The impact of alcohol taxation increase on all-cause mortality inequalities in Lithuania: an interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: Taxation increases which reduce the affordability of alcohol are expected to reduce mortality inequalities. A recent taxation increase in Lithuania offers the unique possibility to test this hypothesis. METHODS: Census-linked mortality data between 2011 and 2019 were used to calculate monthly sex- and education-stratified age-standardized mortality rates for the population aged 40 to 70 years. As primary outcome, we analysed the difference in age-standardized all-cause mortality rates between the population of lowest versus highest educational achievement. The impact of the 2017 taxation increase was evaluated using interrupted time series analyses. To identify whether changes in alcohol use can explain the observed effects on all-cause mortality, the education-based mortality differences were then decomposed into n = 16 cause-of-death groupings. RESULTS: Between 2012 and 2019, education-based all-cause mortality inequalities in Lithuania declined by 18% among men and by 14% among women. Following the alcohol taxation increase, we found a pronounced yet temporary reduction of mortality inequalities among Lithuanian men (- 13%). Subsequent decomposition analyses suggest that the reduction in mortality inequalities between lower and higher educated men was mainly driven by narrowing mortality differences in injuries and infectious diseases. CONCLUSIONS: A marked increase in alcohol excise taxation was associated with a decrease in mortality inequalities among Lithuanian men. More pronounced reductions in deaths from injuries and infectious diseases among lower as compared to higher educated groups could be the result of differential changes in alcohol use in these populations.
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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 | 0.001 | 0.002 |
| 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".