EPA/635/R-13/128a 

www.epa.gov/iris 

Evaluation of the Inhalation Carcinogenicity of
 

Ethylene Oxide
 

(CASRN 75-21-8) 

In Support of Summary Information on the
 

Integrated Risk Information System (IRIS)
 

July 2013 

NOTICE 

This document is a Revised External Peer Review draft. This information is distributed solely for 

the purpose of pre-dissemination peer review under applicable information quality guidelines. It has 

not been formally disseminated by EPA. It does not represent and should not be construed to 

represent any Agency determination or policy. It is being circulated for review of its technical 

accuracy and science policy implications. 

National Center for Environmental Assessment
 

Office of Research and Development
 

U.S. Environmental Protection Agency
 

Washington, DC
 
DISCLAIMER 

This document is a preliminary draft for review purposes only. This information is 

distributed solely for the purpose of pre-dissemination peer review under applicable information 

quality guidelines. It has not been formally disseminated by EPA. It does not represent and 

should not be construed to represent any Agency determination or policy. Mention of trade 

names or commercial products does not constitute endorsement or recommendation for use. 

This document is a draft for review purposes only and does not constitute Agency policy. 

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CONTENTS
 

LIST OF TABLES ...........................................................................................................................v
 

LIST OF FIGURES ...................................................................................................................... vii
 

LIST OF ABBREVIATIONS ...................................................................................................... viii
 

AUTHORS, CONTRIBUTORS, AND REVIEWERS ................................................................. ix
 

1. EXECUTIVE SUMMARY ................................................................................................ 1-1
 

2. INTRODUCTION .............................................................................................................. 2-1
 

3. HAZARD IDENTIFICATION ........................................................................................... 3-1
 

3.1. EVIDENCE OF CANCER IN HUMANS................................................................. 3-1
 

3.1.1. Conclusions Regarding the Evidence of Cancer in Humans .................... 3-12
 

3.2. EVIDENCE OF CANCER IN LABORATORY ANIMALS ................................. 3-19
 

3.2.1. Conclusions Regarding the Evidence of Cancer in Laboratory
 

Animals ..................................................................................................... 3-23
 

3.3. SUPPORTING EVIDENCE .................................................................................... 3-23
 

3.3.1. Metabolism and Kinetics .......................................................................... 3-23
 

3.3.2. Protein Adducts......................................................................................... 3-26
 

3.3.3. Genotoxicity.............................................................................................. 3-28
 

3.4. MODE OF ACTION................................................................................................ 3-38
 

3.4.1. Analysis of the Mode of Action for Ethylene Oxide Carcinogenicity
 

under EPA’s Mode-of-Action Framework ............................................... 3-42
 

3.5. HAZARD CHARACTERIZATION ....................................................................... 3-44
 

3.5.1. Characterization of Cancer Hazard ........................................................... 3-44
 

3.5.2. Susceptible Life stages and Populations ................................................... 3-49
 

4. CANCER DOSE-RESPONSE ASSESSMENT FOR INHALATION EXPOSURE ........ 4-1
 

4.1. INHALATION UNIT RISK ESTIMATES DERIVED FROM HUMAN 

DATA ........................................................................................................................ 4-1
 

4.1.1. Risk Estimates for Lymphohematopoietic Cancer ..................................... 4-4
 

4.1.2. Risk Estimates for Breast Cancer ............................................................. 4-21
 

4.1.3. Total Cancer Risk Estimates ..................................................................... 4-41
 

4.1.4. Sources of Uncertainty in the Cancer Risk Estimates .............................. 4-43
 

4.1.5. Summary ................................................................................................... 4-55
 

4.2. INHALATION UNIT RISK DERIVED FROM EXPERIMENTAL ANIMAL
 

DATA ...................................................................................................................... 4-56
 

4.2.1. Overall Approach ...................................................................................... 4-56
 

4.2.2. Cross-Species Scaling ............................................................................... 4-57
 

4.2.3. Dose-Response Modeling Methods .......................................................... 4-58
 

4.2.4. Description of Experimental Animal Studies ........................................... 4-60
 

4.2.5. Results of Data Analysis of Experimental Animal Studies ...................... 4-62
 

This document is a draft for review purposes only and does not constitute Agency policy. 

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CONTENTS (continued) 

4.3. SUMMARY OF INHALATION UNIT RISK ESTIMATES—NOT
 

ACCOUNTING FOR ASSUMED INCREASED EARLY-LIFE 

SUSCEPTIBILITY .................................................................................................. 4-64
 

4.4. ADJUSTMENTS FOR POTENTIAL INCREASED EARLY-LIFE 

SUSCEPTIBILITY .................................................................................................. 4-65
 

4.5. INHALATION UNIT RISK ESTIMATES—CONCLUSIONS ............................. 4-70
 

4.6. COMPARISON WITH OTHER PUBLISHED RISK ESTIMATES ..................... 4-75
 

4.6.1. Unit Risk Estimates Based on Human Studies ......................................... 4-75
 

4.6.2. Unit Risk Estimates Based on Laboratory Animal Studies ..................... 4-80
 

4.7. RISK ESTIMATES FOR SOME OCCUPATIONAL EXPOSURE
 

SCENARIOS ........................................................................................................... 4-80
 

REFERENCES ............................................................................................................................R-1
 

This document is a draft for review purposes only and does not constitute Agency policy. 

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This document is a draft for review purposes only and does not constitute Agency policy. 

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LIST OF TABLES
 

1-1. Summary of major findings ............................................................................................. 1-6
 

3-1. Epidemiological studies of ethylene oxide and human 

cancer―lymphohematopoietic cancer results ............................................................... 3-13
 

3-2. Summary of epidemiological results on EtO and breast cancer (all sterilizer
 

workers) ......................................................................................................................... 3-17
 

3-3. Tumor incidence data in National Toxicology Program Study of B6C3F1 mice 

(NTP, 1987) ................................................................................................................... 3-21
 

3-4. Tumor incidence data in Lynch et al. (1984a; 1984b) study of male F344 rats ............ 3-22
 

3-5. Tumor incidence data in Snellings et al. (1984) and Garman et al. (1985) reports
 

on F344 rats.................................................................................................................... 3-24
 

3-6. Cytogenetic effects in humans ....................................................................................... 3-33
 

4-1. Considerations used in this assessment for selecting epidemiology studies for
 

quantitative risk estimation .............................................................................................. 4-3
 

4-2. Cox regression results for all lymphohematopoietic cancer and lymphoid cancer
 

mortality in both sexes in the NIOSH cohort, for the models presented by
 

Steenland et al. (2004) ..................................................................................................... 4-5
 

4-3. Exposure-response modeling results for all lymphohematopoietic cancer and 

lymphoid cancer mortality in both sexes in the NIOSH cohort for models not
 

presented by Steenland et al. (2004) .............................................................................. 4-11
 

4-4. Models considered for modeling the exposure-response data for lymphoid cancer
 

mortality in both sexes in the NIOSH cohort for the derivation of unit risk 

estimates......................................................................................................................... 4-13
 

4-5. EC01, LEC01, and unit risk estimates for lymphoid cancer ............................................ 4-14
 

4-6. EC01, LEC01, and unit risk estimates for all lymphohematopoietic cancer.................... 4-18
 

4-7. Cox regression results for breast cancer mortality in females in the NIOSH cohort, 

for models presented in Steenland et al. (2004)............................................................. 4-21
 

4-8. Exposure-response modeling results for breast cancer mortality in females in the
 

NIOSH cohort for models not presented by Steenland et al. (2004) ............................. 4-25
 

4-9. EC01, LEC01, and unit risk estimates for breast cancer mortality in females ................. 4-27
 
LIST OF TABLES (continued) 

4-10. Cox regression results for breast cancer incidence in females from the NIOSH
 

cohort, for the models presented by Steenland et al. (2003).......................................... 4-29
 

4-11. Exposure-response modeling results for breast cancer incidence in females from
 

the NIOSH cohort for models not presented by Steenland et al. (2003) ....................... 4-34
 

4-12. Models considered for modeling the exposure-response data for breast cancer
 

incidence in females in the subcohort with interviews from the NIOSH incidence
 

study cohort for the derivation of unit risk estimates .................................................... 4-36
 

4-13. EC01, LEC01, and unit risk estimates for breast cancer incidence in females—
 

invasive and in situ......................................................................................................... 4-39
 

4-14. Calculation of EC01 for total cancer risk ........................................................................ 4-42
 

4-15. Calculation of total cancer unit risk estimate................................................................. 4-42
 

4-16. Upper-bound unit risks (per μg/m3) obtained by combining tumor sites ...................... 4-61
 

4-17. Unit risk values from multistage Weibull time-to-tumor modeling of mouse tumor
 

incidence in the NTP (1987) study ................................................................................ 4-63
 

4-18. Summary of unit risk estimates (per μg/m3) in animal bioassays .................................. 4-64
 

4-19. EC01, LEC01, and unit risk estimates for adult-only exposures...................................... 4-67
 

4-20. Calculation of EC01 for total cancer risk from adult-only exposure .............................. 4-67
 

4-21. Calculation of total cancer unit risk estimate from adult-only exposure ....................... 4-68
 

4-22. Adult-based unit risk estimates for use in ADAF calculations and risk estimate
 

calculations involving less-than-lifetime exposure scenarios ........................................ 4-69
 

4-23. Adult-based extra risk estimates per ppm based on adult-exposure-only EC01s ........... 4-74
 

4-24. Summary of key unit risk estimates from this assessment (see Section 4.7 for risk
 

estimates based on occupational exposure scenarios) ................................................... 4-76
 

4-25. Comparison of unit risk estimates.................................................................................. 4-78
 

4-26. Extra risk estimates for lymphoid cancer in both sexes for various occupational 

exposure levels ............................................................................................................... 4-83
 

4-27. Extra risk estimates for breast cancer incidence in females for various
 

occupational exposure levels ......................................................................................... 4-86
 

This document is a draft for review purposes only and does not constitute Agency policy. 

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This document is a draft for review purposes only and does not constitute Agency policy. 

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LIST OF FIGURES
 

3-1. Metabolism of ethylene oxide........................................................................................ 3-25
 

3-2. Simulated blood AUCs for EtO following a 6-hour exposure to EtO from the rat, 

mouse, and human PBPK models of Fennell and Brown (2001); based on data
 

presented in Fennell and Brown (2001) ......................................................................... 3-27
 

3-3. Display of 203 data sets, including bacteria, fungi, plants, insects, and mammals
 

(in vitro and in vivo), measuring the full range of genotoxic endpoints........................ 3-29
 

4-1. RR estimate for lymphoid cancer vs. mean exposure (with 15-year lag, unadjusted 

for continuous exposure).................................................................................................. 4-8
 

4-2. RR estimate for all lymphohematopoietic cancer vs. mean exposure (with 15-year
 

lag, unadjusted for continuous exposure). ..................................................................... 4-17
 

4-3. RR estimate for breast cancer mortality vs. mean exposure (with 20-year lag,
 

unadjusted for continuous exposure). ............................................................................ 4-23
 

4-4. RR estimate for breast cancer incidence in full cohort vs. mean exposure (with 15­

year lag, unadjusted for continuous exposure). ............................................................. 4-31
 

4-5. RR estimate for breast cancer incidence in subcohort with interviews vs. mean 

exposure (with 15-year lag, unadjusted for continuous exposure). ............................... 4-32
 

4-6. RR estimate for breast cancer incidence in subcohort with interviews vs. mean
 

exposure (with 15-year lag, unadjusted for continuous exposure); select models
 

compared to deciles........................................................................................................ 4-38
 

4-7. RR estimates for lymphoid cancer from occupational EtO exposures (with 15-year
 

lag). ................................................................................................................................ 4-84
 

4-8. RR estimates for breast cancer incidence from occupational EtO exposures (with
 

15-year lag). ................................................................................................................... 4-89
 
 

LIST OF ABBREVIATIONS
 

ADAF age-dependent adjustment factor 

AIC Akaike information criterion 

AIDS acquired immune deficiency syndrome 

AML acute myeloid leukemia 

AUC areas under the curve 

BEIR Committee on the Biological Effects of Ionizing Radiation 

CI confidence interval 

DSB double-strand breaks 

EC effective concentration 

EOIC Ethylene Oxide Industry Council 

EPA U.S. Environmental Protection Agency 

EtO ethylene oxide 

FRG Federal Republic of Germany 

GST glutathione S-transferase 

HAP hazardous air pollutants 

N7-HEG N7-(2-hydroxyethyl)guanine 

IARC International Agency for Research on Cancer 

ICD International Classification of Diseases 

IRIS Integrated Risk Information System 

LEC lower confidence limit 

MLE maximum likelihood estimate 

NCEA National Center for Environmental Assessment 

NHL non-Hodgkin lymphoma 

NIOSH National Institute for Occupational Safety and Health 

NTP National Toxicology Program 

O6

-HEG O6-hydroxyethylguanine 

OBS observed number 

OR odds ratios 

PBPK physiologically based pharmacokinetic 

POD point of departure 

RR relative rate, i.e., rate ratio 

SCE sister chromatid exchanges 

SE standard error 

SEER Surveillance, Epidemiology, and End Results 

SIR standardized incidence ratio 

SMR standard mortality ratios 

TWA time-weighted average 

UCC Union Carbide Corporation 

UCL upper confidence limit 

WHO World Health Organization 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 viii DRAFT―DO NOT CITE OR QUOTE 
AUTHORS, CONTRIBUTORS, AND REVIEWERS
 

ASSESSMENT AUTHORS AND CONTRIBUTORS 

Jennifer Jinot (Chemical Manager)
 

National Center for Environmental Assessment
 

U.S. Environmental Protection Agency 

Washington, DC 

David Bayliss (retired)
 

National Center for Environmental Assessment
 

U.S. Environmental Protection Agency 

Washington, DC 

Henry D. Kahn (retired)
 

National Center for Environmental Assessment
 

U.S. Environmental Protection Agency 

Washington, DC 

Nagu Keshava 

National Center for Environmental Assessment 

U.S. Environmental Protection Agency 

Washington, DC 

Robert McGaughy (retired)
 

National Center for Environmental Assessment
 

U.S. Environmental Protection Agency 

Washington, DC 

Ravi Subramaniam 

National Center for Environmental Assessment 

U.S. Environmental Protection Agency 

Washington, DC 

Larry Valcovic (retired)
 

National Center for Environmental Assessment
 

U.S. Environmental Protection Agency 

Washington, DC 

Suryanarayana Vulimiri 

National Center for Environmental Assessment 

U.S. Environmental Protection Agency 

Washington, DC 

This document is a draft for review purposes only and does not constitute Agency policy. 

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REVIEWERS 

This document has been provided for review to EPA scientists, interagency reviewers 

from other federal agencies and White House offices, and the public, and peer reviewed by 

independent scientists external to EPA. A summary and EPA’s disposition of the comments 

received from the independent external peer reviewers and from the public is included in 

Appendix H. 

INTERNAL EPA REVIEWERS 

Michele Burgess, OSWER 

Deborah Burgin, OPEI 

Kerry Dearfield, ORD/OSP 

Joyce Donahue, OW 

Rebecca Dzubow, AO/OCHP 

Michael Firestone, AO/OCHP 

Linnea Hansen, OPP 

Karen Hogan, ORD/NCEA-IO 

Aparna Koppikar, ORD/NCEA-W 

Deirdre Murphy, OAR/ESD 

Steve Nesnow, ORD/NHEERL 

Marian Olsen, Region 2 

Brenda Perkovich-Foos, AO/OCHP 

Julian Preston, ORD/NHEERL 

Santhini Ramasamy, OPP/HED, OW 

Nancy Rios-Jafolla, Region 3 

Tracey Woodruff, AO/NCEE 

The authors would like to acknowledge Julian Preston, David Bussard, Cheryl Scott, and 

Paul White of EPA for their contributions during the draft development process. 

This document is a draft for review purposes only and does not constitute Agency policy. 

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EXTERNAL PEER REVIEWERS 

SCIENCE ADVISORY BOARD ETHYLENE OXIDE REVIEW PANEL 

CHAIR 

Dr. Stephen Roberts 

University of Florida 

OTHER SAB MEMBERS 

Dr. Timothy Buckley
 

Ohio State University
 

Dr. Montserrat Fuentes
 

North Carolina State University
 

Dr. Dale Hattis
 

Clark University
 

Dr. James Kehrer
 

Washington State University
 

Dr. Mark Miller
 

California Environmental Protection Agency
 

Dr. Maria Morandi
 

University of Texas—Houston Health Science Center
 

Dr. Robert Schnatter
 

Exxon Biomedical Sciences, Inc.
 

Dr. Anne Sweeney
 

TAMU System Health Science Center
 

This document is a draft for review purposes only and does not constitute Agency policy. 

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CONSULTANTS SERVING ON THE PANEL 

Dr. Steven Alan Belinsky 

University of New Mexico 

Dr. Norman Drinkwater 

University of Wisconsin Medical School 

Dr. Steven Heeringa 

University of Michigan 

Dr. Ulrike Luderer 

University of California 

Dr. James Swenberg 

University of North Carolina 

Dr. Vernon Walker 

Lovelace Respiratory Research Institute 

This document is a draft for review purposes only and does not constitute Agency policy. 

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1. EXECUTIVE SUMMARY 

Ethylene oxide (EtO) is a gas at room temperature. It is manufactured from ethylene and 

used primarily as a chemical intermediate in the manufacture of ethylene glycol. It is also used 

as a sterilizing agent for medical equipment and as a fumigating agent for spices. 

The DNA-damaging properties of EtO have been studied since the 1940s. EtO is known 

to be mutagenic in a large number of living organisms, ranging from bacteriophage to mammals, 

and it also induces chromosome damage. It is carcinogenic in mice and rats, inducing tumors of 

the lymphohematopoietic system, brain, lung, connective tissue, uterus, and mammary gland. In 

humans employed in EtO-manufacturing facilities and in sterilizing facilities, the greatest 

evidence of a cancer risk from exposure is for cancer of the lymphohematopoietic system. 

Increases in the risk of lymphohematopoietic cancer have been seen in most (but not all) of the 

epidemiological studies of EtO-exposed workers, manifested as an increase either in leukemia or 

in cancer of the lymphoid tissue. Of note, in one large epidemiologic study conducted by the 

National Institute for Occupational Safety and Health (NIOSH) of sterilizer workers that had a 

well-defined exposure assessment for individuals, positive exposure-response trends were 

reported for lymphohematopoietic cancer mortality, primarily in males and in particular for 

lymphoid cancer (i.e., non-Hodgkin lymphoma, myeloma, and lymphocytic leukemia), and for 

breast cancer mortality in females (Steenland et al., 2004). The positive exposure-response trend 

for female breast cancer was confirmed in an incidence study based on the same worker cohort 

(Steenland et al., 2003). There is supporting evidence for an association between EtO and breast 

cancer from other studies, but the database is more limited than that for lymphohematopoietic 

cancers. 

Although the evidence of carcinogenicity from human studies was deemed short of 

conclusive on its own, EtO is characterized as “carcinogenic to humans” by the inhalation route 

of exposure based on the total weight of evidence, in accordance with EPA’s 2005 Guidelines for 

Carcinogen Risk Assessment (U.S. EPA, 2005a). The lines of evidence supporting this 

characterization include: (1) strong, but less than conclusive on its own, epidemiological 

evidence of lymphohematopoietic cancers and breast cancer in EtO-exposed workers, 

(2) extensive evidence of carcinogenicity in laboratory animals, including lymphohematopoietic 

cancers in rats and mice and mammary carcinomas in mice following inhalation exposure, 

(3) clear evidence that EtO is genotoxic and sufficient weight of evidence to support a mutagenic 

mode of action for EtO carcinogenicity, and (4) strong evidence that the key precursor events are 

anticipated to occur in humans and progress to tumors, including evidence of chromosome 
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damage in humans exposed to EtO. Overall, there is strong confidence in the hazard 

characterization of EtO as “carcinogenic to humans.” 

This document describes the derivation of inhalation unit risk estimates for cancer 

mortality and incidence based on the human data from the large NIOSH study (Steenland et al., 

2004; 2003). This study was selected for the derivation of risk estimates because it was the 

largest of the available studies and it had exposure estimates for the individual workers from a 

high-quality exposure assessment. Multiple modeling approaches were evaluated for the 

exposure-response data, including modeling the cancer response as a function of either 

categorical exposures or continuous individual exposure levels. Preferred approaches were 

defined for each cancer endpoint in consideration of both the statistical properties and biological 

reasonableness of the resulting model forms (see Tables 4-4 and 4-12 for a summary of models 

investigated in this assessment for lymphoid cancer and breast cancer incidence, respectively, 

and the considerations used in model selection). 

Under the common assumption that relative risk is independent of age, an LEC01 (lower 

95% confidence limit on the EC01, the estimated effective concentration associated with 1% extra 

risk) was calculated using a life-table analysis and linear modeling of the categorical Cox 

regression analysis results for excess lymphoid cancer mortality (Steenland et al., 2004; 

additional results for both sexes combined provided by Dr. Steenland in Appendix D) excluding 

the highest exposure group to mitigate the supralinearity of the exposure-response data. Linear 

low-dose extrapolation below the range of observations is supported by the conclusion that a 

mutagenic mode of action is operative in EtO carcinogenicity. Linear low-dose extrapolation 

from the LEC01 for lymphoid cancer mortality yielded a lifetime extra cancer unit risk estimate 

of 2.2 × 10−4 per μg/m3 (4.0 × 10−4 per ppb)1 of continuous EtO exposure. Applying the same 

linear regression coefficient and life-table analysis to background lymphoid cancer incidence 

rates and applying linear low-dose extrapolation resulted in a preferred lifetime extra lymphoid 

cancer unit risk estimate of 4.8 × 10−4 per μg/m3

 (8.8 × 10−4 per ppb), as cancer incidence 

estimates are generally preferred over mortality estimates. 

Using the same approach, a unit risk estimate of 2.8 × 10−4 per μg/m3

 (5.1 × 10−4 per ppb) 

was derived from the breast cancer mortality results of the same epidemiology study (Steenland 

et al., 2004). Breast cancer incidence risk estimates, on the other hand, were calculated from the 

data from a breast cancer incidence study of the same occupational cohort (Steenland et al., 

2003), and, for these data, a two-piece linear spline model was used for the exposure-response 

modeling. Using the same life-table approach and linear low-dose extrapolation, a unit risk 

estimate of 9.5 × 10−4 per μg/m3

 (1.7 × 10−3 per ppb) was obtained for breast cancer incidence. 

 

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Conversion equation: 1 ppm = 1830 μg/m3 . 
 

 

 

 

 

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Again, the incidence estimate is preferred over the mortality estimate. Combining the incidence 

risk estimates for the two cancer types resulted in a total cancer unit risk estimate of 1.2 × 10−3 

per μg/m3 (2.3 × 10−3 per ppb). 

Unit risk estimates were also derived from the three chronic rodent bioassays for EtO 

reported in the literature. These estimates, ranging from 2.2 × 10−5 per μg/m3 to 4.6 × 10−5 per 

μg/m3, are over an order of magnitude lower than the estimates based on human data. The 

Agency takes the position that human data, if adequate data are available, provide a more 

appropriate basis than rodent data for estimating population risks (U.S. EPA, 2005a), primarily 

because uncertainties in extrapolating quantitative risks from rodents to humans are avoided. 

Although there is a sizeable difference between the rodent-based and the human-based estimates, 

the human data are from a large, high-quality study, with EtO exposure estimates for the 

individual workers and little reported exposure to chemicals other than EtO. Therefore, the 

estimates based on the human data are the preferred estimates for this assessment. 

Because the weight of evidence supports a mutagenic mode of action for EtO 

carcinogenicity, and as there are no chemical-specific data from which to assess early-life 

susceptibility, increased early-life susceptibility should be assumed, according to EPA’s 

Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to 

Carcinogens−hereinafter referred to as “EPA’s Supplemental Guidance” (U.S. EPA, 2005b). 

This mode-of-action-based assumption of increased early-life susceptibility supersedes the 

assumption of age independence under which the human-data-based estimates presented above 

were derived. Thus, using the same approach as for the estimates discussed above but initiating 

exposure in the life-table analysis at age 16 instead of at birth, adult-exposure-only unit risk 

estimates were calculated from the human data under an alternate assumption that relative risk is 

independent of age for adults, which represent the life stage for which the data upon which the 

exposure-response modeling was conducted pertain. These adult-exposure-only unit risk 

estimates were then rescaled to a 70-year basis for use in the standard ADAF calculations and 

risk estimate calculations involving less-than-lifetime exposure scenarios. The resulting 

adult-based unit risk estimates were 4.35 × 10−4 per μg/m3

 (7.95 × 10−4 per ppb) for lymphoid 

cancer incidence, 8.21 × 10−4 per μg/m3 (1.50 × 10−3 per ppb) for breast cancer incidence in 

females, and 1.08 × 10−3 per μg/m3

 (1.98 × 10−3 per ppb) for both cancer types combined. For 

exposure scenarios involving early-life exposure, the age-dependent adjustment factors (ADAFs) 

should be applied, in accordance with EPA’s Supplemental Guidance (U.S. EPA, 2005b). 

Applying the ADAFs to obtain a full lifetime total cancer unit risk estimate yields 1.8 × 10−3 per 

μg/m3 (3.3 × 10−3 per ppb), and the commensurate lifetime chronic (lower-bound) exposure level 

of EtO corresponding to an increased cancer risk of 10−6 is 0.0006 μg/m3 (0.0003 ppb). 

This document is a draft for review purposes only and does not constitute Agency policy. 

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This document is a draft for review purposes only and does not constitute Agency policy. 

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The major sources of uncertainty in the unit risk estimates derived from the human data 

include the low-dose extrapolation, the retrospective exposure assessment conducted for the 

epidemiology study, and the exposure-response modeling of the epidemiological data (see 

Section 4.1.4 for a discussion of these and other sources of uncertainty in the unit risk estimates). 

Although there are uncertainties in the unit risk estimate, confidence in the estimate is 

relatively high. First, there is strong confidence in the hazard characterization of EtO as 

“carcinogenic to humans,” which is based on strong epidemiological evidence supplemented by 

other lines of evidence. Second, the unit risk estimate is based on human data from a large, 

high-quality epidemiology study with individual worker exposures estimated using a 

high-quality regression model. Finally, the use of low-exposure linear extrapolation is strongly 

supported by the conclusion that EtO carcinogenicity has a mutagenic mode of action. 

Confidence in the unit risk estimate is particularly high for the breast cancer component, 

the largest contributor to the total cancer unit risk estimate, which is based on over 200 incident 

cases for which the investigators had information on other potential breast cancer risk factors. 

The selected model for the breast cancer incidence data was the best-fitting model of the models 

investigated as well as the model which provided the best representation of the categorical 

results, particularly in the lower exposure range of greatest relevance for the derivation of a unit 

risk estimate. Alternate estimates calculated from other reasonable models suggest that a unit 

risk estimate for breast cancer incidence fourfold lower (corresponding to a total cancer unit risk 

estimate of twofold lower) is plausible; however, unit risk estimates notably lower than that are 

considered unlikely from the available data. 

There is lower confidence in the lymphoid cancer component of the unit risk estimate 

because it is based on fewer events (40 lymphoid cancer deaths); incidence risk was estimated 

from mortality data; and the exposure-response relationship is exceedingly supralinear, such that 

continuous models yield apparently implausibly steep low-exposure slopes. Although these 

continuous models provided statistically significant slope coefficients, there was low confidence 

in such steep slopes, which, particularly for the two-piece spline models, are highly dependent on 

a small number of cases in the low-exposure range. Thus, a linear regression model of the 

categorical results for the lowest three quartiles was used to derive the unit risk estimate for 

lymphoid cancer, and there was greater confidence in the more moderate slope resulting from 

that model, although it was not statistically significant, because it was based on more data and 

provided a good representation of the categorical results across this larger data range in the 

lower-exposure region. So, while there is lower confidence in the lymphoid cancer unit risk 

estimate than in the breast cancer unit risk estimate, the lymphoid cancer estimate is considered a 
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reasonable estimate from the available data, and overall, there is relatively high confidence in the 

total cancer unit risk estimate. 

The unit risk estimate is intended to provide a reasonable upper bound on cancer risk. 

The estimate was developed for environmental exposure levels (it is considered valid for 

exposures up to 140 μg/m3 [75 ppb]) and is not applicable to higher-level exposures, such as may 

occur occupationally, which appear to have a different exposure-response relationship. 

However, occupational exposure levels of EtO are of concern to EPA when EtO is used as a 

pesticide (e.g., sterilizing agent or fumigant). Therefore, this document also presents extra risk 

estimates for the two cancer types for a range of occupational exposure scenarios (see 

Section 4.7). Maximum likelihood estimates of the extra risk of lymphoid cancer and breast 

cancer combined for the range of occupational exposure scenarios considered (i.e., 0.1 to 1 ppm 

8-hour TWA for 35 years) ranged from 0.047 to 0.14. The overall uncertainty associated with 

the extra risk estimates for occupational exposure scenarios is less than that associated with the 

unit risk estimates for environmental exposures. The extra risk estimates are derived for 

occupational exposure scenarios that yield cumulative exposures well within the range of the 

exposures in the NIOSH study. Moreover, the NIOSH study is a study of sterilizer workers who 

used EtO for the sterilization of medical supplies or spices (Steenland et al., 1991); thus, the 

results are directly applicable to workers in these occupations, and these are among the 

occupations of primary concern for current occupational EtO exposures. 

Table 1-1 provides a summary of the major findings in this assessment. 

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Table 1-1. Summary of major findings 

Hazard Conclusions 

Hazard Characterization 

The weight of evidence from epidemiological 

studies and supporting information is sufficient to 

conclude that ethylene oxide is carcinogenic to 

humans. 

Mode of Action 

The weight of evidence is sufficient to conclude 

that ethylene oxide carcinogenicity has a 

mutagenic mode of action. 

Unit Risk Estimates (for environmental exposures)a 

Basis 

Inhalation unit risk estimatea 

(per μg/m3)

b 

Full lifetime unit risk estimatec 

Total cancer risk based on human data―lymphoid cancer 

incidence and breast cancer incidence in females 1.80 × 10-3 

Adult-based unit risk estimatesd 

Total cancer risk based on human data―lymphoid cancer 

incidence and breast cancer incidence in females 1.08 × 10-3 

Lymphoid cancer incidence in both sexes based on human data 4.35 × 10-4 

Breast cancer incidence in females based on human data 8.21 × 10-4 

Total cancer risk based on human data―lymphoid cancer 

incidence and range of female breast cancer incidence 

estimates from three alternate models 

5.64 × 10-4 – 1.08 × 10-3 

Total cancer incidence risk estimate from rodent data (female 

mouse) 4.6 × 10−5 

Extra risk estimates for occupational exposure scenarios (see Section 4.7) 

Maximum likelihood estimates of the extra risk of lymphoid 

cancer and breast cancer combined for the range of 

occupational exposure scenarios considered (i.e., 0.1 to 1 ppm 

8-hr TWA for 35 yr) 

0.047–0.14 

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a

These unit risk estimates are not intended for use with continuous lifetime exposure levels above about 140 μg/m3 . 

See Section 4.7 for risk estimates based on occupational exposure scenarios. Preferred estimates are in bold. 

b

To convert unit risk estimates to (ppm)−1, multiply the (μg/m3)

−1 estimates by 1,830 (μg/m3

)/ppm. c

Because the weight of evidence supports a mutagenic mode of action for EtO carcinogenicity, and because of the 

lack of chemical-specific data, EPA assumes increased early-life susceptibility and recommends the application of 

ADAFs, in accordance with EPA’s Supplemental Guidance (U.S. EPA, 2005b), for exposure scenarios that include 

early-life exposures. For the full lifetime (upper bound) unit risk estimate presented here, ADAFs have been 

applied, as described in Section 4.4. d

These (upper bound) unit risk estimates are intended for use in ADAF calculations and less-than-lifetime adult 

exposure scenarios (U.S. EPA, 2005b). Note that these are not the same as the unit risk estimates derived directly 

from the human data in Section 4.1 under the assumption that RRs are independent of age. Under that assumption, 

the key unit risk estimates were 4.8 × 10-4 per μg/m3 for lymphoid cancer incidence, 9.5 × 10-4 per μg/m3 for breast 

cancer incidence, and 1.2 × 10-3 per μg/m3 for the combined cancer incidence risk from those two cancers. See 

Section 4.4 for the derivation of the adult-based unit risk estimates. 

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2. INTRODUCTION 

The purpose of this document is to provide scientific support and rationale for the hazard 

and dose-response assessment in IRIS pertaining to carcinogenicity from chronic inhalation 

exposure to ethylene oxide (EtO) (CASRN 75-21-8). It is not intended to be a comprehensive 

treatise on the chemical or toxicological nature of EtO. In general, this IRIS Carcinogenicity 

Assessment provides information on the carcinogenic hazard potential of EtO and quantitative 

estimates of risk from inhalation exposure. The information includes a weight-of-evidence 

judgment of the likelihood that the agent is a human carcinogen and the conditions under which 

the carcinogenic effects may be expressed. Quantitative risk estimates for inhalation exposure 

(inhalation unit risks) are derived. The definition of an inhalation unit risk is a plausible upper 

bound on the estimate of risk per μg/m3

 air breathed. 

Development of the hazard identification and dose-response assessments for EtO has 

followed the general guidelines for risk assessment as set forth by the National Research Council 

(NRC, 1983). United States Environmental Protection Agency (U.S. EPA) Guidelines and Risk 

Assessment Forum Technical Panel Reports that were used in the development of this 

assessment include the following: Guidelines for Mutagenicity Risk Assessment (U.S. EPA, 

1986), Methods for Derivation of Inhalation Reference Concentrations and Application of 

Inhalation Dosimetry (U.S. EPA, 1994), Benchmark Dose Technical Guidance (U.S. EPA, 

2012), Science Policy Council Handbook: Risk Characterization(U.S. EPA, 2000), Guidelines 

for Carcinogen Risk Assessment (U.S. EPA, 2005a), Supplemental Guidance for Assessing 

Susceptibility from Early-Life Exposure to Carcinogens (U.S. EPA, 2005b), and Science Policy 

Council Handbook: Peer Review (U.S. EPA, 2006b). 

An earlier external review draft of this carcinogenicity assessment (U.S. EPA, 2006a) 

was peer reviewed by a panel of EPA’s Science Advisory Board (SAB) in 2007 (SAB, 2007). 

This revised external review draft is being released for public comment and for additional 

external peer review to receive comments primarily on the expanded exposure-response 

modeling of certain epidemiologic data done in response to comments from the 2007 SAB 

review. 

The literature search strategy first employed for this assessment was based on the 

Chemical Abstracts Service Registry Number (CASRN) and at least one common name. Any 

pertinent scientific information submitted by the public to the IRIS Submission Desk was also 

considered in the development of this document. References have been added after the first 

external peer review in response to the reviewers’ and public comments. References have also 

been added for completeness. These references have not changed the overall qualitative or 
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quantitative conclusions. See Appendix I for a list of the references added after the first external 

peer review. The cutoff date for literature inclusion into this carcinogenicity assessment was 

30 June 2010. A systematic literature search was conducted for the time frame from January 

2006 to May 2013 to ensure that no major studies were missed from the time of the first external 

review draft in 2006 until the cutoff date and to determine if any significant new studies had 

been published since the cutoff date that might alter the findings of the assessment. This 

systematic literature search is described in Appendix J. No new studies were identified that 

would impact the assessment’s major conclusions. Nonetheless, two new studies of high 

pertinence to the assessment have been published since the cutoff date for literature inclusion, 

and these studies are reviewed briefly in Appendix J for transparency and completeness. The 

references considered and cited in this document, including abstracts, can be found on the Health and 

Environmental Research Online (HERO) website.2 

On 23 December 2011, the Consolidated Appropriations Act, 2012, was signed into law. 3 

The report language included direction to EPA for the IRIS Program related to recommendations 

provided by the National Research Council (NRC) in their review of EPA’s draft IRIS 

assessment of formaldehyde. The NRC’s recommendations, provided in Chapter 7 of their 

review report, offered suggestions to EPA for improving the development of IRIS assessments. 

The report language included the following: 

The Agency shall incorporate, as appropriate, based on chemical-specific datasets 

and biological effects, the recommendations of Chapter 7 of the National 

Research Council’s Review of the Environmental Protection Agency’s Draft IRIS 

Assessment of Formaldehyde into the IRIS process ... For draft assessments 

released in fiscal year 2012, the Agency shall include documentation describing 

how the Chapter 7 recommendations of the National Academy of Sciences (NAS) 

have been implemented or addressed, including an explanation for why certain 

recommendations were not incorporated. 

Consistent with the direction provided by Congress, documentation of how the 

recommendations from Chapter 7 of the NRC report have been implemented in this assessment 

2

HERO is a database of scientific studies and other references used to develop EPA’s risk assessments, which are 

aimed at understanding the health and environmental effects of pollutants and chemicals. HERO is developed and 

managed in EPA’s Office of Research and Development (ORD) by the National Center for Environmental 

Assessment (NCEA). The database includes more than 750,000 scientific articles from the peer-reviewed literature. 

New studies are added continuously to HERO. 3

Pub. L. No. 112-74, Consolidated Appropriations Act, 2012. 
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is provided in Appendix K. This documentation also includes an explanation for why certain 

recommendations were not incorporated. 

For general information about this assessment or other questions relating to IRIS, the 

reader is referred to EPA’s IRIS Hotline at (202) 566-1676 (phone), (202) 566-1749 (fax), or 

hotline.iris@epa.gov (email address). 

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3. HAZARD IDENTIFICATION 

This chapter presents the evidence considered in the hazard identification of EtO 

carcinogenicity and the hazard characterization resulting from the weight-of-evidence evaluation. 

Section 3.1 summarizes the human evidence (a more detailed discussion of the human cancer 

studies is presented in Appendix A). Section 3.2 describes the evidence from experimental 

animal studies. Section 3.3 discusses supporting evidence, in particular evidence regarding the 

genotoxicity of EtO. Section 3.4 provides the mode-of-action analysis for EtO carcinogenicity. 

To conclude the chapter, Section 3.5 presents the hazard characterization for EtO carcinogenicity 

and a discussion of life stages and populations with potentially increased susceptibility. 

3.1. EVIDENCE OF CANCER IN HUMANS 

The literature from 1988 to present contains numerous epidemiological studies of the 

carcinogenic effects of EtO in occupational cohorts; some of these cohorts were the subject of 

multiple reports. The conclusions about the human evidence of carcinogenicity in this 

assessment are based on the following summary of those studies, which are discussed in more 

detail and critically reviewed in Appendix A. Table A-5 in Appendix A provides a tabular 

summary of the epidemiological studies, including some study details, results, and limitations. 

The strengths and weaknesses of these studies were evaluated individually using standard 

considerations in evaluating epidemiological studies. The major areas of concern are study 

design, exposure assessment, and data analysis. General features of study design considered 

include sample size and assessment of the health endpoint. For case-control studies, design 

considerations include representativeness of cases, selection of controls, participation rates, use 

of proxy respondents, and interview approach (e.g., blinding). For cohort studies, design 

considerations include selection of referent population (e.g., internal comparisons are generally 

preferred to comparisons with an external population), loss to follow-up, and length of 

follow-up. Exposure assessment issues include specificity of exposure (exposure 

misclassification), characterization of exposure (e.g., ever exposed or quantitative estimate of 

exposure level), and potential confounders. Analysis considerations include adjustment for 

potential confounders or effect modifiers and modeling of exposure-response relationships. 

Two primary sources of exposures to EtO are production facilities and sterilization 

operations. There are two types of production facilities (IARC, 1994b): 
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1. Those using the older chlorohydrin process, where ethylene is reacted with hypochlorous 

acid and then with calcium oxide to make EtO (this method produces unwanted 

byproducts, the most toxic of which is ethylene dichloride), and 

2. Those producing EtO via direct oxidation of ethylene in a pressurized vessel, which 

involves less EtO exposure and eliminates the chemical byproducts of the chlorohydrin 

process. 

Exposure in the sterilization of medical equipment and in the direct oxidation process is 

predominantly to EtO, whereas exposure in the chlorohydrin process is to EtO mixed with other 

chemicals.
 

Hogstedt et al. (1986) and Hogstedt (1988) summarized findings of three Swedish 

occupational cohorts (539 men and 170 women) exposed in a plant where hospital equipment
 

was sterilized, in a chlorohydrin production facility, and in a direct oxidation production facility. 

The incidence of leukemia was elevated in all cohorts, although the risk was not statistically 

significant in the cohort from the direct oxidation facility. For the three cohorts combined there 

were statistically significantly elevated standard mortality ratios (SMRs) for leukemia 

(SMR = 9.2; 95% confidence interval [CI] = 3.7–19), based on 7 deaths, and for stomach cancer 

(SMR = 5.5; 95% CI = 2.6–10), based on 10 deaths. Although this study produced high SMRs 

for leukemia, stomach cancer, and total cancer, there are some limitations, such as multiple 

exposures to numerous other chemicals, lack of personal exposure information, and lack of 

latency analysis. No gender differences were separately analyzed. No dose-response 

calculations were possible. This study provides suggestive evidence of the carcinogenicity of 

EtO. 

Coggon et al. (2004) reported the results of a follow-up study of a cohort originally 

studied by Gardner et al. (1989). The cohort included workers in three EtO production facilities 

(two using both chlorohydrin and direct oxidation processes and the third using direct oxidation 

only); in a fourth facility that used EtO in the manufacture of other chemicals; and in eight 

hospitals that used EtO in sterilizing units. The total cohort comprised 1,864 men and 

1,012 women. No statistically significant excesses were observed for any cancer site. Slight 

increases, based on small numbers, were observed for the various lymphohematopoietic cancers: 

Hodgkin lymphoma (2 vs. 1 expected), non-Hodgkin lymphoma (NHL) (7 vs. 4.8), multiple 

myeloma (3 vs. 2.5), and leukemia (5 vs. 4.6). The increases were concentrated in the 

1,471 chemical-manufacturing workers, of whom all but 1 were male. In the 

chemical-manufacturing workers with “definite” exposure, 4 leukemias were observed 

(1.7 expected) and 9 lymphohematopoietic cancers were observed (4.9 expected). A slight 

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deficit in the risk of breast cancer deaths (11 vs. 13.2) was observed in the cohort. No individual 

exposure measurements were obtained from cohort members, and no exposure measurements 

were available before 1977. Multiple exposures to other chemicals, small numbers of deaths, 

and lack of individual EtO measurements make this study only suggestive of a higher risk of 

leukemia from exposure to EtO. 

A series of retrospective mortality studies of about 2,000 male workers who were 

assigned to operations that used or produced EtO in either of two Union Carbide Corporation 

(UCC) chemical production facilities in West Virginia (Valdez-Flores et al., 2010; Swaen et al., 

2009; Teta et al., 1999; Benson and Teta, 1993; Teta et al., 1993; Greenberg et al., 1990) have 

been published. EtO was produced at these facilities until 1971, after which it was imported to 

the facilities. For EtO production, the chlorohydrin process was used from 1925 to 1957, and the 

direct oxidation process was used from 1937 to 1971 (during overlapping years, both processes 

were in use). The cohort was observed from 1940 through 1978 in the original study (Greenberg 

et al., 1990), through 1988 in the Teta et al. (1999); Teta et al. (1993) and Benson and Teta 

(1993) studies, and through 2003 in the latter two studies. A large-scale industrial hygiene 

survey and monitoring of EtO concentrations was carried out in 1976, at which time EtO was in 

use at the facilities but no longer in production. 

Greenberg et al. (1990) found elevated but not statistically significant risks of pancreatic 

cancer (SMR = 1.7) and leukemia (SMR = 2.3) (each based on seven cases) in the entire cohort; 

most of the cases occurred in the chlorohydrin production unit (note that the chlorohydrin 

production unit produced primarily ethylene chlorohydrin, which is used in chlorohydrin-based 

EtO production, but this unit is not where chlorohydrin-based EtO production took place). 

Limitations of this study included multiple exposures to many different chemicals in the facility 

through the years and lack of EtO exposure measurements prior to 1976. Three categories of 

exposure were established for analysis—low, intermediate, and high—based on a qualitative 

characterization of the potential for EtO exposure. The number of workers in each exposure 

category was not reported. No significant findings of a dose-response relationship were 

discernible. No quantitative estimates of individual exposure were made in this study, and no 

latency analysis was conducted (average follow-up was 20 years). Furthermore, EtO is not the 

only chemical to which the observed excesses in cancer mortality could be attributed. 

A follow-up study (Teta et al., 1993) that extended the observation of this cohort 

(excluding the 278 chlorohydrin production unit workers, who reportedly had low EtO 

exposures) for an additional 10 years to 1988 found no significant risk of total cancer; there was 

a slight trend in the risk of leukemia with increasing duration of assignment to departments using 

or processing EtO, but it was not significant (p = 0.28) and was based on only five cases. The 
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average follow-up was 27 years, and at least 10 years had elapsed since first exposure for all 

workers. The same problems of exposure ascertainment exist for this study as for that of 

Greenberg et al. (1990), and furthermore, the follow-up did not update work histories for the 

workers after 1978. EtO production at the plants was discontinued before 1978, as noted by Teta 

et al. (1993); however, according to Greenberg et al. (1990), certain nonproduction areas had 

“intermediate” potential for EtO exposure, although estimates of exposure levels suggest that the 

levels would also be lower during the update period [<1 ppm 8-hour time-weighted average 

(TWA), according to Teta et al. (1993)]. It appears from the Greenberg et al. (1990) publication 

that the high potential exposure group was reserved for EtO production workers, and according 

to Teta et al. (1993), there were only 425 EtO production workers in the cohort. Of these, only 

118 worked in the chlorohydrin-based production process, where exposures were reportedly 

highest. Essentially, the study did not support the earlier studies of cancer in EtO workers; 

however, it was limited by low statistical power and a crude exposure assessment and, thus, is 

not very informative regarding whether exposure to EtO is causally related to cancer. 

