The impact of alcohol control policy on pneumonia mortality in Lithuania: an interrupted time-series analysis
Bibliographic record
Abstract
Abstract Despite the growing body of evidence suggesting that alcohol consumption is associated with an increased risk of and poorer treatment outcomes from pneumonia, little is known about the association between alcohol control policy and pneumonia mortality. As such, this study aimed to assess the impact of three alcohol control policies legislated in 2008, 2017 and 2018 in Lithuania on sex-specific pneumonia mortality rates among individuals 15+ years of age. An interrupted time-series analysis using a generalised additive mixed model was performed for each policy. Of the three policies, only the 2008 policy resulted in a significant slope change (i.e. decline) in pneumonia mortality rates among males; no significant slope change was observed among females. The low R2 values for all sex-specific models suggest that other external factors are likely also influencing the sex-specific pneumonia mortality rates in Lithuania. Overall, the findings from this study suggest alcohol control policy's targeting affordability may be an effective way to reduce pneumonia mortality rates, among males in particular. However, further research is needed to fully explore their impact.
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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".