Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents.
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ABSTRACT:
SUBMITTER: Wang F
PROVIDER: S-EPMC8275938 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Compared with the young, the elderly (age greater than or equal to 60 years old) vulnerable road users (VRUs) face a greater risk of injury or death in a traffic accident. A contributing vulnerability is the aging processes that affect their brain structure. The purpose of this study was to investigate the injury mechanisms and establish head AIS 4+ injury tolerances for the elderly VRUs based on various head injury criteria. A total of 30 elderly VRUs accidents with detailed injury records and video information were selected and the VRUs' kinematics and head injuries were reconstructed by combining a multi-body system model (PC-Crash and MADYMO) and the THUMS (Ver. 4.0.2) FE models. Four head kinematic-based injury predictors (linear acceleration, angular velocity, angular acceleration, and head injury criteria) and three brain tissue injury criteria (coup pressure, maximum principal strain, and cumulative strain damage measure) were studied. The correlation between injury predictors and injury risk was developed using logistical regression models for each criterion. The results show that the calculated thresholds for head injury for the kinematic criteria were lower than those reported in previous literature studies. For the brain tissue level criteria, the thresholds calculated in this study were generally similar to those of previous studies except for the coup pressure. The models had higher (>0.8) area under curve values for receiver operator characteristics, indicating good predictive power. This study could provide additional support for understanding brain injury thresholds in elderly people.
| S-EPMC8261157 | biostudies-literature
Project description:BackgroundBeing injured in a road traffic accident may affect individuals' functional ability and in turn lead to sickness absence (SA) and disability pension (DP). Knowledge regarding long-term consequences in terms of SA and DP following a road traffic accident is lacking, especially comparing different groups of road users and compared to the general population. The aim was to estimate excess diagnosis-specific SA and DP among individuals of different road user groups injured in a road traffic accident compared to matched references without such injury.MethodsA nationwide register-based study, including all working individuals aged 20-59 years and living in Sweden who in 2015 had in- or specialized outpatient healthcare after a new traffic-related injury (n = 20 177) and population-based matched references (matched on: sex, age, level of education, country of birth, living in cities) without any traffic-related injury during 2014-2015 (n = 100 885). Diagnosis-specific (injury and other diagnoses) SA and DP were assessed during 5 years: 1 year before and 4 years following the accident. Mean SA and DP net days/year for each road user group and mean differences of (excess) SA and DP net days/year compared with their matched references were calculated with independent t-tests with bootstrapped 95% confidence intervals (CIs).ResultsA third of all injured road users were bicyclists, 31% were car occupants, 16% were pedestrians (including fall accidents), and 19% were other and unspecified accidents. Pedestrians and other road users were the groups with the highest mean number of SA days during the first year following the accident (51 and 49 days/year respectively). The matched references had between 8 and 13 SA days/year throughout the study period. The excess SA days/year were elevated for all road user groups all five studied years. Excess SA due to injury diagnoses was 15-35 days/year during the first year following the accident. Excess SA due to diagnoses other than injuries were about eight days/year during the whole study period for pedestrians and car occupants and about zero for the bicyclists. The excess DP was low, although it increased every year after the accident for pedestrians and car occupants; for bicyclists no excess DP was seen.ConclusionHigher levels of SA due to injury diagnoses were seen among all road user groups during the first year after the accident compared to their references. Pedestrians and car occupants had more excess SA due to other diagnoses and more excess DP four years after the accident than bicyclists and other road users.
