Alcohol taxation, alcohol consumption and cancers in Lithuania: A case study
Bibliographic record
Abstract
Aims: The aim of this contribution was to estimate the impact of the last significant alcohol taxation increase in Lithuania in 2017 on alcohol consumption, incident cancer cases, and cancer mortality, as well as the number of cancer outcomes that could have potentially been averted in 2018 had larger increases in alcohol excise taxation been applied. Design: Statistical modelling was used to estimate the change in alcohol per capita consumption following the tax increase, and alcohol-attributable fraction methodology was then used to estimate the associated cancer incidence and mortality. Potential increases of current excise duties were modelled in two steps. First, beverage-specific price elasticities of demand were used to predict the associated decreases in consumption and cancer outcomes, and second, the outcomes arising from the actual numbers and the modelled numbers were compared. Method: Data were taken from the following sources: alcohol consumption data from Statistics Lithuania and the WHO, cancer data from the International Agency of Research on Cancer, and risk relations and elasticities of demand from published meta-analyses. Results: A total of 15,857 new cancer cases (8,031 in women and 7,826 in men) and 8,534 cancer deaths (3,757 in women and 4,777 in men) were recorded in Lithuania in 2018. Using the attributable fraction methodology, we estimate that 4.8% of 761 of these new cancer cases were attributable to alcohol use (284 in women; 477 in men), as well as 5.5% or 466 cancer deaths (115 in women; 351 in men). With the taxation increase of 2017, 45 new cases and 24 deaths will be averted over the next 10 years. Further taxation increases of 100% could double the number of new cancer cases averted or saved. Conclusion: In a high-consumption European country like Lithuania, alcohol use is an important and avoidable risk factor for cancer. Taxation is an important measure to reduce the alcohol-attributable cancer burden.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".