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> **UNITED STATES ENVIRONMENTAL PROTECTION AGENCY**

**WASHINGTON D.C. 20460**

> **OFFICE OF THE ADMINISTRATOR**
>
> **SCIENCE ADVISORY BOARD**

November 14, 2008

EPA-SAB-09-004

The Honorable Stephen L. Johnson

Administrator

U.S. Environmental Protection Agency

1200 Pennsylvania Avenue, N.W.

Washington DC. 20460

> Subject: SAB Consultation on EPA's *Proposed Approach for Estimation
> of Bin-Specific Cancer Potency Factors for Inhalation Exposure to
> Asbestos*

Dear Administrator Johnson:

The Environmental Protection Agency's (EPA) current method for
quantifying cancer risk from inhalation exposure to asbestos utilizes
exposure measurements based on phase contrast microscopy. The 1986
method used published epidemiologic studies of miners and manufacturing
workers to select empirical risk models to derive cancer potency factors
for lung cancer and mesothelioma. To address the potential limitations
of EPA's 1986 method, EPA's Office of Solid Waste and Emergency Response
(OSWER) has proposed an interim approach to account for the potential
differences of cancer potency between mineral groups and fiber size
distributions. The proposed method adopts a "multi-bin" mathematical
model to estimate cancer risk according to mineral groups (amphibole or
chrysotile) and measurements of particle dimensions (length and width)
based on transmission electron microscopy (TEM). OSWER asked the Science
Advisory Board (SAB) to conduct a consultation on the proposed method.
OSWER sought SAB advice regarding the soundness of the scientific basis
of the proposed method; the choice of the mathematical models,
statistical methods, epidemiologic and exposure data used; and, what
alternative approaches or methods should be considered.

In response to OSWER's request, the SAB Asbestos Committee (Enclosure 1)
held a public meeting on July 21-22, 2008 in Washington D.C. to consider
the issue and to provide consultative advice on the proposed method. An
SAB consultation is a mechanism for individual technical experts to
provide comments for the Agency's consideration early in the development
of a technical product. Comments from individual committee members (and
subgroups) in response to EPA's charge questions (Enclosure 2) are
included in Enclosure 3. While group consensus was not sought for a
consultation, the Committee would like to underscore major conclusions
that emerged from this consultation as described below.

The general view of the Committee was there is sufficient evidence to
support the need for the Agency's effort in developing risk assessment
method(s) to account for potential differences in risk on the basis of
mineral type and size characteristics of asbestos. As detailed in
individual and subgroup comments, there were divergent views regarding
whether an effort of this type is warranted at this time. The Committee,
however, generally agreed that the scientific basis as laid out in the
technical document in support of the proposed method is weak and
inadequate. A primary concern is the lack of available data to estimate
the TEM specific levels of exposure for the epidemiological studies
utilized in this analysis. The Committee also found that the document
was woefully inadequate with respect to the representation of available
information on epidemiology, toxicology, mechanism of action and
susceptibility.

The Committee urged the Agency to support additional targeted research,
exposure data collection and fiber analysis, and validation of
alternative risk assessment models. In particular, there is a critical
need for analyses of more epidemiologic studies using TEM based fiber
size specific estimates of exposures as was conducted in the recently
published South Carolina textile cohort study. The ongoing research
effort focusing on amphibole asbestos exposure in Libby, Montana would
yield valuable data and insights to further this scientific effort.

The Committee would like to thank the EPA presenters for their
expertise, perspectives and insights that assisted the Committee's
understanding of the proposed method. Thank you for the opportunity to
provide early advice on this important topic. The SAB looks forward to
receiving your response and having further interactions as the Agency
moves forward in this endeavor.

Sincerely,

***/Signed/***

Dr. Agnes Kane, Chair

SAB Asbestos Committee

cc: Dr. Deborah Swackhamer, Chair

EPA Science Advisory Board

Enclosures

ENCLOSURE 1

U.S. Environmental Protection Agency

Science Advisory Board

Asbestos Committee

**CHAIR**

**Dr. Agnes Kane**, Professor and Chair, Department of Pathology and
Laboratory Medicine, Brown University, Providence, RI

**MEMBERS**

**Dr. Louis Anthony (Tony) Cox, Jr.**, President, Cox Associates,
Denver, CO

**Dr. Jeffrey Everitt**, Director, Department of Laboratory Animal
Science, GlaxoSmithKline Pharmaceuticals, Research Triangle Park, NC

**Dr. Murray Finkelstein**, Assistant Professor, Family Medicine Centre,
Mt Sinai Hospital, University of Toronto, Toronto, Ontario, Canada

**Dr. Andrew Gelman\***, Professor of Statistics and Director, Applied
Statistics Center, Department of Statistics and Department of Political
Science, Columbia University, NY, NY

**Dr. George Guthrie**, National Energy Technology Laboratory, US
Department of Energy, Pittsburgh, PA

**Mr. John Harris**, Principal, LabCor Portland, Inc, Portland, OR

**Dr. Karl Kelsey**, Professor, Center for Environmental Health and
Technology, Alpert Medical School, Brown University, Providence, RI

**Dr. Paul J. Lioy**, Deputy Director and Professor, Environmental and
Occupational Health Sciences Institute, Exposure Sciences Division,
UMDNJ - Robert Wood Johnson Medical School, Piscataway, NJ

**Dr. Morton Lippmann**, Professor, Nelson Institute of Environmental
Medicine, New York University School of Medicine, Tuxedo, NY

**Dr. Gary Marsh**, Professor, Department of Biostatistics, Graduate
School of Public Health, University of Pittsburgh, Pittsburgh, PA

**Dr. Gunter Oberdörster**, Professor of Toxicology, Department of
Environmental Medicine, School of Medicine and Dentistry, University of
Rochester, Rochester, NY

**Dr. Luis Ortiz**, Associate Professor of Medicine, Department of
Environmental Health, Director Division of Environmental Medicine,
University of Pittsburgh, Pittsburgh, PA

**Dr. Julian Peto**, Professor, Department of Epidemiology and
Population Health , London School of Hygiene and Tropical Medicine,
London, , UK

**Dr. Christopher Portier**, Associate Director, National Institutes of
Environmental Health Sciences (NIEHS), Director, Office of Risk
Assessment Research, NIEHS, Principle Investigator, Environmental
Systems Biology, NIEHS, Research Triangle Park, NC

**Dr. Carol Rice**, Professor of Environmental Health and Director of
the Environmental and Occupational Hygiene Academic Training Program,
Department of Environmental Health, Kettering Laboratory, University of
Cincinnati, Cincinnati , OH

**Dr. Randal Southard**, Professor of Soils, University of California,
Davis, CA

**Dr. Leslie Stayner**, Director, Epidemiology & Biostatistics,
Epidemiology & Biostatistics, School of Public Health, University of
Illinois, Chicago, IL, USA

**Dr. David Veblen**, Professor of Earth and Planetary Sciences,
Department of Earth and Planetary Sciences, Olin Hall, Johns Hopkins
University, Baltimore, MD

**Dr. James Webber**, Research Scientist, Wadsworth Center, New York
State Department of Health, Albany, NY, USA

**SCIENCE ADVISORY BOARD STAFF**

**Ms. Vivian Turner**, Designated Federal Officer, 1200 Pennsylvania
Avenue, NW

1400F, Washington, DC

\* Dr. Gelman was unable to attend the July 21-22, 2008 public meeting.

**ENCLOSURE 2**

**PROPOSED APPROACH FOR ESTIMATION OF BIN-SPECIFIC CANCER POTENCY
FACTORS FOR INHALATION EXPOSURE TO ASBESTOS**

**CHARGE QUESTIONS TO THE EPA SCIENCE ADVISORY BOARD**

**OVERVIEW**

At present, EPA uses an approach developed in 1986 for quantifying
cancer risk from asbestos exposure based on phase contrast microscopy as
the measure of asbestos exposure. The 1986 method used existing
epidemiological data from cohorts of workers exposed to asbestos in a
variety of mining and manufacturing settings to select quantitative risk
models and estimate potency factors for lung cancer and mesothelioma.
EPA's Office of Solid Waste and Emergency Response (OSWER) is proposing
an interim approach to account for the potential differences of cancer
potency between different mineral types and particle size distributions
at different human exposure conditions. The document submitted for
review describes a "multi-bin" mathematical approach to estimate cancer
risk according to mineral groups (amphibole or chysotile) and particle
size (length and width) based on transmission electron microscopy. There
are a number of issues regarding the statistical methods to be used in
the fitting (these are discussed in Section 8), as well as a number of
issues regarding the epidemiological and exposure data used (these
issues are discussed in Sections 9 and 10). The purpose of the following
charge questions is to identify the key issues that OSWER has
encountered and to seek input from the SAB on the proposed approaches
for addressing these issues, what changes to the proposed approaches may
be needed, and what alternatives should be considered .

**CHARGE QUESTIONS**

The proposed approach is based on the hypothesis that there may be
significant difference in potency for lung cancer and/or mesothelioma as
a function of asbestos mineral type and particle dimensions.

***Charge Question 1:***

1\. Do you agree that the data are sufficient to indicate that such
differences may exist and that an effort of this type is warranted?

**SECTIONS 2-7**

Sections 2-5 of the document provide a synopsis on the physical and
chemical characteristics of asbestos, toxicology, epidemiology, and mode
of action. An overview of EPA's 1986 dose-response method is described
in section 6, and initial EPA efforts to develop bin-specific cancer
potencies are described in section 7

***Charge Question 2:***

2\. Please comment on the adequacy of these sections which serve as the
scientific bases for the proposed dose-response assessment approach.

**SECTION 8**

Section 8 of the document describes the statistical approach that OSWER
is proposing for use in fitting risk models to the available data.
Detailed charge questions related to the proposed fitting process are
provided below.

**Section 8.2 -- Risk Models**

OSWER reviewed work done by others in which the adequacy of the risk
models for lung cancer and mesothelioma were assessed. OSWER concluded
that the existing risk models (i.e., the same models developed by USEPA
1986) were adequate for use in this effort.

***Charge Questions 3a-3c:***

3a. Do you agree that the lung cancer and mesothelioma risk models that
are proposed are a scientifically valid basis for this fitting effort?

3b. Should additional model forms be investigated? If so, what model
forms are recommended for investigation, and what is the basis for
concluding that these forms warrant evaluation?

3c. For lung cancer, the current risk model is multiplicative with the
risk from smoking and other causes of lung cancer. Should the nature of
the interaction between asbestos and smoking be investigated further? If
so, how should this be done? Do you think the model would be sensitive
to additional quantification of the interaction between smoking and
asbestos?

**Section 8.3 -- Fitting Metric**

Fitting of the risk models to the data may occur either at the level of
individual studies, or at the level of individual exposure groups. OSWER
is proposing that fitting occur at the level of exposure groups.

***Charge Questions 4a-4b:***

4a. Is fitting at the group level (based on the number of cancer cases
observed) preferred to fitting at the study level (based on the
study-specific KL or KM values)? What are the advantages and
disadvantages of this approach?

4b. If so, is it scientifically justifiable to use a Poisson likelihood
model for the observed number of cases in each group? Please comment on
any other models that should be considered.

**Sections 8.4 -- Characterizing Uncertainty In Exposure Data**

In most cases, there are multiple sources of uncertainty in the measures
of exposure reported in published epidemiological studies. Section 8.4
provides an overview of how OSWER proposes to characterize these
uncertainties, and the details of the approach are provided in Appendix
C. Application of the proposed methods to each epidemiological study are
presented in Appendix A.

***Charge Questions 5a-5d:***

5a. Have all of the important sources of uncertainty in cumulative
exposure matrices been identified? If not, what other sources should be
accounted for?

5b. Is it appropriate to characterize the uncertainty from each source
in terms of an independent probability density estimated using
professional judgment? If not, what alternative approach is suggested?

5c. Are the general strategies for selecting distributional forms and
parameter values described in Appendix C (and applied in Appendix A)
appropriate for characterizing uncertainty in exposure metrices? If not,
what alternative strategies are recommended?

5d. Based on the assumption that each of the sources of error is
independent, OSWER is proposing an approach where the errors combine in
a multiplicative fashion. Please comment on the scientific validity of
this approach and provide detailed suggestions for other approaches
OSWER should consider.

**Section 8.5. Fitting Approach**

OSWER considered a wide range of strategies for fitting the
epidemiological data to the risk models, including simple minimization
of squared errors, weighted regression, maximum likelihood methods,
measurement error models, Monte Carlo simulation, and Bayes-MCMC. Based
on the recognition that there is substantial error in both the
independent variable (observed number of cases in an exposure group) and
the independent variable (metric of cumulative exposure for the group),
OSWER is proposing Bayes-MCMC as the most robust statistical approach
for fitting the data.

***Charge Questions 6a-6b:***

6a. Is it appropriate to account for measurement error in the exposure
data by using "measurement error" models (weighted regression methods)?
If so, how would the weights assigned to each exposure value be
assigned?

6b. Is the assignment of a PDF for data quality sufficient or should
data quality be factored into a weighted likelihood analysis?

6c. Do you think that the proposed strategy of fitting the risk models
to the available epidemiological data using Bayes-MCMC is scientifically
justifiable? If not, what alternative strategy do you suggest, and why?

**Section 8.6.2 --Specification of Priors**

Assuming that Bayes-MCMC is the method that will be used, it is
necessary to specify prior uncertainty distributions for each of the
fitted parameters, including **α~s\ ~**(the vector of study-specific
relative risks of lung cancer at zero exposure), **KL~b\ ~**(the vector
of bin-specific potency factors for lung cancer), and **KM~b\ ~**(the
vector of bin-specific potency factors for mesothelioma).

***Charge Question 7:***

7\. Are the priors proposed in Section 8.6.2 for **α~s~**, **KL~b~**,
and **KM~b\ ~**consistent with available knowledge? If not, what
alternative priors should be considered, and why?

**Section 8.7 -- Comparing Results For Different Binning Strategies**

OSWER is proposing an approach in which the best binning strategy is
determined empirically (by finding the strategy that yields the best fit
with the data), rather than specifying a binning strategy *a priori*
that is expected to be optimal based on information from other sources.
Conceptually, an infinite number of binning strategies might be
considered. The choice of the size cutoffs for length and width are
judgmental, and are also limited by the availability of particle size
distribution data (see Section 10). OSWER is proposing 20 different
binning strategies for evaluation. Length bins proposed for use include
\<5, 5-10, and \>10 um. Width bins proposed for use are \<0.4 and 0.4 to
1.5 um.

***Charge Questions 8a-8d:***

8a. Do you agree that multiple binning strategies should be evaluated,
or do you believe that a physiological basis exists that can be used to
identify a particular set of length and width cutoffs that should be
assessed? If so, what would those length and width cutoffs be, and can
these bins be implemented considering the limitations in the available
TEM particles size data sets? (see Section 10)

8b. Are there any of these strategies that you feel do not warrant
evaluation? If so, why? Are there any additional strategies that you
recommend for inclusion? If so, why?

8c. Assuming that fitting is performed using Bayes-MCMC, OSWER is
proposing that a comparison of goodness of fit between different binning
strategies be based on the Bayes Factor. Do you agree that this is a
statistically valid method for comparing binning strategies? Are there
any other comparison methods you would recommend? If so, why?

8d. Is it important to account for differences in the number of fitting
parameters (bin-specific potency factors) when comparing 1-bin, 2-bin,
and 4-bin strategies to each other? If so, how should that be done?

**Section 8.8 -- Other Methods For Characterizing Goodness-of-Fit**

OSWER is proposing that the initial evaluation of goodness-of-fit of
different binning strategies be based on the Bayes Factor, but is also
proposing a number of additional evaluations to assess both relative and
absolute goodness-of-fit. These are described in Section 8.8.

***Charge Questions 9a-9e:***

9a. What method(s) is (are) preferred for characterizing the absolute
goodness-of-fit of any selected binning strategy? Should any of these
methods be used to supplement the relative comparisons based on the
Bayes Factor? If so, how?

9b. If different measures of goodness of fit do not yield results that
agree, which method should be preferred, and why?

9c. What methodological options do you recommend for validating the
results of the modeling efforts? What are the strengths and limitations
of these options compared to others that might be available?

9d. In lung cancer studies, it is expected that the value of α~s~ should
be relatively close to 1.0. If the fitted value of any particular value
of α~s~ is substantially higher or lower than 1.0, should this be taken
to reflect that the data set giving rise to the value are somehow flawed
or are too uncertain for use, and should be excluded? If so, what
criteria would you suggest for recognizing values that warrant concern?

9e. Is an examination performed of the residuals from the meta-analysis
a rigorous and scientifically valid assessment of homogeneity?

**Section 8.9 -- Sensitivity Analysis**

OSWER is proposing an approach for evaluating the sensitivity of the
results to the various assumptions and choices used in the effort that
is based on series of "what if" tests. For example, this may include
excluding all or some of the data from one or more of the studies, and
assessing how those exclusions impact the results. Likewise, one or more
of the PDFs used to characterize uncertain input data may be changed to
evaluate if/how the results are altered.

***Charge Questions 10a-10b:***

10a. Is this "what if" approach for evaluating sensitivity
scientifically valid and useful?

10b. Are there other techniques that you recommend for characterizing
the sensitivity of the outcome to the data and methods that are used? If
so, what?

**SECTION 9. EPIDEMIOLOGICAL DATA PROPOSED FOR USE**

Section 9 of the document describes the methods that are proposed for
selecting studies for use in the effort, along with a list of studies
that are proposed for inclusion. Detailed charge questions related to
Section 9 are provided below.

**Section 9.1 -- Criteria For Study Selection**

OSWER has reviewed the published literature and identified studies that
include sufficient exposure-response data to allow the study to be
included in the model fitting effort for lung cancer and/or
mesothelioma. These rules are as follows:

-   The study must be published in a refereed journal.

-   The study must provide data that can be expressed in terms of the
    quantitative risk models for lung cancer and/or mesothelioma

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-   The study cohort must consist of individuals who were exposed to
    approximately the same atmospheric composition of asbestos.

Some members of the 2003 Peer Consultation panel recommended that a
minimum set of data quality requirements be imposed as part of the study
selection procedure, while other members favored inclusion of all
studies and the use of uncertainty factors to account for differences in
data quality. OSWER considered these peer consultation recommendations,
and is proposing that no data quality requirement be imposed because a)
formulation of the data quality rules would be very difficult, and b)
the method for characterizing uncertainty in the data from each study
ensures that data from strong studies has more influence on the results
that data from weak studies.

***Charge Questions 11a-11e:***

11a. Are the study-specific selection rules proposed above
scientifically valid for the intended uses? Should any additional
selection rules be added?

11b. Is it appropriate to assume that all workers in a cohort are
exposed to the an atmosphere with a constant composition (i.e., the
mixture of asbestos types and sizes is constant) unless the authors
report information to the contrary? If this is not an appropriate
assumption, what alternative strategy would be available?

11c. Should a set of minimal data quality requirements (other than those
above) be established for inclusion of a study in the analysis? If so,
what elements of data quality should be considered, and how should those
data quality rules be established?

11d. For lung cancer, OSWER's approach requires that there be at least
two exposure groups per study in order impose some constraint on the
value of the study specific value of α. However, OSWER is proposing to
use data from three cohorts described by Henderson and Enterline (1979),
even though there is only one dose group for each cohort. This is
because a reliable estimate of α for the combined cohort can be derived
from the data of Enterline et al. (1987). Is this approach appropriate
and scientifically justifiable? If not, can you suggest an alternative
strategy for retaining the data from this important study or should this
study be excluded?

11e. One key assumption in any meta-analysis is that the data sets
included in the analysis are homogeneous. How should the assumption of
homogeneity be assessed prior to combining the data from the studies or
groups? If you recommend statistical testing, please provide guidance on
the reliability of a decision based solely on the test statistic. If
testing produces evidence of heterogeneity between some studies, what
steps can be recommended?

**Sections 9.2 and 9.3. Studies Proposed for Use and Studies Excluded**

Section 9.2 lists each of the lung cancer and/or mesothelioma studies
that OSWER has identified as being sufficient for inclusion in the data
fitting effort. There are a number of studies where cumulative exposure
was not reported in the units needed for modeling. In order to utilize
these studies, it was necessary to use the data provided to estimate
cumulative exposure in the needed units (e.g., Yano et al. 2001,
McDonald et al. 1982, 1983, 1984). Section 9.3 identifies several
studies that were considered for use, and the reasons why they are
proposed for exclusion.

***Charge Questions 12a-12c:***

12a. Are you aware of any studies that should be included in the model
fitting effort that are currently excluded or omitted? If so, what are
these studies, and do they meet the requirements for study inclusion?

12b. Are there any studies that are currently proposed for inclusion in
the analysis that you believe should be excluded? If so, why?

12c. In cases where the epidemiological data are not reported in the
form needed for use in the fitting effort, are the methods used to
estimate the exposures scientifically sound, and are the methods used
for characterizing the uncertainty in the estimates appropriate?

**SECTION 10. METHOD PROPOSED FOR ESTIMATING BIN-SPECIFIC EXPOSURES**

One of the largest problems with this effort is that none of the
published studies included bin-specific exposure estimates. Therefore,
the effort is contingent upon methods for estimating bin-specific
exposures based on the data provided. Specific charge questions related
to this process are provided below.

**Section 10.2 -- Extrapolation from Dust to PCM-Based Measures**

A number of studies reported exposure in terms of dust rather than
asbestos. In some cases, data are available to extrapolate from dust to
asbestos levels. In other cases, no data are provided. OSWER is
proposing to use an \"average\" extrapolation factor in this case.

***Charge Questions 13a-13b:***

13a. Is it scientifically justifiable to employ a default dust-to-PCM
conversion factor when there are no site-specific data available?

13b. Are the uncertainty distributions specified in Appendix A to
characterize the uncertainty in this extrapolation consistent with
available information and are they statistically appropriate?

**Section 10.3 -- Extrapolation from PCM to Bin-Specific Measures**

The process of extrapolating from PCM-based measures of exposure to
bin-specific measures of exposure requires two types of data: 1) the
fraction of the atmosphere that is chrysotile and the fraction that is
amphibole, and 2) particle size data for both the chrysotile and the
amphibole components. In the absence of reliable study-specific data,
OSWER is proposing to use published TEM particle size data from similar
workplaces as the basis of the particle size data needed for step 2.

***Charge Questions 14a-14i:***

14a. Are the point estimates and uncertainty distributions for the
fraction amphibole term proposed for each study scientifically valid?

14b. Is it scientifically valid to use surrogate TEM data to estimate
bin-specific concentrations and exposure values in studies where these
data are not reported? If not, what alternative approach could be
followed, or what additional data would be helpful?

14c. Are there any additional bi-variate TEM data sets available that
would be useful in this analysis?

14d. Are the point estimates and uncertainty distributions for the
fraction amphibole term scientifically valid?

14e. Can you suggest any ways to improve the process used to identify
select the best available matching TEM data set(s) to a workplace? How
sensitive would the model output be to these changes?

14f. Would the model benefit by establishing a common lower cut-point in
diameter to normalize the lower detection limit across studies?

14g. Do the studies included in the model have surrogate data of
sufficient quality and similarity to expected exposure conditions to
support the model? If not, what alternative approach could be followed?

14h. Are the PDFs described in Appendix C to characterize the
uncertainty in the extrapolation of TEM particle size data from one
location to another sufficient and helpful in understanding the
implications of the method used?

14i. Are the extrapolation techniques used on the raw TEM data sets to
meet the bin definitions (e.g., 0.4 um diameter) transparent,
objectively presented and scientifically valid? Are there alternative
techniques that you would recommend?

**SECTION 11 -- UTILIZING POTENCY FACTORS TO COMPUTE LIFETIME RISK**

Assuming that it is possible to derive a set of bin-specific potency
factors, it is expected that these will be used to evaluate lifetime
risk of cancer to an individual with a specified exposure history using
the same basic life-table approach used by EPA (1986). However, each
bin-specific potency factor will be uncertain. Therefore, it is
important to specify the uncertainty in the risk predictions that arise
from the uncertainty in the potency factors.

***Charge Questions 15a-15b:***

15a. What method is best for estimating the uncertainty in lifetime
cancer risk predictions that are associated with the uncertainty in the
bin-specific potency factors?

15b. Assuming that estimates of exposure at Superfund sites will also
have uncertainty, how should the overall uncertainty in risk predictions
be characterized?

> **NOTICE**
>
> This report has been written as part of the activities of the EPA
> Science Advisory Board, a public advisory group providing extramural
> scientific information and advice to the Administrator and other
> officials of the Environmental Protection Agency. The SAB is
> structured to provide balanced, expert assessment of scientific
> matters related to the problems facing the Agency. This report has not
> been reviewed for approval by the Agency and, hence, the contents of
> this report do not necessarily represent the views and policies of the
> Environmental Protection Agency, nor of other agencies in the
> Executive Branch of the Federal government, nor does mention of trade
> names or commercial products constitute a recommendation for use.
> Reports of the EPA Science Advisory Board are posted on the EPA
> website at <http://www.epa.gov/sab>.

ENCLOSURE 3

> **Comments from Subgroups and Individual Members**

[Subgroup Comments on Specific Charge Questions]{.underline}

Charge \# 1 (Scientific basis for proposed method) --- see individual
comments

Charge \# 2 (Background Information)

Physical/Chemical Characteristics (section 2)---Drs. Veblen, Southard,
Gutherie.............3-2

Toxicology/Mode of Action (sections 3 & 5) --- Drs. Oberdörster, Ortiz,
Everitt..............3-4

Epidemiology (section 4) --- see individual comment (Dr. Rice)

Charge #3 and 4 (Risk Models) --- Drs. Lippmann**,** Stayner
..............................................3-14

Charge \# 5, 6, 7 (Exposure Data) --- see individual comments (Drs.
Lioy, Portier, Cox)

Charge \# 9, 10 (Statistical Methods and Uncertainty Analysis) --- Drs.
Portier**,** Cox, Gelman ......3-18

Charge \# 11, 12 (Criteria and Selection of Epdemiologic Data) --- see
individual comments

(Drs. Finkelstein and Stayner)

Charge \# 13, 14 (Exposure Measurements and Extrapolation) --- see
individual comments

(Drs. Harris and Webber)

Charge \# 15 (Computation of Lifetime Risks) --- see individual comments
(Drs. Rice and Lioy)

[Individual Comments]{.underline}

Dr. Tony
Cox......................................................................................................3-20

Dr. Murray Finkelstein
.................................................................................\...\...\...\....3-24

Dr. Andrew
Gelman...............................................................................................3-35

Mr. John
Harris....................................................................................................3-37

Dr. Karl
Kelsey....................................................................................................3-43

Dr. Paul
Lioy.......................................................................................................3-46

Dr. Mort
Lippmann.............................................................................................\...3-50

Dr. Gary
Marsh....................................................................................................3-53

Dr. Luis
Ortiz......................................................................................................3-59

Dr. Julian
Peto.....................................................................................................
3-63

Dr. Christopher
Portier............................................................................................3-66

Dr. Carol
Rice......................................................................................................3-71

Dr. Leslie
Stayner..................................................................................................3-74

Dr. James
Webber..................................................................................................3-82

Additional Materials Supplied by Dr. Peto .
...................................................................3-88

[Subgroup responses to Charge Question 2]{.underline}

**Please comment on the adequacy of Sections 2--5 which serve as the
scientific bases for the proposed dose-response assessment approach.**

***[Drs. Veblen, Southard, Gutherie]{.underline}***

Section 2 provides background information on asbestos mineralogy,
particle size variability, and measurement methods. These sections
should (at a minimum) provide the basis for (1) justification of the
proposed approach and (2) application of the approach (including
potential limitations).

Pertaining to justification, the primary points for the proposed binning
on mineral type are that:

-   *Asbestiform amphiboles and chrysotile have different properties
    that may result in different biological responses.* The current
    document does not provide sufficient information on this account.

