The report summarises current knowledge about the effects of cannabis on driving and accident risk based on a review of available literature published since 1994. Cannabis consumption is frequent and increasing. A particular risk group is young males, in whom alcohol consumption is also common. This group is an a priori risk group for traffic accidents. Understanding of the biological mechanism of action of cannabis is far from complete. However, the identification of cannabinoid receptors in the cerebral cortex and the discovery of an endogenous ligand, anandamide, has heightened research activity. Development of new drugs acting on this system are imminent and may have implications for treatment of conditions, which would otherwise compromise ability to drive e.g. motor diseases.
The effects of cannabis on laboratory based tasks show clear impairment with respect to tracking ability, attention and other tasks depending on the dose administered. These effects however, are not as pronounced on tasks of greater ecological validity i.e. real driving or simulator tasks. Indeed, compensatory effort can be invoked to offset impairment in the driving task. This compensatory effort may be one reason for the failure to implicate cannabis consumption as an accident risk factor. However, this claim is difficult to substantiate in the absence of any valid epidemiological estimates of accident risk. Specifically, 4-12% of accident fatalities have detected levels of cannabis. However, most studies report that the majority of fatal cases with detected levels of cannabis are confounded by alcohol although this rate is lower in the UK. Moreover, accident risk cannot be derived in the absence of baseline data for non-fatal cases. Alcohol alone or in combination with cannabis, increases impairment, accident rate and accident responsibility. Dose equivalence estimates suggest that the legal limit of alcohol (0.08% BAC) corresponds to 11ng/ml THC, in terms of impairment.
Key research objectives based on the analysis of the limitations of current knowledge about the effects of cannabis on driving and accident risk are presented. Research in this area has been impeded by methodological, legal and ethical problems. Specifically, there is no standard experimental paradigm, no consistancy in reporting format, dose administration or detection method. In short, the research strategy to evaluate the effect of cannabis has been piecemeal. Consequently, firm and reliable conclusions cannot be drawn.
Traffic safety is a major concern for society (Blanchard & Hickling, 1997). A mandate of responsible government is to implement relevant transport policy to reduce accident risk. Transport policy can relate to the road infrastructure, vehicle design, and the performance and licensing of drivers. Driver impairment is a significant factor that has been associated with accident risk. Policies related to impairment must be justified on the basis of research which explicates factors that increase accident risk (Berghaus, 1997b). For example, research has identified alcohol as a significant factor related to traffic accidents (McIntrye, 1995; Starmer, Vine & Watson, 1988;). This has led to specific legislation and methods of enforcement to prohibit this form of impaired driving. Berghaus (1997b) has provided evidence on which to base legal limit thresholds for cannabis.
Aside from alcohol, there are other sources of driver impairment that may be related to accident risk (Kedjidijian, 1995). Notably, a recent report commissioned by the UK Automobile Association has stated that "the possibility that certain drugs may impair driving skills is a concern shared by the medical community, regulatory bodies, the transport industry and general public alike" (Sherwood, 1998). However, whereas the evidence of driver impairment from alcohol and fatigue has been demonstrated sufficiently to provide a basis for transport policy, the evidence that certain classes of drug may impair driving, and thereby, increase accident risk, is inconclusive (Robbe, 1994).
Drugs can be consumed for therapeutic purposes by prescription to alleviate symptoms of medical conditions. To the extent that these conditions may impair driving if not treated, the use of the medicinal drugs may actually reduce accident risk. However, the unwanted side-effects of these drugs may be to increase impairment. The effect of the drug on accident risk is confounded by any impairment from the treated condition (Sherwood, 1998).
Drugs can also be consumed for 'recreational' purposes. Self-administration to experience the effects induced by the compound is common for a number of classes of drugs. Aside from alcohol, one of the most commonly consumed recreational drugs is cannabis (Border & Norton, 1996). Cannabis has a long history of suspicion as a source of driver impairment associated with increased accident risk (Robbe, 1994). There is evidence that the consumption of cannabis does affect the biochemistry of the central nervous system and thereby produces impairment of certain psychomotor functions under laboratory test conditions (Krüger & Berghaus, 1995). However, the evidence that these effects translate into impaired driving and increased accident risk is equivocal (Drummer, 1995; Crandon, 1997). Therefore, at present it is not possible to formulate a rational transport policy for cannabis and driving on the basis of existing research evidence.
To the extent that there is a sizeable and expanding use of cannabis within the (driving) population for both therapeutic and recreational purposes that may increase the risk of accidents, then transport policy must develop to manage this risk (Border & Norton, 1996).
To this end, further research is needed to provide a definitive basis for transport policy from valid evidence of driver impairment and increased accident risk resulting from the use of cannabis (Crandon, 1997). This report provides a review of current knowledge of the effects of cannabis on driving performance and accident risk in order to identify key research objectives which will support transport policy development.
The purpose of this report is to document current knowledge about cannabis and driving and identify key research objectives for areas where current knowledge is deemed to be insufficient to guide rational transport policy.
The report focuses on the (acute) effects of cannabis use related to driver functioning and accident risk. Cannabis smoking is considered as the most common method of consumption rather than oral ingestion. 1 Chronic effects of prolonged cannabis use are not considered in detail as these are extensively reviewed elsewhere (e.g., Hall et al., 1994; Solowij, 1998). 2 The report does not evaluate the various forms of intervention intended to reduce the prevalence of cannabis use in drivers (see Border & Norton, 1996; Hall et al., 1994).
Research prior to 1994 is summarised from the seminal treatise of Robbe (1994) conducted in the Netherlands and funded by the US Government. In addition, this report incorporates a summary of the Australian Government report on 'The health and psychological consequences of cannabis use' which was published as part of the Australian National Drug Strategy (Hall et al., 1994). The report also includes relevant data from a UK Government report published by the Parliamentary Office of Science and Technology that reviewed information about cannabis from literature available up to 1994 (Border & Norton, 1996).
Research published since 1994 is reviewed from obtained literature identified from relevant databases: 3
In addition to the database searches, requests for relevant information were posted on electronic bulletin boards for international associations involved in transport safety research:
Contact was also made with acknowledged experts in the field of drugs and driving for the provision of relevant literature:
This report is organised into 6 main sections to provide a logical examination of the accident risk associated with cannabis. First, in order to estimate the magnitude of accident risk associated with cannabis, it is necessary to determine the exposure of the population to this risk factor. Exposure to cannabis in the UK can be surmised on the basis of data about the availability and usage of cannabis. Second, to detect those that are at risk, it is necessary to specify the mechanism of action and biological markers of drug consumption. Third, detection must relate not only to the presence of the drug, but also to the psychological indices of impairment resulting from drug consumption. Evidence of consumption and impairment can be indicated from research on the biological and acute psychomotor responses of the individual. Fourth, to explain the basis of the accident risk, it is necessary to demonstrate that the impairment effects of cannabis are manifest in the driving task. This demonstration can be obtained from either simulation based or actual driving studies. Fifth, to demonstrate that cannabis is a risk factor, it is necessary to provide evidence that the resultant driving impairment is associated with an increased probability of (culpable) accidents. This evidence can be interpreted from epidemiological studies of accident involved and injured drivers. Finally, to reduce the accident risk associated with cannabis use it is necessary to specify future research objectives necessary for development of effective interventions and rational transport policy.
1 Passive inhalation is not considered in this report. Cannabinoids are not expected to be detected in non-smokers under normal conditions exposed to cannabis smoke (Hall et al., 1994).
2 Rather than consider these chronic effects of persistent drug use that may persist beyond drug use, it is more pertinent to attend to the acute effect of drug intoxication following consumption for which culpability can be assigned. As a parallel, whereas a drunk driver may be prosecuted for impaired driving, it may not be warranted or justified to sanction a driver for impairment resulting from the effects of alcoholism. However, some countries such as Germany are considering aptitude testing related to chronic effects as well as licence exclusion for regular users that may be dependent on cannabis (Bode, 1998).
3 The keyword search was completed on the current databases accessed by the University of Leeds in May 1998. The searches were completed with the following keyword(s) using Boolean logic modifiers: [cannabis or marijuana or hashish] and [driving or accidents or impairment or cognitive performance].
The extent to which the (UK) population is exposed to cannabis as a risk factor in driving accidents can be inferred on the basis of (i) the availability of cannabis; (ii) the percentage of the population using cannabis; and (iii) the percentage of the using population willing or likely to drive after cannabis has been consumed.
The amount of cannabis available in the UK is not recorded given its status as an illegal, unregulated drug. Thus, the amount of cannabis available within the UK is not directly estimable. Instead, the amount of cannabis seized by regulatory authorities (e.g., police, Customs and Excise, Port Authority) can be viewed as a surrogate measure of the supply of cannabis. Assuming that the law supporting seizures and the applied regulator effort remain constant, the amount of cannabis seized in each year may approximate the trend in the available supply of cannabis.
Figure 1 shows the quantity of herbal and resin cannabis that was seized between 1985 and 1995 (Barber et al., 1996). These forms of cannabis constitute the most common drug seized. 4 Most of the cannabis seized is in the form of resin. Whereas there was some fluctuation in the amount of cannabis seized between 1985 and 1989, there is an apparent steady trend of increasing availability of cannabis, suggested by the amount seized in each year since 1989.
For comparison purposes, Figure 1 includes the amount of amphetamine seized each year which is the second most common drug in the same class (Class B). This comparison suggests that the increased amount of cannabis seized is indicative of a general trend amongst other drugs of the same class.
Figure 1: Quantity of and type of Class B drugs seized between 1985 and 1995 in the UK (Barber et al., 1996)
Exposure data relevant to availability can also be estimated from surveys of which drugs are offered by suppliers to the population. There have been a series of surveys conducted in Wolverhampton every 5 years between 1969 to 1994 (Wright & Pearl, 1995). These surveys have targeted 14-15 year olds in their fourth year of secondary school. The percentage of young respondents indicating that cannabis and amphetamines (the two most common drugs) had been offered to them is illustrated in Figure 2. 5 This comparison indicates that the availability of cannabis to this cohort has increased since 1989. This data is therefore consistent with the seizure data i.e. that the availability of cannabis has increased over recent years.
Figure 2 : Percentage of respondents indicating that the drug type had been offered to them (derived from Wright & Pearl, 1995)
Reported use of cannabis in the UK has been documented by several surveys since 1982 (Border & Norton, 1996). However, trends of usage over time are confounded by differences in methodology employed in each year. At present, the only years that have employed a comparable methodology to permit inferences about usage trends was completed for the Home Office for 1984, 1994 and 1996 (Border & Norton, 1996). 6 These surveys also use different report formats which do not permit direct comparison of exposure data. Moreover, several definitions of 'usage' have been employed in these surveys: (i) ever used; (ii) used last year; (iii) used last month. It is not clear what operational definition of a drug user is most appropriate to estimate the exposure within the population relevant to accident risk. For example, these definitions do not indicate current usage, or more importantly for this report, usage in conjunction with the operation of a motor vehicle.
The percentage of respondents indicating that they had used either of the two most common types of drug in the year preceding the survey are presented in Table 1. The data clearly indicate that cannabis is the most commonly used drug, and its usage has increased since 1984. To date, comparable methodologies allowing inferences about usage trends have only been employed in Home Office surveys conducted in 1984, 1994 and 1996.
|
Table 1 : Drug usage in preceding year for 16-59 age group (from Border & Norton, 1996, and Ramsay & Spiller) |
|||
|
|
1984 |
1994 |
1996 |
|
Cannabis |
2.9% |
8% |
9% |
|
Amphetamine |
- |
2% |
3% |
This section considers the pharmacological properties of cannabis and it's mechanism of action, predominantly for smoking as a route of administration before considering the time course of potential impairment and the potential methods for detection of cannabis intoxication.
Cannabis sativa, the Indian hemp plant is an herbaceous annual. All parts of the plant contain psychoactive substances but highest concentrations of cannabinoids, the active ingredients, are found in the flowering head. Cannabis has been used recreationally and therapeutically for 4000 years (Mechoulam, 1986) yet an understanding of it's pharmacology is recent and incomplete.
Cannabinoids is the collective term for a variety of compounds which can either be:
1. extracted from the plant
2. produced synthetically to mimic the effects of natural cannabis compounds
3. produced within the body after ingestion and metabolism of cannabis
4. found to occur naturally within the body or brain and to bind to cannabinoid receptors (Solowij, 1998).
The cannabis plant contains more than 60 cannabinoid compounds, most of which are either inactive or only weakly active. These may however, interact with tetrahydrocannabinol (THC) and increase or decrease it's potency 9 (Abood and Martin, 1992).
4.2 Delta-9-Tetrahydrocannabinol (
9-THC)
Delta-9-Tetrahydrocannabinol (
9-THC) is the major psychoactive
constituent of cannabis. It's chemical structure was
established in 1964 (Gaoni and Mechoulam, 1964) although it
was first isolated as a "toxic red oil" in the late
nineteenth century. Since 1964, more has been learnt about
the mechanism of action of cannabis and
-9-THC in particular, although the
current state of knowledge is far from complete.
