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Previous studies have indicated that unlicensed drivers are more likely to engage in risky driving behaviors, and are more likely than licensed drivers to be at fault and more seriously injured when involved in a crash. However, the prevalence of unlicensed drivers in the general driving population has not been measured, and the risk of an unlicensed driver being involved in an injury crash has not been quantified. We examined the association between unlicensed driving and car crash injury using data from a population-based case control study. The study population was the drivers of all cars on public roads in the Auckland region. Cases were 571 vehicles involved in a crash resulting in any occupant being hospitalised or killed, from the study base, during the recruitment period. Controls were 588 vehicles selected from the driving population using a random cluster sampling method. The drivers of all vehicles completed a structured interview covering multiple potentially crash-related factors. Driving unlicensed was reported by 12% of case and 1% of control drivers. Unlicensed drivers were at significantly higher risk of car crash injury than those holding a valid licence (odds ratio 11.1, 95% confidence interval 4.2 to 29.7) after adjustment for age and sex. After further adjustment for education level, ethnicity, driving exposure, time of day, sleepiness score, year of vehicle manufacture, passenger carriage, seatbelt use, blood alcohol concentration, and travelling speed at time of crash, the increased risk was still present but no longer significant (OR 3.9, 95% CI 0.7-22.4). Unlicensed drivers are a high risk group for car crash injury after taking other crash-related risk factors into account. Strategies to reduce unlicensed driving may therefore facilitate reductions in road crashes, although further work is needed in this area.
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Traffic Injury Prevention, 6:230–234, 2005
Copyright C
2005 Taylor & Francis Inc.
ISSN: 1538-9588 print / 1538-957X online
DOI: 10.1080/15389580590969175
Unlicensed Drivers and Car Crash Injury
STEPHANIE BLOWS and REBECCA Q. IVERS
The George Institute for International Health, University of Sydney, Sydney, Australia
JENNIE CONNOR and SHANTHI AMERATUNGA
School of Population Health, University of Auckland, Auckland, New Zealand
MARK WOODWARD and ROBYN NORTON
The George Institute for International Health, University of Sydney, Sydney, Australia
Objective.Previous studies have indicated that unlicensed drivers are more likely to engage in risky driving behaviors,
and are more likely than licensed drivers to be at fault and more seriously injured when involved in a crash. However, the
prevalence of unlicensed drivers in the general driving population has not been measured, and the risk of an unlicensed
driver being involved in an injury crash has not been quantified. We examined the association between unlicensed driving
and car crash injury using data from a population-based case control study.
Methods. The study population was the drivers of all cars on public roads in the Auckland region. Cases were 571 vehicles
involved in a crash resulting in any occupant being hospitalised or killed, from the study base, during the recruitment period.
Controls were 588 vehicles selected from the driving population using a random cluster sampling method. The drivers of all
vehicles completed a structured interview covering multiple potentially crash-related factors.
Results. Driving unlicensed was reported by 12% of case and 1% of controldrivers. Unlicensed drivers were at significantly
higher risk of car crash injury than those holding a valid licence (odds ratio 11.1, 95% confidence interval 4.2 to 29.7) after
adjustment for age and sex. After further adjustment for education level, ethnicity, driving exposure, time of day, sleepiness
score, year of vehicle manufacture, passenger carriage, seatbelt use, blood alcohol concentration, and travelling speed at
time of crash, the increased risk was still present but no longer significant (OR 3.9, 95% CI 0.7–22.4).
Conclusions. Unlicensed drivers are a high risk group for car crash injury after taking other crash-related risk factors
into account. Strategies to reduce unlicensed driving may therefore facilitate reductions in road crashes, although further
work is needed in this area.
Keywords Transportation; Motor Vehicle Injury; Unlicensed Drivers; Case-Control Study
Many countries require drivers to hold a licence to legally
operate a motor vehicle, and reserve the right to suspend or dis-
qualify this licence if road rules are not obeyed. Despite such
laws, a number of people drive unlicensed, either having never
obtained a licence or continuing to drive after their licence has
been suspended or disqualified (Griffin & DeLaZerda, 2000;
Knox et al., 2003). These unlicensed drivers are thought to be at
higher risk of motor vehicle injury than their licensed counter-
parts, but this has been difficult to prove (Federal Office of road
Safety, 1997a; Griffin & DeLaZerda, 2000; Knox et al., 2003).
Several studies of unlicensed drivers have suggested that
they are more likely to engage in risky driving behaviors such
as speeding, drink driving, red light running, and non-use of
Received 22 September 2004; accepted 14 March 2005.
Address correspondence to Stephanie Blows, The George Institute for Inter-
national Health, University of Sydney, Level 10, King GeorgeV Building, Royal
Prince Alfred Hospital, Missenden Road, Camperdown, NSW 2050, Australia.
E-mail: sblows@thegeorgeinstitute.org
seatbelts, than those with a valid licence (Federal Office of
Road Safety, 1997; Retting et al., 1999; Griffin & DeLaZerda
2000; Kim & Kim, 2003). It has also been suggested that unli-
censed drivers may be over-represented in crash statistics (Fed-
eral Office of Road Safety, 1997; Griffin & DeLaZerda, 2000),
but this has not been conclusively demonstrated because of a
lack of comparative data on the prevalence of unlicensed driv-
ing in the general driving population (DeYoung et al., 1997;
Knox, 2003). Studies of crashed drivers have found that com-
pared to licensed drivers, unlicensed drivers are more likely to
be at fault (Perneger & Smith, 1991; Land Transport Safety Au-
thority, 2003) and more seriously injured (Harrison, 1997) when
involved in a crash. These studies used control drivers who were
involved in a crash, rather than population-based controls, so
were unable to determine the excess risk of car crash injury for
unlicensed drivers.
Only one previous study has estimated the driving expo-
sure of suspended/revoked and unlicensed drivers, using a
“quasi-induced” exposure estimation method (DeYoung et al.,
230
UNLICENSED DRIVER CRASHES 231
1997). This study found that both groups were at higher risk of
crash involvement compared to validly licensed drivers, but cau-
tion that their exposure estimates are likely to be subject to sig-
nificant bias. There remains a lack of estimates of the prevalence
of unlicensed driving amongst the general driving population,
and because of this the excess risk of an unlicensed driver’s in-
volvement in a crash that leads to injury has not previously been
quantified.
