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Objective: To determine the presence of alcohol and drugs in drivers severely injured in traffic crashes in 6 European countries. Methods: Data were collected from 2492 seriously injured drivers of cars and vans in Belgium, Denmark, Finland, Italy, Lithuania and The Netherlands, between 2007 and 2010. Toxicological analysis was performed with chromatographic techniques on whole blood for 23 substances Results: The percentage of drivers positive for at least one psychoactive substance ranged between 28 (Lithuania) and 53% (Belgium). Alcohol (≥0.1 g/L) was the most common finding with the highest percentage in Belgium (42.5%). Among the alcohol-positive drivers, 90.5% had a BAC ≥0.5 g/L and 65.7% had a BAC ≥1.3 g/L (between 58.2% (NL) and 76.5% (FI)). Benzodiazepines (0.0-10.2%) and medicinal opioids (0.5-7.8%) were the most prevailing medicinal drugs, but half of the concentrations were lower than therapeutic. Cannabis (0.5-7.6%) was the most prevailing illicit drug. Alcohol was found in combination with drugs in 2.3-13.2% of the drivers. Drug combinations were found in 0.5-4.3% of the drivers. Conclusion: This study confirms the high prevalence of psychoactive substances in injured drivers, but we observed large differences between the participating countries. Alcohol was the most common finding, followed by cannabis and benzodiazepines. Notable are the many drivers having a BAC ≥ 1.3 g/L. The majority of the substances were found in combination with another psychoactive substance, mostly alcohol. The high prevalence of high BACs and combinations (compared to roadside surveys) suggest that those drivers are most at risk and that preventive actions should target them preferentially.

The objective of this deliverable is to assess the risk of driving with alcohol, illicit drugs and medicines in various European countries. In total nine countries participated in the study on relative risk of serious injury/fatality while positive for psychoactive substances. Six countries contributed to the study on the relative risk of getting seriously injured: Denmark, Finland, Lithuania, Italy, Belgium and the Netherlands. Four countries contributed to the study on the relative risk of getting killed: Finland, Norway, Sweden and Portugal. The risk for a driver of getting seriously injured or killed in an accident while positive for a given substance was calculated as the ratio between the odds for a driver of being seriously injured/killed in an accident while positive for a given substance and the odds of being seriously injured/killed while negative. The odds ratios were calculated by means of logistic regression using the SAS 9.2 procedure proc logistic. Data from the case study population consisted of samples from the hospital studies of seriously injured drivers and those of killed drivers (Isalberti et al., 2011). In total, 2,490 seriously injured drivers and 1,112 killed drivers were included. Data from the control population came from the roadside surveys in the same countries, in total, 15,832 drivers participated in the control sample of the seriously injured drivers and 21,917 drivers participated in the control samples of killed drivers; data were weighted for the national distribution of traffic in each of eight time periods of the week (Houwing et al., 2011). The relative risk estimates were adjusted for age and gender. An estimation of the overall relative risk by substance group is given. These risk estimates are based on the odds ratios estimated separately for each country, together with aggregated odds ratios estimated on the basis of all countries’ data together or a subset of countries. In the estimate is also taken into account the imprecision of the odds ratios of getting seriously injured and killed as expressed by the confidence intervals of the odds ratio estimates. The main finding of this report is that the highest risk of getting seriously injured or killed is associated with driving with high alcohol concentrations (above 1.2 g/L) and alcohol combined with other psychoactive substances. These two groups indicate extremely high risks of about 20-200 times that of sober drivers. Other high risk groups are drivers with medium blood alcohol concentrations (between 0.8 g/L and 1.2 g/L), multiple drug use and amphetamines. The risks indicated for this group are about 5-30 times that of sober drivers. Medium increased risk was found for alcohol concentrations between 0.5 and 0.8 g/L, for cocaine, benzoylecgonine, illicit opiates and medicinal opioids. Risk for this group was estimated to about 2-10 times that of sober drivers. The risk associated with benzoylecgonine that is not an active agent might be caused by sleep deprivation after cocaine consumption. The risk associated with cannabis seems to be similar to the risk when driving with a low alcohol concentration (between 0.1 g/L and 0.5 g/L), which is slightly increased of about 1-3 times that of sober drivers.

