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Odds of culpability associated with use of impairing drugs in injured drivers in Victoria, Australia
Accident Analysis and Prevention 135 (2020) 105389
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Odds of culpability associated with use of impairing drugs in injured drivers in Victoria, Australia
T
Olaf H. Drummera,*, Dimitri Gerostamoulosa, Matthew Di Ragoa, Noel W. Woodforda, Carla Morrisb, Tania Frederiksenb, Kim Jachnoc, Rory Wolfec a
Victorian Institute of Forensic Medicine and the Department of Forensic Medicine, Monash University, School of Public Health and Preventive Medicine, 65 Kavanagh Street, Southbank 3006, Victoria, Australia b Road Policing Drug and Alcohol Section, Victoria Police, 20 Dawson St., Brunswick 3056, Victoria, Australia c School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd, Melbourne 3004, Victoria, Australia
A R T I C LE I N FO
A B S T R A C T
Keywords: Cannabis Culpability analysis Crash risk Injured drivers Drug impairment Methylamphetamine
Culpability analysis was conducted on 5000 drivers injured as a result of a vehicular collision and in whom comprehensive toxicology testing in blood was conducted. The sample included 1000 drivers for each of 5 years from approximately 5000–6000 drivers injured and taken to hospital in the State of Victoria. Logistic regression was used to investigate differences in the odds of culpability associated with alcohol and drug use and other selected crash attributes using the drug-free driver as the reference group. Adjusted odds ratios were obtained from multivariable logistic regression models in which other potentially explanatory driver and crash attributes were included. Drivers with alcohol present showed large increases in the odds of culpability similar to that seen in other studies investigating associations between blood alcohol concentration and crash risk. Methylamphetamine also showed a large increase in the odds of culpability (OR 19) compared to the reference group at both below and above 0.1 mg/L, whereas those drivers with Δ9-tetrahydrocannabinol (THC) present showed only modest increase in odds when all concentrations were assessed (OR 1.9, 95 %CI 1.2–3.1). Benzodiazepines in drivers also gave an increase in odds (3.2, 95 %CI 1.6–6.1), but not other medicinal drugs such as antidepressants, antipsychotics and opioids. Drivers that had combinations of impairing drugs generally gave a large increase in odds, particularly combinations of alcohol with THC or benzodiazepines, and those drivers using both THC and methamphetamine.
1. Introduction
2012; Morland et al., 2011). While there is little dispute that most of the drugs used recreationally can cause significant reduction in cognitive and psychomotor functions there is still some doubt over the extent that these drugs affect crash risk. Estimates of crash risk arise from three main types of analyses; i) by estimating the prevalence of specific drugs or drug classes in drivers that have crashed compared to drivers that have not had a collision (Movig et al., 2004; Mura et al., 2003; Hels et al., 2013), ii) from culpability analyses (sometimes called responsibility analyses) where the proportion of culpable drivers using a particular drug is compared to the proportion of drivers not using drugs (Gadegbeku et al., 2011; Drummer et al., 2004; Poulsen et al., 2014; de Carvalho et al., 2016; Dubois et al., 2015; Reguly et al., 2014; Laumon et al., 2005) and iii) from data obtained from population databases and registries (Dischinger et al., 2011; Corsenac et al., 2012; Leveille et al., 1994; Meuleners et al., 2011; Monarrez-Espino et al., 2016).
Drugs capable of impairing key functions required for safe driving of motorized vehicles are one of several factors that increase crash risk. Globally, it is estimated that in 2013 there were over 188,000 alcoholrelated road traffic deaths and a further almost 40,000 drug-related road traffic deaths (Poznyak, 2016). Drug use by drivers in the State of Victoria, Australia is also prevalent in both injured and killed drivers (Drummer et al., 2012, 2003; Di Rago et al., 2019b). It is well recognized that around the world the drugs most capable of causing impairment, after alcohol, are illicit substances, particularly amphetamines, cannabis, and cocaine, although opioids and benzodiazepines are commonly associated with traffic injuries and death, and often in combination with other substances, including to some degree the novel psychoactive substances (NPS) (Gadegbeku et al., 2011; Movig et al., 2004; Li et al., 2013; Legrand et al., 2012; Bernhoft et al.,
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Corresponding author at: Department of Forensic Medicine at Victorian Institute of Forensic Medicine, 65 Kavanagh Street, Southbank 3006, Victoria, Australia. E-mail address: [email protected] (O.H. Drummer).
https://doi.org/10.1016/j.aap.2019.105389 Received 12 August 2019; Received in revised form 25 November 2019; Accepted 28 November 2019 0001-4575/ © 2019 Elsevier Ltd. All rights reserved.
Accident Analysis and Prevention 135 (2020) 105389
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the crash occurred off-road (declared and local roads), if the crash was caused by a medical episode, or was found to be intentional. Drugs most likely to have been given by paramedics or staff at hospitals were excluded from the database. These included midazolam and anesthetics such as lignocaine, bupivacaine, ropivacaine as well as ondansetron and metoclopramide. Acetaminophen (paracetamol) detections were also not included since this drug is unlikely to pose a road safety risk. While morphine and ketamine are often given to injured persons, it was not possible to determine if they had been taken or were given post-crash; hence these drugs were not excluded from consideration. Drivers were categorized into those driving cars, motor cycles (including motorized scooters), vans and light trucks (under 4.5 tonnes and not articulated) and trucks (articulated trucks and rigid trucks over 4.5 tonnes, and buses). Crashes were also categorized into either single or multiple vehicle collisions.
Meta-analyses of published studies generally show increases in crash risk, particularly for users of amphetamines (e.g. methylamphetamine and amphetamine) and cocaine (Elvik, 2013). The presence of THC in blood shows a more modest increase in crash risk with estimates (as odds ratios, OR) ranging from not significant to over 2, with most OR showing a less than doubling when adjusted for possible confounders (Gadegbeku et al., 2011; Li et al., 2013; Bernhoft et al., 2012; Hels et al., 2013; Drummer et al., 2004; Poulsen et al., 2014; Elvik, 2013; Martin et al., 2017; Asbridge et al., 2012). Crash risks for benzodiazepines and opioids are not so well established given both their prescribed and recreational use. Meta-analyses of published studies that include case control design, crash involvement or from determination of crash culpability show variable crash risk for these two classes (Elvik, 2013; Chihuri and Li, 2017; Dassanayake et al., 2011). Culpability analysis has been used in several studies as originally reported (Robertson and Drummer, 1994) or with some variations in a number of subsequent studies (Gadegbeku et al., 2011; Drummer et al., 2004; Poulsen et al., 2014; Laumon et al., 2005; Brubacher et al., 2019; Longo et al., 2000). This has allowed an assessment of any changes to the proportion of culpable drivers within a number of categories including the effect of alcohol and various drugs summarized as an OR using the drug-free driver as the reference group. Changes in the OR may provide an estimate of the effect of drugs on crash risk providing any confounding factors can be accommodated in multivariate statistical calculations. In the state of Victoria, the presence of methylamphetamine (MA), 3,4-methylenedioxy-methylamphetamine (MDMA) and Δ9-tetrahydrocannabinol (THC) has been prohibited in drivers of motorized vehicles. Since mid 2009 Victoria has legislated compulsory drug testing of all drivers hospitalized as a result of a road traffic collision. Approximately 5000–6000 blood specimens are analyzed at the Victorian Institute of Forensic Medicine annually for alcohol (ethanol), THC, MA and MDMA in accordance with the Victorian Road Safety Act (1986). Blood specimens collected following a collision under the Victorian Injured Driver Drug Testing Program provide an excellent ongoing opportunity to add to the body of evidence surrounding drug use trends and insight to crash-risk among Victorian drivers through use of culpability analysis. This study estimates the odds of culpability of drivers with detected drugs in Victorian injured-drivers hospitalized over the 5-year period to mid 2018 using a previously validated culpability method of determining driver contribution to a crash.
2.2. Analytical aspects Blood samples were analyzed at the Victorian Institute of Forensic Medicine (VIFM) for a broad range of drugs and metabolites as well as alcohol. A fully validated LC–MS/MS semi-quantitative method was used for drug analyses (Di Rago et al., 2019a) with results of the prevalence of drugs reported previously (Di Rago et al., 2019b) and alcohol had been measured by a fully validated gas chromatography flame ionization detection (GC-FID) technique with a reporting limit of 0.01 g/ 100 mL. The method was able to detect up to 327 drugs, including a large series of NPS drugs. 2.3. Culpability analyses Culpability analyses were conducted on each driver using a validated method (Robertson and Drummer, 1994). Briefly, this involved a consideration of the known circumstances of the collision using an 8point assessment guide. This included scoring each collision for the condition of road, condition of vehicle, driving conditions, accident type, witness observations, road law obedience, difficulty of task, and level of fatigue. Each factor had a predetermined numerical score. Culpable drivers were those with an aggregate score of 12 or less. If a sufficient number of mitigating factors from the sum total of the scores were identified a driver would be found to be either partly or totally exonerated from blameworthiness and scored either as a contributory (score between 13 and 15) or nonculpable driver (score over 15). If drugs present in a driver had contributed to crash causation, it would be expected that they would be overrepresented in culpable drivers. Statistical analyses, as defined below, of the whole dataset was used to identify any associations using the drug-free driver as the control group. These culpability analyses were conducted by trained project officers at Victoria Police (CM, TF) with a sub-set of 100 drivers each year audited by three authors (MDR, DG, OHD) of this manuscript. All of these assessments were conducted blind to any knowledge over the presence or absence of drugs.