In a parallel follow-up study through 1988 of only the chlorohydrin production 

employees, Benson and Teta (1993) found that pancreatic cancer and lymphohematopoietic 

cancer cases continued to accumulate and that the SMRs were statistically significant for 

pancreatic cancer (SMR = 4.9; Obs = 8, p < 0.05) and for lymphohematopoietic cancer 

(SMR = 2.9; Obs = 8, p < 0.05). These investigators interpreted these excesses as possibly due 

to ethylene dichloride, a byproduct in the chlorohydrin process. Again, this small study of only 

278 workers was limited by the same problems as the Greenberg et al. (1990) study and the Teta 

et al. (1993) study. No individual estimates of exposure are available and the workers were 

potentially exposed to many different chemicals (see Table A-5 in Appendix A). Furthermore, 

the chlorohydrin production unit was reportedly considered a low potential EtO exposure 

department. Hence this study has little weight in determining the carcinogenicity of EtO. 

In a later analysis, Teta et al. (1999) fitted Poisson regression dose-response models to 

the UCC data (followed through 1988 and excluding the chlorohydrin production workers) and 

to data (followed through 1987) from a study by the National Institute for Occupational Safety 

and Health (NIOSH) (described below). Because Teta et al. (1999) did not present risk ratios for 

the cumulative exposure categories used to model the dose-response relationships, the only 

comparison that can be made between the UCC and NIOSH data is based on the fitted models. 

These models are almost identical for leukemia, but for the lymphoid category, the 

risk―according to the fitted model for the UCC data—decreased as a function of exposure, 

whereas the risk for the modeled NIOSH data increased as a function of exposure. However, the 

models are based on small numbers of cases (16 [5 UCC, 11 NIOSH] for leukemia; 22 [3 UCC, 
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19 NIOSH] for lymphoid cancers), and no statistics are provided to assess model goodness-of-fit 

or to compare across models. In any event, this analysis is superseded by the more recent 

analysis by the same authors (Valdez-Flores et al., 2010) of the results of more recent follow-up 

studies of these cohorts (see below). 

Swaen et al. (2009) studied the same UCC cohort identified by Teta et al. (1993), i.e., 

without the chlorohydrin production workers, but extended the cohort enumeration period from 

the end of 1978 to the end of 1988, identifying 167 additional workers, and conducted mortality 

follow-up of the resulting cohort of 2,063 male workers through 2003. Work histories were also 

extended through 1988 (exposures after 1988 were considered negligible compared to earlier 

exposure levels). Swaen et al. (2009) used an exposure assessment based on the qualitative 

categorizations of potential EtO exposure in the different departments developed by Greenberg et 

al. (1990) and time-period exposure estimates from Teta et al. (1993). This exposure assessment 

was relatively crude, based on just a small number of department-specific and time-periodspecific categories, and with exposure estimates for only a few of the categories derived from 

actual measurements (see Appendix A.2.20 for details). 

At the end of the 2003 follow-up, 1,048 of the 2,063 workers had died (Swaen et al., 

2009). The all-cause mortality SMR was 0.85 (95% CI = 0.80, 0.90) and the cancer SMR was 

0.95 (95% CI = 0.84, 1.06). None of the SMRs for specific cancer types showed any statistically 

significant increases. In analyses stratified by hire date [pre- (inclusive) or post-1956], the SMR 

for leukemia was elevated but not statistically significant (1.51; 95% CI 0.69, 2.87) in the 

early-hire group, based on nine deaths. In analyses stratified by duration of employment, no 

trends were apparent for any of the lymphohematopoietic cancers, although in the 9+ years of 

employment subgroup, the SMR for NHL was nonsignificantly increased (1.49; 95% CI 0.48, 

3.48), based on five deaths. In SMR analyses stratified by cumulative exposure, no trends were 

apparent for any of the lymphohematopoietic cancers and there were no notable elevations for 

the highest cumulative exposure category. Note that only 27 lymphohematopoietic cancer deaths 

(including 12 leukemias and 11 NHLs) were observed in the cohort. 

Swaen et al. (2009) also did internal Cox proportional hazards modeling for some disease 

categories (all-cause mortality, leukemia mortality, and lymphoid cancer [NHL, lymphocytic 

leukemia, and myeloma] mortality [17 deaths]), using cumulative exposure as the exposure 

metric. These analyses showed no evidence of an exposure-response relationship. Alternate 

Cox proportional hazard analyses and categorical exposure-response analyses of the UCC data 

conducted by Valdez-Flores et al. (2010) for a larger set of cancer endpoints similarly reported 

an absence of any exposure-response relationships. Each of these cancer analyses, however, 
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relies on small numbers of cases and a crude exposure assessment, where there is a high potential 

for exposure misclassification. 

In a study of 2,658 male workers at eight chemical plants where EtO is produced 

(manufacturing process not stated), Kiesselbach et al. (1990) found slightly increased SMRs for 

cancers of the stomach, esophagus, and lung. A latency analysis was done only for stomach 

cancer and total mortality. The investigators considered 71.6% of the cohort to be “weakly” 

exposed; only 2.6% were “strongly exposed.” No data were provided to explain how these 

exposure categories were derived. The workers were followed for a median 15.5 years. Without 

additional information on exposure to EtO, this study is of little help at this time in evaluating the 

carcinogenicity of EtO. 

NIOSH conducted an industry-wide study of 18,254 workers (45% male and 55% 

female) in 14 plants where EtO was used (Steenland et al., 2004; Stayner et al., 1993; Steenland 

et al., 1991). Most of the workers were exposed while sterilizing medical supplies and treating 

spices and in the manufacture and testing of medical sterilizers. Individual exposure estimates 

were derived for workers from 13 of the 14 plants. The procedures for selecting the facilities and 

defining the cohort are described in Steenland et al. (1991), and the exposure model and 

verification procedures are described in Greife et al. (1988) and Hornung et al. (1994). Briefly, a 

regression model was developed, allowing the estimation of exposure levels for time periods, 

facilities, and operations for which industrial hygiene data were unavailable. The data for the 

model consisted of 2,700 individual time-weighted exposure values for workers’ personal 

breathing zones, acquired from 18 facilities between 1976 and 1985. The data were divided into 

two sets, one for developing the regression model and the second for testing it. Seven out of 

23 independent variables tested for inclusion in the regression model were found to be significant 

predictors of EtO exposure and were included in the final model. This model predicted 85% of 

the variation in average EtO exposure levels. (See Appendix A, Section A.2.8, for more details 

on the NIOSH exposure assessment and its evaluation.) Results of the original follow-up study 

through 1987 are presented in Steenland et al. (1991) and Stayner et al. (1993). The cohort 

averaged 26.8 years of follow-up in the extended follow-up study through 1998, and 16% of the 

cohort had died (Steenland et al., 2004). 

The overall SMR for cancer was 0.98, based on 860 deaths (Steenland et al., 2004). The 

SMR for (lympho)hematopoietic cancer was 1.00, based on 79 cases. Exposure-response 

analyses, however, revealed exposure-related increases in hematopoietic cancer mortality risk, 

although the effect was primarily in males, when analyzed by sex. In categorical life-table 

analysis, men with >13,500 ppm-days of cumulative exposure had an SMR of 1.46 (Obs = 13). 

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cohort) with exposure as a continuous variable, statistically significant trends in males for all 

hematopoietic cancer (p = 0.02) and for “lymphoid” cancers (NHL, lymphocytic leukemia, and 

myeloma; p = 0.02) were observed using log cumulative exposure (ppm-days) with a 15-year 

lag. 4  In internal categorical analyses, statistically significant odds ratios (ORs) were observed in 

the highest cumulative exposure quartile (with a 15-year lag) in males for all hematopoietic 

cancer (OR = 3.42; 95% CI = 1.09–10.73) and “lymphoid” cancer (OR = 3.76; 95% 

CI = 1.03−13.64). The exposure metrics of duration of exposure, average concentration, and 

maximum (8-hour TWA) concentration did not predict the hematopoietic cancer results as well 

as did the cumulative exposure metric. 

Although the overall SMR for female breast cancer was 0.99, based on 102 deaths, the 

NIOSH mortality follow-up study reported a significant excess of breast cancer mortality in the 

highest cumulative exposure quartile using a 20-year lag period compared to the U.S. population 

(SMR = 2.07; 95% CI = 1.10–3.54; Obs = 13). Internal exposure-response analyses also noted a 

significant positive trend for breast cancer mortality using the log of cumulative exposure and a 

20-year lag time (p = 0.01). In internal categorical analyses, a statistically significant OR for 

breast cancer mortality was observed in the highest cumulative exposure quartile with a 20-year 

lag (OR = 3.13; 95% CI = 1.42–6.92). 

In summary, although the overall external comparisons did not demonstrate increased 

risks, the NIOSH investigators found significant internal exposure-response relationships 

between exposure to EtO and cancers of the hematopoietic system, as well as breast cancer 

mortality. (Internal comparisons are considered superior to external comparisons in occupational 

epidemiology studies because internal comparisons help control for the healthy worker effect and 

other factors that might be more comparable within a study’s worker population than between 

the workers and the general population.) Exposures to other chemicals in the workplace were 

believed to be minimal or nonexistent. This study is the most useful of the epidemiologic studies 

in terms of carrying out a quantitative dose-response assessment. It possesses more attributes 

than the others for performing risk analysis (e.g., good-quality estimates of individual exposure, 

lack of exposure to other chemicals, and a large and diverse cohort of workers). 

It should be noted that Steenland et al. (2004) used Cox regression models, which are 

log-linear relative rate models, thus providing some low-dose sublinear curvature for doses 

expressed in terms of cumulative exposure. However, the best-fitting dose-response model for 

both male lymphoid cancers and male all hematopoietic cancers was for dose expressed in terms 

of log cumulative exposure, indicating supralinearity of the low-dose data. Supralinearity of the 

 

4

The sex difference is not statistically significant, however, and the trends for both sexes combined are also 

statistically significant [p = 0.01 and p = 0.02, respectively; see Tables D-3e and D-4e in Appendix D]. 
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dose-response data was also indicated by the categorical exposure results. This is in contrast to 

the reported results of Kirman et al. (2004) based on the Teta et al. (1999) analysis combining 

the 1993 UCC leukemia data with the 1993 NIOSH leukemia data, which are claimed by the 

authors to provide empirical evidence supporting a quadratic dose-response relationship. The 

2004 NIOSH dose-response data for hematopoietic cancers clearly do not provide empirical 

evidence in support of a quadratic dose-response relationship. On the contrary, the NIOSH data 

suggest a supralinear dose-response relationship in the observable range. 

Wong and Trent (1993) investigated the same cohort as Steenland et al. (1991) but added 

474 new unexplained subjects and increased the follow-up period by 1 year. They incremented 

the total number of deaths by 176 and added 392.2 more expected deaths. The only positive 

finding was a statistically significantly increased risk of NHL among men (SMR = 2.5; Obs = 6; 

p < 0.05). However, there was a deficit risk of NHL among women. For breast cancer, there 

was no trend of increasing risk by duration of employment or by latency. This study has major 

limitations, not the least of which is a lack of detailed employment histories, making it 

impossible to quantify individual exposures and develop dose-response relationships. 

Furthermore, the addition of more than twice as many expected deaths as observed deaths makes 

the analysis by the authors questionable. 

Valdez-Flores et al. (2010) conducted alternative Cox proportional hazards modeling and 

categorical exposure-response analyses using data from the UCC cohort (Swaen et al., 2009), the 

NIOSH cohort (Steenland et al., 2004) and the two cohorts combined, analyzing the sexes both 

separately and together. These investigators reported that they found no evidence of 

exposure-response relationships for cumulative exposure with either the Cox model or 

categorical analyses for all of the cohort/endpoint data sets examined (endpoints included all 

lymphohematopoietic cancers, lymphoid cancers, and female breast cancer, the latter in the 

NIOSH cohort only). Valdez-Flores et al. (2010) did observe statistically significant increases in 

response rates in the highest exposure quintile relative to the lowest exposure quintile for 

lymphohematopoietic and lymphoid cancers in males in the NIOSH cohort, consistent with the 

categorical results of Steenland et al. (2004), as well as a statistically significant increase in the 

highest exposure quintile for lymphoid cancers in males and females combined in the NIOSH 

cohort, consistent with the results in Appendix D. Because the exposure assessment conducted 

for the UCC cohort is much cruder (see above and Appendix A.2.20), especially for the highest 

exposures, than the NIOSH exposure assessment (which was based on a validated regression 

model; see Appendix A.2.8), EPA considers the results of exposure-response analyses of the 

combined cohort data to have greater uncertainty than those from analyses of the NIOSH cohort 

alone, despite the additional cases contributed by the UCC cohort (e.g., the UCC cohort 
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contributes 17 cases of lymphoid cancer to the 53 from the NIOSH cohort). Furthermore, 

Valdez-Flores et al. (2010) did not use any log cumulative exposure models, and these were the 

models that were statistically significant in the Steenland et al. (2004) analyses, consistent with 

the apparent supralinearity of the NIOSH exposure-response data. See Appendix A.2.20 for a 

more detailed discussion of the Valdez-Flores et al. (2010) analyses and how they compared with 

the Steenland et al. (2004) analyses. 

In a mortality study of 1,971 male chemical workers in Italy, 637 of whom were licensed 

to handle EtO but not other toxic gases, Bisanti et al. (1993) reported statistically significant 

excesses of hematopoietic cancers (SMR = 7.1, Obs = 5, p < 0.05). The study was limited by the 

lack of exposure measurements and by the young age of the cohort. Although this study 

suggests that exposure to EtO leads to a significant excess of hematopoietic cancer, the lack of 

personal exposure measurements and the fact that members were potentially exposed to other 

chemicals in the workplace lessen the study’s usefulness for establishing the carcinogenicity of 

EtO. 

Hagmar et al. (Hagmar et al., 1995; Hagmar et al., 1991) studied cancer incidence in 

2,170 Swedish workers (861 male and 1,309 female) in two medical sterilizing plants. They 

determined concentrations in six job categories and estimated cumulative exposures for each 

worker. They found hematopoietic cancers in 6 individuals versus 3.4 expected (SMR = 1.8) and 

a nonsignificant doubling in the risk when a 10-year latency period was considered. Even 

though the cohort was young, the follow-up time was short (median 11.8 years), and only a small 

fraction of the workers was highly exposed, the report is suggestive of an association between 

EtO exposure and hematopoietic cancers. The risk of breast cancer was less than expected, 

although with such short follow-up, the total numbers of cases was small (standardized incidence 

ratio [SIR] = 0.5, Obs = 5). In the latent category of 10 years or more, the risk was even lower 

(SIR = 0.4, Obs = 2). 

In a large chemical manufacturing plant in Belgium (number of employees not stated), 

Swaen et al. (1996) performed a nested case-control study of Hodgkin lymphoma to determine 

whether a cluster of 10 cases in the active male work force was associated with any particular 

chemical. They found a significant association for benzene and EtO. This study is limited by 

the exclusion of inactive workers and the potential confounding effect of other chemicals besides 

EtO, and it is not useful for quantitative dose-response assessment. 

Olsen et al. (1997) studied 1,361 male employees working in the ethylene and propylene 

chlorohydrin production and processing areas located within the EtO and propylene oxide 

production plants at four Dow Chemical Company sites in the United States. Although these 

investigators found a nonsignificant positive trend between duration of employment as 
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chlorohydrin workers and lymphohematopoietic cancer (Obs = 10), they concluded that there 

was no appreciable risk in these workers, in contrast to the findings of Benson and Teta (1993). 

The small cohort size and the lack of data on EtO exposures limit the usefulness of this study in 

inferring risks due to EtO. 

Norman et al. (1995) studied 1,132 workers (204 male and 928 female) in a medical 

sterilizing plant in the United States. In the women, there was a significant excess incidence of 

breast cancer (SIR = 2.6, Obs = 12, p < 0.05); no other cancer sites were elevated. The risk of 

breast cancer was not noted to be excessive in the few previous studies where adequate numbers 

of females were included and analyzed for breast cancer; however, only one of these studies was 

also an incidence study. The follow-up time was too short to draw meaningful conclusions at 

this time. This study lacks the power to determine whether risks for cancers other than breast 

cancer are statistically significantly elevated. It has no information regarding historical exposure 

and some breast cancer victims had worked for less than 1 month. 

Tompa et al. (1999) reported a cluster of eight breast cancers and eight other cancers in 

98 nurses exposed to EtO in a hospital in Hungary; however, the expected number of cases 

cannot be identified. 

The NIOSH investigators used the NIOSH cohort to conduct a study of breast cancer 

incidence and exposure to EtO (Steenland et al., 2003). The researchers identified 7,576 women 

from the initial cohort who had been employed in the commercial sterilization facilities for at 

least 1 year (76% of the original cohort). Breast cancer incidence was determined from 

interviews (questionnaires), death certificates, and cancer registries. Interviews were obtained 

for 5,139 women (68% of the study cohort). The main reason for nonresponse was inability to 

locate the study subject (22% of cohort). The average duration of exposure for the cohort was 

10.7 years. For the full study cohort, 319 incident breast cancer cases were identified, including 

20 cases of carcinoma in situ. Overall, the SIR was 0.87 (0.94 excluding the in situ cases) using 

Surveillance, Epidemiology, and End Results (SEER) reference rates for comparison. Results 

with the full cohort are expected to be underestimated, however, because of case 

under-ascertainment in the women without interviews. A significant exposure-response trend 

was observed for SIR across cumulative exposure quintiles, using a 15-year lag time (p = 0.002). 

In internal Cox regression analyses, with exposure as a continuous variable, a significant trend 

for breast cancer incidence was obtained for log cumulative exposure with a 15-year lag 

(p = 0.05), taking age, race, and year of birth into account. Using duration of exposure, lagged 

15 years, provided a slightly better fit (p = 0.02), while models with cumulative 

(nontransformed), maximum or average exposure did not fit as well. In the Cox regression 
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analysis with categorical exposures and a 15-year lag, the top cumulative exposure quintile had a 

statistically significant OR for breast cancer incidence of 1.74 (95% CI = 1.16–2.65). 

In the subcohort with interviews, 233 incident breast cancer cases were identified. 

Information on various risk factors for breast cancer was also collected in the interviews, but 

only parity and breast cancer in a first-degree relative turned out to be important predictors of 

breast cancer incidence. In internal analyses with continuous exposure variables, the model with 

duration of exposure (lagged 15 years) again provided the best fit (p = 0.006). Both the 

cumulative exposure and log cumulative exposure models also yielded significant regression 

coefficients with a 15-year lag (p = 0.02 and p = 0.03, respectively), taking age, race, year of 

birth, parity, and breast cancer in a first-degree relative into account. In the Cox regression 

analysis with categorical exposures and a 15-year lag, the top cumulative exposure quintile had a 

statistically significant OR of 1.87 (95% CI = 1.12–3.10). 

Steenland et al. (2003) suggest that their findings are not conclusive of a causal 

association between EtO exposure and breast cancer incidence because of inconsistencies in 

exposure-response trends, possible biases due to nonresponse, and an incomplete cancer 

ascertainment. Although that conclusion seems appropriate, those concerns do not appear to be 

major limitations. As noted by the authors, it is not uncommon for positive exposure-response 

trends not to be strictly monotonically increasing, conceivably due to random fluctuations or 

imprecision in exposure estimates. Furthermore, the consistency of results between the full 

study cohort, which is less subject to nonresponse bias, and the subcohort with interviews, which 

should have full case ascertainment, alleviates some of the concerns about those potential biases. 

In a study of 299 female workers employed in a hospital in Hungary where gas sterilizers 

were used, Kardos et al. (2003) observed 11 cancer deaths, including 3 breast cancer deaths, 

compared with slightly more than 4 expected total cancer deaths. Site-specific expected deaths 

are not available in this study, so RR estimates cannot be determined. However, the observation 

of 3 breast cancer deaths, with at most 4.4 (with Hungarian national rates as the referent) total 

cancer deaths expected, is indicative of an increased risk of breast cancer 5

, and this 

characterization is supported by the reference of Major et al. (2001) to a cluster of breast cancer 

cases in female nurses at the same hospital. 

 

5

Hungarian age-standardized female cancer mortality rates reported by the International Agency for Research on 

Cancer (http://eu-cancer.iarc.fr/country-348-hungary.html,en) suggest that the ratio of breast cancer deaths to total 

cancer deaths in Hungarian females is about 0.16 (28.0/100,000 breast cancer mortality rate versus 

180.0/100,000 total cancer mortality rate). Although a comparison of this general population ratio with the ratio of 

0.68 for breast cancer to total cancer mortality in the Kardos et al. (2003) study is necessarily crude because the 

general population ratio is not based on the age-standardized rates that would correspond to the age distribution of 

the person-time of the women in the study, which are unknown, the large difference between the ratios (0.68 for the 

study versus 0.16 for the general population) indicates an increased risk of breast cancer in the study. 
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3.1.1. Conclusions Regarding the Evidence of Cancer in Humans 

Most of the human studies suggest a possible increased risk of lymphohematopoietic 

cancers, but the total weight of the epidemiological evidence does not provide conclusive proof 

of causality. Of the eight relevant criteria of causality envisioned by Hill (1965), temporality, 

coherence, biological plausibility, and analogy are clearly satisfied. There is also evidence of 

consistency in the response, of a dose-response relationship (biological gradient), and of 

specificity when the loosely defined blood malignancies are combined under the rubric “cancer 

of the hematopoietic system.” On the other hand, most of the relative risk estimates are not large 

(strong) in magnitude. See Section 3.5.1 for a more detailed discussion of the Hill criteria as 

applied to the EtO database. 

The large NIOSH study (Steenland et al., 2004; Stayner et al., 1993; Steenland et al., 

1991) of workers at 14 chemical plants around the country provides the strongest evidence of 

carcinogenicity. A statistically significant positive trend was observed in the risk of 

lymphohematopoietic neoplasms with increasing (log) cumulative exposure to EtO, although the 

results for this model were reported only for males (the sex difference is not statistically 

significant, however, and the trend for both sexes combined is statistically significant; see 

Appendix D). Despite limitations in the data, most other epidemiologic studies have also found 

elevated risks of lymphohematopoietic cancer from exposure to EtO (summarized briefly in 

Section 3.1 and Table 3-1; see also Appendix A for more details, in particular Table A-5 for a 

summary of study results and limitations). Furthermore, when the exposure is relatively pure, 

such as in sterilization workers, there is an elevated risk of lymphohematopoietic cancer that 

cannot be attributed to the presence of confounders such as those that could potentially appear in 

the chlorohydrin process. Moreover, the studies that do not report a significant 

lymphohematopoietic cancer effect from exposure to EtO have major limitations, such as small 

numbers of cases and inadequate exposure information (see Table A-5 in Appendix A). 

In addition, there is evidence of an increase in the risk of both breast cancer mortality and 

incidence in women who are exposed to EtO. Studies have reported increases in the risk of 

breast cancer in women employees of commercial sterilization plants (Steenland et al., 2004; 

Steenland et al., 2003; Norman et al., 1995) as well as in Hungarian hospital workers exposed to 

EtO (Kardos et al., 2003). In several other studies where exposure to EtO would be expected to 

have occurred among female employees, no elevated risks were seen (Coggon et al., 2004; 

Hagmar et al., 1991) or breast cancer results were not reported (Hogstedt, 1988; Hogstedt et al., 

1986). However, these studies had far fewer cases to analyze than the NIOSH studies, and most 

did not have individual exposure estimates and relied on external comparisons (see Table 3-2 for 

a brief summary and Table A-5 in Appendix A for more details). The Steenland et al. (2004) and 
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Table 3-1. Epidemiological studies of ethylene oxide and human cancer―lymphohematopoietic cancer resultsa 

Study/Population/ 

Industry 

Number of 

subjects Lymphohematopoietic cancer results Comments 

Hogstedt (1988) and 709 cancer deaths observed expected SMR (95% CI) Insufficient follow-up; only 12.0% of 

Hogstedt et al. (1986). (539 men, leukemia (ICD-8 204-207) 7 0.8 9.2 (3.7, 19)b cohort had died (85 deaths). 

Sterilizers, production 170 women) lymphohematopoietic 9 2.0 4.6 (2.1, 8.7)b Exposure to other chemicals. 

workers, Sweden. (ICD-8 200-208) 

Coggon et al. (2004). 

Update of Gardner et al. 

(1989). 

Sterilizing workers in 8 

hospitals and users in 4 

companies, Great 

Britain. 

2,876 

(1,864 men, 

1,012 

women) 

cancer deaths observed expected SMR (95% CI) 

leukemia (ICD-9 204-208) 5 4.6 1.1 (0.35, 2.5) 

leukemia 5 2.6 1.9 (0.62, 4.5)b 

(definite or continual exposure) 

NHL (ICD-9 200+202) 7 4.8 1.5 (0.58, 3.0)b 

lymphohematopoietic 17 12.9 1.3 (0.77, 2.1)b 

(ICD-9 200-208) 

Short follow-up; only 19.6% of cohort 

had died (565 deaths). 

Exposure to other chemicals. 

Kiesselbach et al. 

(1990). 

Production workers 

(methods unspecified) 

from 8 chemical plants 

in West Germany. 

2,658 men cancer deaths observed expected SMR (95% CI) 

leukemia (ICD-9 204-208) 2 2.35 0.85 (0.10, 3.1) 

lymphohematopoietic 5 5 1.0 (0.32, 2.3) 

(ICD-9 200-208) 

Insufficient follow-up; only 10.1% of 

cohort had died (268 deaths). 

Exposure to other chemicals. 

Benson and Teta (1993). 

Follow-up of only the 

chlorohydrin-exposed 

employees from 

Greenberg et al. (1990) 

cohort. 

Production workers at a 

chemical plant in West 

Virginia. 

278 men cancer deaths observed expected SMR (95% CI) 

leukemia and aleukemia 4 1.14 3.5 (0.96, 8.9) 

lymphosarcoma 1 0.50 2.0 (0.05, 11) 

and reticulosarcoma 

lymphohematopoietic 8 2.72 2.9 (1.3, 5.8) 

(ICD NS) 

EtO exposures reported to be low in the 

chlorohydrin process. 

Exposure to other chemicals. 

Very small cohort; thus, small numbers 

of specific cancers despite long followup (52.9% had died; 147 deaths). 
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Table 3-1. Epidemiological studies of ethylene oxide and human cancer―lymphohematopoietic cancer resultsa 

(continued) 

Study/Population/ 

Industry 

Number of 

subjects Lymphohematopoietic cancer results Comments 

Swaen et al. (2009). 

Update of Teta et al. 

(1993) [Greenberg et al. 

(1990) cohort minus all 

chlorohydrin-exposed 

employees] plus cohort 

enumeration extended 

an additional 10 years, 

adding 167 workers. 

Production workers and 

users at 2 chemical 

plants in West Virginia. 

2,063 men cancer deaths observed expected SMR (95% CI) 

leukemia 11 11.8 0.93 (0.47, 1.7) 

leukemia 9 NR 1.5 (0.69, 2.9) 

(in workers hired before 1956) 

NHL 12 11.5 1.05 (0.54, 1.8) 

lymphohematopoietic 27 30.4 0.89 (0.59, 1.3) 

(ICD NS) 

Internal Cox regression analyses: 

No statistically significant trends were observed for lymphoid or leukemia 

cancer categories for continuous cumulative exposure. 

Small cohort; thus, small numbers of 

specific cancers even though long 

follow-up time (50.8% had died; 1,048 

deaths). 

Crude exposure assessment, especially 

for the early time periods. 

Exposure to other chemicals. 

Steenland et al. (2004). 18,254 cancer deaths observed expected SMR (95% CI) Large cohort; thus, substantial number of 

Update of Steenland et (45% male, leukemia (ICD-9 204-208) 29 NR 0.99 (0.71, 1.36) deaths (2,852) despite short follow-up 

al. (1991), Stayner et al. 

(1993). 

Sterilizers of medical 

equipment and spices; 

and manufacturers and 

testers of medical 

sterilization equipment, 

in 14 plants in the 

United States. 

55% female) NHL (ICD-9 200+202) 31 NR 1.00 (0.72, 1.35) 

lymphohematopoietic 79 NR 1.00 (0.79, 1.24) 

(ICD-9 200-208) 

Internal Cox regression analyses: 

“lymphoid” cancers (ICD-9 200, 202, 203, 204): OR = 3.0 (p = 0.046) 

in highest cumulative exposure group, with 15-yr lag; significant 

regression coefficient for continuous log cumulative exposure (p = 0.02). 

lymphohematopoietic cancer (ICD-9 200-208): OR = 2.96 (p = 0.03) 

in highest cumulative exposure group, with 15-yr lag; significant 

regression coefficient for continuous log cumulative exposure (p = 0.009). 

(15.6% had died). 

High-quality exposure assessment. 

No evidence of exposure to other 

occupational carcinogens. 

No increase in lymphohematopoietic 

cancer risk with increase in exposure in 

women. 

Results from internal Cox regression 

analyses for both sexes combined from 

Sections D.3 and D.4 of Appendix D. 
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Table 3-1. Epidemiological studies of ethylene oxide and human cancer―lymphohematopoietic cancer resultsa 

(continued) 

Study/Population/ 

Industry 

Number of 

subjects Lymphohematopoietic cancer results Comments 

Bisanti et al. (1993). 1,971 men cancer deaths observed expected SMR (95% CI) Insufficient follow-up; only 3.9% of 

Chemical workers leukemia (ICD-9 204-208) 2 1.0 1.9 (0.23, 7.0) cohort had died (76 deaths). 

licensed to handle EtO lymphosarcoma and 4 0.6 6.8 (1.9, 17) Exposure to other chemicals. 

and other toxic reticulosarcoma (ICD-9 200) 

chemicals, Italy. lymphohematopoietic 6 2.4 2.5 (0.91, 5.5) 

(ICD-9 200-208) 

in group only licensed to handle EtO (n = 637): 

leukemia 2 0.3 6.5 (0.79, 23) 

lymphosarcoma and 3 0.2 17 (3.5, 50) 

reticulosarcoma 

lymphohematopoietic 5 0.7 7.0 (2.3, 16) 

Hagmar et al. (1995) and 

Hagmar et al. (1991). 

Two plants that 

produced disposable 

medical 

equipment, Sweden. 

2,170 

(861 men, 

1,309 

women) 

cancer cases observed expected SIR (95% CI) 

leukemia (ICD-7 204-205) 2 0.82 2.4 (0.30, 8.8) 

NHL (ICD-7 200+202) 2 1.25 1.6 (0.19, 5.8) 

lymphohematopoietic 6 3.37 1.8 (0.65, 3.9) 

(ICD-7 200-209) 

leukemia 2 0.28 7.1 (0.87, 26) 

(among subjects with at least 0.14 ppm-years of cumulative exposure 

and 10 years latency) 

Short follow-up period (only 40 cancer 

cases). 

Norman et al. (1995). 1,132 cancer cases observed expected SIR (95% CI) Short follow-up period and small cohort 

Sterilizers of medical (204 men, leukemia (ICD NS) 1 0.54 1.85 (0.05, 10)b (only 28 cancer cases). 

equipment and supplies 

that were assembled at 

this plant, New York. 

928 women) 

Swaen et al. (1996). 

Nested case-control 

study; cases and controls 

from a large chemical 

production plant, 

Belgium. 

10 cases of 

Hodgkin 

lymphoma (7 

confirmed) 

and 200 

controls; all 

male 

cancer OR (95% CI) 

Hodgkin lymphoma (ICD 201) 8.5 (1.4, 40) 

Hypothesis-generating study to 

investigate a cluster of Hodgkin 

lymphomas observed at a chemical plant. 

Exposure to other chemicals. 
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Table 3-1. Epidemiological studies of ethylene oxide and human cancer―lymphohematopoietic cancer resultsa 

(continued) 

Study/Population/ 

Industry 

Number of 

subjects Lymphohematopoietic cancer results Comments 

Olsen et al. (1997). 

Four EtO production 

plants (chlorohydrin 

process) in 3 states. 

1,361 men cancer deaths observed expected SMR (95% CI) 

leukemia (ICD-8 204-207) 2 3.0 0.67 (0.08, 2.4) 

lymphosarcoma and 1 1.1 0.91 (0.02, 5.1) 

reticulosarcoma (ICD-8 200) 

lymphohematopoietic 10 7.7 1.3 (0.62, 2.4) 

(ICD-8 200-209) 

Short follow-up and small cohort; thus, 

small numbers of specific cancers 

(22.0% had died; 300 deaths). 

Exposure to other chemicals. 

Kardos et al. (2003). 

Female workers from 

pediatric clinic of 

hospital in Eger, 

Hungary. 

299 women 1 lymphoid leukemia death of 11 cancer deaths; expected number not 

reported. 

Short follow-up period and small cohort 

(only 11 cancer deaths). 

Possible exposure to natural radium, 

which permeates the region. 

a

Extracted from Table A-5 of Appendix A, with addition of some summary results (e.g., SMRs). b

Calculated by EPA assuming Poisson distribution.
 

ICD NS: ICD codes not specified; NR: not reported
 
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Table 3-2. Summary of epidemiological results on EtO and breast cancer (all sterilizer workers)a 

Study 

Number of 

Women Breast Cancer Results Comments 

Hogstedt (1988); 

Hogstedt et al. 

(1986) 

Swedish 

incidence and 

mortality study 

170 not reported Only 8 deaths (7 from cancer) had 

occurred among the women. 

37 incident cancer cases (27 

expected) in the total cohort 

(including 539 men); 7 were 

lymphohematopoietic cancers, rest 

not reported. 

Coggon et al. 1,011 women exposure category observed expected SMR (95% CI) 11 breast cancer deaths. 

(2004) hospital continual 5 7.2 only 14% of the cohort of 1,405 

Great Britain workers intermittent 0 0.7 (including males) hospital workers 

mortality study unknown 6 5.2 

ALL 11 13.1 1.04 (0.42, 1.51) 

had died. 

Steenland et al. 9,908 SMR in highest quartile of cumulative exposure (with 20-yr lag) = 2.07 103 breast cancer deaths. 

(2004) (p < 0.05). 

U.S. mortality significant Cox regression coefficient for log cumulative exposure 

study (20-yr lag) (p = 0.01). 

Steenland et al. 

(2003) 

U.S. breast 

cancer incidence 

study 

7,576 

employed for 

≥1 yr; 5,139 

with interviews 

full cohort results: 

Cox regression analysis OR = 1.74 (95% CI: 1.16, 2.65) for highest 

cumulative exposure quintile (15-yr lag). 

p = 0.05 for regression coefficient with log cumulative exposure (15-yr 

lag). 

subcohort results: 

Cox regression analysis OR = 1.87 (95% CI: 1.12, 3.10) for highest 

cumulative exposure quintile (15-yr lag). 

p = 0.02 for regression coefficient with cumulative exposure (15-yr 

lag); p = 0.03 with log cumulative exposure (15-yr lag). 

319 cases in full cohort. 

233 cases in subcohort with 

interviews. 
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Table 3-2. Summary of epidemiological results on EtO and breast cancer (all sterilizer workers)a (continued) 

Study 

Number of 

Women Breast Cancer Results Comments 

Hagmar et al. 

(1995) and 

Hagmar et al. 

(1991) 

Swedish cancer 

incidence study 

1,309 5 cases vs. 10.8 expected SIR = 0.46 (95% CI: 0.15, 1.08). 5 cases. 

Norman et al. 

(1995) 

U.S. cancer 

incidence study 

928 SIRs ranged from 1.72 (95% CI: 0.99, 3.00) to 2.40 (95% CI: 1.32, 

4.37) depending on calendar year of follow-up, assumptions about 

completeness of follow-up, and reference rates used. 

12 cases. 

Kardos et al. 

(2003) 

Hungarian 

mortality study 

299 11 cancer deaths observed compared with 4.38, 4.03, or 4.28 expected 

(p < 0.01), based on comparison populations of Hungary, Heves 

County, and city of Eger, respectively; 3 were breast cancer deaths, i.e., 

3 breast cancer deaths vs. ~4.3 total deaths expected. Although the 

expected number of breast cancer deaths was not reported, the number 

of breast cancer deaths observed for the total deaths expected is 

indicative of an increased risk of breast cancer (see footnote 2 in 

Section 3.1). 

3 deaths. 

a

Extracted from Table A-5 of Appendix A 
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Steenland et al. (2003) studies, on the other hand, used the largest cohort of women potentially 

exposed to EtO and clearly show significantly increased risks of breast cancer incidence and 

mortality based upon internal exposure-response analyses. 

In summary, the most compelling evidence of a cancer risk from human exposure to EtO 

is for cancer of the lymphohematopoietic system. Increases in the risk of lymphohematopoietic 

cancer are present in most of the studies, manifested as an increase in leukemia and/or cancer of 

the lymphoid tissue. The evidence of lymphohematopoietic cancer is strongest in the one study 

(the NIOSH study) that appears to possess the fewest limitations. In this large study, a 

significant dose-response relationship was evident with cumulative exposure to EtO. However, 

this effect was observed primarily in males and the magnitude of the effect was not large. 

Similarly, in most of the other studies, the increased risks are not great, and other chemicals in 

some of the workplaces cannot be ruled out as possible confounders. Thus, the findings of 

increased risks of lymphohematopoietic cancer in the NIOSH and other studies cannot 

conclusively be attributed to exposure to EtO. The few studies that fail to demonstrate any 

increased risks of cancer do not have those strengths of study design that give confidence to the 

reported lack of an exposure-related effect. 

There is also evidence of an elevated risk of breast cancer from exposure to EtO in a few 

studies. The strongest evidence again comes from the NIOSH studies, which found positive 

exposure-response relationships for both breast cancer incidence and mortality. Hopefully, 

future studies will shed more light on this more recent finding. 

3.2. EVIDENCE OF CANCER IN LABORATORY ANIMALS 

The International Agency for Research on Cancer (IARC) monograph (IARC, 1994b) has 

summarized the rodent studies of carcinogenicity, and Health Canada (2001) has used this 

information to derive the levels of concern for human exposure. EPA concludes that the IARC 

summary of the key studies is valid and is not aware of any animal cancer bioassays that have 

been published since 1994. The Ethylene Oxide Industry Council (EOIC, 2001) also reviewed 

the same studies and did not cite additional studies. The qualitative results are described here 

and the incidence data are tabulated in the unit risk derivation section of this document. 

One study of oral administration in rats has been published; there are no oral studies in 

mice. Dunkelberg (1982) administered EtO in vegetable oil to groups of 50 female 

Sprague-Dawley rats by gastric intubation twice weekly for 150 weeks. There were two control 

groups (untreated and oil gavage) and two treated groups (7.5 and 30 mg/kg-day). A 

dose-dependent increase in the incidence of malignant tumors in the forestomach was observed 

in the treated groups (8/50 and 31/50 in the low- and high-dose groups, respectively). Of the 
 

 

 

 

 

 

 

 

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39 tumors, 37 were squamous cell carcinomas, and metastases to other organs were common in 

these animals. This study was not evaluated quantitatively because oral risk estimates are 

beyond the scope of this document. 

One inhalation assay was reported in mice (NTP, 1987) and two inhalation assays were 

reported in rats [(Lynch et al., 1984a; Lynch et al., 1984b) in males; (Garman et al., 1986, 1985; 

Snellings et al., 1984), in both males and females)]. In the National Toxicology Program (NTP) 

mouse bioassay (NTP, 1987), groups of 50 male and 50 female B6C3F1 mice were exposed to 

EtO via inhalation at concentrations of 0, 50, and 100 ppm for 6 hours per day, 5 days per week, 

for 102 weeks. Mean body weights were similar for treated and control animals, and there was 

no decrease in survival associated with treatment. A concentration-dependent increase in the 

incidence of tumors at several sites was observed in both sexes. These data are summarized in 

Table 3-3. Males had carcinomas and adenomas in the lung. Females had carcinomas and 

adenomas in the lung, malignant lymphomas, adenocarcinomas in the uterus, and 

adenocarcinomas in the mammary glands. The NTP also reports that both sexes had dose-related 

increased incidences of cystadenomas of the Harderian glands, but these are benign lesions and 

are not considered further here. 

In the Lynch et al. [Lynch et al. (1984a); Lynch et al. (1984b)] bioassay in male Fischer 

344 (F344) rats, groups of 80 animals were exposed to EtO via inhalation at concentrations of 0, 

50, and 100 ppm for 7 hours per day, 5 days per week, for 2 years. Mean body weights were 

statistically significantly decreased in both treated groups compared with controls (p < 0.05). 

Increased mortality was observed in the treated groups, and the increase was statistically 

significant in the 100-ppm exposure group (p < 0.01). Lynch et al. (1984a) suggest that survival 

was affected by a pulmonary infection alone and in combination with EtO exposure. 

Concentration-dependent increases in the incidence of mononuclear cell leukemia in the spleen, 

peritoneal mesothelioma in the testes, and glioma in the brain were observed (see Table 3-4). 

The fact that the increased incidence of mononuclear cell leukemia was statistically significant in 

the low-exposure group, but not in the high-exposure group, is probably attributable to the 

increased mortality in the high-exposure group. The increased incidence in just the terminal kill 

rats in the 100-ppm group was statistically significant compared with controls. 

In the bioassay conducted by Snellings et al. (1984), 120 male and 120 female F344 rats 

in each sex and dose group were exposed to EtO via inhalation at concentrations of 0 (2 control 

groups of 120 rats of each sex were used), 10, 33, and 100 ppm for 6 hours per day, 5 days per 

week, for 2 years, with scheduled kills at 6 (10 rats per group), 12 (10 rats per group), and 

18 (20 rats per group) months. Significant decreases in mean body weight were observed in the 

100-ppm exposure group in males and in the 100-ppm and 33-ppm exposure groups in females. 

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Table 3-3. Tumor incidence data in National Toxicology Program Study of 

B6C3F1 mice (NTP, 1987)a 

Gender/tumor type 

EtO concentration 

(time-weighted average)b 

EC10 

(LEC10)

c 

, 

(mg/m3) 

Unit risk 

(0.1/LEC10) 

(per mg/m3 0 ppm )

50 ppm 

(16.3 mg/m3) 

100 ppm 

(32.7 mg/m3) 

Males 

Lung adenomas plus 

Carcinomas 

11/49 19/49 26/49d 6.94 

(4.51) 

2.22 × 10-2 

Females 

Lung adenomas plus 

Carcinomas 

2/44 5/44 22/49e 14.8 

(9.12) 

1.1 × 10-2 

Malignant 

Lymphoma 

9/44 6/44 22/49f 21.1 

(13.9) 

7.18 × 10-3 

Uterine 

Carcinoma 

0/44 1/44 5/49g 32.8 

(23.1) 

4.33 × 10-3 

Mammary 

carcinomah 

1/44 8/44f 6/49 9.69 

(5.35) 

1.87 × 10-2 

a

Incidence data were adjusted by eliminating the animals that died prior to the occurrence of the first tumor or prior
 

to 52 weeks, whichever was earlier.
 b

Adjusted to continuous exposure from experimental exposure conditions of 6 hr/d, 5 d/wk; 1 ppm = 1.83 mg/m3 .
 c

Calculated using Tox_Risk program.
 d

p < 0.01 (pairwise Fisher’s exact test).
 e

p < 0.001 (pairwise Fisher’s exact test). f

p < 0.05 (pairwise Fisher’s exact test). g

p = 0.058 by pairwise Fisher’s exact test compared to concurrent controls; however, uterine carcinomas are rare 

tumors in female B6C3F1 mice, and p < 0.0001 by pairwise Fisher’s exact test compared to the NTP historical
 

control incidence of 1/1,077 for inhalation (air) female B6C3F1 mice fed the NIH-07 diet.
 h

Highest dose was deleted in order to fit a model to the dose-response data.
 

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Table 3-4. Tumor incidence data in Lynch et al. (1984a; 1984b) study of 

male F344 rats 

Tumor type 

Concentration (time-weighted average)a 

EC10 

(LEC10)

b 

, 

(mg/m3) 

Unit risk 

(0.1/LEC10) 

(per mg/m3 0 ppm )

50 ppm 

(19.1 mg/m3) 

100 ppm 

(38.1 mg/m3) 

Splenic 

mononuclear 

cell leukemiac 

24/77 38/79d 30/76 7.11 

(3.94) 

2.54 × 10–2. 

Testicular 

peritoneal 

mesothelioma 

3/78 9/79 21/79e 16.7 

(11.8) 

8.5 × 10–3. 

Brain mixedcell glioma 0/76 2/77 5/79e 65.7 

(37.4) 

2.68 × 10–3. 

a

Adjusted to continuous exposure from experimental exposure conditions of 7 hr/d, 5 d/wk; 1 ppm = 1.83 mg/m3 .
 b

Calculated using Tox_Risk program.
 c

Highest dose deleted while fitting the dose-response data.
 d

p < 0.05 (pairwise Fisher’s exact test).
 e

p < 0.01 (pairwise Fisher’s exact test). 

During the 15th month of exposure, an outbreak of viral sialodacryoadenitis occurred, resulting in 

the deaths of 1–5 animals per group. Snellings et al. (1984) claim that it is unlikely that the viral 

outbreak contributed to the EtO-associated tumor findings. After the outbreak, mortality rates 

returned to preoutbreak levels and were similar for all groups until the 20th or 21st month, when 

cumulative mortality in the 33-ppm and 100-ppm exposure groups of each sex remained above 

control values. By the 22nd or 23rd months, mortality was statistically significantly increased in 

the 100-ppm exposure groups of both sexes. 

