Modeling the impact of alcohol dependence on mortality burden and the effect of available treatment interventions in the European Union
Bibliographic record
Abstract
Alcohol consumption is a major risk factor for the burden of disease, and Alcohol Dependence (AD) is the most important disorder attributable to this behavior. The objective of this study was to quantify mortality associated with AD and the potential impact of treatment. For the EU countries, for the age group 15-64 years, mortality attributable to alcohol consumption in general, to heavy drinking, and to AD were estimated based on the latest data on exposure and mortality. Potential effects of AD treatment were modeled based on Cochrane and other systematic reviews of the effectiveness of the best known and most effective interventions. In the EU 88.9% of men and 82.1% of women aged 15-64 years were current drinkers; and 15.3% of men and 3.4% of women in this age group were heavy drinkers. AD affected 5.4% of men and 1.5% of women. The net burden caused by alcohol consumption was 1 in 7 deaths in men and 1 in 13 deaths in women. The majority of this burden was due to heavy drinking (77%), and 71% of this burden was due to AD. Increasing treatment coverage for the most effective treatments to 40% of all people with AD was estimated to reduce alcohol-attributable mortality by 13% for men and 9% for women (annually 10,000 male and 1700 female deaths avoided). Increasing treatment rates for AD was identified as an important issue for future public health strategies to reduce alcohol-attributable harm and to complement the current focus of alcohol policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".