P1203 Spatial mapping and geographic variation of inflammatory bowel disease in Canada: A population-based study using Statistics Canada data
Bibliographic record
Abstract
Abstract Background Crohn’s Disease (CD) and Ulcerative Colitis (UC) pose significant challenges to patients and healthcare systems, with a rising incidence and prevalence globally1,2. To address the challenges of health resource allocation and understand the geographic distribution of these conditions in Canada, we conducted the spatial mapping of IBD across Canada using national population survey data available from Statistics Canada. Methods We utilized data from the 2009-2018 Canadian Community Health Survey (CCHS) to identify individuals with CD or UC. Prevalence rates, both crude and adjusted for age and sex, were calculated for each health region in Canada. Spatial dependencies between health regions were estimated using global Moran’s I and local indicators of spatial association (LISA) were used to identify clusters of health region (hot-spots) where the prevalence was significantly higher than in other areas. We further applied spatial simultaneous autoregressive lag models to explore the influence of region-level sociodemographic factors on identified spatial dependencies. Results A total of 492,560 individuals from 2010-2018 CCHS were included, representing 29,846,350 Canadians across 109 health regions. Survey data revealed that 0.38% of Canadians self-reported having CD and 0.47% reported having UC. The majority of respondents reported being urban-residing, middle-aged, and white. Across Canadian provinces, the age and sex-standardized prevalence of IBD ranged from 742 per 100,000 in Quebec to 1267 per 100,000 in Nova Scotia (Figure 1). Spatial analysis identified significant dependencies among health regions, but these spatial associations were explained by the differing distributions of sociodemographic characteristics in each health region (Table 1). Increased health region-level prevalence of CD and UC was associated with a larger proportion of individuals 30+ years, identifying as a female, immigrants, or as white (p<0.05 for each). Conclusion Data from this large, population-based Canadian national survey demonstrated significant geographic variation in the prevalence of CD and UC. However, after adjusting for patient demographic characteristics, the spatial dependency became non-significant. Key characteristics related to urbanization including the age distribution and proportion of immigrants in a health region were associated with increased prevalence rates. These results suggest that differences in CD and UC prevalence between geographic areas are primarily driven by individual demographics rather than differences in characteristics of local healthcare delivery systems and further support the notion that characteristics of urbanization increase the prevalence of IBD in an area1. References 1.Ng et al., 2017. Doi: 10.1016/s0140-6736(17)32448-0 2.Coward et al., 2023. Doi: 10.1093/jcag/gwad004
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.018 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".