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Cannabis is known to have detrimental effects on human performance and may also affect driving adversely. However, studies designed to examine this issue have provided equivocal findings. We set up this study to further determine the effect of cannabis on driving. We used a cross-sectional, case-control design with drivers aged 20-49 who were involved in a fatal crash in the United States from 1993 to 2003; drivers were included if they had been tested for the presence of cannabis and had a confirmed blood alcohol concentration of zero. Cases were drivers who had at least one potentially unsafe driving action recorded in relation to the crash (e.g., speeding); controls were drivers who had no such driving action recorded. We calculated the crude and adjusted odds ratios (ORs) of any potentially unsafe driving action in drivers who tested positive for cannabis but negative for alcohol consumption. In computing for the adjusted OR, we controlled for age, sex, and prior driving record. Five percent of drivers tested positive for cannabis. The crude OR of a potentially unsafe action was 1.39 (99% CI = 1.21-1.59) for drivers who tested positive for cannabis. Even after controlling for age, sex, and prior driving record, the presence of cannabis remained associated with a higher risk of a potentially unsafe driving action (1.29, 99% CI = 1.11-1.50). Cannabis had a negative effect on driving, as would be predicted from human performance studies. This finding supports the need for interventions to decrease the prevalence of driving under the influence of cannabis, and indicates that further studies should be conducted to investigate the dose-response relationship between cannabis and safe driving.
The Impact of Cannabis on Driving
Michel Bédard,
Sacha Dubois, HBA
Bruce Weaver, MSc
Background: Cannabis is known to have detrimental effects on human performance and
may also affect driving adversely. However, studies designed to examine this issue have
provided equivocal findings. We set up this study to further determine the effect of
cannabis on driving.
Methods: We used a cross-sectional, case-control design with drivers aged 20-49 who
were involved in a fatal crash in the United States from 1993 to 2003; drivers were
included if they had been tested for the presence of cannabis and had a confirmed blood
alcohol concentration of zero. Cases were drivers who had at least one potentially unsafe
driving action recorded in relation to the crash (e.g., speeding); controls were drivers who
had no such driving action recorded. We calculated the crude and adjusted odds ratios
(ORs) of any potentially unsafe driving action in drivers who tested positive for cannabis
but negative for alcohol consumption. In computing for the adjusted OR, we controlled for
age, sex, and prior driving record.
Results: Five percent of drivers tested positive for cannabis. The crude OR of a potentially
unsafe action was 1.39 (99% CI = 1.21-1.59) for drivers who tested positive for cannabis.
Even after controlling for age, sex, and prior driving record, the presence of cannabis
remained associated with a higher risk of a potentially unsafe driving action (1.29, 99% CI
= 1.11-1.50).
Conclusion: Cannabis had a negative effect on driving, as would be predicted from human
performance studies. This finding supports the need for interventions to decrease the
prevalence of driving under the influence of cannabis, and indicates that further studies
should be conducted to investigate the dose-response relationship between cannabis and
safe driving.
MeSH terms: Cannabis; accidents, traffic; alcohol drinking; automobiles
survey revealed that 7.3% of
Canadians used cannabis (marijua-
na) at least once in the previous
year and that 2.0% used it weekly.
Similarly, American (US) surveys revealed
that the prevalence of cannabis use in the
previous year is approximately 4%.
However, cannabis use is more prevalent
among young people. Among Canadian
undergraduate students, 18.2% reported
using cannabis during the academic year.
In the US, the prevalence of past-year use
among adults aged 18-29 in 2000-01 was
10.5%. In a survey of US college students,
15.7% reported having used cannabis in
the past 30 days.
While these trends fuel
many debates about cannabis use, one per-
sistent unresolved issue is the potential
effect of cannabis on driving.
The effect of cannabis on the human
system is wide-ranging, combining
“…many of the properties of alcohol, tran-
quilizers, opiates and hallucinogens; it is
anxiolytic, sedative, analgesic, psychedelic;
it stimulates appetite and has many sys-
temic effects.”
(p. 103-104) Nonetheless,
while the negative effect of cannabis on
cognitive functions supporting safe driving
was documented by some researchers,
have reported that performance is
affected minimally, and often only in con-
junction with alcohol. Epidemiological
studies also provide equivocal findings.
Some have demonstrated a small but statis-
tically significant association between
cannabis use and driving problems.
However, many researchers have not
found an association between cannabis use
and crashes (see refs. 14 and 15 for
It is possible that cannabis has minimal
effects because drivers compensate for their
However, it is also possible
that methodological issues explain discrep-
ancies in these findings. The concentration
of THC (the active ingredient in cannabis)
in drivers is typically not available nor is
the amount of time since it was absorbed.
Recently absorbed cannabis may affect dri-
ving adversely but this effect may wane
once a longer period of time has elapsed
after absorption.
Because THC has a
half-life of approximately seven days,
many drivers who test positive for cannabis
may not be impaired at the time of testing.
This would lead to an underestimation of
the association between cannabis and poor
driving/crashes and possibly the absence of
La traduction du résumé se trouve à la fin de l’article.
1. Public Health Program, Lakehead University, Thunder Bay, ON
2. Division of Human Sciences, Northern Ontario School of Medicine, Thunder Bay
3. St. Joseph’s Care Group, Thunder Bay
Correspondence and reprint requests: Michel Bédard, Public Health Program, Lakehead University,
955 Oliver Road, Thunder Bay, ON P7B 5E1, Fax: 807-346-7734, E-mail: michel.bedard@lake-
Acknowledgements: The authors thank Julie Riendeau for her assistance with the literature search and
Owen Marks for his assistance with data management. Michel Bédard is a Canada Research Chair in
Aging and Health (; he acknowledges the support of the Canada Research Chair
Program. Additional funding for this research was provided through a research grant from AUTO21,
Network of Centres of Excellence. The sponsors were not involved in any aspects of this study.
statistical significance. Furthermore,
because the prevalence of cannabis use is
relatively low in the general population,
small sample sizes yield limited numbers of
users involved in crashes.
One remedy to
this problem is to use large datasets.
Although these would not eliminate the
underestimation of the association, a
greater number of cases would increase the
statistical power of the analyses.
Another problem is that cannabis use is
often highly correlated with other crash
determinants (e.g., alcohol use, high-risk
behaviour). Alcohol use is especially prob-
lematic given that it is correlated with
crashes, and that it may exacerbate the
effects of cannabis.
Other high-risk
behaviours such as speeding, may also con-
found the association between cannabis
and crashes. Drummer and colleagues
reported an association between THC and
responsibility for crashes (OR = 2.7, 95%
CI = 1.0-7.0) but could not take into con-
sideration high-risk behaviours (with the
exception of alcohol use). Hence, the pos-
sibility remains that cannabis users are sim-
ply high-risk drivers who are involved in a
disproportionate number of crashes; this
requires that we separate the respective
effects of cannabis and alcohol, and control
for high-risk driving habits.
