armitage trend test were conducted to analyze temporal trends as expressed as annual percentage of change (APC) with 95% confidential intervals (CIs). Results: From 2013 to 2018, the hospitalization rates for Crohn’s disease (CD) and ulcerative colitis (UC) in China increased from 2.20 (95% CI = 2.17–2.22) to 3.62 (3.59–3.65) per 100,000 inhabitants ( p < 0.0001) with an APC of 10.68% (6.00–15.36%) and from 6.24 (6.20–6.28) to 8.29 (8.23–8.33) per 100,000 inhabitants ( p < 0.0001) with an APC of 5.73% (2.32–9.15%), respectively. Surgical rates decreased from 7.96% (7.29–8.63%) to 5.56% (5.11–6.00%) for CD patients ( p < 0.0001) with APC of −6.30% (−11.33 to −1.27%) and from 3.54% (3.26–3.82%) to 2.52% (2.32–2.72%) for UC patients ( p < 0.0001) with APC of −6.35% (−16.21 to 3.51). In 2018, there were estimated 166,000 IBD patients hospitalized costing a total of $426.37 million ($149.91 + $276.46 million) across the entire China. Conclusion: The population-based hospitalization rate of IBD increased, whereas the surgical rate decreased from 2013 to 2018 in China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".