Development of a predictive model for work-relatedness of MSDs among semiconductor back-end workers

Objective. Limited models are available to predict work-relatedness of musculoskeletal disorders (MSDs) among semiconductor back-end workers. This study aims to develop a model to predict the MSDs development among back-end workers. Method. Potential MSD risk factors were extracted from 277 work compensation investigation reports conducted between 2011-2019. Binary logistic regression approach was used to determine significant predictors. Results. Significant predictors (p < 0.05) include poor posture (odds ratio [OR] = 1.822; 95% confidence interval [CI] [1.261, 2.632]), forceful exertion (OR = 1.741; 95% CI [1.281, 2.367]), static posture (OR = 1.796; 95% CI [1.367, 2.378]), lifting and lowering (OR = 1.438; 95% CI [0.966, 1.880]), transferring (OR = 1.533; 95% CI [1.101, 2.136]), pushing and pulling (OR = 0.990; 95% CI [0.744, 1.317]), repairing machines (OR = 0.845; 95% CI 76 [0.616, 1.159]), preventive maintenance (OR = 1.061; 95% CI [0.765, 1.471]) and quality inspection (OR = 0.982; 95% CI [0.729, 1.322]). Confounding factors and employment duration played crucial roles in the model. Cross-validation of predictive model was 86.2%, while face validation among 30 experts was 7.9/10 (SD 1.9). Conclusion. The model allows practitioners to predict potential MSD cases among semiconductor back-end workers and proactively plan appropriate mitigation measures.