The Epidemiology of Inflammatory Bowel Disease in Canada: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Previously, we have demonstrated a high incidence and prevalence of Crohn's disease (CD) and ulcerative colitis (UC) in the Canadian province of Manitoba. However, the epidemiology of inflammatory bowel disease (IBD) in other regions of Canada has not been defined. The aim of this study was to estimate the incidence and prevalence of CD and UC in diverse regions of Canada and the overall burden of IBD in Canada. METHODS: We applied a common case identification algorithm, previously validated in Manitoba to the provincial health databases in British Columbia (BC), Alberta (AB), Saskatchewan (SK), Manitoba (MB), and Nova Scotia (NS) to determine the age-adjusted incidence rates per 100,000 person-years for 1998-2000 and prevalence per 100,000 for mid 2000 and to estimate the IBD burden in Canada. Poisson regression was used to assess differences in incidence rates and prevalence by gender, age, and province. RESULTS: The incidence rate for CD ranged from 8.8 (BC) to 20.2 (NS), and for UC ranged from 9.9 (BC) to 19.5 (NS). The prevalence of CD was approximately 15- to 20-fold higher than the incidence rate, ranging from 161 (BC) to 319 (NS). This was similar for the prevalence of UC, which ranged from 162 (BC) to 249 (MB). Adjusting for age and province, the female:male ratio for incidence ratio was 1.31 (p < 0.0001) for CD and 1.02 (n.s.) for UC and was mostly stable across the five provinces. CONCLUSIONS: Approximately 0.5% of the Canadian population has IBD. Canada has the highest incidence and prevalence of CD yet reported.
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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 | 0.008 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".