Socio‐economic association of alcohol use disorder and cardiovascular and alcohol‐associated liver disease from 2010 to 2019
Bibliographic record
Abstract
BACKGROUNDS AND AIMS: Alcohol use leads to disabilities and deaths worldwide. It not only harms the liver but also causes alcohol use disorder (AUD) and heart disease. Additionally, alcohol consumption contributes to health disparities among different socio-economic groups. METHODS: We estimated global and regional trends in the burden of AUD, liver disease, and cardiovascular disease from alcohol using the methodology of the Global Burden of Disease study. RESULTS: In 2019, the highest disability-adjusted life years rate per 100,000 population was due to AUD (207.31 [95% Uncertainty interval (UI) 163.71-261.66]), followed by alcohol-associated liver disease (ALD) (133.31 [95% UI 112.68-156.17]). The prevalence rate decreased for AUD (APC [annual percentage change] -0.38%) and alcohol-induced cardiomyopathy (APC -1.85%) but increased for ALD (APC 0.44%) and liver cancer (APC 0.53%). Although the mortality rate for liver cancer from alcohol increased (APC 0.30%), mortality rates from other diseases decreased. Between 2010 and 2019, the burden of alcohol-associated complications increased in countries with low and low-middle sociodemographic index (SDI), contributing more significantly to the global burden. CONCLUSION: The global burden of AUD, liver, and cardiovascular disease has been high and increasing over the past decade, particularly for liver complications. Lower SDI countries are contributing more to this global burden. There is a pressing need for effective strategies to address this escalating burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".