Alcohol-impaired Drivers Research Articles

Modeling of drinking and driving behaviors among adolescents and young adults in the United States: Complexities and Intervention outcomes

Alcohol-impaired driving is a formidable public health problem in the United States, claiming the lives of 37 individuals daily in alcohol-related crashes. Alcohol-impaired driving is affected by a multitude of interconnected factors, coupled with long delays between stakeholders’ actions and their impacts, which not only complicate policy-making but also increase the likelihood of unintended consequences. We developed a system dynamics simulation model of drinking and driving behaviors among adolescents and young adults. This was achieved through group model building sessions with a team of multidisciplinary subject matter experts, and a focused literature review. The model was calibrated with data series from multiple sources and replicated the historical trends for male and female individuals aged 15 to 24 from 1982 to 2020. We simulated the model under different scenarios to examine the impact of a wide range of interventions on alcohol-related crash fatalities. We found that interventions vary in terms of their effectiveness in reducing alcohol-related crash fatalities. In addition, although some interventions reduce alcohol-related crash fatalities, some may increase the number of drinkers who drive after drinking. Based on insights from simulation experiments, we combined three interventions and found that the combined strategy may reduce alcohol-related crash fatalities significantly without increasing the number of alcohol-impaired drivers on US roads. Nevertheless, related fatalities plateau over time despite the combined interventions, underscoring the need for new interventions for a sustained decline in alcohol-related crash deaths beyond a few decades. Finally, through model calibration we estimated time delays between actions and their consequences in the system which provide insights for policymakers and activists when designing strategies to reduce alcohol-related crash fatalities.

The association between single and dual use of cannabis and alcohol and driving under the influence and riding with an impaired driver in a large sample of Canadian adolescents

Objective Dual use of cannabis and alcohol has increased in adolescents, but limited research has examined how it relates to impaired driving or riding with an impaired driver (IDR) compared to single substance use. This study aimed to examine the odds of alcohol- and/or cannabis-IDR among adolescents based on their use of alcohol and/or cannabis, and whether associations differed by gender and age. Methods Cross-sectional survey data were used from a sample of 69,621 students attending 182 Canadian secondary schools in the 2021/22 school year. Multilevel logistic regression estimated the odds of exclusive alcohol-IDR, exclusive cannabis-IDR, and both alcohol and cannabis IDR (alcohol-cannabis-IDR). Substance use interactions with gender and age were tested. Results Overall, 14.7% of participants reported IDR; 7.5% reported exclusive alcohol-IDR, 3.2% reported exclusive cannabis-IDR, 4.0% reported alcohol-cannabis-IDR, and 7.4% were unsure if they had experienced IDR. The prevalence of IDR varied across substance use groups, 8.0% among nonuse, 21.9% among alcohol-only use, 35.9% among cannabis-only use, and 49.6% among dual use groups. Gender diverse, older, and students with lower socioeconomic status exhibited a higher likelihood of reporting alcohol-cannabis-IDR. Dual use was significantly associated with 9.5 times higher odds of alcohol-cannabis-IDR compared to alcohol-only use, and 3.0 times higher odds compared to cannabis-only use. Dual use was also associated with an increased likelihood of either alcohol- or cannabis-IDR. Conclusions This study highlights that all students, regardless of substance use, are at risk of IDR, but students engaged in dual use of alcohol and cannabis face an elevated risk compared to both peers who do not use substances and those who use only a single substance. These findings emphasize the importance of targeted interventions that address the risks associated with IDR.