Figure 5 : The chemical structure of Delta-9-Tetrahydrocannabinol
Low doses of
-9-THCare stimulatory, followed by
sedation. High doses induce sedation. This suggests that
the mechanism of action of cannabinoids differs for low and
high concentrations of the agonist.
Until recently it was thought that cannabis affected neuronal membrane fluidity, an action shared with alcohol. Recent research has shown that effects on neuronal membranes do occur but this is too simplistic to explain it's mechanism of action (Glass and Felder, 1997). In the last decade a cannabinoid receptor CB1 and an endogenous brain molecule, anandamide, have been discovered (Devane et al., 1988; 1992).
Research mapping the distribution of cannabinoid receptors in the brain has shown that they are localised in the cerebellum, hippocampus, basal ganglia and cortex (Herkenham et al., 1990). These brain regions are involved in attention (Solowij, 1998) memory, motor co-ordination and other cognitive functions. Cannabinoids inhibit acetylcholine (ACh) release in the hippocampus. ACh is involved in learning and remembering and in controlling the stage of sleep in which dreams occur. Cannabinoids also inhibit noradrenaline release at the sympathetic nerve terminals and centrally in the hippocampus, cortex and cerebellum. Noradrenalin or norepinephrine is involved in the control of alertness and wakefulness. The cerebellum is involved in coordinated movement. Damage to this region results in jerky, poorly coordinated movement. Impairment of standing and walking and performance of coordinated motor tasks. The hippocampus is involved in learning and memory, while the basal ganglia play an important role in the control of movement. As we shall discuss later, the presence of cannabinoid receptors in these areas relates closely to the types of impairment produced after intake of THC, though the extent and duration of effects are dependent on numerous factors.
Following identification of the CB1 receptor, two additional cannabinoid receptors, CB1A (Shire et al., 1995) and CB2 (Munro et al., 1993) have been identified. While the CB1 receptor is found in highest concentrations in the brain, the CB2 receptor is found in the cells of the immune system. However, both CB1 and CB2 selectively bind anandamide (Axelrod and Felder, 1998).
Cannabinoid receptor binding is sparse or absent from areas of the brain stem, medulla and thalamus which might explain the lack of serious effects of cannabis abuse (Felder and Glass, 1998).
The consequences of cannabis and cannabinoid agonist exposure at varying doses and in the long term requires further research. A very recent development is the breeding of mice bearing genetic deletion of CB1 and CB2 receptors (Axelrod and Felder, 1998) and this is likely to yield new information about the cannabinoid receptor family.
Anandamide is the endogenous cannabinoid
arachidonylethanolamide. It was named after the Indian
Sanskrit word, ananda, meaning "bringer of bliss and
tranquillity" (Devane et al., 1993).
9-THC acts by mimicking anandamide
(Border and Norton, 1996). The mechanism and synthesis of
anandamide is not yet certain (Mechoulam et al., 1994). It
stimulates cannabis receptor function in a similar way to
the action of cannabinoids, suggesting that it may have a
role in cognition and motor control/coordination (Axelrod
and Felder, 1998).
Studies in rodents show that anandamide stimulates
behavioural effects similar to those observed for
cannabinoid agonists such as
9-THC. These behavioural effects include
hypothermia, analgesia, hypomobility and catalepsy.
However, relatively high concentrations of anandamide are
required to produce these effects.
Anandamide may be one of a novel class of lipid neurotransmitters. Classical neurotransmitters are small molecules, stored in synaptic vessicles that are released into the synaptic cleft following an appropriate signal usually a depolarisation of the cell membrane. Neurotransmitters then bind to a receptor protein and elicit functional responses such as the generation of second messengers (e.g. cyclic AMP or adenylate cyclase). Finally, they are removed from the synapse through uptake or metabolism. Anandamide does not appear to function in this way. It is hydrophobic and membrane permeant and probably is not stored in synaptic vessicles but synthesised on demand to act at both the outer plasma membrane and intracellular compartments (Axelrod and Felder, 1998). Interestingly this membrane permeability is similar to the original hypothesis of cannabis action i.e. through effects on neuronal membrane fluidity.
Levels of anandamide in human brain regions are similar to
those of other neurotransmitters such as dopamine (Felder
et al., 1996). It may have functional roles in the brain
and in the periphery, as both CB1 and
CB2 receptors which selectively bind
9-THC, also bind anandamide. Thus,
anandamide acts as an agonist at CB1 and
CB2 receptors. It stimulates signal transduction
pathways in the same manner as cannabinoid agonists (Devane
et al., 1993) and there is some indication of
cardiovascular pressor effects (Lake et al., 1997).
Anandamide has been found throughout the human brain, though highest concentrations are found in the hippocampus, striatum, cerebellum and cortex (Felder et al., 1996). However, anandamide is also found in the thalamus where CB1 receptors are infrequent, which suggests that there may be additional roles for anandamide in the brain (and which may be affected by cannabis consumption in some, as yet unknown, manner): Similarly, the relatively low affinity of anandamide for the CB1 receptor raises the possibility that other more potent agonists exist for the CB1 receptor in the brain (Axelrod and Felder, 1998).
The discovery of cannabinoid receptors and the endogenous cannabinoid, anandamide, has directed research attention to the development of synthetic cannabinoid agonists, with potential therapeutic applications in a number of conditions.
The FDA has approved oral preparations of synthetic
9-THC (Marinol and Dronabinol) to reduce
nausea associated with cancer chemotherapy in 1984 and to
combat weight loss in AIDS patients in 1992. These and
other THC compounds in development aim to harness certain
therapeutic actions of THC without the psychoactive effects
or the harmful effects associated with smoking cannabis.
For example, receptor selective agonists might target only
one type of receptor. Hence a CB2 agonist should not
produce sedative or psychotropic effects while a CB1
agonist should not lead to immunosuppression. Recent
legislation in Arizona and California allows the use of
smoked marijuana for medicinal purposes and such use in
other countries is also known though not condoned.
There is potential application for THC in conditions which affect movement such as multiple sclerosis and Huntingdon's Disease as well as spasticity, glaucoma and wasting diseases such as associated with the AIDS virus. Increasing knowledge of the pharmacology of cannabinoid receptors will lead to further drug development with potential for widespread use 10 . However, research will be required to establish the absence of psychoactive effects of these compounds and their lack of potential effects on cognitive function in much the same way as the benzodiazepines were evaluated in the 1970's and 1980's, when they were shown to have substantial and long-lasting effects on performance related to driving (see for example, Hindmarch, 1991).
Preparation of cannabis in any form requires that it is
heated so that the acid derivative of
9-THC found in cannabis is
decarboxylated to
9-THC. Various forms of the drug are
consumed in different manners e.g. marijuana or grass may
be smoked alone or in conjunction with tobacco. Resin
(hashish) which has greater potency is often mixed with
tobacco as is oil. Alternatively cannabis may be cooked and
eaten. i.v., administration of cannabis is difficult due to
the lack of nitrogen in the molecule (see Figure 5),
cannabinoids are insoluble and infection can appear at the
site of injection. 11
However, i.v. administration of THC extract has been used
to determine the pharmacokinetics of THC.
Oral ingestion produces a bioavailability of only 6% (Sticht and Käferstein, 1998) and onset of effects is delayed. Titration is easier when cannabis is smoked as the effects are more immediate. Around 2-3mg of THC is required to produce a high in occasional users (Solowij, 1998). A typical joint of marijuana contains between 0.3g and 1.0g of plant matter and will have 1-15% THC (2.5-150mg). Estimates of the amount of THC in various forms of cannabis vary, particularly between studies in different countries. Estimates of bioavailability have often been derived from i.v. administration of THC extract because of the difficulty in calculating the amount of THC delivered when cannabis is smoked, due to loss through side stream smoke etc. The toxic dose of THC has been estimated to be 8.45kg in a 65kg human 12 (Hall et al., 1994)
Table 3 shows the estimated concentration of THC in various forms of cannabis. The variation is due to the source of the information e.g. police seizure etc., as well as national differences due to climate, growing techniques and origin of cannabis.
Most of the cited research on the acute psychomotor response has been conducted under laboratory conditions to assess subjective state (mood) and functioning related to vision, cognition and motor control. The experimentation and literature review conducted by Robbe (1994) is the basis for the summary of research prior to 1994. Similar English language reviews for the same period are also provided by Hall et al., (1994), Border & Norton (1996) and Solowij (1998). These reviews often differ in the interpretation of the extent of impairment indicated. However, there is general agreement that the most extensive effect of cannabis is to impair controlled information processing (Robbe, 1994).
5.1.1 Mood
Consumption of cannabis produces
a psychological 'high'. Although the subjective
components which constitute this phenomenological state
have not been empirically specified, it is generally
characterised by euphoria ranging from exhilaration to
introspection. However, dysphoria and even psychotic
symptoms may be experienced by naive users after
consuming large doses, particularly when ingested.
Robbe (1994) reports that the level of high reported by an individual is correlated over time (intra-subject) with the amount of THC present in the blood serum (mean r = .80) 15 . The correlation across individuals (inter-subject) at each time interval is typically lower (<.50)16 . This suggests that there is considerable variation due to individual differences in terms of the experience of cannabis and the relation to THC and its metabolites in the body (see also biological response).
The 'high' experienced is also significantly moderated by the activity and context in which the drug is consumed. Pleasant environments with a positive social context often elevate the experience. Indeed, the motivation to consume the drug and the expectation of the effect can have as much influence on the experience as the pharmacological properties of the drug, particularly at low doses. For example, motivation may overcome the effects of a drug by the exertion of additional compensatory effort. The expectation that the drug will have an impairment effect may amplify the effect of the drug. This suggests that studies of the effects of cannabis should include both a control group (no intervention) and a placebo group. Given that the control group would have neither motivation nor expectation and the placebo group would have only expectation, the effects of these mitigating factors can be isolated from the pharmacological effect of the drug under experimental conditions.
The 'high' is experienced as subjectively distinct from other related moods such as calmness, contentment, and alertness. For example, during a study of ad lib consumption of cannabis (194-524 µg/kg THC), Robbe (1994) recorded subjective measures of mood at different time intervals (expressed as a percentage of the maximum level ever experienced). The maximum reported high was reported within 30 minutes after consumption with a half-life of approximately 140 minutes. By comparison, calmness, contentment and alertness were lowest within 30 minutes after consumption and increased linearly thereafter to a highest level in the final time period (210 minutes). Thus, the experienced 'high' appears to have different phenomenological properties to these other mood components.
5.1.2 Senses
Few studies have considered the
effect of cannabis on senses other than vision
17 . The effect of
cannabis on static or dynamic acuity, binocular fusion,
lateral phoria, glare recovery, colour vision, and
saccadic and ballistic eye movements during eye tracking
has been studied. These studies have considered a range
of doses of up to 300 µg/kg of ingested cannabis and
about 210 µg/kg of inhaled cannabis for periods up to 6
hours after consumption. The effect of cannabis on visual
and oculomotor function is negligible with most studies
indicating little or no effect 18 . Similarly, there is
inconsistent evidence that cannabis affects the
sensitivity of the visual nervous system as measured by
critical flicker fusion threshold (CFFT). CFFT is a
measure of visual information processing capacity,
requiring the detection of flicker threshold resolution
and has well documented sensitivity to many psychoactive
agents. Whereas CFFT has been shown to rise after
consumption of 15 mg THC (opposite to the presumed
depressant action of cannabis on Central Nervous System),
CFFT has been shown to decrease after consumption of 19
mg THC.
5.1.3 Cognition
For this report, cognition will
include topics of attention, memory, time estimation and
effort as well as cognitive based behaviour indexed by
reaction time and dynamic tracking.
