Racial and ethnic disparities in alcohol-associated liver disease in the United States: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Alcohol-associated liver disease (ALD), encompassing alcohol-associated hepatitis and alcohol-associated cirrhosis, is rising in the United States. Racial and ethnic disparities are evident within ALD; however, the precise nature of these disparities is poorly defined. METHODS: We conducted a search of the PubMed/MEDLINE and EMBASE databases to identify studies published from inception through September 2023 that reported ALD incidence, prevalence, and mortality within the United States, stratified by race and ethnicity. We calculated pooled prevalence and incidence by race and ethnicity, including risk ratios and ORs for ALD pooled prevalence and alcohol-associated hepatitis/alcohol-associated cirrhosis pooled proportions, and OR for ALD mortality using the DerSimonian and Laird method for random-effect models. RESULTS: We identified 25 relevant studies (16 for quantitative meta-analysis), comprising 76,867,544 patients. ALD prevalence was highest in Hispanic (4.5%), followed by White (3.1%) and Black (1.4%) individuals. Pooled risk ratios of ALD prevalence were 1.64 (95% CI: 1.12-2.39) for Hispanic and 0.59 (95% CI: 0.35-0.87) for Black compared to White individuals. Mortality among those with ALD did not significantly differ between White and Hispanic (OR: 1.54, 95% CI: 0.9-2.5; I2=0%), Black (OR: 1.2, 95% CI: 0.8-1.6; I2=0%), or Native American (OR: 2.41, 95% CI: 0.9-2.9) individuals, while there was a significant difference between White and Asian (OR: 0.1; 95% CI: 0.03-0.5) individuals. Most data were cross-sectional and assessed to be of poor or fair quality. CONCLUSIONS: Differences were observed in ALD epidemiology, including higher prevalence among Hispanic and lower prevalence among Black individuals, although there were smaller differences in ALD mortality. Differences in ALD prevalence and prognosis remain poorly defined based on existing data, highlighting a need for higher-quality epidemiological studies in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".