Page 1

How Many People Are Injured and Killed as a Result of Aging? Frailty,

Fragility, and the Elderly Risk-Exposure Tradeoff Assessed via a Risk

Saturation Model

Richard Kent, Matthew Trowbridge, Francisco J. Lopez-Valdes

Center for Applied Biomechanics, University of Virginia, U.S.A.

Rafael Heredero Ordoyo, Maria Segui-Gomez

European Center for Injury Prevention, Universidad de Navarra, Spain

__________________________________

ABSTRACT – Crash protection for an aging population is one of the primary drivers of contemporary passive safety research,

yet estimates of the potential benefit of age-optimized systems have not been reported. This study estimates the number killed

and injured in traffic crashes due to the age-related reduction in tolerance to loading. A risk-saturation model is developed and

calibrated using 2000-2007 data for the age distribution of crash-involved adult occupants and drivers and the number of those

injured and killed in 2006. Nonlinear functions describing the relationships between age and risk, adjusted for several

confounders are developed using 10 years of NASS-CDS data and considered along with published risk functions for both

mortality and injury. The numbers killed and injured as a result of age-related fragility and frailty are determined by setting the

risk at all ages equal to the risk at age 20 (i.e., risk is assumed to “saturate” at age 20). The analysis shows that risk saturation at

age 20 corresponds to 7,805-14,939 fewer driver deaths and 10,989-21,132 fewer deaths to all occupants. Furthermore, 1.13-

1.32 million fewer occupants would be injured (0.80-0.93 million fewer drivers) per year. In other words, that number of deaths

and injuries can be attributed to age-related reductions in loading tolerance. As the age of risk saturation increases, the benefit

decreases, but remains substantial even in the age regime typically considered “elderly”. For example, risk saturation at age 60

corresponds to 1,011-3,577 fewer deaths and 73,537-179,396 fewer injured occupants per year. The benefit of risk saturation is

nearly log-linear up to approximately age 70, but drops off quickly thereafter due to the low exposures in the oldest age range.

The key contribution of this study is the quantification of deaths and injuries that can be attributed to aging and the development

of functions describing the relationship between age of risk saturation and the number of deaths and injuries averted.

__________________________________

INTRODUCTION

An increase in mean population age has been well

documented for many developed nations, including

the United States. By 2030, 25% of the U.S.

population will be age 65 or older (OECD 2001) and

the average age of the U.S. population is projected to

increase through 2100 (2000 U.S. Census). People

are also tending to drive later in life. Protecting an

older occupant in a collision presents a unique set of

challenges. It is well documented that, in general,

older people are more susceptible to injury than

younger, and that the morbidity, mortality, and

treatment costs for a given injury are higher (e.g.,

Martinez et al. 1994, Miltner and Salwender 1995,

Peek-Asa et al. 1998, Miller et al. 1998, Bulger et al.

2000, Evans 2001). Motivated by these changing

demographics and by the particular challenge of

protecting an older and more frail occupant, crash

protection for an aging population is one of the

primary drivers of contemporary passive safety

research (e.g., Rouhana et al. 2003, Bostrom and

Haland 2003, Forman et al. 2006). Despite this

focus, estimates of the potential benefit of age-

optimized systems have not been reported. The

purpose of this study is to estimate the number of

automotive deaths and injured occupants in the U.S.

that can be attributed to the well-documented age-

related reduction in tolerance to loading and thus

quantify an upper limit on the benefits that can be

realized by safety systems optimized for older

occupants.

Fragility,

Independent Aspects of Aging

Frailty, and Environment as

Older occupants differ in several respects from young

or even middle-aged occupants in terms of both crash

exposure and outcomes (see e.g., Islam and

Mannering 2006). Morris et al. (2002, 2003) and

Kent et al. (2005) identified several characteristics of

older-driver crashes in the United Kingdom and in

the United States, including the older driver’s

tendency to sustain greater injury for a given crash

severity, and for chest

disproportionately important. These macro-scale

differences in population

independent aspect of aging. First, older people are

injuries to be

outcomes reflect

Page 2

more fragile than younger: they tend to sustain a

greater level of injury for a given magnitude of

loading. This is reflected, for example, in a higher

probability of injury at a given AIS level for a

specified crash condition (delta-V, restraint use, etc.).

