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International Journal of Environmental... May 2021This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among...
This study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration's fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in , , and (i.e., $50,000 or less). The main source of injuries manifested in (46%), followed by (19%), and (16%). The most frequent types of injuries were (31%), (27%), and (14%); the main injured body parts were (25%), (23%), and (18%). Among non-fatal cases, (37%), (36%), and (19%) caused most injuries; among fatal cases, was the leading cause of death (50%), followed by (28%) and (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as , , , , , and . In other words, the statistical probability of a fatal accident-given an accident occurrence-changes significantly based on the degree of these factors. The results of this study, as depicted in the proposed decision tree model, revealed that the most important factor for predicting the nature of injury (electrical or non-electrical) is: whether the source of injury is ; followed by whether the source of injury is . In other words, in predicting (with a 94.31% accuracy) the nature of injury as electrical or non-electrical, whether the source of injury is and whether the source of injury is are very important. Seven decision rules were derived from the proposed decision tree model. Beyond these outcomes, the described methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts to subsequently mitigate more injuries in other fields.
Topics: Accidental Falls; Accidents, Occupational; Electricity; Occupational Health; Reproducibility of Results
PubMed: 34066030
DOI: 10.3390/ijerph18105126 -
Sensors (Basel, Switzerland) Mar 2022An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost...
An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident's location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.
Topics: Accidents, Traffic; First Aid; Geographic Information Systems; Weather
PubMed: 35336248
DOI: 10.3390/s22062077 -
Environment International Aug 2021Many radiation protection actions carry a multitude of direct and indirect consequences that can impact on the welfare of affected populations. Health surveillance...
Many radiation protection actions carry a multitude of direct and indirect consequences that can impact on the welfare of affected populations. Health surveillance raises ethical challenges linked to privacy and data protection, as well as questions about the net benefit of screening. The SHAMISEN project recognized these issues and developed specific recommendations to highlight ethical challenges. Following a brief overview of ethical issues related to accident management, this paper presents the SHAMISEN recommendations: R1 The fundamental ethical principle of doing more good than harm should be central to accident management; and R4 Ensure that health surveillance respects the autonomy and dignity of affected populations, and is sensitive to any inequity in the distribution of risks and impacts. While a holistic approach to accident management means that decisions will be complicated by different values, perceptions and uncertainties about outcomes, addressing ethical issues could help ensure that the assumptions and potential conflicts behind eventual decisions are as transparent as possible.
Topics: Fukushima Nuclear Accident; Radiation Protection
PubMed: 33823460
DOI: 10.1016/j.envint.2021.106537 -
Journal of Neurology Apr 2022Eduard Gamper (1887-1938) was Head of the Department of Neuropsychiatry at the Charles University's German Faculty of Medicine in Prague. He had trained in Innsbruck,... (Review)
Review
Eduard Gamper (1887-1938) was Head of the Department of Neuropsychiatry at the Charles University's German Faculty of Medicine in Prague. He had trained in Innsbruck, where he undertook groundbreaking work on the midbrain in an anencephalic child and in a series of patients who had died from Wernicke's encephalopathy. Gamper identified the mamillary bodies as key in the performance of declarative memory. Considered an expert in memory disorders, he was chosen by the Medical Faculty in Innsbruck to provide expert opinion on the notorious case of Philipp Halsmann, who was suspected of murdering his father. Details of the case remained unresolved and Gamper's opinion caused both professional and political controversy. When in Prague, Gamper made great efforts to improve patient care and clinical services, establishing a special ward for patients with neurological conditions. This task was not nearly completed, when he and his wife died after their car drove over a cliff into the Walchensee in Bavaria. Rumours surrounded his death: that Gamper had just examined Adolf Hitler; that he was a political victim; that the Gestapo were behind the accident. After an investigation of the available evidence, we can report that an unusual 22 cm of snow fell in the area of the Walchensee on April 20, 1938, the day of the Gampers' deaths. We were unable to find any evidence for foul play in what appears to have been a tragic accident.
Topics: Accidents; Child; Ethnicity; History, 20th Century; Humans
PubMed: 34515855
DOI: 10.1007/s00415-021-10795-0 -
Sensors (Basel, Switzerland) Oct 2021Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining...
Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.
Topics: Accidents; Computer Simulation; First Aid; Internet of Things; Transportation
PubMed: 34696118
DOI: 10.3390/s21206905 -
Accident; Analysis and Prevention Jan 2023The relationship between driver mileage and accident involvement has been a controversial topic for at least 20 years. The key issue is whether driver accident...
The relationship between driver mileage and accident involvement has been a controversial topic for at least 20 years. The key issue is whether driver accident involvement rate increases in proportion to miles driven or has a non-linear relationship to miles driven. This paper presents a synthesis of evidence from studies of how the number of accidents per driver per unit of time relates to distance driven in the same period. Most studies of this relationship are methodologically weak and their results highly inconsistent and potentially misleading. Unreliable data and poor control for confounding factors characterise most studies. Only a few studies based on multivariate statistical models control for at least some of the confounding factors that may influence the relationship between distance driven and accident involvement. These studies consistently show that the number of accidents per driver per year increases less than in proportion to distance driven. A good approximation is that the number of accidents per driver per unit of time is proportional to the square root of distance driven. Potential methodological and substantive explanations of this finding are discussed.
Topics: Humans; Accidents, Traffic
PubMed: 36395619
DOI: 10.1016/j.aap.2022.106899 -
International Journal of Environmental... Oct 2021One of the important factors affecting the production safety of a country or region is the level of economic development. Avoiding accidents under the condition of...