2. Methods 2.1. General A blood specimen was taken from each driver admitted to a Victorian hospital (includes attendance at emergency units at hospitals) as a result of a road traffic crash as part of the State’s legislative requirements. Blood was collected by a medical practitioner or registered nurse at each hospital as soon as practicable after admission, and placed in sample tubes containing preservative (1 % sodium fluoride/potassium oxalate). As part of this project 1000 drivers from each of the 5-year periods from beginning of July 2013 to end of June 2018 were randomly selected, using a Microsoft Excel function, for further and comprehensive toxicological analyses as part of an approved funded project investigating the prevalence and involvement of other drugs in Victorian injured drivers. These specimens had been initially analyzed for alcohol and three proscribed drugs (MA, MDMA and THC). Inclusion criteria required the blood sample be obtained from the injured driver of a vehicle involved in a traffic collision irrespective of the severity of the injuries. Cases were excluded from the study if individuals were determined to not be the driver of the vehicle at the time of the crash, if
2.4. Statistical analyses Logistic regression was used to investigate differences in the odds of culpability associated with alcohol and drug use and other selected crash attributes. Initial estimates of unadjusted odds ratios (ORs) and 95 % confidence intervals (CIs) were obtained from a series of univariate logistic regression models. Adjusted ORs for the effect of the drug and alcohol categorisation on driver culpability for the crash in which they were injured were obtained from multivariable logistic regression models in which other potentially explanatory driver and crash attributes were included. A categorisation of drivers was created by drug family categories dependent on the toxicological findings. The control group of drug-free 2
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independent of whether vehicles were cars, motor cyclists, vans or trucks (cases in which no impairing drug was also detected). Drivers with BAC 0.15 g/100 mL and higher, and no other drugs, had odds of culpability that were 73 times those of the control group (P < 0.001). Overall alcohol at any concentration was present in 15.8 % of drivers. The next largest increase in the odds of culpability was for methylamphetamine, an illicit drug seen in 12.8 % of all drivers, particularly drivers of cars and motor cycles (13.1 and 14.3 %, respectively) (Di Rago et al., 2019b). Overall the adjusted OR for methylamphetamine positive injured drivers (who had no alcohol or other drug present) was 19 (P < 0.001) (Table 2). The lack of a clear concentration dependence is not unexpected since persons using this strong stimulant can experience both euphoria and enhanced aggression (amongst other strong stimulant properties) and drowsiness to fatigue without any apparent dose relationship; all of which are detrimental to safe driving (Logan, 2002). A meta-analysis of previously published studies have shown an OR of 6 for injured drivers (Elvik, 2013). Since this review was published a DRUID1 study of drug data in severely injured drivers when compared to controls showed an OR of all amphetamines as 14 (Hels et al., 2013) and a study of fatally injured drivers in New Zealand using the same methodology as this paper gave an OR of 12 for any stimulant positive driver (Poulsen et al., 2014). Our own previous study of fatally-injured drivers in Victoria had shown a lower OR of 2.3 for stimulants, although this was much higher in truck drivers (OR 8.8). Importantly, the prevalence of amphetamines in injured heavy vehicles in this study was much lower than found almost 10 years earlier (Drummer et al., 2012). THC was the next most prevalent drug detected in 11.1 % of all drivers. When THC was present in all driver categories in the absence of any other potentially impairing substance the odds of culpability was 1.9-fold higher than the control group (P = 0.007), however the increase in odds was most apparent at higher blood THC concentrations. At 5 ng/mL and above the OR was 3.2 (P = 0.01), and at THC concentrations of 10 ng/mL and above the OR was 10 (P = 0.03) indicating that the odds of culpability increase with rising concentration. Only 1 % of the 5000 drivers in this THC-only group were 10 ng/mL or higher. In 67 car drivers with only THC present the odds of culpability was 2.8 (95 %CI 1.7–4.5) times greater than car drivers in the control group, and in 47 car drivers with the combination of only THC and alcohol the increased odds of culpability was 21 (95 %CI 6.4–68). The effect of THC could not be examined in the other driver categories due to insufficient sample numbers. The impact of cannabis use on crash risk has been subject to much debate. In our previous multicenter study (Drummer et al., 2004) involving fatally-injured drivers there were comparatively few drivers with only THC present. This analyte had not been measured by all laboratories for the whole study period, albeit the adjusted OR (2.7, 95 %CI 1.02–7) was somewhat higher than calculated in this injured driver study. Meta-analyses of crash risk associated with cannabis and driving have shown variable results; from an OR of 1.6 for culpability studies and higher depending on the type of study (Asbridge et al., 2012). Another review found the OR (1.1) was not significantly elevated (Elvik, 2013). A recent Bayesian analysis of 13 published studies estimated the pooled increased risk of a culpable crash as 1.46 with a 95 % credibility interval of 1.11–1.75 (Rogeberg, 2019). Despite the variations that naturally exist between studies and models chosen the finding from our study appears to be broadly similar to these other studies, and suggests lower odds of culpability than that seen with alcohol and methylamphetamine. Driving simulator studies on cannabis users have generally found a decrease in the ability to control a motor vehicle, both through lane control as measured by the standard deviation of lane position (SDLP) and ability to respond to unexpected situations (Downey et al., 2013;
and alcohol-free injured drivers (“negative” category of drivers) was used as a comparator for other categories of drivers found to contain either alcohol or drugs alone, or combinations of alcohol and drugs. These categories included drivers found to be under the influence of alcohol only, cannabinoids only, amphetamines only, anti-depressants only, sedating anti-histamines only, benzodiazepines only, opioids only and other stimulants or anorectics only. For the alcohol, THC and methylamphetamines categories, further sub-categories based on the detected drug concentration were investigated. Further, the driver categorisation included selected combinations of drug families, specifically drivers found to be under the influence of THC and methylamphetamine, alcohol and THC, and alcohol and benzodiazepines. Two other categorisations of drug involvement were also created. An illicit drug categorisation of drivers was based on the presence of illicit amphetamines, THC, cocaine, heroin and/or NPS. An impairing drug categorisation of drivers used the presence of any of the above plus alcohol, benzodiazepines and/or sedating anti-histamines. Other driver or crash attributes that were potentially explanatory of culpability included driver’s gender, age group (< 18 years, 18–25 years, 26–29 years, 30–39 years, 40–59 years and 60+ years), vehicle type (car, motorcycle, heavy vehicle, van/light truck) and crash location (metropolitan or rural). All potential interactions between pairs of these four driver and crash attributes were tested in extended logistic regression models and were found to be non-significant at a p-value threshold of 0.05 and were thus omitted from further consideration. 2.5. Ethics This project was approved by the Victoria Police Human Research Ethics Committee, application number 66/08. 3. Results and discussion Of the 5000 cases analyzed 12 were subsequently excluded since they were later found not to be drivers. This left 4988 drivers, of which 63.1 % were positive to alcohol and/or one of the detected drugs. Details of the presence of these drugs in injured drivers have been detailed elsewhere (Di Rago et al., 2019b). Both age and gender affected the odds of being responsible among drivers injured in a crash (Drummer et al., 2004; Chihuri and Li, 2019) (Table 1). Drivers aged between 40 and 59 years showed the lowest odds of culpability, while drivers under 30 years old and drivers over 60 years old exhibited relatively higher odds of culpability. Similarly, and not unexpectedly, drivers injured in a single vehicle collision had higher odds of culpability than drivers injured in multiple vehicle collisions. The odds of culpability also varied between categories of vehicle, with injured motor cycle drivers having the lowest odds of culpability for their crash and injured drivers of cars and trucks having the highest odds (Martin-de-Los Reyes et al., 2018) (Table 1). Comparisons between injured drivers with various individual drugs and drug combinations and injured drivers without alcohol and drugs were made in terms of the odds of culpability as shown in Table 2. Drivers with one or more illicit drug present, but no alcohol, had a 10-fold increase in odds of culpability (P < 0.001), while drivers with any potentially impairing drug (but no alcohol) had 8.2-fold increased odds (P < 0.001). The illicit drugs detected included mostly methylamphetamine and THC, but also some cases of MDA, MDMA, cocaine, heroin, LSD and a few novel psychoactive drugs (NPS). The potentially impairing drugs included these illicit drugs as well as benzodiazepines and sedating antihistamines (Table 2). The most common combinations included alcohol with THC, alcohol with a benzodiazepine, alcohol with methylamphetamine, and THC with methylamphetamine. In line with many other studies (Zador et al., 2000; Peck et al., 2008), alcohol showed substantial increases in odds of culpability, particularly at blood alcohol concentrations (BAC) of 0.05 g/100 mL (g %) and above. Odds of culpability also increased with rising BAC
1
3
Driving under the influence of drugs project funded by the European Union.
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Table 1 Summary of drivers injured in crashes in Victoria, Australia: culpability / non-culpability ratio for drug-free drivers with odds ratios, and comparisons of culpability / non-culpability ratio by comparing different categories of drug-free drivers to reference categories for age group, gender, vehicle type and multi-vehicle crash. Group
Age ranges3 0–17 years 18–25 26–29 years 30–39 years 40–59 years 60 years or older Male4 Female Car drivers Motor cyclists Vans/light truck drivers Heavy vehicle drivers Single vehicle crash5 Multiple vehicle crash
All Drivers (N = 4988)
50 1337 531 1015 1385 660 3213 1750 4011 701 107 169 1942 2924
Drug-Free drivers (N = 1837) N1
Culpable / non-culpable
Ratio2
OR (95 % CI)
22 531 171 313 506 291 1079 752 1493 203 53 88 560 1236
19/3 305/190 80/83 136/159 184/288 154/124 529/482 348/364 745/669 70/113 23/26 43/39 307/191 559/640
6.33 1.61 0.96 0.86 0.64 1.24 1.10 0.96 1.11 0.62 0.88 1.10 1.61 0.87
9.91 (2.89–34.0) 2.51 (1.94–3.26) 1.51 (1.05–2.16) 1.34 (1.00–1.80) Reference 1.94 (1.44–2.62) Reference 0.87 (0.72–1.06) Reference 0.56 (0.41-0.76) 0.79 (0.45–1.41) 0.99 (0.63–1.55) 1.84 (1.49–2.28) Reference
OR = odds ratio, i.e. odds of culpability compared between each category and the reference category, CI = 95 % confidence interval, N = number of drivers in each category. 1 Number of alcohol and drug free drivers in each category; 2 Culpable drivers divided by not culpable drivers; contributory drivers make up the difference between the sum of culpable and non-culpable and the total number of cases; 3 10 drivers had unknown age; 4 25 drivers had unknown gender; 5 122 crashes were not classified.