In males, concentration-dependent increases in the incidence of mononuclear cell 

leukemia in the spleen and peritoneal mesothelioma in the testes were observed, and in females 

an increase in mononuclear cell leukemia in the spleen was seen. These data are summarized in 

Table 3-5. Note that these investigators observed the same types of tumors (splenic leukemia 

and peritoneal mesothelioma) seen by Lynch et al. (1984a); Lynch et al. (1984b). Snellings et al. 

(1984) only report incidences (of incidental and nonincidental primary tumors for all exposure 

groups) for the 24-month (terminal) kill. However, in their paper they state that significant 

findings for the mononuclear cell leukemias were also obtained when all rats were included and 

that a mortality-adjusted trend analysis yielded positive findings for the EtO-exposed females 

(p < 0.005) and males (p < 0.05). Similarly, Snellings et al. (1984) report that when male rats 

with unscheduled deaths were included in the analysis of peritoneal mesotheliomas, it appeared 

that EtO exposure was associated with earlier tumor occurrence, and a mortality-adjusted trend 

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analysis yielded a significant positive trend (p < 0.005). In later publications describing brain 

tumors (Garman et al., 1986, 1985), both males and females had a concentration-dependent 

increased incidence of brain tumors (see Table 3-5). Garman et al. (1986, 1985) report 

incidences including all rats from the 18- and 24-month kills and all rats found dead or killed 

moribund. The earliest brain tumors were observed in rats killed at 18 months. 

3.2.1. Conclusions Regarding the Evidence of Cancer in Laboratory Animals 

In conclusion, EtO causes cancer in laboratory animals. After inhalation exposure to 

EtO, statistically significant increased incidences of cancer have been observed in both rats and 

mice, in both males and females, and in multiple tissues (lung, mammary gland, uterus, 

lymphoid cells, brain, tunica vaginalis testis). In addition, one oral study in rats has been 

conducted, and a significant dose-dependent increase in carcinomas of the forestomach was 

reported. 

3.3. SUPPORTING EVIDENCE 

3.3.1. Metabolism and Kinetics 

Information on the kinetics and metabolism of EtO has been derived primarily from 

studies conducted with laboratory animals exposed via inhalation, although some limited data 

from humans have been identified. Details are available in several reviews (Fennell and Brown, 

2001; Csanady et al., 2000; Brown et al., 1998; Brown et al., 1996). 

Following inhalation, EtO is absorbed efficiently into the blood and rapidly distributed to 

all organs and tissues. EtO is metabolized primarily by two pathways (see Figure 3-1): 

(1) hydrolysis to ethylene glycol (1,2-ethanediol), with subsequent conversion to oxalic acid, 

formic acid, and carbon dioxide; and (2) glutathione conjugation and the formation of 

S-(2-hydroxyethyl)cysteine and N-acetylated derivatives (WHO, 2003). From the available data, 

the route involving conjugation with glutathione appears to predominate in mice; in larger 

species (including humans), the conversion of EtO is primarily via hydrolysis through ethylene 

glycol. Because EtO is an epoxide capable of reacting directly with cellular macromolecules, 

both pathways are considered to be detoxifying. 

Among rodent species, there are clear quantitative differences in metabolic rates. The 

rate of clearance of EtO from the blood, brain, muscle, and testes was measured by Brown et al. 

(1998); Brown et al. (1996). Clearance rates were nearly identical across blood and other tissues. 

Following a 4-hour inhalation exposure to 100 ppm EtO in mice and rats, the average blood 

elimination half-lives ranged from 2.4 to 3.2 minutes in mice and 11 to 14 minutes in rats. The 
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Table 3-5. Tumor incidence data in Snellings et al. (1984) and Garman et al. (1985) reports on F344 rats a 

Gender/tumor type 

Concentration (time-weighted average)b 

EC10 

(LEC10)

d 

(mg/m3) 

Unit risk (0.1/LEC10) 

(per mg/m3 0 ppm ) c 

10 ppm 

(3.27 mg/m3) 

33 ppm 

(10.8 mg/m3) 

100 ppm 

(32.7 mg/m3) 

Males 

Splenic mononuclear cell 

leukemia 

13/97 

(13%)e 

9/51 

(18%) 

12/39f 

(32%) 

9/30f 

(30%) 

12.3 

(6.43) 

1.56 × 10-2 

Testicular peritoneal 

mesothelioma 

2/97 

(2.1%) 

2/51 

(3.9%) 

4/39 

(10%) 

4/30f 

(13%) 

22.3 

(11.6) 

8.66 × 10-3 

Primary brain tumors 1/181 

(0.55%) 

1/92 

(1.1%) 

5/85f 

(5.9%) 

7/87g 

(8.1%) 

36.1 

(22.3) 

4.5 × 10-3 

Females 

Splenic mononuclear cell 

leukemia 

11/116 

(9.5%) 

11/54f 

(21%) 

14/48g 

(30%) 

15/26h 

(58%) 

4.46 

(3.1) 

3.23 × 10-2 

Primary brain tumors 1/188 

(0.53%) 

1/94 

(1.1%) 

3/92 

(3.3%) 

4/80f 

(5%) 

63.8 

(32.6) 

3.07 × 10-3 

a

Denominators refer to the number of animals for which histopathological diagnosis was performed. For brain tumors Garman et al. (1985) included animals in
 

the 18-month and the 24-month sacrifice and found dead or euthanized moribund of those alive at the time of the first brain tumor, whereas for the other sites
 

Snellings et al. (1984) included animals only at the 24-month sacrifice. b

Adjusted to continuous exposure from experimental exposure conditions of 6 hr/d, 5 d/wk; 1 ppm = 1.83 mg/m3 .
 c

Results for both control groups combined. d

Using Tox_Risk program.
 e

Numbers in parentheses indicate percentage incidence values.
 f

p < 0.05 (pairwise Fisher’s exact test).
 g

p < 0.01 (pairwise Fisher’s exact test).
 h

p < 0.001 (pairwise Fisher’s exact test).
 
 

 

O 

CH2 CH2 

Ethylene oxide 

Glutathione 

transferase 

GSCH2CH2OH 

S-2-(hydroxyethyl-glutathione) 

CYS-CH2CH2OH
 

S-2-(hydroxyethyl)cysteine
 

N-acethyl-S-(2 -hydroxyethyl) 

cysteine 

Epoxide
 

hydrolase?
 

HOCH2CH2OH 

1,2 -ethanediol 

HOCH2CHO 

hydroxyacetaldehyde 

HOCH2CO2H 

Glycolic acid 

OHCCO2H 

Glyoxylic acid 

HCO2H CO2HCO2H 

Formic acid Oxalic acid 

+ 

CO2 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

Figure 3-1. Metabolism of ethylene oxide. 

elimination half-life in humans is 42 minutes (Filser et al., 1992), and the half-life in salt water is 

4 days (IARC, 1994b). 

In a more detailed study in mice, Brown et al. (1998) measured EtO concentrations in 

mice after 4-hour inhalation exposures at 0, 50, 100, 200, 300, or 400 ppm. They found that 

blood EtO concentration increased linearly with inhaled concentrations of less than 200 ppm, but 

above 200 ppm the blood concentration increased more rapidly. In addition, glutathione levels in 

liver, lung, kidney, and testes decreased as exposures increased above 200 ppm. The 

investigators interpreted this, along with other information, to mean that at low concentrations 

the metabolism and disappearance of EtO is primarily a result of glutathione conjugation, but at 

higher concentrations, when tissue glutathione begins to be depleted, the elimination occurs via a 

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slower nonenzymatic hydrolysis process, leading to a greater-than-linear increase in blood EtO
 

concentration. 

Fennell and Brown (2001) constructed physiologically based pharmacokinetic (PBPK)
 

models of uptake and metabolism in mice, rats, and humans, based on previous studies. They
 

reported that the models adequately predicted blood and tissue EtO concentrations in rats and 

mice, with the exception of the testes, and blood EtO concentrations in humans. Modeling 

6-hour inhalation exposures yielded simulated blood peak concentrations and areas under the 

curve (AUCs) that are similar for mice, rats, and humans (human levels are within about 15% of 

rat and mouse levels; see Figure 3-2). In other words, exposure to a given EtO concentration in 

air results in similar predicted blood EtO AUCs for mice, rats, and humans. 

These studies show that tissue concentrations in mice, rats, and humans exposed to a 

particular air concentration of EtO are approximately equal and that they are linearly related to 

inhalation concentration, at least in the range of exposures used in the rodent cancer bioassays 

(i.e., 100 ppm and below). 

3.3.2. Protein Adducts 

EtO forms DNA (see Section 3.3.3.1) and hemoglobin adducts within tissues throughout 

the body (Walker et al., 1992a; Walker et al., 1992b). Formation of hemoglobin adducts has 

been used as a measure of exposure to EtO. The main sites of alkylation are cysteine, histidine, 

and the N-terminal valine; however, for analytical reasons, the N-(2-hydroxyethyl)valine adduct 

is generally preferred for measurements (Walker et al., 1990). Walker et al. (1992b) reported 

measurements of this hemoglobin adduct and showed how the concentration of the adducts 

changes according to the dynamics of red blood cell turnover. Walker et al. (1992b) measured 

hemoglobin adduct formation in mice and rats exposed to 0, 3, 10, 33, 100, and 300 (rats only) 

ppm of EtO (6 hours/day, 5 days/week, for 4 weeks). Response was linear in both species up to 

33 ppm, after which the slope significantly increased. The exposure-related decrease in 

glutathione concentration in liver, lung, and other tissues observed by Brown et al. (1998) in 

mice is a plausible explanation for the increasing rate of hemoglobin adduct formation at higher 

exposures. 

In humans, hemoglobin adducts can be used as biomarkers of recent exposure to EtO 

(IARC, 2008a; Boogaard, 2002; IARC, 1994b), and several studies have reported 

exposure-response relationships between hemoglobin adduct levels and EtO exposure levels 

(e.g., van Sittert et al., 1993; Schulte et al., 1992). Hemoglobin adducts are good general 

indicators of exposure because they are stable (DNA adducts, on the other hand, may be repaired 

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0 

1 

2 

3 

4 

5 

6 

7 

EtO blood AUC (mg*h/L) 

mouse 

rat1 

rat2 

human 

0 20 40 60 80 100 120 

EtO exposure concentration (ppm) 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

Figure 3-2. Simulated blood AUCs for EtO following a 6-hour exposure to 

EtO from the rat, mouse, and human PBPK models of Fennell and Brown 

(2001); based on data presented in Fennell and Brown (2001). (Rat1 and 

rat2 results use different values for pulmonary uptake.) 

or fixed as mutations and hence are less reliable measures of exposure). However, Föst et al. 

(1991) noted that human erythrocytes showed marked interindividual differences in the amounts 

of EtO bound to hemoglobin, and Yong et al. (2001) reported that levels of 

N-(2-hydroxyethyl)valine were approximately twofold greater in persons with a GSTT1-null 

genotype than in those with positive genotypes. Endogenous ethylene oxide (see 

Section 3.3.3.1) also contributes to hemoglobin adduct levels, making it more difficult to detect 

the impacts of low levels of exogenous EtO exposure. In addition, Walker et al. (1993) reported 

that hemoglobin adducts in mice and rats were lost at a greater rate than would be predicted by 

the erythrocyte life span. 
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3.3.3. Genotoxicity 

Since the first report of EtO induction of sex-linked recessive lethals in Drosophila 

(Rapoport, 1948), numerous papers have been published on the positive genotoxic activity in 

biological systems, spanning the whole range of assay systems, from bacteriophage to higher 

plants and animals. Figure 3-3 shows the 203 test entries in the EPA Genetic Activity Profile 

database in 2001. In prokaryotes and lower eukaryotes, EtO induced DNA damage and gene 

mutations in bacteria, yeast, and fungi and gene conversions in yeast. In mammalian cells (from 

in vitro and/or in vivo exposures), EtO-induced effects include unscheduled DNA synthesis, 

gene mutations, sister chromatid exchanges (SCEs), micronuclei, and chromosomal aberrations. 

Genotoxicity, in particular increased levels of SCEs and chromosomal aberrations, has also been 

observed in blood cells of workers occupationally exposed to EtO. Several publications contain 

details of earlier genetic toxicity studies (e.g., IARC, 2008b; Kolman et al., 2002; Thier and Bolt, 

2000; Natarajan et al., 1995; Preston et al., 1995; IARC, 1994b; Dellarco et al., 1990; Ehrenberg 

and Hussain, 1981). This review briefly summarizes the evidence of the genotoxic potential of 

EtO, focusing primarily on recently published studies that provide information on the mode of 

action of EtO (see Appendix C for more details from some individual studies). 

3.3.3.1. DNA Adducts 

EtO is a direct-acting SN2 (substitution-nucleophilic-bimolecular)-type monofunctional 

alkylating agent that forms adducts with cellular macromolecules such as proteins (e.g., 

hemoglobin, see Section 3.3.2) and DNA (Pauwels and Veulemans, 1998). Alkylating agents 

may produce a variety of different DNA alkylation products (Beranek, 1990) in varying 

proportions, depending primarily on the electrophilic properties of the agent. Reactivity of an 

alkylating agent is estimated by its Swain-Scott substrate constant (s-value), which ranges from 

0 to 1, and EtO has a high s-value of 0.96 (Beranek, 1990; Golberg, 1986; Warwick, 1963). 

Acting by the SN2 mechanism and having a high substrate constant both favor alkylation at the 

N7 position of guanine in the DNA (Walker et al., 1990). The predominant DNA adduct formed 

by EtO and other SN2-type alkylating agents is N7-(2-hydroxyethyl)guanine (N7-HEG). After in 

vitro treatment of DNA with EtO, Segerbäck (1990) identified three adducts, N7-HEG, 

N3-hydroxyethyladenine, and O6- hydroxyethylguanine (O6

-HEG), in the ratios 200:8.8:1; two 

other peaks, suspected of representing other adenine adducts, were also observed at levels well 

below that of N7-HEG. 

Ethylene, an endogenous precursor of EtO, is produced during normal physiological 

processes. Such processes reportedly include oxidation of methionine and hemoglobin, lipid 
 

 

 

 

ETHYLENE OXIDE 

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Figure 3-3. Display of 203 data sets, including bacteria, fungi, plants, insects, 

and mammals (in vitro and in vivo), measuring the full range of genotoxic
 

endpoints. (This is an updated version of the figure in IARC, 1994b)

See Appendix B for list of references.
 

peroxidation of fatty acids, and metabolism of intestinal bacteria (reviewed in Thier and Bolt, 

2000; IARC, 1994a). EtO is then endogenously produced through the cytochrome 

P450-mediated conversion of ethylene (Törnqvist, 1996). This endogenous production of EtO 

contributes significantly to background levels of DNA adducts, making it difficult to detect the 

impacts of low levels of exogenous EtO exposure on DNA adduct levels. For example, in DNA 

extracted from the lymphocytes of unexposed individuals, mean background levels of N7-HEG 

ranged from 2 to 8.5 pmol/mg DNA (Bolt, 1996). Using sensitive detection techniques and an 

approach designed to separately quantify both endogenous N7-HEG adducts and “exogenous” 

N7-HEG adducts induced by EtO treatment in rats, Marsden et al. (2009) reported increases in 

exogenous adducts in DNA of spleen and liver consistent with a linear dose-response 

relationship (p < 0.05), down to the lowest dose administered (0.0001 mg/kg injected i.p. daily 

This document is a draft for review purposes only and does not constitute Agency policy. 

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for 3 days). Note that the whole range of doses studied by Marsden et al. (2009) lies well below 

the dose corresponding to the lowest LOAEL from an EtO cancer bioassay (see Section C.7 of 

Appendix C). Marsden et al. (2009) also observed increases in endogenous N7-HEG adduct 

formation at the two highest doses (0.05 and 0.1 mg/kg), suggesting that, in addition to direct 

adduct formation via alkylation, EtO can induce N7-HEG adduct production indirectly. 

(Marsden et al., 2009) hypothesized that this indirect adduct formation by EtO results from the 

induction of ethylene generation under conditions of oxidative stress. 

In experiments with rats and mice exposed to EtO at concentrations of 0, 3, 10, 33, 100, 

or 300 (rats only) ppm for 6 hours per day, 5 days per week, for 4 weeks, Walker et al. (1992a) 

Walker et al. (1992a) measured N7-HEG adducts in the DNA of lung, brain, kidney, spleen, 

liver, and testes. At 100 ppm, the adduct levels for all tissues except testis were similar (within a 

factor of 3), despite the fact that not all of these tissues are targets for toxicity. The study’s data 

on the persistence of the DNA adducts indicate that DNA repair rates differ in different tissues. 

Although Walker et al. (1992a) suggested that N7-HEG adducts are likely to be removed by 

depurination forming apurinic/apyrimidinic (AP) sites in DNA, a later study from the same 

group showed that EtO-induced DNA damage is repaired without accumulation of AP sites or 

involving base excision repair (Rusyn et al., 2005). Rats exposed to high doses of EtO (300 

ppm) by inhalation showed steady-state levels of O6-HEG adducts that are ~250–300 times 

lower than the N7-HEG levels (Walker et al., 1992a). Even though low levels of O6-HEG 

adducts were detected, they are more mutagenic in nature and may contribute to the tumors 

observed in target organs. 

Two studies provide evidence of N7-HEG DNA adduct formation in human populations 

occupationally exposed to EtO, one reporting a modest increase in white blood cells (van Delft et 

al., 1994) and the other a four- to fivefold increase in granulocytes (Yong et al., 2007) compared 

to unexposed controls. However, these differences were not statistically significant due to high 

interindividual variation in adduct levels. 

3.3.3.2. Point Mutations 

EtO has consistently yielded positive results in in vitro mutation assays from 

bacteriophage, bacteria, fungi, yeast, insects, plants, and mammalian cell cultures (including 

human cells). For example, EtO induces single base pair deletions and base substitutions in the 

HPRT gene in human diploid fibroblasts (Kolman and Chovanec, 2000; Lambert et al., 1994; 

Bastlová et al., 1993) in vitro. The results of in vivo studies on the mutagenicity of EtO have 

also been consistently positive following ingestion, inhalation, or injection (e.g., Tates et al., 

1999). Increases in the frequency of gene mutations in T-lymphocytes (Hprt locus) (Walker et 
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al., 1997) and in bone marrow and testes (LacI locus) (Recio et al., 2004) have been observed in 

transgenic mice exposed to EtO via inhalation at concentrations similar to those in 

carcinogenesis bioassays with this species (NTP, 1987). At somewhat higher concentrations 

than those used in the carcinogenesis bioassays (200 ppm, but for only 4 weeks), increases in the 

frequency of gene mutations have also been observed in the lungs of transgenic mice (LacI 

locus) (Sisk et al., 1997) and in T-lymphocytes of rats (Hprt locus) (van Sittert et al., 2000; Tates 

et al., 1999). In in vivo studies with male mice, EtO also causes heritable mutations and other 

effects in germ cells (Generoso et al., 1990; Lewis et al., 1986). 

In a study of mammary gland carcinomas in EtO-exposed B6C3F1 mice from the 1987 

NTP bioassay (NTP, 1987) and 19 mammary gland carcinomas from concurrent controls in the 

1987 NTP EtO bioassay and a 1986 NTP benzene bioassay, Houle et al. (2006) measured 

mutation frequencies in exons 5−8 of the p53 tumor suppressor gene and in codon 61 of the Hras 

oncogene. Mutation frequencies in the mammary carcinomas of EtO-exposed mice were only 

slightly increased over frequencies in spontaneous mammary carcinomas (33% of the 

carcinomas in the EtO-exposed mice had Hras mutations versus 26% of spontaneous tumors; 

67% of the carcinomas in the EtO-exposed mice had p53 mutations versus 58% of spontaneous 

tumors); however, the EtO-induced tumors exhibited a distinct shift in the mutational spectra of 

the p53 and Hras genes and more commonly displayed concurrent mutations of the two genes 

(Houle et al., 2006). Furthermore, Houle et al. (2006) detected about sixfold higher levels of p53 

protein expression in the mammary carcinomas of EtO-exposed mice than in spontaneous 

mammary carcinomas, and there was an apparent dose-response relationship between EtO 

exposure level and both p53 protein expression and p53 gene mutation (three of the seven tumors 

in the 50-ppm exposure group and all five tumors in the 100-ppm group had increased protein 

expression; also, three p53 gene mutations were found in the seven tumors in the 50-ppm 

exposure group and nine were found in the five tumors in the 100-ppm group). Some of the 

same investigators conducted a similar study of Kras mutations in lung, Harderian gland, and 

uterine tumors (Hong et al., 2007). Substantial increases were observed in Kras mutation 

frequencies in the tumors from the EtO-exposed mice. Kras mutations were reported in 100% of 

the lung tumors from EtO-exposed mice versus 25% of spontaneous lung tumors (108 NTP 

control animal tumors, including 8 from the EtO bioassay), in 86% of Harderian gland tumors 

from EtO-exposed mice versus 7% of spontaneous Harderian gland tumors (27 NTP control 

animal tumors, including 2 from the EtO bioassay), and in 83% of uterine tumors from 

EtO-exposed mice (there were no uterine tumors in control mice in the 1986 NTP bioassay and 

none were examined from other control animals). Furthermore, a specific Kras mutation, a 

G → T transversion in codon 12, was nearly universal in lung tumors from EtO-exposed mice 
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(21/23) but rare in lung tumors from control animals (1/108). Other specific mutations were also 

predominant in the Harderian gland and uterine tumors, but too few Kras mutations were 

available in spontaneous Harderian gland tumors, and no spontaneous uterine tumors were 

examined; thus, meaningful comparisons could not be made for these sites. Overall, these data 

strongly suggest that EtO-induced mutations in oncogenes and tumor-supressor genes play a role 

in EtO-induced carcinogenesis in multiple tissues. 

Only a few studies have investigated gene mutations in people occupationally exposed to 

EtO. In one study, HPRT mutant frequency in peripheral blood lymphocytes was measured in a 

group of 9 EtO-exposed hospital workers, a group of 15 EtO-exposed factory workers, and their 

respective controls (Tates et al., 1991). EtO exposure scenarios suggest higher exposures in the 

factory workers, and this is supported by the measurement of higher hemoglobin adduct levels in 

those workers. HPRT mutant frequencies were 55% increased in the hospital workers, but the 

increase was not statistically significant. In the factory workers, a statistically significant 

increase of 60% was reported. In a study of workers in an EtO production facility (Tates et al., 

1995), HPRT mutations were measured in three exposed groups and one unexposed group (seven 

workers per group). No significant differences in mutant frequencies were observed between the 

groups; however, the authors stated that about 50 subjects per group would have been needed to 

detect a 50% increase. 

Major et al. (2001) measured HPRT mutations in female nurses employed in hospitals in 

Eger and Budapest, Hungary. This study and an earlier study measuring effects on chromosomes 

(see Table 3-6) were conducted to examine a possible causal relationship between EtO exposure 

and a cluster of cancers (mostly breast) in nurses exposed to EtO in the Eger hospital. The 

Budapest hospital was chosen because there was no apparent increase in cancer among nurses 

exposed to EtO. Controls were female hospital workers in the respective cities, and nurses in 

Eger with known cancers were excluded. Mean peak levels of EtO were 5 mg/m3

 (2.7 ppm) in 

Budapest and 10 mg/m3

 (5.4 ppm) in Eger. HPRT variant frequencies in both controls and 

EtO-exposed workers in the Eger hospital were higher than either group in the Budapest hospital, 

but there was no significant increase among the EtO-exposed workers in either hospital when 

compared with the respective controls. The authors noted that the HPRT variant frequencies 

among smoking EtO-exposed nurses in Eger were significantly higher than among smokers in 

the Eger controls; however, the fact that the HPRT variant frequency was almost three times 

higher in nonsmokers than in smokers in the Eger hospital control group raises questions about 

the basis of the claimed EtO effect. 
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Table 3-6. Cytogenetic effects in humans 

Number exposed 

(number of controls) 

Exposure time 

(years) Ethylene oxide level in air (ppm)a Cytogenetic observations 

Range Mean Range Mean (TWA) CA SCE MN Reference 

33 (0) 1–14 ±0.05–8 ±0.01b (+) (Clare et al., 1985) 

Site I: 13 

Site II: 22 

Site III: 25–26 

(171 total) 

0.5c 

5–10c 

5–20c 

– 

– 

+ 

– 

+ 

+ 

(Stolley et al., 1984) 

(Galloway et al., 1986) 

12 (12) ±36 + (Garry et al., 1979) 

14 (14) <0.07–4.3c – (Hansen et al., 1984) 

Factory I: 18 

Factory II: l0 

(20 total) 

0.5–8 

0.5–8 

3.2 

1.7 

<1 

<1 

+ 

+ 

– 

– 

+d (Högstedt et al., 1983) 

15 smokers (7) 

10 nonsmokers (15) 

0.5–10 

0.5–10 

5.7 

4.5 

20–123 

20–123 

+ 

+ 

(Laurent et al., 1984) 

10 (10) 3 60–69c + + (Lerda and Rizzi, 1992) 

Low dose: 9 (48) 

High dose: 27 (10) 

4 

15 

2.7–10.9 

2.7–82 

2.7 

5.5 

+ 

+ 

– 

+ 

(Major et al., 1996) 

34 (23) 8e <0.1–2.4c <0.3 – + (Mayer et al., 1991) 

11 smokers 

14 nonsmokers 

(10 total) 

0.5–4l7f 

0.5–208f 

– 

– 

(Popp et al., 1994) 

75 (22) 3–14 7 2–5c + + (Ribeiro et al., 1994) 

56 (141) 1–10 1–40c + + (Richmond et al., 1985) 
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Table 3-6. Cytogenetic effects in humans (continued) 

Number exposed 

(number of controls) 

Exposure time 

(years) Ethylene oxide level in air (ppm)a Cytogenetic observations 

Range Mean Range Mean (TWA) CA SCE MN Reference 

22 (22) 

19 (19) 

0.6–4 

1.5–15 

3 

6.8 

0.2–0.5c 

3.7–20c 

0.35 

10.7 

(+) 

+ 

+ 

+ 

(Sarto et al., 1984) 

10 (10) 0–9.3c 1.84 + (Sarto et al., 1987) 

9 

3 

(27 total) 

0.5–12 5 0.025–0.38c 

>0.38g 

– 

+h 

(Sarto et al., 1990) 

5 

5 

(10 total) 

0.1–4 

4–12 

2 

8.6 <1–4.4 

0.025 

0.38 

– 

+ 

–i 

–i 

(Sarto et al., 1991) 

32 

11 

(8 total) 

5.1 

9.5 

0–0.3c 

0.1 3–0.3c 

0.04 

0.16 

+ 

+ 

– 

– 

(Schulte et al., 1992) 

9 hospital workers (8) 

15 factory workers (15) 

2–6 

3–27 

4 

12 

20–25 

17–33 

+ 

+ 

+ 

+ 

– 

+ 

(Tates et al., 1991) 

7 

7 

7 

(7 total) 

Accidental 

<5 

>15 

28–429c 

<0.005–0.02 

<0.005–0.01 

– 

– 

– 

– 

– 

– 

(Tates et al., 1995) 

Low exposure: 9 

High exposure: 5 

(13 total) 

13j 

501j 

– 

+ 

(Yager et al., 1983) 

19 

17 

(35 total) 

1–5 

6–14 

<0.05–8 

<0.05–8 

<0.05 

<0.05 

– 

– 

(Van Sittert and de Jong, 

1985) 
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Table 3-6. Cytogenetic effects in humans (continued) 

a

1 ppm = 1.83 mg ethylene oxide/m3 .
 b

Calculated by linear extrapolation.
 c

TWA (8-hr). d

Positive for erythroblasts and polychromatic erythrocytes (negative for lymphocytes).
 e

Maximum years exposed.
 f

Peak concentrations.
 g

Exposed acutely from sterilizer leakage in addition to chronic exposure.

h

Nasal mucosa.
 i

Buccal cells.
 j

Average 6-month cumulative exposure (mg).
 

CA = chromosomal aberrations
 

MN = micronucleus
 

SCE = sister chromatid exchange
 

TWA = time-weighted average
 
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3.3.3.3. Chromosomal Effects 

As discussed by Preston (1999) in an extensive review of the cytogenetic effects of EtO, 

a variety of cytogenetic assays can be used to measure induced chromosome damage. However, 

most of the assays commonly employed measure events that are detectable only in the first (or in 

some cases the second) metaphase after exposure and require DNA synthesis to convert DNA 

damage into a chromosomal aberration. In addition, DNA repair is operating in peripheral 

lymphocytes to repair induced DNA damage. Thus, for acute exposures, the timing of sampling 

is of great importance. For chronic studies, the endpoints measure only the most recent 

exposures, and if the time between last exposure and sampling is long, any induced DNA 

damage not converted to a stable genotoxic alteration is certain to be missed. The events 

measured include all types of chromosomal aberrations, micronuclei, SCE, and numerical 

chromosomal changes. Stable chromosomal aberrations include reciprocal translocations, 

inversions, and some fraction of insertions and deletions as well as some numerical changes. 

However, until the development of fluorescent in situ hybridization (FISH), chromosome 

banding techniques were needed to detect these types of aberrations. 

In in vitro assays, EtO has consistently tested positive in studies for multiple types of 

chromosomal effects, including DNA strand breaks, SCEs, micronuclei, and chromosomal 

aberrations (e.g., see Table 11 of IARC, 2008a). Of note, Adám et al. (2005) measured the 

sensitivity of different human cell types to EtO-induced DNA damage using the comet assay, 

which measures direct strand breaks and/or DNA damage converted to strand breaks during 

alkaline treatment. Adám et al. (2005) reported dose-dependent increases in DNA damage in the 

concentration range 0–100 μM in each of the cell types examined with no notable cytotoxicity. 

At the lowest concentration reported (20 μM), significant increases in DNA damage were 

observed in lymphoblasts, lymphocytes, and breast epithelial cells, but not in keratinocytes or 

cervical epithelial cells, suggesting that breast epithelial cells may have increased sensitivity to 

EtO-induced genotoxicity compared to other nonlymphohematopoietic cell types. In addition, 

Godderis et al. (2006) investigated the effects of genetic polymorphisms on DNA damage 

induced by EtO in peripheral blood lymphocytes of 20 nonsmoking university students. No 

significant increases in micronuclei were observed following EtO treatment; however, 

dose-related increases in DNA strand breaks were seen in the comet assay. GST polymorphisms 

did not have a significant impact on the EtO-induced effects; however, significant increases in 

DNA strand breaks were associated with low-activity alleles of two DNA repair enzymes 

compared to wild-type alleles. 

In vivo, several inhalation studies in laboratory animals have demonstrated that EtO 

exposure levels in the range of those used in the rodent bioassays induce SCEs (see Table 11 of 
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IARC, 2008a); however, evidence for micronuclei and chromosomal aberrations from these 

same exposure levels is less consistent. In particular, studies by van Sittert et al. (2000) and 

Lorenti Garcia et al. (2001) observed increases in micronuclei and chromosomal aberrations in 

splenic lymphocytes of rats exposed to 50, 100, or 200 ppm EtO for 6 hours/day, 5 days/week, 

for 4 weeks compared to levels from control rats, but the increases were not statistically 

significant. IARC (2008a) noted, however, that “strong conclusions cannot be drawn” from 

these two studies because the cytogenetic analyses “were initiated 5 days after the final day of 

exposure, a suboptimal time, and the power of the (FISH) studies were limited by analysis of 

only a single chromosome and the small numbers of rats per group examined,” which was 3 per 

exposure group in both of the studies, although numerous cells/rat were examined. Moreover, a 

recent study by Donner et al. (2010) showed clear, statistically significant increases in 

chromosomal aberrations with longer durations of exposure (≥12 weeks) to the concentration 

levels used in the rodent bioassays. 

In humans, various studies of occupationally exposed workers have reported SCEs and 

other chromosomal effects associated with EtO exposure, including micronuclei and 

chromosomal aberrations. The genotoxicity of EtO was demonstrated in humans as early as 

1979. Table 3-6 summarizes the cytogenetic effects of EtO on human exposures (see also 

Appendix C for more details on some of the studies). 

As illustrated in Table 3-6, numerous studies observed increased SCEs in occupationally 

exposed workers, especially for workers with the highest exposures (e.g., Major et al., 1996; 

Sarto et al., 1991; Tates et al., 1991; Sarto et al., 1987). Several studies of occupationally 

exposed workers have also reported increased micronucleus formation in lymphocytes (Ribeiro 

et al., 1994; Tates et al., 1991), in nasal mucosal cells (Sarto et al., 1990), and in bone marrow 

cells (Högstedt et al., 1983), although this endpoint seems to be less sensitive than SCEs. An 

association between increased micronucleus frequency and cancer risk has been reported in at 

least one large prospective general population study (Bonassi et al., 2007). In addition, 

chromosomal aberrations have been reported in multiple studies of workers occupationally 

exposed to EtO (Ribeiro et al., 1994; Tates et al., 1991; Sarto et al., 1987). Chromosomal 

aberrations have been linked to an increased risk of cancer in several large prospective general 

population studies (e.g., Boffetta et al., 2007; Rossner et al., 2005; Hagmar et al., 2004; Liou et 

al., 1999). 
 

 

 

 

 

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3.3.3.4. Summary 

The available data from in vitro studies, laboratory animal models, and epidemiological 

studies establish that EtO is a mutagenic and genotoxic agent that causes a variety of types of 

genetic damage. 

3.4. MODE OF ACTION 

EtO is an alkylating agent that has consistently been found to produce numerous 

genotoxic effects in a variety of biological systems ranging from bacteriophage to occupationally 

exposed humans. It is carcinogenic in mice and rats, inducing tumors of the 

lymphohematopoietic system, brain, lung, connective tissues, uterus, and mammary gland. In 

addition, epidemiological studies have shown an increased risk of various types of human 

cancers (see Table A-5 in Appendix A), in particular lymphohematopoietic and breast cancers. 

Target tissues for EtO carcinogenicity in laboratory animals are varied, and the cancers are not 

clearly attributable to any specific type of genetic alteration. Although the precise mechanisms 

by which the multisite carcinogenicity in mice, rats, and humans occurs are unknown, EtO is 

clearly a mutagenic and genotoxic agent, as discussed in Section 3.3.3, and mutagenicity and 

genotoxicity are well established as playing a key role in carcinogenicity. 

Exposure of cells to DNA-reactive agents results in the formation of carcinogen-DNA 

adducts. The formation of DNA adducts results from a sequence of events involving absorption 

of the agent, distribution to different tissues, and accessibility of the molecular target (Swenberg 

et al., 1990). Alkylating agents may induce several different DNA alkylation products (Beranek, 

1990) with varying proportions, depending primarily on the electrophilic properties of the agent. 

The predominant DNA adduct formed by EtO is N7-HEG, although other adducts, such as 

N3-hydroxyethyladenine and O6

-HEG, have also been observed, in much lesser amounts 

(Segerbäck, 1990). In addition to direct DNA adduct formation via alkylation, Marsden et al. 

(2009) observed an indirect effect of EtO exposure on endogenous N7-HEG adduct formation 

and hypothesized that EtO could also indirectly cause adduct formation via oxidative stress (see 

also Section 3.3.3.1 and Appendix C). The various adducts are processed by different repair 

pathways, and the subsequent genotoxic responses elicited by unrepaired DNA adducts are 

dependent on a wide range of variables. The specific adduct(s) responsible for EtO-induced 

genotoxicity and the mechanism(s) by which this adduct(s) induces the genotoxic damage are 

unknown. 

It had been postulated that the predominant EtO-DNA adduct, N7-HEG, although 

unlikely to be directly promutagenic, could be subject to depurination, resulting in an apurinic 

site which could be vulnerable to miscoding during cell replication (e.g., Walker and Skopek, 

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1993). However, in a study designed to test this hypothesis, Rusyn et al. (2005) failed to detect 

an accumulation of abasic sites in brain, spleen, and liver tissues of rats exposed to EtO. Rusyn 

et al. (2005) conclude that the accumulation of abasic sites is unlikely to be a primary 

mechanism for EtO mutagenicity, although they note that it is also possible that their assay was 

not sufficiently sensitive to detect small increases in abasic sites or that abasic sites are only 

mutagenic under conditions of rapid cell turnover, when cell replication may occur before repair 

of the abasic site (the tissues examined in their study were relatively quiescent). Another 

potential mechanism for EtO-induced mutagenicity is the direct mutagenicity of the 

promutagenic adducts such as O6

-HEG, although these adducts are generally considered to occur 

at levels too low to explain all of the observed mutagenicity (IARC, 2008a). In an in vitro study, 

Tompkins et al. (2009) exposed plasmid DNA to a range of EtO concentrations in water and 

reported that only the N7-HEG adduct was detectable after exposure to EtO concentrations up to 

2,000 μM; at higher EtO concentrations (≥10 mM), N1-hydroxyethyladenine and O6-HEG 

adducts were also quantifiable but at much lower levels than the N7-HEG adducts. Tompkins et 

al. (2009) then examined the mutagenicity of these adducts in a supF forward mutation assay and 

reported that the relative mutation frequencies were significantly elevated only for plasmids 

exposed to these higher EtO concentrations (see Appendix C, Sections C.1.2 and C.2.2, for a 

more detailed discussion of this study). 

The events involved in the formation of chromosomal damage by EtO are similarly 

unknown. N-alklylated bases are removed from DNA by base excision repair pathways. A 

review by Memisoglu and Samson (2000) notes that the action of DNA glycosylase and apurinic 

endonuclease creates a DNA single-strand break, which can in turn lead to DNA double-strand 

breaks (DSBs). DSBs can also be produced by normal cellular functions, such as during V(D)J 

recombination in the development of lymphoid cells or topoisomerase II-mediated cleavage at 

defined sites. A review of mechanisms of DSB repair indicates that the molecular mechanisms 

are not fully understood (Pfeiffer et al., 2000). This review provides a thorough discussion of 

both sources (endogenous and exogenous) of DSBs and the variety of repair pathways that have 

evolved to process the breaks. Although homology-directed repair generally restores the original 

sequence, during nonhomologous end-joining, the ends of the breaks are frequently modified by 

addition or deletion of nucleotides. The lack of accumulation of abasic sites observed in the 

Rusyn et al. (2005) study discussed above argues against a mechanism involving abasic sites as 

hot spots for strand breaks, although it is possible that abasic sites accumulate more readily in 

replicating lymphocytes, which were not examined in the study of Rusyn et al. (2005). Another 

postulated mechanism for EtO-induced strand breaks is via the formation of hydroxyethyl 
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adducts on the phosphate backbone of the DNA, but this mechanism requires further study 

(IARC, 2008a). 

Lymphohematopoietic malignancies, like all other cancers, are considered to be a 

consequence of an accumulation of genetic and epigenetic changes involving multiple genes and 

chromosomal alterations. Although it is clear that chromosome translocations are common 

features of some hematopoietic cancers, there is evidence that mutations in p53 or NRAS are 

involved in certain types of leukemia (U.S. EPA, 1997). It should also be noted that 

therapy-related leukemias exhibiting reciprocal translocations are generally only seen in patients 

who have previously been treated with chemotherapeutic agents that act as topoisomerase II 

inhibitors (U.S. EPA, 1997). In NHL, the BCL6 gene is frequently activated by translocations 

(Chaganti et al., 1998) as well as by mutations within the gene coding sequence (Lossos and 

Levy, 2000). Preudhomme et al. (2000) observed point mutations in the AML1 gene in 9 of 

22 patients with the M0 type (minimally differentiated acute myeloblastic leukemia) of acute 

myeloid leukemia (AML), and Harada et al. (2003) identified AML1 point mutations in cases of 

radiation-associated and therapy-related myelodysplastic syndrome (MDS)/AML. In both 

reports, point mutations within the coding sequence were found in patients with normal 

karyotypes as well as some with translocations or other chromosomal abnormalities. 

Zharlyganova et al. (2008) identified AML1 mutations in 7 of 18 radiation-exposed MDS/AML 

patients but in none of 13 unexposed MDS/AML cases. Other point mutations have also been 

identified in therapy-related MDS/AML patients, including p53 gene mutations after exposure to 

alkylating agents (Christiansen et al., 2001) and mutations in RAS and other genes in the receptor 

tyrosine kinase signal transduction pathway (Christiansen et al., 2005). Several models have 

been developed to integrate these various types of genetic alterations. One recent model 

suggests that the pathogenesis of MDS/AML can be subdivided into at least eight genetic 

pathways that have different etiologies and different biologic characteristics (Pedersen-Bjergaard 

et al., 2006). 

A mode-of-action-motivated modeling approach based solely on chromosome 

translocations has been proposed by Kirman et al. (2004). The authors suggested a nonlinear 

dose-response relationship for EtO and leukemia, based on a consideration that “chromosomal 

aberrations are the characteristic initiating events in chemically induced acute leukemia and gene 

mutations are not characteristic initiating events.” They proposed that EtO must be responsible 

for two nearly simultaneous DNA adducts, yielding a dose-squared (quadratic) relationship 

between EtO exposure and leukemia risk. However, as discussed above, there is evidence that 

does not support the assumption that chromosomal aberrations represent the sole initiating event. 

In fact, these aberrations or translocations could be a downstream event resulting from genomic 
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instability. In addition, it is not clear that acute leukemia is the lymphohematopoietic cancer 

subtype associated with EtO exposure; in the large NIOSH study, increases in 

lymphohematopoietic cancer risk were driven by increases in lymphoid cancer subtypes. 

Furthermore, even if two reactions with DNA resulting in chromosomal aberrations or 

translocations are early-occurring events in some EtO-induced lymphohematopoietic cancers, it 

is not necessary that both events be associated with EtO exposure (e.g., background error repair 

rates or exposure to other alkylating agents may be the cause). Moreover, EtO could also 

produce translocations indirectly by forming DNA or protein adducts that affect the normally 

occurring recombination activities of lymphocytes or the repair of spontaneous double-strand 

breaks. Thus, broader mode-of-action considerations were not regarded as supportive of the 

hypothesis that the exposure-response relationship is purely quadratic. 

Breast cancer is similarly considered to be a consequence of an accumulation of genetic 

and epigenetic changes involving multiple genes and chromosomal alterations (Ingvarsson, 

1999). Again, the precise mechanisms by which EtO induces breast cancer are unknown. As 

discussed in Section 3.3.3.2, in a study of mammary gland carcinomas in EtO-exposed mice, 

Houle et al. (2006) noted that the EtO-induced tumors exhibited a distinct shift in the mutational 

spectra of the p53 and Hras genes and more commonly displayed concurrent mutations of the 

two genes. The comet assay results of Adám et al. (2005) suggest that human breast epithelial 

cells may have increased sensitivity to EtO-induced genotoxicity compared to other 

nonlymphohematopoietic cell types (see Section 3.3.3.3); however, the basis for any increased 

sensitivity of breast epithelial cells is similarly unknown. 

In summary, EtO induces a variety of types of genetic damage. It directly interacts with 

DNA, resulting in DNA adducts, gene mutations, and chromosome damage. Depending on a 

number of variables, EtO-induced DNA adducts (1) may be repaired, (2) may result in a 

base-pair mutation during replication, or (3) may be converted to a DSB, which also may be 

repaired or result in unstable (micronuclei) or stable (translocation) cytogenetic damage. All of 

the available data are strongly supportive of a mutagenic mode of action involving gene 

mutations and chromosomal aberrations (translocations, deletions, or inversions) that critically 

alter the function of oncogenes or tumor suppressor genes. Although it is clear that chromosome 

translocations are common features of many hematopoietic cancers, there is evidence that 

mutations in p53, AML1, or Nras are also involved in some leukemias. The current scientific 

consensus is that there is very good correspondence between ability of an agent to cause 

mutations, as does EtO, and carcinogenicity. All of the above scientific evidence provides 

support for a mutagenic mode of action. 
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3.4.1. Analysis of the Mode of Action for Ethylene Oxide Carcinogenicity under EPA’s 

Mode-of-Action Framework 

In this section, the mode of action evidence for EtO carcinogenicity is analyzed under the 

mode of action framework in EPA’s 2005 Guidelines for Carcinogen Risk Assessment (U.S. 

EPA, 2005a, Section 2.4.3). 

The hypothesis is that EtO carcinogenicity has a mutagenic mode of action. This 

hypothesized mode of action is presumed to apply to all of the tumor types. 

The key events in the hypothesized mutagenic mode of action are DNA adduct formation 

by EtO, which is a direct-acting alkylating agent, and the resulting genetic damage, including the 

formation of point mutations as well as chromosomal alterations. Mutagenicity is a well 

established cause of carcinogenicity. 

1. Is the hypothesized mode of action sufficiently supported in the test animals? 

Numerous studies have demonstrated that EtO forms protein and DNA adducts, in mice 

and rats (see Sections 3.3.1 and 3.4 and Figure 3-2). For example, Walker et al. (1992b) and 

Walker et al. (1992a) demonstrated that EtO forms protein adducts with hemoglobin in the blood 

and DNA adducts with tissues throughout the body, including in the lung, brain, kidney, spleen, 

liver, and testes. 

In addition, there is incontrovertible evidence that EtO is mutagenic (see Section 3.3.3). 

