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Hierarchical Cluster Analysis Applied to Workers Exposures in Fiberglass Insulation Manufacturing

Annals of Occupational Hygeine , Volume 43 (1): 43 – Jan 1, 1999

Details

Publisher
Oxford University Press
Copyright
Copyright © 1999 Oxford University Press
ISSN
0003-4878
eISSN
1475-3162
D.O.I.
10.1093/annhyg/43.1.43
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Hierarchical Cluster Analysis Applied to Workers Exposures in Fiberglass Insulation Manufacturing

Abstract

Abstract The objectives of this study were to explore the application of cluster analysis to the characterization of multiple exposures in industrial hygiene practice and to compare exposure groupings based on the result from cluster analysis with that based on non-measurement-based approaches commonly used in epidemiology. Cluster analysis was performed for 37 workers simultaneously exposed to three agents (endotoxin, phenolic compounds and formaldehyde) in fiberglass insulation manufacturing. Different clustering algorithms, including complete-linkage (or farthest-neighbor), single-linkage (or nearest-neighbor), group-average and model-based clustering approaches, were used to construct the tree structures from which clusters can be formed. Differences were observed between the exposure clusters constructed by these different clustering algorithms. When contrasting the exposure classification based on tree structures with that based on non-measurement-based information, the results indicate that the exposure clusters identified from the tree structures had little in common with the classification results from either the traditional exposure zone or the work group classification approach. In terms of the defining homogeneous exposure groups or from the standpoint of health risk, some toxicological normalization in the components of the exposure vector appears to be required in order to form meaningful exposure groupings from cluster analysis. Finally, it remains important to see if the lack of correspondence between exposure groups based on epidemiological classification and measurement data is a peculiarity of the data or a more general problem in multivariate exposure analysis.
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