Short-term physical limitations in children following motor vehicle crashes

This study describes frequency of injury and short-term physical limitation among child occupants ≤15 years in motor vehicle crashes and examines the association between age, restraint use, seating position, and type of crash on the presence of physical limitations. Conducted from 1/1/2005–11/30/2007, as part of a child-specific crash surveillance system in 15 U.S. states; data were collected using claims records and parent/driver telephone surveys. Respondents were asked whether children sustained physical limitations from the crash and the duration limitations persisted. Overall, 3.3% had ≥1 physical limitations. Limitations increased with age, from 0.7% for children ≤3 years to 7.6% for adolescents 13–15 years (p < 0.001). Among children with AIS ≥2 injuries, the proportion with physical limitations ranged from 58% to 91% depending on injury diagnosis. Among children with whiplash, 47% resulted in physical limitations. Suboptimally restrained children were nearly twice as likely to have a limitation compared to optimally restrained children. After adjusting for driver characteristics and vehicle type, child's age, restraint use, and type of initial impact were independently associated with the presence of physical limitations. Our results show the importance of assessing children for physical limitations following motor vehicle crashes. We also observed that children with whiplash were at risk for physical limitations.     

Partners for child passenger safety: a unique child-specific crash surveillance system

Insurance claims data were combined with telephone survey and on-site crash investigation data to create the first large scale, child-focused motor vehicle crash surveillance system in the US. Novel data management and transfer techniques were used to create a nearly real-time data collection system. In the first year of this on-going project, known as Partners for Child Passenger Safety, over 1200 children < or = 15 years of age per week were identified in crashes reported to State Farm Insurance Co. from 15 states and Washington, D.C. Partners for Child Passenger Safety is similar in its design and overall objectives to National Automotive Sampling System (NASS), the only other population-based crash surveillance system currently operating in the US.     

Modeling motor vehicle crashes for street racers using zero-inflated models

Motor vehicle crashes are a leading cause of death for young people in the United States. Assessing which drivers are at a high risk of experiencing a crash is important for the implementation of traffic regulations. Illegal street racing has been associated with a high rate of motor vehicle crashes. In this study, we link Utah statewide driver license citations and motor vehicle crash data to evaluate the rate of crashes for drivers with street racing citations relative to other drivers. Using a zero-inflated negative binomial model we found that drivers with no citations are approximately three times more likely to be at zero risk of a crash compared to drivers with street racing citations. Moreover, among drivers at non-negligible risk of crash, cited street racers are more likely to be involved in a crash compared to drivers without citations or those cited for violations other than street racing. However, drivers with increased numbers of non-street-racing citations experience crash risks approaching those of the cited street racers.     

The effects of age,gender, and crash types on drivers’ injury-related health care costs

There are many studies that evaluate the effects of age, gender, and crash types on crash related injury severity. However, few studies investigate the effects of those crash factors on the crash related health care costs for drivers that are transported to hospital. The purpose of this study is to examine the relationships between drivers’ age, gender, and the crash types, as well as other crash characteristics (e.g., not wearing a seatbelt, weather condition, and fatigued driving), on the crash related health care costs. The South Carolina Crash Outcome Data Evaluation System (SC CODES) from 2005 to 2007 was used to construct six separate hierarchical linear regression models based on drivers’ age and gender. The results suggest that older drivers have higher health care costs than younger drivers and male drivers tend to have higher health care costs than female drivers in the same age group. Overall, single vehicle crashes had the highest health care costs for all drivers. For males older than 64-years old sideswipe crashes are as costly as single vehicle crashes. In general, not wearing a seatbelt, airbag deployment, and speeding were found to be associated with higher health care costs. Distraction-related crashes are more likely to be associated with lower health care costs in most cases. Furthermore this study highlights the value of considering drivers in subgroups, as some factors have different effects on health care costs in different driver groups. Developing an understanding of longer term outcomes of crashes and their characteristics can lead to improvements in vehicle technology, educational materials, and interventions to reduce crash-related health care costs.     

