CHARACTERISTICS OF DWI/DUI DRIVERS;
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Patterns of Traffic Violations with Special Emphasis
to DUI in Tennessee, USA
Department of Sociology, Tennessee Technological University, Cookeville, Tennessee, U.S.A.
Drinking-driving, DUI offences, DUI offenders, Traffic violations
The primary objective of this paper is to study the driving patterns of the Tennessee DUI drivers,
in order to understand alcohol-related crashes and fatalities in Tennessee. Data were obtained
from the Tennessee Department of Highway Safety. Driving records were analyzed for drivers
who received a citation for a driving violation in 1999, since they received their driving licenses.
Out of a total 4,355,230 valid licensed drivers in 1999, 671,544 drivers were cited for traffic
movement violations. These cited drivers were recorded to have a total of 989,848 traffic
movement violations since they received their driving license. The analyses revealed that among
these drivers 68% were males, 78% were whites, and about 15% (99,388) were cited for DUI
offences. In addition to the typologies of the drinking-drivers’ traffic violations, this study
explores the possibility of detecting indirectly the drivers’ driving skills and attitudes toward
Studies describing the characteristics of drinking-drivers and DUI offenders (1,3,4,7,12) reveal
that unemployed, single, separated or divorced individuals, with a lower level of formal
education and lower socioeconomic status are over-represented for drinking-driving and DUI
offences. Most are males between the ages of 20-50. However, the results on lifestyle behavior
studies of DUI offenders are not consistent. Some have indicated a relationship between higher
rates of traffic movement violations and automobile crashes among crash involved and/or DUI
offenders (2), but others have found no relationship between drinking-driving and auto accidents
(6). Studies also identify a higher level of tobacco and drug use among the crash involved drivers
(2,8), where the events occur mostly in relation to sports, bar attendance, spending time with
friends and other social activities (10).
Studies on motivations to drink and drive indicate that these activities are affected by sub-cultural
norms, expectations, and values implemented by individuals (10). Some studies have also
revealed that work-related stress might have been the pre-conditional factor in drinking-driving,
which then leads to auto-crashes. Attitudinal studies, suggest that personal attitudes and decisions
made well in advance of drinking-driving are highly predictive of impaired driving and crashes
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Most of the above studies are derived from the implied assumption that the impaired drivers are
mostly involved in auto-accidents, and their impairments are directly related to their alcohol
addiction. In short, impaired drivers are alcoholics. As a result, researchers have developed
typologies of impaired drivers (11,12), where “the delinquency group”(11) or “the hard core
drinking-drivers”(1,3,9) are found to be more likely to be involved in “crashes” or “drinking-
driving crashes.” This “delinquency group” consists of mostly male drivers who are less likely to
be heavy drinkers with less driving exposure. They exhibit the highest drinking-driving problems,
even more than those of the heavy drinkers (12:61).
These findings suggest that the heavy drinkers are not always the problem drivers, who are
involved in drinking-driving crashes. We believe that attitudes of the drivers are the important
factor, which lead to their drinking-driving behavior and auto- crashes. We assume that the
drivers’ driving attitudes and skills are reflected through the patterns of their driving movement
violations. We will explore the patterns of driving violations of Tennessee DUI drivers, as a test
case, to better understand the alcohol-related motor vehicle crashes and fatalities in Tennessee.
The data were collected from the Tennessee Department of Highway Safety. Driving records of
the drivers who received a driving citation in 1999 for driving violation, as recorded, since they
received their driving license, were analyzed. Driving violations were categorized according to
(1) minor traffic violations, e.g., reckless driving, speeding, driving through red light, and related
violations; (2) driving under the influence (DUI) and related violations; (3) accidents- property
damage; (4) accidents- bodily injuries; and (5) fatal accidents.
During 1999 in Tennessee, out of a total 4,355,230 valid licensed drivers, 671,544 drivers were
cited for traffic movement violations. These cited drivers were recorded to have a total of
989,848 traffic movement violations since they received their driving license. Among these
671,544 cited drivers about 15% (99,388) were cited for drinking-driving (DUI) offences. Our
main interest was to find out the patterns of driving offences among these DUI offenders. We
analyzed the driving records of these DUI offenders (99,388), since they received their driving
The analyses revealed that among these 99,388 DUI offenders in Tennessee during 1999, 78.5%
were males and 77.4% were white adults. Comparatively, more non-white males than white
males (93% vs. 74%), but more white females than non-white females (26% vs. 7%) were
convicted for a DUI offence. However, regarding the total number of traffic violations in 1999,
both whites and non-whites received almost equal proportions of citations in DUI and related
offences (15% vs. 15%), accidents-bodily injuries (3% vs. 3%), and fatal accidents (0.2% vs.
0.1%), while in accidents-property damage whites (10%) received more citations than that of the
non-whites (7%). Interestingly, for minor traffic violations non-whites (75%) received more
citations in comparison to whites (72%).
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When considering the age of the DUI offenders, it was noted that the majority of the offenders
were from the 21-29 age-group (38%), and next to that were the 30-39 age-group (33%). The
DUI convictions were lower among the higher age-groups (40-49 =14%; 50-59 =5%; 60 & over
=3%). However, the DUI related traffic violations among the age-group 21 & below (9%) were
lower in comparison to that of the other age-groups.
Table 1 presents the different types of traffic violations committed by the 1999 DUI offenders,
since they received their driving license. The severity of drinking-driving in connection with
accidents is vividly revealed in this table (Table 1). The record shows that these 99,388 DUI
offenders were cited with a total of 394,283 traffic violations. Among these DUI offenders, 59%
were cited with one-DUI offence, 21% were charged with two-DUI offences, and the remaining
20% were multiple-DUI offenders. The record also shows that the one-time DUI offenders were
cited for a total of 152,630 traffic movement violations, indicating their “somewhat reluctant”
driving pattern. On the other hand, these one-time DUI offenders had only about 38.4% of the
total traffic violations in DUI offences, while the two-times or multiple-DUI offenders had the
most traffic violations in DUI offences (45% - 75%). Again, these one-time drinking-driving
offenders’ minor traffic violations were only 33% of their total traffic violations, the remaining
67% of the traffic violations were serious offences, including accidents- property damage, bodily
injuries, fatal accidents, and drinking-driving. In addition, these one-time DUI offenders
committed on an average 1.6 other traffic violations, while the repeat DUI offenders committed
on an average more than 5.2 other traffic violations. These records of traffic violations not only
reflect these drivers pattern of driving, they also suggest to take caution about the repeat DUI
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Table 1: DUI offenders’ number of DUI offences by all traffic violations
Number DUI Minor traffic Accidents- Accidents- Fatal Total
of DUI Offenders violations property bodily acci- viola-
damage injuries dents tions
One 58,639 50,369 27,473 15,386 763 152,630
(38.4%) (33.0%) (18.0%) (10.0%) (0.5%) (100%)
Two 21,103 25,016 14,015 6,404 79 87,720
(48.1%) (28.5%) (16.0%) ( 7.3%) (0.09%)(100%)
Three 9,929 19,095 6,052 3,815 5 58,754
(50.7%) (32.5%) (10.3%) ( 6.5%) (0.01%)(100%)
Four 4,261 8,817 3,198 1,987 - 31,046
(54.9%) (28.4%) (10.3%) ( 6.4%) (100%)
Five 2,485 9,532 1,264 849 - 24,070
(51.6%) (39.6%) ( 5.3%) ( 3.5%) (100%)
Six 1,391 7,154 1, 533 612 - 17,645
(47.2%) (40.5%) ( 8.7%) ( 3.5%) (100%)
Seven 696 2,835 - 78 - 7,785
(62.6%) (36.4%) - ( 1.0%) (100%)
Eight 396 573 217 248 - 4,206
(75.3%) (13.6%) ( 5.2%) ( 5.9%) (100%)
Nine & more 488 3,348 977 340 30 10,427
(55.0%)(32.1%)( 9.4%) (3.3%) (0.3%) (100%)
Total 99,388(182,219) 126,739 54,729 29,719 877 394,283
(Total DUI offences committed by DUI offenders are presented in italics)
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Table 2: DUI offenders and their total traffic violations
Number Total # Number of traffic violations committed by DUI offenders Total # Mean #
of DUI of DUI __________________________________________of viola- of viola-
offences offenders (1) (2) (3) (4) (5) (6) (7 & +) tions tions
One 58,639(59.0%)12,314 21,755 13,370 6,216 2,580 1,114 1,290 152,630 2.6
(100) (21.0) (37.1) (22.8) (10.6) ( 4.4) ( 1.9) ( 2.2)
Two 21,103(21.2%) 3,482 5,529 5,529 2,912 1,583 2,068 87,720 4.2
(100) (16.5) (26.2) (26.2) (13.8) ( 7.5) ( 9.8)
Three 9,929(10.0%) 1,708 1,857 1.857 933 3,574 58,754 5.9
(100) (17.2) (18.7) (18.7) ( 9.4) (36.0)
Four 4,261( 4.3%) 230 835 1,065 2,131 31,046 7.3
(100) ( 5.4) (19.6) (25.0) (50.0)
Five 2,485( 2.5%) 152 226 2,107 24,070 9.7
(100) ( 6.1) ( 9.1) (84.8)
Six 1,391( 1.4%) 1,391 17,645 12.7
Seven 696( 0.7%) 696 7,785 11.2
Eight & 884( 0.9%) 884 14,633 16.6
more (100) (100)
Total 99,388(100%) 12,314 25,237 20,607 13,832 8,336 4,921 14,141 394,283 4.0
(100) (12.4) (25.4) (20.7) (13.9) ( 8.4) ( 5.0) ( 14.2)
(Percentages are presented in parentheses and in italics.)
Table 2 presents the total number of traffic violations committed by the 1999 DUI offenders.
Among all these DUI offenders (99,388) about 59% were first-time DUI offenders, and their total
traffic violations numbered 152,630, which is an average of 2.6 traffic violations committed per
offender until 1999. Data revealed that the convicted second, third, fourth, fifth, and sixth-time
DUI offenders, on an average, were involved in 4, 6, 7, 10, and 13 traffic violations per person,
respectively. The convicted eighth-time or more DUI offenders were involved in 14,633 or an
average of 17 traffic violations per person, including the eight or more DUI offences.
The data further indicated that among the 58.639 first-time DUI offenders, 12,314 were involved
only in DUI offence, where the remaining 48,325 were involved in one DUI offence each plus
other traffic violations for a total of 140,316 traffic violations. Similarly, among the 21,103
second-time DUI offenders, 3,482 were convicted of two-DUI offences each, the remaining
17,621 offenders being involved in 80,756 traffic violations including their 2-times DUI offences.
In other words, the multiple DUI offenders (40,749) committed a total of 206,269 traffic
violations in addition to their DUI offences.
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In comparison, it is clear that where the convicted first-time DUI offenders, on an average, were
involved 2.6 times in traffic violations, the multiple DUI offenders were, on an average, involved
5.9 times in traffic violations. Therefore, it can be concluded that these multiple DUI offenders
were not only highly involved in traffic violations, but their driving behavior were also highly
dangerous for themselves as well as for their communities.
Results of this study of convicted drinking-drivers in Tennessee coincide with earlier findings
characterizing the alarming situation of the DUI offenders, especially the multiple-DUI offenders
(1-3,9,11,12). It should be noted that the DUI offences committed (182,219) by these convicted
drinking-drivers were 46% of their total traffic violations (394,283) until 1999. Although the
number of fatal accidents was not remarkably high, the accidents involved in property damage
and bodily injuries (85,324) were accounted for about 22% of the total traffic violations
committed by these DUI offenders. At the same time, minor traffic violations were not negligible,
which made up about 32% of all traffic violations.
Although we do not have the information about the pre-conditional factors in drinking-driving,
and attitudes of these DUI offenders, the pattern of their traffic violations draw a clear picture of
their negligent driving behavior, which is especially true for the multiple-DUI offenders. The
pattern of traffic violations further identifies the risk pattern of this DUI population. It is clear
that whether it is one-time DUI offender or multiple-DUI offender, none of them could drive
without committing any traffic violations. All of the DUI offenders were high risk drinking-
drivers, but the multiple-DUI offenders were the highest risk drinking-drivers. Hence, it can be
concluded that there are no experienced DUI drivers, who can drive safely after being impaired
by alcohol and/or other drug intake.
It is true that this study was confined to Tennessee DUI drivers of 1999, so the implications of the
results will be limited. However, it will not be unwise to conclude, based on these results, that the
DUI offenders should not be treated lightly; they should be considered with more caution. The
data indicated that on average the higher the number of DUI offences committed, the higher the
number of total traffic violations committed per person. On the other hand, it can be suggested
that these DUI offenders appear to be dangerous drivers, who did not show any responsibility to
traffic regulations and/or human safety. Some programs should be developed to control their
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Drunk Driving in Injury and Fatal Accidents in France in 2000
- Interaction between Alcohol and other Offences –
2, Avenue du général Malleret Joinville, 94114 ARCUEIL Cedex France
This article relates the French situation of alcohol in injury accidents and examines especially the
relations between alcohol and other offences.
The French statistics related to alcohol levels of drivers involved in injury accidents are
incomplete. Indeed, they affect only the number of breath tests and positive tests performed by
police after all accidents (injury and damage). The results of alcohol levels by checking after
positive tests and by direct blood sample (impossible breath test) are not taken into account.
Objectives and methods
An exact representation of the drunk driving (legal and illegal) in injury accidents must take in
consideration the alcohol levels of all accident-involved drivers.
Two data were available in 2000: first, the national exhaustive injury accident file holding two
variables according to "alcohol": method of detection (breath test, breathalyser, blood sample)
and result (alcohol level) which is elaborated by police rapidly after the accident; secondly, the
INRETS accident report file (1/50 of all reports) where the data and the practices are more
detailed and precise which has been performed later. We made an interrogation of these two files.
Two types of analysis may be introduced: analysis at individual driver and analysis at accident
taking into account all drivers involved in the accident. Analysis at whole accident level is
performed on the basis that an accident "without alcohol" corresponds to an accident in which no
driver was over the legal limit and an accident is considered "with alcohol" if at least one driver
was over the legal limit.
It contains 121,223 injury accidents (6,811 fatal).
Some results (according to the number of vehicle involved, the category and the sex of driver) are
available, More than one injury accident out of ten (11%) is an accident "with alcohol". This ratio
increases to more than one out of three (35%) for fatal accidents and 45% for the single vehicle
involved in fatal accidents.
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10,658 drivers (5.5%) had an illegal (over 0.49 g/l) alcohol rate (1,584 or 16.2% in fatal). For the
pedestrian, the ratio is smaller in injury accidents (5.2%) but bigger in fatal one's (28.6%).
