Racial and ethnic disparities in alcohol-associated liver disease hospitalizations in Brazil before and after the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: The COVID-19 pandemic has resulted in a greater incidence of alcohol-associated liver disease (ALD) and simultaneously magnified health-related inequalities. We evaluated the impact of race and ethnicity on ALD-related hospitalizations in Brazil. MATERIALS AND METHODS: An interrupted time series analysis was used to estimate ALD-related hospitalization in public hospitals in Brazil. Monthly hospitalization rates for 34 consecutive months before and after the point of interruption (March 2020) were calculated using the Sistema de Informações Hospitalares database across four ethnic groups: Black, Pardo, Black, and Pardo combined, and Others (White and Unknown Ethnicity). RESULTS: A total of 84,787 ALD-related hospitalizations were recorded during the study period. The mean age of hospitalized patients was 53 years (SD=12.5); 83.6% were male. Immediately after the start of the pandemic, there was a statistically significant decrease in monthly ALD-related hospitalization rates for the whole population and for all ethnic groups. Subsequently, compared to pre-pandemic rates, there was a statistically significant trend increase in the referred hospitalization rates for the total population (0.065, 95% CI= 0.045 to 0.085, p<0.01), black population (0.0028, 95% CI= 0.006 to 0.050, p<0.05), pardo population (0.077, 95% CI= 0.063 to 0.090, p<0.01), and for black and pardo combined population (0.066, 95% CI= 0.053 to 0.079, p<0.01); however, the increase in hospitalization rates among the Others population (0.059, 95% CI= -0,014 to 0.133, p>0.1) was not statistically significant. CONCLUSIONS: The pandemic impacted ALD-related monthly hospitalization rates and disproportionately impacted Black and Pardo populations in Brazil.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".