The Impact of Increasing the Minimum Legal Drinking Age from 18 to 20 Years in Lithuania on All-Cause Mortality in Young Adults—An Interrupted Time-Series Analysis
Bibliographic record
Abstract
AIMS: To determine the effect of an alcohol policy change, which increased the minimum legal drinking age (MLDA) from 18 years of age to 20 years of age on all-cause mortality rates in young adults (18-19 years old) in Lithuania. METHODS: An interrupted time series analysis was conducted on a dataset from 2001 to 2019 (n = 228 months). The model tested the effects of the MLDA on all-cause mortality rates (deaths per 100,000 individuals) in three age categories (15-17 years old, 18-19 years old, 20-22 years old) in order to control for general mortality trends in young adults, and to isolate the effects of the MLDA from other alcohol control policies. Additional models that included GDP as a covariate and a taxation policy were tested as well. RESULTS: There was a significant effect of the MLDA on all-cause mortality rates in those 18-19 years old, when modelled alone. Additional analyses controlling for the mortality rate of other age groups showed similar findings. Inclusion of confounding factors (policies on alcohol taxation, GDP) eliminated the effects of MLDA. CONCLUSIONS: Although there was a notable decline in all-cause mortality rates among young adults in Lithuania, a direct causal impact of MLDA on all-cause mortality rates in young adults was not definitively found.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".