Impact of the program life in traffic and new zero-tolerance drinking and driving law on the prevalence of driving after alcohol abuse in Brazilian capitals: An interrupted time series analysis
Bibliographic record
Abstract
Introduction Driving under the influence of alcohol is one of the main factors for morbidity and mortality from traffic accidents. In 2010 and 2013, the Program Life in Traffic was implemented in Brazil, including the international initiative " Road Safety in Ten Countries ", which established actions to reduce one of the main risk factors for road traffic injuries, the driving under the influence of alcohol. In 2012, a new zero-tolerance drinking and driving law (new dry law) was implemented, establishing a zero-tolerance limit for the drivers' blood alcohol concentration, and increasing punitive measures. This study aimed at analyzing the impact of these measures on the prevalence of driving under the influence of alcohol abuse in Brazilian capitals. Methods An interrupted time series study was conducted using the models of autoregressive integrated moving average or seasonal autoregressive integrated moving average. The main outcome was the prevalence of driving after alcohol abuse in the adult population (≥ 18 years). The model's predictors were the interventions "Program Life in Traffic" and "New Dry Law". The former was implemented in the first quarter of 2011, initially in five capitals: Belo Horizonte, Campo Grande, Palmas, Teresina, and Curitiba, being expanded to the other capitals in the first quarter of 2013. The latter was implemented in the country on the first quarter of 2013. Data source for the study was the records of the surveillance system for risk and protection factors of chronic diseases through telephone survey (Vigitel) from 2007 to 2016. Results The time intervals considered in the study were the quarters. Thirty-eight units were considered in the analysis, corresponding to time series points. It was found that after the implementation of the Program Life in Traffic, in the first quarter of 2011, there was a reduction in the prevalence in Belo Horizonte and Curitiba. Because the introduction of the New Dry Law and the Program Life in Traffic took place in similar periods in the other cities, there was a significant reduction in the outcome prevalence in the cities of Aracaju, Belo Horizonte, Boa Vista, Fortaleza, João Pessoa, Maceió, Manaus, Palmas, Porto Alegre, Recife, Teresina, Rio Branco, and Vitória following the law application. Conclusion The present study identified an immediate impact of the Program Life in Traffic in two capitals (Belo Horizonte and Curitiba) and a joint impact of the New Dry Law in 13 capitals. The results of this study have implications for strengthening interventions aimed at reducing the burden of morbidity and mortality from traffic accidents in Brazil.
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 | 0.003 | 0.012 |
| 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".