EPA 1505
Proposed asbestos cancer risk assessment
methodology
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Proposed Asbestos Cancer Risk Assessment...logy, Risk Assessment, Superfund, US\Ej?!&wyg://95/http://www.epa.gov/superfund/programs/risk/asbestos/method.hl
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EPA, Home > Superfund > Health & Safety > Risk Assessment > Proposed Asbestos Cancer Risk
Assessment Methodology
roposed Asbestos Cancer Risk
Assessment Methodology
^;PA issues a draft report to provide a firm basis for completing a
vtate-of-the-art-protocol to assess potential human-health risks associated with
-xposure to asbestos. Such a protocol is intended specifically for use in
„ erforming risk assessments at Superfund sites, although it may be applicable to a
,road range of situations. Please see below for links to the Federal Register
;|otice of the Peer Consultation Workshop On a Proposed Asbestos Cancer Risk
Assessment and the Proposed Asbestos Cancer Risk Assessment Methodology.
Federal Register Notice (48 KB, 3 pages)
Proposed Asbestos Cancer Risk Assessment Methodology
KB, 5 pages)
ntrQyction.-yersiQ,ril (34 KB, 2 pages)
IntrQduction.- version. 2 (34 KB, 2 pages)
Overview (42 KB, 3 pages)
'Backgrou.nd. (1 15 KB, 21 pages)
The .Asbestos.. Ljteratu re. (83 KB, 1 1 pages)
Epide.mj.Qiogy_StydJes (440 KB, 90 pages)
S U p po rti n g Experimental ... Stud I eg (739 KB, 1 57 pages)
Discussion , Conclusions, and RecommendatiQng (1 06 KB, 1 6 pages)
References (252 KB, 29 pages)
Appendix_A (305 KB, 38 pages)
Tables 1,2 (2,3 M, 2 pages)
Iables.3t...4,..5 (1 .6 M, 3 pages)
lablese, 7. 8 (2.2 M, 3 pages)
Tables 9, 10. 11 (1 .7 M, 3 pages)
Tables 12. 13. 14 (1 .9 M, 3 pages)
Tables 15, 16. 17 (1 .9 M, 3 pages) .
Tables 18, 19. 20 (2.2 M, 3 pages)
Tables 21 , 22 (2 M, 2 pages)
Tables 23. 24, 25 (2.2 M, 3 pages)
Appendix B (63 KB, 10 pages)
Appendix C (353 KB, 27 pages)
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Notice of Public Meeting, Risk Assessment, Superfund, US EPA
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EPA Home > Superfund > Health & Safety:> Risk Assessment > Notice of Public Meeting
Notice of Public Meeting
ENVIRONMENTAL PROTECTION AGENCY
Peer Consultation Workshop On a Proposed Asbestos
Cancer Risk Assessment
About!
• Federal Register Notice - PDF version (48 KB, 3 pages) PDF/ «
• Proposed Asbestos Cancer Risk Assessment Methodology
[FRN-7445-7]
AGENCY: Environmental Protection Agency (EPA).
ACTION: Notice of Public Meetings
SUMMARY: This notice announces a peer consultation workshop on a proposed
asbestos cancer risk assessment methodology. The purpose of the workshop is to
discuss the scientific merit of the proposed methodology developed for EPA by Dr.
Wayne Berman and Dr. Kenny Crump. The proposed methodology distinguishes
carcinogenic potency by asbestos fiber size and asbestos fiber type and
advocates use of a new exposure index to characterize carcinogenic risk. Expert
panelists will discuss many relevant technical issues at the workshop, and
observers also will be invited to comment. A contractor will prepare a summary
report documenting the discussions of the peer consultation workshop, and this
report will be publicly available and become part of EPA's administrative record for
IRIS. This meeting is being sponsored by EPA's Office of Solid Waste and
Emergency Response and by EPA's Office of Research and Development.
DATES: The workshop will be held on February 25-27, 2003. The workshop
hours will be from 9:00 AM to 5:30 PM on Tuesday, February 25; from 8:30 AM to
5:00 PM on Wednesday, February 26; and from 8:00 AM to 12:00 noon on
Thursday, February 27. Observer comment periods are currently scheduled on
Tuesday and Wednesday.
ADDRESSES: The peer consultation workshop will be held at the Westin St.
Francis Hotel, 335 Powell Street, San Francisco, California. To attend the
workshop as an observer, contact Eastern Research Group (ERG) either in
writing, by electronic mail, or by telephone. ERG's contact information for this
workshop is: Eastern Research Group, Conference Registration, 110 Hartwell
Avenue, Lexington, MA 02421-3136; phone, 781-674-7374; fax: 781-674-2906;
meetings@erg.com.
There is no charge for attending this workshop as an observer, but observers are
encouraged to register early as the number of seats will be limited. Each registrant
will receive a confirmation notice, a preliminary agenda, and a logistical fact sheet
that contains directions to the meeting location. Copies of the proposed asbestos
cancer risk assessment methodology can be obtained prior to the meeting from
the EPA, OERR web page.
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Notice of Public Meeting, Risk Assessment, Superfund, US EPA
wysiwyg://7/http://www.epa.gov/superfund/programs/risk/asbestos/
SUPPLEMENTARY INFORMATION: EPA's current assessment of asbestos
toxicity is based primarily on an asbestos assessment completed in 1986, and
EPA's assessment has not changed substantially since that time. The 1986
assessment considers all mineral forms of asbestos and all asbestos fiber sizes
(i.e., all fibers longer than 5 micrometers) to be of equal carcinogenic potency.
However, since 1986, there have been substantial improvements in asbestos
. measurement techniques and in the understanding of how asbestos exposure
contributes to disease. To incorporate the knowledge gained over the last 17
years into the agency's toxicity assessment for asbestos, EPA oversaw the
development of a revised methodology for conducting risk assessments of
asbestos. The proposed risk assessment methodology distinguishes between
fiber sizes and fiber types in estimating potential health risks related to asbestos
exposure. EPA is convening this peer consultation workshop to seek input from a
panel of experts on the scientific merit of the proposed methodology. The experts
will include scientists with extensive expertise in relevant fields, such as
biostatistics, fiber identification, inhalation toxicology, and carcinogenic
mechanisms. The panelists will be asked to respond to several charge questions
that address key issues in the proposed methodology, including interpretations of
epidemiology and toxicology literature, the proposed exposure index, and general
topics. The product of the peer consultation workshop will be a report that
summarizes the panelists' and observers' comments, conclusions, and
recommendations on the proposed methodology.
FOR FURTHER INFORMATION CONTACT: For general information, contact the
RCRA/CERCLA Call Center at 800-424-9346 or TDD 800-553-7672 (hearing
impaired). In the Washington, D.C. metropolitan area, call 703-412-9810 or TDD
703-412-3323. For more detailed technical information on this conference call
Richard Troast (703-603-901-9) Office of Emergency and Remedial Response,
U.S. Environmental Protection Agency, 1200 Pennsylvania Avenue, NW,
Washington, D.C. 20460-0002, Mail Code 5204G.
David Lopez
Director, Region 3/8 Support Center, OERR
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1.0 EXECUTIVE SUMMARY
The purpose of this report is to provide a firm basis for completing a state-of-the-art-
protocol to assess potential human-health risks associated with exposure to asbestos.
Such a protocol is intended specifically for use in performing risk assessments at
Superfund sites, although it may be applicable to a broad range of situations.
The approach currently employed at the U.S. Environmental Protection Agency (U.S.
EPA) to evaluate asbestos-related risks (IRIS 1988) is based primarily on a document
completed in 1986 (U.S. EPA 1986) and has not been changed substantially in the past
15 years, despite substantial improvements in asbestos measurement techniques and
in the understanding of the manner in which asbestos exposure contributes to disease.
Therefore, this document provides an overview and evaluation of the more recent
studies and presents proposed modifications to the protocol for assessing asbestos-
related risks that can be justified based on the more recent work.
The studies relevant to developing a protocol are reviewed in this document and
combined with supporting analysis to resolve issues and identify the best candidate
procedures for assessing asbestos-related risks. Although the objective of this
evaluation was to identify the single best procedures, when current knowledge is
inadequate for distinguishing among alternatives, options are presented along with a
discussion of their relative advantages and limitations. In a few cases, limited and
focused additional research studies are recommended, which may enhance the current
state of knowledge sufficiently to resolve one or more of the important, remaining
issues.
Inhalation of asbestos dusts has been linked to several adverse health effects including
primarily asbestosis, lung cancer, and mesothelioma (U.S. EPA 1986). Asbestosis, a
chronic, degenerative lung disease, has been documented among asbestos workers
from a wide variety of industries. Although asbestosis cases have been observed at
some locations of current interest to the U.S. EPA, the disease is generally expected to
be associated only with the higher levels of exposure commonly found in workplace
settings and is not expected to contribute substantially to potential risks associated with
environmental asbestos exposure. Therefore, asbestosis is only considered in this
document to the extent required to address it's putative association with lung cancer.
Overall, the majority of evidence indicates that lung cancer and mesothelioma are the
most important sources of risk associated with exposure to low levels of asbestos.
A variety of human, animal, and tissue studies have provided insight into the nature of
the relationship between asbestos exposure and disease. Ideally, human epidemiology
studies are employed to determine the quantitative dose/response relationships and the
attendant risk coefficients for asbestos exposure. Risk coefficients have been
estimated for asbestos from approximately 20 epidemiology studies for which adequate
1.1 Revision 1 - 9/3/01
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dose-response data exist. Such factors vary widely, however, and the observed
variation has not been reconciled. Among the objectives addressed in this study is to
evaluate and account for the sources of uncertainty that contribute to the variation
among the risk coefficients derived from the literature so that these estimates can be
reasonably interpreted and recommendations for their use in risk assessment
developed.
Animal and tissue studies indicate that asbestos potency is a complex function of
several characteristics of asbestos dusts including fiber size and fibertype (i.e., fiber
mineralogy). Moreover, the influence of fiber size is a complex function of both
diameter and length as critical parameters (among others). Therefore, whenever the
goal is to compare across samples with differing characteristics, it is not sufficient to
report asbestos concentrations simply as a function of mass (or any other single
parameter) and this stands in stark contrast to the treatment of chemical toxins. It has
generally been difficult to distinguish among the effects of fiber size and type in many
studies because such effects are confounded and the materials studied have not been
adequately characterized. However, several adequate studies do exist and these have
been highlighted.
The influence of such effects cannot be adequately evaluated in the existing
epidemiological studies because the analytical techniques used to monitor asbestos
exposure in these studies are not capable of resolving all of the characteristics of
asbestos dusts that other studies indicate are important. Moreover, the exposure
indices (the range of structure sizes and shapes included in an analysis) that are
employed in the existing epidemiology studies may not correspond predsely with the
characteristics of asbestos that best relate to biological activity. This hinders the ability
to compare the risk (dose-response) coefficients derived from the different studies. It
also limits the confidence with which risk coefficients derived from the existing
epidemiology studies can be applied to assess risks from asbestos exposure in other
environments. Such limitations are explored in this study, along with potential
remedies. At the same time, the existing epidemiology studies provide the most
appropriate data from which to determine the relationship between asbestos dose and
response in humans.
Briefly, the major kinds of limitations that potentially contribute to uncertainty in the
available epidemiology studies (and the effect such limitations likely produce on trends
in potency estimates) include;
• limitations in the manner that exposure concentrations were estimated
(likely increases random variation across studies);
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« limitations in the manner that the character of exposure (i.e. the
mineralogical types of fibers and the range and distribution of fiber
dimensions) was delineated (likely increases systematic variation between
industry types and, potentially, between fiber types);
• limitations in the accuracy of mortality determinations or incompleteness
in the extent of tracing of cohort members (likely increases random
variation across studies);
• limitations in the adequacy of the match between cohort subjects and the
selected control population (likely increases random variation across
studies and may have a substantial effect on particular studies); and
• inadequate characterization of confounding factors, such as smoking
histories for individual workers (likely increases random variation across
studies and may have a substantial effect on particular studies).
Importantly, this new analysis of the epidemiology database differs from the evaluation
conducted in the 1986 Health Effects Assessment Update (U.S. EPA 1986). Not only
does it incorporate studies containing the latest available followup for the exposure
settings previously evaluated and several additional studies addressing new exposure
settings, but the manner in which the analysis was conducted incorporates important,
new features including estimation of more realistic confidence bounds for the dose-
response factors derived from each study,
Confidence bounds were adjusted to account for uncertainty contributed by the manner
that exposure was estimated, by the manner that work histories were assigned, and by
limitations in the degree of followup, in addition to the traditional practice of accounting
for the statistical uncertainty associated with the observed incidence of disease
mortality. Thus, most of the major contributors to the overall uncertainty of each study
are now addressed, at least in qualitative fashion. By better accounting for overall
uncertainty, we were better abie to distinguish what can and cannot be reasonably
concluded from the existing studies.
Separately, we also completed an evaluation to consider questions concerning the
appropriateness of the range, of fiber sizes characterized in the published epidemiology
studies and whether adjusted exposure indices might improve the quality of asbestos
risk assessments. This was motivated by conclusions from our review of the
supplemental literature, which indicate that the methods used to assess exposure in the
existing epidemiology studies do not adequately reflect the characteristics of asbestos
that determine biological activity.
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Results from our evaluation of the available epidemiology studies indicate that:
(1) the results of individual epidemiology studies are uncertain, especially when one
considers all of the major potential sources of uncertainty (rather than simply
considering the statistical uncertainty associated with the number of deaths, as is
traditionally done). However, more robust conclusions can be drawn from an C
analysis of the set of epidemiology studies taken as a whole than results derived
from individual studies;
(2) by adjusting for fiber size and fiber type, the existing database of studies can be
reconciled adequately to reasonably support risk assessment; ,-
(3) the procedures recommended as a result of our analysis offer substantial
improvement over EPA's current approach to evaluating asbestos-related risks;
(4) while there is some indication that the existing EPA models for lung cancer and,
potentially, mesothelioma may not entirely reflect the time-dependence of f
disease at long times following cessation of exposure, such effects appear to be
modest so that they are unlikely to adversely affect the proposed approach.
Prudence dictates, however, that limited additional study may be warranted to
adequately dismiss related concerns; and
t:
(5) results from our review of the supplemental literature provide additional support
for the approach recommended in this document and indicate additional, specific
modifications that (if supported by limited additional study) could result in
substantial improvement even over the approach currently recommended, which
(in turn) provides substantial improvement over the current approach.
Based on our analysis, we recommend the following.
(1) Asbestos concentrations should be analyzed for structures that correspond to a
new (interim) exposure index that is defined in this document. As previously
indicated, an exposure index is a description of the sizes and shapes of fibers (
(and the relative weights to be assigned to each category of size and shape) that
need to be counted in an analysis to determine asbestos concentrations.
(2) Because amphiboles, fiber-for-fiber, were found to be substantially more potent
than chrysotile, the individual contributions from chrysotile and the combined
amphiboles to any particular exposure need to be separately delineated and,
because amphibole exposures are the primary drivers, the analytical sensitivity
required for each particular study should be set based on amphiboles.
1.4 Revision 1-9/3/01
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A set of new dose-response coefficients for chrysotile and the amphiboles are also
recommended in this document, which indicate the relative potency of each toward the
induction of lung cancer and mesothelioma, respectively.
Three options are presented in this document for incorporating the new exposure index,
the new dose-response coefficients, and the other recommendations in this document
into a general protocol for assessing asbestos-related risks:
• the first option involves performing lifetable analyses using the dose-
response models that EPA currently recommends for lung cancer and
mesothelioma. When sufficient data are available from a particular
project to support this type of analysis, it should provide the best site-
specific estimates of asbestos-related risks;
• the second option involves use of a risk table, which can be adapted to
estimate risk for most cases of interest, even ones for which exposure
estimates are crude and descriptions of the circumstances of exposure
are sparse; and
• the third option involves development of a new unit risk factor for asbestos
(based on the latest data presented in this document), which would
replace the unit risk, factor in current use.
A couple of focused, additional studies are also recommended in this document, due to
a small number of important knowledge gaps that remain to be resolved, that can be
resolved cost-effectively, and that, if resolved, can substantially increase both the
overall confidence in the proposed approach and, potentially, improve the approach as
well. Importantly, even without conducting the new studies, the approach currently
recommended was shown in this document to provide substantial improvement in the
ability to assess asbestos-related risks (in terms of reducing error) over the approach in
current use. The objectives of the proposed studies are:
(1) to expand the test of the ability of the current EPA models to adequately track
the time-dependence of disease; and
(2) to develop the supporting data needed to define adjustments for potency factors
that will allow them to be used with an exposure index that even more closely
captures the criteria that determine biological activity than the "interim" index
recommended in this document.
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2.0 INTRODUCTION
The purpose of this report is to provide a firm basis for completing a state-of-the-art-
protocol to assess potential human-health risks associated with exposure to asbestos.
It is designed specifically for use in performing risk assessments at Superfund sites,
although it may be applicable to a broad range of situatbns.
The approach currently employed at the U.S. Environmental Protection Agency (U.S.
EPA) to evaluate asbestos-related risks (REF IRIS) is based primarily on a document '
completed in 1986 (U.S. EPA 1986) and has not been changed substantially in the past
15 years, despite substantial improvements in asbestos measurement techniques and
in the understanding of the manner in which asbestos exposure contributes to disease.
Therefore, this document provides an overview and evaluation of the more recent
studies and presents proposed modifications to the protocol for assessing asbestos-
related risks that can be justified based on the more recent work.
In May, 2001, the U.S. EPA along with the California Environmental Protection Agency
(Cal EPA), the National Institute for Occupational Safety and Health (NIOSH), the
American Toxic Substances Disease Registry (ATSDR), and the Mine Safety and
Health Administration (MSHA) hosted an international conference on asbestos in
Oakland. The current state of knowledge (including, particularly, the status of
knowledge gaps) was reviewed during this conference by many of the leading
researchers, Consequently, several issues were raised concerning the state of
knowledge of asbestos. To the extent that such issues are relevant to the assessment
of asbestos-related risks, they are considered in this document.
Among the asbestos-related issues that have been the focus of recent attention;
» whether the dose-response models currently in use by the EPA for
describing the incidence of asbestos-related diseases adequately reflect
the time-dependence for the developme nt of these diseases;
» whether the relative in-vivo durability, of different asbestos mineral types
affect their relative potency;
» whether the set of minerals included in the current definition of asbestos
adequately covers the range of minerals that potentially contribute to
asbestos-related diseases;
2.1 Revision 0- 7/31/01
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• whether the analytical techniques and methods currently used for
determining asbestos concentrations adequately capture the biologically
relevant characteristics of asbestos (particularly with regard to the sizes of
the structures included in the various analyses) so that they can be used
to support risk assessment; and
• whether reasonable confidence can be placed in the cross-study
extrapolation of dose-response relationships that are required to assess
asbestos-related risks in new environments of interest.
These and other asbestos-related issues are described in greater detail in Chapter 3
(Overview) and Chapter 4 (Background).
The studies relevant to developing a protocol are reviewed in this document and
combined with supporting analysis to resolve issues and identify the best candidate
procedures for assessing asbestos-related risks. Although the objective of this
evaluation was to identify the single best procedures, when current knowledge is
inadequate for distinguishing among alternatives, options are presented along with a
discussion of their relative advantages and limitations. In a few cases, limited and
focused additional research studies are recommended, which may enhance the current
state of knowledge sufficiently to resolve one or more of the important, remaining
issues.
This document is the last in a series of documents developed as part of a multi-task
project to develop a set of mutually consistent methods for determining asbestos
concentrations in a manner useful for assessing risk and a companion protocol for
conducting such risk assessments. A method for the determination of asbestos in air
(Chatfield and Berman 1990) and a companion technical background document
(Chatfield and Berman 1990) were published by the U.S. EPA in 1990. The air method
has since been superceded (improved) by the ISO Method (ISO 10312). A method for
the determination of asbestos in soils and bulk materials (Berman and Kolk 1997) was
also published by the U.S. EPA with the draft of an improved version also recently
completed (Berman and Kolk 2000). The recommendations in this document should
serve as the basis for development of the companion risk-assessment protocol.
2.2 Revision 0- 7/31/01
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2.0 INTRODUCTION
The purpose of this report is to provide a firm basis for completing a state-of-the-art-
protocol to assess potential human-health risks associated with exposure to asbestos.
Such a protocol is intended specifically for use in performing risk assessments at
Superfund sites, although it may be applicable to a broad range of situations.
The approach currently employed at the U.S. Environmental Protection Agency (U.S.
EPA) to evaluate asbestos-related risks (IRIS 1988) is based primarily on a document
completed in 1986 (U.S. EPA 1986) and has not been changed substantially in the past
15 years, despite substantial improvements in asbestos measurement techniques and
in the understanding of the manner in which asbestos exposure contributes to disease.
Therefore, this document provides an overview and evaluation of the more recent
studies and presents proposed modifications to the protocol for assessing asbestos-
related risks that can be justified based on the more recent work.
In May, 2001, the U.S. EPA along with the California Environmental Protection Agency
(Cal EPA), the National Institute for Occupational Safety and Health (NIOSH), the
American Toxic Substances Disease Registry (ATSDR), and the Mine Safety and
Health Administration (MSHA) hosted an international conference on asbestos in
Oakland. The current state of knowledge (including, particularly, the status of
knowledge gaps) was reviewed during this conference by many of the leading
researchers. Consequently, several issues were raised concerning the state of
knowledge of asbestos. To the extent that such issues are relevant to the assessment
of asbestos-related risks, they are considered in this document.
Among the asbestos-related issues that have been the focus of recent attention:
• whether the dose-response models currently in use by the EPA for
describing the incidence of asbestos-related diseases adequately reflect
the time-dependence for the development of these diseases;
• whether the relative in-vivo durability of different asbestos mineral types
affect their relative potency; .
• whether the set of minerals included in the current definition of asbestos
adequately covers the range of minerals that potentially contribute to
asbestos-related diseases;
2.1 Revision 1 - 8/27/01
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• whether the analytical techniques and methods currently used for
determining asbestos concentrations adequately capture the biologically
relevant characteristics of asbestos (particularly with regard to the sizes of
the structures included in the various analyses) so that they can be used
to support risk assessment; and
• whether reasonable confidence can be placed in the cross-study
extrapolation of dose-response relationships that are required to assess
asbestos-related risks in new environments of interest.
These and other asbestos-related issues are described in greater detail in Chapter 3
(Overview) and Chapter 4 (Background).
The studies relevant to developing a protocol are reviewed in this document and
combined with supporting analysis to resolve issues and identify the best candidate
procedures for assessing asbestos-related risks. Although the objective of this
evaluation was to identify the single best procedures, when current knowledge is
inadequate for distinguishing among alternatives, options are presented along with a
discussion of their relative advantages and limitations. In a few cases, limited and
focused additional research studies are recommended, which may enhance the current
state of knowledge sufficiently to resolve one or more of the important, remaining
issues.
This document is an update to a previous review (Berman and Crump 1999) and
primarily incorporates information developed subsequent to the earlier review. It is also
the last in a series of documents developed as part of a multi-task project to develop a
set of mutually consistent methods for determining asbestos concentrations in a
manner useful for assessing risk and'a companion protocol for conducting such risk
assessments. A method for the determination of asbestos in air (Chatfield and Berman
1990) and a companion technical background document (Berman and Chatfield 1990)
were published by the U.S. EPA in 1990. The air method has since been superceded
(improved) by the ISO Method (ISO 10312). A method for the determination of
asbestos in soils and bulk materials (Berman and Kolk 1997) was also published by the
U.S. EPA and the draft of an improved version was also recently completed (Berman
and Kolk 2000). The recommendations in this document should serve as the basis for
development of the companion risk-assessment protocol.
C
2.2 Revision 1 - 8/27/01
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3.0 OVERVIEW
Inhalation of asbestos dusts has been linked to several adverse health effects including
primarily asbestosis, lung cancer, and mesothelioma (U.S. EPA 1986). Asbestosis, a
chronic, degenerative lung disease, has been documented among asbestos-workers
from a wide variety of industries. Although asbestosis cases have been observed at
some locations of current interest to the U.S. EPA, the disease is generally expected to
be associated only with the higher levels of exposure commonly found in workplace
settings and is not expected to contribute substantially to potential risks associated with
environmental asbestos exposure. Therefore, asbestosis is only considered in this
document to the extent required to address it's putative association with lung cancer.
Overall, the majority of evidence indicates that lung cancer and mesothelioma are the
most important sources of risk associated with exposure to low levels of asbestos.
Note that the primary route of exposure of concern in association with asbestos
is inhalation. There is little evidence that ingestion of asbestos induces disease
(see, for example, U.S. EPA 1986, IRIS 1988). Therefore, this study is focused
on inhalation hazards and other routes of exposure are not addressed.
Gastrointestinal cancers and cancers of other organs (e.g. larynx, kidney, and ovaries)
have also been linked with asbestos exposures (by inhalation) in some studies.
However, such assodations are not as compelling as those for the primary health
effects listed above and the potential risks from asbestos exposures associated with
these other cancers are much lower (U.S. EPA 1986). Consequently, this document is
focused on risks associated with the induction of lung cancer and mesothelioma.
A variety of human, animal, and tissue studies have provided insight into the nature of
the relationship between asbestos exposure and disease. Ideally, human epidemiology
studies are employed to determine the quantitative dose/response relationships and the
attendant risk coefficients for asbestos exposure. Risk coefficients have been
estimated for asbestos from approximately 20 epidemiology studies for which adequate
dose-response data exist. Such factors vary widely, however, and the observed
variation has not been reconciled. Among the objectives addressed in this study is to
evaluate and account for the sources of uncertainty that contribute to the variation
among the risk coefficients derived from the literature so that these estimates can be
reasonably interpreted and recommendations for their use in risk assessment
developed.
Animal and tissue studies indicate that asbestos potency is a complex function of
several characteristics of asbestos dusts including fiber size and fibertype (i.e., fiber
mineralogy). Moreover, the influence of fiber size is a complex function of both
diameter and length as critical parameters (among others). Therefore, whenever the
goal is to compare across samples with differing characteristics, it is not sufficient to
3.1 Revision 1 - 8/28/01
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PRELIMINARY WORKING DRAFT -DO NOT COPY OR QUOTE
report asbestos concentrations simply as a function of mass (or any other single
parameter) and this stands in stark contrast to the treatment of chemical toxins. It has
generally been difficult to distinguish among the effects of fiber size and type in many
studies because such effects are confounded and the materials studied have not been
adequately characterized. However, several adequate studies do exist and these have
been highlighted.
The influence of such effects cannot be adequately evaluated in the existing
epidemiological studies because the analytical techniques used to monitor asbestos
exposure in these studies are not capable of resolving all of the characteristics of
asbestos dusts that other studies indicate are important. Moreover, the exposure
indices (the range of structure sizes and shapes included in an analysis) that are
employed in the existing epidemiology studies may not correspond predsely with the
characteristics of asbestos that best relate to biological activity. This hinders the ability
to compare the risk (dose-response) coefficients derived from the different studies, It
also limits the confidence with which risk coefficients derived from the existing
epidemiology studies can be applied to assess risks from asbestos exposure in other
environments. Such limitations are explored in this study, along with potential
remedies.
Based on the approach developed for evaluating asbestos-related cancer risk by the
U.S. EPA (1986), risk is estimated as the product of a risk coefficient and a
mathematical function that depends on the level of exposure, the duration of exposure,
and time. The risk coefficient for lung cancer is generally denoted, "KL" and the one for
mesothelioma is "KM."
A detailed description of both the lung cancer and mesothelioma models is provided in
Chapter 6. The models differ depending on whether lung cancer or mesothelioma is
being considered.
For lung cancer, the model estimates relative risk, which means that the increase in
lung cancer incidence that is attributable to asbestos exposure is proportional to the
background lung cancer incidence in the exposed population. The background cancer
incidence is the rate of lung cancer that would be expected to occur in the population in
the absence of asbestos exposure. In other words, background lung cancer incidence
is the lung cancer rate for the exposed population that is attributable to all causes other
than asbestos.
The model for mesothelioma is an absolute risk model. This means that the increase in
mesothelioma attributable to asbestos is independent of the background rate of
mesothelioma, which is negligible in the general population.
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Ideally, the risk coefficients derived from the existing epidemiology studies could be
combined with measurements from other exposure settings (using the corresponding
models) to estimate lung cancer and mesothelioma risks in these other exposure
settings. However, such risk estimates are only valid if both of the following conditions
are met:
(1) asbestos is measured in the exposure setting of interest in the identical
manner in which it was measured in the study from which the
corresponding risk coefficients are derived; and
(2) such measurements reflect the characteristics of asbestos exposures that
determine risk.
A growing body of evidence indicates that the way in which asbestos concentrations
were measured in the existing epidemiology studies do not reflect the characteristics of
asbestos exposure that determine risk. Therefore, measuring asbestos concentrations
in the same way in exposure settings of interest may not be sufficient to assure validity
of risk estimates derived using the published risk coefficients (and the corresponding
models). This is because the second of the above-listed conditions would not be
satisfied.
Considerations necessary to compare risk coefficients derived in different exposure
settings (or to apply a coefficient to predict risk in a setting different from the one in
which the coefficient was derived) have been elucidated clearly in a mathematical
model (Chesson et al 1989). The consequences of the model indicate that adjusting
the existing risk coefficients so that they reflect asbestos characteristics that determine
biological activity requires knowledge of the fiber size distributions of the dusts studied
in the original epidemiology studies. To the extent they exist, such data may be used to
normalize each of the published risk coefficients so that they relate to a common
exposure index reflecting asbestos characteristics that determine biological activity.
Among the goals of this evaluation is to assess the relative importance of such
limitations and to attempt to construct "fixes" as appropriate (within the confines of the
current state of knowledge). Consequently, we explored the possibility of defining an
improved exposure index (that better reflects biological activity) and of applying such an
improved index to the epidemiology data. We also evaluated improved ways of
simultaneously accounting for the effects of both fiber size and type.
Unfortunately, some of the issues that need to be resolved to support development of a
protocol for assessing asbestos-related risks cannot be entirely resolved with existing
data. We have attempted to identify such issues, to assess their relative importance,
and, when deemed appropriate, to propose limited and focused research projects
designed to provide the data required to reduce the impacts of such knowledge gaps.
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4.0 BACKGROUND
Asbestos is a term used to describe the fibrous habit of a family of hydrated metal
silicate minerals. The most widely accepted definition of asbestos includes the fibrous
habits of six of these minerals (IARC 1977). The most common type of asbestos is
chrysotile, which is the fibrous habit of the mineral serpentine. The other five asbestos
minerals are all amphiboles (i.e. all partially hydrolyzed, magnesium silicates). These
are: fibrous reibeckite (crocidolite), fibrous grunerite (amosite), anthophyllite asbestos,
tremolite asbestos, and actinolite asbestos.
All six of the minerals whose fibrous habits are termed asbestos occur most commonly
in non-fibrous, massive habits. While unique names have been assigned to the
asbestiform varieties of three of the six minerals (noted parenthetically above) to
distinguish them from their massive forms, such nomenclature has not been developed
for anthophyllite, tremolite, or actinolite. Therefore, when discussing these latter three
minerals, it is important to specify whether a massive habit of the mineral or the fibrous
(asbestiform) habit is intended.
Although other minerals may also occur in a fibrous habit, they are not generally
included in the definition of asbestos either because they do not exhibit properties
typically ascribed to asbestos (e.g. high tensile strength, the ability to be woven, heat
stability, and resistence to attack by acid or alkali) or because they do not occur in
sufficient quantities to be exploited commercially.
The first four of the six asbestos minerals listed above have been exploited
commercially (IARC 1977). Of these, chrysotile alone accounts for more than 90% of
the asbestos found in commercial products.
Importantly, it is neither clear whether the term asbestos maps reasonably onto the
range of fibrous minerals that can contribute to asbestos-like health effects nor whether.
individual structures of the requisite mineralogy must formally be asbestiform to
contribute to such health effects.
Regarding whether the term asbestos is a useful discriminator for health effects, it is
well established that en'onite (a fibrous zeolite not related to asbestos) is a potent
inducer of mesothelioma (Baris et al. 1987), which is one of the two primary asbestos-
induced cancers (see Chapters). It is therefore possible that the fibrous habits of at
least some other minerals not formally included in the current definition of asbestos
may contribute to the induction of asbestos-related diseases. Therefore, what needs to
be developed, is an efficient procedure for separating potentially hazardous materials
from those that are most likely benign.
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There are two issues related to the question of whether fibers must formally be
asbestiform to contribute to health effects. The first involves the relationship between
fiber structure and disease induction and the second involves measurement. Although
the evidence is overwhelming that the size and shape of a fiber affects the degree to
which it contributes to the induction of disease (see Chapter 7 and, specifically, Section
7.5), it does not appear that sizes inducing biological activity are well distinguished by f
criteria that define the asbestiform habit. For example, determining the concentration of
a specific, common size range of structures was shown to adequately predict the
observed tumorigenicity of six forms of tremolite (three asbestiform varieties and three
non-asbestiform varieties) that were intrapleurally injected into rats (see Section
Appendix B). Once again, therefore, what is needed is an efficient procedure for (
distinguishing between the potentially hazardous size range of fibrous structures and
those that do not contribute to disease.
Importantly, depending on the definition employed for the fibers (or fibrous structures)
that are counted during an analysis, it may or may not be necessary to distinguish
formally between asbestiform and non-asbestiform structures for the concentrations ^
derived from such a count to adequately reflect biological activity.
Note, the dimensions of an asbestiform fiber are determined by the manner in
which the fiber grows (Addison, J. private communication). In contrast, the
massive forms of various minerals, when cleaved, also form elongated particles (
(termed "cleavage fragments") and, depending on the definition employed for
fibrous structures during an analysis, such cleavage fragments may or may not
be included along with asbestiform fibers in a count (see, Section 4.3). Although
it is clear from the manner in which they are each formed that the surface
properties of asbestiform fibers and cleavage fragments are likely to be very
different (for example, the latter will have many "unsatisified" chemical bonds), ^
the degree to which such differences affect potency for comparable sized
structures is not currently known.
Although it is beyond the scope of this document to present a detailed treatise on
asbestos mineralogy, the morphology of asbestos dusts, or the nature and limitations of C
analytical techniques and methods used to determine asbestos concentrations, a brief
overview of these topics is presented in the following sections both to identify issues
that need to be addressed as part of the development of an appropriate protocol for
assessing asbestos risks and to provide the background required to facilitate evaluation
of the relevant issues. In that regard, a section on lung physiology and function is also ,-
provided.
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4.1 THE MINERALOGY OF ASBESTOS
As previously indicated, the six asbestos minerals can be divided into two general
classes.
Chrysotile is the fibrous habit of the mineral serpentine (Hodgson 1965). The smallest
fibrils of chrysotile occur as rolled sheets or hollow tubules of this magnesium silicate
mineral. The larger fibers of chrysotile form as tightly packed bundles of the unit fibrils.
Chrysotile fibrils typically range from 20 nm to approximately 300 or 400 nm (0.02 to
0.3 or 0.4 urn) in diameter. Occasionally, structures up to 1000 nm (1 um) have been
observed, but these are unusual. At some point the curvature induced by the mismatch
between the, magnesium and silicon layers of the fibril becomes thermodynamically
unstable, so that production of thicker fibrils is prohibited (Addison, J. private
communication).
Chrysotile bundles are held together primarily by Van der Waals forces and will readily
disaggregate in aqueous solutions containing wetting agents (e.g. soap). They will also
readily disaggregate in lung surfactant (Addision, J. private communication).
The general chemical composition of serpentine is reported as Mg3.(Si2O5)(OH)4
(Hodgson 1965). However, the exact composition in any particular sample may vary
somewhat from the general composition. For example, aluminum may occasionally
replace silicon and iron, nickel, manganese, zinc or cobalt may occasionally replace
magnesium in the crystal lattice of chrysotile (serpentine).
The five other common varieties of asbestos are all fibrous forms of amphibole minerals
(Hodgson 1965). These are ferro-magnesium silicates of the general composition:
A2.3B5(Si,AI)8022(OH)2
where:
A = Mg, Fe, Ca, Na, or K; and
B = Mg, Fe, or Al.
Some of these elements may also be partially substituted by Mn, Cr, Li, Pb, Ti, or Zn.
The fibrous habits of the amphibole mirierals tend to occur as extended chains of silica
tetrahedra that are interconnected by bands of cations (Hodgson 1965). Each unit cell
typically contains eight silica tetrahedra and the resulting fibers tend to be rhomboid in
cross-section. Amphibole fibrils are generally thicker than chrysotile fibrils and may
typically range from approximately 100 nm to 700 or 800 nm in diameter (Addison, J
private communication). Substantially thicker fibrils have also been observed.
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4.2 MORPHOLOGY OF ASBESTOS DUSTS
Structures comprising the fibrous habits of the asbestos minerals come in a variety of
shapes and sizes. Not only do single, isolated fibers vary in length and thickness but
such fibers may be found combined with other fibers to form bundles (aggregates of
closely packed fibers arranged in parallel) or clusters (aggregates of randomly oriented
fibers) or combined with equant particles to form matrices (asbestos fibers embedded in
non-asbestos materials). Consequently, dusts (even of one mineral variety) are
complex mixtures of structures. For precise definitions of the types of fibrous structures
typically found in asbestos dusts, see Chatfield and Berman (1990).
Detailed descriptions of the characteristics of dusts typically encountered at
environmental and occupational asbestos sites have been reported in the literature and
the following summary is based on a previously published review (Berman and
Chatfield 1990), Typically, the major components of the dust observed in most
environments are non-fibrous, isometric particles. Fibrous structures consistently
represent only a minor fraction of total dust. Asbestos structures represent a subset of
the fibrous structures.
The magnitude of the fraction of total dust represented by fibers and the fraction of
fibers composed of asbestos minerals vary from site to site, However, the fraction of
asbestos in total dusts has been quantified only in a very limited number of
occupational and environmental settings (see, for example, Lynch et al 1970 or Cherrie
et all 987).
The gross features of structure size distributions appear to be similar among asbestos
dusts characterized to date (Berman and Chatfield 1990). The major asbestos fraction
of all such dusts are small structures less than 5 urn in length. Length distributions
generally exhibit a mode (maximum) between 0.8 and 1.5 urn with larger fibers
occurring with decreasing frequency. Fibrous structures longer than 5 um constitute no
more than approximately 25% of total asbestos structures in any particular dust and
generally constitute less than 10%.
In some environments, the diameteiB of asbestos fibers exhibit a narrow distribution
that is largely independent of length. In other environments, diameters appear to
exhibit a narrow distribution about a mean for each spedfic length. In the latter case,
both the mean and the spread of the diameter distribution increases as the length of the
structures increase. The increase in diameter with length appears to be more
pronounced for chrysotile than for the amphiboles, presumably due to an increase in
the fraction of chrysotile bundles contributing to the overall distribution as length
increases.
C
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Only a few studies have been published that indicate the number of complex structures
in asbestos size distributions. The limited data available indicate that complex
structures may constitute a substantial fraction (up to one third) of total structures, at
least for chrysotile dusts (see, for example, Sebastien et al 1984). Similar results were
also obtained during a re-analysis of dusts generated from the asbestos samples
evaluated in the animal inhalation studies conducted by Davis et al. (Berman et al.
unpublished). This is the same re-analysis used to support a study to identify asbestos
characteristics that promote biological activity (Berman et al. 1995), which is discussed
further in Section 7.4.3.
Historically, fibrous structures have been arbitrarily defined as structures exhibiting
aspect ratios (the ratio of length to width) greater than 3:1 to distinguish them from
isometric particles (Walton, 1982). However, alternate definitions for fibers have also
been proposed, which are believed to better relate to biological activity (see, for
example, Wylie et al. 1993 or Berman et al. 1995). The degree to which fibers are
combined within complex structures in a particular dust may also affect the biological
activity of the dust (Berman et al. 1995). Therefore, proper characterization of
asbestos exposure requires that the relative contributions from each of many
components of exposure be simultaneously considered. Factors that need to be
addressed include the distribution of structure sizes, shapes, and mineralogy in addition
to the absolute concentration of structures. Such considerations are addressed further
in Chapter 7. Thus, unlike the majority of other chemicals frequently monitored at
hazardous wastes sites, asbestos exposures cannot be adequately characterized by a
single concentration parameter.
4.3 CAPABILITIES OF ANALYTICAL TECHNIQUES USED TO MONITOR
ASBESTOS
Due to a complex history, a range of analytical techniques and methods have been
employed to measure asbestos in the various studies conducted over time (Walton
1982), Use of these various methods has affected the comparability of results across
the relevant asbestos studies (Berman and Chatfield 1990). Therefore, the relative
capabilities and limitations of the most important methods used to measure asbestos
are summarized here. Later sections of this report incorporate attempts to reconcile
effects that are attributable to the limitations of the different methods employed in the
various studies evaluated.
Analytical techniques used to measure airborne asbestos concentrations vary greatly in
their ability to fully characterize asbestos exposure. The capabilities and limitations of
four analytical techniques (midget impinger, phase contrast microscopy, scanning
electron microscopy, and transmission electron microscopy) need to be considered
here.
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Midget impinger (Ml) and phase contrast microscopy (PCM) are the two analytical
techniques used to derive exposure estimates in the majority of epidemiology studies
from which the existing risk factors are derived. Scanning electron microscopy (SEM) is
an analytical technique that has been employed in several key animal studies,
Transmission electron microscopy (TEM) provokes interest because it is the only
analytical technique that is potentially capable of distinguishing all of the characteristics
of asbestos that potentially affect biological activity.
Although PCM is widely used to characterize occupational exposures, its inability to
distinguish between asbestos and non-asbestos and its lack of sensitivity limits its
usefulness in environmental settings (Berman and Chatfield 1990). In fact, PCM
analyses and TEM analyses showed no correlation among measurements collected
during the cleanup of the Oakland Hills fire (Berman, unpublished). Such lack of
correlation is expected to be observed generally whenever measurements are collected
at sites where no single, obvious source of asbestos is expected to dominate.
Consequently, TEM is the technique that has been recommended for use at Superfund
sites (Chatfield and Berman 1990 and Berman and Kolk 1997).
A general comparison of the relative capabilities and limitations of the analytical
techniques introduced above is presented in Table 4-1.
Importantly, the physical limitations of the various analytical techniques is only part of
the problem. To provide reproducible results that can be compared meaningfully to
other analyses in other studies, one must also consider the choice of procedures
(methods) that address everything from sample collection and preparation to rules for
counting and quantifying asbestos structures.
Multiple methods have been published for use in conjunction with several of the
analytical techniques mentioned above (particularly TEM). Such methods differ in the
procedures incorporated for sample preparation and for the manner in which asbestos
structures are counted. The sample preparation requirements, conditions of analysis,
and structure counting rules for several of the most commonly employed methods are
presented in Table 4-2 to illustrate how the choice of method can result in substantially
different measurements (even on duplicate or split samples).
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Table 4-1:
Capabilities and Limitations of Analytical Techniques Used for
Asbestos Measurements
Parameter
Range of
Magnification
Particles Counted
Minimum Diameter
(size) Visible
Resolve Internal
Structure
Distinguish
Mineralogy4
Midget
Impinger
100
All
1 pm
No
No
Phase
Contrast
Microscopy
400
Fibrous
Structures2
0.3 urn
No
No
Scanning
Electron
Microscopy
2,000-10,000
Fibrous
Structures2
0.1 um
Maybe
Yes
Transmissio
n Electron
Microscopy
5,000-
20,000
Fibrous
Structures2'3
< 0.01 um
Yes
Yes
1. The capabilities a.nd limitations in this table are based primarily on the physical constraints of the
indicated instrumentation. Differences attributable to the associated procedures and practices of
methods in common use over the Iast25 years are highlighted in Table 4-2.
2. Fibrous structures are-defined here as particles exhibiting aspect ratios (the ratio of length to
width) greater than 3 (see Walton 1982).
3. TEM counts frequently resolve individual fibrous structures within larger, complex structures.
Based on internal structure, several different counting rules have been developed for handling
complex structures. See the discussion of methods presented bebw.
4. Most SEM and TEM instruments are equipped with the capability to record selected area electron
diffraction (SAED) spectra and perform energy dispersive X-ray analysis (EDXA), which are used
to distinguish the mineralogy of structures observed.
The second column of Table 4-2 describes the specifications of the PCM method
currently mandated by the Occupational Safety and Health Agency (OSHA) for
characterizing asbestos exposure in occupational settings. Although this method is in
common use today, several alternate methods for counting fibrous structures by PCM
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have also been used historically. Therefore, PCM measurements reported in earlier
studies (including the available epidemiology studies) may not be comparable to PCM
results collected today.
The last four columns of Table 4-2 describe TEM methods that are in current use.
Comparison across these methods indicates:
» the shortest lengths included in counts using these methods vary
between 0,06 and 1 urn Given that structures shorter than 1 urn
represent a substantial fraction of total asbestos structures in almost any
environment (Section 4.2), this difference alone contributes substantially
to variation in measurement results across methods;
• the definitions and procedures for counting complex structures (i.e.
bundles, clusters, and matrices) vary substantially across methods, which
further contribute to variation in measurement results. For example, the
ISO Method requires that component fibers of clusters and matrices be
counted separately, if theycan.be readily distinguished. In contrast,
clusters are counted as single structures under the AHERA Method; and
• although all of the methods listed incorporate sample preparation by a
direct transfer process, several of the methods have also been paired with
an optional indirect transfer process. Measurements derived from split
samples that are prepared, respectively, by direct and indirect transfer,
can vary by factors as large as several 100 (Berman and Chatfield 1990),
More typically, however, counts of asbestos structures on samples
prepared by an indirect transfer procedure are greater than those derived
from directly prepared samples by factors of between 5 and 50.
Given the combined effects from the physical limitations of the various techniques
employed to analyze for asbestos and the varying attributes of the methods developed
to guide use of these techniques, the relative capabilities and limitations of asbestos
measurements derived, respectively, from paired methods and techniques in common
use can be summarized as follows:
». all four techniques are particle counting techniques;
• neither Ml nor PCM are capable of distinguishing asbestos from non-
asbestos (i.e. they are incapable of determining structure mineralogy);
• counting rules used in conjunction with Ml do not distinguish isometric
particles from fibers;
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« counting rules used in conjunction with PCM limits counting to fibrous
structures longer than 5 urn with aspect ratios greater than 3:1;
• the range of visibility associated with PCM limits counting to fibers thicker
than approximately 0,3 urn;
• under conditions typically employed for asbestos analysis, the range of
visibility associated with SEM limits counting to fibers thicker than
approximately 0,1 urn, which is only marginally better than PCM;
» SEM is capable of distinguishing asbestos structures from non-asbestos
structures;
• TEM is capable of resolving asbestos structures over their entire size
range (down to thicknesses of 0.01 urn);
• TEM is capable of distinguishing the internal components of complex
asbestos structures; and
• TEM is capable of distinguishing asbestos structures from non-asbestos
structures.
More detailed treatments of the similarities and differences between asbestos
techniques and methods can also be found in the literature (see, for example, Berman
and Chatfield 1990).
Due to the differences indicated, measurements from a particular environment (even
from duplicate samples) that are derived using different analytical techniques and
methods can vary substantially and are not comparable. In fact, results can differ by
two or three orders of magnitude (Berman and Chatfield 1990). More importantly,
because the relative distributions of structure sizes and shapes vary from environment
to environment, measurements derived using different analytical techniques and
methods do not even remain proportional from one environment to the next.
Therefore, the results from multiple asbestos studies can only be meaningfully
compared if the effects that are attributable to use of differing analytical techniques and
methods can be quantified and reconciled. To complicate the situation, however, few of
the existing studies document analytical procedures in sufficient detail to reconstruct
exactly what was done.
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4.4 THE STRUCTURE AND FUNCTION OF THE HUMAN LUNG
4,4,1 Lung Structure
The lungs are the organs of the body in which gas exchange occurs to replenish the
supply of oxygen and eliminate carbon dioxide. To reach the gas exchange regions of
the lung, inhaled air (and any associated toxins) must first traverse the proximal
conducting (non-respiratory) airways of the body and the lung induding the nose (or
mouth), pharynx, larynx, trachea, and the various branching bronchi of the lungs down
to the smallest (non-respiratory) bronchioles. Air then enters the distal (respiratory)
portion of the lung, where gas exchange occurs.
In humans, the respiratory portion of the lungs are composed of the respiratory or
aveolarized bronchioles, the alveolar ducts, and the alveoli (or alveolar sacs). There
are approximately 3 x 108 alveoli in human lungs (about 20 per alveolar duct) with a
cumulative volume of 3.9 x 103 cm3 (Yeh and Harkema 1993). This represents
approximately 65% of the total volume capacity of human lungs at full inspiration.
Yeh and Harkema also report that the ratio of lung volume to body weight is
approximately constant across a broad range of mammals (from a shrew - 0.007 kg to a
horse - 500 kg). Human lung volumes average a little more than 5 L.
Each human alveolus has a diameter of approximately 0.03 cm (300 urn) and a length
of 0.025 cm (Yeh and Herkema 1993). The typical path length from the trachea to an
alveolus is approximately 25 cm. The bronchi leading to each alveolus have branched
an average of 16 times from the trachea (with a range of 9 to 22 branches).
Importantly, the mean path length, the number of branches between trachea and
alveolus, and the detailed architecture (branching pattern) of the respiratory region of
the lung vary across mammalian species. For example, rats and mice lack respiratory
bronchioles while macaque monkeys exhibit similar numbers of respiratory bronchiole
generations as humans (Nikula et al. 1997). Furthermore, branching in humans tends
to be symmetric (each daughter branch being approximately the same size and the
angle of branching for each is similar but not quite equal) while rodents tend to exhibit
monopodal branching in which smaller branches tend to come off at angles from a main
trunk (Lippmann and Schlesinger 1984).
The gas exchange regions of the lung are contained within the lung parenchyma, which
constitute approximately 82% of the total volume of the lungs (Gehr et al. 1993).
Importantly, the lung parenchyma is not a "portion" of the lung; it fills virtually the entire
volume of the organ traditionally visualized as the "whole" lung. Embedded within the
parenchyma are the larger conducting airways of the lungs and the conducting blood
vessels that transport blood to and from the capillaries that are associated with each
alveolus. In the human lung, approximately 213 ml of blood are distributed over 143 m2
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of gas-exchange (alveolar) surface area (about the size of a tennis court). The gas-
exchange surface area of lungs scale linearly with body weight over most mammalian
species. The slope of the line is 0.95. Figure 4-1 is a photomicrograph showing two
views of a portion of lung parenchyma in the vidnity of a terminal bronchiole and an
avleolar duct, which are labeled. The circular spaces in the left portion of the figure and
the cavities in the right portion of the figure are alveoli. Note the thinness of the walls ^
(septa) separating alveoli.
Alveoli are separated from each other by alveolar septa that average only a few
micrometers in thickness (Gehr et al. 1993). Gas-exchange capillaries run within these
septa and the air-blood barrier, which averages only 0.62 um in thickness, is composed (
of three layers: the alveolar epithelium, the interstitium, and endothelium. The
epithelium is described in detail below. The interstitium is primarily composed of a
collagenous, extracellular matrix, which constitute about two-thirds of the volume with a
collection of fibroblasts, macrophages, and other cells interspersed within the matrix
(Miller et al. 1993). The cells of the interstitium constitute about one third of its volume.
The endothelial layer is composed of the smooth muscle cells and connective tissue *•'
that constitute the walls of vascular capillaries. The amount of connective tissue in the
septa between alveoli also varies between animal species; small rodents have less and
primates more (Nikula et al. 1997).
Figures 4-2 and 4-3 show, respectively, a typical alveolar septum andla closeup of one (
portion of such a septum. In Figure 4-2, one can see that the septa themselves are thin
and are filled almost entirely with capillaries. Figure 4-3 shows that the epithelial lining
of an alveolus is no more than 1 urn thick, that the underlying interstitium is no thicker,
and that the endothelium of the adjacent capillary is similarly thin. These three layers
constitute the major tissues of the air-blood barrier (the rest of the barrier includes the
limited quantity of blood plasma between the endothelial wall of a capillary and a red
blood cell and the outer membrane of the red blood cell itself).
Gehr et al. (1993) also report that interalveolar septa constitute approximately 14% of
the volume of the gas-exchange region of the lung (i.e. the lung parenchyma). Of this
tissue mass, approximately 20% is endothelium, 55% is interstitium, and the rest is (
composed of cells associated with the alveolar epithelium. The remainder of the gas-
exchange region is air space. Gehr et al. also report that the interalveolar septa fold to
accommodate changes in lung volume during respiration, although the major
contribution to lung volume changes (over the range of normal inspiration) appears to
be the collapsing (folding) of alveolar ducts (Mercer and Crapo 1993). r
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Figure 4-1
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Figure 4-3
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According to Gehr et al. (1993), human alveoli contain approximately 380 cells and the
total number reportedly increases as a power function with body mass for other
mammals. In humans, this includes approximately 12 alveolar macrophages (although
this number is larger in smokers, apparently due to chronic inflammation), 40 Type I
epithelial cells, 67 Type II epithelial cells, 106 interstitial cells, and 148 endothelial cells.
The relative number of the various types of cells associated with alveoli varies across
mammalian species (Gehr et al. 1993). In the rat, for example, Type I epithelium is
approximately 20% of the total (rather than 12% in humans) with less interstitium and
more endothelium.
According to Gehr et al. (1993), Type I epithelial cells, which constitute approximately
95% of the surface area of alveolar epithelium, are flat and platy(squamous), and
average less than 1 urn in thickness (except where cell nuclei exist, which are
approximately 7.5 urn in diameter and protrude into the alveolar space). Type II
epithelial cells, which are cuboidal, constitute no more than 5% of the epithelial surface.
Despite the small fraction of surface that they occupy, Type II cells serve to maintain
the integrity of the overall epithelial lining so that, for example, they limit the tissue's
permeability and control/prevent transport of macromolecules from the interstitium to
the alveolar space, or the reverse (Leikauf and Driscoll 1993). Type II cells also secrete
lung surfactant. The basement membrane of alveolar epithelium is a collagenous
structure.
In contrast, the epithelial cells lining the trachea and bronchi (including the respiratory
bronchioles) are ciliated and columnar and averages between 10 and 15 urn in
thickness (based on photomicrographs presented in St. George et al. 1993).
Tracheobronchial epithelium reportedly contains at least 8 distinct cell types (St.
George et al. 1993): ciliated epithelium, basal cells (small flat cells situated along the
basal lamina and not reaching the luminal surface), mucous goblet cells, serous cells,
nonciliated bronchiolar (clara) cells, small mucous granule (SMG) cells, brush cells, and
neuroendocrine cells. The relative abundance of the various cells varies across
mammalian species as well as across the various airway generations (branches) and
even the opposing sides of specific airways. The number of cells per length of basal
lamina also varies across mammalian species.
4.4.2 The Structure of the Mesothelium
The mesothelium is a double layered membrane and each layer is a single-cell thick.
The two layers of the mesothelium are separated by a space (e.g. the pleural space),
which contains extracellular fluid and free macrophages (Kane and McDonald 1993).
The pleural space is drained at fixed locations by lymphatic ducts. Each layer of the
mesothelium overlies a collagenous basement membrane containing dispersed spindle
cells. Depending on the location of the mesothelium, the basement membrane may
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overlie the skeletal muscle of the diaphragm or the rib cage (in the case of the parietal
pleura, which is the outer layer), For the inner layer or visceral pleura, the basement
membrane overlies visceral organs (including the lungs) within the rib cage. Healthy
mesothelium is quiescent, meaning that cells are not actively dividing.
The relative size and thickness of mesothelial tissue varies across mammalian species
(Nikula et al. 1997). For example, rats have relatively thin pleura with limited lymphatic
ducts. In contrast, nonhuman primates have thicker pleura with greater lymphatic
drainage than rats and humans have even thicker pleura and relatively abundant
lymphatic ducts.
4.4.3 Cytology
Alveolar Epithelium. In the respiratory region of the lung, Type II epithelial cells are
progenitor cells for Type I epithelium (Leikauf and Driscoll 1993), Type I epithelial cells
are not proliferation competent (Nehls et al. 1997), After injury to Type I cells, Type II
cells proliferate and reestablish the continuous epithelial surface. Type II cells also
secrete surfactant. It appears that the identity and location of the progenitor cells for
Type II epithelial cells are not currently known.
Injury or alteration of Type II cell function are associated with several diseases included
idiopathic pulmonary fibrosis (Leikauf and Driscoll 1993). Also, crystalline silica and
other toxic agents have been shown to directly modify Type 11 cellular activity. For
example, crystalline silica (at sub-cytotoxic levels, < 100 ug/ml) stimulates Type II
proliferation in tissue culture. In contrast, neither titanium dioxide nor aluminum coated
silica induce proliferation at corresponding concentrations.
In culture, Type II epithelial cells transform slowly into Type I cells and thus have limited
population doubling capacity (Leikauf and Driscoll 1993). Rarely can the number of
Type II cells expand past three to 10 passages (20-30 doublings). During this time,
cells terminally differentiate, develop cross-linked envelopes, and appear squamous,
enlarged, and multinucleated. The process is noted to'be accelerated by the presence
of transforming growith factor beta (TGF-(3).
Macrophages. Alveolar macrophages (the largest population of macrophages in the
lung) are mobile, avidly phagocytic, present antigens, and release cytokines that trigger
various other immune responses (Leikauf and Driscoll 1993), They also initiate
inflammatory responses and other repair mechanisms that are designed to restore
tissue homeostasis.
The next largest populatbn of macrophages in the lung are the interstitial macrophages
(Leikauf and Driscoll 1993), These are localized in the peribronchial and perivascular
spaces, the interstitial spaces of the lung parenchyma, the lymphatic channels, and the
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visceral pleura. The various populations of macrophages in the lung express different
surface proteins, show different proliferative capacity, and show differences in
metabolism.
The sizes of alveolar macrophages varies substantially across mammalian species
(Krombach et al. 1997), Krombach and co-workers provide a table summary of the
relative sizes across several species of interest:
Animal Mean diameter (um) Mean Volume (um3)
Rats 13.1+/-0.2 1166+7-42
Syrian Golden Hamsters 13.6 +/-0.4 1328 +/-123
Cyanomolgus Monkeys 15.3+/-0.5
Healthy Humans 21.2+/-0.3 4990+/-174
As indicated later (Section 7.2), the relative size of various macrophages has direct
implications regarding the dependence of clearance mechanisms on fiber size.
Mesothelium. Importantly, mesothelial cells are proliferation competent and may be
their own progenitor cells (Kane and McDonald 1993). It is also possible, however, that
as yet to be identified progenitor cells are located along opposite walls of pleura or at
other locations within the pleural space.
Tracheo-bronchiolar epithelium. As previously indicated, tracheo-bronchiolar (i.e.
non-respiratory) epithelium is composed primarily of ciliated, columnar cells that are 10
to 15 urn thick. Although some report that Clara cells serve as progenitor cells for
tracheo-bronchiolar epithelium (Finkelstein et al. 1997), others report that both Clara
cells and bronchiolar epithelium are proliferation competent (Nehls et al. 1997). It
appears that the identity and location of the progenitor cells for Clara cells are not
currently known.
4.4.4 Implications
The potential implications of the above observations concerning lung structure and
cytology are:
• that the thicknesses of Type I epithelial cells, endothelial cells, and the
interstitium in the alveolar septa are all very small relative to the lengths of
the putative asbestos fibers that contribute to disease;
• that an entire alveolus is only 300 um across and a typical Type I cell is 46
in radius by < 1 um thick;
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• that distances across alveolar septa are only on the order of a few urn and
such septa contain both the endothelium and the interstitum. Thus, the
distances that have to be traversed to get to these structures are also
small relative even to the length of a fiber;
• that the alveolar septa and the walls of the alveolar ducts fold during
respiration, which may provide mechanical forces that facilitate movement
of fibers into and through the alveolar epithelium;
• that Type I epithelium do not proliferate so they cannot be the cells that
lead to cancer. It is the Type II epithelial cells (and potentially
macrophages, basal cells, or endothelial cells) that contribute to cancer in
the pulmonary portion of the lung. Type II cells eventually terminally
differentiate to Type I cells;
• that other cells in the lung that have variously been reported to be
proliferation competent (so that they potentially serve as target cells for
the induction of cancer) include Clara cells and bronchiolar epithelial cells;
• that the distance from the most distal airways and alveoli to the pleura is
small relative to the lengths of a fiber; and
• that mesothelial cells are proliferation competent and thus serve as
potential targets for the induction of cancer;
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5.0 THE ASBESTOS LITERATURE
This is a description of the common types of studies in the asbestos literature and an
overview of the sources of potential uncertainty typically associated with each. Such
[imitations must be considered when drawing conclusions from these studies and, more
importantly, when comparing inferences across studies, Throughout this document, we
have endeavored to identify the major sources of uncertainty in the studies we
examined and have endeavored to account for such uncertainties during our evaluatbn
and interpretation of study results.
The types of studies available for examining relationships between risk and asbestos
exposure include human epidemiology studies, human pathology studies, a broad
variety of animal studies, and a broad variety of in-vitro studies in both tissue cultures
and cell-free systems. To properly compare and contrast the results from such
studies;
» the method(s) employed for asbestos characterization in each study need
to be reconciled;
» the procedures employed for evaluating study end points need to be
compared and contrasted;
• the relationship between the route of exposure employed in each type of
study and the exposure route of interest (i.e. human inhalation) needs to
be examined; and
» other major, study-specific sources of uncertainty need to identified and
addressed.
Among study conditions and procedures that must be considered before evaluating
study conclusions, it is particularly important to address the analytical methodologies
employed to characterize the nature of exposures (or doses) in each study and such
considerations are common to virtually all of the various types of studies of interest.
As indicated in Section 4.3, the only instrument capable of completely delineating
asbestos structure size distributions'is TEIV1 (or TEM combined with other techniques).
Thus, for example, conclusions regarding variations in biological effects due to
differences in such things as fiber size must be viewed with caution when fiber sizes are
characterized using only PCM, SEM, or other, cruder analytical techniques. Similarly,
the ability to adequately determine fiber mineralogy (fiber type), particularly of what may
be minor constituents of various dusts or bulk materials, aiso depends strongly on the
instrumentation employed for analysis as well as the strategy for sampling and for
conducting the actual structure count. All of these factors must be considered.
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f
Not only does the specific instrumentation (analytical technique) that is employed in an
asbestos measurement affect the outcome of that measurement, but the particular
method employed to guide the measurement affects the outcome. As previously
indicated (Section 4.3), details concerning the definition of structures to be included in a
count, the strategy for counting, and the minimum number of specific types of structures (
to be included in a count (all features that vary across analytical methods) affect the
precision with which fiber concentrations (particularly of longer and thinner fibers) are
delineated. It is not uncommon, for example, that asbestos concentrations measured in
the same sample may vary by several orders of magnitude, due simply to a difference
in the analytical method employed for the analysis (even when the same analytical (
technique is employed, see Section 4.3).
Other important sources of uncertainty tend to be study-type specific and are thus
addressed separately below.
5.1 HUMAN EPIDEMIOLOGY STUDIES (
A good overview of the kinds of limitations that contribute to uncertainty in the available
epidemiology studies was presented in the Health Effects Assessment Update (U.S.
EPA 1986). As described in Appendix A of this document, while evaluating dose-
response factors derived from the human epidemiology studies, we tried to address (
most of the major sources of uncertainty commonly associated with such studies, which
are described briefly below.
Epidemiology studies, which track the incidence of disease (or mortality) within a
defined group (cohort) sharing comparable exposures, have, been performed on cohorts
of workers exposed to asbestos and other mineral fibers in a variety of occupational
and environmental settings. Among these, studies that include quantification of
exposures are particularly useful for evaluating dose/response relationships and
deriving risk factors.
Generally, the most severe limitations in an epidemiology study involve the exposure C
data. Both estimates of the level of exposure and determination of the character of
exposure are affected by such limitations. Regarding the character of exposure,
because exposure measurements from most of the available quantitative epidemiology
studies are based on midget impinger measurements or PCM measurements, detailed
characterization of the size distribution or the mineral type of fibrous structures ^
(particularly of minor constituents) that contributed to exposure in such studies is
generally lacking (Section 4.3). This is particularly important because of the evidence
that neither midget impinger or PCM are capable of providing measurements that
remain proportional (across study environments) to the biologically-relevant
characteristics of an asbestos dust (see Section 7.5 and Appendix B). This limits the
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ability both to compare results across the existing studies and to extrapolate such
results to new environments for which risks need to be estimated. At the same time,
effects on the ability to. observe dose-response trends within a single study are not
typically impaired.
Samples collected prior to the mid-1960s were often analyzed by measuring total dust
in units of millions of particles per cubic foot (MPCF) using implngers or thermal
precipitators, A description of the relative strengths and weaknesses of these
techniques is provided in Section 4,3, The fibrous portion of the dust was not
monitored. Impinger measurements are sometimes related to fiber counts (based on
PCM) using side-by-side measurements of total dustand fiber counts collected during a
relatively brief period of time (e.g., McDonald et al, 1980b, Dement et al, 1983a).
However, the correlation between fiber counts and total dust is sometimes poor within a
plant and generally poor between plants (see, for example, U.S. EPA 1986). Thus,
conversions based on limited sets of paired measurements are of questionable validity,
In some studies (e.g., McDonald et al, 1983b) the only available measurements are
impinger measurements in MPCF and these have been related to f/ml by PCM using
conversion factors derived in other plants, which raises further questions concerning
validity,
Even conversion to PCM may not be adequate for assessing risk in a manner that
allows extrapolation across exposure environments or studies. As indicated in
Section 7.5, comparing dose-response factors derived in different exposure
environments (or extrapolating to new environments to predict risk) requires that
asbestos measurements reflect the characteristics of asbestos structures (size, shape,
mineralogy) that determine biological activity. If surrogate measures are employed
(e.g. measures of asbestos structures displaying characteristics other than those that
determine biological activity), there is no guarantee that concentrations of such
surrogate measures and the true biologically active structures will remain proportional
from one environment to the next, As a consequence, the relationship between
exposure (measured by surrogate) and risk may not remain constant from one
environment to the next. Importantly, several studies suggest that PCM may, at best,
be no more than a surrogate measure (see, for example, Berman et al. 1995),
Moreover, the technique was adapted to asbestos in an ad hoc fashion with only limited
thought given to biological relevance (Walton 1982),
Use of surrogate measures of asbestos exposure may be less of a problem within a
single exposure environment (where airborne asbestos structures likely have been
generated in a similar manner from similar source material). Thus, surrogate measures
of asbestos exposure may remain approximately proportional to the true biologically
active structures, which suggests why monotonically increasing dose-response
relationships have likely been observed with PCM-measured concentrations in single
exposure environments. In different exposure environments, however, airborne
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asbestos structures likely are generated in different processes from different source
material, There is thus little reason.to expect surrogate measures of exposure to
remain proportional across such environments.
None of the published epidemiology studies incorporate TEM measurements of
asbestos and such measurements are not widely available in occupational settings
(Section 6,2.4), However, TEM is the method currently used (and recommended) to
assess exposure in environmental settings (due both to questions concerning biological
relevance (Section 7.5) and to problems with measuring environmental asbestos
concentrations by PCM (Section 4.3 and Appendix B),
In some cases, the limited exposure characterization presented in specific epidemiology
studies can be augmented by pairing such studies with published TEM
characterizations of dusts from the same or similar exposure settings, to the extent the
appropriate supplemental studies are available. In fact, this is the procedure adopted in
this document to adjust the existing risk factors to exposure indices that are thought to
better relate to biological activity (Sections 6.2.4 and 6.3.3), Such an approach is
limited, however, to the extent that the published asbestos characterizations actually
represent exposure conditions in the corresponding epidemiology studies. To the
extent reasonable, the limitations of this approach have been addressed in this study by
assigning and incorporating additional factors into the calculation of the confidence
intervals associated with the adjusted potency factors (Sections 6,2.4 and 6.3.3).
Regarding levels of exposure in the epidemiology studies, in most cases, air
measurements were collected only at limited points in time and measurements may be
entirely lacking from the earliest time periods, when exposures may have been
heaviest. In such cases, exposures are typically estimated either by extrapolation from
periods when measurements are available or by expert judgement based on personal
accounts and records of changes in plant operations, industrial hygiene procedures, air
standards, etc. Moreover, the majority of exposure measurements used in these
studies are based on area (ambient) rather than personal samples. Typically, only a few
areas of a plant have been sampled so that levels in other areas must be approximated
using expert judgement by persons familiar with operations at the plant.
It is difficult to judge the degree that available asbestos concentration measurements
are representative of actual exposures in the existing studies. In some cases, it seems
likely that operations were shut down or otherwise modified in preparation for sampling.
Likewise, in some operations there are brief episodes of very intense exposure and it is
questionable whether such episodes are adequately represented in the available data.
Most of the asbestos measurements used in the published epidemiology studies were
collected for insurance or compliance purposes. They were not intended to provide
representative estimates of the direct level of exposure to workers. Some of the
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published epidemiology studies lack any direct exposure data. For example, exposures
were estimated for the cohort studied by Seidman (1984) based on conditions
simulated many years later in a similar plant to the one from which Seidman studied the
original cohort. In fact, the equipment in the plant from which Seidman obtained
exposure estimates came originally from the plant where Seidman studied the workers;
it was purchased and moved. Recently, an epidemiology study was also completed for
a cohort working at that new plant (Levin et al. 1998).
In addition to problems with the actual analysis of asbestos concentrations, individual
exposures are generally estimated in the existing epidemiology studies by relating
ambient asbestos measurements to job descriptions and integrating the duration of
exposure over the recorded time that each worker spent in each job category.
However, sometimes there are no records of specific areas in which an employee
worked, so that work areas must be assumed based on job title. Some types of
workers (e.g., maintenance workers) may have spent time in many different areas of a
plant so their exposure varies from what might otherwise be assumed.
Although the greatest problems with the data in existing epidemiology studies likely lies
within the estimates of exposure, problems with disease-response data also exist.
Mesothelioma is rare and this disease may have been under-reported as a cause of
death in older studies. This is probably less of a problem in more recent studies, since
the association of mesothelioma with asbestos exposure is now well known. In fact, the
opposite tendency (over-reporting) may now be occurring because an asbestos worker
with mesothelioma is probably eligible for compensation. Some studies have
re-diagnosed causes of death from all of the available data (e.g., Selikoff et al, 1979);
however, this creates the problem of lack of comparability to control populations (for
which such re-diagnosis is not generally performed).
The choice of an appropriate control population is also an important consideration.
Local cancer rates may differ substantially from regional or national rates and the
choice of an appropriate control is not always clear. A related problem is the lack of
smoking data in many of the studies. Because of the interrelation between smoking
and asbestos in lung cancer, errors could occur in lung cancer risk estimates if the
smoking patterns of the cohort are substantially different from those of the control
population.
In some of the studies, a substantial portion of the population is lost to follow-up (e.g.,
Armstrong 1988), and this adds additional uncertainty to the analysis. Also, the effect
of exposure may be inaccurately evaluated if the follow-up of the population is too brief.
This may be a limitation, for example, of the Levin et al, (1998) study.
Another problem frequently associated with these studies is that available data are not
reported in a form that is well-suited to risk assessment. The EPA lung cancer model,
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for example, requires that exposure be estimated as cumulative exposure in f/ml-years
excluding the most recent 10 years (Section 6.2); generally the data are not published
in this form. The data are also frequently not available in a form that permits study of
the shape of the lung cancer dose/response curve, so it is not possible to determine
how well the EPA model describes the data. The form of the data for mesothelioma is
generally even less appropriate for risk assessment. Ideally, what is needed is the C
incidence of mesothelioma subdivided according to exposure level, age at beginning of
exposure, and duration of exposure (Section 6.3). Such data are almost never
available in published studies and crude approximations must be made to account for
this lack.
It is important to appreciate the type and magnitude of effect that each of these sources
of uncertainties are likely to have on the distribution of potency estimates derived from
the set of available studies for lung cancer and mesothelioma, respectively. Some of
the above-described limitations likely introduce random errors that simply decrease the
overall precision of a potency estimate. However, other types of limitations may cause
systematic errors in particular studies, which potentially biases the potency estimate ^
either high or low. Some of the limitations may only affect between-study differences
and some may introduce a systematic bias between either industry types or fiber types.
Examples of some of these types of variation are provided in Section 6.1.
We also note that, although individual estimates of potency factors from individual (
studies may be highly uncertain, by combining results across multiple studies while
properly addressing such uncertainties, it may be possible to draw condusions with
greater precision than reasonable for any individual study. This is the essential
advantage of the type of meta analysis discussed in Section 6.2.4 for lung cancer and
6.3.3 for mesothelioma.
5.2 HUMAN PATHOLOGY STUDIES
Human pathology studies provide a characterization of disease morphology and
correlations between causes of death and the types of asbestos fibers retained in the
lungs and other bodily tissues. These studies generally involve microscopic (
examination of tissue samples for indications of morphologic changes characteristic of
disease and/or microscopic examination of digested tissue specimens to characterize
the mineral fibers extracted from such tissue.
The results of human pathology studies need to be evaluated carefully by addressing ,
effects that are attributable to:
* the way that tissue samples are fixed for preservation;
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• the way that tissue samples are prepared for analysis (e.g. ashing, bleach
digestion, digestion in alkali, or some combination);
. • the choice of methods employed for characterization of asbestos; and
• the choice of locations within tissues from which samples are collected for
analysis.
Because tissue samples obtained from deceased individuals are typically stored for
long periods of time before they may be analyzed as part of a human pathology study,
such samples are commonly fixed by treatment with chemical preservatives prior to
storage. However, Law et al. (1991) studied the effects of two common fixatives
(Karnovsky's fixative and formalin fixative) on asbestos fibers and concluded that such
fixatives degrade and dissolve asbestos fibers (including both chrysotile and crocidolite)
at measurable rates (Section 7.2.4). Therefore, particularly for samples that are stored
"wet" (as opposed, for example, to storage in paraffin blocks), the concentrations and
character of the tissue burden of asbestos may be altered during storage. Even for
studies in which relative (as opposed) to absolute concentrations are being compared,
alterations associated with preservation may limit the ability to make such comparisons,
particularly among samples stored for widely disparate periods of time or stored using
widely disparate procedures.
Fiber concentrations in tissue samples have also been shown to vary as a function of
the method employed for preparing such samples for analysis. Historically, samples
that are digested in bleach or alkali have tended to exhibit lower recovery of asbestos
fibers than samples that are ashed (REF). However, more recent studies suggest that
improving technique has narrowed these differences so that this is no longer a major
consideration (REF). Thus, when comparing results across studies, due consideration
needs to be given for the time frame during which such studies were conducted and the
comparability/differences in the techniques employed for tissue sample preparation.
Once prepared, both the character and the concentration of the tissue burden
measured in a tissue sample will also depend heavily on the particular analytical
method employed to characterize asbestos and differences attributable to such
techniques must be reconciled before measurements across samples or conclusions
across studies can be reasonably compared. A more detailed description of the effects
attributable to asbestos measurement was presented in the previous section on human
epidemiology studies (Section 5.1) and the same issues obtain for human pathology
studies. Unless measurements are made using comparable instrumentation with
comparable methodology, comparisons across such measurements can be very
misleading,
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Perhaps the biggest limitation hindering the kinds of evaluation that can be conducted
based on human pathology studies is that due to the strong dependence of asbestos
concentrations on the specific location within a tissue from which a sample is obtained,
Numerous authors have reported that asbestos is non-uniformly distributed in lung
parenchyma and other tissues following exposure (see, for example: Davis et al. 1986a,
Bignon et al. 1979, or Pooley 1982). The incidence of lesions and other pathological
effects attributed to asbestos exposure correspondingly exhibit a non-uniform
distribution.
For lung tissue samples (which tend to be among the primary interests in human
pathology studies) the relationship between sample location and asbestos
concentration is particularly important. To sample deep lung tissue reproducibly, it has
been shown necessary to select a specific section of lung parenchyma from a defined
portion of the bronchio-alveolar tree. Pinkerton et al. (1986) showed that the deposition
of asbestos in the lungs is an inverse function both of the pathlength and the number of
bifurcations between the trachea and the site. Thus, analyses of samples from different
animals of the same species can only be compared meaningfully if the samples are
collected from identical locations in the bronchio-alveolar tree., Similar, nonuniform
depositional patterns have also been observed in humans (Raabe 1984). Futhermore,
due to the complex branching and folding pattern of the lung, adjacent sections of lung
parenchyma frequently represent disparate portions of the bronchio-alveolar tree (REF).
Consequently, lung burdens derived even from adjacent samples of lung parenchyma
can show broadly varying concentrations (dffering by orders of magnitude).
Unfortunately, however, tissue samples that are available for analysis in support of a
human pathology study are typically "opportunistic" samples, which means that they
were selected and stored for an entirely different purpose, usually with little or no
thought given to the specific location from which they were collected (except in the most
general way). It is therefore seldom possible to address the effects of sample location
directly. Consequently, comparisons of tissue burden concentrations across samples
from different individuals in a human pathology study are at best qualitative and may
only be useful when averaged over large numbers of individuals and only when large
differences in concentrations (several orders of magnitude) are being distinguished.
Moreover, because the parts of a tissue that undergoes morphologic changes induced
by asbestos typically corresponds to the parts of a tissue where asbestos burdens are
the highest, even comparison of morphologic effects across tissue samples requires
proper consideration of the effects of the locations from which such tissue samples
were derived.
5.3 ANIMAL STUDIES
In animal studies, various species are exposed to measured doses of size-selected
mineral fibers and the resultant biological responses are monitored. Animals may be
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dosed either by inhalation, intratracheal installation, implantation, or injection (U.S. EPA
1986). Such studies are conducted for several purposes. As with human pathology
studies, animal pathology studies are those in which the transport of asbestos
structures is tracked through the various organs and tissues of the animal and the
attendent cellular and molecular changes are characterized. In parallel with quantitative
epidemiology studies, animal dose/response studies track the incidence of disease
among a population that has been exposed in a controlled manner. One of the
advantages of animal dose/response studies over epidemiology studies is that
exposures are controlled and can be well characterized. The major disadvantage is
that there are many uncertainties introduced when extrapolating the results of animal
data to predict effects in humans. Therefore, attempts to adapt such things as animal
derived dose-response factors to humans are not generally recommended.
As with human epidemiology and pathology studies, the validity of conclusions drawn
from animal studies depends strongly on the techniques and methods used to
characterize and quantify asbestos structures either in the delivered dose or in the
tissues of the dosed animals (see Section 5.1). The ability to reconcile conclusions
derived from many animal studies with the rest of the asbestos literature is limited
because SEM was commonly employed to measure asbestos in animal studies but not
other kinds of studies. Even many of those studies in which TEM was employed for
asbestos analysis suffer from use of non-standard methods that cannot be easily
reconciled with the more traditional TEM methods, particularly because such
specialized methods are seldom adequately documented to allow comparison.
As with human pathology studies, the location of a tissue sample excised for analysis is
a critical factor that also governs the quality of an animal pathology study (Section 5.2).
However, one potential advantage frequently available in animal pathology studies over
human studies is the ability to carefully identify and select the precise tissue samples to
be analyzed. The extent that a particular animal pathology study exploits this capability
can affect the overall utility of the study. Thus, such issues neejd to be addressed
carefully when evaluating and comparing the results across animal pathology studies.
The route of exposure employed in a particular animal study is also important to
consider. Each of the routes of exposure commonly employed in these studies
(inhalation, intratracheal installation, and injection or implantation) delivers different size
fractions of asbestos to a target tissue with varying efficiencies. For example, injection
or implantation studies deliver 1.00% of all size categories of structures to the target
tissue. However, the efficiency that each size category is delivered by inhalation is a
function of the aerodynamic properties of the asbestos structures and the air flow
characteristics of the lungs (Section 7,1). Thus, the relationship between dose and
exposure depends upon the route of exposure employed. Importantly, because the
ultimate goal is to understand the effects that inhaled fibers have on humans,
differences between the character of the delivered dose in an animal study and the
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character that such a dose would have, had it originally been inhaled, typically need to
be addressed.
Regarding the measurement of health effects, many of the results in animal studies
suffer from a lack of statistical significance because of small numbers of observed
tumors. Consequently, trends cannot be established conclusively. Interestingly,
several studies draw conclusions regarding the relative potency of different sample
types when variations within the dose/response trend of a single sample type is larger
than observed differences between types (REFERENCE).
For animal inhalation studies, meaningful comparison of the relative deposition of
asbestos dusts across species is not direct. To extrapolate results across species, the
detailed differences in the physiology of the respiratory tracts between the species need
to be addressed (Sections 4.4 and 7.1). However, if measurements are available for
both species, differences in physiology are addressed, and the manner in which tissue
burdens are analyzed is considered, it may be possible to compare relative tissue
doses (mass of asbestos per mass of tissue) across species.
5.4 IN-VITRO STUDIES
A broad range of in-vitro studies provide useful insight on the effects of asbestos.
These include, for example, studies in cell-free systems (which have been used to
evaluate such things as asbestos dissolution rates or the kinetics of free radical
formation on the surface of asbestos fibers) and studies of the effects of asbestos on
cultures of a broad variety of cell types and tissues.
As with other studies, the potential limitations and sources of uncertainty associated
with in-vitro studies need to be considered when evaluating the validity of study results
or comparing such results to those of other studies, particular studies of varying type.
Also, as with other studies, among the primary sources of uncertainty that need to be
addressed for in-vitro studies is the manner in which asbestos doses are characterized
and quantified (Section 5.1). For in-vitro studies (as with animal studies dosed by
routes other than inhalation), this also extends to the need to consider the relationship
between the character of the asbestos dose applied in vitro and the character that a
similar exposure might possess following inhalation exposure in vivo (Section 5.3). This
can be particularly problematic for studies in tissue cultures because it is not clear how
the application of a suspension of fibers (with known concentration) to a dish containing
cultured cells can be related to doses that reach corresponding tissues following
administration to whole animals.
In-vitro studies, of necessity, represent isolated components of living systems observed
under conditions that may vary radically from those under which such components
operate in vivo. Consequently, the behavior of such components may also vary
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radically from the behavior of the same components in vivo. Therefore, additional
study-specific considerations (concerning the design of a study and the conditions
under which a study is conducted) also need to be addressed before evaluating the
validity or relevance of the results from an in-vitro study to what might otherwise be
observed in a whole animals. Examples of such considerations include:
for cell-free systems:
• whether conditions under which the study is conducted are sufficiently
similar to conditions in vivo to expect that the observed effect is likely to
occur in vivo; and
• if the observed variables describing the nature or magnitude of the effect
are also likely to reflect what may occur in vivo;
for tissue cultures:
• whether conditions under which the study is conducted are sufficiently
similar to conditions in vivo to expect that the observed effect is likely to
occur in vivo;
• whether responses by specific tissues or cells in culture are likely to
behave similarly in vivo where their behavior may be suppressed,
enhanced, or modified in some other manner due to additional stimuli
provided by responses of other tissues and cells that are components of a
complete organism but that may be lacking in culture; and
• whether the conditions required to establish and maintain a tissue culture
for experimentation sufficiently alters the characteristics and behavior of
the cells being studied to minimize the relevance of results from such a
study to conditions in vivo.
Among the most important examples of the last consideration relates to the general
need to create immortalized cells to maintain tissue cultures. Thus, questions must
always be raised concerning whether the alterations required to create immortalized
cells for culture (from what are normally mortal cells in vivo) also alter the nature of the
responses being studied.
Note, many studies in the current literature also incorporate combined aspects of
several of the four general study types described in this chapter. For these studies, a
corresponding combination of the considerations described must therefore be
addressed when evaluating such studies and comparing their results with inferences
derived from the rest of the literature.
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6.0 EPIDEMIOLOGY STUDIES
The existing epidemiology studies provide Hie most appropriate data from which to
determine the relationship between asbestos dose and response in humans. As
previously indicated, however, due to a variety of methodological limitations
(Section 5.1) , the ability to compare and contrast results across studies needs to be
evaluated to determine the confidence with which risk may be predicted by
extrapolating from the "reference" epidemiology studies to new environments where risk
needs to be assessed. This requires both that the uncertainties contributed by such
methodological limitations and that several ancillary issues (identified in Chapter 2) be
adequately addressed.
A detailed discussion of the methodological limitations inherent to the available
epidemiology studies was provided in the Health Effects Assessment Update (U.S. EPA
1986) and additional perspectives are provided in this document (Section 5.1). The
manner in which the uncertainties associated with these limitations are addressed in
this document are described in Appendix A. As previously indicated (Chapter 2), the
ancillary issues that need to be addressed include:
• whether the models currently employed to assess asbestos-related risk
adequately predict the time-dependence of disease;
• whether the relative in-vivo durability of different asbestos mineral types
affect their relative potency;
• whether the set of minerals included in the current definition for asbestos
adequately covers the range of minerals that potentially contribute to
asbestos-related diseases; and
• whether the analytical techniques and methods used to characterize
exposures in the available epidemiology studies adequately capture the
characteristics of exposure that affect biological activity.
All but the third of the above issues are addressed in this Chapter. Currently, the third
issue can best be addressed by evaluating inferences from the broader literature (see
Chapter 7). The remaining issues are addressed separately for lung cancer and
mesothelioma following a brief overview of the approach adopted for evaluating the
epidemiology literature.
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6.1 APPROACH FOR EVALUATING THE EPIDEMIOLOGY LITERATURE
To develop dose/response relationships (and corresponding risk coefficients) for use in
risk assessment from epidemiological data, two basic types of information are
necessary: information on disease mortality in the study population (cohort) and
information on the asbestos exposure experienced by each member of the cohort. So
that disease mortality attributable to asbestos can be distinguished from other
(background) causes of .death, it is also necessary to have knowledge of the rates of
mortality that would be expected in the study population, absent exposure. Normally,
such information must be determined based on a "reference" or "control" population.
Ideally, one would like to have complete knowledge of exposure at any period of time
for each individual in the cohort and complete access to the data in order to fit different
types of dose/response models to the data so that the approach for evaluating the
relationship between dose and response can be optimized. In most instances,
unfortunately, the data suffer from multiple limitations (see Section 5.1 and Appendix A)
and the epidemiologist is further constrained by less than complete access to the data.
Briefly, the major kinds of limitations that potentially contribute to uncertainty in the
available epidemiology studies (and the effect such limitations likely produce on trends
in potency estimates) include;
• limitations in the manner that exposure concentrations were estimated
(likely increases random variation across studies);
• limitations in the manner that the character of exposure (i.e. the
mineralogical types of fibers and the range and distribution of fiber
dimensions) was delineated (likely .increases systematic variation between
industry types and, potentially, between fiber types);
• limitations in the accuracy of mortality determinations or incompleteness
in the extent of tracing of cohort members (likely increases random
variation across studies);
• limitations in the adequacy of the match between cohort subjects and the
selected control population (likely increases random variation across
studies and may have a substantial effect on particular studies); and
• inadequate characterization of confounding factors, such as smoking
histories for individual workers (likely increases random variation across
studies and may have a substantial effect on particular studies).
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More detailed discussion of the above limitations is provided in Section 5.1. The
manner in which these limitations are being addressed in this evaluation are described
briefly below and in more detail in Appendix A.
The existing asbestos epidemiology database consists of approximately 150 studies of
which approximately 35 contain exposure data sufficient to derive quantitative
dose/response relationships. A detailed evaluation of 20 of the most recent of these
studies, which includes the most recent followup for all of the cohorts evaluated in the
35 studies, based on the considerations presented in this overview, is provided in
Appendix A.
Importantly, this new analysis of the epidemiology database differs from the evaluation
conducted in the 1986 Health Effects Assessment Update (U.S. EPA 1986). Not only
does it incorporate studies containing the latest available followup for the exposure
settings previously evaluated and several additional studies addressing new exposure
settings, but the manner in which the analysis was conducted incorporates important,
new features. These new features include:
• estimation of more realistic confidence bounds for the dose-response
factors derived from each study; and
• for the lung cancer model, introducing a parameter, "a", which represents
the ratio of the background rate of lung cancer in the cohort relative to the
control population.
Confidence bounds were adjusted to account for uncertainty contributed by the manner
that exposure was estimated, by the manner that work histories were assigned, and by
limitations in the degree of followup, in addition to the traditional practice of accounting
for the statistical uncertainty associated with the observed incidence of disease
mortality. Thus, most of the major contributors to the overall uncertainty of each study
are now addressed, at least in qualitative fashion. A detailed description of how the
new confidence bounds were constructed is provided in Appendix A.
Note, in the text of this Chapter, we occasionally refer to significant differences between
certain measured or estimated values. When such differences are determined
informally (for example, by comparing confidence intervals) they are simply referred to
as "significant." When they are determined formally (by statistical test), they are
denoted as "statistically significant."
In this analysis, dose-response coefficients were estimated for each cohort both by
requiring that a =1 (the traditional approach) and by allowing a to vary while optimizing
the fit of the data. Allowing a to vary addresses such potential problems as differences
in the relative fraction of smokers among cohort and controls, respectively, or
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differences in background cancer rates due to lack of the appropriateness of the
selected control population for the cohort. As indicated in Appendix A, such
adjustments have a substantial effect on the fit of the EPA model to the data for several
specific cohorts and a corresponding effect on the estimates of the lung cancer dose-
response coefficients for those cohorts.
To better evaluate the sources of variation in estimated dose-response coefficients
observed across the available studies and potentially reconcile such variation so that
the precision of asbestos risk assessments might be improved (Sections 6.2 and 6.3),
we were also able to obtain the original, raw data for selected cohorts from a limited
number of some of the most important of the published epidemiology studies. This
allowed us to more formally evaluate the appropriateness of the existing U.S. EPA
models for lung cancer and mesothelioma, respectively, and to evaluate the effects of
such things as fiber biodurability. The models were evaluated by determining the fit of
the models to various representations of the time-dependence of mortality observed in
these raw data sets. A biologically based model, the two-stage model developed by
Moolgavkar et al. (Moolgavkar and Luebeck 1990) was also fit to the observed time-
dependence and contrasted with results from the U.S. EPA models.
Separately, we also completed an evaluation to consider questions concerning the
appropriateness of the range of fiber sizes characterized in the'published epidemiology
studies and whether adjusted exposure indices might improve the quality of asbestos
risk assessments.
6.2 LUNG CANCER
The Airborne Health Effects Assessment Update (U.S. EPA 1986) utilizes a model for
lung cancer in which the asbestos-related age-specific incidence of lung cancer, "t"
years from onset of exposure, is proportional to cumulative asbestos exposure at time t-
10 years, multiplied by the age and calendar year incidence of lung cancer in the
absence of asbestos exposure, A linear relationship between cumulative dose and
response was assumed based on the ten epidemiology studies identified (in the 1986
EPA document) as containing sufficient information to establish a dose/response curve
for asbestos induced lung cancer:
IL= lE[1+KLfd(MO)] (6.1)
where:
"IL" is the overall incidence of lung cancer (expected new cancers per year per
person) adjusted for age and calendar year;
"IE" is the corresponding cancer incidence in a population not exposed to
asbestos;
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"f is the concentration of asbestos (expressed as PCM fibers longer than
5 urn in the existing evaluations of the published epidemiology data);
"t" is the time since onset of exposure in years;
"d(t.10)" is the duration of exposure excluding the most recent 10 years; and
"KL" is the proportionality constant between dose and response. This is the
risk coefficient that represents the potency of asbestos.
The above model is a relative risk model in that it assumes that the excess incidence of
lung cancer from asbestos is proportional to the incidence in an unexposed population.
Since smokers have a much higher incidence of lung cancer, if smoking-specif ic
incidence rates are applied, the model predicts a higher excess incidence of asbestos-
related lung cancer in smokers than in non-smokers. This is consistent with the
multiplicative relationship between smoking and asbestos that has been observed in
epidemiological studies (see, for example, Hammond et al. 1979). Note that the KL in
the model pertains to an occupational pattern of exposure (e.g., 8 hours per day, 240
days per year) and must be modified before application to environmental exposure
patterns.
Equation 6.1 is generally written as follows to indicate that KL is a proportionality
constant between dose and response:
KL(f)(d') = RR-1 (6.2)
where:
"RR" is the relative risk and is equal to (IL/IE) and d' is now the duration
excluding the final 10 years before observation of total lung cancer
deaths.
6.2.1 The Adequacy of the Current EPA Model for Lung Cancer
Access to the raw epidemiology data from a small number of key studies allowed us to
evaluate the adequacy of the EPA model for describing the time-dependence for lung
cancer in asbestos exposed cohorts. For this analysis, the raw data for the cohort of
crocidolite miners in Whittenoom, Australia was graciously provided by Nick DeKlerk
(unpublished) and the raw data for the cohort of chrysotile textile workers (described by
Dement et al. 1994) was graciously provided by Terri Schnorr of NIOSH. The
Whittenoom cohort was originally described by Armstrong et al. (1988), but the data
provided by DeKlerk included additional followup through 1999.
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The EPA lung cancer model was fit to the raw data from both South Carolina and
Wittenoom. In these analyses, each person-year of followup was categorized by
cumulative exposure defined using a lag of 10 years, in accordance with the EPA lung
cancer model. The data were then grouped into a set of cumulative exposure
categories and the observed and expected numbers of lung cancers were computed for
each category. For South Carolina, expected numbers were based on sex-race-age-
and calendar-year-spedfi.c U.S. rates and separate analyses were conducted for white
males, black males, and white females, as well as for the combined group. For
Wittenoom expected rates were based on age- and calendar-year-specific rates for
Australian men. These data were also categorized by cumulative exposure lagged 10
years. The resulting fit of the EPA model to the data from Wittenoom and South
Carolina are presented in Tables 6-1 and 6-2, respectively.
The data in these tables were fit assuming that the observed cancers in each
cumulative exposure category had a Poisson distribution with mean response equal to
the expected response in the comparison population times a (1 + KL*CE10), where the
linear slope, KL, is the "lung cancer potency factor" in the EPA lung cancer model, and
CE10 is the mean cumulative exposure for a category computed using a lag of 10 years.
For the Wittenoom data (Table 6-1), the fit with a = 1 is poor (p < 0.0001), as the model
overpredicts the number of cancers in the highest exposure category and greatly
underpredicts at lower exposures. By contrast the fit of the model with a variable is
adequate (p = 0.1). This model predicts a high background of lung cancer in this cohort
(a = 2.1) and a fairly shallow slope, with the relative risk increasing only from 2.1 at
background to 3.6 in the highest exposure category. A test of the hypothesis that a = 1
for this cohort is rejected (P < 0.01) and the model fit to the data (with a variable)
predicts KL= 0.0047.
The fit of the EPA model to the South Carolina lung cancer data categorized by
cumulative exposure is shown in Table 6-2. The model with a = 1 cannot be rejected
both when the model is applied to white males only (p = 0.54) or with all data combined
(p = 0.92). Since the values a and KL estimated from white males only are similar to
those estimated using the complete cohort (black and white males and white females),
the fit to the complete data is emphasized in this analysis. This model fit to the data
predicts a = 1.2 and KL = 0.021 (f/ml-y)'1. A test of the hypothesis that a =1 for this
cohort cannot be rejected (P = 0.21).
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Table 6-1
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6.2.1.1 Time Dependence
The EPA model was next evaluated to determine whether it adequately describes the
time-dependence of the mortality observed in the Wittenoom and South Carolina
cohorts. The model predicts that relative risk will remain constant following 10 years
from the time of last exposure.
Table 6-3, shows the fit of the EPA model to the Wittenoom data categorized by time
since last exposure. In each category of time since last exposure, the number of lung
cancer deaths are predicted by the EPA model using the parameter values calculated
in Table 6-1, based on the expected cancers and the average cumulative exposure for
each category. This categorization allows investigation of the assumption inherent in
the EPA model that the relative risk remains constant following 10 years from last
exposure. The Wittenoom data appear to be consistent with this assumption, as the
excess relative risk per f/ml-y of cumulative exposure varies only between 0.059 and
0.073 for categories representing more than ten years from last exposure.
The conclusion that relative risk remains constant following '10 years from last exposure
among Wittenoom miners is further illustrated in Figure 6-1. Figure 6-1 is a plot of the
quantity: (relative risk - a)/(cumulative~exposure)~versus~time since last exposure.
Based on the modified form of the EPA model (in which a is allowed to vary), this
quantity, (RR-a)/(CE10), should be constant and equal to a*KL (beyond 10 years from
last exposure), figure 6-1 demonstrates that this is exactly the behavior observed
among Wittenoom miners, where the value of the quantity plotted on the Y-axis rises
rapidly until 10 years after exposure ends and then remains constant for all longer
times. Moreover, a test of the hypothesis that the slope of the data depicted in
Figure 6-1 (beyond 10 years after the end of exposure) differs from zero cannot be
rejected
Because it has been reported that asbestos fibers (especially chrysotile but including
amphiboles) dissolve in biological fluids and that animal studies suggest that the
tumorigenicity of fibers is proportional to in-vivo durability (Section 7.2.4), the
Wittenoom data categorized by time since last exposure was also fit to a modification of
the EPA lung cancer model in which the cumulative exposure variable is replaced by an
estimate of internal dose derived assuming first-order clearance of fibers. Results are
presented in Table 6-4
In developing Table 6-4, a preliminary investigation, based upon an "exact" likelihood
that did not involve categorization of exposures, indicated that this model provided a
best fit using a half-life of 11 years and a lag of 12 years. As this table indicates,
despite optimizing the model with respect to the lag and half-life, it did not fit the data as
well as the standard EPA model based upon cumulative number of inhaled fibers.
Thus, there is no evidence from the Wittenoom data that crocidolite fibers degrade (or
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Table 6-3
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Table 6-4
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Figure 6-1
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that the effects of crocidolite fibers are diminished) in vivo over time scales that are
short relative to a human life time and this is consistent with the data presented in
Section 7.2.4 that suggest crocidolite fibers likely last many decades in human lungs, at
a minimum. It is also consistent with the appearance of Figure 6-1, which shows no
decrease in the effect of crocidolite following cessation of exposure.
Table 6-5 shows the fit of the EPA model to the South Carolina data categorized by
time since last exposure. This table was constructed in the same manner as Table 6-3.
In contrast to the data from Wittenoom, the time dependence of the South Carolina
data cannot be adequately fit with the EPA model, whether a is held equal to one
(P < 0.0001) or allowed to vary (P < 0.0007). A plot of (RR-a)/CE10 versus time (Figure
6-2) indicates why.
In Figure 6-2 (as with the Wittenoom data), the quantity plotted on the Y-axis increases
sharply to a maximum at approximately 10 years following the end of exposure. In
contrast to what is observed for Wittenoom, however, the value of this quantity
decreases with time beyond the first 10 years following the end of exposure. This
decrease is clear from the figure, despite some scatter in the data. Thus, in contrast to
the assumption inherent to the EPA model, relative risk decreases precipitously in this
cohort after 10 years following the cessation of exposure. This suggests that the
effects of chrysotile are somehow diminished after long periods of time.
There are several reasonable hypotheses that .might explain the decrease in relative
risk with time within the South Carolina cohort. Among those that have attracted
interest is the possibility that risk is a function of biodurability (for example, see
Section 7.2.4). We therefore decided to explore this possibility.
Given that the expected lifetime for respirable chrysotile fibers in biological fluids is
anticipated to be only on the order of a year (see Section 7.2.4), further analysis of the
decreasing trend observed in Figure 6-2 is warranted. Therefore, the South Carolina
data were also fit using the modified form of the EPA model that accounts for internal
dose (rather than cumulative exposure). In this model, as indicated above, the internal
dose is derived from cumulative exposure, except it is assumed that, from the moment
that a fiber is deposited in the lung, it dissolves by a first order process. Actually, the
best analysis of the dissolution process indicates that it is likely zero order, but the first
order decay (see Section 7.2.4) is assumed as an approximation because this provides
an easier description of the lifetime of the material.
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Table 6-5
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Figure 6-2
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Table 6-6 shows the fit to the South Carolina data to the EPA model using internal
dose. This Table was derived from the South Carolina data in the same manner that
Table 6-4 was derived from the Wittenoom data. Since data appeared to be about
equally consistent with a range of lags and half-lives, for consistency, the same values
were used as with the Wittenoom data. This table indicates that this model provides an
adequate fit to the South Carolina data (P= 0.16).
Assuming a first order decay process, the best fit trend line to the data beyond 10 years
following cessation of exposure in Figure 6-2 suggests a half-life for chrysotile of
approximately 20 years. A similar analysis of the Whittenoom data suggests a half-life
for crocidolite that is > 700 years, which is no different than infinity.
Although this is in stark contrast to the dissolution half-life for chrysotile fibers estimated
based on in-vitro data, these results may not be inconsistent. There are at least two
reasonable explanations for this apparent discrepancy. First, the data in Figure 6-2
track the risk attributable to the presence of chrysotile, not the presence of chrysotile
itself. It is therefore reasonable that, even if the chrysotile fibers disappear readily, the
risk from their initial presence may decay only much more slowly; the biological
changes induced by the initial presence of the fibers may persist.
Second, as indicated in the in-vitro studies (see Section 7.2.4), the dissolution rate of
chrysotile is limited by the rate at which fluid flows past the material. The maximum
dissolution rate (i.e. the rate reported in the in-vitro studies) is observed only when flow
is adequate. Otherwise, partial saturation of the solution surrounding the chrysotile
fibers will slow the dissolution rate until it is only a fraction of what is observed in the
optimally designed studies. Therefore, that a slower dissolution rate is suggested by
the South Carolina data may simply indicate that the various biological compartments in
which the biologically active fibers are located do not allow for adequate flow of an
adequate reservoir of biological fluids to maintain the dissolution rate at its theoretical
maximum.
An alternate hypothesis is that the fibers are not dissolving at all but are simply being
sequestered in some manner that reduces their biological activity with time. Such a
hypothesis, however, does not appear consistent with the animal data that suggests
that tumorigenicity is indeed a function of the in-vivo durability of fibrous materials (see
Section 7.2.4). However, other hypotheses may also reasonably explain the data.
Therefore, although relative risk clearly decreases with time after cessation of exposure
among the South Carolina workers and such observations are not inconsistent with the
possibility that this is an effect of biodurability, it is premature to conclude such a link
without further study.
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Table 6-6
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Importantly, the time-dependent behavior described above for crocidolite and chrysotile
are each based on analysis of data from only a single study. Thus, although such
trends are also consistent with additional evidence from animal studies and other
implications from the literature, it would be useful to confirm these observations by
analyzing additional raw epidemiology data sets. If they can be acquired, the raw data
from Libby may serve as a second example for amphiboles and the lung cancer data
from Quebec may serve as a second example for chrysotile.
6.2.1.2 Comparison with biologically-based model
To further evaluate the behavior of the EPA model, results were compared with those of
a biologically based (two-stage) model.
Definition of the two-stage model. The two-stage model of cancer implemented in
this report (Moolgavkar and Luebeck, 1990) assumes that there is a population of
normal cells under homeostatic control whose size remains fixed at X. A normal cell
mutates at a given rate to an intermediate cell. (Mathematically, intermediate cells are
generated according to a non-homogeneous Poisson process with intensity v, which
implies that the per-cell mutation rate is v/X. For a given value of v the hazard function
does not depend upon X and, without loss of generality, X was fixed at 107 cells.) An
intermediate cell divides into two intermediate cells at a rate a, dies at a rate p, and
divides into one intermediate and one malignant cell at a rate u. (Mathematically, a
clone of intermediate cells is a non-homogeneous birth, death and mutation process.)
