Global and regional burden of alcohol-associated liver disease and alcohol use disorder in the elderly
Bibliographic record
Abstract
Background & Aims: Alcohol-associated liver diseases (ALDs) and alcohol use disorder (AUD) pose a global health risk. AUD is underrecognized in the elderly, and the burden of AUD complications, including ALD, may increase with aging populations and rising alcohol intake. However, there is a lack of epidemiological evidence on AUD and ALD in the elderly. Methods: Using the Global Burden of Disease Study 2019, we analyzed the prevalence, mortality, disability-adjusted life years (DALYs), age-standardized rates (ASRs), and temporal change from 2000 to 2019 of ALD and AUD in the overall population and the elderly (65-89 years). The findings were categorized by sex, region, nation, and sociodemographic index. Results: The prevalence rates of ALD in the elderly were higher than those in adolescents and young adults, whereas AUD levels were lower than those in adolescents and young adults. In 2019, there were 9.39 million cases (8.69% of cases in the overall population) of AUD, 3.23 million cases (21.8% of cases in the overall population) of alcohol-associated cirrhosis, and 68,468 cases (51.27% of cases in the overall population) of liver cancer from alcohol among the elderly. ASRs of the prevalence of ALD and AUD in the elderly increased in most regions; on the contrary, ASRs of death and DALYs decreased in most regions. Nevertheless, ASRs of death and DALYs from liver cancer from alcohol increased in many areas. Conclusions: Our findings highlighted the increased prevalence of ALD in the elderly, with a burden of AUD comparable with that in the overall population. Public health strategies on ALD and AUD targeting the elderly are urgently needed. Impact and implications: The burden of alcohol-associated liver disease (ALD) and alcohol use disorder (AUD) is increasing. Advances in healthcare and education have resulted in a remarkable spike in life expectancy and a consequential population aging. Nevertheless, little is known about the epidemiology of ALD and AUD in the elderly. Our study indicates the increasing burden of ALD and AUD in the elderly population, necessitating early detection, intervention, and tailored care to the unique needs and complexities faced by older individuals grappling with these conditions.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".