> Information is needed on properties that may relate to biopersistence,
> which has been shown to differ between the two groups of minerals.
> This includes a discussion of solubility and dissolution rates (e.g.,
> Hume and Rimstidt, 1992, Am. Mineral., 77:1125-1128 and references
> therein for chrysotile; Nagy, 1995, Rev. in Mineral., 31:173-234 for
> chrysotile; Brantley and Chen, 1995, Rev. in Mineral., 31:119-172 for
> amphibole) and cleavage/parting that can alter particle length (e.g.,
> Veblen and Wylie, 1993, Rev. in Mineral., 28:61-137). This discussion
> must also include aspects such as dissolution behavior (such as
> leaching of surface; e.g., Jaurand et al., 1977, Env. Res.,
> 14:245-254), which impacts the potential release of iron (believed to
> be an important factor in some aspects of pathogenesis).
>
> Information is needed on factors related to surface properties, which
> have been shown to differ between these materials (e.g., see Veblen
> and Wylie, 1993). Fubini (1997, Env. Health Persp., 105:1013-1020) is
> a good link between surface properties and biological response.

-   *Asbestiform amphiboles and chrysotile can be differentiated.*
    Section 2.3 addresses this in part. A helpful addition would be to
    discuss critically the ability of each technique to differentiate
    amphibole from serpentine (chrysotile).

-   *Binning occupational exposures into the two groups is meaningful.*
    At least two short additions are needed to allow the evaluation of
    this: background exposures and nature of mineral deposits. Klein
    (1993, Rev. in Mineral., 28:7-59) provides a discussion on factors
    related to background exposures, which provides context for
    assessing studies where the occupational exposures reported for a
    given asbestos type were zero.. The nature of mineral deposits is
    important in assessing the likelihood of multiple exposures (e.g.,
    some chrysotile deposits contain small amounts of tremolite
    asbestos, whereas others do not).

Pertaining to potential application of the approach, it is important to
note potential differences between the exposures used in the assessment
and those to which this might be applied. This includes the nature of
the mineral compositions explored and the nature of commercial exposures
to amphiboles compared to exposures to materials from the environment.

-   *The human data proposed for use relate to a specific set of
    materials (specifically to chrysotile and a subset of asbestiform
    amphiboles).* Yet, in the amphibole category, there are mineral
    species that are not represented in the proposed epidemiological
    studies. For example, winchite and richterite are two examples of
    amphiboles that can have asbestiform habits and that are implicated
    in asbestos-related disease at Libby, MT (see Wylie and Verkouteren,
    2000, Am. Mineral., 85:1540-1542). Another example is fibrous (not
    necessarily asbestiform) fluoro-edenite, which is an amphibole that
    has been implicated in studies of environmental exposures in
    Bianavilla, Italy (Gianfagna et al., 2003, Min. Mag., 67:1221-1229).
    These amphiboles differ chemically (and hence in their properties)
    from those in the proposed epidemiological studies. Can/should the
    results of the proposed approach be applied to these other types of
    environmental exposures to amphiboles?

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-   *The human data proposed for use relate to materials that are mined
    and processed.* In contrast, environmental exposures are often to
    materials that have been exposed to the environment, which could
    include factors such as weathering and concurrent exposure to other
    materials. In some cases, the environmental conditions may lessen
    the impact; in others they may augment the impact. IARC recognizes a
    distinction in risk between exposures to silica in occupational
    settings versus silica exposure under other conditions (in part due
    to differences in the surface reactivity). For example, Horwell et
    al. (2003, Env. Res., 93:202-215) show that generation potential of
    free radicals diminishes for volcanic ash following environmental
    aging. Although the proposed approach is not assessing any
    relationships related to occupational versus environmental
    exposures, the results may find application beyond the narrow
    conditions represented in the epidemiological studies. Hence, a
    discussion of the differences between the processed/commercial
    exposures and environmental exposures would aid in assessing
    potential applicability (and/or limitations) of the proposed method.

There are some minor modifications that are also necessary in order to
provide a more accurate base:

-   Section 2.1-Intro Like it or not, many standard mineralogy texts do
    not equate "fibrous" and "asbestiform." To qualify as fibrous, a
    mineral can merely look fibrous and still be hard, brittle, and not
    separable into thin, flexible fragments. It would be better here
    simply to define the term asbestiform.

-   Section 2.1-Serpentine Modify the sentence on elemental
    substitutions to note that the most common substitutions involve
    small amounts of aluminum and ferrous iron; the additional elements
    noted are minor in comparison to these subsitutions. Veblen and
    Wylie (1993) is a good reference to serpentine composition.\
    \
    The word "lattice" in the first paragraph of this section should be
    replaced by "structure," so that it reads "in the crystal
    structure." A lattice is a mathematical construct used to describe
    crystal structure but is not the structure itself.\
    \
    The crystal structure(s) for chrysotile should be described briefly.
    A description is given for amphibole, so why not chrysotile?\
    Section 2.1-Amphibole The first sentence of this section is poor. I
    suggest replacing it with "Amphiboles possess double chains of
    silicate tetrahedra that are interconnected by bands of 6- to
    8-coordinated cations."\
    \
    The elements listed for the B site should be modified to include Mg
    and Fe, which are the dominant B-site elements for the amosite
    minerals (for example). Veblen and Wylie (1993) is a good reference
    to amphibole? composition. (I would add at least Mn, since this
    section is discussing amphiboles in general, not just those that can
    adopt asbestiform habit.---DRV)

Section 2.1-Amphibole The list of amphibole species should read
"grunerite" not "gruenerite".\
Also "riebeckite" not "rebeckite."\
Though perhaps a matter of taste, some might find it useful to include
chemical formulat for these five amphiboles. (An end-member formula *is*
given in the section on serpentine.)\
\
Last word in section 2.1: The proper mineral name is fluor-edenite, not
fluro-edenite.

-   Section 2.1 Provide a reference to a more comprehensive discussion
    of properties, such as the recent publication by IOM on Asbestos:
    Selected Cancers.

-   Section 2.3-TEM Change "irradiate" to "illuminate" in the first
    sentence

-   Section 2.3-TEM Change "provides the x-ray diffraction pattern" to
    "provides the electron diffraction pattern."

-   Section 2.3-TEM It is stated that "most TEM instruments" have an
    accessory that allows the formation of SAED patterns. In fact, an
    electron diffraction pattern forms in the back focal plane of the
    objective lens whether one likes it or not. It's true that an
    aperture is required to make this DP correspond to a specific
    specimen area, but I know of no TEMs that have ever been
    manufactured without at least one SA aperture. Biologists may not
    use it, but they get it anyway, whether they like it or not!

***[Drs. Oberdörster, Ortiz, Everitt]{.underline}***

Sections 3-5 are designed as a backbone to illustrate the needs of the
OSWER report. These sections should facilitate analysis of the OSWER
document by the members of SAB and the broader public. A potential
consequences of endorsing such document is that the general public may
equate this support with that of an official change in EPA policy toward
asbestos carcinogenic potency. It may be advisable that such document be
supplemented to cover the following items.

1\. There must be ample discussion in the introduction of these sections
stating that this is an interim approach to assess the question of how
physical difference in the composition of asbestos fibers modify its
cancer inducing capacity. A clear description of the historical and
current EPA's needs that motivate the OSWER report should be stated.
Specifically, the document should make reference to the current EPA
priorities in the clean up efforts of superfund sites such as Libby,
Montana and other sites around the US (as discussed during the SAB
meeting in Washington, D.C.).

2\. A more detailed discussion of the importance of the physical aspects
of the asbestos fibers (length as well as width) and whether or not
these properties bear directly in the carcinogenic (lung or
mesothelioma) capacity of the asbestos fibers is necessary.

3\. The report should also clearly state that currently there is a
definitive paucity of scientific information, both in animals (Stanton
and Wrench , 1972) as well as humans (Stayner LT *et al*., 2007)
regarding the use of TEM to characterize fiber size specific asbestos
exposure. In the case of Libby there are samples that could be subjected
to TEM analysis to properly address this deficiency. Therefore, this
aspect could be incorporated in the report as a scientific and
investigational priority to the agency.

4\. Similarly, there appears to be room for improvement regarding the
description of a body of published work describing the factors that
modify the environmental host interaction and determine individual
susceptibility to asbestos-induced cancer. Specifically, there is little
description of epidemiological data addressing the relationship
(additive versus multiplicative) between smoking and asbestos exposure
on its carcinogenic effects.

5\. Finally, the approach to the biology of the carcinogenic mechanisms
of asbestos is timid, lacking in molecular depth and not providing a
biologic foundation to back the epidemiologic approach map (that could
be experimentally adopted to support the imminent fitting of the human
epidemiological data) to indicate how the proposed differences in the
physical properties of the asbestos fibers determine their carcinogenic
potency.

The toxicology section is wholly inadequate as described as it doesn\'t
address the important role of biopersistence in fiber-induced
carcinogenesis. It fails to address the synthetic vitreous fiber
database which points strongly to the role of fiber biopersistence as
being an important factor in fiber potency and cancer risk. The fiber
biopersistence issue for chrysotile and amphiboles has been reviewed
(most recently by Bernstein and Hoskins, 2006) and should be discussed
in this light.

There is also need to review the refractory ceramic fiber studies to
discuss the fiber lengths that were associated with rodent fiber-induced
disease as this will emphasize the relatively short nature of the 10mm
proposed bin and the inadequacy of the existing exposure database in the
epidemiology studies.

Another major gap is the lack of discussion of the role of mixed dust
exposures on the pathogenicity of any given fiber exposure. I think the
study of Davis and colleagues (1991) should be specifically referenced
with respect to this.

The charge question gives the impression that the cancer risk assessment
approach described in the document is based on a Dose-Response
assessment model. Although that would indeed be the most desirable
approach, it was never attempted anywhere in the document, but rather
the analysis is based on Exposure-Response. There is a significant
difference between these 2 approaches, an exposure concentration is not
a dose, and only the dose is obviously most directly correlated with the
response. Figure 1 depicts the interrelationship between Exposure, Dose
and Response and some of the important factors involved. A more detailed
description is provided in an earlier review (Oberdörster, 2003,
attached). Deposition, nasal filtering capacity, breathing parameters,
fiber length and diameter, and clearance behavior are among the
important factors that determine dose; in particular the deposition and
clearance characteristics of inhaled fibers have been well described by
C.P. Yu's group (Dai and Yu, 1998; Yu *et al.,* 1990; 1991; 1998) and
most recently by Balashazy *et al.* (2005). The document suffers from
not discussing these important very basic issues.

It would also be helpful to mention and briefly describe the classical
risk assessment paradigm (NRC, 1983) for further clarification. An
attempt to adapt this paradigm together with risk management
consideration, specifically for asbestos, is shown in Figure 2.

The document text (p.3) emphasizes that OSWER focuses only on
epidemiological exposure-response data, and no attempts are made to
integrate results from other sources, including animal data, mode of
action, *etc*. However, the numerous animal toxicological studies that
have been performed over several decades on different types of asbestos
can give important information about differences in biokinetics and
biopersistence (retention halftimes, see attached Table 1) of different
asbestos types as well as providing information about dose-response
relationships and the importance of fiber dimensions for translocation
to pleural sites, and translocation pathways. These include several
long-term inhalation studies in rats and hamsters with refractory
ceramic fibers including asbestos as a positive control by Hesterberg
and colleagues (1993; 1994; 1995; 1997; 1998; 1999) that describe
retention kinetics, effects, and tissue distribution.

In addition, the classic studies by Stanton *et al*. (1977; 1981), Pott
*et al*. (1974, 1976), Wagner *et al*. (1974, 1984) and Davis *et al.*
(1986, 1988, 1991, 1999) related to different types of asbestos,
including amosite, chrysotile, tremolite should be described as valuable
background information on fiber dimension and associated tumorigenic
effects and the importance of durability and biopersistence. A *caveat*
of these earlier studies relates to the very high exposure
concentrations and doses used, which even with more benign particles
would have resulted in lung overload conditions, including significant
inflammation. Also, the impact of co-exposure of asbestos plus a
granular dust, whether a benign particle type (TiO~2~) or a cytotoxic
one (quartz), on cancer induction (increase of both lung cancer and
mesothelioma) and altered retention and biokinetics in rat studies as
reported by Davis *et al*. (1991), should be included because they
highlight the impact of a mixed dust exposure in exposed human workers
(see studies by Finkelstein, 1983; Rey *et al*., 1994). This is an
important concept to be mentioned and considered in the document
especially with regard to asbestos present in superfund sites
potentially resulting in combined exposures.

More recent data point to the enormous differences in the biopersistence
between chrysotile and amphiboles: Chrysotile from Canada, California
and Brazil was cleared from the lungs in rat studies very rapidly,
including fibers longer than 20 µm (Bernstein *et al*., 2005a; 2008;
2004; 2005b). Retention halftimes for chrysotile fibers \>20 µm ranged
from less than 1 day to about 11 days, indicating their low
biopersistence. The authors report also less or no pathological
responses in rats after subchronic and chronic inhalation exposure to
chrysotile, and suggest that low level exposure to chrysotile may not be
hazardous and the risk much lower than assumed (Bernstein and Hoskins,
2006). An earlier study in rats on deposition, clearance and
translocation of inhaled chrysotile fibers reported a fast fiber length
dependent pulmonary clearance, with retention halftimes (T½) from below
10 days to 30 days for fibers up to 8 µm long, and about 100 days for
fibers \>16 µm in length (Coin *et al.,* 1992). Because retention was
measured only for 29 days post-exposure, estimates of the short T½ have
a greater level of certainty -- and are consistent with significant
chrysotile dissolution -- than the 100 day T½, which, however, is also
consistent with significant dissolution. Although these studies do not
exculpate chrysotile from being labeled as a known human carcinogen,
they point to a lower carcinogenic potency than amphiboles because of
the much lower biopersistence of chrysotile. This should be considered
in the risk assessment process.

One suggestion is to compile data of the tox studies in a table form, if
available with inclusion of doses retained in the lung. Animal to human
extrapolation models for fiber deposition, retention and clearance could
be mentioned, as developed by C.P. Yu.

Section 5 focuses mainly on the oxidative stress hypothesis of
asbestos-induced tumorigenesis, which is certainly a major mechanism in
high dose studies (all animal data) where significant and persistent
inflammation is induced. While a number of mechanistic studies are
cited, a more structured approach through listing and brief discussion
of 5 major mechanistic hypotheses as proposed by Kane (1996) would be
desirable. (Fiber generated radicals damage DNA; fibers interfere with
mitosis; fibers stimulate proliferation of target cells; fibers provoke
chronic inflammation with release of ROS and cytokines; fibers act as
co-carcinogens or carriers of chemical carcinogens).

Either here or in the Toxicology section, the importance of fiber
biokinetics should be discussed, which is different between induction of
lung cancer and mesothelioma. Once inhaled fibers have deposited in the
different regions of the lung (see earlier comment on fiber size
dependent deposition in tracheobronchial *vs*. alveolar region), they
need to translocate to pleural sites to induce effects (plaques,
fibrosis, mesothelioma). The accepted dogma is that fibers \>15/20 µm
are the most carcinogenic ones. While this may be true for lung cancer
(alveolar macrophages cannot fully phagocytose long fibers so they are
not cleared), the longer fibers are also least likely to translocate to
the pleura. If lymphatic translocation pathways are involved as
suggested by Jones, (1987), lymphatic clearance is limited by length and
diameter, and this is different for pre-nodal and post-nodal lymph
(\~9-16 µm long; 0.5 µm thick; Oberdörster *et al*., 1988); thus, long
fibers are excluded from this pathway. Indeed, animal studies and human
tissue analyses show that by far mostly short fibers (\<5-8 µm) were
found in pleural tissue, and few, if any, fibers longer than 10-15 µm
(Gelzleichter *et al*., 1996; 1999; Boutin *et al*., 1996; Churg and
Vedal, 1994; McDonald *et al*., 2001; Sebastien *et al*., 1980; Dodson
*et al*., 1990; Suzuki and Yuen, 2001, 2002, 2005). Studies reporting
fiber dimensions found in autopsy samples of exposed workers are
summarized in Dodson et al., 2003. These authors also suggested to
consider other factors than length for identifying a hazardous fiber,
such as surface properties (charge, area, activity), chemical
composition (Fe, other transition models, biopersistence), physiological
factors. Summary findings from all of these studies showed that
chrysotile is less prevalent or even absent in human mesothelial
tissues; and these studies indicate also that even short fibers have a
carcinogenic potential for inducing mesothelioma and contributing to
lung cancer.

Other studies raise additional issues: Timbrell (1982) found fiber
surfaced area to best correlate with lung pathology (asbestosis) in
exposed workers from different mines, introducing the aspect of the most
appropriate dose-metric, which may not necessarily be fiber number.
Dogan *et al*. (2006) suggested the influence of genetic disposition as
susceptibility factor for mesothelioma. He analyzed the familial history
of erionite-induced mesothelioma in several Turkish villages where high
of erionite levels exist in whitewash slurry used as wall cover on their
houses.

Conclusions from the toxicology, fiber tissue burden and mode of action
data summarized in the foregoing paragraphs are that (*i*) fiber types
of asbestos have different potencies (hazard level) with respect to
tumor induction (*e.g.,* low biopersistence for chrysotile); (*ii*) the
risks for lung tumors and mesothelioma are different (*e.g*.,
translocation of long fibers to pleura); and (*iii*) short asbestos
fibers should not be considered as harmless (are most prevalent in tumor
tissue; inflammation as initiating condition in tox studies). With
respect to different types of asbestos, even among amphiboles
differences in potencies seem to exist, *e.g*., tremolite, erionite as
more hazardous types. Exposure-Dose relationships ought to be clarified
in the document. Issues of confounders, mixed exposures and
susceptibility should be discussed. Regarding bin selection for the risk
model calculations, tumor type, asbestos types, and fiber length
categories (\<5 µm; 5-20 µm; ≤15 or 20 µm)seem quite appropriate.

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Figure 1:

![](media/image2.wmf){width="5.4375in" height="3.6819444444444445in"}

![](media/image3.wmf){width="5.660416666666666in"
height="4.1097222222222225in"}

[Subgroup responses to Charge Questions 3 & 4]{.underline} ***(Drs.
Lippmann and Stayner)***

**3a) Do you agree that the lung cancer and mesothelioma risk models
that are proposed are a scientifically valid basis for this fitting
effort?**

If \"a scientifically valid basis for this fitting effort\" means \"a
basis that allows correct comparisons of the risks at different sites\",
then the answer is that we do "not" have sufficient information to know
that the proposed models will provide valid answers. In a broader sense,
the EPA 1986 models were reasonable starting points for this effort in
that Crump and Berman \[Aeolus 2003\], in their 2003 report for the EPA,
tested the EPA 1986 models using raw data from the South Carolina,
Quebec and Wittenoom, Australia study cohorts, and concluded that these
models provided reasonably good predictions for these studies.

On the other hand, we must recognize that these models were based on the
best available science in 1986. Even in 1986, they provided rather
marginal fits to the available data in the literature, with individual
study K~L~s and K~M~s varying by an order of magnitude or more from the
models. With the additional knowledge gained since 1986, these models no
longer provide reasonable representations of the available data. At the
same time, the formulations of some of the modeled variables remain
valid, and can be incorporated into improved models.

**3b) Should additional model forms be investigated?**

It would also be desirable to test the assumption that cumulative
exposure (duration x intensity) is an appropriate metric for the lung
cancer model. This should be done if published data exist to allow
separate modeling of duration and intensity. It may only be possible to
conduct such an analysis by obtaining and combining (pooling) the raw
data from these studies, which is one of the research recommendations
that has been made.

New models should reflect: 1) the Stayner et al. 2008 analyses of the
Charlestown, SC textile worker cohort, including the evidence that long,
thin chrysotile fibers are particularly influential in lung cancer
causation. This provides a human analog to the already well established
results from the 2-year rat inhalation bioassays with chrysotile and
amosite of Davis et al. 1986a,b) for the lung cancer model. In the
amosite bioassay, studies, short amosite fibers produced no lung
cancers, while long amosite fibers produced more lung cancers than UICC
amosite. In the chrysotile bioassay, short chrysotile fibers produced a
much lower cancer yield than UICC and the even longer chrysotile fibers,
but the short chrysotile was not as free of long fibers as was intended.
With regard to the human experience,

At this point, it may be prudent to assume that long-thin chrysotile
fibers are more biopersistent within the lungs than shorter chrysotile
fibers and comparable in biopersistence to amphibole fibers, so the
revised lung cancer model can be assumed to apply to long fibers of both
fiber types. The results from the Charlestown cohort may represent a
worst-case example, but would be a scientifically defensible one.

The model for lung cancer implies that age at asbestos exposure is
irrelevant, and this should be tested in the available data. In
addition, two-stage clonal expansion (TSCE) or other biologically
motivated risk models that have been developed specifically for lung
cancer should be considered to address the effects of exposure on
changing cancer risks over time. Such analyses can only be conducted if
a pooled rather than a metanalysis approach were used.

For the mesothelioma model, where fibers must translocate from the lung
to the pleura or peritoneal surfaces, it must be recognized that it
really only applies to amphibole fibers insofar as it assumes long-term
retention there (over decades) of fibers. While chronic chrysotle fiber
exposures have been associated with mesothelioma, it is now
well-established that such fibers are much less potent in terms of
causing mesothelioma than are amphibole fibers (Berman and Crump 2003,
ERG 2003, Hodgson and Darnton 2000, Lippmann 1988) and the revised model
must reflect this knowledge.

**3c) For lung cancer, the current risk model is multiplicative with the
risk from smoking and other cause of lung cancer. Should the nature of
the interaction between asbestos and smoking be investigated further?**

Yes, this relationship should be further evaluated. The observation that
there is a multiplicative effect of smoking and asbestos is largely
based on the results from the study of insulation workers \[Selikoff et
al. 1968, Seidman et al. 1979\]. These workers were exposed to
relatively thick amosite asbestos fibers, and were mostly heavy smokers
at a time when most lung cancer was squamous cell cancer in the large
airways. It is known that heavy smokers have increased particle
deposition in the tracheobronchial tree (Lippmann and Albert (1969), and
abnormal particle clearance from these airways (Albert et al. (1969).
Since then, cigarettes have changed (lower tar content), most lung
cancers are now adenocarcinomas, and most asbestos exposures are to
thinner fibers that deposit primarily in smaller lung airways. Also, we
now know that there is a tremendous variation in the evidence regarding
whether this relationship is multiplicative or additive \[see Steenland
and Thun 1986 and Vainio and Boffetta 1994\]. A recent analyses of this
issue suggests that in fact the relationship may be somewhere between
additive and multiplicative \[Wraith and Mengersen 2008\].

**If so, how should this be done?**

A more thorough investigation of chrysotile asbestos exposed workers
(smokers and non-smokers) cancer experience may be productive. The
sub-multiplicative effect may be due largely to the inclusion of
ex-smokers among the non-smoking group in many studies. Studies with
prospective data in which lifelong non-smokers were identified at
recruitment are rare, and ex-smokers are increasingly common. Careful
evaluation of the quality of smoking histories in different cohorts is
an important aspect of further research that is needed. A
submultiplicative effect will reduce the predicted risk in smokers.
However, it will greatly increase it among non-smokers, so in an
extrapolation from old cohorts with a lot of smokers to today\'s
population will increase the overall predicted risk. The predicted
overall risk would not be affected by model or measurement
misclassification in a population with the same smoking habits.

**Do you think the model would be sensitive to additional quantification
of the interaction between smoking and asbestos?**

The dependence of risk on other parameters (fiber type, size, age,
duration, etc.) in the model is unlikely to be strongly affected by
assumptions about smoking.

**4a) Is fitting at the group level (based on the number of cancer cases
observed *\[In answering, we will assume this refers to subgroups based
on differing levels of exposure\])* preferred to fitting at the study
level (based on the study-specific KL or KM values)?**

The committee was divided on this question. Some believed that it was
preferable in those cases where the subgroups are sufficiently large for
statistically valid inference. It provides the opportunity to compare
and contrast differing results that might reveal causal characteristics
(length, width, fiber type) associated with the differing fiber
characteristics at a specific workplace operation within each study,
that was reported from analyses of some studies (South Caroina, Quebec
and Wittenoom). Subgroup analyses would be particularly valuable in any
in-depth review of the completed epidemiology studies based on
experience at Libby, MT.

Other panelists believed that an approach fitting at the individual
level was preferable because it more fully utilized the data available
in the studies. This was particularly true for studies where there is
extensive exposure-response information available for modeling such as
the South Carolina, Quebec and Wittenoon studies that were analyzed in
the Aeolus report for EPA in 2003.

**What are the advantages and disadvantages of this approach?**

If grouping can be done on the exposure-specific results, then there are
advantages to an initial fitting on a group level, while preserving the
opportunity to combine the results for the study group as a whole.

**4b) If so, is it scientifically justifiable to use a Poisson
likelihood model for the observed number of cases in each group?**

Yes, it is scientifically justifiable and appropriate to use a Poisson
likelihood model for the analysis of grouped data. If the individual
study group results are modeled, one would probably assume a normal or
more likely a log normal distribution applies. In any case, it will be
important to test the adequacy of the distributional assumption made.
While the assumption of Poisson variation may be correct, it should be
evaluated by testing for over-dispersion.  If there is over-dispersion,
an appropriate change should be made to allow for extra-Poisson
variation. In any case, the number of cases will almost always be small,
and a Poisson likelihood model would most likely be appropriate.

**[References]{.underline}**

Aeolus 2003. Report to U.S. Environmental Protection Agency, Washington,
DC 20460. May 2003.

Albert, R.E., Lippmann, M., and Briscoe, W. The characteristics of
bronchial clearance in humans and the effects of cigarette smoking*.
Arch. Environ. Health* 1969;18:738-755.

Davis JMG, Addison J, Bolton RE, Donaldson K, Jones AD, and Smith T.
1986a. The pathogenicity of long versus short fiber samples of amosite
asbestos administered to rats by inhalation and intraperitoneal
injection. *Br. J. Exp. Pathol.* 67:415-430.

Davis JMG, Addison J, Bolton RE, Donaldson K, and Jones AD 1986b.
Inhalation and injection studies in rats using dust samples from
chrysotile asbestosprepared by a wet dispersion process. *Br. J. Exp.
Pathol.* 67:113-129.

Eastern Research Group, Inc. (ERG). Report on the Peer Consultation
Workshop to Discuss a Proposed Protocol to Assess Asbestos-Related Risk.
Final Report. Prepared for the Office of Solid Waste and Emergency
Response. U.S. Environmental Protection Agency, Washington, DC 20460.
May 2003.

Hodgson j and Darnton A. The quantitative risk of mesothelioma and lung
cancer in relation to asbestos exposure. *Ann Occup Hyg* 44:565-601.

Lippmann, M. and Albert, R.E. The effect of particle size on the
regional deposition of inhaled aerosols in the human respiratory tract.
*Am. Ind. Hyg. Assoc. J.* ; 1969;30:257-275.

Lippmann, M. 1988. Asbestos exposure indices. *Environ. Res.* 46:86-106.

Seidman, H., Selikoff, I.J., Hammond, E.C. Short-term asbestos work
exposure and long-term observation. *Ann. N.Y. Acad. Sci.* 1979,
330 :61-89.

Selikoff. I, Hammond E., Churg J. Asbestos exposure, smoking and
neoplasia. *Journal of the American Medical Association* 1968;
204:104--110.

Steenland. K., Thun M. Interaction between tobacco smoking and
occupational exposures in the causation of lung cancer. *Journal of
Occupational Medicine* 1986; 28:110--118.

## [**Vainio H**](http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term="Vainio H"%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus), [**Boffetta P**](http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term="Boffetta P"%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus). Mechanisms of the combined effect of asbestos and smoking in the etiology of lung cancer. **[*Scand J Work Environ Health.*](javascript:AL_get(this, 'jour', 'Scand J Work Environ Health.');) 1994 Aug;20(4):235-42.** 

[Wraith
D](http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term="Wraith D"%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus),
[Mengersen
K](http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term="Mengersen K"%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus).Assessing
the combined effect of asbestos exposure and smoking on lung cancer: a
Bayesian approach. [*Stat
Med.*](javascript:AL_get(this, 'jour', 'Stat Med.');) 2007 Feb
28;26(5):1150-69.

