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Asbestos Abatement >> Asbestos Cancer
Abstract The most recent update of the U.S. Environmental Protection Agency (EPA) health assessment document for asbestos (Nicholson, 1986, referred to as Asbestos Cancer "the EPA 1986 update") is now 20 years old.
That document contains estimates of "potency factors" for asbestos in causing lung cancer (K(L)'s) and Asbestos Cancer mesothelioma (K(M)'s) derived by fitting mathematical models to data from studies of occupational cohorts. The present paper provides a parallel analysis that incorporates data from studies published since the EPA 1986 update.
The EPA lung cancer model assumes that the relative risk varies linearly with cumulative exposure lagged 10 years. This implies that the relative Asbestos Cancer risk remains constant after 10 years from last exposure.
The EPA mesothelioma model predicts that the mortality rate from mesothelioma increases linearly with the intensity of exposure and, for a given intensity, increases indefinitely after exposure ceases, Asbestos Cancer approximately as the square of time since first exposure lagged 10 years.
These assumptions were evaluated using raw data from cohorts where exposures were principally to chrysotile; mesothelioma only data from Quebec miners and millers, and crocidolite (Wittenoom Gorge, Australia miners and millers, and Asbestos Cancer using published data from a cohort exposed to amosite (Paterson, NJ, insulation manufacturers, Seidman et al., 1986).
Although the linear EPA model generally provided a good description of exposure response for lung cancer, Asbestos Cancer in some cases it did so only by estimating a large background risk relative to the comparison population. Some of these relative risks seem too large to be due to differences in smoking rates and are probably due at least in part to errors in exposure estimates.
There was some equivocal evidence that the relative risk decreased with increasing time since last exposure in the Wittenoom cohort, Asbestos Cancer but none either in the South Carolina cohort up to 50 years from last exposure or in the New Jersey cohort up to 35 years from last exposure.
The mesothelioma model provided good descriptions of the observed patterns of mortality after exposure ends, Asbestos Cancer with no evidence that risk increases with long times since last exposure at rates that vary from that predicted by the model (i.e., with the square of time).
In particular, the model adequately described the mortality rate in Quebec chrysotile miners and Asbestos Cancer millers up through >50 years from last exposure. There was statistically significant evidence in both the Wittenoom and Quebec cohorts that the exposure intensity-response is supralinear(1) rather than linear.
The best-fitting models predicted that the mortality rate varies as [intensity](0.47) for Wittenoom and Asbestos Cancer as [intensity](0.19) for Quebec and, in both cases, the exponent was significantly less than 1 (p< .0001).
Using the EPA models, K(L)'s and K(M)'s were estimated from the three sets of raw data and also from published data covering a broader range of environments than those originally addressed in the EPA 1986 update. Uncertainty in these estimates was quantified using "uncertainty bounds" that reflect both statistical and Asbestos Cancer nonstatistical uncertainties.
Lung cancer potency factors (K(L)'s) were developed from 20 studies from 18 locations, Asbestos Cancer compared to 13 locations covered in the EPA 1986 update. Mesothelioma potency factors (K(M)'s) were developed for 12 locations compared to four locations in the EPA 1986 update.
Although the 4 locations used to calculate K(M) in the EPA 1986 update include one location with exposures to amosite and three with exposures to mixed fiber types, the 14 K(M)'s derived in the present analysis also include 6 locations in which exposures were predominantly to chrysotile and Asbestos Cancer 1 where exposures were only to crocidolite.
The K(M)'s showed evidence of a trend, with lowest K(M)'s obtained from cohorts exposed predominantly to chrysotile and highest K(M)'s from cohorts exposed only to amphibole asbestos , Asbestos Cancer with K(M)'s from cohorts exposed to mixed fiber types being intermediate between the K(M)'s obtained from chrysotile and amphibole environments.
Despite the considerable uncertainty in the K(M) estimates, the K(M) from the Quebec mines and mills was clearly smaller than those from several cohorts exposed to amphibole asbestos or a mixture of amphibole asbestos and Asbestos Cancer chrysotile.
For lung cancer, although there is some evidence of larger K(L)'s from amphibole asbestos exposure, there is a good deal of dispersion in the data, and Asbestos Cancer one of the largest K(L)'s is from the South Carolina textile mill where exposures were almost exclusively to chrysotile.
This K(L) is clearly inconsistent with the K(L) obtained from the cohort of Quebec chrysotile miners and millers. The K(L)'s and K(M)'s derived herein are defined in terms of concentrations of airborne fibers measured by phase-contrast microscopy (PCM), which only counts all structures longer than 5 microm, Asbestos Cancer thicker than about 0.25 microm, and with an aspect ratio > or =3:1.
Moreover, PCM does not distinguish between asbestos and nonasbestos particles. One possible reason for the discrepancies between the K(L)'s and Asbestos Cancer K(M)'s from different studies is that the category of structures included in PCM counts does not correspond closely to biological activity.
In the accompanying article (Berman and Crump, 2008) the K(L)'s and Asbestos Cancer K(M)'s and related uncertainty bounds obtained in this article are paired with fiber size distributions from the literature obtained using transmission electron microscopy (TEM).
The resulting database is used to define K(L)'s and K(M)'s that depend on both the size (e.g., length and width) and Asbestos Cancer mineralogical type (e.g., chrysotile or crocidolite) of an asbestos structure.
An analysis is conducted to determine how well different K(L) and Asbestos Cancer K(M) definitions are able to reconcile the discrepancies observed herein among values obtained from different environments.
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