The evidence is strong and consistent; EtO has invariably yielded positive results in in vitro 

mutation assays from bacteriophage, bacteria, fungi, yeast, insects, plants, and mammalian cell 

cultures. The results of in vivo studies on the mutagenicity and genotoxicity of EtO have also 

been consistently positive following ingestion, inhalation, or injection. Increases in the 

frequency of gene mutations in the lung, in T-lymphocytes, in bone marrow, and in testes have 

been observed in transgenic mice exposed to EtO via inhalation at concentrations similar to those 

in the mouse carcinogenesis bioassays. Furthermore, in a study of p53 (tumor supressor gene) 

and Hras (oncogene) mutations in mammary gland carcinomas of EtO-exposed and control 

mice, Houle et al. (2006) noted that the EtO-induced tumors exhibited a distinct shift in the 

mutational spectra of the p53 and Hras genes and more commonly displayed concurrent 

mutations of the two genes, and in a similar study of Kras (oncogene) mutations in lung, 

Harderian gland, and uterine tumors, substantial increases were observed in Kras mutation 

frequencies in the tumors from the EtO-exposed mice (Hong et al., 2007). 

Several inhalation studies in laboratory animals have demonstrated that EtO exposure 

levels in the range of those used in the rodent bioassays induce SCEs. Evidence for micronuclei 

and chromosomal aberrations from these same exposure levels has been less consistent; 
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however, IARC (2008a) has noted analytical limitations with some of these analyses (see 

Section 3.3.3.3). Moreover, a recent study by Donner et al. (2010) showed clear, statistically 

significant increases in chromosomal aberrations with exposure durations of ≥12 weeks to the 

concentration levels used in the rodent bioassays. 

Ethylene oxide induces a variety of mutagenic and genotoxic effects, including 

chromosome breaks, micronuclei, SCEs, and gene mutations; however, the more general effect 

of mutagenicity/genotoxicity is specific and occurs in the absence of cytotoxicity or other overt 

toxicity. A temporal relationship is also clearly evident, with adducts and mutagenicity 

observed in subchronic assays. 

Dose-response relationships have been observed between EtO exposure in vivo and 

hemoglobin adducts (e.g., Walker et al., 1992b) , as well as DNA adducts, SCEs, and Hprt 

mutations (e.g., van Sittert et al., 2000) (see also Sections 3.3 and 3.4). A mutagenic mode of 

action for EtO carcinogenicity also clearly comports with notions of biological plausibility and 

coherence because EtO is a direct-acting alkylating agent. Such agents are generally capable of 

forming DNA adducts, which in turn have the potential to cause genetic damage, including 

mutations; and mutagenicity, in its turn, is a well-established cause of carcinogenicity. This 

chain of key events is consistent with current understanding of the biology of cancer. 

In addition to the clear evidence supporting a mutagenic mode of action in test animals, 

there are no compelling alternative or additional hypothesized modes of action for EtO 

carcinogenicity. 

2. Is the hypothesized mode of action relevant to humans? 

The evidence discussed above demonstrates that EtO is a systemic mutagen in test 

animals; thus, there is the presumption that it would also be a mutagen in humans. Moreover, 

there is human evidence directly supporting a mutagenic mode of action for EtO carcinogenicity. 

Several studies of humans have reported exposure-response relationships between hemoglobin 

adduct levels and EtO exposure levels (e.g., van Sittert et al., 1993; Schulte et al., 1992; see 

Section 3.3.2), demonstrating the ability of EtO to bind covalently in systemic human cells, as it 

does in rodent cells. DNA adducts in EtO-exposed humans have not been well studied, and the 

evidence of increased DNA adducts is limited. 

In addition, EtO has yielded positive results in in vitro mutagenicity studies of human 

cells (see Figure 3-3). Although the studies of point mutations in EtO-exposed humans are few 

and insensitive and the evidence for mutations is limited, there is clear evidence from a number 

of human studies that EtO causes chromosomal aberrations, SCEs, and micronucleus formation 

in peripheral blood lymphocytes (see Section 3.3.3.3 and Table 3-6). At least one study 
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suggested an exposure-response relationship for the formation of SCEs in peripheral blood 

lymphocytes (Major et al., 1996). Another study reported a statistically significant increase in 

micronuclei in bone marrow cells in EtO-exposed workers (Högstedt et al., 1983). 

Finally, there is strong evidence that EtO causes cancer in humans, including cancer 

types observed in rodent studies (i.e., lymphohematopoietic cancers and breast cancer), 

providing further weight to the relevance of the aforementioned events to the development of 

cancer in humans (see Sections 3.1 and 3.5.1). 

In conclusion, the weight of evidence supports a mutagenic mode of action for EtO 

carcinogenicity. 

3. Which populations or lifestages can be particularly susceptible to the hypothesized mode of 

action? 

The mutagenic mode of action is considered relevant to all populations and lifestages. 

According to EPA’s Supplemental Guidance (U.S. EPA, 2005b), there may be increased 

susceptibility to early-life exposures to carcinogens with a mutagenic mode of action. Therefore, 

because the weight of evidence supports a mutagenic mode of action for EtO carcinogenicity, 

and in the absence of chemical-specific data to evaluate differences in susceptibility, increased 

early-life susceptibility should be assumed and, if there is early-life exposure, the age-dependent 

adjustment factors should be applied, in accordance with the Supplemental Guidance (see 

Section 4.4). 

In addition, as discussed in Section 3.5.2, people with DNA repair deficiencies or genetic 

polymorphisms conveying a decreased efficiency in detoxifying enzymes may have increased 

susceptibility to EtO-induced carcinogenicity. 

3.5. HAZARD CHARACTERIZATION 

3.5.1. Characterization of Cancer Hazard 

In studies of humans there is substantial evidence that EtO exposure is causally 

associated with lymphohematopoietic cancer, but the evidence is not strong enough to be 

conclusive. There is also evidence that EtO exposure is causally associated with breast cancer, 

but the database for breast cancer is more limited. Of the eight relevant 6 Hill “criteria” (or 

considerations) for causality (Hill, 1965), temporality, coherence, biological plausibility and 

analogy are readily satisfied, and the other four criteria (specificity, consistency, biological 

gradient, and strength of association) are satisfied to varying degrees, as discussed below. 

 

6

The ninth consideration is experimental evidence, which is seldom available for human populations and is not 

available in the case of human exposures to EtO. 
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Temporality, the sole necessary criterion, is satisfied because the subjects of all the 

epidemiology studies of EtO were workers who were exposed to EtO before the cancers of 

interest were observed, i.e., exposure preceded the development of the disease. 

The related criteria of coherence, biological plausibility and analogy are fulfilled by the 

well-established knowledge that EtO is mutagenic and genotoxic, which are common 

mechanistic features of many carcinogens; that EtO is carcinogenic in rodents, with 

lymphohematopoietic cancers being observed in both rats and mice and mammary carcinomas 

being observed in female mice; and that EtO is an epoxide and epoxides are capable of directly 

interacting with DNA and are the active metabolites of many carcinogens. 

There is some specificity with respect to the lymphohematopoietic system. Most of the 

studies focus on examining risks associated with subcategories of the lymphohematopoietic 

system. These cancers include leukemia, Hodgkin lymphoma, NHL, reticulosarcoma, and 

myeloma. (Note that, with the exception of the Steenland et al. (2004) study, which includes 

lymphocytic leukemia in a lymphoid cancer category, the studies do not subcategorize leukemia 

into its distinct myeloid and lymphocytic subtypes.) In most of the studies, an enhanced risk of 

cancer of the lymphohematopoietic system is evident, and in some studies, it is statistically 

significant. There also appears to be specificity across the epidemiological database for an 

increased risk of breast cancer. It should be noted, however, that the specificity criterion is not 

expected to be strictly satisfied by agents, such as EtO, that are widely distributed in all tissues 

and are direct-acting chemicals. 

As just alluded to, there is evidence of consistency between studies with respect to cancer 

of the lymphohematopoietic system as a whole. Most of the available epidemiologic studies of 

EtO exposure have reported elevated risks of lymphohematopoietic cancer, and the studies that 

do not report a significant lymphohematopoietic cancer effect have major limitations, such as 

small numbers of cases (from small study size and/or insufficient follow-up time), inadequate 

exposure information, and/or reliance on external analyses (see Table 3-1 and Table A-5 in 

Appendix A). Overall, about 9 of 11 studies (including only the last follow-up of independent 

cohorts) with adequate information to determine RR estimates reported an increased risk of 

lymphohematopoietic cancers or a subgroup thereof, although not all were statistically 

significant, possibly due to the limitations noted above (see Table 3-1 and Table A-5 in 

Appendix A). The large, high-quality NIOSH study shows statistically significant 

exposure-response trends for lymphoid cancers and all lymphohematopoietic cancers (Steenland 

et al., 2004; see Sections D.3 and D.4 of Appendix D for results for both sexes combined). Four 

other studies reported statistically significant increases in risk (Swaen et al., 1996; Benson and 

Teta, 1993; Bisanti et al., 1993; Hogstedt, 1988; Hogstedt et al., 1986), although EtO exposures 
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were reportedly low in the Benson and Teta (1993) study and the increased risks may be due to 

other chemical exposures. Nonsignificant increases in lymphohematopoietic cancer risk were 

observed in four other studies, based on small numbers of cases (Coggon et al., 2004; Olsen et 

al., 1997; Hagmar et al., 1995; Norman et al., 1995 [with only 1 case]; Hagmar et al., 1991). 

Only 2 of the 11 studies showed no evidence of an increase in lymphohematopoietic cancer risk 

(Swaen et al., 2009; Kiesselbach et al., 1990). 

Regarding consistency in the breast cancer studies, the large, high-quality NIOSH study 

shows statistically significant increased risks for both breast cancer mortality (n = 103 deaths; 

(Steenland et al., 2004)) and breast cancer incidence (n = 319 cases; (Steenland et al., 2003)). 

Two other studies suggest an increased risk of breast cancer despite their small size ((Norman et 

al., 1995), n = 12 cases; (Kardos et al., 2003), n = 3 deaths). No elevated risks were seen in the 

only other two studies reporting breast cancer results; however these studies had few cases, 

owing to their small size and/or inadequate follow-up time ((Hagmar et al., 1991), n = 5 cases; 

(Coggon et al., 2004), n = 11 deaths) (see Table 3-2 and Table A-5 in Appendix A). 

There is also some evidence of dose-response relationships (biological gradient). In the 

large, high-quality NIOSH study, a statistically significant positive trend was observed in the risk 

of lymphohematopoietic cancers with increasing (log) cumulative exposure to EtO, although 

results for this model were reported only for males (Steenland et al., 2004) [the sex difference is 

not statistically significant, however, and the trend for both sexes combined is also statistically 

significant (see Tables D-3e and D-4e in Appendix D)]. For only two other cohorts were results 

for exposure-response analyses reported, probably because most cohorts had too few cases 

and/or lacked adequate exposure information. In the Swaen et al. (2009) study of the UCC 

cohort, no statistically significant trends were observed for leukemia or lymphoid cancer using a 

Cox proportional hazards model with cumulative exposure, a model which notably did not yield 

statistically significant trends in the NIOSH study, either. In the small study of Hagmar et al. 

(1995), an SIR for leukemia of 7.14 was reported for subjects with at least 0.14 ppm × years of 

cumulative exposure and 10 years latency, but this result was based on only two cases and was 

not statistically significant. For breast cancer, exposure-response analyses were reported only for 

the NIOSH cohort, again presumably because most cohorts had too few cases and/or lacked 

adequate exposure information. These analyses yielded clear, statistically significant trends for 

both breast cancer mortality (Steenland et al., 2004) and breast cancer incidence (Steenland et al., 

2003) for a variety of models. 

Whereas most of the considerations are largely satisfied, as discussed above, there is little 

strength in the associations, as reflected by the modest magnitude of most of the RR estimates. 

For example, in the large NIOSH study, the RR estimate for lymphoid cancer mortality in the 
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highest exposure quartile is about 3.0 and the RR estimate for breast cancer incidence in the 

highest exposure quintile in the subcohort with interviews is on the order of 1.9. While large RR 

estimates increase the confidence that an observed association is not likely due to chance, bias, 

or confounding, modest RR estimates, such as those observed with EtO, do not preclude a causal 

association (U.S. EPA, 2005a). With EtO, the modest RR estimates may, in part, reflect the 

relatively high background rates of these cancers, particularly of breast cancer incidence. 

In addition to the Hill criteria, other factors such as chance, bias, and confounding are 

considered in analyzing the weight of epidemiological evidence. Given the consistency of the 

findings across studies and the exposure-response relationships observed in the largest study, 

none of these factors is likely to explain the associations between these cancers and EtO 

exposure. Coexposures to other chemicals are expected to have occurred for workers in the 

chemical industry cohorts but would have been much less likely in the sterilizer worker cohorts, 

such as the NIOSH cohort, which reported no evidence of confounding exposures to other 

occupational carcinogens (Steenland et al., 1991). For breast cancer in the NIOSH subcohort 

with interviews (Steenland et al., 2003), other risk factors for breast cancer were assessed, and 

statistically significant factors were included in the exposure-response models. 

In conclusion, the overall epidemiological evidence for a causal association between EtO 

exposure and lymphohematopoietic cancer was judged to be strong but less than conclusive. For 

breast cancer, the existing evidence was strong, but there were few studies and, thus, overall, the 

epidemiological evidence was judged to be more limited. 

There is inadequate evidence for other cancer types (e.g., stomach cancer and pancreatic 

cancer) in the epidemiology studies. 

The experimental animal evidence for carcinogenicity is concluded to be “sufficient” 

based on findings of tumors at multiple sites, by both oral and inhalation routes of exposure, and 

in both sexes of both rats and mice. Tumor types resulting from inhalation exposure included 

mononuclear cell leukemia in male and female rats and malignant lymphoma and mammary 

carcinoma in female mice, suggesting some site concordance with the lymphohematopoietic and 

breast cancers observed in humans, also exposed by inhalation. 

The evidence of EtO genotoxicity and mutagenicity is unequivocal. EtO is a 

direct-acting alkylating agent and has invariably tested positive in in vitro mutation assays from 

bacteriophage, bacteria, fungi, yeast, insects, plants, and mammalian cell cultures (including 

human cells). In mammalian cells (including human cells), EtO-induced genotoxic effects 

include unscheduled DNA synthesis, gene mutations, SCEs, and chromosomal aberrations. The 

results of in vivo genotoxicity studies of EtO have also been largely positive, following 

ingestion, inhalation, or injection. Increases in frequencies of gene mutations have been reported 
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in the lung, T-lymphocytes, bone marrow, and testes of EtO-exposed mice. In particular, 

increases in frequencies of oncogene mutations have been observed in several tumor types from 

EtO-exposed mice compared to spontaneous mouse tumors of the same types. Several inhalation 

studies in laboratory animals have demonstrated that EtO exposure levels in the range of those 

used in the rodent bioassays (i.e., 10–100 ppm, 6–7 hours/day, 5 days/week) induce SCEs. 

Evidence for micronuclei and chromosomal aberrations from these same exposure levels in 

short-term studies (4 weeks or less) is less consistent, although concerns have been raised about 

some of the negative studies. A recent study showed clear, statistically significant increases in 

chromosomal aberrations with longer durations of exposure (≥12 weeks) to the concentration 

levels used in the rodent bioassays. The studies of point mutations in EtO-exposed humans are 

few and insensitive and the evidence for mutations is limited; however, there is clear evidence 

from a number of human studies that EtO causes chromosomal aberrations, SCEs, and 

micronucleus formation in peripheral blood lymphocytes, and one study has reported increased 

levels of micronuclei in bone marrow cells in EtO-exposed workers. 

Under EPA’s 2005 Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), the 

conclusion can be made that EtO is “carcinogenic to humans.” In general, the descriptor 

“carcinogenic to humans” is appropriate when there is convincing epidemiologic evidence of a 

causal association between human exposure and cancer. This descriptor is also appropriate when 

there is a lesser weight of epidemiologic evidence that is strengthened by specific lines of 

evidence set forth in the Guidelines, which are satisfied for EtO. The lines of evidence 

supporting the characterization of “carcinogenic to humans” include the following: (1) there is 

strong, although less than conclusive on its own, evidence of cancer in humans associated with 

EtO exposure via inhalation, specifically, evidence of lymphohematopoietic cancers and female 

breast cancer in EtO-exposed workers; (2) there is extensive evidence of EtO-induced 

carcinogenicity in laboratory animals, including lymphohematopoietic cancers in rats and mice 

and mammary carcinomas in mice following inhalation exposure; (3) EtO is a direct-acting 

alkylating agent whose mutagenic and genotoxic capabilities have been well established in a 

variety of experimental systems, and a mutagenic mode of carcinogenic action has been 

identified in animals involving the key precursor events of DNA adduct formation and 

subsequent DNA damage, including point mutations and chromosomal effects; and (4) there is 

strong evidence that the key precursor events are anticipated to occur in humans and progress to 

tumors, including evidence of chromosome damage, such as chromosomal aberrations, SCEs, 

and micronuclei in EtO-exposed workers. 
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3.5.2. Susceptible Life stages and Populations 

There are no data on the relative susceptibility of children and the elderly when compared 

with adult workers, in whom the evidence of hazard has been gathered, but because EtO does not 

have to be metabolized before binding to DNA and proteins, the maturing of enzyme systems in 

very young children is thought not to be a predominant factor in its hazard, at least for activation. 

However, the immaturity of detoxifying enzymes in very young children may increase children’s 

susceptibility because children may clear EtO at a slower rate than adults. As discussed in 

Section 3.3.1, EtO is metabolized (i.e., detoxified) primarily by hydrolysis in humans but also by 

glutathione conjugation. Both hydrolytic activity and glutathione-S-transferase activity 

apparently develop after birth (Clewell et al., 2002); thus, very young children might have a 

decreased capacity to detoxify EtO compared to adults. In the absence of data on the relative 

susceptibility associated with EtO exposure in early life, increased early-life susceptibility is 

assumed, in accordance with EPA's Supplemental Guidance (U.S. EPA, 2005b), because the 

weight of evidence supports the conclusion of a mutagenic mode of action for EtO 

carcinogenicity (see Section 3.4). 

Other than the occurrence of sex-specific cancers (e.g., breast cancer in human females, 

mammary and uterine carcinomas in female mice, and testicular peritoneal mesotheliomas in 

male rats; see Section 3.2), there is no clear sex difference in EtO-induced carcinogenicity. With 

the exception of the sex-specific cancers and the observation of malignant lymphomas in female 

but not male mice, there is no sex difference in EtO-induced cancer types in the rat and mouse 

bioassays. Cancer potency estimates for females are roughly 50% higher than those for males 

for both mice and rats (see Table 4-18 in Section 4.2.5). In humans, in the large NIOSH study 

(Steenland et al., 2004), the association between lymphoid cancers and EtO exposure was seen 

primarily in males, but the sex difference was not statistically significant (see Appendix D), and 

the SAB panel that reviewed an earlier draft of this assessment recommended that data from both 

sexes be combined for the derivation of quantitative risk estimates for the lymphohematopoietic 

cancers (SAB, 2007). 

Brown et al. (1996) reported that sex differences in EtO toxicokinetics were observed in 

mice but not in rats; female mice had a significantly higher steady-state blood EtO concentration 

after 4 hours of exposure to either 100 or 330 ppm than male mice. As noted above and 

discussed in Section 3.3.1, EtO is metabolized primarily by hydrolysis in humans. Mertes et al. 

(1985) reported no sex difference in microsomal or cytosolic epoxide hydrolase activities in 

human liver in vitro using benzo[a]pyrene 4,5-oxide or trans-stilbene oxide, respectively, as 

substrates. Using EtO as a substrate, but with far fewer subjects, Fennell and Brown (2001) 
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reported similar values for males and females for epoxide hydrolase activity in human liver 

microsomes and for GSH transferase in human liver cytosolic fractions. 

Because EtO is detoxified by glutathione conjugation or hydrolysis, people with 

genotypes conveying deficiencies in glutathione-S-transferase or epoxide hydrolase activities 

may be at increased risk of cancer from EtO exposure. Yong et al. (2001) measured 

approximately twofold greater EtO-hemoglobin adduct levels in occupationally exposed persons 

with a null GSTT1 genotype than in those with positive genotypes. Similarly, in a study of 

hospital workers, Haufroid et al. (2007) reported increased urinary excretion of a glutathione 

conjugate of EtO, reflecting increased detoxification of EtO, associated with a nonnull GSTT1 

genotype, although the increase was not statistically significant in all the regression models 

tested; associations were less clear for other glutathione-S-transferase or epoxide hydrolase 

polymorphisms. 

In addition, people with DNA repair deficiencies such as xeroderma pigmentosum, 

Bloom’s syndrome, Fanconi anemia, and ataxia telangiectasia (Gelehrter et al., 1990) are 

expected to be especially sensitive to the damaging effects of EtO exposure. Paz-y-Miño et al. 

(2002) have recently identified a specific polymorphism in the excision repair pathway gene 

hMSH2. The polymorphism was present in 7.5% of normal individuals and in 22.7% of NHL 

patients, suggesting that this polymorphism may be associated with an increased risk of 

developing NHL. 

This document is a draft for review purposes only and does not constitute Agency policy. 

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4. CANCER DOSE-RESPONSE ASSESSMENT FOR INHALATION EXPOSURE 

This chapter presents the derivation of cancer unit risk estimates from human and rodent 

data. Section 4.1 discusses the derivation of unit risk estimates for lymphohematopoietic 

cancers, breast cancer, and total cancer from human data, as well as sources of uncertainty in 

these estimates. Section 4.2 presents the derivation of unit risk estimates from rodent data. 

Section 4.3 summarizes the unit risk estimates derived from the different data sets. Section 4.4 

discusses adjustments for assumed increased early-life susceptibility, based on recommendations 

from EPA's Supplemental Guidance (U.S. EPA, 2005b), because the weight of evidence supports 

the conclusion of a mutagenic mode of action for EtO carcinogenicity (see Section 3.4). 

Section 4.5 presents conclusions about the unit risk estimates. Section 4.6 compares the unit risk 

estimates derived in this EPA assessment to those derived in other assessments. Finally, 

Section 4.7 provides risk estimates derived for some general occupational exposure scenarios. 

4.1. INHALATION UNIT RISK ESTIMATES DERIVED FROM HUMAN DATA 

The NIOSH retrospective cohort study of more than 18,000 workers in 13 sterilizing 

facilities (most recent update by Steenland et al., 2004; Steenland et al., 2003) provides the most 

appropriate data sets for deriving quantitative cancer risk estimates in humans for several 

reasons: (1) exposure estimates were derived for the individual workers using a comprehensive 

exposure assessment, (2) the cohort was large and diverse (e.g., 55% female), and (3) there was 

little reported exposure to chemicals other than EtO. Exposure estimates, including estimates for 

early exposures for which no measurements were available, were determined using a regression 

model that estimated exposures to each individual as a function of facility, exposure category, 

and time period. The regression model was based on extensive personal monitoring data from 

18 facilities spanning a number of years as well as information on factors influencing exposure, 

such as engineering controls (Hornung et al., 1994; see also Section A.2.8 in Appendix A). 

When evaluated against test data, the model accounted for 85% of the variation in average EtO 

exposure levels. The investigators were then able to estimate the cumulative exposure 

(ppm × days) for each individual worker by multiplying the estimated exposure for each job 

(exposure category) held by the worker by the number of days spent in that job and summing 

over all the jobs held by the worker. Steenland et al. (2004) present follow-up results for the 

cohort mortality study previously discussed by Steenland et al. (1991) and Stayner et al. (1993). 

Positive findings in the current follow-up include increased rates of (lympho)hematopoietic 

cancer mortality and of breast cancer mortality in females. Steenland et al. (2003) present results 

of a breast cancer incidence study of a subcohort of 7,576 women from the NIOSH cohort. 
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The other major occupational study (most recent update by Swaen et al., 2009) described 

risks to Union Carbide workers exposed to EtO at two chemical plants in West Virginia, but this 

study is less useful for estimating quantitative cancer risks for a number of reasons. First, the 

exposure assessment is much less extensive than that used for the NIOSH cohort, with greater 

likelihood for exposure misclassification, especially in the earlier time periods when no 

measurements were available (1925–1973). Exposure estimation for the individual workers was 

based on a relatively crude exposure matrix which cross-classified 3 levels of exposure intensity 

with 4 time periods. The exposure estimates for 1974–1988 were based on measurements from 

air sampling at the West Virginia plants since 1976. The exposure estimates for 1957–1973 were 

based on measurements in a similar plant in Texas. The exposure estimates for 1940–1956 were 

based loosely on “rough” estimates reported for chlorohydrin-based EtO production in a Swedish 

facility in the 1940s. The exposure estimates for 1925–1939 were essentially guesses. Thus, for 

the two earliest time periods (1925–1939 and 1940–1956) at least, the exposure estimates are 

highly uncertain. (See Section A.2.20 of Appendix A for a more detailed discussion of the 

exposure assessment for the Union Carbide cohort.) This is in contrast to the NIOSH exposure 

assessment in which exposure estimates were based on extensive sampling data and regression 

modeling. In addition, the sterilization processes used by the NIOSH cohort workers were fairly 

constant back in time, unlike chemical production processes, which likely involved much higher 

and more variable exposure levels in the past. Furthermore, the Union Carbide cohort is of much 

smaller size and has far fewer deaths than the NIOSH cohort, it is restricted to males and so 

cannot be used to investigate breast cancer risk in females, and there are coexposures to other 

chemicals. 

A third study (Hagmar et al., 1995; Hagmar et al., 1991) estimated cumulative exposures 

for individual workers; however, insufficient exposure-response data are presented for the 

derivation of unit risk estimates. Exposure-response results for specific cancers are provided 

only in the 1991 paper and then only for two lymphohematopoietic cancers across two 

categorical exposure groups. 

Table 4-1 provides a summary of the judgments made in selecting the NIOSH study as 

the basis for the derivation of unit risk estimates. The NIOSH EtO cohort data can be obtained 

from the Industrywide Studies Branch of NIOSH.7 

The derivation of unit risk estimates, defined as the lifetime risk of cancer from chronic 

inhalation of EtO per unit of air concentration, for lymphohematopoietic cancer mortality and 

 

7

Industrywide Studies Branch; Division of Surveillance, Hazard Evaluations and Field Studies: NIOSH; 

Centers for Disease Control and Prevention, 4676 Columbia Parkway MS R-13,Cincinnati, Ohio 45226, telephone: 

513-841-4203. 
 

 

 

 

 

 

 

 

 

 

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Table 4-1. Considerations used in this assessment for selecting epidemiology 

studies for quantitative risk estimation 

Consideration Studies Selected Comments 

Availability of 1. Hagmar et al. (1995) and Hagmar These are the only 3 studies with quantitative 

quantitative exposure et al. (1991) [Swedish sterilizer exposure estimates, which is an essential 

estimates cohort] 

2. Swaen et al. (2009) [latest 

follow-up of Union Carbide 

Corporation (UCC) cohort] 

3. Steenland et al. (2004) and 

Steenland et al. (2003) [latest 

follow-up of NIOSH cohort] 

criterion for quantitative risk estimation. 

Availability of exposure­ 1. Swaen et al. (2009) Hagmar et al. (1995) and Hagmar et al. (1991) 

response information 2. Steenland et al. (2004) and did not present sufficient exposure-response 

Steenland et al. (2003) results, presumably because they had a short 

follow-up time and thus few cases of specific 

cancers (5 breast cancer cases; 

6 lymphohematopoietic cancer cases). 

Other factors affecting 

the utility of 

epidemiology studies for 

quantitative risk 

estimation 

Steenland et al. (2004) and Steenland 

et al. (2003) 

The NIOSH study [Steenland et al. (2004) and 

Steenland et al. (2003)] alone was selected for 

quantitative risk estimation, as it was judged to 

be substantially superior to the UCC study 

(Swaen et al., 2009) with respect to a number of 

key considerations [in particular, in order of 

importance: (1) quality of the exposure 

estimates, (2) cohort size, and (3) the absence of 

coexposures and the inclusion of women]. 

incidence and for breast cancer mortality and incidence in females, based on results of the recent 

analyses of the NIOSH cohort, is presented in the following subsections. 

The exposure-response models used to fit the epidemiological data are empirical 

“curve-fitting” models. Considerations used in the selection of the exposure-response models 

upon which to base the derivation of unit risk estimates included statistical fit (as reflected by 

p-values), visual fit of the models to the categorical results, and biological plausibility. When 

multiple models were deemed to be reasonable candidates for selection based on those 

considerations, AIC 8 was also considered in selecting the “preferred” model. 

8

Akaike Information Criteria. The AIC is a measure of information loss from a dose-response model that can be 

used to compare a specified set of models. The AIC is defined as 2p – 2ln(L), where p is the number of estimated 

parameters included in the model and L is the maximized value of the likelihood function. Among a set of specified 

models, the model with the lowest AIC is the preferred model. 

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4.1.1. Risk Estimates for Lymphohematopoietic Cancer 

4.1.1.1. Lymphohematopoietic Cancer Results From the NIOSH Study 

Steenland et al. (2004) investigated the relationship between (any) EtO exposure and 

mortality from cancer at a number of sites using life-table analyses with the U.S. population as 

the comparison population. Categorical SMR analyses were also done by quartiles of cumulative 

exposure. Then, to further investigate apparent exposure-response relationships observed for 

(lympho)hematopoietic cancer and breast cancer, internal exposure-response analyses were 

conducted using Cox proportional hazards models, which have the form 

Relative rate (RR) = eβX , (4-1) 

where β represents the regression coefficient and X is the exposure (or some function of 

exposure, e.g., the natural log of exposure). Internal analyses were done two ways―with 

exposure as a categorical variable and with exposure as a continuous variable. A nested 

case-control approach was used, with age as the time variable used to form the risk sets. Risk 

sets were constructed with 100 controls randomly selected for each case from the pool of those 

surviving to at least the age of the index case. According to the authors, use of 100 controls per 

case has been shown to result in ORs virtually identical to the RR estimates obtained with full 

cohorts. Cases and controls were matched on race (white/nonwhite), sex, and date of birth 

(within 5 years). Exposure was the only covariate in the model, so the p-value for the model also 

serves as a p-value for the regression coefficient, β, as well as for a test of exposure-response 

trend. 

For lymphohematopoietic cancer mortality, Steenland et al. (2004) analyzed both all 

lymphohematopoietic cancers combined and a subcategory of lymphohematopoietic cancers that 

they called “lymphoid” cancers; these included NHL, myeloma, and lymphocytic leukemia. 

Their exposure-response analyses focused on cumulative exposure and (natural) log cumulative 

exposure, with various lag periods. Other EtO exposure metrics (duration of exposure, average 

exposure, and peak exposure) were also examined, but models using these metrics did not 

generally predict lymphohematopoietic cancer as well as models using cumulative exposure. A 

lag period defines an interval before death, or end of follow-up, during which any exposure is 

disregarded because it is not considered relevant to the outcome under investigation. For 

lymphohematopoietic (and lymphoid) cancer mortality, a 15-year lag provided the best fit to the 

data, based on the likelihood ratio test. One ppm × day was added to cumulative exposures in 

lagged analyses to avoid taking the log of 0. For both all lymphohematopoietic and lymphoid 

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cancers, Steenland et al. (2004) found stronger positive exposure-response trends in males and so 

presented the results for some of the regression models separately by sex. The apparent sex 

difference was not statistically significant (see Appendix D), however, and results for both sexes 

combined were subsequently obtained from Dr. Steenland (see Appendix D; Section 3 for 

lymphoid cancer, Section 4 for all lymphohematopoietic cancer). These results are presented in 

Table 4-2. For additional details and discussion of the Steenland et al. (2004) study, see 

Appendix A. 

Table 4-2. Cox regression results for all lymphohematopoietic cancer and 

lymphoid cancer mortality in both sexes in the NIOSH cohort, for the models 

presented by Steenland et al. (2004) 

Exposure variablea p valueb Coefficient (SE) ORs by categoryc (95% CI) 

All lymphohematopoietic cancerd 

Cumulative exposure, 15-yr 

lag 

0.40 0.00000326 

(0.00000349) 

Log cumulative exposure, 15­

yr lag 

0.009 0.107 (0.0418) 

Categorical cumulative 

exposure, 15-yr lag 

0.10 1.00, 2.33 (0.93–5.86), 3.46 (1.33−8.95), 

3.02 (1.16–7.89), 2.96 (1.12–7.81) 

Lymphoid cancere 

Cumulative exposure, 15-yr 

lag 

0.22 0.00000474 

(0.00000335) 

Log cumulative exposure, 15­

yr lag 

0.02 0.112 (0.0486) 

Categorical cumulative 

exposure, 15-yr lag 

0.21 1.00, 1.75 (0.59–5.25), 3.15 (1.04−9.49), 

2.44 (0.80–7.50), 3.00 (1.02–8.45) 

a

Cumulative exposure is in ppm × days.
 b

P-values from likelihood ratio test.
 c

Exposure categories are 0, >0–1,199, 1,200–3,679, 3,680–13,499, ≥13,500 ppm × days.
	 d

9th revision ICD codes 200–208; results based on 74 cases.
 e

NHL, myeloma, and lymphocytic leukemia (9th revision ICD codes 200, 202, 203, 204); results based on 53 cases.
 

Source: Additional analyses performed by Dr. Steenland (see Sections D.3 and D.4 of Appendix D).
 

4.1.1.2. Prediction of Lifetime Extra Risk of Lymphohematopoietic Cancer Mortality 

The exposure-response trends for lymphohematopoietic cancers observed by Steenland et 

al. (2004) appear to be driven largely by the lymphoid cancers; therefore, the primary risk 

analyses for lymphohematopoietic cancer are based on the lymphoid cancer results. 

Lymphohematopoietic cancers are a diverse group of diseases with diverse etiologies, and 

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myeloid and lymphoid cells develop from different progenitor cells; thus, there is stronger 

support for an etiologic role of EtO in the development of lymphoid cancers than in the 

development of the cancers in the aggregate all lymphohematopoietic cancer category. The 

consideration of NHL, (plasma cell) myeloma, and lymphocytic leukemia together as 

“lymphoid” cancers is consistent with the current World Health Organization classification of 

such cancers based on their derivation from B-cells, T-cells, and NK-cells rather than previous 

distinctions (Harris et al., 1999). Nonetheless, for comprehensiveness and for the reasons listed 

below, risk estimates based on the all lymphohematopoietic cancer results are presented for 

comparison. Judging roughly from the p-values, the model fits do not appear notably better for 

lymphoid cancers than for all lymphohematopoietic cancers (see Table 4-2, p-values for log 

cumulative exposure models), and the “lymphoid” category did not include Hodgkin lymphoma, 

which also exhibited evidence of exposure-response trends, although based on few cases 

(Steenland et al., 2004). In addition, misclassification or nonclassification of tumor type is more 

likely to occur for subcategories of lymphohematopoietic cancer (e.g., 4 of the 25 leukemias in 

the analyses were classified as “not specified” and so could not be considered for the lymphoid 

cancer analysis). 

The results of internal exposure-response analyses of lymphoid cancer in the NIOSH 

cohort (Cox regression analyses, summarized in Table 4-2) were used for predicting the extra 

risks of lymphoid cancer mortality from continuous environmental exposure to EtO. Extra risk 

is defined as 

Extra risk = (Rx − Ro)/(1 − Ro), (4-2) 

where Rx is the lifetime risk in the exposed population and Ro is the lifetime risk in an 

unexposed population (i.e., the background risk). These risk estimates were calculated using the 

β regression coefficients and an actuarial program (life-table analysis) that accounts for 

competing causes of death.9

 An inherent assumption in the Cox regression model and its 

application in the life-table analyses is that RR is independent of age. (An alternate assumption 

of increased susceptibility from early-life exposure to EtO, as recommended in EPA's 

Supplemental Guidance (U.S. EPA, 2005b) for chemicals, such as EtO [see Section 3.4], with a 

mutagenic mode of action, is considered in Section 4.4. This alternate assumption is the 

 

9

This program is an adaptation of the approach previously used by the Committee on the Biological Effects of 

Ionizing Radiation (BEIR, 1988). A spreadsheet illustrating the extra risk calculation for the derivation of the LEC01 

for lymphoid cancer incidence (see Section 4.1.1.3) is presented in Appendix E. 
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prevailing assumption in this assessment, based on the recommendations in the Supplemental 

Guidance. Risk estimates are first developed under the assumption of age independence, 

however, because that is the standard approach in the absence of evidence to the contrary or of 

sufficient evidence of a mutagenic mode of action to invoke the divergent assumption of 

increased early-life susceptibility.) 

U.S. age-specific all-cause mortality rates for 2004 for both sexes of all race groups 

combined (Arias, 2007) were used to specify the all-cause background mortality rates in the 

actuarial program. For the cause-specific background mortality rates for lymphoid cancers, 

age-specific mortality rates for the relevant subcategories of lymphohematopoietic cancer (NHL 

[C82-C85 of 10th revision of the International Classification of Diseases (ICD)], multiple 

myeloma [C88, C90], and lymphoid leukemia [C91]) for the year 2004 were obtained from the 

National Center for Health Statistics Data Warehouse website 

(http://www.cdc.gov/nchs/datawh/statab/unpubd/mortabs.htm). The risks were computed up to 

age 85 for continuous exposures to EtO beginning at birth.10 Conversions between occupational 

EtO exposures and continuous environmental exposures were made to account for differences in 

the number of days exposed per year (240 vs. 365 days) and in the amount of EtO-contaminated 

air inhaled per day (10 vs. 20 m3

; (U.S. EPA, 1994)). An adjustment was also made for the lag 

period. The reported standard errors for the regression coefficients from Table 4-2 were used to 

compute the 95% upper confidence limits (UCLs) for the relative rates, based on a normal 

approximation. 

The only statistically significant Cox regression model presented by Steenland et al. 

(2004) for lymphoid cancer mortality in males was for log cumulative exposure with a 15-year 

lag (p = 0.02). This was similarly true for the analyses of lymphoid cancer using the data for 

both sexes (see Table 4-2). However, using the log cumulative exposure model to estimate the 

risks from low environmental exposures is problematic because this model, which is intended to 

fit the full range of occupational exposures in the study, is inherently supralinear (i.e., risk 

increases steeply with increasing exposures in the low exposure range and then plateaus), and 

results are unstable for low exposures (i.e., small changes in exposure correspond to large 

changes in risk; see Figure 4-1). Some consideration was thus given to the cumulative exposure 

model, which is typically used and which is stable at low exposures, although the fit to these data 

was not statistically significant (p = 0.22). However, the Cox regression model with cumulative 

10Rates above age 85 years are not included because cause-specific disease rates are less stable for those ages. Note 

that 85 years is not employed here as an average lifespan but, rather, as a cutoff point for the life-table analysis, 

which uses actual age-specific mortality rates. The average lifespan for males and females combined in a life-table 

analysis truncated at age 85 years is about 75 years. 
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1.5 

2 

2.5 

3 

3.5 

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 

lymphoid cancer RR estimate 

mean cumulative exposure (ppm * days) 

e^(β*ex p) 

e^(β*logex p) 

categorical 

linear 

spline100 

spline1600 

Figure 4-1. RR estimate for lymphoid cancer vs. mean exposure (with 15-year lag, unadjusted for continuous 

exposure). 

e^(β*exp): Cox regression results for RR = e(β × exposure); e^(β*logexp): Cox regression results for RR = e(β × ln(exposure)); 

categorical: Cox regression results for RR = e(β × exposure) with categorical exposures; linear: weighted linear regression 

of categorical results, excluding highest exposure group (see text); spline100(1600): 2-piece log-linear spline model 

with knot at 100 (1,600) ppm × days (see text). 

Source: Steenland reanalyses for male and female combined; see Appendix D (except for linear regression of 

categorical results, which was done by EPA). 
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exposure is inherently sublinear (i.e., risk increases gradually in the low exposure range and then 

with increasing steepness as exposure increases) and does not reflect the apparent supralinearity 

of the data demonstrated by the categorical results and the superior fit of the log cumulative 

exposure model; thus, this model was not considered further. (Note that all the models discussed 

in Chapter 4 treat exposure as a continuous variable except for the categorical models and the 

linear regressions of categorical data, which are specifically described as such.) 

In a 2006 external review draft of this assessment (U.S. EPA, 2006a), which relied on the 

original published results of Steenland et al. (2004), EPA proposed that the best way to represent 

the exposure-response relationship in the lower exposure region, which is the region of interest 

for low-exposure extrapolation, was through the use of a weighted linear regression of the results 

from the Cox regression model with categorical cumulative exposure and a 15-year lag (for 

males only, as this was the significant finding in the published paper of Steenland et al., 2004). 

In addition, the highest exposure group was not included in the regression to alleviate some of 

the “plateauing” in the exposure-response relationship at higher exposure levels and to provide a 

better fit to the lower exposure data. Linear modeling of categorical (i.e., grouped) 

epidemiologic data and elimination of the highest exposure group(s) under certain circumstances 

to obtain a better fit of low-exposure data are both standard techniques used in EPA 

dose-response assessments (U.S. EPA, 2012, 2005a). An established methodology was 

employed for the weighted linear regression of the categorical epidemiologic data, as described 

by Rothman (1986) and used by others (e.g., van Wijngaarden and Hertz-Picciotto, 2004). 

However, the SAB panel that reviewed the draft assessment recommended that EPA employ 

models using the individual exposure data as an alternative to modeling the published grouped 

data. The SAB also recommended that both males and females be included in the modeling of 

lymphohematopoietic cancer mortality (SAB, 2007). 

In response to these recommendations and in consultation with Dr. Steenland, one of the 

investigators from the NIOSH cohort studies, EPA determined that, using the full data set, an 

alternative way to address the supralinearity of the data (while avoiding the extreme 

low-exposure curvature obtained with the log cumulative exposure model) might be to use a 

two-piece log-linear spline model. Spline models have been used previously for 

exposure-response analyses of epidemiological data (Steenland and Deddens, 2004; Steenland et 

al., 2001). These models are particularly useful for exposure-response data such as the EtO 

lymphoid cancer data, for which RR initially increases with increasing exposure but then tends to 

plateau, or level off, at higher exposures. Such plateauing exposure-response relationships have 

been seen with other occupational carcinogens and may occur for various reasons, including the 

depletion of susceptible subpopulations at high exposures, mismeasurement of high exposures, 
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or a healthy worker survivor effect (Stayner et al., 2003). No other traditional exposure-response 

models for continuous data which might suitably fit the observed exposure-response pattern were 

apparent. Dr. Steenland was commissioned to do the spline analyses using the full data set with 

cumulative exposure as a continuous variable, and his findings are included in Appendix D (see 

Section D.3 for lymphoid cancer, Section D.4 for all lymphohematopoietic cancer). The results 

of the spline analyses are presented below. 

For the two-piece log-linear spline modeling approach, the Cox regression model (eq 4-1) 

was the underlying basis for the splines which were fit to the lymphoid cancer exposure-response 

data.

11 Taking the log of both sides of eq 4-1, log RR is a linear function of exposure 

(cumulative exposure is used here), and, with the two-piece log-linear spline approach, log RR is 

a function of two lines which join at a single point of inflection, called a “knot”. The shape of 

the two-piece log-linear spline model, in particular the slope in the low-exposure region, depends 

on the location of the knot. For this assessment, the knot was generally selected by trying 

different knots in increments of 1,000 ppm × days, starting at 1,000 ppm × days, and choosing 

the one that resulted in the largest model likelihood. In some cases, increments of 100 ppm × 

days were used between the increments of 1,000 ppm × days to fine-tune the knot selection. The 

model likelihood did not change much across the different trial knots (see Figure D-3a of 

Appendix D), but it did change slightly; therefore, the largest calculated likelihood was used as a 

basis for knot selection. For more discussion of the two-piece spline approach, see Appendix D. 

Using this approach, the largest likelihood was observed with the knot at 

1,600 ppm × days. However, the graphical results for the two-piece log-linear spline model with 

a knot at 1,600 ppm × days suggested that the model was underestimating RR in the region 

where the data were plateauing (see Figure 4-1).12 Therefore, knots below 1,000 ppm × days 

were also evaluated in increments of 100 ppm × days, and a likelihood was observed with the 

knot at 100 ppm × days that exceeded the likelihood with the knot at 1,600 ppm × days, 

although, again, the model likelihood did not actually change much across the different trial 

knots. See Table 4-3 and Section D.3 of Appendix D for parameter estimates and fit statistics for 

the two spline models. The graphical results for the two-piece spline model with a knot at 

100 ppm × days suggested that this model provided a better fit to the region where the data were 

plateauing (see Figure 4-1). Furthermore, the overall fit of this two-piece spline model was 

statistically significant (p = 0.048), whereas the p-value for the two-piece spline model with the 

knot at 1,600 ppm × days exceeded 0.05, although minimally (p = 0.072). Thus, for the 

 

11As parameterized in Appendix D, for cumulative exposures less than the value of the knot, RR = e(β1 × exposure); for 

cumulative exposures greater than the value of the knot, RR = e(β1 × exposure + β2 × (exposure-knot)). 12The loglinear spline segments appear fairly linear in the plotted range; however, they are not strictly linear. 
 