Longitudinal associations between adult-supervised drinking during adolescence and alcohol misuse from ages 25–31 years: A comparison of Australia and the United States

Prior studies suggest that adult supervised drinking in adolescence predicts greater adolescent alcohol misuse. Long-term follow up data examining how adult supervised drinking during adolescence relates to alcohol misuse in adulthood are lacking. Longitudinal data from the International Youth Development Study tested associations between adult supervised drinking during adolescence (ages 13–16; 2002–2004) and adult alcohol misuse (ages 25–31; 2014, 2018, 2020). Cross-nationally matched samples were compared in Washington State, USA (n = 961) and Victoria, Australia (n = 1,957; total N = 2,918, 55 % female, 83 % White), where adult-supervised adolescent alcohol use was more common. Multilevel analyses adjusted for state, sex, adolescent drinking, parent education, family management, family history of substance use problems, and parent alcohol-related norms. Adult supervised drinking in adolescence (at dinner or parties, on holidays) predicted more adult alcohol misuse (mean Alcohol Use Disorders Identification Test score; b[SE] 0.07[0.03]; p = 0.004) and higher rates of alcohol-impaired driving (Odds Ratio [OR] 1.501, p = 0.034) and riding with an alcohol-impaired driver (OR 1.669, p = 0.005), but not the use of strategies to moderate alcohol intake (e.g., counting drinks). Better family management (monitoring, clear rules) in adolescence predicted less adult alcohol misuse. Associations were similar in the two states. Reducing the frequency of adult supervised drinking and improving family management practices in adolescence may help to decrease alcohol misuse well into adulthood. Findings support the widespread implementation of substance use prevention and family management training programs.

Child Passenger Deaths in Traffic Crashes Involving Alcohol-Impaired Drivers: 2011-2020.

Child Passenger Deaths in Traffic Crashes Involving Alcohol-Impaired Drivers: 2011-2020.

Using behavioral economics to optimize safer undergraduate late-night transportation.

Many universities sponsor student-oriented transit services that could reduce alcohol-induced risks but only if services adequately anticipate and adapt to student needs. Human choice data offer an optimal foundation for planning and executing late-night transit services. In this simulated choice experiment, respondents opted to either (a) wait an escalating delay for a free university-sponsored "safe" option, (b) pay an escalating fee for an on-demand rideshare service, or (c) pick a free, immediately available "unsafe" option (e.g., ride with an alcohol-impaired driver). Behavioral-economic nonlinear models of averaged-choice data describe preference across arrangements. Best-fit metrics indicate adequate sensitivity to contextual factors (i.e., wait time, preceding late-night activity). At short delays, students preferred the free transit option. As delays extend beyond 30 min, most students preferred competing alternatives. These data depict a policy-relevant delay threshold to better safeguard undergraduate student safety.

Single-vehicle roadway departure crashes at rural two-lane highway curved segments: A diagnosis using pattern recognition

Curved segments account for a disproportionately high proportion of fatal and serious injury crashes, with most of these crashes occurring on rural two-lane (R2L) highways. During the 10-year period from 2008 to 2017, a total of 1 234 fatal single-vehicle roadway departure (SV-RwD) crashes occurred on R2L roads in Louisiana, out of which 635 (51.5 %) crashes occurred on curved segments. Therefore, it is critical to investigate the causes of SV-RwD crashes, specifically those that occur on curved segments. This study aimed to investigate the ‘association knowledge’ of the factors contributing to SV-RwD crashes on R2L curved segments in Louisiana using fatal and injury crash data collected from 2008 to 2017. The study utilized Cluster Correspondence Analysis (CCA), a robust joint dimension reduction and clustering method for handling high-dimensionality and multicollinearity of crash data, to achieve this objective. Based on the cluster validation measures, the study identified five clusters with specific traits, including alcohol-impaired male drivers with no seatbelt usage, young (15–24 years old) female drivers’ crash involvement in cloudy weather conditions, animal-involved crashes in rainy weather conditions, crashes occurring on hillcrest locations under cloudy weather conditions, and crashes in the dark with the presence of streetlights and higher traffic volume. Furthermore, young (15–24 years) female drivers were identified in most clusters, implying that this specific age group of female drivers requires special consideration when dealing with SV-RwD collisions on R2L curved segments. To improve safety on R2L curved segments, policymakers can use the findings of this study to develop targeted countermeasures.

