Lower Extremity Injuries



Lower Extremity Injuries

John Hill

Josh Hoffman

December 6, 2004

Introduction

Automobile crashes lead to a variety of different injuries based on the severity and type of crash. In particular, Morgan et al. (1991) showed that lower extremity injuries account for 26% of AIS >=2 (moderately severe) injures for belted occupants and a similar proportion of unbelted occupants. Given the prevalence of these types of injuries, it is important to know the particular risk factors associated with them, as well as which populations are more prone to these injuries.

Prior analyses of crash outcomes have used a variety of statistical measures to analyze crash data, including frequencies and proportions of lower extremity injuries. As seen in the graph below, previous analysis from the Wisconsin CODES database, a standardized summary of crash data from the state, shows that older female occupants involved in vehicle crashes are at higher risk for lower extremity injury.

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Figure 1. Plot of Lower Extremity Injury Rates from the State of Wisconsin

(Source: Wisconsin CODES)

Given the similar driving populations and roadway environments in Iowa and Wisconsin, we evaluated the crash characteristics associated with lower extremity injuries in Iowa. These included gender, age, vehicle type, seating position, and type of collision.

Purpose

The goal of this study was to determine the likelihood of suffering a lower extremity injury when involved in an automobile crash.

Data

Data was obtained from the Iowa Department of Public Health pertaining to injuries in vehicle crashes in the state of Iowa from the year 2000. The database, a nationally standardized dataset known as CODES, contains biographical information on injured occupants, the type of injuries, accident description, and vehicle and environmental characteristics on roughly 1500 injured individuals. The specific variables we included in our model and their corresponding values are listed below. Each classification had a corresponding dummy variable coding.

Gender – Male, female

Age – Younger or older than 55

Vehicle Type – SUV, Truck, Van, Car

Seating Position – Driver, Front passenger, Rear passenger

Collision Type – Head on, Rear end, Side, Single vehicle

After reducing the data to eliminate pedestrians, bicycles, motorcycles, and recreational and commercial vehicles there were about 1200 data points left in the set to analyze.

Analysis

A logistic regression model was used to evaluate the effect of these characteristics. The likelihood of lower extremity injury based on each of the prediction variables above was determined based on odds ratios. This provided a quantitative assessment of risk for each of the crash scenarios.

The SAS code used to conduct this analysis can be seen in Appendix A.

Results

The results of the logistic regression analysis can be found in Table 1. Only those variables which were statistically significant are included.

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The results show that the following variables increased the likelihood of suffering a lower extremity injury when involved in an automobile accident: females, the driver of the vehicle, the front right passenger, and those involved in head-on collisions. Table 2 shows the odds ratios for each crash condition calculated from the parameter estimates. Each variable is discussed independently below.

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Females

Figure 2 shows that females were 1.34 times as likely to suffer a lower extremity injury in an accident. This may be a result of the smaller stature in the female population, as mentioned by Dischinger (1996), which may require them to place their seat closer to the dash. In this position they would be more likely to hit their legs in an accident.

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Figure 2. Effect of Gender on Likelihood of Suffering a Lower Extremity Injury

Seniors

Figure 3 shows that seniors were only 0.72 times as likely to suffer a lower extremity injury in an accident. This result may reflect more cautious driving on the part of the elderly, resulting in lower collision velocities when they crash. Older drivers may tend to drive larger and heavier vehicles that protect them from such injuries.

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Figure 3. Effect of Age on Likelihood of Suffering a Lower Extremity Injury

Drivers and Passengers

Figure 4 shows that drivers were 1.98 times as likely and front seat passengers were 1.79 times as likely to suffer a lower extremity injury in an accident. This is in contrast to rear seat passengers, who were only 0.28 times as likely to suffer a lower extremity injury. The fact that front seat passengers overall are more likely to be injured in this manner reflects the vulnerability of front seat occupants in crashes. They are more likely to come in contact with hard objects including the knee bolster of the dash and the pedals, as suggested by Rudd et al., 1998. Rear seat occupants instead collide with the backs of cushioned seats in high speed frontal collisions.

