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Cross-validation and refinement of the Stoffenmanager as a first tier exposure assessment tool for REACH

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Author: Schinkel, J. · Fransman, W. · Heussen, H. · Kromhout, H. · Marquart, H. · Tielemans, E.
Type:article
Date:2010
Institution: TNO Kwaliteit van Leven
Source:Occupational and Environmental Medicine, 2, 67, 125-132
Identifier: 347468
doi: DOI:10.1136/oem.2008.045500
Keywords: Research · volatile agent · accuracy · algorithm · article · calibration · controlled study · drug granule · occupational exposure · powder · prediction · priority journal · risk assessment · stoffenmanager algorithm · validation study · work environment · workplace · Algorithms · Bias (Epidemiology) · Environmental Monitoring · Hazardous Substances · Humans · Inhalation Exposure · Models, Statistical · Occupational Exposure · Reproducibility of Results · Risk Assessment

Abstract

Objectives: For regulatory risk assessment under REACH a tiered approach is proposed in which the first tier models should provide a conservative exposure estimate that can discriminate between scenarios which are of concern and those which are not. The Stoffenmanager is mentioned as a first tier approach in the REACH guidance. In an attempt to investigate the validity of the Stoffenmanager algorithms, a cross-validation study was performed. Methods: Exposure estimates using the Stoffenmanager algorithms were compared with exposure measurement results (n=254). Correlations between observed and predicted exposures, bias and precision were calculated. Stratified analyses were performed for the scenarios "handling of powders and granules" (n=82), "handling solids resulting in comminuting" (n=60), "handling of low-volatile liquids" (n=40) and "handling of volatile liquids" (n=72). Results: The relative bias of the four algorithms ranged between -9% and -77% with a precision of approximately 1.7. The 90th percentile estimate of one out of four algorithms was not conservative enough. Based on these statistics and analyses of residual plots the underlying algorithm was adapted. Subsequently, the calibration and the cross-validation dataset were merged into one dataset (n=952) used for calibrating the adapted Stoffenmanager algorithms. This new calibration resulted in new exposure algorithms for the four scenarios. Conclusions: The Stoffenmanager is capable of discriminating among exposure levels mainly between scenarios in different companies. The 90th percentile estimates of the Stoffenmanager are verified to be sufficiently conservative. Therefore, the Stoffenmanager could be a useful tier 1 exposure assessment tool for REACH.