Second, older people are more frail than younger:

they tend to have worse outcomes for the same

injury. Both fragility and frailty contribute, for

example, to the age-related shift in delta-V

distribution in fatal frontal crashes (Figure 1, Kent et

al. 2005). Finally, older people have different

exposure patterns than younger. For example,

NHTSA (Cerelli 1998) found that older drivers have

slightly higher belt usage than younger and Viano

and Ridella (1996) concluded that older drivers are

over-represented in lateral impacts at intersections.

Cumulative Delta-V Frequency for All Drivers in Frontal

Crashes (Weighted)

0

10

20

30

40

50

60

70

80

90

100

Cumulative Frequency

Young adults

Middle age

Seniors

Cumulative Delta-V Frequency for Fatally Injured Drivers in

Frontal Crashes (Weighted)

0

10

20

30

40

50

60

70

80

90

100

0 1020304050

Delta-V (km/h)

60708090100 110 120

Cumulative Frequency

Young adults

Middle age

Seniors

Figure 1. Cumulative delta-V distribution of all drivers

(top) and of fatally injured drivers (bottom) in frontal

crashes (Kent et al. 2005).

All of these aspects (fragility, frailty, and

environment) are important to consider as the

population ages and remains mobile later in life.

This paper attempts to quantify the number of deaths

and injuries that can be attributed to increased

fragility and frailty of older people involved in

crashes. A risk-saturation model is developed and

calibrated using 2000-2007 data for the age

distribution of crash-involved adult occupants and

drivers and the number of those injured and killed.

Nonlinear functions describing the distribution of risk

by age, adjusted for several confounders are

developed using 10 years of NASS-CDS data and

considered along with published risk distributions for

both mortality and injury. The relationship between

the assumed age of risk saturation and the number of

deaths and injuries averted is then determined to

quantify potential gains for safety countermeasures

targeted at certain age groups.

METHODS

Governing Equations

The number of people who die or sustain an injury in

crashes each year, N, can be described as a sum over

two distributions:

(

age

)

ˆ ˆ

R E

⋅

N

=

∑

[1]

Where ˆR is the risk distribution by age and ˆE is the

crash exposure distribution by age. Recognizing that

20

ˆˆ

rel

RRR

=⋅

[2]

where ˆ

rel

R

is the relative risk distribution by age

and

20

R is the absolute risk at age 20, and that

ˆˆ

EP E

⋅=

[3]

where ˆP is the proportional age distribution of crash-

involved people and E is the total number of people

exposed to a crash, the following equation can be

written:

(

20

rel

age

)

ˆˆ

NRRP E

⋅=⋅⋅

∑

[4].

Determination of Exposures, Outcomes, and Age

Distributions

Drivers and all occupants (including drivers) were

considered separately. For this study, exposure and

outcome data for calendar year 2006 were used. The

values shown in Table 1 were used for N and E. The

number of drivers in all crashes was taken from

Traffic Safety Facts (2006) less 1% to remove

motorcycle drivers from the total. An analogous

figure for all occupants is not published, so it was

estimated from the ratio of drivers to all occupants in

the tow-away crashes sampled in 2006 for the NASS-

CDS database (ratio = 1/1.301, weighted).

Page 3

Table 1 – Estimates of N and E in the United States, Calendar Year 2006

Drivers

All occupants

Number in crashes, E

10.452 million1

Number killed, N

22,8302

Number injured, N

1.666 million2

13.599 million3 32,0922 2.375 million2

1Table 63 in Traffic Safety Facts (2006) less 1% for motorcycle drivers

2Table 53 in Traffic Safety Facts (2006)

3Number of drivers multiplied by 1.301 (see text)

The age distributions, ˆP , for drivers and for all

occupants were taken from the NASS-CDS database

for the calendar years 2000-2007 with the weighting

variable RATWGT used to estimate a nationally

representative distribution (Figure 2).

0.00

0.01

0.02

0.03

0.04

0.05

0.06

1525354555 657585

Age

Proportion

Crash-involved drivers

All crash-involved occupants

Figure 2. Age distributions of crash-involved adults (age

≥16) in NASS-CDS 2000-2007.

Estimation of Fatality Risk Distribution

With N, E, and ˆP defined as described above, the

only remaining unknowns in Equation [4] are ˆ

rel

R

and

components facilitates comparison with other studies

that have assessed age-related changes in risk using

different methods, age ranges, or populations. The

relative fatality risk distribution used in this paper

was estimated from two sources. The first is from

Evans (2001), who estimated risks using a double-

pair comparison approach on data for over 250,000

people killed in traffic crashes. Twenty-eight

combinations of gender, restraint use, helmet use, and

seating location were considered and a consistent

non-linear increase in fatality risk with age was

observed. The risk of death was found to increase at

a compound rate of 2.52±0.08 percent per year for

males and 2.16±0.10 percent per year for females.

