Collision Involvement Research Articles

Driving impairment due to psychoactive substances and attention deficit disorder: A pilot study

ObjectiveAttention disorder and substance use disorder are linked to driving impairment and increased road crash involvement. This study explores attention deficits in a population of drivers found driving under the influence (DUI) of psychoactive substances. MethodsA case-control study was conducted comparing subjects with a previous DUI episode (cases) to subjects who were negative for DUI offenses (controls). Personal, socio-demographic, and DUI data were collected for both groups. All subjects were administered the Continuous Performance Test–third edition (CPT-3), which measures dimensions of attention, including inattentiveness, impulsivity, sustained attention, and vigilance. Possible associations with a previous DUI episode, the use of illicit substances or excessive alcohol use, and road crash involvement were analyzed statistically. ResultsOverall, the study included 147 subjects (100 cases, 47 controls). The parameter distributions of detectability, probability of ADHD, and inattentiveness indicated statistical differences between the two groups. No attention deficits predicted substance use disorder or excessive alcohol consumption. Inattentiveness was an independent risk factor for previous road collision involvement. ConclusionsThe results suggest that alterations exist in some attention dimensions in a population of DUI subjects who were users of alcohol or other psychoactive substances and involved in road traffic crashes. The CPT-3 had successfully distinguished between the two study groups, and after validation, it could be useful in the process of reinstating a driver's license. Future research should expand the study sample to better understand the relevance of the proposed methodological approach in terms of prevention, rehabilitation, and the monitoring of subjects evaluated for driving eligibility requirements.

The expression of anger while driving – The role of personality and self-consciousness in a sample of Chinese drivers

Driving anger among Chinese drivers is common leading to aggressive and risky driving behaviours and potentially increasing involvement in road collisions. This study adopted an online survey to explore the relationship between personality, self-consciousness and driving anger expression. 559 participants completed a questionnaire consisting of the Driving Anger Scale (14-item DAS), the short version of the Driving Anger Expression Inventory (15 item DAX), the Brief HEXACO Inventory (BHI), and the Self-Consciousness Scale (SCS). A Confirmatory Factor Analysis yielded a reliable and valid three-factor structure of the Chinese 15 item DAX, labelled as “Adaptive Expression”, “Verbal Expression” and “Physical and Vehicle Expression”. Physical and Vehicle expression of anger was reported more by males and by experienced drivers compared to females and novice drivers. Traffic offenders showed more inclination towards exhibiting verbal anger expression than non-traffic offenders. In terms of dispositional traits, Humility-Honesty had a negative effect on both verbal expression and physical and vehicle expression. However, private self-consciousness was related to an increase in verbal expression and physical and vehicle expression. Importantly, Humility-Honesty and private self-consciousness moderated the relationship between trait driving anger and non-adaptive anger expressions in opposite ways. The findings could provide some support for the development of strategies to mitigate driving anger in China.

Does age matter? Examining age-dependent differences in at-fault collisions after attending a refresher course for older drivers

The “Driver 65+” course is a voluntary refresher course offered to all drivers aged 65 years or older in Norway. The current study estimated differences in at-fault motor vehicle collisions (MVCs) between older drivers who had attended in the course and older drivers who had not attended the course. MethodsTwo samples of drivers were selected from the database of an insurance company and were sent a questionnaire in the mail. The first sample consisted of 2039 car owners aged 70 years or older who had reported a collision to the insurance company during the last 24 months. The second sample consisted of 1569 drivers aged 70 or older who had not reported any collisions during the last 24 months. ResultsThe results indicated an age-dependent effect; drivers attending the course before 75 years of age had a significantly lower risk of being the at-fault driver in a multi-MVC than older drivers who did not attend the course. ConclusionThe results indicate that the refresher course had a beneficial effect on collision risk for drivers who attended the course before reaching 75 years of age. One possible explanation of this age-dependent effect is that a certain level of visual, cognitive, and motor functioning is needed to implement the strategies learned in the course. However, the design of the study makes it difficult to draw definite conclusions about the causal relationship between course attendance and later collision involvement.

