1. IntroductionIn 2010, motor vehicle crashes (MVC) were responsible forover 32,000
fatalities and 2.2 million injuries, with crash occupant injuries accounting for 15%
of all non-fatal emergency department injuries treated annually. (CDC 2010, CDC
2011, NHTSA 2012) The annual
economic cost of MVC related injury is substantial, estimated at $230 billion.
(Blincoe, Seay et al. 2000) Crash related
injury patterns and severity results from the complex interaction of many
biomechanical factors, including seatbelt use, crash severity (measured using
deltaV, the reconstructed change in velocity of the center of gravity of the vehicle
determined based on measurement of post crash vehicle damage), airbag deployment and
collision type (frontal, side impact, rollover).(Arbabi, Wahl et al. 2003, Zhu, Layde et
al. 2006) Public health and automotive safety experts attempt to address
these various factors through improvements in roadway engineering, driver behavior
modification and improved automotive design.
Occupant factors, including age, body habitus and size, injury tolerance and
mechanical response of affected body regions, arean important component of the
complex interactions that determine injury severity.(Bedard, Guyatt et al. 2002, Bose,
Segui-Gomez et al. 2011) Elderly drivers have higher fatality rates per
vehicle miles driven than all other age groups except young drivers and have a
significantly increased risk of injury that rises steeply after age 50. (Augenstein, Perdeck et al. 2003, Austin and Faigin 2003, Newgard 2008, Insurance
Institute on Highway Safety 2010, Ridella, Rupp et al. 2012) Obesity also increases the risk of death and
serious injury, although males and females may be affected differently. (Choban, Weireter et al. 1991, Boulanger, Milzman et al. 1992, Mock, Grossman et al. 2002, Arbabi, Wahl et al. 2003, Neville, Brown et al. 2004, Zhu, Layde et al. 2006, Ryb and
Dischinger 2008, Viano, Parenteau et al.
2008, Sivak, Schoettle et al.
2010, Jehle, Gemme et al. 2012, Rupp, Flannagan et al. 2013) Some studies find
an increased risk only for male obese drivers,(Zhu,
Kim et al. 2010, Ma, Laud et al.
2011) while others find an increased risk for both obese male and female
drivers, although greater for obese males. (Viano,
Parenteau et al. 2008) On average, male drivers experience more severe
crashes than female drivers (Insurance Institute on
Highway Safety 2010), but prior research has also shown that in crashes
of equal severity, women are more likely than men to be injured or killed. (Evans 2001, Evans 2001, Evans and Gerrish
2001, Bedard, Guyatt et al. 2002)
This prior research examining occupant factors is limited, however, by a focus on
specific body regions and challenges combining datasets for direct comparison of
occupant factors. Thus, the relative influence of age, weight and dimensions of the
occupant on the likelihood of injury or death in motor vehicle crashes is
incompletely understood. (Mock, Grossman et al.
2002)
Current motor-vehiclesafety systems (vehicle structures, seatbelt, airbags
and other passive safety devices) are designed and tested with crash dummies
representing a mid-sized male (stature=175cm, BMI = 24.3 kg/m3) and small
female (stature = 151 cm, BMI = 21 kg/m3) (Zhu 2006; Bose 2011).
Demographic trends continue to emphasize the increasing disparity in body dimensions
between the current driving population and these standards for occupant safety
testing, with an increasing proportion of the population that is elderly and obese.
(United Nations 2011, Ogden, Carroll et al. 2012) Previous studies
have suggested that vehicle design and testing without adequate consideration of the
relative effects of occupant factors may contribute to higher fatality rates and
serious injury among populations that deviate from the standard test models.(Zhu, Layde et al. 2006, Bose, Segui-Gomez et al. 2011) However, before devoting
substantial resources to developing crash test dummies and other human surrogates,
such as computational models that can be used to assess the ability to vehicle
safety systems to protect a wider range of occupant types, a detailed quantification
of the effects of occupant characteristics on injury is needed.
As indicated above, previous efforts have focused on characterizing the
effects of age, gender, and BMI on the risk of injury while controlling for other
factors that affect the probability of injury given that a crash has occurred.