study, 12 % of injured drivers had one or more benzodiazepine detected with most of these cases (67 %) positive to diazepam; a drug occasionally given post-crash before and during medical treatment. Nevertheless, those drivers with only benzodiazepines present had odds of culpability that were elevated 3.2-fold compared to the control group (P < 0.001). Alprazolam was the next most common benzodiazepine detected (1.6 % of all cases), however given the multiplicity of other potentially impairing drugs present in these cases any change in the odds of culpability specific to a benzodiazepine could not be calculated. Drivers with opiates and the larger opioid class of drugs and without other impairing drugs showed similar odds of culpability to the control group (adjusted OR 1.1). However, a large number of the morphine positive cases may have been the result of post-crash administration of this opiate to relieve pain for injuries sustained in the collision. Unfortunately, we were unable to access patient records to distinguish medical use from personal use of morphine and heroin. The ORs of drivers with only oxycodone gave slightly higher OR to those with the weaker opioid codeine but with overlapping confidence ranges (Table 2). None of the other detected opioids gave any different results, but all had a lower prevalence than morphine, codeine and oxycodone (Di Rago et al., 2019b). Meta-analyses have shown an association to exist between prescription opioids and crash involvement but not between use of prescription opioids and odds of culpability (Chihuri and Li, 2017). A recent study investigating driver culpability in over 18,000 2-vehicle crashes using the FARS database in the USA opioid-using drivers had an adjusted OR of 2.18 compared to control drivers (Chihuri and Li, 2019). Fifteen antihistamines were targeted by the toxicological analyses performed in this study, 9 of which were detected in one or more drivers. A number of the sedating antihistamines (often available over-thecounter) were the most common particularly doxylamine, chlorpheniramine, promethazine and diphenhydramine (in decreasing order of prevalence). However, while these showed some increase in odds of culpability (OR 1.7), this was not statistically significant. Over 80 % of these cases were present with other drugs. Published reviews have also shown little or no increase in crash risk with these drugs (Elvik, 2013; Rudisill et al., 2016). There are several limitations with this type of culpability analysis to
Lenne et al., 2010; Micallef et al., 2018; Tank et al., 2019). Similarly, studies using volunteers and instrumented cars have generally also shown deficits in performance shortly after smoking cannabis (Ramaekers et al., 2000; Hartman et al., 2015; Arkell et al., 2019). However, it may not always be possible to differentiate the direct effects of cannabis use on driving performance since users of this drug tend to be inherently riskier drivers (Bergeron and Paquette, 2014). Behavioral changes and the effects seen in various psychomotor performance and cognitive testing following cannabis use are well known and documented (Ramaekers et al., 2004). However, this does not mean that a driver positive to THC is necessarily impaired to a degree that can be detected using standard sobriety assessments. However, the concentrations of THC detected in blood in this study are similar to those found in drivers who had been assessed as being impaired compared to those in whom only THC was detected (Jones et al., 2008; Khiabani et al., 2006; Bramness et al., 2010). While cocaine is a strong stimulant and might be expected to have similar driving impairing effects to methylamphetamine there were too few cases to form a meaningful assessment in this study. Almost all of 0.8 % of drivers positive to cocaine or one if its metabolites, were also positive to another impairing drug. Other (weaker) stimulants or anorectics detected included some cases with amphetamine only possibly from use of legally available dexamphetamine since very little if any amphetamine is available as a street drug (Phelan, 2018). Additional stimulants detected were phentermine (19 cases), modafinil (4 cases) and some ephedrine and high caffeine (greater than 10 mg/L in blood) cases. However, collectively these drivers did not show an elevated OR (Table 2). Similarly, drivers with antidepressants did not show evidence of increased odds of culpability. While almost 15 % of injured drivers had an antidepressant detected, these were mostly of the serotonin or mixed serotonin-norepinephrine reuptake inhibitors, which would not be expected to elevate crash risk, rather, it is possible their effect may be to improve driver behavior by elevating mood. A neutral effect of these anti-depressants on crash risk has been identified elsewhere (Elvik, 2013). In contrast, benzodiazepines are generally regarded as drugs that increase crash risk, although modestly (Chihuri and Li, 2019). In this 4
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Table 2 Unadjusted and adjusted odds ratios (OR) for groups of injured drivers who satisfy various drug only positive categories compared to the group of injured drivers without alcohol and without other drugs; adjustment for gender, age group, type of vehicle (car/motorbike/heavy vehicle/van or light truck) and location (metro/ rural). Group of drivers
N1 (total)
N2 (drug subgroup)
Number of drivers culpable/non culpable3
Ratio (culpable/non culpable)
OR unadjusted3
OR adjusted (95 %CI)4
Control group (no alcohol/drugs) Alcohol (all concentrations) BAC (< 0.05 %) BAC (0.05-0.099 %) BAC (0.10-0.149 %) BAC (≥0.15 %) Any illicit drug (no alcohol)6 Any impairing drug7 THC (all concentrations) THC (1–4.9 ng/mL) THC (5–9.9 ng/mL) THC (≥5 ng/mL) THC (≥10 ng/mL) Amphetamines (all concentrations) Methylamphetamine (all concentrations) Methylamphetamine (< 0.1 mg/L) Methylamphetamine (≥0.1 mg/L) Other stimulants/anorectics Antidepressants Antihistamines5 Benzodiazepines Opioids Codeine Oxycodone THC plus methylamphetamine Alcohol plus THC Alcohol plus benzodiazepine
1837 789 90 125 194 380
– 305 24 44 79 158 1057 1917 98 62 22 36 14 169 152 17 135 21 177 21 50 615 47 37 44 56 25
881/847 284/17 18/5 37/5 75/4 154/3 927/93 1654/201 62/28 38/21 13/6 24/7 11/1 155/9 141/6 16/1 125/5 11/8 86/79 12/8 35/13 299/278 25/19 19/15 41/3 52/3 25/0
1.04 16.7 3.6 7.4 18.8 51.3 10 8.2 2.2 1.8 2.2 3.4 11 17 23.5 16 25 1.4 1.1 1.5 2.7 1.1 0.8 1.3 13.7 17.3 **
1.0 (reference) 16 (9.7–28) 3.5 (1.2–12) 7.1 (2.8–23) 18 (6.7–68) 49 (16–243) 9.6 (7.6–12) 7.9 (6.6–9.5) 2.1 (1.3–3.5) 1.7 (1.0–3.1) 2.1 (0.7–6.7) 3.3 (1.4–9.1) 11 (1.5–456) 17 (8.4–37) 23 (10–63) 15 (2.4–646) 24 (9.9–76) 1.3 (0.5–3.8) 1.0 (0.8–1.5) 1.4 (0.5–4.1) 2.6 (1.3–5.4) 1.0 (0.9–1.2) 0.7 (0.4–1.4) 1.2 (0.6–2.6) 13 (14–67) 17 (5.4–84) **
1.0 (reference) 16 (9.4–26) 3.2 (1.1–8.7) 5.7 (2.2–15) 16 (5.7–43) 73 (18–294) 10 (7.9–13) 8.2 (6.8–9.8) 1.9 (1.2–3.1) 1.6 (0.9–2.7) 1.9 (0.7–5.0) 3.2 (1.3–7.2) 10 (1.3–82) 14 (6.9–28) 19 (8.4–44) 12 (1.6–95) 21 (8.3–51) 1.4 (0.6–3.6) 1.2 (0.9–1.7) 1.7 (0.7–4.3) 3.2 (1.6–6.1) 1.1 (0.9–1.4) 0.8 (0.4–1.5) 1.3 (0.6–2.6) 12 (3.6–38) 14 (4.4–46) **
553 373 130 180 50 705 593 128 465 67 729 116 594 1541 189 120 997 1194 1237
1
Total number of drivers with presence of the specific drug or drug combination, other than control cases in which no substances were detected. Drivers with presence of the specified drug or drug combination, includes drivers that were determined to be contributory and only drivers included where there were more than 20 cases. 3 Ratio of number of culpable drivers over number of non-culpable drivers divided by the ratio of the reference group. 4 OR calculated following multivariate analysis as presented in methods section. 5 Only sedating antihistamines were targeted by analytic method. 6 Includes THC, methylamphetamine, MDMA, MDA, heroin, cocaine, NPS. 7 Includes alcohol, THC, methylamphetamine, MDMA, heroin, cocaine, NPS, benzodiazepines, sedating antihistamines. ** OR cannot be calculated since no non-culpable drivers. 2
blood specimen. While a little over 2 h was the median delay this alone would affect the concentration of rapidly eliminated drugs; particularly for alcohol and THC. This delay could cause the blood concentration to drop below the limit of detection of the assay. Nevertheless, the results have shown odds of culpability broadly similar to other studies including those that have used different methodology to assess crash risk.
assess crash causation of drugs. Foremost is the difficulty in assessing all of the factors that could distract drivers or place drivers at an increased risk of an injurious collision independently of their drug use. While some factors can be assessed and then adjusted for it is clear that any findings from this epidemiological approach can only provide some indication of culpability risks associated with drug use. Additionally, it will not necessarily be the case that culpability risk translates to crash risk. Since a little over two-thirds of the drug-positive drivers used more than one impairing substance this further complicates any assessment of individual drug contribution. The use of sedating drugs, such as in particular alcohol, opioids, benzodiazepines, and even amphetamines in the elimination and withdrawal phases will exacerbate existing driver fatigue and even cause micro-sleeps (Logan, 2002; Seppala et al., 1979; Drummer, 2002; NHTSA, 1998). However, in most cases it was not possible to assess with any certainty whether a driver necessarily fell asleep at the wheel given there are many factors that cause collisions. Individual assessments of dose and tolerance to the drug(s) could not be made and will clearly be limitations in all epidemiological studies of this type although it is unclear the direction of bias that would be imparted. A host of factors could exist to differentiate the type of driver who has, say only alcohol, from the type of driver who has, say no impairing drugs at all. We have adjusted for some of the more likely among these factors but simply did not have access to others and again it would be unclear the direction of the bias that may be imparted by other confounders. A further complication is the time delay from crash to collection of a
4. Conclusion This culpability analysis of almost 5000 injured drivers has provided further evidence of the elevated odds of culpability associated with alcohol and methylamphetamine using drivers as well as drivers positive to THC, particularly those with higher blood concentrations. Of all the legally-available drugs benzodiazepines used in isolation show a modest increase in culpability but are most often associated with other potentially impairing drugs. Submission declaration and verification This manuscript has not been published previously (except in the form of an abstract, or lecture), and it is not under consideration for publication elsewhere. This publication is approved by all authors and explicitly by the responsible authorities where the work was carried out, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder 5
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Author contributions
