Belgian Road Safety Institute

Belgian, Belgium

Belgian Road Safety Institute

Belgian, Belgium
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Devos H.,Georgia Regents University | Devos H.,Catholic University of Leuven | Tant M.,Belgian Road Safety Institute | Akinwuntan A.E.,Georgia Regents University
Cerebrovascular Diseases | Year: 2014

Background: Little is known about the critical on-road driving skills that get affected after a stroke. The purpose of this study was to investigate the key on-road driving impairments and their associated cognitive deficits after a stroke. A second aim was to investigate if lateralization of stroke impacts results of the cognitive and on-road driving tests. Methods: In this cross-sectional study, 99 participants with a first-ever stroke who were actively driving prior to stroke underwent a cognitive battery and a standardized road test that evaluated 13 specific on-road driving skills. These onroad driving skills were mapped onto an existing, theoretical framework that categorized the on-road items into hierarchic clusters of operational, tactical, visuo-integrative, and mixed driving skills. The total score on the road test and the on-road decision, made by a certified fitness-to-drive expert, decided the main outcome. The critical on-road driving skills predicting the on-road decision were identified using logistic regression analysis. Linear regression analysis was employed to determine the cognitive impairments leading to poor total on-road scores. Analyses were repeated for rightand left-sided strokes.Results: In all, 37 persons scored poorly on the road test. These participants performed worse in all hierarchic clusters of on-road driving. Performances on the operational cluster and the visuo-integrative cluster best predicted on-road decisions (R 2 = 0.60). 'Lane changing' and 'understanding, insight, and quality of traffic participation' were the critical skill deficits leading to poor performance on the road test (R2 = 0.65). Divided attention was the main determinant of on-road scores in the total group (R2 = 0.06). Participants with right-sided stroke performed worse on visual field, visual neglect, visual scanning, visuo-constructive skills, and divided attention compared with those with left-sided stroke. Divided attention was the main determinant of total on-road scores in the right-sided stroke group (R2 = 0.10). A combination of visual scanning, speed of processing, and executive dysfunction yielded the best model to predict on-road scores in left-sided strokes (R2 = 0.46).Conclusions: Poor performance in the road test after stroke is determined by critical operational and visuo-integrative driving impairments. Specific and different driving evaluation and training programs are needed for right- and leftsided strokes. © 2014 S. Karger AG, Basel. © 2014 S. Karger AG, Basel.

Devos H.,Catholic University of Leuven | Akinwuntan A.E.,Georgia Regents University | Nieuwboer A.,Catholic University of Leuven | Truijen S.,University of Antwerp | And 2 more authors.
Neurology | Year: 2011

OBJECTIVE: To identify the best determinants of fitness to drive after stroke, following a systematic review and meta-analysis. METHODS: Twenty databases were searched, from inception until May 1, 2010. Potentially relevant studies were reviewed by 2 authors for eligibility. Methodologic quality was assessed by Newcastle-Ottawa scores. The fitness-to-drive outcome was a pass-fail decision following an on-road evaluation. Differences in off-road performance between the pass and fail groups were calculated using weighted mean effect sizes (dw). Statistical heterogeneity was determined with the I statistic. Random-effects models were performed when the assumption of homogeneity was not met. Cutoff scores of accurate determinants were estimated via receiver operating characteristic analyses. RESULTS: Thirty studies were included in the systematic review and 27 in the meta-analysis. Out of 1,728 participants, 938 (54%) passed the on-road evaluation. The best determinants were Road Sign Recognition (dw 1.22; 95% confidence interval [CI] 1.01-1.44; I, 58%), Compass (dw 1.06; 95% CI 0.74-1.39; I, 36%), and Trail Making Test B (TMT B; dw 0.81; 95% CI 0.48-1.15; I, 49%). Cutoff values of 8.5 points for Road Sign Recognition, 25 points for Compass, and 90 seconds for TMT B were identified to classify unsafe drivers with accuracies of 84%, 85%, and 80%, respectively. Three out of 4 studies found no increased risk of accident involvement in persons cleared to resume driving after stroke. CONCLUSIONS: The Road Sign Recognition, Compass, and TMT B are clinically administrable office-based tests that can be used to identify persons with stroke at risk of failing an on-road assessment. ©2011 American Academy of Neurology.

