A variety of dimensions (lengths and widths) of elongate mineral particles (EMPs) have been proposed as being related to health effects. In this paper, we develop a mathematical approach for deriving numerical conversion factors (CFs) between these EMP exposure metrics and applied it to the Minnesota Taconite Health Worker study which contains 196 different job exposure groups (28 similar exposure groups times 7 taconite mines). This approach comprises four steps: for each group (i) obtain EMP dimension information using ISO-TEM 10312/13794 analysis; (ii) use bivariate lognormal distribution to characterize overall EMP size distribution; (iii) use a Bayesian approach to facilitate the formation of the bivariate lognormal distribution; (iv) derive conversion factors between any pair of EMP definitions. The final CFs allow the creation of job exposure matrices (JEMs) for alternative EMP metrics using existing EMP exposures already characterized according to the National Institute of Occupational Safety and Health (NIOSH)-defined EMP exposure metric (length >5 μm with an aspect ratio ≥3.0). The relationships between the NIOSH EMP and other EMP definitions provide the basis of classification of workers into JEMs based on alternate definitions of EMP for epidemiological studies of mesothelioma, lung cancer, and non-malignant respiratory disease.
Bibliographical noteFunding Information:
This research was supported by an award number 1R01OH010418-01A1 from the National Institute for Occupational Safety and Health. We would also like to thank the State of Minnesota for funding the Taconite Workers Health Study.
© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.
Copyright 2020 Elsevier B.V., All rights reserved.
- Bayesian approach
- EMP exposure metrics
- bivariate lognormal distribution
- elongate mineral particles (EMPs)
PubMed: MeSH publication types
- Journal Article
- Research Support, U.S. Gov't, P.H.S.