Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression

• Published in: Huan jing yu zhi ye yi xue = Journal of environmental & occupational medicine Vol. 42; no. 2; pp. 133 - 139
• Main Authors: Xiao, Yingheng, Lu, Chunhua, Qian, Juan, Chen, Ying, Gu, Yishuo, Yang, Zeyun, Ding, Daozheng, Li, Liping, Zhu, Xiaojun
• Format: Journal Article
• Language: Chinese; English
• Published: Shanghai Shanghai Municipal Center For Disease Control and Prevention 2025; Editorial Committee of Journal of Environmental and Occupational Medicine
• Subjects: Burnout; Cardiovascular diseases; Disorders; Gender; Industrial safety; influencing factor; Injuries; lasso regression; Low temperature; machine learning; manufacture; Manufacturing; Manufacturing industry; Musculoskeletal diseases; Neurological diseases; non-fatal occupational injury; Occupational accidents; Occupational health; Occupational safety; Regression models; Resampling; Risk factors; Sense organs; Smoking; Training; Variables; Workers; Working conditions; Working hours; Workpieces
• ISSN: 2095-9982
• DOI: 10.11836/JEOM24318

[Background] As a pillar industry in China, the manufacturing sector has a high incidence of nonfatal occupational injuries. The factors influencing non-fatal occupational injuries in this industry are closely related at various levels, including individual, equipment, environment, and management, making the analysis of these influencing factors complex. [Objective] To identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. [Methods] A total of 2 243 frontline workers from cable and shipbuilding enterprises were selected as study subjects to investigate the incidence of non-fatal occupational injuries and collect information at four levels: individual, equipment, management, and environment in past 12 months. Data balancing was performed using resampling, and LASSO regression was used to select factors of non-fatal occupational injuries. The influence degree and type of variables were judged based on the magnitude of the estimated coefficients of each variable, where variables with estimated coefficients > 0 are risk factors, and those < 0 are protective factors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to test the performance of the model, with an AUC value > 0.7 indicating good model performance. [Results] Among the 2 243 frontline workers, males accounted for 77.7% (1 742 out of 2 243), with the main age range being 40-49 years old, representing 29.5% (661 out of 2 243), 82.7% of the workers (1854 out of 2 243) were married, and 55.6% (1 248 out of 2 243) had a junior middle school education level. The average monthly income for 51.0% (1 144 out of 2 243) of the workers was between 5 000 and 6 999 Chinese Yuan. The incidence of non-fatal occupational injuries among the manufacturing workers was 8.4% (189/2 243) in the past 12 months. Among the 22 factors associated with the occurrence of non-fatal occupational injuries (P < 0.05), 10 were individual-level factors, including gender, smoking, alcohol consumption, colleague relationships, average exercise duration, job burnout, work fatigue, musculoskeletal disorders, cardiovascular diseases, and neurological and sensory organ diseases; 3 were equipment-level factors, including equipment operability, hazardous workpieces, and safety hazards; 5 were environmental-level factors, including low temperatures, special operations, noise, workspace size, and dirty and disorderly environment; and 4 were management-level factors, including daily working hours, weekly working days, overtime, and pre-job technical training. The AUC value of the LASSO regression model was 0.704 and the final model retained a total of 10 variables. Among them, there were 7 risk factors for non-fatal occupational injuries (coefficient > 0), including safety hazards, musculoskeletal disorders, dangerous workpieces, job burnout, dirty and disorderly environment, smoking, and male gender; and 3 protective factors (coefficient < 0), including pre-job technical training, good colleague relationship, and long working days per week. [Conclusion] Manufacturing enterprises need to focus on the incidence of non-fatal occupational injuries and conduct targeted interventions for non-fatal occupational injuries by controlling potential safety hazards, providing pre-job technical training, reducing dangerous workpieces, rectifying working environment, and reasonably arranging working hours.