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EPA Document# EPA-740-S-23-001
August 2023
Office of Chemical Safety and
Pollution Prevention
&ERA
United States
Environmental Protection Agency
White Paper:
Quantitative Human Health Approach to be Applied in the
Risk Evaluation for Asbestos Part 2 -
Supplemental Evaluation including Legacy Uses and
Associated Disposals of Asbestos
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33 TABLE OF CONTENTS
34	ACKNOWLEDGEMENTS	4
35	1 INTRODUCTION	5
36	1.1 Overview	5
37	1.2 Summary of Part 1 of the Risk Evaluation	6
38	1.3 Scope and Purpose of Part 2 of the Risk Evaluation	6
39	2 STRUCTURE OF THE WHITE PAPER	9
40	3 SYSTEMATIC APPROACH TO IDENTIFY DOSE-RESPONSE INFORMATION	10
41	3.1 Step 1: Comprehensive Literature Search	11
42	3.2 Steps 2 & 3: Studies Meeting PECO Criteria at Title/Abstract and Full-Text Screening	12
43	3.3 Steps 4 & 5: Filtering of Studies for Dose-Response Consideration	12
44	3.3.1 Step 4: Standardized Mortality Ratios and Regression Analysis	12
45	3.3.2 Step 5: Exposure Measurement and Exposure Assignment in Analysis	12
46	3.3.2.1 Exposure Measurement	12
47	3.3.2.2 Exposure Assignment in Analysis	13
48	3.4 Step 6: Consideration of Cohorts for Dose-Response Analysis	13
49	4 NON-CANCER DOSE-RESPONSE FOR ASBESTOS	15
50	4.1 Systematic Approach for Identification of Epidemiologic Cohorts for Non-cancer Effects	15
51	4.2 IRIS Libby Amphibole Assessment: Non-cancer Dose-Response	17
52	4.3 Quantitative Non-cancer Approach for the Risk Evaluation for Asbestos Part 2	18
53	5 CANCER DOSE-RESPONSE FOR ASBESTOS	20
54	5.1 Identification of Epidemiologic Cohort for Cancer Dose-Response	20
55	5.2 1988 IRIS Asbestos Assessment	25
56	5.3 IRIS Libby Amphibole Assessment Cancer Dose-Response	26
57	5.4 Part 1 Risk Evaluation for Asbestos: Dose-Response	27
58	5.5 Part 2 Risk Evaluation for Asbestos: Quantitative Cancer Approach	29
59	6 SUMMARY AND NEXT STEPS	32
60	REFERENCES	33
61	APPENDICES	38
62	Appendix A ABBREVIATIONS AND ACRONYMS	38
63	Appendix B SYSTEMATIC REVIEW APPROACH	40
64	B.l Data Search and Screening	43
65	B.l.l Data Search	43
66	B.l.2 Data Screening	43
67	B,2 Identification of Studies Potentially Informative for Dose-Response Analysis	44
68	B.3 Data Quality Evaluation	49
69	B.4 Consideration of Epidemiologic Cohorts for Dose-Response Analysis	50
70	Appendix C NON-CANCER EPIDEMIOLOGIC COHORTS	51
71	C.l Cohorts Included in the IRIS Libby Amphibole Assessment	51
72	C.2 Cohorts Not Previously Considered in Non-cancer Assessments	52
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73	Appendix D CANCER EPIDEMIOLOGIC COHORTS	54
74	D. 1 Cohorts Included in the Risk Evaluation for Asbestos Part 1	54
75	D.2 Cohorts Included in the IRIS Libby Amphibole Asbestos Assessment	59
76	D.3 Cohorts (Mixed-Fiber) Included in the IRIS Asbestos Assessment	59
77	D.4 Cohorts Not Included in Existing EPA Assessments	61
78	Appendix E LITERATURE INVENTORY FORM	63
79	Appendix F POPULATIONS, EXPOSURES, COMPARATORS, AND OUTCOMES
80	(PECO) CRITERIA FOR PART 2 OF THE RISK EVALUATION FOR ASBESTOS	65
81	Appendix G DATA QUALITY EVALUATION CRITERIA	68
82
83	LIST OF TABLES	
84	Table 4-1. Cohorts Identified for Consideration in Asbestos Part 2 Non-cancer Dose-Response
85	Analysis	15
86	Table 5-1. Cohorts Identified for Consideration in Asbestos Part 2 Cancer Dose-Response Analysis.... 21
87	Table 5-2. Comparison of EPA Inhalation Unit Risk Values for Asbestos	30
88
89	LIST OF FIGURES	
90	Figure 3-1. Schematic of the Approach Used to Identify Epidemiologic Studies for Dose-Response
91	Consideration	11
92
93	LIST OF APPENDIX TABLES	
94	TableApx F-l. PECO Criteria for Asbestos Part 2 (Legacy Uses and Associated Disposals)	65
95	Table Apx F-2. Major Categories of "Potentially Relevant Supplemental Material"	67
96	Table_Apx G-l. Mesothelioma Criteria	68
97	Table Apx G-2. Other Outcomes Data Quality Evaluation Criteria	83
98
99	LIST OF APPENDIX FIGURES	
100	FigureApx B-l. Literature Inventory Tree - Environmental and Human Health Hazard for Asbestos
101	Part 2	41
102	Figure Apx B-2. Literature Flow Diagram Presenting the Identification, Screening, and Evaluation of
103	Literature	42
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ACKNOWLEDGEMENTS	
This report was developed by the United States Environmental Protection Agency (U.S. EPA), Office of
Chemical Safety and Pollution Prevention (OCSPP), Office of Pollution Prevention and Toxics (OPPT).
Acknowledgements
The Assessment Team gratefully acknowledges the participation, input, and review comments from
OPPT and OCSPP senior managers and science advisors as well as assistance from EPA contractors
Battelle (Contract No. EPW16017), ERG (Contract No. 68HERD20A0002), ICF (Contract No.
68HERC19D0003), SpecPro Professional Services, LLC (Contract No. 68HERC20D0021), General
Dynamics Information Technology, Inc. (Contract No. HHSN316201200013W), and SRC (Contract No.
68HERH19D0022). Special acknowledgement is given for the contributions of technical experts from
EPA's Office of Research and Development (ORD), including Tom Bateson and Leonid Kopylev.
Docket
Supporting information can be found in the public docket (Docket ID: EPA-HQ-QPPT-2023-0309).
Disclaimer
Reference herein to any specific commercial products, process, or service by trade name, trademark,
manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring
by the United States Government.
Authors: Jennifer Nichols (Assessment Lead), Christelene Horton, Ryan Klein, and Leora Vegosen
Contributors: Rony Arauz Melendez, Sarah Au, Jone Corrales, Ann Huang, Ross Geredien
Technical Support: Mark Gibson, Hillary Hollinger, Grace Kaupas
Direction, input, and approval was given by OCSPP scientific and executive leadership.
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1 INTRODUCTION
1.1 Overview	
EPA's programs have evaluated various aspects of asbestos hazard and exposure over many decades.
Pursuant to TSCA section 6(b)(2)(A), asbestos was designated as one of the first 10 chemical substances
for the OPPT's initial risk evaluations in December 2016 (81 FR 91927). EPA's Integrated Risk
Information System (IRIS) in ORD completed an Asbestos Assessment and Libby Amphibole Asbestos
(LAA) Assessment in 1988 and 2014, respectively, which are used by EPA program offices such as risk
assessments conducted under the Superfund program in the Office of Land and Emergency Management
(OLEM).
OPPT's Risk Evaluation for Asbestos Part 1: Chrysotile Asbestos (hereafter "Part 1 of the Risk
Evaluation" or "Part 1") was released in December 2020 (U.S. EPA. 2020). Part 1 focused on inhalation
exposures and mesothelioma and lung, laryngeal, and ovarian cancer and did not evaluate oral or dermal
exposures or non-cancer effects. Part 1 also excluded consideration of all asbestos fiber types besides
chrysotile and is solely focused on ongoing uses. EPA is currently developing Part 2 of the Risk
Evaluation for Asbestos (hereafter "Part 2 of the Risk Evaluation" or "Part 2") that will provide a more
comprehensive evaluation of the human health risks of asbestos, including all fiber types as well as
cancer and non-cancer effects from all relevant routes of exposure, which EPA agreed to consider as
part of an agreement that was reached for the purpose of resolving a petition for review of Part 1 of the
Risk Evaluation (see AD AO, et al. v. EPA, No. 21-70160 (9th Cir. Oct. 2021)).
For the human health assessment in Part 2, OPPT has continued to focus on epidemiologic evidence and
evaluated cancer and non-cancer evidence and conclusions from the existing EPA assessments in
addition to other studies identified from a recently conducted systematic review approach.1 The purpose
of this white paper is to describe the systematic review considerations and criteria for identifying studies
for dose-response analysis, to evaluate and compare existing cancer inhalation unit risks (IURs, see also
Footnote 3) and the non-cancer point of departure (POD) with the results of the new systematic review,
and to propose a cancer IUR and non-cancer POD for use in Part 2.
In summary, OPPT has made the following findings:
•	OPPT conducted systematic review to identify the reasonably available information relevant for
consideration in the quantitative human health approach to be applied in Part 2 of the Risk
Evaluation for Asbestos. This included identification of cancer and non-cancer epidemiologic
studies from oral, dermal, and inhalation routes of exposure.
•	OPPT has not identified any cancer or non-cancer epidemiologic studies from oral or dermal
exposures that support dose-response analysis; therefore, OPPT is not proposing cancer or non-
cancer values for these routes.
•	For inhalation exposures, OPPT has identified several inhalation epidemiologic studies (or
cohorts) for non-cancer effects, including some that were considered in the IRIS LAA
Assessment (U.S. EPA. 2014b). However, none of those studies warranted an updated dose-
response analysis for the non-cancer POD. OPPT is proposing to use the existing POD of
2.6x 10~2 fiber/cc from the IRIS LAA Assessment to assess non-cancer risks in Part 2 with
application of appropriate uncertainty factors (UFs).
1 While the white paper specifically focuses on the quantitative human health assessment and dose-response considerations.
Part 2 of the Risk Evaluation for Asbestos will address studies relevant to hazard identification but not informative for dose-
response assessment.
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•	OPPT did not identify any inhalation cancer cohorts beyond those considered by previous EPA
assessments, including for cancers other than mesothelioma and lung cancer, which would
warrant an updated dose-response assessment.
•	The existing IURs derived by EPA, 0.23, 0.17, and 0.16 per fiber/cc, are based on lung cancer
and mesothelioma with quantitative adjustment for laryngeal and ovarian cancers in the
development of the IUR of 0.16 per fiber/cc in the Part 1 Risk Evaluation. Despite each value
being derived from different information and epidemiologic cohorts, and therefore having
different strengths and uncertainties, the values are notably similar and round to 0.2 per fiber/cc.
OPPT is proposing to use an IUR of 0.2 per fiber/cc in Part 2 of the Risk Evaluation for
Asbestos.
EPA is soliciting comment on these proposals and associated analyses. This document, and associated
independent, expert peer review, are solely focused on the human hazard characterization and dose
response to support Part 2 of the Risk Evaluation for Asbestos. OPPT will subsequently release a draft
Part 2 risk evaluation, including a complete risk characterization and presentation of risk determination,
which will be made available for public comment pursuant to TSCA section 6(15 U.S.C. 2605(b)(4)(H)
(U.S. EPA. 2017a). OPPT will also release an accompanying Systematic Review Protocol for Asbestos
at that time.
1.2	Summary of Part 1 of the Risk Evaluation	
For Part 1 of OPPT's Risk Evaluation for Asbestos, EPA initially adopted the definition of asbestos as
defined by TSCA Title II (added to TSCA in 1986), section 202 as the "asbestiform varieties of six fiber
types - chrysotile (serpentine), crocidolite (riebeckite), amosite (cummingtonite-grunerite),
anthophyllite, tremolite or actinolite." However, a choice was made to focus Part 1 solely on chrysotile
asbestos as this is the only asbestos fiber type that is currently imported, processed, or distributed in the
United States. EPA informed the public of this decision to focus on ongoing uses of asbestos and
exclude legacy uses and disposals in the Scope of the Risk Evaluation for Asbestos, released in June
2017 (U.S. EPA. 2017b). However, in late 2019, the court in Safer Chemicals, Healthy Families v. EPA,
943 F.3d 397 (9th Cir. 2019) held that EPA's Risk Evaluation Rule (82 FR 33726 [July 20, 2017])
should not have excluded "legacy uses" (i.e., uses without ongoing or prospective manufacturing,
processing, or distribution for use) or "associated disposals" (i.e., future disposal of legacy uses) from
the definition of conditions of use—although the court did uphold EPA's exclusion of "legacy
disposals" (i.e., past disposals). Following that court ruling, EPA continued development of the risk
evaluation for the ongoing uses of chrysotile asbestos and determined that the complete Risk Evaluation
for Asbestos would be issued in two parts. The Risk Evaluation for Asbestos Part 1: Chrysotile Asbestos
was released in December (2020). allowing the Agency to expeditiously move into risk management for
the unreasonable risk identified in Part 1.
1.3	Scope and Purpose of Part 2 of the Risk Evaluation	
Following the finalization of Part 1 of the Risk Evaluation for Asbestos, EPA OPPT immediately began
development of Part 2, starting with the issuance of a draft scope document. The Final Scope of the Risk
Evaluation for Asbestos Part 2: Supplemental Evaluation Including Legacy Uses and Associated
Disposals of Asbestos (87 FR 38746) (EP A-HQ-2021 -0254-0044; hereafter "Final Scope") was released
in June 2021, reflecting consideration of public comments on a draft scope document. Although Part 1
of the Risk Evaluation adopted the TSCA Title II definition of asbestos, the consideration of legacy uses
and associated disposals that will be evaluated in Part 2 warrant broader considerations as asbestos can
be co-located geologically with commercially mined substances. In particular, LAA is known to have
been present with vermiculite, extracted from an open pit mine near Libby, Montana, until the mine
closed in 1990. Vermiculite was widely used in building materials which are an important focus of the
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evaluation of legacy uses of asbestos. Thus, LAA (and its tremolite, winchite, and richterite constituents)
will be considered in Part 2 of the Risk Evaluation. EPA will also determine the relevant conditions of
use of asbestos-containing talc, including any "legacy use" and "associated disposal" where asbestos is
implicated in Part 2 of the Risk Evaluation. Where the Agency identifies reasonably available
information demonstrating asbestos-containing talc conditions of use that fall under TSCA authority,
these will be evaluated in Part 2 of the Risk Evaluation for Asbestos.
An additional expansion of considerations in Part 2, as described in the Final Scope, pertains to the
evaluation of human health effects. Although Part 1 focused on certain cancer outcomes known to be
causally related to asbestos exposure (I ARC, 2012,1977), Part 2 will consider non-cancer outcomes at
the system level or higher. Historically, there has been a focus on inhalation exposures in health
assessments conducted by the EPA and other organizations, but there has also been interest in the
updated literature on dermal and oral exposures. These routes of exposure are being considered in Part 2,
which EPA agreed to consider as part of an agreement that was reached for the purpose of resolving a
petition for review of Part 1 of the Risk Evaluation (see AD AO, et al. v. EPA, No. 21-70160 (9th Cir.
Oct. 2021)). A broad range of health effects are examined in the asbestos epidemiologic literature
including cancer (e.g., mesothelioma, lung, ovarian, laryngeal, gastrointestinal cancers) and non-cancer
(e.g., asbestosis, lung function decrements, pleural plaques/abnormalities, immune-related effects,
cardiovascular effects) outcomes. This range of human health outcomes was presented in Figure 2-10 in
the Final Scope, and an interactive version of this diagram is available Heat Map of Hazard Screening
Results for Asbestos.2
In considering the broad range of health effects and routes of exposure, EPA will continue to focus on
the epidemiologic evidence for dose-response as was done in Part 1 and supported by EPA's Science
Advisory Committee on Chemicals (SACC). Prior assessments of asbestos conducted by EPA and other
agencies have conducted extensive reviews of the literature including epidemiologic and toxicological
studies in animals (U.S. EPA. 2020. 2014b: IARC. 2012; AT SDR. 2001; U.S. EPA. 1988. 1986; IARC.
1977). The human health hazards related to asbestos exposure are well-established and there is a robust
epidemiologic evidence base. In 1977 and 2012, an International Agency for Research on Cancer
(IARC) Working Group reviewed a large body of evidence that covered all fiber types in various
epidemiologic studies and settings and found that there is a causal relationship between asbestos
inhalation exposure and cancer (mesothelioma and lung, ovarian and laryngeal cancers) and mortality
(IARC. 2012. 1977). Additionally, respiratory effects including histopathologic changes (e.g., pleural
thickening [LPT], fibrosis, inflammation, etc.) and lung function decrements are consistently observed
following asbestos exposure. Some studies have described cardiovascular and immune-related effects,
but these effects are demonstrated to occur subsequent to observed respiratory effects (U.S. EPA.
2014b). From a qualitative point of view, the hazards for asbestos are well characterized. Thus, EPA is
focusing its efforts on Part 2 on epidemiologic evidence that support quantitative dose-response
relationships as needed for the risk evaluation.
EPA has conducted an updated systematic review of the literature to identify and evaluate relevant
information. In addition, there are three peer-reviewed, existing Agency assessments on asbestos that
2 Details on how the Heat Map of Hazard Screening Results for Asbestos and evidence tables were generated are described in
Section 4.7.5 of Draft Systematic Review Protocol Supporting TSCA Risk Evaluations for Chemical Substances (U.S. EPA.
2021a).
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264	have derived cancer inhalation unit risk (IUR)3 values and a reference concentration (RfC) for non-
265	cancer effects based on a POD:
266	1. The IRIS Asbestos Assessment (U.S. EPA. 1988) - presenting an IUR of 0.23 per fiber/cc based
267	on combined risk for lung cancer and mesothelioma;
268	2. The IRIS Libby Amphibole Asbestos (LAA) Assessment (U.S. EPA. 2014b) - presenting an
269	IUR of 0.17 per fiber/cc based on combined risk for lung cancer and mesothelioma and an RfC
270	of 9x 10~5 mg/m3 based on a POD of 2.6x 10~2 fiber/cc for LPT in the lungs; and
271	3. The Risk Evaluation for Asbestos Part 1: Chrysotile Asbestos (U.S. EPA. 2020) - presenting an
272	IUR of 0.16 per fiber/cc based on combined risk for lung cancer and mesothelioma, including a
273	quantitative adjustment for laryngeal and ovarian cancer.
3 An IUR is a value representing the upper-bound excess lifetime cancer risk estimated to result from continuous exposure to
an agent per fiber/cc of exposure. The IUR can be multiplied by an estimate of lifetime exposure (in fibers/cc) to estimate the
lifetime cancer risk.
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2 STRUCTURE OF THE WHITE PAPER	
This white paper presents the approach taken to identify and evaluate the most relevant of the reasonably
available information to inform human health dose-response considerations in Part 2 of the Risk
Evaluation for Asbestos. The remainder of the document is organized into the following major sections:
•	Section 3 presents an overview of the systematic approach employed to identify the relevant
reasonably available information and how the information was screened and categorized to
efficiently identify the epidemiologic studies informative for dose-response assessment.
•	Section 4 presents an overview of identification of non-cancer dose-response information, a
synopsis of the selection of the POD and associated evidence from the IRIS LAA Assessment
(U.S. EPA. 2014bI and the proposed quantitative non-cancer approach to be applied in Part 2.
•	Section 5 presents an overview of the cancer dose-response information, a synopsis of the
existing IURs from the IRIS Asbestos Assessment (U.S. EPA. 19881 the IRIS LAA Assessment
(U.S. EPA. 2014b). the Risk Evaluation for Asbestos Part 1: Chrysotile Asbestos (U.S. EPA.
2020). and the proposed quantitative cancer approach to be applied in Part 2.
•	Section 6 describes the next steps in this process resulting in the release of a draft Part 2 of the
Risk Evaluation for Asbestos for public comment.
Additional details on the systematic review approach OPPT used and the underlying evidence for each
of the IURs and POD are included in the following seven appendices and one supplemental document:
•	Appendix A: Abbreviations and Acronyms
•	Appendix B: Systematic Review Approach
•	Appendix C: Non-cancer Epidemiologic Cohorts
•	Appendix D: Cancer Epidemiologic Cohorts
•	Appendix E: Literature Inventory Form
•	Appendix F: Populations, Exposures, Comparators, and Outcomes (PECO) Criteria for Part 2 of
the Risk Evaluation for Asbestos
•	Appendix G: Data Quality Evaluation Criteria
•	Supplemental File: Systematic Review of Data Quality Evaluation Information for Human
Health Hazard Epidemiology (U.S. EPA. 2023)
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3 SYSTEMATIC APPROACH TO IDENTIFY DOSE-RESPONSE
INFORMATION	
This section presents an overview of the process used to identify, screen, and evaluate the reasonably
available information in accordance with TSCA section 6. Details of the TSCA systematic review
process are described in EPA's Draft Systematic Review Protocol Supporting TSCA Risk Evaluations for
Chemical Substances (hereafter "2021 Draft Systematic Review Protocol") (U.S. EPA. 202la),
including Appendix A, which describes updates made to that Protocol in response to recommendations
from the National Academies of Sciences, Engineering, and Medicine (NASEM), SACC, and public.
Subsequent comments from the April 2022 SACC Meeting on the Draft TSCA Systematic Review
Protocol included a recommendation of developing chemical-specific protocols. Therefore, an asbestos-
specific, supplemental protocol will be included in the forthcoming Part 2 of the Risk Evaluation that
will address asbestos-specific updates for all disciplines. Appendix B in this white paper provides details
on the systematic review process for epidemiologic studies for asbestos, including updates to and fit-for-
purpose application of the methods described in the 2021 Draft Systematic Review Protocol. Figure 3-1
presents a schematic of the process, beginning with a comprehensive literature search (including all
disciplines), followed by successive steps to screen the studies, and ultimately considers the most
relevant studies for dose-response assessment.
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Figure 3-1. Schematic of the Approach Used to Identify Epidemiologic Studies for Dose-Response
Consideration
TIAB = title/abstract (screening); PCM = phase-contrast microscopy; TEM = transmission electron microscopy
3.1 Step 1: Comprehensive Literature Search
For each risk evaluation conducted under TSCA, EPA conducts a comprehensive literature search for
reasonably available information (Step 1 in Figure 3-1; see also Appendix B in this document and
Section 4 of the 2021 Draft Systematic Review Protocol Supporting TSCA Risk Evaluations for
Chemical Substances (U.S. EPA, 2021a). For asbestos, literature searches were conducted for Part 1 of
the Risk Evaluation for Asbestos in 2016 and then updated in April 2021 for Part 2 (see Appendix
Section C.l .24 of the 2021 Draft Systematic Review Protocol). The comprehensive literature search
casts a broad net and includes references for hazard (epidemiology, human health toxicology, and
environmental hazard).
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3.2	Steps 2 & 3: Studies Meeting PECO Criteria at Title/Abstract and
Full-Text Screening
Following the literature search, initial screening for relevance was conducted at the title/abstract (TIAB)
screening level and then subsequently conducted at the full-text level (Steps 2 and 3, respectively, in
Figure 3-1). These processes are more thoroughly described in Appendix B in this white paper and the
2021 Draft Systematic Review Protocol (U.S. EPA. 2021a). TIAB and full-text screening was conducted
based on criteria specified in the hazard PECO statement. Generally, for the epidemiologic literature,
studies on any human population with exposure to one of the fibers included in the asbestos definition
(specific to Part 2 of the Risk Evaluation, see PECO in Appendix F) and examining any outcome or
route of exposure (inhalation, dermal, oral) were selected for inclusion. The full PECO statement
applied for hazard is included in Appendix F. After screening for these criteria at TIAB and full-text, a
total of 343 epidemiologic studies were identified as relevant (Step 3 in Figure 3-1).
3.3	Steps 4 & 5: Filtering of Studies for Dose-Response Consideration
Following the PECO-based screening of the epidemiologic studies, studies were further characterized
according to route of exposure, outcome assessed, analysis type and cohort. In an effort to streamline the
identification of dose-response information, OPPT identified criteria to filter the literature that met
PECO screening criteria. These modifications to the process described in the 2021 Draft Systematic
Review Protocol (U.S. EPA. 2021a) were implemented to efficiently identify studies with dose-response
data for full data quality evaluation. They included consideration of the data analysis methods used in
the study, exposure measurement methods, and use of exposure assignment in analysis. These
modifications and the rationale for their development and use are briefly described below and more
thoroughly in Appendix B.
3.3.1	Step 4: Standardized Mortality Ratios and Regression Analysis
Given the approach to dose-response analysis conducted in prior asbestos assessments, including Part 1
of the Risk Evaluation for Asbestos, identification of studies that either used standardized mortality
ratios (SMRs) or conducted analyses with regression models were determined most likely to be
informative for dose-response (Step 4 in Figure 3-1). An SMR is a ratio or percentage describing the
increase or decrease in mortality in a given study population relative to the general population and is
typically used in studies examining cancer. Regression analyses, in general, describe quantitatively the
relationship between an exposure and a response and are typically used in studies examining non-cancer
effects. The outputs from studies using SMRs and regression analyses can be used in assessing dose-
response. Overall, there were 213 studies using either SMR or regression analyses.
3.3.2	Step 5: Exposure Measurement and Exposure Assignment in Analysis
3.3.2.1 Exposure Measurement
It is well-established that the most reliable methods to detect and accurately quantify asbestos fibers are
phase-contrast microscopy (PCM)4 and transmission electron microscopy (TEM) (U.S. EPA. 1985).
Multiple measurements taken by PCM or TEM for a given exposure setting is preferred over a single
measurement. In addition, some studies have utilized measurements of dust from midget impingers, and
if a combination of methods are used such that an appropriate conversion factor is available to yield
fiber concentrations from dust measurements, these data can also be informative for dose-response.
4 PCM was recommended by the National Institute for Occupational Safety and Health (NIOSH) and Occupational Safety
and Health Administration (OSHA) as the preferred asbestos measurement method in 1979 as there was a recognized need
for reliable measurement and evaluation of occupational exposure to asbestos to put practices into place to prevent asbestos-
related disease (Leidel et al.. 1979).
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OPPT evaluated exposure measurement methods in studies before evaluating other data quality
evaluation criteria to identify those with reliable methods for dose-response (Step 5 in Figure 3-1).
Notably, some epidemiologic cohorts considered in the 1988 IRIS Asbestos Assessment were not
initially identified in the systematic review approach because the individual publications for these
cohorts lacked sufficient detail to meet PECO criteria, including for exposure measurement; however,
additional related publications were identified through citations and the information in the 1988 IRIS
Asbestos Assessment (U.S. EPA. 1988) and the 1986 Airborne Asbestos Health Assessment Update
(U.S. EPA. 1986) provided important information about these cohorts and analyses such that these
cohorts warranted consideration in this white paper for dose-response (see Appendix D.3).
Studies were considered by cohort groupings. For example, if multiple publications were available on a
particular occupational cohort, they were considered as a set of information rather than as independent
publications. For the 343 studies that met PECO screening criteria, a total of 156 epidemiologic cohorts
were identified, and 66 of these cohorts were the subject of multiple publications.
3.3.2.2 Exposure Assignment in Analysis
A variety of approaches can be used in the quantitative analysis within an epidemiologic study;
however, understanding the exposure-response relationship in a given population/cohort is best informed
when the analysis is conducted with consideration of three or more exposure levels or a model using a
continuous exposure measure (Step 5 in Figure 3-1). For example, analyses presenting results based on
only an unexposed and an exposed group is minimally informative for dose-response relative to studies
presenting responses for a broader range of exposure levels. Thus, studies using appropriate exposure
measurement methods and containing three or more exposure groups or a continuous measure of
exposure were identified to undergo data quality evaluation.
A total of 43 cohorts meeting these additional criteria of using regression or SMR and having
appropriate exposure measurement and exposure assignment in analysis were identified for further
consideration. These cohorts subsequently underwent data quality evaluation (Step 5 in Figure 3-1), as
explained in Appendix B of this white paper and in Appendix R of the Draft Systematic Review Protocol
Supporting TSCA Risk Evaluations for Chemical Substances (U.S. EPA. 2021a). Study quality
evaluations were conducted using DistillerSR, and the summary of the data evaluation results are
included in a Supplemental File (U.S. EPA. 2023). Briefly, the evaluation of study quality includes
consideration of 22 different metrics that are rated as High, Medium, Low, or Critically Deficient based
on pre-defined criteria. The assessment of each of the metrics contributes to an overall quality
determination (OQD) of High, Medium, Low, or Uninformative. Cohorts with an OQD of Medium or
High were further considered for dose-response assessment, of this white paper and in Appendix R of
the Draft Systematic Review Protocol Supporting TSCA Risk Evaluations for Chemical Substances (U.S.
EPA. 2021a). Study quality evaluations were conducted using Distiller SR, and the summary of the data
evaluation results are included in a Supplemental File (U.S. EPA. 2023). Briefly, the evaluation of study
quality includes consideration of 22 different metrics that are rated as High, Medium, Low, or Critically
Deficient based on pre-defined criteria. The assessment of each of the metrics contributes to an overall
quality determination (OQD) of High, Medium, Low, or Uninformative. Cohorts with an OQD of
Medium or High were further considered for dose-response assessment.
3.4 Step 6: Consideration of Cohorts for Dose-Response Analysis	
Cohorts with studies receiving an OQD of Medium or High were categorized for examination of cancer
and/or non-cancer outcomes. Review of the exposure and outcome data and analysis performed was
done to confirm (1) the use of PCM or TEM for measurement of asbestos fibers or application of
appropriate conversion factors to dust measurements, (2) the use of air measurements in the analysis, (3)
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the analysis was conducted with health outcome data, and (4) there was adequate assessment of the
outcome (e.g., sufficient follow-up time). While these four aspects were considered as part of the data
quality evaluation, considering these factors in light of dose-response analysis provides a more detailed
perspective. Ultimately, 32 cohorts were removed from further consideration at this point because the
quantitative analyses were done with dust measurements or fiber measurements not using PCM or TEM
and did not have conversion factors or because they had received a Low or Uninformative OQD rating
in data quality evaluation. As noted previously, in the case of some cohorts considered in the Airborne
Asbestos Health Assessment Update (U.S. EPA. 1986). additional information on conversion of dust
measurements to fiber counts was available to enable use and consideration of these studies in the
context of dose-response (see Appendix D.3).
Finally, the extent to which cohorts may inform an exposure-response relationship was evaluated using
considerations primarily aimed at the identification of high-quality exposure and outcome data to inform
the estimation of an IUR and/or a POD. The list of considerations provided below was used to aid in
making judgements regarding which studies or studies from a group of studies quantitatively evaluated
the exposure-response relationship for asbestos to derive an estimation of its effect on the outcome in the
studied population. EPA considered time since first exposure (TSFE) because it is a predictor of risk.
The job exposure metric (JEM) was used because the table provides estimated exposure levels in air
(fibers/cc) for workers in each job for each year. The Agency utilized these considerations, which were
identified in the IRIS LAA Assessment as characteristics necessary for identifying principal studies with
the greatest confidence that might inform the dose-response assessment (U.S. EPA. 2014b). A total of
19 cohorts were under consideration at this stage. Cohorts that were deemed most useful for dose-
response assessment adhered to the following considerations:
1.	Medium or High OQD;
2.	Asbestos fibers collected on membrane filters and analyzed using PCM or TEM or a conversion
factor from early measurement of total dust particles in million particles per cubic foot (mppcf)
to estimate fiber/mL or the equivalent fiber/cc;
3.	Used continuous measure of exposure rather than categorical exposure levels (e.g., quartiles) to
provide more granular details on the exposure-response relationship;
4.	Models that used individual-level exposure assignment methods;
5.	Availability of data on TSFE matched to the exposure data, as this is needed to model asbestos-
related outcomes in dose-response analysis (U.S. EPA. 2014b);
6.	Timing of exposure relative to the outcome;
7.	Sufficient length of follow-up for outcome assessment, recognizing the extended latency of
asbestos-related outcomes;
8.	Studies that provide information on the exposure-response relationship between asbestos
exposure and outcome; and
9.	Use of a JEM to accurately reconstruct workers' exposure histories to derive a cumulative
exposure for each individual over the course of the relevant exposure period.
While Appendix C and Appendix D provide a description of each of the non-cancer and cancer cohorts,
respectively, Sections 4 and 5 focus more specifically on the key dose-response information for cancer
and non-cancer, respectively, for Part 2 of the Risk Evaluation. Each of these sections provides an
overview of cohorts available and describes the relevant non-cancer POD or IURs and the underlying
data and specific cohort upon which they are based. The approach to be applied in Part 2 of the Risk
Evaluation for Asbestos for non-cancer and cancer outcomes is also described in each of these sections.
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4 NON-CANCER DOSE-RESPONSE FOR ASBESTOS	
Section 4.1 presents an overview of the literature identified for non-cancer dose-response information
for asbestos exposures. Section 4.2 presents an overview of the non-cancer dose-response analysis from
the IRIS LAA Assessment (U.S. EPA. 2014b). while Appendix C provides additional discussion of
other cohorts for which dose-response data were available. Ultimately, new dose-response analyses were
not warranted for Part 2. Section 4.3 describes the non-cancer quantitative approach to be applied in Part
2 of the Risk Evaluation for Asbestos.
4.1 Systematic Approach for Identification of Epidemiologic Cohorts for
Non-cancer Effects	
Application of the systematic review approach described in Section 3 resulted in the identification of
seven cohorts for consideration in assessing dose response of non-cancer outcomes related to asbestos
exposures. All of the cohorts identified examined inhalation exposures. Epidemiologic studies
examining oral or dermal exposures with dose-response information were not identified by the
systematic review approach. The outcomes assessed in the identified cohorts included non-cancer
mortality (including asbestosis and pneumoconiosis), pleural changes/thickening, and lung function
changes. Some of these cohorts were identified and considered in the IRIS LAA Assessment (U.S. EPA.
2014a). which is the only EPA assessment that quantitatively considered non-cancer effects. The cohorts
are listed and briefly described in Table 4-1 and are more thoroughly presented in Appendix C. Based on
the considerations described in Appendix C, it was determined that the O.M. Scott Marysville, OH,
Plant Cohort provides the most robust data for dose-response assessment for non-cancer outcomes. This
determination was based on reliable individual-level measurements of asbestos exposures and detection
of pleural thickening, an early adverse effect. This cohort and the selection of the POD, uncertainty
factors, and derivation of RfC are described further in Section 4.2. The other six cohorts OPPT
identified, which were not within the scope of the IRIS LAA Assessment, were less suitable for non-
cancer dose-response assessment because the outcomes examined were less sensitive (i.e., mortality-
related outcomes) and/or because there was greater uncertainty in the exposure data (e.g., community-
based measurements rather than personal sampling). Generally, for dose-response assessment,
preference is given to studies examining the most sensitive outcome(s), so although mortality can be
used in the assessment, it is less sensitive than a well-described outcome preceding mortality from a
disease state. Appendix C provides more details on the dose-response considerations for each cohort.
Table 4-1. Cohorts Identified for Consideration in Asbestos Part 2 Non-cancer Dose-Response
Analysis				
Cohort Name
(Reference [s])
Cohort Description
Non-cancer
Outcome(s)
Data Quality
Evaluation Rating
IRIS Libby Amphibole Asbestos Assessment, 2014
O.M. Scott Marysville,
OH, Plant Cohort
(Lockev et al.. 1984)
(Rohs et al.. 2008)
•	Cohort included 530 workers with
known vermiculite exposure participated
in the 1980 investigation. Eight different
worksite operations at the ore processing
plant were represented.
•	Monitoring of industrial hygiene at the
facility started in 1972, including
personal breathing zone sampling. PCM
measurements beginning after 1976.
•	Job exposure matrix used to determine
cumulative exposures.
Pulmonary function
Mortality
High
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Cohort Name
(Reference [s])
Cohort Description
Non-cancer
Outcome(s)
Data Quality
Evaluation Rating