Another methodological problem is the
reliance on self-reports to identify cannabis
use and crashes.
Given the social and
legal implications of cannabis use, self-
reports may also result in the underestima-
tion of the association between cannabis
and crashes. Ideally, the presence of THC
in the body and crash status should be
determined through objective means (e.g.,
blood tests, police reports).
Our goal was to clarify the association
between cannabis use and driver behav-
iour. Specifically, we aimed to determine if
cannabis, in the absence of alcohol, is relat-
ed to driving actions that may have result-
ed in fatal crashes. We used the Fatality
Analysis Reporting System (FARS), an
administrative database where information
on all crashes involving a fatality in the US
is recorded. This database has several
advantages. First, all crashes involving at
least one fatality are included, therefore
reducing selection bias. Second, the infor-
mation is obtained by investigators, there-
fore eliminating biases that may arise from
self-reports. Third, it contains actual blood
alcohol concentration (BAC). Fourth, it
contains information on prior driving inci-
dents (e.g., crashes, violations) to control
for high-risk driving behaviours. Finally, it
is sufficiently large to allow us to focus
only on drivers who were tested for drugs
and alcohol, and to examine the drivers’
actions that may have resulted in the crash.
Based on previous research and the
acknowledged impact of cannabis on cog-
nitive functions, we hypothesized that
cannabis use would be related to poorer
driver behaviour.
Data source
The FARS database is one of the most
comprehensive databases on crashes.
every traffic fatality in the US since 1975,
information is compiled about crash situa-
tions, drivers and passengers, and about
the vehicles involved. The quantity of
information coded in the database, and
number of crashes recorded, allows for the
control of numerous potential con-
founders, and the calculation of crash esti-
mates more easily generalized to all drivers
involved in fatal crashes.
We used data from 1993 to 2003 (inclu-
sive) because drug information has been
collected more comprehensively since
1993. The database also contains informa-
tion to identify risk factors pertinent to
crash initiation and not only crash involve-
ment. This information is contained in the
“driver-related factors” (DRF). Briefly, for
every driver, up to three (four since 1997)
driver-related factors were coded according
to police reports. Driver-related codes from
20 to 59 (inclusive) were considered
actions that may have contributed to the
crash (e.g., driving too fast for the condi-
tions; these codes are presented in the
Appendix). Drivers for whom no DRFs
were specified were assumed to not have
contributed to crash initiation. This
approach is not a perfect substitute for the
assessment of “responsibility” but has been
used successfully by other researchers.
Our aim was to identify predictors of any
We used other data from FARS, includ-
ing: age, sex, drug test results (blood or
urine), alcohol tests results (blood), drivers’
past driving record, and the type of vehicle
driven. For each driver, a maximum of
three drug results were provided (in no
particular order). The cannabinoid drug
compounds (FARS 600 series) were
included: Delta 9 (600), Hashish Oil
(601), Hashish (602), Marijuana (603),
Marinol (604), Tetrahydrocannabinoid
(605), THC (606), Cannabinoid, Type
Unknown (695); the concentration of the
drug is not available. For alcohol, the actu-
al BAC is available. This allowed us to
identify drivers who were tested for alcohol
use, to verify the validity of our approach
with alcohol data, and to examine the con-
tribution of cannabis in drivers free of
alcohol. We elected to use only drivers for
whom both alcohol and drug tests were
performed and reported because one
Descriptive Statistics for Drivers Involved in Fatal Automobile Collision
Demographics (N=314,636)
Age, mean (SD) 32.48 (8.59)
Male, No. (%) 222,671 (71)
Driving Record – Any in the Past Three Years (N=314,636) No. (%)
Crashes 50,430 (16)
DWI 15,873 (05)
Other conviction 59,983 (19)
Speeding 79,000 (26)
License suspension/revocation 52,171 (17)
Any of the above 153,240 (49)
Odds Ratios and Confidence Intervals of a DRF by BAC
Model 1 – Unadjusted Model 2 – Adjusted
BAC Level Odds Ratio (95% CI; 99% CI) Odds Ratio (95% CI; 99% CI)
0 Reference category Reference category
0.05 2.20 (2.08-2.32; 2.05-2.36) 2.01 (1.90-2.13; 1.87-2.16)
0.10 3.37 (3.21-3.54; 3.17-3.59) 3.06 (2.91-3.22; 2.87-3.27)
0.15 4.73 (4.52-4.94; 4.46-5.01) 4.40 (4.20-4.61; 4.14-4.68)
0.20 5.74 (5.50-5.98; 5.43-6.06) 5.61 (5.36-5.86; 5.29-5.95)
0.30 6.00 (5.69-6.33; 5.59-6.44) 6.16 (5.82-6.52; 5.71-6.64)
potential bias is that only drivers suspected
of impairment may be tested.
We also used data on drivers’ past three
years’ driving record, including: number of
accidents (crashes), number of recorded
convictions for driving while impaired
(DWI; includes both alcohol and drug),
speeding convictions (going too fast or too
slow), other harmful moving violation con-
victions, and licence suspensions and revo-
cations. Because controlling for driving
habits through these variables is important
to rule out the confounding effect of high-
risk driving habits, we excluded drivers
aged less than 20 because they may not
have had sufficient opportunity (years) to
incur such records. We excluded drivers
aged 50 and over because cannabis use
becomes much less frequent with advanc-
ing age. Finally, we limited our analyses to
drivers of passenger vehicles, sport-utility
vehicles and light trucks (pick-up trucks)
only. The focus on these drivers will facili-
tate the interpretation of the findings.
Analytical plan
We first sought to confirm known findings
regarding alcohol use and crashes by
demonstrating a dose-response relationship
between BAC and DRF. To this effect, we
used two logistic regressions using the
report of any DRF as the dependent vari-
able. In the first model, the only predictor
variable was BAC coded as the following
categories: 0 (0-0.02; the reference catego-
ry), 0.05 (0.03-0.07), 0.10 (0.08-0.12),
0.15 (0.13-0.17), 0.20 (0.18-0.24), 0.30
or more (0.25-0.94). In the second model,
we added the following list of potential
confounding variables: age, sex, and the
past three years’ driving record.
Our second set of analyses focused solely
on cannabis. To achieve this goal, we
looked exclusively at drivers who had a
confirmed BAC of zero. We present demo-
graphic characteristics and the 10 most fre-
quent DRFs for these drivers, and used
independent t-tests and Pearson’s Chi-
square tests to compare drivers who tested
positive for cannabis and those who did
not. We then present regression models
(crude and adjusted) to determine the con-
tribution of cannabis to DRFs. We report
95% and 99% confidence intervals (CI).
Alcohol data
Briefly, approximately two thirds of the
sample were male, the mean age was 32.5
(SD = 8.6), and one of every two drivers in
the sample had a driving record (see Table
I). Table II and Figure 1 present the OR
and CI of any DRF by BAC category. In
Model 1, we present the crude association
between DRFs and BAC. In Model 2, we
present the OR adjusted for potential con-
founders (age, sex, prior driving record).
