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The construction industry is one of the most important socio-economic sectors of the Spanish economy and one of the most affected by workplace accidents. An analysis of the data on accident rates is needed, in order to identify variables related with workplace accidents and to define the measures that need to be taken for their reduction. In this study, an analysis is conducted using Bayesian Networks and data from the 7th National Survey on Working Conditions (VII NSWC), to study the relations between workplace accidents, visiting a doctor for occupational reasons, time in the company/sector, information that workers have on workplace risks in the workplace, and information and training on workplace risks that workers have received over the past two years. The NSWC survey, which is conducted every four years, was administered to 8892 workers, in Spain, in 2011. The values derived from the analysis yield certain implications involving the aforementioned variables and how to reduce the probability of workplace accidents. From among the variables under study, information on workplace risks is the most important, with the probability of suffering an accident in the construction industry doubling when such information is insufficient. In accordance with the results, these implications could also help with decision-making focused on improvements to training and on-the-job information, intended both to prevent and to reduce workplace accidents.

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions, (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions, (iii) distracted motorcycles and squad riders are found more likely to speed, (iv) the probability of committing infractions by distracted drivers increases on streets and highways, and, finally, (v) drivers’ infractions lead to serious injuries.

The use of communication technologies, e.g., mobile phones, has increased dramatically in recent years, and their use among drivers has become a great risk to traffic safety. The present study assessed the workload and road ordinary violations, utilizing driving data collected from 39 young participants who underwent a dual-task while driving a simulator, i.e., respond to a call, text on WhatsApp, and check Instagram. Findings confirmed that there are significant differences in the driving performance of young drivers in terms of vehicle control (i.e., lateral distance and hard shoulder line violations) between distracted and non-distracted drivers. Furthermore, the overall workload score of young drivers increases with the use of their mobile phones while driving. The obtained results contribute to a better understanding of the driving performance of distracted young drivers and thus they could be useful for further improvements to traffic safety strategies.

Road traffic plays a vital role in countries&rsquo, economic growth and future development. However, traffic accidents are considered a major public health issue affecting humankind. Despite efforts by governments to improve traffic safety, the misalignment between the policy efforts and on-ground infringements, distractions and breaches reflect the regulatory failure. This paper uses the Bayesian network method to investigate unsafe behaviors and traffic accidents involving unlicensed drivers as a perspective for the regulatory alignment assessment. The findings suggest that: (1) unlicensed drivers are more likely to have unsafe driving behaviors, (2) the probability of being involved in a severe traffic accident increases when the drivers are unlicensed and decreases in the case of licensed drivers, (3) young drivers are noticeably more likely to engage in unsafe behaviors, usually leading to serious injuries and deaths, when their driving licenses are invalid, (4) women are more likely to engage in right-of-way violations and to have collisions with no serious injuries, contrary to unlicensed men drivers, who are involved in other types of traffic accidents resulting in serious injuries.

In this research we identify the main factors believed to trigger occupational hypoacusia in an effort to increase our knowledge of how this occupational disease occurs and develops. With this goal in mind, we have gathered various demographic/personal, occupational and non-occupational data from a heterogeneous sample of 1418 workers. The data selected include the noise levels to which the individuals in the sample are exposed. This entailed taking measurements at their respective jobs, as well as doing an objective assessment of their hearing ability, which required administering medical hearing tests. Lastly, the workers completed a survey on various habits and other factors deemed to be influential, and on the respondents’ own perception of their hearing. Bayesian networks were used to obtain the conditioned probability of developing hypoacusia based on the data collected from the sample. Specifically, for this study we used the general network created by the relationships between all of the factors associated with developing hypoacusia in order to analyze the influence individually and by grouping three specific variables: activity sector, noise level and time on the job. This work yielded a considerable database that can be used to conduct a multitude of analyses intended to study and predict the hearing acuity of the working population under different scenarios. Specifically, in the case at hand, the Bayesian network obtained indicates that the three factors analyzed influence the hearing of the individuals, though to different extents. The least influential factor involves the sector of activity, followed by the noise level on the job, which varies noticeably in favor of better hearing for workers in jobs whose noise levels are rated as low. Finally, we deemed time on the job (which is also related to age), as the most influential factor as it exhibits the largest differences among its potential states, with workers whose time on the job is rated as low or medium exhibiting the best likelihood of having good hearing.

To reduce traffic accidents, an accurately estimated model is needed to capture the true relationships between the injury severity and risk factors. This study aims to propose a robust procedure to address the biases in police-reported accident data and subsequently to conduct sensitivity analyzes in order to estimate the variations in injury severity and distraction probability based on drivers’ behaviors/characteristics and psychophysical conditions. The results show that: (i) the excess speed will likely increase the probability of serious/fatal injury for drivers of all age groups by 10%; (ii) distraction and driver’ errors will likely increase the probability of serious/fatal injury in all drivers driving at a proper speed up to 1.5%; (iii) alcohol and drug consumption can significantly increase the probability of being distracted and making errors by 28.5% and 33.5% respectively; (iv) Alcohol consumption reduces the probability of driving at an appropriate speed in drivers under 25 by 40%. However, the results for drugs consumption are not as significant as the ones for alcohol consumption., This study was supported by funds from the following projects: “Modelo cuantitativo de Red Bayesiana con capacidad predictiva de la gravedad del accidente en función de los comportamientos y actuaciones de las personas” [Qualitative Bayesian Network Model for Predicting Accident Severity Based on Personal Behaviors and Actions]. Ref. SPIP2015-01852 supported by funds from DGT (Directorate-General for Traffic). “Modelización mediante técnicas de machine learning de la influencia de las distracciones del conductor en la seguridad vial” [Modeling the influence of driver distractions on road safety using machine learning techniques]. Ref. BU300P18 supported by funds from FEDER (Fondo Europeo de Desarrollo Regional, Junta de Castilla y León).

