National estimates of the burden of inflammatory bowel disease among racial and ethnic groups in the United States
Bibliographic record
Abstract
BACKGROUND: The epidemiology of inflammatory bowel disease (IBD) is poorly characterized in minorities in the U.S. We sought to enumerate the burden of IBD among racial and ethnic groups using national-level data. METHODS: Data from the National Health Interview Survey was used to calculate prevalence and incidence of IBD among adults (≥ 18 years) in 1999. The Nationwide Inpatient Sample was queried to ascertain rates of IBD-related hospitalizations and the Underlying Cause of Death Database was accessed to quantify IBD-related mortality. RESULTS: An estimated 1,810,773 adult Americans were affected by IBD yielding a prevalence of 908/100,000, which was higher in Non-Hispanic Whites (1099/100,000) compared with Non-Hispanic Blacks (324/100,000), Hispanics (383/100,000), and non-Hispanic Other (314/100,000). Relative to Non-Hispanic Whites, the odds ratios for having a diagnosis of IBD associated with being Non-Hispanic Black, Hispanic, and Other Non-Hispanic race after adjusting for age, sex, and geographic region were 0.33 (95% CI: 0.19 - 0.57), 0.45 (95% CI: 0.26 - 0.77), and 0.34 (95% CI: 0.12 - 0.93), respectively. IBD incidence was similarly lower in Non-Hispanic Blacks (24.9/100,000) and Hispanics (9.9/100,000) compared to Non-Hispanic Whites (70.2/100,000). The ratio of IBD hospitalizations to prevalence was disproportionately higher among Non-Hispanic Blacks (7.3%) compared with Non-Hispanic Whites (3.0%) and Hispanics (2.7%). Similarly, the ratio of IBD-related mortality was greater in Non-Hispanic Blacks (0.061%) compared to Non-Hispanic Whites (0.036%) and Hispanics (0.026%). CONCLUSIONS: IBD disease burden is lower in ethnic minorities compared to Non-Hispanic Whites. However, IBD-related hospitalizations and deaths seem disproportionately high in Non-Hispanic Blacks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".