Importantly, the "a" in this model is in no way related to the "a" variable used to adjust
for background mortality in the EPA lung cancer model and the two should not be
confused.
The mortality hazard function (rate of dying of cancer at age t among persons who
survive to that age) is assumed to be the hazard for the occurrence of the first
malignant cell, with a delay of L years. That is, the mortality hazard at t years of age is
assumed to be h(t-L), where h is the hazard for the occurrence of the first malignant
cell. Thus, L represents the delay between the occurrence of the first malignant cell
and the subsequent death from the resulting cancer. Following the assumption of Doll
and Peto (1978) and Moolgavkar et al, (1993), a lag of L = 4 years was generally
assumed. We note that the lag in this model is conceptually different from the lag often
years used in the EPA models. Whereas the lags in the latter model were empirically
derived based on goodness of fit, the lag in the two-stage model has a mechanistic
interpretation and, consequently, should not necessarily have the same value.
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To introduce the effect of exposure to asbestos into the model, one or more of the
parameters v, a, p or u were assumed to be linearfunctions of the instantaneous
whole-body fiber burden, d(t), where t indicates age:
v(t) = v0 + v, * d(t),
aft) = a0 + a, * d(t),
P(t) = P0 + p1 * d(t),
The hazard function, h(t) and the corresponding survival function (probability of
surviving to age t without death from lung cancer in the absence of competing causes
of death), S(t) = exp(-JV h(s)ds, predicted by this model, although mathematically
complex, can be solved for explicitly whenever the parameters are piecewise constant
(Moolgavkar and Luebeck, 1990; Heidenreich et a/., 1997).
In application of this model to lung cancer (and mesothelioma) data from Wittenoom,
South Carolina, and Quebec: the fiber body burden, d(t), was computed (up to a
multiplicative constant) from a subject's exposure pattern, assuming a first-order
elimination process with an elimination rate of r (half-life of -Ln(.5)/r). In these
calculations, an average exposure rate during work hours was computed for each year
of work. Based on the first-order assumption, the body burden at time t resulting from
exposure to an average of f f/ml during the year between s and s+1 is
d(t) = (f/r)[1 - exp(-r(t-s)] fort^s+1
= (f/r)[1 - exp(-r)]exp[-r(t-s-1 )] for t > s+1 .
This expression does not include the multiplicative constant needed to convert from the
air concentration of asbestos to internal deposition rate, but without loss of generality is
assumed to be included in the parameters u1( a.,, p.,, and v.,.
The average body burden between t and t+1 years due to exposure between s and s+1
years was computed by integrating this expression from t to t+1 , The average body
burden during each yearwas then computed by summing the contributions to the
average from each year (by summing over s^t). The body burden d(t) was
approximated by the step function formed by these yearly average body burdens. With
this approximation the two-stage parameters v(t), a(t), P(t) and u(t) are piecewise
constant and the exact solution to the two-stage model for this case (Heidenreich et a/.,
1997) was used to calculate the hazard, h(t) and survival function, S(t). This calculation
was made using a FORTRAN subroutine kindly provided by Drs. Suresh Moolgavkar
and Georg Luebeck.
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This model was fit to the raw lung cancer and mesothelioma data from Wittenoom and
South Carolina, and to the raw mesothelioma data from Quebec using the method of
maximum likelihood. The contribution to the likelihood for a subject who began work at
age t0 and died of cancer at age t, was h^^S^/S^), and the corresponding
expression for a subject whose followup ended at t, without resulting in death from
cancer was S(t1)/S(t0). The complete likelihood was computed as the product of these
individual contributions. The software program, MINUIT, developed by CERN in
Geneva, was used to maximize the log-likelihood.
The full two-stage model described above has eight parameters (nine if the elimination
rate, r, is included), which is too many to be estimated reliably using the epidemiologic
data available. Moreover, the general model is not fully identifiable, as different
combinations of parameters can lead to the same hazard (Heidenreich etal., 1997).
Accordingly, in applications of this model, not all of the parameters were estimated
independently. For example, in most cases only one of the two-stage parameters (v, a,
(3, u) was allowed to be dose-related. Also, to reduce the number of estimated
parameters, in most cases either a or (3 was fixed at zero, because, when the mortality
rate is low, h(t) is practically a function of the difference between these two parameters.
Also, the elimination rate, r, was not estimated from the epidemiologic data, but was
determined from evidence on the durability of different types of asbestos fibers (see
Section 7.2.4).
Although the parameters in the two-stage model at least nominally represent
physiological phenomena, it must be realized that the model is, at best, likely to be a
highly idealized representation of a very complex process (see Section 7.3.1).
Accordingly, the results from this model are probably best used to investigate the
relative contributions to risk from exposures at different ages (e.g., if the model predicts
that v is dose-related, then earlier exposures are more important in determining risk,
whereas if the model predicts that u is dose-related, then more recent exposures are
more important). Moreover, if the model predicts that v is dose-related, the offending
toxic substance can be considered to be acting as a classical initiator; if a (or, more
precisely, a - (3) is dose-related, the toxic substance is more likely a promoter. If u is
dose related, the toxin is likely a late-stage actor, which also would present different
behavior than an initiator. An important advantage of the two-stage model is that, since
it models risk as a function of internal dose, it can naturally incorporate consideration of
fiber clearance.
Applying the two-stage model to lung cancer. Table 6-7 summarizes the results of
three applications of the two-stage model to the lung cancer data from Wittenoom, and
Table 6-8 summarizes a similar application of the model to the South Carolina lung
cancer data. With each set of data, the model is fit allowing either v, a, or u to be a
linear function of the fiber body burden. In keeping with the half-life estimates based on
the durability of chrysotile and amphibole fibers in Section 7.2.4, for the South
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Table 6-7
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Table 6-8
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Carolina data, the half-life of internally deposited fibers was assumed to be 0.5 years (r
= -ln(0.5)/20 = 1.39/year) and, for the Wittenoom data, the half-life was assumed to be
20 years (r = 0.035/year).
Note that these analyses were completed before the analysis of the time-dependence
'of the data using the EPA model, so that we did not have the opportunity to go back
and run this biological model using half-lives that might better represent what has been
observed with the time-dependence of the Wittenoom and South Carolina data
(Section 6.2.1.1). However, now that we have developed an approach that allows
evaluation of the time-dependence of lung cancer mortality based on estimates of
internal dose using the EPA model, further analysis using the two-stage model may not
be warranted (see Section 6.3.1).
When applied to the Wittenoom data the model provided comparable descriptions of
the data (i.e., similar likelihoods) when either the proliferation rate of intermediate cells,
a, or the mutation rate of intermediate cells, u, was assumed to be dose related.
These models provided a much larger likelihood than the model in which the mutation
rate of normal cells, v, was assumed to be dose-related. Note that the "likelihoods"
reported in the table are "negative likelihoods" so that the smaller the number in the
table, the larger the actual likelihood.
Table 6-9 shows the fit of the best-fitting 2-stage model to the Wittenoom data
categorized by time since last exposure, compared to the fit of the EPA lung cancer
model. As this Table shows, the simpler EPA model fits these data better than the 2-
stage model, although the fit of both models is adequate (lack of fit p-value = 0.12 for 2-
stage model and 0.35 for EPA model).
The above indicates that the best-fitting model runs for Wittenoom adequately describe
the time-dependence for lung cancer and further indicate that asbestos primarily affects
a (and potentially u). This suggests that crocidolite may be acting primarily as a
promoter (or at least a late-stage actor) rather than an initiator toward the induction of
lung cancer, which is consistent with inferences from several recent in-vitro studies
(Section 7.3.3.4 and 7.3.4.7) and a recent retrospective cohort study of retired workers
(Sanden etal. 1992).
Table 6-10 shows the fit of the 2-stage model (in which v, the mutation rate from normal
to intermediate cells is dose-related, i.e. the best-fitting model) to the totality of the
South Carolina data categorized by time since last exposure, compared to the fit of the
EPA model. Although the 2-stage model fits the data only marginally well (p = 0.05), it
fits the data much better than the EPA model (p < 0.001). The EPA model
underpredicts the number of cases for short times since end of exposure.
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Table 6-9 f
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The results with the South Carolina data provide a stark contrast to the Wittenoom
results. With the South Carolina data, the model with v dose-related provided the best
fit. Thus, the results from South Carolina suggest that chrysotile may be acting
primarily as an initiator. Such an interpretation must be tempered, however, by several
inconsistencies and potential problems with the South Carolina fits that may be
attributed, among other possibilities, to the short half-lives assumed for chrysotile.
The two fits (for South Carolina and Wittenoom) also provide very different estimates of
the background parameters (v0, a0 and u0). The u0 and v0 estimated from the South
Carolina data both are about 250 times larger than those estimated from the Wittenoom
data. Although these rates could differ somewhat in these cohorts, owing to different
lifestyles and genetic traits in the two populations, these differences appear too large to
be attributable to such causes.
Due to the apparently conflicting results from the analyses of the lung cancer data from
Wittenoom and South Carolina, we conducted several additional analyses to attempt to
identify the source of the apparent conflict. We initially explored the possibility that the
assumed half-life for crocidolite was too long and completed additional model runs
assuming a 0,5 yr half-life (identical to what was assumed for South Carolina) and
these results are described below. Subsequently, we discovered during our analysis of
time-dependence using the EPA model (see Section 6.2.1.1), that the half-lives
measured in vitro for these fibers are likely much too short, which suggests a solution to
the apparent conflict and this is also described below. Unfortunately, however, this
revelation came too late to allow additional, confirmatory runs with the two-stage model
to be completed in time for this report. Therefore, the apparent conflict between the
Wittenoom and South Carolina results (at least with regard to the two-stage model)
remains to be definitively resolved.
The results of the two-stage model are based on the assumption that one of the two-
stage parameters is a linear function of the instantaneous body burden predicted by a
first order retention model that relies on dissolution lifetimes for the asbestos fibers that
are observed in vitro (Section 7.2.4). Alternately, it could be the case that an inhaled
fiber is soon neutralized in some manner (e.g. by some type of sequestration
mechanism) so that any contribution to cancer induction must occur soon after
exposure. To explore this possibility, the two-stage model was also fit to the Wittenoom
data assuming a half-life of only 0.5 year (the same value used for the South Carolina
cohort) and results are indicated in Table 6-11.
When the Wittenoom data are evaluated assuming a fiber half-life of 0.5 yrs, as was
the case using a 20-year half life, the best-fitting of the three submodels considered
was the one with a dose-related. This fit using this submodel was not appreciably
worse than that obtained assuming a 20-year half life (difference in likelihoods of 1.16).
C
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Table 6-11
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However, with a 0.5-year half-life, the fit of the model that assumed u was dose-related
was appreciably worse than the comparable model assuming a 20-year half life
(change in likelihoods of 5). As this analysis demonstrates, choice of an appropriate
internal dose can be confounded with selection of the appropriate two-stage parameter
that is affected by that dose. The analysis also implies that, due to the number of
variables that can be optimized, the two-stage model does not appear to be a good
discriminator for evaluating the biodurability of fibers.
These results do not suggest that asbestos acts at short times and is then sequestered.
However, they do suggest that favored dose-dependent variables may be strong
functions of other constraints placed on the model (such as the assumed half-life of the
fibers), This further implies that the South Carolina results reported above might be
anomalous, due to the assumption of a half-life that is short relative to the actual rate at
which risk is observed to decrease for this cohort (based on the results from the
analysis using the EPA model, Section 6.2.1.1).
It is unfortunate that it now appears that we may have evaluated these data using the
wrong estimates of dissolution half-lives (which were, nevertheless, based on the best
estimates from the literature). Our analysis of the time-dependence of the data using
the EPA model (Section 6.2.1.1) suggests that both crocidolite and chyrsotile dissolve
in vivo much more slowly than in vitro studies suggest and that this should not be
surprising given that such materials only dissolve at their optimum rates when the fluid
they are dissolving in is rapidly and continually renewed. Fluid flow rates in most
compartments of the lung may simply not be adequate to facilitate dissolution of
asbestos at the maximum rate.
It would be interesting to rerun the Moolgavkar model using half-lives based on what
was observed with the analyses using the EPA model (approximately 20 years for
chrysotile and > 700 yrs or virtually infinite for crocidolite). The goal would be to
determine whether the fitting the South Carolina data might then show that chrysotile
also acts primarily as a promoter (in agreement both with what is observed for
Wittenoom and with what is indicated by the most current mechanism studies, see
Sections 7.3,3.4 and 7.3.4.7).
That the two-stage model with v dose-dependent only marginally fits the time-
dependence of the South Carolina data, may further reflect the inadequacy of this
model (because the assumed half-life for asbestos is much too short). Incorporating a
longer half life may improve this fit. In contrast, model fits for Wittenoom in which
longer half-lives were assumed (although still short based on observations reported
from our analysis using the EPA model), do adequately describe the time-dependence
for lung cancer mortality observed in that cohort. As previously indicated, however, the
marginal benefit derived from the two-stage model in this application may not be
justified compared to the cost and effort that may be required to obtain the new results.
Especially given that we can now address questions concerning bio-durability with the
adaptation we developed for the EPA lung cancer model (Section 6.2.1.1).
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6.2.2 Estimating KL's from the Published Epidemiology Studies
The EPA model for lung cancer (described in Section 6.2.1.1) was applied to each of
the available epidemiology data sets to obtain study-specific estimates for the lung
cancer risk coefficient, h^. Based on the results presented in Section 6.2.1.1, there is
no indication that the current model does not provide an adequate description of lung
cancer mortality, at least in association with exposure to amphiboles. There is some
indication that the current model may not provide an adequate description of the time-
dependence of mortality following exposure to chrysotile. However, it is believed that
the effect that such deviations might have on estimates of K^ derived using the model
are likely to be small, except in rare cases when studies incorporate long periods of
followup after cessation of exposure. It will be prudent to evaluate the observed
deviations further (if additional raw data sets can be obtained) and to adapt the current
model to account for the observed effect, should it be confirmed in other studies.
However, at this point in time, we see no reason not to proceed with the analysis of
published studies using, the existing model.
The set of KL values derived from available epidemiology studies are presented in
Table 6-12. Note that this table is a reproduction of Table A-1 in Appendix A.
In Table 6-12, Column 1 lists the fiber types for the various studies, Column 2 lists the
exposure settings (industry type), and Column 3 indicates the specific locations studied.
Column 4 presents the best estimate of each KL value derived for studies, as reported
in the original 1986 Health Effects Update and Column 5 presents the reference for
each respective study. Columns 6, 7, and 8 present, respectively; the best-estimates
for the KL values derived for all of the studies currently available (including studies
corresponding to those in the original Health Effects Update); the estimated lower and
upper bounds for each KL value (derived as described in Appendix A); and the
reference for the respective study from which the data were derived. To assure
comparability across studies, values for all studies (even those that have not been
updated since their inclusion in the 1986 Health Effects Update) were re-derived using
the modified procedures described in Appendix A. The remaining columns of the table
present information relevant to risk coefficients for mesothelioma and are discussed in
Section 6.3.2.
The Kt values derived In this study and the corresponding values derived In the original
1986 Health Effects Update generally agree; most vary be no more than a factor of 2, a
couple vary by a factor of 3, and one varies by a factor of about 5. In no case,
however, are the differences significant (based on visual comparison of confidence
intervals).
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Table 6-12 (
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Perhaps the most interesting of the changes between the 1986 KL value estimates and
the current KL value estimates involves the friction products plant in Connecticut
(McDonald et al. 1984). Although a relatively small, positive dose-response was
estimated from this study in the 1986 Health Effects Update, the best current estimate
is that this is essentially a negative study (no excess risk attributable to asbestos). The
difference derives primarily from allowing a to vary in the current analysis. The
exposure groups in this cohort do not exhibit a monotonically increasing dose-response
relationship and, in fact, the highest response is observed among the group with the
lowest overall exposure. Thus, the most likely explanation for the observed relative risk
in this cohort is that there is poor agreement in the background cancer rates between
the study cohort and the control population. As indicated in Appendix A, lack of a
monotonically increasing dose-response relationship is a problem observed in several
of the studies evaluated.
Among the KL values derived in the current study, the lowest and highest of the best-
estimate values differ by a factor of 90 (excluding the two negative studies, which would
make the spread even larger) and several of the pair-wise comparisons across this
spectrum are significant (based on a comparison of their confidence intervals). For
example, the KL value derived for the chrysotile miners in Quebec is significantly
smaller than the KL values derived for ch rysotile textile workers (in eithe r of the two
studies for South Carolina, which are of highly redundant cohorts in the same plant), for
textile workers in the Roachdale, UK plant, and amosite insulation manufacturers (in the
Seidman study).
The KL values and the associated confidence bounds derived in the current study are
plotted in Figure 6-3. Each exposure environment is plotted along the X-axis of the
figure and is labeled with a four-digit code that indicates fiber type (chrysotile, mixed,
crocidolite, or tremolite), industry (mining, friction products, asbestos-cement pipe,
textiles, insulation manufacturing, or insulation application); and a two-digit numerical
value indicating the study from which the data were derived. A key is also provided. In
the Figure, the chrysotile studies are grouped on the left, amphibole studies are
grouped on the right, and mixed studies are in the middle. Note also that studies
conducted in the same facility (generally among highly overlapping cohorts), such as
the Dement et al. (1994) and the McDonald et al. (1983a) studies of the same South
Carolina textile facility, are combined (averaged) and presented as a single entry in the
Figure. The two Libby Vermiculite studies were similarly combined.
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Figure 6-3
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Key for Figure 6-3
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Comparisons of KL values across the available studies are instructive. Within chrysotile f
studies alone, lowest and highest KL values vary by approximately a factor of 80 (again,
excluding the negative friction products study), which is little different than the range
observed across all studies. Moreover, as indicated above, the differences between the
lowest (non-zero) value (for Quebec miners) and the highest value (textile workers) is
significant. Differences between the negative friction product study and the other KL f
estimates for chrysotile are not significant, primarily due to the wide confidence interval
associated with the negative study.
Among the apparent variations, differences in lung cancer potency observed among
Quebec miners versus that observed among South Carolina textile workers has been
the subject of much discussion and evaluation, which is worthy of review (Section <
6.2.3). In fact, the inability to reconcile these differences, appears to be among the
biggest obstacles to reliably estimating an overall chyrsotile potency factor for lung
cancer.
Among "pure" amphibole studies, the lowest and highest of the best-estimate KL values ^
vary by a factor of approximately 20 and, interestingly, the two extremes both derive
from within the same industry (amosite insulation). However, these two estimates for
amosite insulation are not significantly different (based on a comparison of their
confidence intervals) Taking the arithmetic mean of the values for the two amosite
insulation studies, results indicate that KL values estimated, respectively, for crocidolite
mining, amosite insulation manufacture, and mining of vermiculite contaminated with ^
tremolite vary by less than a factor of 3, which constitutes stellar agreement (given the
magnitude of the associated confidence intervals, as shown in Figure 6-3), if this is
indeed the appropriate interpretation. However, alternate interpretations cannot be
excluded.
(
As indicated in Section 6.2.3, for example, it is possible that mining studies tend to
exhibit low KL values relative to studies of asbestos products industries. As indicated in
that section, this may be due to the presence of large numbers of cleavage fragments
in the dusts, which may not contribute to biological activity (because the majority of
these may not exhibit the requisite size to be biologically active) but which,
nevertheless, are included in estimates of asbestos concentrations in the original ^
epidemiology studies. Under this interpretation, the KL estimates for Libby and for
Wittenoom might both be raised substantially, if they could be adjusted for similar
effects, so that they are closer in value to that obtained from the Seidman study. Then
the "outlier" Levin study might be re-evaluated to see if inadequate consideration of lag,
the relatively young age of the cohort studied, or other factors might contribute to a low c
KL estimate for this study. As indicated in Section 6.2.3, such considerations have
implications both for identifying appropriate size ranges of structures to be included in
the analysis of asbestos and for evaluating the relative potency of chrysotile and the
amphiboles. Of course, because the difference between the KL values from the Levin
et al. study and the Seidman study do not appear significant, such differences may ,
simply be due to unavoidable statistical variation.
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It is also instructive to compare variation within and between industries. Within
industries (especially for a single fiber type), the data are limited. The studies from the
two chrysotile mines (Quebec and Italy) show remarkably close agreement, varying by
less than about 20%. However, the studies of the two amosite insulation plants
(Paterson, New Jersey and Tyler, Texas) show Kt values that vary by a factor of 20;
due primarily to the uncertainty of the KL value for the Tyler facility; these differences
are not significant. As previously mentioned, the same equipment (literally) was used at
both of these insulation plants and the sources of asbestos were also apparently
similar, although the cohorts at the two plants are entirely independent.
The differences between the KL values from the New Jersey and Texas facilities may
represent the practical limit to agreement within industries that can be expected.
However, it is also possible that an "explanation" for the difference may eventually
present itself.
Across all asbestos types (including mixed), the asbestos-cement pipe industry shows
the greatest variation, including a negative study (best estimate KL = 0) and three
positive studies with KL values that range amongst themselves by a factor of 17. The
friction products industry includes one negative and one positive study. Better
agreement is observed among textiles. The two mixed textile plants show KL values
that vary by no more than a factor of 2.5 from each other and from the pure chrysotile
textile plant value. However, given the variation observed within other industry groups,
such agreement may be fortuitous.
KL estimates within the mining industry (but between fiber types) range over a factor of
20, from a low of 0.029 for Quebec miners to a high of 0.62 for tremolite in vermiculite
miners. Based on inspection of confidence intervals, however, none of the pairwise
differences within this industry appear significant.
6.2.3 The Variation in Kt's Derived for Chrysotile Miners and Chrysotile
Textile Workers
The difference between the observed risk of lung cancer for comparable levels of
chrysotile exposure among Quebec miners (most recent followup: Liddell et al. 1997)
and South Carolina textile workers (McDonald et al, 1983a and Dement et al. 1994) has
been the focus of much attention. Although the discrepancy in lung cancer risk
between Connecticut friction products workers (McDonald et al. 1984) and South
Carolina textile workers is potentially even greater (Table 6-12), the large uncertainties
associated with the risk estimate in the friction products study make comparisons with
friction products more difficult to evaluate (Section 6.2,2). Moreover, reasonably good
agreement between results from the Quebec studies and another study of chrysotile
miners in Italy (Piolatto et al. 1990) coupled with reasonably good agreement between
results from the South Carolina Plant and results from two other textile plants: one in
Mannheim, Pennsylvania (McDonald et al. 1983b)and one in Roachdale, England (Peto
1980), suggest that the difference between Quebec and South Carolina may reflect a
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general difference between the two industries, rather than specific locations (see Table f
6-12 and Section 6.2.2). This appears true despite the fact, for example, that cohorts at
the two other textile plants may have been exposed to mixed fiber types (amphiboles as
well as chrysotile), see Appendix A and Section 6,2.2. Importantly, this "discrepancy"
has been recognized as an issue for lung cancer only, the differences in mesothelioma
rates observed in the Quebec mines and the South Carolina textile plant are not widely
disparate (see Table 6-12).
Three main hypotheses have been advanced to explain the difference in the risk per
unit exposure observed among miners and textile workers (see, for example, Sebastien
etal. 1989). These are:
(
(1) the low reliability of exposure estimates in the various studies;
(2) differences in fiber size distributions in the two industries (with textile-related
exposures presumably involving greater fractions of longer fibers); or
(3) simultaneous exposure to a co-carcinogen (oil that may have been sprayed on
the asbestos fibers) in the textile industry.
It has also been proposed that differences in the concentration of long tremolite
(amphibole) fibers in dusts from each of the two industries might represent an
explanatory factor (see, for example, McDonald 1998b). However, this would also t.
require a large relative difference between the potencies of tremolite (amphiboles) and
chrysotile toward the induction of lung cancer. This latter issue is addressed further in
Section 6.2.4. McDonald (1998b) also presents an overview of the current status of
each of the hypotheses described above.
(
In an attempt to distinguish among the above-listed hypotheses, Sebastien et al. (1989)
conducted a study to determine lung fiber concentrations in tissue samples from
deceased members of the cohorts studied from both the Quebec mines (specifically,
from the Thetford mine) and the South Carolina textile plant. These researchers
ultimately analyzed tissue samples from 72 members of the South Carolina cohort and
89 members of the Thetford (Quebec) cohort. Because the tissue samples came from ^
cohort members, they could be matched with estimates of the exposure experienced by
each of the individuals as well as details concerning the age at first employment, the
age at death, the years of employment, and the number of years following employment
until death.
C
In the Sebastien et al. study, tissue samples were obtained in formalin-fixed or paraffin
blocks, which were then digested in bleach, filtered, and analyzed by TEM. Tissue
samples were apparently "opportunistic." Only fibers longer than 5 urn with an aspect
ratio greater than 3:1 were included in the count. For consideration of the limitations
associated with such preparations, see Section 5.2.
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Results from matching of tissue samples with the histories of corresponding cohort
members indicate that tissue samples obtained from each cohort covered a broad
range of exposure levels, duration of exposure, and years since the end of exposure.
They also indicate that South Carolina cohort members included in the Sebastien et al,
study experienced, on average, 13.5 years of exposure with 18.1 years between the
end of exposure and death. In contrast, Thetford workers included in this study
experienced an average of 32.6 years of exposure with only 11.6 years between the
end of exposure and death, Corresponding to differences in exposure levels observed
across the two cohorts in the original epidemiology studies, mean exposure levels
experienced by Thetford cohort members included in this study were about 10 times
mean exposure levels experienced by South Carolina workers (19.5 mpcf vs. 1.9 mpcf).
Because Sebastien and coworkers recognized the general lack of a good model
describing the retention and clearance of asbestos fibers in the lungs at the time their
study was conducted, they performed most of their analyses either on pairs of members
(one from each cohort) matched for duration of exposure and time since end of
exposure or on groups of members from each cohort similarly stratified by duratbn of
exposure and time since end of exposure.
Results from their study indicate that, overall, lung burdens observed among Thetford
cohort members are substantially higher than those observed among South Carolina
cohort members. Geometric mean lung chrysotile concentrations are reported to be 5.3
and 0.63 fibers/[jg dry lung tissue in Thetford workers and South Carolina workers,
respectively. Furthermore, despite tremolite representing only a minor contaminant in
the chrysotiie from Quebec and the dusts to which the miners were exposed (Sebastien
et al. 1986), the majority of fibers observed in the lungs of Thetford miners were in fact
tremolite (mean concentration 18.4 f/ug dry lung). Since the raw material used in the
South Carolina plant came largely from Quebec, tremolite was also expected to be a
minor contaminant in the dusts to which textile workers were exposed, Yet among
these workers also, tremolite represented a substantial fraction of the lung fibers
observed (mean concentration 0.36 f/ug). Thus, the ratio of tremolite concentrations
observed among Thetford miners and that observed among South Carolina workers
(18.4:0.36 or 51) is even more extreme than the ratio observed for chrysotile (8.4),
To evaluate the first of the above-listed hypotheses, It is instructive to compare the
ratios of chrysotile or tremolite fibers observed in the lungs of deceased workers from
Thetford and South Carolina, respectively, with the overall exposures that each
received, A rough estimate of cumulative exposure for each set of workers in the
Sebastien et al, (1989) study representing each cohort can be derived as the product of
the mean duration of exposure and the mean intensity of exposure, Thus, for example,
mean cumulative exposure in Thetford was 32.6 years x 19.5 mpcf or 635.7 mpcf-yrs.
Similarly, for South Carolina, mean cumulative exposure was 25.65 mpcf-yrs, which
gives a Thetford/South Carolina ratio of 24,8. This presumably represents the relative
cumulative exposure to chrysotile, For tremolite, Sebastien et al..report that, based on
a regression analysis, air concentrations of tremolite were likely only 0.4 times as much
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in South Carolina than in Thetford (where they likely averaged 1% of total fibers).
Therefore, the ratio of cumulative exposures to tremolite for the sets of cohort members
studied by Sebastien is likely 62.
Comparing the ratio of Thetford:South Carolina lung burden estimates with the ratios of
the corresponding cumulative exposures, it appears that the chrysotile lung burden ratio
(8.4) is only a third of the ratio predicted based on cumulative exposure (24.8).
However, the ratio of lung tremolite concentrations (51) is much closer to the
corresponding cumulative exposure ratio (62). It thus appears that, although, airborne
concentrations may not closely track the exposures that led to the observed lung
burdens, the overall trend in exposures predicted by airborne measurements is
approximately correct. It is therefore likely that overall exposure concentrations in
Thetford were in fact substantially higher than in South Carolina (in agreement with
airborne measurements). Thus, we concur with Sebastien et al. that the unreliability of
exposure estimates in these two cohorts is unlikely to explain the observed difference in
the risk per unit of exposure observed for each cohort.
Importantly, although the general trend in relative overall exposure levels predicted by
airborne measurements between Thetford and South Carolina appear to have been
confirmed by mean lung fiber concentrations in the Sebastien et al. (1989) study,
estimated exposures appear to do a very poor job of predicting lung burdens for any
particular individual. To demonstrate this, we analyzed the Thetford: South Carolina
ratios of lung chrysotile concentrations and, separately, lung tremolite concentrations
reported by Sebastien et al. for their set of 32 matched pairs of cohort workers to
determine whether trends in these ratios adequately matched trends in the
corresponding estimated airborne exposure level ratios for the same matched pairs. To
do this, we subjected the ratios presented in Table 7 of the Sebastien et al. (1989)
study to a Rank Von Neuman test (Gilbert 1987). Results indicate that trends in
neither lung chrysotile concentration ratios nor lung tremolite concentration ratios can
be predicted by the observed trend in the estimated airborne concentration ratios
among these 32 matched pairs.
There are numerous sources of potential uncertainty that may mask the relationship
between airborne exposure estimates and resulting lung burdens (Section 5.2).
Potentially the largest of these is the variation expected among lung burden estimates
derived from use of "opportunistic" tissue samples, which are not controlled for the
portion of the respiratory tree represented by the sample, Even for samples collected
from adjacent locations in lung parenchyma, observed fiber concentrations may vary
substantially and such variation is magnified between samples taken from different
individuals at locations in the lung that may not in any way correspond to their relative
position in the respiratory tree.
Other potentially important sources of variation that may mask the relationship between
airborne exposure concentrations and resulting lung burden estimates may primarily
involve limitations in the degree to which the airborne estimates from an epidemiology
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study represent actual exposures to the individual members of a study cohort (Section
5.1). Thus, such things as: potential differences between individual exposures versus
area concentrations (which are what is typically measured), the adequacy of
extrapolation to the earliest exposures in a cohort (when measurements were generally
not available), or the adequacy of estimating job x time matrices for individual workers
that can then be integrated with work area exposure estimates to derive individual
exposure estimates may all contribute to the uncertainty of exposure estimates.
The second of the above-listed hypotheses, involves potential differences in the size of
structures that may have been present in the airborne concentrations in Thetford and
South Carolina, which may not have been adequately represented by the exposure
measurements. More generally, this is a question of the degree to which measured
exposures in the two environments adequately reflect potential differences in the
character of exposure that relate to biological activity.
Sebastien et al. (1989) considered this second hypothesis by generating and comparing
size distributions for the fibers observed in the lungs of workers from Thetford and,
separately, South Carolina. Importantly, the size distributions for each cohort were
generated by including the first five fibers observed from every member of that cohort,
without regard to the duration of exposure, level of exposure, or time since exposure
experienced by each cohort member. Therefore, the size distributions obtained are
"averaged" over very different time frames during which differing degrees of fiber
retention and clearance will have taken place, each of which potentially alters the
distributions of fiber sizes (Section 7.2). Thus, the two distributions generated are each
actually collections of samples from multiple, varied size distributions (rather than single
distributions) and this likely masks distinctions between the two work environments. It
is therefore not surprising that the authors found relatively little differences in the two
size distributions.
The portion of the generated size distributions that are least likely to have been affected
by the limitations due to the manner in which they are generated (as Sebastien et al.
suggest) is the fraction of tremolite (amphibole) fibers longer than 20 urn. This is
because (1) tremolite fibers (unlike chrysotile) are biodurable and (2) biodurable fibers
longer than approximately 20 urn have been shown to clear from the lung only very
slowly, if at all (Section 7.2). Thus, the Thetford:South Carolina ratio of long tremolite
fibers may provide the best indication of the relative size distributions in the two
environments.
Table 6-13 presents a table of the estimated, relative concentrations of specific lengths
of fibers observed in lung tissue among Thetford miners and South Carolina workers,
respectively. The length category for various fibers is presented in the last column of
the table. The estimated concentrations, presented in Columns 2 (for Thetford) and 3
(for South Carolina) of this table were derived as follows. For the first length category
(L > 5 urn), concentrations are taken directly from Table 5 of the Sebastien et al. paper
(the geometric means are presented). Concentrations for the remaining length
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categories were estimated by multiplying the concentrations for this first length category
by the fraction of the size distribution represented by each succeeding length category
(as provided in Table 4 of the Sebastien et'al. paper). So that the relative precision of
these concentration estimates can be evaluated, an estimate of the numbers of fibers
included in each length category (from the total used to derive the size distribution in
Table 4 of Sebastien et al.) are provided in Columns 6 (for Thetford) and 7 (for South
Carolina), respectively. The Thetford:South Carolina ratios of the concentrations of
fibers in each length category (for each fiber type) are provided in Column 5 of the
table.
It is instructive to compare the ratios presented in Table 6-13 to the Thetford :South
Carolina ratios of mean cumulative exposures estimated above for chrysotile and
tremolite among the cohort members included in the Sebastien etal. study (24.8 and
62, respectively). As indicated in Table 6-13, for chrysotile, the ratio remains
approximately constant at about 9 (varying only between 8.4 and 10) for all of the size
ranges reported except the longest. For the longest category (L > 20), however, the
ratio drops to 5. Because fibers longer than 20 urn are expected to be the most
persistent in the body (Section 7.2), it may be that the ratio of 5 best represents the
relative concentration of long chrysotile structures among the two sets of cohort
members.
Because this ratio (for the long fibers found in the lung) is only approximately one fifth
of the estimated ratio for the cumulative exposure to chrysotile (24.8), this suggests that
the South Carolina cohort may indeed have been exposed to dusts enriched in long
fibers relative to dusts experienced at Thetford. Because the estimate of this ratio is
based on counts of at least 11 fibers from Thetford and South Carolina, respectively, it
is unlikely that this ratio will vary by more than a factor of two or three (the 95%
confidence interval around 11 fibers, based on a Poisson distribution is 6 to 19).
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Table 6-13
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The trend with tremolite is even more striking. Moreover, as previously indicated,
because tremolite fibers are biodurable, it is the tremolite fibers longer than 20 urn that
may best represent the ratio of long fibers to which these two groups of cohort
members were exposed. The ratios observed among tremolite fibers steadily decrease
from approximately 50 for fibers longer than 5 urn to 4.4 for fibers longer than 20 urn,
although this last value is uncertain (due to it being based on only 1 fiber observed
among Thetford-derived lungs and only 4 fibers among South Carolina-derived lungs).
In fact these data are statistically consistent even with a ratio considerably less than
one, (i.e., with a considerably higher concentration of long tremolite fibers in South
Carolina than in Quebec). Given that the ratio of the original cumulative exposures for
tremolite was estimated to be 62, that the ratio of long tremolite fibers is only 4.4
suggests that dusts in South Carolina may have been highly enriched in long fibers.
Observations that the fibers to which textile workers were exposed were longer and
thinner than those found in mining are further supported by various published size
distributions of fibers determined in air samples collected in these environments (see,
for example, Gibbs and Hwang 1975, 1980). Also, as noted in Curmp et al. (1985), the
raw fiber purchased by textile plants was commonly described as the longest grade of
product (see Table 22 of Crump et al.) Size issues are addressed further in Section
6.2.4.
At this point it is worth mentioning some of the potential differences in the
characteristics of mining dusts and textile mill dusts that may affect biological activity,
but that may not be adequately delineated when measuring exposures by PCM (in f/ml)
and almost certainly not delineated when exposures are measured by midget impinger
(in mpcf), see Section 4.3. During the mining of asbestos, only a small fraction of the
rock (generally no more than 10%) that is mined is typically composed of the fibers of
interest.
While the host rock in a mine may be of similar chemical composition, it generally
represents an entirely different crystalline habit. Nevertheless, a large fraction of the
dust that is created during mining is likely composed of fragments from the host rock
and many of these fragments will be of a size that would be included in the particles
counted by midget impinger. Furthermore, at least some fraction of the fragments
created by the crushing and cutting of the host rock will be elongated "cleavage"
fragments (Section 4.0) so that at least some fraction of these may be included even in
PCM counts, despite many of them being either too thick to be respirable or too short or
thick to be biologically active (see Section 7.2). Note, although Sebastien et al.
employed TEM to characterize fibers in their 1989 study, they apparently employed a
fiber definition that was sufficiently broad that they too would have counted large
numbers of structures that may not contribute to biological activity.
In comparison, the dusts created in a textile factory are likely composed almost
exclusively of true asbestos fibers. The raw material received by the factory will already
have been milled and beneficiated to remove the vast majority of non-fibrous material.
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It is therefore, much less likely that extraneous fragments (even cleavage fragments)
exist that might be counted either by midget impinger or PCM. We make this point
because, if this represents the true situation, it would be expected that risk per unit
exposure estimates (i.e. dose-response factors) derived from any mining site, may be
smaller than estimates derived for the same fiber type in occupational environments
where only finished fiber is used. Thus, another interpretation of the variation observed
among estimated K£ and KM values for amphiboles (reported in Section 6.2.2 and
Section 6.3.2, respectively) is that the mining values are somewhat low. The
implications of this possibility are discussed further in each respective section.
Note, although the Sebastien et al. paper suggests that (mpcf) exposure estimates from
Thetford and South Carolina grossly suggest the relative range of lung burdens
observed, there is too much scatter in the data to determine how closely the air ratios
track the lung burden ratios. For example, ratbs derived from arithmetic means (rather
than the geometric means) for the Sebastien et al. data are substantially different.
Moreover, as indicated above, there may be substantially different size distributions in
the two environments, which might at least in part be explained by the inclusion of large
numbers of cleavage fragments (with dimensions inappropriate for biological activity) in
the mining environment.
Although the third of the above-listed hypotheses was not addressed by Sebastien and
coworkers, the question of whether a co-carcinogen contributes to the overall observed
lung cancer rate among textile workers has been considered by several other
researchers. To test the hypothesis of whether oils potentially contributed to disease in
South Carolina, Dement and Brown (1994) performed a nested case-control study
among a subset of the cohort members previously studied by Dement et al. (most
recent update, 1994). In this analysis, Dement and Brown qualitatively assessed the
probability of mineral oil exposure for cases and controls based on knowledge of
historic descriptions of mineral oil use. The extent of such exposure was then further
categorized into three strata: none or little, moderate, or heavy, based on where each
worker was longest employed. Cases and controls were then further categorized based
on years at risk and level of asbestos exposure. Results from this nested analysis
indicated no significant change in the estimated exposure-response slope for asbestos
after adjusting for mineral oil exposure.
Additional, albeit qualitative, evidence that oils may not represent an adequate
explanation for the relative lung cancer risks observed in mining and textiles is provided
by McDonald (1998b). McDonald suggests that oils were not used in the Roachdale
plant until 1974. Therefore, due to latency, it is unlikely that the use of such oils would
have had a substantial impact on the observed lung cancer cases at the point in time
that the study was conducted (Peto 1980).
Taken as a whole, the evidence presented in this Section suggests that the relative
distribution of fiber sizes found in dusts in the textile industry and the mining industry,
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respectively, may be the leading hypothesis for explaining the observed differences in (
lung cancer risk per unit of exposure between these two industries,
6.2.4 Evaluating Effects Associated with the Character of Exposure
As indicated in Chapter 3(Overview), valid extrapolation of risk coefficients derived from
a reference site for use at a new site (to predict risk) requires that both of the following
two conditions be satisfied:
(1) that asbestos be measured in both environments in an identical manner;
and
C
(2) that such measurements reflect (or at least remain proportional to) the
characteristics of asbestos exposure that determine biological activity.
Because there is mounting evidence that the manner in which asbestos was measured
in the available epidemiology studies may not adequately reflect the characteristics that ^
relate to biological activity (Section 7.4 and 7.5), the second of the above two criteria
may not be satisfied when dose-response factors (i.e. K^'s and KM's) derived from these
studies are used to predict risk in new environments, without first applying some type of
adjustment. Therefore, we explored the possibility that an improved index of exposure
could be identified and that dose-response factors from the available human
epidemiology studies could be adjusted so that they could be matched with *
measurements based on an improved exposure index to predict risk.
Based on an extensive evaluation of the broader literature and the results from a series
of supplemental studies (Chapter 7), the primary features required of an improved index
of exposure were identified and an "optimum" exposure index was defined. As (
indicated below, however, compromises were required to adapt the risk coefficients
derived from human epidemiology studies to match measurements using the improved
index. Due to the limitations of the data available for adjusting the existing KL's and
KM's, an improved, but less than optimum exposure index was defined, which
nevertheless represents the best of adjustments that can be justified, based on existing
data. (
Following a description of the procedures employed to adjust the existing KL's and KM's,
the effects of such adjustments and their appurtenant implications for risk assessment
are explored.
C
6.2.4.1 Adjusting KL's for size
r
As previously indicated, considerations necessary to compare risk coefficients derived
in different exposure settings (or to apply a coefficient to predict risk in a setting
different from the one in which the coefficient was derived) have been elucidated clearly <
in a mathematical model (Chesson et al 1989). The consequences of the model
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indicate that adjusting the existing risk coefficients so that they reflect asbestos
characteristics that determine biological activity requires knowledge of the fiber size
distributions of the dusts studied in the original epidemiology studies. To the extent
they exist, such data may be used to normalize each of the published risk coefficients
so that they relate to a common exposure index reflecting asbestos characteristics that
determine biological activity.
Adjustment of the existing risk coefficients is necessary because they have all been
derived based primarily on PCM and Ml measurements and neither PCM nor Ml
measurements relate adequately to biological activity (Section 7.5). Thus, analytical
techniques employed at the time these studies were conducted were not capable of
providing asbestos measurements that directly reflect biological activity.
Ideally, the existing risk coefficients should be adjusted so that they are all normalized
to an exposure index such as that defined by Equation 7.12, which has been shown to
adequately reflect the characteristics of asbestos that determine biological activity
(Section 7.5), However, as indicated below, the database available for describing
structure-size distributions for exposures in the original epidemiology studies do not
contain sufficient information to individually characterize frequencies for structures in
separate length categories longer than approximately 10 urn, while Equation 7.12
requires that fibers longer than 40 urn be separately enumerated. Therefore, an ad hoc
exposure index is presented, which represents a compromise between theoretical
needs defined in Chapter 7 (Section 7.5) and the limitations of the available data.
Importantly, the conclusions that can be drawn from a review of the general asbestos
literature (Section 7.5) is that the asbestos fibers that drive risk are almost certainly
longer than 10 urn with potency likely increasing for longer structures, at least up to a
length of 20 urn. Therefore, due to the fact that structures longer than 20 urn have not
been adequately characterized in any of the epidemiologically studied environments, it
is not possible to test optimal exposure indices such as the one defined in the earlier
analysis of animal inhalation studies (Berman et al. 1995). At the same time, it is
apparent that an exposure index focusing on structures longer than 10 urn as the
primary contributors to risk should be superior to the arbitrary index originally defined
for PCM analysis and employed in the original epidemiology studies. Moreover, the
new index proposed here is also limited to structures that are clearly respirable and
includes counting of even the thinnest of respirable structures while the index defined
for PCM does neither (Appendix B).
In the remainder of this section, the KL's derived in Section 6.2.2 (and summarized in
Table 6-12) are normalized (using a series of published size distributions derived from
TEM measurements) to an exposure index that is expected to better reflect biological
activity. The distributions are defined for exposure settings that correspond to several
of the settings evaluated in the epidemiology studies from which the published risk
coefficients were derived. The coefficients are then combined with the asbestos risk
models presented previously (U.S. EPA 1986) and evaluated above (Section 6.2.1) to
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derive a set of adjusted Kt values (Kr's), which are appropriately matched for use with
the proposed, new exposure index. The proposed, new index is also defined below.
For the lung cancer model and the associated risk coefficient, "Kt)" to be applicable
generally, the asbestos concentration, "f," needs to be expressed in an exposure index
that incorporates the characteristics of asbestos that determine risk. Correspondingly,
the risk coefficient, "Kt" needs to be normalized to a model in which "f" is properly
expressed.
As indicated previously, published structure size distributions derived from TEM
measurements are used to adjust the existing Kt's by normalizing them to an exposure
index that is expected to better relate to biological activity than the measurements in the
epidemiology studies from which the Kt's were obtained.
Assuming that the available TEM size distributions are representative of dust
characteristics for the exposure settings (industries) studied, these were paired with
corresponding epidemiology studies. The TEM size distributions are then used to
convert the exposure measurements used in the epidemiology studies to the new
exposure index that is potentially more characteristic of biological activity. Studies were
paired as indicated in Table 6-14
Only a subset of the TEM size distributions listed in Table 6-14 were actually employed
in the effort to normalize ^ values. To minimize uncertainty introduced bybetween-
study variability, it was decided to employ distributions from common studies, to the
extent that this could be accomplished without reducing the number of "size-distribution
-- Kt" pairs available for inclusion in the analysis. Also, studies containing the best
documented procedures were favored over those in which the limitations of the
distributions were less clear. Ultimately, the size distributions selected for use (with one
exception) came from only two studies, which were reported in three publications:
Dement and Harris (1979), Gibbs and Hwang (1980), and Hwang and Gibbs (1981). In
the case of the one exception, size distributions for Libby (tremolite asbestos in
vermiculite) were derived from TEM data recently acquired directly from the site.
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Table 6-14
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Table 6-15 presents bivariate fiber size distributions derived from the published TEM
data that are paired with representative KL values from the corresponding epidemiology
studies. These data were used to adjust KL values in the manner described below.
The procedure for adjusting risk coefficients is straightforward. First, Equation 6.2 is re-
written to reflect the fact that the fiber concentrations, "f," in the original epidemiology
studies were typically measured by PCM:
KL(CPCM)(dO = RR-1 6.3
Where: C
"CPCM" is the concentration of asbestos structures that are typically included in a
PCM measurement (Section 4.3); and
all other parameters have been previously defined.
The left side of Equation 6.3 is then multiplied by one (expressed as a product of
reciprocal ratios):
K E (C)d ,} . RR _
,TOpt*Utot )\{ TPCME*Utotj
where:
"fPCME" is the fraction of asbestos structures in the published size distribution that
are typically included in PCM measurements;
"fopt" is the fraction of asbestos structures in the published size distribution that
represent the optimal exposure index (i.e. the index of exposure that
determines biological activity); and
"Ctot" is the concentration of total structures from which the fractions derive.
Remembering that the product of any fraction and the total concentration of a
distribution is equal to the concentration of that fraction and that CPCME is supposed to
equal CPCM (Section 4.3) and canceling terms accordingly, one is left with a new
equation for risk in which asbestos concentrations are expressed in terms of the optimal
exposure index:
K,,(C0pi)(d') - RR-1 6'5
where:
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"Kr" is now the adjusted risk coefficient, defined (and derived) as described in
Equation 6.6:
6.6
oPt
where all terms have been previously defined.
The adjusted risk coefficients, the "Krs", derived from the data provided in Table 6-15 in
the manner described above are presented in Table 6-16. Adjusted risk coefficients for
mesothelioma, "KM.'s", are also provided. KM*'s and KL.'s are derived in precisely the
same manner. The model adopted for mesothelioma risk is described in the next
section.
Ideally, Copt would be set equal to the concentration of asbestos structures defined as
described in Equation 7.12. However, as previously described, published size
distributions cannot be used to adjust risk coefficients to be consistent with the
exposure index described by Equation 7.12. This is because the longest size category
individually represented in the published distributions is for all structures longer than 10
urn and the exposure index described by Equation 7.12 requires that structures longer
than 40 urn-be individually enumerated. Therefore, Equation 7.12 was modified in an
ad hoc fashion to generate the exposure index that is evaluated in this document:
Casb = 0.003CS + 0.997CL 6.7
where:
"Casb" is the concentration of asbestos to be used to estimate risk;
"Cs" is the concentration of asbestos structures between 5 and 10 urn in length
that are also thinner than 0.5 urn; and
"CL" is the concentration of asbestos structures longer than 10 urn that are
also thinner than 0.5 urn.
Although not optimized, as previously indicated, this index is nevertheless expected to
represent a substantial improvement over the PCM-related index in current use,
because it reflects more of the general criteria for biological activity defined in Section
7.5.
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Table 6-15
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Table 6-16
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The justification for selecting the above exposure index and the potential limitations
associated with its use are described in Appendix B. Until such time that a study is
completed in which size distributions are generated that allows use of an exposure
index incorporating consideration of longer structures (such as that defined by
Equation 7.12), it is recommended that asbestos concentrations be defined in terms of
the exposure index defined by Equation 6.7 when evaluating asbestos risks. We
therefore recommend that this index be incorporated into an interim protocol for
conducting asbestos risk assessments.
6.2.4.2 Evaluating the implications of adjusting Kr's for size
To compare the effects of adjusting KL values for fiber size, the adjusted values (the
Kr's), are plotted (along with their associated confidence intervals) in Figure 6-4, which
exhibits a format identical to that of Figure 6-3, to facilitate direct comparison. The
"key" provided in Figure 6-3 is also directly transferable to Figure 6-4.
At first glance, the two figures look quite similar. However, closer inspection indicates
that: (1) two of the points plotted in Figure 6-3 (for MP11 and MX13) are missing in
Figure 6-4; (2) the confidence intervals are larger in Figure 6-4 than in Figure 6-3; and
(3) the relative location of the best estimate values (central points within each
confidence interval) have changed slightly relative to one another. The implications of
these observations are discussed below.
Regarding Figure 6-4, the two points missing (for MP11 and MX13)were omitted
because it was felt that none of the available size distributions used for adjusting the KL
values were suitably applicable for these study sites, so no conversions were possible.
The confidence intervals are larger in Figure 6-4 because, as previously indicated, we
have attempted throughout our analysis of the epidemiology data to account for major
sources of uncertainty. Thus, the confidence intervals depicted in Figure 6-4 are
modified from those depicted in Figure 6-3 to account for the uncertainty of making the
adjustment for fiber size using paired data from published size distributions. The
intervals were adjusted by multiplying the upper bound and dividing the lower bound by
a factor thought to represent the relative contribution to uncertainty contributed by the
need to match data from separate studies to perform the conversions. The factors
employed are provided in Table 6-17.
The visual impressions from a comparison between Figures 6-3 and 6-4 regarding
changes in the relative locations of the central values (best estimate) KL and adjusted
KL values are subtle. Thus, numerical representations are provided in Table 6-18.
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Figure 6-4
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Table 6-17 f
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Table 6-18
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Table 6-18 presents the magnitude of the spread in the range of original and adjusted f
KL values (and also original and adjusted KM values) estimated as the quotient of the
maximum and minimum values of each range. Note that, of necessity, such an
analysis requires that the zero values obtained for the Connecticut friction products
plant (CF4) be omitted. Note further that the data sets evaluated in Table 6-18 for the
original and adjusted values are identical (i.e., the two studies for which no suitable
conversion factor could be found were excluded).
In Table 6-18, it is apparent that the spread in values among "pure" fiber types (i.e.
chrysotile only or amphibole only) are both smaller than those for mixed data sets
(containing both fiber types). This suggests that there may be differences in the
potencies of each fiber type toward each respective disease (lung cancer and (
mesothelioma). In fact, the effect appears to be even more extreme among
mesothelioma values than among lung cancer values (discussed further in
Section 6.3.3.2). Moreover, that the spread in adjusted values for the data sets
containing only the pure fiber types are smaller than for the unadjusted values, but (at
least for lung cancer) larger than the unadjusted values for mixed data sets suggests ,
that the effects from adjusting the KL (and KJ values for fiber size may be confounded
with differences in the potency of fiber types. Therefore, we endeavored to develop a
procedure for evaluating these data that would simultaneously account for both fiber
size and type.
To address these issues, we considered that the mixed exposure environments C
represent situations in which there would be combined contributions to risk from both
amphiboles and chrysotile. It is also known that chrysotile from the Quebec mines
contains small amounts of tremolite (amphibole) asbestos (Sebastien etal. 1986). We
therefore set up the following formalism to separate the effects of chrysotile asbestos
from the effects of the amphiboles. Note, based on results of a previous study (Berman ^
et al. 1995) and lack of compelling evidence elsewhere in the literature (assuming that
size effects are adequately addressed, see Chapter 7), we assume in this formalism
that all similarly-sized amphibole fibers are equipotent.
We start by defining potency factors for truly pure fiber types: KLa for pure amphiboles
and K/c for pure chrysotile. Next we define the relative potency of chrysotile, "RPC" as *-'-'
the ratio of these factors. Thus:
Ktc = RPC*Kta 6.8
If one then knows the fraction of amphibole asbestos, famp/7 in an exposure setting, the ^
relationship between the observed, overall potency factors (e.g. the K^'s) and these
fiber-specific factors is given by:
6.9
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Note that, if RFC = 1 then Equation 6,9 reduces to the identity KL = KLa and
Equation 6,8 reduces to KLc = KLg, meaning that all fibers are equipotent and there is no
distinction for fiber type. Also, if RPC = 0, then chrysotile is non-potent and
Equation 6.9 reduces to KL = KLa*famph. Moreover, because famph is always less than
one, this latter equation means that the true potency of the amphibole fibers is greater
than would be indicated by the overall KL in a study (whenever RPC = 0).
Finally, solving Equation 6.9 for KLa yields;
K
fimph)l
and we further define the denominator on the right side of Equatbn 6,10 as the
function "Q".
Because the value for KLa should be constant across all study environments, if we also
know the fraction of exposure in each environment contributed by amphiboles, we can
apply Equation 6.10 to each of the available KL's and search for the value of RPC that
minimizes the spread in the resulting KLa values. Furthermore, by applying this same
procedure both to the original KL values and to the size adjusted KL values (the KL*'s)
allows simultaneous consideration of fiber size and type. Results of applying this
analysis are presented in Table 6-19.
The results provided in Table 6-19 are based on the estimates of the fraction of
exposures in each environment that is represented by amphiboles presented in
Table 6-20, In Table 6-20, the best estimate of the fraction of amphiboles is provided in
Column 3 along with an estimate of the range (in Column 4), which indicates the degree
of uncertainty that should be associated with each estimate of the fraction of
amphiboles. The source of information from which each estimate was derived is
provided in Column 5, Note that, depending on the particular study environment, such
fractions either represent a time/operations average (e.g. commercial amphiboles were
employed in a certain fractbn of the plant for a certain period of time) or a composition
average (e.g. tremolite constitutes some defined fraction of the chrysotile used).
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Table 6-19
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Table 6-20
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In Table 6-19, the different data sets evaluated (i.e. the KLa's, K,_a.*s, KMa's, and KMa,*s)
are identified in Column 1. The number of studies included in each data set is listed in
Column 2. Column 3 presents the values of the optimized RPC's (the RPC's for which
the spread in Kxa values across each data set is smallest). Column 4 presents the
minimized (optimum) range in the corresponding Kxa values for each data set
(expressed as the ratio of the maximum/minimum values). Column 5 presents the
corresponding ratio of the original and adjusted K^ values. Column 6 presents this
same ratio of original and adjusted Kxa values with RPC held fixed at one, and
Column 7 indicates which study environments represent the maximum and minimum
values for the optimized range of each data set.
Several conclusions can be drawn from the data presented in Table 6-19. First, it
appears that, for both lung cancer and mesothelioma, the best estimate is that there is
a difference in potency between chrysotile and amphiboles. For lung cancer, chrysotile
may only be one third to one half as potent as the amphiboles, depending on whether
one also adjusts risk factors for fiber size. For mesothelioma, no matter whether risk
factors are adjusted for fiber size or not, it appears that chrysotile is much less potent
than amphiboles and, in fact, may be non-potent. However, because improvement in
the reconciliation of potency estimates across studies becomes marginal for RPC's
smaller than about 1/1000 (and changes are no longer noticeable for RPC's smaller
than about 1/10,000), a conservative estimate suggests that, at most, chrysotile is no
more than about 1/1000 as potent as amphiboles toward the induction of
mesothelioma. Other inferences concerning mesothelioma are addressed further in
Section 6.3.3.2.
Although the improvements in reconciliation across potency estimates for the different
studies are modest when one adjusts for fiber size (the ranges are decreased by about
30%), these reductions are based on spreads over the entire, available data sets (15
studies for lung cancer and 10 studies for mesothelioma), Moreover, such reductions
are achieved using the relatively crude procedure of matching published size
distributions to published epidemiology studies (rather than by obtaining true study-
specific size distributions). Thus, combined with the strong inferences from the rest of
the literature (see Chapter/) that such size adjustments better reflect the
characteristics of biological activity, adjusting values for fiber size is well justified and
will improve the confidence with which risks can be extrapolated to new sites.
In this analysis, the range in lung cancer potency estimates is reduced from about a
factor of 90 (when data are neither adjusted for fiber size or type, Table 6-18) to 59
(when data are adjusted for both fiber size and type). Interestingly, the extremes of this
range are contributed by South Carolina textiles (CT6 - the maximum value) and
Quebec mining (CM1 - the minimum value). - Once adjusted for fiber size and type, the
lung cancer potency estimates from all other studies (including those both for mixed
exposures and for nominally pure amphibole exposures) falls between these two
extremes. As previously indicated (Section 6.2.3) the differences in potency estimates
in these two environments has been the focus of much attention. Based on the
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analysis presented in Section 6,2,3, the best of candidate explanations for the
differences between mining and textile production is the range of sizes of fibers in these
two environments. Further evidence that this is the case is apparent from Table 6-19,
As indicated from a comparison of the range in KL and KL* values in the table, adjusting
values for fiber size (even given the crude approach currently available for making such
adjustments) already reduces the difference in the potency estimates from these two
environments by about 30%.
Although useful, the above analysis of ranges is potentially limited by the need to
exclude the nominally zero value from the Connecticut friction products plant (CF4) and
the lack of formal consideration of uncertainty. Therefore, an additbnal analysis was
conducted in which uncertainty is addressed formally and the value from the
Connecticut study was included,
In this analysis, we assumed that the log of the measured KL values (or KL,, KM, KM.
values) were normally distributed with mean Ln{KLa*[famph + rpe*(1-famph)}, where Kia,
RPC, and famph were all as previously defined. The variance of KL was assumed to be
composed of two or three independent components. The first component, a,, was
assumed to be equal to one-half of the difference between the log transforms of the
upper limit of the confidence interval and the point estimate of KL (see Table 6-12),
This variance was assumed to represent statistical uncertainty in response and
exposure measurements (i.e, uncertainty that the confidence intervals were designed to
represent, see Appendix A). Note that these values represent the irreducible fraction of
uncertainty in the potency estimates.
A second component, o, of the standard deviation was assumed to be constant for all
studies, and may be thought of as representing the uncertainty in the estimate resulting
from differences in exposure conditions, such as fiber size distributions, which are not
represented in the G|. The overall standard deviation of the KL from study i was
assumed to be (of + o2)%. As an alternate approach, the uncertainty in famphi was
incorporated by adding a-third independent component of exposure, oa!, given by one-
half of the difference between the log transforms of the upper limit of the confidence
interval for famph, and the best estimate of famphl (see Table 6-20). Thus, when the
uncertainty in famph was accounted for, the overall standard deviation of the KL from
study i was (o,2 + o2+ oa,2)1/a.
Using this model, a likelihood test was applied to estimate the parameters, Ln (KLa),
RPC, and o, and to conduct likelihood tests of the hypotheses that chrysotile was non-
potent (RPC=0) or equally potent (RPC = 1) with amphibole. The same test was
applied to the KL,, KM, and KM, values. When applied to the adjusted (starred)
variables, the uncertainty in the fiber size distribution data was not accounted for so that
the relative effect of adjusting for fiber size could be evaluated.
In this approach, the overall likelihood is estimated as the product of a series of terms
that each represent the likelihood of obtaining a Kt value equal to that obtained from
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one of each of the available studies, given that they are derived from a common normal (
distribution with mean, KLa and standard deviation .(a,2 + a2)'/!, as indicated above. The
optimized (best estimate values) for KLa and a are those for which the overall likelihood
is maximized. Results from the analysis with the two parameter standard deviation are
summarized in Table 6-21.
C
In Table 6-21, the first and fifth columns identify the specific data set being considered
and the size of each data set is also presented. Columns 2 and 6 indicate the specific
variables estimated. Columns 3 and 7 indicate the values for the estimated parameters
with the value for RPC optimized. Columns 4 and 8 indicate the values for the
estimated parameters with the value for RPC fixed at 1 and Columns 5 and 9 indicate
the values for the estimated parameters with the values for RPC fixed at 0. (
The results presented in Table 6-21 closely parallel the results obtained from the
analysis of the range of values presented in Table 6-19. The best estimated value of
relative potency for lung cancer indicates that chrysotile is only between a fifth and.a
half as potent as the amphiboles toward the induction of lung cancer, depending on (
whether potency factors are adjusted for fiber size. The results of a hypothesis test
indicate that this is not significantly different from the hypothesis that chrysotile and
amphiboles are equipotent (in either the original or the size-adjusted data sets).
However, the hypothesis that chrysotile is non-potent toward the induction of lung
cancer is clearly rejected. Size-adjusting the potency values provides a small
improvement (reduction) in the overall variance of the data set of about 3% (although ^
substantially larger improvements are noted for mesothelioma). Assuming the optimal
difference in potency for the different fiber types, best estimate H^ and Kr values for
chrysotile and amphiboles are also presented in Rows 8 and 9, respectively, of the
table. Although the data are not shown, the analysis using the three parameter
estimate of standard deviation gave entirely comparable results. (
Discussion of the results from the analysis of the mesothelioma values (also presented
in the table) is provided in Section 6.3.3.2. Based on this analysis and the rest of the
evaluation described in this chapter, a recommended set of lung cancer (and
mesothelioma) potency factors has been developed and is presented in Section 6.4.
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Table 6-21
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6.3 MESOTHELIOMA f
The EPA model used to describe the incidence of mesothelioma in relation to asbestos
exposure is the model proposed in the Airborne Health Assessment Update (U.S. EPA
1986). This model assumes that the incidence of asbestos induced mesothelioma is
independent of age at first exposure and increases according to a power of time from s
onset of exposure, as described in the following relationship:
lM = KMf[(T-10)3-(T-10-d)3] forT>10+d (6.11)
= KMf(T-10)3 for10+d>T>10
= 0 for10>T
where:
"IM" is the mesothelioma mortality observed at "T" years from onset of ^
exposure to asbestos for duration "d" and concentration "f";
"KM" is the proportionality constant between dose and mesothelioma
response and represents the potency of asbestos; and
all other factors have been previously defined.
This is an absolute risk model, which means that the incidence of mesothelioma
predicted by the model is a direct function of asbestos exposure that does not depend
on the background incidence of the disease. Background mesothelioma cases are rare
in the general population in any case.
The EPA model for mesothelioma is the solution to the following integral equation,
assuming that exposure remains constant:
(6-12)
This expression is more general than Equation 6.11, as it applies to both constant and
variable exposures (see Appendix A). C
6.3.1 The Adequacy of the Current EPA Model for Mesothelioma
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Access to the raw epidemiology data from a small number of key studies allowed us to
evaluate the adequacy of the EPA model for describing the time-dependence for
mesothelioma in asbestos exposed cohorts. For this analysis, the raw data for the
cohorts for the chrysotile miners in Quebec was graciously provided by Drs. Liddeli and
McDonald (described in Liddeli et al. 1997), the raw data for the cohort of crocidolite
miners in Whittenoom, Australia was graciously provided by Nick DeKlerk (unpublished)
and the raw data for the cohort of chrysotile textile workers (described by Dement et al.
1994) was graciously provided by Terri Schnorr of NIOSH. The Whittenoom cohort was
originally described by Armstrong et al. (1988), but the data provided by DeKlerk
included additional followup through 1999.
To identify potential effects due to varying procedures, different methods for fitting the
EPA mesothelioma model to epidemiological data were evaluated. In this evaluation,
three methods were used to fit the EPA mesothelioma model to data from Wittenoom.
In the first approach the data were categorized in a manner often available in published
form, so this method mimics the method generally used when raw data are not
available. The observed mesotheliomas and person years of observation were
categorized by time since first exposure, and the mean exposure level and duration of
exposure were calculated for each such category. The EPA model is then applied to
such data using the approach for the typical situation (as described in Appendix A) and
results for the Wittenoom cohort are presented in Table 6-22. The KM value estimated
for Wittenoom using this approach is 7.15E-8 (90% Cl: 6.27, 8.11). The fit of this
model to data categorized by time since first exposure is good (p = 0.65).
Note that, for most of the published epidemiology data sets, the average level and
duration of exposure are not available so that these have to be estimated from cruder
data, such as cohort-wide averages.
A second approach to fitting the EPA model to epidemiology data, exploits the fact that
the mesothelioma model (Equation 6-12) expresses the mesothelioma mortality rate as
the product of KM and an integral involving the exposure pattern, the time of
observation, and the ten-year time lag, but not any variable parameters. The value of
this integral was calculated for each year of followup of each subject. Person-years of
followup and mesothelioma deaths were then categorized according to the values of
the integral, and the average value of the integral determined for each category.
Results are presented in Table 6-23.
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Table 6-22 f
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Table 6-23
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Kw was estimated by maximum likelihood from Table 6-23 assuming that the observed
numbers of cancer deaths werelndependently Poisson distributed with a mean equal to
the mean value of the integral for that category times KM. The value of KM obtained in
this fashion was KM = 9.00E-8 (90% Cl: 7.89, 10.2). The fit of the model to data
categorized by the integral is not good (P < 0.00001), as the model predicts too few
mesotheliomas for small values of the integral and too many mesotheliomas for large
values of the integral. However, the estimates for KM obtained from applying the model
in this manner are still very similar to those obtained using the exact method (described
below).
A third method of estimating KM (termed the "exact method") employs a likelihood that
does not involve any categorization of data. With this method, the hazard function, h(t)
= lM(t) and the corresponding survival function (probability of surviving to age t without
death from mesothelioma in the absence of competing causes of death),
S(t) = exp(-JV h(s)ds, are computed for time at the end of followup. The contribution to
the likelihood of a subject who died of mesothelioma t, years after beginning of followup
is h^^S^), and the contribution of a subject whose followup was not terminated by
death from mesothelioma is S(t.,). The complete likelihood is the product of such terms
over all members of the cohort. .The estimate of KM obtained by maximizing the
logarithm of this likelihood was 7.95E-8 (90% Cl: 7.0, 9.0).
We consider the exact method of computing KM to be the most accurate and results
from this method are reported in the summary tables (see, for example, Table 6-12),
The Quebec and South Carolina data sets were thus evaluated using the exact method.
However, it is noteworthy that the two other methods described above, one of which is
often applied to published data, give similar estimates of K^ at least for this data set,
The Quebec cohort was subdivided into three sutocohorts, believed to correspond to
differing amounts of amphibole exposure due to tremolite contamination within the
chrysotile (Liddell et al. 1997). Location I consisted of workers at the mine at Asbestos
where the ore reportedly had less tremolite contamination. Locations 3 and 4 consisted
of workers at the large central mine and at smaller mines, respectively, nearThedford,
where the ore was more heavily contaminated with tremolite. Location 2 consisted of
workers at an asbestos products factory at Asbestos, which processed some
commercial amphibole fibers in addition to chrysotile, The exact method of calculating
KM produced the following estimates: Location 1 (8 cases): KM = 1.3E-10 (90% Cl:
0.3E-10, 4.9E-10); Location 2 (5 cases): 9.2E-10, (90% Cl: 2.0E-10, 35E-10); Locations
3 and 4 (22 cases): KM = 2.1E-10 (95% Cl: 0.65E-10, 6.5E-10. The relative magnitudes
of these estimates track with the relative amounts of amphibole exposure estimated for
these locations (Liddell et al. 1997), which is consistent with the hypothesis that the
mesothelioma risk in this cohort is due, at least in large measure, to exposure to
amphiboles.
There were only two confirmed mesothelioma deaths in the South Carolina cohort and
four additional suspected deaths. These were too few to permit detailed analysis.
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Based on both confirmed and suspected mesothelioma deaths, the exact method of
analysis gave an estimate for KM of KM = 0.43x1O'8, 90% Cl: (0.20x10'8, 0.79x10'8).
Using only the two confirmed mesotheliomas, the same analysis yielded KM =
0.14x1Q-8, 90% Cl: (0.034x1Q-8, 0.38x10"8). The same estimates were obtained by
estimating KM from data categorized by time since first exposure and fitting a linear
model to the categorized value of the integral in the definition of the EPA model. Thus,
for this cohort (in parallel with the cohort from Wittenoom) comparable KM values are
estimated no matter which of the three methods described above are used for fitting the
EPA mesothelioma model to the epidemiology data.
6.3.1.1 Time dependence
The EPA mesothelioma model was next evaluated to determine whether it adequately
describes the time-dependence of mesothelioma mortality following cessation of
exposure in the Wittenoom and Quebec cohorts. The small number of mesotheliomas
observed among the South Carolina cohort precluded our completing this analysis for
that cohort. The model predicts that risk rises as the difference between the third
power of time since the start of exposure and the third power of time since the
cessation of exposure (both lagged by 10 years).
Table 6-24 shows the fit of the EPA mesothelioma model to Wittenoom data
characterized by time since last exposure, based on the KM estimated from the exact
analysis. Although this fit appears adequate (P = 0.21), there appears to be a slight
tendency for the EPA model to under-predict the mesothelioma rate at long times
following cessation of exposure. However, given that the EPA model does not appear
to under-predict the mesothelioma rate at long times for the other two cohorts evaluated
(see below), it is not clear that this is a real limitation in the model. At the same time,
the number of mesotheliomas in the other two cohorts are relatively small. Moreover,
both of these other cohorts nominally involve chrysotile exposure (as opposed to
crocidolite). Therefore, it may prove useful to obtain additional cohorts (preferably with
relatively large numbers of mesotheliomas and preferably involving amphibole
exposure) before concluding definitively whether the current EPA mesothelioma model
adequately describes the time dependence for mesothelioma.
Table 6-25 shows the observed and expected mesotheliomas in the three separate
locations at Quebec, categorized by time since last exposure, and computed using the
KM values obtained using the exact fitting method. Although the small numbers of
mesotheliomas make it difficult to draw definite conclusions about the adequacy of the
model, there is little evidence that the model under or over-predicts the numbers of
mesotheliomas at long periods after the end of exposure.
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Table 6-24
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Table 6-25
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Table 6-26 similarly shows the fit of the EPA model to the South Carolina mesothelioma
data (including all four suspected mesotheliomas as real). As with the Quebec cohort,
there is little evidence from this cohort that the model under or over-predicts the number
of mesotheliomas at long times following the end of exposure. Particularly with this
cohort, however, condusions must be drawn with caution due to the very small number
of mesotheliomas observed.
Based on the analysis performed, we see marginal evidence that the current EPA
model for mesothelioma may not provide an adequate description of the long term risk
of mesothelioma in cohorts exposed to asbestos (even for extended periods of time
following cessation of exposure); the fit of the model to the Wittenoom cohort is
adequate, despite the small apparent discrepancy at long times. There also appears to
be little evidence that there is a difference in the time development of mesothelioma
between the Wittenoom and Quebec cohorts, although it is acknowledged that the data
from the Quebec cohorts were divided into sets containing only small numbers of
mesotheliomas. This latter observation contrasts with what was observed for the lung
cancer model, where there is evidence that the effect that chrysotile has on relative risk
appears to decrease with time following cessation of exposure while the effects of
.crocidolite appear to remain constant (Section 6.2.1.1).
If the observed difference in the time dependence for lung cancer risk observed for
chrysotile (among South Carolina textile workers and Quebec miners) and crocidolite
(among Wittenoom miners) is indeed due to fiber type, that a similar effect is not
observed for mesothelioma suggests, among other possibilities, that similar fiber types
may be primarily responsible for driving mesothelioma risk. This provides further, albeit
circumstantial, support for the "amphibole hypothesis" (that amphiboles are primarily
responsible for the induction of mesothelioma, even when present as minor
contaminants in chrysotile).
Given the importance of these two issues: (1) the relative potencies of chrysotile and
the amphiboles and (2) the adequacy of EPA models for predicting the time
dependence of disease, limited analysis of raw data from a small number of additional
cohorts is warranted. Such recommendations are discussed further in the conclusions
. to this Chapter (Section 6.4).
6.3.1.2 Comparison with a biologically based model
Definition of the two-stage model. The two-stage model of cancer implemented in
this report (Moolgavkar and Luebeck, 1990) was previously described (Section 6.2.1.2).
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Table 6-26
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Applying the two-stage model to mesothelioma. The 2-stage model was fit to the
mesothelioma data using the same general approach applied to the lung cancer data
and described earlier (Section 6.2.1 .2). Since the background risk of mesothelioma .is
very small, the first stage mutation rate, v0, was fixed at zero in these analyses. Also,
the death rate for intermediate cells, (30, was likewise fixed at zero. (This is not
expected to affect the range of hazards predicted by the model, since so long as the
hazard is small, it is approximately a function of the difference a - (3.). Four situations
were modeled:
(1) assuming only the mutation rate of normal cells into intermediate cells is
dose-related [v = v^t), a = a0, u = u0j;
(2) assuming both the mutation rate of normal cells and the proliferation rate
of intermediate cells are dose-related [v = v.,d(t), a = a0 + a^t), u = u0];
(3) assuming both mutation rates are dose-related [v = v.,d(t.), a = a0, u = u0 +
]; and
(4) assuming that both mutation rates are dose-related and equal on a per-
cell basis [v = uX = v^t), a = a0, where X = 107 is the assumed number
of normal cells].
The two-stage model was fit to the mesothelioma data from both Wittenoom and
Quebec in a variety of situations. Table 6-27 shows results of fitting the two-stage
model to the Wittenoom mesothelioma data assuming either an infinite half-life of
internally deposited fibers (r = 0) or a ten-year half-life ( r = 0.069). This Table shows
the results of fitting allowing each one of the four parameters a, (3, v, and [j individually
to be related to internal exposure (Model 1-4) and allowing all four simultaneously to be
exposure-related (Models 5,6). The best fit (largest likelihood) using a single exposure-
related parameter came from allowing v to depend upon exposure. This was true when
either an infinite half-life or a ten-year half-life was assumed. The likelihoods from the
two assumed half-lives were similar. The estimated parameters were also similar,
except for v1( which was larger using to ten-year half life in order to compensate for the
smaller estimated exposures in this case.
The fit with an infinite half-life allowing all four parameters to be exposure-related
(Model 5) did not improve the fit, even though parameter estimates differed
considerably. This suggests that the likelihood is quite flat and diverse parameter
estimates can provide comparable fits, However, the differences a0 - (30 are nearly
constant, which is consistent with the observation by Moolgavkar et a/. (1988) that in
many cases the model depends upon these parameters mainly through their difference,
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Table 6-27
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Allowing each of the parameters to be exposure-related did significantly improve the fit
of the model assuming a ten-year half life over that obtained by assuming any one of
the parameters to be exposure-related, based on the improvement in the log-likelihood.
The model assuming an infinite half-life and all four two-stage parameters dose-related
predicts that the background hazard increases sharply between age 50 and 60 and
reaches a plateau of about 7x10"5 per year from age 60 onward. This seems
somewhat implausible given the low mortality rate for mesothelioma in persons without
asbestos exposure. Consequently, this model was refit with the background
mesothelioma risk of zero, by assuming both v0 and u0 were fixed at zero (Model 6).
This model, although being perhaps more realistic, gave a poorer fit (p = 0.03 for infinite
half-life and p=0.08 for ten-year half-life, based on likelihood ratio test) than the model
with the high background.
That the highest likelihoods for the Wittenoom data arise when the mutation rate from
normal to intermediate cells (v) is dose related, suggests that asbestos is acting as an
initiator for mesothelioma. That the fit improves when either the proliferation rate or the
mutation rate of intermediate cells is also allowed to vary with dose suggests that
asbestos may also be serving as either a promoter or a late-stage actor for
mesothelioma. This contrasts with the two-stage model results for lung cancer, in
which having v dose dependent did not improve the fits. Given the apparent limitations
of the two-stage model, the interpretation of such results needs to be taken with great
caution. However, the implication that asbestos serve primarily as a promoter (or late
stage actor) toward lung cancer but also acts as an initiator toward mesothelioma is
consistent with inferences from the broader literature (see Section 7.3).
Table 6-28 shows the fit of the two-stage model with an an infinite half-life and without
background (v0 = u0 = 0) to the Wittnoom data categorized as to time since end of
exposure, The fit of these data to the two-stage model is poor (P = 0.03). For
comparison purposes the fit of the EPA mesothelioma model to the same data is also
provided. The EPA model provides an adequate fit (p = 0.21), which is the same as
previously indicated (Table 6-24).
Based on this limited exploration of the application of the two-stage model to the
Wittenoom data, as well as the Quebec data (not shown), the decision has been
reached that this model is probably not suitable for routine use in asbestos risk
assessment. There are several reasons for this conclusion. The two-stage model was
originally considered because there was some evidence from the literature that the
mesothelioma risk did not continue to rise after the end of exposure, as predicted by the
EPA model. The hazard function from the two-stage model predicts that risk will
eventually fall back to baseline after exposure ceases, and consequently can predict
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Table 6-28
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this behavior. However, a detailed analysis of the mesothelioma data from both f
Wittnoom and Quebec indicates that neither demonstrates such behavior
(Section 6.3.1.1). In fact, if anything, the EPA model may tend to under-predict the risk
at the longest time periods following cessation of exposure (based on the Wittenoom
data), although the results with the other two cohorts may not support this observation
(see Tables 6-24, 6-25, and 6-26). ,
Although the two-stage model has been shown to be a useful research tool, some of its
features make it less suitable for routine use in risk assessment. The model is complex
and would be more difficult to comprehend by stakeholders in an asbestos risk
assessment. Because of the numerous parameters in the model, numerical
calculations are time-consuming and the results are sometimes problematic. Data are C
sometimes compatible with a range of possible outcomes under this model (e.g.,
exposure affecting either the first mutation rate or the cell death rate); currently the
mechanism of mesothelioma causation is not well enough understood to permit choice
among these possibilities with any degree of confidence, although our findings are not
inconsistent with inferences from the literature (see Section 7.3). Moreover, the f.
absence of a single "potency parameter" in the two-stage model would complicate
simple comparisons of potency across studies or environments. For these reasons, the
decision was reached to not pursue the two-stage model further in this project, but
instead to consider simple modifications to the existing EPA model for cases in which
this model was not adequate (Section 6.2.1.1).
An advantage of the two-stage model is that, since it models risk as a function of
internal dose, it can naturally incorporate effects of fiber clearance. However, we found
that, due to the complexity of the model, it was neither particularly useful at predicting
fiber half-lives nor distinguishing among assumed half-lives. We also found it possible
to develop simpler adaptations to the EPA model that could reasonably address this (
consideration (Section 6.2.1.1) and found this latter approach more fruitful.
6.3.2 Estimating KM's from Published Epidemiology Studies
At the time that the Health Effects Update was published (U.S. EPA 1986), four studies
were found to provide suitable quantitative data for estimating a value for KM and six
additional studies provide corroborative support for the mesothelioma model applied.
Currently, there are 14 published studies with adequate data for deriving an estimate of
KM (including updates to all four of the quantitative studies evaluated in 1986).
The EPA mesothelioma model described at the.beginning of Section 6.3 was applied to C
each of these data sets to obtain study-specific estimates for the mesothelioma risk
coefficient, KM. The resulting set of KM values are presented in Table 6-12 (which has
been previously described, Section 6.2.2. In Table 6-12, Columns 9 and 10 list,
respectively, the best estimate of each KM value derived for studies reported in the
original 1986 Health Effects Update and the reference for each respective study. ,
Columns 11, 12, and 13 present, respectively: the best-estimates for the KM values
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derived for all of the studies currently available (including studies corresponding to
those in the original Health Effects Update); the estimated lower and upper bounds for
each KM value (derived as described in Appendix A); and the reference for the
respective study from which the data were obtained. To assure comparability across
studies, values for all studies (even those that have not been updated since their
inclusion in the 1986 Health Effects Update) were re-derived using the modified
procedures described in Appendix A.
The KM values derived in this study and the corresponding values derived in the original
1986 Health Effects Update are in close agreement, none vary by more than a factor of
2 and none of the differences appear significant (based on a comparison of their
relative confidence intervals).
Among the KM values derived in the current study, the lowest and highest of the best-
estimate values differ by a factor of approximately 1400 (excluding the one negative
study) and many of the pair-wise comparisons across this spectrum appear significant
(because their confidence intervals do not overlap). For example, none of the
confidence intervals for the KM values derived for any of the environments involving
exposure to chrysotile overlap the confidence intervals associated with the KM values
derived for either crocidolite mining or asbestos-cement manufacture using mxed fibers
at the Ontario plant (Finkelstein 1984). Furthermore, neither of the confidence intervals
for the KM values derived for each of the two sets of chrysotile mines in Quebec
(Asbestos and Thetford) overlap the intervals around KM values for any of the
amphibole environments or any of the mixed environments, except the Quebec factory
that is associated with the Asbestos mine (Liddell et al. 1997).
The KM values and the associated confidence bounds derived in the current study are
plotted in Figure 6-5. Each exposure environment is plotted along the X-axis of the
figure and is labeled with a four-digit code that indicates fiber type (chrysotile, mixed,
crocidolite, or tremolite), industry (mining, friction products, asbestos-cement pipe,
textiles, insulation manufacturing, or insulation application); and a two-digit numerical
value indicating the study from which the data were derived. The same key provided
for Figure 6-3 also applies to this figure. In the Figure, the chrysotile studies are
grouped on the left, amphibole studies are grouped on the right, and mixed studies are
in the middle. Note also that studies conducted in the same facility (generally among
highly overlapping cohorts), such as the Dement et al. (1994) and the McDonald et al.
(1983a) studies of the same South Carolina textile facility, are combined as a single
entry in the Figure. The cohorts from Asbestos Quebec and Thetford are also
combined in the Figure.
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Figure 6-5 f
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As with the KL values, comparison of KM values across the available studies are
instructive. Within chrysotile studies alone, lowest and highest KM values vary by
approximately a factor of 15 (again, excluding the negative friction products study),
which is minimal variation relative to the spread across the values from all of the studies
in the table. Based on the overlap of their confidence intervals, none of these
differences appear to be significant.
The KM values for the two "pure" amphibole'studies (crocidolite mining and amosite
manufacturing) agree to within a factor of 2 and, with the exception of the value from
the Ontario asbestos-cement plant (Study No. 8 in the Figure) and the factory plant
associated with the Asbestos, Quebec mines (Study No. 14), the mixed exposures fall
in between the high values for the "pure" amphibole exposures and the lower values
reported for all of the chrysotile sites. Although the KM value for the asbestos-cement
plant (Study No. 8) appears high, it does not appear to be significantly different than the
values from either of the "pure" amphibole studies or values for many of the other mixed
exposures (based on the degree of overlap of their respective confidence intervals).
The KM value reported for the factory associated with the Asbestos, Quebec mine
(Study No. 14) is the lowest of those reported for mixed exposures, but (again based on
the overlap of confidence intervals) it does not appear to differ significantly from values
for several of the other mixed exposures nor the highest values among the chrysotile
studies.
As with KL values, the asbestos-cement pipe industry shows the greatest variation in KM
values across all asbestos types, with a range that varies over a factor of 90. Moreover,
the difference between the highest and lowest values in this industry may be significant
(due to the lack of overlap of their intervals). Within the textile industry, the KM value for
the South Carolina chrysotile plant is only a tenth of the values reported for the two
textile plants using mixed fibers, but these differences do not appear to be pairwise
significant. The potential sources of variation across all of the KM values are likely
attributable to the same sources of variation identified for the KL values and previously
described in detail (Section 6.22).
6.3,3 Evaluating Effects Associated with the Character of Exposure
To evaluate the effects of fiber size on the KM values listed in Table 6-12 (in parallel
with the manner in which the KL values were evaluated) KM values were also adjusted to
match them to the improved exposure index defined in Equation 6.7 and the
implications of these adjustments were evaluated (in a manner entirely analogous to
that described in Section 6.2.4.2). The justification for the development of the improved
exposure index is provided in Appendix B.
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6.3.3.1 Adjusting KA/s for size
The KM values were adjusted for size in the same manner described in Section 6.2.4.1
for the KL values. To accomplish this, the same, published fiber size distributions were
matched to the same epidemiology studies (Table 6-14) and the conversions performed
as described by Equations 6.4 to 6.6 using the data presented in Table 6-15, The
resulting adjusted values, the KM.'s are listed in Table 6-16.
6.3.3.2 Evaluating the implications of adjusting KM's for size
To compare the effects of adjusting KM values for fiber size, the adjusted values (the
KM,'s), are plotted (along with their associated confidence intervals) in Figure 6-6, which
exhibits a format identical to that of Figure 6-5. In fact, the formats of Figures 6-3
(unadjusted KL's), 6-4 (the adjusted, Kr's), 6-5 (unadjusted KM's), and Figure 6-6
(adjusted, KM,'s) are also designed for direct comparison. The "key" provided for
Figure 6-3 is also directly transferable to all of the figures, including Figure 6-6.
Although Figures 6-5 and 6-6 may look similar at first glance, there are several
differences. First, the value for study MF14 had to be omitted from Figure 6-6 because
no suitable size distribution is available to allow the potency factor from this study to be
adjusted. The confidence intervals presented in Figure 6-6 are also larger than those
depicted in Figure 6-5. This is because the one's depicted in Figure 6-6 incorporate an
additional factor to account for the uncertainty of adjusting for fiber size using paired
data from published size distributions. Table 6-17 lists the factors by which the upper
bounds are multiplied and lower bounds are divided to derive the confidence intervals
depicted in Figure 6-6.
The most important differences between Figures 6-5 and 6-6 are the changes in the
relative positions of the central value estimates for the potency factors depicted.
Therefore, numerical representations of these changes are provided in Table 6-18.
Table 6-18 presents the magnitude of the spread in the range of original and adjusted
KM values (original and adjusted KL values are also presented, see Section 6.2.4,2).
Such spreads are estimated as the quotient of the maximum and minimum values of
each range. As previously indicated, this type of analysis (of necessity) requires that
the zero value obtained for the Connecticut friction products plant study (CF4) be
omitted, The one study for which no suitable conversion factor could be found for the
corresponding KM, (Study No. MF14) was also omitted.
t
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Figure 6-6
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The trends apparent in Table 6-18 that were described for a comparison between KL f
and Kt. values are similar but exaggerated between the KM and KM. values, Briefly, the
spread in values among "pure"fiber types (i.e. chrysotile or amphiboles only) are much
smaller than the spread observed over all values (or over data sets that include mixed
exposures) and this is true for both the adjusted and unadjusted KM values. This
suggests that chrysotile and the amphiboles exhibit different potencies toward the
induction of mesothelioma. Also, the spreads for all of the data sets presented in
Table 6-18 (except the one for pure amphiboles, which is already minuscule) are
reduced when values are adjusted for fiber size. However, even for adjusted values,
the spreads in data sets including mixed exposures are still rather large. As for lung
cancer, this implies that the effects of fiber type and fiber size are also confounded for
mesothelioma. Therefore, an evaluation similar to that conducted .for lung cancer C
potency factors was also conducted for mesothelioma potency factors to
simultaneously address the effects of fiber size and fiber type. The manner in which
the evaluation was conducted is described in Section 6.2.4.2.
Table 6-19 presents the results of the analysis in which the KM estimates are adjusted ^
both for fiber size and for the fraction of the exposures in each environment contributed
by amphiboles, As previously described, KM/, values are the estimated potencies of
pure amphiboles, KM/,,'s are the estimated potency of pure amphiboles adjusted for fiber
size, and RFC is the ratio of the potency of pure chrysotile to pure amphibole toward
the induction of mesothelioma, in this case (see Section 6.2.4.2). In the analysis, the
spread of the range in values for each data set was minimized by optimizing the value *
for RFC.
That the optimum values for RFC (presented in Column 3 of Table 6-19) are
substantially smaller than one for both the unadjusted KM/, values and the KM/r values
(which are adjusted for fiber size) suggests that chrysotile is substantially less potent (
toward the induction of mesothelioma than amphiboles and may be non-potent.
However, because Improvement (reduction) in the spread in values for these data sets
becomes marginal for values of RFC less than about 1/1000, a conservative estimate
suggests that, at most, chrysotile is no more than 1/1000 as potent as amphiboles
toward the induction of mesothelioma.
Based on a comparison of the magnitude of the spreads in range for each data set
(listed in Column 4), adjusting mesothelioma potency factors for fiber size results in a
30% improvement in agreement across estimated values, Moreover, simultaneously
accounting for the effects of fiber type and fiber size results in very effective
reconciliation of potency estimates for mesothelioma. Once adjusted in ths manner, the (
spread in potency estimates across all 10 of the studies available for this analysis is
only a factor of 26 (only a little larger than an order of magnitude). This should provide
reasonable confidence that a representative value for amphibole potency that might be
selected from this range can be applied to new environments to estimate risk.
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As with lung cancer, a more sophisticated analysis of mesothelioma potency values
was also completed, which incorporates formal consideration of uncertainty and (due to
the manner in which the analysis is performed) allows us to include the nominally zero
value from the Connecticut friction products plant (Study No. CF4). The details of the
analysis are provided in Section 6.2.4.2. Results are provided in Table 6-21.
As with lung cancer, the results of this more formal analysis of mesothelioma potency
factors closely parallels the results described in the above analysis. The best estimated
values of the relative potency of chrysotile toward mesothelioma lies between 1/250th
(0.004) and 1/570th (0.002) of that for the amphiboles and these values are not
statistically different from an estimate of zero potency for chrysotile (P = 0.54 for the
unadjusted data set and P = 0.72 for the size adjusted data set).
Adjusting the mesothelioma potency factors for fiber size results in an about a 23%
improvement in the overall agreement among values (determined by comparing the
relative magnitudes of the optimized "a" estimated for the size adjusted data set
(0.6561) and the unadjusted data sets (0.8123), respectively. Thus, even using the
relatively crude procedure of adjusting KM values based on published (rather than
study-specific) size distributions, results in moderate improvement in the agreement in
values across studies.
6.4 GENERAL CONCLUSIONS FROM HUMAN EPIDEMIOLOGY STUDIES
Results from our evaluation of the available epidemiology studies indicate that:
(1) the results of individual epidemiology studies are uncertain, especially when one
considers all of the major potential sources of uncertainty (rather than simply
considering the statistical uncertainty associated with the number of deaths, as is
traditionally done). ' However, more robust conclusions can be drawn from an
analysis of the set of epidemiology studies taken as a whole than results derived
from individual studies;
(2) by adjusting for fiber size and fiber type, the existing database of studies can be
reconciled adequately to reasonably support risk assessment;
(3) the procedures recommended as a result of our analysis offer substantial
improvement over EPA's current approach to evaluating asbestos-related risks;
(4) while there is some indication that the existing EPA models for lung cancer and,
potentially, mesothelioma may not entirely reflect the time-dependence of
disease at long times following cessation of exposure, such effects appear to be
modest so that they are unlikely to adversely affect the proposed approach.
Prudence dictates, however, that limited additional study may be warranted to
adequately dismiss related concerns; and
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(5) results from our review of the supplemental literature provide additional support (
for the approach recommended in this chapter and indicate additional, specific
modifications that (if supported by limited additional study) could result in
substantial improvement even over the approach currently recommended, which
(in turn) provides substantial improvement over the current approach.
More detailed conclusions and recommendations are described below,
Conclusions and Recommendations For Lung Cancer
Without adjustments, the KL values estimated from the 18 existing studies vary by a
factor of 90 and pairwise differences between several individual studies are likely <'.'
significant (based on a comparison of their associated confidence intervals), By
simultaneously adjusting these values for fiber size and type, variation across the 16
studies (for which data are available to make the required adjustments) is reduced to a
factor of about 60.
Especially given the crude manner available for performing size adjustments coupled
with the very consistent picture that such adjustments improve matters and that there is
a strong theoretical basis for expecting that such adjustments should improve cross-
study comparison or extrapolation, we believe that such adjustments increase overall
confidence in cross-study comparison/extrapolation. We therefore recommend their
use. ^
The maximum and minimum values driving the factor of 60 come from cohorts of South
Carolina textile workers and Quebec miners, respectively, and the difference in the lung
cancer potency estimated between these studies has long been the subject of much
attention. A detailed evaluation of the studies addressing this difference, the results of (
our analysis of the overall epidemiology literature, and implications from the broader
literature, indicate that the most likely cause of the difference between these studies is
the relative distribution of fiber sizes in the two study environments. It is therefore likely
that the variation between these studies can be further reduced by implementing one or
a combination of the following:
• deriving improved characterizations of the exposures experienced by cohort
members in each, respective environment based on study-specific size
distributions (instead of relying on the cruder use of published distributions, as
currently required);
C
« deriving improved characterization of the exposures experienced by cohort
members in each, respective environment based on improved analysis of
relevant material (instead of relying on the existing, published distributions, which
are insufficiently detailed to capture all of the biologically-relevant aspects of size
distributions); (
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• from a selected subset of key studies, developing the Improved characterization
data required to allow the existing lung cancer potency factors to be adjusted to
an exposure index that is even closer to an optimized index than the one
recommended in this document;
• procedures for accomplishing some or all of the these objectives are described
below.
Importantly, the exposure index recommended in this document (and used throughout
this analysis), while not perfect, when coupled with the recommended adjustments for
fiber type, already provides substantial improvement over EPA's current approach to
evaluating asbestos-related risks. This is evident, for example, if one compares certain
values of the variance (a) provided in Table 6-21. Because, under the current
approach, chrysotile and amphiboles are considered equipotent and lung cancer and
mesothelioma risks are combined, the overall variance estimated for the unadjusted KM
values with RPC =1 (1,817} should be compared with the variance estimated for
adjusted KM. values with RPC optimized (0,6561), which represents the recommended
approach. Thus, the new approach reduces variance by approximately a factor of three
over the current approach. Actually, the improvement is even larger because the KM
values employed in our analysis include a greater number of studies than available to
the U.S. EPA in 1986. Therefore, despite our recommendations below to conduct
limited, additional study that may lead to further improvements, there is no reason not
to implement the recommendations from our current analysis now.
Although the EPA lung cancer model appears to adequately describe the time
dependence of disease for amphiboles, it may not adequately describe risks for
chrysotile at long times following cessation of exposure. In contrast to predictions from
the model (potentially due to the observed, lower biodurability of chrysotile), lung cancer
risks from chrysotile decrease with time following cessation of exposure. Thus, if the
model is applied to any new site to predict risks, if anything, risk estimates will be
conservative. However, particularly for studies with long followup times after cessation
of exposure, the KL's estimated from existing studies (using the EPA model) may be
underestimated, importantly, these conclusions are based on raw observations from
only one chrysotife and one amphibole study. Thus, it may be prudent to evaluate raw
data from a minimum of one additional chrysotile study and one additional amphibole
study to confirm that such observations are indeed general. However, it is likely that
these time-dependent effects are relatively small.
Conclusions and Recommendations For Mesothelioma
Without adjustments, the KM values estimated from the 12 existing studies vary by more
than a factor of 1000 and pairwise differences between several individual studies are
likely significant (based on a comparison of their associated confidence intervals). By
simultaneously adjusting these values for fiber size and type, variation across the 11
studies (for which data are available to make the required adjustments) is reduced to a
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factor of about 26, Given that this is little larger than an order of magnitude, it appears f
that we have essentially reconciled the mesothelioma studies.
Although the EPA mesothelioma model appears to adequately describe the time
dependence of disease in general, there was some indication that it may under
estimate risks at long times following cessation of exposure for amphiboles, Therefore,
as part of the analysis recommended for further testing the time dependence of the
lung cancer model, it may also be prudent to further evaluate the behavior of the
mesothelioma model.
Conclusions and Recommendations for Risk Assessment
f
Based on our analysis,.we recommend the following.
(1) Asbestos concentrations should be analyzed for structures that correspond to
the exposure index defined in Equation 6.7. Thus, count only structures longer
than 5 urn and thinner than 0.5 urn with structures longer than 10 urn weighted ^
as indicated in Equation 6.7. Further, count both parent fibers and bundles and
fibers and bundles that are components of more complex structures, but that
individually satisfy the above-defined dimensional criteria. Importantly, analysis
needs to be performed by TEM following preparation by a direct-transfer
procedure.
(2) When mixed, the individual contributions from chrysotile and the combined
amphiboles to any particular exposure need to be separately delineated and,
because amphibole exposures are the primary drivers, the analytical sensitivity
required for each particular study should be set based on amphiboles.
C;
(3) Combine such counts with the matched potency factors for lung cancer and
mesothelioma described in Tables 6-29 or 6-30 below using the appropriate EPA
model for each disease.
Based on our analysis, the optimized potency coefficients that are adjusted for size (so
that they are appropriately matched to the exposure index defined above) are obtained (
from Table 6-21 and reproduced here:
Table 6-29:
Optimized Risk Coefficients
for Pure Fiber Types
Fiber Type
Chrysotile
Amphiboles
KL,x100
0.85 .
4.5
KM,x108
0.05
30
€
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In keeping with the tradition of incorporating conservative estimates to minimize the
chance of under-estimating risks, conservative estimates for the potency factors listed
in Table 6-29 are derived by up-adjusting the amphibole potency values for both lung
cancer and mesothelioma so that they match the largest value from the set of available
studies and adjusting the chrysotile values proportionally. Results from this calculation
are presented in Table 6-30.
Table 6-30:
Conservative Risk Coefficients
for Pure Fiber Types
Fiber Type
Chrysotile
Amphiboles
KL,x100
3.0
15
KM,x108
0.1
50
We leave it as an option for deciding which values to employ. Interestingly, however,
due to the great degree of reconciliation among the disparate values for the potency
estimates already achieved in this document, the ratio of the conservative and optimum
KL, estimates is only a little larger than a factor of 3 while the conservative and optimum
KM. values vary by less than a factor of 2.
Recommendations for Limited, Further Study
The two major objectives identified above for further study are:
(1) to expand the test of the ability of the current EPA models to adequately track
the time-dependence of disease; and
(2) to develop the supporting data needed to define adjustments for potency factors
that will allow them to be used with an exposure index that even more closely
captures the criteria that determine biological activity (see Section 7.5). Among
other things, this may ultimately provide the data allowing complete reconciliation
of the potency factors derived for Quebec miners and South Carolina textile
workers, one of the major outstanding issues identified among asbestos
researchers.
The first of the above objectives requires that we gain access to raw data from a small
number of selected, additional epidemiology studies. The best candidate studies
include: (for chrysotile) the lung cancer data from Quebec (best) or, potentially, from the
New Orleans asbestos-cement pipe plant studied by Hughes et al. For amphiboles,
the best candidate studies indude: the lung cancer and newest mesothelioma data
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from Libby (best), or, potentially the lung cancer and mesothelipma data from the
Paterson, New Jersey insulation manufacturing plant studied by Seidman. The
possibility of obtaining some or all of these data sets needs to be further explored.
The second of the above objectives requires more detailed size characterization data
for the environments of interest. Although archived air samples do not appear to be
available from any of the study locations of interest, we believe that suitable data can
be developed from appropriate bulk samples. Thus, for example, it would be useful to
obtain samples of the raw ore from Libby, Wittenoom, and Quebec and the textile,
asbestos-cement pipe, and friction-product grade products from Quebec. We have
already approached individuals who can provide access to these materials and they
have all expressed interest in collaborating.
Results from our review of the supporting literature suggest that the optimum cutoff for
increased potency occurs at a length that is closer to 20 um thanIO urn, (the latter of
which is the cutoff in the exposure index provided in this study). Data do not currently
exist to improve on this latter cutoff. However, provided that study-specific size
distribution data could be obtained as indicated above, with the appropriate analyses, it
will be possible to develop the size distributions necessary to evaluate a range of
considerations including:
• delineation of size fractions among individual length categories out to
lengths as long as 30 or40 um;
• determination of the relative presence and importance of cleavage
fragments (of non-biologically relevant sizes) in mine ores vs. finished
fibers; and
• the relative fraction of fibrous material vs. non-fibrous particles in the
various exposure dusts of interest.
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7.0 SUPPORTING EXPERIMENTAL STUDIES
To evaluate the plausibility of cancer risk models for asbestos it is useful to examine
what is currently known (and what is not known) about (1) the mechanisms that
facilitate the transport of asbestos to the various target tissues of interest (in'the lungs
and mesothelium) and (2) the mechanisms that contribute to the development of cancer
in these target tissues. Accordingly, a detailed review of the relevant literature is
provided in this Chapter.
Although much progress has been made over the last decade toward elucidating the
fiber/particle mechanisms that contribute to transport and subsequent cancer induction,
at least two critical data gaps remain:
• no one has yet been able to track a specific lesion induced by asbestos in a
specific ceil through to the development of a specific tumor. There have been
experiments' that show altered DNA and other types of cellular and tissue
damage that are produced in association with exposure to asbestos and other
studies demonstrating that various tumors of the kinds that result from asbestos
exposure exhibit specific patterns of DNA alteration or other damage, There are
also studies that show that exposure to asbestos can lead ultimately to
development of tumors. However, these types of studies have yet to be linked;
and
» although the target tissues in which asbestos-induced cancers develop have
generally been identified, the specific target cells that serve as precursors to
these tumors are not known with certainty.
Because of the first of the above limitations, researchers have tended to report on a
broad range of tissue and cellular effects induced by asbestos that may lead generally
to various kinds of cellular damage or injury. Direct cytotoxicity, for example, is one of
the end points typically tracked as a marker for asbestos-induced injury. However, not
all of these effects necessarily contribute (either directly or indirectly) to the
development of cancer. Therefore, one of the goals of the following discussion is to
distinguish (within the limitations of the current state of knowledge) among effects that
likely contribute to the development of cancer from those that are less likely or unlikely
to contribute.
In addition, because the relative effects of fiber size, shape, and mineralogy need to be
elucidated to better indicate how asbestos concentrations should be characterized to
support risk assessment, studies that address these topics are highlighted. Of
particular interest are studies that contrast the effects of different sized fibers or the
effects of fibers from the effects of non-fibrous particles of similar mineralogy and
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studies that contrast the effects of fibers of comparable morphology (size and shape)
but differing mineralogy.
The types of studies that have contributed to the state of knowledge of the effects of
asbestos (in addition to the human epidemiology studies previously reviewed, see
Chapter 6) include:
whole animal inhalation studies;
whole animal instillation studies;
whole animal injection, implantation studies;
human pathological studies;
in-vitro studies in cell cultures; and
in-vitro studies in cell-free systems.
Note, depending on the outcome(s) monitored, the animal studies may alternately be
categorized as retention studies, histopathology studies, or dose-response studies.
