Quantitative Risk Assessment of Seafarers' Nonfatal Injuries Due to Occupational Accidents Based on Bayesian Network Modeling

Teleconsultation at Sea and Acute Wound Management Onboard

Introduction: The French Tele-Medical Assistance Service (TMAS) provides medical teleconsultation for any ship at sea. At the end of the consultation, the doctor decides whether the problem can be taken care of onboard or not. In this study, we determined the factors associated with the decision for disembarkation or evacuation in case of wounds. Methods: We conducted a retrospective epidemiological study between 2011 and 2019 from consultations with the French TMAS. The inclusion criterion was the presence of an acute wound. Results: One thousand six patients (n = 1,006) were analyzed and 586 (58%) patients were disembarked or evacuated. Factors associated with disembarkation or evacuation are wound characteristics (severity and location), the onboard staff's medical training, the availability of photography, and the ship's location. Wound severity is a risk factor for disembarkation or evacuation. The availability of photography, staff with advanced training, and being at >1 day of navigation from a harbor are protective factors against being disembarked or evacuated. Conclusion: The added value of photography and an update of medium medical training could increase the number of wounds taken care of onboard.

A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H

Unsafe crew acts (UCAs) related to human errors are the main contributors to maritime accidents. The prediction of unsafe crew acts will provide an early warning for maritime accidents, which is significant to shipping companies. However, there exist gaps between the prediction models developed by researchers and those adopted by practitioners in human risk analysis (HRA) of the maritime industry. In addition, most research regarding human factors of maritime safety has concentrated on hazard identification or accident analysis, but not on early warning of UCAs. This paper proposes a Bayesian network (BN) version of the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method to predict the probability of seafarers' unsafe acts. After the identification of performance-shaping factors (PSFs) that influence seafarers' unsafe acts during navigation, the developed prediction model, which integrates the practicability of SPAR-H and the forward and backward inference functions of BN, is adopted to evaluate the probabilistic risk of unsafe acts and PSFs. The model can also be used when the available information is insufficient. Case studies demonstrate the practicability of the model in quantitatively predicting unsafe crew acts. The method allows evaluating whether a seafarer is capable of fulfilling their responsibility and providing an early warning for decision-makers, thereby avoiding human errors and sequentially preventing maritime accidents. The method can also be considered as a starting point for applying the efforts of HRA researchers to the real world for practitioners.

Human-Related Hazardous Events Assessment for Suffocation on Ships by Integrating Bayesian Network and Complex Network

To investigate the human-related factors associated with suffocation on ships during docking repair, a comprehensive analysis model composed of a Bayesian network (BN) and a complex network (CN) is proposed in the present study. The principle of event tree analysis (ETA) is firstly applied to identify the hazardous events involved in the accident according to the accident report, based on which the CN would then be developed with the logic relationships among the hazardous events. The improved K-shell decomposition algorithm is utilized to determine the criticality of nodes in the CN, the results of which are then used to develop the BN model within the framework of a human factor analysis classification system (HFACS). Then, the developed BN model can be simulated with the probability distribution of all the nodes within the BN, which are obtained on the basis of node criticality. Finally, the results of the BN simulation are interpreted from the perspectives of a brief analysis, backward analysis and sensitivity analysis. The results are verified with existing studies and the accident investigation report issued by authority, which are presented as evidence to verify the effectiveness of the proposed methodology to evaluate the human-related risk involved in the suffocation on ships. The methodology proposed in this study integrates the advantages of BN and CN to investigate the human-related hazardous events involved in maritime accidents, which can be seen as the main innovation of this work.