[Subgroup Responses to Charge Questions 9 and 10]{.underline} ***(Drs.
Portier, Cox, Gelman)***

**9a. What method(s) is(are) preferred for characterizing the absolute
goodness-of-fit of any selected binning strategy? Should any of these
methods be used to supplement the relative comparisons based on the
Bayes Factor? If so, how?**

The use of Bayes Factors for initial comparisons of different binning
strategies seems appropriate, but additional evaluation methods may be
useful.  The key question is:  When is one binning strategy
unambiguously better than another?  Goodness-of-fit tests may not
provide clear answers to this question.  Some other techniques that may
help are as follows.\
\
(a)  You could use conditional independence tests.  If binning method A
is \"more informative than\" method B, in the sense that model
predictions are conditionally independent of the information provided by
B, given the information provided by A (but not conversely), then method
A is preferable to Method B.\
\
(b)  There is also an opportunity to use simulation-based validation.
 The Agency could use the hypothesized relative risk model to generate
simulated data for multiple sites, with people having different
distributions of individual exposure histories.  Each of the different
proposed binning strategies could be applied to the simulated data. The
Agency could then compare the results, and use them to identify which
binning strategies work best (based on criteria such as number of errors
or ordinal correlations between the rank-ordering of the sites using
binning and the rank-ordering based on the detailed simulated data.)

Finaly, the Agency could do posterior predictive checks, i.e.,
simulating replicated datasets from the model and seeing if they look
like actual data. To do this, it would be first necessary to create some
useful graphical displays of the data that are being used to fit the
model.

**9b. If different measures of goodness of fit do not yield results that
agree, which method should be preferred, and why?**

This particular question cannot be answered in the abstract since the
answer would depend entirely upon what was seen and why the methods
differed. Beyond that, the Agency could use conditional independence
tests and relative performance in comparing simulated sites to provide a
different approach from standard goodness-of-fit that may help to
resolve ambiguous cases. Baysian Model Averaging (BMA) may also prove
useful in combining results from multiple plausible models.

**9c. What methodological options do you recommend for validating the
results of the modeling efforts? What are the strengths and limitations
of these options compared to others that might be available?**

Simulation-based validation discussed under 9a is also applicable here.

**9d. In lung cancer studies, it is expected that the value of α~s~
should be relatively close to 1.0. If the fitted value of any particular
value of α~s~ is substantially higher or lower than 1.0, should this be
taken to reflect that the data set giving rise to the value are somehow
flawed or are too uncertain for use, and should be excluded? If so, what
criteria would you suggest for recognizing values that warrant
concern?**

You are placing too much focus on the nature of this statistic and not
asking yourself what it really means. If this value is substantially
different than 1, it means you should return to your data set and
examine *why* this one data set is so different. Find what that
difference is and THEN decide whether it warrants exclusion from the
overall analysis. Does it change your inclusion/exclusion criteria? Do
you need to reevaluate all of the cohorts? The Agency should also focus
on ways to explain the differences that are related to the model
structure such as model specification errors, omitted explanatory
variables, and omitted confounders.

**9e. Is an examination performed of the residuals from the
meta-analysis a rigorous and scientifically valid assessment of
homogeneity?**

These types of analyses are not necessarily scientifically (or
statistically) valid unless certain assumptions hold, but they are a
reasonable starting point. It would also be useful to also display raw
data and replications of the raw data.

It would also be useful to do these same evaluations after making
predictions for new data sets such as that emerging from Libby, Montana.
The Agency could also attempt to apply the model to develop fiber
distributions; these could then be evaluated to see if they are
reasonable.

**10a. Is this "what if" approach for evaluating sensitivity
scientifically valid and useful?**

Sensitivity analyses are indeed a valid and useful technique for
understanding the importance of assumptions on the primary predictions
from the modeling exercise. However, many times we approach these types
of exercise without any idea of what we plan to do with the results. The
Agency needs to give some thought to what will be done with the results
of the sensitivity analysis.

The Agency has a fairly comprehensive approach to the sensitivity issue.
The only additional analysis we would suggest is that they consider
varying the actual form of the model being applied to these data. Some
careful thought should go into this decision for alternative models
prior to doing it, since alternate models may demand alternate prior
structures and the Agency would basically be conducting multiple
complete analyses of these data. The purpose of our suggestion is for
the Agency to get a solid "feel" for the impact of alternatives, not
necessarily to do multiple full analyses of these data.

Finally, rather than varying one input distribution at a time, try
varying the joint distribution of inputs. For example, one might attempt
to solve the optimization problem of choosing joint inputs to the model
(within their allowed or plausible joint distribution) to maximize the
prediction error from the model (using simulated data if necessary.) If
the maximized prediction error is small, then this would build
confidence in model predictions.

**10b. Are there other techniques that you recommend for characterizing
the sensitivity of the outcome to the data and methods that are used? If
so, what?** Model cross-validation provides another technique with a
somewhat similar goal (See Maldonado G, Greenland, (1996) Impact of
model-form selection on the accuracy of rate estimation. *Epidemiology*
7(1): 46-54).

**Individual Responses to Charge Questions**

***Dr. Tony Cox***

*Responses to Charge Questions 1, 5-10, 15*

1.  *Do you agree that the data are sufficient to indicate that such
    differences may exist and that an effort of this type is warranted?*

This is really more than one question.

To the question "*Do you agree that the data are sufficient to indicate
that such differences\[in potencies of different fiber types\] may
exist?*", my answer is: Yes.

To the question "*Do you agree that the data are sufficient to indicate
that an effort of this type \[i.e., refined risk modeling with exposure
variables that take into account differences in fiber characteristics\]
is warranted"*, my answer is: Yes, provided that relevant data on fiber
characteristics and risks are used. However, it may be very desirable to
develop additional data to support such refined risk modeling.

To the question "*Do you agree that the data are sufficient to warrant
the proposed binning strategies as valid approaches to risk
estimation?*, my answer would be: Not at present. More data, analysis,
and validation are needed.

A decision analysis perspective may be useful in considering what to do
next. It seems very plausible that different types of fibers (and
different types of asbestos) have very different potencies. Failing to
consider these differences could lead to a misallocation of cleanup
resources and priorities. The key decision analysis questions now are:

1.  Can EPA allocate resources and set priorities for Superfund cleanups
    *better* by using the proposed binning strategies than by not using
    them?

2.  Would other strategies (e.g., treating fiber characteristics as
    continuous variables and estimating the joint distribution of
    characteristics using nonparametric smoothing or other methods) work
    better than binning (and better than ignoring information about
    fiber characteristics)?

The most useful question is *not* (or at least should not be) "Does the
proposed approach yield correct answers with high confidence using
available data?", but rather: "Does the proposed approach support more
effective risk management and resource allocation decisions in deciding
which Superfund sites should receive highest priority for cleanup?" The
answer to the first (irrelevant) question may be no, while the answer to
the second may be yes. Simply ignoring differences between exposures
with very different compositions of asbestos fiber types, posing very
different health risks, is almost certainly *not* the most effective way
to improve EPA's ability to make good risk management decisions based on
available information.

Many public comments stressed that refined risk analysis is less
important than banning activities that create asbestos risks for workers
(or others). In light of these comments, it may be worth emphasizing the
obvious: EPA has asked us about *how to use data to set Superfund
priorities most effectively,* not about whether asbestos hazards should
exist to begin with.

*5a. Have all of the important sources of uncertainty in cumulative
exposure matrices been identified?*

(a) No. Other sources include: model form uncertainty, uncertainty about
    smoking (and other covariates) that may modify the effects of
    asbestos exposure, omitted explanatory variables, errors and biases
    induced by bin boundaries and discretization, and use of cumulative
    exposure (which is probably not a sufficient statistic for
    predicting risk.)

(b) The relevance of uncertainties about cumulative exposure matrices
    depends very much on uncertainties about the true exposure-response
    relation. Thus, uncertainties about dose-response (e.g., possible
    response thresholds or nonlinearities) should interact with
    uncertainties about cumulative exposure (e.g., is it above or below
    a threshold value). (See e.g., [Pierce JS, McKinley MA, Paustenbach
    DJ, Finley
    BL.](http://www.ncbi.nlm.nih.gov/pubmed/18324516?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)
    An evaluation of reported no-effect chrysotile asbestos exposures
    for lung cancer and mesothelioma. *Crit Rev Toxicol*.
    2008;38(3):191-214.) Ideally, uncertainties in cumulative exposure
    would be assessed in the context of knowledge and uncertainties in
    the exposure-response relation.

(c) There is some uncertainty about whether cumulative exposure provides
    a sufficient statistic for predicting risk. For example, suppose
    that some effects that contribute to induction of lung cancer or
    mesothelioma (such as apoptosis or production of specific cytokines
    in the lung) are disproportionately triggered by certain
    concentrations of asbestos (see e.g., [Nishimura Y, Nishiike-Wada T,
    Wada Y, Miura Y, Otsuki T, Iguchi
    H.](http://www.ncbi.nlm.nih.gov/pubmed/18179739?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum),
    Long-lasting production of TGF-beta1 by alveolar macrophages exposed
    to low doses of asbestos without apoptosis. *Int J Immunopathol
    Pharmacol*. 2007 Oct-Dec;20(4):661-71.) Then *cumulative exposure
    metrics may not contain enough information needed to accurately
    predict risks.* In other words, different detailed exposure
    histories that correspond to identical cumulative exposures might
    have significantly different effects on risk.

(d) Using discrete bins may introduce biases and uncertainties in
    estimated exposure-response relations (see e.g., Streiner DL.
    Breaking up is hard to do: the heartbreak of dichotomizing
    continuous data. *Can J Psychiatry* 2002; 47(3): 262-6.)

(e) Smoking (and perhaps other exposures) may change the effective
    cumulative exposure metric for asbestos, e.g., by changing the
    distribution, retention, and effects of fibers in the lung ([Vainio
    H, Husgafvel-Pursiainen K, Anttila S, Karjalainen A, Hackman P,
    Partanen
    T.](http://www.ncbi.nlm.nih.gov/pubmed/8143615?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)
    Interaction between smoking and asbestos in human lung
    adenocarcinoma: role of K-ras mutations. Environ Health Perspect.
    1993 Oct;101 Suppl 3:189-92. [Albin M, Pooley FD, Strömberg U,
    Attewell R, Mitha R, Johansson L, Welinder
    H.](http://www.ncbi.nlm.nih.gov/pubmed/8130851?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)
    Retention patterns of asbestos fibres in lung tissue among asbestos
    cement workers. Occup Environ Med. 1994 Mar; 51(3):205-11.). Thus,
    uncertainty about effective cumulative exposure should incorporate
    the effects of uncertainty about smoking and other modulators of
    asbestos exposure effects.

(f) Variability (interindividual heterogeneity) and uncertainty in
    cumulative exposure metrics should be modeled separately.

*5b. Is it appropriate to characterize uncertainty from each source as
an independent random variable, using professional judgment? If not,
then what instead?*

No. I doubt that uncertainties about these different sources are
statistically independent. (For example, group average exposure
durations and group average exposures for unbounded bins might be
correlated. Extrapolations from dust measurements to PCM-based
measurements and extrapolations from values based on PCM to values based
on bin-specific concentrations might be correlated with each other and
with use of bin midpoints to represent average exposures.)

Whether professional judgment is valid or useful in this context is an
empirical question. It can perhaps be addressed by independently
calibrating different experts and then eliciting joint uncertainty
distributions from them independently, using several different methods
(e.g., different factorings of the joint distribution into products of
marginal and conditional distributions). If the different sources are
not considered mutually statistically independent, then copulas or
conditional distributions might be used instead of modeling the
uncertainties for different sources as independent random variables.
Also, elicitation techniques can be used that do not assume specific
parametric forms for the uncertainty distributions.

Is there an advantage to quantifying the uncertainty for each source
separately, instead of quantifying uncertainty directly about exposure?
If so, can that advantage be captured in the form of known or assumed
algebraic constraints on the possible uncertainty distributions for
exposure? This might be easier and give tighter bounds than quantifying
uncertainty separately for each of a bunch of factors and then combining
them. (Here is a simple analogy. Suppose that *Y = aX* and *Z = bY*.
Each of *a* and *b* may be very uncertain. In that case, quantifying
uncertainty directly about the reduced parameter *a\*b*, e.g., by
regressing observed *Z* values against observed *X* values, may be
easier and more informative than quantifying uncertainty about each of
*a* and *b* separately and then combining.)

*5c and 5d* It seems to me that any selection of specific parametric
forms for univariate uncertainty distributions in somewhat *ad hoc*.
Also, expert estimates of different quantities (sources of error) are
not necessarily independent (since an expert who guesses too high on one
item may tend to guess high on others, for example.) Having experts
quantify uncertainty distributions directly for the output (exposure
metric) and then for the inputs (perhaps represented as a network of
variables from which the output can be calculated) may give more chances
to validate the internal consistency of judgments (and to resolve any
inconsistencies) than simply quantifying univariate distributions for
the individual sources of error and then combining them. (In general,
there is no unique way to combine marginal distributions to get a joint
distribution when independence cannot be assumed. Copulas and Bayesian
network representations of dependencies among estimated values of
variables may help to better structure and validate the uncertainty
analysis.)

*6a-c. Measurement error modeling.* I don't think this proposed approach
is sufficiently well explained, as there are several different
computational Bayesian methods for missing data that use MCMC and
related methods. I would be interested in seeing a more detailed
comparison and evaluation of different mainstream approaches (e.g.,
bias-correction algorithms, SIMEX, EM algorithm, Data Augmentation
algorithm for errors-in-explanatory-variables) and a more detailed
rationale for (and explanation of) the proposed approach.

**[References]{.underline}**

[Berry SM, Carroll RJ, Ruppert
D.](http://citeseer.ist.psu.edu/309111.html) Bayesian smoothing and
regression splines for measurement error problems. *Journal of the
American Statistical Association* 2002 March; 97(457):160-169

<http://citeseer.ist.psu.edu/309111.html>

[Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu
C.](http://www.stat.tamu.edu/~carroll/eiv.SecondEdition/index.php)
*Measurement Error in Nonlinear Models: A Modern Perspective, Second
Edition* Chapman & Hall. New York. 2006
<http://www.stat.tamu.edu/~carroll/eiv.SecondEdition/index.php>

7.  *Priors.* Yes, alternative priors should be used (e.g., uniform over
    different ranges, exponential, log-normal, etc.) to reveal the
    sensitivity of conclusions to the assumed priors. The chosen priors
    may be reasonable, but they are surely not the only possible
    reasonable ones. (A multiple-priors or robust analysis may also be
    useful; see <http://www.princeton.edu/~noahw/palgrave2.pdf>.)

8.  *Binning strategies.* Yes, multiple binning strategies should be
    evaluated. In addition, a binning-optimization approach might be
    used (with simulated data, if necessary) to discover what binning
    strategies minimize k-fold cross-validation errors. Rather than
    selecting a single strategy, more robust results might be achieved
    by using a Bayesian Model Averaging (BMA) approach that combines
    predictions from the several best binning strategies.

9.  *a and b. Goodness-of-fit and competing binning strategies.*
    Consider using *conditional independence tests* (is one binning
    approach more informative than another?), *model cross-validation*
    and *BMA* (or bagging) to combine results of multiple binning
    models, rather than just using goodness-of-fit criteria and
    selecting one binning strategy. (When model uncertainty is
    considered, as it ought to be, it often turns out that *selecting*
    any single model or strategy increases the risk of erroneous
    conclusions compared to that from *combining* results from several
    plausible models.)

*9c.* Validate the modeling approach and results using
*simulation-validation* (i.e., simulate a data set on fiber
characteristics and risks for several sites, apply the selected binning
strategy or strategies to the simulated data, then evaluate and compare
the performance of the selected strategies in setting cleanup priorities
among the simulated sites. Note that the correct answers are known for
simulated data, since all details on fiber characteristics and risks are
known.)

9d. If alpha is unexpectedly far from 1, consider model misspecification
as a possible explanation. (It may be more sensible to blame the model
-- and improve it -- rather than blaming the data.)

10. *What-if approach.* (a) Rather than varying one input distribution
    at a time, try varying the joint distribution of inputs. For
    example, one might attempt to solve the optimization problem of
    choosing joint inputs to the model (within their allowed or
    plausible joint distribution) to maximize the prediction error from
    the model (using simulated data if necessary.) If the maximized
    prediction error is small, then this would build confidence in model
    predictions. (b) Model cross-validation provides another technique
    with a somewhat similar goal.

*15a. How to estimate the uncertainty in lifetime cancer risk
predictions from uncertain bin-specific potency factors?*

*15b. How to incorporate exposure uncertainties at Superfund sites?* One
simple framework for answering both questions is: Use Monte Carlo
uncertainty analysis, i.e., sample from the joint distribution of
uncertain model-input quantities (including bin-specific potency factors
and exposure uncertainties), then push the sampled values through the
rest of the model (using a tool such as *Analytica*) to obtain a
corresponding distribution for predicted cancer risks.

Model uncertainty is a more difficult (and probably more important)
issue. The basic model of lung cancer used here is a relative risk
model, not a biologically-based model that predicts effects of exposures
on age-specific hazard functions by quantifying effects on underlying
mechanistic (e.g., cell transition and proliferation) rate parameters.
To make defensible predictions of effects on lifetime risks (and
uncertainties about those effects), it might be very desirable to use a
more biologically-based model of lifetime cancer risks. (The current
report's claim that model specification errors are unlikely to be
significant does not seem to me to be very self-evident. Considering
other model forms that more directly reflect relevant lung cancer and
mesothelioma biology might be valuable.) Without considering how risks
*change* over time, given exposure histories, there may be no sound
basis for deciding which sites to clean first. The goal of cleaning
sites is to reduce health risks, so the relation between reduced
exposure and reduced age-specific health risks is crucial for informing
EPA's decisions. The relative risk model is not necessarily the best
approach for quantifying this risk. TSCE and other models might give
better results, and should be considered as well.

***Dr. Murray Finkelstein***

Charge Question 1*: Do you agree that the data are sufficient to
indicate that such*

*differences may exist and that an effort of this type is warranted.*

I believe that animal and human data suggest that there are potency
differences related to

asbestos mineral type and dimension. I also believe that an effort by
the EPA to study these potency differences would be worthwhile.

I do not believe that an effort of the type outlined in the Proposed
Approach is warranted because it is based solely on human epidemiologic
data. With the exception of the recently published update of the South
Carolina textile plant, none of the human studies provide the data
required to analyse the proposed model. In essence, all of the input
data would consist of "guesses" and the output from the model would not
be credible.

Charge Question 2: *Comment on adequacy of these sections which serve as
the scientific*

*basis for the proposed dose-response approach.*

Section 4:

This section is an overview of human studies and covers non-cancer and
malignant effects. The section is brief and superficial, but covers
outcomes of asbestos exposure in humans.

The material is generally adequate. I will suggest a few editorial
changes:

Asbestosis \-- Paragraph 2.

The authors write that difficulty breathing is often accompanied by
"coughing and rales".

Coughing is a symptom, rales are a sound audible through a stethoscope.
Rales are often an early sign of asbestosis. I would suggest deleting
mention of rales (which few non-physicians will understand).

Pleural abnormalities

"Pleural effusions are areas"\.... no.

Pleural effusions are the fluid which collects in the pleural spaces.

Lung Cancer

"The risk of developing lung cancer from asbestos exposure is
substantially higher in smokers than non-smokers" \... no.

While it is certainly true that smokers exposed to asbestos have a
higher overall risk of lung cancer than do non-smokers, under the
multiplicative model, the asbestos-attributable risk of lung cancer is
unrelated to smoking status.

Charge Question 11

11a. *Are the study specific selection rules proposed above (Section
9.1) scientifically valid for the intended uses? Should any additional
selection rules be added?*

OSWER must decide whether to exclude internally-controlled studies. The
OSWER author has misinterpreted ALL internally controlled studies,
confusing Odds Ratios and Relative Risks with SMRs. There is no
confidence interval for the Reference Category and no value of **α** for
any of these studies.

Criterion 3 is unclear in that "atmospheric composition" is not defined.
If atmospheric

composition means the mix of asbestos fibre types, then this is
reasonably assessed. If

atmospheric composition means, in addition, the distribution of fibre
sizes (length and diameter) then most study cohorts don't provide enough
information to enforce this criterion. In practice, OSWER proposes to
"average over" the varying processes that create the varying fibre-size
distributions.

From an epidemiologic perspective, I would propose the addition of the
following new rules:

New rules: 1) Source of outcome (mortality) data must be the same for
study subjects and the reference population (not satisfied in Lacquet
study)

2\) Must stratify or account for latency. Since lung cancer or
mesothelioma excesses

are not usually seen before 15-20 years from start of exposure,
inclusion of early PYR will dilute apparent risk.

11b*. Is it appropriate to assume that all workers in a cohort are
exposed to the same*

*atmosphere with a constant composition (i.e. the mixture of asbestos
types and sizes is*

*constant) unless the authors report information to the contrary? If
this is not an*

*appropriate assumption, what alternative strategy would be available?*

It is not appropriate to assume that all workers in a cohort are exposed
to the same atmosphere with a constant composition. OSWER acknowledges
that the fibre size distribution is process dependent, and this has been
re-demonstrated in the recent work of Stayner and colleagues.

Averaging over work processes, as OSWER proposes to do, will mask the
very risk differences that OSWER is wanting to determine. The best
strategy, as implemented by Stayner and colleagues, is to undertake
TEM-based exposure-response analyses. Failing this, I think that the
best strategy is ignore the fibre size question and to compute
sector-dependent risks, eg, mining, textile, friction materials etc.

11c. *Should a set of minimal data quality requirements (other than
those above) be*

*established for inclusion of a study in the analysis? If so, what
elements of data quality*

*should be considered, and how should those data quality rules be
established?*

No, I agree that these will be difficult to establish, and that the
OSWER approach is acceptable.

11d. *For lung cancer, OSWER's approach requires that there be at least
2 exposure groups per study in order to impose some constraint on the
value of the study specific value of alpha. However, OSWER is proposing
to use data from 3 cohorts described by Henderson and Enterline (1979)
even though there is only one dose group for each cohort. This is
because a reliable estimate of alpha for the combined cohort can be
derived from the data of Enterline et al. Is this approach appropriate
and scientifically justifiable? If not, can you suggest an alternative
strategy for retaining the data from this important study or should this
study be excluded?*

I am not sure that there is a reliable estimate for **α** for the
combined cohort.

The Enterline cohort is composed of retirees from a large company with
many work locations.

There is no rationale to believe that a single value of alpha is
appropriate for all subcohorts.

Also, the deaths are coded to ICD 7.

OSWER has failed to subtract the mesothelioma deaths to get the number
of lung cancer cases.

Using linear regression, I calculate a different value for the intercept
(116), same as Enterline's.

From Poisson regression log(Obs/Exp) = 0.523 at CumExp = 0. ==\>
intercept is 1.68. Neither of these is the same as the alpha displayed
on Figure A3-4.

I suspect that the data from chrysotile only and asbestos-cement pipe
subcohorts is captured in the Hughes and Weill study.

As for values of **α** in other studies:

1\. Berry and Newhouse (A1). This is based upon case-control data. There
is no value of alpha.

2\. Albin at al (A12): The OSWER author treats this study as a standard
SMR-type study, which it is not. There was a defined industrial
reference population of 1233 men, not a national reference population.
Relative risk was computed by age and calendar period adjusted Poisson
regression modeling. The data presented in Figure A12-1 are thus not
meaningful and the value of **α** is not compatible.

3\. Libby vermiculite. The OSWER author treats this study as a standard
SMR-type study, which it is not. Comparisons were internal to the
cohort, not external to a national reference population.

Relative risk was computed by adjusted Poisson regression modeling. The
data presented in Figure A12-1 are thus not meaningful. There is no
measure of "expected deaths" for the referents. There is no 95%
confidence limit for the Reference Category and no value of **α**.

4\. Witennoom crocidolite miners. OSWER fails to recognize that this is
a case-control study. Figure A14-1 is thus not meaningful. There is no
confidence interval around the Odds Ratio for the reference category.
The concept of observed and expected is not well defined. The 90%
confidence Intervals are probably inaccurate and there is no value of
**α.**

5\. China Asbestos Products Factory. The OSWER author treats this study
as a standard SMR-type study, which it is not. Comparisons were internal
to the cohort, not external to a national reference population. Relative
risk was computed by Cox proportional hazards regression modeling. The
data presented in Figure A17-1 are thus not meaningful. There is no
measure of "expected deaths" for the referents. There is no 95%
confidence limit for the Reference Category and no value of **α**.

In summary: Many of the values OSWER derives for **α** are invalid.

11e. *One key assumption in any meta-analysis is that the data sets
included in the analysis are homogeneous. How should the assumption of
homogeneity be assessed prior to combining the data from the studies or
groups? If you recommend statistical testing, please provide guidance on
the reliability of a decision based solely on the test statistic. If
testing produces evidence of heterogeneity between some studies, what
steps can be recommended?*

I think that there is a more serious problem. I don't think that the
individual data sets are

internally homogeneous. There are different process-dependent fibre size
distributions, and OSWER proposes to smear over them by combining the
size distributions from the various processes.

There is also no reason to believe that the inter-study dose estimates
are consistent.

Charge Questions 12:

12a: *Are you aware of any studies that should be included in the model
fitting effort that*

*are currently excluded or omitted? If so, what are these studies, and
do they meet the*

*requirements for study inclusion?*

New report on South Carolina textile factory by Stayner et al in OEM

(doi:10.1136/oem.2007.035584) needs to be considered.

Finkelstein has also published a mesothelioma dose-response study:

Finkelstein M. Analysis of the exposure-response relationship for
mesothelioma among

asbestos-cement factory workers. Ann NY Acad Sci 1991;643:85-89.

Finkelstein M. The exposure-response relationship for mesothelioma among
asbestos-

cement factory workers. Toxicology and Industrial Health 1990;6:623-627.

Italian Balangero Mesothelioma

(Mirabelli D, Calisti R, Barone Adesi F, Fornero E, Merletti F, Magnani
C.

Excess of Mesotheliomas after Exposure to Chrysotile in Balangero,
Italy.

Occup Environ Med. 2008 Jun 4. )

12b. *Are there any studies that are currently proposed for inclusion in
the analysis that you believe should be excluded? If so, why?*

The Belgian (Lacquet) study should be omitted because the mortality data
for the cases (family doctor or social workers) are derived from a
different source than the reference data (death certificates).

12c. *In cases where the epidemiological data are not reported in the
form needed for use in the fitting effort, are the methods used to
estimate the exposures scientifically sound, and are the methods used
for characterizing the uncertainty in the estimates appropriate?*

See study specific comments below.

A1. British Friction Products Factory (Berry and Newhouse 1983)

Text of Draft Report

Fraction Amphibole: OSWER proposes a screening level value of 0.5% for
the average fraction amphibole in the workplace. However, exposure was
likely to be binary (Yes/No). OSWER proposal thus introduces
misclassification.

Estimating Cumulative Exposure: OSWER states that occupational histories
were extracted from employee personnel files and used to estimate levels
of cumulative exposure for each individual. This is not correct.
Cumulative exposures were estimated only for subjects in the
case-control study.

Smoking Data: OSWER states that the workers demonstrated a reduction in
smoking compared to the national population. This is not correct. Berry
and Newhouse speculated in their discussion that a reduction in smoking
might account for lower SMR, but would not influence case-control study.

Lung Cancer Results: Berry and Newhouse fit a linear model to the
case-control data. There was no significant dose-response relationship.

It must be realized that the dose-response results for this study are
based upon case-control and not cohort data. The Expected deaths in
Figure A1-1 are meaningless in the context of a case-control study.
There is no explanation how the author derived his Confidence Limits in
the Table. They are meaningless. There is no confidence interval about
the baseline in a case-control study.

Uncertainty in Particle Size data: It is likely that the particle size
distribution depends upon the job and the expenditure of mechanical
energy to disrupt the asbestos fibres. Combining data from mixing,
forming, and finishing, averages out all distinctions and is
noninformative.

Discussion: This study provides some estimates of risk in a primarily
chrysotile environment. There is little useful information about fibre
size distribution. OSWER has misinterpreted this case-control study.

A2. South Carolina Textile Plant (Hein et al, 2007)

Text of Draft Report

PCM - f/cc Conversion factors

Dement and McDonald disagree on conversion factors (by about a factor of
2). These are not reconciled.

Uncertainty in Particle Size Data

Data sets for different operations combined. Recent analysis by Dement
et al (OEM, 2007) demonstrate differences between operational areas.
Combining different operations smears out size distribution differences.

Discussion

This is one of the important studies in asbestos epidemiology. Risk, in
relation to fibre dimension data, is available in updated analyses from
Stayner and colleagues.

New report in OEM (doi:10.1136/oem.2007.035584) needs to be considered.