Figure 1: Illegal alcohol levels in accidents
0.50-0.79 g/l 0.80-1.19 g/l >= 1.20 g/l
Injury accident Fatal accident
The proportion of drivers with "illegal alcohol" rate is more important for men (6.7%) than for
women (1.8%) but the ratio is reduced in fatal accidents (18.2% vs. 6.3%). Illegal alcohol
fluctuates according to the vehicle.
Table 1: Drivers with "illegal alcohol" rate in accidents according to the vehicle
Vehicles Fatal accidents Injury accidents
Bicycle 16.0% 2.0%
Moped 28.3% 4.3%
Motorcycle 20.6% 3.8%
Car 18.0% 6.3%
Delivery truck 10.0% 4.4%
Truck 1.5% 1.1%
Bus 0% 0.2%
Others 11.6% 6.8%
Car drivers have the greatest proportion of "illegal alcohol" rate in injury accidents. But in fatal
accident, the maximum concerns moped's drivers and the rate of motorcyclists is too greater than
the one of car drivers. Whatever severity of the accident, bus and truck drivers are very respectful
with the law on the alcohol.
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1,000 reports were examined. They involved 1,743 drivers and 138 pedestrians. Alcohol level is
known in 80% accidents for 86% drivers.
Figure 2: Alcohol investigation practices for injury accidents
Drivers involved in injury accidents
screening screening screening screening
impossible unknown NOT performed
or refused performed
8.2% 2.4% 8.0% 81.4%
blood test blood test positive negative
impossible result result
3.7% 4.5% 3.9% 77.5%
result negative positive
unknown result result
0.3% 2.2% 2.0%
The search of alcohol was carried out for only 86% drivers (81.4% by screening test with
confirmation if the result was positive, confirmation by breathalyser or blood test and 4.5% by
direct blood test).
We notes than when the screening had a positive result, first an illegal rate concerns only 93%
drivers and then, the alcohol level is higher if the confirmation is measured by blood test than by
breathalyser and those in spite of a longer delay for the measure.
Table 2: Measures of the alcohol level after positive result by screening test
Alcohol level (g/l) Blood test Breathalyser
<0.50 4% 9%
0.50-0.79 8% 19%
0.80-1.19 4% 14%
1.19-1.99 42% 37%
>= 2.00 42% 21%
Mean 1.81 g/l 0.71 mg/l
Delay 1h 45mn 1h 20mn
Variations are also observed between accidents "with alcohol" or "without alcohol" according to
the localisation, the day and the time.
The proportion of drivers with "illegal alcohol" rate fluctuates with the age. It is maximum for
18-39 years old and minimum for younger.
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Figure 3: Drivers with illegal alcohol rate according to the age
0 to 17 y. 18 to 24 y. 25 to 39 y. 40 to 54 y. over 55 y.
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Figure 4: Injury accident "with alcohol" according to the network
Motorway Main road Secondary road Other road
Injury accidents "with alcohol" were more frequent on rural than in urban area and this especially
on main roads (that is new) but also on secondary roads. It is on the motorway that the legislation
is the most respected.
Table 3: Injury accident "with alcohol" according to the day
Day of the week Injury accidents with alcohol
Bank holiday 14%
The injury accidents "with alcohol" are more frequent during the night especially between
midnight and 4h.
The proportion of injury accidents "with alcohol" is bigger during the weekend especially on
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Figure 5: Injury accident "with alcohol" according to the time
0-4h 4-8h 8-12h 12-16h 16-20h 20-24h
In the reports, police indicates the traffic offences committed by the drivers involved in injury
accidents (speed, priority, manoeuvre, overtaking, stamped papers, others…).
One or more "other" offence is founded among 47% drivers with "illegal alcohol" rate (vs. only
31% drivers with legal alcohol rate. Speed offence is correlated with alcohol infraction in 31%
cases. Lake of priority, helmet or seat belt not wearing and no insurance are less frequent.
The bigger proportion of drivers with legal alcohol rate who do not respect the priority must be
explained: drivers with "illegal alcohol" rate are more involved in single accident out of
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Figure 6: Drivers with other offences in injury accidents
Speed offence Lake of priority Helmet or sealt belt No insurance
Illegal alcohol Legal alcohol
In 1995, the alcohol legal limit was reduced to 0.5 g/l. It seems that this legislation has no influence on the
number of drivers involved in injury accidents with "illegal alcohol" rate. Moreover, their mean alcohol
rate did not decrease (2.15 g/l).
In the French legislation, the search of alcohol is compulsory for all drivers involved in injury accident. In
fact, this search is carried out for only 88%. So, the searchers are very anxious to know the results about
the search of drug in fatal accidents, which is compulsory since October 2001.
The link between illegal alcohol and speed offence in injury accidents is not surprising. However, the
measures carried out on the road showed that the drivers with alcohol below the legal limit (between 0.01
and 0.24 g/l) exceed more the speed limits than those with "illegal alcohol" rate.
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Criminal Profiles of Drinking Drivers in Ontario
S. Stewart1, P. Boase2, A. Reid3
Ministry of the Attorney General, Toronto, Ontario, Canada. Ontario Court of Justice, Room
155, Old City Hall, 60 Queen Street West, Toronto, Ontario, Canada M5H 2M4,
Transport Canada, Ottawa, Ontario, Canada,
Ontario Provincial Police, Orillia, Ontario, Canada
Accident, Alcohol Intoxication, Criminals, Driving Under the Influence, Conviction
The paper reviews the criminal and driving history of a sub-sample of 99 drivers drawn randomly
from drivers charged with criminal alcohol related driving offences in Toronto, Canada after the
implementation of the immediate ninety- day administrative driver licence suspension (A.D.L.S.)
in November, 1996. These data, referred to herein as the 1998 sample, and results are compared
to the 1996 sample, which was reported on previously (1), to determine changes, if any, in the
characteristics, as reflected in the criminal and driving records of those charged with drinking and
driving related criminal offences in the face of the deterrent of the immediate ninety- day licence
suspension. The paper reviews whether the immediate ninety day A.D.L.S. has deterred drivers
with previous criminal or significant driver histories. Findings from the 1998 sub-sample that are
distinct from the 1996 sub-sample findings are reported on. Some of the measurements in the
1998 sample are either the same as, or have little differential with the 1996 sub-sample and are
not reported on.
In Canada, driving while impaired and having a blood alcohol content (BAC) in excess of 80mgs,
as well as refusing to provide a breath sample upon request of a police officer, are criminal
offences under the Criminal Code of Canada. The criminal sanctions (minimum fine of $300 and
a minimum three-month driving prohibition for a first conviction, with increasing penalties
including minimum jail terms for subsequent convictions) were unchanged in 1998; however, the
Province of Ontario implemented the immediate ninety- day A.D.L.S. Subsequent to the time of
this sub-sample, the Criminal Code was amended with regard to penalties and sentencing. For
example, as a result of the 1999 amendments, the minimum fine is $600 with a minimum 12
month driving prohibition and a BAC of over 160mgs became an aggravating factor in
sentencing. Unlike those in the 1996 sample, the 1998 drivers faced an immediate and relatively
significant consequence, upon detection, of losing their driver’s licence for ninety days
irrespective of the outcome of any criminal charges.
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The relevant document flow for a drinking and driving related criminal conviction in Ontario
changed. The process remains the same, starting with the police charging the driver and serving
the applicable notice related to the A.D.L.S. Charging documents are entered into the integrated
court offences network and proceed through the judicial system. If a conviction is registered, for
either a criminal or provincial driving related offence, the law requires the court office to notify
the Registrar of Motor Vehicles. The notification process changed from a manual one to an
automated one in 1998, with the exception of criminal conviction information from the Superior
Court of Justice which is still transmitted manually. Very few drinking and driving charges are
heard in this court; however those that are generally involve death or serious bodily harm. In
order to ensure that the charges and dispositions appear on the driver’s criminal record, the
charging police service that receives the information from the court office must notify the
Canadian Police Information Centre. Merging the records should provide a profile on the
complete driving and criminal history of an individual.
Materials and Method
Information on 957 drivers who had been stopped and charged for a drinking and driving related
criminal offence by the Toronto Police Service in 1997 and 1998 was obtained and compared to
the 880 drivers in the 1996 sample. A sub-sample of 99 drivers was randomly selected from the
1998 sample and the criminal and driving records were manually retrieved and assessed, with the
results compared to the 1996 sub-sample.
Some of the information reported on for the 1996 sub-sample, such as the reason for the arrest
and the blood alcohol concentrations is not available for the 1998 sample. Where possible, a
similar analysis of the 1998 data was undertaken. The results were compared with the 1996
measurements and differences reported on. Of the 99 drivers in the sub-sample, 6% were female
and 94% were male. They ranged in age from 18 to 79 years of age (22- 73 in 1996), with the
median age category being thirty to forty years old.
Of the 99 drivers sampled, provincial driver records for all but one were located, a substantial
improvement over the 1996 result of 15 unmatched records. A similar improvement was seen in
the number of drivers without either a criminal or a provincial driver record match, which went
from 7 in 1996 to only 1 in 1998. Conversely, the failure rate for the criminal record match
increased from 2 to 9 drivers. Ten drivers (6 in 1996) had no record of the 1998 arrest on neither
the criminal nor provincial driver record. Eliminating the drivers with record retrieval issues
produces a sub-sample of 88 drivers with both criminal and provincial driver records unless
Of the 98 drivers with provincial driving records, 30 % (31% in 1996) were not licenced or
suspended at the time of the driver record search (February, 2000) and an additional 3 drivers
were never licenced. One driver was suspended for life as a result of a 1999 conviction (Ontario
law was amended in 1998 to provide for escalating suspensions upon conviction including
suspension for life on a fourth conviction. The record search period for calculating the
convictions cannot go back further than September 30, 1993).
Twenty-three percent (20% in 1996) of the suspended drivers were suspended as a result of an
unpaid fine and 36% (50% in 1996) were under suspension because they were convicted of a
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drinking and driving criminal offence. Three drivers were suspended for both a drinking and
driving conviction and driving while disqualified or suspended and one for medical reasons. Five
drivers (2 in 1996) remained unlicenced or suspended at the time of the search despite the fact
that the reinstatement date preceded the search date. Three drivers who should have been
unlicenced appeared as licenced.
Of the 89 drivers in 1998 with matching criminal records, 8% refused to provide a sample on
request (11% in 1996). In addition to the drinking and driving related criminal charges, one driver
was charged at the same time with a drug-related offence, down significantly from the nine
drivers in 1996, although 11 of the drivers had drug related matters on their criminal record (two
with extensive drug related records). A number of other criminal or provincial charges were also
laid arising out of the same incident including failing to stop for police (two drivers), fail to
remain, drive while disqualified and care and control (one driver). As with the drug offences, this
is markedly different from the earlier sub-sample in that fewer drivers faced additional criminal
charges at the same time and the charges that they did face were not as assorted. The 1996 sample
included criminal harassment, resisting arrest, obstruction and assault.
In reviewing the criminal profile of the sampled drivers (89), just over 30% (45% in 1996) of the
drivers were found to have other, non-drinking and driving related criminal charges or
convictions by charge date, including parole violation, unlawfully at large, sexual assault, fail to
appear, obstruction, assault and narcotic offences. One driver had been convicted on 18 prior
occasions of one or more offences, but only one was a driving related criminal offence. Four of
the drivers also had convictions for non-drinking and driving criminal offences post-1998. In
reviewing the provincial driver records for the 98 drivers, 86% (58% in 1996) had non-alcohol
related, provincial offence convictions such as careless driving, speeding, failing to report
collision and drive with no insurance.
The following tables show the incidence of selected convictions, as a percentage, on the driver
records for the entire 1996 (721) and 1998 (748) driver samples respectively. The results seem
similar for the two samples, except there appears to be more drivers with a previous alcohol
infraction on record. This likely reflects improved automated record exchange related to the new
processes associated with ADLS.
Table 1: Convictions for Selected Charges
Offence/Number 0 1 Multiple 0 1 Multiple
Careless 84% 14% 2% 82% 14% 4%
Driving While Suspended 86% 9% 4% 85% 9% 6%
Impaired/Blow over .08 26% 42% 32% 20% 49% 31%
Fail to Report Collision 90% 9% 2% 87% 11% 2%
Speeding 30% 19% 52% 28% 17% 55%
Other HTA Convictions 22% 19% 59% 24% 17% 59%
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With regard to drinking and driving charges and dispositions, 34% (36% in 1996) of the drivers
with criminal records available had drinking and driving related charges or convictions prior to
the 1998 arrest. Thirty percent (10% of the total) had multiple priors, ranging from 5 drivers (12
in 1996) with 2 priors to 3 drivers with 3 priors to 1 driver (3 in 1996) with 5 priors. This
represents a significant decline over the 1996 finding of 52 percent or 18% of the total drivers.
The number of multiple priors per driver has also declined sharply over 1996; however, in
contrast to the one driver in 1996, five drivers in 1998 had drinking and driving related
convictions subsequent to the 1998 arrest. Three drivers were convicted of other criminal
offences at the same court appearance as the drinking and driving charge, one each of break and
enter & possession; theft and escape lawful custody.
Of the number of drivers with criminal records, twelve (13%) had no indication or record of the
1998 sub-sample charges. This represents a decrease over the 1996 result of 23%. Three of the
seven drivers charged with impaired and refuse to provide a breath sample were only convicted
of impaired. One driver was convicted of impaired driving but had the charges of dangerous
driving and fail to stop withdrawn. Eight drivers (3 in 1996) had the charges dismissed, stayed or
withdrawn. The balance of the drivers (77%) were subsequently convicted of the drinking and
driving related criminal charges – either drive over, impaired or impaired and refuse-an increase
from 1996 of over twenty percent.
Eight-five of the drivers arrested in 1998 had a corresponding A.D.L.S. on the driver record. One
of the drivers had a subsequent A.D.L.S. imposed while under a drinking and driving conviction
related suspension. Four drivers who should have had an A.D.L.S. on the driver record did not,
with four more drivers having no record of an A.D.L.S. or of the arrest. One driver charged with
impaired and refused did not have the suspension on record even though the refusal qualifies the
driver for the suspension. The balance of the drivers appear to have been charged with only
impaired driving, which does not attract the suspension under provincial law. A review of the
driver records indicates that 14 drivers (16% of those with a suspension) have had two
administrative driver licence suspensions imposed between the start date of November 1996 and
the record search date (February, 2000).
A number of drivers had an A.D.L.S. on the provincial driver record but had no indication on the
corresponding criminal record of the 1998 related charges. Nine drivers had an administrative
driver licence suspension on the driver record but no subsequent record, criminal or provincial, of
any related charges or outcomes. Seven of these drivers (one driver in 1996) had a conviction for
careless driving on the driver record that appeared to correspond to the A.D.L.S.
