Exploring multisite musculoskeletal symptoms among sewing machine operators in a tunisian leather and footwear industry using decision tree models

• DOI: 10.1016/j.cegh.2024.101575
• Publication Date: 2024-04-17T16:51:30Z
• Authors: Nehla Rmadi; Imen Sellami; Afef Feki; Kaouthar Jmal Ham...; Mohamed Larbi Mas...; Mounira Hajjaji
• Keywords: 03 medical and health sciences; 0302 clinical medicine; Epidemiology; Prevalence; Decision tree; Public aspects of medicine; RA1-1270; Musculoskeletal disorders

Background/objectivesSewing machine operators (SMO) are the most likely workers to experience a high prevalence of musculoskeletal disorders in textile, clothing, and footwear industries. We conducted cross-sectional exhaustive study among SMO working leather industry describe multi-site symptoms (MMS) evaluate factors associated with their occurrence.MethodsMusculoskeletal declared by these were assessed through modified Nordic questionnaire. The psychosocial work environment was using Karasek model. variables MMS issued from binary logistic regression decision tree R software.ResultsOf 145 operators, 65.5 % men 72.4 women had MMS. Based on regression, history (MSDs) increased risk developing 8 folds. identified five main nodes: MSDs, professional seniority, often finding pace restrictive male gender.ConclusionIdentifying homogeneous profiles MMS's occurrence will help implementation an effective targeted preventive strategy.