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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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; both teacher heads agree on what is shown here.
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".