Each type of study possesses certain advantages and exhibits certain limitations, which
have previously been described (Chapter 5) along with descriptions of the nature of
each of these study types. In addition to the advantages and limitations that are
attributable to the type of study, the quality of the characterization of asbestos (or other
particulate matter) determines the utility of the study for addressing issues associated
with fiber morphology and mineralogy. Unfortunately, for many published studies, both
the characterization of the asbestos (or other particulate matter) and descriptions of the
manner in which such materials were handled are insufficient to establish the detailed
morphology or mineralogy. Thus, such limitations need to be considered when
comparing across study results or evaluating the validity of study conclusions.
As previously indicated (Chapter 3), the primary route of asbestos exposure of concern
for humans is inhalation. Moreover, the biological activity of inhaled asbestos depends
on the following factors:
• the extent that asbestos structures are respirable and the pattern of
deposition of inhaled structures;
• the extent that deposited structures are subsequently cleared or
degraded;
• the extent that deposited structures are transported or migrate to the
various target tissues; and
• the extent that retained structures induce a biological response in each
target tissue.
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7.1 FACTORS AFFECTING RESPIRABILITY AND DEPOSITION
Discounting systemic affects resulting from other forms of exposure, factors affecting
respirability are common to all of the toxic end points associated with asbestos
exposure considered in this study (asbestosis, pulmonary carcinomas, and
mesothelioma). Moreover, respirability is common to the factors affecting the toxicity of
inhaled, insoluble particles in general, To be respirable, an inhaled particle must pass
the blocking hairs and tortuous passageways of the nose and throat and be deposited
in the lungs. Particles deposited in the naso-pharyngeal portion of the respiratory tract
are not considered respirable.
Not all of the inhaled partides that reach the lungs will be deposited. Small particles
may not impact lung surfaces during inhalation and are subsequently exhaled. Once a
particle impacts on a surface, however, it is likely to remain because the surfaces of the
lungs are wetted with a surfactant (Raabe 1984).
Adverse health effects potentially result when particles are deposited in the lungs and
remain in contact with tissue within the lungs for a sufficient period of time to provoke a
biological response. To affect the mesothelium, an offending particle may also need to
migrate or be transported from the lung to this surrounding tissue. However, due to the
proximity of the mesothelium to peripheral portions of the lung parenchyma (which
include locations where particles are typically deposited), it is also possible that
diffusable molecules produced in lung tissue in response to deposited particles can
have an adverse effect on the mesothelium (see, for example, Adamson 1997). Such
effects are considered among the mechanisms of disease induction addressed in the
discussion of biological responses (Section 7.3).
It is long-term retention of particles in the respiratory tract that potentially leads to
adverse health effects. Consequently, the interplay between deposition and removal
(clearance) is an important determinant of biological activity and separating the impact
of these two processes is difficult. The term "retention" is used here to represent the
fraction of particles remaining in the lungs beyond the time frame over which only the
most rapid removal processes are active (i.e. muco-ciliary clearance). The factors
affecting retention are addressed further in Section 7.2.
Published inhalation studies divide the respiratory tract into three units (see, for
example, Raabe 1984). The naso-pharyngeal portion of the respiratory tract extends
from the nares in the nose through the entrance to the trachea. The tracheo-bronchial
portion of the respiratory tract includes the trachea and all of the branching bronchi
down to the terminal bronchioles. The respiratory bronchioles and the alveoli, which
are collectively referred to as the "deep lung", are the bronchio-alveolar (or pulmonary)
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portion of the respiratory tract. For a more detailed description of the features of the
respiratory tract, see Section 4.4.
The dimensional requirements forrespirability have been studied and reviewed by
several authors (see, for example, Raabe or 1984 or U.S. EPA 1986), A more recent
review is also presented by Stober et ai. (1993). Much of the data reviewed by these (
authors is based on earlier studies in which researchers exposed animals or human
volunteers to a series of monodisperse spherical particles (although Stober et a I. also
reviews fiber experiments). In this manner, the impact of the diameter of spherical
particles on respirability was elucidated. The respirability of fibrous materials (such as
asbestos) tends to be described in terms closely associated with those employed for ^
spherical particles, but with adjustments for density and shape. Importantly, because
respirability is a mechanical process: the size, shape, and density of a particle (or fiber)
determine its respirability along with the morphometry of the airways through which the
particle passes (Stober et al. 1993). Other than affecting the particle's density or the
distribution of fiber shapes, the chemical composition (mineralogy) of a particle (or fiber)
does not influence respirability. *-•
The respirability of particles and fibers by humans and a variety of other mammals of
experimental interest has also been the subject of increasingly sophisticated-modeling
efforts (Stober et al. 1993). The latest refinements of such models predict particle
deposition with a degree of accuracy that is beyond what can be validated with existing, (
experimental data. The application of several of these models to asbestos (and other
fibrous materials) are considered throughout this chapter. However, a detailed
overview of the state of the art of such modeling is beyond the scope of this document.
Such an overview is presented by Stober etal. (1993).
7.1.1 Respirability of Spherical Particles
Spherical particles larger than 10 urn in diameter are considered non-respirable
because virtually all particles in this size range are trapped in naso-pharyngeal
passageways and blocked from entering the lungs. As the diameter of the particles fall,
an increasing fraction traverses the nose and throat and may be deposited in the lungs. C
About half of particles 5 urn in diameter are blocked before entering the lungs. Virtually
all particles smaller than 1 urn enter the lungs, although other factors determine
whether they are in fact deposited or simply exhaled. Figure 7-1 (Source: Raabe 1984)
is a representation of the relative deposition in the various compartments of the
respiratory tract as a function of particle diameter. ,
Within the lungs (Figure 7-1), the greatest fraction of respirable particles (over the entire
range of diameters down to less than 0.01 urn) are deposited in the deep lung (the
broncho-alveolar portion of the respiratory tract), primarily at alveolar duct bifurcations
(see, for example, Brody et a!. 1981, Davis et al. 1987, Johnson 1987, and Sussman et
C
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al. 1991 a). These studies also indicate that biological responses appear to be initiated
where deposition is heaviest. Generally, the fraction of particles deposited in the deep
lung increases regularly with decreasing diameter until a maximum of 60% deposition in
the deep lung is reached at about 0.1 um diameter.
As indicated in Figure 7-1, a transition occurs at particle diameters between 0.5 and
1 um. For particles in this range and smaller, deposition in the deep lung competes
primarily with deposition in the tracheo-bronchial tree and with exhalation; smaller
particles have an increasing probability of being exhaled without ever impacting the
surface of an air passageway. For particles larger than this transition range, broncho-
alveolar deposition is limited chiefly by the fraction of particles that are removed from
the air stream prior to reaching the deep lung (either by deposition in the naso-
pharyngeal or the tracheo-bronchial portions of the respiratory tract).
The transition between naso-pharyngeal competition with deep-lung deposition and
competition from other removal processes is important because, during mouth
breathing, a process that bypasses the tortuous pathways of the nose and throat, it has
been observed that larger particles (up to several micrometers in diameter) may be
deposited in the deep lung (Raabe 1984). Studies of the effects of mouth breathing are
also reviewed by Stober et al. (1993). Because most people spend at least small
amounts of time mouth breathing, especially during exertion or during snoring, this
mechanism for allowing larger particles to settle in the deep lung should not be ignored.
A diameter of 0.5 um also happens to represent the transition between the regime
where inertial flow and the regime where diffusional flow becomes the major factor
controlling deposition in the lungs. Below the 0.5 um transition, the diffusional diameter
becomes more important in determining deposition than the aerodynamic equivalent
diameter (defined below).
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Figure 7-1. OLD Figure 5-1 .
NEED TO GET PERMISSION TO REPRODUCE FIGURE AND NEED TO DEVELOP
FIGURE.
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7.1.2 Respirability of Fibrous Structures
Several authors have investigated the effect of the shape of non-spherical particles
(including fibers) on respirability and deposition (see, for example, Harris and Timbrell
1977, Sussman et al. 1991 a and b, Strom and Yu 1994, or Yu et al. 1995a and b). It
has been found that the behavior of non-spherical particles can be related to the
behavior of spherical particles by introducing a concept known as the aerodynamic
equivalent diameter. The aerodynamic equivalent diameter is the diameter of a
hypothetical spherical particle of unit density that would exhibit the same settling
velocities and aerodynamic behavior as the real, non-spherical particle of interest.
Factors that affect the aerodynamic equivalent diameter are density, true diameter, true
length (for elongated particles such as fibers) and the regularity of the particle shape.
Harris and Timbrell Findings
Because fibrous particles tend to align primarily along the axis of travel under the flow
conditions found in the lungs, respirability is predominantly a function of the diameter of
a fiber and the effect of length is secondary (Harris and Timbrell 1977). Fibrous
structures (of unit density) with aspect ratios greater than 3:1 behave like spherical
particles with diameters up to 3 times larger (the aerodynamic equivalent diameter) and
exhibit only a very weak dependence on length. This is demonstrated in Figure 7-2
where the true diameter of a fiber is graphed on the top horizontal axis against spherical
(aerodynamic equivalent) diameters on the bottom horizontal axis. Figure 7-2 is an
overlay of Figure 7-1. Note that, to adjust for the density of asbestos, the true
diameters listed in the figure have been shifted to the right of where they would appear
if the relationship was exactly one third of the aerodynamic equivalent diameter.
Two vertical dashed lines in Figure 7-2 represent effective limits to the range of
respirable asbestos. The left line in the figure represents the limiting diameter of the
smallest chrysotile fibril (about 0.02 urn true diameter) and thus represents a lower limit
to the deposition chart that is of concern when considering asbestos. The right vertical
line represents the cutoff where deposition in the deep lung becomes unimportant due
to removal of such particles by the naso-pharyngeal passageways. This latter cutoff
corresponds to a true fiber diameter of 2.0 urn, which theoretically represents the upper
limit to the size of asbestos that is respirable. As indicated in the figure, however,
deposition in the deep lung drops precipitously for fibers thicker than about 0.7 urn so
that no more than a few percent of asbestos fibers thicker than approximately 1 urn
actually reach the deep lung.
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Figure 7-2 (old Figure 5-2)
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Harris and Timbrell (1977) also evaluated the relationship between the overall shape of
a particle and the extent of deposition. Over the range of diameters that potentially
represent the range of asbestos fibers likely to be encountered, pulmonary deposition
decreases with increasing complexity of shape beyond simple cylinders (such as
clusters and matrices) at the expense of increasing naso-pharyngeal or tracheo-
bronchial deposition. This change also becomes increasingly important as the length of
the structure increases.
For structures less than 25 um in length, the difference in deposition between simple
fibers and complex clusters or matrices may vary by up to a factor of 2 with the complex
structures being more likely to be removed in the naso-pharyngeal portion of the
respiratory tract and the fibers more likely to be deposited in the deep lung. At 100 um
lengths, the fraction of complex structures that survive passage through the nose and
throat in comparison with simple fibers may vary by a factor of five. This means that
large structures become relatively less respirable as their complexity increases.
However, during mouth breathing large clusters and matrices may enter the deep lung.
When all of the factors that Harris and Timbrell (1977) addressed are considered, the
efficiency of the deposition of asbestos structures in the deep lung is maximal for short,
thin, single fibers (less than 10 um in length with a true diameter less than 0.7 um).
The efficiency decreases slowly with increasing length (up to an effective limit of 200
um), moderately with increasing complexity of shape, and rapidly with increasing
diameter (up to an effective limit of 2.0 um, true diameter). Thinner fibers, down to the
lower limit of the range for asbestos fibers (0.02 um, true diameter), are deposited with
roughly the same efficiency. Approximately 20 to 25% of the fibers between 0.7 and
0.02 um in diameter (and less than 10 um in length) are deposited in the deep lung.
Sussman et al. Findings
Based on a series of experiments on human tracheal bronchial casts, Sussman et al.
(1991 a and b), also developed models of fiber deposition in the human lung. Such
experiments are in fact illustrative of several research groups who have developed
deposition models based on results from experiments on airway casts (for a review, see
Stoberetal. 1993).
The results reported by Sussman et al. (1991 a and b) appear to be generally consistent
with the results reported by Harris and Timbrell (above) and Yu and coworkers (below),
although the manner in which their results are reported make them somewhat less
directly comparable. Briefly, Sussman et al. report that deposition increases along most
generations of the bronchial tree with increasing fiber length and increasing airflow rate
for any fixed aerodynamic diameter. This increased deposition efficiency is
demonstrated for airway generations at least through the ninth bifurcation and is implied
to continue through to airway generations that would be representative of the
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respiratory (pulmonary) portion of the lung (i.e. airway bifurcations greater than
approximately 16 to 22). For definitions and a description of airway generations, see
Section 4.4.
Findings of Yu and Coworkers
In a series of studies, Yu and coworkers combined an improved model of human lung
physiology (Asgharian and Yu 1988) with a series of more rigorous equations to
describe fiber mobility (Chen 1992) and used these to evaluate the deposition of
various types of fibrous materials in the lung. The trends indicated in their studies show
general agreement with those reported by Harris and Timbrell, but with several notable
refinements.
In a study of refractory ceramic fibers (Yu et al. 1995a), a maximum deposition
efficiency of 15% is reported for fibers that are approximately 6 urn long and
approximately 1 urn in diameter. This is close to the fiber size at which maximal
deposition is reported by Harris and Timbrell (1977). As with Harris and Timbrell, Yu et
al. also report that deposition efficiency decreases precipitously for thicker structures
and more slowly for thinner structures. For thinner structures, deposition efficiency
increases with both decreasing width and length. As fibers get longer, optimum
deposition occurs with decreasing thickness. Thus, for example, a maximum deposition
rate of 10% occurs for fibers that are 20 urn long at a thickness of 0,8 urn..
In a study of silicon-carbide whiskers (Strom and Yu 1994), the deposition model is
extended to fiber widths as narrow as 0.01 urn. Results from this study indicate that
fibers between 0.01 and 0.1 urn in thickness are deposited with a minimum efficiency of
5% up to lengths of approximately 40 urn before efficiency drops below 5%. For thin
fibers (thinner than 0.5 um), shorter fibers tend to be deposited in the deep lung much
more efficiently than longer fibers. More than 25% of thin fibers shorter than 1 urn are
deposited in the deep lung following inhalation. Strom and Yu also report that the
efficiency of deposition in the deep lung of long structures increases substantially
during mouth breathing.
Comparing the results reported for refractory ceramic fibers (density = 2.7 g/cm3) and
silicon-carbide whiskers (density = 3.2 g/cm3), it also appears that the efficiency of
deep-lung deposition increases for thinner and for longer structures as the density of
the structures increases. Given the observed density effect, longer and thinner
chrysotile fibers would be deposited in the deep lung less efficiently than (denser)
amphibole fibers of the same size. However, shorter and thicker chrysotile structures
would be deposited somewhat more efficiently than similarly sized amphiboles. This
suggests that a greater fraction of the mass of chrysotile that gets deposited in the deep
lung will be composed of very short fibers and somewhat longer bundles than the mass
fraction of short fibers or longer bundles in the air breathed. Also, to the extent that
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chrysotile fibers are curved, these would be deposited somewhat less efficiently than
straighter (amphibole) fibers of comparable size.
Based on the deposition efficiencies predicted by Yu and coworkers, fibrous structures
that reach the deep lung in humans are effectively limited to those thinner than
approximately 1 urn. It is also apparent that aspect ratio constraints affect only the
shortest fibers (i.e., those shorter than approximately 3 urn). The thickness constraint
for all longer structures is best described as a maximum width (rather than an aspect
ratio).
Rats versus Humans
Yu and coworkers also modified their models to evaluate the rates that fibrous materials
are deposited in rat lungs and compared these with results for humans. Such
comparisons have implications for the manner in which results from animal inhalation
studies are extrapolated to humans.
Results from Yu et al. (1994) suggest that pulmonary deposition of all fibrous structures
with lengths between about 1 and 100 (Jim and thinner than approximately 1 urn occurs
at much higher rates in rats than in humans. Fibers as long as 90 i-im are deposited in
rat lungs at efficiencies exceeding 20% while fewer than 5% of structures this long are
deposited in the pulmonary region of human lungs. In fact, it is only structures
between 1 and about 20 urn within a very narrow range of thicknesses (centered
around 1 urn) that are deposited more efficiently in the deep lungs of humans than in
rats.
Yu et al. (1995a) also indicate-that, even when deposition efficiencies are comparable
in rats and humans, due to differences in the total lung mass and breathing dynamics
across species, the resulting lung burdens (i.e. the mass or number of structures per
mass of lung tissue) are five to 10 times higher in the rat than in humans for any given
exposure. Lung burden per lung surface area are also higher in the rat than in humans.
To illustrate, assume rats and humans are similarly exposed to a concentration of
0,1 f/cm3 (100 f/L)of some fibrous material with a length at which both species retain
approximately 10% of the fibers inhaled. The Following Table 7.1 then indicates the
calculations required to determine the relative rates at which the lung (volume and
surface area) burdens in each species would develop,
Table 7-1:
ESTIMATION OF LUNG VOLUME AND LUNG SURFACE AREA
LOADING RATES FOR RATS AND HUMANS
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Species
Human
Rat
Species
Human
Rat
Body
Weight
(kg)
70
0.15
Breathing
Rate
(L/min)
21.7
0.13
Lung
Volume
(L)
5
0.01
No. Fibers
Inhaled
per
Minute
(f/min)
2170
13,3
Lung
Surface Area
(m2)
140
0.4
No. Fibers
Deposited
per Minute
(f/min)
217
1.3
Rest Breaths
per Minute
(bpm)
1.5
70
Lung Volume
Loading Rate
(f/L-min)
43.4
130
Tidal Lung
Volume
(L)
1.5
0.0019
Lung Surface
Area Loading
Rate
(f/m2-min)
1.6
3.3
From the above table, it is clear that rats exposed to comparable airborne
concentrations as humans will increase their loading of fibers per volume (or mass) of
lung at a rate that is approximately three times that of humans (for fibers in sizes that
are deposited with 10% efficiency in both species). Similarly, the fiber load per surface
area of lung will increase in rats at a rate that is approximately twice that of humans.
Moreover, even higher relative mass or surface area loading rates are expected for the
rat than shown in the above table, due to the greater efficiency with which most fiber
sizes are deposited in rat lungs. Data used to compute the loading rates in the table
(which are also presented) are derived from Gehret al. (1993) and supplemented with
information from Stober et al (1993). A more detailed description of this information is
provided in Section 4.4,
7.1.3 The Effects of Electrostatic Charge on Particle Respirability
Electrostatic charge has been shown to affect the retention of particles within the lungs
(see, for example, Vincent 1985). Since processes that generate airborne particles
generally involve some form of abrasion, airborne dust particles frequently exhibit
varying degrees of electrostatic charge. Although this potentially leads to variation in
the efficiency of particle retention in the lungs as a function of the source of the dust, a
detailed relationship between surface charge and retention was not described in this
paper. A more detailed and quantitative treatment was developed by Chen and Yu
(1993) and the implications of the Chen and Yu model are described below (following
discussion of results reported by Davis et al.).
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Davis et al (1988) report that animals exposed to dusts containing fibrous chrysotile,
whose surface charge is reduced with a beta minus source, retain significantly less
chrysotile than animals dosed with dusts containing particles whose surface charge has
not been reduced. However, the magnitude of the difference in the mass of fibers
retained is less than a factor of two, implying that the absolute variation due to this
effect may be small. Further research in this area is needed.
Chen and Yu (1993) report that, based on modeling of lung deposition, overall
deposition increases with increasing charge density on the particles inhaled. However,
due to the pre-filtering by the naso-pharyngeal and and tracheo-bronchial portions of
the respiratory tract, the effects of electrostatic charge on deep lung deposition appear
to be only slight to modest.
Given the results of the above studies, the overall effects of electrostatic charge on
particle deposition in the deep lung appear to be relatively minor. Therefore, such
effects do not need to be considered explicitly when evaluating the health
consequences of asbestos.
7.1.4 General Conclusions Concerning Particle Respirability
Based on the information provided in the last several sections, it is apparent that in
humans:
• deposition of asbestos fibers in the pulmonary portion of the lung occurs
primarily at alveolar duct bifurcations;
• electrostatic effects on pulmonary deposition are likely minor;
• fibers that are deposited in the pulmonary portion of the lung are largely thinner
than approximately 0.7 urn and virtually all are thinner than 1 urn (except during
mouth breathing, when thicker and more complex structures may be respired);
• the length of a fiber has limited impact on respirability up to a length of
approximately 20 urn, but the efficiency of deposition of longer fibers decrease
slowly with increasing length for longer fibers;
• as the length of the fibers that are inhaled increases, the thinner fibers are
deposited with greater efficiency. Thus, the longer the fibers inhaled, the thinner
the fibers retained;
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• due to differences in density, shorter and thicker chrysotile structures will be
deposited more efficiently in the pulmonary portion of the lung than
corresponding amphibole structures and longer and thinner amphibole structures
will be deposited more efficiently than corresponding chrysotile structures;
• curly chrysotile structures are less likely to reach the pulmonary portion of the
lung than straight amphibole (or chrysotile) structures;
• except for a very narrow range of fiber sizes (centered around 6 um in length and
1 urn in diameter), virtually all size fibers are deposited with greater efficiency in
rat lungs than human lungs;
• due to body morphology and the dynamics of breathing, rats exposed to similar
air concentrations will accumulate fiber burdens both per mass (volume) of lung
tissue and per lung surface area at a rate that is several times the rate such
burdens accumulate in humans; and
• the dynamics of fiber lung deposition can now be accurately predicted in great
detail using currently available models.
7.2 FACTORS AFFECTING DEGRADATION, TRANSLOCATION, AND CLEARANCE
Degradation and clearance mechanisms compete with deposition to determine the
fraction of asbestos that is retained in the lungs. Other (translocation) mechanisms
mediate the movement of asbestos from sites of initial deposition to various target
tissues within the lung and mesothelium. These factors affect all of the toxic end points
of interest. Studies indicating the dependence of the various contributing mechanisms
on fiber size and mineralogy are highlighted as well as studies indicating differences
between mechanisms in humans and laboratory animals.
The three units of the respiratory tract defined in the last section (naso-pharyngeal,
tracheo-bronchial, and bronchio-alveolar units) differ primarily by the types of clearance
(and translocation) mechanisms operating in each unit (Raabe, 1984). These are
summarized in Table 7-2 along with rough estimates of the time frames over which
each mechanism may operate.
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TABLE 7-2
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Briefly, the structures of the nose and throat are bathed in a continual flow of mucous,
which is ultimately swallowed or expectorated. The mucous traps deposited particles
and carries them out of the respiratory tract. The air channels of the tracheo-bronchial
section of the respiratory tract are lined with cilia and mucous secreting cells, As in the
nose and throat, the mucous traps particles deposited in these air pathways and the
ciliary escalator transports the mucous up to the throat where it may be swallowed or f
expectorated.
Neither the alveolar ducts nor the alveoli of the pulmonary compartment of the lung are
ciliated (inferred from St. George et al, 1993). Therefore, particles deposited in this
section of the respiratory tract can only be cleared by the following mechanisms: (
« if the deposited particles are soluble, they may dissolve and be
transported away from the lungs in blood or lymph; or
• if they are sufficiently compact, they may be taken up by alveolar
macrophages and transported outward to the muco-ciliary escalator of the *•-
tracheo-bronchial portion of the respiratory tract.
Due to a combination of chemical and physical stresses in the environment of the lung,
deposited asbestos structures may degrade by splitting. Longitudinal splitting, primarily
of bundles, produces thinner structures and transverse splitting produces shorter c
structures. In both cases, the number of structures produced may be larger than the
number of structures initially deposited.
By changing the size and number of structures that were initially deposited in the lungs,
splitting may affect the rates and efficiency with which the various other degradation f
and clearance mechanisms operate.
Particles and fibers that are deposited in the pulmonary portion of the lung may also be
transported by a variety of mechanisms into and through the epithelium lining the
alveolar ducts and alveoli to the underlying interstitium and endothelium that are located
within the interalveolar septa (See Section 4.4). In those portions of the lung C
parenchyma that lie proximal to the pleura, such mechanisms may also facilitate
transport to the mesothelium. Putative mechanisms by which such transport may
occur include:
• if particles are sufficiently compact to be phagocytized by alveolar ^
macrophages, they may be transported within macrophage "hosts"
through the epithelium to the interstitium;
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• if particles are sufficiently compact to be phagocytized by the epithelial
cells lining the air passageways of the deep lung, they may be transported
into cell interiors or transported through to the basement membrane, the
interstitium, the endothelium, and (eventually) the pleura;
• particularly when associated biological effects cause changes in the
morphology of epithelial cells (Section 7.3.6), particles may diffuse
between the cells of the epithelium to underlying tissues; and/or
• particles may be transported through respiratory epithelium mechanically
due to physical stresses associated with respiration within the lung.
Although the transport of fibers and particles from airway lumena to the interstitium is
apparent in many studies (see below), the precise mechanisms by which such transport
actually occurs has yet to be delineated with certainty.
Particles-deposited in the interstitium can also be cleared and the processes by which
these particles are ultimately cleared are similar to, but may be substantially slower
than, the mechanisms by which particles deposited in airway spaces can be cleared.
Such mechanisms include:
• if the deposited particles are soluble, they may dissolve and be
transported away from the lungs in blood or lymph; or
• if the particles of the interstitium are sufficiently compact to be
phagocytized by interstitial macrophages, they may be taken up and
transported to the lymphatic system for removal.
The mechanisms by which particles that reach the pleura and mesothelium may be
cleared are also similar to those operating in the interstitium:
• if the deposited particles are soluble, they may dissolve and be
transported away from the lungs in blood or lymph; or
• if the particles that reach the pleura are sufficiently compact to be
phagocytized by pleural macrophages, they may be taken up and
transported to the lymphatic system for removal.
Note, particles cleared from the pleura by macrophages appear subsequently to be
deposited at sites of lymphatic drainage along the pleura (i.e. at lymphatic ducts) from
where they are ultimately cleared in lymph (Kane and McDonald 1993).
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The various degradation, clearance, and transport mechanisms that affect the retention
of asbestos in the lung and other target tissues (identified above) exhibit disparate
kinetics that may be further altered by the size, shape, mineralogy, and concentration of
the particles affected. Therefore, the kinetics of these mechanisms are considered
below. The mechanisms evaluated include:
• . dissolution;
• muco-ciliary transport;
• macrophage phagocytosis and transport; and
• diffusional transport.
Evidence for the existence of these mechanisms and inferences concerning their
kinetics derive primarily from retention studies, which may include both studies of
retained structures in animals following either short-term or chronic exposure or human
pathology studies in which the lung burdens of deceased individuals are correlated with
their exposure history. Other information also comes from in-vitro studies. Various,
increasingly sophisticated models have also been developed to predict the individual
and combined effects of these mechanisms.
7.2.1 Animal Retention Studies
Retention studies track the time-dependence of the lung burden of asbestos or other
particulate matter (i.e. the concentration of particles in the lung) during orfollowing
exposure. Thus, such studies are designed to indicate the degree to which inhaled
structures are retained. Depending on the time frame evaluated, however, effects due
to deposition and those due to clearance may not easily be distinguished in such
studies. Moreover, due to the near impossibility of isolating the various compartments
of the lung when preparing for quantitative anlaysis of tissue burden (e.g. the pure
respiratory components vs. the larger airways or the tissues directly associated with
airway lumena vs, the underlying interstitium or endothelium), it is nearly impossible to
separate the effects of the various dearance mechanisms, which typically operate over
vastly different time scales (Table 7-2). This is why modeling has proven so important
to distinguishing effects attributable to individual mechanisms.
Results from retention studies must be evaluated carefully. In addition to the limitations
highlighted above, the lung burden estimates from such studies may be affected by the
manner in which asbestos is isolated from lung tissue for measurement and the manner
in which the concentration of asbestos is quantified (Chapter 5). For example, lung
burden estimates may vary substantially depending on what portions of lung
parenchyma are sampled or whether whole lungs are homogenized. Results may also
vary depending on whether lung tissue is ashed or dissolved in bleach during sample
preparation. More importantly, because several clearance mechanisms are affected by
the size and even the mineralogy of the structures being cleared, studies (particularly
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older studies) that track lung burden by mass or by total fiber number may not
adequately capture such distinctions.
7.2.1.1 Studies involving short-term exposures
The latest retention studies tend to focus on the fate of long fibers (typically those
longer than 20 urn) in support of the generally emerging recognition that these are the
fibers that cannot be readily cleared from the pulmonary compartment of the lung and
that, not coincidentally, contribute most to disease (Section 7.4).
Hesterberg et al. (1998a), for example, tracked the time-dependent retention in rats of
two fiber categories: (1) WHO fibers1 and (2) WHO fibers longer than 20 urn for a range
of manmade vitreous fibers (MMVF's), a refractory ceramic fiber (RCF1 a), and amosite
following a 5-day (6 hr/day), nose-only exposure. Rats were sacrificed at intervals up to
a year following exposure. The amosite was size selected to contain a high proportion
of fibers longer than 20 urn. Aerosol concentrations were also adjusted to maintain
target concentrations of 150 f/cm3 for long fibers for each sample tested. Airborne
mass concentrations varied between 17 mg/m3 for amosite to as much as 60 mg/m3 for
the other fiber types. Lungs (without trachea or main bronchi) were weighed and stored
frozen. For analysis, each lung was dried to constant weight, minced and a portion was
ashed. The ashed portion was further washed with filtered, household bleach, then
filtered and.applied to an SEM stub. Fiber numbers and dimensions (in both aerosols
and tissue) were determined by SEM with a minimum of 200 fibers counted. In
addition, analysis continued until a minimum of 30 fibers longer than 20 urn were
counted.
Hesterberg et al. tracked the ratios of retained fiber concentrations with time to the
concentration retained 1-day following cessation of exposure, The observed time-
dependent decay in these ratios were then fit to one-pool (single first order decay) or
two-pool (weighted sum of two first order decays) models. With zero time assumed to
be the time immediately following cessation of exposure. The authors recognize that at
least some clearance likely takes place during the five days of exposure so they
expected the assumption that retained concentrations at the end of exposure on day 5
to be equal to deposited concentrations would cause their analysis to slightly
underestimate clearance rates. They also recognized that waiting 24-hours after
cessation of exposure to measure retention allows some short-term clearance of upper
airways, so that they expected their analysis would better focus on slower clearance
from deeper in the lung.
Results reported by Hesterberg et al. indicate that the dimensions and concentrations
of fibers in aerosols from the five synthetic fibrous materials were all similar but that the
WH O fibers are those longer than 5 pm, thinner than 3 |jm with an aspect (length to
width) ratio greater than 3 (WHO 1985).
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amosite aerosol contained a substantially greater number of fibers (including the
longest fibers) but that the fibers, on average, were somewhat shorter and substantially
thinner than the other aerosols. Of the fibers initially deposited in the lung (based on
measurements made 1-day following cessation of exposure, comparable fiber numbers
of long (> 20 urn) fibers were retained across all six fiber types. Deposited
concentrations of fibers 5-20 urn in length were more variable, but values within one
standard deviation still overlapped. About 6 times as many short amosite fibers (< 5
urn) were initially deposited than for any of the other fiber types. The authors also
indicate that the dimensions of retained fibers were generally shorter and thinner than
the original aerosol and were much more similar across retained fiber types than the
original aerosols.
Clearance of long fibers (>20 urn) for all six fiber types could best be described using a
two-pool model. The first pool cleared relatively rapidly (within the first 90 days) and
represented a minimum of 65% of the lung burden observed 1-day following exposure.
The second pool cleared much more slowly. For amosite fibers in the second pool,
during the approximately 275 days of clearance, retention was only reduced to 80% of
the 90-day value. In contrast, all five of the synthetic fibers were reduced to less than
30% of their 90-day value during this period. For amosite, the first pool decayed with a
half-life of 20 days (90%CL: 13-27) and all of the other fibers with half-lives of 5-7 days
(with varying confidence bounds). For the slower pool, amosite fibers exhibited a half-
life of 1160 days (90% CL: 420-°°) with the other fibers showing half lives varying
between 24 and 179 days. The combined, weighted half-life for amosite was 418 days
(90%CL: 0-1060). The authors also note that data reanalyzed from an earlier study
(Hesterberg et al. 1996) indicate a corresponding weighted half life for croddolite of 817
days (246-°°) and indicate that this was best fit using a single exponential (a one-pool
model).
Hesterberg et al. indicate that in this and previous studies approximately 20 to 60% of
long fibers typically clear from the lung within 2-weeks post exposure. They further
suggest that this rapid clearance may be attributable to muco-ciliary clearance from the
upper respiratory tract. They further report from the present study that short amosite
fibers cleared much more rapidly than long fibers. Fibers < 5 urn in length were reduced
by 90% in the first 90 days (in comparison to 65% for long fibers). However, from 90 to
365 days, little or no dearance was observed for amosite fibers of any length.
For four of the synthetic fibers, long fibers cleared at the same rate as short fibers (all
more rapidly than amosite) and the authors report that the data suggest transverse
breakage for these fibers. Moreover, they attribute the more rapid clearance of long
fibers among the MMVF's to dissolution, since these fibers exhibit in-vitro dissolution
rates that are rapid relative to the time scale of macrophage clearance. One synthetic
fiber MMVF34, which is a stonewool) disappeared much more rapidly than any other
fiber and the long fibers disappeared more rapidly than the short fibers. MMVF34
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shows the greatest in-vitro dissolution rate at neutral pH for any of the fibers tested in
ths study and dissolves particularly rapidly at pH 4.5 (the pH found in the phagosomes
of macrophages). The authors postulate that clearance of all of the synthetic fibers are
enhanced over amosite by dissolution and breakage.
In summary, Hesterberg et al. observed that:
• multiple clearance mechanisms (operating over multiple time scales)
contribute to clearance;
• for sufficiently soluble fibers, long fibers clear more rapidly than short
fibers;
• for insoluble fibers, a subset of long fibers clears rapidly within the first few
months following exposure and the remaining long fibers clear only
extremely slowly, if at all;
• short fibers of all types are cleared at approximately the same rate (much
more rapidly than long, insoluble fibers);
• a small, residual concentration of short fibers may not always clear and
may remain in the lungs (sequestered in alveolar macrophages) for
extended periods; and
• in this study, there is some suggestion that short amosite fibers clear
somewhat more slowly than short fibers of the other, non-asbestos
mineral types studied.
Regarding the last observation, whether this is attributable to differences in fiber
thicknesses among the various mineral types, due to partial contributions (even among
short structures) to dissolution, or due to a unique, toxic effect of amosite is unclear.
However, the likeliest of these candidate hypotheses is that the effect is due to partial
dissolution.
This general pattern of observations are consistent with the findings of most, recent
retention studies following short-term exposure.
In an earlier study of similar design, Bernstein et al. (1996) evaluated the deposition
and clearance of a series of 9 glass and rock wools. These authors similarly found that
clearance could be modeled using-a double exponential for all length fibers (in similar
length categories of < 5, 5-20, and > 20 urn) and that for soluble fibers, long fibers clear
more rapidly than short fibers (with the intermediate length fibers in between).
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For the Bernstein et al. study, if one assumes that the pool of longer-lived fibers is
representative of macrophage clearance, this suggests that the efficiency of clearance
by macrophages decreases with increasing fiber length and that the longest structures
are not phagocytized at all, so that they remain exposed to the extracellular medium
where dissolution occurs. Lending further support to this interpretation, the authors also
report that the clearance rate for long fibers correlate with measured in-vitro dissolution
rates at neutral pH while the clearance rates for short fibers neither correlate with in-
vitro dissolution nates at neutral pH or at pH 4,5, Although the latter pH corresponds to
the pH found in the phagosomes of macrophages, there is likely too little fluid available
in such organelles to support efficient dissolution. The authors also indicate that a
sufficient number of fibers were counted during the study to suggest that breakage is
not playing a role in clearance (except at very early times) and that the clearance rate
for short fibers appears to be the same or slower than that observed for nuisance dusts.
In another, earlier study of similar design Eastesand Hadley (1995) evaluated four
types of manmade mineral fibers (MMMF's) and crocidolite. All of the samples
(including crocidolite) had been size-selected to assure a large fraction of fibers longer
than 5 urn. Unfortunately, due to differences in reporting, it is not possible to compare
the initial loading of crocidolite fibers to those reported for amosite in the Hesterberg et
al. (1998a) study, However, results from this study further support the physical
interpretation of clearance suggested in the studies discussed above. In fact, the
authors report that the time-dependent size distribution of retained fibers observed in
this study agree well with a computer simulation of fiber clearance, The simulation
assumes that long fibers dissolve at the rate measured for such fibers in vitro and that
short fibers of every type are removed at the same rate as short fiber crocidolite (which
is practically insoluble). This is strong evidence that short fibers are cleared by
macrophage phagocytosis and that long fibers cannot be cleared by macrophages, but
may dissolve in extracellular fluid provided that they are sufficiently soluble.
Regarding crocidolite, the data from the Eastes and Hadley study suggest that short
crocidolite fibers appear to clear at a rate that is somewhat slower than observed for
any of the short MMVF fibers. Importantly, however, the interpretation of short fiber
clearance in this paper is somewhat confounded because, unlike the studies discussed
above, short fibers in this paper are defined as all fibers < 20 urn so there maybe some
confounding with MMVF fibers that are dissolving. As previously indicated, the
Hesterberg et al. (1998) work also suggests that short asbestos (amosite) fibers may
clear more slowly than short fibers of differing mineralogy and Hesterberg et al. only
includes fibers < 5 um in their definition. Nevertheless, it is still possible that some
effects due to dissolution may still be affecting the clearance of these shorter fibers.
Surprisingly, a visual inspection of the data presented in their table suggests a lack of
any long-term clearance for long fiber crocidolite (> 20 urn). Yet, the authors model
long fiber crocidolite clearance using a single exponential (suggesting no rapidly
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clearing compartment). The long-term half-life reported for crocidolite in this study is
approximately 220 days (with estimated confidence limits of 165 to 566 days). This
overlaps with the long term clearance half-life reported by Hesterberg et al. (1996) for
crocidolite of approximately 820 days (246-°°).
Equally surprising, Hesterberg et al. also modeled crocidolite clearance as a single
exponential, which might suggest better penetration to the deep lung by crocidolite, less
clearance by muco-ciliary transport or alveolar macrophage transport, or better
penetration to the interstitium than other fibers. More likely, however, it may simply
indicate that the two-pool model does not represent a statistically significant
improvement in model fit over the one pool model. However, relative size distributions
would need to be evaluated carefully before drawing any such conclusions. Bastes and
Hadley also report clearance of short fiber crocidolite is modeled as a double
exponential with short and long half-lives of: 25 and 112 days, respectively. Since this
fiber category contains fibers up to 20 um in length (in this study only), this does
suggest at least some contribution from muco-ciliary and alveolar macrophage
mediated clearance for crocidolite.
In two studies, Coin et al. (1992 and 1994) evaluated the fate of chrysotile fibers in rats
exposed for 3 hrs to 10 mg/m3 (reportedly containing > 5000 fibers longer than
5 um/cm3). For lung analysis, the left lung was separated into peripheral and central
regions under a dissecting microscope. Slices of peripheral and central portions were
separately weighed and minced. Tissue was digested in sodium hypochlorite and then
filtered. A quality control test indicated that the digestion process caused a slight
(~10%) decrease in fiber number and slight decreases in fiber diameter and fiber
length. Fiber size distributions were evaluated by SEM. A stratified counting procedure
was employed to assure equal precision for each length category of interest.
Measurements for each category were then converted to mass equivalents.
Results from the Coin et al. studies indicates no difference between deposition in
central or peripheral regions of the lung. They also confirm that chrysotile splits
longitudinally in the lungs with a half life that is competitive with the clearance rates
measured in this study. Clearance was found to be very length dependent so that
rates decrease from a half life of about 10 days for fibers about 4 urn in length through
30 days for fibers 8 um to 112 days (which is no different from zero) for fibers longer
than 16 um (all after adjusting for longitudinal splitting). Importantly, the brief followup
period (30 days) is too short to provide an adequate evaluation of the longer term
clearance pools observed in other studies and certainly too short to evaluate any
effects potentially associated with chrysotile dissolution. Also, that the decay curves for
clearance were limited to four points, makes evaluation of the slopes for these curves
highly uncertain.
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Coin et al. also report that the mass of chrysotile deposited during these short
exposures (i.e. no more than 20 ug) is very small compared to levels at which overload
has been reported to occur (approximately 1 mg, see, for example, Yu and Yoon 1991)
and that the volume of the 16 urn fibers, which have an average diameter of 0.2 urn
and therefore a mean volume of 0.5 urn3, is small relative to the volume at which
macrophage clearance of non-fibrous particles is reported to be hindered (Morrow C
1988). Thus, the authors conclude that fiber length presents an additional constraint
on macrophage clearance, independent of any other overload. They also indicate that
inhibition of clearance due to fiber length is independent of fibrosis.
Coin et al. also discuss the effect of fibrosis on clearance. They indicate that, although f
increased concentrations of short fibers are observed in focal areas of fibrosis, it is
more likely that such fibers accumulate because clearance is hindered by fibrosis in
these areas than the hypothesis that the short fibers are causing fibrosis. This is
because, as they point out, there are too many studies demonstrating the lack of ability
of short fibers to induce fibrosis.
(
Evidence in the Coin et al. studies suggests that no translocation from central to
peripheral regions of the lung were detected. An upper bound rate that is about 20% of
clearance is reported. However, the short followup time in this study would have
precluded slower processes from being detected. Despite the lack of evidence of
translocation, the authors report that duct bifurcations in peripheral regions of the lung (
where fibers are deposited are no more than 1-2 mm from the visceral pleura. In fact,
in the 1994 study, the authors show that 50% of the primary duct bifurcations in the
peripheral portion of the rat lung occur within 1 mm of the visceral pleura and some
occur as close as 220 urn. Deposited fibers may also affect the pleura by inducing
generation of diffusable, inflammatory agents.
A short term inhalation study by Warheit et al. (1997) evaluated retention of chrysotile
and aramid fibers. In this study, rats (and hamsters) were exposed nose-only for
6 hrs/day, 5 days/wk for 2 weeks by inhalation to UICC chrysotile and p-aramid fibers
(each at two doses of 460 or 780 fibers/ml, although the size range of these fibers is not
stated nor is the manner in which they were analyzed). Fixed lungs were digested in C
chlorox during preparation for asbestos analysis. Animals were followed for up to a
year post exposure.
As in studies described above, results from the Warheit et al. study indicate rapid
clearance of short chrysotile fibers but slow to non-existent clearance of fibers longer f
than 20 urn. In contrast, aramid fibers apparently degrade and are subsequently
cleared fairly rapidly in vivo. Based on the data provided in figures, although the
reported concentrations of chrysotile and aramid fibers to which animals were exposed
were equivalent, at both the lower and higher concentrations, it appears that rats initially
retain three to five times as many aramid fibers as chrysotile fibers (at least for the size
C
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range counted, which was not reported). For both fiber types, clearance appears rapid
for an initial period of approximately 90 days post exposure, During this time, the mean
length of chrysotile fibers also appears to increase steadily, which suggests rapid,
preferential clearance of short structures. After the initial period, it appears that (as the
authors suggest), a residual concentration of longer fibers are cleared only very slowly,
if at all.
Oberdorster et al. (1988) instilled a 3 ml suspension of irradiated amosite into the
bronchio-alveolar space of the right diaphragmatic lobe of the lungs of dogs to evaluate
clearance and transport. The amosite used was modified by sedimentation from UICC
amosite to contain only fibers shorter than 20 urn. One dog also had unmodified UICC
amosite instilled directly into a lymph node in the thigh. The dogs had been cannulated
to allow collection of lymph from the right lymph duct-RLD and the thoracic duct- TD
(both in the neck).
Results from Orberdorsteret al. indicate that within 4 hours following instillation in the
lung, low activity was noted in postnodal lung lymph but not in either the RLD or TD.
Within 24 hours, however, activity and fibers (determined by SEM) were observed in
both the RLD and the TD. The median length of fibers observed in the lymph were
significantly longer than the instilled material, although there appeared to be a cutoff
length of 16 urn in fibers observed at nodes and 9 urn in fibers observed directly in
lymph. Fibers recovered from lymph were also significantly thinner and appeared to
exhibit an absolute cutoff at a maximum width of 0.5 urn. Fibers recovered from the
TD and RLD in the dog that had unmodified UICC amosite instilled directly into leg
lymph were all short (with a maximum length of 6 urn). Since collection times were all
short, the authors indicate that it is unknown whether longer fibers would have been
observed at later times. The authors also note the almost total absence of fibers
shorter than 1 urn in lymph, which they assume are cleared rapidly and efficiently by
alveolar macrophages.
Oberdosrster et al. also report that a rough calculation, based on the fraction of the
material originally instilled that was recovered in the first 24 hours, it would take
approximately 6 years to clear all of the instilled asbestos (assuming no other clearance
mechanisms were active).
Everitt et al. (1997) performed a short-term inhalation study that is interesting
particularly because it focused on pleural (as opposed to lung) fiber burden. The
authors exposed rats and hamsters to one type of refractory ceramic fiber (RCF-1) by
nose-only inhalation for periods of 0, 4 and 12 weeks and animals were held for
observation for up to an additional 12 weeks post exposure. Exposures were
conducted for 4 hrs/day, 5 days/wk, at 45.6 +/- 10 mg/m3 Groups of 6 animals were
held for 0,4, 12, and 24 weeks to determine pleural fiber burden. An agarose casting
method was reportedly used to recover fibers from the pleura. Analysis was by electron
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microscopy. Fibers were observed in the pleura at each time point examined (including
samples from rats sacrificed immediately following the last day of a five-day exposure).
Fibers were all reported to be short and thin (geometric mean length: 1.6 [jm with GSD:
1.8, geometric mean diameter: 0.1 urn with GSD; 1.5), Concentrations averaged
approximately 40,000 fibers (per whole pleura??? - units not reported). The authors
indicate that such fibers would not typically be visible by optical microscopy. They also C
indicate that use of casts may be a more efficient method of recovering fibers from the
pleura.
Everitt et al. indicate that observation of rapid translocation of short, thin fibers to the
pleura has also been observed in studies of chrysotile so these results are not unique. f-
Although it is stated that the mechanisms facilitating translocation are currently
unknown, the authors indicate that their finding of site-specific mesothelial proliferation
supports observations by Boutin et al. (1996) that asbestos fibers accumulate in the
parietal pleura of humans at sites associated with lymphatic drainage. Kane and
McDonald (1993) have suggested that fibers are transported to these locations by
pleural macrophages. However, the mechanisms by which fibers are transported from *-•'
the lung to the pleura are still unconfirmed.
Older Retention Studies
Although the older retention studies generally support the results of newer studies (such (;
as those cited above), older studies are sometimes limited by such things as the
tracking of lung burden in terms of fiber mass or use of analytical techniques such as
infrared spectroscopy for detection of asbestos, which are neither capable of
distinguishing individual fibers nor provide any information on their sizes. Tracking of
lung burdens in terms of mass may not reflect the fate of long, thin fibers, which (by
increasing concurrence) appear to be the legitimate focus of studies evaluating
biological hazards attributable to asbestos.
In two studies (Roggli and Brody 1984 and Roggli et al. 1987), Roggli and coworkers
tracked the behavior of chrysotile (not UICC) and UICC crocidolite in rats following 1 hr
exposure by inhalation to 3.5-4.5 mg/m3 dusts. The authors indicate that this results in C
deposition of approximately 21 ug of dust. Portions of the lower lung lobes of selected
rats were collected and digested for asbestos analysis using a scheme that was shown
to be representative. To evaluate size distributions, more than 400 fibers from each
sample were characterized by SEM. Fiber dimensions were then used to estimate total
fiber mass. ^
Based on their study, Roggli and coworkers indicate that similar fractions of inhaled
chrysotile and crocidolite dust are deposited in the lung during inhalation (23% and
19%, respectively). The authors therefore concluded that respirability and deposition
do not depend on fiber type. Importantly, however, the manner in which this study was
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conducted does not facilitate distinguishing deposition in the deep lung from deposition
in the upper respiratory tract.
Roggli and coworkers further indicate that dearance rates for the two fiber types appear
comparable. Of the chrysotile initially deposited, they report that 81% of this material is
cleared after four weeks. Similarly, 75% of the crocidolite Is cleared. Importantly,
because this is based on total mass (estimated by summing volume contributions from
observed fibers), it may not reflect the specific behavior of long, thin structures.
Therefore, it is difficult to compare such results with those of more recent studies.
However, the authors do report that short structures are cleared more readily than long
structures and that chrysotile is observed to split longitudinally in vivo (based on
observation that the total number of chrysotile structures initially increases and the
mean length increases). The authors further conclude that clearance rates appear to
be independent of fiber type.
In another short-term study, Kauffer et al. (1987) report that the average length of
retained chrysotile structures increases in rat lungs following 5-hrs inhalation of
chrysotile dust. Based on their results, the authors report that fibers shorter than
approximately 8 urn are preferentially cleared. Kauffer and coworkers. also confirm that
chrysotile fibrs split longitudinally in the lung. In fact, several other studies (Le Bouffant
et al. 1978, Kimizuka et al. 1987) also provide supporting observations that chrysotile
fibers (or bundles) split longitudinally in the lung.
In two studies (Morgan et al. 1978 and 1980), Morgan and coworkers report on the fate
of fibers following short term inhalation of radio-labeled fibers by rats. In the first study
(Morgan et al, 1978), rats inhaled UICC anthophyllite at 35 mg/m3 fora total of 8.4-hrs
spread over three days. The authors report that the rats retained approximately 190 ug
of dust at the end of exposure, mostly in the alveolar region; the authors assumed that
conducting airway clearance is sufficiently rapid to clear this portion of the lung within a
few days. Beginning about 7 days following exposure, the rats were then sacrificed
serially for a period up to 205 days following exposure. Because anthophyllite fibers
are relatively thick, fibers were analyzed by optical microscopy. Fibers were determined
both in free cells (mostly macrophages) recovered in bronchopulmonary lavage and in
lung tissue, Tissue samples and cells were digested in KOH and peroxide in
preparation for fiber analysis.
Based on this first study, Morgan et al. report that anthophyilite lung content declined
steadily by a process that could be described as a simple first order decay with a half
life of approximately 76 days. Free macrophages recovered by lavage, initially
contained about 8 pg and this too declined steadily with a half life of about 49 days.
The authors further indicate that, if the number of macrophages remains constant with
time (i.e. they are replaced at the same rate they are cleared), then the decay of the
load in the macrophages should match what is observed in the rest of the lung. They
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suggest that the discrepancy may be due either to an influx of an increasing number of
macrophages in response to injury with time and/or to transfer of some fibers through
the alveolar wall. They also cite unpublished work indicating that uptake of fibers by
alveolar macrophages is essentially complete within hours after cessation of exposure.
The authors also report that, initially, the lengths of fibers recovered in lung lavage was
greater than in the original aerosol but that the prevalence of the longest fibers
decreased after the first seven days. In lung tissue, however, the fraction of longer
fibers (among total fibers) steadily increased with time. This suggests rapid clearance
of naked fibers by muco-ciliary transport (which is a length independent process) with
later times dominated by slower clearance in the deep lung by alveolar macrophages,
which is a length-dependent process.
Morgan and coworkers also radiometrically determined the fraction of fibers in rat
faeces (prior to sacrifice). They assumed that after 14 days, this would represent the
fraction of asbestos cleared primarily from the alveolar region of the lung. Initially 1.4%
of lung anthophyllite content was excreted daily but this fell to 0.5% after 120 days.
The authors indicate that this suggests that the elimination from asbestos in lung tissue
cannot be described by a single exponential because multiple processes are involved
and that, over longer periods of time, the slower processes become increasingly
important.
In the second study, Morgan et al. track the fate of several size-selected radiolabeled
glasses in rats, again following short term inhalation. From an analysis of the size
dependence of deposited fibers in this study, the authors suggest that alveolar
deposition in the rat is limited to structures with aerodynamic equivalent diameters less
than about 6 urn and that deposition in this region of the lung falls precipitously for
fibers with thicknesses between about 2 and 3 urn (aerodynamic equivalent diameter).
For fibers that are the density of asbestos, this represents an upper bound limit to
alveolar deposition for the absolute thickness of a fiber of approximately 1.5 urn with
fibers deposition of fibers thicker than approximately 0.7 urn being drastically reduced.
This is in concordance with conclusions concerning deposition provided in
Section 7.1.4. Alveolar deposition efficiency is also shown to decrease with increasing
fiber length, at least for fibers longer than approximately 8 urn, also in concordance with
findings presented in Section 7.1.4.
Intratracheal Instillation
The fate of fibers following intratracheal instillation into the lungs has also proven
informative in some studies. For example, Wright and Kushner (1975) intratracheally
instilled paired samples each of several types of glass fibers, fluoramphibole, and
crocidolite into guinea pigs. For each mineral tested, a sample with predominantly short
structures (25 mg total dose for crocidolite, reportedly 99% < 5 urn) and another with
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predominantly long structures (4 mg total dose forcrocidolite, reportedly 80% > 10 urn)
were evaluated. Unfortunately, the authors do not report how fibrous structures were
characterized. Results in the cited paper report observations only after 2-years
following the last injection.
Wright and Kuschner report that long structures uniformly caused fibrosis (primarily
involving the respiratory bronchioles and alveoli and abutting the terminal bronchioles)
while the short structures were uniformly phagocytized and generally removed to
thoracic lymph nodes. Among other things, clearance to lymph suggests that fibers
reached the interstitium (Section 7.2.5). It is interesting that, even after 2 years of
recovery, the authors observe elevated levels of macrophages in the alveoli of animals
dosed with short structures, Based on the relative size distributions of the samples
analyzed, the authors report that structures up to 10 urn in length appear to be
efficiently scavenged by macrophages. Based on the observation of larger numbers of
short structures than expected in comparison with their fractions in the original samples,
the authors further conclude that glass structures underwent biodegradation so that
longer structures broke down into shorter structures that could be phagocytized.
Wright and Kuschner also report that long fibers are occasionally visible within the
fibrotic interstitium of dosed animals. The long-fiber dosed animals also show
macrophages in hilar lymph nodes containing fibers that are too small to resolve and all
of them are short. In short-fiber dosed animals, some fibers are seen to remain in the
lung within aggregates of macrophages, both in alveoli and the interstitium. Short-fiber
dosed animals also show many more macrophages within the hilar lymph nodes than
long-fiber dosed animals.
In two reports of the same study (Bellmann et al. 1986 and 1987), Bellmann and
coworkers followed the fate of UICC chrysotile, UICC crocidolite, several fibrous
glasses, and other manmade mineral fibers following a single intratracheal instillation of
0.3 ml of fibrous material in rats. Groups of rats were then sacrificed at 1, 6, 12, 18,
and 24 months following instillation. Lungs were low temperature ashed and the
resulting, filtered suspension analyzed by transmission electron microscopy. Some of
the fiber types were also acid treated with 0.1 M oxalic acid for 24 hrs prior to
instillation.
Bellmann and coworkers report that short fibers (< 5 urn) from all of the fiber types were
shown to be cleared from the lungs with half-lives of approximately 100 days, with the
asbestos varieties tending to exhibit slightly longer half lives than the other fibers.
Short crocidolite fibers exhibited a half life of 160 days The half-life for clearance of
short chrysotile was reported to be 196 days (the longest of all). However, this was
attributed to positive contributions from breakage of longer fibers.
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Bellmann and coworkers report that the behavior of the different long fibers (> 5 pirn) for
the different fiber types was radically different. The authors report no observed net
decline in long crocidolite fibers over the 2 yrs of followup. The also report no
observable changes in width of these fibers with time. In contrast, long chrysotile fibers
increased in number with time throughout the 18 month followup period and this was
attributed to longitudinal splitting. The width of these fibers reportedly decreased with
time.
Bellmann et al. also report that a more detailed examination of the time dependence of
the width of chrystoile fibers indicates a rapid increase in the number of thin fibrils (<
0.05 urn in width) and thin bundles (< 0.1 urn in width) within 100 days (at the expense
of thicker bundles). The authors suggest that this would result in rapid decrease in the
number of chrysotile structures visible by optical microscopy and, possibly, increased
clearance of the thinnest fibrils by dissolution, but this study shows no increased rate of
clearance for thinner chrysotile structures compared to thicker structures (when viewed
by electron microscopy). In contrast, long chrysotile fibers that were acid-leached prior
to instillation reportedly disappeared with a half life of 2 days.
Generally, the rate of clearance of the long fractions of the other fibers reported in the
Bellmann et al. papers varies as a function of solubility and overall thickness.
Importantly, all half lives are reported to have high standard errors in this study, due to
the small number of animals included for examination.
In summation, virtually all short-term retention studies indicate that:
• fibers retained in the lung tend to be shorter and thinner than the aerosols
from which they derive and the size distributions of retained structures
tend to be more similar overall than the size distributions observed in the
original aerosols;
• chrysotile asbestos undergoes rapid, longitudinal splitting in the lung while
amphiboles do not;
• by mass, chrysotile and amphibole asbestos are deposited in the lung
with comparable efficiencies, although it is not clear whether chrysotile
dusts tend to contain sufficient numbers of curly fibers to limit deposition
in the deep lung;
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• multiple clearance processes operate over different time frames and
some of these processes are strongly length-dependent. Fibers shorter
than approximately 10 um appear to be cleared rapidly relative to longer
fibers and those longer than approximately 20 um are not cleared
efficiently at all (if the fibers are insoluble). The Bellmann et al. studies
appear to contrast with other studies in this regard in that they suggest
fibers longer than 5 um do not readily clear;
• the quickest clearance process (presumably muco-dliary clearance) is not
dependent on length; and
• the effects of fiber diameter on clearance have not been well delineated
overall, although fibers that reach the deep lung appear to be largely
limited to those thinner than approximately 0.7 um.
These findings are in addition to those mentioned previously from the newer studies:
• multiple clearance mechanisms (operating over multiple time scales)
contribute to clearance;
• for sufficiently soluble fibers, long fibers clear more rapidly than short
fibers;
• for insoluble fibers, a subset of long fibers clears rapidly while the
remaining long fibers clear only extremely slowly, if at all;
• short fibers of all types are cleared at approximately the same rate (much
more rapidly than long, insoluble fibers);
• a small fraction of short fibers may be retained for long periods under
certain circumstances (sequestered in alveolar macrophages) despite
overall rapid clearance of these structures; and
• there is some suggestion that short asbestos fibers clear somewhat more
slowly than short fibers of the other, non-asbestos mineral types studied.
Regarding specifically the clearance of long fibers, it appears that a component of all
such fibers clears rapidly within the first two weeks and this likely represents muco-
ciliary clearance. A second component (representing as much as 60% of the fibers)
clears within 90 days and this likely represents clearance by alveolar macrophages.
The remaining long fibers are cleared only very slowly, if at all, and this likely represents
fibers that are sequestered in granulomas or that escape into the interstitium.
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7.2.1.2 Studies involving chronic or sub-chronic exposures
Although the results of older retention studies following longer term (sub-chronic or
chronic) exposure were difficult to reconcile with the results following shorter-term
exposures, newer studies suggest greater consistency and a clearer picture of the fate .
of fibers in the lung. Moreover, although there are further suggestions of mineralogy- f
(fiber type) dependent effects with some clearance mechanisms, it is important that size
effects be considered simultaneously, if the dynamics of these processes are to be
understood.
In some of the latest studies, for example, Heterberg et al. (1993, 1995, and 1998b) (
exposed rats (nose-only) by inhalation to a series of manmade vitreous fibers (including
a variety of fibrous glasses, rock wools, and refractory ceramic fibers) and two kinds of
asbestos: chrysotile (intermediate length NIEHS fiber) and crocidolite (size selected).
Animals were dosed for 6 hrs/day, 5 days/wkfor up to two years at target
concentrations of 10 - 60 mg/m3. The target concentration for chrysotile and crocidolite
was 10 mg/m3. Animals were periodically sacrificed during the exposure regimen to ^
determine the character of the retained fibers. Vitreous fiber aerosols were
characterized by PCM, SEM or, for chrysotile, by TEM. The right accessory lung lobe
of sacrificed animals was tied off, frozen, and stored for lung burden analysis.
For analysis, lung lobes were dried to constant weight, ashed, the residue suspended in ('
distilled water, and then filtered on Millipore filters (for examination by optical
microscopy) or Nuclepore filters (for analysis by SEM or TEM for chrysotile).
Approximately 100 fibers were reportedly characterized to establish fiber size
distributions. However, this is problematic for this study because chrysotile asbestos
concentrations in the aerosols to which the animals were exposed contained
approximately 100 times as many fibers as the other aerosols. Thus, although no fibers
longer than 20 urn were observed during characterization of the chrysotile, the
concentration of such long fibers could still have been larger in this aerosol than the
other aerosols and it would not necessarily have been observed. This is also true of
lung burden analyses especially because indirect preparation tends to magnify the
number of short chrysotile structures observed in a sample. C
A comparison of the retention patterns of chrysotile and RCF-1 from the Hesterberg et
al. studies is particularly instructive. First, it should be noted that, in contrast to the
values reported by the authors of this study, chrysotile and RCF-1 in fact appear to
exhibit comparable in-vitro dissolution rates (12.7 vs. 8 ng/cm2-hr, respectively) when ^
rates are measured using comparable techniques (see discussion in Section 7.2.4).
The dissolution rates quoted in the Hesterberg study are not derived in comparable
studies.
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Although a full set of time-dependent analyses are not apparently available for
chrysotile, it is reported that approximately 14% of those chrysotile WHO fibers
observed to be retained after 104 days of exposure continue to be retained after 23
days of recovery. Under the same conditions, it is reported that 43% of RCF-1 fibers
are retained, which suggests more rapid clearance for chrysotile. Even adjusted for the
fraction of RCF-1 structures that are longer than 20 Mm (and that are presumably
cleared even more slowly), approximately 37% of the RCF-1 WHO fibers (< 20 urn) are
apparently retained over this period, which is still more than twice the rate reported for
chrysotile. Still, more detailed characterization of the size distributions of these two
fiber types would need to be evaluated before it could be concluded with confidence
that chrysotile is deared more rapidly than RCF-1 or that dissolution plays a role. In
fact, dissolution would tend to cause more rapid clearance only of the longest fibers
(i..e. the ones that cannot be cleared by macrophages see Section 7.2.1.1), which
would further reduce the apparent retention rate of the shorter RCF fibers, making it
even more comparable to the chrysotile number.
Based on these studies, Hesterberg et al. report that fibers deposited and retained in
the lung tend to be shorter and thinner on average than the sizes found in the original
aerosol. It is also apparent from their data that long RCF-1 fibers clear more rapidly
than short RCF-1 fibers (although a small fraction of long structures are retained at all
time points following a recovery period after cessation of exposure), which is consistent
with observations in other studies for fibers that dissolve at reasonable rates. In the
short term study performed by the same laboratory, Hesterberg et al. 1998a, long fiber
(> 20 urn) RCF-1 a appears to clear at approximately the same rate as the shorter
structures (< 5 urn), although the scatter in the data (and an unexplained initial rise in
long-fiber RCF) prevent a more careful comparison. Similar results are also apparent in
the data presented for MMVF21. It should be noted that the dissolution rates for RCF-1
and MMVF21 bracket the estimated dissolution rate for chrysotile asbestos (when the
three are derived from comparable studies, see Section 7.2.4).
The data presented in Table 3 of Hesterberg et al. (1998b), which are reproduced in the
following Table 7-3, can also be used to evaluate the time-trend of retention during
chronic exposure.
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Table 7-3:
Fraction of Fibers Retained Following Chronic Exposure1
Chrysotile
Exposure Lung/Aerosol
Period WHO Fibers
(wks) f/lung x 10A6
0.0357
13 250
26 180
52 1020
78 853
104 1600
Aerosol Concentration:
(f/ml) 10600
Ratio
0.024
0.017
0.096
0.080
0.151
RCF-1
Lung/Aerosol
WHO Fibers
f/lung x 10A6
0.009
39
56
119
173
143
187
Ratio
4.81E-05
0.209
0.299
0.636
0.925
0.765
Long Lung/Aerosol
WHO fibers
f/lung x 10A6
0.002
3-
6
20
21
25
101
Ratio
1.98E-05
0.030
0.059
0.198
0.208
0.248
1. Source: Hesterberg et al, (1998b)
The values presented in Columns 2, 4, and 6 of the above Table present, respectively,
measurements of the lung burden for chrysotile WHO fibers, RCF-1 WHO fibers, and
RCF-1 long WHO fibers (> 20 urn) in animals sacrificed immediately following cessation
of exposure for the time period indicated in Column 1. Unlike results reported in some
earlier chronic studies based on mass (see below), there is no evidence from this table
(based on fiber number) that chrysotile lung burdens reach a plateau. Rather chrysotile
lung burdens (as well as RCF-1 lung burdens) continue to increase with increasing
exposure.
The data presented in Table 7-3 can also be used to gauge the relative efficiency with
which the chrysotile and RCF fibers are retained. Considering that the number of fibers
inhaled (N,nh) over the period of exposure would be equal to the product of the aerosol
concentration (Cair in f/cm3), the breathing rate of the exposed animal (RB in cm3/wk),
and time (in weeks):
Ninh = Cair*RB*t 7.1
and the efficiency of retention is simply equal to the quotient of the number of fibers
retained (N,ung) and the total number inhaled: Nlung/Ninh, then the efficiency of retention is
estimated by the following simple relationship:
Efficiency of retention = Nlung/(Calr*RB*t) (7.2)
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By rearranaging Equation 7.2, one obtains:
Efficiency of retention*! = (Nlung/(Cair)*(1/RB) (7.3)
Because the breathing rate for the rats in the Hesterberg et al. study can be considered
a constant for all experiments, Equation 7.3 indicates that the slope of a plot of N|Ung/Cair
versus time should yield estimates of the relative efficiency of retention for each of the
fiber types evaluated. The plot for chrysotile is presented in Figure 7-3. Results from
this plot and similar plots for RCF-1 WHO fibers and long WHO fibers (data not shown),
result in the following estimates of the relative efficiencies of retention (along with the
corresponding R2 value for the fit of the linear trend line):
chrysotile WHO fibers: 0.0014, R2 = 0.856
RCF-1 WHO fibers: 0.0095, R2 = 0.694
RCF-1 long, WHO fibers 0.0026, R2 = 0.880
Thus, it appears that chrysotile WHO fibers are retained somewhat less efficiently than
either RCF-1 WHO fibers or RCF-1 long WHO fibers. However, whether this is due to
less efficient deposition or more efficient clearance cannot be determined from this
analysis. It is also not possible to determine whether such differences are due to the
effects of differences in size distributions among the various fiber types. Interestingly,
based on the data presented by Hesterberg et al., which indicates that long RCF-1
WHO fibers clear more rapidly than regular RCF-1 WHO fibers, the differences in the
relative retention of these two length categories of fibers is due primarily to relative
efficiency of clearance.
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r
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In a similar study involving chronic exposure to syrian golden hamsters (Hesterberg et
al. 1997), fiber retention and biological effects associated with exposure to amosite and
a series of MMVFs were evaluated. The amosite was size selected and hamsters were
exposed to one of three levels (0.8+/-0.2, 3.7+/-0.6, and 7.3+/-1.0 mg/m3). Amosite
lung burdens were shown to increase regularly with dose and time of exposure. The
time dependence for accumulation of some of the MMVF's was more complicated.
None of the animals were apparently followed for any recovery periods following
cessation of exposure. The authors also indicate that the severity of the effects
observed (inflammation, cellular proliferation, fibrosis, and eventually several
mesotheliomas), appear to correlate well with the concentration of fibers longer than
20 Mm.
Earlier Studies
Earlier studies, in which asbestos concentrations tend to be monitored as total mass
tend commonly to show that chrysotile asbestos is neither deposited as efficiently as
various amphibole asbestos types nor is it retained as long (i.e. it is cleared much more
rapidly from the lungs). In fact, several such studies tend to show that chrysotile
asbestos concentrations eventually reach a plateau despite continuing exposure, which
suggests that clearance and deposition come into balance and a steady state is
reached. In contrast, amphibole asbestos concentrations continue to rise with
increasing exposure, even at the lowest exposure levels at which experimental animals
have been dosed.
Such observations do not appear to be entirely consistent with those reported in newer
studies (see above) that track fiber number concentrations (in specific size categories).
In these newer studies, chrysotile retention is not observed to level off, but continues to
increase in a manner paralleling amosite or other fibers. As indicated below, however,
the limitations associated with these older studies suggest that, although it may not be
easy to reconcile them quantitatively with the newer studies, results from these studies
are not necessarily inconsistent with those of the newer studies. Moreover, the trends
observed in the newer studies are likely more directly relevant to issues associated with
the induction of asbestos related disease.
The problems with the older studies are that:
• the trends seen in the older studies (based on mass) may mask the more
important trends associated with deposition and retention of long, thin
fibers. Thus, results from such studies may not be directly relevant to
considerations of risk; and
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• the observed differences between chrysotile and the amphiboles may be
attributed to differences in size distribution (among other possibilities).
Thus, lacking detailed information on size distributions, it is difficult to
reconcile the results from the older studies with results from the newer
studies, which explicitly track spedfic size ranges of fibers.
f
Given these limitations, the earlier studies are only mentioned briefly.
Middleton et al. (1979) tracked the fate of asbestos (as mass measured by infrared
spectroscopy) in rats following inhalation of several asbestos aerosols (UICC chrysotile
A, UICC amosite, and UlCCcrocidolite) at multiple concentrations (reported at 1, 5 or ^
10 mg/m3). To account for possible differences in the nocturnal (vs. daytime) activity
level of rats, several groups of rats were also exposed in a "reversed daylight" regimen
(in which cages were darkened during the real day and bathed in light during the real
night). Acclimatized rats in these groups were thus dosed at times corresponding to
their night. Exposure continued for 7 hrs per day, 5 days per week, for six weeks.
Results from the Middleton et al. study were fit to a three compartment model (originally
proposed by Morgan et al. 1977) and the authors concluded that clearance was
independent of fiber type, but that the initial deposition of fibers was very dependent on
fiber type. This was indicated by a "K-factor" representing the efficiency of initial
deposition. Chrysotile showed K factors that range between 0.17 and 0.36 and vary (
inversely with the initial exposure concentration. In contrast, amosite exhibits a K factor
of 0.69 and crocidolite a K factor of 1.0 and both are independent of exposure level.
Although the design of this experiment precluded fitting of the shortest two
compartments of the model (with half lives of 0.33 and 8 days, from Morgan et al.
1977), they did optimize the half life of the longest compartment. Fibers in this
compartment were cleared with a half life of 170 days. '-
In a series of studies, Davis and coworkers (1978, 1980, 1988a, and 1988b) report that
retention of asbestos (measured in terms of mass) appears to be a function of fiber
type and surface charge in addition to fiber size. With regard to fiber type, for example,
Davis et al (1978) report that substantially more amphibole (amosite) asbestos appears C
to be deposited and retained in the lungs of exposed rats than chrysotile. Chrysotile is
also apparently cleared more readily than amosite. However, mineralogical effects
should only be judged after adjusting for fiber size.
Rats in the Davis et al. studies were dosed at 6hrs/day, 5 days/wk for up to one year at ,
dust concentrations of 2, 5 or 10 mg/m3 (depending on the specific experiment). Right
lungs (used for determining lung burden) were ashed and the residue was washed in
distilled water and filtered. The residue was formed into a potassium bromide disc and
asbestos (mass) content was determined by infrared spectroscopy.
C
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Jones et al, (1988) report that the lung-tissue concentration of amosite increases
continually with exposure (at 7 hrs/day, 5 days/wk for up to 18 months) and the rate of
increase is proportional to the level of exposure. A leveling off of amphibole
concentrations in lung-tissue was not observed in this study as long as exposure
continued, even for the lowest level of exposure (0.1 mg/m3) studied. The lowest
exposure concentration evaluated in this study is only 1% of the concentration at which
chrysotiie lung burdens were shown to reach equilibrium in other retention studies (see
below). Importantly, however, these are only the older studies in which fiber burden is
tracked by mass, The newer studies don't show this effect.
The authors also report lack of any apparent change in size distribution with time
among the fibers recovered from the animal's lungs, which suggests lack of substantial
clearance even of short fibers. However, the longest recovery period following the
cessation of exposure evaluated in this study is only 38 days, which may be too short to
allow evidence for differential clearance as a function of size to become apparent (at
least in a chronic study; the time dependence in chronic studies such as this are more
complicated than for short-term studies). Moreover, the apparent inclusion of lymph
nodes as part of the lung homogenate may have caused short fibers initially cleared
from the lung to be added back in. In this study, lungs were recovered intact including
the associated mediastinal and hilar lyrnph nodes, which were ashed in toto. Ash
residue was washed in acid and water, ultrasonicated and filtered for electron
microscope analysis. Note that such a procedure would include any fibers cleared to
local lymph nodes.
In a widely cited study, Wagner et al. (1974), report that amphibole lung burdens
increase continually as long as exposure to amphiboles continues and that amphibole
concentrations in lung tissue decrease only slowly following cessation of exposure. In
contrast, chrysotiie lung burdens reach a plateau despite continued exposure
Importantly, asbestos content was estimated by determining total lung silica content
and adjusting for similar analysis on filtered samples of the original aerosols. Thus, in
addition to suffering from the limitations associated with tracking fiber burden by mass,
there are questions concerning the validity of using total silica to represent asbestos
content. Therefore, for these reasons and the additional reason of the lack of
controlling for fiber size, the ability to interpret this study and reconcile its conclusions
with those of newer studies is severely limited,
Chrome Inhalation of Non-fibrous Particulate Matter
A recent study involving chronic inhalation of non-fibrous materials is helpful at
elucidating the relative localization of particles in rats and primates. Nikula et al. (1997)
studied lung tissue from a two-year bioassay originally conducted by Lewis et al. (1989),
in which cyanomolgus monkeys and F344 rats were exposed to filtered, ambient air or
air containing one of three particulate materials: diesel exhaust (2 mg/m3),
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coal dust (2 mg/mS, particles < 7 um in diameter), or a 50-50 mix of diesel exhaust and
coal dust (combined concentration: 2 mg/mS),
Results from Nikula et al. (1997) indicate that responses to all three particulate
materials were similar. The particles tended to localize in different compartments of the
lung in a species-specific manner:
• 73% of particles remain in the alveolar lumen of rats but only 43% in
monkeys. The remainder can be found in the interstitium;
• in both the alveolar lumen and in the interstitium, virtually all of the
particles are observed to be isolated within macrophages; and
• the particles in the interstitium reside in macrophages within the alveolar
septa, the interstitium of respiratory bronchioles, the adventitia and
lymphatic capillaries surrounding arterioles and veins of pulmonary
parenchyma, or in the pleura.
It is not known whether free particles penetrate the epithelial lining of the airway lumena
and escape into the interstitium or whether such particles are first engulfed by
macrophages and then transported in their macrophage "hosts" into the interstitium.
Importantly, even after two years of exposure, the particles in the interstitium do not
appear to have elicited a tissue response. Also, the aggregates of particle-laden
macrophages observed in alveolar lumena elicited significantly less of a tissue
response im monkeys than in rats. Such responses included: alveolar epithelial
hyperplasia, inflammation, and focal septal fibrosis.
The authors further indicate that "epithelial hyperplasia concomitant with aggregation of
particle-laden macrophages in alveolar lumen is a characteristic response to many
poorly soluble particles in the rat lung, both at exposure concentrations that result in
lung tumors and at concentrations below those resulting in tumors. Such a response,
however, was not characteristic of what was observed in monkeys. Among other
things, these differences in responses suggest that rats may not represent a good
model for human responses to inhalation of poorly soluble particulate matter. It would
also have been interesting had they tested a "benign" dust such as Ti02.
In summation:
results of (newer) sub-chronic and chronic retention studies are generally
consistent with those of retention studies that track lung burden following
short-term exposure (Section 7.2.1.1);
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• there is some indication in these sub-chronic and chronic studies that
chrysotile asbestos may not be retained as efficiently as amphibole
asbestos. It is likely, however, that such distinctions are due more to fiber
size than fiber type so that definitive conclusions concerning such effects
cannot be reached until better studies that properly account for both size
and type are conducted;
• although earlier studies that track mass instead of fiber num ber suggest
otherwise, chrysotile and amphibole asbestos concentrations (when
measured by fiber number) continue to increase with time as long as
exposure continues. Due to a lack in the ability to distinguish among size-
dependent effects when lung burdens are tracked by mass, the results of
the earlier studies are not necessarily inconsistent with the results of the
later studies;
7.2.2 Animal Histopathological Studies
Studies in which the lungs of dosed animals are examined to determine the fate and
effects of inhaled asbestos are helpful for understanding the movement and distribution
of retained particles within the lung and surrounding tissue. Both the newest retention
studies and the older retention studies tend to include at least some of this type of
information. They also tend to indicate a consistent picture of the fate and effects of
asbestos. While such studies tend to confirm that translocation in fact occurs, they are
less helpful for elucidating the specific mechanisms by which translocation occurs.
Newer Studies
llgren and Chatfield (1998) studied the biopersistence of three types of chrysotile
("short chrysotile" from Coalinga in California, "long1 Jeffrey fiber from the Jeffrey mine
in Asbestos, Quebec, and UICC- B (??) Canadian Chrysotile, a blend from several
mines in Quebec). Both the Coalinga fiber and the Jeffrey fiber were subjected to
further milling prior to use. In this study, rats were exposed via inhalation for 7 hrs/day,
5 days/wk for up to two years. Concentrations were: 7.78 +/-1.46 mg/m3 for Coalinga
fiber, 11.36+/-2.18 mg/m3 for Jeffrey fiber, and 10.99+/-2.11 mg/m3forthe UICC-B
fiber. An additional group of rats was also dosed for a single 24-hour period with
Jeffrey fiber at a concentration of 5,000 f/ml > 5 urn. Estimates of lung content of
chrysotile were based on measurements of total silica content. The character of the
three chrysotile types evaluated in this study was previously reported (Campbell et al.
1980, Pinkerton et al. 1983), The animal studies were conducted previously with the
overall approach reported by McConnell et al. (1983a and b ) and Pinkerton et al.
(1984). Based on the characterization presented in these papers, Coalinga fiber is
short, but not as extremely short as suggested by the authors:
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Ratio of fibers longer than: 5 urn: 10 urn: 20 um
Coalinga fiber: 200: 78: 0.98
Jeffrey fiber: 591: 220: 78
Such calculations also suggest that the single, "high" dose in this experiment was
equivalent only to a concentration that is approximately 10 times the other
concentrations studied, so that it is equivalent only to a 10-day exposure and small
relative to the longer term (up to 2 yrs) exposures considered in this study.
Results reported by llgren and Chatfield indicate that the lung burden for short fiber
chrysotile initially increases with exposure, reaches a steady state, and then decreases
steadily following cessation of exposure. Approximately 95% of this material is cleared
within 2 years. Thus, short fibers appear to exhibit the trend suggested by the older
chronic retention studies that tracked burden by mass and is consistent with the newer
studies indicating rapid clearance of short structures (Section 7,2.1.2).
In the studies reported by llgren and Chatfield, most short fibers are found initially within
alveolar macrophages and, while concentrations in Type I epithelium, interstitial cells,
and interstitial matrix increase with time, little appears to be taken up by Type II
epithelium. Small amounts of short fiber chrysotile are also observed to be taken up by
endothelial cells. There is also little sign of inflammation orfibrosis following exposure
to the Calidria chrysotile.
Jeffrey chrysotile also initially appears to be taken up primarily by alveolar macrophages
but later becomes most prevalent in the interstitial matrix and, to a lesser extent, in
interstitial cells. Substantial numbers of fibers are also taken up by Type I epithelium
and small amounts by endothelial cells. Similar, but slightly delayed effects were seen
for UICC-B chrysotile (which has a smaller fraction of long fibers and therefore, the
authors suggest, takes longer to accumulate). Note that, by 12 months, the majority of
long fibers from both these types were found in interstitial matrix while the majority of
Calidria material was still found in alveolar macrophages This is consistent with
observations concerning behavior between short and long fibers reported by Wright and
Kushner (1975), Section 7.2,1.1. At this point, the Jeffrey material also caused
substantial thickening of the basement membrane and most fibers in the interstitium
were trapped within the collagenous matrix. The authors note that some of the most
severe interstitial changes occurred adjacent to areas of bronchiolar metaplasia. Such
effects were not seen with Calidria exposure.
Thickened basement membranes, calcium deposits, metaplastic changes, and
structural abnormalities were all observed with long fiber exposure but not with calidria
exposure. Interstitial macrophages also showed morphological changes following
phagocytosis of fibers. While calidria material was about evenly distributed between
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interstitial matrix and cells, the vast majority of long fiber material was found in the
matrix. Movement into the matrix was also observed to increase even after exposure
ceased. With time, the number of long fibers in interstitial cells dedined modestly but
declined precipitously for Calidria material
Jeffrey fibers accumulated in Type 1 epithelial cells during exposure and then levels
decreased slowly after exposure ceased. UICC-B fibers accumulated more slowly,
never reaching the same levels as for Jeffrey and decreased more rapidly,
Concentrations of Calidria fibers in Type 1 epithelial cells was low at all time points,
Type II epithelial cells accumulated very few fibers of any type (although they took up
slightly more Jeffrey fiber than the others). All three fiber types caused substantial
increases in interstitial cells (mostly macrophages) at three months and this increase
persisted for the Jeffrey fiber but decreased to background after 24 months for the
other two fiber types. Fibroblast numbers also increased with the long fiber types but
not Calidria chrysotile.
Type II epithelial cells showed decreases in volume and number that persisted until
exposure to long fiber ceased and these cells displayed dramatic structural aberrations
despite absence of a fiber load. One possible explanation for the observed changes in
Type II cells, especially the reduction in their number, is that they were undergoing
terminal differentiation to Type I cells (see Section 4.4) In fact, the apparent absence of
fibers observed within Type II cells might be explained by such cells taking up fibers but
.being induced to terminally differentiate, once fibers are accumulated. Overall, Type II
cells displayed greater cellular response than Type I cells (which might also suggests a
role for cytokines), All effects were observed to be fiber length dependent and all were
exaggerated following exposure to long fiber material,
Rats exposed to long fiber had numerous accumulations of dust-laden interstitial
macrophages and/or small focal accumulations of dust within the interstitium at the end
of the lifetime study, but such changes were not observed for Galidria exposed animals
The lung burden for rats exposed to the single, "high" Jeffrey fiber exposure (based on
total silica) at 12 months (i.e. 12 months post exposure) was not different from controls,
Therefore, the authors conclude that short-term, "high" exposures are rapidly deared
(even exposures containing substantial quantities of long fibers). Other changes
induced by the single, short-term high exposure of Jeffrey fiber that was followed for
24 months also showed reversion to close to background status. Importantly, these
observations are not based on quantitation of fiber burden in lung tissue. Rather they
are inferred by observing the effects caused by the presence of fibers. This may
suggest, for example, that more than 10 day's worth of exposure would be required at
this level of exposure before irreversible lesions develop.
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In the study by Hesterberg et al, (1997), which was previously discussed (Section
7,2,1,2), the authors note (among dosed hamsters) that the magnitude of cellular
effects appeared to differ among the fibrous glasses as a function of their relative
biodurability. For animals dosed with the least biopersistent glass, only transient effects
(influx of macrophages, development of microgranulomas) were observed and these
did not progress further. For the more persistent glasses, injury progressed through
more intense inflammation, interstitial fibrosis, pleural collagen deposition, mesothelial
hypertrophy and hyperplasia and eventually, mesothelioma, The authors also suggest
that amosite appears to be more potent than chrysotile, even when aerosols contain
comparable numbers of fibers longerthan 20 urn.
Choe et al. (1997) exposed rats to chrysotile and crocidolite (both NIEHS samples) by
inhalation for 6 h/d, 5 d/wk, for 2 weeks. The rats were then sacrificed and their pleural
cavities iavaged. Results indicate that significantly more pleural macrophages were
recovered in plueral lavage fluid at one and six weeks following exposure than sham
exposed rats. The centrifuged pellet from pleural lavage fluid from one of four rats also
exhibited long (> 8 urn), thin (< 0,5 um) crocidolite fibers (one week following exposure).
The concentration of fibers in this pellet suggested approximately 1 f per 4000 cells in
the pleura. Note that chrysotile rats were not examined for fiber content,
Older Studies
In the series of studies by Davis et al. (1978, 1980, 1985, and 1986aj, the authors
generally report similar histopathological observations that emphasizes a marked
distinction between effects from long and short fibers. From the 1986 study, for
example, Davis et al. report that at the end of 12 months of exposure, rats exposed to
long fibers (amosite in this case) exhibited deposits of granulation tissue around
terminal and respiratory bronchioles. They further indicate that the granulation tissue
consists primarily of macrophages and fibroblasts with occasional foreign body giant
cells.
As the animals aged, there was increased evidence of collagen deposits in these
lesions and the oldest lesions consist mainly of aceilular, fibrous tissue. The alveolar
septa in these older animals showed progressive thickening. Initially, this was
apparently due primarily to hyperplasia of Type II epithelial cells but with time was
increasingly due, first, to reticulin and, later, to coliagenous deposits in the septal walls.
Asbestos dust was frequently visible in these deposits. Epithelial cells lining alveoli
adjacent to the oldest lesions also tended to become cuboidal in shape. As the animals
aged, these areas of interstitial fibrosis became more extensive.
In contrast, animals exposed to short fibers (also amosite in this case) showed no such
lesions (peribronchial fibrosis) at any point in time. At the end of exposure, the lungs of
these animals contained large numbers of pulmonary macrophages packed with fibers,
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but these cells remained free in the avleolar spaces. The authors report that large
numbers of laden macrophages sometimes aggregated in alveoli close to respiratory
bronchioles, but that there would be no formation of graunulation tissue or thickening of
alveolar septa at these locations. Thus, with the exception of the presence of dust-
laden macrophages, the structure of the lung of these rats was not altered.
In the Davis et al. 1987 study of chrysotile, a slight variation of the above scenario is
worth noting. In this study, the development of peribronchial fibrosis was reported for
animals dosed both with the long fiber material and with the short fiber material.
However, the authors also report that the short fiber chrysotile in this study in fact
contains a sizable fraction of longer structures and this finding was corroborated by
more formal size characterization (Berman et al. unpublished) later conducted in
support of a study to evaluate the effects of size (Berman et al. 1995).
In this study, Davis et al. also reported observations on the morphological changes
observed in the mesothelium during these studies. The authors indicate that the older
animals in these studies exhibit "areas of vesicular pleural metaplasia consisting of
loose, fibrous tissue containing large vesicular spaces lined with flattened cells." The
authors also report that examination in previous studies indicates that these cells are of
a mesothelial type.
Davis et al. report that, "...occasionally the walls between vesicular spaces were so thin
that they consisted of two closely opposed layers of extended and flattened cells with
no basement membrane in between them. Where cells were supported by areas of
fibrous tissue, a basement membrane was present. While no method for the direct
quantification of this pleural metaplasia has been developed, its occurrence is closely
related to the presence of advanced interstitial fibrosis or adenomatosis in the lung
tissue and it is particularly common where patches of this type of parenchyma! lesion
have reached the surface. It is not known whether such lesions are precursors to
mesothelioma." Davis et al. also note that neither of the two mesotheliomas observed
in this study showed histological patterns consistent with the observed vesicular
hyperplasia.
Brody et al (1981) tracked the distribution of chrysotile following inhalation by rats.
Asbestos was initially deposited almost exdusively at alveolar duct bifurcations. In
agreement with Pinkerton et al (1986), the degree of deposition appeared to be an
inverse function of the pathlength and bifurcation number for each alveolar duct.
Uptake by macrophages and type 1 epithelial cells were observed following deposition.
Asbestos was observed both in lipid vesicles and free in the cytoplasm of type 1 cells.
After 8 days, alveolar duct bifurcations became thickened with an influx of
macrophages. Asbestos was also observed in basement membrane below the
epithelium. Apparently, structures had been transported through type 1 cells to the
basement membrane. Once in the basement membrane, asbestos may enter the
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interstitium, Predominantly short structures were monitored in this study, Long
structures were not readily observed (but this is likely a counting problem; under such
circumstances, short structures may serve as surrogates for the presence of other
structures).
Intratracheal Instillation
Bignon et ai (1979) studied the rate of translocation of various materials in rats.
Chrysolite, crocidolite, and glass fibers were intrapleurally injected into rats and their
concentration was monitored as a function of time in lung parenchyma and other
tissues removed from the pleura, Within one day following injection, asbestos was
detectable in lung parenchyma. After 90 days, asbestos was found in all of the tissues
analyzed. Based on the rate of translocation to the lung, crocidolite migrates about 10
times more rapidly than chrysotile (on a mass basis). The rate of migration of glass is
in between the two asbestos types. Structures initially found in the lung were
significantly shorter than the average size of structures injected. After seven months,
however, the average lengths of structures in all tissues monitored were longer than the
average length of structures originally injected. Thus, short structures migrate more
rapidly than longer structures (possibly by a different mechanism) but long structures
eventually translocate as well. Within a target tissue, preferential clearance of short
structures also contributes to observed increases in the average length of the structures
with time.
Studies of Non-Fibrous Particulate Matter
Studies of the fate and effects of respirabie, non-fibrous particulate matter provide
evidence for at least one mechanism by which particles (and fibers) may be transported
to the interstitium.
Li et al. (1997) evaluated the effect of urban PM 10, carbon black, and ultrafine carbon
black on rats following Intratracheal instillation (0.2 ml volume instilled containing
between 50 and 125 ug of particles). After 6 hrs, there was a noted influx of neutrophils
(up to 15% of total cells observed in bronchioalveolar lavage BAL fluid) and
increases in epithelial permeability was surmised based on increased total protein
(including increases in levels of lactate dehydrogenase, which is a marker for cell
membrane damage) in BAL fluid.
Conclusions
Overall, observatbns among both the newer and the older studies (including the study
by Wright and Kuschner 1975, see Section 7.2.1,2) tend to be highly consistent,
particularly with regard to the distinction between the effects of short and of long fibers,
Typically, long fibers initially produce substantial inflammation characterized by an influx
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of macrophages and other inflammatory cells. Ultimately, exposure to such fibers
cause thickening of alveolar septa (particularly near avleolar duct bifurcations) due to a
combination of epithelial hypeiplasia and deposition of reticulin and, later, collagen
resulting in interstitial fibrosis. In contrast, short fibers cause an initial influx of
macrophages and, long term, show persistent accumulations of fiber-laden
macrophages both in alveolar lumena and in pulmonary lymph nodes, but otherwise no
structural changes are observed in the lung tissue of these animals.
These studies also provide ready evidence of the effects of fiber translocation, but
generally offer only limited evidence for elucidating the mechanisms by which such
translocation occurs. There is evidence that Type I (and possibly Type II) epithelium
phagocytize particles and fibers and it is possible that such fibers maybe passed
through to the basement membrane and the interstitium. For Type I cells, the distance
between the alveolar lumen and the basement membrane averages less than 1 urn in
any case (Section 4.4). Certainly fibers are also observed in the interstitium.
Particulate studies also indicate that oxidative stress induced by particulate matter and
fibers may cause morphological changes in Type II cells with consequent loss of
integrity of the epithelium, which increases it's permeability overall and may also allow
diffusional passage of particules and fibers.
7.2.3 Human Pathology Studies
Human pathology studies provide additional information concerning the nature of
asbestos deposition, clearance, and retention. These are the studies in which lung
burdens are measured in samples of lung tissue and correlated with the exposures
received by the individuals from which the lung samples derive.
Among the advantages of human pathology studies is that they provide direct insight
into the behavior of asbestos in humans. They are also limited, however, by the lack of
ability to obtain time-dependent estimates of lung burden (because samples are derived
from deceased individuals), by the manner in which lung tissue is.stored (several of the
fixatives employed to store tissue samples have been shown to enhance dissolution of
asbestos, Law et al. 1990 and 1991), by the manner in which samples are prepared for
asbestos analysis, by the manner in which asbestos is analyzed, and by the limited
ability to re-construct the uncontrolled exposures experienced by study subjects
(Section 5.2).
Perhaps most importantly, the ability to construct anything but the coarsest quantitative
comparisons across subjects is also typically limited by use of "opportunistic" tissue
samples (i.e. use of samples that happened to have been collected and stored during
autopsy or necropsy) because such samples are not controlled for location on the
respiratory tree (i.e. the linear distance and branch number from the trachea) that is
represented by the sample. Because it has previously been shown that deposition is a
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strong function of such location (see, for example, Yu et al. 1991), comparisons across
lung samples not controlled for these variables are problematic. In fact, it has also
been shown that samples collected from adjacent locations in lung parenchyma can in
fact exhibit strikingly different fiber concentrations due specifically to the differences in
the location of the respiratory tree represented by the alveoli and respiratory
bronchioles in the spatially adjacent samples (Pinkerton et al. 1986, Brody et al. 1981,
Section 5.2).
Despite the above indicated cautions, when interpreted carefully, human pathology
studies can provide useful evidence regarding fiber deposition, clearance, and retention
in the human lung.
Newer Studies
Among the most recent studies, Finkelstein and Dufresne (1999) evaluated trends in
the relationship between lung burdens for different fiber types and different size ranges
as a function of historical exposure, the duration of such exposure, the time since last
exposure, and other variables. The analyses were performed among 72 cases from
which tissue samples could be obtained (including 36 asbestosis cases, 25 lung cancer
with asbestosis cases, and 11 mesothelioma cases).
Due to the excessive scatter in the data, most of the analyses presented depend on
"Lowess Scatterplot Smoothers." NEED TO COMMENT ON THIS Moreover, although
not stated, it is likely that the tissue samples obtained were "opportunistic" in that they
were not matched or controlled for relative position in the respiratory tree.
The authors employed the multi-compartment model developed by Vincent et al. (1985)
to evaluate trends in their data. The features of this model include:
• a compartment representing conducting airways that are cleared within
minutes to hours by muco-ciliary transport;
• a compartment representing the subset of fibers reaching the pulmonary
portion of the lung that are cleared by alveolar macrophages and
transported to the muco-ciliary escalator. This type of clearance is also
considered relatively rapid with half lives of no more than several days to
several weeks. Macrophage clearance is also considered size-dependent
and long fibers are cleared less efficiently than short fibers;
• when sufficient dust is inhaled (or dust is sufficiently cytotoxic) to impair
the motility of macrophages (either by volumetric overload or by toxicity), a
sequestration compartment form's that consists of laden but immobile
macrophages. Although this compartment may ultimately be cleared to
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lymphatic drainage, such clearance is assumed to be slow and size
dependent (with half lives of 2 or 3 years for short fibers and 8 years for
fibers longer than 10 urn); and
• once the macrophage system is overloaded, fibers may cross the alveolar
epithelium and reach the interstitium and this compartment must be
cleared by transport to lymphatic drainage, which is assumed to be an
extremely slow process,
Finkelstein and Dufresne indicate that chrysotile splits both longitudinally and
transversely in the lung and that chrysotile lung burdens decrease significantly with time
since last exposure (with short fibers clearing even faster than long fibers) while
tremolite burdens do not appear to decrease with time since last exposure. They also
suggest that smoking does not appear to affect clearance rates.
Finkelstein and Dufresne indicate that these type of studies are not useful for examining
the behavior in rapidly clearing compartments of the lung but they may provide insight
concerning the more slowly clearing compartments. Based on their modeling, they
suggest that tremolite is transferred to the sequestration compartment at rates that are
6 to 20 times that of chrysotile (which they indicate is comparable to what was found for
crocidolite by DeKlerk). The authors suggest that retained chrysotile concentrations
tend to plateau after accumulation of about 35 years of exposure while tremolite
concentrations continue to increase. They also, suggest, however, that chrysotile
concentrations may begin to increase again after 40 years (suggesting the overload is
eventually reached for chrysotile as well). Reported half-lives from the long-term
compartment are:
chrysotile
fibers shorter than 5 urn 3.8 yrs
fibers 5-10 urn in length 5.7 yrs
fibers longer than 10 urn 7.9 yrs
tremolite
fibers shorter than 5 urn 14.3 yrs (not different from °°)
fibers 5-10 urn in length 15.8 yrs (not different from «>)
fibers longer than 10 urn 150 yrs (not different from °=)
In a case-control study, Albin et al. (1994) examined the lung burdens of deceased
workers from the asbestos-cement plant previously studied for mortality (Albin et al.
1990). In this study, details of the procedures used to prepare lung tissue for analysis
were not provided. It is also assumed that available tissue samples were
"opportunistic" in that they were not matched or controlled for relative position in the
respiratory tree.
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Results from Albin et al. (1994) are consistent with, but do not necessarily demonstrate
that, chrysotile is cleared more readily from a long-term sequestration compartment
than amphiboles. The authors also report that chrysotile fibers observed in this study
are much shorter than the amphibole fibers observed, so that differences in clearance
rates might be attributable to size differences. The authors also suggest that clearance
is impaired by fibrosis. (
Studies of Quebec Miners
Several authors also studied the lung content of various groups of deceased chrysotile
miners in Quebec and found, despite overwhelming exposure to chrysotile from the ore (
(which contains only trace quantities of tremolite, see Section 6.2.3 and Sebastien et al,
1986), a substantial number of fibers (in some cases the majority of fibers) observed in
the lungs of deceased miners from this area are tremolite. Thus, for example:
• in a study of lung burdens in 6 mesothelioma victims, Churg et al (1984)
showed that amphiboles structures were 5 to 15 times as plentiful as ^
chrysotile despite the predominantly chrysotile exposure;
• in a study of lung tissue from 20 asbestosis cases, Pooley (1976) found
substantial concentrations of tremolite in the lungs of deceased Quebec
chrysotile workers; and (
• in a much larger study comparing lung burdens of Quebec workers with
those from the South Carolina textile mill, which has also been extensively
studied for asbestos-related mortality (Section 6.2.3), Sebastien et al.
(1989) examined 161 lung tissue samples (89 from the Quebec mines).
Results from this study indicate that geometric mean tremolite fiber
concentrations were more than three times mean chrysotile fiber
concentrations (18.4 vs. 5.3 f/ug dry lung tissue) among the deceased
Quebec miners evaluated. It was also found that, despite these
differences, the overall size distributions of tremolite and chrysotile fibers
observed in lung tissue were approximately the same. A more detailed C
discussion of the results of this study is provided in Section 6.2.3.
• McDonald, Gibbs and Rowlands (1985) suggest that lung burden data
from quebec indicate little evidence of decreasing chrysotile concentration
with time since last exposure. Rather they suggest simply that tremolite is ,
initially deposited in the deep lung more efficiently. These authors also
indicate that tremolite fibers are mostly optical while chrysotlie are mostly
"Stanton" or thinner.
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These authors report good correlation of both tremolite and chrysotile with
estimated past exposures. Reported geometric mean measurements for
the various fibers in lung are: tremolite: 1x 106 - 18.2 x 106, chrysotile : 1.5
x 106 -15.7 x 106, respectively, when exposure varied from < 30 mpcf to
>300 mpcf. Note that, if these are PCM measurements, this may not be
telling the whole story, The authors also report that 66% of those who
died 10 yrs since first exposure and half of those who died 30 yrs since
first exposure showed high chrysotile concentrations in their lungs.
These observations provide evidence that either amphibole (tremolite) asbestos is
deposited more efficiently in the compartments of the lung where clearance is slower
chrysotile asbestos is cleared more rapidly and efficiently from even the slowest
clearing compartments of the lung (or both). Moreover, this conclusion appears to
apply similarly to both short and long fibers.
Some of the pathology studies that have been published suggest that at least some
clearance mechanisms show a dependence on fiber size, which is consistent with what
is observed in animal studies (Section 7.2.1), Notably, for example, Timbrell (1982)
studied deceased workers and relatives from the Paakkila anthophyllite mine in Finland.
He found that structures shorter than 4 urn and less than 0.6 urn in diameter are
completely cleared from healthy lungs. The efficiency of clearance decreases slowly
with increasing size. Structures longer than 17 urn and thicker than 0.8 urn in diameter
are not significantly cleared. The study is based on a comparison of structure size
distributions in lungs compared to the structure size of the material in the original dust
exposure. Timbrell also noted that asbestosis suppresses the removal process.
When considering the dependence of clearance on size (particularly via mechanisms
involving phagocytosis), it is necessary to address differences in human and animal
physiology. Due to differences in the morphology, for example, human macrophages
have been shown capable of phagocytizing larger particles and longer fibers than
macrophages found in mice and rats (Krombach et al. 1996, for details, see Section
4.4). Thus, the range of fibrous structures that are efficiently cleared from human lungs
is expected to include longer fibers than the range efficiently cleared in mice or rats.
Unfortunately, given the limited precision of the available data, the size ranges that are
reported to be cleared efficiently in rats and humans, respectively, cannot be easily
distinguished.
Several human pathology studies also support observations from animal studies
indicating that clearance may be inhibited by the development of f ibrosis (Albin et al.
1994, Churg et al. 1990, Morgan and Holmes 1980) or by heavy smoking. However,
other studies do not indicate such hindered clearance either with smoking (Finkelstein
and Dufresne 1998) or with fibrosis.
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Older Studies
Morgan and Holmes (1980) examined tissue samples from 21 patients in England (10
who died of mesothelioma, 3 who died of lung cancer, and 8 who died of other causes),
In this study, formalin-fixed tissue samples were digested with hypochlorite, The
residue was then rinsed, diluted, and an aliquot filtered. The filter was mounted on a C
microscope slide and clarified for analysis by phase contrast optical microscopy,
Importantly, the authors note that ch'rysotile fibers were ignored in this study because
they would not generally have been detected by this technique, Portions of the filters
were also carbon coated and prepared for TEM analysis, Based on the observation
that only 19% of the fibers observed in this study were between 2.5 and 5 urn, when the f
authors expect airborne distributions to contain closer to 90% of the fibers within this
size range, the authors conclude that short fibers are preferentially cleared from the
lung. They also conclude, based on one subject with asbestosis whose lung tissue
exhibited 72% short fibers, that asbestosis hinders dearance. The authors also'note
that fewer than 1 % of ferrugenous bodies (iron-coated asbestos bodies) are less than
10 urn in length, which indicates (in agreement with previously published work) that ^
such bodies seldom form on short fibers. They also suggest that virtually all fibers
longer than 20 urn tend to be coated in the distributions they observe.
l_e Bouffant (1980) studied the concentrations, mineralogy, and size distributions of
asbestos fibers found in the lungs and pleura of deceased asbestos workers. Based on .f
the analysis, he found that the average ratio of chrysotile fiber concentrations found in
the lung versus the pleura is 1,8 while for amosite the ratio is 34, This indicates that
chrysotiie migrates from the lung to the pleura more rapidly than amphiboles resulting in
a higher fraction of total fibers in the pleura being composed of chrysotile (3% in the
lungs versus 30% in the pleura). With regard to size, the researchers found the size
distribution of amosite is virtually identical in the lung and pleura while chrysotile fibers
found in the pleura are much shorter than chrysotile fibers found in lung tissue. This
suggests that the movement of chrysotile is a result of a combination of translocation
and degradation to shorter fibers (or that tissue samples have been contaminated with
environmentally ubiquitous short, chrysotile structures). The authors indicate that
chrysotile fibers apparently degrade to shorter fibers more rapidly than amosite and C
translocate to the pleura more rapidly than amosite. Thus, a greater fraction of
chrysotile fibers (albeit short fibers) reach the pleura than amosite fibers over fixed time
intervals. However, the results of this study also confirm that the longer amosite fibers
do eventually translocate, although on a much more extended time scale than the
translocation of chrysotile. ,-.