Attention
Dose related impairment effects of cannabis have been consistently demonstrated for both divided attention and sustained attention (vigilance). Divided attention tasks, using the dual-task paradigm, typically involve a central primary task with a concurrent peripheral task. Whereas there may be some variation amongst different methodologies, common measures of attention include the speed (latency) and accuracy of task performance. Cannabis may affect both central and peripheral tasks, but the most common dose related impairment is increased latency and decreased accuracy in the peripheral (secondary) task. This suggests reduced ability to efficiently divide attention across tasks and increased mental workload to maintain performance on the central (primary) task. Sustained attention is impaired in a similar dose related manner. Impairment of sustained attention has been studied with vigilance tasks whereby a target region must be continuously monitored over time to detect a signal from non-signal events. The effect of cannabis appears to be reduced sensitivity to discriminate between signals and non-signals rather than from the distraction of attention.Memory
Cannabis has been consistently shown to impair memory processes, particularly during the acquisition phase. It has been suggested that this impairment resides within working memory. Working memory has several functions including (i) the acceptance of incoming information; (ii) serial encoding information in relation to time; and (iii) retaining information to form associations in long-term memory. Cannabis may interfere with any of these functions. For example, THC may retard the function of the hippocampus to inhibit recall of inappropriate associations from long term memory such that there is interference with the processing of working memory. This would explain reports that cannabis use results in omissions from the memorisation of stories and intrusions during free recall.Time Estimation
Cannabis results in a distortion of time perception such that elapsed time is over-estimated. As discussed above, there may also be temporal disintegration as evident by the interference with the serial indexing of information and recall with respect to time. The accurate estimation of time is important to the processing of information and the co-ordination of activities to support task performance. The effect of cannabis may impair two categories of task dependent on temporal processing. First, the distortion of time perception may impair tasks requiring the estimation or reproduction of a specific time interval. Second, the disintegration of temporal memory may impair tasks requiring the prediction to an event based on preceding events.Compensatory Effort/Response Style
Although cannabis may impair performance, individuals intoxicated with cannabis appear to be aware of their potential impairment and exert compensatory effort or adopt a cautious response style. By contrast, individuals intoxicated with alcohol do not generally appreciate their level of impairment and may even adopt a riskier response style (see also Alcohol and Cannabis: Dose equivalence, below and Berghaus et al., 1998). For example, Robbe (1994) reports a study of individuals who drove a test route in an urban area after consuming either cannabis (mean THC level at 20 minutes measured in blood at 20 ng/ml) or alcohol (mean ethanol level at 20 minutes measured in blood at 0.034g%). Performance was compared to a placebo condition without any intoxicant. Figure 5 shows the subjective level of intoxication, impairment and compensatory effort reported by subjects after completing the test route. Intoxication was reported 95 minutes after consumption. Performance quality and effort is expressed as a percentage change relative to the baseline condition (placebo). The alcohol group showed significantly more driving impairment compared to both the baseline condition (no intoxicant) and the cannabis group. In fact, there was no evidence for driving impairment in the cannabis group. However, it is apparent that whereas both groups felt comparably intoxicated, the cannabis group perceived greater impairment and reported greater effort to maintain performance.
Figure 7 : Level of perceived intoxication, performance and compensatory effort reported after consumption of cannabis and alcohol (adapted from Robbe, 1994, Figures 8.2/8.3/8.4)
Reaction Time
Simple reaction time is only marginally affected by cannabis. Choice reaction time, a more demanding task, appears to be more sensitive to the effect of cannabis. Specifically, the variation of response latency in choice reaction time tasks has been shown to increase after consumption of cannabis. This suggests that attention to the task may be impaired. However, because of the variety of test paradigms used, it is difficult to draw firm conclusions with respect to the impairment mechanism. In terms of the trade-off between speed and accuracy of performance, cannabis does not seem to affect latency for correct responses. Rather, cannabis increases the latency for incorrect responses instead of the number of errors. This suggests that cannabis results in a more cautious response style. This is in contrast to the risky response style adopted with alcohol whereby latency increases for correct responses but not incorrect responses, resulting in more errors.Dynamic Tracking
There is clear and consistent evidence that most forms of dynamic tracking task are impaired by cannabis. Whereas impairment can be observed at low doses (3 mg) and has been noted to persist for up to 8 hours, the effect may not be dose related. However, performance tasks analogous to dynamic tracking within the driving task have not consistently shown similar impairment.
5.1.4
Motor Control
There is consistent evidence that consumption of cannabis
impairs motor control and balance. Even for low doses (10
µg/kg), hand and body instability increases. The effect
is dose related such that higher levels of consumption
produce proportionally greater instability. This effect
suggests that the consumption of cannabis interferes with
processing of proprioceptive feedback involved in motor
control.
It should be noted that most of the evidence for motor control impairment has been related to static control as measured by tasks such as maintaining a stylus within a fixed target area or remaining balanced while stationary on a platform supported by a fulcrum. Robbe (1994) considers the impairment of fine and gross motor control to be non-critical. "Unless the cannabis user is also a watchmaker or tightrope walker, it seems unlikely that the person would suffer greatly from this effect in any practical sense, including driving" (p. 57). Thus, whereas measures of motor control may be useful as an index of intoxication, they do not necessarily imply impaired driving performance or increased accident risk.
Since the seminal work of Robbe (1994) the major research development in the evaluation of the effects of cannabis on performance related to driving has been the meta-analytic studies conducted by Berghaus (Berghaus, 1995; 1997a; 1997b; Berghaus, Scheer and Schmidt, 1995; Berghaus, Schutz and Szegedi, 1998; Berghaus, Krüger and Vollrath, 1998; Krüger and Berghaus, 1995).
Before this approach was taken, researchers seeking to draw conclusions about the effects of cannabis on cognitive performance had two sources of information available. Firstly and of primary importance, are epidemiological studies. The epidemiological approach depends however, on accident data of which there is not sufficient to make accurate estimations of risk from cannabis (see also section epidemiological studies above). Secondly, experimental studies, the results of which have been summarised above, are conducted in controlled conditions to estimate the effects of cannabis for driving performance. To draw general conclusions, the researcher must draw together results of studies using different subjects (in terms of numbers, sex, age, experience of cannabis), performing different tests of varying duration and difficulty, administered following different doses of cannabis via different routes, with or without measurement of plasma THC. It is not an easy task to decide the relevant weight to be given to any particular study or to provide a synopsis of this information. In addition, performing further studies in Europe is difficult for ethical and legal reasons.
Meta analysis techniques summarise information from single studies in a standard form to allow results to be both compared and combined to determine the weight that should be accorded to a particular finding. Details of THC and time of testing allow for determination of the concentration-effect relationship which can be used to weight performance areas and produce an overall profile which is time dependent. However, many published single studies do not provide information in sufficient detail to allow results to be compared in a meaningful manner. Berghaus' studies represent the first attempt to summarise single studies in a structured and objective fashion.
These meta-analytic studies have examined more than 120 experimental studies with over 700 test results on the effects of cannabis on various aspects of performance, categorised using the same classification system developed by Krüger (1990;1993) in his meta-analysis of alcohol effects. The use of a similar methodology also meant that meta-analytic studies comparing the effects of alcohol and cannabis have been possible (Berghaus, Krüger and Vollrath, 1998; Krüger and Berghaus, 1995) and recommendations for comparative cut-off thresholds for alcohol and cannabis have been proposed (Berghaus, 1997b) (see section alcohol and cannabis dose equivalence below).
The following discussion therefore, is based on these meta-analytic studies. This type of study represents a major research advance, bringing together disparate measures of performance and often conflicting results in a comprehensive and comparable manner. A number of studies conducted in the 1990's have not been considered in the Berghaus' meta-analyses. Given that these are often better controlled than earlier studies and use sensitive measures of performance, an extension of the meta-analysis to include these is merited.
Classification of Performance
Krüger (1990;1997) formulated a highly differentiated
performance classification with 8 main areas of
performance (see Table 6). These areas were further
categorised based on the type of psychological processes
demanded by each task i.e. automatic and control
processes. This classification system formed the basis
for the meta-analysis discussed below.
Automatic processes are those which can be performed without central control. These include easy compensatory tracking, easy pursuit tracking, simple reaction time and choice reaction time, mental arithmetic, crossing out tests, categorisation tasks and attention tasks. Control processes are characterised by high consciousness and the use of central capacity. Simultaneous use of multiple control processes is not possible. Control processes include difficult compensatory tracking, difficult pursuit tracking, hand-eye coordination, information processing (en and decoding), reaction to 2 stimuli/tasks, and driving performance.
|
Performance Type |
Tasks included |
|
Tracking |
Easy compensatory tracking |
|
Psychomotor |
Hand-eye coordination |
|
Reaction Time |
Simple Reaction Time |
|
Visual Function |
Eye physiology |
|
Attention |
Simple attention |
|
Divided Attention |
Reaction to two stimuli |
|
En- and Decoding |
Information processing |
|
Driving |
Driving Simulator |
Robbe (1994) has commented that "whereas the acute effects of cannabis are relatively well established, consensus concerning the consequences of long-term use is lacking" (p. 37). This lack of consensus may be attributed to limitations of research design that only indicate associations between cannabis use and observed effects. Such designs can not specify causality because unmeasured variables may confound or create a spurious relationship between use and effect.
This section does not consider the chronic effects of cannabis in detail as the primary accident risk is associated with acute effects of cannabis consumption. Chronic effects are those which occur as a consequence of repeated administration of cannabis over a prolonged period. The acute effects are a result of recent consumption that implies culpability for traffic accidents resulting from impairment. Chronic effects may be present at this time, but are not related to a specific consumption episode that may be litigated. These chronic effects persist beyond the elimination phase of cannabinoids from the body. As a parallel, whereas a drunk driver may be prosecuted for impaired driving, it may not be warranted or justified to sanction a driver for impairment resulting from the effects of alcoholism. Moreover, Robbe (1994) cites evidence that "chronic use of cannabis may be associated with psychomotor and cognitive deficiencies, although, at the same time, deficits in chronic users seem to be less pervasive than those occurring during acute intoxication" (emphasis in original, p. 41).
The recent UK parliamentary report (Border & Norton, 1996) provides a table indicating the potential chronic health effects of cannabis. The report is based primarily on a review of the 1994 Australian review of health and psychological consequences of cannabis use (Hall et al., 1994). Table 10 lists the evidence and conclusions regarding the main category of health effects related to chronic use of cannabis. It is apparent from this summary that the chronic use of cannabis is primarily related to carcinogenic and respiratory disease as well as foetal hypoxia. In this regard, the chronic effects of cannabis may be attributed as much to the form of consumption (smoking) as to the psychopharmacological properties of the drug (Hall et al., 1994). Moreover, the source of these effects may be confounded by unmeasured variables that relate to both chronic use and chronic effects.
Hall et al., (1994) consider evidence that (chronic) use of cannabis retards adolescent and adult development. In cross-sectional studies, chronic heavy use of cannabis has been associated with poor adjustment including delinquency (non-conformity), poor educational attainment, and job instability. However, any causal relationship can not be asserted because many of these indicators of poor adjustment precede cannabis use i.e. adolescents who show indications of poor adjustment are those who are more likely to undertake chronic use of cannabis.
Smiley (1986) produced a review article of all simulator and on road studies of cannabis and driving that had been published up to 1986. This has since been updated for a forthcoming World Health Organisation (WHO) report on cannabis to include all simulator and on road studies published up to 1997 (Smiley, 1998). It is notable that no simulator or road studies of cannabis have been published since 1994. Indeed, the most recent simulator study was published over 15 years ago in 1983. The 1994 treatise by Robbe is the most recently published road study of cannabis. The limited number of simulation and road studies of cannabis may be attributed to the ethical, legal and practical encumbrances inherent in this type of research.
A modified presentation of the tabulated summary of simulator studies reviewed by Smiley (1998) is shown in Table 11. 22 In addition to the main effects on driving performance reported for each study, the table includes information about the methodology of each study. Most of the studies have used a within subject design including a placebo condition (control). A number of these studies have used doses comparable to that indicated by Robbe (1994) to be typical (i.e., average) for regular cannabis users (300 µg/kg). It is generally these higher doses that have effects on performance. Moreover, all the effects were observed when the task was initiated within 1/2 an hour of consumption. Given the length of the simulated tasks, this suggests that the effects of cannabis were observed between 15 minutes and approximately 1 hour after consumption (Smiley, 1998). Indeed, the earliest simulation study did not observe any effect of cannabis at periods of 2.5 and 4 hours after consumption (Crancer et al., 1974, cited by Smiley, 1998).
The effects of cannabis can be described by three general performance characteristics. First, cannabis increases variability of longitudinal (speed and headway) and lateral control (lane position). This may be more evident under high workload and unexpected conditions such as curve negotiation and compensation for lateral displacement (wind gusts). These conditions resemble pursuit and compensatory tracking tasks. The increased variability may be attributed to specific acute effects of cannabis consumption including unstable motor control and reduced capacity to divide attention between task elements (e.g., maintain lane position and monitor speed) and sustain attention (vigilance) to task feedback (e.g., lane position deviation).
Second, decision times may increase to evaluate a situation and determine an appropriate response. This has been observed with respect to overtaking. The time taken to decide to attempt to overtake may increase. To the extent that this increase is considered in the decision to respond, it may also explain the effect of increased distance accepted to overtake. Although speculative, the case of cannabis resulting in fewer attempts to overtake under "cued" emergency conditions may indicate a strategy to avoid a complex decision process in deference to the external signal. Such a strategy would reduce cognitive workload. This is rational given that some subsidiary tasks have indicated less spare mental capacity after cannabis consumption. Strategies that do not impose upon limited resources are 'economical'.
Third, the style of driving performance after consumption of cannabis can be interpreted as cautious. Evidence of increased caution includes fewer overtaking attempts, larger distances required for overtaking, slower speeds, and larger headways. This caution can describe either the behaviour or the strategy of the driver. For example, cautious behaviour may arise without deliberation as a result of alterations in perception and control (e.g., distorted perception of time and space). Alternatively, a driver may decide upon a deliberate strategy to act cautiously by adopting a reduced threshold of acceptable risk. This decision may be motivated by the recognition of performance impairment. Of course, neither basis is mutually exclusive; changes in behaviour may be a result of both (unconscious) psychomotor impairment and (conscious) cognitive strategy.