We found a clear relationship between
increasing BAC and the OR of any DRF.
These results replicate the known dose-
response relationship between BAC and
crash risk.
Cannabis data
Of the 32,543 drivers tested, 1,647 (5%)
tested positive for cannabis. Drivers who
tested positive were generally younger,
male, and had a poorer driving record in
the past three years (see Table III; all dri-
vers had a confirmed BAC of zero). The 10
most frequently reported DRFs are pre-
sented in Table IV. A greater proportion of
drivers who tested positive for cannabis
had a DRF related to speeding or
erratic/reckless driving.
The crude OR between DRFs and
cannabis was 1.39 (99% CI = 1.21-1.59).
After adjusting this association for age, sex,
and driving record (see Table V) the OR
for THC was 1.29 (99% CI = 1.11-1.50).
Younger age and poorer driving records,
but not sex, were also associated with a
higher risk of a reported DRF.
The findings point to cannabis as a poten-
tial risk factor in fatal crashes. Individuals
Figure 1. OR and 99% CI for any DRF by BAC (categorical) level
Panel A: Unadjusted Odds Ratios Panel B: Adjusted Odds Ratios
Alcohol Level (BAC = 0 to 0.02 is the reference) Alcohol Level (BAC = 0 to 0.02 is the reference)
who tested positive for cannabis but nega-
tive for alcohol had 29% excess risk (99%
CI = 11-50) of having driven in a fashion
that may have contributed to the crash,
compared to drivers who tested negative
for cannabis. This association was found
after controlling for age, sex, and prior dri-
ving record. However, our findings likely
reflect an underestimation of the actual
effect of cannabis on driving. Given the
long half-life of THC, it is possible that
many drivers tested positive for cannabis
without being impaired at the time of the
crash. Furthermore, it is difficult to make
the distinction between the presence of the
active component of cannabis (THC), and
its metabolites, which have no effects on
the brain.
We found that 5% of the drivers tested
positive for cannabis, but this number is
not based on a representative sample. The
authors of a study based on a representa-
tive sample of adults living in Ontario
reported that only 1.9% of adults drove
under the influence of cannabis in the pre-
vious year.
However, this number was
19.7% in a survey of Ontario high school
students who had a driver’s licence.
In a
study of impairment in reckless drivers,
more than half of those tested but not
impaired by alcohol were impaired by
some drug (cannabis was the most fre-
These numbers may be related to
young people’s perception of the risks asso-
ciated with cannabis use. Contrary to what
many of them would report regarding alco-
hol, several consider cannabis to have a
negligible effect on driving, and some even
believe that cannabis may enhance
which suggests that we need
to deal with these perceptions and atti-
We also replicated findings regarding
While this is not new, it increas-
es our confidence in our results regarding
cannabis and also allows us to put these
findings in perspective. The frequency of
drinking and driving and the severe impact
of alcohol on driving abilities are well
beyond what has been shown with
cannabis, even if we consider that we may
be underestimating the association. We
also found, once more, that young males,
and especially those with a bad driving
record, were at greater risk of driving in an
unsafe fashion, and controlled for these
variables. This was important given that
others have reported statistical associations
between cannabis use and traffic
and in one study the excess
risk posed by cannabis was eliminated once
drinking and driving behaviour and sex
were considered.
Hence, we are confident
that the higher risk found in drivers who
tested positive for cannabis cannot be
explained by aggressive driving patterns or
alcohol consumption.
One unanswered question is whether we
could identify dosages at which cannabis
may start to pose a crash risk, as has been
done with alcohol.
This question points
to a lack of knowledge regarding the dose-
response relationship between cannabis
and driving, knowledge which is important
to better educate drivers and support policy-
making. For example, new procedures for
roadside screening of cannabis use (using
saliva) are highly predictive of actual use.
However, the successful implementation of
such roadside testing may require the
determination of the minimum blood con-
centration at which one does become
“impaired” to safely drive an automobile.
The issue of safe driving and cannabis
use is not restricted to recreational users;
cannabis is increasingly used for medicinal
and long-term use of cannabis
may create residual cognitive impair-
Moreover, many conditions for
which cannabis may be useful (e.g., glauco-
ma, cancer) are seen in older people; hence
the effect of cannabis may be exacerbated
by age-related changes in pharmacokinetics
and/or the presence of other medications.
Further studies are required to better
understand the impact of cannabis on dri-
vers who are using it as a medication.
We see our findings as additional evi-
dence suggesting that cannabis may
adversely affect drivers. The results are
consistent with our knowledge of cannabis’
effect on human performance and all
mechanisms known to support safe dri-
Furthermore, the sample size
allowed us to obtain narrow confidence
Descriptive Statistics for Drivers Tested for THC and a BAC of Zero
Demographics Cannabis Cannabis
/t* p-value
Absent Present
(N=30,896) (N=1,647)
Age, mean (SD) 32.94 (8.88) 30.2 (8.44) 12.23 <0.001
Male, No. (%) 19,791 (64) 1296 (79) 146.72 <0.001
Driving Record – Any in the
past three years No. (%) No. (%)
Crashes 5174 (17) 295 (18) 1.52 0.22
DWI 767 (2) 74 (4) 25.12 <0.001
Other conviction 5831 (19) 502 (30) 134.40 <0.001
Speeding 7801 (26) 535 (33) 43.76 <0.001
License suspension/revocation 4229 (14) 419 (26) 177.86 <0.001
Any of the above 14,690 (48) 1013 (62) 122.02 <0.001
* Chi square values are presented for all variables with the exception of Age where the t-statistic is
The Top 10 Reported DRF from 1993 to 2003
Driver-related Factor Cannabis Cannabis
Absent Present
(N=30,896) (N=1,647)
No. (%) No. (%)
Failure to keep in proper lane 9074 (29.4) 494 (30.0) 0.29 0.59
Driving too fast for conditions or
in excess of posted maximum 6072 (19.7) 428 (26.0) 39.24 0.001
Failure to yield right of way, obey
signs or other safety zone traffic
laws 3613 (11.7) 189 (11.5) 0.07 0.79
Erratic, reckless, careless or
negligent vehicle operation 1715 (5.6) 146 (8.9) 31.85 <0.001
Making improper turn 1718 (5.6) 89 (5.4) 0.07 0.79
Overcorrecting 1270 (4.1) 57 (3.5) 1.69 0.19
Driving on wrong side of road
(intentional or unintentional) 736 (2.4) 28 (1.7) 3.17 0.08
Passing with insufficient distance,
or visibility, or failing to yield
to overtaking vehicle 400 (1.3) 25 (1.5) 0.61 0.44
Improper or erratic lane changing 381 (1.2) 21 (1.3) 0.02 0.88
Following improperly 291 (0.9) 19 (1.2) 0.74 0.39
Any 18,405 (59.6) 1106 (67.2) 37.44 <0.001
intervals around our point estimates and
allowed for the control of important con-
founders; others have reported similar risk
estimates but could not rule out the play of
chance because of small sample sizes.