 

 

 

 

 

 

 

 

 

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lymphoid cancer mortality data, the optimal two-piece log-linear spline model appeared to be the 

one with the knot at 100 ppm × days. This model provided the largest calculated likelihood, was 

statistically significant, and presented the best apparent graphical fit to the majority of the range 

of the data. However, this model yielded a very steep slope in the exposure range below the knot 

of 100 ppm × days (see Figure 4-1), and, as such, there was low confidence in the slope, given 

that it is based primarily on a relatively small number of cases in the low-exposure region. Thus, 

after examining the new modeling analyses, it was determined that the weighted linear regression 

of the categorical data still provided the best available approach for risk estimates for 

lymphohematopoietic cancer.13 

Table 4-3. Exposure-response modeling results for all lymphohematopoietic 

cancer and lymphoid cancer mortality in both sexes in the NIOSH cohort for 

models not presented by Steenland et al. (2004) 

Modela p valueb Coefficient (SE) 

All lymphohematopoietic cancerc 

2-piece log-linear spline (knot at 500 ppm × 

days) 

0.02 low-exposure spline segment: 

B1 = 0.00201 (0.0007731) 

Linear regression of categorical results, 

excluding highest exposure group 

0.08 0.0003459 (0.0001944) 

Lymphoid cancerd 

Optimal 2-piece log-linear spline (knot at 100 

ppm × days) 

0.048 low-exposure spline segment: 

B1 = 0.01010 (0.00493) 

Alternate 2-piece log-linear spline (knot at 1,600 

ppm × days) 

0.07 low-exposure spline segment: 

B1 = 0.0004893 (0.0002554) 

Linear regression of categorical results, 

excluding the highest exposure quartile 

0.18 0.000247 (0.000185) 

a

All with cumulative exposure in ppm × days as the exposure variable and with a 15-yr lag.
 b

p-values from likelihood ratio test, except for linear regressions of categorical results, where Wald p-values are 

reported.
 c

9th revision ICD codes 200–208; results based on 74 cases.
 d

NHL, myeloma, and lymphocytic leukemia (9th revision ICD codes 200, 202, 203, 204); results based on 53 cases.
 

Source: Additional analyses performed by Dr. Steenland (see Sections D.3 and D.4 of Appendix D), except for the 

linear regression of the categorical results, which was performed by EPA.
 

13When this assessment was near completion, a two-piece linear spline model (with a linear model, i.e., RR = 1 + β 

× exposure, as the underlying basis for the spline pieces) was attempted, using the just-published approach of 

Langholz and Richardson (2010) to model the individual data with cumulative exposure as a continuous variable; 

however, this model did not alleviate the problem of the excessively steep low-exposure spline segment (see 

Figure D-3c in Appendix D) and was not pursued further for the lymphoid cancer data. The Langholz and 

Richardson (2010) approach was also employed to model the lymphoid cancer data using linear RR models with 

cumulative exposure and log cumulative exposure as continuous variables; however, these linear models similarly 

did not alleviate the problems of the corresponding log-linear RR models (see Figure D-3c in Appendix D). 

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For the weighted linear regression, the Cox regression results from the model with 

categorical cumulative exposure and a 15-year lag (see Table 4-2) was used, excluding the 

highest exposure group, as discussed above. 14 The weights used for the ORs were the inverses 

of the variances, which were calculated from the confidence intervals.15 Mean and median 

exposures for the cumulative exposure groups were provided by Dr. Steenland (see Table D-3a 

of Appendix D).16 The mean values were used for the weighted regression analysis because the 

cancer response is presumed to be a function of cumulative exposure, which is expected to be 

best represented by mean exposures. If the median values had been used, a slightly larger 

regression coefficient would have been obtained, resulting in slightly larger risk estimates. See 

Table 4-3 for the results obtained from the weighted linear regression and Figure 4-1 for a 

depiction of the resulting model. 

As the lymphoid cancer data set is the primary data set used for the derivation of unit risk 

estimates for lymphohematopoietic cancers, a summary of all the models considered for 

modeling the lymphoid cancer exposure-response data and the judgments made about model 

selection is provided in Table 4-4. See Figures 4-1 and D-3c in Appendix D for visual 

representations of the models. See Tables 4-2 and 4-3 and Section D.3 of Appendix D for 

p-values and other fit statistics. 

The linear regression of the categorical results for males and females combined and the 

actuarial program (life-table analysis) were used to estimate the exposure level (ECx; “effective 

concentration”) and the associated 95% lower confidence limit (LECx) corresponding to an extra 

risk of 1% (x = 0.01). A 1% extra risk level is commonly used for the determination of the point 

of departure (POD) for low-exposure extrapolation from epidemiological data; higher extra risk 

levels, such as 10%, would be an upward extrapolation for these data. Thus, 1% extra risk was 

selected for determination of the POD, and, consistent with EPA's Guidelines for Carcinogen 

Risk Assessment (U.S. EPA, 2005a), the LEC value corresponding to that risk level was used as 

the POD to derive the cancer unit risk estimates. 

14Concerns have been raised that this approach of dropping high-dose data appears arbitrary. It should be noted, 

however, that only the highest exposure group was omitted from the linear regression, and the exposure groupings 

were derived a priori by the NIOSH investigators and not by U.S. EPA in the course of its analyses. 15Equations for this weighted linear regression approach are presented in Rothman (1986) and summarized in 

Appendix F.

16Mean exposures for both sexes combined with a 15-year lag for the categorical exposure quartiles in Table 4-1 

were 446; 2,143; 7,335; and 39,927 ppm × days. Median values were 374; 1,985; 6,755; and 26,373 ppm × days. 

These values are for the full cohort, not just the risk sets. 
 

 

 

 

 

 

 

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Table 4-4. Models considered for modeling the exposure-response data for 

lymphoid cancer mortality in both sexes in the NIOSH cohort for the 

derivation of unit risk estimates 

Modela Comments 

Cox regression (log-linear) model Inadequate overall statistical fit and poor visual fit in the 

low-exposure region 

Cox regression model with log cumulative exposure Good overall statistical fit but too steep in the lowexposure region 

Optimal 2-piece log-linear spline (knot at 100 ppm × 

days) 

Good overall statistical fit but too steep in the lowexposure region 

Alternate 2-piece log-linear spline (knot at 1,600 

ppm × days) 

Nonsignificant statistical fit and too steep in the lowexposure region 

linear model (RR = 1 + β × exposure) Inadequate overall statistical fit (p = 0.13) and poor visual 

fit in the low-exposure region 

linear model with log cumulative exposure Good overall statistical fit (p = 0.02) but too steep in the 

low-exposure region 

2-piece linear spline model Good overall statistical fit (p = 0.04) but too steep in the 

low-exposure region 

Linear regression of categorical results, 

excluding the highest exposure quartile 

SELECTED. The continuous supralinear models (e.g., 

the log-cumulative-exposure models and the optimal 2­

piece log-linear spline model) are statistically significant 

for lymphoid cancer mortality; however, they are too 

steep in the low-exposure region for the derivation of 

stable unit risk estimates. Thus, the linear regression 

model of categorical results, excluding the highest 

exposure quartile, was used for the derivation of unit risk 

estimates, despite the lack of statistical significance, as it 

was considered a better representation of the data in the 

low-exposure region. Lack of statistical significance is 

not critical given the low statistical power with 

categorical data and the statistical significance of the 

continuous supralinear models, which establishes the 

significance of the exposure-response correlation for the 

underlying data. 

a

All with cumulative exposure as the exposure variable, except where noted, and with a 15-yr lag.
 

Because EtO is DNA-reactive and has direct mutagenic activity (see Section 3.3.3), 

which is one of the cases cited by EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 

2005a) for the use of linear low-dose extrapolation, a linear low-exposure extrapolation was 

performed. The EC01, LEC01, and inhalation unit risk estimate calculated for lymphoid cancer 

mortality from the linear regression of the categorical results are presented in Table 4-5 (the 

incidence results also presented in Table 4-5 are discussed in Section 4.1.1.3 below). The 

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Table 4-5. EC01, LEC01, and unit risk estimates for lymphoid cancera 

Mortality Incidenceb 

EC01 LEC01 Unit risk EC01 LEC01 Unit risk 

Modelc (ppm) (ppm) (per ppm) (ppm) (ppm) (per ppm) 

Cox regression model, log 

cumulative exposure, 

15-yr lag 

0.00441 0.000428 -­d 0.000288 0.0000898 -­d 

Optimal low-exposure log-linear 

spline (knot at 100 ppm × days),e 

cumulative exposure, 

15-yr lag 

0.000982 0.000545 -­d 0.000525 0.000291 -­d 

Alternate low-exposure log-linear 

spline (knot at 1,600 ppm × days),

f 

cumulative exposure, 

15-yr lag 

0.0203 0.0109 -­f 0.0108 0.00583 -­f 

Linear regression of categorical 

results, cumulative exposure, 

15-yr lagg 

0.0564 0.0252 0.397 0.0254 0.0114 0.877 

a

From lifetime continuous exposure. Unit risk = 0.01/LEC01. b

Incidence estimates presented here for comparison; they are derived in Section 4.1.1.3. c

From Dr. Steenland's analyses for males and females combined (see Section D.3 of Appendix D), Cox regression 

models. Note that the EC01 and LEC01 results presented here will not exactly match those presented in Appendix D 

because, although EPA used the regression coefficients reported by Dr. Steenland in Appendix D, the life-table 

analyses using 2004 all-cause mortality rates were redone to be more up-to-date and consistent with the 

cause-specific mortality rates; the results presented in Appendix D were based on life-table analyses using 2000 

all-cause mortality rates. d

Unit risk estimates are not presented for these models because these models were deemed unsuitable for the 

derivation of risks from (low) environmental exposure levels (see text). 

e

Using regression coefficient from low-exposure segment of optimal two-piece log-linear spline model (largest 

likelihood) with knot at 100 ppm × days; see text and Appendix D. Each of the EC01 values is below the value of 

0.0013 ppm roughly corresponding to the knot of 100 ppm × days [(100 ppm × days) × (10 m3

/20 m3) × 

(240 d/365 d) × (365 d/yr)/70 yr = 0.0013 ppm] and, thus, appropriately in the range of the low-exposure segment. f

Using regression coefficient from low-exposure segment of alternate two-piece log-linear spline model (local 

largest likelihood) with a knot at 1,600 ppm × days. Each of the EC01 values is below the value of 0.021 ppm 

roughly corresponding to the knot of 1,600 ppm × days (see footnote d for calculation) and, thus, appropriately in 

the range of the low-exposure segment. Unit risk estimates were not calculated from this model because the fit was 

inferior to that of the optimal model (see text). 

g

Regression coefficient derived from linear regression of categorical Cox regression results from Table 4-2, as 

described in Section 4.1.1.2. Each of the EC01 values is appropriately below the value of 0.090 ppm roughly 

corresponding to the value of about 7,000 ppm × days (see footnote d for calculation) above which the linear 

regression model of the categorical results does not apply (see Figure 4-1). 

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resulting unit risk estimate for lymphoid cancer mortality based on the linear regression of the 

categorical results for both sexes using cumulative exposure with a 15-year lag is 0.397 per ppm. 

EC01 and LEC01 estimates from the other models considered are presented for comparison only, 

to illustrate the differences in model behavior at the low end of the exposure-response range. 

Unit risk estimates are not presented for these other models because, as discussed above, these 

models were deemed unsuitable for the derivation of risks from (low) environmental exposure 

levels. The log cumulative exposure model, with its extreme supralinearity in the lower 

exposure region, and the optimal two-piece log-linear spline model, with its very steep 

low-exposure slope, yield substantially lower EC01 estimates (0.00441 ppm and 0.000982 ppm, 

respectively). Converting the units, the resulting unit risk estimate of 0.397 per ppm from the 

linear regression model of the categorical results corresponds to a unit risk estimate of 

2.17 × 10−4 per μg/m3 for lymphoid cancer mortality.17 

As discussed above, risk estimates based on the all lymphohematopoietic cancer results 

are also derived for comparison. The same methodology presented above for the lymphoid 

cancer results was used for the all lymphohematopoietic cancer risk estimates. Age-specific 

background mortality rates for all lymphohematopoietic cancers for the year 2004 were obtained 

from the NCHS Data Warehouse website (http://www.cdc.gov/nchs/datawh/statab/unpubd/ 

mortabs.htm). The results of Dr. Steenland's reanalyses using the Cox regression models 

presented in the Steenland et al. (2004) paper with data for males and females combined are 

presented in Table 4-2. As for lymphoid cancer and for all hematopoietic cancer in males 

presented in the Steenland et al. (2004) paper, the only statistically significant Cox regression 

model was for log cumulative exposure with a 15-year lag (p = 0.01). The cumulative exposure 

model did not provide an adequate fit to the data and is not considered further here (p = 0.35). 

Because of the problems with the supralinear log cumulative exposure model which are 

discussed for the lymphoid cancers above, EPA again investigated the use of a two-piece 

log-linear spline model to attempt to address the supralinearity of the data while avoiding the 

extreme low-exposure curvature obtained with the log cumulative exposure model. For the all 

lymphohematopoietic cancer mortality data, the largest calculated likelihood was obtained with a 

knot of 500 ppm × days (see Figure D-4a of Appendix D). See Table 4-3 and Section D.4 of 

Appendix D for parameter estimates and fit statistics for the two-piece spline model. As with the 

lymphoid cancer mortality results, however, this model resulted in an apparently excessively 

17Conversion equation: 1 ppm = 1,830 μg/m3 

. 

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steep low-exposure spline (see Figure 4-2), so, again, the linear regression model of the 

categorical results was used to derive the cancer unit risk estimate for this data set.18 

For the weighted linear regression, the results from the Cox regression model with 

categorical cumulative exposure and a 15-year lag (see Table 4-2) were used, excluding the 

highest exposure group, and the approach discussed above for lymphoid cancer mortality. See 

Table 4-3 for the results obtained from the weighted linear regression and Figure 4-2 for a 

graphical presentation of the resulting linear regression model. As discussed above, this linear 

regression model was used to derive the unit risk estimates for all lymphohematopoietic cancer. 

The EC01, LEC01, and inhalation unit risk estimate calculated for all 

lymphohematopoietic cancer mortality from the linear regression model of the categorical results 

are presented in Table 4-6 (the incidence results also presented in Table 4-6 are discussed in 

Section 4.1.1.3 below). The resulting unit risk estimate for all lymphohematopoietic cancer 

mortality based on the linear regression of the categorical results for both sexes using cumulative 

exposure with a 15-year lag is 0.680 per ppm. EC01 and LEC01 estimates from the other models 

considered are presented for comparison only, to illustrate the differences in model behavior at 

the low end of the exposure-response range. Unit risk estimates are not presented for these other 

models because, as discussed above, these models were deemed unsuitable for the derivation of 

risks from (low) environmental exposure levels. The resulting unit risk estimate for all 

lymphohematopoietic cancer mortality from the linear regression model of the categorical results 

is similar to that for lymphoid cancer mortality (70% higher; see Table 4-5). Converting the 

units, the resulting unit risk estimate of 0.680 per ppm corresponds to a unit risk estimate of 

3.72 × 10−4 per μg/m3 for all lymphohematopoietic cancer mortality. 

4.1.1.3. Prediction of Lifetime Extra Risk of Lymphohematopoietic Cancer Incidence 

EPA cancer risk estimates are typically derived to represent an upper bound on increased 

risk of cancer incidence, as from experimental animal incidence data. Cancer data from 

epidemiologic studies are more generally mortality data, as is the case in the Steenland et al. 

(2004) study. For tumor sites with low survival rates, mortality-based estimates are reasonable 

 

18When this assessment was near completion, a two-piece linear spline model (with a linear model, i.e., RR = 1 + β 

× exposure, as the underlying basis for the spline pieces) was attempted, using the just-published approach of 

Langholz and Richardson (2010) to model the individual data with cumulative exposure as a continuous variable; 

however, this model did not alleviate the problem of the excessively steep low-exposure spline segment (see 

Figure D-4c in Appendix D) and was not pursued further for the all lymphohematopoietic cancer data. The 

Langholz and Richardson (2010) approach was also employed to model the all lymphohematopoietic cancer data 

using linear RR models with cumulative exposure and log cumulative exposure as continuous variables; however, 

these linear models similarly did not alleviate the problems of the corresponding log-linear RR models (see 

Figure D-4c in Appendix D). 
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2.0 

2.5 

3.0 

3.5 

4.0 

0 5000 10000 15000 20000 25000 30000 35000 40000 

RR estimate 

cumulative exposure (ppm*days) 

e^(β*exp) 

e^(β*logexp) 

categorical 

2-piec e spline 

linear 

Figure 4-2. RR estimate for all lymphohematopoietic cancer vs. mean exposure (with 15-year lag, unadjusted 

for continuous exposure). 

e^(β*exp): Cox regression results for RR = e(β × exposure); e^(β*logexp): Cox regression results for RR = e(β × ln(exposure)); 

categorical: Cox regression results for RR = e(β × exposure) with categorical exposures; linear: weighted linear regression 

of categorical results, excluding highest exposure group (see text); 2-piece spline: 2-piece log-linear spline model with
 

knot at 500 ppm × days (see text)
 

Source: Steenland reanalyses for male and female combined; see Appendix D (except for linear regression of the
 

categorical results, which was done by EPA).
 
 

 

 

 

 

 

 

 

 

 

 

 

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Table 4-6. EC01, LEC01, and unit risk estimates for all lymphohematopoietic 

cancera 

Mortality Incidenceb 

EC01 LEC01 Unit risk EC01 LEC01 Unit risk 

Modelc (ppm) (ppm) (per ppm) (ppm) (ppm) (per ppm) 

Log cumulative 

exposure, 

15-yr lag 

0.00140 0.000245 -­d 0.000190 0.0000753 -­d 

Low-exposure 

log-linear spline;e 

cumulative 

exposure, 

15-yr lag 

0.00377 0.00231 -­d 0.00216 0.00132 -­d 

Linear regression 

of categorical 

results, 

cumulative 

exposure, 

15-yr lagf 

0.0283 0.0147 0.680 0.0144 0.00746 1.34g 

a

From lifetime continuous exposure. Unit risk = 0.01/LEC01.
 b

Incidence estimates presented here for comparison; they are derived in Section 4.1.1.3.
 c

From Dr. Steenland's analyses for males and females combined (see Appendix D), Cox regression models. Note
 

that the EC01 and LEC01 results presented here will not exactly match those presented in Appendix D because,
 

although EPA used the regression coefficients reported by Dr. Steenland in Appendix D, the life-table analyses
 

using 2004 all-cause mortality rates were redone to be more up-to-date and consistent with the cause-specific 

mortality rates; the results presented in Appendix D were based on life-table analyses using 2000 all-cause mortality 

rates. 

d

Unit risk estimates are not presented for these models because these models were deemed unsuitable for the 

derivation of risks from (low) environmental exposure levels (see text). 

e

Using regression coefficient from low-exposure segment of two-piece log-linear spline model with knot at 500 ppm 

× days; see text and Appendix D. Each of the EC01 values is below the value of 0.0064 ppm roughly corresponding 

to the knot of 500 ppm × days [(500 ppm × days) × (10 m3

/20 m3) × (240 d/365 d) × (365 d/yr)/70 yr = 0.0064 ppm] 

and, thus, appropriately in the range of the low-exposure segment. f

Regression coefficient derived from linear regression of categorical Cox regression results from Table 4-2, as 

described in Section 4.1.1.2. Each of the EC01 values is appropriately below the value of 0.064 ppm roughly 

corresponding to the value of about 5,000 ppm × days (see footnote d for calculation) above which the linear 

regression model of the categorical results does not apply (see Figure 4-2). g

For unit risk estimates below 1, convert to risk per ppb (e.g., 1.34 per ppm = 1.34 × 10−3 per ppb). 

approximations of cancer incidence risk; however, for many lymphohematopoietic cancers, the 

survival rate is substantial, and incidence-based risks are preferred because EPA endeavors to 

protect against cancer occurrence, not just mortality (U.S. EPA, 2005a). 

Therefore, another calculation was done using the same regression coefficients presented 

above (see Section 4.1.1.2), but with age-specific lymphoid cancer incidence rates for the 

relevant subcategories of lymphohematopoietic cancer (NHL, myeloma, and lymphocytic 

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leukemia) for 2000–2004 from SEER (Ries et al., 2007); Tables XIX, XVIII, XIII: both sexes, 

all races) in place of the lymphoid cancer mortality rates in the actuarial program. SEER collects 

good-quality cancer incidence data from a variety of geographical areas in the United States. 

The incidence data used here are from “SEER 17,” a registry of seventeen states, regions, and 

cities covering about 26% of the U.S. population. 

The incidence-based calculation assumes that lymphoid cancer incidence and mortality 

have the same exposure-response relationship for the relative rate of effect from EtO exposure 

and that the incidence data are for first occurrences of primary lymphoid cancer or that relapses 

and secondary lymphoid cancers provide a negligible contribution. (The latter assumption is 

probably sound; the former assumption is more potentially problematic. Because various 

lymphoid subtypes with different survival rates are included in the categorization of lymphoid 

cancers, if the EtO-associated relative rates of the subtypes differ and if the relative 

rate-weighted survival rates for the lymphoid cancers are different from those for the combined 

subtypes, a bias could occur, resulting in either an underestimation or overestimation of the extra 

risk for lymphoid cancer incidence.) 19 Potential concern that the incidence estimates might be 

overestimated would come primarily from the inclusion of multiple myeloma, because that 

subtype has the lowest incidence:mortality ratios (and, thus, if that subtype were driving the 

increased mortality observed for the lymphoid cancer grouping, then including the incidence 

estimates). Multiple myelomas, however, constitute only 25% of the lymphoid cancer cases in 

the cohort, and there is no evidence that multiple myeloma is driving the EtO-induced rates for 

the other subtypes, which have higher incidence:mortality ratios, might inflate the not expected 

to result in an overestimation of the incidence risk estimates; if anything, the incidence risks 

would likely be diluted with the inclusion of the multiple myeloma rates. The incidence-based 

calculation also relies on the fact that the lymphoid cancer incidence rates are excess in lymphoid 

cancer mortality. 20 Thus, using the total lymphoid cancer incidence rates is small when 

 

19Sielken and Valdez-Flores (2009) reject the assumption that lymphohematopoietic cancer incidence and mortality 

have the same exposure-response relationship, reporting that, except at high exposure levels, the exposure-response 

data in the male workers in the NIOSH cohort are consistent with a decreased survival time and suggesting that this 

could explain the observed increases in mortality. However, they do not establish that this is what is occurring, and 

the mechanistic data support an exposure-related increase in incident cancers. See Appendix A.2.20 for a more 

detailed discussion of this issue. 

20According to data from SEER (www.seer.cancer.gov), 25% is below the proportion of multiple myeloma deaths 

one would expect based on age-adjusted U.S. background mortality rates of multiple myeloma, NHL, and chronic 

lymphocytic leukemia, and these 3 subtypes have the same pattern for mortality rates increasing as a function of age 

mostly above age 50, so the comparison with lifetime background rates is reasonable. In addition, the low 

proportion of multiple myeloma deaths in the lymphoid cancer subgrouping cannot be attributed to an 

underrepresentation of blacks, who have incidence rates of multiple myeloma over twice those of whites 

(http://seer.cancer.gov/statfacts/html/mulmy.html), in the cohort, because blacks comprise 16% of the cohort versus 

12.3% in the U.S. population. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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compared with the all-cause mortality rates.21 The resulting EC01 and LEC01 estimates for 

lymphoid cancer incidence from the various models examined are presented in Table 4-5. The 

unit risk estimate for lymphoid cancer incidence from the selected linear regression model of the 

categorical results is 0.877 per ppm. 

The EC01 estimates for cancer incidence range from about 6.5% (log cumulative exposure 

Cox regression model) to 54% (cumulative exposure Cox regression model) of the corresponding 

mortality-based estimates. The difference between incidence and mortality rates cannot explain 

the large discrepancy in EC01 estimates for the log cumulative exposure model. Instead, the 

discrepancy probably reflects the very different results that can occur from a small shift along the 

dose-response curve for the log cumulative exposure model, illustrating the low-dose instability 

of the results from this model. The incidence unit risk estimate from the linear regression model 

of the categorical results is about 120% higher than (i.e., 2.2 times) the mortality-based estimate. 

Overall, as discussed above, the preferred estimate for the unit risk for lymphoid cancer is 

the estimate of 0.877 per ppm (4.79 × 10−4 per μg/m3

) derived, using incidence rates for the 

cause-specific background rates, from the weighted linear regression of the categorical results, 

dropping the highest exposure group. 

As discussed in Section 4.1.1.2, risk estimates based on the results of Dr. Steenland’s 

reanalyses of the all lymphohematopoietic cancer data (see Appendix D and Table 4-2) are also 

derived for comparison. The same methodology presented above for the lymphoid cancer 

incidence results was used for the all lymphohematopoietic cancer incidence risk estimates, and 

the same assumptions apply. Age-specific SEER incidence rates for all lymphohematopoietic 

cancer for the years 2000−2004 were used (Ries et al., 2007); Tables XIX, IX, XVIII, and XIII: 

both sexes, all races). The EC01 and LEC01 estimates for all lymphohematopoietic cancer 

incidence from the different all lymphohematopoietic cancer mortality models examined are 

presented in Table 4-6. The resulting unit risk estimate for all lymphohematopoietic cancer 

incidence from the linear regression of the categorical results is about 2.0-times the 

mortality-based estimate and about 1.5-times the lymphoid cancer incidence estimate (see 

Table 4-5). 

 

21Sielken and Valdez-Flores (2009) suggest that the methods used by EPA to calculate incidence risk estimates in 

the life-table analysis are inappropriate; however, as explained in more detail in Appendix A.2.20, we disagree. For 

the situation where the cause-specific incidence rates are small compared to the all-cause mortality rates, as with 

lymphoid cancer, there is no problem, as Sielken and Valdez-Flores (2009) themselves demonstrate, and, for the 

situation where the cause-specific incidence rates are not negligible compared to the all-cause mortality rates, as 

with breast cancer, an adjustment was made in the analysis to remove those with incident cases from the population 

at risk, i.e., those "surviving" each interval without a diagnosis of breast cancer (see Section 4.1.2.3). See 

Appendix A.2.20 for a more detailed discussion of this issue. 
 

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4.1.2. Risk Estimates for Breast Cancer 

4.1.2.1. Breast Cancer Results From the NIOSH Study 

The Steenland et al. (2004) study discussed above in Section 4.1.1.1 also presents results 

from exposure-response analyses for breast cancer mortality in female workers. Steenland et al. 

(2003) present results of a breast cancer incidence study of a subcohort of the female workers 

from the NIOSH cohort. In addition to the analyses presented in the Steenland et al. (2003) and 

Steenland et al. (2004) papers, Dr. Steenland did subsequent analyses of the breast cancer 

incidence and mortality data sets for EPA; these are discussed below and reported in 

Sections D.1 and D.2 of Appendix D, respectively. 

4.1.2.2. Prediction of Lifetime Extra Risk of Breast Cancer Mortality 

Results from the Cox regression models presented by Steenland et al. (2004), with some 

reanalyses reported by Dr. Steenland in Appendix D (see Section D.2), are summarized in 

Table 4-7. These models were considered for the derivation of unit risk estimates for breast 

cancer mortality in females from continuous environmental exposure to EtO, applying the 

methodologies described in Section 4.1.1.2. 

Table 4-7. Cox regression results for breast cancer mortality in females in 

the NIOSH cohorta

, for models presented in Steenland et al. (2004) 

Exposure variableb p-valuec Coefficient (SE) ORs by categoryd (95% CI) 

Cumulative exposure, 20-yr 

lage 

0.06 0.0000122 

(0.00000641) 

Log cumulative exposure, 20­

yr lagf 

0.01 0.084 (0.035) 

Categorical cumulative 

exposure, 20-yr lagf 

0.07 1.00, 1.76 (0.91–3.43), 1.77 (0.88−3.56), 

1.97 (0.94–4.06), 3.13 (1.42–6.92) 

a

Based on 103 breast cancer (ICD-9 174,175) deaths.
 b

Cumulative exposure is in ppm × days.
 c

p-values reported by Steenland et al. (2004). d

Exposure categories are 0; >0–646; 647–2,779; 2,780–12,321; >12,322 ppm × days. e

From reanalyses in Section D.2 of Appendix D; Steenland et al. (2004) reported the Cox regression results for 

cumulative exposure with no lag. f

From Table 8 of Steenland et al. (2004). 

United States age-specific all-cause mortality rates for 2000 for females of all race groups 

combined (Miniño et al., 2002) were used to specify the all-cause background mortality rates in 

This document is a draft for review purposes only and does not constitute Agency policy. 

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This document is a draft for review purposes only and does not constitute Agency policy. 

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the actuarial program (life-table analysis). The National Center for Health Statistics 1997–2001 

cause-specific background mortality rates for invasive breast cancers in females were obtained 

from a SEER report (Ries et al., 2004). The risks were computed up to age 85 for continuous 

exposures to EtO, conversions were made between occupational EtO exposures and continuous 

environmental exposures, and 95% UCLs were calculated for the relative rates, as described 

above. 

The only statistically significant Cox regression model presented by Steenland et al. 

(2004) for breast cancer mortality in females was for log cumulative exposure with a 20-year lag 

(p = 0.01). However, as for the lymphohematopoietic cancers in Section 4.1.1, using the log 

cumulative exposure model to estimate the risks from low environmental exposures is 

problematic because this model is highly supralinear and results are unstable for low exposures 

(see Figure 4-3). The cumulative exposure model, which is typically used and which is stable at 

low exposures, was nearly statistically significant (p = 0.06 with a 20-year lag; see Section D.2 

of Appendix D) in terms of the global fit to the data; however, at low exposures, the Cox 

regression model with cumulative exposure is sublinear and does not reflect the apparent 

supralinearity of the breast cancer mortality data (see Figure 4-3). 

In a 2006 external review draft of this assessment (U.S. EPA, 2006a), which relied on the 

original published results of Steenland et al. (2004), EPA proposed that the best way to reflect 

the exposure-response relationship in the lower exposure region, which is the region of interest 

for low-exposure extrapolation, was to do a weighted linear regression of the results from the 

Cox regression model with categorical cumulative exposure and a 20-year lag. In addition, the 

highest exposure group was not included in the regression to alleviate some of the “plateauing” 

in the exposure-response relationship at higher exposure levels and to provide a better fit to the 

lower exposure data. Linear modeling of categorical epidemiologic data and elimination of the 

highest exposure group(s) in certain circumstances to obtain a better fit of low-exposure data are 

both standard techniques used in EPA dose-response assessments (U.S. EPA, 2005a). However, 

as discussed in Section 4.1.1.2 for the similarly supralinear lymphohematopoietic cancer data, 

the SAB panel that reviewed the draft assessment recommended that EPA employ models using 

the individual exposure data as an alternative to modeling the published grouped data (SAB, 

2007). Consequently, it was determined that, using the full data set, an alternative way to 

address the supralinearity of the data (while avoiding the extreme low-exposure curvature 

obtained with the log cumulative exposure model) might be to use a two-piece spline model, and 

Dr. Steenland was commissioned to do the spline analyses using the full data set with cumulative 

exposure as a continuous variable. His findings are reported in Section D.2 of Appendix D, and 

the results for the breast cancer mortality analyses are summarized below. 
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0 5000 10000 15000 20000 25000 30000 35000 

RR estimate 

mean cumulative exposure (ppm*days) 

e^(B*logexp) 

linear 

categorical 

e^(B*exp) 

spline13000 

spline700 

Figure 4-3. RR estimate for breast cancer mortality vs. mean exposure (with 20-year lag, unadjusted for 

continuous exposure). 

e^(B*exp): Cox regression results for RR = e(β × exposure); e^(B*logexp): Cox regression results for RR = e(β × ln(exposure)); 

categorical: Cox regression results for RR = e(β × exposure) with categorical exposures; linear: weighted linear regression 

of categorical results, excluding highest exposure group (see text); spline700(13000): 2-piece log-linear spline model 

with knot at 700 (13,000) ppm × days (see text).
 

Source: Steenland reanalyses with 20-year lag; see Section D.2 of Appendix D (except for linear regression of the
 

categorical results, which was done by EPA).
 
This document is a draft for review purposes only and does not constitute Agency policy. 

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For the two-piece log-linear spline modeling approach, as described in Section 4.1.1.2 

and discussed more fully in Section D.2 of Appendix D, the Cox regression model was the 

underlying basis for the splines which were fit to the breast cancer mortality exposure-response 

data (cumulative exposure is used here, with a 20-year lag), and thus, log RR is a function of two 

lines which join at a single point of inflection, called a “knot.” The shape of the two-piece 

log-linear spline model, in particular the slope in the low-exposure region, depends on the 

location of the knot. For this assessment, knot selection was first attempted by trying different 

knots in increments of 1,000 ppm × days, starting at 1,000 ppm × days, and choosing the one that 

resulted in the largest model likelihood. The model likelihood did not actually change much 

across the different trial knots (see Figure D-2a of Appendix D), but it did change slightly, and 

this approach indicated that a knot of 13,000 ppm × days for the breast cancer mortality data 

yielded the largest likelihood.22 However, a visual inspection of the model fit suggested that the 

two-piece log-linear spline model with a knot at 13,000 ppm × days underestimates the 

low-exposure results (see Figure 4-3). Thus, knots below 1,000 ppm × days in increments of 

100 ppm × days were investigated, and it was revealed that a knot at 700 ppm × days yielded a 

model with a likelihood that exceeded that for the model with the knot at 13,000 ppm × days (see 

Figures D-2a and D-2a' of Appendix D).23 The model with the knot at 700 ppm × days, 

however, has a seemingly implausibly steep low-exposure slope, as was the case with the largest 

likelihood models for the lymphohematopoietic cancers above. Moreover, neither the model 

with the knot at 700 ppm × days nor the one with the knot at 13,000 ppm × days was statistically 

significant overall, although both were nearly so (p = 0.067 and 0.074, respectively). See 

Table 4-8 and Section D.2 of Appendix D for parameter estimates and fit statistics for the two 

spline models. Because there was low confidence in the steep low-exposure slope from the 

two-piece spline model with the largest likelihood, which is based on a relatively small number 

of cases in that exposure range, and because the model with the knot at 13,000 ppm × days, 

which had a local largest likelihood, appeared to have a poor fit to the low-exposure data, it was 

determined that the weighted linear regression of the categorical results was more appropriate as 

the basis for the unit risk estimates. For more discussion of the breast cancer mortality 

exposure-response modeling using the continuous data, see Section D.2 of Appendix D. 

For the weighted linear regression, the results from the Cox regression model with 

categorical cumulative exposure (and a 20-year lag) presented in Table 4-7 were used, excluding 

 

22Using the log-linear spline model with the knot at 13,000 ppm × days, a regression coefficient of 0.0000607 per 

ppm × day (SE = 0.0000309 per ppm × day) was obtained for the low-exposure spline segment (see Appendix D). 23Using the optimal two-piece log-linear spline model with the knot at 700 ppm × days, a regression coefficient of 

0.0006877 per ppm × day (SE = 0.0004171 per ppm × day) was obtained for the low-exposure spline segment (see 

Appendix D). 
 

 

 

 

 

 

 

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the highest exposure group, and the approach discussed above for the lymphoid cancers (see 

Section 4.1.1.2).24 Mean and median exposures for the cumulative exposure groups were
 

provided by Dr. Steenland (see Appendix D).25 See Table 4-8 for the results obtained from the 

weighted linear regression of the categorical results and mean exposures and Figure 4-3 for a 

depiction of the resulting linear regression model.
 

The linear regression of the categorical results and the actuarial program (life-table 

analysis) were used to estimate the exposure level (ECx) and the associated 95% lower
 

confidence limit (LECx) corresponding to an extra risk of 1% (x = 0.01). As discussed in 

Section 4.1.1.2, a 1% extra risk level is a more reasonable response level for defining the POD 

for these epidemiologic data than 10%. 

Table 4-8. Exposure-response modeling results for breast cancer mortality 

in females in the NIOSH cohort for models not presented by Steenland et al. 

(2004) 

Modela p value Coefficient (SE) 

Optimal 2-piece log-linear spline (knot at 700 

ppm × days) 

0.067 low-exposure spline segment: 

B1 = 0.000688 (0.000417) 

Alternate 2-piece log-linear spline (knot at 

13,000 ppm × days) 

0.074 low-exposure spline segment: 

B1 = 0.0000607 (0.0000309) 

Linear regression of categorical results, 

excluding the highest exposure quartile 

0.09 0.000201 (0.000120) 

a

All with cumulative exposure in ppm × days as the exposure variable and with a 20-yr lag; based on 103 breast 

cancer deaths. 

b

p-values from likelihood ratio test, except for linear regression of categorical results, where Wald p-values are 

reported. 

Source: Additional analyses performed by Dr. Steenland (see Section D.2 of Appendix D), except for the linear 

regression of the categorical results, which was performed by EPA. 

Because EtO is DNA-reactive and has direct mutagenic activity (see Section 3.3.3), 

which is one of the cases cited by EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 

2005a) for the use of linear low-dose extrapolation, a linear low-exposure extrapolation was
 

24Equations for this weighted linear regression approach are presented in Rothman (1986) and summarized in
 

Appendix F.

25Mean exposures for females with a 20-year lag for the categorical exposure quartiles in Table 8 of Steenland et al.
 

(2004) were 276; 1,453; 5,869; and 26,391 ppm × days. Median values were 250; 1,340; 5,300; and 

26,676 ppm × days. These values are for the risk sets but should provide a good approximation to the full cohort
 

values.
 

This document is a draft for review purposes only and does not constitute Agency policy. 

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This document is a draft for review purposes only and does not constitute Agency policy. 

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performed. The EC01, LEC01, and inhalation unit risk estimate calculated for breast cancer 

mortality from the linear regression model of the categorical results are presented in Table 4-9. 

The resulting unit risk estimate for breast cancer mortality based on the linear regression of the 

categorical results using cumulative exposure with a 20-year lag is 0.513 per ppm. EC01 and 

LEC01 estimates from the other models considered are presented for comparison only, to 

illustrate the differences in model behavior at the low end of the exposure-response range. Unit 

risk estimates are not presented for these other models because, as discussed above, these models 

were deemed unsuitable for the derivation of risks from (low) environmental exposure levels. 

As one can see, the standard Cox regression cumulative exposure model, with its extreme 

sublinearity in the lower exposure region, yields a substantially higher EC01 estimate 

(0.530 ppm) than the EC01 estimate of 0.0387 ppm from the linear regression of the categorical 

results, while the log cumulative exposure Cox regression model, with its extreme supralinearity 

in the lower exposure region, yields a substantially lower EC01 estimates (0.00112 ppm). The 

estimates from the two-piece log-linear spline models flank the result from the linear regression 

of the categorical results more closely. The steep low-exposure segment of the two-piece 

log-linear spline model with the optimal knot at 700 ppm × days yields an EC01 estimate of 

0.00941 ppm, whereas the shallower low-exposure slope from the two-piece log-linear spline 

model with the local maximum likelihood suggesting a knot at 13,000 ppm × days yields an EC01 

estimate of 0.107 ppm. Converting the units, the unit risk estimate of 0.513 per ppm for breast 

cancer mortality from the linear regression model of the categorical results corresponds to a unit 

risk estimate of 2.80 × 10−4 per μg/m3 . 

4.1.2.3. Prediction of Lifetime Extra Risk of Breast Cancer Incidence 

As discussed in Section 4.1.1.3, risk estimates for cancer incidence are preferred to 

estimates for cancer mortality, especially for cancer types with good survival rates, such as breast 

cancer. In the case of female breast cancer in the NIOSH cohort, there is a corresponding 

incidence study (Steenland et al., 2003) with exposure-response results for breast cancer 

incidence, so one can estimate cancer incidence risks directly rather than estimate them from 

mortality data. The incidence study used a (sub)cohort of 7,576 (76%) of the female workers 

from the original cohort. Cohort eligibility for the incidence study was restricted to the female 

workers who had been employed at 1 of the 14 plants for at least 1 year, owing to cost 

considerations and the greater difficulties in locating workers with short-term employment. 

Interviews were sought from all the women in the incidence study cohort or their next-of-kin 

(18% of the cohort had died). Completed interviews were obtained for 5,139 (68%) of the 
 

 

 

 

 

 

 

 

 

 

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Table 4-9. EC01, LEC01, and unit risk estimates for breast cancer mortality 

in femalesa 

Model 

EC01 

(ppm) 

LEC01 

(ppm) 

Unit risk 

(per ppm) 

Log cumulative exposure, 20­

yr lagb 0.00112 0.000219 -­c 

Cumulative exposure, 20-yr 

lagd 0.530 0.285 -­c 

Low-exposure log-linear 

spline, cumulative exposure 

with knot at 700 ppm × days, 

20-yr lage 

0.00941 0.00471 -­c 

Low-exposure log-linear 

spline, cumulative exposure 

with knot at 13,000 ppm × 

days, 20-yr lagf 

0.107 0.0580 -­c 

Categorical; cumulative 

exposure, 20-yr lagg 0.0387 0.0195 0.513 

a

From lifetime continuous exposure. Unit risk = 0.01/LEC01.
 b

From Table 8 of Steenland et al. (2004), Cox regression model.
 c

Unit risk estimates are not presented for these models because these models were deemed unsuitable for the 

derivation of risks from (low) environmental exposure levels (see text).
 d

From Dr. Steenland's reanalyses (see Table D-2d of Appendix D), Cox regression model.
 e

From low-exposure segment of two-piece log-linear spline model with largest model likelihood and a knot at 

700 ppm × days; see text and Table D-2c of Appendix D. The EC01 value is below the value of 0.009 ppm roughly 

corresponding to the knot of 700 ppm × days [(700 ppm × days) × (10 m3

/20 m3) × (240 d/365 d) × (365 d/yr)/70 yr 

= 0.0013 ppm] and, thus, appropriately in the range of the low-exposure segment. f

From low-exposure segment of two-piece log-linear spline model with a local largest likelihood for knot at 

13,000 ppm × days; see text and Table D-2f of Appendix D. The EC01 value is below the value of 0.17 ppm roughly 

corresponding to the knot of 13,000 ppm × days (see calculation in footnote e) and, thus, appropriately in the range 

of the low-exposure segment. g

Regression coefficient derived from linear regression of categorical Cox regression results from Table 8 of 

Steenland et al. (2004), as described in Section 4.1.2.2. The EC01 value is appropriately below the value of 0.064 

ppm roughly corresponding to the value of about 5,000 ppm × days (see footnote e for calculation) above which the 

linear regression model of the categorical results does not apply (see Figure 4-3). 

7,576 women in the cohort. The investigators also attempted to acquire breast cancer incidence 

data for the cohort from cancer registries (available for 9 of the 11 states in which the plants were 

located) and death certificates; thus, results are presented for both the full cohort (n = 7,576) and 

the subcohort of women with interviews (n = 5,139). For additional details and discussion of the 

Steenland et al. (2003) study, see Section A.2.16 of Appendix A. 

Steenland et al. (2003) identified 319 incident cases of breast cancer in the cohort through 

1998. Interview (questionnaire) data were available for 73% (233 cases). Six percent were 

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carcinoma in situ (20 cases). Steenland et al. (2003) performed internal exposure-response 

analyses similar to those described in their 2004 paper and in Section 4.1.1.1 above. Controls for 

each case were selected from the cohort members without breast cancer at the age of diagnosis of 

the case. Cases and controls were matched on race. Of the potential confounders evaluated for 

those with interviews, only parity and breast cancer in a first-degree relative were important 

predictors of breast cancer, and only these variables were included in the final models for the 

subcohort analyses. In situ cases were included with invasive breast cancer cases in the analyses; 

however, the in situ cases represent just 6% of the total, and excluding them reportedly did not 

greatly affect the results. 

From the Steenland et al. (2003) internal analyses (Cox regression) using the full cohort, 

the best-fitting model with exposure as a continuous variable was for (natural) log cumulative 

exposure, lagged 15 years (p = 0.05). Duration of exposure, lagged 15 years, provided a slightly 

better fitting model. Models using maximum or average exposure did not fit as well. In 

addition, use of a threshold model did not provide a statistically significant improvement in fit. 

For internal analyses using the subcohort with interviews, the cumulative exposure and log 

cumulative exposure models, both lagged 15 years, and the log cumulative exposure model with 

no lag all fit almost equally well, and the duration of exposure (also lagged 15 years) model fit 

slightly better. Results of the Cox regression analyses for the cumulative and log cumulative 

exposure models, with 15-year lags, are shown in Table 4-10, and these are the results 

considered for the unit risk calculations. The models using duration of exposure are less useful 

for estimating exposure-related risks, duration of exposure and cumulative exposure are 

correlated, and the fits for these models are only marginally better than those with cumulative 

exposure. The log cumulative exposure model with no lag was considered less biologically 

realistic than the corresponding model with a 15-year lag because some lag period would be 

expected for the development of breast cancer. Furthermore, although initial risk estimates 

based on the full cohort results are calculated for comparison, the preferred estimates are those 

based on the subcohort with interviews because the subcohort should have more complete case 

ascertainment and has additional information available on potential breast cancer confounders. 

For the actuarial program (life-table analysis), U.S. age-specific all-cause mortality rates 

for 2004 for females of all race groups combined (Arias, 2007) were used to specify the all-cause 

background mortality rates. Because breast cancer incidence rates are not negligible compared 

to all-cause mortality rates, the all-cause mortality rates in the life-table analysis were adjusted to 

reflect women dying or being diagnosed with breast cancer in a given age interval. All-cause 

mortality rates and breast cancer incidence rates were summed, and breast cancer mortality rates 
 

 

 

 

 

 

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Table 4-10. Cox regression results for breast cancer incidence in females 

from the NIOSH cohort, for the models presented by Steenland et al. 