However, such approaches do not consider the exposures to crashes of young and old,
men and women, and high- and low-BMI occupants.For example, older occupants may be
at greater risk in crashes, but they may be less likely than younger occupants to be
exposed to crashes, reducing the number of injuries that could be prevented by
improving protection for older occupants. Two prior studies have explored the effect
of occupant characteristics on the number of injured occupants. Kent et al.(2009) used
information on the distribution of occupants involved in crashes by age and the risk
of injury as a function of age normalized to the risk of a twenty year old to
characterize the effects of age on the number of occupants killed and injured in
crashes. Rupp et al.(2013)
modeled the risk of serious-to-fatal injury to different body regions in frontal,
nearside, farside, and rollover crashes as functions of significant predictors of
injury and then applied these models to a probability sample of occupants in
crashes, adjusting the BMI distribution in this sample to estimate the effect of BMI
in terms of the numbers of occupants with injury to different body regions in
different crash modes.
In the current study, we expand upon the methods used by Rupp et al. (2013), with the objective of
determining the relative effects of age, gender, and BMI on the numbers of occupants
with serious-to-fatal injuryto different body regions in MVCs. These estimates
describe the magnitude of the individual and combined effects of age, gender, and
BMI and there by aid in prioritizing the development of tools to assess vehicle
safety performance for different occupant types as well as countermeasures to better
protect these occupants.
3. Results3.1 Sample CharacteristicsThe application of vehicle model year, vehicle type, and occupant age
resulted in a dataset of 25,246 vehicle occupants. Removal of occupants with
missing height or weight information, heavy trucks, buses or motorcycle
occupants and those in frontal and side impact crashes that were missing deltaV
reduced occupant number to 18,371 in the unweighted sample, which corresponds to
a weighted sample of 6,011,375.
Table 1 lists the key predictors
used in the regression models. Tables
2-4 demonstrate the
distribution of key predictor variables in the dataset used in this study
stratified by age, BMI and gender categories, respectively. Among age categories
(Table 2), we found a significant
difference in belt use and seat location, with younger crash-involved occupants
less likely to use a seatbelt than older occupants andmore likely to be a
passenger than middle-aged occupants. Younger and older occupants were also more
likely to be driving passenger vehicles rather than utility vehicles or vans and
were less likely to be in higher BMI categories. Middle-aged occupants were also
less likely to be involved in high severity (i.e. deltaV) crashes for farside
impacts than younger or older occupants.
Among BMI categories (Table 3),
we found significant differences in age and gender, with higher percentages of
males and middle-aged crash-involved occupants in higher BMI categories. Younger
and Older occupants were less likely to be among the higher BMI categories. Low
BMI occupants were more likely to be passengers. Higher BMI occupants were less
likely to drive passenger cars and more likely to drive larger vehicles such as
utility vehicles or vans. With respect to crash mode, lower BMI occupants were
less likely to be involved in nearside crashes, while other crash modes didn't
demonstrate significant differences by BMI category. Lower BMI occupants were
also more involved in higher severity (i.e. higher deltaV) nearside crashes. No
meaningful differences in crash severity were noted for frontal or farside
impacts.
Between men and women (Table 4),
men were more likely to be unbelted and occupying the driver position than women
in crashes. Men were also more likely to be driving pickups or utility vehicles
than women. Men were less likely to have a normal BMI (<25
kg/m2) than women occupants in the dataset. With respect to crash
mode, men were more likely to be involved in rollover crashes. No other
significant crash mode differences were noted. Men were more likely involved in
lower severity farside impacts than females, while no other meaningful
differences were noted in crash severity.
3.2 Models of the Effects of Occupant Characteristics on Injury RiskLogistic regression models predicting AIS 3+ injury by body region
(head, spine, thorax, abdomen, upper extremity, lower extremity) and crash mode
(frontal, nearside, farside and rollover), which have previously been reported
in the literature by Ridella et.
al.(2012) were recreated and are available in the
accompanying appendix (Table A2, A3, A4,
A5) along with a table demonstrating the baseline risks of AIS 3+
injury by crash mode and body region (Table
A1). A summary table showing the occupant characteristics that were
significant predictors of AIS 3+ injury risk is presented in Table 5. Age was a significant predictor
for AIS 3+ injury risk for more body regions by crash type than either BMI or
gender. Multiple significant effects of occupant characteristics and variables
that interact with occupant characteristics were observed for some body regions,
particularly the thorax and upper extremities in frontal crashes and head in
nearside crashes, indicating that the effects of occupant characteristics are
complex.
