Decision Strategies while Intoxicated relate to Alcohol-Impaired Driving Attitudes and Intentions

Objective: Approximately 28 million individuals engage in alcohol-impaired driving (AID) every year. This study investigated individuals’ AID decision making strategies under intoxication, their variability across the breath alcohol concentration curve (BrAC), and the association between strategy and AID attitudes and intentions. Method: 79 adults (23.9 years, 57% women) who drank alcohol ≥2 days per week and lived >2 miles away from their typical drinking locations completed an alcohol administration protocol and AID decision making task. AID attitudes, intentions, and behaviors were assessed repeatedly across the BrAC curve. Bayesian cognitive modeling identified decision strategies used by individuals on the AID decision making task, revealing whether alcohol consumption level and/or ride service cost factored into individuals’ decisions to drive while impaired or obtain a ride. Additional analyses tested whether AID attitudes and intentions were related to individuals’ decision strategies. Results: Two decision strategies were examined on the ascending and descending limb of the BrAC curve: compensatory (both consumption level and ride service cost factored into AID decisions) and non-compensatory (only consumption level factored into AID decisions). Switching to a compensatory strategy on the descending limb was associated with lower perceived intoxication, perceiving AID as less dangerous, and being willing to drive above the legal BrAC limit. Conclusions: Results suggest that risk for engaging in AID is higher for those using a cost-sensitive, compensatory strategy when making AID decisions under intoxication. Future research is needed to test whether AID countermeasures (e.g., subsidized ride services) are differentially effective according to decision strategy type.

Implementation of Road Safety Countermeasures in Four Cities to Reduce the Harmful Effects of Alcohol: A Progress Report

BackgroundBeginning in 2016, the Anheuser-Busch InBev Foundation (ABIF) provided funding to six pilot cities to implement evidence-based interventions to reduce the harmful use of alcohol and its deleterious consequences such as alcohol-impaired driving. The cities receiving funding are Alexandra Township in Johannesburg, South Africa; Brasilia, Brazil; Columbus, Ohio, United States; Jiangshan, China; Leuven, Belgium; and Zacatecas, Mexico.MethodsFour of the city pilot coalitions are implementing a wide array of interventions to deter driving under the influence (DUI). Columbus made efforts to get more judges to apply Ohio’s alcohol ignition interlock law and implemented and evaluated a Safe Rides program. Brasilia increased the number of roadside checkpoints and planned an educational campaign about the dangers of impaired driving to be delivered at bars by firefighters and paramedics. Alexandra expanded and upgraded the Metropolitan Police Department’s Alcohol Evidence Center (AEC). In Zacatecas, among other interventions, a new Driving While Intoxicated (DWI) facility is being constructed to expedite case processing and adjudication.ResultsIn Columbus, the evaluation of the Safe Rides program showed an estimated reduction of 2.9 impaired driving crashes but also an average increase of 0.4 alcoholic drink per program participant. There was a reduction in harmful alcohol use of .02% in 2017 associated with the Safe Rides campaign, but with no carryover to 2018. In Brasilia, the combined effect of the road safety measures and other factors resulted in a 35% decrease in traffic deaths between 2016 and 2019. In Alexandra, there were 46 fatal crashes over the Easter weekend in 2018, 25 fatal crashes in 2019 (46% reduction), and 3 fatal crashes in 2020 (88% reduction from 2019). To date, Zacatecas’ road safety measures have not yet been evaluated.ConclusionsFull implementation of the city pilots’ planned road safety interventions has been slow, and presently the COVID-19 pandemic has halted most operations. Interim evaluations can be conducted once a pilot city’s countermeasures are fully implemented and have operated for at least one year. ABIF should continue to donate funds to increase or enhance evidence-based DUI enforcement strategies while also implementing awareness campaigns to inform the public and enhance the deterrent effect of those efforts.

Fentanyl, Acetylfentanyl, and Carfentanil in Impaired Driving Cases: A Review of 270 Cases

The detection of fentanyl and fentanyl analogs has been widely communicated throughout the scientific community. While most of the reporting has been in relation to overdose deaths, these drugs are commonly detected in impaired driving cases. A retrospective study of impaired driving cases analyzed between 2017 and 2019 produced 270 cases positive for fentanyl, carfentanil, and/or acetylfentanyl. Fentanyl was the predominant drug found in these 270 cases (65.5%) with concentrations ranging from less than 1.0 ng/mL to 64 ng/mL. Carfentanil was found alone in 6.6% cases with three concentrations above 1.0 ng/mL. Acetylfentanyl was always found when fentanyl was positive with concentrations ranging from less than 1.0 ng/mL to 9.2 ng/mL. Detailed case histories are provided with corresponding toxicology results. Toxicology results show impaired drivers using multiple drugs with a wide range of observed behaviors. The inclusion of these drugs in routine impaired driver toxicology testing is extremely important when attempting to determine their overall prevalence.

Two-Lane Highway Crash Severities: Correlated Random Parameters Modeling Versus Incorporating Interaction Effects

Two-lane highways represent the majority of highways in the U.S. and their safety is of crucial concern. Even though road safety researchers intensively evaluated two-lane highway safety, past studies were challenged by a methodological hindrance, namely that of correlated random parameters (CRP) modeling methods. Random parameters models capture unobserved heterogeneity effects of crash contributing factors, while CRP models offer the additional benefit of capturing correlations among variables inducing such unobserved heterogeneity effects. However, CRP models do not permit specifying pairs of regressors, with statistically insignificant correlations, to be uncorrelated. In this research, it was demonstrated that the conventional uncorrelated random parameters ordinal probit (URPOP) structure with interaction effects outperformed the correlated random parameters ordinal probit (CRPOP) structure when modeling injury severity risks of two-lane highway crashes in Wyoming. As per the former model’s results, speeding, head-on collisions, sideswipe opposite-direction collisions, intersecting-direction collisions, motorcycle involvement, impaired driving, distracted driving, the interaction effect of speeding with motorcycle involvement, that of head-on collisions with impaired driving, and that of head-on collisions with commercial vehicle involvement all raised the likelihood of sustaining severe injuries. Conversely, leaving the crash scene, proper seat belt use, wet road surfaces, and the interaction effect of impaired driving with motorcycle involvement alleviated the risk of incurring severe injuries. The superiority of the proposed model and its reduced computation time warrant its recommendation for implementation in future studies. Also, from a practical perspective, safety mitigation measures are suggested based on this research’s findings.