A number of studies have included alcohol as a verum condition. Alcohol is a standard impairment agent that can be used to indicate the relative impairment effect of cannabis. With few exceptions, alcohol generally produces a greater impairment of performance effectiveness and efficiency than cannabis (for the range of doses administered). Moreover, the form of impairment from alcohol consumption appears to be qualitatively different than for cannabis. Notably, alcohol seems to result in a 'riskier' driving style (e.g., faster speeds) rather than one that is more cautious. There is also some indication that alcohol may affect oculomotor activity which is an earlier processing stage than distortion of perception.
There are a few inconsistencies in the results summarised in Table 11. For example, the earliest study (Crancer et al., 1969 cited in Smiley, 1998) found few effects of cannabis despite using similar doses to later studies. This has been related to the possibility that the potency of the cannabis may have been over-estimated such that the actual dose was 14-36% of what was indicated (Rafaelsen et al., 1973 cited in Smiley, 1998). Moreover, Smiley et al. (1981 cited in Smiley, 1998) found more evidence of cannabis impairment than did Stein et al., (1983 cited in Smiley, 1998) despite similar methodologies and doses of cannabis. 23 This may relate to Smiley et al.'s, (1981) use of performance incentives that may have increased motivation as well as dose levels that produced greater than the normal 'high' for the subject sample. Also, Stein et al. (1983) tested cannabis over a shorter period and included a non-random subsidiary task (Smiley, 1998). This shorter period and the ability of subjects to anticipate (secondary) task demands may have limited the effects observed.
The reported effects of cannabis on simulated driving performance must be interpreted within the limitations of the methodologies represented by the range of simulator studies. Some of the evidence has been observed for tasks and environments that have very limited realism or interaction. The relevance of this evidence to actual driving is equivocal. Indeed, in the absence of a standard test protocol, including a specified driving task and simulation environment, it is not possible to directly compare results between studies. There is also no standard format for reporting results such as the relation between dose and levels present at the time of testing. 24 This is compounded by differences between studies in dose, time elapses since testing, and measures analysed. There is further variation in terms of the type of subject used whereby the demographic and psychosocial variables may interact with the effects of cannabis. As a result, it is not possible to directly or accurately ascertain the effects of cannabis dose on driving performance, or the relative effect compared to alcohol from the existing set of published simulation studies.
Epidemiological studies are employed to provide evidence of the effect of cannabis use on traffic accident risk. The accident risk is defined by two factors: (i) prevalence of cannabis in accident involved drivers; and (ii) prevalence of cannabis in non-accident driving population. An increased accident risk may be attributed to cannabis if its presence is over-represented amongst accident involved cases relative to the general driving population.
Table 13 lists the main epidemiological evidence of cannabis prevalence amongst accident involved drivers reviewed by Robbe (1994) and Hall et al. (1994). Additional studies are also included that were either omitted (e.g., Crouch et al., 1993; Gerostamoulos & Drummer, 1993) or have since been published (see also Hunter et al., 1998b). The table indicates the time period and country of origin of the data set, the type of cases considered (injured or fatal drivers), the cannabinoid detected, the percentage of cases with detected cannabinoids, and the percentage of those positive cases that also indicated alcohol.
The reported data indicates cannabis was detected in approximately 4 to 12% of accident involved cases. This is consistent with the extensive review of epidemiological studies reported by Krüger and Löbman (1998) that indicated median rates of detected THC in 10.9% and 3% of fatal and injured cases, respectively. The median values for THC metabolites were 14.5% and 9.5%.
The few extreme rates of detection may be attributed to the definition of a positive case. For example, the higher incidence rate for fatal accidents reported by Williams et al., (1985 cited by Robbe, 1994) for the US in 1982-83 (36.8%) has been attributed to the use of a sample with a high a priori accident risk (males under 35 years) and inclusion of cases with trace detection (1 ng/ml). When the data is adjusted to estimate prevalence amongst the general driving population with trace detection treated as false positives the incident rate is more comparable to that of other studies (12.4%).
The rate of detection is also dependent on which cannabinoid is used to indicate the presence of cannabis. For example, together the studies by Crouch et al., (1993) and Gerostamouos & Drummer (1993) analyse THC, THC-COOH, and 11-OH-THC. The detection rates for these individual cannabinoids indicate prevalence between 4.1 and 13%. This variation, due to the choice of identifying cannabinoid, accounts for the same overall range of cannabis detection for all accident involvement cases (4-12%). Similarly, it is apparent from the review of epidemiological studies by Krüger and Löbman (1998) that the detection rate is dependent on the measurement of either THC or its metabolites. Moreover, the apparent rate of detection for cannabis may be higher if the rate for all cannabinoids is summed.
There are few replicated surveys that permit the estimation of trends over time. For example, Soderstrom et al.(1995) compare the detection rate for injured automobile and motorcycle drivers for the 1985-86 and 1990-91 data sets. Whereas the prevalence of cannabis in motorcycle drivers was similar for both periods (38.6% and 32.0%), the prevalence in automobile drivers was significantly less during 1990-91 (2.7%) than for the earlier period between 1985-86 (31.8%). Detected cases of alcohol did not change for either driver group during this time period. This dramatic reduction could not be explained by the researchers.
It is evident that between 50 and 90% of these positive cases also had indications of consumed alcohol. Thus, the combination of cannabis and alcohol precludes conclusions about the effect of either drug alone on accident involvement. In addition to the impairment effect of alcohol, Mercer and Jeffery (1995) have reported certain demographic features of accidents involving alcohol. Specifically, alcohol alone and in combination with cannabis/other drugs was detected significantly more often in younger drivers. Young drivers already have a greater accident risk than the general driving population. The alcohol cases also involved single vehicle accidents significantly more often than did drug only or no drug/alcohol cases, and were more frequent at night and on the weekends. Thus, the presence of alcohol, the circumstances of the accident, and the type of driving consuming drugs all confound assessment of the independent effect of cannabis on accident risk.
Detection indicates consumption, but not impairment. Moreover, not only is the association between the level of detected THC or THC-COOH and driving performance weak (Robbe, 1994), but the detection threshold may not be sufficient to impair performance. For example, Robbe (1994) indicated minimum detected levels above 1 ng/ml during city driving from a dose of 200 µg/kg with no observed change in driving performance relative to control conditions. Thus, presence of cannabis cannot be asserted as a causal factor. Indeed, there are many occasions where the presence of cannabis is confounded with other factors that may have contributed to the accident. For example, Gerostamoulos & Drummer (1993) report that other contributing factors (e.g., wet roads, poor visibility, other drugs) were identified for 60% of the cases testing positive for cannabis.
Whereas the data reported in Table 13 indicates the prevalence of cannabis in accident involved cases, it does not indicate the accident risk associated with cannabis use. In order to estimate this accident risk, it is necessary to relate this data to baseline data indicating the prevalence of cannabis amongst non-accident involved cases. In order to be valid, this baseline data must apply to a random sample from a comparable population within the same time period as the accident involved data, preferably in parallel with investigations of accident involvement cases. At present, there are no valid baseline data that may be applied.
There are very few sources of baseline data. The earliest data exist from roadside surveys were conducted in Canada (4%) and Italy (1.2%) in 1974 and 1982 respectively (Robbe, 1994). The most recent roadside survey was conducted in Germany between 1992 and 1994. During this period, 10,000 drivers were stopped on a random basis to provide a sample of saliva for the detection of cannabinoids. Compliance was obtained from 90% of these drivers. The data set was weighted to represent the detection rate in the general driving population. The calculated detection rate was 0.6%. However, given that 1/3 of the saliva tests may fail to detect cannabis verified by blood samples, the actual detection rate may be as great as 1.8% in this population. 25
The preceding sections of this report have reviewed research on the effects of cannabis on driving and accident risk. It is apparent from this review that this research is not definitive. There are some substantive weaknesses in the existing research and other areas have not been investigated. As a result, a number of significant issues relevant to the formulation of transport policy for cannabis remain unresolved (Hall et al., 1994). These issues identify key objectives for future research (e.g., DGVII, 1995; Friedel, 1995; Sweedler & Vingilis, 1992; TRB, 1993). This section of the report will identify the main research objectives necessary for the development of relevant transport policy (and interventions). These objectives are specified in the form of stated research questions relating to each of the main report headings.
Exposure:
Biological Response:
Acute Psychomotor Response:
Chronic Effects:
Driving Response:
Accident Risk:
Rather than rely on the ethos of alcohol to determine the research agenda and development of transport policy for cannabis and driving, the accident risk posed by cannabis must be determined on the basis of considerations relevant to both the 'recreational' and medicinal use of the drug class. Starmer et al., (1988) have suggested some guidelines for assessing the accident risk associated with groups of drugs. Whereas these guidelines do not quantify the accident risk of cannabis, they do provide a rational framework to structure and summarise evidence relevant to the consideration of the accident risk associated with cannabis.
1. Does the drug have effects which may impair human skilled performance? Yes. The evidence presented for laboratory tests of human skill indicate that certain memory, attention and psychomotor functions may be impaired by cannabis. However, because different tests are used to measure these functions rather than single standard tests specific to each function, there are some inconsistencies between studies. Moreover, because the tests used have not been selected to measure functions that are related to driving based on an accepted model of driving performance, it is not clear to what extent the impairment of these skills may relate to driving. Indeed, whereas there is evidence of increased variability, effort and decision times from simulation and road studies, the effects of cannabis are generally more pervasive under laboratory rather than 'natural' conditions.
2. If so, what is the nature of the effects which occur? Given the idiosyncratic nature of the tests used to measure impairment of human skill, it is not possible to provide a comprehensive description of the effects of cannabis. The effects that are evident may be specific to the particular tasks used rather than describing a general process that may be related to a model of driving. The implication of the impairment of memory, attention and psychomotor control evident from the laboratory studies is that aspects of driving may be jeopardised including the perception of time, elements of pursuit or compensatory tracking, and periods of divided attention and sustained attention to monitor critical (unexpected) events and task feedback. Such evidence is apparent from simulation and road studies for periods shortly after consumption of larger doses of cannabis. However, there have not been systematic studies to examine the factors that influence the nature of these effects in terms of dose and time profile parameters, nor the influence of context and user characteristics. This evidence is needed to provide more comprehensive descriptions of the nature of the effect of cannabis on driving. Without this evidence it will not be possible to accurately predict the effect of cannabis under specific conditions.
3. Are these effects manifest at therapeutic doses? At present, the range of medicinal uses is expanding. Medicinal dosages are currently experimental or unspecified. It is therefore, not possible to comment on the issue of impairment at medicinal dose levels. Moreover, the impairment at a particular dose is also dependent on time elapsed since consumption. Thus, it is necessary to consider not only the therapeutic dose, but also the period of convalescence after treatment prior to an opportunity to drive. Also, the medicinal use of cannabis reduces symptoms that might otherwise interfere with driving. It is not clear what the net effect on driving performance would be due to the intoxicating effect of cannabis as opposed to the symptoms of the conditions for which it is taken.
Developments in pharmacology imply that there will be an increase in the use of THC for medicinal purposes. This coupled with the increased recreational use of the drug will mean that the number of people whose functioning may be affected by THC and related compounds is likely to increase. Thus effective means of measuring effects and level of intoxication (if any) are critical for road safety. A further as yet unconsidered issue is that given that THC has therapeutic potential in disorders of movement, it may be necessary to examine whether the therapeutic use of THC related compounds might prolong the ability to drive in for example, sufferers of multiple sclerosis and other motor disorders, whose disease often precludes this activity.
4. Do these effects occur in all or only in certain individuals? There is evidence to suggest that the context, use history and expectations of the user may influence the effect of cannabis. To date, only one study has tested the effect of cannabis and driving in a social ('party') context which is likely to produce the most pronounced effects (Smiley et al., 1986). Comparisons of 'casual' and chronic users with non-users have not been systematically undertaken. Such comparisons would examine the influence of tolerance and expectation on the effect of cannabis. These investigations are required to identify and predict impairment from cannabis consumption for types of individuals.
5. What conditions is the drug used to treat? Cannabis derivatives or synthetic analogues are not currently approved in the UK but are in use in some american states for nausea reduction in chemotherapy and wasting conditions associated with the AIDS virus. Research is flourishing for other potential applications.
6. Is the drug available only on medical prescriptions? Cannabis derivatives or synthetic analogues are not currently available in the UK.
7. Is the drug used recreationally? The most common purpose for use of cannabis is recreational. It is the most common form of illicit recreational drug. It is most commonly used by younger persons (16-29 years), particularly males. Its use is also associated with a propensity for 'problem behaviours' including delinquency and alcohol use. Its use may lead to other forms of drug taking by virtue of the character of the user and the social context of use which exposes the individual to other drug forms and portrays drug taking as the 'norm'.
