Driving under the influence of drugs

A retrospective study of traffic offenses associated with driving under the influence of drugs in China

OBJECTIVE

This study aimed to investigate the prevalence and pattern of traffic offenses associated with driving under the influence of drugs (DUID) in 31 provinces of mainland China.

METHODS

Traffic offenses were extracted from the nationally representative court file disclosure website. Drivers convicted of traffic offenses associated with DUID were included. The monthly percentage change (MPC) in rates was estimated by fitting negative binomial regression models to assess the time trends. Moran's I was used to determine the correlations of DUID-related rates among surrounding provinces.

RESULTS

In total, 896 individuals convicted of DUID offenses were identified (April 2007 to March 2022). Of these, 769 individuals were involved in traffic crashes, and they killed 629 people. Regarding the types of drugs, 638 used amphetamine-type stimulants (ATS; methamphetamine, 3,4-methylenedioxymethamphetamine, amphetamine, and tenamfetamine), 104 used ketamine, 104 used opioids (heroin, morphine, methadone, codeine, and tramadol), 20 used caffeine, 4 used tetrahydrocannabinol, and 1 used gamma-hydroxybutyric acid. The offense trends among overall drug users (MPC, -1.1; p < .001) declined from 2014 to 2020. The trends also decreased for DUID-related traffic crashes (-1.5; p < .001) and deaths (-1.7; p < .001) during the same period. Except in three provinces, ATS-related offense rates were higher than opioid-related offense rates. Ketamine-related offense rates showed a significant positive correlation among the surrounding provinces (Moran's I, 0.22; p < .01), and South China had the highest ketamine-related offense rates.

CONCLUSIONS

In the sample, the case fatality rate was 70.7%, which was a strong indicator of underreporting, and most minor DUID-related offenses could not be captured by Chinese court files. The frequency of drug screening tests for people should be increased. The traffic police in Qinghai, Tibet, and Xinjiang should always be equipped with fast screening test kits for drugs, which could increase the possibility of identifying DUID offenders.

Overview of the bioanalytical methods used for the determination of benzodiazepines in biological samples and their suitability for emergency toxicological analysis

Benzodiazepines are one of the most widely used classes of drugs around the world. They are medically used in different therapeutic areas including insomnia, anxiety, epilepsy, and anesthesia. Unfortunately, these drugs are very widespread in the illicit market for recreational purposes and cause drug dependence, traffic accidents, and criminality. Furthermore, benzodiazepine misuse leads to acute poisoning cases that often end up in hospital emergency rooms. Therefore, it is crucial for hospitals to possess straightforward and efficient bioanalytical techniques that enable the swift detection of benzodiazepines in biological samples. This review provides a general overview of the different bioanalytical techniques used for the detection and quantification of benzodiazepines in biological samples and emphasizes their suitability for emergency toxicological analyzes.

Toward a typology of driving under the influence of alcohol and drugs

PURPOSE

Most research on driving under the influence (DUI) has relied upon variable-centered methods that examine predictors/correlates of DUI. In the present study, we utilize a person-level approach-latent class analysis (LCA)-to model a typology of individuals reporting DUI. This allows us to understand the degree to which individuals drive under the influence of a particular substance or do so across multiple substance types.

METHODS

We use public-use data collected between 2016 and 2019 from the National Survey on Drug Use and Health. The analytic sample was 189,472 participants with a focus on those reporting DUI of psychoactive substances in the past-year (n = 24,619). LCA was conducted using self-reported DUI of past-year alcohol, cannabis, cocaine, heroin, hallucinogens, and methamphetamine as indicator variables.

RESULTS

More than 1 in 10 Americans reported a DUI within the past-year. One in five people who reported DUI of one substance also reported DUI of at least one additional substance. Using LCA to model heterogeneity among individuals reporting DUI, four classes emerged: "Alcohol Only" (55%), "Cannabis and Alcohol" (36%), "Polydrug" (5%), and "Methamphetamine" (3%). Rates of risk propensity, drug involvement, illicit drug use disorders, and criminal justice system involvement were highest among members of the "Polydrug" and "Methamphetamine" classes.

CONCLUSION

Drug treatment centers should take care to include discussions of the dangers and decision-making processes related to DUI of the full spectrum of illicit substances. Greater investment in drug treatment across the service continuum, including the justice system, could prevent/reduce future DUI episodes.

Categorization and labeling systems concerning driving-impairing medicines: A scoping review

OBJECTIVE

Classification systems concerning driving-impairing medicines can help healthcare providers identify medicinal drugs with no or the least impairing effects and inform patients of the potential risks of certain medicines to safe driving. This study aimed to comprehensively assess the characteristics of classifications and labeling systems regarding driving-impairing medicines.

METHODS

Google Scholar and several databases, including PubMed, Scopus, Web of Science, EMBASE, safetylit.org, and TRID were searched to identify the relevant published material. The retrieved material was assessed for eligibility. Data extraction was done to compare the categorization/labeling systems concerning driving-impairing medicines in terms of characteristics such as the number of categories, description of each category, and description of pictograms.

