0079 Examining multiple exposure pathways of beryllium using mixed and structural equation moding techniques. (23rd June 2014)

• Record Type: Journal Article
• Authors: Armstrong, Jenna; Virji, Abbas; Davis, Mary; Day, Gregory; Stefaniak, Aleksandr; Stanton, Marcia; Deubner, David; Schuler, Christine
• Is Part Of: Occupational and environmental medicine. Volume 71(2014)Supplement 1
• Page Start: A8
• Page End: A8
• Publication Date: 2014-06-23
• Subjects: Medicine, Industrial -- Periodicals; Environmental health -- Periodicals; 616.980305
• DOI: 10.1136/oemed-2014-102362.24
• Languages: English
• ISSNs: 1351-0711

Abstract

Abstract : Objectives: Inhalation beryllium exposures are associated with sensitisation, however dermal exposures are also important. In a previous study, we identified strong correlations between dermal-air, dermal- surface, and air- surface measurements. The aim of this study was to investigate workplace factors associated with exposures using mixed-effects models and structural equation modelling (SEM). Method: Beryllium was measured in personal air, on gloves, and on surfaces at three manufacturing facilities. Predictor variables included substance and activity emission potential (REACH classification), dilution, segregation, PPE, personal behaviour, and work shift. Results: The mixed model described 57 and 59% of total variance for air and dermal, respectively. The total variance explained by the SEM model for air and dermal was 0.51 and 0.48% respectively. In both models activity and substance emission potential, surface contamination, dilution, and personal behaviour were significant predictors of air concentrations (p ≤ 0.05); and surface contamination and air concentrations were significant predictors of dermal loading on cotton gloves (p ≤ 0.05). However, work shift and personal behaviour were predictive of dermal loading in the SEM (p ≤ 0.03), but not in the mixed model. In addition, the SEM reported a parameter estimate for air concentration as a predictor of dermal loading that was an order of magnitude higher than in the mixed model. Conclusions: Although SEM requires relatively large sample sizes, it is useful for modelling multiple, correlated dependent variables. In addition, full-information maximum likelihood (FIML) methods can be used in SEM to include missing predictor variable data. Although we found both models to be useful, SEM has the potential to illustrate indirect pathways of outcome variables.