Insights resulting from quantitative bioanalysis in studies of drugs and driving

In this study we examined the value of quantitative bioanalysis of drugs in studies on drugs and driving. Based on the fact that the use of psychoactive substances should not be combined with driving, one could argue that, especially for routine testing for driving under influence of drugs, reporting quantitative results is not necessary and that it is sufficient to determine the presence or absence of a drug (class) to study the prevalence of drugged driving or to estimate the crash risk. The example of alcohol, where crash risk is increasing exponentially with increasing blood alcohol concentrations (hence using a low cut-off for the presence of alcohol underestimates crash risk) suggests that using quantitative drug concentrations in bio-fluids might have added value. In the Belgian DUID-legislation both oral fluid and blood samples are taken and analysed with similar cut-offs. An objective of this thesis was to investigate the effect of using these similar cut-offs in paired samples of oral fluid and blood in a population of general drivers. For this purpose a dataset of almost 3000 paired samples was investigated. Quantitative analysis showed that 2.6 times more drivers confirmed positive in oral fluid compared to blood. Although the Belgian traffic law amendment (introducing both oral fluid screening and confirmation) went into force on October 1st, 2010, confirmation is still performed on blood samples but with lower cut-offs than before. To assess the influence of this change in enforcement procedure on the number of false positive screenings, two datasets of approximately 4000 positive screening cases each were compared. Quantitative analysis disclosed that fewer false positive screenings were observed since the implementation of the new legislation and that more recent drug use was targeted. The total number of positive screenings has dropped with the new legislation, but this is solely due to a lower number of positive screenings for cannabis, which can be explained by the much longer detection window of THCCOOH in urine (previous legislation) than THC in oral fluid (current legislation). Most risk estimations are calculated based on nominal categorisation (positive or negative) of the cases and controls. The concentration found in the biological matrices can also be of interest. We compared the distribution of plasma concentrations of several psychoactive substances between the general driving population (n= 2750) and seriously injured drivers (n= 377). Quantitative analysis illustrated that higher amphetamine and benzoylecgonine concentrations were found in injured drivers. In addition, a trend towards higher concentrations of benzodiazepines and Z-drugs was also observed. Accident risks should therefore also be assessed in relation to substance concentrations not only the presence or absence of a drug in a biological sample. Self-reporting is the most widely used method to measure prevalence of use of psychoactive substances. In the Belgian roadside study both questionnaire data and results of bioanalysis were collected. An objective of this thesis was to investigate the consistency between self-report and results of bioanalysis. Data on 2949 respondents providing questionnaire data and the results of bioanalysis of blood and/or oral fluid samples were investigated. Quantitative analysis revealed that the self-reported data underestimated the use of cannabis and that this underestimation was most obvious for recent use. Besides the fact that the DUID legislation requires quantitative bioanalysis of drug concentrations, quantitative bioanalysis is also of significant importance in epidemiological research on drug use and in research on accident risks associated with psychoactive substances

Self reported (il)licit drug use in Belgian drivers

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

Analytical evaluation of five oral fluid drug testing devices

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 therei

DUID : oral fluid and blood confirmation compared in Belgium

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

Effect van ondersteunende richtlijnen en instrumenten op houding van apothekers bij aflevering van rijgevaarlijke geneesmiddelen

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

Prevalence of alcohol, illicit drugs and psychoactive medicines in killed drivers in four European countries

Our objective was to determine the presence of psychoactive substances in blood of drivers killed in road crashes in four European countries. Data from 1118 drivers of car and vans, killed between 2006 and 2009, were collected in Finland, Norway, Portugal and Sweden. The prevalence of any psychoactive substance ranged between 31 and 48%. Alcohol (>= 0.1 g/L) was the most common finding, 87% had a blood alcohol concentration (BAC)>=.5 g/L. Benzodiazepines (1.8-13.3%) and amphetamines (0-7.4%) were the most prevalent psychoactive medicines and illicit drugs, respectively. Alcohol-drug and drug-drug combinations were rather prevalent. Differences in alcohol/drug findings seemed to reflect differences in use in the countries. More research should be done to develop preventive strategies to reduce the number of alcohol- and drug-related traffic accidents targeting at-risk groups, such as drivers with very high BACs and novice drivers