Impact of Safety-Net Burden on In-Hospital Mortality and Hospitalization Costs Among Patients with Alcoholic Hepatitis and Alcoholic Cirrhosis
Bibliographic record
Abstract
AIMS: Alcoholic hepatitis (AH) and alcoholic cirrhosis disproportionately affect ethnic minority and safety-net populations. We evaluate the impact of a hospital's safety net burden (SNB) on in-hospital mortality and costs among patients with AH and alcoholic cirrhosis. METHODS: We performed a cross-sectional analysis of 2012-2016 National Inpatient Sample. SNB was calculated as percentage of hospitalizations with Medicaid or uninsured payer status. Associations between hospital SNB and in-hospital mortality and costs were evaluated with adjusted multivariable logistic regression and linear regression models. RESULTS: Among 21,898 AH-related hospitalizations, compared to low SNB hospitals (LBH), patients hospitalized in high SNB hospitals (HBH) were younger (44.4 y vs. 47.4 y, P < 0.001) and more likely to be African American (11.3% vs. 7.7%, P < 0.001) or Hispanic (15.4% vs. 8.4%, P < 0.001). AH-related hospitalizations in HBH had a non-significant trend towards higher odds of mortality (OR 1.27, 95% CI 0.98-1.65, P = 0.07) and higher mean hospitalizations costs. Among 108,669 alcoholic cirrhosis-related hospitalizations, patients in HBH were younger (53.3 y vs. 55.8 y, P < 0.001) and more likely to be African American (8.2% vs. 7.3%, P < 0.001) or Hispanic (24.4% vs. 12.0%, P < 0.001) compared to LBH. Compared to alcoholic cirrhosis-related hospitalizations in LBH, mortality was higher among medium SNB (OR 1.10, 95% CI 1.03-1.17, P = 0.007) and HBH (OR 1.07, 95% CI 1.00-1.15, P = 0.05). Mean hospitalization costs were not different by SNB status. CONCLUSIONS: HBH hospitals predominantly serve ethnic minorities and underinsured/uninsured populations. The higher in-hospital mortality associated HBH particularly for alcoholic cirrhosis patients is alarming given its increasing burden in the USA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".