Evaluating the Impact of Alcohol Control Interventions on Suicide Mortality Rates
Bibliographic record
Abstract
Introduction & Objective: Given that the impact of regulatory and public policy initiatives cannot usually be tested through traditional randomized controlled trial designs, well-selected, -designed, and -analyzed natural experiments are the method of choice when examining the effects of such enactments on a variety of outcomes. The classic methodology for such evaluations is interrupted time-series (ITS) analysis, which is considered as one of the quasi-experimental designs that use both pre- and post-policy data without randomization. This study tests the impact of alcohol control interventions implemented in different period of times on suicide mortality rates among people 25-74 years of age using ITS. Methods: We mainly use the generalized additive mixed model (GAMM) to capture trend and seasonality in suicide mortality rates while controlling for unemployment rates, financial crisis during 2007-2008, and legal alcohol consumption records. Given the notable differences in alcohol consumption and suicide mortality between males and females, all analyses are conducted gender-specifically. Results: The ITS shows that the intervention introduced in 2017 has a significant effect on reducing suicide mortality rates for males between 25 and 74. Following the implementation of the intervention, suicide mortality rates decreased by 23.8% (95% CI: 10.2% - 35.4%) on average. Conclusion: The alcohol control intervention that strictly increased the excise tax on alcohol products has been shown to have a strong impact on reducing suicide mortality rates among male adults 25-74 years of age. ITS analyses are one of the strongest evaluative designs and allow a more detailed assessment of the longitudinal impact of an intervention than may be possible with a randomized control trial.
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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".