Yet, these estimates appear small compared
to alcohol and some prescription medica-
tions (e.g., long-acting benzodiazepines).
Nonetheless, we remain cautious because
we lacked knowledge about drug concen-
trations and we could not fully ascertain
responsibility for crashes. Possibly the best
approach to resolve the issue will be to
determine the dose-response relationship
between THC and driving performance.
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Odds Ratios with 95% and 99% CI for Models Predicting DRF
Main Variables Odds Ratio (95% CI; 99% CI)
THC – Adjusted Model 1.29 (1.15, 1.45; 1.11, 1.50)
Confounding Variables
Age 0.98 (0.98, 0.98; 0.98, 0.98)
Male 1.01 (0.96, 1.06; 0.94, 1.07)
Previous Accident
None (reference)
One in past three years 1.11 (1.04, 1.19; 1.02, 1.22)
Two or more in past three years 1.32 (1.16, 1.51; 1.11, 1.57)
Previous DWI
None (reference)
One in past three years 1.02 (0.86, 1.22; 0.81, 1.29)
Two or more in past three years 1.03 (0.67, 1.58; 0.59, 1.81)
Previous Other Moving Violation
None (reference)
One in past three years 1.12 (1.04, 1.21; 1.02, 1.23)
Two or more in past three years 1.25 (1.12, 1.40; 1.08, 1.45)
Previous Speeding Conviction
None (reference)
One in past three years 1.11 (1.04, 1.18; 1.02, 1.20)
Two or more in past three years 1.11 (1.01, 1.21; 0.99, 1.25)
Previous Licence Suspension/Revocation
None (reference)
One in past three years 1.36 (1.23, 1.50; 1.20, 1.54)
Two or more in past three years 1.67 (1.49, 1.87; 1.44, 1.93)
Driver-related Factors
20 Leaving Vehicle Unattended in Roadway
21 Overloading or Improper Loading of Vehicle with Passengers or Cargo
22 Towing or Pushing Vehicle Improperly
23 Failing to [Dim Lights or, Since 1995] Have Lights on When Required
24 Operating without Required Equipment
25 Creating Unlawful Noise or Using Equipment Prohibited by Law
26 Following Improperly
27 Improper or Erratic Lane Changing
28 Failure to Keep in Proper Lane or Running off Road
29 Illegal Driving on Road Shoulder, in Ditch, on Sidewalk, on Median
30 Making Improper Entry to or Exit from Trafficway
33 Passing where Prohibited by Posted Signs, Pavement Markings, Hill or Curve, or School Bus
Displaying Warning not to Pass
34 Passing on Wrong Side
35 Passing with Insufficient Distance or Inadequate Visibility or Failing to Yield to Overtaking
36 Operating the Vehicle in Other Erratic, Reckless, Careless or Negligent Manner [or
Operating at Erratic or Suddenly Changing Speeds, Since 1995]
37 Traveling on Prohibited Trafficway (Since 1995)
38 Failure to Yield Right of Way
39 Failure to Obey Traffic Signs, Traffic Control Devices or Traffic Officers, Failure to Observe
Safety Zone Traffic Laws
40 Passing Through or Around Barrier Positioned to Prohibit or Channel Traffic
41 Failure to Observe Warnings or Instructions on Vehicles Displaying Them
42 Failure to Signal Intentions
43 Giving Wrong Signal
44 Driving Too Fast for Conditions or in Excess of Posted Speed Limit
45 Driving Less than Posted Maximum
46 Operating at Erratic or Suddenly Changing Speeds
47 Making Right Turn from Left Turn Lane or Making Left Turn from Right Turn Lane
48 Making Improper Turn
49 Driving Wrong Way on One-Way Trafficway
50 Driving on Wrong Side of Road [(Intentionally or Unintentionally) Since 1995]
51 Operator Inexperience
52 Unfamiliar with Roadway
53 Stopping in Roadway (Vehicle not Abandoned)
54 Underriding a Parked Truck
55 Getting Off/Out of or On/Into Moving Transport Vehicle
56 Getting Off/Out of or On/Into Non-Moving Transport Vehicle
57 Improper Tire Pressure (Since 1995)
58 Locked Wheel (Since 1995)
59 Overcorrecting (Since 1995)
20. Dischinger PC, Ho SM, Kufera JA. Medical con-
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25. Walsh G, Mann RE. On the high road: Driving
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dents. CMAJ 2003;168:564-66.
27. Brookoff D, Cook CS, Williams C, Mann CS.
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na. N Engl J Med 1994;331:518-22.
28. Albery IP, Strang J, Gossop M, Griffiths P. Illicit
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29. Terry P, Wright KA. Self-reported driving behav-
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groups in England. Addict Behav 2005;30:619-
30. Davey JD, Davey T, Obst P. Drug and drink dri-
ving by university students: An exploration of the
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31. Begg DJ, Langley JD, Stephenson S. Identifying
factors that predict persistent driving after drink-
ing, unsafe driving after drinking, and driving
after using cannabis among young adults. Accid
Anal Prev 2003;35:669-75.
32. Consensus Development Panel. Drug concentra-
tions and driving impairment. JAMA
33. Toennes SW, Steinmeyer S, Maurer HJ, Moeller
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serum in forensic cases. J Anal Toxicol
34. Williamson EM, Evans FJ. Cannabinoids in clin-
ical practice. Drugs 2000;60:1303-14.
35. Solowij N, Stephens RS, Roffman RA, Babor T,
Kadden R, Miller M, et al. Cognitive functioning
of long-term heavy cannabis users seeking treat-
ment. JAMA 2002;287:1123-31.
36. Movig KLL, Mathijssen MPM, Nagel PHA, van
Egmond T, de Gier JJ, Leufkens HGM, et al.
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vehicle accidents. Accid Anal Prev 2004;36:636.
37. Lowenstein SR, Koziol-McLain J. Drugs and traf-
fic crash responsibility: A study of injured
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Received: November 25, 2005
Accepted: May 25, 2006
Contexte : On sait que le cannabis a des effets nuisibles sur les performances humaines et qu'il
pourrait aussi nuire à la conduite d'un véhicule. Cependant, les études sur le sujet donnent des
résultats peu probants. Dans notre étude, nous avons cherché à approfondir la question des effets
du cannabis sur la conduite.
Méthode : Nous avons mené une étude cas-témoin transversale auprès de conducteurs de 20 à 49
ans impliqués dans des accidents mortels aux États-Unis entre 1993 et 2003; nous n'avons inclus
que les conducteurs ayant fait l'objet d'un test de dépistage du cannabis, mais dont le taux
d'alcoolémie était nul. Les cas étaient des conducteurs ayant commis au moins un acte de conduite
potentiellement dangereux dans le contexte de l'accident (p. ex., dépasser la limite de vitesse); les
témoins étaient des conducteurs dont la conduite n'avait pas été dangereuse lors de l'accident.