A3: Retirees from US Asbestos Products Factory

Draft Report Text

Conversion from mppcf to f-yr: Use default factor of 3 for all processes
and all jobs

Lung Cancer Results:

Henderson and Enterline coded to ICD 7:

ICD 162 and 163 include mesothelioma (table 3, 1987)

ICD 7 coding table:

162 Malignant neoplasm of bronchus and trachea, and of lung specified as
primary

162.0 Trachea

162.1 Bronchus and lung

162.2 Pleura

162.8 Multiple sites

163 Malignant neoplasm of lung, unspecified as to whether primary or
secondary

OSWER has failed to subtract the mesothelioma deaths to get the number
of lung cancer cases.

Using linear regression, I calculate a different value for the intercept
(116), same as Enterline's.

From Poisson regression log(Obs/Exp) = 0.523 at CumExp = 0. ==\>
intercept is 1.68. Neither of these is the same as the alpha displayed
on Figure A3-4.

Uncertainty in Particle Size data: Lumping all jobs, and factories,
together is suspect.

A4. Ontario Asbestos cement

There is a mesothelioma exposure-response study:

Finkelstein M. The exposure-response relationship for mesothelioma among
asbestos-

cement factory workers. Toxicology and Industrial Health 1990;6:623-627.
, and

Finkelstein M. Analysis of the exposure-response relationship for
mesothelioma among

asbestos-cement factory workers. Ann NY Acad Sci 1991;643:85-89.

A5: New Orleans Cement Products Manufacturing Plants

Uncertainty in Particle Size Distributions

Chrysotile: Averaging over jobs and processes

Amphibole: Combined mining/milling and insulation data sets

Discussion:

Major difficulty is with particle size distributions. All jobs and
processes assigned similar

distributions. Smearing over distributions averages out all distinctions
and is noninformative.

A6: Quebec Mines and Mills

Conversion Factor:

Gibbs (1994): "there were definite patterns with Membrane Filter/Midget
Impinger ratios

increasing from the predominantly dust-generating operations such as
drilling and crushing to fibre-releasing operations such as fibre
screening and bagging."

==\> substantial uncertainty in conversion factor

Cumulative Exposure:

OSWER states that the "study reports CE rather than CE10. This is not
correct. The study reports CE to age 55. This introduces comparability
issues with studies reporting CE or CE10.

SMRs are calculated from age 55 onwards.

Uncertainty in Particle Size Distributions

Chrysotile: Averaged over mining and bagging. Uncertainty stated to be
low, but differences are smeared out.

Amphibole: Data from talc mining and milling applied to chrysotile
mining and milling. Needs to be demonstrated that this is reasonable.

Discussion:

Major difficulty is with particle size distributions. All jobs and
processes assigned similar. Smearing over distributions averages out all
distinctions and is noninformative. Needs to be demonstrated that using
tremolite size distribution from talc mining is relevant for Quebec
chrysotile mining.

A7: Pennsylvania Textile Factory

ICD Coding of Malignant Neoplasms

Death certificates obtained and coded by nosologist. The nosologist
coded to ICD 7:

162 Malignant neoplasm of bronchus and trachea, and of lung specified as
primary

162.0 Trachea

162.1 Bronchus and lung

162.2 Pleura

162.8 Multiple sites

163 Malignant neoplasm of lung, unspecified as to whether primary or
secondary

164 Malignant neoplasm of mediastinum

McDonald et al classified lung cancer as ICD 162-164

In ICD 7, mesothelioma and lung cancer both coded to ICD 162. The
authors state that

there were 10 pleural tumours.

Lung Cancer Results:

The author of the OSWER report ignores the fact that Table 5 in the
McDonald report

(FigureA7-1) combined lung cancer + mesothelioma + ICD 160 + ICD 161

Mesothelioma:

Because of the combination of endpoints, many of these meso deaths were
included with the lung cancer analysis above.

The computations to produce a meso risk estimate are tenuous.

Bias Correction Factor CE vs CE10

McDonald et al present table 5 in relation to dust exposure accumulated
to 10 years before death. They do not state how they dealt with subjects
who were still alive.

Uncertainty in Particle Size Data

Again OSWER averages over departments and jobs, smearing out any
differences.

Discussion

OWSER has failed to recognize that mesothelioma deaths have been
combined with lung cancer and other respiratory sites in Table 5. The
numbers of observed lung cancer deaths are thus in error.

A8: Connecticut Friction Products

Cumulative Exposure Estimates

Estimates made for Departments rather than jobs

Mesothelioma Data

No cases observed

OWSER make approximations and assumptions about PYR and cumulative
exposure.

Uncertainty in Particle Size Data

Again OSWER averages over departments and jobs, smearing out any
differences.

A9: British Textile Factory

Uncertainty in Particle Size Data

Again OSWER averages over departments and jobs, smearing out any
differences.

A10: Italian Chrysotile Mine

Fraction Amphibole: OSWER guesses that amphibole was present despite the
authors report that none was present and that a fibrous silicate,
balangeroite, was present.

Lung Cancer Results: It is not clear from the published reports whether
or not PYR were

assigned to dust categories sequentially as work time accumulated, or
whether all PYR were attributed to final category. Failure to assign PYR
sequentially would flatten dose-response curve.

Mesothelioma Results: OSWER estimates duration and exposure data. The
Piolatto data are superceded by those of Mirabelli et al 2008.

(Mirabelli D, Calisti R, Barone Adesi F, Fornero E, Merletti F, Magnani
C.

Excess of Mesotheliomas after Exposure to Chrysotile in Balangero,
Italy.

Occup Environ Med. 2008 Jun 4. )

Uncertainty in Cumulative Exposure for Meso: Not certain whether authors
moved PYR

through exposure categories or assigned all PYR to final one achieved

Uncertainty in Fraction Amphibole: There is no tremolite, but another
fibrous silicate in the ore. This was said to be 0.2 - 0.5% of the mass
of chrysotile.

Applying size data for tremolite is inappropriate.

Uncertainty in Particle Size Data

Again OSWER averages over departments and jobs, smearing out any
differences.

A12: Swedish Cement Plant

Relative Risk: The OSWER author treats this study as a standard SMR-type
study, which it is not. There was a defined industrial reference
population of 1233 men, not a national reference population. Relative
risk was computed by age and calendar period adjusted Poisson regression
modeling. The data presented in Figure A12-1 is thus not meaningful.
There is no measure of "expected deaths" for the referents, and where
the author derives a RR of 1.8 for the referents is a mystery. He has
apparently misinterpreted Table 2. Allocation of Observed deaths based
upon person-years is not necessarily valid as the relative risk is a
regression-adjusted estimate.

Uncertainty in Particle Size Data

Again OSWER averages over departments and jobs, smearing out any
differences.

The assignment of particle size data for the amphiboles is highly
speculative.

A13: Libby Montana Vermiculite Mine

Pg A13-4. OSWER states that exposure is expressed in terms of f/cc
lagged by 10 yrs. This is not true. There is no lag.

Relative Risk: The OSWER author treats this study as a standard SMR-type
study, which it is not. Comparisons were internal to the cohort, not
external to a national reference population. Relative risk was computed
by adjusted Poisson regression modeling. The data presented in Figure
A12-1 is thus not meaningful. There is no measure of "expected deaths"
for the referents. There is no 95% confidence limit for the Reference
Category and no value of **α**.

A14: Wittenoom Australia Crocidolite Miners

OSWER fails to recognize that this is a case-control study. Figure A14-1
is thus not meaningful. There is no confidence interval around the Odds
Ratio for the reference category. The concept of observed and expected
is not well defined. The 90% confidence Intervals are probably
inaccurate.

OWSER estimates cumulative exposure by multiplying concentration by
estimate of average duration. This is bound to introduce
misclassification.

Uncertainty in Particle Size Data

Again OSWER averages over departments and jobs, smearing out any
differences. The statement that the uncertainty in f~size~ is low is not
very credible.

A15: Belgian Asbestos Cement Factory

Issues:

Cause of death not based on official records, but from family doctor or
social workers who visited relatives. This is unreliable.

Exposure concentration: Fibre counts available 1970-76. Exposures
estimated 1928-77 using a logistic function. Would expect levels to be
better modeled by step-function with changes occurring with ventilation,
layout, or work practice changes.

Recommendation: This study should be omitted because the mortality data
for the cases (family doctor or social workers) are derived from a
different source than the reference data (death certificates).

A16: Austrian Cement Factory

Lung Cancer SMRs. Figure 16-1 uses smoking-adjusted SMRs. This is one of
the few studies where this desirable adjustment is possible, but the use
of smoking adjusted SMRs is not consistent with the SMRs abstracted from
the other studies in the database.

Uncertainty in Particle Size Data

Again OSWER averages over departments and jobs, smearing out any
differences. The statement that the uncertainty in f~size~ is low is not
very credible.

A17: China Asbestos Products Factory

Relative Risk: The OSWER author treats this study as a standard SMR-type
study, which it is not. Comparisons were internal to the cohort, not
external to a national reference population. Relative risk was computed
by Cox proportional hazards regression modeling. The data presented in
Figure A17-1 are thus not meaningful. There is no measure of "expected
deaths" for the referents. There is no 95% confidence limit for the
Reference Category and no value of **α**.

***Dr. Andrew Gelman***

Below are my responses to the charge questions. Before answering these,
let me emphasize that I am not an asbestos expert and, although I have
looked at the EPA report ("Proposed approach..."), I have not looked at
the original research that is cited in the report. Also, as you can see,
I've focused my comments in the areas where I am more expert.

1\. Yes, I agree that the data indicate that differences in effects of
different sorts of asbestos may exist and that an effort of this type is
warranted.

2\. The scientific basis for the approach seems reasonable to me. There
are certainly challenges here in combining data from studies that have
different sorts of measurements. In general, I'd recommend including
more categories rather than fewer. I can't comment on the biomedical
models of health effects.

3a. The mixture model approach makes sense to me. I cannot really
comment one way or the other on the specific risk models that are being
used (the models of risk given exposure and how this is affected by the
time of exposure).

3b. I don't have any particular suggestions of other model forms to be
investigated; however, if other reasonable models are proposed, I agree
that they should be looked into.

3c. Interactions of smoking and other cancer risks can be large. For
example, I know that the added cancer risk from radon exposure is much
higher among smokers than nonsmokers. If the data are available, it
would make sense to fit separate models for smokers and nonsmokers.
Otherwise it might make sense to use a model in which the added risk
from asbestos is higher (by some multiple) for smokers than for
nonsmokers.

4a. The recommendation in Section 8.3 appears to be to fit separate
rates for each group in each study. This makes sense to me. As noted in
Section 8.3, this allows more direct modeling of the data. Perhaps this
approach has a disadvantage if it is not easy to replicate the methods
that were used in preparing the derived statistics for each study.

4b. The Poisson likelihood might be reasonable; however, in practice we
almost always use overdispersed models. If the Poisson model is indeed
used, it is important to check for overdispersion in the differences
between data and fitted models. In some settings, there is a group-level
variance parameter which catches the extra-Poisson variation and
eliminates the need for an overdispersed model. That may be happening
here (through the alpha_s and Q_sg parameters in the model in Section
8.6.1) but I am not completely sure.

5a. Section 8.4 discusses many potential sources of uncertainty. I do
not know of other sources that should be accounted for, but, again, I am
not an expert here.

5b. Characterizing the uncertainties as independent probability
densities can't be correct, but perhaps it is a reasonable thing to do
in practice. The use of "professional judgment" is problematic because
such judgments are generally found to be overconfident (that is, often
not containing the true values); however it's not clear what the
alternative is. It could be useful to propagate each source of
uncertainty to see how it contributes to the uncertainty in the final
recommendations.

5c. The general approach for characterizing uncertainty used in Appendix
C seems reasonable, but, again, with all the details, I could imagine
that something important could have been left out.

5d. The assumption that errors combine in a multiplicative fashion could
be reasonable. I am not sure how this assumption could be tested given
the available data.

6a. I'm not sure why "measurement error models" are labeled as "weighted
regression models." My impression is that measurement error models are
simultaneous equation models (e.g., a regression model for y given x,
along with a measurement model for x.observed given x, where in both
cases, x is the true predictor and x.observed is the predictor measured
with error). I don't see where "weighted regression comes in."

6b. I don't think weighted likelihood analyses are helpful here. For one
thing, there's no direct way to get standard errors from weighted
likelihood analysis; for another, the weights should, to be correct,
include model error as well as measurement error, so that the
appropriate weights depend on estimated parameters such as group-level
variances.

6c. I think the Bayesian approach is most appropriate.

7\. I don't fully understand the prior distributions in Section 8.6.2.
It says that the alphas are likely to fall between 0.5 and 2, but then
it puts a Uniform\[0.1, 10\] prior distribution on each. I have two
suggestions:

\(a\) Check the inferences for the parameters after fitting the model.
If the estimates are far from the originally suggested range of (0.5,
2), then see what's going on. Is it just a matter of there being a large
posterior uncertainty? If so, perhaps more prior information would be
useful. Or are the alphas estimated with precision to be far from their
prior range (e.g., an estimate such as 4.0 with posterior sd of 0.5)? If
the latter, you have to think harder about what is going on here. I have
a similar comment for the priors on the KL_b and KM_b parameters.

\(b\) Consider a hierarchical model for these parameters. If you have
many alpha_s parameters, you can give them a prior dist with parameters
estimated from the data. That could make more sense than giving them
independent noninformative distributions.

8\. The bins seem to be chosen in a somewhat ad hoc, data-based manner.
This is fine, but I'd suggest including more bins rather than fewer. I
don't see the advantage in reducing the number of bins. I mean, I can
see why you wouldn't want hundreds of bins, but I'd think that 5 or 10
bins wouldn't be a problem.

9\. Again, I wouldn't focus so much on the binning strategy. I'd choose
a reasonably large number of bins that could include whatever variation
might be expected, then fit the model from there. I could see going back
to the binning question at the end, when you get to policy questions,
but I think it makes more sense to estimate for many bins and then do
some smoothing at the end, rather than trying to combine bins in the
main analysis.

Beyond this, I'd recommend posterior predictive checks, i.e., simulating
replicated datasets from the model and seeing if they look like actual
data. To do this, it would be first necessary to create some useful
graphical displays of the data that are being used to fit the model.
Analysis of residuals is fine, but it would be useful to also display
raw data and replications of the raw data.

10\. The idea of sensitivity analysis is fine although in practice I
don't know that much is learned from these things. Still, I think it has
to be done.

11\. I don't really have any comments on the rules for selecting studies
in the meta-analysis. I defer to others who are more familiar with the
biomedical literature.

12\. I defer to others on whether there are other relevant studies of
lung cancer or mesothelioma that should be considered.

13\. The extrapolation from dust to asbestos might be questionable but I
don't know that it can be avoided. You could see what happens if these
studies are excluded entirely, but if that leaves inferences that are
too vague, maybe you have to go with the assumption.

14\. I can't judge the validity of the assumptions underlying the
exposure interpolations, but it seems reasonable to use the published
data if that is the closest thing available.

15\. Given the methods that are being used, the uncertainty about
lifetime cancer risks can be summarized using simulations. However, I
would also recommend estimating life-years lost, not just lifetime
cancer risks. That way, costs can be expressed as dollars per life-year,
which is a slightly different measure than dollars per life. Also could
be broken down as estimated lives and life-years lost among smokers and
among non-smokers.

***Mr. John Harris***

General response

The microscopic techniques used by EPA for risk evaluations have both
benefits and limitations. The chief advantage to microscopy is to supply
important size data information used for binning purposes. There are
some modifications to these methods that critical information for risk
assessment researchers.

PCM has historically been criticized for not providing any information
of the fiber types counted. For mineral identification of asbestos in
bulk materials, polarized light microscopy (PLM) has been the standard.
However, for smaller fibers, PLM is limited since the analysis is done
at 100x as compared to 400x by PCM. Recently, there are new innovations
that allow modified PCM microscopes to utilize the mineral
identification techniques of PLM on a PCM microscope. This can be a
useful tool for investigators that have historical PCM slides that could
be recounted to determine chrysotile:amphibole ratios at the PCM level.
For studies with limited funding, this might be a good alternative
approach to standard PCM alone.

TEM is the best approach for studies needing definitive analysis as
described in the documentation provided for this advisory board.
Typically for risk assessments, the ISO method (ISO 10312) is preferred
as it offers the most analytical intensive method to define and count
all fibrous particles regardless of size. The counting rules can be
modified to allow analysts to include all fibers that would be normally
counted by PCM, or PCM equivalent sized fibers.

One drawback to TEM is the reduced amount of area analyzed. Most
asbestos fiber size distributions typically have very few long, thin
fibers compared to a very high number of shorter fiber sizes. The
reduced analytical area covered by TEM limits the detection of longer,
thinner fibers under these conditions. Therefore, a stratified analysis
approach makes better sense for TEM studies for historical or current
risk assessments. The stratified analysis would use both lower
magnification to search only for longer fiber sizes and higher
magnification to analysis for all fiber sizes. The analyzed area at low
magnification would be relatively large while the analyzed area for high
magnification would be standardized to an established sensitivity
required.

TEM studies provide a wealth of information of each mineral type
present. TEM identification of amphiboles requires more rigorous
mineralogical identification using the International Mineralogical
Association (IMA) protocol proposed by Leake (1997). TEM chemical
systems must be calibrated to better standards, such as microprobe
standards, in order to more accurately identify differences in mineral
types, especially with amphibole minerals.

In addition to having the correct chemical identification of the mineral
type, accurate diffraction data is needed to differentiate minerals with
similar chemical compositions to the mineral types of interest. Use of
internal aperture standards is not sufficient. Measuring diffraction
spacings over several rows or diffraction points are needed to
accurately measure crystallographic dimensions to differentiate
different mineral classes. This requires more astute attention to
details for TEM analysts than routine samples containing commercial
grade asbestos.

Some of the ISO counting rules may affect fiber dimensions and bias
fiber dimensions for binning. For example, fibers with one end
sufficiently embedded in a particle are given an estimated fiber length
based primarily on the size of the particle and not the fiber itself.
Fibers intersecting with TEM grid bars are recorded as twice their
visible length. Complex structures with more than 9 individual fibers
are noted as "+" instead of allowing an analyst to measure and count all
structures. For complex structures with more than 5 substructures, the
additional structures after the first 5 are recorded as "residual" and
are estimated based on an average length and width.

Using a TEM combined with SEM capabilities (STEM) solves most of these
fiber dimension problems. This enhanced capability allows an analyst to
determine whether a fiber is actually embedded in the particle or lying
free on the surface or under a particle. By looking at the surface
features, we often see round particles composed of asbestos fibers
tightly bound that are not countable by any method. The ability to
analyze a complex fiber arrangement completely by diffraction, chemistry
and surface features provide an accurate description of that structure
for binning and identification purposes. Using SEM only for an
evaluation of Superfund sites is problematic since only surface features
and chemistry are available. Without the higher resolution of TEM,
higher penetration of electrons through the sample at higher energies
and diffraction capabilities, some particles may be identified
definitively by SEM.

I hope this helps guide EPA in its efforts to accurately assess the
environmental exposure at Superfund sites.

*[Charge question 14a:]{.underline}*

*Are the point estimates and uncertainty distributions for the fraction
amphibole term proposed for each study scientifically valid?*

Defer to statisticians, risk assessors.

*[Charge question 14b:]{.underline}*

*Is it scientifically valid to use surrogate TEM data to estimate
bin-specific concentrations and exposure values in studies where these
data are not reported?*

No comment

If not, what alternative approach could be followed , or what additional
data would be helpful?

No comment

*[Charge question 14c:]{.underline}*

*Are there any additional bi-variate TEM data sets available that would
be useful in this analysis?*

Some additional variables could include properties such as surface area
and mineralogy.

*[Charge question 14d:]{.underline}*

*Are the point estimates and uncertainty distributions for the fraction
amphibole term scientifically valid?*

No comment

*[Charge question 14e:]{.underline}*

*Can you suggest any ways to improve the process used to identify select
the best available matching TEM data set(s) to a workplace?*

Eliminate impinger data and PCM data as surrogate data for TEM. Use only
TEM data sets only.

How sensitive would the model output be to these changes?

*[Charge question 14f:]{.underline}*

*Would the model benefit by establishing a common lower cut-point in
diameter to normalize the lower detection limit across studies?*

*[Charge question 14g]{.underline}*

*Do the studies included in the model have surrogate data of sufficient
quality and similarity to expected exposure conditions to support the
model?*

No comment

If not, what alternative approach could be followed?

*[Charge question 14h:]{.underline}*

*Are the PDFs described in Appendix C to characterize the uncertainty in
the extrapolation of TEM particle size data from one location to another
sufficient and helpful in understanding the implications of the method
used?*

No comment

*[Charge question 14i:]{.underline}*

*Are the extrapolation techniques used on the raw TEM data sets to meet
bin definitions (e.g., 0.4 µm diameter) transparent, objectively
presented and scientifically valid?*

No comment

*Are there alternative techniques that you would recommend?*

I would recommend that EPA either consider modifying ISO TEM methodology
to better fit a more accurate identification of particle types and sizes
common to Superfund sites or develop TEM methodologies of their own.
Described below are some observations of our experiences with
difficulties encountered at some of these sites.

a.  Current use of the ISO counting rules for PCM equivalency using TEM
    procedures can create biases. These biases include:

    i.  ISO 10312 method does not include nonasbestos fibers in its
        counts. They are noted as comments. Investigators should be
        aware of this bias when comparing ISO data with PCM data.

    ii. Matrices (fibers embedded in particulates) need surface imaging
        capabilities (i.e., STEM) to accurately determine the true fiber
        length as well as whether the fiber is truly embedded or simply
        landed on the surface (see image below). For fibers lying
        underneath particles, the TEM grid can be turned over and the
        fiber visualized on the bottom surface of the replica. There may
        be possible methods to determine fiber length within a matrix if
        needed. Under ISO counting rules, this structure length would be
        measured by the analyst at more twice the true length since more
        than 30% of the virtual length of the protruding fiber is
        obscured. By increasing a fiber length, the aspect ratio of the
        fiber is also increased and incorrectly reported.

> ![](media/image4.png){width="1.5590277777777777in"
> height="1.8791666666666667in"}
> ![](media/image5.png){width="2.297222222222222in"
> height="1.7333333333333334in"}

iii. For investigations of activity-based sampling involving risk
     assessments, our laboratories looks for noncountable particles with
     compacted fibrous asbestos that would not be included in either PCM
     counts nor ISO counting rules. These types of unique asbestos-laden
     particles need to be included in counts until future investigations
     determine the health effects of these types of particles. Shown
     below is an example of one of these particles from a
     naturally-occurring asbestos site in Swift Creek, Washington:

> ![](media/image6.png){width="2.0in"
> height="1.5834645669291338in"}![](media/image7.jpeg){width="2.0in"
> height="1.5834645669291338in"}

iv. Structures touching grid bars are given a measurement of twice its
    visible length. Most often, this creates a longer structure than
    actually exists.

> ![](media/image8.png){width="1.9256944444444444in"
> height="1.4388888888888889in"}
> ![](media/image9.png){width="1.3041666666666667in"
> height="1.5611111111111111in"}

v.  There are limitations in recording data for complex structures that
    may bias reported numbers of fibers detected by PCM but not recorded
    effectively in ISO method studies. Substructure (or "total
    structure") counts larger than 10 are given a "+" and are no
    indication of the actual fiber loading. When more than 5 total
    structures are part of a primary structure, a designation of
    "residual" is applied and total structure counts are estimated. All
    total structures should be included. For example, the chrysotile
    structure below from the Atlas mine in California shows a complex
    structure that would be defined primarily using a "+" designation
    instead of detailed numerical values.

![](media/image10.jpeg){width="2.9925196850393703in"
height="2.1838582677165355in"}![](media/image11.wmf){width="0.7338582677165354in"
height="3.818897637795276e-2in"}