•	Follow-up including chest x-rays and
interview information from 280 of the
431 workers who were known to be alive
between 2002 and 2005.
•	Followed up on the respiratory effects in
the cohort conducted in 2012.


Libby, MT, Vermiculite
Mining and Milling
Cohort
•	Participants were white men who had
worked for at least 1 year in the mine
and mill.
•	Reports based on follow-up data from
1960 to 2006.
•	Air sampling data were used to build a
job-exposure matrix assigning daily
exposures (8-hour time-weighted
average [TWA]) for selected job codes.
•	Individual work histories and the mine
and mill job-exposure matrix were used
to determine individual exposure
metrics.
Mortality
Medium
Cohorts not included in previous EPA assessments for non-cancer effects
SC Textiles Cohort
•	Textile plant in Charleston, SC and used
asbestos from 1909 to 1977.
•	Original cohort of textile workers limited
to white males employed for at least 1
month between 1940 and 1965. Later
expanded to included non-whites and
females.
•	Individual-level exposures estimates
derived from detailed work histories and
extensive air measurements using PCM
and conversion of dust measurements
from analysis of paired sampling.
Mortality
Medium
SC Vermiculite Miners
Cohort
(W. R. Grace & Co.
1988)
•	Cohort composed of 194 men hired
between 1949 and 1974 in
mining/milling of vermiculite in Enoree,
SC.
•	58 air samples collected in 1986 and
analyzed by PCM.
Mortality,
parenchymal
abnormalities
including pleural
thickening and
sputum analysis
Medium
Anatolia, Turkey,
Villagers Cohort
(Metintas et al.. 2005)
•	Field-based, cross-sectional study of 991
villagers from 10 randomly selected
villages with known asbestos-containing
white soil.
•	Indoor and outdoor air sample taken for
each village; fibers counted by PCM.
Pleural plaques,
asbestosis, diffuse
pleural fibrosis
High
Wittenoom, Australia,
Residents Cohort
• Residential cohort included 4659
individuals residing for at least 1 month
in Wittenoom between 1943 and 1992.
Mine workers excluded.
Mortality
Medium
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Cohort Name
(Reference [s])
Cohort Description
Non-cancer
Outcome(s)
Data Quality
Evaluation Rating

•	Follow-up in 1993, 2000, and 2004
•	Ambient exposures from nearby
crocidolite assigned based on dates of
residence, assigned exposure intensity,
and period personal monitoring after
operations ceased.


Chinese Chrysotile
Textile Factory Cohort
(Huana. 1990)
•	Cohort of 776 workers employed for at
least 3 years in chrysotile textile product
factory; Shanghai.
•	17 workplaces in the factory selected for
routine sampling; dust and fiber
measurements collected by membrane
filters.
•	Follow-up through September 1982 for
asbestos diagnosis.
Asbestosis
incidence
Medium
4.2 IRIS Libby Amphibole Assessment: Non-cancer Dose-Response	
The IRIS LAA Assessment conducted a dose-response assessment for non-cancer effects utilizing data
from a cohort of workers in the O.M. Scott plant in Marysville, Ohio. The O.M. Scott plant was a site
that received vermiculite from Libby, Montana, by rail where it was processed into expanded form for
use as an inert carrier for herbicides and fertilizers. A total of 512 workers participated in the 1980
investigation of pulmonary effects in Ohio plant workers (Lockev et al.. 1984). Workers were drawn
from a variety of departments/facilities, including production and packaging of commercial products,
maintenance, research, the front office, and the polyform plant. The initial study of this cohort utilized
air sample measurements collected in 1972 to assign cumulative worker exposures based on individual
job histories. Outcomes were assessed by radiologist readings of chest x-ray films and spirometry for
lung function measures. A follow-up of this cohort was conducted nearly 25 years later, providing more
robust exposure-response analyses (Rohs et al.. 2008).
In this follow-up analysis (Rohs et al.. 2008). the cohort was limited to men hired after 1972 as there
was more certainty in the exposure estimates; post-1972 measurements were taken by industrial
hygienists who followed employees during the course of their work with sampling devices. Sampling
data were also collected within personal breathing zones beginning in 1977. Detailed employee records
were used to construct exposure histories and estimate cumulative asbestos exposures for each
individual. Health outcomes were assessed in 1980 and between 2002 and 2005; however, the use of
different protocols was considered an uncertainty and the later film readings were deemed more reliable.
In addition, the later radiographic films extended the follow-up time by roughly 25 years, which is
important given the latency of effects. These considerations resulted in a sub-cohort of 119 men for
which robust exposure and outcome data were available for dose-response modeling.
With the data from the sub-cohort, a range of dose-response model forms were evaluated, but the most
suitable model fitting results were obtained using the Dichotomous Hill model using the mean exposure
and pleural thickening. Various covariates were examined in model-fitting; however, none appeared to
be a confounder or a significant predictor of outcome risk in the model. One covariate examined, TSFE,
has been demonstrated to be an important predictor of asbestos-related effects (Loomis et al.. 2019).
However, TSFE in the model did not improve model-fitting results, presumably due to the low
variability across the dataset. Given the known importance of TSFE, its impact on outcome was
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determined using the broader set of cohort data (including those hired prior to 1972), which was then
incorporated as a fixed regression coefficient in the model. In the modeling, a benchmark response
(BMR) of 10 percent was used based on considerations of adversity for LPT. The benchmark
concentration is the level of exposure expected to result in the excess risk defined by the BMR. More
specific details and results of model-fitting are presented in Section 5.2.2.6.1 in the IRIS LAA
Assessment (U.S. EPA. 2014b). A POD based on a 10 percent BMR for LPT was calculated to be
2.6xl0~2 fiber/cc.
The IRIS program noted important uncertainties related to the underlying evidence base for this POD
and applied UFs to account for intraspecies variability (UFh of 10), database uncertainty (UFd of 3), and
data-informed sub chronic-to-chronic uncertainty (UFs of 10) in the 2014 LAA Assessment (U.S. EPA.
2014b).
•	Regarding the UFh, the occupational cohort included individuals healthy enough to work, and
when taking into account human variability, it is plausible that there are more sensitive
individuals in the population. This uncertainty remains at this time; thus, UFh of 10 continues to
be applied.
•	Regarding the UFd of 3, applied in the IRIS LAA Assessment because of the limited number of
cohort studies evaluating the most sensitive non-cancer effects of chronic asbestos exposure, the
Agency has reevaluated the appropriateness of UFd of 3 in light of the systematic review. As
described in Section 4, no new cohort studies have been published that would inform the dose
response relationship for hazards beyond pleural effects and asbestosis for the non-cancer POD.
Therefore, the Agency will continue to apply a UFd of 3.
•	Regarding the UFs, it was anticipated that if the cohort had been followed for longer, even more
cases of LPT would have been identified. The cohort used to derive the 2014 IRIS RfC, O.M.
Scott Marysville, OH, was followed for approximately 30 years. The IRIS LAA Assessment
determined that it was appropriate to apply a UFs because even 30 years of observation is
insufficient to describe lifetime risk of LPT, which continues to increase over a person's lifetime
(see page 5-42 of the IRIS LAA Assessment for further rationale for applying the UFs (U.S.
EPA. 2014a)). The IRIS LAA Assessment, therefore, derived a data informed UFs of 10 based
on the fact that "the central estimate of the risk at TSFE = 70 years is ~10-fold greater than the
central estimate of the risk at TSFE = 28 years (from 6% to 61%)" (see page 5-43 of the IRIS
LAA Assessment for further details (U.S. EPA. 2014a)). TSFE in the model was set at 28 years
due to limitations in the statistical uncertainty.
4.3 Quantitative Non-cancer Approach for the Risk Evaluation for
Asbestos Part 2	
As described in Section 3.1, seven epidemiologic cohorts were identified for consideration in dose-
response analysis (Table 4-1): two occupational cohorts considered in the IRIS LAA Assessment as well
as three additional occupational cohorts and two community-based cohorts. When considering specific
attributes of the cohorts and available data (see Appendix B), the two occupational cohorts from the
Libby assessment were the most informative for dose-response, and the O.M. Scott Marysville, OH,
Fertilizer Plant Workers Cohort continues to be the most robust. This is because of the confidence in the
individual-level exposure and outcome data in addition to having sufficient follow-up time, as described
more fully in the IRIS LAA Assessment and as summarized in the preceding section (4.2) (U.S. EPA.
2014b). Also of note is that dose-response assessment for non-cancer effects is typically conducted for
the most sensitive endpoint or the earliest observed adverse effect.
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Given the above, use of the LAA POD from the IRIS assessment in Part 2 of the Risk Evaluation is a
reliable approach to quantitatively consider non-cancer risks from asbestos exposures. While there is
some uncertainty in application of a Libby-specific POD for exposures to a broader range of asbestos
fibers, the uncertainty of using other studies for quantitative assessment would be even greater given the
limited exposure characterization for those cohorts (SC Vermiculite Miners Cohort; Anatolia, Turkey,
Villagers Cohort) (see Appendix C). For example, for the SC Vermiculite Miners Cohort, non-cancer
outcomes were only categorically analyzed as exposed and unexposed. In addition, details of the
exposure assessment are insufficient for dose-response assessment, and there is a lack of information on
TSFE. The Anatolia, Turkey, Villagers Cohort constructed individual-level exposure estimates, but
these were based on broad assumptions of time spent indoors, outdoors, and sleeping. The other cohorts
available for dose-response assessment similarly had exposures to a single fiber type and examined
mortality as the outcome, which would not be representative of the most sensitive effects known to
result from asbestos exposures.
Based on the comprehensive approach to identify and evaluate the relevant epidemiologic literature for
dose-response assessment of non-cancer effects resulting from asbestos exposures, use of the POD
presented in the IRIS LAA Assessment for Part 2 of the Risk Evaluation is proposed. In the IRIS LAA
Assessment, LPT was selected as the critical non-cancer effect for POD selection with a BMR of 10
percent extra risk. LPT, as indicated by the presence of pleural plaques is the most effective endpoint to
select because it is the outcome that generally appears at lower doses after asbestos inhalation exposure.
In summary, EPA is proposing use of the IRIS LAA POD, 2.6x10-2, in Part 2 of the Risk Evaluation
and will compare this value to MOEs that will take into account asbestos concentrations from the
different exposure scenarios and a benchmark of 300 (UFh =10, UFd = 3, UFs =10) based on the IRIS
LAA Assessment as described in Section 4.2. Those specific details will be further developed and
described in the draft Part 2 Risk Evaluation that will subsequently be released for public comment.
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5 CANCER DOSE-RESPONSE FOR ASBESTOS	
5.1 Identification of Epidemiologic Cohort for Cancer Dose-Response
As described in Section 3 and Appendix B, epidemiologic cohorts providing information for dose-
response assessment were identified for non-cancer and cancer outcomes. This process included a
comprehensive literature search, PECO-based screening at the TIAB and full-text level, and further
filtering of epidemiologic cohorts for exposure measurement and assignment methods, as well as the
study analysis. Studies identified describing hazards but not informative for dose-response will be
addressed in Part 2 of the Risk Evaluation for Asbestos.
Overall, 16 cohorts were identified for consideration in assessing dose response of cancer outcomes
related to asbestos exposures. Most of these cohorts were identified and considered in previous
assessments, including the 1988 IRIS Asbestos Assessment, the 2014 IRIS LAA Assessment, and the
2020 Part 1 of the Risk Evaluation for Asbestos. Only one cohort was identified that was not previously
considered in an EPA assessment—and as a community-based cohort (Wittenoom, Australia, Residents
Cohort), rather than an occupational cohort—was unique. All 16 cohorts are listed and briefly described
in Table 5-1 and are more thoroughly presented in Appendix C.
Because the cohorts identified for dose-response were considered in the derivation of the existing IURs,
OPPT focused on these existing IURs and their derivation, as described below in Section 5.2. The single
cohort identified that was not considered in any of the existing IURs, while meeting systematic review
criteria, did not have exposure data that was better suited for dose-response analysis given the
uncertainties in community-based exposure assignment (see Appendix D.4). Thus, this study did not
warrant an updated quantitative analysis. The proposed quantitative approach for cancer in Part 2 of the
Risk Evaluation is described in Section 5.3 and accounts for each of the existing IURs (see Section 5.2).
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623 Table 5-1. Cohorts Identified for Consideration in Asbestos Part 2 Cancer Dose-Response Analysis
Cohort Name
Cohort Description
Cancer Outcomes*
Overall Quality Determination
(OQD) Rating
Risk Evaluation for Asbestos Part 1: Chrysotile Asbestos, 2020
NC Textiles Cohort
•	Four textile plants imported raw chrysotile fibers to make yarns
and woven goods.
•	5,770 workers employed for at least 1 day between 1950 and
1973.
•	Cohort followed through 2003.
Mesothelioma, pleural
cancer, lung cancer
High
SC Textiles Cohort
•	Textile plant in Charleston, SC, and used asbestos from 1909 to
1977.
•	Original cohort of textile workers limited to white males
employed for at least 1 month between 1940 and 1965. Later
expanded to included non-white and females.
•	Individual-level exposures estimates derived from detailed
work histories and extensive air measurements using PCM and
conversion of dust measurements from analysis of paired
sampling.
Lung cancer, mesothelioma
Medium
Quebec, Canada
Asbestos Mines
and Mills Cohort
•	Study of chrysotile miners and mill in Thetford mines in
Quebec, Canada.
•	The original cohort was made up of men who were born
between 1891 and 1920 and who had worked for at least 1
month in the mines and mills.
•	Cohort followed from first employment in 1904 to May 1992.
•	Detail work histories as well as total dust measurement from
4,000 midget impinger dust counts in mppcf per year were
analyzed.
Mesothelioma, lung cancer
Medium
Qinghai, China
Asbestos Mine
Cohort
•	Study of chrysotile mine in Qinghai Province, China.
•	Cohort made up of 1,539 male workers who were on the
registry January 1, 1981, and who had worked for at least 1
year.
•	Occupational and work history of cohort was obtained from
personnel records and employee.
•	Cohort followed for vital stats from 1981 to 2006.
•	Total dust concentrations were measured by area sampling in
fixed locations and converted to fiber/cc.
Lung cancer, gastrointestinal
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Cohort Name
Cohort Description
Cancer Outcomes*
Overall Quality Determination
(OQD) Rating
Chongqing, China
Asbestos Products
Factory Cohort
•	Chrysotile asbestos plant in Chongqin, China, which produces
textile, asbestos cement products, friction materials, rubber
products and heat-resistant materials.
•	Cohort of 515 men were followed from January 1, 1972, to
December 31, 1996; workers (men and women) who had
worked for less than 1 year were excluded.
•	Cohort followed until 2008 when women who were employed
between 1970 and 1972 were added to analysis.
•	Airborne dust and fiber concentrations were measured from
personal samplers.
Lung cancer
High
Balangero, Italy
Mining Cohort
•	Balangero mine and mill of the Amiantifera Company started
in 1916 and produced pure chrysotile asbestos.
•	Cohort consisted of 1,056 men who worked in mines for at
least 1 year between January 1, 1930, and December 31, 1975.
•	Cohort followed up from January 1, 1946, or date of first
employment, to December 31, 2003, or when subjects reached
80 years of age.
•	Information on cohort collected from mine records.
•	First fiber counts were first carried out in 1969 and exposure
levels before 1969 were reconstructed to represent earlier years.
Lung cancer, laryngeal
cancer, gastrointestinal
cancer, lip cancer, oral
cavity and pharynx cancer,
esophageal cancer, liver
cancer, stomach cancer,
colon cancer, rectal cancer
peritoneal cancer, pleural
cancer, bladder cancer,
nervous system cancer,
kidney cancer,
mesothelioma
Medium (lung cancer, laryngeal
cancer, oral cavity and pharynx
cancer, esophageal cancer, liver
cancer, peritoneal cancer, pleural
cancer, kidney cancer,
mesothelioma)
Salonit Anhovo,
Slovenia Asbestos
Factory Cohort
•	Salonit Anhovo factory in western Slovenia produced asbestos-
cement products made from chrysotile and amphibole asbestos.
•	Cohort made up of 6,714 workers who had worked for at least
1 day between 1964 and 1994.
•	Air sampling measurements taken at fixed location close to
worker's breathing zone.
•	Work histories were obtained from personnel files.
Lung cancer
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Cohort Name
Cohort Description
Cancer Outcomes*
Overall Quality Determination
(OQD) Rating
IRIS Libby Amphibole Asbestos Assessment, 2014
Libby, MT,
Vermiculite Mining
and Milling Cohort
•	Cohort included 1,871 vermiculite miners, millers, and
processors hired prior to 1970 and employed for at least 1 year
at the Montana site.
•	Subjects followed through December 2006.
•	Historical air sampling data used to estimate 8-hour TWA.
•	Work histories including job title and dates of employment
were obtained and used to calculate cumulative fiber exposures.
Lung cancer, mesothelioma
Medium (lung cancer)
High (mesothelioma)
IRIS Asbestos Assessment, 1988
US Asbestos
Company
Employees Cohort
•	Cohort consisted of 1,075 men obtained from company records.
•	Subjects were retired between 1941 and 1967 and receiving a
pension from company.
•	Cohort followed through 1973.
•	Total dust measured in mppcf.
Mesothelioma, lung cancer,
digestive cancer
Medium
New Orleans
Asbestos Cement
Building Material
Plants Cohort
•	Includes two asbestos cement building material plant producing
products containing chrysotile, crocidolite, and amosite
asbestos.
•	Cohort consisted of 5,645 men who had worked in either plant
and had at least 20 years of follow up.
•	Detail work history obtained from plant records.
Lung cancer, mesothelioma,
digestive cancer
High
Ontario, Canada
Asbestos Cement
Factory Cohort
•	Cohort included 241 production and maintenance employees
who worked for at least 9 years at the factory prior to 1960.
•	Impingers were used to prior to 1973 and membranes fiber
counts used thereafter.
•	Mortality was followed through October 1980.
Lung cancer, mesothelioma,
gastrointestinal cancer
Medium
NY-NJ Asbestos
Insulation Workers
Cohort
•	Cohort located in Paterson, NJ, and manufactured amosite
products.
•	Cohort included 820 men that worked for at least 5 years in
factory.
•	Cohort followed through 1982.
•	No fiber counts available, but used counts for similar plant in
Tyler, TX.
Lung cancer
Medium
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Cohort Name
Cohort Description
Cancer Outcomes*
Overall Quality Determination
(OQD) Rating
Asbestos Textile
Workers Cohort
•	Cohort consisted of white males who worked at the plant for at
least 1 month prior to January 1, 1959.
•	Work histories obtained from this U.S. textile cohort included
all 1,261 white males who worked at the plant for at least a
month between January 1, 1940, and December 31, 1965. All
workers who had a social security administration (SSA) record
and had worked for at least 1 month prior to January 1, 1959,
were considered to be part of the cohort. The cumulative dust
exposures were assigned to each study participant using the
same data that (Dement et al.. 2008) used to calculate historical
exposures.
Lung cancer, mesothelioma
Medium
International
Association of Heat
and Frost Insulators
and Asbestos
Workers Cohort
•	Plant located in the NY-NJ metro area and produced chrysotile
and amosite products between 1943 and 1976.
•	Cohort included 623 men employed prior to 1943 and 833 men
employed after 1943.
•	Follow-up in 1962 and 1976.
•	Asbestos concentration in facilities not measured but used
counts from other U.S. insulation facilities that operated
between 1968 and 1971.
Mesothelioma
Medium
Cohort not included in existing EPA assessments
Wittenoom,
Australia,
Residents Cohort
•	Residential cohort included 4,659 individuals residing for at
least 1 month in Wittenoom between 1943 and 1992. Mine
workers excluded.
•	Follow-up in 1993, 2000, and 2004.
•	Ambient exposures from nearby crocidolite assigned based on
dates of residence, assigned exposure intensity, and period
personal monitoring after operations ceased.
Lung cancer, ovarian cancer,
mesothelioma, brain cancer,
leukemia
Medium
*As indicated in Section 1.3 and the Final Scope document. Part 2 of the Risk Evaluation will focus on mesothelioma and lung, ovarian and laryngeal cancers.
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5.2 1988 IRIS Asbestos Assessment	
The IRIS Asbestos Assessment, released in 1988 (U.S. EPA. 19881 utilizes the Airborne Asbestos
Health Assessment Update from 1986 (U.S. EPA. 1986). The latter was developed as the scientific
foundation to support EPA's review and revision of the designation of asbestos as a hazardous air
pollutant under the 1973 National Emission Standards for Hazardous Air Pollutants (NESHAP) under
the 1977 Clean Air Act Amendments (U.S. EPA. 1986). The original designation of asbestos was based
upon a qualitative review of the evidence prior to 1972 establishing associations between exposure and
carcinogenicity. The objectives of the Airborne Asbestos Health Assessment Update (U.S. EPA. 1986)
were to identify any new asbestos-related health effects from studies published after 1972, examine the
dose-response relationship, and establish unit risk values for asbestos, if warranted.
At the time of assessment, the prevailing thought was that creating an exposure-response relationship for
asbestos could be done in one of two ways. The first would be to choose the study or studies that have
the best exposure data, presuming a sufficient measure of effect. The second approach would use all
studies that provide exposure-response information along with estimates of the uncertainty of the data.
In this approach, an overall exposure-response relationship is produced by taking an appropriate
weighted average of the relationships discovered across studies accounting for observable variations in
exposure conditions. The benefits of taking into account all research for which exposure-response data
can be generated are as follows:
1.	any bias in the selection of the research to be analyzed is largely eliminated;
2.	information on the degree of uncertainty in the estimate of the average Kl value can be acquired;
and
3.	more accurate estimations of the impact of different fiber types or manufacturing processes can
be made.
Based on this information, the assessment utilized data from all studies that provided exposure response
data, rather than basing the assessment on a single study with the strongest exposure assessment (as was
done in the later EPA assessments on Libby and chrysotile). The assessment included occupational
studies with exposures to any of the principal commercial varieties of asbestos fibers (i.e., amosite,
anthophyllite, crocidolite, and chrysotile). A total of 14 occupational studies for lung cancer and 4
occupational studies for mesothelioma provided data for a dose-response assessment. The data for a best
estimate of increased risk of lung cancer per unit exposure are provided by 14 studies across a range of
occupational activities. The mixed fiber cohorts are explicitly described in Appendix D.3; however, the
cohorts in the 1988 Asbestos Assessment that were chrysotile-specific were not explicitly described
because they had been extended and encompassed by studies included in Part 1 of the Risk Evaluation
for Asbestos (see also Appendix D.4). In the 1988 Asbestos Assessment, studies of mining and milling
were excluded due to a substantial difference in risk observed and the notion that exposure assessment in
these operations is significantly more challenging due to a wide array of fibers being present. Factories
have a more limited set of sources of dust and fibers, making fiber counts more straightforward. In
deriving the overall Kl (slope factor for lung cancer), the geometric mean was calculated from the 14
epidemiologic studies, representing exposures to chrysotile, amosite, and crocidolite.
Of the four studies examining mesothelioma mortality in occupational cohorts (see Table II.C.2 in the
IRIS Asbestos Summary (U.S. EPA. 1988)). three of these cohorts had mixed-fiber exposures and also
examined lung cancer mortality. However, mesothelioma risk was calculated for the 10 studies
examining lung cancer and not mortality by developing an adjustment factor (the ratio of Km [slope
factor for mesothelioma] to Kl in the 4 studies examining both mortality outcomes) and applying that