Nous avons calculé les rapports de cotes (RC) bruts et ajustés de tout acte de conduite
potentiellement dangereux chez les conducteurs déclarés positifs pour le cannabis, mais négatifs
pour la consommation d'alcool. Dans notre calcul des RC ajustés, nous avons tenu compte de
l'âge, du sexe et du dossier de conduite antérieur.
Résultats : Cinq p. cent des conducteurs avaient été déclarés positifs pour le cannabis. Le RC brut
d'un acte de conduite potentiellement dangereux était de 1,39 (IC de 99 % = 1,21-1,59) pour les
conducteurs déclarés positifs. Même compte tenu de l'âge, du sexe et du dossier de conduite
antérieur, la présence de cannabis demeurait associée à un risque plus élevé d'avoir eu une
conduite potentiellement dangereuse (1,29, IC de 99 % = 1,11-1,50).
Conclusion : Le cannabis a eu un effet néfaste sur la conduite, comme on pouvait le prédire d'après
les études de performance humaine. Cette constatation confirme la nécessité d'intervenir pour
réduire la prévalence de la conduite avec facultés affaiblies par le cannabis et montre qu'il faudrait
pousser la recherche sur la relation dose-réponse entre le cannabis et la prudence au volant.
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... Driving after cannabis use (DACU) could be a risky behavior, considering that delta-9-tetrahydrocannabinol (THC), the main psychoactive molecule in cannabis, can alter motor coordination, short-term memory, shared attention, concentration, reaction time, and time perception (Bondallaz et al., 2016;Capler et al., 2017;Doroudgar et al., 2018;Hartman & Huestis, 2013;Mikulskaya & Martin, 2018a, 2018bRogeberg & Elvik, 2016). Cognitive and psychomotor impairments could explain associations between recent cannabis use and a greater likelihood of collisions (Asbridge et al., 2012;Bédard et al., 2007). Although DACU may not lead systematically to accidents and fatalities, 4407 Canadians were physically injured and 75 died in a road accident involving cannabis in 2012 (Wettlaufer et al., 2017). ...
... Many sociodemographic and psychological characteristics have been associated with higher probabilities of engaging in DACU: age under 35 years old (Domingo-Salvany et al., 2017;Voas et al., 2013), male gender (Arterberry et al., 2013;Voas et al., 2013), high frequency of cannabis use (Arterberry et al., 2013;Arterberry et al., 2017;Berg et al., 2018;Borodovsky, Marsch et al., 2020;Cuttler et al., 2018;Matthews et al., 2014;Sukhawathanakul et al., 2019;Whitehill et al., 2019), presence of cannabis-related problems or disorders (Choi et al., 2019;Cook et al., 2017;Le Strat et al., 2015;Scherer et al., 2013), participation in other risky behaviors, for example, driving under the influence of alcohol (DUI-A), speeding, and erratic driving (Bédard et al., 2007;Bingham et al., 2008), and peer approval of engaging in DACU (Aston et al., 2016;Ward et al., 2018). However, these risk factors may not be shared by all individuals who have DACU, as they constitute a heterogeneous population. ...
... In North America where "car culture" is still dominant, suburban and rural communities are spread out across a vast territory, requiring most individuals to rely on their car if they need to go somewhere (Filion, 2014). Nonetheless, there remains a potential risk of road accidents associated with DACU (Asbridge et al., 2012;Bédard et al., 2007). From a macroscopic perspective, policymakers should consider implementing transportation strategies that would permit these frequent cannabis users to move around conveniently while reducing road accidents. ...
Full-text available
Young adults that drive after cannabis use (DACU) may not share all the same characteristics. This study aimed to identify typologies of Canadians who engage in DACU. About 910 cannabis users with a driver's license (17–35 years old) who have engaged in DACU completed an online questionnaire. Two‐step cluster analysis identified four subgroups, based on driving‐related behaviors, cannabis use and related problems, and psychological distress. Complementary comparative analysis among the identified subgroups was performed as external validation. The identified subgroups were: (1) frequent cannabis users who regularly DACU; (2) individuals with generalized deviance with diverse risky road behaviors and high levels of psychological distress; (3) alcohol and drug‐impaired drivers who were also heavy frequent drinkers; and (4) well‐adjusted youths with mild depressive‐anxious symptoms. Individuals who engaged in DACU were not a homogenous group. When required, prevention and treatment need to be tailored according to the different profiles.
... Individuals who engage in DACU are also more likely to present other risky road behaviours (Bergeron & Paquette, 2014). These include heavy drinking, drunk driving and dangerous driving such as speeding or being erratic on the road (Bédard et al., 2007;Bingham, Shope, & Zhu, 2008). Another explanation for DACU is that individuals do not have other alternative transportation. ...
... Our results show that a higher score in risky driving is associated with DACU; this finding has been reported in previous studies (Bédard et al., 2007;Bergeron, Langlois, & Cheang, 2014;Fergusson, Horwood, & Boden, 2008). Given that DACU appears to be associated with impulsive road behaviours (e.g. ...
... Individuals who engage in DACU are also more likely to present other risky road behaviours (Bergeron & Paquette, 2014). These include heavy drinking, drunk driving and dangerous driving such as speeding or being erratic on the road (Bédard et al., 2007;Bingham, Shope, & Zhu, 2008). Another explanation for DACU is that individuals do not have other alternative transportation. ...
... Our results show that a higher score in risky driving is associated with DACU; this finding has been reported in previous studies (Bédard et al., 2007;Bergeron, Langlois, & Cheang, 2014;Fergusson, Horwood, & Boden, 2008). Given that DACU appears to be associated with impulsive road behaviours (e.g. ...
Full-text available
This study identifid the most prominent risk factors associated with driving after cannabis use (DACU). 1,126 Canadian drivers (17-35 years old) who have used cannabis in the past 12 months completed an online questionnaire about sociodemographic information, substance use habits, cannabis effect expectancies, driving behaviours and peers' behaviours and attitudes concerning DACU. A hierarchical logistic regression allowed identifying variables that were associated with DACU. Income (CA$30,000-CA$69,000), weekly-to-daily cannabis use, higher level of cannabis-related problems, expectation that cannabis facilitates social interactions, drunk driving, belief that DACU is safe, general risky driving behaviours, having a few friends who had DACU and injunctive norms predicted past 12-month DACU. Older age, holding negative expectations concerning cannabis, driving aggressively and perceived accessibility of public transportation decreased the probability of DACU. With restricted resources, programmes will be more efficient by targeting Canadian young adults most inclined to DACU by focussing on these risk factors.
... More specifically, driving after cannabis use may be a particularly hazardous behavior considering that cannabis can alter cognitive functioning in adolescents and young adults (Dellazizzo et al., 2022). Such cognitive and psychomotor impairments may explain associations between recent cannabis use and a greater likelihood of collisions (Asbridge et al., 2012;Bédard et al., 2007). Yet, individuals who are engaged in driving after cannabis use may not be a homogenous group. ...
