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R E S E A R C H A R T I C L E Open Access
Risk of death in crashes on Ontario’s highways
Damian Rzeznikiewiz, Hala Tamim and Alison K Macpherson
*
Abstract
Background: Motor vehicle collisions (MVCs) that result in one or more fatalities on the 400-series Highways
represent a serious public health problem in Ontario, and were estimated to have cost $11 billion in 2004. To date,
no studies have examined risk factors for fatal MVCs on Ontario’s 400 series highways.
The investigate how demographic and environmental risk factors are associated with fatal MVCs on Ontario’s
400-Series Highways.
Methods: Data were provided from the Ontario Ministry of Transport database, and included driver demographics,
vehicle information, environmental descriptors, structural descriptors, as well as collision information (date and
time), and severity of the collision. Multivariate analysis was used to identify factors significantly associated with the
odds of dying in a collision.
Results: There were 53,526 vehicles involved in collisions from 2001 to 2006 included in our analysis. Results from
the multivariate analysis suggest that collisions with older age and male drivers were associated with an increased
risk of involving a fatality. Highway 405 and an undivided 2-way design proved to be the most fatal structural
configurations. Collisions in the summer, Fridays, between 12 am-4 am, and in drifting snow conditions during the
wintertime were also shown to have a significantly increased risk of fatality.
Conclusion: Our results suggest that interventions to reduce deaths as a result of MVCs should focus on both
driver-related and road-related modifications.
Background
Unintentional injuries, many of which are attributable to
motor vehicle collisions, are the 6th leading cause of
death for Canadians [1]. With close to nine million dri-
vers on Ontario’s roads, the costs arising from motor ve-
hicle collisions (MVCs) on the 400-series highways in
Ontario which result in one or more fatalities are im-
mense when all aspects are taken into account. A report
released by the Ministry of Transportation of Ontario
(MTO) in 2007, calculated the social costs of MVCs to
be $18 billion in the year 2004 [2]. Additionally, fatalities
were reported to have cost $11 billion (64% of the total
social costs) even though fatal collisions represented less
than 1% of the 231,548 Highway Traffic Act reportable
collisions in 2004. The average social cost of a fatal colli-
sion in 2004 was $15.7 million.
Conversely, even though the costs of MVCs are aston-
ishing, it is important to highlight the advances the
province has made in road safety, which include a
decline in fatality rates over the past three decades. Since
1980, the number of licensed drivers has increased by al-
most 80% but the number of fatalities has decreased by
49% [3]. However, in the six-year period spanning from
2001 to 2006, there were 815 fatal MVCs on Ontario’s
400-Series Highways that in turn cost the province $12.8
billion, or almost $2.6 billion per year.
As a result of these astounding costs, the province has
invested and continues to invest billions of dollars every
year in engineering and restructuring plans in order to
improve our highways and subsequently decrease the
number of MVCs.
There has been previous research investigating driver
characteristics and environmental risk factors associated
with fatal motor vehicle collisions in Ontario. For ex-
ample, studies suggest that the older driver population is
at an elevated risk for being involved in fatal MVCs
[4,5]. Further, younger drivers are at an elevated risk as
well due to their risk-taking behavior which may be
expressed by going at higher speeds or by using alcohol
and/or drugs when driving [6-8].
* Correspondence: alison3@yorku.ca
School of Kinesiology and Health Science, York University, 4700 Keele Street,
Toronto M3J 1P3, Canada
© 2012 Rzeznikiewiz et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Rzeznikiewiz et al. BMC Public Health 2012, 12:1125
http://www.biomedcentral.com/1471-2458/12/1125
In addition to age, sex has been studied as a possible
risk-factor for the involvement in fatal MVCs. Zhang
found no difference between male and female fatality
rates in MVCs [9]. Interestingly, Singleton et al. found
that females were 60% more likely than males to be ser-
iously injured in MVCs, and Bedard et al. explained that
females in the United States were 54% more likely to be
involved in a fatal accident than their male counterparts
[10,11]. Moreover, Lemieux found that when looking at
MVCs in the Hamilton region of Ontario, weather did
not play a significant role in the collisions that resulted
in one or more fatalities [12].
Objective
To date, there have been no studies conducted which
examine Ontario’s 400-Series highways and the risk of
dying in a crash. As a result, the objective of this study
is to investigate how driver demographics and environ-
mental risk factors are associated with fatal motor ve-
hicle collisions on Ontario’s 400-Series Highways.