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adjustment factor to the Kl for each study (see Table 3-31 in the Airborne Asbestos Health Assessment
Update (U.S. EPA. 1986). The resulting relative mesothelioma hazard was closely examined across
cohorts and occupational categories (e.g., mining/milling, insulation workers, textiles, etc.) and because
there were no obvious outliers, a geometric mean was calculated considering all studies. The assessment
discusses the postulation that crocidolite was thought to have higher potency with regard to
mesothelioma, but quantitative investigation of this concern demonstrated that the overall impact of this
uncertainty was minimal, and an overall adjustment was not made for cohorts with potential crocidolite
exposures. Because under-ascertainment of mesothelioma was also a concern, a quantitative adjustment
was made to account for this uncertainty.
The cancer slope factors for lung cancer and mesothelioma were separately derived and then statistically
combined. Subsequently, a life table analysis was conducted using the Kl and Km to represent the
epidemiologic data, a relative risk model for lung cancer, and an absolute risk model for mesothelioma
with linear low dose extrapolation to arrive at an IUR of 0.23 per fiber/cc. It is important to note that in
the original studies identified in this assessment, exposure data was commonly collected as a measure of
dust, and some studies additionally presented fiber counts using filter or membrane-based techniques,
allowing for the development of a conversion factor. This conversion factor is necessary in order to
conduct quantitative assessment of asbestos exposure in studies where measurements were initially
taken for dust. These are further described in Appendix D.4, where applicable. Additionally, the
assessment found that the risk from lung cancer increased with time since first exposure and death from
mesothelioma increased rapidly after onset of exposure—an important observation. Limitations of the
analysis that were described include (1) variability in the exposure-response relationship at high
exposure; (2) uncertainty in extrapolating to much lower exposures (i.e., background exposures that can
be l/100th the levels seen in occupational settings); and (3) uncertainties in converting between
detection methods (e.g., optical fiber counts, mass determination). The asbestos IUR is widely
recognized and is used in other EPA programs, including Superfund risk assessments conducted under
the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) (U.S. EPA.
2021b).
5.3 IRIS Libby Amphibole Assessment Cancer Dose-Response	
The IRIS LAA Assessment, released in 2014, included a detailed toxicological review that provides the
scientific foundation to support the risk and dose-response assessment of chronic inhalation exposure
specific to LAA in the Rainy Creek complex and from the vermiculite mine near Libby, Montana (U.S.
EPA. 2014b). The LAA Assessment evaluated the possible risks associated with exposure to LAA,
including those related to cancer and non-cancer health effects, and presents risk values for use in risk
assessments, including an RfC for non-cancer health effects (summarized in Section 4.2 above) and an
IUR to address cancer risk. The LAA Assessment considered several occupational and community-
based cohorts for dose-response assessment (see Figure 4-1 in the LAA Assessment); however, OPPT
identified two of those occupational cohorts as being most relevant for dose-response consideration
(Appendix C.2).
For derivation of the IUR, the Libby, Montana, workers cohort (including miners and millers) was
ultimately selected as the cohort with the most robust data for dose-response assessment (i.e., individual-
level exposure data based on impinger and PCM measurements, complete demographic data, and vital
status with extended follow-up through 2006).
For mesothelioma mortality in this dataset, Poisson modeling was conducted to fit mortality data and
exposure data with a range of exposure metrics. The best model was based upon a subcohort with
employment beginning in 1959 and a cumulative exposure metric with a 5-year half-life and a 10-year
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lag time. The central estimate for Km was 3.11 x 10~4 per fibers/cc. Following selection of the Km, a life
table procedure was applied to the U.S. general population using age-specific mortality statistics to
estimate the exposure levels that would be expected to result in a 1 percent increase in absolute risk of
mesothelioma over a lifetime of continuous exposure. Linear low-dose extrapolation was used to find an
effective concentration corresponding to the central tendency, which was estimated to be 0.032 per
fiber/cc and 0.074 per fiber/cc when adjusted to account for under-ascertainment of mesothelioma.
Lung cancer unit risk values were also calculated separately and based on a subcohort of the Libby,
Montana, workers hired after 1959. Multivariate extended Cox models were run with a range of
exposure metrics, and the best fit was based on cumulative exposure with a 10-year half-life and a 10-
year lag. The resulting Kl from this model was 0.0126 per fiber/cc-yr. As was done for the
mesothelioma cancer slope factor, a life-table analysis was applied to the KLto determine an exposure
level of asbestos expected to result in a 1 percent increase in relative cancer risks when taking into
account age-specific background risk. The corresponding effective concentration relating to the central
tendency was 0.0399 per fiber/cc for a lifetime continuous exposure with an upper bound unit risk of
0.0679 per fiber/cc.
The upper bound unit risks for mesothelioma and lung cancer were statistically combined to yield an
appropriate upper bound value representing overall cancer risk for continuous lifetime asbestos
exposure. Importantly, the statistical derivation of a combined upper bound unit risk value accounted for
overprediction resulting from combining individual upper bound estimates. The upper bound combined
risk from the best fitting models applied to individual-level data from the Libby, Montana, workers was
0.17 per fiber/cc. The 2014 IRIS LAA Assessment notes some limitations, including the difficulty in
controlling for smoking as a confounder, the potential for under-ascertainment of mesothelioma, and
uncertainties in the exposure measurements in the facility. The LAA IUR is widely recognized and is
specifically used in Superfund risk assessments conducted under the Comprehensive Environmental
Response, Compensation, and Liability Act (CERCLA) (U.S. EPA. 2021b).
5.4 Part 1 Risk Evaluation for Asbestos: Dose-Response	
The most recent asbestos IUR was developed as part of the Risk Evaluation for Asbestos Part 1:
Chrysotile Asbestos that was finalized in 2020 (U.S. EPA. 2020). As previously described, asbestos was
identified as one of the first 10 substances to undergo risk evaluation under the amended TSCA. The
consideration and evaluation of human health evidence primarily focused epidemiologic studies of lung
cancer or mesothelioma resulting from inhalation exposures to chrysotile asbestos. Thus, OPPT made a
distinction between (1) studies of exposure settings where only commercial chrysotile asbestos was used
or where workers exposed only to commercial chrysotile asbestos could be identified, and (2) situations
where chrysotile asbestos was used in combination with amphibole asbestos forms and the available
information would not allow exposures to chrysotile and amphibole asbestos forms to be separated. The
studies that were found to be useful for the study of mesothelioma and lung cancer were all based on
historical occupational cohorts with use of the longest follow-up for each cohort or the most pertinent
exposure-response when a cohort had been the subject of more than one publication.
In Part 1, an IUR of 0.16 per fiber/cc was derived based upon thorough consideration and analysis of
data from epidemiological studies on mesothelioma and lung cancer in cohorts of workers using
chrysotile. As described in Appendix D.l and presented in Table 5-1, data from several cohorts was
available for dose-response modeling following a systematic approach to literature identification and
evaluation. Ultimately, data from cohorts of workers in textile plants in North and South Carolina were
selected for IUR derivation. For the NC cohort, individual-level exposure-response data was available
for lung cancer in Loomis et al. (2009) and Elliott et al. (2012) as well as mesothelioma in Loomis et al.
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(2019). For these studies, the Part 1 Risk Evaluation presents cancer potency values based on Poisson
regressions of the individual-level data using both logistical and additive relative rate model forms with
adjustment for age, sex, race, calendar period, and birth cohort (see Table 3-4 in (U.S. EPA. 2020)). For
the SC cohort, individual-level data was available for lung cancer in Hein et al. (2007) and Elliott et al.
(2012) as well as for mesothelioma from Berman and Crump (2008). Lung cancer potency values for
these studies were based on Poisson regression models using a linear relative rate model form with
adjustment for sex, race, and age. Mesothelioma cancer potency values were reported in Berman and
Crump (2008) based on analyses of the original cohort data using the Peto model (see Table 3-3 in U.S.
EPA (2020)).
Part 1 also describes uncertainty related to under-ascertainment of mesothelioma as an International
Classification of Diseases (ICD) code specific to mesothelioma that was not available prior to 1999.
Thus, some cases of mesothelioma are missed on death certificates prior to 1999 and likely even during
the initial use of the ICD code. This uncertainty was also considered in the IRIS LAA Assessment (U.S.
EPA. 2014b) and a multiplier was derived (1.39) based on data from the Libby cohort that was not fiber-
specific, but rather specific to outcome ascertainment for mesothelioma. This multiplier was used to
adjust IURs in Part 1 of the Risk Evaluation (see Section 3.2.3.8.1 in U.S. EPA (2020)). Part 1 also
describes uncertainty related to under-ascertainment of mesothelioma as an International Classification
of Diseases (ICD) code specific to mesothelioma that was not available prior to 1999. Thus, some cases
of mesothelioma are missed on death certificates prior to 1999 and likely even during the initial use of
the ICD code. This uncertainty was also considered in the IRIS LAA Assessment (U.S. EPA. 2014b)
and a multiplier was derived (1.39) based on data from the Libby Cohort that was not fiber-specific, but
rather specific to outcome ascertainment for mesothelioma. This multiplier was used to adjust IURs in
Part 1 of the Risk Evaluation (see Section 3.2.3.8.1 in U.S. EPA (2020)).
Additionally, the IUR was adjusted to account for cancer risk from other cancer endpoints beyond lung
cancer and mesothelioma. As explained in Section 3.2.3.8.1 of Part 1, IARC concluded that exposure to
asbestos is causally related to lung cancer and mesothelioma as well as laryngeal and ovarian cancer
(U.S. EPA. 2020; Straif et al.. 2009). Data was not available to derive potency factors for laryngeal and
ovarian cancer, so an adjustment factor was developed to account for potential underestimation of
cancer risk when only considering data for lung cancer and mesothelioma. The combined adjustment
factor applied to lung cancer to address other cancers was 1.06 (see Table 3-11 in U.S. EPA (2020)).
For each modeling result from the NC and SC datasets, the unit risks were calculated separately for lung
cancer and mesothelioma. Lung cancer unit risks were adjusted to account for other cancers and
mesothelioma unit risks were adjusted to account for under-ascertainment. The unit risks were then
statistically combined for central unit risk and upper bound risk. Overall, six IUR values were available
for the datasets and modeling results, and the median IUR was ultimately selected because there was
low model uncertainty (see Table 3-12 in U.S. EPA (2020)). The median lifetime cancer incidence IUR
was 0.16 per fiber/cc based upon a linear model of the data from the NC textile workers cohort (Elliott et
al.. 2012).
Part 1 notes a few important uncertainties in the IUR (see Section 4.3.5 in U.S. EPA (2020)). First, PCM
measurements were used despite TEM being a more precise analytical technique. However, it was
determined that when TEM and PCM were available in the same dataset, TEM and PCM model results
were similar. Thus, this uncertainty was considered to be low for the NC textile worker cohort. Another
source of uncertainty in exposure measurements is the use of impinger sampling data for early asbestos
exposures. The most robust approach to account for this is to use paired and concurrent sampling data to
derive a conversation factor, and this was performed in the analysis of the NC and SC textile cohorts
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resulting in low uncertainty. When considering uncertainties related to outcome data, use of mortality
data rather than incidence, which was not available, was of concern. To account for this, background
rates of lung cancer incidence were used in lifetable analyses. However, this was not possible for
mesothelioma. While this remains a bias, it is noteworthy that median survival for mesothelioma is less
than 1 year. Finally, confounding must be considered with regard to uncertainties. Smoking is
considered a strong confounder for lung cancer related to asbestos exposure, but in the NC and SC
cohorts, confounding was deemed to be low because regression models accounted for birth cohort that
would reflect changes in smoking rates over time. Additionally, it is likely that smoking rates among
workers were similar across facilities and occupations. Smoking is not a confounder for mesothelioma.
In Part 1 of the Risk Evaluation, this IUR was applied for all chrysotile asbestos exposure scenarios,
with less-than-lifetime adjustments applied where appropriate for less-than-lifetime exposures. Risk
determinations were based, in part, on quantitative risk characterization computer with this IUR. Risk
management rulemaking that is currently underway will address the unreasonable risk identified in Part
1 of the Risk Evaluation for Asbestos (U.S. EPA. 2020).
5.5 Part 2 Risk Evaluation for Asbestos: Quantitative Cancer Approach
Across decades of epidemiologic research in various occupational settings, employing diverse exposure
measurement methods and approaches to exposure assignment, and based upon a wide range of dose-
response modeling with application of adjustment factors, all three IURs are numerically very similar
(Table).
Inherent strengths and uncertainties pertain to each IUR, and all were developed for a distinct purpose
and application. The IUR of 0.16 per fiber/cc presented in Part 1 of the Risk Evaluation for Asbestos
(U.S. EPA. 2020) benefits from the most recent data available and generally, the longest follow-up
periods. Advanced exposure measurement methods are reflected in the underlying data resulting in
exposure estimates that are of high confidence. Furthermore, longer follow-up times increase the
statistical power of the study as more mortality is observed. Other notable strengths include accounting
for laryngeal and ovarian cancers, which are causally associated with asbestos exposure, and accounting
for under-ascertainment of mesothelioma. However, this IUR was strictly limited to exposures to
chrysotile asbestos and is therefore most appropriately applied in cases where exposures are chrysotile-
specific.
The IUR of 0.17 per fiber/cc presented in the IRIS LAA Assessment (U.S. EPA. 2014b) has similar
strengths and limitations as the chrysotile IUR. EPA ORD was able to conduct robust analyses based on
very detailed individual-level exposure measurements and outcome data for lung cancer and
mesothelioma as the cohort was established from one operation, the mine in Libby, Montana. There
were not sufficient data on laryngeal or ovarian cancers in this cohort for quantitative consideration5, but
under-ascertainment of mesothelioma was accounted for. As described in Section 5.2, herein, the
comprehensiveness of the data yielded quantitative analyses of high confidence. However, this IUR is
based on data specific to scenarios of exposure to only LAA, and therefore, is most appropriately
applied in risk estimates based on Libby-specific exposures.
5 The quantitative adjustment for lung cancer to address laryngeal and ovarian cancers developed in Part 1 of the Risk
Evaluation for Asbestos would not have impacted the LAA IUR and proposed IUR for application in Part 2 because it was
small and is only appropriate for lung cancer, which accounts for the minority of risk relative to mesothelioma in the Libby
IUR.
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The earliest IUR of 0.23 per fiber/cc presented in the IRIS Asbestos Assessment (U.S. EPA. 1988) was
developed to describe risks related to all asbestos fiber types. Development of this IUR was based on
historically robust data at a time when standard fiber measurement methods had not yet been established
and reporting and publication standards were highly variable. Although additional uncertainty exists in
the exposure measurement provided in these published studies, it is important to note that EPA technical
experts were diligent in advancing their understanding and use of data beyond what was available in
original publications to reduce uncertainties, as reflected in the 1988 Asbestos Assessment and related
publications. A major strength of this IUR is that it represents exposures to a range of fiber types and is
most appropriately applied to describe risks related to mixed-fiber exposures, which is pertinent to
exposure scenarios in Part 2 of the Risk Evaluation for Asbestos. The authors of the report
acknowledged this objective when they described the use of data from all cohorts and not isolating data
from the cohort with the most detailed exposure assessment that may have been specific to only a single
fiber.
Table 5-2. Comparison of EPA Inhalation Unit Risk Values for Asbestos
IUR per
fiber/cc
EPA Assessment
Fiber Type
Cancer Outcomes
0.23
IRIS Asbestos Assessment
(U.S. EPA. 1988)
Mixed fiber (chrysotile,
amosite, crocidolite)
Lung cancer and mesothelioma
0.17
IRIS LAA Assessment
(U.S. EPA. 2014b)
Libby Amphibole
Asbestos fiber
Lung cancer and mesothelioma
0.16
Risk Evaluation for Asbestos
Part 1: Chrysotile Asbestos
(U.S. EPA. 2020)
Chrysotile fiber
Lung cancer and mesothelioma, with
quantitative adjustment to account
for laryngeal and ovarian cancers
When considering the strengths and uncertainties of each IUR, OPPT is proposing to use an IUR of 0.2
per fiber/cc in Part 2 of the Risk Evaluation for Asbestos based on the existing IURs. When considering
standard practice of reporting IURs with precision to one significant digit, each of the existing IURs
would round to 0.2 per fiber/cc. This approach is well-supported in taking into account a broad range of
information that is applicable to Part 2. This value reflects exposures in a variety of settings and levels,
an array of asbestos fibers, and relevant cancer outcomes. Furthermore, the exposures that will be
analyzed based on the conditions of use in Part 2 (U.S. EPA. 2022) will predominantly be for legacy
uses of asbestos, or those uses for which there is no current manufacture, process, or distribution. These
exposure scenarios will not pertain to specific fiber types (e.g., chrysotile and LAA). Specifically, for
asbestos-containing building materials, exposure to mixed fiber types is described.
In applying an IUR of 0.2 per fiber/cc in the Part 2 of the Risk Evaluation for Asbestos, it is recognized
that this value applies to risks associated with a continuous lifetime exposure, which will not be
expected for all exposure scenarios in Part 2. Thus, as was done in Part 1 of the Risk Evaluation, partial
or less-than-lifetime (LTL) values corresponding to the IUR will be applied. The general equation for
estimating cancer risks for LTL exposure from inhalation of asbestos, from the OLEM Framework for
Investigating Asbestos-contaminated Superfund Sites (U.S. EPA. 2008). is:
ELCR = EPC x TWF x IURLtl
where:
ELCR = Excess lifetime cancer risk, the risk of developing cancer as a consequence of the site-
related exposure
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EPC = Exposure point concentration, the concentration of asbestos fibers in air (fiber/cc) for the
specific activity being assessed
IURltl = Less-than4ifetime inhalation unit risk per fiber/cc
For example: the notation for the LTL IUR could start at age 16 with 40 years duration IUR(i6.40).
TWF = Time weighting factor, this factor accounts for less-than-continuous exposure during a
one-year exposure, and is given by:
TWF = [Exposure time (hours per day) / 24 hours] x [Exposure frequency (days
per year) / 365 days]
For more information on the general approach for estimating cancer risk for less-than-lifetime exposure
from inhalation of asbestos, see Section 4.4.1 in Part 1 of the Risk Evaluation (U.S. EPA. 2020).
Assessing asbestos-related health effects is unique because of the timing of exposure related to outcomes
as TSFE plays an important role in risk modeling. Exposures occuring decades prior to the observed
outcome are most relevant—particularly for understanding risk. Following the approach described in the
Part 1 of the Risk Evaluation (see Appendix K), which was reviewed by the SACC, LTL values will be
determined based on age of first exposure and duration of exposure. These will be presented in the risk
characterization of the draft Part 2 of the Risk Evaluation for Asbestos.
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6 SUMMARY AND NEXT STEPS	
As described in preceding sections of this white paper, prior to OPPT's efforts to develop Part 2 of the
Risk Evaluation, the Agency has developed three IURs describing the relationship between cancer and
asbestos exposure and an RfC for non-cancer effects related to asbestos exposure. To ensure that the
consideration of human health effects in Part 2 is based upon the best available science, OPPT employed
a systematic approach to identify and evaluate the epidemiologic evidence available for dose-response
assessment and to consider if an updated IUR is warranted.
OPPT determined that the most appropriate epidemiologic cohorts available for dose-response
assessment were previously considered in deriving the existing IURs and RfC. Thus, OPPT is proposing
that an updated dose-response assessment for cancer and non-cancer effects related to asbestos
exposures is not needed at this time and that the existing, peer-reviewed EPA values are appropriate for
application in Part 2 of the Risk Evaluation for Asbestos. As described in Section 4.3, for non-cancer
effects, application of the LAA POD of 2.6x 10~2 fiber/cc is proposed for application in Part 2 with three
associated UFs (UFh= 10, UFd = 3, UFs = 10). Because there are three relevant IURs for cancer effects
that are all numerically similar, EPA is proposing use of an IUR of 0.2 per fiber/cc in Part 2 as this value
at one significant figure reflects an appropriate level of precision when considering the range of IURs
(Section 5.5).
OPPT is soliciting input through a letter peer-review. Following peer review of this proposed approach,
OPPT will release a draft Part 2 Risk Evaluation for Asbestos that will be made available for public
comment. Peer reviewer input and public comment will be taken into consideration and appropriate
revisions will be made to finalize the Part 2 Risk Evaluation for Asbestos on or before December 1,
2024, consistent with the consent decree timeline in ADAO, et al. v. Regan, No. 4:21-cv-03716 (N.D.
Cal. Oct. 2021). Ultimately, in the finalized Part 2 risk evaluation, OPPT will determine, based on
assessments of risk for the conditions of use examined, whether or not unreasonable risks are posed to
human health or the environment. As required by TSCA, any unreasonable risk must be addressed via
subsequent risk management rulemaking.
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REFERENCES	
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Leidel. NA; Bayer. SG; Zumwalde. RD; Busch. KA. (1979). USPHS/NIOSH membrane filter method
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NTP. (2015). Handbook for conducting a literature-based health assessment using OHAT approach for
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Musk. A. (2007). Age and sex differences in malignant mesothelioma after residential exposure
to blue asbestos (crocidolite). Chest 131: 376-382. http://dx.doi.org/10.1378/chest.06-1690
Reid. A: Franklin. P; Olsen. N: Sleith. J: Samuel. L; Aboagye-Sarfo. P; de Klerk. N: Musk. AW. (2013).
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SAB. (2008). SAB consultation on EPA's proposed approach for estimation of bin-specific cancer
potency factors for inhalation exposure to asbestos. (EPA-SAB-09-004). U.S. Environmental
Protection Agency. http://nepis.epa.gov/exe/ZyPURL.cgi?Dockev=P 1002EAG.txt
Seidman. H. (1984). Short-term asbestos work exposure and long-term observation. Seidman, H.
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Selikoff. IJ: Hammond. EC: Seidman. H. (1979). Mortality experience of insulation workers in the
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chrysotile asbestos fibre dimensions in determining respiratory disease risk in exposed workers.
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.pdf
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amphibole asbestos; CASRN not applicable, https://www.epa.gov/iris/supporting-documents-
libbv-amphibole-asbestos
U.S. EPA. (2014b). Toxicological review of libby amphibole asbestos: In support of summary
information on the Integrated Risk Information System (IRIS) [EPA Report], (EPA/635/R-
11/002F). Washington, DC: Integrated Risk Information System, National Center for
Environmental Assessment, Office of Research and Development.
https://cfpub.epa.gov/ncea/iris/iris documents/documents/toxreviews/1026tr.pdf
U.S. EPA. (2017a). Procedures for chemical risk evaluation under the amended Toxic Substances
Control Act. Fed Reg 82: 33726-33753.
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Washington, DC: Office of Chemical Safety and Pollution Prevention.
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8012). Washington, DC: Office of Chemical Safety and Pollution Prevention.
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Response, Compensation, and Liability Act act sites. (OLEM Directive No. 9200.0-90).
Asbestos Committee of the Technical Review Workgroup, Office of Land and Emergency
Management. https://semspub.epa.gov/work/HQ/100002942.pdf
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including legacy uses and associated disposals of asbestos [EPA Report], (EPA Document#
EPA-740-R-21-002). n.p.: U.S. Environmental Protection Agency, Office of Chemical Pollution
and Safety Prevention, https://www.epa.gov/svstem/files/documents/2022-
06/Asbestos%20Part%202 FinalScope.pdf
U.S. EPA. (2023). Quantitative approach to the human health assessment for the Risk Evaluation for
Asbestos Part 2: Supplemental evaluation including legacy uses and associated disposals of
asbestos — Systematic review of data quality evaluation information for human health hazard
epidemiology. Washington, DC: Office of Pollution Prevention and Toxics (OPPT).
Von Elm. E; Altman. DG: Egger. M; Pocock. SJ: G0tzsche. PC: Vandenbroucke. JP. (2008). The
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement:
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Wang. X: Yano. E; Lin. S: Yu. IT: Lan. Y; Tse. LA: Qiu. H; Christiani. DC. (2013). Cancer mortality in
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1128	Weill H; Hughes. J; Waggenspack. C. (1979). Influence of dose and fiber type on respiratory
1129	malignancy risk in asbestos cement manufacturing. Am Rev Respir Dis 120: 345-354.
1130	http://dx.doi.Org/10.1164/arrd.1979.120.2.345
1131
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APPENDICES
Appendix A ABBREVIATIONS AND ACRONYMS
ADME
ATSDR
BMR
CAA
CERCLA
COPD
Cr6+
CT
DLCO
DPT
ELCR
EPA
EPC
f/cc
f/mL
FEV
FT
FVC
GC-ECD
GC-FID
GC-HRMS
GC-MS
GC-MS/MS
HRCT
IARC
ICD
ILO
IRIS
IUR
JEM
KL
KM
LAA
LC-MS/MS
LPT
LTL
Me so
[j,m
mppcf
MT
NC
NASEM
NESHAP
NIOSH
NJ
Absorption, distribution, metabolism, and excretion
Agency for Toxic Substances and Disease Registry
Benchmark response
Clean Air Act
Comprehensive Environmental Response, Compensation, and Liability Act
Chronic obstructive pulmonary disease
Hexavalent chromium
Computerized tomography
Diffusing capacity of the lungs for carbon monoxide
Diffuse pleural thickening
Excess lifetime cancer risk
Environmental Protection Agency
Exposure point concentration
Fibers per cubic centimeter
Fibers per milliliter
Forced expiratory volume
Full text
Forced vital capacity
Gas chromatography with electron capture detector
Gas chromatography with flame-ionization detection spectrometry
Gas chromatography/high-resolution mass spectrometry
Gas chromatography mass spectrometry
Gas chromatography with tandem mass spectrometry
High resolution computed tomography
International Agency for Research on Cancer
International Classification of Diseases
International Labour Organization
Integrated Risk Information System
Inhalation unit risk
Job exposure metric
Lung cancer potency factor
Mesothelioma potency factor
Libby Amphibole Asbestos
Liquid chromatography with tandem mass spectrometry
Localized pleural thickening
Less-than-lifetime
Mesothelioma
Micrometers
Million particles per cubic foot of air
Montana
North Carolina
National Academies of Sciences, Engineering, and Medicine
National Emission Standards for Hazardous Air Pollutants
National Institute for Occupational Safety and Health
New Jersey
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NY
New York
OCSPP
Office of Chemical Safety and Pollution Prevention
OH
Ohio
OLEM
Office of Land and Emergency Management
OPPT
Office of Pollution Prevention and Toxics
OQD
Overall quality determination
ORD
Office of Research and Development
OSHA
Occupational Safety and Health Administration
PA
Pennsylvania
PBPK
Physiologically based pharmacokinetic
PCM
Phase-contrast microscopy
PCMe
Phase-contrast microscopy equivalent
PECO
Population, exposure, comparator, and outcome
POD
Point of departure
QC
Quality control
RfC
Reference concentration
SACC
Science Advisory Committee on Chemicals
SC
South Carolina
SIR
Standardized incidence ratio
SMR
Standardized mortality ratio
SSA
Social Security Administration
TSFE
Time since first exposure
TEM
Transmission electron microscopy
TIAB
Title/abstract (screening)
TLV
Total Liquid Ventilation
TSCA
Toxic Substances Control Act
TWA
Time-weighted average
TWF
Time weighting factor
TX
Texas
UF
Uncertainty factor
UFd
Database uncertainty factor
UFh
Intraspecies uncertainty factor
UFs
Subchronic uncertainty factor
U.S.
United States
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Appendix B SYSTEMATIC REVIEW APPROACH	
The sections below describe the process used to identify, screen, and evaluate the reasonably available
information. Many aspects of this process are described thoroughly in the 2021 Draft Systematic Review
Protocol (U.S. EPA. 2021a). However, some aspects of the process were modified or extended in a fit-
for-purpose manner. The modifications were performed to build off of systematic review efforts from
Asbestos Part 1 and utilize data evaluation elements from the prior assessment while providing a similar
structure for evaluating new and existing studies for other noncancer and cancer endpoints of concern
not evaluated in Asbestos 1. In addition, based upon recommendations from NASEM and SACC on
systematic review methodology, OPPT identified high quality studies based on previous assessments by
the IRIS program and evaluated these critical studies in a systematic way leading to robust set of cohort
studies for this dose response analysis. FigureApx B-l and FigureApx B-2 present schematics of the
process. Further descriptions below in B.1.2 explain how the 338 peer-reviewed, 3 gray literature, and 2
data sources pursuant to TSCA (total 343 data sources) that met PECO screening criteria (Figure Apx
B-l) were considered for dose-response screening (Figure Apx B-2).
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0
Supplemental
at TIAB Screening
ฉ
aso Stud;
Case Sen
ฉ
No Original Data
ฉ
Conference Abstract
ฉ
Susceptible Population
ฉ
ฉ
Human
ฉ
ฉ
ฉ
Plant
ฉ
Meets PECO
Criteria at
FT Screening