Discrepancies in vehicular crash injury reporting: Northeastern Ohio Trauma Study. IV

People injured in motor vehicle traffic crashes were identified from a population-representative incidence sample of hospital emergency department visits. Matched police reports of crashes were sought in official state records of motor vehicle traffic crashes. Of the emergency department cases, 55% had matched police reports. The frequency of matched reports was highest for drivers (74%), people transported to the hospital by emergency vehicle (69%), and those requiring hospital admission (74%). The frequency was lowest for people younger than 16 years (28%), people injured as occupants of vehicles other than passenger cars (24%), medicaid recipients (33%), and nonresidents of the study region (40%). Motor vehicle traffic injuries are undercounted in police-reported statistics. For many groups, police reporting is less than 50% of the cases identified through emergency departments. The likelihood that a case of motor vehicle traffic injury will have a matched police report depends on demographic, social and crash factors as well as on injury severity.     

Bicycling crash characteristics: An in-depth crash investigation study

The aim of this study was to describe the crash characteristics and patient outcomes of a sample of patients admitted to hospital following bicycle crashes. Injured cyclists were recruited from the two major trauma services for the state of Victoria, Australia. Enrolled cyclists completed a structured interview, and injury details and patient outcomes were extracted from the Victorian State Trauma Registry (VSTR) and the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR). 186 cyclists consented to participate in the study. Crashes commonly occurred during daylight hours and in clear weather conditions. Two-thirds of crashes occurred on-road (69%) and were a combination of single cyclist-only events (56%) and multi-vehicle crashes (44%). Of the multi-vehicle crashes, a motor vehicle was the most common impact partner (72%) and distinct pre-crash directional interactions were observed between the cyclist and motor vehicle. Nearly a quarter of on-road crashes occurred when the cyclist was in a marked bicycle lane. Of the 31% of crashes that were not on-road, 28 (15%) occurred on bicycle paths and 29 (16%) occurred in other locations. Crashes on bicycle paths commonly occurred on shared bicycle and pedestrian paths (83%) and did not involve another person or vehicle. Other crash locations included mountain bike trails (39%), BMX parks (21%) and footpaths (18%). While differences in impact partners and crash characteristics were observed between crashes occurring on-road, on bicycle paths and in other locations, injury patterns and severity were similar. Most cyclists had returned to work at 6 months post-injury, however only a third of participants reported a complete functional recovery. Further research is required to develop targeted countermeasures to address the risk factors identified in this study.     

A simple method for identifying and correcting crash problems on urban arterial streets

Urban arterials by their nature carry heavy traffic volumes and generate large numbers of motor vehicle crashes. The present study involved review of police crash reports to identify precrash events and driver actions for a sample of crashes on urban arterials and describes a method for reducing such crashes based on analyses of collision patterns and identification of locations with excessive numbers of crashes of a particular type. Police-reported crash data were obtained for three urban arterials in the Washington, DC metropolitan area. A total of 2013 crash reports were analyzed. Seven crash types accounted for nearly 90% of these reports. On each arterial studied, several locations with excessive numbers of crashes of a particular type were identified, and corresponding engineering countermeasures were recommended. Differences between the approach employed in this study and traditional blackspot analyses are discussed.     

Factors affecting hospital charges and length of stay from teenage motor vehicle crash-related hospitalizations among United States teenagers, 2002-2007

Motor vehicle crashes are the leading cause of death for all teenagers, and each year a far greater number of teens are hospitalized with non-fatal injuries. This retrospective cohort study used the National Inpatient Sample data to examine hospitalizations from the years 2002 to 2007 for 15–18-year-old teenagers who had been admitted due to a motor vehicle crash. More than 23,000 teens were hospitalized for motor vehicle-related crash injuries each year, for a total of 139,880 over the 6-year period. Total hospital charges exceeded $1 billion almost every year, with a median hospital charge of more than $25,000. Older teens, boys, those with fractures, internal injuries or intracranial injuries, and Medicaid/Medicare as a payer were associated with higher hospital charges and longer lengths of stay. These high charges and hospitalization periods pose a significant burden on teens, their families, and the health care system.     

