Interdisciplinary Graduate School, Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore.
School of Business IT & Logistics, RMIT University, Melbourne, Australia.
Risk Anal. 2020 Jan;40(1):8-23. doi: 10.1111/risa.13374. Epub 2019 Jul 17.
Reducing the incidence of seafarers' workplace injuries is of great importance to shipping and ship management companies. The objective of this study is to identify the important influencing factors and to build a quantitative model for the injury risk analysis aboard ships, so as to provide a decision support framework for effective injury prevention and management. Most of the previous research on seafarers' occupational accidents either adopts a qualitative approach or applies simple descriptive statistics for analyses. In this study, the advanced method of a Bayesian network (BN) is used for the predictive modeling of seafarer injuries for its interpretative power as well as predictive capacity. The modeling is data driven and based on an extensive empirical survey to collect data on seafarers' working practice and their injury records during the latest tour of duty, which could overcome the limitation of historical injury databases that mostly contain only data about the injured group instead of the entire population. Using the survey data, a BN model was developed consisting of nine major variables, including "PPE availability," "Age," and "Experience" of the seafarers, which were identified to be the most influential risk factors. The model was validated further with several tests through sensitivity analyses and logical axiom test. Finally, implementation of the result toward decision support for safety management in the global shipping industry was discussed.
降低海员工作场所受伤的发生率对航运和船舶管理公司至关重要。本研究的目的是确定重要的影响因素，并建立一个船舶伤害风险分析的定量模型，为有效的伤害预防和管理提供决策支持框架。以前大多数关于海员职业事故的研究要么采用定性方法，要么只进行简单的描述性统计分析。在这项研究中，先进的贝叶斯网络（BN）方法被用于海员伤害的预测建模，因为它具有解释能力和预测能力。该建模是数据驱动的，基于广泛的实证调查，收集海员工作实践和最近一次轮班期间受伤记录的数据，这可以克服历史伤害数据库的局限性，历史伤害数据库大多只包含受伤群体的数据，而不是整个人口的数据。利用调查数据，开发了一个由九个主要变量组成的 BN 模型，包括海员的“个人防护设备的可用性”、“年龄”和“经验”，这些变量被确定为最具影响力的风险因素。该模型还通过敏感性分析和逻辑公理测试进行了进一步验证。最后，讨论了将结果应用于全球航运业安全管理的决策支持。