RESULTS

After screening 5,852 records, 20 studies were selected for inclusion in the review. This review identified 22 categorization/labeling systems regarding medicines and driving. Classification systems had different characteristics, but most of them were designed based on the graded categorization system described by Wolschrijn. Initially, categorization systems had seven levels, but later the medicine impacts were summarized into 3 or 4 levels.

CONCLUSIONS

Although different categorization/labeling systems regarding driving-impairing medicines are available, the most effective systems in changing driver's behavior are the simple and understandable ones. Besides, health care providers should consider patient's socio-demographic features when informing them about driving under the influence.

A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning

According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor vehicle accidents. Preventing highly intoxicated persons from driving could potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from six alcohol sensors (MQ-3 alcohol sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system, scoring a 99.8% detection accuracy with a very short inferencing delay of 2.22 μs. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at the massive deployment of alcohol-sensing systems that could potentially save thousands of lives annually.

Evaluation of RapidSTAT®, DrugWipe® 6S, DrugScreen® 5TK and DrugScreen® 7TR for on-site drug testing in German police roadside traffic patrol

Driving under the influence of drugs (DUID) remains a subject of concern worldwide, and its increasing trend is likely to continue. Therefore, there is a constant need for reliable on-site drug tests to identify drugged drivers during roadside patrols. Performance and reliability of four on-site drug tests were evaluated among a high number of DUID cases in Germany. Results of oral fluid (OF) (RapidSTAT®, DrugWipe® 6S) and urine (DrugScreen® 5TK and 7TR) test devices were compared with corresponding serum/plasma results obtained by confirmation analyses in consideration of recommended analytical limits for substances pertaining the annex of the German Road Traffic Code ('Straßenverkehrsgesetz', StVG) s. 24a (2). Overall, the screening devices performed well for individual drugs; however, none of the test devices assessed in this study fulfilled the ROSITA-1 criteria (sensitivity, specificity ≥ 90%, and accuracy ≥ 95%) for all substances. Our data demonstrated that both urine tests showed high sensitivities for most compounds. DrugWipe® 6S (94%) and RapidSTAT® (93%) revealed high sensitivities, especially for amphetamine screening. Poor specificities (< 90%) and accuracies (< 95%) were observed for all tests except for low-prevalent substances (e.g. opiates). For drug testing in OF, Δ⁹ -tetrahydrocannabinol (THC) still seems to be a compound of concern due to poor sensitivity (RapidSTAT®, 77%; DrugWipe® 6S, 85%), although the results indicate improvements compared to previously reported data. Although the obtained data indicate reliable detection for some substances, deployment of trained police officers is inevitable to identify DUID suspects by signs of recent use and recognizing impairment.

Effects of synthetic cannabinoids on psychomotor, sensory and cognitive functions relevant for safe driving

Recreational use of Synthetic Cannabinoids (SCs), one of the largest groups of New Psychoactive Substances (NPS), has increased globally over the past few years. Driving is a structured process requiring the cooperation of several cognitive and psychomotor functions, organized in different levels of complexity. Each of these functions can be affected when Driving Under the Influence (DUI) of SCs. In order to reduce the likelihood of SC-related road accidents, it is essential to understand which areas of psychomotor performance are most affected by these substances, as well as the severity of impairment. For this purpose, a multiple database- literature review of recent experimental studies in humans and animals regarding the psychomotor effects of SCs has been performed. Despite the many limitations connected to experimental studies on humans, results showed a consistency between animal and human data. SCs appear to impair psychomotor performance in humans, affecting different domains related to safe driving even at low doses. Cases of DUI of SC have been repeatedly reported, although the exact prevalence is likely to be underestimated due to current analytical and interpretative issues. For this reason, an accurate physical examination performed by trained and experienced personnel has a primary role in recognizing signs of impairment in case of strong suspicion of SC consumption. The identification of a suspected case should be followed by reliable laboratory examination.

Survey and Synthesis of State of the Art in Driver Monitoring

Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

Road traffic injuries and substance use among emergency department patients in the Dominican Republic and Peru

Objective.

To report demographic and substance use characteristics and risk of road traffic injury (RTI) from alcohol use, cannabis use, and combined use in a sample of emergency department patients from two countries in Latin America and the Caribbean.

Methods.

A cross-sectional study in which patients 18 years and older admitted within six hours of suffering an RTI to one emergency department in Santa Domingo, Dominican Republic (n = 501) and in Lima, Peru (n = 431) were interviewed. Case-crossover analysis, based on self-reported use prior to the RTI, was used to analyze risk from alcohol, cannabis, and co-use.

Results.

Overall, 15.3% reported alcohol use prior to the event and 2.5% cannabis use. Drivers using alcohol only were over twice as likely to have an RTI (OR = 2.46, p < 0.001), and nearly eight times more likely if using both alcohol and cannabis (OR = 6.89, p < 0.01), but risk was not elevated for cannabis alone. Significant differences were not found for passengers or pedestrians.