Age and gender patterns in motor vehicle crash injuries: importance of type of crash and occupant role

To evaluate the interaction of gender, age, type of crash, and occupant role in motor vehicle crash injuries leading to hospitalization, we analyzed 1997 Wisconsin hospital discharge data for patients with primary E-code diagnoses of motor vehicle injuries. The overall ratio of males to females (M/F ratio) hospitalized for motor vehicle crash injuries was 1.33 (95% confidence interval (CI): 1.26-1.41). The M/F ratio varied by type of crash and differed for passengers and drivers. For injuries sustained in collisions between vehicles, the M/F ratio was 0.96 (95% CI: 0.87-1.05); in loss of control accidents the M/F ratio was 1.95 (95% CI: 1.76-2.17). Within each type of crash, the M/F ratio for drivers was similar to that for the entire type; the M/F ratio for passengers was about half of the type total. Expressed as rates of hospitalization per 100,000 people in the general population, hospitalizations of drivers in collisions with another motor vehicle increased steeply in males, but not in females, beginning at about age 70. For drivers in loss of control crashes, male rates exceeded female rates in all age groups, with peaks in the groups 15-24 and 85-89. For passengers, injury rates from collisions with other motor vehicles were greater for females, especially in the elderly, and injury rates from loss of control crashes were similar for both genders, with peaks at 15-24 and 85-94. The higher fatality of men in loss of control motor vehicle crashes, compared to women, suggests an important area for further investigation.     

Predicting motor vehicle crashes using Support Vector Machine models

Crash prediction models have been very popular in highway safety analyses. However, in highway safety research, the prediction of outcomes is seldom, if ever, the only research objective when estimating crash prediction models. Only very few existing methods can be used to efficiently predict motor vehicle crashes. Thus, there is a need to examine new methods for better predicting motor vehicle crashes. The objective of this study is to evaluate the application of Support Vector Machine (SVM) models for predicting motor vehicle crashes. SVM models, which are based on the statistical learning theory, are a new class of models that can be used for predicting values. To accomplish the objective of this study, Negative Binomial (NB) regression and SVM models were developed and compared using data collected on rural frontage roads in Texas. Several models were estimated using different sample sizes. The study shows that SVM models predict crash data more effectively and accurately than traditional NB models. In addition, SVM models do not over-fit the data and offer similar, if not better, performance than Back-Propagation Neural Network (BPNN) models documented in previous research. Given this characteristic and the fact that SVM models are faster to implement than BPNN models, it is suggested to use these models if the sole purpose of the study consists of predicting motor vehicle crashes.     

Who is driving when unrestrained children and teenagers are hurt?

This paper examines driver characteristics in crashes where child and teenage motor vehicle crash victims were injured, in particular factors that determine whether or not the victim was restrained. Analyzing the data on children and teenagers who are injured revealed that the presence of a second adult in a vehicle increased the likelihood that these passengers were unrestrained. Other findings are more predictable: victim restraint use generally mirrored driver restraint use; a male driver, a young driver, a drinking driver, a speeding or reckless driver, an unlicenced or suspended driver, and a night-time trip each independently raised the odds that child and teenage passengers were not restrained when they were injured.     

How similar are two-unit bicycle and motorcycle crashes?

This paper explores the similarities and differences between bicycle and motorcycle crashes with other motor vehicles. If similar treatments can be effective for both bicycle and motorcycle crashes, then greater benefits in terms of crash costs saved may be possible for the same investment in treatments. To reduce the biases associated with under-reporting of these crashes to police, property damage and minor injury crashes were excluded. The most common crash type for both bicycles (31.1%) and motorcycles (24.5%) was intersection from adjacent approaches. Drivers of other vehicles were coded most at fault in the majority of two-unit bicycle (57.0%) and motorcycle crashes (62.7%). The crash types, patterns of fault and factors affecting fault were generally similar for bicycle and motorcycle crashes. This confirms the need to combat the factors contributing to failure of other drivers to yield right of way to two-wheelers, and suggest that some of these actions should prove beneficial to the safety of both motorized and non-motorized two-wheelers. In contrast, child bicyclists were more often at fault, particularly in crashes involving a vehicle leaving the driveway or footpath. The greater reporting of violations by riders and drivers in motorcycle crashes also deserves further investigation.     

Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.     

Comparison of factors influencing emergency department visits and hospitalization among drivers in work and nonwork-related motor vehicle crashes in Utah, 1999-2005

This study identified contributing factors in the occurrence of motor vehicle crashes (MVCs) and the severity of crashes according to work-related status in Utah. Analyses were based on probabilistically linked data involving police crash reports and hospital inpatient and emergency department (ED) records for the years1999–2005. Of 643,647 drivers involved in crashes, 73,437 (11.4%) went to the emergency department (ED) and 4989 (0.8%) were hospitalized. Of the drivers in crashes visiting the ED, 2330 (3.2%) were working at the time of the crash and of drivers in crashes who were hospitalized, 235 (4.7%) were working at the time of the crash. There was no significant difference between those working versus not working at the time of the crash in safety belt use (82% [53,947/66,188] for ED cases and 60% [2,489/4,176] for hospitalized cases) or fatigue (4% [2,697/70,536] for ED cases and 9% [450/4,824] for hospitalized cases) among drivers in crashes, but there was a significant difference with respect to alcohol drinking between workers versus nonworkers (ED: 1% [31/2,237] vs. 5% [3,455/68,299], P < 0.001; hospitalized: 3% [7/228] vs. 15% [673/4,596], P < 0.001). Of those attending the ED because of a crash, workers were significantly more likely to have broken bones, bleeding wounds, or to die. Of those hospitalized because of a crash, workers were significantly less likely to have caused the crash (65% [145/223] vs. 73% [3,315/4,566], P < 0.001). Yet although those drivers who were working at the time of the crash compared with those not working were less likely to have alcohol involved or to have caused the crash, there remains room for improvement among workers with respect to these factors, as well as safety belt use and fatigue.     

An empirical analysis of farm vehicle crash injury severities on Iowa's public road system

Farm vehicle crashes are a major safety concern for farmers as well as all other users of the public road system in agricultural states. Using data on farm vehicle crashes that occurred on Iowa's public roads between 2004 and 2006, we estimate a multinomial logit model to identify crash-, farm vehicle-, and driver-specific factors that determine farm vehicle crash injury severity outcomes. Estimation findings indicate that there are crash patterns (rear-end manner of collision; single-vehicle crash; farm vehicle crossed the centerline or median) and conditions (obstructed vision and crash in rural area; dry road, dark lighting, speed limit 55 mph or higher, and harvesting season), as well as farm vehicle and driver-contributing characteristics (old farm vehicle, young farm vehicle driver), where targeted intervention can help reduce the severity of crash outcomes. Determining these contributing factors and their effect is the first step to identifying countermeasures and safety strategies in a bid to improve transportation safety for all users on the public road system in Iowa as well as other agricultural states.     

Rural motor vehicle crash mortality: The role of crash severity and medical resources

We did a retrospective case control study to examine the relationship between the risk of dying for Michigan motor vehicle crash (MVC) drivers and the type of county (rural/nonrural) of crash occurrence, while adjusting for crash characteristics, age, sex, and the medical resources in the county of crash occurrence. The 1987 Michigan Accident Census was used to obtain data regarding all MVC driver nonsurvivors (733) and a random sample of all surviving drivers (2,483). County of crash occurrence was defined as rural or nonrural. The crash characteristics analyzed were vehicle deformity, seat belt use, and drivability of the vehicle from the scene. Age and sex of the driver were also analyzed. Medical resource characteristics for the county of crash occurrence were measured as the number of resources per square mile for each of the following: ambulances, emergency medical technicians (EMT), acute care hospital beds, and operating rooms, surgeons and emergency physicians. Also considered were the number and level of emergency rooms in the county of crash occurrence along with the maximum level of prehospital care available (basic life support versus advanced life support) in a county. Before adjusting, the relative risk (RR) for rural MVC drivers dying, compared to their nonrural counterparts, was 1.96. Adjustment for crash characteristics, age, and sex (using logistic regression) decreased the RR to 1.51. An attempt to add medical resource variables to the model resulted in high correlation with the rural/nonrural variable, as well as with each other. This multi-collinearity prevented us from providing a simple explanation of the role of medical resource variables as predictors of survival. We conclude that almost 50% of excess rural MVC mortality, as measured by the RR, can be accounted for by difference in crash characteristics and age. Delineation of the role of medical resources will require further investigation.     