(2003)a,b 

Cohort Exposure variablec 

Coefficient (SE), 

p-valued ORs by categorye (95% CI) 

Full incidence 

study cohort 

n = 7,576 

319 cases 

Cumulative exposure, 15-yr 

lag 

0.0000054 

(0.0000035), 

p = 0.12 

Log cumulative exposure, 15­

yr lag 

0.037 (0.019), 

p = 0.05 

Categorical cumulative 

exposure, 15-yr lag 

1.00, 1.07 (0.72–1.59), 1.00 

(0.67−1.50), 1.24 (0.85–1.90), 1.17 

(0.78–1.78), 1.74 (1.16−2.65) 

Subcohort with 

interviews 

n = 5,139 

233 cases 

Cumulative exposure, 15-yr 

lag 

0.0000095 

(0.0000041), 

p = 0.02 

Log cumulative exposure, 15­

yr lag 

0.050 (0.023), 

p = 0.03 

Categorical cumulative 

exposure, 15-yr lag 

-­f 1.00, 1.06 (0.66–1.71), 0.99 

(0.61−1.60), 1.24 (0.76–2.00), 1.42 

(0.88–2.29), 1.87 (1.12−3.10) 

a

Invasive breast cancer (ICD-9 174) and carcinoma in situ (ICD-9 233.0).
 b

Cases and controls matched on age and race (white/nonwhite). Full cohort models include cumulative exposure 

and categorical variable for yr of birth (quartiles). Subcohort models include cumulative exposure, categorical
 

variables for yr of birth (quartiles), breast cancer in first-degree relative, and parity.
 c

Cumulative exposure is in ppm × days.
 d

p-values for exposure variable from Wald test, as reported by Steenland et al. (2003).
 e

Exposure categories are 0, >0–647, 647–2,026, 2,026–4,919, 4,919–14,620, >14,620 ppm × days.
 f

p-value for the addition of the categorical exposure variables = 0.11 (email dated 5 March 2010 from Kyle 

Steenland, Emory University, to Jennifer Jinot, EPA).
 

Source: Tables 4 and 5 of Steenland et al. (2003).
 

were subtracted so that those dying of breast cancer were not counted twice (i.e., as deaths and as 

incident cases of breast cancer). The National Center for Health Statistics 2002–2006 mortality 

rates for invasive breast cancer in females were obtained from a SEER report (Horner et al., 

2009). The SEER report also provided SEER-17 incidence rates for invasive and in situ breast 

cancer. The Cox regression results reported by Steenland et al. (2003) are for invasive and in 

situ breast cancers combined. It is consistent with EPA’s Guidelines for Carcinogen Risk 

Assessment (U.S. EPA, 2005a) to combine these two tumor types because the in situ tumors can 

progress to invasive tumors. Thus, the primary risk calculations in this assessment use the sum 

of invasive and in situ breast cancer incidence rates for the cause-specific background rates. 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 4-29 DRAFT―DO NOT CITE OR QUOTE
 
This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 4-30 DRAFT―DO NOT CITE OR QUOTE
 

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Comparison calculations were performed using just the invasive breast cancer incidence rates for 

the cause-specific rates; this issue is further discussed in Section 4.1.3 on sources of uncertainty. 

The risks were computed up to age 85 for continuous exposures to EtO, conversions were made 

between occupational EtO exposures and continuous environmental exposures, and 95% UCLs 

were calculated for the relative rates, as described in Section 4.1.1.2 above. 

For breast cancer incidence in both the full cohort (see Figure 4-4) and the subcohort with 

interviews (see Figure 4-5), the low-exposure categorical results suggest a more linear 

low-exposure exposure-response relationship than that obtained with either the continuous 

variable log cumulative exposure (supralinear) or cumulative exposure (sublinear) Cox 

regression models, the two of which lie on opposite sides of the low-exposure categorical results. 

Thus, as with the lymphohematopoietic cancer and the breast cancer mortality results above, 

EPA proposed in the 2006 Draft Assessment (U.S. EPA, 2006a), which relied on the original 

published results of Steenland et al. (2003), that the best way to reflect the data in the lower 

exposure region, which is the region of interest for low-exposure extrapolation, was to do a 

weighted linear regression of the results from the model with categorical cumulative exposure 

(with a 15-year lag). In addition, the highest exposure group was not included in the regression 

to provide a better fit to the lower-exposure data (The RR estimates for the highest exposure 

quintiles suggest somewhat supralinear exposure-response relationships for both the full cohort 

and the subcohort with interviews and supralinearity is evidenced in the subcohort with 

interviews by the strong influence of the top 5% of cumulative exposures on dampening the 

slope of the [cumulative exposure] Cox regression model [see Section D.1 and Figure D-1d of 

Appendix D]. Moreover, there is more uncertainty in using the mean cumulative exposure to 

represent the range of exposures in a highest exposure categorical group because such groups 

contain a wider range of exposures; for example, for the subcohort with interviews, the highest 

exposure quintile contains exposures ranging from about 14,500 ppm × days to over 

250,000 ppm × days). Linear modeling of categorical (i.e., grouped) epidemiologic data and 

elimination of the highest exposure group(s) under certain circumstances to obtain a better fit of 

low-exposure data are both standard techniques used in EPA dose-response assessments (U.S. 

EPA, 2012, 2005a). However, as discussed in Section 4.1.1.2 for the lymphohematopoietic 

cancer data, the SAB panel that reviewed the draft assessment recommended that EPA not rely 

on the published grouped data but, rather, do additional analyses using the individual data (SAB, 

2007). 

Consequently, it was determined that using the individual data, a better way to address 

the apparent supralinearity of the data (while avoiding the extreme low-exposure curvature 

obtained with the log cumulative exposure Cox regression model) might be to use a two-piece 
This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 4-31 DRAFT―DO NOT CITE OR QUOTE

1 

1.1 

1.2 

1.3 

1.4 

1.5 

1.6 

1.7 

1.8 

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 

RR estimate 

mean cumulative exposure (ppm * days) 

e^(β*exp) 

e^(β*logexp) 

linear 

categorical 

Figure 4-4. RR estimate for breast cancer incidence in full cohort vs. mean exposure (with 15-year lag, 

unadjusted for continuous exposure). 

e^(β*exp): Cox regression results for RR = e(β × exposure); e^(β*logexp): Cox regression results for RR = e(β × ln(exposure)); 

categorical: Cox regression results for RR = e(β × exposure) with categorical exposures; linear: weighted linear regression 

of categorical results, excluding highest exposure group (see text). 

Source: Steenland et al. (2003) (except for linear regression of the categorical results, which was done by EPA). 
This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 4-32 DRAFT―DO NOT CITE OR QUOTE

Figure 4-5. RR estimate for breast cancer incidence in subcohort with interviews vs. mean exposure (with 

15-year lag, unadjusted for continuous exposure). 

e^(β*exp): Cox regression results for RR = e(β × exposure); e^(β*logexp): Cox regression results for RR = e(β × ln(exposure)); 

categorical: Cox regression results for RR = e(β × exposure) with categorical exposures; e^(β*sqrtexp): Cox regression 

results for RR = e(β × sqrt(exposure)); linear regression: weighted linear regression of categorical results, excluding highest 

exposure group (see text); log-linear and linear spline: 2-piece spline models, both with knots at 5,800 ppm × days (see
 

text); linear: RR = 1 + β × exposure, with exposure as a continuous variable; 1 + β*logexp: RR = 1 + β ×
	

ln(exposure), exposure continuous. 

Sources: Steenland et al. (2003) except for Steenland 2-piece spline models (see Appendix D) and linear regression of
 

the categorical results, which was done by EPA.
 
This document is a draft for review purposes only and does not constitute Agency policy. 

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spline model, and Dr. Steenland was commissioned to do the spline analyses. His findings are 

reported in Appendix D (see Section D.1), and the results for the breast cancer incidence 

analyses are summarized below. Note that, for the two-piece spline analyses, only the data from 

the subcohort with interviews and for the invasive and in situ breast cancers combined were 

analyzed, because this was the preferred data set, as discussed above. (Dr. Steenland also 

employed a cubic spline model as a semiparametric approach to visualize the underlying 

exposure-response relationship; however, this approach produces an overly complicated function 

for an empirical model, as opposed to a biologically based model, and was not used for risk 

assessment purposes. In addition, Dr. Steenland investigated the use of a Cox regression model 

with a square-root transformation of cumulative exposure; however, this approach, though less 

extreme than using the log transformation of cumulative exposure, also yields a notably 

supralinear model [see Figure 4-5], which can result in unstable low-exposure risk estimates. 

The model results for both the cubic spline and square-root transformation models are included 

in Appendix D, Section D.1, but are not considered further here. EPA chose to pursue the 

development of two-piece spline models, which avoid the problem of unstable risk estimates 

from supralinear curvature in the low-exposure region and provide a more general approach to 

modeling supralinear exposure-response data, as opposed to using random, arbitrary 

power-transformations of the exposure variable.) 

For the two-piece log-linear spline modeling approach, as described in Section 4.1.1.2 

and discussed more fully in Appendix D, the Cox regression model was the underlying basis for 

the splines which were fit to the breast cancer incidence exposure-response data (cumulative 

exposure is used here, with a 15-year lag), and, thus, log RR is a function of two lines which join 

at a single point of inflection, called a “knot.” The shape of the two-piece spline model, in 

particular the slope in the low-exposure region, depends on the location of the knot. For this 

assessment, the knot was generally selected by trying different knots in increments of 1,000 ppm 

× days, starting at 1,000 ppm × days, and choosing the one that resulted in the largest model 

likelihood. In some cases, increments of 100 ppm × days were used between the increments of 

1,000 ppm × days to fine-tune the knot selection. The model likelihood did not actually change 

much across the different trial knots (see Figure D-1a of Appendix D), but it did change slightly, 

and a knot of 5,800 ppm × days for the breast cancer incidence data based on the largest 

likelihood was chosen. The two-piece log-linear spline model with this knot provided a 

statistically significant fit to the data (p = 0.01 for the addition of the exposure terms), as well as 

a good visual fit (see Figure 4-5). 

A two-piece linear spline model was also fitted, using the just-published approach of 

Langholz and Richardson (2010). This model is similar to the log-linear spline model discussed 
 

 

 

 

 

 

 

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above; however, for the linear spline model, the underlying basis for the splines is a linear model 

(i.e., RR = 1 + β × z, where z represents the covariate data, including exposure, and β are the 

parameters being estimated). The knot was selected as for the log-linear spline model, and the 

same knot of 5,800 ppm × days yielded the largest likelihood (see Figure D-1h of Appendix D) 

and was also chosen for the two-piece linear spline model. The two-piece linear spline model 

with this knot provided a statistically significant fit to the data (p = 0.002 for the addition of the 

exposure terms), as well as a good visual fit (see Figure 4-5). Because this model provided a 

better fit than the log-linear spline model, i.e., it had a lower AIC, the two-piece linear spline 

model was selected as the preferred model for the unit risk estimates for breast cancer incidence. 

See Table 4-11 and Section D.1 of Appendix D for parameter estimates and fit statistics for the 

two spline models. 

Table 4-11. Exposure-response modeling results for breast cancer incidence 

in females from the NIOSH cohort for models not presented by Steenland et 

al. (2003) 

Modela p valueb Coefficient (SE) 

Full incidence study cohortc 

Linear regression of categorical results, 

excluding the highest exposure quintile 

0.33 0.0000264 (0.0000269) 

Subcohort with interviewsd 

2-piece log-linear spline (knot at 5,800 ppm × 

days) 

0.01 low-exposure spline segment: 

B1 = 0.0000770 (0.0000317) 

2-piece linear spline (knot at 5,800 ppm × days) 0.002 low-exposure spline segment: 

B1 = 0.000119 (0.0000677) 

linear 0.003 0.0000304 (0.0000175) 

linear with log cumulative exposure 0.01 0.0713 (0.0392) 

Linear regression of categorical results, 

excluding the highest exposure quintile 

0.16 0.0000517 (0.0000369) 

a

All with cumulative exposure in ppm × days as the exposure variable and with a 15-yr lag.
 b

p-value for addition of exposure variables from likelihood ratio test, except for the linear regressions of categorical
 

results, where Wald p-values are reported.
 c

319 breast cancer cases.
 d

233 breast cancer cases.
 

Source: Additional analyses performed by Dr. Steenland (see Section D.2 of Appendix D), except for the linear
 

regressions of categorical results, which were performed by EPA using the equations of Rothman (1986) presented
 

in Appendix F.
 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 4-34 DRAFT―DO NOT CITE OR QUOTE
 
This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 4-35 DRAFT―DO NOT CITE OR QUOTE 

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Linear RR models with cumulative exposure and log cumulative exposure as continuous 

variables were also investigated using the approach of Langholz and Richardson (2010), and 

these models fit better than the corresponding log RR models (see Table 4-11 and Section D.1 of 

Appendix D) although not as well as the two-piece linear spline model, which had the lowest 

AIC. Risk estimates based on the linear model with cumulative exposure are developed for 

comparison, but the linear model with log cumulative exposure is too steep in the low-exposure 

region (see Figure 4-5) and is not considered further. For more details of the breast cancer 

incidence exposure-response modeling, see Section D.1 of Appendix D. 

Risk estimates based on the original linear regression analyses of the categorical results 

are also presented for comparison. For the approach of using a weighted linear regression of the 

results from the Cox regression model with categorical cumulative exposure (and a 15-year lag), 

excluding the highest exposure group, the weights used for the ORs were the inverses of the 

variances, which were calculated from the confidence intervals.26 Mean and median exposures 

for the cumulative exposure groups for the full cohort were kindly provided by Dr. Steenland 

(email dated April 21, 2004, from Kyle Steenland, Emory University, to Jennifer Jinot, EPA).27 

The mean values were used for the weighted regression analysis because the (arithmetic) mean 

exposures best represent the model’s linear relationship between exposure and cancer response. 

Differences between means and medians were not large for the females, especially for the lower 

four quintiles. If the median values had been used, a slightly larger regression coefficient would 

have been obtained, resulting in slightly larger risk estimates. Although the exposure values are 

for risk sets from the full cohort, they should be reasonably close to the values for the subcohort 

with interviews. See Table 4-11 for the results from the weighted linear regressions of the 

categorical results and Figures 4-4 and 4-5 for a depiction of the resulting linear regression 

models. 

As the subcohort with interviews from the NIOSH incidence study cohort provides the 

preferred data set for the derivation of unit risk estimates for breast cancer, a summary of all the 

models considered for modeling the breast cancer exposure-response data from the subcohort 

and the judgments made about model selection is provided in Table 4-12. See Figure 4-5 for 

visual representations of the models. See Tables 4-10 and 4-11 and Section D.1 of Appendix D 

for parameter estimates, p-values, and other fit statistics. Three of the models presented in 

 

26Equations for this weighted linear regression approach are presented in Rothman (1986) and summarized in 

Appendix F.

27Mean exposures for females with a 15-year lag for the exposure categories in Table 3 of Steenland et al. (2003) 

were 280; 1,241; 3,304; 8,423; and 36,022 ppm × days. Median values were 253; 1,193; 3,241; 7,741; and 26,597 

ppm × days. These values are for the risk sets but should provide a good approximation to the full cohort values. 
 

 

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Table 4-12. Models considered for modeling the exposure-response data for 

breast cancer incidence in females in the subcohort with interviews from the 

NIOSH incidence study cohort for the derivation of unit risk estimates 

Modela AICb Comments 

Cox regression (log-linear) 

model 

1956.675 Good overall statistical fit but poor visual fit (too 

shallow) in the low-exposure region. 

Cox regression model with log 

cumulative exposure 

1956.176 Good overall statistical fit but too steep in the lowexposure region. 

Cox regression model with 

square-root transformation of 

exposure 

1953.028 Good overall statistical fit but still notably supralinear 

(steep) in the low-exposure region, though less so than 

with the log transformation; also preference was given to 

the two-piece spline models as providing a more general 

approach to modeling supralinear data. 

Linear regression of categorical 

results, excluding the highest 

exposure quintile 

-­c Not statistically significant, though that is unsurprising 

since the approach, which is based on categorical data, 

has low statistical power; preference given to models that 

treated exposure as a continuous variable, as 

recommended by the SAB, and that also provided 

reasonable representations of the low-exposure region. 

2-piece log-linear spline model 

(knot at 5,800 ppm × days) 

1954.485 Good overall statistical fit and good visual fit; preference 

given to the 2-piece linear spline model because it had a 

better statistical fit (lower AIC) and better apparent fit to 

the lower-exposure data. 

linear model (RR = 1 + β × 

exposure) 

1952.260 Good overall statistical fit and good visual fit; preference 

given to the 2-piece linear spline model because it had a 

better statistical fit (lower AIC) and better apparent fit to 

the lower-exposure data. 

linear model with log 

cumulative exposure 

1954.267 Good overall statistical fit but too steep in the lowexposure region. 

2-piece linear spline model 

(knot at 5,800 ppm × days) 

1950.935 SELECTED. Good overall statistical fit and good visual 

fit; lower AIC than 2-piece log-linear spline and linear 

model and better apparent fit to the lower-exposure data. 

a

All with cumulative exposure as the exposure variable, except where noted, and with a 15-yr lag. b

AIC = 2p-2LL, where p = # of parameters and LL = ln(likelihood), assuming two exposure parameters for the 

two-piece spline models. c

Not calculated. 

This document is a draft for review purposes only and does not constitute Agency policy. 

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This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 4-37 DRAFT―DO NOT CITE OR QUOTE
 

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Table 4-12 had a good overall statistical fit, a good visual fit, and a credible low-exposure slope 

(the linear and log-linear two-piece spline models and the [continuous] linear RR model). To 

better compare these models, they are plotted again in Figure 4-6 this time against the categorical 

data in deciles. Earlier categorical results in this assessment were based on the (log-linear) Cox 

regression model; however, the deciles in Figure 4-6 are based on a linear RR categorical 

model―this model had a lower AIC than the log-linear decile model (1963.94 vs. 1966.91), and 

it provides a statistically significant fit to the data (p = 0.004), so the deciles should provide a 

good representation of the data for the purposes of comparing the models (the decile results from 

the log-linear and linear RR categorical models and the mean cumulative exposure estimates for 

the deciles are presented in Section D.1 of Appendix D). As can be seen in Figure 4-6, the 

two-piece linear spline model, in addition to having the lowest AIC (see Table 4-12), appears to 

have a better fit to the lower-exposure data, which are of the greatest interest in estimating 

low-exposure risk. It also appears from Figure 4-6 that the linear model has a poorer fit to the 

lower-exposure data than either of the two-piece spline models. This is consistent with the 

analysis presented in Section D.1 of Appendix D showing the strong influence of the upper tail 

of cumulative exposures on the results of the cumulative exposure Cox regression model. The 

responses in the upper tail of exposures are relatively dampened, such that when the highest 5% 

of exposures are excluded, the slope of the Cox regression model is substantially increased (e.g., 

at 10,000 ppm × days, the RR estimate increases from about 1.1 to almost 1.5; see Figure D-1d 

in Appendix D). This strong influence of the upper tail of exposures would similarly attenuate 

the slope of the (continuous) linear model. The two-piece spline models, on the other hand, are 

more flexible, and the influence of the upper tail of exposures would be primarily on the upper 

spline segment; thus, the two-piece models are able to provide a better fit to the lower-exposure 

data. 

The exposure level (ECx) and the associated 95% lower confidence limit (LECx) 

corresponding to an extra risk of 1% (x = 0.01) for breast cancer incidence in females (based on 

invasive + in situ tumors in the subcohort with interviews) for the models discussed above were 

estimated using the actuarial program (life-table analysis). As noted in Section 4.1.1.2, a 1% 

extra risk level is a more reasonable response level for defining the POD for these epidemiologic 

data than a 10% level. The results are presented in Table 4-13. 

Because EtO is DNA-reactive and has direct mutagenic activity (see Section 3.3.3), 

which is one of the cases cited by EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 

2005a) for the use of linear low-dose extrapolation, a linear low-exposure extrapolation was 

performed. The inhalation unit risk estimates for the different breast cancer incidence models 

considered suitable for low-exposure extrapolation are presented in Table 4-13. As discussed 
This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 4-38 DRAFT―DO NOT CITE OR QUOTE

 

 

Figure 4-6. RR estimate for breast cancer incidence in subcohort with interviews vs. mean exposure (with 

15-year lag, unadjusted for continuous exposure); select models compared to deciles. 

Categorical: linear model (RR = 1 + β × exposure) with categorical exposures; log-linear and linear spline: 2-piece 

spline models, both with knots at 5,800 ppm × days (see text); linear: RR = 1 + β × exposure, with exposure as a
	

continuous variable. 

Sources: Steenland analyses in Appendix D.
 
 

 

 

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Table 4-13. EC01, LEC01, and unit risk estimates for breast cancer incidence 

in females—invasive and in situa 

Model 

With interviews Full cohort 

EC01 

(ppm) 

LEC01 

(ppm) 

Unit risk 

(per ppm) 

EC01 

(ppm) 

LEC01 

(ppm) 

Unit risk 

(per ppm) 

Cox regression, 

cumulative 

exposure, 

15-yr lagb 

0.135 0.0788 -­c 0.237 0.115 -­c 

Cox regression, log 

cumulative 

exposure, 

15-yr lagb 

0.0000765 0.0000422 -­c 0.000124 0.0000529 -­c 

Linear regression of 

categorical results, 

excluding highest 

exposure quintile; 

cumulative 

exposure, 

15-yr lagb,d 

0.0257 0.0118 0.847 0.0503 0.0188 0.532 

Low-exposure loglinear spline, 

cumulative 

exposure, 

15-yr lage 

0.0166 0.00991 1.01f -­g 

Linear model with 

continuous 

cumulative 

exposure, 15-yr lagh 

0.0437 0.0224 0.446i -­g 

Low-exposure 

linear spline, 

cumulative 

exposure, 

15-yr lage 

0.0112 0.00576 1.74f,j -­g 

a

All-cause mortality adjusted (to dying of something other than breast cancer or developing breast cancer). Unit 

risk = 0.01/LEC01. Note that the EC01 and LEC01 results presented here will not exactly match those presented in 

Appendix D because, although the regression coefficients reported by Dr. Steenland in Appendix D were used, the 

life-table analyses using 2004 all-cause mortality and 2002–2006 cause-specific mortality and incidence rates were 

redone to be more up-to-date; the results presented in Appendix D were based on life-table analyses using 2000 

all-cause mortality rates and comparable cause-specific rates. b

From Tables 4 and 5 of Steenland et al. (2003), Cox regression models. c

Unit risk estimates are not presented for these models because these models were deemed unsuitable for the 

derivation of risks from (low) environmental exposure levels (see text).

d

Regression coefficient derived from linear regression of categorical results, as described in Section 4.1.2.3. 

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Table 4-13. EC01, LEC01, and unit risk estimates for breast cancer incidence 

in females—invasive and in situa (continued) 

e

From low-exposure segment of two-piece spline analysis; see text and Table D-1c of Appendix D for log-linear 

model or Table D-1i for linear model; two-piece spline analyses not performed for the full cohort. The EC01 value is 

below the value of 0.075 ppm roughly corresponding to the knot of 5,800 ppm × days [(5,800 ppm × days) × 

(10 m3

/20 m3) × (240 d/365 d) × (365 d/yr)/70 yr = 0.075 ppm] and, thus, appropriately in the range of the 

low-exposure segment. f

For unit risk estimates above 1, convert to risk per ppb (e.g., 1.74 per ppm = 1.74 × 10−3 per ppb). g

Not estimated. 

h

From linear analyses in Section D.1.b.2 and Table D-1i of Appendix D. i

Confidence intervals used in deriving the LEC01s were estimated employing the Wald approach. Confidence 

intervals for linear RR models, however, in contrast to those for the log-linear RR models, may not be symmetrical. 

EPA also evaluated application of a profile likelihood approach for the linear RR models (Langholz and Richardson, 

2010), which allows for asymmetric CIs, for comparison with the Wald approach. The MLE for the regression 

coefficient of the linear model is 0.0000304 per ppm × day. Using the profile likelihood method, the (95% 

one-sided) upper bound on the regression coefficient is 0.0000745 per ppm × day and the (95% one-sided) lower 

bound on the regression coefficient is 0.00000975 per ppm × day. Based on these profile likelihood estimates, the 

LEC01 estimate is 0.0174 ppm, the UEC01 estimate is 0.133 ppm, and the unit risk estimate for breast cancer 

incidence from the linear model would have been 0.575 per ppm, slightly higher (29%) than the value of 0.446 per 

ppm obtained using the Wald approach.

j

Confidence intervals used in deriving the LEC01s were estimated employing the Wald approach. Confidence 

intervals for linear RR models, however, in contrast to those for the log-linear RR models, may not be symmetrical. 

EPA also evaluated application of a profile likelihood approach for the linear RR models (Langholz and Richardson, 

2010), which allows for asymmetric CIs, for comparison with the Wald approach. The MLE for the regression 

coefficient of the first spline segment is 0.000119 per ppm × day. Using the profile likelihood method, the (95% 

one-sided) upper bound on the regression coefficient is 0.000309 per ppm × day and the (95% one-sided) lower 

bound on the regression coefficient is 0.000032 per ppm × day. Based on these profile likelihood estimates, the 

LEC01 estimate is 0.00430 ppm, the UEC01 estimate is 0.0415 ppm, and the unit risk estimate for breast cancer 

incidence from the low-exposure linear spline would have been 2.33 per ppm, slightly higher (34%) than the value 

of 1.74 per ppm obtained using the Wald approach. 

above, the unit risk estimate based on the two-piece linear spline model using cumulative
 

exposure with a 15-year lag (i.e., 1.74 per ppm, or 1.74 × 10−3 per ppb) is the preferred estimate. 

The two-piece log-linear spline model resulted in a unit risk estimate of 1.01 per ppm, while the 

linear regression of categorical results yielded a unit risk estimate of 0.847 per ppm and the 

continuous linear model produced a unit risk estimate of 0.446 per ppm; these alternate estimates 

are about 60%, 50%, and 25%, respectively, of the estimate based on the preferred two-piece 

linear spline model. EC01 and LEC01 estimates from the other models examined are presented 

for comparison only, to illustrate the differences in model behavior at the low end of the 

exposure-response range. Unit risk estimates are not presented for these other models because, 

as discussed above, the log cumulative exposure Cox regression model was considered overly 

supralinear and the cumulative exposure Cox regression model was considered overly sublinear 

for the data in the lower exposure range (e.g., first 4 quintiles of exposure). As one can see from 

the results for the subcohort with interviews, the standard Cox regression cumulative exposure 

This document is a draft for review purposes only and does not constitute Agency policy. 

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model, with its extreme sublinearity in the lower exposure region, yields a notably higher EC01 

estimate (0.135 ppm) than that from the two-piece linear spline model (0.0112 ppm), while the 

log cumulative exposure model, with its extreme supralinearity in the lower exposure region, 

yields a substantially lower EC01 estimate (0.0000765 ppm). Converting the units, the preferred 

unit risk estimate of 1.74 per ppm corresponds to an estimate of 9.51 × 10−4 per μg/m3 for breast 

cancer incidence. 

As discussed above, the primary risk calculations for breast cancer incidence were based 

on invasive and in situ tumors in the subcohort of women with interviews, and the primary 

model was the two-piece linear spline model. For this assessment, the two-piece spline analyses 

were not performed with the full cohort and the life-table analyses were not replicated for the 

invasive cancers only. In the 2006 Draft Assessment (U.S. EPA, 2006a), however, comparison 

analyses were done. Using the linear regression of the categorical results, the comparable unit 

risk estimate for the full cohort was about 40% lower than the estimate based on the subcohort 

with interviews. The corresponding unit risk estimate derived based on the subcohort results but 

using invasive breast cancer only for the background incidence rates was about 17% lower than 

the estimate based on invasive and in situ tumors, reflecting the difference between incidence 

rates for invasive breast cancer only and for combined in situ and invasive breast cancer. 

The unit risk estimate of 1.74 per ppm (1.74 × 10−3 per ppb) is the preferred estimate for 

female breast cancer risk because it is based on incidence data versus mortality data, it is based 

on more cases (n = 233) than the mortality estimate (n = 103), and information on personal 

breast cancer risk factors obtained from the interviews is taken into account. Furthermore, the 

two-piece linear spline model, which uses the complete data set with exposure as a continuous 

variable, was statistically significant and had the lowest AIC and the best apparent visual fit to 

the lower-exposure data of the models considered. Converting the units, 1.74 per ppm 

corresponds to a unit risk of 9.51 × 10−4 per μg/m3 

. 

4.1.3. Total Cancer Risk Estimates 

According to EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), 

cancer risk estimates are intended to reflect total cancer risk, not site-specific cancer risk; 

therefore, an additional calculation was made to estimate the combined risk for (incident) 

lymphoid and breast cancers, because females would be at risk for both cancer types. Assuming 

that the cancer types are independent and that the risk estimates are approximately normally 

distributed, one can estimate the 95% UCL (one-sided) on the total risk as the 95% UCL on the 

sum of the maximum likelihood estimates (MLEs) of the risk estimates according to the formula 
95% UCL = MLE + 1.645(SE), (4-3) 

where MLE is the MLE of total cancer risk (i.e., the sum of the individual MLEs) and the SE of 

the sum of the MLEs is the square root of the sum of the individual variances (i.e., the variance 

of the sum is the sum of the variances, and the SE is the square root of the variance). First, an 

EC01 of 0.0078 ppm for the total cancer risk (i.e., lymphoid cancer incidence + breast cancer 

incidence) was estimated, as summarized in Table 4-14. 

Table 4-14. Calculation of EC01 for total cancer risk 

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Cancer type 

EC01 

(ppm) 

0.01/EC01 

(per ppm) 

EC01 for total 

cancer risk 

(ppm) 

Lymphoid 0.0254 0.394 -­

Breast 0.0112 0.893 --

Totala -­ 1.29 0.00775 

a

The total 0.01/EC01 value equals the sum of the individual 0.01/EC01 values; the EC01 for the total 

cancer risk then equals 0.01/(0.01/EC01). 

Then, a unit risk estimate of 2.3 per ppm for the total cancer risk (i.e., lymphoid cancer 

incidence + breast cancer incidence) was derived, as shown in Table 4-15. An LEC01 estimate of 

0.00441 ppm for the total cancer risk can be calculated as 0.01/(2.27 per ppm). 

Table 4-15. Calculation of total cancer unit risk estimate 

Cancer type 

Unit risk 

estimate 

(per ppm) 

0.01/EC01 

(per ppm) 

SEa 

(per ppm) Variance 

Total cancer 

unit risk 

estimate 

(per ppm) 

LEC01 for total 

cancer riskd 

(ppm) 

Lymphoid 0.877 0.394 0.294 0.0864 -­ -­

Breast 1.74 0.893 0.515 0.265 -­ -­

Total -­ 1.29 (0.593)b 0.351 2.27c 0.00441 

a

SE = (unit risk – 0.01/EC01)/1.645.
 b

The SE of the total cancer risk is calculated as the square root of the sum of the variances (next column), not as the 

sum of the SEs.
 c

Total cancer unit risk = 1.29 + 1.645 × 0.593.
 d

The LEC01 for the total cancer risk equals 0.01/(total cancer unit risk estimate).
 

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Thus, the total cancer unit risk estimate is 2.3 per ppm (or 2.3 × 10−3 per ppb; 

1.2 × 10−3 per μg/m3). Recall that this is the unit risk estimate derived under the assumption that 

RR is independent of age (see Section 4.1.1.2). The preferred assumption of increased early-life 

susceptibility, in accordance with EPA's Supplemental Guidance (U.S. EPA, 2005b), is 

considered in Section 4.4. While there are uncertainties regarding the assumption of a normal 

distribution of risk estimates, the resulting unit risk estimate is appropriately bounded in the 

roughly twofold range between estimates based on the sum of the individual MLEs (i.e., 1.29) 

and the sum of the individual 95% UCLs (i.e., unit risk estimates, 2.6), or more precisely in this 

case, between the largest individual unit risk estimate (1.74) and the sum of the unit risk 

estimates (2.6). Thus, any inaccuracy in the total cancer risk estimate resulting from the 

approach used to combine risk estimates across cancer types is relatively minor. 

4.1.4. Sources of Uncertainty in the Cancer Risk Estimates 

The two major sources of uncertainty in quantitative cancer risk estimates are generally 

interspecies extrapolation and high-dose to low-dose extrapolation. The risk estimates derived 

from the Steenland et al. (2003) and Steenland et al. (2004) and additional Steenland (see 

Appendix D) analyses are not subject to interspecies uncertainty because they are based on 

human data. Furthermore, the human-based estimates are less affected by high-dose to low-dose 

extrapolation than are rodent-based estimates and, thus, uncertainty from that source is reduced 

somewhat. For example, the average exposure in the NIOSH cohort was more than 10 times 

lower than the lowest exposure level in a rodent bioassay after adjustment to continuous lifetime 

exposure. Nonetheless, uncertainty remains in the extrapolation from occupational exposures to 

lower environmental exposures. Although the actual exposure-response relationship at low 

exposure levels is unknown, the clear evidence of EtO mutagenicity supports the linear 

low-exposure extrapolation that was used (U.S. EPA, 2005a). 

Because of the existence of endogenous EtO (see Section 3.3.3.1), several members of 

the SAB panel that reviewed EPA’s external review draft assessment felt that the 

exposure-response relationship for cancer at low exposures would be nonlinear and suggested 

that it would be consistent with EPA’s 2005 Guidelines for Carcinogen Risk Assessment (U.S. 

EPA, 2005a) to present a nonlinear approach for “extrapolation” to lower exposures (SAB, 

2007). EPA considered this suggestion but judged that the support for a nonlinear approach was 

inadequate. In brief, as discussed in Sections 3.1 through 3.3.3, EtO is a DNA-reactive, 

mutagenic, multisite carcinogen in humans and experimental species; as such, it has the 

hallmarks of a compound for which low-dose linear extrapolation is strongly supported under 

EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a). EPA’s Guidelines for 
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Carcinogen Risk Assessment (U.S. EPA, 2005a) do provide for presenting alternate approaches 

when those alternatives have significant biological support; however, EPA’s analysis of the 

arguments for using a nonlinear approach presented on page 23 and in Appendix C of the SAB 

report (SAB, 2007) did not find these arguments to be persuasive. The arguments posited by the 

SAB panel members who supported using a nonlinear approach were largely that (1) DNA 

adducts may show a nonlinear response when identical adducts are formed endogenously and 

(2) mutations do not have linear relationships with exposure but exhibit an “inflection point.” 

However, as discussed in Section 3.3.3.1, recent data from Marsden et al. (2009) support a linear 

exposure-response relationship for EtO exposure and DNA adducts (p < 0.05) and demonstrate 

increases of DNA adducts from exogenous EtO exposure above those from endogenous EtO for 

very low exposures to exogenous EtO, providing direct evidence against argument (1). 

Moreover, Appendix C of the SAB report (SAB, 2007) presents two EtO-specific mutation data 

sets in support of argument (2); however, EPA’s analysis of these data sets finds that they are in 

fact consistent with low-dose linearity. See the response to this comment under charge question 

2.b in Appendix H for a more comprehensive discussion of EPA’s consideration and rejection of 

a nonlinear approach and for the details of EPA’s analysis of the two EtO mutation data sets. 

Other sources of uncertainty emanate from the epidemiologic studies and their analyses 

(Steenland et al., 2004; Steenland et al., 2003; Steenland analyses in Appendix D), including the 

retrospective estimation of EtO exposures in the cohort, the modeling of the epidemiologic 

exposure-response data, the proper dose metric for exposure-response analysis, and potential 

confounding or modifying factors. Although these are common areas of uncertainty in 

epidemiologic studies, they were generally well addressed in the NIOSH studies. 

Regarding exposure estimation, the NIOSH investigators conducted a detailed 

retrospective exposure assessment to estimate the individual worker exposures. They used 

extensive data from 18 facilities, spanning a number of years, to develop a regression model 

(Hornung et al., 1994; Greife et al., 1988) [see also Section A.2.8 for more details about the 

development and evaluation of the regression model]. The model accounted for 85% of the 

variation in average EtO exposure levels in an independent set of test data. In addition, the 

modeled estimates were not highly biased nor biased in one direction when compared to the 

predictions of a panel of 11 industrial hygiene experts familiar with EtO levels in the sterilization 

industry. Detailed work history data for the individual workers were collected for the 1987 

follow-up (Steenland et al., 1991). For the extended follow-up (Steenland et al., 2004; Steenland 

et al., 2003), additional information on the date last employed was obtained for those workers 

still employed and exposed at the time of the original work history collection for the plants still 

using EtO (25% of the cohort). It was then assumed that exposure for these workers continued 
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until the date of last employment and that their exposure level stayed the same as that in their last 

job held at the time of the original data collection. Thus, there would be more exposure 

misclassification in the extended follow-up. However, when the investigators compared 

cumulative exposures estimated with and without the extended work histories, they found little 

difference because exposure levels were very low by the mid-1980s and, therefore, had little 

impact on cumulative exposure (Steenland et al., 2004; Steenland et al., 2003). While the 

NIOSH regression model performed well in estimating exposures in validation tests (Hornung et 

al., 1994), there is, nonetheless, uncertainty associated with any retrospective exposure 

assessment, and this can affect the ability to discriminate among exposure-response models. 

With respect to the lymphohematopoietic cancer response, it is not clear exactly which 

lymphohematopoietic cancer subtypes are related to EtO exposure, so analyses were done for 

both lymphoid cancers and all lymphohematopoietic cancers (Steenland et al., 2004). The 

associations observed for all lymphohematopoietic cancers was largely driven by the lymphoid 

cancer responses, and biologically, there is stronger support for an etiologic role for EtO in the 

development of the more closely related lymphoid cancers than in the development of the more 

diverse cancers in the aggregate all lymphohematopoietic cancer grouping; thus, the lymphoid 

cancer analysis is the preferred analysis for the lymphohematopoietic cancers. Nonetheless, the 

preferred unit risk estimate for all lymphohematopoietic cancers was similar to (about 50% 

greater than) that for the lymphoid cancers. 

For the lymphoid cancer response (Steenland et al., 2004), modeling the 

exposure-response relationship is limited by the small number of cases (n = 53). The Cox 

proportional hazards model used by Steenland et al. (2004) is commonly used for this type of 

analysis because exposure can be modeled as a continuous variable, competing causes of 

mortality can be taken into account, and potential confounding factors can be controlled for in 

the regression. Normally, model dependence should be minimized by the practice, under EPA’s 

2005 Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a), of modeling only in the 

observable range and then performing a linear extrapolation from the “POD” (in this case the 

LEC01). However, the log cumulative exposure Cox regression model with 15-year lag, which 

provides the best fit to the overall data, is too steep in the low-exposure region and then plateaus 

rapidly at higher exposures, making it difficult to derive stable risk estimates (i.e., estimates that 

are not highly dependent on the POD). And the alternative cumulative exposure model, though 

typically used for epidemiologic data, is too sublinear in the low-exposure region for these data, 

which exhibit supralinearity. EPA attempted to fit two-piece log-linear and linear spline models 

to the individual continuous data to address the supralinearity of the data while avoiding the 

extreme low-exposure curvature of the log cumulative exposure model; however, these models 
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resulted in low-exposure slopes that appeared to be implausibly steep (i.e., they suggested 

excessively large changes in risk from small changes in exposure). The steep low-exposure 

slopes are a manifestation of apparently high risks in workers with relatively low exposures; 

however, this elevation is based on small numbers of cancer cases in that exposure range, and 

EPA has low confidence in the low-exposure slopes. The two-piece spline model with the knot 

at a higher exposure level could have been used, but without model likelihood as a basis for knot 

selection, such selection becomes arbitrary, and with the knot at the higher exposure level which 

had an apparent local maximum for the log-linear model (1,600 ppm × days rather than 100 ppm 

× days), the visual fit was poor (see Figure 4-1). Thus, EPA opted for a weighted linear 

regression model based on the Cox regression categorical results, excluding the highest exposure 

group, to reflect the exposure-response relationship in the exposure region below the “plateau.” 

The all lymphohematopoietic cancer data set had more cases (n = 74) but was heavily dominated 

by the lymphoid cancer response and conveyed the same problems for exposure-response 

modeling; thus, a linear regression model, excluding the highest exposure group, was used for 

this data set as well. 

The linear model is a parsimonious choice that assumes neither a sublinear nor a 

supralinear exposure-response relationship and acknowledges the inherent imprecision in the 

epidemiological data. The highest exposure group was excluded because it is less relevant to the 

low-exposure risks of interest for low-exposure extrapolation and its inclusion would have overly 

influenced the linear regression, resulting in a slope that would have substantially underestimated 

the apparent low-exposure risks. Excluding data can appear arbitrary, but EPA aimed to avoid 

an arbitrary selection by using the a priori exposure groups presented by Steenland et al. (2004) 

and excluding only the highest exposure group, with the exposures least relevant to low 

environmental exposure levels. The linear regression has its own limitations (e.g., it is based on 

categorical rather than continuous data and the slopes were not statistically significant); 

nonetheless, it was judged to be the most reasonable approach for deriving low-exposure risk 

estimates from the available lymphohematopoietic cancer data. 

Although the linear regression model of the categorical results seems to be a reasonable 

approach for best reflecting the exposure-response results at the lower end of the exposure range, 

clearly there is uncertainty regarding the exposure-response model. The log cumulative 

exposure Cox regression model, which was the best-fitting model overall of the models 

investigated, yields lower EC01 and LEC01 estimates than the linear regression model of the 

categorical results (see Table 4-6), but the estimates based on the linear regression model are 

preferred because the linear regression model is more stable. 
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Another, more minor area of uncertainty related to the exposure-response modeling is the 

lag period. The best-fitting models presented by Steenland et al. (2004) for 

lymphohematopoietic cancer mortality had a 15-year lag (lag periods of 0, 5, 10, 15, and 

20 years were considered). A 15-year lag period means that exposures in the 15 years prior to 

death or the end of follow-up are not taken into account. In other words, in the best-fitting 

models, relevant exposures for the development of the lymphohematopoietic cancers occurred 

over 15 years before death. For the best-fitting continuous model for lymphoid cancer reported 

by Steenland et al. (2004), the log cumulative exposure Cox regression model, the actual 

difference between the regression coefficients from the 15-year-lagged and the unlagged models 

was negligible (the regression coefficient from the unlagged model was about 8% lower than that 

from the 15-year-lagged model; however, it should be noted that the unlagged model did not 

provide a statistically significant fit to the data (p = 0.17) (the results for the unlagged model are 

presented in Section D.3.e of Appendix D). 

In addition, the analyses of the NIOSH investigators indicate that the regression 

coefficient for cumulative exposure might have decreased with increasing follow-up, suggesting 

that the higher exposure levels encountered by the workers in the more distant past are having 

less of an impact on more recent risk. The regression coefficient for lymphoid cancers was 

1.2 × 10-5 per ppm × day, for both sexes with a 10-year lag, in the 1987 follow-up (Stayner et al., 

1993) versus 4.7 × 10−6 per ppm × day, for both sexes with a 15-year lag, in the 1998 follow-up 

(see Steenland reanalyses in Appendix D). A similar decrease was found in the regression 

coefficient for cumulative exposure for all lymphohematopoietic cancers. The life-table analysis 

used in this dose-response assessment assumes exposure accrues over the full lifetime for the 

cumulative exposure metric. If, in fact, exposures in the distant past cease to have a meaningful 

impact on the risk of lymphohematopoietic cancers, this approach would tend to overestimate the 

unit risk. Thus, a comparison analysis was conducted to evaluate the impact of ignoring 

exposures over 55 years in the past in the life-table analysis. The actual value of such a cut 

point, if warranted, is unknown. A value less than 55 years might not be appropriate because 

exposures for some of the workers began in 1943, so any diminution of potency for past 

exposures occurring since 1943 is already reflected in the regression coefficient with follow-up 

through 1998, at least for those workers, although it is unknown what proportion of workers had 

such early exposures and how long they survived. The comparison analysis for lymphoid cancer 

yielded an LEC01 of 0.0156 ppm and a unit risk estimate of 0.64 per ppm, which is about 

27% less than the estimate obtained from the unrestricted life-table analysis. Because the 

appropriate cut point for excluding past exposures is unknown and the unit risk estimate from the 

linear regression model of the categorical results is already substantially less than that obtained 
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from the best-fitting log cumulative exposure Cox regression model, the estimate from the full 

life-table analysis is preferred. In any event, the preferred estimate is not appreciably different 

from the estimate from the analysis which considered only the most recent 55 years of exposure 

in the life-table analysis. 

Several dose metrics (cumulative exposure, duration of exposure, maximum [8-hour 

TWA] exposure, and average exposure) were analyzed by Steenland et al. (2004), and 

cumulative exposure was the best predictor of mortality from lymphohematopoietic cancers. 

Cumulative exposure is considered a good measure of total exposure because it integrates 

exposure (levels) over time. 

Also, the important potential modifying/confounding factors of age, sex, race, and 

calendar time were taken into account in the analysis, and the plants included in this cohort were 

specifically selected for the absence of any known confounding exposures (Stayner et al., 1993). 

With respect to the breast cancer mortality response (Steenland et al., 2004), the 

exposure-response modeling was based on 103 deaths. As for the lymphohematopoietic cancer 

responses, the exposure-response data for breast cancer mortality are fairly supralinear, 

especially for the low-exposure groups. An attempt was again made to fit two-piece log-linear 

and linear spline models to the individual continuous data to address the supralinearity of the 

data while avoiding the extreme low-exposure curvature of the log cumulative exposure Cox 

regression model; however, these models resulted in low-exposure slopes that appeared to be 

implausibly steep and the model fits were not convincing (i.e., they were neither statistically 

significant nor visually compelling; see Figure 4-3). Thus, the same linear regression approach, 

excluding the highest exposure group, was taken to obtain a regression coefficient for the 

life-table analysis. As discussed above, the linear regression has its own limitations (e.g., it is 

based on categorical rather than continuous data and the slope is not statistically significant); 

nonetheless, it was judged to be the most reasonable approach for deriving low-exposure risk 

estimates from the available breast cancer mortality data. 