Devos H.,Catholic University of Leuven | Devos H.,University of Iowa | Vandenberghe W.,University Hospitals Leuven | Vandenberghe W.,Catholic University of Leuven | And 6 more authors.
Movement Disorders | Year: 2013

Background: We previously developed a short clinical battery, consisting of contrast sensitivity, Clinical Dementia Rating, the Unified Parkinson's Disease Rating Scale-motor section (UPDRS III), and disease duration, which correctly classified 90% of drivers with Parkinson's Disease (PD). The aim of this study was to validate that screening battery in a different sample of PD drivers. Methods: Sixty drivers with PD were enrolled to validate our original screening battery to predict driving fitness decisions (pass-fail) by a state agency where drivers underwent detailed visual, cognitive, and on-road testing. Results: Twenty-four participants (40%) failed the driving evaluation. The screening battery correctly classified 46 (77%) participants (sensitivity and negative predictive value=96%; specificity and positive predictive value=64%). Adding other clinical predictors (e.g., age of onset, Hoehn-Yahr stage instead of UPDRS III) failed to improve the specificity of the model when the sensitivity was kept constant at 96%. However, a driving simulator evaluation improved the specificity of the model to 94%. Conclusions: The original clinical battery proved to be a valid screening tool that accurately identifies fit drivers with PD and select those who need more detailed testing at specialized centers. © 2013 Movement Disorder Society.

Bowers A.R.,Schepens Eye Research Institute | Tant M.,Belgian Road Safety Institute | Peli E.,Schepens Eye Research Institute
Stroke Research and Treatment | Year: 2012

Aims. Homonymous hemianopia (HH), a severe visual consequence of stroke, causes difficulties in detecting obstacles on the nonseeing (blind) side. We conducted a pilot study to evaluate the effects of oblique peripheral prisms, a novel development in optical treatments for HH, on detection of unexpected hazards when driving. Methods. Twelve people with complete HH (median 49 years, range 29-68) completed road tests with sham oblique prism glasses (SP) and real oblique prism glasses (RP). A masked evaluator rated driving performance along the 25 km routes on busy streets in Ghent, Belgium. Results. The proportion of satisfactory responses to unexpected hazards on the blind side was higher in the RP than the SP drive (80% versus 30%; P = 0.001), but similar for unexpected hazards on the seeing side. Conclusions. These pilot data suggest that oblique peripheral prisms may improve responses of people with HH to blindside hazards when driving and provide the basis for a future, larger-sample clinical trial. Testing responses to unexpected hazards in areas of heavy vehicle and pedestrian traffic appears promising as a real-world outcome measure for future evaluations of HH rehabilitation interventions aimed at improving detection when driving. © 2012 Alex R. Bowers et al.

Van der Linden T.,Ghent University | Van der Linden T.,National Institute of Criminalistics and Criminology | Silverans P.,Belgian Road Safety Institute | Verstraete A.G.,Ghent University
Drug Testing and Analysis | Year: 2014

The objective of this study was to compare the number of drivers who self-reported cannabis use by questionnaires to the results of toxicological analysis. During roadside surveys, 2957 respondents driving a personal car or van completed a questionnaire to report their use of drugs and medicines during the previous two weeks and to indicate the time of their last intake. Cannabis was analyzed in oral fluid by ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), in blood by gas chromatography-mass spectrometry (GC-MS). Frequencies in the time categories were calculated and compared with toxicological results. Diagnostic values were calculated for the time categories in which positive findings were to be expected (<4 h and <2 4h, respectively for tetrahydrocannabinol (THC) and delta9-tetrahydrocannabinol (THCCOOH) in blood, <12 h for THC in oral fluid). Most self-reported cannabis use was more than 12 h before driving. The sensitivity of the questionnaire was low, while the specificity and accuracy were high. Kappa statistics revealed a fair agreement between self-report and positive findings for THC in oral fluid and blood and moderate agreement with THCCOOH in blood. Self-report largely underestimates driving under the influence of cannabis, particularly recent cannabis use; therefore analysis of biological samples is necessary. © 2013 John Wiley & Sons, Ltd. Self-reporting could be an indicative measurement for driving under the influence of cannabis, but analysis of biological samples is still necessary. © 2013 John Wiley & Sons, Ltd.