vi. All structures should be digitally imaged for future investigators
    as binning categories and other morphological characteristics are
    modified.

```{=html}
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```
1.  TEM EDS chemical analysis of amphiboles need to be standardized
    based on mineralogical methods such as the International
    Mineralogical Association method by Leake from 1997 There is no
    standardization among asbestos labs providing analysis which can
    create biases between PCM and TEM results.

The determination of asbestiform or nonasbestiform varieties of minerals
should not be done during the analysis by the analyst. Instead, it must
be done post-analysis by epidemiologists. Since the definition of
cleavage fragments has been and will continue to be a moving target, all
potential structures that fit 3:1 aspect ratio with minimum fiber
lengths should be included in counts for future binning to determine
suitability for current and future studies.

***Dr. Karl Kelsey***

*1) Do you agree that the data are sufficient to indicate that such
differences may exist and that an effort of this type is warranted?*

The committee chose to rephrase this question. I believe this to be a
wise choice, as the question, as posed is confusing. I would agree that
the epidemiologic data are consistent with the hypothesis that asbestos
mineral type and particle dimensions have different potency for
mesothelioma. This is based upon the multiple, consistent epidemiologic
observations demonstrating differences in the occurrence of MPM in
workers whose exposure was known to be to different types of asbestos.

However, I do not agree that the data are sufficient to indicate that
differences in potency by mineral type or dimension exist in the case of
lung cancer. The data are not consistent for lung cancer, with the South
Carolina workers particularly standing out as a group primarily exposed
to crysotile with very dramatic lung cancer risks.

In sum, the data are coherent for the suggestion that fiber type and
dimension are important for MPM

I am (and I believe the committee was) also supportive of an effort to
represent this scientific consensus in the risk assessment process. The
approach that is proposed, however, is flawed. The flaws are potentially
fatal ones. Chief among the flaws is the attempt to use the occupational
exposure data. These data are sparse and truly not amenable, in the vast
majority of cases, to the multiple binning approach being proposed. Even
the two bin approach is problematic, based upon the nature of the
exposure data.

Finally, and importantly, the document presented for review by the
committee was profoundly inadequate with respect to its representation
of the animal and mechanistic data. Indeed, for example, there are data
that suggest that there may be heritable susceptibility to the action of
asbestos in generating MPM (see multiple publications from
Carbone,.including: [Dogan AU, Baris YI, Dogan M, Emri S, Steele I,
Elmishad AG, Carbone M]{.underline}; Genetic predisposition to fiber
carcinogenesis causes a mesothelioma epidemic in Turkey. Cancer Res.
2006 May 15;66(10):5063-8 and [[Roushdy-Hammady I, Siegel J, Emri S,
Testa JR, Carbone
M.]{.underline}](http://www.ncbi.nlm.nih.gov/pubmed/11273069?ordinalpos=48&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)
Genetic-susceptibility factor and malignant mesothelioma in the
Cappadocian region of Turkey. Lancet. 2001 Feb 10;357(9254):444-5). That
is, there may be a susceptible subgroup of the population and, if that
is the case, assuming that this subgroup responds to asbestos in the
same fashion as the population as a whole is not conservative.

The work by the EPA, while laudable in its intent, is flawed in its
execution. I am supportive of going forward with investigation of the
impact of fiber type and dimension on mesothelioma risk, but any
modeling exercise must be clearly labeled as what it is -- the document
reviewed has only the very loosest of biologic ties, does not consider
susceptibility, includes uncertainty in exposure assessment that cannot
conceivably be scientifically estimated and, in essence, seeks only to
fit epidemiologic data to a better underlying mathematical construct.
This effort is well intended and is an earnest and important research
enterprise but it is not supportable as an endeavor that will contribute
to better estimation of disease risk; that is, it is not an effort that
is in the interest of overall public health at this time.

*2) Please comment on the adequacy of the overview of EPA's OSWER Draft
Report.*

The sections of the report that serve as the scientific basis for the
proposed dose-response modeling are very poor. As noted in my response
to charge question 1, I would advocate that this document acknowledge
its tenuous relationship to biologic mechanism and not include the
mechanistic sections. They are manifestly inadequate as written.

Simple examples: nowhere in this document is there any reference to the
now large body of data that suggests that there may be a portion of the
population that is susceptible to the toxic effects of asbestos
(Carbone, M -- multiple references above). The data describing the
toxicology and "mode of action" of asbestos ignores completely the fact
that both lung cancer and mesothelioma are known to harbor significant
epigenetic gene alterations that contribute to the genesis of these
diseases ([[Suzuki M, Toyooka S, Shivapurkar N, Shigematsu H, Miyajima
K, Takahashi T, Stastny V, Zern AL, Fujisawa T, Pass HI, Carbone M,
Gazdar
AF.]{.underline}](http://www.ncbi.nlm.nih.gov/pubmed/15592515?ordinalpos=26&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)Aberrant
methylation profile of human malignant mesotheliomas and its
relationship to SV40 infection. Oncogene. 2005 Feb 10;24(7):1302-8.;
[[Christensen BC, Godleski JJ, Marsit CJ, Houseman EA, Lopez-Fagundo CY,
Longacker JL, Bueno R, Sugarbaker DJ, Nelson HH, Kelsey
KT.]{.underline}](http://www.ncbi.nlm.nih.gov/pubmed/18310086?ordinalpos=4&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)Asbestos
exposure predicts cell cycle control gene promoter methylation in
pleural mesothelioma.Carcinogenesis. 2008 Feb 28.;[[Destro A, Ceresoli
GL, Baryshnikova E, Garassino I, Zucali PA, De Vincenzo F, Bianchi P,
Morenghi E, Testori A, Alloisio M, Santoro A, Roncalli
M.]{.underline}](http://www.ncbi.nlm.nih.gov/pubmed/17920725?ordinalpos=5&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)Gene
methylation in pleural mesothelioma: correlations with
clinico-pathological features and patient\'s follow-up.Lung Cancer. 2008
Mar;59(3):369-76.;[[Tsou JA, Galler JS, Wali A, Ye W, Siegmund KD,
Groshen S, Laird PW, Turla S, Koss MN, Pass HI, Laird-Offringa
IA.]{.underline}](http://www.ncbi.nlm.nih.gov/pubmed/17659810?ordinalpos=6&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)DNA
methylation profile of 28 potential marker loci in malignant
mesothelioma.Lung Cancer. 2007 Nov;58(2):220-30.

[[Pu RT, Sheng ZM, Michael CW, Rhode MG, Clark DP, O\'Leary
TJ.]{.underline}](http://www.ncbi.nlm.nih.gov/pubmed/17636483?ordinalpos=7&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum)Methylation
profiling of mesothelioma using real-time methylation-specific PCR: a
pilot study.

Diagn Cytopathol. 2007 Aug;35(8):498-502.)

There are also omissions of major animal works germane to the question
of how fiber size and type impact mesothelioma and lung cancer risk.

*3a) Do you agree that the lung cancer and mesothelioma risk models that
are proposed are a scientifically valid basis for this fitting effort?*

This is a complex question. The models are an attempt to parse crude
data in a parsimonious fashion consistent with a set of assumptions.
Uncertainty in exposure makes testing for validity impossible. Fitting
the data are possible, but this does not necessarily imply the models
are valid.

*3b) Should additional model forms be investigated?*

Yes; Dr. Peto had several suggestions that might be profitably pursued.

*3c) For lung cancer, the current risk model is multiplicative with the
risk from smoking and other cause of lung cancer. Should the nature of
the interaction between asbestos and smoking be investigated further?*

Given that there is no consensus on the mode of action of asbestos in
generating the multiplicative interaction with tobacco, additional
investigation is most certainly needed. The epidemiology strongly
suggests that the interaction is multiplicative, but awaits data that
might allow for a more complete description of the biology and the
nature of this association.

*If so, how should this be done?*

The research community might be asked to investigate the nature of this
interaction in grant funded research with and RFA that precisely
articulated the questions of interest.

*Do you think the model would be sensitive to additional quantification
of the interaction between smoking and asbestos?*

Yes. Any synergy will skew models and be very sensitive to this
interaction in a modeled dose-response.

*4a) Is fitting at the group level (based on the number of cancer cases
observed) preferred to fitting at the study level (based on the
study-specific KL or KM values)?*

Fitting should be done in a fashion that is least prone to propagate
error and misclassification.

*What are the advantages and disadvantages of this approach?*

As above**.**

*4b) If so, is it scientifically justifiable to use a Poisson likelihood
model for the observed number of cases in each group?*

Poisson models seem appropriate for sparse data.

*8a) Do you agree that multiple binning strategies should be evaluated,
or do you believe that a physiological basis exists that can be used to
identify a particular set of length and width cutoffs that should be
assessed?*

I believe that multiple binning strategies are an attractive
intellectual advance in risk assessment. However, there is really only
one study that has compared anywhere near the number of exposure
assessment techniques that would be needed to accurately assess the
effects of using a multiple binning strategy. On the face of it, I
believe that this is not possible. It will amplify uncertainty in
unknown ways and is simply not good science.

*If so, what would these length and width cutoffs be, and can these bins
be implemented considering the limitations in the available TEM particle
size data sets?*

The human data primarily address below and above 10 um only. The data
cannot address width adequately, as prior methods were unable to assess
small width fibers. Hence, this cannot be done with any scientific
validity.

*8b) Are there any of these strategies that you feel do not warrant
evaluation?*

Yes**.**

*If so, why?*

The binning approach is not feasible as it propagates uncertainty, is
based upon selective and incomplete data, has not been subjected to
sensitivity analysis, and does not consider susceptibility.

8c) Assuming that fitting is performed using Bayes-MCMC, OSWER is
proposing that a comparison of goodness of fit between different
strategies be based on the Bayes Factor.

Do you agree that this is a statistically valid method for comparing
binning strategies?

NA

*Are there any other comparison methods you would recommend?*

No.

*8d) Is it important to account for differences in the number of fitting
parameters (bin-specific potency factors) when comparing 1-bin, 2-bin,
and 4-bin strategies to each other?*

NA.

*If so, how should that be done?*

***Dr. Paul Lioy***

*Section 8.4 -- Characterizing Uncertainty in Exposure Data*

*5a. Have all of the important sources of uncertainty in cumulative
exposure matrices been identified? If not, what other sources should be
accounted for?*

Based upon the observations of Dr. Peto, no. However, the most pressing
issues relate to the need of a defined research program, especially for
bullets 7-9. Experimental data is required to provide values other than
default factors for long term applications of the Bin approach to
Asbestos at Superfund sites and other fibers. For Asbestos alone, at
superfund sites, the ratio of dust to fibers will be variable and not
all associated with the asbestos fibers. Thus the need for data on
actual fiber counts in the selected Bins, or any other Bins.

*5b. Is it appropriate to characterize the uncertainty from each source
in terms of an independent probability density estimated using
professional judgment? If not, what alternative approach is suggested?*

Yes, this is fine for the initial application, but not necessarily
reality. For the most important issues you will need better designed
studies, and appropriate data collected to minimize uncertainties. You
will be hard pressed to make totally justifiable assumptions, thus the
need to do research at this time.

*5c. Are the general strategies for selecting distributional forms and
parameter values described in Appendix C (and applied in Appendix A)
appropriate for characterizing uncertainty in exposure matrices? If not,
what alternative strategies are recommended?*

Yes, comprehensive.

5d. Based on the assumption that each of the sources of error is
independent, OSWER is proposing an approach where the errors combine in
a multiplicative fashion. Please comment on the scientific validity of
this approach and provide detailed suggestions for other approaches
OSWER should consider.

For uniform processes the error can be assumed to be independent, but it
is not necessarily true for asbestos at superfund sites, which is a
mixed waste from disparate origins.

Sum of absolute values of errors is more conservative, as well as the
root mean square of the uncertainties.

*Section 8.5. Fitting Approach*

*6a. Is it appropriate to account for measurement error in the exposure
data by using "measurement error" models (weighted regression methods)?
If so, how would the weights assigned to each exposure value be
assigned?*

Yes, weighing is reasonable but should be conditional based upon those
variables having the highest impact on the estimates of exposure.

*6b. Is the assignment of a PDF for data quality sufficient or should
data quality be factored into a weighted likelihood analysis?*

No comment.

*6c. Do you think that the proposed strategy of fitting the risk models
to the available epidemiological data using Bayes-MCMC is scientifically
justifiable? If not, what alternative strategy do you suggest, and why?*

Yes. An excellent application for a modeling procedure that will improve
results and evolve understanding *over time based upon new data*. Based
upon the discussions at the meeting, new data is essential since dust to
PCM ratios from industrial and occupational studies are not directly
applicable.

*Section 8.62 -- Specification of Priors*

*7. Do you think that the proposed strategy of fitting the risk models
to the available epidemiological data using Bayes-MCMC is scientifically
justifiable? If not, what alternative strategy do you suggest, and why?*

The Bayes approach requires appropriate probability density functions.
Given the level of understanding a uniform distribution with wide
boundaries is a reasonable start for sensitivity analysis. These need to
be augmented with more appropriate data on asbestos at superfund sites
to achieve a normal or log normal PDF. The Agency should take advantage
of all the work being funded at Libby to begin to validate the Bin
approach. This is an opportunity that should not be lost!

*Section 10.2 -- Extrapolation from Dust to PCM-Based Measures*

*13a. Is it scientifically justifiable to employ a default dust-to-PCM
conversion factor when there are not site-specific data available?*

After careful consideration of the extensive discussions made by fellow
committee members at the meeting, No, it is not justified to use a
default. The basic premise of transferability of dust in an industrial
setting to superfund application has too much uncertainty. This problem
needs to be eliminated by better data and determining the strength and
utility of TEM measurements in the overall "bin" approach.

The agency needs to use the South Carolina data as a first level attempt
at understanding of the utility of the Bin approach. However, it must
look for opportunities, like Libby, to establish a comprehensive
framework for the future validation of the Bin approach.

*13b. Are the uncertainty distributions specified in Appendix A to
characterize the uncertainty in this extrapolation consistent with
available information and are they statistically appropriate?*

Yes.

*Section 10.3 -- Extrapolation from PCM to Bin-Specific Measures*

*14a. Are the point estimates and uncertainty distributions for the
fraction amphibole term proposed for each study scientifically valid?*

No, uncertainties in the measurement and estimation techniques. Again
the Libby research and the South Carolina data must be fully exploited
in validation of the Bin approach.

*14b. Is it scientifically valid to use surrogate TEM data to estimate
bin-specific concentrations and exposure values in studies where these
data are not reported? If not, what alternative approach could be
followed, or what additional data would be helpful?*

I have no answer, except what are the biases that are introduced, and
how does one reasonably select the error PDF without data? The analyses
will be highly uncertain, and could lead to poor conclusions. You need
to obtain the TEM data to reduce uncertainties. Sort of an obvious
answer.

*14c. Are there any additional bi-variate TEM data sets available that
would be useful in this analysis?*

No answer, I am not knowledgeable on this point.

*14d. Are the point estimates and uncertainty distributions for the
fraction amphibole term scientifically valid?*

See answer to 14a.

*14e. Can you suggest any ways to improve the process used to identify
select the best available matching TEM data set(s) to a workplace? How
sensitive would the model output be to these changes?*

No. However, this is related to 14b**.**

*14f. Would the model benefit by establishing a common lower cut-point
in diameter to normalize the lower detection limit across studies?*

Yes, but it should be based upon inter-comparison studies of blind
samples among the TEM analysts.

*14g. Do the studies included in the model have surrogate data of
sufficient quality and similarity to expected exposure conditions to
support the model? If not, what alternative approach could be followed?*

No, need to obtain data that can be of value in validation studies

*14h. Are the PDFs described in Appendix C to characterize the
uncertainty in the extrapolation of TEM particle size data from one
location to another sufficient and helpful in understanding the
implications of the method used?*

Yes, because of the ability to complete sensitivity analyses.

*14i. Are the extrapolation techniques used on the raw TEM data sets to
meet the bin definitions (e.g. 0.4 µm diameter) transparent, objectively
presented and scientifically valid? Are there alternative techniques
that you would recommend?*

Yes.

*Section 11 -- Utilizing Potency Factors to Compute Life Time Risk*

*15a. What method is best for estimating the uncertainty in lifetime
cancer risk prediction?*

A validation study must be completed to get a much better handle on
uncertainties, and the overall utility of the bin method.

*15b. Assuming that estimates of exposure at Superfund sites will also
have uncertainty, how should the overall uncertainty in risk predictions
be characterized?*

Ambiguous question. For all risks or just asbestos risk? Site specific
applications and data are usually the best way to minimize
uncertainties.

Note, as discussed at the meeting:

The agency needs to do a better job of explaining the purpose of the
proposed appraoch, initially superfund sites, and any intention of
applying it to other asbestos issues, and fibers. As written, the
committee was not given a full understanding of the over all conceptual
framework

General Comment:

I agree with Dr. Lippmann on the need to have the Bin approach
generalized for all fibers. In the US, Asbestos is primarily a Superfund
and removal issue, however, other fibers are in use and being produced.
*[Note the aftermath of the WTC did not have any reasonable standards
for clean up of many vitreous fibers etc. , and we still do not in
2008.]{.underline}* I cannot re-emphasize this point more.

***Dr. Mort Lippmann***

Overview Remarks

While the document being reviewed is narrowly focused on OSWER needs,
any endorsement will lead to a document that has much wider implications
to risk assessments for airborne fibers within and beyond EPA. Thus, it
is important that the document be based on a broader review of the
effects of fibers on human diseases, and of the properties of fibers
that affect these diseases. In order to provide this perspective, the
document should:

-   Discuss fiber properties (lengths, widths, and biopersistance), and
    whether they differ as they influence mesothelioma, and lung cancer
    in humans and rats (Rodelsperger 2004).

-   Discuss lessons learned from long-term animal inhalation studies to
    asbestos, other mineral fibers (Wagner et al. 1990), and synthetic
    vitreous fibers (SVFs) (Miller et al., 1999,), as well as those
    learned from human experience.

-   Discuss biopersistence data from animal inhalation studies involving
    synthetic vitreous fibers (SVFs) with varying *in vitro*
    solubilities and *in vivo* solubilities in the lung as they inform
    differential cancer risks of chrysotile and amphibole asbestos
    fibers (Eastes and Hadley 1995, 1996), and commercial chrysotle
    asbestos (Davis et al. 1985) and chrysotile asbestos that is
    contaminated with tremolite asbestos (Pooley and Wagner 1988).

-   Discuss relevance of fiber dimensions and biopersitence to risks
    from exposure to multi-walled carbon nanotubes. The Poland et
    al. (2008) paper demonstrates that nanotube bundles with \>24% of
    lengths \>15 *u*m were as or more toxic in short term assays as the
    long asbestos fibers shown by Davis et al. (1985) to be more
    carcinogenic than UICC amosite, while amosite with virtually no
    fibers \<5 *u*m in length produced no tumors..

The risks of exposure to chrysotile fibers should be considered
separately from those of amphibole fibers. Both are important, but they
are not the same, even when fiber length and width are considered. It is
important to consider both, because:

-   Amphibole fibers in Libby, MT are of immediate concern, and can best
    be addressed by considering analogous human risks associated with
    other amphibole exposures. Also, a focus on exposures to fibers from
    the Libby mines and the human experience from those exposures is
    warranted because there are membrane filters that can be analyzed by
    modern measurement methods that can provide the most comprehensive
    data set on the lengths, widths, and compositions of amphibole
    fibers associated with a significant number of cancer cases that
    were well diagnosed.

-   Exposures to chrysotile asbestos remain the most common exposures to
    carcinogenic fibers in the US, and EPA's guidance is needed on the
    risks associated with building maintenance and demolition.

-   Much of the chrysotile asbestos in-place in buildings came from the
    Thetford mines in Quebec or other mining regions where a significant
    fraction of the raw fibrous material was tremolite asbestos, an
    amphibole form of asbestos that is much more biopersistent than
    chrysotile. The risks are therefore greater than those from
    amphibole-free chrysotle.

The concept of binning by fiber composition (e.g., amphiboles vs.
chrysotile) and by fiber dimensions (e.g., length and/or width
intervals) is sound and desirable in terms of the use of descriptors of
measurable exposure variables that are much more closely related to the
health risks associated with fiber inhalation. While the draft that we
reviewed did not provide a sufficient amount of data on fiber size to
justify any specific size-related bins, the SAB Panel's discussions
pointed the way to data resources that would enable the authors to do so
in the next draft document, and we urge that they do so. They must seize
the opportunity to do so, because this area of the overall challenge is
the one most amenable to significant progress. The literature on the
influence fiber size and composition on toxicity and cancer is very
extensive, albeit primarily in rodent models. However, in this case,
interspecies extrapolation has much less uncertainty than in most cases
because the physiological and anatomical differences affecting fiber
deposition in, and clearance from, the airways are well known (Lippmann
1988, 1994, Case et al. 2000, Berman et al. 1995, Brody et al. 1981).
Fortuitously, the recent publication by Stayner et al. (2007) on the
associations of long chrysotile fibers with lung cancer in chrysotile
textile workers in Charlestown, SC provides important new support for
the importance of long fibers in carcinogenesis that was first
demonstrated by Stanton and Wrench (1972) in rat instillation studies,
and later by Davis and colleagues (1978, 1985, 1986, 1987) in a long
series of 2-year inhalation studies in rats. By contrast, the
uncertainties inherent in the risk models are much greater, with K~L~s
and K~M~s varying among the historic studies by an order of magnitude or
more, and with little prospect that retrospective re-analyses can refine
them further.

Finally, it is important that EPA gets the exposure-related risk issues
right this time and there would be no excuse if it doesn't, because:

-   Our ability to routinely sample and identify airborne fiber
    distributions by length, width, and chemical and crystallographic
    composition is much more mature than in 1986.

-   Our understanding of the influence of fiber length, width, and
    biopersistence on fiber toxicity is much more mature than in 1986.

-   There would be continued reliance on outmoded risk models and
    grossly inappropriate air monitoring methods, which would therefore
    continue to cause unnecessary public concerns about de-minimus
    risks, unwarranted litigation, and asbestos risks that increase from
    unwarranted removals of in-place asbestos.

**[References]{.underline}**

Berman DW, Crump KS, Chatfield EJ, Davis JMG, and Jones, AD. 1995. The
sizes, shapes, and mineralogy of asbestos structures that induce lung
tumors or mesothelioma in AF-HAN rats following inhalation. *Risk
Anal.*15:181-195.

Brody AR, Hill LH, Adkins B Jr, and O\'Connor RW. 1981. Chrysotile
asbestos inhalation in rats: Deposition pattern and reaction of alveolar
epithelium and pulmonary macrophages. *Am Rev Respir Dis.* 123:670-679.

Case BW, Dufresne A, McDonald AD, McDonald JC, and Sébastien P. 2000.
Asbestos fiber type and length in lungs of chrysotile textile and
production workers: Fibers longer than 18μm. *Inhal Toxicol*. 12(suppl.
3):411-418.

Davis JMG. 1987. Experimental data relating to the importance of fibre
type, size, deposition, dissolution and migration. In: Proceedings of
1987 Mineral Fiber Symposium, Lyons, IARC.

Davis JMG, Addison J, Bolton RE, Donaldson K, Jones AD, and Miller BG.
1985. Inhalation studies on the effects of tremolite and brucite dust in
rats. *Carcinogenesis* 6:667-674.

Davis JMG, Addison J, Bolton RE, Donaldson K, Jones AD, and Smith T.
1986. The pathogenicity of long versus short fiber samples of amosite
asbestos administered to rats by inhalation and intraperitoneal
injection. *Br J Exp Pathol.* 67:415-430.

Davis JMG, Addison J, McIntosh C, Miller BG, and Niven K. 1991.
Variations in the carcinogenicity of tremolite dust samples of differing
morphology. *Ann NY Acad Sci* 643:473-490.

Davis JMG, Beckett ST, Bolton RE, Collings P, and Middleton AP. 1978.
Mass and number of fibers in the pathogenesis of asbestos-related lung
disease in rats. *Br J Cancer* 37:673-688.

Eastes W, and Hadley JG. 1995. Dissolution of fibers inhaled by rats.
*Inhal Toxicol* 7:179-196.

Eastes W, and Hadley JG. 1996. A mathematical model of fiber
carcinogenicity and fibrosis in inhalation and intraperitoneal
experiments in rats. *Inhal Toxicol.* 8:323-343.

Lippmann M. 1988. Asbestos exposure indices. *Environ. Res.* 46:86-106

Lippmann M. 1994. Deposition and retention of fibres: Effects on
incidence of lung cancer and mesothelioma. *Occup. Environ. Med.*
51:793-798.

Miller BG, Jones AD, Searl A, Buchanan D, Cullen RT, Soutar SA, Davis
JMG, Donaldson K. (1999). Influence of characteristics of inhaled fibers
on development of tumours in the rat lung. *Ann Occup Hyg* 43:167-179.

Poland CA, Duffin, R, Kinloch I, Maynard A, Wallace WAH, Seaton A, Stone
V, Brown S, MacNee W, Donaldson K. (2008). Carbon nanotubes introduced
into the abdominal cavity of mice show asbestos-like pathogenicity in a
pilot study. *Nature Nanotechnology Online*, 20 May, 2008.

Pooley FD, and Wagner JC. 1988. The significance of the selective
retention of mineral dusts. *Ann Occup Hyg*. 32(Suppl. 1):187-194.

Rodelsperger K. (2004). Extrapolation of the carcinogenic potency of
fibers from rats to humans. *Inhal Toxicol.* 16:801-807.

Stanton MF, and Wrench C. 1972. Mechanisms of mesothelioma induction
with asbestos and fibrous glass. *J Natl Cancer Inst.* 48:797-821.

Stayner LT, Kuempel E, Gilbert S, Hein M, Dement J. (2007). An
epidemiologic study of the role of chrysotile asbestos fiber dimensions
in determining respiratory disease risk in exposed workers. *Occup
Environ Med Online* 20, Dec. 2007.

Wagner JC (1990). Biological effects of short fibers. Pp. 835-840 in:
Proceedings of the VIIth International Pneumoconiosis Conference, Part
II. DHHS (NIOSH) Publication No. 90-108 Part II.

***Dr. Gary Marsh***

*1- Do you agree that the data are sufficient to indicate that such
differences may exist and an effort of this type is warranted?*

I would agree that the available experimental and human epidemiology
data support the hypothesis that the mineral type and size
characteristics of asbestos are associated with markedly different risks
for malignant mesothelioma. While support for this hypothesis as it
relates to the risk for lung cancer is much less consistent, some recent
work, such as the meta-analysis of Hodgson and Darnton (2000), provides
some compelling support for the hypothesis that lung cancer risks are
related to asbestos fiber type. Much of the evidence against this
hypothesis for lung cancer stems from the epidemiology study of the
Charleston, South Carolina asbestos textile workers, and the meaning and
significance of these anomalous results remain a subject of scientific
debate.

While the available epidemiology data provide support for this type of
effort, it is apparent that we do not have adequate environmental data
within the epidemiology studies to enable scientifically sound model
fitting by fiber size bins. In particular, aside from the recent work of
Stayner et al. (2007), we do not have TEM-equivalent data for any of the
existing epidemiology studies and the proposed PCM to TEM conversion
methods have been deemed by the SAB as too unreliable for practical
purposes. This effort should be re-visited as more TEM or TEM-equivalent
data become available that are directly related to human health
outcomes.

Model fitting by asbestos fiber type (i.e., 2 bins) using the proposed
methods appears to be feasible based on the available epidemiological
data. An alternative approach to this latter effort might be an update
of the meta-analysis performed by Hodgson and Darnton (2000).

*2) Please comment on the adequacy of Section 4 (Overview of Human
Studies) which serves as part of the scientific bases for the proposed
dose-response assessment approach.*

As the authors admit, this section is relatively brief, providing a
general overview of the published studies that have reported adverse
effects from human exposure to asbestos. The reader is referred to
several government agency reports (IARC, 1977), WHO (2000) and ATSDR
(2001; 2004) for more detailed reviews (it should be noted that many
other excellent reviews are available in the peer-reviewed literature).
This brief summary succeeds in providing some basic background
information on the main non-cancer and cancer health outcomes as well as
a brief overview of the role of fiber type with focus on relatively more
recent studies. The section on mesothelioma is particularly brief,
especially regarding studies of worker populations occupationally
exposed to asbestos.

While the brevity of Section 4 may be appropriate for the larger goals
of the overall document, an unnecessary and disconcerting disconnect
exists between the literature for lung cancer and mesothelioma
summarized in Section 4 and the literature actually used in the modeling
protocol, which is described in general in Section 9 and in considerable
detail in Appendix A. That is, many of the "primary" and "other"
literature cited in Appendix A and used in the modeling of these two
health outcomes were not cited in the general epidemiology overview in
Section 4.

Because the epidemiological literature of human health effects from
asbestos exposure is vast and can defy attempts to summarize adequately,
a logical and consistent approach is needed when selecting a
representative set of articles for background purposes. One simple
alternative approach to the choice of lung cancer and mesothelioma
studies in Section 4 might be to include, as a minimum subset, all the
studies cited in Appendix A, as well as a representative selection of
studies that did not meet the criteria for inclusion in Section 9, but
are nonetheless relevant. For example, as described in two recent review
articles (Goodman et al., 2004; Laden et al., 2004), a substantial
number of epidemiological studies have consistently demonstrated the
absence of risk from lung cancer and mesothelioma among vehicle
mechanics exposed to low levels of chrysotile. The meta-analysis of
Hodgson and Darnton (2000) should also be discussed in detail in this
document as it bears directly on the objectives of this risk assessment.

Other areas that should be further developed in this section include:
(1) the influence of fiber properties (composition, length, wide,
durability) on the risks of human lung cancer and mesothelioma; (2) an
overview of the animal toxicology data, especially the long-term
inhalation studies, pertaining to asbestos, other mineral fibers and
synthetic vitreous fibers, and their implications regarding human health
risks; and (3) background information on the relevant asbestos exposures
and perceived human health risks related to Superfund sites (including
an in-depth review of the background and completed epidemiology studies
at the Libby, Montana site) as this is the purported impetus behind this
entire risk assessment exercise.

*3a Do you agree that the lung cancer and mesothelioma risk*

*models that are proposed are a scientifically valid basis for this
model fitting?*

This is a complex question, whose meaning was discussed extensively at
the SAB meeting. My response to what I believe this question is asking
was essentially included in my response to question (1) above. To
reiterate, while the available epidemiology data provide support for
this type of effort, it is apparent that we do not have adequate
environmental data within the epidemiology studies to enable
scientifically sound model fitting by fiber size bins. In particular,
aside from the recent work of Stayner et al. (2007), we do not have
TEM-equivalent data for any of the existing epidemiology studies and the
