Background Falls are one of the most important types of occupational injuries. The incidence of falls is high in manufacturing workers. However, most of the studies on falls in China focus on primary and secondary school students and the elderly, and there are few studies on falls in the occupational population. Objective To evaluate efficiency of Bayesian network model in predicting fall injury risks in manufacturing enterprise staff, and impacts from work content, work environment, enterprise status, and health management on falls and their mutual relationships, and provide a scientific basis for enterprises to carry out fall-associated injury intervention. Methods Data from the European Survey of Enterprises on New and Emerging Risks (ESENER) were used. The survey provided data on work content, working environment, enterprise status, and health management of enterprises in European countries. The outcome indicator, was fall injury risks reported in enterprises. A total of 23 potential impact factors covering work content, working environment, enterprise status, and health management were screened by least absolute shrinkage and selection operator (LASSO) regression, followed by Bayesian network model for structure learning and parameter learning and area under the curve (AUC) for model fitness evaluation, using R and Netica 5.18. Diagnostic inference analysis was also conducted to identify key influencing factors and key influencing chains of fall injury risks based on the change rate of fall injury risks. Results In 5997 enterprises surveyed, 2573 (42.9%) enterprises reported fall injury risks. Ordered by their coefficient estimates from high to low, the 14 variables (mean-squared error=0.20) selected by LASSO regression were: manual handling, repetitive arm movement, poor posture, using desktop computers, and using robots in the category of work content; abnormal temperature and noise in the category of working environment; company size and employee quality in the category of enterprise status; mental health training, regular risk assessment, availability of psychologists, health and safety procedures, and provision of psychological counseling in the category of health management. The fitting result of Bayesian network model for fall injury risks was good (AUC=0.779). The Bayesian network diagnostic inference identified five key influencing factors, including abnormal temperature (change rate=35.9%), poor posture (change rate=27.3%), noise (change rate=23.4%), manual handling (change rate=18.2%), and repetitive arm movement (change rate=5.1%). The key influencing chain was "manual handling - poor posture - repetitive arm movement - fall injury risks" (combined change rate=16.9%). Conclusion The Bayesian network model has a good predictive performance in predicting the risk of falls in manufacturing enterprises. Manufacturing enterprises need to focus on jobs involving manual handling and repetitive arm movement, identify and improve workers' poor posture and mental health problems, and avoid workers working in harsh temperature or noise environment.

Background Occupational injuries, which can result in absenteeism, disability, or death, are closely related to poor working conditions. However, the improvement of operating conditions are often time-consuming and require significant economic inputs. Both occupational psychology and enterprise risk factors have been proved to be related to the occurrence of occupational injuries, but their roles in the influence path of adverse working conditions leading to occupational injuries remain unclear. Objective To explore the roles of occupational psychology and enterprise risk factors in the impact of adverse working conditions on occupational injury, so as to provide a scientific basis for enterprises with adverse working conditions to carry out targeted occupational injury intervention programs. Methods The survey data of 5997 manufacturing enterprises were obtained from the European Survey of Enterprises on New and Emerging Risks (ESENER) database. The data on enterprise risk characteristics, occupational injuries, working conditions, and occupational psychological factors were extracted and assigned. Occupational injury differences by enterprise categories were examined by chi-square test. Correlations between interest variables were evaluated by Spearman test. Path analysis with Bootstrap method was conducted using AMOS 26 software, and ratio of chi-square statistic to degree of freedom (χ²/ν), comparative fit index (CFI), Tucker-Lewis index (TLI), and root mean square error of approximation (RMSEA) were used to evaluate the path model candidates. The effect size and its proportion were calculated for variables (occupational psychological factors, enterprise risk factors, and adverse working conditions) included in the final model. Results The M (P₂₅, P₇₅) scores of occupational injuries, adverse working conditions, and occupational psychological factors were 40 (20, 50), 50 (30, 60), and 20 (10, 30), respectively. The enterprises that reported occupational injuries accounted for 25.5% (1550 enterprises) of the total enterprises. Proportions of the enterprises that reported occupational injuries varied significantly by company scale, branch companies, temporary employment, language barriers, and establishment time (P<0.05). The results of Spearman test showed that occupational injuries were positively correlated with working conditions (r_s=0.440), occupational psychological factors (r_s=0.205), company scale (r_s=0.307), temporary employment (r_s=0.282), and language barriers (r_s=0.158); but negatively correlated with branch companies (r_s=−0.180) and establishment time (r_s=−0.176) (P<0.05). In the path analysis, the fitness indexes of the final model were χ²/ν=2.85, CFI=0.997, TLI=0.993, and RMSEA=0.018 (90%CI: 0.011, 0.025). The indirect effect size values and constituent ratios of enterprise risk factors and occupational psychological factors in the effect of adverse working conditions on occupational injuries were 0.166 (30.01%) and 0.013 (3.13%) respectively. The indirect effect size value of occupational psychological factors in the effect of enterprise risk factors on occupational injuries and its constituent ratio were 0.022 and 6.85%. Conclusion Enterprises with adverse working conditions may control the risk of occupational injuries by offering better solutions to surmount language barriers and temporary employment, developing occupational psychological intervention and optimization programs such as improving working hours system. At the same time, large enterprises, enterprises without branches, or enterprises with a long history are the focus of occupational injury prevention and control.

Globally, manufacturing workers are one of the most vulnerable groups to occupational injuries. Occupational injuries can lead to absenteeism, disability or even death, and most of the inflicted workers involve young adults aged 18-40 years, suggesting a safety and health problem that needs close attention. In the working environment of manufacturing industry, there are a variety of occupational injury risk factors, involving individuals, equipment, environment, and management, which should be considered comprehensively. This study found comprehensive research coverage on the influencing factors of occupational injuries in manufacturing industry at individual, environmental, and management levels at home and abroad, and rich research results on the impacts of psychological factors on occupational injuries. However, factors associated with occupational injuries in equipment safety and engineering like man-machine environment need further research. Influencing factors at all levels should be comprehensively considered in the surveillance and intervention of occupational injuries in manufacturing industry to protect health and safety of workers.

Background As a pillar industry in China, the manufacturing sector has a high incidence of non-fatal 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 2243 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 2243 frontline workers, males accounted for 77.7% (1742 out of 2243), with the main age range being 40-49 years old, representing 29.5% (661 out of 2243), 82.7% of the workers (1854 out of 2243) were married, and 55.6% (1248 out of 2243) had a junior middle school education level. The average monthly income for 51.0% (1144 out of 2243) of the workers was between 5000 and 6999 Chinese Yuan. The incidence of non-fatal occupational injuries among the manufacturing workers was 8.4% (189/2243) 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.