Methods
Data
Road Traffic Accident Data from the Ministry of Trans-
portation of Ontario (MTO) included all Highway Traffic
Act (HTA) reportable accidents on Ontario 400-Series
highways between January 1st, 2001, and December 31st,
2006. The data, compiled by MTO, are composed of infor-
mation extracted from police reports of MVCs that take
place on the province’s 400-Series highway system. These
controlled-access freeways are similar to the interstate
network in the United States and are located in the south-
ern and central regions of Ontario, Canada. All of the
highways are at least four lanes, have posted speed limits
of between 80 and 100 kilometres an hour, and generally
restrict access to pedestrians and non-motorized vehicles.
No data that could identify anyone involved in the colli-
sions were requested; therefore approval from an Ethics
Committee was not necessary. We used Highway 401 as
the reference group because it included the largest num-
ber of collisions.
A total of 53,526 vehicles involved in collisions from
2001 to 2006 were included in our analysis. Two-wheel
vehicles (motorcycles, mopeds and bicycles), and vehi-
cles for whom type of vehicle was not recorded were
removed from the dataset in order to avoid confounding
throughout the analyses. A sub-analysis comparing fatal-
ities in these aforementioned vehicles to cars/trucks
found that there was a 54% increased chance of a fatal
outcome when a motorcycle, moped or bicycle were
involved in a collision when compared to larger vehicles
(OR = 1.54, 95% CI 1.07-2.20). Previous research sug-
gests that this may be due to a decreased amount of pro-
tection when driving a two wheeled vehicle in addition
to greater risk taking behavior profile shown by motor-
cycle drivers [13,14].
Previous literature has found that the time of the day
of a collision is related to one’s chances of a fatal out-
come [5,11]. In order to investigate this relationship, we
chose to examine four-hour time periods in order to
gain insight into specific time frames when driving pat-
terns might be different, rather than examining broad
categories of daytime, evening, and night.
Data included driver characteristics (e.g. age and sex),
vehicle information (e.g. number of passengers and ve-
hicle type), environmental (e.g. weather and road surface
conditions) and highway characteristics (e.g. road char-
acteristics, location on a highway, and highway number),
as well as collision information (e.g. time and day) and
severity of the collision which ranged from 0–4, but was
dichotomized into death vs. no death for the analysis.
Age groups were defined according to Erikson’s develop-
mental stages [15]. Adolescent drivers were between 16
and 18 years old, early adults between 19 and 35 years
old, middle adults between 36 and 65 years of age, and
older adult drivers were defined as being older than
65 years of age. Road designs included: undivided 1-way,
undivided 2-way (uses solid lines to keep directions sep-
arate), dividing barrier, divided (usually by an area of un-
paved land), ramp road (part of an interchange allowing
traffic to enter or exit the highway), collector (segment
of highway which allows for entry and exit at inter-
changes), expressway, and transfer. We decided to in-
clude weather conditions in our analysis, even though it
had not been previously identified as a risk factor, be-
cause Canadian weather conditions can be more extreme
than those in some other parts of the world, and we
wanted to examine if weather was associated with fatal-
ities in Ontario.
After eliminating two-wheel vehicle cases (n = 1,395), a
total of 52,131 vehicles involved in MVCs between 2001
and 2006 on the 400-series highways in Ontario were
available for analyses.
Statistical analyses
Descriptive statistics were calculated for nine variables:
Highway name, Season, Day of the week, Time period,
Sex, Age group, Environment/Weather, Road Character-
istic, and Road Surface Condition; they were categorized
into three groups: Driver Characteristics (Sex and Age
group), Highway Characteristics (Highway name and
Road Characteristic), and Environmental Characteristics
(Season, Day of the week, Time period, Environment/
Weather, and Road Surface Condition).
Chi-Square tests were conducted in order to detect
differences in fatal injury rates among variables. Crude
odds ratios (OR) and 95% confidence intervals (95% CI)
were calculated using simple logistic regression in order
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to examine the relationships between each of the nine
variables and a fatality as a result of the collision.