For the lag period, the best-fitting model had a lag of 20 years, which was the longest lag 

period investigated. This is a commonly used lag period for solid tumors, which typically have 

longer latency periods than lymphohematopoietic cancers. It is unknown whether a lag period 

longer than 20 years would have provided a better model fit. The Steenland et al. (2004) 

analysis took into account age, race, and calendar time. Other risk factors for breast cancer could 

not be included in the mortality analysis, but many of these factors were considered in the breast 

cancer incidence study (Steenland et al., 2003), as discussed below, and the preferred breast 

cancer risk estimates are based on the breast cancer incidence data. 
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Steenland et al. (2003) conducted an incidence study for breast cancer; therefore, it was 

not necessary to calculate unit risk estimates for breast cancer incidence indirectly from the 

mortality data as was done for lymphohematopoietic cancer. Further advantages to using the 

results from the incidence study are that more cases were available for the exposure-response 

modeling (319 cases) and that the investigators were able to include data on potential 

confounders in the modeling for the subcohort with interviews (233 cases). Because the 

subcohort with interviews had complete case ascertainment and provided data on potential 

confounders, it was the preferred breast cancer incidence data set, although some results based 

on the full cohort are presented for comparison. For the full cohort, the continuous exposure Cox 

regression model providing the best fit to the data was again the log cumulative exposure model. 

With breast cancer incidence, a 15-year lag provided the best model fits. For the subcohort, the 

cumulative exposure and log cumulative exposure Cox regression models fit nearly equally well. 

For both groups, the categorical Cox regression results suggest that a linear model lying between 

the supralinear log cumulative exposure model and the sublinear cumulative exposure model 

would better represent the low-exposure data than either of the two presented 

continuous-variable models (see Figures 4-4 and 4-5). Thus, for both groups, in EPA’s original 

draft analyses based on the published summary data, a linear regression was fitted to the 

categorical results, dropping the highest exposure group to provide a better fit to the 

lower-exposure data (U.S. EPA, 2006a). In addition, in subsequent analyses by Dr. Steenland 

(see Appendix D) of the individual data using exposure as a continuous variable, two-piece 

log-linear and linear spline models and other linear RR models were used to model the subcohort 

data; the two-piece linear spline model was the best-fitting of these models and provided the 

preferred breast cancer incidence risk estimates. 

Confidence intervals were determined using the Wald approach. Confidence intervals for 

linear RR models, however, in contrast to those for the log-linear RR models, may not be 

symmetrical. EPA also evaluated application of a profile likelihood approach for the linear RR 

models (Langholz and Richardson, 2010), which allows for asymmetric CIs, for comparison with 

the Wald approach. Using the profile likelihood method and the two-piece linear spline model, 

the resulting unit risk estimate for breast cancer incidence would have been 2.33 per ppm, 

slightly higher (34%) than the value of 1.74 per ppm obtained as the unit risk estimate for breast 

cancer incidence in this assessment. These results suggest that if the profile likelihood method 

had been used for the linear RR models in this assessment, the total cancer risk estimate, which 

incorporates the breast cancer incidence estimate as a component, would be less than 34% higher 

than the total cancer risk estimate presented here. 
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With respect to the two-piece spline models, the use of this model form is not intended to 

imply that an abrupt change in biological response occurs at the knot but, rather, to allow 

description of an exposure-response relationship in which the slope of the relationship differs 

notably in the low-exposure versus high-exposure regions. The two-piece model is used here 

primarily for its representation of the low-exposure data. The main uncertainty in the two-piece 

spline models is in the selection of the knot, and the location of the knot is critical in defining the 

low-exposure slope. The model likelihood was used to provide a statistical basis for knot 

selection; although, as shown in Appendix D (see Figure D-1a), the likelihood did not generally 

change appreciably over a range of possible knots. Thus, because of the importance of knot 

selection, a sensitivity analysis was done to examine the impacts of selecting different knots (see 

Section D.6 of Appendix D). For the sensitivity analysis, the two-piece log-linear model was run 

with knots roughly one increment (1,000 ppm × days) below and one increment above the 

selected knot. For breast cancer incidence, this sensitivity analysis yielded EC01 estimates of 

0.0133 ppm and 0.0176 ppm, respectively (i.e., about 14% lower and 14% higher, respectively, 

than the EC01 of 0.0154 ppm obtained with the originally selected knot of 6,000 ppm × days). 28 

As can be seen in Table 4-13, there is substantial variation in the EC01 estimates obtained 

from the different models. Although some plateauing is apparent with the highest exposure 

group and is evidenced in the subcohort with interviews by the strong influence of the top 5% of 

cumulative exposures on dampening the slope of the (cumulative exposure) Cox regression 

model (see Section D.1 and Figure D-1d of Appendix D), the categorical data for breast cancer 

incidence do not display the supralinearity in the lower exposure groups seen in the cases 

discussed above (i.e., lymphohematopoietic cancers and breast cancer mortality). Thus, for the 

subcohort with interviews, the difference between the EC01 estimates from the standard 

cumulative exposure Cox regression model and the two-piece spline models or the linear 

regression of the categorical results or continuous linear models are not as dramatic as seen in 

those cases (the EC01 estimates from the latter four approaches are nearly within an order of 

magnitude of that of the cumulative exposure model). For the subcohort with interviews, the 

two-piece spline models, the continuous linear model, and the linear regression of the categorical 

results gave similar results—the unit risk estimates spanned less than a fourfold range. This 

range is bounded by the two best-fitting (based on AIC) continuous models—the two-piece 

linear spline model and the continuous linear model. If the continuous linear model had been 

selected rather than the two-piece linear spline model, which had a slightly lower AIC value and 

 

28 About 12% lower and 17% higher, respectively, than the EC01 of 0.0151 ppm obtained with the more finely tuned 

knot of 5,800 ppm × days (see Appendix D). The EC01 value of 0.0166 presented in this assessment (see 

Table 4−13) is not directly comparable to the values in the sensitivity analysis because more recent background 

incidence and mortality rates were used in the lifetable analyses upon which the assessment estimates were based. 
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a better apparent visual fit of the lower-exposure data, the breast cancer incidence unit risk 

estimate would have been 0.446 per ppm rather than 1.74 per ppm, and the total cancer unit risk 

estimate would have been 1.15 per ppm rather than 2.27 per ppm. In other words, of the models 

investigated, the total cancer unit risk estimate from the best-fitting alternate model (based on 

AIC) is about 50% lower than that of the best-fitting model. However, data in the lower 

exposure range of greatest relevance for the derivation of a unit risk estimate support a steeper 

slope in the lower exposure range; thus, although the lower estimate obtained from the 

continuous linear model is plausible, unit risk estimates notably lower than that are considered 

unlikely from the available data. 

The best-fitting models presented by Steenland et al. (2003) for breast cancer incidence 

generally had a 15-year lag (lag periods of 0, 5, 10, 15, and 20 years were considered). A 

15-year lag period means that exposures in the 15 years prior to diagnosis or the end of 

follow-up are not taken into account. For the various continuous models for breast cancer 

incidence in the full cohort and the subcohort with interviews reported by Steenland et al. (2003, 

Tables 4 and 5), none of the unlagged models provided a statistically significant fit to the data, 

with the exception of the log cumulative exposure Cox regression model for the subcohort, 

where the unlagged model fit marginally better than the 15-year-lagged model. However, as 

noted in Section 4.1.2.3, the log cumulative exposure model with no lag was considered less 

biologically realistic than the corresponding model with a 15-year lag because some lag period 

would be expected for the development of breast cancer; thus, the 15-year-lagged model was 

used in this assessment. The regression coefficient from the unlagged log cumulative exposure 

Cox regression model was about 90% higher than that from the 15-year-lagged model. 

With respect to dose metrics for breast cancer incidence, models using duration provided 

better model fits than those using cumulative exposure (Steenland et al., 2003); however, 

duration is less useful for estimating unit risks and the cumulative exposure models also provided 

statistically significant fits to the data, thus the cumulative exposure metric was used for the 

quantitative risk estimates. Models using peak or average exposure did not fit as well. 

Regarding potential confounders/modifying factors, analyses for the full cohort were 

adjusted for age, race, and calendar time, and exposures to other chemicals in these plants were 

reportedly minimal. For the subcohort with interviews, a number of specific breast cancer risk 

factors were investigated, including body mass index, breast cancer in a first-degree relative, 

parity, age at menopause, age at menarche, socioeconomic status, and diet; however, only parity 

and breast cancer in a first-degree relative were determined to be important predictors of breast 

cancer and were included in the final models. 
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An area of uncertainty in the life-table analysis for breast cancer incidence pertains to the 

rates used for the cause-specific background rate. The regression coefficients presented by 

Steenland et al. (2003) represent invasive and in situ cases combined, where 6% of the cases are 

in situ, and the preferred unit risk estimates in this assessment are calculated similarly using 

background rates for invasive and in situ cases combined. The regression coefficients for 

invasive and in situ cases combined should be good approximations for regression coefficients 

for invasive cases alone; however, it is uncertain how well they reflect the exposure-response 

relationships for in situ cases alone. Diagnosed cases of in situ breast cancer would presumably 

be remedied and not progress to invasive breast cancer, so double-counting is unlikely to be a 

significant problem. Carcinoma in situ is a risk factor for invasive breast cancer; however, this 

observation is most likely explained by the fact that these two types of breast cancer have other 

breast cancer risk factors in common, some of which have been considered in the subcohort 

analysis. One might hypothesize that EtO exposure could cause a more rapid progression to 

invasive tumors; however, there is no specific evidence that this occurs. On the other hand, there 

is some indication that in situ cases in the incidence study might have been diagnosed at 

relatively low rates in comparison to the invasive cases. Steenland et al. (2003) reported that 6% 

of the cases in their study are in situ; according to the National Cancer Institute, however, ductal 

carcinoma in situ accounted for about 18% of newly diagnosed cases of breast cancer in 1998 

(NCI, 2004). 

There are several possible explanations for this difference. One is that it reflects 

differences in diagnosis with calendar time because the rate of diagnosis of carcinoma in situ has 

increased over time with increased use of mammography. Another is that the difference is 

partially a reflection of the age distribution in the cohort because the proportion of new cases 

diagnosed as carcinoma in situ varies by age. A third possible explanation is that the low 

proportion of in situ cases is at least partially a consequence of underascertainment of cases 

because in situ cases will not be reported on death certificates, although, even if all 20 in situ 

cases were in the subcohort with interviews, that would still be only 8.6% of the cases. In any 

event, this is a relatively minor source of uncertainty, and a comparison of the unit risk estimates 

using invasive + in situ breast cancer background rates and invasive-only background rates, 

using EPA’s original analyses in the 2006 Draft Assessment, found that the estimate based on the 

invasive + in situ background rates was less than 20% higher than the corresponding estimate 

using only invasive breast cancer background rates (U.S. EPA, 2006a). 

The results for the subcohort with interviews are used for the primary breast cancer unit 

risk calculations because, in addition to including the data on potential confounders, the 

subcohort is considered to have full ascertainment of the breast cancer cases, whereas the full 
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cohort for the incidence study has incomplete case ascertainment, as illustrated by the fact that 

death certificates were the only source of case ascertainment for 14% of the cases. Complete 

interviews were available for only 68% of the 7,576 women in the full incidence cohort, and 

thus, some potential exists for participation selection bias in the subcohort. There is, however, 

no basis for considering participation to be associated with breast cancer or EtO exposure, and 

the major reason for nonparticipation was a failure to locate (22% of full incidence cohort) and 

not lack of response (3% of cohort) or refusal to participate (7% of cohort). Risk estimates based 

on the full cohort were calculated for comparison with the subcohort estimates using the original 

linear regression analyses of the categorical results. The unit risk estimate based on the 

subcohort was about 60% higher than the corresponding estimate from the full cohort (U.S. EPA, 

2006a). 

Some additional sources of uncertainty are not so much inherent in the exposure-response 

modeling or in the epidemiologic data themselves but, rather, arise in the process of obtaining 

more general Agency risk estimates from the epidemiologic results. EPA cancer risk estimates 

are typically derived to represent an upper bound on increased risk of cancer incidence for all 

sites affected by an agent for the general population. From experimental animal studies, this is 

accomplished by using tumor incidence data and summing across all the tumor sites that 

demonstrate significantly increased incidences, customarily for the most sensitive sex and 

species, to be protective of the general human population. However, in estimating comparable 

risks from the NIOSH epidemiologic data, certain limitations are encountered. First, the study 

reported by Steenland et al. (2004) is a retrospective mortality study, and cancer incidence data 

are not available for lymphohematopoietic cancer (for breast cancer, a separate incidence study 

[Steenland et al., 2003] was available). Second, these occupational epidemiology data represent 

a healthy-worker cohort. Third, the epidemiologic study may not have sufficient statistical 

power and follow-up time to observe associations for all the tumor sites that may be affected by 

EtO. 

The first limitation was addressed quantitatively in the life-table analysis for the 

lymphohematopoietic cancer risk estimates. Although assumptions are made in using incidence 

rates for the cause-specific background rates, as discussed in Section 4.1.1.3, the resulting 

incidence-based estimates are believed to be better estimates of cancer incidence risk than are the 

mortality-based estimates. The incidence unit risk estimate is about 120% higher than (i.e., 

2.2 times) the mortality-based estimate, which seems reasonable given the relatively high 

survival rates for lymphoid cancers (according to SEER data [www.seer.cancer.gov], 5-year 

survival rates are 65% for NHL; 78% for chronic lymphocytic leukemia, which are the vast 

majority of the lymphocytic leukemias in adults; and 40% for multiple myeloma). 
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The healthy-worker effect is often an issue in occupational epidemiology studies, but the 

internal exposure-response analyses conducted by these investigators help address this concern, 

at least partially. In terms of representing the general population, the NIOSH study cohort was 

relatively diverse. It contained both female (55%) and male workers, and the workers were 79% 

white, 16% black, and 5% “other.” Furthermore, because of EtO's mutagenic mode of action, 

increased early-life susceptibility is assumed and ADAFs are applied for exposure scenarios 

involving early life (see Section 4.4). 

With respect to other possible tumor sites of concern, the rodent data suggest that 

lymphohematopoietic cancers are a major tumor type associated with EtO exposure in female 

mice and in male and female rats. Thus, it is reasonable that this might be a cancer type of 

concern in humans also. Likewise, the mouse data suggest an increased risk of mammary gland 

tumors from EtO exposure, and evidence of that can be seen in the Steenland et al. (2004) and 

Steenland et al. (2003) studies. However, the rodent data suggest associations between EtO 

exposure and other tumor types as well, and although site concordance across species is not 

generally assumed, it is possible that the NIOSH study, despite its relatively large size and long 

follow-up (mean length of follow-up was 26.8 years), had insufficient power to observe small 

increases in risk in certain other sites. For example, the tumor site with the highest potency 

estimate in both male and female mice was the lung. In the NIOSH study, one cannot rule out a 

small increase in the risk of lung cancer, which has a high background rate. 

To obtain the risk estimate for total cancer risk (2.3 per ppm, or 2.3 × 10−3

 per ppb), the 

preferred estimates for lymphoid cancer incidence and breast cancer incidence were combined. 

While there are uncertainties in the approach used to combine the individual estimates, the 

resulting unit risk estimate is appropriately bounded in the roughly twofold range between 

estimates based on the sum of the individual MLEs of risk and the sum of the individual 95% 

UCLs, and thus, any inaccuracy in the total cancer unit risk estimate resulting from the approach 

used is relatively minor. Because the breast cancer component of the total cancer risk estimate 

applies only to females, the total cancer risk estimate is expected to overestimate the cancer risk 

to males somewhat (the preferred unit risk estimate for lymphoid cancer alone was 0.877 per 

ppm [or 8.77 × 10−4

 per ppb], which is about 40% of the total cancer risk estimate). 

Despite these uncertainties, the inhalation cancer unit risk estimate of 2.3 per ppm (or 

2.3 × 10−3

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cancer incidence has the advantages of being based on human data from a large, high-quality 

epidemiologic study with individual exposure estimates for each worker. Furthermore, the breast 

cancer component of the risk estimate, which contributes approximately 60% of the total cancer 

risk, is based on a substantial number of incident cases (233 total, the vast majority of which 
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were in the exposure range below the knot of 5,800 ppm × days [see Table D.1a of 

Appendix D]). 

A further area of uncertainty pertains to the assumption that RR is independent of age, 

which is a common assumption in the dose-response modeling of epidemiological data and is an 

underlying assumption in the Cox regression model. For the NIOSH worker cohort, the 

proportional hazards model assumption of RR being independent of age was tested by checking 

the significance of an interaction between age and cumulative exposure, and none of the models 

had a significant interaction term. This suggests that, for adults at least, the assumption that RR 

is independent of age is valid. However, the worker cohort contains no children and is 

uninformative on the issue of early-life susceptibility. In the absence of data on early-life 

susceptibility, EPA's Supplemental Guidance (U.S. EPA, 2005b) recommends that increased 

early-life susceptibility be assumed for carcinogens with a mutagenic mode of action, and the 

conclusion was made in Section 3.4 that the weight of evidence supports a mutagenic mode of 

action for EtO. Thus, in accordance with the Supplemental Guidance, the alternate assumption 

of increased early-life susceptibility is preferred as the basis for risk estimates in this assessment, 

and risk estimates derived under this preferred assumption are presented in Section 4.4. 

4.1.5. Summary 

Under the common assumption that RR is independent of age, an inhalation unit risk 

estimate for lymphoid cancer incidence of 0.877 per ppm (or 8.77 × 10−4

 per ppb; 4.79 × 10−4 per 

μg/m3) was calculated using a life-table analysis and a weighted linear regression of the 

categorical Cox regression results, excluding the highest exposure group, for excess lymphoid 

cancer mortality from a high-quality occupational epidemiology study. Similarly, an inhalation 

unit risk estimate for female breast cancer incidence of 1.74 per ppm (or 1.74 × 10−3

 per ppb; 

9.51 × 10−4 per μg/m3) was calculated using a life-table analysis and two-piece linear spline 

modeling of the continuous data for excess breast cancer incidence from the same high-quality 

occupational epidemiology study. The linear regression of the categorical results with the 

exclusion of the highest exposure group for the lymphoid cancer results and the two-piece linear 

spline analysis for the breast cancer incidence data were different modeling approaches used to 

address the supralinearity of the exposure-response data in the two data sets. Low-dose linear 

extrapolation was used, as warranted by the clear mutagenicity of EtO. An EC01 estimate of 

0.0078 ppm, a LEC01 estimate of 0.0044 ppm, and a unit risk estimate of 2.3 per ppm (or 

2.3 × 10−3 per ppb; 1.2 × 10−3 per μg/m3) were obtained for the total cancer risk combined across 

both cancer types. Despite the uncertainties discussed above, this inhalation unit risk estimate 
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has the advantages of being based on human data from a high-quality epidemiologic study with 

individual exposure estimates for each worker. 

In the absence of data on early-life susceptibility, EPA's Supplemental Guidance (U.S. 

EPA, 2005a) recommends that increased early-life susceptibility be assumed for carcinogens 

with a mutagenic mode of action, and the conclusion was made in Section 3.4 that the weight of 

evidence supports a mutagenic mode of action for EtO. Thus, in accordance with the 

Supplemental Guidance, the alternate assumption of increased early-life susceptibility is 

preferred as the basis for risk estimates in this assessment, and risk estimates derived under this 

preferred assumption are presented in Section 4.4. Other than the use of the alternate assumption 

about early-life susceptibility, the approach used to derive the estimates presented in Section 4.4 

is identical to the approach used for the estimates derived here in Section 4.1, and the 

comparisons made between various options and the issues and uncertainties discussed here in 

Section 4.1 are applicable to the estimates derived in Section 4.4. 

4.2. INHALATION UNIT RISK DERIVED FROM EXPERIMENTAL ANIMAL DATA 

4.2.1. Overall Approach 

Lifetime animal cancer bioassays of inhaled EtO have been carried out in three 

laboratories, as described in Section 3.2. The data from these reports are presented in Tables 3-3 

through 3-5. These studies have also been reviewed by the IARC (1994b) and Health Canada 

(2001). Health Canada calculated the ED05 for each data set using the benchmark dose 

methodology. The EOIC report (EOIC, 2001) tabulated only lymphatic tumors because they 

constituted the predominant risk. 

The overall approach in this derivation is to find a unit risk for each of the 

bioassays―keeping data on males and females separate—from data on the incidence of all tumor 

types and then to use the maximum of these values as the summary measure of the unit risk from 

animal studies (i.e., the unit risk represents the most sensitive species and sex). The unit risk for 

the animals in these bioassays is converted to a unit risk in humans by first determining the 

continuous exposures in humans that are equivalent to the rodent bioassay exposures and then by 

assuming that the lifetime incidence in humans is equivalent to lifetime incidence in rodents, as 

is commonly accepted in interspecies risk extrapolations. For cross-species scaling of exposure 

levels (see Section 4.2.2 below), an assumption of ppm equivalence is used; thus, no interspecies 

conversion is needed for the exposure concentrations. Bioassay exposure levels are adjusted to 

equivalent continuous exposures by multiplying by (hours of exposure/24 hours) and by (5/7) for 

the number of days exposed per week. The unit risk in humans (risk per unit air concentration) 
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is then assumed to be numerically equal to that in rodents (after adjustment to continuous 

exposures); the calculations from the rodent bioassay data are shown in Tables 3-3 through 3-5. 

4.2.2. Cross-Species Scaling 

In the absence of chemical-specific information, EPA’s 1994 inhalation dosimetry 

methods (U.S. EPA, 1994) provide standard methods and default scaling factors for 

cross-species scaling. Under EPA’s methodology, EtO would be considered a Category 2 gas 

because it is reactive and water soluble and has clear systemic distribution and effects. 

Dosimetry equations for Category 2 gases are undergoing EPA re-evaluation and are not being 

used at this time. For cross-species scaling of extrarespiratory effects, current practice is to treat 

Category 2 gases as Category 3 gases. For Category 3 gases, ppm equivalence is assumed (i.e., 

responses across species are equivalent on a ppm exposure basis), unless the air:blood partition 

coefficient for the experimental species is less than the coefficient for humans (U.S. EPA, 1994, 

p. 4−61). In the case of EtO, measured air:blood partition coefficients are 78 in the mouse 

(Fennell and Brown, 2001), 64 in the rat (Krishnan et al., 1992), and 61 in the human (Csanady 

et al., 2000); thus, ppm equivalence for cross-species scaling to humans can be assumed for 

extrarespiratory effects observed in mice and rats. The assumption of ppm equivalence is further 

supported by the PBPK modeling of Fennell and Brown (2001), who reported that simulated 

blood AUCs for EtO after 6 hours of exposure to concentrations between 1 ppm and 100 ppm 

were similar for mice, rats, and humans and were linearly related to the exposure concentration 

(see Section 3.3.1 and Figure 3-2). This modeling was validated against measured blood EtO 

concentrations for rodents and humans. For Category 2 gases with respiratory effects, there is no 

clear guidance on an interim approach. One suggested approach is to do cross-species scaling 

using both Category 1 and Category 3 gas equations and then decide which is most appropriate. 

In this document, the preferred approach was to assume ppm equivalence was also valid for the 

lung tumors in mice because of the clear systemic distribution of EtO (e.g., see Section 3.1). 

Treating EtO as a Category 1 gas for cross-species scaling of the lung tumors would presume 

that the lung tumors are arising only from the immediate and direct action of EtO as it comes into 

first contact with the lung. In fact, some of the EtO dose contributing to lung tumors is likely 

attributable to recirculation of systemic EtO through the lung. 

If one were to treat EtO as a Category 1 gas for the cross-species scaling of the lung 

tumor response as a bounding exercise, EPA’s 1994 inhalation dosimetry methods present 

equations for estimating the RGDRPU, i.e., the regional gas dose ratio for the pulmonary region, 

which acts as an adjustment factor for estimating human equivalent exposure concentrations 

from experimental animal exposure concentrations (adjusted for continuous exposure) (U.S. 
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EPA, 1994, pp. 4−49 to 4−51). These equations rely on parameters describing mass transport of 

the gas (EtO) in the extrathoracic and tracheobronchial regions for both the experimental animal 

species (mouse) and humans. Without experimental data for these parameters, it seems 

reasonable to estimate RGDRPU using a simplified equation and the adjusted alveolar ventilation 

rates of Fennell and Brown (2001). Fennell and Brown adjusted the alveolar ventilation rates to 

reflect limited pulmonary uptake of EtO, a phenomenon commonly observed for highly 

water-soluble gases (Johanson and Filser, 1992). The adjusted ventilation rates were then used 

by Fennell and Brown in their PBPK modeling simulations, and good fits to blood concentration 

data were reported for both the mouse and human models. In this document, the adjusted 

alveolar ventilation rates were used to estimate the RGDRPU as follows: 

RGDRPU = (RGDPU)m/(RGDPU)h = (Qalv/SAPU)m/(Qalv/SAPU)h, (4-4) 

where: 

RGDPU = regional gas dose to the pulmonary region, 

Qalv = (adjusted) alveolar ventilation rate, 

SAPU = surface area of the pulmonary region, and 

the subscripts “m” and “h” denote mouse and human values. 

Then, using adjusted alveolar ventilation rates from Fennell and Brown (2001) and surface area 

values from EPA (U.S. EPA, 1994, p. 4−26), 

RGDRPU = ((0.78 L/h)/(0.05 m2))/((255 L/h)/(54.0 m2)) = 3.3. (4-5) 

Using this value for the RGDRPU would increase the human equivalent concentration about 

threefold, resulting in a decreased risk for lung tumors of about threefold, as a lower bound. The 

true value of the RGDRPU is expected to be between 1 and 3, and any adjustment to the lung 

tumor risks would still be expected to result in unit risk estimates roughly within the range of the 

rodent unit risk estimates derived later in Section 4.2 under the assumption of ppm equivalence. 

4.2.3. Dose-Response Modeling Methods 

In this document the following steps were used: 

1. Extract the incidence data presented in the original studies. In order to crudely adjust 

for early mortality in the analysis of the NTP (1987) data, the incidence data have been corrected 
 

 

 

 

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for a specific tumor type by eliminating the animals that died prior to the occurrence of the first 

tumor or prior to 52 weeks, whichever was earlier. It was not possible to make this adjustment 

with the other studies where data on individual animals were not available. With these 

exceptions, the tumor incidence data in Tables 3-3 through 3-5 match the original data. 

2. Fit the multistage model to the dose-response data using the Tox_Risk program. 

The likelihood-ratio test was used to determine the lowest value of the multistage 

polynomial degree that provided the best fit to the data while requiring selection of the most 

parsimonious model. In this procedure, if a good fit to the data in the neighborhood of the POD 

is not obtained with the multistage model because of a nonmonotonic reduction in risk at the 

highest dose tested (as sometimes occurs when there is early mortality from other causes), that 

data point is eliminated and the model is fit again to the remaining data. Such a deletion was 

found necessary in two cases (mammary tumors in the NTP study and mononuclear cell 

leukemia in the Lynch study). The goodness-of-fit measures for the dose-response curves and 

the parameters derived from them are shown in Appendix G. 

In the NTP bioassay, where the individual animal data were available, a time-to-tumor 

analysis was undertaken to account for early mortality. The general model used in this analysis 

is the multistage Weibull model: 

P(d,t) = 1 − exp[−(q0 + q1d + q2d2 + ... + qkdk) × (t − t0)

z

], (4-6) 

where P(d,t) represents the probability of a tumor by age t (in bioassay weeks) for dose d (i.e., 

human equivalent exposure), and the parameter ranges are restricted as follows: z > 1, t0 > 0, 

and qi > 0 for i = 0, 1, ..., k. The parameter t0 represents the time between when a potentially 

fatal tumor becomes observable and when it causes death. The analyses were conducted using 

the computer software Tox_Risk version 3.5, which is based on methods developed by Krewski 

et al. (1983). Parameters are estimated in Tox_Risk using the method of maximum likelihood. 

Tumor types can be categorized by tumor context as either fatal or incidental. Incidental 

tumors are those tumors thought not to have caused the death of an animal, whereas fatal tumors 

are thought to have resulted in animal death. Tumors at all sites were treated as incidental 

(although it was recognized that this may not have been the case, the experimental data are not 

detailed enough to conclude otherwise). The parameter t0 was set equal to 0 because there were 

insufficient data to reliably estimate it. 

The likelihood-ratio test was used to determine the lowest value of the multistage 

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parsimonious model. The one-stage Weibull (i.e., k = 1) was determined to be the most optimal 

value for all the tumor types analyzed. 

3. Select the POD and calculate the unit risk for each tumor site. The effective 

concentration that causes a 10% extra risk for tumor incidence, EC10, and the 95% lower bound 

of that concentration, LEC10, are derived from the dose-response model. The LEC10 is then used 

as the POD for a linear low-dose extrapolation, and the unit risk is calculated as 0.1/LEC10. This 

is the procedure specified in the EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 

2005a) for agents such as EtO that have direct mutagenic activity. See Section 3.4 for a 

discussion of the mode of action for EtO. Tables 3-3 through 3-5 present the unit risk estimates 

for the individual tumor sites in each bioassay. 

4. Develop a unit risk estimate based on the incidence of all tumors combined. This 

method assumes that occurrences of tumors at multiple sites are independent and, further, that 

the risk estimate for each tumor type is normally distributed. Then, at a given exposure level, the 

MLEs of extra risk due to each tumor type are added to obtain the MLE of total cancer risk. The 

variances corresponding to each tumor type are added to give the variance associated with the 

sum of the MLEs. The one-sided 95% UCL of the MLE for the combined risk is then calculated 

as: 

95% UCL = MLE + 1.645(SE), (4-7) 

where SE is the standard error and is the square root of the summed variance. (Note that as a 

precursor to this step, when Tox _Risk is used to fit the incidence of a single tumor type, it 

provides the MLE and 95% UCL of extra risk at a specific dose. The standard error in the MLE 

is determined using the above formula). The calculation is repeated for a few exposure levels, 

and the exposure yielding a value of 0.1 for the upper bound on extra risk is determined by 

interpolation. The unit risk is then the slope of the linear extrapolation from this POD. The 

results are given in Table 4-16. 

4.2.4. Description of Experimental Animal Studies 

NTP (1987) exposed male and female B6C3F1 mice to concentrations of 0, 50, and 

100 ppm for 6 hours per day, 5 days per week, for 102 weeks. An elevated incidence of lung 

carcinomas was found in males, and elevated lung carcinomas, malignant lymphomas, uterine 
 

 

 

 

 

 

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Table 4-16. Upper-bound unit risks (per μg/m3) obtained by combining 

tumor sites 

Combination methoda 

NTP (1987) 

female mouse 

Lynch et al. (1984a); 

Lynch et al. (1984b) 

male rat 

Snellings et al. (1984)

b 

Male rat Female rat 

UCL on sum of risksc 2.71 × 10-5 4.17 × 10-5 2.19 × 10-5 3.37 × 10-5 

Sum of unit risksd 4.12 × 10-5 3.66 × 10-5 2.88 × 10-5 3.54 × 10-5 

Time-to-tumor analysis and 

u.c.b on sum of risksc 

4.55 × 10-5 – – – 

a

Unit risk in these methods is the slope of the straight line extrapolation from a point of departure at the dose
 

corresponding to a value of 0.1 for the 95% upper confidence bound on total extra risk.
 b

Includes data on brain tumors from the analysis by Garman et al. (1985). See Table 3-3.
 c

UCL = 95% upper confidence bound. At a given dose, the MLE of the combined extra risk was determined by
 

summing the MLE of risk due to each tumor type. The variance associated with this value was determined by
 

summing over the variances due to each tumor type. 

d

Sum of values in last column of Tables 3-1 through 3-3. 

adenocarcinomas, and mammary carcinomas were found in females. These data are shown in 

Table 3-3. 

Lynch et al. [Lynch et al. (1984a); Lynch et al. (1984b)] exposed male F344 rats to 0, 50, 

and 100 ppm for 7 hours per day, 5 days per week, for 2 years. They found excess incidence of 

tumors at three sites: mononuclear cell leukemia in the spleen, testicular peritoneal 

mesothelioma, and brain glioma. In this study the survival in the high-dose group (19%) was 

less than that of controls (49%), which reduced the incidence of leukemias. In the animals in the 

high-dose group that survived to the termination of the experiment, the incidence of leukemias 

was statistically significantly higher than for controls (p < 0.01). The incidence data are shown 

in Table 3-4, uncorrected for the high-dose-group mortality. If the individual animal data were 

available to perform the correction, the incidence would be higher. Therefore, using these data 

results in an underestimate of risk. 

Snellings et al. (1984) exposed male and female F344 rats to 0, 10, 33, and 100 ppm for 

6 hours per day, 5 days per week, for 2 years and described their results for all sites except the 

brain. In two subsequent publications for the same study, Garman et al. (1986, 1985) described 

the development of brain tumors in a different set of F344 rats. The Snellings et al. (1984) 

publication reported an elevated incidence of splenic mononuclear cell leukemia and peritoneal 

mesothelioma in males and an elevated incidence of splenic mononuclear cell leukemia in 

females. The mortality was higher in the 100-ppm groups than the other three groups for both 

males and females. The incidences in the animals killed after 24 months in Snellings et al. 

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(1984) are shown in Table 3-5. Table 3-5 also presents the brain tumor incidence data for male 

and female rats from the Garman et al. (1986, 1985) publications. The brain tumor incidence 

was lower than that of the other tumors, particularly the splenic mononuclear cell leukemias. 

4.2.5. Results of Data Analysis of Experimental Animal Studies 

The unit risks calculated from the individual site-sex-bioassay data sets are presented in 

Tables 3-3 through 3-5. The highest unit risk of any individual site is 3.23 × 10−5 per μg/m3 

, 

which is for mononuclear cell leukemia in the female rats of the Snellings et al. (1984) study. 

Table 4-17 presents the results of the time-to-tumor method applied to the individual 

animals in the NTP bioassay, compared with the results from the dose group incidence data in 

Table 3-3. This comparison was done for each tumor type separately. The time-to-tumor 

method of analyzing the individual animals results in generally higher unit risk estimates than 

does the analysis of dose group data, as shown in Table 4-17. The ratio is not large (less than 

2.2) across the tumor types. (In the case of mammary tumors this ratio is actually less than 1. It 

must be noted that the incidence at the highest dose [where the incidence was substantially less 

than at the intermediate dose] was deleted from the analysis of grouped data, whereas it was 

retained in the time-to-tumor analysis. Therefore, the comparison for the mammary tumors is 

not a strictly valid comparison of methods.) The results also show the extent to which a time-totumor analysis of individual animal data increases the risk estimated from data on dose groups. 

It is expected that if individual animal data were available for the Lynch et al. (1984a); Lynch et 

al. (1984b) and the Snellings et al. (1984) bioassays, then the time-to-tumor analysis would also 

result in higher estimates because both those studies also showed early mortality in the highest 

dose group. 

The results of combining tumor types are summarized in Table 4-16. The sums of the 

individual unit risks tabulated in Tables 3-3 to 3-5 are given in the second row of Table 4-16. 

Note that as expected they are greater than the unit risks computed from the upper bound on the 

sum of risks for all data sets except for the Lynch et al. [Lynch et al. (1984a); Lynch et al. 

(1984b)] data. The reason for this exception is not known, but the differences are small. It is 

likely that the problem arises from the methodology used to combine the risks across tumor sites. 

In an attempt to be consistent with the new two-step methodology (i.e., modeling in the 

observable range to a POD and then doing a linear extrapolation to zero extra risk at zero 

exposure), the exposure concentration at which the sum of the independent tumor site risks 

yielded a 95% upper bound on 10% extra risk was estimated and used as the POD. Summing 

risks in this way results in a POD for the combined tumor risk that is different (lower) than the 

points of departure for each individual tumor site risk. Thus, the risk estimate for the sum is not 
Table 4-17. Unit risk values from multistage Weibulla time-to-tumor 

modeling of mouse tumor incidence in the NTP (1987) study 

 

 

 

 

 

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Tumor type 

Unit risk, 0.1/LEC10 

(per μg/m3) 

from time-to-tumor 

analysis 

Unit risk, 

0.1/LEC10 

(per μg/m3

) 

(Table 3-3)

b 

Ratio of unit risks 

time-totumor/grouped data 

Males 

Lung: alveolar/bronchiolar 

adenoma and carcinoma 

3.01 × 10-5 2.22 × 10-5 1.4 

Females 

Lung: alveolar/bronchiolar 

adenoma and carcinoma 

2.40 × 10-5 1.10 × 10-5 2.2 

Malignant lymphoma 1.43 × 10-5 7.18 × 10-6 2.0 

Uterine carcinoma 6.69 × 10-6 4.33 × 10-6 1.5 

Mammary carcinoma 8.69 × 10-6 1.87 × 10-5 0.5 

a

P(d,t) 1 − exp[−(q0 + q1d + q2d2 + ... + qkdk) × (t − t0)

z

], where d is inhaled ethylene oxide concentration in ppm, t
 

is weeks until death with tumor. In all cases, k = 1 provided the optimal model.
 b

Incidence data modeled using multistage model without taking time to tumor into account.
 

strictly comparable to the individual risks that constitute it. These tumor-site-specific risks were 

based on points of departure individually calculated to correspond with a 10% extra risk. In any 

event, adding the upper bound risks of individual tumor sites should overestimate the upper 

bound of the sum, and the latter is the preferred measure of the total cancer risk because it avoids 

the overestimate. However, for the exceptional Lynch et al. [Lynch et al. (1984a); Lynch et al. 

(1984b)] data, the sum of upper bounds, 3.66 × 10−5 per μg/m3

, is already an overestimate of the 

total risk, and this value is preferred over the anomalously high value of 4.17 × 10−5 per μg/m3 

corresponding to the upper bound on the sum of risks. The latter value is considered to be an 

excessive overestimate and is therefore not carried over into the summary Table 4-18. For the 

Snellings et al. (1984) data sets, the upper confidence bound on the sum of risks is used in the 

summary Table 4-18. The results of the sum-of-risks calculations on the NTP bioassay time-totumor data are included in the third row of Table 4-16. The estimate for the NTP female mice is 

4.55 × 10−5 per μg/m3, which is higher than the other two measures of total tumor risk in that 

bioassay. This value is preferable to the other measures because it utilizes the individual animal 

data available for that bioassay. 

Summary of results. The summary of unit risks from the five data sets is shown in 

Table 4-18. The data set giving the highest risk (4.55 × 10−5 per μg/m3

) is the NTP (1987) data 

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Table 4-18. Summary of unit risk estimates (per μg/m3

) in animal bioassays 

Assay Males Females 

NTP (1987), B6C3F1 mice 3.01 × 10-5 a 4.55 × 10-5 b 

Lynch et al. (1984a); Lynch et al. (1984b), 

F344 rats 3.66 × 10-5 c – 

Snellings et al. (1984), F344 rats 2.19 × 10-5 d 3.37 × 10-5 d 

a

From time-to-tumor analysis of lung adenomas and carcinomas, Table 4-17.
 b

Upper bound on sum of risks from the time-to-tumor analysis of the NTP data, Table 4-16.
 c

Sum of (upper bound) unit risks (see text for explanation), Table 4-16.
 d

Upper bound on sum of risks, Table 4-16.
 

on combined tumors in female mice. The other values are within about a factor of 2 of the
 

highest value. 

4.3. SUMMARY OF INHALATION UNIT RISK ESTIMATES—NOT ACCOUNTING 

FOR ASSUMED INCREASED EARLY-LIFE SUSCEPTIBILITY 

For both humans and laboratory animals, tumors occur at multiple sites. In humans, there 

was a combination of tumors having lymphohematopoietic, in particular lymphoid, origins in 

both sexes and breast cancer in females, and, in rodents, lymphohematopoietic tumors, mammary 

carcinomas, and tumors of other sites were observed. From human data, an extra cancer unit risk 

estimate of 4.79 × 10−4 per μg/m3

 (8.77 × 10−4 per ppb) was calculated for lymphoid cancer 

incidence, and a unit risk estimate of 9.31 × 10−4 per μg/m3 (1.74 × 10−3 per ppb) was calculated 

for breast cancer incidence in females. The total extra cancer unit risk estimate was 1.2 × 10−3 

per μg/m3 (2.3 × 10−3 per ppb) for both cancer types combined (EC01 = 0.0078 ppm; 

LEC01 = 0.0043 ppm). Unit risk estimates derived from the three chronic rodent bioassays for 

EtO ranged from 2.2 × 10−5 per μg/m3

 to 4.6 × 10−5 per μg/m3, over an order of magnitude lower 

than the estimates based on human data. 

Adequate human data, if available, are considered to provide a more appropriate basis 

than rodent data for estimating human risks (U.S. EPA, 2005a), primarily because uncertainties 

in extrapolating quantitative risks from rodents to humans are avoided. Although there is a 

sizeable difference between the rodent-based and the human-based estimates, the human data are 

from a large, high-quality study, with EtO exposure estimates for the individual workers and 

little reported exposure to chemicals other than EtO. Therefore, the total extra cancer unit risk 

estimate of 1.2 × 10−3 per μg/m3

 (2.3 × 10−3 per ppb) calculated for lymphoid cancers and breast 

cancer combined is the preferred estimate of those estimates not taking assumed increased earlyThis document is a draft for review purposes only and does not constitute Agency policy. 

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life susceptibility into account (estimates accounting for assumed increased early-life 

susceptibility are presented in Section 4.4). The unit risk estimate is intended to be an upper 

bound on cancer risk for use with exposures below the POD (i.e., the LEC01). The unit risk 

estimate should not generally be used above the POD; however, in the case of this total extra 

cancer unit risk, which is based on cancer type-specific unit risk estimates from two linear 

models, the estimate should be valid for exposures up to about 0.075 ppm (140 μg/m3), which is 

the minimum of the limits for the lymphoid cancer unit risk estimate (0.090 ppm; see 

Section 4.1.1.2) and the breast cancer unit risk estimate (0.075 ppm; see Section 4.1.2.3). 

Because a mutagenic mode of action for EtO carcinogenicity (see Section 3.3.2) is 

“sufficiently supported in (laboratory) animals” and “relevant to humans”, and as there are no 

chemical-specific data to evaluate the differences between adults and children, increased 

early-life susceptibility should be assumed and, if there is early-life exposure, the age-dependent 

adjustment factors (ADAFs) should be applied, as appropriate, in accordance with EPA’s 

Supplemental Guidance (U.S. EPA, 2005b; see Section 4.4 below for more details on the 

application of ADAFs). 

4.4. ADJUSTMENTS FOR POTENTIAL INCREASED EARLY-LIFE 

SUSCEPTIBILITY 

There are no chemical-specific data on age-specific susceptibility to EtO-induced 

carcinogenesis. However, there is sufficient weight of evidence to conclude that EtO operates 

through a mutagenic mode of action (see Section 3.4.1). In such circumstances (i.e., the absence 

of chemical-specific data on age-specific susceptibility but sufficient evidence of a mutagenic 

mode of action), EPA’s Supplemental Guidance for Assessing Susceptibility from Early-Life 

Exposure to Carcinogens (U.S. EPA, 2005b) recommends the assumption of increased early-life 

susceptibility and the application of default age-dependent adjustment factors (ADAFs) to adjust 

for this potential increased susceptibility from early-life exposure. See the Supplemental 

Guidance for detailed information on the general application of these adjustment factors. In 

brief, the Supplemental Guidance establishes ADAFs for three specific age groups. The current 

ADAFs and their age groupings are 10 for <2 years, 3 for 2 to <16 years, and 1 for 16 years and 

above (U.S. EPA, 2005b). For risk assessments based on specific exposure assessments, the 

10-fold and 3-fold adjustments to the unit risk estimates are to be combined with age-specific 

exposure estimates when estimating cancer risks from early-life (<16 years of age) exposure. 

These ADAFs, however, were formulated based on comparisons of the ratios of cancer 

potency estimates from juvenile-only exposures to cancer potency estimates from adult-only 

exposures from rodent bioassay data sets with appropriate exposure scenarios, and they are 
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designed to be applied to cancer potency estimates derived from adult-only exposures. Thus, 

alternate life-table analyses were conducted to derive comparable adult-exposure-only unit risk 

estimates to which ADAFs would be applied to account for early-life exposure. For these 

alternate life-table analyses, it was assumed that RR is independent of age for adults, which 

represent the life stage for which the exposure-response data and the Cox regression modeling 

results from the NIOSH cohort study specifically pertain, but that there is increased early-life 

susceptibility, based on the weight-of-evidence-based conclusion that EtO carcinogenicity has a 

mutagenic mode of action (see Section 3.4), which supersedes the assumption that RR is 

independent of age for all ages including children. 

In the alternate analyses, exposure in the life table was taken to start at age 16 years, the 

age cut point that was established in EPA’s Supplemental Guidance (U.S. EPA, 2005b), to derive 

an adult-exposure-only unit risk estimate to which ADAFs would be applied to account for 

early-life exposure. Other than the age at which exposure was initiated, the life-table analyses 

are identical to those conducted for the results presented in Section 4.1. Adult-exposure-only 

unit risk estimates were derived for both cancer incidence and mortality for both lymphoid and 

breast cancers. Alternate estimates were not derived for all lymphohematopoietic cancers 

because lymphoid cancer was the preferred endpoint (see Section 4.1.1.2). Incidence estimates 

are preferred over mortality estimates, but both are calculated here for comparison and because 

mortality estimates are sometimes used in addition to incidence estimates in benefit-cost 

analyses. For each cancer endpoint, the same exposure-response model was used as that which 

was selected for the unit risk estimates in Section 4.1 (i.e., linear regression of the categorical 

results, excluding the highest exposure category, for lymphoid cancer and breast cancer mortality 

and two-piece linear spline model for breast cancer incidence). The results are presented in 

Table 4-19 along with the unit risk estimates derived assuming that RR was independent of age 

for all ages (see Section 4.1) for comparison. As can be seen in Table 4-19, the unit risk 

estimates for adult-only exposures range from about 66% to about 72% of the unit risk estimates 

derived under the assumption of age independence across all ages. 