3.3 Effects of Occupant Characteristics on the Percentage of Occupants with
InjuryFigure 1 describes the effects of
occupant characteristics in terms of percent change in the number of occupants
with severe injury to different body regions predicted to occur as limits are
placed on the defining variable. In Figure
1a, the percent reduction in injury is modeled as the maximum age of
the crash population is limited to progressively lower values, such that
occupants above that age are modeled using the maximum age instead of their true
(older) age. The results demonstrate that for every crash mode and body region
shown, injury decreases substantially as the maximum age of the population
decreases. Figure 1b shows similar plots
for the percent reduction in injury as the maximum value for BMI is limited to
progressively lower values. In frontal crashes, limiting BMI to progressively
smaller values leads to substantial reduction in upper and lower extremity
injuries. In nearside crashes, limiting BMI leads to a reduction in thorax
injuries, but an increase in head injuries among this population. Figure 1c demonstrates the percent reduction
in injury expected if all occupants in the crash were male, with evidence of
substantial reductions in nearside and farside head injuries, frontal thorax,
upper extremity and lower extremity injuries. Less substantial reductions were
noted in rollover upper extremity injuries, and an increase in spinal injuries
in rollover crashes was observed.
3.4 Estimating Total Number of Occupants with Severe Injury (AIS 3+) due to
Age, BMI, and GenderFigure 2 applies the predicted
changes in percentage of occupants with severe injury to the NASS-CDS 2007-2008
dataset to estimate the decreases in numbers of occupants with severe injury if
all occupants were male and if age and BMI were limited to their 5th
percentile values (i.e. 17 years old and 19 kg/m2). Table 6 provides the mean predicted
decreases in number of occupants with injuries and the 95% confidence intervals
on these estimates. For all crash modes limiting age results in 8,396 [95% CI
6,871-9,070] fewer head, 17,961 [95% CI 15,960 – 18,859] fewer thorax,
3,843 [95% CI 3,065 – 4,242] fewer spine, 3,578 [95% CI 1,402 –
4,439] fewer upper extremity and 4,584 [95% CI 4,012 – 4,995] fewer lower
extremity injuries. Similarly, limiting BMI results in 2,069 [95% CI 1,107
– 2,775] fewer thorax,and 5,304 [95% CI 5,818 – 6,777] fewer lower
extremity injuries. Finally, modeling all occupants as males results in 1,999
[95% CI 844 – 2,685] fewer head, 5,618 [95% CI 4,212 – 6,272]
fewer thorax, 3,804 [95% CI 1,781 – 4,803] fewer upper extremity and
2,791 [95% CI 2,216 – 3,256] fewer lower extremity injuries.
4. DiscussionThis is the first analysis that comprehensively estimates the impact of age,
BMI and gender variations on the numbers of occupants in MVC's with serious to fatal
injuries to different body regions in different crash modes. The results describe
the effects in terms of the change in number of occupants with severe injury (AIS
3+) that would be expected if all other predictors of injury were held constant.
Because of this, the results do not consider changes in exposure that would be
expected because of associations between predictor variables (e.g., if all high BMI
occupants became normal BMI occupants they would be more likely to drive passenger
cars). However, the estimates of the numbers of injured occupants associated with
age, gender, and BMI are an estimate of the theoretical benefits if an occupant
protection system were changed to provide similar protection to occupants of all
sexes, ages, and BMIs. As a result, the estimates in this paper can reasonably be
used to identify the need for human surrogates to represent specific populations and
the types of crash tests that these surrogates should be used in for testing.
Results are also useful in prioritizing public health interventions related to
occupants of motor vehicle crashes, including additional safety countermeasures,
improved crash prevention testing parameters and public awareness campaigns focusing
attention on vulnerable populations as well as promoting effective interventions for
modifiable risk factors.
The trends in the distributions of predictor variables with age, gender, and
BMI have important implications on the need for vehicle safety systems that account
for the increased vulnerability associated with particular occupant characteristics.
For example, older occupants were noted to be more likely to travel in passenger
cars than light trucks, vans or utility vehicles; suggesting that investment in
countermeasures to reduce the likelihood of injuries associated with aging, like
thorax and head injuries in all crash modes may be most effectively targeted at
passenger vehicles and in particular passenger vehicles tend to be driven by an
older demographic. Because higher BMI occupants were less likely to drive passenger
vehicles and more likely to occupy utility vehicles, trucks or vans, countermeasures
to prevent injuries associated with BMI, such as lower extremity injuries in frontal
crashes, may be of higher value in SUVs. The effects of occupant characteristics on
injury to different body regions observed in this study are generally consistent
with results of previous crash database analyses and biomechanical studies.