Finally, a multivariate analysis was conducted to esti-
mate the odds of a fatality in each collision for the risk
factors, adjusting for all other variables in the model,
and for the clustering of vehicles in collisions where
more than one vehicle was involved. This was done
using the GENMOD procedure in SAS, with the unique
identifier for each collision as the repeated variable. Ini-
tially, all nine risk factors were included in the model;
however, collinearity between the Environment/Weather
and Road surface condition variables was evident, there-
fore, we decided to eliminate the latter from the model.
The outcome variable was if anyone (passenger or
driver) died in the collision, compared to collisions
where no-one died. Thus the numerator was the number
of vehicles involving a fatality, while the denominator
was all vehicles involved in collisions. Analyses were
conducted using SAS v 9.0 [16].
Results
Across all Ontario highways, the fatality rate among
vehicles was 1.5%. This included 753 deaths among
the reported 52,131 total vehicles involved in colli-
sions. The distribution of fatality varied by age, sex,
highway and environmental risk factors. Table 1 shows
the distribution of non-fatal and fatal injuries among
all categories for the eight included variables and
Table 2 displays the results of the multivariate regres-
sion analysis.
The analysis compared fatality rates in the four differ-
ent age groups, broadly based on Erikson’s developmen-
tal stages [15]. An increasing trend was observed; as one
got older, the fatality rate when involved in a MVC be-
came greater. As shown in Table 1, the fatality rates
were 1.1%, 1.4%, 1.6%, and 2.3% for teenagers, early
adults, middle adults, and older adults respectively.
Multivariate analysis suggested that as age progresses,
the odds of dying in a motor vehicle collision increases.
Compared to teenaged drivers, cars with young adults
drivers were more likely to suffer a fatality in a collision
(AOR = 1.52; 95% CI, 1.01-2.29), middle and older adults
drivers had an increased odds of being involved in a fatal
crash (AOR = 1.82; 95% CI, 1.20 –2.75) and 1.37%
(AOR = 2.36; 95% CI, 1.46 –4.06).
Sex was also associated with the odds of dying in a
motor vehicle collision. Initial results showed that male
drivers were involved in a collision with a fatal outcome
1.7% compared to 1.1% of cases for females. After ad-
justment for the other variables in the model, the odds
of being involved in a fatal crash for cars driven by a fe-
male compared to those driven by a male were signifi-
cantly lower (AOR = 0.84, 95% CI, 0.76-0.91).
Highway characteristics
After adjustment, our analysis found that Highway 405
posed a significantly greater odds of fatality a 7-fold
increased risk of fatal crash compared to Highway 401
(reference group) (AOR = 6.89, 95% CI 2.16-21.98).
Seven of the 11 remaining highways showed a protective
effect while the other 4 were not statistically different
from Highway 401 (Table 2).
Undivided 2-way segments and divided 2-way seg-
ments without barrier showed higher fatality rates than
segments with any other design (3.9% and 5.0% respect-
ively). In multivariate analysis, the odds of fatality on an
undivided 2-way design was three times greater than the
risk on an undivided 1-way segment (OR =3.74 (95% CI,
1.37-10.28)). A divided road without a barrier was also
associated with an increased odds of fatality(AOR = 4.76
(95% CI, 1.92-11.59)) compared to an undivided unidir-
ectional design.
Environmental characteristics
The distribution of collisions among all four seasons of
the year (winter, spring, summer, and fall) was similar.
However, fatalities seem to make up a higher percent-
age of collisions during the summer (30.4%). Initially,
fewer collisions were reported in the summer months
(n = 13,128) compared to the winter season (n = 13,980).
However, once all other variables were added to the
model, driving in the summer was associated with an
increased odds of fatality compared to the wintertime
(AOR = 1.58; 95% CI, 1.08-2.29).
Another purpose of the data analysis was to identify if
there were any days of the week that had higher rates of
fatality than others. The number of collisions per day of
the week were similarly distributed (mean = 7,335).
However, there appeared to be an increased risk of colli-
sion on Fridays, with a total of 8,566 over the span of six
years. After adjusting for all other variables, no days
showed significant differences in the odds of fatality,
with Friday being associated with a slightly, but not sig-
nificantly increased risk of death for those involved in
MVCs (AOR = 1.28, 95% CI, 0.79 –2.10).
From 4 am until 8 pm (early morning, morning, after-
noon, and early evening), fatality rates amongst people
involved in MVCs ranged from 1.2% to 1.4%. However,
from 8 pm to 12 am (evening) it was elevated to 2.0%
and during the nighttime (12 am to 4 am), it increased
to 3.4%. Driving at night was associated with the highest
risk of fatality compared to evening (AOR = 1.67, 95%
CI, 1.07-2.59).
