Department of Preventive Medicine, School of Public Health, Fujian Medical University; and Key Laboratory of Environment and Health, Fujian Province University, 1 North Xue-Fu Rd, Minhou, Fuzhou, 350122, Fujian Province, China.
Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, Fujian Province, China.
BMC Public Health. 2024 Nov 25;24(1):3269. doi: 10.1186/s12889-024-20713-4.
Workplace may not only increase the risk of heat-related illnesses and injuries but also compromise work efficiency, particularly in a warming climate. This study aimed to utilize machine learning (ML) and deep learning (DL) algorithms to quantify the impact of temperature discomfort on productivity loss among petrochemical workers and to identify key influencing factors.
A cross-sectional face-to-face questionnaire survey was conducted among petrochemical workers between May and September 2023 in Fujian Province, China. Initial feature selection was performed using Lasso regression. The dataset was divided into training (70%), validation (20%), and testing (10%) sets. Six predictive models were evaluated: support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), Gaussian Naive Bayes (GNB), multilayer perceptron (MLP), and logistic regression (LR). The most effective model was further analyzed with SHapley Additive exPlanations (SHAP).
Among the 2393 workers surveyed, 58.4% (1,747) reported productivity loss when working in high temperatures. Lasso regression identified twenty-seven predictive factors such as educational level and smoking. All six models displayed strong prediction accuracy (SVM = 0.775, RF = 0.760, XGBoost = 0.727, GNB = 0.863, MLP = 0.738, LR = 0.680). GNB model showed the best performance, with a cutoff of 0.869, accuracy of 0.863, precision of 0.897, sensitivity of 0.918, specificity of 0.715, and an F1-score of 0.642, indicating its efficacy as a predictive tool. SHAP analysis showed that occupational health training (SHAP value: -3.56), protective measures (-2.61), and less physically demanding jobs (-1.75) were negatively associated with heat-attributed productivity loss, whereas lack of air conditioning (1.92), noise (2.64), vibration (1.15), and dust (0.95) increased the risk of heat-induced productivity loss.
Temperature discomfort significantly undermined labor productivity in the petrochemical sector, and this impact may worsen in a warming climate if adaptation and prevention measures are insufficient. To effectively reduce heat-related productivity loss, there is a need to strengthen occupational health training and implement strict controls for occupational hazards, minimizing the potential combined effects of heat with other exposures.
工作场所不仅会增加与热相关的疾病和伤害的风险，还会降低工作效率，尤其是在气候变暖的情况下。本研究旨在利用机器学习 (ML) 和深度学习 (DL) 算法来量化温度不适对石化工人生产力损失的影响，并确定关键影响因素。
2023 年 5 月至 9 月期间，在中国福建省对石化工人进行了一项横断面面对面问卷调查。使用套索回归进行初始特征选择。数据集分为训练 (70%)、验证 (20%) 和测试 (10%) 集。评估了六种预测模型：支持向量机 (SVM)、随机森林 (RF)、极端梯度提升 (XGBoost)、高斯朴素贝叶斯 (GNB)、多层感知机 (MLP) 和逻辑回归 (LR)。对最有效的模型进行了 SHapley Additive exPlanations (SHAP) 分析。
在所调查的 2393 名工人中，58.4% (1747 人) 在高温下工作时报告生产力下降。套索回归确定了教育水平和吸烟等 27 个预测因素。所有六种模型均表现出较强的预测准确性（SVM=0.775、RF=0.760、XGBoost=0.727、GNB=0.863、MLP=0.738、LR=0.680）。GNB 模型表现最佳，截断值为 0.869，准确率为 0.863，精度为 0.897，灵敏度为 0.918，特异性为 0.715，F1 得分为 0.642，表明其作为预测工具的有效性。SHAP 分析表明，职业健康培训 (SHAP 值：-3.56)、防护措施 (-2.61) 和体力要求较低的工作 (-1.75) 与归因于热的生产力损失呈负相关，而缺乏空调 (1.92)、噪音 (2.64)、振动 (1.15) 和灰尘 (0.95) 会增加热引起的生产力损失的风险。
温度不适显著降低了石化行业的劳动力生产力，如果适应和预防措施不足，这种影响在气候变暖的情况下可能会恶化。为了有效降低与热相关的生产力损失，需要加强职业健康培训，并对职业危害实施严格控制，最大限度地减少热与其他暴露因素的潜在综合影响。