The records were reviewed to determine the number of months (to the nearest month) between
the 1998 arrest and the disposition (conviction or dismissal) of the resulting charges. The range
was from one day to 25 months, somewhat lower than in 1996. In 1998, 40% of the 76 drivers in
the group had dispositions in six months or less; 47% in 12 months or less, with the balance
taking from 13 - 25 months. Two of the drivers appear to have plead guilty to secure release
from custody after arrest as the dispositions were one and five days from the arrest date. Accused
persons released from custody by the police would not appear in court for at least seven days. A
person may be held in custody for a judicial interim release hearing before a judicial officer.
Individuals who have little chance of release for whatever reason will sometimes plead guilty to
the offence charged rather than remain in custody until trial, which could take three months or
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more. Where a conviction resulted, the average time from charge to disposition was just over
seven (9 in 1996) months (The two drivers convicted in one and five days were eliminated); for
those not convicted it was ten months (11.6 months in 1996). The criminal and driver records of
convicted drivers were reviewed to determine if the drivers received the appropriate criminal
sentence as well as the appropriate administrative consequence. Of the convicted drivers with a
criminal record, fifty had a prohibition order recorded as part of the sentence while twenty-one
(just under 30%) drivers had no prohibition recorded. This is an increase over the 23% of
convicted drivers with no corresponding prohibition in the 1996 sub-sample. Administratively, all
drivers with the convictions on the driver record appear to have received the appropriate
Fore the full sample of drivers in the 1998 sample , by individual driver, 163 had no collision
involvement on record, 224 had one collision, 152 had two collisions, 95 had 3 collisions and the
remaining 114 drivers had more than 3 collisions on the record including 3 drivers with 12 or
more on the record. This is almost identical to the 1996 data except that there was only one
driver with 12 or more collisions during the time period.
The results indicate that there has been a substantial improvement in record keeping with the
introduction of automated transmittal between the court offices and the Registrar of Motor
Vehicles. This improvement ensures that more convicted drinking drivers will receive the full
scope of provincial, administrative consequences of any driving related criminal conviction as
well as the corresponding treatment. Ontario has a remedial measures program, consisting of
education and treatment units and will soon implement a post-conviction program of alcohol
ignition interlock. Of concern, however, is the increased failure rate in matching drivers with
criminal records. This could be reflective of a number of issues, such as fingerprinting or record
follow up with the charging police agency or the Canadian Police Information Centre. It would
appear that the driver record has become the best record of previous alcohol related driving
convictions. This is not likely a serious issue, as long as the police check both records for
previous conviction information. However, if only the criminal record is searched for non-
driving related matters, such as at a bail hearing, then a drinking driver may benefit from the lack
of a complete record. This situation is also likely to support the perception that drinking and
driving criminal offences are not really criminal, but driving, in nature.
Significantly fewer drivers were detected in the 1998 sub-sample with drugs and fewer drivers in
the 1998 sub-sample refused to provide a sample on demand. Those in the later group, as in 1996,
appeared to have a reasonable prospect of not being convicted of the refusal charge. The number
of drivers who had the charges stayed, dismissed or withdrawn has increased (8 versus 3).
Overall, the percentage of drivers convicted as a result of the arrest has increased by over 20%
over 1996. The time taken to convict those drivers decreased by almost two months over 1996.
Even for those who had the charges dismissed, the time to disposition decreased by a month and
a half. The decrease in time from arrest to disposition is difficult to attribute directly to A.D.L.S.
Fourteen drivers had the charges disposed of in two months or less, possibly because drivers
under an A.D.L.S are permitted to have the suspension run concurrent (at the same time) as any
conviction related suspension. The record information does not indicate whether a plea of guilty
was entered or a trial held; however, it would be reasonable to infer that convictions entered
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within three months of arrest are pleas of guilty as the criminal justice system in Ontario does not
accommodate trials for those not in custody within three months.
The introduction of A.D.L.S. and the corresponding entry onto the provincial driver record allows
for charged drivers to be monitored irrespective of the outcome of the criminal charges. In the
context of the Canadian criminal justice system, this allows for a fuller assessment of the number
of drinking drivers who meet or exceed the criminal standard. Alcohol involvement on the part of
drivers subsequently convicted of careless driving, with the same offence date, can be monitored
for research purposes. However, pleading to careless is an advantage to the driver as the driver
avoids the possibility of being identified as a repeat offender on a subsequent offence and also
avoids the more immediate consequence of the onerous provincial sanctions that flow from
conviction for a criminal driving offence.
While the percentage of drivers with prior drinking and driving charges or convictions did not
change appreciably, the percentage with multiple priors decreased over 20%. The number of
drivers with subsequent arrests increased in the 1998 sub-sample. Five drivers, as opposed to the
one in 1996, had post-1998 charges or convictions. Sixteen percent (14) of the drivers with an
A.D.L.S. imposed have incurred two A.D.L.S.’s subsequent to the implementation in November
1996 and the record search (February, 2000). Clearly, A.D.L.S. allows for the immediate, point in
time identification of the drinking and driving behavior as opposed to the pre-A.D.L.S.
environment. A.D.L.S. is only available when the driver is charged with drive over or refuse, not
impaired driving or other criminal driving related offences.
In conclusion, the comparison of the two sub-samples primarily showed an improvement. Fewer
drivers faced other charges at the same time, fewer had a previous criminal record and fewer had
prior drinking and driving charges. The fact that the number of drivers unlicenecd or suspended
at the time of search remained the same may indicate that something happens to drivers charged
with drinking and driving criminal offences. Some of these drivers should have been licenced,
leaving open the possibility that they become disinterested in remaining in the driver licencing
regime or that they subsequently leave the jurisdiction or willfully choose to be unlicenced.
The authors would like to acknowledge the assistance of Constable Jeff Patrick of the Toronto
Police Service, the Ontario Provincial Police and the Royal Canadian Mounted Police for the
considerable work in providing the samples and all necessary subsequent record retrieval.
1. Stewart, S., Boase, P. and Lamble, R.W. (2000). Criminal Profiles of Drinking Drivers in
Ontario. International Conference on Alcohol, Drugs and Traffic, Stockholm, Sweden.
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Mark B. Johnson, James E. Lange, & Tara Kelley Baker
Pacific Institute for Research and Evaluation
11710 Beltsville Drive, Suite 300, Calverton, Maryland, 20705 USA
Heavy drinking, motivation, sex differences, designated driver, groups, normative influence
Young people do the majority of their drinking within small groups. This research examines the
intersection between individuals’ intentions to drink and the perceived group norm for drinking on actual
drinking behavior. The paper suggests that individuals’ unique drinking motivations may be expressions
of different group roles. The data show that the role of “driver” within groups may protect individuals
from normative group pressure. The importance of understanding the processes of group construction is
It has been widely acknowledged that young people do the majority of their drinking within
groups of peers. Given the social nature of drinking, researchers have examined the influence of
social factors – such as group norms and group characteristics – on the drinking behavior of
individuals (e.g., Collins, Parks, & Marlatt ; Aitken ). Although evidence suggests that
normative group drinking pressure does exist, the unique motivations and attitudes of individuals
within groups clearly play a role in drinking behavior as well. For example, individuals’
intentions to get drunk, their belief that alcohol facilitates social interaction (3), and their belief
that drinking was an important part of college (4) all predict drinking behavior.
However, the characteristics of the group and the individual motivations of group members are
not completely independent. Part of the unique individual variations within groups may be
expressions of different group roles. Limited attention has been paid to the social construction of
groups, and researchers have failed to examine closely how individual group members – with
their unique motivations and beliefs – select other members to fill specific group roles. Social
roles within naturally formed groups of friends may be functional, and although not necessarily
stable or easy to define, they may have specific purposes for facilitating the group’s goals.
Within drinking groups, perhaps the role of driver is easiest to study because the role is salient,
consciously known, functional, and usually acknowledged by all group members. Further, when
it comes to drinking, the role of driver is predictive of very different drinking behaviors. Among
groups of young people, the driver is more likely to refrain from drinking or to drink considerably
less than their peers (e.g., Foss and Beirness ; Lange & Voas ). It is interesting that drivers
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enter into the same drinking environment as their peers – where normative pressures to drink
should be at their peek – and yet are able to maintain sobriety.
The lower BACs typically found among drivers may occur (a) because situational factors that
influence drinking (i.e., alcohol availability, loose regulation, normative environment that fosters
‘party’ attitude) affect drivers differently than they do passengers, or (b) because individuals
whose motivations and beliefs make them likely to drink less are selected by the group to be the
driver. This research examines both possibilities. The paper first reports on role of the driver
within groups and how this role relates to drinking behavior and drinking intentions; the paper
then address how the role of driver may attenuate normative pressures to drink. Finally, the
paper examines factors that may predispose individuals to be assigned to role of driver.
An opportunity to examine situational influences, individual intentions, and the interaction
between the two became available from ongoing survey of drinkers at the U.S.-Mexico border.
Beginning in the fall of 1997, researchers at the crossing between San Diego County and Tijuana,
Mexico, have randomly sampled whole groups of U.S. residents (aged 18–30) en route to the bars
and nightclubs south of the border. These participants were asked to provide basic demographic
information about themselves, whether or not they drove to the border, information on their
drinking history, their drinking intentions for the evening, and their perceptions of the drinking
intentions of other group members. The researchers gave a hospital-style identification bracelet
and took a breathalyzer test from each participant. These groups of participants were re-sampled
upon their return across the border into San Diego, asked to provide information on their drinking
behavior while in Tijuana, and given a second breath test.
Between October 1997 and March 2000, entry and exit data were collected from 687 groups that
contained at least one driver (some groups reach the border via taxi, trolley, or some other
transportation). These groups comprised 2,369 participants. Because participants were clustered
in naturally occurring groups, statistical assumptions of independent observations were tenuous.
Therefore, most analyses were conducted using PROC MIXED in SAS (Version 8.0) to account
for the clustered nature of the data. For purposes of analyses, the sample was split into two
halves: one exploratory, the other confirmatory. Thus, attempts were made to replicate analyses
in both samples. Although the results of analyses conducted on the confirmatory sample are not
described below, only those findings from the exploratory sample that were replicated are
The first analysis was designed to test for differences in returning BACs between participants
crossing the border as drivers and as passengers. As predicted, analysis of the first sample
revealed that for both men and women, BACs of drivers were significantly lower than they were
for passengers [F (1, 517) = 19.3, p< .01; F (1, 439) = 8.82, p<.01, for men and women
respectively]. The average BAC of male drivers was .023, relative to .061 for male passengers.
Similarly, the average BAC of female drivers was .020, relative to .050 for passengers.
Next, we examined the extent to which participants’ drinking intentions (i.e., the extent to which
they planned to drink before actually going to the bars) predicted their BACs. Participants
indicated their intentions to “Not Drink,” “Get a Slight Buzz,” or “Get Drunk.” For both men and
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women, analysis revealed a statistically significant relationship between drinking intentions
before entering Tijuana and returning BACs [F (2, 517) = 53.2, p<.01; F (2, 439) = 35.7, p<.01].
Participants who intended to drink more heavily did indeed return with significantly higher
BACs. Mean BACs were .016, .038, and .068; female BACs were .010, .040, and .067 for the
three levels of drinking intentions, respectively.
However, an analysis that was conducted to examine the interaction between drinking intentions
and driving status failed to produce a statistically significant effect. Thus, both drivers and
passengers tended to drink to the same extent as indicated by their drinking plans. A subsequent
analysis, which treated drinking plans as a three-point scale variable, revealed that drivers
planned to drink less than passengers, F (1, 1056) = 182.9, p< .01. The mean drinking-intentions
score was 1.8 for drivers and 2.4 for passengers. There were no differences in drinking intentions
between men and women.
Because drinking intentions was shown to be predictive of returning BACs regardless of driver
status, a series of analyses were conducted to examine one normative factor that might predict
drinking intentions. It is possible that bar-goers base their drinking intentions in part on the
Perceived Drinking Climate (PDC; i.e., on their expectations regarding how much alcohol others
in their immediate social group plan to drink). Individuals who believe that their drinking
companions will consume heavily may consume heavily as well. However, if the designated
driver concept is used appropriately, we might predict that drivers’ drinking intentions will
remain low regardless of the PDC.
Analysis of group size revealed no statistically reliable results across the two samples. However,
regressing drinking intentions onto driver status, PDC, and their interaction did reveal statistically
significant effects, and these differed between men and women. For men, the main effect of PDC,
F (2, 513) = 35.6, p< .01, was statistically significant;1 those who perceived that others in their
group would drink heavily planned to drink more heavily as well. The interaction between PDC
and driver status was not significant (p<.09). However, a planned contrast was conducted to
compare the drinking intentions of drivers who indicated that others in their group did not plan to
drink with drivers who indicated that others in their group planned to get very drunk. The results
revealed that for male drivers, PDC did indeed predict drinking intentions, F (1, 513) = 17.5,
p<.01. This pattern is depicted in Figure 1a.
For women, however, the results differed. As with men, PDC did predict drinking intentions, F
(2, 434) = 18.2, p< .01.1 However, in contrast to the men, the interaction was statistically
significant as well, F (2, 434) = 9.2, p< .01. The nature of this interaction was revealed through
the planned contrast. Although PDC was related to the drinking intentions of female passengers,
the relationship between PDC and drinking intentions for female drivers was not statistically
significant. This pattern is illustrated in Figure 1b.
The main effect for driver status was statistically significant as well, but this effect was produced and discussed
in earlier analyses as well.
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Figure 1: Predicting Drinking Plans from Drinking Climate and Driver Status
1a. Men 1b. Women
Driver Passenger Driver Passenger 2.6
2.0 2.0 2.0
2.0 2.0 1.8 1.8 1.8
1.5 1.4 1.5
1 2 3 1 2 3
Not drink Slight buzz Get drunk Not drink Slight buzz Get drunk
Drinking Climate Drinking Climate
To examine factors that might predict who will be the driver after the group has been drinking,
analyses examined two driver characteristics. Analyses were conducted to determine whether
recent history of heavy drinking and implicit alcohol attitudes predicted driver status within
groups. First, we hypothesized that individuals who did not have a recent history of heavy
drinking (defined by consuming five or more drinks on a single occasion over the past 4 weeks)
were more likely to be the driver. However, chi-square analysis (adjusting the expected
frequencies according to group size) failed to find a statistically significant relationship between
driver status and recent heavy drinking.
Second, we examined drivers’ implicit alcohol attitudes. Implicit alcohol attitudes were measured
using the Alcohol Association Scale (AAS; 7), a word association instrument designed to tap into
implicit attitudes and mental representations of alcohol. Scores on this measure ranged from .00
to 1.00. Participants with higher scores were thought to associate alcohol with aggressive
concepts, whereas participants with lower scores were thought to associate alcohol with amiable
experiences. We predicted that drivers would tend to have higher AAS scores (i.e., a more
negative association with alcohol) than would passengers. For men, analysis revealed that drivers
did indeed have higher (more negative) AAS scores than did passengers, F (1, 522) = 5.23, p<.05,
driver M = .45, passenger M = .39. However, the pattern was not statistically reliable across
samples from women.