In New Zealand, a driver’s licence is obtained by completing
a series of applications and driving tests, and payment of a num-
ber of fees. Unlicensed drivers may have not undertaken this
licensing process, or they may have had their licence suspended
or disqualified for a variable period of time as a result of offences
such as drink driving, speeding, or other serious infringements of
the road rules (Land Transport Safety Authority, 2004). We used
data from a population-based case-control study conducted in
the Auckland region of New Zealand to examine the prevalence
of unlicensed driving in the regional driving population, and the
relationship between unlicensed driving and car crash injury.
METHODS
A complete description of the methodology of the study has
been published previously (Connor et al., 2002). Recruitment
took place from 1998 to 1999 in the Auckland region, which
contains the largest city in New Zealand and a mixture of other
urban, suburban and rural areas. The regional population is about
1.1 million people (Statistics New Zealand, 2001). The study
base was defined as all light vehicles driving on non-local pub-
lic roads in the region. Case vehicles were identified when any
occupant of a vehicle from the study base was hospitalized or
killed in a crash during the recruitment period. Case identifica-
tion took place through surveillance at the four hospitals serving
the Auckland region, and through the Auckland Coroner.
During the study period 615 eligible case drivers were iden-
tified and interviews were completed for 571 (93%) of these.
Control selection aimed to achieve a representative sample of
all driving time for the study base during the recruitment period.
Control vehicles were identified during the same time interval
and at approximately the same rate as cases. To select controls, a
list of roads in the Auckland region was obtained and 69 roadside
sites were randomly selected from this list. A day of the week,
time of day, and direction of travel were randomly assigned to
each site. Study staff then visited the site at the selected time
and vehicles that passed the site during a defined period were
randomly selected as control vehicles. The number of vehicles
selected from each site was proportional to the volume of traffic
at the site. These vehicles were stopped at the roadside and a suit-
able time for a telephone interview was arranged. During these
roadside surveys 746 control cars were identified, and of these,
interviews were completed with 588 drivers (79% response rate).
Interviews for the drivers of case and control vehicles were
conducted by telephone for 204 (36%) case drivers and 576
(98%) control drivers; the remaining interviews were conducted
in person. Proxy respondents were interviewed for 57 case
drivers and two control drivers who were fatally injured or un-
able to be interviewed for other reasons. All interviews were
based on a structured questionnaire that included characteristics
of the driver, circumstances of the crash, and vehicle character-
istics. For control drivers, the interview was referenced to the
time of being sampled in the roadside survey. Licence status was
determined by asking drivers what type of car licence they held
at the time of the crash or survey.
For these analyses, “unlicensed” drivers were those that had
never held a car licence or whose licence was disqualified or sus-
pended at the time of the crash/survey; other types of licence,
including full licences, learner licences, and overseas licences,
were considered valid. Blood alcohol level wasdetermined using
a breathalyser for controls and from hospital and police records
for cases. Missing data for blood alcohol level was imputed ac-
cording to self reported alcohol consumption prior to the crash
and the suspicions of ambulance and hospital staff. Details of al-
cohol imputation have been previously published (Connor et al.,
2004). Environmental surveys of crash and control recruitment
sites were conducted to measure environmental factors poten-
tially related to crashes.
Odds ratios (OR’s) and 95% confidence intervals (CIs) were
calculated from linear logistic regression models using SU-
DAAN software, which accounts for intra-cluster correlation of
control data sampled from the same site (Shah et al., 1997). For
the multivariable analyses, we identified potential confounders
from the epidemiological literature and adjusted for these if they
were significantly associated with car crash injury in our data
after controlling for driver’s age and sex. Because unlicensed
driving may influence crash risk indirectly through its associa-
tions with other risky driving, we examined the association be-
tween unlicensed driving and car crash injury by first adjusting
only for age and sex.
We then adjusted for ethnicity, education level, and driving
exposure (average hours spent driving per week), plus acute
driving-related exposures at the time of the crash/survey (pas-
senger carriage, time of day, Stanford sleepiness score, year of
vehicle manufacture, blood alcohol concentration, seatbelt use,
and travelling speed). We also examined the contribution that
each of these confounders made to the age and sex adjusted odds
ratio for the association between unlicensed driving and car crash
injury. This was done by adding each variable to this model in-
dividually and estimating the percentage by which this changed
the odds ratio, using the formula 100 ([ORU-ORA]/[ORU– 1])%
where ORUand ORAare, respectively, the odds ratios for unli-
censed driving and car crash injury unadjusted (except by age
and sex) and after further adjustment for each risk factor alone.
RESULTS
The mean age of case drivers was 36.6 years, and control
drivers 40.8 years. The case group was 65% male and the control
group was 59% male. There were no significant differences in
age group, sex, or driving conditions, between drivers who were
interviewed and all eligible drivers, for both case and control
vehicles.
232 S. BLOWS ET AL.
Figure 1 Unadjusted, age and sex adjusted, and multivariable adjusted odds
ratios (95% confidence intervals) for the association between unlicensed driving
and car crash injury, Auckland Car Crash Injury Study. The multivariable odds
ratio is adjusted for age, sex, ethnicity, education, driving exposure, passenger
carriage, time of day, sleepiness score, year of car manufacture, blood alcohol
concentration, seatbelt use, and traveling speed at the time of crash.
Table I shows the frequency distributions of licence status
and confounding variables by case/control status. The preva-
lence of unlicensed driving at the time of the crash/survey was
11.6% (n =66) amongst cases and 1.1% (n =7) amongst con-
trols. Missing data for both cases and controls was less than 1%
for licence status, and less than 10% for all variables used in
these analyses after imputation for blood alcohol level.
Figure 1 shows the unadjusted, age and sex adjusted, and mul-
tivariable adjusted odds ratios and 95% confidence intervals for
the association between unlicensed driving and car crash injury.
Unlicensed drivers were at significantly higher risk of car crash
injury than those holding a valid licence in the unadjusted model
(OR 12.1, 95% CI 3.8 to 38.4), and after adjustment for age and
sex (OR 11.1, 95% CI 4.2 to 29.7) However, after adding edu-
cation level, ethnicity, driving exposure, time of day, sleepiness
score, year of vehicle manufacture, passenger carriage, seatbelt
use, blood alcohol concentration, and travelling speed at time
of crash, the increased risk was no longer significant (OR 3.9,
95% confidence interval 0.7 to 22.4). These adjustments had a
similar effect when restricted to the subset of participants with
complete data.
Figure 2 shows the proportion of the age and sex adjusted odds
ratio for unlicensed driving and car crash injury explained by the
confounders we examined. Ethnicity and education level were
the two largest contributors, accounting for 47% and 34% of
the odds ratio respectively, followed by Stanford sleepiness score
(32%), blood alcohol level (25%) and driving exposure (22%).
