Racial Disparities in Utilization of Specialist Care and Medications in Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVES: Optimization of medical therapy and specialist care for inflammatory bowel disease (IBD) may reduce morbidity. We sought to characterize racial disparities in utilization of healthcare and medical therapy for IBD. METHODS: We performed a cross-sectional study of black (n=137) and white (n=149) IBD patients recruited from an outpatient IBD clinic and through medical record review and telephone interview, compared utilization of IBD specialist services, emergency department (ED) services, and medications. We adjusted racial comparisons for demographic, socioeconomic, and clinical factors. RESULTS: After adjustment for confounders, blacks were less likely than whites to be under the regular care (defined as at least annual visit) of a gastroenterologist (adjusted odds ratio (aOR) 0.43; 95% confidence interval (CI): 0.25-0.75) or IBD specialist (aOR 0.37; 95% CI: 0.22-0.61). Follow-up with a primary care provider was, however, similar between blacks and whites. Over the preceding 12 months, blacks were more likely than whites to have at least one visit to the ED (aOR 2.02; 95% CI: 1.22-3.35), but there was no difference in hospitalization. Among CD patients with prolonged steroid use, blacks were less likely than whites to have been on infliximab (aOR 0.41; 95% CI: 0.21-0.77), but there were no racial differences in the use of immunomodulators (aOR 0.87; 95% CI: 0.48-1.60). CONCLUSIONS: There are racial differences in utilization of IBD-related specialist services, ED visits, and infliximab that are independent of income and education. Modifiable barriers to health-care access may have a role in these disparities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".