... Similarly, male drivers injured or killed in road crashes were more likely to test positive for drugs than females Bogstrand et al., 2015;Brady and Li, 2014;Grondel, 2016;Hoye, 2020;Jones et al., 2009;Poulsen et al., 2012). Deceased drivers testing positive for THC (the short-lived active ingredient in cannabis) tended to be younger males (Azagba et al., 2020;Bédard et al., 2007;Beirness et al., 2021;Davey et al., 2020;Dubois et al., 2015;Tefft and Arnold, 2021;Tefft et al., 2016), whereas psychoactive prescription drugs were common in female drivers aged 50 and older (Li and Chihuri, 2020;Mathijssen and Houwing, 2005). ...
Drug driving is a serious problem worldwide that can increase the risk of road crashes. This systematic review seeks to identify factors associated with drug driving (i.e., driving after consuming drugs other than alcohol) to highlight gaps in existing knowledge and inform the design of more effective countermeasures. A search of the literature was conducted for the period January 1, 2005 to July 31, 2021 using six different databases. The search protocol followed PRISMA guidelines and was registered in PROSPERO (#CRD42021234616). Studies that met inclusion criteria compared drug drivers with either non-drug drivers, alcohol-only drivers or drug drivers from an earlier time period, to identify factors specifically associated with drug driving, rather than common to all drivers. Two hundred and nineteen publications met the inclusion criteria and were included within the review. Based on the findings, a logic model was developed that presents the factors associated with drug driving. Various sociodemographic, psychosocial and legal factors emerged as the main factors associated with illegal drug driving. At the sociodemographic and psychological levels, drug drivers were more likely to be single, young males who often drive after using cannabis and who score high on sensation-seeking and impul-sivity scales. The key social factor found to be associated with drug driving was peer acceptance/disapproval of the behaviour. At the legal level, the review suggested that the effectiveness of current enforcement approaches to drug driving vary among jurisdictions around the world due to differences in the level of perceived certainty of apprehension and the chances of punishment avoidance. Future research into the anticipated and actual rewards for drug driving is needed to inform the development of more effective countermeasures.
... Driver characteristics such as age, gender, or personality traits can also contribute to adapting driving behavior. Drivers with higher frequency of DUIC generally present a risky driving style, including reckless onroad behaviors such as speeding (Bergeron & Paquette, 2014;Bédard et al., 2007). In addition, previous research suggests that DUIC is associated with demographics such as gender and age, with young males being more likely to drive under the influence of cannabis (Jones et al., 2007;Richer & Bergeron, 2009). ...
Objective This study analyzed the self-regulation behaviors of drivers under the influence of cannabis and its relationship with road complexity and some driver traits, including visual deterioration. Background Cannabis is the illicit drug most often detected in drivers; its use results in significant negative effects in terms of visual function. Self-regulation behaviors involve the mechanisms used by drivers to maintain or reduce the risk resulting from different circumstances or the driving environment. Methods Thirty-one young, occasional cannabis users were assessed both in a baseline session and after smoking cannabis. We evaluated the visual function (visual acuity and contrast sensitivity) and driver self-regulation variables of both longitudinal and lateral control as the speed adaptation and standard deviation of lateral position (SDLP). Results Visual function was significantly impaired after cannabis use. Recreational cannabis use did not result in self-regulation, although some road features such as curved roads did determine self-regulation. Male participants adopted mean faster driving speeds with respect to the speed limit. Driver age also determined better lateral control with lower SDLPs. In addition, visual impairment resulting from cannabis use (contrast sensitivity) was linked with self-regulation by changes in longitudinal and lateral control. Conclusion Contrast sensitivity could be a good indicator of individual visual status to help determine how drivers self-regulate their driving both in normal conditions and while under the influence of cannabis. Application The findings provide new insights about driver self-regulation under cannabis effects and are useful for policy making and awareness campaigns.
... In their systematic review of the effectiveness of mass media campaigns, Elder et al. (2004) find a median decrease of 10% in injury crashes. There has been an increase in drug-related impaired driving in recent years in Canada, particularly among young adults (Bédard, Dubois, & Weaver, 2007). Although alcohol-impaired driving is declining in Alberta, drug-related impaired driving may be on the rise. ...
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This study explores the changes in alcohol-impaired driving among a representative sample of Edmontonians from 1991 to 2009. Based on self-report data from the annual Alberta Surveys of 1991, 1992, 1997, and 2009, this study also traces the shift in the impact of standard demographic factors on alcohol-impaired driving in Edmonton, Alberta. Findings show that self-reported alcohol-impaired driving has decreased substantially over the years (10.6% in 1991, 8.4% in 1992, 7.2% in 1997, and 3.7% in 2009). However, there have been little changes in designated driving. In addition, there have been a shift in age-related impaired driving, i.e., people aged 55-65+ report impaired driving more in 2009 (4.8%) compared to 1991 (2.0%) and 1992 (2.2%); while individuals aged 18-34 and 35-54 report impaired driving less in 2009 (4.8% and 2.6%, respectively) compared to 1991 (12.7% and 13.0%, respectively). Logistic regression analyses indicate that gender is a significant predictor of alcohol-impaired driving in 1991 (OR = 3.29, 95% CI = 1.52–7.16), but not in 2009. However, the interaction between age and gender is not significant. The policy implications of the findings are discussed. Cette étude porte sur les changements dans la conduite affaiblie par l’alcool chez un échantillon représentatif de résidents d’Edmonton entre 1991 et 2009. Fondée sur des déclarations fournies par les automobilistes à l’occasion d’enquêtes annuelles menées en Alberta en 1991, 1992, 1997 et 2009, l’étude décrit aussi des fluctuations dans la portée de facteurs démographiques standard sur la conduite avec facultés affaiblies par l’alcool à Edmonton (Alberta). Les conclusions démontrent une diminution considérable des déclarations de conduite avec facultés affaiblies par l’alcool sur plusieurs années (10,6% en 1991, 8,4% en 1992, 7,2% en 1997, et 3,7% en 2009). Néanmoins, peu de changements ont été observés dans le recours aux conducteurs désignés. En outre, la fréquence de la conduite avec facultés affaiblies a connu des variations dans les différents groupes d’âge. Par exemple, les personnes âgées de 55 ans et plus ont plus souvent déclaré avoir conduit avec des facultés affaiblies en 2009 (4,8%) qu’en 1991 (2,0%) et en 1992 (2,2%), tandis que les personnes âgées de 18 à 34 ans et de 35 à 54 ans ont déclaré moins souvent avoir conduit avec les facultés affaiblies en 2009 (4,8% et 2,6%, respectivement) qu’en 1991 (12,7% et 13,0%, respectivement). Des analyses de régression logistique indiquent que le sexe a été une importante variable explicative de la conduits avec facultés affaiblies en 1991 (OR = 3,29, 95% CI = 1,52–7.16), ce qui n’a pas été le cas en 2009. Malgré cela, l’interaction entre l’âge et le sexe n’est pas significative. L’étude comprend une analyse des répercussions de ses conclusions sur certaines politiques.