antidepressants and opioids on driving: a systematic review and meta-analysis of epidemiological and experimental evidence. Drug Saf. 34 (2), 125–156. de Carvalho, H.B., Andreuccetti, G., Rezende, M.R., Bernini, C., Silva, J.S., Leyton, V., D’Andrea Greve, J.M., 2016. Alcohol and drug involvement in motorcycle driver injuries in the city of Sao Paulo, Brazil: analysis of crash culpability and other associated factors. Drug Alcohol Depend. 162, 199–205. DiRago, M., Pantatan, S., Hargreaves, M., Wong, K., Mantinieks, D., Kotsos, A., Glowacki, L., Drummer, O.H., Gerostamoulos, D., 2019a. High throughput detection of 327 drugs by LC-MS/MS with automated data processing. J. Anal. Toxicol submitted. diRago, M., Gerostamoulos, D., Morris, C., Frederickson, T., Woodford, N., Drummer, O.H., 2019b. Prevalence of drugs in injured drivers in Victoria, Australia. Austr. J. Forensic Sci. https://doi.org/10.1080/00450618.2019.1687753. epub 17/11/2019. Dischinger, P., Li, J., Smith, G.S., Ho, S., Auman, K., Shojai, D., 2011. Prescription medication usage and crash culpability in a population of injured drivers. Ann. Adv. Automot. Med. 55, 207–216. Downey, L.A., King, R., Papafotiou, K., Swann, P., Ogden, E., Boorman, M., Stough, C., 2013. The effects of cannabis and alcohol on simulated driving: influences of dose and experience. Accid. Anal. Prev. 50, 879–886. Drummer, O.H., 2002. Benzodiazepines – effects on human performance and behavior. Forensic Sci. Rev. 14 (1–2), 1–14. Drummer, O.H., Kourtis, I., Beyer, J., Tayler, P., Boorman, M., Gerostamoulos, D., 2012. The prevalence of drugs in injured drivers. Forensic Sci. Int. 215 (1–3), 14–17. Drummer, O.H., Gerostamoulos, J., Batziris, H., Chu, M., Caplehorn, J.R., Robertson, M.D., Swann, P., 2003. The incidence of drugs in drivers killed in Australian road traffic crashes. Forensic Sci. Int. 134 (2–3), 154–162. Drummer, O.H., Gerostamoulos, J., Batziris, H., Chu, M., Caplehorn, J., Robertson, M.D., Swann, P., 2004. The involvement of drugs in drivers of motor vehicles killed in Australian road traffic crashes. Accid. Anal. Prev. 36 (2), 239–248. Dubois, S., Mullen, N., Weaver, B., Bedard, M., 2015. The combined effects of alcohol and cannabis on driving: impact on crash risk. Forensic Sci. Int. 248, 94–100. Elvik, R., 2013. Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accid. Anal. Prev. 60, 254–267. Gadegbeku, B., Amoros, E., Laumon, B., 2011. Responsibility study: main illicit psychoactive substances among car drivers involved in fatal road crashes. Ann. Adv. Automot. Med. 55, 293–300. Hartman, R.L., Brown, T.L., Milavetz, G., Spurgin, A., Pierce, R.S., Gorelick, D.A., Gaffney, G., Huestis, M.A., 2015. Cannabis effects on driving lateral control with and without alcohol. Drug Alcohol Depend. 154, 25–37. Hels, T., Lyckegaard, A., Simonsen, K.W., Steentoft, A., Bernhoft, I.M., 2013. Risk of severe driver injury by driving with psychoactive substances. Accid. Anal. Prev. 59, 346–356. Jones, A.W., Holmgren, A., Kugelberg, F.C., 2008. Driving under the influence of cannabis: a 10-year study of age and gender differences in the concentrations of tetrahydrocannabinol in blood. Addiction 103 (3), 452–461. Khiabani, H.Z., Bramness, J.G., Bjorneboe, A., Morland, J., 2006. Relationship between THC concentration in blood and impairment in apprehended drivers. Traffic Inj. Prev. 7 (2), 111–116. Laumon, B., Gadegbeku, B., Martin, J.L., Biecheler, M.B., Group, S.A.M., 2005. Cannabis intoxication and fatal road crashes in France: population based case-control study. BMJ 331 (7529), 1371. Legrand, S.A., Houwing, S., Hagenzieker, M., Verstraete, A.G., 2012. Prevalence of alcohol and other psychoactive substances in injured drivers: comparison between Belgium and the Netherlands. Forensic Sci. Int. 220 (1–3), 224–231. Lenne, M.G., Dietze, P.M., Triggs, T.J., Walmsley, S., Murphy, B., Redman, J.R., 2010. The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand. Accid. Anal. Prev. 42 (3), 859–866. Leveille, S.G., Buchner, D.M., Koepsell, T.D., McCloskey, L.W., Wolf, M.E., Wagner, E.H., 1994. Psychoactive medications and injurious motor vehicle collisions involving older drivers. Epidemiology 5 (6), 591–598. Li, G., Brady, J.E., Chen, Q., 2013. Drug use and fatal motor vehicle crashes: a casecontrol study. Accid. Anal. Prev. 60, 205–210. Logan, B.K., 2002. Methamphetamine – effects on human performance and behavior. Forensic Sci. Rev. 14 (1–2), 133–151. Longo, M.C., Hunter, C.E., Lokan, R.J., White, J.M., White, M.A., 2000. The prevalence of alcohol, cannabinoids, benzodiazepines and stimulants amongst injured drivers and their role in driver culpability: part ii: the relationship between drug prevalence and drug concentration, and driver culpability. Accid. Anal. Prev. 32 (5), 623–632. Martin, J.L., Gadegbeku, B., Wu, D., Viallon, V., Laumon, B., 2017. Cannabis, alcohol and fatal road accidents. PLoS One 12 (11), e0187320. Martin-de-Los Reyes, L.M., Martinez-Ruiz, V., Lardelli-Claret, P., Moreno-Roldan, E., Molina-Soberanes, D., Jimenez-Mejias, E., 2018. Association between type of vehicle and the risk of provoking a collision between vehicles. Gac. Sanit. https://doi.org/10. 1016/j gaceta.2018.10.00. S0213-9111(18) 30245-0. Meuleners, L.B., Duke, J., Lee, A.H., Palamara, P., Hildebrand, J., Ng, J.Q., 2011. Psychoactive medications and crash involvement requiring hospitalization for older drivers: a population-based study. J. Am. Geriatr. Soc. 59 (9), 1575–1580. Micallef, J., Dupouey, J., Jouve, E., Truillet, R., Lacarelle, B., Taillard, J., Daurat, A., Authie, C., Blin, O., Rascol, O., Philip, P., Mestre, D., 2018. Cannabis smoking impairs driving performance on the simulator and real driving: a randomized, double-blind, placebo-controlled, crossover trial. Fundam. Clin. Pharmacol. 32 (5), 558–570. Monarrez-Espino, J., Laflamme, L., Rausch, C., Elling, B., Moller, J., 2016. New opioid analgesic use and the risk of injurious single-vehicle crashes in drivers aged 50–80 years: a population-based matched case-control study. Age Ageing 45 (5), 628–634. Morland, J., Steentoft, A., Simonsen, K.W., Ojanpera, I., Vuori, E., Magnusdottir, K., Kristinsson, J., Ceder, G., Kronstrand, R., Christophersen, A., 2011. Drugs related to
Dimitri Gerostamoulos: Substantial contribution to the study conception and design, data acquisition, supervision of analyses and interpretation, drafting or revising the article for intellectual content. Revision of the article for intellectual content, approval of the final version. Matthew Di Rago: Analytical analyses, data acquisition and analysis, revision of the article for intellectual content, approval of the final version. Noel W. Woodford: Contribution to the study conception and funding and availability of resources within Institute, revision of the article for intellectual content. Approval of the final version. Carla Morris: Specimen and data acquisition and analysis, revision of the article for intellectual content, approval of the final version. Tania Fredericksen: Specimen and data acquisition and analysis, revision of the article for intellectual content, approval of the final version. Kim Jachno: Conducted the statistical analyses. Rory Wolfe: Supervised the statistical analyses, revision of the article for intellectual content. Olaf H. Drummer: Substantial contribution to the study conception and design and interpretation of results. Drafting and revision of the article, approval of the final version. Project administration and agreement to be accountable for all aspects of the work related to the accuracy or integrity of any part of the work. Declaration of Competing Interest None. Acknowledgements The authors gratefully acknowledge the support of the Department of Justice, VicRoads, the Transport Accident Scheme (TAC) and Victoria Police that formed the Road Safety Executive (Victoria) and funded the study. In particular, gratitude is extended to William Gibbons (Department of Justice) and Inspector Tom McGillian (Victoria Police) for their support. We also thank Gemma Wynd of the Road Policing Drug and Alcohol Section of Victoria Police who assisted with data entry and culpability analyses. References Arkell, T.R., Lintzeris, N., Kevin, R.C., Ramaekers, J.G., Vandrey, R., Irwin, C., Haber, P.S., McGregor, I.S., 2019. Cannabidiol (CBD) content in vaporized cannabis does not prevent tetrahydrocannabinol (THC)-induced impairment of driving and cognition. Psychopharmacology (Berl.) 236 (9), 2713–2724. Asbridge, M., Hayden, J.A., Cartwright, J.L., 2012. Acute cannabis consumption and motor vehicle collision risk: systematic review of observational studies and metaanalysis. BMJ 344, e536. Bergeron, J., Paquette, M., 2014. Relationships between frequency of driving under the influence of cannabis, self-reported reckless driving and risk-taking behavior observed in a driving simulator. J. Saf. Res. 49, 19–24. Bernhoft, I.M., Hels, T., Lyckegaard, A., Houwing, S., Verstraete, A.G., 2012. Prevalence and risk of injury in Europe by driving with alcohol, illicit drugs and medicines. Procedia - Soc. Behav. Sci. 48, 2907–2916. Bramness, J.G., Khiabani, H.Z., Morland, J., 2010. Impairment due to cannabis and ethanol: clinical signs and additive effects. Addiction 105 (6), 1080–1087. Brubacher, J.R., Chan, H., Erdelyi, S., Macdonald, S., Asbridge, M., Mann, R.E., Eppler, J., Lund, A., MacPherson, A., Martz, W., Schreiber, W.E., Brant, R., Purssell, R.A., 2019. Cannabis use as a risk factor for causing motor vehicle crashes: a prospective study. Addiction 114 (9), 1616–1626. Chihuri, S., Li, G., 2017. Use of prescription opioids and motor vehicle crashes: a meta analysis. Accid. Anal. Prev. 109, 123–131. Chihuri, S., Li, G., 2019. Use of prescription opioids and initiation of fatal 2-vehicle crashes. JAMA Netw Open 2 (2), e188081. Corsenac, P., Lagarde, E., Gadegbeku, B., Delorme, B., Tricotel, A., Castot, A., Moore, N., Philip, P., Laumon, B., Orriols, L., 2012. Road traffic crashes and prescribed methadone and buprenorphine: a French registry-based case-control study. Drug Alcohol Depend. 123 (1–3), 91–97. Dassanayake, T., Michie, P., Carter, G., Jones, A., 2011. Effects of benzodiazepines,
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O.H. Drummer, et al.
Ramaekers, J.G., Berghaus, G., van Laar, M., Drummer, O.H., 2004. Dose related risk of motor vehicle crashes after cannabis use. Drug Alcohol Depend. 73 (2), 109–119. Reguly, P., Dubois, S., Bedard, M., 2014. Examining the impact of opioid analgesics on crash responsibility in truck drivers involved in fatal crashes. Forensic Sci. Int. 234, 154–161. Robertson, M.D., Drummer, O.H., 1994. Responsibility analysis: a methodology to study the effects of drugs in driving. Accid. Anal. Prev. 26 (2), 243–247. Rogeberg, O., 2019. A meta-analysis of the crash risk of cannabis-positive drivers in culpability studies-Avoiding interpretational bias. Accid. Anal. Prev. 123, 69–78. Rudisill, T.M., Zhu, M., Kelley, G.A., Pilkerton, C., Rudisill, B.R., 2016. Medication use and the risk of motor vehicle collisions among licensed drivers: a systematic review. Accid. Anal. Prev. 96, 255–270. Seppala, T., Linnoila, M., Mattila, M.J., 1979. Drugs, alcohol and driving. Drugs 17 (5), 389–408. Tank, A., Tietz, T., Daldrup, T., Schwender, H., Hellen, F., Ritz-Timme, S., Hartung, B., 2019. On the impact of cannabis consumption on traffic safety: a driving simulator study with habitual cannabis consumers. Int. J. Legal Med. 133 (5), 1411–1420. Zador, P.L., Krawchuk, S.A., Voas, R.B., 2000. Alcohol-related relative risk of driver fatalities and driver involvement in fatal crashes in relation to driver age and gender: an update using 1996 data. J. Stud. Alcohol 61 (3), 387–395.
motor vehicle crashes in northern European countries: a study of fatally injured drivers. Accid. Anal. Prev. 43 (6), 1920–1926. Movig, K.L., Mathijssen, M.P., Nagel, P.H., van Egmond, T., de Gier, J.J., Leufkens, H.G., Egberts, A.C., 2004. Psychoactive substance use and the risk of motor vehicle accidents. Accid. Anal. Prev. 36 (4), 631–636. Mura, P., Kintz, P., Ludes, B., Gaulier, J.M., Marquet, P., Martin-Dupont, S., Vincent, F., Kaddour, A., Goulle, J.P., Nouveau, J., Moulsma, M., Tilhet-Coartet, S., Pourrat, O., 2003. Comparison of the prevalence of alcohol, cannabis and other drugs between 900 injured drivers and 900 control subjects: results of a French collaborative study. Forensic Sci. Int. 133 (1–2), 79–85. NHTSA, 1998. Expert Panel on Driver Fatigue and Sleepiness. NHTSA. Peck, R.C., Gebers, M.A., Voas, R.B., Romano, E., 2008. The relationship between blood alcohol concentration (BAC), age, and crash risk. J. Safety Res. 39 (3), 311–319. Phelan, M., 2018. Illicit Drug Data Report 2016–17. Australian Criminal Intelligence Commission, Canberra p. 184. Poulsen, H., Moar, R., Pirie, R., 2014. The culpability of drivers killed in New Zealand road crashes and their use of alcohol and other drugs. Accid. Anal. Prev. 67, 119–128. Poznyak, V., 2016. Drug Use and Road Safety: A Policy Brief. World Health Organisation: Geneva, Switzerland. Ramaekers, J.G., Robbe, H.W., O’Hanlon, J.F., 2000. Marijuana, alcohol and actual driving performance. Hum. Psychopharmacol. 15 (7), 551–558.