Vision Screening for Motor Vehicle Collision Involvement among Older Drivers

To evaluate the epidemiologic association between visual acuity and other measures of visual function and motor vehicle collisions (MVCs) as well as their performance as screening tests for MVCs. Prospective cohort study. A total of 2000 licensed drivers aged 70 years and older who resided in the environs of Jefferson County, Alabama. Visual acuity, contrast sensitivity, useful field of view, Motor-free Visual Perception Test, and visual field sensitivity were measured at a baseline visit. Study participants were followed for up to 4 years for the occurrence of MVC involvement. Area under the curve (AUC), sensitivity, and specificity were calculated to determine the screening performance of each visual function measure with respect to MVC occurrence. Poisson regression was used to estimate rate ratios (RRs) for the association between each visual function measure and MVC occurrence. Police-reported MVCs. For all visual function measures, the AUC values were only slightly higher than 0.50; additionally, none of the measures exhibited adequate values for both sensitivity and specificity (i.e., > 80%). For all visual function measures except visual acuity, there were statistically significant positive RRs for the association between vision impairment and MVC occurrence, although the magnitude of the associations was weak (i.e., < 2.0). The negative impact of involuntary driving cessation on mobility and the associated mental health implications likely outweigh the safety benefit of vision screening. Alternative approaches to improving older driver safety should be considered.

Intense Infrared Laser-Induced Radiation–Collision Involvement of Molecules That Do Not Absorb Laser Radiation in Resonance with a Laser Field in a Two-Component Molecular Medium

Intense Infrared Laser-Induced Radiation–Collision Involvement of Molecules That Do Not Absorb Laser Radiation in Resonance with a Laser Field in a Two-Component Molecular Medium

To what extent do our walking and cycling behaviours relate to each other, and do we cycle as well as we think we do? Piloting the walking and cycling behaviour questionnaires in the UK

Greater uptake of active transport has been argued as necessary for the transport system to achieve relevant sustainability and public health goals; however, the research tools used to investigate behaviour when using these modes are far less well-developed than those used to investigate driving behaviour. This study takes two self-report behavioural measures, the Walking Behaviour Questionnaire (WBQ) and the Cycling Behaviour Questionnaire (WBQ), and pilots them in the UK. Exploratory and confirmatory factor analyses with data from 428 respondents revealed factor structures different to those described in the limited number of previous studies that used the CBQ and WBQ. Across both questionnaires, scales measuring intentional behaviour differed from original descriptions to a greater extent than did the scale concerning unintentional attention or memory errors. In addition to a validation exercise, this research explored the relationships between variables, finding a correlation between the reported performance of unintentional errors when walking and cycling. Looking in more detail at cycling behaviours, we found that those who rated themselves as more proficient cyclists also reported performing fewer unintentional cycling errors. Results also showed self-reported helmet use to bear little to no relationship with other self-reported cycling behaviours or self-rated cycling proficiency. Finally, using structural equation modelling, we demonstrated that responses to the CBQ add very little (over and above age, gender, and exposure to the road environment) to the explanation of self-reported past collision involvement. In total, only 7% of the variation in past collision involvement was explained by the included variables. We urge caution when using self-report behavioural measures that have not been validated in the context of intended use, and the importance of using such measures in combination with other approaches rather than in isolation when trying to develop an understanding of overall system performance.