proposed PCM to TEM conversion methods have been deemed by the SAB as
too unreliable for practical purposes. This effort should be re-visited
as more TEM or TEM-equivalent data become available that are directly
related to human health outcomes.

Model fitting by asbestos fiber type (i.e., 2 bins) using the proposed
methods appears to be feasible based on the available epidemiological
data. An alternative approach to this latter effort might be an update
of the meta-analysis performed by Hodgson and Darnton (2000).

[Section 9 -- Epidemiological Data Proposed for Use]{.underline}

[Section 9.1 -- Criteria for Study Selection]{.underline}

*11a) Are the study specific selection rules proposed scientifically
valid for the intended use?*

The first and second proposed study selection rules (published in
refereed journal and must provide data that can be expressed in terms of
the quantitative risk models for lung cancer and/or mesothelioma) are
straightforward, logical and scientifically valid. The wording of the
second rule might be revised as "The study must provide data that can be
*used directly or to provide accurate (reliable and valid) estimates*
that can be expressed in terms of the quantitative ....", as this
conforms better to the actual procedures that are proposed.

The third rule (the study cohort must consist of individuals who were
exposed to approximately the same atmospheric composition of asbestos)
is problematic as it is ill defined and does not seem to have been
applied consistently to all candidate studies to include/exclude
subjects. Moreover, confusion arises between the rule used to exclude
studies due to exposure mixes and the detailed procedure described in
Section 10.3 to estimate the "fraction amphibole data" (as well as the
detailed procedure used to characterize the uncertainty in the fraction
amphibole described in Appendix C). For example, contrast the first
statement on page 74 under "cohorts with mixed exposure",

*". . . OSWER recommends that studies in which health statistics were
combined across two or more sub-cohorts exposed to substantially
different workplace atmospheres should be excluded."*

and the statement on page 72, Section 9.1.3:

*"Hence, studies in which the cohort is known to be composed of
individuals who are exposed to differing types of atmospheres are
excluded from the data fitting process."\
*

with the procedure on page 79 under "chrysotile plus amphibole":

*"In some studies, the description of the workplace and its operations
makes clear that both chrysotile and amphibole were used in the
workplace. Ideally, data from TEM studies of air samples collected from
the workplace would serve as a basis for estimation of the relative
amounts of chrysotile and amphibole in the exposure atmosphere. However,
in the absence of such data, information on the relative amounts of the
different types of asbestos purchased or processed can be used as a
rough surrogate for the relative amounts in the atmosphere."*

The authors need to clarify the distinction between excluding studies
based on mixed atmospheres, and including studies with mixed atmospheres
and estimating the fraction amphibole. The clarification of this
distinction should include a detailed explanation of the reasons why
each of the excluded candidate studies was excluded.

*Should any additional selection rules be applied?*

Another selection rule that might be considered by OSWER is that the
studies meet some specified level of data quality. As noted on page 69,
this was recommended by some peer consultation panel members who
reviewed the approach of Aeolus (1999, 2001), but was rejected by OSWER
as an impracticable requirement. As noted on page 70, OSWER believes
that the assignment of PDFs around each data input item should account
for any differing levels of data quality between groups and studies

*11b) Is it appropriate to assume that all workers in a cohort are
exposed to an atmosphere with a constant composition (i.e., the mixture
of asbestos types and sizes is constant) unless the authors report
information to the contrary? If this is not the appropriate assumption,
what alternative strategy would be available?*

See the response to 11a

*11c) Should a set of minimum data quality requirements (other than
those above) be established for inclusion of a study in the analysis? If
so, what elements of data quality should be considered and how should
those data quality rules be established?*

One approach to this would be to require that included studies meet the
minimum "Good Epidemiology Practices or GEPs" described by Cook (1991).
GEPs, which are now commonly applied in occupational epidemiology
projects, were modeled after their "Good Laboratory Practices or GLPs"
counterparts in the toxicology area, and provide standardized guidance
regarding protocol development, reporting, quality assurance of data
maintenance and documentation of analytic procedures.

*11d) For lung cancer, OSWER's approach requires that there be at least
two exposure groups per study in order to impose some constraint on the
value of the study specific value of α. However, OSWER is proposing to
use data from three cohorts of described by Enterline and Henderson
(1979) event though there is only one dose group for each cohort. This
is because a reliable estimate of α for the combined cohort can be
derived from the data of Enterline et al. (1987). Is this approach
appropriate and scientifically justifiable? If not, can you suggest an
alternative strategy for retaining the data from this important study or
should this study excluded?*

I believe this approach is appropriate and scientifically defensible
although it may not be the best approach. The error associated with this
estimation should be adequately handled adequately by the weakly
informed prior for α described in Section 8.6.2. (i.e., each α~s~ is
UNIFORM (0.1, 10)).

Another approach to estimating a study-specific α~s~ or global α would
be to adjust the general population rate for lung cancer by the likely
positive confounding by smoking that would occur in a blue collar
working population. This could be done on a study-specific basis using
the lung cancer rates of the local population or more globally by using
national lung cancer rates. Estimates of the positive confounding by
smoking could be obtained from available government reports or published
epidemiology studies and incorporated into the adjustment using Monte
Carlo-based sensitivity analysis (e.g., see Steenland and Greenland,
2004).

*11e) One key assumption in any meta-analysis is that the data sets
included in the analysis are homogeneous. How should the assumption of
homogeneity be assessed prior to combining the data from the studies or
groups?*

First, some of the better detailed discussions of basic meta-analysis
methods and related tests of homogeneity can be found in Bailey, 1987;
Berlin et al., 1989; Deeks et al., 2001; DerSimonian and Laird, 1986;
Fleiss, 1993; Higgins et al., 2003 and Petitti, 2001. However, it
remains unclear exactly how these methods would apply or need to be
modified for incorporation within the proposed risk assessment methods.

With the above qualification in mind, briefly, a basic meta-analysis
should include a formal test of homogeneity of the risk estimates
comprising a given meta-RR. Statistical heterogeneity of the RRs is
assessed by the I^2^ index, which describes the percentage of total
variance across a given set of studies that is greater than that
expected by chance. The I^2^ index is calculated from the heterogeneity
chi-square (Q) and degrees of freedom (df) statistics from a
meta-analysis (I^2^ = 100% x (Q-df)/Q), and is compared to the
chi-squared distribution with n-1 degrees of freedom, where n is the
number of studies. Many software packages are available to perform
meta-analyses, including Stata10 (Stata Corp., 2007).

*If you recommend statistical testing, please provide guidance on the
reliability of a decision based solely on the test statistic.*

With the above qualification in mind, because heterogeneity tests for
meta-analysis of studies are generally conservative (i.e., they have low
power) and to avoid type II errors, it is often recommended that a
significance level of 0.10 instead of the more traditional 0.05. Also,
some authors use these qualitative terms to characterize values for
I^2^: low (0-24%), moderate (25-49%), high (50-74%) and very high
(\>75%) (Fayerweather, 2007).

*If testing produces evidence of heterogeneity between some studies,
what steps can be recommended?*

With the above qualification in mind, when the null hypothesis of
homogeneity is not rejected, then a fixed effects model is preferred. If
there is significant heterogeneity, a random effects model is preferred.
The fixed effects model assumes that any differences between study
results are due solely to chance, while in the random effects model the
observed effect is assumed to vary around some average with sampling
error.

Random effect models should not be used to "explain away" heterogeneity.
In the presence of significant heterogeneity, efforts should be made to
explore the reasons for the heterogeneity and/or to reduce the level of
heterogeneity. These efforts might include stratification by other study
factors to identify homogeneous subgroups or meta-regression, in which
the characteristics of the studies or the subjects of the studies are
used as explanatory variables in a multivariate regression with the
effect size (or some measure of the deviation from the summary measure
of effect) as the dependent variable.

[Section 9.2 and 9.3 -- Studies Proposed for Use and Studies
Excluded]{.underline}

*12a) Are you aware of any studies that should be included in the model
fitting effort that are currently excluded or omitted? If so, what are
these studies, and do they meet the requirements for study inclusion?*

The above recommendation that the model fitting be limited to fiber type
bins notwithstanding, it seems that the Stayner et al. (2007) paper that
used TEM data to reevaluate risks in the South Carolina cohort should be
considered for inclusion in any model fitting effort that attempts to
address fiber size. Otherwise, I am not currently aware of other studies
that should be included in the model fitting effort.

Per my response to question 11a, it would be helpful to see a detailed
discussion of the reasons why each of the excluded candidate studies
were excluded from the model fitting exercise. In other words, it would
be useful to see the entire list of candidate studies considered by
OSWER for inclusion.

*12b) Are there any studies that are currently proposed for inclusion in
the analysis that you believe should be excluded? If so, why?*

As noted during the SAB meeting, a number of the proposed studies for
evaluating lung cancer risks utilized internal comparisons and thus do
not provide an estimate of alpha. All the studies currently included do
appear to have satisfied the first two selection criteria, and as noted
above, uncertainties exist in the current document regarding the
selection of studies based on mixed atmospheres.

*12c) In cases where the epidemiological data are not reported in the
form needed for use in the fitting effort, are the methods used to
estimate the exposures scientifically sound, and are the methods used
for characterizing the uncertainty in the estimates appropriate?*

As noted above, while the available experimental animal and human
epidemiology data provide underlying support for this type of effort
(i.e., support regarding differential risks for mesothelioma and
possibly lung cancer by fiber type and size), it is apparent that we do
not have adequate environmental data within the available epidemiology
studies to enable scientifically sound model fitting by fiber size bins.
In particular, aside from the recent work of Stayner et al. (2007), we
do not have TEM-equivalent data for any of the existing epidemiology
studies and the proposed PCM to TEM conversion methods have been deemed
by the SAB as too unreliable for practical purposes. This effort should
be re-visited as more TEM or TEM-equivalent data become available that
are directly related to human health outcomes.

One questionable area related to the availability of epidemiology data
is how the model fitting process for lung cancer will explicitly account
for available (or unavailable) data on tobacco smoking histories.
Appendix A of the draft document describes the disposition of smoking
history data for each of the included studies. These data range from
none to detailed data at the individual subject level. Without any
provision for the available data and considering the effect modification
evidence for smoking and asbestos, the model fitting will be combining
higher risks from asbestos among smokers with lower risks among
non-smokers. It is also not clear why smoking data were not considered
as a source of uncertainty in Appendix C?

Also, given the uncertainties surrounding the inclusion or exclusion of
studies involving worker exposures to mixed atmospheres (see response to
question 11a), it is not entirely clear how the modeling of the potency
factors will explicitly account for mixed atmospheres when they occur.

***Dr. Luis Ortiz***

*Question 1) Do you agree that the data are sufficient to indicate that
such differences may exist and that an effort of this type is
warranted?*

The data available supports the notion that differences in asbestos type
and particle dimension confer significant differences in carcinogenic
(as it pertains to lung cancer and mesothelioma) potency and therefore,
the proposed new approach appear to be warranted to more definitively
resolve the issue. However, this new proposed approach levels on the
harmonization of pre-existent data (mostly epidemiological reports from
well characterized cohorts of asbestos exposed subjects) to adopt a
multi bin mathematical approach to estimate cancer risk according to
mineral groups and measurements of particle size based on transmission
electron microscopy. Therefore, it appears that a number of simplifying
assumptions that were necessary during the harmonization process deserve
further analysis (even prospective validation) and as such the new
approach should be considered interim.

*Question 2) Please comment on the adequacy of Sections 2-5 of the
overview of EPA's OSWER Draft Report.*

Sections 2-5 are designed as a backbone to illustrate the needs of the
OSWER report. These sections should facilitate analysis of the OSWER
document by the members of SAB and the broader public. A potential
consequences of endorsing such document is that the general public may
equate this support with that of and official change in EPA policy
toward asbestos carcinogenic potency then it may be advisable that such
document be supplemented to cover a number of item as discussed below.

There must be ample discussion in the introduction of these sections
stating that this is an interim approach to assess the question of how
physical difference in the composition of asbestos fibers modify its
cancer inducing capacity. A clear description of the historical and
current EPA's needs that motivate the OSWER report should be stated.
Specifically, the document should make reference to the current EPA
priorities in the clean up efforts of superfund sites such as Libby,
Montana and other sites around the US (as discussed during the SAB
meeting in Washington, D.C.).

Section 2 is adequate in length for the purpose of the OSWER report as
it describes both the fundamental aspects of the differences in asbestos
fibers and clarifies basic aspects of the methodology used to study the
question at hand. However, a more detailed discussion of the importance
of the physical aspects of the asbestos fibers (length as well as width)
and whether or not these properties bear directly in the carcinogenic
(lung or mesothelioma) capacity of the asbestos fibers is necessary.

Although Section 3 provides a summary of pertinent data linking the
experimental exposure of animals to asbestos with the subsequent
development of cancer this section does not provide description of the
in-depth biology necessary to support the larger questions addressing
the fitting of the published human data as it pertains to the evaluation
of whether differences in asbestos type and fiber dimension confer
significant differences in their ability to induce tumors (which is the
question at hand).

This section of the OSWER report should also clearly describe that
currently there is a definitive paucity of scientific information, both
in animals (Stanton and Wrench J. Natl. Cancer Inst. 48:797-821,1972) as
well as humans (Stayner LT et al. Occup Environ Med. 2007 Dec 20; \[Epub
ahead of print\]) regarding the use of TEM to characterize fiber size
specific asbestos exposure. In the case of Libby there are samples that
could be subjected to TEM analysis to properly address this deficiency.
Therefore, this aspect could be incorporated in the report as a
scientific and investigational priority to the agency

Similarly, there appears to be room for improvement regarding the
description of a body of published work describing the factors that
modify the environmental host interaction and determine individual
susceptibility to asbestos-induced cancer. Surprisingly, there is little
mention of experimental animal data addressing the relationship between
smoking and its effects on the asbestos induced malignancy (which is
charged in question 3).

Sections 4-5 constitute a summary of studies describing the effects of
asbestos in human beings and provide an overview of the potential
biological mechanism of the asbestos mode of action. Although these
sections are intended as an introductory summary to the reader of the
OSWER draft report these sections are superficial in relation to the
importance of the work proposed by the OSWER and although they summarize
some of the key published data linking asbestos to both lung cancer and
mesothelioma they do not address in detail the issue of whether
variability of fiber type confer differences in potency towards
carcinogenesis. The summary of the data linking asbestos to mesothelioma
is particularly brief and would benefit from adding additional
references (some of which are subsequently included in Appendixes). As
was the case of the animal data, there are almost no descriptions of the
potential interactions of asbestos with smoking and certainly, no
consideration to the relevance of individual susceptibility as it
pertains to the pathogenesis of lung cancer and mesothelioma.

Finally, the approach to the biology of the carcinogenic mechanisms of
asbestos is timid, lacking in molecular depth and not providing a
biologic foundation to back the epidemiologic approach map (that could
be experimentally adopted to support the imminent fitting of the human
epidemiological data) to indicate how the proposed differences in the
physical properties of the asbestos fibers determine their carcinogenic
potency. Therefore, although the number of references could be adequate
for a report of this magnitude, a more clear articulation of these
references could be structured to reflect the historical perspective
that motivated the adoption of the fitting methodology proposed in the
current report.

*Question 3: Do you agree that the lung cancer and mesothelioma risk
models that are proposed are a scientifically valid basis for this
fitting effort? Should additional model forms be investigated?*

In general the approach is sound and so the modeling is scientifically
valid. However, the development of a progressive approach to determine
the importance that asbestos fiber type and size confer to specific risk
estimates requires the availability of data sets derived with the use of
transmission electron microscopy (TEM) to appropriately describe the
asbestos fiber size and length distribution to which subjects are
exposed. Currently, it appears that the only published data available is
that of Stayner et al (Occup Environ Med. 2007 Dec 20) characterizing
the South Carolina textile cohort. Therefore, further validation may be
pertinent at this point and it appears that the superfund at Libby could
offer a great opportunity to validate the measurements of the amphibole
particles by using TEM as appropriate filters are available.

As presented in the OSWER document, these data are not available and
therefore the working data sets had been extrapolated from pre-existent
cohorts (17 in total). These calculations assume a number of simplifying
assumptions that introduce bias. While the current OSWER report
acknowledge most of these bias and adopted stringent protocols to limit
and correct them questions remain regarding the ability of this fiber
size specific TEM matrixes to account for within industry variation.
Thus, as demonstrated in Dement et al 2007, application of TEM to a well
described (previously characterized by use of PCM-based fiber count)
cohort of subjects from the Charleston, SC, textile plant identified
differences in asbestos fiber size among the different operations in
this plant. Thus, the TEM-generated matrix did not accurately predict
inter occupational and industry specific variations of these
measurements.

TEM data sets appear to more adequately provide evidence that suits
their use in assessing the risk of asbestos-induced lung cancer.
However, this does not appear to be the case with mesothelioma and fewer
TEM derived matrixes are available due to the prevailing limitations in
inferring the equivalent calculations.

Similarly, there appear to be inconsistencies in judging the rational
for excluding the value of data sets from specific cohorts to conduct
risk assessment. A clear example of this, identified by the
correspondence shared by the members of the asbestos SAB, is the
exclusion of the studies by Selikoff and Seidman on insulator workers.

As stated in the OSWER report the current notion regarding the
interaction between asbestos and lung cancer is that this relationship
is multiplicative. However, given the ongoing changes in the biology of
the lung cancer (increased number of subjects with lung cancer had not
been smokers), the decrease in the population exposed to smoking, then
it appears that it is imperative that the nature of this interrelation
be revisited at this point. The proposed model appears adequate to do so
and it appears premature to look for alternative methods as no other
strategy (ies) has been validated to assess the asbestos conferred risk
of lung cancer or mesothelioma models that are likely to be superior to
the current one.

*Question 4: Is fitting at the group level (based on the number of
cancer cases observed) preferred to fitting at the study level (based on
the study-specific KL or KM values)?*

The rational to support binning by fiber composition (amphiboles vs.
chrysotile) and by fiber dimensions (length or width intervals) is sound
as the information available support the concept that these variables
may directly influence the biological effects of asbestos fibers inhaled
into the lung or deposited in the pleura. Otherwise, it was the general
consensus at the SAB meeting that the OSWER report did not provide a
sufficient data to support binning based on fiber size. However, as
stated above, the SAB consider that the current efforts at Libby offer a
unique opportunity to prospectively study this important question.

*Question 8: Do you agree that multiple binning strategies should be
evaluated, or do you believe that a physiological basis exists that can
be used to identify a particular set of length and width cutoffs that
should be assessed? If so, what would these length and width cutoffs be,
and can these bins be implemented considering the limitations in the
available TEM particle size data sets?*

As discussed above the rational for this approach appears to be
reasonable. However, the fundamental issue that limits this approach is
the lack of current available data to support the methodological
approach. As discussed in the SAB meeting currently, there is only one
well-characterized cohort of asbestos exposed subjects in which fiber
characteristics have been analyzed by TEM (Stayner LT et al. Occup
Environ Med. 2007 Dec 20) as such this limitation (paucity of validated
data) decreases enthusiasm for the approach.

I would also like to defer in this questions to the comments consigned
at the SAB by Dr. M. Lippmann in which he stressed the fact that while
descriptions of bins according to length make sense for lung cancer
(Lippmann, M. 1994. Occup. Environ. Med, 51:793-8) they may not be
suitable for studying the causation of mesothelioma as fiber diameter
determine the ability of asbestos fiber to traffic via lymphatic
channels to deposit into the pleura.

*Questions 11 and 12: Are the study-specific selection rules proposed
above scientifically valid for the intended uses? Should any additional
selection rules be added? Is it appropriate to assume that all workers
in a cohort are exposed to the same atmosphere with a constant
composition (mixture of asbestos types and sizes is constant) unless the
authors report information to the contrary?*

The reality is that there is a paucity of data to support such an
enterprise and only one recently published article has comprehensible
TEM characterized asbestos fiber properties and correlated them to
relevant epidemiological data (Stayner LT et al. Occup Environ Med. 2007
Dec 20). Therefore, every effort should be made to consider available
information (transparent and scientifically valid data that could
undergo stringent and un-bias scrutiny) and incorporate this data if
there is a valid scientific reason.

As mentioned above, recent information indicate that even data set
obtained by using fiber size specific TEM matrixes do not account for
within industry variation (Dement et al 2007). Thus, this appears to be
an area ripe for research application to pertinent cohorts such as the
Libby mining sites.

*Questions 13 and 14: Is it scientifically justifiable to employ a
default dust-to-PCM conversion factor when there are no site-specific
data available? Is it scientifically valid to use surrogate TEM data to
estimate bin-specific concentrations and exposure values in studies
where these data are not reported? If not, what alternative approach
could be followed, or what additional data would be helpful?*

The answer to this question is not because previous experience has shown
that impinger data cannot reliable used to generate PCM comparisons.
Therefore, it appears that this limitation will also limit the use of
such data to estimate TEM fiber size distributions. Similarly, as
discussed during the SAB meeting, there are limitations to the ability
of PCM estimates to produce consistent fiber size distributions as
measured by TEM.

*Asbestos Charge Questions Section 3 (Toxicology) & Section 5 (mode of
action) and*

*Please comment on the adequacy of these sections (3-5), which serve as
the scientific bases for the proposed dose-response assessment
approach.*

Sections 3-5 are designed as a backbone to illustrate the needs of the
OSWER report. These sections should facilitate analysis of the OSWER
document by the members of SAB and the broader public. A potential
consequences of endorsing such document is that the general public may
equate this support with that of and official change in EPA policy
toward asbestos carcinogenic potency then it may be advisable that such
document be supplemented to cover the following items.

1\. There must be ample discussion in the introduction of these sections
stating that this is an interim approach to assess the question of how
physical difference in the composition of asbestos fibers modify its
cancer inducing capacity. A clear description of the historical and
current EPA's needs that motivate the OSWER report should be stated.
Specifically, the document should make reference to the current EPA
priorities in the clean up efforts of superfund sites such as Libby,
Montana and other sites around the US (as discussed during the SAB
meeting in Washington, D.C.).

2\. A more detailed discussion of the importance of the physical aspects
of the asbestos fibers (length as well as width) and whether or not
these properties bear directly in the carcinogenic (lung or
mesothelioma) capacity of the asbestos fibers is necessary.

3\. The report should also clearly state that currently there is a
definitive paucity of scientific information, both in animals (Stanton
and Wrench J. Natl. Cancer Inst. 48:797-821,1972) as well as humans
(Stayner LT et al. Occup Environ Med. 2007 Dec 20; \[Epub ahead of
print\]) regarding the use of TEM to characterize fiber size specific
asbestos exposure. In the case of Libby there are samples that could be
subjected to TEM analysis to properly address this deficiency.
Therefore, this aspect could be incorporated in the report as a
scientific and investigational priority to the agency.

4\. Similarly, there appears to be room for improvement regarding the
description of a body of published work describing the factors that
modify the environmental host interaction and determine individual
susceptibility to asbestos-induced cancer. Specifically, there is little
description of experimental animal data addressing the relationship
(additive versus multiplicative) between smoking and asbestos exposure
on its carcinogenic effects.

5\. Finally, the approach to the biology of the carcinogenic mechanisms
of asbestos is timid, lacking in molecular depth and not providing a
biologic foundation to back the epidemiologic approach map (that could
be experimentally adopted to support the imminent fitting of the human
epidemiological data) to indicate how the proposed differences in the
physical properties of the asbestos fibers determine their carcinogenic
potency.

***Dr. Julian Peto***

> Leslie Stayner recognises that his response raises various crucial
> points in relation to fibre type and other issues on which the Panel
> do not agree. I have therefore addressed his remarks directly in my
> response (see below) rather than writing a separate commentary. Our
> response to the EPA should identify crucial areas of disagreement.
> Whether amosite causes a greater lung cancer risk than chrysotile is
> one such issue, although in the absence of adequate exposure data in
> fibre/ml longer than 5 microns in any of the epidemiological studies,
> let alone in subdivisions of length, this is not a well-defined
> question.
>
> This approach would clarify the points of disagreement and give the
> public an idea of who holds which opinions, and why they hold them. My
> only plea is that we agree to distinguish clearly between scientific
> evidence and consideration of the social or legal effects of the
> EPA\'s position. Both are proper issues for discussion, but they must
> not be confused.
>
> Two fundamental weaknesses in the proposed analysis were discussed by
> the Panel but are perhaps not generally appreciated. The first is the
> assumption that the lung cancer risk is proportional to duration of
> exposure. The draft EPA report states that this was established in the
> 1986 EPA report, but it would be useful for the evidence on this to be
> reexamined systematically, particularly in cohorts exposed to
> chrysotile. Amphibole exposures of less than 5 years duration have
> caused substantial lung cancer risks, but this does not seem to be
> true of chrysotile, perhaps because it disappears from the lung much
> more rapidly.
>
> The second and more important weakness is the virtual absence of any
> reliable measurements of the historical exposure levels that caused
> substantial cancer risks in any of the cohorts. Fibre counts, let
> alone counts in each range of fibre size, were non-existent before the
> 1960s, different methods of measuring particles gave very different
> readings, and parallel measurements of particles and fibres gave
> different ratios in different workplaces, and even in different areas
> within the same industry using the same instruments. Last but not
> least, the most heavily exposed workers were not monitored at all.
> Richard Doll and I discussed these problems at length in our UK report
> (Doll R and Peto J: Asbestos: Effects on health of exposure to
> asbestos. Health and Safety Commission. Her Majesty's Stationery
> Office, London 1985). There were no systematic historical data on most
> amphibole workers, and the following extracts from our report
> illustrate the weakness of the best of the chrysotile studies.
>
> S Carolina chrysotile textile factory: 'The exposure data obtained in
> this factory are less extensive than appears at first sight. A total
> of 5576 samples were taken before 1975, but only 376 midget impinger
> samples were taken before 1960, including 112 by a life insurance
> company and 81 by the US public health service or state board of
> health between 1930 and 1945. It is difficult to know how
> representative these were, and many activities, including fibre mixing
> with pitch forks in an area where there was no dust suppression, were
> unmonitored.'
>
> Quebec chrysotile mines and mills: 'The Quebec \[exposure\] data,
> which are the most extensive, began to be collected in
> 1949.......There were no routine measurements in the open pits, and
> few in the underground mines, and the exposure estimates assigned to
> the substantial proportion of the cohort who worked in these areas may
> be particularly unreliable. The conversion of high particle counts to
> fibre counts is also difficult, as only 34 (5%) of the parallel
> samples \[taken in the1970s\]... exceeded 3 mppcf, while the estimated
> average exposure levels of men with 20 or more years service ranged
> from 4.2 mppcf for "low" exposure to 46.8 mppcf for "very high"
> exposure....As in our own study \[the Rochdale textile factory\] the
> lack of contemporary particle and fibre counts during the period when
> the exposures that caused the highest observed excess risks occurred,
> together with the poor correlation subsequently observed between
> particle and regulated fibre counts, make it impossible to quantify
> the dose-specific effect at low fibre counts with much confidence.'
>
> The basic data on which the proposed analyses depend are thus so
> unreliable even for chrysotile that we concluded: 'We hesitate to
> suggest that the \[exposure data\] are insufficiently reliable to
> justify making any quantitative extrapolation from past experience to
> the effects of current exposures. Nevertheless, this may in fact be
> the case, and we may have to be satisfied with qualitative conclusions
> based on knowledge of the direction in which progress has been made
> and epidemiological observations of the effects of qualitatively
> different types of exposure.' In relation to the amphiboles we
> concluded: 'The use of crocidolite and amosite (and, we assume, other
> types of amphibole asbestos) is, in practice, more hazardous than the
> use of chrysotile, possibly because of the longer residence time of
> amphibole fibres in the lungs, but possibly also for other reasons
> ....No worthwhile data are, however, available to enable quantitative
> comparisons to be made between the effects on humans of the different
> fibre types. It can be concluded only that the use of amphiboles
> should be avoided whenever possible and that extra precautions need to
> be taken when exposure to them occurs. It is possible, though we
> believe unlikely, that the hazardous effects of chrysotile are mainly
> due to contamination with small amounts of tremolite.' I am not at all
