OBJECTIVE: To examine the relationship between schizophrenia, antipsychotic medication adherence and driver responsibility for motor vehicle crash.
METHODS: Retrospective observational cohort study using 20 years of population-based administrative health and driving data.
METHODS: British Columbia, Canada.
METHODS: Licensed drivers who were involved in a police-attended motor vehicle crash in British Columbia over a 17-year study interval (2000-16).
METHODS: Incident schizophrenia was identified using hospitalisation and physician services data. Antipsychotic adherence was estimated using prescription fill data to calculate the \'medication possession ratio\' (MPR) in the 30 days prior to crash.
METHODS: We deemed drivers \'responsible\' or \'non-responsible\' for their crash by applying a validated scoring tool to police-reported crash data. We used logistic regression to evaluate the association between crash responsibility and exposures of interest.
RESULTS: Our cohort included 808 432 drivers involved in a police-attended crash and for whom crash responsibility could be established. In total, 1689 of the 2551 drivers with schizophrenia and 432 430 of the 805 881 drivers without schizophrenia were deemed responsible for their crash, corresponding to a significant association between schizophrenia and crash responsibility (66.2% vs 53.7%; adjusted OR (aOR), 1.67; 95% CI, 1.53 to 1.82; p<0.001). The magnitude of this association was modest relative to established crash risk factors (eg, learner license, age ≥65 years, impairment at time of crash). Among the 1833 drivers with schizophrenia, near-optimal antipsychotic adherence (MPR ≥0.8) in the 30 days prior to crash was not associated with lower crash responsibility (aOR, 1.04; 95% CI, 0.83 to 1.30; p=0.55).
CONCLUSIONS: Crash-involved drivers with schizophrenia are more likely to be responsible for their crash, but the magnitude of risk is similar to socially acceptable risk factors such as older age or possession of a learner license. Contemporary driving restrictions for individuals with schizophrenia appear to adequately mitigate road risks, suggesting more stringent driving restrictions are not warranted.

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.

Drugged driving, the act of driving a vehicle under the influence of illicit drugs, by adolescents is a serious public health concern. Many factors contribute to this risk behavior, but much less is known regarding the role of parenting behaviors in this phenomenon. The purpose of this study was to examine specific parenting behaviors and their influence among a nationally representative sample of adolescents. Pooled data from the 2016-2019 National Survey on Drug Use and Health (NSDUH) among 17,520 adolescents ages 16-17 years old were analyzed. Differences were found in specific parenting behaviors and adolescent drugged/drunk driving, with parents not checking homework and not telling their children they are proud of them being the most influential. Findings from the present study may inform drugged driving prevention programs for parents and adolescents and enhance road safety interventions.

BACKGROUND: Germany is reforming its legal approach to cannabis, allowing the possession and cultivation of cannabis for recreational purposes. The objective of this study is to investigate the impact of the policy reform on (1) The prevalence of cannabis use in the general population and (2) Driving under the influence of cannabis (DUIC) among regular users.
METHODS: A quasi-experimental research design will be employed, with repeated cross-sectional surveys on self-reported DUIC and cannabis use conducted at three measurement points in Germany (intervention group) and Austria (control group) over a 2-year observation period (2023-2025). Data will be collected from approximately 50 000 individuals aged between 18 years and 64 years. To minimise reporting biases in the measurement of DUIC, we will use direct and indirect assessments via crosswise model and motor vehicle accident data from official statistics. In a difference-in-difference framework, regression analyses and interrupted time series analysis will be carried out for hypothesis testing.
BACKGROUND: Participants will be informed about voluntary participation, data protection laws and the option to delete data on request. Ethical approval was obtained from the Local Psychological Ethics Committee of the Centre for Psychosocial Medicine in Hamburg, Germany (reference number: 0686). Findings will be disseminated through scientific networks and will be key for a comprehensive evaluation of the cannabis law reform. The findings will facilitate the design and implementation of road safety measures.

Drink driving is an infamous factor in road crashes and fatalities. Alcohol testing is a major countermeasure, and random breath tests (RBTs) deter tested drivers and passersby (observers who are not tested). We propose a genetic algorithm (GA)-based RBT scheduling optimisation method to achieve maximal deterrence of drink driving. The RBT schedule denotes the daily plan of where, when, and for how long tests should occur in the road network. The test results (positive and negative) and observing drivers are considered in the fitness function. The limited testing resource capacity is modeled by a number of constraints that consider the total duration of tests, the minimum and maximum duration of a single test site, and the total number of test sites during the day. Clustering of the alcohol-related crash data is used to estimate the matrix for drink driving on the scheduled day. The crash data and traffic flow data from Victoria, Australia are analysed and used to describe sober/drink driving. A detailed synthetic example is developed and a significant improvement with 150% more positive results and 59% more overall tests is observed using the proposed scheduling optimisation method.

