The Head AIS 4+ Injury Thresholds for the Elderly Vulnerable Road User Based on Detailed Accident Reconstructions.
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ABSTRACT:
SUBMITTER: Wu H
PROVIDER: S-EPMC8261157 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head-neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson's correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.
| S-EPMC8275938 | biostudies-literature
Project description:Background: Post-traumatic stress disorder (PTSD) occurs after exposure to actual or threatened death, serious injury, or sexual violence. Road traffic accident (RTA) is one of the traumatic experiences, which may result in PTSD. But treatment is mainly concentrated on physical health. This may be due to a lack of evidence in low-income countries. Aim: To determine the incidence level and identify risk factors of PTSD after RTA. Methods: Longitudinal panel study was done to assess the incidence of PTSD after RTA. The study was conducted in three orthopedic settings of Bahir Dar town Northwest, Ethiopia. The study was on 299 adult car accident survivors. PTSD Checklist (PCL) civilian version, Sheehan disability assessment scale, Patient Health Question (PHQ-2), and Alcohol Use Disorder Identification Test (AUDIT) were instruments to assess the outcome and associated factors. The generalized linear model with Poisson log-linear method was applied to identify associated factors. Ethical clearance was obtained from Bahir Dar University. Individuals with PTSD symptoms were linked to the psychiatric clinic. Result: One hundred thirty-nine (46.5%) participants had at least three extremely severe symptoms that fulfil criteria B, C, and D of Diagnostic Statistical Manual IV of PTSD. The most frequent severe symptoms were having repeated, disturbing memories, thoughts, or images. Two hundred ten (70.2%) participants reported the extreme impact of the accident on work or schooling and 156 (51.9%) reported extreme problems in social functioning. Alcohol dependence, hazardous alcohol consumption, and harmful use were reported by 7.9%, 15.1%, and 4.7% of the participants, respectively. In the final model witnessing death, severe sleep problem and severe impairment in family functioning were significantly associated with PTSD. Conclusion: Nearly half of RTA survivors develop PTSD. Clinicians need to link these patients to the psychiatry clinic. Special attention should be given to patients who witnessed death, with a serious disability, and previous psychiatric history.
| S-EPMC6659351 | 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:It is expected that most accidents occurring due to human mistakes will be eliminated by autonomous vehicles. Their control is based on real-time data obtained from the various sensors, processed by sophisticated algorithms and the operation of actuators. However, it is worth noting that this process flow cannot handle unexpected accident situations like a child running out in front of the vehicle or an unexpectedly slippery road surface. A comprehensive analysis of historical accident data can help to forecast these situations. For example, it is possible to localize areas of the public road network, where the number of accidents related to careless pedestrians or bad road surface conditions is significantly higher than expected. This information can help the control of the autonomous vehicle to prepare for dangerous situations long before the real-time sensors provide any related information. This manuscript presents a data-mining method working on the already existing road accident database records to find the black spots of the road network. As a next step, a further statistical approach is used to find the significant risk factors of these zones, which result can be built into the controlling strategy of self-driven cars to prepare them for these situations to decrease the probability of the potential further incidents. The evaluation part of this paper shows that the robustness of the proposed method is similar to the already existing black spot searching algorithms. However, it provides additional information about the main accident patterns.
| S-EPMC7959616 | biostudies-literature
Project description:Increased traffic volumes worldwide have resulted in an increased number of road accident injuries and mortalities. This global phenomenon motivated the United Nations (UN) to initiate a decade-long global road safety plan in 2010. In response, Saudi Arabia concurrently initiated a comprehensive road safety program, supported by detailed and comprehensive road safety data for the Eastern Province (EP) of Saudi Arabia. The contributed EP-Traffic-Mortality-and-Policy-Interventions Dataset provides multidimensional road safety data for 2010-2020 via two primary and five secondary data subsets. The first primary subset provides road accident mortality data. The five secondary data subsets reflect road accident mortalities at different time scales and administrative (provincial or governorate) levels. The second primary subset provides details of traffic safety policy interventions implemented during the same period. Researchers and policymakers can use this comprehensive dataset to study accident mortality patterns across various geospatial and time scales and analyze the effectiveness of policies intended to mitigate road accident mortalities.
| S-EPMC9363955 | biostudies-literature
Project description:Road accidents are one of the primary causes of death worldwide; hence, they constitute an important research field. Taiwan is a small country with a high-density population. It particularly has a considerable number of locomotives. Furthermore, Taiwan’s traffic accident fatality rate increased by 23.84% in 2019 compared with 2018, primarily because of human factors. Road safety has long been a challenging problem in Taiwanese cities. This study collected public data pertaining to traffic accidents from the Taoyuan city government in Taiwan and generated six datasets based on the various accident frequencies at the same location. To find key attributes, this study proposes a three-stage dimension reduction to filter attributes, which includes removing multicollinear attributes, the integrated attribute selection method, and statistical factor analysis. We applied five rule-based classifiers to classify six different frequency datasets and generate the rules of accident severity. The order of top ten key attributes was hit vehicle > certificate type > vehicle > action type > drive quality > escape > accident type > gender > job > trip purposes in the maximum accident frequency CF ≥ 10 dataset. When locomotives, bicycles, and people collide with other locomotives or trucks, injury or death can easily occur, and the motorcycle riders are at the highest risk. The findings of this study provide a reference for governments and stakeholders to reduce the road accident risk factors.
