EPA Contract No. 68-W9-0059
Work Assignment No. 59-06-D800
FINAL
METHODOLOGY FOR CONDUCTING RISK
ASSESSMENTS AT ASBESTOS SUPERFUND SITES
PART 2: TECHNICAL BACKGROUND DOCUMENT
INTERIM VERSION
Prepared fon
Kent Kitchingrnan
U.S. Environmental Protection Agency
Region 9
75 Hawthorne
San Francisco, California 94105
Prepared by:
D. Wayne Berman
Aeolus, Inc.
751 Taft St
Albany, CA 94706
and
Kenny Crump
ICF Kaiser Engineers, Inc.
602 E. Georgia Ave
Ruston, LA 71270
February 15,1999
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ACKNOWLEDGMENTS
This technical background document was prepared by D. Wayne Bemnan of
Aeolus, Inc., Albany, California and Kenny Crump, ICF Kaiser Engineers, Inc., Ruston,
Louisiana under USEPA Contract No. 68-W9-0059, Work Assignment No. 59-06-D800
for the U.S. Environmental Protection Agency (USEPA), Office of Emergency and
Remedial Response (OERR). The USEPA Task Manager for this project is Kent
Kitchingman. The USEPA project officer is Linda Ma.
This work could not have been completed without the assistance of John Davis and
Alan Jones (Institute of Occupational Medicine, Edinburgh, United Kingdom) and Eric
Chatfield (Chatfield Technical Consulting Limited, Mississauga, Ontario, Canada) and
their collaboration in several supporting studies. Support for data manipulation and
analysis were also provided by Chris Rambin and Tammie Covington of ICF Kaiser,
Ruston.
We would also like to thank Jean Chesson, John Dement, and Phil Cook for their
valuable and stimulating discussions on this work.
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DISCLAIMER
This report was prepared under contract to the U. S.
Environmental Protection Agency. Such support, however,
does not signify that the contents necessarily reflect the
views and policies of the U.S. Environmental Protection
Agency, nor does the mention of trade or commercial
products constitute endorsement of recommendations for
use.
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Table of Contents
1. INTRODUCTION 1-1
2. OVERVIEW 2-1
3. BACKGROUND 3-1
3.1 MORPHOLOGY OF ASBESTOS DUSTS 3-1
3.2 CAPABILITIES OF ANALYTICAL TECHNIQUES USED TO
MONITOR ASBESTOS 3-3
4. THE ASBESTOS LITERATURE 4-1
5. ASBESTOS CHARACTERISTICS THAT RELATED TO BIOLOGICAL
ACTIVITY 5-1
5.1 FACTORS AFFECTING RESPIRABIUTY AND DEPOSITION 5-2
5.1.1 Respirability of Spherical Particles 5-3
5.1.2 Respirability of Fibrous Structures 5-6
5.1.3 Effects of Electrostatic Charge on Panicle Respirability 5-10
5.2 FACTORS AFFECTING DEGRADATION AND CLEARANCE 5-10
5.2.1 Retention Studies 5-12
5.2.2 Human Pathology Studies 5-16
5.2.3 Dynamic Models 5-17
5.3 FACTORS AFFECTING TRANSLOCATION 5-19
5.4 FACTORS GOVERNING BIOLOGICAL RESPONSE 5-21
5.4.1 Gross Features of Biological Response Elucidated
by Injection and Implantation Studies 5-21
5.4.2 Implications Regarding the Mechanics of Cancer
Induction Derived from In-Vitro Studies 5-27
5.5 OTHER FACTORS 5-29
5.6 ANIMAL INHALATION STUDIES 5-30
5.6.1 The Existing Animal Inhalation Studies 5-30
5.6.2 A Re-analysis of the Davis et al. Studies 5-33
5.7 CONCLUSIONS: ASBESTOS CHARACTERISTICS THAT BEST
RELATE TO BIOLOGICAL ACTIVITY 5-40
5.7.1 Addressing Outstanding Issues 5-40
5.7.2 Defining an Exposure Index for Asbestos 5-45
6. ASBESTOS RISK FACTORS 6-1
6.1 OVERVIEW OF THE EPIDEMIOLOGY LITERATURE 6-1
6.2 ADJUSTING ASBESTOS RISK COEFFICIENTS 6-4
6.2.1 Assessing the Risk of Lung Cancer , 6-5
6.2.2 Assessing the Risk of Mesothelioma 6-17
7. RECOMMENDATIONS FOR ASSESSING THE HUMAN HEALTH
RISKS ASSOCIATED WITH EXPOSURE TO ASBESTOS 7-1
in
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8. REFERENCES 8-1
APPENDIX A REVIEW OF RELEVANT EPIDEMIOLOGY STUDIES A-1
APPENDIX B BACKGROUND FOR DEVELOPMENT OF AN AD HOC
EXPOSURE INDEX FOR ASBESTOS B-1
APPENDIX C DERIVATION OF LIFETIME RISKS FOR LUNG CANCER
AND MESOTHELIOMA FROM MODELS USING KL AND
K« ESTIMATES FOR POTENCY C-1
IV
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List of Tables
TABLE 3-1: CAPABILITIES AND LIMITATIONS OF ANALYTICAL
TECHNIQUES USED FOR ASBESTOS MEASUREMENTS 3-4
TABLE 3-2: COMPARISON OF APPLICABLE METHODS FOR MEASURING
ASBESTOS IN AIR 3-6
TABLE 5-1: SUMMARY DATA FOR ANIMAL INHALATION EXPERIMENTS
CONDUCTED BY DAVIS AND COWORKERS 5-34
TABLE 6-1: K, AND Ktf VALUES DERIVED FROM PUBLISHED
EPIDEMIOLOGY STUDIES 6-7
TABLE 6-2: CORRELATION BETWEEN PUBLISHED QUANTITATIVE
EPIDEMIOLOGY STUDIES AND AVAILABLE TEM FIBER
SIZE DISTRIBUTIONS 6-11
TABLE 6-3: REPRESENTATIVE Kt AND KM VALUES PAIRED WITH
AVERAGED TEM FIBER SIZE DISTRIBUTIONS FROM
PUBLISHED PAPERS 6-12
TABLE 6-4: COMPARISON OF ORIGINAL AND ADJUSTED K, AND
RVALUES 6-14
TABLE 6-5: COMPARISON OF SPREAD IN RANGE OF ORIGINAL
AND ADJUSTED Kt AND KM VALUES FOR SPECIFIC
FIBER TYPES 6-16
TABLE 7-1: RECOMMENDED RISK COEFFICIENTS 7-1
TABLE 7-2: ADDITIONAL RISK PER ONE HUNDRED THOUSAND
PERSONS FROM LIFETIME CONTINUOUS EXPOSURE
TO 0.0005 TEM F/ML LONGER THAN 5.0 pm AND
THINNER THAN 0.5 jjm 7-3
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List of Figures
FIGURE 5-1: FRACTIONS OF RESPIRABLE PARTICLES DEPOSITED
IN THE VARIOUS COMPARTMENTS OF THE HUMAN
RESPIRATORY TRACT AS A FUNCTION OF
AERODYNAMIC EQUIVALENT DIAMETER 5-5
FIGURE 5-2: FRACTIONS OF RESPIRABLE PARTICLES DEPOSITED
IN THE VARIOUS COMPARTMENTS OF THE HUMAN
RESPIRATORY TRACT AS A FUNCTION OF THE TRUE
DIAMETER OF ASBESTOS FIBERS 5-7
FIGURE 5-3: FIT OF MODEL: TUMOR INCIDENCE VERSUS
STRUCTURE CONCENTRATION BY TEM 5-38
VI
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1.0 INTRODUCTION
This report presents a protocol for assessing potential human-health risks associated
with exposure to airborne asbestos. It is designed specifically for use in performing
risk assessments at Superfund sites, although it may be applicable to a broad range of
situations.
The protocol itself is presented in Part 1 of the report, under separate cover.
Considerations addressed during the development of the protocol are presented in this
companion technical background document (Part 2 of the report).
In the protocol presented in Part 1, the risk associated with asbestos exposure can be
estimated using either of two procedures. The first procedure, which is preferred when
sufficient data exist to support the required inputs, is to apply an appropriate risk model
(selected from among those presented, based on the end point health effect of interest)
using case-specific data as inputs. The models, the types of data required to support
the models, and the procedures to* use for evaluating each model are defined within the
protocol presented in Part 1.
The second approach, which can be used when supporting data are limited, is to
estimate risk by extrapolation from a risk table. Both the risk table and instructions on
how to use it are provided in the protocol (Part 1 of this document). Limits to the
validity of this approach are also discussed, so that the user can evaluate the
confidence that may be placed in risk estimates derived using this latter technique.
Importantly, the protocol also includes guidelines for proper collection and analysis of
samples to be used to support estimation of asbestos exposure. Estimates of asbestos
exposure in a particular setting can vary by orders of magnitude depending on the
particular method(s) employed to collect, prepare, and analyze samples and to report
results (Berman and Chatfield 1990). Therefore, both the method(s) to be used to
develop exposure data and the exposure index to be used to report results are
specified in the protocol. Correspondingly, the risk coefficients employed in the
protocol have been adapted for use with the specified exposure index.
The models employed for assessing asbestos-related risks (and for deriving the
corresponding risk coefficients) are adapted from those proposed in the Airborne
Asbestos Health Assessment Update (U.S. EPA 1986). The approach has been
modified, however, to better account for the limitations imposed by asbestos analytical
techniques. Studies published since the appearance of the Update have also provided
new insights into the relationship between asbestos measurement and biological
1-1
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activity. Consequently, a review and evaluation of the new studies and key studies
published earlier are presented in this report1.
The purpose for documenting the data and assumptions used to develop the protocol
proposed in Part 1 is to facilitate critical evaluation while highlighting needs for
additional research. Considerations addressed in this report that have been
documented in the literature are cited accordingly. Considerations that remain largely
a subject of conjecture are also noted. Due to the current level of interest and activity
provoked by asbestos, further improvements in asbestos sampling, analysis, and
evaluation are anticipated.
Due to a combination of time and budget constraints, the literature review presented in
this document is not comprehensive. However, we did attempt to identify and include
Key studies and reports that were published at least through 1994.
1-2
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2.0 OVERVIEW
Exposure to 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. However, the disease is 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. 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.
Gastrointestinal cancers and cancers of other organs (e.g. larynx, kidney, and ovaries)
have also been linked with asbestos exposures in some studies. However, such
associations 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, the protocol presented in Part 1 is
focused on risks associated with the induction of lung cancer and mesothelioma.
A variety of human and animal 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 15 epidemiology studies for which adequate
dose-response data exist. Such factors vary widely, however, and the observed
variation has not been reconciled.
Animal studies indicate that asbestos potency is a complex function of several
characteristics of asbestos dusts including fiber size and, possibly, fiber type (i.e., fiber
mineralogy). The influence of such effects cannot be evaluated in the existing
epidemioiogical 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. Thus, the exposure indices
employed in the existing epidemiology studies do not correspond precisely with the
characteristics of asbestos that best relate to biological activity. This hinders the ability
to compare the risk (dose-response) factors 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.
Ideally, it should be possible to define an exposure index that correlates precisely with
biological activity. Using such an index would assure that risk and exposure are linked
by a single, universal relationship in any exposure environment. Asbestos exposures
expressed in terms of such an index would therefore provide an accurate measure of
relative risks. Further, if risk coefficients could also be derived based on such an
2-1
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index, they could be paired directly with exposure estimates (using appropriate models)
to provide estimates of absolute risk that would remain comparable across exposure
environments.
Based on the approach developed for evaluating asbestos-related cancer risk in U.S.
EPA (1986), risk is estimated by a mathematical function that depends on time, the
level of exposure, the duration of exposure, and a risk coefficient that is appropriate for
the disease endpoint of interest The risk coefficient for lung cancer is generally
denoted, "K^ and the one for mesothelioma is "
Detailed descriptions of both the lung cancer and mesothelioma models are provided in
Section 6.2. The models differ depending on whether lung cancer or mesothelioma is
being considered.
For lung cancer, the mode! estimates relative risk, which means that the increase in
lung cancer mortality that is attributable to asbestos exposure is a function of the
background lung cancer mortality in the exposed population. Background cancer
mortality is the rate of deaths from cancer that would be expected to occur in the
population in the absence of asbestos exposure. In other words, background lung
cancer mortality is the rate of lung cancer mortality for an exposed population that is
attributable to all causes other than asbestos.
The mode] for mesothelioma is an absolute risk model. This means that the increase in
mesothelioma attributable to asbestos is independent of the background rats of
mesothelioma, which is negligible in the general population.
Ideally, the risk coefficients derived from the existing epidemiology studies can 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 exposure settings of interest would not be sufficient to assure validity of risk
2-2
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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.
An alternate approach, which was adopted to develop this protocol, is to:
reconcile existing literature by addressing the capabilities and limitations
of the analytical techniques used to measure asbestos in the existing
studies;
evaluate the literature and perform studies to define the characteristics of
asbestos that determine risk,
define a method for measuring asbestos that adequately reflects such
characteristics;
adjust published risk coefficients for asbestos so that they are normalized
and applicable to the same method for measuring asbestos; and
identify the appropriate dose-response models (incorporating
appropriately adjusted risk coefficients) that indicate how to relate
asbestos exposure (that is properly measured arid reported) to risk.
Due to the interrelationship between the steps identified above, the process is
necessarily iterative.
Note that asbestos measurements that are derived using a well-defined and
standardized analytical method are said to reflect a specific index of exposure. Such a
method must specify the sizes, shapes, and mineralogical characteristics of the
asbestos structures that are to be included in the determination of asbestos
concentrations. Therefore, the second of the above-listed steps for developing this
protocol is to define an exposure index for asbestos that adequately reflects the various
asbestos characteristics that determine risk.
Unfortunately, several of the details concerning the characteristics of asbestos that
determine biological activity remain a subject of controversy that cannot be entirely
resolved with existing data. Also, the data set required to adjust existing risk
coefficients for the limitations of analytical methods is incomplete at this time.
Consequently, the approach adopted in this report for deriving asbestos risk
coefficients and applying them using the recommended protocol represent
compromises among several alternatives. Where major uncertainties remain, research
needs are identified.
2-3
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In the remaining chapters of this report:
Chapter 3 provides background information that includes the definition of
asbestos, a characterization of asbestos dusts, and a comparison of the
capabilities and limitations of the various analytical techniques and
methods that are used to measure asbestos exposures.
Chapter 4 presents a characterization of the asbestos literature. It
includes definitions and descriptions of the types of asbestos-related
studies that are available and their relative strengths and limitations.
Chapter 5 presents an evaluation of the asbestos literature, which was
conducted to develop an exposure index for asbestos that reflects
asbestos characteristics that best relate to biological activity.
Chapter 6 presents models that are recommended for estimating
asbestos risks. The models incorporate risk coefficients that are derived
by adjusting published coefficients for use with an asbestos exposure
index that relates to risk.
Conclusions and recommendations are summarized in Chapter 7.
References are provided in Chapter 8.
2-4
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3.0 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: chrysotile, amosite, crocidolite, anthophyllite, tremolite,
and actinolite (IARC 1977). 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.
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.
The general chemical composition of serpentine is reported as Mg3(Si2Os)(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 minerals 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.
3.1 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
3-1
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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 Benman (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 (Herman 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
Cherrieetal. 1987).
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 0.6 and 1.5 urn with larger fibers
occurring with decreasing frequency. Fibrous structures longer than 5 urn constitute no
more than approximately 25% of total asbestos structures in any particular dust and
generally constitute less than 10%.
Diameters appear to exhibit a narrow distribution about a mean for each specific length.
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.
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 recent study of asbestos
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Table 3-2: COMPARISON OF APPLICABLE METHODS FOR MEASURING ASBESTOS IN AIR
Analytical
Technique
Preparation
Methodology
Magnification
Dimensions
Counted
Length (L):
Width (W):
Aspect Ratio
(AR):
Sensitivity:
s/cm'
s/mm*
Mineralogy
Determined
Maximum
Number
Counted
Maximum
Area Scanned
NIOSH 74001
PCM
Direct (no transfer)
450x
L .> 5 urn
W> 0.25pm
AR>3
-';;'!.
No
100 structures
100 fields
NIOSH 7402*
TEM
Direct
10,000x
L > 1 pm
3.0 >W> 0.04pm
AR>3
Yes
100 structures
100 grid openings
YAMATE1
TEM
Direct
(Indirect Optional)
ZO.OOOx
L > 0.06 pm
W > 0.02 pm
AR>3
Yes, except matrix particles
100 structures
10 grid openings
AHERA4
TEM
Direct
15,000x-20.000x
L > 0.5 pm
W > 0.02 pm
AR>5
0.005
70
Yes, except matrix particles
50 structures
Blanks: 10 openings
Samples: 10 openings (assuming
ISO"
TEM
Direct
(Indirect Optional)
20.000x (total structures)
1 0.OOOx (structures longer than 5 pm)
L > 0.5 pm, total structures
L > 5 pm. long structures
W < 3.0 pm (respfrabillty)
AR>5
Adjustable
10 for total structures
0.1 for long structures
Yes
100 total structures
100 long structures
Adjustable
defined sensitivity is achieved with the
collected air volumes).
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Table 3-2 (Page 2)
NIOSH7400 NIOSH7402 YAMATE AHERA
Statistically No No No No
Balanced
Counting'
Counting
Rules
ISO
Yes
Structures
Bundles
Clusters
Count all structures
exhibiting L > 5 urn.
W < 3.0 pm. and
AR>3.
Count all structures exhibiting L > 1
um, W < 3.0 pm. and AR > 3. Note
PCME' fraction within count.
Bundles meeting
overall dimensional
criteria generally
counted as single
fibers unless up to 10
Individual fiber ends
can be distinguished
within the bundle
(representing 5
Individual fibers).
Within a duster, count
up to 10 Individual
fiber ends from (up to
S) fibers that meet the
overall dimensional
criteria. Otherwise,
count a cluster as a
single entity.
Bundles meeting overall
dimensional criteria generally
counted as single fibers.
Within a cluster, count up to 3
individual fibers that meet the
overall dimensional criteria.
Otherwise, clusters that contain
more than 3 fibers that meet the
overall dimensional criteria are
counted as single clusters.
Count all structures exhibiting an
AR>3.
Bundles meeting overall
dimensional criteria generally
counted as single entitles and
noted as bundles on the count
sheet.
Within a duster, count up to 3
Individual fibers that meet the
overall dimensional criteria.
Otherwise, dusters that contain
more than 3 fibers that meet the
overall dimensional criteria are
counted as single dusters.
Count all structures with L > 0.5 um
exhibiting an AR > 5. Record
Individual fibers within all groupings
with fewer than 3 Intersections. Count
structures with L > 5 um separately
(PCME8).
Bundles of 3 or more fibers that meet
the overall dimensional criteria are
counted as single entities and noted
as bundles on the count sheet.
A collection of fibers with more than 2
intersections where at least one
individual projection meets the overall
dimensional criteria Is counted as a
single duster.
Count afl structures wtth L > 0.5 um or
containing components with L > 0.5 pm
that also exhibit an AR> 5. Separately
Identifiable components of parent
structures that satisfy dimensional criteria
are also separately enumerated.
Conduct a similar count to that Indicated
above for structures with L > 5 pm.
Count parent bundles with L > 0.5 pm
containing at least one component fiber
that exhibits an AR > 5.
Qualifying bundles that are components
of other parent structures are also
separately enumerated.
For counts of structures wtth L > 5 pm,
Indude only bundles longer than 5 pm.
Distinguish 'disperse" and compact*
dusters. Count all dusters containing at
least one component fiber or bundle
satisfying appropriate dimensional
criteria. Separately enumerate up to
10 component structures satisfying '
appropriate dimensional criteria. ..., :.:
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Table 3-2 (Page 3)
NIOSH 7400 NIO3H 7402 YAMATE AHERA ISO
Matrices Count up to 5 fibers Count individually identifiable fibers Count as a single matrix, all Count as a single matrix, ail matrices Distinguish "disperse* and "compact*
emanating from a within a matrix. Fibers must matrices with at least one with at least one protruding fiber such matrices. Count all matrices containing
dump (matrix). Each individually meet the dimensional protruding or embedded fiber that that the protruding section meets the at least one component fiber or bundle
Individual fiber must criteria. meets the dimensional criteria. dimensional criteria. satisfying appropriate dimensional
meet the dimensional criteria. Separately enumerate up to
criteria. 10 component structures satisfying
appropriate dimensional criteria.
1. National Institute for Occupational Safety and Health (1985). Method for Determination of'Asbestos In Air Using Positive Phase Contrast Microscopy. NIOSH Method 7400. NIOSH,
Cincinnati, Ohio, U.S.A.
2. National Institute for Occupational Safety and Health (1086). Method for Detemination of Asbestos in Air Using Transmission Electron Microscopy. NIOSH Method 7402. NIOSH.
Cincinnati, Ohio, U.S.A.
3. Yamate, G., Agarwal, S.C., and Gibbons, R.D. (1984). Methodology for the Measurement of Airborne Asbestos by Electron Microscopy. U.S. EPA Report No. 68-02-3266. U.S.
Environmental Protection Agency, Washington, D.C., U.S.A.
4. U.S. Environmental Protection Agency (1987). Asbestos Hazard Emergency Response Act: Asbestos-Containing Materials in Schools. Final Rule and Notice (Appendix A: AHERA
Method). Federal Register, 40 CFR 763, Vol. 52, No. 2, pp. 41826-41903, October.
5. IS010312: Ambient Air Determination of Asbestos Fibres - Direct-Transfer Transmission Electron Microscopy Method. (Developed by Chatfield, E. J. 1993).
6. Note that the ISO Method is closely related to to the Interim Superfund Method: Chatfield, E.J. and Berman, D. W. (1990). Superfund Method for the Determination of Asbestos In
Ambient Air. Part 1: Method Interim Version.. Prepared for the U.S. Environmental Protection Agency, Office of Emergency and Remedial Response, Washington, D.C.
EPA/540-2-90/005a. May. Both methods derive from a common development effort headed by Eric Chatfield. The ISO Method incorporates counting rules that are further refined over
what was presented in the Superfund Method, which was published first.
7. Statistically balanced counting is a procedure Incorporated into some asbestos methods (e.g. the Superfund Methods and the ISO Methods) in which long structures (typically longer than
5 urn) are counted separately during a lower magnification scan than used to count total structures (which are predominantly short). This procedure assures that the relatively rare longer
structures are enumerated with comparable precision to that of the shorter structures.
8. PCME stands for phase contrast microscope equivalent and indicates the fraction of structures observed by transmission electron micrscopy that would also be visible by phase contrast
microscopy.
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characteristics that promote biological activity (Berman et al. 1995), which is discussed
further in Section 5.6.2.
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 5. Thus, unlike the majority of other chemicals frequently
monitored at hazardous wastes sites, asbestos exposures cannot be adequately
characterized by a single concentration parameter.
3.2 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.
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 coefficients 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.
3-3
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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 (Serman and Chatfield 1990). 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 3-1.
Table 3-1:
Capabilities and Limitations of Analytical Techniques Used for
Asbestos Measurements
1
Parameter
Range of
Magnification
Particles Counted
Minimum Diameter
Midget Phase Scanning
Impinger Contrast Electron
Microscopy Microscopy
100 400
All Fibrous
Structures2
1 urn 0.3 urn
2,000 -
10,000
Fibrous
Structures2
0.1 urn
Transmission
Electron
Microscopy
5,000 - 20,000
Fibrous
Structures2-3
< 0.01 urn
(size) Visible
Resolve Internal
Structure
Distinguish
Mineralogy4
No
No
No
No
Maybe
Yes
Yes
Yes
1. The capabilities and 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 last 25 years are highlighted in Table 3-2.
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 below.
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.
3-4
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Importantly, the physical limitations of the various analytical techniques
(instrumentation) 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 3-2 to illustrate how the choice of method can result in substantially
different measurements (even on duplicate or split samples).