Importantly, the results of this study need to be evaluated carefully. Boutin et a I. (1996)
showed that the majority of asbestos fibers in the pleura (particularly the long fibers) are
aggregated in localized "black spots" (which surround the sites of lymphatic drainage).
€
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Thus, if the tissue samples analyzed by Le Bouffant do not contain representative sets
of such spots, the conclusions drawn by Le Bouffant may be subject to question.
In summation, human pathology studies tend generally to support the findings of other
studies regarding the size effects of asbestos (i.e. short fibers tend to clear more rapidly
than long fibers, which can be retained in pulmonary tissues for extended periods).
They also appear to highlight drastically different behavior between chrysotile and the
amphibole asbestos types (particularly tremolite) regarding the heavily favored retention
of the latter, which has also been indicated in animal studies. Unfortunately, the ability
to draw quantitative conclusions from human pathology studies is hampered by the
severe limitations of these studies (Section 5.2).
7.2.4 Studies of Dissolution/BioDurability
Although asbestos minerals are relatively insoluble in vivo in comparison, for example,
to various fibrous glasses or other manmade mineral fibers (see, for example,
Hesterberg et al. 1998a or Estes and Hadley 1996), they do eventually dissolve in the
body. Therefore, this pathway may contribute importantly to the overall biological
clearance of'asbestos. Moreover, it has been suggested by several researchers (see,
for example, McDonald 1998 and other references cited below) that differences in
biodurability between chrysotile and the amphiboles may at least partially explain the
disparate potencies observed for these fiber types toward the induction of
mesothelioma and, potentially, lung cancer (see Sections 6.2.4.2 and 6.3.3.2).
Note that the term "biodurability" is used here to indicate the persistence of a
particle or fiber attributable specifically to solubility (in the absence of other
clearance or degradation mechanisms). In contrast, the term "biopersistence" is
used to indicate the overall persistence of a particle or fiber in the body
attributable to the combined effect of all mechanisms by which it might be
removed. Thus, for example, while biopersistence can be evaluated in vivo,
biodurability can best be inferred from in vitro dissolution studies so that effects
from other clearance mechanisms can be eliminated.
Several studies further indicate that both the in-vivo biopersistence and the bio-activity
(including carcinogenicity) of various fiber types may be linked to their observed, in-vitro
dissolution rates (Eastesand Hadley 1995, 1996, Bernstein etal. 1996, Hesterberg et
al. 1998a, 1998b). Such studies, however, typically involve fiber types with dissolution
rates that are rapid relative to the rates of clearance by other mechanisms (Sections
7.2.1.1 and 7,2.1.2). In such studies, moreover, the various types of asbestos are
typically employed as negative (insoluble) controls. In fact, most of these studies are
based on experiments with rats and the 2-yr lifetime of a rat is comparable to the
anticipated lifetime of chrysotile asbestos in the body and short compared to the
anticipated lifetimes for the amphiboles (see below). Therefore, such studies are not
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particularly sensitive to differences in the relative biodurability of the different asbestos
types. In fact, in the majority of these studies, the dissolution rates reported for
asbestos were derived indirectly by analogy with other minerals or are quoted from
other studies that derive rates similarly and may therefore be somewhat unreliable.
Nevertheless, a review of a subset of these studies is instructive.
f
» Bastes and Hadley (1995 and 1996) report a simple model that
reasonably predicts the relative fibrogenicity and tumorigenicity for a
range of synthetic fibers based on the dissolution rates of the fibers
measured in vitro. The authors found that they could explain observations
by assuming that the effects of the various fibers are a function of an (
adjusted dose that accounts for biodurability. Thus,
F = f(ax)
where "F" is the observed incidence of the end point, "f" is the dose-
response function proposed for the effect, "x" is the measured dose, and ^
"a" is an adjustment factor that accounts for durability.
In the model, "a" is determined simply as "td/tL" where ^ is the time that a
fiber of diameter, "D" remains in the lung and { is the lifetime of the
exposed animal (e.g. 2 years for rats). This simple model reasonably p
reconciles the results observed in animal inhalation and injection studies
of manma.de mineral fibers (MMVF's), refractory ceramic fibers (RCF's),
and asbestos for end points including lung tumors, degree of fibrosis, and
(for intrapleural injection studies) mesothelioma. Based on a chi-square
test, the simple model is shown to adequately fit the data to a number of .
databases reviewed. In contrast, the unadjusted doses do not.
Importantly, the dissolution rates used for the various asbestos minerals in
this study were estimated by analogy with similar minerals and therefore
may be unreliable.
» In studies comparing in-vivo biopersistence with dissolution rates C
measured in-vitro, Bernstein et al. (1996) and Hesterberg et al. (1998a),
indicate that it is necessary to consider only long fibers (typically longer
than 20 urn), because shorter structures are typically cleared by other
mechanisms. They also indicate, at least for this type of study in which
whole lungs were homogenized and dissolved prior to preparation for ,
asbestos analysis, that clearance is initially rapid. This reflects muco-
ciliary clearance from the upper respiratory tract. Therefore, it is
clearance.of long fibers from a longer term pool that tracts in-vitro
dissolution rates.
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These authors also report that long, soluble fibers (longer than 20 urn) are
actually cleared more rapidly than short fibers in these studies. They
indicate that this is likely due to long fibers being too long to be effectively
phagocytized by macrophages so that they are left to dissolve in the
extracellular fluid at neutral pH, Shorter fibers are effectively taken up by
macrophages so that dissolution is hindered by the more acidic
environment of the phagosomes (pH 4.5) and by the limited volume of
fluid within which to dissolve.
• Law et al. (1991) studied the dissolution of a range of fibers in solutions
used as common fixatives for biological samples. The authors report that
chrysotile and crocidolite, as well as many other fibers, dissolve at
measureable rates in the fixatives studied (Karnovsky's fixative and
formalin fixative). They therefore recommend that fiber concentrations
and size distributions obtained from tissue samples stored in such
fixatives should be evaluated carefully to account for the possible effects
of the fixatives.
• Although Coin et al. (1994) reported seeing no effective reduction in long
fiber (> 16 urn) chrysotile (nor other evidence of dissolution) in their study
of fiber biopersistence, the limited time frame of this study (30-days) may
have been too short to allow detectable changes to accumulate.
The most consistent data for the comparative biodurability of chrysotile and the
amphiboles (specifically crocidolite) is found in two in-vitro studies of the dissolution
rates of fibers that were conducted under comparable conditions. In the first of these
studies, Hume and Rimstidt (1992) measured the dissolution rate for chrysotile
asbestos at neutral pH under conditions analogous to biological systems. The
dissolution rate that they report for chrysotile converts to: K^ = 12.7 ng/cm2-hr and this
is reportedly independent of pH. In a comparable study Zoitus et al. (1997) report the
following dissolution rate for crocidolite: K^ = 0.3 ng/cm2-hr, which is 40 times slower
than for chrysotile. Dissolution rates for several MMVF's and RCF-1 are also reported
in the latter paper, which are listed from fastest dissolving to slowest in Table 7-4.
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Table 7-4:
Measured In-Vitro Dissolution Rates for Various Fibers1
Fiber Type K^- (ng/cm2-hrt
MMVF 10 259
MMVF 11 142
MMVF 22 119
MMVF 21 23
Chrysotile 12.72
RCF 1 . 8
Crocidolite 0.3
1 Source: Zoitus etal. (1997)
2 Source: Hume and Rimstidt (1992)
Note that dissolution rates for other amphiboles, such as amosite are probably
no more than a factor of two or three different than that reported above for
crocidolite (see, for example, Hesterberg et al. 1998a,b).
To compare the effect of biodurability on the in-vivo biopersistence of asbestos and
other fiber types, both the detailed kinetics of dissolution and the distribution of fiber
sizes must be considered.
As reported by Zoitus etal. (1997), at a sufficiently high rate of fluid flow, the rate of
mass loss from a fiber is proportional to its surface area, A. Thus:
-dM/dt = -kA. (7.4)
This means that for a uniform mass fiber dissolving congruently:
1-(M/M0fs = 2kt/D0p (7.5)
where:
M is the mass at time t;
M is the initial mass at time t = 0;
0
D0 is the initial diameter of the fiber; and
p is the density of the fiber.
Substituting the equation relating the mass and the diameter of a fiber (M = prrd2h/4)
into the above equation, cancelling terms, and rearranging indicates that (during
dissolution) the diameter of a fiber decreases linearly with time:
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D = D0 -2kt/p (7.6)
where:
D is the diameter at time t; and
all other terms have been previously defined.
Furthermore, the rate of reduction in radius is given by: k/p. Based on the dissolution
rates given above for chrysotile and crocidolite, the radius reduction rates (vrad) for these
fiber types are determined to be: 1.26 x 10"8 urn/sec and 2.6 x 10"10 urn/sec,
respectively. Thus, the dissolution of each fiber is a zero order process (i.e. the rate is
constant with time and independent of concentration). Given these rates, a chrysotile
fiber 1 urn in diameter will disappear in approximately a year (3.9 x 107 sec) and a
crocidolite fiber of the same diameter in approximately 60 years (1.9x109 sec).
The number rate of disappearance of a population of fibers due to dissolution is a
function of the rate of radial reduction for the fiber type and the distribution of fiber
diameters in the population. The time at which the entire population finally dissolves
can be estimated simply by dividing the radius of the largest fiber by the radius
reduction rate, vmd, that is appropriate for the fiber type. The number of fibers
remaining from the population at time t will be equal to the number of fibers in the
original distribution with radii larger than vradt for the reduction rate that is appropriate for
the fiber type.
Note that dissolution will not cause an immediate reduction in fiber
concentration. The number of fibers will not begin to decrease until sufficient
time has elapsed for the thinnest fibers to completely dissolve. Bastes and
Hadley (1994) therefore recommend tracking the time dependence of the mode
of the distribution of fiber diameters to best gauge the effects of dissolution in
vivo.
Importantly, fibers in vivo will only dissolve at the rates predicted by the above
equations if the fluid in which they are dissolving flows past the fibers sufficiently rapidly
to prevent saturation from limiting the rate (Mattson 1992). Especially for slow
dissolving materials like asbestos, however, it is expected that the observed dissolution
rate in vivo will generally be slower than the rates predicted based on in vitro
measurements. Even for more rapidly dissolving fibers like most fibrous glasses and
manmade mineral fibers, dissolution is hindered in compartments of the body in which
the volume of available solute is limited. •
In summary, dissolution is a zero-order (i.e. constant with time, independent of
concentration) clearance mechanism that is dependent on fiber mineralogy, that the
effect it has on fiber populations (concentrations) is a function of the distribution of fiber
diameters within the population, and that the theoretical rate of dissolution may not be
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achieved in all tissues in all compartments of the lung or mesothelium due to limits in
the rate of in-vivo solute flow.
7.2.5 Dynamic Models
Unlike particle deposition in the lungs, which is an entirely mechanical process,
clearance, transport, and degradation mechanisms tend to be complex biochemical
processes. Due to the incomplete understanding of such processes, state-of-the-art of
degradation and clearance modeling is not as advanced as that for deposition. Even
the most sophisticated of these models remain semi-empirical. Although, current
models in this area show general agreement with the sparse, available data, there are
clearly areas of weakness that require additional research. Nevertheless, the models
provide a good indication of the kinds of processes that are important in the body and
their overall constraints. It is also noted that models for degradation and clearance in
humans tend to be better developed than those for animals primarily due to
confounding uncertainties associated with animal Ventilation rates. An overview of the
state of the art, which was current as of the date of publication, can be found in Stober
etal. (1993).
According to Stober et a I. (1993), the general conclusions that can be drawn from the
current models are that:
• clearance from ciliated airways is rapid, independent of particle/fiber type,
apparently independent of partide size, and can be described as the sum of two,
weighted exponentials (i.e. an assumed combination of two first-order decay
processes), although the process may in fact be zero order (i.e. the reduction in
concentration with time is constant and independent of concentration) with rates
that differ primarily by the distance that a particle must traverse to return to the
trachea.
Based on studies of particle clearance reported by Raabe (1984), muco-ciliary
transport in the nose and throat generally exhibits a half life for clearance of 4
minutes. Clearance of the tracheo-bronchial section of the respiratory tract is a
function of the distance from the trachea and generally varies from a half life of
30 minutes for the largest bronchi to approximately 5 hours for the smallest and
most remote bronchi. In healthy humans, material deposited in this region is
generally cleared within 24 hours. In contrast, the clearance mechanisms
operating in the deep lung, beyond the muco-ciliary escalator, operate over time
frames of many days to years (see Table 7-2);
• clearance of insoluble particles from the pulmonary portion of the lung occurs
primarily by macrophage transport and such transport has several components.
One component represents the population of "free" macrophages located within
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the alveoli that engulf particles and transports them to the muco-ciliary escalator.
Macrophages are also renewed at some rate of recruitment that may be
dependent on particle concentrations. In fact, numerous studies have
demonstrated that macrophage recruitment is induced by the deposition of
asbestos and other particles in the lung (Section 7.3.5). Particles also migrate
into the interstitium where another population of macrophages clears these
particles to lymph. This second component (interstitial clearance) is much
slower than the first (see Table 7-2);
• each macrophage can carry a maximum load and the mobility of each
macrophage decreases with increasing load. At sufficient loading, macrophages
become immobile and aggregates of overloaded macrophages in the alveoli may
then sequester particles for some period of time as this clearance mechanism is
shut down. In the interstitium, masses of immobile macrophages may trigger
development of granulomas that sequester particles for extended periods of time
by effectively preventing clearance of the particles within such tissue, at least
until or unless the granulomas resolve. Thus/these models incorporate overload
mechanisms and the incorporation of such overload mechanisms are required to
explain observed trends in experimental results; and
• in various published studies, overload (immobilization of laden macrophages)
has been modeled as dependent on the total volume or mass of phagocytized
material (for compact partides) and (additionally) on the length of phagocytized
material (for fibers). It is also possible that the motility of macrophages and the
consequent overall rate of this clearance process is additionally a function of
fiber diameter and/or particle toxicity (the latter for special cases).
Interestingly, while it is reported that large particles are not readily cleared by this
mechanism, the range of sizes over which clearance becomes hindered
corresponds reasonably well to the limits of overall respirability. In contrast,
fibers that are clearly too long to be cleared by macrophages, if they are
sufficiently thin, are quite respirable. Thus fibrous materials present a unique
challenge to the respiratory tract based solely on the dimensions of these
materials.
Stober et al. (1993) also notes that many models incorporate the assumption that most
clearance processes are first order (i.e. that the rate of reduction of mass or fiber
number is proportional to the remaining mass or fiber number, respectively, and
independent of other factors) and that the combined effects of multiple clearance
processes can therefore be expressed as a weighted sum of exponentials, has been
fairly successful. This means, however, that the half-lives "t1/2 's" attributed to the
various first order decays are empirical and do not necessarily correspond to any
specific physiological or biochemical features of the processes being modeled.
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Depending on the specific process, clearance rates may be zero order or may be a
more complicated function of multiple variables than can be described by a first order
decay. Nevertheless, models incorporating these simplifying assumptions have shown
good success at adequately describing observed effects.
Note that half lives for first order decay processes represent the time required for half of
the initial mass to decay (or be transported or whatever) and can be estimated as: t1/2 =
(In2)/k with k being the first order rate coefficient or proportionality constant between
rate and mass. This is why so many of the retention studies cited above provide
estimates of a series of decay constants or half-lives that are assumed to correspond
approximately to the major clearance processes contributing to the observed, overall
reduction in concentration.
Due to the complexity of the processes involved, only a small number of dynamic
models for fiber retention have been developed. Interpretation of the results of these
models requires that the meaning of the term "retention" first be reconciled across
studies,
• Dement and Harris (1979) report that, based on a mathematical model,
the fraction of structures retained in the deep lung is unlikely to vary by
more than a factor of 2 for different asbestos mineral types. In this study,
however, the term retention appears to refer primarily to a very short time
period that primarily includes consideration of deposition but not clearance
processes;
• Using a definition for retention that reflects long-term residence in the
lung, Yiretal. (1990a) developed a model of chrysotile retention that
explicitly incorporates longitudinal splitting, dissolution, and size-
dependent clearance. Time-dependent lung burden estimates derived
using the model were shown to compare reasonably well with published
data (Abraham et al. 1988, as cited by Yu etal.) both in terms of fiber
concentrations and fiber size-distributions.
• In a later modification of their retention model for chrysotile, Yu et al.
(1991) also considered the effect of airway asymmetry on fiber retention.
In this version of the model, Yu and coworkers incorporated information
concerning the geometry of the bronchio-alveolartree (including mean
distance and the mean number of airway bifurcations between the trachea
and the alveoli in each section of the lung) and studied the effects of such
considerations. The modified model predicts a non-uniform distribution of
the asbestos that is retained in the lung and the predictions reasonably
reproduce the distributions observed by various researchers and
measured formally by Pinkerton et al. (as cited by Yu et al.).
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• Yu et al. (1990b) also modeled the long term retention of amosite in rat
lungs. In contrast to the models employed for chrysotile, the model
presented for amosite incorporates a term for the clearance rate that is
not a constant but, rather, is a function of the lung concentration of
asbestos, which was adapted from an earlier model for diesel soot. This
modification was incorporated to adequately mimic the suppression of
clearance with increasing lung burden that has been observed by several
research groups (e.g. Wagner et al. 1974 or Davis et al. 1978) for
amphiboles. Conditions underwhich elevated asbestos (or dust)
concentrations are observed to reduce clearance are referred to as
"overload" conditions. Model predictions were shown to reasonably
reproduce the time-dependence of amosite lung burdens (in terms of
mass) in several studies.
Importantly, the overload conditions addressed in theYu etal. (1990b)
model were primarily observed among older retention studies where lung
burden was tracked as total asbestos mass (Section 7.2.1). Such studies
tend to suggest a difference in the behavior of chrysotile and the
amphiboles. As indicated in Section 7.2.1, however, later retention
studies, which track lung burden as a function of fibers number (in specific
size categories) tend to show this effect is a function of fiber length more
than fiber type and newer models may need to incorporate such factors
that indicate reduced macrophage motility as a function of fiber length.
Moreover, it is important to consider the major, confounding effects, if the
goal is to develop a model that not only reproduces the time-dependence
of clearance but also captures relevant physical phenomena. Thus, for
example, Yu et al. (1994) were able to reproduce the time-dependence of
the retention in rats of inhaled RCF-1 (as a function of fiber size) using a
model in which macrophage motility was limited only be total lung burden
and not dependent on fiber size. However, these authors also failed to
consider that long RCF-1 fibers in fact dissolve at rates competitive with
the clearance of short fibers (see Section 7.2.1), which is probably why
they did not find a dependence on length; the two effects cancelled out.
7.2.6 General Conclusions Regarding Deposition, Translocation, and
Clearance
The current literature on deposition, translocation, and clearance paint a consistent
picture of the fate of fibrous structures in the lung. The ultimate fate of biodurable fibers
depend overwhelmingly on their size. Although there may be additional effects due to
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t 1
mineralogy (addressed further in Section 7,3) and for rare, special cases this may be
important: generally such effects appear to be minor.
The primary effect attributable to mineralogy that is important to consider in relation to
clearance is that associated with biodurability. Fibers that dissolve in the lung at rates
that are competitive with the other clearance mechanisms described below may be
cleared sufficiently rapidly to preclude adverse effects, even when such fibers are too
long to be cleared efficiently by macrophages (see Section 7,2,1).
A schematic representation of the complex set of mechanisms that contribute to the
translocation and clearance of fibrous structures that have been deposited in the deep
lung is presented in Figure 7-4. This description was developed based on the complete
spectrum of observations reported in each of the previous sections of this Chapter
including, primarily, the descriptions of the most sophisticated of the models reviewed
by Stober et al. (1993).
As shown in Figure 7-4, briefly, the first reaction to the introduction of fibers (or other
particulate matter) into tie alveolar lumen is scavenging by alveolar macrophages. It
has been reported that the initial uptake by macrophages is a rapid process that is
essentially complete within hours after initial deposition. Rates for several of the
mechanisms depicted in Figure 7-4 have been estimated in the literature and are
summarized in Table 7-2.
FIGURE 7-4
i
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FIGURE 7-4, PAGE 2
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FIGURE" 7-4, PAGE 4
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The rate of removal (to the muco-ciliary escalator) by alveolar macrophages is then
determined by a variety of effects. Macrophage motility Is a size dependent-process so
that only fibers that are sufficiently compact (< -20 urn) can be removed from the lung.
The rate of removal by this process may also be suppressed both for fibers of
intermediate lengths (10-20 urn, which are short enough to be phagocytized but long
enough to suppress macrophage motility) and by the overall mass/volume of particles
deposited (and, proportionally, taken up by each macrophage). Note that the
dimensions provided are the ones that are apparently appropriate for humans. For rats,
the corresponding dimensions may be somewhat smaller,
Likely competing with scavenging by macrophages are (1) phagocytosis by the
epithelial cells lining the alveolus and (2) diffusive transport to the intersititium. Both
Type I and Type II epithelial cells appear to phagocytize fibers, Although relatively few
fibers are observed to be taken up by Type II cells, as previously discussed (Section
7.2,2), onepossible explanation for the limited observation of fibers in Type II cells is
that uptake of fibers induces terminal differentiation to Type I cells. It is expected that
phagyctosis by epithelial, cells is a size-dependent process,
Especially when the presence of fibers (or other particulate matter) induces
morphological changes in Type II cells that increase the overall permeability of the
epithelial lining (Section 7.3.7), fibers can apparently diffuse into the interstitium. This
process, potentially supplemented with expulsion of phagocytized fibers by epithelial
cells and/or transport of fiber laden macrophages through the epithelial lining,
represents the set of putative mechanisms by which fibers may reach the interstitium. It
is expected that these processes are somewhat slower than uptake by alveolar
macrophages (see Table 7-2). Therefore, if this latter mechanism is operating at peak
efficiency, relatively few fibers may reach the interstitium. Note that diffusive transport
is likely independent of fiber length, but may be dependent on fiber width with thinner
fibers more rapidly diffusing to the interstitium.
Fibers reaching the interstitium are likely cleared primarily by interstitial macrophages,
which phagocytize the fibers and transport them to the lymphatic system. Both the
efficiency of phagocytosis and motility of macrophage transport in the interstitium likely
depend on fiber size and the total volume/mass of fibers in the same way described
above for transport by alveolar macrophages. However, all such mechanisms are
substantially slower in the interstitium than in the alveolar lumen (see Table 7-2).
Macrophages that have been immobilized (due to fiber size or volume/mass) in the
interstitium tend to aggregate and induce formation of granulomas, which may
sequester the.fibers in these cells. Although there is less evidence for this, fibers free
in the interstitial matrix might also trigger such a process. Such fibers would typically be
too large to have been effectively phagocytized by any of the cells of the interstitium.
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Fibers may also reach the endothelium and be taken up by endothelial cells lining the
capillaries of the deep lung. Because fibers have also been observed in capillary
lumena, mechanisms similar to those described for transport through the alveolar
epithelium to the interstitium may be operating to transport fibers into capillary lumena.
While it is expected that such mechanisms will also show size dependence similar to
that previously described, little is known about the details or the rates of such
processes.
Also by mechanisms similar to some or all of the putative mechanisms described for
translocation of fibers from the alveolar lumen to the interstitium, fibers may reach the
pleura. Whether fibers can also reach the pleura via transport in blood or lymph has
not been definitively determined. Fibers reaching the pleura may be phagocytized by
mesothelial cells or may pass through such cells to the pleural cavity. Fibers reaching
the pleural cavity are apparently phagocytized by pleural macrophages (probably
showing a similar size or volume/mass effect as described above for similar
mechanisms) and are apparently transported to (and deposited at) sites of lymphatic
drainage along the pleura. If such fibers are then too large to pass through the
lymphatic ducts, they may trigger accumulation of additional macrophages and other
inflammatory cells.
Overall, the effects of size appear to be:
• few fibers thicker than approximately 0.7 urn and virtually none thicker
than approximately 1.5 urn appear to reach the deep lung. Of these, the
longer the fiber, the thinner the fiber. Importantly, the distribution of sizes
of structures deposited in the deep lung tend to be much more similar
across studies than the distributions in the aerosols originally inhaled.
Thus, deposition is a very effective filtering process; and
• short fibers (or compact particles) that are shorter than somewhere
between 10 and 20 urn tend to be taken up almost entirely by
macrophages and are either cleared via the muco-ciliary escalator,
isolated in immobilized macrophages that remain within alveolar lumena,
or transported to lymphatics (presumably after first reaching the
interstitum). These processes appear to be efficient for the shortest of the
fibers in this range (and all shorter fibers) so that no further effects are
manifest. In contrast, longer fibers, which are not efficiently cleared or
isolated by macrophages either in the alveolar lumen or the interstitium
appear to trigger a range of additional responses, some of which appear
to lead to disease.
Therefore, based on deposition, translocation, and clearance, it is fibers thinner than
approximately 0.7 urn and longer than a minimum of approximately 10 urn (with relative
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contributions increasing with increasing length up to at least 20 urn) that likely
contribute to disease. Modifications to this range of structure sizes due to effects
attributable to direct biological responses are addressed further in Section 7.3.
Regarding putative differences in behavior between chrysotile asbestos and amphibole
asbestos, such effects are adequately addressed by the unifying discussion provided
above. To make sense of such differences, the effects of fiber size must first be
explicitly considered. Thus, chrysotile fibers may not be as efficiently deposited in the
deep lung to the extent that they are curlier or occur in thicker bundles than amphibole
fibers. The overall load of chrysotile deposited in the deep lung may also be cleared
more rapidly than amphiboles to the extent that (1) short, thin fibrils ultimately represent
a greater fraction of the total load of chrysotile than the amphiboles and (2) a subset of
long chrysotile fibers, not sequestered in an environment with limited fluid flow, may be
cleared more rapidly than similarly long amphibole fibers due to contributions from
dissolution. .
Importantly, the concentrations of asbestos to which humans are exposed are much
lower than the concentrations to which animals were exposed in the various literature
studies cited. Moreover, as indicated in Section 7,1.2, for virtually any exposure of
interest, the resulting (volumetric or surface area) lung burden will be substantially
higher in rats than in humans. Therefore, overload conditions or other processes that
might impede or alter the clearance mechanisms described above, will never occur in
humans without first having affected the results of the animal studies reported. Thus,
conclusions concerning size-dependence and related effects (except to the extent that
they need to be adjusted for cross-species differences) should remain valid when
extrapolated between animals and humans.
7.3 FACTORS GOVERNING CELLULAR AND TISSUE RESPONSE
For inhaled structures that survive degradation and clearance, a series of complex
reactions between the structures retained in the lung and surrounding tissue may
induce a biological response. Asbestosis (fibrosis), pulmonary carcinomas, or
mesotheliomas may result. Mesotheliomas are likely associated with structures that are
translocated from the lung to the mesenchyme, although diffusable growth promoters
and other chemical signals produced by asbestos exposed cells in lung tissue
immediately proximal to the mesothelium may also play a role (see Adamson 1997, as
described in Section 7.3.4.1).
That the specific biochemical triggers for asbestos-related diseases have not been
definitively delineated as of yet is not surprising. Despite great progress in elucidating
candidate mechanisms, the number of candidate mechanisms is large and confounded
by "cross-talk" between mechanisms. Moreover, similar toxic end points may result
from entirely independent mechanisms that exhibit disparate dose-response
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characteristics but, nevertheless, may be triggered by the same or similar toxins. In
such cases, however, the relative contributions from each mechanism to a particular
end point may vary substantially. Unfortunately, the ability to compare results across
studies of different mechanisms is currently limited due to the inability to reconcile the
quantitative effects of dose and response across dissimilar studies.
Table 7-5 illustrates the range and complexity of the biological responses triggered by
asbestos in lung tissue. The table was developed based on the information gleaned
from the studies described in this Section. Importantly, not all of the mechanisms listed
contribute equally to the toxic end points that are attributable to asbestos, but their
relative importance has yet to be entirely delineated. The toxic end points of potential
interest to which each of the listed mechanisms potentially contribute are indicated in
bold italics.
As indicated previously (Section 7.0), although much has been learned about specific
components of the underlying mechanisms by which asbestos causes the above-listed
diseases, substantial knowledge gaps remain. Moreover, because of these gaps,
multiple candidate effects have been explored as potential contributors to
carcinogenicity (or fibrogenicity) and one of the goals of this document is to distinguish
among those effects that are likely to contribute to the induction of cancer from those
that are less likely or unlikely to contribute (given the current state of knowledge).
Accordingly, an overview of recent studies is presented below following a brief
description of the current model of the general mechanism for cancer. Note that, due to
the availability of several recent reviews (including, for example, Floyd 1990, Kamp et
al. 1992, Kane and McDonald 1993, Economou etal. 1994, Oberdorster 1994,
Mossman et al. 1996, Mossman and Churg 1998 and Robledo and Mossman 1999),
only the most recent primary articles are induded in the following review.
Also, as indicated below, many of the biological responses provoked by retained
asbestos are in fact dependent on fiber size and type. Therefore, studies that
distinguish among effects induced by different size fibers (or fibers and non-fibrous
particles) of the same mineralogy and studies that distinguish among effects induced by
comparably sized fibers (or non-fibrous particles) but differing mineralogy are
highlighted. However, due to the limits with which fibrous materials have tended to be
characterized in many of these studies, the database from which to distinguish among
the effects of size and mineralogy are limited and conclusions from differing studies
must be compared with caution.
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Table 7-5, Page 2
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A large body of evidence supports the conclusion that it is primarily (if not exclusively)
long fibers (those longer than a minimum of 5 to 10 urn) that contribute to disease (see
Sections...7.2.1, 7,2.2 and 7.4). Much evidence also indicates that the potency of long
fibers increases with length at least up to a length of approximately 20 urn. Therefore,
because short and long fibers are both respirable (for fibers that are sufficiently thin,
less than approximately 0.7 urn in diameter), differences in the ultimate response to C
short and long fibers must be attributable to differences in tissue and cellular responses
to the retained fibers in each size range.
At least at a histopathological level, clear differences have been observed in the
responses evoked by short and long structures (see Sections 7.2.1. and 7.2.2). It is a (
goal of this section to determine whether the biochemical triggers that mediate the
disparate responses to short and long fibers can also be identified. Unfortunately, while
a large body of knowledge has been amassed, definitive conclusions are not yet
possible, This is because the specific mechanisms by which asbestos acts have still
not been definitively determined, although many candidate mechanisms have been
elucidated (see above). However, important inferences can still be gleaned from the ^
available studies.
Evidence for the relationship between fiber diameter and disease is somewhat less
clear. Although there appears to be a fairly sharp cutoff in the diameter of fibers that
are respirable (see Section 7.1.4), several studies suggest that the most potent fibers (
are substantially thinner than the sharp cutoff in respirability (see Section 7.4). If these
latter observations are valid then, as with length, differences in the ultimate response to
thick and thin fibers must be attributable to differences in the tissue and cellular
responses elicited by retained fibers of each width. As indicated with length, however,
definitive conclusions regarding biochemical triggers and the effect of size on such
triggers are not yet possible because the specific triggers that lead to specific asbestos-
related diseases have not been definitively identified. Still, useful inferences can be
developed.
7.3.1 The Current Cancer Model
C
The following description of the current model for cancer is derived from the ideas
presented in Moolgavkar et al. 1988, Mossman 1993, and Economou et al, 1994.
In the current model of cancer, normal cells must accumulate specific, multiple
mutations before a tumorigenic .cell is created that can lead to the development of .
cancer. Each successive mutation produces an initiated cell (a cell that is transformed
from normal cells because it incorporates one or more of the requisite mutations, but
that has not yet acquired all of the changes needed to produce cancer). Each initiated
cell may then proliferate to generate a population of similarly initiated cells, which
increases the probability that other events will lead to further mutation in at least one of
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these cells. Individual mutations may occur spontaneously or may be induced by
exposure to mutagens.
Generally, the minimum, heritable changes required before a normal cell is transformed
into a metastatic tumor cell include, but may not be limited to (see, for example, Hei et
al. 1997 or Kravchenko et al. 1998):
• escape from terminal differentiation or programmed cell death (especially in
response to DMA damage);
• escape from anchorage/neighbor dependent growth inhibition;
• development of self-promoting growth and proliferation; and
• active expression of cytokines needed to promote angiogenesis and allow tissue
invasion.
It is not clear whetherthe mutations associated with these changes need to occur in a
particular order, although the first of the above-listed changes would facilitate
accumulation of all later changes. It is also unclear which of the above changes
contribute to the time dependence of tumor development and this may vary among
tumor types. It is likely that only a subset of the required mutations determine the
ultimate time development of the associated cancer. For example, once a cell begins
self-promoting growth, later mutations (even relatively rare ones) may become
incorporated rather quickly. Also, some mutations may be rare or may require
intervention by a toxic agent, while other mutations may occur spontaneously and may
thus occur frequently, once a sufficient number of initiated precursor cells are
generated. This is one of the reasons that models with as few as one or two stages
have proven successful at predicting the time course of many types of cancer (see, for
example, Moolgavkar et al. 1988).
To produce cancer, the mutations that occur must also be "heritable" meaning that they
must be preserved and passed on to daughter cells during mitosis. Thus, it is not only
necessary to cause alteration in the DMA of a cell (genetic damage), but the
mechanisms by which the cell subsequently repairs such changes or prevents cell
division (e.g. arrest of the cell cycle, programmed cell death, terminal differentiation) in
the presence of such changes must also be defeated. Generally, if a cell proceeds
through DMA synthesis (in preparation for mitosis) before accumulated alterations to
DMA are repaired, the sites of such alterations can lead to errors in replication during
synthesis, which in turn result in permanent, heritable mutations in one or both of the
daughter cells that are created from mitosis.
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Toxic agents that (directly or indirectly) cause DMA alterations may contribute to the
development of cancer by inducing one or more of the set of requisite mutations
required for cancer to develop. In traditional parlance, such agents are termed
"initiators". In addition, any toxic agent that enhances proliferation also facilitates the
development of cancer both by increasing the probability of creating spontaneous
mutations (due to errors that infrequently but unavoidably occur whenever DMA is
synthesized in support of mitosis) and by increasing the numbers of any initiated cells
that may be present, which may then serve as additional targets for initiators or may
incorporate additional spontaneous mutations. Agents that facilitate the development
of cancer by inducing proliferation are traditionally termed "promoters."
Multiple mechanisms have been identified by which both initiators and promoters may
act. Initiators, for example, may react directly with DMA to cause genetic damage, or
may induce generation of other reactive species (such as reactive oxygen species or
reactive nitrogen species) that, in turn, react with DNA to cause genetic damage, in
addition, fibrous materials may uniquely damage chromosomes by interfering with
mitosis causing aneuploidy (incorporation of an incorrect number of chromosome
copies in cells) and/or various clastogenic changes (alterations in the organization and
structure of specific chromosomes).
Promoters may also induce proliferation via a variety of mechanisms. Cytotoxic agents,
for example, may induce proliferation in a tissue by damaging or killing cells and
thereby induce stem cells to proliferate to replace the damaged cells. Promoters may
also induce release of various growth factors that, in turn, induce proliferation in
targeted tissues. This may occur, for example, as part of the inflammatory response to
tissue insult.
Promoters that are biopersistent (such as long asbestos fibers) or promoters that are
continually reintroduced (through chronic, external exposure) may also chronically up
regulate (or down regulate) certain cell signaling cascades that may contribute to
cancer development in a variety of ways including: (1) activation of genes that mediate
proliferation or production of various growth factors (including any of various
oncogenes) or (2) suppression of genes that inhibit proliferation or growth (including
any of various tumor-suppressor genes).
There is growing evidence that all varieties of asbestos fibers (and certain other fibrous
materials) can act both as cancer initiators and promoters. However, the biological
responses to these materials appear to vary in different tissues so that it may be
important to separately evaluate the behavior of asbestos in specific tissues. Biological
responses to varying fiber types also appear to vary, particularly in relation to a fiber's
biopersistence (Sections 7.2.1 and 7.2.4). Accordingly, an overview of the generic
evidence that specific types of asbestos may act as an initiator and, separately, as a
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promoter is reviewed below, followed by consideration of the more limited data
suggesting tissue-specific variation in biological responses.
7.3.2 Evidence for Transformation
Several recent studies provide evidence that specific types of asbestos can induce
transformation of cells in specific tissues (both lung epithelium and mesothelium) to
create tumor cells. This provides further, confirmatory support for the whole animal
studies in which cancer is induced by exposure to asbestos (see Sections 7.4).
• An immortalized but non-tumorigenic cell line of human bronchial epithelial cells
(BEP2D cells) was transformed by a single exposure to 4 ug/cm2 UICC
chrysotile B (Rhodesian). Surviving cells (the treatment caused 18% cell death)
went through several transformations including: altered growth kinetics,
resistence to serum induced terminal differentiation, and loss of anchorage
dependent growth, before becoming tumorigenic (Hei et a I. 1997).
Tumorigenicity developed in the various exposed cell lines over a period of
several to 11 weeks following exposure. When injected into nude mice,
secondary tumors developed with a latency of 8 to 10 weeks.
The authors indicate that there were no mutations in these cells at either codon
12 or 13 or the ras gene (mutations that have sometimes been observed in
asbestos-induced lung cancers. Also, because this cell line already contains
alterations in genes for p53 (a protein that plays a role in cell-cycle control,
among other things, see Table 7-6) and Rb (Table 7-6), the authors speculate
that such changes are not rate controlling for transformation to cancer.
It should also be noted that cultures of Type II cells have been particularly
difficult to maintain due to the tendency of these cells to undergo terminal
differentiation (to Type I cells) once they are removed from their natural
environment in the epithelial lining of lung alveoli (see Leikoff and Driscoll 1993,
as described in Section 4.4).
• Kravchenko et al. (1998) indicates that, unlike cultures of lung epithelial cells, in-
vitro cultures of rat mesothelial cells tend to transform spontaneously to
tumorigenic cells. The major changes that occur with time include: altered
response to epithelial growth factor (from growth-proliferation inhibition by this
factor to growth-proliferation stimulation), morphological changes (from polygonal
epithelial-like cells to elongated fibroblast-like cells and, eventually,
polynucleated cells exhibiting broad polymorphism), and multi-layered growth
and an ability to grow as colonies and masses in semi-solid agar. Eventually,
these cells become immortal and induce cancer when harvested and injected
into other rats. The authors indicate that asbestos-induced transformations in
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such cultures proceed through identical stages but that they occur much more
rapidly. For example, incorporation of the stimulating response to EGF occurs
spontaneously at passages 9 or 10 but at passages 6 or 7 when induced by
asbestos. Asbestos was applied at 5 ug/cm2, which was noted to be sub-lethal
(95% cell survival was noted at this rate of application).
» in a study in which p53 deficient mice were intrapieurally injected with UICC
crocidolite (200 ng/week), Marsella et al, (1997) showed that p53 deficient mice
exhibited substantially increased susceptibility to development of mesothelioma,
In this study, 12.5% of homozygous mice (p53 deficient) developed
mesothelioma and the rest died of lymphomas or hemangiosarcomas that
develop spontaneously in these mice; 76% of heterozygous mice died of
mesothelioma, and only 32% of wild-type mice (p53 competent) died of
mesothelioma. The authors suggest that p53 deficient mice are susceptible to
excess proliferation induced by crocidolite due to loss of control at the G1/S
check point that is normally mediated by p53.
In further confirmation of this hypothesis, Marsella et al, report in the same study
that 7,5 |jg/cm2 of crocidolite applied to wild murine mesothelial cells in culture
induced substantial apoptosis while p53-deficient cells were resistant to
apoptosis. The authors also note that most of the p53-deficient cells are
tetraploid (suggesting a loss of a spindle check point) while the wild type cells are c
all diploid.
Note that, although these studies suggest that asbestos alone can induce complete
transformation of both lung epithelial cells and mesothelial cells, such studies must be
evaluated with caution. In the case of the Hei et al. study, for example, the effects of
the (asbestos-independent) mutations required to initially establish the immortal line of
lung epithelial cells are not entirely known. Therefore, the response to asbestos of
epithelial cells in vivo may be substantially different.
In the case of the Kravchenko et al. study, it is clear that mesothelial cells in vitro do not
behave in the same manner as those in vivo; in vitro, they spontaneously transform to €
tumorigenic cells. This suggests that one or more growth inhibitory signals exist in vivo
(which are absent in vitro) that are critical to maintaining the health of the mesothelium,
7.3.3 Evidence that Asbestos Acts As a Cancer Initiator
As indicated in a review by Jaurand (1997), historically, the status of asbestos as a
mutagen was questioned, due primarily to the failure to produce detectable gene
mutations in short-term assays. However, more recent studies provide clear evidence
that asbestos (and other fibrous materials) can produce mutations. Moreover, fibrous
materials may induce mutations by multiple mechanisms including, for example:
• t
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• direct interference with mitosis;
• production and release of reactive oxygen species (ROS); or
• production and release of reactive nitrogen species (RNS).
Of these, the most consistent, positive evidence that asbestos can act as a cancer
initiator (that is, a genotoxin or mutagen) has been from assays designed to detect the
kinds of genetic damage that result from interference with mitosis (Jaurand 1997).
Although generation of ROS and RNS plays a clear role in mediating asbestos-induced
disease, the direct link to a role of asbestos as an initiator is somewhat more tenuous
(see Sections 7.3.3.2 and 7.3.3.3).
7.3.3.1 Interference with mitosis. Apparently, the physical presence of asbestos
fibers can interfere with proper spindle formation and the function of other structural
units required for mitosis (Jaurand 1997). This typically leads to aneuploidy (an
incorrect number of copies of the chromosomes contained within a cell), development
of micronuclei (fragments of chromosomes enclosed in a membrane that are isolated
from the main nucleus of the cell), and has also been shown to lead to clastogenic
effects (changes in the organization and structure of the chromosomes). Assays for
these kinds of genetic alterations have consistently shown asbestos capable of indudng
these effects.
Jaurand (1997) also indicates that:
• fibers must be phagocytized by the target cells before they can interfere
with mitosis;
• once phagocytized, all asbestos types are observed to interfere with
mitotic activity; and
• samples enriched in long, thin fibers enhance these effects. In contrast,
short fibers do not appear to contribute to these effects.
Jaurand notes, however, that results related to fiber size have not been entirely
consistent, primarily because not all studies have rigorously controlled for or even
adequately characterized the sizes of the fibrous structures in test materials. Jaurand
also notes that this mechanism is not dependent on the formation of reactive oxygen
species or any other reactive free radicals.
Among studies that suggest that surface chemistry (or presumably fiber type) may not
be an important factor in determining the degree to which asbestos (or other fibrous
materials) interfere with mitosis, Keane et al. (1999), exposed cultured V79 cells
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(hamster lung fibroblasts) to untreated and HCI-treated chrysotile asbestos. The acid
treatment substantially reduces the magnesium content on the surface of the chrysotile.
The authors also noted a "small" effect on fiber size due to treatment (treated fibers
showed a 20% excess of short fibers). Cells were exposed to doses ranging between
0.4 and 12.7 ug/em2 (each dose left in place for 24 hrs and then rinsed off). Ceils were
harvested after an additional 24 hrs and evaluated for cytotoxlcity and the presence of
micronuclei,
Results from the Keane et al. study indicate that untreated fibers were shown to be •
slightly more cytotoxic than treated fibers but both treated and untreated fibers were
observed to increase the abundance of micronuclei in a similar, dose-dependent
fashion. The induction of micronuclei appeared to saturate at approximately 35/1000
cells observed at an applied asbestos concentration of 40 ug/cm2. In contrast,
substantial cytotoxicity was only observed at the highest doses employed, The authors
thus concluded that surface chemistry (at least in terms of magnesium content) does
not appear to have a major affect on induction of micronuclei and that fie observed
genetic damage and cytotoxicity appear to occur through entirely independent
mechanisms.
In other studies, cultured cells were assayed for a variety of genotoxic effects following
exposure to a range of fibrous materials.
• For example, Dopp and Shiffmann (1998) dosed human amniotic fluid (HAF)
cells or Syrian Hamster Embryo (SHE) cells with UICC amosite, Rhodesian
chrysotife, crocidolite, or ceramic fibers at concentrations of 0.5, 1.0, 5.0, and
10,0 ug/em2 and assayed the cultures for formation of micronuclei and a variety
of clastogenic effects.
Based on their study, Dopp and Shiffmann report that all asbestos types induced
formation of micronuclei in SHE cells in a dose-dependent fashion at rates that
were significantly elevated over control animals. The effect appeared to
saturate at doses between 1 and 5 ug/cm2 and rates appeared to peak at
between 48 and 66 hrs post exposure. Ceramic fibers, which were noted to be
longer but thicker than the asbestos fibers tested, also showed,significantly
increased induction of micronudei, but at rates less pronounced than for
asbestos, However, it is difficult to judge whether this is due to differences in
fiber type because the data in the paper are not adequate to distinguish effects
of fiber size and number from effects of fiber type. Similar results were obtained
with HAF cells, but overall rates were about one third those observed in SHE
cells.
The authors also note observing various disturbance of chromatin structure
during interphase. They report observing formation of chromatin bridges and
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chromosome displacements in meta and anaphases and impaired chromatin
separation in mitosis. They also report that cytokinesis was frequently blocked.
In addition to the effects that they observed that are attributable to fiber
interference with mitosis, Dopp and Shiffmann report observing a variety of
clastogenic effects. In all cases where authors labeled specific regions of
specific chromosomes, fiber-exposed cells showed significantly greater
frequencies of DMA breaks over controls or gypsum-exposed animals. Different
regions of various chromosomes also showed significantly different frequencies
of breakage with patterns that were specific to the different fiber types. The
authors hypothesize that the observed clastogenic effects may be due to
production of reactive oxygen species, to formation of some type of clastogenic
factor, or to the direct interactions between fibers and chromosomes during
mitosis that are the apparent cause of the disturbances discussed in the
previous paragraphs. However, It was not possible to distinguish among
hypothetical causes of the observed clastogenic effects in this study.
Kodama et al. (1993) exposed cultures of human bronchiolar epithelial (HBE)
cells to asbestos (chrysotile at 0 to 4 ug/cm2 and crocidolite at 0 to 300 ug/cm2).
They then examined cells at 24, 48, 72, and 96 hrs following exposure for
cytotoxic effects and cytogerretic effects. Results indicate that both fiber.types
induced concentration-dependent inhibition of cell proliferation and colony-
forming ability but chrysotile was 100 to 300 times more toxic. The authors
report this translates to a 40 fold increase in toxicity on a fiber for fiber basis
(although the range of sizes included in this count is not indicated).
Kodama et al. also report that, at 72 hrs, chrysotile (4 ug/cm2) caused a 2.7 fold
increase in binuclei and a 1.6 fold increase in micronuclei. Over the same time
interval, at 300 ug/cm2, crocidolite caused a 1.9 fold increase in binuclei but did
not cause micronuclei, They also report that chrysotile failed to produce
significant numerical chromosome changes in HBE cells and increased structural
aberrations only at the 24 hr time point. The frequency of neither aneuploidy or
polyploidy was increased at any time point following exposure to asbestos in this
study. The authors indicate that this contrasts with observations of relatively
high incidences of asbestos-induced chromosome changes observed in some
rodent cell cultures and clastogenic effects observed in human mesothelial cell
lines. They further speculate that phagocytic cells with high mitogenetic activity
are likely most susceptible to the effects of asbestos, which primarily interferes
with mitosis. However, they suggest that epithelial cells that are exposed to
fibers may undergo terminal differentiation (from Type II to Type I cells) and thus
cease mitosis. This would effectively prevent adverse genetic effects from
asbestos exposure. Such pathways are not available to mesothelial cells.
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Hart et al. (1994) studied the effects of a range of fiber types (with varying size
distributions) on Chinese hamster ovary (CHO) cells. The authors indicate that
such cells are very different from cells in pulmonary tissues in that they are
immortalized, aneuploid, undifferentiated, and preneoplasic. They also note that
the responses observed in these cells differs from responses observed in cells
from pulmonary tissues. Nevertheless, the implications concerning fiber size are
instructive. Fibers evaluated included long, medium, short, and UICC crocidolite
and chrysotile along with a range of manmade vitreous fibers (MMVF's),
refractory ceramic fibers (RCF's), and fibrous glasses, Exposure concentrations
ranged between 10 and 225 ug/cm2.
Results from the Hart et al. study indicate that all of the fibers caused
qualitatively similar toxic effects: concentration-dependent reduction in cell
numbers and an increase in the incidence of abnormal nuclei with little or no loss
in viability. Fiber-induced cell death in CHO cells appears to be minor, even at
relatively high exposure concentrations, Based on mean dimensions (which is
problematic), the diameter dependence on the observed toxic effects, particularly
on the formation of aberrant nuclei, was slight or absent. However, the effect
with length was striking. For lengths up to at least 20 urn, potency toward both
cytotoxicity and the induction of aberrant nuclei increased dramatically with
increasing length. The authors also note that the lack of an observed fiber
composition associated effect on the toxicity of CHO cells does not correlate with
findings from recent rodent inhalation studies using the same test fibers. The
authors therefore speculate that CHO cells may not represent an appropriate in-
vitro model for fiber effects. However, it may also be that effects in vitro occur
over time scales that are rapid relative to those that occur in vivo so that
biodurability is not important in vitro.
In a study by Takeuchi et al. (1999) cultured human mesothelioma cells
(MSTO211H) and, separately, cultured human promyelocytic leukemia cells
(HL60), which are notphagocytic, were dosed with crocidolite between 0.6 and
6.6 ug/cm2 (no size range indicated). Studies with latex beads confirmed that
the mesothelioma cells are actively phagocytic but that the leukemia cells are
not, The authors indicate that dosed mesothelioma cells showed significantly
increased numbers of polynucleated cells, tetraploid cells, and cells with variable
DMA content at the GO/G1 transition in the cell cycle and that the extent of
effects was dose-dependent. Leukemia cells showed no such effects,
The authors further indicate that, when the mesothelioma cells were sorted by
fiber content, those with the highest fiber content showed the greatest effects.
The authors also indicate that cells are stimulated neitherto release superoxide
nor NO at the concentrations of crocidolite studied. However, they hypothesize
that intracellular ROS may have been generated because they report finding
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increased levels of 8-OH-dGua (and oxidized form of one of the DMA bases)
following crocidolite exposure. Nevertheless, the authors conclude that the
mechanisms by which crocidolite induce cytotoxicity and, potentially,
carcinogenicity is related to phagocytosis.- Importantly, the effects described in
the Takeuchi et al. study are entirely consistent with effects attributable to
interference with mitosis, despite any speculation by the authors.
7,3.3.2 Generation of reactive oxygen species (ROS). ROS have been implicated
as mediators in a variety of toxic effects (including cancer initiation) associated with a
broad range of toxins (see, for example, Floyd 1990). Moreover, substantial evidence
indicates that asbestos can induce generation of ROS by several mechanisms and that
asbestos-induced ROS plays a role in several of the toxic effects attributable to
asbestos (see below). However, whether ROS plays an important role in asbestos-
associated cancer initiation is less clear and needs to be evaluated carefully.
Therefore, evidence that asbestos induces the production of ROS and that ROS
contributes to the adverse health affects attributable to asbestos are reviewed below
with particular attention to effects that may contribute to the initiation of cancer.
Contributions by ROS to other asbestos-related toxic effects are also evaluated in later
sections of this chapter.
Note, that generation of certain reactive nitrogen species (specifically the peroxinitrite
ion) is closely associated with ROS generation so that evidence for the generation of
certain reactive nitrogen species (Section 7.3.3.3) constitutes additional evidence for
the generation of ROS.
Asbestos-Induced Generation of ROS
Asbestos has been shown capable of generating a variety of reactive oxygen species
(ROS) including hydrogen peroxide (H2O2), superoxide (O2"), and hydroxyl radical (OH )
via several mechanisms (see, for example, Kamp et al. 1992, Jaurand 1997, Fubini
1997) including:
• catalytic production of superoxide from oxygen in aqueous solution;
• catalytic production hydrogen peroxide from oxygen in aqueous solution;
• catalytic production of hydroxyl radical by the Fenton reaction
(degradation of hydrogen peroxide catalyzed by iron on the surface of
fibers or mobilized from the surface of fibers);
• catalytic production of several ROS by redox cycling of iron on the surface
of fibers or mobilized from the surface of fibers (Haber-Weiss type
reactions);
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• catalytic production of ROS by release of heme and heme protein from
various cellular components;
• by inducing release of various ROS species from phagocytes during
"frustrated" phagocytosis; and
• by binding to cell receptors, integrins for such receptors, or other features
of surface membranes that trigger signaling cascades mediating the
production and release of various ROS (and RNS).
For a general review of the chemistry involved in these processes, see Floyd (1990).
Fenton, Haber-Weiss, and related reactions. Most of the evidence for Fenton and
Haber-Weiss reactions (and related free radical generating reactions) that take place on
the surface of asbestos fibers comes from experiments in cell-free systems (see below)
Therefore, their relevance to the conditions found in vivo may be limited. Moreover, the
size of the fibers (especially in terms of their cumulative surface area) and the history of
their surfaces (in terms of metal contaminants or coatings that might be present) may
substantially alter the effects of such experiments (Fubini 1997). Unfortunately,
however, few of the available studies report characterization of fiber sizes or surface
conditions in sufficient detail to judge the importance of these effects.
For fibers:
• Zalma et al. (1987) evaluated a range of fibers (LJICCcrocidolite, UICC amosite,
UICC Canadian and Zimbobwean chrysotile, industrial chrysotile, and magnetite)
for their ability to produce free radicals by the direct reduction of oxygen in
aqueous solution. In some cases, hydrogen peroxide was also added to the
solution. Results indicate that all of the fibers tested were able to generate
hydroxyl radicals (even in the absence of hydrogen peroxide) but that the
efficiency of production was a strong function of the activation (by grinding) or
pacification (by coating with benzene or other agents) of the fiber surface.
Chrysotile was reported to be the most efficient at generating radicals and the
authors assumed that this is due to iron contamination on the surface (since iron
is not a component of the "ideal" chrysotile fiber). However, such conclusions
are difficult to evaluate in the absence of simultaneous consideration of fiber
size.
• Governa et al. (1998) evaluated the ability of wollastonite fibers to generate ROS
in both a cell free system and a suspension of polymorphonucleocytes (PMN's).
The fibers were observed to produce ROS in both systems and that ground
wollastonite produced substantially more ROS than unground. The efficiency of
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ROS generation in PMN suspension is also reported to be greater for
wollastonite than for either chrysotile or crocidolite (tested in previous studies).
However, no size information is given. Based on additional work with various
inhibitors added to the system, the authors also indicate that only a fraction of
the ROS generated was composed of hydroxyl radicals.
Brown et al. 1998 subjected several different fiber types (amosite, silicon carbide
whiskers, RCF-1, and various fibrous glasses) to two standard chemical assays
for free-radical production (in cell-free systems). The authors indicate that, of the
fiber types tested, only amosite showed free radical activity significantly above
controls in both assays and only RCF-1 additionally showed significantly
elevated free radical activity in one assay, However, there is not enough
information provided in this study to determine whether the observed differences
are due to differences in fiber sizes, sample preparation (i.e. surface condition),
or fiber type. In apparent contrast, for example, Gold et al. (1997) report that
amosite and crocidolite produce few free radicals in cell free systems, unless
they are ground.
Weitzman and Graceffa (1984) indicate that chrysotile, crocidolite, and amosite
are all capable of catalyzing the generation of hydroxyl radicals and superoxide
from hydrogen peroxide in vitro and that, based on experiments with various iron
chelators, these reactions are iron dependent. The authors further indicate that
hydrogen peroxide is produced in large quantities as a normal bi-product of
tissue metabolism, but that it is effectively scavenged by various enzymes. The
authors speculate that, by physically damaging cell membranes, asbestos may
allow release of the precursor hydrogen peroxide before it can be scavenged.
For particles:
Silica, residual fly ash, and ambient air dusts also can create ROS in vitro and
the efficiency of production correlates with ionizable concentrations of various
transition metals complexed on the dust (Martin et al. 1997). In vivo, such metal
containing particles also cause release of ROS from macrophages (Martin et al.
1997). Additionally, binding of silica to plasma membranes of airway lung cells
and phagosomes provokes generation of ROS.
Castronova et al. (1997) further indicate that it is the concentration of
contaminating iron on the surface of freshly fractured quartz that enhances free
radical production in aqueous solution (in cell free systems). These authors also
showed that high iron-containing (430 ppm) quartz dust inhaled by rats (at 20
mg/m3 for 5 hrs/day for 10 days) induced five times more leukocyte recruitment,
two times more lavageable red blood cells, 30 to 90% increase in macrophage
production of ROS, 71% increase in nitric oxide production by macrophages, and
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38% increase in lipid peroxidation of lung tissue than observed in rats exposed to
low iron-containing (56 ppm) quartz.
Although iron is required for the reactions considered here, studies indicate that the iron
content of the fiber itself is not a good indicator of reactivity (Gold et al. 1997), Studies
also indicate that the iron that participates in these reactions need not originate with the <"
fiber (Jaurand 1997, Fubini 1997) and biological systems contain abundant sources of
iron. Therefore, both iron-containing fibers and iron-free fibers have been shown to
participate in these reactions in vivo.
Release ofheme and heme protein. At least one research group (Rahman et al. (
1997) indicates that heme and herne protein cause extensive DMA damage in the
presence of asbestos in vitro and, based on previous studies, that this may involve
heme catalyzed production of ROS following asbestos-induced release of heme from
cytochrome P-450, from prostaglandin H synthetase (or perhaps from other heme
containing proteins). Importantly, the authors indicate that such observations relate to a
nuclear pool of heme, which suggests that ROS generation via this mechanism may . . *
occur in the immediate vicinity of DNA, The work by this group suggests at least one
additional pathway by which asbestos may induce the production of ROS and by which
ROS-mediated damage to DNA might occur.
Frustrated phagocytosis. Numerous studies indicate that long asbestos fibers (longer (
than somewhere between 10 and 20 urn) cannot be efficiently phagoycitized by
macrophages (see, for example, Sections 4,4 and 7.2) and that macrophages that are
damaged by such "frustrated" phagocytosis release ROS (see, for example, Mossman .
and Marsh 1991 or Kamp et al. 1992), Due to the differences in the size of
macrophages across species (see discussion of Krombach et al. 1997 in Section 4.4)
The minimum length beyond which phagocytosis may become frustrated may differ in
different animals. However, it is clearly longer fibers that contribute to this mechanism
for generating ROS. Shorter fibers (< 10 urn) are unlikely to cause frustrated
phagocytosis in any of the mammalian species of potential interest.
• Lim et al, (1997) showed that alveolar macorphages in culture (after stimulation C
with lippopolysaccharide) generated free radicals (ROS) when subsequently
exposed to chrysotile, crocidolite, or amosite (all UICC), They found chrysotile to
be the most potent inducer of free radical activity (which is not surprising given
that UICC chrysotile contains the highest fraction of long fibers of the UICC
samples tested (Berman unpublished). Based on tests with various inhibitors, (-
the authors indicated that the free radicals generated by the alveolar
macrophages occurred through a pathway mediated by protein tyrosine kinase,
phospholipase C, and protein kinase C and that the effects are dose-related.
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• Kostyuk et al. (1998) cultured peritoneal macrophages and showed that
treatment with chrysotile asbestos (1 ug, no size data given) resulted in
production of frustrated phagocytosis and cell injury (the latter as evidenced by
release of lipid dehydrogenase - LDH, a marker for membrane damage). By
working with various chelators and flavonoids (natural plant products, some of
which quench superoxide and some of which chelate iron), the authors indicate
that cell injury was likely induced by superoxide and that the superoxide was
likely produced by an iron-dependent mechanism. Note that this contrasts with
the above studies that suggests production of radicals by frustrated phagocytosis
in culture is an iron-independent mechanism.
• At least for one kind of phagocyte: polymorphonucleocytes (PMN's), a study by
Ishizaki et al. (1997) suggests that crocidolite and erionite may induce production
of ROS from PMN's by each of two mechanisms. The first requires phagocytosis
and may represent the traditional, "frustrated" phagocytosis pathway indicated
above. The second pathway is triggered by an interaction between the fiber and
the cell surface and is mediated by NADPH. The authors also cite evidence that
chrysotile may similarly act through both of these pathways.
• Afaq et al. (1998) cultured alveolar macrophages and peripheral red blood cells
(RBC's) that were harvested from rats 30-days following a single 5 mg
intratracheal instillation of UICC crocidolite, UICC chrysotile, or ultrafine titanium
dioxide. The authors indicate that populations of alveolar macrophages were
significantly increased (over sham-exposed rats) for all three particle types and
that acid phosphatase and lipid dehydrogenase (LDH), which are markers of cell
membrane damage, were observed in cell-free lung lavage from animals
exposed to all three particle types. Both alveolar macrophages taken from
asbestos-exposed animals (both types) showed significantly elevated lipid
peroxidation and hydrogen peroxide production over titanium dioxide exposed
animals. However, the latter also showed elevated peroxidation and peroxide
production that were significantly elevated over sham-exposed animals. Similar
results were observed among RBC's from asbestos-exposed animals but not
from titanium dioxide-exposed animals. Note, it is possible that ROS production
induced by TiO2 occurs by a different mechanism (or set of mechanisms) than
that for asbestos (see, for example, Palekar et al. 1979, Section 7.3.4.4)
In-vivo evidence for asbestos-generated ROS. Several studies involving whole
animals also indicate that asbestos exposure induces the generation of ROS.
Importantly, in such studies, evidence for generation of ROS is generally determined
based on observation of the putative effects of ROS, rather than ROS directly.
• Ohio et al. (1998) intratracheally instilled 500 |jg of crocidolite (NIEHS) into rats.
This was observed to induce a neutrophilic inflammatory response within 24
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hours (in contrast to saline-exposed rats). The authors collected chloroform
extracts from exposed lungs and subjected them to electron spin residence
(ESR) spectrometry. Results indicate the presence of a carbon-centered radical
adduct that has a structure consistent with products of lipid-peroxidation. The
radical signal was only observed in asbestos-exposed animals and persisted
even after one-month following exposure. The authors also report that depletion
of neutrophils did not affect the signal and that dextrin-induced inflammation did
not produce the signal.
• ' Yamaguchi et al. (1999) studied effects in rat lung tissue at 1, 3, 5, 7, and 9 days
following a single intratracheal instillation of 2mg of either glass fiber or UICC
crocidolite. The authors indicate significantly increased levels of 8-OH-Guanine
(an oxidized form of the DMA base Guanine) one day after crocidolite instillation
and increasing repair activity for this oxidized form of guanine with time that
became significant at days 7 and 9 following instillation, Glass fibers (noted to
be non-fibrogenic and non-tumorigenic) did not produce either increases in 8- .
OH-Gua or its repair activity, The effects associated with crocidolite were all
noted to be dose related.
Several of these studies also suggest distinctions in ROS generation (either the
absolute generation of ROS or generation of specific ROS species by specific tissues)
due to differences in fiber (or particle) size or type :
• Nehls et al. (1997) intratracheally instillled rats either with quartz (2.5 mg) or
- corundum (2.5 mg). The latter mineralis reportedly non-tumorigenic. Results
indicate that lung epithelial cells in quartz exposed rats exhibited increased 8-
oxo-Guanine levels (a DNA adduct generated by reaction with ROS, see above)
that persisted for up to 90 days post exposure. Elevated levels of the DNA
adduct appeared in all cell types in all areas of the lung. The authors suggest
that the observed persistence of the elevated levels of 8-oxo-Guanine suggests
that it was produced at a rate in excess of the lung's capacity for repair. The
authors also report enhanced and persistent inflammation, cell proliferation, and
an increase in neutrophil population in bronchio-alveolar lavage (BAL) fluid and
an increase in in tumor necrosis factor alpha (TNF-a) in BAL fluid in the quartz
exposed animals. TNF-a is a cytokine linked to a variety of effects including the
recruitment of inflammatory cells (see Table 7-6) In contrast, exposure to
corundum produced none of the effects observed with quartz.
• Timblin et al. (1998b).report that ROS induced responses by rat lung epithelial
cells vary depending on whether exposure is to crocidolite, hydrogen peroxide, or
cadmium chloride (CdCI2). In response to ROS generation induced by the first
two agents increase levels of cJun protein (a protooncogene, Table 7-6) and
crocidolite, but not hydrogen peroxide, causes elevation in the levels of
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manganese-containing superoxide (MnSOD) dismutase (an enzyme that
catalyzes dismutation of superoxide, Table 7-6). Neither of these agents affect
levels of either of two common stress proteins (Table 7-6): GRP78 or HSP72/73,
nor do they affect cellular glutathione levels. In contrast, cadmium chloride does
not alter MnSOD levels but increases levels of GRP78 and HSP72/73 in addition
to cJun protein. Therefore, ROS-related mechanisms may be complex and may
be toxicant-specific. Thus, it may not be correct to assume that all fibers and
particles act through common ROS-related pathways.
In contrast to the results of the above study (which showed no affect on
glutathione levels), Golladay et al. (1997) showed that human lung epithelial cells
(cultured A549 cells) exposed to crocidolite (NIEHS sample) showed substantial
reduction in intracellular levels of glutathione (without increases in the oxidized
forms of glutathione). Rather, an associated increase in extracellular, reduced
glutathione was observed, suggesting that crocidolite induces release of
glutathione from the interior of these cells to the environment. The authors also
indicate that, given that the half-life for reduced glutathione outside of cells is on
the order of an hour, while extracellular reduced glutathione levels remained
elevated for more than 24 hrs following exposure, cells must have be releasing
reduced glutathione continuously. Because no concomitant release of lipid
dehygrogenase (LDH) or labeled adenine was observed (despite loading of cells
with labeled adenine prior to the experiment), the authors conclude that release
of glutathione is not associated with membrane disruption or apoptosis (which is
induced to some degree by exposure to crocidolite). Also, all of the effects
described above were similarly associated with exposure to de-ironized
crocidolite. Thus, the iron content of the fibers does not play a role in this
process.
Kaiglova et al. (1999)intrapleurally injected rats with 10 mg of long amosite and
collected bronchio-alveolar lavage (BAL)fluid 24 hours following exposure and at
later time intervals. They indicate that total protein and alkaline phophatase (AP)
were both elevated in BAL 24 hrs after exposure and that AP remained elevated
for at least 3 mo following exposure. They also noted increased levels of lipid
peroxides in BAL at 24 hrs but not 3 mo following exposure. The authors
indicate that antioxidants were significantly decreased following exposure:
glutathione was significantly decreased in lung tissue at both 24 hrs and 3 mo
following exposure but was normal in BAL fluid at all time points; a-tokopherol
and retinol were significantly decreased at 3 mo in lung tissue; and ascorbic acid
was significantly decreased in both lung tissue and BAL at 24 hrs and remained
low at 3 mo. The authors indicate that decreases in antioxidants implies a role
for ROS (or RNS) in lung tissue injury. It is also possible that the varied
responses of specific antioxidants may suggest a role for toxin- and injury-
specific ROS/RNS.
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Note that the apparent difference in the reported effect of crocidolite exposure on
intracellular glutathione levels in the above two studies might be due (individually or in
combination) to differences in the cell-types studied, differences in the size distribution
of the crocidolite employed, differences in study design, or other factors. Insufficient
information is available to distinguish among these possibilities.
Conclusions concerning generation of ROS, Except for frustrated phagocytosis
(which is unique to long fibers), ROS generation by the mechanisms discussed above
are generally considered a common response to respiration of particles in general (see,
for example, Martin et al. 1997 who suggest that ROS "...may be a global signaling
mechanism mediating response to particulate insult mostly by activation of kinases and
transcription factors common to many response genes." They further indicate that if the
load of ROS generated is to great, or the airway in which it is generated has been
previously impaired, "...these same mechanisms can result in deleterious respiratory
lesions and outright pathology"). However, not all non-fibrous particles are similarly
capable of inducing production of ROS. As indicated above, for example, while
crystalline silica is a potent inducer of ROS production, carundum is not (Nehls et al.
1997). Moreover, the spectrum of ROS species that are induced by particular toxicants
are generally specific to the offending toxicant (Timblin et al. 1998b). Therefore, the
generic grouping of ROS mediated pathways by particles and, especially, by particles
and fibers, does not appear justified. These mechanisms are more complex and
individualized than such generic grouping suggests.
ROS can be generated by multiple pathways that are variously dependent on particle
size, whether a particle is a fiber, fiber size, and particle or fiber type (i.e. chemical
composition). The primary mechanism(s) through which is ROS generated in response
to one type of particle or fiber may be very different than that through which ROS
generation is induced by another and the resulting suite of ROS may also differ.
Importantly, the relationship between dose and response for each mechanism may also
differ (see, for example, Palekaret al. 1979, Section 7.3.4.4).
Given the above, comparing among the ability of fibrous materials and non-fibrous
analogs to induce generation of ROS requires that such analogs be properly matched
before valid conclusions can be drawn. Thus, for example, the appropriate non-fibrous
analog for crocidolite is the massive habit of reibeckite and the appropriate analog for
chrysotile is the massive habit of antigorite or lizardite. Due to differences in both
chemistry and crystal structure, crystalline silica is not an appropriate non-fibrous
analog for any of the asbestos types. Moreover, because ROS can be generated by
different mechanisms, critical comparison across analogs requires more than the
simple confirmation that ROS is generated or even whether the relative efficiency with
which ROS is generated is comparable. It is also necessary to contrast the specific
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complexion of ROS generated and the specific tissue/cellular environments (i.e.
locations within a cell) in which they are generated in response to each analog.
It is also clear that ROS is generated by both iron-dependent and iron-independent
pathways. Even for the iron-dependent pathways, however, the source of iron need not
derive from the fibers or particles themselves. Therefore, since iron is abundant in vivo
(and the environment), both iron-containing and iron-free fibers (or particles) can
potentially participate in both the iron-independent and the iron-dependent pathways.
Effects Mediated by ROS
ROS have been implicated as mediators in a variety of toxic effects (including cancer
initiation) associated with a broad range of toxins (see, for example, Floyd 1990, Martin
et al. 1997). Cellular and tissue effects that have been associated with the effects of
ROS include:
enhancement of overall uptake of particles by epithelium;
stimulation of inflammatory responses;
stimulation of various signaling cascades and production of cytokines;
inducement of apoptosis;
cytotoxicity;
mediation of cell proliferation;
formation of oxidized macromolecule (including DMA) adducts; and
induction of DMA strand breaks.
However, only the last two of the above list of ROS effects are potentially relevant to the
initiation of cancer (the topic of this section). The other effects in the above list likely
contribute to the induction of other asbestos-related diseases and may even promote
(but not initiate) asbestos-related cancer. Thus, they are addressed further in later
sections of this chapter (see below).
Although, ROS generation is associated with exposure to various particles and fibers
(including all forms of asbestos), generation of ROS does not necessarily imply
carcinogenesis. For example, Zhu et al. (1998) indicate that ROS generation is induced
in response to exposure to asbestos, crystalline silica, and coal mine dust. However,
based on an extensive record of human exposure, the latter (coal mine dust) is not
carcinogenic in humans. Therefore, evidence related to the last two of ROS-associated
effects listed above, are examined in more detail below.
Several studies indicate that, once generated by exposure to asbestos, ROS can
interact with DMA in vitro and in vivo to produce oxygenated adducts, primarily 8-oxo-
Guanine. Of the studies reviewed above, for example, Yamaguchi' et al. (1999)
observed that crocidolite but not (non-tumorigenic) glass produced dose-dependent
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increases in 8-oxo-Guanine in rat lung tissue following intratraeheal instillation. Also,
Leanderson et al. (1988), Park and Aust (1998), Keane et al, (1999) indicate that
asbestos induces formation of oxidized DNA adducts in in-vitro assays. Brown et al.
(1998) indicate that asbestos induces ROS-mediated DNA strand breaks.
However, not all DNA strand breaks attributable to asbestos occur through pathways
involving ROS, OHikainen et al. (1999) exposed cultures of human mesothelial cells
(MeT-5A, transfected with SV-40 but nontumorigenic) to hyrdrogen peroxide (100 uM)
or crocidolite (2-4 u.g/cm2), either alone or in combination with TNF-oc and performed
assays for DNA strand breaks. The authors note that the concentrations of asbestos
evaluated are well below those that have been associated with cytotoxic effects.
Crocidolite alone was shown to produce DNA strand breaks at the concentrations
tested. The authors also note that, at lower concentrations, only reversible effects were
observed (presumably indicating DNA repair). Co-exposure to crocidolite and TNF-oc
increased the observed incidence of DNA damage, but the effect was less than
additive. The authors indicate that additional studies with antioxidants indicate that the
DNA damage Induced by crocidolite in this study occurs through a mechanism that
does not involve ROS. In fact, a potentially much more substantial mechanism by
which asbestos may induce DNA breaks and various clastogenic alterations involves its
ability to interfere with mitosis (Section 7.3.3.1).
There is also evidence that different tissues may respond to asbestos-induced ROS
generation differently. For example, Zhti et al. (1998) indicate that MnSOD is found in
the mitochondria of Type II epithelial cells of rats exposed to crocidolite. The authors
further indicate that, because fibroblasts, alveolar m'acrphages, or endotheiial ceils do
not display this protein when stimulated by exposure, this suggests a difference in the
susceptibility of epithelial cells to certain types of asbestos-induced injury.
Importantly, although these studies provide evidence that indicates asbestos is capable
of producing oxidized DNA adducts (or strand breaks) through ROS mediated
processes, they do not address the question of whether such adducts can lead to
heritable mutations in DNA in vivo nor do they indicate whether such adducts lead to
tumor production. Therefore, such studies should only be construed to suggest a
potential that asbestos can act as a cancer initiator through ROS-mediated pathways.
As previously indicated, not all mechanisms involving the generation or effects of ROS
are similarly fiber-size fiber-type dependent and those that are do not necessarily
depend on these variables in the same way. At this time, it is not possible to distinguish
among the relative importance of the different mechanisms so that it is difficult to judge
the importance of the relative effects. However, the overall general implication (with the
few exceptions noted) is that ROS more likely contributes to other asbestos-related
diseases and cancer promotion than cancer initiation.
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One final note concerning the effects of ROS that specifically involves the behavior of
the hydroxyl radical is also warranted. The hydroxyl radical, is an extremely reactive
species. Whether in vitro or in vivo, this species will react with virtually 100% efficiency
with every organic molecule it encounters, Therefore the effects attributable to the
hydroxyl radical are limited to those involving reactions in the immediate vicinity of the
location at which it is generated. Thus, unless asbestos-induced generation of this
radical occurs within the nucleus and in the immediate vicinity of susceptible strands of
DMA, it is unlikely that these radicals are the direct cause of DMA damage.
Rather, hydroxyl radicals tend to react with cell membranes and other cellular
components to produce further intermediate radicals (primarily lipid peroxides), which
are much more stable than the hydroxyl radical and may migrate substantial distances
before having an effect. It is likely that these intermediate radicals are ultimately
responsible for any ROS-mediated DMA damage that may be attributed to asbestos.
7.3.3.3 Generation of reactive nitorgen species (RNS). Nitric oxide (NO) is
produced ubiquitously in biological systems and serves many functions (REF). It is
highly reactive and, therefore, generally a short-lived in.vivo. However, nitric oxide has
been shown to react with superoxide (O2") to form the peroxynitrite ion (ONOO") at near
diffusion-limited rates (see, for example, Zhu et al. 1998). This RNS may represent the
primary species responsible for the effects attributed to RNS.
Asbestos-induced Generation of RNS
Alveolar macrophages, lung endothelial and epithelial cells, and alveolar epithelium (in
both rats and humans), when stimulated by inflammatory agents generate both
superoxide and over-produce nitric oxide that then combine to produce the perxoynitrite
ion (Zhu et al. 1998, Martin et al. 1997). These cells up-regulate production of NO
when stimulated by various cytokines, lipopolysaccharide, and interferon Y- Because
the peroxynitrite ion is a strong oxidantand nitrating agent and is extremely reactive,
evidence for its production is generally indicated in most studies by the presence of
nitrotyrosine, the stable product of tyrosine nitration, (Zhu et al. 1998). Evidence for
production of NO is frequently indicated by observation of nitrite. Numerous studies
also provide evidence of nitric oxide and peroxynitrite ion production specifically in
response to exposure to asbestos in various tissues.
• Both chrysotile and crocidolite up-regulate the production of nitric oxide by
alveolar macrophages in the presence of interferon-y and the interaction
between asbestos and interferon is synergistic. Non-fbrogenic carbonyl iron did
not induce nitric oxide formation (Zhu et al. 1998). These authors also cite a
study in which intratracheal instillation of silica and coal mine dust caused more
inflammation and nitric oxide formation than TiO2 or carbonyl iron (on an equal
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particle basis) This suggests both the geometry and chemical composition of
particles determine their ability to up-regulate nitric oxide.
Inhalation of chrysotile or crocdo'lite induce secretion of both TNF-a and nitric
oxide by pleural macrophages (Tanaka et al. 1998) Tanaka et al. (1998) studied
the effects of RNS in rats exposed by inhalation to 6 to 8 mg/m3 crocidolite or
chrysotile (both NIEHS samples) for 6 hrs/day, 5 days/wk for 2 wks. Rats were
sacrificed at 1 and 6 weeks following exposure. The authors indicate that
asbestos induces formation of stable products of nitric oxide in cells obtained by
lung lavage 1 wk after asbestos exposure. Nitrotyrosine (a marker for ONOO"
formation) was also observed. Also, a greater number of alveolar macrophages
and pleural macrophages were shown to express iNOS protein (the inducible
form of nitric oxide synlhase, Table 7-6) than sham exposed animals. Exposed
rats showed significantly elevated immuno-staining for nitrotyrosine in the region
of thickened duct bifurcations as well as within bronchiolar epithelium, alveolar
macrophages, and mesothelial cells of both the visceral and parietal pleura.
Nitrotyrosine staining was persistent, being observed at both 1 and 6 wks
following exposure.
Quinlin et al. (1998) studied the production of nitric oxide in rats exposed to
crocidolite or chrysotile asbestos (both NlESH reference samples). Rats were
exposed by inhalation at 6 hrs/day, 5 days/wk and lavaged at 3, 9, and 20 days.
Lavaged macrophages showed significantly increased nitrite/nitrate (indicating
production of nitric oxide) and this was suppressed with inhibitors to iNOS.
Thus, nitric oxide is produced via an iNOS pathway. The authors also note that
nitric oxide production correlated temporally with neutrophil influx in the lavage
fluid. They also indicate that asbestos exposed animals showed a 3 to 4 fold .
increase in iNOS positive macrophages in their lungs.
Quinlin et al. also exposed cultured murine alveolar macrophages (RAW 264.7
cells) to crocidolite, riebeckite, and crstobalite silica in vitro. These cells showed
increased iNOS mRNA following exposure to asbestos and even greater
increases if the cells were also stimulated with lipopolysaccharide (LPS). Both
crocidolite and riebeckite (but not cristobalite silica) stimulated increased iNOS
promoter activity when applied in combination with LPS. Thus, in this case, their
appears to be a mechanism that is sensitive to particle composition but not size.
Park and Aust (1998) treated cultures of human lung epithelial (A549) cells with
crocidolite and observed induction of iNOS and reduction of intracellular
glutathione (GSH) levels. Based on studies with inhibitors, the authors further
indicate that iron mobilized from crocidolite was required both for formation of
nitric oxide and to generate 2'-deoxy-7-hydro-8-oxoguanosine but not for the
observed decrease in intracellular glutathione. The authors note that
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approximately five times as much chrysotile (containing approximately 3% iron)
as crocidolite was required to produce the same level of nitric oxide formation.
Importantly, these experiments were conducted in vitro in serum-free medium, so
that no extra-biological source of iron was present.
• Choe et al. (1998) dosed cultured rat pleural mesothelial cells with either
chrysotile or croddolite (both NIEHS samples) at concentrations between 1.05
and 8.4 ug/cm2 with or without co-stimulation with 50 ng/ml of interleukin-1 (3 (IL-
1P). The authors report that mRNA for iNOS in asbestos and IL-1 p dosed cells
increased progressively from 2 to 12 hours following exposure. Both types of
asbestos also stimulated production of nitric oxide (measured as nitrite) in IL-1P
stimulated cells in a dose- and time- dependent fashion. Both types of asbestos
also induced expression of iNOS protein and formation of nitrotyrosine (based on
nitrate detection) in IL stimulated cells. In contrast, carbonyl iron particles did not
induce any of the effects observed for asbestos in IL stimulated cells. Thus,
formation of RNS appears to be either fiber size dependent (not induced by
particles) or mineralogy-dependent (or both).
As with the production of ROS, RNS production is apparently a function of multiple,
complex mechanisms. Also, as with production of ROS, the dose-response
characteristics of the various mechanisms differ. Several of the mechanisms show a
strong dependence on fiber size and some dependence on fiber type. However, there
are other mechanisms that are dependent primarily on fiber (or particle) type, but may
not be dependent on size (or at least not dependent on fiber size). At this point in time,
it is not possible to gauge the relative importance of the various mechanisms by which
RNS may be generated, except that it is likely that the importance of the various
mechanisms likely differ in different cells and tissues and likely differ as a function of
the specific toxin whose presence is inducing RNS production.
There are also indications that production of RNS may be species specific. For
example, Jesch et al. (1997) report that alveolar macrophages harvested from rats
expressed iNOS when stimulated with either LPS (lippopolysaccharide) or interferon-Y-
In contrast, iNOS expression could not be induced in hamster, monkey or human
macrophages.
Effects Mediated by RNS
Based on Zhu et al. (1998), over-production of nitric oxide can:
• inactivate critical enzymes;
• cause DNA strand breaks that result in activation of poly-ADP-ribosyl
transferase (PARS);
• inhibit both DNA and protein synthesis; and
• form peroxynitrite by reaction with superoxide.
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In turn, peroxynitrite ion may:
• initiate iron-dependent lipid peroxidation;
• oxidize thiols;
• damage the mitochondria! electron transport chain; and
• nitrate phenolics (including tyrosine).
f
Also, some of the damage to alveolar epithelium and pulmonary surfactant system
previously attributed to reactive oxygen species may actually be caused through ROS
generation of peroxynitrite. For example, Chao et al. (1996) report that crocidolite
treatment of human lung epithelial cells (A549 cells) results in formation of 8-hydroxy-2'-
deoxyguanosine (8-OHdG) in DMA and synthesis of mRNA for iNOS. An iNOS inhibitor ^
reduces intracellular nitrite and eliminates production of 8-OHdG. Addition of
independent NO donor, recovers production of 8-OhdG. Thus, production of the
oxygenated DMA adduct in this case appears to be generated by reaction with RNS.
As with ROS, it is primarily the potential for RNS to contribute to DNA damage (e.g.
strand breaks or generation of oxygenated adducts) that represent the primary *
pathways by which RNS might participate in cancer initiation (as opposed to cancer
promotion or other asbestos-related diseases). It appears that RNS-mediated DNA
damage is closely associated with ROS generation and mediation of DNA damage (see
Section 7.3.3.2). Thus, there is little to add here.
(
7.3.3.4 Conclusions concerning asbestos as a cancer initiator.
The strongest, most consistent evidence that asbestos can act as a cancer initiator
relates to the tendency of asbestos to interfere with mitosis. Although there is evidence
that asbestos may induce production of DNA adducts and DNA strand breaks (through
ROS and RNS mediated pathways), whether such adducts or breaks ultimately lead to
permanent, heritable changes to DNA remain to be demonstrated. The relative
importance of the ROS/RNS mediated pathways compared to the pathway involving
interference with mitosis also remains to be determined-. As indicated in later sections,
however, ROS/RNS mediated pathways may play substantial roles in cancer promotion
and induction of other asbestos-related diseases. C
Regarding the primary mechanism by which asbestos may initiate cancer (interference
with mitosis), the pathway is length dependent (short fibers do not appear to contribute
to the effect). Further, although there may also be a dependence on fiber type
(chemical composition), it is apparent that all types of asbestos can act through this ,
pathway. The dependence of this pathway on fiber diameter is less clear. Thus, other
than to suggest that fibers must be respirable (and therefore thinner than approximately
0.7 urn to have an opportunity to act, Section 7.1), whether further diameter constraints
are associated specifically with the mechanism of cancer initiation is not known.
L
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Importantly, although crystalline silica may also act to produce some of the same
effects as asbestos (including potentially carcinogenicity), there is substantial evidence
that this material does not act through the same pathways and that the characteristics
of its respective dose/response relationships may differ. Thus, for example, while
asbestos likely initiates cancer through a mechanism that favors long (and potentially
thin) fibers, silica more likely acts through a mechanism that is dependent on total
surface area, with freshly and finely ground material likely being the most potent. In
contrast, grinding asbestos fibers tends to lesson its carcinogenicity overall. Due to
differences in chemistry and crystallinity (reinforced by studies indicating a lack of
correspondence in behavior) crystalline silica does not appear to be an appropriate
analog for any of the asbestos types. Rather, for example, the appropriate non-fibrous
analog for crocidolite is riebeckite and the appropriate non-fibrous analog for chrysotile
is antigorite or lizardite.
There is also some evidence that the relative importance of asbestos as a cancer
initiator may differ in differing tissues. Thus, the results of Zhu et al. (1998) suggests
that Type II epithelial cells (but not alveolar macrophages, fibroblasts, orendothelial
cells) express MnSOD (manganese-dependent superoxide dismutase) in mitochondria
in response to exposure to asbestos, which may serve to mitigate effects associated
with ROS mediated mechanisms.
More importantly, the results reported by Kodama etal. (1993) indicate that human
bronchiolar epithelial cells are relatively resistant to the overall ability of asbestos to
induce genotoxic effects (including those associated with interference with mitosis) in
comparison, for example, to human meosthelial cells or various animal cells lines that
have been studied by others.
Further, Sanden et al. (1992) indicates from a prospective cohort study of shipyard
workers that the risk of lung cancer decreases with time since last exposure and the
authors argue that the time-dependence of disease is more consistent with asbestos as
a promoter than an initiator.
Taken as a whole, the above studies suggest that asbestos may serve primarily as a
promoter of lung cancer, rather than an initiator. In contrast, it appears that asbestos
may serve as both an initiator and a promoter of mesothelioma. Among other things,
this may suggest a reason for the observed difference in the time-development of these
two diseases among asbestos exposed individuals.
7.3.4 Evidence that Asbestos Acts As a Cancer Promoter
Primarily, asbestos may promote cancer by facilitating tissue proliferation. However,
additional mechanisms associated with the observed synergism between asbestos
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exposure and smoking (see, for example, Hammond et al. 1979) also need to be
considered.
Substantial evidence exists indicating that asbestos induces proliferation in target
tissues associated with lung cancer and mesothelioma and this is summarized below
followed by an overview of studies that suggest the various mechanisms by which C'
asbestos may facilitate such proliferation. Evidence suggesting the various
mechanisms related to the synergism between smoking and asbestos exposure are
also briefly reviewed.
There are numerous mechanisms by which asbestos may facilitate proliferation (
including:
• direct cell signaling to induce proliferation. This may occur by:
direct interactions between fibers and receptors on the cell surface;
interaction with integrins for such receptors;
interactions between phagocytized fibers and intracellular ^
components of signaling cascades;
or interactions between cells and intermediate species (e.g. ROS or
RNS) whose generation and release has been induced by
asbestos; or
(
• response to induced cell death in target tissues, which then stimulates
stem cells to proliferate to replace killed cells. Cell death may be induced
either through:
inducing apoptosis (programmed cell death); or
direct cytotoxic effects, which leads to necrosis.
These pathways are summarized in Table 7-5, which provides a perspective on the
complexity of the interactions between asbestos and the cells and tissues of the body.
Regarding asbestos-induced cell death, asbestos-induced apoptosis (and all of the
other effects described above) typically occurs at exposure concentrations that are (
much lower than required to induce frank cytotoxic effects (Sections 7.3,4,3 and
7.3.4.4). Therefore, it is primarily the former that is of potential interest in terms of
implications for asbestos-induced diseases in humans. Due to the high exposure
concentrations typically required, the importance of contributions from frank cytotoxicity
to human disease is unclear (Section 7.3.4.4). ,
7.3.4.1 Asbestos-induced proliferation. Numerous in-vivo studies indicate that all
types of asbestos induce proliferation in target tissues relevant to lung cancer and
mesothelioma. Such proliferation is also suggested by the animal histopathological
observations previously described (Section 7.2.2). Moreover, many of these studies
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suggest that the underlying mechanisms may be fiber size- and fiber-type specific.
Responses may also be species-specific.
Importantly, there are some studies (primarily in-vitro studies) that suggest asbestos
acts to inhibit (rather than induce) proliferation. Although it is likely that the contrasting
observations found in such studies are due to differences in conditions, timing, dose, or
the type of asbestos employed, the underlying reason for the contrasting observations
is not always apparent.
The evidence for proliferation is summarized by tissue below.
In lung epithelium:
• Brody et al, (1997) showed that rats and mice exposed for a brief (5 hr) period to
chrysotile asbestos at 1000 fibers/cm3 (no size indicated) exhibited focal scarring
at bronchio-alveolar duct junctions that are identical to those seen in asbestos-
exposed humans. After 3-consecutive exposures, the lesions persisted for 6
months. In regions where fibers are deposited, macrophages are observed to
accumulate, epithelium is injured, and proliferation is observed to occur. In this
study, the authors also showed by immunohybridization staining that the four
genes required to express three peptide growth factors (TGF-a, TGF-P, and the
A and B chains of PDGF) and the proteins themselves are expressed in
bronchio-alveolar tissue within 24 hrs of exposure. PDGF is expressed almost
immediately and expression remains elevated for 2-weeks post exposure, but
only in regions where fibers are deposited.
The authors report that PDGF is a potent growth factor for mesenchymal cells,
TGF-a is a potent mitogen for epithelial cells, and TGF-p inhibits fibroblast
proliferation but stimulates synthesis of extra-cellular matrix (Table 7-6). The
authors also report additional experiments with knockout mice indicate that TNF-
a is required to induce the early stages of proliferation. The authors also
indicate that Type II cells produce TGF-P1 and TGF-P2 (two of three isoforms of
this protein) and that they are stimulated to do so when co-cultured with
macrophages.'
• Adamson (1997) intratracheally instilled size-separated (by sedimentation) long
and short crocidolite fibers into rats (0.1 mg in a single dose) and noted that the
long fibers damaged the bronchiolar epithelium and that fibers were incorporated
into the resulting connective tissue; granulomas formed with giant cells
containing fibers), The long fibers also appeared to escape into the interstitium.
Labeled thymidine uptake (which indicates DMA synthesis and suggests
proliferation) following long fiber exposure was seen in lung epithelial cells,
fibroblasts, and pleural mesothelial cells. Such labeling peaked at 2% in
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mesothelial cells and 3% in epithelial cells within one week following exposure.
Proliferation appeared to end shortly beyond one week. Short fibers were
observed to have been efficiently phagocytized by alveolar macrophages and a
small increase in macrophage population appeared to have been induced.
Otherwise, none of the other effects attributable to long fibers (described above)
were observed with short fibers. Note that such observations are entirely
consistent with those reported for the range of studies described in
Section 7.2.2.
• McGavran et al. (1989) exposed both normal (C5+) mice and compliment
deficient (C5-) mice to asbestos (at 4 mg/m3) by inhalation and observed
proliferation of bronchio-alveolar epithelium and interstitial cells at alveolar duct
bifurcations (based on incorporation of tritiated thymidine) between 19 and 72
hrs after a single, 5-hr exposure. Sham exposed rats showed fewer than 1% of
epithelial and interstitial cells at alveolar duct bifurcations incorporate labeled
thymidine. In contrast, thymidine uptake in asbestos exposed animals is
significantly elevated for the first few days, begins to decrease at 8 days, and
returns to normal by one month following exposure. Both C5+ and C5- mice
show similar increases in volume density of epithelial and interstitial cells at 48
hrs post exposure. However, one month following exposure C5+ mice
developed fibrotic lesions while C5- mice were no different than controls. The
authors conclude that the depressed macrophage response in C5- mice does
not appear to change the early mitogenic (and proliferative) response to
asbestos but apparently attenuates later fibrogenesis.
• Chang et al. (1989) describes the morphometric changes observed in rats
. following 1 hr inhalation exposure to chrysotile (at 13 mg/m3). Within 48 hrs
following exposure: the volume of the epithelium increased by 78% and the
interstitium by 28% at alveolar duct bifurcations relative to sham-exposed
animals. Alveolar macrophages increased 10 fold and interstital macrophages 3
. fold. Numbers of Type I and Type II epithelial cells increased by 82% and 29%,
respectively. Atone month following exposure, the numbers of Type I and Type
II pneumocytes were still elevated, but not significantly. However, the volume of
the interstitium had increased by 67% accompanied by persistently high
numbers of interstitial macrophages, accumulation of myofibroblasts, smooth
muscle cells, and an increased volume of interstitial matrix.
In lung endothelium:
In addition to the general evidence for proliferation of endothelial cells provided by
Adamson (1997), McGavran et al. (1989), and Chang et al. (1989), as cited above, a
more detailed description of the nature of asbestos-induced proliferation of endothelial
cells is also available.
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Proliferation of endothelial cells and smooth muscle cells of arterioles and
venules near alveolar duct bifurcations is induced in rats inhaling chrysotile (no
size information given) for 5 hrs at 4 mg/m3 (McGavran et al. 1990), This is
based on the observed uptake of labeled thymidine (which indicates DNA
synthesis and suggests proliferation) that is significantly increased over controls
between 19 and 72 hrs following exposure. 28% of vessels near bifurcations
exhibited labeled cells 31 hrs after exposure. One month following exposure, the
thickness of the smooth muscle layers around these blood vessels is significantly
increased (doubled). In contrast, labeling of these same endothelial and smooth
muscle cells in sham exposed rats is zero. The authors indicate that endothelial
cells and smooth muscles associated with pulmonary blood vessels are normally
quiescent with turnover rates on the order of years.
In mesothelium:
In the second part of the study addressing epithelial proliferation, Adamson
(1997) reports that rats instilled with 0.5 mg of unmodified, UICC crocidolite were
sacrificed at one week and six weeks following exposure and subjected to
bronchiolar lavage and pleural cavity lavage. Lavaged alveolar macrophages
were observed to contain fibers but pleural macrophages did not. At one week,
collected pleural macrophages were shown to induce proliferation of fresh
mesothelial cells in culture and pleural lavage fluid showed an even greater
effect. No effects were observed at six weeks. Further work with anti-bodies to
various cytokines indicated that early, transient proliferation of mesothelial cells
was dependent on kertinocyte growth factor (KGF) but not on PDGF, FGF, or
TNF-a (Table 7-6). This suggests that early, transient proliferation is induced by
diffusing cytokines rather than direct fiber exposure. Adamson further reports
that KGF is a fibroblast-derived cytokine that acts on epithelial cells so that it's
up-regulation likely results from epithelial injury with penetration of asbestos to
the interstitium (where fibroblasts are found, Section 4.4). A similar transient
proliferative response in mesothelial cells was also observed following exposure
to chrysotile and in response to crystalline silica exposure. Thus, it appears that
the mesenchymal proliferative response may be mineralogy specific for particles
and is size-specific for fibers.
Everitt et al. (1997) exposed rats and hamsters by inhalation to RCF-1 (45 mg/m3
- 650 f/cm3) for 12 weeks and then let them recover for up to an additional 12
weeks prior to sacrifice. The authors indicate that both rats and hamsters
showed qualitatively similar levels of inflammation at time examined (4 wks and
12 wks). They also indicate the mesothelial cell proliferation was observed in
both animal types but was more pronounced in hamsters at all time points
examined. The greatest proliferation in both species was in the parietal pleura
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lining the diaphragm. The authors also report that fibers (primarily short and
thin) were also observed in the pleural cavities of both species at all time points.
Several in vitro studies provide evidence that asbestos either induces or inhibits
proliferation in lung tissues of interest and that at least some of the mechanisms
involved are fiber size and mineralogy dependent. As previously indicated, the specific
reasons for the apparent contrast between results observed In vivo (where asbestos
consistently promotes proliferation) and the inhibition sometimes observed in in-vitro
studies is not always apparent. However, it must be due to the special conditions that
must be created to conduct in-vitro studies, which may not support certain mechanisms
that are Important in vivo.
• Timblin et al. (1998a) completed a study in which rat pleural mesothelioma
(RPM) cells in culture were dosed with crocidolite or various cation-substituted
erionites. The expression of several gene and gene products were then tracked,
Cultures in this study were exposed to 1, 5 or 10 ug/cm2 of the various fibrous
materials. Analysis of the fibrous materials indicated that crocidolite contained
many more fibers per gram of material (probably because they are thinner) and
that the preparation contains somewhat longer fibers than the erionites
evaluated. In crocidolite, for example, 88% of the fibers are longer than 5 um,
68% longer than 10 um, and 37,5% longer than 20 urn. All of the cation
substituted erionites showed approximately the same size distribution: 50%
longer than 5 um, 10-20% longer than 10 um, and 1-5% longer than 20 um,
Results indicate that the various cation substituted erionites behave differently
and that the Na substituted erionite shows the largest overall potency, at least for
some end points but not others, Fe, Na, and Ca substituted erbnite all appear to
Induce c-fos expression in a dose-dependent fashion (Increasing regularly
among the 1, 5, and 10 ug applications). K-erionite may also show the same
pattern, but the changes were not indicated as significant over controls
(apparently due to greater variability). Only Na-erionite showed significantly
increased expression of c-jun (at 1 and 5 ug applicatbns but not significantly at
10 |jg, apparently due to greater variability. However, the mean result for 10 ug
shows a consistent trend with the lower concentration application results),
Na-erionite induces c-fos at the same or greater rates as crocidolite asbestos for
the same mass application (but not the same fiber number), Crocidolite also
appears to show a dose-response trend for c-jun expression, but only the result
for the highest application (10 ug) is significantly different from controls. In
contrast, Na-erionite appears to show greater induction of c-jun expression at
lower dose than crocidolite, but the increase with increasing dose is much lower
for Na-erionite, Crocidolite also appears to induce substantial apoptosis (even at
the low dose of 5 ug) and that the induction is dose-dependent. In contrast, non-
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fibrous riebeckite does not appear to induce apoptosis. Comparison between
crocidolite dosed cultures and Na-erionite dosed cultures indicate that crocidolite
induces substantial apoptosis at all time periods following application but that
Na-erionite induces little apoptosis even at higher mass dose and longer time
periods than crocidolite. The authors also indicate that crocidolite and Na-
erionite appear to stimulate DNA-synthesis, which appears to be a compensating
mechanism to fiber cell toxicity. The authors indicate that chemistry is important
in fiber toxicity as Na-erionite was a strong inducer of c-jun, even at relatively low
concentrations, but several of the other cation substituted erionites (including Fe-
erionite) were not. They further suggest that, given the difference in the fiber
lengths of the crocidolite samples and Na-erionite samples, that fiber length may
be a less important consideration than fiber surface chemistry. However, despite
the author's assertion, considering that non-fibrous riebeckite does not induce
any of the effects observed for crocidolite, there appears to be a clear size effect.
It may simply require that a defined, minimum length is necessary to induce the
effect.
The authors also indicate that balance between proliferation and apoptosis is
required to maintain homeostatis in healthy tissue. They further indicate that
other studies suggest that c-Jun expression is linked to proliferation and
induction of cancer, while c-fos expression is linked to apoptosis. Thus,
suppression of c-fos may be linked to carcinogenesis by allowing establishment
or maintenance of a transformed cellular phenotype. This is in fact an early step
in carcinogensis. Many environmental agents stimulate both apoptosis and
proliferation and, depending on the degree, may cause imbalances that lead to
disease. Relative stimulation of c-fos and c-jun may reflect some of these
pathways. Since crocidolite induces both c-fos and c-jun in this study, the
implication is that it mediates both apoptosis and proliferation.
Wylie et al. (1997) dosed hamster tracheal epithelial (HTE) cells and rat pleural
mesothelial (RPM) cells with various asbestos and talc samples and evaluated
proliferation based on a colony-forming efficiency (CFE) assay. The samples
were NIEHS crocidolite and chrysotile and three different talcs. Samples were
characterized in the paper by mineralogical composition, surface area, and size
distributions.
The authors indicate that both asbestos samples increased colony formation of
HTE cells (suggesting induction of proliferation) but talc samples did not. RPM
cells, in contrast, showed only dose-dependent decreases in colony forming
efficiency for all samples, which the authors indicate is a sign of cytotoxicity. The
authors report that all samples show corresponding effects when concentrations
are expressed as fibers longer than 5 um or by total surface area. They also
suggest that the "unique" proliferative response by HTE cells could not be
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explained by either fiber dimension or surface area and suggests a mineralogical
effect. NOTE: MY OBSERVATIONS FROM RESULTS OF THIS STUDY ARE
THAT ABESTOS AFFECTS BOTH HTE AND RPM CELLS, BUT THAT TALC
ONLY APPEARS TO AFFECT RPM CELLS. EXPRESSING EXPOSURE AS
LONG FIBER NUMBER CONC OR SURFACE AREA TENDS TO PARTIALLY
BUT NOT COMPLETELY NORMALIZE DOSE-RESPONSE ACROSS SAMPLE
TYPES FOR RPM CELLS, BUT NOT FOR HTE CELLS. THE TWO
ASBESTOS SAMPLE TYPES BEHAVE SIMILARLY TWARD BOTH CELL
TYPES WHEN EXPOSURE IS EXPRESSED EITHER AS LONG FIBERS OR
SURFACE AREA (NEED TO GO BACK AND DEMONSTRATE THIS WITH
TABLES FROM THE PAPER.)
• Barchowsky et al, (1997) dosed cultured (low passage) endothelial cells to
NIEHS chrysotile, crocidolite, or RCF-1. After 1 to 3 hrs exposure to 5 ug/cm2
(non-lethal concentrations), asbestos (but not RCF-1) causes changes in cell
morphology (cells elongate), increases in cell motility, and increases in gene
expression. Furtherwork by the authors Indicate that these effects are mediated
by interaction between asbestos and the receptor for urakinase-type pasminogen
activator (uPAR). The authors also suggest that attachment of asbestos to cell
membranes, internalization of asbestos fibers by the cells, and the morphological
changes induced by asbestos are each mediated by different proteins.
Examples of in vitro studies that indicate asbestos (and other fibrous materials) m'ay
inhibit proliferation in culture include the following.
