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Research Article

A Meta-Analysis of Asbestos-Related Cancer Risk That Addresses Fiber Size and Mineral Type

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Pages 49-73 | Received 11 Jun 2008, Accepted 15 Jun 2008, Published online: 20 Oct 2008
 

Abstract

Quantitative estimates of the risk of lung cancer or mesothelioma in humans from asbestos exposure made by the U.S. Environmental Protection Agency (EPA) make use of estimates of potency factors based on phase-contrast microscopy (PCM) and obtained from cohorts exposed to asbestos in different occupational environments. These potency factors exhibit substantial variability. The most likely reasons for this variability appear to be differences among environments in fiber size and mineralogy not accounted for by PCM.

In this article, the U.S. Environmental Protection Agency (EPA) models for asbestos-related lung cancer and mesothelioma are expanded to allow the potency of fibers to depend upon their mineralogical types and sizes. This is accomplished by positing exposure metrics composed of nonoverlapping fiber categories and assigning each category its own unique potency. These category-specific potencies are estimated in a meta-analysis that fits the expanded models to potencies for lung cancer (KL's) or mesothelioma (KM's) based on PCM that were calculated for multiple epidemiological studies in our previous paper (Citation). Epidemiological study-specific estimates of exposures to fibers in the different fiber size categories of an exposure metric are estimated using distributions for fiber size based on transmission electron microscopy (TEM) obtained from the literature and matched to the individual epidemiological studies. The fraction of total asbestos exposure in a given environment respectively represented by chrysotile and amphibole asbestos is also estimated from information in the literature for that environment. Adequate information was found to allow KL's from 15 epidemiological studies and KM's from 11 studies to be included in the meta-analysis.

Since the range of exposure metrics that could be considered was severely restricted by limitations in the published TEM fiber size distributions, it was decided to focus attention on four exposure metrics distinguished by fiber width: “all widths,” widths > 0.2 μ m, widths < 0.4 μ m, and widths < 0.2 μ m, each of which has historical relevance. Each such metric defined by width was composed of four categories of fibers: chrysotile or amphibole asbestos with lengths between 5 μ m and 10 μ m or longer than 10 μ m. Using these metrics three parameters were estimated for lung cancer and, separately, for mesothelioma: KLA, the potency of longer (length > 10 μ m) amphibole fibers; rpc, the potency of pure chrysotile (uncontaminated by amphibole) relative to amphibole asbestos; and rps, the potency of shorter fibers (5 μ m < length < 10 μ m) relative to longer fibers.

For mesothelioma, the hypothesis that chrysotile and amphibole asbestos are equally potent (rpc = 1) was strongly rejected by every metric and the hypothesis that (pure) chrysotile is nonpotent for mesothelioma was not rejected by any metric. Best estimates for the relative potency of chrysotile ranged from zero to about 1/200th that of amphibole asbestos (depending on metric). For lung cancer, the hypothesis that chrysotile and amphibole asbestos are equally potent (rpc = 1) was rejected (p ≤ .05) by the two metrics based on thin fibers (length < 0.4 μ m and < 0.2 μ m) but not by the metrics based on thicker fibers.

The “all widths” and widths < 0.4 μ m metrics provide the best fits to both the lung cancer and mesothelioma data over the other metrics evaluated, although the improvements are only marginal for lung cancer. That these two metrics provide equivalent (for mesothelioma) and nearly equivalent (for lung cancer) fits to the data suggests that the available data sets may not be sufficiently rich (in variation of exposure characteristics) to fully evaluate the effects of fiber width on potency. Compared to the metric with widths > 0.2 μ m with both rps and rpc fixed at 1 (which is nominally equivalent to the traditional PCM metric), the “all widths” and widths < 0.4 μ m metrics provide substantially better fits for both lung cancer and, especially, mesothelioma.

Although the best estimates of the potency of shorter fibers (5 < length < 10 μ m) is zero for the “all widths” and widths < 0.4 μ m metrics (or a small fraction of that of longer fibers for the widths > 0.2 μ m metric for mesothelioma), the hypothesis that these shorter fibers were nonpotent could not be rejected for any of these metrics. Expansion of these metrics to include a category for fibers with lengths < 5 μ m did not find any consistent evidence for any potency of these shortest fibers for either lung cancer or mesothelioma.

