Impact of alcohol control policy on hemorrhagic and ischemic stroke mortality rates in Lithuania: An interrupted time series analysis
Bibliographic record
Abstract
Given the causal impact of alcohol use on stroke, alcohol control policies should presumably reduce stroke mortality rates. This study aimed to test the impact of three major Lithuanian alcohol control policies implemented in 2008, 2017 and 2018 on sex- and stroke subtype-specific mortality rates, among individuals 15+ years-old. Joinpoint regression analyses were performed for each sex- and stroke subtype-specific group to identify timepoints corresponding with significant changes in mortality rate trends. To estimate the impact of each policy, interrupted time series analyses using a generalized additive mixed model were performed on monthly sex- and stroke subtype-specific age-standardized mortality rates from January 2001-December 2018. Significant average annual percent decreases were found for all sex- and stroke subtype-specific mortality rate trends. The alcohol control policies were most impactful on ischemic stroke mortality rates among women. The 2008 policy was followed by a positive level change of 4,498 ischemic stroke deaths per 100,000 women and a negative monthly slope change of -0.048 ischemic stroke deaths per 100,000 women. Both the 2017 and 2018 policy enactment timepoints coincided with a significant negative level change for ischemic stroke mortality rates among women, at -0.901 deaths and -1.431 deaths per 100,000 population, respectively. Hemorrhagic stroke mortality among men was not affected by any of the policies, and hemorrhagic stroke mortality among women and ischemic stroke mortality among men were only associated with the 2008 policy. Our study findings suggest that the impact of alcohol control policies on stroke mortality may vary by sex and subtype.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".