DISCUSSION
This population based case control study allowed us to exam-
ine the prevalence of unlicensed driving the Auckland regional
driving population and the excess risk of car crash injury for
unlicensed drivers. Driving unlicensed was reported by 12%
of cases in this study, of whom 10% had never held a licence
and 2% held a licence that was currently suspended or disquali-
fied. Unlicensed drivers had about 11 times higher risk of being
Table I Frequency distributions of licence status and confounding variables
by case/control status, Auckland Car Crash Injury Study
Cases (n =571) Controls1(n =588)
No. (%) No. (%)
Licence status
Unlicensed (total) 66 (11.6) 7 (1.1)
Never held 55 (9.6) 6 (1.1)
Disqualified/suspended 11 (1.9) 1 (0.02)
Valid 502 (87.9) 580 (98.8)
Don’t know/missing 3 (0.5) 1 (0.1)
Age of driver (years)
<25 195 (34.2) 91 (13.7)
25–34 133 (23.3) 125 (22.3)
35–44 85 (14.9) 154 (24.5)
45–54 61 (10.7) 107 (19.6)
55–64 39 (6.8) 80 (14.2)
65+58 (10.2) 31 (5.6)
Sex
Female 198 (34.7) 226 (41.3)
Male 373 (65.3) 362 (58.7)
Education level
Post secondary 178 (31.5) 276 (49.3)
Secondary school, >3 years 137 (24.2) 154 (25.1)
Secondary school, 3 years 252 (44.4) 157 (25.6)
Ethnicity
White/European 313 (54.8) 444 (74.7)
Maori 117 (20.5) 61 (9.2)
Pacific Islander 86 (15.1) 36 (6.1)
Other 55 (9.6) 47 (10.0)
Driving exposure
(average hours per week)
5 219 (42.1) 171 (30.5)
6–10 205 (39.4) 216 (39.3)
11–20 63 (12.1) 135 (22.3)
21–30 11 (2.1) 32 (3.8)
>30 22 (4.2) 27 (4.1)
Time of day
Not between 2–5 am 525 (91.9) 571 (99.6)
Between 2–5 am 46 (8.1) 17 (0.4)
Stanford Sleepiness Score
1–3 (sleepy) 447 (87.7) 578 (99.0)
4–7 (not sleepy) 63 (12.3) 8 (1.0)
Year of vehicle manufacture
<1984 118 (23.9) 58 (8.9)
1984 to 1988 157 (31.8) 164 (31.0)
1989 to 1993 169 (34.2) 207 (37.8)
1994 50 (10.1) 133 (22.4)
Number of passengers
0 285 (50.3) 355 (62.9)
1 140 (24.7) 144 (23.4)
2ormore 142 (25.0) 88 (13.7)
Seatbelt use
Yes 469 (82.1) 568 (97.4)
No 81 (14.2) 4 (0.8)
Blood alcohol concentration (mg %)2
<3 397 (69.7) 565 (96.6)
3–50 41 (7.2) 16 (2.6)
>50 132 (23.2) 6 (0.8)
Travelling speed
0–30 kph 87 (16.9) 78 (13.9)
31–50 kph 113 (21.9) 229 (41.5)
51–80 kph 196 (38.1) 220 (33.7)
>80 kph 119 (23.1) 55 (11.0)
1Proportions of controls are adjusted for the clustered sampling design.
2Missing data imputed (Connor et al., 2004).
UNLICENSED DRIVER CRASHES 233
Figure 2 Proportion of the age and sex adjusted odds ratio for unlicensed
driving and car crash injury explained by other risk factors, Auckland Car Crash
Injury Study.
involved in a serious injury crash compared to drivers holding a
valid licence after adjustment for age and sex. This positive as-
sociation was still present after adjusting for other crash-related
factors, although it was no longer significant probably due to
lack of power.
Our prevalence estimates are consistent with several previous
studies. Crash statistics from New Zealand indicate that in 1998,
11% of fatal crashes involved a disqualified or unlicensed driver
(Land Transport Safety Authority, 2000). Griffin and DeLaZerda
(2000) examined 278,078 drivers involved in fatal crashes us-
ing the Fatal Accident Reporting System in the United States.
Of these drivers, 11% of these held an invalid licence or had
no known licence. Harrison (1997) found that of fatal crashes
in Victoria, Australia, 2% had a disqualified licence. However,
there are few previous estimates of the frequency of unlicensed
driving amongst the general driving population. In the control
population of our study, conceptually representing the Auckland
regional driving population, 1% reported being unlicensed or
disqualified/suspended at the time of the roadside survey. Us-
ing a “quasi-induced” exposure method, also using data from
the Fatal Accident Reporting System, DeYoung et al. (1997)
estimated that 9% of the driving population in California had a
suspended or revoked licence. This is higher than our estimate,
but the authors of this study note several serious limitations of
their estimation methods. Further representative surveys are re-
quired to measure the prevalence of unlicensed driving more
accurately.
We also found a strong association between unlicensed driv-
ing and car crash injury after adjustment for age and sex. Even
after adjustment for other crash-related risk factors, the point
estimate for the odds ratio was about four, although this was no
longer significant. This lack of significance is likely to be due to
small numbers producing wide confidence intervals. Our find-
ing that unlicensed drivers are a high-risk driving population is
consistent with previous research. Two studies have examined
the effect of driving unlicensed on crash severity. Shibata &
Fukuda (1994) used data from police-reported traffic crashes
of both cars and motorcycles in Fukuoka Prefecture, Japan,
comparing characteristics of crash-involved drivers who died
to those who were uninjured (Shibata & Fukuda, 1994). Af-
ter adjustment for age, unlicensed drivers of cars were three
times more likely, and drivers of motorcycles nine times more
likely, to be killed when involved in a car crash compared to
licensed drivers. The association was significant only for motor-
cycle drivers.
Harrison (1997) conducted a similar study of crash-involved
drivers in Victoria, Australia, and found a significant difference
between the frequency of disqualified licences amongst fatally
injured drivers (4.6%) compared to uninjured drivers (0.7%)
in unadjusted analyses (Harrison, 1997). Another study exam-
ined the effect of various driver characteristics, including licence
status, on involvement in an at-fault crash (Perneger & Smith,
1991). Using paired crash data from the Fatal Accident Report-
ing System in the United States, this study found that drivers
with an invalid licence were about twice as likely to have ini-
tiated the crash compared to those holding a valid licence. Our
results confirm unlicensed drivers to be a population at high
risk of serious car crashes and suggest that unlicensed drivers
have three times excess risk of involvement in an injury crash
compared to licensed drivers.
