S1157 Geographic Hot Spot Analysis of a Pediatric Inflammatory Bowel Disease Registry in British Columbia
Bibliographic record
Abstract
Introduction: High and increasing incidence of pediatric inflammatory bowel disease (IBD) in Canada presents a considerable challenge to both patient wellness and the healthcare system. One of the most notable populations at risk in the province of British Columbia (BC) is people of South Asian (SA) descent. Geographic hot spot analysis can be used to statistically identify areas of high incidence to direct service delivery and target for followup studies. Methods: This study used data from a clinical registry of patients seen at BC Children’s Hospital and diagnosed before age 17 during the period of 2003 - 2016 in the Vancouver Coastal or Fraser Health Authorities. Cases were directly age-standardized for small Community Health Services Areas (CHSAs) using 2011 BC population as the reference population. Standardized incidence ratios were adaptively smoothed toward regional averages to adjust for areas with small populations. The local Moran’s I statistic was used to locate IBD, Crohn’s disease (CD), and ulcerative colitis (UC) hot spots (relatively high incidence), while the bivariate local Moran’s I was used to determine the location of shared UC and CD clusters. Monte Carlo simulation with a Holm correction was used to approximate statistical significance. This study was approved by the UBC Children's and Women's Research Ethics Board. Results: Within the Greater Vancouver area [Figure 1A], hot spots of relatively high incidence [Figure 1B] were identified for IBD, CD, and UC, with shared hot spot clusters of CD and UC. We observed differential SA population distribution [Figure 1C] across the study area and identified hot spots. Geographical variations in IBD subtype were observed, with a univariate spatial outlier of relatively low incidence UC surrounded by high incidence UC [Figure 1D] and a bivariate spatial outlier of high UC surrounded by low CD [Figure 1D]. Conclusion: Geospatial hot spot analysis is a valuable tool for quantifying geographic patterns of pediatric IBD. Identified geographic hot spots were often located in areas with large SA populations who we have previously identified as a population at risk of developing IBD. However, not all areas with a high proportion of SA residents were part of identified hot spots. Environmental determinants are likely extremely important for further understanding this differential expression of disease in areas with large SA populations. Studies to investigate environmental determinants of IBD in BC are underway.Figure 1.: A) Greater Vancouver population density reference map. Darker color indicates higher population density. B) Identified hot spots of relatively high incidence for IBD, CD, and UC. Darker color indicates hot spots identified in multiple analyses. C) South Asian ethnic origin of the population. Percentages are categorized into intervals spanning 10%, with the palest green color representing 0 - 10% South Asian population and the darkest green color representing 60 - 70%. D) Identified spatial outliers (low UC surrounded by high UC and high UC surrounded by low CD).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".