8. Does the drug interact adversely with other drugs or with alcohol? There are few occasions of cannabis used in conjunction with other 'illicit' drugs. Cannabis is frequently used in conjunction with alcohol. The combined effect of alcohol and cannabis produces more impairment and a higher accident risk than either alone. However, the combined effect of consuming both cannabis and alcohol together may or may not be synergistic. The variation for the combined effect may be attributed to the different time profile of intoxication for these drugs. For example, the maximum impairment for each drug may occur at different times after consumption. Therefore, the magnitude of the combined effect will depend on when they are consumed in relation to each other. Given the prevalence of alcohol in combination with cannabis, this is an important area of research.
9. To what extent is the drug used by the driving population? There is no valid data to estimate exposure within the driving population. Whereas surveys have estimated the recency of use in the general population and the percentage of the general population that may be defined 'regular' users, there is no estimate of those who drive after consuming cannabis. Even if this data were available, it would need to be specific to a time period after consumption in which the intoxication effect of cannabis was sufficient to impair performance. There have been few road side surveys of detected cannabinoids in random samples of drivers. Those that have been completed vary too much in terms of survey date, methodology and national region to provide a valid baseline. Such data is necessary to provide valid calculation of accident risk for cannabis. Because such evidence is necessary to vindicate any transport policy, it should be a priority to conduct epidemiological research with valid baseline data to estimate accident risk. This research should use a methodology similar to the Grand Rapids method to derive threshold values of cannabinoids (or BAC equivalence) for specified levels of accident risk.
10. Can the drug be detected in body fluids? There is considerable debate about not only which body fluid to use (blood, urine, sweat, saliva), but also which cannabinoid to detect. There is also variation in the recommended methods of detection and the minimum detection level. These issues may be resolved differently depending on the particular application. For example, one set of recommendations may be applied to accommodate the practical considerations of road side testing, while another set may be applied to the legal demands of accident investigation. The most promising measures, pragmatically and pharmacologically, are saliva and sweat. However, multiple sampling of metabolites may be required for ratio calculations to determine recency of consumption. Also, legal corroborative measures necessary will imply that research specifies suitable storage and reanalysis protocols. Standard methods and reporting formats should be proposed and verified for the range of relevant applications.
11. How often is the drug detected? Drugs are detected at a high rate amongst drivers stopped by police for impaired driving. However, these rates are elevated by the exclusion of cases testing positive for alcohol and attest to the skill of the police in detecting impairment (Burns & Alder, 1995; Fell, 1995) rather than to the incident rate in the general population. Incident rates amongst hospitalised or fatally injured drivers ranges between 4-12%. Higher rates tend to relate to high risk driver groups (e.g., under 35 years) or high use regions. There are few valid indications of the detection rate in the general (non-accident) driving population (0.6-4%). Research is needed to provide relevant data for these different areas of detection. The form of research should be specific to the needs of informing and supporting transport policy (rather than be surmised from other sources). The research should be ongoing to monitor trends over time and by region (including analysis of demographic groups). Here, the method and threshold level for detection is critical to calculation of prevalence.
12. Is there reliable information linking the blood concentration of a drug with the expected degree of impairment? There is only inconsistent (and weak) evidence that detected levels of cannabis correlate with impairment. There have been no attempts to relate detection level to accident risk. Thus, at present there may be limited practical significance of using detection levels to indicate impairment and accident risk. Moreover, there is no agreement on which cannabinoid to detect. Indeed, THC-COOH which is rapidly detectable, is not psychoactive. Since different cannabinoids may have different relation to impairment over time, it is not possible to specify a single reliable model to link blood concentrations and impairment. Given that impairment is a function of time and dose, a valid model must consider both the time and dose functions. Moreover, it may also be necessary to include parameters reflecting the influence of relevant physiological, demographic and psychosocial variables. However, in the absence of a reliable and valid measure of 'impairment', no practical model can be specified. Research is needed to provide standardised impairment tests with which validated models of impairment can be formalised.
13. Can a case be advanced that a driver is safer with the medicinal use of the drug than in the absence of the drug? The current therapeutic benefit of cannabis is not clear with respect to psychomotor effects relevant to driving, especially since derived therapeutic agents will be developed with or without psychoactive properties.
14. Is the drug representative of its class and are the alternative (medicinal) drugs available? Currently cannabis is not classified as a medicinal drug and amongst illicit drugs of its class it has a different mechanism of action.
This overview of the main evidence for the effects of cannabis on accident risk does not provide a definitive conclusion. Whereas cannabis does impair certain skill functions under laboratory test conditions, these effects are less manifest in simulated and road studies largely because the impairment effects are recognised and compensated for by greater caution and longer deliberation. Moreover, the level of impairment has not been shown to increase accident risk either in absolute terms or in relation to alcohol. Indeed, the evidence does not exist at present to quantify accident risk from the detection of cannabis or the measurement of associated impairment. Therefore, it is not scientifically justified at present to specify a transport policy regarding a threshold value for cannabis detection in relation to accident risk.
This report has summarised available research on cannabis and driving. The report has included the main review documents for research published before 1994 (e.g., Hall et al., 1994; Robbe, 1994) as well as primary sources for research published from 1994 using keyword searches of relevant databases. This synthesis of research was directed to identify key research objectives to develop a rational transport policy for cannabis and driving.
It is apparent that cannabis is the most common 'illicit' drug. Indeed, there is some evidence to indicate an increasing trend in its availability and use in the general population. Thus, in terms of drug use and traffic safety, this would suggest that cannabis represents the major drug type to be addressed by transport safety policy.
However, there is not sufficient evidence indicating the percentage of drivers that operate a vehicle after consuming cannabis, particularly during the time period of any intoxicating effect. As a result, there is no precise estimate of the percentage of drivers exposed to cannabis as an accident risk factor. Indeed, it is problematic to estimate the extent of exposure independent of other risk factors associated with cannabis use such as alcohol. Moreover, the demographic group most frequently using cannabis already has the greatest a priori accident risk due to driving inexperience and factors associated with youth relating to risk taking, delinquency and motivation. These demographic and psychosocial variables may relate to both drug use and accident risk, thereby presenting an artificial relationship between use of drugs and accident involvement.
The recent developments and discoveries in pharmacology such as cannabinoid receptors and endogenous ligands are important and exciting. These and the increased understanding of the mechanism of action of cannabis will mean that new or improved methods of detection are likely. Most promising to date in terms of reliability, detection of recent consumption and practical application are methods for determining presence of metabolites in saliva and sweat. These developments and ongoing research also have implications for therapeutic drug development. New compounds based on cannabinoids will need careful evaluation to confirm their lack of psychoactive and psychomotor effects.
Evidence of impairment from the consumption of cannabis has been reported by studies using laboratory tests, driving simulators and on-road observation. The laboratory tests generally indicate acute impairment of memory, attention and psychomotor control. Both simulation and road trials generally find that driving behaviour shortly after consumption of larger doses of cannabis results in (i) a more cautious driving style; (ii) increased variability in lane position (and headway); and (iii) longer decision times. Whereas these results indicate a 'change' from normal conditions, they do not necessarily reflect 'impairment' in terms of performance effectiveness since few studies report increased accident risk. However, the results do suggest 'impairment' in terms of performance efficiency given that the increased compensatory effort resulting from cannabis use limits the available resources to cope with any additional, unexpected or high demand, events.
In conclusion, cannabis impairs driving behaviour. However, this impairment is mediated in that subjects under cannabis treatment appear to perceive that they are indeed impaired. Where they can compensate, they do, for example, by not overtaking, by slowing down and by focusing their attention when they know a response will be required. However, such compensation is not possible where events are unexpected or where continuous attention is required. Effects of driving behaviour are present up to an hour after smoking but do not continue for extended periods.
"With respect to comparisons between alcohol and marijuana effects, these substances tend to differ in their effects. In contrast to the compensatory behaviour exhibited by subjects under cannabis treatment, subjects who have received alcohol tend to drive in a more risky manner. Both substances impair performance, however, the more cautious behaviour of subjects who have received cannabis decreases the impact of the drug on performance, where the opposite holds true for alcohol." (Smiley, 1998, p. 19)
It is notable that the studies based on laboratory tests tend to indicate more effects of cannabis consumption than those using simulation and road observation methods. The higher incidence of effects under laboratory test conditions relative to the 'natural' conditions of simulation and road studies has been attributed to (i) reduced error variance from greater control of test conditions; (ii) higher task demand under novel test conditions; (iii) irrelevance or non-equivalence of laboratory test to component of driving; (iv) greater latitude for compensatory effort under 'natural' conditions; and (v) self-selection under 'natural' conditions not to be exposed to risk (e.g., not drive).
"It is exceedingly difficult to explain the disparity in results obtained by laboratory tests and in driving situations. Rather than try, it seems better for the moment to assume that both sets of results are valid for the particular circumstances under which they were obtained. It demonstrates, however, that performance decrements obtained under the artificial and non-life threatening conditions in the laboratory do not automatically predict similar decrements in driving situations that are closer to real-world driving." (emphasis added, Robbe, 1994, p. 66).
The greater propensity for cannabis effects under laboratory test conditions is somewhat paradoxical given that the laboratory tests have typically used smaller doses of cannabis than the simulation and road studies. It is also controversial since the limited number of studies and absence of demonstrable effects under natural driving conditions has impeded the development of transport policy regarding cannabis use. Whereas evidence of drug impairment under laboratory test conditions is not sufficient to provide an increase in accident risk, it does demonstrate cause for concern. This concern should then guide subsequent research under simulation and road conditions to investigate more valid evidence of impairment. Such efforts should be guided by relevant laboratory tests that relate to a model of driving, and use of a standard test methodology and reporting format for both simulation and road based research. This will provide a logical sequence of inquiry that can include both the replication of key findings, and the comparison of effects between a range of study designs.
Attempts to estimate the accident risk associated with cannabis use have relied on epidemiological evidence from accident involved drivers. Whereas this evidence has identified the presence of cannabis amongst accident involved drivers, accident risk can not be calculated given the absence of valid baseline data for cannabis detected in the non-involved population. Moreover, the presence of cannabis is often confounded by alcohol, as well as demographic and psychosocial risk factors associated with both drug and alcohol use. Current methodologies can only determine the presence of cannabinoids, but not evidence of impairment.
Thus, not only is it problematic to estimate the percentage of accident involvements associated with cannabis use alone, there is no evidence that impairment resulting from cannabis use causes accidents. Attempts to alleviate these problems by calculating risk of culpability for an accident (rather than the risk of having an accident) suggest that cannabis may actually reduce responsibility for accidents. It is evident that further epidemiological research is necessary. Such research must adopt a 'Grand Rapids' methodology of obtaining valid baseline data matched to positive cases, as well as including sufficient sample sizes and a valid operational definition of 'responsibility'. Such research may benefit from differentiating between accident types and accounting for relevant covariates including driver age and sex.
Much of the interest in cannabis as a potential accident risk factor is related to the concern about alcohol. Both alcohol and cannabis have an intoxicating effect that alters the psychological state of the individual. However, the mechanism of action and form of intoxication of these drugs are distinct. Alcohol may provide a useful metric to evaluate the effect of cannabis. Moreover, given the existence of a set legal limit for alcohol, research of the dose equivalence between alcohol and cannabis for performance relevant to accident risk may provide a method of determining a safety critical limit for cannabis. German research based on meta-analyses has concluded that 50% of performance is impaired at 11ng/ml THC, making this an equivalent level of intoxication to 0.08% BAC, although more recent and driving specific studies need to be compared with respect to effect size to confirm these suggested dose equivalences.
However, it is important not to use parallel reasoning between alcohol and cannabis to dictate the research agenda and transport policy for cannabis alone. Such reasoning is particularly inappropriate for medicinal applications of cannabis derivatives.
"There has developed an understandable but regrettable tendency to separate alcohol from other impairing agents and at the same time to enact tough drugs-driving legislation which remains firmly based on experience with alcohol. This is illogical, inappropriate and usually quite unenforceable. There is often pressure to define, for legal purposes, critical body fluid concentrations above which all would be impaired and below which no impairment would be demonstrable. At present, this is not possible. In addition to the considerably more complex pharmacokinetic and pharmacodynamic effects of most drugs compared with those of ethanol, there is also the proposition that therapeutic drugs, used for legitimate purposes, may improve the driving ability of certain patients despite their ability to impair performance normal individuals." (Starmer et al., 1988, p. 35-36)
One approach to deriving a legal limit for cannabis during driving has been to set the threshold to the level at which 50% of results show impairment. For alcohol, Berghaus showed a BAC of 0.073% corresponded to impairment on 50% of 923 performance measures examined. The corresponding threshold for THC was 11ng/ml. This is the closest estimate of dose equivalence to date, although there are recent, well-controlled studies which have not been included in such meta-analyses. A necessary research undertaking would be a thorough meta-analysis of results to date, using statistical measures of effect size related to dose.
