Assessing spinal movement during four extrication methods: a biomechanical study using healthy volunteers

Background Motor vehicle collisions are a common cause of death and serious injury. Many casualties will remain in their vehicle following a collision. Trapped patients have more injuries and are more likely to die than their untrapped counterparts. Current extrication methods are time consuming and have a focus on movement minimisation and mitigation. The optimal extrication strategy and the effect this extrication method has on spinal movement is unknown. The aim of this study was to evaluate the movement at the cervical and lumbar spine for four commonly utilised extrication techniques. Methods Biomechanical data was collected using inertial Measurement Units on 6 healthy volunteers. The extrication types examined were: roof removal, b-post rip, rapid removal and self-extrication. Measurements were recorded at the cervical and lumbar spine, and in the anteroposterior (AP) and lateral (LAT) planes. Total movement (travel), maximal movement, mean, standard deviation and confidence intervals are reported for each extrication type. Results Data from a total of 230 extrications were collected for analysis. The smallest maximal and total movement (travel) were seen when the volunteer self-extricated (AP max = 2.6 mm, travel 4.9 mm). The largest maximal movement and travel were seen in rapid extrication extricated (AP max = 6.21 mm, travel 20.51 mm). The differences between self-extrication and all other methods were significant (p < 0.001), small non-significant differences existed between roof removal, b-post rip and rapid removal. Self-extrication was significantly quicker than the other extrication methods (mean 6.4 s). Conclusions In healthy volunteers, self-extrication is associated with the smallest spinal movement and the fastest time to complete extrication. Rapid, B-post rip and roof off extrication types are all associated with similar movements and time to extrication in prepared vehicles.

The Influence of Landscape Structure on Wildlife–Vehicle Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity Relations

Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the factors that influence wildlife–vehicle collisions (WVCs) and provide mitigation methods. Most of these studies examined road density, traffic volume, seasonal fluctuations, etc. However, in analysing the distribution of WVC, few studies have considered a spatial and significant distance geostatistical analysis approach that includes how different land-use categories are associated with the distance to WVCs. Our study investigated the spatial distribution of agricultural land, meadows and pastures, forests, built-up areas, rivers, lakes, and ponds, to highlight the most dangerous sections of roadways where WVCs occur. We examined six potential ‘hot spot’ distances (5–10–25–50–100–200 m) to evaluate the role different landscape elements play in the occurrence of WVC. The near analysis tool showed that a distance of 10–25 m to different landscape elements provided the most sensitive results. Hot spots associated with agricultural land, forests, as well as meadows and pastures, peaked on roadways in close proximity (10 m), while hot spots associated with built-up areas, rivers, lakes, and ponds peaked on roadways farther (200 m) from these land-use types. We found that the order of habitat importance in WVC hot spots was agricultural land < forests < meadows and pastures < built-up areas < rivers < lakes and ponds. This methodological approach includes general hot-spot analysis as well as differentiated distance analysis which helps to better reveal the influence of landscape structure on WVCs.

Factors Affecting the Severity of Placental Abruption in Pregnant Vehicle Drivers: Analysis with a Novel Finite Element Model

We clarified factors affecting the severity of placental abruption in motor vehicle collisions by quantitively analyzing the area of placental abruption in a numerical simulation of an unrestrained pregnant vehicle driver at collision velocities of 3 and 6 m/s. For the simulation, we constructed a novel finite element model of a small 30-week pregnant woman, which was validated anthropometrically using computed tomography data and biomechanically using previous examinations of post-mortem human subjects. In the simulation, stress in the elements of the utero–placental interface was computed, and those elements exceeding a failure criterion were considered to be abrupted. It was found that a doubling of the collision velocity increased the area of placental abruption 10-fold, and the abruption area was approximately 20% for a collision velocity of 6 m/s, which is lower than the speed limit for general roads. This result implies that even low-speed vehicle collisions have negative maternal and fetal outcomes owing to placental abruption without a seatbelt restraint. Additionally, contact to the abdomen, 30 mm below the umbilicus, led to a larger placental abruption area than contact at the umbilicus level when the placenta was located at the uterus fundus. The results support that a reduction in the collision speed and seatbelt restraint at a suitable position are important to decrease the placental abruption area and therefore protect a pregnant woman and her fetus in a motor vehicle collision.

Examining the impact of both legal and nonlegal factors on following a vehicle too closely utilizing three deterrence-based theories

Following a vehicle too closely (otherwise known as tailgating) is a high-risk behavior and major contributor to motor-vehicle collisions and injuries. Both legal and nonlegal countermeasures are currently in place in an attempt to prevent this behavior, yet there has been limited research that has examined the effectiveness of both legal and nonlegal factors on engagement in the behavior. Therefore, this research utilized a combination of the three most salient deterrence-based theories used in road safety to understand the impact of both legal and nonlegal sanctions on following a vehicle too closely. A survey was completed by 887 Queensland drivers aged 17–84 years ( Mage = 49 years; 55% males). Variables from Classical Deterrence Theory, the reconceptualized deterrence theory and the extended deterrence-based model (that includes perceived internal loss, physical loss, and social sanctions), as well as measures of following a vehicle too closely were used. The majority of the sample (98%) reported following a vehicle too closely at some point, with the average frequency ranging from rarely to sometimes. Significant predictors of more frequent unsafe following distances included: being male, younger in age, and avoiding punishment for the behavior. Meanwhile, significant predictors of less frequent unsafe following distances included knowing others who have been punished for the behavior, as well as fearing the physical and internal losses resulting from unsafe following distances. Notably, the severity of the punishment was also a significant deterrent, while the perceived certainty of being apprehended for the offence was low and did not impact engagement in behavior. These results have a number of important implications on how to maximize both legal and nonlegal countermeasures to further prevent following a vehicle too closely.

