20 Racial Disparities in Surgical Rates Among Hospitalized Patients With Inflammatory Bowel Disease: A Meta-Analysis
Bibliographic record
Abstract
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.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.087 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".