An alternative is to specify a zero limit threshold, where any level of detected drug is prohibited. However, such an approach is premised on the philosophy that any drug which alters the state of the driver is inconsistent with the responsibility of the driver to operate the vehicle only when in an optimal state. This approach is associated with its own impracticalities of defining an 'optimal' state and deciding if reasons for impairment (i.e. deviation from the optimal state) other than drug use can be prosecuted (e.g., fatigue, poor driving skills, age related decline in capacity to drive etc.).
Ultimately, the direction of transport policy will be decided by an assignment of relative priorities. On one hand, any drug that affects the alertness and capability of a driver to safely operate a vehicle must be precluded. On the other hand, there are other factors such as alcohol which have a stronger association with accidents.
"Of the many psychotropic drugs, licit and illicit, that are available and used by people who subsequently drive, cannabis may well be amongst the least harmful. Campaigns to discourage the use of cannabis by drivers are certainly warranted. But concentrating a campaign on cannabis alone may not be in proportion to the safety problem it causes" (Robbe, 1994, p. 177).
The main conclusion from this report is that there is insufficient evidence of the accident risk associated with cannabis. Future research directed to the formulation of transport policy is required to resolve many key issues that remain unresolved in relation to cannabis and driving. However, it must be recognised that these issues may not be readily resolved given the ethical, legal, and technical impediments of the research domain (Hall et al., 1994).
This call for additional research was set forth by Robbe (1994) at the conclusion of his oft cited treatise on cannabis and driving:
"This dissertation should not be considered as the final word. It should, however, remain for a while as a point of departure for subsequent studies that will ultimately complete the picture of cannabis effects on driving performance" (p. 177).
It is now imperative that funding is made available to facilitate further research. However, such research must also be supported by mechanisms to accommodate legal and ethical requirements in this area. "In the meantime, cannabis users should be urged not to drive while intoxicated by cannabis, and they should be particularly warned of the dangers of driving after combining both alcohol and cannabis" (Hall et al., 1994, p. 50).
Abood, M.E. & Martin, B.R. (1992). Neurobiology of marijuana abuse. Trends in Pharmacological Science, 13, 201-6.
Adams, I.B. & Martin, B.R. (1996). Cannabis: pharmacology and toxicology in animals and humans. Addiction, 91, 575-9.
Agurell, S., Halldin, M., Lindgren, J., Ohlsson,
A., Widman, M., Gillespie, H. & Hollister, L.
(1986). Pharmacokinetics and metabolism of
1 Tetrahydrocannabinol
and other cannabinoids with emphasis on man.
Pharmacological Reviews,
38, 21-43.
Attwood, D., Williams, R., McBurney, L. & Frecker, R. (1981). Cannabis, alcohol and driving: Effects on selected closed course tasks. In L. Goldberg (Ed.), Alcohol, Drugs, and Safety, Vol. III.
Axelrod, J. & Felder, C.C. (1998). Cannabinoid Receptors and Their Endogenous Agonist, Anandamide. Neurochemical Research, 23 (5) 575-581.
Barber, A., Corkery, J. & Ogunjuyigbe, K. (1996). Statistics of drugs seizures and offenders dealt with, United Kingdom, 1995. Home Office Statistical Bulletin.
Berghaus, G. (1994). Cannabis und Fahrtüchtigkeit Relation zwischen Blutalkohol und Tetrahydrocannabiol (THC) Konzentrationen auf der Basis einer Metaanalyse experimenteller Studien. Symposion der Bundesanstalt für Strassenwesen und des Instituts für Rechtsmedizin der Universität Köln, Mensch und Sicherheit, Heft M41:44-51. Bergisch Gladbach.
Berghaus G. & Schulz, E. (1998). Cannabis und Fahrtüchtigkeit. Ergebnisse der experimentellen Forshung. Cannabis im Straßenverkehr. Stuttgart: Gustav Fischer Verlag.
Berghaus, G., Krüger, H-P. & Vollrath, M. (1998). Beeinträchtigung fahrrelevanter Leistungsprofile nach Rauchen von Cannabis und nach Alkoholkonsum eine vergleichende Metaanalyse experimenteller Studien. In G. Berghaus & H-P. Krüger (eds.) Cannabis im Straßenverkehr. Stuttgart: Gustav Fischer Verlag.
Biegon, A. & Kerman, I. (1995). Quantitative autoradiography of cannabinoid receptors in the human brain post-mortem. In A Biegon & N.D. Volkow (eds.), Sites of Drug Action in the Human Brain, pp 65-74,. Boca Raton: CRC Press.
Blanchard, E.B. & Hickling, E.J. (1997). After the crash. Washington, DC: American Psychological Association.
Block, R.I. (1996). Does heavy marijuana use impair human cognition and brain function? Journal of the American Medical Association., 275, 560-1.
Block, R.I. & Ghoneim, M.M. (1993). Effects of chronic marijuana use on human cognition. Psychopharmacoogy, 110, 219-28.
Border, P. & Norton, M. (1996). Common Illegal Drugs and Their Effects: Cannabis, Ecstasy, Amphetamines and LSD. Report 79. London: Parliamentary Office of Science and Technology.
Brookoff, D. et al. (1994) Testing reckless drivers for cocaine and marijuana. New England Journal of Medicine, 331 (8) 518-522.
Burns, M. & Adler, E.V. (1995). Study of a drug recognition expert (DRE) program. In C.N. Kloeden & A.J. McLean. Proceedings of the 13th International Conference on Alcohol, drugs and Traffic Safety, pp.437-441, Adelaide, Australia.
Casswell, S. (1977). Cannabis and alcohol: Effects on closed course driving behaviour. In Johnson, I., (Ed.), Seventh International Conference on Alcohol, Drugs, and Traffic Safety, Melbourne, Australia.
Castle, D.J. & Ames, F.R. (1996). Cannabis and the brain. Australian and New Zealand Journal of Psychiatry, 30 (2), 179-183.
Chait, L.D. & Pierri, J. (1992). Effects of smoked marijuana on human performance: a critical review. In Marijuanal Cannabinoids: Neurobiology and Neurophysiology, ed. L. Murphy & A. Bartke, pp. 387-423. Boca Raton: CRC Press.
Chesher, G.B. (1995). Cannabis and road safety: An outline of the research studies to examine the effects of cannabis on driving skills and on actual driving performance. In Road Safety Committee, Parliament of Victoria (Eds.) Inquiry into the effects of drugs (other than alcohol) on road and safety in Victoria. Melbourne, L.V. North, Government Printer.
Chiang, C.N. & Barnett, G. (1984). Marihuana effect and delta-9-tetrahydrocannabinol plasma level. Clinical Pharmacology and Therapeutics, 36, 234-238.
Crandon, L. (1997). Driving High. Police Review, 12 September, pp. 24-25.
Crandon, L. (1997). Signs of Drugs. Police Review, 19 Septenber, pp. 22-23.
Crancer, A.J., Dille, J.M., Delay, J.C., Wallace, J.E., & Haken, M. (1969). Comparison of the effects of marijuana and alcohol on simulated driving performance. Science, 164 , 851-845.
Crawley, J., Corwin, R., Robinson, J., Felder, C., Devance, W. & Axelrod, J. (1993). Anandamide, an endogenous ligand of the cannabinoid receptor, induces hypomotility and hyperthermia in vivo in rodents. Pharmacology Biochemistry and Behaviour, 46, 967-72.
Crouch, D.J. et al. (1993) The prevalence of drugs and alcohol in fatally injured truck drivers. Journal of Forensic Sciences, 38 (6), 1342-1353.
Deadwyler, S.A., Bunn, T. & Hampson, R.E. (1996a). Hippocampal ensemble activity during spatial delayed-nonmatch-to-sample performance in rats. Journal of Neuroscience, 16, 354-72.
Deadwyler, S.A., Byrd, D.R., Konstantopoulos, J.A., Evans, G.J.O., Rogers, G. & Hampson, R.E. (1996b). Enhancement of rat hippocampal ensemble activity by CX516 protects against errors in spatial DNMS. Society of Neuroscience Abstracts, 2, 1131.
Deadwyler, S.A. & Hampson, R.E. (1995). Ensemble activity and behaviour: what's the code? Science, 270, 1316-18.
Deadwyler, S.A. & Hampson, R.E. (1997). The significance of neural ensemble codes during behaviour and cognition. Annual Review of Neuroscience, 20, 217-44.
Deadwyler, S.A., Heyser, C.J. & Hampson, R.E. (1995). Complete adaptation to the memory disruptive effects of delta-9-THC following 35 days exposure. Neuroscience Research Communications, 17 , 9-18.
Delong, F.L. & Levy, B.I. (1974). A model of attention describing the cognitive effects of marijuana. In L.L. Miller, (Ed.) Marijuana, Effects on Human Behaviour. Academic Press, Inc.
DETR (1998). Government acts on drug-driving enforcement. DETR News Release (94/Transport), 11 February.
Directorate General for Transport of the European Commission. (1995). Alcohol, Drugs and Medicines and Driving.
Devane, W.A., Dysarz, F.A., Johnson, M.R., Melvin, L.S., and Howlett, A.C. (1988). Determination and characterization of a cannabinoid receptor in rat brain. Mol. Pharmacology, 34, 605-613.
Devane, W.A., Hanus, L., Breuer, A., Pertwee, R.G., Stevenson, L.A., Griffin, G., Gibson, D., Mandelbaum, A., Etinger, A., & Mechoulam, R. (1993). Isolation and structure of a brain constituent that binds to the cannabinoid receptor. Science 258, 1946-1949.
Devane, W.A. & Axelrod, J. (1994). Enzymatic synthesis of anandamide, an endogenous ligand for the cannabinoid receptor, by brain membranes. Proceedings of the National Academy of Sciences of the USA, 91, 6698-701.
Donovan, J.E. (1993). Young adult drinking-driving: Behavioral and psychosocial correlates. Journal of Studies on Alcohol, 54 (5) 600-613.
Dott, A.B. (1972). Effect of marijuana on risk acceptance in a simulated passing task. Public Health Service Report ICRL-RR-71-3, DHEW Publication No. HSM-72-10010, Washington, D.C.
Drummer, O.H. (1995). Drugs and accident risk in fatally-injured drivers. In C.N. Kloeden & A.J. McLean. Proceedings of the 13th International Conference on Alcohol, drugs and Traffic Safety, pp. 426-429. Adelaide, Australia.
Ellingstad, V.S., McFarling, L.H. & Struckman, D.L. (1973). Alcohol, marijuana and risk taking. Vermillion Human Factors Laboratory, South Dakota University.
Evans, L. (1991). Traffic safety and the driver. New York, NY: von Norstrand Reinhold.
Everest, J.T., Tunbridge, R.J., & Widdop, B. (1989). The incidence of drugs in road traffic accident fatalities (TRRL Research Report 202). Crowthorne; TRRL.
Felder, C.C., Briley, E.M., Axelrod, J., Simpson, J.T., Mackie, K. & Devane, W.A. (1993). Anandamide, an endogenous cannabinoid agonist, couples to receptor mediated signla transduction. Proc. Natl. Acad. Sci. USA, 90, 7656-7660.
Felder, C.C. & Glass, M. (1998). Cannabinoid Receptors and their Endogenous Agonists. Annu. Rev. Pharmacol. Toxicol., 38,179-200.
Felder, C.C., Nielsen, A., Briley, E.M., Palkovits, M., Priller, J., Axelrod, J., Nguyen, D.N., Richardson, J.M., Riggin, R.M., Koppel, G.A., Paul, S.M. & Becker, G.W. (1996). Isolation and measurement of the endogenous cannabinoid receptor agonist, anandamide, in brain and peripheral tissues of human and rat. FEBS Lett., 393, 231-235.
Fell, J.C. (1995). What's new in alcohol, drugs and traffic safety in the U.S. In C.N. Kloeden & A.J. McLean. Proceedings of the 13th International Conference on Alcohol, drugs and Traffic Safety, pp.329-335. University of Adelaide, Australia.
Fell, J.C. (1995). What's new in alcohol, drugs and traffic safety? In C.N. Kloeden & A.J. McLean. Proceedings of the 13th International Conference on Alcohol, drugs and Traffic Safety pp.579. University of Adelaide, Australia.
Fletcher, J.M., Page, J.B., Francis, D.J., Copeland, K., Naus, M.J., Davis, C.M., Morris, R., Krauskopf, D. & Satz, P. (1996). Cognitive correlates of long-term cannabis use in Costa Rican men. Archives of General Psychiatry, 53, 1051-7.
Francis, M., Eldemire, D. & Clifford, R. (1995). A pilot study of alcohol and drug-related traffic accidents and death in two Jamaican parishes, 1991. West Indian Medical Journal, 44 (3), 99-101.
Friedel, B. (1995). Legal limits for the assessment of illegal drug use with regard to driving in the Federal Republic of Germany. Proceedings of the 13th International Conference on Alcohol, Drugs and Traffic Safety, 1 , 422-425, Adelaide.
Gaoni, Y. & Mechoulam, R. (1964). Isolation structure and partial synthesis of an active constituent of hashish. J. Am. Chem. Soc., 861, 646-47.