Use of cannabis and/or prescription opioids among adult drivers in Ontario, Canada: Prevalence and association with motor vehicle collisions

ObjectiveIn response to the opioid crisis, health practitioners and patients are seeking effective non-opioid analgesic alternatives for treatment of non-cancer pain. Cannabis is being considered as an alternative to opioids for this purpose. This shift may result in an increase in combined use of these drugs, which may have important health implications, including those related to road safety. This study was designed to provide a population-based estimate of the prevalence of cannabis and/or prescription opioid use and their impact on collision risk. MethodBased on data from a regionally stratified cross-sectional general population telephone survey of adults (18+ years) in Ontario, Canada conducted from 2010 through 2018 (N = 13,552), a binary logistic regression was conducted of self-reported past-year collision involvement, assessing demographic characteristics, year of interview, driving exposure, hazardous drinking, perceived general and mental health, and past-year use of cannabis and/or prescription opioids. ResultsApproximately 29.5 percent of respondents reported using either cannabis (11.1 percent) or prescription opioids (18.4 percent), whereas 3.8 percent reported using both cannabis and prescription opioids. Adjusting for potential covariates, relative to those who reported using either cannabis or prescription opioids, drivers who reported being users of both drugs faced more than a two-fold increase in their odds of past-year collision involvement (AdjOR = 2.29, 95% CI = 1.46–3.58, p < 0.001). ConclusionsThe identified increase in the odds of a crash among drivers who report use of both cannabis and of prescription opioids has important implications. Further research examining the impact of combined use on driver skills and behaviours is needed.

Reducing optimism bias in the driver’s seat: Comparing two interventions

Optimism bias combined with sensation-seeking and risky driving have been proposed to be the main contributing factors to young drivers’ involvement in road traffic collisions. The present study aimed to evaluate how two brief interventions, one based on an unambiguous definition of “good” driving and the other on a hazard perception test, might reduce young drivers’ optimism bias. One hundred and twenty-eight university students were randomly allocated to one of three groups: standard definition, hazard perception or control. Measures evaluating optimism bias were completed before and after the intervention, and questions regarding their sensation-seeking and past risk-taking tendencies were asked at follow-up. Both brief interventions reduced optimism bias levels, but hazard perception had the strongest effect. The effectiveness of the two interventions also differed across individuals depending on their sensation-seeking and past risky driving tendencies. The results provide evidence for the effectiveness of brief interventions to reduce optimism bias.

Motion perception as a risk factor for motor vehicle collision involvement in drivers ≥ 70 years

PurposeTo evaluate the relationship between visual function and a five-year history of motor vehicle collision rates in older adults. Motion perception impairment was explored as a risk factor for motor vehicle collisions for the first time in this study. Materials and methodsParticipants were licensed drivers ≥70 years old enrolled in the Alabama VIP Older Driver Study who underwent functional assessments for motion perception, distance visual acuity, contrast sensitivity, visual field sensitivity, and visual processing speed. Participants were recruited based on their being patients in an ophthalmology clinic in the year prior to enrollment or had participated in an earlier driving study. Crash reports were obtained from the Alabama Law Enforcement Agency for the 5 years prior to enrollment and mileage estimated using the Driving Habits Questionnaire. Crude and age-adjusted rate ratios (RRs) and 95 % confidence intervals (95 % CIs) were calculated using Poisson regression. Results159 participants enrolled with a mean age of 79 years. The age-adjusted crash rate was higher among those with worse motion perception (RR: 2.7, 95 % CI: 1.4−5.2), severe slowing in visual processing speed (RR: 3.6, 95 % CI: 1.5−8.5), and impaired peripheral visual field sensitivity (RR: 2.4, 95 % CI: 1.3−4.4). ConclusionsAmong a sample of older drivers, crash rates were higher for those with impaired motion perception, severely slowed visual processing speed, and impaired peripheral visual field sensitivity. The association between motion perception and crash risk in older drivers has not been previously reported. Prospective analysis of the Alabama VIP Older Driver Study will examine these risk factors for future collision involvement based on naturalistic driving data.

Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification

Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psychological disorders can be identified from driving data, and usually analyses utilize simple models and aggregated data. This study aims at utilizing microscopic data from a driving simulator to detect sessions belonging to “depressed” drivers by utilizing powerful machine learning classifiers. Driving simulator sessions from 11 older drivers with symptoms of depression and 65 healthy drivers were utilized towards that aim. Random Forests, an ensemble classifier, with proven efficiency among transportation applications, are then trained on highly disaggregated data describing the mean and standard deviation of speed and lateral or longitudinal acceleration of drivers in the simulator. The kinematic data were aggregated in 30-seconds, 1-minute and 5-minute intervals, but the corresponding time-series of the measurements were also taken into account. Furthermore, classifiers were treated with imbalanced learning techniques to address the scarcity of depressed drivers among the healthy. Time-series of mean speed and the standard deviation of longitudinal acceleration even with a duration of 30-seconds have proven to be the best predictors of driving sessions belonging to depressed drivers with a very low rate of false alarms. The results outperform previous approaches, and indicate that naturalistic driving data or deep learning could prove even more efficient in detecting depression.

Relationship between Overconfidence and Risky Behavior among Ship Crew

Human factors are a primary cause of maritime accidents. This paper explores the relationship between risky behavior and overconfidence among members of a ship crew. Data on confidence and risky behaviors were collected through experiments carried out on a navigation simulator and an associated questionnaire. Contingency table analysis and chi-square test were then performed to clarify the relationships between these variables. The results indicated that both overconfidence and underconfidence were potential causes of risky behaviors among crew members; the type of risky behavior undertaken was also found to be related to both overconfidence and underconfidence. Levels of confidence varied with individual characteristics such as age and sailing experience. The major findings were as follows. Crew members aged 25 years or younger were more likely to be overconfident; crew members with less sailing experience were more likely to be either highly overconfident or underconfident; highly overconfident and underconfident crew members were more prone to involvement in collisions; skill-based risky behaviors were most associated with underconfidence; rule-based risky behaviors were more likely to be exhibited by highly overconfident or underconfident crew members; knowledge-based risky behavior was primarily observed in highly overconfident crew members. This paper fills a current research gap by identifying the individual characteristics that induce risky behaviors, which will be beneficial in enabling the maritime sector to develop targeted interventions to prevent maritime accidents.

Safety equipment use and crash involvement among cyclists – Behavioral adaptation, precaution or learning?

The present study has investigated relationships between cyclists’ safety equipment use, crash involvement, and other safety relevant behavior. The main focus is on relationships that indicate either behavioral adaptation (safety equipment use leads to riskier behavior) or precautionary behavior (safety equipment is used for cycling in risky situations). Three consecutive surveys were conducted in 2015, 2016, and 2017 years among 650 Norwegian cyclists. Most items were dichotomized and analyzed with logistic regression models. In contrast to the behavioral adaptation hypothesis, regular use of safety equipment (bicycle lights, high-visibility clothing, and helmets) was found to be negatively related to some types of high-risk behavior (listening to music and taking chances while cycling). Regular use of bicycle lights and high-visibility clothing is also negatively related to collision involvement. Safety equipment use was found to be positively related to regular winter cycling and cycling in mixed traffic (not on sidewalks), and it is most likely used as a precautionary measure in such situations. Some cyclists learn from crash involvement by starting to use safety equipment after a crash, but the results do not indicate that crash involvement deters from cycling. The main conclusion from the study is that recommending, promoting or even mandating safety equipment for cyclists can be expected to improve safety and that behavioral adaptation is not likely to occur, at least not to an extent that will outweigh the positive safety effects. The results do not support reservations against the use of “sporty” (well-equipped) models in campaigns for promoting cycling.

Differences in severity of injuries between motorcyclist and bicyclist fatalities in single vehicle collisions

To understand the injury severity of bicyclists and motorcyclists in single vehicle collisions, we performed a retrospective analysis of forensic autopsy cases of 25 motorcyclists and 14 bicyclists performed from 1999 to 2018. Collision details, injury characteristics, and involvement of alcohol were examined. The injury severity between bicyclists and motorcyclists was compared. The average age of victims was 62.3 years. All motorcyclists and no bicyclists wore a helmet. Motorcyclists had more severe injuries than bicyclists (mean Injury Severity Score of 42 and 27, respectively). The motorcyclists had more severe chest injuries but fewer severe facial injuries than bicyclists, owing to the difference in collision velocity or rate of helmet use (p < 0.05). Alcohol was present in the blood of 52.0% of bicyclists but no motorcyclists. The mean blood alcohol concentration of these bicyclists was 1.59 mg/mL. The bicyclists under the influence of alcohol had more severe injuries to neck and upper extremities than non-drunken bicyclists (p < 0.05). Our results may be useful for determining the cause of death and reconstructing the mechanisms of fatal injuries in bicyclists and motorcyclists.

