Population Density and Risk of Inflammatory Bowel Disease: A Prospective Population-Based Study in 13 Countries or Regions in Asia-Pacific
Bibliographic record
Abstract
INTRODUCTION: Living in an urban environment may increase the risk of developing inflammatory bowel disease (IBD). It is unclear if this observation is seen globally. We conducted a population-based study to assess the relationship between urbanization and incidence of IBD in the Asia-Pacific region. METHODS: Newly diagnosed IBD cases between 2011 and 2013 from 13 countries or regions in Asia-Pacific were included. Incidence was calculated with 95% confidence interval (CI) and pooled using random-effects model. Meta-regression analysis was used to assess incidence rates and their association with population density, latitude, and longitude. RESULTS: We identified 1175 ulcerative colitis (UC), 656 Crohn's disease (CD), and 37 IBD undetermined (IBD-U). Mean annual IBD incidence per 100 000 was 1.50 (95% CI: 1.43-1.57). India (9.31; 95% CI: 8.38-10.31) and China (3.64; 95% CI, 2.97-4.42) had the highest IBD incidence in Asia. Incidence of overall IBD (incidence rate ratio [IRR]: 2.19; 95% CI: 1.01-4.76]) and CD (IRR: 3.28; 95% CI: 1.83-9.12) was higher across 19 areas of Asia with a higher population density. In China, incidence of IBD (IRR: 2.37; 95% CI: 1.10-5.16) and UC (IRR: 2.63; 95% CI: 1.2-5.8) was positively associated with gross domestic product. A south-to-north disease gradient (IRR: 0.94; 95% CI: 0.91-0.98) was observed for IBD incidence and a west-to-east gradient (IRR: 1.14; 95% CI: 1.05-1.24) was observed for CD incidence in China. This study received IRB approval. CONCLUSIONS: Regions in Asia with a high population density had a higher CD and UC incidence. Coastal areas within China had higher IBD incidence. With increasing urbanization and a shift from rural areas to cities, disease incidence may continue to climb in Asia.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".