Driving unlicensed is unlikely to directly increase the risk
of car crash injury and the mechanism by which this popu-
lation is at risk is probably through associations with other
crash-related factors. Of the acute risky driving behaviors we
examined, sleepiness and blood alcohol level accounted for the
largest proportion of the age and sex adjusted odds ratio. Other
studies have also found evidence that unlicensed drivers may be
more likely to display risky driving behaviors, including speed-
ing, drink driving, red-light running, and non-use of seatbelts
(Federal Office of Road Safety, 1997b; Retting et al., 1999; Grif-
fin & DeLaZerda, 2000; Kim & Kim, 2003). In our data, educa-
tion level, ethnicity, and driving exposure were also important
contributors to the relationship between unlicensed driving and
car crash injury.
There were significant associations between unlicensed driv-
ing and ethnicity (p =0.01), with more Maori and Pacific Is-
landers being both never licensed and holding a disqualified or
suspended licence. There were no significant associations be-
tween licence status and other demographic variables, including
age (p =0.2). Because of small numbers we were not able to
fully investigate the relationships between licence status and
other associated variables; this will be an interesting area for
future research. However, although a variety of crash-related
variables may account for the relationship between unlicensed
driving and car crash injury, explicit knowledge of these need
not be a prerequisite for the implementation of countermeasures
aimed at unlicensed drivers.
Our study has several potential limitations. Licence status
and many of the other variables were self reported. Because
driving unlicensed is illegal in New Zealand, this may be un-
derreported and is therefore a potential source of measurement
234 S. BLOWS ET AL.
bias. However, questions on illegal behaviors were embedded in
a large questionnaire containing multiple items relating to driv-
ing; interviewers were highly trained and assured participants of
complete confidentiality. Our data on excess alcohol consump-
tion prior to driving (also illegal in New Zealand) suggest that
self report is a valid measure, as the correlation between self re-
ported and objective measures of alcohol consumption was high
(Spearman correlation coefficient =0.77).
The differential response rate between cases and controls may
have introduced selection bias, particularly if non-responders
amongst the control population tended to be unlicensed. If this
is the case, we are likely to have underestimated the prevalence
of unlicensed driving in controls, which would result in an over-
estimate of the risk of injury crashes. Confounding variables,
particularly those that relate to risky and illegal driving, may
have also been inaccurately measured. Case control studies using
self-reported data may also be subject to recall bias (Woodward,
2005), although it is difficult to predict what effect this would
have on our estimate of effect.
The increased risk of injury to vehicle occupants when the
driver is unlicensed supports the need for interventions aimed at
this population. Other authors have suggested a variety of mea-
sures that may be effective, including increasing police resources
to enable enforcement, reviewing and broadening the penalties
applied, and increasing public awareness of the dangers of un-
licensed driving and the penalties involved (Knox et al., 2003).
Others have proposed applying barriers to driving including ve-
hicle impoundment, electronic driver licences, and ignition in-
terlocks when licences are suspended or disqualified (Griffin &
DeLaZerda, 2000).
For most jurisdictions, interventions in the short term are
likely to focus on enforcement and education strategies. Unli-
censed driving is unlikely to be a randomly distributed charac-
teristic and the identification of high-risk groups and risk fac-
tors for unlicensed driving, for example, age, gender, ethnicity,
socioeconomic status, or lifestyle factors such as hazardous al-
cohol use, would aid in targeting health promotion strategies.
There may also be value in reviewing the suitability of current
licensing processes and driver training for these specific groups.
Although the primary aim of such interventions should be to re-
duce unlicensed driving, a harm minimisation approach aiming
to decrease risky driving amongst unlicensed drivers may also
achieve a reduction in injuries.
ACKNOWLEDGMENTS
The Auckland Car Crash Injury Study was funded by the Health Research
Council of New Zealand.
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... The studies that focused on all age groups also cover traffic regulation violation topics. For example, texting while driving and violations at the stop sign and beating red traffic light (Bener et al., 2013;Blows et al., 2005;Chliaoutakis et al., 2005;Debnam & Beck, 2011;Elias & Shiftan, 2017;Factor et al., 2012;Jahanfar, 2018;Matthews & Norris, 2002;Romano et al., 2005Romano et al., , 2006Rudisill & Zhu, 2017;Sloan et al., 2013;Smith et al., 2019). In addition to that, Almallah et al. (2020) attempted to make a comparison between ethnicities for green phase acceleration reaction at a traffic light. ...
... Traffic accidents and police reports were also used to investigate the pattern of violations made by drivers (Blows et al., 2005;Gau & Brunson, 2012;Romano et al., 2005Romano et al., , 2006Smith et al., 2019). As it is different from a mere self-report study, the data also presented a true incident that has taken place. ...
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Reckless driving behavior might result in a higher risk of an accident. Many factors are known to be the cause of this driving behavior. One of the factors is the socio-demographic background of the driver. This study aims to review currently available literature that investigates the relationship between driving behavior and any known socio-demographic characteristics. The review also focuses on the method used in the data collection as well as the tools used to perform the analysis to correlate the driving behavior and socio-demographic background. The review found that the influence of socio-demographic background on driving behavior study has not been explored in detail especially from the ethnicity point of view. With regards to the data collection, most of the study utilised the self- report survey, in which the targeted respondents are young adults. There are also studies covering all age groups that made use of the Driving Behaviour Questionnaire, data of traffic accidents or police reports, and virtual reality to collect the data. SAS/STAT statistical software package was found to be a popular choice among researchers when analyzing the data. This review concludes that driving behavior study in the multi-racial country for instance in Malaysia should explore further the relationship between driving behavior and socio-demographic background, especially from the ethnic perspective.
... In the past few decades, several investigations have provided evidence of a relationship between driving without a valid license (DWVL) (e.g., unlicensed drivers, driving with a suspended, revoked or expired license) and an increased risk of causing or being involved in a road crash (DeYoung et al., 1997;Knox et al., 2003;Blows et al., 2005a, b;Watson et al., 2011a, b;Brar, 2014;Sagberg, 2018). The recognized association between DWVL and other classic risk factors for road crash, including driving under the influence of alcohol or other drugs, speeding, lower age/inexperience or driving after midnight (Harrison, 1997;Federal Office of Road Safety, 1997;Griffin and DelaZerda, 2000;Malenfant et al., 2002;Knox et al., 2003;Blows et al., 2005b;Hanna et al., 2010Hanna et al., , 2013Watson et al., 2011aWatson et al., , b, 2012Sagberg, 2018;Boulagouas et al., 2020), have been usually invoked to explain the above relationship. In addition, there is evidence supporting an association between unlicensed drivers and young male drivers (Griffin and Dela-Zerda, 2000;Knox et al., 2003;Watson et al., 2011aWatson et al., , b, 2012Boulagouas et al., 2020). ...