ฉ
Human
ฉ
ฉ
Human
(um)
Meets PECO
Criteria at
TIAB Screening
ฉ
ฉ
Mechanistic
ฉ
ADME/TK/PBPK
ฉ
ฉ
Case Study
or Case Series
ฉ
No Original Data

Conference Abstract
( xioxa \
Asbestos Part 2
Risk Evaluation:
Environmental and
Human Health Hazard
^19748^
Does Not Meet PECO Criteria
at TIAB Screening
ฉ
PDF Unavailable
(1970J
Mechanistic
ฉ
ADME/TK/PBPK
ฉ
Susceptible Population
ฉ
Field Study
ฉ
Non-English Record
ฉ
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\\ " ฎ
Field Study
ฎ
Non-English Record
ฉ
Tate or
Magnesium Silicate
Figure Apx B-l. Literature Inventory Tree - Environmental and Human Health Hazard for
Asbestos Part 2
View the interactive literature inventory tree in HAWC. Data in this figure represent all references obtained from
the publicly available databases and gray literature references searches that were included in systematic review as
of March 20, 2023. Additional data may be added to the interactive version as they become available.
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Literature Search
T1AB Screening
FT Screening
Epidemiologic studies
	^ with PFCO
	^ with PFCO


Include N = 343 references


from 156 cohorts
_C
*c
ai
CD
U
 3
exposure groups	
1
Cohorts WITHOUT appropriate
exposure measurementor
assignment, N = 127 references
from 62 cohorts
r
CANCER
Cohorts selected for final dose-
response consideration
•	Libby, MT, Mining and Milling
Cohort
•	NC Textile Cohort
•	SC Textile Workers
ฆ	Balangero, Italy, Mining Cohort
•	Chongqing, China, Asbestos
Products Factory Cohort
•	Qinghai, China, Asbestos Mine
Cohort
ฆ	Quebec, Canada,Asbestos
Mines and Mills Cohort
•	Salonit Anhovo, Slovenia,
Asbestos Factory Cohort
ฆ	Wittenoom, Australia,
Residents Cohort
T
NON-CANCER
Cohorts selected for final dose-
response consideration for
non-cancer
•	O.M. Scott Marysville, OH,
Plant Cohort
•	Libby, MT, Mining and Milling
Cohort
•	SC Textiles Cohort
ฆ	SCVermiculite Miners Cohort
ฆ	Anatolia, Turkey, Villagers
Cohort
•	Wittenoom, Australia.
Residents Cohort
•	Chinese Chrysotile Textile
Factory Cohort
1
Cohorts not selected for
final dose-responSe
consideration, N = 32
•	Exposure assessment
is for dust
•	Exposure assessment
is for lung tissue
•	Confirmation -
exposure not
assessed using PCM
or TEM
•	Rated low or
un in formative in SR
•	Confirmation - <3
exposure groups
	1	
References with mixed-fiber
exposures from EPA IRIS
Asbestos Assessment, 1988
N = 6
1234
1235	FigureApx B-2. Literature Flow Diagram Presenting the Identification, Screening, and
1236	Evaluation of Literature
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B.l Data Search and Screening	
B.l.l Data Search	
As described in Section 4 of the 2021 Draft Systematic Review Protocol Supporting TSCA Risk
Evaluations for Chemical Substances (U.S. EPA. 202 la). EPA conducts a comprehensive search for
reasonably available information to support TSCA risk evaluations. Details on the methodology used to
search for chemical-specific peer-reviewed and gray literature are available in Sections 4.2 and 4.3 of
the 2021 Draft Systematic Review Protocol (U.S. EPA. 2021a). Of note, the search for and screening of
hazard information considered for Part 2 of the Risk Evaluation for Asbestos includes all receptors
(humans, animals, plants, and other organisms); however, this section focuses on specific details for the
systematic review of epidemiologic (human) data to identify the most relevant information for informing
both the cancer and non-cancer dose-response human health hazard assessments.
Appendix Section C.1.24 of the 2021 Draft Systematic Review Protocol contains the specific strategy
and search string used to identify reasonably available hazard information for asbestos in Part 2 (U.S.
EPA. 2021a). Literature searches for asbestos hazard information were conducted in April 2021 (U.S.
EPA. 2021a). As stated in the 2021 Draft Systematic Review Protocol, "[t]he literature strategy for
Asbestos Part 2 is composed of three pieces: (1) reevaluation of all references used in Part 1 [of the Risk
Evaluation for Asbestos]; (2) evaluation of new literature produced by performing a Part 1 search
update; and (3) evaluation of new literature produced by inclusion of additional asbestos fiber types."
(U.S. EPA. 2021a p. 240). Although references from Part 1 were included in the literature search for
Part 2, these references were only reevaluated for outcomes that had not been previously evaluated in
Part 1. All reasonably available information submitted to EPA under TSCA authorities was also
considered for Part 2 of the Risk Evaluation. Appendix Section C.1.24 of the 2021 Draft Systematic
Review Protocol contains the specific strategy and search string used to identify reasonably available
hazard information for asbestos in Part 2 (U.S. EPA. 2021a). Literature searches for asbestos hazard
information were conducted in April 2021 (U.S. EPA. 2021a). As stated in the 2021 Draft Systematic
Review Protocol, "[t]he literature strategy for Asbestos Part 2 is composed of three pieces: (1)
reevaluation of all references used in Part 1 [of the Risk Evaluation for Asbestos]; (2) evaluation of new
literature produced by performing a Part 1 search update; and (3) evaluation of new literature produced
by inclusion of additional asbestos fiber types." (U.S. EPA. 2021a p. 240). Although references from
Part 1 were included in the literature search for Part 2, these references were only reevaluated for
outcomes that had not been previously evaluated in Part 1. All reasonably available information
submitted to EPA under TSCA authorities was also considered for Part 2.
Following the data search, SWIFT-Review was used to identify peer-reviewed references predicted to be
relevant for human health hazard (epidemiology) for asbestos. SWIFT-Review is a freely available text
mining and machine learning software that can be used for topic modeling, categorization, and
prioritization of search results (Howard et al.. 2016). Search strings were developed and validated in
collaboration with ORD and Sciome. The generic search strings used in SWIFT-Review to
automatically tag and categorize references can be found on the SWIFT-Review website. Peer-reviewed
references proceeded to TIAB screening if the SWIFT-Review search string terms were present in the
title, abstract, or keywords of a given reference. Additional details about the SWIFT-Review application
itself are described in the 2021 Draft Systematic Review Protocol (U.S. EPA. 2021a)
B.1.2 Data Screening	
Sections 4.2.5 and 4.3.2 of the 2021 Draft Systematic Review Protocol describe TIAB and full-text
screening, respectively, were conducted to identify references that may contain relevant information for
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use in risk evaluations under TSCA using discipline-specific screening criteria (U.S. EPA. 2021a).
Screening of environmental and human health hazard data sources was conducted using the specialized
web-based software programs: SWIFT-Active-Screener6-7 and DistillerSR.8 Specifically, for Part 2,
TIAB screening was conducted using SWIFT-Active-Screener that utilizes a machine-learning
algorithm to automatically compute which unscreened documents are most likely to be relevant based on
the results of manual screening conducted by two independent screeners. Subsequent to TIAB screening,
full-text screening was conducted manually by two independent reviewers for each reference using
DistillerSR, and conflict resolution was conducted for any discrepancies in screening results.
The same PECO screening criteria (presented in Appendix F) were utilized during both TIAB and full-
text screening of data sources containing environmental and human health hazard information relevant
for Part 2. During screening, calibration was conducted to increase consistency in interpretation of
PECO screening criteria between reviewers. Calibration allowed for clarifying modifications to be made
to the PECO screening criteria, published in Appendix H.5.13 of the 2021 Draft Systematic Review, to
reduce discrepancies in interpretation where identified (U.S. EPA. 2021a). The PECO screening criteria
for asbestos include a requirement for quantitative asbestos exposure concentration. Although the PECO
screening criteria encompass considerations and updates following screening calibration for both
environmental and human health hazard data, the PECO screening criteria modifications relevant for the
screening of environmental hazard data will be described in the forthcoming systematic review protocol
supplemental document included in the Part 2 of the Risk Evaluation for Asbestos.
As shown in the literature inventory tree above in Figure Apx B-l, 343 references met full-text PECO
criteria (338 peer-reviewed studies, 3 gray literature references, and 2 data sources pursuant to TSCA).
These references were further screened as described in Section 3.3 to identify a subset of these studies
potentially informative for dose-response that proceeded to data quality evaluation and extraction.
Studies were considered by cohort groupings. For example, if multiple publications were available on a
particular occupational cohort, they were considered as a set of information rather than as independent
publications.
B.2 Identification of Studies Potentially Informative for Dose-Response
Analysis	
An additional screening was conducted after full-text screening to identify the subset of studies that met
PECO screening criteria that contained dose-response data. In an effort to streamline the identification
of studies relevant to dose-response assessment, EPA implemented modifications to the process
described in the 2021 Draft Systematic Review Protocol (U.S. EPA. 2021a). The modifications included
conducting further screening of studies that met PECO criteria to identify the most relevant evidence
6	SWIFT-Active Screener is another systematic review software that EPA uses in the TSCA systematic review process. From
Sciome's SWIFT-Active Screener web page: "As screening proceeds, reviewers designate articles as having met or not
having met criteria, while an underlying statistical model in SWIFT-Active Screener automatically computes which of the
remaining unscreened documents are most likely to be relevant. This 'Active Learning' model is continuously updated during
screening, improving its performance with each reference reviewed. Meanwhile, a separate statistical model estimates the
number of relevant articles remaining in the unscreened document list."
7	SWIFT is an acronym for "Sciome Workbench for Interactive Computer-Facilitated Text-mining." SWIFT-Active Screener
uses machine learning approaches.
8	As noted on the DistillerSR web page, this systematic review software "automates the management of literature collection,
triage, and assessment using AI and intelligent workflows...to produce transparent, audit ready, and compliant literature
reviews." EPA uses DistillerSR to manage the workflow for screening and evaluating references; the literature search is
conducted external to DistillerSR.
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prior to conducting data quality evaluation. The further screening was based on the data analysis method
used in the study (regression and SMR studies were included), the method of exposure measurement
(based on Data Quality Evaluation Metric 4), and the range, distribution, and levels of exposure in the
analysis (based on Data Quality Evaluation Metric 5).
Step 1 of Further Screening for Fit for Purpose Context: Identification of Studies that Used
Standardized Mortality Ratios and Regression Analysis
Prior asbestos assessments, including Part 1 of the Risk Evaluation for Asbestos (U.S. EPA. 20201
focused their dose-response analyses on studies that assessed exposure-response relationships using
either SMRs or multivariate regression analyses.
An SMR is a ratio or percentage of the observed mortality in a given study sample relative to the
mortality in a specified general population (examples include males in Montana, U.S. adults, etc.).
Multivariate regression analyses generally estimate the average relationship between an exposure and an
outcome in a given study population, while holding other factors constant (adjusting for other variables).
Both SMRs and regression analyses can be used to assess a dose-response relationship, particularly
when the modeled relationship has either three or more exposure groups or is continuous.
Because of the utility of SMR and regression studies in dose-response assessment, EPA further screened
PECO-relevant studies to identify the subset of these studies that used SMR and/or regression analyses.
During this screening, study inventorying was also conducted, capturing details on route of exposure,
endpoint analyzed, study type, study design, cohort name/location, and analysis characterization. The
Distiller Form for this binning/inventory is included in Appendix E. Studies that were tagged as SMR
studies or regression analyses based on this binning/inventory process moved on to the next step of
further screening.
Step 2 of Further Screening for Fit for Purpose Context: Identification of Studies with Sufficient
Exposure Measurement and Range
For all studies identified as either regression or SMR studies, for each outcome in the paper or cohort
group, Metrics 4 and 5 were evaluated before other data quality evaluation metrics. Each paper or cohort
group of papers was evaluated by two epidemiologists: an initial evaluator and a quality control (QC)
reviewer. If the paper or cohort group was rated as Medium or High for Metrics 4 and 5, then the initial
evaluator moved on to data quality evaluation for all metrics, and then all data quality evaluation metrics
and comments went on to QC review. If either Metric 4 or 5 was rated Low or Uninformative, then the
initial reviewer submitted for QC without evaluation of the remaining metrics. If the QC reviewer
determined that Metrics 4 and 5 should have been rated Medium or High, then the paper or cohort group
was sent back to the initial reviewer for evaluation of the remaining metrics prior to completion of QC.
Exposure Measurement: In epidemiology studies, asbestos exposure is typically expressed as the
product of the amount of asbestos dust in the air (fibers or particles per mL) and the total amount of time
(years) exposed to each concentration (fibers/mL-years). Prior to 1968, the midget impinger method was
(Dement et al.. 2008) the most commonly used method for determining the level of asbestos in
occupational air. With no details on fiber type or particle size distribution, data from midget impingers
only give a rough estimation of the amount of asbestos in the air (SAB. 2008). With advancement in
methodological techniques, it was later determined that use of PCM was a more accurate method to
detect and quantify asbestos fibers in air samples (Leidel et al.. 1979). PCM identifies fibers according
to the NIOSH 7400 Method. More specific characterization of asbestos can be achieved using TEM. In
contrast to optical microscopy, which uses a beam of light, TEM uses a high-energy electron beam to
view structures that are considerably smaller. Compared to PCM, the majority of TEM instruments used
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for asbestos analysis feature technology that enables a more thorough characterization of a particle. The
total number of fibers counted on a sample grid as well as the number of PCM equivalent (PCMe) fibers
are typically recorded and estimated using TEM in order to measure the fiber size, distribution, and
dimension. TEM examination of mineral fibers is often used to confirm fiber analysis by PCM. By
comparing the fiber's ionic spectrum to a recognized standard and determining the mineralogy of a
target fiber, TEM analysis enables microscopists to identify the target fiber (U.S. EPA. 2014a). In
addition, multiple measurements taken by PCM or TEM for a given exposure setting is preferred over a
single measurement.
Although some studies collect measurements of dust using midget impingers, these exposure
measurements alone are less reliable in the context of dose-response assessment because the
differentiation of fiber types is not possible. In cases where exposure data collected by midget impingers
was used in analyses, it is strongly preferred that a conversion factor is applied based on paired sampling
measurements using impingers and PCM.
Because of the importance of the of exposure measurement in dose-response assessment, OPPT
evaluated the exposure measurement (Metric 4) before evaluating other data quality evaluation metrics
to focus on the subset of studies with the most reliable asbestos fiber detection and quantification
methods (i.e., PCM or TEM). Studies that were rated Low or Uninformative for Metric 4 did not move
on to data quality evaluation.
The data quality evaluation criteria for Metric 4 are as follows:
Mark as High if:
For all study types:
Quantitative estimates of exposure were consistently assessed (i.e., using the same method and sampling
time-frame) during multiple time periods and using either PCM or TEM.
OR
A combination of methods were used over time (i.e., midget impinger, PCM or TEM), but side-by-side
sampling and analyses were conducted to develop appropriate conversion criteria.
AND
For an occupational population, contains detailed employment records and quantitative estimates of
exposure using either PCM or TEM which allows for construction of job-matrix for entire work history
of exposure (i.e., cumulative or peak exposures and time since first exposure).
Mark as Medium if:
For all study types:
Exposure was assessed during one time period but this time period is judged to be reasonably
representative of the entire study time period.
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AND
Exposure was assessed using a combination of midget impingers, PCM, and/or TEM measurements, but
side-by-side sampling and analyses were not conducted for all operations and thus there is a lack of
confidence in the conversion factors.)
OR
For an occupational study population, contains detailed employment records and quantitative estimates
of exposure using a combination of midget impingers and PCM or TEM measurements for only a
portion of participant's work history of exposure (i.e., only early years or later years), such that
extrapolation of the missing years is required.
Mark as Low if:
For all study types:
Exposure was estimated solely using professional judgement.
OR
Exposure was directly measured and assessed using a quantitative method other than PCM or TEM and
conversion factors were not determined.
OR
The method of quantifying/counting fibers was not specified (PCM, TEM, or other method not
specified).
*If "acceptable," refer to the evaluation guide to see confidence level criteria.
Mark as Uninformative if:
For all study types:
Methods used to quantify the exposure were not well defined, and sources of data and detailed methods
of exposure assessment were not reported (STrengthening the Reporting of OBservational studies in
Epidemiology rSTROBE! Checklist 7 and 8 ("Von Elm et al.. 2008).
OR
There was no quantitative measure or estimate of exposure.
OR
There is evidence of substantial exposure misclassification that would significantly bias the results.
Mark asN/A if:
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Do not select for this metric.
Range, Distribution, and Levels of Exposure: To derive a dose-response relationship from an
epidemiologic study, it is necessary for the study analysis to inform how a unit change in exposure
relates to a unit change in risk for a health outcome. This is most easily accomplished with studies that
estimate the relationship between a continuous measure of exposure and a health outcome. However, a
dose-response relationship can also be estimated for studies that report the relationship between a
categorical measure of exposure and a health outcome as long as there are a sufficient number of
exposure groups to approximate a continuous relationship. This is done by estimating a dose-response
line that passes through the mid-points of each of the exposure categories. Three or more exposure
groups, including one unexposed or lower-exposed group and at least two additional exposed groups, is
considered the minimum for being able to adequately approximate a dose-response relationship in this
manner. Thus, studies that were rated Low or Uninformative for Metric 5 did not move on to data
quality evaluation.
Metric 5 explicitly evaluates whether the study includes sufficient exposure data for dose-response
assessment, regardless of potential bias or lack of bias in the study methodology. Thus, Metric 5 was
evaluated before the other data quality evaluation metrics, and only those studies that were rated as
Medium (High is not an option) for Metric 5 moved on to data quality evaluation. The data quality
evaluation criteria for Metric 5 are:
Mark as High if:
Do not select for this metric.
Mark as Medium if:
For all study types:
The range and distribution of exposure is sufficient or adequate to develop an exposure-response
estimate (Cooper et al.. 2016).
AND
Reports 3 or more levels of exposure (i.e., referent group +2 or more) or an exposure-response model
using a continuous measure of exposure.
Mark as Low if:
For all study types:
The range of exposure in the population is limited.
OR
Reports 2 levels of exposure (e.g., exposed/unexposed)) (Cooper et al.. 2016) (Source: IRIS)
Mark as Uninformative if:
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For all study types:
The range and distribution of exposure are not adequate to determine an exposure-response relationship
(Cooper et al.. 2016).
OR
No description is provided on the levels or range of exposure.
Mark asN/A if:
Do not select for this metric.
B.3 Data Quality Evaluation	
All references that met PECO screening criteria, as described above in Section 3.2 and that used
regression or SMR analyses and were rated as Medium or High for Metrics 4 and 5 underwent full data
quality evaluation as an individual reference or as part of a cohort group, as described in Appendix R of
the 2021 Draft Systematic Review Protocol and the Draft Risk Evaluation for Asbestos Part 1
Systematic Review Supplemental File: Data Quality Evaluation of Raman Health Hazard Studies:
Mesothelioma and Lang Cancer Studies (March 2020), with some modifications described below (U.S.
EPA. 2021a).
Part 1 of the Risk Evaluation for Asbestos evaluated the association between inhalation exposures to
asbestos and the outcomes of mesothelioma, lung cancer, laryngeal cancer, and ovarian cancer. Part 2
included additional outcomes including other cancers and asbestosis, pulmonary function/spirometry
results, pleural plaques, and other non-cancer outcomes.
For mesothelioma, the mesothelioma data quality evaluation form used in Part 1 of the Risk Evaluation
for Asbestos was used for Part 2, with some modifications based on the calibration for data quality
evaluation. For other outcomes, the lung cancer data quality evaluation form from Part 1 was used with
additional modifications to evaluate other outcomes that were not considered in Part 1.
Prior to beginning calibration and then data quality evaluation for asbestos, the data quality evaluation
criteria from the Draft Risk Evaluation for Asbestos: Systematic Review Supplemental File: Data
Quality Evaluation of Human Health Hazard Studies: Mesothelioma and Lang Cancer Studies (March
2020) were reviewed, and changes were made to the criteria to address the additional outcomes included
in Part 2. In Part 1 of the Risk Evaluation for Asbestos, there were separate data quality evaluation forms
for mesothelioma and lung cancer due to the differences between these health outcomes. In comparison
to lung cancer and other health outcomes, mesothelioma has a lower incidence and a longer latency
period. Furthermore, mesothelioma has few known causes other than asbestos and few potential
confounders, and thus has different data quality considerations than lung cancer as well as other
outcomes. Therefore, for Part 2 of the Risk Evaluation, a separate data quality evaluation form was
maintained for mesothelioma, and the lung cancer data quality evaluation form was modified to include
considerations of other cancer and non-cancer outcomes. Calibration was then conducted, resulting in
additional clarifying modifications to the data quality evaluation criteria. The data quality evaluation
criteria for Asbestos Part 2 are presented in Appendix G. TableApx G-l presents the data quality
evaluation criteria for mesothelioma and Table Apx G-2 presents the data quality evaluation criteria for
other outcomes.
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B.4 Consideration of Epidemiologic Cohorts for Dose-Response Analysis
Following the data quality evaluation of each cohort, those receiving Medium or High OQD ratings
were further reviewed to confirm suitability for dose-response assessment. The cohorts were categorized
for examination of cancer and/or non-cancer outcomes. Additionally, the exposure and outcome data
and analysis performed were reviewed to confirm the use of PCM or TEM for measurement of asbestos
fibers or application of an appropriate conversion factor, use of air measurements in the analysis,
analysis conducted with outcome data, and adequate assessment of the outcome (e.g., sufficient follow-
up time).
At this point, some cohorts were removed from further consideration because the quantitative analyses
were not done with PCM or TEM measurements or a conversion factor even though the study may have
presented some PCM or TEM data (e.g., passing Metric 4). Other cohorts were removed from
consideration because they had received a Low or Uninformative OQD rating in data quality evaluation.
Cohorts that were used in the derivation of the existing IURs or RfC were automatically included for
dose-response consideration so that a complete assessment of each IUR and RfC could be achieved,
noting strengths and uncertainties related to the underlying data. Sections 4 and 5 provide detailed
descriptions of the cohorts and the existing IURs and RfC, respectively.
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Appendix C NON-CANCER EPIDEMIOLOGIC COHORTS
C.l Cohorts Included in the IRIS Libby Amphibole Assessment	
The IRIS LAA Assessment presents the cohorts considered in Figure 4-1 of the Toxicological Review
(U.S. EPA. 2014b). There were two distinct occupational cohorts including miners and millers in Libby,
Montana, and fertilizer plant workers in Marysville, Ohio, where vermiculite from Libby was received,
processed, and packaged for distribution.
Libby, MT, Mining and Milling Cohort
As described in Section 5.2.2, the Libby, MT, Mining and Milling Cohort included men who worked in
the open-pit vermiculite mine outside of Libby in either mining or milling operations. There were
several different investigations of this cohort that differed in inclusion criteria; however, each examined
non-cancer morbidity and mortality. The exposure assessment data used in analyses the non-cancer
outcomes are the same as those described for the cancer mortality as described in Section 5.2.2 and in
greater detail in Table 4-1 and Section 4.1.1.1 of the IRIS LAA Assessment (U.S. EPA. 2014b). For
outcome assessment in all investigations, mortality was determined by death certificates with a certified
underlying cause of death. Examination of pulmonary outcomes in workers were assessed by chest x-
ray. Films were randomized and independently read by three qualified readers using the 1980 ILO
classification system to identify parenchymal abnormalities.
O.M. Scott, Marysville, OH, Fertilizer Plant Workers
The O.M. Scott plant in Marysville, Ohio, was a site that received vermiculite ore by rail where it was
process into expanded form for use as an inert carrier for herbicides and fertilizers. A total of 512
workers participated in the 1980 investigation on the pulmonary effects in Ohio plant workers (Lockev
et al.. 1984). Follow-up of the original cohort including chest x-rays and interview was conducted in
2004 (Rohs et al.. 2008) and vital status for mortality in 2011 (Dunning et al.. 2012).
For this cohort, there were eight main departments at the vermiculite ore processing plant in Marysville,
Ohio, including production and packaging of commercial products, maintenance, research, the front
office, and the polyform plant. The vermiculite ore was delivered by train or truck to the facility,
processed and packaged, and stored. Dust controls were implemented beginning in 1967 leading to a
marked improvement in dust management during the course of the 1970s. Monitoring of industrial
hygiene at the facility started in 1972 which consisted of an industrial hygienist following a worker with
a sampling device. After 1976, personal breathing-zone samples were collected and analyzed by PCM.
Cumulative exposures for each worker were estimated using detailed work histories and industrial
hygiene data. Overall, employees were divided into three different exposure groups: nonexposed
workers (chemical processing, research, front office), low exposed workers (central maintenance,
packing, and warehouse), and high exposed workers (expander, plant maintenance, and pilot plant) (U.S.
EPA. 2014b; Lockev et al.. 1984). In 2009, the exposure analyses were updated based on the inclusion
of newly available information on sampling and industrial hygiene records resulting from litigation
records related to Libby vermiculite (U.S. EPA. 2014b; Borton et al.. 2012).
Exposure-response analyses were conducted for respiratory outcomes and mortality based on the
detailed exposure estimates in 2004, and 2009, respectively. Comprehensive, individual-level data was
available from physical examination and interviews with each participant, allowing more control for
confounding in the analysis. Also notable is that the extended follow-up periods provided time from first
exposure that ranged from 23 to 47 years (U.S. EPA. 2014b).
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C.2 Cohorts Not Previously Considered in Non-cancer Assessments	
SC Textiles Cohort
The workers included in the SC Textile Cohort studies described in Appendix D.l and included in Part 1
of the Risk Evaluation for Asbestos were also followed for non-cancer outcomes, primarily asbestosis
pneumoconiosis mortality. The exposure measurement and assignment methods for the non-cancer
analyses are the same as those used in the exposure-response analyses for cancer. Hein et al. (2007) and
Stavner et al. (2008) included the longest follow-up for non-cancer mortality in this cohort with vital
status through 2001. These studies included an extension of the original cohort to include non-white
workers and females. Strong associations between asbestos exposure and asbestosis and
pneumoconiosis-related mortality were demonstrated in the analysis of this cohort.
SC Vermiculite Miners and Millers Cohort
W.R. Grace & Company conducted a study of vermiculite miners in Enoree, South Carolina, in 1988
drawing comparisons to the health effects observed in the Libby, Montana, mines (W. R. Grace & Co.
1988). The study included a cohort of 194 men involving in milling and mining vermiculite with
exposures to tremolite fibers. The mine opened in 1946 and employment was at 80 men in the 1960s.
Dust control procedures were implemented in 1970. In 1985 and 1986, 21 bulk samples and 58 static air
samples were collected. Bulk sample analysis showed the presence of tremolite-actinolite, vermiculite
fragments, talc/anthophyllite, and iron rich fibers. Air samples form 10 different areas were analyzed by
PCM, all below 0.01 f/cc. Additionally, the study references other exposure measurement data,
including 125 air samples from Mine Safety and Health Administration and personal samples of longer
durations than static samples, but details are not provided. Estimates of exposure were calculated based
on work history and calculated fibers concentrations in wet and dry zones. Mortality data was collected
through 1985, providing a minimum latency of 15 years. Radiographic films were taken and sputum
collected in April to May 1986. Overall, mean length of employment for the cohort was 9.2 years and
mean length of time between start of employment and death was 19.7 years. Exposure-response analyses
were conducted for mortality and excess mortality was observed. Results for sputum and parenchymal
abnormalities were only categorically reported for exposed and unexposed employees.
Anatolia, Turkey, Villagers Cohort
In Anatolia, Turkey, there are deposits of asbestos, known as white soil, that has been used in as many
as 196 villages in the past, Metintas et al. (2005) conducted a study to examine respiratory outcomes
among villagers in a subset of villages with ongoing environmental exposures to asbestos. Ten villages
were randomly selected and 991 residents at least 30 years of age were included in the cohort.
Assessment of soil samples showed the presence of tremolite, anthophyllite, actinolite, and chrysotile
asbestos. For each village, indoor and outdoor air samples were collected and fibers counted by PCM.
Cumulative fiber estimates for each villager were calculated based on the assumption of an 8-hour
workday outside of the home, 8 hours sleeping within the home, 8 hours of household activity, and 11
months spent in the village each year. Villagers completed questionnaires and had clinical and
radiological examining conducted with a portable roentgenogram and had additional follow-up if
abnormalities were detected. Outcomes of interest included pleural plaques, diffuse pleural fibrosis, and
asbestosis. Multivariate logistic regression analysis was performed, but few details of the analysis are
provided in the study. Additionally, TSFE was not characterized for the cohort.
Chinese Chrysotile Textile Factory Cohort
In the suburb of Shanghai, China, a chrysotile textile product factory opened in 1958 that employed
1,059 workers between opening and follow-up in September of 1982. Huang (1990) examined
exposures to workers and asbestosis. In the exposure-response analysis, exposures for each of the 776
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workers with at least 3 years of employment with sufficient documentation for study inclusion were
determined by combining detailed work histories with asbestos routine air measurements collected from
17 worksites across the factory using membrane filters. For earlier asbestos exposures, fiber estimates
were derived from dust concentrations converted based on site-specific conversion factors and linear
regression. Onset of asbestosis was assessed based on chest x-ray films using ILO classification. Linear
regression showed strong correlation between asbestos exposure and asbestosis in this cohort.
Wittenoom, Australia, Residents Cohort
As described in Appendix D.4, the Wittenoom, Australia, Residence Cohort comprised all individuals
residing in Wittenoom for at least 1 month between 1943 and 1992. The exposure assessment data used
in analyses the non-cancer mortality outcomes are the same as those described for the cancer mortality.
Only one study identified for this this cohort examined non-cancer mortality; Reid et al. (2008)
described excess mortality in women and girls of the cohort for a variety of causes including
pneumoconiosis. Overall, there is only limited non-cancer data available from this cohort for dose-
response consideration.
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Appendix D CANCER EPIDEMIOLOGIC COHORTS
D.1 Cohorts Included in the Risk Evaluation for Asbestos Part 1	
South Carolina Textiles Cohort, U.S.
Many publications have reported on the mortality of a group of workers at a textile plant in Charleston,
South Carolina, which produced asbestos. The plant produced textiles from raw chrysotile asbestos
fibers that were imported from Rhodesia (Zimbabwe) and Canada. Crocidolite yarns were also used in a
small operation within the plants, but overall, only accounted for 0.03 percent of the annual asbestos
processed.
In terms of exposure assessment for the cohort, beginning in the 1930s, the facility implemented
engineering measures to manage dust levels, and at the time, it was regarded as the industry's "gold
standard." Based on 5,952 industrial hygiene air samples taken between 1930 and 1975, estimates of
personal exposure were derived. Prior to 1965, only midget impinger samplers were used to collect all
samples. From 1965 to 1971, both impinger and membrane filter samplers were employed. Post-1971,
only membrane filter samplers were employed (U.S. EPA. 2020).
To determine the concentrations of fibers 5 [j,m or longer, PCM and membrane filter sampling were
used. Conversion factors between membrane and impinger samples were derived to calculate job and
operation-specific asbestos measurements. In 1965, 120 paired samples were collected, and between
1968 and 1971, 986 concurrent samples were also collected, and statistical analysis showed no
significant changes in the fiber/dust ratios over time or between operations. Overall, asbestos
measurements were estimated for nine departments and four job categories using linear regression with
adjustment for time-related changes in process and dust control, and individual cumulative exposures for
workers were determined based on detailed occupation histories and the constructed job exposure matrix
(U.S. EPA. 2020V
A follow-up of 3,072 workers through 2001 provided the most recent data for lung cancer and
mesothelioma in the cohort. For study inclusion, workers needed to be employed for at least 1 month
between 1940 and 1965, which primarily consisted of white men initially, but later study years included
non-white men and women. Using Poisson regression modeling and a linear relative rate form,
quantitative exposure-response associations for lung cancer were calculated. Chrysotile asbestos
exposure cumulative in f/cc-yr was entered as a continuous variable with sex, race, and age as variables,
and it was lagged by 10 years (U.S. EPA. 2020).
Of the available information and data in publications, individual-level lung cancer and mesothelioma
data from Hein et al. (2007). Elliot et al. (2012). and Berman and Crump (2008) were used in linear and