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Odds of culpability associated with use of impairing drugs in injured drivers in Victoria, Australia
T
Olaf H. Drummera,*, Dimitri Gerostamoulosa, Matthew Di Ragoa, Noel W. Woodforda, Carla Morrisb, Tania Frederiksenb, Kim Jachnoc, Rory Wolfec a
Victorian Institute of Forensic Medicine and the Department of Forensic Medicine, Monash University, School of Public Health and Preventive Medicine, 65 Kavanagh Street, Southbank 3006, Victoria, Australia b Road Policing Drug and Alcohol Section, Victoria Police, 20 Dawson St., Brunswick 3056, Victoria, Australia c School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd, Melbourne 3004, Victoria, Australia
A R T I C LE I N FO
A B S T R A C T
Keywords: Cannabis Culpability analysis Crash risk Injured drivers Drug impairment Methylamphetamine
Culpability analysis was conducted on 5000 drivers injured as a result of a vehicular collision and in whom comprehensive toxicology testing in blood was conducted. The sample included 1000 drivers for each of 5 years from approximately 5000–6000 drivers injured and taken to hospital in the State of Victoria. Logistic regression was used to investigate differences in the odds of culpability associated with alcohol and drug use and other selected crash attributes using the drug-free driver as the reference group. Adjusted odds ratios were obtained from multivariable logistic regression models in which other potentially explanatory driver and crash attributes were included. Drivers with alcohol present showed large increases in the odds of culpability similar to that seen in other studies investigating associations between blood alcohol concentration and crash risk. Methylamphetamine also showed a large increase in the odds of culpability (OR 19) compared to the reference group at both below and above 0.1 mg/L, whereas those drivers with Δ9-tetrahydrocannabinol (THC) present showed only modest increase in odds when all concentrations were assessed (OR 1.9, 95 %CI 1.2–3.1). Benzodiazepines in drivers also gave an increase in odds (3.2, 95 %CI 1.6–6.1), but not other medicinal drugs such as antidepressants, antipsychotics and opioids. Drivers that had combinations of impairing drugs generally gave a large increase in odds, particularly combinations of alcohol with THC or benzodiazepines, and those drivers using both THC and methamphetamine.
1. Introduction
2012; Morland et al., 2011). While there is little dispute that most of the drugs used recreationally can cause significant reduction in cognitive and psychomotor functions there is still some doubt over the extent that these drugs affect crash risk. Estimates of crash risk arise from three main types of analyses; i) by estimating the prevalence of specific drugs or drug classes in drivers that have crashed compared to drivers that have not had a collision (Movig et al., 2004; Mura et al., 2003; Hels et al., 2013), ii) from culpability analyses (sometimes called responsibility analyses) where the proportion of culpable drivers using a particular drug is compared to the proportion of drivers not using drugs (Gadegbeku et al., 2011; Drummer et al., 2004; Poulsen et al., 2014; de Carvalho et al., 2016; Dubois et al., 2015; Reguly et al., 2014; Laumon et al., 2005) and iii) from data obtained from population databases and registries (Dischinger et al., 2011; Corsenac et al., 2012; Leveille et al., 1994; Meuleners et al., 2011; Monarrez-Espino et al., 2016).
Drugs capable of impairing key functions required for safe driving of motorized vehicles are one of several factors that increase crash risk. Globally, it is estimated that in 2013 there were over 188,000 alcoholrelated road traffic deaths and a further almost 40,000 drug-related road traffic deaths (Poznyak, 2016). Drug use by drivers in the State of Victoria, Australia is also prevalent in both injured and killed drivers (Drummer et al., 2012, 2003; Di Rago et al., 2019b). It is well recognized that around the world the drugs most capable of causing impairment, after alcohol, are illicit substances, particularly amphetamines, cannabis, and cocaine, although opioids and benzodiazepines are commonly associated with traffic injuries and death, and often in combination with other substances, including to some degree the novel psychoactive substances (NPS) (Gadegbeku et al., 2011; Movig et al., 2004; Li et al., 2013; Legrand et al., 2012; Bernhoft et al.,
⁎
Corresponding author at: Department of Forensic Medicine at Victorian Institute of Forensic Medicine, 65 Kavanagh Street, Southbank 3006, Victoria, Australia. E-mail address: [email protected] (O.H. Drummer).
https://doi.org/10.1016/j.aap.2019.105389 Received 12 August 2019; Received in revised form 25 November 2019; Accepted 28 November 2019 0001-4575/ © 2019 Elsevier Ltd. All rights reserved.
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the crash occurred off-road (declared and local roads), if the crash was caused by a medical episode, or was found to be intentional. Drugs most likely to have been given by paramedics or staff at hospitals were excluded from the database. These included midazolam and anesthetics such as lignocaine, bupivacaine, ropivacaine as well as ondansetron and metoclopramide. Acetaminophen (paracetamol) detections were also not included since this drug is unlikely to pose a road safety risk. While morphine and ketamine are often given to injured persons, it was not possible to determine if they had been taken or were given post-crash; hence these drugs were not excluded from consideration. Drivers were categorized into those driving cars, motor cycles (including motorized scooters), vans and light trucks (under 4.5 tonnes and not articulated) and trucks (articulated trucks and rigid trucks over 4.5 tonnes, and buses). Crashes were also categorized into either single or multiple vehicle collisions.
Meta-analyses of published studies generally show increases in crash risk, particularly for users of amphetamines (e.g. methylamphetamine and amphetamine) and cocaine (Elvik, 2013). The presence of THC in blood shows a more modest increase in crash risk with estimates (as odds ratios, OR) ranging from not significant to over 2, with most OR showing a less than doubling when adjusted for possible confounders (Gadegbeku et al., 2011; Li et al., 2013; Bernhoft et al., 2012; Hels et al., 2013; Drummer et al., 2004; Poulsen et al., 2014; Elvik, 2013; Martin et al., 2017; Asbridge et al., 2012). Crash risks for benzodiazepines and opioids are not so well established given both their prescribed and recreational use. Meta-analyses of published studies that include case control design, crash involvement or from determination of crash culpability show variable crash risk for these two classes (Elvik, 2013; Chihuri and Li, 2017; Dassanayake et al., 2011). Culpability analysis has been used in several studies as originally reported (Robertson and Drummer, 1994) or with some variations in a number of subsequent studies (Gadegbeku et al., 2011; Drummer et al., 2004; Poulsen et al., 2014; Laumon et al., 2005; Brubacher et al., 2019; Longo et al., 2000). This has allowed an assessment of any changes to the proportion of culpable drivers within a number of categories including the effect of alcohol and various drugs summarized as an OR using the drug-free driver as the reference group. Changes in the OR may provide an estimate of the effect of drugs on crash risk providing any confounding factors can be accommodated in multivariate statistical calculations. In the state of Victoria, the presence of methylamphetamine (MA), 3,4-methylenedioxy-methylamphetamine (MDMA) and Δ9-tetrahydrocannabinol (THC) has been prohibited in drivers of motorized vehicles. Since mid 2009 Victoria has legislated compulsory drug testing of all drivers hospitalized as a result of a road traffic collision. Approximately 5000–6000 blood specimens are analyzed at the Victorian Institute of Forensic Medicine annually for alcohol (ethanol), THC, MA and MDMA in accordance with the Victorian Road Safety Act (1986). Blood specimens collected following a collision under the Victorian Injured Driver Drug Testing Program provide an excellent ongoing opportunity to add to the body of evidence surrounding drug use trends and insight to crash-risk among Victorian drivers through use of culpability analysis. This study estimates the odds of culpability of drivers with detected drugs in Victorian injured-drivers hospitalized over the 5-year period to mid 2018 using a previously validated culpability method of determining driver contribution to a crash.
2.2. Analytical aspects Blood samples were analyzed at the Victorian Institute of Forensic Medicine (VIFM) for a broad range of drugs and metabolites as well as alcohol. A fully validated LC–MS/MS semi-quantitative method was used for drug analyses (Di Rago et al., 2019a) with results of the prevalence of drugs reported previously (Di Rago et al., 2019b) and alcohol had been measured by a fully validated gas chromatography flame ionization detection (GC-FID) technique with a reporting limit of 0.01 g/ 100 mL. The method was able to detect up to 327 drugs, including a large series of NPS drugs. 2.3. Culpability analyses Culpability analyses were conducted on each driver using a validated method (Robertson and Drummer, 1994). Briefly, this involved a consideration of the known circumstances of the collision using an 8point assessment guide. This included scoring each collision for the condition of road, condition of vehicle, driving conditions, accident type, witness observations, road law obedience, difficulty of task, and level of fatigue. Each factor had a predetermined numerical score. Culpable drivers were those with an aggregate score of 12 or less. If a sufficient number of mitigating factors from the sum total of the scores were identified a driver would be found to be either partly or totally exonerated from blameworthiness and scored either as a contributory (score between 13 and 15) or nonculpable driver (score over 15). If drugs present in a driver had contributed to crash causation, it would be expected that they would be overrepresented in culpable drivers. Statistical analyses, as defined below, of the whole dataset was used to identify any associations using the drug-free driver as the control group. These culpability analyses were conducted by trained project officers at Victoria Police (CM, TF) with a sub-set of 100 drivers each year audited by three authors (MDR, DG, OHD) of this manuscript. All of these assessments were conducted blind to any knowledge over the presence or absence of drugs.