Finally, the association between weather condition and
fatal MVCs on the 400-series highways in Ontario between
2001 and 2006 was examined. Fatality rates ranged from
1.4% in the ‘clear’reference condition, to 12.8% under ‘drift-
ing snow’conditions. In the multivariate analysis, ‘drifting
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snow’,‘wind’,and‘fog’were the only categories that
remained significantly different from the ‘clear’weather
condition. ‘Drifting snow’conditions were associated with
an increased odds of death if involved in a MVC compared
to MVCs in clear conditions (AOR = 7.39, 95% CI, 2.21-
24.78). Windy conditions were associated with a greater
odds (AOR = 5.00, 95% CI, 1.34-18.73) while fog was asso-
ciated with an almost 3 times greater odds of fatality than
clear conditions (AOR = 2.78, 95% CI, 1.11-6.96).
Discussion
We found an increased risk of fatality as a result of a
motor vehicle collision for older drivers and males. Fur-
ther, undivided 2-way segments as well as divided 2-way
segments were associated with increased fatality risk com-
pared to divided 1-way segments. Driving on Ontario’s
Highway 405 was associated with the highest odds of fatal-
ity in a collision. In addition, various environmental condi-
tions were associated with an increased risk of dying as a
result of a motor vehicle collision and they included driv-
ing in the summertime, at nighttime, and in drifting snow
weather.
The results in this study found that an increase in
driver age was strongly associated with an increased
odds of fatality in the vehicle as a result of a motor
vehicle collision. These findings are consistent with
Table 1 Distribution of fatal and non-fatal injuries by risk
factor for 52,131 vehicles involved in collisions between
2001 and 2006 on Ontario’s 400-series highways
Outcome –N (%) Total
Fatal Injury
Total 783 (1.5) 51,348 (98.5) 52,131
Highway
400 102 (1.9) 5,347 (98.1) 5,449
401 456 (1.4) 31,094 (98.6) 31,550
402 20 (3.4) 573(96.6) 593
403 40 (1.3) 3,124 (98.7) 3,164
404 13 (0.4) 3,088 (99.6) 3,101
405 9 (15.8) 48 (84.2) 57
406 18 (2,7) 640 (97.3) 658
409 1 (0.3) 352 (99.7) 353
410 10 (1.1) 907 (98.9) 917
416 5 (1.2) 416 (98.8) 421
417 82 (2.6) 3,013(97.4) 3,095
420 3 (1.7) 170 (98.3) 173
427 24 (0.9) 2,576 (99.1) 2,600
Season
Winter 218 (1.6) 13,762 (98.4) 13,980
Spring 165 (1.4) 11,280 (98.6) 11,445
Summer 238 (1.8) 12,890 (98.2) 13,128
Fall 162 (1.5) 13,416 (98.5) 13,578
Day of the Week
Sunday 109 (1.7) 6,300 (98.3) 6,409
Monday 114 (1.5) 7,292 (98.5) 7,406
Tuesday 81 (1.1) 7,452 (98.9) 7,533
Wednesday 98 (1.3) 7,290 (98.7) 7,388
Thursday 103 (1.3) 7,624 (98.7) 7,727
Friday 166 (1.9) 8,599 (98.1) 8,765
Saturday 112 (1.6) 6,791 (98.4) 6,903
Time Period*
Evening (8 pm-12 am) 114 (2.0) 5,722 (98.0) 5,836
Night (12 am-4 am) 146 (3.4) 4,137 (96.6) 4,283
Early Morning (4 am-8 am) 99 (1.3) 7,619 (98.7) 7,718
Morning (8 am-12 pm) 107 (1.1) 9,525 (98.1) 9,632
Afternoon (12 pm-4 pm) 144 (1.4) 9,901 (98.6) 10,045
Early Evening (4 pm-8 pm) 173 (1.2) 14,256 (98.8) 14,429
Sex
Female 176 (1.1) 15,464 (98.9) 15,640
Male 607 (1.7) 35,884 (98.3) 36,491
Age Group
Adolescence 26 (1.1) 2,267 (98.9) 2,293
Early Adulthood 363 (1.4) 25,742 (98.6) 26,105
Middle Adulthood 326 (1.6) 19,483 (98.4) 19,809
Late Adulthood 49 (2.3) 2,055 (97.7) 2,104
Table 1 Distribution of fatal and non-fatal injuries by risk
factor for 52,131 vehicles involved in collisions between
2001 and 2006 on Ontario’s 400-series highways
(Continued)
Environment
Clear 549 (1.4) 39,598 (98.6) 40,147
Rain 45 (1.0) 4,567 (99.0) 4,612
Snow 82 (1.5) 5,354 (98.5) 5,436
Frozen Rain 23 (2.7) 842 (97.3) 865