exponential modeling to derive Kl and Km values.
North Carolina Textiles Cohort, U.S.
In four North Carolina textile mills that used asbestos, authors reported on mortality in a cohort of
workers that had not been previously researched. Three of these plants produced yarns and woven goods
from raw chrysotile fibers while one, smaller plant produced asbestos products using purchased yarns.
One of the larger factories also used amosite fibers, however, this was a separate operation from that
using raw chrysotile. These factories, unlike the South Carolina plants, did not use exposure controls.
Company records listed 5,770 workers (3,975 men and 1,795 women) with at least 1 day of employment
between 1950 and 1973 and vital status and state or national health agency records were collected
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through 2003. These records included ICD codes indicating cause of death, including intermediate
causes and any relevant conditions. Of note, prior to the introduction of a unique code for mesothelioma
in 1999, death certificate data were reviewed for any mention of mesothelioma and for ICD codes
frequently used to indicate mesothelioma (U.S. EPA. 2020).
Between 1935 and 1986, 3,420 air samples were collected and the presence of asbestos fibers was
assessed. Both impinger sampling and membrane filter sampling were utilized up until 1971, when
impinger sampling was no longer used. Sampling prior to 1964 was done using impingers. To estimate
concentrations, fibers longer than 5 [j,m were counted on membrane filters. To determine plant-,
operation-, and period-specific parameters for converting dust to PCM-equivalent fiber concentrations,
paired and contemporaneous samples by both methods were used. Fiber/dust ratios did not change
significantly (U.S. EPA. 2020).
Multivariable mixed models were used to assess fiber concentration data and estimate average
concentrations by factory, department, job, and time period. The employment-exposure matrix's
functioning and job categories were the same as those created for South Carolina. To determine each
worker's average and cumulative exposure to asbestos fibers, these estimations were correlated with
their individual work history records. Where records lacked detailed job titles within departments (27%
of employees, primarily those with short-term positions), exposure was calculated using the averages for
the plant, time, and department. Exposures during the years before 1935, when there were no exposure
measurements and little work history records available, were presumed to be the same as those in 1935,
before dust restrictions were put in place (U.S. EPA. 2020).
A Poisson regression analysis with both log-linear and additive relative rate model types, was used to
examine exposure-response relationships for lung cancer in the North Carolina cohort. Age, sex, race,
the year of birth, and birth cohort were taken into account during modeling. With lags of 0, 10, or 20
years, the results were presented per 100 f/cc-yr of cumulative fiber exposure. Kl and Km values were
reported for the individual-level data presented in Loomis et al. (2009) and Elliott et al. (2012) based on
linear and exponential model results. A Poisson regression analysis with both log-linear and additive
relative rate model types, was used to examine exposure-response relationships for lung cancer in the
North Carolina cohort. Age, sex, race, the year of birth, and birth cohort were taken into account during
modeling. With lags of 0, 10, or 20 years, the results were presented per 100 f/cc-yrs of cumulative fiber
exposure. Kl and Km values were reported for the individual-level data presented in Loomis et al. (2009)
and Elliott et al. (2012) based on linear and exponential model results.
Quebec, Canada, Asbestos Mines and Mills Cohort
Several investigations of workers at various mining, milling, and production facilities in Quebec,
Canada, are available. The oldest publication included 11,379 Canadian miners and mill workers from
Quebec who were born between 1891 and 1920 and had worked for at least a month in the mines and
mills. The cohort was followed to 1975 where additional findings were published based on the cohort's
follow-up through 1988, and extended analysis to include data through 1992 (U.S. EPA. 2020).
In these studies, exposure assessment methods varied. Midget impinger readings from 1948 to 1966
were used to estimate total dust concentrations in mppcf, and studies report a range of 3,096 to 10,205
samples for 5,782 unique job assignments according to a 13-point scale ranging from 0.5 to 140 mppcf.
Although the categories are described by the authors as "approximating the mean," the procedures used
to analyze the exposure measures and assign categories are not described. Different methods were
employed to estimate exposures in earlier and later years when dust data were deemed to be insufficient
or not available. Exposures in years prior to 1948 were based on expert assessment from interviews with
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employees and company personnel, while those in years following 1966 were extrapolated from the
previously measured levels (U.S. EPA. 2020).
The initial publications reported exposure-response analyses based on dust concentrations in mppcf.
Some of the later investigations applied conversion factors ranging from approximately 3 to 7 f/cc per
mppcf. The basis for these conversion factors, however, is not well described and the reported
confidence in these conversion factors also varies. In addition, later examination of dust samples from
Quebec mines reported by (Berman. 2010). demonstrated that a third of the structures in samples were
not asbestos in PCM and TEM analysis. These findings raise serious doubts about the accuracy of the
f/cc estimates of exposure from the Quebec investigations, combined with issues surrounding the
selection of an appropriate conversion factor. Ultimately, Kl values were estimated based on modeling
with data from Berman and Crump (2008). but because of uncertainties, they were not used in final IUR
derivations (U.S. EPA. 2020).
Qinghai, China, Asbestos Mine Cohort
The Qinghai Mine first opened in 1958 and produced raw commercial chrysotile. The examination of
workers from this mine included individuals that were on the registry in 1981 and were employed for at
least 1 year. They were followed from 1981 to 2006. Periodically between 1984 and 1995, area
sampling at specified places was used to measure total dust concentrations, though the number of
measurements was not reported. In addition, 28 measurements in 6 different workshops were taken in
2006. Dust concentrations were converted to f/cc using a linear regression model built from 35 paired
measurements taken in 1991. Fiber concentrations were determined for each workshop and job
description from 1984 to 2006 using a single conversion factor, though the estimation techniques are not
fully explained in English-language publications.
In the Part 1 of the Risk Evaluation for Asbestos, Kl values were calculated using data from Wang et al.
(2013) and Wang et al. (2014). A strength of the analysis in these studies was the use of continuous
exposure variables in log-linear Cox proportional hazards models adjusted for age and smoking. Despite
the statistically robust analysis, results from these investigations were not selected for final IUR
derivations due to uncertainties in the exposure measurements and assignment.
Balangero, Italy, Mining Cohort
This historical cohort was the subject of four relevant publications (Pira et al.. 2017; Pira et al.. 2009;
Piolatto et al.. 1990; Rubino et al.. 1979); however, the cohort studies from Balangero, Italy, were
omitted due to the models' failure to produce findings when exposure was measured continuously. The
Balangero Mine and Mill, was located northwest of Turin, and workers were exposed to chrysotile
asbestos. The mine began operations in 1916, expanded to produce an average of 130,000 to 160,000
tons of chrysotile asbestos per year in the 1970s, and shut down in 1990, before all forms of asbestos,
including chrysotile, were outlawed in Italy in 1992. The cohort included 952 workers who had each
worked at least 30 calendar days between January 1, 1930, and December 31, 1965, and were still living
on January 1, 1946. Additionally, a small number of contract workers who were occasionally employed
on the Balangero site and subjects who worked for less than a year were not included in the cohort.
The factory's personnel records provided information on employment, and population registrations and
copies of death certificates from municipal registration offices provided information on vital status and
causes of death for this cohort. Date of birth, employment history, cause of death (including contributing
factors for deaths that happened since 1988), job category, and latest information for subjects who were
lost to follow-up were all accessible. Since researchers were unable to determine when subjects'
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employment ended after December 31, 1987, they used the assumption that those who were still
employed at the mine on that day would continue there until production stopped in 1990.
Data on exposure were quantified using the cumulative dose of inhaled fibers reported in fiber-years.
This was calculated using environmental observations from 1969 onward and synthetically
reconstructed working conditions for earlier times.
In order to determine the cohort's mortality experience through 1975, 98 percent of the cohort was
tracked down. Overall, 332 deaths were recorded versus 214.4 predicted, which is an extraordinarily
high mortality rate. Nevertheless, non-malignant respiratory disorders, cardiovascular diseases, and
accidents accounted for the majority of the extra mortality. Only laryngeal cancer was found to be
considerably overrepresented in the entire sample, with the overall SMR for all malignant neoplasms
being 106.
Chongqing, China, Asbestos Products Factory
This cohort started with a preliminary study on worker fatalities at a Chongqing, China, facility that
manufactured a range of asbestos-containing items. Using plant data, a fixed cohort of 515 males who
had been working for at least a year and were active as of January 1, 1972, was formed. Since no women
were hired before 1970, none were part of the founding cohort. In later studies, additional analyses
based on extensive follow-up were presented. The cohort's 2008 follow-up included 279 more women
who had jobs between 1970 and 1972 (U.S. EPA. 2020).
The Chongqing Plant produced a variety of asbestos-containing items including textiles, friction
materials, rubber-impregnated commodities, and cement after it first opened in 1939 and then expanded
in the 1950s. The plant reportedly used chrysotile asbestos from two mines in Sichuan Province, and it is
unlikely that there was amphibole or tremolite contamination.
Techniques of exposure assessment that were reported in this cohort were based on 556 area
measurements at 4-year intervals between 1970 and 2006. Fiber concentrations for four activities
(processing raw materials, textile carding and spinning, textile weaving and maintenance, and
manufacturing rubber and cement) were estimated. Prior to 1999, only total dust was recorded; after that
year, measurements of both dust and fibers were done in tandem. In total, there were 223 measurements
of fiber concentration made using PCM. To estimate dust to PCM fiber-equivalent concentrations for the
period 1970 to 1994, paired dust and fiber samples from 1999 to 2006 was used; however, no
information was provided on what operations and jobs these estimations reflect. Cumulative individual
fiber exposures were calculated based on the concentration information and the length of time
employees spent in each section of the factory, which was generally stable over time (U.S. EPA. 2020).
Several articles have presented exposure-response information for lung cancer in the Chongqing cohort
for various time periods of the study, and Kl values were estimated. However, model fitting could not
be conducted for the minimal amount of data on mesothelioma. Furthermore, due to potential for
exposure misclassification resulting from the low number of exposure measures, the absence of fiber
measurements prior to 1999, and the use of area sampling as opposed to personal sampling, this cohort
was not selected for use in IUR derivation (U.S. EPA. 2020).
Salonit Anhovo, Slovenia, Asbestos Factory Cohort
This historical cohort was the subject of two relevant publications examining asbestos exposure to
workers in asbestos cement factory that included factories producing cement, cement pipes, and
corrugated sheets. The factory opened in 1921 and began using asbestos in 1922. In 1996, asbestos was
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banned by law in Slovenia. Uniquely, the plant kept record of asbestos use separately for chrysotile and
amphibole.
The cohort comprised all 6,714 employees who started working at the Salonit Anhovo factory after
December 31, 1946, and who did so for at least 1 day between 1964 and 1994. From the cohort, 58
primary lung cancer cases with histological confirmation and 290 healthy controls were chosen. The
working life exposure histories to the asbestos form amphibole (10% exposure) and chrysotile (90%
exposure) were estimated independently. Some employees in Salonit Anhovo were also exposed to
cement dust, which contains hexavalent chromium (Cr6+), and silica dust, which is free SiC>2. For either
silica or chromium, airborne concentration data were not available; nonetheless, each contaminant's
presence or absence could be determined for each work and each year.
The facility-maintained records and tracked of the amount of asbestos utilized throughout production
(separately for chrysotile and amphibole). Chrysotile was blended with amphiboles in minor but
recognized quantities after being primarily acquired from Canada, Rhodesia, Italy, Russia, and then
Yugoslavia. The first records of employment are from 1939, when the factory employed 731 people.
The total workforce was down to 520 by the end of World War II, although it quickly increased after the
war. By 1953, there were more than 1,000 employees, and in 1981, that number peaked at 2,651.
Women made up about 30 percent of the employee population. Between 300 and 800 workers were
directly exposed to asbestos each year, with the number fluctuating.
From 1961 until 1996, the facility's airborne fiber concentrations were observed for compliance. It was
not until 1986 that the workers' exposure conditions significantly changed as a result of the installation
of an efficient ventilation system and the introduction of respirators (although they were not used
consistently at the time). A total of 1,030 air measurements were taken at the asbestos facility between
1961 and 1995, using a variety of monitoring techniques, including 78 pairs of measurements where the
gravimetric and membrane filter methods were utilized side-by-side. Every air sampling measurement
was made at a set point that was close to the worker's breathing zone. The side-by-side samples were
used to develop conversion factors, which incorporated the information acquired by the various
exposure assessment techniques.
Part 1 of the Risk Evaluation considered this cohort for exposure to commercial chrysotile and found
that it was uninformative for further consideration because it did not adequately allow exposures to
chrysotile and amphibole asbestos forms to be separated. However, this limitation is not relevant to Part
2.
Thus, these studies were considered further for use in dose-response assessment. Additional limitations
in the data are available from these cohorts relevant to the criteria described in Section 5.1. Job exposure
matrices were constructed based on worker histories and fiber concentrations from area sampling
measurements. However, some jobs did not have relevant air sampling data as they moved between or
outside of facilities, and in these instances, a consultation group was used to develop exposure matrices.
It is unclear what percentage of study participants for which this applied. Another limitation of this
cohort for use in dose-response assessment is the use dichotomous exposure or categorical exposures
based on the 90th percentile. As described in Section 5.1, preference is for studies with continuous
exposure based on individual-level data (Fikfak et al.. 2007; Fikfak. 2003).
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D.2 Cohorts Included in the IRIS Libby Amphibole Asbestos Assessment
Libby, MT, Vermiculite Mining and Milling Cohort
Several studies are available that examine occupational asbestos exposures to LAA. These studies were
conducted in Libby, Montana to assess the mining and milling operations or at a plant in Marysville,
Ohio, which received vermiculite mined in Libby, Montana. The Libby vermiculite mine opened in
1923 and remained open until 1990. The operations in the open pit mine produced high dust exposures
that were reduced in 1970 with new drilling technology. Vermiculite from the mine was shipped by rail
beginning in 1935 and enclosed hoppers were only used beginning in 1960.
The relevant studies examining this occupational cohort are summarized in Table 4-2 of the IRIS LAA
Assessment (U.S. EPA. 2014b). The studies were similar in examining asbestos exposure and outcomes
in male workers, but varied in the inclusion criteria (e.g., length of employment, employment date),
asbestos quantification, and job-exposure classification.
However, in all studies, the asbestos quantification included fiber counts by PCM in later study years
and impinger measurements in earlier study years that were converted to f/cc based on analysis of
location-specific sampling. Publications on the cohort included various follow-up periods for mortality
and pulmonary outcomes, with the longest follow-up in 2006.
For lung cancer and mesothelioma, exposure-response relationships were analyzed to derive an IUR. By
2006, approximately 54 percent of the cohort had died, and a detailed individual-level work history and
asbestos exposure measurements were available. As described in Section 6.2.2 of the IRIS LAA
Assessment (U.S. EPA. 2014b). the data were fit with various models with a range of exposure metrics
because there was not a biological basis for model selection. Ultimately, a subcohort was established
that included workers hired after 1959, which improved model fitting. Data prior to 1959 did not include
as detailed work history which likely contributed to exposure misclassification in the dataset. This
subcohort included 880 workers, of which 26 percent had died at time of follow-up. These model fitting
results were retained for consideration in the IUR derivation.
D.3 Cohorts (Mixed-Fiber) Included in the IRIS Asbestos Assessment
Insulation Manufacturing, Paterson, NJ (Amosite)
Between 1941 and 1945, men were recruited to work at an amosite asbestos factory in Paterson, New
Jersey, to supply the U.S. Navy with insulation materials for ships in World War II. Seidman et al.
(1979) and Seidman (1984) examined the mortality among 820 of these men that met study inclusion
criteria, including attaining 5 years of employment at the factory. The cohort was followed through 1982
and mortality data was collected. While no air concentrations were available for the Paterson, New
Jersey, plant, fiber counts were available from similar plants located in Tyler, Texas, and Port Allegany,
Pennsylvania. Data collection in these other plants was conducted by the U.S. Public Health Service in
1967, 1970, and 1971 and reported in the Asbestos Criteria Document of the National Institute for
Occupational Safety and Health. Although the number of samples collected and the methods used for
fiber counting are not described, it is known that dust control measures were not in place. Exposure-
response analysis was conducted with data for this cohort using SMR based on expected and observed
cancer deaths in the population. For this cohort, workers with less than 6 months of history had an
abnormally high observed mortality rate; thus, adjustments were made yielding a Kl of 0.043 and a Km
of 3.2xl0~8(U.S. EPA. 1986).
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Insulation Application, United States (Chrysotile and Amosite)
Selikoff et al. (1979) and Peto et al. (1982) studied the mortality experience in members of the
International Association of Heat and Frost Insulators and Asbestos Workers in the New York-New
Jersey metropolitan area between 1943 and 1976. The cohort included 623 men employed prior to 1943
and 833 men employed after 1943, the latter group reflecting work experience in post-war conditions.
Expected and observed cancer deaths were estimated at follow-up in 1962 and 1976. Asbestos
concentrations in these specific work facilities were not measured; however, asbestos air concentration
measurements were obtained through study of insulation work facilities by three different laboratories in
the United States between 1968 and 1971 using the NIOSH and OSHA method (published in 1979;
phase contrast illumination) (Leidel et al.. 1979). The average fiber concentration of asbestos dust in
insulation work, ranged from roughly 3 to 6 f/mL with 2 to 5 minutes peak concentrations exceeding
100 f/mL. However, it was recognized that asbestos exposures prior to these measurement dates could
have been significantly higher due to changes in asbestos products over time (e.g., less asbestos in later
years). Because of this, the overall average concentration used was 15 f/mL. For this cohort, a Kl of
0.0075 per fiber/cc was estimated, which included reduction to adjust for death certificate diagnoses
rather than best estimates as well as substantial smoking rates in insulation workers. For this cohort, a
Km of 1.5x1 0-8 was estimated (U.S. EPA. 1986; Peto et al.. 1982)
Asbestos Products Manufacturing, United States (Chrysotile and Crocidolite)
Henderson and Enterline (1979) studied a cohort of men who had worked in product or maintenance for
a U.S. asbestos company. This cohort was established from company records, including those who
retired between 1941 and 1967 and were receiving a company pension. The average length of
employment in the asbestos industry for these 1,075 men was 25 years. The cohort was followed
through 1973, using company records and SSA files for tracing. For this cohort, total dust concentrations
were measured in mppcf and no specific conversion factor was available to present air concentrations in
f/mL or f/cc. Thus, in U.S. EPA (1986). air concentration data from other relevant studies was
considered. It was determined conversion factors from other industrial settings (i.e., cement plants) was
useful and a conversion factor of 1.5 f/mL/mppcf was used. In deriving the KLfor this cohort, it was
additionally noted that a retrospective analysis starting from retirement would likely underestimate the
actual deaths. After adjustment to account for this, a Kl of 0.0049 was presented. (U.S. EPA. 1986).
New Orleans Asbestos Cement Building Material Plants Cohort (Chrysotile and Crocidolite)
In the early 1920s, two asbestos cement building materials plants opened in New Orleans, Louisiana,
producing flat shingles and corrugating sheets in one plant, and shingles, pipes, and asphalt flooring
materials in the other plant. Overall, products contained between 15 and 28 percent asbestos,
predominantly chrysotile with crocidolite and amosite in some products. Weill et al. (1979) studied the
mortality experience in 5,645 men who had worked in either or both of these plants that had at least 20
years of follow-up from beginning employment. Plant records included demographic information and
complete work history for each person and were mostly complete with the exception of poor records
before 1942 in one plant. Tracing of the cohort was done in 1974 through SSA records, and only 75
percent could be verified as deceased or living. While study authors considered the ages and potential
occupations of those loss to follow-up, there is likely an underestimation of mortality especially when
considering that the deaths prior to 1970, more so for blacks, were not reported to SSA.
Expected and observed mortality rates were used in exposure-response calculations. Exposure data for
this cohort consisted of dust measurements collected with impingers, reported in mppcf. Sampling was
initiated in the 1950s and impinger measurements were taken at various locations in both plants.
Exposure profiles for each workers were developed using impinger sampling data combined with
estimated fiber content for each job by month and year. The dose-response modeling of this data
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resulted in a Kl of 0.0053, which included adjustment for loss to follow-up and application of a fiber-
particle conversion factor of 1.4.
Ontario, Canada, Asbestos Cement Factory Cohort (Chrysotile and Crocidolite)
An Ontario asbestos-cement factory that began production in 1948 was the manufacturing site for a
variety of product including cement board and insulation materials made with both chrysotile and
crocidolite. Finkelstein (1983) examined mortality in a cohort of men hired before 1960 and who had
been employed for nine or more years. The cohort included production and maintenance workers in
asbestos operations as well as workers in rock wool operations that had minimal asbestos exposure.
Workers who could not be classified based on work history were excluded from the cohort.
Air measurements were collected in the factory using impingers for area sampling from 1949 through
the 1960s and membrane filters in personal sampling starting in 1969. Based on crude analysis of the
impinger data, fiber concentrations from 1955 to 1961 were assumed to be 30 percent higher and from
1948 to 1954 twice as high. These exposure estimates were matched with detailed work history for each
workers based on company records to calculate an annual exposure concentration; however,
extrapolations were used for maintenance workers. Even with these uncertainties, exposure estimates
were assumed to be accurate to within a factor of 3 to 5. Exposure-response analysis was conducted
based on individual-level cumulative exposures over an 18-year period with follow-up through 1980.
Local tracing and Statistic Canada were used to determine confirm the deceased and living. Of note,
only 2 to 7 percent of the cohort were lost to follow-up and smoking status was obtained for 70 percent
of men. Calculations resulted in a Kl of 0.067 and Km of 1,2x 10~7 (U.S. EPA. 1986).
D.4 Cohorts Not Included in Existing EPA Assessments	
Wittenoom, Australia, Residents Cohort
From 1937 to 1966, crocidolite (blue asbestos) was mined in Western Australia's Wittenoom Gorge. A
single proprietor, the Australian Blue Asbestos firm, which employed about 7,000 people during that
time period, owned the plant. The township of Wittenoom was established in 1946 and initially situated
just 1.6 km from the mine but was relocated to 12 km away in 1947. Tailings from the mine were high
in crocidolite fibers and distributed throughout the town for a variety of uses through the 1960s.
The Wittenoom, Australia, Residents Cohort comprised all individuals residing within the town for at
least a month between 1943 and 1992 and were not employed in asbestos work. Of the 4,659 former
residents in the cohort, follow-up by questionnaire in 1993 resulted in 2,173 responses, confirmed 460
deaths and 549 that could not be traced. By 1993, there only 45 residents remained in the town.
The Mines Department of Western Australia used a konimeter to measure dust levels in the mine and
mill on a number of occasions between 1948 and 1958. A Casella long running thermal precipitator was
used to conduct the first fiber count of the mine, mill, and Wittenoom area in 1966. Using a combination
of personal and fixed positional monitors, additional monitoring was conducted in and around the
township in 1973, 1977, 1978, 1980, 1984, 1986, and 1992. Based on the monitoring conducted in 1966,
inhabitants were allocated an intensity of exposure of 0.5 fiber/milliliter (f/mL) of air between 1958 and
1966, when the mine closed. In light of the assumption that fiber levels were roughly twice as high when
the original mill was in operation, a level of 1.0 f/mL was assigned for the period 1943 to 1957.
Exposures were interpolated from 0.5 f/ml in 1966 to 0.01 f/mL in 1992 based on dust surveys that
employed personal monitors. The product of the fiber content for each year and the amount of time spent
in Wittenoom during that year was multiplied by the number of years each resident lived there to
determine their cumulative exposure, adjusted to account for a continuous 24-hour exposure. By
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2059	demonstrating concordance with lung fiber burdens, the estimations of asbestos exposure have been
2060	internally validated.
2061
2062	The earliest identified publication on the cohort was conducted by Hansen et al. (1998) and
2063	demonstrated a strong relationship between mesothelioma mortality that increased with time from first
2064	exposure and duration of exposure. Additional publications examined differences between age and sex
2065	in mesothelioma mortality in the cohort (Reid et al.. 2007). mortality observed only in women and girls
2066	in the cohort (Reid et al.. 2008). as well as childhood exposures and adult mortality (Reid et al.. 2013).
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Appendix E LITERATURE INVENTORY FORM
Asbestos Human Lit Inventory Distiller Form
Is this study a candidate for re-screening? {i.e., PECO-relevance related issues) If yes, please stop
inventorying.
•	Case-only, case-case, or other case-report
•	No quantitative exposure concentration
•	Other
Exposure routes (check all that apply)
•	Inhalation
•	Dermal
•	Oral
Endpoints analyzed (check all that apply)
•	Cancer (check all that apply)
o Mesothelioma (ICD-9: 163)
o Lung (ICD-9: 162)
o Laryngeal (ICD-9: 161)
o Ovarian
o Other
•	Non-cancer (check all that apply)
o Pleural Plaques
o Asbestosis
•	Other Respiratory (check all that apply)
o Spirometry (forced expiratory volume [FEV], total liquid ventilation [TLV], FVC, etc.)
o Chest x-ray
o Asthma/wheeze
o Chronic obstructive pulmonary disease (COPD)
o Other
•	Non-respiratory
Study type (focus on the study population)
•	Occupational
o Study Design
ฆ	Prospective Cohort
•	Study Identifiers
o Cohort/Study Name:	
o Cohort/Study Location:	
ฆ	Retrospective Cohort
•	Study Identifiers
o Cohort/Study Name:	
o Cohort/Study Location:	
ฆ	Case-control
ฆ	Other
•	Other
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o Study Design
ฆ	Prospective Cohort
•	Study Identifiers
o Cohort/Study Name:	
o Cohort/Study Location:	
ฆ	Retrospective Cohort
•	Study Identifiers
o Cohort/Study Name:	
o Cohort/Study Location:	
ฆ	Case-control
ฆ	Other
Analysis characterization
•	SMR studies
•	Incidence rate or number of cases of the outcome and person-years for each interval - Are the
incidence rates broken out by? (check all that apply)
o Interval of time since first exposure (TSFE)
o Cumulative exposure
o Duration of employment or exposure
o Other
•	Regression analyses - What was the unit of analysis for the regression (i.e., form of the exposure
term)? (check all that apply)
o Analyzed by intervals of times since first exposure (TSFE)
o Analyzed by intervals of cumulative exposure
o Analyzed by duration of employment/exposure
o Other
•	Other
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2141	Appendix F POPULATIONS, EXPOSURES, COMPARATORS, AND
2142	OUTCOMES (PECO) CRITERIA FOR PART 2 OF THE
2143		RISK EVALUATION FOR ASBESTOS	
2144
2145	Table Apx F-l. PECO Criteria for Asbestos Part 2 (Legacy Uses and Associated Disposals)	
PECO Element
Evidence
P
Human: Any population and lifestage (e.g., occupational or general population, including
children and other sensitive populations).
Animal: Aquatic and terrestrial species (live, whole organism) from any lifestage (e.g.,
preconception, in utero, lactation, peripubertal, and adult stages). Animal models will be
inventoried according to the categorization below:
•	Ecotoxicoloaical models: invertebrates (e.g., insects, soidcrs. crustaceans, mollusks. and
worms) and vertebrates (e.g., mammals and all amphibians, birds, fish, and reptiles).
Plants: All aquatic and terrestrial species (live), including algal, moss, lichen, and fungi species.
Screener notes:
•	All non-human animal (e.g., rodents, rabbits, hens, amphibians, fish, insects) and plant
models listed above are relevant as an ecotoxicological model.
•	PECO considerations should be directed toward effects on target species only and not on
the indirect effects expressed in taxa as a result of chemical treatment (e.g., substance is
lethal to a targeted pest species leading to positive effects on plant growth due to diminished
presence of the targeted pest species).
Tests of single toxicants in in vitro and ex vivo systems or on gametes, embryos, or plant or fungal
sections capable of forming whole, new organisms will be tagged as potentially supplemental
(mechanistic studies). Bacteria and yeast studies specific for assessing genotoxicity or
mutagenicity (e.g., Ames assay) will also be tagged as potentially supplemental (mechanistic
studies) but are otherwise excluded. Studies on viruses will be excluded.
E
Relevant forms:
Asbestos, as defined by the following fiber types (or mixtures of fiber types):
•	Asbestos: 1332-21-4
•	Chrysotile (serpentine): 12001-29-5
•	Crocidolite (riebeckite): 12001-28-4
•	Amosite (grunerite): 12172-73-5
•	Anthophy llite: 17068-78-9
•	Tremolite: 14567-73-8
•	Actinolite: 12172-67-7
•	Winchite: 12425-92-2
•	Richterite: 17068-76-7
•	Libby amphibole: 1318-09-8
•	Exposure reported as PCM or TEM (including conversion factors for dust)
•	Talc (or magnesium silicate) contaminated with asbestos
For svnonvms see and a list of validated svnonvms on the EPA Chemistrv Dashboard.
Human: Any exposure to one or more of the nine asbestos fiber types, singularly or mixed, that
meets the following conditions:
•	Exposure based on quantitative (measured or estimated) concentrations of asbestos, such
as exposure biomonitoring data (e.g., lung tissue specimens), environmental or occupational
monitoring data (e.g., ambient air levels). This may be combined with estimates of duration
of exposure. (Generally, studies with quantitative exposure data are included; however,
studies that included a quantitative measurement of exposure but did not use that
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PECO Element
Evidence
E
quantitative measurement in the analysis of the association between exposure and outcome
are excluded.)
•	For categorical exposures, a minimum of two exposure groups (referent group + 1)
Eco Animal: Anv oral exDosure to one or more of the nine asbestos fiber tvDcs. regardless of the
exposure media (e.g., water, diet, soil, sediment), singularly or mixed. All other exposure
pathways (e.g., dermal, inhalation, injection) are designated as not meeting screening criteria
(please select the correct supplemental tag: apical/mechanistic and the non-oral exposure
pathway). For organism exposures to asbestos or PECO-relevant asbestos fibers where oral
exposures cannot be discerned from other exposure pathways that are more characteristic
of mammalian and avian studies, please select include (e.gfish or invertebrates exposed to
asbestos in surface water, sediment, and/or soil.
Plants: Any exposure to one or more of the 9 asbestos fiber types, regardless of the exposure
media (e.g., water, soil, sediment), singularly or mixed
Screener notes:
•	Field studies with media concentrations (e.g., surface water, interstitial water, soil,
sediment) and/or body/tissue concentrations of animals or plants are to be identified as
Supplemental if anv biological effects are reported.
•	Controlled outdoor experimental studies (e.g., controlled crop/greenhouse studies,
mesocosm studies, artificial stream studies) are considered to be laboratory studies (not field
studies) because there is a known and prescribed exposure dose(s) and an evaluation of
hazardous effect(s). Whereas field studies (e.g., biomonitoring) where there is no prescribed
exposure dose(s) do not meet screening criteria if there is no evaluated hazardous effect,
and tagged as Supplemental field, if there is an evaluated hazardous effect.
Papers reporting exposure to "asbestos" generally and not specific fiber type of asbestos will be
included for further consideration.
C
Human: The source meets either of the following conditions:
•	Contains a comparison or referent population exposed to lower levels (or no
exposure/exposure below detection limits) of asbestos, and other relevant forms listed
above.
Eco Animal and Plants: A concurrent control group exposed to vehicle-only treatment and/or
untreated control (control could be a baseline measurement).
Screener note:
•	If no control group is explicitly stated or implied (e.g., by mention of statistical results that
could only be obtained if a control group was present), the study will be marked as Unclear
during TIAB screening.
0
Human: Health outcomes including cancer (e.g., lung cancer, mesothelioma, laryngeal cancer,
ovarian cancer) and all non-cancer endpoints at the organ level (e.g., immune, cardiovascular,
respiratory) or higher.
Eco Animal and Plants: All apical biological effects (effects measured at the organ level or
higher) and bioaccumulation from laboratory studies with concurrently measured media and/or
tissue concentrations. Apical endpoints include but are not limited to reproduction, survival, and
growth.
Screener notes:
• For Active Screener only: INCLUDE Supplemental references: mechanistic (including in
vitro/in silico studies and studies with genotoxicity/mutagenicity assays in yeast/bactcria):
absorption, distribution, metabolism, and excretion (ADME)/physiologically based
pharmacokinetic (PBPK)/toxicokinetic; case reports or case series; susceptible populations
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PECO Element
Evidence