Individuals’ drinking intentions for that evening was a strong predictor of drinking behavior as
measured by BACs. These intentions not only may reflect unique and personal motivations, but
also could reflect role norms (e.g., being the designated driver) as well as group norms. One
interpretation of the results presented herein is that group norms and role norms can be
countervailing. The role of the driver amongst drinking groups dictates maintaining sobriety to
ensure a safe drive home. However, the perception that the drinking climate for that evening will
be heavy, moderate, light, and so on may implicitly establish a group norm; normative pressures
may influence drinking behavior. The relationship between PDC and passenger drinking
intentions is consistent with this contention.
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How does PDC relate to drinking intentions of drivers? The results suggest that women are better
able to resist normative pressures to drink; the drinking intentions of female drivers remained low
regardless of the PDC. Perhaps for women, the role and responsibility of being a driver was
clearer and better defined, and this attenuated other normative influence. Conversely, male
drivers planned to engage in heavier drinking to the extent that they perceived that other group
members would drink heavily. It appears that male driver were less able to resist normative
pressures to drink.
The analysis of individual’s implicit alcohol attitudes suggests that, at least for men, individuals
who have a negative association with alcohol are more likely to be drivers within groups. This
pattern was not found among women drivers. The results also failed to reveal a relationship
between driver status and recent heavy drinking history.
These results are consistent with the interpretation that role norms and larger group norms can
compete for influence of drinking intentions. Perhaps, by more clearly defining the role and
responsibilities of being a driver, drivers may be better able to resist normative pressures. Our
understanding of drinking behavior may benefit from further research that addresses the way
drinking groups are constructed and the nature of roles within groups.
1. Collins RL, Parks GA, Marlatt GA. Social determinants of alcohol consumption: The effects
of social interaction and model status on the self-administration of alcohol. J Consult Clin
Psychol. 1985; 53:189–200.
2. Aitken PP. An observational study of young adults’ drinking groups—II. Drink purchasing
procedures, group pressures and alcohol consumption by companions as predictors of alcohol
consumption. Alcohol Alcohol. 1985; 20:445–457.
3. Beck KH, Treiman KA. The relationship of social context of drinking, perceived social
norms, and parental influence to various drinking patterns of adolescents. Addictive
Behaviors. 1996; 21:633–644.
4. Wechsler H, Davenport A, Dowdall G, Moeykens B, Castillo S. Health and behavioral
consequences of binge drinking in college: A national survey of students at 140 campuses.
JAMA. 1994; 272:1672–1677.
5. Foss RD, Beirness DJ. Drinking passengers and their drivers: Roadside survey results. 40th
Annual Proceedings of the Association for the Advancement of Automotive Medicine.
Chicago: Association for the Advancement of Automotive Medicine; 1996:263–273.
6. Lange JE, Voas RB. Youth escaping limits on drinking: Binging in Mexico. Addiction. 2000;
7. Lange JE. Alcohol's effect on aggression identification: A Two-channel Theory. Psychology
of Addictive Behaviors. 2002; 16:47-55.
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Social-cultural characteristics of DWI drivers compared
with drug-alcohol-free drivers
S. Zancaner, R. Giorgetti, G. Frison, Boscolo Mia, S.D. Ferrara
Dipartimento di Medicina Ambientale e Sanità Pubblica, sede di Medicina Legale, Università
degli Studi di Padova, via Falloppio n. 50, 35121 PADOVA, Italy.
The study analyzes a population of drivers stopped during some highway police checks. The
population has been divided into subgroups according to the presence of alcohol and drugs in the
biological fluids. Social and cultural characteristics have been considered for each group.
Some toxicological forensic tests have been carried out from 1997 to 2000 to prevent driving
under the influence of alcohol or psychoactive substances, in collaboration with the Highway
Police Department of Veneto and the Italian Red Cross. The tests have been done especially
during weekend nights; the activity has been partially done within the project ROSITA (ROad
SIde Testing Assessment), which is a multicentric European study.
Car drivers have been stopped by the police, submitted to medical examinations, and samples of
blood and/or urine have been taken. Toxicological tests have been done on biological samples to
detect alcohol and psychoactive substances.
The population studied has been divided into subgroups on the basis of the results of the
toxicological analyses; the present work makes a comparison of social and cultural characteristics
of car drivers belonging to each group.
Materials and methods
The population studied is made up of 1,977 subjects driving motor vehicles (1,894 males, 83
females); at least one sample of the biological fluids has been examined for each subject.
Each subject has been informed about the methodology of the tests and the consequences of a
possible refusal (punishment as if driving in a state of intoxication); then they have been
submitted to a medical examination including collecting of general clinical details and samples of
biological fluids; 1,748 blood samples and 1,702 urine samples have been taken.
The following groups of subjects have been considered:
BAC from 10 to 80 mg/100 ml;
BAC over 80 mg/100 ml, sanctioned under Italian law for driving under the influence of
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subjects sanctioned under Italian law for driving under the influence of psychoactive
subjects drug addicts but who were not found in a state of acute poisoning at the moment
of the tests (n.78);
drivers intoxicated by cocaine (n.98);
drivers intoxicated by cannabis (n.165);
drivers with BAC < 10 mg/100 ml, selected as control sample (n.500);
total population examined (n.1,977).
The following parameters have been considered in each group: gender, age, driving licence
category, driving experience, marital status, job, place of origin, education.
Social and cultural characteristics of the car drivers are reported in the following tables, in
relation to the presence of alcohol or psychoactive substances in the biological fluids.
Table 1: lcohol and drugs in relation to the gender of drivers
Gender Total NAND BAC BAC DUID Drug DUID DUID
Pop. 10-80 >80 users cocaine cannabis
Male 95,8% 97,8% 94.48% 97,3% 97,1% 98,7% 97,96% 96,36%
Female 4,2% 2,2% 5.52% 2,7% 2,9% 1,3% 2,04% 3,64%
NAND: no alcohol, no drugs
BAC: blood alcohol concentration
DUID: driving under the influence of drugs
Drug users: drugs in urine; no drugs in blood, no clinical signs of intoxication.
Table 2: Alcohol and drugs in relation to the age of drivers
Age Total NAND BAC BAC DUID Drug DUID DUID
(years) Pop. 10-80 >80 users cocaine cannabis
<20 12,44% 14,8% 12,1% 8,2% 13,28% 7,69% 9,18% 16,97%
21-25 39,65% 38,8% 39,7% 35,32% 46,47% 47,44% 47,96% 49,70%
26-30 25,24% 22,2% 29,51% 28,83% 23,24% 26,92% 23,47% 21,21%
31-35 13,35% 16,2% 10,62% 14,77% 13,28% 11,54% 15,31% 10,30%
>35 9% 7,8% 7,86% 12,43% 3,32% 5,13% 4,08% 1,82%
Unknown 0,30% 0,2% 0,21% 0,54% 0,41% 1,28% 0% 0%
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Table 3: Alcohol and drugs in relation to the driving experience
Driver Total NAND BAC BAC DUID Drug DUID DUID
Experience Pop. 10-80 >80 users Cocaine Cannabis
<1 5,97% 7,8% 5,73% 3,42% 7,47% 5,13% 3,06% 9,70%
1-5 37,13% 36,4% 38,22% 29,19% 44,40% 41,02% 38,78% 52,12%
5-10 29,23% 26,6% 32,27% 34,06% 25,31% 35,90% 27,55% 23,03%
>10 25,95% 28,4% 21,87% 30,81% 20,75% 16,67% 27,55% 13,94%
Unknown 1,72% 0,8% 1,91% 2,52% 2,07% 1,28% 3,06% 1,21%
Table 4: Alcohol and drugs in relation to the driving licence
Driver Total NAND BAC BAC DUID Drug DUID DUID
Licence Pop. 10-80 >80 users cocaine cannabis
motorcycle 0,35% 0,2% 0,42% 0.36% 0,41% 1,28% 0% 0,61%
car 83,8% 87,2% 83,23% 79,82% 87,97% 85,9% 88,77% 88,48%
little truck 7,19% 3,8% 7,22% 10,45% 7,06% 7,69% 7,15% 6,06%
Bus 3,04% 4% 3,61% 2,70% 2,08% 1,28% 2,04% 1,82%
Heavy truck 3,50% 3,2% 3,19% 4,69% 1,66% 1,28% 1,02% 2,42%
None 0,76% 0,2% 0,85% 0,72% 0,41% 0% 1,02% 0%
Other 0,45% 1% 0,42% 0,18% 0% 0% 0% 0%
Unknown 0,91% 0,4% 1,06% 1,08% 0,41% 2,57% 0% 0,61%
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Table 5: Alcohol and drugs in relation to the place of provenance
Place of Total NAND BAC BAC DUID Drug DUID DUID DUID
provenance Pop. 10-80 >80 users cocaine cannabis amphet.
Disco 37,13% 29,8% 43,31% 40,18% 35,69% 28,21% 41,84% 30,30% 68,19%
place 31,10% 28,4% 32,06% 36,58% 31,95% 26,92% 32,65% 32,73% 13,63%
Private 16,55% 18,4% 12,95% 12,61% 18,67% 29,49% 18,37% 20,61% 18,18%
Other 14,56% 22,8% 10,62% 10,09% 13,69% 14,10% 7,14% 16,36% 0%
Unknown 0,66% 0,6% 1,06% 0,54% 0% 1,28% 0% 0% 0%
Table 6: Alcohol and drugs in relation to the marital status
Marital Total NAND BAC BAC DUID Drug DUID DUID
Status Populat 10-80 >80 users cocaine cannabis
Unmarried 86,9% 89% 85,57% 81,98% 92,12% 97,44% 92,86% 92,12%
Married 9,20% 7,8% 10,19% 12,80% 6,22% 1,28% 4,08% 6,66%
Legally 2,69% 2,2% 2,76% 3,06% 0,83% 0% 2,04% 0,61%
Divorced 0,25% 0,2% 0,21% 1,08% 0% 0% 0% 0%
Other 0,1% 0,2% 0,21% 0,36% 0,83% 0% 1,02% 0,61%
Unknown 0,86% 0,6% 1,06% 0,72% 0% 1,28% 0% 0%
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Table 7: Alcohol and drugs in relation to the employment status
Employment Total NAND BAC BAC DUID Drug DUID DUID
Status Pop. 10-80 >80 users cocaine cannabis
Employed 87,26% 86% 87,48% 89,55% 90,04% 75,64% 93,88% 87,27%
Unemployed 4,55% 3% 5,10% 3,42% 4,15% 7,70% 2,04% 5,45%
Student 6,38% 9% 5,52% 4,87% 4,57% 15,38% 2,04% 6,67%
Pensioner 0,15% 0,2% 0,21% 0,18% 0% 0% 0% 0%
Other 0,80% 1,2% 0,42% 0,72% 0,83% 0% 1,02% 0,61%
Unknown 0,86% 0,6% 1,27% 1,26% 0,41% 1,28% 1,02% 0%
Table 8: Alcohol and drugs in relation to the educational level
Total NAND BAC BAC DUID Drug DUID DUID
Schooling Pop. 10-80 >80 users Cocaine cannabis
None 0,15% 0% 0% 0,36% 0% 0% 0% 0%
3,60% 2,6% 1,91% 6,31% 4,56% 1,28% 6,12% 1,82%
Middle school 46,49% 42% 46,92% 47,57% 55,19% 43,59% 52,04% 56,97%
School 17,35% 16,4% 18,05% 18,20% 19,09% 12,83% 20,41% 18,79%
High school 27,57% 33,6% 27,60% 21,98% 18,67% 35,90% 19,39% 20%
degree 0,30% 0,4% 0,42% 0,54% 0% 1,28% 0% 0%
degree 2,88% 3,2% 3,61% 3,42% 1,25% 2,56% 1,02% 1,22%
Other 0,55% 0,6% 0,64% 0,36% 0,41% 1,28% 0% 0,60%
Unknown 1,11% 1,2% 0,85% 1,26% 0,83% 1,28% 1,02% 0,60%
Discussion and conclusion
Nearly the whole population examined is composed of males (Table 1) who present intoxication
by alcohol and psychoactive substances more often than females. Table n.2 describes the age
parameter: there is a high prevalence of drug intoxication, in particular by cannabis, among the
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youngest subjects, who have even less experience in driving. Six percent of the whole population
and 9.7 % of the subjects poisoned by cannabis have their driving licence less than a year.
In the oldest subjects and in particular in subjects over 35, intoxication by alcohol is found more
frequently. Eighteen percent of the subjects punished for driving in a state of dtunkenness had a
driving licence for heavy transport (table 4).
There is no difference in the place of origin among the groups of subjects: the use of alcohol and
drugs is shared equally in all the environmental settings; in any case, to analyze the typology of
the identified substances is more meaningful: 41.84 % of the subjects under the influence of
cocaine and 70 % of the subjects positive for amphetamines (22) were coming from a disco. In
the general population 37,13 % of subjects were coming from a disco.
Thus the population studied presents a high prevalence of subjects using psychoactive substances
in a disco setting.
Most subjects submitting to the saturday-night tests were single; they were more frequently found
intoxicated by drugs than by alcohol. Those subjects married or separated, on the other hand,
were more frequently found under the influence of alcohol than of drugs.
Data about the employment of subjects are not meaningful. The social and cultural level of the
examined population is not very high. Most subjects found intoxicated by alcohol and drugs had
the lowest social and cultural level.
The study outlines two kinds of people: the first made up of subjects from 18 to 25 years old,
characterized by a more frequent use of psychoactive substances, cannabis in particular; the
second made up of drivers over 35; most of them driving in a state of intoxication by alcohol.
Identification of social and cultural characteristics of drivers represents an attempt to prevent
driving in a state of intoxication or under the influence of psychoactive substances: a higher level
of education seems to be a protective factor; yet no one can be considered completely exempt
from the phenomenon.
Data collected identify discos as places where psychoactive substances are frequently used; this
fact is quite worrying since we know that intoxication by drugs can increase the risk of being
involved in road accidents.
Even drivers with a driving licence for heavy transport frequently consume alcohol and drugs.
Thus we can conclude that the rare Italian street surveys are not sufficient to discourage these at-
Data collected, in relation to the peculiar population studied, are very different to data collected
in other works carried out in Quebec (4), New Zealand (5) end Denmark (6).
The procedures are not standardized, so data are difficult to compare.
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In Italy street surveys are rare and not uniform in procedures and tests, so it is very difficult to
collect data that really describe phenomenon incidence, social and cultural characteristics of the
drivers etc.; reliable data would be important to plan efficient protocols to educate and survey
drivers and at least to prevent a lot of road accidents.