20

R . Decomposing the distribution into these

For comparison with the Evans findings, the NASS-

CDS data files for the calendar years 1992-2002 were

analyzed to develop a fatality risk distribution by age.

Since relative risk estimation was the goal of the

study, a well-defined population with relatively large

exposure was chosen, viz. adult drivers age >15 years

in a planar frontal (principal direction of force 10:00-

2:00) crash without a fire. Drivers with unknown or

unreported injury outcome or restraint usage or with

a belt other than a lap-shoulder belt were excluded

from the data set. Females in the 2nd or 3rd term of

pregnancy were excluded as they were considered a

special at-risk group that was beyond of the scope of

this investigation. The mortality outcome was used

in its dichotomous mode (died = 1 and survived = 0).

Adjusted statistical models were developed to

account for the confounders Delta-V (km/h), vehicle

body type (0=passenger car, 1=light truck), vehicle

curb weight (kg), vehicle age (years, defined as the

calendar year of the crash minus the model year of

the vehicle), occupant gender (1=female), occupant

height (cm) and weight (kg), airbag deployment

(1=present and deployed), and belt use (1=belt in

use). The weighting variable RATWGT was used to

get national estimates. Multiple logistic regression

models were used to adjust for the confounders and

to calculate the logit estimates and the probability of

death. These probabilities were calculated via the

maximum likelihood method of logistic regression

models. The logit multivariate regression model is

written as

loge[P(Yi=1) /(1-P(Yi=1))] = (β0 + Σ (βij . χij) [5]

Odds (Yi=1) = exp -(β0 + Σ (βij . χij) [6]

P(Yi=1) = 1/ [1+ exp -(β0 + Σ (βij . χij)] [7]

where loge is the natural log (logit estimate). P(Yi=1) is

the probability of the event occurring (i.e., when Yi=1

for a dichotomous outcome) given χij . β0

model intercept (Log odds (Yi= 1), given all χs = 0).

χij

model. βij

included covariates within the logistic regression

model. For the set of model parameters described

above, Equation [7] can be written specifically as

is the

are the selected predictors (covariates) within the

are the coefficient’s estimates for the

Page 4

P(Yi=1) = 1/ [1+ exp -(β0 + Σ (β1 * airbag) + (β2 * belt

use) + (β3 * DV-total) + (β4 * vehicle type) + (β5 *

vehicle age) + (β6 * vehicle weight) + (β7 * driver

gender) + (β8 * driver height) + (β9 * driver weight) +

(β10 * driver age))]

[8]

where Yi=1 if the measurable event occurred and β1,

2,……10 are the coefficients for each included covariate.

From Equation [8] the relative risk distribution can

be determined by normalizing the probability

function to its value when driver age is set to 20

years. The relative fatality risk distributions based on

the Evans (2001) study and on the NASS-CDS study

are shown in Figure 3.

Estimation of Injury Risk Distribution

The relative risk of death cannot be used for the

injury portion of the study. The risk of death is

determined by the combined effects of two factors

that both change with aging. First, the risk of death

depends on the risk that an injury is sustained in the

first place (fragility). Second, the risk of death

depends on the risk of a person dying given a certain

injury (frailty). Both fragility and frailty increase

with aging and the relative death risk distributions

described above include both of those factors. The

risk of an injury occurring is by definition the first of

those two factors (fragility) and is unaffected by the

second (frailty), so it is to be expected that the

distribution of injury risk with aging should be

different than the distribution of fatality risk. In fact,

since frailty is known to increase with aging, the

relative risk of injury would be expected to increase

at a somewhat lower rate than the relative risk of

death.

One estimate of the relative risk distribution, ˆ

for injury as an outcome can be made using the

NASS-CDS data and analysis described above with

an injury outcome measure other than fatality. For

the development of this relative risk function, Yi was

defined as equaling 1 if the driver sustained an injury

severity score (ISS) ≥ 16. Other definitions of injury

were considered, but the shape of the relative risk

distribution with age was found to be relatively

insensitive to the definition of injury (i.e., the degree

to which risk increased with age was not strongly

dependent on the severity of injury chosen for

consideration).

rel

R

,

Another estimate of the relative risk distribution can

be derived from the analysis of Zhou et al. (1996).

