S1157 Geographic Hot Spot Analysis of a Pediatric Inflammatory Bowel Disease Registry in British Columbia
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,005 | 0,013 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».