Collision Risk Assessment Using Naturalistic Data from a Rent-A-Car Fleet

We analyzed commercial fleet operations data collected by a South Korean rent-a-car company, SK Networks Co. Ltd., to evaluate the differences between collision-free and collision-involved drivers with the ultimate goal of predicting driver collision risk. The first objective was to identify critical variables related to collision risk. The second objective was to build and compare classification models to predict the colli-sion involvement of a driver. Data used in the analysis were collected through Long-Range (LoRa) Internet of Things (IoT) modem-Fleet management system (FMS) devices, a first commercial implementation of LoRa modems in the vehicle. These devices have five main built-in modules, i.e., On-Board Diagnostics (OBD-II) Connector, GPS, LoRa modem, Gravity sensor, and Bluetooth. They can communicate with the vehi-cle, the driver’s smartphone, and the host server. Data from 3,854 drivers with a total of 2.19 million trips recorded in 2018 were explored. Out of these 3,854 drivers, 514 (13.3%) were involved in at least one collision. Predictor variables were selected based on previous research that uti-lized naturalistic data to identify factors affecting collision risk (Dingus et al., 2016; Tselentis, Yannis, &amp; Vlahogianni, 2016; Bian, Yang, Zhao, &amp; Liang, 2018; Jin et al., 2018). Forty-eight predictor variables that may affect collision risk were selected, which can be categorized into two groups: 27 variables were related to the business and the environment, characterized by how much drivers traveled, in what type of vehicles, on what types of roads, and during what times of the day; the other 22 variables were driving behavior-related variables, capturing overspeeding, potential fatigue, rapid speed changes, and counts of traffic regulation violations. After a feature selection phase based on univariate analysis, nine variables were select-ed to be used in the classification models. These selected vari-ables are running driving time (driving time excluding idling time), trip frequency per thousand kilometer driving, accumu-lated count of violation, accumulated amount of fine, the per-centage of trips driving a compact car (&lt;1,000 cc), the per-centage of trips driving older than a 2016 car model, the per-centage of trips during 6 a.m.-9 a.m., the percentage of trips that ended during 2 a.m.-7 a.m., and the sum of rapid accelera-tion and deceleration frequencies per kilometer. A total of twenty classification models were built and compared to classify collision-involved and non-collision in-volved drivers: 5 classification modeling techniques (Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbor (kNN), Support Vector Machine (SVM) and Gradient boosted trees (GBT)) x 4 sampling methods (Up, Down, Smote, and No-sampling). The GBT-down sampled model showed the best classification performance according to Area under the Curve (0.804) and Area under the Precision and Recall Curve (0.406) statistics. Comparing relative variable importance val-ues for the best three classification models (GBT, RF, and LR), both running driving time and violation count were found to be the most influential variables, followed by the sum of rapid acceleration and deceleration frequencies, accumulated amount of fine, trip frequency per thousand kilometer driving, and the percentage of trips driving a compact car. These re-sults agree with the results of previous naturalistic studies: driver behavior-related variables are highly related to collision likelihood, although running driving time in our dataset was likely dictated by businesses. This dataset provided us with a unique opportunity to take an in-depth look at the relationship between collisions and business, environment, and driving behavior-related variables by using naturalistic data from newly-invented LoRa IoT-FMS devices. To the best of our knowledge, this study was the first naturalistic study connecting both driving data and various types of traffic violations (e.g., overspeeding, lane, sign, park-ing, toll fees, and fine amount). Interestingly, non-driving-related violation types such as parking or toll-fee violation counts were also strongly correlated with collision involve-ment; suggesting that collision-involvement is likely not just a skill issue but also an attitude issue regarding the law. In terms of industrial applications, this study suggests multiple oppor-tunities. Through a better understanding of the influential vari-ables related to collision-involvement (e.g., accumulated vio-lations), fleet operators can build policies to enhance their fleet safety, reducing collision rates and the associated costs. Further, in the long-term, this study can provide a framework for developing a Usage-Based Rent-a-car (UBR) service for car rental field, similar to Usage-Based Insurance (UBI), which can reduce drivers’ rental fees based on their driving behaviors.