Van der Linden T.,Ghent University | Van der Linden T.,National Institute of Criminalistics and Criminology | Legrand S.-A.,Ghent University | Silverans P.,Belgian Road Safety Institute | Verstraete A.G.,Ghent University
Journal of Analytical Toxicology | Year: 2012

The objective of this study was to compare the number of drivers with drug concentrations above the legal cutoffs for driving under the influence of illicit substances in paired samples of blood and oral fluid. Between January 2008 and September 2009, 2,949 randomly selected drivers participated in a roadside survey. Each was asked to provide blood and oral fluid. Samples were analyzed for 11 illicit substances or metabolites by ultra-performance liquid chromatography-tandem mass spectrometry and gas chromatography- tandem mass spectrometry. Out of the 2,750 drivers who gave both blood and oral fluid, 28 (1.0%) had drug concentrations above the legal cutoff in blood and 71 (2.6%) were above the legal cutoff in oral fluid. Fifteen (7.5%) of the 199 drivers who gave an oral fluid sample but refused to provide blood tested positive, significantly more than drivers who provided both samples. Based on oral fluid analysis, 2.6 times more subjects tested positive for drugs compared to blood analysis. Those that refused to give a blood sample were 3 times more likely to test positive for drugs. Even in a survey that guaranteed total anonymity, people fearing a positive test result might have been more likely to refuse to give a blood sample. © The Author [2012]. Published by Oxford University Press. All rights reserved.

Legrand S.-A.,Ghent University | Silverans P.,Belgian Road Safety Institute | de Paepe P.,Ghent University | Buylaert W.,Ghent University | Verstraete A.G.,Ghent University
Traffic Injury Prevention | Year: 2013

Objective: To estimate the percentage of drivers involved in a traffic crash in Belgium who have alcohol and drugs in their blood. Methods: Blood samples of the drivers injured in a traffic crash and admitted to the emergency departments of 5 hospitals in Belgium between January 2008 and May 2010 were analyzed for ethanol (with an enzymatic method) and 22 other psychoactive substances (with ultra-performance liquid chromatography with tandem mass spectrometry or gas chromatography-mass spectrometry). Results: One thousand seventy-eight drivers were included in the study. Alcohol (≥0.1 g/L) was the most common substance (26.2%). A large majority of the drivers (64%) who were positive for alcohol had a blood alcohol concentration (BAC) ≥1.3 g/L (legal limit in Belgium: 0.5 g/L). These high BACs were most frequent among male injured drivers. Cannabis was the most prevalent illicit drug (5.3%) and benzodiazepines (5.3%) were the most prevalent medicinal drugs. Approximately 1 percent of the drivers were positive for cocaine and amphetamines. No drivers tested positive for illicit opioids. Medicinal drugs were more likely to be found among female drivers and drivers older than 35 years, and alcohol and illicit drugs were more likely to be found among male drivers and drivers younger than 35 years. Conclusion: A high percentage of the injured drivers were positive for a psychoactive substance at the time of injury. Alcohol was the most common substance, with 80 percent of the positive drivers having a BAC ≥0.5 g/L. Compared to a roadside survey in the same area, drivers/riders with high BACs and combinations of drugs were overrepresented. Efforts should be made to increase alcohol and drug enforcement. The introduction of a categorization and labeling system might reduce driving under the influence of medicinal drugs by informing health care professionals and patients. © 2013 Copyright Taylor and Francis Group, LLC.

Devos H.,Catholic University of Leuven | Nieuwboer A.,Catholic University of Leuven | Tant M.,Belgian Road Safety Institute | De Weerdt W.,Catholic University of Leuven | And 2 more authors.
Neurology | Year: 2012