... Finally, neither of these studies provided adjusted estimates of the strength of the association between DWVL and the risk of crash, which involves other potentially explanatory factors related to the driver, vehicle or environment. In fact, we only found one study which, using a different approach (a case-control design), provided an estimate of the association between DWVL and the risk of crash adjusted by other risk factors of road crash (Blows et al., 2005b). ...
Article
The aim of this study was to estimate the association between each cause of driving without a valid license (DWVL) and the risk of causing a road crash, considering driver, vehicle and environmental factors. A case-control study based on data from the Spanish Register of Road Accidents with Victims was carried out between 2014 and 2017. Cases included 28,620 drivers of moving private cars, vans and off-road vehicles involved in single crashes plus 50,100 drivers deemed responsible for clean collisions (i.e. those in which only one driver was labeled as responsible). In accordance with the quasi-induce exposure approach, drivers not responsible for clean collisions comprised the control group (N = 51,656). Logistic and multinomial regression models were used to estimate crude and adjusted Odds Ratios or Relative Risk Ratios between each reason for DWVL and the risk of being a case of all, single and multi-vehicle collisions. A significant association was found between all reasons for DWVL and the risk of causing a road crash. This association was particularly high for drivers with a suspended license and drivers who had never obtained a license. In these subgroups of drivers, the proportion of the relationship explained by high-risk driving behaviors is high. Our results support the need for applying continued strategies to identify and control these subgroups of drivers.
... Analyses of crash data provide another, albeit also problematic, way to estimate unlicensed driving. A few studies have reported the proportion of crashes that involve unlicensed drivers (AAA Foundation for Traffic Safety 2011; Blows et al. 2005;Møller & Janstrup 2021;NHTSA 2014). However, lack of a license is associated with crash risk, so the prevalence of unlicensed drivers on the road in general would be different (lower). ...
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Background Little is known about the prevalence of driving among teenagers who have not yet obtained a license. The primary objective of the present study was to estimate the prevalence of unlicensed driving among young drivers using the quasi-induced exposure (QIE) approach and to determine whether unlicensed driving was more common among minority and lower-income teenagers. Additionally, we examined whether unlicensed driving among adolescents increased following the implementation of a graduated driver licensing (GDL) system and whether GDL differentially affected minority and low-income adolescents. Methods Using North Carolina crash and driver license data, we identified 90,267 two-vehicle crashes from 1991 through 2016 where only one driver was considered contributory and the non-contributory driver was a White or Black 16 or 17 years old. In the QIE approach, these non-contributory young drivers are assumed to be representative of all adolescents driving in the state during this time period. The prevalence of unlicensed driving among adolescents by age and year was estimated by identifying the proportion of non-contributory drivers who had never been licensed by the time of their involvement in these two-vehicle crashes. We further conducted logistic regression analyses to examine the likelihood of a non-contributory young driver being unlicensed as a function of race, neighborhood income level, and licensing era (prior to or after GDL was implemented). Results During the 26 years for which data were available, the mean annual prevalence of unlicensed driving was 1.2% for 16-year-olds and 1.7% among 17-year-olds. Young Black drivers and individuals living in lower-income neighborhoods were somewhat more likely to drive before obtaining a license, but the rates of unlicensed driving among these groups were also quite low. Unlicensed driving increased slightly for 17-year-olds following the implementation of GDL, but returned to previous levels after a few years. Conclusion Unlicensed driving among adolescents in North Carolina is substantially less common than suggested by previous self-report studies and analyses of fatal crash data.
... Similarly, although failure to use seatbelt does not in itself cause crashes, it increases the probability of being injured in a crash (Evans, 1996;Abdel-Aty, 2003;Wang and Jiang, 2003;Kim et al., 2013;. Nonetheless, many studies have found a strong correlation between serious injury crash outcomes and risky behaviors such as DUI (e.g., Tavris et al., 2001;Abdel-Aty, 2003;Dabbour, 2017), aggressive driving (Paleti et al., 2010;Dahlen et al., 2012;Islam and Mannering, 2020), and driving without a valid license (Blows et al., 2005;Adanu et al., 2018). The propensity of certain road user groups to engage in risky driving behaviors have been linked to many factors such as age (e.g., Elander et al., 1993;Chliaoutakis et al., 2000;, gender (e.g., Miller et al., 1998;Turner and McClure, 2003;Adanu et al., 2018), socioeconomic status (e. g., Abdalla et al., 1997;Liu et al., 1998), personality (e.g., Yu and Williford, 1993;Nicholson et al., 2005), type of vehicle being driven (e.g., Ulfarsson and Mannering, 2004), and even regional culture and systems (e.g., Lund and Rundmo, 2009;Atchley et al., 2014;Adanu et al., 2019). ...
Article
With the rising number of cases and deaths from the COVID-19 pandemic, nations and local governments, including many across the U.S., imposed travel restrictions on their citizens. This travel restriction order led to a significant reduction in traffic volumes and a generally lower exposure to crashes. However, recent preliminary statistics in the US suggest an increase in fatal crashes over the period of lockdown in comparison to the same period in previous years. This study sought to investigate how the pandemic affected road crashes and crash outcomes in Alabama. Daily vehicle miles traveled and crashes were obtained and explored. To understand the factors associated with crash outcomes, four crash-severity models were developed: (1) Single-vehicle (SV) crashes prior to lockdown order (Normal times SV); (2) multi-vehicle (MV) crashes prior to lockdown order (Normal times MV); (3) Single-vehicle crashes after lockdown order (COVID times SV); and (4) Multi-vehicle crashes after lockdown order (COVID times MV). The models were developed using the first 28 weeks of crashes recorded in 2020. The findings of the study reveal that although traffic volumes and vehicle miles traveled had significantly dropped during the lockdown, there was an increase in the total number of crashes and major injury crashes compared to the period prior to the lockdown order, with speeding, DUI, and weekends accounting for a significant proportion of these crashes. These observations provide useful lessons for road safety improvements during extreme events that may require statewide lockdown, as has been done with the COVID-19 pandemic. Traffic management around shopping areas and other areas that may experience increased traffic volumes provide opportunities for road safety stakeholders to reduce the occurrence of crashes in the weeks leading to an announcement of any future statewide or local lockdowns. Additionally, increased law enforcement efforts can help to reduce risky driving activities as traffic volumes decrease.