Hearing loss affects hundreds of millions of people all over the world, leading to several types of disabilities, ranging from purely physical to psychological and/or social aspects. A proper analysis to ascertain the main risk factors is essential in order to diagnose early and treat adequately. An exploratory analysis based on a heterogeneous sample of 1418 workers is presented in order to identify the main trigger factors for hearing loss. On the one hand, we recorded several medical and environmental parameters, and on the other, we created a model based on Bayesian networks in order to be able to infer the probability of hearing loss considering different scenarios. This paper focuses on three parameters: gender, age, and a family history of hearing problems. The results obtained allow us to infer or predict the best or worst auditory level for an individual under several different scenarios. The least relevant factor is the existence of a family history of deafness, followed by the gender factor, which slopes considerably toward better hearing for females, and most prominent of all, the age factor, given the large differences identified between the various age groups when the gender and family history of deafness variables remain constant.

This study analyses factors associated with cyclist injury severity, focusing on vehicle type, route environment, and interactions between them. Data analysed was collected by Spanish police during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to model relationships between cyclist injury severity and circumstances related to the crash, with the outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly injured. Factors in the model included those relating to the injured cyclist, the route environment, and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph zones, and urban zones) were associated with lower injury severity. Interactions exist between the two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a collision were more likely than the injured cyclists to have committed an error or infraction. The study supports the creation of infrastructure that separates cyclists from motor tra c. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk., European Regional Development Fund (Ref. FEDER BU300P18).

Each day thousands of workers suffer occupational accidents of varying degrees of severity. Accidents at work render workers incapable of carrying out their day to day activities, either temporarily or permanently, and they also have detrimental effects on family life, the company, and the general public. In order to reduce the occupational accident rate, it is necessary to determine the causes of those accidents. Although there are many different types of accidents, they generally stem from poor working conditions. The purpose of this study was to analyze the influence of working conditions on occupational accidents from data gathered in the VI National Survey of Working Conditions (VI NSWCs) in 2007. This survey utilized a random sample of the active population of Spain. The sample comprised 11,054 people (5917 males and 5137 females). In order to carry out the study, a probabilistic model was built using Bayesian networks. The model included the following variables: hygiene conditions, ergonomic conditions, job demands, physical symptoms, psychological symptoms, and occupational accidents. The study demonstrated that there were strong relationships between hygiene conditions and occupational accidents; it has been shown that poor hygienic conditions duplicate the probability of accident. Physical symptoms increased almost 50% due to poor ergonomic conditions. And finally, high job demands almost duplicated the psychological symptoms. The investigation also showed a high degree of interdependence between physical and psychological symptomatologies and the relationship between these and occupational accidents.

Many high-hazard industries around the world have explicitly recognized the critical role that human, management and organizational risk factors play in major accidents. The findings of accident investigations and risk assessments demonstrate a growing recognition that the cultural context of work practices may influence safety just as much as technology.The objective of this paper is to establish a relationship between the concepts of safety culture and organizational culture in a Nuclear Power Plant (NPP). This study permits the identification and quantification of the possible mechanisms for improving the safety culture in the NPP acting on organizational culture. It therefore provides a methodology to identify potential strategies for safety improvement.Probabilistic (Bayesian) Networks (BNs) have been used to determine the relationships between the organizational culture and safety culture in a quantitative form. To this aim, we considered data from a survey conducted of every employee at a Spanish NPP. The resulting data-driven models allow us to establish the probabilistic relationship among organizational culture factors, including the 12 OCI (Organizational Culture Inventory) scales, that have an influence on safety culture. The study yielded a ranking of organizational cultures that can be used to improve safety culture in a NPP. © 2012 Elsevier Ltd.

Occupational stress is a major health hazard and a serious challenge to the effective operation of any company and represents a major problem for both individuals and organizations. Previous researches have shown that high demands (e.g. workload, emotional) combined with low resources (e.g. support, control, rewards) are associated with adverse health (e.g. psychological, physical) and organizational impacts (e.g. reduced job satisfaction, sickness absence). The objective of the present work is to create a model to analyze how social support reduces the occupational stress caused by work demands. This study used existing Spanish national data on working conditions collected by the Spanish Ministry of Labour and Immigration in 2007, where 11,054 workers were interviewed by questionnaire. A probabilistic model was built using Bayesian networks to explain the relationships between work demands and occupational stress. The model also explains how social support contributes positively to reducing stress levels. The variables studied were intellectually demanding work, overwork, workday, stress, and social support. The results show the importance of social support and of receiving help from supervisors and co-workers in preventing occupational stress. The study provides a new methodology that explains and quantifies the effects of intellectually demanding work, overwork, and workday in occupational stress. Also, the study quantifies the importance of social support to reduce occupational stress. © 2013 Elsevier Ltd. All rights reserved., This study was supported by funds from the following project: “The Influence of Workplace Organization on Safety and Health”. Ministry of Labour and Immigration. Ref. 07/08.08 PH/mc.

The authors regret providing the incorrect affiliation of Dr. Susana Garcia-Herrero and Dr. M.A. Mariscal. The correct affiliations are given below: Susana Garcia-Herrero, Ph.D. Escuela Politecnica Superior, Universidad de Burgos, Spain M.A. Mariscal, Ph.D. Escuela Politecnica Superior, Universidad de Burgos, Spain J.M. Gutierrez, Ph.D. Instituto de Fisica de Cantabria (CSIC-UC), Spanish National Research Council, Spain