• In contrast to the above, Levresse et al. (1997) found that chrysotile and
crocidolite act to inhibit proliferation in cultured rat pieural mesothelioma (RPM)
cells. In this study, RPM cells (diploid, no more than 25 passages) were dosed
with either UICC crocidolite or NIEHS Zimbabwean Chrysotile at concentrations
varying between 0.5 and 20 ug/cmz. The authors also note that the chrysotile
sample contains approximately four times the number of fibers as the crocidolite
samples. Cells were then examined at 4, 24 and 48 hrs following treatment. In
untreated cultures, the number of cells in replicative phase decrease with time,
which indicates that such cells are headed for confluence (completion of a "
monolayer on the culture medium). At 48 hrs, for example, 10% of untreated
control cells were observed to be in replicative stage. Chrysotiie (but not
crocidolite) decreased the fraction of cells in replicative phase in a time- and
dose-dependent manner. At 48 hrs, for example, cells treated with 10 ug/cm2
chrysotile showed only 1.5% in replicative phase. Further tests confirmed that
this was due to blockage of cells at the G1/S boundary of the cell cycle. Both
chrysotile and crocidofite appears to induce a time-dependent increase in the
number of cells at G2/M in the cell cycle, although this effect was not observed to
be dose-dependent for chrysotiie. Even on a fiber-number basis, chrysotile
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appears to be elicit a greater response (arrest a greater percentage of cells) than
crocidolite.
The authors also indicate that chrysotile caused nuclear-localized, time-
dependent increases in p53 concentrations. Crocidolite produced much lower
levels that were not detectable in the nucleus. Chrysotile was also observed to
produce blockage at the GO/G1 transition of the cell cycle, but crocidolite did not.
They also note that p53 is known to mediate arrest at this stage in the cycle so
observing that chrysotile induces arrest at this transition in the cell cycle may be
consistent with the observed increased expression of p53. The authors also
note that chrysotile triggers apoptosis in this study and that crocidolite shows a
smaller, but detectable effect. Spontaneous apoptosis in untreated cultures ran
between 0.5 and 1% at 24 to 48 hours whereas chrysotile induced 4% apoptosis,
peaking at 72 hrs following exposure to 10 ug/cm2. The authors indicate that the
lower level of effects observed with crocidolite could be due to the substantially
smaller number of long fibers in UICC crocidolite compared to NIEHS chrysotile.
• The previously reviewed (Section 7.3.3.1) study by Hart et al. (1994) also
suggests that long, medium, short, and UIGC crocidolite and chrysotile along
with a range of MMVF's show a dose-dependent inhibition on proliferation of
cultured CHO cells and that potency toward the effect is a direct function of fiber
length.
Several of the above-described studies, in addition to providing evidence that asbestos
induces proliferation in various lung tissues, also suggests certain mechanisms.
Asbestos may induce proliferation, for example, by inducing production of specific
cytokine growth factors (Brody et al. 1997, Adamson 1997), or by inducing certain
signaling cascades (Barchowsky et al. 1997, Timblin et al. 1998b). It is also possible
that the two effects may be related (i.e. that stimulation of a particular signaling cascade
may result in production of certain growth-stimulating cytokines). Other mechanisms
may also be important (Table 7-5).
7.3.4.2 Asbestos induced cell signaling. Asbestos has been shown to induce a
variety of cell signaling cascades in a variety of target cell and tissue types. Such
signaling may then trigger effects in the stimulated cells that may include:
• proliferation;
• morphological changes;
• generation and release of various cytokines, enzymes, or extracellular
matrix; or
• programmed cell death.
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Note, due to the large number of chemical species that need to be considered in this
discussion, Table 7-6 provides a summary of the sources of such species (Table 7-6A)
and the effects attributable to such species (Table 7-6B).
In specific cases, asbestos may initiate cell signaling by interacting directly with
receptors on the cell surface, by interacting with integrins for such receptors, by causing
generation and release of intermediate species (e.g. ROS or RNS) that trigger cell
signaling, or (for phagocytized fibers) by interacting with intracellular components of a
particular signaling cascade. The specific responses to cell signaling induced by
asbestos are frequently cell- or tissue- type specific. Moreover, depending on the
specific mechanism, cell signaling by asbestos may be dependent on fiber size and/or
type.
• Barchowsky et al. (1998) showed in a set of studies that long chrysotile and long
crocidolite but not RCF-1 fibers (at concentrations between 1 and 10 ug/cm2),
which is reportedly below levels that typically induce cytotoxic effects) induced
up-regulation of urokinase-type plasminogen activator (uPA) and its receptor
(uPAR) in both lung endothelial cells (vascular cells) and lung epithelial cells.
They also showed that the increased pericellular protolytic activity (requiring
cleavage of plasminogen to plasmin) that is induced by asbestos in these cells is
mediated by uPA.
In prior studies, chrysotile has been shown to cause endothelial cells to elongate and
increase expression of adhesion molecules for phagocytes. They also show enhanced
proteolytic activity and matrix interactions. Chrysotile has also been shown to stimulate
fibrinolytic activity in lung epithelial cells and extravasating macrophages. All such
stimulation appears to result from up-regulation of uPA. Thus, this mechanism may
explain the observed asbestos-induced changes in lung endothelial and epithelial cells
including vascular remodeling, development of vascularized granular tissue, increased
matrix turnover, and leukocyte extravasation (which in turn may be caused by cell
activation and elaboration of proteases and adhesion molecules). The authors suggest
that asbestos-induced up-regulation of uPA and uPAR expression may represent a
global mechanism for pulmonary toxicity and fibrosis induced by crystalline fibers.
Importantly, chrysotile was shown to induce uPA and uPAR expression in the absence
of serum, so the effect is apparently due either to direct binding of fibers to cell-surface
receptors or integrins for such receptors.
• Mossman et al. (1997) observed that concentrations of 1,25 to 5 ug/cm2 of
crodidolite (sample from TIMA) caused expression of c-jun and AP-1 in both
cultured hamster tracheal epithelial (HTE) cells and cultured rat pleural
mesothelial (RPM) cells. Crocidolite was also observed to trigger the EGFR-
regulated kinase (ERK) and mitogen activated protein kinase (MAPK) pathways
in RPM cells. The authors indicate that these pathways ahe also stimulated by
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hydrogen peroxide and that NF-K(3 induction stimulated by crocidolite is also
stimulated by crystalline silica (although silica stimulation of the MARK pathway
was not investigated), They also note that the non-fibrous analog to crocidolite,
riebeckite does not elicit these activities.
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Table 7-6A, page 1
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Table 7-6A, page 2
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Table 7-6A, Page 3
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Table 7-6B, Page 1
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c
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Table 7-6B, Page 3
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The authors indicate that induction of the NF-KP cascade was inhibited by
excess glutathione, which is stimulated by N-acetylcysteine (NAG), suggesting
that this pathway is induced by asbestos-caused oxidative stress (perhaps
through ROS or RNS intermediates). Application of MAC also diminished
crocidolite induced c-fosand c-jun RNA levels and inhibited activation of the
ERK-MAPK cascade. Further work suggests that asbestos triggers the MARK f
pathway by interaction with the Epithelial Growth Factor Receptor (EGFR), either
directly or by phosphorylation of this receptor by ROS. At the concentrations
examined, crocidolite induces substantial apoptosis (apparently through
activation of the ERK-MAPK cascade). In contrast, the authors note that TNF-a
induces the JNK arm of the ERK-MAPK cascade, which leads to proliferation. ^
Asbestos does not elevate JNK over the time-period of the study. The authors
note that it has been shown in some studies that inhibition of ERK in some cells,
also inhibits asbestos-induced apoptosis. Importantly, given that these
processes are induced by crocidolite but not by its non-fibrous analog, riebeckite,
induction of the ERK-MAPK cascade appears to be a fiber-size dependent
process. (
In a related study to that conducted by Mossman et al. (1997), Zanella et al.
(1999) report that the (TIMA) crocidolite (at concentrations of 2.5 to 10 ug/cm2,
but not its non-fibrous analog, riebeckite, eliminated binding of EGF to its
receptor EGFR. Because EGF does not bind to crocidolite in the absence of (
membrane, this is not simply a case of crocidolite tying up ligand. Croa'dolite
also induces a greater than twofold increase in steady-state message and
protetin levels of EGFR.
The authors also note that the tyrphostin, AG-1478 (which specifically inhibits the
tyrosine kinase activity of EGFR), significantly mitigated asbstos-induced *--'
increases in mRNA levels of c-fos but not c-jun and that the asbestos action was
not blocked by a non-specific tyrphostin, AG-10. Moreover, pretreatment of RPM
cells with AG1478 significantly reduced asbestos-induced apoptosis. Therefore,
the authors concluded that asbestos -induced binding to EGFR initiates signaling
pathways responsible for increased expression of the protooncogene c-fos and (
the development of apoptosis. This apparently occurs through the EGFR-
extracellular signaling regulated kinase (ERK). It is hypothesized that asbestos
may induce dimerization and activation (phosphorylation) of EGFR, which also
prevents binding of EGF. Asbestos apparently serves the same role as EGF in
that it promotes aggregation of EGFR, which in turn promotes binding to the
extracellular domain of tyrosine kinase receptors and the activation of their
intracellular kinases. The authors also indicate that other work suggests that
crocidolite fiber exposure leads to aggregation and accumulation of EGFR at
sites of fiber contact and that asbestos also stimulates biosynthesis of the EGFR
and activates ERK in an EGFR-dependent manner.
C
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The authors speculate that asbestos binding may not be ligand-site specific but
may be charge related or may induce EGFR phophorylation by local production
of ROS, which has previously been demonstrated to cause EGFR activation. As
previously indicated, that this effect is driven by crocidolite exposure but not by
exposure to riebeckite indicates that this mechanism is fiber-size specific.
Johnson et al. (1997) showed that UICC crocidolite, but not JM-100 glass,
applied to a culture of immortalized human Type II epithelial (A549) cells at non-
cytotoxic concentrations (for 20 hours) results in increased expression of p53,
Cip1, and GADD153 in a dose- and time-dependent fashion. Expression was
observed to be maximum at 18 hours. The crocidoiite treatment was also shown
to cause an increase in the number of cells arrested in Stage G2 of the cell cycle
(with a persistent decrease in the number of cells in G1), This was considered
surprising because both p53 and Cip1 are known to mediate arrest in Stage G1.
The authors suggest that these findings indicate a strong dependence on both
fiber type and fiber size (JM-100 glass contains substantially more long fibers
than crocidolite). However, it is not possible to separate the effects of fiber type
versus fiber size in this study.
Luster and Simeonova (1998) indicate that at high concentrations, ROS may
induce frank cytotoxicity. At low or moderate levels, ROS is more likely to induce
cell,signaling cascades that may, in turn, contribute to asbestos-related disease.
The authors dosed cultures of immortalized human Type II epithelial (A549)
cells, originally derived from a lung carcinoma, and normal human
bronchioepithelial (NHBE) cells with long (Certain-Teed supplied) crocidolite
(reported mean length: 19 urn) at concentrations ranging between 0 and
24 ug/ml. Results indicate that secretion of both lnterluken-8 (IL-8) and IL-6 was
stimulated by crocidolite exposure in a dose-dependent manner. In contrast,
increases in LDH levels (which indicate cell damage) was only detected at the
highest exposure concentrations tested. Further work indicates that stimulation
of IL-6 and IL-8 secretion occurs through ROS that is generated in an iron-
dependent process (that may also include NF-«p induction). Note that the trend
of cell signal induction at low and moderate levels of asbestos exposure with
evidence for cytotoxieity observed only at the highest exposure concentrations is
common to many of these kinds of studies.
Choe et al. (1999) conducted a combined in-vivo/in-vitro study of the effects of
low level exposure to chrysotile or crocidolite at inducing leukocyte attachment to
rat pleural mesothelial cells. The authors note that similar populations of rat
pleural leukocytes (74% macrophages, 2% neutrophils, 10% mast cells, and
10% eosinohils) were observed in both asbestos-exposed and unexposed rats.
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In the second part of the study, cultured RPM cells were exposed to either
crocidolite or chrysotile (both NIEHS samples) at concentrations ranging
between 1.25 and 10 ug/cm2, which was noted to be below levels at which
substantial cytotoxicity is observed. Attachment of rat pleural leukocytes to RPM
cells was then observed to increase with increasing dose of asbestos to the RPM
cells. In contrast, carbonyl Iron (a non-fibrous particle) also induced enhanced
attachment but at much lower levels and the effect was not dose-dependent,
Further analysis indicated that asbestos-induced adhesion is mediated by up-
regulation of IL-1P (but not dependent on TNF-a or nitric oxide production,
although it is noted that TNF-a independently increases attachment). Asbestos
also induces increased expression of vacular cell adhesion molecule (VCAM-1).
The authors also note that rat pleural leukocytes harvested from asbestos-
exposed rats also showed increased adhesion to in-vitro RPM cells over
leukocytes harvested from sham exposed rats. Thus, asbestos appears to
trigger alterations in these cells as well,
In a recent paper, Driscoll et al. (1997) reviewed the roie of tumor necrosis factor
alpha (TNF-a) in mediating the inflammatory response to lung insult by
particulate matter. The authors indicate that quartz, coal dust, crocidolite, and
chrysotiie are all potent inducers of TNF-a production. Titanium dioxide (Ti02),
corundum (aluminum oxide: AI2O3), and latex beads are not. Pulmonary
macrophages have been shown to secrete TNF-a in vivo in response to
exposure to some of the dusts listed above (including asbestos). This is also
observed among macrophages from asbestosis patients.
The authors indicate that rats immunized against TNF-a show reduced
recruitment of neutrophils, which demonstrates that TNF-a is involved in the
recruitment of inflammatory cells, TNF-a stimulates macrophages, epithelial
cells, endothelial cells, and fibroblasts to release chemokines that include
adhesion molecules (Eselectin, ICAM, VCAM). Inflammatory cells then interact
with such molecules and migrate along gradients from vascular structures in the
lung to the lung interstitium and even the lung air spaces. It has also been
shown that release of TNF-a by macrophages is apparently dependent on
oxygen stress (i.e. exposure to ROS/RNS) that is induced in pathways that
require iron. Production of TNF-a and other compounds that mediate the
inflammatory response are regulated by the oxidant-sensitive transcription factor
NF-Kp, This factor exists as a heterodimer in the cytoplasm in an inactive form
because it is bound to the inhibitory 1-kappaBalpha (1-KBa), which masks a
nuclear translocation signal. Appropriate stimulation of a cell induces
phosphoryiation of 1-KBa, which then marks it for proteolytic degradation. Then,
NF-Kp translocates to the nucleus and induces transcription. The process is
stimulated by oxidants and inhibited by antioxidants.
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In another review, Finkelstein et al. (1997) indicated that Type II epithelial cells
and Clara cells (non-ciliated bronchiolar epithelial cells) respond to and produce
specific cytokines during the inflammatory process. Early responses to particle
challenge include increases in mRNA and protein for IL-1 p, IL-6, and TNF-a.
These are also accompanied by changes in specific epithelial genes including
those for surfactant protein C and Clara cell secretory protein. The authors
further indicate that these responses are due to direct interaction with particles
rather than a result of macrophage-derived mediators and they suggest a more
significant role for epithelial secretions in the overall pulmonary response than
previously suspected. Results also suggest that Type II pneumocyte-derived
growth factors may play a significant role in the pathogenesis of pulmonary
fibrosis.
Also in this paper, Finkelstein et al. report thatintratracheal instillation of
lipopolysaccharide (a potent inflammatory agent) caused increases in both
lavage fluid and plasma levels of TNF-a and IL-6. Intrapleural injection induced
primarily increases in plasma levels. The authors indicate that this suggests that
the observed cytokines are produced primarily at the site of injury. The authors
further indicate that IL-6 is elevated in lavage fluids following exposure to Ni2S3, a
suspected human carcinogen, but not following exposure to TiO2 or NiO. In vitro
studies indicate that release of IL-1 p and TNF-a by Type II cells occurred only
following exposure to crocidolite or ultrafine TiO2, but not pigment grade TiO2.
The authors also indicate that protein C and Clara cell secretory protein were
both expressed in their respective source cells following exposure only to the
fibrogenic of the above particles. They also report that crystalline silica has been
shown to promote cytokine release and hypertrophy in Type II cells.
Jagirdar et al. (1997) used immunohistochemistry in a study to show that all
three isoforms of TGF-P (1,2, and 3) are expressed in the fibrotic lesions of
asbestosis and pleural fibrosis patients from the Quebec mines, primarily by
Type II pneumocytes. The cases examined averaged 38 years of exposure to
the Quebec chrysotile. The authors also indicate that the hyperplastic epithelium
of silicosis patients also show elevated expression of all three isoforms. They
further indicate from previous studies that mesothelioma tumor cells frequently
express TGF-P2 while the cells in the stroma of such tumors frequently express
TGF-P1. It is also noted in this study that jun and fos are both transcription
factors that activate the TGF-P1 promoter.
Zhang et al. (1993) indicate that macrophages obtained in BAL fluid from
idiopathic pulmonary fibrosis (IFF) patients and asbestosis patients show
significantly increased secretion of TNF-a and asbestosis patients also showed
significantly increased secretion of IL-1 p. Macrophages and monocytes obtained
from both kinds of patients also show elevated expression of mRNA for these
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cytokines. In an in-vitro part of this study, Zhang and coworkers, showed that
chrysotile, crocidolite, amosite, and crystalline silica all stimulated IL-1P and
TNF-a release and up-regulated their respective mRNAin both macrophages
and monocytes. The authors also report that these two cytokines have been
shown to up-regulate collagen Types I and III and fibronectin gene expression in
human diploid lung fibroblasts after short term, serum free exposure in vitro.
• Holian et al. (1997) exposed cultures of normal human alveolar macrophages
(AM) (obtained by lavage) to varying concentrations (up to 25 ug/ml) of short
chrysotile, UICC crocidolite, ground silica, wollastonite, and titanium dioxide to
determine whether these materials cause a phenotypic shift in macrophage
populations by inducing selective apoptosis. The authors indicate that normal
lungs contain: 40-50% RFD1+7+ suppressor AM, and 5-10% RFD1+ immune
activator. In this study, the fibrogenic subset of the particles tested (not
wollastonite. or titanium dioxide) increased the ratio of activator/suppnessor AM
by a factor of 4 within a few hours and the effect was seen to increase with time.
The authors also note that fibrogenic particles decrease the abundance of
RFD7+ AM (phagocytic), but the consequences of this phenotypic shift are
unclear.
The authors indicate that AM taken from fibrotic patients release a variety of
proinflammatory mediators capable of stimulating fibroblast proliferation and
collagen synthesis. Even in the absence of evidence of fibrosis, workers who
have been heavily exposed to asbestos yield similarly activated AM. In contrast,
they also note that, in vitro studies in which AM are stimulated with fibrogenic
particles, while such AM are activated to release inflammatory cytokines, such
releases are orders of magnitude less than that seen from AM derived from
fibrotic patients. The authors indicate that the apoptosis-driven phenotypic shift
in AM that is indicated by this study may explain the apparent discrepancy.
« In a previously described study, Timblin et a I. (1998a), see Section 7.3.4.1,
showed that crocidolite asbestos and several cation substituted erionites all
stimulate c-jun and c-fos in rat pleural mesothelial cells, but to varying degrees
depending on fiber chemistry. The effect also appears to be size dependent as
crocidolite but not its non-fibrous analog riebeckite induces the effect.
In general, the kinds of signaling cascades that are potentially stimulated by exposure
to asbestos are important due to their potential to contribute to the promotion of cancer.
Such pathways, for example, may mediate proliferation or may suppress apoptosis.
Alternately, they may mediate an inflammatory response that in turn may lead to
proliferation or to production and release of other, mutagenic agents (e.g. ROS or
RNS). Pathways that facilitate development of fibrosis may also contribute to cancer
promotion, given the apparent link between fibrosis and the development of lung
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cancer, which may relate (among other possibilities) to inhibition of fiber clearance
(Section 7.3.4,5).
The ERK-MAPK signaling pathway evaluated in multiple studies by Mossman and
coworkers (Mossman et al. 1997, Timblin et al. 1998a and b, Zanella et al. 1999) in rat
pleural mesothelial (RPM) cells is a case in point (see above). These studies suggest
that crocidolite stimulates the ERK-MAPK cascade through interaction with the EOF
receptor. This ultimately leads to transcription of mRNA for c-fos. Crocidolite has also
been shown to induce c-jun (apparently through a separate mechanism) and the
balance between c-jun and c-fos has been implicated in guiding a cell toward either
proliferation or apoptosis. Although the direct connection between c-jun/c-fos and
apoptosis has not been established, it is observed that crocidolite induces substantial
apoptosis in RPM cells at the same concentrations at which it induces substantial
expression of c-fos and c-jun. The link is also implied because inhibition of the ERK
pathway has been shown in some studies to inhibit asbestos-induced apoptosis. Na-
erionite has also been shown to induce c-fos at levels comparable or higher than :
crocidolite for comparable exposures (at least on a mass basis) and induce c-jun at
higher levels. However, it is not known whether Na-erionite and crocidolite act via the
same pathways. Potentially due to the increased, relative expression of c-jun induced
by Na-erioinite, increased apoptosis is not observed in association with exposure to Na-
erionite. However, the link between increased c-jun expression and inhibition of
apoptosis has not been demonstrated explicitly.
Both crocidolite and Na-erionite were also shown by Mossman and coworkers to induce
uptake of bromodeoxyuridine (BrdU) by RPM cells in these same experiments. Uptake
of BrdU is an indicator of DNA synthesis. Since it has also been shown that crocidolite
is capable of damaging DNA via ROS and other pathways (Sections 7.3.3) and both
crocidolite and erionite are known to induce mesotheliomas in any case, the balance
between proliferation and apoptosis in this cell population that is struck by exposure to
these toxins may very well determine whether development of cancers are promoted or
prevented. Of course, both proliferation and apoptosis may also be mediated by other
pathways independent of the ones described here.
The problem is that the range of responses that are induced by asbestos in the lung are
varied and complex (see Table 7-5) so that it has not yet been possible to definitively
identify the biochemical triggers that lead to lung cancer or mesothelioma. It is even
likely, for example, that different mechanisms (or combinations of mechanisms)
predominate under different exposure conditions or in association with differing fiber
types or particle sizes. Still, examination of the dependence of candidate mechanisms
on fiber type and particle size can be instructive, especially to the degree that such
indications are consistent with observations in whole animal studies (see, for example,
Section 7.2.2). For the signaling cascade described above by Mossman and
coworkers, for example, the effects attributable to croddolite are clearly dependent on
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fiber size because the non-fibrous analog to crocidolite, riebeckite does not induce any
of the effects, It also appears that the chemistry of the fibers is important, given the
observed differences in responses among the various, substituted erionites,
7.3.4.3 Asbestos-induced apoptosis. Apoptosis (programmed cell death) is generally
triggered when a cell accumulates certain types of genetic damage, when cell signaling
cascades are triggered by external stimuli that may occur, for example, as part of the
need to maintain tissue homeostasis or to cause a phenotypic shift in response to toxic
challenge (see, for example, Holian et al. 1993, Section 7.3.4.2), or when a cell has
completed a pre-programmed number of divisions. Asbestos can induce apoptosis in a
variety of cells by several mechanisms including primarily:
• by causing sufficient genetic damage to trigger apoptosis; or
• by triggering a signal cascade that leads to apoptosis.
As previously indicated, asbestos may trigger signaling cascades by interacting directly
with receptors on the ceil surface, by reacting with integrins for such receptors, or by
inducing production of intermediate species (such as ROS or RNS) that may in turn
induce cell signaling.
Some of the mechanisms by which asbestos may act to induce apoptosis may be fiber
size- or type-dependent. Also, responses may vary in different target tissues.
Fibers:
As indicated in Section 7.4.3.2, crocidolite .(but not the non-fibrous analog
riebeckite) induces apoptosis in hamster tracheal epithelial cells and rat pleural
mesothelial cells when applied at non-cytotoxic concentrations (Mossman et al.
1997). Results from the study also indicate that apoptosis is triggered in this
case by inducing an ERK-MAPK signaling cascade as a consequence of
interaction with EGF receptors on the cell surface. The interaction may be direct
or may be caused by asbestos-induced ROS. In a related study (also previously C
summarized, Section 7.3.4.1), Timblin et al. (1998a) indicate that the asbestos-
induced apoptosis reported in the Mossman et al. work is fiber-type specific and
the pathway involved appears to stimulate expression of c-fos.
In a study previously reported in greater detail (Section 7.3.4.1), Levresse et al, /
(1997) indicate that chrysotile induces apoptosis in cultured rat pleural
mesothelioma cells with the effect peaking at 4% at 72 hours following exposure
to 10 ug/cm2. Although the authors observed a much smaller effect with
crocidolite, they indicate that the difference is likely due to the much smaller
number of long fibers in the particular crocidolite sample evaluated.
C
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• Broaddus et al. (1997) indicates that crocidolite (not UICC) but not wollastonite,
glass beads, or non-fibrous riebeckite cause substantial apoptosis in rabbit
pleural mesothelioma cells in culture in a dose-dependent fashion. The extent of
apoptosis induced was inhibited by treatment with catalase and by
3-minobenzamide (an inhibitor of poly(ADP-ribosyl) polymerase. The former
indicates a role for ROS mediation and the latter indicates that this enzyme,
which mediates DNA repair, also mediates asbestos-induced apoptosis (perhaps
triggered by asbestos-induced DNA damage). Asbestos induced apoptosis was
also inhibited by treatment with desfeoxamine, but effects were restored by
adding iron to the medium. The authors note that in other studies, crocidolite
has been shown to induce DNA strand breaks within 2 hrs after exposure and
induces unscheduled DNA synthesis within 24 hrs following exposure. Asbestos
also induces production of poly(ADP-ribosyl) polymerase
Non-fibrous particles:
• Leigh et al. (1997) intratracheally instilled rats with silica (a non-fibrous particle)
at doses varying between 2 and 22 mg. They then collected cells by bronchio-
alveolar lavage (BAL) 10 days after instillation. The authors observed large
numbers of apoptotic cells in BAL fluid and that the number of such cells was
dose-dependent. The dead cells were primarily neutrophils (so that this might
represent some type of mechanism to restore homeostasis). Engulfment of
apoptotic cells by macrophages was also observed. The authors report that, 56
days after instillation, apoptotic cells were observed in granulomatous tissue
within the lungs of rats exposed to silica. This suggests that apoptosis may also
occur in response to chronic inflammation. The authors conclude that silica
induces apoptosis among granulomatous cells and alveolar cells and that such
apoptosis and the subsequent engulfment of apoptotic cells by macrophages
may play a role in the evolution of silica-related disease. The authors also note
that granuloma formation is a hyperplasia-related event.
At least some of the mechanisms suggested above for asbestos-induced apoptosis are
dependent on fiber size (the non-fibrous analog of crocidolite does not induce the
effect) and dependent on the chemistry of the fibers involved (various, cation-
substituted analogs of erionite exhibit disparate ability to induce the effect). Although
non-fibrous particles (such as crystalline silica) may also induce apoptosis, as
previously suggested, this may be through separate mechanisms from those
responsible for asbestos-related effects, even if the same end point results.
7.3,4.4 Asbestos-induced cytotoxicity. While there is ample evidence from various
in-vitro studies that asbestos is cytotoxic, such effects are observed almost exclusively
at the highest concentrations evaluated in an experiment (for example, Luster and
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Simeonova 1998, Section 7,3.4.2 and Choe et al. 1999, Section 7.3.4.2). Many
experiments are conducted at concentrations below those for which cytotoxicity is
important because the 'other toxic effects attributable to asbestos occur at substantially
lower exposure levels and researchers prefer to study such effects in the absence of
potentially confounding cytotoxicity. For in-vitro studies, for example, non-cyotoxic
effects are typically studied at concentrations less than approximately 10 [jg/cm2 (or 20 ('
ug/ml) while substantial cytotoxicity is not typically observed until exposure
concentrations are several times higher.
Because most of the other effects attributable to asbestos occur at concentrations that
are substantially lower, this begs the question as to whether frank cytotoxicity is an <-••
effect that is relevant to human exposures. There is also substantial evidence that the
mechanisms associated with asbestos-induced cytotoxicity are separate from the
mechanisms that mediate most of the other asbestos-related effects of interest
• Kamp et al. (1993) dosed cultured pulmonary epithelial (PE) cells with UICC
amosite asbestos. In some studies, polymorphonuclear leukocytes (PMN) were *-•'
also added to the culture. Typical doses in this experiment were on the order of
250 ug/cm2, which is quite high for these types of studies. For example,
compare this level with the levels reported for studies of ROS/RNS generation
(Section 7.3.3), cell signaling (Section 7.3.4.2), proliferation (Section 7.3.4.1) or
apoptosis (Section 7.3.4.3). (
Kamp and coworkers indicate that the effect of amosite exposure on cultured PE
cells (at the concentrations studied) was to induce substantial cell lysis
(cytotoxicity) and little cell detachment (from the culture medium), which would
indicate increased cell motility. Addition of PMN to the culture resulted in both
increased cell lysis and cell detachment for comparable exposures to amosite.
The observed cell detachment was mitigated in a dose-dependent fashion by
adding protease inhibitors. Further work indicated that asbestos induces release
of human neutrophil elastase (HNE), which may mediate the combined effects
with PMN. PE cell exposure to HNE alone causes increases in cell detachment
in a dose-dependent fashion. However, when combined with asbestos exposure, (
cell lysis increases at the expense of cell detachment. The authors suggest that
HNE becomes bound to asbestos, which also becomes bound to PE cells and
this facilitates augmented cytotoxicity by proteases that are secreted by PMN's.
• Blake et al. (1998) studied the effect of fiber size on the cytotoxicity of alveolar f
macrophages in vitro. Cultured cells were dosed with concentrations varying
between 0 and 500 ug/ml of each of 5 different length preparations of JM 100
glass fiber. Cytotoxicity was monitored by assays for extracellular LDH and by
chemiluminescence following zymosan addition. The latter assy is intended to
show macrophage stimulation. Results indicate that all samples showed dose-
€
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dependent increases in toxicity (i.e. increasing LDH and decreasing
chemiluminescence). Comparing across samples, relatively long fibers
(mean = 17 |jm) showed the greatest toxicity. The authors further indicate that
microscopic examination suggests that frustrated phagocytosis plays a role in
cytotoxicity.
Goodglick and Kane (1990) studied the effect of three different length
preparations of crocidolite(long, short, and UICC) on elicited macrophages
(stimulated initially with thioglycolate) in vitro and in vivo. The long and short
samples were reportedly prepared from the UICC sample by repeated
centrification, Goodglick and Kane report that all three types of crocidolite
stimulated release of ROS from macrophages. At sufficiently high
concentrations, all three also caused substantial cytotoxicity, although apparently
due to the longer time required to settle in culture, the full effects from short
fibers take longer to develop. They suggest that, on a total fiber number or
surface area basis, long and short crocidolite appear to exhibit approximately
equal potency toward the cytotoxicity of macrophages. Further work with
various inhibitors indicates that cytotoxicity is mediated by production of ROS
and that ROS is produced via an iron-dependent pathway. They also indicate
that, among the effects of crocidolite exposure is that macrophage mitochondria!
membranes are depolarized
Goodglick and Kane also evaluated the effects of long and short crocidolite in
vivo. This was done by evaluating the effects of intraperitoneal injection of the
various samples (long, short, or mixed crocidolite or titanium dioxide particles) in
C57B1/6 mice, Results indicate that a single injection of long crocidolite (480
ug) induced an intense inflammatory response, leakage of albumin, and fibers
observed scattered across the diaphragm. In contrast, a single injection of short
crocidolite (600 ug) induced only a relatively mild inflammatory response and
only limited clusters of fibers observed on the surface of the diaphragm.
To test whether short fibers would show a greater response, if they were not
cleared more readily than long fibers, Goodglick and Kane also subjected mice
to 5 consecutive, daily injections of 120 ug of short crocidolite and noted more
substantial aggregations of fiber clusters along the diaphragm as well as a more
pronounced inflammatory response. Cell injury was also assessed by Trypan
blue staining (which indicates cell death). All mice singly or multiply injected with
mixed or long crocidolite showed marked Trypan blue staining. Single injections
of short structures showed only limited Trypan blue staining, However, following
5 daily injections of short fibers, multiple Trypan blue stained cells were observed
on the diaphragm in the vicinity of the locations were clusters of fibers were also
observed. The authors also indicate (in contrast) that neither single injections of
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160 or 800 ug nor 5 consecutive (160 ug) injections of titanium dioxide produced
any Trypan blue staining.
The authors conclude from this study that both short and long crocidolite fibers
appear to be cytotoxicto macrophages while titanium dioxide particles are not
(suggesting that not only fiber length, but fiber type is important to cytotoxicity).
They further suggest that, while short fibers tend to be cleared rapidly in vivo,
when such clearance mechanisms are overwhelmed (such as by repeated insult
through repeated, daily injections in this study), then the toxic effects of short
structures becomes apparent. As indicated in other studies, however, there may
be multiple mechanisms working to produce similar responses, that such
mechanisms may exhibit varying dose-response characteristics, and that
cytotoxicity may not generally be directly related to mechanisms that contribute
to carcinogenesis, Moreover, there almost certainly are at least some longer
fibers in the short fiber preparation and extended analysis to determine their
relative concentration with adequate precision would be helpful to see if the
relative magnitude of the observed effects correlate.
Palekar et al. (1979) studied the ability of four different samples of
commingtonite-grunerite, each also subjected to varying degrees of grinding, to
induce hemolysis of mammalian erythrocytes and cytotoxicity to Chinese
hamster ovary (CHO) cells. The samples studied include: UICC amosite (4.13
m2/g surface area/mass), which is denoted as "asbestiform grunerite; "semi-
asbestiform" commingtonite (3.88 m2/g); acicular commingtonite (ground to three
particle sizes: 3.76, 2.45, and 0.82 m2/g), and acicular grunerite (2.82 rrf/g).
Results from this study indicate that amosite induced the greatest hemolysis of
erythrocytes by far (approximately 50%) while acicular, unground grunerite
caused no hemolysis. However, grinding the acicular grunerite to increasingly
smaller particle sizes and greater surface area ultimately results in some
hemolysis. Both semi-asbestiform and acicular, unground commingtonite show
hemolytic activity between amosite and unground, acicular grunerite and grinding
acicular commingtonite also increased its hemolytic activity.
Similar results were also observed for cytotoxicity. Amosite was by far the most
cytotoxic and the effect was dose-dependent. A dose of 0.05 mg/ml caused
approximately 75% cell death for CHO cells. At 0.2 mg/ml, only 1% of cells
survived. Acicular grunerite was nontoxic even at 0.5 mg/ml. With grinding,
acicular grunerite cytotoxicity increased, albeit only slowly. The most heavily
ground sample killed fewer than 25% of cells at 0.2 mg/ml and killed only 65% at
0.5 mg/ml. The cytotoxicity of semi-asbestiform commingtonite was substantially
less than amosite but greater even than ground, acicular grunerite. For this
material, 0.2 mg/ml killed approximately 65% of cells and 0.5 mg/ml killed
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approximately 90%. Interestingly, approximately the same dose-response curve
for cytotoxicitywas observed for the 3,88 and 1.61 specific surface area samples
of this material. The 1.21 samples was somewhat less cytotoxic. Acicular
commingtonite was somewhat less cytotoxic (for corresponding doses) than
semiasbestiform commingtonite at the highest specific surface area (3.76) and
its toxicity decreased with decreasing surface area.
The authors also indicate that neither surface charges on crystal particles nor
Magnesium ion content appear to correlate with biological activity. The authors
conclude that the degree of "asbestiform" character of a mineral has a dominant
effect on biological activity. Moreover, although non-fibrous particles may also
be biologically active (and their activity increases with increasing specific surface
area), the effects of particles and fibers lie along entirely separate dose-
response curves. The biological activity of fibrous materials does not appear to
depend directly on specific surface area.
Importantly, the results of the Palekar et al. (1979) study are also consistent with the
possibility that fibrous structures within a specific range of sizes and shapes contribute
strongly to biological activity while largely non-fibrous particles act through a separate
mechanism that depends primarily on total surface area but that, particle-for-particle,
elicits a substantially lower overall response than the mechanism by which fibers act.
Such a scenario is supported by several studies. Jaurand (1997), for example, indicate
that ROS is implicated in the cytotoxicity of long but not short fibers on tracheal
epithelial cells. Although the evidence for distinct mechanisms for fibers and particles
discussed here is specific to cytotoxic and hemolytic effects, evidence in other studies
suggest similar scenarios for other toxic end points (potentially including end points that
contribute to carcinogenicity).
Comparisons of the rate and extent of effects observed in epidemiology studies, whole
animal dose-response studies, and in vitro studies suggests that cytotoxicity may not be
important to human exposures. Unfortunately, however, there is currently insufficient
information to compare doses and exposures across these studies in a more
quantitative fashion, Therefore, the importance of cytotoxicity to human asbestos
exposure cannot be definitively determined at this time.
7.3.4.5 Association between fibres is and carcinogenicity. The hypothesis that lung
tumor induction is associated with the fibrosis has been examined by several authors.
There appears to be a debate as to whether fibrosis is a necessary precursor for
development of lung cancer (associated with exposure to fibers), whether the presence
of fibrosis is an additional factor contributing to increased risk for lung cancer, or
whether the two diseases are largely unrelated. This is an important consideration
because the characteristics of the dose-response relationship between asbestos and
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lung cancer or asbestosis (fibrosis) apparently differ (see Chapter 6 and Sections 7.3,6
and 7,4).
Based on animal studies, Davis and Cowie (1990) found that rats that developed
pulmonary tumors during inhalation experiments exhibited a significantly greater clinical
degree of fibrosis than rats that did not develop tumors. Furthermore, Davis and Cowie
reported suggestive evidence that the pulmonary tumors that did develop in the dosed
rats tended to develop within portions of the rat's lungs that were already scarred by
fibrosis.
As part of a review, Mossman and Churg (1998) indicate that fibrosis of any cause
(including diffuse idiopathic fibrosis) appears to be associated with an increased risk of
lung cancer and that this is observed in both human and animal studies. They also
indicate that only those strains of mice and hamsters that develop fibrotic lesions
following exposure to crystalline silica show an increased risk of developing cancer.
They also report that, in parallel to what is reported for asbestos by Davis and Cowie
(1990), lung tumors that develop following exposure to crystalline silica tend to occur
primarily (if not exclusively) in those portions of the lung where fibrotic lesions
predominate.
In contrast, Case and Dufresne (1997) from their study of lung burdens among Quebec
miners and millers indicate that there is high overlap in the range of concentrations that
lead to both lung cancer and asbestosis and those that lead to iung cancer alone, which
the authors suggest show a lack of relationship between the two diseases (one is not
predictive of the other). The authors indicate that, based on regression of the 111
cases they examined, the only indicator that reasonably tracks lung cancer is severity
of smoking and they indicate that this is true despite the level of fiber content in the
lung.
€
Although a definitive determination concerning the relationship between fibrosis
(asbestosis) and lung cancer cannot be developed at this time, it does appear that
there is some association between the two diseases. Most likely, fibrosis is an
additional risk factor for lung cancer and thus represents an additional set of €
mechanisms that may contribute to the overall risk of developing lung cancer in
association with exposure to asbestos. However, based on the evidence as a whole,
including the evidence that the character of the dose-response relationship for lung
cancer and asbestosis differ, it is not clear that development of fibrosis is an absolute
precursor that is required before asbestos-related lung tumors can develop. ,
Interestingly, based on the studies reviewed by Mossman and Churg (1998), the
relationship between fibrosis and lung cancer induced by silica may be substantially
stronger than that between fibrosis and lung cancer induced by asbestos.
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7.3.4.6 Synergism between asbestos and smoking. Numerous studies have
confirmed the synergistic relationship between smoking and asbestos exposure toward
the induction of lung cancer (see, for example, Hammond et al. 1979, Kamp et al. 1992,
Kamp et al. 1998, Mossman et al. 1996). Smoking is also suspected to facilitate
development of asbestos-induced fibrosis (Kamp et al. 1992).
Putative mechanisms that may contribute to synergism between asbestos exposure
and smoking include;
« facilitated transport of carcinogenic components of smoke that may be
adsorbed on the surface of asbestos fibers, which may then serve as
vehicles to transport these materials through cell membranes to cell
interiors and even to locations adjacent to or within the nucleus (see, for
example, Fubini 1997, Mossman et al. 1996);
« asbestos-catalyzed production of more highly mutagenic metabolites of
the various components of smoke, including benzo(a)pyrene (see, for
example, Mossman etal. 1996);
« smoke-product induced inhibition of clearance of asbestos fibers and/or
asbestos induced inhibition of clearance of smoke products (see, for
example, Mossman et al. 1996); and
« smoke-product induced facilitation of uptake of asbestos by lung
epithelium (see, for example, Mossman et al. 1996).
Although much progress has been made at elucidating the nature of these mechanisms
and other candidate mechanisms, at this point in time it is possible to indicate
definitively neither what mechanisms are important to the observed synergism between
smoking and asbestos exposure nor to indicate the relative magnitude of the
contributions from such mechanisms. Moreover, a detailed review of such
mechanisms is beyond the scope of this document.
7.3.4,7 Conclusions concerning asbestos as a promoter. There is strong evidence
that asbestos acts as a promoter for cancer. While this may primarily involve
mechanisms the contribute to the induction of proliferation, mechanisms associated
with the well-established synergism between smoking and exposure to asbestos (for
the induction of lung cancer; smoking does not appear to affect mesothelioma) are also
important. There also appears to be an association between the development of
fibrosis and an increased risk for lung cancer.
Evidence indicates that multiple mechanisms may be involved with asbestos-induced
cancer promotion and that such mechanisms may be complex and interacting. The
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different mechanisms also appear to exhibit dose-response relationships with differing
characteristics. While there are indications that the most important among these
mechanisms may be strong functions of fiber size (with long fibers contributing most to
the induction of disease), mechanisms that depend primarily on surface area or total
fiber (particle) number (for any size range) may also contribute to overall cancer
promotion. Importantly, these latter mechanisms also appear to be strongly assodated ^
with the composition of fibers (particles) and may therefore contribute more
substantially to the disease induction of agents that have been shown to be particularly
toxic (such as crystalline silica), as opposed to particles, fibers, or asbestos in general.
At this point in time, the available data may not be sufficient to distinguish among the (
relative contributions from the various mechanisms to the overall promotion of cancer,
at least in terms of the mechanistic data itself. Importantly, however, the mechanistic
data should not be considered to be inconsistent with the results from whole animal
studies, where there are clearer indications that fiber size plays a major role in
carcinogenicity and fiber (particle) type is also important (see Sections 7.2 and 7.4).
Such studies indicate, for asbestos (and other biodurable fibers) that: *•
• short fibers (less than somewhere between 5 and 10 urn) do not appear to
contribute to disease;
• potency likely increases regularly for fibers between 10 urn and a C
minimum of 20 urn (and, perhaps, continues to increase up to lengths of
at least 40 urn); and
• fiber type may be important primarily in determining biodurability.
They further indicate that particularly (or uniquely) toxic particles (such as crystalline
silica) may act through a different set of mechanisms that are not dependent on fiber
length but that induce toxic end points paralleling those observed for asbestos.
Importantly, the mechanisms by which asbestos may act as a promoter appear to occur
in cell lines that may contribute both to the induction of lung cancer and mesothelioma. i
7.3.5 Evidence that Asbestos Induces an Inflammatory Response
There is ample evidence that asbestos induces an inflammatory response in pulmonary
tissues and the pleura (see, for example, Sections 7.2.2 and 7.3). Moreover, there ,
appears to be multiple biochemical triggers that mediate this response and various
mechanisms may be fiber size- and/or fiber type-specific (Table 7-5). Because the role
that inflammation plays in the induction of cancer has been addressed elsewhere
(Sections 7.3.3 and 7.3.4), it is beyond the scope of this document to provide a detailed
review of the mechanisms that lead specifically to inflammation.
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7.3.6 Evidence that Asbestos Induces Fibrosis
There is ample evidence that asbestos induces fibrosis in pulmonary tissues (see, for
example, Sections 7.2.2 and 7.3). Moreover, there appears to be multiple biochemical
triggers that mediate this response and various mechanisms may be fiber size- and/or
fiber type-specific (Table 7-5). Because the role that fibrosis plays in the induction of
cancer has been addressed elsewhere (Sections 7.3.3 and 7.3.4), it is beyond the
scope of this document to provide a detailed review of the mechanisms that lead
specifically to fibrosis. Such mechanisms have also been the subject of recent reviews
(see, for example, Mossman and Churg 1998 and Robledo and Mossman 1999).
7.3.7 Evidence that Asbestos Mediates Changes in Epithelial Permeability
As previously indicated (Section 4.4), maintaining the overall integrity of the epithelial
surface of the lung is among the various functions of Type II epithelial cells (Leikauf and
Driscoll 1993), It has been shown that asbestos induces changes in the morphology of
Type II epithelial cells (see, for example, llgren and Chatfield 1998), which has the
effect (among others) of increasing the overall permeability of lung epithelial tissue to
various macromolecules and, potentially to asbestos fibers themselves. The former
plays a role in asbestos-induced fibrosis (by allowing cytokines that stimulate fibroblast
proliferation or stimulate fibroblasts to generate extracellular matrix to pass through the
epithelium and reach the underlying fibroblasts, Section 7.3.6). The latter may be
important to facilitating transport of asbestos from the alveolar lumen to the interstitium
(see, for example, Lippmann 1994).
Changes in epithelial permeability may be triggered by cytokines released from other
cells or by the action of asbestos fibers on epithelial cells directly. Moreover, some of
the mechanisms that mediate this response may be sensitive to fiber size and/or fiber
type. For example, Gross et al. (1994) showed that monolayers of human bronchial
epithelial cells cultured over a porous medium and exposed to cryogenically ground
chrysotile (average length: 1 urn, average aspect ratio:14 at 15 ug/culture plate)
became permeable to fibrin breakdown products (FBP's). The cultures were grown
over human serum with labeled fibrinogen. This was based on observed increased
concentrations of FBP's (double in 24 hrs) in the ablumenal chambers of exposed cells
compared to cells in control cultures. Because the epithelium showed greater
permeability to all concentrations, the increased concentrations were not due to
increased breakdown. The observed FDP flux was not vectoral, not saturable, and
required neither proteolytic processing nor active transport. Thus, asbestos increases
the paracellular flux of intact FDP across airway epithelium.
7.3.8 Conclusions Regarding the Biochemical Mechanisms of Asbestos-
Related Diseases
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That the specific biochemical triggers for asbestos-related diseases (particularly, the
asbestos-related cancers) have not been definitively delineated as of yet is not
surprising. The detailed interactions between fibers (and particles) and the cells and
tissues of the lung are complex and there are complex, multiple, Interacting
mechanisms by which such interactions may contribute to disease. Despite great
progress in elucidating candidate mechanisms, the number of candidate mechanisms is
large and distinguishing among their relative contributions has been difficult. This Is
because, among other things, the ability to compare results across studies of different
mechanisms Is currently limited due to the inability to reconcile the quantitative effects
of dose and response across dissimilar studies.
Nevertheless, a number of important implications can be gleaned from the available
literature, First, it appears that asbestos can function both as a cancer initiator and a
promoter, It also appears that both the initiation and promotion of cancer may occur
through more than one mechanism.
Regarding cancer initiation, asbestos likely acts primarily through a mechanism
involving interference with mitosis... By this mechanism, asbestos fibers are
phagocytized by target cells, migrate to perinuclear locations, and interact with the
spindle apparatus and other cell assemblages required to complete mitosis. This tends
to result in aneuploidy and may cause various clastogenic effects. This mechanism is
driven by long fibers; short fibers do not appear to contribute to the effect. It also
appears that all asbestos fiber types (and potentially other durable fibers with sufficient
dimensions) cause genetic damage via this mechanisms. If there are effects due to
fiber type, they appear only to play a secondary role,
Although there Is also evidence that asbestos may induce production of DMA adducts
and DMA strand breaks (through ROS and RNS mediated pathways), whether such
adducts or breaks ultimately lead to permanent, heritable changes to DNA remain to be
demonstrated. The relative importance of ROS/RNS mediated .pathways for initiating
cancer, compared to the pathway involving interference with mitosis, also remains to be
determined.
There Is also some evidence that the relative importance of asbestos as a cancer
initiator may differ in different tissues. Lung epithelial cells, for example, appear to be
relatively resistant to the mechanisms by which asbestos may initiate cancer.
Mesothelial ceils are not. Among several possibilities, this may be due to the ability of
proliferation-competent lung epithelial cells (Type II cells) to undergo terminal
differentiation when challenged with certain toxins and this is a pathway not available to
mesothelial cells,
t
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The mechanisms by which asbestos may promote cancer primarily involve mechanisms
that contribute to the induction of proliferation, although mechanisms associated with
the well-established synergism between smoking and exposure to asbestos to induce
lung cancer are also important. There also appears to be an association between the
development of fibrosis (including asbestosis) and an increased risk of lung cancer.
Evidence indicates that multiple mechanisms may be involved with asbestos-induced
cancer promotion and that such mechanisms may be complex and interacting. The
different mechanisms also appear to exhibit dose-response relationships with differing
characteristics. While there are indications that the most important of these may be
strong functions of fiber size (with long fibers contributing the most to carcinogenicity),
mechanisms that depend primarily on surface area or total fiber number (for any size
range) may also contribute to overall cancer promotion. These latter mechanisms also
appear to be strongly associated with the composition of fibers and may therefore
contribute more substantially to the disease induction of agents that have been shown
to be particularly toxic, as opposed to particles, fibers, or asbestos in general.
Although crystalline silica may act to produce some of the same effects as asbestos
(including carcinogenicity), there is substantial evidence that this family of materials do
not act through the same pathways and that the characteristics of their respective dose-
response relationships may differ. Thus, for example, while asbestos likely induces
cancer through mechanisms that favor long (and potentially thin) fibers, silica more
likely acts through a mechanism that is dependent on total surface area, with freshly
and finely ground material likely being the most potent. In contrast, grinding asbestos
fibers tends to lesson its carcinogenicity overall. Due to differences in chemistry and
crystallinity (reinforced by studies indicating a lack of correspondence in behavior),
crystalline silica does not appear to be an appropriate analog for any of the asbestos
fiber types. Rather, for example, the appropriate non-fibrous analog for crocidolite is
riebeckite and the appropriate non-fibrous analog for chrysotile is antigorite or lizardite.
7.4 ANIMAL DOSE RESPONSE STUDIES
Ideally, human epidemiology studies (reviewed in Chapter 6) provide the best data
from which to judge the effects of asbestos in humans and they certainly provide the
only reliable information from which to derive dose-response factors for humans.
However, animal dose-response studies have proven useful for elucidating certain
features of the relationship between asbestos dose and response that cannot be
adequately explored in the human studies, primarily due to limitations in the manner
that exposures were characterized in the human studies (see Chapter 5).
Unlike human epidemiology studies, exposures in animal studies are controlled and
better quantified. Frequently, the characteristics (in terms of fiber size, shape, and
type) of such exposures have also been better quantified and this has allowed
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exploration of the effects that such characteristics (fiber size, shape, type) have on
disease response. Accordingly, an overview of animal dose-response studies is
provided in this section. Both injection-implantation studies and inhalation studies are
reviewed. Particular attention is also focused on a "supplemental" animal inhalation
study that we conducted with the specific aim of identifying the characteristics of
asbestos that best relate to risk. The strengths and limitations of these kinds of studies (
are described in Chapter 5.
7.4.1 Injection-Implantation Studies
Because the fibrous materials in injection and implantation studies are placed (
immediately against the target tissue, the effects of processes associated with
inhalation, retention, and translocation are avoided. The only active mechanisms that
need to be considered in these studies are those that occur directly in the target tissue
(including degradation, clearance, and biological responses of the types described in
the previous sections of this chapter) Fibrous materials placed against the tissue
surface are subject to dissolution, phagocytosis bymacrophages, and phagocytosis by ' •'
the cells of the target tissue. These mechanisms are described in greater detail in
Section 7.2. A range of biologic responses have also been observed (described in
Section 7.3).
Numerous researchers have performed these types of studies. (
The work of Stanton et a I. In a series of studies, Stanton et al. (1972, 1977, and
1981) implanted fibrous materials and induced mesotheliomas in rats. In the studies, a
pledgette composed of coarse glass is loaded with hardened gelatin containing sample
material and is surgically implanted immediately against the left pleura of the rats. f..
Control studies demonstrate that the coarse glass of the pledgette does not induce
significant tumors in the absence of other tumorigenic agents in the gelatin.
Although the mass dose of material implanted was the same for all experiments
(40 mg), the observed incidence of mesothelioma varied among samples. By
characterizing the dimensions of fibrous structures in the samples using a microscope, <
the researchers were able to explore the relationship between fiber size and the
incidence of mesothelioma. By studying a wide range of fibrous materials, Stanton and
his coworkers concluded that the induction of mesothelioma is determined primarily by
the physical dimensions of fibers and that mineral composition is secondary. Further,
potency appears to increase with the length and decrease with the diameter of fibrous ^
structures. The researchers also concluded that the incidence of malignant tumors
correlates with the degree of fibrosis induced by the presence of the fibrous materials.
This does not necessarily imply, however, that fibrosis is a necessary step in the
induction of asbestos-induced tumors (see Section 7.3.4.5).
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Conclusions from the Stanton et al. studies indicating that mineralogy is not a factor in
biological response conflicts with evidence provided in Chapter 6 and implications
gleaned from mechanism studies presented in Section 7.3. However, the Stanton et al.
studies have been shown to suffer from certain methodological limitations (Berman et
al. 1995) so that results from these studies should be considered more qualitative than
quantitative.
Due to limitations in the ability to produce samples composed of uniform fibers,
quantitative relationships between size and potency were explored by Stanton et al.
(1972, 1977, and 1981) using a regression analysis. Structures longer than 8 urn with
diameters less than 0,25 urn or longer structures with diameters less than 1.5 urn were
found to represent the range of sizes that best correlate with carcinogenicity, It was
further stated that such correlations did not eliminate the possibility that other size
ranges also contribute to potency, only that the two size ranges identified appear to
correlate best. Samples that varied significantly from the reported correlations were
attributed to errors in the characterization of structure size distributions in those
samples. However, other methodological limitations might also have contributed to the
observed deviations or such "outliers" may also suggest evidence for a mineralogical
effect that is similar to what is reported in other studies (see Section 7.3 and Berman et
al. 1995).
The precision of estimates for the ranges of sizes that contribute to biological activity
that are derived from the Stanton et al. studies is limited so that such estimates should
also be considered qualitative. Size distributions were determined by characterizing
200 to 1,000 structures using TEM and there is no indication that statistically balanced
counting rules were employed (Section 4.3). Under such conditions, counts of
structures longer than 8 urn are likely small and subject to large uncertainties for most
of the samples characterized. Confidence intervals are not provided for any of the
counts presented in these studies.
^
Potentially larger errors in the Stanton et al. studies could have been introduced by the
method employed to relate fiber counts to sample mass. As indicated in Chapter 5,
estimating contributions to mass by sizing total particles and assuming that this is
proportional to total sample mass is subject to error from the limit to the precision of
characterizing structure dimensions (particularly diameter) and by not accounting for
nonasbestos (and possibly nonfibrous) material in the samples.
Thus, for example, there is no discussion of the precision with which the cut point of
8 urn was determined in these studies. Based on the manner in which size distribution
data are presented, it is unlikely that cut points that vary by up to a factor of 2 would
lead to substantial difference in the quality of the correlation. For example, a cut point
of 5 urn may not be distinguishable from 8 urn within the database examined.
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Re-analysis and extension of the Stanton studies. Several researchers have re-
evaluated data from the implantation studies to test additional hypotheses. Using the
Stanton data, Bertrand and Pezerat (1980) examined the relationship between
mesothelioma incidence and several characteristics not evaluated by Stanton et al.
including: average fiber length, average fiber diameter, average fiber aspect ratio, total
fiber surface area and total fiber volume. Results from the regression analysis indicate (-'
that potency varies directly with average length and inversely with average diameter but
that neither parameter is a good indicator alone. Combining the effects of length and
diameter, average aspect ratio is highly correlated with potency. Biological activity does
not correlate highly with structure count, surface area or volume except when fiber sizes
are restricted to the long, thin structures that Stanton defined. Results of this study are p
not inconsistent with those originally presented by Stanton except that they emphasize
a set of characteristics that relate parametrically to biological activity rather than
expressing exposure as a single restricted size range of structures.
It is important to note that Bertrand and Pezerat were able to find good correlations
between response and specific "average" characteristics of the samples that are not *-
proportional to the quantify of the material present in the sample ("intensive"
characteristics). Such intensive characteristics as average aspect ratio, average length,
or average diameter are properties that are independent of the mass of material in a
sample. Since response must be a function of the quantity of sample present, intensive
characteristics should have to be multiplied by characteristics that are proportional to (
the mass of a sample (e.g. fiber number, sample mass, or sample volume) in order to
relate them to response. Properties that vary with the mass of a sample are termed
"extensive" properties.
The correlations between intensive properties and response reported by Bertrand and
Pezerat likely succeed within the Stanton database because a constant sample mass *
(40 mg) was employed for all of the implantation experiments. However, to apply
dose/response relationships that are dependent only on intensive characteristics
beyond the Stanton data (where mass dose will not be constant), it is necessary to pair
intensive characteristics with extensive characteristics (such as mass or number of
fibers per sample). Therefore, it is unclear how the conclusions from this paper may be C>
generalized to other data sets.
In a similar study, Bonneau, et al. (1986) also examined parametric relationships
between structure characteristics and mesothelioma induction. The paper examined
specifically correlations between carcinogenicity and dose in terms of two specific ^
relationships: dose expressed as fibers longer than 8 urn that are thinner than 0.25 urn
("Stanton" fibers) and dose expressed as mean aspect ratio. The researchers conclude
that mean aspect ratio provides an excellent indication of carcinogenicity for individual
fiber types but that each fiber type must be treated separately. Poorer correlations are
found for the relationship between the concentration of "Stanton" fibers and
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mesothelioma, even when fiber types are considered independently. Although these
results appear to be consistent with findings reported from mechanistic studies
(Chapter 6, Sections 7.2 and 7.3) in that they posit a role for fiber mineralogy, the
relationships evaluated by Bonneau et al. also suffer from the limitation of expressing
dose only in terms of intensive quantities, as discussed above. Direct comparison with
other studies is therefore difficult.
Following up on the reported problems characterizing crocidolite in Stanton's work,
Wylie et al (1987) reanalyzed seven crocidolite samples originally studied by Stanton.
She and coworkers then used the new size distributions to reevaluate the "Stanton
hypothesis" (that the concentration of Stanton fibers in a sample correlates with
carcinogenicity). Wylie and coworkers note that substantial deviations from the Stanton
hypothesis occur for specific samples. They conclude that a specific structure size
range alone is not sufficient to characterize biological activity and that a parametric
relationship with other structure characteristics (potentially including mineral type) may
be necessary to sufficiently describe biological activity.
Conclusions from the Wylie et al. paper must be interpreted carefully because the
researchers evaluated only the relationship between carcinogenicity and the single
specific size range indicated ("Stanton" fibers). Thus, the possibility that improved
correlations exist between biological activity and different size ranges or a combination
of size ranges cannot be ruled out. Qualitatively, conclusions presented in this paper
are not inconsistent with the conclusions reported by Stanton et al. regarding the
general relationship between response and fiber dimensions,
The Wylie et al. study appears to suffer from several methodological problems. These
relate to the manner in which the sample reanalysiswas performed. The drop method
for preparing electron microscopy grids (used in this study) is not satisfactory for
preparing grids. In fact, as reported in the study itself, grids prepared as duplicates by
this method were shown to be non-uniform at the 95% confidence interval using a chi
squared test. In addition, only 100 to 300 fibers were counted for each sample. Since
there is no indication that statistically balanced counting was performed, the uncertainty
associated with counts of Stanton fibers may be substantial. Such errors would be
further multiplied by uncertainty introduced during the sizing of total particles to
determine the number of fibers per unit mass.
In a later study, Wylie et al. (1993) examined the effect of width on fiber potency. In
this latter study, results from animal injection and implantation studies were pooled and
subjected to regression analyses to identify correlations between exposure and tumor
incidence. The animal studies selected for inclusion in this analysis were performed on
a variety of tremolite samples exhibiting a range of morphological and dimensional
characteristics.
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In their regression analyses, Wylie et al, evaluated a range of exposure indices that
emphasize different morphological or size characteristics to help elucidate the
characteristics of asbestos that induce a biological response. Because all of the animal
studies included in their analyses involved tremolite, mineralogy was not an issue.
Results from the Wylie et al. study suggest that fibers longer than 5 urn and thinner
than 1 urn best correlate with tumor incidence among the animal Injection and
implantation studies examined. Further, they suggest that a width limit, rather than a
limit on aspect ratio, better reflects the bounds of the asbestos characteristics that
determine biological activity. They also suggest that complex structures (bundles and
clusters) need to be evaluated as part of the determination of exposure because such
structures can breakdown and contribute to the population of thinner fibers.
Although the results of the Wylie et al. study are interesting and tend to support the
general conclusions in this document related to width, if not to length (see Section 7.5
and Appendix B), as the authors themselves indicate, such results should be
considered qualitative due to the limitations imposed on their study by the methodology
employed. Their study was conducted by:
combining results from multiple studies without careful consideration of
variation introduced by methodological differences across the studies;
employing asbestos concentrations determined by SEM and without
careful consideration of differences in the counting methodologies
employed by differing research groups across studies; and
considering injection and implantation studies, which (as opposed to
inhalation studies) do not account for all of the mechanisms that affect the
dose-response relationship in humans.
The limitations imposed by the above constraints are highlighted in Chapters 4 and 5 of
this report.
Other injection studies. A series of injection studies were conducted by several
research groups. In these studies, fibrous materials were suspended in saline and
injected into rats immediately adjacent either to the pleura or peritoneum, A large
number of fibrous materials have now been studied by this process, as reported by Pott
et al. (1974, 1976, 1978, 1982, and 1987), Muhle et al. (1987), Bolton et al. (1982,
1984, and 1986), Davis et al. (1985, 1986a, 1986d, 1987, and 1988a), and Wagner et
al. (1976, 1980, 1982, 1984, and 1985). Newer studies are also discussed in
Section 7.2. Results confirm that it is the fibrous nature of the materials that is the
primary factor leading to the induction of tumors and that potency appears to depend
directly on length and inversely on diameter.
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The authors of these studies tend to indicate that, except where fibers are not
persistent in vivo due to solubility or other degradation processes, the mineralogy of the
fibers appears to play only a secondary role in determining disease incidence.
Researchers conducting injection experiments also tended to report a correlation
between tumor incidence and the degree of fibrosis induced by the sample. These
observations are consistent with the ideas originally articulated by Stanton.
Pott developed Stanton's ideas further by suggesting that carcinogenicity is a
continuous function of fiber dimensions, which decreases rapidly for lengths less than
10 urn and also decreases with increasing diameter. The possibility was also raised
that the apparent inverse dependence on diameter may be an artifact due to the limited
number of thick fibers that can be injected in a sample of fixed mass.
Although the published injection studies indicate that potency decreases with
decreasing length, researchers in these earlier studies were reluctant to identify a
length below which contributions to carcinogenicity can be considered inconsequential.
This may be due in part to the skewed distribution of fiber sizes typical of asbestos
dusts. Thus, for example, even if structures less than 5 urn are only 1% as potent as
structures longer than 5 urn, they may be as much as 100 times as plentiful in some
asbestos dusts, so that the total contribution to potency would be equal for both size
fractions.
Reasonable dose/response curves have been generated using various sample masses
of a single material in some of these studies. This has been demonstrated for UICC
crocidolite and UICC chrysotile "A" (Bolton 1984). Results indicate that the relationship
between tumor incidence and the log of the dose may be linear and there is no effective
threshold. A consistent difference between the two dusts is apparent; the points lie
along separate curves and chrysotile appears to be more potent per unit sample mass.
In general, details of the analytical techniques used for quantifying size distributions in
these studies are not fully documented. To the extent that they are, it appears that
similar approaches were adopted to those described for the implantation studies above.
Consequently, similar limitations apply to the interpretation of results. Briefly, large
uncertainties are likely associated with counts of long fibers and estimates of the
number of fibers per unit sample mass. Counts in several of the studies also suffer
from limitations in the ability of SEM or PCM to detect thin fibers (Section 4.3);
whenever SEM or PCM was employed, the thinner fibers were likely under-represented
in reported fiber size distributions.
Because samples are placed against mesenchyme in the published implantation and
injection studies, results of these studies most directly represent processes associated
with the induction of mesothelioma. Assuming, however, that clearance and
degradation processes are similar in the deep lung, once a fiber reaches a target
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tissue, results from the implantation and injection studies may also provide a model of
biological response in lung tissue and the factors that lead to the induction of pulmonary
tumors, Such a model must be considered qualitative at best, however, because it has
been shown that the mechanisms of tissue response to the presence of asbestos in
lung parenchyma and in the mesenchyme differ in detail (Section 7.3), The time
periods over which the various clearance mechanisms operate in the deep lung and the C
mesenchyme also differ (Section 7,2), although, it is apparent that the general nature of
the clearance and degradation processes in the two tissue types are generally similar,
7.4.2 Animal Inhalation Studies
(
Animal inhalation studies measure response to exposure in controlled systems that
model all of the relevant variables associated with asbestos disease mechanisms in
humans (including respirability, retention, degradation, clearance, transloeation, and
tissue-specific response). Thus, the available inhalation studies are the best database
from which to evaluate the integrated effects that lead to the development of asbestos-
related disease. Such studies can be used both to identify tie characteristics of ^
asbestos that determine biological activity and to qualitatively elucidate the nature of the
corresponding relationship between exposure (via inhalation) and the induction of
disease.
In this section, the nature and results of the existing animal inhalation studies are (
described. In the following section, a project undertaken to overcome the limitations of
the existing animal inhalation studies is described (the supplemental inhalation study),
Because this latter project was specifically designed to support the risk protocol
presented in this document, the nature and results of this latter project are described in
detail, .
The existing animal inhalation database consists of approximately 30 studies of which
approximately 20 contain dose/response information based on lifetime monitoring of
exposed animals, including the work of Davis et al, (1978, 1980, 1985, 1986a, 1986d,
1988a, and 1988b), Wagner et al. (1974, 1982, 1985, and 1987), Bellman et al. (1987,
1995), Boltonetal. (1982), Le Bouffant et al, (1987), Leeetal. (1981), McConnel et al. €
,(1982), Muhleetal. (1987), Plateket al. (1985), Smith etal. (1987), and Goldstein et al,
(1983). The studies are similar in overall-design, although detailed differences
potentially affect the comparability of results from separate studies,
In the inhalation studies, plugs are formed from bulk samples of fibrous asbestos and ,
related materials, which are placed in a dust generator to be aerosolized. The
generators (Beckett 1975), usually a modified version of the apparatus originally
designed by Timbrell (Timbrell et al. 1968), consist of a rotating brush that sweeps over
an advancing plug of bulk material liberating fibers that are entrained in the controlled
air flow passing through the device. The airborne dust is then passed either into a
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delivery system for nose-only exposure or into an exposure chamber where animals are
kept for fixed periods of time (usually 7 hours per day) on a weekly routine (typically 5
days per week). The exposure routine is continued for as long as 2 years in some of
the studies. In some, but not all of the studies, fiber-containing air is passed through a
cyclone or elutriator prior to the exposure chamber so that exposure consists primarily
of particles sized within the respirable range.
Asbestos concentrations in the animal inhalation experiments are monitored by a
combination of techniques. The concentration of total dust in the chamber is generally
monitored gravimetrically. Simultaneously, membrane filter samples are collected and
fibers counted by PCM. The quotient of these two measurements yields the number of
(PCM) fibers per unit mass of dust (Section 4.3). The distribution of fiber sizes within
the dusts introduced into the animal exposure chambers may also be determined in
these studies by any of a variety of methods. As indicated previously (Section 4.3),
however, the utility of such measurements depends on the precise manner in which
they are derived.
To derive fiber size distributions, dust samples from these studies have generally been
collected on polycarbonate filters for analysis by SEM. However, such distributions
suffer both from the limitations of SEM (Section 4.3) and from the manner in which they
are tied to the inhalation experiments (see Chapter 5).
Theoretically, the dose of any fiber size fraction can be estimated in a two-step process.
The procedure incorporates consideration of a size fraction termed the PCM-equivalent
fraction (PCME), which is the fraction of structures measured by SEM (or TEM) that
correspond to the size range of structures known to be visible and therefore countable
by PCM. First, the concentration of the PCM-equivalent fraction of the fiber size
distribution (measured by SEM) is normalized by dividing its value by the PCM-
measured concentration per unit dust mass observed in the inhalation experiment.
This ratio is then multiplied by the fractional concentration of any specified size range of
interest within the distribution (measured by SEM) to determine the exposure level for
that size fraction, However, because bivariate (length by diameter) size distributions
have not typically been developed in the available studies and because the number of
total fibers longer than 5 urn observed by SEM (without adjustment for width) does not
correspond to the number of total fibers longer than 5 urn observed by PCM, it is not
possible to derive a true PCME fraction from the SEM data. Therefore, the theoretical
approach described above for estimating exposure to specific.size fractions cannot
generally be applied in the existing studies.
As indicated above, the data within the published animal inhalation studies are further
constrained by the limitations of the analytical methods employed to generate the data
(Section 4.3). Comparison of data between studies is also hindered by the lack of
sufficient documentation to indicate the specific methods and procedures employed in
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each study. Frequently, for example, it is unclear whether respirable dusts or total
dusts have been monitored. Also, several studies fail to report one or both of two
critical pieces of information: fiber-number-to-mass conversion factors and fiber size
distributions. In addition, few studies indicate the precise counting rules employed for
generating size distributions.
When structure-number-to-mass conversion factors are provided, unless the
conversion factor is derived by counting fibers in a specific size range in a known mass
of sample and fiber concentrations in other size ranges are normalized to this count,
several types of error may be introduced. For example, if total sample mass is
assumed proportional to calculated mass derived from volume characterizations of the
particles counted, unless isometric particles are sized along with fibers and both
asbestos and nonasbestos partides are included in the count, a bias will be introduced
in the conversion factor because total sample mass will have been under represented
to the extent that such particles are ignored in the estimation of fiber mass. Even if
such particles are included, significant uncertainty may result from estimating the
volumes of irregular particles and the limited precision associated with the count of the
largest particles (due to their limited number). The uncertainty in the measurement of a
fiber's diameter is squared in contributing to the uncertainty associated with a mass
estimate.
Among reported variations in study design, differences in the detailed design and
operation of the aerosolization chamber and the frequency and duration of exposure
also potentially contribute to variation in results between studies. Also, use of differing
animal strains and species across the various studies suggest the possibility that
physiological differences may contribute to the observed variation in study results.
Such differences are discussed further in Chapter 5.
A small subset of the asbestos dusts evaluated in the animal inhalation studies have
been analyzed by TEM. However, even the published fiber size distributions from these
TEM studies are subject to variation from differences in procedures used for sample
preparation, from differences in counting rules, and from precision limitations due to the
limited number of fibers actually characterized (Section 4.3). This latter limitation
particularly affects the precision with which longer fibers are counted.
Although fiber size distributions are primarily based on SEM analyses rather than TEM
analyses in the existing animal inhalation studies, results generally echo the results of
the injection and implantation studies. Thus, longer fibrous structures are observed to
contribute most to asbestos biological activity, at least qualitatively. For example, dusts
containing predominantly long amosite or long chrysotile fibers induce far more
pulmonary tumors than samples containing predominantly short structures (Davis et al
1986a and 1988b). However, dusts evaluated in the existing inhalation experiments
have not been characterized sufficiently to distinguish the dependence of biological
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activity on fiber diameter. Neither are the existing studies sufficient to evaluate the
importance of mineralogy (or other potentially important asbestos characteristics) in
determining risk.
7.4.3 Supplemental Inhalation Study
Given the problems with the existing animal inhalation studies, a project was
undertaken to overcome some of the attendant limitations. To control for effects from
variation in study design and execution (including choice of animal strain, animal
handling procedures, equipment design, sample handling procedures, dosing regimen,
and pathology protocols), the project focused on a set of studies generated from a
single laboratory (i.e., the studies published by Davis et al.). Ultimately, the results from
six studies covering nine different asbestos samples (including four types of asbestos
with samples exhibiting multiple size distributions for two asbestos types) and a total of
13 separate experiments (some samples were studied at multiple exposure levels or in
duplicate runs) were pooled for analysis. The database of experiments employed in the
project is described in Table 7-7.
To overcome the limitations in the Davis et al. studies associated with the
characterization of asbestos itself, the dusts studied in the thirteen experiments listed in
Table 7-7 were regenerated by the same group who performed the original studies,
from the same starting materials, using the same equipment, and reproducing the same
conditions under which the original studies were conducted. Samples of the
regenerated dusts were then collected and analyzed by TEM using a modified version
of the Superfund air method (Chatfield and Berman 1990) to generate bi-variate size
distributions that also include detailed characterization of the shapes and complexity of
fibrous structures observed.
The total mass concentration of the regenerated dusts and fiber measurements by
PCM were also collected to provide the data required to link size distributions in the
regenerated dusts to absolute structure concentrations in the original inhalation
experiments. The manner in which such calculations are performed has been
published (Berman et al. 1995).
The concentration estimates (for asbestos structures exhibiting a range of
characteristics of interest) that were derived from the TEM analyses of the regenerated
dusts were then combined with the tumor response data from the set of inhalation
experiments listed in Table 7-7 and a statistical analysis was completed to determine if
a measure of asbestos exposure could be identified that satisfactorily predicts the lung
tumor incidence observed. A more limited analysis was also performed to address
mesothelioma; the small number of mesotheliomas observed in the Davis et al. data
constrained the types of analyses that could be completed for this disease. The
detailed procedures employed in this analysis and the results from the first part of the
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study have been published (Berman et al. 1995). These are summarized below along
with results from the parts of the study that remain to be published.
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Table 7-7
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In the statistical analysis performed in this study, the individual dose/response profiles
from each of the two data sets were fit to a linear dose/response model:
P, = 1 - EXP( - Q0 -Ljb.ajXj) (7.7)
where: f
"P," is the probability of inducing pulmonary tumors observed in the "ith" study.
A probability of one is equivalent to 100% incidence among the animals
dosed in study i;
c
"Q0" is the correction factor for background derived from the background
incidence of pulmonary tumors observed among the control populations
(pooled from all studies);
IK, II
Xj" is the concentration of the "jth" size fraction of fibers in the "ith" study;
"aj" is the coefficient of potency for the "jth" size fraction of fibers in the "ith"
study; and
MI- n
b" is a coefficient representing a normalization factor accounting for
differences between the dose/response factors found for the summed
contributions of the "j" exposure indices in the "ith" study.
The "a/'s in this analysis are constrained to be positive because it is assumed that no
fiber prevents cancer. The "a/'s are also constrained to sum to one so that
contributions to overall potency from individual size fractions are normalized to the
overall concentration of asbestos. Correspondingly, size fractions evaluated represent
disjoint (mutually exclusive) sets.
The dose/response model was setup as indicated in Equation 7.7 to provide flexibility.
The model allows separate potency coefficients to be assigned to individual size
fractions in a dose/response relationship that depends on multiple size fractions.
Simultaneously, the "b" coefficients allows separate potency coefficients to be assigned
to different fiber types or to results from different studies performed under different
experimental conditions.
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Several investigators (Stanton et al. 1977; Bertrand and Pezerat 1980; Bonneau et al.
1986; and Wylie, et al, 1987) have used a logit curve to investigate the dose/response
relating various measures of asbestos exposure to tumor response. The logit formula
specifies that the tumor probabilities satisfy the relation:
log[P/(1-P)] = a + b-logx (7.8)
where log x is some measure of asbestos exposure, such as log of concentration of
fibers in some size range. In some instances, the logit model was expanded by
replacing b-log x with a term representing a linear combination of exposure indices so
that multiple exposure indices could be explored simultaneously. The models were fit
using standard linear regression based on normal theory.
An equivalent form for the logit model is:
P = eaxb/(1 + eaxb) (7.9)
Written in this form, it is clear that this model does not permit a background response
(i.e. P = 0, whenever x = 0). This is not a serious limitation when there are no tumors in
control animals, such as was the case in Stanton et al. (1977). However, the model
will not adequately fit data in which tumors are found in control animals, This was one
reason for adopting the linear model (Equation 7.7) used in the investigation of the
animal data reported in the study described here.
There is no evidence from this study that the linear model is inadequate. For cases in
this study in which the fit between exposure and response is shown to be inadequate,
the lack of fit is typically observed to be due to an inconsistent (non-monotonic)
dose/response curve so that there is no indication that a non-linear model, such as the
logit, would provide a better fit,
The linear model (Equation 7.7) used in this study was fit using a maximum likelihood
(Cox and Lindley 1974) approach that utilizes the actual underlying binomial
probabilities. This is a more efficient estimation method model than use of regression
methods based on normal theory, which was the fitting method used in the earlier
studies (described above). The regression procedure indicates only whether the
exposure measures that were studied are significantly correlated with tumor response.
In contrast, the statistical analyses performed in this study indicate whether exposures
that are described by a particular characteristic (or combination of characteristics)
satisfactorily predict the observed tumor incidence. To illustrate, it is apparent from
Text Figure 2 of Stanton et al. (1981) that the exposure measure they identify as being
most highly correlated with tumor incidence (fibers longer than 8 urn and thinner than
0.25 urn) does not provide an acceptable fit to the observed tumor incidence. Similarly,
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although all of the univariate exposure measures listed in Table 2 of Berman et al,
(1995) are highly correlated with tumor incidence, none of them adequately describe
(fit) lung tumor incidence.
To test for the goodness of fit in this study, each relationship was subjected to the
standard "P" test where the model was rejected if P was significant at the 5% level
indicating that the true model would provide a worse fit only 5% of the time. Among
models that were not rejected based on a P test, several hypotheses concerning the
relative merit of the various models were also examined according to the method of
maximum likelihoods (Cox and Lindley 1974).
An example of an adequate fit to the tumor response data is provided in Figure 7-5
(Figure 3 of Berman et al. 1995). Note that Figures 2 and 3 of the original'paper were
inadvertently switched during publication; the correct Figures is reproduced here, The
exposure index plotted in Figure 7-5 is the sum:
Exposure = 0.0017C, + 0.853C2 + 0.145C3 (7.10)
where;
"C/' is the concentration of structures between 5 and 40 urn in length that are
thinner than 0.3 um;
"C2" is the concentration of structures longer than 40 um that are thinner than
0.3 pm; and
"C3" is the concentration of structures longer than 40 um that are thicker than 5
um.
This index of exposure represents one of the optimum indices reported in Berman et al.
1995.
As is clear from the figure, when exposure is expressed in the manner described above,
the tumor responses observed in the 13 separate experiments that were evaluated
increase monotonically with increasing exposure, It is also apparent that the data
points representing each study fall reasonably close to the line representing the
optimized model for this exposure index. Thus, exposure adequately predicts
response.
Results obtained from completing more than 200 statistical analyses to determine
whether various measures of asbestos exposure adequately predict lung tumor
response (Berman et al. 1995) indicate that:
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• neither total dust mass nor fiber concentrations determined by PCM
adequately predict lung tumor incidence;
• no univariate measure of exposure (i.e. exposure represented by the
concentration of a single size category of structures as measured by
TEM) was found to adequately predict lung tumor incidence. Of the
univariate measures of exposure examined, the concentration of total
structures longer than 20 um provides the best fit (although still
inadequate); and
• lung tumor incidence can be adequately predicted with measures of
exposure representing a weighted sum of size categories in which longer
structures are assigned greater potency than shorter structures.
The set of analyses completed in support of this work are summarized in Appendix C.
One example of an exposure measure that adequately describes lung tumor incidence
is presented in Figure 7-5. Another exposure measure shown to provide an adequate
fit is:
Exposure = 0.0024Ca + 0.9976Cb (7.11)
where:
"Ca" is the concentration of structures between 5 and 40 um in length that are
thinner than 0.4 um; and
"Cb" is the concentration of structures longer than 40 urn that are thinner than
0.4 urn.
The fit of this index is depicted in Figure 7-6.
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C
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In addition to the above, a series of hypotheses tests were also conducted to test such
questions as: whether fiber type affects potency or whether the component fibers in
complex clusters and matrices should be counted individually. Questions concerning
whether mesothelioma incidence can be adequately described by the same measure(s)
of exposure that describe lung tumor incidence were also addressed. Taken as a
whole, the results presented in Berman et al. (1995) support the following, general
conclusions:
• structures contributing to lung tumor incidence are thin (< 0.5 urn) and
long (> 5 um) with structures longer than 20 um being the most potent;
• the best estimate is that short structures (< 5 um) are non-potent. There
is no evidence from this study that these structures contribute anything to
risk;
• among long structures, those shorter than 40 urn appear individually to
contribute no more than a few percent of the potency of the structures
longer than 40 um;
• lung tumor incidence is best predicted by measurements in which the
component fibers and bundles of complex structures are individually
counted;
• at least for lung tumor induction in rats, the best estimate is that chrysotile
and the amphiboles are equipotent;
• for equivalent size and shape structures, amphiboles are more potent
toward the induction of mesothelioma than chrysotile; and
• after adjusting for the relative potencies of fiber type, the size categories
that contribute to lung tumor incidence appear also to adequately describe
mesothelioma incidence.
A number of supplemental analyses were also conducted, primarily to identify optimal
procedures for performing asbestos analysis and for estimating concentrations. These
analyses have not yet been published, but they are included in the summaries in
Appendix C. The most important results of the supplemental analyses are that:
• tumor incidence can only be adequately fit by data derived from TEM
analysis of samples prepared by a direct transfer procedure.
Measurements derived from indirectly prepared samples could not be fit to
lung tumor incidence in any coherent fashion; and
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Figure 7-6
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• it was not possible to identify an exposure measure in which potency is
expressed in terms of a single, continuous function of structure length.
Regarding the last point, although we were not able to identify a continuous function of
length that provides an adequate fit to the tumor incidence data, the general results
from the above analysis are not inconsistent with the hypothesis that potency is a
continuous function of length (i.e. the Pott hypothesis, Pott 1982). The Pott hypothesis
suggests that relative potency is low for short fibers, rises rapidly over an intermediate
range of length, and approaches a constant for the longest fibers.
7.4.4 Conclusions Concerning Animal Dose-Response Studies
Results from our evaluation of the animal dose-response data for asbestos (including
the existing injection/implantation studies, the existing inhalation studies, and our
supplemental study) indicate that:
• short structures (less than somewhere between 5 and 10 urn in length) do
not appear to contribute to cancer risk;
• beyond a fixed, minimum length, potency increases with increasing length,
at least up to a length of 20 urn (and possibly up to a length of as much as
40 um);
• the majority of structures that contribute to cancer risk are thin with
diameters less than 0.5 um and the most potent structures may be even
thinner. In fact, it appears that the structures that are most potent are
substantially thinner than the upper limit defined by respirability;
• identifiable components (fibers and bundles) of complex structures
(clusters and matrices) that exhibit the requisite size range may contribute
to overall cancer risk because such structures likely disaggregate in the
lung. Therefore, such structures should be individually enumerated when
analyzing to determine the concentration of asbestos;
• for asbestos analyses to adequately represent biological activity, samples
need to be prepared by a direct-transfer procedure; and
• based on animal dose-response studies alone, fiber type (i.e. fiber
mineralogy) appears to impart only a modest effect on cancer risk (at least
among the various asbestos types).
Regarding the last of the above bullets, that only a modest effect of fiber mineralogy
was observed in the available animal dose-response studies (when large effects are
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observed among human studies, Chapter 6), may be due at least in part to the limited
lifetime of the rat relative to the biodurability of the asbestos fiber types evaluated in
these studies, although it is also possible that different mechanisms drive the effects
observed in the animal studies than those that dominate for asbestos-induced cancers
in humans and that such mechanisms depend more strongly on mineralogy. Other
explanations are also possible. Issues relating to both fiber mineralogy and fiber size
are addressed further in Section 7.5 and Chapter 8.
7.5 CONCLUSIONS FROM AN EVALUATION OF SUPPORTING STUDIES
Although gaps in knowledge remain, a review of the literature addressing the health-
related effects of asbestos (and related materials) provides a generally consistent
picture of the relationship between asbestos exposure and the induction of disease
(lung cancer and mesothelioma). Therefore, the general characteristics of asbestos
exposure that drive the induction of cancer can be inferred from the existing studies and
can be applied to define appropriate procedures for evaluating asbestos-related risk.
Furthermore, although it would be helpful to definitively identify the underlying
biochemical triggers and associated mechanisms that drive asbestos-induced cancer,
this is not an absolute prerequisite for the development of a technically sound protocol
for assessing asbestos-related risk.
As previously indicated (Sections 7.3.8), the biochemical mechanisms that potentially
contribute to the induction of asbestos-induced cancer are complex and varied.
Moreover, different mechanisms appear to exhibit differing dose-response
characteristics (i.e. the various mechanisms do not all show the same kind of
dependence on fiber size or fibertype). Some mechanisms, for example, suggest that
fiber length is important and that only structures that are sufficiently long induce a
response. In contrast, other mechanisms suggest that fibers (and even non-fibrous
particles) may all contribute to response and that the magnitude of the response is a
function of the total surface area of the offending fibers (or particles). Among these
mechanisms, additionally, some suggest that fiber type (i.e. mineralogy) is not an
important determinant of potency while other mechanisms indicate that fiber type is the
predominant determinant of potency.
Unfortunately, the existing studies are not currently adequate to support definitive
identification of the specific mechanisms that drive the induction of asbestos-related
cancer (versus other mechanisms that may contribute only modestly or not at all).
However, whatever mechanisms in fact contribute to the induction of disease, they must
be consistent with the gross characteristics of exposure that are observed to predict
response in the available whole-animal dose-response studies and human
epidemiology studies. Therefore, the implications from these latter studies regarding the
dependence of asbestos-induced cancers on fiber size and type are reviewed here in
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some detail. Further, in Chapter 8, they are used to support development of a protocol
for evaluating asbestos-associated risks.
Fiber Length
Fibers less than a minimum length between 5 and 10 urn do not appear to contribute to
risk. This is supported both by the results of our re-analysis of the animal inhalation
studies conducted by Davis et al. (Section 7.4.3 and Berman et al. 1995), in which this
hypothesis was tested formally, and by inferences from the broader literature. As long
as fiber size is adequately characterized, the animal inhalation studies (Section 7.4.2)
and injection/implantation (Section 7.4.1) studies consistently indicate lack of ability of
short structures to contribute to the induction of cancer. Furthermore, animal retention
studies (Section 7.2.1) and histopathology studies (Section 7.2.2) provide strong
mechanistic evidence that explains the lack of potency for short structures; they are
readily cleared from the respiratory tract. Even when sequestered in large numbers in
macrophages within the lung, there is little indication that such structures induce the
kinds of tissue damage and related mechanisms that appear to be closely associated
with the induction of cancer.
Although there are mechanism studies that may suggest a role for short fibers in the
induction of asbestos-related disease (see, for example, Goodglick and Kane 1990,
Section 7.3.4.4), such studies do not track cancer as an end point. Therefore, the
relationship between the toxic end point observed and the induction of cancer needs to
be adequately addressed before it can be concluded definitively that short structures
can contribute to cancer.
Beyond the minimum length below which structures may be non-potent, potency
appears to increase with increasing length, at least up to a length of 20 urn and
potentially up to a length of 40 urn. The latter limit is suggested by our re-analysis of
the Davis et al, studies (Section 7.4.3)-in which it was also found that structures longer
than 40 urn may be as much as 500 times as potent as those between 5 and 40 urn in
length. The former limit is suggested by broader inferences from the literature that
suggest the cutoff in the length of structures that are at least partially cleared by
macrophages from the lung may lie close to 20 urn and that the efficiency of clearance
likely decreases rapidly for structures between 10 and 20 um in length (Section 7.2).
Such inferences are further reinforced by measurements of the overall dimensions of
macrophages in various mammals by Krombach et al. (1997), as reported in
Section 4.4.
Importantly, the inferences -that potency increases for structures longer than 10 um (up
to some limiting length) from these various studies are strongly reinforcing, even though
the upper limits to the points at which potency stops increasing do not precisely
correspond. Furthermore, that the longest structures are substantially more potent
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than shorter structures (and that the shortest structures are likely non-potent) dictates
that asbestos analyses performed in support of risk assessment need to provide
adequate sensitivity and precision for counts of the longest structures.
Fiber Diameter
Because fibers that contribute to the induction of cancer must be respirable, they must
also be thin. The studies reviewed in Section 7.1 indicate that respirable fibers are
thinner than 1.5 urn and the vast majority of such structures are thinnerthan 0.7 urn. In
fact, the results of injection, implantation, and inhalation studies reviewed in
Sections 7,4.1 and 7.4.2 and the results of our supplemental re-analysis of the Davis et
al. studies (Section 7.4.3) indicate that the fibers that contribute most to the induction of
asbestos-related cancers are substantially thinner than the limit suggested by
respirability alone.
Importantly, the results of all of the studies cited above indicate that it is a cutoff in
absolute width that defines the bounds of biological activity rather than a cutoff in
aspect ratio (the ratio of length to width) that has been used to define fibrous structures
heretofore. That is why the exposure index recommended based on our review of the
Davis et al. studies incorporates a maximum width as a cutoff, rather than a minimum
aspect ratio.
Fiber Complexity
In our supplemental evaluation of the Davis et al. studies, the tumor incidence data
from the animal inhalation studies were best fit (predicted) by exposure indices in which
the component fibers and bundles of complex structures (clusters and matrices) were
separately enumerated and induded in the exposure index used to represent
concentration (Section 7.4.3). The appropriateness of such an approach is further
supported by the observation that loosely bound structures (including, for example,
chrysotile bundles) readily disaggregate in vivo (Section 7.2). Therefore, it is
recommended in this report that those components of complex structures that
individually exhibit the required dimensional criteria be individually enumerated and
included as part of the count during analyses to determine the concentration of
asbestos in support of risk assessment.
Fiber Type (Mineralogy)
Two separate but interrelated issues need to be addressed with regard to the effects of
fiber mineralogy. The first is that mineralogy appears to be an important determinant of
cancer risk. The second is that the magnitude of the mineralogical effect appears to be
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at best modest in animal dose-response studies but appears to be substantial in human
epidemiology studies. It must also be emphasized that, due to confounding, the effects
of fiber size and fiber mineralogy need to be addressed simultaneously, if one is
interested in drawing useful conclusions concerning fiber mineralogy.
Results from some (but not all) of the animal injection, implantation, and inhalation
studies previously reviewed (Sections7.4.1 and 7.4.2) suggest that mineralogy plays an
important role in determining biological activity. However, the nature of the effects of
mineralogy are not easily separated from size effects, due to the methodological
limitations of the studies cited. Therefore, the evidence from these studies can be
considered ambiguous. Formal hypothesis testing during our re-analysis of animal
inhalation studies (Section 7.4.3) indicates that, when size effects are addressed,
chrysotile and the amphiboles exhibit comparable potency toward the induction of lung
cancer. In contrast, amphiboles appear to be approximately three times more potent
than chrysotile toward the induction of mesothelioma, once fiber size effects are
addressed.
Several of the human pathology studies cited previously (Section 7.2.3) suggest that
mineralogy is an important factor in determining cancer risk, but these studies similarly
suffer from methodological difficulties that introduce ambiguity into the inferences
drawn. However, it is clear from the human epidemiology data (Chapter 6) that
mineralogy plays a substantial role in the determination of risk for human cancer
(primarily, mesothelioma).
The underlying cause(s)for the observed difference in potency between chrysotile and
the amphiboles may relate to differences in fiber durability (Section 7.2), to size/shape
related differences in fibers that are a function of mineralogy and that cause differences
in deposition, retention, ortranslocation (Sections 7.1 and 7.2), and/or to the
dependence on mineralogy of the specific mechanisms underlying the biological
responses of specific tissues (Section 7.3). The relative magnitudes of such effects on
animal and human pathology also need to be considered, if the observed differences in
potency among animal and human studies, respectively, is to be reconciled. Such
considerations are addressed further in Chapter 8.
Importantly, whether the observed differences in the role of mineralogy toward animal
and human pathology can be reconciled, the effects of mineralogy can be adequately
addressed when assessing asbestos-related cancer risk for humans by incorporating
dose-response coefficients explicitly derived from the human epidemiology data.
Because this is the approach proposed in this document, effects due to mineralogy are
properly addressed.
An Appropriate Exposure Index for Risk Assessment
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The optimum exposure index defined based on our re-analysis of the Davis et al.
animal inhalation studies is a weighted sum of two fiber size categories:
Copt = 0.0024Ca + 0.9976Cb (7.12)
where:
"Copt" is the concentration of asbestos expressed in terms of the optimum
exposure index (i.e. as the weighted sum of two size categories);
"Ca" is the concentration of structures between 5 and 40 urn in length that are
thinner than 0.4 urn; and
"Cb" is the concentration of structures longer than 40 urn that are thinner than
0.4 (jm.
This index was shown to adequately fit (predict) the tumor incidence data across the 13
separate animal inhalation experiments evaluated (P = 0.09). Whether the exposure
index defined in Equation 7.12 is also optimal for capturing the relevant characteristics
of fibers that contribute to the induction of human cancer is an open question. Because
it captures the major characteristics (concerning length and diameter) identified above
that are indicated to be important for human exposures, it represents a promising
candidate. Unfortunately, however, the data required to match this index to a set of
human-derived dose response coefficients does not currently exist (Section 6.2.4).
Therefore, until such time that the data required to adapt a set of human-derived
coefficients to the above index become available, the following interim index (defined
originally in Equation 6.7 and reproduced here), should be used to evaluate asbestos-
related cancer risks for humans:
Casb = 0.003CS + 0.997CL (7.13)
where:
"Casb" is the concentration of asbestos to be used to estimate risk;
"Cs" is the concentration of asbestos structures between 5 and 10 urn in length
that are also thinner than 0.5 um; and
"CL" is the concentration of asbestos structures longer than 10 um that are
also thinner than 0.5 um.
Although not optimized (so that, for example, it does not provide an adequate fit to the
animal inhalation studies), this index nevertheless captures substantially more of the
characteristics (concerning length and diameter) that are indicated to be important for
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human exposures than the index (traditionally defined for phase contrast microscopy) in
current use by EPA (which does not provide an adequate fit to the animal inhalation
studies either). The index currently recommended by EPA is defined as "CPCM" and is
the concentration of asbestos structures longer than 5 urn, thicker than 0.25 urn, with
an aspect ratio greater than 3. A comparison between this index and the "interim" index
defined by Equation 7.13 is instructive.
First, we will ignore the limitations in PCM instrumentation (Section 4.3), which among
other things has been shown incapable of providing reliable measurements of asbestos
concentrations in outdoor settings that are removed from obvious, dominating sources
of asbestos (Berman unpublished). Therefore, we will begin this comparison by
assuming that measurements for the index currently in use by EPA are actually derived
using TEM (so that they can be considered to be "PCM-equivalent concentrations" or
Comparing the dimensions of structures incorporated in CPCMEand Casb, respectively, it
is clear that:
• the interim index, recommended in this document, is weighted toward
longer structures (> 10 urn vs. > 5 urn) than the current EPA approach
and this better captures the expected trend with length;
• the interim index is focused on the thinnest structures, which also tracks
the expected trends in biological activity. In contrast, the current index
excludes the thinnest structures (which are likely more potent and more
numerous than thicker structures in most environments of interest); and
• even more important, the interim index includes only structures that are
respirable while the current index potentially includes counting of a large
fraction of structures that are not respirable (which clearly cannot
contribute to risk).
As indicated above, the size criteria for the interim index proposed in this document
provides substantially better agreement with the characteristics expected of biologically
active fibers (described above) relative to the index currently recommended by EPA
(which is also the index to which the existing epidemiology studies are currently
normalized). It is likely for this reason that human dose-response coefficients adjusted
to match the interim index show improved cross-study agreement relative to unadjusted
coefficients (Sections 6.2.4.2 and 6.3.3.2). Additional advantages of the interim index
are also discussed in Appendix B, where, for example, the interim index is shown to
adequately fit (predict) the relative tumorigenicity of six different tremolite samples that
vary in the degree of their asbestiform character. In contrast, the index currently
recommended by EPA does not adequately fit the tremolite data.
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Although the interim index appears to provide substantial improvement in performance
for use in risk assessment over the index in current use, conclusions from the literature
reviewed in this Chapter indicate that additional, substantial improvement may be
gained by adopting an index that more heavily weights structures even longer
thanIO urn, Although the ideal may be to incorporate separate weights for structures as
long as 40 urn, even incorporating separate weights for structures longer than 20 urn (
would likely achieve most of the desired improvement. However, this will require
completion of limited additional study to develop the data needed to adjust the existing
human dose-response coefficients to match the improved index. Importantly, it is also
likely that, additional improvement over the interim index can be achieved by
conducting the additional study in a manner that would provide size data appropriate for ^
the specific facilities evaluated in the existing epidemiology studies (rather than
generically similar sites, matched by industry), The adjustments used in this document
to match the existing dose-response coefficients to the interim index are industry
generic (Section 6.2,4). It is therefore interesting that, even given this cruder approach
for adjusting coefficients, improved agreement across the adjusted coefficients is still
observed (Sections 6.2.4.2 and 6.3.3,2). (
Interpreting Exposure Indices
It is instructive to examine the relationship between the true (but unknowable) function
that represents biological activity and the functions represented, respectively, by each (
of the exposure indices considered in this Section. All of the indices considered above
(the current index, the interim index, and the proposed, optimum index) all represent
step functions with precise dimensional cutoffs. The existing index is a single-
parameter function with cutoffs for minimum length, minimum width, and minimum
aspect ratio. Both the interim index and the proposed, optimal index are two-parameter
indices with cutoffs for minimum lengths, an interim length (at which potency
incrementally increases), and maximum width.
In contrast, the true relationship between fiber dimension and biological activity likely
represents a continuous function without distinct, hard boundaries (although relatively
steep declines in potency for minimum length and maximum width are likely C
approximated reasonably well by a hard boundary). Unfortunately, due to the
limitations of the studies conducted to date, it has not been possible heretofore to
define a continuous function that adequately predicts risk. However, as long as the
sensitivity of the associated analysis is set sufficiently low so that a minimum of several
structures (say four or five) would be counted to estimate concentrations the relate to ,
levels of risk of potential concern, exposure indices that are step functions
(incorporating hard, dimensional boundaries) will provide a reasonably good
approximation to results that would be obtained, should the true, continuous function be
known. Moreover, agreement between results from a step function and the true,
continuous function will improve as the number of structures counted increases (i.e. as
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the estimated concentration and associated risk increases), due to the averaging of
potency over the multiple fibers that are observed.
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8.0 DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS
Although gaps in knowledge remain, a review of the literature addressing the health-
related effects of asbestos (and related materials) provides a generally consistent
picture of the relationship between asbestos exposure and the induction of disease
(lung cancer and mesothelioma). Therefore, the general characteristics of asbestos
exposure that drive the induction of cancer can be inferred from the existing studies and
can be applied to define appropriate procedures for evaluating asbestos-related risk.
Moreover, such procedures provide substantial improvement in the confidence that can
be placed in predicting risks in exposure environments of interest compared to
predictions derived based on procedures in current use.
Following a general discussions of the findings of this study, specific recommendations
(including options) for finalizing a protocol for assessing asbestos-related risks using
the procedures identified in this document are provided in Section 8.2.
Recommendations are described in Section 8.3 for limited, focused, additional studies
to:
(1) settle a small number of outstanding issues (concerning whether the
current dose-response models adequately track the time-dependence of
lung cancer and mesothelioma) and
(2) provide the data required to fully optimize the approach recommended in
this document.
8.1 DISCUSSION AND CONCLUSIONS
8.2.1 Addressing Issues
The issues identified in the introduction (Chapter 2) as part of the focus of this study
can now be addressed. These are:
• whether the dose-response models currently in use by the EPA for
describing the incidence of asbestos-related diseases adequately reflect
the time-dependence for the development of these diseases;
• whether the relative in-vivo durability of different asbestos mineral types
affect their relative potency;
• whether the set of minerals included in the current definition of asbestos
adequately covers the range of minerals that potentially contribute to
asbestos-related diseases;
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• whether the analytical techniques and methods currently used for
determining asbestos concentrations adequately capture the biologically
relevant characteristics of asbestos (particularly with regard to the sizes of
the structures included in the various analyses) so that they can be used
to support risk assessment; and
• whether reasonable confidence can be placed in the cross-study
extrapolation of dose-response relationships that are required to assess
asbestos-related risks in new environments of interest.
Time dependence (
Regarding the first of the above-listed issues, the lung cancer model appears to
adequately reflect long-term risk associated with exposure to amphiboles (for which
relative risk remains constant beyond 10 yrs after the end of exposure) but may over-
predict long-term risk for chrysotile (Section 6.2.1). Relative risk for chrysotile-exposed
individuals appears to decrease substantially with time following 10 yrs after the end of
exposure, rather than remain constant, as the model predicts. However, these findings
are based on evaluation of only a single chrysotile cohort and a single amphibole cohort
so that further evaluation of a minimum of two additional cohorts (one each of
amphibole and chrysotile), to determine whether these trends are general, maybe
warranted (Section 8.3). C
Although there are hints from our evaluation that the mesothelioma model may
somewhat under-predict long term mesothelioma risk following exposure to amphiboles
(Section 6.3.1), the effect appears small and may not be important. Still, because this
finding is based on evaluation of only a single amphibole study, evaluation of at least (-
one additional cohort may prove prudent. There was no indication that the
mesothelioma model under-predicts risk for chrysotile-exposed individuals, although
only small numbers of mesotheliomas were observed in the data sets evaluated for
chrysotile.
Biodurability (
Because the in-vitro dissolution rate for chrysotile in biological fluids is substantially less
than for crocidolite and, likely, other amphiboles (Section 7.2.4), effects potentially
attributable to differences in the relative biodurabiliy of these asbestos types are
addressed in several sections of this document. Among other things, this difference ^
may explain the reduction in relative risk for lung cancer following cessation of exposure
observed for chrysotile-exposed workers that was not apparent for amphibole-exposed
workers (Section 6.2.1). Both of these affects may also explain the small difference in
potency toward the induction of lung cancer between chrysotile and the amphiboles
observed in this study (Section 6.2.4). Importantly, however, the observed difference is
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based only on best estimates of relative potency; formally, the difference was not found
to be significant,
Although the relative biodurability of chrysotile and the amphiboles may potentially
underlie the substantial difference in potency observed between these fiber types
toward the induction of human mesothelioma, which is significant, (Sections 6.2.4.2 and
6.3.3.2), direct evidence for the specific effects contributing to this difference are not
apparent. Thus, for example, the current mesothelioma model appears to adequately
describe the time-dependence for mesothelioma following exposure to both chrysotile
and amphiboles (Section 6.3.1), although the chrysotile data sets examined were fairly
small, which limits the power of this analysis. Moreover, there is evidence suggesting
that, even among the chrysotile-exposed cohorts, the observed mesotheliomas maybe
attributable to the small concentration of tremolite (amphibole) asbestos that was found
as a contaminant in the material to which these cohorts were exposed (Section 6.3.3).
