Exploring spatio-temporal patterns and environmental determinants of pediatric Inflammatory Bowel Disease in British Columbia
Bibliographic record
Abstract
Canada has some of the highest rates of pediatric Inflammatory Bowel Diseases (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), in the world. Environmental factors are known to be important for disease development but are not well understood. This study used two forms of analysis to examine the epidemiology and potential causes of IBD diagnosed before age 17 in the Canadian province of British Columbia from 2001 to 2016. A spatial cluster detection methodology was used to locate disease clusters of high and low incidence rates, the presence of which would highlight potential environmental risk and protective factors. Logistic regression models of case-control data were used to measure the relationship between IBD diagnosis and NO₂ air pollution, density of residential and neighborhood vegetation greenness (green spaces), vitamin D adjusted ultraviolet solar radiation, area South Asian and Jewish ethnicity, area self-identification as Aboriginal, and area social and material deprivation. The spatial distributions of IBD, CD, and UC were significantly clustered, with consistent IBD hot spots identified near the main urban centre of the province and cold spots identified in rural areas of south-eastern British Columbia. CD and UC had similar and different hot and cold spots, suggesting both shared and distinct environmental determinants. Most measured associations between variables of interest and IBD were moderate or small; as IBD is a multifactorial disease, these variables may still have a population-level effect on disease risk or interact with other risk factors and should be studied further. NO₂ air pollution was a significant risk factor for UC. Area South Asian ethnicity was only a significant risk factor in the univariate analysis, though a small and similar effect was observed in the multivariate analysis which included social and material deprivation. Ultraviolet vitamin D exposure was a protective factor for UC and IBD, especially in winter months. Area Aboriginal identity and area material deprivation (areas with lower socioeconomic status) were significant protective factors for CD, though Aboriginal identity was not significant in a multivariate analysis that included social and material deprivation. No reliable relationship was observed for greenness or area Jewish ethnicity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| 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".