Blackwell Science, LtdOxford, UKADDAddiction0965-2140© 2005 Society for the Study of Addiction100••••
Original Article
Marijuana use and car crash injury
Stephanie Blowset al.

RESEARCH REPORTMarijuana use and car crash injury
Stephanie Blows1, Rebecca Q. Ivers1, Jennie Connor2, Shanthi Ameratunga2, Mark Woodward1 &Robyn Norton1
The George Institute For International Health, Sydney1 and School of Population Health, University of Auckland, New Zealand2

Correspondence to:Stephanie Blows
The George Institute For International
HealthLevel 10 King George V Building
Royal Prince Alfred Hospital
PO Box M201
Missenden Road
SydneyNSW 2050
Australia
Tel: +61 29993 4585
Fax: +61 29993 4501
E-mail: sblows@thegeorgeinstitute.org
Submitted 2 July 2004;
initial review completed 15 October 2004;
final version accepted 16 December 2004

RESEARCH REPORTABSTRACT
Aims To investigate the relationship between marijuana use prior to driving,
habitual marijuana use and car crash injury
Design and setting Population based case–control study in Auckland, New
Zealand.
Participants Case vehicles were all cars involved in crashes in which at least
one occupant was hospitalized or killed anywhere in the Auckland region, andcontrol vehicles were a random sample of cars driving on Auckland roads. The
drivers of 571 case and 588 control vehicles completed a structured interview.
Measurements Self reported marijuana use in the 3 hours prior to the crash/survey and habitual marijuana use over the previous 12 months were recorded,along with a range of other variables potentially related to crash risk. The main
outcome measure was hospitalization or death of a vehicle occupant due to car
crash injury.
Findings Acute marijuana use was significantly associated with car crashinjury, after controlling for the confounders age, gender, ethnicity, educationlevel, passenger carriage, driving exposure and time of day (OR 3.9, 95% CI
1.2–12.9). However, after adjustment for these confounders plus other risky
driving at the time of the crash (blood alcohol concentration, seat-belt use, travelling speed and sleepiness score), the effect of acute marijuana intake was nolonger significant (OR 0.8, 95% CI 0.2–3.3). There was a strong significant
association between habitual use and car crash injury after adjustment for
all the above confounders plus acute use prior to driving (OR 9.5, 95% CI
2.8–32.3).
Conclusions This population-based case–control study indicates that habitualuse of marijuana is strongly associated with car crash injury. The nature of therelationship between marijuana use and risk-taking is unclear and needs further research. The prevalence of marijuana use in this driving population waslow, and acute use was associated with habitual marijuana use, suggesting thatintervention strategies may be more effective if they are targeted towards highuse groups.
KEYWORDSINTRODUCTION
Marijuana is an increasingly widely used drug, particularly among young people [1–3], and there is concern© 2005 Society for the Study of Addiction

Cannabis, case control study, motor vehicle injury.

about its effects on road safety. Many research studies andseveral reviews of the literature have examined the relationships between marijuana use, driving ability andcrashes [4–11]. Driving ability and crash risk may be

doi:10.1111/j.1360-0443.2005.01100.x

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affected by both acute marijuana use prior to driving andhabitual use of marijuana [9]. The effects of acute marijuana use have been studied in both laboratory/simulator
studies and epidemiological studies of drivers. Laboratory
studies of the effect of acute marijuana intake on various
tasks show impairment in reaction time, attention, coordination and motor skills, which are likely to be important in driving [1]. However, there is less evidence that
this translates into actual driving impairment in simulator studies [9]. There have been few studies of the prevalence of acute marijuana use among on-road drivers, butthese estimate it to be around 1–6% in those driving
populations that have been studied [4,9,12]. Studies ofcrash-involved and/or injured drivers have found evidence that marijuana use is higher is these populations,at 4–12% [4,9,12–14]. This suggests that there is a positive relationship between marijuana use and car crashes
and injury. However, epidemiological studies of the effectof acute marijuana intake on crash risk have had variable
results. Some studies have suggested marijuana use may
increase the likelihood of culpability in a crash; in particular, a recent case–control study by Drummer et al. foundthat drivers who tested positive for marijuana use weremore likely than drug-free drivers to be culpable for thecrash [7,11]. However, other studies have failed to find an
association between marijuana use and car crash injury,
and recent literature reviews conclude that overall theevidence for the role of marijuana use in car crashes
remains inconclusive [5,6,9,10,15]. These reviews notethat it is difficult to draw conclusions because of methodological problems, including selection bias, due particularly to low response rates; measurement bias relating todifficulty in accurately measuring marijuana use; andfailure to adjust for important confounders, including
alcohol consumption.
The effect of habitual marijuana use on car crash riskhas been studied in several cohort studies [8,16–18], but
not all have suggested a significant positive relationship.
Many of these studies also have methodological problems, including long time delays between measurementof exposure and outcome [18] and use of self-reported
and non-injury crashes as outcomes [16]. Furthermore,
although these studies adjust for confounding variables
such as age and gender, due to their prospective nature
the results are usually not adjusted for important variables, such as speed and blood alcohol level, at the time of
the crash. The joint associations between habitual andacute marijuana use and car crash injury have not been
described.
This analysis examines the relationships between
acute marijuana use, habitual marijuana use and car
crash injury, using data from a population-based case–control study with 1159 participants. A wide range ofcrash-related variables were measured in this study,© 2005 Society for the Study of Addiction