That tremolite fibers represent a substantial (sometimes predominant) fraction of the
fibers found in the lungs of deceased workers from these cohorts (Sections 6.2.3 and
7.2.3), despite its trace-level presence in the material handled, suggests either that
chrysotile is not deposited as efficiently in the deep lung (which would not be affected
by biodurability) or that it is cleared much more rapidly (which may imply a role for
biodurability, although size effects may also dominate). Animal retention studies
addressing these effects are somewhat ambiguous (Section 7.2.1 and 7.2.2), although
they too support the general impression that chrysotile (on a mass basis) is either not
deposited as efficiently in the deep lung or cleared more readily from the deep lung (or
both). Still, the fact that long fibers of chrysotile (which are expected to contribute most
to potency) are observed to be retained for extended periods of time, clouds the
potential role for biodurability.
Overall, the substantially larger potency toward the induction of mesothelioma observed
in humans may have several causes, but direct evidence for such causes is not
definitive either among the supporting animals studies or the in-vitro studies. Thus,
while this effect can be adequately addressed by the procedures recommended in this
document for risk assessment (because the recommended risk coefficients are based
on human data), it cannot be entirely explained.
Minerals of concern
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Regarding the range of fibrous minerals that potentially contribute to lung cancer and
mesothelioma, available evidence (Sections 7.2 and 7.4)suggests that:
« several minerals and the most biodurable among synthetic fibers (such as
erionite or refractory ceramic fibers) in addition to those included strictly
within the definition for asbestos have been shown capable of inducing f
lung cancer and/or mesothelioma (as long as the corresponding fibers fall
within the appropriate size range);
• fibrous minerals that differ radically in chemical composition or crystal
structure (such as erionite, chrysotile, and the amphibole asbestos types) (
appear to exhibit substantially different potencies (even after adjusting for
size), which maybe largely, but not necessarily entirely, explained by
relative biodurability; and
• fibrous minerals that exhibit closely related chemical compositions and
crystal structures (such as the family of amphibole asbestos types) appear ^
to exhibit relatively similar potencies (once effects are adjusted for size).
Note, to illustrate that biodurability may not entirely describe relative potencies across
the different fiber types, the biodurability-based model byEastes and Hadley (1995 and
1996), which is described in Section 7.2.4, would (properly) predict different potencies (
for chrysotile and amphiboles for both lung cancer and mesothelioma. However,
because the dose-response models for both lung cancer and mesothelioma are linear
in fiber concentration, the Eastes and Hadley model would incorrectly predict that the
ratio of relative potencies of chrysotile and the amphiboles would be the same in
humans for these two diseases, which is definitely not what is observed (Section 6.4).
Given the above-described observations, it is clear that fibrous minerals beyond those
included in the definition for asbestos can contribute to lung cancer and mesothelioma.
It is also likely that potencies for minerals that exhibit similar chemistry and crystal
structure (and which therefore likely exhibit similar physical-chemical properties) also
exhibit corresponding potency (for similarly sized fibers). However, the carcinogenicity (
of fibers exhibiting radically different chemical compositions and crystal structures than
those already identified as carcinogenic, should likely be evaluated on a case-by-case
basis. Two additional considerations may be useful for focusing such evaluations.
First, the size distribution for fibers composed of the mineral of concern should be
shown to include substantial numbers within the range of structures that potentially (
contribute to biological activity (i.e. that fall within the size range defined by the interim
index, Equation 7.13). Second, such fibers should also be shown to be relatively
biodurable (i.e. that they exhibit dissolution rates less than approximately 100 ng/cm2-
hr, Lippmann 2000). Note that, as long as the interim index of exposure is used to
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define fiber sizes, whether a fiber is strictly asbestiform appears to be irrelevant
(Appendix B)
Analytical methods
Given the need to detect the thinnest fibers and the need for reliable measurements in
outdoor settings, the only analytical technique that appears to be capable of providing
quantitative data useful for supporting risk assessment is TEM (Sections 4.3 and 7.5).
Further, given the specific size range of structures that need to be evaluated and the
specific manner in which they need to be counted (to assure both cross-study
comparability and compatibility with the recommended dose-response coefficients),
analyses should be performed using the specific analytical methods recommended in
this document, These are ISO 10312 (modified to focus on interim index structures) for
air and the Modified Elutriator method (Berman and Kolk 2000) for soils or bulk
materials. Note that, on a study-specific basis, other methods may be shown to provide
comparable results so that they can also be used as part of a properly integrated
investigation.
Confidence in risk assessment
Given the degree of reconciliation achieved across the various epidemiology studies
(with the adjustments proposed in this document, Section 6.4), it appears that
asbestos-related risks estimated using the recommended procedures may be
considered good to within one to two orders of magnitude. Further, if proper
procedures are employed to derive exposure estimates that are then properly matched
with the recommended dose-response coefficients, the chance of severely
underestimating risk can be minimized without the need to introduce overly
conservative assumptions.
8.1.2 Comparison with Other, Recent Risk Reviews
Although several other reviews have also recently been published that nominally
address risk-related issues for asbestos (including questions concerning the
identification of an appropriate exposure index and the relative potency of varying fiber
types), these studies are either qualitative or involve analysis of data in a manner that
does not allow formal evaluation of nature of the specific dose-response relationships
for the various diseases. Therefore, they are not well suited to support development of
a protocol for conducting formal assessment of asbestos-related risks. Nevertheless,
the general conclusions from these reviews are not inconsistent with our findings.
Hodgson and Darnton (2000)
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Hodgson and Darnton (2000) conducted a comprehensive quantitative review of
potency of asbestos for causing lung cancer and mesothelioma in relation to fiber type.
They concluded that amosite and crocidolite were, respectively, on the order of 100 and
500 times more potent for causing mesothelioma than chrysotile. They regarded the
evidence for lung cancer to be less clear cut, but concluded nevertheless that
amphiboles (amosite and crocidolite) were between 10 and 50 times more potent for C
causing lung cancer than chrysotile. In reaching this latter condusion they discounted
the high estimate of chrysotile potency obtained from the South Carolina cohort.
Hodgson and Darnton concluded that inter-study comparisons for amphibole fibers
suggested non-linear dose response relationships for lung cancer and mesothelioma,
although a linear relationship was possible for pleural mesothelioma and lung tumors, ^
but not for peritoneal mesothelioma.
The Hodgson and Darnton study was based on 17 cohorts, 14 of which were among
the 20 included in the present evaluation. This study had different goals from the
present evaluation and used different methods of analysis. Hodgson and Darnton did
not use the dose response information within a study. Instead, lung cancer potency ^
was expressed as a cohort-wide excess mortality divided by the cohort mean exposure.
Likewise, mesothelioma potency was expressed as the number of mesothelioma
deaths divided by the expected total number of deaths, normalized to an age of first
exposure of 30, and by the mean exposure for the cohort. These measures have the
advantage of being generally calculatable from the summarized data available from a (
study. However, since they are not model-based, it is not clear how they could be used
to assess lifetime risk from a specified exposure pattern, which is an important goal of
the current project. Use of average cohort exposure could cause biases in the
estimates, if, e.g., a large number of subjects were minimally exposed. Also, the
recognized differences between studies in factors, in addition to level of exposure, that
may affect risk will also affect the reliability of conclusions concerning the dose ^
response shape based on comparisons of results across studies,
Lippmann (1994 and 2000?)
In the most recent of these reviews, although based on a qualitative, anecdotal i
treatment of the literature, Lippmann reinforces our general findings that it is longer
fibers (those longer than a minimum of approximately 5 urn) that contribute to lung
cancer and mesothelioma. He further indicates that, based primarily on the limits
observed for fibers that can be phagocytized, fibers that contribute most to lung cancer
are likely longerthan a minimum of 10 urn. In his review, based on a series of ,
comparisons of mean and median dimensions reported for the relevant exposures
across a broad range of studies, Lippmann draws several fairly specific conclusions on
the ranges of fiber sizes that may contribute to various diseases (i.e. that the minimum
length fibers that contribute to asbestosis, lung cancer, and mesothelioma are 2, 5, and
10 urn, respectively). He also suggests that fibers that contribute to mesothelioma may
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need to be thinner than 0.1 urn while those that contribute to lung cancer may need to
be thicker than 0,15 urn. While it is not clear that drawing such specific conclusions
can be firmly supported by the kinds of qualitative comparisons across reported mean
and median dimensions for exposures in various studies that are described in this
paper, the author indicates that further, more formal study of the dose-response
relationships that he posits is warranted. It is noted that many of the studies reviewed
by Lippmann are the same studies also incorporated in our analysis.
In the earlier review, Lippmann plots lung tumor incidence as a function of inhaled
animal dose for data from a series of broadly varying studies based, respectively, on
fibers longer than 5, 10, and 20 urn (no widths considered) and suggests that the
quality of the fits are comparable. The author further suggests, based on this
evaluation, that PCM seems to provide a reasonable index of exposure. However, no
formal goodness of fit tests were performed in this analysis and, based on visual
inspection, none of the plots would likely show an adequate fit. Moreover, the plot of
the tumor response vs. dose as a function of fibers longer than 5 urn appears to be
substantially worse than the other two plots; if one removes the single highest point in
this plot, it appears that any sign of correlation will largely disappear.
Stavneretal. 1996
In the context of evaluating the "amphibole hypothesis", Stayner et a/, computed the
excess relative risk of lung cancer per fiber/ml/yr from 10 studies categorized by the
fiber types to which the cohort was exposed. Each of these studies was also included
in the present evaluation. Both the lowest and highest excess relative risks came from
cohorts exposed exclusively to chrysotile. Based on their evaluation, they concluded
that the epidemiologic evidence did not support the hypothesis that chrysotile asbestos
is less potent than amphibole for inducing lung cancer. However, based on a review of
the percentage of deaths in various cohorts from mesothelioma, they concluded that
amphiboles were likely to be more potent than chrysotile in the induction of
mesothelioma. They also noted that comparison of the potency of different forms of
asbestos are severely limited by uncontrolled differences in fiber sizes. None of these
conclusions are inconsistent with our general findings.
8.2 RECOMMENDATIONS FOR ASSESSING ASBESTOS-RELATED RISKS
Recommended risk coefficients for lung cancer and mesothelioma (the adjusted KL
values and KM values) are provided in Tables 6-29 and 6-30, which present best-
estimate recommendations and conservative recommendations, respectively. Once it
is decided which of these sets of risk coefficients to adopt for general use, the selected
set can be incorporated into the procedures described below to evaluate asbestos-
related risk. Consistent with the evidence presented elsewhere in this report of a
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different potency for chrysotile and amphibole (Chapter 6), separate risk coefficients are
recommended for chrysotile and the amphiboles.
Importantly, the risk coefficients provided in Tables 6-29 and 6-30 are adjusted to be
specifically combined with exposure estimates expressed in terms of the interim
exposure index defined by Equation 7-13. Such exposure estimates include only C
asbestos structures longer than 5 urn and thinner than 0.5 urn with those longer than 10
urn weighted more heavily (as described in the equation). Combining the risk
coefficients with measurements expressed in terms of any other index of exposure
(such as the PCME index in current use) will result in invalid estimates of risk. Note, as
indicated in Appendix B, all structures satisfying the dimensional criteria for this index ^
(and exhibiting the requisite mineralogy) should be included in analyses for determining
asbestos concentrations; it is not necessary (for example) to distinguish between true
asbestiform fibers and cleavage fragments when the interim index is employed.
Three options are described below for evaluating asbestos-related lung cancer and
mesothelioma risks using the risk coefficients defined in Tables 6-29 or 6-30 with ^
appropriately derived exposure estimates (expressed in terms of the interim exposure
index defined by Equation 7-13). Additional considerations for determining asbestos
exposure concentrations are also provided.
C
Option 1: Using the EPA Dose-Response Models
If there is interest in estimating risk for a particular population that involves time-varying
exposures (or a cohort that potentially experiences background mortality rates
substantially different from the general population), the EPA models for lung cancer
(Equation 6.2) and for mesothelioma (Equation 6.11, for constant exposure, or Equation *••
6.12 for time-varying exposure) may be directly used to assess risk. This requires
knowledge of both the age-specific background lung cancer mortality rate for the
population of interest and the age-specific total mortality rate for this population, in
addition to the data describing the overall pattern of exposure. The mortality rates can
then be used in a lifetable analysis, along with the given exposure pattern and the (
appropriate values of KL, and KM, (either from Table 6-29 or 6-30) to estimate the
additional probability of dying of lung cancer or mesothelioma. Such a lifetable analysis
is described in Appendix D.
C
Option 2: Estimating Risk from a Risk Table
Once it is decided which of the two sets of recommended risk coefficients (Table 6-29
or 6-30) to adopt for general use, a risk table can be constructed (using general U.S.
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mortality rates) that can then be applied broadly to assess risk. To illustrate this
approach, Table 8-1 is a risk table (for lifetime, continuous exposure) that was
constructed using the risk coefficients from Table 6-30 (i.e. the "conservative" values).
Note that the values in the table have already been adjusted for lifetime, continuous
exposure (so conversions from an occupational exposure scenario are not required).
The only data required to assess risks using this risk table are estimates of long-term
average exposure (derived from appropriate measurements, as described below) for
each particular exposure scenario and population of interest.
Table 8-1 presents estimates of the additional risk of death from lung cancer and
mesothelioma attributable to lifetime exposure at an asbestos concentration of 0.0005
f/ml (for fibrous structures bnger than 5 urn and thinner than 0.5 urn) as determined
using TEM methods recommended below. In the table, separate risk estimates are
provided for males and females and for smokers and non-smokers, Separate
estimates are also presented for exposures containing varying fractions (in percent) of
fibrous structures longer than 10 urn.
Separate estimates are presented for smokers and nonsrnokers because the lifetime
asbestos-induced risk of both lung cancer and mesothelioma differ between smokers
and non-smokers. The asbestos-induced risk of lung cancer is higher among smokers
because the lung cancer model (Equation 6.2) assumes that the increased mortality
rate from lung cancer risk due to asbestos exposure is proportional to background lung
cancer mortality, which is higher among smokers. This is consistent with the
muitplicative effect between smoking and asbestos exposure reported by several
researchers (see, for example, Hammond et al. 1979).
The asbestos-induced risk of mesothelioma is smaller among smokers because the
mesothelioma model (Equation 6.11) assumes that risk from constant exposure
increases with the cube of age, with the result that the predicted mortality rate is highest
among the elderly. Thus, since smokers have a shorter life span than non-smokers,
their risk of dying from mesothelioma is also predicted to be smaller.
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f
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Separate estimates are provided for different fractions of fibrous structures longer than
10 urn because the model assumes that structures longer than 10 um are more potent
than structures between 5 and 10 um in length (in a manner consistent with
Equation 7.13). The derivation of this model is described in detail in Chapters 6 and 7.
Risks from lifetime exposures to asbestos levels other than 0.0005 may be estimated
from the appropriate entry in Table 8-1 by multiplying the value in the selected cell from
the Table by the airborne asbestos concentration of interest and dividing by 0.0005
(i.e., by assuming that the additional risk is proportional to the asbestos exposure level).
Airborne asbestos concentrations to be used in this manner must be estimates of
lifetime average exposure and must be expressed as structures longer than 5 um and
thinner than 0.5 um derived as described below. Estimates of the fraction of these
structures that are also longer than 10 um must also be determined to select the
appropriate cell of the table from which to derive the risk estimate. Note that the two
size fractions that are combined to determine Casb (Equation 7.13) are separately
enumerated (not combined) when they are to be used in conjunction with Table 8-1.
The procedure described above for estimating risks using Table 8-1 should provide
good approximations as long as the projected risk is no greater than 1,000 per 100,000.
Risks greater than 1,000 per 100,000 (i.e. 1 in 100) that are derived from the Table are
likely to be over-estimated. However, for risks associated with short-duration exposures
or exposures that differ radically over time, it may be better to use a lifetable analysis or
a modified version of Table 8-1. This is to avoid substantially under- or over-estimating
risk (depending on how the table might otherwise be applied).
Table 8-1 was derived using the approach described in Appendix D by incorporating the
age-, sex-, and smoking-specific death rates reported for the general U.S. population
and assuming that exposure is constant and continuous at the level indicated in the
table. The underlying models are provided in Chapter 6 for cases in which exposure is
either not constant or not continuous and for which sufficient data exist to characterize
the time-dependence of such exposure. If available, there may also be cases in which
it is advantageous to employ mortality data from a control population that better
matches the exposed population of interest than the U.S. population as a whole.
Option 3: Estimating Risk from a Unit Risk Factor
A third option for estimating risks based on the approach recommended in this
document would be to derive a combined, unit risk factor for asbestos cancer risk
similar to the unit risk factor currently recommended by EPA. The new unit risk factor
would be derived using the selected set of new risk coefficients (either those in Table 6-
29 or Table 6-30) and would be appropriate for use with exposure estimates expressed
in terms of the interim exposure index (rather than the PCME index, as currently
recommended). As with the unit risk factor currently in use, some estimate of the
fraction of smokers in the general population would also need to be provided as an
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input. This approach, however, may prove to be somewhat less flexible than the
second option above and therefore maybe prone to greater errors.
Requirements for Asbestos Measurements
One additional advantage of the approach for evaluating asbestos-related risks
recommended in this document (in comparison to the current approach) is that the
procedure for assessing risks is tied unambiguously to a specific index for measuring
and expressing exposure (i.e. the interim index) and this, in turn, is tied unambiguously
to requirements for analyzing asbestos samples.
Estimates of airborne asbestos concentrations that are required to support risk
assessment can be derived either by extrapolation from airborne measurements or by
modeling release and dispersion of asbestos from sources (soils or other bulk
materials). In either case, exposure estimates must be representative of actual (time-
dependent or time-integrated) exposure and must provide measurements of the specific
size fractions of asbestos that are components of the interim exposure index defined by
Equation 7.13. Additional considerations that need to be addressed to assure the
validity of risk estimates derived using this protocol include:
• the array of samples collected for estimating airborne asbestos
concentrations must be representative of the exposure environment;
• the time variation of airborne asbestos concentrations must be properly
addressed;
• airborne samples must to be collected on membrane filters that are
suitable for preparation for analysis by transmission electron microscopy
(TEM). Appropriate procedures for sample collection are described in
Chatfield and Berman (1990) or the ISO Method (ISO 10312)1;
• . sample filters must be prepared for analysis using a direct transfer
procedure (e.g. ISO 10312). Should indirect preparation be required (due,
for example, to problems with overloading of sample filters), a sufficient
number of paired samples will need to be collected and analyzed to
Note that the ISO Method (ISO 10312) is a refinement of the method
originally published as the Interim Superfund Method (Chatfield and
Berman 1990). It incorporates improved rules for evaluating fiber
morphology. Both methods derive from a common development effort
headed by Eric Chatfield.
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establish a site-specific correlation between directly and indirectly
prepared samples;
samples must be analyzed by TEM;
samples must be analyzed using the counting and characterization rules
defined in ISO 10312 with one modification: only structures longer than 5
urn and thinner than 0.5 urn need to be enumerated. Separate scans for
counts of total structures longer than 5 urn and longer than 10 urn are
recommended to increase the precision with which the longest structures
are enumerated. Importantly, ISO Method rules require separate
enumeration and characterization of component fibers and bundles that
are observed within more complex clusters and matrices. Such
components, if they meet the dimensional criteria defined in Equation 7.13
must be included in the structure count;
if risks are to be estimated using the risk models (as described in the first
option), asbestos concentrations derived from the above-described
measurements must be expressed as the weighted sum of structures
between 5 and 10 urn in length and structures longer than 10 urn in
length, per the exposure index defined in Equation 7.13. Only structures
thinner than 0.5 urn are to be included in these counts. Both fibers and
bundles that are isolated structures and fibers and bundles that are
components of more complex structures are to be included in structure
counts (as long as each structure counted satisfies the defined size
criteria for the size category in which it is included);
if risks are to be estimated using the risk models (the first option), the risk
coefficient(s) selected eitherfrom Table 6-29 or 6-30 must be appropriate
for the fiber type (i.e. chrysotile or amphibole) and the disease end point
(i.e. lung cancer or mesothelioma) relevant to the situation of interest; and
if risks are to be estimated using Table 8-1 (The second option), rather
than deriving the weighted sum described in Equation 7.13, the
concentration of asbestos structures longer than 10 urn and thinner than
0.5 urn must be derived to determine the appropriate column of the Table
from which to estimate risk and the concentration of total asbestos
structures longer than 5 urn and thinner than 0.5 urn must be derived,
divided by 0.0005, and multiplied by the risk estimate listed in the
appropriate cell of the Table to generate the risk estimate of interest.
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Considerations that need to be addressed to assure the validity of risk estimates
derived from soil or bulk measurements combined with release and transport modeling
include:
• the array of samples collected for estimating source concentrations must
be representative of the surface area or volume of source material from
which asbestos is expected to be released and contribute to exposure;
• samples must to be prepared and analyzed using the Modified Elutriator
Method for soils and bulk materials (Berman and Kolk 1997, 2000), which
is the only method capable of providing bulk measurements that can be
related to risk; .
• membrane filters samples prepared using the tumbler and vertical
elutriator per the Superfund method must themselves be prepared for
TEM analysis using a direct transfer procedure;
• TEM analysis must be conducted using the counting and characterization
rules defined in the ISO Method (ISO 10312) in predsely the same
manner that is described above for air measurements. Also, the same
size categories need to be evaluated in the same manner described
above, whether results are to be used to support assessment using risk
models or using the risk table; and
• release and dispersion models that are selected for assessing risks must
be appropriate to the exposure scenario and environmental conditions of
interest. Such models must also be adapted properly so that they accept
input estimates expressed in terms of fiber number concentrations.
Procedures suggested for adapting such models are illustrated in a recent
publication (Berman 2000).
Note, if we design analytical procedures to focus on long structures, we can evaluate
risks cost-effectively. Moreover, even to the extent that shorter structures or other
structures might contribute to risk should generally be addressed by default. The
alternate approach of spending most time and money counting lots of (potentially non-
potent or marginally potent) short structures while not characterizing longer structures
with adequate sensitivity or precision (which is embodied in the current EPA approach)
leaves the much greater and more probable danger of falsely missing potentially
serious hazards because a small population of extremely potent, long fibers were
missed in a particular environment.
L
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8.3 RECOMMENDATIONS FOR FURTHER STUDY
A small number of limited and focused studies (described previously) are previously
recommended in this document because they are likely to provide very cost-effective
improvements to the quality of this document and may support substantial improvement
to the recommended procedures for assessing asbestos-related risks. The
recommended studies are:
(1) a focused study to expand our evaluation of the ability of the current EPA
models to adequately track the time-dependence of disease (Sections
6.2.2 and 6.3.2); and
(2) a focused study to develop the supporting data needed to define
adjustments for potency factors that will allow them to be used with an
exposure index that even more closely captures asbestos characteristics
that determine biological activity than the currently proposed "interim
index" (Section 7.5),
Note that, by properly designing the second of the above-listed studies, ft may also be
possible to further address another outstanding issue that was previously identified: the
question of whether dose-response coefficients derived from mining studies are under-
estimated relative to studies involving asbestos products because exposures in the
mining studies may contain large numbers of non-asbestos particles contributed by the
disturbance of host rock (Section 6.2.3).
For the first of the above-recommended studies, we would need to acquire the raw data
from at least one additional cohort exposed primarily to chrysotile and one additoinal
cohort exposed primarily to amphiboles to determine whether our observation that the
current EPA model for lung cancer adequately describes the time dependence of
relative risk associated with exposure to amphiboles but not to chrysotile. Should this
finding be confirmed, the potential magnitude and importance of the deviation would
also be evaluated along with a candidate adjustment to the model that would likely
solve the problem. Simultaneous, additional analysis of the mesothelioma model may
also be useful,
Candidate cohorts that may be useful for this evaluation, if they can be acquired,
include: (for amphibole exposures) the lung cancer data and newest mesothelioma data
from Libby (best) or, potentially the lung cancer and mesothelioma data from the
Paterson, New Jersey insulation manufacturing plant studied by Seidman and (for
chrysotile exposures) the lung cancer data from Quebec (best) or, potentially, the lung
cancer and mesothelioma data from the New Orleans asbestos-cement pipe plant
studied by Hughes et al. The possibility of obtaining some or all of these data sets
needs to be further explored.
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For the second of the above-listed studies, we would need to obtain suitable bulk
samples of raw ore from the Wittenoom, Libby, and Quebec mines and bulk samples of
textile grade (and several other grade) products from the Quebec mines. We have
already approached individuals who can provide access to these materials and they
have all expressed interest in collaborating.
Ideally, a well-designed field sampling plan would be used to collect and composite raw
ore samples and multiple samples (from widely dispersed time-periods of production)
would be collected and composited for the fiber product samples. The resulting, limited
number of well-constructed composite samples would then be prepared using the
Modified Elutriator Method (Berman and Kolk 2000) and subjected to a rigorous
analysis by TEM (covering counting and characterization of a very broad range of
fibrous structures and non-fibrous particles). The data from these analyses would then
be used both to evaluate questions concerning the quality of exposure at mining sites
and to adjust the dose-response coefficients derived from studies of these mining sites
(and several other studies of facilities that used the products from Quebec). The
coefficients would be adjusted to a better optimized exposure index (in which fibers at
least as long as 20 urn would be separately weighted) and the degree to which use of
this new index Improves agreement across certain selected studies would be evaluated.
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Yu, C.P. and Asgharian, B; "A Kinetic Model of Alveolar Clearance of Amosite Asbestos
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Yu, C.P.; Asgharian, B; and Pinkerton, K.E.; "Intrapulmonary Deposition and Retention
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Yu, C.P.; Zhang, L; Oberdoster, G.; Mast, R.W.; Glass, L.R..; and Utell, M.J.,
"Deposition Modeling of Refractory Ceramic Fibers in the Rat Lung" Journal of Aerosol
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Yu, C.P.; Zhang, L.; Oberdoster, G.; Mast, R.W.; Maxim, D.; and Utell, M.J., "Deposition
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APPENDIX A:
Update of Potency Factors for Lung Cancer (KL) and Mesothelioma (KM)
Estimates of risk of dying of lung cancer or mesothelioma from asbestos exposure are
quantified by means of mathematical models that express risk as a function of
exposure. The models utilized in the 1986 EPA Airborne Asbestos Health Assessment
Update (USEPA, 1986) contain parameters (KL for lung cancer and KM for
mesothelioma) that gauge the potency of asbestos for causing these health effects.
USEPA calculated KL and KM values from a number of studies. In this section these
KL and KM calculations are revised using the same models as in the EPA 1986 update,
but incorporating newer data from more recent publications. Since the 1986 update,
additional cohorts have been studied from several new exposure settings, and the
followup periods have been extended for several of the previously studied cohorts.
In the 1986 update KM values were not calculated from all of the available studies,
perhaps owing to the limited number of mesotheliomas observed in some of these
studies. In this update, an attempt has been made to utilize any study with suitable
health and exposure data, regardless of the number of mesotheliomas reported, and to
quantify the statistical uncertainty attributable to small numbers using statistical
confidence limits. Since the present work utilizes somewhat different methods from the
1986 update, for consistency, all of the KL and KM values were recalculated, even from
studies for which no new data were available. Table 1 contains a summary of these
new KL and KM calculations. The original values from the 1986 update are also
provided for comparison.
Lung Cancer Model
The 1986 EPA lung cancer model (USEPA, 1986) assumes that the relative risk, RR, of
mortality from lung cancer at any given age is a linear function of cumulative asbestos
exposure (fiber-years/ml, orf-y/ml, as measured by PCM), omitting any exposure in the
most recent ten years. This exposure variable is denoted by CE10. The ten-year lag
embodies the assumption that exposure does not influence lung cancer mortality until
ten years has elapsed. The mathematical expression for this model is
RR = 1 + KL*CE10I (1)
where the linear slope, KL, is the "lung cancer potency factor." Sometimes allowance is
made for the possibility that the background lung cancer risk in the exposed population
differs from that of the comparison population by applying the expanded model,
= a(1+-KL*CE10). (2)
With this form of the model the relative risk at zero exposure is a rather than 1 .0. Both
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KL and a are estimated by fitting the model to data. The type of data most appropriate
for applying this model are from cohort studies in which observed and expected (based
on an appropriate comparison population, e.g., U.S. males) numbers of lung cancers
are categorized by cumulative exposure incorporating a ten-year lag. To explore the
adequacy of the model, it is useful to have the data cross-classified by one or more
other variables, such as latency,
Frequently the cumulative exposure variable available from the published report of a
study does not incorporate a lag (or, less frequently, incorporates a lag of less than ten
years). In this report, rather than attempting an ad hoc correction, no correction for lag
has been made. Although this tends to cause KL values to be slightly underestimated,
this is unlikely to be a serious problem. For most cohorts, exposures decreased
significantly over time. Also, in many studies, followup didn't begin until several years
after the start of exposure, and the bulk of the lung cancers occurred at older ages. All
of these factors tend to mitigate the error created from use of data with no lag.
Moreover, use of an ad hoc correction for lag could hinder comparisons of KL values
among studies that do not employ a lag (which indudes the majority of studies).
Mesothelioma Model
The 1986 EPA mesothelioma model (USEPA, 1986) assumes that the mortality rate at
time t after the beginning of exposure can be calculated by summing the contributions
from exposure at each increment of time, du, in the past. The contribution to the
mortality rate at time t from exposure to E(u) f/ml (as measured by PCM) at time-u is
assumed to be proportional to the product of the exposure rate, E(u), and (t - u - 10f,
the square of the elapsed time minus a lag of ten years. Thus, as with the lung cancer
model, the mesothelioma model assumes a ten-year lag before exposure has any
effect upon risk. With the additional assumption that the background rate of
mesothelioma is zero, the mesothelioma mortality rate at time t since the beginning of
exposure is given by
lH(t) = 3*KM*Int"1DE(u)*(t™u-10)2du, (3)
where t,u are in years, lM(t) is the rate per year at year t after the beginning of exposure,
and the proportionality factor, KM,.is the "mesothelioma potency factor." The factor of
"3" is needed to retain the same meaning of KM as in USEPA (1986),
If exposure is at a constant level, E, for a fixed duration, DUR, this model can be written
as
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0 0<,t±1Q
lM(t) = KM*E*(? - 10)3 10zt<10 + DUR (4)
KM*E*[(t -10)3- (t-10- DUR)3] t ^ DUR
The genesis of this model and its agreement with data was discussed in USEPA
(1986).
Through the courtesy of Dr. Corbett McDonald, Professor Douglass Liddell, Dr.
Nicholas DeKlerk, and the National Institute for Safety and Health (NIOSH), raw data on
mesothelioma mortality were obtained from a cohort of Quebec chrysotile. miners and
millers (McDonald et a/., 1980a; Liddell et a/., 1997), a cohort of Wittenoom, Australia
crocidolite miners and millers (Armstrong et a/., 1988; DeKlerk et a/., 1994), and a
cohort of workers from a plant Charleston, South Carolina that manufactured textiles
from chrysotile (Dement et a/., 1983a,b, 1994; Dement and Brown, 1998). These data
were used to calculate KM values in a more accurate manner (using the "exact"
approach described below) for these cohorts, and to explore the potential magnitude of
the errors incurred by the crude application of cohort-wide averages when fitting the
mesothelioma model.
Statistical fitting methods
The method of maximum likelihood (Cox and Oakes, 1984; Venzon and Moolgavkar,
1988) was used herein to fit the lung cancer and mesothelioma models to data and to
estimate KL and KM. The profile likelihood method was used to calculate statistical
confidence intervals, and likelihood ratio tests were used to assess goodness-of-fit and
test hypotheses.
Typically the data for calculating a lung cancer potency factor, KL, consist of observed
and expected (based on an external control group, such as U.S. males) numbers of
cancer deaths categorized by cumulative exposure, The likelihood of these data is
determined by assuming that the deaths in different exposure categories are
independent and the number of deaths in a particular category has a Poisson
distribution with expected number given by the expected number predicted by the
control group times the relative risk given by either expression (1) or (2).
In the typical situation, the published data most useful for calculating the mesothelioma
potency factor, KM, consist the number of mesothelioma deaths and person-years of
observation categorized by time since first exposure. The likelihood of these data is
determined by assuming statistical independence of the number of mesothelioma
deaths in different categories, and that the number of mesothelioma deaths in a
category has a Poisson distribution with mean equal the number of person-years in the
category times expression (4), using average values for E, DUR, and t appropriate for
that category.
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The fitting of the mesothelioma model (3) to raw mesothelioma data was accomplished
using an "exact" maximum likelihood method. The cumulative mesothelioma hazard
was defined as
(5)
(
The contribution to the likelihood of a person whose followup terminated at t was S(t) =
1 - exp(-H(t) if the followup did not terminate in death from mesothelioma, and lM(t)S(t) if
the person died of mesothelioma. The complete likelihood was defined as the product
of these individual contributions. The integrals in expressions (3) and (5) were
evaluated numerically. (
Selection of a "best estimate" of KL and KM
For each study for which a KL or KM was estimated, a "best estimate" was developed.
For mesothelioma, this estimate was generally the maximum likelihood estimate ^
derived from the best-fitting model in the form (3) for raw data and (4) for published
data. For lung cancer, the best estimate of KL was generally assumed to be the
maximum likelihood (MLE) estimate obtained with a = 1 if this model fit the data
adequately and the hypothesis a = 1 could not be rejected. If this hypothesis could be
rejected and the model with a estimated fit adequately, the MLE from this model was
generally used as the best estimate. If neither model fit adequately, or if the result of *
the hypothesis test of a = 1 was marginal, the geometric mean of the MLE with a = 1
and a estimated was generally used as the best estimate. In other cases these general
rules had to be adapted to fit the particular form of the data available.
Uncertainty in KL and KM (
Statistical uncertainty in KL and KM estimates is expressed using statistical confidence
limits. However, other sources of uncertainty, such as model uncertainty and
uncertainty in exposure, are likely to also be very important. Although non-statistical
uncertainties are difficult to quantify, it is important to attempt quantification, since ,
presentation of statistical uncertainty alone may provide a misleading picture of the
reliability of the estimates. Consequently, an ad-hoc approach to quantifying non-
statistical uncertainty was adopted in this report. In this approach, a few general
sources of uncertainty are identified. For each study, a factor for each uncertainty
source was selected using loose guidelines. The individual factors were combined with
the statistical confidence bounds to arrive at a "likely range" for KL or KM for each (.
particular cohort. A "best estimate" within the range is also provided for each cohort.
The most serious uncertainties are often related to exposure. Air samples may not
exist for certain departments or periods of time. Sampling was often sparsest in the
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more distant past when exposures were generally highest. Often samples were not
collected routinely and the context of historical samples may no longer be known. (E.g.,
were they representative, or were they worst-case?) Some asbestos jobs involved
short and infrequent, but extremely dusty, conditions that were difficult to evaluate. In
some studies historical exposure levels were estimated from samples collected during
an attempt to recreate past exposure conditions. An uncertainty factor, F1, was
selected for each study to reflect uncertainty in KL and KM estimates stemming from
these issues. This factor is at least 1.5 and is 2 or greater in most cases.
Samples from the 1960's and earlierwere collected using an impinger or similar
apparatus that counted particles rather than fibers, and a conversion factor must be
used to convert from, say, particle concentrations in million particles per cubic foot
(mppcf) measured by an impinger to fiber concentrations in fibers per milliliter (f/ml)
measured by phase contrast microscopy (PCM). Since different operations had
different degrees of dustiness, a common conversion factor may not be appropriate for
different cohorts or even for different jobs within the same plant. Some studies had
available side-by-side samples by impinger and PCM, or concurrent samples collected
over a period of years in different operations, which could be used to determine
appropriate conversion factors for different operations. Other studies only had samples
collected by an impinger, and no conversion factor was estimated. An uncertainty
factor, F2, is used to represent the uncertainty in the conversion factor.
Some studies had highly variable working conditions, with little or no sampling data, and
exposure levels were estimated from attempts to recreate typical working conditions.
Other studies did not have individual work histories, and a crude estimate of average
duration was applied to all members of the cohort. A third uncertainty factor, F3, is
used to account for these special conditions.
In addition to uncertainty related to exposure, there are non-statistical uncertainties
stemming from 1) lack of information on potential confounders, 2) questionable
appropriateness of the comparison population, and 3) incomplete or inaccurate
mortality ascertainment. Many studies do not have information on smoking, an
important potential confounder for lung cancer. In some studies a sizable proportion of
the cohort was loss tofollowup. Mesothelioma deaths were sometimes misclassified on
death certificates as due to other types of cancer. To handle the appropriateness of the
comparison population for lung cancer, the reported lower (upper) confidence bound
selected was the smaller (larger) of the two bounds calculated with the background
parameter, a, in the lung cancer model (2) estimated, and with this parameter fixed at
a = 1. Since all three non-statistical uncertainties imply an improper control population,
this approach addresses to some extent all three problems. In addition, when it was
deemed to be warranted, an additional non-exposure-related uncertainty factor for lung
cancer, F4L, and/or mesothelioma, F4M, was proposed.
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In addition to upper and lower statistical confidence intervals, four uncertainty factors
have been proposed: F1: Exposure, general; F2: exposure conversion factor; F3: lack
of individual work histories; F4L (lung cancer), F4M (mesothelioma); non-exposure
related. Since it is unlikely that all of the uncertainty sources caused errors in the same
direction, rather than multiplying the uncertainty factors, an overall uncertainty factor, F,
was calculated as F =exp{[Ln2(F1) + Ln2(F2) + Ln2(F3) + Ln2(F4)f'}, where 1.0 is the
default value for any factor not explicitly provided. The overall "reasonable range" for
KL or KM was calculated by dividing the statistical lower bound by F and multiplying the
upper bound by F.
Analysis of Individual Studies
Predominately Chrysotile Exposure
Quebec Mines and Mills
Liddell et al., 1997 extended the followup into 1992 of a cohort of about eleven
thousand workers at two chrysotile asbestos mines and related mills in Quebec that had
been studied earlier by McDonald et al., 1980 (followup through 1975) and McDonald
et al. 1993 (followup through 1988). Production at the mines began before 1900. The
cohort consisted of workers who worked one month or more and who were born {
between the years of 1891 and 1920. Follow-up began for each individual after 20
years from first employment. The most recent follpwup (Liddell et al., 1997) traced
9780 men through May 1992, whereas 1138 (10%) were lost to vjew, most of whom
worked for only a few months prior to 1935. Of those traced, 8009 (82%) were
deceased as of 1992. ^
Estimates of dust levels in specific jobs were made from some 4,000 midget impinger
measurements collected systematically starting in 1948 and periodically in the factory
beginning in 1944. Estimates for the period prior to 1949 utilized interviews with long-
term employees and comparison with more recent conditions. These dust-level
estimates were matched to individual job histories to produce estimates of cumulative (-•
exposure for each worker (mppcf-years). Conversions between dust levels and PCM
concentrations were derived from side-by-side samples. On the basis of over six
hundred side-by-side midget impinger and optical microscopy measurements, it was
estimated that 3.14 fibers/ml was, on the average, equivalent to 1.0 mppcf (McDonald
etal., 1980b). c
Liddell et al. (1997) categorized .cancer deaths after age 55 from of lung, trachea, and
bronchus by cumulative asbestos exposure to that age (Liddell et al., 1997, Table 8).
Standardized mortality ratios (SMRs) were calculated based on Quebec rates from
1950 onward, and Canadian, or a combination of Canadian and Quebec rates, for
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earlier years, Table 2 shows the fit of the lung cancer model to these data. Although
the models both with a = 1 and a variable provided reasonably adequate fits to the
data, the hypothesis a = 1 can be rejected (p = 0.007). The model with a estimated
yields a best estimate of KL of 0.00029 (f-y/ml)'1, 90% Cl: (0.00019, 0.00041). With a =
1, the estimate was KL = 0.00041 (f-y/ml)"1, 90% Cl: (0.00032, 0.00051).
Smoking history was obtained in 1970 by a questionnaire administered to current
workers at that time, and to proxies of those who had died after 1950. Although no
analyses of lung cancer and asbestos exposure were presented for the 1992 followup
(Liddell et al,, 1997) that controlled for smoking, such an analysis was conducted for the
followup that continued through 1975 (McDonald et al,, 1980a). Table 9 of McDonald et
al, (1980a) contained data on lung cancer categorized jointly by cumulative exposure to
asbestos and by smoking habit. Two models were fit to these data: the multiplicative
model for relative risk
- RR = ct(l + bd)(l+cx),
and the additive model
RR = a(l + bd + ex),
where d is cumulative exposure to asbestos to age 45, x is number of cigarettes
smoked per day, and a,b,c are parameters estimated from the data. The multiplicative
model fit the data well, but the fit of the additive model was inadequate. This
'corroborates the multiplicative interaction between smoking and asbestos exposure in
causing lung cancer (Hammond et al., 1979). The estimate of potency using the
multiplicative model was 0.00051 (f-y/ml)"1, which was very close to that of 0.00045 (f-
y/ml)"1 estimated from Table 5 of McDonald et al, (1980a), which did not utilize smoking
data. This suggests that smoking is not strongly confounded with exposure in this
cohort.
The best estimate of KL was assumed to be the MLE estimate with a variable. The
uncertainty factors selected for this study were F1 = 2, F2 = 1.5, which, when coupled
with the statistical confidence limits, resulted in the likely range for KL shown in Table 1,
By 1993, 38 deaths from mesothelioma had occurred in this cohort (Liddell et al., 1997).
Through the courtesy of Dr. Corbett McDonald and Professor Douglass Liddell, the
underlying mesothelioma data from this study were, provided to us (Liddell, 2001,
personal communication). These data contained the following information on each
worker: the date of birth, asbestos exposure history, last date of followup, whether
followup ended as a result of death from mesothelioma, location where they first
worked, and whether they worked in more than one location.
Nine distinct locations for first employment were coded. Locations 5-9 referred to small
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operations, some having very heterogeneous exposures, and were omitted from the
analysis. Also, workers who worked at more than one location were omitted. After
these exclusions, there remained 9244 workers who worked at Locations 1 -4, and
among whom 35 deaths from rnesothelioma occurred. Location 1 (4195 men, 8 deaths
from rnesothelioma) was the mine and mill at the town of Asbestos. Location 2 (758
men, 5 deaths) was a factory at the town of Asbestos that, in-addition to processing
chrysotiie, had also processed some crocidolite. Location 3 (4032 men, 20 deaths)
comprised a major mining and milling company complex near Thedford Mines.
Location 4 (259 men, 2 deaths) comprised a number of smaller mines and mills also in
the vicinity of Thedford Mines. Because of the small number of workers at Location 4,
the fact that both locations were near Thedford Mines, and the fact that the separate '
KM values obtained from Locations 3 and 4 were similar, data from these locations
were combined. The remaining groups were analyzed separately, because of the
crocidolite used at Location 2, and because of evidence of greater amounts of tremolite
in the ore at Thedford Mines that at Asbestos (Liddell et a/,, 1997).
The availability of the raw data from this study made it possible calculate KM from this
study using an "exact" likelihood approach based on expression (3) that did not involve
any grouping of data, or use of average values. For Location 1 (Asbestos mine and
mill), KM = 0.013x1 a8, 90% Cl: (0.0068x10'8, 0.022x10'8). For Location 2 (Asbestos
factory), KM = 0.092x1Q-8, 90% Cl: (0.040x1 a8, 0.18x1Q-8). For Locations 3 and 4,
KM = 0.021x10-8, 90% Cl; (0.014x1Q-8, Q,029x10-8), The KM estimate from Location 1
(whose ore was reported to have a lower tremolite content) was about one-half that
from Locations 3 and 4, although this difference was not significant (p = 0.22). The KM
estimated from Location 2, the mill where substantial croddolite was used, was four to
seven times higher than the KL estimated from Location 1 and Locations 3 and 4.
For comparison purposes, KM were also calculated using grouped data and applying
expression (4), since this is the method that must be used with most studies. For
Location 1 (3&4) the KM estimate based on the "exact" analysis was 34% (25%) higher
than that based upon grouped data. This suggests that reliance upon published data
for calculating KM may introduce some significant errors in some cases. Such errors
may be further compounded by the failure of some studies to report the needed data on
levels and durations of exposure in different categories of time since first exposure.
The best estimate of KM for each location was assumed to be the MLE estimate. The
uncertainty factors described earlier, when coupled with the statistical confidence limits,
resulted in the likely range for KM shown in Table 1.
Italian Mine and Mill
Piolatto eta/, (1990) conducted additional followup of workers at a chrysotiie mine and
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mill in Italy that was earlier studied by Rubino et al. (1979), The cohort consisted of
1058 workers with at least one year of employment between 1946 and 1987. Followup
extended from 1946 through 1987, which is 12 more years of followup than in Rubino
et al. (1979), Lung cancer mortality was compared to that of Italian men.
As described in Rubino et a/. (1979), fiber levels were measured by PCM in 1969. In
order to estimate earlier exposures, information on daily production, equipment
changes, number of hours worked per day, etc. were used to create conditions at the
plant during earlier years. PCM samples were obtained under these simulated
conditions and combined with work histories to create individual exposure histories.
Piolatto et al, (1990) observed 22 lung cancers compared to 11 in the earlier study
(Rubino et al., 1979). Lung cancer was neither significantly in excess nor significantly
related to cumulative asbestos exposure. Piolatto ef a/. (1990, Table 1) presented
observed and expected lung cancers (based on age- and calendar-year-specific rates
for Italian men) categorized by cumulative exposure in f-y/ml. The lung cancer model
with fixed a provided an adequate fit to these data ( Table 3, p = 0.42) and allowing a
to vary did not significantly improve the fit. The KL estimate with a =1 was 0.00035 (f-
y/ml)"1, with 90% Cl: (0,0.0015). With a allowed to vary the estimate was KL = 0.00051
(f-y/ml)-1 with 90% Cl: (0,0.0057).
The best estimate of KL was assumed to be the MLE'estimate with a = 1. The
uncertainty factors selected for this study were F1 = 2, which, when coupled with the
statistical confidence limits, resulted in the likely range for KL shown in Table 1.
Two mesotheliomas were observed by Piolatto ef a/. (1990), compared to one found by
Rubino ef al. (1979). However, data were not presented in a form from which KM could
be estimated.
Connecticut Friction Product Plant
McDonald et al. (1984) evaluated the mortality of workers employed in a Connecticut
plant that manufactured asbestos friction products. The plant began operation in 1913
and used only chrysotile until 1957, when a little anthophyllite was used. Also, a small
amount of crocidolite (about 400 pounds) was handled experimentally between 1964
and 1972. Brake linings and clutch facings were made beginning in the 1930s, and
production of automatic transmission friction materials, friction disks and bands was
begun in the 1940s.
The cohort was defined to include any man who had been employed at the plant for at
least one month before 1959, omitting all that had worked at a nearby asbestos textile
plant that closed in 1939. This cohort consisted of 3515 men, of whom 36% had died
by the end of follow-up (December 31, 1977). Follow-up of each worker was only
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begun past 20 years from first employment.
Information on dust levels from impinger measurements were available for the years
1930, 1935, 1936, and 1939. There was little other exposure information available until
the 1970s. An industrial hygienist used these measurements and information on
processes and jobs, environmental conditions and dust controls to estimate exposures
by process and by period in units of mppcf. No conversion from mppcf to f/ml value
was suggested by the authors, a conversion factor or between 1.4 and 10 is suggested
by other studies. The most common value seems to be around 3 f/ml per mppcf, which
has been observed in diverse environments such as mining and textile manufacture.
This value was provisionally applied to this cohort, although this conversion has
considerable uncertainty associated with it,
Total deaths and deaths from most individual causes investigated were elevated; these
elevations were due primarily to increased deaths in the group working for less than
one year. This pattern holds for lung cancer in particular; the SMR for lung cancer was
highest (180) for persons exposed for less than one year. A similar pattern holds when
the analysis was carried out by cumulative exposure (Table 4); the SMR in the lowest
exposure category is higher than in any other category. The linear relative risk lung
cancer model provided a poor fit (p = 0.01) to these data when the Connecticut rates
were assumed to be appropriate for this cohort (fixing the parameter a = 1); use of U.S.
rates gave similar results. However, the fit was adequate (p = 0.28) if the background
response is allowed to rise above that of Connecticut men (allowing the parameter a to
vary). Although the reason for this increased response in persons that worked for a
short period or have low exposures is not clear, the analysis in which the background
response is allowed to vary appears to be the most appropriate. This analysis yields an
estimate of KL = 0.0 (f-y/ml)'1, 90% Cl: (0, 0.0017). The analysis with a = 1 yielded
KL = 0.0019(f-y/ml)-1, 90% Cl: (0, 0.0061).
The best estimate of KL was assumed to be the MLE estimate with a = 1 . The
uncertainty factors selected for this study were F1 = 2, F2 = 3, which, when coupled
with the statistical confidence limits, resulted in the likely range for KL shown in Table 1 .
McDonald et al. did not find any mesotheliomas in this cohort. It is useful to determine
the range of mesothelioma risk that is consistent with this negative finding. Although
McDonald et al. do not furnish data in the form needed for this calculation, these data
can be approximated from McDonald et al.'s Table 1. In this table they list 511 deaths
occurring after age 65. Assuming that the overall SMR of 108.5 held for persons over
65 years of age, the expected number of deaths is 511/1 .085 = 471 . The death rate in
U.S. white males between 65 and 75 years of age is approximately 0.050 per year
(from 1971 vital statistics). Therefore the number of person years observed in persons
post 65 years of age is estimated as 471/0.050 = 9420.
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A lower bound on the person-years of followup between ages 45 and 65 can be
estimated by assuming that followup was complete for this age group. First we
estimate the number of persons that would have had to have been in the cohort to
experience the observed deaths. Assuming that x persons in the cohort are alive at
age 45, we have the following estimates of the number entering each successive five-
year age interval and the corresponding number of deaths (based on death rates in
1971 white males).
Age
45-50
50-55
55-60
60-65
65+
TOTALS
Number Entering Interval
X
x(1-0.00638)5=0.97x
0.97x(1-0.01072)5=0,92x
0.92x(1-0.01718)5=0.84x
0.84x(1-0.02681)5=0.73x
Number of Deaths
in Interval
0.032x
0.052X
0.076x
0.11x
0.27x
Person-Years in
Interval
4.9x
4.7x
4.4x
3.9x
18. Ox
Since there were 616 deaths in men between the ages of 45 and 65, the expected
number of deaths is estimated as 616/1.085 = 567.7 expected deaths between ages of
45 and 60, the number of persons entering this age interval is estimated as
x = 567,7/0.27 = 2100. The person-years is then estimated as (2100)(17.964) = 38000.
Using the average age of beginning work of 30.95 years (McDonald et al., 1984, Table
3) yields the data in Table 5. Moreover, the average duration of exposure in this cohort
was 8.04 years and the average exposure level was 1.84 mppcf (McDonald et al, Table
3), which is equivalent to 1.84x3 = 5.52 fibers/ml. These data yields an estimate of KM
= 0.0 and a 90% upper bound of KM = 1.2xlCT9.
The best estimate of KM was assumed to be zero. In addition to the uncertainty factors
described earlier, an additional factor (F4M = 3) was applied to account for the crude
method of analysis. When coupled with the statistical confidence limits, these resulted
in the likely range for KM shown in Table 1.
New Orleans Asbestos-Cement Plants
Hughes et al, (1987) report on followup through 1981 of a cohort of Louisiana workers
from two asbestos cement plants studied previously by Weill et al. (1979). Although
chrysotile, amosite and crocidolite were used at these plants, a group of workers at one
of the plants were only exposed to chrysotile. The cohort contained 6,931 workers, of
whom 95% were traced, compared to a 75% success in tracing by Weill et al, (1979).
This improved trace was the result both of greater access to Social Security
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Administration records and greater availability of computerized secondary information
sources (Dr. Hughes, personal communication).
is*,
Both of the plants have operated since the 1920s. Chrysotile was used predominantly
in both plants. Some amosite was used in Plant 1 from the early 1940s until the late
1960s, constituting about 1% of some products, and crocidolite was used occasionally
for approximately 10 years beginning in 1962. Plant 2 utilized only chrysotile, except
that pipe production, which began in 1946 and was housed in a separate building,
produced a final product that contained about 3% crocidolite. Since the total
percentage of asbestos fiber in most asbestos cement products ranges from fifteen to
28 per cent, it is estimated that crocidolite constituted between ten and twenty per cent
of the asbestos used to make cement pipe (Ontario Royal Commission, 1984).
Workers from Plant 2 that did not work in pipe production were exposed only to
chrysotile.
Estimates of airborne dust levels were made for each job by month and year from
midget impinger measurements initiated in the early 1950s. Levels estimated from
initial samples in the 1950s were also assumed to hold for all earlier periods because
no major dust control measures had been introduced prior to that time. New exposure
data from Plant 2 became become available after the earlier study (Weili et al., 1979)
was completed, and these, along with a complete review of all the exposure data, were
used to revise the previous estimates of exposure. In Plant 1 the earlier and revised
estimates were reasonably similar, but in Plant 2, the revised estimates tended to be
about one-third of the previous estimates through the 1940s and about one-half the
previous estimates thereafter. Based on 102 side-by-side measurements by midget
impinger and PCM in various areas of one of the plants, Hammond et al. (1979)
estimated an overall conversion factor of 1.4 fibers/ml per mppcf. There were
substantial variations in this factor among different areas of the plant.
The principal cohort studied consisted of all workers who, according to company
records, were employed for at least one month prior to 1970, had a valid Social Security
number, and were first employed in 1942 or later (Plant 1) or in 1937 or later (Plant 2).
Mortality experience was compared with that expected based on Louisiana rates.
Hughes et al. found no significant difference between the dose responses for lung
cancer in Plant 2 among workers exposed to chrysotile only and those who were also
exposed to crocidolite in pipe production. A single lung cancer dose response model
adequately describes the lung cancer data from Plants 1 and 2 combined (p >= 0.42,
Table 6). The fit of this model is good when Louisiana men are assumed to be an
appropriate control group (fixing the parameter a = 1). This fit provides an estimate of
KL = 0.004 (fiber-y/ml)-1, 90% Cl; (0.001, 0.007) With a allowed to vary, the estimate is
0.003 (fiber-y/ml)-1, 90% Cl: (0, 0.007 ).
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The best estimate of KL was assumed to be the MLE estimate with a = 1. The
uncertainty factors selected for this study were F1 = 2, F2 = 1,5, which, when coupled
with the statistical confidence limits, resulted in the likely range for KL shown in Table 1.
Six mesotheliomas were identified in the primary cohort studied by Hughes et al., two in
Plant 1 and four in Plant 2. Four other mesotheliomas are known to have occurred, one
among those initially employed in Plant 2 before 1937 and three among Plant 2 workers
shortly after followup ended in 1981. A case control analysis conducted among Plant 2
workers found a relationship between mesothelioma risk and length of employment and
proportion of time spent in the pipe area after controlling for length of exposure, which
is consistent with a greater risk of mesothelioma from crocidolite exposure.
Data were not presented in the paper in the form required for estimating KM. However,
Hughes and Weill (1986) present estimates of mesotheliomas potency from several
data sets, including the cohort studied in Hughes et al. and containing six
mesotheliomas, but using a model slightly different from the 1986 EPA model (3).
Estimating KM by multiplying the potency estimated by Hughes and Weill model by the
ratio of the potency values estimated for another study using the 1986 EPA model and
the Hughes-Weill model yielded the following estimates of KM for the Hughes et al.
data:0.25xlO-8(Selikoffetal., 1979); 0.21xlO-8(Dement et al,, 1983b);
0.27xlCT8(Seidman, et al., 1979); and 0.43xl(r8 (Finkelstein, 1983). Based on these
calculations, KM = O.SOxlO"8 seems to be a reasonable estimate for the Hughes et al.
cohort.
It would be worthwhile to estimate mesothelioma risk using additional followup that
included the three cases that occurred shortly afterfollowup ended. However, such an
estimate should be no larger than about KM = 0.45xlO"8. This is because, since there
were six mesotheliomas in the cohort studied by Hughes et al., even if the additional
person years of followup post 1981 is not taken into account, the three additional
mesotheliomas would increase the estimate of KM by only about 50%.
Hughes et al.'s finding of an association with crocidolite exposure implies that a smaller
KM would correspond to the chrysotile-only exposed group in Plant 2. Although Hughes
et al. didn't furnish the data needed for precise estimation of KM from this cohort, it is
possible to make some reasonable approximations to this KM. Since none of the six
mesotheliomas occurred among workers exposed only to chrysotile, KM =0 would be
the point estimate derived from the data used by Hughes et al.
However, one mesothelioma was discovered in a person whose employment began in
1927 and thus was not eligible for inclusion in the cohort. This person was employed
continuously for 43 years in the shingle production area, where only chrysotile was
used. In an attempt to compute an alternative KM using this one case, it was noted that
the duration of observation of the Hughes et al. cohort was roughly equivalent to that of
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the Dement et al. (1983b) cohort. If the person-years from this cohort, categorized by
years since first exposure, are adjusted by the ratio of the sizes of Dement et a I. and
the Hughes et al. non-crocidolite-exposed cohort from Plant 2, one mesothelioma is
assumed to occur (in 30+ years from first exposure category) and the average duration
of exposure (2.5 years) and fiber level (11.2 fibers/ml) appropriate for the Hughes et al.
cohort are applied to these data, a KM = 0.2xlO"8 is obtained.
The best estimate of KM was assumed to be 0.2xlO~8 for workers exposed only to
chrysotile and O.SxIO"8 for workers exposed to both chrysotile and amphibole. Since no
confidence interval was available for these values, additional uncertainty factors were
included (F4M = 5, for chrysotile exposures and 2.5 for mixed exposures), which, when
coupled with the other uncertainty factors discussed earlier, resulted in the likely range
for KM shown in Table 1.
South Carolina Textile Factory
Dement and coworkers (Dement et al., 1994; Dement and Brown, 1998) conducted
a retrospective cohort study of employees of a chrysotile textile plant in South Carolina.
In an earlier study of this plant (Dement et al. 1982, 1983a, 1983b), the cohort was
defined as all white male workers who worked for one or more months between 1940
and 1965, and followup was through 1975. Dement et al. (1994) expanded the cohort
to include black male and white female workers who met the entrance requirements,
and extended followup through 1990, an additional 15 years. This expanded cohort
included 1247 white males (2.8% lost to followup), 1229 white females (22.8% lost to
followup) and 546 black males (7.8% lost to followup). A total of 1259 deaths were
identified, and a death certificate was located for all but 79 (6.2%) of the deaths.
Based on data from 5,952 air samples taken at the plant between 1930 and 1975,
linear statistical models were used to reconstruct exposure levels, while taking into
account textile processes, dust control methods, and job assignments (Dement et al.,
1983a). For each worker, time spent in each job was multiplied by the estimated
exposure level for that job to estimate cumulative exposure (f/ml-days). Based on
regression analyses applied to 120 side-by-side particle and fiber counts, Dement
(1980) estimated a f/ml to mppcf ratio of 2.9, 95% Cl: (2.4, 3.5), Also, between 1968
and 1971 both impinger and PCM samples were collected (a total of 986 samples).
Based upon a regression analysis of these data, Dement (1980) determined that a
common conversion factor could be used for jobs except fiber preparation, For fiber
preparation, a conversion factor of 7.8 was found, 95% Cl: (4,7-9.1). For all other
operations, a value of 2.5, 95% Cl: (2.1-3.0) was calculated. Based on this information,
Dement et al. (1983a) concluded that a conversion factor of 3 was appropriate for all
operations except preparation, for which a factor of 8 was adopted.
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The underlying data for this cohort were obtained from the National Institute for Safety
and Health (NIOSH). These data consisted of a work history file and a file with
exposure levels by job category and time period. The work history file contained codes
for race, sex, month and year of birth, vital status, month and year of death, and the
department, operation, start date, and stop date for each job worked. The exposure
level file contained the exposure start and stop dates and the exposure level (fiber/ml)
by the plant code, the department code, and the operation code.
The cohort was defined as the white and black males and the white females who met
the employment requirements described above. This cohort included 1244 white males
(1.5% lost to followup), 550 black males (7.5% lost to followup), and 1228 white
females (22.1 % lost to followup).
Table 7 shows observed and expected deaths for lung cancer among white males/
black males and white females, categorized by cumulative exposure. This table shows
an excess of lung cancers that exhibited a dose response relationship. U.S. rates were
used for calculating expected deaths, whereas South Carolina lung cancer rates are
higher for white men but slightly lowerfor white women and black men. Whereas
twelve categories of cumulative exposure were used for fitting the model, these were
been combined into seven categories for display in Table 7. The model with a = 1 and
a variable fit the data adequately (p z 0.2), and the hypothesis that a = 1 cannot be
rejected (p = 0.21). The estimate of KL with a = 1 was 0.028 (f-y/ml)'1, 90% Cl: (0.021,
0.037), and the estimate with a variable was KL = 0.021 (f-y/ml)'1, 90% Cl: (0.012,
0.034). An analysis applied to white men alone gave somewhat higher estimates
(KL = 0.040 (f-y/rnl)-1 with a = 1, and KL = 0.026 (f-y/ml)'1 with a variable).
Because the chi-square goodness of fit test could not reject the model with a = 1 even
though a = 1 could be rejected the best estimate of KL was assumed to be the
geometric mean of the MLE estimates with a = 1 and a variable. The uncertainly
factors selected for this study were F1 = 1.5, which, when coupled with the statistical
confidence limits, resulted in the likely range forKL shown in Table 1.
Two deaths were certified as due to mesothelioma on the death certificates. In
addition, Dement et al. (1994) considered four other deaths as likely due to
mesothelioma. The availability of the raw data from this study made it possible
calculate KM from this study using an "exact" likelihood approach based on expression
(3) that did not involve any grouping of data, or use of average values, Using the six
confirmed and suspected mesotheliomas, KM = 0.43x10'8, 90% Cl: (0.20x10'8,
0.79X10'8). Using the two confirmed mesotheliomas, KM = 0.14x1Q-8, 90% Cl:
(0.034x1 Q-8, 0.38x10'8).
For comparison purposes, KM were also calculated using grouped data and applying
expression (4), since this is the method that must be used with most studies. The data
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were divided into 10 categories by the tabulated values of expression (4), The KM
estimate based on the "exact" analysis was 2% greater than that based upon grouped
data.
The best estimate of KM was assumed to be the geometric mean of the MLE estimates
computed using either confirmed or both confirmed and suspected mesotheliomas
(0.25x10~8). The statistical lower bound used for this estimate was the one based on
confirmed cases and the upper bound used was the one based on confirmed and
suspected cases. The uncertainty factors described earlier, when coupled with these
statistical confidence limits, resulted in the likely range for KM shown in Table 1.
McDonald et al. (1983a) conducted a cohort mortality study in the same South
Carolina textile plant that was studied by Dement et al. (1994). Their cohort consisted
of all men employed for at least one month before 1959 and for whom a valid social
security record existed. This cohort consisted of 2410 men, of whom 36% had died by
the end of followup (December 31, 1977). Followup of each worker was begun past 20
years from first employment.
McDonald et al. had available the same exposure measurements as Dement et al.
(1983b) and used these to estimate cumulative exposures for each man in mppcf-y. In'
their review of the environmental measurements in which both dust and fiber
concentrations were assessed, they found a particle to fiber conversion range of from
1.3 to 10.0 with an average of about 6 fibers/ml per mppcf. This value, which is
intermediate between the values of 3 and 8 found by Dement et al. for different areas of
the same plant, will be used in the calculations involving the McDonald et al. (1983a)
study.
McDonald et al. describe two practices at the plant that entailed very high exposures
arid which were not reflected in either their's or Dement et al.'s estimates: cleaning of
burlap bags used in the air filtration system by beating them with buggy whips during
the years 1937-53, and the mixing of fibers, which was carried out between 1945 and
1964 by men with pitch forks and no dust suppression equipment.
A strong dose response for lung cancer was observed (Table 8), which parallels the
results of Dement et al. (1994). Unlike Dement et al,, McDonald et al. used South
Carolina men as the control group rather than U.S. men. Use of this control group
provided an adequate description of the data and lung cancer potency values estimated
both with a = 1 and allowing a to vary provided excellent descriptions of the data (p
>= 0.88) and the hypothesis a = 1 could not be rejected (p = 0,36). Assuming a = 1
resulted in KL = 0.012 (f-y/ml)-1, 90% Cl: (0.0075, 0.016), and when a was allowed to
vary, KL = 0.010 (f-y/ml)-1, 90% Cl: (0.0044, 0.025). These results are reasonably
consistent with the potency estimated from Dement et al. (1994), and the differences
can be largely accounted for by the different assumptions regarding the fiber/particle
.
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ratio.
The best estimate of KL was assumed to be the MLE estimate with a = 1. The
uncertainty factors selected for this study were F1 = 2, which, when coupled with the
statistical confidence limits, resulted in the likely range for KL shown in Table 1.
McDonald etal, found one case of mesothelioma in this cohort, apparently the same
one discovered by Dement et ah (1983b): a man born in 1904 who died in 1967 and
worked at the plant for over 30 years. Since this study was conducted exactly as
McDonald et al. (1984), fie same method used there to reconstruct person-years by
years from first exposure can be applied to this cohort as well. The reconstructed data
are listed in Table 9. The estimated potency MLE is KM = 0.088 xlO'8, with a 90%
confidence interval of (0.0093xlO~8, 0.32xlO"8).
The MLE estimate of KM was assumed to be the best estimate. The uncertainty factors
described earlier, when coupled with the statistical confidence limits and an additional
factor to account for the reconstructed person years (F4M = 3) resulted in the likely
range for KM shown in Table 1,
Predominant Crocidolite Exposure
Wittenoom, Australia Mine and Mill
de Klerk et al., 1994 followed a cohort of 6904 men and women employed at a
crocidolite mine and mill in Wittenoom, Australia. This cohort was followed through
1999 and the raw data were obtained through the courtesy of Dr, de Klerk. The data
consisted of a record number, date of birth, sex, employment start date, total days of '
employment, average exposure level (f/cc), cumulative exposure (f-Yr/cc), date of last
contact, ICD code for cause of death, indicator variable for mesothelioma death, and
date of death if applicable.
A number of subjects from the full cohort were removed from the analysis reported
herein; .412 because the sex was not designated as male; One because the date of
last contact was missing; 1275 subjects because the followup period was less than five
years; 41 because the number of days worked was 0 or missing. After these subjects
were removed, the cohort consisted of 5173 men who were employed at Wittenoom
Gorge between 1943 and 1966.
The concentrations of fibers greater than 5 mm in length as measured by PCM were
measured at various work sites in a survey conducted in 1966. Job category data were
obtained from employment records and supplemented by records from the Perth Chest
Clinic and the Western Australian Mineworkers Relief Fund. The concentration
measurements and job category information were used to estimate the exposure level
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for each subject in the cohort (DeKlerk et a/., 1989). The exposure levels were high
with a median of 17.8 (fiber/ml). The durations of employment were low with a median
of 128 days.
There were 251 lung cancer deaths in the cohort. Table 10 shows the observed,
expected, and predicted lung cancer deaths among the males categorized by C
cumulative exposure (fiber-year/ml). The number of expected lung cancer deaths are
based on Australian lung cancer mortality rates. With no allowance for difference
between the background lung cancer death rates among Australia and the members of
this cohort (a = 1), the fit of the model is poor (p < 0.01). Allowing for difference in the
background lung cancer death rates (a variable), the model provides a reasonably good (
fit to the data (p = 0.10) and estimates KL = 0.0047 (fiber-year/ml)-1, 90% Cl: (0.0017,
0.0087). The hypothesis a = 1 can be rejected with high confidence (p < 0.01).
The best estimate of KL was assumed to be the MLE estimate with a variable. The
uncertainty factors selected for this study were F1 = 2, which, when coupled with the
statistical confidence limits, resulted in the likely range for KL shown in Table 1. ^
There were 165 mesotheliomas in the cohort. The availability of the raw data from this
study made it possible calculate KM from this study using an "exact" likelihood
approach based on expression (3) that did not involve any grouping of data, or use of
average values. With this approach, KM = 7.95x10'8, 90% Cl: (6.97x1Q-8, 9.01x10'8). c
For comparison purposes, KM were also calculated using grouped data and applying
expression (4), since this is the method that must be used with most studies. The KM
estimate based on the "exact" analysis was 12% lower than the estimate based upon
grouped data.
The MLE estimate of KM was assumed to be the best estimate. The uncertainty factors
described earlier, when coupled with the statistical confidence limits, resulted in the
likely range for KM shown in Table 1.
Predominant Amosite Exposure (
Patterson, N.J. Insulation Factory
Seidman et a/. (1986) studied a cohort of 820 men (mostly white) who worked at an
amosite asbestos factory that operated in Patterson, New Jersey from 1941 through ^
1954. The men began work between 1941 and 1945 and followup was through 1982.
The followup of a worker began five years following the beginning of employment.
Workers who had prior asbestos exposure were not included in the cohort, and followup
was stopped when a worker was known to have begun asbestos work elsewhere (6
men). Exposures were generally brief, as 76% were exposed for two years or less,
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although-a few were exposed for as long as 10 years.
No asbestos exposure measurements are available for this plant, Estimates of
exposures in particular jobs were made based on air measurements made between
1967 and 1970 at plants in Tyler, Texas and Port Allegheny, Pennsylvania that were
operated by the same company and made the same products using some of the same
machinery as the Patterson facility, The estimated median exposure level was 50 f/ml.
Amoslte was the only type of asbestos used at the plant,
Seidman etal. cross-categorized lung cancer deaths by cumulative exposure (eight
categories of f-y/ml) and length of time worked (seven categories, Seidman et a/., 1986,
Table XXXIV). Although this table apparently was created by categorizing workers by
their final cumulative exposure (rather than categorizing person-years of followup by the
cumulative exposure to that point in time, which is more appropriate for calculating a
KL), because exposures were brief this likely made little difference, Expected number
of lung cancer deaths were based on age- and year-specific rates for New Jersey white
males.
Table 11 shows the results of applying the lung cancer model to these data, after
collapsing the table by summing over length of time worked. Results were highly
dependent upon whether or not the background lung cancer mortality rate was
assumed to be equal to that predicted by the comparison population of New Jersey
white males (equivalent to a = 1). The test for departure from the null hypothesis, oc=1,
was highly significant, and the maximum likelihood estimate was a = 3.3. Similarly, the
model gave a poor overall fit to the data with oc= 1 (p < 0,01), but the fit was quite good
when a was allowed to vary (p = 0.90). The estimated potency parameter, KL, also
was highly dependent upon the assumption regarding the parameter, a. The estimate
of KL was 0.082 (f-y/miy1, 90% Cl: (0,050, 0,076), when a was fixed at a = 1, and
0,011 (f-y/ml)"1, 90% Cl: (0,0058, 0,019), when a was allowed to vary, a six-fold
difference. The lung cancer model was also fit to the data cross-classified by both
cumulative exposure and length of time worked, allowing a to assume a different value
in each category of time worked. Although the estimated values of a tended to
increase with increasing duration of exposure, allowing different values of a did not
significantly improve the fit (p = 0.64),
The reason for this behavior is not clear. There is no indication that workers with
shorter durations experienced disproportionately high mortality, since, as noted above,
a tended to increase with increasing duration of exposure. Although it is possible that
cumulative exposure is not the appropriate exposure metric, it is difficult to envision
what metric would predict this response, so long as a linear model is assumed. It is
also possible that a linear model for relative risk is not correct and a supralinear mode!
is more appropriate, or that the increased risk is not proportional to the background risk,
as assumed by this simple relative risk model. Finally, it is possible that the
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background rate in this population is significantly greater than that in the comparison
population, although it seems unlikely that it could be three times greater as suggested
by the model.
It is not clear what is the best estimate of KL in this case. If a is fixed at a = 1, the
model underpredicts risk at low exposures and overpredicts at high exposures. On the (
other hand if a is estimated, the resulting estimate of 3.3 seems unrealistically high.
Provisionally, 0.026 (f-y/ml)"1, the geometric mean of the two MLE estimates with a = 1
and a estimated will be used as the best estimate of KL for this cohort. The uncertainty
factors selected for this study were F1 = 3.5, which, when coupled with the statistical
confidence limits, resulted in the likely range for KL shown in Table 1, (
Seidman etal. (1986) discovered 17 deaths from mesothelioma in this population.
Table III of Seidman etal. categorized mesothelioma deaths and person-years of
observation by years since onset of work. In order to apply the 1986 EPA
mesothelioma model it is necessary to have estimates of the duration of exposure and
level of exposure for each category. Using the categorization of the members of the ^
cohort by duration of work in Table XXI11 of Seidman et a/., it was estimated that the
mean duration of work was 1.5 years. Using data from Seidman et a/., Table XIV, an
average cumulative exposure was for each category of time from onset of exposure by
weighting exposures according to the expected total number of deaths. These
averages were divided by 1.5 years to obtain the average fiber concentrations in Table <
12. The estimated exposure levels decrease with time since onset, which is consistent
with higher mortality among more heavily exposed workers.
The 1986 mesothelioma model provided an adequate fit to these data (p = 0.35),
although it over-predicted somewhat the number of cases in the highest latency
category (> 35 years). The estimate of KM was 3.9x10"8, 90% Cl: (2.6x10'8, 5.7x10'8). (
The MLE estimate of KM was assumed to be the best estimate. The uncertainty factors
described earlier, when coupled with the statistical confidence limits, resulted in the
likely range for KM shown in Table 1.
C
Tyler, Texas Insulation Factory
Levin etal. (1998) studied the mortality experience of 1121 men who formerly worked
at a plant in Tyler, Texas that manufactured asbestos pipe insulation. The plant ^
operated from 1954 through February, 1972. The plant used the same raw materials
and some of the same equipment that was used in the Patterson, New Jersey plant that
was studied by Seidman etal. (1986). The asbestos used was amosite from the
Transvaal region of SoUth Africa. The insulation was manufactured from a mixture that
contained 90% amosite asbestos.
C
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Environmental surveys were conducted at the plant in 1967, 1970 and 1971, with
average fiber concentrations ranging from 15.9 through 91.4 f/ml. An average exposure
of 45 f/ml is assumed for this plant, which is near the middle of this range obtained in
the three surveys. It is also consistent with average levels assumed for the Patterson,
New Jersey plant, which operated under very similar conditions.
The cohort consisted of 744 whites, 305 non-white (mostly black), and 72 with missing
race (assumed to be white, based on hiring practices at that time). For the entire
cohort, the median age of first employment was 25 years, and the mean duration of
employment was 12.7 months (range of one day to 17.3 years), Followup was through
1993, Death certificates were obtained for 304 of the 315 men known to be dead. In
the mortality analysis only white men were evaluated and followup started ten years
after first employment. After additional exclusions of men with missing birth dates or
missing employment information, the cohort analyzed in the mortality analysis consisted
of 753 former workers, among whom 222 deaths were recorded. These deaths were
compared with those expected based on age, race and sex-specific U.S. rates,
There was an excess of deaths from respiratory cancer (SMR = 277, based on 36
deaths, not including four deaths from mesothelioma). Table 13 contains observed and
expected numbers of deaths from respiratory cancer, categorized by duration of
exposure. Cumulative exposure in f-y/ml was estimated by multiplying the duration of
exposure times the assumed average fiber level of 45 f/ml. There was an excess of
lung cancer deaths in the lowest exposure group (23 observed, 8.9 expected), and
consequently the model with a = 1 did not fit these data (p < 0.01), and the hypothesis
a = 1 could be rejected (p < 0.01). The KL with a variable was KL = 0.0013, 90% Cl:
(0, 0,006). With a = 1, KL = 0,013 (f-y/ml)-1, 90% CI: (0.0055, 0,022),
The best estimate of KL was assumed to be the MLE estimate with a variable, The
uncertainty factors selected for this study were F1 = 3, which, when coupled with the
statistical confidence limits, resulted in the likely range for KL shown in Table 1.
Four mesotheliomas were reported in this study. However, the data are not presented
in a form that would permit application of the EPA 1986 mesotheiioma model,
Predominant Tremolite-Actinolite Exposure
Libby, Montana Vermiculite Mine
Amandus and Wheeler (1987) conducted a retrospective cohort study of 575 men who
were exposed to tremolite-actinolite while working at a vermiculite mine and mill in
Libby, Montana. A dry mill began operation in 1935 and a wet mill began operating in
the same building as the dry mill in 1950 (Amandus etal., 1987).
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A total of 376 impinger samples were available that had been collected during
1950-1969, although only 40 of these were collected prior to 1965. In addition 4118
PCM samples were available from the period 1967-1982. Exposure estimates for years
later than 1968 were based on historical measures of fiber concentrations (f/ml), and
those for earlier years were based on concentrations measured by midget impinger
(mppcf) and converted to f/ml assuming a conversion ratio of 4 f/ml per mppcf. This
conversion factor was derived from 336 impinger samples collected during 1965-1969
and 81 filter samples collected during 1967-1971, Individual cumulative fiber exposure
estimates (f-y/ml) were computed from job-specific exposure estimates and work
histories (Amandus et a/., 1987).
The cohort consisted of all men hired prior to 1970 and employed for at least one year
in either the mine or the mill. Followup was through December 31, 1981. The vital
statuses of 569 of the men (99%) were determined and death certificates were obtained
for 159 of the 161 who were deceased.
Smoking information was available for 161 men employed between 1975 and 1982 and
with at least five years of tenure. The proportion of these workers who smoked (current
or former) was 84% compared to 67% among U.S. white males during the same time
period.
A total of 20 deaths from lung cancer were observed (9 expected, SMR = 223.2, using
U.S. white males as the comparison population). Table 14 (based on Amandus and
Wheeler, 1987, Table II) shows that the excess occurred mainly in workers whose
cumulative exposure exceeded 400 f-y/ml (10 observed, 1.7 expected). The 1986 EPA
lung cancer model fit these data adequately (p ^ 0.25) both with a = 1 and a variable,
and the hypothesis a = 1 could not be rejected (p = 0.4). With a = 1, KL was estimated
as 0.0061 (f-y/ml)-1, 90% Gl: (0.0029, 0.010), and with a variable, KL = 0.0051
(f-y/ml)-1, 90% Cl: (0.0011, 0.020).
The best estimate of KL was assumed to be the MLE estimate with a =1. The
uncertainty factors selected for this study were F1 = 2.5, F2 = 1,5, which, when coupled
with the statistical confidence limits, resulted in the likely range for KL shown in Table 1.
Amandus and Wheeler (1987) observed 2 deaths from mesothelioma in this cohort.
However, information on these cases was not sufficient to permit application of the
1986 EPA mesothelioma model.
McDonald et al. (1986) also conducted a cohort study of workers at the Libby, Montana
vermiculite mine and mill. Their cohort was composed of 406 workers employed prior
to 1963 for at least one year. Followup was until July 1983. Vital status was
determined for ail but one man and death certificates were obtained for 163 of the 165
men who had died, Cumulative exposures (f-y/ml) were estimated for each worker
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using work histories based on 42 job categories, and 1363 environmental
measurements, including samples analyzed by PCM (f/ml) and by midget impinger
(mppcf).
A total of 23 deaths from lung cancer were observed (SMR = 303, based on Montana
rates). Table 15 shows these deaths categorized by cumulative exposure (based on
Table 4 of McDonald et al., 1986). Both the models with a = 1 and a variable fit these
data adequately (p > 0.16) although the hypothesis a = 1 could almost be rejected
(p = 0.057). The estimate of KL with a = 1 was 0.011, (f-y/ml)'1, 90% Cl: (0.0055,
0.017), and with a variable, KL = 0.0039 (f-y/ml)'1, 90% Cl: (0.00067, 0.012).
Since the test of the hypothesis a = 1 was close to significance, KL was assumed to be
the geometric means of the MLE estimates from a = 1 and a variable. KL estimate
with MLE estimate with a variable. The uncertainty factors selected for this study were
F1 = 2,5, F2 = 1.5, which, when coupled with the statistical confidence limits, resulted in
the likely range for KL shown in Table 1.
McDonald et al. (1986) observed 2 deaths from mesothelioma. However, information
on these cases was not sufficient to permit application of the 1986 EPA mesothelioma
model.
Exposure to Mixed Fiber Types
British Friction Products Factory
Berry and Newhouse (1983) conducted a mortality study of 13,460 workers in a
factory in Britain that manufactured brake blocks, brake and clutch linings, and other
friction materials. Only chrysotile was used at the plant except for two relatively short
periods before 1945 when crocidolite was used in the production of railway blocks.
The cohort studied consisted of all men or women employed at the plant between 1941
and 1977. Follow-up was to the end of 1979 and the mortality experience was
examined after 10 years from first exposure. Airborne dust measurements were only
available from 1967 onward and these were made using the PCM method. Fiber
concentrations in earlier years were estimated by reproducing earlier working conditions
using knowledge of when processes were changed and exhaust ventilation introduced.
Deaths from all causes were less than expected both prior to ten years from first
employment (185 observed versus 195.7 expected) and afterward (432 observed
versus 450.8 expected). There was no indication of an effect of employment at the
plant upon lung cancer; there were 51 lung cancers more than ten years from first
employment compared to 47.4 expected. A significant deficit of gastrointestinal
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cancers was observed after ten years from first employment (25 observed versus 35.8
expected, p = 0. 04).
A linear dose response model relating cumulative exposure and lung cancer was fit to
case-control data presented by Berry and Newhouse. The resulting KL was 0.00058
(f-y/ml)"1 and the 95% upper limit was 0.0080 (f-y/ml)"1. This estimate was used as the
best estimate of KL, and and the lower confidence bound was assumed to be zero.
The uncertainty factors selected for this study were F1 = 3, which, when coupled with
the statistical confidence limits, resulted in the likely range for KL shown in Table 1.
A case control study on mesothelioma deaths showed that eight of the eleven cases
had been exposed to crocidolrte and another possibly had intermittent exposure to
crocidolite, The other two had been employed mostly outside the factory and possibly
had other occupational exposures to asbestos. The case control analysis showed that
the distribution of cases and controls in respect to exposure to crocidolite was quite
unlikely assuming no association with crocidolite. This indicates that some, and
possibly all, of the eight mesotheliomas with crocidolite exposure were related to this
exposure. The data were not presented in a form that permitted a quantitative estimate
of mesothelioma risk.
Ontario Asbestos-Cement Plant
Finkelstein (1984) studied mortality among a group of 535 exposed and 205
unexposed employees of an Ontario asbestos-cement factory who had been hired
before 1960 and who had been employed for at least one year. This cohort contained
the cohort studied by Finkelstein (1983) and which required at least nine years of
employment for membership. Follow-up continued until 1977 or 1981.
The plant produced asbestos cement pipe from 1948, asbestos cement board from
1955-1970, and manufacture of asbestos insulation materials was added in 1960. Both
chrysotile and crocidolite were used in each batch processed in the pipe process, but
only chrysotile was used in the cement board operation. Crocidolite constituted
approximately 20% of the asbestos used in the pipe process (Ontario Royal
Commission, 1984).
Fiber concentrations in various work areas and for various epochs were estimated from
membrane filter samples taken after 1969, impinger measurements taken during 1949,
1954, 1956, 1957 and semiannually during the 1960s, and information on changes in
dust control methods. Finkelstein judged that the resulting exposure estimates were
"probably accurate to within a factor of three or five." Exposures of maintenance
workers were not estimated, and the exposure response analysis consequently involved
only the unexposed workers (N = 205) and the production workers (N = 428).
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Only 21 deaths from lung cancer were observed among production workers. Based on
these deaths, Finkelstein compared age-standardized lung cancer mortality rates in
production workers after a 20-year latency, categorized into five groups according to
their cumulative exposure through 18 years from date of first employment (Finkelstein,
1984, Table 7), Mortality rates were standardized with respect to age and latency using
the man-years distribution in the cohort as a whole as the standard. Using similarly
standardized mortality rates in Ontario males as the comparison population, lung
cancer rates were elevated in all five categories, and Finkelstein found a significant
exposure-response trend. However, the trend was not monotone, as rates increased
up to the middle exposure category and decreased thereafter (Table 16).
These data may be put into a form roughly equivalent to the more conventional
age-adjusted comparison of observed and expected lung cancer deaths by dividing the
rates in the exposed group by that, of Ontario men, (The rate for unexposed workers
was not used because it was based on only 3 deaths,) The results of this are shown in
Table 16, which also shows the results of fitting the 1986 EPA lung cancer model both
assuming the Ontario rates were appropriate for this cohort (fixing the parameter a = 1)
and not making this assumption (allowing the parameter a to vary). Neither approach
provided an adequate fit to these data (p <; 0.05) and the hypothesis a = 1 was rejected
(p = 0.03). The maximum likelihood estimate of a was 4.26, which seems too large to
be due to differences in smoking habits. The KL estimate with a =1 was 0.048 [f-y/ml]"
\ 90% Cl: (0,028,0.074). With a allowed to vary the estimate was KL = 0.0029 [f-y/ml]-
\ 90% Cl: (0,0.037). The fact that the lower limit was zero indicates that the dose-
response trend was not significant when the background was allowed to vary.
Because the hypothesis a = 1 was rejected but neither a = 1 nor a estimated gave an
adequate fit to these data, the best estimate of KL was assumed to be the geometric
means of the MLE estimates from a = 1 and a variable. The uncertainty factors
selected for this study were F1 = 4, which, when coupled with the statistical confidence
limits, resulted in the likely range for KL shown in Table 1,
Based on a "best evidence" classification of cause of death, Finkelstein identified 17
deaths from mesothelioma among production workers. Table 3 of Finkelstein (1984)
gives these mesotheliomas categorized by years since first exposure. This table also
provides the mortality rate, from which can be calculated the person-years of
observation, Finkelstein states that the average cumulative exposure for production
workers was about 60 f-y/ml, but does not provide information for determining duration
and level of exposure separately. CHAP (1983) used an average exposure of 9 f/ml for
a subcohort of production workers, although they provided no support for this
assumption. If this value is assumed to be appropriate for the expanded cohort, the
average duration is estimated as about 60/9 = 6.7 years. However these values are
uncertain. Table 17 presents the result of applying the 1986 EPA mesothelioma model
to the Finkelstein (1984) data based on these assumptions. The mesolhelioma model
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describes these data adequately (p = 0.26) and provides an estimate of KM = 18x10"8,
90%CI:(13x10-8,24x10-8).
The MLE estimate of KM was assumed to be the best estimate. The uncertainty factors
described earlier, when coupled with the statistical confidence limits and an additional
factor to account for the estimated exposure level (F4M = 2), resulted in the likely range
for KM shown in Table 1.
Swedish Asbestos-Cement Plant
Albin et a/. (1990) studied workers at a Swedish plant that operated from 1907 to 1978
and produced various asbestos cement products, including sheets, shingles, and
ventilation pipes. The asbestos handled was mainly chrysotile (> 95%). Crocidolite
was used before 1966 but never exceeded 3-4% of the total asbestos. Amosite was
used for a few years in the 1950s but never exceeded 18% of the total asbestos used.
Fiber length classes were the commercial grades 3-7, and all asbestos was milled prior
to incorporation into products.
Impinger and gravimetric dust measurements were available for 1956-1969, and PCM
measurements after 1969. These data, along with information on production and dust
control, were used to estimate exposures for different jobs and periods of time.
The cohort contained 2898 men and was defined as all male employees who worked
for at least three months between 1907 and 1977. A reference cohort was composed
of 1233 men who worked in other industries in the region and who were not known to
have worked with asbestos. Vital status of both groups was determined through 1986.
Followup of both began after 20 years from first employment.
Excluding mesothelioma, other respiratory cancers were not significantly increased.
Albin et al. present relative risks of these respiratory cancers and corresponding 95%
confidence intervals for three categories of cumulative exposure (Table 18), based on
Poisson regression with control for age and calendar year. In order to obtain crude
estimates of the range of KL that are consistent with these data, the 1986 EPA lung
cancer relative risk model was fit, assuming that the Ln (RR) were normally distributed
with fixed variances computed from the reported confidence intervals for the RR.
Although elevated, the RR did not exhibit a dose response, and the hypothesis a - 1
was rejected (p = 0.02). In this analysis KL was not significantly different from zero,
regardless of whether a was fixed at 1.0 or estimated. With a =1 the estimate of KL
was 0.019 (f-y/ml)-1, 90% Cl: (0, 0.065), and KL = 0.00067 (f-y/ml)"1, 90% Cl: (0, 0.036)
with a estimated.
Because the hypothesis a = 1 was rejected, the best estimate of KL was assumed to
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be the geometric means of the MLE estimates from a = 1 and a variable. The
uncertainty factors selected for this study were F1 = 4, which, when coupled with the
statistical confidence limits, resulted in the likely range for KL shown in Table 1.
Thirteen mesotheliomas were identified among exposed workers and one in the
referent population, and a significant dose response was observed with increasing
cumulative exposure. Unfortunately, the mesothelioma data were not presented in a
format that would permit application of the 1986 EPA mesothelioma model.
Belgium Asbestos-Cement Plant
Lacquet et al. (1980) conducted a roentgenologic, asbestosis, and mortality study in a
Belgium asbestos cement factory employing about 2400 employees that annually
processed about 39,000 tons of asbestos, of which 90% was chrysotile, 8% crocidolite,
and 2% amosite. The mortality study considered male workers who worked in the
factory for at least 12 months during the 15-year period 1963-1977. Apparently no
minimal latency was required before followup began.
Fiber counts were available for the years 1970-1976; fiber levels were estimated for as
far back as 1928, but these estimates were considered to be "only good guesses at
best." Individual exposures were estimated in fiber-years from work histories and
estimated yearly concentrations in four work areas.
The incidence of respiratory cancer was very close to that which was expected in a
Belgium population of matched age and sex (Table 19). The models with a = 1 (p =
0.51) and a variable (p = 0.39) gave similar results and the hypothesis a = 1 was not
rejected (p = 0.34). With a = 1, the estimate of KL was 0.0 (f-y/ml)'1, 90% Cl: (0,
0.0010). With a estimated, KL = 6.8X10-6 (f-y/ml)'1, 90% Cl: (0, 0.0021).
The estimate of KL with a = 1 was assumed to be the best estimate. The uncertainty
factors selected for this study were F1 = 4, which, when coupled with the statistical
confidence limits, resulted in the likely range forKL shown in Table 1.
One death was due to pleural mesothelioma. Unfortunately, the data were not
presented in a way that allowed the estimation of KM.
Retirees from U.S. Asbestos Products Company
Enterline et al, (1986) extended followup through 1980 fora cohort of U.S. retirees
from a large asbestos products company that had been the subject of an earlier report
(Henderson and Enterline, 1979). Products manufactured by the company included
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textiles, cement shingles, sheets, insulation and cement pipe. Exposure was
predominately to chrysotile in most operations, although amosite predominated in
insulation production, and crocidolite in manufacture of cement pipe, Each worker's
exposure was estimated from dust measurements in mppcf obtained from
environmental surveys that started in the mid-1950's and were extrapolated back in
time by the company industrial hygienist. No data are provided for conversion from
mppcf to PCM in f/ml. Given the wide range of products manufactured, this conversion
likely varied according to operation. Conversions calculated in different environments
have ranged from 1.4 to 10, the most common value.being around 3 f/ml per mppcf,
which has been observed in diverse environments such as mining and textile
manufacture. This value was provisionally applied to this cohort.
The cohort consisted of 1074 white males who retired from the company during 1941-
1967, and who were exposed to asbestos in production or maintenance jobs. The
average duration of employment was 25 years. Followup started at age 65 or at
retirement if work continued past age 65, By the end of followup in 1980, 88% were
deceased.
Overall, respiratory cancer was significantly increased (SMR = 258 in comparison to
U.S. rates, based on 79 observed deaths). Enterline etal. (1986) categorized lung
cancer deaths by cumulative exposure (their Table 4). Results of applying the 1986
EPA lung cancer model to these data are shown in Table 20. Although both the model
with a = 1 and a variable fit the data adequately (p ;> 0.75), the test of a = 1 was
marginally significant (p = 0.05). With a = 1 the estimate of KL was 0.0021 (f-y/ml)"1,
90% Cl: (0.0015,0.0027). With a variable, KL = 0.0011 (f-y/ml)'1, 90% Cl: (0.00041,
0.0028).
Since the test of the hypothesis a = 1 was close to significance, KL was assumed to be
the geometric means of the MLE estimates from a = 1 and a variable. The uncertainty
factors selected for this study were F1 = 2, F2 = 3, which, when coupled with the
statistical confidence limits, resulted in the likely range for KL shown in Table 1.
From the death certificates Enterline et al. identified eight deaths from mesothelioma.
These data were not presented in a form that permitted application of the 1986 EPA
mesothelioma model.
U.S. Insulation Applicators
Selikoff and Seidman (1991) reported on followup through 1986 of a cohort of 17,800
asbestos insulation applicators that had been followed through 1976 by Selikoff et al.
(1979). The cohort consisted of men enrolled as members of the insulator's union in
the United States and Canada. Deaths were classified both based on the information
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the death certificate, and using "best evidence," in which death certificate information
was augmented by clinical data, histopathological material and X-rays.
Based on the composition of insulation material, it seems likely that these workers were
exposed to substantial amounts of chrysotile and amosite. Data on insulator's
exposures were reviewed by Nicholson (1976), who concluded that average exposures
of insulation workers in past years could have ranged 10-15 f/ml.and could have been
15-20 f/ml in marine construction. USEPA (1986) assumed a value of 15 f/ml as an
overall average, with an associated 3-fold uncertainty. This estimate of 15 f/ml will be
used provisionally here as well.
The form of the data provided in Selikoff and Seidman (1991) is not particularly suitable
for calculating KL. Table 4 of Selikoff and Seidman (1991) contain observed and
expected deaths from lung cancer (determined from either death certificates or best
information) categorized by years from first exposure (<15, 15-19, 20-24, .,., 50+).
Death certificate information was utilized herein to facilitate comparisons with expected
deaths (based on the mortality experience of U.S. white males), which were also based
on death certificates. Lung cancer was significantly increased over expected, except
for the category of < 15 years from onset of exposure. Selikoff and Seidman did not
provide information on the duration of exposure. The USEPA (1986, page 90)
assumed an average exposure duration of 25 years. Assuming that all workers worked
exactly 25 years and were exposed to 15 f/ml, the data in Table 4 of Selikoff and
Seidman (1991) can be used to categorize lung cancer deaths by cumulative exposure
lagged 10 years. The result is shown in Table 21. The 1986 EPA lung cancer model
provided a reasonable fit to these data with a variable (p = 0.12), but not with a = 1
(p < 0.01). Also, the hypothesis that a = 1 could be rejected (p < 0.01). The estimate
of KL with a variable was 0.0018 (f-y/ml)'1, 90% Cl: (0.00065, 0.0038). With a = 1, KL
= 0.0087 (f-y/ml)-1, 90% Cl: (0.0081, 0.0093).
Although the hypothesis a = 1 could be rejected, the estimated value of a (2.3) seemed
somewhat large. Accordingly, the best estimate of KL was assumed to be the
geometric means of the MLE estimates from a = 1 and a variable. The uncertainty
factors selected for this study were F1 = 4, F3 = 2 (no individual work histories) and F4L
= 2 (data not summarized in appropriate form for fitting model), which, when coupled
with the statistical confidence limits, resulted in the likely range for KL shown in Table 1.
Based on best evidence, Selikoff and Seidman (1991) found 458 mesotheliomas in this
cohort, Table 22 shows these deaths categorized by years from onset (based on
Selikoff and Seidman, 1991, Tables 5 and 6). Table 22 also shows the results of fitting
the 1986 EPA mesothelioma model to these data, assuming, as above, that workers
worked for 25 years and were exposed to 15 f/ml. The 1986 EPA mesothelioma model
provided a poor fit to these data (p < 0.01), as it overestimates by more than a factor of
2 the number of mesothelioma deaths after 50+ years from first exposure. The
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estimate of KM was 1.3x1 CT8, 90% Cl: (1.2x10-8, 1.4X10'8).
The MLE estimate of KM was assumed to be the best estimate. The uncertainty factors
for exposure described earlier, when coupled with the statistical confidence limits,
resulted in the likely range for KM shown in Table 1.
Pennsylvania Textile Plant
McDonald et al. (1983b) report on mortality in an asbestos plant located near
Lancaster, Pennsylvania that produced mainly textiles, but also some friction materials.
About 3,000 to 6,000 tons of chrysotile were processed annually at the plant, which
began operation in the early 1900s. Crocidoiite and amosite were used from 1924
onward; about three to five tons of raw crocidolite were processed annually and the use
of amosite reached a peak of 600 tons during World War II.
The cohort consisted of all men employed for at least one month prior to 1959 and who
had a valid record with the Social Security Administration. This group consisted of 4022
men, of whom 35% had died by the end of follow-up (December 31, 1977). Follow-up
of each worker was only begun past 20 years from first employment.
To estimate exposures, McDonald et al. had available reports of surveys conducted by
the Metropolitan Life Insurance Company during the period 1930-1939, Public Health
Service surveys conducted during 1967 and 1970, and company measurements made
routinely from 1956 onward. These data were used to estimate by department and
year in units of mppcf.
The lung cancer mortality in this cohort exhibited a significant dose response trend
(Table 23), which was partially due to a deficit of cancers in the group exposed to <10
mppcf-y (21 with 31.4 expected). A survey of those employed in the plant in 1978
revealed a larger percent of nonsmokers (25%) than were found in the other plants
studied by these researchers (McDonald et al., 1983a,1984), although this finding was
based on a sample of only 36 workers. Regardless of the reason for this shortfall in the
number of lung cancers, it appears that the most appropriate analysis is that in which
the background is allowed to vary; this analysis fits the data well (p > 0.7), whereas the
analysis which assumes the Pennsylvania rates are appropriate provides a marginal fit
(p = 0.08). The hypothesis a = 1 was rejected (p < 0.01). Consequently, the former
analysis is judged to be the most appropriate (allowing the parameter a to vary).
McDonald et al. did not provide a factor for converting from mppcf to f/ml. Assuming
that 3 f/ml is equivalent to one mppcf, the resulting estimate of lung cancer potency with
a variable was 0.018 (f-y/ml)'1, 90% Cl: (0.0075, 0.045). With a = 1, KL = 0.0057
(f-y/rni)-1, 90% Cl: (0.0027, 0.0094).
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The best estimate of KL was assumed to be from the analysis with a variable, The
uncertainty factors selected for this study were F1 = 2, F2 = 3, which, when coupled
with the statistical confidence limits, resulted in the likely range for KL shown in Table 1.
A diagnosis of mesothelioma was specified on fourteen death certificates (ten pleural
and four peritoneal). Thirty other deaths were given the ICD code 199 (malignant
neoplasms of other and unspecified sites) and the diagnosis given in many of these
cases was said to be consistent with an unrecognized mesothelioma, McDonald et al.'s
Table 3 lists the average age .at beginning of employment as 28,92 and the average
duration of employment as 9.i8 years, and their Table 1 lists 191, 667 and 534 deaths
as occurring before age 45, between 45 and 65, and after 65 years of age, respectively.
Assuming that 1/2 of the deaths given the ICD code 199 might have been due to
mesotheliomas, the total number of mesotheliomas in this cohort is estimated to be 23,
Proceeding as in the mesothelioma analysis carried out for the McDonald et al. (1984)
data, the data in Table 24 were generated. Noting that the age since first exposure
categories in which the mesotheliomas occurred is irrelevant as far as estimating KM is
concerned, the estimate of KM is 1,1x1Cr8, 90% Cl; (0,76x10"8, 1,5x1(r8), These
estimates are uncertain due to the uncertainty regarding the number of mesotheiiomas
in the cohort,
The MLE estimate of KM was assumed to be the best estimate. The uncertainty factors
described earlier, when coupled with the statistical confidence limits and an additional
uncertainty factor (F4M = 2) to account for uncertainty in the number of mesotheliomas,
resulted in the likely range for KM shown in Table 1.
Rochdale, England Textile Factory
Peto et al. (1985) studied a textile factory In Rochdaie, England that has been the
subject of a number of earlier reports (Peto, et al., 1977; Peto, 1980a,b). Peioetal.
(1985) has the most complete follow-up (through 1983) and emphasizes assessment of
risk. The factory, which began working with asbestos in 1879, used principally
chrysotile, but approximately five percent crocidolite was used between 1932 and 1968,
Quantitative estimates of risk were based on a subgroup of Peto et al.'s "principal
cohort" consisting of all men first employed in 1933 or later who had worked in
scheduled areas or on maintenance and had completed five years of service by the end
of 1974. In the analyses of interest relating to lung cancer, follow-up only begins 20
years after the beginning of employment and exposure during the last five years of
follow-up is not counted.
Routine sampling using a thermal precipitator began at 23 fixed sampling points in
1951, Comparisons of particle counts and fiber counts taken in 1960 and 1961 were
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used to convert between particles/ml and f/ml. Dust levels prior to 1951 were assumed
to be the same as those observed during 1951-1955 for departments for which no
major changes had been made. In departments in which conditions had improved,
higher levels were assigned. These levels and work histories were used to assign
individual exposure estimates. A conversion factor of 34 particles/ml per f/ml was
determined by comparing average results obtained by the Casella thermal precipitator f
(particles/ml) with Ottway long running thermal precipitator (f/ml) at the same sampling
point during 1 960 and 1 961 . However, a conversion factor of 35.3 was used by Peto et
al. for the sake of consistency with earlier work, and this factor will be used here as
well.
After 20 years from first employment, there were 93 lung cancer deaths with only 64.6
expected. Using a lung cancer model essentially the same as the 1986 EPA model,
Peto et al. estimated KL = 0,0054 (f-y/ml)-1 for the entire cohort, and KL = 0.015
(f-y/ml)-1 when the analysis was restricted to men first employed in 1951 or later. Peto
et al, felt that the most plausible explanation for this difference was that it was largely
due to chance and also possibly to the chance that exposure to the most carcinogenic *-
fibers was not reduced as much as changes in particle counts from 1951 and 1960
would suggest.
The best estimate of KL was assumed to be the geometric mean of the two estimates
obtained by Peto et al. Since Peto et al. did not provide confidence intervals, the upper (
and lower statistical bounds were assumed to be equal to the larger and smaller of the
two estimates, respectively, with an additional uncertainty factor added (F4M = 2). The
best estimate of KL was assumed to be from the analysis with a variable. The
remaining uncertainty factors selected for this study were F1 = 2, F3 =2, which resulted
in the likely range for KL shown in Table 1 .
Ten mesotheliomas were observed in the cohort used by Peto et al. for quantitative
analysis (an eleventh case who was exposed for four months and died four years later
was omitted because the short latency made it unlikely that this case was related to
exposure at the factory). Observed mesotheliomas and corresponding person years of
observation by duration of service and years since first employment (Peto et al., 1985, (
Table 8) are shown in Table 25. An overall average exposure was estimated by
applying the Peto mesothelioma model to the data in Table 25 with a single exposure
estimate selecting the value that gave the smallest least squares fit of this model to the
mesothelioma data. The fitting was carried out both unweighted and by weighting by
the person years, with resulting estimates of 360 and 322 particles/ml, respectively; the
latter value was the one selected. Using the conversion factor of 35.3 particles/ml per
f/ml, the estimated average exposure is 322/35.2 = 9.1 f/ml. The 1986 EPA
mesothelioma model fit these data well (p = 0.80), and the resulting estimate of
mesothelioma potency (Table 25) was KM ='1.3x1 a8, 90% Cl: (0.74x10-8, 2.1x1Q-8).