... Knowledge on unlicensed crash involvement is limited, but the existing studies indicate that the proportion of this crash type is small (Lam, 2003). However, the risk of serious injury is 11 times higher for unlicensed young drivers compared to licensed young drivers (Blows et al., 2005). The existing studies indicate that more young men than women are involved in unlicensed crashes (Lam, 2003;Huber et al., 2006;McDowell et al., 2009), and that a number of the crashes occur while the car is being pursued by the police. ...
Article
Unlicensed driving among youth is associated with increased crash risk, and partly motivated by a wish to learn to drive. In this paper we examine whether crash involvement among 17-year-old unlicensed drivers changed after post-licence accompanied driving from the age of 17 was allowed in Denmark in 2017. The study includes police-registered crashes occurring three years before and three years after the change (2014-2019). Results show an increase in crash involvement among 17-year-olds and a small increase in crash involvement among unlicensed 17-year-olds, if population size is taken into account, but no differences in the crash and person characteristics before and after the change. Being male, speeding, and impairment at the time of the crash predicted unlicensed crash involvement. A latent class clustering analysis (LCCA) identified seven clusters of crashes involving an unlicensed 17-year-old. The cluster characteristics reveal different patterns in the associated factors such as females and parked vehicles being more likely to be included in C1, alcohol impaired in C2 and drug impaired in C7. Brief crash descriptions provided by the police indicate that driving with extra motives such as showing-off or pleasure are prevalent in all clusters. Results confirm, that unlicensed crash involvement among 17-year olds is associated with risk-taking behaviours such as speeding, impaired driving, showing-off, and the car being pursued by the police. However, unfortunate manoeuvres and loss of control of the vehicle possibly related to poor driving skills are also associated with the crashes. Crash characteristics such as impairment by alcohol and drugs indicate that unlicensed crash involvement is a distinct safety challenge associated with health risk behaviours rather than a transport related need for a driver's license. Additional studies exploring the motivations and circumstances associated with unlicensed driving among 17-year olds are needed along with measures to prevent car access among unlicensed youth..
Article
The relationship between license-related infractions (LRIs) and the severity of road crashes has been scarcely addressed in previous research. This study estimates the association between each LRI and the severity of driver injuries and the partial severity of the crash (i.e., crash severity after excluding the severity of the driver’s own injuries) in a cohort comprising 78,720 drivers who were considered responsible for crashes in the Spanish National Register for Road Traffic Accident Victims, from 2014 to 2017. Adjusted Relative Risk Ratios for each LRI and severity level were obtained through multinomial logistic regression models. Age- and sex-adjusted estimates revealed an increased severity for almost all LRIs. Additional adjustment for seat belt use showed a decrease in the magnitude of the associations, particularly regarding driver injury severity, suggesting that part of these associations was related to increased vulnerability of the driver. Adjustment for other vehicle- and environment-related variables showed a further decrease in the associations but remained significant for “never having obtained a license” and other specific LRIs. These results support the need for maintaining police surveillance and legal measures to identify these subgroups of drivers, remove them from the road and adopt strategies for their safe return to driving.
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Background Prevention of Road traffic deaths as a critical public health issue requires coordinated efforts. We aimed to determine influential factors related to traffic safety in Iran as a low-income country (LIC). Methods In this study with a cross-sectional design, the information of 384,614 road traffic crashes recorded in Integrated Road Traffic Injury Registry System (IRTIRS) in a one-year period (March 2015 - March 2016) was analyzed. All registered crashes in Tehran, Isfan, Fras, Razavi Khorasan, Khuzestan and East Azerbaijan provinces, the six most populated provinces in Iran was included in this study. The data was in five main section namely crash scene, vehicle-, driver-, passenger- and pedestrian- related information. Multiple logistic regression applied through STATA software was used for data analysis. Results Over all the final model could identified thirty-two out of seventy-one different variables to be effective in road collisions. The following factors were found to increase the rate of fatal crashes at least by two time: be the most five significant in predicting fatal outcome in road traffic crashes: presence of passenger, unlicensed driving, illegal driving maneuver, head-on collision, crashes in suburban areas, occurrence of multiple causes for collision, vehicles with not personal-regional plaques, presence of pedestrians, drivers with low-income jobs, driver misconduct, roads with double solid lines, non-residential areas, multiple road defects. Conclusion This study reveals that driving behavior, infrastructure design and geometric road factors must be considered to avoid fatal crashes. Our results provide support for compulsory interventions in these areas.
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Background Several campaigns on road traffic accidents have been launched by the Federal Road Safety Corps (FRSC) of Nigeria in collaboration with the Beer Sectoral Group (BSG). One such campaign is the "Don't Drink and Drive" intervention launched in 2008. This intervention was initiated to discourage drunk-driving and to improve safety on Nigerian roads through awareness creation. While it is a fact that the the combined enforcement and public education roles of the stakeholders has enormous potentials to tackle drunk driving problems, indigenous empirical evaluation on drivers' exposure and compliance to such DDD messages is scanty. This paper therefore examined motorists' exposure to FRSC's "Don't Drink and Drive" media campaign in South-east, Nigeria and its effects on their compliance levels. Methods A descriptive survey was used to elicit information from 360 registered commercial and private drivers in three selected states (Anambra, Ebonyi and Enugu) in South-eastern region of Nigeria. A multistage sampling approach was adopted in the study. Specifically, a combination of simple random sampling techniques and a purposive sampling procedure was adopted to access respondents across the region under study. Participants volunteered to complete paper version of the questionnaire at their convenience. An independent-samples t-test, a one-way between-groups analysis of variance (ANOVA) and a chi-square test for odd ratio (OR) and relative risks (RR) were performed to test the variables of interests in the study. Results Among other findings, data revealed that in terms of the participants' level of exposure to the DDD campaign, significant differences were observed in age, education, income and number of years spent driving. We also found that drivers' agreement level of the contents of the DDD campaigns was appreciable. Results particularly suggest that the DDD campaigns had a significant impact on drivers' likelihood of avoiding alcohol when driving, reducing alcohol intake at other times and educating others on the danger of drunk driving. Conclusion We conclude that the DDD campaigns might be more effective when policy designers and interventionists concerned with road safety begin to focus on the differences in the demographic characteristics of the drivers. Nonetheless, we advised that the intervention should continue alongside the use of legal back-up (i.e., by imposing some sanctions on drunk drivers) for optimal performance, while campaign efforts should factor in the roles of multiple variables that have been raised in this study.