Objectives: To identify the most accurate clinical predictors of fitness to drive (FTDr) in Huntington disease (HD). Methods: This cross-sectional study included 60 active drivers: 30 patients with manifest HD (8 women) and 30 age- and gender-matched healthy controls. Mean (SD) age of the HD group was 50 (12) years and median (Q1-Q3) disease duration was 24 (12-48) months. A clinical battery consisting of a driving history questionnaire, the cognitive section of the Unified Huntington's Disease Rating Scale (UHDRS), Trail Making Test, and Mini-Mental State Examination, as well as a driving simulator evaluation, were administered to all participants. Additionally, the subjects with HD completed the motor, behavioral, and Total Functional Capacity sections of the UHDRS and underwent an official FTDr evaluation comprising visual, neuropsychological, and on-road tests. The blinded neurologist's appraisal of FTDr and the 3 most predictive clinical tests were compared with the official pass/fail FTDr decision. Results: The patients with HD performed worse on all tests of the clinical battery and driving simulator than the healthy controls. Fifteen patients with HD (50%) failed the FTDr evaluation. The blinded neurologist correctly classified 21 patients (70%). The Symbol Digit Modalities Test, Stroop word reading, and Trail Making Test B provided the best model (R2 - 0.49) to predict FTDr, correctly classifying 26 patients (87%). Conclusions: Half of active drivers with HD fail a driving evaluation and pose a potential hazard on the road. Our results suggest that those at risk can be accurately identified using a clinical screening tool. © 2012 American Academy of Neurology.

Dupont E.,Belgian Road Safety Institute | Papadimitriou E.,National Technical University of Athens | Martensen H.,Belgian Road Safety Institute | Yannis G.,National Technical University of Athens
Accident Analysis and Prevention | Year: 2013

Hierarchical structures in road safety data are receiving increasing attention in the literature and multilevel (ML) models are proposed for appropriately handling the resulting dependences among the observations. However, so far no empirical synthesis exists of the actual added value of ML modelling techniques as compared to other modelling approaches. This paper summarizes the statistical and conceptual background and motivations for multilevel analyses in road safety research. It then provides a review of several ML analyses applied to aggregate and disaggregate (accident) data. In each case, the relevance of ML modelling techniques is assessed by examining whether ML model formulations (i) allow improving the fit of the model to the data, (ii) allow identifying and explaining random variation at specific levels of the hierarchy considered, and (iii) yield different (more correct) conclusions than single-level model formulations with respect to the significance of the parameter estimates. The evidence reviewed offers different conclusions depending on whether the analysis concerns aggregate data or disaggregate data. In the first case, the application of ML analysis techniques appears straightforward and relevant. The studies based on disaggregate accident data, on the other hand, offer mixed findings: computational problems can be encountered, and ML applications are not systematically necessary. The general recommendation concerning disaggregate accident data is to proceed to a preliminary investigation of the necessity of ML analyses and of the additional information to be expected from their application. © 2013 Elsevier Ltd. All rights reserved.

Yannis G.,National Technical University of Athens | Papadimitriou E.,National Technical University of Athens | Dupont E.,Belgian Road Safety Institute | Martensen H.,Belgian Road Safety Institute
Traffic Injury Prevention | Year: 2010

Objective: In this article the factors affecting fatality and injury risk of road users involved in fatal accidents are analyzed by means of in-depth accident investigation data, with emphasis on parameters not extensively explored in previous research. Methods: A fatal accident investigation (FAI) database is used, which includes intermediate-level in-depth data for a harmonized representative sample of 1300 fatal accidents in 7 European countries. The FAI database offers improved potential for analysis, because it includes information on a number of variables that are seldom available, complete, or accurately recorded in road accident databases. However, the fact that only fatal accidents are examined requires for methodological adjustments, namely, the correction for two types of effects on a road user's baseline risk: "accident size" effects, and "relative vulnerability" effects. Fatality and injury risk can be then modeled through multilevel logistic regression models, which account for the hierarchical dependences of the road accident process. Results: The results show that the baseline fatality risk of road users involved in fatal accidents decreases with accident size and increases with the vulnerability of the road user. On the contrary, accident size increases nonfatal injury risk of road users involved in fatal accidents. Other significant effects on fatality and injury risk in fatal accidents include road user age, vehicle type, speed limit, the chain of accident events, vehicle maneuver, and safety equipment. In particular, the presence and use of safety equipment such as seat belt, antilock braking system (ABS), and electronic stability program (ESP) are protection factors for car occupants, especially for those seated at the front seats. Conclusions: Although ABS and ESP systems are typically associated with positive effects on accident occurrence, the results of this research revealed significant related effects on accident severity as well. Moreover, accident consequences are more severe when the most harmful event of the accident occurs later within the accident chain. © 2010 Taylor & Francis Group, LLC.

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