> sure that the limitations in the exposure data that we drew attention
> to in 1985 have been overcome, but they should certainly be discussed
> more critically in a new EPA report than they were in the last one.
>
> I would like two further documents that are mentioned in my response
> to be attached to the Committee's final report:
>
> \(1\) My 1985 comments on the first draft of the 1986 EPA report
> (attached at the end of this document) directly addresses the main
> points of disagreement.
>
> \(2\) My 1981 report to the EPA on asbestos in schools (also attached
> at the end of this document) used the same models for lung cancer and
> mesothelioma as the 1986 report and the current draft, and is also
> directly relevant. It was never made public, and by 1985 it could not
> be traced in the EPA\'s archives. Incidentally, I would like the fact
> that I developed these models to be acknowledged.

**Response by Julian Peto to Leslie Stayner's comments on the EPA draft
report:**

**Proposed Approach for Estimation of Bin Specific Cancer Potency
Factors for Inhalation Exposure to Asbestos**

I agree with Leslie Stayner\'s comments on fibre dimension but I
disagree with his statement that there is little or no difference in
lung cancer risk between chrysotile and the amphiboles. He offers no
evidence apart from a reference to the factory in S Carolina to explain
why he disagrees with the majority of independent scientists and the
pooled data on this issue. The idea that even a few years of exposure to
chrysotile at levels averaging 1 fibre/ml or less could cause a
substantial risk of either mesothelioma or lung cancer flies in the face
of all evidence. Amosite exposure can increase the lung cancer risk
substantially after brief exposure (Seidman et al 1979), but substantial
chrysotile exposure has not been shown to do so. As we mentioned in our
1985 report to the UK Health and Safety Commission (Doll and Peto 1985),
the studies that Richard Doll and I conducted in a Rochdale textile
factory using mainly chrysotile gave a dose-specific estimate for lung
cancer of the same order as the S Carolina study (Peto et al 1985). The
SMR for lung cancer was about two in workers exposed for between 10 and
20 years at levels that probably averaged between 5 and 10 fibres/ml,
but in men who worked for 1-5 years the SMR was not detectably increased
(7 lung cancer deaths observed, 9.9 expected). Note that these analyses
were restricted to men employed after 1932, when conditions had greatly
improved. There were 13 lung cancer deaths compared with only 1.6
expected and 2 mesotheliomas in men with more than 10 years of very
heavy chrysotile exposure in this factory before 1932 (Peto et al 1985).
There was a large excess of respiratory cancer in the S Carolina
factory, but the excess in men exposed for 1-5 years (McDonald et al
1983: 10 observed, 6.1 expected) was not statistically significant. In
Quebec chrysotile miners the lung cancer SMR was 2.4 in workers who were
heavily exposed for more than 20 years in the 1930s and 1940s and 1.5
for 5-20 years of exposure (23 lung cancer deaths) but was not
detectably increased for less than 5 years (27 deaths) (McDonald et al
1980). Such data convinced most independent scientists many years ago
that amphiboles are more dangerous than chrysotile for lung cancer as
well as for mesothelioma, and the 1986 EPA report was wrong in its fixed
view that there is little difference in hazard. My formal comments to
the EPA on that report (attached letter to Dennis Kotchmar dated 13^th^
Aug 1985) are directly relevant to the questions that we are now
readdressing 23 years later. The report that EPA commissioned me to
write on asbestos in schools in 1981 is also attached. In it I applied
the lung cancer and mesothelioma dose and time models that I had
published in 1979, and these have been used in most subsequent
Government reports in the US and Western Europe, including the 1986 EPA
report and the current draft. Assuming that dose-response is linear,
that the mixture of fibre types in schools would be similar to that of
US insulators' exposure, and that insulators were on average exposed at
30 fibre/ml, I showed that the lifetime cancer risk at an average of
0.002 fibre/ml in schools would be of the order of 1 in 100,000. This
report was not published, and a copy could not be found in the EPA's
archives when I mentioned this in 1985 while reviewing the draft 1986
report.

Leslie concludes by stating that any research by the EPA on differences
in risk between asbestos fibres that is published before a ban on the
few remaining uses of chrysotile has been introduced is an insult to
those now dying of mesothelioma. They and their generation will in fact
be completely unaffected by any change in current exposure, and we
cannot base our scientific conclusions on this dubious prediction of
their reaction. Some estimate of the number of future deaths that this
ban might prevent or cause in the US would be more relevant. That
requires either more research or a consensus statement by panel members
on the plausible range of health effects of different policies on
asbestos management based on existing models and evidence. My personal
view is that a ban is now irrelevant, and the important issue is whether
the risk from the amosite and crocidolite that remains in some ships and
waste sites and in many buildings can and should be abated.

Our report should be open about these scientific and political
differences of opinion. We should include an appendix comprising
Leslie's statement, this one from me, and comments from other Panel
members on fibre type differences in hazard so that the public can judge
the spectrum and credibility of expert opinion for themselves. We cannot
write a report that conceals these disagreements, and they need to be
clarified. The reservations about the weak epidemiological evidence on
fibre size that we all expressed at the Panel's public meeting on July
21-22 are already being misrepresented as doubts about differences
between chrysotile and amphiboles.

***Dr. Christopher Portier***

**General Comments:** Two key elements are not readily addressed in this
proposal. First, what is the scientific support for the underlying
model. Because the entire analysis is based upon the assumed model and
models within this class, there should be some discussion of the
scientific basis for this model. This is confounded by the second issue,
and that is the loss of data, especially for mesothelioma, that results
from the need to divide the exposure into the two separate forms. I view
the epidemiological data as falling into three separate datasets;
everything appropriate for 1 bin analysis (group A), everything
appropriate for a 2 bin analysis (Group B) and everything appropriate
for a 4 bin analysis (Group C). I assume that group C is a subset of
Group B which is a subset of group A. I would suggest you do three
analyses for the 1 bin case (Groups A, B and C), two analyses for the 2
bin case (groups B and C) and a single analysis for the 4 bin case
(group C). Comparing A to B to C for the 1 bin case tells me what is the
impact of study selection in this situation. Same for the 2 bin case.
These types of comparison will tell about the impact of study selection
on the posterior distribution for the potencies. If there is a
substantial difference in central tendency or variance in the
posteriors, the Agency should consider not using the binning approach or
develop a method that allows for all of the data to be used in the
analysis or in the validation of the analysis.

**Charge Question 1:** I have insufficient detailed knowledge of all of
the studies being evaluated, especially on the clarity of the exposure,
to provide any definitive opinion on this question.

**Charge Question 2:** My answer to this question is no. I feel the
authors could have spent a bit more space on the toxicological data
because the clarity of the differences in response for fiber size and
type could be better supported through a careful examination of these
data.

**Charge Question 3:** There was considerable discussion of potential
differences in absorption, distribution and elimination of fibers as a
function of size, chemistry and fiber type. As a long-term goal, the
Agency might wish to pursue the development of models for ADME as a tool
for potentially addressing differences in risk due to the different
types of particles.

On the question of whether the models that are proposed are a
scientifically valid basis for the fitting effort, I believe it is.
Given that all they have to work with is population-based
epidemiological data, the exercise is nothing more than a curve-fitting
exercise to explore trends in the data. As such, these models are as
good any one might wish to use and better than some due to the long
history of these models in asbestos-related epidemiological studies.
This is not to say the model is correct; there is no doubt that this
multiplicative model is a simplification of a much more complicated
biological process. The authors may wish to try other models to explore
the implications of model choice on the eventual predicted risks. Seeing
no differences from a broad spectrum of models gives more confidence in
the results. Seeing large differences may lead to a discussion of
potential assumptions that support one model in favor of another or to
additional data/experimentation that could lead to one specific answer.

**Charge Question 4:** The assumption of Poisson variation may be
correct, but should be evaluated by testing for over-dispersion. If
there is over-dispersion, an appropriate change should be made to allow
for extra-Poisson variation.

**Sections 8.4 -- Characterizing Uncertainty In Exposure Data**

In most cases, there are multiple sources of uncertainty in the measures
of exposure reported in published epidemiological studies. Section 8.4
provides an overview of how OSWER proposes to characterize these
uncertainties, and the details of the approach are provided in Appendix
C. Application of the proposed methods to each epidemiological study are
presented in Appendix A.

***Charge Questions 5a-5d:***

5a. Have all of the important sources of uncertainty in cumulative
exposure matrices been identified? If not, what other sources should be
accounted for?

As far as I can tell, yes. However, I am no expert on this.

5b. Is it appropriate to characterize the uncertainty from each source
in terms of an independent probability density estimated using
professional judgment? If not, what alternative approach is suggested?

Of course this assumption is wrong. The real question one has to ask is
if this is appropriate in this case or whether there is sufficient
correlation between uncertainties that by treating these as independent
one is inflating the overall uncertainty. A sensitivity analysis of the
distributions chosen for each factor will tell you something about the
relative importance of that factor. It would be wise to search for a
data set

that might tell you something about correlations between the most
important factors and try using these observed correlations to look at
the sensitivity to correlation.

5c. Are the general strategies for selecting distributional forms and
parameter values described in Appendix C (and applied in Appendix A)
appropriate for characterizing uncertainty in exposure metrices? If not,
what alternative strategies are recommended?

The approach seems appropriate. I am concerned that several
distributions are based upon expert judgment and ranges that seem to
have no direct reference associated with them. These types of judgments
tend to be fairly optimistic and I suggest the sensitivity analysis look
at this carefully.

In trying to find priors, my first choice would be to identify data that
informs the prior directly, start with an uninformed prior and use a
hierarchical Bayesian model and update the prior in each iteration. If
this is not possible, use the scant data to develop an informed prior
that is not updated. Failure to do this, I would use an uninformed
prior, do some sensitivity analysis and, for priors that seem to be
important, possibly se expert solicitation to identify a prior.

5d. Based on the assumption that each of the sources of error is
independent, OSWER is proposing an approach where the errors combine in
a multiplicative fashion. Please comment on the scientific validity of
this approach and provide detailed suggestions for other approaches
OSWER should consider.

There is really not much that can be said here about this approach.
There does not seem to be a way to test the assumption. Clearly other
approaches (e.g. additive) could be used and would likely yield
different results. But these would also not appear to be testable with
the available data.

**Section 8.5. Fitting Approach**

OSWER considered a wide range of strategies for fitting the
epidemiological data to the risk models, including simple minimization
of squared errors, weighted regression, maximum likelihood methods,
measurement error models, Monte Carlo simulation, and Bayes-MCMC. Based
on the recognition that there is substantial error in both the
independent variable (observed number of cases in an exposure group) and
the independent variable (metric of cumulative exposure for the group),
OSWER is proposing Bayes-MCMC as the most robust statistical approach
for fitting the data.

***Charge Questions 6a-6b:***

6a. Is it appropriate to account for measurement error in the exposure
data by using "measurement error" models (weighted regression methods)?
If so, how would the weights assigned to each exposure value be
assigned?

I am not certain why "weighted regression" enters into this? It is
appropriate to use a measurement error model to account for measurement
error in exposure data from an epidemiological study.

6b. Is the assignment of a PDF for data quality sufficient or should
data quality be factored into a weighted likelihood analysis?

Not sure what is being asked here since you have chosen the Bayesian
approach. For the Bayesian approach, the approach for including data
quality should be fine.

6c. Do you think that the proposed strategy of fitting the risk models
to the available epidemiological data using Bayes-MCMC is scientifically
justifiable? If not, what alternative strategy do you suggest, and why?

The authors are very critical of the ML method but do not apply the same
scrutiny to the Bayesian methods. You basically can't get something from
nothing. The Bayesian approach is giving you a different answer and is
based on a range of assumptions that may be very wrong but generally are
not testable. The rosie picture painted for the Bayesian approach should
be more critically reviewed in this document. That said, the Bayesian
approach is appropriate.

**Section 8.6.2 --Specification of Priors**

Assuming that Bayes-MCMC is the method that will be used, it is
necessary to specify prior uncertainty distributions for each of the
fitted parameters, including **α~s\ ~**(the vector of study-specific
relative risks of lung cancer at zero exposure), **KL~b\ ~**(the vector
of bin-specific potency factors for lung cancer), and **KM~b\ ~**(the
vector of bin-specific potency factors for mesothelioma).

***Charge Question 7:***

7\. Are the priors proposed in Section 8.6.2 for **α~s~**, **KL~b~**,
and **KM~b\ ~**consistent with available knowledge? If not, what
alternative priors should be considered, and why?

As far as this document goes, these priors seem appropriate. However, it
seems to me that there might be enough data to use an informed prior or
even to set up a second hierarchy where the parameters for the priors
are informed by data. This would use more of the available data in a
direct fashion.

**Section 8.8 -- Other Methods For Characterizing Goodness-of-Fit**

OSWER is proposing that the initial evaluation of goodness-of-fit of
different binning strategies be based on the Bayes Factor, but is also
proposing a number of additional evaluations to assess both relative and
absolute goodness-of-fit. These are described in Section 8.8.

***Charge Questions 9a-9e:***

9a. What method(s) is(are) preferred for characterizing the absolute
goodness-of-fit of any selected binning strategy? Should any of these
methods be used to supplement the relative comparisons based on the
Bayes Factor? If so, how?

The proposed approaches seem appropriate.

9b. If different measures of goodness of fit do not yield results that
agree, which method should be preferred, and why?

That would depend entirely upon what was seen and why they differed.
Question cannot be answered in the abstract.

9c. What methodological options do you recommend for validating the
results of the modeling efforts? What are the strengths and limitations
of these options compared to others that might be available?

No comment.

9d. In lung cancer studies, it is expected that the value of α~s~ should
be relatively close to 1.0. If the fitted value of any particular value
of α~s~ is substantially higher or lower than 1.0, should this be taken
to reflect that the data set giving rise to the value are somehow flawed
or are too uncertain for use, and should be excluded? If so, what
criteria would you suggest for recognizing values that warrant concern?

You are placing too much focus on the nature of this statistic and not
asking yourself what it really means. If this value is substantially
different than 1, it means you should return to your data set and
examine why this one data set is so different. Find what that difference
is and THEN decide whether it warrants exclusion from the overall
analysis. Does it change your inclusion/exclusion criteria? Do you need
to reevaluate all of the cohorts?

9e. Is an examination performed of the residuals from the meta-analysis
a rigorous and scientifically valid assessment of homogeneity?

It is not rigorous, but it is useful.

**Section 8.9 -- Sensitivity Analysis**

OSWER is proposing an approach for evaluating the sensitivity of the
results to the various assumptions and choices used in the effort that
is based on series of "what if" tests. For example, this may include
excluding all or some of the data from one or more of the studies, and
assessing how those exclusions impact the results. Likewise, one or more
of the PDFs used to characterize uncertain input data may be changed to
evaluate if/how the results are altered.

***Charge Questions 10a-10b:***

10a. Is this "what if" approach for evaluating sensitivity
scientifically valid and useful?

Sensitivity analyses are indeed a valid and useful technigue for
understanding the importance of assumptions on the primary predictions
from the modeling exercise. However, many times we approach these types
of exercise without any idea of what we plan to do with the results. The
Agency needs to give some thought to what will be done with the results
of the sensitivity analysis.

The Agency has a fairly comprehensive approach to the sensitivity issue.
The only additional analysis we would suggest is that they consider
varying the actual form of the model being applied to these data. Some
careful thought should go into this decision for alternative models
prior to doing it, since alternate models may demand alternate prior
structures and the Agency would basically be conducting multiple
complete analyses of these data. The purpose of our suggestion is for
the Agency to get a solid "feel" for the impact of alternatives, not do
multiple full analyses of these data.

***Dr. Carol Rice***

*[Charge Question 2, section 4 (epidemiology)]{.underline}*

*Please comment on the adequacy of these sections which serve as the
scientific bases for the proposed dose-response assessment approach.
\[NOTE: is dose-response evaluated, or exposure-response?\]*

Non-cancer effects: section 4.1

It is not clear whether this section has been updated since the last
version was presented to EPA.

One useful update is Rohs et al (2008). A PubMed search shows other
citations which may be considered for radiographic changes. Non-cancer
mortality is also described in many of the cohort studies, including an
update of the Libby workers provided by Sullivan (2007).

Cancer effects: Section 4.2

The selection of the cohorts should be carefully evaluated in relation
to the usefulness of the exposure estimates in a "grouped analysis".
While the methods in an individual study may provide useful internal
comparisons, the amount of estimation needed may lead to serious
misclassification when studies are combined, as is proposed. A careful
review by industrial hygienists familiar with the sampling methods used
over time (especially impinger, MCE filter) and analyses of collected
samples (particle counting, fiber counting, fiber identification and
sizing) is needed. Work provided to us by Stayner (see Dement et al and
references) provides a benchmark of procedures against which other
exposure metrics can be compared.

The Agency might consider seeking original data from studies other than
the Charleston cohort, to determine if the original metrics might be
validated with additional data or other metrics might be created. For
example, exposures experienced by the Enterline cohort might be
described by data collected and stored by the corporation. A careful
review by hygienists of original exposure data might reveal other
alternatives: comparison temporally in time, if not side-by-side
measurements; examination of differences between particle count by
operation to replace a linear regression across all operations. Should
expert assessment be undertaken, the work of Dr. Gurumurthy Ramachandran
would be very important to consider (Ramachandran et al 2003; Hewett et
al 2006).

Dr. Peto referred to a report describing further the Canadian exposure
metric formulation; I have asked him to forward that to me. An
informative review of data available for many of the asbestos cohorts is
found in Gibbs (1994).

Role of fiber type: Section 4.3

From an industrial hygiene point of view, comparison of the exposure
estimates is a challenge from study to study. The proposal to use few
conversion factors (including industry-wide factors for various metrics)
can be expected to introduce substantial error. It is likely that this
will be non-differential leading to an inability to find any real
difference, but possible that an extreme example might result that could
lead to an erroneous conclusion (Wacholder, Dosemeci, Lubin,1991;
Wacholder, 1995).

The need for application of multiple conversion factors is illustrated
in the recent Dement study, where 10 factors were used to convert from
PCM-based estimates to estimates of exposure to fiber length and width
as would have been achieved if TEM analysis had been conducted on the
original samples. A recent report by Dodic-Fikfak (2007) of
determination of conversion factors from mppcf (using a Konimeter, not
impinger) and filter samples for a single plant resulted in five
different values, depending upon operation.

The summary by Gibbs (1994), illustrates the need for multiple
conversion factors. He describes the 4152 midget impinger samples from
the mines and mills and 3096 mill dust samples used in the Canadian
studies. In later studies looking into conversion factors between
impinger and filter samples for the Canadian work locations, he noted
that overall, the relation was only about 13% better than random
assignment; however, much of the variability was explained if job-site
and mine-site was considered. A review of papers cited by Gibbs may also
be informative; see especially Ayer, Lynch and Fanny (1965). The
discussion in Enterline (1981) provides a perspective in terms of
environmental epidemiology.

*[Charge Questions 5,6,7]{.underline}*

I was asked to provide some comment on the "inputs" to the model. See
the sections above regarding uncertainty in the exposure data, need for
IH review and input into study selection and need for IH input into the
conversion factors.

*[Charge Questions 15 a, b.]{.underline}*

*15a.* *What method is best for estimating the uncertainty in lifetime
cancer risk predications that are associated with the uncertainty in the
bin-specific potency factors?*

Defer to Dr. Cox. The Agency might consider using only animal data as a
"test of the model", as exposure in many cases is more carefully
characterized. This is of course not without uncertainty (e.g,
contamination, sizing bins) or experimental issues (e.g., dosing regime,
time to sacrifice) or animal selection/differences.

*15b. Assuming that estimates of exposure at Superfund sites will also
have uncertainty, how should the overall uncertainty in risk
predications be characterized?*

The level of uncertainty depends on the nature and extent of exposure
information, and the context of 'estimates of exposure'.

At a generic Superfund site, there will be qualitative records of what
has been placed in the site, core samples, perimeter air samples and
perhaps personal measurements of airborne particulate during
remediation. These provide little data in general to compare with a risk
prediction. Prudent actions to protect workers and public health will
include limiting potential, unknown exposure.

At Libby, there are well-characterized bulk and airborne material. These
data are better than the input into the risk prediction.

[Citations for Charge Question 2]{.underline}

[Epidemiology]{.underline}

Rohs et al. 2008. Low-level fiber-induced radiographic changes caused by
Libby vermiculite, Am J Respir Crit Care Med 177:630-637.

Sullivan PA. 2007.Vermiculite, respiratory disease and asbestos exposure
in Libby, Montana: update of a cohort mortality study.Environ Health
Persp 115:579-585.

Wacholder S, Dosemeci M, Lubin JH. 1991.Blind assignment of exposure
does not always prevent differential misclassification. Am J Epid
134:433-437.

Wacholder S. 1995 When measurement errors correlate with truth:
surprising effects of non-differential misclassification. Epidemiology
6:157-61.

[Baysian approaches to exposure assessment]{.underline}

Ramachandran G, Banerjee S, Vincent JH. 2003. Expert Judgment and
Occupational Hygiene:Application to Aerosol Speciation in the Nickel
Primary Production Industry. Ann Occup Hyg 47:461-475.

Hewett P, Logan P, Muhausen J, Ramachandran G, Banerjee S. 2006. Rating
exposure control using Bayesian decision analysis. J Occup Environ Hyg
3:568-81.

[Conversion factors for asbestos exposure measurements]{.underline}

Ayer HE, Lynch JR, Fanny JH. 1965. A comparison of impinger and
memberane filter techniques for evaluating air samples in asbestos
plants. Ann NY Acad Sci 132:274-287. (Note, incorrect volume number
cited in Gibbs---132 is correct).

Dement JM, Kuempel ED, Zumwalde RD, Smith RJ, Stayner LT, Loomis D.
2007. Development of a Fiber Size-Specific Job-Exposure Matrix for
Airborne Asbestos Fibers. OEM. 11/5/2007 (online).

Dodic-Fikfak M. 2007. An experiment to develop conversion factors to
standardise measurements to airborne dust. Arh Hig Rada Toksikol
58:179-185.

Enterline PE. 1981. Extrapolation from Occupational Studies: a
Substitute for Environmental Epidemiology. Env Health Persp 42:39-44.

Gibbs GW. 1994. The Assessment of Exposure in Terms of Fibres. Ann occup
Hyg 38:477-487.

***Dr. Leslie Stayner***

Following are my specific comments on the charge questions that I was
asked to respond to (1, 2, 3, 4, 11 and 12). As requested, I have also
included at the end of my comments several recommendations for future
research. I also want to make some general overarching comments, since I
am afraid that my main concerns might be lost in the details of my
responses to these questions.

First of all, I unfortunately do not believe that there is adequate
epidemiologic data to support the development of the bin specific risk
assessment models that EPA is seeking to develop at this time. I believe
that most if not all of the participants in our meeting agreed on this
point. The use of data on fiber size dimensions from one study to
estimate fiber size dimensions for another is simply not credible. At
this point there is only one epidemiologic study that provides fiber
size specific results, which is our reanalysis of the South Carolina
cohort \[Stayner et al. 2007\]. Although this study provides support for
the concern that long and thin fibers may be more strongly associated
with lung cancer mortality than short and thick fibers, the correlations
in the exposures to different fiber sizes make it impossible to
determine their relative potency. There is one more study underway by
Dr. John Dement at Duke University of North Carolina textile workers
that will be using essentially the same methodology as our study to
conduct fiber size specific analyses. It is possible that in the future
we may combine the data from these 2 studies and be able to obtain more
definitive estimates of the relative potency of different fiber sizes.
However, this may still not be a sufficient amount of data and both
studies are of primarily chrysotile exposed workers and thus will not
provide information on amphiboles. This is why I strongly recommend that
EPA support efforts to study other populations where it would be
possible to characterize the fiber size distributions, and in particular
of workers at the Libby facility.

Secondly, I do not believe that a good case can made that the potency of
asbestos fibers for lung cancer varies by fiber type. The only analysis
that supports this conclusion is the Hodson and Darnton paper. However,
in reaching this conclusion they disregarded the findings from South
Carolina cohort as an outlier even though most observers would rate this
study as having one of the best exposure assessments of any of the
studies. The fundamental and yet unresolved issue is that there is a
large degree of disagreement in the results from the studies (i.e.,
heterogeneity), and particularly between the findings from the South
Carolina and Quebec cohorts. I believe that the disagreement in the
results between the two studies may in part be explained by differences
in the fiber size distributions. In fact in our study we did find a
higher percentage of long and thin fibers in South Carolina \[Stayner et
al. 2007\] than what had been reported in previous studies of Quebec
mines and mills \[Gibbs and Hwang 1975, Gibbs and Hwang 1980\]. If
possible, further analyses of samples from the Quebec mines and mills
would be extremely helpful in attempting to resolve this issue. It is
also quite possible that the discrepancy in findings between these two
studies may be explained by errors in the exposure estimates for the
Quebec study, which could have weakened the observed exposure-response
relationship. The lack of correlation between mppcf and fiber/cc
measurements in the Quebec studies does suggest that there would have
been substantial misclassification of exposures from the conversion to
fiber counts. In any case, I do not believe at this time that is
appropriate for EPA to make an assumption in its modeling that there is
a difference in lung cancer potency for different fiber types.

Finally, I want to make a comment and suggestion to EPA regarding the
extremely hostile reactions we heard from many of the public
commentators at our meeting regarding this proposal. I believe that in
moving forward with this proposal while not supporting a ban on asbestos
this administration is in a sense adding insult to injury to the workers
and communities who have been victims of past exposures to asbestos. The
primary effort of the EPA at this time should be to convince this
administration and members of our Congress to pass proposed legislation
banning the use and production of asbestos products in our country. This
ban would not only insure that we will never repeat our tragic mistakes
of the past, but will also send a strong signal to other countries in
the world who are currently importing and using asbestos in large
quantities. Of course, I recognize that a ban will not address the
issues of natural occurring asbestos or the remnants of asbestos in
hazardous waste and buildings in our country and that a more reliable
risk assessment model would be extremely useful for prioritizing the
cleanup of these situations. However, until such an asbestos ban is
passed it is difficult to see how modifications in the risk assessment
models for asbestos will not be viewed with deep suspicion by the
victims of asbestos in this country.

*[Charge Question 1]{.underline}*

*Do you agree that the data are sufficient to indicate that such
differences may exist and that an effort of this type is warranted?*

This question needs to be divided into two questions. The first part
being is there sufficient information to suggest that there are
differences in potency of asbestos by fiber type and dimensions. The
second part of this question is whether such an effort as described in
this proposal is warranted. I will provide my views on each of these
questions separately below.

*Is there sufficient information to suggest that there are differences
in potency of asbestos by fiber type and dimensions?*

I agree that there is evidence that asbestos fiber potency varies by
mineral type and particle dimensions. However, my confidence in this
statement varies by cancer type. There is I believe a general consensus
in the scientific community that chrysotile asbestos is less potent than
amphiboles for mesothelioma. Support for this was discussed in a review
we published in 1997 \[Stayner et al. 1997\]. This view was strongly
supported by the reanalysis of the Davis rat inhalation studies based on
TEM data by Berman et al. \[1995\], by the meta-analyses of the
epidemiologic literature that was conducted by Hodson and Darnton
\[2000\], and by Berman and Crump \[Aeolus\] in their 2003 report
developed for the EPA. All of these analyses revealed highly
statistically significant evidence that chrysotile is less potent than
amphiboles for mesothelioma. It also worth noting that the analysis by
Hodson and Darnton also indicated that amosite is less potent than
crocidolite for mesothelioma.

The role of fiber size in mesothelioma induction is less clear. As
recently reviewed by Dodson \[2003\], the results from toxicological
studies have been conflicting with some studies indicating long fibers,
and other studies indicating short fibers may be play a role in
mesothelioma induction in animals. Short thin fibers are the predominant
type found in pleural tissues in studies of workers by Suzuki \[2001\].
However, I don't believe that what is found in the either pleural or
lung tissues can necessarily be inferred to be the fibers that actually
cause the disease (either for mesothelioma or lung cancer).

For lung cancer, I do not believe the available evidence convincingly
demonstrates that mineral type is an important determinant of potency.
My basis for this conclusion was discussed in the 1997 review article
\[Stayner et al. 1997\]. Nothing that has been published since our
review article has changed my views on this subject and if anything I
believe there is now further support for this position. The analysis of
the Davis rodent inhalation studies using a TEM analysis by Berman et
al. \[1995\] failed to demonstrate evidence of a difference in lung
tumor potency by fiber type. In their meta-analysis of the epidemiologic
studies that they performed for EPA in 2003 \[Aeolus\], Berman and Crump
also failed to find significant evidence that lung cancer mortality
varied by fiber type. Although their analysis did suggest that potency
was slightly less than amphiboles (26 to 42% in 3 different models)
these differences are likely to be explained by random error given the