1. Zancaner S, Giorgetti R, Fenato F, Rossi A, Tedeschi L, Snenghi R, Frison G, Montisci M,
Tagliaro F, Meroni M, Giron G, Marigo M, Ferrara SD. Psychoactive substances and driving
disability: epidemiological roadside survey in north-east Italy. In: Kloeden CN, McLean AJ
(Eds). Alcohol, Drugs and Traffic Safety. Proceedings of the 13th International Conference,
Adelaide, Australia, 1995, pp 773-779.
2. Zancaner S, Giorgetti R, Dal Pozzo C, Molinari G, Snenghi R, Ferrara SD. Driving under the
influence of drugs. Correlation between clinical signs and type of intoxication. In: Mercier-
Guyon C (Ed). Alcohol, Drugs and Traffic Safety, Proceedings of the 14th International
Conference, Annecy France, 1997, pp 757-767.
3. Ferrara SD. Alcol, droga, farmaci e incidenti stradali. CLEUP, Padova, 1999.
4. Dussault C, Lemire AM, Bouchard J, Brault M. Drug use among Québec drivers: The 1999
roadside survey. In: Fred H, Smith RB (Ed). Alcohol, Drugs and Traffic Safety, Proceedings
of the 15th International Conference, Stockholm, Sweden, 2000, paper 309.
5. Keall MD, Frith WJ. Drink driving behaviour and its strategic implications in New Zealand.
In: Mercier-Guyon C (Ed). Alcohol, Drugs and Traffic Safety. Proceedings of the 14th
International Conference, Annecy, France, 1997, pp 401-408.
6. Bernhoft IM. Characteristics of drunken drivers in Denmark. Alcohol, Drugs and Traffic
Safety. In: Mercier-Guyon C (Ed). Alcohol, Drugs and Traffic Safety. Proceedings of the
14th International Conference, Annecy France, 1997, pp 1129-1133.
7. Riley SC, James C, Gregory D, Dingle H, Cadger M. Patterns of recreational drug use at
dance events in Edinburgh, Scotland. Addiction 96, 1035-1047, 2001.
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Identifying Possible Sources of Bias Introduced in
Traffic Safety Research: Comparison of Blind Linkage
with Volunteer Clinical Samples
R.E. Mann, 3S. Macdonald, 2M.L. Chipman, 1,2E. Adlaf, 4K. Anglin-Bodrug and 1,2J. Zhao
Centre for Addiction and Mental Health, Toronto, Ontario; 2Department of Public Health
Sciences, University of Toronto; 3Centre for Addiction and Mental Health, London, Ontario;
Department of Psychology, University of Western Ontario, London, Ontario.
In research on alcohol, drugs and traffic safety, and on road safety in general, investigators have
employed a variety of measures to construct samples for research. Differing sample construction
procedures may introduce bias into the resulting samples, but this possibility has rarely if ever
been assessed empirically. In this research we compare two samples of individuals who obtained
treatment for a substance abuse problem. One sample was obtained by blind linkage procedures,
that is, groups were identified in the clinical records. A second sample was identified in the
clinical records, and then tracked, contacted, and asked to consent to participate in a research
study. Comparisons of the two samples on psychosocial and problem indicators derived from
clinical records revealed a large number of significant differences between the samples. In all
cases, the differences indicated that the group tracked and asked for consent had higher levels of
functioning than the blind linkage group. The possible reasons for these differences, and their
implications for research and research policy, are discussed.
In research on drinking and drug using drivers, and other areas in the road safety field, a variety
of research approaches have been used over the years. Two of these approaches can be broadly
characterized as those which involve complete samples of individuals drawn from records (e.g.,
1-3), and those which involve samples of individuals who have been asked to provide their
consent to participate in research (e.g., 4-6). Increasing concerns with ethical issues in research,
among other factors, have meant that researchers are increasingly following the latter route. An
important concern that is highlighted where researchers must choose between procedures in
which complete samples are used, versus those in which incomplete samples are used due to the
inability to find or obtain consent from all eligible participants, is the introduction of potentially
serious bias into the latter type of study. This bias may occur for a variety of reasons. For
example, in studies involving driving records of clients in treatment for a substance abuse
problem, it can be speculated that individuals with the characteristics of most interest (e.g., those
who use drugs most frequently, or who have the highest levels of dependence) will be the
individuals most likely not to volunteer to participate, or who will be least likely to be found in
order to seek agreement to participate. In assessing the effects of treatment for drug dependence
on traffic safety measures, it is reasonable to suppose that individuals with the most serious drug
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problems, or the most serious driving problems, will also be those who are most likely not to
agree to participate or those who are most likely not to be found when sought. However, while
this is a general concern in the research community, virtually no studies have compared the
characteristics of the two types of samples.
In a recent investigation of the collision experiences of individuals in treatment for alcohol,
cannabis and/or cocaine problems (7,8), we had a unique opportunity to compare a complete
samples of individuals with a sample that had to be tracked and from whom consent had to be
obtained. We therefore compared the two groups on a variety of demographic and psychosocial
measures, to test the hypothesis that the group from whom we had to obtain consent would be
biased towards excluding individuals with more extreme characteristics and higher levels of
Two samples were drawn from clinical records, involving clients who sought help for an alcohol
or drug problem in 1994, 1995, 1996 or 1997 from the Centre for Addiction and Mental Health
(CAMH - formerly the Clinical Research and Treatment Institute of the Addiction Research
Foundation). All clients selected were at least 20 years of age at the time treatment began and
lived in the greater Toronto area, thus eliminating confounding influences associated with place
of residence. About 75% of the clients seen at CAMH meet this criterion. The substance abuse
groups were composed of 7 sub-groups: alcohol only; cannabis only; cocaine only; alcohol and
cannabis; alcohol and cocaine; cannabis and cocaine; and alcohol, cannabis, and cocaine.
The first sample, for telephone interview purposes, was a random sample of 160 subjects in each
of the seven sub-groups noted above, and was selected from patient records (projected N=1120).
These people were targeted for a telephone interview. Patients must have been at least 20 years
old at their assessment and have had addresses within the Greater Toronto area. It was found that
many subjects did not have a valid address, phone number, lived in a group house, did not speak
English, or did not provide a last name. These people also were excluded from the initial
sampling frame. Patient records were first sampled from 1995. Sufficient numbers that met the
selection criteria were obtained for the alcohol only and cocaine only groups. Subsequent random
samples were taken from 1996 and 1997 to obtain the desired sample size of 160 patients per
group. Insufficient numbers could not be obtained for the cannabis + cocaine and cannabis =
cocaine + alcohol groups (i.e., 80 and 96 subject respectively were collected), and the final total
number available for the telephone interview sample was 971.
The second sample, drawn for blind linkage purposes, of 527 patients was randomly selected
from each of the seven drug groups from 1994. We attempted to retrieve 80 patients in each of
the 7 groups selected at random from the list of all clients seen initially for a substance abuse
problem in 1994, for a projected total sample of 560. However, only 47 patients met the criteria
of having a drug problem with all 3 substances. No interviews were conducted with this group.
Data sources and measurement
The data described here were obtained from clinical intake forms, patient records, and
supplementary telephone interviews. When clients first arrive at CAMH they are all asked
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routine questions (i.e., name, address, birth date, marital status and other demographic
characteristics) and given a standardized drug use assessment interview from which their
substance problems are identified.
Over the course of a client's contact with CAMH, detailed clinical files are maintained. These
files covered the following topic areas: presenting problem, alcohol and drug use history, family
alcohol and drug use, social relationships, accommodation, educational attainment, financial,
leisure, legal, physical, emotional concerns, previous treatment of any kind, crisis issues and
treatment recommendations. Progress notes were also available and described each visit by the
client, types of treatment received and information on the therapist’s impression of the client's
progress. In order to develop the coding scheme for the study, 40 patient files were reviewed and
a coding form, the Detailed Client Coding Form, was developed and used for coding the
information in the files. Following the development of the Detailed Client Coding Form, the
sample files were reviewed and relevant information extracted onto the coding forms for data
Measures of severity of the substance abuse problem were drawn from the alcohol and drug use
history chart, which was also useful for assessing whether other drugs were being used in
addition to those reported in the face sheets. An interval level variable was constructed
measuring intensity of treatment based on the number of hours in contact with the Clinical
Institute. Type of program attended was treated as a nominal variable and could include: a youth
program, an adult abstinent lifestyle program, individual counseling, group counseling, family
counseling, guided self change, an Employee Assistance Program, low intensity outpatient
program (i.e., one hour per week), and a high intensity day patient and residential program (i.e., 7
hours per day for 14 to 28 days). Since at least 5 years of follow-up data were available for each
client, indicators of subsequent treatment and relapse were recorded. Additional variables
extracted included suicide attempts, depression, anger, social supports and other emotional
Tracking of individuals in the telephone interview sample began in the fall of 1999. The initial
task was to obtain the telephone numbers of 971 patients, based on the first and last names of
each subject as well as their phone number and address at the time they were initially assessed.
Direct or likely telephone matches were made for 396 subjects (i.e., 40.8% of the initial sample)
and these people, termed the telephone match sample, were telephoned for an interview. At least
20 phone call attempts were made for each telephone number in order to resolve their status. We
were unable to make a positive identification in the telephone interview for 80 of these people
and 3 were clearly the wrong person. For those where positive identification could be made 63
people were ineligible for the study because they did not have a drivers license (31), had died (8)
or moved away from the Greater Toronto area (24). Another 13 people were identified by
someone at the residence as being the correct person but were never able to be contacted. Finally,
63 people were successfully contacted but refused to participate in the study. The final response
rate, comparing the participants with whom interviews were successfully completed (111) to the
to the total participants and refusals among the posively identified group, was 64%, or 28% of the
telephone match sample.
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We compared the telephone interview group (TI) with the blind linkage group (BL) on 58
demographic, substance use, and psychosocial problem measures derived from the Detailed
Client Coding Form. Variables on which significant differences between groups were found are
summarized in Table 1. It is clear that the samples differed substantially on a large number of
variables. It is also clear that in every instance, the differences revealed that the TI group had
lower levels of problems, or fewer difficulties, than the BI group.
This study provides one of the first empirical assessments of the types of bias introduced by
differing procedures used to obtain samples for road safety research. The differences in
procedures were substantial, and it is important to note that not all comparisons between groups
were significant. However, there is clear support for the general hypothesis that the way in which
research samples are constructed can have a very large impact on the nature of the eventual
sample obtained. All the significant differences observed support the specific hypothesis that
sampling procedures requiring contact and consent to participate will act to exclude individuals
who have more serious problems, or whose levels of dysfunction are higher that those who are
eventually contacted and provide consent.
The factors influencing or responsible for this biasing process cannot be identified specifically,
but several could be operating. First, a substantial proportion of the potential TI sample was
unable to be located for telephone calls. This could reflect a higher level of mobility over the
follow-up interval, or insufficient resources to maintain residential stability or a telephone,
among other factors. Additionally, although the participation rate among individuals who were
successfully contacted (64%) is considered very good (9), it is possible that biasing factors were
operating here as well. For example, individuals with higher levels of dysfunction may have
been less likely to agree to the interview. Higher levels of substance abuse could be the direct or
underlying cause for the various sources of bias identified here. Thus, since the interest of the
research was in the impact of substance abuse on collisions and injuries, in addition to there being
an indirect bias introduced by the TI interview procedures, the bias could be more direct in
screening out individuals with the highest levels of exactly those characteristics or qualities we
are interested in. However, although the factors mentioned here are all associated with higher
levels of dysfunction or substance abuse, that need not necessarily be the case and additional
work is needed to understand this biasing process.
The likely impact of this bias on collisions and convictions can easily be hypothesized. We have
observed, in general, that individuals with a substance abuse problem have increased collision
risk (4-9). Thus, in a sample which is biased to exclude those who may have the highest levels of
substance abuse problems, we would seem to be excluding those with the highest
collision risk (analyses to assess this hypothesis are underway). If this is the case, it would seem
to underscore the significance of differences found in studies using such biasing procedures.
That is, the biasing procedures would make the comparisons between groups more conservative,
and thus any significant differences that are found would likely be smaller that those that really
exist. Thus, while our results highlight the bias that might be implemented by procedures which
require tracking and obtaining consent, they do not necessarily imply that results from such
studies are invalid. Instead, the results may be conservative and underestimate differences that
really exist. However, this hypothesis requires more work to confirm.
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Table 1: Significant comparisons between Telephone Interview (TI) and Blind Linkage
(BL) groups on measures derived from the Detailed Patient Coding Form
Measure Test of Significance Direction of Result
Problems with family χ21 = 3.90, p<.05 BL group had higher levels of family problems
Likelihood of seeking χ21 = 6.79, p<.01 BL group were more likely to seek help at other
help at another agencies
Alcohol consumption t = 4.41, p<.001 BL group had higher alcohol consumption
Emotional health χ21 = 5.57, p<.05 BL group had more emotional health concerns
Likelihood of χ21 = 4.82, p<.05 BL group was less likely to receive outpatient
receiving outpatient counseling, and more likely to be assigned to an
counseling inpatient program
Likelihood of having χ21 = 5.96, p<.05 BL group was more likely to have legally
legally imposed imposed treatment
Likelihood of having χ21 = 8.81, p<.05 BL group was more likely to have used
injected drugs injection drugs
Likelihood of having χ21 = 15.35, p<.05 BL group was more likely to have used cocaine
used cocaine in the in the past year
Likelihood of having χ21 = 3.90, p<.05 BL group was more likely to have used other
used other drugs in drugs in the past year
the past year.
Total opioid use t = 2.62, p<.05 BL opioid users had higher total levels of
Total other drug use t = 3.80, p<.01 BL other drug users had higher total levels of
other drug use
Our results also underscore the value of blind linkage procedures in research on this and related
questions. Ethical issues in conducting research are never simple, and problems of consent and
confidentiality in dealing with government, transportation and health datasets are particularly
complex. One trend in recent years has been to deny access to these databanks for research
purposes, or to introduce a blanket consent policy where any access requires individual consent.
Our data demonstrate that such procedures can result in significant bias in the resulting sample.
Thus, the solution to the consent and confidentiality problem of requiring individual consent in
all instances will result in biased and inaccurate results. This may create problems that are worse
than those a blanket consent policy is meant to solve.
This work was supported in part by a grant from the Canadian Institutes for Health Research.
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driver: Relationships with traffic safety outcome. Accid Anal Prevention 1991; 23: 483-491.
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substance users. In: Utzelmann, HD, Berghaus, G and Kroj, G. editors, Alcohol, drugs and
traffic safety, Proceedings of the 12th International Conference, Cologne, Germany, TUV
Rheinland, 1993, pp. 860-865.
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addiction treatment programs. Amer J Epid 2001; 153: 225.
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treatment groups for alcohol, cocaine and cannabis abuse, and a matched control group.
Presented at the 24th Alcohol Epidemiology Symposium of the Kettil Bruun Society,
Toronto, May 28, 2001.
