Racial Disparities in Utilization of Medications and Disease Outcomes in Inflammatory Bowel Disease Patients
Bibliographic record
Abstract
Background: Although traditionally associated with White European ancestry, inflammatory bowel disease (IBD) has increased among different races and ethnicities. Large studies conducted in the United States and Canada have identified more complex disease phenotypes among Black patients. Our study aimed to investigate disparities in IBD treatments and outcomes between Black and White patients in the United States. Methods: Using the TriNetX database, adult IBD patients were divided into 2 groups based on race: Black and White patients with IBD, Crohn's disease (CD), or ulcerative colitis (UC). Medical therapy and disease outcomes were evaluated in both groups with 1:1 propensity-score matching. Methodologic limitations include the potential for missing data, lack of information on socioeconomic strata, and patient-level medication coverage plans. Results: In comparison to White patients, Black patients with CD were less likely to receive advanced therapies; Adalimumab (adjusted odds ratio- aOR 0.89), Certolizumab (0.81), Vedolizumab (0.66), Ustekinumab (0.82), or Tofacitinib (0.58). Black patients with UC were less likely to receive advanced therapies; Adalimumab (0.83), Golimumab (0.62), Vedolizumab (0.69), Ustekinumab (0.73), or Tofacitinib (0.55). Black patients with IBD were at higher odds of utilizing corticosteroids (CD 1.18 and UC 1.20) and opioids (CD 1.26 and UC 1.09). Black patients with CD had higher rates of hospitalization (1.35) and perianal abscess (1.56), perianal fistula (1.28), and intestinal fistula (1.38). Black patients with UC had higher rates of hospitalization (1.29), Clostridioides difficile infection (1.11), and toxic megacolon (1.34). Conclusions: There were racial disparities in IBD medical therapy and disease outcomes. Black IBD patients had lower treatment with advanced therapies, higher opioid and corticosteroid use, and higher IBD-related complications.
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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 | 0.001 |
| 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) | 0.002 | 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".