The Epidemiology of Inflammatory Bowel Disease in Oceania: A Systematic Review and Meta-Analysis of Incidence and Prevalence
Bibliographic record
Abstract
BACKGROUND: Past studies have shown high rates of inflammatory bowel disease (IBD) in Australia and New Zealand (NZ). We aimed to describe the epidemiology of IBD in Australia, NZ, and the surrounding region (collectively termed Oceania) by conducting a systematic review and meta-analysis. METHODS: Electronic databases were searched from inception to April 2023 for studies reporting incidence or prevalence rates of IBD, Crohn's disease (CD), or ulcerative colitis (UC) in Oceania. All study designs were included. A meta-analysis calculated pooled estimates of incidence and prevalence, and a sensitivity analysis compared the pooled population-based studies with the non-population-based studies and the Australian and NZ studies separately. RESULTS: Nineteen incidence and 11 prevalence studies were included; 2 studies were from the Pacific Islands, with the rest coming from Australia and NZ. Pooled estimates showed high incidence rates of 19.8 (95% confidence interval [CI], 15.8-23.7) for IBD, 8.3 (95% CI, 6.9-9.8) for CD, and 7.4 (95% CI, 5.7-9.1) for CD per 100 000 person-years. CD was more common than UC in most studies. The pooled estimates for the prevalence studies were 303.3 (95% CI, 128.1-478.4) for IBD, 149.8 (95% CI, 71.0-228.5) for CD, and 142.2 (95% CI, 63.1-221.4) for UC per 100 000 persons. Studies using population-based data collection methods showed higher pooled rates for both incidence and prevalence. CONCLUSIONS: The incidence and prevalence of IBD in Oceania is high. The studies were heterogeneous and there were several geographic areas with no information, highlighting the need for more epidemiological studies of IBD.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".