The second column of Table 3-2 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
have also been used historically (see Walton 1982). 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 3-2 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 3.1), 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 they can be readily distinguished. In contrast,
clusters are counted as single structures under the AH ERA 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
3-5
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Table 3-2: COMPARISON OF APPLICABLE METHODS FOR MEASURING ASBESTOS IN AIR
.Analytical
technique
Preparation
Methodology
Magnification
Dimensions
Counted
Length (L):
Width (W):
Aspect Ratio
5pm ' *
< 1
-W> 0.25pm i
AR>3 "
* r ~>f ~* -f
"*f * -1 V
f t 1 "^ *-
! .j ''HL.jf. J. J
1 1 * '"'tS-aC ;f
j j- r ^ **. ^-^
I
I r ^~ f
No ,'
i
100 structures ~
100 fields
NIOSH74021
"i TEM
Direct
10,000x
L>1pm
3.0 > W > 0.04 pm
J AR>3
^ s
J
Yes
100 structures
100 grid openings
YAMATE3 :
TEM ': :'..-' »: .' ' '
Direct ',:'. ; ""I ;'.;.'
(Indirect Optional) >':. ::
20,000x '';-'. :
' '-; ; ' : ':'<],... '
L> 0.06pm : , - "'''- J.': ;
W> 0.02pm ; "i
Yes, except matrix particles ;
100 structures :''''. --.''
10 grid openings
AHERA4
.-.. TEM
: Direct
':: i5.ooox-20,ooox
.':': ; L> 0.5 pm
-.:.. W> 0.02pm
1 AR>5
0.005
70
Yes, except matrix particles
50 structures
Blanks: 10 openings
Samples: 10 openings (assuming
ISO1*
;TEM
Direct '
i (Indirect Optional)
!20.000x potal structures)
' lO.OOOx (structures longer than 5 pm)
;." ; , :
L > 0.5 pm, total structures
L > 5 pm, long structures
W < 3.0 pm (resplrabillty)
;,AR > 5 , '
Adjustable
10 for total structures
0.1 for long structures
Yes
100 total structures
100 long structures
Adjustable
denned sensitivity is achieved with the
collected air volumes).
-------
Table 3-2 (Page 2)
NIOSH7400 NIOSH7402 YAMATE ;; i AHERA
Statistically No '':-' : No No !< No
Balanced ; ' , ',
Counting' : ' : : : ,
Counting ..-.,.,.. . \:.,. .->' >. ' . : ' ' ' l ' ' -\ :
Rules ; : '" . . . ' ''" ;:':'- : ' . ; ;: '. ' .
ISO
Yes
* ' :
Structures
Count all structures
exhibiting L> 5pm.
W< 3.0pm, and
AR>3
Count all structures exhibiting L > 1
\im, W < 3.0 pm, and AR > 3. Note
PCME' fraction within count.
Bundles Bundles meeting
overall dimensional
criteria generally ',
counted as single^
fibers unless up to 10
individual fiber ends
^ can be distinguished
1 within the bundle f
(representing 5 '
Individual fibers)
Clusters i Within a cluster, count
up to 10 Individual
fiber ends from (up to
5) fibers that meet the,
overall dimensional :
: criteria. Otherwise,
count a cluster as a '
: single entity, r* :.
Bundles meeting overall
dimensional criteria generally
counted as single fibers.
Within a cluster, count up to 3
individual fibers that meet the
overall dimensional criteria.
Otherwise, clusters that contain
more than 3 fibers that meet the
overall dimensional criteria are
counted as single clusters.
Count all structures exhibiting an
AR>3. . ;: : ; ;''vf..,.-
Bundles meeting overall
dimensional criteria generally
counted as single entities and
noted as bundles on the count
sheet. ' ; :'r, . ''"'; .
Within a cluster, count up to 3
Individual fibers that meet the
overall dimensional criteria.
Otherwise, clusters that contain
more than 3 fibers that meet the
overall dimensional criteria are
counted as single clusters.'
Count all structures with L > 0.5 urn
exhibiting an AR > 5. Record
individual fibers wilhin all groupings
with fewer than 3 intersections. Count
structures with L > 5 pm separately
(PCME8).
Count all structures with L > 0.5 urn or
.containing components with L > 0.5 pm
that also exhibit an AR > 5. Separately
Identifiable components of parent
structures that satisfy dimensional criteria
j are also separately enumerated.
Conduct a similar count to that Indicated
: above for structures with L > 5 um.
Bundles of 3 or more fibers that meet -Count parent bundles with L > 0.5 pm
the overall dimensional criteria are
counted as single entities and noted
as bundles on the count sheet.
A collection of fibers with more than 2
intersections where at least one
individual projection meets the overall
dimensional criteria is counted as a
single cluster.
- containing at least one component fiber
that exhibits an AR > 5.
Qualifying bundles that are components
of other parent structures are also
separately enumerated.
.' . '
For counts of structures with L > 5 pm,
Include only bundles longer than 5 pm.
Distinguish 'disperse* and 'compact*
clusters. Count all clusters containing at
, least one component fiber or bundle
satisfying appropriate dimensional
.criteria. Separately enumerate up to
10 component structures satisfying
appropriate dimensional criteria.
-------
Table3-2 (Paged)
.:NjpSH 7400 f>v NIOSH7402 ._. YAMATE AHERA ISO :
Matrices Count up to 5 fibers ! Count individually identifiable fibers : Count as a single matrix, all Count as a single matrix, all matrices Distinguish 'disperse* and 'compact*
emanating from a within a matrix. Fibers must matrices with at least one. with at least one protruding fiber such matrices. Count all matrices containing
clump (matrix). Each i individually meet the dimensional . protruding or embedded fiber that : that the protruding section meets the ; at least one component fiber or bundle
individual fiber must criteria. j meets the dimensional criteria. ; dimensional criteria. satisfying appropriate dimensional
meet the dimensional ? j , : . ; ; : criteria. Separately enumerate up to
criteria. ," :: . . 10 component structures satisfying
' ' appropriate dimensional criteria.
1. National Institute for Occupational Safety and Health (1985). Method for Determination of Asbestos in Air Using Positive Phase Contrast Microscopy. NIOSH Method 7400. NIOSH.
Cincinnati, Ohio, U.S.A.
2. National Institute for Occupational Safety and Health (1986). Method for Determination of Asbestos in Air Using Transmission Electron Microscopy. NIOSH Method 7402. NIOSH,
Cincinnati. Ohio, U.S.A.
3. Yamate, G., Agarwal, S.C., and Gibbons, R.D. (1984). Methodology for the Measurement of Airborne Asbestos by Electron Microscopy. U.S. EPA Report No. 68-02-3266. U.S.
Environmental Protection Agency, Washington, D.C., U.S.A.
4. U.S. Environmental Protection Agency (1987). Asbestos Hazard Emergency Response Act: Asbestos-Containing Materials in Schools. Final Rule and Notice (Appendix A: AHERA
Method). Federal Register, 40 CFR 763, Vol. 52, No. 2, pp. 41826-41903. October.
5. ISO 10312: Ambient AJr - Determination of Asbestos Fibres Direct-Transfer Transmission Electron Microscopy Method. (Developed by Chatfield, E.J. 1993).
8. Note that the ISO Method is closely related to to the Interim Superfund Method: Chatfield, E.J. and Berman, D.W. (1990). Superfund Method for the Determination of Asbestos in
Ambient Air. Part 1: Method Interim Version.. Prapored for the U.S. Environmental Protection Agency, Office of Emergency and Remedial Response. Washington, D.C.
EPA/540-2-90/005a. May. Both methods derive from a common development effort headed by Eric Chatfield. The ISO Method incorporates counting rules that are further refined over
what was presented in the Superfund Method, which was published first.
7. Statistically balanced counting Is a procedure incorporated into some asbestos methods (e.g. the Superfund Methods and the ISO Methods) in which long structures (typically longer than
5 urn) are counted separately during a lower magnification scan than used to count total structures (which are predominantly short). This procedure assures that the relatively rare longer
structures are enumerated with comparable precision to that of the shorter structures.
8. PCME stands for phase contrast microscope equivalent and indicates the fraction of structures observed by transmission electron micrscopy that would also be visible by phase contrast
microscopy.
-------
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:
Ml, PCM, SEM, and JEM are all particle counting techniques and the
latter three count fibrous particles;
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;
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).
3-9
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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.
3-10
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4.0 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 error commonly associated with each. Such
limitations must be considered when drawing conclusions from the available studies
and, more importantly, when comparing inferences across studies.
The types of studies available for examining relationships between risk and asbestos
exposure include human epidemiology studies, human pathology studies, animal
studies, and in-vitro tissue or cultured cell studies. Because results from such studies
need to be compared and contrasted, the methods employed for asbestos
characterization in each of these studies need to be reconciled and the procedures
employed for evaluating study effects need to be compared and contrasted.
When evaluating the asbestos literature, it is particularly important to assess the
analytical methodologies employed in each study (Section 3.2). The only instrument
capable of completely delineating structure size distributions is TEM (or TEM combined
with other techniques). Thus, conclusions regarding variations in biological effects due
to differences in size distributions must be viewed with caution when size distributions
are derived using only PCM, SEM, or other analytical techniques. As also indicated
previously, the specific methods chosen to define the protocols for sample preparation
and structure counting also affect the outcome of measurements so that structure size
distributions derived using different methods cannot be directly compared (even, for
example, when the measurements are all obtained using TEM).
Epidemiology studies, which track the incidence of disease 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
coefficients. However, 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 is generally lacking.
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 protocol to adapt the existing risk coefficients to exposure indices thought to
better relate to biological activity (Section 6.2). Such an approach is limited, however,
to the extent that the published asbestos characterizations actually represent exposure
conditions in the corresponding epidemiology studies.
4-1
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Human pathology studies provide a characterization of disease morphology and
correlations between causes of death and the types of asbestos fibers retained in the
Jungs 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 the 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 prepared for analysis
(e.g. ashing or bleach digestion), the choice of methods employed for characterization
of asbestos, and the choice of locations from which tissue samples are collected for
analysis. Fiber concentration estimates have been shown to vary substantially
depending on whether tissue samples are ashed or digested in bleach prior to
asbestos analysis (HEI-AR 1991). The ways in which choice of analytical methods can
affect the outcome of these kinds of studies was described in the last Chapter
(Section 3.2). Effects attributabJe to choice of tissue location are described further
below.
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
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 attendant 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 human epidemiology studies is that
exposures are controlled and can be well characterized. The major disadvantage is
that uncertainties are introduced when extrapolating the results of animal data to
predict effects in humans.
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 asbestos structures. 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 the animal studies but not other
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.
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
4-2
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injection or implantation studies deliver 100% 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. Thus, the relationship between dose and exposure
depends upon the route of exposure employed.
Regarding the measurement of health effects, many of the results in animal studies
suffer from a lack of statistical significance because only small numbers of tumors are
observed. Consequently, trends cannot be established conclusively. Interestingly,
some 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.
In both human and animal pathology studies, the location of a tissue sample excised for
analysis is a critical factor that governs the quality of the study. Numerous authors
have reported that asbestos is non-uniformly distributed in lung parenchyma and other
tissues following exposure (see, for example: Davis et al. 1986, 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.
To sample deep lung tissue reproducibly, it is 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). Unfortunately, few of the available animal and
human pathology studies have considered this important factor when selecting tissue
samples for analysis.
For animal inhalation studies, meaningful comparison of the relative deposition of
asbestos dusts between species is not direct. To extrapolate results between species,
differences in physiology between species need to be addressed. However, if
measurements are available for both species, it is possible to compare relative tissue
doses (mass of asbestos per mass of tissue).
In-vrtro tissue studies typically involve the dosing of isolated tissue cultures with
various types and quantities of asbestos while monitoring for changes in levels of
various enzymes or metabolites within the system. In some cases, cultures are
monitored for morphological changes within the cells of the system or for changes in
the growth or survivability of the culture as a whole. Limitations in such studies may
include:
4-3
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uncertainties introduced by the methods employed for characterizing the
asbestos samples used for dosing;
uncertainties introduced when attempting to equate dosing carried out in
in-vitro cultures and dosing associated with real-world exposures of
interest; and
effects attributable to uncontrolled differences between the environment
of the tissue culture and the in-vivo environment being simulated.
At the same time these types of studies are particularly useful for elucidating the
biophysical and biochemical mechanisms that contribute to the induction of asbestos-
related disease.
Many studies in the current literature also incorporate combined aspects of several of
the four general study types described above. For these studies, a corresponding
combination of the considerations described above must be addressed when
evaluating such studies and comparing their results with inferences derived from the
rest of the literature.
4-4
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5.0 ASBESTOS CHARACTERISTICS THAT RELATE TO BIOLOGICAL ACTIVITY
As indicated in Chapter 2, the first two steps of the process required to develop a
protocol for assessing asbestos risk are: (1) to reconcile the existing literature by
considering the capabilities and limitations of the methods employed for data
development and (2) to identify the characteristics of the asbestos that determine risk.
Accordingly, the principal processes that determine the biological response to asbestos
are identified below and the relevant literature characterizing such processes is
evaluated. Based on the analysis presented, the characteristics of asbestos that
determine biological activity are also identified.
It is generally recognized that risks posed by exposure to asbestos dusts depend
predominantly on the physical dimensions of the fibrous structures in the dust (e.g. U.S.
EPA 1986, Mossman et al. 1990). However, other factors that relate to mineralogy and,
therefore, fiber type (such as surface chemistry and in-vivo durability) have also been
shown to affect biological activity in at least some studies (Mossman 1993).
Traditional methods of characterizing exposure have not proven adequate to develop a
dose/response relationship for asbestos that uniformly applies under all exposure
circumstances. This is likely due to inadequate understanding both of the critical range
of dimensional parameters that best correlate with risk and of the degree to which fiber
type affects risk.
The principal outstanding issues are:
whether short fibers (which were excluded from the asbestos analyses
performed in the majority of available epidemiology studies) contribute
substantially to risk;
whether thin fibers (which cannot be detected by the analytical methods
used in the majority of the available epidemiology studies) contribute
substantially to risk;
whether asbestos aggregates (bundles, clusters, and matrices) should be
ignored, counted as single entities, or weighted in proportion to the
number of individual fibers present in each aggregate;
whether fibers exhibiting the same dimensions but of varying mineralogy
(i.e. varying fiber type) contribute uniquely to risk;
whether fiber types in addition to those included in the currently accepted
definition of asbestos contribute to risk (assuming that they also exhibit
appropriate dimensional characteristics);
5-1
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whether factors addressing fiber size and fiber type relate to the induction
of each of the asbestos-related diseases (primarily asbestosis, pulmonary
cancer, and mesothelioma) in the same manner; and
whether the currently accepted dose-response models for each of the
asbestos-related diseases adequately describe the induction of each
respective disease.
To date, the main emphasis has been placed on animal implantation and injection
studies for identifying the characteristics of asbestos that determine biological activity.
Consequently, there are questions concerning the exact relationship between effects
observed in such studies and effects that obtain when exposure is via inhalation.
Another issue that remains unresolved is the extent to which animal data can be
extrapolated to human exposures. These issues are considered more fully in the
following sections of this Chapter.
Given the considerations addressed in Chapters 3 and 4, selected studies from the
available literature provide a consistent picture of asbestos characteristics that relate to
biological activity. The objectives are to identify the specific morphological
characteristics (e.g. length, surface area, mass, surface charge, chemical composition)
of asbestos structures that best correlate with biological activity and to delineate the
range of values for each characteristic over which biological activity is important.
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 translocated from the lung to
neighboring tissues; and
the extent that retained structures induce a biological response in the
surrounding tissue.
5.1 FACTORS AFFECTING RESPIRABIUTY 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). To be respirable, an inhaled particle must pass the blocking hairs and
5-2
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tortuous passageways of the nose and throat and be deposited in the lungs. Particles
deposited in the naso-pharengeal portion of the respiratory tract are not considered
respirable.
Not all of the inhaled particles 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 for biological
effects to occur. For mesothelioma, an offending particle must also be translocated
from the lung to surrounding mesenchyme.
It is the 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. The factors affecting retention are addressed
further in Section 5.2.
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 terminal bronchioles and the alveoli, which are
collectively referred to as the "deep lung", are the bronchio-alveolar (or pulmonary)
portion of the respiratory tract
The dimensional requirements for respirability have been studied and reviewed by
several authors (see, for example, Raabe, 1984 or U.S. EPA 1986). 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. 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.
5.1.1 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,
5-3
-------
an increasing fraction traverses the nose and throat and may be deposited in the lungs.
About half of particles 5 pm 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 5-1 (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 5-1), 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). 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 urn diameter.
As indicated in Figure 5-1, a transition occurs at particle diameters between 0.5 and
1 pm. 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). 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 pm 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 pm transition, the diffusional diameter
becomes more important in determining deposition than the aerodynamic equivalent
diameter (defined below).
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FIGURE 5-1
FRACTIONS OF RESPIRABLE PARTICLES DEPOSITED IN THE VARIOUS
COMPARTMENTS OF THE HUMAN RESPIRATORY TRACT AS A FUNCTION
OF AERODYNAMIC EQUIVALENT DIAMETER-*
1.0
i i i ii niViiin nil 1 i .1 1/1
10.0
o
0)
"I
»-*
0)
C
c
o
t5
nJ
0.01
Diameter of Spherical Particle with Density Equal to One g/cm (urn) c
Source: Raabe 1984.
b 3
Assumes a typical tidal value of 1450 cm and a rate of 15 breaths per minute.
° Aerodynamic equivalent diameter.
-------
5.1.2 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 Timbrel!
1977, Strom and Yu 1994, or Yu et al. 1995). 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.
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 5-2
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 5-2
is an overlay of Figure 5-1. 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 5-2 represent effective limits to the range of
respirable asbestos. The left line in the figure represents the limiting diameter of the
smallest chrysolite 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 deap 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.
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 at the expense
of increasing naso-pharyngeal or tracheo-bronchial deposition. Thus, fewer clusters
and matrices, which exhibit complex shapes, are deposited in the deep lung than fibers
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FIGURE 5-2
FRACTIONS OF RESPIRABLE PARTICLES DEPOSITED IN THE VARIOUS
COMPARTMENTS OF THE HUMAN RESPIRATORY TRACT AS A FUNCTION
OF THE TRUE DIAMETER OF ASBESTOS FIBERS'66
.002
The Diameter of Asbestos Fiber
.01 .02 .05 .1 .2 .5
1.0
0.01
0.1 1.0 10.0
Diameter of Spherical Partide with Density Equal to One o/cm (um) e
Source of original: Raabe 1984.
Assumes a typical tidal value of 1450 cm and a rate of 15 breaths per minute.
° The relationship between true diameters and aerodynamic equivalent
diameters derived from Harris & Timbrell (1977). Diameters
adjusted for shape and density of asbestos fibers.
Aerodynamic equivalent diameter.
-------
and bundles, which are essentially cylinders. This change also becomes increasingly
important as the length of the structure increases.
For structures less than 25 urn in length, the difference in deposition between simple
fibers and complex dusters 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 urn
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 respirabie 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 urn in length with a true diameter less than 0.7 urn).
The efficiency decreases slowly with increasing length (up to an effective limit of
200 urn), moderately with increasing complexity of shape, and rapidly with increasing
diameter (up to an effective limit of 2.0 urn, true diameter). Thinner fibers, down to the
lower limit of the range for asbestos fibers (0.02 urn, true diameter), are deposited with
roughly the same efficiency. Approximately 20 to 25% of the fibers within this range
are deposited in the deep lung.
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. 1995), 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 urn), shorter fibers tend to be deposited in the deep lung much
5-8
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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 bundles than the mass fraction of bundles in the air
breathed.
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).
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 urn and thinner than approximately 1 urn occurs
at much higher rates in rats than in humans. Fibers as long as 90 urn 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. (1995) 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.
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5.1.3 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 has not been elucidated.
Davis et al. (1988b) report that animals exposed to dusts containing fibrous chrysotile,
whose surface charge is reduced with a beta minus source, retain substantially 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 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.
5.2 FACTORS AFFECTING DEGRADATION AND CLEARANCE
Degradation and clearance mechanisms compete with deposition to determine the
fraction of asbestos that is retained in the lungs. These factors affect all of the toxic
end points of interest
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 mechanisms operating in each section (Raabe, 1984). 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
expectorated.
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The terminal bronchioles and alveoli of the deep lung are not ciliated. Particles
deposited in this section of the respiratory tract can only be cleared by the following
mechanisms:
if the deposited particles are soluble or if their structural integrity is
insufficient to withstand the physical stresses of the lung environment,
they may be degraded by splitting or dissolution and their components
transported away from the lungs by blood or lymph;
if they are sufficiently compact to be taken up through phagocytosis into
the cells lining the air passageways of the deep lung, they may be
transported into cell interiors or transported through to the basement
membranes of the lungs or to the lymphatic system; or
if they are sufficiently compact, they may be taken up by macrophages
and transported into the lymphatic system or transported outward to the
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,
asbestos structures may degrade by splitting. Longitudinal splitting, primarily of
bundles, produces thinner structures and transverse splitting produces shorter
structures. Observations from several studies suggest that chrysotile asbestos
commonly undergoes longitudinal splitting in the deep lung while amphiboles appear
relatively resistant to splitting (see, for example, Roggli et al. 1987). By changing the
size and number of structures that were initially deposited in the lungs, splitting may
affect the rates at which the various other degradation and clearance mechanisms
operate.
The clearance mechanisms described above for the deep lung may be common to
other tissues potentially invaded by asbestos such as the mesenchyme (see, for
example, Stanton 1977).
The different clearance mechanisms that are active within the respiratory tract operate
over different time frames (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.
Once in the lungs, the majority of fibers with diameters greater than 2 urn tend to be
deposited in the upper bronchioles, which efficiently clear such particles. For asbestos
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to remain within the lung long enough to cause a biological response, it generally must
be deposited in the terminal bronchioles or the alveoli beyond the ciliary escalator. As
indicated in the last section, more than one half of asbestos fibers less than 3 urn in
diameter that are deposited in the lungs are expected to be removed by ciliary actions
and subsequently swallowed.
It is primarily the asbestos particles that are deposited in the deep lung, beyond the
ciliary escalator, that present a substantial potential for causing adverse health effects.
Clearance in the other portions of the respiratory tract is sufficiently rapid that biological
effects associated with asbestos exposure are seldom manifested. Although there are
a small number of studies that indicate a potential connection between asbestos
exposure and cancers of the nose and throat the evidence is much less compelling
than the overwhelming evidence supporting a link between asbestos inhalation and
lung cancer or mesothelioma (U.S. EPA 1986). Similarly, a number of studies show
that asbestos deposition in the deep lung are heaviest at alveolar duct bifurcations and
biological responses appear to be initiated where deposition is heaviest (see, for
example, Brody et al. 1981, Davis et al. 1987, and Johnson 1987).
Detailed knowledge of clearance and degradation mechanisms have been developed
primarily from animal retention studies and human pathology studies. Dynamic models
have also been developed.
5.2.1 Retention Studies
Retention studies track the time-dependence of the lung burden of asbestos (the
concentration of asbestos in the lung) during or following 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.
Results from retention studies must be evaluated carefully. This is because 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. For example, lung burden estimates may vary substantially
depending on whether lung tissue is ashed or dissolved in bleach during sample
preparation (Chapter 4).
Retention studies that track lung burden following a single, short term exposure tend to
confirm that deposition rates for similarly sized amphibole and chrysotile structures are
comparable (see, for example, Roggli et al. 1987). Thus, respirability and deposition
are processes that do not depend on mineral type. Roggli and coworkers report that
19% of the mass of crocidolrte structures inhaled by rats is retained in the lungs
immediately following exposure. Similarly, 23% of the mass of inhaled chrysotile is
retained.
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Like several other studies, Roggli et al. observed in this study that clearance
mechanisms are size dependent and that short structures are cleared more readily than
long structures. They also observe that chrysotile splits longitudinally in the lung
environment, which increases the number concentration of (thinner) structures in the
short term (before clearance mechanisms become dominant). In contrast with several
other studies, they also report that clearance rates are independent of mineral type.