The increase in thoracic injuries for elderly occupants relative to younger
occupants in similar crashes has been widely reported. (Morris, Welsh et al. 2002, Morris, Welsh et al. 2003, Kent, Henary
et al. 2005) The increased risk of chest injuries is primarily from
increased rib fractures and the accompanying intra-thoracic injuries. These injuries
are more prevalent in older occupants because of age-related bone loss and
reductions in fracture toughness. (Kent, Henary et
al. 2005) Ultimately, elderly crash victims are less able to tolerate the
effects of intra-thoracic injury, with less efficient oxygen exchange, decreased
pain tolerance and increased stiffening of the chest wall that prohibits adequate
clearance of secretions and increases infection risk.(Kent, Woods et al. 2008) Consistent with this mechanism and
prior NASS-CDS crash analysis, (Ridella, Rupp et al.
2012) we found that risk of serious injury increases with age for all
body regions and crash modes, with thorax injuries most prominent in frontal,
nearside and farside crashes. Our finding that serious-to-fatal head injuries were
more common in frontal and nearside crashes mirrors the findings of Ridella et al.
and is consistent with Mallory's finding that elderly occupants are at higher risk
for bleeding type head injuries, even at low crash severities. (Mallory 2010, Ridella, Rupp et al. 2012) Comparing age, BMI and gender effects on the
numbers of occupants with AIS 3+ injuries to different body regions, the age effect
substantially overwhelms BMI and gender for all crash modes and regions, signifying
an urgent need to address injury in elderly crash victims, especially thorax and
head injuries, with improved testing and occupant safety systems.
Obese occupants are at increased risk for severe injury due to anatomical
and physiological variations that alter normal occupant and safety belt response
during a crash. (Zhu, Layde et al. 2006,
Turkovich and van Roosmalen 2010) Greater
occupant mass increases kinetic energy, increasing forward hip/pelvis movement
before adequate safety belt restraint (Viano,
Parenteau et al. 2008, Kent, Forman et
al. 2010, Turkovich and van Roosmalen
2010, Rupp, Flannagan et al. 2013)
and decreases normal pitch forward during impact. (Kent, Forman et al. 2010) Thus, the increased lower extremity injuries
in frontal crashes observed in our study likely results from increased hip excursion
and a higher knee impact against the lower instrument panel. This increases
knee-thigh-hip fractures and below the knee injury.(Wang, Bednarski et al. 2003, Kent,
Forman et al. 2010, Rupp, Flannagan et
al. 2010, Rupp, Flannagan et al.
2013) As the majority of nearside impacts usually have some associated
component of frontal impact, the mechanism for the observed increase in obese
occupants with nearside thoracic injuriesmay be similar to that observed in frontal
crash cadaver studies where there is increased seatbelt loading on the more
compliant and vulnerable lower thorax region and not on the stiffer upper thorax
region.(Kent, Trowbridge et al. 2009)
This has been shown to increase rib fractures, pulmonary contusions and thoracic
injuries among obese occupants.(Boulanger, Milzman et
al. 1992, Mock, Grossman et al.
2002, Moran, Rue et al. 2002,
Zhu, Kim et al. 2010) Previous research
has identified a conflicting relationship between obesity and severe abdominal
injuries. Some authors find a protective effect from the adipose tissue
“cushion”(Arbabi, Wahl et al.
2003, Wang, Bednarski et al. 2003)
while others find increased abdominal injury and mortality.(Ryb and Dischinger 2008, Zarzaur and Marshall 2008) Zhu et
al(2010) found a U shaped relationship, concluding that the protective
effect that may be overcome by increased momentumas BMI increases. This relationship
was not apparent in our study, which may be due to our inclusion of interaction
effects (e.g. BMI*Vehicle Type) that were not accounted for in prior studies.