| S-EPMC10988042 | biostudies-literature
Project description:IntroductionThis research aims to estimate the relative risks of responsibility for a fatal accident linked to driving under the influence of cannabis or alcohol, the prevalence of these influences among drivers and the corresponding attributable risk ratios. A secondary goal is to estimate the same items for three other groups of illicit drugs (amphetamines, cocaine and opiates), and to compare the results to a similar study carried out in France between 2001 and 2003.MethodologyPolice procedures for fatal accidents in Metropolitan France during 2011 were analyzed and 300 characteristics encoded to provide a database of 4,059 drivers. Information on alcohol and four groups of illicit drugs derived from tests for positivity and potential confirmation through blood analysis. The study compares drivers responsible for causing the accident, that is to say having directly contributed to its occurrence, to drivers involved in an accident for which they were not responsible, and who can be assimilated to drivers in general.ResultsThe proportion of persons driving under the influence of alcohol is estimated at 2.1% (95% CI: 1.4-2.8) and under the influence of cannabis at 3.4% (2.9%-3.9%). Drivers under the influence of alcohol are 17.8 times (12.1-26.1) more likely to be responsible for a fatal accident, and the proportion of fatal accidents which would be prevented if no drivers ever exceeded the legal limit for alcohol is estimated at 27.7% (26.0%-29.4%). Drivers under the influence of cannabis multiply their risk of being responsible for causing a fatal accident by 1.65 (1.16-2.34), and the proportion of fatal accidents which would be prevented if no drivers ever drove under the influence of cannabis is estimated at 4.2% (3.7%-4.8%). An increased risk linked to opiate use has also been found to be significant, but with low prevalence, requiring caution in interpreting this finding. Other groups of narcotics have even lower prevalence, and the associated extra risks cannot be assessed.ConclusionAlmost a decade separates the present study from a similar one previously conducted in France, and there have been numerous developments in the intervening years. Even so, the prevalence of drivers responsible for causing fatal accidents under the influence of alcohol or narcotics has stayed remarkably stable, as have the proportion of fatal accidents which could in theory be prevented if no drivers ever exceeded the legal limits. The overall number of deaths from traffic accidents has dropped sharply during this period, and the number of victims attributable to alcohol and/or cannabis declined proportionally. Alcohol remains the main problem in France. It is just as important to note that one in two drivers considered to be under the influence of cannabis was also under the influence of alcohol. With risks cumulating between the two, it is particularly important to point out the danger of consuming them together.
| S-EPMC5678710 | biostudies-literature
Project description:Different patterns in the incidence of road accidents are revealed when considering areas with increased levels of urbanization. To understand these patterns, road accident data from England and Wales is explored. In particular, the data are used to (i) generate time series for comparison of the incidence of road accidents in urban as opposed to rural areas, (ii) analyse the relationship between the number of road accidents and the population size of a set of urban areas, and (iii) model the likelihood of suffering an accident in an urban area and its dependence with population size. It is observed that minor and serious accidents are more frequent in urban areas, whereas fatal accidents are more likely in rural areas. It is also shown that, generally, the number of accidents in an urban area depends on population size superlinearly, with this superlinear behaviour becoming stronger for lower degrees of severity. Finally, given an accident in an urban area, the probability that the accident is fatal or serious decreases with population size and the probability that it is minor, increases sublinearly. These findings promote the question as to why such behaviours exist, the answer to which will lead to more sustainable urban policies.
| S-EPMC7211831 | biostudies-literature
Project description:Previous assessments of wildlife road mortality have not used directly comparable methods and, at present, there is no standardized protocol for the collection of such data. Consequently, there are no internationally comparative statistics documenting roadkill rates. In this study, we used a combination of experimental trials and road transects to design a standardized protocol to assess roadkill rates on both paved and unpaved roads. Simulated roadkill were positioned over a 1 km distance, and trials were conducted at eight different speeds (20-100 km·h(-1)). The recommended protocol was then tested on a 100-km transect, driven daily over a 40-day period. This recorded 413 vertebrate roadkill, comprising 106 species. We recommend the protocol be adopted for future road ecology studies to enable robust statistical comparisons between studies.
| S-EPMC4161179 | biostudies-literature
Project description:The World Health Organization has highlighted that the number of deaths worldwide due to road accidents increases every year. It recommends that countries improve road safety for all people by providing sustainable and safe transport systems by 2030, efforts are especially required within Low Middle-Income Countries (LMICS). This study is the first to investigate the impact of rainfall on road accidents in Thailand. Thai emergency data were collected from the National Institute for Emergency Medicine (NIEM) between 2012 to 2018. A time-series design with generalized linear model (GLM) was applied to analyse the associations between road accidents and rainfall. The results are reported using relative risk (RR) at 95% confidence intervals compared with dry days. The effects of long-term trends, seasonality, day of the weeks, public holidays and other meteorological factors were controlled in the GLM. A meta-analysis was applied to summarise the estimate effect of rain groups stratified by the Northern and Southern provinces. Findings reported a significant increase in road accidents due to high rainfall levels both in the Southern and the Northern provinces. The pooled estimate risks in the Southern provinces have higher estimated risks than the Northern provinces. Both Northern and Southern provinces showed the rain group with 10-20 mm/day having the highest pooled estimated risk with RR = 1.052, (95% CI: 1.026-1.079) and RR = 1.062, (95% CI: 1.043-1.082), respectively, while surprisingly, heavy rain with more than 20 mm/day reported a reduction of risks. Road accidents can therefore be associated with rainfall. It is recommended that rainfall is factored into ambulance forecast models and warning systems, allowing for improvements in ambulance service efficiency. Policymakers need to integrate road safety policies that reduce road accidents in wet weather.