(with no health outcome; only at full text screening); mixture studies (tagged separately for
human health animal and eco animal/plant studies); non-English records, records with no
original data (e.g., reviews, editorials, commentaries, assessments); conference abstracts;
field studies.
• For citations with no abstract, use the following to screen: title relevance and page numbers
(articles two pages in length or less are assumed to be conference reports, editorials, or
letters and can be tagged as supplemental material). Reviews that do not suggest a specific
focus on the chemical of interest can be excluded rather than marked as supplemental
material.
2146
2147
2148
2149
Table Apx F-2. Major Categories of "Potentially Relevant Supplemental Material
Category
Evidence
Mechanistic studies
All studies that report results at the cellular level and lower in both
mammalian and non-mammalian model systems, including in vitro, in vivo,
ex vivo, and in silico studies. These studies include assays for genotoxicity or
mutagenicity using bacteria or yeast.
ADME, PBPK, and
toxicokinetic
Studies designed to capture information regarding ADME, toxicokinetic
studies, or PBPK models.
Case reports, case series, case-
case, or case-only study
designs
Case reports, case series, case-case, and case-only study designs will be
tracked as potentially relevant supplemental information. (Does NOT include
case-control, case-referent, or case-crossover study designs, which would be
PECO includes if they meet criteria).
Susceptible populations
(no health outcome)
Studies that identify potentially susceptible subgroups; for example, studies
that focus on a specific demographic, lifestage, or genotype. This tag applies
primarily during full text screening.
Screener note:
• If biological susceptibility issues are clearly present or strongly
implied in the title/abstract, this supplemental tag may be applied at
the title/abstract level. If uncertain at title/abstract, do not apply this
tag to the reference during title/abstract screening.
Non-English records
Non-English records will be tracked as potentially relevant supplemental
information.
Records with no original data
Records that do not contain original data, such as other agency assessments,
informative scientific literature reviews, editorials, or commentaries.
Conference abstracts
Records that do not contain sufficient documentation to support study
evaluation and data extraction.
Field Studies
Field studies with media concentrations (e.g., surface water, interstitial water,
soil, sediment) and/or body/tissue concentrations of animals or plants if
biological effects reported
Other relevant structures
If another asbestos fiber type or talc/magnesium silicate are mentioned with
resulting biological effects reported. However, please exclude synthetic
magnesium silicate (lab-synthesized and thus, not asbestos-relevant) or
synthetic magnesium silicate-products.
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Appendix G DATA QUALITY EVALUATION CRITERIA	
As described above in Appendix Section B.3, data quality evaluation forms originally used in Part 1 of
the Risk Evaluation for Asbestos were updated and used to evaluate references containing
epidemiological data for Part 2. In short, the mesothelioma data quality evaluation form used in Part 1,
with updates based on calibration, was used for mesothelioma studies in Part 2. The lung cancer data
quality evaluation form from Part 1 was modified to include considerations of other cancer and non-
cancer outcomes for Part 2. Additional description of the updates to the data quality evaluation forms
will be provided in the forthcoming Draft Risk Evaluation for Asbestos Part 2: Supplemental Evaluation
including Legacy Uses and Associated Disposals of Asbestos - Systematic Review Protocol.
Table Apx G-l. Mesothelioma Criteria
Data Quality
Rating
Description
Domain 1. Studv Participation
Metric 1. Participant Selection (selection, performance biases)
High
For all studv tvves:
-	All key elements of the study design are reported (e.g., setting, participation rate
described at all steps of the study, inclusion and exclusion criteria, and methods of
participant selection or case ascertainment)
AND
-	The reported information indicates that participant selection in or out of the study (or
analysis sample) and participation was not likely to be biased {i.e., the exposure-outcome
distribution of the participants is likely representative of the exposure-outcome
distributions in the population of persons eligible for inclusion in the study.)
Medium
For all studv tvves:
- Some key elements of the study design were not present but available information
indicates a low risk of selection bias (/'. e., the exposure-outcome distribution of the
participants is likely representative of the exposure-outcome distributions in the
population of persons eligible for inclusion in the study.)
Low
For all studv tvves:
- Key elements of the study design and information on the population (e.g., setting,
participation rate described at most steps of the study, inclusion and exclusion criteria, and
methods of participant selection or case ascertainment) are not reported (STROBE
checklist 4. 5 and 6 (Von Elm et al.. 2008)).
-If the study provides little to no information about selection criteria, then rate this metric
as Low.
Critically Deficient
For all studv tvves:
The reported information indicates that selection in or out of the study (or analysis
sample) and participation was likely to be significantly biased (/'. e., the exposure-outcome
distribution of the participants is likely not representative of the exposure-outcome
distributions of the population of persons eligible for inclusion in the study).
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality
Rating
Description
Metric 2. Attrition (missing data/attrition/exclusion, reporting biases)
High
For cohort studies:
-	There was minimal subject loss to follow up during the study (or exclusion from the
analysis sample) and outcome and exposure data were largely complete.
OR
-	Any loss of subjects (i.e., incomplete outcome data) or missing exposure and outcome
data were adequately* addressed (as described below) and reasons were documented
when human subjects were removed from a study (NTP. 2015).
OR
-	Missing data have been imputed using appropriate methods (e.g., multiple imputation
methods), and characteristics of subjects lost to follow up or with unavailable records are
not significantly different from those of the study participants (NTP. 2015).
For case-control studies and cross-sectional studies:
-	There was minimal subject withdrawal from the study (or exclusion from the analysis
sample) and outcome data and exposure were largely complete.
OR
-	Any exclusion of subjects from analyses was adequately* addressed (as described
below), and reasons were documented when subjects were removed from the study or
excluded from analyses (NTP. 2015).
*NOTE for all study types: Adequate handling of subject attrition can include: Use of
imputation methods for missing outcome and exposure data; reasons for missing subjects
unlikely to be related to outcome (for survival data, censoring was unlikely to introduce
bias); missing outcome data balanced in numbers across study groups, with similar
reasons for missing data across groups.
Medium
For cohort studies:
-	There was moderate subject loss to follow up during the study (or exclusion from the
analysis sample) or outcome and exposure data were nearly complete.
AND
-	Any loss or exclusion of subjects was adequately addressed (as described in the
acceptable handling of subject attrition in the high confidence category) and reasons were
documented when human subjects were removed from a study.
For case-control studies and cross-sectional studies:
-	There was moderate subject withdrawal from the study (or exclusion from the analysis
sample), but outcome and exposure data were largely complete
AND
-	Any exclusion of subjects from analyses was adequately addressed (as described above),
and reasons were documented when subjects were removed from the study or excluded
from analyses (NTP. 2015).
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Data Quality
Rating
Description
Low
For cohort studies:
-	The loss of subjects (e.g., loss to follow up, incomplete outcome or exposure data) was
moderate and unacceptably handled (as described below in the unacceptable confidence
category) (Source: OHAT).
OR
-	Numbers of individuals were not reported at important stages of study (e.g., numbers of
eligible participants included in the study or analysis sample, completing follow-up, and
analyzed). Reasons were not provided for non-participation at each stage (STROBE
Checklist Item 13 (Von Elm et al.. 2008)).
For case-control and cross-sectional studies:
-	The exclusion of subjects from analyses was moderate and unacceptably handled (as
described below in the unacceptable confidence category).
OR
-	Numbers of individuals were not reported at important stages of study (e.g., numbers of
eligible participants included in the study or analysis sample, completing follow-up, and
analyzed). Reasons were not provided for non-participation at each stage (STROBE
Checklist Item 13 (Von Elm et al., 2008)).
Critically Deficient
For cohort studies:
-	There was large subject attrition during the study (or exclusion from the analysis
sample).
OR
-	Unacceptable handling of subject attrition: reason for missing outcome data likely to be
related to true outcome, with either imbalance in numbers or reasons for missing data
across study groups; or potentially inappropriate application of imputation (Source:
OHAT).
For case-control and cross-sectional studies:
-	There was large subject withdrawal from the study (or exclusion from the analysis
sample).
OR
-	Unacceptable handling of subject attrition: reason for missing outcome data likely to be
related to true outcome, with either imbalance in numbers or reasons for missing data
across study groups; or potentially inappropriate application of imputation.
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality
Rating
Description
Metric 3. Comparison Group (selection, performance biases)
High
For ALL stuclv tvves:
-	Any differences in baseline characteristics of groups were considered as potential
confounding or stratification variables and were thereby controlled by statistical analysis
(Source: OHAT).
OR
For cohort and cross-sectional studies:
-	Key elements of the study design are reported {i.e., setting, inclusion and exclusion
criteria, and methods of participant selection), and indicate that subjects were similar (e.g.,
recruited from the same eligible population with the same method of ascertainment and
within the same time frame using the same inclusion and exclusion criteria, and were of
similar aae and health status) (NTP. 2015).
For case-control studies:
-	Key elements of the study design are reported indicate that that cases and controls were
similar (e.g., recruited from the same eligible population with the number of controls
described, and eligibility criteria and are recruited within the same time frame (NTP.
2015).
For studies revortins Standardized Mortality Ratios (SMRs) or Standardized Incidence
Ratios (SLRs):
- Age, sex (if applicable), and race (if applicable) adjustment or stratification is described
and choice of reference population (e.g., general population) is reported.
Medium
For cohort studies and cross-sectional studies:
-	There is only indirect evidence (e.g., stated by the authors without providing a
description of methods) that groups are similar (as described above for the high
confidence rating).
OR
-	If there is potential for healthy worker effect.
For case-control studies:
-	There is indirect evidence (i.e., stated by the authors without providing a description of
methods) that cases and controls are similar (as described above for the high confidence
rating).
For studies revortins SMRs or SLRs:
-	Age, sex (if applicable), and race (if applicable) adjustment or stratification is not
specifically described in the text, but results tables are stratified by age and/or sex (i.e.,
indirect evidence); choice of reference population (e.g., general population) is reported.
Low
For cohort and cross-sectional studies:
-	There is indirect evidence (i.e., stated by the authors without providing a description of
methods) that groups were not similar (as described above for the high confidence
rating).
AND
-	Differences between the exposure groups are not adequately controlled for in the
statistical analysis.
For case-control studies:
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Data Quality
Rating
Description

-	There is indirect evidence {i.e., stated by the authors without providing a description of
methods) that cases and controls were not similar (as described above for the high
confidence rating).
AND
-	The characteristics of cases and controls are not reported (NTP. 2015).
AND
-	Differences in groups is not adequately controlled for in the statistical analysis.
For studies revortins SMRs or SIRs:
- Indirect evidence of a lack of adjustment or stratification for age or sex (if applicable);
indirect evidence that choice of reference population (e.g., general population) is
inappropriate.
Critically Deficient
For cohort studies:
-	Subjects in all exposure groups were not similar.
OR
-	Information was not reported to determine if participants in all exposure groups were
similar (STROBE Checklist 6 (Von Elm et al.. 2008)).
AND
-	Potential differences in exposure groups were for a factor that was related to the
outcome and not controlled for in the statistical analysis.
OR
-	Subjects in the exposure erouos had verv different participation/response rates (NTP.
2015).
AND
-	Participation rates were related to exposure and outcome
For case-control studies:
-Controls were drawn from a very dissimilar population than cases or recruited within
verv different time frames (NTP. 2015).
AND
-Potential differences in the case and control groups were not controlled for in the
statistical analysis.
OR
- Rationale and/or methods for case and control selection, matching criteria including
number of controls per case (if relevant) were not reported (STROBE Checklist 6 (Von
Elm et al.. 2008)).
For cross-sectional studies:
-	Subjects in all exposure groups were not similar, recruited within very different time
frames, or had verv different participation/response rates (NTP. 2015).
AND
-	Potential differences in exposure groups were not controlled for in the statistical
analysis.
OR
-	Sources and methods of selection of participants in all exposure groups were not
reported (STROBE Checklist Item 13 (Von Elm et al.. 2008)).
For studies revortins SMRs or SIRs:
- Lack of adjustment or stratification for both age and sex (if applicable), race (if
applicable), and calendar time or choice of reference population (e.g., general population)
is not reported.
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Data Quality
Rating
Description
Not Rated/Not
Applicable
- For mesothelioma studies, a comparison population is not required, as EPA's interest is
in the absolute risk and not the relative risk. All studies of mesothelioma allowing for
evaluation of absolute risk should be labeled as "Not rated/not applicable"
-Only rate as NA if there is no mesothelioma comparison group. Otherwise, if the study
includes a comparison group, rate this metric H, M, L, or U.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Domain 2. Exposure Characterization
Metric 4. Measurement of Exposure (detection/measurement/information, performance biases)
High
For all stuclv tvpes:
-	Quantitative estimates of exposure were consistently assessed (i.e., using the same
method and sampling timeframe) during multiple time periods and using either PCM or
TEM.
OR
-	A combination of methods were used over time (i.e., midget impinger, PCM or TEM),
but side by side sampling and analyses were conducted to develop appropriate conversion
criteria.
AND
-	For an occupational population, contains detailed employment records and quantitative
estimates of exposure using either PCM or TEM which allows for construction of job-
matrix for entire work history of exposure (i.e.. Cumulative or peak exposures, and time
since first exposure).
Medium
For all stuclv tvpes:
- (Exposure was assessed during one time period but this time period is judged to be
reasonably representative of the entire study time period.

