Geographical Variation and Factors Associated With Inflammatory Bowel Disease in a Central Canadian Province
Bibliographic record
Abstract
BACKGROUND: We investigated temporal trends, geographical variation, and geographical risk factors for incidence of inflammatory bowel disease (IBD). METHODS: We used the University of Manitoba IBD Epidemiology Database to identify incident IBD cases diagnosed between 1990 and 2012, which were then geocoded to 296 small geographic areas (SGAs). Sociodemographic characteristics of the SGAs (proportions of immigrants, visible minorities, Indigenous people, and average household income) were obtained from the 2006 Canadian Census. The geographical variation of IBD incidence was modeled using a Bayesian spatial Poisson model. Time trends of IBD incidence were plotted using Joinpoint regression. RESULTS: The incidence of IBD decreased over the study years from 23.6 (per 100,000 population) in 1990 to 16.3 (per 100,000 population) in 2012. For both Crohn's disease (CD) and ulcerative colitis (UC), the highest incidence was in Winnipeg and the southern and central regions of Manitoba, whereas most of northern Manitoba had lower incidence. There was no effect of sociodemographic characteristics of SGAs, other than the proportion of Indigenous people, which was associated with lower IBD incidence. CONCLUSIONS: Although the incidence of IBD in Manitoba is decreasing over time, we have identified geographic areas with persistently higher IBD incidence that warrant further study for etiologic clues.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".