Supplemental Material for Using Mobile Technology to Influence Alcohol-Impaired Driving Risk Perceptions and Decisions

Supplemental Material for Using Mobile Technology to Influence Alcohol-Impaired Driving Risk Perceptions and Decisions

Exploring association of contributing factors to pedestrian fatal and severe injury crashes under dark-no-streetlight condition

The pedestrian crash pattern in the dark-no-streetlight condition is a noteworthy ongoing traffic safety concern. The persistently high percentage of pedestrian fatalities at night in the U.S., as well as in the state of Louisiana, necessitates new research to supplement existing studies. This study utilized 10 years (2010–2019) of pedestrian fatal and severe injury crashes in Louisiana that occurred in the dark without streetlights to identify the associated crash patterns. The methodology is based on Multiple Correspondence Analysis (MCA), an exploratory approach used to discover the association of multiple categorical variables from a crash dataset. The findings suggest that driver characteristics (age, gender, and physical condition), pedestrian action, pedestrian alcohol impairment, and physical settings (posted speed limit, location, and roadway type) have a substantial impact on pedestrian collisions at night without streetlights. Moreover, the obtained combination clouds of MCA reveal associations such as elderly pedestrian (>64 years) alcohol impairment resulting in fatalities, crashes in an open country location with a high posted speed limit, crashes involving pedestrians in dark clothing on high-speed (50–55 mph) roadways, alcohol-impaired driver involvement in crashes on two-way roads without physical separation, severe injury crashes at intersections, male pedestrian crashes on midblock locations during weekends, and young (15–24 years) female driver's involvement in crashes while pedestrians were walking against the traffic. Based on the findings, this research also suggests safety recommendations that can assist highway safety practitioners in determining appropriate countermeasures to reduce pedestrian crashes in the dark-no-streetlight condition.

Combining emerging hotspots analysis with XGBoost for modeling pedestrian injuries in pedestrian-vehicle crashes: a case study of North Carolina

Pedestrians might face more dangers and sustain severer injuries in crashes than others. Also, the crash data has inherent patterns related to both space and time. Crashes that happened in locations with highly aggregated uptrend patterns should be worth exploring to examine the most recently deteriorative factors affecting pedestrian-injury severities in crashes. Therefore, applying proper modeling approaches is needed to identify the causes of pedestrian-vehicle crashes to improve pedestrian safety. In this study, an emerging hotspot analysis is firstly utilized to identify the most targeted hotspots, followed by a proposed XGBoost model that analyzes the most recently deteriorative factors affecting pedestrian injury severities. The overall accuracy of the best model on the hotspot dataset is 94.49%, which shows a relatively high performance compared to conventional models. Seven factors are identified to increase the likelihood of fatal injury, including “land development: farm, wood and pasture” (FWP), “interstate”, “US route”, “hit and run”, “alcohol-impaired driver” (AID), “urban”, and “alcohol-impaired-pedestrian”. While for incapacitating injury, there are five significant factors including “work zone”, “interstate”, “US route”, “curved roadway” and “alcohol-impaired-pedestrian”. The results of this research could give a solid reference for the identification of contributing factors affecting pedestrian-injury severities to policymakers and researchers.

Decision strategies while intoxicated relate to alcohol-impaired driving attitudes and intentions.

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 (BrAC) curve, and the association between strategy and AID attitudes, intentions, and behavior. Seventy-nine adults (mean 23.9 years, 57% female) 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. Two decision strategies were examined on the ascending and descending limbs 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. 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. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Between- and Within-Person Correlates of Alcohol-Impaired Driving in First-Year University Students: The Roles of Impulsivity, Binge Drinking, Depression, and Anxiety.

