Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression
BackgroundAs 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, mak...
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Editorial Committee of Journal of Environmental and Occupational Medicine
2025-02-01
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| Series: | 环境与职业医学 |
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| Online Access: | http://www.jeom.org/article/cn/10.11836/JEOM24318 |
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| author | Yingheng XIAO Chunhua LU Juan QIAN Ying CHEN Yishuo GU Zeyun YANG Daozheng DING Liping LI Xiaojun ZHU |
| author_facet | Yingheng XIAO Chunhua LU Juan QIAN Ying CHEN Yishuo GU Zeyun YANG Daozheng DING Liping LI Xiaojun ZHU |
| author_sort | Yingheng XIAO |
| collection | DOAJ |
| description | BackgroundAs 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. ObjectiveTo identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. MethodsA 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.ResultsAmong 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.ConclusionManufacturing 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. |
| format | Article |
| id | doaj-art-98a5bb27a0474cada6ddb9f7f699e11a |
| institution | DOAJ |
| issn | 2095-9982 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Editorial Committee of Journal of Environmental and Occupational Medicine |
| record_format | Article |
| series | 环境与职业医学 |
| spelling | doaj-art-98a5bb27a0474cada6ddb9f7f699e11a2025-08-20T03:15:26ZengEditorial Committee of Journal of Environmental and Occupational Medicine环境与职业医学2095-99822025-02-0142213313910.11836/JEOM2431824318Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regressionYingheng XIAO0Chunhua LU1Juan QIAN2Ying CHEN3Yishuo GU4Zeyun YANG5Daozheng DING6Liping LI7Xiaojun ZHU8National Center for Occupational Safety and Health, National Health Commission of the People's Republic of China, NHC Key Laboratory for Engineering Control of Dust Hazard, Beijing 102308, ChinaNantong Center for Disease Control and Prevention, Nantong, Jiangsu 226007, ChinaYixing Center for Disease Control and Prevention, Yixing, Jiangsu 214206, ChinaNantong Center for Disease Control and Prevention, Nantong, Jiangsu 226007, ChinaNational Center for Occupational Safety and Health, National Health Commission of the People's Republic of China, NHC Key Laboratory for Engineering Control of Dust Hazard, Beijing 102308, ChinaNantong Center for Disease Control and Prevention, Nantong, Jiangsu 226007, ChinaYixing Center for Disease Control and Prevention, Yixing, Jiangsu 214206, ChinaSchool of Public Health, Shantou University, Shantou, Guangdong 515041, ChinaNational Center for Occupational Safety and Health, National Health Commission of the People's Republic of China, NHC Key Laboratory for Engineering Control of Dust Hazard, Beijing 102308, ChinaBackgroundAs 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. ObjectiveTo identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. MethodsA 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.ResultsAmong 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.ConclusionManufacturing 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.http://www.jeom.org/article/cn/10.11836/JEOM24318non-fatal occupational injuryinfluencing factorlasso regressionmachine learningmanufacture |
| spellingShingle | Yingheng XIAO Chunhua LU Juan QIAN Ying CHEN Yishuo GU Zeyun YANG Daozheng DING Liping LI Xiaojun ZHU Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression 环境与职业医学 non-fatal occupational injury influencing factor lasso regression machine learning manufacture |
| title | Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression |
| title_full | Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression |
| title_fullStr | Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression |
| title_full_unstemmed | Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression |
| title_short | Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression |
| title_sort | impact factor selection for non fatal occupational injuries among manufacturing workers by lasso regression |
| topic | non-fatal occupational injury influencing factor lasso regression machine learning manufacture |
| url | http://www.jeom.org/article/cn/10.11836/JEOM24318 |
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