allowing us to adjust for many important confounding
factors. The outcome factor was severe injury or death of
a vehicle occupant as a result of a crash.

METHODS
Recruitment and data collection
The study was conducted in the Auckland region of New
Zealand, which has a population of about 1.1 million[19] and includes both urban and rural areas. Details of
the study methodology have been published previously[20]. Recruitment of participants occurred from March
1998 to July 1999. Case vehicles were defined as all cars
involved in crashes in which at least one occupant (driveror passenger) of the car was hospitalized with injuries, orkilled, during the study period, in the Auckland region.
For control selection, random cluster sampling was used
to obtain a representative sample of all driving in the
study region. Random points were selected from a list ofroads in the region and control cars, identified randomly
proportional to the volume of traffic at the site, were
stopped as close as possible to these points. One control
participant per case was recruited at approximately thesame rate and over the same time period as cases.
Drivers of all vehicles completed a face-to-face or
telephone-administered questionnaire-based structured
interview. For controls, this was related to the time thatthey were recruited during the roadside surveys. The nextof kin or other suitable person (proxy respondent) wasinterviewed when a driver was fatally injured or otherwise unable to complete the interview. The interview
contained 155 questions on potential risk factors for car
crashes, including demographics; circumstances of thecrash; and personal, vehicle and environmental factors.
An environmental survey of the crash and control
recruitment sites was also conducted to record road andtraffic characteristics. The medical records of case participants were examined for information on injuries andother relevant variables. Blood alcohol levels were estimated using a breathalyser for controls and from hospitaland police records for cases. Missing data for blood alcohol level were imputed according to self-reported alcohol
consumption and whether or not consumption andimpairment were suspected by ambulance and hospital
staff. Full details of blood alcohol measurement andimputation have been published previously [21]. Marijuana use was measured using two questions. For acuteuse, participants were asked if they had used any marijuana in the 3 hours prior to the crash/roadside survey.
For habitual use, participants were asked about frequency of marijuana use during the past 12 months. Theinterviewer reminded participants that all information
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Marijuana use and car crash injury

was strictly confidential and would not be disclosed toauthorities.

Study population
Interviews were completed for 571 drivers of case vehicles, a response rate of 92.8%. Non-responders included
30 (4.9%) case vehicles for which the selected participant
declined to participate in an interview and 14 (2.3%) that
could not be contacted.
From the roadside surveys, 746 cars were identified
as control vehicles. Of these, interviews were completed
for 588 drivers (78.8%). Non-responders included
92 (12.3% of total) who declined to participate, 60(8.0%) who could not be contacted and six (0.8%) who
could not participate for other reasons.