University students are a high-risk demographic for alcohol-impaired driving (AID), a leading contributor to death and injury on Canadian roads. Although between-person correlates of AID are well established, little research has identified within-person correlates that elucidate when AID occurs. Accordingly, this study investigated whether between- and within-person variability in impulsivity, binge drinking, depression, and anxiety are associated with AID in university students. Participants were 633 first-year students (75% female, mean age = 17.97 [SD = 0.76]) from a Canadian university who completed seven monthly surveys. Multilevel models disaggregated between- and within-person associations. Between-person elevations in negative and positive urgency, sensation seeking, lack of premeditation, binge drinking, and depression were associated with greater odds of AID. Within-person elevations in negative urgency, sensation seeking, and binge drinking were associated with greater odds of AID, whereas within-person elevations in depression were associated with lower odds of AID. These results support existing research regarding who is most likely to engage in AID (students with elevated impulsivity, binge drinking, and depression) and extend this research by identifying under what conditions AID is likeliest to occur (when impulsivity and binge drinking are higher than usual, and depression is lower than usual). The opposing between- and within-person associations of depression with AID highlight the need for careful specification of hypotheses, as findings at the between-person level may not generalize to the within-person level. Moving forward, research that elucidates not only for whom but also when AID occurs may be best positioned to inform intervention and prevention efforts among university students.

Waiting for the Stop Sign to Turn Green: Contemporary Issues on Drug and Alcohol Impaired Driving Policy.

Impaired driving has been a considerable social problem in the U.S. for decades, but efforts to reduce it have stalled after the initial reductions in the 1980's. As a result, legislators continue to develop more polices aimed at deterring impaired driving. Although alcohol has historically been the focus of these efforts, recently there has been increased concern about marijuana impaired driving policies as well. However, alcohol and marijuana impaired driving differ in many ways. This paper explores the costs and benefits of new zero-tolerance policies such as the reduction of the per-se Blood Alcohol Concentration (BAC) level from .08 to .05 for alcohol and the establishment of similar per-se limits for marijuana. These policies are not based on actual impairment and reflect a net widening effect that will criminalize unimpaired drivers, divert criminal justice resources away from the most problematic impaired drivers, and will have little impact on impaired driving crashes. As such, they have the potential to do more harm than good.

Modeling the temporal relationship between contributing factors and injury severities in rural single-vehicle alcohol-impaired driving crashes: Insights from random parameters logit models in the means and variances

Objectives Alcohol-impaired driving (A-ID) crashes have been acknowledged as fatality-concentrated while there is a limited understanding of how contributors relating to A-ID influence crash severity and lead to more severe injuries in rural areas. The current paper utilized North Carolina crash data to investigate the unobserved heterogeneity and temporal stability of the rural single-vehicle A-ID crash injury-severity determinants over a five-year period from 2014–2018. Methods Crash injury severities were estimated using a group of random parameters logit models in the means and variances with three categories of injury-severity determined as outcome variables including no injury, minor injury, and severe injury. Explanatory variables were selected across multiple factors that could be classified as roadway characteristics, environmental characteristics, crash characteristics, temporal characteristics, vehicle characteristics and driver characteristics. The temporal stability of the models was examined through a series of likelihood ratio tests. Marginal effects were also adopted to analyze the temporal stability of the explanatory variables. Results The result uncovers an overall temporal instability. Some contributors present relatively temporal stability such as female, turning, passenger car, motorcycle, vehicle age (5-9 years old), speed limit (<45 mph), curved segment, dry road surface, animal collision and overturned collision. Curved segment and dry road surface are found to consistently increase the possibility of severe injuries in rural alcohol-involved crashes. Conclusions This paper can provide insights into preventing single-vehicle A-ID crashes and could potentially facilitate the development of single-vehicle A-ID crash injury mitigation policies in rural areas. More studies could be conducted adopting the advanced data-driven methods for A-ID crash prediction.

Ride-Hailing and Road Traffic Crashes: A Critical Review.

Ride-hailing businesses, including Uber and Lyft, have reshaped road traffic since they first began operating in the United States approximately a decade ago. It follows that ride-hailing may also alter the incidence and distribution of road traffic crash injuries and deaths. The available evidence relating ride-hailing to crashes is critically reviewed in this article. We present a theoretical model that synthesizes the hypothesized mechanisms, and we identify common methodological challenges and suggest priorities for future research. Mixed results have been reported for the overall incidence of road traffic crash injuries and deaths, likely due to heterogeneous impacts on vehicular traffic flow (e.g., increasing the volume of vehicles); on vehicle-, person-, and event-level characteristics (e.g., reducing alcohol-impaired driver crashes); on road-user types (e.g., increasing pedestrian crashes); and on environmental conditions (e.g., reducing crashes most substantially where public transit access is poorest). The lack of a well-developed theory of human mobility and methodological challenges that are common to many ecological studies impede exploration of these sources of moderation. Innovative solutions are required to explicate ride-hailing's heterogeneous impacts, to guide policy that can take advantage of the public health benefits of ride-hailing, and to ensure that research keeps pace with technological advances that continue to reshape road traffic use.