Thus, for example, after four weeks, Roggli et al. report that 75% of crocidolite
structures and a comparable 81% of chrysotile structures were cleared from the lungs
of the rats studied in this experiment.
In contrast to the above, retention studies that track lung burden during chronic
exposure suggest that clearance mechanisms may depend on mineralogy (and, thus,
fiber type). Wagner et al. (1974), for example, 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, chrysotile lung burdens reach a plateau despite continued exposure. This
indicates that a steady state between deposition and clearance is reached for
chrysotile while removal processes for amphiboles are much slower. The
concentration at which chrysotile reaches a plateau is shown to be a function of the
level of exposure. Corresponding results have also been reported in other, similar
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 to be deposited
and retained in the lungs of exposed rats than chrysotile. Chrysotile is
also apparently cleared more readily than amosite.
Jones et al. (1988) report that the lung-tissue concentration of amosite
(an amphibole asbestos) increases continually with exposure 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 chrysotile lung burdens
were shown to reach equilibrium in other retention studies.
Several related animal studies suggest ways in which mineralogy may affect the
efficiency of clearance, particularly with regard to susceptibility to degradation. For
example, Bellman et al. (1986), Roggli et al. (1984 and 1987), and Wright and Kushner
(1975) all indicate that glass and certain other materials (including chrysotile asbestos)
appear to dissolve and degrade in vivo.
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Bellman et al. (1986) showed that chrysotile and glass structures instilled
into rats rapidly biodegrade. Acid treated structures degrade even more
rapidly. Chrysotile was also observed to split longitudinally in this study.
In contrast, long crocidolite structures, which do not degrade in vivo,
were removed only slowly, with a half life greater than 1000 days.
While amphiboles appear relatively immune to chemical attack,
Thommassin et al. (1980) have demonstrated that chrysotile readily loses
magnesium in acid solution. In strong acid, the loss occurs in stepwise
fashion where the surface magnesium is lost first. Although the process
is slower, acids of the Krebs cycle (found in living tissue) also leach
magnesium from chrysotile.
Le Bouffant et al. (1978) also confirm chrysotile bundles dissociate in vivo
over time.
A range of studies also indicate that chrysotile asbestos undergoes longitudinal
splitting in the environment of the lung (see, for example, Roggli et al. 1987, Kauffer et
al. 1987, or Kimizuka et al. 1987). In contrast, amphiboles are not observed to undergo
longitudinal splitting in the lung.
Several retention studies indicate that clearance mechanisms operating in the deep
lung exhibit dimensional selectivity. For example, a number of animal studies indicate
that short structures are preferentially cleared from the deep lung.
Bellmann et al. (1986) found that fibrous structures shorter than 5 pm
were cleared with a half life of 150 days from rats receiving glass,
chrysotile, or crocidolite via intratracheal installation.
In two studies (Roggli et al. 1987 and Roggli and Brady 1984), short
crocidolite and chrysotile structures were preferentially cleared from rat
lungs following inhalation.
Wright and Kushner (1975) intratracheally instilled paired samples each
of glass, fluoramphibole, and crocidolite into guinea pigs. For each
mineral tested, a sample with predominantly short structures and another
with predominantly long structures were evaluated. The long structures
uniformly caused fibrosis while the short structures were uniformly
phagocytized and removed to thoracic lymph nodes. Based on the
relative size distributions of the samples analyzed, structures up to 10 urn
in length appear to be efficiently scavenged by macrophages. Glass
structures also underwent biodegradation so that longer structures broke
down into shorter structures that could be phagocytized.
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In a study by Morgan et al. (1978), structures shorter than 10 urn were
efficiently scavenged from the lungs of rats inhaling radiolabled
anthophyllite. Structures up to 40 urn were partially removed by the
process. However, the efficiency of phagocytosis appears to decrease
substantially for structures longer than 10 urn, based on the observation
that such structures remain in the lung for extended periods.
In a study of rats inhaling chrysotile for a single, 5-hour period, Kauffer et
al. (1987) report that the average length of fibers observed in lungs
increases over time (suggesting that shorter structures are preferentially
cleared). In this study, fibers shorter than approximately 8 urn are
preferentially cleared.
In contrast, a small number of studies present a lack of evidence that clearance
mechanisms are dependent on size (see, for example, Roggli et al. 1987). However,
the manner in which the Roggli study was conducted may limit the ability to detect time-
dependent changes in size distributions (see below).
Among the considerations that must be addressed when interpreting the results of
retention studies is the limitations attributed to the procedures employed to count and
to characterize the dimensions of the fibers encountered. For example, studies that
employ optical microscopic techniques to characterize lung burdens may report higher
clearance rates for chrysotile asbestos than the true rate of clearance because (with
time) longitudinal splitting simply causes the remaining chrysotile fibers to become too
thin to observe (see Section 3.2). However, because documentation of the procedures
and analytical techniques employed is severely limited in most of these studies, it is not
possible to evaluate the effects that may be due to limitations in measurement.
Another limitation in these studies (except Kauffer et al. 1987) is the lack of
consideration of the effect of fiber diameter. Therefore, results relating to size
dependence should likely be interpreted qualitatively rather than quantitatively. A strict
cutoff in the length of fibers below which removal by phagocytosis is 100% effective
has not been identified. Rather, the effectiveness of phagocytosis appears to decrease
rapidly for structures between 5 to 20 urn. Structures longer than 20 urn are not
effectively cleared. The selectivity of clearance with respect to diameter is less clear.
Given the body of evidence provided by the available retention studies, it appears that:
deposition efficiency is dependent on structure size and shape but
independent of chemistry or mineralogy;
clearance mechanisms are dependent on mineralogy at least to the extent
that mineral type determines susceptibility to mechanical splitting or
dissolution; and
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at least a subset of clearance mechanisms exhibit a size dependence with
smaller fibers being preferentially cleared.
The last of the above conclusions, however, remains somewhat controversial, due to
conflicting results from different studies. Also unresolved is the mechanism(s)
contributing to the observed difference in retention of chrysotile and amphiboles.
Given limits to the rates of dissolution, solubility is unlikely to account for the difference.
Among other possibilities, two are worth noting:
(1) in some studies, longitudinal splitting of chrysotile fibers may rapidly
reduce the bulk of chrysotile to structures that are not detectable by many
of the procedures traditionally employed to measure them; or
(2) amphiboles may induce a stronger toxic response than chrysotile in the
tissues that the asbestos structures contact. This, in turn, may facilitate
sequestration and hinder clearance of the amphibole as repair
mechanisms respond to the damaged tissue surrounding the offending
fibers. Alternately, such responses may contribute to overload
mechanisms that have been reported in several studies to hinder
clearance in general (see Sections 5.2.3 and 5.4).
Further research is required to distinguish among these and other possible
explanations for the observed difference in retention of chrysotile and the amphiboles.
5.2.2 Human Pathology Studies
Human pathology studies provide additional information concerning the nature of
asbestos deposition, clearance, and retention. These are 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 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
(Chapter 4).
Observations from human pathology studies generally reinforce conclusions drawn
from animal retention studies. For example, several studies indicate that chrysotile is
cleared much more rapidly and completely than amphiboles from the human lung.
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In a study of lung burdens in mesothelioma victims, Churg et al. (1984)
showed that amphiboles structures were 5 to 15 times as plentiful as
chrysotile despite the predominantly chrysotile exposure. This is likely
due to differential clearance since deposition efficiencies appear to be
similar (Section 5.1).
Similarly, Pooley (1976) found substantial concentrations of tremolite (an
amphibole) in the lungs of deceased Quebec chrysotile workers despite
the fact that tremolite is an extremely minor contaminant of the chrysotile
ore being mined. Based on this observation, chrysotile is preferentially
cleared from the lung.
Similarly, some pathology studies indicate that at least some clearance mechanisms
show a dependence on fiber size. 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 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). 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 compared.
5.2.3 Dynamic Models
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 two for different asbestos mineral types. In this
study, however, the term retention appears to refer primarily to a very
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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, Yu et al. (1990a) developed a model of chrysolite 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) 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-alveolar tree
(including the 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
byYuetal.).
« 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. This modification was apparently required to adequately mimic
the suppression of clearance with increasing lung burdens that has been
observed by several research groups (e.g. Wagner et al. 1974 or
Davis et al., 1978). Conditions under which 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 several
studies.
That Yu and coworkers found it necessary to consider the effects of overloading in
modeling amosite retention but not when modeling chrysotile is consistent with
observations that amphibole lung burdens tend to increase with chronic exposure while
chrysotile lung burdens tend to level off (see, for example, Wagner et al. 1974 or
Davis et al, 1978). These observations may suggest, among other possibilities, that
the tissue-specific responses induced by the amphiboles are more pronounced than
those induced by chrysotile, which might therefore contribute to the suppression of
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clearance mechanisms at lower concentrations for amphiboles than for chrysotile (see
Section 5.4.2).
The general conclusions that can be drawn from the dynamic models cited above serve
to reinforce the conclusions drawn from the animal and human pathology studies also
reviewed and discussed in this Chapter. Briefly:
during inhalation, deposition efficiency depends on the size, shape, and
complexity of a fibrous structure but is entirely independent of chemistry
(mineralogy);
mechanisms that contribute to clearance from the deep lung show a
dependence on fiber size in which short fibers (i.e. those shorter than
approximately 10 urn) are cleared more rapidly and efficiently than longer
structures;
mineralogy may also effect clearance mechanisms in at least three ways:
first, chrysotile structures are soluble and dissolve in vivo over a
time scale that is important to clearance, at least in humans.
Amphiboles are insoluble over such time scales;
second, chrysotile structures apparently degrade by splitting
longitudinally in vivo so that complex structures ultimately degrade
to their component fibrils. Amphiboles do not similarly degrade;
and
third, possibly due to differences in tissue toxicity (Section 5.4),
amphiboles are retained more effectively than chrysotile during
chronic exposure due to an overload effect in which clearance
mechanisms are suppressed as amphibole tissue burdens
increase. Such an overload effect may only occur at relatively
higher concentrations for chrysotile;
5.3 FACTORS AFFECTING TRANSLOCATION
To induce mesothelioma, asbestos structures retained in the lung must be translocated
to the surrounding mesenchyme. A number of mechanisms have been proposed for
this process. Evidence indicates that fibrous structures are quite mobile in the lungs
and surrounding tissues.
Brady et al. (1981) tracked the distribution of chrysotile following
inhalation by rats. Asbestos was initially deposited almost exclusively at
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alveolar duel 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 interstitium. Predominantly
short structures were monitored in this study. Long structures were not
readily observed.
Bignon et al. (1979) studied the rate of translocation of various materials
in rats. Chrysotile, 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 remote 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.
Le Bouffant (1980) studied the concentrations, mineralogy, and size
distributions of asbestos fibers found in the lungs and pleura of deceased
asbestos workers. Based on 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 chrysotile 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. Chrysotile fibers apparently degrade to
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shorter fibers more rapidly than amosite and 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 transiocation of chrysotile.
Based on the above studies, the efficiency of transiocation is a function both of
mineralogy and fiber size. Chrysotile appears to translocate more rapidly than amosite
possibly due to chemical reactivity and shorter fibers appear to translocate more rapidly
also. Transiocation of the shortest fibers (less than 10 um) likely competes with
clearance to the lymphatic system and clearance becomes increasingly important as
the length of the fiber decreases. Longer fibers (up to 20 um in length) also translocate
from the lung to the pleura but over a much longer time scale than the movement of
short fibers.
5.4 FACTORS GOVERNING BIOLOGICAL RESPONSE
For inhaled structures that survive degradation or clearance, a series of complex
reactions between the deposited structure and surrounding tissue may induce a
biological response. Asbestosis (fibrosis), pulmonary carcinomas, or mesotheliomas
may result. Mesotheliomas would be associated with structures translocated to the
mesenchyme following retention in the lung.
The best studies available for addressing the gross effects of biological responses in
target tissues are the series of implantation and injection studies performed by several
groups of researchers. Many of the details of the mechanisms that mediate biological
response have also been elucidated in a number of in-vitro studies of isolated tissues,
cells, or chemical assemblies.
5.4.1 Gross Features of Biological Response Elucidated by Injection and
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 transiocation 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) Fibrous materials placed
against the tissue surface are subject to dissolution, phagocytosis by macrophages,
and phagocytosis by the cells of the target tissue. A range of biologic responses have
also been observed.
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Numerous researchers have performed these types of studies.
The work of Stanton et al. 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.
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.
The hypothesis that lung tumor induction is associated with the onset of fibrosis was
further examined by Davis and Cowie (1990). These researchers 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.
Lack of a strong relationship between tumor incidence and mineral type for fibrous
dusts is in direct contrast to the behavior of hazardous isometric dusts where toxicity is
a strong function of the chemical nature of the dust (Elmes 1982). Effects observed in
association with fibers occur at much lower doses than the doses at which hazardous
dusts exhibit toxicity.
Conclusions from the Stanton et al. studies indicating that mineralogy is not a factor in
biological response also conflicts with evidence concerning mechanisms presented in
Section 5.4.2. 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.
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(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 substantially 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 5.4.2 and
Bermanetal. 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 (Chapter 4). 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 4,
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 is 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.
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
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fiber sizes are restricted to the long, thin structures that Stanton defined. Results of
this study are not inconsistent with those originally presented by Stanton, except that
they emphasize a set of characteristics that relate parametricaliy 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 quantity 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 ail 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 hew the conclusions from this paper may be
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 mesothelioma, even when fiber types are considered independently. Although
these results appear to be consistent with findings reported from mechanistic studies
(Section 5.4.2) 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
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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 reanalysis was 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.
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 analysis 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.
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Although the results of the Wylie et al. study are interesting and tend to support the
general conclusions in this document (see Section 5.6.2), as the authors themselves
indicate, such results should be considered qualitative due to the limitations imposed
on their study by the methodology employed. This 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 3 and 4 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, 1986b, 1987, and 1988a), and Wagner et
al. (1976,1980,1982,1984, and 1985). 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.
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 tend 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 et al. Also, the possibility of
a relationship between tumor induction and fibrosis is further addressed by Davis and
Cowie(1990).
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 um 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. However, this
contrasts with conclusions drawn by Wylie et al. 1993 (see above)..
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Although the published injection studies indicate that potency decreases with
decreasing length, researchers have been 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 3.2);
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
tissue, results from the implantation and injection studies may also provide a model
biological response in lung tissue and the factors that lead to the induction of
pulmonary tumors. Such a model must be considered qualitative, 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 5.4.2). At the
same time, It is known that the general nature of clearance and degradation processes
in the two tissue types are generally similar.
5.4.2 Implications Regarding the Mechanics of Cancer Induction Derived
from In-Vitro Studies
In a 1993 review, Mossman describes the currently accepted model for cancer. In this
model, development of cancer requires both that genetic alterations occur in a
particular cell line and that the affected cells be stimulated to grow and divide so that
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the mutations are inherited and preserved. Such genetic alterations may either occur
randomly and spontaneously during cell division or may be induced by one or more
specific biological or chemical agents. If the affected cells are chronically stimulated to
divide, sufficient genetic mutations (of the right type) eventually accumulate in the cell
line to cause growth to become uncontrolled so that tumors develop. Agents that cause
genetic alterations are cancer initiators in this model and agents that chronically
stimulate cell division are promoters. It is also noted that the long latency periods
between an initiating event and the development of clinically observable tumors is a
result of the substantial time required for sufficient mutations of the right type to
accumulate in the affected cell line to lead to neoplastic behavior.
Mossman (1993) then highlights the results from various in-vitro studies, which indicate
that
asbestos serves primarily as a promoter of lung cancer although evidence
suggests it may serve both as a promoter and an initiator of
mesothelioma. Mesothelial cells have been shown to be much more
susceptible to genetic damage by asbestos than bronchial and alveolar
epithelial cells;
promotion is facilitated by the genotoxic and cytotoxic effects of asbestos
where the resulting cell death stimulates cell division to replace damaged
tissue;
asbestos genotoxicity and cytotoxicity is mediated by production of
reactive oxygen species generated either during phagocytosis by
monocytes or in reactions catalyzed by iron on the surface of asbestos
fibers; and
the genotoxic and cytotoxic effects of asbestos are size dependent with
longer structures showing greater potency than shorter structures. In
many of these studies, structures shorter than approximately 10 urn are
shown largely to be inactive.
Consistent with the above, evidence from several of the studies cited by Mossman in
this and an earlier review (Mossman et al. 1990) indicate that amphiboles are more
potent carcinogens than chrysotile, particularly toward the induction of mesothelioma.
This is apparently due both to the increased persistence of amphiboles in vivo (relative
to chrysotile) and to the higher concentration of iron in most amphibole matrices
(particularly crocidolite). The greater persistence suggests that amphibole structures
may provide chronic stimulation of cell division in target tissues for a longer period of
time than chrysotile and thus serve as a more potent promoter. The greater
concentration of iron suggests a greater number of active sites on the surface of each
structure af which formation of reactive oxygen species may be catalyzed.
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Taken as a whole, these data suggest that it may be prudent to establish distinct risk
coefficients for each of the different asbestos fiber types. Because these findings are
further reinforced by results of the re-analysis of animal inhalation studies described
later (Section 5.6.2), the protocol recommended in this document incorporates separate
consideration of fiber types.
5.5 OTHER FACTORS
A range of studies provide further, though indirect, evidence of the overall dependence
of biological activity on structure characteristics without distinguishing among the
effects due to respirability, clearance, degradation, translocation, or mechanism. Such
studies generally confirm conclusions drawn from the specialized studies above, except
that the effect of chrysotile biodegradability is emphasized. In addition, a wider range
of durable minerals that exist as fibrous structures within the appropriate size range are
shown to be biologically active.
Pooley (1982) showed that the amphibole lung burdens of deceased
asbestos workers, which correlate with disease incidence are longer than
the chrysotile found in the lungs of workers and controls, which do not
correlate with disease.
In another study, (Pooley 1972) showed that the incidence of
mesothelioma appears to correlate best with the incidence of amphibole.
The relationship between chrysotile counts and mesothelioma is not clear,
but none of the mesotheliomas are associated with high chrysotile
concentrations.
Baris et al. (1987) showed that the distribution of erionite fibers in sheep
lungs correlate with the incidence of mesothelioma in various regions of
Turkey. Short chrysotile fibers also found in Turkish sheep lungs do not
correlate with the incidence of disease.
Churg et al. (1984) found that the longer amphibole fibers present in the
lungs of mesothelioma victims correlate with the incidence of the disease
between cases and controls. The relationship between disease and the
distribution of shorter chrysotile fibers, also found in the lungs, is less
clear.
When evaluating these studies, it is important to consider that, due to the in-vivo
degradability of chrysotile, valid tests for correlations between chrysotile lung burdens
and any toxic end point require that time since exposure be considered explicitly.
Since the studies above do not formally address the time issue, rather than concluding
a lack of correlation between chrysotile exposure and disease, there might simply be a
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less pronounced effect per unit of chrysotile exposure than per unit of amphibole
exposure. Such an interpretation (though not directly testable due to the limitations of
the designs of these studies) would place them in closer agreement with the results of
the mechanistic studies discussed in Section 5.4.2.
5.6 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, translocation, 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 the 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.
The remainder of this section is divided into two parts. The nature and results of
existing animal inhalation studies are described first. Then a project undertaken to
overcome the limitations of the existing animal inhalation studies is described.
Because this latter project was specifically designed to support the risk protocol
presented in this document, the nature and results of this project are described in
detail.
5.6.1 The Existing Animal Inhalation Studies
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, 1986b,
1988a, and 1988b), Wagner et al. (1974,1982,1985, and 1987), Bellman et al. (1987),
Bolton et al. (1982), Le Bouffant et al. (1987), Lee et al. (1981), McConnel et al. (1982,
1995), Muhle et al. (1987), Platek et al. (1985), Smith et al. (1987), Goldstein et al.
(1983) and Mast et al. (1995a and b). 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. 1970), consist of a rotating brush that sweeps over
an advancing plug of bulk material liberating fibers that become entrained in the
controlled air flow passing through the device. The airborne dust is then passed either
into a delivery system for nose-only exposure or into an exposure chamber where
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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 3.2). 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 3.2),
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 3.2) and from the manner in which they
are tied to the inhalation experiments (see Chapter 4).
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). This 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
concentration of the same size fraction per unit dust mass that was derived by PCM in
the original 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 3.2). Comparison of data between studies is also hindered by the lack of
sufficient documentation to indicate the specific methods and procedures employed in
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
5-31
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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 then 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 particles 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 any types of particles are ignored in the estimation of fiber mass.
Even if all types of particles are included, substantial uncertainty may result due to the
difficulty of 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. Further, 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 4.
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 3.2). 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.
1986 and 1988b). However, dusts evaluated in the existing inhalation experiments
have not been characterized sufficiently to distinguish the dependence of biological
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.
5-32
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5.6.2 A Re-analysis of the Davis et al. Studies
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 5-1
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 5-1 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 5-1 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
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.
5-33
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TABLE 6-1:
SUMMARY DATA FOR ANIMAL INHALATION EXPERIMENTS CONDUCTED BY DAVIS AND COWORKERS"
Fiber
Type
Mats
Concentration
Description Abbreviations (mg/m1)
PCM
f/ml
Number of
Animal*
Number of
Benign
Pulmonary
Tumors
Number of
Malignant
Pulmonary
Tumors
Total Number
of Pulmonary
Tumors
Mesotheliomas
Reference*
Chrysolite UICC-A UC
ChrysoUle UICC-A UC
Chrysolite Long LC
Chrysotlle Short SC
Chrysotlle UICC-A UC
ChrysoUle UICC-A (Discharged)1 DC
Chrysotlle WDC Yam4 WC
Amostte UICC UA
AmosHe Long LA
Amoslle Short SA
CrbddoIHe UICC UR
Croddolite UICC UR
Tremolite Korean KT
2
10
10
10
0.8
0.9
3.6
10
10
10
4.0
10
10
300
1.050
S.S10
1,170
2.500
2.670
670
550
2.060
70
430
860
1.600
42
40
40
40
36
30
41
43
40
42
43
40
30
6
7
S
1
6
4
5
2
3
0
2
1
2
8
12
6
8
6
13
0
8
0
0
0
16
8
15
20
7
14
10
18
2
11
0
2
1
18
1
0
3
1
0
1
0
0
3
1
1
0
Davis etal.
Davis etal.
Davis et al.
Davis et al.
Davis etal.
Davis el al.
Davis et al,
1076
1078
1088a
10888
1088b
1086b
1088b
Davis etal, 1078
Davis et at. 1088a
Davis etal. 1086e
Davis etal. 1078
Davis etal. 1078
Davis et al. 1085
None
None
None
None
UAMM
riono
i.
2.
3.
Control
Control
Control
Control
Control
C
C
C
C
C
0
0
0
0
0
20
36
61
64
47
0
0
1
1
1
0
0
. 1
1
1
0
0
2
2
2
0
0
0
0
0
Davis et al. 1078
Davis etal. 1085
Davis etal. 10868
Davis et al. 1086b
Davis etal. 10888
Source: Berman et al. 1095
Exposure occurred for 7 hours per day, 5 days per week for 1 year.
UICC-A chrysolite in this experiment was treated with mixed polarity air (produced with a source of beta radiation) following generation to reduce the surface charge on Individual particles
within the dust.
Chrysotlle samples used for dust generation In this experiment were obtained from material treated by a commercial wet dispersion source.
-------
In the statistical analysis performed in this study, the individual dose/response profiles
included in the Davis et al. data (Table 5-1) were fit to a linear dose/response model:
P, = 1 - EXP( - 0.-Ow) <5-1)
where:
"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;
"Q0" is the correction factor for background derived from the background
incidence of pulmonary tumors observed among the control populations
(pooled from all studies);
"Xj" is the concentration of the "jth" size fraction of fibers in the "ith" study;
"a/1 is the coefficient of potency for the "jth" size fraction of fibers in the "ith"
study; and
"fa" 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 set up as indicated in Equation 5.1 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.