Analysis
Frequencies, odds ratios and 95% confidence intervals
were calculated from linear logistic regression models
using SUDAAN software, which accounts for intracluster
correlation of control data sampled from the same site.
Proportions of controls were adjusted for the clusteredsampling design. This was conducted by weighting control data by the inverse of the sampling fraction at therecruitment site and adjusting the variances of the estimates to account for intracluster correlation of data fromthe same site. We identified potential confounders fromthe epidemiological literature and adjusted for these in
the analyses if they were associated significantly with car
crash injury in our data after adjusting for driver’s age
and sex. To investigate the relationship between marijuana use and other acute risky driving variables, and toallow for the possibility that risky driving variables may
be on the pathway between marijuana use and car crashinjury, we obtained odds ratios for the association
between acute and habitual marijuana use first adjusting
only for other non-risk taking variables (age, sex, ethnicity, education level and driving exposure of the driver; ageof vehicle; number of passengers; and time of day) thenadding other variables measuring acute risky driving at
the time of the crash/survey (sleepiness score, blood alcohol level, seat-belt use and speed) to each model.

RESULTS
The mean age of case drivers was 36.6 years and of control drivers 40.8 years. The case group was 65% maleand the control group was 59% male. There were no significant differences in age group, sex or driving conditions, between drivers who were interviewed and all
eligible drivers, for both cases and controls. Table 1 shows© 2005 Society for the Study of Addiction

607

the distributions of acute and habitual marijuana use
and all confounding variables by case–control status.
Proportions of controls in this table are adjusted for theclustered sampling design. The proportion of drivers
reporting marijuana use in the 3 hours prior to thecrash/survey was 5.6% among cases and 0.5% among
controls. Habitual marijuana use of an average of at least
once per week over the past 12 months was reported by
10.0% of cases and 0.9% of controls. Of those who had
used marijuana acutely in the 3 hours prior to the crash/survey, 87% of cases and 88% of controls were also habitual users, compared to 6% and 0.5%, respectively, forthose who had not used it acutely. Missing data were lessthan 10% for all variables included in these analyses after
imputation for blood alcohol level [21].
Table 2 shows the associations between acute andhabitual marijuana use and car crash injury. Acute use of
marijuana in the 3 hours prior to the crash was significantly associated with car crash injury in the univariate
model (odds ratio (OR) 11.4, 95% confidence interval (CI)
3.6–35.4). This association remained significant after
adjustment for the non-risk taking confounders age, sex,
education, ethnicity, driving exposure, age of vehicle,
time of day and number of passengers (OR 3.9, 95% CI
1.2–12.9). However, after adjustment for these confounders plus other risky driving at the time of the crash(sleepiness, blood alcohol concentration, seat-belt use
and travelling speed), the effect of acute marijuana intake
was no longer significant (OR 0.8, 95% CI 0.2–3.3). The
association between habitual marijuana use and car
crash injury was significant in both the univariable andage and sex-adjusted models, and remained significant
after adjustment for all risk-taking and non-risk taking
confounders, plus acute marijuana use prior to thecrash/survey (OR 9.5, 95% CI 2.8–32.3). Restricting theanalyses to cases in which the driver was hospitalized or
killed did not alter the significance of the odds ratios.

DISCUSSION
This population-based case–control study is notable formeasurement of multiple confounding factors, bothacute and chronic; random selection of controls from a
regional driving population; and the use of validated
records on injury-related hospitalizations or death as theoutcome. The results suggest that habitual users of marijuana have about 10 times the risk of car crash injury or
death compared to infrequent or non-users, after adjustment for other crash-related variables including an objective measure of blood alcohol level. In addition, this study
measured self-reported acute marijuana use prior to driving in a random selection of the Auckland regional driving population (our control participants) and compared
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Table 1 Frequency distributions of acute and habitual marijuana use and other confounding variables by case–control status, Auckland Car Crash Injury Study.
Controls1 (n = 588)

Cases (n = 571)
No.

(%)

No.

(%)

Acute marijuana use (in past 3 hours)
No
YesDon’t know/missing

520
32
19

(91.1)
(5.6)
(3.3)

582
5
1

(99.2)(0.5)(0.2)

Habitual marijuana use (in past 12 months)
Less than once per week
Once per week or more
Don’t know/missing

494
57
20

(86.5)(10.0)
(3.5)

579
7
1

(98.7)(0.9)(0.4)

Age of driver (years)
< 25
25–34
35–44
45–54
55–64
65 +

195
133
85
61
39
58

(34.2)(23.3)(14.9)(10.7)(6.8)(10.2)

91
125
154
107
80
31

(13.7)(22.3)(24.5)(19.6)(14.2)(5.6)

Sex
Female
Male

198
373

(34.7)(65.3)

226
362(41.3)(58.7)

Education level
Post-secondary
Secondary school, > 3 years
Secondary school, £ 3 years

178
137
252

(31.5)(24.2)(44.4)

276
154
157

(49.3)(25.1)(25.6)

Ethnicity
White/European
Maori
Pacific Islander
Other

313
117
86
55

(54.8)(20.5)(15.1)
(9.6)

444
61
36
47

(74.7)(9.2)(6.1)(10.0)

Time of day
Not between 2 and 5 a.m.
Between 2 and 5 a.m.