This study makes for a particularly interesting

comparison with the NASS-CDS approach since

cadavers in a controlled laboratory setting, rather

than living humans in the field, were used to define

the risk increase associated with aging. Zhou and

colleagues divided into three age ranges (16-35, 36-

65, 66-85 years) a cohort of 107 cadavers exposed to

3-point seatbelt loading, 24 cadavers exposed to

anterior blunt impacts, and 29 cadavers exposed to

lateral blunt impacts and determined the age-

associated reduction in AIS 3+ injury tolerance. The

force tolerance for the subjects exposed to belt

loading was found to decrease by approximately half

from the youngest age group to the middle age group

while the force tolerance of the oldest age group was

28% of the youngest. The chest deformation

tolerance under blunt loading was found to decrease

much less dramatically with aging, with the middle

age group retaining about 82% of the chest deflection

tolerance of the youngest group and the oldest group

retaining about 75%. For the current study, the

inverse of the tolerance reduction was plotted at the

midpoint of each age range and a 2nd order

polynomial was fit to the three data points for the belt

loading case and then normalized to have the value of

1 at age 20. The blunt impact condition was not used

in the current study since the criterion used to define

injury threshold, chest deflection, depends on both

the intrinsic characteristics of the cadaver and on the

severity of the extrinsic loading. For this reason,

several studies in addition to the Zhou blunt hub

experiments, which relied upon chest deformation to

quantify age-related changes in risk, were also

excluded (e.g., Kent and Patrie 2003, Laituri et al.

2005). In contrast, if belt force is used to define the

tolerance reduction, then the relative risk function

describes the risk change associated with aging for a

similar severity external load. The relative injury risk

distributions based on the Zhou et al. (1996) study

and on the NASS-CDS study described above are

shown in Figure 3.

Analytical Approach

Once relative risk distributions are defined, the only

remaining unknown in Equation [4] is the absolute

risk term

20

R . The analytical approach taken here

included four steps:

1. Solve for

known, a range of values for ˆ

the literature).

2. Set

ˆ

ˆ

1

rel

R

= to represent the situation where

there is no increase in relative risk associated

with aging.

3. Recalculate N with

20

20

R for the baseline case (N, E, and ˆP

rel

R

estimated from

R , E, and ˆP unchanged.

Page 5

Figure 3. Relative risk distributions, ˆrel

4. The difference between the baseline value of N

(derived from 2006 field data) and the value of N

determined in Step 3 is the number of killed or

injured people that can be attributed to the age-

related reduction in tolerance to external loading.

R

, for injury (left) and for death (right) using different populations and methodologies.

For calculating N, the relative risk of drivers age 16-

19 was assumed to be one (i.e., there was assumed to

be no age-related reduction in tolerance between age

16 and age 20). The 4-step algorithm described

above yields estimates of the total number of killed or

injured occupants that can be attributed to the age-

related reduction in tolerance to external loading.

This is the number that could be averted if the crash

environment could be modified such that there was

no increase in relative risk for ages greater than 20

years. In other words, this is the number that could

be averted if risk saturated (became constant) at age

20.

Sensitivity to Risk Saturation Age

Of course, attaining a crash environment in which

risk does not increase beyond age 20 is an extremely

ambitious and perhaps unrealistic goal, so it may be

more useful to quantify the numbers if risk were to

saturate at ages greater than age 20. For example, it

may be a more realistic short-term goal to create a

crash environment where risk saturates at age 80 (i.e.,

risk for all occupants older than 80 years becomes

equal to the risk at age 80). In fact, the number of

killed or injured occupants can be determined for any

risk saturation age. To determine this relationship,

the relative risk distribution

assumed to become constant at the age of risk

saturation and to remain unchanged at all ages less

than that (Figure 4). Steps 3 and 4 above were then

performed with the relative risk curve saturated at

each year of age. The resulting number of injured or

ˆ

rel

R

was simply

killed occupants who would be saved can then be

plotted against the age of risk saturation to define the

sensitivity.

Fragility vs. Frailty

The NASS-CDS analysis is also useful for

apportioning the contributions of fragility and frailty

to the increased death risk associated with aging. If

the relative risk of injury is indeed insensitive to

either the environment or to the particular definition

of injury, then that relative risk defines the increase

in fragility with age:

(

rel

injury

)

ˆ

R

=“fragility” [9]

Furthermore, the conditional probability of death

given an injury, assuming they are independent

events, can be considered as a definition of “frailty”.