Drifting Snow 55 (12.8) 375 (87.2) 430
Wind 10 (7.9) 117 (92.1) 127
Fog 16 (4.1) 372 (95.9) 388
Other 3 (2.4) 123 (97.6) 126
Road Characteristic
Undivided 1-Way 10 (1.3) 732 (98.7) 742
Undivided 2-Way 42 (3.9) 1,026 (96.1) 1,068
Dividing Barrier 318 (1.5) 21,463 (98.5) 21,781
Divided 301 (5.0) 5,706 (95.0) 6,008
Ramp Road 37 (0.6) 6,096 (99.4) 6,133
Collector 30 (0.3) 8,799 (99.7) 8,828
Expressway 43 (0.6) 7,067 (99.4) 7,110
Transfer 2 (0.5) 416 (99.5) 418
*Missing data for: time period = 188, sex = 7, road characteristic = 43.
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previous literature [5,10,11,17]. The magnitude of the ef-
fect age has on fatal outcomes varies throughout the lit-
erature; Bedard reported a five times higher risk of death
among vehicles driven by 80+ year old drivers compared
to middle aged counterparts and Singleton reported a
65% increase in chances of fatality when comparing
adults drivers 60 years or older to younger 20 year old
drivers [10,11]. Most importantly, the direction of the
relationship remains constant; however, the magnitude
may differ due to the use of different reference and com-
parison groups, different settings (city vs. highway
MVCs), differing health practices in different geograph-
ical regions, and different adjustment for confounding
variables in the multivariate analyses. The increased risk
of fatality may be related to other age-related conditions,
including co-morbid health conditions (e.g. epilepsy, de-
mentia, diabetes mellitus, heart disease and hyperten-
sion) [5]. Observing the increased risk that older drivers
are at, the Canadian Association of Occupational
Therapists released the “National Blueprint for Injury
Prevention in Older Drivers”in 2010, which by vari-
ous methods, is designed to increase awareness of
the issue. For example, the blueprint outlines various
partnerships established to promote older driver
safety throughout the country in addition to inter-
ventions that include media coverage, brochures for
Table 2 Results of multivariate analyses for the odds of
fatality among 52,131 vehicles involved in collisions
between 2001 and 2006 on Ontario’s 400-series
highways
Risk factor AOR* 95%CI
Highway
401 1.00 ref
400 0.97 0.67 - 1.39
402 0.44 0.20 - 0.99
403 0.48 0.29 - 0.80
404 0.20 0.10 - 0.41
405 6.89 2.16 - 21.98
406 0.78 0.36 - 1.67
409 0.20 0.03 - 1.49
410 0.33 0.14 - 0.78
416 0.25 0.08 - 0.70
417 0.83 0.44 - 1.51
420 0.68 0.09 - 5.21
427 0.75 0.36 - 1.65
Season
Winter 1.00 ref
Spring 1.23 0.84 - 1.82
Summer 1.58 1.08 - 2.29
Fall 1.62 0.71 - 1.54
Day of the Week
Sunday 1.00 ref
Monday 1.06 0.66 - 1.70
Tuesday 0.77 0.46 - 1.25
Wednesday 0.97 0.61 - 1.55
Thursday 1.00 0.64 - 1.57
Friday 1.28 0.79 - 2.10
Saturday 1.01 0.64 - 1.57
Time Period
Evening (8 pm-12 am) 1.00 ref
Night (12 am-4 am) 1.67 1.07 - 2.59
Early Morning (4 am-8 am) 0.63 0.39 - 1.00
Morning (8 am-12 pm) 0.55 0.36 - 0.85
Afternoon (12 pm-4 pm) 0.63 0.38 - 1.04
Early Evening (4 pm-8 pm) 0.69 0.45 - 1.05
Sex
Male 1.00 ref
Female 0.84 0.76 - 0.91
Age Group
Adolescence 1.00 ref
Early Adulthood 1.52 1.01 - 2.29
Middle Adulthood 1.82 1.20 - 2.75
Late Adulthood 2.36 1.46 - 4.06
Table 2 Results of multivariate analyses for the odds of
fatality among 52,131 vehicles involved in collisions
between 2001 and 2006 on Ontario’s 400-series
highways (Continued)
Environment
Clear 1.00 ref
Rain 0.76 0.49 - 1.15
Snow 0.93 0.57 - 1.51
Frozen Rain 1.49 0.66 - 3.39
Drifting Snow 7.39 2.21 - 24.78
Wind 5.00 1.34 - 18.73
Fog 2.78 1.11 - 6.96
Other 1.72 0.34 - 8.58
Road Characteristic
Undivided 1-Way 1.00 ref
Undivided 2-Way 3.74 1.37 - 10.28
