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

Abstract

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

Keywords

• Musculoskeletal disorders
• Prevalence
• Epidemiology
• Decision tree