Several investigators (Stanton et al. 1977; Bertrand and Pezerat 1980; Bonneau et al.
1986; and Wylie, et al. 1987 and 1993) have used a logit curve to investigate
dose/response relating various measures of asbestos exposure to tumor response.
The logit formula specifies that the tumor probabilities satisfy the relation:
5-35
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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 mode! 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 = eV/CI + eV)
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 5.1 ) 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 5.1 ) 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,
although all of the univariate exposure measures listed in Table 2 of
Berman et al. (1995) are highly correlated with lung tumor incidence, none of them
adequately describe (fit) the lung tumor incidence.
To test for the goodness of fit in this study, each relationship was subjected to the
standard "P" test whore 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
5-36
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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 5-3
(Figure 3 of Berman et al. 1995). Note that Figures 2 and 3 of the original paper were
inadvertently switched during publication; the correct Figure 3 is reproduced here. The
exposure index plotted in Figure 5-3 is the sum:
Exposure = 0.0017C, + 0.853C2 + 0.145C3
where: .
"C," is the concentration of structures between 5 and 40 urn in length that are
thinner than 0.3 urn;
"C2" is the concentration of structures longer than 40 urn that are thinner than
0.3 urn; and
"C3" is the concentration of structures longer than 40 urn that are thicker than
5pm.
This index of exposure represents one of the optimum indices reported in
Berman etal. 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 indicate that:
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 urn provides the best fit (although still
inadequate); and
5-37
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FIGURE 5-3
FIT OF MODEL
TUMOR INCIDENCE VERSUS STRUCTURE CONCENTRATION BY TEM
Length Categories: 5/
-------
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.
One example of an exposure measure that adequately describes lung tumor incidence
is presented in Figure 5-3. Another exposure measure shown to provide an adequate
fit is:
Exposure = 0.0024C, + 0.9976Cb
where:
u
C," 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 urn.
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 urn) with structures longer than 20 urn being the most potent;
the best estimate is that short structures (< 5 urn) are non-potent. There
is no evidence from this study that these structures contribute anything to
risk;
among long structures, those shorter than 40 um appear individually to
contribute no more than a few percent of the potency of the structures
longer than 40 urn;
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;
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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 (after publication of the first
part of the study), primarily to identify optimal procedures for performing asbestos
analysis and for estimating concentrations. These analyses have not yet been
published. 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
to1 lung tumor incidence in any coherent fashion; and
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 lengths, and approaches a constant for the longest fibers.
5.7 CONCLUSIONS: ASBESTOS CHARACTERISTICS THAT BEST RELATE TO
BIOLOGICAL ACTIVITY
By considering the studies that address the effects of deposition, clearance,
degradation, and biological response (which are discussed in the previous sections of
this chapter), the outstanding issues identified in Section 5.0 can now be addressed.
Furthermore, an exposure index that captures the principal characteristics of asbestos
that determine biological activity and, therefore, risk can now be defined
(Section 5.7.2).
5.7.1 Addressing Outstanding Issues
The seven outstanding issues concerning the characteristics of asbestos that relate to
biological activity identified in Section 5.0 can be addressed based on conclusions
drawn from the literature review presented in this Chapter.
5-40
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Short structures. Short, fibrous structures (less than 5 urn in length) do not appear to
contribute to risk. This is supported both by the results of our re-analysis of the animal
inhalation studies (Section 5.6.2), in which this hypothesis was tested formally, and by
inference from the broad body of other literature reviewed in this document.
The results of the vast majority of injection and implantation studies that are discussed
in Sections 5.4.1, many of the pathology studies discussed in Section 5.5, and the
animal inhalation studies discussed in Section 5.6.1 consistently indicate that short
structures do not contribute substantially to the induction of disease. In fact, there is no
positive evidence from any of the studies reviewed that suggests short structures do
contribute measurably to risk.
Results from several injection and implantation studies (Section 5.4.1) and other
mechanism studies presented in Section 5.4.2 also suggest a reason that short
structures do not contribute substantially to disease: they are efficiently cleared from
lung tissue relative to longer'Structures. Based on the relative sizes of rat and human
macrophages (Krombach et al. 1996), structures shorter than some cutoff between
5 urn and 10 pm appear to be efficiently cleared from rat lungs and structures shorter
than 10 pm to 15 pm may be efficiently cleared from human lungs.
Based on formal hypothesis testing concerning short structures, the best estimate of
the potency of short structures is precisely zero. Even an upper bound estimate (based
on a 90% confidence interval) for the relative potency of these structures is that they
are no more than 4% as potent as structures longer than 5 pm and no more than
0.009% as potent as structures longer than 40 pm. This indicates that, for virtually all
of the size distributions observed to date, contributions to overall potency from short
structures is inconsequential.
Given the above, the asbestos exposure index recommended in this report excludes
consideration of structures shorter than 5 pm. This cutoff is selected because it is still
sufficiently short to allow for the possibility that a slightly shorter set of structures
contributes to the induction of mesothelioma than the set of structures that contributes
to lung cancer, as some have hypothesized (e.g. Lippmann 1988).
Because the results of our re-analysis of the animal inhalation studies indicates
formally that structures longer ithan 40 pm are even more potent than structures of
intermediate length (i.e. those ibetween 5 pm and 40 pm), the recommended exposure
index incorporates a sum in Which the potency of longer structures is weighted more
heavily than those of intermediate length. As indicated previously (Section 5.6.2), This
is entirely consistent with the Pott hypothesis (Pott et al. 1982) and other inferences
that can be gleaned from the animal injection, implantation, and inhalation studies.
Thin structures. The fibrous structures that contribute to risk are apparently thin (less
than 0.5 pm in diameter). This conclusion is supported by the formal hypothesis testing
5-41
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performed during our re-analysis of the animal inhalation data (Section 5.6.2), by less
formal analyses that have been conducted by others (e.g. Wylie et al. 1993), and by
inference from the majority of injection and implantation studies reviewed in
Section 5.4.1.
Dynamic models of respirability and retention (Section 5.2.3) further suggest that the
respirability of fibrous structures thicker than approximately 0.7 urn is severely limited
when such structures are as dense as the various asbestos minerals. Therefore,
thicker structures may simply not reach the deep lung where they might otherwise be
retained and potentially contribute to the induction of disease. Note that the indicated
cutoff in respirability refers primarily to relatively solid structures (such as fibers or
bundles). Complex structures such as clusters or matrices may contain substantial
empty space so that, because of their lower density, they tend to be more respirable
than their size might otherwise indicate. The importance of complex structures is
addressed further below.
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. This is why the exposure index recommended in this report is defined by a
maximum width rather than a minimum aspect ratio.
Based on formal hypothesis testing to evaluate the relationship between width and
biological activity (Section 5.6.2), the maximum width above which fibers and bundles
do not appear to contribute to risk lies somewhat below 0.5 urn; the best fits to the
animal inhalation data all suggested narrower width cutoffs. Therefore, a maximum
width of 0.5 urn is incorporated in the definition of the exposure index recommended in
this report. Note, however, that this cutoff applies primarily to fibers and bundles. As
discussed further below, this limit is not applied to complex structures (clusters and
matrices), which may contain thinner components that potentially contribute to risk and
are therefore included in the exposure index.
Complex structures (asbestos aggregates). 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 included in the exposure index used to represent
concentration (Section 5.6.2). Therefore, it is recommended in this report that those
components of complex structures that exhibit the required dimensional criteria be
individually enumerated and included within the exposure index defined for asbestos.
The need to include components of complex structures in the exposure index for
asbestos is further supported by inferences that such structures may degrade in vivo to
their component fibers and bundles. Otherwise, studies other than our re-analysis of
the animal inhalation data (Section 5.6.2) have not tended to address consideration of
5-42
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complex structures. Regarding injection and implantation studies, this is largely due to
the nature of the sample. Complex structures such as clusters and matrices can
neither be readily identified nor easily defined within the solid matrices of the samples
employed in these studies. Their consideration may also have been ignored during
most of the published inhalation studies due to the wide use of SEM to characterize
asbestos exposure and the limited ability to distinguish the internal details of asbestos
structures with such instrumentation (Section 3.2).
Fiber mineralogy. The mineralogy (i.e. chemical composition and crystal structure) of
a fibrous material is an important determinant of the potency of that material toward the
induction of disease, particularly in humans. This appears due both to the dependence
of in-vivo durability on mineralogy and to the dependence of the mechanisms of
biological responses on mineralogy (Section 5.4).
Results from some (but not all) of the animal injection, implantation, and inhalation
studies previously reviewed (Sections 5.4 and 5.6) 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. Several of the human pathology studies cited previously
(Sections 5.2 and 5.5) also suggest that mineralogy is an important factor in
determining risk, but these studies similarly suffer from methodological difficulties that
introduce ambiguity into the inferences drawn.
Formal hypothesis testing during our re-analysis of animal inhalation studies
(Section 5.6.2) 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.
Although these results suggest a modest role for mineralogy in determining risk, it is
also recognized that the effects may be magnified in humans relative to animals (as
discussed below).
More directly, many in-vitro studies (Section 5.4.2) indicate that mineralogy plays an
important role in the mechanisms by which fibrous structures induce cancer so that
mineralogy would be expected to be an important determinant of risk. Both the
relationship between mineralogy and in-vivo durability and the effect of mineralogy on
the rate and mechanism of production of reactive oxygen species appear to cause
different mineral types to exhibit different degrees of carcinogenicity. That mineralogy
may be a more important determinant of risk in humans than disease induction in rats
has also been highlighted in these studies. Differences in durability between mineral
types may have a greater effect in humans than rats because the time scales over
which degradation occurs is long relative to the life-span of a rat but important over the
time scale of a human lifetime (Section 5.2).
5-43
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The strongest evidence that mineralogy is an important factor in determining risk comes
from epidemiology studies. Therefore, the effects of mineralogy are addressed more
fully in our review and reconciliation of the epidemiology database (Chapter 6).
Formal consideration of the effects of mineralogy are incorporated into the procedures
recommended in this document for assessing asbestos-related risks in the manner
described in Chapter 6.
Non-asbestos fibers. There is some evidence that certain other fibrous materials may
exhibit similar biological activity to that ascribed to the fibrous materials included
formally under the definition of asbestos. For example, fibrous erionite has been
causally associated with the mesothelioma cases that have been observed in several
cities in Turkey (Ban's et al. 1987).
Some have thus suggested that any refractory fibrous material be regulated in the
same manner as asbestos, if the material is known both to be durable in-vivo and to
occur in a distribution of sizes that includes the biologically active range. However,
evidence presented in various studies reviewed in this chapter (and highlighted further
in Chapter 6) indicates that mineralogy is an important determinant of potency beyond
it's impact on bio-durability. Therefore, while prudence dictates that the fibrous habits
of other refractory materials deserve scrutiny, potency estimates for those found to
induce disease will need to be established on a case-by-case basis.
Disease end-points. At this point in time, there is no compelling evidence that the
size range of fibrous structures that contribute to the induction of lung cancer and
mesothelioma are substantially different. While such differences have been
hypothesized by various researchers (e.g. Lippmann 1988), quantitative evidence
demonstrating such differences appears to be lacking. At the same time, results of
hypothesis tests conducted during the re-analysis of the animal inhalation studies
(Section 5.6.2) indicates that, as long as the effects of mineralogy are adequately
addressed, the same asbestos exposure index can be used to fit (predict) both the lung
tumor and the mesothelioma data.
Despite the above, the size range defined by the asbestos exposure index
recommended in this document is sufficiently broad to incorporate structures that
contribute most strongly to both lung cancer and mesothelioma, even if small
differences do exist in the range of structures that contribute to each disease.
Dose-response models. Although the validity of the dose-response models employed
for lung cancer and mesothelioma in this report was not evaluated formally, there is no
strong evidence to suggest that the existing models are inadequate or, if they are, at
least they are not expected to lead to the underestimation of risk. This issue is
discussed further in Chapter 6. Given the situation, the models recommended in the
Health Effects Assessment Update (U.S. EPA 1986) were adopted without modification.
5-44
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Other general considerations. Several additional considerations developed from the
literature review and evaluation documented in this Chapter also contributed to the
nature of the asbestos exposure index recommended in this document. These
considerations include:
it is clear both from the literature review and the re-analysis of the animal
inhalation studies discussed above that asbestos risk is best described as
a function of the number of structures of a specific type and size range
that contribute to an individual's exposure. Estimates of exposure and
dose that are based on asbestos mass cannot be used to predict risk;
both because the structures that contribute most to risk are thin and
because appropriately sized components of complex structures need to
be included in the characterization of exposure, use of TEM in
combination with a method that incorporates appropriate structure
counting and characterization rules appears to be the only analytical
procedure that is adequate for determining asbestos concentrations that
can be related to risk;
the results from our formal re-analysis of the animal inhalation data
indicate clearly that samples to be prepared for TEM analysis must be
prepared using a direct-transfer procedure, if the characteristics of the
sample that determine risk are to be preserved. Measurements derived
from samples prepared using indirect transfer cannot be related to risk.
Therefore, any method employed to support asbestos risk assessment
needs to incorporate sample preparation by direct transfer; and
because longer structures (at least as long as 20 urn) have been shown
to be more potent than shorter structures, it is important that the longer
structures be enumerated with adequate precision when measuring
asbestos concentrations. Therefore, since such structures are also rarer
than shorter structures in size distributions typically encountered, long
structures need to be counted exclusively in a separate scan from that
employed for the more numerous, shorter structures. Any method to be
employed for measuring asbestos concentrations to support risk
assessment must therefore incorporate statistically balanced counting
(see Table 3-2).
Section 5.7.2 Defining an Exposure Index for Asbestos
Given the results of our re-analysis of the animal inhalation studies coupled with the
findings of the literature review presented in this chapter, we believe that the following
5-45
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index of exposure best captures the characteristics of asbestos that determine risk:
^asbestos= 0.003CS + 0.997CL ' 5.2
where:
"Cs" is the concentration of structures between 5 and 40 urn in length that are
also thinner than 0.5 um; and
"CL" is the concentration of structures longer than 40 um that are also thinner
than 0.5 pm.
It is acknowledged that humans and rats may not respond to asbestos in precisely the
same manner. However, it is believed that (due primarily to differences in the
physiology of respirability) any changes in the fiber characteristics that relate to
carcinogenicity in humans may shift the break point in this equation to longer fibers.
Humans appear to respire and retain a greater fraction of longer fibers in the deep lung
(Section 5.1.2). It is also known that human macrophages, which mediate deep lung
clearance, are larger than their counterparts in the rat (Krombach et a!. 1996). Thus, it
is likely that the maximum length of structures effectively cleared from the lungs is
greater for humans than for rats. Therefore, since the shortest fibers represent the
most numerous in all asbestos dusts so far encountered, the above equation is
believed to be conservative for humans relative to rats.
As indicated in the previous sections, estimates of asbestos exposure using the above
defined exposure index should be derived from asbestos measurements obtained by
TEM analysis of samples prepared by a direct-transfer procedure. Further, the analysis
should be conducted based on a method that incorporates statistically-balanced
counting and incorporates counting and characterization rules that require enumeration
of appropriately sized fibers and bundles that occur either as isolated structures or as
components of more complex structures. The general procedures of the current ISO
Method (IS010312) are recommended. However, the structures recorded in the
analysis can be restricted to those that satisfy the dimensional criteria defined by the
recommended exposure index. This will simplify the analysis substantially.
5-46
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6.0 ASBESTOS RISK COEFFICIENTS
As indicated in Section 2, the last two steps required for developing a protocol to
evaluate asbestos risks are to adjust existing risk coefficients so that they can be
applied to current measurements and to identify appropriate models to relate asbestos
exposure to risk.
The risk coefficients and the corresponding dose-response models that are used to
assess asbestos-related risks are derived from the existing epidemiology literature. An
overview of the literature is provided below to highlight the considerations that need to
be addressed to define a mutually-consistent set of risk coefficients. The specific risk
coefficients that were selected for adjustment to support this protocol were developed
from a detailed evaluation of the subset of epidemiology studies that contain sufficient
information to evaluate dose-response relationships quantitatively. The evaluation is
described in Appendix A.
The procedures employed for adjusting selected risk coefficients so that they can be
applied to predict asbestos-related risks in exposure settings of interest are described
in Section 6.2. A detailed description of the corresponding dose-response models is
also provided.
6.1 OVERVIEW OF 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 the health response in the study population (cohort) and
information on the asbestos exposure experienced by the cohort. 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 can be optimized. In most
instances, unfortunately, the data suffer from multiple limitations and the analyst is
further constrained by less than complete access to the data.
Generally, the most severe limitations in an epidemiology study involve the exposure
data. 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 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. The majority of analysis results 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.
6-1
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It is difficult to judge the degree that available asbestos concentration measurements
are representative of actual exposures. 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 a representative
estimate of the direct level of exposure to workers. Some of the 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
Samples collected prior to the mid-1960s were often analyzed by measuring total dust
in units of millions of particles per cubic foot (MPPCF) using impingers or thermal
precipitators. A description of the relative strengths and weaknesses of these
techniques'is provided in Section 3.2. 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 dust and fiber counts collected during
a relatively brief period of time (e.g., McDonald et al, 1980, Dement et al, 1983).
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 MPPCF and these have been related to f/ml by PCM using
conversion factors derived in other plants, which raises further questions concerning
validity.
None of the published epidemiology studies incorporate TEM measurements of
asbestos because such measurements are not widely available in occupational settings
(Section 6.2). However, TEM is the method currently used (and recommended) to
assess exposure in environmental settings. Thus, even accepting the conversions from
dust counts to fiber counts, the exposure estimates that are available from existing
epidemiology studies (based on PCM or impinger) do not reflect thin fibers (thinner
than about 0.3 urn) and do not distinguish asbestos from non-asbestos fibers, which
may also be present in the exposure settings studied (Section 3.1), because PCM is
incapable of addressing such factors (Section 3.2).
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
6-2
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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.
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,
for example, requires that exposure be estimated as cumulative exposure in f/ml-years
excluding the most recent 10 years (see Section 6.2.1); 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 incidence of mesothelioma subdivided according to exposure level, age
at beginning of exposure, and duration of exposure (Section 6.2.2). Such data are
almost never available and crude approximations must be made to account for this
lack.
The existing asbestos epidemiology database consists of approximately 100 studies of
which approximately 15 contain exposure data sufficient to derive quantitative
dose/response relationships. A detailed evaluation of 15 key studies, based oh the
considerations presented in this overview, is provided in Appendix A.
6-3
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It is important to note that the evaluation reported in Appendix A was completed in 1986
and has not been updated despite appearance in the literature of several follow-up
studies to the key studies evaluated (e.g., McDonald et al.1993, Dement et al. 1994,
Dement and Brown 1994, and Brown et al. 1994). Also, at least one study has been
published (Sluis-Cremer et al. 1992) that addresses a new exposure setting not
previously considered. Incorporation of this study would broaden the diversity of the
epidemiology database. Due to the coincident exposure of amphibole asbestos with
talc, it might also be helpful to add selected epidemiology studies of talc minors (Brown
et al. 1979, Stille and Tabershaw 1982, Brown et al. 1983, and Gamble and Greife
1983) to the set evaluated in Appendix A.
A brief review of the new epidemiology literature suggests that there are no surprises.
Thus, the risk coefficients presented in this document are unlikely to change
substantially even if the newest follow-up studies are incorporated in our analysis. At
the same time, if there is interest in further refining the ability to predict asbestos-
related risks, it is recommended that the detailed analysis of the quantitative
epidemiology studies (presented in Appendix A) be updated in the future to incorporate
the newly available data, which would provide a richer database for testing hypotheses.
Even more useful: to the extent that archived samples are available from environments
evaluated in the existing epidemiology studies, we highly recommend a study be
completed that is similar in form to the one we completed for the animal inhalation
database (Section 5.6.2). Specifically, we recommend that archived samples be re-
analyzed to provide a better indication of the nature of exposure experienced by
cohorts evaluated in the epidemiology studies and that a statistical analysis then be
completed to better define the characteristics of asbestos that determine human risk.
This would complete our effort to reconcile the published risk coefficients, the results of
which are reflected in the design of the protocol that we recommend in this document
(Part 1) for estimating asbestos-related risks.
6.2 ADJUSTING ASBESTOS RISK COEFFICIENTS
As indicated previously, risk coefficients derived from existing epidemiology studies
must be modified to reflect the relationship between measurements by the analytical
techniques used in such studies and the characteristics of asbestos that determine
biological activity. Risk coefficients based on exposure indices that do not relate
directly to biological activity cannot be readily applied to exposure settings different
from the one in which the risk coefficients were initially derived. This is because the
relationship between biological activity and any indirect measure (index) of exposure
may vary between environments. Thus, risk coefficients based on indirect indices of
exposure would also vary between environments, which minimizes their utility as
predictive tools.
6-4
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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 at. 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.
Adjustment of the existing risk coefficients is necessary because they have all been
derived based primarily on PCM and Ml measurements and, as previously indicated,
neither PCM nor Ml measurements relate adequately to biological activity
(Section 5.6.2). 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 5.2, which was designed to
adequately reflect the characteristics of asbestos that determine biological activity
(Section 5.7). 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
longer than approximately 10 urn. Therefore, an ad hoc exposure index is presented,
which represents a compromise between theoretical needs defined in Chapter 5 and
the limitations of the available data.
In the following sections, published risk coefficients are normalized to an exposure
index that better reflects biological activity using a series of published size distributions
derived from TEM measurements. 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) to complete
design of a general protocol for assessing asbestos-related risks. In keeping with
general risk management practices under Superfund, the approach adopted is
conservative so that potential errors will not likely result in the underestimation of risk.
6.2.1 Assessing the Risk of 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 mortality from lung cancer at
age t is proportional to cumulative asbestos exposure at time t-10 years
(i.e., cumulative exposure lagged 10 years), multiplied by the age- and calendar year-
specific background mortality rate of lung cancer in the absence of asbestos exposure.
6-5
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A linear relationship between cumulative dose and response has been 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= yi+K.-PCMd^] (6.1)
where:
"IL" is the overall lung cancer mortality (expected lung cancer deaths per year
per person) adjusted for age and calendar year;
"IE° is the corresponding lung cancer mortality in a population not exposed to
asbestos;
"PCM" is the concentration of asbestos (expressed as PCM fibers longer
than 5 urn;
U1B
t" is age;
is the duration of exposure up to age t, excluding the most recent 10
years; and
"IV 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 mode! in that it assumes that the excess mortality of
lung cancer from asbestos is proportional to the mortality in an unexposed population.
Since smokers have a much higher mortality from lung cancer, if smoking-specific
mortality rates are applied, the model predicts a higher excess mortality from asbestos-
related lung cancer in smokers than in non-smokers. This is consistent with the
multiplicative relationships between smoking and asbestos that have been observed in
epidemiological studies. Note that the K<. 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.
The series of unadjusted KL values derived from the existing epidemiology study.
database are presented in Table 6-1. In Table 6-1, Column 1 lists the fiber types for
the various studies. Column 2 lists the exposure settings (industries) studied and
Column 3 lists the specific epidemiology study used to derive the K^ values listed in
Columns 4, 5, and 6. The Column headed "EPA" lists KL values reported in the
Airborne Health Effects Assessment Update (U.S. EPA 1986). Columns 5 and 6
present, respectively, median level estimates and upper bound estimates derived as
part of this study (see Appendix A). In general, the median levels presented in
6-6
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TABLE 6-1: Kt AND KM VALUES DERIVED FROM PUBLISHED EPIDEMIOLOGY STUDIES
KL KMfrlO8)
Fiber Exposure Setting
Type
Chrysolite Textile*
Friction Product*
Mining and Milling
Epidemiology Reference
Dement et al. 1983b
McDonald et al. 1983*
Representative Value:
McDonald et al. 1964
Representative Value:
McDonald et al. I960
Nicholson et al. 1979
Rublno et al. 1979
EPA 1986"
0.028
0.025
0.0001
0.0006
0.0017
0.00081
Present
MLE
MLE
0.028
0.012
0.02
0.0006b
0.00045
Study
Upper
Limit
0.073
0.017
0.0017
0.0008
Present Study
EPA 1966" MLE
0.3
0.051
O2
0
O2
0.011
Upper
Limit
1.1
0.19
0.12
0.017
Asbestos Cement Manu.
