Associations of Occupational Heat Stress and Noise Exposure with Carotid Atherosclerosis among Chinese Steelworkers: A Cross-Sectional Survey

The use of machine and deep learning to model the relationship between discomfort temperature and labor productivity loss among petrochemical workers

BACKGROUND

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.

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.

Heat and health of occupational workers: a short summary of literature

Globally, occupational workers suffer various health impacts due to extreme heat. In this short review, we examine the literature discussing health impacts of heat on occupational workers, and then discuss certain individual and institutional measures needed to address the problem. Though the available literature in the recent decade discusses health impacts of heat on workers as various heat-related illnesses, we found very few studies examining how occupational workers suffer from issues concerning cardiovascular health, neurological health, respiratory health, and mental health. In this regard, we highlight the need for more studies to examine how occupational workers exposed to extreme heat conditions suffer from fatal health issues like cardiovascular attack, brain stroke, and other ailments impacting vital organs of the body. Occupational workers across the world should be made aware of measures to protect themselves from extreme heat. Further, countries should develop occupational heat safety guidelines with statutory effect.