Spatial mapping and geographic variation of inflammatory bowel disease in Canada: a population-based study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) poses significant challenges to healthcare systems, with rising prevalence globally. To address challenges of health resource allocation and understand the geographic distribution of disease in Canada, we explored spatial mapping of IBD across Canada. METHODS: Data from 2009 to 2018 Canadian Community Health Survey identified individuals with IBD. Crude and adjusted prevalence rates were calculated, and spatial dependencies were analyzed using Moran's I. Local indicators of spatial association identified hot-spots where the prevalence of IBD was significantly different than neighboring areas. Spatial autoregressive models assessed the influence of sociodemographic factors on spatial dependencies. RESULTS: Across 109 health regions, 492 560 individuals were included, representing 29 846 350 Canadians. Crohn's disease and ulcerative colitis were reported in 0.38% and 0.47% of Canadians, respectively. Age and sex-standardized prevalence ranged from 742 to 1267 per 100 000. Significant dependencies among health regions were found, but dependencies were explained by the distribution of sociodemographic characteristics in a region. CONCLUSIONS: Results demonstrated significant geographic variations in the prevalence of IBD in Canada. Evidence suggests that differences in prevalence between regions are primarily driven by individual demographics rather than geographical characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".