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The general use of cocaine is increasing in recent years, while the trend for 3,4-methylenedioxymethamphetamine (MDMA) is less clear. The relationship between blood concentrations and impairment is poorly understood, which complicates interpretation. The aims of this study were to report prevalence and blood concentrations of cocaine and MDMA in drugged drivers, and to investigate the relationship between blood concentrations and impairment. Samples of whole blood were collected from apprehended drivers in the period 2000-2022, and a clinical test of impairment (CTI) was simultaneously performed. The samples were initially analyzed for cocaine and MDMA using gas chromatography-mass spectrometry (until 2009 and 2012, respectively), and later using ultra-high-performance liquid chromatography-tandem mass spectrometry. Overall, cocaine was detected in 2,331 cases and MDMA in 2,569 cases. There were 377 and 85 mono cases of cocaine and MDMA, respectively. In the mono cases, the median cocaine concentration was 0.09 mg/L (range: 0.02-1.15 mg/L), and 54% of the drivers were clinically impaired. The median MDMA concentration was 0.19 mg/L (range: 0.04-1.36 mg/L), and 38% were clinically impaired. There was a statistically significant difference in the median cocaine concentration between drivers assessed as not impaired (0.07 mg/L) and drivers assessed as impaired (0.10 mg/L) (P = 0.009). There was also a significant effect of the blood concentration of cocaine (adjusted odds ratio [aOR] = 6.42, 95% confidence interval [CI] = 1.13-36.53, P = 0.036) and driving during the evening/night-time (aOR = 2.17, 95% CI = 1.34-3.51, P = 0.002) on the probability of being assessed as impaired on the CTI. No significant differences were found for MDMA. Many drivers are not assessed as impaired on a CTI following cocaine or especially MDMA use. For cocaine, a relationship between blood concentrations and impairment was demonstrated, but this could not be shown for MDMA.

Alcohol acts as a central nervous system depressant and falls under the category of psychoactive drugs. It has the potential to impair vital bodily functions, including cognitive alertness, muscle coordination, and induce fatigue. Taking the wheel after consuming alcohol can lead to delayed responses in emergency situations and increases the likelihood of collisions with obstacles or suddenly appearing objects. Statistically, drivers under the influence of alcohol are seven times more likely to cause accidents compared to sober individuals. Various techniques and methods for alcohol measurement have been developed. The widely used breathalyzer, which requires direct contact with the mouth, raises concerns about hygiene. Methods like chromatography require skilled examiners, while semiconductor sensors exhibit instability in sensitivity over measurement time and has a short lifespan, posing structural challenges. Non-dispersive infrared analyzers face structural limitations, and in-vehicle air detection methods are susceptible to external influences, necessitating periodic calibration. Despite existing research and technologies, there remain several limitations, including sensitivity to external factors such as temperature, humidity, hygiene consideration, and the requirement for periodic calibration. Hence, there is a demand for a novel technology that can address these shortcomings. This study delved into the near-infrared wavelength range to investigate optimal wavelengths for non-invasively measuring blood alcohol concentration. Furthermore, we conducted an analysis of the optical characteristics of biological substances, integrated these data into a mathematical model, and demonstrated that alcohol concentration can be accurately sensed using the first-order modeling equation at the optimal wavelength. The goal is to minimize user infection and hygiene issues through a non-destructive and non-invasive method, while applying a compact spectrometer sensor suitable for button-type ignition devices in vehicles. Anticipated applications of this study encompass diverse industrial sectors, including the development of non-invasive ignition button-based alcohol prevention systems, surgeon\'s alcohol consumption status in the operating room, screening heavy equipment operators for alcohol use, and detecting alcohol use in close proximity to hazardous machinery within factories.

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.

OBJECTIVE: Little is known about the consequences of substance-related advertising on driving under the influence of alcohol (DUIA) or cannabis (DUIC). This study seeks to estimate associations between substance-related digital advertising exposures and DUIA and DUIC in a sample of young adults.
METHODS: The Rhode Island Young Adult Survey 2022 was a cross-sectional web-based survey of 1,022 adults ages 18-25 years. Analyses were conducted among lifetime alcohol users (n = 824) and lifetime cannabis users (n = 471). Prevalence of past-30-day digital ad exposure to alcohol and cannabis was assessed, as well as the prevalence of past-month DUIA and DUIC. Multivariable logistic regressions examined the association between digital ad exposure and substance use among lifetime users, and ad exposure and DUI among current users.
RESULTS: In all, 54.4% of participants reported past-30-day exposure to digital alcohol advertising, and 33.5% reported exposure to digital cannabis advertising. Exposure to social media alcohol advertising was associated with higher odds of DUIA after adjustment for sociodemographic variables (odds ratio [95% CI] = 2.14 [1.07, 4.28]) but not after accounting for frequency of use. Odds of DUIC remained significantly higher for those exposed to app-based cannabis advertising after adjusting for sociodemographic variables (odds ratio [95% CI] = 4.36 [1.76, 10.8]) and frequency of use (odds ratio [95% CI] = 3.96 [1.53, 10.2]).
CONCLUSIONS: Since social media alcohol ad exposure may be indirectly linked with DUIA, and app-based cannabis ad exposure may be directly linked to frequency of use and DUIC, legislators should consider comprehensive substance-related ad regulations and bans.