Mapping the complex causal mechanisms of drinking and driving behaviors among adolescents and young adults

BackgroundThe proportion of motor vehicle crash fatalities involving alcohol-impaired drivers declined substantially between 1982 and 1997, but progress stopped after 1997. The systemic complexity of alcohol-impaired driving contributes to the persistence of this problem. This study aims to identify and map key feedback mechanisms that affect alcohol-impaired driving among adolescents and young adults in the U.S. MethodsWe apply the system dynamics approach to the problem of alcohol-impaired driving and bring a feedback perspective for understanding drivers and inhibitors of the problem. The causal loop diagram (i.e., map of dynamic hypotheses about the structure of the system producing observed behaviors over time) developed in this study is based on the output of two group model building sessions conducted with multidisciplinary subject-matter experts bolstered with extensive literature review. ResultsThe causal loop diagram depicts diverse influences on youth impaired driving including parents, peers, policies, law enforcement, and the alcohol industry. Embedded in these feedback loops are the physical flow of youth between the categories of abstainers, drinkers who do not drive after drinking, and drinkers who drive after drinking. We identify key inertial factors, discuss how delay and feedback processes affect observed behaviors over time, and suggest strategies to reduce youth impaired driving. ConclusionThis review presents the first causal loop diagram of alcohol-impaired driving among adolescents and it is a vital first step toward quantitative simulation modeling of the problem. Through continued research, this model could provide a powerful tool for understanding the systemic complexity of impaired driving among adolescents, and identifying effective prevention practices and policies to reduce youth impaired driving.

Rideshare Trips and Alcohol-Involved Motor Vehicle Crashes in Chicago

Rideshare companies such as Uber and Lyft have substantially changed transportation markets in the United States and globally. The aim of this study was to examine whether ridesharing is associated with reductions in alcohol-involved crashes. This case-series study used highly spatially and temporally resolved trip-level rideshare data and motor vehicle crash data from the Chicago Data Portal from November 2018 to December 2019. The units of analysis were motor vehicle crashes in Chicago. Events of interest were 962 crashes that police indicated were alcohol involved. The comparison group was 962 non-alcohol-involved crashes that occurred in the same census tract, matched 1:1. The exposure of interest was the density per square mile of rideshare trips that were in progress at the time of the crash, calculated using a kernel density function around the estimated route paths of active trips. A conditional logistic regression compared alcohol involvement to rideshare trip density while adjusting for matching and relevant time-varying covariates (taxi trips, precipitation, temperature, holidays). Mean rideshare trip density was 69.0 per square mile (SD = 129.7) at the time and location of alcohol-involved crashes and 105.7 per square mile (SD = 192.6) at the time and location of non-alcohol-involved crashes. After controlling for covariates, the conditional logistic regression model identified that a standard deviation increase in rideshare trips per square mile at the crash location was associated with 23% decreased odds that the crash location was alcohol involved (odds ratio = 0.771; 95% confidence interval [0.594, 0.878]). Ridesharing may replace motor vehicle trips by alcohol-impaired drivers.