Men were less susceptible to thorax and upper/lower extremity injury in
frontal crashes and less likely to sustain head injury in farside crashes in our
analysis, consistent with previous findings that men and women experience crashes
differently.(Evans 2001, Evans and Gerrish 2001, Bose, Segui-Gomez et al. 2011) Some authors argue that the
shorter female stature and the tendency for women to sit more forward in the cabin
may decrease the protection provided by standard safety devices, increasing the
potential for lower extremity injuries and thorax injuries in frontal crashes.(Dischinger, Kerns et al. 1995, Crandall and Martin 1997, Bose, Segui-Gomez et al. 2011) Others suggest that additional
mechanisms are at play, given that anthropometric data demonstrates a consistent
stature difference between men and women throughout life, while injury risk changes
over time, with greater risk for elderly females.(Evans 2001) We observed an interaction between gender and head injury
with increased risk for severe-to-fatal head injury in females involved in farside
crashes. This may be a result of shorter stature prompting seat position to be more
forward for females than males and increasing the risk for women to be injured by
the striking vehicle in a side impact. Despite the low contribution of gender effect
to overall injury risk compared to BMI and age, more study is needed to understand
these gender variations and the disparity in protection offered by current safety
devices.
The effects of occupant characteristics on injury observed in this study
indicate a need for improved human computational models that better represent
different sets of occupant characteristics that can be used to identify the
mechanisms and biomechanical factors that are associated with the observed effects
of occupant characteristics. Specifically, the finding that the effect of BMI is
largest in frontal crashes and on lower extremity injuries suggests that development
of computational models of obese occupants should focus on this crash mode and
injury. Further, the association between the body shape changes associated with
obesity and poor belt fit (as well as more adipose tissue over the anterior pelvis)
indicates that such models should have a humanlike external body shape.The increase
in the risk and incidence of injuries to almost every body region and every crash
model with increasing age indicates a broad need for computational models that
represent elderly occupants for use in multiple modes of loading. Like computational
models of obese occupants, these models should consider the differences in body
shape associated with aging as well as the changes in skeletal geometry and failure
characteristics associated with increasing age. Because thoracic injury (rib
fracture) is a major contributor to the effects of age in all crash modes,
computational models of older occupants should emphasize appropriate representation
of age-related changes in rib geometry, costal cartilage mineralization, and tissue
level failure characteristics. The finding that women are more likely to sustain
thoracic and extremity injuries in frontal crashes than men suggests that frontal
crash simulations with female computational models are needed to better understand
whether the effect of gender is related to differences in body size, shape, skeletal
geometry, or injury tolerance between men and women.
This analysis highlights the importance of improving occupant protection
with specific targeted population interventions that reflect population variations
in BMI, age and gender.For all crash modes, age was found to have the largest effect
on injury, especially for the thorax and head regions. Despite the age effect, the
obesity effect on lower extremity and thorax injuries and the differential injury
findings among men and women need to be addressed. One potential intervention for
the opposing effect of age and obesity on thoracic injuries is adaptive seat belt
restraint systems that provide increased loading on obese occupants while decreasing
the thoracic load on elderly occupants. Four point restraint systems and inflatable
seatbelts have also been proposed for elderly occupants that will reduce or
distribute chest loading. For obese occupants, knee airbags may help counter
thelower extremity impact against the dashboard during a crash, limiting injury
potential. Regardless, the introduction of new safety measures will require
extensive physical and virtual testing to ensure that additional protection to limit
occupant injuries among one occupant subgroup (e.g. elderly patients) doesn't
adversely affect other subgroups (e.g. obese drivers).
Several analysis limitations are noted. Our analysis did not control for
structural intrusion into the vehicle compartment. Although unlikely to affect the
analysis, controlling for intrusion may reduce the effects of related variables such
as deltaV and vehicle type. For a similar reason, the analysis did not control for
airbag deployment, which has been shown to be a cause of upper extremity injuries
(Hardy, Schneider et al. 2001) and may
explain some of the upper extremity findings in this analysis. We did not consider
crash direction within each crash type or the effects of subtypes of crashes due to
small sample sizes and this may miss important relationships between predictor
variables and injury. Height and weight data for uninjured occupants is
self-reported in the NASS-CDS database. These reporting biases combined with more
accurate data obtained for injured occupants may both overestimate increased injury
risk underestimate decreased injury risk associated with gender and BMI. In
addition, 30% of frontal, nearside and farside crashes in the NASS-CDS database are
missing deltaV estimates, which has previously been shown to occur in those crashes
with more severe injuries, multiple impacts and more often with trucks than other
vehicles.(Kononen, Flannagan et al. 2011)
This likely affects the estimates of the numbers of occupants sustaining injuries to
different body regions but should not affect the relationship between the injury
risk, body regions and predictors of risk.