Article
Background: Fractures in children can be caused by a long term disability and decreasing quality of life in every people that involved. Factors that affect fractures incidences must be identified so that we can create prevention management. This study aims to evaluate the fractures pattern of children in orthopaedic and traumatology in dr. Wahidin Sudirohusodo Central General Hospital Makassar.Methods: A cross-sectional study was conducted among children under 19 years old by collecting a medical record of patients. The prevalence and patterns of fractures were reviewed for details, such sex, range of age, causes, place of injury, single or multiple fractures, types or location of single fracture, and treatment of fractures. Data were analysed using SPSS version 17 for Windows.Results: There were 152 children in the study, and 72.3% were boys. Most common occur at the 12-18 years age group (92.8%), most of them were caused by traffic accidents (73.6%). Consequently, the location in which fractures were most prevalent was the street (76.9%). Most of them were presented as a single fracture (72.3%) dominated by closed fracture (63.6%), while distal radius/ulna (12.7%) was the most common fracture sites in this study and most patients have undergone surgery for their treatment (84.8%).Conclusion: Most of the patients were boys and caused by traffic accidents. A single and close fracture were the most common types of fracture.
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Rural highways are an important component of highway networks in developing countries. The high fatality rates of single-vehicle crashes in these highways recently attracted increasing attention. Given that most studies on the factors that affect the severity of single-vehicle crashes in rural highways were conducted in developing countries, the present study investigated this issue in a Chinese setting by analyzing the single-vehicle crash data of rural highways in Anhui Province, China from 2014 to 2017. First, in consideration of the unobserved heterogeneity of crash data, a method that combines latent class analysis (LCA) and binary logistic regression (BLR), which is called LC-BLR, was applied to identify the significant factors that affect the severity of single-vehicle crashes in rural highways. Second, the goodness-of-fit and prediction accuracy of the LC-BLR model and the BLR model were compared. Results revealed that the performance of the former was more satisfactory than that of the latter. Finally, countermeasures were proposed based on the analysis of the main factors that affect each sub-class crash in the LC-BLR model. The LC-BLR model results indicated that collision typewas significant in all three sub-class models considered in the analysis, but the effects on crash severity varied. Several variables (e.g., driving license state, time of week, driver age) demonstrated a significant effect in a specific sub-class model, thereby indicating that these factors were only effective in mitigating the crash severity of one sub-class. The findings of this study can facilitate the development of cost-effective policies or countermeasures for reducing the severity of single-vehicle crashes in rural highways.
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This population-based study examines drivers' characteristics associated with driving errors that resulted in fatal motor vehicle crashes. Routinely collected data from the Fatal Accident Reporting System were used to assess whether a driver initiated the crash (case) or was passively involved (control) in 6,506 two-car collisions (81% of 7,993 eligible events). A paired comparison of cases and controls avoided confounding by environmental factors, exposure to traffic, and differences in case fatality. The strongest predictor of crash initiation is alcohol (odds ratio (OR) = 11.5; 95% confidence interval (CI) 9.57-13.9). Odds ratios are elevated even at the lowest blood alcohol concentration levels and increase dramatically as alcohol levels rise. Drivers aged 40-49 years are least likely to initiate crashes; odds ratios rise in a U-shaped manner to 3.35 in teenagers (95% CI 2.72-4.13) and to 22.1 in drivers over 80 years (95% CI 14.2-34.5). Other risk factors for initiating a fatal crash are the following: not wearing a seat belt (OR = 1.54; 95% CI 1.35-1.75), driving without a valid driver's license (OR = 2.16; 95% CI 1.72-2.73), and having had a crash within the last year (OR = 1.21; 95% CI 1.07-1.38). Driving errors leading to fatal crashes do not occur at random, but are associated with specific driver characteristics. The risk factors for crash initiation among crash-involved drivers are similar to risk factors for crash involvement found in other studies. These findings suggest that driving errors often explain high rates of crash involvement, invite further use of crash initiation in traffic injury research, and underscore the value of population-based registries for analytic epidemiology.
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To estimate the contribution of driver sleepiness to the causes of car crash injuries. Population based case control study. Auckland region of New Zealand, April 1998 to July 1999. 571 car drivers involved in crashes where at least one occupant was admitted to hospital or killed ("injury crash"); 588 car drivers recruited while driving on public roads (controls), representative of all time spent driving in the study region during the study period. Relative risk for injury crash associated with driver characteristics related to sleep, and the population attributable risk for driver sleepiness. There was a strong association between measures of acute sleepiness and the risk of an injury crash. After adjustment for major confounders significantly increased risk was associated with drivers who identified themselves as sleepy (Stanford sleepiness score 4-7 v 1-3; odds ratio 8.2, 95% confidence interval 3.4 to 19.7); with drivers who reported five hours or less of sleep in the previous 24 hours compared with more than five hours (2.7, 1.4 to 5.4); and with driving between 2 am and 5 am compared with other times of day (5.6, 1.4 to 22.7). No increase in risk was associated with measures of chronic sleepiness. The population attributable risk for driving with one or more of the acute sleepiness risk factors was 19% (15% to 25%). Acute sleepiness in car drivers significantly increases the risk of a crash in which a car occupant is injured or killed. Reductions in road traffic injuries may be achieved if fewer people drive when they are sleepy or have been deprived of sleep or drive between 2 am and 5 am.
Article
Objectives To estimate the contribution of driver sleepiness to the causes of car crash injuries. Design Population based case control study. Setting Auckland region of New Zealand, April 1998 to July 1999. Participants 571 car drivers involved in crashes where at least one occupant was admitted to hospital or killed ("injury crash"); 588 car drivers recruited while driving on public roads (controls), representative of all time spent driving in the study region during the study period. Main outcome measures Relative risk for injury crash associated with driver characteristics related to sleep, and the population attributable risk for driver sleepiness. Results There was a strong association between measures of acute sleepiness and the risk of an injury crash. After adjustment for major confounders significantly increased risk was associated with drivers who identified themselves as sleepy (Stanford sleepiness score 4-7 v 1-3; odds ratio 8.2, 95% confidence interval 3.4 to 19.7); with drivers who reported five hours or less of sleep in the previous 24 hours compared with more than five hours (2.7, 1.4 to 5.4); and with driving between 2 am and 5 am compared with other times of day (5.6, 1.4 to 22.7 No increase in risk was associated with measures of chronic sleepiness. The population attributable risk for driving with one or more of the acute sleepiness risk factors was 19% (15% to 25%). Conclusions Acute sleepiness in car drivers significantly increases the risk of a crash in which a car occupant is injured or killed. Reductions in road traffic injuries may be achieved if fewer people drive when they are sleepy or have been deprived of sleep or drive between 2 am and 5 am.