Gerostamoulos, J. & Drummer, O.H. (1993). Incidence of psychoactive cannabinoids in drivers killed in motor vehicle accidents. Journal of Forensic Sciences, 38 (3) 649-656.
Gifford, A.N. & Ashby, C.R.J. (1996). Inhibition of acetylcholine release from hippocampal slices by the cannabimimetic aminoalkylindole WIN 55212-2, and evidence for the release of an endogenous cannabinoid inhibitor. Journal of Pharmacology and Experimental Therapeutics, 277, 1431-6.
Glass, M., & Felder, C.C. (1997). Concurrent stimulation of cannabinoid CBI and dopamine D2 receptors augments cAMP accumulation in striatal neurons: Evidence for a Gs linkage to the CBI receptor. J. Neurosci., 17, 5327-5333.
Graß, H. (1989). Computergestützte Analyse pharmakokinetischer Profile psychotroper Wirkstoffe unter besonderer Berucksichtigung rechtsmedizinischer Fragestellungen. Inaugural-Dissertation. Institut fur Rechtsmedizin der Universitat zu Köln.
Hall, W. (1995). The health risks of cannabis. Australian Family Physician, 24 (7) 1237-40.
Hall, W, Solwij, N. & Lemon, J. The health and psychological consequences of cannabis use. National Drug Strategy: Australian Government Publishing Service, Canberra.
Hall, W., Johnston, L. & Donnelly, N. (In press) Epidemiological evidence on patterns of cannabis use and their health consequences. World Health Organisation Project on Health Implications of Cannabis Use.
Hall, W. & Solowij, N. (1996). Steering a course between the Charybdis of credulity and the Scylla of scepticism. Response to comments on Hall et al.'s Australian National Drug strategy Monograph No. 25 "The Health and Psychological Consequences of Cannabis Use". Addiction, 91, 770-3.
Hall, W. & Solowij, N. (1997). Long-term cannabis use and mental health. British Journal of Psychiatry, 171, 107-8.
Hall, W., Solowij, N., & Lemon, J. (1994). The Health and Psychological Consequences of Cannabis Use. Canberra: Australian Government Publishing Service.
Hampson, R.E., Evans, G.J., Mu, J., Zhuang, S.Y., King, V.C., Childers, S.R. & Deadwyler, S.A. (1995). Role of cyclic AMP dependent protein kinase in cannabinoid receptor modulation of potassium "A-current" in cultured rat hippocampal neuroens. Life Science. 56, 2081-2088.
Hampson, R.E., Byrd, D.R., Konstantopoulos, J.K., Bunn, T., & Deadwyler, S.A. (1996). Delta-9-Tetrahydrocannabinol influences sequential memory in rats performing a delayed-nonmatch-to-sample task. Society of Neuroscience Abstracts, 2, 1131.
Hansen, A.C., Kristensen, I.B., Dragsholt, C. & Brangstrup Hansen, J.P. (1996). Alcohol and drugs (medical and illicit) in fatal road accidents in a city of 300 000 inhabitants. Forensic Science International, 79, 49-52.
Hansteen, R.W., Miller, R.D. & Lonero, L. (1976). Effects of cannabis and alcohol on automobile driving and psychomotor tracking. Ann. N.Y. Acad. Sci, 282 , 240-456.
Herkenham, M., Lynn, A.B., Little, M.D., Johnson, M.R., Melvin, L.D., de Costa, B.R. & Rice, K.C. (1990). Cannabinoid receptor localization in brain. Proc. Natl. Acad. Sci. USA 87: 1932-1936.
Heyser, C.J., Hampson, R.E. & Deadwyler, S.A.
(1993). Effects of
9- tetrahydrocannabinol
on delayed match-to-sample performance in rats:
Alterations in short-term memory associated with
changes in task specific firing of hippocampal
cells. Journal of Pharmacology and
Experimental Therapeutics,
264, 294-307.
Hindmarch, I.K., & Sherwood, N. (1991). The effects of alcohol and other drugs on psychomotor performance and cognitive function. Alcohol and Alcoholism, 26, 71-79.
Hollister, L.E., Gillespie, H.K., Ohlsson, A., Lindgren, J.E., Wahlen, A. & Agurell, S. (1981). Do plasma concentrations of delta-9-tetrahydrocannabinol reflect the degree of intoxications? Journal of Clinical Pharmacology, 21, 171-177.
Huestis, M.A., Henningfield, J.E. & Cone, E.J. (1992a). Blood Cannabinoids: I. Absorption of THC and formation of 11-OH-THC and THCCOOH during and after smoking marijuana. Journal of Analytical Toxicology, 16, 276-82.
Huestis, M.A., Sampson, A.H., Holicky, B.J., Henningfield, J.E. & Cone, E.J. (1992b). Characterisation of the absorption phase of marijuana smoking. Clinical Pharmacology and Therapeutics, 52, 31-41.
Huestis, M.A., Henningfield, J.E. & Cone, E.J. (1992b). Blood Cannabinoids.II. Models for the prediction of time of marijuana exposure from plasma concentrations of delta-9-tetrahydrocannabinol (THC) and 11-nor-9-carboxy-9-tetrahydro-cannabinol (THC-COOH). Journal of Analytical Toxicology, 16, 283-290.
Hunter, C.E., Lokan, R.J., Longo, M.C., White, J.M., & White, M.A. (1998a). The prevalence and role of alcohol, cannabinoids, benzodiazepines and stimulants in non-fatal crashes. Adelaide, SA: Forensic Science, Department for Administrative and Information Services.
Hunter, C.E., Lokan, R.J., Longo, M.C., White, J.M., & White, M.A. (1998b). Appendices to : The prevalence and role of alcohol, cannabinoids, benzodiazepines and stimulants in non-fatal crashes. Adelaide, SA: Forensic Science, Department for Administrative and Information Services.
Johnson, V. & White, H.R. (1989). An investigation of factors related to intoxicated driving behaviors among youth. Journal of Studies on Alcohol, 50 (4), 320-330.
Johansson, E., Sjovall, J., Noren, K., Agurell, S., Hollister, L.E. & Halldin, M.M. (1987). Analysis of 1-Tetrahydrocannabinol (?1-THC) in human plasma and fat after smoking. In Marijuana: An International Research Report, ed. G. Chesher, P. Consroe,& R. Musty, National Campaign Against Drug Abuse Monograph Series No.7. pp. 291-6. Canberra: Australian Government Printing Service.
Johansson, E., Agurell, S., Hollister, L.E. &
Halldin, M.M. (1988). Prolonged apparent
half-life of
1-tetrahyrocannabinol in
plasma of chronic marijuana users. Journal of
Pharmacy and Pharmacology,
40, 374-5.
Jones, M.H. (1978). Safe performance curriculum. Performance Measures, Los Angeles: University of Southern California, Final Report No. DOT-HS-461, March, 1978.
Jones, R.T. (1987). Drug of abuse profile: cannabis,. Clinical Chemistry, 33, 72B-81B.
Junger, M. (1994). Accidents. In T. Hirschi & M.R. Gottfredson (Eds.), The Generality of Deviance, 81-112. NJ: Transaction Publishing.
Junger, M. & Wiegersma, A. (1995). The relations between accidents, deviance and leisure time. Criminal Behaviour and Mental Health, 5, 144-174.
Junger, M., Terlouw, G. & Van der Heijden, P.G.M. (1995). Crime, accidents and social control. Criminal Behaviour and Mental Health, 5 (4) 386-410.
Kalant, H. (1996). Good report but scanty research. Comments on Hall et al.'s Australian National Drug Strategy Monograph No. 25 "The Health and Psychological Consequences of Cannabis Use". Addiction, 91, 759-73.
Käferstein, H., Sticht, G. & Staak, M. (1990). Psychotropically active substances and driving incapability A pharmaco-kinetic computer model. In M. W. Perrine (ed.), Alcohol, Drugs and Traffic Safety, pp.265-269. Chicago: National Safety Council.
Kedjidjian, C.B. (1995). Drug-impaired driving: The other DUI. Traffic Safety, 95 (5), 12-15.
Klonoff. H. (1974). Marijuana and driving in real-life situations. Science, 186 , 317-324.
Krüger, H., Schulz, E. & Magerl, H. (1995). The German Roadside Survey 1992-1994. Saliva analyses from an unselected driver population: licit and illicit drugs. Proceedings of the 13 International Conference on Alcohol, Drugs and Traffic Safety, pp. 55-62, Australia.
Krüger, H. & Berghaus, G. (1995). Behavioral effects of alcohol and cannabis: Can equipotencies be established? Proceedings of the 13 International Conference on Alcohol, Drugs and Traffic Safety, pp. 410-415, Australia.
Krüger, H.P. (1995). Auftreten und Risken von Cannabis im Strassenverkehr. Eine Epidemiologische Studie. Drogen und Verkehrssicherheit. Symposion der Bundesanstalt für Strassenwesen und des Instituts für Rechtsmedizin der Universität Köln. Bundesanstalt für Strassenwesen, Heft M41. Bergisch Gladbach.
Krüger, H-P. & Löbmann (1998). Auftreten und Risiken von Cannabis im Straßenverkehr. In Berhaus, G. & Krüger, H-P. (Eds), Cannabis im Straßenverkehr. Stuttgart: Gustav Fischer Verlag.
Krüger, H-P. & Löbmann, R. Auswirkungen des Beigebrauchs von Alkohol zu Cannabis. In Berhaus, G. & Krüger, H-P. (Eds), Cannabis im Straßenverkehr. Stuttgart: Gustav Fischer Verlag.
Laixuthai, A. (1994). Youth alcohol use and abuse: An examination of public policy and laws on alcohol, Marijuana, and drunk driving. Doctoral dissertation, University of Illinois at Chicago.
Lake, K.D., Martin, B.R., Kunos, G. & Varga, K. (1997). Cardiovascular effects of anandamide in anesthetized and conscious normotensive and hypertensive rats. Hypertension, 29, 1204-10.
Leitner, M., Shapland, & Miles (1993). Drug useage and drugs prevention: Review of habits of the general population. HMSO Publication Office.
Lichtman, A.H. & Martin, B.R. (1996). Delta-9-Tetrahydrocannabinol impairs spatial memory through a cannabinoid receptor mechanism. Psychopharmacology, 126, 125-31.
Lindgren, J.E., Ohlsson, A., Agurell, S., Hollister, L. & Gillespie, H.K. (1981). Clinical effects and plasma levels of delta-9-Tetrahydrocannabinol (delta-9-THC) in heavy and light users of cannabis. Psychopharmacology, 74, 208-212.
Louwerens, J.W., Gloerich, A.B.M., De Vries, G., Brookhuis, K.A., & O'Hanlon, J.F. (1987). The relationship between drivers' blood alcohol concentration (BAC) and actual driving performance during high speed travel. In P.C. Noordzij (Ed.), Proceedings of the 10th International Conference on Alcohol, Drugs, and Traffic Safety. Amsterdam: Elsevier.
Louwerens, J.W., Gloerich, A.B.M., De Vries, G., Brookhuis, K.A., & O'Hanlon, J.F. (1985). De Invloed van Verschillende Bloedalcoholspiegels op Objectief Meetbare Aspekten van Feitelijk Rijgedrag. Technical Report No. VK 85-03, Traffic Research Centre, University of Groningen.
Macdonald, S. & Dooley, S. (1993). A case-control study of driving-while-impaired offenders. Drug and Alcohol Dependence, 33 , 61-71.
Maisey, G. (1995). Drugs, Driving and enforcement. In L. Hartley (Ed.), Fatigue and Driving: Driver Impairment, Driver Fatigue and Driving Simulation. Taylor & Francis.
Marowitz, L.A. (1995). Drug arrests and driving risk. Alcohol, Drugs and Driving, 11(1) 1-22.
Maycock, G. (1997). Drinking and driving in Great Britain A review. TRL Report 232, TRL: Berkshire.
McIntyre, K.D. (1995). Drugs and driving: the issues from the motorists' perspective. In Road Safety Committee, Parliament of Victoria (Eds.) Inquiry into the effects of drugs (other than alcohol) on road safety in Victoria. Melbourne: L.V. North, Government Printer.
Mechoulam, R. (1986). The pharmacohistory of Cannabis Sativa. In Mechoulam, R. (ed.) Cannabinoids as Therapeutic Agents (1-19). CRC Press, Boca Raton, FL.
Mechoulam, R., Fride, E., Hanus, L., Sheskin, T., Bisogno, T., di Marzo, V., Bayewitch, M. & Vogel, Z. (1997). Anandamide may mediate sleep induction. Nature, 389, 25-6.
Mechoulam, R., Hanus, L. & Martin, B.R. (1994). Search for endogenous ligands of the cannabinoid receptor. Biochemical Pharmacology, 48, 1537-44.
Menkes, D.B., Howard, R.C., Spears, G.F.S. & Cairns, E.R. (1991). Salivary THC following cannabis smoking correlates with subjective intoxication and heart rate. Psychopharmacology, 103, 277-9.