A. 32
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
The MLE estimate of KM was assumed to be the best estimate. The uncertainty factors
described earlier, when coupled with the statistical confidence limits, resulted in the
likely range for KM shown in Table 1.
A.33
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Preliminary Working Draft - Do Not Copy or Quote
References
Albin, M., Jakobsson, K., Attewell, R., Johansson,!., Welinder, H. "Mortality and
Cancer Morbidity in Cohorts of Asbestos Cement Workers and Referents." British
Journal of Industrial Medidne 79:602-610. September, 1990.
Amandus, H.E., Wheeler, R., "The Morbidity and Mortality of Vermiculite Miners and
Millers Exposed to Tremolite-Actinolite: Part II. Mortality." American Journal of
Industrial Medicine 11:15-26, 1987.
Amandus, H.E., Wheeler, R., Jankovic, J., Tucker, J., "The Morbidity and Mortality of
Vermiculite Miners and Millers Exposed to Tremolite-Actinolite: Part I. Exposure
Estimates." American Journal of Industrial Medicine 11:1-14, 1987.
Armstrong, B.K., DeKlerk, N.H., Musk, A.W., Hobbs, M.S.T., "Mortality in Miners and
Millers of Crocidolite in Western Australia," British Journal of Industrial Medicine, Vol.
45, pp. 5-13, 1988.
Berry, G., Newhouse, M.L., "Mortality of Workers Manufacturing Friction Materials
Using Asbestos." British Journal of Industrial Medicine 40:1-7, 1983.
CHAP (Chronic Hazard Advisory Panel on Asbestos), "Report to the U.S. Consumer
Product Safety Commission." July 1983.
Cox and D. Oakes, Analysis of Survival Data. Chapman and Hall, London, 1984.
DeKlerk, NH; Musk, AW; Armstrong, BK; and Hobbs, MST; "Diseases in Miners and
Millers of Crocidolite from Wittenoom, Western Australia: A further Followup to
December 1986." Annals of Occupational Hygiene, Vol. 38, Supplement 1, pp.
647-655. 1994.
Dement, J.M., "Estimation of dose and evaluation of dose-response in a retrospective
cohort mortality study of chrysotile asbestos textile workers." Ph.D. Thesis, The
University of North Carolina at Chapel Hill, 1980.
Dement, J.M., Brown, DP., "Cohort Mortality and Case-Control Studies of White Male
Chrysotile Asbestos Textile Workers", Journal of Clean Technology, Environmental
Toxicology, and Occupational Medicine 7:1052-1062,1998.
Dement, J.M., Harris, R.L., Symons, M.J., Shy, C.M., "Estimates of Dose-Response for
Respiratory Cancer Among Chrysotile Asbestos Textile Workers." Annals Occupational
Hygiene 26:869-887, 1982.
A.34
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Preliminary Wo rklng Draft - Do N ot Copy or Q uote
Dement, J.M., Harris, R.L., Symons, M.J,, Shy, C.M., "Exposures and Mortality Among
Chrysotile Workers. Part I; Exposure Estimates." American Journal of Industrial
Medicine 4:399-419, 1983a.
Dement, J.M., Harris, R.L., Symons, M.J., Shy, C.M., "Exposures and Mortality Among
Chrysotile Workers. Part II: Mortality." American Journal of Industrial Medicine 4:421-
433, 1983b,
Dement, J.M., Brown, DP., Okun, A,, "Follow-up Study of Chrysotile Asbestos Textile
Workers: Cohort Mortality and Case-Control Analysis." American Journal of Industrial
Medicine 26:431-447, 1994.
Doll, R., Peto, J., "Asbestos: Effects on Health of Exposure to Asbestos." Health and
Safety Commission, London, United Kingdom, 1985.
Enterline, P.E., Harley, J,, Henderson, V., "Asbestos and Cancer - A Cohort Followed
to Death." Graduate School of Public Health, University of Pittsburgh, 1986.
Finkelsteln, M.M., "Mortality Among Long-Term Employees of an Ontario Asbestos-
Cement Factory." British Journal of Industrial Medicine 40:138-144, 1983.
Finkelstein, M.M., "Mortality Among Employees of an Ontario Asbestos-Cement
Factory", American Review of Respiratory.Disease 129:754-761, 1984.
Hammond, E.G., Selikoff, I.J., Seidman, H. "Asbestos Exposure, Cigarette Smoking
and Death Rates." Annals New York Academy of Sciences 330:473-490, 1979.
Henderson, V.L., Enterline, P.E., "Asbestos Exposure: Factors Associated with Excess
Cancer and Respiratory Disease Mortality." Annals New York Academy of Sciences
330:117-126, 1979.
Hughes, J.M., Weill, H., "Asbestos Exposure: Quantitative Assessment of Risk,"
American Review of Respiratory Disease 133:5-13, 1986.
Hughes, J.M., Weill, H., Hammad, Y.Y., "Mortality of Workers Employed at Two
Asbestos Cement Plants." British Journal of Industrial Medicine 44:161-174, 1987.
Lacquet, L.M., VanderLinden, L, Lepoutre, J., "Roentgenographic Lung Changes,
Asbestosis and Mortality in a Belgian Asbestos-Cement Factory." Biological Effects of
Mineral Fibres, Wagner, J.C., ed., IARC Sci Publ., pp. 783-793, 1980.
Levin, J.L., McLarty, J.W., Hurst, G.A., Smith, A.N., Frank, A.L, "Tyler Asbestos
Workers: Mortality Experience in a Cohort Exposed toAmosite," Occupptional and
A.35
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Preliminary Working Draft - Do Not Copy or Quote
Environmental Medicine 55:155-160, 1998.
Liddell, F.D.K., McDonald, A.D., McDonald, J.C., "The 1891-1920 Birth Cohort of
Quebec Chrysotile Miners and Millers: Development From 1904 and Mortality to 1992."
Annals of Occupational Hygiene 41:13-36,1997.
McDonald, J.C., Liddell, F.D.K., Gibbs, G.W., Eyssen, G.E., McDonald, A.D., "Dust
Exposure and Mortality in Chrysotile Mining, 1910-1975." British Journal of Industrial
Medicine 37:11-24, 1980.
McDonald, J.C., Gibbs, G.W., Liddell, F.D.K., "Chrysotile Fibre Concentration and Lung
Cancer Mortality: A Preliminary Report." Biological Effects of Mineral Fibres, Wagner,
J.C., ed., IARC Scientific Publications, pp. 811-817, 1980b.
McDonald, A.D., Fry, J.S., Wooley, A.J., McDonald, J.C., "Dust Exposure and Mortality
in an American Chrysotile Textile Plant." British Journal of Industrial Medicine 39:361-
367, 1983a.
McDonald, A.D., Fry, J.S., Woolley, A.J., McDonald, J.C., "Dust Exposure and Mortality
in an American Factory Using Chrysotile, Amosite, and Crocidolite in Mainly Textile
Manufacture." British Journal of Industrial Medicine 40:368-374, 1.983b.
McDonald, A.D., Fry, J.S., Woolley, A.J., McDonald, J.C., "Dust Exposure and Mortality
in an American Chrysotile Asbestos Friction Products Plant." British Journal of
Industrial Medicine 41:151-157, 1984.
McDonald J.C., McDonald, A.D., Armstrong, B., Sebastien, P., "Cohort Study of
Mortality of Vermiculite Miners Exposed to Tremolite." British Journal of Industrial
Medicine 43:436-444, 1986.
McDonald, J.C., Liddell, F.D.K., Dufresne, A., McDonald, A.D., "The 1891-1920 Birth
Cohort of Quebec Chrysotile Miners and Millers: Mortality 1976-1988." British Journal
of Industrial Medicine 50:1073-1081, 1993.
Nicholson, W.J., "Part III. Recent Approaches to the Control of Carcinogenic
Exposures. Case Study 1: Asbestos - The TLV Approach." Annals New York
Academy of Science 271:152-169, 1976.
Ontario Royal Commission, "Report of the Royal Commission on Matters of Health and
Safety Arising form the Use of Asbestos in Ontario." Vol. 3, 1984.
Peto, J., "Lung Cancer Mortality in Relation to Measured Dust Levels in an Asbestos
Textile Factory." Biological Effects of Mineral Fibres, Wagner, J.C., ed., IARC Scientific
A.36
c
-------
Preliminary Working Draft - Do Not Copy or Quote
Publications, pp. 829-836, 1980a,
Peto, J., "The Incidence of Pleural Mesothelioma in Chrysotile Asbestos Textile
Workers," Biological Effects of Mineral Fibres, Wagner, J.C., ed., IARC Scientific
Publications, pp. 703-711, 1980b,
Peto, J., Doll, R., Howard, S.V., Kinlen, L.J., Lewinsohn, H.C., "A Mortality Study
Among Workers in an English Asbestos Factory," British Journal of Industrial Medicine
34:169-173, 1977,
Peto, J,, Doll, R,, Hermon, C., Binns, W., Clayton, R., Goffe, T., "Relationship of
Mortality to Measures of Environmental Asbestos Pollution in an Asbestos Textile
Factory." Annals Occupational Hygiene 29:305-355, 1985.
Piolatto, G., Negri, E,, LaVecchia, C., Pira, E., Decarii, A., Peto, J.,"An Update of
Cancer Mortality Among Chrysotile Asbestos Miners in Balangero, Northern Italy,"
British Journal of Industrial Medicine 47:810-814, 1990.
Rubino, G.F., Piolatto, G.W., Newhouse, M.L., Scansetti, G., Aresini, G.A., Murray, R.,
"Mortality of Chrysotile Asbestos Workers at the Balangero Mine, Northern Italy."
British Journal of Industrial Medicine 36:187-194, 1979.
Seidman, H., Selikoff, I.J., Hammond, E.G., "Short-Term Asbestos Work Exposure and
Long-Term Observation," Annals New York Academy of Sciences 330:61-89, 1979,
Seidman, H,, Selikoff, I.J., Gelb, S.K., "Mortality Experience of Amosite Asbestos
Factory Workers: Dose-Response Relationships 5 to 40 Years After Onset of Short-
Term Work Exposure," American Journal of Industrial Medicine 10:479-514, 1986.
Selikoff, I.J,, Seidman, H,, Hammond, E.G., "Mortality Experience of Insulation Workers
in the United States and Canada 1943-1976." Annals New York Academy of Sciences
330:91-116,1979.
Selikoff, I.J., Seidman, H., "Asbestos-Associated Deaths among Insulation Workers in
the United States and Canada, 1967-1987," Annals of the New York Academy of
Sciences 643:1-14, 1991.
USEPA (U.S. Environmental Protection Agency), "Airborne Asbestos Health
Assessment Update," Report 600/8-84-003F, U.S. EPA, 1986.
Venzon, D., Moolgavkar, S, "A method for computing profile-likelihood-based
confidence intervals." Appl Stat 37:87-94, 1988,
A,37
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Weill, H., Hughes, J., Waggenspack, C., "Influence of Dose and Fibre Type on
Respiratory Malignancy Risk in Asbestos Cement Manufacturing." American Review of
Respiratory Disease 120:345-354,1979.
A.38
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Preliminary Working Draft - Do Not Copy or Quote
Figure 1
Fiber Typ Operation
.Cohort
EPAf-1986]
KLxIQO Reference
This update
.KLxlOO
Reference
EPAf13881
,..KMx1(f.j.Referi:riOji_
This update
; Reference
Quebec mines'
Chrysotil Mning and Mlling mills
j%bestos, Qua
Thedford Mine
Quebec
Italian mine ar
mill
Friction Producti Lonnej;tie|J:jsJi
0,0611* Donate* al, (1980j
aJ. (1979)
DD81 Ptelattoei 3^(1990)
., 0.01! MteDonatet al. (1J841_,
New Orleans
Cement manufacturi plants
' Uddeltf al
0029 10.0085,0.11) (19971
_ 0.035 j. (0... 1.1) Piolatto.et. al^flWi
Oi(p,2,2) ,,,,.,,119841....
' Hugheet al
04 iO, 1 61 119871 I
DJ)13.(D.OD?, DH49j bddell et al (1997) A
0.021 iO 0065, 0.085 Uddell et al f1997i(i
L4) j_Hunheefa.C198Tl
South Carolin;
plant
'tWiitenoom ,
Textiles
Crocidoli Mning and Mllinq ^
Patterson, M.
fffiQ site insulation jrnanufacuj jactori/
! Tyler, Texas
: f actgrjr _
2.5 MeDonaleia;, (19S3a)
2 4 i0.81, 5 61 ram data;
! bteDonalafiF
1.16j (B.|2t4.81_ ,(J9833)
Da Klerk, unpublish
.JLlli.fiyUP'?. 0.251 per. oornrn. Dernerit
.0.088 3 'iufsitef a!. (1978X'ini'eilli
061 (004,53) 119871
0.64 (p.025. 4 71 (.19061
Berry and Nemhou
1 2 (0,28,1
.Finkelftein (1884)
12 Finkelstein (1983'
U 4 ffl, 1 6) tiMgheef a/j'18'37)
007 (0,259)"" ' «re"fa'r(T890)
0 iOi084) laguefef.^(19801
04M Henderson and Bttertm. 015 (0011,1) (1988)
Textiles
Pennsylvania p
Rochedale,
Bipland plant
07*5 Mikot vtal, (1979i
1 4 MoDonalef al. (1983bj
1 T Peto£198D),
Selikoff and Seidrr
0 4 rO 012, 5.1) r.19911
1 0 IDD73, 16,5). (1983bi
OJ.'.tPJe.S) . Petp(1985)
A.39
etal (197i
Peto t.19801. Pelt
_et al..
18 (2.1,149) FmkeiJtein_(1984)
0 3 I.O 082,1 4) Huqheef al 11987). _,
0.092 i002,Li iflj bddell etal
13 ill 25, 6 5i Wihottand
1.1 1,013001 M;DonaWa,'(1983b
, 1..3.1 i,(0.28, 6.6) : Peto (1985)
-------
Preliminary Working Draft— Do Not Copy or Quote
Figure 2
Cancer of Lung, Trachea, or Bronchus by Cumulative Exposure
Level among Workers In Quebec chrysotile Mines and Mills
Liddellet a!., (1997)
Deaths from Cancer of lung, trachea, or bronchus
Observer SMR ; Expected j Predicted by model
'• n
! 1-5
;6.5
: 20
[45
!80 '
!"T50""
f'25tr
[350
I 700
j 1500
nppfc-y \
: (<3) \
;(3r.iQL.LL
(10-30) j
J(30-60)
((60-100) ;
ft 100-200) '
("(200-300)
1 (300-400) :
1 (400- 1000'
It>1000) :
f-y/ml
4.71 ;
20.41 :'
62.8 :
141.3
251.2
471
785
1099
2198
4710
75
64
61
60
61
67
35
29
88
47
1.12
i 1.27
; 1 .03
1.32
1.45
1.27
T.f
J.46
1.84
2.97
67.0 -;
50.4 ;
59.2 \
' 45.5
42.1 '
52.8
31.8
19.9
47.8
15.8 '
(0=1)
67.1
50.8
60.8
"48.1
46.4
63.0
4"2".T
2'C8'
91.1
46.5
(a estimated)
77.0
5' 872
69.2
54.3
51.8
68.8
_„
3071
89.9
43.0
Goodness of fit p-value j
Test of l-i: a=1 \ ;
p = o.oi ; ...... ;
Estimates of KL (f-y/ml;1
(a=1)
KL= 0.00041
90% Cl: (0.00032,0.00051)
; (a variable )
'~W_"= 0-00029 ...... ~
r^-~..^.|:. — —
0.18
0.58
A.40
-------
Preliminary Working Draft - Do Not Copy or Quote
Figure 1
Fjber Typ Operation
Chrysotil Mning and Mlling
Friction jjoduets , . j
Cement.ma,nufacturj
Textiles, _._<
LCrooidoli Mning and yjling
; Amosite Insulation manufactv
Trernolite \4nnicuifte mines ar
[Mixed ; Friction Products
; Cement mariufactur;
Factory uioAerr
Insulation application
Textiles
Cohort
Quebec mines
mills
Asbestos, Que
Thedford Mine:
Quebec
Italian mine .ar
mill.
Connecticut plj.
New Orleans
plants.
South CaraliS
jjjant
WUhitenoom,
.Australia
Patterson, N_
factory
"Tyler, Texas
factory
LJbby , fyfontan
British f actor'
Ontario factor
New Orleans
p[ants
Swedish plant
Belgium factoi
U.S. retirees
Asbestos, Qua
U.S. insulatio
workers
Pennsylvania p
Rochedale ,
Bigland plant
EPAf1386i This update
KLxIQQ i Reference KLxIQO ! Range
i f \ '-
• i
0.06 MoDonalefai, (1980) 0.029 1,0.0085,0.11)
.0,17 Nicholses? a!. (1979)
0.081 \ Piobttoe* al (1980) 0.035 i (0, 1 .1)
O.OjJj!;fcDgnalfl,aif,,fl9.8.4,) 0 ' (P, 2.2)
i i
0.4 (0, 1.6)
2.8 DemensrfaJ. (1883) . 2.4 iD.81,5.6)
2 .5 i Mo Donalef al_, £1 983a) 1.15 tP -22 , 4.9)
- \
! 0.47 (P.08, 6.9)
43 Seidrnani 1984i . J,tffl.-.11,Jll
0.13 (D, 6.6)
0.61 (0.04,5.3)
0.64 110.025, 4.7)
0.058 Berry and Newhouse (1 OJ58 (0,2.4),,
4.8 Finkelstein (1983) 1.2 tD,,29)
B.53 Weilef al, (1979); Weill i U4 I'D, 1.6'L
0 07 (0,25.9)""" '"
0 iK. 0,.8.4)
0.49 Hendecon and Brtertini 0 15 fD.Q11, 1 1
0.75 Selikofsf al, (1979) 0.4 $.012.5,1)
1.4 fifcDonalefa,', (1983bj 1.8 (0.073, 18 5'i
1.1 Peto(1980) 0.9; (0,1.6, ,5,)
EPflf1386l
Reference KMx1(? s Reference
i "I
Uddeif al "• '
(1997,1
Plolattg et al. Q991
11984) _
Tiughesf ai
(1937)
Demertira(.(1994
raw data)
Mb Donald t a,1
(1983a1
DeMerk, unpublish
vdata^..
.Seidman (1986) 32 Seidman (1984)
Levirefa. (1998)
(19B7)
(1988)
Berry and Neuihou
(1983)
Fink-elstein(1984) , _12 Finkelstein (1983)
Huaheeta.l(1987)
Sbiretai'OggD)
-Laguefeta.'.tt9Jp)
(1988)
Seltkoff and Seidrr
(1991) 1.5 Sslikoff etali!97!
(1903b)
Pato (1980); Pet<
. Peto (1 985) 1 J etjLd 982)
Thiis update
.KMx1(?l.jRanfli ,_ [
0.013 (D.003, DU49)
0.021 (B.00n5, 0065
D : (D. OJ) ,
0.2 C0.028,_1.4)
0.11 (0.037,0,25)
D.088 (D.002, 1.6)
7.9jj^3.5, 18)
3,9j,£0,74,20) ;
' "n (2 1, 149)
....,,,D,3: (0.062, ,1,,4.X i
0 092 CB 02,,0..35)
1 3 (D2S, 6.5 1
1.1: (B.13~8.81
1.31;itl,28,.5..8).
Reference i
" j
s
Uddeli et al i1997)ff
b'ddelletal (1897 in
^OonateMHSM
Huflha,fia,,,a.?J7)
- ' 1
per .oorijm. Dement
"'" """" ~1
McDo,naleia.,(1|i83aj
De Kterjj , uniyb|i.s,hec
.Seidmin_£19_88). _ ;
I
i
Finkelstein (1984) |
Hupheeta£fj987) i
Lidddl et al (199JXr|
Selidotf and Oeidrnaf
Wfc Donates- aJ£1983b':
.Petp_(1985).
A.39
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 2
Cancer of Lung, Trachea, or Bronchus.by CumulativeExposure
Level among Workers In Quebec Chrysotile Mines and Mills
Uddeliefarr(T997)
| Deaths from Cancer of lung, trachea, or bronchus :
; Observed SMR i Expected! Predicted by model '
] mppfc-y
1 5
65
20
45
80
150
250
350
700
1500
(<3)
(3-10)
(10-30)
(30-60) .
(60-100)
(100-200)
(200-300)
(300-400) '
(400-1000'
(>1000)
f-y/ml
4.71
20.41
628
141 3
251 2
471
785
1099
2198
4710
75
64
61
60
61
67
35
29
88
47
1.12
1.27
1.03
1.32
1.45
1 27
1 1
1.46
1.84
297
67.0
504
59.2
45.5
42.1
52.8
31 8
19.9
478 '
15.8
(OF1) [
67.1
50.8
60.8
48 1
46.4
63.0
42.1
28.8
91.1
46.5
(a estimated)
77.0 i
58.2 ;
69.2 !
54.3
51.8
68.8
44.8 !
30.1
89.9
"43.0
Goodness of fit p-value
Test of
= 0.0f
ce
Estimates of KL (f-y/rni;1
(a=1)
KL = 0.00041
90% Cl: (0.00032,0.00051).
(a variable)
1C'=TX06029
90% Cl: (0.00019. Q.00041)i
0.18
0.58
A.40
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 3
Lung Cancer Mortality among Chrysotiie Asbestos
^ '-
Piolattoef a/.."('1990) "
• ' 1
I • jf-y/mf
i 50 I (<100)
250 ; ( 1 00 - 400 )
j 600 [ ( >400 )
| Goodness of Fit P-valu
i Test of hb: o=1
ip = 0.42 ;
Number of Lu
"Observe cj'Expected
4 " 5.1
8 6 1
TO 8 7
ng Cancer Deaths |
Predicted i
(ot=1.)
5.2
6.6
'"TM"'
0,75
I ( a variable]
! 4.9 i
P 64 ' ']
! To .7 1
j 0.45 |
"Estimafes"6f KL (f-y/ml;1
(a= 1 ) .
KL= 0.00035
90% Cl = (0 0,0.0015
( a variable)
KL = 0".00051
90% Cl = ( 0.0. 0.0057
A.41
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Preliminary Working Draft - Do Not Copy or Quote
Figure 4
Lung C ancer Mortality among Workers in a Chrysotile
Asbestos Friction Products Plant in Connecticut
McDonald efal.".' (1984)
Number of Lung Cancer Deaths
Predicted
mppcf-y f-y/mi : SMR , dbservecs Expected .( a = 1 ) (a variable
: ' s * ' '
15
: 30
60
fid]
(<10)
( 10-20
(20-40
( ,40 - 80
1>=80)
15
45
90
180
330]
167.4
101.7
105.4
162.8
55.22
55|
B'
5
6
1}
32.9
5.9~;
" 4.7
3.7;
1.8 i'
33.8
6.4
5.5
4.9'
2.9
49.0:
§11
73 !
5"5l
2JJ
Goodness of Fit P-value ' • i : 0.0 V 0.28S
Test of hp: OF
p < o!01
Estimates of hL (f-y/rni}
(a = "i )
KL = 00019
"90% Cl = ( 0 0, 0.0061
1
(a variable) I
"ML =0-0 ; ;
90%CI = (6^,0-0017)
A.42
-------
Preliminary Working Draft - Do Not Copy or Quote
Figure 5
Misotheiloma Mortality among
Connecticut Friction Product Plant Workers |
McDonaldef a/., (1984) I
\ =
i
! ! | Number of Mesothelioma: |
Years After First Exposure \
22
39
14-34
" 34+
Duration or \
Exposure i
8.04
8.04"
5.5
5.5
Goodness of Fit P-value = 1
" Estimate of KM (f-v3 / ml)-1
Kmxig8 =
|P ., :
Person-
Years
37742
9420
Observed
0
"o '
Predicted j
by model |
0.4 ' j
TOj ;
f*
:: ,
A.43
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 6
Lung Cancer Mortality among Workers Employed in Two Asbestos Cement
Manufacturing Plants in New Orleans, Louisiana
"_ I Hughes ef at. (1987) " "" ~J~
mppcf-y
Number of Lung Cancer [
IIIIIIT I IIIr
f-y/ml Observed I Expected (o=1)
Plant 1 Employees I
4.0
"13.0
35 0
74.0
183.0
(<
6)
(6-24)
(25
(50
(>=
-49)
-99)
100)
5.6
18.2
49.0
103.6
256.2
3
9
i
3
5
2.9
"8.0
3.7
3.8
4.1
3
" 8
; 4
5
8
0
I'll
.4
.4
.3
Deaths
edicted
(a variable) j
a
~ 9.
""" "4".
5.
7.
4 1
6 t
8" I
5
7
Plant 2 Employees ;
3.0
12.0
36.0
71.0
164.0
= Goodness
( <
6)
(6-24) '
(25
(50
i ( >=
of Fit
-49)
-99)
100) i
D- value
4.2
168
504
994
2296
20
19
12
10
12""T
18.9
14.5
60
5.5
5.2
19.2
15.5
7
7
9
0.
.2
.7
.9
44
21
17
7
7
9
.8
3
7
9
4
042
Test of Ho: c=1
P
= 0.10
Estimates of KL (f-y/ml)'1
(a=1)
KL-0.0040
90% Cl=( 0,0015, 00070)
i (gt variable)
!'"KL"=aoo25
:'90~%"ci"=( 6 , 0 0066 )
A.44
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 7
Lung Cancer Mortality among South Carolina Chrysotile Asbestos Textile VVorke
Cumulative Exposuri } \
Mean i Category! Observed; Expected ; (
0.14
1 .33
2 90
6.53
1 9 35
54.73
143.35'
Totals
Goodness
0
[08,2)
[2,4)
[4,10)
[ 10,35)
[35,85)
( >= 85 ) •
Of Fit
7
11
12
19
19
21
33
122
6.8
9.3
9.2i
11.0i
11.9|
8.5;
e'.e1""
63.4
Predicted ;
a - i )"" T "( a "variable );
6.9: .
9.6;
10.0
13.0:
18.4i
21.7:
33.5'
113.2
0.92
8 3
11.6
11.9
15.1
20.2
22.1
32.0
121.2,
"""0.96
PYR l
34959.4
13213.7
11964.5
14615.1
14793.3
"88M7l
4e49^9|
102996.4)
Test of hb: a = 1
p value ............ 0.21
"Estimates of KL
(q= 1 )"i ...... "
KL'=0"irJ28
( a variable )__
|.L = 0021 ~~ ......
90% Cl = ( 0.012,0.034)
A.45
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Preliminary Working Draft - Do Not Copy or Quote
Figure 8
; 1
\ mppcf-y
\ 5
i 15 ~
\ 30
LZlLlI
i Goodness
i Test of Ho
1 p = 0.36
u<
Lurii
Cancer Mortality ami
Asbestos Textiles P
McDonald
fy/ml
10) '
- 19 )
i ( 20 - 39 )
f( 40 -"79 ) :
ff>=80j '
of Fit
•"OF!
P- value
30
90
180
360
660
SMR
143 1
182,7
• 304.2
• " 419^5"
1031.9 "'
••=•
)ng Workers
lant in South
et a/.", (1983s
in a Chrysotile
Carolina
) " " " ~
Number of Lung Cancer Deaths
~[™ ~; 11,L lllEi?
Observed Eapected i! ((x- 1 j
31
5
8
8
21.7
2.7
2.6
T.7 i
61 """i "
29.2
5.6
8.1
8.6
"177"""" "
0.95
dieted
( a variable }
30.4
5.7
ao
6.4
"""6.5
0.88
Estimates'of Ril j-y/ml) :T
(a=1)
KL = 0.012
i 90%'Cl = ( 0.007570.016)
! ( a variable )
i Ml = 0.010 (
I 90% Cl ='( D'"0044.' 0.025 )
; Assuming that 6 f/rnl = 1 mppct
A.46
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 9
Mesothelioma Mortality among South Carolina Textile Plant Workers
III"~'''~'7I McDpnaTd^
Mesotheliomas
Years After First Exposure! Duration ; f/ml
Mesotheliomas pei I Predicted
10OP Person Year: by model
28.
44
( 19-39) 10 | 5.4 } 26280 I 0
(" >39 ) 10 | 5!4 | "2787' 1 1 '
0.00
0.36
0.66
0.34
I.Gooclness_of_F t Pvalue = 0.14
fEstimate'of K
.m*1Q3= o 088 '••
90%Clx1(?= (Q 0093, 0.32)
A.47
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f*.
Preliminary Working Draft - Do Not Copy or Quote
Figure 10
Asbestos Workers in Wittenoom, Australia
"' DeKIerk ' "
\ " , Predicted ;
f-y/ml j Observed j Expected { (a=1) i( o; variable'
s- o i
' 0.1 92681
0.692009
1.58918
""3 "27249
6 19474
11 8112
. 21.5285
' 41.0693
, 142.283 ;
[Totals ;
- Goodness
' 5 ;
" "27
1 1
"22
""28
38
" 31
21
25
43
251 j
of Fit P- value
4.6 _£
J~§~ I
8.2 \
'ii".G r
12.9
14.3 " "'
13.2 '
9.2
11.6
1 1 .6 ;
105.1 I
4.6 . !
8.0"""
8.3
1Z1 .
14.0
16.7
17.4
14.5
24.5
56.5
176.6;
< 0.01
' 9.8 • ;
~""~J"Q
I7.6
24.9
279
31 4
29.8
21.6
29.6
41 6
250.99999
010
Test of Ho: o=1 '•
'• p < 0.01
: Estimates of KL (f-y/ml) "1
(a=1)
:KL = 0.027
i 90% Cl=( 0.020, 0.035)
'. (a'variable)
' KL = 0.0047
j 90% C! = (0.0017,0.00877
•A.48
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Preliminary Working Draft - Do Not Copy or Quote
Figure 1 1
Lung Cancer Mortality by
Cumulative
Exposure
Factory Workers in Patterson,
| ;
Seidman
among Amosite Asbestos
New Jersey
et ai. ,(1986)
I ' 1
j I Deaths from Cancer of Lung
3
: 9
"18,5
375
75
125
1 200
r "'175'
Ty/hiF ~ j
(
-------
Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 12
Mespthel'igma Mortality among Amosite insulation Workers In New Jersey
~"s?idl^anerar"(T986)
I Years After
! 7.5
12.5
17.5
22.5
27.5
i 32.5
i 37.5
First Exposure
; ( 5-9 )
I ( 10-14 )
i (15-19)
! (20-24)
i (25-29)
\ ( 30-34 )
! ( 35-39 )
j Duration o- i
I Exposure I
1.5
1.5
1.5
1-5
1.5 i
1.5 I
1.5 I
f/ml
j Number of Mesothelioma: s
Person- !
Years j
46.9 3952 I
48.3 3628
44.1 3198
43.2 2656
40.3 | 2094
33.5 1 1576
. 31.1 | 1086
Observed
0
0
0
2
5
8
2
Predicted bv I
model
0.0
0 1
1 1
2 8
4.2
4.4
4.3
\ Goodness of Fit P-value = 0.35
Estimate of KM ff-f /miy1
I'M > 1 0s = ' 33
90% C1> 1 ?"=":"(" 2. 6 ,5. 7)
A.50
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Preliminary Working Draft - Do Not Copy or Quote
Figure 13
Lung Cancer Deaths among Asbestos Workers in Tyler, Texas
Levinef"aM1998'j
! Duration
; 0.25 i (<0.5)
' 6~75 f f535-1J
3 1 ": ( T- 5 )
7.5 I (>5)
f/mi
45
45
45
45
f-y/mT
11.25
33.75
135
Observed
23
3
4
337.5 j 6 i
Expected;
8.9 ;
1.1 !
1.8 ;
1.5 ;
Predicted :
ot= 1 go variable;
10.15
1.56
4.84
"T'83
\ 22.45 =
' 2.86 j
1 V"5T2"8 "]
\ "§342 ;
Goodness of fit p value
Test of hb: a - 1 :
p value < 0.01
Estimates of KL (f-y/ml;1
( a = 1 )
KL=0.bl3 "'__
90% C( = ( 0.0055 ,0.022
( a variable )
KL =""0.001 3
90% ci"= o7
<0.01
0.81
A.51
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Preliminary Working Draft - Do Not Copy or Quote
Figure 14
I
Lung Cancer
Mortality by Cumulative Exposure among [
Vermicullte Mine and Mill Workers near Libby, Montana
Amandus and Wheeler, (1987)
' Deaths from Cancer of Lung ;
25
75
250
600
Goodness
Test of HO
p = 0.40"
f-y/ml
I C <50 )
. .„__ „ _
"( 1 00-399 )
( >=400 ) ""
of fit p-value
a=\
Observed
6
' ' 1
1
10
Expected
! 4.0
T4
1.9
1 7
(0=1
4.6
20
48
8.1
041
Predicted
} (a estimated)
f 5.0
2.1
48
8.0
025
1 • •••- •
Estimates "ol KL (f-y/m!;1
KL = 0.0061
90%cr=|n.oo297o5T[
( a variable ) '
KL= "0.0051 ; "
90% 01^(0.0011,1120)
A.52
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 15
Lung Cancer Mortality by Cumulative Exposure among
Ven^cuiite
^cDonaj^ef ajr|;|gggj
i Deaths from Cancer of Lung
j Observecj Expected! Predicted
f-y/ml T""" f"" I (g=Ty I (a estimated)
I 12.5
= 773
i 332.4
i 836.1
c
,(
<<
( 0-25 )
25-200 ;
200-500,
>=500 )|
1
5
7
4
3
2
0
0
.4
.5
.9
.7
3
4
4
7
9 1
6 \
2 1
o :
6
6
4
5
9
3
1
8
Goodness of fit p-valu e j j 0.16 0.26
test of ho: a=1 :
p>0.06 33 '. ' ~.
Estimates of KL (f-y/mi;1
'KL="Uo'l1
"loCc'Ff 00055,0.017)
( a variable)
KL = p_0039
'90%"CI: f "
A.53
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Preliminary Working Draft - Do Not Copy or Quote
Figure 16
IVItrdumtlliU
rna ivionaniy amung employees OT an untano «SDesios wemem raciory ;
"_ "" Fin'kelstem, (1984) ..."..... I
^ '' :; '•
i j Mesotheliornas ' j
Years After First Exposure i Duration of f/m|
i Exposure \
12
17
22
27
32
Goodness
(10-
: (15-
(20-
(25-
(30-
of Fit P-ralue
MIL.
19) i
24) .
29)
34) -
= 0.26
i
B.7
6.7
6.7
6.7
6.7
I 9.0
} 9.0
. 9.0
9.0
, 9.0
— j - -
Person- i Number per 10 3
Years j Person-Years
2500 :
2500 !
:
2063.49 ;
625 :
0
0
4
4
2.7
6
9
3
6
• Observed
1
; 1
8
13
6
Predicted |
by model ;
0.
1.
7.
o i
4 s
6
12.8
7
--•j -
2
KM*1Q8= 18
.90%Clx 10 g= (13', 24)
A.54
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Preliminary Working Draft - Do Not Copy or Quote
Figure 17
.,...,,,
Mesothelioma
h
Years After First Exposure
12
17 "
22" "
27"
32
Goodness
! ( 10-
\'\ Ts-
(20:
, '( 25 -
, (30-
14
19
24
29
34
of Fit P-value =
ortality
among
Employees of an Ontario Asbestos Cement Facto
Finkelstein, (1984)
K I
i. i i i
Duration of
Exposure
)! 6
)' 6
) 6
) 6
), 6
0.26
7
7
7
7
7
f/ml
9.0
9.0
9.0
9.0
9.0
' \ Mesotheliomas
Person- i Number per 103 | nh_prvpl
- Years j Person-Years | UDservei
2600
2500
2962.96
2063.49
. 625 ,
0.4
'" 0.4
"" 27
"" 6".3
9.6
1
1
8
13
6
Predicted
by
0.0
1.4
7.6
128
72
^
KrnxlO8 ....... [ |8JM5667]
LL ........ T"29.09596f"
[18.045667
'L(K')-L(K)-1 .64! - 1 .3530 1 2£
Estimate of \ 'M (f-'i3/ml)-1
"''
13,24)
Km*1 CP LoweFiTTupper 1 (?
"l|^04567" 13.0797 24 13306271964,
29JD9597 "27.743 277429521844?
A.55
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 18
Lung Cancer Mortality among Asbestos Cement Workers in Sweden
Albinef a/..(i99'
f-y/ml
3.1
25.6
"88.2
Goodness
"tesfof—
p = 0.02
*" r
RR i
1.8 i
1.9 j
"T9 f
of Fit P-valii
«... .,!..,„ !,
} j
3)
Relative Risk of Dying of Lung
Lower Bounc
0.8
0.7
115
Cancer
Predicted
Upper Bound St. Dev.
3.9
5.3
7.1
0.4
0.5
0.7
• - - -
(a=1) (a
i 1 .06 i
I 1 .49 :
I 2.69 i
j 0.32 \
variable
1.82
1,85
1.92
0.95
Estimates ofKLJf-y/mi;1
(°~=1) """"
KL-0.019 {
90% Ci = ( 0, 0.065 ) f
( a variable;
KL = 0.00067
90%Cr=(0.0.036) ;
A.56
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Preliminary Working Draft - Do Not Copy or Quote
Figure 19
Lung Cancer Mortality among Belgian Asbestos-Cement Factory Workers
Laquetef a£,(f98D) ;
IMumber of Lung Cancer Deaths i
Predicted !
f-y/ml ,_ Observed Expected ' (g = 1 ) a (g variable jj
i 25
I " 75
! 150
{' 300
; 600
| 1200
f 2400" '
I Goodness
! Test of \-Q.
rp = 0'.34
I ( 0 - 49 )
\ (50-93)
\ (100-199)
; (200- 399 )
; ( 400 - 799 )
; (800- 1599)
; (1600-3200)
of Fit P-value
Qt=1
6.
' " 3
5
4
1
2
o
.
: 5.2
24
4.6
; 7:5
; 2.0
: 0.57
• 0 17
5.2 S
2.4
4.6
7.4
1.9
0.5
02
0.51
• - -1
4.8 i
2.3
4.3 :
7.0
1.9
0.6 \
0.39 \
Estimates of KL (f-y/rni;1
(a=1)
KL = 0
90% Cl = (0^, 0.0010 )
'"(a variable )
KL = 0.000068
0.0021
A.57
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 20
a" Us Asbestos'Company'
"~ Enterllneef ai, (1986) ™~~
mppcf-y
62 (< 125)
1 82 ( 1 25 - 249
352 (250-499
606 ( 500 - 749
976 ( •>= 750 )
f-y/ml
186 |
546 !
1056 '
1818
2928
SMR
1823
203.1
322
405
698,7
Observed i
23
14
24
10
8
Expected
12.6
6.9
7.5
25
1.1
(ot= 1
17.5
14.7
23.7
11.7
8.1
I
Predicted
} I (a variable
21.8
15.9
23.4
10.8
7.1
Goodness of Fit P-valui
0.04727^
Test of h: =1
'EstirnateTof KL (f-y/ml- 1
Io;=1)
KL= 0.0021
90% CT= ( OMl 5," 0.0027 )
( a variable )
KL = 0.0011 ;
90% Cl = ( 0'.Qb6'4l""oT0028 j
0.75
0.92
A.58
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Preliminary Working Draft- Do Not Copy or Quote
Figure 21
j Lung Cancer Deaths
,y ,->,-,,.,
among Insulation Workers in the United States and Canada !
Selikoff and Seidman, (1991) . j
1 Years After First Duration o ,. .
! ,- r- f/ml
; Exposure Exposure :
125
175
22.5
27.5
325
35,
~ "" ~3iih*
' ; -HP'
35
1 Goodness
i Test of ho:
\ p value '
; Estimates
1=15)
f 15-19 J
( 20-24 )
( 25-29 )
( 30-34 )
f 35-38 T T-"?
(4JM4)' •,
1 4gi4g'1 !
•tw^i/v*
( 35+ )
of Fit p value
EX '
<0.01
of KL (f-y/rrfj
1"7
2.5
7.5
125
17.5
225
76
25"
•25-1
2s:
25
150
150
150
150
150
-.-^.16.0
"ip^o
'/»>jc'*15 Q>
^j|-'tCo-
150
t \
I Predicted j
Person- , . . dbserve n i 1
Years ^m (DC) ! ExPectec ! ( a = 1 ) (
816554 375
527095 1125
575954 1875
505186 2825
371658 3375
375"
10210,5, '-S75 --"I.
^|)2S8"5 '9?S' <> "
"fflfff/.' "375 ;
41948 375
7
34
85
172
252
.189-'
^fifi?
' 71,-
459
1 81 3.87
293 11 .60
309 27.51
369 48.61
4 38 57 53
4 1CT ;j«3 "7*3
' '3 S1 '": 'ti8'^&%>
28'-
121 75
I i
5.13
2296
7239
15307
22649
-gpj^ss?-.,^, ,
s^'.fs'f*
51901
1 <0.01 |
a varying [ \
9.87
3342
8825
18481
222 29
».'.'"-v - »
,-f1
* | ** *v*^i ^
49038
0.12 i
— - ;
! KL = 0.0087
1 90%'CI ='( 0.0081, 0|p9fl
: (g variable ) •
KL'=1.0018 :
90% Q = ( 0.00065, 51038)
A.59
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Preliminary Working Draft - Do Not Copy or Quote
Figure 22
Mesotheiioma Deaths among Asbestos Insulation Workers
" Sellkoff
! ;
Years After
12.5
W.5
22.5
27.5
32,5
37.5
42.5
; " " 47.5
55
First Exposure
(<15)
: (15-19)
( 20-24 j
: (25-29)"
; (30-34)
: ( 35-39 )
(40-44 )
( 45-49 )
j (50+3
j I I ; . • { I
Duration ofT
Exposure j
25 |
25 ;
25 r
25" |
" 25 f"
25 ! "'
25
25
25 \
f/ml
15.0
...„.._
15.0
is',o
'Ts.tT
Ts.o"
15.0
15.0
15.0
| Person-
j Years
I 61655.4!
~'T52709l"r
r] 57595.4 '
"I 50518.6
"1" 37 165.8 '
'T 20340
: 10200.5
' 5256.5
i 6151
Pleural
0
2
10
33 '
40 ' '
33
17
27
11
Number of Mesotheliornas j
: Peritonea: total f™1^
i ; by model |
; 0
' 3"
: 8
40
"65
58
42
31
38
i 0
' 5
'' IB
; 73
105"
91
59
58
49
0.2 i
' " 4£ i
23".4"""~:
' '56.3
66.0
87 79
71.9
55.5
106.3
Goodness of Fit P-value = < 0,01
Estimate of KM (f-y3/mi)-1
KMxlo§ = fj73
'm%C\xW= if 1.2. 1 4
A.60
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 23
Lung Cancer Mortality among Workers in a Pennsylvania Textile
McDonald ef a!., (1 9B3b)
1 " f j " " | 1 "~
Factory j
| . ! l I ' Predicted !
mppcf-y I f-y/rnl SMR Observecj Expected ( a = 1 )
5 S (< 10) \ 15 66.9
"" 30 ; ( 20 - 40 J 90 156"
60 j"(' 40 -80; 180 160
" il'O" | ('>= 80 ) 330 416.1
Goodness of Fit P-va!ue :
test of Ft a=1 . i
Estimates of KL (f-y/rni;1 i
(a - 1 ) I I
KL= 00057 ~~"": ' |
90% Cl = ( 0.0027, 0.0094 )
( a variable ) " "
21 | 31.4 34.1
5" f™" ~6XT 7.5
10""'* 64" 9.7
6 3.8 7.6
11 2.6 7.6
: O.OB
KL- 0.018 : | | i
190% Cl=TOJ06751'd.045) 1 ; 1 \
( a variable ;
20.7
5.8
8.8
8.3
9.6 '"
0.76
A,61
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Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 24
M'esothelioma Mortality 'among Pennsylvania Textile Plant Workers
'"" '''sf,'(1983b)" " i '
Years After First ;
'. Exposure |
i 15.5 j
) 24 ;
! EsttmateTofT
-------
Preliminary Wo rking Draft - Do N ot Copy or Q uote
Figure 25
Asbestos Texli|e Factory
1
: Years After First Exposure :
11.5
225
275
325
37.5
42
22.5
27.5
32.5
37.5
42
11.5
225
275
325
37.5
42
i 11.5
: 22.5
; 27.5
(™""ZIj£5]
i 42:
225
275
325
375
42
325
; ~~" "37 5
j " 42
(0-19)
(20-24)
( 25-29 )
(30-341
( 35-39 )
( ==40 )
(20-24)
( 25-29 )
(30-34) :
( 3fii;o
(0-19)
(20-241
(25=29)
(30-34)
( 35-39 )
f ==40 )
(0-1.9.) ;
(20:24) ,
(25-29) ;
(30-34)
(35-39)
( >=40 ) ;
( 20-24 ) j
(25-29) "7
[30-34) i
( 35-39 )
( ==40 )
( 30-34 )
(35-39)
(==40)
Pet oef
Duration of i
E,,po=uie
0.5
05
05
05
0.5
05
3
3'
3)
3"'
3V"
75
7.5
7.5
7.5
7.5,
7.5
15:
15 i
15""
15
15
25
25
25
25
25
35
35
35
a!., (1985)
f/ml
91
9 1
9,1
9.1
9,1
9.1
9.1
9.1
91
9.1
9.1
9.1
91
9.1
9.1
9.1
91
9.1
9.1
9'T
9'T
'9"1
91
91
9.1
9 1
9.1
9 1
9 1
9.1
9 1
9 1
Person-
Years
' 28015
4668 2
3469 8
2041 2
840.4
4023
' 4785.6 '
, 877.4,
631.7
' 421.3
237.7".'
i'4'a4""
, 8520.5
, 1416.8
11035
70? 2
3833
2491
I 4814.1 i
5 1423.37
'•: 869. 7T
"469.7:"
204 :
'102.3':
8483
. 9353
5997
257.1
'121.7
862
1069
1034
Number of Mesothelioma •
.... , i Predicted ;
Observed , , , •,
: by model
0
0
0
0
0
0
G\
_|
. .....„,,
-fl
0
0
0
0"
0
0
0
711 _o;;
3:
i':
1 ;
11
2"!
1'!
01
o!
0:
o
00
02
0 3
0.3
0.2
0.1
0.0
0.2
~ 0.3
0.3
03
"0.2
0.0
0.5
0.9
1.1
09.
09:
0,0;
Q.5\
"'""'"" °-9^
0.7:
0.5
0.3.
1 :a
1.3
1 ,0 :
"0.8;
Q.2:
""OJ;
Goodness of Fit P-value = 0.8
M.3
A.63
-------
PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
APPENDIX B:
BACKGROUND FOR DEVELOPMENT OF AN AD HOC
EXPOSURE INDEX FOR ASBESTOS
The asbestos exposure index recommended for supporting risk assessment in this
report (i.e. the interim index, Casb, as defined by Equation 7.13) represents a
compromise. The index preserves most of the important features of the optimum
exposure index (Equation 7.12) that is recommended based on the results of our
supplemental literature review (Chapter 7) combined with our formal statistical re-
analysis of the animal inhalation studies conducted by Davis et al. (Section 7.4.3).
These features include:
• a maximum structure width similar to;
• the same minimum structure length as; and
• the same analytical requirements for obtaining the required counts as
the optimum index. However, due to the limitations in the published size distributions
available for re-evaluating the human epidemiology studies (Section 6.2.4.2), the
longest category of structures had to be shortened from that incorporated in the
optimum index (40 urn) to 10 urn, which is incorporated in the interim index
(Section 7.5).
Nevertheless, we expect Casb to provide somewhat conservative (in a health protective
sense) estimates of asbestos exposure because we believe that:
• the minimum length for the structures included in Casb (5 Mm) is sufficiently short
to capture the range of structures that contribute both to lung cancer and to
mesothelioma in humans;
• the maximum width for the structures included in Casb (0.5 Mm) is greater than the
greatest width observed to contribute in our formal analysis of the Davis et al,
studies and is expected to be sufficiently wide to capture the bulk of the range of
structures that contribute both to lung cancer and to mesothelioma. Importantly,
contributions from thicker, complex structures are also included because the
counting rules adopted to provide measurements for generating estimates of Casb
require that the thinner components of these complex structures be individually
enumerated and included in the overall count of structures; and
• the weighting factors incorporated in Equation 7.13 are conservative (in a health
protective sense) in that they are adopted directly from the optimum exposure
index (Equation 7.12) but they are applied so that a greater number of structures
(i.e. those between 10 and 40 urn in addition to those greater than 40 um) are
included within the concentration that is assigned the greater potency value.
B-1 • Revision 1 - 9/3/01
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PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
Despite the compromises adopted to define and apply Casb, the analyses reported in
Sections 6.2.4.2 and 6.3.3.2 of this document indicate that it indeed represents an
improved index of exposure over the index in current use. by EPA, CPCME, apparently
because it better captures the characteristics of asbestos that determine biological
activity than the current index (Section 7.5). The analyses presented in Sections
6.2.4.2 and 6.3.3.2 demonstrate that, when human dose-response coefficients are (
adjusted to match the exposure expressed as Casb, the variation observed in the
published values across studies is reduced in comparison to unadjusted coefficients
(which are matched with CPCME).
Remarkably, the improved across-study agreement observed when risk coefficients are ^
adjusted to Casb is achieved despite the limitations of the manner in which the
coefficients are adjusted, including:
• that the definition of Casb itself is a compromise that does not fully account
for the effects of structure size. The optimum exposure index
recommended in Section 7.5 of this document (Equation 7.12) could not C
be applied to the epidemiology studies because available TEM size
distributions would not support it (Section 6.2.4.2). Therefore, as
indicated above, the length dimensions of the longest size category
incorporated into Casb is substantially shorter than what is considered
optimal; ^
• that the size distributions employed to adjust risk coefficients to match Casb
were obtained from analyses performed in separate studies than those
from which the corresponding risk coefficients were derived. Thus, the
size distributions employed for the adjustments were typically derived
under time-frames and conditions that differed from those that obtained *
during the studies from which the risk coefficients were derived, even if
such studies were conducted in the same facility (which was not always
the case);
• that the same size adjustment was applied to each of the multiple risk <
coefficients representing a particular fiber type in a particular type of
industrial setting (e.g. chrysotile in textile production) even when such risk
coefficients were derived from studies at different facilities, which would
typically exhibit somewhat varying conditions; and
• that each risk coefficient was subjected to a single, average adjustment *--
for fiber size despite the fact that each such coefficient was derived from a
long-term study during which exposure conditions (potentially including
fiber size distribution) typically changed substantially over the course of
the study.
C
B-2 Revision 1-9/3/01
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PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
Due to the limitations described above, a small number of follow-on studies are
recommended in the text of this document (Chapter 8), which would provide the
additional data required to allow use of a better optimized exposure index and may
reduce some of the uncertainty associated with use of generic-industry-based
adjustments rather than study-specific adjustments.
As a further test of the relative performance of the recommended, interim Index (vs. the
index in current use), we compared the ability of the interim index (Casb) and the current
index (CPCME) to fit (predict) the relative tumorigenicity of six tremolite samples that were
intraperitoneally injected into rats in a study by Davis et al. (1991). This is a study to
evaluate the relative tumorigenicity of trernolite samples that vary primarily by the
difference in the degree of their "asbestiform character" (i.e. the difference in the
degree that each contains asbestos-type fibers vs deavage fragments of acicular
tremolite),
The data from this study were selected for evaluation for two reasons:
(1) because the published study includes detailed bivariate size distributions
for each of the samples, which allows us to derive concentration estimates
based on each of the exposure indices of interest; and
(2) because the study provided an opportunity to evaluate the importance of
considering the degree of "asbestiform character" of a sample when
analyzing such samples for risk assessment,
The data used to develop estimates of the magnitude of the response (i.e. frequency of
tumors) and the magnitude of the dose injected for each sample in the Davis et al.
study, based on the current index of exposure, CPCME, is provided in Table B-1, The
first column of the Table indicates the identification of the sample. The second and
third columns, respectively, provide the number of animals dosed and the number of
mesotheliomas observed. The fourth column is an estimate of the rate of
mesotheliomas observed for each sample tested (equal to the number of animals with
tumors divided by the number of animals dosed). Columns five and six provide,
respectively, estimates of the upper bound and lower bound mesothelioma rates for
each sample (derived assuming that the observed frequency of mesotheliomas among
the population of dosed animals is binomiaily distributed).
B-3 Revision 1 - 9/3/01
-------
Table B-1
r
PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
B-4 Revision 1 - 9/3/01
c
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PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
Columns seven and eight of Table B-1 present, respectively, the mass dose
administered and the estimated number of PCME fibers administered to each animal
for each sample. Columns 9 and 10 (the last two columns of the table) present,
respectively, the upper and lower confidence bounds on the estimated number of fibers
in each sample dose. These confidence bounds are derived assuming that the number
of fibers observed in each sample is Poisson distributed.
The fit of the estimated doses (based on PCME) to the observed tumor incidence is
provided in Figure B-1. In this figure, the observed mesothelioma incidence is plotted
on the Y-axis and the estimated PCME dose is plotted on the X-axis. The solid squares
are points representing the observed tumor incidence and the best estimate of average
dose for each experiment. The hollow rectangles surrounding each solid square
represent the estimated confidence bounds for each point (i.e. the confidence bounds
for tumor incidence on the vertical axis and the confidence bounds for dose on the
horizontal axis). The curve in the figure represents the best-fit dose-response model
(which has the same form as that described to evaluate the Davis et al. inhalation data
(Equation 7.7, Section 7.4.3), except that the symbols have been changed and the
equation simplified for this application:
IM = 1 - exp(a - (3d) (B.1)
where:
'M
is the observed incidence of mesotheliomas in each experiment;
a is a coefficient used to adjuste for any estimated background rate
of mesotheliomas among unexposed rats;
P is a coefficient representing the potency for PCME fibers; and
d is the estimated dose of PCME fibers.
Because background rates from an appropriate control population was not provided in
this paper, the background incidence of mesothelioma was optimized as an adjustable
parameter. The fits of this model to the data was optimized visually by trial and error.
In this case, the best estimate model indicates a background mesothelioma incidence
rate, a = 0 and an estimated potency coefficient, P = 0.004.
B-5 Revision 1 - 9/3/01
-------
PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
Figure B-1
c
c
t
B-6 Revision 1 - 9/3/01
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PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
As is obvious from the figure, the fit is entirely inadequate, as the "best fit" line does not
even touch two of the six hollow boxes in the figure. Moreover, it should be obvious
from the relative location of the hollow boxes representing the samples with the three
lowest tumor responses in this figure, that no smooth dose-response curve can be
constructed that can pass through all three of these boxes. Therefore, it is not possible
to fit these data using this index.
In comparison, the data used to develop estimates of the magnitude of the response
(i.e. frequency of tumors, which is the same as in Table B-1) and the magnitude of the
dose injected for each sample in the Davis et al. study, based on the interim index of
exposure, Casb, is provided in Table B-2. The information provided in each of the
columns of Table B-2 is the same as the information provided in the corresponding
column from Table B-1.
In further comparison, the fit of the estimated doses (based on the interim index
recommended in this document) to the observed tumor incidence is provided in
Figure B-2. The format for this figure is identical to that described for Figure B-1. The
best fit of the model to the observed mesothelioma incidence in these experiments
using the interim index indicates a coefficient for the background rate, a = 0 and an
estimated potency coefficient, p = 0.19. That the resulting curve passes well within the
boundaries of the hollow boxes for each of the six samples indicates that this model
provides an adequate fit to these data.
Note that the appearance that the boxes representing the confidence intervals in this
figure are larger than in Figure B-1 is an artifact created by the difference in the scales
of the two figures. Because the magnitude of the doses for the interim index are
smaller than those for PCME, the X-axis scale in Figure B-2 is expanded relative to
Figure B-1. Thus the boxes appear wider in Figure B-2.
When dose is expressed in terms of the interim index recommended in this document,
the observed tumor incidence for the six tremolite samples studied by Davis et al.
(1991), each with vastly differing degree of asbestiform character, can be adequately
predicted. This means, among other things, that it is not necessary to distinguish
fibers from cleavage fragments when evaluating potency using the recommended,
interim index. All structures that exhibit the requisite dimensions, whether asbestiform
or not, should be included in the count.
In contrast, doses estimated using the current EPA index cannot predict the relative
potency of these six tremolite samples. Whether, by adjusting the dose estimates for
the fraction of asbestiform fibers vs. cleavage fragments might improve the fit remains
to be seen. The data provided in the paper were not sufficient to allow for such
adjustments. Even if it were, this additional complication, which is not beyond
B-7 Revision 1 - 9/3/01
-------
PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
Table B-2
B-8 Revision 1 - 9/3/01
-------
PRELIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
Figure B-2
B-9 ' Revision 1 - 9/3/01
-------
PRELIIMINARY WORKING DRAFT - DO NOT COPY OR QUOTE
controversy, makes use of the current EPA index even less favorable relative to use of
the recommended interim index, in addition to the reasons stated in Section 7.5 and the
beginning of this appendix.
r
c
c
B-10 Revision 1-9/3/01
C
-------
APPENDIX C:
COMPENDIUM OF MODEL FITS TO ANIMAL INHALATION DATA IN SUPPORT
OF THE BERMAN ET AL (1995) STUDY AND POST-STUDY WORK
The attached tables are a compendium of raw outputs for the fits of various (exposure
index) models to the Davis et al. animal inhalation studies. Each entry lists the date of
the run, the size categories included in the run, the maximum likelihood estimate for the
run, the degrees of freedom, the P-value for the fit, and the coefficients representing
the relative potency assigned to each size category for the model.
NOTE THAT THE OF NOT IN OUR
ADDITIONAL OF THE INFORMATION -
WOULD AND AND CURRENTLY BE
-------
"05/29/1992
.7472 : : 5
"05/29/1992
.0000 ' ' 3
"05/29/1992
5.1206E-03,'
"05/29/1992
.2085 ' j-
"05/29/1992
2.0226E-11
"05/29/1992
.0000
"05/29/1992
.0000 '
"05/29/1992
.7619 : •' 4
"05/29/1992
.0000 ' i
"05/29/19.92
.0000-
"05/29/19-92
.0000' .;
'^05/29/1932
.0000 • , 3
"05/29/1992
.'8224
'"05/2.9/1992
1.2200E-02;
"05/29/19:92
.0000 '. • • 9
"05/29/1992
.1732 •'
"05/29/1992
.0000
"05/29/1992
.0000 •
"05/29/1992
1.0128E-11 '"•
:" 0572 9/1 992
3.1396E-03. •
"05/29/1992
4.2799E-13
" "17:52:38"
.2207E-03 6
" "17:52:42"
.7528E-02
" "17:53:01"
6.2222E-04
" "17:53:06"
.0000 •' . 4
" "17:53:16"
4.2164E-03
" "17:53:23"
.0000
" "17:53:42"
.0000
" "17:54:06"
.7177E-03 7
" "17:54;:11"
.0000 . '2
" "17:55:00"
.0000 1
" "17:55:34"
.0000
11 "17:55:52"
.6828E-03 ' 3
" "17:56:15"
.0000
" "17:56:20"
.2542
". "17:56:25"
.7985E-03
" "17:56:50"
.1111
" "17:56:54"
.0000 4
" "17:57:02"
.0000
" "17:57>16"
4.0590E-03
" "17:57:20"
.0000
11 "17:57:37"
4.2822E-03
1 Equation?
"PS PCM <5, 5-10, 10-20, >2Q
.7064E-05 ' . • ' ;
"SC PCM 20-30, >30, <0.1, i 0.1-0.2, 0.2-0.3
.0000 .0000 .0000 .5381
"•SC .PCM 20-30, 30-40 >40 \ '
}
"SC PCM 20-30, >30, MK1060
1.8379E-04 . L
"SC PCM <5, 5-10, 10-20, 20-J30, 30-40, 40-50,
.OO'OO .0000 -.7552' • .0000
0.3-0.4, >0.4
3.7282E-11 4.
5-60, >60, <0.
.0000
3022E-03 5.1364E-03
2, >0.2
2448 .0000
"SC PCM 10-20, 20-30, 30-40,| >40, AR<100, 100-200, AR>200
.0000 .2265 8.0142^-02 .0000
"SC PCM <5, 5-10, 10-20, >2ti'
.9308E-05 f
•"PS PCM <5, 5-10, 10-20, 20-(30, 30-40,- 40-50,
.763SE-02 4.6469E-04 .5748; .0000 •
• .4841
50-60, >60, <0
J3849 •" 1.
"PS(no C or M) PCM <5, 5-10, J10-20, 20-30, 30-40, 40-50, 50-6
.7508E-02 4.3916E-02 .692-0- .2466
"SC'(no C or M) PCM <5, 5-10, [.10-2, 20-30, 30-40
.0000- '7..8183E.-03 '.6538; .0000
"SC PCM. 20-30, 30-40, 40-50,j <0.15, 0.15-0.25
.8012E-05 '.0000 .0000 ' 1.4520E-02-
"PS PCM 20-30, 30-40, 40-50,1 <0.15', 0.15-0.25
.0000- .0000 .OOOOj .1776
"PS(no C -or M) PCM 20-30, 30-J40, 40-50, <0.15,
'.1467 .0000- .OOQO .0000
"SC(no C or M) PCM 20-30, 30-J40, 40-50, <0.15,
.0000 ; JOOOO .0000'' 1.5661E-03
"PS PCM 20-30, 30-40, 40-50,} <0.2, 0.2-0.3,
.0000 ... .0000 6..4495;E-02 .5343
"SC PCM 20-30, 30-40, 40-50,1 <0.2, 0.2-0.3, '
.9612E-02 - .1239 .OOOCJ 1.2642E-10
"SC PCM <5, 5-10, 10-20, >2a, <0 .25, • >0 ..25
.0000 2.5766E-02 •2.3627IE-10 4.0282E-03
"SC PCME(SC) <5, 5-10, 10-20|, >20
3:4073E-05 ' • "..[
"EEC PCM <5, 5-10, 10-20, >20 , <0.25, >0.25
4:8616E-02 .0000 6.299J6E-03 4.2436E-03
"SC PCM 5-10, 10-20, 20-30,j >30
2.1632E-04 " '- . '^-' j
-.0000
, :40-50, 50-60
JOOOO
, ! 0.25-0.35,
.:0000v;
, 0.25-0.35,
JOOOO
0.15-0.25, 0
.1467
0.15-0.25, 0
JO'OOO
0.3-0.4
7J5079E-03 9.
OJ3-0.4
5.0996E-03 1.
1.0973E-03
1.6013E-03
0000 -6.0482E-08
.2, :>0.2
2251E-02 ;0000
0, >60, <0. 2, >0.2
0000 .0000
, >60,: <0.2, >0.2 .
3383 .0000. :
.>0.35
3824 , -1.7330E-08
>0.35
0000 4.3492E-10
.25-0.35, >0.35
.1467 3.5740E-11
.25-0.'35, >0.35
4401 -3.8840E-08
4402E-03 . .
1890E-02 .
MLE
" -62.1949
" -60.5687
" -60.7224
" -60.9782
" -61.1790
" -60.1211
.0000
" -59.8032
4.1242E-03
" -61.7831
" -59.6135
.0000
" -59.5425
.0000
11 -60.5424
.0000
" -59.1475
4.4091E-03
" -59.3960
4.2216E-03
" -59.8255
3:.4723E-03
" -58.9800
4.1576E-03
" -59.6295
" -59.0333
" -61.4230
" -60.4908
" -6-1.5539
" -61.6197
ChlSquare DF
18
13
13
14
15
13
.0000
12
14
56
89
77
12
15
80
11
8
10
10
11
'10
.0000
8
P-Value Coefficients?
7.8315E-02
9.4038E-02
.1782 7.
.1407
.1772
.2155 .
1.3025E-10
.1189
0000
0000
6677E-02. 3.
0000
0000 1
0000
2.9064E-03
0000
0000
2042
2929E-13
7915
.000
0000
'2.4831E-03
2092
6.7554E-03
17
12-
.'0000
13
.0000
14
.0000
11
00
25
53 ,
20
74
11
7
.0000
8
.0000
9
.0000
6
1.9861E-02
12
11
10
.1080
9.2727E-02
-1.5614E-08
9.4748E-02
1.97-32E-10
.1155
-2.8277E-10
6.7916E-02
[
.2776
0000
0000',
5.6502E-03
0000
4--.4931E-03
0000
3.8208E-0.3
1315
0000
0000
0000
9.6600E-03
0000
1.5296E-03
0000 ^ -
1.8162E-03
0000
0000"
2.6240E-03
13
54
5
1.8790E-02
1467
OO'OO
4-9456E-03
11
67
7
.1120 9.
8451E-02
0000
2.1421E-02
12
11
15
14
15
16
.32
22
59
03
72
18
7
8
10
11
9
10
9.0528E-02
.1897 9.
.1119 ' .
.2312
7.3068E-02
9.4624E-02
0000 5.
2764E-02
0000
0000.
0000'
0000 1.
2419E-02
OO'OO
0000
oo:oo
0000
7645E-02 '
-------
"OS/29/1992" "17:57:42" "SC PCM, AR>10, 5-10,! 10-20, 20-30, >30
4.2799E-13 4.2822E-03 2.1632E-04 j
"05/29/1992" "17:57:46" "SC PCM, AR>10, 5-10,! 10-20, >20, <0.25, > 0.25
.0000 I 2.5766E-02 1.9480E-11 4.0282E-03J 1.0973E-03
"05/29/11992" "17:57:54" "SC PCM, 10-20, >20, <0.1, 0.1-0.2, 0.2-0.5, ' >0.5
3.9377E-02 .3581
.0000
-2.6301E-10 3.7544E-03 1.9807E-03
-61..6197 16.18 10 9.4624E-02 .0000 1.7645E-02
-61.4230 15.59 10 .1119 .0000 .0000
-60.8243 15.06 9 8.9374E-02 .6025 .0000
-------
"06/04/1992
.0000
"06/04/19:92
.oooo ' ;
"06/04/1992
1.0642E-02y
'"06/04/1992
.0000 ;
"06/04/1992
1.8243E-03
"06/04/1992
9.7585-E-03'.
"06/04/1992
1.1391E-02
"06/04/1992
1.0616E-02
"06/04/1992
1.9830E-02
"06/04/1992
9.758-5E-03
"06/08/1992
9.1772E-07:
• "06/08/1992
3.5523E-07,:
"06/08/1992
..0000 - ;
""06/68/19'92
.0000 '
"06/08/1992
.0000 ' ;
"06/0:8/1992
7. 9477.E-02:
"06/08/1992
6.589QE-02:<
"06/08/1992
8-. 9823E-02-
"06/08/19:92
.0000 ; i
"06/08/1992
1.8013E-02;
"06/08/1992
." "15:
.0000
" "16:
.0000
" "16:
• .0000
" ';16:
.0000
" "16:
.0000
" "16:
.0000
."' "16:
-. .0000
" "16:
.0000
" "16:
.odo'o
" "17:
' .0000
" "14:
" "14:
" "15:
.3697
" "15:
.8647
" "15:
.9434
" "15:
.0000
" "15:
.0000
" "15:
.0000
" ""15:
.•oooo
" "15:
.6000
" "15:
59:
00:
00:
09:
09:
10:
25:
25^:
25:
01:
23:
23':
17:
17:
17:
"
17:
IS-
IS:
18:
18:
19:
:53
:06
:34
:39
:53
:22
:02
:16
:29
:04
:24
:27
:31
:38
:43
:50
:00
:16
:33
:48
:15
' \ Equation?
" "PS M <5, 5-10, 10-20, 20-30:,, 30-40, 40-50, ''50-60, >60- : .
.3350 .0000' .66471 ' 3.3093E-02 2.-7314E-02
" "PS M <5, 5-10, 10-20, 20-30), 30-40, 40-50, 50-60, >60 (without): :
•.3131" • :0000 ' .6868J 2-.3751E-02 3.0330E-02 '
" "PS PCM <5, 5-10, 10-20, 20430, 30-40, 40-50, 50-60,. >60
.1925 .0000 .3962 2.6431E-02 1.3497E-02 . • : ' -
11 "SC M <5, 5-10, 10-20, 20-30;, 30-40, 40-50, 50-60, >60 '
.4181 ..OOOO .5818; 3.3111E-0'2 3.'1739E-02 '. • '
" "SC M <5, 5-10, 10-20, 20-30', 30-40, 40-50, 50-60, >60 (without);
.3599 • .0000 . .638-2 2.3345E-02 3.4731E-02
" "SC PCM <5, 5-10, 10-20, 20--30, 30-40, 40-50, .50-6.0, >60 . • .
' -.1659 .0000 .126:6 2.5546E-02 1.7443E-02 ' , • '
" "FBC M <5, '5-10, "10-20, 20-3;0, 30-40, 40-50, 50-60, >60
. • .0000 .4578 .530;8 3.2501E-02 7.0673E-02 •
" "FBC M <5, '5-10, 10-20, 20~3;0, 30-40, .40-50, 50-60, >60 (without) •
.0000 .4798 .509:5 '2.3348E-02 7.4414E-02 ' :
." "FBC PCM <5,' 5-10, '10-20, 2QJ-30, 30-40, 40-50, ' 50-60, >60
' .0000 • ' .9802 • 2.163'8E-13 2/4967E-02 2.1750E-02 :
"'"•SC PCM <5, 5-10, 10-20, 20-J30, 30-40, 40-50, 50-60, >60
'.1659 .0000 .697:8 . 2.5546E-02 1.7443E-Q2
" "SC PCM, USING SUM OF AR INSTEAD OF ' THE SUM OF THE NUMBER OF STRUCTURES
' ' I •
" "SC PCM, USING SUM OF (LA2/W) INSTEAD OF THE SUM OF THE NUMBER OF STRUCTURES
" "SC PCM 10-20, 2.0-30, 30-40,j 40-50, 50-60, >60, IAR>100 " ' . : •
.1637 2.7318E-02 8.6314JE-02 ^ - . ' .
11 "SC PCM 10-20, 20-30, 30-40,[' 40-50, -50-60, >60, :AR>50 . • -
1.5222E-11 2'.4334E-02 2.8640^-02 '
" "SC PCM 10-20, 20-30, 30-40,f 40-50, 50-60, >60, AR>30
6.3283E-14 2.5224E-02 2.2209^-02 ;
" "SC PCM 10-20-, 20-30, 30-40,1 40-50, 50-60, >60, ,AK>10 ' . •
-7.6669E-10 2.4802E-02 2.834;:5E-02 "
" "SC PCM 10-20, . 20-30, 30-40,] 40-50, 50-60, >60, <0.1,. >0 . 1
.0000 -4.0257E-02 .00o|o .0000 :.0000 .-.OOOO .3413
" "SC- PCM 10-20, 20-30, 30-40,[ 40-50, 50-60, >60,!<0.2, >0.2 '
..9063 .0000 .000^0 .0000 .0000 .0000 : 3.9421E-11
" "SC PCM 10-20, 20-30, 30-40,} 40-50, 50-60, >60, -XQ.3, >0.3
.0000 .0000 - .8731; .0000 ' .0000 1.6883E--02 .1024
11 "SC PCM IO-2'O, 20-30, ,30-40,! 40-50, 50-60, >60, |<0.5, >0.5 -.•...''
3.6580E-02 .0000 - .7005 .' .0000 .0000 .0000 .2300
" "SC PCM 10-20, 20-30, 30-40,| 40-50, 50-60, >60., .1 -
MLE
" -325.
11 -289.
" -279.
" -324.
" -288.
" -279.
" -327.
" -292.
" -280.
" -279.
" -320
11 -304.
" -282.
" -282.
" -284,
" -282.
11 -277.
ChiSquare DF
.410
.768
.861
.088
.190
.958
.491
.240
.941
.958
.998
,439
.372
.195
.342
.527
.397
2.6604E-02.
" -274.
.192
2.4470E-02
" -273.
.570
2.5219E-02
" -277.
.042
2.3656E-0.2
" -276,
.804-
72.32
40.91
24/45
69.36
- 37.50-
24.27 -
76.80
• 48.03"
27.21
24.27
128.1
92.38
31.86
29.24
33.22
28.52
,21.06
3.2997E-02
14.21
9.5869E-02
13.47
7.8163E-02-
18.60
3.0589E-02
18.60
12
!0
9.
12
9
_
' 9
12
10
10
9
11
11
9
9
9
9
8 -
9
9
7
9
P-Value
.0000
.0000
2.8979E-03
.0000'
.0000
3. 1396E-03
'-
ioooo
.0000
1.5744E-03
3.1396E-03
.0000 '
--
'.OOOO
.0000
-
.0000
.0000
2.6697E-05
6.1426E-03
.1142
-.1417
-8.7967E-03
2.8109E-02
Coefficients?
2.5437E-04
1.2826E-04
'.OOOO
1.2222E-04
2.5206E-05
.0000
.0000
.0000
' .0000 -
.0000
.5002 ''
.5002
.3233
2.9482E-02
2.5570E-02
1.0006E-02
.5520
' .0000
7.5561E-03
7.1726E-03
1.0869E-02
.0000
.0000
.0000
.0000
. .0000
.0000
.0000
.0000
. .0000
' .-0000
.1054
4.9910E-02
.1434
.1058
3.1076E-02
.0000
5.5765E-04
3.8278E-03
.0000-
7.'7336E-03
' ' .0000
-------
•°000 .0000 .1691 .1142 ; .0000 .0000 .0000
"06/08/1992" "15:19:23" "SC PCM 10-20, 20-30,! 30-40, 40-50, 50-60, >60, AR>200
.4112 • .0000 4.5280E-02 .1237 | .2896
.0000
.7058
2.5210E-02
" -314.551
1.7712E-02
102.3
.0000
3.2936E-02
"06/09/1992
.0000 ;
"06/09/1992
.000-0
"06/09/1992
4.9270E-02
"06/09/1992
.1396 '..
"06/09/1992
8. 9222E-02
"06/09/1992
9. 9701E-02'
"06/09/1992
.2282 . 2
"06/09/1992
.2383
"06/09/1992
.0000 •
"06/09/1992
.0000
"06/09/1992
.0000 :
"06/09/1992
1.3378E-04
"06/09/1992
.0000
"06/09/1992
.0000 j
"06/10/1992
.0000 i
"06/10/1992
.0000
"06/10/1992
.0000
"06/10/1992
.3992 '•
"06/10/1992
.4357
"06/10/1992
" "09:36:19"
.0000
" "09:36:44"
.9754 5.
" "09:36:50"
.0000 4
" "09:37:02"
.0000 9.
" "09:51:30"
2.54.53E-04
" "09:51:53"
.0000
" "09:52:08"
. 9401E-03
" "09:52:36"
.0000
" "10:49:30"
.0000
" "10:49:54"
.0000
" "10:50:29"
.0000 '
" "10:42:08"
.0000
" "10:43:04"
.0000
" "10:43:44"
.0000 5.
" "09:42:52"
.0000
" "10:46:34"
.0000 3.
" "10:47:09"
. ODOO 9.
" "10:47:42"
.1441
" "10:47:44"
.1441
" "10:47:46"
"SC PCM 10-20, 20-30,
0000 .0000
"SC PCM 10-20, 20-30,
0787E-11 2.4409E-02
"SC PCM 10-20, 20-30,
30-40, 40-50, 50-60,
.9036 .0000
30-40, 40-50, 50-60,
2.0780E-02
30-40, 40-50, 50-60,
>60, <0.4, >0.4
.0000 3.
>60, AR>20
>60, AR>5
9774E-02
4..6900E-02
" -273.752
2.4767E-02
" -282.130
" -280.428
13.77
5.8149E-02
28.46
24.35
8 8.7091E-02 7.
10 6.8359E-04 2.
9 3.0278E-03 1.
5744E-03
4648E-02
0293E-02
.6592E-02 2.4754E-02J 2.5388E-02
"SC PCM 10-20, 20-30,
8428E-.02 2.5738E-02
"PS PCM 10-20, 20-30,
.9088 .0000
30-40, 40-50, 50-60,
1.9572E-02
30-40, 40-50, 50-60,
.0000 .0000
"FBC PCM 10-20, 20-30, 30-40, 40-50, 50-60
.8806 .0000
"PS PCM 10-20, 20-30,
3567 .0000
1.5773E-02 .0000
30-40, 40-50, 50-60,
.0000 .0000
"FBC PCM 10-20, 20-30J, 30-40, 40-50, 50-60
0000 .2039
"SC PCM <5, >5, <0.1,
0000 .0000
"SC PCM <10, >10, <0.
0000 .0000
"SC PCM <20, >20, <0.
0000 ."0000
"SC PCM <30, >30, <0.
.0000 .0000
"SC PCM <40, >40, <0.
0000 .0000
"SC PCM <50, >50, <0.
0791E-04 .0000
.5428 .0000
0.1-0.2, 0.2-0.3,
.0000 .0000
1, 0.1-0.2, 0.2-0.3
.0000 .7228
1, 0.1-0.2, 0.2-0.3
.0000 .3188
1, 0.1-0.2, 0.2-0.3
.0000 .0000
1, 0.1-0.2, 0.2-0.3
2.8445E-04 .0000
1, 0.1-0.2, 0.2-0.3
.0000 .0000
"SC. PCMfdifferent order)<40, >40, <0.1, 0.
0000 .0000
2.8445E-04 .0000
>60, AR>3
>60, <0.2, >0.2
.0000
, >60, <0.2, >0.2
.0000
>60, <0.3, >0.3
.0000
, >60, <0.3, >0.3
-.0000
0.3-0.5, 0.5-1,
.0000
, 0.3-0.5, 0.5-1
.0000
, 0.3-0.5, 0.5-1
.5973
0.3-0.5, 0.5-1
.4087
, 0.3-0.5, 0.5-1
.0000
0.3-0.5, 0.5-1
.0000
1-0.2, 0.2-0.3, 0.
.0000
.0000
.0000
0000
0000
1-2, >2
0000
, 1-2, >2
0000 -
, 1-2, >2
0000
,1-2, >2
.4290
, 1-2, >2
9300
, 1-2, >2
9601 '
3-0.5, 0.
9300
-2.0902E-08
-7.8124E-08
.4087
-2.1203E-06
1.0368E-02
.0000
4.5484E-02
5.4099E-02
.0000
.0000
5-1, 1-2, >2
.'0000
"SC M 10-20, 20-30, 30-40, >40, AR<100, 100200
6258E-03 .0000
.0000 .0000
.7186 2.
4978E-03
.1761
"SC M 10-20, 20-30, 30-40, >40, AR<100, 100200 (without)
5295E-03 .0000
"SC PCM 30-40, 40-50,
1207
.0000 .0000
<0.2, 0.2-0.3
.7246 4.
7535E-03
.1814
"SC PCM 30-40, >40, <0.2, 0.2-0.3
1141
"SC PCM 30-40 AND <0.2, 40-50 AND 0.2-0
.3
" -280.621
" -273.982
2.4482E-02
" -276.214
2.5377E-02
" -276.577
2.5828E-02
11 -285.851
1.7593E-02
" -306.621
.2379
" -288.155
8.3293E-02
" -211. 141
.0000
" -278.516
.0000
" -274.113
.0000
" -285.544
.0000
" -274.113
.0000
" -301.303
2.6786E-02
" -271.722
2.3813E-02
" -328.264
" -328.362
11 -328.264
25.34
. 14.15
.2017
18.66
.1086
19.01
2.8763E-02
39.03
2.3597E-02
96.83
.3233
44.97
.1248
18.94
3.8390E-02
21.96
.1080
13.89
6.9566E-02
42.63
3.9410E-02
13.89
6.9566E-02
21.78
.6618
9.472
.6408
132.1
132.3
132.1
9 1.8654E-03 9.
8 7.7092E-02
8 1.5954E-02
8 1.3996E-02
8 .0000
9 .0000
5.4390E-02 9.2242E-04
9 .0000
2.6367E-02 9.8343E-03
9 2.4998E-02
2.2548E-02 4.7097E-02
8 4.1566E-03
1834E-03
0000
0000
0000
0000
0000
0000
0000
0000
2.6150E-02 5.0063E-02
9 .1256 1.
2.6644E-02 9 . 1119E-02
10 .0000 - .
3.3774E-02 .1998
9 .1256 • 1.
2.6644E-02 9.1119E-02
10 1.5441E-02
8 .3034
10 .0000
10 .0000
11. .0000
4143E-04
0000
4143E-04
0000
0000
4994
4486
4443
2.1524E-03
.0000
.0000
.0000
1.7037E-03
3.9259E-03
3.5134E-03
1.4957E-02
.0000
.0000
.0000
.0000
.0000
.0000
,0000
-9.9225E-02
7.9714E-02
.0000
.0000
.1441
-------
1
4
5
6
-
1
1084
"'06/10/1992
1009
"06/10/1992
1226
"06/10/1992
.7299E-04
"06/10/1992
.5248E-02
"06/10/1992
.9914E-02
"06/10/1992
. 6660E-02
"06/10/1992
1224 2
"06/10/1992
1842 2
"06/10/1992
1.5012E-12
"06/10/1992
.7035E-02
"06/10/1992
2507 2
"06/10/1992
-7.5830E-12
5
1
2
5
2
5
"06/10/1992
:0000 •
"06/11/1992
.9240E-03
"06/11/1992
.0718E-02'
"06/11/1992
.4070E-03
"06/11/1992
.1824E-03:
"06/11/1992
-4093E-02-,
"06/11/1992
.4238E-02
"06/11/1992
0000
"06/11/1992
0000
m>
" "10:47:47"
" "10:47:49"
" "10:. 47:: 55"
.0000
" "15': 23: 45"
2.5944E-02
" "15:24:05'"
2.7518E-02
" "15:24:25"
2.5536E-02
" "15:24:36"
.6666E-02 8
" "15:24:48"
.9171E-02 8
" "15:25:11"
2.5325E-02
" "15:25:17"
2.4296E-02
" "15:25:24"
.6694E-02 2
" "15:25:27"
_ 2.4133E-02
" "15:25:34"
.0000 4
" "15:31:51"
" "15:31:56"
2.5619E-02
"' "15:32:29"
.0000
" "15:32:48"
5.4184E-03
" "15:33:31"
1.6167E-02
" "15:33:37"
2.0059E-02
" "16:05:03"
.0000
" "16:06:22"
.0000
A
"SC PCM 30-40: AND <0.2, S40 AND 0.2-.0.3 : "
"SC PCM <40
"SC PCM'<40
.0000
"SC PCM <40
9.8491E-02
"SC PCM <40
4. 8515E-02
11 SC PCM <40
9.0266E-02
"SC PCM <40
.0706E-02
"SC PCM <40
.2945E-02
, >40,
, >40,
.'0000
, >40,
, >40/
, >40,
," >40,
, >40,
"SC- PCM' 20-40, >40
8.2712E-02
"SC PCM 20-40-, >40
3.6304E-02
"SC PCM 20-35, >35
.8991E-02
"SC PCM 20-45, >45
.2121
"SC PCM <20
.5312E-02 2
"'S'C' PCM <40
"SC P£M <29
".1163
":SC PCM <10
4.'5564E-02
"SC PCM <10
.1346
"SC PCM <20
"SC PCM <20
"SC'M <10,"
.-0000
"SC M-<20,
.0000 -
, -20-40
' :
•0.2-0.3 ; ."
'
<0.1, O.lf-0.2, 0.2-0.3, Q.3-0.4, 0.4-0.6, 0.6-1, >1
1.6'964E'-04 .0000 .0000- '.7574 :2283
2 ; •-•-.- •. ,!.... •-
t • :
<0.4, >2 I • ' . ' "
<0,3, >3 i : - „
f • - ' ;
<0.3, >5 f : ' ' ;
<0.3, >10 j , ! '.-"''."
f - . • - -
, <0.3, >2i - : ; ;
', ! ' •
, <0.4, >2 - -' ' ' ' "
( .'•••:"• ; ' .
, 2; ;•• ;i ;. . ;- ' . -.
f " •
:, <0.3, >2; : -; ; .
5 . • ' '
, >40, <0.';3, >2 '..-.'•• ; ; ' '• !;-
-328.
-277.
362
294
-274,. 426
.0000
' -274.
-275 i
-274.
-274.
-274.
-283.
-276.
-282.
-286.
-274,
857
922
995
006'
370
427
234
898
727
850
132.3
: 19.14
14.08 '
1.3866E-02
14'. 75
17.20
14.95
13.41
14'. 68
31.70
17.96
''30.25
36.13
14.73
.:.-''--' :
11 .0000 i
11 5.7976E-02
7 . 4.9035E-02 5.
2.6012E-02 .1065
9 9.7473E-02 5.
9 4.4919E-02 1'.
9 9.1591E-02 ' 5.
-
9 "'" .1442 ! 7.
• • ! .
9 9.9294E-02 7.
'' ' 1-
- 1 •
9 .0000 3.
9 3..4871E-02 9.
!
10 .0000- '
\ •
9 .0000 ' .2'.
J -
9 9.7945E-02 '5.
4927
9997 -
2545E-05
4764E-05
0871E-04
9029E-05
1369E-05
5858E-05
7466E-02
9336E-02
1302
3230E-.02"
4613E-05
.1441
2.7463E-02
. . OOO'O
2.5762E-04
1.0373E-03
3.1457E-03
4.9037E-03
1.2275E-02"
9.1336E-02
7.5388E-02
..0000 .
3.3283E-02
2.7873E-04
.5936E-02 9.8583'E-02 : ' • • ' • ' '' • . . i -•••.
1 . ' ]-'••.• • . • i . .-
, >40
20-40,
2.5487E
10-40,
1 .'•''••• '
! •:.''.-'
>40, <0.3, >5 \ ' ' "
-02 7.6466E-02 ' ! -
>40, <0.3, >2 • : '
-289.
-273.
-274.
830
833
688
50.85
13.04
14.64
11 .0000
8 .1098" | '7.
,
9 .1005 '- 4.
9995 -
4307E-05
4249E-05 '.
4.0751E-02
.0000
.0000
2.5144E-02 '9.342*2E-02 ' : • ' '. ' ..
10-40,
2.4875E
20-40,
20-40,
10-20,
.0.000
>20, <0
.0000
e
>40, <0.3', >5 . • '"
-02 7.032'9E-02 '
>40, with; AR>100 or w>5 : ' ' '
>40, with; AR>200 or w>5 - . "
.20-30, 30-;40, 40-50, 50-60, >60, <0.4, >0.4 . . ' '"
2.0091IE-02 .0000 .3376 .0000 .6323
.2, 0.2-0;. 3, 0.3-0.4, 0.4-2, 2-5, 5-8, >8 (without) ' "
.0000' .0000 .0000 .4395 1.5005E-02.
i ' •' '
-273.
-279.
-287.
-279;
.0000
-290.
.0000
9
349
057
783
728
051
12.40
23.32
57.24
23.25
9.9335E-03
43.31
.5454 '
8 .1337 ''! . 4.
10 ' 8.7947E-03
.
10 .0000 4.
- . j •
.8 2.2192E-03 3.
2. 1964E-02 .3872
9 .0000 1.
2.3980E-02 3.3505E-02
• i
5873E-05
1284
7695E-02
687'8E-05
0731E-04
a
• .0000
.4497
.6415
.0000
- -.0000
>
-------
1
4
1
2
7
7
3
5
1
2
7
5
6
6
"06/11/1992'
OOOO ;
"06/11/1992"
OOOO ;
"06/11/1992"
.4206E-02
"06/11/1992"
.3934E-02
"06/12/1992"
.0976E-07
"06/12/1992"
.5583E-b8
"06/12/1992"
oooo ; 3.
"06/12/1992"
.5561E-03
"06/12/1992"
.5744E-03 2
"06/12/1992"
OOOO ;
"06/12/1992"
oooo ;
"06/12/1-992"
. 9080E-02 3
"06/12/1992"
.5882E-04 3
"06/12/1992"
.7176E-03
"06/12/1992"
. 6148E-02 2
"06/12/1992"
.5831E-03 5
"06/12/1992"
2053 ;
"06/12/1992"
.5410E-02
"06/12/1992"
.0514E-02 1
"06/12/1992"
1835 - 2.
"06/12/1992"
.8066E-02
"06/12/1992"
"16:06:57
OOOO
"16:08:07
OOOO
"16:08:44
.0000
"16:0-9:50
.0000
"12:45:29
"12:46:51
"14:25:51
7448E-03
"14:26:39
.0000
"14:27:33
.1524E-03
"14:28:40
OOOO
"14:30:01
OOOO
"14:31:34
.7648E-02
"14:32:09
.2760E-04
"14:33:06
.0000
"14:33:22
.3396E-02
"14:33:37
.6428E-02
"14:33:58
OOOO
"14:34:10
.0000
"14:34:49
.3755E-02
"14:36:04
0935E-02
"14:36:54
.0000
"14:36:59
" "SC M <30,
.0000
" "SC M <40,
.0000
" "SC M <10,
.0000
" "SC M <10,
.0000
" "SC PCM,
" "SC PCM
" "SC PCM
8.7979E-02
" "SC PCM
.0000
" "SC PCM
.0000
11 "SC PCM
2.8541E-03
" "SC PCM
3.1038E-03
" "SC PCM
.0000
" "SC PCM
.0000
" "SC PCM
.1453
" "SC PCM
>30, <0.2,
OOOO
>40, <0.2,
oooo
0.2-0.3, 0
.0000
0.2-0.3, 0
.0000
.3-0.4, ; 0.4
.8490
.3-0.4, 0.4
.0000
10-20, 20-30, 30-40, 40-50, 50-60,
.0000
10-20, 20-
.0000
.9699
1.3334E-02
30, 30-40, 40-50, 50-60,
.4960
9.9726E-02
USING SUM OF (AR)~1.8 INSTEAD OF THE
-2, 2-5, 5-8
.0000
-2, 2-5, 5-8
.5169
>60, <0.2, >
.0000
>60, <0.3, >
5.7563E-02
, >8 (without)
9.9544E-02
, >8 (without)
.4323
0.2
.0000
0 . 3
.0000
OOOO
OOOO
.0000
.0000
SUM OF THE NUMBER OF STRUCTURES
MIMICS RJ LEE
<10, 10-20, 2^0-30, 30-40,
oooo
.8868
<10, 10-20, 20-30, 30-40,
<10
<20
<30
8
<40
<50
f
,
r
r
r
.0000
10-20, 2
.0000
>20 and
OOOO
>30 and
3599E-02
>40 and
.0000
>50 and
.0000
.0000
0-30, 30-40,
.0000
<0.2, 0.2-0.
.0000
<0.2, 0.2-0.
.0000
<0.2, 0.2-0.
.0000
<0.2, '0.2-0.
.0000
40-50, 50-60
.0000
40-50, 50-60
.0000
40-50, 50-60
.0000
3, 0.3-0.4,
.3170
3, 0.3-0.4,
.0000
3, 0.3-0.4,
.0000
3, 0.3-0.4,
.0000
, >60 and ,0
2.1443E-02
, >60 and ,0
.8731
, >60 and ,0
.9036
0.4-2, 2-5
8.8019E-03
0.4-2, 2-5
1.5371E-02
0.4-2, 2-5
.0000
0.4-2, 2-5
.0000
.2, >0.2
.0000
.3, >0.3
.0000
.4, >0.4
.0000
, 5-8, >8
.2178 . .
, 5-8, >8
.5256
, 5-8, >8
.0000
, 5-8, >8
.7592
OOOO
.0000
.0000
oooo
oooo
.0000
.0000
<5, 5-40, >40| and <0.3, >5
2
. 5612E-02I 7.0424E-02
<5, 5-40, >40! and <0.4, >5 ' .
6.1184E-02
" "SC PCM
6
-3939E-02 5.3121E-03
<10, 10-40, >40 and <0.4,
3.3751E-14
" "SC PCM
.7667
" "SC PCM
<20
3
<20
6.1022E-02
11 "SC PCM
.1895
" "SC PCM
.4459
" "SC PCM
.9319
" "SC PCM
<20
<20
2
2
>5
.4070E-02I 4.5074E-02
, 20-40, >40 and <0.4,
>5
.7371E-02 3.2598E-04
, 20-40, >40 and <0.3,
3
2
. 8161E-02
20-50, >
-4667E-02
4.0910E-05
50 and <0.3,
6.5813E-02
, 20-50, ^50 and <0.4,
>8
>5
>5
.9928E-02 2.6012E-05
<10, 10-40, >40, with AR>=50 or WIDTH>=5 (6 categories)
<10
2
.4370E-02
10-40, >
1.4888E-02
40, with AR>=100 or WIDTH>=5 (6 categories)
-278.334
1.6664E-04
-277.968
.0000
-280.135
.0000
-298.689
.0000
-316.759
-314.904
-274.192
.0000
-273.570
1.6883E-02
-273.752
3.9774E-02
-273.104
.0000
-274.535
.0000
-318.182
.0000
-282.887
.0000
-272.935
-301.510
-273.662
-337.543
-345.304
-277.024
-349.979
-274.917
-275.017
19.29
9.9650E-04
19.74
5.0666E-02
24.37
2.5917E-03
63.37
.3028
120.5
103.8
14.22
4.0984E-06
13.47
.1024
13.77
4.6900E-02
12.30
.4536
15.28
.3723
153.4
.9233
38.07
.2399
12.19
78.09
13.27
281.4
353.5
18.84
404.9
15.55
15.94
7 6.5724E-03 5.7529E-05
2.1948E-02 .3498
9 1.8859E-02 8.5360E-05
2.2914E-02 .2941
8 1.1420E-03 5.8051E-06
2.1348E-02 .'3422
8 .0000 .0000
4.6309E-02 3.6312E-02
11 .00-00 .5002
11 .0000 .4998
8 7.5497E-02 .0000
2.4473E-02 9.7935E-02
9 .1417 .0000
2.5219E-02 7.8163E-02
8 8.7091E-02 .0000
2.4767E-02 5'.8149E-02
8 .1378 .0000
2.4370E-02 2.3120E-02
8 5.3177E-02 .0000
2.5364E-02 3.0722E-02
10 .0000 .0000
3.8196E-02 1.0287E-03
9 .0000 .0000
3.7498E-02 6.3278E-02
10 .2716 .0000
9 .0000 .0000
8 .1023 2.6086E-05
10 .0000 2.7954E-02
9 .0000 .2255
8 1.4921E-02 6.7283E-05
9 .0000 .3496
11 .1582 .0000
10 .1006 .4137
.0000
.0000
.oooo
.0000
.6106E-02
.1020
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
-------
.2309
"06/12/1992"
.3874 4.
"06/12/1992"
.2309
"06/12/1992"
.2258 ' , .
"06/12/1992"
2.2757E-02 .2
"06/12/1992"
7.5406E-02' 2
"06/15/1992"
3.1957E-02
"06/15/1992"
4.7120E-02
"06/15/1992"
9.6472E-04-
"06/15/1992"
.0000 ' .
"06/15/1992"
.0000 ' .
"06/15/1992"
.0000
"06/15/19.92"
3.3972B-04
"06/15/1992"
3.2317E-04
"06/15/1992"
8.7093E-02
"06/15/1992"
5.6553E-02~
"06/15/1992"
.0000
"06/15/1992"
.0000
"06/15/1992"
.0000 " .
"06/15/1992"
.0000
"06/15/1992"
.0000
"06/15/1992"
.0000
0000
"14:37:
:04
7852E-02
-"14:37:
0000
"14:37:
0000
"14:37:
:11
:15
:21
.5509E-02
"16:47:
:32
.7.083E-02
"11:06:
.0000
"11:08:
.0000
"11:08:
.0000
"11:10:
0000
"11:12:
0000
"11:14:
0000
"11:16:
.0000
"11:18-:
.0000
"12:16:
.0000
"12:16:
.0000
"12:16
0000
"12:18
0000
"12:19':
0000
"12 :20':
0000
"12:22
0000
"12:24
0000
:50
:'19
:49
:43
:36
:25
:10
:03
:10
:21
:44
:06
:28
:57
:28
:05
.3555 .2.
" "SC PCM <10,
.2899 5.
" "SC AR>=100
.3555' 2.
" "EEC AR>=100
.3623 2.
" "SC PCM <40,
9.4588E-02
" "SC PCM <20,
.'7421 2
" "SC PCM <5,
.0000
" "SC PCM <5,
2.9985E-02
" "SC PCM <5,
4.3486E-03-
" "SC PCM <5,
1.3893E-03
" "SC PCM <5,
3.7626E-03
" "SC PCM <5,
7.6795E^03
" "SC PCM <3,
4.1296E-03
" "SC PCM ,<5,
2.2255E-03
" "SC PCM <5 ,
8.7093E-02
" "SC PCM <5, '
.2414
" "SC PCM <5,
1.3801E-02
" "SC PCM <5,
4.9098E-03
" "SC PCM <5,
6.9703E-03
" "SC PCM <5,
8.1095E-03
" "SC PCM <5,
5.0759E-03
" "SC PCM <5,
5.8288E^03
5908E-02 3.
10-40, >40,
2688E-02 4.
OR (C and CS
5908E-02 3.
OR, SC(C and
5958E-02 3.
>40, and <0
20-40, >40
.5097E-02 6
5-10, 10-20,
.0000 4
5-10, 10-20,
.0000
5^-10, 10-20,
.0000
5-10, 10-20,
0000 -5.
5-10, 10-20,
0000 3.
5-10, 10-20,
0000 3.
3-10, 10-20,
.0000 -
4688JE-02 - ;
with AR>=200 or WIDTH>=5 (6 categories)' "
5503;E-02
only) WIDTH>-5, PCM
4688E-02
CS {only) WIDTH>=5,
48'41iE-02
.3, ->0.3
and !>8,- <0 . 3 .
.9122E-02
20-J30, 30-40, 40-50
.334J1E-02 .0000 '
20-J30, 30-40, 40-50
.000)0 .0000
20-[30, 30-40, 40-50
.ooo'o .0000
20-J30, - 30-40, 40-50
2414IE-02 .0000
20-j30, 30-40, '40-50
8885JE-02 .0000
20-'30, 30-40, 40-50
4904E-03 .0000
20-;30, 30-40,^40-50
.000*0 .0000
5-8, 8-20, 20-30, 30-40, 40-50,
.0000
5-10, 10-20,
.0000
5-10, 10-20,
.00-00
5-10, 10-20,
0000 3.
5-1-0, 10-20,
oooo ' i.
5-10, 10-20,
0000
5-10, 10-20,
000ft
5-10, 10-20,
0000 4.
5-10, 10-20,
0000 7'.
.00050 .0000
20-130, 30-40, 40-50
.14ltg .0000
20-;30, 30-40; 40-50
.ooo;o ,0000
20-130, 30-40, 40-50
7693JE-02 .0000
20-J30, 30-40, 40-50
9981|E-02 .0000
20-[30, 30-40, 40-50
oooo) .0000
20430, 30-40, 40^50
0000 .0000
20430, 30-40, 40-50
7770JE-03 .0000
20-30, 30-40, 40-50
2078>-04 .0000
, <10, 10-40, >40
PCM, <10, 10-40, >4Q '
, >50 and <0.2, >8 -
, .6315 • 1.6485E-03
,- >50 and <0.3, >8 ' .
.0000 . .0000
, >50 and <0.4, >8 ;
.0000 .1754
, >5Q and <0.5, >8 ;
6.0335E-02 .3280
, >50 and <0.6, >8 :
.1015 .'4142
, >50 and <0.8, ->8 !
6.0160E-02- .3190 ;
, >50 and <0.3, >8
.0000 . 4.2215E-02
>5'0'-and <0 .3, >8 i
- ' .0000 . 6.5672E-02
11
"
.
"
'„
.0000
"
.0000
11
.7476
"
.0000
„
.0000 7
".
.0000
"
.4837
„
.3431
,' >50 with AR>=200 or !W>=8 (14 cat.) "
.0000 ,. .2037
, >50 with AR>=100 or ;W>=
.000.0 .2577
, >50 with AR>=50 or W>=8
5.3310E-02 .3375
, >50 with AR>-30 or W>=8
6J4082E-03 .2060
, >50 with AR>=20 or W>=8
3.00'64E-02 .2439 -
, >50 with AR>=10 or W>=8
2:5890E-02 " .2019
, ,>50 with AR>=5 oriw>=8'
,0000 . .10.90 ,
, >50 with AR>=3 or:W>=8
1J0573E-02 .1299
8.7093E-02
8 (14 -cat.) "
- .0000
(14 cat.-)
.0000
(14 cat.) "
- .0000
(14 cat.)
.0000
. (14 cat.) "
.0000
(14 cat.)
.0000
(14 cat.)
.0000
-286.452
-275'.017
-275.121
-276.137
^273.463
-296.708
.0000
-301.032
.0000
-272.385
.0000
-272.858
.3594
-273:029
. 0023E-02
-273.276'
.2732
'-272.209
.0000
-272.224
.0000
-285.792
.0000
-272.200
.2205
-273.057
.3226
-273.632
.6920
-273.360
.5410
-273.866
.4075
-274:167
.7382
-274.231
.6530 '
56.19
1.5.94
1.6.15
17.09
12.58
70.84
.2916
82.76
.9229
11.16
7.1640E-02
11.87
.1984
12.11
.3717
12.53
.3365-
10.93
.1544
11.04
8.4681E-02
53.75
.1092
11.02
2.2922E-02
12.26.
.2351
13.16
7.0740E-02
12.73
.1781
' 13.72
.3565
14.40
.1430-
14.54
.2000
9 .0000 I .0000
10 -.1006 ! .4137
10 9.4586E-02 .4119
9 4.6680E-02 1.6030E-05
, 8 .1262 .0000'
!
8 .0000 '' ! .0000
2.8411B-02 1.6238E-02
10 .0000 i . .0000
2.5358E-02 5.3733E-03 .