525
46

(91.9)
(8.1)

571
17

(99.6)(0.4)

Stanford Sleepiness Score
1–3 (sleepy)
4–7 (not sleepy)447
63(87.7)(12.3)

578
8(99.0)(1.0)

Number of passengers
0
1
2 or more

285
140
142

(50.3)(24.7)(25.0)

355
144
88(62.9)(23.4)(13.7)

Seat-belt use
Yes
No

469
81

(82.1)(14.2)

5684(97.4)(0.8)

Blood alcohol concentration (mg percentage) 2
<3
3–50
> 50

397
41
132

(69.7)(7.2)(23.2)

565
166(96.6)(2.6)(0.8)

Travelling speed at time of crash
0–30 kph
31–50 kph
51–80 kph
> 80 kph

87
113
196
119

(16.9)(21.9)(38.1)(23.1)

78
229
220
55

(13.9)(41.5)(33.7)(11.0)

1

Proportions of controls are adjusted for the clustered sampling design. 2Missing data imputed [16].

© 2005 Society for the Study of Addiction

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609

Table 2 Univariable, age- and sex-adjusted and multivariable adjusted odds ratios (95% confidence intervals) for the associations
between acute marijuana use, habitual marijuana use and car crash injury, Auckland Car Crash Injury Study.
Age- and sex-adjusted

Multivariable 1

Multivariable2

95% CIOR

95% CIOR

95% CIOR

95% CI

(3.6–35.4)
Reference

6.0
1.0(1.8–20.3)

3.9
1.0

(1.2–12.9)

0.8
1.0

(0.2–3.3)

Habitual marijuana use (in past 12 months)
Less than once per week
12.7
(5.3–30.6)
Once per week or more
1.0
Reference

8.6
1.0(3.3–22.0)

9.1
1.0

(3.8–22.0)

9.5
1.0

(2.8–32.3)

Univariable
OR
Acute marijuana use (in past 3 hours)
Yes
11.4
No
1.0

1
Adjusted for age, sex, education, ethnicity, driving exposure, age of vehicle, time of day and number of passengers. 2Adjusted for the above confounders,plus blood alcohol concentration, seat-belt use and travelling speed. The model for habitual marijuana use is also adjusted for acute marijuana use.

this to crashed drivers from the same population, which
has been achieved by few previous studies [9]. Marijuana
use prior to the crash/survey was reported by 5.6% ofcase drivers and 0.5% of control drivers. Acute use of
marijuana prior to the crash was associated with fourtimes the risk of car crash injury with adjustment for
non-risk taking variables, but after adjustment for speed,
seat-belt use, blood alcohol level and sleepiness this association was no longer present. Acute use of marijuana
was also highly correlated with habitual use: 87% ofcases and 88% of controls who had used marijuanaacutely prior to the crash/survey were also habitual
users, compared to less than 10% who had not.
The relationship we observed between habitual marijuana use and car crash injury has been found previously
in a number of cohort studies [17,18,22]. These havesuggested associations between frequent habitual marijuana use and alcohol-related, single vehicle and at-fault
crashes [17]; and crashes causing injury [18] and hospitalization [22]. Unlike most of these studies, we were able
to control for marijuana use prior to the crash, minimizing the possibility that habitual users were at increased
risk because they had been smoking marijuana prior todriving. However, the mechanism by which habitualmarijuana use increases the risk of car crash injury has
not been identified. For example, habitual users may be
more likely to engage in other risk taking behaviours. Fergusson & Horwood [16], in a New Zealand-based cohort
study, found that habitual marijuana users were more
likely to report risky/illegal driving behaviors and attitudes, which were associated with increased risk of car
crashes. We included the risky driving variables speed,
non-use of seat-belts, sleepiness and high blood alcohol
level in our models but found that this did not remove thesignificant effect of habitual marijuana use. However,heavy users may be more likely to engage in other risky
driving behaviours associated with car crashes that we
did not measure, and our analysis may thus be subject to© 2005 Society for the Study of Addiction