Conclusions.

Risk of RTI for drivers in these two samples is significantly elevated from alcohol use, and more so for co-use with cannabis. Differences between the two countries underscore the need for similar data from the region to determine risk of RTI from substance use, including risk for passengers and pedestrians. Data suggest that alcohol contributes significantly to the burden of RTI, which calls for more stringent enforcement of alcohol control policy related to drink driving in the region.

Driving under the influence of drugs or alcohol: Predicting the intentions of young drivers

Objectives: Driving under the influence (DUI) of drugs or alcohol impairs driving performance and, as a result, increases the risk of crashes. The risk of DUI is five-fold higher for young drivers (aged 18-25 years), but little is known about what determines their DUI intentions. This study applied an extended model of the Theory of Planned Behavior (TPB) to address the research question of what factors might influence young drivers' future intentions to DUI. Methods: This study used a survey obtaining data from 329 young drivers (Mage = 20.92 years, SD = 2.16) in Australia. Beyond the standard TPB measures of attitudes, subjective norms and perceived behavioral control (PBC), the current study included demographic variables and additional predictors (i.e., moral norm, peers' norm, perceived risk, impulsivity and past DUI behavior). Results: A vast majority of the participants (85.1%) selected the maximum (9, never), meaning that they had no intention to DUI in the future. Overall, a stepwise multilevel logistic regression analysis (Step 1: demographics, Step 2: TPB measures, and Step 3: additional predictors) explained between 30.1% and 52.9% of the variance in DUI intentions. It showed past DUI behavior as the strongest predictor of DUI intention, followed by instrumental attitude and descriptive norms. Conclusions: This study explored an extended TPB model to understand young drivers' DUI intentions better. With this new knowledge of understanding the factors that influence DUI, researchers and practitioners can create interventions and strategies that are better tailored to the population of young drivers at higher risk.

Development and validation of quantitative analytical method for 50 drugs of antidepressants, benzodiazepines and opioids in oral fluid samples by liquid chromatography–tandem mass spectrometry

Purpose

We developed and validated a method for quantitative analysis of 50 psychoactive substances and metabolites (antidepressants, benzodiazepines and opioids) in oral fluid samples using simple liquid–liquid extraction procedure followed by liquid chromatography–tandem mass spectrometry (LC–MS/MS).

Method

Oral fluid samples were collected using Quantisal™ device and extracted by liquid–liquid extraction with 1.0 mL of methyl tert-butyl ether and then analyzed using LC–MS/MS.

Results

The method attended method validation criteria, with limits of quantification as low as 0.5 and 1.0 ng/mL, and linearity between 0.5–50.0 ng/mL for antidepressants, 0.5–25.0 ng/mL for benzodiazepines and 1.0–50.0 ng/mL to opioids. During method validation, bias and imprecision values were not greater than 16 and 20%, respectively. Ionization suppression/enhancement bias results were not greater than 25%. No evidence of carryover was observed. Sample stability studies showed that almost all analytes were stable at 25 °C for 3 days and at 4 °C for 7 days. Freeze–thaw cycles stability showed that most antidepressants and opioids were stable under these conditions. Autosampler stability study showed that all analytes were stable for 24 h, except for nitrazepam and 7-aminoclonazepam. Thirty-eight authentic oral fluid samples were analyzed; 36.8% of the samples were positive for 2 drugs. Citalopram was the most common drug found, followed by venlafaxine.

Conclusions

The method was validated according to international recommendations for the 50 analytes, showing low limits of quantification, good imprecision and bias values, using simple liquid–liquid extraction, and was successfully applied to authentic oral fluid samples analysis.

Simultaneous detection of salivary Δ9-tetrahydrocannabinol and alcohol using a Wearable Electrochemical Ring Sensor

Driving under the influence of cannabis and alcohol represents a major safety concern due to the synergistic or additive effect of these substances of abuse. Hence, rapid road-site testing of these substances is highly desired to reduce risks of fatal accidents. Here we describe a wearable electrochemical sensing device for the simultaneous direct, decentralized, detection of salivary THC and alcohol. The new ring-based sensing platform contains a voltammetric THC sensor and an amperometric alcohol biosensor on the ring cap, along with the wireless electronics embedded within the ring case. Rapid replacement of the disposable sensing-electrode ring cap following each saliva assay is accomplished by aligning spring-loaded pins, mounted on the electronic board (PCB), with the current collectors of the sensing electrodes. The printed dual-analyte sensor ring cover is based on a MWCNT/carbon electrode for the THC detection along with a Prussian-blue transducer, coated with alcohol oxidase/chitosan reagent layer, for the biosensing of alcohol. THC and alcohol can thus be detected simultaneously in the same diluted saliva sample within 3 min, with no cross talk and no interferences from the saliva matrix. The new wearable ring sensor platform should enable law enforcement personnel to screen drivers in a single traffic stop and offers considerable promise for addressing growing concerns of drug-impaired driving.