' ' 8, .1922 ; .0000
2.5317E-02 6.412QE-02
7 -.1043 i .0000
2.4085E-02 4.6887E-02
7 9,6208E-02 .0000
2.4532E-02 3.5003E-02
" 7 8.'3861E-02 ' .0000
2.4465E-02 3.7891Ej-02
7 .1412 '' .0000
2.5667E-02 9.9115E-02
"6 8.6318E-02 . .0000
2.5656E-02 '.1^29
4 .0000 ' 8.7093E-02
5.5273E-02 .1341
5 5.0335E-&2 .4.9881E-02
2.5260E-02' 9".4228E-02
- - 6 5.5674E-02 .0000
2.3665E-02 4.4856E-02 -
7 6.7616E-02 - ' .0000
2.3745E-02 6.6264E-02
8 .1208 .0000
2.4490E-02 5.1541E-02
8 8.8611E-02 .0000
2.5024E-02 3.8497E-02
" 8 7.1042E-i)2' ..0000
2.5059E-02 5.8887E-02
7 4.1630E-02 .0000
2. 5215E-02 5.1238E-0-2
.0000
.0000
. .0000
1.8285E-04
8.1316E-05
.0000
.0000
.0000
.0000
.0000
.0000
.00.00
6.2016E-07
8.7093E-02
'4.9881E-02
1.3179E-07
.0000
.0000
.0000
.0000
.0000
-------
"06/15/1992
.0000 , 6
"06/16/1992
.0000 : 4
"06/16/1992
.0000 ; 2
"06/16/1992
1.0688EH02
"06/16/1992
1.1243E-B2
"06/16/1992
7.3058E-!02
"06/16/1992
.1135 ; 4
"06/16/1992
9.8803E-J02
"06/16/1992
.1482 :
"06/16/1992
.2372
"06/16/1992
9.3549E-XI2
"06/16/1992
.0000 ! 4
"06/16/1992
.0000 : 7
"06/17/1992
.0000 ; 6
"06/17/;1992
.0000 ; 6
"06/17/1992
.0000 ;
"06/17/1992
.0000
"06/17/1992
.0000 ;
"06/17/1992
.0000
"06/17/1992
2.0354E-02
"06/17/1992
.0000 '; 3
" "15:17:15" "SC PCM <5, 5-10, 10-20, 20-30, 30-40, 40-50, >50 and >8,
.4848E-04 .0000 3.2479E-03 .0000 .0000 4.9158E-02
" "15:57:13" "SC PCM <5, 5-30, >30 and >8, <0-3
.7435E-03 .5686 2.5768E-02 2.5232E-02
" "15:57:21" "SC PCM <5, 5-40, >40 and >8, <0.3
.0072E-03 .7917 2.7267E-02 6.5788E-02
" "15:57:28" "SC PCM <8, 8-40, >40 and >8, <0.3
8.2511E-03 .7160. 2.5565E-02 5.4117E-02
" "16:20:15" "SC PCM <10, 10-40, J-40 and <0.3, >8
5.4704E-02 .2171 2.4137E-02 5.6716E-02
" "16:20:42" "SC PCM <15, 15-40, '>40 and <0.3, >8
.1151 .3195 2.3B06E-02 3.7739E-02
" "15:58:04" "SC PCM <10, 10-30, :>30 and >8 , <0.3
.1740E-02 .2284 2.3632E-02 1.8589E-02
" "15:58:14" "SC PCM <10, 10-50, >50 and >8, <0 . 3 '
3.6726E-02 1. 3111E-11 2.4535E-02 2.2019E-02
" "16:37:21" "SC PCM >50, 20-5-0, -t20 and >8, <0 . 3
.1710 1
11 "15:58:39"
.1225
" "15:58:46"
2.4707E-02
11 "15:58:54"
.5497E-04
11 "15:59:39"
.6856E-03
" "14:24:32"
.4848E-04
" "14:25:51"
.4849E-04
" "14:28:12"
.0000
" "14:32:50"
.0000
" "14:33:09"
.0000 7
" "14:33:41"
.0000
" "14:33:51"
.2327
" "14:34:07"
.9432E-02 8
8609E-04
"SC PCM
3545
"SC PCM
.2394
"SC PCM
0000
"SC PCM
7128
"SC PCM
0000
"SC PCM
0000
"SC PCM
2027
"SC PCM
0000
"SC PCM
7411E-03
"SC PCM
0000
"SC PCM
.5564
"SC PCM
5786E-02
2.3804E-02 2
<10, 10-40, >40
2.4533E-02 3
<5, 5-10, 10-20
.0000
<5, 5-10, 10-20
2.2876E-03
<10, 10-40, >40
2.6190E-02 6
<5, 5-10, 10-20
3.2479E-03
<5, 5-10, 10-20
3.2479E-03
<5, 5-10, 10-20
.0000
<5, 5-10, 10-20
.0000 9
<40, >40 and <0
.0000
<40, >40 and <0
.0000 3
<10, 10-40, >40
3.6817E-02
<10, 10-40, >40
2.3356E-02 8
.4382E-02
and AR>=100 or W>=8 (6 categories)
. 6979E-02
<0.3
.0000 .0000
"
, 20-30, 30-40, 40-50, >50 and AR>=150 or ' W>=8 (14 cat.)
.0000 2.3420E-02 8.5612E-02
, 20-30, 30-40, 40-50, >50 and >8,
.0000 ' .0000 .0000
and >8, <0.3 (with AR>=3)
.5721E-02
, 20-30, 30-40, 40-50, >50 and >8,
.0000 .0000 4.9158E-02
, 20-30, 30-40, 40-50, >50 and >8,
.0000 .0000 4.9158E-02
, 20-30, 30-40, 40-50, >50 and >8,
.0000 .0000 .3859
, 20-30, 30-40, 40-50, >50 and >8,
.3260E-02 .0000 .0000
.2, 0.2-0.3, 0.3-0.4, 0.4-2, 2-5
.4238 • • .0000 .0000
.2, 0.2-0.3, 0.3-0.4, 0.4-2, 2-5
.1406E-02 .0000 .8748
and<0.3, >8 (chrysotile)
4.5930E-05
and <0.3, >8 (amphiboles)
. 8092E-02
7.6892E-02 .1257
<0.3 (with AR>=3)
.0000 .0000
<0.3 and AR>=10 "
.0000 .0000
<0.3 and AR>=3
.0000 .0000
<0.3 (chrysotile)
.2964 .0000 '
<0.3 (amphiboles) "
5.5979E-02 .0000
, 5-8, >8 (chrysotile) "
.0000 6.5665E-02
, 5-8, >8 (amphiboles) "
.0000 .0000
"
"
-272
.4088
-275
-272
-272
-272
-272
-274
-274
-275
-274
-272
198
834
871
805
895
886
890
641
523
361
330
8.5612E-02
-272
.2874
-272
-272
.4088
-272
.4088
-199
.0000
-100
.7515
-199
.0000
-100
.0000
-231
-100
197
968
198
198
146
336
147
379
202
344
10.89
.4276
18.83
12.51
12.41
12.21
' 12.13
15.82
15,42
16.03
15.16
11.22
.1054
10.83
.5971
11.90
10.89
.4276
10.89
.4276
8.429
1.1885E-11
1.656
7.0580E-11
8.432
.5028 '
1.697
9.3811E-02
165.7
1.655
7 .1430
2.5808E-02 .1227
10 4.1618E-02
10 .2515
9 .1906
8 .1414 1.
8 .1446 2.
9 7.0146E-02
9 7.9344E-02
8 4.1166E-02
9 8.5993E-02
1 5.8425E-05 5.
2.7771E-02 .1274
8 .2109
2.6318E-02 .1745
9 .2183
7 .14,30
2.5808E-02 .1227
7 .1430
2.5808E-02 .1227
2 1.3952E-02
3.2157E-02 7.5085E-02
1 .1976
2.3542E-02 .1025
3 3.7153E-02
3.2162E-02 2.7220E-02
3 .6374
2.3351E-02 8.8094E-02
3 .0000
3 .6468
0000
0000
0000
0000
5538E-,05
1089E-05
0000
0000
6671
2858
6473E-02
0000
0000
0000
0000
0000
0000
0000
0000
1906
0000
.0000
.0000
• .0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
5.6473E-02
.0000
3.9534E-05
.0000
' .0000
.0000
.0000
.0000
.0000
.0000
.0000
-------
"06/17/19-92
.1034 1
"06/17/19-92
3.7724E-04
"06/17/1992
.0000 1
"06/17/1992
.0000 -j 1
"06/17/1992
.0000
"06/17/1992
."oooe-
"06/17/1992
.1094 3
"06/17/1992
.1094 •' 2
"06/22/1992
9.7750E-04
"06/22/1992
3.3816E-03|
"06/22/1992
.0000
"06/22/1992
6.0850E-04
"06/22/1992
8.4954E-04.
"06/22/1992
.0000
"06/22/19*92
.0000
"06/22/1992
3.9108E-03'
"06/22/1992
.0000
"06/22/1992
8.6521E-03
"06/22/1992
7.0798E-04"
11 "14:34:13
.4351E-02
" ™T4:45::23
.0000
" "15:45:33
.5961E-03
" "15:45:33
.5961E-03
" "15:45:50
.0000
" "15:45:50
.0000
" "15:47:24
.2465E-02
" "15:47:24
.0771E-02 ~
11 "15:33:41
.0000
" "15:35:19
.0000
" "15:36:20
.0000
" "15:37:36
.0000
" "15": 40:55
.0000
" "15: 43-: 58
.0000
" "15:46:08
.0000
" "15:55:24
.0000
" "16:30:35
.0000
" "15:49:00
.1246
" "15:50:08
7.2592E-03
" "SC PCM <10,
.6578 2.
" "SC PCM ,<5,
6.8825E-04
11 "SC PCM <5,
.0000 4.
" "SC PCM <5,
.0000 4.
" "SC PCM <40,
7.8049E-04
" "SC PCM <40,
7.8049E-04
" "SC PCM <10,
8.7587E-02
" "SC PCM <10,
6.6119E-02
" "SC M(14) <5
.0000
" "PS M(14) <5
.0000-
10-40, >40
4091E-02 3.
5-10, • 10-20,
.0000 7
5-1.0, 10-20,
1331E-02
5-10, 10-20,
1331E-02
>40 and <0.
0000 '. 4.
>40 and <0 .
0000 • 4.
10-40, >40
•10-40, >40
and.j>8, <0 . 4
4458E-02
20-J30, 30-40, 40-50,
-1379E-03 .0000
20-pO, 30-40, 40-50,
0000 .0000
20430, 30-40, 40-50,
0000 .00.00
2, JO. 2-0. 3, 0.3-0.4,
1073tE-02 • .0000
2, J0.2-0.3, 0.3-0.4,
1073E-02 .0000
>50 and <0.3
.0000
>50 and >8,
.0000
>50 and >8,
.0000
'.0.4-2, 2-5
.8914
Ol4-2, 2-5
.8914
, >8 (with AR>=10)
.0000 .2738
<0.3 '(2 studies)
.0000 -2. 1416E-05
<0.3 (2 'studies) ..
.0000 -2.1416E-05
, 5-8; >8 (2 'studies)
.0000 4.1637E-02
, 5-s; >8 (2 studies)
.0000 4.1637E-02
anoLK0.3, >8 (2 studies) •
(
and|<0.3, >:8 (2 studies) .
\
, 5-10, 10-20-, 20-30, 30-40, 40-50
.0000
.0000 .0000
, 5-10, 10-20, 2*0-30, 30-40, 40-50
.0000 1
. 6148E-02 .0000
, >50 and <0
-.0000
, >50 and <0
.0000
.3, >8
.0000 .2541
.3, >8
.0000 ' .0000
" "FBC M(14) <5, 5-10, 10-20, J20-30, 30-40, 40-50, >50 and <0.3, >8 •
.0000
" "SC PCM <5,
3.1381E-03
" "SC PCM <5,
2.3397E-03
" "SC PCM <5,
.0000
" "SC PCM <40,
5.5468E-03
" "SC PCM <5,
1.7452E-02
" "SC PCM <40,
1.4347E-02
" "PS PCM <5,
.0000
" "FBC PCM <5,
.0000
8194 2.
5-10, 10-20,
.0000
5-10, 10-20,
.0000
5-10, 10-20,
1688
>40 and <0.
0000
5-10, 10-20,
.0000 "l
>40 and <0.
.0000
5-10, 10-20,
.0000
5-10, 10-20
.7180 2
274QE-02 1.290
20-[30, 30-40, 40-50,
.0000 . .0000
20-J30, 30-40, 40-50,
.0000 .0000
20-^30, 30-40, 40-50,
0000* .0000 ~
2, io.2-0.3, 0.3-0.4,
3469^ .0000
20-[30, 30-40, 40-50,
. 0756E-02.- .0000
2, JO. 2-0. 3, 0.3-0.4,
.0000 .0000 -
20-S30, 30-40, 40-50,
.oodo .0000
, 2q-30, 30-40, 40-50,
-591J6E-02 .3137
>50 and <0.3
.0000
>50 and <0.3
.0000
>50 and <0.3
.2492
0.4-2, 2-5
.0000
>50 and"<0.3
.0000
0.4-2, 2-5
.0000
>50 and <0 . 3
.1227
• >50 and <0.
and 5R>=20, >8
5.6291E^02 .3825
and AR>=30, >8 .
7.1845E-02 .3003
, >8 (chrysotile)
.5106 .0000
, 5-8; >8 (chrysotile)
.0000 .0000
, >8 (chrysotile) (avg)
.0000 • .0000
, 5-8, >8 (chrysotile)
.0000 ' .8395
, >8 '
.4444 - .0000
3, >8
" -273.352
" -275.830
.0000
" -301.025
3.1086E-02
" -301.025
2.1682E-02
" -300.792
.3.2830E-02
" -300.792 •
2.0805E-02
" -300.139 '•
" . -300.139
•"• -277.733,
.7450
" -277,324
.9455
" .-282:927
" -272.197
.0000 '
" -272.256
.0000
" -174'. 591
.0000
" -174.5.91
.0000
" -126.207
. .0000 .
(avg) " ' -126.
.1459
" -272.711
.0000
" -275'. 871
13.00
18.52
-1.0060E-10 2
12.62
1.8208E-02
12.62
2.9493E-02
12.06
.1416
12.06
8.7989E-0'2
11.32
11.32
. 20.96
1.9854E-10 2
19. 3' 7 .
3.4942E-02 2
31.48
. 10.92
9.9690E-02. 2
11.12
5.9915E-02 2
3.392
7.1357E-02 3.
3.395
.3974 3.
1.2755E-15
.9679 ' 2
9 .161.9
7' 9.0747E-Q3
.5949E-02 .3M2 •
8 '.12-49 .
•
8 .1249' .!
1 !
6 5.9787E-02 2.
-
6 5.9787E-02- 2.
...
8 .1837 :
8 .1837 |
•
!
9 1.2108E-Q2
.1741E-02 .4278
9 2.1452E-ID2
-2094E-02 .3327
9 .0000 | 2.
5 5.2268E-02 • 1.
.5851E-02 .1301
5 4'. 830lE-b2 8.
.5516E-02 .1679-
2 .1827 '
0137E-0^ 9.0414E-02
. ' 2 ' .1825 j • .
0140E-02 3.8470E-02
0 .0000
0000
0000
0000
0000
6205E-05
6205E-05
0000
O'OOO
0000 .
0000 -
0191E-06
•7829E-05
7621E-05
0000. -
0000 -
oooo •'
.0000
.0000
•*
'.0000
.0000
.0000
...ooop
.0000
.0000
.0000 .
.0000
2.8155E-04
1.3.304E-09--
1.3116E-06.
.0000
.0000
.0000 .
..6668E-02 2.1269E-02
.207 2.9177E-13 -1 .0000
^1.9596E-10 2
12.25
-.2996' 2
18.62
.0000
' 2.8851E-04
.6668E-02 -1.3955E-02
8 .1395
0000
.0000
.5616E-02 3.4242E-02
8 1.6231E-02
0000
3.7774E-04
"06/23/1992" "14:32:26" "SC PCM <20, >20 (chrysotile only - averaged K013) . .'.' -127.858 - 3.449 3 ".3270. ; 1 000
2-.D218E-03' i ' . . . ! ' '
"06/23/1992" "15:20:51" "SC PCM <5,5-10,10T20,20-30,130-40,40-50,>50 with AR>=100 or W>=8 (14 CAT.)(no discharged)" -247.715 6.231 5 i .2839
5.3902E-02 .0000 .2203 .0000 .JOOOO .0000 .0000 12507 .0000 ' .0000 6.1001E-02 2.4264E-02 1.1097
•- 2.9297E-02
.1653 '.1653'
o
-------
"06/23/1992" "15:21:06" "SC PCM <5, 5-10, lo420, 20-30, 30-40, 40-50, >50 with <0.3, >8 (no discharged) "
7.7664E-04 .0000 3.3297E-03 .0000 j 1.1683E-02 .0000 .0000 .1023
"06/23/1992" "15:21:59" "SC PCM <5, 5-10, 10^20, 20-30, 30-40, 40-50, >50 with <0.4, >8 (no discharged)
9.2037E-04 .0000 5.3676E-03 .0000 j 2.3489E-02 .0000 • .0000 .2528
"06/23/1992" "15:22:39" "SC PCM <5, 5-10, 10^20, 20-30, 30-40, 40-50, >50 with <0.5, >8 (no discharged)
•000° ; .0000 4.4608E-03 .0000 j 7.6930E-02 .0000 3.6633E-02 .4443
"06/23/1992" "15:43:52" "SC PCM <5, 5-10, 10-2CJ, 20-30, 30-40, 40-50, >50 with <0.5 and AR>=3,>8(no discharged)"
•0000 ; .0000 4.4608E-03 .0000 j 7.6930E-02 .0000 3.6633E-02 .4443 .0000
"06/23/1992" "15:50:53" "SC PCM <5,5-10,10-20,20-30,30-40,40-50,>50 with >8,<0.5and AR>*10(no discharged)
.0000 : .0000 .0000 .0000 I .0000 .1014 .4356 6.'
"06/23/1992" "15:45:06" "SC PCM <5,5-10,10-20.20-30,30-40,40-50,>50 with <0.Sand AR>=20,>8(no discharged)
.0000 | .0000 .0000 .0.000 | .1019 .0000 .0000 .4509
"06/23/1992" "16:04:30" "SC PCM <20, >20 with <0.3, >8 (no discharged)
.6923 | 2.1630E-02 1.6030E-02 i
"06/23/1992" "16:04:37" "SC PCM <40, >40 with1 <0.3, >8 (no discharged)
.1952 2.8847E-02 7.4959E-02 ' j
"06/23/1992" "16:04:49" "SC PCM <40, >40 with1 <0.3, >5 (no discharged)
.1310 ! 2.4517E-02 7.8915E-02 ;
"06/23/1992" "16:39:42" "SC PCM >10 (all with AR >= 3) (no WDC chrysotile or tremolite)
4.4491E-03
"06/23/1992" "16:39:44" "SC PCM <10, 10-20, ^20 (all with AR >= 3) (no WDC chrysotile or
2.5926E-t)2 3.8301E-04
"06/23/1992" "16:39:46" "PS PCM >10 (all with' AR >= 3) (no WDC chrysotile or tremolite)
4.6048E-03
"06/23/1992" "16:40:17" "PS PCM <10, 10-20, >20 (all with AR >= 3) (no WDC chrysotile or-
2.6163E-02 4.7643E-04 !
"06/23/1992" "16:39:49" "SC(no C,CS,M,or MS) PCM >10 (all with AR >= 3) (no WDC chrysotile or tremolite) "
1.1936E-t>3 ' • !
"06/23/1992" "16:39:51" "SC(no C,CS,M,or MS) JPCM <10,10-20,>20 (all with AR >= 3) (no WDC or
•2.6733E-02 '4.2818E-04 j
"06/24/1992" "14:59:32" "SC PCM >10 (all with1 AR>=3 and W>=0.2) (no tremolite or WDC chrysotile)
1.0459E-02
"06/24/1992" "14:59:34" "SC PCM <10,10-20, >20'(all with AR>=3 and W>=0.2) (no tremolite or WDC chrysotile)
2.5513E-02 4.4249E-04 . '
"06/24/1992" "14:59:36" "FBC PCM >10 (all'witjh AR>=3 and W>=0.2) (no tremolite or WDC chrysotile)
1.0404E-02
"06/24/1992" "14:59:38" "FBC PCM <10,10-20, >2|o (all with AR>=3 and W>=0.2) (no tremolite or WDC chrysotile)"
2.6426E-02 4.8211E-04 !
"06/24/1992" "14:59:41" "SC PCM <5, 5-40, >4o', with W <0.5, >5
4.6480E-03 .0000 .8946 2.9880E-02| 1.5430E-02
"06/30/1992" "10:00:07" "SC PCM 5-40, >40, w.ith W >5, <0.3 .
.8530 2.5612E-02 7.0424E-02 |
"06/24/1992" "14:59:58" "SC PCM 5-40, >40, with W<0.5, >5
no discharged) "
.3292
no discharged) "
.5300
no discharged) "
.0000
8 (no discharged) "
.0000
0 (no discharged) "
-03 .0000
8 (no discharged) "
.0000
"
'
"
tremolite) "
"
tremolite} "
e or tremolite) "
or tremolite) "
sotile)
WDC chrysotile) "
ysotile) "
WDC chrysotile) "
"
"
"
-247.
.0000
-247.
.0000
-248.
.0000
-248.
.0000
-248.
.0000
-248.
.0000
-250.
-251.
-250.
-220.
-219.
-220.
-220.
-220.
-220.
-219.
-219.
-220.
-220.
-279.
-272.
-279'.
916
977
237
237 -
223
-3
139
437
384
175
082
772
532
468
925
428
932
402
855
072
469
935
469
6.
.1214
6.
.1875
6.
4377
6.
4377
6.
347
5
2733
0000
.0000
2.3640E-02 .1100
302
6
3897
0000
.0000
2.3356E-02 5.1364E-02
697
7
2.2722E-02
697
7
2.2722E-02
690
7956E-06
6.
4468
9.
12
9.
16
16
17
17
. 18
17
16
15
18
16
27
12
27
630
6
2.2698E-02
8
2.2774E-02
979
.81
585
.49
.07
.26
.36
.92
.89
.02
.14
.70
.90
.19
.19
.19
9
8
8
9
9
9
9
9
9
9
9
9
8
10
10
10
5.
6.
4.
4.
2.
3.
6.
8.
2.
3.
1.
1.
4607
3.3184E-02
4607
3.3184E-02
3499
3.2980E-02
5767 4.
3.3466E-02
3517 3.
1178 9.
2948 7.
6634E-02
4832E-02
4107E-02
2634E-02 . .
5190E-02
5777E-02
5704E-02
6504E-02
7090E-02
0312E-02 1.
5875E-03
2716
5875E-03 4.
0000
0000
0000
1766E-04
1512E-04
4338E-05
0228E-05
9283
0000
9300
0000
6933
0000
9655
0000
9594
9053E-03
0000
0000
6480E-03
.0000
.0000
1.0298E-04
.0000
.0000
3.9873E-03
1.5561E-02
2.5391E-02
3.9618E-12
2.5829E-02
.1894
2.6422E-02
5.4710E-11
2.5115E-02
2.4662E-11
2.6243E-02
1.9158E-12
.0000
1.7176E-03
.0000
-------
.8946 '2.9880E-02 1.5430E-02 1 ' , '
"06/24/1992" "15:00:08" "SC PCM 5-40, >40, with W -S0.3, >5 (and all structures AR>=3) :
.1517 ,.2.5388E-02 7.8347E-02 {. " ' '..'..
"06/24/1992" "15:00':17"'"SC PCM 5-40, >40, with W <'0.3, >5 . (and all structures AR>=5) i '."
.1902 2.5505E-02 8.6887E-02 ' i .'';-"
"06/30/1992" "10:00:12" "SC PCM 5-40, >40, with W >J5, <0. 3 and AB>=3 > !
.8530 2.5612E-02 7.0424E-02 . j. ' ' . ' : ;
"06/30/1992" "10:00':17" "SC PCM .5-40, >40, with W >|s, <0.3 and AB>=5 '
.8530; 2.5612E-02 7.0424E-02 j '' . ; . ; '
"06/24/1992" "15:00:35" "SC PCM( 5-40, >40, with W <;0.5 and AR>=5, '>5' : ' ' ' : /
.8946 2.9880E-02 1.5430E-02 j , , ' ' • ' .
"06/24/1992" "15:00:46" "SC PCM 5-40, >40, with W =10, >5 •
.8946 2.9880E-02 1.5430E-02 j . ;
"06/24/19,92'' "15:00':56" "SC PCM 5-40,. >40, with W <0.5 and AR>=20, >5 ; '
.9347 2.8885E-02 1.5186E-02 ! ' - ' '
".06/24/1952" "15:01:00" "SC PCM .5-40, >40, with W <{0 .5 and AR>=30, >5 . : ' -
.9274 ';2.7747E-02 1.5152E-02 } .'' i .
"06/2'4/1992" "15:01:03" "SC PCM <5, 5-10, 10-20, 20-^30, 30-40, 40-50, >50 .(no discharged chrysotile)
2.3031E-02:; 9.2428E-02 4.8158E-02 .8364' 2.459J4E-02 7.7840E-03 " ' ' : ' '
"06/2'4/1992" "15:01:13" "SC PCM 5-40, >40, with W 5 (and all structures AR>=3) (no: discharged)
.2102 2.4254E-02 7.1338E-02 I . '.-'-.
"06/24/1992" "15:01:18" "SC PCM 5-40, >40, with W <|0.5'and AR>=20, >5 (no discharged cfirysotile)
.9926 :2.6968E-02 1.6049E-02 1 ; '. . '•
"06/25/1992" "10:28:00" "SC M(16) 5-40, >40 and <0.3|, >5 ' • !
5.5987E-02 2.9951E-02 .2335 \ . '• •
"06/25/1992" "10:28:07" "SC M(14) 5-40, >40 and<0.3, >5
7.1229E-02' 2.3175E-02 .2276 j .
"06/2'5/19'92" "10:28:13" "SCM(16) 5-40, >40 and <0.3;, >5 (no discharged ;chrysotile) • •' '
.1014 2.6934E-02 .2021 j :
"06/25/1992" "10:28:17" "SC M(14) 5-40, >40 and'<0.3;, >5 (no discharged rchrysotile) ;
.1164 2.1490E-02 .1998 I . ' •
"06/25/19'92" "10:28:21" "SC PCM <5, 5-10, 10-20, 20-130, 30-40, ' 40-50, >50, and <0.3, >5
4.2994E-04 .0000 1.7850E-03 .0000 .OOo'o •'. .0000 '.0000 3.3391E-02 .2812
"06/25/1992" i110:30::25"""SC PCM 5-40, >40 and 0.1-O.J3, >5 -1 :
.1407 ^2.5473E-02 7.0764E-02 1 , ' .
"06/25/1992" "10:30:30" "SC PCM 5-40, >40 and 0.2-OJ3, >5 , ' :
.;1429 »2.5396E-02 6.8760E-02 • ' I "
"06/25/1992'''"10:53:03" "SC PCM 5-40, >40 with >5, '<0.3 and AR>=10
.8530 '2.5612E-02 7.0424E-02 ] :
".06/25/1992" "10:30.: 41" "SC PCM 5-40, >40 with <0.3 iand AR>=20, >5 }'••'-'•'•
.1478 2.5544E-02 6.9820E-02 j ' ' ;
"06/25/1992" "10:30:47" "SC PCM 5-40, >40 with <0.4 land AR>=5, >5 . • :'
.1005- .; 2.56.52E-02 4.2275E-02 j ' •,.
-273.
-274.
-272.
-272.
-279.
-279.
-278.
-277.
-255.
-248.
-253.
-320.
-286.
-294.
-260.
-272.
.0000
-272.
-272.
-272.
-272.
-274.
227
576
935
935
469
4.69
500
782
492
899
154
420
209
603
648
188
938
985
935
902
247
12
15
12
12
. 27
27
24
22
20
8.
18
65
40
56
32
..61
.49
'.19
.19
.19
.19 •>
.89
.81
.01
027
.13
.65
.07
.81
.11
10.78
4.0372E-02
12.17
12
12
-12
14
.21
.19
.14
.82
10
10
10
10
10
10
10
10
8'
' 9
10
12
10
11
9
.2459
.1144 i .
.2716 i
.2716
1.5875E-03 .
1.5875E-03
4.7281E-03
. 1.0646E-02
9.4712E-03
'' .5311 !
5.2069E-02
.0000
.0000
.0000 j '
-.0000.
7 .1480
2.57B3E-02 .1943
10 .2734
10
10
10
9
.2706
.2716
.2751
9.-5423E-02
1
1
4
4
6
1
1
7
1
1
1
1
1
3
1
2
.4845E-03
.4548E-03
.0000
.0000
.6480E-03
.6480E-03
.8172E-03
.2458E-02.
.0000.
. 7527E-03
.4254E-03
.2664E-03
. 1853E-03
."6400E-03
.4970E-03
.0000
. 8856E-03
. 4336E-03
.0000
.8812E-03
.2016E-03
. .0000
.0000
1.7176E-03
1.7176E-03
.0000'
-.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
1.7176E-03
.0000
1.5835E-02
-------
1
7
7
2
7
7
r" :
"06/25/1992
1296 ' 2
"06/25/1992
1140 i
"06/25/1992
. 5134E-02
"06/25/1992
9573 < 2
"06/25/1992
1139
"06/25/1992
1770 ! 2
"06/25/1992
9285 : 2
"06/25/1992
1133 i
"06/25/1992
1332 ; 3
"06/25/1992
8193 : 3
"06/26/1992
9799 : 3
"-06/26/1992
9779 ! 3
"06/26/1992
9929 : 2
"06/26/1992
0000 :
"06/26/1992
1453 . i 2
"06/26/1992
.8526E4o2
"06/26/1992
-2764E-02
"06/26/1992
.7287E4,02
"06/26/1992
1014 \ 2
"06/26/1992
.7677E^02
"06/26/1992
.2156E-02
"06/26/1992
" "10:30:
.5808E-02
11 "10:31:
" "10:31:
" "10:53:
.7270E-02
" "10:32:
" "10:32:
.4251E-02
" "10:32:
.4971E-02
" "10:32:
" "10:33:
.9379E-02
" "10:53:
.7799E-02
" "15:45:
.5380E-02
11 "15:45:
.4927E-02
" "15:45:
.7947E-02
11 "15:45:
.8530
" "15:46:
.5612E--02
" "15:46:
" "1-5:46:
" "15:46:
3.2171E-0
" "15:46:
.4772E-02
" "15:46:
" "15:46:
" "15:46:
58"
4
15"
18"
"SC PCM 5-40, ->40 wiph <0.4 and AR>=20, • >5 ' " -274.223
0611E-02
"SC PCM 5-40, >40 with <0.3 , " -277.690
"SC PCM 5-4-0, >40 with >5 - -299.923
08" "PS PCM 5-40, >40 and <0.3, >5 " -276.320
1
46"
49"
6
56"
1
58"'
01"
6
11"
6
4.2"
4
47"
4
49"
1
52"
03"
7
08"
08"
20"
2
30"
8
32"
32"
42"
2536E-02
"FBC PCM 5-40, >40 akd <0.3 (there are no fibers >5) ' ' " -277.767
"SC PCM 5-40, >40 an
5809E-02
1 <0.3, >5 (no discharged chrysotile) " -248 966
-
"PS PCM 5-40, >40 and <0.3, >5 (no discharged chrysotile) • " -252.063
2388E-02
"FBC PCM 5-40, >40 and <0 . 3 (there are no fibers >5) (no discharged chrysotile) " -255.100
"SC PCM(16) 5-40, >40 and <0.3, >5 " -309.395
9539E-02
"SC PCM(16) 5-40, >40 with >5, <0 . 3 (no discharged chrysotile) " -285.380
2884E-02
"SC PCM <5, >5 with W <0.3, >5 ' -294.370
1829E-03
"SC'PCM <5, >5 with W <0.3 and AR>=20, >5 " -294.170
1831E-03
"SC PCM 5-40, >40 with AR>=20, >5 (4 categories) " -277.958
4448E-02
"SC PCM 5-40, >40 wi
1453 2.5612E-02
:h <0.3, 0.3-5, >5 . ' • . " -272.935
7.0424E-02
"SC PCM 5-40, >40 with <0.3, >5 (but exclude all- M and MS) " -272.935
0424E-02
"SC PCM 5-40, >40 with <0.3, >5 (2 studies) • . " -300.312
"SC PCM 5-40, >40 wi
:h <0.3, >5 (2 studies) " -300.312
"SC PCM 5-40, >40 wifh <0.3, >5 (chrysostile only) - " -199.150
.1319
"SC PCM 5-40, >40 with <0.3, >5 (amphiboles only) - " -100.725
5164E-02
"SC PCM 5-40, >40 with <0.3 and AR>=20, >5 (2 studies) " -300.286
"SC PCM 5-40, >40 with <0 . 3 and AR>=20, >5 (2 studies) " -300.286
"SC PCM 5-40, >40 wi
;h <0.3 and AR>=20, >5 (chrysotile only) " -199.150
14.82 9 9.5218E-02 2.8966E-03
22.79 11 1.8207E-02 .9986
81.71 11 .0000 .7287
19.51 10 3.3427E-02 2.1545E-02
22.97 11 1.7084E-02 .9986
8.273 - 8 .4067 1.9186E-03
14.90 9 9.3166E-02 1.7562E-02
21.82 10 1.5245E-02 .9985
49.04 12 .0000 1.7750E-03
45.29 11 .0000 .0000
61.95 11 .0000 .0000
61.28 11 .0000 - ' .0000
23.15 11 1.6070E-02 7.1418E-03
12.19 10 .2716 . 1.7176E-03
12.19 10 .2716 1.7176E-03
11.46 9 .2451 .1256
11.46 . 9 .2451 .1256
8.438 3 3.7044E-02 7.3631E-04
- 2.344 3 -.5038 2.2569E-04
11.41 9 .2481-- .1283
11.41 9 .2481 .1283
8.438 3 3.7048E-02 8.5321E-04
1.3154E-02
2.8284E-02
5.5902E-02
2 .1116E-02
2 .8292E-02
4 .6766E-03
5.3984E-02
2.7767E-02
.0000
2.1511E-03
.0000
.0000
.0000
.0000
.0000
3.3596E-02
2.1933E-02
1.5232E-02
.0000
3.3499E-02
2.1909E-02
l.-4942E^-02
-------
<0
<0
3.3975E-02 3.2170E-02 ., .1252
"06/26/199'2" '"15:46:50" "SC'PCM 5-40, >40 with <0 .3
.1018 _ 2.4718E-02 8.4964E-02
"06/26/1992" "17:18:46" "SC PCM 5-40, >40 with >5 (C
8.6976E-02
"06/26/1992" "17:18:46" "SC PCM 5-40, >40 with >5 (C
7.5613E-02
"06/26/19'92" "17:19:12" "SC PCM 5-40, >40 with >5 (C
.9546' 3.2171E-02 .1304
"06/26/19*92" "17:19:48" "SC PCM 5-40, >40 with >5 (C
.8983" 2.4743E-02 8.5096E-02
"06/26/19'92" "17:19:51" "SC PCM 5-40, >40 with >5,
9.5056E-02
"06/2671992" "17:19:51" "SC PCM 5-40, >40 with >5,
7.3280E-02
"06/26/19'92" "17:20:27" "SC PCM 5-40, >40 with >5, <
.7876^' : 3.0145E-02 6.0929E-02
"06/26A992" "15:47:32" "SC PCM 5-40, >40 with <0.3
9.3412E-02
"06/26/1992" "15:47:32" "SC PCM 5-40, >40 -with <0.3
7.2605E-02
•"06/26/19"92" "15:47:47" "SC PCM 5-40, >40 with <0.3
.2255 l 3.0158E-02 5.1641E-02
"06/26/19'92" "15:48:06" "SC PCM 5-40, >40 with AR>=:
1.3384E-02
"06/26/1992" "15:48:06" "SC PCM 5-40, >40 with AB>=:
1.9317E-02
"06/26/1992" "15:48:13" "SC PCM 5-40, >40 with AR>=:
2.1254'E-ll 3.0232E-02 1.2891E-02
"06/26/1992" "15:48:16" "SC PCM 5-40, >40 with AR>
.9863- ' 1.9872E-02 1.5860E-02
"06/26/19'92" "15:48:17" "SC PCM 5-40, >40 with AR>=;
.9919 2.0367E-02 1.3821E-02
"06/26/1992" "15:48:19" "SC PCM 5-40, >40 with AR>=5
.9915' ' 2.0225E-02 1.3745E-02
"06/26/1992" "15:48:21" "SC PCM 5-40, >40 with AR>=
.9908 2.0025E-02 1.43S4E-02
"06/26/1992" "15:48:23" "SC PCM 5-40, >40 with AR>
.9770- '2.0138E-02 1.6260E-02
"06/26/1992" "1'5:48':24" "SC PCM 5-40, >40 with AR>=
.9403' 2.0684E-02 1.7099E-02
"06/26/1992" "15:48:26" "SC PCM <5, 5-40, >40 with
1.91i86E-03'- :4.6766E-03 .1770 2.4251E-02 6.
"06/26/1992" "15:4'8':36" "SC PCM 5-40, >40 with <0.3,
and AR>=20, >5 (amphiboies only) '
and CS'only), <0.3 (FBC only) (2 studies)
and CS only), <0.3:(FBC only) (2 studies) •
and CS only), <0.3i(FBC only) (chrysotile only)
and CS .only), <0.3 (FBC only)- (amphiboies only) '
.3 (no discharged) (2 studies) • '
.3 (no 'discharged) ; (2 studies) *
0.3 {chrysotile only - no discharged) '
and AR>=20, >5 (no discharged) (2 studies) '
and AR>=20, >5 (no discharged) (2 studies) . '
and AR>=20, >5 (chrysotile only - no discharged) '
0, >5 (4 categories) (no discharged) -(2 studies) '
, >5 (4 categories), (no discharged) (-2 studies) '
, >5 (4 categories): (chrysotile • only - no discharged)'
, >5 (4- categories) (amphiboies only) '
, >5- (4 categories) (amphiboies only) '
, >5 (4 categories) (amphiboies only) '
, >5 (4 categories)1 (amphiboies only)
, >5 (4 ' categories): (amphiboies only); '
>5 (4 categories) (amphiboies only) '
^20
10
•30
-50
<0
.3, >5 (no discharged chrysotile}
E-02 - ' "
0.3-5, >5 (no discharged chrysotile)
-100'.
-300.
-300.
-199.
-100.
-276.
-276.
-174.
-276.
-276.
-174.
-279.
-279.
-174.,
-103.
-105.
-105.
-104'.
-103.
-103.
-248.
--248-.
.723
.289
.289
.150 "
.724
.253
.253
.593 •
.253
.253
.593
.340
.340
.600
.580'
.610
.251
.569.
.778
.554
.966
.966
2.337
' 11.34
11.34
8.438
2.341
7.248
7.248
3.399
7.262
7.262
' 3.397
13.11
13,11-
3.396
6.615
10.10
9.464 ,
8.284
6.865
6.499
8.273
8.273.
3 .5051 '
8 .1824
8 .1824
3 3.7044E-02
3 .5043
7 .4031 I
7 • '.4031 j;
2 .1821
•1 .4017 ;
7 .4017
2 .1823
9 .1571
9 .1571 ; '
2 .1823 ;
4 .1569 ;
4 3-7986E-02
4 4.9692E-02
1- 8.0934E-02
4 .1425
4' .1641 \
8 .4067
8 .4067
2 . 6046E-04
./8903
.8903
1.5135E-02
.0000
.8852
.8852
3.4305E-02
.1027
M027
2.0719E-03
.9916
.9916
1.1242E-03
1.3691E-02
8.0680E-03
8.4672E-03
9.2017E-03
2.3034E-02
5.9731E-02
.0000
1.9186E-03
.0000 •
3.3130E-02
2.17B4E-02
7.5254E-04
2.3382E-04
3.1198E-02
2.1220E-02
1.5901E-03
3.1189E-02
2.1218E-02
3.6781E-02
3.1665E-02
2.1102E-02.
.3689'
.0000
.0000
.000.0
..0000
• ..ooo'o
.0000
' .0000'
.0000
-------
4.6766E-03 .8164 .1770 2.4251E-02 6.5809E-02
"06/26/1992" "18:00:06" "SC PCMQ >10 (all structures AR>=3,W>0.2) (no WDC chrysotlle or tremollte)
1.0063E-02 |
"06/26/1992" "18:10:08" "SC PCMQ <10,10-20,>20 (all structures AR>=3,W>0.2) (no WDC chrysotile/tremolite)"
2.3184E-02 4.3772E-04 |
"06/30/1992" "10:44:26" "PS PCM, USING SUM oi1 SURFACE AREA INSTEAD OF THE SUM OF THE NUMBER OF STRUCTURES"
1.0475EH06 " ' I
"06/30/1992" "10:44:27" "PS PCM, USING SUM OF VOLUME INSTEAD OF THE SUM OF THE NUMBER OF STRUCTURES
1.0362E-I06 j
"07/01/1992" "15:49;07" "SC PCM 5-40, >50 ani W >5, <0.3 (all structures AR>=20) (no discharged)
.3941 2.7504E-02 .2366 !
"07/02/1992" "11:56:05" "SC PCM <5, 5-10, 10J20, 20-30, 30-40, >=40 and W <0.3r >=5
1.1629E-03 .0000 5.3055E-03 .0000 j .0000 .0000 .0000 6.1057E-02 .1258
"07/02/1992" "11:57:25" "SC PCM <5, 5-10, 10J20, 20-30, 30-40, >=40 and W <0.3, >=5 (no discharged)
"9.5393E-04 .0000 6.7145E-03 1.9149E-02 .0000 .0000 .0000 8.2033E-02 .1382
"07/02/1992" "13:14:24" "SC PCM >=5 and W <0i3, >=5 (no discharged chrysotile)
5.0646E-03 j .
"07/02/1992" "13:14:26" "SC PCM 5-40, >=40 (rjo discharged chrysotile)
5.4757EH03 . j .
"07/02/1992"'"13:14:29" "SC PCM >=5 (no discharged chrysotile)
7.0975E^03
"07/02/1992" "13:14:31" "SC PCM 5-40 and WJ<0.3, >=5 (no discharged chrysotile)
4.4280E-03 j
"07/02/1992" "13:14:33" "SC PCM 5-40 (no discharged chrysotile)
2.6253E-03 . . ' |
"07/02/1992" "13:14:50" "SC PCM <5, 5-40, >=40 and W <0.3, >=5 (no discharged chrysotile)
1.9186E-03 4.6766E-03 .1770 2.4251E-02 6.5809E-02
"07/02/1992" "13:15:02" "SC PCM <5, 5-40, >=40 (no discharged chrysotile)
2.8947EH02 5.4757E-03
"07/02/1992" "13:14:35" "SC PCM <5, >=5 andjW <0.3, >=5 (no discharged chrysotile)
.9815 3.0991E-02 5.0646E-03 !
"07/02/1992" "13:14:39" "SC PCM <5 and W <0.3 (there are no fibers W >= 5) (no discharged chrysotile)
2.0883E-05 j
"07/02/1992" "13:14:41" "SC PCM <5, >=5 (no discharged chrysotile) • "
8.8654E-^05 . |
"07/02/1992" "13:14:43" "SC PCM <5 (no discharged chrysotile)
•1.6960E-05
"07/02/1992" "13:14:45" "SC PCM 5-40, >=40 and W >=5 (no discharged chrysotile)
1.6962E-02
"07/02/1992" "13:14:46" "SC PCM 5-40, >=40 and W <0.3 (no discharged chrysotile)
.1069 ; !
"07/02/1992" "14:50:19" "SC PCM <5,5-10,10-20,20-40,>=40 and <0.15,0.15-0.3,0.3-1,1-5,>=5 (no discharged)"
-223.907
-221.562
-285.826.
-291.704
-253.886
-272.586
2.4594E-02
-248.218
2.2930E-02
-267.012
-261.069
-285.450
-283.414
-286.783
-248.966
-261.069
-267.012
-301.986
-285.450
"-301.288
-267.213
-260.510
-248.178
24.09
19.60
39.16
55.77
19.50
11.47
6.9386E-02
6.779
6.7034E-02
49.63
31.75
103.2
99.33
107.2
8.273
31.75
49. 63
132.5
103.2
132.0
60.09
33.58
6.674
9
8
11
11
9
8
6
10
10
10
10
10
8
10
10
'10
11
10
10
10
6
3.4107E-03
1.1142E-02
.0000
.0000
2.0509E-02
.1757
.3412
.0000
.0000
.0000
.0000
.0000
.4067
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.3515
.9521
.1793
.5002
.5002
.0000
.0000
.0000
.9815
.9907
.9875
.9753
.9671
.0000
.0000
.0000
.6775
1.000
.6657
.9371
.998-2
.0000
'2
3.
2 ,
4.
7.
3.
2.
5.
5.
6.
5
5
3
. 3551E-02
. 3351E-13
.9496E-02
.7021E-02
. 6482E-04
.0000
.0000'
.0991E-02
.8947E-02
.7855E-02
.5205E-02
.2602E-02
.0000
.9907
.0000
.1194
.7855E-02
.1169
.1633E-02
.3808E-02
.0000
-------
.0000
.0000 '
"07/02/1992
/OOOO '
.0000
"07/02/1992
.0000 _
.0000 '
"07/02/1992
.0000
.0000
"07/02/1992
.0000
.0000
"07/02/1992
.0000 "
.0000
"07/02/1992
.0000
.0000
"07/06/1952
5.7162E-02
"07/ti6/19'92
1.3791'E-05
"07/06/1992
2.5174E-03,
"07/06/1992
1.3791-E-05
"07/06/1992
2.5174E-03
"07/06/1952
.0000
.0000
"07/06/1992
.0000
.0000
"07/06/1992
.0000
.0000
"07/07/1992
.0000
.0000
•I
.0000
.0000
.0000
6.0103E-02
.0000 2.5839^-03
.0000- .5370J
.0000
.0000
" "15:42:07" "SCM(14) <5, 5-10, 10-20, 20-40, >=40 and <0.15
.0000
.0000
" "15:
.0000
.0000
" "16:
.0000
.0000
" "16:
.0000
-.0000
11 "17:
.0000
.1431
" "17;
.0000
.1431
" "09:
" "10:
" "10:
11 "11:
11 "11:
" "11:
.0000
.0000
" "13:
.0000
.0000
" "15:
.0000
.0000
" "13:
.0000
.0000
.0000
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: 43:31" "SC PCM
.0000
1.7003E-02
1.5838E-02 ' .0000
.0000 .8857J
<5, 5-10, 10-20, 20-40,;>=40
.0000 1.817$E-03
.0000 .7188
:13:3'3" "SC M(14) <5, 5-10, 10-20, 20-40
.0000
.0000
:30:29" "SC PCM
.0000
' .1020
:'20:59" "SC PCM
1.2014E-03
2.4572E-02
:18:45" "SC PCM
1.2014E-03
2.4572E-02
:32:37" "SC PCM
:57:33" "SC PCM
:57:42". -"SC PCM
: 18:57" "SC PCM
:19:00" "SC PCM
:33:31" "PS PCM
.0000
7.3795E-02
.0000 .0000
7.3297E-02 .0000
<5, 5-10, 10-20', 20-J40,
.0000' - .0000
.1269 .0000
<5, 5-10, 10-20, 20-J40,
.0000 .0000
6.7829E-02 !
<5, 5-10, 10-20, 20-^40,
.0000 . .oooq
6.7829E-02 • 1 {
>=40 and W <0.3, >=5
I
.0000
2.5155E-02
and <0.15,0
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7.3297E-02 2
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.0000
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, >=40 and >=5, -1-5, 0.3-1,
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.0000
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-1.2727E-09 2
, 1^5, 0.3-1, 0
1.8178E-03
-1.9608E-09 2
3, 0.3-1, 1-5,
5J4344E-03
3/:Q.3-l, 1-5,
5.4344E-03
.0000
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0.1S-0:3
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.0000
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>=5
.0000
>=5 (with
.0000
. 3.-3464E-02
7.7920E-02
,' <0.1.5
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<0.15
.0000
7.7920E-02
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CI) - .
.0000
(no length or width categories)
;
length >= 5 !
(no length or width categories) '
!
length >- 5 1
1
i
<5, 5-10, 10-20, 20-40,
5.4514-E-03 . OOOO!
.0000 ..0000
>=40 and W
.0000
.0000
:44:41" "PS M(14) <5, 5-10, 10-20, J20-40, >=40 and
.0000
.0000
:20:12" "SC PCM
.0000
1.2887E-02
28:40" -"SC PCM
.0000
.1038
7.'9976E-02 .0000
.0000^ .0000*
<5, 5-10, 10-20, 20'-40,
.0000 1.7628JE-03
.'0000 .7253;
<5, 5-10, 10-20, 20-140,
.0000 .0000:
4.4982E-02 .OOOOJ
I
.0000
.2160
>=40 and W
.0000
.0000
<0.15, 0.15-0.3
.0000
.4188 2
W =40 arid Complex, >-l, 0.3-
.0000
6.8896E-03
l.!2681E-03.
-l.:8720E-10 2
, 0.3-1,
.0000
.3729E-02
.3, Q. 3-1
.0000
.1931E-02
0 3-1 - 1-
.0000
.4639E-02
1,' OJ15-0
.0000
.3811E-02
1-5, >=5
.4190
2.3807E-02
, 1-5, >=5
.3.587
8.7607E-02
S >-5
3.8041E-02
7.9469E-Q2
.3, <0.15
.0000
'9.7120E-.02
-.0000
V -279.614
' .0000
""' -272.616
. .0000 .
" -279.614
.0000 . .
> -272.616
; -.0000
!' -272.680
,: .0000
" -272.680
"• .0000
." -310.173
" -322.668
V -309^474
'!• -322.668
" '-309.474
" -273.275
3.9527E-02
'V -275.607
5.1851E-03
" -272.609
.0000
" -274.051
' 7.6933E-03
.0000
23.57-
.0000
11.60
.0000
23.57 •
.0000
11.60
.0000
11.64
.0000
11.64
.0000
118.6
131.4
' 107.2
131.4
' 107.2
12.67
.0000
15.73
.0000
11.58
.0000
14.09
.0000
.-0000 .0000
9 4.2818E-03
.0000' .0000
7 - .1137
\0000 .0000
•.
8 1.8615E-03
.0000 .0000
' 6 7.0614E-02
.0000 -3.34fe4E.-02
8 .1674
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8 ' .1674 | . ' .
.0000 .0000
11 .0000 :.
:
- 11 .0000
11 ,_..oooo - .
11 .0000- !
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7 7.9868E-Q2
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- 8 4.5597E-i)2
.0000 .0000
•j
7 .1146
.0000 .0000
!
"6 2.7817E-02
.0.000 • .0000
2.5171E-02 .0000
0000. .. .0000
.0000 ' .0000
0000 • .0000 .-
.1020 . .0000
0000 ' .0000
.0000 • .0000
0000 .0000
1.7003E-02 .0000
0000 . .00.00
- 1.1976E-02 ' .8382
0000 .0000 . -•'
1.1976E-02 .8382
2645 9.1781E-02
6354 .1125 •
96.91 6.3273E-02
6354 - .1125
9691 ' 6.3273E-02
0000 • .'0000
.0000 .0000
0000 .0000
.0000 .0000-'
0000 • 0000
9.6416E-02 .0000
0000 .0000
.0000 .0000
-------
"07/07/1992
.0000 ,
.oooo :
"07/07/1992
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.0000 !
"07/07/1992
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"07/07/1992
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"07/07/1992
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.0000 ' 1
"07/07/1992
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"07/07/1992
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"07/08/1992
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"07/08/1992
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"07/08/1992
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"07/08/1992
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.0000 -2
"07/08/1992
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"07/08/1992
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.0000 : -1
"07/08/11932
.0000 \
"07/08/1992
.0000 •
"07/08/1992
" "14:46:55"
.0000
.0000
" "15:06:25"
.0000
.0000 1
" "15:24:54"
.0000
. 8055E-07 2
" '"15:30:50"
.0000
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" "15:50:02"
.0000
.4806E-10 2
". "16:31:01"
.5795E-03
.5002E-10 2
" "16:56:39"
.0000
" "08:36:46"
.0000
. 3093E-07 2
" "08:42:38"
,0000
.9329 2
" "09:04:18"
.0000
.5399E-07 3
" "09:09:10"
; OOOO
.5203E-07 4
11 "09:16:06"
.0000
-6822E-10 2
" "09:24:17"
.0000
.2442E-06 3
" "09:27:24"
.0000 3
" "09:57:02"
.0000
" "10:17:31"
"PS PCM <5, 5-10, 10J-20, 20-40, >
OOOO .000-0
OOOO .0000
.0000
.0000
"PS PCM <5, 5-10, 10-20, 20-40,
OOOO .0000
0417E-02 .0000
"PS PCM 5-10, 10-20
OOOO .2655
6883E-02 3.3488E-02
"PS PCM 5-10, 10-20
OOOO 2.8399E-03
4136E-02 4.6405E-02
"PS PCM 5-10, 10-20
OOOO ' 3.1200E-03
4043E-02 .1039
"SC PCM 5-10, 10-20
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4148E-02 3_. 9445E-02
"PS PCM 5-10, 10-20
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.0000
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20-40, >=40
.0000
20-40, >=40
.2072
20-40, >=40
5.-6519E-02
20-40, >=40
4.9393E-02
20-40, >=40
.0000
"PS PCM <5, 5-10, 10-^20, 20-40, >
OOOO .0000
5732E-02 7.1660E-02
.0000
"PS PCM <5, 5-10, 10-20, 20-40,-
OOOO .0000
4067E-02 9.1268E-02
"PS PCM >=40, 20-40
6995 .0000
0207E-02 2.7264E-03
"PS PCM >-40, 20-40
OOOO .0000'
4540E-02 2.2204E-03
"PS PCM <5, 5-10, 10-
0000 .0000
4067E-02 9.1268E-02
,0000
10-20, 5-10
.0000
10-20, 5-10
,0000
-20, 20-40, >
.0000
"PS PCM <5/ 5-10, 10J20, 20-40, >
OOOO ' .0000
0200E-02 2.7271E-03
"PS PCM 5-10, 10-20,
9B37E-03 .0000
"PS PCM- <5, 5-10, 10-
oooo .0000--
.0000
20-40, >=40
,0000
-20, 20-40, >=
.0000 :
=40 and Complex, >=1, 0.
.0000 .0000
.0000 -1.0440E-08
>=43 and W <0.15, 0.15-0
.0000 .0000
.3577 -1.1622E-08
and W <0.15, 0.15-0.3,
.0000 .23,17
and Complex, >=1, 0.3-1
.0000 .0000
and Complex, >=1, 0.4-1
.0000 .0000
and Complex, >=1, 0.4-1
.0000 1.1903E-02
and Complex, >=1, 0,4-1
.0000 .0000
=40 and W <0.4, 0.4-1, >
.0000 .0000
>=40 and Complex, >=1 , 0
.0000 3.9S37E-03"
, <5 and Complex, >=1, 0
.0000 .0000
, <5 and W <0 . 4 , 0.4-1,
.9993 .0000
=40 and Complex, <0,4, 0
.0000 3.9837E-03
=40 and Complex, <0.4, >
.0000 .0000
and Complex, <0.4, 0.4-1
3.0906E-02 .0000
40 and W <0 . 4 , 0.4-1, >=
' .0000 4.5807E-02
"PS PCM <5, ,5-10,, lO-jZO, 20-40, >=40 and B >=1, 0.4-1, <0.
3-1, 0.15-0.
.0000
3.4370E-02
3, <0.15 "
, .oooo
2.4421E-03
.3, 0.3-1, >=1, Complex "
.0000
2.4136E-02
0.3-1, >=1,
4.4614E-04
, 0.15-0.3,
,4218
.4217
4.6418E-02
Complex "
.0000
<0.15 "
.1.0419E-02
, 0.2-0.4, <0.2 "
.0000
, 0.2-0.4, <
.0000
, <0.4
.0000-
=1 , Complex
.0000
.4-1, <0,4
3.0906E-02
.4-1, <0.4
.0000
>=1, Complex
.0000
.4-1, >=i
.0000'
=1, 0.4-1
.3006
, >=1
3.2175E-02
1 (no complex
1.8.9491-02
4 trio complex
" .0000
0.2
.0000
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.0000
,,
6.1242E-02 8
»
.0000
'
.0000
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i "
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;
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structures) "
3.391J4E-02
structures) "
-294. 903
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-275.101'
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-274,044
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-273.078
OOOO
-272 ,'998
'0000
-278.198
1909
-274.519-
8340E-04
-273.814
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-292.314
3005
-298.842
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-273.814
0906E-02
-292.314
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-273.814
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-275.298
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-275.298
61.42
.2615
14.95.
.0000
17.10
2.5093E-02
14.95
.0000
12.73
.0000
12.43
.0000
21.79
.3745
14.77
2. 0129E-02
14.11
.0000
55.42
.0000
69.62
.0000
14.11
.0000
55.42
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14.11
.0000
16.34
.9000
16.34
10
.0000
6 1.
.2071
6 8.
.0000
7 3,
.0000
8
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6 5..
2.1272E-03
9 8.
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6 2,
5.4116E-02
9
- .0000
10
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8 7.
3.2175E-02
10
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8 7.
.9329
7 2.
-1.1481E-05
8 3.
0000
.0000
9811E-02
2.8387E-03
0952E-03
2.7684E-02
5898E-02-
.0000
1208
.2888
2239E-02
.0000
8203E-03
-1.0420E-06
1279E-02
.0000
1177
.0000
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.0000
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.0000
8127E-02
.0000
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.0000
8127E-02 ;
1.4118E-10
1478E-02
2.7020E-02
693SE-02
000.0 .0000
.7385 .0000
OOOO .0000
2.8200E-04 .0000
OOOO .0000
.0000 .0000
OOOO - .0000
.0000 .0000
OOOO .0000
.oooo .oooo
OOOO .0000
3.0210E-02 .0000
0000 .0000
2.2334E-02 1.39181-02
OOOO .0000
1.2797E-02 , .8508
OOOO .0000
3.2175E-02 .0000 ,
OOOO .0000
'.OOOO .0000
OOOO .0000
.0000 6.9620E-04
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-.0000 .0000
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2.4067E-02 9. 12681-02
OOOO 1.2928E-03
8.8065E-02
OOOO 1.2930E-03
-------
.0000
"07/08/1992"
.0000
5.9101E-03
"07/08/1992"
.0000
.0000
"07/08/1982"
.0000
.0000
"07/08/1992"
.0000' 6.
.0000
"07/08/1992"
.0000 7.
"07/08/1932"
.0000 6.
.0000
"07/09/1992"
.0000 6.
.0000
"07/09/1992"
-.1140'
"07/09/1992"
1.5134E-02
"07/09/1992"
3.3178E-03
"07/09/1932"
.6999' -'-1.
"07/09/1992"
5.6375E-03
"07/09/19'92"
.0000 8.
"07/09/1992"
.0000,,
"07/09/1992"
.1450 2.
"07/09/1992"
8.1133E-02
"07/09/1992"
7.1689E-02"
"07/09/1392"
.0000
0000
' "13:34:
0000
."0000
"14:44:
0000
0000
"14:48:
0000
0000
"14:58:
56'66E-04
0000
"15:13:
4658E-02
"15:45:
3774E-04
0000
"09:11:
5666E-04
0000
"11:30:
"11:30:
"11:30:
"11:30:
6245E-07
"12:05:
.:0000
"12:05:
5144E-03
"14:31:
0000
"14:55:
6668E-02
"15': 18:
"15:18:
"15:46:
0000
26"
47"
-9
02"
-1
17"
9
14"
37"
7
08"
9
24"
28"
29"
31"
1
26"
47"
48"
1
48"
7
32"
32"
24"
.0000 .0000 . 4.581QE-T02 .0000 , .0000 .0000 3.3927E-02
"PS PCM <5, 5-10, 10-20, 20-40,]>=40 and >=1, 0.4-1,. <0. 4 and Complex with 6 lengths
.0000 .0000 3.7007SE-02 .0000 ,0000 .0000 .0000
5.3636E-10 2.3957E-02 8.8626E-02
"SC PCM <5, 5-10, 10-20, 20-40,|>=40 and '>=!, 0.4-1, <0.4 and Complex with 6 lengths
-.0000 .0000 .0000 5.'4027E-05 :9999 .0000 .0000
.9125E-08 -1.4853E-02 9.5085E-04
"SC PCM Complex with 6 lengths and <5, 5-10, 10-20, 20-40, >=40 and!>=l, 0.4-1, <0.4
.0000 .0000 . .0000* .0000 .0000 , .0000 .0000
.8353E-07 4.1522E-02 3.4939E-03
"SC PCM <5,5-10,10-20,20-40,?>=40 and <0.4, 0.4-1, >=1 arid Complex with 6 lengths
.0000 ".0000 6.0087E-03 .0000 5V8557E-02 2 . 6'025E-02 .0000
.1494E-02 2.4199E-02 3.6281E-02
"SC PCM <5, 5-10, 10-20, 20-30,J>30 f or< . 3 and 5-10/ 10-20, 20-30, 30-40,:40-50,>50 for>5
.4261 . • .0000 '.0000 .0000 .2326 .2633 2.3275E-02'
"SC PCM <5, 5-10, 10-20, 20-40,>>=40 and <0.3, 0.3-1, >=1 and Complex with 6 lengths
.0000 '. .,0000 4.1770E-03 .0000 1.8608E-02 .0000 ' .0000
.4421E-02 2:4148E-02 7.5365E-02 ^
1 .
l • .
"SC PCM <5, 5-10, 10-20, 20-40,i>=40 and <0.4, 0.4-1, >=1 and C andiCS with 6 lengths
.0000 .0000 6.0087E-03 .0000 5.8557E-02 2.6025E-02 .0000
.1494E-02 2.4199E-02 3.6281E-02 .
"SC PCM 5-40, >-40 and w!<0.3 : ' .
! • ' ' • • ."••'••
"SC PCM 5-40, >=40 and W i>5 '.
[ •
"SC PCM 5-40 and- W <0.3, i>5 ' " •
- ; . -
"SC PCM 5-40, >-4'0 and WJ<0.3, >5 • and Length < 5 (no widths) (with 95% CI)
.5694E-02 2.9785E-03 ', '
"SC PCM <8, 8-^15, 15--25, 25-t40, >=40 and W <0.3, >=5 i
.0000 3.8505E-02 .0000 .0000 :.1430 2V3986E-02 7.0573E-02
"SC PCM 10-20, 20-40, >=4o! and W <0.3, >=5 '
.1634 2.3902E-02 5.9924E-02 :
"SC PCM <5, 5-10, 10-20, 20-740, >=40 and <0.4, 0.4-1. and Complex with 6 lerigths
.0729E-02 3.2242E-03 .0000 .0000 :8210 .0000 .0000
"SC PCM 5-40, >=40 and w'<0.3, >5 (multipy control animals/response by 10~6)
.0300E-02 ; ! -:-••'•!•••.'
"SC PCM 5-40, >=40 ;and: W5 (2 studies) (eontrol animals/response * 10"6)
"SC PCM 5-40, >=40 and 'W<0.3,>5 (2 studies) (control animals/response * 10A6)
I : . ' '
"SC M(16) <5, 5-10, 10-20, 20-40, >=40 and <0..15, 0.15-0.3, 0 . 3L1 ,' 1-5 ., >=5
.0000 1.7778E-02 '.0000 .0000 . :0000 .0000 .0000
1.8952E-02
" -273
774
3.4493E-02
" -311
.0000
" -2 93
.0000
" -272
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176
905
749
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0000
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17
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1.5413E-05
7
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2.6966E-02
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2.8166E-03
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7287 5.
9697. 6.
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2.4809E-02
7130E-03
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.0000
000 0
.0000
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.0000
8284E-02
5902E-02
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3.9978E-02
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6668E-02
6668E-02.
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.0000
-------
"07/09/1992"
.0000 :
.0000 \
"07/09/1992"
.0000 ;
"07/09/1992"
.•oooo
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"07/10/1992"
1.7176E-03
"07/10/1992"
.0000 ; 1.
.4446 ;
"07/10/1992"
.0000 :
.0000
"07/10/1992"
.0000
.0000 ;
"07/10/1992"
.0000 -
.0000 ; 1.
"07/10/1992"
.0000
.0000 :
"07/10/1992"
.0000 -
.0000 ;
"07/10/1992"
.0000
.0000 !
"07/10/1992"
2.5406E-i02 vl
"07/10/1992"
2.6927E-02 1
"07/16/1992"
1.0898E-:02 4
"07/16/1992"
.0000 ! 6.
"15:47:33"
0000
0000
"16:06:08"
1453 2
"17:22:46"
0000 2
8618E-04
"09:34:47"
.8530
"10:28:12"
5443E-03
0000
"11:30:52"
0000 7
0000
"12:20:58"
0000
0000
"12:15:35"
0000
0111E-02
"12:29:04"
0000
0000
"12:32:22"
0000
0000
"13:46:21"
0000
0000
"14:23:02"
.0021E-02
"15:20:11"
.2277E-02
"09:13:25"
.2320E-03
"09:14:13"
5172E-04
"PS M(16) <5, 5-10,
.0000 .1318
.0000 .0000
"SC PCM Length < 5
.0-20, 20-40
.0000
.0000
;no widths)
, >=40 and <0.15, 0.15-0.3, 0.3-1, 1-5, >=5
.0000 .0000 .0000 .1955
.1961
and 5-40, >=
.3931 2.1022E-02 7.2003E-02
40 and W <0.3, >5 (with 95% CI)
-304. 903
.0000
-272.935
25.27
.0000
12.19
10
.0000
10
4.0203E-03
.0000
.2716
.5612E-02 7.0424E-02
"PS PCM (FSB) 5-10,1 0-20, 20-4 0,>40
.1747E-03 7.1876E-02
.0000 .0000
11 SC PCM Length < 5
5.3176E-02
.0000
[no widths)
and<.4, .4-1
.0000
-5.1981E-08
,>1 C only and CS with 6 length cat."
.0000 .7673 .0000
2.4947E-02 6.3726E-02
and 5-40, >=40 and W >5, <0.3 (with 95% CI) "
1 -272.433
.0000
-272.935-
11.32
.0000 .
12.19
6
.0000
10
7.8319E-02
.0000
.2716
2.5612E-02 7.0424E-02 - -
"SC PCM <5, 5-10, 10-20, 20-40, >=40
.0000 ,0000
.5538 .0000
.0000
.0000
"SC PCM <5, 5-10, 10-20, 20-40, >=40
.4663E-05 7.7508E-02
.0000 .0000
.0000
.0000
"SC PCM <5, 5-10, 10-20, 20-40, >=40
.0000 .0000
.0000 -1.9458E-08
5.4696E-02
'4.3684E-03
"SC PCM <5, 5-10, 10-20, 20-40, >=40
.0000 . .0000
.8618
.0000 -5.4691E-08 4.2054E-03
"SC PCM Complex w/6 lengths s <5
.0000 .0000
.9970 -1.6111E-06
9.2717E-05
4.7046E-03
"SC PCM Complex w/6 lengths & <5
.0000 .0000
.0000 -1.5832E-08
.0000
4.6132E-03
"SC PCM Complex w/6 lengths S <5
.0000 .0000
.0000 .8977
"SC PCM >= 20
7.8080E-05
4.6447E-03
and <0. 15, 0
.0000
.0000
and >=5,l-5
.0000
.0000
S >=.3, ,15-
.0000
7.3528E-04
S <.15, .15-
.0000
9.5365E-04
,5-10,10-20,
.0000
1.0201E-02
,5-10,10-20,
.0000
2.6062E-04
,5-10,10-20,
.0000
1.1447E-02
.15-0.3,0.3-1, 1-5, >=5 (Mesotheliomas) "
.0000 .0000 .0000
-5.0341E-08 4.6448E-03 6.0542E-04
, 0.3-1, 0.15-0. 3, <0. 15 (Mesotheliomas) "
.0000 .0000 .0000
4.2322E-10 1.4830E-03 1.1808E-02
. 3,<.15 S Complex w/6 lengths (Meso.)"
.8768 6.8520E-02 .0000
-3,>-.3 &. Complex w/6 lengths {Meso.}"
3.1225E-02 .0000 .0000
20-40, >=40 S <.15, .15-.3,>=.3 (Meso.)1"
.0000 .0000 .0000
20-40, >=40 & >=.3, .15-.3,<.15 (Meso.)"
.0000 .0000 .0000
20-40, >=40 S <.15,>=.3, .15-.3 (Meso.)"
.0000 .0000 .0000
n
"SC PCM >= 20 and < 0'. 4
"SC PCM <5, 5-10, 10-
.8006 .0000
"SC PCM <5, 5-10, 10-
.0000 1.4206E-02
•20, 20-40-,
.0000
•20, ' 20-40,
.0000
>=40 S <0.4
.0000
>=40 S <0.2,
.0000
and Complex only with 6 lengths "
.1114 .0000 7.2753E-02
0.2-0.4 and Complex only w/6 lengths"
.0000 .8095 ' .0000
-60.6932
.0000
-59.0535
.0000
-60.6227
.0000
-60.6067
9.6821E-02
-59.3875
.0000
-60.7657
.0000
-59.3238
.0000
-282.315
-285.310
-273.250
2.4602E-02
-273.150
.0000
14.64
.0000
12.20
.0000
14.73
.0000
14.72
.0000
11.93
2. 9416E-03
15.50
.1489
12.04
.0000
28.39
39.84
12.63
4.1308E-02
12.42.
.0000
9
.0000
9
.0000
9
.0000
8
.0000
9
.0000
9
.0000
10
.0000
11
11
7
8
.1250 '
.1005
.0000
.2015
.0000
9.7866E-02
.0000
6.4088E-02
.0000
.2167
.0000
7.7300E-02
.5814
.2815
.0000
2.0676E-03
.0000
8.0924E-02
.1328
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.0000
.2055
' .4983
.0000
.0000
5.
.0000
0000 8.3400
1 .7176E-03
.0000
0000 .0000
. 0000
.0000
0000 .0000
.0000
0000 .0000
.0000
0000 .0000
.0000
0000 .0000
.0000
0000 .0000
.0000 '.
2697 .0000
.0000
1022 .0000
.7945
.5017
1.5099E-04
.0000
0575E-02 2.4376
4.2294E-02
"07/16/1992" "16:17:33
"SC PCM <5, 5-10, 10J20, 20-40, >=40 & >=1, 0.4-1, <0.4 (no complex structures) " -273.681
13. f
7 5.2712E-02
.0000
2.4004E-04
-------
.0000 : .0000 .0000 ' . .0000 -5. 073^-02 .0000 1.1999E-02 2.7987E-02 .0000
"07/20/1992" "11:16:23" "SC PCM Length >=20 and Width <0.2
7.8262E-02 8.3237E-02 !
"07/20/1992" "11:16:24" "SC PCM Length >=30 j
3.6883E-02 9.8082E-03 ;
"07/20/19'92" "11:16:25" "SC PCM Length >=30 and Wdjdth <0.4
.1132 ' 2.6895E-02 j -
"07/^0/1992" "11:16:26" "SC PCM Length >=30 and Wijdth <0.2
.1544 ' .1622 j
"07/20/19-92" "11:16:27" "SC PCM Length >=20 and width >=0.4 '
2.9009E-02 1.0020E-02 [
"07/20/1992" "11:16:28" "SC PCM L,ength >=10 j
3.0399E-02 1.0603E-02 ;
"07/20/1992" "16:59:38" "SC PCM FSB with 5-40r<0.3, FSB with >=40,<0.3,: and Complex only with >=40,;
.1467 2.5581E-02 7.0175E-02 , i . "'
"07/20/1992" "17:02:26" "SCPCM F=>40,<.3, B=>40,<.3, Compiex=>40,>5, F=5-40,<.3, B=5-40,<.3
5.9873E-02 '8.8266E-06 8.3673E-04 2.9366E-02 .1525
•f
"07/2,1/1992" "13:14:11" "SC PCM 5-40 & <0.3, >=40[&<0.3, and->=40 E >=5
.1453 2.5612E-02 7.0424E-02 |
"07/21/1992" "13:19:20" "SC PCM >=40 & >=5, 5-40 £ <0.3, and >=40 &<0.:3 "
.8530 2.5612E-02 7.0424E-02 i
"08/06/1992" "15:38:36" "SC PCM length >=
5.4171E-02 4.4551E-03
and width < 0.25'
"12/14/1992" "16:30:46" "SC PCM Length < 5 (no widths) and 5-40, >=40 and W <0.3, >5 (with 95% CI)
.0000 '.8530 .1453 2.5612E-02 7.0424E-02 .
"12/15/1992" "10':16:36" "(all except chrysotile) SC JPCM <5, 5-iO, 10-20, 20-40, >=40 and <0 115, 0 .15-0 . 3, 0 . 3-1,1-5, >=5
.'0000 .0000 .0000 .0000 .0000( .0000 .0000 .0000 .0000
.0000 % .0000 .0000 .0000 7.6546E-02 .0000 .9027 .0000 .0000 2.0714E-02 2.2772E-02 i
"12/15/1992" "10:17:47" "("chrysotile only) SC PCM <5_, 5-10,10-20, 20-40, >=40 and <0 .15/0 .15-0 . 3, 0 .3-1,1-5, >=5. " -199.
1.9054E-08 • .0000 lOOOO .0000 .000*0 .0000 . ' .0000 •' .0000; .0000 .171-9 .0000
-0000 .0000 2.0494E-02 " .0000 .5626 .0000 .0000 ;,. .2450 .0000 -6.1822E-05 3.2157E-02
f
E
"12/17/1992" "14:09:01" " (chrysotile only) SC PCM <5f, 5-10, 10-20, 20-40, >~40 and >=5, 1-5, 0 . 3-1, 0 .15-0 . 3, <0 .15 " -224,
.0000 .0000 .1402 9.5597E-03 .0000 .0000 .0000 .0000 .OOO'O .0000 .0000
.0000 : .0000 . .0000 .'OBOO- .0000 .0000 i .0000 .0000 1.2193E-07 5.9591E-02 1.1295E-04
t • •
"12/18/1992" "09:34:18" "(chrysotile only) SC PCM >==40, <5, 5-10, 10-20, 20-40 and <0 .15, 0 .15-0 . 3, 0 . 3-1,1-5, >=5 " -199.
.0000 .2450 .0000 .0000 .00*00 .0000 .0000 ' .0000 .0000 .0000 .0000
.0000 .0000 .0000 .0000 .00*00 .0000 2.0501E-02 .0000 .5625 3.2158E-02 3.7206E-02
6.2087E-04
" -313.
11 -287.
11 -316.
" -331.
" -285.
" . -292.
" -272.
" -272.
" -272.
" -272.
" -306,
" -272.
, 1-5, >=5
.0000
069
475
358
356
590
283
933
537
935
935
530
935
.0000
137.2
40.36
110.2
132.3"
34.13
56.96
12.20
11.55
12.19
12.19 •
100.2
12.19
.0000
.0000
11
11
• 11
11
11
. 11
10
8
10
10
11
10
-100.
.0000
.9084.
.0000
.0000
.'0000.
.0000
.0000 •
.0000 •
.2712 .
.1718
.2716
.2716
.0000
.1
.2716
.359 ... . 1
.0000
2.4677E-02 4.07421
.5000
.4976
.5702
.5175
.3007
3. 6654E-02
1.7376E-03
.4988 "'
1.7176E-03
.1453
.3319
^OOO'O
.685 ' 3'
.CTOOO
.5000 '.
.5024.
.4298
.4825
.6993
.9633"
.8516
..4405
.8530
"1.7176E-03
.6681
l".7176E-Q3
.6399
.'0000
i . 691,6E-02 ;
146 8".430j. 1 2.9414E-03
.0000 • .0000 ' - ..0000 •'•' .0000
3.7205E-02 ;
r
122 109.7| 3 .0000 - .0000
.0000 .': .0000 .0000 ' .8502
146
.0000
i.430;' 3 3.7196E-02, ./OOOO
.0000 .1719 .0000
-------
"10/08/1993" "16:02:37" "SC PCM 5-40, >=40 with >=5, < 0.3 (All except chrysotile)
9.8000E-02 .9019 2.6668E-02 8.6399E-02
|
"10/12/1993" "17:03:34" "SC PCM >=40, 5-40 with <0.3, >=5 (Chrysotile only) (dl*1000,d2*100,d4*30)
-2883 ] .2214 2.6668E-02 3.3475E-04
"03/23/1994" "14:46:29" "SC - Length: <5, 5-40; Width: <.3, >5
0.9161 ; 8.1337E-02 2.3018E-02 8.4603E-02
"03/23/1994" "14:52:42" "SC - Length: 5-20, ^20; Width: <.3, >5
0.3639 ! 0.6204 2.1557E-02 1.0547E-02
"03/23/1994" "15:13:12" "SC - Length: 5-40, ^40; Width: >5
6.1680E-02 1.6153E-02
"03/23/1994" "15:13:26" "SC - Length: 5-20, :>20; Width: <.3
2.2466E-02 1.0049E-02 j .
"03/23/1994" "15:35:48" "SC - Length: 5-20, >20; Width: <.3 and Length: 5-40, >40; Width: >5
O.OOOOE+00 0.7780 2.2547E-02 1.5203E-02
"06/28/1994" "15:56:48" "SC (smooth): L: 5-10, 10-20, 20-40, >40; W: 0-.15, .15-.3, .3-1, 1-5, >5
O.OOOOE+!00 O.OOOOE+00 O.OOOOE+00 9.3741E-02 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 1.7745E-02
O.OOOOE+00 O.OOOOE+00 0.3401 |
2.1446E-02 6.0462E-02 i
-2.774594E+07 2.321
-2.774604E+07 8.826
.5082
3 3.0963E-02 .3958
7.1027E-05
9.4552E-02
•271.
•276.
•303.
•284.
•275.
.156
.444
.315
.330
.192
8.005
17.53
90.11
35.14
15.27
10 0.6280 2.5270E-03 O.OOOOE+00
10 6.2729E-02 1.5736E-02 O.OOOOE+00
11 O.OOOOE+00 0.3142 0.6858
11 O.OOOOE+00 1.5189E-02 0.9848
"10 0.1217 1.1560E-02 0.2105
-274.350 13.01 8 0.1108 O.OOOOE+00 O.OOOOE+00
O.OOOOE+00 2.8592E-02 O.OOOOE+00 O.OOOOE+00 0.5198 O.OOOOE+00
"06/30/1994" "08:40:56" "SC (smooth): L: <5,J5-10, 10-20, 20-40, >40; W: 0-.15, .15-.3, .3-1, 1-5, >5
O.OOOOE+IOO O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE + 00 O.OOOOE+00
O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 |
0.7434 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 0.2563 4.5062E-02 6.3313E-02
!
"06/30/1994" -"09:27:22" "SC (smooth): L: <5, J5-10, 10-20, 20-40, >40; W: 0-.15, .15-.3, .3-1, 1-5, >5
O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 0'.3393
2.2450EH02 O.OOOOE+00 O.OOOOE+00
O.OOOOE+00 0.1061 O.OOOOE+00 O.OOOOE+00 0.5322 2.1610E-02 2.6262E-02
"07/05/1994" "12:25:40" "SC (smooth): L: <5,J5-10, 10-20, 20-40, >40; W: 0-.15, .15-.3, .3-1, 1-5, >5
O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 8.6406E-04 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 9.4722E-02
1.8899E-02 O.OOOOE+00 O.OOOOE+00
O.OOOOE+00 0.4309 O.OOOOE+00 O.OOOOE+00 0.4546 2.3140E-02 2.9659E-02
"07/05/1994" "13:42:25" "SC (smooth) - Length: 5-40, >40; Width: <.3, >5
0.6729- : 0.3216 2.1457E-02 3.1507E-02
"07/05/1994" "13:46:50" "SC (smooth) - Length: 5-40, >40; Width: <.3, >5
0.6166 ' 0.3803 2.2734E-02 3.0645E-02
-
"07/11/1994" "09:30:21" "Davis Studies - Using mass to calculate the constant for the dose "
O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 5.9335E-02 O.OOOOE+00 O.OOOOE+00
-285.085 51.34 9 O.OOOOE+00 2.0498E-04 7.1561E-05
O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00
-272.652 10.23 9 0.3313 O.OOOOE+00 O.OOOOE+00
O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00
-274.379 14.21 8 7.5695E-02 O.OOOOE+00 O.OOOOE+00
O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00
-273.849 12.39 10 0.2589 5.4909E-03 O.OOOOE+00
-274.767 14.82 10 0.1382 3.1150E-03 O.OOOOE+00
-271.857 9.198 7 0.2382 O.OOOOE+00 O.OOOOE+00
O.OOOOE+00 O.OOOOE+00 7.7491E-02 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00
-------
O.OOOOE+00 0.3107 O.OOOOE+00
O.OOOOE+00 O.OOOOE+00 0.5525
O.OOOOE+00 -7.lf447E-05 2.1965E-02 2.1994E-02
"07/11/19.94" ?09.:48;:26". ."PS. (smooth): L: <5, 5-10, lp-.2.0, 20-40, >40; W: :<.15, .15-.3, .3-1, 1-5, >5 •" -274.561' 15.30 8 5.2770E-Q2 O.OOOOE+00 0-. OO.OOE+00
O.OOOOE+00, O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00 0.2206 O.OOOOE+00 O.OOOOE+00 1.5167E-03 0.1926 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00
O.OOOOE+00. O.OOOOE+00 O.OOOOE+00 ;. ' " 1 • : i
0.0'OOOE+OO O.OOOOE+00 0.5853 O.OOOOE+00 -2 .9093E-07, 2.3400E-02 1.7669E-02
9 6.3160E-p2 O.OOOOE+00 O.OOOOE+00
9 5.9294E-02 O.OOOOE+00 O.OOOOE+00
.1
11 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00
11 O.OOOOE+00 "O.OOOOE+00 O.OOOOE+00
'9 O.OOOOE+00 O.OOOOE+00- O.OOOOE+00
9'O.OOOOE+OO •O.OOOOE+00 O.OOOOE+00
11- O.OOOOE+pO O.OOOOE+00 0-. OOOOE+00
8 2.4088E-02 O.OOOOE+00 O.OOOOE+00
| "
9 0.3545 i O.OOOOE+00 O.OOOOE+00
10 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00
•
11 O.OOOOE+00 O.OOOOE+00 O.OOOOE+00
9 O.OOOOE+00 1.1927E-04 O.OOOOE+00
8 O.OOOOE+00 2.1660E-07 O.OOOOE+00
10 2.1803E-03 .O.OOOOB+00 O.OOOOE+00'
10 2.1803E-03 O.OOOOE+00 O.OOOOE+00
8- 5.5433E-0-2 5.3701E-05 '2.6475E-03
"09/06/1994"
O.OOOOE+00 0.
"09/06/19'94"
O.OOOOE+00 0.
"09/08/1994"
O.OOOOE+00 1.
"09/08/1994"
0. OOOOE+00- ' 2.
"09/08/1994"
1.1306E-03'' 0.
"09/09/1994"
1.1306E-03 0.
"09/09/19'94"
O.OOOOE+00 '2,
"09/09/1994"
O.OOOOE+00 -0
"09/09/19'94"
1.7669E-03 0.
"0,9/15/19'94"
1.6818E-04, 5,
"09/15/1994"
O.OOOOE+00 2
"09/15/1994"
1.3661E-03 0
1'09/15/1994"
8.6262E-04 0
"09/29/1994"
O.OOOOE+00 0
"09/29/1994"
O.OOOOE+00 0
"09/29/19'94"
"14:33:11"
.OOOOE+00
"14:44:46"
.OOOOE+00
"09:59:21"
. 9526E-02
"10:40:42"
. 0334E-02
"10:40:54"
.6515
"15:25:33"
.6515
"15:25:54"
. 0334E-02
"15:28:54"
.OOOOE+00
"15:29:19"
.OOOOE+00
"15:28:41"
.2736E-02
"15:29:00"
.0334E-02
"15:29:18"
.OOOOE+00
"15:29:34"
.6668 '
"15:1-4.:20"
.1713
"15:14:30"
.1713
"15:14:38"'
" SC - Length: <5, 5-10,
5.2769E-02
2.5228E-02 0
" PS - Length: <5, 5-10,
6.4922E-02
" (Indirect)
O.OOOOE+00
" (Indirect)
O.OOOOE+00
" (Indirect)
O.OOOOE+00
" (Indirect)
O.OOOOE+00
" (Indirect)
O.OOOOE+00
" (Direct)
2.0403E-02
" (Direct)
3.8221E-03
" (Indirect)
O.OOOOE+00
" (Indirect)
O.OOOOE+00
" (Indirect)
O.OOOOE+00
" (Indirect)
O.OOOOE+00
" (Indirect).
O.OOOOE+00
" (Indirect)
O.OOOOE+00
" (Indirect)
1.4464E-02 0
PS - Length:
O.OOOOE+00 0
PS - Length:
O.OOOOE+00 0
SC - Length:
0.3474 0
SC - Length:
0.3474 -0
'•PS - Length:
O.OOOOE+00 0
PS - Length:
9.0581E-04 0
SC - Length:
O.OOOOE+00 0
PS - Length:
O.OOOOE+00 0
PS - Length:
O.OOOOE+00 -0
SC - Length:
0.9985 0
SC - 'Length:
0.33-24 -0
PS - Length:
0.1244 0
PS - Length:
0.1244 0
SC - Length:
10-J20 20-40, >40; F&B,
.OOOOE+00
O.OOOOE+00
10-J20 20-40, >40; F&B,
.OOOOE+00
|
<5,j 5-10,
.000*OE+00
<5,j 5-10,
.ooboE+oo
<5,J '5-10,
.OOOOE+00
\
J
<5,| 5-10,
.ooqoE+oo
<5,f 5-10,
.ooqqE+oo
i
<5, J5-10,
.ooqoE+oo
<5, 15-10,
.OOO'OE+OO
<5,! 5-10,
.ooqoE+oo
<5,| 5-10,
.OOOOE+00
<5,| 5-10,
.ooqoE+oo
<5,[ 5-10,
.-OOO!OE+OO
<5J 5-10,
.OOOOE+OO
<5,i 5-10,
.OOO'OE+OO
<5J 5-10,
O.OOOOE+00
10-20 20-4'0,
O.OOOOE+00
10-20 20-40,
O.OOOOE+00
10-20 20-40,
O.OOOOE+00
10-20 20-40,
O.OOOOE+00
10-20 20-40,
O.OOOOE+00
10-20 20-40,
4.6108E-03
10-20 20-40,
O.OOOOE+00
10-20 20-40,
O.OOOOE+00
10-20 20-40,
0., OOOOE+00
10-20 20-40,
O.OOOOE+00
10-20 20-40,
O.OOOOE+00
10-20 20-40,
O.OOOOE+00
10-20 20-40,
o.oqooE+oo
10-20 20-40,
C&M
0.8311
C&M
0.7031
>40; F&B,
O.OOOOE+00-
>40; F&B,
O.OOOOE+00
>40; FSB,
O.OOOOE+00
9.0947E-02
0.2175
C&M
0.9805
C&M j
. 0.9797
C&M
-O.OOOOE+00
"
2.2160E-02
"
2.2220E-02
..
2.7743E-02
"
2.7748E-02
"
6.7921E-02
>40; FSB (w<.3), C&M "
O.OOOOE+00
O.OOOOE+00
6.7921E-02
>40; F&B (w<.3) j. C&M P
O.OOOOE+00-
>40; F&B (w
0-9375
>40; F&B (w
O."9790 -
>40; width
0. OOOOE+00
0.9797
<.3) , 'C&M
3.6547E-02
<.3) , iCSM
1.5448E-02
: <.3, >= .
0.9471
>40; FSB (w<.3); C&M
0.00'OOE+OO
>40; width
O.OOOOE+00
0.9797
: <.3^ >= .
O.OOOOE+00
>40; F&B (w<.3) J C5M
0. OOQOE+OO
>40; width
O.OOOOE+00
>40; width
0. OOOOE+00 '
>40; width
O.OOOOE+00
: <.3, >= 1
0.7044
: = 1
0.7044
: <.3, >= 1
2.7748E-02
"
2.2579E-02
"
2.2709E-02
3
2.6545E-02
(w>=.3) "
2.7748E-02
3
8.5000E-02
(w>=.3)
8.1093E-02
,,
2.8964E-02
"
2.8964E-02
-275.569
1.5963E-02
-2761055
1.4475E-02
-281.736
0.1171
-281.698
0.1125
-301.014
2.4401E-02
-301.014
2.4401E-02
-281.698
0.1125
-277.173
0.1224
-272.162
0.1088
-282.522
0.1388
-281.698
0.1125
-312.657
1.2810E-02
-304.851
2.541-8E-02
-278.476
1.1187E-02
-278.476
1.1187E-02
-273.829
16.
16.
33,
33.
90.
90.
33.
17.
15
35
23
16
67
67-
16
54
9.944
33.
33.
116
103
26,
26'.
15.
70
16
.4
.5
59
59
15
-------
O.OOOOE+00 0-
"09/29X1994"
0..OOOOE+00 0.
"10/08/1994"
0.9525 • -4,
"10/08/1994"
0.9933 ; 1.
"10/08/1994"
0.3818 i 0.
"10/08/1994"
0.9539 ; 1.
"10/08/1994"
0.7112 ; 0.
"10/08/1994"
0.9238 ; 2.
"10/08/1994"
0.9586 ' -2.
"10/08/1994"
0.9062 8.
"10/08/1994"
0.1544 0.
"10/08/1994"
5.6862E-02 3.
"10/08/1994"
0.9525 .'. -4.
"10/08/1994"
0.9933 1.
"10/08/1994"
0.3818 ' 0.
"10/08/1994"
0.9539 ' 1,
"10/08/1994"
0.7112 : 0.
"10/08/1994"
0.9238 ; 2.
"10/08/1994"
0.9586 -2.
"10/08/1994"
0.9062 ; , 8.
"10/08/11994"
0.1544 : - 0.
"10/08/1994"
5.6B62E-I02 3.
B 6.8763E-02 5.1098E-OS 2.3654E-03
" (Direct) PS - Length: 5-35, >35; width: <.4, >-.4
'.2537E-02 4.9013E-02
" (Direct.) SC - Length: 5-35, >35; width: <.4, >=.4
'.2452E-02 2.6220E-0£
" (Direct) PS - Length: 10-35, >35; width: <,4, >=. 4
?.4778E-02 "1.6989E-02
" (Direct) SC - Length: 10-35, >35; width: <.4, >=. 4
Z.1312E-02 2.0133E-02 •
" (Direct) PS - Length: 10-30, >30; width: <.4, >=.4
2.2471E-02 2.1357E-Q2
" CDirect) SC - Length: 10-30, >30; width: =.4
2.1867E-02 1.4365E-02
" (Direct) PS - Length: 5-30, >30; width: <.4, >=.4
2.0451E-02- 3.4136E-0?
" (Direct) SC - Length: 5-30, >30; width: <. 4 ,• >=.4
'.3156E-Q2 1.5083E-02
"• (Direct) PS - Length: >40; width: <.4, >=.4
i
" (Direct) SC - Lengih: >40; width: <.4, >=.4
" (Direct) PS - Length: 5-35, >35; width: <.4, >=,4
>.2537E-02 4.9013E-02
" (Direct) SC - Lengih: 5-35, >35; width: <.4, >=.4
J.2452E-02 2.6220E-02
" (Direct) PS - Length: 10-35, >35; width: <.4, >=.4
2.4778E-02 1.6989E-02
" (Direct) SC - Lengih: 10-35,'>35; width: <.4, >=,4
?.1312E-02 2.0133E-02
" (Direct) PS - Length: 10-30, >30; width: <.4, >=.4
?.2471E-02 2.1357E-02
" (Direct) SC - Length: 10-30,' >30; width: <.4, >=. 4
?.1867E-02 1.4365E-OJ
" (Direct) PS - Length: 5-30, >30; width: <.4, >=,4
i.0451E-02 3.4136E-02
" (Direct) SC - Length: 5-30, >30; width: <.4, >=.4 '
?.3156E-02 1.50B3E-0?
" (Direct) PS - Length: >40; width: <.4, >=.4
-273
-274.
-27 B,
-274 ,
-278,
-275.
-273.
-277,
-331.
-300.
-273.
-274.
-278.
-274.
-278.
-275.
-273,
-2-77.
-331.
-300.
.526
.549
.769
. 577
.770
.485
.993
.242
.352
.376
.526
.549
,769
.577
.770
,485
.993
.242
.352,
.376
12.
13.
21.
13 .
21.
15.
12.
19.
132
70.
12.
13.
21,
13.
21.
15.
12.
19.
132
70.
55
85
65
29
61
94
89
78
.3
55
55
85
65
29
61
94
89
78
.3
55
9
10
9
9
9
9
9
10
11
11
. 9
10
9
'9
9
9
9
10
11
11
0.1832
0.1792
9. 30991-03
0.1492
9.4643E-03
6.7477E-02
0.1669
3.0632E-02
O.OOOOE+00
, o.oboos+oo
0.1832
0.1792
9.3099E-03
0.1492
9.4643E-03
6.7477E-02
0.1669
3.0632E-02
O.OOOOE+00
O".OOOOE+OO
3.6855E-02
6.7383E-03
0,3404
2.7992E-02
0 1"24
3.2623E-02
1.9502E-02
8.8853E-03
1.000
0.7468
3.6855E-02
6.7383E-03
0.3404
2.7992E-02
0.1124
3.2623E-02
1.9502E-02
8.8853E-03
1.000
0.7468
1
0'
4
1
5
2
2
0.,
3.
0.
1.
0
4.
1
5
2
2
0
3
0
..0638E-02
. OOOOE+00
. 9499E-02
.8079E-02
. 9273E-02
.3444E-02
.1946E-02
. OOOOE+00
. 8068E-14'
.2532
.Q638E-02
.OOOOE+00
. 9499E-02
. B079E-02
. 9273E-02
.3444E-02
. 1946E.-02
.OOOOE+00
.8068E-14
.2532
-------
"10/1:0/1994" "08:00:28" " (Direct) PS - Length: 5-33, >35; width: <.4, >=. 4
0.9523 _ 1.1095E-13 2.2538E-02 4.9012E-02 !
"10/10/1994" "08:00:31" " (Direct) SC - Length: 5-35', >35; width: <.4, >=. 4
0.9933 1.9284E-13 2.2454E-02 2.6220E-02 ' ' ' •' •
"10/10/1994" "08:00:33" " (Direct) PS - Length: 10-35, >35; width: <.4, >=.4 ' •
0.3817 0.2283 2.4778E-02 1.6987E-02
"10/10/1994" "08:00:36" " (Direct) SC - Length: 10-3,5, >35; width: <-. 4, '>=. 4
0.9539 1.0979E-13 2.1314E-02 2.0134E-02 f .
"10/1*0/1994" "08:00:38" " (Direct) PS - Length: 10-3,0, >30; width: <.4, >=.-. 4
0.7113. 0.1171 2.2472E-02 2.1356E-02 \
"10/1-0/1994" "08:00:41" " (Direct) SC - Length: 10-3,0, >30; width: <.4, >=. 4
0.9238 2.0172E-02 2.1869E-02 1.4365E-02 i
"10/10/1994" "08:00:44" " (Direct) PS - Length: 5-30., >30; width: <.4, >=. 4 •
0.9586 ' 2.7876E-13 2.0451E-02 3.4131E-02 ',
"10/10/1994" "08:00:47" " (Direct) SC - Length: 5-30*, >30; width: <.4, >=.4
0.9062 8.4873E-02 2.3157E-02 1.5084E-02 ;
" "10/1*0/1994" "08:00:49" " (Direct) PS - Length: >40;^ width: <.4, >=.4
0.1544 0.1680 .;
"10/10/1994" "08:00:52" " (Direct) SC - Length: >40;; width: <.4, >=.4 ;
5.6857E-02 3'. 1165E-02 j ^
"10/18/1994" "10:52:04" " (Direct) SC - Length: 5-4 Q, >40; width: <.4, 4=. 4 (not adjusted)
0.9963 _ 1.2560E-03 2.7391E-02 4.7399E-02 (
"10/18/1994" "10:52:22" " (Direct) SC - Length: 5-4CI, >40; width: <.3, >-. 3 (not adjusted)
0.9684 3.0243E-02 2.5583E-02 8.5973E-02 i
"11/10/1994" "13:06:25" " (Direct) SC - L: 5, W: <.4*; L:5, W: >=. 4; L:' >40, wr <.4 (not I adjusted)
0.9_976 2.7465E-02 4.7575E-02 ,
t : . .
lengtK-width category followed by 2 equation coefficients ,
"10/22/1996" "19:32:55" "PS PCM lengths <10, >=10 | :"
3.4161e-02' 3.5653e-04 :
"10/22/1996" "19:32:56" "PS PCM lengths >=10 j •...•'
3.5653e-04 J
"10/22/19,96" "19:32:56" "PS PCM lengths >=10 and wijdths < 0;3
2.6484e-03 j
"10/22/1996" "19:32:56" "IPS PCM lengths >=10 and wijdths < 0.4 • •
1.9852e-03 I • '' .
"10/22/1996" "19:32:57" "PS PCM lengths >=10 and widths < 0.5 - : •
1.4187e-03 j
"10/22/1996" "19:32:57" "PS PCM lengths >=10 and wjdths >=0.3
3.8243e-04 f :
"10/22/1996" "19:32:57" "PS PCM lengths >-10 and widths >=0.4
4.0886e-04 i
"10/22/1996" "19:32:57" "PS PCM lengths >=10 and widths >=0.5 '
-273.
-274.
-278,
-274,
-278
-275
-273
-277
-331
-300
-275
-275
-275
.526
.549
.768
.577
.771
.485
.992
.241
.357
.376
.345
.510
.346
Log-Like
-296
-296
-325
-312
-306
-297
-298
-298
.320
.320
.595
.334
.968
.031
.067
.522
12.
13.
21.
13.
21.
- 15.
12..
19.
132
70.
17.
17.
17.'
55
85
65 '
29
61
94
89
77
.3
55'
84
22
86
Chi-S
69.
69.
129
•no
96.
70.
72.
73.
01
or
.6
.2
93
29
82
82
9
- 10
9
9
9
. 9
9 .
10 '
11
11
10.
10
11
DF
•12
12
12
. 12
12
1.2
12
12
0.
0.
9,
•0.
• 9
6
0
3
0
0
5
6
8
.1832
.1793
.3202E-03
.1493
.4607E-03
.7504E-02
.1670 ;
.0658E-02-
.OOOOE+00
.OOOOE+00
. 6943E-02
.8761E-02
. 4167E-02
p-value
0
0
o'
0
0
0
0
0
. OOOOE+00
. OOOOE+00
.OOOOE+00
. OOOOE+PO
. OOOOE+00
. OOOOE+00
.OOOOE+00
.OOOOE+00
3.-6855E-02 1
6". 7384E-03 -0
0.3404 4
2.7991E-02 1
0.1123 5
3.2620E-02 2
1.9478E-02 2
'8.."BB50E-03 0
1.000 3
0.7468 0
2.'4333E-03 Q
1.3829E-03 0
2.4379E-03' 0
coefficients
0. OOOOE+00
1.000 3
l.OQO 0
1.000 ' 6
1.000 5
1.00.0 3
1.000 3
1.000 ' 3
.0638E-02
.OOOOE+00
.9500E-02
.8080E-02
.9294E-02
.3446E-02
.1954E-02
. OOOOE+00
.8065E-14
.2532
.OOOOE+00
.OOOOE+00
.OOOOE+00
for each
1.000
.4161e-02
.1064
.6970e-02
.5436e-02
,5080e-02
.6791e-02
.72'27e-02
-------
.4693e-04
•10/22/1996"
.6171 : 0,
•10/22/1996"
.6218 i 0,
'10722/1996"
.5800 : 0.
•10/22/1996"
.COOOE+OQ 0.
•10/22/1996"
.OQCOE+00 0.
•10/22/1996"
.OOOOE+00 0.
•10/22/1996"
.0399e-02 3.
•10/22/1996"
.88646-04
'10/22/1996"
.28B7e-03
'10/22/1996"
.01266-04
'10/22/1996"
.9682e-04
•10/22/1996"
.3901e-04
•10/22/1996"
.0335e-04
'10/22/1996"
.Q040e-03
'10/22/1996"
. OOOOE-HOO 1
'10/22/1996"
.1906 : 0.
'10/22/1996"
."87016-02 0.
'10/22^1996"
.OOOOE+00 , 0.
'10/22/1996"
. OQOOE+QO 0.
'10/22/1996"
.OOOCE+00 0.
"19:32:58"
3829
"19:32:58"
3782
"19:32:58"
4200
"19:32:59"
COQOE+00
"19:32:59"
OOOOE+00
"19:32:59"
OOOOE+00
"19:33:01"
"19:33:01"
"19:33:02"
"19:33:02"
"19:33:02"
"19:33:03"
"19:33:03"
.000
"19:33:03"
8094
"19:33:03"
9313
"19:33:0.4"
OOOOE+00
"19:33:04"
OOOOE+00
"19:33:05"
OOOOE+00
"PS PCM lengths <10> >=10 and widths <0.3,' >=o.
. 4C84e-02 8.9173e-Cfl
"PS PCM lengths =10 and widths <0.4, >=0.
.3966e-02 8.6454e--09
"PS PCM lengths <10^ >=10 and widths <0.5, >=0.
.4167e-02 7.8386e-o!l
"PS PCM lengths <5, 5-10, >=10 and widths
.6171 0.3829 ! 3.4084e-02 8.9173e-
"PS PCM lengths <5,;5-10, >=10 and widths
.6218 0.3782 ''• 3.3966e-02 8.6454e-
"PS PCM lengths <5,15-10, >=10 and widths
.5800 0.4200 ; 3.4167e-02 7:8386e-
"SC PCM lengths <101 >-10
i
"SC PCM lengths >=lp
"SC PCM lengths >-l6 and widths < 0.3
"SC PCM lengths >=1Q and'widths < 0.4
"SC PCM lengths >=10 and widths < 0.5
"SC PCM lengths >=10 and widths >=0.3
"SC PCK lengths >=10 and widths >=0.4 '
"SC PCM lengths >=10 and widths .>=0.5
"SC PCM lengths <10| >=10 and widths <0.3, >-0.3
.0016e-02 5.3901e-04
"SC PCM lengths <10, >=10 and widths <0.4, >=0.4
.9857e-02 T.3280e-04
"SC PCK lengths <10j >=10 and widths <0.5, >=0.5
.8234e-02 9.6970e-04
"SC PCM lengths <5,|5-10, >=10 and widths
.OOOOE+00 1.000 | 3.0016e-02 5.3901e-
"SC PCM lengths <5, J5-10, >=10 and widths
.1906 0.8094 : 2.9857e-02 7.3280e-
"SC PCM lengths <5,:5-10, >=10 and widths
.8701e-02 0.9313 2.8234e-02 3.697Ce-
<0.3,
04 .
<0.4,
04
<0.5,
•04
>=0.3
>=0.4
>=0.5
-296.
-296.
-296.
-296.
^296.
-296.
-292.
-292.
-301.
-300.
-299.
-291.
-291.
-289.
-291.
-291.
-289.
-291.
-291.
-289.
.228
.124
.226
228
124
.226
283
283
778
564
230
282
615
538
282
297
410
282
297
410
68.
68.
68.
68,
68.
68.
56.
56.
90.
87.
80.
53.
53,
47,
53,
53.
47.
53.
53.
47.
.97
.75
. 95
.97
.75
.95
.96
.96
.24
,30
.93
.08
.13
.01
.08
.07
.20
,08
.07
,20
11
11
11
11
11
11
12
12
12
12
- 12
12
12
12
12
11.
11
12
11
11
0 .OOOOE+00
O.OOOOB
O.OOOOE
+ 00
+00
O.OOOOE+OC
O.OOOOE
+00
0. OOOOE+00
O.OOOOE
O.OOOOE
+00
+00
0. OOOOE+00
O.OOOOE
O.OOOOE
O.OOOOE
O.OOOOE
+00
+00
+00
+ 00
0. OOOOE+00
O.OOOOE
+0.0
0. OOOOE+00
O.OOOOE
0.00'OOE
O.OOOOE
O.OOOOE
+00
+00
+00
+00
0. OOOOE+00
O.OOOOE+OC
0. COQOE+00
0-. OOOOE+00
0. OOOOE+00
0. OOOOE+00
O.COOOE+00
1.000
1,000
1.000
l.'OOO
1.000
1.000
l.-OOO .'
O.OOOCE+00
0. OOOOE+00
0. OOOOE+00
O.QOOOE+OC
0. OOOOE+00
0. OOOOE+00
0
0
0
0
0
0
3
4
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------- |