Lee Ju-Yeun, Lee Woojoo, Cho Sung-Il
The Department of Public Health, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.
Institute of Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.
Heliyon. 2023 Sep 14;9(9):e20138. doi: 10.1016/j.heliyon.2023.e20138. eCollection 2023 Sep.
Analysis of occupational injuries is essential for developing preventive strategies. However, few studies have evaluated severe occupational injuries in migrant workers from the perspective of gender. Therefore, using a new analytical method, this study was performed to identify gender-specific characteristics associated with fatal occupational injuries among migrant workers; the interactions between these factors, were also analyzed. In addition, we compared the utility of explainable artificial intelligence (XAI) using SHapley Additive exPlanations (SHAP) with logistic regression (LR) and discuss caveats regarding its use.
We analyzed national statistics for occupational injuries among migrant workers ( = 67,576) in South Korea between January 1, 2007, and September 30, 2018. We applied an extreme gradient boosting model and developed SHAP and LR models for comparison.
We found clear gender differences in fatal occupational injuries among migrant workers, with males in the same occupation having a higher risk of death than females. These gender differences suggest the need for gender-specific occupational injury prevention interventions for migrant workers to reduce the mortality rate. Occupation was a significant predictor of death among female migrant workers only, with care jobs having the highest fatality risk. The occupational fatality risk of female workers would not have been identified without the performance of detailed job-specific analyses stratified by gender. The major advantages of SHAP identified in the present study were the automatic identification and analysis of interactions, ability to determine the relative contributions of each feature, and high overall performance. The major caveat when using SHAP is that causality cannot be established.
Detailed job-specific analyses stratified by gender, and interventions considering the gender of migrant workers, are necessary to reduce occupational fatality rates. The XAI approach should be considered as a complementary analytical method for epidemiological studies, as it overcomes the limitations of traditional statistical analyses.
分析职业伤害对于制定预防策略至关重要。然而，很少有研究从性别角度评估农民工的严重职业伤害。因此，本研究采用一种新的分析方法，以确定与农民工致命职业伤害相关的性别特异性特征；同时还分析了这些因素之间的相互作用。此外，我们比较了使用SHapley加性解释（SHAP）的可解释人工智能（XAI）与逻辑回归（LR）的效用，并讨论了其使用的注意事项。
我们分析了2007年1月1日至2018年9月30日期间韩国农民工（n = 67,576）职业伤害的国家统计数据。我们应用了极端梯度提升模型，并开发了SHAP和LR模型进行比较。
我们发现农民工致命职业伤害存在明显的性别差异，从事相同职业的男性死亡风险高于女性。这些性别差异表明，需要针对农民工采取针对性别的职业伤害预防干预措施，以降低死亡率。职业仅是女性农民工死亡的一个重要预测因素，护理工作的死亡风险最高。如果没有按性别进行详细的特定工作分析，就无法识别女性工人的职业死亡风险。本研究中确定的SHAP的主要优点是能够自动识别和分析相互作用、确定每个特征的相对贡献以及整体性能较高。使用SHAP的主要注意事项是无法建立因果关系。
按性别进行详细的特定工作分析，以及考虑农民工性别的干预措施，对于降低职业死亡率是必要的。XAI方法应被视为流行病学研究的一种补充分析方法，因为它克服了传统统计分析的局限性。