Associations between Job Characteristics, Mental Health and Driving: A Secondary Analysis

Background: Human factors are widely acknowledged as major contributors in road traffic collision (RTC) involvement. The aim of the present study, using secondary data analysis was to analyse associations between job characteristics, mental health, personality, fatigue and driving behaviour and their potential connection to RTCs, as well as their potential links with some of the risk factors as outcome variables (driving behaviour, driver fatigue and risk-taking).&#x0D; Methodology: This study used a cross-sectional approach, with 2856 clients of an insurance company completing an online survey in which they were asked about their driving and a range of other factors, such as personality, job characteristics and mental health.&#x0D; Results: The results revealed that whereas the extant literature points to personality traits as directly causal of RTCs, they actually impact driving behaviour and risk-taking behaviour. In addition, an association was found between higher salary and risk-taking (the latter predictive of RTC involvement). Using the Demands, Resources and Individual Effects model (DRIVE) it was possible to examine associations between particular job characteristics and driving behaviour, driver fatigue, and risk-taking. Associations between poor levels of driving behaviour and high levels of decision making, perceived job stress, long working hours and issues of work-life balance were uncovered. Moreover, a combined effects approach revealed a 16.73-fold increase in driver fatigue for younger, single drivers who often drive in heavy traffic, on the motorway and in adverse weather, with stressful, noisy, pressurised jobs, lower in levels of respect (typical of the blue-collar worker).&#x0D; Conclusion: Based on the current findings, further longitudinal research is recommended to assess causality.

The association of childhood symptoms of conduct disorder and collision risk in adulthood

IntroductionDespite limited research, existing evidence suggests that there is an association between conduct disorder (CD) and various risky driver behaviours, such as driver aggression and driving after drinking. The current study sought to estimate the association between probable CD during childhood and past-year collision risk. MethodsData were based on interviews with 5297 respondents who reported having driven in the past year, derived from the 2011–2013 cycles of the CAMH Monitor, a cross-sectional survey of adults in Ontario, Canada aged 18 years and older. A binary logistic regression was conducted of self-reported collision involvement in the previous 12 months, assessing demographic characteristics, driving exposure, demonstrated symptoms of ADHD, mild driver aggression, driving after drinking, and childhood (before age 15 years) symptoms of CD. ResultsThe unadjusted odds ratio of past-year collision involvement for those reporting childhood symptoms of CD was 1.76 (95% CI = 1.10, 2.82). Adjusting for potential covariates, self-reporting childhood symptoms of CD was significantly associated with a 77% increase in the odds of a crash (OR = 1.77; 95% CI = 1.01, 3.11). ConclusionThese findings add to a growing literature and suggest that treatment for CD should include a focus on driver safety.

Validating Driver Behavior and Attitude Measure for Older Italian Drivers and Investigating Their Link to Rare Collision Events.

