Li H R, Yao Y, Liu S F, Ma H, Mei Y, Wu J B
Department of Physical Examination, Xiangyang Hospital of Integrated Traditional Chinese and Western Medicine, Xiangyang 441004, China.
Department of Physical Examination, Xiangyang Hospital of Integrated Traditional Chinese and Western Medicine, Xiangyang 441004, China School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China.
Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi. 2024 Aug 20;42(8):573-580. doi: 10.3760/cma.j.cn121094-20230412-00129.
To explore the risk factors of neck work-related musculoskeletal disorders (WMSDs) among automobile manufacturing enterprise workers, and construct the risk prediction model. In May 2022, a cluster convenience sampling method was used to selet all front-line workers from an automobile manufacturing factory in Xiangyang City as the research objects. And a questionnaire survey was conducted using the modified Musculoskeletal Disorders Questionnaire to analyze the occurrence and exposure to risk factors of neck WMSDs. Logistic regression was used to analyze the influencing factors of workers' neck WMSDs symptoms, and Nomogram column charts was used to construct the risk prediction model. The accuracy of the model was evaluated by the receiver operating characteristic (ROC) curve, the Bootstrap resampling method was used to verify the model, Hosmer-Lemeshow goodness of fit test was used to evaluate the model, and the Calibration curve was drawn. A total of 1783 workers were surveyed, and the incidence of neck WMSDs symptoms was 24.8% (442/1783). Univariate logistic regression showed that age, female, smoking, working in uncomfortable postures, repetitive head movement, feeling constantly stressed at work, and completing conflicting tasks in work could increase the risk of neck WMSDs symptoms in automobile manufacturing enterprise workers (=1.37, 95%: 1.16-1.62; =2.85, 95%: 1.56-5.20; =1.50, 95%: 1.18-1.91; =1.18, 95%: 1.02-1.37; =1.34, 95%: 1.04-1.72; =1.62, 95%: 1.21-2.17; =1.48, 95%: 1.13-1.92; <0.05). While adequate rest time could reduce the risk of neck WMSDs symptoms (=0.56, 95%: 0.52-0.86, <0.05). The risk prediction model of neck WMSDs of workers in automobile manutacturing factory had good prediction efficiency, and the area under the ROC curve was 0.72 (95%: 0.70-0.75, <0.001) . The occurrence of neck WMSDs symptoms of workers in automobile manufacturing factory is relatively high. The risk prediction model constructed in this study can play a certain auxiliary role in predicting neck WMSDs symptoms of workers in automobile manufacturing enterprise workers.
为探究汽车制造企业工人颈部工作相关肌肉骨骼疾病（WMSDs）的危险因素，并构建风险预测模型。2022年5月，采用整群便利抽样方法，选取襄阳市一家汽车制造工厂的所有一线工人作为研究对象。使用改良的肌肉骨骼疾病问卷进行问卷调查，以分析颈部WMSDs的发生情况和危险因素暴露情况。采用Logistic回归分析工人颈部WMSDs症状的影响因素，并使用列线图构建风险预测模型。通过受试者工作特征（ROC）曲线评估模型的准确性，采用Bootstrap重采样方法验证模型，使用Hosmer-Lemeshow拟合优度检验评估模型，并绘制校准曲线。共调查1783名工人，颈部WMSDs症状发生率为24.8%（442/1783）。单因素Logistic回归显示，年龄、女性、吸烟、工作姿势不舒服、头部重复运动、工作中经常感到压力以及工作中完成相互冲突的任务会增加汽车制造企业工人颈部WMSDs症状的风险（=1.37，95%：1.16 - 1.62；=2.85，95%：1.56 - 5.20；=1.50，95%：1.18 - 1.91；=1.18，95%：(1.02 - 1.37；=1.34，95%：1.04 - 1.72；=1.62，95%：1.21 - 2.17；=1.48，95%：1.13 - 1.92；<0.05）。而充足的休息时间可降低颈部WMSDs症状的风险（=0.56，95%：0.52 - 0.86，<0.05）。汽车制造工厂工人颈部WMSDs的风险预测模型具有良好的预测效率，ROC曲线下面积为0.72（95%：0.70 - 0.75，<0.001）。汽车制造工厂工人颈部WMSDs症状的发生率较高。本研究构建的风险预测模型可为预测汽车制造企业工人颈部WMSDs症状起到一定的辅助作用。
