Fatal Motor Vehicle-Pedestrian Collision Injury Patterns-A Systematic Literature Review

Injury patterns in motor vehicle collision-youth pedestrian deaths

OBJECTIVE

The objective of this study was to describe fatal pedestrian injury patterns in youth aged 15 to 24 years old and correlate them with motor vehicle collision (MVC) dynamics and pedestrian kinematics using data from medicolegal death investigations of MVCs occurring in the current Canadian motor vehicle (MV) fleet.

METHODS

Based on a systematic literature review, MVC-pedestrian injuries were collated in an injury data collection form (IDCF). The IDCF was coded using the Abbreviated Injury Scale (AIS) 2015 revision. The AIS of the most frequent severe injury was noted for individual body regions. The Maximum AIS (MAIS) was used to define the most severe injury to the body overall and by body regions (MAISBR). This study focused on serious to maximal injuries (AIS 3-6) that had an increasing likelihood of causing death. The IDCF was used to extract collision and injury data from the Office of the Chief Coroner for Ontario (OCCO) database of postmortem examinations done at the Provincial Forensic Pathology Unit (PFPU) in Toronto, Canada, and other provincial facilities between 2013 and 2019. Injury data were correlated with data about the MVs and MV dynamics and pedestrian kinematics.The study was approved by the Western University Health Science Research Ethics Board (Project ID: 113440; Lawson Health Research Institute Approval No. R-19-066).

RESULTS

There were 88 youth, including 54 (61.4%) males and 34 (38.6%) females. Youth pedestrians comprised 13.1% (88/670) of all autopsied pedestrians. Cars (n = 25/88, 28.4%) were the most frequent type of vehicle in single-vehicle impacts, but collectively vehicles with high hood edges (i.e., greater distance between the ground and hood edge) were in the majority. Forward projection (n = 34/88, 38.6%) was the most frequent type of pedestrian kinematics. Regardless of the type of vehicle, there was a tendency in most cases for the median MAISBR ≥ 3 to involve the head and thorax. A similar trend was seen in most of the pedestrian kinematics involving the various frontal impacts. Of the 88 cases, at least 63 (71.6%) were known to be engaged in risk-taking behaviors (e.g., activity on roadway). At least 12 deaths were nonaccidental (8 suicides and 4 homicides). Some activities may have been impairment related, because 26/63 (41.3%) pedestrians undertaking risk-taking behavior on the roadway were impaired. Toxicological analyses revealed that over half of the cases (47/88, 53.4%) tested positive for a drug that could have affected behavior. Ethanol was the most common. Thirty-one had positive blood results.

CONCLUSION

A fatal dyad of head and thorax trauma was observed for pedestrians struck by cars. For those pedestrians hit by vehicles with high hood edges, which were involved in the majority of cases, a fatal triad of injuries to the head, thorax, and abdomen/retroperitoneum was observed. Most deaths occurred from frontal collisions and at speeds more than 35 km/h.

A methodology for the public health surveillance and epidemiologic analysis of outdoor falls that require an emergency medical services response

BACKGROUND

Falls are a common cause of injury with significantly associated medical costs yet public health surveillance of injuries from falls is underdeveloped. In addition, the epidemiologic understanding of outdoor falls, which have been reported to account for 47% of all injurious falls, is scant. Here we present methods to use emergency medical services (EMS) data as a public health surveillance tool for fall injuries, including those that occur secondary to syncope and heat illness, with a focus on the scope and epidemiology of outdoor fall injuries.

METHODS

Using the 2019 National Emergency Medical Services Information System (NEMSIS) data, we developed an approach to identify EMS encounters for fall injuries, syncope and heat illness. NEMSIS variables used in our algorithm included the EMS respondent's impression of the encounter, the reported major symptoms and the cause of injury. With these data we identified injuries from falls and, using the NEMSIS data on the location of the encounter, we identified fall injuries as occurring indoors or outdoors. We present the descriptive epidemiology of the identified patients.

