Occupational skin health risk among nurses: development and validation of a nomogram-based prediction model

Background: Occupational contact dermatitis (OCD) is a prevalent work-related skin condition among nurses and remains a significant occupational health issue due to its impact on well-being, productivity, and workforce sustainability. However, reliable tools for early risk stratification in this population are lacking. This study aimed to develop and validate a nomogram-based prediction model to estimate the individual risk of OCD among nurses. Methods: = 852). Independent predictors were identified using univariate and multivariable logistic regression analyses. A nomogram was constructed based on the final multivariable model. Model performance was assessed using the area under the ROC curve (AUC), bootstrapped calibration plots, and decision curve analysis (DCA). Results: Nine predictors were independently associated with OCD: age, dermatitis history, glove type, glove-wearing hours, handwashing frequency during work, hospital level, hand-cream habit, baseline skin condition, and sleep duration. The model showed excellent discrimination (AUC = 0.925 in the training set; 0.931 in the validation set). Calibration curves demonstrated close agreement between predicted and observed risks. DCA indicated consistently higher net benefit compared with the "treat-all" and "treat-none" strategies across wide threshold probability ranges (0.01-0.98 in the training set; 0.02-0.96 in the validation set). The resulting nomogram provides an intuitive, point-based tool for individualized OCD risk prediction. Conclusion: A robust, well-validated prediction model and nomogram were developed to estimate OCD risk among nurses. This tool may support occupational health screening, early risk identification, and targeted preventive strategies in healthcare institutions.