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The Influence of MDMA on Cognition and Psychomotor Function,
and the Importance for Driving Capacity
CTJ Lamers, 1JG Ramaekers, 2N Samyn, 3NL Read and 1WJ Riedel
Experimental Psychopharmacology Unit, Brain and Behaviour Institute, Dep. of
Neurocognition, University of Maastricht, Maastricht, The Netherlands,
National Institute of Criminalistics and Criminology, section Toxicology, Brussels, Belgium,
Institute for Transport Studies, University of Leeds, United Kingdom
MDMA, alcohol, driving capacity, task performance
In this study the influence of single doses of MDMA 75 mg and alcohol 0.5 g/kg on cognitive
and psychomotor performance was assessed in 12 healthy recreational MDMA users. A single
recreational dose of MDMA improved tracking performance under single and double task
conditions. Movement speed in the choice RT paradigm improved increased after MDMA, while
the ability to estimate time to collision was impaired after MDMA. Alcohol impaired tracking
performance 1 and 2 hrs after alcohol but returned to baseline levels 3 and 5 hrs after drinking.
While MDMA produced both stimulating and impairing effects on skills related to driving, it is
concluded that further research employing real driving paradigms and interaction studies with
alcohol and possibly THC are needed.
(±)-3,4-Methylenedioxymethamphetamine (MDMA; ecstasy), is a commonly used psychoactive
recreational drug related to mescaline and stimulant drugs such as amphetamine (1,2). Surveys
among young people visiting raves, indicate that as much as 64-96% of visitors of raves and big
parties uses ecstasy (3,4). Up to 60% of the people that planned to drive a motorized vehicle
after a rave were under the influence of ecstasy (3). Not much is known about the influence of
MDMA on driving performance but case reports seem to justify concern about traffic safety
behavior and involvement in traffic accidents after the use of MDMA (5).
Driving performance consists of a complex combination of cognitive and psychomotor functions.
MDMA is often associated with poor cognitive function. Cognitive impairment, such as memory
impairment in abstinent users has been demonstrated in many studies (6,7) but only a few studies
addressed the acute effects of MDMA on cognition and psychomotor function. When acute
effects of ecstasy were studied under uncontrolled circumstances at a party, a significant
reduction in verbal recall and visual scanning was observed (8). In studies conducted under
controlled conditions a single dose of MDMA (range 0.9 – 1.9 mg/kg) did not affect immediate
recall, digit repetition and selective attention. One prior placebo-controlled study addressed the
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acute effects of MDMA on psychomotor performance. No effect of a single dose of MDMA (75
or 125 mg) on simple reaction time was observed (9).
The current study investigated the acute effects of a recreational dose of MDMA (75mg) on
psychomotor and cognitive performance, vital signs, mood and cortisol concentrations in
recreational ecstasy users under experimentally controlled conditions. The tests described in this
abstract comprise a variety of tasks measuring skills related to driving. They have been
previously shown to be sensitive to a variety of psychoactive drugs (10). Furthermore an effort is
made to investigate the relation between MDMA concentrations and task performance,
physiology and mood. Alcohol treatment was implemented in the design as an active control.
Design & procedure
The study was conducted according to a placebo controlled, 3-way crossover, double blind, and
double dummy design. Subjects underwent all 3 treatment conditions on three separate days,
spaced at least two weeks apart, receiving placebo, alcohol or MDMA. Twelve healthy
recreational MDMA and alcohol users, with no current or history of physical or psychiatric
disease, completed the study. Drug use was prohibited throughout the study. Before entering a
test day subjects were tested for recent drug use and females were also tested for pregnancy using
a urine drugs screen and urine pregnancy test. Before treatment administration subjects consumed
2 sandwiches for lunch. Throughout the day subjects had free access to water, isotonic drinks,
orange juice and sugar free chewing gum. At 1.15 p.m. subjects received study treatments.
Placebo was administered as 400 ml orange juice and 25 ml bitter orange syrup. MDMA was
administered by replacing part of the bitter orange syrup by MDMA 75 mg. During alcohol
administration part of the orange juice was replaced by pure alcohol (.5mg/kg body weight).
Driving-related task performance:
The Critical Tracking Test (CTT) measures the ability to control an inherently unstable error
signal in a 1st-order compensatory tracking task. Subjects attempt to keep a cursor centered in the
middle of a display using a joystick while the cursor tends to move away from the center. The
point where the subjects losses control is defined as the 'critical frequency' or lambda-c (λc)
expressed in radians per second (rad/sec). Theoretically, λc is the reciprocal of the operating
delay lag in human closed-loop control. The CTT was conducted at 0, 1, 2, 3 and 5 h post
The Divided Attention Task (DAT) assesses the subjects’ ability to divide attention between
tracking and monitoring tasks performed simultaneously. The tracking subtask is similar to the
CTT, but the error signal velocity is fixed at 50% of individual’s optimal performance during
training (λc/2). Tracking error is measured as the average absolute distance (mm) between the
cursor's position and display center. The other subtask consists of monitoring 24 peripheral LED
displays fixed to the corners of the screen, each presenting numerals, 0-9, which change
asynchronously every 5 seconds. The subject removes his foot from a pedal as quickly as
possible after detecting the target numeral, "2". Median correct reaction time (msec) to targets is
the second response measure. The DAT was conducted at 1 and 3 h post treatment.
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The Motor Choice Reaction Time (MCRT) is a test in which reaction time (RT) is studied as a
function of task complexity. Reaction time is divided into initiation time (time between target and
onset of response) and movement time (time of movement execution). The MCRT was conducted
at 1 and 3 h post treatment.
The Object Movement Estimation under Divided Attention (OMEDA) task is in essence a task to
estimate time to contact (TTC) of a moving object to a specific location. The subject is seated in
front of a computer screen on which a yellow circle occludes the center. The circle varies in size
per trial (2, 100 or 200 pixels). From one of the corners, a red dot (target) travels towards the
center of the screen and travels underneath the yellow circle and will no longer be visible. The
subject estimates when exactly the target reaches the center of the screen by pressing a foot pedal.
During the trial, 5 geometrical shapes appear; one on top of the occlusion circle and one in each
of the corners. For the secondary task the subject has to press a button in case the geometric
shape at the occlusion circle matches one of the others. Absolute TTC error and the number of
correct responses to the geometric targets (divided attention) were combined using Z-scores.
The Signal Detection Task (SDT) is a visual search task. Small white squares are presented in a
pseudo-random fashion on a computer screen. Twenty squares are randomly assigned being 2.5
cm apart. Squares move to a different location on the screen and subjects are required to respond
to the target stimuli, defined as a set of four stimuli forming a square of 2.5 x 2.5 cm, by pressing
a button as fast as possible. Sensitivity (A’) defined as the non-parametric proportion of correctly
identified targets corrected for false positives is the dependent measure.
Starting from baseline, pulse rate, blood pressure and body temperature were assessed in a
relaxed sitting position every 30 min. until 5.5 h after drug intake, using an automated vital signs
monitor (Dinamap 1800 BP; Critikon Inc., Tampa FL and an in-ear thermometer, respectively).
MDMA was determined in plasma, saliva, sweat and urine, at baseline, and every hour until 5
hours after drug intake. All samples were frozen at -20°C until analyzed. Detailed information
about the collection and analyses of samples are reported elsewhere (11).
Subject’s breath alcohol concentration (BAC) was assessed every 30 min., starting at baseline
until 5.5 h after drug intake, by means of a Lion SD-4 Breath Alcohol Analyzer.
Dependent variables representing task performance were tested for the main effect and
interactions between Treatment and Time using a multivariate repeated measures analysis of
variance (General Linear Model). Exceptions occurred for Initiation Time in the MCRT where
Task Complexity was added as an extra within subjects Factor. Analyses of performance on the
OMEDA task were accomplished by entering both dependent variables (TTC error and divided
attention error) in a bivariate 3 x 3 (treatment x occlusion diameter) repeated measures design. A
polynomial contrast was used for Time and Complexity, while a simple contrast was used for
Treatment for univariate comparisons of all drug-placebo differences.
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Correlations are analyzed using the intra-subject correlations. Pearson’s r of all individual
correlations between two factors are averaged and tested for significance using an independent
one-sample t-test. The _-probability criterion for determining the significance of mean
differences and correlation was defined as (p < .05).
8 Male and 4 female subjects completed the study (mean age 23.5, range 21-30; mean weight
65.9 kg, range 60-73 kg). All subjects used ecstasy (mean lifetime use: 39, range 5-125),
marijuana (mean lifetime use: 760, range 3-3500) and alcohol (mean units/week: 17, range 4-50).
Mean plasma Cmax of MDMA was 178 ng/ml (range 85-295 ng/ml) at 3 hours after drug intake.
Mean BAC peak concentration was reached 60 minutes after alcohol intake (.31 mg/ml) and
during the second repetition of the test battery BAC had dropped to 0.01 g/ml. During last
assessments the BAC’s had dropped to 0 in all subjects (12).
Driving-related task performance:
Tracking performance in the CTT improved after MDMA as compared to placebo, resulting in
higher λc. Alcohol had no main effect on λc. There was an interaction of Treatment by Time
between alcohol and placebo caused by the fact that λc was lower at 1 and 2 h after alcohol
relative to placebo, while λc was higher than post placebo 3 and 5 h after alcohol (p=.001).
In the DAT, MDMA improved sub-critical tracking performance, represented by tracking error,
as compared to placebo. Alcohol did not affect tracking error in the DAT. Movement Time
improved after MDMA as compared to placebo (100 and 108 msec respectively while movement
time after alcohol (111 msec) did not differ from placebo. There was no effect of MDMA or
alcohol on initiation time. In the OMEDA task, there was a trend towards an impairing effect of
MDMA on performance. MDMA also tended to impair TTC estimation more when occlusion
diameter was larger (p=.066). Alcohol did not affect TTC error compared to placebo.
Alcohol or MDMA did not affect visual search performance on the SDT.
Table 1: Mean (sd) performance averaged over all assessments after treatment of the
CTT, DAT, MCRT and OMEDA and the outcome of the univariate analyses
under the influence of placebo, alcohol and MDMA (_ = improvement, _=
Dependent variable Placebo Alcohol Mdma Placebo Placebo
Tracking (CTT) 4.52 (.14) 4.54 (.15) 4.87 (.16) p=ns p=.03 _
Tracking error (DAT 17.32 (1.25) 18.35 (.94) 15.18 (1.14) p=ns p=.02 _
Movement Time (MCRT) 107.6 (5.47) 110.4 (7.27) 99.40 (4.87) p=ns p=.01 _
TTC error (Z-transformated Units; Omeda) -.076 (.08) -.021 (.06) .103 (.13) p=ns p=.055 . _
Vital signs, presented by pulse rate, body temperature and blood pressure all rose as a function of
MDMA as compared to placebo. Alcohol had no effect of vital signs.
Intra-subject correlations between the MDMA levels in blood plasma, urine, saliva, sweat and
physiology, and performance are presented in table 2.
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Table 2: The intra-subject correlations between blood plasma, urine, saliva and sweat
with body temperature, pulse rate, blood pressure and task performance over all
assessments after placebo and MDMA treatment. (Significance: * p<.05, **
Variable Blood plasma Urine Saliva Sweat
Body temperature .24* .30** .20 .20
Pulse rate .40** .39** .49** .27*
Diastolic blood pressure .37** .39** .35** .29**
Systolic blood pressure .53** .49** .51** .39**
Critical tracking .32* .33** .34* .27*
Tracking error -.37* -.35* -.48** -.37*
Movement time -.57** -.31 -.63** -.66**
Time to contact error .25 .28 .28 .25
The purpose of the study was to investigate the effect of a single dose of MDMA 75 mg and a
small dose of alcohol .5mg/kg psychomotor performance, cognition, physiology and mood. An
important part of the study was to investigate the influence of MDMA and alcohol on driving
related behavior and to make a first attempt to analyze the correlations between MDMA levels
and task performance and vital signs. Although there was no main effect of alcohol on λc and
tracking error, there seemed to be an effect of alcohol on tracking performance relative to placebo
as indicated by a significant treatment x time interaction. In the CTT λc was lower after alcohol as
compared to placebo1 and 2 h post treatment, indicating impaired performance. When the test
was repeated 3 and 5 h post treatment performance on the CTT returned to baseline after alcohol,
while performance after placebo seemed slightly impaired. Alcohol had no effect on task
performance of the MCRT.
A single dose of MDMA improved psychomotor performance. Critical tracking performance, i.e.
λc, increased by 5.6-9.1% as compared to placebo. Tracking error in the Divided Attention Task
decreased by 11.4% after MDMA as compared to placebo. In the MCRT, movement time
increased after MDMA use, while initiation time –the time between presentation of the target and
the onset of responds- was not affected by MDMA. A single dose of MDMA impaired
performance on the primary OMEDA task, the time to collision (TTC) task. The unique
component of the OMEDA task is the perception and correct estimation of object movement.
After MDMA the error between estimated and actual TTC increased and subjects had more
difficulty predicting the TTC as the occlusion of the center of the screen increased in size relative
to placebo. The increment in TTC error after MDMA may reflect a disturbance in perception of
time and space, also observed by other researchers (13,14). The decreased ability to estimate and
predict movement can result in impaired estimation of other traffic movements at crossroads,
leading to acceptance of smaller gap between vehicles, indicating increased risk taking behavior.
Dangerous driving and accidents after MDMA use have already been reported in the past (5,15).
Furthermore, impaired co-ordination, difficulty concentrating and hallucinations have been
observed in people while under the influence of MDMA (13-15). These factors, sometimes
combined with exhaustion after a night of dancing, have already led to involvement in -even
fatal- car accidents of MDMA users in the past (5,16). Furthermore, MDMA dosages may be
higher than in the present study and are often used in combination with sedative psychoactive
substances such as marijuana and alcohol. Often combining 2 or more psychoactive substances,
e.g. marijuana and alcohol, often increases the impairment caused by a drug when taken alone
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(17). The current study was part of a larger experiment assessing aspects of driving and simulated
driving under the influence of MDMA with and without other drugs and/or alcohol. The study
assessing simulated driving confirms these findings. While MDMA alone only had minor effects
on driving performance, MDMA combined with other drugs (mostly THC) and/or alcohol
increased driving speed and smaller gap acceptance (18).
Alcohol had no effect on vital signs while MDMA increased body temperature, pulse rate and
The increased vital signs correlated with MDMA levels in most body fluids. MDMA levels also
correlated with improved psychomotor function. MDMA levels did not significantly correlate
with impaired task performance observed in this study. The analyses of correlations in this study
were a first attempt to get more insight in the relation between MDMA levels and task
performance. However, duplication of these findings in a larger study sample is necessary. Based
on current and future results, saliva may proof itself a good and easy collectable alternative for
blood samples in off-road screening.