2. Methods 2.1. General A blood specimen was taken from each driver admitted to a Victorian hospital (includes attendance at emergency units at hospitals) as a result of a road traffic crash as part of the State’s legislative requirements. Blood was collected by a medical practitioner or registered nurse at each hospital as soon as practicable after admission, and placed in sample tubes containing preservative (1 % sodium fluoride/potassium oxalate). As part of this project 1000 drivers from each of the 5-year periods from beginning of July 2013 to end of June 2018 were randomly selected, using a Microsoft Excel function, for further and comprehensive toxicological analyses as part of an approved funded project investigating the prevalence and involvement of other drugs in Victorian injured drivers. These specimens had been initially analyzed for alcohol and three proscribed drugs (MA, MDMA and THC). Inclusion criteria required the blood sample be obtained from the injured driver of a vehicle involved in a traffic collision irrespective of the severity of the injuries. Cases were excluded from the study if individuals were determined to not be the driver of the vehicle at the time of the crash, if
2.4. Statistical analyses Logistic regression was used to investigate differences in the odds of culpability associated with alcohol and drug use and other selected crash attributes. Initial estimates of unadjusted odds ratios (ORs) and 95 % confidence intervals (CIs) were obtained from a series of univariate logistic regression models. Adjusted ORs for the effect of the drug and alcohol categorisation on driver culpability for the crash in which they were injured were obtained from multivariable logistic regression models in which other potentially explanatory driver and crash attributes were included. A categorisation of drivers was created by drug family categories dependent on the toxicological findings. The control group of drug-free 2
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independent of whether vehicles were cars, motor cyclists, vans or trucks (cases in which no impairing drug was also detected). Drivers with BAC 0.15 g/100 mL and higher, and no other drugs, had odds of culpability that were 73 times those of the control group (P < 0.001). Overall alcohol at any concentration was present in 15.8 % of drivers. The next largest increase in the odds of culpability was for methylamphetamine, an illicit drug seen in 12.8 % of all drivers, particularly drivers of cars and motor cycles (13.1 and 14.3 %, respectively) (Di Rago et al., 2019b). Overall the adjusted OR for methylamphetamine positive injured drivers (who had no alcohol or other drug present) was 19 (P < 0.001) (Table 2). The lack of a clear concentration dependence is not unexpected since persons using this strong stimulant can experience both euphoria and enhanced aggression (amongst other strong stimulant properties) and drowsiness to fatigue without any apparent dose relationship; all of which are detrimental to safe driving (Logan, 2002). A meta-analysis of previously published studies have shown an OR of 6 for injured drivers (Elvik, 2013). Since this review was published a DRUID1 study of drug data in severely injured drivers when compared to controls showed an OR of all amphetamines as 14 (Hels et al., 2013) and a study of fatally injured drivers in New Zealand using the same methodology as this paper gave an OR of 12 for any stimulant positive driver (Poulsen et al., 2014). Our own previous study of fatally-injured drivers in Victoria had shown a lower OR of 2.3 for stimulants, although this was much higher in truck drivers (OR 8.8). Importantly, the prevalence of amphetamines in injured heavy vehicles in this study was much lower than found almost 10 years earlier (Drummer et al., 2012). THC was the next most prevalent drug detected in 11.1 % of all drivers. When THC was present in all driver categories in the absence of any other potentially impairing substance the odds of culpability was 1.9-fold higher than the control group (P = 0.007), however the increase in odds was most apparent at higher blood THC concentrations. At 5 ng/mL and above the OR was 3.2 (P = 0.01), and at THC concentrations of 10 ng/mL and above the OR was 10 (P = 0.03) indicating that the odds of culpability increase with rising concentration. Only 1 % of the 5000 drivers in this THC-only group were 10 ng/mL or higher. In 67 car drivers with only THC present the odds of culpability was 2.8 (95 %CI 1.7–4.5) times greater than car drivers in the control group, and in 47 car drivers with the combination of only THC and alcohol the increased odds of culpability was 21 (95 %CI 6.4–68). The effect of THC could not be examined in the other driver categories due to insufficient sample numbers. The impact of cannabis use on crash risk has been subject to much debate. In our previous multicenter study (Drummer et al., 2004) involving fatally-injured drivers there were comparatively few drivers with only THC present. This analyte had not been measured by all laboratories for the whole study period, albeit the adjusted OR (2.7, 95 %CI 1.02–7) was somewhat higher than calculated in this injured driver study. Meta-analyses of crash risk associated with cannabis and driving have shown variable results; from an OR of 1.6 for culpability studies and higher depending on the type of study (Asbridge et al., 2012). Another review found the OR (1.1) was not significantly elevated (Elvik, 2013). A recent Bayesian analysis of 13 published studies estimated the pooled increased risk of a culpable crash as 1.46 with a 95 % credibility interval of 1.11–1.75 (Rogeberg, 2019). Despite the variations that naturally exist between studies and models chosen the finding from our study appears to be broadly similar to these other studies, and suggests lower odds of culpability than that seen with alcohol and methylamphetamine. Driving simulator studies on cannabis users have generally found a decrease in the ability to control a motor vehicle, both through lane control as measured by the standard deviation of lane position (SDLP) and ability to respond to unexpected situations (Downey et al., 2013;
and alcohol-free injured drivers (“negative” category of drivers) was used as a comparator for other categories of drivers found to contain either alcohol or drugs alone, or combinations of alcohol and drugs. These categories included drivers found to be under the influence of alcohol only, cannabinoids only, amphetamines only, anti-depressants only, sedating anti-histamines only, benzodiazepines only, opioids only and other stimulants or anorectics only. For the alcohol, THC and methylamphetamines categories, further sub-categories based on the detected drug concentration were investigated. Further, the driver categorisation included selected combinations of drug families, specifically drivers found to be under the influence of THC and methylamphetamine, alcohol and THC, and alcohol and benzodiazepines. Two other categorisations of drug involvement were also created. An illicit drug categorisation of drivers was based on the presence of illicit amphetamines, THC, cocaine, heroin and/or NPS. An impairing drug categorisation of drivers used the presence of any of the above plus alcohol, benzodiazepines and/or sedating anti-histamines. Other driver or crash attributes that were potentially explanatory of culpability included driver’s gender, age group (< 18 years, 18–25 years, 26–29 years, 30–39 years, 40–59 years and 60+ years), vehicle type (car, motorcycle, heavy vehicle, van/light truck) and crash location (metropolitan or rural). All potential interactions between pairs of these four driver and crash attributes were tested in extended logistic regression models and were found to be non-significant at a p-value threshold of 0.05 and were thus omitted from further consideration. 2.5. Ethics This project was approved by the Victoria Police Human Research Ethics Committee, application number 66/08. 3. Results and discussion Of the 5000 cases analyzed 12 were subsequently excluded since they were later found not to be drivers. This left 4988 drivers, of which 63.1 % were positive to alcohol and/or one of the detected drugs. Details of the presence of these drugs in injured drivers have been detailed elsewhere (Di Rago et al., 2019b). Both age and gender affected the odds of being responsible among drivers injured in a crash (Drummer et al., 2004; Chihuri and Li, 2019) (Table 1). Drivers aged between 40 and 59 years showed the lowest odds of culpability, while drivers under 30 years old and drivers over 60 years old exhibited relatively higher odds of culpability. Similarly, and not unexpectedly, drivers injured in a single vehicle collision had higher odds of culpability than drivers injured in multiple vehicle collisions. The odds of culpability also varied between categories of vehicle, with injured motor cycle drivers having the lowest odds of culpability for their crash and injured drivers of cars and trucks having the highest odds (Martin-de-Los Reyes et al., 2018) (Table 1). Comparisons between injured drivers with various individual drugs and drug combinations and injured drivers without alcohol and drugs were made in terms of the odds of culpability as shown in Table 2. Drivers with one or more illicit drug present, but no alcohol, had a 10-fold increase in odds of culpability (P < 0.001), while drivers with any potentially impairing drug (but no alcohol) had 8.2-fold increased odds (P < 0.001). The illicit drugs detected included mostly methylamphetamine and THC, but also some cases of MDA, MDMA, cocaine, heroin, LSD and a few novel psychoactive drugs (NPS). The potentially impairing drugs included these illicit drugs as well as benzodiazepines and sedating antihistamines (Table 2). The most common combinations included alcohol with THC, alcohol with a benzodiazepine, alcohol with methylamphetamine, and THC with methylamphetamine. In line with many other studies (Zador et al., 2000; Peck et al., 2008), alcohol showed substantial increases in odds of culpability, particularly at blood alcohol concentrations (BAC) of 0.05 g/100 mL (g %) and above. Odds of culpability also increased with rising BAC
1
3
Driving under the influence of drugs project funded by the European Union.
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Table 1 Summary of drivers injured in crashes in Victoria, Australia: culpability / non-culpability ratio for drug-free drivers with odds ratios, and comparisons of culpability / non-culpability ratio by comparing different categories of drug-free drivers to reference categories for age group, gender, vehicle type and multi-vehicle crash. Group
Age ranges3 0–17 years 18–25 26–29 years 30–39 years 40–59 years 60 years or older Male4 Female Car drivers Motor cyclists Vans/light truck drivers Heavy vehicle drivers Single vehicle crash5 Multiple vehicle crash
All Drivers (N = 4988)
50 1337 531 1015 1385 660 3213 1750 4011 701 107 169 1942 2924
Drug-Free drivers (N = 1837) N1
Culpable / non-culpable
Ratio2
OR (95 % CI)
22 531 171 313 506 291 1079 752 1493 203 53 88 560 1236
19/3 305/190 80/83 136/159 184/288 154/124 529/482 348/364 745/669 70/113 23/26 43/39 307/191 559/640
6.33 1.61 0.96 0.86 0.64 1.24 1.10 0.96 1.11 0.62 0.88 1.10 1.61 0.87
9.91 (2.89–34.0) 2.51 (1.94–3.26) 1.51 (1.05–2.16) 1.34 (1.00–1.80) Reference 1.94 (1.44–2.62) Reference 0.87 (0.72–1.06) Reference 0.56 (0.41-0.76) 0.79 (0.45–1.41) 0.99 (0.63–1.55) 1.84 (1.49–2.28) Reference
OR = odds ratio, i.e. odds of culpability compared between each category and the reference category, CI = 95 % confidence interval, N = number of drivers in each category. 1 Number of alcohol and drug free drivers in each category; 2 Culpable drivers divided by not culpable drivers; contributory drivers make up the difference between the sum of culpable and non-culpable and the total number of cases; 3 10 drivers had unknown age; 4 25 drivers had unknown gender; 5 122 crashes were not classified.