DRUID (Driving under the Influence of Drugs, Alcohol and Medicines) aimed to combat the problem of driving under the influence of psychoactive substances by providing a solid scientific base for European policy makers. It brought together experienced organisations in Europe to assemble a coordinated set of data resources and measures. DRUID is an integrated European research project which consisted of different sub-projects (Work Packages) that were aimed at different topics such as the prevalence and risk of psychoactive substances, enforcement, classification of medicines, rehabilitation of offenders and withdrawal of driving licenses (www.druid-project.eu). The main objective of WP2 of DRUID was to assess the situation in Europe regarding the prevalence and risk of the use of illicit drugs, alcohol and psychoactive medicinal drugs by drivers. The main aim of this study was to obtain more insight in the use of psychoactive substances among drivers in European traffic. Thirteen countries participated in this study by conducting roadside surveys according to a general design. In total almost 50,000 randomly selected drivers participated between January 2007 and July 2009. All participating countries are members of the European Union (EU) except for Norway, which is associated with the European Union as a member of the European Economic Area (EEA). Participants, i.e. drivers of passenger cars and vans, were randomly selected using a stratified multistage sampling design. In the first stage, one or more regions per country were selected. These regions were meant to be representative for the country with regard to substance use and traffic distribution. Within the selected regions smaller research areas were selected, and within these areas, survey locations were selected, where subjects were stopped at random, and were requested to participate in the study. With regard to days of the week and times of the day, the study population sample was stratified into eight time periods over the week, for each of the survey areas. The time periods did not overlap each other and covered all the days of the week and all times of the day.

De toepassing van alcohol ontstekingsinterlocksystemen beter bekend als alcoholsloten is in opmars in de Westerse wereld alsook bij ons in België. De vraag is of deze systemen het doel bereiken waarvoor ze ontworpen zijn: namelijk een vermindering van het aantal verkeersongevallen en –slachtoffers door een daling van de recidivismegraad van rijden onder invloed (ROI). Om hier een bevestigend antwoord op te geven is verder onderzoek in de vorm van gerandomiseerde gecontroleerde studies noodzakelijk. De resultaten beschikbaar op dit moment tonen dat dit systeem in staat is een daling van het ROI te bewerkstelligen wanneer het toestel aanwezig is in de wagen; echter na verwijdering wordt geen langdurig effect gezien en hervallen de meeste overtreders. De combinatie met een omkaderingsprogramma zou wel in staat zijn een gedragsverandering te bereiken en zo ook een effect op lange termijn bewerkstelligen. Het is echter bijzonder moeilijk om een statistisch significant verschil aan te tonen en extrapolatie toe te laten gezien de lage participatiegraad van de overtreders, de selectiebias aanwezig in niet gerandomiseerde studies en het aantal geobserveerde dropouts. Een alcoholslot heeft mogelijkheden in de aanpak van ROI. Om dit effect aan te tonen dient het op grotere schaal geïmplementeerd en geëvalueerd te worden. Op heden kan besloten worden dat dit middel effectief is in de preventie van ROI zolang het aanwezig is in het voertuig. Het heeft als belangrijk voordeel dat het overtreders niet limiteert in hun professionele en sociale activiteiten. Bovendien kan een positieve gedragsverandering met effect op lange termijn verwacht worden indien het geïmplementeerd wordt in combinatie met de reeds bestaande hulpprogramma’

The European Integrated Project DRUID (Driving Under the Influence of Drugs, Alcohol and Medicines) is a part of the 6th Framework Program, the European Community Framework Program for Research, Technological Development and Demonstration. The objective of DRUID is to give scientific support to the EU transport policy by providing a solid basis to generate harmonised, EU-wide regulations for driving under the influence of alcohol, drugs and medicine. This study is a part of Work Package 2, Epidemiology, of the DRUID project. The objective of the study is to assess the situation in Europe regarding the prevalence of alcohol and other psychoactive substances in drivers who have been injured/killed in traffic accidents. Part 1 of this report presents the general results of the hospital & killed driver studies. After a short introduction, the representativeness of the populations in the EU countries as well as the representativeness of hospitalised and killed driver samples are addressed. An overview of the non-response issues in the various countries is also included. Based on the toxicological findings, a general summary of the prevalence of drug use is given for the 9 participating countries. This is followed by a discussion and a conclusions part. Part 2 and 3 of this report include all the country reports. In this part more detailed information regarding study design, methods, representativeness of the sampling and results on a national level can be found.

