Hospitalizations for inflammatory bowel disease: Profile of the uninsured in the United States
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease (IBD) patients may be at increased risk for having no health insurance. Our objectives were to assess the prevalence of hospitalized IBD patients without insurance in the US and to characterize predictive factors. METHODS: We identified IBD admissions in the Nationwide Inpatient Sample (1999-2005) and a 1% sample of general medical patients. We used population estimates from the US Census Bureau to calculate hospitalization rates, and logistic regression to determine predictors of being uninsured. RESULTS: Although uninsured IBD patients were less likely to be hospitalized than those privately insured (incidence rate ratio [IRR] 0.41; 95% confidence interval [CI]: 0.38-0.45), their hospitalization rate increased from 8.3/100,000 to 12.5/100,000 (P < 0.001) over 7 years, outpacing private admissions. The proportion of uninsured IBD inpatients increased from 4.6% to 6.5% (P < 0.001), and IBD patients were more likely than general medical patients to be uninsured (5.1% vs. 4.1%, P < 0.0001). Predictors of being uninsured were being 21 to 40 years (odds ratio [OR] 1.95; 95% CI: 1.64-2.31), African American (OR 1.51; 95% CI: 1.29-0.76) or Hispanic (OR 2.21; 95% CI: 1.79-2.74), or residing in the southern US (OR 1.63; 95% CI: 1.27-2.11). Being female (OR 0.65; 95% CI: 0.61-0.70), residing in higher income neighborhoods (OR 0.69; 95% CI: 0.62-0.77), and higher comorbidity were protective factors. CONCLUSIONS: The rate of uninsured IBD admissions has risen disproportionately relative to the privately insured and general medical populations. We need measures to alleviate the burden of being uninsured among young and otherwise healthy adults with IBD who are most vulnerable.
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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 | 0.001 |
| 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) | 0.001 | 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".