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The crash involvement of drivers and motorcyclists who were disqualified from driving was investigated using the Victorian database of reported injury crashes. Disqualified drivers and motorcyclists were overrepresented in more serious crashes and in crashes that occurred during recreational times, such as night time and weekends. They were also over-represented in single-vehicle crashes and crashes involving a collision with a stationary object. The possibility that this pattern represents an ongoing road safety risk, and the impact of this road-user behavior on the effectiveness of license cancellation and suspension penalties for serious traffic offenses are discussed.
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The present study was conducted to evaluate the effect of potential risk factors--such as driving without a license, alcohol use, speed, seat belt, and helmet--use on fatality in motor vehicle traffic accidents. Unconditional multiple logistic regression analysis was employed to take these factors and age into account, simultaneously. The effect of driving without a license was not significant after controlling for other factors. The deleterious effect of alcohol use remained significant for male motorcar drivers after controlling for speed and seat belt use. Magnitude of the risk due to speed was slightly reduced after controlling for alcohol use and seat belt use, but the striking effect remained highly significant. Speed was the strongest risk factor of fatality for both motorcycles and motorcars and for both sexes and seemed to be more critical for motorcyclists than motorcar drivers. The protective effect of seat belt use was unchanged after adjustment for alcohol and speed, and the effectiveness of seat belt use was demonstrated for motorcar drivers. The effectiveness of helmet use for male motorcyclists was dependent upon speed at the time of the accidents, suggesting an interaction between helmet use and speed. Helmet use was definitely protective at a low speed of < or = 50 km/h, but ineffective at high speeds of over 50 km/h.
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There have been a number of studies conducted during the past three decades which show that most suspended/revoked (S/R) drivers violate their license action and continue to drive during their period of disqualification. Traffic safety researchers also suspect that S/R drivers are overinvolved in traffic crashes, but this is difficult to demonstrate because of the lack of good data on their prevalence among all road users. This paper applies the quasi-induced exposure method to fatal crash data obtained from the National Highway Traffic Safety Administration's Fatal Accident Reporting System, to generate exposure and crash rate estimates for S/R drivers in California. The results show exposure rates of 8.8% and 3.3% for suspended/revoked and unlicensed drivers, respectively, and that, compared to validly licensed drivers, the former are overinvolved in fatal crashes by a factor of 3.7:1, and the latter 4.9:1. These findings provide support for efforts to better control S/R and unlicensed drivers. The paper also discusses serious limitations to using quasi-induced exposure to estimate the numbers of such drivers on California roads, and concludes that it is not suited to this task.
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About 40% of motor vehicle crashes occur at intersections. In recent years, the number of crashes at traffic signals has increased considerably. A major cause of such crashes is drivers disregarding traffic signals. Despite concerns about the frequent occurrence of red light violations and the significant crash consequences, relatively little is known about the overall prevalence and characteristics of red light running crashes. The present study examines the prevalence of red light running crashes on a national basis and identifies the characteristics of such crashes and the drivers involved. Cities with especially high rates of fatal red light running crashes are identified. Countermeasures to reduce red light running crashes based on collision patterns and characteristics of drivers involved are discussed. It was estimated that about 260000 red light running crashes occur annually in the United States, of which approximately 750 result in fatalities. Comparisons were made between red light running drivers and drivers deemed not to have run red lights in these same crashes. As a group, red light runners were more likely than other drivers to be younger than age 30, male, have prior moving violations and convictions for driving while intoxicated, have invalid driver's licenses, and have consumed alcohol prior to the crash. Comparisons also were made between characteristics of red light runners involved in daytime and nighttime crashes. Nighttime red light runners were more likely than daytime runners to be young, male, and have more deviant characteristics, 53% having high blood alcohol concentrations.
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The characteristics of crash-involved seat belt non-users in a high use state (Hawaii) are examined in order to better design enforcement and education programs. Using police crash report data over a 10-year period (1986-1995), we compare belted and unbelted drivers and front seat occupants, who were seriously injured in crashes, in terms of personal (age, gender, alcohol involvement, etc.) and crash characteristics (time, location, roadway factors, etc.). A logistic regression model combined with the spline method is used to analyze and categorize the salient differences between users and non-users. We find that unbelted occupants are more likely to be male, younger, unlicensed, intoxicated and driving pickup trucks versus other vehicles. Moreover, non-users are more likely than users to be involved in speed-related crashes in rural areas during the nighttime. Passengers are 70 times more likely to be unbelted if the driver is also unbelted than passengers of vehicles with belted drivers. While our general findings are similar to other seat belt studies, the contribution of this paper is in terms of a deeper understanding of the relative importance of various factors associated with non-use among seriously injured occupants as well as demonstrating a powerful methodology for analyzing safety problems entailing the categorization of various groups. While the former has implication for seat belt enforcement and education programs, the latter is relevant to a host of other research questions.
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Alcohol impairment of drivers is considered the most important contributing cause of car crash injuries. The burden of injury attributable to drinking drivers has been estimated only indirectly. We conducted a population-based case-control study in Auckland, New Zealand between April 1998 and July 1999. Cases were 571 car drivers involved in crashes in which at least 1 occupant was admitted to the hospital or killed. Control subjects were 588 car drivers recruited on public roads, representative of driving in the region during the study period. Participants completed a structured interview and had blood or breath alcohol measurements. Drinking alcohol before driving was strongly associated with injury crashes after controlling for known confounders. This was true for several measures of alcohol consumption: for self-report of 2 or more 12-g alcoholic drinks in the preceding 6 hours compared with none, the odds ratio (OR) was 7.9 (95% confidence interval = 3.4-18); for blood alcohol concentration 3 to 50 mg/100 mL compared with <3 mg/100 mL, the OR was 3.2 (1.1-10); and for blood alcohol concentration greater than 50 mg/100 mL compared with <3 mg/100 mL, the OR was 23 (9-56). Approximately 30% of car crash injuries in this population were attributable to alcohol, with two-thirds involving drivers with blood alcohol concentration in excess of 150 mg/100 mL. Equal proportions of alcohol-related injury crashes were attributable to drivers with blood alcohol concentrations of 3 to 50 mg/100 mL as those with levels of 51 to 150 mg/100 mL. Evidence about the proportion of crashes attributable to drivers at different blood alcohol concentrations can inform the prioritization of interventions that target different groups of drivers. These data indicate where there is the most potential for reduction of the injury burden.