Global epidemiology of alcohol-related liver disease, liver cancer, and alcohol use disorder, 2000–2021
Bibliographic record
Abstract
BACKGROUND/AIMS: Alcohol represents a leading burden of disease worldwide, including alcohol use disorder (AUD) and alcohol-related liver disease (ALD). We aim to assess the global burden of AUD, ALD, and alcohol-attributable primary liver cancer between 2000-2021. METHODS: We registered the global and regional trends of AUD, ALD, and alcohol-related liver cancer using data from the Global Burden of Disease 2021 Study, the largest and most up-to-date global epidemiology database. We estimated the annual percent change (APC) and its 95% confidence interval (CI) to assess changes in age-standardized rates over time. RESULTS: In 2021, there were 111.12 million cases of AUD, 3.02 million cases of ALD, and 132,030 cases of alcohol-attributable primary liver cancer. Between 2000 and 2021, there was a 14.66% increase in AUD, a 38.68% increase in ALD, and a 94.12% increase in alcohol-attributable primary liver cancer prevalence. While the age-standardized prevalence rate for liver cancer from alcohol increased (APC 0.59%; 95% confidence interval [CI] 0.52 to 0.67%) over these years, it decreased for ALD (APC -0.71%; 95% CI -0.75 to -0.67%) and AUD (APC -0.90%; 95% CI -0.94 to -0.86%). There was significant variation by region, socioeconomic development level, and sex. During the last years (2019-2021), the prevalence, incidence, and death of ALD increased to a greater extent in females. CONCLUSION: Given the high burden of AUD, ALD, and alcohol-attributable primary liver cancer, urgent measures are needed to prevent them at both global and national levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".