The injury and death

determined from the NASS-CDS data can be used to

determine how frailty changes with aging through a

simple conditional probability relationship:

probability functions

()

( and )

( )

P B

P AB

A

P

B

=

[10]

where (

(A) given an injury (B),

probability of injury and death, which is the

probability function of Equation [8] with the death

outcome, and

( )

P B is the probability of injury but

not death.

)

A

P

B is the conditional probability of death

P A

( and )

B is the

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

20304050

Age

607080

Risk Relative to Age 20

AIS 3+ belt force tolerance (Zhou et al. 1996)

NASS-CDS, ISS 16+, belt and bag

NASS-CDS, ISS 16+, belt, no bag

NASS-CDS, ISS 16+, bag, no belt

NASS-CDS, ISS 16+, no belt, no bag

20 304050

Age

607080

NASS-CDS, belt and bag

NASS-CDS, belt, no bag

NASS-CDS, bag, no belt

NASS-CDS, no belt, no bag

FARS males (Evans 2001)

FARS females (Evans 2001)

Conservative estimate

Upper estimate

Upper estimate

Conservative estimate

Page 6

Baseline Relative Risk Function

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

Risk Relative to Age 20

Risk Saturation at Age 30

Risk Saturation at Age 50

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

203040 50

Age

607080

Risk Relative to Age 20

Risk Saturation at Age 70

20 30 40 50

Age

60 7080

Figure 4. Illustration of changes to a relative risk distribution, ˆ

rel

R , to reflect risk saturation at ages 30, 50, and 70.

Equation [10], then, gives the conditional probability

of death given a certain injury, B. In order to

determine the conditional probability of death for any

injury, it is necessary to normalize the injury

probability function. This can be accomplished by

recognizing that

()

20

20

20

( and )

( )

P B

P AB

A

P

B

=

[11]

where the subscript indicates the age. The left-hand

side of Equation [10] can then be divided by the left-

hand side of Equation [11], and the right-hand side

by the right-hand side to yield

(

(

20

B

)

)

(

(

)

)

20

20

ˆ

( and )

( and )

P A

( )

P B

P B

⋅

ˆ

( )

rel

death

rel

injury

A

P

R

P AB

B

B

A

R

P

⋅

==

= “frailty” [12].

RESULTS

A wide range in the relative risk distributions by age

was found when the different methods for estimating

them were compared. The injury risk at age 70

relative to age 20 ranged from 4.06 based on the

Zhou et al. (1996) study to 6.22 in the NASS-CDS

analysis of belted occupants with an airbag. The

NASS-CDS-based fatality risk functions exhibited a

sharper increase with aging than the injury risk

functions, as expected due to the combined effects of

fragility and frailty discussed above. The relative

risk of death at age 70 based on the NASS-CDS

analysis was 9.42. The double-pair comparison study

of Evans (2001) suggested a much less pronounced

increase with aging, with the relative risk at age 70

being only 2.91 for females (the most conservative

estimate found). As a result of this discordance in the

literature, the estimated numbers of killed and injured

exhibited a large range of uncertainty.

Drivers and All Occupants Injured and Killed as a

Result of Aging

Of the 10.452 million drivers involved in crashes in

the U.S. in 2006, 1.666 million were injured and

22,830 were killed. If the age of risk saturation is set

to 20 years (i.e., if there was no age-related increase

in risk), those totals drop to 739,552-869,801 injured

and 7,891-15,025 killed. In other words, 47.8%-

55.6% of injured drivers and 24.3%-46.5% of driver

deaths can be attributed to the age-related increase in

risk that starts at age 20 (Table 2).

Page 7

Table 2 – Estimated Annual Lives Lost and People Injured as a Result of Aging

Drivers

Injured annually Percent of all injured Killed annually Percent of all killed

796,199-926,449 47.8%-55.6% 7,805-14,939 24.3%-46.5%

All occupants age ≥ 16 1,133,242-1,327,607 47.7%-55.9% 10,989-21,132

34.2%-65.8%

The percentages are slightly greater when all adult

occupants are considered. This is a result of the age

distribution for all adult occupants being shifted

slightly to the right compared to drivers (Figure 2).