One of the important factors affecting the production safety of a country or region is the level of economic development. Avoiding accidents under the condition of ensuring economic development is a problem that needs in-depth research. On the basis of collecting the data of occupational accidents and economic development indicators in China from 2000 to 2020, this paper studies the relationship between occupational accidents and five economic indicators, such as resident consumption, energy consumption, education funds, wage level and research input. The grey working accident model of Gaussian function is established, the occurrence trend of occupational accidents is quantitatively analyzed, and the accident reduction measures are suggested based on the relationship between accidents and economy. The results show that there is a strong correlation between accident and economic indicators, and the comprehensive correlation coefficient among scientific research investment, education funds and accident indicators is significantly higher than that of other economic indicators. Increasing investment in scientific research and education is conducive to improving the quality of workers and training safety professionals and can effectively reduce workplace accidents.
Topics: Accidents, Occupational; China; Economic Development; Economic Factors; Humans
PubMed: 34682524
DOI: 10.3390/ijerph182010781 -
International Journal of Environmental... Sep 2019Traffic accidents impart both economic and social costs upon communities around the world, hence the desire for accident rates to be reduced. For this reduction to... (Review)
Review
Traffic accidents impart both economic and social costs upon communities around the world, hence the desire for accident rates to be reduced. For this reduction to occur, the factors influencing the occurrence of accidents must be understood. The role of congestion in modifying accident risk has been widely studied, but consensus has not been reached, with conflicting results leaving open questions. An inverse relationship between accidents and congestion would imply a benefit of congested conditions for road safety, posing a difficult situation for traffic management. This paper assesses articles that reveal the shape of the relationship between traffic accidents and congestion. We find a positive linear response to dominate the literature. However, studies with higher numbers of statistical units tend to show a U-shaped relationship. This suggests an important role of high spatio-temporal traffic data in understanding factors causing accidents and identifying the combination of real-time conditions which may lead to increased accident risk. Modern advancements in traffic measurement systems provide the ability for real-time alleviation of accident-prone conditions before they can fully develop.
Topics: Accidents, Traffic; Automobile Driving; Humans
PubMed: 31540246
DOI: 10.3390/ijerph16183400 -
BMC Emergency Medicine Jul 2022This study aims to estimate and compare the parameters of some univariate and bivariate count models to identify the factors affecting the number of mortality and the...
BACKGROUNDS
This study aims to estimate and compare the parameters of some univariate and bivariate count models to identify the factors affecting the number of mortality and the number of injured in road accidents.
METHODS
The accident data used in this study are related to Kermanshah province in march2020 to march2021. Accidents areas were divided into 125 areas based on density characteristics. In a one-year period, 3090 accidents happened on the suburban roads of Kermanshah province, which resulted in 398 deaths and 4805 injuries. Accident information, including longitude and latitude of accident location, type of accident (fatal and injury), number of deaths, number of injuries, accident type, the reason of the accident, and the kind of accident were all included as population-level variables in the regression models. We investigated four frequently used bivariate count regression models for accident data in the literature.
RESULTS
In bivariate analysis, except for the DNM model, there is a reasonable decrease in the AIC measures of the saturated model compared to the reduced model for the other three models. For the injury models, MSE is lowest, respectively for DIBP (137.87), BNB (289.46), BP (412.36) and DNM (3640.89) models. These results are also established for death models. But, in univariate analysis, only injury models almost present reasonable results.
CONCLUSIONS
Our findings show that the IDBP model is better suitable for evaluating accident datasets than other models. Motorcycle accidents, pedestrian accidents, left turn deviance, and dangerous speeding were all significant variables in the IDBP death model, and these parameters were linked to accident mortality.
Topics: Accidents, Traffic; Humans; Iran
PubMed: 35843936
DOI: 10.1186/s12873-022-00686-6 -
International Journal of Environmental... Jun 2019In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the... (Review)
Review
In order to clearly understand the risky riding behaviors of electric bicycles (e-bikes) and analyze the riding characteristics, we review the research results of the e-bike risky riding behavior from three aspects: the characteristics and causes of e-bike accidents, the characteristics of users' traffic behavior, and the prevention and intervention of traffic accidents. The analysis results show that the existing research methods on risky riding behavior of e-bikes mainly involve questionnaire survey methods, structural equation models, and binary probability models. The illegal occupation of motor vehicle lanes, over-speed cycling, red-light running, and illegal manned and reverse cycling are the main risky riding behaviors seen with e-bikes. Due to the difference in physiological and psychological characteristics such as gender, age, audiovisual ability, responsiveness, patience when waiting for a red light, congregation, etc., there are differences in risky cycling behaviors of different users. Accident prevention measures, such as uniform registration of licenses, the implementation of quasi-drive systems, improvements of the riding environment, enhancements of safety awareness and training, are considered effective measures for preventing e-bike accidents and protecting the traffic safety of users. Finally, in view of the shortcomings of the current research, the authors point out three research directions that can be further explored in the future. The strong association rules between risky riding behavior and traffic accidents should be explored using big data analysis. The relationships between risk awareness, risky cycling, and traffic accidents should be studied using the scales of risk perception, risk attitude, and risk tolerance. In a variety of complex mixed scenes, the risk degree, coupling characteristics, interventions, and the coupling effects of various combination intervention measures of e-bike riding behaviors should be researched using coupling theory in the future.
Topics: Accident Prevention; Accidents, Traffic; Adult; Awareness; Bicycling; Electricity; Female; Humans; Male; Motorcycles; Probability; Risk-Taking; Safety; Young Adult
PubMed: 31261838
DOI: 10.3390/ijerph16132308