Using Direct and Indirect Estimates for Alcohol-Attributable Mortality: A Modelling Study Using the Example of Lithuania
Bibliographic record
Abstract
Introduction: Comparative risk assessments (CRAs) for alcohol use are based on indirect estimates of attributable harm, and usually combine country-specific exposure estimates and global risk relations derived from meta-analyses. CRAs for Eastern European countries, such as Lithuania, base their risk relations not on global risk relations, but on a large Russian cohort study. The availability of a direct estimate of alcohol-attributable mortality following the 2017 implementation of a large increase in alcohol excise taxes in Lithuania has allowed a comparison of these indirect estimates with a country-specific gold standard. Methods: A statistical modelling study compared direct (predictions based on a time-series methodology) and indirect (predictions based on an attributable-fraction methodology) estimates of alcohol-attributable mortality before and after a large increase in alcohol excise taxes in Lithuania. Specifically, Russia-specific versus global relative risks were compared against the gold standard of time-series based predictions. Results: Compared to direct estimates, indirect estimates markedly underestimated the reduction of alcohol-attributable mortality 12 months post intervention by at least 63%. While both of the indirect estimates differed markedly from the direct estimates, the Russia-specific estimates were closer to the direct estimates, primarily due to higher estimates for alcohol-attributable cardiovascular mortality. Discussion: As all indirect estimates were markedly lower than direct estimates, current overall relative risks and price elasticities should be re-evaluated. In particular, global estimates should be replaced by new regional estimates based on cohort studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".