The relationship between cannabis legalization and traffic safety remains unclear. Physiological measures of cannabis impairment remain imperfect. This analysis used self-report data to examine the relationship between cannabis legalization and driving under the influence of cannabis (DUIC)¹. Using a cross-sectional national sample (2016–2017) of 1,249 past–30-day cannabis users, we regressed self-reported DUIC (driving within three hours of “getting high”) on cannabis legalization (recreational and medical (recreational), medical only (medical), or no legal cannabis), adjusting for demographics, days of use (past 30 days), days of use*legal status, calibration weights, and geographic clustering. The risk of DUIC in recreational (risk ratio [RR] = 0.41, 95% confidence interval (CI):0.23–0.72) and medical (RR = 0.39, 95% CI:0.20–0.79) states was lower than in states without legal cannabis, with one exception. Among frequent cannabis users (≥20 days per month), there was a significantly lower risk of DUIC for those living in recreational states (RR = 0.70, 95% CI: 0.49–0.99), but not for those living in medical states (RR = 0.87, 95% CI: 0.60–1.24), compared to users living in states without legal cannabis. The risk of self-reported DUIC was lower in recreational and medical cannabis states compared to states without legal cannabis. The only exception was for frequent users in medical states, for whom there was no difference in risk compared to frequent users living in states without legal cannabis.
Recreational use of cannabis has been recently legalized in Canada, however, its impact on driving performance and safety is not well-defined. One experimental tool that can be implemented to better examine the link between cannabis use and driving impairment is driving simulation. Customized driving scenarios can be created to target and evaluate hypothesized effects of cannabis on driving behaviors. This paper presents a framework for the evidence-based design of driving scenarios that aim to characterize cannabis-related driving impairment. The framework begins by considering the effects of cannabis on sensory, motor, and cognitive abilities, as well as how impairments in these abilities could negatively affect driving performance. Next, we examine how these negative effects on driving could be measured in a controlled, repeatable, and safe manner using simulators, to map the specific effects of cannabis on particular aspects of driving performance. Last, we describe how customized driving simulator scenario elements could be designed to challenge the targeted driving abilities that are affected by cannabis. In addition to detailing this experimental framework, a prototype scenario developed for DriverLab at KITE - Toronto Rehabilitation Institute is presented, but the expectation is that the proposed approaches could be broadly implemented across a range of simulators.
For this meta-analysis, the aim is to estimate the prevalence and to determine the factors associated with changes in this prevalence in the world. A systematic review of PubMed/Medline, ISI Web of Knowledge, Scopus, and Science Direct, SID, and Google Scholar from 2011 to April 2020 was performed. Point prevalence was analyzed with a 95% confidence interval. The variances of each study were calculated using a two-sentence distribution formula, and the heterogeneity between the studies was assessed by the Q-Cochran test with a significance level of less than 0.1. Also, the index of changes attributed to I² was examined. Due to the heterogeneity between the studies, the random-effects model was used to combine the results of the studies. All statistical analyses were performed by the STATA software version 11 using Meta-Analysis commands. The significance level of the tests was considered to be P < 0.05. The prevalence of mortality in motorcyclists in six studies was reported to be 12% (0.05-0.18 = 95% CI). We stratified this study by participant geographical region and age group. In this study, the highest prevalence of mortality was in the under-30 age group. Furthermore, planning educational campaigns and advertisements by state authorities that are pointing out the harmful effects of motorcyclists' cocaine and/or other substances consumption is necessary.
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This population-based study examines drivers' characteristics associated with driving errors that resulted in fatal motor vehicle crashes. Routinely collected data from the Fatal Accident Reporting System were used to assess whether a driver initiated the crash (case) or was passively involved (control) in 6,506 two-car collisions (81% of 7,993 eligible events). A paired comparison of cases and controls avoided confounding by environmental factors, exposure to traffic, and differences in case fatality. The strongest predictor of crash initiation is alcohol (odds ratio (OR) = 11.5; 95% confidence interval (CI) 9.57-13.9). Odds ratios are elevated even at the lowest blood alcohol concentration levels and increase dramatically as alcohol levels rise. Drivers aged 40-49 years are least likely to initiate crashes; odds ratios rise in a U-shaped manner to 3.35 in teenagers (95% CI 2.72-4.13) and to 22.1 in drivers over 80 years (95% CI 14.2-34.5). Other risk factors for initiating a fatal crash are the following: not wearing a seat belt (OR = 1.54; 95% CI 1.35-1.75), driving without a valid driver's license (OR = 2.16; 95% CI 1.72-2.73), and having had a crash within the last year (OR = 1.21; 95% CI 1.07-1.38). Driving errors leading to fatal crashes do not occur at random, but are associated with specific driver characteristics. The risk factors for crash initiation among crash-involved drivers are similar to risk factors for crash involvement found in other studies. These findings suggest that driving errors often explain high rates of crash involvement, invite further use of crash initiation in traffic injury research, and underscore the value of population-based registries for analytic epidemiology.
The policy debate on cannabis has moved back into prominence in Britain and elsewhere after reports of increases in use during the early 1990s1 and renewed claims about the therapeutic value of marijuana. 2 3 Rational debate has often been obstructed because the media present a forced choice between two sets of views. One of these constructed views is that cannabis is harmless when used recreationally, is therapeutically useful, and hence should be legalised. The other is that recreational use is harmful to health and that cannabis should continue to be prohibited for recreational or therapeutic purposes.4 This oversimplification of the cannabis debate has prevented a more considered examination of eight conceptually separate issues (box). We believe that a competent consideration of these issues would contribute to a more informed debate about the appropriate public policies that could be adopted towards cannabis use for recreational or therapeutic purposes. #### Summary points Cannabis use is increasing steadily in many countries and is most prevalent among young people The value of the debate on cannabis is seriously diminished by heated contributions that obstruct rational consideration of important public health and policy issues The different domains of the debate should be considered in isolation at first to allow a more objective analysis of the evidence Substantial public investment in research into the different areas is a prerequisite of rational consideration of public policies More than 60 different cannabinoids and over 400 active components have been identified in samples of cannabis.2 However, our interest and concerns about associated harms could be much more focused. Should we be especially concerned about the use of new cannabis preparations with higher concentrations of tetrahydrocannabinol? Does using cannabis that has a higher tetrahydrocannabinol content result in a higher intake of tetrahydrocannabinol or do smokers consciously or subconsciously titrate …
Context Cognitive impairments are associated with long-term cannabis use, but the parameters of use that contribute to impairments and the nature and endurance of cognitive dysfunction remain uncertain.Objective To examine the effects of duration of cannabis use on specific areas of cognitive functioning among users seeking treatment for cannabis dependence.Design, Setting, and Participants Multisite retrospective cross-sectional neuropsychological study conducted in the United States (Seattle, Wash; Farmington, Conn; and Miami, Fla) between 1997 and 2000 among 102 near-daily cannabis users (51 long-term users: mean, 23.9 years of use; 51 shorter-term users: mean, 10.2 years of use) compared with 33 nonuser controls.Main Outcome Measures Measures from 9 standard neuropsychological tests that assessed attention, memory, and executive functioning, and were administered prior to entry to a treatment program and following a median 17-hour abstinence.Results Long-term cannabis users performed significantly less well than shorter-term users and controls on tests of memory and attention. On the Rey Auditory Verbal Learning Test, long-term users recalled significantly fewer words than either shorter-term users (P = .001) or controls (P = .005); there was no difference between shorter-term users and controls. Long-term users showed impaired learning (P = .007), retention (P = .003), and retrieval (P = .002) compared with controls. Both user groups performed poorly on a time estimation task (P<.001 vs controls). Performance measures often correlated significantly with the duration of cannabis use, being worse with increasing years of use, but were unrelated to withdrawal symptoms and persisted after controlling for recent cannabis use and other drug use.Conclusions These results confirm that long-term heavy cannabis users show impairments in memory and attention that endure beyond the period of intoxication and worsen with increasing years of regular cannabis use.