DRUID (Driving under the Influence of Drugs, Alcohol and Medicines) aimed to combat the problem of driving under the influence of psychoactive substances by providing a solid scientific base for European policy makers. It brought together experienced organisations in Europe to assemble a coordinated set of data resources and measures. DRUID is an integrated European research project which consisted of different sub-projects (Work Packages) that were aimed at different topics such as the prevalence and risk of psychoactive substances, enforcement, classification of medicines, rehabilitation of offenders and withdrawal of driving licenses (www.druid-project.eu). The main objective of WP2 of DRUID was to assess the situation in Europe regarding the prevalence and risk of the use of illicit drugs, alcohol and psychoactive medicinal drugs by drivers. The prevalence of drug driving was estimated by means of roadside surveys and the prevalence of drugs in injury accidents was estimated by means of hospital surveys of seriously injured and/or killed drivers. Accident risk estimates for drug driving were assessed by relating the prevalence of drugs among the general driving population to the prevalence among seriously injured and/or killed drivers, by relating medication records to accident data and by relating substance use among accidentinvolved drivers to accident culpability.

Inleiding: DRUID (Driving Under the Influence of Drugs,alcohol and medicines) is een Europees onderzoeksproject waarin onder meer studies worden uitgevoerd over het effect van geneesmiddelen op de rijvaardigheid. Er werden richtlijnen voor apothekers voor het afleveren van mogelijk rijgevaarlijke geneesmiddelen ontwikkeld alsook ook een classificatiesysteem dat aanduidt in welke mate een geneesmiddel invloed heeft op de rijvaardigheid. Deze richtlijnen en classificatiesysteem worden geïmplementeerd binnen bestaande software om ze in de praktijk te gebruiken om patiënten beter te informeren over de mogelijke risico’s. Doelstelling: Het doel van de studie is om de impact van het gebruik van de richtlijnen en het classificatiesysteem te meten in de dagelijkse praktijk. De impact wordt op twee manieren gemeten: vanuit de geïntegreerde software zullen anonieme data geëxtraheerd worden en verandering van kennis en attitude zal geanalyseerd worden via enquêtes. Methode: 84 apothekers die gebruik maken van de ViaNova software doen mee aan de studie. Aan deze groep wordt gevraagd om: - De functies m.b.t. rijvaardigheid in te schakelen in de Vianova software. - Deel te nemen aan een trainingsavond over de invloed van geneesmiddelen op de rijvaardigheid. - Een enquête in te vullen. - Zes maand de geïntegreerde functies m.b.t. rijvaardigheid te gebruiken. - Na deze periode de gegevens anoniem te laten ophalen uit de software. - Na deze periode een tweede enquête in te vullen Resultaten: Resultaten worden verwacht in het voorjaar van 2011. Apothekers vinden het moeilijk om deel te nemen indien artsen niet geïnformeerd zijn. Conclusies: Met een goede, wetenschappelijk onderbouwde classificatie moeten artsen en apothekers in de toekomst in staat zijn de patiënt optimaal in te lichten. De talrijke opkomst bij de trainingsavonden geeft aan dat er vanuit de apothekersgroep grote belangstelling is voor een implementatie van een systeem dat ze kunnen gebruiken om de patiënt begrijpelijke informatie mee te geven.

Objectives: In Belgium and some other countries, the police performs on-site drug screening when a driver is suspected of being drug impaired, since the test procedure is time consuming and the screening devices are relatively expensive. The objective of the study is to compare the results of a checklist with drug concentrations in saliva. Method: The checklist used in the DRUID study in the Netherlands was used. Two fifth-year medical students performed the tests on 250 subjects, 50 drivers and 200 subjects attending a methadone clinic. Saliva analysis was performed by UPLC-MS/MS. As several signs were rarely observed, the parameters were reduced to those that were positive in at least 3 out of 250 test subjects. This selection led to a reduction to 13 (out of 28) parameters. A statistical test (Fisher’s exact test) was used to test for correlations between the checklist parameters and the presence of substances in oral fluid. Results: Most parameters did not correlate significantly with drug intake. The pupil tests seemed to be the best predicting parameters, especially for amphetamine and THC. Remarkably, some correlations were found between parameters and drugs where no correlation was expected, e.g. sleepiness and amphetamines. This can possibly be caused by the presence of combination use of drugs in a lot of subjects. The signs were often observed when high drug concentrations were seen in saliva, but in many cases with high saliva drug concentrations, no signs were observed. Conclusions: In general, the checklist correlated badly with drug presence in this population of chronic drug users, but our results also confirm other studies that found that checklists are not very sensitive.

