20 Racial Disparities in Surgical Rates Among Hospitalized Patients With Inflammatory Bowel Disease: A Meta-Analysis
Notice bibliographique
Résumé
Background: Surgical intervention is a critical component of care for hospitalized patients with inflammatory bowel disease (IBD). Whether surgical utilization is influenced by race remains unclear, with the overall impact yet to be clearly defined due to inconsistent evidence in the literature. Methods: This meta-analysis, following PRISMA guidelines, assessed racial disparities in surgical interventions among hospitalized IBD patients. A systematic search of PubMed, Google Scholar, and Scopus (2020–2025) identified cohort, case-control, and cross-sectional studies. After screening 1,155 records, 4 studies were included. Random-effects models compared surgical intervention rates between Black, Asian, Hispanic, and White patients. Study quality was evaluated using the Newcastle-Ottawa Scale, and heterogeneity was assessed using I2 and Chi2 statistics. Publication bias was examined with funnel plots (P < 0.05), and effect sizes were estimated using the generic inverse variance method. Results: A total of 4 observational studies (n = 566,209 participants) were included, comprising 32,026 Black, 8,868 Asian, 27,327 Hispanic, and 271,452 White patients. The pooled analysis using a random-effects model showed no statistically significant difference in surgical rates between Black and White patients (pooled OR: 0.81, 95% CI [0.54–1.21], P = 0.30), with substantial heterogeneity (I2 = 98%, Chi-square P = 101.65). Subgroup analysis revealed a statistically significant increase for Black patients (pooled OR: 0.70, 95% CI [0.48–1.03], P = 0.03), with reduced heterogeneity (I2 = 72.1%, Chi-square P = 7.17), and point estimates trended toward a higher surgical rate among Black patients. For Hispanic versus non-Hispanic White patients, no significant overall difference was observed (pooled OR: 0.7, 95% CI [0.53–1.06], P = 0.11), with high heterogeneity (I2 = 96%, Chi-square P = 53.43). Sensitivity analysis excluding (Shustak 2024) showed a modest but significant increase for Hispanic patients (pooled OR: 0.65, 95% CI [0.51–0.83], P = 0.003), with reduced heterogeneity (I2 = 89%, Chi-square P = 9.11). Asian patients showed no significant difference versus White patients (pooled OR: 0.78, 95% CI [0.57–1.06], P = 0.11), with high heterogeneity (I2 = 93%, Chi-square P = 13.96). Subgroup analysis showed a significant increase in surgical rate (pooled OR: 0.75, 95% CI [0.61–0.92], P = 0.01), with reduced heterogeneity (I2 = 56.2%, Chi-square P = 4.57), and point estimates also trended higher surgical rate among Asian patients. Conclusions: This meta-analysis suggests that racial minorities with IBD may experience slightly higher surgical utilization compared to White patients, particularly among Black, Hispanic, and Asian populations in subgroup and sensitivity analyses. These findings likely reflect underlying healthcare disparities, including delayed diagnosis, limited access to medical therapy, and challenges with follow-up and health literacy. While heterogeneity across studies and a small sample size limit definitive conclusions, the consistent trend highlights the need for interventions to improve equitable access to timely diagnosis and medical therapy. Future prospective studies should further explore these disparities to better inform strategies that reduce surgical burden in minority populations.
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 | 0,018 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,014 | 0,087 |
| Bibliométrie | 0,007 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».