RESULTS

There were 1,854,909 injuries from falls that required an EMS response identified in the NEMSIS data, with 4% of those injuries secondary to episodes of syncope (n = 73,126) and heat illness. Sufficient data were available from 94% of injurious falls that they could be assigned to indoor or outdoor locations, with 9% of these fall injuries occurring outdoors. Among fall injuries identified as occurring outdoors, 85% occurred on streets and sidewalks. Patient age was the primary sociodemographic characteristic that varied by location of the injurious fall. Sixty-six percent of fall injuries that occurred indoors were among those age 65 years or older, while this figure was 34% for fall injuries occurring outdoors on a street or sidewalk.

CONCLUSION

The occurrence of outdoor fall injuries identified in the NEMSIS data were substantially lower than reported in other data sets. However, numerically fall injuries occurring outdoors represent a substantial public health burden. The strengths and weaknesses of using this approach for routine public health surveillance of injuries from falls, syncope and heat illness are discussed.

Injury patterns in motor vehicle collision-pediatric pedestrian deaths

OBJECTIVE

To describe fatal pediatric pedestrian injury patterns and correlate them with motor vehicle collision (MVC) characteristics and pedestrian kinematics using data from medicolegal death investigations of MVCs occurring in the current Canadian MV fleet and determine the applicability of the classical "Waddell's triad" comprising knee, hip (femur) or pelvis and craniocerebral injuries to present data injury patterns.

METHODS

An Injury Data Collection Form was used to extract MV, MVC, pedestrian demographic and injury information from the Office of the Chief Coroner for Ontario database using autopsy data from 2013 to 2018. Injuries were coded using the Abbreviated Injury Scale (AIS) 2015 revision. The study focused on AIS ≥3 injuries utilizing the Maximum Abbreviated Injury Scale (MAIS), MAIS by Body Region (MAISBR) and Injury Severity Score (ISS).

RESULTS

Between 2013 and 2018, there were 25 pediatric deaths. The pedestrians were either struck and run over (n = 17, 68%; 56% low speed) or struck and projected (n = 8, 32%). Twenty-two deaths were from frontal impacts; three were from reversing vehicles. Fourteen of the 17 (82.4%) run over cases occurred at low speed (<30 km/h). In 9 (36%) cases, the vehicle was turning at impact (right n = 3, left n = 6). A majority of the vehicles had a high hood edge. The head was the most severely injured (median MAISBR = 5 overall and ≤10 years; median MAISBR = 6 for 11-14 years old) followed by the neck (MAISBR = 3 overall; 6-14 years old), and the thorax (median MAISBR = 3 overall; all age groups). For the early adolescents (11-14 years old), the serious injury pattern included the abdomen (median MAISBR = 3.5). Nearly half (n = 11, 44%) sustained brainstem injuries. Over fifty percent of the 16 cases with neck injuries (n = 9, 56.3%) had atlanto-occipital or axial dislocation.

CONCLUSION

More than half of the deaths occurred during low speed run overs. MAIS ≥3 injuries trended to a dyad of head and thorax in ≤5 years old, a triad of head, neck, and thorax injuries in children 6-10 years old and a tetrad with the addition of abdominal injuries in pedestrians ≥11 years old. Waddell's triad was not applicable to the fatal cases in the present study.

A pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles

This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. Specifically, the vehicle type is predicted on the basis of traumatic injury suffered by casualties, exploiting machine learning algorithms. In the present study, AI-assisted diagnosis was shown to have correct prediction about 70% of the time. In pedestrians struck by trucks, more severe injuries were appreciated in the facial skeleton, lungs, major airways, liver, and spleen as well as in the sternum/clavicle/rib complex, whereas the lower extremities were more affected by fractures in pedestrians struck by cars. Although the distinction of the striking vehicle should develop beyond autopsy evidence alone, the presented approach which is novel in the realm of forensic science, is shown to be effective in building automated decision support systems. Outcomes from this system can provide valuable information after the execution of autoptic examinations supporting the forensic investigation. Preliminary results from the application of machine learning algorithms with real-world datasets seem to highlight the efficacy of the proposed approach, which could be used for further studies concerning this topic.