| S-EPMC7895724 | biostudies-literature
Project description:BackgroundIn the past decades, extremely serious road accidents with a death toll over ten in each have become a severe public health problem in China. This study investigates risk factors contributing to extremely serious road accidents, which will be crucial for accident prevention.MethodsCollecting data from The Road Accident Statistical Annual Report openly issued by China's Traffic Management Bureau of the Public Security Ministry for the time period 2004-2015, we used the monthly case number of extreme serious road accidents as the dependent variable. We then selected ten risk factors as primary independent variables: professional driver, driving under influence (alcohol or drug), fatigue, vehicle type, overload, brake problem, weather, road classification, terrain, and region. The method of negative binominal regression was implemented to investigate the association between these risk factors and extremely serious road accidents.ResultsA total of 346 extremely serious road accidents were included in our analysis. On a national scale, we found that professional driver [incidence rate ratio (IRR): 1.10, 95% CI: 1.02-1.19], fatigue (IRR: 1.15, 95% CI: 1.03-1.29), large vehicle type (IRR: 1.11, 95% CI: 1.03-1.21), overload (IRR: 1.09, 95% CI: 1.03-1.16), and terrain (IRR: 1.09, 95% CI: 1.01-1.18) were significantly associated with extremely serious road accidents. Besides, separate analyses on western and non-western region indicated that both regions had shared risk factors as well as distinct factors.ConclusionsOur study identifies professional driver, fatigue, large vehicle type, overload, and terrain as significant risk factors of extremely serious road accidents in China, and targeted and preventative measures could be taken based on our findings.
| S-EPMC6070265 | biostudies-literature
Project description:Introduction: The severity of injury from motor vehicle crashes (MVCs) depends on complex biomechanical factors, and the bodily features of the injured person account for some of these factors. By assuming that vulnerable road users (VRUs) have limited protection resulting from vehicles and safety equipment, the current study analyzed the characteristics of fat distribution measured by computed tomography (CT) imaging and investigated the existence of a "cushion effect" in VRUs. Materials and Methods: This retrospective study enrolled 592 VRUs involved in MVCs who underwent CT scans. Visceral fat area and subcutaneous fat cross-sectional area were measured and adjusted according to total body area (TBA) and are presented as the visceral fat ratio and the subQ fat ratio (subcutaneous fat ratio). Risk factors for serious abdominal injury (maximum abbreviated injury scale (MAISabd ≥ 3)) resulting from MVCs were determined by univariate and multivariate analysis. Results: MAISabd ≥ 3 was observed in 104 (17.6%) of the patients. The subQ fat ratio at the L4 vertebral level was significantly lower in the MAISabd ≥ 3 group than in the MAISabd < 3 group (24.9 ± 12.0 vs. 28.1 ± 11.9%; p = 0.015). A decreased L4 subQ fat ratio was associated with a higher risk for MAISabd ≥ 3 in multivariate analysis (odds ratio 0.063; 95% CI 0.008-0.509; p = 0.009). Conclusion: The current study supported the "cushion effect" theory, and protection was apparently provided by subcutaneous fat tissue. This concept may further improve vehicle and safety designation in the future.
| S-EPMC8393396 | biostudies-literature
Project description:Studies carried out in Moscow residents have revealed that the prevalence of chronic toxoplasmosis is very close to those in countries of Eastern and Central Europe. Our findings also demonstrated a statistically significant relationship between the rate of traffic accidents and the seroprevalence of chronic toxoplasmosis in drivers who were held responsible for accidents. The latter was 2.37 times higher in drivers who were involved in road accidents compared with control groups. These results suggest that the consequences of chronic toxoplasmosis (particularly a slower reaction time and decreased concentration) might contribute to the peculiarities of the epidemiology of road traffic accidents in the Russian Federation and might interfere with the successful implementation of the Federal Programme named "Increase road traffic safety". Suggestions for how to address overcome this problem are discussed in this paper.
| S-EPMC5619715 | biostudies-literature
Project description:Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users-pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups-for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.
| S-EPMC8341492 | biostudies-literature