study, 12 % of injured drivers had one or more benzodiazepine detected with most of these cases (67 %) positive to diazepam; a drug occasionally given post-crash before and during medical treatment. Nevertheless, those drivers with only benzodiazepines present had odds of culpability that were elevated 3.2-fold compared to the control group (P < 0.001). Alprazolam was the next most common benzodiazepine detected (1.6 % of all cases), however given the multiplicity of other potentially impairing drugs present in these cases any change in the odds of culpability specific to a benzodiazepine could not be calculated. Drivers with opiates and the larger opioid class of drugs and without other impairing drugs showed similar odds of culpability to the control group (adjusted OR 1.1). However, a large number of the morphine positive cases may have been the result of post-crash administration of this opiate to relieve pain for injuries sustained in the collision. Unfortunately, we were unable to access patient records to distinguish medical use from personal use of morphine and heroin. The ORs of drivers with only oxycodone gave slightly higher OR to those with the weaker opioid codeine but with overlapping confidence ranges (Table 2). None of the other detected opioids gave any different results, but all had a lower prevalence than morphine, codeine and oxycodone (Di Rago et al., 2019b). Meta-analyses have shown an association to exist between prescription opioids and crash involvement but not between use of prescription opioids and odds of culpability (Chihuri and Li, 2017). A recent study investigating driver culpability in over 18,000 2-vehicle crashes using the FARS database in the USA opioid-using drivers had an adjusted OR of 2.18 compared to control drivers (Chihuri and Li, 2019). Fifteen antihistamines were targeted by the toxicological analyses performed in this study, 9 of which were detected in one or more drivers. A number of the sedating antihistamines (often available over-thecounter) were the most common particularly doxylamine, chlorpheniramine, promethazine and diphenhydramine (in decreasing order of prevalence). However, while these showed some increase in odds of culpability (OR 1.7), this was not statistically significant. Over 80 % of these cases were present with other drugs. Published reviews have also shown little or no increase in crash risk with these drugs (Elvik, 2013; Rudisill et al., 2016). There are several limitations with this type of culpability analysis to
Lenne et al., 2010; Micallef et al., 2018; Tank et al., 2019). Similarly, studies using volunteers and instrumented cars have generally also shown deficits in performance shortly after smoking cannabis (Ramaekers et al., 2000; Hartman et al., 2015; Arkell et al., 2019). However, it may not always be possible to differentiate the direct effects of cannabis use on driving performance since users of this drug tend to be inherently riskier drivers (Bergeron and Paquette, 2014). Behavioral changes and the effects seen in various psychomotor performance and cognitive testing following cannabis use are well known and documented (Ramaekers et al., 2004). However, this does not mean that a driver positive to THC is necessarily impaired to a degree that can be detected using standard sobriety assessments. However, the concentrations of THC detected in blood in this study are similar to those found in drivers who had been assessed as being impaired compared to those in whom only THC was detected (Jones et al., 2008; Khiabani et al., 2006; Bramness et al., 2010). While cocaine is a strong stimulant and might be expected to have similar driving impairing effects to methylamphetamine there were too few cases to form a meaningful assessment in this study. Almost all of 0.8 % of drivers positive to cocaine or one if its metabolites, were also positive to another impairing drug. Other (weaker) stimulants or anorectics detected included some cases with amphetamine only possibly from use of legally available dexamphetamine since very little if any amphetamine is available as a street drug (Phelan, 2018). Additional stimulants detected were phentermine (19 cases), modafinil (4 cases) and some ephedrine and high caffeine (greater than 10 mg/L in blood) cases. However, collectively these drivers did not show an elevated OR (Table 2). Similarly, drivers with antidepressants did not show evidence of increased odds of culpability. While almost 15 % of injured drivers had an antidepressant detected, these were mostly of the serotonin or mixed serotonin-norepinephrine reuptake inhibitors, which would not be expected to elevate crash risk, rather, it is possible their effect may be to improve driver behavior by elevating mood. A neutral effect of these anti-depressants on crash risk has been identified elsewhere (Elvik, 2013). In contrast, benzodiazepines are generally regarded as drugs that increase crash risk, although modestly (Chihuri and Li, 2019). In this 4
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Table 2 Unadjusted and adjusted odds ratios (OR) for groups of injured drivers who satisfy various drug only positive categories compared to the group of injured drivers without alcohol and without other drugs; adjustment for gender, age group, type of vehicle (car/motorbike/heavy vehicle/van or light truck) and location (metro/ rural). Group of drivers
N1 (total)
N2 (drug subgroup)
Number of drivers culpable/non culpable3
Ratio (culpable/non culpable)
OR unadjusted3
OR adjusted (95 %CI)4
Control group (no alcohol/drugs) Alcohol (all concentrations) BAC (< 0.05 %) BAC (0.05-0.099 %) BAC (0.10-0.149 %) BAC (≥0.15 %) Any illicit drug (no alcohol)6 Any impairing drug7 THC (all concentrations) THC (1–4.9 ng/mL) THC (5–9.9 ng/mL) THC (≥5 ng/mL) THC (≥10 ng/mL) Amphetamines (all concentrations) Methylamphetamine (all concentrations) Methylamphetamine (< 0.1 mg/L) Methylamphetamine (≥0.1 mg/L) Other stimulants/anorectics Antidepressants Antihistamines5 Benzodiazepines Opioids Codeine Oxycodone THC plus methylamphetamine Alcohol plus THC Alcohol plus benzodiazepine
1837 789 90 125 194 380
– 305 24 44 79 158 1057 1917 98 62 22 36 14 169 152 17 135 21 177 21 50 615 47 37 44 56 25
881/847 284/17 18/5 37/5 75/4 154/3 927/93 1654/201 62/28 38/21 13/6 24/7 11/1 155/9 141/6 16/1 125/5 11/8 86/79 12/8 35/13 299/278 25/19 19/15 41/3 52/3 25/0
1.04 16.7 3.6 7.4 18.8 51.3 10 8.2 2.2 1.8 2.2 3.4 11 17 23.5 16 25 1.4 1.1 1.5 2.7 1.1 0.8 1.3 13.7 17.3 **
1.0 (reference) 16 (9.7–28) 3.5 (1.2–12) 7.1 (2.8–23) 18 (6.7–68) 49 (16–243) 9.6 (7.6–12) 7.9 (6.6–9.5) 2.1 (1.3–3.5) 1.7 (1.0–3.1) 2.1 (0.7–6.7) 3.3 (1.4–9.1) 11 (1.5–456) 17 (8.4–37) 23 (10–63) 15 (2.4–646) 24 (9.9–76) 1.3 (0.5–3.8) 1.0 (0.8–1.5) 1.4 (0.5–4.1) 2.6 (1.3–5.4) 1.0 (0.9–1.2) 0.7 (0.4–1.4) 1.2 (0.6–2.6) 13 (14–67) 17 (5.4–84) **
1.0 (reference) 16 (9.4–26) 3.2 (1.1–8.7) 5.7 (2.2–15) 16 (5.7–43) 73 (18–294) 10 (7.9–13) 8.2 (6.8–9.8) 1.9 (1.2–3.1) 1.6 (0.9–2.7) 1.9 (0.7–5.0) 3.2 (1.3–7.2) 10 (1.3–82) 14 (6.9–28) 19 (8.4–44) 12 (1.6–95) 21 (8.3–51) 1.4 (0.6–3.6) 1.2 (0.9–1.7) 1.7 (0.7–4.3) 3.2 (1.6–6.1) 1.1 (0.9–1.4) 0.8 (0.4–1.5) 1.3 (0.6–2.6) 12 (3.6–38) 14 (4.4–46) **
553 373 130 180 50 705 593 128 465 67 729 116 594 1541 189 120 997 1194 1237
1
Total number of drivers with presence of the specific drug or drug combination, other than control cases in which no substances were detected. Drivers with presence of the specified drug or drug combination, includes drivers that were determined to be contributory and only drivers included where there were more than 20 cases. 3 Ratio of number of culpable drivers over number of non-culpable drivers divided by the ratio of the reference group. 4 OR calculated following multivariate analysis as presented in methods section. 5 Only sedating antihistamines were targeted by analytic method. 6 Includes THC, methylamphetamine, MDMA, MDA, heroin, cocaine, NPS. 7 Includes alcohol, THC, methylamphetamine, MDMA, heroin, cocaine, NPS, benzodiazepines, sedating antihistamines. ** OR cannot be calculated since no non-culpable drivers. 2
blood specimen. While a little over 2 h was the median delay this alone would affect the concentration of rapidly eliminated drugs; particularly for alcohol and THC. This delay could cause the blood concentration to drop below the limit of detection of the assay. Nevertheless, the results have shown odds of culpability broadly similar to other studies including those that have used different methodology to assess crash risk.
assess crash causation of drugs. Foremost is the difficulty in assessing all of the factors that could distract drivers or place drivers at an increased risk of an injurious collision independently of their drug use. While some factors can be assessed and then adjusted for it is clear that any findings from this epidemiological approach can only provide some indication of culpability risks associated with drug use. Additionally, it will not necessarily be the case that culpability risk translates to crash risk. Since a little over two-thirds of the drug-positive drivers used more than one impairing substance this further complicates any assessment of individual drug contribution. The use of sedating drugs, such as in particular alcohol, opioids, benzodiazepines, and even amphetamines in the elimination and withdrawal phases will exacerbate existing driver fatigue and even cause micro-sleeps (Logan, 2002; Seppala et al., 1979; Drummer, 2002; NHTSA, 1998). However, in most cases it was not possible to assess with any certainty whether a driver necessarily fell asleep at the wheel given there are many factors that cause collisions. Individual assessments of dose and tolerance to the drug(s) could not be made and will clearly be limitations in all epidemiological studies of this type although it is unclear the direction of bias that would be imparted. A host of factors could exist to differentiate the type of driver who has, say only alcohol, from the type of driver who has, say no impairing drugs at all. We have adjusted for some of the more likely among these factors but simply did not have access to others and again it would be unclear the direction of the bias that may be imparted by other confounders. A further complication is the time delay from crash to collection of a
4. Conclusion This culpability analysis of almost 5000 injured drivers has provided further evidence of the elevated odds of culpability associated with alcohol and methylamphetamine using drivers as well as drivers positive to THC, particularly those with higher blood concentrations. Of all the legally-available drugs benzodiazepines used in isolation show a modest increase in culpability but are most often associated with other potentially impairing drugs. Submission declaration and verification This manuscript has not been published previously (except in the form of an abstract, or lecture), and it is not under consideration for publication elsewhere. This publication is approved by all authors and explicitly by the responsible authorities where the work was carried out, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder 5
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Author contributions