The purpose of the present study was to assess the effects of low doses of marijuana and alcohol, and their combination, on visual search at intersections and on general driving proficiency in the City Driving Test. Sixteen recreational users of alcohol and marijuana (eight males and eight females) were treated with these substances or placebo according to a balanced, 4-way, cross-over, observer- and subject-blind design. On separate evenings, subjects received weight-calibrated doses of THC, alcohol or placebo in each of the following treatment conditions: alcohol placebo + THC placebo, alcohol + THC placebo, THC 100 μg/kg + alcohol placebo, THC 100 μg/kg + alcohol. Alcohol doses administered were sufficient for achieving a blood alcohol concentration (BAC) of about 0.05 g/dl. Initial drinking preceded smoking by one hour. The City Driving Test commenced 15 minutes after smoking and lasted 45 minutes. The test was conducted over a fixed route within the city limits of Maastricht. An eye movement recording system was mounted on each subject's head for providing relative frequency measures of appropriate visual search at intersections. General driving quality was rated by a licensed driving instructor on a shortened version of the Royal Dutch Tourist Association's Driving Proficiency Test. After placebo treatment subjects searched for traffic approaching from side streets on the right in 84% of all cases. Visual search frequency in these subjects did not change when they were treated with alcohol or marijuana alone. However, when treated with the combination of alcohol and marijuana, the frequency of visual search dropped by 3%. Performance as rated on the Driving Proficiency Scale did not differ between treatments. It was concluded that the effects of low doses of THC (100 μg/kg) and alcohol (BAC < 0.05 g/dl) on higher-level driving skills as measured in the present study are minimal. Copyright © 2001 John Wiley & Sons, Ltd.
The role of Delta(9)-tetrahydrocannabinol (THC) in driver impairment and motor vehicle crashes has traditionally been established in experimental and epidemiological studies. Experimental studies have repeatedly shown that THC impairs cognition, psychomotor function and actual driving performance in a dose related manner. The degree of performance impairment observed in experimental studies after doses up to 300 microg/kg THC were equivalent to the impairing effect of an alcohol dose producing a blood alcohol concentration (BAC) >/=0.05 g/dl, the legal limit for driving under the influence in most European countries. Higher doses of THC, i.e. >300 microg/kg THC have not been systematically studied but can be predicted to produce even larger impairment. Detrimental effects of THC were more prominent in certain driving tasks than others. Highly automated behaviors, such as road tracking control, were more affected by THC as compared to more complex driving tasks requiring conscious control. Epidemiological findings on the role of THC in vehicle crashes have sometimes contrasted findings from experimental research. Case-control studies generally confirmed experimental data, but culpability surveys showed little evidence that crashed drivers who only used cannabis are more likely to cause accidents than drug free drivers. However, most culpability surveys have established cannabis use among crashed drivers by determining the presence of an inactive metabolite of THC in blood or urine that can be detected for days after smoking and can only be taken as evidence for past use of cannabis. Surveys that established recent use of cannabis by directly measuring THC in blood showed that THC positives, particularly at higher doses, are about three to seven times more likely to be responsible for their crash as compared to drivers that had not used drugs or alcohol. Together these epidemiological data suggests that recent use of cannabis may increase crash risk, whereas past use of cannabis does not. Experimental and epidemiological research provided similar findings concerning the combined use of THC and alcohol in traffic. Combined use of THC and alcohol produced severe impairment of cognitive, psychomotor, and actual driving performance in experimental studies and sharply increased the crash risk in epidemiological analyses.
Motor vehicle traffic fatalities in the United States are described by two major data sources, the Fatal Accident Reporting System (FARS) and the National Center for Health Statistics Multiple Cause of Death data (NCHS). Certain data, such as the age and sex of the fatality, are reported by both sources. However, each source contains data absent from the other. For example, only the FARS describes the precise circumstances of injury, and only the NCHS identifies the anatomic injuries listed on the death certificate. Thus, it would be useful to have a single file that offers for each case all of the data provided in each of the separate files. Creation of such a file is impeded by the fact that neither file contains personal identifiers for the cases listed. The present paper describes a method of matching cases from the two files based on simultaneous agreement of several variables common to both files (age, sex, date of death, role in the crash, and state in which the injury occurred). Using this method, 85% of the FARS cases can be uniquely matched with a case in the NCHS data.
Driving under the influence of intoxicating drugs other than alcohol may be an important cause of traffic injuries. We used a rapid urine test to identify reckless drivers who were under the influence of cocaine or marijuana. We conducted a consecutive-sample study in Memphis, Tennessee, in the summer of 1993. Subjects arrested for reckless driving who were not apparently impaired by alcohol (did not have an odor of alcohol, tested negative on breath analysis, or both) were tested for cocaine and marijuana at the scene of arrest. The results of the drug tests were compared with clinical evaluations of intoxication made at the scene by a police officer. A total of 175 subjects were stopped for reckless driving, and 150 (86 percent) submitted urine samples for drug testing at the scene of arrest. Eighty-eight of the 150 (59 percent) tested positive: 20 (13 percent) for cocaine, 50 (33 percent) for marijuana, and 18 (12 percent) for both drugs. Ninety-four of the 150 tested drivers were clinically considered to be intoxicated, and 80 of them (85 percent) tested positive for cocaine or marijuana. The intoxicated drivers had a broad range of affects and appearances. Nearly half the drivers intoxicated with cocaine performed normally on standard sobriety tests. Over half of the reckless drivers who were not intoxicated with alcohol were found to be intoxicated with other drugs. Toxicologic testing at the scene is a practical means of identifying drivers under the influence of drugs and is a useful adjunct to standard behavioral sobriety testing.