Dividing Barrier 1.28 0.54 - 3.03
Divided 4.76 1.92 - 11.59
Ramp Road 0.52 0.20 - 1.36
Collector 0.27 0.09 - 0.79
Expressway 0.44 0.17 - 1.26
Transfer 0.37 0.04 - 3.13
*AOR = Adjusted Odds Ratio, CI = Confidence interval.
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families with older adult drivers and information for
health care practitioners taking care of them [18].
Our study suggested that female drivers were involved
in fewer fatal motor vehicle collisions than men, a find-
ing consistent with previous literature in which female
drivers were 62% less likely to die in a collision com-
pared to male drivers [19]. Further, Turner and McClure
revealed that men scored significantly higher than
females in the ‘Driver Aggression’and ‘Risk Acceptance’
scales [19]. Lemieux reported an increased rate of fatal-
ities amongst male drivers compared to females [12].
However, this finding is not completely consistent. For
example, Bedard and Levine show up to a 50% increased
risk for fatal injuries in women compared with men
[10,20]. However, they state that this difference seems to
be present only in younger drivers and that it disappears
once older adults are added to the model.
Our results suggest that the undivided two-way design
poses an increased fatality risk, as does the divided road
with no barrier design; these results are consistent with
previous research by Zhang [9]. Re-evaluating these two
designs on Ontario’s highways, and installing barriers in
both of these designs may improve road safety in some
areas.
Further, our results showed that Highway 405 posed a
significantly increased fatality risk when involved in
MVCs on this 8.5 kilometer segment which connects the
Queen Elizabeth Way to the Queenston-Lewiston Bridge
(Canada-US Border). No previous research has been
done examining specific highways and the risk involved
in driving on them. Although we have no evidence for
why the fatality rate is higher than on most other high-
ways, we hypothesize that there may be an elevated
number of trucks on this highway because it is one of
the arteries that connects Canada to the United States
and therefore heavy transport vehicles are common. Col-
lisions involving large trucks have been shown to be
deadly more often than cars and small trucks [21]. This
possibility warrants further investigation.
Our finding that summer was associated with a signifi-
cantly higher likelihood of fatality is consistent with
Zhang who reported that young drivers were at a 25%
increased risk for fatality compared to middle aged dri-
vers involved in MVCs during the summer [5].
The lack of association between day of the week and
fatal collisions on Ontario’s highways is not consistent
with previous research by Lemieux who reported that
MVCs in the Hamilton-Wentworth Niagara Region of
Ontario that resulted in fatalities occurred during Fri-
days and Sundays, on city and rural roads as well as
highways [12]. Other studies compared fatality rates in
different days of the week; however, they compared
weekdays vs. weekend days rather than each day indi-
vidually [5,17,22].
Similarly to Valent, who found that deaths increased
as a results of MVCs between 1:00 and 5:00 a.m. by a
factor of two, this study found that night (0:00–4:00 am)
was the most fatal period of the day with a 67% higher
risk of fatal MVCs than the evening [17]. Possible rea-
sons for an increased occurrence of fatal accidents dur-
ing the nighttime/early morning hours include an
increased use of alcohol and/or drugs while driving and
tiredness or drowsiness while driving during these
hours [23,24].
