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Objectives. Several studies have shown that one of the most common causes of collision is driver fatigue since fatigue causes drowsiness while driving and this decreases the driver's ability to maneuver the vehicle and increases the probability of their nodding off and falling asleep at the wheel. This may be due to a variety of personal reasons and specific factors connected to working conditions. In the present work we therefore intend to develop a predictive model for fatigue in professional drivers using the following indicators: age, gender, personality, burnout, characteristics and job content. Method. The participants were 516 professional drivers from different transport sectors, obtained through non-probabilistic sampling. SPSS version 25.0 was used for data analysis. Results. The predictive capacity of a number of variables that affect drivers by causing fatigue is determined. Fatigue can be predicted through certain variables, with the best predictor being exhaustion (48.8%). Conclusions. This research contributes to a greater knowledge of the factors that produce fatigue in professional drivers. It highlights the importance of designing interventions to reduce the incidence of fatigue, resulting in greater well-being for the driver and a lower incidence of collisions.

Objectives . Professional drivers drive for many hours without rest. This factor, in addition to the characteristics of the job, the vehicle, the environment and the driver, causes driver fatigue. Fatigue is one of the most common risk factors when driving because it causes drowsiness, decreases drivers' attention and may make them fall asleep at the wheel. In this article we propose a predictive model for professional drivers using the following variables: age, number of children, time spent at work, time spent inside the vehicle, personality, job characteristics (JDS), job content (JCQ) and burnout. Method . Participants were 509 professional drivers from various transport sectors recruited by non-probabilistic sampling. SPSS version 25.0 was used for statistical analysis. Results . The predictive capacity of variables that cause driver fatigue was determined. Exhaustion best predicts fatigue positively, while openness to experience best predicts it negatively. Burnout and certain personality characteristics are good predictors, whereas other variables, such as JCQ and JDS, are weak predictors. Conclusions . This study extends our knowledge of the factors that cause fatigue in professional drivers and underlines the importance of designing interventions aimed at reducing the incidence of fatigue, promoting greater driver well-being and lowering the incidence of accidents.

Objectives. Stress maintained over time leads to a state of exhaustion known as burnout syndrome. This syndrome constitutes an occupational health problem, leading to high absenteeism. It can also mean that workers come to the workplace feeling unwell, which increases occupational collisions and injuries at work. In this study, we developed a predictive model of burnout in professional drivers using the following indicators: age, hours worked, seniority, educational level, fatigue, personality, attitudes toward driving, safety behaviors in the vehicle, and work characteristics and content. Method . A total of 523 professional drivers from different transport sectors, obtained through non-probability sampling, participated in the study. We used SPSS version 25.0 to analyze the data. Results . We determined the predictive capacity of certain variables that affect drivers and cause burnout. Exhaustion can be predicted with fatigue (48.8%), professional efficiency with emotional stability (39.8%) and cynicism with lack of motivation (28%) as the best predictors. Conclusions . The results contribute to a better knowledge of those factors that cause burnout in professional drivers. It is important to design individual interventions to reduce burnout, which would help reduce sick leave and possible collisions, in addition to providing greater well-being for drivers.

The primary vector of the dengue fever virus, the Aedes aegypti mosquito, is distributed across the tropical and sub-tropical latitudes; however, the area at risk of infection has been expanding steadily. This study aimed to identify the industries most vulnerable to the effects of dengue fever by 2030. The assessment was done by considering the international supply chain, with aspects such as the labor intensity, and the relevant geographical and socioeconomic aspects being taken into account. In addition, multi-regional input-output tables were employed to analyze the ripple effects of productivity losses resulting from workers contracting the disease. The results indicate that more than 10% of the workers involved in the supply chain of all the major industries in the United States (USA), China, Japan, and Germany could be considered at risk of contracting dengue fever by 2030. Moreover, the risk was even higher in India and Brazil, namely, more than 70%. The effect of widespread dengue fever infection could influence industrial activities severely, not only in the regions most at risk (India and Brazil) but also in the other regions (USA, Japan, and Germany). Labor-intensive industries, such as agriculture, fisheries, and the distribution sector are particularly at risk and will have to consider appropriate contingency measures. It is recommended that the downstream side of the supply chain, the industries in the USA, Japan, and Germany, supports the introduction of worker's health management system against the infectious disease into their business partners. This study employed limited data and only estimated the possible effects of the disease by 2030. Further comprehensive analysis is required with more data modeled for the future to verify and enhance the reliability of the present results., Competing Interests: Conflict of interestThe author declares that there is no conflict of interest., (© Springer Science+Business Media Dordrecht 2017.)