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Examining multiple exposure pathways of beryllium using mixed and structural equation moding techniques.

Authors
Armstrong-J; Virji-A; Davis-M; Day-G; Stefaniak-A; Stanton-M; Deubner-D; Schuler-C
Source
Occup Environ Med 2014 Jun; 71(Suppl 1):A8
NIOSHTIC No.
20044583
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.
Keywords
Beryllium-compounds; Exposure-levels; Risk-factors; Respiration; Models; Emission-sources; Personal-protective-equipment; Behavior; Shift-work; Statistical-analysis
CODEN
OEMEEM
CAS No.
7440-41-7
Publication Date
20140601
Document Type
Abstract
Fiscal Year
2014
NTIS Accession No.
NTIS Price
Identifying No.
M062014
ISSN
1351-0711
NIOSH Division
DRDS
Source Name
Occupational and Environmental Medicine
State
OH; MA; OR
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