We found that ‘drifting snow’,‘wind’, and ‘fog’were sig-
nificantly associated with an increased fatality risk. A
study by Khattak et al. which compared collision rates
on American Interstate freeways for periods with snowy
conditions to those with clear weather found that during
snowstorms the crash rate was 5.86 crashes per million
vehicle kilometers (vs. 0.41 during clear conditions).
These results represented a 13 times increased collision
risk during snowy weather [25]. These findings comple-
ment those by Qui and Nixon (2008) although their sys-
tematic review found that in the last four decades, the
effect of adverse weather on fatal collisions has
decreased substantially [26].
Strengths and limitations
The main strength of this study is that it was able to
examine the relative contribution of driver-related and
environmental risk factors to fatal motor vehicle colli-
sions on Ontario’s 400-Series Highways. All major pro-
vincial highways were included in the analyses and the
most comprehensive source for MVC data was used
(Ministry of Transportation of Ontario) whose database
included 96.1% of cases with no missing data, leaving
only 3.9% of cases ineligible for some of the analyses.
Vehicles which were evidently at an increased risk for
fatality when involved in a MVC (e.g. motorcycles and
bicycles) were excluded in order to allow the analyses to
approximate a more accurate fatality risk posed to occu-
pants of vehicles with similar safety mechanisms and
driving conditions.
The limitations of this study include a potential for
misclassification bias since the data is entirely based on
police reports and non-fatal crashes may therefore be
underreported. Also, the reports only include outcome
data up to 30 days after the collision. Therefore, colli-
sions that resulted in fatalities 30 days after they took
place would have not been classified as fatal collisions.
We had no information on driver distraction, kilometers
driven, or other potentially confounding variables not
included in the database, including use of restraints and
impaired driving, and access to trauma care. However,
we believe it is unlikely that these variables are systemat-
ically different across different highways. The results of
this study may only be generalizable to other jurisdictions
Rzeznikiewiz et al. BMC Public Health 2012, 12:1125 Page 6 of 7
http://www.biomedcentral.com/1471-2458/12/1125
with similar highway structures. Finally, the most recent
data available were from 2006 and there have been
improvements to the highways since then. These improve-
ments include widening Highway 401 and 417, and
improvements to bridges. Future research can evaluate the
impact of these changes.
Conclusions
Even after controlling for characteristics of the driver,
the highway and the environment contribute to the like-
lihood of someone dying in a motor vehicle collision.
Interventions to reduce deaths may focus on structural
road redesign, as well as driver-related interventions tar-
geted at reviewing driving practices within different age
groups and for drivers under different conditions. Our
research suggests that both driver-level and environmen-
tal interventions may help reduce the risk of fatality in a
motor vehicle collision.
Competing interests
The authors declare that they have no competing interests.
Authors’contributions
DR participated in study design, obtained study data, performed the
statistical analysis, drafted, and edited the manuscript. HT participated in
study design, reviewed statistical analysis, aided in draft and final manuscript
compilation. AM conceived of the study, designed the study, performed the
statistical analysis, drafted and edited the manuscript. All authors read and
approved the final manuscript.
Acknowledgements
We would like to acknowledge support received from the Ministry of
Transportation of Ontario, through the invaluable assistance from Susan
Nichol and John Zajac at the Traffic Office in St. Catherine’s.
Received: 26 March 2012 Accepted: 22 December 2012
Published: 28 December 2012
References
1. Ministry of Transportation of Ontario: Ministry of transportation of Ontario
road safety. 2010, Retrieved November, 2009, from http://www.mto.gov.on.
ca/english/safety/.
2. Vodden K, Smith D, Eaton F, Mayhew D: Analysis and estimation of the social
cost of motor vehicle collisions in Ontario. Transport Canada: 2007.
3. Road Safety Program Office: Ontario road safety annual report 2006. Annual
Report. Ontario, Canada: Service Ontario Publications; 2006.
4. Evans L, Frick MC, Frick MC: Safety belt effectiveness in preventing driver
fatalities versus a number of vehicular, accident, roadway, and
environmental factors. J Safety Res 1986, 17(4):143–154. doi:10.1016/0022-
4375(86)90065-4.
5. Zhang J, Lindsay J, Clarke K, Robbins G, Mao Y: Factors affecting the
severity of motor vehicle traffic crashes involving elderly drivers in
Ontario. Accident Anal Prev 2000, 32(1):117–125. doi:10.1016/S0001-4575(99)
00039-1.