Introduction: There are relatively few data on the prevalence of driving under the influence of drugs in the general population. Aim: To determine the number of drivers who took drugs and medicines by using questionnaires, and comparing to the results of toxicological analysis. Method : 2957 respondents driving a personal car or van completed a questionnaire during roadside surveys to report their use of drugs and medicines during the last two weeks and indicate the time of last intake. The drug classes were combined to benzodiazepines and Z-drugs, antidepressants, codeine, alcohol, cannabis, cocaine, heroin and amphetamines. Drugs were analysed in oral fluid by UPLC-MS/MS. Frequencies in the time categories were calculated and compared with toxicological results. Results : Drug class Self-report/ toxicology Use 24h Unknown Alcohol 1614/196 138/95 180/56 182/15 370/9 713/14 31/7 Antidepressants 110/41 6/3 14/5 50/19 24/8 8/0 8/6 Benzodiazepines and Z-drugs 98/40 4/2 10/9 33/14 30/9 12/4 9/2 Cannabis 79/32 5/4 3/1 10/8 7/3 46/14 8/2 Codeine 60/6 4/2 7/3 9/0 6/0 25/0 9/1 Cocaine 7/5 2/2 0 0 0 4/2 1/1 Amphetamines 5/2 0 0 0 0 3/1 2/1 Heroin 2/1 1/1 0 0 0 1/0 0 Conclusions : Alcohol, antidepressants, cannabis, benzodiazepines and codeine were most commonly used. Most drugs were last used >4h or more before driving. Self-report yielded more positives than toxicological analysis. The percentages of positives were higher among the subjects who reported more recent drug consumption. Disclaimer: This abstract has been produced under the project “Driving Under the Influence of Drugs, Alcohol and Medicines” (DRUID) financed by the European Community within the framework of the EU 6th Framework Program. This abstract reflects only the author's view. The European Community is not liable for any use that may be made of the information contained therein.

Introduction: The correlation with blood drug presence and the easiness of sample collection make oral fluid an ideal matrix for roadside drug tests targeting impaired drivers. Aim: To evaluate the reliability of five oral fluid testing devices: Varian OraLab®6, Dräger DrugTest® 5000, Cozart® DDS 806, Mavand RapidSTAT® and Innovacon OrAlert. Method: More than 760 samples were collected from volunteers either at drug addiction treatment centres or during roadside sessions. Target drug classes were amphetamines, cannabinoids, cocaine and opiates for all devices. Dräger DrugTest® 5000 (137 samples tested), Cozart® DDS 806 (n=138) and Mavand Rapid STAT® (n=133) could also detect the presence of benzodiazepines, while phencyclidine could be detected using Varian OraLab® 6 (n=249) and Innovacon OrAlert (n=110). Samples were tested on-site with one of the selected devices. Volunteers provided an additional oral fluid sample for confirmation analysis by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS). Sensitivity, specificity, accuracy and prevalence were calculated applying Belgian legal confirmation cut-offs. Results : All devices showed good specificity for all drug classes. Sensitivity and accuracy were very variable among devices and drug classes. Overall, when applying Belgian law cut-offs, sensitivity was always unsatisfactory for cocaine (highest sensitivity 50%), moderate or very low for cannabis and amphetamines (23-80% and 17-75% respectively). Conclusions : Considering that cannabis, followed by amphetamines, is the most prevalent drug among impaired drivers in Belgium, only one device was sensitive enough to be used during roadside police controls. This abstract has been produced under the project “Driving Under the Influence of Drugs, Alcohol and Medicines” (DRUID) financed by the European Community within the framework of the EU 6th Framework Program. This abstract reflects only the author's view. The European Community is not liable for any use that may be made of the information contained therein

Objective: To determine the influence of saliva sampling methods on drug concentrations. Methods: Saliva was obtained from 249 subjects (who had given informed consent) by Varian OraLab and Statsure Saliva?Sampler. OraLab consists of foam-tipped saliva collector. The sponge contains an acid that stimulates salivation. Statsure consists of a collector with a blue indication when 1ml of saliva is collected. After sampling, the collector is transferred to a tube that contains 1ml of buffer. Saliva was analysed with UPLC-MSMS. Results: For all the drugs, the concentrations in the saliva collected with OraLab are 50-70% compared to Statsure, except for morphine (80%) and codeine (92%). Possible explanations are: a buffer could explain a better extraction recovery with Statsure (particularly THC); the stimulation of salivation by an acid in OraLab could also explain the lower concentrations. Conclusions: The correlation coefficients are relatively low (0.61-0.90). For all drugs, the concentrations measured in the saliva collected by OraLab are lower. This could have consequences for the determination of legal cut-offs.