Crocldoltte Mining and Milling
Amostte Insulation Manu.
Mixed Textiles
Friction Product*
Asbestoe Cement Manu.
(Chrysotlla and
Crocldolfte)
Manufacturing
Insulation Application
(Chiysolile and
Amosrte)
Representative Value: 0.0005
Hughe* et al. 1967 0.004 0.01
Representative Value: 0.005
Armstrong et al. 1968 0.1
Seldman 1984 0.043 32
McDonald et at, 1963b 0014 0.0! 8 0045 1.0
Peto 1980. 1985 0011 00054b 0015
Berry & Newhous* 1983 000058 0.0006 0008
Finkelstein 1983 0.048 0.036 12
Hughe* et al. 1987 0.004 0.0098
Well! et al. 1979 0.0064 0.019
Wain el al. 1979: 0.0053
Welll 1984
Henderson & Enterilne 1979 0.0049
Selikoff et al. 1979 00075 1.5
0.01
O.OS
PJL
14
S.SS
1.3
19
0.3
0.07
0.2, <0.3
1.1
30
0.18
-------
Column 5 agree closely with the values estimated by EPA. The two columns differ by
less than +/- 30% except for the McDonald 1984 study of friction products, which was a
negative study. The Armstrong (1988) study of crocidolite mining was published after
the EPA document so that it was not evaluated by EPA. Additional columns present KM
values for mesothelioma. The representative values presented in Column 5 of
Table 6-1 were used for this evaluation. Where values are not presented in Column 5,
the EPA values for these studies were adopted (Column 4).
Note that the KL values listed in Table 6-1 vary by over a factor of 200 when all fiber
types are considered. Values for chrysotile alone vary by over a factor of 40 between
exposure settings. As indicated previously (Chapter 4), the variation in K^ values from
the different industries presented in Table 6-1 potentially derive from one or a
combination of several factors including:
fiber type;
differences in the distribution of asbestos fiber sizes;
the ratio of asbestos to nonasbestos in the total dust;
the ratio of asbestos to nonasbestos in the fibrous dust;
contributions from other etiologic agents present in specific exposure
settings;
the age of the population and period of follow-up;
differences in the underlying cancer rates within the population under
study (due to smoking habits and other factors);
differences in the methods used for defining exposure groups and
assigning exposure estimates to each group; and
the representativeness of dust sampling in each study to the actual
exposures.
The first four factors listed above involve characteristics of asbestos exposure that
affect the degree with which measurements relate to biological activity and were
discussed in detail in Chapter 5. It is due to these factors that we suggest the need for
adjusting the existing risk coefficients so that they predict asbestos potency more
consistently across exposure settings.
The last five factors listed above relate to variation in parameters not associated with
exposure measurement. Although a short follow-up period should not bias the estimate
6-8
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of KL, if the model is correct and is applied correctly, it would cause K^ to be estimated
with less precision. Data on smoking habits in the epidemiological populations would
permit adjustment for smoking differences between this and the control population;
however, such data are often not available. Except possibly for contributions from
other etiologic agents, these last five factors would be unique to the set of data and
epidemiological methods employed in a particular study and would not be expected to
vary systematically with exposure setting.
If the variation in KL values contributed by all of the factors not related to asbestos
measurement could be systematically addressed, the remaining variation presumably
would be due entirely to differences in the characteristics of asbestos dusts present in
the various exposure settings (the first four factors listed above). As mentioned above,
except for the presence of other etiologic agents, the effects of such non-measurement
related factors would not be expected to correlate with exposure setting. Rather, such
differences should contribute to variation "within" each exposure setting as well as
"between" exposure settings.
Since the data in Table 6-1 indicates that (with one exception) KL values "within"
exposure settings vary by less than a factor of 4, the larger differences observed
"between" exposure settings (over a factor of 200) can likely be attributed either to
characteristics of the asbestos dusts (including fiber type) or to the presence of
additional etiologic agents that are specific to each industry. Therefore, assuming that
other etiologic agents are not important, it appears that factors relating to the manner in
which asbestos is measured, are indeed among the major sources of variation among
the observed ^ values. This is consistent with the findings reported in Chapter 5.
As for the one exception, the K^ derived from the Finkelstein study is a factor of 10
larger than other asbestos/cement values derived from other studies. The cohort
followed in this study may have been exposed to higher levels of crocidolite (25%) in
combination with chrysotile than the other studies, which also reported mixed
exposures. Also, the Finkelstein study exhibits an unexplained non-monotonic
relationship between asbestos exposure and lung cancer. Therefore, the Finkelstein
study is handled separately from the other studies in this evaluation in an attempt to
account for the larger contribution from crocidolite and the unusual dose/response
relationship.
The potential importance of contributions from etiologic agents other than asbestos has
been raised to date in association with only one of the exposure environments
considered here. Some have argued that the elevated potency of chrysotile asbestos
that is observed in association with textile manufacturing may be due to the spraying of
asbestos fibers with oil and the consequent contributions to carcinogenicity by
components of the oil (U.S. EPA 1986). However, more recent: studies have tended to
case doubt on this hypothesis (Dement et al. 1994b). Thus, contributions to etiologic
agents other than asbestos are not considered further in this document.
6-9
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Adjusted Risk Coefficients for Lung Cancer
For the lung cancer model and the associated risk coefficient, "K^," 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, "KJ needs to be normalized to a model in which T is properly
expressed.
As indicated previously, published structure size distributions derived from TEM
measurements are used to adjust the existing K^'s by normalizing them to an exposure
index that better relates to biological activity than the measurements in the
epidemiology studies from which the Kj/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 other
exposure indices that are potentially more characteristic of biological activity. Studies
were paired as indicated in Table 6-2.
Only a subset of the TEM size distributions listed in Table 6-2 were actually employed
in the effort to normalize K,_ values. To minimize uncertainty introduced by between-
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 dear. Ultimately, the size distributions selected for use came
from only two studies, which were reported in three publications: Dement and Hams
(1979), Gibbs and Hwang (1980), and Hwang and Gibbs (1981).
Table 6-3 presents bivariate fiber size distributions derived from the published TEM
data that are paired with representative K^ values from the corresponding epidemiology
studies. These data were used to adjust Kj, values in the manner described below.
The procedure for adjusting risk coefficients is straightforward. First, note that
Equation 6.1 can be rewritten as:
= RR-1 (6.2)
where RR is the relative risk and is equal to (IL/IE) and d' is the duration excluding the
final 10 years before observation of total lung cancer deaths.
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TABLE 6-2:
CORRELATION BETWEEN PUBLISHED QUANTITATIVE EPIDEMIOLOGY
STUDIES AND AVAILABLE TEM FIBER SIZE DISTRIBUTIONS
Fiber Type Exposure Setting Distribution Reference Epidemiology Reference
Chrysolite Textiles
Dement and Harris 1979
Cherrieetal. 1979
Dement etal. 1983b
McDonald etal. 1983a
McDonald etal. 19S3b
Peto 1980 and 1965
Friction Products
Crocidolite
Amosite
Mining and Milling
Asbestos Cement Manu.
Mine Environment
Construction
Asbestos Cement Manu.
Tremolite
Mining and Milling
Insulation Manu.
Insulation Application
Insulation Clearance
Mining and Milling
Talc Mining
Dement and Hams 1979
Marconi etal. 1934
Winer and Cossett 1979
Roberts and Zumwalde 1982
Gibbs and Hwang 1960
Winer and Cossett 1979
Dement and Harris 1979
Snyder etal. 1987
Winer and Cossett 1979
Sebastian et al. 1964 and 1986
Snyder etal. 1987
Hwang and Gibbs 1981
Gibbs and Hwang 1960
Hwang and Gibbs 1981
Dement and Harris 1979
Snyder etal. 1937
Cherrie et al. 1979
Gibbs and Hwang 1980
Dement and Harris 1979
Berry and Newhouse 1963
McDonald et al. 1964
McDonald etal. 1980
Nicholson etal. 1979
Rubino etal. 1979
Hughs et al. 1986
Finkerstein 1983
Hughs etal. 1986
Weill etal. 1979
Weil) etal. 1984
Armstrong et al. 1988
Seidman 1984
Henderson and Entertine 1979
Selikoff etal. 1979
-------
TABLE 6-3:
REPRESENTATIVE K, AND KM VALUES PAIRED WITH AVERAGED TEM FIBER SIZE DISTRIBUTIONS FROM PUBLISHED
PAPERS
Exposure Setting Reference «t KM
(x108)
Chyrsotile Textile Factory D&H 1979 0.02 0.2
Chrysotile Friction Products D&H 1979 0.0006 0.12
Predominantly Chrys Pipe Manuf D&H 1979 0.005 0.1
Mixed Asb/Cmt Pipe Manuf H&G 1931 0.036 19
Chrysotile Mining G&H 1980 0.0006 0.01
Amostte Ins Manuf D&H 1979 0.043 3.2
AmosftelnsApplte* D&H 1979 0.0075 1.5
Crocidoltte Mining" H&G 1931 0.1 14
Talc Mining (actin-trem) D&H 1979 TBD TBD
Fraction of Distribution Represented by Size Categories Indicated
PCME
0.1296
0.0724
0.0607
00076
0.0140
0.3520
0.3275
0.0117
0.0702
W<0.2
L<5 5
-------
By multiplying Equation 6.2 by one (expressed as a product of reciprocal ratios), we
obtain:
Kj
'PCME
'Opt
(6-3)
where:
"CPCME" is tne concentration of asbestos structures in the published size
distribution (Table 6.3) that are typically included in PCM
measurements; and
"Copt" is the concentration of asbestos structures in the published size
distribution that represent the optimal exposure index (i.e. the index of
exposure that determines biological activity).
Note that the equation incorporates concentrations but that the ratio of concentrations
of specific size categories is always equal to the ratio of the fractions of the
corresponding categories within a defined size distribution. Thus, the data in Table 6-3
can be used to derive estimates of the ratios of concentrations.
Remembering that CpcME is supposed to equal CpcM (Section 5.6.1) 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:
Kl(COPT)(d') = RR-1
(6.4)
where:
y is the adjusted risk coefficient, defined (and derived) as:
(6.5)
where all terms have been previously defined.
The adjusted risk coefficients, the "KL's", derived from the data provided in Table 6-3 in
the manner described above are presented in Table 6-4. Adjusted risk coefficients for
mesothelioma, uKM'sn, are also provided. KM's and KJs are derived in precisely the
6-13
-------
TABLE 6-4:
COMPARISON OF ORIGINAL AND ADJUSTED KL AND KM VALUES
Exposure Setting
Chyrsotile Textile Factory
Chrysotile Friction Products
Predominantly Chrys Pipe
Manuf
Mixed Asb/Cmt Pipe Manuf
Chrysotile Mining
Amosite Ins Manuf
Amosite Ins Applic*
Reference
O&H 1979
O&H 1979
O&H 1979
H&G 1981
G&H 1980
D&H 1979
D&H 1979
Kt
0.02
0.0006
0.005
0.036
0.0005
0.043
0.0075
K*
(x108)
0.2
0.12
0.1
19
0.01
3.2
1.5
K,'
0.0531
0.0014
0.0108
0.0974
0.0019
0.2871
0.1390
KM
(x108)
0.5313
0.2778
0.2160
51.4038
0.0373
21.3671
27.7915
Crocidolite Mining** H&G 1981 0.1 14 0.1827 25.5752
Talc Mining (actin-trem) D&H 1979 TBD TBD
-------
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 5.2. However, published size distributions cannot be used to
adjust risk coefficients to be consistent with the exposure index described by
Equation 5.2. 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 5.2 requires that structures longer than 40 urn be individually
enumerated. Therefore, Equation 5.2 was modified in an ad hoc fashion to generate
the exposure index that forms the basis of this protocol:
Copt = 0.003CS + 0.997CL (6.6)
where:
"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.
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 5.2), it is recommended that asbestos concentrations be defined in terms of
the exposure index defined by Equation 6.6 when evaluating asbestos risks and that
the procedures described in the companion Protocol (Part 1 of this document) be
employed to assess such risks.
It is interesting to consider the effect of adjusting the asbestos risk coefficients in the
manner described above. If such adjustments were truly optimal (in that they
adequately reflected all of the characteristics of asbestos that determine risk), one
would expect the variation observed among the adjusted risk coefficients from all
exposure settings to shrink to a level no greater than that observed within each single
exposure settings (approximately a factor of 4).
To examine variability, the magnitude of the ratio of the maximum and minimum values
(i.e. the spread of the values) for the adjusted and the unadjusted K^'s from Table 6-4
are presented in Table 6-5. Such ratios provide a rough indication of the variability or
spread in these coefficients. The same adjustment is made to KM's (risk coefficients for
mesothelioma, as described below) and these results are also presented in Table 6-5.
6-15
-------
TABLE 6-5:
COMPARISON OF SPREAD IN RANGE OF ORIGINAL AND ADJUSTED KL AND
RVALUES FOR SPECIFIC FIBER TYPES
Fiber Type Number of
Values in Set
All fiber types combined
All chrysotile exposure settings
Chrysotile only (excluding mixed)
All amphibole exposure settings
Amphiboles only (excluding mixed)
8
5
4
4
3
Ranges of Values
Kt
200
72
40
13.3
13.3
KM
1900
1900
20
12.7
9.3
Kt'
207
70
38
2.9
2.1
KM'
1376
1376
14
2.4
1.3
-------
It is apparent from Table 6-5, that the total variation (spread) in adjusted K^ values is no
different than for the unadjusted values (when all fiber types and exposure settings are
considered together). Although there is an apparent reduction in total variation in
adjusted KM values (compared to the unadjusted values), the improvement is relatively
small. However, if one also assumes that fiber type is an important factor that affects
potency (as indicated in Section 5.7), the impression changes.
Although the spread in adjusted K^ values among chrysotile-related exposures is no
different than that for unadjusted values (a range of 72 versus a range of 70), the
spread among K^ values for amphibole exposures is reduced substantially when the
values are adjusted (a range of 13.3 versus a range of 2.9). In fact, the spread in
adjusted Kj. values among amphibole-related exposures is reduced to the point where it
is indistinguishable from the variability that is observed within single exposure settings.
Similar effects are also observed among KM values, when fiber type is considered.
Among chrysotile associated values, the spread in adjusted KM's is reduced marginally.
Again, however, the spread in adjusted KM values among amphibole-related values is
reduced substantially so that the variability observed between exposure settings
becomes indistinguishable from that observed within single exposure settings.
The apparent reconciliation in published risk coefficients described above is
particularly interesting, given that the exposure index to which the adjusted values are
normalized is not optimal, due to limitations in the available data (see Appendix B).
One wonders whether further reconciliation might occur if the exposure index employed
can be further optimized using data from a future study. However, it is also possible
that the variation observed among chrysotile-related coefficients is due to factors
unrelated to the manner in which asbestos exposure is characterized so that it cannot
be further reduced. Such factors might include, for example, any of the last five factors
that contribute to variability that were listed at the beginning of this section.
Importantly, in no case do the current adjustments make the spread among risk
coefficients worse.
Although the observations concerning variability discussed above are qualitative, they
do tend to support the more substantive findings of the extensive literature review and
the re-analysis of the animal inhalation studies presented in Chapter 5.
6.2.2 Assessing the Risk of Mesothelioma
The model used here to describe mesothelioma mortality in relation to asbestos
exposure is the model proposed in the Airborne Health Assessment Update
(U.S. EPA 1986). This model assumes that asbestos-induced mesothelioma mortality
6-17
-------
is independent of age at first exposure and increases according to a power of time from
onset of exposure, as described in the following relationship:
forT>10+d (6.7)
= KM Cpcn, (T-1 0)3 for 10+d > T > 1 0
= 0 for10>T
where:
"1M" is the mesothelioma mortality observed at 'T' years from onset of
exposure to asbestos for duration "d" and concentration C^ of
PCM fibers;
"KM" is the risk coefficient (proportionality constant between dose and
response) for mesothelioma 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 does not depend on the background incidence of the disease.
Background mesotheiioma cases are rare in the general population in any case.
This model assumes that the mesothelioma risk from exposure in any increment of time
increases forever, even after exposure ceases. Information for evaluating this
assumption is available in a long-term follow-up study of workers in a factory that was
only open during a five year period in the 1940's (Seidman 1984). The data from this
study suggest that mesothelioma risk from exposure during a given time increment
peaks about 40 years after exposure and decreases thereafter (Crump et al. 1987).
Data from a large study of insulation workers (Selikoff et al. 1979) also supports this
point of view.
The derivation of K^'s (Appendix A) was conducted in 1986 and the model has not
been revised to account for the apparent drop off in risk after 40 years that is described
above. Nevertheless this model is used herein to estimate the risk from constant
lifetime exposure to asbestos, which requires using the model to predict risk from a
given jncrement of exposure 70 or more years into the future. This is far longer than
the model has been shown to adequately represent the mesothelioma response to
asbestos. If future mesothelioma risk does peak 40 years after exposure, as indicated
by the data currently available, use of the model (Equation 6.7) will overstate
mesothelioma risk.
6-18
-------
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.
Five of the additional studies are considered in this evaluation to derive estimated
values for KM.
Although the characteristics of asbestos that best relate to the induction of
mesothelioma were not investigated directly in the re-evaluation of the animal
inhalation experiments described in Section 5.6.2, results from this study suggest that,
as long as the potency of chrysotile and the amphiboles are separately considered, the
exposure index found to adequately predict lung cancer was also found to adequately
predict mesothelioma incidence (Berman et al. 1995).
The results from our animal inhalation study are also reinforced by the findings of the
literature review reported in Chapter 5. In terms of fiber dimensions, there are no clear
indications of major differences between the range of fiber sizes that lead to lung
cancer and those that lead to mesothelioma, although such differences have been
hypothesized (e.g., Lippmann 1988). However, several studies suggest that induction
of mesothelioma is a stronger function of fiber type than the induction of lung cancer.
Therefore, fiber types are treated separately in our model of risk for mesothelioma (as
well as for lung cancer).
Given the above, the same two-parameter exposure index adopted for lung cancer
(defined in Equation 6.6) is also assumed to reasonably represent the fiber dimensional
criteria that are important to the induction of mesothelioma and KM values are adjusted
accordingly. The set of adjusted KM values (derived as described in Section 6.2.1) is
presented in Table 6-4.
That the exposure index defined in Equation 6.6 adequately represents the
characteristics of asbestos that determine potency toward the induction of
mesothelioma is further suggested by the data presented in Table 6-5. The spread in
unadjusted KM values associated with amphibole-related exposures decreases
substantially when the KM values are adjusted as described in Section 6.2.1. The
variation in the adjusted amphibole-related KM values appears to decrease to a level
that is no greater than that observed within single exposure settings. Thus, in essence,
the adjusted KM values reported for the set of epidemiology studies considered in this
evaluation converge to a single value.
Reconciliation of chrysotile-related KM values is not as striking as observed for the
amphibole-related values. However, some improvement is still apparent. The spread
in adjusted KM values for chrysotile-related environments is about 70% of the variation
observed for unadjusted KM values but remains about three times the level of variation
observed within single exposure settings.
6-19
-------
7.0 RECOMMENDATIONS FOR ASSESSING THE HUMAN HEALTH RISKS
ASSOCIATED WITH EXPOSURE TO ASBESTOS
Recommended risk coefficients for lung cancer and mesothelioma (the adjusted K^ and
KM, respectively) are presented in Table 7-1:
TABLE 7-1: RECOMMENDED RISK COEFFICIENTS
Fiber Type Kt' K,"
(X108)
Chrysotile 0.05 0.5
Amphiboles 0.3 50
These risk coefficients are designed for use with exposure estimates derived from the
measurement methods (based on TEM) recommended for use at Superfund sites (ISO
103122 and Berman and Kolk 1997) and expressed in terms of the exposure index
defined by Equation 6.6. Such exposure estimates include only asbestos structures
longer than 5 pm and thinner than 0.5 urn. Consistent with the evidence presented
elsewhere in this report of a different potency for chrysotile and amphibole (which we
have not been able to reconcile using the information on physical dimensions of
asbestos structures presently available), separate risk coefficients are recommended
for chrysotile and amphibole. The recommended values are conservative (in a health-
protective sense) in that the largest of the adjusted values is recommended for each
fiber type.
Estimating Cancer Risks
To assess the lifetime cancer risks using the models and procedures recommended in
this document from a given exposure pattern 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. These mortality rates can then be
used in a lifetable analysis, along with the given exposure pattern and the appropriate
values of K^1 and KM' (from Table 7-1) to estimate the additional probability of dying of
lung cancer or mesothelioma. Such a lifetable analysis is described in Appendix C.
The ISO Method (ISO 10312) is a refinement of the method originally published as a
Superfund Method (Chatfield and Berman 1990). It incorporates improved rules for
evaluating fiber morphology and is therefore recommended for use here. Both methods
derive from a common development effort headed by Eric Chatfield.
7-1
-------
Table 7-2 presents estimates of the additional risk of death from lung cancer and
mesothelioma attributable to lifetime exposure to an asbestos concentration of 0.0005
f/ml (for fibrous structures longer than 5 urn and thinner than 0.5 um) as determined
using the TEM methods recommended for use at Superfund sites (ISO 10312 and
Berman and Kolk 1997). These risk estimates are derived using the lifetable method
described in Appendix C.
In Table 7-2, separate risk estimates are provided for males and females and for
smokers and non-smokers. The background rates for lung cancer and total mortality
used in these calculations are listed in Appendix C. Separate estimates are also
presented for exposures containing varying fractions (in percent) of fibrous structures
greater than 10 um in length.
Separate estimates are presented for smokers and nonsmokers because the lifetime
asbestos-induced risk of both lung cancer and mesothelioma differ between smokers
and nonsmokers. The asbestos-induced risk of lung cancer is higher among smokers
because the lung cancer model (Equation 6.1) 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.
The asbestos-induced risk of mesothelioma is smaller among smokers because the
mesothelioma model (Equation 6.7) 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.
Separate estimates are provided for different fractions of fibrous structures longer than
10 um 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 6.6).
Risks from lifetime exposures to asbestos levels other than 0.0005 may be estimated
from the appropriate entry in Table 7-2 by multiplying the risk listed in 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). This procedure
should provide a good approximation as long as the projected risk is no greater than
0.01 (1,000 per 100,000). Risks greater than 0.01 that are derived from the table are
likely to be over-estimated.