fact that these differences were not found to be even close to
statistically significant (p=0.23 to 0.51 in 3 models). In addition,
these differences are so small that they are not important from a public
health viewpoint.

A much larger difference in lung cancer potency was reported in the
metanalysis reported by Hodson and Darnton \[2000\]. They suggested that
chrysotile asbestos was 10 to 50 times less potent than amphiboles.
However, as they suggest the interpretation of their findings is greatly
complicated by the large difference in lung cancer potency derived from
the studies of South Carolina textile workers, and Quebec miners and
millers. There was strong evidence of heterogeneity in the slope for
lung cancer in the Hodson and Darnton metanalysis that was largely
attributable to the differences between the Quebec and South Carolina
studies. Hodson and Darnton chose to in effect ignore the results from
the South Carolina study in developing their estimates of lung cancer
potency for chrysotile asbestos. They reasoned that "Taking account of
the excess risk recorded by cohorts with mixed fibre exposures
(generally,1%), the Carolina experience looks untypically high."
However, I think it is a very questionable judgment to ignore the
findings from the South Carolina study when in fact, as was noted in the
Berman and Crump report for EPA in 2003, this was one of the studies
with the best information on asbestos exposure levels. Actually I would
think a stronger justification could be made for deleting the Quebec
study because of the large degree of uncertainty that exists in their
exposure estimation procedures related to their conversion from particle
count measurements to fiber counts as discussed at our meeting. I have
previously demonstrated in an analysis that I presented at the EPA 2003
meeting in San Francisco that if one deletes the Quebec study the result
of the meta analysis would indicate that the potency for chrysotile
appears to be greater for chrysotile than amphiboles. I would not
advocate deleting the Quebec study, but this merely suggests that the
results from meta-analyses are not robust and dependent on whether one
includes the Quebec or the South Carolina studies. Drawing conclusions
on relative potency for lung cancer will not be possible in meta
analyses of the epidemiologic data until we have explained the large
differences in lung cancer potency between these two studies.

In contrast to fiber mineralogy, I do believe that there is substantial
evidence to indicate that lung cancer potency is likely to vary by fiber
dimensions. Stanton \[1981\] was the first to suggest that long and thin
fibers might be more potent than thick and short fibers in inducing lung
cancer based on pleural implementation studies in rats. Inhalation
studies of chrysotile asbestos exposure in rats conducted by Davis et
al. \[1988\] confirmed that long fibers were more potent in inducing
lung tumors than short fibers. The reanalysis of the Davis studies using
TEM analysis by Berman and Crump \[1995\] provided strong evidence that
long and thin fibers were most strongly related to lung cancer risk.
These toxicologic findings are supported by the results from our
recently published study of the South Carolina cohort using TEM based
size specific estimates of chrysotile exposure \[Dement et al. 2007 and
Stayner et al. 2007\]. In this study we reported that cumulative
exposure to long and thin fibers were most strongly associated with lung
cancer mortality. It should be noted, however, that short and thick
fibers were also associated with lung cancer mortality and that the
interpretation of the study findings were complicated by the strong
correlations between the different size specific exposure measures.
Although I agree that the potency of asbestos fibers is likely to vary
by fiber dimensions, I do not believe that we have adequate data at this
time to quantify these differences.

*Is the effort as described in this proposal warranted?*

Although I am sympathetic to the need for modification to our current
risk assessment paradigms for asbestos, I do not believe that the
approach outlined in this proposal would yield valid or useful results.
The simple reason for my skepticism in this regard is that the data
currently available to modify these models is grossly inadequate for
this purpose. The primary problem lies with the data available to
estimate the TEM specific levels of exposure for the cohort studies
included in this analysis. Using TEM data from one study setting to
estimate fiber size distributions at another facility and even worse for
another industry is simply not reliable. This was clearly the opinion of
the members of our panel with expertise on TEM analysis who answered no
to question 4 b in our charge (Is it scientifically valid to use
surrogate TEM data to estimate bin specific concentrations and exposure
values in studies where data are not reported?). The 2003 report
prepared for EPA by Berman and Crump \[Aeolus\] also provides empiric
evidence that this approach doe not work since the addition of fiber
size to their models resulted in at best a marginally significant
improvement in model fit for lung cancer \[p=0.04 to 0.1\] depending on
model\], and mesothelioma \[p=0.05 to 0.24\].

As far as estimating difference in potency by fiber type for lung cancer
the EPA approach will face the same problems as encountered in the
previous meta-analyses with regard to the large extent of heterogeneity
in the study findings and the unexplained differences in potency for
lung cancer risk observed in the South Carolina and Quebec studies.

It is important to recognize that although the report includes an
extensive effort to estimate the uncertainties underlying the estimation
of bin specific and fiber type exposures, that this effort can not
correct for these errors and could even conceivably introduce biases
into the estimation of the model parameters. There is so much
uncertainly about how to estimate the pdfs for their uncertainty
analysis that I am concerned that their misspecification of the pdfs
could actually introduce error into the estimation process.

Given these concerns, I think it would be irresponsible for EPA to move
forward with this risk assessment model until we have more reliable data
on which to base such an assessment. I do not accept the argument that
if EPA conducts this analysis it would be viewed by the agency and
perhaps more importantly by the public as "hypothesis generating
exercise". Coming from a government agency, the results from this
assessment will undoubtedly be used by lawyers and policy makers. The
testimonies of several lawyers at our meeting demonstrated how even the
2003 Berman and Crump analysis is being currently used in legal cases.
Furthermore, it is not even credible that results from the analysis
would not be used by EPA since it is stated in the overview of the
report (page 1) that OSWER is proposing to use this as "an interim
approach to account for the potential differences of cancer potency
between different mineral types and particle size distributions."

*[Charge Question 2- Sections 6 & 7]{.underline}**: ***

Sections 6 and 7 provide an accurate summary of the risk assessment
methods that were used in the EPA 1986 and the Berman and Crump analysis
that was conducted for EPA. Berman and Crump have developed manuscripts
for three papers based on their work for EPA, which I believe they are
submitting for publication in a peer review journal. There may be some
slight modifications in these papers that should have been noted in this
document.

As a Co-Chair of the 2003 panel that reviewed the EPA proposal in 2003,
I do not feel the description of our conclusions is accurately portrayed
in this report. This report suggests (page 46) that the "consultation
panel generally endorsed the basic idea of a multi-bin approach".
Although I think this is true conceptually, several members of the panel
had very serious misgivings about our ability to develop such a model
based on the data available at that time. This report then lists a
number of issues and limitations of the 2003 report, but it seems to
attribute these comments to our panel. Our panel made individual
comments and I don't believe this is a fair summary of all of our
concerns. One over riding concern that I had at the time was that there
was insufficient epidemiologic data to support the development of a
fiber size specific model. This unfortunately remains my chief concern
today. I also expressed a concern that the 2003 model provided fiber
type specific slopes for lung cancer risk even though their own analysis
did not support their being significant evidence for this.

*[Charge Questions 3a-c]{.underline}*

*3a. Do you agree that the lung cancer and mesothelioma risk models that
are proposed are a scientifically valid basis for this fitting effort?*

I believe the EPA 1986 models are a reasonable starting point for this
effort. Crump and Berman \[Aeolus 2003\] in their 2003 report for the
EPA tested the the EPA 1986 models using raw data from the South
Carolina, Quebec and Wittenoon study cohorts and found that these models
provided reasonably good predictions for these studies.

*3b. Should additional model forms be investigated?*

While it would be nice to evaluate biologically based models, such as
the 2 stage clonal expansion model, as was suggested by Dr. Cox, I do
not believe that this is possible given the data that is available to
EPA. It would also be desirable to test the assumption that cumulative
exposure (duration x intensity) is an appropriate metric for the lung
cancer model as was suggested by Dr. Peto. Again I don't think the data
exists in the papers to allow separate modeling of duration and
intensity.

3c. For lung cancer, the current risk model is multiplicative with the
risk from smoking and other causes of lung cancer. Should the nature of
the interaction between asbestos and smoking be further evaluated?

Yes I believe this relationship should be further evaluated. The
observation that there is a multiplicative effect of smoking and
asbestos is largely based on the results from the study by Selikoff of
insulator workers \[Selikoff et al. 1968\]. There in fact is a
tremendous variation in the evidence regarding whether this relationship
is multiplicative or additive \[see Steenland and Thun 1986 and Vainio
and Boffetta 1994\]. A recent analyses of this issue suggests that in
fact the relationship may be somewhere between additive and
multiplicative \[Wraith and Mengersen 2008\].

*4a. Is fitting at the group level preferred to fitting at the study
level?*

I do not believe that fitting at the group level should be preferred to
fitting at the study level. The primary advantage of fitting at the
group level appear to be that it permits a better description of the
uncertainties involved. However, this advantage does not outweigh the
serious loss of information that results from grouping of the data. For
several of the studies (South Carolina, Quebec and Wittenoon), it is
possible to obtain the raw data and to directly fit a slope (K~L~ or
K~M~) for these studies. Using group categories for these studies
results in substantial misclassification of exposures since it is
necessary to assign a midpoint or mean for each of the categories. In
act Berman and Crump in 2003 were able to obtain the raw data from these
studies, and I would urge the EPA to use these individual data in
subsequent analyses.

*4b. If so, is it scientifically justifiable to use a Poisson likelihood
model for the observed number of cases in each group?*

Yes it is scientifically justifiable and appropriate to use a Poisson
likelihood for the analysis of grouped data. If the individual study
results are modeled instead, as I would suggest, one could probably
assume a normal or more likely a log normal distribution applies. In any
case, it will be important to test the adequacy of the distributional
assumption made.

*[Charge Questions 11 & 12]{.underline}*

*11a. Are the study-specific selection rules proposed above
scientifically valid for the intended uses? Should any additional
selection rules be added?*

I do not believe these selection rules are appropriate. The first rule
that a study has to be published in a peer reviewed journal is in
principle okay, but is not okay as it was applied. This rule was used as
a basis for rejecting the use of the raw data from South Carolina,
Quebec, and Wittenoon which was used in the previous analysis conducted
by Berman and Crump for EPA in 2003. Although the findings from these
studies have all been published in peer review journals, the report
rejected using the data from these studies because the new analysis is
not published. I believe this is being too restrictive and results in
the loss of some valuable information for the purposes of this risk
assessment.

The second criterion requires studies to provide information that can be
expressed in terms of the models selected is reasonable in principle,
but it also too restrictive as applied. Case-control studies and other
studies that could not be used to estimate the alpha were rejected on
this basis. However, the alpha is a correction parameter and one that is
not necessary in studies where there is not likely to be a difference in
the background rates of diseases in the exposed and unexposed groups.
For most case-control studies alpha would be expected to be one if the
study is properly designed.

The third criterion is unclearly written, but my understanding from the
meeting was that this criterion was primarily intended to exclude
studies where the percentage of different fiber types or dimensions was
not constant over time. This was the basis for the exclusion of the
Selikoff study of insulators. This study was used in the 2003 report by
Berman and Crump. It seems it would be preferable to include it, and
express this issue is an uncertainty in the analysis.

As a general principle, I believe it is better not to exclude too many
studies in a meta-analysis. Rather I think the approach should be to
investigate whether weaknesses and strengths of the study designs are
explanations of the heterogeneity observed in the analysis.

*11b. Is it appropriate to assume that all workers in a cohort are
exposed to the an atmosphere with a constant composition (i.e., the
mixture of asbestos types and sizes is constant) unless the authors
report information to the contrary? If this is not an appropriate
assumption, what alternative strategy would be available?*

No, I do not believe it is appropriate to assume the composition (fiber
size and type) is a constant in the study if the authors do not state
otherwise. Fiber dimensions were not found to be constant over different
operations in our study of South Carolina chrysotile textile workers
\[Dement 2008\]. In many study situations the fiber size dimensions
would be likely to vary over time. The use of fiber types also would be
expected to have varied substantially in many industries over time. The
only alternative would be to limit the analysis to studies with well
documented information on how the fiber size and type varies over time
and by operations in the study facilities. Unfortunately this only
available for one study, which is the recent study of South Carolina
textile workers \[Stayner et al. 2007\] or from the toxicologic studies?

[Research Recommendations]{.underline}

1\. Meta and Pooled Analyses

The Agency should consider conducting or funding a new meta-analysis to
further evaluate the evidence that asbestos cancer potency varies by
fiber type. This effort is important because there are new studies that
were not incorporated in the most recent meta-analyses conducted by
Hodson and Darnton or Berman and Crump \[2003\]. It is critically
important that these analyses fully assess heterogeneity and possible
explanations for any heterogeneity.

The Agency should also consider the possibility of funding a pooled
analysis in which the raw data from the individual epidemiologic studies
are combined. Such an effort would be a substantial improvement over a
meta-analysis since the analysis would not be restricted to the
categories of exposure and other covariates that were reported in the
different studies. It would also be possible in this analysis to
evaluate the assumption that cumulative exposure (product of duration
and intensity of exposure) is an appropriate metric for the development
of the lung cancer model. A good model of how this study might be
conducted is the IARC pooled analysis for exposure to silica \[Steenland
et al.

2001\]. In fact the IARC is uniquely well positioned to conduct such
international collaborative efforts, and I would highly recommend
pursuing this possibility with them. I have already had a preliminary
discussion with one scientist at IARC (Dr. Kurt Straif) and he was very
interested in this possibility particularly since IARC is re-evaluating
asbestos next year.

2\. Libby Montana

There is a critical need for analyses of more epidemiologic studies
using TEM based fiber size specific estimates of exposures as was
conducted in the recently published South Carolina textile cohort study
\[Stayner et al. 2007\]. Based on discussions at our meeting it appears
that there are old sampling filters and slides that may be reanalyzed
using TEM methods to develop a fiber size specific exposure matrix for
the study of workers that was recently updated by Sullivan \[2007\]. It
may also be possible to do fiber size specific analyses with the
community but I think this is unlikely given the limited number of cases
of respiratory disease that have been reported. It may also be possible
to find other asbestos cohorts where a fiber size specific analysis
could be conducted.

3\. Medical Screening & Treatment

There is to my knowledge hardly any current research on treatment for
asbestos related diseases. I believe this is a very important and
promising area for future research. Mesothelioma incidence in the U.S.
has just begun to peak and thus there are still many workers and
community exposed individuals who will develop this disease in the
future. There also remains a large burden from lung cancer and
asbestosis in our country. Research on secondary prevention of these
diseases either through early detection or treatment at an early
preclinical stage of the disease has great potential for preventing the
further development of asbestos related diseases.

References

Bermann DW, Crump KS, Chatfield EJ, Davis JM, Jones AD. The sizes,
shapes, and mineralogy of asbestos structures that induce lung tumors or
mesothelioma in AF/HAN rats following inhalation. Risk Anal. 1995 Apr
(15(2):181-95.

Berman DW and Crump KS. New metrics for assessing asbestos-related
cancer risk that address fiber size and mineral type. Unpublished
report. 2003.

Davis JM, Jones AD. Comparisons of the pathogenicity of long and short

7 fibres of chrysotile asbestos in rats. Br J Exp Pathol
1988;69(5):717-37.

Dement JM, Kuempel E, Zumwalde R, Smith R, Stayner L and Loomis D.
Development of a fiber size specific job-exposure matrix for airborne
asbestos fibers. *Occup. Environ. Med.* published online 5 Nov 2007.

Dodson Asbestos fiber length as related to potential pathogenicity: a
critical review. AJIM 44:291--297 (2003).

Gibbs GW and Hwang CY. Physical parameters of airborne asbestos fibres

in various work environments-Preliminary findings. Am Ind Hyg Assoc J
1975;36(6):459.

Gibbs GW and Hwang CY. Dimensions of airborne asbestos fibres. In:

Biological Effects of Mineral Fibers. JC Wagner, Ed. IARC Scientific

Publications, Lyon, France 1980.

Selikoff I, Hammond E, Churg J. Asbestos exposure, smoking and
neoplasia. *Journal of the American Medical Association* 1968;
204:104--110.

Stanton MF, Layard M, Tegeris E, Miller E, May M, Morgan E, Smith A.
1981.

3 Relation of particle dimension to carcinogenicity in amphibole
asbestoses and

4 other fibrous minerals. J Natl Cancer Inst 1981;67:965.

Stayner LT, Dankovic D, and Lemen R. Response to letters from Wagner JC
on Asbestos Related Cancer and the Amphibole Hypothesis. Am J Pub
Health, 1997:87(4);687-8.

Stayner LT, Kuempel E, Gilbert S, Hein M, Dement J. An epidemiologic
study of the role of chrysotile asbestos fiber dimensions in determining
respiratory disease risk in exposed workers. Occup Environ Med. 2007 Dec
20; \[Epub ahead of print\]

Steenland K, Mannetje A, Boffetta P, Stayner L et al. Pooled
exposure-response analyses and risk assessment for lung cancer in 10
cohorts of silica-exposed workers: an IARC multicentre study. Cancer
Causes and Controls, 2001;12;773-784.

Steenland K, Thun M. Interaction between tobacco smoking and
occupational exposures in the causation of lung cancer. *Journal of
Occupational Medicine* 1986; 28:110--118.

Sullivan PA. Vermiculite, respiratory disease, and asbestos exposure in
Libby, Montana: update of a cohort mortality study. [Environ Health
Perspect.](javascript:AL_get(this, 'jour', 'Environ Health Perspect.');)
2007 Apr;115(4):579-85. Epub 2007 Jan 3. .

Suzuki Y, Yuen SR. 2001. Asbestos tissue burden study on human malignant
mesothelioma. Ind Health 39:150--160.

## [**Vainio H**](http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term="Vainio H"%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus), [**Boffetta P**](http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term="Boffetta P"%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus). Mechanisms of the combined effect of asbestos and smoking in the etiology of lung cancer. **[Scand J Work Environ Health.](javascript:AL_get(this, 'jour', 'Scand J Work Environ Health.');) 1994 Aug;20(4):235-42.** 

[Wraith
D](http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term="Wraith D"%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus),
[Mengersen
K](http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&Cmd=Search&Term="Mengersen K"%5BAuthor%5D&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DiscoveryPanel.Pubmed_RVAbstractPlus).Assessing
the combined effect of asbestos exposure and smoking on lung cancer: a
Bayesian approach. [Stat
Med.](javascript:AL_get(this, 'jour', 'Stat Med.');) 2007 Feb
28;26(5):1150-69.

***Dr. James Webber***

I have split my comments into two sections. The first section, *A.
Charge Questions*, is devoted to answering specific charge questions.
The second section, *B. Need for Future Research*, is on the final page
and sketches my thoughts on what must be done before the EPA can
consider going forward with their OSWER risk-assessment proposal.

*A. Charge Questions*

Below, I have answered the charge questions on which I serve as primary
and secondary lead. In addition, I have responded to other charge
questions where I felt I could make meaningful contributions.

Charge Question 1: Do you agree that the data are sufficient to indicate
that such differences may exist and that an effort of this type is
warranted?

No. A careful review of existing data reveals that environmental
exposure measurements are currently insufficient and/or inadequate for
developing a new risk-assessment model. Conversions of impinger (dust)
concentrations and even PCM (fiber) data into (TEM-equivalent)
fiber-size distributions cannot be considered reliable because of the
orders-of-magnitude uncertainty at each conversion step. At present,
only the data of Stayner *et al.* (2007) provide dependable fiber-size
distributions that are associated with known lung cancer and
mesothelioma outcomes. As more TEM-based environmental exposure data
that are directly associated with health outcomes become available, this
effort could be re-visited.

References

Leslie T Stayner, Eileen Kuempel, Steve Gilbert, Misty Hein and John
Dement. An epidemiologic study of the role of chrysotile asbestos fiber
dimensions in determining respiratory disease risk in exposed workers.
*Occup. Environ. Med.* published online 20 Dec 2007;

*Charge Question 2*: Please comment on the adequacy of these sections
which serve as the scientific bases for the proposed dose-response
assessment approach.

Section 6

> The conversion factor in Section 6.2 ("k", page 34) for "relative
> mesothelioma hazard" includes RMH~all~ (page 35). RMH~all~ excludes
> friction-product data because of their "uncertainty in the KL~PCM~
> values", where KL~PCM~ is lung cancer potency. This exclusion is
> unwarranted in that the physical/biological/chemical mechanisms that
> cause lung cancer have not been convincingly related to mesothelioma
> mechanisms.

Section 7

> 1\) The Aeolus model that lumps amosite and crocidolite into one
> amphibole category must be re-evaluated. The fiber size distributions
> of the two amphiboles are extremely disparate, as seen in the tables
> and figures of Appendix B. For example, crocidolite has a lower PCME,
> \~0.007, versus \~0.3 for amosite. Chrysotile and the minor amphiboles
> are midway at \~0.04-0.12.
>
> 2\) Equations 7-1 and 7-4 use the percentage of PCME fibers as
> denominators of lung cancer potency. This reduces the significance of
> fibers shorter than 5 µm and/or narrower than 0.25 µm. A review of the
> raw data in Appendix B reveals that using PCME as a denominator
> disregards the role of more than 90% of the airborne asbestos fibers.
> This extreme bias drives the modeled potency of these numerous small
> fibers to be self-fulfillingly minor. This also creates positive bias
> for amosite potency when one considers that the PCME for amosite is
> much greater than for other asbestos types. Apparently a similar
> approach was used for mesothelioma, where downplaying these small
> fibers may be an even more egregious oversight. Evidence continues to
> build that smaller fibers are more likely to reach extrapulmonary
> sites and be associated with mesothelioma (Dodson *et al., 1990;
> Dodson, 2006; Suzuki & Yuen, 2001).*
>
> 3\) Yes, I agree with EPA 2003 decision, as outlined in Section 7.8,
> to not pursue the Aeolus method.

References

Dodson, R.F. *et al.* Asbestos content of lung tissue, lymph nodes and
pleural plaques from former shipyard workers, *Am. Rev. Respir. Dis.*
142, 843-847, 1990.

Dodson, R.F. Analysis and relevance of asbestos burden in tissue. In
*Asbestos: Risk\
Assessment, Epidemiology, and Health Effects*, Dodson, R.F and Hammar,
S.P. ed., CRC Press, Boca Raton, FL, 2006.

Suzuki, Y., and Yuen, S.R. Asbestos tissue burden study on human
malignant mesothelioma, *Ind. Health*, 39, 150-160, 2001.

*Charge Question 3c: For lung cancer, the current risk model is
multiplicative with the risk from smoking and other causes of lung
cancer. Should the nature of the interaction between asbestos and
smoking be investigated further? If so, how should this be done? Do you
think the model would be sensitive to additional quantification of the
interaction between smoking and asbestos?*

> One possible investigation would be the potential interaction of
> fiber-width exposure and smoking. Fiber width is the primary
> determinant of aerodynamic diameter, which causes thicker fibers to be
> intercepted in the upper portions of the bronchial tree. The
> compromised mucociliary escalator in smokers probably enhances
> residence time of these thicker fibers in this upper region. Hence an
> epidemiologic study using fiber-width bins for asbestos-worker
> exposure might reveal different lung cancer potencies for specific
> fiber widths for smokers versus non-smokers.

*Charge Question 8a. Do you agree that multiple binning strategies
should be evaluated, or do you believe that a physiological basis exists
that can be used to identify a particular set of length and width
cutoffs that should be assessed? If so, what would those length and
width cutoffs be, and can these bins be implemented considering the
limitations in the available TEM particles size data sets? (see Section
10)*

> Multiple binning should be evaluated, ***but only using TEM-analyzed
> environmental exposure data that is directly associated with health
> outcomes***. Studies continue to reveal the importance of fiber width
> in potency. Fiber width is the most critical dimension in determining
> deposition site in the respiratory system, plays a significant role in
> determining surface area exposed to tissue, and may be a factor in
> mobilizing fibers from alveoli to pleural space. Future attempts to
> model fiber potency should have at least two bins for width. One
> possible width division could be an aerodynamic diameter of 2.5 µm,
> which is the cutpoint for EPA fine (\~respirable) particles. This
> would be \~0.5 µm width for amphibole asbestos and \~0.65 µm width for
> chrysotile.

*Charge Question 11a: Are the study-specific selection rules proposed
above scientifically valid for the intended uses? Should any additional
selection rules be added?*

The first criterion, "published in a refereed journal" is too
restrictive given the paucity of studies that have been suggested by
OSWER for this undertaking. While publication in a peer-reviewed journal
is a good indicator of quality, one can't be certain that lack of such
publication is an indicator of poor quality. It would be appropriate to
let a subset of the Science Advisory Board (SAB) act as reviewers of
unpublished candidate studies to determine if they meet certain quality
criteria. Probably all members of the SAB have served as reviewers for
publications and would certainly wield a fine-toothed comb on any
studies that are proposed for inclusion. However, to retain the
transparency of this effort, it would be appropriate to continue
exclusion of those studies whose details cannot be disclosed to the
public, as suggested in Section 9.3.

*Charge Question 12a: Are you aware of any studies that should be
included in the model fitting effort that are currently excluded or
omitted? If so, what are these studies, and do they meet the
requirements for study inclusion?*

Stayner et al.'s (2007) recent study, using TEM results from archived
filters from a South Carolina textile plant during the 1960s, provides
the only set of data that can currently be used for reliable risk
assessment.

References

Leslie T Stayner, Eileen Kuempel, Steve Gilbert, Misty Hein and John
Dement. An epidemiologic study of the role of chrysotile asbestos fiber
dimensions in determining respiratory disease risk in exposed workers.
*Occup. Environ. Med.* published online 20 Dec 2007;

*Charge Question 13a: Is it scientifically justifiable to employ a
default dust-to-PCM conversion factor when there are no site-specific
data available?*

No. The OSWER statement in Section 10.2 "However, most values of CF are
found to range between 1 and 10 PCM s/cc per mppcf (USEPA 1986)." is
misleading when compared to the data presented in Table C-1, where CF
ranges from 0.1 to 21.9. Thus the potential for error in using any
default value is enormous when on-site impinger/PCM comparisons are not
available. Impinger data cannot be reliably used to derive PCM
concentrations, let alone TEM fiber-size distribution.

*Charge Question 14a: Are the point estimates and uncertainty
distributions for the fraction amphibole term proposed for each study
scientifically valid?*

No. The data from Addison and Davis for 81 samples of chrysotile were
produced by XRD, which cannot distinguish asbestiform tremolite from
non-asbestiform tremolite. Furthermore, since XRD measurements are made
largely on the basis of mass, a single moderate tremolite fiber could
weigh as much as a ten, or even a thousand, chrysotile fibers. Given the
unknown impact of any tremolite found in these chrysotile samples, no
amphibole fraction should be given to chrysotile that was not mixed with
amphibole asbestos on-site. But this skirts the issue that ***only
TEM-analyzed environmental exposure data that is directly correlated
with health outcome should be used for risk assessment**.* TEM analysis
will unequivocally determine what fraction of airborne asbestos, if any,
was amphibole.

*Charge Question 14b: Is it scientifically valid to use surrogate TEM
data to estimate bin-specific concentrations and exposure values in
studies where these data are not reported? If not, what alternative
approach could be followed, or what additional data would be helpful?*

No. Comparisons of concurrently collected TEM and PCM concentrations
have produced inconsistent conversion factors. Hence, PCM data cannot be
used to create TEM-equivalent fiber-size distributions.

*Charge Question 14d: Are the point estimates and uncertainty
distributions for the fraction amphibole term scientifically valid?*

No. Summarizing my comments for 14.a: The tremolite contamination of
chrysotile remians completely indeterminate in a fiber-population sense.
An amphibole fraction should not be applied to chrysotile. ***Only TEM
analysis of exposure environments can reliably determine the extent of
exposure to amphiboles.***

*Charge Question 14f: Would the model benefit by establishing a common
lower cut-point in diameter to normalize the lower detection limit
across studies?*

No. TEM analysts are capable of detecting even the thinnest asbestos
fiber. Given the probable

importance of width in potency, raw diameter measurements from TEM
should be utilized.

B. Need for Future Research

[TEM Analsysis of Retrospective Environmental Exposure]{.underline}

The greatest need for the type of risk assessment outlined in the OSWER
proposal is additional environmental exposure data produced by TEM
analysis. Only TEM can reliably reconstruct the bivariate fiber-size
distributions needed for modeling and unequivocally determine whether or
not amphibole asbestos was present along with chrysotile in the
environments under study. Archived filters representing occupational
environments that produced lung cancer and mesothelioma should be sought
and analyzed by TEM, as done by Stayner et al. (2007). Even archived PCM
slides from these environments should be sought for TEM analysis. This
would, of course, require development of a methodology to transfer
asbestos reliably from under the cover slip to a TEM grid.

[Animal Studies]{.underline}

I leave it up to SAB panel members who have more expertise in this area
to make recommendations. However, I would strongly caution that
non-inhalation studies be performed with fiber-size distributions that
are appropriate to the targeted cell types. Throwing big fibers onto
pleural mesothelial cells does not replicate what happens in people or
animals, as evidenced by the works of Dodson (2006) and Suzuki et al.
(2001). Elutriation has proven to be an effective means of producing
fibers of the desired aerodynamic diameters (Webber et al. 2008).

References

Dodson, R.F. Analysis and relevance of asbestos burden in tissue. In
*Asbestos: Risk\
Assessment, Epidemiology, and Health Effects*, Dodson, R.F and Hammar,
S.P. ed., CRC Press, Boca Raton, FL, 2006.

Leslie T Stayner, Eileen Kuempel, Steve Gilbert, Misty Hein and John
Dement. An epidemiologic study of the role of chrysotile asbestos fiber
dimensions in determining respiratory disease risk in exposed workers.
*Occup. Environ. Med.* published online 20 Dec 2007.

Suzuki, Y., and Yuen, S.R. Asbestos tissue burden study on human
malignant mesothelioma, *Ind. Health,* 39, 150-160, 2001.

Webber, J.S., D.J. Blake, T.J. Ward, and J.C. Pfau. 2008. Separation and
Characterization of Respirable Amphibole Fibers from Libby, Montana.
*Inhal. Toxicol.* 20:8, 733 --- 740.

**Additional Materials Supplied by Dr. Julian Peto**

> **Are Attached**
>
> **1985 Comments on the first draft of the 1986 EPA Report**
>
> **1981 Report to the EPA on Asbestos in Schools**