Despite the substantial improvements in fit over that provided by the traditional use of PCM, neither the “all widths” nor the widths < 0.4 μ m metrics (or any of the other metrics evaluated) completely resolve the differences in potency factors estimated in different occupational studies. Unresolved in particular is the discrepancy in potency factors for lung cancer from Quebec chrysotile miners and workers at the Charleston, SC, textile mill, which mainly processed chrysotile from Quebec. A leading hypothesis for this discrepancy is limitations in the fiber size distributions available for this analysis. Citation recently analyzed by TEM archived air samples from the South Carolina plant to determine a detailed distribution of fiber lengths up to lengths of 40 μ m and greater. If similar data become available for Quebec, perhaps these two size distributions can be used to eliminate the discrepancy between these two studies.

ACKNOWLEDGMENTS

We thank Corbett McDonald and acknowledge the help of Douglass Liddell (deceased) for making available the raw data on mesothelioma mortality for the cohort of Quebec chrysotile miners and millers. We thank Nicholas de Klerk for making available the raw data for the cohort of Wittenoom, Australia, crocidolite miners. We thank NIOSH, and Everett (Chip) Lehman in particular, for making available the raw data for the cohort of workers from the textile plant in Charleston, SC. We thank Misty Hein for her valuable and very timely assistance in providing data on background cancer rates and additional information on the South Carolina cohort. We gratefully acknowledge the guidance and support provided by Aparna Koppikar, Richard Troast, Chris Weis, and Paul Peronard. We are indebted to John Addison, John Dement, Agnes Kane, Bruce Case, Michel Camus, and Vanessa Vu for their input and assistance. We thank Phillip Benge for his technical assistance. Finally, we gratefully acknowledge the financial support for the earlier work provided by the U.S. EPA and the grant provided by the National Stone Sand and Gravel Association to prepare this manuscript. This project was supported in part by an appointment to the Research Participation Program at the National Center for Environmental Assessment, U.S. EPA, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. EPA. The financial support provided by any group should not be construed as endorsement of the results of this work.

Notes

1. As used here, the term “fiber” is intended to include not only single-crystal fibers (fibrils), but the bundles, clusters, and matrices that make up the full set of fibrous particles in an asbestos dust (ISO, 1995).

2. Distinguishing among the differing crystalline habits of true asbestos fibers (i.e., the asbestiform habit) and nonasbestos particles (i.e., cleavage fragments) potentially affects potency along with size and mineralogical type. However, as data for distinguishing among the crystalline habits of fibers in different environments are lacking, this consideration is not further addressed.

3. The distribution derived from Dement et al. (Citation2007) for the South Carolina plant also represents the unweighted average of distributions observed among the multiple plant processes analyzed in this study (Dement et al., Citation2007, ).

4. As a very approximate and informal marker, an improvement in a likelihood of 1.9 units is barely significant (p = .05) when adding a single estimated parameter to a model.

5. To illustrate how an exposure metric can be used in conjunction with exposure estimates from environments of interest to quantify human risk, see Chapter 8 and Appendix E of Berman and Crump (Citation2003).

6. Even whether various analyses are intended to include or exclude nonasbestiform particles is controversial. Documents in which PCM or PCME is proposed for estimating asbestos concentrations appear to vary in this regard. For example, NIOSH Method 7402, when first issued (NIOSH, Citation1986), explicitly listed “nonasbestiform amphiboles” as interferences. Later revisions of this method (NIOSH Citation1989, 1994) still list “massive amphiboles” as potential interferences, but provide varying CAS numbers to define what is supposed to be determined. More recently, the stated position of NIOSH is that (since 1990) the NIOSH definition of asbestos includes the nonasbestiform amphibole analogs (Middendorf et al., Citation2007). In contrast. nonasbestiform amphiboles were excluded from regulation under the OSHA final asbestos rule (OSHA, Citation1992). Yet compliance with the OSHA rule is typically based on use of NIOSH Methods.

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