7-2
-------
TABLE 7-2:
ADDITIONAL RISK PER ONE HUNDRED THOUSAND PERSONS FROM LIFETIME
CONTINUOUS EXPOSURE TO 0.0005 TEM f/ml LONGER THAN 5.0 jim
AND THINNER THAN 0.5
Percent of Fibers Greater Than 10
0.5%
1%
2%
4%
6%
10%
15%
Mm in Length
20%
30%
40%
50%
CHRYSOTILE
MALE NONSMOKERS
Lung Cancer
Mesotheliomas
0.052
0.057
0.084
0.093
0.15
0.16
0.28
0.31
0.41
0.45
0.67
0.74
0.99
1.1
1.3
1.4
2.0
2.2
2.6
2.9
3.3
3.6
FEMALE NONSMOKERS
Lung Cancer
Mesotheliomas
MALE SMOKERS
Lung Cancer
Mesotheliomas
FEMALE SMOKERS
Lung Cancer
Mesotheliomas
0.039
0.064
0.48
0.038
0.32
0.057
0.063
0.10
0.77
0.062
0.52
0.093
0.11
0.18
1.4
0.11
0.93
0.16
0.21
0.34
2.6
0.21
1.7
0.31
0.30
0.50
3.7
0.30
2.5
0.45
0.50
0.83
6.1
0.49
4.2
0.74
AMPHIBOL
MALE NONSMOKERS
Lung Cancer
Mesotheliomas
0.51
5.7
0.82
9.3
1.5
16
2.7
31
4.0
45
6.5
74
0.74
1.2
9.1
0.73
6.2
1.1
.ES
9.7
109
0.98
1.6
12
1.0
8.2
1.5
13
145
1.5
2.4
18
1.4
12
2.2
19
216
1.9
3.2
24
1.9
16
2.9
25
288
2.4
4.0
30
2.4
20
3.6
32
359
FEMALE NONSMOKERS
Lung Cancer
Mesotheliomas
MALE SMOKERS
Lung Cancer
Mesotheliomas
FEMALE SMOKERS
Lung Cancer
Mesotheliomas
0.38
6.4
4.7
3.8
3.2
5.7
0.61
10
7.6
6.2
5.2
9.3
1.1
18
13
11
9.1
16
2.0
34
25
21
17
31
3.0
50
37
30
25
45
4.8
83
60
49
41
74
7.2
123
89
73
61
110
9.6
163
118
97
81
145
14
243
176
144
120
217
19
323
235
192
160
288
24
403
293
239
199
360
-------
Other Considerations
Based on the information presented in this technical background document, the
following considerations also need to be addressed when collecting data to assess
asbestos-related health risks:
samples collected for evaluating asbestos exposure need to be
representative of the exposure environment;
samples need to be prepared for analysis using a direct transfer
procedure. 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 establish a site-specific
correlation and corresponding conversion factor between directly and
indirectly prepared samples;
samples need to be analyzed by JEM;
samples should be analyzed using the counting and characterization
rules defined in the ISO Method (ISO 10312) with one modification: only
structures longer than 5 urn need to be enumerated. Separate scans for
counts of total structures longer than 5 urn and structures 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 stated here,
should be included in the structure count;
if risks are to be estimated using the risk models described in Chapter 6,
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 6.6. 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); and
if risks are to be estimated using Table 7-2, rather than deriving the
weighted sum described in Equation 6.6, 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
7-4
-------
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.
7-5
-------
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APPENDIX A:
REVIEW OF RELEVANT EPIDEMIOLOGY STUDIES
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In this appendix, an evaluation is provided of the epidemiology studies used to derive KL and
values listed in Table 16. Potency estimates derived for lung cancer and mesothelioma are based
on accepted dose/response models as reported in the text and summarized here.
Risk Assessment Model for Lung Cancer
Previously, it has been assumed (EPA, 1986) that the relative risk of lung cancer risk is given by
the following linear function of dose:
RR a 1 -f KLd, (1)
where d is cumulative exposure to asbestos in fiber-years/ml accumulated up to ten years prior to the
time of observation, and Kj. is the lung cancer potency in units of (fiber-years/ml)'7, measured by PCM.
This model will extended slightly here to account for the possibility that the background rates in the
studied population may be different from those of the control population used. This could occur, for
example, if smoking rates in the studied population were different from those in the control population.
The modified model is of the form
RR = a(l + KLd). (2)
The parameters of this model (Kg. and a) will be estimated by fitting the model to epidemiological dose-
response data for lung cancer. Allowing the background parameter, a, in the model allows one to adjust
for differences in smoking rates between the control population and studied population without actually
having data on smoking rates available for the studied population. The multiplicative form in which the
background parameter appears is also consistent with the observed multiplicitive relation between
smoking and asbestos (Hammond et a/., 1979). Whenever the model adequately describes
epidemiololgical data with a=l, the corresponding K^'will be used. This KL will be identical with the
KL that would be obtained using (1).
Risk Assessment Model for Mesothelioma
To estimate risk from mesothelioma, it will be assumed EPA (1986) that the absolute risk or
death from mesothelioma in the t-th year after the beginning of exposure (assuming survival to that
time) is given by
RM = KM fl(t-10)j - (t-10-D)J] for t > 10+D
= KM f(t-10)5 for 10+D > t > 10 (3)
= 0 for t < 10,
where f is the average occupational fiber concentration in fibers/ml, D is the duration of exposure in
years, and KM is the mesothelioma potency in units of (fiber-years/ml)'7 estimated by fitting the model
to epidemiological data.
Application of this mesothelioma model is difficult for many studies because the data needed to
apply the model were not published. In one case (Dement et a/., 1982) the required data were obtained
from the authors in a personal communication. In other cases the needed data were reconstructed
based upon reasonable assumptions. Although it would have been preferable to have had direct access
A-l
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to the data, it is considered unlikely that having this data would cause the estimates to change by more
than a factor of 2, which is relatively small in comparison the differences between estimates of K^
obtained from different studies.
Developing Estimates of Lifetime Risk from Estimates of Kr and KM
Hie lung cancer model (2) provides estimates of the increase in the relative risk of lung cancer due to
asbestos exposure. The relative risk is the instantaneous risk of death from lung cancer in the presence
of asbestos exposure divided by the corresponding instantaneous risk in the absense of asbestos
exposure. On the other hand, the additional lifetime risk of lung cancer death from an asbestos
exposure pattern denoted by d is defined as the difference,
where P//d) is the lifetime risk of death from lung cancer under exposure pattern d, and P^(0) is the
lifetime risk of death from lung cancer in the absense of exposure to asbestos. To estimate the
additional lifetime risk of lung cancer risk from asbestos exposure requires taking into account the
backgound risk of lung cancer in the population as well as completing risks of death from causes other
than lung cancer. Similarly, the mesothelioma model (3) provides estimates of the instantaneous
absolute risk of mesothelioma following the the onset of exposure, and to estimate the additional
lifetime risk of mesothelioma .death from asbestos exposure requires data on competing causes of death.
Since death rates from lung cancer, as well as from certain other causes, are higher in smokers than in
non-smokers, risks from asbestos exposure will be. different in smokers and in non-smokers. Risk of
lung cancer from asbestos exposure will be higher in smokers because of the multiplicative interaction
between asbestos and smoking. However, risk of mesoihdioma will be less in smokers because of their
shorter lifespans. In order to take these differences into account, smoking-specific death rates are
needed. Appendix 8 describes the death rates used in the calculations in this document, and how these
rates are used to estimate addition lifetime risk of asbestos-related cancer death.
A-2
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ANALYSIS OF INDIVIDUAL STUDIES
Production of Chrysotile Friction Products
perry and Newhouse (1983) conducted a mortality study of 13,460 workers in a factory in
Britain that manufactured brake blocks, brake a dutch linings, and other friction materials. Only
Chrysotile was used at the plant except for two relatively short periods before 1945 when crocidoliie 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 membrane filter 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
cancers was observed after ten years from first employment (25 observed versus 35.8 expected, p = 0.0-1).
A linear dose response model between cumulative exposure and lung cancer was fit to case
control data by Berry and Newhouse. The resulting potency estimate7 was 0.00058 (fiber-y/m!)"; and the
90% upper limit was 0.0080 (fiber-y/ml)";.
A case control study on mesothelioma deaths showed that eight of the eleven workers had been
exposed to crocidolite and another possible 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 unlikely assuming no association with crocidolite. The data were not presented in a
form that permitted a quantitative estimate of mesothelioma risk, in a Connecticut plant that
manufactured asbestos friction products.
McDonald era/. (1984) evaluated the mortality of workers employed. The plant began
operation in 1913 and used only Chrysotile until 1957, when a little anthophylite 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 cohon studied 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 cohon consisted of 3515 men, of which 36% 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.
1 Unless otherwise qualified, estimate" refers to maximum likelihood
estimate.
A-3
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Information on dust levels from impinger measurements were available for the years 1930, 1935,
1936, and 1939. There was little other information available until the 1970s. An industrial hygienisi
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.
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 relative risk for lung cancer were highest (180) for persons exposed for
less than one year. A similar pattern holds when the analysis is carried out by cumulative exposure
(Table A-3). The linear relative risk lung cancer model (2) provides a very poor fit to this data (p <
0.01) when the Connecticut rates are assumed to be appropriate for this cohort (fixing the parameter a
= 1); use of US. rates gives similar results. However, the fit is adequate (p = 03) 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 (mppcf-y)*7, and a 95% upper limit of
KL « 0.0051 (mppcf-y)';. To convert estimates to units of (fiber-y/ml)';, a conversion from mppcf to
fibers/ml is needed. Although no value was suggested by the authors, a conversion factor or between 1.4
and 6 is suggested by other studies. Assuming, as a median value, that that 3 fibers/ml is equivalent to
1 mppcf, the 95% limit is equivalent to 0.0017 (fiber-y/ml)';.
McDonald ef a/, 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 ef a/, do
not furnish data in the needed form for this calculation, the needed data can be approximated from
McDonald etal.'s Table A-l. 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
past 65 years of age is estimated as 471/0.050 = 9420.
A lower bound to the person years observed between ages 45 and 65 can be estimated by
assuming that the 616 observed deaths arose in a cohort that was followed up completely. 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).
Number Entering Number of Deaths Person-Years
Age Interval in Interval in Interval
45-50
50-55 x(l-.00638)5 =
55-60 .967x(l-.01072)5 =
6(W5 .9167x(l-.01718)* =
65+ .8406x(l-.02681)5 =
Totals
x
.9685x
.9177x
.8416x
= .7298x
0.0315x
0.0508X
0.0761X
0.1118X
0.2702x
4.921X
4.716x
4398x
3.929X
17.964x
Given 616/1.085 = 567.7 expected deaths between ages of 45 and 60, the number of persons entering
the interval is estimated as x = 567.7/0.2702 « 2101. The person years is then estimated as
(2101)(17.964) m 37742.
.A-4
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Using the average age of beginning work of 30.95 years (McDonald et a/., 1984, Table A-3)
yields the data in Table A-4. Moreover, the average duration or exposure in this cohort was 8.04 years
and the average exposure level was 1.84 mppcf (McDonald et a/.'s Table A-3), which is equivalent to
1.84x3 = 5.52 fibers/ml. This data yields an estimate of KM « 0.0 (Gber-y/ml)'7 and a 95% upper limit
of KM = 1.2 x 10"9 (fiber-y/ml)''. This upper limit is possibly too large because person-years in the
45-65 age group are probably overestimated.
Chrvsotile Mining
McDonald ef a/. fl980a^ followed a cohort of 11379 workers exposed almost exclusively to
chrysotile in two asbestos mines and related mills in Quebec Production at the mines began before
1900. The cohort consisted of everyone registered as working 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 and continued the end of 1975. Smoking habits were determined for over 95% of those
who died after 1950.
Estimates of dust levels were made from over 4,000 midget impinger measurements made
starting in 1949. Estimates for the period prior to 1949 were based on interviews with long term
employees and comparison with more recent conditions. On the basis of over six hundred side-by-sidc
measurements made from midget impinger and PCM methods, it was estimated that 3.14 Gbers/ml was.
on the average, equivalent to one mppcf (McDonald ef a/., 1980b).
Table A-5 shows the lung cancer in the cohort divided by length of employment and by
exposure level. Although there is a highly significant dose response for lung cancer, the slope is smaller
than for many other cohorts. The model lung cancer provides an excellent fit to this data without
modification of the background rate (fixing the parameter a = 1), yielding a potency estimate of 0.00045
(Gber-y/ml)'7 and a 95% upper limit of 0.00061 (fiber-y/ml)';).
McDonald ef a/.'s Table A-9 contains 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 = a(l + bd)(l+cx),
and the additive model
RR = a(l + bd + a),
where d is cumulative exposure to asbestos by 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 ef a/., 1979). The estimate of potency using
the multiplicative model is 0.00051, which is very close to that of 0.00045 estimated from Table A-5,
which did not utilize smoking data. This latter estimate is preferred because the the estimate which
used the smoking data did not take account of asbestos exposures after age 45 and therefore would be
expected to overestimate lung cancer potency somewhat.
McDonald ef a/, discovered 10 mesothelioma deaths in the cohort, all plueral. From Table A-4
of McDonald ef a/. (1980a), the average age at beginning of employment was 24.9 years, the average net
employment was 10.5 years, and the average concentration was 59.5 fibers/ml. Proceeding exactly as with
A-5
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the data from McDonald etal. (1984), the person years of observation between ages 45 and 65 and pasi
age 65 are estimated as 127,663 and 29651, respectively. Unlike the cohort studied in McDonald ef a/.
(1984), a sizeable number of deaths (754) were recorded in persons under 45 years of age. Using the
average age at beginning of estimated that 40% of exposure of 24.9 years and the death rates for males
ages 25-45, it is these deaths occurred between age 40 and 45. Using the death rate for males between
ages 40 and 45 of 0.00408 per year, the person years of observation between ages 40 and 45 is estimated
.as (754)(0.40)/l(0.00408)(1.09)J - 67818, where 1.09 is the overall relative risk observed for the cohort.
These calculations provide all of the data needed for the mesothelioma model except that, although we
know that ten mesotheliomas occurred in all, McDonald ef a/, do not furnish the ages at which the
deaths occurred (Table A-6). However, the estimate of KM is unaffected by the age groups into which
the mesothelioma death belong; the estimate is K^ = 1.1 x Iff70 (fiber-y/ml)';, no matter how the ten
mesotheliomas are placed |n intervals corresponding to years since first exposure.
Asbestos-Cement Products
Finkelstein (1982) studied mortality among a group of 328 employees of an Ontario asbestos-
cement factory who had been hired before 1960 and who had been employed for at least nine years.
Follow-up continued until October, 1980. The plant produced asbestos cement pipe from 1948, asbestos
cement board from 1955 to 1970, and manufacture of asbestos insulation materials was added in 1960.
Both chrysolite and crocidolite were used in each batch processed in the pipe process, but only
chrysoiile 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 were estimated from membrane filter samples taken
after 1969, impinger measurements taken during 1949, 1954, 1956, 195? and regularly 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."
Finkelstein compared lung cancer mortality rates in production workers divided into three
exposure groups to that of Ontario men in general (Table A-7). Mortality rates were standardized to
that of Group C, the most highly exposed group. Although lung cancer rates are elevated in all three
exposed groups, there is no apparent trend of increasing rates with exposure. In fact, the highest
exposed group experienced the lowest lung cancer mortality. This peculiar response pattern does not
appear to be due to misclassification of workers into dose groups because Finkelstein found a clear dose
response trend for both mesothelioma and asbestosis (cf. Finkelstein, 1983).
These data may be put into a form roughly equivalent to the more conventional age-adjusted
comparison of observed and expected lung cancer by dividing the rates in the exposed group by that or
Ontario men. The results of this are shown in Table A-8, which also shows the results of fitting the
lung cancer model both assuming the Ontario rates are appropriate for this cohort (fixing the parameter
a «= 1) and not making this assumption (allowing the parameter a to vary). The results are clearly
inconsistent with Ontario men being the appropriate control population, with the discrepancies being
due to random variation only (p < 0.01). On the other hand, the data are consistent with the model if
the background response is allowed to rise above that of Ontario men (p = 0.3). However, it seems
unlikely that the background lung cancer risk in this cohort was 10 times that in Ontario men in
general, as suggested by the analysis (a = 9.9). The best explanation possibly is that the background
risk was somewhat elevated in this cohort and that random variation and misclassification into exposure
groups played some role, also. It seems quite likely that the estimate KL = 0.073 (fiber-y/ml)';,
obtained ignoring the lack of fit, is too large, and a more appropriate estimate is somewhere between
this and the 95% upper limit K£ « 0.011 (fiber-y/ml)'7 obtained by letting the background vary.
A-6
-------
Accordingly, the estimate K£, = 0.036 (fiber-y/ml)"7 will be used, which is one half the higher KL and
more than three times the lower value. However, the lack of a dose response for lung cancer in this
cohort makes estimates derived from this study less reliable than those from other studies.
Eleven mesotheliomas were observed in this cohort (Table A-9) and, unlike the lung cancer
data, mesotheliomas showed a dose response trend. The mesothelioma model describes these data
adequately and provides an estimate of K# = 1.9 x Iff7 (fiber-y/ml)'7, based on an average duration of
exposure of 12 years and an average exposure level of 9 fibers/ml (CPSC, 1983).
"Weill etal. (1979) studied 5,645 men employed for at least one month before 1970 in either of
two asbestos cement plants in New Orleans and who had been followed up for at least twenty years.
Sixty per cent of the population were employed for less than one year and sixteen per cent for more
than ten years.
This population was traced in 1975 through the Social Security Administration, and 601 persons
were identified as dead. All other persons were assumed to be alive. However, only 64 per cent were
known to be alive in 1974 (by consequence of having a transaction with the Social Security
Administration), and consequently only 75% of the population was traced.
Both of the plants have operated since the 1920s. Chrysolite was used predominantly in both
plants. The first plant used some amosite, which constituted one per cent of various products, and also
used crocidolite infrequently. Chrysotile was used exclusively in the second plant, except in the pipe
production area, where crocidoliie constituted three per cent of the final product. Since the total
percentage of asbestos fiber in most asbestos cement products ranges from fifteen to 23 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).
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 1930*
were also assumed to hold for all earlier periods because no major dust control measures had been
introduced prior to that time. Based on 102 side-by-side measurements collected in various areas of one
of the plants of panicles using impinger methods and fibers using optical microscopy methods, Hammad
etal. (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.
Lung cancers were below expected in the lowest exposure categories, and were only elevated at
exposures above 100 mppcf-years (Table A-10). Although assuming non-traced individuals to be alive
would have the tendency to make observed less than expected, it seems unlikely that this could cause
observed cancers to fall as much below expected as was observed in this cohort. At any rate, such a
tendency should not bias estimates of potency estimated by allowing the background parameter "a" to
vary. The estimate of KL = 0.0064 (fiber-y/ml/; obtained allowing the background parameter to vary is
therefore judged to be the most appropriate estimate of potency. Although the fit of the lung cancer
model in this situation is marginal (p = 0.04), this lack of fit is due mainly to a shortfall of cancer in
the 51-100 mppfc-y group and an excess in the 101-200 group; this could have been due to
misalignment of persons within these groups.
Table A-ll contains the mesothelioma data from the Weill etal. cohort. The numbers of
mesothelioma were obtained from the observation by Weill er a/, that only two mesothelioma deaths
were recorded (both pleural) one 18 years and one 19 years after initial employment. The person-years
in each five-year age interval were estimated from Weill etal. (1979) Table A-3 by averaging the number
A-7
-------
of workers entering the interval and the number entering the subsequent interval, and multiplying the
result by 5. In the 35+ age group the number entering was multiplied by 5, assuming an average
follow-up of five years. From Table A-5 of Weill era/, the average employment was d = 4.5 years.
This same table provides an average dust concentration of 16.15 mppcf or, upon convening to fiber/ml
by multiplying by 1.4, f «= 216 fiber/ml The resulting potency estimate is KM «= 7.0 x I0']0
(fiber-y/ml)';, which is considerably smaller than the estimate from the Finkelstein cohort. Although
this value is based on only two mesotheliomas, the 95% upper limit is still only KM = 1.8 x 10*9
(fiber-y/ml)-;.
According to Table A-7 of Weill era/., 77% of the workers were exposed solely to chrysotile,
whereas only one of the mesotheliomas came from this group. Thus, assuming that the duration, level
of exposure, and amount of followup were similar in the chrysotile and non-chrysotile exposed groups,
the estimate of KU for chrysotfle exposed workers only would be (7.0 x l(r;0)(0.5)/(0.77) = 4.5 x W10
(fiber-y/miy;.
Hughes. Weill and Hamad d9871 report on followup through 1981 of an expanded cohort of
Louisiana workers from the same two asbestos cement plants studied by Weill era/. (1979). The study
contains 6,931 workers, of whom 95% were traced. This improved trace was the result both of greater
access to Social Security Administration records and greater availability of computerized secondary
information sources (Dr. Hughes, personal communication).
Chrysotfle was the primary type of asbestos used in both plants. Some amosite was used in
Plant 1 from the early 1940s until the late 1960s and crocidolite was used occasionally for approximately
10 years beginning in 1962. Previously (Weill ef a/., 1979) it was thought that amosite was not
introduced to this plant until the late 1950s. Plant 2 utilized only chrysotile, except that pipe
production, which began in 1946 and was housed in a separate building, utilized crocidolite. Thus,
workers from Plant 2 that did not work in pipe production were exposed only to chrysotile.
New exposure data from Plant 2 have become available since the earlier study, and these, alone
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 reasonable 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. The estimate by Hammad ef a/. (1979) of 1.4. mppcf per fibers/ml will be
used here.
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 era/, 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. Therefore this study does not indicate a difference in risk of lung cancer between
chrysotile and crocidolite. Further, a single lung cancer dose response model dose adequately describes
the lung cancer data from Plants 1 and 2 combined (Table A-12). 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 K£. = 0.0040 (fiber-y/ml)'7 and a 95% upper confidence limit of 0.0070
(fiber-y/miy7. These estimates are lower than those estimated from Wei!l ef a/. (1979) despite the fact
that Hughes ef a/. (1986) estimated lower exposures in Plant 2 than were used in the earlier study. This
seems to be due chiefly to the fact that the control population appears appropriate in Hughes ef a/.,
A-8
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whereas there was a deficit of lung cancer in the Weil! er a/. study in the lower exposure groups. This
deficit served both to make the control population appear to be inappropriate and to increase the
apparent dose response slope, K.L. It is notable that the KL estimated from Hughes ef a/. is the same as
that estimated from Weill era/, assuming the control population used by Weill era/, was appropriate,
although that model didn't fit the Weill er a/. data.
Six mesotheliomas have occurred in the primary cohort studied by Hughes er a/., 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 since 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 mesothelioma potency from several data sets, including
the cohort studied in Hughes er a/, and containing six mesotheliomas, but using a model slightly
different from (3). Estimating Kw by multiplying the potency estimated by Hughes and Weill model by
the ratio of the potency values estimated for another study using (3) and the Hughes- Weill model
yielded the following estimates of Ku for the Hughes era/, data: 0.25 x 10'* (fiber-y/ml)'7 (Selikoff
era/., 1979); 0.21 x 1(T* (fiber-y/mlV' (Dement era/., I983b); 0.27 x KT* (fiber-y/ml)'7 (Seidman, era/..
1979); and 0.43 x 10"* (Gber-y/ml)'' (Finkelstein, 1983). Based on these calculations, K*/ = 0.30 x 10 *
(fiber-y/ml)'' is a reasonable estimate for the Hughes er a/, cohort.
Three additional mesotheliomas have occurred in this cohort since followup ended in 1981. It
would be worthwhile to estimate mesothelioma risk using additional followup that included these cases.
However, we can be assured that such an estimate would be no larger than K.M = 0.45 x 10**
(fiber-y/ml)";. This is because, since there were six mesotheliomas in the cohort studied by Hughes
er a/., even if the additional person years of followup past 1981 is not taken into account, the three
additional mesotheliomas would increase the estimate of Kw = 0.30 x 10** (fiber-y/ml)'; obtained from
followup through 1981 by only 50%.
Hughes era/.'s finding of an association with crocidolite exposure implies that a smaller
would correspond to the chrysotile-only exposed group in Plant 2. Although Hughes era/, didn't furnish
the data needed for precise estimation of Kw 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, K^ = 0 would be the point estimate derived from the data used by Hughes er a/.
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, indicating exposure only to chrysotile. In addition, three more
mesotheliomas have occurred in former workers in the pipe department since followup ended in 19S2.
Considering these additional four mesotheliomas, there have been eight mesotheliomas in Plant 2
workers, seven of whom were exposed to crocidolite. Since the cohort from Plant 2 is about 1.8 times
as large as that of Plant 1 (based either on number of men or number of deaths) and twice as many
mesotheliomas were discovered in the Plant 2 as in the Plant 1 cohort, it is reasonable to estimate that
Hughes and Weill (1986) would have obtained about the same estimate of mesothelioma potency- had
they used only data from Plant 2. Since only 37% of Plant 2 were exposed to crocidolite and these
workers worked an average of four times as long, a reasonable adjustment of the earlier estimate of
A-9
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to apply only 10 chrysotile exposed workers would be KM ^ 0.3 x Iff* x (1/8) x (0.37) x (4) = 0.05 x
Iff* (fiber-y/miy;.
