Relationships of State Alcohol Policy Environments With Homicides and Suicides
Bibliographic record
Abstract
Introduction Alcohol use is involved in a large proportion of homicides and suicides each year in the U.S., but there is limited evidence on how policies targeting alcohol influence violence in the U.S. context. Extant studies generally focus on individual policies in isolation of each other. This study examines the impacts of changes in states' alcohol policy restrictions on overall homicide and suicide rates and firearm-related homicide and suicide rates using a holistic measure of states' alcohol policy environments. Methods Using a composite measure of state-level alcohol policies (Alcohol Policy Scale) and data from the National Vital Statistics System from 2002 to 2018, this study applied a Bayesian time series model to estimate the impacts of alcohol policy changes on overall and firearm-involved homicide and suicide rates. The analysis was performed in 2023 and 2024. Results A 1 SD change in the Alcohol Policy Scale was associated with a 6% decline in homicide rates both overall (incident rate ratio=0.94; 95% credible interval=0.89, 1.00) and for firearm homicides specifically (incident rate ratio=0.94, 95% CI=0.88, 1.01). There was no clear association of alcohol policy with suicides. The model predicts that a nationwide increase in alcohol restrictions equivalent to a shift from the 25 th to 75 th percentile of the scale's distribution would result in almost 1,200 fewer homicides annually. Conclusions Increases in the restrictiveness of state-level alcohol policies are associated with reductions in homicides. More restrictive alcohol policy environments may offer an opportunity to reduce homicides.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".