Potenzielle Auswirkungen erhöhter Alkoholsteuern auf die alkoholbedingte Krankheitslast in Deutschland: Eine Modellierungsstudie
Bibliographic record
Abstract
BACKGROUND: In 2019, Germany was among the countries with the highest alcohol per capita consumption in the world, which contributes significantly to the burden of disease. AIM: In this modelling study, we estimate how many alcohol-attributable diseases and deaths in Germany could have been avoided in 2019 if current alcohol excise taxes were increased by 20%, 50%, and 100%. METHODS: The starting point for the modelling was the national beverage-specific alcohol taxes. Three scenarios were modelled under the assumption that the resulting tax increase would be fully transferred to the retail prices. Beverage-specific price elasticities were used. Based on the estimated resulting decline in annual per capita consumption and the disease-specific risk functions, we modelled the avoidable incidence and mortality for alcohol-attributable diseases for 2019. Alcohol-attributable diseases of the cardiovascular and digestive systems, alcohol dependence, epilepsy, and infectious diseases as well as injuries and accidents were considered. RESULTS: Overall, doubling the beverage-specific alcohol taxes could have avoided up to 200,400 alcohol-attributable cases of disease and injury as well as 2800 deaths in Germany in 2019. This corresponds to just under 7% of the modelled new alcohol-attributable cases of disease and death in Germany. DISCUSSION: Alcohol-attributable diseases and injuries are preventable and an increase in the alcohol taxes could substantially reduce the alcohol-attributable burden of disease in Germany.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".