| S-EPMC9394815 | 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
Project description:BACKGROUND:Road traffic accident (RTA), an unexpected traumatic event, may not only lead to death and serious physical injuries, but also could put survivors at an increased risk for a wide range of psychiatric disorders, particularly acute stress disorder (ASD). Early assessment of trauma-related psychological responses is important because acute trauma responses in the early post-traumatic period are among the robust predictors of long-term mental health problems. However, estimates of the prevalence of ASD among RTA survivors varied considerably across studies. Therefore, this meta-analysis aimed to identify the pooled prevalence of ASD among RTA survivors. METHODS:A systematic literature search in the databases of PubMed, PsycINFO, PsycARTICLES, Embase and Web of Science was performed from their inception dates to December 2017. Subject headings were used to identify relevant articles, and the search strategy was adjusted across databases. Heterogeneity across studies was evaluated by Cochran's χ2 test and quantified by the I2 statistic. Subgroup analyses were performed to identify the pooled prevalence in relation to the country of study, instrument used to identify ASD, age, gender and traumatic brain injury. When significant heterogeneity was observed, the influence of some potential moderators was explored using meta-regression analyses. RESULTS:Thirteen eligible studies conducted in 8 countries were included. A total of 2989 RTA survivors were assessed, of which 287 were identified with ASD. The overall heterogeneity was high across studies (I2=96.8%, P < 0.001), and the pooled prevalence of ASD among RTA survivors was 15.81% (95% confidence interval: 8.27-25.14%). Subgroup analyses indicated that the prevalence of ASD among RTA survivors differed significantly with regard to the country of study, instrument used to identify ASD, age and gender (P < 0.05). Meta-regression analyses showed that mean age of participants and quality assessment score were significant moderators for heterogeneity (P < 0.05). CONCLUSIONS:Nearly one-sixth of RTA survivors suffer from ASD, indicating the need for regular assessment of early trauma responses among RTA survivors, as well as the importance of implementing early psychological interventions.
| S-EPMC5998549 | biostudies-literature
Project description:Road traffic accident (RTA) victims also suffer from different types of injuries and disabilities, which can affect their quality of life. They usually face with various physical, mental, and social problems. Most traffic accident victims had difficulty to return to normal life.This study aimed to understand the experiences of return to normal life in RTA victims.This qualitative study with content analysis approach was conducted on 18 Iranian patients with disability in the upper or lower limbs caused by traffic accidents, who had passed a time between 3 months till 2 years. A purposeful sampling method was applied until reaching data saturation. Data were collected using semi-structured interviews. Afterwards, the gathered data were analyzed through conventional content analysis.By analyzing 498 primary codes, four main categories, including supportive needs, adaptation to the new situation, seeking information, and transition from functional limitation, were extracted from traffic accident victims' experiences of reintegration to normal life.The results of this study may help policy-makers to take steps toward health promotion and recovery of RTA victims. Considering the results of this study, it is a need for further research to investigate RTAs victims' needs for reintegration to home and community. Access to training and supportive facilities like strong therapeutic, nursing and social support, and the possibility to participate in self-care activities is essential for reintegration to community in RTA victims.
| S-EPMC4893411 | biostudies-other
Project description:Due to low counts in an (111)In single photon emission computed tomography (SPECT) scan, a large part of the head was missing in the reconstructed images on Philips Extended Brilliance Workspace (EBW) and IntelliSpace Portal (ISP) workstations. This problem occurred for the ordered subsets expectation maximization (OSEM) algorithm with and without resolution recovery (Astonish), but not for filtered backprojection (FBP) or maximum likelihood expectation maximization (MLEM). There were also underflow problems because the images are stored as integers resulting in a loss of intensity resolution and color banding.Philips EBW2.0 and ISP5.02 workstations upscale low-count images, but the result is not always optimal, for example, in the case of low counts in one part and more counts in another part of an image. On these workstations, the missing head artefact problem could be resolved by applying a Hann pre-filter (with a cutoff at the Nyquist frequency, which only influences the filtering) in the reconstruction process. Upscaling of the projection data prior to reconstruction did not recover the head in the images, neither did limiting the reconstructed volume to the low-count part of interest.Underflow problems were partially solved by the new version 2.0 of the Philips EBW and ISP stations, although situations could arise where underflow still poses a problem. A solution for the underflow problems is to upscale the raw projection data before reconstruction. While this results in a pure upscaling of the FBP reconstruction, the effect in iterative statistical reconstruction is not only upscaling of the intensities because the assumption of Poisson statistics of the data is violated. However, the influence of this last matter seems limited.Reconstruction of studies with low counts in relevant areas should be performed with care. Reconstruction artefacts and scaling issues can easily arise.
| S-EPMC6890917 | biostudies-literature