In sum we can conclude that certain aspects of vehicle control, e.g. tracking capacity improved
after MDMA. Although tracking capacity is an important aspect of driving, improved tracking by
no means automatically indicate increased driving safety. The increased risk taking and the
users’ decreased ability to estimate and predict movement can result in impaired estimation of
other traffic movements at crossroads, leading to acceptance of shorter gaps, especially when
MDMA is used in combination with other drugs. This form of increased risk taking makes traffic
safety under the influence of MDMA questionable.
1. de Man, R.A., Morbiditeit en sterfte als gevolg van ecstacygebruik. Ned Tijdschr Geneeskd,
1994. 138(37): p. 1850-5.
2. Morgan, M.J., Ecstasy (MDMA): a review of its possible persistent psychological effects.
Psychopharmacology (Berl), 2000. 152(3): p. 230-48.
3. Spruit, I.P., XTC in Nederland. 1997, Trimbos-Instituut in opdracht van ministerie van
Volksgezondheid, Welzijn en Sport: Den Haag. p. 49.
4. Winstock, A.R., P. Griffiths, and D. Stewart, Drugs and the dance music scene: a survey of
current drug use patterns among a sample of dance music enthusiasts in the UK. Drug and
alcohol dependence, 2001. 64(1): p. 9-17.
5. Henry, J.A., K.J. Jeffreys, and S. Dawling, Toxicity and deaths from 3,4-
methylenedioxymethamphetamine ("ecstasy") [see comments]. Lancet, 1992. 340(8816): p.
6. McCann, U.D., Mertl, M. Eligulashvili, V. and Ricaurte, G.A., Cognitive performance in (+-)
3,4-methylenedioxymethampetamine (MDMA, 'ecstacy') users: a controlled study.
Psychopharmacology, 1999. 143: p. 417-425.
7. Morgan, M.J., Memory deficits associated with recreational use of "ecstasy" (MDMA).
Psychopharmacology Berl, 1999. 141(1): p. 30-6.
8. Parrott, A.C. and J. Lasky, Ecstasy (MDMA) effects upon mood and cognition: before, during
and after a Saterday night dance. Psychopharmacology, 1998. 139: p. 261-268.
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9. Cami, J., Farre, M. Mas, M., Roset P.N., et al., Human pharmacology of 3,4-
methylenedioxymethamphetamine ("ecstasy"): psychomotor performance and subjective
effects. J Clin Psychopharmacol, 2000. 20(4): p. 455-66.
10. Ramaekers, J.G., Muntjewerff, N.D., Uiterwijk, M.M.C. van Veggel, L.M.A. et al., A study of
the pharmacodynamic interaction between befloxatone and ethanol on performance and
mood in healthy volunteers. Journal of Psychopharmacology, 1996. 10(4): p. 288-294.
11. Samyn, N., De Boeck, G., Wood, M., Lamers, C.T.J., et al., Plasma, oral fluid and sweat
wipe ecstasy concentrations in controlled and real life conditions. Forensic Sci. Int., 2002, in
12. Lamers, C.T.J., Ramaekers, J.G., Muntjewerff, N.D. Sikkema, K. et al., Dissociable effects of
a single dose of MDMA on psychomotor skills and attentional performance. Submitted.
13. Downing, J., The psychological and physiological effects of MDMA on normal volunteers. J
Psychoactive Drugs, 1986. 18(4): p. 335-40.
14. Vollenweider, F.X., Gamma, A. Liechti, M, Huber, T. et al., Psychological and
cardiovascular effects and short-term sequelae of MDMA ("Ecstasy") in MDMA-naieve
healthy volunteers. Neuropsychopharmacology, 1998. 19(4): p. 241-251.
15. Schifano, F., Dangerous driving and MDMA ('Ecstacy') abuse. Journal of Serotonin
Research, 1995. 1: p. 53-57.
16. Morland, J., Toxicity of drug abuse--amphetamine designer drugs (ecstasy): mental effects
and consequences of single dose use. Toxicol Lett, 2000. 112-113: p. 147-52.
17. Lamers, C.T.J. and J.G. Ramaekers, Visual search and urban city driving under the influence
of marijuana and alcohol. Human Psychopharmacology Clinical and Experimental, 2001.
16(5): p. 393-401.
18. Brookhuis, K.A., D. De Waard, and L.M.C. Pernot, A driving simulator study on driving
performance and traffic safety after multiple drug use, consisting of MDMA (Ecstasy) and
various other psychoactive compounds., in T’2000 Alcohol, Drugs and Traffic Safety., I.H.
Laurell, Editor. 2000: Stockholm, Sweden.
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Alcohol-Impaired Driving Recidivism Among First Offenders More
Closely Resembles That of Multiple Offenders
W.J. Rauch1, P.L. Zador1, E.M. Ahlin1, H. Baum1, D. Duncan1,
R. Raleigh2, J. Joyce2 and N. Gretsinger2
Center for Studies on Alcohol, Westat, Rockville, Maryland, USA,
Maryland Motor Vehicle Administration, Glen Burnie, Maryland, USA.
Alcohol-impaired driving legislation and sanctions have historically been aimed at the offender
with multiple driving while intoxicated (DWI) convictions, with little or no attention paid to the
first-time offender or to alcohol-related events other than DWI [such as administrative per se
(APS) violations involving breath alcohol content (BrAC) of 0.10 or more, APS breath test
refusal and probation before judgment (PBJ)]. It is a widely held belief among the legislature and
judicial branches of state government that first offenders criminally convicted of an alcohol-
related traffic law are drivers with a single and isolated alcohol-related violation that results in
arrest. This finding is inconsistent with published estimates that a person can drive while
impaired by alcohol 200 to 2,000 times before being arrested once for alcohol-impaired driving
(1-6). Moreover, some drivers manage to have their records expunged under certain conditions,
and many state motor vehicle administration (MVA) offices routinely purge driving records after
a set number of years. Therefore, it is reasonable to assume that the typical so-called first-time
offender will have had an extensive history of alcohol-impaired driving by the time he or she
makes it into the MVA’s record system. The current research examines the relative risk of
alcohol-related recidivism among drivers with one, two and three or more alcohol-related events
(not just convictions) and expands prior research (7) using an updated data set. Our findings
suggest that first-time alcohol-related traffic offenders are at a high and significant risk of
recidivating even after one alcohol-related event and that alcohol-impaired driving recidivism
among first offenders more closely resembles that of multiple alcohol offenders. The results
demonstrate that any alcohol-related traffic event (APS BrAC of 0.10 or more, APS breath
refusal, and PBJ), not just convictions, should be perceived by the courts, the MVA, and
physicians as a marker for future alcohol-related recidivism. The results also suggest that relative
risk among females is similar to the risk among males once females have had one alcohol-related
The purpose of this study was to determine the statewide alcohol-impaired recidivism rate among
Maryland drivers with no, one, two and three or more prior alcohol-related traffic violations
(events). In the State of Maryland, an alcohol-related traffic event may result in administrative
penalties mandated under APS regulations for failing or refusing the breath alcohol test and
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criminal penalties mandated for a conviction. In addition, under certain conditions, an offender
can be placed on PBJ following a conviction or nolo contendere plea.
A closer look at recidivism rates among drivers with so-called first, second and third or more
alcohol-related traffic events is warranted because of four factors: 1) the low probability of arrest
for alcohol-impaired driving; 2) the practice of expunging and/or purging driver records; 3) our
finding in a companion paper (8) that any alcohol-related event (whether administrative in nature
or through criminal sanctions and/or diversion programs) significantly and substantially increases
the future risk of an alcohol-related event and therefore should be considered a marker for future
recidivism; and 4) the perceived leniency with which state legislative and judicial systems handle
so-called first-time offenders.
We considered all alcohol-related traffic events (DWI/DUI, PBJ and two APS events: BAC of
0.10 or more and breath test refusal). Data were extracted from the Maryland MVA’s driver
record database after personal identifying information had been deleted from drivers’ records.
Since PBJs are maintained by the MVA as a segregated record, those records were also extracted
and linked for analysis. Drivers who had died or moved from the state were excluded. Records
were also removed if the driver’s license had expired 6 months before December 31, 1999. For
many driver records, multiple APS, conviction and PBJ records were found for the same date of
an alcohol-related event, and these duplicates were removed from the database. Analyses were
restricted to drivers who were included in the Maryland Driver License Record or Segregated
files and events with dispositions between January 1, 1994, and December 31, 1999. All alcohol-
related events from 1973 to 1999 were counted among the prior alcohol-related events. Data on
alcohol-related traffic events occurring between 1973 and 1999 were analyzed. Possibly because
of administrative and/or procedural factors, conviction counts prior to 1973 were small; therefore,
disregarding alcohol-related events prior to 1973, which occurred 20 or more years before the
start of the study period, was unlikely to affect any of the estimates. More than 55 million driver
records were screened for the study period 1994-1999, representing more than 23 million drivers
of interest. Research was conducted using a Maryland driver record database extract from March
2001 and updates prior estimates (7). It can take up to 12 months or more for cases to work their
way through the administrative and judicial systems and reach a final adjudication and there is an
additional lag period before the outcomes appear on a driver record so using this extract more
accurately reflects alcohol-related events occurring during 1999.
In Tables 1-4, estimates based on all Maryland drivers are tabulated by prior alcohol-related
event and year, total number of prior alcohol-related events by number of prior events (0, 1, 2,
3+) and year, rate of alcohol-related events per 1,000 Maryland drivers by number of prior events
(0, 1, 2, 3+) and year, and proportion of female drivers by number of prior events and year. The
tables present summary statistics for the number of drivers and sex at the middle of each year
(June 30). Disposition counts are reported for the whole calendar year, and disposition rates are
reported as the ratios of disposition counts to the corresponding mid-year driver counts.
Table 1 displays the count of drivers by the number of prior alcohol-related events and year. The
number of drivers in Maryland increased during this period from about 3.75 million in 1994 to
about 4.03 million in 1999. In 1999, more than 3.75 million drivers had no prior alcohol-related
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traffic events; 188,769 drivers had committed one event; 56,971 different drivers had committed
two events; and 34,451 different drivers had committed three or more events.
Table 1: Number of Drivers by Prior Alcohol-Related Events and Year
Events 1994 1995 1996 1997 1998 1999 All
0 3,533,047 3,553,554 3,584,750 3,625,251 3,678,828 3,752,584 21,728,014
1 155,408 161,576 167,975 174,700 181,572 188,769 1,030,000
2 44,063 46,673 49,272 51,749 54,396 56,971 303,124
3+ 23,105 25,275 27,490 29,854 32,106 34,451 172,281
All 3,755,623 3,787,078 3,829,487 3,881,554 3,946,902 4,032,775 23,233,419
Table 2 displays the number of alcohol-related events by number of prior events and year. The
number of alcohol-related traffic arrests that resulted in a disposition of any type (APS, criminal,
PBJ) increased from 18,628 in 1994 to 21,568 in 1999. In 1999, 12,236 events were for first-time
offenders; 4,966 were for drivers who had one prior alcohol-related event; 2,279 were for drivers
who had two prior events; and 2,087 were for drivers with three or more prior events.
Table 2: Number of Alcohol-Related Events by Number of Prior Events and Year
Events 1994 1995 1996 1997 1998 1999 All
0 10,487 11,310 11,140 11,733 12,110 12,236 69,016
1 4,579 4,828 4,805 4,892 4,903 4,966 28,973
2 2,003 2,218 2,299 2,331 2,329 2,279 13,459
3+ 1,559 1,758 1,715 1,969 1,955 2,087 11,043
All 18,628 20,114 19,959 20,925 21,297 21,568 122,491
The number of drivers in Maryland increased by 7% from 1994 to 1999 (Table 1) while the
number of alcohol-related events increased 14% (Table 2). Thus the number of alcohol-related
events in Maryland is increasing twice as fast as the number of drivers.
Table 3 displays a rate analysis of the number of alcohol-related events per 1,000 drivers by
number of prior events and year. Among drivers with no prior events, the average annual rate of
alcohol-related first-time offenders was 3 per 1,000 drivers. Among drivers with one, two and
three or more prior alcohol-related events, the average annual rates were, respectively, 28, 44 and
64 per 1,000 drivers.
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Table 3: Rate of Alcohol-Related Events Per 1,000 Drivers by Number of Prior Alcohol-
Related Events and Year
Number of Year
Prior Events 1994 1995 1996 1997 1998 1999 All
0 3 3 3 3 3 3 3
1 29 30 29 28 27 26 28
2 45 48 47 45 43 40 44
3+ 67 70 62 66 61 61 64
All 5 5 5 5 5 5 5
Remarkably, the average rate of alcohol-related events was increased almost 10-fold (933
percent) by the first prior event, by 1,467 percent by the second prior event and 2,133 percent by
the third or additional prior event, relative to drivers with no prior events (Figure 1). However,
the rate of a subsequent alcohol-related event only increased by about 230% for drivers with three
or more prior events relative to drivers with 1 prior event. Thus the first alcohol-related event
results in a larger change in risk for subsequent events than multiple offenses. Furthermore, the
rates of subsequent events for first offenders is more similar to that of multiple offenders than
drivers with no prior events. Overall, the rates among drivers with no prior events receiving their
first event changed relatively little during the study period. However, among drivers with one or
more prior alcohol-related event, annual rates declined by about 10 percent between 1994 and
Figure 1: Percent Increase of Alcohol-Related Events Relative to Drivers with No Prior
1 Event 2 Events 3+ Events
Number of Prior Alcohol-Related Events
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Table 4 displays the proportion of female drivers by the number of prior alcohol-related events
and year. Females accounted for 50 percent of all drivers during the study period. Females
represented 52 percent of drivers with no prior alcohol-related event, but only 17 percent of
drivers with one prior event, 11 percent of drivers with two prior events and 7 percent of drivers
with three or more prior events.
Table 4: Proportion of Female Drivers by Number of Prior Alcohol-Related
Events and Year
Number of YEAR
Prior Events 1994 1995 1996 1997 1998 1999 All
0 0.52 0.52 0.52 0.52 0.52 0.52 0.52
1 0.16 0.17 0.17 0.17 0.17 0.18 0.17
2 0.10 0.11 0.11 0.11 0.12 0.12 0.11
3+ 0.06 0.07 0.07 0.07 0.08 0.08 0.07
All 0.50 0.50 0.50 0.49 0.49 0.49 0.50
The rate of alcohol-related events was examined by sex. The relative risk of a first offense (i.e.,
an individual had no prior alcohol-related events) is 4.3 times higher for males than for females.
However, once a first offense has occurred, the relative risk is almost the same for males and
females (1.2 for one prior event, 1.1 for two prior events and 1.1 for three or more prior events).
Males are four times more likely than females to have a first alcohol-related event, but once a
first event has occurred, males and females are at a similar relative risk of recidivating.