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Figure 4. . Effect of Seating Position on Likelihood of Suffering a Lower Extremity Injury

Collision Type

Figure 5 shows that single vehicle collisions were only 0.74 times as likely to result in a lower extremity injury. In contrast, head-on multi-vehicle accidents were 3.45 times as likely to have this result. Other multi-vehicle crashes such as rear impacts or side impacts were only 0.21 times as likely to generate lower extremity injury. This result is consistent with the previous results showing that front seat occupants were more prone to these injuries. Head-on collisions are also higher energy crashes that transmit a greater amount of energy to the occupants.

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Figure 5. Effect of Collision Type on Likelihood of Suffering a Lower Extremity Injury

Conclusions

We found that age, gender, seating position, and collision type were significant factors in determining the likelihood of lower extremity injury in vehicle crashes. We did not find any significant interactions between the predictor variables in the model. Females were more likely to suffer lower extremity injuries. Contrary to our hypothesis, Seniors (over 55) were less likely to suffer lower extremity injuries, and there was no significant interaction with Female and Senior. Front seat passengers and drivers were more likely to suffer lower extremity injury, as were occupants involved in head on collisions and multi-car accidents.

References

CHSRA/UW-Madison. (2003). Wisconsin CODES injury outcome report. Found online at:

Morgan, R.M. & Eppinger, R.H., (1991). Ankle joint injury mechanism for adults in frontal automotive impact. SAE Paper No. 912902.

Dischinger, P.C. The risk of ankle/foot fractures among women drivers. Proceedings of the 2nd National Conference on Women’s Travel Issues. Baltimore, MD, October, 1996.

Rudd, R.W., Crandall, J.R., Bass, C.R., (1998). Lower extremity and brake pedal interaction in frontal collisions: Sled tests. SAE Technical Paper Series 980359. Warrendale, PA.

Appendix A

data ALR.CODES2 ;

set ALR.CODES ;

if Vehicle_Type = 'Bus' then delete;

if Vehicle_Type = 'Moped' then delete;

if Vehicle_Type = 'Bicycle' then delete;

if Vehicle_Type = 'Farm Vehicle' then delete;

if Vehicle_Type = 'Motorcycle' then delete;

if Vehicle_Type = 'Other' then delete;

if Vehicle_Type = 'Unknown' then delete;

if Vehicle_Type = 'Rec. Vehicle' then delete;

if Vehicle_Type = 'Maintenance Vehicle' then delete;

if Seating_Position = 'Pedestrian' then delete;

if Lower_Extreme_tries = 1 then LEI = 1;

else LEI = 0;

if SEX = 'F' then Female = 1;

else Female = 0;

if Sex = 'M' then Male = 1;

else Male = 0 ;

if Age < 55 then Senior = 0;

else Senior = 1;

if Vehicle_Type = 'Utility Vehicle' then LTUV = 1;

else if Vehicle_Type = 'Pickup' then LTUV = 1;

else if Vehicle_Type = 'Truck' then LTUV = 1;

else if Vehicle_Type = 'Light Van' then LTUV = 1;

else LTUV = 0;

if Seating_Position = 'Driver' then Driver = 1;

else Driver = 0;

if Seating_Position = 'Front Seat Passenger Side' then FrtPassSeat = 1;

else FrtPassSeat = 0;

if Collision_Type = 'Single' then Col_Single = 1 ;

else Col_Single = 0 ;

if Collision_Type = 'Head-on' then Col_Headon = 1 ;

else if Collision_Type = 'Head-on/Left Entering' then Col_Headon = 1 ;

else if Collision_Type = 'Headon/Left Entering' then Col_Headon = 1 ;

else Col_Headon = 0 ;

if Collision_Type = 'Broadside/Left Entering' then Col_BSide = 1 ;

else if Collision_Type = 'Broadside/Left Turn' then Col_BSide = 1 ;

else if Collision_Type = 'Broadside/Right Entering' then Col_BSide = 1 ;

else if Collision_Type = 'Broadside/Right Angle' then Col_BSide = 1 ;

else Col_BSide = 0 ;

if Collision_Type = 'Rear End' then Col_RearEnd = 1 ;

else if Collision_Type = 'Rear End/Left Turn' then Col_RearEnd = 1 ;

else if Collision_Type = 'Rear End/Right Turn' then Col_RearEnd = 1 ;

else Col_RearEnd = 0 ;

run;

Final code for the regression model after eliminating insignificant variables:

proc logistic data = ALR.CODES2 descending;

model LEI = Female Senior Driver FrtPassSeat Col_Single Col_Headon ;

run;

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