Of the 13.559 million adult occupants involved in

crashes in the U.S. in 2006, 2.375 million were

injured and 32,092 were killed. If the age of risk

saturation is set to 20 years, those totals drop to

1.047-1.242 million injured and 10,960-21,103

killed. In other words, 47.7%-55.9% of injured adult

occupants and 34.2%-65.8% of occupant deaths can

be attributed to the age-related increase in risk that

starts at age 20 (Table 2).

Risk Saturation

As expected due to the drop-off in exposure as age

increases, the number of killed and injured that are

averted when risk saturates also drops off as the age

of risk saturation increases. The decrease is non-

linear on a log-linear scale (Figure 5). From the

approximately 10,000 deaths and 1,000,000 injuries

that could be averted by saturating risk at age twenty,

the benefit of risk saturation decreases to

approximately 200,000 injured and 3,500 deaths

prevented with risk saturation at age 60, and 3,000

injuries and 100 deaths with saturation at age 85.

The benefit to risk saturation increases rapidly as the

age of saturation decreases. Beyond the trend with

age, however, the results indicate that substantial

benefits can be gained with relatively modest

reductions in risk for the oldest occupants. For

example, approximately 100 annual deaths could be

prevented if occupants over the age of 85 had the risk

of an 85-year old. If the crash environment could be

modified in such a way that all occupants over age 70

had the risk of a 70-year-old, the expected benefit is

the elimination of some 90,000 injured occupants and

1,700 deaths.

Fragility vs. Frailty

The changes in fragility and in frailty with aging,

using the definitions of Equations [9] and [12], are

plotted in Figure 6. This shows that fragility is the

dominant factor, increasing by a factor of over eight

from age 20 to age 80, while frailty increases by a

factor of less than two over that range.

DISCUSSION

Crash exposure clearly decreases with aging beyond

the teen years (see Figure 2). This fact may be used

to justify prioritization of other at-risk populations. It

is important to realize, however, that the number of

people killed or injured is the product of both the

exposure and the risk, that risk increases significantly

with age, and that the U.S. population will continue

to age for decades. The fundamental purpose of the

current study was to quantify the interplay between

exposure and risk as a function of occupant age.

Based on the analysis presented here, it is reasonable

to conclude that frailty and fragility related to aging

are responsible for approximately half of all injured

and killed occupants. Even the most conservative

estimates of risk distribution indicate that intrinsic

aspects of aging are responsible for at least 10,989

deaths and 1.13 million injured occupants every year

in the United States.

As described in the Introduction, aging is associated

with changes in the crash environment in addition to

intrinsic changes in

Interestingly, Evans (2001) concluded that his

relative risk distribution by age was insensitive to the

specifics of the blunt trauma that generated the death,

even suggesting that the relationship between age and

relative risk in automotive crashes could be applied

generally to blunt trauma (e.g., falls). The NASS-

CDS analysis presented here is consistent with that

conclusion in that the relative risk functions are

insensitive to restraint condition, delta-v, and other

confounders that were considered in the statistical

model (Figure 3, Figure 7). For the purposes of the

study presented in this paper, a relative risk

distribution that is independent of environment or

exposure (crash severity, restraint use, etc.) is a

fortuitous finding since it allows the increased risk

that is due to fragility (injury outcome) or the

combination of fragility and frailty (death outcome)

to be isolated from the environmental aspects of risk

(changing restraint usage, increase in side impacts,

etc.). The fact that both the Evans distribution and

frailty and fragility.

Page 8

Drivers Age >15

10

100

1,000

10,000

100,000

1,000,000

Injured Averted Annually.

1

10

100

1,000

10,000

100,000

Deaths Averted Annually.

Injured

Deaths

All Occupants Age >15

10

100

1,000

10,000

100,000

1,000,000

203040 506070 80

Age at which Risk Saturates (Years)

Injured Averted Annually.

1

10

100

1,000

10,000

100,000

Deaths Averted Annually.

Injured

Deaths

Figure 5. Number of averted deaths and injured drivers (top) and all occupants (bottom) as a function of the age at which risk

saturates. Error bars represent the conservative and upper estimates of relative risk (see Figure 3).

