The Global Evolution of Inflammatory Bowel Disease across Four Epidemiologic Stages
Bibliographic record
Abstract
Abstract During the 20th century, inflammatory bowel disease (IBD) was considered a disease of early-industrialized regions in North America, Europe, and Oceania. At the turn of the 21st century, incidence of IBD increased in newly-industrialized and emerging regions in Africa, Asia, and Latin America, while prevalence in early-industrialized regions started to grow steadily. Changes in incidence and prevalence denote evolution of IBD across four epidemiologic stages: Stage 1 (Emergence), characterized by low incidence and prevalence; stage 2 (Acceleration in Incidence), characterized by rapidly rising incidence and low prevalence; and stage 3 (Compounding Prevalence), characterized by decelerating, plateauing, or declining incidence and steadily rising prevalence. A fourth stage (Prevalence Equilibrium) has been proposed where the slope of prevalence plateaus due to shifting demographics of an aging IBD population, but it has not yet been evidenced. To date, these stages have been theoretical without specific incidence/prevalence indicators defining transition points. Using real-world data from 490 population-based studies comprising 80 global regions and spanning over a century (1920–2023), we show spatiotemporal transitions across stages 1–3 and model transition towards stage 4. Understanding the evolution of IBD across epidemiologic stages allows healthcare systems to better prepare for the future worldwide burden 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".