Mercer, G.W. & Jeffery, W.K. (1995). Alcohol, drugs, and impairment in fatal traffic accidents in British Columbia. Accident, Analysis and Prevention., 27 (3) 335-343.
Möller, M.R., Dörr, G. & Warth, S. (1992). Simultaneous quantitation of delta-9-tetrahydrocannabinol (THC) and 11-Nor-9-carboxy-delta-9-tetrahydrocannabinol (THC-COOH) in serum by GC/MS using deuterated internal standards and its application to a smoking study and forensic cases. Journal of Forensic Sciences, 37, 969-983.
Moskowitz, H. (1985). Marijuana and driving. Accident Analysis and Prevention, 17, 323-345.
Moskowitz, H., Hulbert, S., & McGlothlin, W. (1976). Marijuana: Effects on simulated driving performance. Accident Analysis and Prevention, 8 , 45-50.
Moskowitz, H., Ziedman, K. & Sharma, S. (1976). Visual search behavior while viewing driving scenes under the influence of alcohol and marijuana. Human Factors 18 (5),.417-432.
Moskowitz, H. (1973). A marijuana dose response study of performance in a driving simulator. Paper presented at the First International Conference on Driver Behaviour in Zurich, Switzerland, September.
Munro, S., Thomas, K.L. & Abu-Shaar, M. (1993). Molecular characterization of a peripheral receptor for cannabinoids. Nature. 365, 61-65.
Musselman, D.L. , Haden, C., Caudle, J. & Kalin, N.H. (1994). Cerebrospinal fluid study of cannabinoid users and normal controls. Psychiatry Research, 52, 103-5.
National Institute on Drug Abuse. (1991). National Household Survey of Drug Abuse: Population Estimates 1991. NIDA, U.S. Department of Health and Human Services, Rockville, MD, DHHS Publication No. (ADM) 92-1887.
National Institute on Drug Abuse. (1989). National Household Survey of Drug Abuse: Population Estimates 1988. NIDA, U.S. Department of Health and Human Services, Rockville, MD, DHHS Publication No. (ADM) 89-1636.
National Institute on Drug Abuse (1988). National Household Survey of Drug Abuse: Main Findings, 1985. NIDA, U.S. Department of Health and Human Services, Rockville, MD, DHHS Publication No. (ADM) 88-1586.
Ohlsson, A., Lindgren, J-E., Wahlen, A., Agurell, S., Hollister, L.E. & Gillespie, H.K. (1980). Plasma delta-9-tetrahydrocannabinol concentrations and clinical effects after oral and intravenous administration and smoking. Clinical Pharmacology and Therapeutics, 28, 409-16.
Parliament of Victoria, Road Safety Committee. (1995). Inquiry into the effects of drugs (other than alcohol) on road safety in Victoria. L.V. North: Melbourne.
Parliamentary Office of Science and Technology. (1996). Common Illegal Drugs and Their Effects: Cannabis, Ecstasy, Amphetamines and LSD.
Peat, M.A. (1989). Distribution of D-9-tetrahydrocannabinol and its metabolites. In R.C. Baselt (ed.), Advances in analytical Toxicology (Vol. II, pp. 186-217). Chicago: Yearbook Medical Publishers.
Peck, R.C., Biasotti, A., Boland, P.N., Mallory, C. & Reeve, V. (1986). The effects of marijuana and alcohol on actual driving performance. Alcohol, Drugs and Driving, Abstracts and Reviews, 2, 3-4.
Perez-Reyes, M. (1990). Marijuana smoking: Factors that influence the bioavailability of tetrahydrocannabinol. In N. Chiang & R. Hawks (Eds). Research Findings on Smoking of Abused Substances. Rockville: NIDA Research Monograph, 99, pp.42-62. Washington DC:U.S. Government Printing Office.
Perez-Reyes, M., Di Guiseppi, S., Davis, K.H., Schindler, V.H. & Cook, C.E. (1982). Comparison of effects of marihuana cigarettes of three different potencies. Clinical Pharmacology and Therapeutics, 31, 617-24.
Perez-Reyes, M., Di Guiseppi, S., Mason, A.M. & Davis, K.H. (1983). Passive inhalation of marihuana smoke and urinary excretion of cannabinoids. Clinical Pharmacology and Therapeutics, 34, 36-41.
Perez-Reyes, M., Hicks, R.E., Bumberry, J., Jeffcoat, A.R. & Cook, E.E. (1988). Interaction between marihuana and ethanol: effects on psychomotor performance. Alcoholism: Clinical and Experimental Research, 12, 268-76.
Perez-Reyes, M., Timmons, M.C. & Wall, M.E. (1974). Long-term use of marijuana and the development of tolerance or sensitivity to ?9Tetrahydrocannabinol. Archives of General Psychiatry, 31, 89-91.
Perl, J., Mascord, D.J., Moynham, A.F., & Starmer, G.A. (1995). Drug usage by persons suspected of driving under the influence of a drug. Proceedings of the 13 International Conference on Alcohol, Drugs and Traffic Safety pp.88-86, Australia.
Plomp, H.N., Kuipers, H. & Van Oers, M.L. (1990). Roken, Alcohol- en Drugsgebruik onder Scholieren vanaf 10 jaar. VU Uitgeverij, Amsterdam. Cited by Robbe and O'Hanlon (1993).
Pope, H.G., Gruber, A.J. & Yurgelun-Todd, D. (1995). The residual neuropsychological effects of cannabis: the current status of research. Drug and Alcohol Dependence, 38, 25-34.
Pope, H.G., Gruber, A.J. & Yrgelun-Todd, D. (1996). The residual cognitive effects of heavy marijuana use in college students. Journal of the American Medical Association, 275, 521-7.
Rafaelsen, L., Christup, H., & Bech, P. (1973). Effects of cannabis and alcohol on psychological tests. Nature, 242 , 117-118.
Ramsay, M. & Percy, A. (1994). Drug misuse declared: results of the 1994 British Crime Survey. Home Office Research and Statistics Directorate.
Ramsay, M. & Spiller, J. (1996). Drug misuse declared in 1996: latest results from the British Crime Survey. Home Office Research and Statistics Directorate.
Robbe, H.W.J. & O'Hanlon, J.F. (1993). Marijuana and actual driving performance. U.S. Department of Transportation, NHTSA, Final Report DOT HS 808078.
Robbe, H.W.J. (1994). Influence of Marijuana on Driving. Thesis: Maastricht.
Rosencrantz, H. (1983). Cannabis, marihuana and cannabinoid toxicology manifestations in man and animals. In K.O. Fehr & H. Kalant (Eds.) Cannabis and Health Hazards. Toronto: Addiction Research Foundation.
Rosenthal, R. (1991) Meta-Analytic Procedures For Social Research.. Revised Edition. Newbury Park, California: Sage.
Schultz, E. et al. (1997). Fahrtüchtigkeit durch Cannabis, Amphetamine und Cocain. Literaturanalyse. Berichte der Bundesanstalt für Strassenwesen. Mensch und Sicherheit, Heft M81. Bergish Gladbach.
Sherwood, N. (1998). A critical review of the effects of drugs other than alcohol on driving. PACTS seminar "Illegal drugs and driving", London, UK, February.
Shire, D., Carillon, C., Kaghad, M., Callandra, B., Rinaldi-Carmona, M., Le Fur, G., Caput, D., and Ferrara, P. (1995). An amino-terminal variant of the central cannabinoid receptor resulting from alternative splicing. J. Biol. Chem. 270, 3726-3731.
Skurtveit, S., Christophersen, A.S. & Morland, J. (1995). Driving under influence of alcohol and other drugs in Norway. In Proceedings of the Road Safety in Europe and Strategic Highway Research Programme (39-43), Prague.
Smiley, A. (1986). Marijuana: On-road and driving simulator studies. Alcohol Drugs, and Driving, 2 (3-4), 121-34.
Smiley, A. (1998). Marijuana: On road and driving simulator studies. Prepared for World Health Organisation, Geneva, Switzerland.
Soderstrom, C.A., Dischinger, P.C., Kerns, T.J. & Trifillis, A.L. (1995). Marijuana and other drug use among automobile and motorcycle drivers treated at a trauma center. Accident, Analysis and Prevention, 27 (1) 131-135.
Solowij, N. (1995). Do cognitive impairments recover following cessation of cannabis use? Life Sciences, 56, 2119-26.
Solowij, N., Grenyer, B.F.S., Chesher, G. & Lewis, J. (1995a). Biopsychosocial changes associated with cessation of cannabis use: a single case study of acute and chronic cognitive effects, withdrawal and treatment. Life Sciences, 56, 2127-34.
Solowij, N., Grenyer, B.F.S., Peters, R. & Chesher, G. (1997). Long-term cannabis use impairs memory processes and frontal lobe function. In 1997 Symposium on the Cannabinoids, p.84. International Cannabinoid Research Society, Burlington, Vermont.
Solowij, N., Michie, P.T. & Fox, A.M. (1991). Effects of long-term cannabis use on selective attention: an event-related potential study. Pharmacology Biochemistry and Behaviour, 40, 683-8.
Solowij, N., Michie, P.T. & Fox, A.M. (1995b). Differential impairments of selective attention due to frequency and duration of cannabis use. Biological Psychiatry, 37, 731-9.
Solowij, N., Michie, P.T. & Fox, A.M. (1995c). ERP indices of selective attention in ex-cannabis users. Biological Psychology, 39, 201.
Starmer, G.A., Vine, J.H. & Watson, T.R. (1988). A co-ordinated approach to the drugs and traffic safety problem. In D. Burley & T. Silverstone (Eds) Medicines and Road Traffic Safety. CNS Publishers: London.
Steentoft, A., Worm, K. & Toft, J. (1997). Other drugs than alcohol in Danish traffic cases, requested by the police. In C. Mercier-Guyon (Ed.), Proceedings of the 14th International conference on Alcohol, drugs and traffic safety, 3, 1345-1349.
Sticht, G. & Käferstein, H. (1995). Pharmacokinetic evaluation of published studies on controlled smoking of marijuana. In N. Kloeden & A. J. McLean (eds.), Alcohol, drugs and traffic safety (Vol.1, pp. 397-402). Adelaide: University of Adelaide, NHMRC Road Accident Research Unit.
Sticht, G., Staak, M. & Käferstein, H.(1986), Die Ermittlung pharmakokinetischer Konstanten aus Wirkstoffspiegeln am Beispiel des Tetrazepam. In W. Eisenmenger, E. Liebhardt & M. Schuck (eds.), Festschrift fur W. Spann (S. 596-607). Berlin, Heidelbergn, New York: Springer.
Sticht, G. & Käferstein, H. (1998). Grundbegriffe, Toxikokinetik und Toxikodynamik. In Berhaus, G. & Krüger, H-P. (Eds), Cannabis im Straßenverkehr. Stuttgart: Gustav Fischer Verlag.
Sweedler, B. & Vingilis, E. (1992). Alcohol and other drugs in transportation: Research needs for the next decade. Proceedings of the 12th International conference on Alcohol, drugs and traffic safety, 1088-1095, Cologne,
Transportation Research Board. (1993). Alcohol and other drugs in transportation: Research needs for the next decade. Transportation Research circular 408, June 93.
Tunbridge, R.J., & Rowe, D. (1998). The incidence of drugs in road accident fatalities in Great Britain. 9th International Conference on Road Safety in Europe, Cologne (September).
Vogel, Z., Barg, J., Levy, R., Saya, D., Heldman, E. & Mechoulam, R. (1993). Anandamide, a brain endogenous compound, interacts specifically with cannabinoid receptors and inhibits adenylate cyclase. Journal of Neurochemistry, 61, 352-5.
Volkow, N.D., Gillespie, H., Mullani, N., Tancredi, L., Grant, C., Valentine, A. & Hollister, L. (1996). Brain glucose-metabolism in chronic marijuana users at baseline and during marijuana intoxication. Psychiatry Research: Neuroimaging, 67, 29-38.
Volkow, N.D., Gillespie, H., Tancredi, L. & Hollister, L. (1995). The effects of marijuana in the human brain measured with regional brain glucose metabolism. In Sites of Drug Action in the Human Brain, ed., A Biegon & N.D. Volkow, pp. 75-86. Boca Raton: CRC Press.
Wall, M.E., Sadler, B.M., Brine, D., Taylor, H. & Perez-Reyes, M. (1983). Metabolism, disposition, and kinetics of ?9-tetrahydrocannabinol in men and women. Clinical Pharmacology and Therapeutics, 34, 352-63.
Wanner Lang, S., Waller, P.F. & Thatcher Shope, J. (1996). Adolescent driving: Characteristics associated with single-vehicle and injury crashes. Journal of Safety Research, 27 (4) 241-257.
Wright, J.D. & Pearl, L. (1995). Knowledge and experience of young people regarding drug misuse, 1969-94. British Medical Journal, 310, 20-24.