BackgroundWelding fumes are a risk factor for welder pneumoconiosis. However, there is a lack of population information on the occurrence of welding fume-induced lung cancer, and little is known about the welding fume pathogenesis.MethodsWelding fume and metal ion concentrations were assessed in a vehicle factory in Wuhan. A Cox regression model estimated lung-related disease risk in workers by independent and combined factors.ResultsWorkers' exposures were divided into four grades; the highest exposure was among the welders in the maintenance workshop, the highest Mn and Fe exposure was 4 grades, and the highest Cr exposure was 3 grades. Subgroup analysis found that the risk of lung-related disease was 2.17 (95% CI: 1.31–3.57, p < 0.05) in welders compared with non-welders, and the risk of pulmonary disease in male welders was 2.24 (95% CI: 1.34–3.73, p < 0.05) compared to non-welders. Smoking welders had a 2.44 (95% CI: 1.32–4.51, p < 0.01) higher incidence of lung-related diseases than non-welders. Total years of work as an independent protective factor for lung-related disease risk was 0.72 (95% CI: 0.66–0.78, p < 0.01). As an independent risk factor, high-high and high-low exposure had a 5.39 (95% CI: 2.52–11.52, p < 0.001) and 2.17 (95% CI: 1.07–4.41, p < 0.05) higher risk for lung-related diseases, respectively.ConclusionsHigh welding fume exposure is a significant risk factor for lung-related disease in workers.
ObjectiveTo develop a prediction nomogram for the risk of lung-related diseases (LRD) in construction workers.MethodsSeven hundred and fifty-two construction workers were recruited. A self- designed questionnaire was performed to collected relevant information. Chest X-ray was taken to judge builders' lung health. The potential predictors subsets of the risk of LRD were screened by the least absolute shrinkage and selection operator regression and univariate analysis, and determined by using multivariate logistic regression analysis, then were used for developing a prediction nomogram for the risk of LRD. C-index, calibration curve, receiver operating characteristic curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to evaluation the identification, calibration, predictive ability and clinical effectiveness of the nomogram.ResultsFive hundred and twenty-six construction workers were allocated to training group and 226 to validation group. The predictors included in the nomogram were symptoms, years of dust exposure, work in shifts and labor intensity. Our model showed good discrimination ability, with a bootstrap-corrected C index of 0.931 (95% CI = 0.906–0.956), and had well-fitted calibration curves. The area under the curve (AUC) of the nomogram were (95% CI = 0.906–0.956) and 0.945 (95% CI = 0.891–0.999) in the training and validation groups, respectively. The results of DCA and CICA indicated that the nomogram may have clinical usefulness.ConclusionWe established and validated a novel nomogram that can provide individual prediction of LRD for construction workers. This practical prediction model may help occupational physicians in decision making and design of occupational health examination.
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