OC6 Sustained increase in paediatric inflammatory bowel disease incidence across the South-West of the United Kingdom over the last 10 years
Bibliographic record
Abstract
Paediatric inflammatory bowel disease (pIBD) incidence has increased over the last 25-years.1 Datasets demonstrating this change in the United Kingdom have come from single centres or regions.2 3 Heterogeneity is described in national and international cohorts; particularly in age group and disease subtype.1 4 This work collected data over a greater population and multiple centres, in order to capture better resolution of incidence. We aimed to establish developing trends in incidence and highlight the demand for changes in service provision. Data were provided from five centres covering the South-West of the United Kingdom, with a total area at-risk population (<18-years) of 2,947,534.5 Cases were retrieved for 2013–2022. Incident rates were calculated based on referral area populations, with temporal trends analysed through correlation. Subgroup analysis was undertaken for age groups - Very-Early Onset Inflammatory Bowel Disease (VEOIBD) (0–6 years); Early Onset Inflammatory Bowel Disease (EOIBD) (7–11 years); Paediatric-onset Inflammatory Bowel Disease (PIBD) (12–17 years) - gender and disease subtype. Choropleth maps were created for local districts (figure 1). In total 2,497 cases were diagnosed between 2013–2022, mean age 12.6 years (38.7% female). Diagnosis numbers increased from 187 to 376, with corresponding incidence rates of 6.0/100,000/year (2013) and 12.4/100,000/year (2022) (b=0.918, p<0.01) (table 1). Female IBD rose from 5.1/100,000/year (2013) to 11.0/100,000/year (2022) (b=0.865, p=0.01). Male rates increased from 5.7/100,000/year to 14.4/100,000/year (b=0.832, p=0.03). Crohn’s disease incidence increased from 3.1/100,000/year to 6.3/100,000/year (b=0.897, p<0.01). Ulcerative Colitis increased from 2.3/100,000/year to 4.3/100,000/year (b = 0.813, p=0.04). IBD-Unclassified (IBDU) rates also increased, 0.6/100,000/year to 1.8/100,000/year (b= 0.851, p=0.02). Statistically significant increases were seen in PIBD 11.2/100,000/year to 24.6/100,000/year (b=0.912, p<0.01), and EOIBD, with incidence rising from 4.4/100,000/year to 7.6/100,000/year (b=0.878, p=0.01). There was no statistically significant increase in VEOIBD (b=0.417, p=0.231). We demonstrate significant growth in pIBD incidence across a large geographical area and over multiple sites. We report continued increases, particularly in older children, across gender and disease subtype. We note stable incidence in VEOIBD. Rising incidence has significant implications for service provision for healthcare providers managing IBD. Further socioeconomic analysis of this cohort may provide greater insight into the causation of pIBD. References Kuenzig ME, Fung SG, Marderfeld L, et al. Twenty-first century trends in the global epidemiology of pediatric-onset inflammatory bowel disease: systematic review. Gastroenterology 2022;162:1147–59. doi:10.1053/J.GASTRO.2021.12.282 Ashton JJ, Barakat FM, Barnes C, et al. Incidence and prevalence of paediatric inflammatory bowel disease continues to increase in the South of England. J Pediatr Gastroenterol Nutr 2022;75:E20–4. doi:10.1097/MPG.0000000000003511 Hamilton B, Green H, Heerasing N, et al. Incidence and prevalence of inflammatory bowel disease in Devon, UK. Frontline Gastroenterol 2021;12:461–70. doi:10.1136/flgastro-2019–101369 Benchimol EI, Bernstein CN, Bitton A, et al. Trends in epidemiology of pediatric inflammatory bowel disease in Canada: distributed network analysis of multiple population-based provincial health administrative databases. American Journal of Gastroenterology 2017;112:1120–34. doi:10.1038/ajg.2017.97 Anon. Office for National Statistics - Census 2021. https://gov.wales/population-and-household-estimates-wales-census-2021. 2022.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".