Quality Risk Management Algorithm for Cold Storage Construction Based on Bayesian Networks

In the cold storage construction project, only by controlling the quality risk of the project can ensure that the cold storage can meet the expected use function and achieve the expected economic benefits after the completion of the cold storage. In order to effectively ensure the key pivot role of cold storage in cold chain logistics, a cold storage construction quality risk management system is constructed to identify and analyze quality risk factors from three dimensions: construction procedures, participating units, and work processes, construct a cold storage construction quality risk evaluation model based on Bayesian network, and through reverse reasoning analysis and sensitivity analysis, key quality risk factors are derived: inadequate quality assurance system, technical delivery not in place, mismatch of building materials and equipment, inadequate training of skilled workers, completion acceptance not careful or acceptance standards unreasonable, and duration not meeting the requirements. Finally, in view of the above quality risks, suggestions and measures are put forward from five aspects: man, material, machine, method, and environment.

The Predictive Role of ADRA2A rs1800544 and HTR3B rs3758987 Polymorphisms in Motion Sickness Susceptibility

Motion sickness is a common central nervous system response, the primary sign of which is vomiting. Its susceptibility varies between individuals. To find predictive factors, we investigated the association of ADRA2A rs1800544 and HTR3B rs3758987 with motion sickness susceptibility and examined their mRNA changes during actual voyages. A total of 315 healthy college students were enrolled for SNP genotyping by the PCR-RFLP method. Blood samples were collected from another 42 subjects during two separate voyages to detect their mRNA expression changes at three time points. The frequency of the rs1800544 GG genotype in the susceptibility group was significantly higher (52.26%), and allele G increased the risk of motion sickness (OR = 1.585, 95% CI = 1.136-2.208). In the logistic regression model, the rs3758987 CC+TC genotype and rs1800544 GG genotype increased the risk of motion sickness-induced vomiting (OR = 2.105, 95% CI = 1.112-3.984; OR = 1.992, 95% CI = 1.114-3.571). The ADRA2A mRNA baseline was lower in the GG carriers and the HTR3B mRNA baseline was lower in the TC/CC carriers before sailing, then increased significantly within 24 h and then decreased after a long-term voyage. People carrying the rs1800544 GG genotype seem more susceptible to motion sickness. In combination with the incidence of vomiting during the actual-voyage experiments, our results indicate the involvement of rs1800544 and rs3758987 in motion sickness-induced vomiting.

Lessons Learned from the Development and Demonstration of a PPE Inventory Monitoring System for US Hospitals

An international system should be established to support personal protective equipment (PPE) inventory monitoring, particularly within the healthcare industry. In this article, the authors discuss the development and 15-week deployment of a proof-of-concept prototype that included the use of a Healthcare Trust Data Platform to secure and transmit PPE-related data. Seventy-eight hospitals participated, including 66 large hospital systems, 11 medium-sized hospital systems, and a single hospital. Hospitals reported near-daily inventory information for N95 respirators, surgical masks, and face shields, ultimately providing 159 different PPE model numbers. Researchers cross-checked the data to ensure the PPE could be accurately identified. In cases where the model number was inaccurately reported, researchers corrected the numbers whenever possible. Of the PPE model numbers reported, 74.2% were verified-60.5% of N95 respirators, 40.0% of face shields, and 84.0% of surgical masks. The authors discuss the need to standardize how PPE is reported, possible aspects of a PPE data standard, and standards groups who may assist with this effort. Having such PPE data standards would enable better communication across hospital systems and assist in emergency preparedness efforts during pandemics or natural disasters.

Exploring Probabilistic Network-Based Modeling of Multidimensional Factors Associated with Country Risk

Assessment of country risk provides a vital source of information to organizations for expanding and globalizing their operations. Various rating agencies are involved in developing models for assessing country risk, which utilize different statistical techniques for establishing the overall impact of individual factors on country risk. The main limitation of existing studies on country risk is their limited focus on exploring the relative contribution of individual factors to country risk in a probabilistic network setting. Utilizing real data, we develop a probabilistic network model that captures dependencies among multidimensional factors associated with country risk. Further, we assess the network-wide vulnerability and resilience potential of individual factors to identify critical factors. The findings of this study provide policy-makers with some unique insights into prioritizing strategies to mitigate country risk. Further, this study provides the context for multinational enterprises to utilize the proposed methodology for prioritizing key factors associated with the relative variables of interest such as regional stability and business environment among others.