Background: Construction workers exposed to silica-containing dust are at risk of developing silicosis even at low exposure levels. Health surveillance among these workers is commonly advised but the exact diagnostic work-up is not specified and therefore may result in abundant unnecessary chest X-ray investigations. Aim: To develop a simple diagnostic model to estimate for an individual worker the probability of having pneumoconiosis from questionnaire and spirometry results in order to accurately rule out workers without pneumoconiosis. Methods: The study was performed using cross sectional data of 1291 Dutch natural stone and construction workers with potentially high quartz dust exposure. A multivariable logistic regression model was developed using chest X-ray with ILO profusion category > 1/1 as the reference standard. The model’s calibration was evaluated with the Hosmer-Lemeshow (HL) test; the discriminative ability was determined by calculating the area under the receiver operating characteristic curve (ROC area). Internal validity of the final model was assessed by a bootstrapping procedure. For clinical application, the diagnostic model was transformed into an easy-to-use score chart. Results: Age 40 years or older, current smoker, high exposed job, working 15 years or longer in the construction industry, ‘feeling unhealthy’, and FEV1 were the independent predictors in the diagnostic model. The model showed good calibration (a non-significant HL-test) and discriminative ability (ROC area 0.81, 95% CI 0.74-0.85). Internal validity was reasonable; the optimism corrected ROC area was 0.76. By using a cut-off point with a high negative predictive value the occupational physician can efficiently detect a large proportion of workers with a low probability of having pneumoconiosis and exclude them from unnecessary X-ray investigations. Conclusions: Our diagnostic model is an efficient and effective instrument to rule out pneumoconiosis among construction workers. Its use in health surveillance among these workers can reduce the number of redundant X-ray investigations.
- construction workers
- diagnostic model
- health surveillance
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