uncontrolled confounding. The possibility that habitual
heavy marijuana use induces mental impairment, that inturn affects driving ability, is less likely. Although there issome evidence that habitual marijuana use can cause
long-term changes in brain functioning [23,24] theresults are inconclusive and this has not been studied inrelation to driving. There is some evidence that heavy
habitual marijuana users build up continuously high levels of the drug and may display ongoing acute effects[25]. If so, the association we observed may be due tothese acute effects rather than due solely to the effect of
habitual marijuana use.
Marijuana use prior to driving was reported by 0.5%of control drivers in this study. These figures may be subject to under-reporting, but are consistent with the estimates of 1–6% that have been reported previously forsimilar populations [4,9]. They suggest that the prevalence of marijuana use prior to driving in the general population is low. More cases than controls reported using
marijuana acutely in the 3 hours prior to the crash; however, acute marijuana use was not associated with car
crash injury after adjustment for the crash-related risky
driving variables of speed, seat-belt use, blood alcohol
level and sleepiness. This supports several previous studies and literature reviews that have not found an effect ofacute marijuana intake on crash risk [4,7,16]. However,some studies have suggested that acute marijuana use
does have an effect on crash risk [6,12] and in particular,a recent large, well-conducted study of fatally injured
drivers by Drummer et al. [11] suggested that those whotested positive for marijuana were almost three times
more likely to be culpable for the crash. Our odds ratio forthis relationship is close to 1 and the confidence intervals
are wide, so we cannot rule out an effect. Furthermore,these risky driving variables may be on the causal pathway between acute marijuana use and car crash injury;that is, because drivers are under the influence of marijuana, they may be less likely to wear seat-belts and more
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Stephanie Blows et al.

likely to be sleepy and drive at higher speeds due to the
influence of the drug. In this case, it may be inappropriate
to adjust for these variables [26]. In particular, sleepinessis known to be an acute effect of marijuana use [27]. It
would be appropriate to test for an association between
sleepiness, risky driving and marijuana use in the control
group, but because of small numbers of controls with positive risk taking outcomes in this study we were not able
to do so. Other studies have also indicated that there may
be a relationship between marijuana use, risky driving
and car crash injury [16,28] and this area needs further
research.
This study has several limitations and the results may
be subject to bias. We relied on a retrospective, selfreported measure of marijuana consumption in the past
3 hours and did not differentiate between levels of consumption. We also did not measure marijuana levels inthe body or any resulting impairment. This may have produced an inaccurate result for marijuana consumption
or resulted in differential reporting of marijuana consumption for cases and controls. It is difficult to estimate
to what extent and in what direction this may have biased
our results. It will remain a challenge for future investigators to obtain an accurate measure of both marijuanaconsumption and impairment within the short time window available following a crash (or roadside survey) in
which these variables can be measured. Although theamount of missing data in this study were small, there
were more missing data for cases than controls. If caseswith missing data were more likely to have been using
marijuana than cases who answered the question, thenwe may have underestimated the effect of marijuana use
on car crash injury. The response rate in cases was also
higher than that for controls, and although there were nodifferences in age, gender or driving conditions between
case and control non-responders we were not able toassess differences in marijuana use by case–control status in non-responders. The result may be subject to selection bias as a result of these missing participants.

CONCLUSIONSThis population-based case–control study suggests that
habitual marijuana use is associated with a 10-foldincrease in the risk of car crash injury. The relationshipbetween both habitual and acute marijuana use and car
crashes is complex and is likely to be related to other risktaking behaviours, particularly risky driving. Because of
the challenges involved in conducting high-quality
research into these relationships, converging evidence
from a variety of sources is required before potentiallycostly policy and practice decisions are made. In thisstudy the prevalence of self-reported recent marijuana© 2005 Society for the Study of Addiction

use in the Auckland driving population was less than 1%,
and those who did use marijuana prior to driving werehighly likely to be habitual users. This suggests that interventions targeting high-risk marijuana use groups may
be more cost-effective than general population interventions such as random roadside testing.
Acknowledgements
The Auckland Car Crash Injury Study was funded by the
Health Research Council of New Zealand.
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