Pedestrian crashes in Washington,DC and Baltimore

Police crash reports were obtained for pedestrian-motor vehicle crashes in Washington, DC (N = 852) and Baltimore (N = 1234) for the year 1998. Reports were coded using procedures developed and applied in these two cities during the 1970s, including the determination of pedestrian crash type, primary precipitating factor, and culpability. Results indicated substantial differences between crash patterns observed during the 1970s and those observed during 1998. Midblock dart-dash crashes, which typically involve a precipitating factor or critical error by a child pedestrian, decreased (from 37% to 15% in Washington). Across all crashes in both cities, the number of drivers who made a critical error leading to the crash was nearly equivalent to the number of pedestrians who made a critical error. Overall, pedestrians were slightly more likely to be judged culpable (50% vs. 39%). Turning vehicle crashes, which typically involve a driver's failure to grant a pedestrian the right of way at a signalized intersection, increased (from 9% to 25% in Washington). Countermeasures to reduce the number of pedestrians hit by turning vehicles are discussed.     

Attributions of responsibility for motor vehicle crashes

A sample of 321 motor vehicle crash survivors completed a survey in which they provided attribution ratings of the extent to which they were responsible for their crashes, other people (drivers) were responsible, or road/weather conditions were responsible. The attribution ratings were consistent with the predictions of defensive attribution theory (DAT; [Walster, E., 1966. Assignment of responsibility for an accident. J. Pers. Soc. Psychol. 3, 73-79]) in that people who experienced crashes of greater severity (necessitating medical treatment for injuries) attributed greater responsibility to other drivers than to self or to weather/road conditions. People who were in crashes of lesser severity attributed approximately the same amount of responsibility to themselves as they did to others. An actor-observer effect also appeared in survivors' attribution ratings in that self-acceptance of responsibility for the crash was positively correlated with attributions to the situation (road/weather conditions) whereas such attributions to the situation were negatively correlated with attributions of responsibility to other drivers. Consistent with results of prior research, survivors who assigned crash responsibility to other drivers reported increased levels of driving and riding avoidance compared to people who accepted responsibility for their crashes.     

Prevalence of teen driver errors leading to serious motor vehicle crashes

Objectives

Motor vehicle crashes are the leading cause of adolescent deaths. Programs and policies should target the most common and modifiable reasons for crashes. We estimated the frequency of critical reasons for crashes involving teen drivers, and examined in more depth specific teen driver errors.

Methods

The National Highway Traffic Safety Administration's (NHTSA) National Motor Vehicle Crash Causation Survey collected data at the scene of a nationally representative sample of 5470 serious crashes between 7/05 and 12/07. NHTSA researchers assigned a single driver, vehicle, or environmental factor as the critical reason for the event immediately leading to each crash. We analyzed crashes involving 15–18 year old drivers.

Results

822 teen drivers were involved in 795 serious crashes, representing 335,667 teens in 325,291 crashes. Driver error was by far the most common reason for crashes (95.6%), as opposed to vehicle or environmental factors. Among crashes with a driver error, a teen made the error 79.3% of the time (75.8% of all teen-involved crashes). Recognition errors (e.g., inadequate surveillance, distraction) accounted for 46.3% of all teen errors, followed by decision errors (e.g., following too closely, too fast for conditions) (40.1%) and performance errors (e.g., loss of control) (8.0%). Inadequate surveillance, driving too fast for conditions, and distracted driving together accounted for almost half of all crashes. Aggressive driving behavior, drowsy driving, and physical impairments were less commonly cited as critical reasons. Males and females had similar proportions of broadly classified errors, although females were specifically more likely to make inadequate surveillance errors.

Conclusions

Our findings support prioritization of interventions targeting driver distraction and surveillance and hazard awareness training.