AND
-	Exposure was assessed using a combination of midget impingers, PCM, and/or TEM
measurements, but side by side sampling and analyses were not conducted for all
operations and thus there is a lack of confidence in the conversion factors.)
OR
-	For an occupational study population, contains detailed employment records and
quantitative estimates of exposure using a combination of midget impingers and PCM or
TEM measurements for only a portion of participant's work history of exposure (i.e., only
early years or later years), such that extrapolation of the missing years is required.

Low
For all stuclv tvpes:
-Exposure was estimated solely using professional judgement.
OR
-The method of quantifying/counting fibers was not specified.
OR
- Exposure was directly measured (e.g., midget impinger) and assessed using a
quantitative method other than PCM or TEM and conversion factors were not determined.


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Data Quality
Rating
Description
Critically Deficient
For all study types:
- Methods used to quantify the exposure were not well defined, and sources of data and
detailed methods of exposure assessment were not reported (STROBE Checklist 7 and 8
(Von ElmetaL 2008)).
OR

-	There was no quantitative measure or estimate of exposure.
OR
-	There is evidence of substantial exposure misclassification that would significantly bias
the results.

Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 5. Exposure Levels (detection/measurement/information biases)
High
- Do not select for this metric
Medium
For all study types:

- The range and distribution of exposure is sufficient or adequate to develop an exposure-
response estimate (CooDer et al.. 2016).
Low
For all study types:

- The range of exposure in the population is limited
Critically Deficient
For all studv tvpes:
-	The range and distribution of exposure are not adequate to determine an exposure-
response relationship (Cooper et al.. 2016).
OR
-	No description is provided on the levels or range of exposure.

Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 6. Temporalitv
High
For all studv tvpes:
-	The study presents an appropriate temporality between exposure and outcome (i.e., the
exposure precedes the disease).
AND
-	The interval between the exposure (or reconstructed exposure) and the outcome is
sufficiently long considering the latency of the disease (i.e.. study follow-up is more than
20 vears for mesothelioma) (LaKind et al.. 2014).
Medium
For all studv tvpes except cross-sectional studies:

- Temporality is established, but it is unclear whether there is adequate follow-up for
consideration of latencv (i.e.. onlv 15-20 vears of follow-up) (LaKind et al.. 2014).
Low
For all studv tvpes:
-	The temporality of exposure and outcome is uncertain (10-15 years).
OR
-	There is inadequate follow-up of the cohort considering the latency period.

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Data Quality
Rating
Description
Critically Deficient
For all study types:
-	Study lacks an established time order, such that exposure is not likely to have occurred
priorto outcome (LaKind et al.. 2014).
OR
-	There was inadequate follow-up of the cohort for the expected latency period (<10
years).
OR
-	Sources of data and details of methods of assessment were not sufficiently reported (e.g.,
duration of follow-up, periods of exposure, dates of outcome ascertainment, etc.) (Source:
STROBE Checklist 8 (Von Elm et al.. 2008)).

Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Domain 3. Outcome Assessment
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases)
High
For all study types:
The outcome was assessed using one or a combination of the following well-established
methods:
-	Mesothelioma cases confirmed by histological or cytological means (including subtypes
of mesothelioma) and/or
-	ICD-10 codes (3-digit) C45 or (4-digit) C45.x (C45.0, C45.1, C45.2, C45.7, C45.9)
-	All fields on the death certificates of cohort searched for 'mesothelioma"
-	Appropriate Pre-ICD 10 codes supplemented by additional evidence (e.g.,
patholoav/autopsv) see Table 1 of (Kopvlev et al.. 2011)
-	International Classification of Diseases for Oncology Third Edition (ICD-O-3) and
Second Edition (ICD-O-2) codes are acceptable because ICD-O-3 and ICD-O-2 include
mesothelioma-specific codes.
-	ICD-O-3 and ICD-O-2 codes 9050-9055 (note if designated as benign or malignant) are
acceptable.
Medium
For all study types:

- Examined death certificates searched for mesothelioma for pre-ICD-10 codes that
include pleura, peritoneum and site unspecified (ICD code 199)
Low
- Do not select for this metric.
Critically Deficient
For all studv tvpes:
-	Numbers of outcome events or summary measures were not reported (Source: STROBE
Checklist 15 (Von Elm et al.. 2008)
OR
-	Only pre ICD-10 codes (without additional information) were used for ascertainment of
mesothelioma.
OR
-	Examined death certificates searched for mesothelioma for codes that included only
pleura and/or peritoneum
OR
-	Study lacks individual assessment of mesothelioma (i.e, mesothelioma is assessed as a
combination with other cancer types, excluding lung and bronchus or trachea)
OR
-	Any self-reported information


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Data Quality
Rating
Description
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 8. Reporting Bias
High
For all stuclv tvves:
- Mesothelioma findings are reported in the abstract, results or discussion. Effect estimates
are reported with confidence intervals and/or standard errors, number of cases/controls or
exposed/unexposed reported for each analysis, to be included in exposure-response analysis
or fullv tabulated during data extraction and analvses (NTP. 2015).
Medium
For all stuclv tvves:
- All of the study's findings (primary and secondary) outlined in the abstract, results or
discussion (that are relevant for the evaluation) are reported but not in a way that would
allow for detailed extraction (e.g., results were discussed in the text but accompanying
data were not shown).
Low
For all stuclv tvves:
- Mesothelioma outcomes outlined in the methods, abstract, and/or introduction (that are
relevant for the evaluation) have not been reported (NTP. 2015).
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Domain 4. Potential Confounding/Variability Control'1
Metric 9. Covariate Adjustment (confounding)
High
For all stuclv tvves:
-	Appropriate adjustments or explicit considerations were made for potential confounders
(e.g., age, sex, SES, race, etc.) (excluding co-exposures, which are evaluated in metric 11)
in the final analyses through the use of statistical models to reduce research-specific bias,
including matching, adjustment in multivariate models, stratification, or other methods
that were appropriately justified (NTP. 2015).
For studies revortins SMRs or SIRs:
-	Adjustments are described and results are age-, race-, and sex-adjusted (or stratified) if
applicable.
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Data Quality
Rating
Description
Medium
For all study types:
-	There is indirect evidence that appropriate adjustments were made (i.e., considerations
were made for primary covariates (excluding co-exposures) and potential confounders
adjustment) without providing a description of methods.
OR
-	The distribution of potential confounders (excluding co-exposures) did not differ
significantly between exposure groups or between cases and controls.
OR
-	The major potential confounders (excluding co-exposures) were appropriately adjusted
and any not adjusted for are considered not to appreciably bias the results (e.g., smoking
rates in an occupational cohort are expected to be generally similar in different
departments and thus confounding by smoking is unlikely when internal analyses are
applied).
For studies reporting SMRs or SIRs:
-	Results are adjusted (or stratified) for age and sex, unless adjustment or stratification is
not necessary because the exposed and control groups are sufficiently similar on the
particular demographic variable.
Low
For all study types:
-	There is indirect evidence (i.e., no description is provided in the study) that
considerations were not made for potential confounders adjustment in the final
analyses (NTP. 2015).
AND
-	The distribution of primary covariates (excluding co-exposures) and potential
confounders was not reported between the exposure groups or between cases and controls
(NTP. 2015).
For studies reportins SMRs or SIRs:
-	Results are adjusted or stratified for age, race, OR sex (any one of the three), unless
adjustment or stratification is not necessary because the exposed and control groups are
sufficiently similar on the particular demographic variable.
Critically Deficient
For all study types:
-	The distribution of potential confounders differed significantly between the exposure
groups.
AND
-	Confounding was demonstrated and was not appropriately adjusted for in the final
analyses (NTP. 2015).
For studies reportins SMRs or SIRs:
-	No discussion of adjustments. Results are not adjusted for both age and sex (or stratified)
if applicable.
Not Rated/Not
Applicable
- Rate this metric as "N/A" if no analyses of the association between exposure and
outcome were performed or if there are no potential confounders.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality
Rating
Description
Metric 10. Covariate Characterization (measurement/information, confounding biases)
For occupational studies, it can be assumed that personnel records were used to obtain covariate data if not
otherwise specified.
High
For all stuclv tvpes:
- Potential confounders (excluding co-exposures; e.g., age, sex, SES) were assessed using
valid and reliable methodology where appropriate (e.g., validated questionnaires,
biomarker).
Medium
For all stuclv tvpes:
- A less-established method was used to assess confounders (excluding co-exposures) and
no method validation was conducted against well-established methods, but there was little
to no evidence that that the method had poor validity and little to no evidence of
confounding.
Low
For all stuclv tvpes:
- The confounder assessment method is an insensitive instrument or measure or a method
of unknown validity.
Critically Deficient
For all stuclv tvpes:
- Confounders were assessed using a method or instrument known to be invalid.
Not Rated/Not
For all study types:
Applicable
-	Covariates were not assessed.
OR
-	Metric 9 is rated "Not applicable"

Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 11. Co-exposure Reliability (measurement/information, confounding biases)
High
- Do not select for this metric.
Medium
For all stuclv tvpes:
-	Any co-exposures to pollutants that are not the target exposure that would likely bias the
results were not likely to be present.
OR
-	Co-exposures to pollutants were appropriately measured or either directly or indirectly
adjusted for.
-	Example: There is confirmation of the likely absence of known co-exposures via
mechanisms such as engineering controls (closed systems) for co-pollutants or
confirmation of the absence of co-pollutants through monitoring.
Low
For cohort ancl cross-sectional studies:

- There is direct evidence that there was an unbalanced provision of additional co-
exposures across the primary study groups, which were not appropriately adjusted for.

For case-control studies:

- There is direct evidence that there was an unbalanced provision of additional co-
exposures across cases and controls, which were not appropriately adjusted for, and
significant indication a biased exposure-outcome association.
OR

For all study types:

In an occupational setting, potential co-exposures are not discussed.
Critically Deficient
- Do not select for this metric.
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Data Quality
Rating
Description
Not Rated/Not
Applicable
- For mesothelioma studies, evaluations of potential confounders are not required as there
are few other causes of mesothelioma (zeolites, viruses, therapeutic or diagnostic
radiation) and none that are likely to be correlated in a dose-dependent manner with
asbestos. Evaluation of potential confounding in mesothelioma studies should be
labeled as "Not rated/applicable" unless there is substantial information to indicate
otherwise.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Domain 5. Analysis
Metric 12. Studv Desian and Methods
High
- Do not select for this metric.
Medium
For all study types:

-	The study design chosen was appropriate for the research question.
OR
-	The study uses an appropriate statistical method to address the research question(s) (e.g.,
Cox and Poisson regression for cohort studies and logistic regression analysis for case-
control studies.

Low
- Do not select for this metric.
Critically Deficient
For all study types:
- The study design chosen was not appropriate for the research question.
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 13. Statistical Power (sensitivity)
High
- Do not select for this metric.
Medium
For cohort and cross-sectional studies:
-	The number of participants are adequate to detect an effect in the exposed population
and/or subgroups of the total population.
OR
-	The paper reported statistical power is high enough (> 80%) to detect an effect in the
exposure population and/or subgroups of the total population.


For case-control studies:
-	The number of cases and controls are adequate to detect an effect in the exposed
population and/or subgroups of the total population.
OR
-	The paper reported statistical power is high enough (> 80%) to detect an effect in the
exposure population and/or subgroups of the total population.

Low
- Do not select for this metric.
Critically Deficient
For cohort and cross-sectional studies:
-	The number of participants is inadequate to detect an effect in the exposed population
and/or subgroups of the total population and the study was negative.
For case-control studies:
-	The number of cases and controls are inadequate to detect an effect in the exposed
population and/or subgroups of the total population and the study was negative.
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Data Quality
Rating
Description
Not Rated/Not
Applicable
-	For mesothelioma, EPA is primarily interested in the presentation of data collected in the
study, rather than the statistical analysis. EPA will pool data across asbestos studies to
conduct for the analysis of mesothelioma risk. Therefore, the power of individual studies
will not be considered. This metric may be marked as not rated/applicable.
-	Mark as NA if there were no statistical analyses or models for mesothelioma. If no
analyses were performed because (whether stated or implied) there wasn't sufficient
statistical power to do analyses, be sure to note this in the comments.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 14. Reproducibility of Analyses (adapted from Blettner et al. (2001))
High
- Do not select for this metric.
Medium
For all stuclv tvves:
- The description of the analysis is sufficient to understand how to conceptually reproduce
the analysis with access to the analytic data.
Low
or all stuclv tvves:
- The description of the analysis is insufficient to understand what has been done and to be
reproducible OR a description of analyses are not present (e.g., statistical tests and
estimation procedures were not described, variables used in the analysis were not listed,
transformations of continuous variables (e.g., logarithmic) were not explained, rules for
categorization of continuous variables were not presented, exclusion of outliers was not
elucidated and how missing values are dealt with was not mentioned).
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
-	For mesothelioma, EPA is primarily interested in the presentation of data collected in the
study, rather than the statistical analysis. If individual data elements (e.g., time since first
exposure, number of person-years, etc.) are present in the study that will allow EPA to
conduct its own analysis, this metric may be marked as not rated/applicable.
-	Mark as NA if there were no statistical analyses or models for mesothelioma.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 15. Statistical Models (confounding bias)
High
- Do not select for this metric.
Medium
For all stuclv tvves:
- The model or method for calculating the risk estimates (e.g., odds ratios, SMRs, SIR) is
transparent (i.e., it is stated how/why variables were included or excluded).
Low
For all stuclv tvves:
- The statistical model building process is not fully appropriate OR model assumptions
were not met OR a description of analyses and assumptions are not present (STROBE
Checklist 12e (Von Elm et al., 2008)).
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
-	For mesothelioma, EPA is primarily interested in the presentation of data collected in the
study, rather than the statistical analysis. If individual data elements (e.g., time since first
exposure, number of person-years, etc.) are present in the study that will allow EPA to
conduct its own analysis, this metric may be marked as not rated/applicable.
-	Mark as NA if there were no statistical analyses or models for mesothelioma.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality
Rating
Description
Domain 6. Other (if applicable) Considerations for Biomarker Selection and Measurement (LaKind et al.. 2014)
Metric 16. Use of Biomarker of Exposure (detection/measurement/information biases)
High
-	Biomarker in a specified matrix has accurate and precise quantitative relationship with
external exposure, internal dose, or target dose.
AND
-	Biomarker is derived from exposure to one parent chemical.
Medium
-	Biomarker in a specified matrix has accurate and precise quantitative relationship with
external exposure, internal dose, or target dose.
AND
-	Biomarker is derived from multiple parent chemicals.
Low
- Evidence exists for a relationship between biomarker in a specified matrix and external
exposure, internal dose or target dose, but there has been no assessment of accuracy and
precision or none was reported.
Critically Deficient
- Biomarker in a specified matrix is a poor surrogate (low accuracy, specificity, and
precision) for exposure/dose.
Not Rated/Not
Applicable
- Select "N/A" if no human biological samples were assessed or if the only biomarkers
assessed were biomarkers of effect or biomarkers of susceptibility.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 17. Effect Biomarker (detection/measurement/information biases)
High
- Effect biomarker measured is an indicator of a key event in an adverse outcome pathway
(AOP).
Medium
- Biomarkers of effect shown to have a relationship to health outcomes using well
validated methods, but the mechanism of action is not understood.
Low
- Biomarkers of effect shown to have a relationship to health outcomes, but the method is
not well validated and mechanism of action is not understood.
Critically Deficient
- Biomarker has undetermined consequences (e.g., biomarker is not specific to a health
outcome).
Not Rated/Not
Applicable
- Select "N/A" if no human biological samples were assessed or if the only biomarkers
assessed were biomarkers of exposure or biomarkers of susceptibility.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 18. Method Sensitivitv (detection/measurement/information biases)
High
- Do not select for this metric.
Medium
- Limits of detection are low enough to detect chemicals in a sufficient percentage of the
samples to address the research question. Analytical methods measuring biomarker are
adequately reported. The limit of detection (LOD) and limit of quantification (LOQ)
(value or %) are reported.
Low
-	Frequency of detection too low to address the research hypothesis.
OR
-	LOD/LOQ (value or %) are not stated.

Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Do not select "N/A" for this metric if the study assessed biomarkers. If LOD/LOQ are
not stated then select Low.
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Data Quality
Rating
Description
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 19. Biomarker Stability (detection/measurement/information biases)
High
- Samples with a known storage history and documented stability data or those using real-
time measurements.
Medium
- Samples have known losses during storage, but the difference between low and high
exposures can be qualitatively assessed.
Low
- Samples with either unknown storage history and/or no stability data for target analytes
and high likelihood of instability for the biomarker under consideration.
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Do not select "N/A" for this metric if the study assessed biomarkers.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 20. Sample Contamination (detection/measurement/information biases)
High
-	Samples are contamination-free from the time of collection to the time of measurement
(e.g., by use of certified analyte free collection supplies and reference materials, and
appropriate use of blanks both in the field and lab).
AND
-	Documentation of the steps taken to provide the necessary assurance that the study data
are reliable is included.
Medium
-	Samples are stated to be contamination-free from the time of collection to the time of
measurement.
AND
-	There is incomplete documentation of the steps taken to provide the necessary assurance
that the study data are reliable.
OR
-	Samples are known to have contamination issues, but steps have been taken to address
and correct contamination issues.
OR
-	There is no information included about contamination (only allowed for biomarker
samples not susceptible to contamination).
Low
-	Samples are known to have contamination issues, but steps have been taken to address
and correct contamination issues.
OR
-	Samples are stated to be contamination-free from the time of collection to the time of
measurement, but there is no use or documentation of the steps taken to provide the
necessary assurance that the study data are reliable.
Critically Deficient
- There are known contamination issues (e.g., phthalate study that used plastic sample
collection vials) and no documentation that the issues were addressed.
Not Rated/Not
Applicable
- Do not select "N/A" for this metric if the study assessed biomarkers.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality
Rating
Description
Metric 21. Method Requirements (detection/measurement/information biases)
High
- Instrumentation that provides unambiguous identification and quantitation of the
biomarker at the required sensitivity (e.g., gas chromatography/high-resolution mass
spectrometry [GC-HRMS]; gas chromatography with tandem mass spectrometry [GC-
MS/MS]; liquid chromatography with tandem mass spectrometry [LC-MS/MS]).
Medium
- Instrumentation that allows for identification of the biomarker with a high degree of
confidence and the required sensitivity (e.g., gas chromatography mass spectrometry
[GC-MS], gas chromatography with electron capture detector [GC-ECD]).
Low
- Instrumentation that only allows for possible quantification of the biomarker, but the
method has known interferants (e.g., gas chromatography with flame-ionization detection
[GC-FID], spectroscopy).
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Do not select "N/A" for this metric if the study assessed biomarkers.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 22. Matrix Adjustment (detection/measurement/information biases)
High
- If applicable for the biomarker under consideration, study provides results, either in the
main publication or as a supplement, for both adjusted and unadjusted matrix
concentrations (e.g., creatinine-adjusted or specific gravity-adjusted and non-adjusted
urine concentrations) and reasons are given for adjustment approach.
Medium
- If applicable for the biomarker under consideration, study only provides results using
one method (matrix-adjusted or not).
Low
- If applicable for the biomarker under consideration, no established method for matrix
adjustment was conducted.
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- If metrics 16 and 17 are both NA, then the remaining biomarker metrics are
automatically not rated. Otherwise:
Select ""N/A" if matrix adjustment is not required for assessment of the biomarker.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
2161
2162
2163	Table Apx G-2. Other Outcomes Data Quality Evaluation Criteria
Data Quality Rating
Description
Domain 1. Studv Participation
Metric 1. Participant Selection (selection, performance biases)
High
For all studv tvpes:
-	All key elements of the study design are reported (e.g., setting, participation rate
described at all steps of the study, inclusion and exclusion criteria, and methods of
participant selection or case ascertainment)
AND
-	The reported information indicates that participant selection in or out of the study (or
analysis sample) and participation was not likely to be biased (i.e., the exposure-
outcome distribution of the participants is likely representative of the exposure-outcome
distributions in the population of persons eligible for inclusion in the study.)
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Data Quality Rating
Description
Medium
For all study types:

- Some key elements of the study design were not present but available information
indicates a low risk of selection bias (/'. e., the exposure-outcome distribution of the
participants is likely representative of the exposure-outcome distributions in the
population of persons eligible for inclusion in the study.)
Low
For all study types:

-	Key elements of the study design and information on the population (e.g., setting,
participation rate described at most steps of the study, inclusion and exclusion criteria,
and methods of participant selection or case ascertainment) are not reported (STROBE
Checklist 4. 5. and 6 (Von Elm et al.. 2008)).
-	If the study provides little to no information about selection criteria, then rate this
metric as Low.
Critically Deficient
For all study types:
The reported information indicates that selection in or out of the study (or analysis
sample) and participation was likely to be significantly biased (/'. e., the exposure-
outcome distribution of the participants is likely not representative of the exposure-
outcome distributions of the population of persons eligible for inclusion in the study).
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 2. Attrition (missing data/attrition/exclusion, reporting biases)
High
For cohort studies:
-	There was minimal subject loss to follow up during the study (or exclusion from the
analysis sample) and outcome and exposure data were largely complete.
OR
-	Any loss of subjects (i.e.. incomplete outcome data) or missing exposure and outcome
data were adequately* addressed (as described below) and reasons were documented
when human subjects were removed from a studv (NTP. 2015).
OR
-	Missing data have been imputed using appropriate methods (e.g., multiple imputation
methods), and characteristics of subjects lost to follow up or with unavailable records are
not sianificantlv different from those of the studv participants (NTP. 2015).



For case-control studies and cross-sectional studies:
-	There was minimal subject withdrawal from the study (or exclusion from the analysis
sample) and outcome data and exposure were largely complete.
OR
-	Any exclusion of subjects from analyses was adequately* addressed (as described
below), and reasons were documented when subjects were removed from the study or
excluded from analvses (NTP. 2015).


*NOTE for all study types: Adequate handling of subject attrition can include: Use of
imputation methods for missing outcome and exposure data; reasons for missing
subjects unlikely to be related to outcome (for survival data, censoring was unlikely to
introduce bias); missing outcome data balanced in numbers across study groups, with
similar reasons for missing data across groups.
Medium
For cohort studies:

- There was moderate subject loss to follow up during the study (or exclusion from the
analysis sample) or outcome and exposure data were nearly complete.
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Data Quality Rating
Description

AND
- Any loss or exclusion of subjects was adequately addressed (as described in the
acceptable handling of subject attrition in the high confidence category) and reasons
were documented when human subjects were removed from a study.
For case-control studies and cross-sectional studies:
-	There was moderate subject withdrawal from the study (or exclusion from the analysis
sample), but outcome and exposure data were largely complete
AND
-	Any exclusion of subjects from analyses was adequately addressed (as described
above), and reasons were documented when subjects were removed from the study or
excluded from analvses (NTP. 2015).
Low
For cohort studies:
-	The loss of subjects (e.g., loss to follow up, incomplete outcome or exposure data) was
moderate and unacceptably handled (as described below in the unacceptable confidence
category) (Source: OHAT).
OR
-	Numbers of individuals were not reported at important stages of study (e.g., numbers
of eligible participants included in the study or analysis sample, completing follow-up,
and analyzed). Reasons were not provided for non-participation at each stage (STROBE
Checklist Item 13 (Von Elm et al.. 2008)).
For case-control and cross-sectional studies:
-	The exclusion of subjects from analyses was moderate and unacceptably handled (as
described below in the unacceptable confidence category).
OR
-	Numbers of individuals were not reported at important stages of study (e.g., numbers
of eligible participants included in the study or analysis sample, completing follow-up,
and analyzed). Reasons were not provided for non-participation at each stage (STROBE
Checklist Item 13 (Von Elm et al.. 2008)).
Critically Deficient
For cohort studies: There was larae subject attrition during the studv (or exclusion from
the analysis sample).
OR
- Unacceptable handling of subject attrition: reason for missing outcome data likely to be
related to true outcome, with either imbalance in numbers or reasons for missing data
across study groups; or potentially inappropriate application of imputation (Source:
OHAT).
For case-control and cross-sectional studies:
-	There was large subject withdrawal from the study (or exclusion from the analysis
sample).
OR
-	Unacceptable handling of subject attrition: reason for missing outcome data likely to be
related to true outcome, with either imbalance in numbers or reasons for missing data
across study groups; or potentially inappropriate application of imputation.
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality Rating
Description
Metric 3. Comparison Group (selection, performance biases)
High
For ALL study tvves:
- Any differences in baseline characteristics of groups were considered as potential
confounding or stratification variables and were thereby controlled by statistical analysis
(Source: OHAT).
OR
For cohort and cross-sectional studies:
-	Key elements of the study design are reported {i.e., setting, inclusion and exclusion
criteria, and methods of participant selection), and indicate that subjects were similar
(e.g., recruited from the same eligible population with the same method of ascertainment
and within the same time frame using the same inclusion and exclusion criteria, and
were of similar age and health status) (NTP. 2015).
For case-control studies:
-	Key elements of the study design are reported indicate that that cases and controls were
similar (e.g., recruited from the same eligible population with the number of controls
described, and eliaibilitv criteria and are recruited within the same time frame (NTP.
2015).
For studies revortins SMRs or SLRs:
-	Age, sex (if applicable), and race (if applicable) adjustment or stratification is
described and choice of reference population (e.g., general population) is reported.
Medium
-If there is substantial potential for healthy worker effect.
OR
For cohort studies and cross-sectional studies:
-	There is only indirect evidence (e.g., stated by the authors without providing a
description of methods) that groups are similar (as described above for the high
confidence rating).
For case-control studies:
-	There is indirect evidence (i.e., stated by the authors without providing a description of
methods) that cases and controls are similar (as described above for the high confidence
rating).
For studies revortins SMRs or SLRs:
-	Age, sex (if applicable), and race (if applicable) adjustment or stratification is not
specifically described in the text, but results tables are stratified by age and/or sex (i.e.,
indirect evidence); choice of reference population (e.g., general population) is reported.
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Data Quality Rating
Description
Low
For cohort and cross-sectional studies:
-	There is indirect evidence (i.e., stated by the authors without providing a description of
methods) that groups were not similar (as described above for the high confidence
rating).
AND
-	Differences between the exposure groups are not adequately controlled for in the
statistical analysis.
For case-control studies:
-	There is indirect evidence {i.e., stated by the authors without providing a description of
methods) that cases and controls were not similar (as described above for the high
confidence rating).
AND
-	The characteristics of cases and controls are not reported (NTP. 2015).
AND
-	Differences in groups is not adequately controlled for in the statistical analysis.
For studies revortins SMRs or SIRs:
-	Indirect evidence of a lack of adjustment or stratification for age or sex (if applicable);
indirect evidence that choice of reference population (e.g., general population) is
inappropriate.
Critically Deficient
For cohort studies:
-	Subjects in all exposure groups were not similar.
OR
-	Information was not reported to determine if participants in all exposure groups were
similar (STROBE Checklist 6 (Von Elm et al.. 2008)).
AND
-	Potential differences in exposure groups were for a factor that was related to the
outcome and not controlled for in the statistical analysis.
OR
-	Subjects in the exposure aroups had verv different participation/response rates (NTP.
2015).
AND
-	Participation rates were related to exposure and outcome
For case-control studies:
-Controls were drawn from a very dissimilar population than cases or recruited within
verv different time frames (NTP. 2015).
AND
-Potential differences in the case and control groups were not controlled for in the
statistical analysis.
OR
-	Rationale and/or methods for case and control selection, matching criteria including
number of controls per case (if relevant) were not reported (STROBE Checklist 6 (Von
Elm et al.. 2008)).
For cross-sectional studies:
-	Subjects in all exposure groups were not similar, recruited within very different time
frames, or had verv different participation/response rates (NTP. 2015).
AND
-	Potential differences in exposure groups were not controlled for in the statistical
analysis.
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Data Quality Rating
Description

OR
- Sources and methods of selection of participants in all exposure groups were not
reported (STROBE Checklist 6 (Von Elm et al.. 2008)).