As a third approach to estimating KM from the non-crocidolite exposed group, it was observed
that the duration of observation of the Hughes ef a/, cohort was roughly equivalent to that of the
Dement ef el. (1983b) cohort Using the person years in the Dement ef el. cohort, adjusted by the ratio
of the sizes of Dement ef a/, and the Hughes ef el. non-crocidolite-exposed cohort from Plant 2, and
applying the average duration of exposure (25 years) and fiber level (11.2 fibers/ml) appropriate for the
Hughes ef a/, cohort yields K^ = 0.2 x 10* (fiber-y/ml)''.
Each of these approaches imply that the KM for chrysotile exposure in asbestos cement
production should be less than the value of 03 x 10** (fiber-y/ml)'; estimated for the Hughes ef a/.
cohort as a whole.
Chrysolite Textile Products
Dement etel. (1982. 1983a. 1983b) conducted a retrospective cohort study of 1261 white male
employees of a chrysotile textile plant in South Carolina who worked for one or more months between
1940 and 1965. Followup was through 1975, thus insuring at least a ten year latency.
A considerable excess of lung cancers which exhibited a dose response relationship were
observed in this population (Table A-14). Dement ef a/, used U.S. rates for calculating expected deaths
even though local are 75% higher. Dement ef a/, argues that local were probably influenced by a World
War II shipyard and also by this plant; however, these sources of elevated lung cancer risk seem unlikely
to have influenced local rates greatly. Furthermore, if the background is not held fixed when fitting the
model, the estimated background is 56% higher than that derived from U.S. rates (Table A-14). Thus
the most accurate potency estimate from this cohort is considered to be K.L = 0.0285 (fibers-y/ml)"7
estimated from the analysis in which the background is allowed to vary.
Only one mesothelioma was detected in this cohort (Table A-15). The person-years in this
table, as well as the details of the mesothelioma case (20+ years of employment and a latency of about
40 years) were furnished through the courtesy of Dr. Dement. Table IV of Dement ef el. (1983b)
indicates that the average duration of employment was about ten years. Dement ef el. do not furnish
data that are particularly appropriate for determining average exposure. However, in a study of the
same mill McDonald ef el. (1983a) estimated the average exposure to be 1.8 mppcf. Using the
conversion factor of three fibers/ml per mppcf estimated by Dement for this mill, the average exposure
in fiber/ml is estimated as 5.4. Based on this data the estimate is KM = 3.0 x Iff9 (fiber-y/ml)'^ and the
95% upper limit is Kw = 1.1 x Iff* (fiber-y/ml)-7.
McDonald ef a/. H983a^ conducted a cohort mortality study in the same South Carolina textile
plant that was studied by Dement ef el. (1983b). 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 only begun past 20 years from first employment
McDonald etel. had available the same exposure measurements as Dement ef el. (1983b) and
used these to estimate cumulative exposures for each man in mppcf-y. In thr.ir review of the
environmental measurements in which both dust and fiber concentrations were assessed, they found a
panicle to fiber conversion range of from 1.3 to 10.0 with an average of about 6 fibers/ml per mppcf.
A-10
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This value, which is intermediate between the values of 3 and 8 found by Dement ef a/, for different
areas of the same plant, will be used in the calculations involving the McDonald ef a/. (1983a) study.
McDonald ef a/, describe two practices at the plant that entailed very high exposures and which
were not reflected in either their's or Dement ef a/.'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 A-16), which parallels the results of
Dement et a/. Unlike Dement et a/., McDonald et a/, used South Carolina men as the control group
rather than U.S. men. Use of this control group provides an adequate description of the data and lung
cancer potency values estimated both assuming that the control population is appropriate (fixing the
parameter a «= 1) and not making this assumption (allowing the parameter a to vary) yield practically
identical results (Kj. = 0.012 (fiber-y/ml)"7 in the former case and 0.010 (fiber-y/ml)"' in the latter).
These results are reasonably consistent with the potency estimated from the Dement ef a/, study with a
allowed to vary [KL = 0.028 (fiber-y/ml)'7), but not with the results with a = 1 [KL = 0.050
(fiber-y/miy7).
McDonald ef a/, found one case of mesothelioma in this cohort, apparently the same one
discovered by Dement ef a/.: 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 ef a/. (1984), the 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 A-17. The estimated potency is KM = 5.1 x 10';o (fibcr-y/ml)';,
with a 95% upper limit of 1.9 x Iff9 (fiber-y/ml)~;. This estimate is about six-fold smaller than than
estimated from the Dement cohort. However, McDonald ef a/, found the same number of
mesotheliomas as Dement despite observing almost three times as many deaths (857 versus 308), and
used a larger panicle-to-fiber conversion factor overall than that used by Dement ef a/. Also, since
Dement ef a/, restricted their cohort to persons working after 1940, the McDonald ef a/, cohort should
contain considerably more person-years observed more than 30 years from first exposure, when
mesothelioma risk is estimated by the EPA model to be greatest.
Peto efa/. (1985^: A textile factory in Rochdale, England has been the subject of a number of
investigations (Doll, 1955; Knox efa/., 1965; Knox efa/., 1968; Peto efa/., 1977; Peto, 1978, 1980a and
1980b; and Peto efa/., 1985). This analysis will be based on the latter study, which has the most
complete follow-up (through 1983) and emphasizes assessment of risk. The factory, which began
working with asbestos in 1879, used principally chrysolite, but approximately five per cent crocidolite was
used between 1932 and 1968.
Quantitative estimates of risk will be based on a subgroup of Peto ef a/.'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 used to convert between
panicles/ml and fibers/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 to periods prior to 1951. These levels and work
histories were used to assign individual exposure estimates. A conversion factor of 34 particles/ml per
A-ll
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fibers/ml was determined by comparing average results obtained by the Casella thermal precipitator
(panicles/ml) with Ottway long running thermal precipitator (fibers/ml) at the same sampling point
during 1960 and 1961. However, a conversion factor of 353 was used by Peto era/, for the sake of
consistency with earlier work, and this factor will be used here as well.
After 20 years front first employment, there were 93 lung cancer deaths with only 64.6 expected.
Using a lung cancer model essentially the same as (1), Peto era/, estimated KL « 0.0054 (fiber-y/ml)';
for the entire cohort, and KL « 0.015 (fiber-y/ml)'7 when the analysis is restricted to men first employed
in 1951 or later. Peto era/, felt that the most plausible explanation for this difference is 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.
Ten mesotheliomas were observed in the cohort used by Peto et a/, 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
are shown in Table A-18 (person years were not provided in Peto et a/. (1985) and were obtained from
Table A-20 in Doll and Peto, 1985). Table A-18 also shows the mesotheliomas predicted using a 'cubic
residence time' model of mesothelioma, which is different from the mesothelioma model defined by (3).
However, all the data needed for application of (3) are contained in the table except for average
exposure. An overall average exposure was estimated by applying the Peto model to the data in
Table A-18 with a single exposure estimate, and selecting the value that gave the smallest least squares
fit of this mode] to the mesothelioma data. The fitting was carried out both unweighted and by
weighting by the person yean, with resulting estimates of 360 and 322 particles/ml, respectively; the
latter value was the one selected. Using this estimate of average dust level, the mesothelioma model (3)
was fit to the data, utilizing the different estimates of duration of work. The resulting estimate of
mesothelioma potency was X*/ = 3.7 x Iff10 (particle-y/ml)"; or, using the conversion factor of 353
panicles/ml per fibers/ml, KM = 13 x 10** (fiber-y/ml)'7. The predicted mesotheliomas derived using
this approach are also shown in Table A-18. This fitting approach, although crude in that individual
panicle levels were not available for each entry in the table, yields a better fit than the model used by
Peto era/, (goodness of fit p-value > 025 based on (3) and = 0.05 for Peto era/, model).
McDonald er af. f!983b> 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. Crocidoliic
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 reported on in McDonald er a/. (1983b) 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 er a/, 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 exposures by department and year in units of mppcf.
The lung cancer mortality in this cohort exhibited a significant dose response trend
(Table A-19), which was partially due to a deficit of cancers in the group exposed to <10 mppcf-y (21
A-12
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with 31.4 expected). A survey of those employed ID the plant in 1978 revealed a larger per cent of
nonsmokers (25%) than were found in the other plants studied by these researchers (McDonald era/.,
1983a and 1984); however this finding is 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 describes the data well (p > 0.7), whereas
the analysis which assumes the Pennsylvania rates are appropriate provides a marginal fit (p = 0.1).
Consequently, the former analysis is judged to be the most appropriate (allowing the parameter a to
vary). Assuming that 3 fiber/ml is equivalent to one mppcf, the resulting estimate of lung cancer
potency is Kj. = 0.018 (fiber-y/ml)'7 and the 95% upper limit is Kj. - 0.045 (fiber-y/ml)-7.
A diagnosis of mesothelioma was specified on fourteen death certificates (ten pleural and four
peritoneal). Seventeen 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 ef a/.'s Table A-3 lists the average age at beginning of
employment as 28.92 and the average duration of employment as 9.18 years, and their Table A-l 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 1CD code 199 might have been due to
mesotheliomas, the total number of mesotheliomas in this cohort is estimate to be between 14 and 23.
Proceeding exactly as in the mesothelioma analysis carried out for the McDonald era/. (1980a) data, the
data in Table A-20 are generated. Noting again that the age since first exposure categories in which the
mesotheliomas occurred is irrelevant as far as estimating KM is concerned:, the estimates are KM =
6.7 x Iff9 (fiber-y/ml)"; (assuming 14 mesotheliomas) and KM = 1.1 x Iff8 (fiber-y/ml)"; (assuming 23
mesotheliomas).
Armstrong er a/, f 1988^ studied a cohort consisting of 6505 men and 411 women who were
employed in the mining and milling of crocidolite in Western Australia between 1943 and 1966.
Followup was through 1980. Vital status at this point in time was known for 73% of the men and 58T,
of the women. Expected deaths were calculated in two ways: assuming all subjects lost to followup were
alive at the close of 1980 (SMR1) and followup of individuals only until the last date known to be alive
(SMR2), which was generally the last date of employment for those lost to followup. Ascertainment of
deaths were considered to be relatively complete, and if so, SMR1 would be the most accurate.
Measurements of airborne dust concentrations were made periodically between 1948 and 1958 using a
koniometer. Concentrations of airborne respirable fibers longer than five microns were measured in
various workplaces in 1966 using thermal precipators. Based on these data, fiber concentrations were
estimated for different job/workplace combinations. Each subject's cumulative exposure was calculated
from these data and work histories.
The total number of deaths observed was 820, which corresponded to standardized mortality
ratios of SMR1 = 0.96 and SMR2 = 1.53. The corresponding standardized mortality ratios for
neoplasms of trachea, bronchus and lung were SMR1 = 1.60 and SMR2 = 2.64, based on 91 observed
deaths.
There are no dose response data reported that are suitable for developing a risk assessment, so
KL and KM will be estimated from summary data from the entire cohort. Although the median
duration of exposure was four months, and the median exposure was six f/ml-years, data from Table A-l
of Armstrong er a/, indicate that average duration of employment was about 7.5 months and average
cumulative exposure was 10 f/ml-years. This suggests an average exposure of 10 f/ml-years/(7.5/12 years)
= 16 Uml. Using this average cumulative exposure and SMR1 = 1.6, the estimate obtained is
KL = (1.60-1)/10 = 0.06 (f/ml-years)-'. If SMR2 is used, the estimate is KL = (162-1)/10 = 0.16.
A-13
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There were 32 deaths from mesothelioma in this cohort. The figure in Armstrong ef a/.
indicates that, based on all exposure groups combined, the incidence of mesothemioma increased
monotonically to a value of approximately 90 per 100,000 man-years 25 years since beginning of
exposure. Using an average exposure of 16 f/ml and an average duration of 7.5 months, the estimated
potency for mesothelioma is '
(90tltf)l[(25-10)3 - (25-10-7.5/12)5J/16 = 14 x Iff8 (ttnl-years)'7.
Selection of Representative Potency Values for Specific Fiber Types and Operations
Table A-21 summarizes K& and KM values computed from various studies and compares them
with corresponding values calculated in the EPA Health Effects Update for Asbestos (EPA, 1986).
Values obtained herein agree generally with those in EPA (1986). However, a number of potency
values calculated here were not calcualated in EPA (1986). Eg., the Armstrong ef a/. (1988) was not
published in in 1986. Also, KM have been calculated for a number of studies which were not used for
this purpose in EPA (1986).
Table xxx21 categorizes studies according to process (e.g., mining, textiles, friction product
manufacture, etc) and also according to fiber type. We note that, with the exception of exposure to
mixture of chrysolite and crocidolite in asbestos cement manufacture, potency values calculated for
similar operations and fiber types are in general agreement, whereas there are large differences in
potencies obtained for different operations (e.g., exposure to chrysolite in texiile mills or mines) and for
differeni fiber types in the same type of operation (e.g., exposure to chrysolite or crocidolite in mines).
One goal of this analysis is to develop potency estimates specific for operation and fiber lype to match
with fiber size distributions obtained by TEM from similar settings. Accordingly, representative values
for poiencies will be derived for each of the processes and fiber types.
Chrvsotlle Textile Manufacturing
The K£.S from four textile studies are quite similar, regardless of whether they involve exposure
to chrysolite alone or to mixed fiber types, as they range from 0.0054 to 0.028 (f/ml)';. As a
representative value the value of 0.2 (f/ml)"; will be used, which is about the average of the values
obtained from the individual studies.
Unlike lung cancer, it appears that there may be an effect of fiber type within textiles in
producing mesothelioma. K^s are larger in cohorts exposed to mixed fiber types over those exposed to
chrysolite only by factors ranging from 23 to 25. For a representaiive value for chrysolite exposure,
KM = 1 x iff9 will be used. This value is between the KM estimated from the McDonald era/, and
Dement era/, studies, and is below the upper confidence limit obtained from either of these studies.
ChrvsotHe Friction Products
Although McDonald era/. (1984) is classifed as chrysotile exposure and and Berry ef a/. (1983)
as mixed exposure, in fact, both studies involved exposure to some amounts of amphiboles, alihough the
amount in the former siudy was apparently minimal Neither of these studies demonstrate a clear
relationship between asbestos and lung cancer, the former study shows no dose response trend (KL = 0)
while the latter study shows a small but nonsignificant positive trend. As a representative value for
friction products, the value of KL - 0.0006 obtained in Berry era/, will be used. This value is within
the range consisiem with the McDonald era/, study, and there is no evidence of a fiber type effect upon
lung cancer rates in these studies.
A-14
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In contrast. Berry etal. found an association between mesotheliotna and crocidolite, so only the
McDonald et al. study will be used to derive an estimate for K# based on chrysotile exposure. The
value of 0.1 t/ml'} will be used, which is somewhat less than the upper limit calcululated for this study.
This value should be used with caution and considered an upper bound, since no mesotheliomas were
found in this cohort
ChrysotHe Mining
For chrysotile mining, the point estimates from the McDonald ef a/. (1980a) will be used as
representative values: Kj. = 0.0005 and K^ = 1 x 10"'0.
Asbestos Cement
The lung cancer data for asbestos cement do not show a consistent relationship among studies.
The K£ from the Finkelstein (1983) study is six to nine times larger than those from the studies of
Weil! and Hughes. Among eight studies of asbestos cement workers surveyed by Hughes era/. (1986).
including several for which K^s could not be estimated, the relative risk for lung cancer was considerably
higher for the Finkelstein study than for any of the other seven; thus the Finkelstein study is an outlier.
Further, as as discussed earlier, the dose response for lung cancer from this study was not consistent
with an asbestos effect; the most highly exposed group had the lowest lung cancer rate. As a
representative value for chrysotile exposure, the value of 0.005 (f/ml)*; will be used, based on study by
.Hughes et al. (1987), recalling that no difference was found in this study in lung cancer dose responses
between workers exposed only to chrysotile and workers also exposed to crocidolite. As a representative
value for KM from exposure to chrysotile, the value of KM - 1.0 x 10*9, which is based on the Hughes
etal. (1986) study.
Representative values selected for crocidolite mining and milling and for amosite insulation
manufacture will be those obtained from the Armstrong et al. (1988) and Scidman (1984) studies,
respectively.
Seidman et al. (1979) and Seidman et al. (1986) studied the mortality experience of 820 men
employed in a plant in Patterson, New Jersey that manufacture insulation for ships during World War
II. Only amosite asbestos was used in the plant, which operated from 1941 to 1954. Seidman (1986)
reports on follow-up through 1982, at which time 72% of the cohort was deceased. Exposure estimates
for particular jobs were estimated based on PCM air measurements obtained in similar plants in Tyler,
Texas and Port Alleghers, Pennsylvania in the late 1960's and the 1970's. Estimates of 1^=0.043 and
KM~ 32 x 10"* will be used for this cohort; these were developed by EPA (1986) from a draft report of
Seidman et al. (1986).
Selikoff et al. (1979) reported on the mortality experience of 17,800 insulators on the roles of
the asbestos workers union in the United States and Canada. Information available included date of
birth, date of first insulation work, employment status on January 1, 1967, respirator use, smoking habits
and work practices. This cohort was followed by Selikoff et al. from 1967 through 1976. Exposure were
to chrysotile and amosite insulation. Assuming an average exposure of 15 £/ml for this cohort, based on
limited surveys of exposure to insulation workers, EPA (1986) estimated risk factors of K/=0.0075 and
1.5 x iff*.
A-15
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Table A-3
Lung Cancer by Exposure Level in a
Connecticut Chrysotile Friction Plant
(McDonald era/., 1984)
Expected
Observed based on
Conn, rates fa«=lV fa allowed to varvV
Expect
r\5""^
ed from lung cancer model mppcf-v
Cancers
5 (<10)
15 (10-20)
30 (20-40)
60 (40-80)
110 (>80)
55
6
5
6
1
Goodness of fit p-value
319
5.9
4.7
3.7
1.8
33.8
6.4
5.5
4.9
2.9
<0.01 <0.01
49.0
8.8
7.0
5.5
2.7
>0.2
" Assumes Conneaicut men are appropriate control group.
* Assumes Conneaicut men are not necessarily appropriate control group.
Estimates with background estimated fa variable')
Maximum likelihood estimates
a - 1.49 K£. - 0.0 (mppcf-y)'7 = 0.0 (fiber-y/ml)';
Chisquare = 33
95% upper confidence limit on KL = 0.00507 (mppcf-y)'7
0.00169 (fiber-y/ml)-7 (a = 137)
Estimates with background fixed fa = 11
Maximum likelihood estimates
KL = 0.00564 (mppcf-y)'-' 0.00188 (fiber-y/ml)'7
Chisquare = 15
95% upper confidence limit on K^ = 0.0183 (mppcf-y)*7 «
0.00610 (fiber-y/ml)'7
A-16
-------
Table A-4
Reconstructed Mesothelioma Data from a
Connecticut Chrysotile Friction Plant
(McDonald era/., 1984)
Years since Person Observed Mesotheliomas Expected
First Years of Mesothe- per 1000 Mesotheliomas
Exposure Observation liomas Person Years Under Model
22 (14-34) 37742 00 0
39 (34+) 9420 00 0
Maximum likelihood estimate: KM = 0.0 (fiber-y/ml)"7
Chisquare = 0.0
95% upper confidence limit on KM = 1.19 x 10"9 (fiber-y/ml)'7
A-17
-------
Table A-5
Lung Cancer by Length of Service and by Exposure Level
in Workers in Quebec Chrysolite Mines and Mills
(McDonald era/., 1980a)
Expected
Years of Observed based on Expected from lung cancer model
Service moocf-v Cancers Quebec rates ra=lV ra allowed to
0.5 19 16.2 16.3 153
1.7 12 13.2 13.2 12.4
5.8 9 10.2 103 9.7
39 7 8.75 9.23 8.8
1-5
33 5 7.58 7.61 7.17
13.6 13 13.68 13.9 13.2
59 6 732 7.93 7.54
2313 5 6.41 8.50 8.2S
5-20
16 13 9.22 9.43 8.90
58.2 14 11.48 12.4 11.8
178.5 7 8.43 10.6 10.2
704 16 737 14.7 14.8
>20
104.6 28 23.1- 26.6 25.5
2613 20 18.5 25.4 24.8
549.1 24 10.9 19.4 19.4
1141.4 32 12.1 31.5 323
Goodness of fit p-value <0.01 >0.8 >0.8
* Assumes control group is appropriate.
b Assumes control group is not necessarily appropriate.
Estimates with background estimated fa variable^
Maximum likelihood estimates
a .m 0.941 KL - 0.00161 (mppcf-y)'; = 0.000513 (fiber-y/ml)-;
Chisquare = 9.28
95% upper confidence limit on Kj. = 0.00247 (mppcf-y)'; =
0.000787 (fiber-y/ml)-' (a = 0.833)
Estimates with background fixed fa = It
Maximum likelihood estimates
IQ. m 0.00141 (mppcf)'7 0.000449 (fiber-y/ml)'7
Chisquare = 934
95% upper confidence limit on Kj. = 0.00192 (mppcf-y)'7 =
0.000611 rfiber-v/mnca
A-18
-------
Table A-6
Reconstructed Mesothelioma Data from
Cohort of Quebec Chrysotile Miners
(McDonald e/a/., 1980a)
Years Since
First
Exposure
17.5 (15-20)
28 (20-40)
45 (45+)
Person
Years of
Observation
67818
127663
29651
Observed
Mesotheliomas
?
7
?
Total: 10
Maximum likelihood estimate: K# = 1.08 x Iff10 (fiber-y/ml)'7
95% upper confidence limit on KM = 1.73 x 10";o (fiber-y/ml)';
A-19
-------
Table A-7
Lung Cancer Rates in Production Workers
at an Ontario Asbestos-Cement Plant
(Finkelstein, 1983)
Observed Rates Per
Fiber-v/ml Cancers 1000 Man-Years
44 5 13.6
92 7 26.1
181 6 11.9
Ontario men 1.6
A-20
-------
Table A-8
Observed and Expected Lung Cancer Rates in Production
Workers at an Ontario Asbestos-Cement Plant
(Finkelstein, 1983)
Observed
Fiber-v/ml Cancers
Expected
Based on Expected from lung cancer model
Ontario Men (3=11° fa allowed to
44
92
181
5
7
6
.588 15
.429 3.3
.807 11.5
5.8
4.2
8.0
Goodness of fit p-value <0.001 <0.01
0.3
0 Assumes Ontario men are appropriate control group.
b Assumes Ontario men are not necessarily appropriate control group.
Estimates with background estimated fa variable^
Maximum likelihood estimates
a = 9.87 KL = 0.0 (fiber-y/ml)';
Chisquare = 2.4
95% upper confidence limit on KL = 0.0110 (fiber-y/ml)'J (a = 4.34)
Estimates with background fixed Ca = It
Maximum likelihood estimates
KL 0.0733 (fiber-y/ml)'7
Chisquare = 9.26
95% upper confidence limit on K^ = 0.111 (fiber-y/ml)';
A-21
-------
Table A-9
Mesothelioma in Production Workers at
an Ontario Asbestos-Cement Plant
(Finkelstein, 1983)
Years Since
First
Exposure
Person Observed
Years of Mesothe-
Observation liomas
Mesotbeliomas Expected
per 1000 Mesotheliomas
Person Years Under Model
17.5 (15-20)
215(20-25)
27.5 (25-30)
(30-35)
1182 1
1061 4
555 5
104 1
0.85
3.77
9.01
9.62
Goodness of fit p-value
.84
3.50
4.86
1.80
>0.3
Maximum likelihood estimate:
Cbisquare = 0.46
95% upper confidence limit on
1.87 x Iff7 (fiber-y/ml)"'
= 2.97 x Iff7 (fiber-y/miy7
A-22
-------
Table A-10
Lung Cancer by Exposure Level in Two
Louisiana Asbestos-Cement Plants
(Weil! e/a/., 1979)
Total dust
within 20 yr. Expected
of first exp. Observed Based on Expected from lung cancer model fmppcf-vt _ Cancers
Louisiana Rates fa=l)a fa allowed to
5 (0-10) 19 24.7 25.2 17.2 30 (111-50) 8
11.4 118 9.6 75 (51-100) 1 3.8 5.0 4.2
150 (101-200) 9 3.1 5.0 4.8 300 (>200) 14
6.2 13.9 15.1
Goodness of fit p-value < 0.001 0.08 0.04
" Assumes Louisiana men are appropriate control group.