Our research, based on driver data from the entire State of Maryland, produced four major
findings: (1) during the study period 1994-1999, the first alcohol-related event proved to be a
more powerful statistical risk factor for recidivism than did subsequent alcohol-related events; (2)
recidivism among first offenders more closely resembles that of multiple offenders than among
drivers with no priors; (3) any alcohol-related traffic event, not just convictions, should be
perceived by the courts, the MVA, and physicians as a marker for future alcohol-related
recidivism; and (4) the relative risk among females is similar to risk among males--once females
have had one alcohol-related event.
These findings call into question the use of segregated records, expungement, and the purging of
any driver history of alcohol-related violations. It would be unconscionable for a woman with a
history of breast cancer to exclude this information from her attending physician because she had
gone for a period of years without a related health problem, or to purge or expunge her medical
history. However, the traditional approach to deterring alcohol-impaired driving not only allows
for expungement of a history of alcohol-impaired driving, or the purging of driver records, but
routinely focuses only on convictions for determining highway safety risk. This analysis (and
companion papers) definitively demonstrate that, like other diseases, no history of an alcohol-
impaired driving event, whether handled through administrative procedures, the criminal justice
system, or a diversion program, should be expunged, purged, or segregated from a driver’s
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record. Alcohol-impaired driving is a national health problem and if we are to decrease injuries
and deaths on the nation’s highways, public health policy should classify first offenders using a
broader definition of alcohol-related events [as defined in this paper and a companion paper (8)]
instead of the legal criminal definition used by state licensing agencies, state legislative and
judicial branches of government, physicians, and public health policy analysts. Once first
offenders are properly identified, public policy needs to focus on early intervention and treatment
of these first offenders.
This work was supported by Grant R01 AA11897 from the National Institute on Alcohol Abuse
1. Borkenstein RF. Problems of enforcement, adjudication and sanctioning. Proceedings of the
Sixth International Council on Alcohol, Drugs and Traffic Safety. Toronto, Ontario, Canada,
2. Beitel GA, Sharp MC, Glauz WD. Probability of arrest while driving under the influence of
alcohol. J Stud Alcohol 1975; 36(1): 109-16.
3. Anda RF, Remington PL, Williamson DF. A sobering perspective on a lower blood alcohol
limit (letter to the editor). JAMA 1986; 256(23): 3213.
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Res Circ 1995; (437): 25-32.
6. National Highway Traffic Safety Administration. Crash Course on Impaired Driving:
Maryland Collegiate Conference. Timonium, Maryland, 1999.
7. Rauch WJ, Zador PL, Ahlin EM, Raleigh R, et al. Alcohol-impaired driving recidivism
among first offenders more closely resembles that of multiple offenders. Alcoholism Clin
Exp Res 2001; 25(5):150A.
8. Rauch WJ, Zador PL, Ahlin EM, Raleigh R, et al. Any first alcohol-impaired driving event is
a significant and substantial predictor of future recidivism. Proceedings of the 16th
International Conference on Alcohol, Drugs and Traffic Safety. Montréal, Quebec, Canada,
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A Geographic Analysis of DWI Offenders
W.F. Wieczorek and A.Y. Naumov
Center for Health and Social Research, Buffalo State College, Buffalo, New York, USA
Alcohol, DWI, cluster analysis, geography
There are almost no studies of the geographic distribution of DWI offenders. Basic information
such as whether DWI offenders are randomly distributed in the population or tend to come from
specific neighborhoods could have important implications for DWI prevention and interventions.
If geographic clusters are identified, anti-DWI efforts can be targeted at specific areas, whereas
this type of geographic targeting would not be appropriate if the DWI population is randomly
distributed. The objective of this study is to determine whether the home locations of DWI
offenders are spatially clustered by using appropriate spatial analytic methodology. All DWI
offenders (i.e., any drinking-driving conviction) from 1990-1994 in Erie County, New York form
the database for this study. Over 15,500 DWI offender home addresses were geocoded and
allocated to census tracts and block groups. A spatial scan methodology based on a case-control
approach was used to determine whether census tracts or block groups formed significant
geographic clusters. Results based on the analysis of DWI offenders at the census tract level and
block group level identified a number of statistically significant spatial clusters. The geographic
analysis found that specific high and low rate areas could be identified based on official DWI
conviction information. The clusters based on block groups provided a refinement of the clusters
found at the tract level. The geographic distribution of DWI offenders is clustered and not
random, which could be used to target intervention programs.
Drinking and driving offenses (DWI) occur in a number of geographic contexts, which include
the road system, places where alcohol is consumed, locations of crashes, locations of arrests, and
the home locations where the DWI offenders reside. Despite the various geographic links with
DWI, there are relatively few studies that examine these geographic factors. Gruenewald and
associates have examined the role of alcohol availability in drinking and driving (1). Wieczorek
and Coyle (2) used a spatial regression technique to identify factors associated with a DWI rate in
census tracts such as non-skilled occupations, high school education level, percent male
population, and white ethnicity (2). In addition, simple spatial cluster analysis was used to
suggest that DWI offender home locations were not randomly distributed (3); however, the
technique was not able to identify specific clusters of either high or low rate locations. There is
great potential for advanced geographic cluster analysis to provide guidance for DWI prevention
by identifying specific low and high DWI rate neighborhoods. DWI prevention has not usually
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been focused on areas that generate a disproportionate number of DWI offenders. The purpose of
this study is to determine whether the home locations of DWI offenders are geographically
clustered by utilizing advanced spatial cluster analysis.
The study was conducted in Erie County, New York, with a population of about 968,000 at the
time of the study. The county includes a large urban area (Buffalo), suburban areas and semi-
rural towns. The home address information for all persons (15,551) convicted of DWI (any
drinking and driving offense) from 1990-1994 in Erie County form the database for the study.
The home addresses of the DWI offenders were geocoded using TIGER line files and Erie
County tax parcel information. After geocoding, the individuals were allocated to census tracts
and block groups. Census population data were used to create DWI rates for the tracts and block
groups. The block groups and tracts were analyzed separately to assess the impact of the
geographic unit used for analysis and to determine if the cluster solutions showed convergent
The spatial cluster analytic techniques used was Kulldorff’s (4) SatScan algorithm. This is a
spatial scan method that uses a scan window to move over the tracts and block groups to sum the
number of cases (i.e., DWIs) in the overlapping scan windows to identify specific clusters. The
method is a case-control approach that compares the expected number of cases in an area with the
number of controls to identify the clusters. The method uses a likelihood ratio statistic that
accounts for the multiple comparisons made during the spatial scan to identify statistically
significant clusters. SatScan identifies clusters of any shape or size, the specific members of each
cluster, and both high and low rate areas. This case-control cluster method controls for the
underlying population distribution so that clusters are a not merely a reflection of population
The two main cluster analyses (one at the block group level, the other at the tract level) both
found significant clusters of DWI offenders. It is important to compare the simple DWI rate
maps with the cluster analysis results. Figure 1 shows the DWI rate map for DWIs at the census
tract level. The mean DWI rate at the tract level is 151.96/10,000 persons. Visual inspection of
this map shows that identifiable groups of tracts have similar DWI rates. Note that the natural
breaks method was used to create the intervals on this map (and in Figure 3). The natural breaks
method utilizes the variance of the rates to create the specified number of categories by using
standard deviation units to define the intervals. This creates intervals with meaningful break
points, but with a different number of tracts in each interval group, as compared to quantile
interval methods that place the same number of tracts in each interval group. Despite the visual
impression of the rates shown in Figure 1, there is no way to know which groups, if any, of the
tracts actually form statistically significant clusters.
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Figure 1: DWI conviction rate for census tracts in Erie County, New York
Figure 2: DWI clusters at the census tract level
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The significant clusters identified at the tract level are shown in Figure 2. Cluster 1 is a low rate
cluster located in the central urban area (p=.001, 1,123 DWI cases found, 1,812 cases expected).
A small geographic area is typical for census tracts in highly urbanized areas, such as those in
cluster 1. Cluster 2 is another high rate cluster that includes a large portion of the major
suburban areas of the county (5,265 DWI cases found, 4,461 cases expected). Cluster 2 also
includes some rural tracts (recognized by their large geographic areas). Cluster 3 is a high rate
cluster found in a suburban-urban transition area with substantial heavy industry (p=.001, 367
DWI cases found, 246 cases expected). A visual comparison between Figures 1 and 2 show that
the significant clusters overlap with the simple rate map, but are also substantially different.
Figure 3 shows the DWI rates at the block group level. The mean DWI rate at the block group
level is 147.62/10,000 persons. Note that block groups are smaller geographic units than census
tracts; census tracts (population range 2,500-8,000) are usually composed of fewer than ten block
groups. Greater geographic variability is found at the scale of block groups as compared to tracts
(see Figures 1 and 3). More nuances are present in the geographic distribution because estimates
of DWI rates for smaller populations are more variable. Visualization of clusters among the
block groups is much more difficult than for the tract-level map. The results of the spatial cluster
analysis are shown in Figure 4. The finer-grained information from the block group-level
analysis resulted in substantially more clusters in comparison to the tract-level analysis (7 clusters
vs. 3 clusters). All of the block group clusters were statistically significant at the p=.001 level.
At the block group-level analysis, cluster 1 coincides well with the first cluster of the tract-level
analysis. They are located in the same urban area and are low rate clusters. Cluster 1 had an
expected number of 2,879 DWI cases, whereas only 2,042 were found, which indicates that this
cluster includes a larger population than the same cluster at the tract level. Clusters 2 (4,995
DWI cases vs. 4,132 expected) and 5 (787 DWI cases vs. 575 expected) are high rate clusters that
show a refinement of the large suburban high rate cluster at the tract-level into two distinct
groups. Cluster 5 added some areas that were not included at the tract level. Cluster 2 extended
to additional areas towards the urban core, whereas some rural tracts that were included in the
high rate tract cluster are no longer in any cluster in the block group analysis.
In figure 4, Cluster 3 is a low rate group in a suburban area (273 DWI cases vs. 522 expected)
that does not have a comparable cluster at the tract level. Cluster 4 is a low rate group that is an
artifact of data reporting because it includes an Indian reservation for which reliable addresses for
DWI convictions are not available. At the tract level, this artifact was not identified as a cluster
with a possible explanation that the single tract was divided into multiple block groups, which
then were identified as a cluster. Cluster 6 is a high rate group (471 DWI cases vs. 321 expected)
that coincides with the third cluster at the tract level. Cluster 7 is a small low rate group (18 DWI
cases vs. 54 expected) in the central urban area that does not have a corresponding group at the
One noteworthy difference between the two cluster analyses is that the block group clusters tend
to include more cases and population than do the comparable clusters at the tract level. Even
when the large high-rate suburban tract cluster was split into two clusters of block groups, the
total number of cases for the two block-group clusters was greater ((5,782 vs. 5,265).
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Figure 3: DWI conviction rate for block groups in Erie County
Figure 4: DWI clusters at the block group level
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The results at the tract and block levels of analysis provide strong evidence that the spatial
distribution of DWI offenders is clustered, with some areas having significantly higher rates and
other areas marked by lower rates. These clusters are not an artifact of population density
because the case-control methodology used for the analysis controls for density and makes
appropriate statistical tests to identify the specific cluster members. The results of the analyses at
two geographic scales (tracts and block groups) resulted in convergent and complementary
findings. The analysis with smaller geographic units provided a greater number of significant
clusters, most of which coincided with the results at the tract level. The results suggest that a
greater amount of geographic information is available from smaller geographic units, with point
level data (i.e., exact home addresses) being the most preferred for spatial analysis.
DWI clusters were found in urban, suburban, and rural areas of the county, indicating that
clusters are possible within any type of residential area. Although specific analysis of
socioeconomic status variables such as income and poverty were not conducted for this study, the
cluster results tend to reflect socioeconomic patterns. The low DWI rate urban cluster is found in
the poorest areas of the city, while the suburban higher rate clusters are in working class and
middle class towns. The results provide a basis to make rational decisions on targeting of
resources for DWI prevention and interventions, especially if those interventions focus on areas
that generate a disproportionate number of DWI offenders. Primary prevention can be focused at
these areas to reduce general alcohol consumption and to provide safe-ride alternatives. Public
service announcements (e.g., billboards) should also be targeted toward the high rate cluster
areas. Enforcement also could be targeted to the higher rate locations.
Although the results strongly support the existence of DWI clusters, there are a number of
limitations and issues that require future research. It is possible that enforcement practices may
be differential and could cause DWI clusters; however, the data are from a five-year period,
which minimizes the impact of short-term DWI enforcement blitzes, and many of the clusters
cross police jurisdictions. Future analyses need to examine the relationships between location of
the DWIs, alcohol-related crashes, and alcohol outlets with the home locations of the offenders.
In additional, spatial cluster analysis that controls for ethnicity, gender, and age are necessary to
provide a more complete view of the geographic distribution of DWI offenders.
1. Gruenewald, P.J., Millar, A.B., Treno, A.J., Yang, Z., et al.. The geography of availability
and driving after drinking. Addiction 1996; 91(7):967-983.
2. Wieczorek, W.F. & Coyle, J.J. Targeting DWI prevention. Journal of Prevention and
Intervention in the Community 1998; 17(1):17-32.
3. Wieczorek, W.F. & Hanson, C.E. New modeling methods: Geographic information systems
and spatial analysis. Alcohol Health & Research World, 1997; 21(4):331-339.
4. Kulldorff, M., Rand, K., Gherman, G., Williams, G., and DeFrancesco, D. SaTScan v2.1:
Software for the Spatial and Space-Time Scan Statistics. National Cancer Institute, Bethesda,
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Road Users Behaviour Monitoring in Estonia
Antov, D., Rõivas, T.
Rapid increases in motorization have raised concerns regarding accidents and fatalities
internationally. Even nations experiencing declining numbers of fatalities, such as Estonia, have
made it a major public policy goal to decrease fatalities. Traffic fatalities declined from 491 in
1991 to 204 in 2000 in Estonia. Nonetheless, the Federal Government is intent on another 50%
decrease by 2010.
This paper studies two steps necessary to achieve this decrease, (1) understanding the perceptions
of road-use behavior and (2) field observations of driving and pedestrian practices. In May and
June 2001 655 subjects were surveyed to assess their perceptions of road-use behavior. Drunken
driving, lack of seat-belt use in the rear seat and speeding on rural roads were all perceived to be
problems. Generational differences accounted for the greatest differences in perception. The
young gave higher scores on most road-use activities, suggesting that better driver education may
be needed. The number of fatalities and field observation indicates that treatment of pedestrians
is an area in need of attention, especially since the perception is that it is not a major problem.
Pedestrians accounted for 39% of the motor-vehicle related fatalities in Estonia in 2000. And
70% of drivers were observed to be in violation of the law requiring them to yield to pedestrians.
Pedestrians themselves are not without blame, with 26% of pedestrians observed to be in
violation at signalized crossings. Finally, speeding continues to be a perceived and observed
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