For studies reporting SMRs or SIRs:

- Lack of adjustment or stratification for both age and sex (if applicable), race (if
applicable), and calendar time or choice of reference population (e.g., general
population) is not reported.
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Domain 2. Exposure Characterization
Metric 4. Measurement of Exposure (detection/measurement/information, performance biases)
High
For all stuclv tvpes:
-	Quantitative estimates of exposure were consistently assessed (i.e., using the same
method and sampling timeframe) during multiple time periods and using either PCM or
TEM.
OR
-	A combination of methods were used over time (i.e., midget impinger, PCM or TEM),
but side by side sampling and analyses were conducted to develop appropriate
conversion criteria.
AND
-	For an occupational population, contains detailed employment records and quantitative
estimates of exposure using either PCM or TEM which allows for construction of job-
matrix for entire work history of exposure (i.e.. Cumulative or peak exposures, and time
since first exposure).
Medium
For all studv tvpes:
-	(Exposure was assessed during one time period but this time period is judged to be
reasonably representative of the entire study time period.
AND
-	Exposure was assessed using a combination of midget impingers, PCM, and/or TEM
measurements, but side by side sampling and analyses were not conducted for all
operations and thus there is a lack of confidence in the conversion factors.)
OR
-	For an occupational study population, contains detailed employment records and
quantitative estimates of exposure using a combination of midget impingers and PCM or
TEM measurements for only a portion of participant's work history of exposure (i.e.,
only early years or later years), such that extrapolation of the missing years is required.

Low
For all studv tvpes:
-Exposure was estimated solely using professional judgement.
OR
- Exposure was directly measured and assessed using a quantitative method other than
PCM or TEM and conversion factors were not determined.
OR
-The method of quantifying/counting fibers was not specified (PCM, TEM, or other
method not specified)


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Data Quality Rating
Description
Critically Deficient
For all study types:
- Methods used to quantify the exposure were not well defined, and sources of data and
detailed methods of exposure assessment were not reported (STROBE Checklist 7 and 8
(Von ElmetaL 2008)).
OR

-	There was no quantitative measure or estimate of exposure.
OR
-	There is evidence of substantial exposure misclassification that would significantly
bias the results.

Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 5. Exposure Levels (detection/measurement/information biases)
High
- Do not select for this metric
Medium
For all study types:

-	The range and distribution of exposure is sufficient or adequate to develop an
exposure-response estimate (Cooper et al.. 2016).
AND
-	Reports 3 or more levels of exposure {i.e., referent group +2 or more) or an exposure-
response model using a continuous measure of exposure.
Low
For all studv tvpes:
-	The range of exposure in the population is limited
OR
-	Reports 2 levels of exposure (e.g., exposed/unexposed)) (Cooper et al.. 2016) (Source:
IRIS)

Critically Deficient
For all study types:
-	The range and distribution of exposure are not adequate to determine an exposure-
response relationship (Cooper et al.. 2016).
OR
-	No description is provided on the levels or range of exposure.

Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 6. Temporalitv 1
detection/measurement/information biases)
High
For all study types:
-	The study presents an appropriate temporality between exposure and outcome (i.e.. the
exposure precedes the disease).
AND
-	The interval between the exposure (or reconstructed exposure) and the outcome is
sufficiently long considering the latency of the disease (i.e., study follow-up is more than
15 vears for luna cancer) (LaKind et al.. 2014).
Medium
For all study types except cross-sectional studies:
- Temporality is established, but it is unclear whether there is adequate follow-up for
consideration of latencv (i.e., onlv 10 vears of follow-up) (LaKind et al.. 2014).
Low
For all studv tvves:
- The temporality of exposure and outcome is uncertain.
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Data Quality Rating
Description

OR
- There is inadequate follow-up of the cohort considering the latency period (5-10 years
of follow-up).
Critically Deficient
For all study types:
-	Study lacks an established time order, such that exposure is not likely to have occurred
priorto outcome (LaKind et al.. 2014).
OR
-	There was inadequate follow-up of the cohort for the expected latency period (<5
years).
OR
-	Sources of data and details of methods of assessment were not sufficiently reported
(e.g., duration of follow-up, periods of exposure, dates of outcome ascertainment, etc.)
(Source: STROBE Checklist 8 (Von Elm et al.. 2008)).

Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Domain 3. Outcome Assessment
Each of the following outcomes has separate criteria for Metric 7: Lung Cancer, Ovarian Cancer, Laryngeal
Cancer, Other Cancer(s), Asbestosis, Pulmonary Function/Spirometry Results, Pleural Plaques, and Other Non-
cancer Outcomes (Mesothelioma criteria are on the Mesothelioma Form)
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases): Lung Cancer
High
For all study types:
- The outcome was assessed using one or a combination of the following well-
established methods:
o Lung cancer cases confirmed by histological or cytological means (including
subtypes of lung cancer)
o ICD-10 C34 (lung and bronchus with or without C33 (trachea)
o ICD-9 (5-digit code) 162.2-162.9 or
o ICD-8 (4-digit code) 162.1 or
o ICD-7 (4-digit code) 162.1 and 163
o ICD-9 (3-digit code) 162
o ICD-8 (3-digit code) 162
o ICD-7 (3-digit code) 162 and 163
Medium
For all studv tvpes:
- Although authors state they identified lung cancer cases they did not use or report the
ICD codes or cases were not confirmed by histological or cytological means.
Low
- Do not select for this metric
Critically Deficient
For all study types:
-	Any self-reported information.
OR
-	Study lacks individual assessment of lung cancer {i.e., lung cancer is assessed as a
combination of cancer types, excluding lung and bronchus or trachea).

Not Rated/Not
Applicable
- The study did not assess lung cancer.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality Rating
Description
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases): Ovarian Cancer
High
For all stuclv tvves:
-The outcome was assessed using one or a combination of the following well-established
methods:
o Ovarian cancer cases confirmed by tissue biopsy
o ICD-11 2C73 Malignant neoplasm of ovary
o ICD-10 C56 Malignant neoplasm of ovary
o ICD-9 183 Malignant neoplasm of ovary
o ICD-8 183 Malignant neoplasm of ovary, fallopian tube and broad ligament,
supplemented by additional information to validate a diagnosis of ovarian cancer,
o Pre-ICD-8 codes supplemented by additional information to validate a diagnosis
of ovarian cancer.
o All fields on the death certificate were searched for a diagnosis of ovarian cancer.
Medium
For all stuclv tvves:
-	Other diagnostic methods such as imaging tests (ultrasound or CT scan) or CA-125
blood tests were used without confirmation by tissue biopsy.
OR
-	The study reports a doctor diagnosis without additional details or validation.
Low
- Do not select for this metric
Critically Deficient
For all stuclv tvves:
- The only included information is a self-reported diagnosis of ovarian cancer without
any additional validation.
Not Rated/Not
Applicable
- The study did not assess ovarian cancer.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality Rating
Description
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases): Laryngeal Cancer
High
For all study types:
- The outcome was assessed using one or a combination of the following well-
established methods:
o Laryngeal cancer cases confirmed by tissue biopsy,
o ICD-11 2C23 Malignant neoplasm of larynx
o ICD-10 C32 Malignant neoplasm of larynx
o ICD-9 161 Malignant neoplasm of larynx
o ICD-8 132 Malignant neoplasm of larynx
o ICD-7 161 Malignant neoplasm of larynx
o Pre-ICD-7 codes supplemented by additional information to validate a diagnosis
of laryngeal cancer.
o All fields on the death certificate were searched for a diagnosis of laryngeal
cancer.
Medium
For all stuclv tvpes:
-	Other diagnostic methods were used without confirmation by tissue biopsy.
OR
-	Doctor diagnosis without additional details or validation.

Low
- Do not select for this metric
Critically Deficient
For all studv tvpes:
- The only included information is a self-reported diagnosis of laryngeal cancer without
any additional validation.
Not Rated/Not
Applicable
- The study did not assess laryngeal cancer.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases): Other Cancer Outcomes
High
For all studv tvpes:
-	The cancer was assessed using well-established methods, such as one or a combination
of the following: specific ICD Codes cases confirmed using histological or cytological
methods, other lab tests, or diagnostic imaging.
OR
-	All fields on the death certificate were searched for the specific diagnosis.

Medium
For all study types:

-	The authors state that they identified a specific health outcome, but less-established
methods were used and they did not conduct method validation.
AND
-	There is little to no evidence that the method had poor validity and little to no evidence
of outcome misclassification.
OR
-	There was a doctor's report or diagnosis, but no ICD code and no additional
confirmation or validation of the diagnosis.
Low
- Do not select for this metric
Critically Deficient
For all study types:
- The study lacks individual assessment of specific cancer types (/'. e., the specific cancer
is assessed as a combination with other cancer types).
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Data Quality Rating
Description

OR
- Only self-reported information was included, without any validation.
Not Rated/Not
Applicable
- The study did not assess other cancer outcomes.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases): Asbestosis
High
For all stuclv tvves:
- The outcome was assessed using one or a combination of the following well-
established methods:
Diagnostic imaging tests (such as chest x-rays or computed tomography (CT) scans)
showing pulmonary fibrosis or scarring of the lung tissue. ICD-11 code CA60.2
Pneumoconiosis due to mineral fibers including asbestos
o ICD-10 Code J61 Pneumoconiosis due to asbestos and other mineral fibers
o ICD-9 Code 501 Asbestosis
o ICD-8 515.2 Asbestosis
o Pre-ICD-8 codes supplemented by additional information to validate a diagnosis
of asbestosis
o All fields on the death certificate were searched for a diagnosis of asbestosis.
Medium
For all stuclv tvves:
- The authors report doctor-diagnosed asbestosis but do not report specific evidence of
lung tissue scarring or ICD codes.
Low
- A less valid method was used to diagnose asbestosis without confirmation using
imaging tests.
Critically Deficient
For all stuclv tvves:
- The only included information is a self-reported diagnosis of asbestosis without any
additional validation.
Not Rated/Not
Applicable
- The study did not assess asbestosis.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases): Pulmonary Function/Spirometry Testing Results
High
For all stuclv tvves:
- The outcome was assessed using well established methods that include standardized
spirometric measurements (FEV1, FVC) and/or diffusing capacity of the lungs for
carbon monoxide (DLCO) measurements. Forced expiratory Volume in Is (FEV1) and
Forced Vital Capacitv (FVC) (Finnish Institute of Occupational Health. 2014).
Medium
For all stuclv tvves:
-	Use of less sensitive and standard methods such as low scanning electron microscopy
(SEM), which lacks sensitivity and standardization as it relates to pulmonary function.
-	There is little to no evidence that the method had poor validity and little to no evidence
of outcome misclassification.
Low
- Do not select for this metric
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Data Quality Rating
Description
Critically Deficient
For all study types:
-	Any self-reported information without additional validation.
-	Study lacks individual assessment of pulmonary function and does not use spirometry
testing
Not Rated/Not
Applicable
- The study did not assess pulmonary function.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases): Pleural Abnormalities, Pleural Plaques, or Parenchymal Opacities
High
For all stuclv tvpes:
- The outcome was assessed using well-established methods such as x-rays or high-
resolution computed tomography (HRCT), with cases defined based on consensus of two
or more B-readers* (blinded) for any pleural abnormality or parenchymal opacities
QLO. 2000).
OR
o ICD-11 Code CB20 Pleural Plaque
o ICD-10 Code CM J92 Pleural Plaque OR
o All fields on the death certificate were searched for the specific diagnosis.
Medium
For all studv tvpes:
-	The outcome was assessed using x-rays or HRCT methods: cases defined as one B-
reader assessment (with either blinding reported or not) for any pleural abnormality or
parenchymal opacities.
OR
-	There was a doctor's report or diagnosis but using other less-established methods.
Low
- Do not select for this metric
Critically Deficient
For all study types:
-	The study lacks assessment of any of the specific pleural abnormality types {i.e.,
costophrenic angle obliteration or diffuse pleural thickening) or parenchymal opacities
{i.e., small opacities or large opacities).
OR
-	Only self-reported information without any validation.

Not Rated/Not
Applicable
- The study did not assess pleural abnormalities, pleural plaques, or parenchymal
opacities.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality Rating
Description
Metric 7. Outcome Measurement or Characterization (detection/measurement/information, performance,
reporting biases): Other Non-cancer Outcomes
High
For all study types:
-	The outcome was assessed using well-established methods, such as one or a
combination of the following: specific ICD Codes, cases confirmed using histological or
cytological methods, other lab tests, or diagnostic imaging.
OR
-	All fields on the death certificate were searched for the specific diagnosis.

Medium
For all stuclv tvpes:
-	The authors state that they identified a specific health outcome, but less-established
methods were used and they did not conduct method validation.
AND
-	There is little to no evidence that the method had poor validity and little to no evidence
of outcome misclassification.
OR
-	There was a doctor's report or diagnosis, but no ICD code and no additional
confirmation or validation of the diagnosis.
Low
- Do not select for this metric
Critically Deficient
For all study types:
- Only self-reported information was included, without any validation.
Not Rated/Not
Applicable
- The study did not assess other non-cancer outcomes.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 8. Reporting Bias
High
For all studv tvpes:
- Findings are reported in the abstract, results or discussion. Effect estimates are reported
with confidence intervals and/or standard errors, number of cases/controls or
exposed/unexposed reported for each analysis, to be included in exposure-response
analvsis or fullv tabulated during data extraction and analvses (NTP. 2015).
Medium
For all studv tvpes:
- All of the study's findings (primary and secondary) outlined in the abstract, results or
discussion (that are relevant for the evaluation) are reported but not in a way that would
allow for detailed extraction (e.g., results were discussed in the text but accompanying
data were not shown).
Low
For all study types:

- Outcomes outlined in the methods, abstract, and/or introduction (that are relevant for
the evaluation) have not been reported (NTP. 2015).
Critically Deficient
- Do not select for this metric
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality Rating
Description
Domain 4. Potential Confounding/Variability Control'1
Metric 9. Covariate Adjustment (confounding)
High
For all stuclv tvpes:
-	Appropriate adjustments or explicit considerations were made for potential
confounders (e.g., age, sex, SES, race, etc.) (excluding co-exposures, which are
evaluated in metric 11) in the final analyses through the use of statistical models to
reduce research-specific bias, including matching, adjustment in multivariate models,
stratification, or other methods that were appropriatelv justified (NTP. 2015).
For studies reporting SMRs or SIRs:
-	Adjustments are described and results are age-, race-, and sex-adjusted (or stratified) if
applicable.
Medium
For all study types:

-	There is indirect evidence that appropriate adjustments were made (i.e.. considerations
were made for primary covariates (excluding co-exposures) and potential confounders
adjustment) without providing a description of methods.
OR
-	The distribution of potential confounders (excluding co-exposures) did not differ
significantly between exposure groups or between cases and controls.
OR
-	The major potential confounders (excluding co-exposures) were appropriately adjusted
(e.g., SMRs, SIRs, etc.) and any not adjusted for are considered not to appreciably bias
the results (e.g., smoking rates in an occupational cohort are expected to be generally
similar in different departments and thus confounding by smoking is unlikely when
internal analyses are applied).
For studies reporting SMRs or SIRs:



- Results are adjusted (or stratified) for age and sex, unless adjustment or stratification is
not necessary because the exposed and control groups are sufficiently similar on the
particular demographic variable.
Low
For all study types:

-	There is indirect evidence (i.e., no description is provided in the study) that
considerations were not made for potential confounders adjustment in the final analyses
(NTP. 2015).
AND
-	The distribution of primary covariates (excluding co-exposures) and potential
confounders was not reported between the exposure groups or between cases and
controls (NTP. 2015).
For studies reporting SMRs or SIRs:
-	Results are adjusted or stratified for age, race, OR sex (any one of the three), unless
adjustment or stratification is not necessary because the exposed and control groups are
sufficiently similar on the particular demographic variable.
Critically Deficient
For all studv tvpes:
-	The distribution of potential confounders differed significantly between the exposure
groups.
AND
-	Confounding was demonstrated and was not appropriately adjusted for in the final
analvses (NTP. 2015).
For studies reporting SMRs or SIRs:

- No discussion of adjustments. Results are not adjusted for both age and sex (or
stratified) if applicable.
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Data Quality Rating
Description
Not Rated/Not
Applicable
- Do not select for this metric
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 10. Covariate Characterization (measurement/information, confounding biases)
High
For all stuclv tvpes:
- Potential confounders (e.g., age, sex, SES), excluding co-exposures, were assessed
using valid and reliable methodology where appropriate (e.g., validated questionnaires,
biomarker).
Medium
For all stuclv tvpes:
- A less-established method was used to assess confounders (excluding co-exposures)
and no method validation was conducted against well-established methods, but there was
little to no evidence that that the method had poor validity and little to no evidence of
confounding.
Low
For all stuclv tvpes:
- The confounder assessment method is an insensitive instrument or measure or a
method of unknown validity.
Critically Deficient
For all stuclv tvpes:
- Confounders were assessed using a method or instrument known to be invalid.
Not Rated/Not
For all study types:
Applicable
- Covariates were not assessed.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 11. Co-exposure Confounding (measurement/information, confounding biases)
High
- Do not select for this metric.
Medium
For all study types:

- Any co-exposures to pollutants that are not the target exposure that would likely bias
the results were not likely to be present.
OR

-	Co-exposures to pollutants were appropriately measured and either directly or
indirectly adjusted for.
-	Example: There is confirmation of the likely absence of known co-exposures via
mechanisms such as engineering controls (closed systems) for co-pollutants or
confirmation of the absence of co-pollutants through monitoring.
Low
For cohort and cross-sectional studies:
- There is direct evidence that there was an unbalanced provision of additional co-
exposures across the primary study groups, which were not appropriately adjusted for.

For case-control studies:
- There is direct evidence that there was an unbalanced provision of additional co-
exposures across cases and controls, which were not appropriately adjusted for, and
significant indication a biased exposure-outcome association.
OR
For all study types:


- In an occupational setting, potential co-exposures are not discussed.
Critically Deficient
- Do not select for this metric
Not Rated/Not
Applicable
- Enter ""N/A" and do not score this metric.
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Data Quality Rating
Description
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Domain 5. Analysis
Metric 12. Studv Desian and Methods
High
- Do not select for this metric.
Medium
For all study types:

-	The study design chosen was appropriate for the research question.
OR
-	The study uses an appropriate statistical method to address the research question(s)
(e.g., Cox and Poisson regression for cohort studies and logistic regression analysis for
case-control studies.

Low
- Do not select for this metric.
Critically Deficient
For all study types:
-	The study design chosen was not appropriate for the research question.
OR
-	Inappropriate statistical analyses were applied to assess the research questions.

Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 13. Statistical Power (sensitivity)
High
- Do not select for this metric.
Medium
For cohort and cross-sectional studies:

-	The number of participants are adequate to detect an effect in the exposed population
and/or subgroups of the total population.
OR
-	The paper reported statistical power is high enough (> 80%) to detect an effect in the
exposure population and/or subgroups of the total population.
For case-control studies:


-	The number of cases and controls are adequate to detect an effect in the exposed
population and/or subgroups of the total population.
OR
-	The paper reported statistical power is high enough (>80%) to detect an effect in the
exposure population and/or subgroups of the total population.

Low
- Do not select for this metric.
Critically Deficient
For cohort and cross-sectional studies:
-	The number of participants is inadequate to detect an effect in the exposed population
and/or subgroups of the total population and the study was negative.
For case-control studies:
-	The number of cases and controls are inadequate to detect an effect in the exposed
population and/or subgroups of the total population and the study was negative.
Not Rated/Not
Applicable
- Do not select for this metric.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance
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Data Quality Rating
Description
Metric 14. Reproducibility of Analyses (adapted from Blettner et al. (2001))
High
- Do not select for this metric.
Medium
For all stuclv tvpes:
- The description of the analysis is sufficient to understand how to conceptually
reproduce the analysis with access to the analytic data.
Low
For all stuclv tvpes:
- The description of the analysis is insufficient to understand what has been done and to
be reproducible OR a description of analyses are not present (e.g., statistical tests and
estimation procedures were not described, variables used in the analysis were not listed,
transformations of continuous variables (e.g., logarithmic) were not explained, rules for
categorization of continuous variables were not presented, exclusion of outliers was not
elucidated and how missing values are dealt with was not mentioned).
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Do not select for this metric
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 15. Statistical Models (confounding bias)
High
- Do not select for this metric.
Medium
For all stuclv tvpes:
-	The model or method for calculating the risk estimates (e.g., odds ratios, SMRs, SIR) is
transparent (i.e., it is stated how/why variables were included or excluded).
AND
-	Model assumptions were met.
Low
For all stuclv tvves:
- The statistical model building process is not fully appropriate OR model assumptions
were not met OR a description of analyses and assumptions are not present (STROBE
Checklist 12e (Von Elm et al., 2008)).
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Enter ""N/A" if the study did not use a statistical model.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Domain 6. Other (if applicable) Considerations for Biomarker Selection and Measurement (LaKind et al.. 2014)
Metric 16. Use of Biomarker of Exposure (detection/measurement/information biases)
High
-	Biomarker in a specified matrix has accurate and precise quantitative relationship with
external exposure, internal dose, or target dose.
AND
-	Biomarker is derived from exposure to one parent chemical.
Medium
-	Biomarker in a specified matrix has accurate and precise quantitative relationship with
external exposure, internal dose, or target dose.
AND
-	Biomarker is derived from multiple parent chemicals.
Low
- Evidence exists for a relationship between biomarker in a specified matrix and external
exposure, internal dose or target dose, but there has been no assessment of accuracy and
precision or none was reported.
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Data Quality Rating
Description
Critically Deficient
- Biomarker in a specified matrix is a poor surrogate (low accuracy, specificity, and
precision) for exposure/dose.
Not Rated/Not
Applicable
- Select "N/A" if no human biological samples were assessed or if the only biomarkers
assessed were biomarkers of effect or biomarkers of susceptibility.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 17. Effect Biomarker (detection/measurement/information biases)
High
- Effect biomarker measured is an indicator of a key event in an adverse outcome
pathway (AOP).
Medium
- Biomarkers of effect shown to have a relationship to health outcomes using well
validated methods, but the mechanism of action is not understood.
Low
- Biomarkers of effect shown to have a relationship to health outcomes, but the method
is not well validated and mechanism of action is not understood.
Critically Deficient
- Biomarker has undetermined consequences (e.g., biomarker is not specific to a health
outcome).
Not Rated/Not
Applicable
- Select "N/A" if no human biological samples were assessed or if the only biomarkers
assessed were biomarkers of exposure or biomarkers of susceptibility.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 18. Method Sensitivitv (detection/measurement/information biases)
High
- Do not select for this metric.
Medium
- Limits of detection are low enough to detect chemicals in a sufficient percentage of the
samples to address the research question. Analytical methods measuring biomarker are
adequately reported. The limit of detection (LOD) and limit of quantification (LOQ)
(value or %) are reported.
Low
-	Frequency of detection too low to address the research hypothesis.
OR
-	LOD/LOQ (value or %) are not stated.

Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Do not select "N/A" for this metric if the study assessed biomarkers. If LOD/LOQ are
not stated then select Low.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 19. Biomarker Stabilitv (detection/measurement/information biases)
High
- Samples with a known storage history and documented stability data or those using
real-time measurements.
Medium
- Samples have known losses during storage, but the difference between low and high
exposures can be qualitatively assessed.
Low
- Samples with either unknown storage history and/or no stability data for target analytes
and high likelihood of instability for the biomarker under consideration.
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Do not select "N/A" for this metric if the study assessed biomarkers.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality Rating
Description
Metric 20. Sample Contamination (detection/measurement/information biases)
High
-	Samples are contamination-free from the time of collection to the time of measurement
(e.g., by use of certified analyte free collection supplies and reference materials, and
appropriate use of blanks both in the field and lab).
AND
-	Documentation of the steps taken to provide the necessary assurance that the study data
are reliable is included.
Medium
-	Samples are stated to be contamination-free from the time of collection to the time of
measurement.
AND
-	There is incomplete documentation of the steps taken to provide the necessary
assurance that the study data are reliable.
OR
-	Samples are known to have contamination issues, but steps have been taken to address
and correct contamination issues.
OR
-	There is no information included about contamination (only allowed for biomarker
samples not susceptible to contamination).
Low
-	Samples are known to have contamination issues, but steps have been taken to address
and correct contamination issues.
OR
-	Samples are stated to be contamination-free from the time of collection to the time of
measurement, but there is no use or documentation of the steps taken to provide the
necessary assurance that the study data are reliable.
Critically Deficient
- There are known contamination issues (e.g., phthalate study that used plastic sample
collection vials) and no documentation that the issues were addressed.
Not Rated/Not
Applicable
- Do not select "N/A" for this metric if the study assessed biomarkers.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
Metric 21. Method Requirements (detection/measurement/information biases)
High
- Instrumentation that provides unambiguous identification and quantitation of the
biomarker at the required sensitivity (e.g., gas chromatography/high-resolution mass
spectrometry [GC-HRMS]; gas chromatography with tandem mass spectrometry [GC-
MS/MS]; liquid chromatography with tandem mass spectrometry [LC-MS/MS]).
Medium
- Instrumentation that allows for identification of the biomarker with a high degree of
confidence and the required sensitivity (e.g., gas chromatography mass spectrometry
[GC-MS], gas chromatography with electron capture detector [GC-ECD]).
Low
- Instrumentation that only allows for possible quantification of the biomarker, but the
method has known interferants (e.g., gas chromatography with flame-ionization
detection [GC-FID], spectroscopy).
Critically Deficient
- Do not select for this metric.
Not Rated/Not
Applicable
- Do not select "N/A" for this metric if the study assessed biomarkers.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
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Data Quality Rating
Description
Metric 22. Matrix Adjustment (detection/measurement/information biases)
High
- If applicable for the biomarker under consideration, study provides results, either in the
main publication or as a supplement, for both adjusted and unadjusted matrix
concentrations (e.g., creatinine-adjusted or specific gravity-adjusted and non-adjusted
urine concentrations) and reasons are given for adjustment approach.
Medium
- If applicable for the biomarker under consideration, study only provides results using
one method (matrix-adjusted or not).
Low
- If applicable for the biomarker under consideration, no established method for matrix
adjustment was conducted.
Critically Deficient
- Do not select for this metric
Not Rated/Not
Applicable
- If metrics 16 and 17 are both NA, then the remaining biomarker metrics are
automatically not rated. Otherwise:
Select ""N/A" if matrix adjustment is not required for assessment of the biomarker.
Reviewer's
Comments
Document concerns, uncertainties, limitations, and deficiencies and any additional
comments that may highlight study strengths or important elements such as relevance.
11 Smoking fits in Metrics 9 and 10, not Metric 11; Metric 9 addresses whether there was appropriate adjustment or
consideration of confounders (such as stratification) (other than co-exposures); Metric 10 addresses how the potential
confounders (other than co-exposures) were measured; Metric 11 assesses co-exposure confounding.
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