* Assumes Louisiana men are not necessarily appropriate control group.
Estimates with background estimated fa variable1!
Maximum likelihood estimates
a = 0.666 KL = 0.00889 (mppcf-y)-; = 0.00635 (fiber-y/ml)'7. Chisquare = 6.6
95% upper confidence limit on Kg, = 0.0185 (mppcf-y)*; =
0.0132 (fiber-y/ml)-; (a = 0.480)
Estimates with background fixed fa = 1)
Maximum likelihood estimates
KL = 0.00412 (mppcf-y)-' = 0.00294 (fiber-y/ml)'7
Chisquare = 9.7
95% upper confidence limit on K/. = 0.00728 (mppcf-yr)"; =
0.00520 (fiber-y/ml)'7
A-23
-------
Table A-ll
Mesothelioma hi Two Louisiana Asbestos-Cement Plants
(Weill era/.. 1979)
Years Since Person Mesotbeliomas Expected
Pint Years of Observed per 1000 Mesotheliomas Exposure
Mesotheliomas Person Years Under Model
Observation
TLS (10-15)
17.5 (15-20)
2Z5 (20-25)
27.5 (25-30)
325 (30-35)
37.5 (35+)
31180
29473
25080
14018
3832
1565
0
2
0
0
0
0
0
0.068
0
0
0
0
0.0076
0.18
0.57
0.70
0.33
0.21
= 6.95 x W° (fiber-y/mI)-;
Chisquare = 20
95% upper confidence limit on
Maximum likelihood estimate:
1.84 x 10"9 (fiber-y/miy;
A-24
-------
Table A-12
Lung Cancer by Exposure Level in Two Louisiana
Asbestos Cement Plants After 20 Years from Initial Exposure
(Hughes era/., 1987)
Total Dust to
Within 10 Yrs.
of Observation Observed
Cancers Louisiana Rates
4
13
35
74
183
3
9
2
3
5
Plant 1
2.9
8
3.7
3.8
4.1
2.96
8.58
4.42
536
8.25
3J
9,
4.
5.-
7.
Expected
Based on Expected from lung cancer model fmppcf-v)
fa allowed to vary)6
3
12
36
71
164
20
19
12
10
12
Goodness of fit p-value
Plant 2
18.9
14.5
6
5.5
5.2
19.2
15.5
7.19
7.66
9.92
21.84
17.28
7.73
7.87
9J9
>0.25
0.25
appropriate control group.
* Assumes Louisiana men are not necessarily appropriate control group.
Estimates with background estimated fa variable^
Maximum likelihood estimates
a = 1.14 KL = 0.00354 (mppcf-y)'J = 0.00253 (fiber-y/ml)'7.
Chisquare = 7.7
95% upper confidence limit on KL = 0.00927 (mppcf-y)'7 =
0.00662 (fiber-y/ml)-7
(a «= 0.949)
Estimates with background fixed fa as p
Maximum likelihood estimates
KL = 0.00553 (mppcf-y)'7 o 0.00395 (fiber-y/ml)'7
Chisquare = 8.9
95% upper confidence limit on Kj, = 0.00978 (mppcf-y)'J =
0.00699 (fiber-y/ml)-7
Assumes Louisiana men are
A-25
-------
Table A-14
Lung Cancer by Exposure Level in a
South Carolina Chrysolite Textile Plant
(Dement era/., 1983b)
Expected
Observed Based on Expected from lung cancer model Fiber-v/ml Cancers
U.S. rates fagIV (a allowed to varvl*
5.81
7.15
8.98
8.94
2.12
Goodness of fit p-value <0.001 >0.5 >0.5
1.4 (<2.74)
15.1 (2.74-27.4)
68.5 (27.4-110)
192 (110-274)
411 (>274)
5
9
7
10
2
3.58
3.23
1.99
0.91
an
3.83
5.68
8.84
9.70
238
0 Assumes U.S. men are appropriate control group.
* Assumes U.S. men are not necessarily appropriate control group.
Estimates with background estimated (a variable1)
Maximum likelihood estimates
a = 1.56 KL = 0.0275 (fiber-y/mi,r;
Chisquare = 1.2
95% upper confidence limit on K.L « 0.0730 (fiber-y/m!)*; (a = 0.830)
Estimates with background fixed fa = 1)
Maximum likelihood estimate
KL m 0.0503 (fiber-y/ml)'7
Chisquare = 2.7
95% upper confidence limit on K/. = 0.0734 (fiber-y/ml)";
A-26
-------
Table A-15
Mesothelioma in a South Carolina Chrysotile Textile Plant
(Dement et a/., 1983b)
Years Since Person
First Years of
Exposure Observation
Mesotheliomas Expected
Observed per 1000 Mesotheliomas
Mesotheliomas Person Years Under Model
5 (<10)
15 (10-20)
25 (20-30)
35 (30+)
11390
10921
8055
2775
Goodness of fit p-value
0
0
0
1
3.01 x Iff9 (fiber-y/ml)-;
Chisquare = 0.81
95% upper confidence limit on
0
0
0
036
0.0
0.022
0.43
0.55
>0.5
Maximum likelihood estimate:
1.10 x 10-* (fiber-y/ml)-;
A-27
-------
5 (<10)
15 (10-20)
30 (20-40)
60 (40-80)
110 (>80)
31
5
8
7
8
21.7
2,7
2.6
1.7
0.78
292
5.6
8.1
8.6
6.7
Table A-16
Lung Cancer by Exposure Level in a
South Carolina Chrysolite Textile Plant
(McDonald efa/., 1983a)
Expected
Observed based on Expected from lung cancer model mppcf-y Cancers
S.C rates fa»lV* fa allowed to varvr
30.4
5.7
8.0
8.4
6.5
Goodness of fit p-value <0.01 >0.9 >0.8
" Assumes South Carolina men are appropriate control group.
* Assumes South Carolina men are not necessarily appropriate control group.
Estimates with background estimated fa variable"!
Maximum likelihood estimates
a = 1.07 KL = 0.0618 (mppcf-y)'7 = 0.0103 (fiber-y/miy7
Chisquare = 0.69
95% upper confidence limit on Kj, = 0.147 (mppcf-y)'7
= 0.0245 (fiber-y/miy7 (a = 0.653)
Estimates with background fixed fa = 1^
Maximum likelihood estimates
K£ = 0.0692 (mppcf-y)'7 « 0.0115 (fiber-y/ml)*7
Chisquare = 0.74
95% upper confidence limit on KL = 0.0987 (mppcf-y)"7
= 0.0165 (fiber-y/miy'
A-28
-------
Table A-17
Reconstructed Mesothelioma Data from a
South Carolina Chrysotile Textile Plant
(McDonald etal., 1983a)
Years Since Person Mesotheliomas Expected
First Years of Observed per 1000 Mesotheliomas Exposure Observation
Mesotheliomas Person Years Under Model
28(19-39) 26280 0 0 0.68
44 (39+) 2787 1 036 032
Maximum likelihood estimate: K^/
«= 5.09 x ID'70 (fiber-y/ml)-;
Chisquare = 2.1
95% upper confidence limit on K^ = 1.86 x 10"9 (fiber-y/ml)";
A-29
-------
Table A-18
Observed and Predicted Numbers of Mesotheliomas
(Peto era/., 1985)
Years Since First Employment
Less
40 or
Duration of Than 20 20-24 25-29 30-34 35-49 More
Service (Yr.) (11.5) (22.5) (27.5) (32.5) (31.5) (42) Total
Less than 1 Obs. 0000
3469.8 2041.2 840.4 4023 39437
0.12 0.84 PEPAC 0.0080 0.13
1-4 Obs. 0000
877.4 631.7 4213 237.7 148.4 7102
0.08 0.57 PEPA 0.0019 0.11
5-9 Obs. 0 0 0 0
1103.5 707.2 3833 249.1 12380
0.51 2.86 PEPA 0.0034 031
10-19
1423.3
031
20-29
9353
2.59
Obs. 0003
869.7 469.7 204 102.3 7883
0.21 2.28 PEPA 0.0019
Obs. 112
599.7 257.1 121.7 2762
*EPA 0.20 0.60
30 or more Obs. 0
86.2 106.9 103.4 297
0.90 PEPA
Total
9234
1.80
Obs. 0115
7010 43253 2029.4 1127.269861
1.69 10.01
Pro, 0.016 1.08 2.09 2.61
0
P/
0.19
0
0.17
0
Pj
0.57
0
033
1
Pj
0.82
0
Pj
0.12
1
2.23
0 0 (0.5) P-Yr." 28015 4668.2
0.10 0.13 0.19 0.19 0.11
0.18 ail 0.07
1 1 (3) P-Yr. 4785.6
Pj 0.07 0.09 0.12 0.12 0.09
0.20 0.17 0.15
0 0 (7.5) P-Yr. 8520.5 1416.S
0.29 0.37 0.55 0.63 0.51
0.68 0.58 0.54
1 1 (15)
Pj 0.40 0.40
0.55 0.62 0.46
0 5 (25)
031 0.70
0.64 0.47
0 0 (35)
0.27 0.40
P-Yr. 4814.1
0.49 0.47
034
P-Yr.
0.76
P-Yr.
0.16
848.3
0.48 0.34
0.31 0.43
2 10 P-Yr. 46135.2
Pj 0.86 130 2.04 2.32
1.98 10.01 "Person-years.
^Predicted numbers based upon Peto etal.'s cubic residence lime model. 'Predicted numbers based upon
mesothelioma model employed herein (equation (3)).
A-30
-------
Table A-19
Lung Cancer by Exposure Level in a Pennsylvania Asbestos Plant
(McDonald et el. 1983b)
Penn. Rates
5 (<10)
15 (10-20)
30 (20-40)
60 (40-80)
110 (>80)
Observed
fa=lV fa
21
5
10
6
11
Expected
Based on Expected
allowed to
31.4
5.98
6.41
3.75
2.64
vaM*
34.1
7.51
9.68
7.58
7.58
from lune cancer model mppcf-v
20.7
5.64
8.76
830
9.57
Cancers
Goodness of fit p-value <0.01 0.10 >0.7
a Assumes control group is appropriate.
* Assumes control group is not necessarily appropriate.
Estimates with background estimated (a variable1)
Maximum likelihood estimates
a = 0.519 Kj. = 0.0544 (mppcf-y)'7 = 0.0181 (fiber-y/miy;
Chisquare = 1.1
95% upper confidence limit on KL = 0.135 (mppcf-y)"; = 0.0450 (fiber-y/ml)'; (a = 0.296)
Estimates with background fixed (a = 1)
Maximum likelihood estimates
KL = 0.0170 (mppcf-y)-; = 0.00567 (fiber-y/ml)'7
Chisquare = 7.7
95% upper confidence limit on KL = 0.0282 (mppcf-y)'7 = 0.0940 (fiber-y/ml)';
A-31
-------
Table A-20
Reconstructed Mesothelioma Data from Pennsylvania Asbestos Plant
(McDonald era/., 1983b)
Years Since
First
Exposure
Person
Years of
Observation
Observed
Mesotheliomas
15.5 (15-16)
24 (16-38)
41 (36+)
17179
40868
9840
?
?
Total mesotheliomas: between 14 and 23
Maximum likelihood estimate:
6.70 x 10*9 (fiber-y/ml)'J (assuming 14 mesotheliomas)
1.1 x 10"* (fiber-y/ml)"7 (assuming 23 mesotheliomas)
A-32
-------
APPENDIX B:
BACKGROUND FOR DEVELOPMENT OF
AN AD HOC EXPOSURE INDEX FOR ASBESTOS
-------
BACKGROUND FOR DEVELOPMENT OF AN AD HOC
EXPOSURE INDEX FOR ASBESTOS
The asbestos exposure index recommended for supporting risk assessment in this
report (Cop,, as defined by Equation 6.6) represents a compromise. The index
preserves most of the important features (including the maximum structure width, the
minimum structure length, and the analytical requirements for obtaining the required
counts) of the optimum exposure index (Equation 5.2), which is recommended based
on the results of our literature review combined with our formal statistical re-analysis of
the animal inhalation studies conducted by Davis et al. (Section 5.6.2 of the main text).
However, due to the limitations in the published size distributions available for re-
evaluating the human epidemiology studies, the longest category of structures had to
be shortened from that incorporated in Equation 5.2 (Section 6.2).
Nevertheless, we expect C^ 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 C^ (5 um) is sufficiently short
to capture the bulk of the range of structures that contribute both to lung cancer
and to mesothelioma in humans;
the maximum width for the structures included in C^ (0.5 um) 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 Copt
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 6.6 are conservative (in a health
protective sense) in that they are adopted directly from the optimum exposure
index (Equation 5.2) but they are applied so that a greater number of structures
(i.e. those between 10 and 40 um in addition to those greater than 40 um) are
included within the concentration that is assigned the greater potency value.
Despite the compromises adopted to define and apply Copt, the analysis reported in
Chapter 6 of this document indicates that it indeed represents an improved index of
exposure (over traditional indices), which better captures the characteristics of
asbestos that determine biological activity. The analysis presented demonstrates that,
when published risk coefficients are adjusted to account for exposure expressed as
Cop,, the variation observed in the published values across studies is substantially
reduced. In fact, the between-environment variation in published risk coefficients for
the amphiboles is completely eliminated by adapting the coefficients to Cop,.
B-1
-------
Remarkably, the improved across-study agreement observed when risk coefficients are
adapted to C^ is achieved despite the limitations of the manner in which the
coefficients are adjusted, including:
that the definition of C^ itself is a compromise that does not fully account
for the effects of structure size. The specific exposure index
recommended in Chapter 5 of this document could not be applied to the
epidemiology studies because available TEM size distributions would not
support it. Therefore, the length dimensions of the longest size category
incorporated into C^ is substantially shorter than what is considered
optimal (see Section 6.2);
that the size distributions employed to adapt risk coefficients to Copt 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;
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 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.
Due to the limitations described above, a small number of follow-on studies are
recommended in the text of this document, which would establish the relative
importance of these limitations and, in some cases, control for their effects. By
completing such studies, it is possible that the remaining variation still observed among
published risk coefficients for chrysotile would be further reduced. In any case, such
studies would add substantially to the understanding of the relationship between
asbestos exposure and risk.
B-2
-------
APPENDIX C:
DERIVATION OF LIFETIME RISKS FOR LUNG CANCER
AND MESOTHELIOMA FROM MODELS USING K, AND KM ESTIMATES
FOR POTENCY
-------
This appendix shows how additional lifetime risk of lung cancer or mesothelioma are calculated
from the models from which Kg,, the potency for lung cancer, and K^, the potency for mesotheliomas,
are derived. First a general model is developed that allows a variable exposure pattern, and the lung
cancer and mesothelioma models are shown to be special cases of the more general expression. Next
the procedure used to implement these models based on human mortality rates is explained. Finally,
the mortality rates used in these calculations are derived.
Let D B {D(t); t> £ 0} represent exposure to asbestos (i.e., exposure at age t is D(t) I/ml). lei
S0(t | x) be the probability of surviving to age t given survival to age x < t. Let M/>(t) be the mortality
rate for a given cause at age L The probability of dying of the given cause during a small age interval
At at age t is the probability of surviving to age t times the probability of dying from the given cause
given survival to age t, or
S/>(t I x)M£>(t)At.
The probability of dying of the given cause is given survival to age x therefore given by the integral
CO
(Bi)
The corresponding probability of dying of the given cause without any exposure to asbestos is given by
P000 « J S0(t)M0(t)dt, (B2)
x
where the subscript O indicates no exposure, and the additional probability of dying from the given
cause as a result of exposure pattern D is
(B3)
The lung cancer and mesothelioma models in Section 6.2 basically model the mortality rate
M0(t). It is shown below how expressions (Bl), (B2), and (B3) are used to convert estimates from the
models in Section 62 into estimates of additional risk.
It will be assumed that the increase in the mortality rate at age t from an exposure of D(v)
between ages v and v+ AV, v
-------
Thus g(u,t) is an intensity function that relates an exposure u years prior to age t to the resulting
mortality rate at age t. It is further assumed that the total mortality rate at age t is the sum of the
contributions from all doses prior to age t, plus the background mortality rate M0(t); i.e.,
t
M/>(t) = Mo(0 + / D(v)g(t-v,t)dv.; (B4)
0
To obtain the relative risk model for lung cancer in Section 6.11, let
M0(t)KL u > 10
S(tM) = (B5)
0 u < 10.
By applying (B5) to (B4) and performing the integration, it follows that
MO
M0W « M0(t) (1+KL J" D(v)dv). (B6)
0
Thus, the relative risk at age t, M0(t)/M0(t), is given by
1 + Kj. [total exposure up to 10 years prior to age t], (B7)
which agrees with expression (E.4) in Section 6.11. However (B7) holds generally for any exposure
pattern D(v), whereas (E.4) is more specialized in that it presupposes a constant exposure.
To obtain the absolute risk model for mesothelioma in Section 6.12 from (B4), define the
intensity function
3K^ (u-10)2 u > 10
8(u.9 - (B8)
0 u < 10
Thus the intensity function is proportional to the square of elapsed time since exposure less 10 years. It
then follows that
MO
- Mo(t) + 3K« J D(v)(t-v-10)2dv. (B9)
0
'This expression assumes a linear dose response. For a non-linear response, replace D(v) by
H(D(v)) where H is a non=linear function (e.g. H^v2).
-------
If a constant exposure rate is assumed over a Cxcd age interval,
f t; < v < \2 (B1°)
D(v) =
0 otherwise,
then
Mo(t) + Kwf(t-tr10)J for t;+10t2+10,
which agrees with the mesothelioma model (EJ) in Section 6.2.2.
To implement these models the integral (Bl) must be evaluated using the appropriate
expression for the mortality rate Mj>(t) (expression (B6) for lung cancer and (Bll) for mesothelioma).
Let blt b>... bjfi be the mortality rates (expected number of deaths) for all causes per year per 100,000
persons for the age intervals 0-5, 5-10,...,80-85, and 85+ years, respectively, and let a;..-a;a be the
corresponding rates for lung cancer. Given survival to age x=5k, the probability of survival to t=5i
years is estimated as
i
[l-5b./100,000]. (B12)
Given survival to age 5(i-l), the probability of dying of lung cancer by age Si is estimated as
5^00,000. (B13)
The probability of dying of lung cancer given survival to age 85 is estimated as a;fl/b;a. Therefore, the
probability of dying of lung cancer in the absence of asbestos exposure, given survival to age x=5k is
estimated as
17 i-1
T K5a//100,000) TT(l-5b/100,000)] (B14)
i=k+l ]=k-H
17
IT (l-5b/100,000),
which represents a discrete approximation to the integral (B2).
To estimate the probability P^x) of dying of lung cancer when exposed to a particular pattern
D of asbestos exposure, expression (B14) is again used, but a, and b, are replaced by a, -t- E, and b, +
E,, where, following (B7),
-------
Ef = a/Kj. (total exposure up to 10 years prior (B15j
to mid-point of ith age interval],
where K£, is the potency parameter (risk factor) for lung cancer. (Here a,+E,- is playing the role of
Mo(0 >n equation (B6).) The additional lifetime risk of lung cancer is estimated by the difference
Pj>(x)-Po(x). For example, to estimate the future risk to a person presently 20 years of age, we would
use x=20 (i.e., k=4) in (B14).
The additional lifetime risk of death from mesothelioma is estimated using the same formulas,
except a,- is replaced by zero (background rate of mesothelioma is so small as to be unimportant), and
(following equation B9) E, is replaced by a discrete approximation to
3K* J D(v)(trv-10)2dv,
0
where t, is the mid-point of the itb age interval. Appropriate modificatioas are made to these
expressions when x is not a multiple of 5.
Sex- and smoking-spedfic estimates are used for the mortality rates required in the above
calculations (a, and b,). Lung cancer mortality rates for nonsmokers are obtained by averaging rates for
nonsmokers are obtained by averaging rates for three different time periods calculated from the
American Cancer Society prospective study (Garfinkel 1981). Lung cancer mortality rates in smokers,
{P(LCF | S)], are calculated using the equation
P(LCD) = P(LCF| S)P(S) + PfLCD | NS)Il-P(S)], (B16)
where P(LCD) is a 1980 age- and sex-specific death rate from lung cancer in the genera] U.S.
population, P(S) is the fraction of smokers in the population, P(LCD | NS) is an age- and sex-specific
death rate from lung cancer in nonsmokers computed from Garfinkel (1981), and P(LCD | S) is a
corresponding rate in smokers. The proportion of smokers, P(S) is assumed to be 0.67 for males and
033 for females, which is consistent with the U.S. EPA (1986) approach. Smoking-specific rates for all
causes are calculated from 1980 U.S. rates for all causes assuming that the mortality rate in smokers is a
factor, f, times the mortality rate in nonsmokers. An age-specific mortality rate, P(AC | NS). in
nonsmokers is then calculated using the formula
P(AC) = fP(AC|NS)P(S) + P(AC|NS)I1-P(S)J,
where P(AC) is a 1980 age- and sex-specific death rate from all causes in the general U.S. population.
Following Hammond (1966), the' factor f is taken as 1.83 for males and 1.26 for females. This
procedure is followed for all age groups despite the fact that smokers generally do not begin smoking
until teenage years and the effects upon mortality will not occur until still later. This makes little
difference in the risk calculations because mortality rates are relatively low at early ages.
The resulting mortality rates are listed in Table Bl.
-------
Table Bl
Smoking- and Sex-Specific Mortality Rates Per Year Peir 100,000
Population for Respiratory Cancer and Total Mortality
0-1
1-5
5-10
10-15
15-20
20-25
25-30
30-35
35-40
40-45
45-50
50-55
55-60
60-65
65-70
70-75
75-80
80-85
85+
0-1
1-5
5-10
10-15
15-20
20-25
25-30
30-35
35-40
40-45
45-50
50-55
55-60
60-65
65-70
70-75
75-80
80-85
85+
1679.0
85.4
41.2
45.0
1663
2393
230.7
230.5
288.4
4283
686.8
1109.0
1717.8
2623.7
3991.2
5972.2
8796.8
13218.0
22110.4
1324.9
63.5
29.7
26.6
61.6
71.8
79.1
98.1
144.4
233.0
372.8
578.7
869.2
13273
19933
3101.6
49393
8424.9
17112.8
Males
918.0
46.7
223
24.6
90.9
130.8
126.1
126.0
157.6
234.0
3753
606.0
938.7
1433.7
2181.0
3263.5
4807.0
72219
12082.2
10513
50.4
23.6
21.1
48.9
57.0
62.8
77.8
114.6
184.9
295.9
4593
689.8
1053.6
1582.0
2461.6
3920.2
6686.4
13581.6
Respiratorv Cancer
Smokers
.4
.0
.0
.0
.1
.4
.7
12
93
26.2
76.1
155.1
263.2
4018
556.7
698.5
750.6
711.0
527.1
Nonsmokers
0
0
0
0
0
0
0
0
0
83
3.1
7.9
10.2
173
28.2
25.2
44.9
72.5
100.5
3
3
3
.0
.0
3
.9
17
10.6
27.9
67.4
124.0
178.8
234.8
2816
286.4
240.8
1812
184.8
0
0
0
0
0
0
0
0
0
2.4
3.5
5.2
7.0
13.6
16.2
20.9
34.7
45.5
517
5
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