Significant Racial and Ethnic Disparities Exist in Health Care Utilization in Inflammatory Bowel Disease: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The incidence of inflammatory bowel disease (IBD) is rising worldwide, though the differences in health care utilization among different races and ethnicities remains uncertain. We aimed to better define this through a systematic review and meta-analysis. METHODS: We explored the impact of race or ethnicity on the likelihood of needing an IBD-related surgery, hospitalization, and emergency department visit. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated with I2 values reporting heterogeneity. Differences in IBD phenotype and treatment between racial and ethnic groups of IBD were reported. RESULTS: Fifty-eight studies were included. Compared with White patients, Black patients were less likely to undergo a Crohn's disease (CD; OR, 0.69; 95% CI, 0.50-0.95; I2 = 68.0%) or ulcerative colitis (OR, 0.58; 95% CI, 0.40-0.83; I2 = 85.0%) surgery, more likely to have an IBD-hospitalization (OR, 1.54; 95% CI, 1.06-2.24; I2 = 77.0%), and more likely to visit the emergency department (OR, 1.74; 95% CI, 1.32-2.30; I2 = 0%). There were no significant differences in disease behavior or biologic exposure between Black and White patients. Hispanic patients were less likely to undergo a CD surgery (OR, 0.57; 95% CI, 0.48-0.68; I2 = 0%) but more likely to be hospitalized (OR, 1.38; 95% CI, 1.01-1.88; I2 = 37.0%) compared with White patients. There were no differences in health care utilization between White and Asian or South Asian patients with IBD. CONCLUSIONS: There remain significant differences in health care utilization among races and ethnicities in IBD. Future research is required to determine factors behind these differences to achieve equitable care for persons living with IBD.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".