Predicting the Impact of Alcohol Taxation Increases on Mortality—A Comparison of Different Estimation Techniques
Bibliographic record
Abstract
AIMS: To examine how standard analytical approaches to model mortality outcomes of alcohol use compare to the true results using the impact of the March 2017 alcohol taxation increase in Lithuania on all-cause mortality as an example. METHODS: Four methodologies were used: two direct methodologies: (a) interrupted time-series on mortality and (b) comparing predictions based on time-series modeling with the real number of deaths for the year following the implementation of the tax increase; and two indirect methodologies: (c) combining a regression-based estimate for the impact of taxation on alcohol consumption with attributable-fraction methodology and (d) using price elasticities from meta-analyses to estimate the impact on alcohol consumption before applying attributable-fraction methodology. RESULTS AND CONCLUSIONS: While all methodologies estimated reductions in all-cause mortality, especially for men, there was substantial variability in the level of mortality reductions predicted. The indirect methodologies had lower predictions as the meta-analyses on elasticities and risk relations seem to underestimate the true values for Lithuania. Directly estimated effects of taxation based on the actual mortalities seem to best represent the true reductions in alcohol-attributable mortality. A significant increase in alcohol excise taxation had a marked impact on all-cause mortality in Lithuania.
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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 | 0.084 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".