Ethnic Variation in the Annual Rates of Adult Inflammatory Bowel Disease in Hospitalized Patients in Vancouver, British Columbia
Bibliographic record
Abstract
BACKGROUND: There is currently little available information regarding the impact of ethnicity on the clinical features of inflammatory bowel disease (IBD). Migrating populations and changing demographics in Vancouver, British Columbia (BC) provide a unique opportunity to examine the role of ethnicity in the prevalence, expression and complications of IBD. OBJECTIVES: To determine the demographics of IBD and its subtypes leading to hospitalization in the adult population of BC. METHODS: A one-year retrospective study was performed for all patients who presented acutely with IBD to Vancouver General Hospital from January 1, 2006 to December 31, 2006. Data regarding sex, age, ethnicity, IBD type and extent of disease, complications and management strategies were collected. Clinical data were confirmed by pathology and radiology reports. RESULTS: There were 186 cases of IBD comprising Crohn's disease (CD) 56%, ulcerative colitis (UC) 43% and indeterminate colitis (1%) 1%. The annual rate of IBD cases warranting hospitalization in Caucasians was 12.9 per 100,000 persons (7.9 per 100,000 persons for CD and 5.0 per 100,000 persons for UC). This was in contrast to the annual rate of IBD in South Asians at 7.7 per 100,000 persons (1.0 per 100,000 persons for CD and 6.8 per 100,000 persons for UC) and in Pacific Asians at 2.1 per 100,000 persons (1.3 per 100,000 persons for CD, 0.8 per 100,000 persons for UC). The male to female ratio was higher in South Asians and Pacific Asians than in Caucasians. The extent of disease was significantly different across racial groups, as was the rate of complications. CONCLUSIONS: These early results suggest that there are ethnic disparities in the annual rates of IBD warranting hospitalization in the adult population of BC. There was a significantly higher rate of CD in the Caucasian population than in South Asian and Pacific Asian populations. The South Asian population had a higher rate of UC, with an increased rate of complications and male predominance. Interestingly, the rate of CD and UC was lowest in the Pacific Asian population. These racial differences - which were statistically significant - suggest a role for ethnodiversity and environmental changes in the prevalence of IBD in Vancouver.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".