The present study aimed to: (a) validate the factor structures of three scales assessing driving behavior, attitudes toward traffic safety (ATTS) and self-regulation in driving, in a sample of Italian older adults, through confirmatory factor analyses and (b) to determine the effectiveness of these measures in predicting the likelihood and the frequency of collision involvements in the following year. A 28-item driver behavior questionnaire (DBQ), a 16-item ATTS, a 21-item extended driving mobility questionnaire (DMQ-A) were administered to 369 active Italian drivers, aged between 60 and 91 years. Results showed a four-factor structure for the DBQ, a five-factor structure for the ATTS and a two-factor structure for the Extended DMQ-A, as the best fitting models. Hurdle model analysis of count data with extra-zeros showed that all factors of DBQ predicted the likelihood of road collisions. Risky behavior, except for aggressive violations, self-regulation and attitudes toward traffic rules were associated with the frequency of collision involvement. The aforementioned three scales seemed to be a useful and concise suite of instruments assessing risky as well as protective factors of driving behavior in elderly.

Analysis of relationships between drivers’ perception of road infrastructure in Poland and their involvement in road accidents and collisions

Road accidents and collisions are very common in Poland and, although for the last several years their number has been constantly decreasing, it is still one of the highest in Europe. This article attempts to provide an answer to the question whether drivers’ overrating the changes in the Polish road transport infrastructure might contribute to the status quo – the factor that has so far been left uninvestigated. In [21], the authors demonstrate that drivers take notice of the changes and perceive them as changes for the better.

The relationship between motor vehicle collisions and cigarette smoking in Ontario: Analysis of CAMH survey data from 2002 to 2016.

Research has shown that tobacco users have an increased risk of collisions compared to nonsmokers. Studies from 1967 through 2013 documented a crude relative risk of collision involvement of about 1.5 among smokers compared to nonsmokers. In January 2009, in response to concerns about the health risks associated with potentially high concentrations of secondhand smoke resulting from smoking in vehicles, the provincial government in Ontario, Canada, introduced legislation restricting smoking in vehicles where children and adolescents are present. We examined the association between reported smoking and involvement in a motor vehicle collision in a large representative sample of adult drivers in Ontario, Canada, from 2002 and 2016, with particular focus on 2002–2008 and 2010–2016, periods before and after the legislation. Data are based on the Centre for Addiction and Mental Health (CAMH) Monitor. Among licensed drivers, prevalence of self-reported collision involvement within the past year for 2002–2008 was 9.39% among those who currently smoked compared to 7.08% of nonsmokers. Following implementation of the legislation, for 2010–2016, the prevalence of collisions for smokers was 7.01% and for nonsmokers was 6.02%. The overall difference for both smokers and nonsmokers between the two time periods was statistically significant; however, the difference between the two groups for the pre-legislation period was significant even after adjusting for potential confounders, while post legislation the difference was not significant. Prior to the legislation, the prevalence of collision was higher among smokers than nonsmokers; following the introduction of the legislation the prevalence was similar for the two groups.

Driving under the influence of prescription opioids: Self-reported prevalence and association with collision risk in a large Canadian jurisdiction

BackgroundMotor vehicle collisions are an important contributor to prescription opioid use-related morbidity and mortality. The purpose of the current study was to estimate the prevalence of driving under the influence of prescription opioids (DUIPO) in Ontario, Canada, and to measure the association between this behaviour and the risk of a motor vehicle collision. MethodsData were based on telephone interviews with 7857 respondents who reported having driven in the past year. Data were derived from the 2011–2016 cycles of the CAMH Monitor, an ongoing cross-sectional representative survey of adults aged 18 years and older. A binary logistic regression analysis of collision involvement in the previous 12 months was conducted and included demographic characteristics (sex, age, marital status, education, income, region), driving exposure, poor mental health, non-medical use of prescription opioids, and driving after use of alcohol. ResultsThe prevalence of past-year DUIPO was 3.1%. Controlling for demographic characteristics, driving exposure, and other risk factors, self-reported DUIPO significantly increased the odds of a collision (AdjOR = 1.97; 95% CI 1.08, 3.60; p = 0.026). ConclusionBased on these findings, DUIPO is a notable road safety issue. Research focused on better understanding the impact of prescription opioids on driver behaviour, reducing the prevalence of DUIPO, and improving drug-impaired driving policy and interventions should be prioritized in public health strategies.