A65 HOSPITALIZATION RATES OF INFLAMMATORY BOWEL DISEASE DURING THE COVID-19 PANDEMIC: A POPULATION-BASED STUDY OF THE ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT.
Bibliographic record
Abstract
Abstract Background Inflammatory bowel disease (IBD) places a significant burden on healthcare resources, requiring medical and surgical management in hospital. The COVID-19 pandemic further strained healthcare systems, but the impact of the global pandemic on IBD hospitalization rates remain unclear. Aims To examine hospitalization rates for IBD prior to the COVID-19 pandemic (2017-2019) and during the pandemic (2020-2022) in Organisation for Economic Co-Operation and Development (OECD) countries. Methods A population-based study was conducted using data from the 34 OECD member countries. Country-level data on annual IBD-related hospital discharge counts and rates (per 100,000 persons) were obtained. Mean hospitalization rates were calculated before (2017 to 2019) and during the pandemic (2020 to 2022). Incident rate ratios (IRR) comparing pandemic to pre-pandemic rates were calculated using Poisson regression, with 95% confidence intervals (CI). Countries without complete data during this the study period were excluded. Results Overall, 22 countries were included, with 20 showing a statistically significant decrease in hospitalization rates during the pandemic. These decreases in hospitalization rates during the pandemic ranged from Lithuania’s IRR of 0.57 (95%CI: 0.53, 0.62) to Spain’s IRR of 0.94 (95%CI: 0.92, 0.95). Of the remaining two countries, Korea had an increase in hospitalization rates (IRR: 1.02; 95%CI: 1.00, 1.04; p=0.015), while Costa Rica showed no significant difference (IRR:0.93; 95% CI: 0.82, 1.04). Conclusions IBD-related hospital discharge rates significantly decreased in most OECD countries during the pandemic. Future studies should explore whether the increased strain on healthcare systems during the pandemic shifted the management of IBD from hospitals to community-based care. Table 1. IBD hospitalization discharge rates in OECD countries prior to (2017-2019) and during (2020-2022) the COVID-19 pandemic per 100,000 person-years. Funding Agencies None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".