antidepressants and opioids on driving: a systematic review and meta-analysis of epidemiological and experimental evidence. Drug Saf. 34 (2), 125–156. de Carvalho, H.B., Andreuccetti, G., Rezende, M.R., Bernini, C., Silva, J.S., Leyton, V., D’Andrea Greve, J.M., 2016. Alcohol and drug involvement in motorcycle driver injuries in the city of Sao Paulo, Brazil: analysis of crash culpability and other associated factors. Drug Alcohol Depend. 162, 199–205. DiRago, M., Pantatan, S., Hargreaves, M., Wong, K., Mantinieks, D., Kotsos, A., Glowacki, L., Drummer, O.H., Gerostamoulos, D., 2019a. High throughput detection of 327 drugs by LC-MS/MS with automated data processing. J. Anal. Toxicol submitted. diRago, M., Gerostamoulos, D., Morris, C., Frederickson, T., Woodford, N., Drummer, O.H., 2019b. Prevalence of drugs in injured drivers in Victoria, Australia. Austr. J. Forensic Sci. https://doi.org/10.1080/00450618.2019.1687753. epub 17/11/2019. Dischinger, P., Li, J., Smith, G.S., Ho, S., Auman, K., Shojai, D., 2011. Prescription medication usage and crash culpability in a population of injured drivers. Ann. Adv. Automot. Med. 55, 207–216. Downey, L.A., King, R., Papafotiou, K., Swann, P., Ogden, E., Boorman, M., Stough, C., 2013. The effects of cannabis and alcohol on simulated driving: influences of dose and experience. Accid. Anal. Prev. 50, 879–886. Drummer, O.H., 2002. Benzodiazepines – effects on human performance and behavior. Forensic Sci. Rev. 14 (1–2), 1–14. Drummer, O.H., Kourtis, I., Beyer, J., Tayler, P., Boorman, M., Gerostamoulos, D., 2012. The prevalence of drugs in injured drivers. Forensic Sci. Int. 215 (1–3), 14–17. Drummer, O.H., Gerostamoulos, J., Batziris, H., Chu, M., Caplehorn, J.R., Robertson, M.D., Swann, P., 2003. The incidence of drugs in drivers killed in Australian road traffic crashes. Forensic Sci. Int. 134 (2–3), 154–162. Drummer, O.H., Gerostamoulos, J., Batziris, H., Chu, M., Caplehorn, J., Robertson, M.D., Swann, P., 2004. The involvement of drugs in drivers of motor vehicles killed in Australian road traffic crashes. Accid. Anal. Prev. 36 (2), 239–248. Dubois, S., Mullen, N., Weaver, B., Bedard, M., 2015. The combined effects of alcohol and cannabis on driving: impact on crash risk. Forensic Sci. Int. 248, 94–100. Elvik, R., 2013. Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies. Accid. Anal. Prev. 60, 254–267. Gadegbeku, B., Amoros, E., Laumon, B., 2011. Responsibility study: main illicit psychoactive substances among car drivers involved in fatal road crashes. Ann. Adv. Automot. Med. 55, 293–300. Hartman, R.L., Brown, T.L., Milavetz, G., Spurgin, A., Pierce, R.S., Gorelick, D.A., Gaffney, G., Huestis, M.A., 2015. Cannabis effects on driving lateral control with and without alcohol. Drug Alcohol Depend. 154, 25–37. Hels, T., Lyckegaard, A., Simonsen, K.W., Steentoft, A., Bernhoft, I.M., 2013. Risk of severe driver injury by driving with psychoactive substances. Accid. Anal. Prev. 59, 346–356. Jones, A.W., Holmgren, A., Kugelberg, F.C., 2008. Driving under the influence of cannabis: a 10-year study of age and gender differences in the concentrations of tetrahydrocannabinol in blood. Addiction 103 (3), 452–461. Khiabani, H.Z., Bramness, J.G., Bjorneboe, A., Morland, J., 2006. Relationship between THC concentration in blood and impairment in apprehended drivers. Traffic Inj. Prev. 7 (2), 111–116. Laumon, B., Gadegbeku, B., Martin, J.L., Biecheler, M.B., Group, S.A.M., 2005. Cannabis intoxication and fatal road crashes in France: population based case-control study. BMJ 331 (7529), 1371. Legrand, S.A., Houwing, S., Hagenzieker, M., Verstraete, A.G., 2012. Prevalence of alcohol and other psychoactive substances in injured drivers: comparison between Belgium and the Netherlands. Forensic Sci. Int. 220 (1–3), 224–231. Lenne, M.G., Dietze, P.M., Triggs, T.J., Walmsley, S., Murphy, B., Redman, J.R., 2010. The effects of cannabis and alcohol on simulated arterial driving: influences of driving experience and task demand. Accid. Anal. Prev. 42 (3), 859–866. Leveille, S.G., Buchner, D.M., Koepsell, T.D., McCloskey, L.W., Wolf, M.E., Wagner, E.H., 1994. Psychoactive medications and injurious motor vehicle collisions involving older drivers. Epidemiology 5 (6), 591–598. Li, G., Brady, J.E., Chen, Q., 2013. Drug use and fatal motor vehicle crashes: a casecontrol study. Accid. Anal. Prev. 60, 205–210. Logan, B.K., 2002. Methamphetamine – effects on human performance and behavior. Forensic Sci. Rev. 14 (1–2), 133–151. Longo, M.C., Hunter, C.E., Lokan, R.J., White, J.M., White, M.A., 2000. The prevalence of alcohol, cannabinoids, benzodiazepines and stimulants amongst injured drivers and their role in driver culpability: part ii: the relationship between drug prevalence and drug concentration, and driver culpability. Accid. Anal. Prev. 32 (5), 623–632. Martin, J.L., Gadegbeku, B., Wu, D., Viallon, V., Laumon, B., 2017. Cannabis, alcohol and fatal road accidents. PLoS One 12 (11), e0187320. Martin-de-Los Reyes, L.M., Martinez-Ruiz, V., Lardelli-Claret, P., Moreno-Roldan, E., Molina-Soberanes, D., Jimenez-Mejias, E., 2018. Association between type of vehicle and the risk of provoking a collision between vehicles. Gac. Sanit. https://doi.org/10. 1016/j gaceta.2018.10.00. S0213-9111(18) 30245-0. Meuleners, L.B., Duke, J., Lee, A.H., Palamara, P., Hildebrand, J., Ng, J.Q., 2011. Psychoactive medications and crash involvement requiring hospitalization for older drivers: a population-based study. J. Am. Geriatr. Soc. 59 (9), 1575–1580. Micallef, J., Dupouey, J., Jouve, E., Truillet, R., Lacarelle, B., Taillard, J., Daurat, A., Authie, C., Blin, O., Rascol, O., Philip, P., Mestre, D., 2018. Cannabis smoking impairs driving performance on the simulator and real driving: a randomized, double-blind, placebo-controlled, crossover trial. Fundam. Clin. Pharmacol. 32 (5), 558–570. Monarrez-Espino, J., Laflamme, L., Rausch, C., Elling, B., Moller, J., 2016. New opioid analgesic use and the risk of injurious single-vehicle crashes in drivers aged 50–80 years: a population-based matched case-control study. Age Ageing 45 (5), 628–634. Morland, J., Steentoft, A., Simonsen, K.W., Ojanpera, I., Vuori, E., Magnusdottir, K., Kristinsson, J., Ceder, G., Kronstrand, R., Christophersen, A., 2011. Drugs related to
Dimitri Gerostamoulos: Substantial contribution to the study conception and design, data acquisition, supervision of analyses and interpretation, drafting or revising the article for intellectual content. Revision of the article for intellectual content, approval of the final version. Matthew Di Rago: Analytical analyses, data acquisition and analysis, revision of the article for intellectual content, approval of the final version. Noel W. Woodford: Contribution to the study conception and funding and availability of resources within Institute, revision of the article for intellectual content. Approval of the final version. Carla Morris: Specimen and data acquisition and analysis, revision of the article for intellectual content, approval of the final version. Tania Fredericksen: Specimen and data acquisition and analysis, revision of the article for intellectual content, approval of the final version. Kim Jachno: Conducted the statistical analyses. Rory Wolfe: Supervised the statistical analyses, revision of the article for intellectual content. Olaf H. Drummer: Substantial contribution to the study conception and design and interpretation of results. Drafting and revision of the article, approval of the final version. Project administration and agreement to be accountable for all aspects of the work related to the accuracy or integrity of any part of the work. Declaration of Competing Interest None. Acknowledgements The authors gratefully acknowledge the support of the Department of Justice, VicRoads, the Transport Accident Scheme (TAC) and Victoria Police that formed the Road Safety Executive (Victoria) and funded the study. In particular, gratitude is extended to William Gibbons (Department of Justice) and Inspector Tom McGillian (Victoria Police) for their support. We also thank Gemma Wynd of the Road Policing Drug and Alcohol Section of Victoria Police who assisted with data entry and culpability analyses. References Arkell, T.R., Lintzeris, N., Kevin, R.C., Ramaekers, J.G., Vandrey, R., Irwin, C., Haber, P.S., McGregor, I.S., 2019. Cannabidiol (CBD) content in vaporized cannabis does not prevent tetrahydrocannabinol (THC)-induced impairment of driving and cognition. Psychopharmacology (Berl.) 236 (9), 2713–2724. Asbridge, M., Hayden, J.A., Cartwright, J.L., 2012. Acute cannabis consumption and motor vehicle collision risk: systematic review of observational studies and metaanalysis. BMJ 344, e536. Bergeron, J., Paquette, M., 2014. Relationships between frequency of driving under the influence of cannabis, self-reported reckless driving and risk-taking behavior observed in a driving simulator. J. Saf. Res. 49, 19–24. Bernhoft, I.M., Hels, T., Lyckegaard, A., Houwing, S., Verstraete, A.G., 2012. Prevalence and risk of injury in Europe by driving with alcohol, illicit drugs and medicines. Procedia - Soc. Behav. Sci. 48, 2907–2916. Bramness, J.G., Khiabani, H.Z., Morland, J., 2010. Impairment due to cannabis and ethanol: clinical signs and additive effects. Addiction 105 (6), 1080–1087. Brubacher, J.R., Chan, H., Erdelyi, S., Macdonald, S., Asbridge, M., Mann, R.E., Eppler, J., Lund, A., MacPherson, A., Martz, W., Schreiber, W.E., Brant, R., Purssell, R.A., 2019. Cannabis use as a risk factor for causing motor vehicle crashes: a prospective study. Addiction 114 (9), 1616–1626. Chihuri, S., Li, G., 2017. Use of prescription opioids and motor vehicle crashes: a meta analysis. Accid. Anal. Prev. 109, 123–131. Chihuri, S., Li, G., 2019. Use of prescription opioids and initiation of fatal 2-vehicle crashes. JAMA Netw Open 2 (2), e188081. Corsenac, P., Lagarde, E., Gadegbeku, B., Delorme, B., Tricotel, A., Castot, A., Moore, N., Philip, P., Laumon, B., Orriols, L., 2012. Road traffic crashes and prescribed methadone and buprenorphine: a French registry-based case-control study. Drug Alcohol Depend. 123 (1–3), 91–97. Dassanayake, T., Michie, P., Carter, G., Jones, A., 2011. Effects of benzodiazepines,
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O.H. Drummer, et al.
Ramaekers, J.G., Berghaus, G., van Laar, M., Drummer, O.H., 2004. Dose related risk of motor vehicle crashes after cannabis use. Drug Alcohol Depend. 73 (2), 109–119. Reguly, P., Dubois, S., Bedard, M., 2014. Examining the impact of opioid analgesics on crash responsibility in truck drivers involved in fatal crashes. Forensic Sci. Int. 234, 154–161. Robertson, M.D., Drummer, O.H., 1994. Responsibility analysis: a methodology to study the effects of drugs in driving. Accid. Anal. Prev. 26 (2), 243–247. Rogeberg, O., 2019. A meta-analysis of the crash risk of cannabis-positive drivers in culpability studies-Avoiding interpretational bias. Accid. Anal. Prev. 123, 69–78. Rudisill, T.M., Zhu, M., Kelley, G.A., Pilkerton, C., Rudisill, B.R., 2016. Medication use and the risk of motor vehicle collisions among licensed drivers: a systematic review. Accid. Anal. Prev. 96, 255–270. Seppala, T., Linnoila, M., Mattila, M.J., 1979. Drugs, alcohol and driving. Drugs 17 (5), 389–408. Tank, A., Tietz, T., Daldrup, T., Schwender, H., Hellen, F., Ritz-Timme, S., Hartung, B., 2019. On the impact of cannabis consumption on traffic safety: a driving simulator study with habitual cannabis consumers. Int. J. Legal Med. 133 (5), 1411–1420. Zador, P.L., Krawchuk, S.A., Voas, R.B., 2000. Alcohol-related relative risk of driver fatalities and driver involvement in fatal crashes in relation to driver age and gender: an update using 1996 data. J. Stud. Alcohol 61 (3), 387–395.
motor vehicle crashes in northern European countries: a study of fatally injured drivers. Accid. Anal. Prev. 43 (6), 1920–1926. Movig, K.L., Mathijssen, M.P., Nagel, P.H., van Egmond, T., de Gier, J.J., Leufkens, H.G., Egberts, A.C., 2004. Psychoactive substance use and the risk of motor vehicle accidents. Accid. Anal. Prev. 36 (4), 631–636. Mura, P., Kintz, P., Ludes, B., Gaulier, J.M., Marquet, P., Martin-Dupont, S., Vincent, F., Kaddour, A., Goulle, J.P., Nouveau, J., Moulsma, M., Tilhet-Coartet, S., Pourrat, O., 2003. Comparison of the prevalence of alcohol, cannabis and other drugs between 900 injured drivers and 900 control subjects: results of a French collaborative study. Forensic Sci. Int. 133 (1–2), 79–85. NHTSA, 1998. Expert Panel on Driver Fatigue and Sleepiness. NHTSA. Peck, R.C., Gebers, M.A., Voas, R.B., Romano, E., 2008. The relationship between blood alcohol concentration (BAC), age, and crash risk. J. Safety Res. 39 (3), 311–319. Phelan, M., 2018. Illicit Drug Data Report 2016–17. Australian Criminal Intelligence Commission, Canberra p. 184. Poulsen, H., Moar, R., Pirie, R., 2014. The culpability of drivers killed in New Zealand road crashes and their use of alcohol and other drugs. Accid. Anal. Prev. 67, 119–128. Poznyak, V., 2016. Drug Use and Road Safety: A Policy Brief. World Health Organisation: Geneva, Switzerland. Ramaekers, J.G., Robbe, H.W., O’Hanlon, J.F., 2000. Marijuana, alcohol and actual driving performance. Hum. Psychopharmacol. 15 (7), 551–558.
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