6. Jonah BA: Accident risk and risk-taking behaviour among young drivers.
Accident Anal Prev 1986, 18(4):255–271. doi:10.1016/0001-4575(86)90041-2.
7. Mercer GW, Jeffery WK: Alcohol, drugs, and impairment in fatal traffic
accidents in British Columbia. Accident Anal Prev 1995, 27(3):335–343.
doi:10.1016/0001-4575(94)00069-X.
8. Seppala T, Linnoila M, Mattila MJ: Drugs, alcohol and driving. Drugs 1979,
17(5):389.
9. Zhang J, Fraser S, Lindsay J, Clarke K, Mao Y: Age-specific patterns of
factors related to fatal motor vehicle traffic crashes: focus on young and
elderly drivers. Public Health 1998, 112(5):289–295. doi:10.1038/sj.
ph.1900485.
10. Bédard M, Guyatt GH, Stones MJ, Hirdes JP: The independent contribution
of driver, crash, and vehicle characteristics to driver fatalities. Accident
Anal Prev 2002, 34(6):717–727. doi:10.1016/S0001-4575(01)00072-0.
11. Singleton M, Huifang Q, Jingyu L: Factors associated with higher levels of
injury severity in occupants of motor vehicles that were severely
damaged in traffic crashes in Kentucky, 2000–2001. Traffic Inj Prev 2004,
5(2):144–150.
12. Lemieux C, Fernandes J, Rao C: Motor vehicle collisions and their
demographics: a 5-year retrospective study of the Hamilton-Wentworth
Niagara region. J Forensic Sci 2008, 53(3):709–715.
13. Horswill MS, Helman S: A behavioral comparison between motorcyclists
and a matched group of non-motorcycling car drivers: factors
influencing accident risk. Accident Anal Prev 2003, 35(4):589–597.
14. Kweon JJ, Kockelman KM: Overall injury risk to different drivers:
combining exposure, frequency, and severity models. Accident Anal Prev
2003, 35(4):441–450.
15. Erikson EH: Childhood and society. Norton & Company: W. W; 1950.
16. SAS Institute Inc: SAS 9.1.3. Cary, NC: SAS Institute Inc; 2010.
17. Valent F, Schiava F, Savonitto C, Gallo T, Brusaferro S, Barbone F: Risk factors
for fatal road traffic accidents in Udine, Italy. Accident Anal Prev 2002,
34(1):71–84. doi:10.1016/S0001-4575(00)00104-4.
18. Canadian Association of Occupational Therapists: National blueprint for injury
prevention in older drivers activity report 2010. Ottawa: CAOT Publications
ACE; 2010.
19. Turner C, McClure R: Age and gender differences in risk-taking behaviour
as an explanation for high incidence of motor vehicle crashes as a driver
in young males. Inj Control Saf Promot 2003, 10(3):123.
20. Levine E, Bedard M, Molloy DW, Basilevsky A: Determinants of driver
fatality risk in front impact fixed object collisions. Mature Med Canada
1999, 2:239–242.
21. McCartt AT, Shabanova V, Retting RA: Types and characteristics of ramp-
related motor vehicle crashes on urban interstate roadways in northern
Virginia. J Safety Res 2004, 35(1):107–114.
22. Doherty ST, Andrey JC, MacGregor C: The situational risks of young
drivers: the influence of passengers, time of day and day of week on
accident rates. Accident Anal Prev 1998, 30(1):45–52. doi:10.1016/S0001-4575
(97)00060-2.
23. Rice TM, Peek-Asa C, Kraus JF: Nighttime driving, passenger transport, and
injury crash rates of young drivers. Inj Prev 2003, 9:245–250.
24. Akerstedt T, Kecklund G, Horte LG: Night driving, season, and risk of
highway accidents. Sleep 2001, 24(4):401–406.
25. Khattakm AJ, Knapp KK, Giese K, Smithson LD: Safety implications of
snowstorms on interstate highways. Washington, D.C: Presented at 79th
Annual Meeting of the Transportation Research Board; 2000.
26. Qui L, Nixon WA: Effects of adverse weather on traffic crashes:
systematic review and meta-analysis. Transportation Research Record
2008, 2055:139–146. 2055.
doi:10.1186/1471-2458-12-1125
Cite this article as: Rzeznikiewiz et al.:Risk of death in crashes on
Ontario’s highways. BMC Public Health 2012 12:1125.
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