Spatial Characteristics of Wildlife-Vehicle Collisions of Water Deer in Korea Expressway

In recent decades, rapid industrial growth has accelerated the construction of new roads, which has led to the destruction and isolation of wildlife habitats. Newly constructed roads affect wildlife in many ways. In particular, fatal wildlife-vehicle collisions (WVCs) have a direct impact on wildlife. A substantial number of WVCs occur every year on expressways, where vehicle speeds and vehicle traffic are significant. However, our understanding of the relative importance of the factors associated with areas in which large numbers of WVCs occur on the expressway remains poor. Therefore, herein, we analyze the spatial characteristics of WVCs. The effect of spatial distribution on the occurrence of WVCs was analyzed using the types of land cover in the areas where water deer appear (Cheongju, Boeun, and Sangju) and the areas in which WVCs occur along the Cheongju–Sangju Expressway (CSE). We identified the WVC hotspots by using CSE patrol data recorded between January 2008 and December 2019, and we analyzed the corresponding distribution patterns and land cover characteristics. Along the CSE, a total of 1082 WVCs occurred, out of which collisions involving water deer (Hydropotes inermis argyropus) accounted for 91%. Water deer appear frequently in Forested Areas and Agricultural Land, but the WVC distribution in the Hotspots followed a highly clustered pattern, with a higher proportion of WVCs occurring in Used Areas (areas including buildings such as residential facilities, commercial and industrial facilities, and transportation facilities). Used Areas have a smaller cut slope compared to Forested Areas, and Used Areas are open terrains. Therefore, the occurrence of WVCs will be high given that wildlife can easily access the expressway. Based on these results, we can infer that the landscapes near the expressway influence the occurrence of WVCs. To establish an effective policy for reducing WVCs on a road, the WVC characteristics and spatial distribution of the road should be considered together. Further research on the wildlife ecology and land-use status of WVC hotspots is required to mitigate WVCs on expressways and protect human and animal life. Therefore, if the characteristics of WVC hotspots are analyzed considering the characteristics of various ecosystems, an appropriate WVC reduction plan can be established.

Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data

First responders including firefighters, paramedics, and police officers are among the first to respond to vehicle collisions on roads and highways. Police officers conduct regular roadside Please check if the country name is correct traffic controls and checks on urban and rural roads, and highways. Once first responders begin such operations, they are vulnerable to motor vehicle collisions by oncoming traffic, a circumstance that calls for a better understanding of contributing factors and the extent to which they affect tragic outcomes. In light of factors identified in the literature, this paper applies machine learning methods including decision tree and random forest to a subset of the National Collision Database (NCDB) of Canada that includes information on collisions between two vehicles (one in parked position) and the severity of these collisions as measured by having or not having injuries. Findings reveal that key measurable, predictable, and sensible factors such as time, location, and weather conditions, as well as the interconnections among them, can explain the severity of collisions that may happen between motor vehicles and first responders who are working alongside the roads. Analysis from longitudinal data is rich and the use of automated methods can be used to predict and assess the risk and vulnerability of first responders while responding to or operating on different roads and conditions.

Assessing Spinal Movement During Four Extrication Methods: A Biomechanical Study using Healthy Volunteers

BackgroundMotor vehicle collisions are a common cause of death and serious injury. Many casualties will remain in their vehicle following a collision. Trapped patients have more injuries and are more likely to die than their untrapped counterparts. Current extrication methods are time consuming and have a focus on movement minimisation and mitigation. The optimal extrication strategy and the effect this extrication method has on spinal movement is unknown. The aim of this study was to evaluate the movement at the cervical and lumbar spine for four commonly utilised extrication techniques. MethodsBiomechanical data was collected using inertial Measurement Units on 6 healthy volunteers. The extrication types examined were: roof removal, b-post rip, rapid removal and self-extrication. Measurements were recorded at the cervical and lumbar spine, and in the anteroposterior (AP) and lateral (LAT) planes. Total movement (travel), maximal movement, mean, standard deviation and confidence intervals are reported for each extrication type.ResultsData from a total of 230 extrications were collected for analysis. The smallest maximal and total movement (travel) were seen when the volunteer self-extricated (AP max = 2.6mm, travel 4.9mm). The largest maximal movement and travel were seen in rapid extrication extricated (AP max = 6.21mm, travel 20.51 mm). The differences between self-extrication and all other methods were significant (p<0.001), small non-significant differences existed between roof removal, b-post rip and rapid removal.Self-extrication was significantly quicker than the other extrication methods (mean 6.4s).ConclusionsIn healthy volunteers, self-extrication is associated with the smallest spinal movement and the fastest time to complete extrication. Rapid, B-post rip and roof off extrication types are all associated with similar movements and time to extrication in prepared vehicles.