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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".