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Application of predictive models for estimation of health care workers exposure to sevoflurane

Objectives. The aim of this study was to assess the potential use of predictive models to estimate professional exposure to chemicals in the workplace, such as the operating room, by simultaneous determination of the levels of exposure using a model and measurements. Methods. Measurements included d...

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Bibliographic Details
Published in:International journal of occupational safety and ergonomics 2015-01, Vol.21 (4), p.471-479
Main Authors: Jankowska, Agnieszka, Czerczak, Sławomir, Kucharska, Małgorzata, Wesołowski, Wiktor, Maciaszek, Piotr, Kupczewska-Dobecka, Małgorzata
Format: Article
Language:English
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Summary:Objectives. The aim of this study was to assess the potential use of predictive models to estimate professional exposure to chemicals in the workplace, such as the operating room, by simultaneous determination of the levels of exposure using a model and measurements. Methods. Measurements included determinations of sevoflurane (SEV) in the workplace air of 117 operating rooms of 31 hospitals in one Polish region. Measurements were carried out at the time of various surgical procedures during administration of anaesthetics by endotracheal intubation. The measurement results were compared with the values estimated using two models: ECETOC TRA and Stoffenmanager. Results. In one case the ECETOC TRA estimated the exposure concentration almost equal to the measured concentration but, because of the need to maintain a margin of safety in case of modelling, it can be concluded that the model underestimated the concentration. The Stoffenmanager model provided accurate exposure estimates in the examined case, and it can be used as a screening tool for the assessment of occupational inhalation exposure of medical personnel to anaesthetics. Conclusions. The results are of particular importance to the circumstances in Eastern Europe, where the levels of anaesthetics often exceed the relevant occupational exposure limits.
ISSN:1080-3548
2376-9130
DOI:10.1080/10803548.2015.1086183