1

2

3

4

5

6

7

8

9

20 3040 50

Age

60 7080

(Fragility)

injury

(Frailty)

(

20

B

()

ˆrel

R

(

A

)

)

(

(

)

)

ˆ

ˆ

rel

death

rel

injury

A

RP

B

R

P

=

Figure 6. Frailty and fragility with increasing age. Fragility

is defined as the probability of an injury occurring and

frailty is defined as the conditional probability of death

given an injury. Both are normalized to age 20.

the NASS-CDS-based distribution were insensitive to

environmental factors such as restraint use and crash

severity suggests that the net effect of environment

on the age-related change in risk is relatively small

and that the numbers of deaths and injured occupants

that were found to result from aging actually

represent primarily the fragility and frailty aspects of

aging, not any change in crash environment. The

apportionment exercise indicates that both fragility

and frailty increase with aging, but that frailty

increases by much less. One interpretation of this

finding is that injury mitigation efforts for older

drivers have a greater potential benefit than improved

treatment in terms of preventing automotive deaths.

The studies used here to define the range of relative

risk distributions employed different methods. Evans

(2001) used a double-pair comparison approach with

Page 9

Figure 7. Illustration of insensitivity of NASS-CDS-based

relative risk distribution to crash severity. The distribution

is similarly insensitive to occupant parameters, vehicle

parameters, and restraint use (see Figure 3 for sensitivity to

restraint).

occupants grouped by selected environmental factors

that affect risk (helmet use, restraint use, gender).

Zhou et al. (2006) considered cadavers that were

tested in a controlled laboratory setting where the

environment was well defined and the parameter

defining injury tolerance (belt force) innately

accounts for variability in impact severity. The

NASS-CDS study presented here controlled for

several environmental factors within a logistic

regression model in order to isolate the part of the

risk that is attributed to aging independently of

environment. Each method has strengths and

limitations. The double pair comparison method

proposes to compare rates (of fatality in this case)

between subjects who have been exposed to similar

crash severities. In fact, this could be viewed as a

case-control type of method wherein there is

matching of crash severity for the cases (i.e., those

who died) and the control (i.e., those who did not).

This method has been used in a number of motor

vehicle safety-related publications aiming to assess

the effectiveness of different safety measures (e.g.,

safety belts, airbags, vehicle mass) and it is superior

to the situation where no other information on the

severity of the crash is available and effectiveness

estimates could be confounded as a result. There is

no evidence that this method is superior to

performing multivariate analysis where crash severity

has been controlled for as a covariate. More

importantly, using multivariate regression models

allows for additional control for other possible

confounders, such as those considered in our NASS-

CDS model. The use of cadavers in the Zhou study

is limited in the nature and range of injuries that can

be sustained by an ex vivo model (in the current case

injury was defined exclusively by rib fractures).

Logistic regression models of NASS-CDS data are

limited by the sampling and documentation accuracy

of the database as well as by the restrictions imposed

by the use of a linear logit function and by the

particular parametric form chosen (logistic). As a

result of the limitations inherent in all of the relative

risk distributions available for study, a conservatively

wide range was considered.

CONCLUSIONS

This study has attempted to quantify the number of

killed and injured occupants that can be related to

aging. Exposure data for the U.S. in 2006 were

combined with estimates of age-related risk from a

variety of sources. Risk was then saturated (made

constant) at different ages to elucidate the potential

savings in terms of the number of injured and killed

occupants who would be saved. If the relative risk

distribution is set to one for all ages (i.e., there is no

age-related increase in injury or death risk), then

47.8%-55.6% of injured drivers and 24.3%-46.5% of

driver deaths would be eliminated and 47.7%-55.9%

of injured adult occupants and 34.2%-65.8% of

deaths among all adult occupants would be

eliminated. In other words, on the order of half of the

injured or killed are so as a result of decreased

tolerance due to aging. The benefit of saturating risk

drops off quickly with increasing age, but the

potential benefits to even modest targets for risk

saturation are worth pursuing. For example, if the

crash environment could be modified in such a way

that all occupants over age 70 had the risk of a 70-

year-old, the expected benefit is the elimination of

some 90,000 injured occupants and 1,700 occupant

deaths each year in the U.S. Conditional probability

analysis suggests that most of these deaths are due to

increased fragility associated with aging rather than

increased frailty.

ACKNOWLEDGMENTS

Basem Hanary assisted in the development of the

NASS-CDS risk functions. Thanks also to Jeff

Crandall and Jason Kerrigan for helpful discussions

on the methods employed in this paper and to Ola

Bostrom for insights on crash protection for older

people.

20

30

40

50

60

Age (years)

0

20

40

60

Delta-v

(km/h)

ˆrel

R

2

4

6

Page 10

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