Alcohol-impaired motorcyclists versus car drivers: A comparison of crash involvement and legal consequence from adjudication data

Introduction: Driving under the influence (DUI) increases the probability of motor-vehicle collisions, especially for motorcycles with less protections. This study aimed to identify commonalities and differences between criminally DUI offenses (i.e., with a blood alcohol concentration (BAC) of 80 mg/dL or higher) committed by motorcyclists and car drivers. Methods: A total of 10,457 motorcycle DUIs and 8,402 car DUIs were compared using a series of logistic regression models, using data extracted from the documents of adjudication decisions by the courts of Jiangsu, China. Results: The results revealed that offenders from the high-BAC group (i.e., 200 mg/dL or higher) accounted for more than 20% of the total DUI offenses, and were more likely to be involved in a crash and punished with a longer detention. Motorcyclists had a higher likelihood of crash involvement, and were also more likely to be responsible for single-vehicle crashes associated with higher odds of injury sustained, compared to alcohol-impaired car drivers. In the verdict, motorcycle offenders were more likely to receive a less severe penalty. Conclusions: Interventions are clearly required to focus on reducing in the high-BAC group of offenders. For alcohol-impaired motorcyclists, their risks of crash and injury against BAC climb more steeply than the risks for car drivers. The factors including frequent occurrences, uncertainty of detection, and short-term sentences may weaken the deterrence effect of the criminalization of motorcycle DUI. Practical Applications: The traffic-related adjudication data support traffic safety analysis. Strategies such as combating motorcycle violations (e.g., unlicensed operators or driving unsafe vehicles), undertaking education and awareness campaigns, are expected for DUI prevention.

State alcohol ignition interlock laws and fatal crashes

Objective Alcohol-impaired driving results in thousands of deaths annually. Alcohol ignition interlocks require a negative breath test to start a vehicle’s engine, and 44 states have mandated some form of interlock law for drivers convicted of driving while intoxicated (DWI). The objective of this study was to estimate the association between interlock laws and fatal impaired-driving crashes. Methods Differences in three interlock laws were evaluated by comparing alcohol-impaired passenger vehicle drivers involved in fatal crashes between 2001 and 2019 in the United States across state and time. State/time differences unrelated to interlock laws were controlled for by fitting a Poisson model. The exposure measure was the number of passenger vehicle drivers in fatal crashes that did not involve impaired drivers. Laws requiring interlocks for drivers convicted of DWI covered: repeat offenders, repeat offenders and high-BAC offenders, all offenders, or none. Results The number of states with all-offender interlock laws during the study period went from three in 2001 to 29 in 2019, and the number of states with any of the three laws increased from 16 to 44. All-offender laws were associated with 26% fewer drivers with 0.08+ BAC involved in fatal crashes, compared with no law. Repeat-offender laws were associated with a 9% reduction in impaired drivers, compared with no law. Repeat and high-BAC laws were associated with a 20% reduction in impaired drivers in fatal crashes, compared with no law. Conclusion Laws mandating alcohol ignition interlocks, especially those covering all offenders, are an effective impaired-driving countermeasure that reduces the number of impaired drivers in fatal crashes.

Alcohol-related traffic laws and drunk-driving fatal accidents

In the United States, about 28 lives are lost daily in motor vehicle accidents that involve an alcohol-impaired driver. While most states have enacted various traffic laws to address this phenomenon, little consensus exists on the causal impact of these laws in reducing alcohol-induced fatalities. This paper exploits quasi-random variation in state-level laws to estimate the causal effect of alcohol-related traffic laws on the frequency of fatal accidents. This is identified from the discontinuities in traffic laws among contiguous counties that are separated by a shared state border. We present robust evidence that the conventional approaches that are typically utilized in the literature may erroneously estimate the effectiveness of several alcohol-related laws.

The effects of false identification laws on underage alcohol-related traffic fatalities.

We examine the effects of policies aimed at restricting the use of false identification to purchase alcohol on traffic fatalities involving alcohol-impaired underage drivers. We find that the implementation of policies that incentivize alcohol retailers to adopt ID scanners reduces traffic fatalities from accidents involving 16-18 year old drivers with a BAC >0, but we do not find that similar policies like vertical ID laws lead to statistically significant changes in traffic fatalities involving underage impaired drivers. A back-of-the-envelope calculation suggests that if all remaining states passed ID scanner laws, the reduction in underage alcohol-related fatal accidents would generate over $400 million in annual economic benefits.