Department of Smart City Research, Seoul Institute of Technology, Seoul, Republic of Korea.
Department of Occupational and Environmental Medicine, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
J Occup Health. 2023 Jan;65(1):e12392. doi: 10.1002/1348-9585.12392.
Workers' diseases and injuries are often highly related to work. However, due to limited resources and unclear work relatedness, workers' compensation insurance cannot cover all diseases or injuries among workers. This study aimed to estimate the status and probability of disapproval from national workers' compensation insurance using basic information from Korean workers' compensation system.
The compensation insurance data for Korean workers consists of personal, occupational, and claims data. We describe the status of disapproval by workers' compensation insurance according to the type of disease or injury. A prediction model for disapproval by workers' compensation insurance was established by applying two machine-learning methods with a logistic regression model.
Among 42 219 cases, there were significantly higher risks of disapproval by workers' compensation insurance for women, younger workers, technicians, and associate professionals. We established a disapproval model for workers' compensation insurance after the feature selection. The prediction model for workers' disease disapproval by the workers' compensation insurance showed a good performance, and the prediction model for workers' injury disapproval showed a moderate performance.
This study is the first attempt to demonstrate the status and prediction of disapproval by workers' compensation insurance using basic information from the Korean workers' compensation data. These findings suggest that diseases or injuries have a low level of evidence of work relatedness or there is a lack of research on occupational health. It is also expected to contribute to the efficiency of the management of workers' diseases or injuries.
劳动者的疾病和伤害通常与工作高度相关。然而，由于资源有限且工作关联性不明确，劳动者工伤保险无法涵盖所有劳动者的疾病或伤害。本研究旨在利用韩国劳动者工伤保险系统的基本信息，评估国家劳动者工伤保险的拒付状况和拒付概率。
韩国劳动者工伤保险数据包括个人、职业和理赔数据。我们根据疾病或伤害类型描述了工伤保险拒付的状况。应用逻辑回归模型的两种机器学习方法建立了工伤保险拒付预测模型。
在 42219 例中，女性、年轻劳动者、技术人员和助理专业人员的工伤保险拒付风险显著较高。我们在特征选择后建立了工伤保险拒付模型。劳动者疾病工伤保险拒付预测模型表现良好，劳动者伤害工伤保险拒付预测模型表现中等。
本研究首次尝试利用韩国劳动者工伤保险数据的基本信息，展示工伤保险拒付的状况和预测。这些发现表明，某些疾病或伤害与工作关联性的证据水平较低，或者对职业健康的研究不足。这也有望提高劳动者疾病或伤害管理的效率。
