Black and White Patients with Inflammatory Bowel Disease Show Similar Biologic Use Patterns with Medicaid Insurance
Bibliographic record
Abstract
Background: Prior studies have identified racial disparities in the treatment and outcomes of inflammatory bowel disease (IBD). These disparities could be secondary to differences in biology, care delivery, or access to appropriate therapy. The primary aim of this study was to compare medication use among Medicaid-insured black and white patients with IBD, given uniform access to gastroenterologists and therapies. Methods: We analyzed Medicaid Analytic eXtract data from 4 states (California, Georgia, North Carolina, and Texas) between 2006 and 2011. We compared the use of IBD-specific therapies, including analyses of postoperative therapy among patients with Crohn disease (CD). We performed bivariate analyses and multivariable logistic regression, adjusting for potential confounders. Results: We identified 14,735 patients with IBD (4672 black [32%], 8277 with CD [58%]). In multivariable analysis, there was no significant difference in the odds of anti-tumor necrosis factor use by race for CD (adjusted odds ratio [aOR] = 1.13; 95% confidence interval [CI], 0.99-1.28] or ulcerative colitis (aOR = 1.12; 95% CI, 0.96-1.32). Black patients with CD were more likely than white patients to receive combination therapy (aOR = 1.50; 95% CI, 1.15-1.96), and black patients were more likely than white patients to receive immunomodulator monotherapy after surgery for CD (31% vs 18%; P = 0.004). Conclusions: In patients with Medicaid insurance, where access to IBD-specific therapy should be similar for all individuals, there was no significant disparity by race in the utilization of IBD-specific therapies. Disparities in IBD treatment discussed in prior literature seem to be driven by socioeconomic or other issues affecting access to care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".