Fewer Cancer Cases in 4 Countries of the WHO European Region in 2018 through Increased Alcohol Excise Taxation: A Modelling Study
Bibliographic record
Abstract
INTRODUCTION: Prevention of cancer has been identified as a major public health priority for Europe, and alcohol is a leading risk factor for various types of cancer. This contribution estimates the number of cancer cases that could have potentially been averted in 2018 in 4 European countries if an increase in alcohol excise taxation had been applied. METHODS: Current country and beverage-specific excise taxation of 4 member states of the WHO European Region (Germany, Italy, Kazakhstan, and Sweden) was used as a baseline, and the potential impacts of increases of 20, 50, and 100% to current excise duties were modelled. A sensitivity analysis was performed, replacing the current tax rates in the 4 countries by those levied in Finland. The resulting increase in tax was assumed to be fully incorporated into the consumer price, and beverage-specific price elasticities of demand were obtained from meta-analyses, assuming less elasticity for heavy drinkers. Model estimates were applied to cancer incidence rates for the year 2018. RESULTS: In the 4 countries, >35,000 cancer cases in 2018 were caused by alcohol consumption, with the highest rate of alcohol-attributable cancers recorded in Germany and the lowest in Sweden. An increase in excise duties on alcohol would have significantly reduced these numbers, with between 3 and 7% of all alcohol-attributable cancer cases being averted if taxation had been increased by 100%. If the 4 countries were to adopt an excise taxation level equivalent to the one currently imposed in Finland, an even higher proportion of alcohol-attributable cancers could be avoided, with Germany alone experiencing 1,600 fewer cancer cases in 1 year. DISCUSSION/CONCLUSION: Increasing excise duties can markedly reduce cancer incidence in European countries.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.015 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".