According to EPA’s Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005a) 

cancer risk estimates are intended to reflect total cancer risk, not site-specific cancer risk; 

therefore, an additional calculation was made to estimate the combined risk for (incident) 

lymphoid and breast cancers from adult-only exposures, because females would be at risk for 

both cancer types. Assuming that the tumor types are independent and that the risk estimates are 

approximately normally distributed, this calculation can be made as described in Section 4.1.3. 

First, an EC01 of 0.0114 ppm for the total cancer risk (i.e., lymphoid cancer incidence + breast 

cancer incidence) from adult-only exposure was estimated, as summarized in Table 4-20. 
 

 

 

 

 

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Table 4-19. EC01, LEC01, and unit risk estimates for adult-only exposures* 

Cancer response EC01 (ppm) 

LEC01 

(ppm) 

Adult-exposureonly unit risk 

estimatea 

(per ppm) 

Lifetime-exposure unit risk estimate 

under assumption of age 

independenceb 

(per ppm) 

Lymphoid cancer mortality 

(both sexes) 0.0787 0.0352 0.284 0.397 

Lymphoid cancer 

incidence (both sexes) 0.0364 0.0163 0.613 0.877 

Breast cancer mortality 

(females) 0.0590 0.0297 0.337 0.513 

Breast cancer incidence 

(females) 0.0167 0.00863 1.16c 1.74c 

a

Unit risk estimate = 0.01/LEC01.
 b

From Tables 4–5, 4–9, and 4–13 of Section 4.1.
 c

For unit risk estimates above 1, convert to risk per ppb (e.g., 1.16 per ppm = 1.16 × 10-3 per ppb).
 

*These are intermediate values. See Table 4-22 below for the final adult-based cancer-type-specific unit risk
 

estimates.
 

Table 4-20. Calculation of EC01 for total cancer risk from adult-only 

exposure 

Cancer type 

EC01 

(ppm) 

0.01/EC01 

(per ppm) 

EC01 for total risk 

(ppm) 

Lymphoid 0.0364 0.275 -­

Breast 0.0167 0.599 --

Totala -­ 0.874 0.0114 

a

The total 0.01/EC01 value equals the sum of the individual 0.01/EC01 values; the EC01 for the 

total cancer risk then equals 0.01/(0.01/EC01). 

Then, a unit risk estimate of 1.5 per ppm for the total cancer risk (i.e., lymphoid cancer 

incidence + breast cancer incidence) from adult-only exposure was derived, as shown in 

Table 4−21. An LEC01 estimate of 0.00654 ppm for the total cancer risk can be calculated as 

0.01/(1.53 per ppm). 

Thus, the total cancer unit risk estimate from adult-only exposure is 1.53 per ppm (or 

1.53 × 10−3

 per ppb; 8.36 × 10−4 per μg/m3). While there are uncertainties regarding the 

assumption of a normal distribution of risk estimates, the resulting unit risk estimate is 

appropriately bounded in the roughly twofold range between estimates based on the sum of the 

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Table 4-21. Calculation of total cancer unit risk estimate from adult-only 

exposure* 

Cancer type 

Adult-exposureonly unit risk 

estimate 

(per ppm) 

0.01/EC01 

(per ppm) 

SEa 

(per ppm) Variance 

Adult-exposure-only 

total cancer unit risk 

estimate 

(per ppm) 

Lymphoid 0.613 0.275 0.205 0.0422 -­

Breast 1.16 0.599 0.340 0.115 -­

Total -­ 0.874 (0.397)b 0.158 1.53c 

*These are intermediate values. See Table 4-22 below for the final adult-based cancer-type-specific unit risk
 

estimates.
 a

SE = (unit risk – 0.01/EC01)/1.645.
 b

The SE of the total cancer risk is calculated as the square root of the sum of the variances (next column), not as the 

sum of the SEs.
 c

Total cancer unit risk = 0.874 + 1.645 × 0.397.
 

individual MLEs (i.e., 0.874) and the sum of the individual 95% UCLs (i.e., unit risk estimates,
 

1.77), or more precisely in this case, between the largest individual unit risk estimate (1.16) and 

the sum of the unit risk estimates (1.77), and thus, any inaccuracy in the total cancer risk estimate 

resulting from the approach used to combine risk estimates across cancer types is relatively
 

minor. 

When EPA derives unit risk estimates from rodent bioassay data, there is a blurring of the
 

distinction between lifetime and adult-only exposures because the relative amount of time that a 

rodent spends as a juvenile is negligible (<8%) compared to its lifespan. [According to EPA’s
 

Supplemental Guidance, puberty begins around 5−7 weeks of age in rats and around 4−6 weeks
 

in mice (U.S. EPA, 2005b)]. Thus, when exposure in a rodent is initiated at 5–8 weeks, as in the
 

typical rodent bioassay, and the bioassay is terminated after 104 weeks of exposure, the unit risk 

estimate derived from the resulting cancer incidence data is considered a unit risk estimate from
 

lifetime exposure, except when the ADAFs were formulated and are applied, in which case the
 

same estimate is considered to apply to adult-only exposure. Yet, when adult exposures are 

considered in the application of ADAFs, the adult-exposure-only unit risk estimate is pro-rated
 

over the full default human lifespan of 70 years, presumably because that is how adult exposures
 

are treated when a unit risk estimate calculated in the same manner from the same bioassay
 

exposure paradigm is taken as a lifetime unit risk estimate.
 

However, in humans, a greater proportion of time is spent in childhood (e.g., 16 of
 

70 years = 23%), and the distinction between lifetime exposure and adult-only exposure cannot
 

be ignored when human data are used as the basis for the unit risk estimates. Thus, as described
 

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above, adult-exposure-only unit risk estimates were calculated distinct from the lifetime 

estimates that were derived in Section 4.1 under the assumption of age independence for all ages. 

In addition, the adult-exposure-only unit risk estimates need to be rescaled to a 70-year lifespan 

in order to be used in the ADAF calculations and risk estimate calculations involving less-thanlifetime exposure scenarios in the standard manner, which includes prorating even adult-based 

unit risk estimates over 70 years. Thus, the adult-exposure-only unit risk estimates are 

multiplied by 70/54 to rescale the 54-year adult period of the 70-year default lifespan to 70 years. 

Then, for example, if a risk estimate were calculated for a less-than-lifetime exposure scenario 

involving exposure only for the full adult period of 54 years, the rescaled unit risk estimate 

would be multiplied by 54/70 in the standard calculation and the adult-exposure-only unit risk 

estimate would be appropriately reproduced. Without rescaling the adult-exposure-only unit risk 

estimates, the example calculation just described for exposure only for the full adult period of 

54 years would result in a risk estimate 77% (i.e., 54/70) of that obtained directly from the 

adult-exposure-only unit risk estimates, which would be illogical. The rescaled adult-based unit 

risk estimates for use in ADAF calculations and risk estimate calculations involving less-thanlifetime exposure scenarios are presented in Table 4-22. Rescaled LEC01 and EC01 estimates for 

adult-based total cancer risk are 5.0 × 10−3 ppm (9.2 μg/m3) and 8.8 × 10−3 ppm (16 μg/m3). 

Table 4-22. Adult-based unit risk estimates for use in ADAF calculations 

and risk estimate calculations involving less-than-lifetime exposure scenarios 

Cancer response 

Adult-based unit risk estimate (per 

ppm) 

Adult-based unit risk estimate (per 

μg/m3) 

Lymphoid cancer mortality 0.368 2.01 × 10-4 

Lymphoid cancer incidence 0.795 4.35 × 10-4 

Breast cancer mortality 0.436 2.39 × 10-4 

Breast cancer incidence 1.50a 8.21 × 10-4 

Total cancer incidence 1.98a 1.08 × 10-3 

a

For unit risk estimates above 1, convert to risk per ppb (e.g., 1.16 per ppm = 1.16 × 10-3 per ppb). 

An example calculation illustrating the application of the ADAFs to the human-dataderived adult-based (rescaled as discussed above) unit risk estimate for EtO for a lifetime 

exposure scenario is presented below. For inhalation exposures, assuming ppm equivalence 

across age groups, i.e., equivalent risk from equivalent exposure levels, independent of body 

This document is a draft for review purposes only and does not constitute Agency policy. 

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size, the ADAF calculation is fairly straightforward. Thus, the ADAF-adjusted lifetime totalcancer unit risk estimate is calculated as follows: 

total cancer risk from exposure to constant EtO exposure level of 1 μg/m3 from ages 0–70 years: 

 unit risk exposure duration partial 

Age group ADAF (per μg/m3

) conc (μg/m3) adjustment risk 

0 to <2 years 10 1.08 × 10−3 1 2 years/70 years 3.09 × 10−4 

2 to <16 years 3 1.08 × 10−3 1 14 years/70 years 6.48 × 10−4 

≥16 years 1 1.08 × 10−3 1 54 years/70 years 8.33 × 10−4 

total lifetime risk = 1.80 × 10−3 

The partial risk for each age group is the product of the values in columns 2–5 [e.g., 

10 × (1.08 × 10−3

) × 1 × 2/70 = 3.09 × 10−4], and the total risk is the sum of the partial risks. 

This 70-year risk estimate for a constant exposure of 1 μg/m3 is equivalent to a lifetime 

unit risk estimate of 1.8 × 10−3 per μg/m3 (3.3 per ppm, or 3.3 × 10−3 per ppb), adjusted for 

potential increased early-life susceptibility, assuming a 70-year lifetime and constant exposure 

across age groups. Note that because of the use of the rescaled adult-based unit risk estimate, the 

partial risk for the ≥16 years age group is the same as would be obtained for a 1 μg/m3

 constant 

exposure directly from the total cancer adult-exposure-only unit risk estimate of 8.36 × 10-4 per 

μg/m3 that was presented above, as it should be (the small difference in the second decimal place 

is due to round-off error). 

In addition to the uncertainties discussed above for the inhalation unit risk estimate, there 

are uncertainties in the application of ADAFs to adjust for potential increased early-life 

susceptibility. The ADAFs reflect an expectation of increased risk from early-life exposure to 

carcinogens with a mutagenic mode of action (U.S. EPA, 2005b), but they are general 

adjustment factors and are not specific to EtO. With respect to the breast cancer estimates, for 

example, evidence suggests that puberty/early adulthood is a particularly susceptible life stage 

for breast cancer induction (U.S. EPA, 2005b; Russo and Russo, 1999); however, EPA has not, 

at this time, developed alternate ADAFs to reflect such a pattern of increased early-life 

susceptibility, and there is currently no EPA guidance on an alternate approach for adjusting for 

early-life susceptibility to potential breast carcinogens. 

4.5. INHALATION UNIT RISK ESTIMATES—CONCLUSIONS 

For both humans and laboratory animals, tumors occur at multiple sites. In humans, there 

was a combination of tumors having lymphohematopoietic, in particular lymphoid, origins in 
 

 

 

 

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both sexes and breast cancer in females, and, in rodents, lymphohematopoietic tumors, mammary 

carcinomas, and tumors of other sites were observed. From human data, an extra cancer unit risk 

estimate of 4.79 × 10−4 per μg/m3

 (8.77 × 10−4 per ppb) was calculated for lymphoid cancer 

incidence, and a unit risk estimate of 9.49 × 10−4 per μg/m3 (1.74 × 10−3 per ppb) was calculated 

for breast cancer incidence in females, under the assumption that RR is independent of age for all 

ages (see Section 4.1). The total extra cancer unit risk estimate was 1.24 × 10−3 per μg/m3 

(2.27 × 10−3 per ppb) for both cancer types combined (EC01 = 0.00775 ppm; LEC01 = 

0.00441 ppm). Unit risk estimates derived from the three chronic rodent bioassays for EtO 

ranged from 2.2 × 10−5 per μg/m3

 to 4.6 × 10−5 per μg/m3, over an order of magnitude lower than 

the estimates based on human data. 

Because a mutagenic mode of action for EtO carcinogenicity (see Section 3.4.1) is 

“sufficiently supported in (laboratory) animals” and “relevant to humans,” and as there are no 

chemical-specific data to evaluate the differences between adults and children, increased 

early-life susceptibility should be assumed, in accordance with EPA’s Supplemental Guidance 

(U.S. EPA, 2005b). This assumption of increased early-life susceptibility supersedes the 

assumption of age independence under which the human-data-based estimates presented in the 

previous paragraph were derived. Thus, as described in Section 4.4, adult-exposure-only unit 

risk estimates were calculated from the human data under an alternate assumption that RR is 

independent of age for adults, which represent the life stage for which the data upon which the 

exposure-response modeling was conducted pertain. These adult-exposure-only unit risk 

estimates were then rescaled to a 70-year basis for use in the standard ADAF calculations and 

risk estimate calculations involving less-than-lifetime exposure scenarios. The resulting 

adult-based unit risk estimates were 4.35 × 10−4 per μg/m3

 (7.95 × 10−4 per ppb) for lymphoid 

cancer incidence and 8.21 × 10−4 per μg/m3 (1.50 × 10−3 per ppb) for breast cancer incidence in 

females. The adult-based total extra cancer unit risk estimate for use in ADAF calculations and 

risk estimate calculations involving less-than-lifetime exposure scenarios was 1.08 × 10−3 per 

μg/m3 (1.98 × 10−3 per ppb) for both cancer types combined. 

For exposure scenarios involving early-life exposure, the age-dependent adjustment 

factors (ADAFs) should be applied, in accordance with EPA’s Supplemental Guidance (U.S. 

EPA, 2005b). Applying the ADAFs to obtain a full lifetime unit risk estimate yields 

1.98/ppm × ((10 × 2 years/70 years) + (3 × 14/70) + (1 × 54/70)) (4-8)
 

= 3.29/ppm = 1.80 × 10−3

/(μg/m3).
 

This document is a draft for review purposes only and does not constitute Agency policy. 

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Applying the ADAFs to the unit risk estimates derived from the three chronic rodent bioassays 

for EtO yields estimates ranging from 3.7 × 10−5 per μg/m3

 to 7.6 × 10−5 per μg/m3, still over an 

order of magnitude lower than the estimate based on human data. 

Adequate human data, if available, are considered to provide a more appropriate basis 

than rodent data for estimating human risks (U.S. EPA, 2005a), primarily because uncertainties 

in extrapolating quantitative risks from rodents to humans are avoided. Although there is a 

sizeable difference between the rodent-based and the human-based estimates, the human data are 

from a large, high-quality study, with EtO exposure estimates for the individual workers and 

little reported exposure to chemicals other than EtO. Therefore, the human-based full lifetime 

total extra cancer unit risk estimate of 1.8 × 10−3 per μg/m3

 (3.3 × 10−3 per ppb) calculated 

for lymphoid cancers and breast cancer combined and applying the ADAFs is the preferred 

lifetime unit risk estimate. For less-than-lifetime exposure scenarios, the human-data-derived 

(rescaled) adult-based unit risk estimate of 1.1 × 10−3 per μg/m3

 (2.0 × 10−3 per ppb) should be 

used, in conjunction with the ADAFs if early-life exposures occur. 

Although there are uncertainties in this unit risk estimate, primarily related to exposure 

misclassification, model uncertainty, and low-dose extrapolation, as discussed in Section 4.1.4, 

confidence in the unit risk estimate is relatively high. First, there is strong confidence in the 

hazard characterization of EtO as “carcinogenic to humans,” which is based on strong 

epidemiological evidence supplemented by other lines of evidence, such as genotoxicity in both 

rodents and humans (see Section 3.5.1). Second, the unit risk estimate is based on human data 

from a large, high-quality epidemiology study with individual worker exposures estimated using 

a high-quality regression model (see Section 4.1 and Section A.2.8 of Appendix A). Finally, the 

use of low-exposure linear extrapolation is strongly supported by the conclusion that EtO 

carcinogenicity has a mutagenic mode of action (see Section 3.4.1). 

Confidence in the unit risk estimate is particularly high for the breast cancer component, 

the largest contributor to the total cancer unit risk estimate, which is based on over 200 incident 

cases for which the investigators had information on other potential breast cancer risk factors 

(see Section 4.1.2.3). The selected model for the breast cancer incidence data was the 

best-fitting model of the models investigated as well as the model that provided the best 

representation of the categorical results, particularly in the lower exposure range of greatest 

relevance for the derivation of a unit risk estimate. Alternate estimates calculated from other 

reasonable models suggest that a unit risk estimate for breast cancer incidence that is fourfold 

lower (corresponding to a total cancer unit risk estimate of twofold lower) is plausible; however, 

unit risk estimates notably lower than that are considered unlikely from the available data. 
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There is lower confidence in the lymphoid cancer component of the unit risk estimate 

because it is based on fewer events (40 lymphoid cancer deaths); incidence risk was estimated 

from mortality data; and the exposure-response relationship is exceedingly supralinear, such that 

continuous models yield apparently implausibly steep low-exposure slopes (see Figure 4-1). 

Although these continuous models provided statistically significant slope coefficients, there was 

low confidence in such steep slopes, which, particularly for the two-piece spline models, are 

highly dependent on a small number of cases in the low-exposure range. Thus, a linear 

regression model of the categorical results for the lowest three quartiles was used to derive the 

unit risk estimate for lymphoid cancer, and there was greater confidence in the more moderate 

slope resulting from that model, although it was not statistically significant, because it was based 

on more data and provided a good representation of the categorical results across this larger data 

range in the lower-exposure region (see Section 4.1.1.2). So, while there is lower confidence in 

the lymphoid cancer unit risk estimate than in the breast cancer unit risk estimate, the lymphoid 

cancer estimate is considered a reasonable estimate from the available data, and overall, there is 

relatively high confidence in the total cancer unit risk estimate. 

The unit risk estimate is intended to be an upper bound on cancer risk for use with 

exposures below the POD (i.e., the LEC01). The unit risk estimate should not generally be used 

above the POD; however, in the case of this total extra cancer unit risk, which is based on cancer 

type-specific unit risk estimates from two linear models, the estimate should be valid for 

exposures up to about 0.075 ppm (140 μg/m3), which is the minimum of the limits for the 

lymphoid cancer unit risk estimate (0.090 ppm: see Section 4.1.1.2) and the breast cancer unit 

risk estimate (0.075 ppm; see Section 4.1.2.3). (See Section 4.7 for risk estimates based on 

occupational exposure scenarios.) 

Using the above full lifetime unit risk estimate of 3.3 × 10−3

 per ppb (1.8 × 10−3 per 

μg/m3), the lifetime chronic exposure level of EtO corresponding to an increased cancer risk of 

10−6 can be estimated as follows: 

(10−6)/(3.3/ppm) = 3.0 × 10−7

 ppm = 0.00030 ppb = 0.0006 μg/m3 . (4-9) 

The inhalation unit risk estimate presented above, which is calculated based on a linear 

extrapolation from the POD (LEC01), is expected to provide an upper bound on the risk of cancer 

incidence. However, estimates of “central tendency” for the risk below the POD are also 

presented. Adult-based extra risk estimates per ppm for some of the cancer responses, based on 

linear extrapolation from the adult-exposure-only EC01 (i.e., 0.01/EC01) and rescaling to a 
 

 

 

 

 

 

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70-year basis for use in ADAF calculations and risk estimate calculations involving less-thanlifetime exposure scenarios (see Section 4.4), are reported in Table 4-23. The adult-exposureonly EC01s were from the linear regression models of the categorical results for lymphoid 

cancers and breast cancer mortality and from the two-piece linear spline model (low-dose
 

segment) for breast cancer incidence. (Note that, for each of these models, the low-exposure
 

extrapolated estimates are a straight linear continuation of the linear models used above the
 

PODs, and thus, the statistical properties of the models are preserved.) These estimates are 

dependent on the suitability of the EC01 estimates as well as on the applicability of the linear
 

low-dose extrapolation. The assumption of low-dose linearity is supported by the mutagenicity 

of EtO (see Section 3.4). If these estimates are to be used, ADAFs should be applied if early-life 

exposure occurs, in accordance with EPA’s Supplemental Guidance (U.S. EPA, 2005b). 

As can be seen by comparing the adult-based rescaled 0.01/EC01estimates in Table 4-23 

with the adult-based unit risk estimates in Table 4-22, the 0.01/EC01 estimates are about 45% of 

the unit risk estimates for the lymphoid cancer responses and about 50% of the unit risk 

estimates for the breast cancer responses. 

Table 4-23. Adult-based extra risk estimates per ppm based on 

adult-exposure-only EC01s

a 

Cancer response EC01 (ppm) 

Adult-based 

0.01/EC01 (per ppm)b 

Lymphoid cancer mortality (both sexes) 0.0787 0.165 

Lymphoid cancer incidence (both sexes) 0.0364 0.356 

Breast cancer mortality (females) 0.0590 0.219 

Breast cancer incidence (females) 0.0167 0.776 

Total cancer incidence 0.0114 1.14c 

a

ADAFs should be applied if early-life exposure occurs, in accordance with EPA’s Supplemental
 

Guidance.
 b

These estimates are calculated as 0.01/EC01 for the adult-exposure-only extra risk estimate per
 

ppm rescaled to a 70-yr basis by multiplying by 70/54 (see Section 4.4).
 c

For unit risk estimates above 1, convert to risk per ppb (e.g., 1.14 per ppm = 1.14 × 10–3 per ppb).
 

Finally, it should be noted that some investigators have posited that the high and variable 

background levels of endogenous EtO-induced DNA damage in the body (see Section 3.3.3.1) 

may overwhelm any contribution from low levels of exogenous EtO exposure (Marsden et al., 

2009; SAB, 2007). It is true that the existence of these high and variable background levels may 

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make it hard to observe statistically significant increases in risk from low levels of exogenous 

exposure. However, there is clear evidence of carcinogenic hazard from the rodent bioassays 

and strong evidence from human studies (see Section 3.5), and the genotoxicity/mutagenicity of 

EtO (see Section 3.4) supports low-dose linear extrapolation of risk estimates from those studies 

(U.S. EPA, 2005a). In fact, as noted in Section 3.3.3.1, Marsden et al. (2009), using sensitive 

detection techniques and an approach designed to separately quantify both endogenous N7-HEG 

adducts and “exogenous” N7-HEG adducts induced by EtO treatment in rats, reported increases 

in exogenous adducts in DNA of spleen and liver consistent with a linear dose-response 

relationship (p < 0.05), down to the lowest dose administered (0.0001 mg/kg injected i.p. daily 

for 3 days, which is a very low dose compared to the LOAELs in the carcinogenicity bioassays; 

see Section C.7 of Appendix C). Furthermore, while the contributions to DNA damage from low 

exogenous EtO exposures may be relatively small compared to those from endogenous EtO 

exposure, low levels of exogenous EtO may nonetheless be responsible for levels of risk (above 

background risk). This is not inconsistent with the much higher levels of background cancer 

risk, to which endogenous EtO may contribute, for the two cancer types observed in the human 

studieslymphoid cancers have a background lifetime incidence risk on the order of 3%, while 

the background lifetime incidence risk for breast cancer is on the order of 15%.29 

See Table 4-24 for a summary of key unit risk estimates derived in this assessment. See 

Section 4.7 for risk estimates based on occupational exposure scenarios. 

4.6. COMPARISON WITH OTHER PUBLISHED RISK ESTIMATES 

The unit risk values derived in this document are compared with other recent risk 

estimates presented in the published literature (see Table 4-25). 

4.6.1. Unit Risk Estimates Based on Human Studies 

Kirman et al. (2004) used leukemia data only and pooled data from both the Stayner et al. 

(1993) and the UCC studies (Teta et al., 1999; Teta et al., 1993). Based on the assumption that 

leukemias are due to chromosome translocations, requiring two independent events 

(chromosome breaks), the Kirman et al. (2004) proposed that two independent EtO-induced 

events are required for EtO-induced leukemias and used a dose-squared model, yielding a unit 

risk value of 4.5 × 10−8 (μg/m3)

−1 as their preferred estimate. 

 

29These background lifetime incidence values were obtained from the lifetable analysis, based on SEER rates, as 

discussed in Sections 4.1.1.3 and 4.1.2.3. For lymphoid cancer, for example, see the value of Ro at the bottom of 

the lifetable analysis in Appendix E. 
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Table 4-24. Summary of key unit risk estimates from this assessment (see 

Section 4.7 for risk estimates based on occupational exposure scenarios) 

Basis 

Inhalation unit risk estimatea 

(per μg/m3)

b 

Full lifetime unit risk estimatec 

Total cancer risk based on human data (NIOSH cohort of 

sterilizer workers)―lymphoid cancer incidence (linear 

regression of categorical results) and breast cancer incidence in 

females (2-piece linear spline model) 

1.80 × 10-3 

Adult-based unit risk estimatesd 

Total cancer risk based on human data (NIOSH 

cohort)―lymphoid cancer incidence (linear regression of 

categorical results) and breast cancer incidence in females (2­

piece linear spline model) 

1.08 × 10-3 

Lymphoid cancer incidence based on human data 

(NIOSH)―linear regression of categorical results 4.35 × 10-4 

Breast cancer incidence in females based on human data 

(NIOSH)―stimate based on best-fitting model: the 2-piece 

linear spline model 

8.21 × 10-4 

Breast cancer incidence in females based on human data 

(NIOSH)―range based on 3 reasonable statistically significant 

continuous models: 2-piece linear spline model, 2-piece loglinear spline model, and linear model 

2.10 × 10-4 to 8.21 × 10-4 

Total cancer risk based on human data (NIOSH 

cohort)―lymphoid cancer incidence (linear regression of 

categorical results) and range of female breast cancer incidence 

estimates (2-piece linear spline model, 2-piece log-linear spline 

model, and linear model) 

5.64 × 10-4 to 1.08 × 10-3 

Lymphoid cancer mortality based on human data 

(NIOSH)―linear regression of categorical results 2.01 × 10-4 

Breast cancer mortality in females based on human data 

(NIOSH)―linear regression of categorical results 2.39 × 10-4 

Preferred total cancer incidence risk estimate from rodent data 

(female mouse) 4.6 × 10-5 

Range of total cancer incidence risk estimates from rodent data 

(mouse and rat) 2.2 × 10-5 to 4.6 × 10-5 

0.01/EC01estimatese 

Lymphoid cancer incidence based on human data 

(NIOSH)―linear regression of categorical results 1.9 × 10-4 

Breast cancer incidence in females based on human data 

(NIOSH)―estimate based on best-fitting model: the 2-piece 

linear spline model 

4.2 × 10-4 

Lymphoid cancer mortality based on human data 

(NIOSH)―linear regression of categorical results 9.0 × 10-5 

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Table 4 24. Summary of key unit risk estimates from this assessment (see 

Section 4.7 for risk estimates based on occupational exposure scenarios) 

(continued) 

Basis 

Inhalation unit risk estimatea 

(per μg/m3)

b 

Breast cancer mortality in females based on human data 

(NIOSH)―linear regression of categorical results 1.2 × 10−4 

Total cancer incidence based on human data (NIOSH) 6.2 × 10−4 

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a

Technically, the values listed in this table are not all unit risk estimates as defined by EPA, but they are all potency 

estimates that, when multiplied by an exposure value, give an estimate of extra cancer risk. These potency estimates 

are not intended for use with continuous lifetime exposure levels above 140 μg/m3 . See Section 4.7 for risk 

estimates based on occupational exposure scenarios. Preferred estimates are in bold. 

b

To convert unit risk estimates to (ppm)−1, multiply the (μg/m3)

−1 estimates by 1,830 (μg/m3

)/ppm. c Because the weight of evidence supports a mutagenic mode of action for EtO carcinogenicity, and because of the 

lack of chemical-specific data, EPA assumes increased early-life susceptibility and recommends the application of 

ADAFs, in accordance with EPA’s Supplemental Guidance (U.S. EPA, 2005b), for exposure scenarios that include 

early-life exposures. For the full lifetime (upper bound) unit risk estimate presented here, ADAFs have been 

applied, as described in Section 4.4. d

These (upper bound) unit risk estimates are intended for use in ADAF calculations and less-than-lifetime adult 

exposure scenarios (U.S. EPA, 2005b). Note that these are not the same as the unit risk estimates derived directly 

from the human data in Section 4.1 under the assumption that RRs are independent of age. Under that assumption, 

the key unit risk estimates were 4.8 × 10-4 per μg/m3 for lymphoid cancer incidence, 9.5 × 10-4 per μg/m3 for breast 

cancer incidence from the best-fitting 2-piece linear spline model, and 1.2 × 10-3 per μg/m3 for the combined cancer 

incidence risk from those two cancers. See Section 4.4 for the derivation of the adult-based unit risk estimates. e

These are not upper-bound risk estimates but, rather, estimates based on linear extrapolation from the EC01. 

ADAFs should be applied if early-life exposure occurs, in accordance with EPA’s Supplemental Guidance (U.S. 

EPA, 2005b). 

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Table 4-25. Comparison of unit risk estimatesa 

Assessments Data source 

Inhalation unit risk estimateb 

(per μg/m3) 

Based on human data 

EPA 

(this document)c 

Lymphoid cancer incidence in sterilizer 

workers (NIOSH)d 

7.2 × 10-4 

Breast cancer incidence in female 

sterilizer workers (NIOSH)e 

1.4 × 10-3 

Total cancer risk based on the NIOSH 

data 

1.8 × 10-3 

Kirman et al. (2004) Leukemia mortality in combined NIOSH 

and UCC cohorts (earlier follow-ups) 

4.5 × 10-8 

Range of 1.4 × 10-8 to 1.4 × 10-7 f 

Valdez-Flores et al. (2010) multiple individual cancer endpoints, 

including all lymphohematopoietic, 

lymphoid, and breast cancers, in 

combined updated NIOSH and updated 

UCC cohorts 

5.5 × 10-7 to 1.6 × 10-6 g 

Based on rodent data 

EPA (this document)c Female mouse tumors 7.6 × 10-5 

Kirman et al. (2004) Mononuclear cell leukemia in 

rats and lymphomas in mice 

2.6 × 10-8 to 1.5 × 10-5 h 

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a

Upper-bound estimates except where footnoted to indicate that estimates are based on EC values (i.e., estimates 

with footnotes f and g). 

b

Because the weight of evidence supports a mutagenic mode of action for EtO carcinogenicity, and in the absence of 

chemical-specific data, EPA assumes increased early-life susceptibility, in accordance with EPA’s Supplemental 

Guidance (U.S. EPA, 2005b), and for the EPA lifetime unit risk estimates presented in this table, ADAFs have been 

applied, as described in Section 4.4. The corresponding adult-based unit risk estimates are 4.4 × 10-4 (μg/m3

)

−1 for 

human-based lymphoid cancer incidence, 8.2 × 10-4 (μg/m3

) -1 for human-based breast cancer incidence, 

1.1 × 10-3 (μg/m3

) -1 for human-based total cancer incidence, and 4.6 × 10-5 (μg/m3) -1 for rodent-based total cancer 

incidence. The non-EPA estimates in the table are shown as reported and do not account for potential increased 

early-life susceptibility for lifetime exposures that include childhood, with the exception of the Valdez-Flores et al. 

(2010) estimates, which are purported to include the ADAFs, but the ADAFs were in fact misapplied and have 

essentially no impact (see Appendix A.2.20). c

See Table 4-24 in Section 4.5 for a more complete summary of estimates from this assessment. See Section 4.7 for 

risk estimates for occupational exposure scenarios. 

d

For lymphoid cancer mortality, the ADAF-adjusted lifetime unit risk estimate is 3.3 × 10-4 (μg/m3) -1 and the adultbased unit risk estimate is 2.0 × 10-4 (μg/m3

) -1 . e

For breast cancer mortality, the ADAF-adjusted lifetime unit risk estimate is 4.0 × 10-4 (μg/m3) -1 and the adultbased unit risk estimate is 2.4 × 10-4 (μg/m3

) -1 . f

Estimates based on linear extrapolation from EC0001−EC000001 obtained from the quadratic model. g

Estimates based on range of EC(1/million)s of 0.001−0.003 ppm obtained from the model RR = e(β × exposure) for 

relevant cancer endpoints. 

h

Estimates based on quadratic extrapolation model below the observable range of the data (i.e., below the LEC10 or 

LEC01 obtained using multistage model) with various points of departure (LEC01–LEC000001) for final linear 

extrapolation (see Section 4.4.2). 

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The Kirman et al. (2004) values are different from those in the current document because 

of the different assumptions inherent in the Kirman et al. (2004) approach and because the study 

used unpublished data from earlier follow-ups of the two cohorts. A key difference is that EPA 

uses a linear model rather than a quadratic (dose-squared) model in the range of observation. 

Then, EPA uses a higher extra risk level (1%) for establishing the POD, whereas Kirman et al. 

(2004) used a risk level of 10-5 for their best estimate and a risk range of 10-4 to 10-6 for their 

range of values. The extra risk level and the corresponding POD are not critical with the linear 

model; however, with the quadratic model used by Kirman et al. (2004), the lower the risk level 

(and hence the POD), the greater the impact of the quadratic model and the lower the resulting 

unit risk estimates. 

In addition, EPA (1) uses data for lymphoid cancers (and female breast cancers) rather 

than leukemias, (2) includes ages up to 85 years in the life-table analysis rather than stopping at 

70 years, (3) calculates unit risk estimates for cancer incidence as well as mortality, (4) uses a 

lower bound as the POD rather than the maximum likelihood estimate, (5) uses the results of 

lagged analyses rather than unlagged analyses, and (6) uses adult-based unit risk estimates in 

cojunction with ADAFs (see Section 4.4) to derive the lifetime unit risk estimates. 

Another key difference is that Kirman et al. (2004) relied on earlier NIOSH results 

(Stayner et al., 1993), whereas EPA uses the results of NIOSH's more recent follow-up of the 

cohort (Steenland et al., 2004). Kirman et al. (2004) claim that a quadratic dose-response model 

provided the best fit to the data in the observable range and that this provides support for their 

assumed mode of action. However, the 2004 NIOSH data for lymphohematopoietic cancers 

suggest a supralinear exposure-response relationship (see Section 4.1.1.2 and Figures 4-1 and 

4-2), which is inconsistent with a dose-squared model. Furthermore, EPA’s review of the mode 

of action evidence does not support the mode of action assumed by Kirman et al. (2004) (see 

Section 3.4). 

The Valdez-Flores et al. (2010) unit risk estimates (see Table 4−25) are similarly much 

lower than those in the current document because of the different assumptions used. A key 

difference is that EPA uses a linear model or a two-piece linear spline model in the range of 

observation rather than an exponential model (RR = eβ × exposure), which was used by ValdezFlores et al. (2010) despite its lack of fit. Then, EPA uses a higher extra risk level (1%) for 

establishing the POD for linear extrapolation, whereas Valdez-Flores et al. (2010) used a risk 

level of 10-6 . In addition, EPA (1) includes ages up to 85 years in the life-table analysis rather 

than stopping at 70 years, (2) calculates unit risk estimates for cancer incidence as well as 

mortality, (3) uses a lower bound as the POD rather than the maximum likelihood estimate, and 

(4) uses the results of lagged analyses rather than unlagged analyses. See Appendix A.2.20 for a 
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more detailed discussion of the differences between the EPA and Valdez-Flores et al. (2010) 

analyses. 

4.6.2. Unit Risk Estimates Based on Laboratory Animal Studies 

Kirman et al. (2004) also used linear and dose-squared extrapolation models to derive 

unit risk estimates based on the rat mononuclear cell leukemia data and the mouse lymphoma 

data. First, they used the multistage model to calculate the LEC10 (LEC01 for the male mouse 

lymphoma data) for the POD from the observable range. Then, using these PODs for linear 

extrapolation, Kirman et al. (2004) obtained a unit risk range of 3.9 × 10−6 (μg/m3

)

-1 to 

1.5 × 10−5 (μg/m3)

-1 . Alternatively, Kirman et al. (2004) used a quadratic extrapolation model 

below the observable range to estimate secondary points of departure (LEC01–LEC000001; 

LEC001–LEC000001 for the male mouse) for final linear low-dose extrapolation, yielding unit risks 

ranging from 2.6 × 10−8 (μg/m3)

-1 to 4.9 × 10−6 (μg/m3

)

-1 . These values are all smaller than the 

unit risks derived from the rodent data in this document. 

4.7. RISK ESTIMATES FOR SOME OCCUPATIONAL EXPOSURE SCENARIOS 

The unit risk estimates derived in the preceding sections were developed for 

environmental exposure levels, where maximum modeled levels are on the order of 1−2 μg/m3 

(email dated October 3, 2005, from Mark Morris, EPA, to Jennifer Jinot, EPA), i.e., roughly 

0.5−1 ppb, and are not applicable to higher exposures, including some occupational exposure 

levels. However, occupational exposure levels of EtO are of concern to EPA when EtO is used 

as a pesticide (e.g., sterilizing agent or fumigant). The occupational exposure scenarios of 

interest to EPA include some cumulative exposures corresponding to exposure levels in the 

nonlinear range of some of the models (i.e., above the maximum exposure level at which the 

low-dose-linear unit risk estimates apply). Therefore, extra risk estimates were calculated for a 

number of occupational exposure scenarios of possible concern. Extra risk estimates are 

estimates of the extra cancer risk above background and are the same type of estimate that one 

gets from multiplying a unit risk estimate by an exposure level. In this case, the exposure level is 

used directly in the exposure-response model, thus accounting for any nonlinearities in the model 

above the range of exposure levels for which the linear unit risk estimate is applicable. For these 

occupational exposure scenarios, exposure-response models based on data from the NIOSH 

cohort were used in conjunction with the life-table program, as previously discussed in 

Section 4.1. A 35-year exposure occurring between ages 20 and 55 years was assumed, and 

exposure levels ranging from 0.1 to 1 ppm 8-hour TWA were examined (i.e., ranging from about 
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1,300 to 13,000 ppm × days). (Note that the current Occupational Safety and Health 

Administration Permissible Exposure Limit is 1 ppm [8-hour TWA].) 

For lymphoid cancer mortality in both sexes, the best-fitting (natural) log cumulative 

exposure Cox regression model (see Steenland reanalyses in Appendix D; see also 

Section 4.1.1.2), lagged 15 years, was used. The log cumulative exposure Cox regression model 

was the best-fitting model for lymphoid cancer in males in the Steenland et al. (2004) study, and 

the same model form is used here but with the data from both sexes. Although this model was 

deemed too steep in the low-exposure region to be useful for the derivation of unit risk estimates 

for lower (environmental) exposures, the model is well suited for the occupational exposure 

scenarios of interest in this assessment because the corresponding cumulative exposures are well 

within the range of the cumulative exposures in the NIOSH cohort. The model was statistically 

significant (p = 0.02) and provided a better fit, based on AIC, than the two-piece spline models 

that were considered as alternative models in Section 4.1.1.2 (the AICs were 460.426, 461.847, 

and 461.48 for the log cumulative exposure Cox regression, log-linear two-piece spline and 

linear two-piece spline models, respectively [as reported in Section D.3 of Appendix D]; a lower 

AIC indicates a better fit), as well as a more plausible, smoothly curved exposure-response 

relationship than the two-piece spline models, both of which, with knots at 100 ppm × days, had 

a very steep rise and then a very sharp change in slope at the knot. In addition, the log 

cumulative exposure Cox regression model had a slightly lower AIC (460.426 versus 460.54) 

than the log cumulative exposure linear model (see Section D.3.c of Appendix D) and has the 

advantage of being a standard epidemiological model for continuous exposure data (the Cox 

regression model, albeit with log cumulative exposure to accommodate the supralinearity of the 

exposure-response data). The log cumulative exposure linear model yields slightly higher RR 

estimates than the log cumulative exposure Cox regression model, as can be seen by comparing 

the log cumulative exposure models in Figures D-3b and D-3c in Appendix D, and would thus 

result in slightly higher extra risk estimates than the log cumulative exposure Cox regression 

model. For example, the MLEs of extra risk from the log cumulative exposure linear model 

would range from about 23% higher for the 0.1 ppm 8-hour TWA to 6% higher for the 1 ppm 

8-hour TWA. 

The extra risk results for lymphoid cancer mortality and incidence in both sexes for the 

log cumulative exposure Cox regression model are presented in Table 4-26. For lymphoid 

cancer incidence, the exposure-response relationship was assumed to be the same as for mortality 

(see Section 4.1.1.3). As can be seen in Table 4-26, the extra risks for these occupational 

exposure levels are in the “plateau” region of the exposure-response relationships and increase 

less than proportionately with exposure. For occupational exposures less than about 1,000 ppm 
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× days, or about 0.08 ppm 8-hour TWA for 35 years, risk estimates are no longer in the plateau 

region (see Figure 4-1) but rather in the steep low-exposure region, which is a region of greater 

uncertainty for the log cumulative exposure model, and one might want to use the linear 

regression of the categorical results that was used for lower exposures (see Section 4.1.1.2; 

Appendix D). Furthermore, if one is using the linear regression model in this range and also 

estimating risks for exposure levels in the range between about 0.08 and 0.6 ppm (near where the 

linear regression and log cumulative exposure Cox regression models meet) 8-hour TWA, then 

one might want to use the linear regression model for the entire range up to 0.6 ppm 8-hour 

TWA to avoid a discontinuity between the two models; thus, results for the linear regression 

model for exposure levels up to 0.6 ppm 8-hour TWA are also presented in Table 4-26. While 

the best-fitting continuous exposure model, the log cumulative exposure Cox regression model, 

would generally be preferred in the exposure range between 0.08 and 0.6 ppm 8-hour TWA, 

there is model uncertainty, so the use of either model could be justified. For exposures higher 

than where the linear regression and log cumulative exposure Cox regression models meet, the 

log cumulative exposure model exclusively is recommended. The models used to derive the 

extra risk estimates presented in Table 4-26 for lymphoid cancer for the occupational exposure 

scenarios are displayed in Figure 4-7 over the range of occupational cumulative exposures of 

interest; the categorical results are included for comparison. 

For breast cancer, incidence data were available from the NIOSH incidence study; thus, 

only incidence estimates were calculated. In addition to being the preferred type of cancer risk 

estimate, the breast cancer incidence risk estimates are based on more cases than were available 

in the mortality study and the incidence data (for the subcohort with interviews) are adjusted for 

a number of breast cancer risk factors (see Section 4.1.2.3). In terms of the incidence data, the 

subcohort data are preferred to the full cohort data because the subcohort data are adjusted for 

these potential confounders and also because the full cohort data have incomplete ascertainment 

of breast cancer cases. 

For breast cancer incidence in the subcohort with interviews, a number of Cox regression 

exposure-response models from the Steenland et al. (2003) breast cancer incidence study fit 

almost equally well (see Section 4.1.2.3). These include a log cumulative exposure model and a 

cumulative exposure model, both with a 15-year lag, and a log cumulative exposure model with 

no lag. The latter model was omitted from the calculations because the inclusion of a 15-year lag 

for the development of breast cancer was considered more biologically realistic than not 

including a lag. Steenland et al. (2003) also provide a duration-of-exposure Cox regression 

model with a marginally better fit; however, models using duration of exposure are less useful 
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Table 4-26. Extra risk estimates for lymphoid cancer in both sexes for various occupational exposure levelsa 

8-hr TWA 

(ppm) 

Lymphoid cancer mortality Lymphoid cancer incidenceb 

Log cumulative exposure Cox 

regression modelc Linear regression modeld 

Log cumulative exposure Cox 

regression modelc Linear regression modeld 

MLE 95% UCL MLE 95% UCL MLE 95% UCL MLE 95% UCL 

0.1 0.014 0.032 0.003 0.007 0.031 0.071 0.007 0.016 

0.2 0.016 0.038 0.007 0.014 0.035 0.084 0.014 0.031 

0.3 0.017 0.042 0.010 0.022 0.038 0.093 0.021 0.047 

0.4 0.018 0.045 0.013 0.029 0.040 0.099 0.028 0.062 

0.5 0.018 0.047 0.016 0.036 0.042 0.10 0.035 0.076 

0.6 0.019 0.049 0.019 0.042 0.043 0.11 0.042 0.090 

0.7 0.019 0.051 -­ 0.049 0.044 0.11 -­ -­

0.8 0.020 0.052 -­ -­ 0.045 0.12 -­ -­

0.9 0.020 0.054 -­ -­ 0.046 0.12 -­ -­

1.0 0.021 0.055 -­ -­ 0.047 0.12 -­ -­

a

Assuming a 35-yr exposure between ages 20 and 55 years (see Section 4.7). b

Assumes same exposure-response relationship as for lymphoid cancer mortality.
 c

From the best-fitting log cumulative exposure Cox regression model for lymphoid cancer mortality in both sexes; 15-yr lag (see Appendix D; see also
 

Section 4.1.1.2). d

Linear regression of categorical results for both sexes (see Appendix D; 15-yr lag), excluding the highest exposure group (see Section 4.1.1.2); extra risk
 

estimates from the linear model are provided only up to the exposure level where the linear model meets the log cumulative Cox regression model.
 

MLE: maximum likelihood estimate; UCL: (one-sided) upper confidence limit estimate. 
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Relat ive Risk 3.5 

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2.5 

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1.5 

1 

0.1 ppm x 35 years 

log exposure Cox 

regression model 

linear regression 

of categorical 

res ults 

lymphoid cancer 

quartiles 

1 ppm x 35 years 

0.5
 

0 2000 4000 6000 8000 10000 12000
 

Cumulative Exposure (ppm x days) 

Figure 4-7. RR estimates for lymphoid cancer from occupational EtO exposures (with 15-year lag). 

Lymphoid cancer models (see Section 4.1.1.2): log cumulative exposure Cox regression model; categorical results from Cox 

regression model; linear regression of categorical results, excluding highest exposure group. 
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for estimating exposure-related risks, and duration of exposure and cumulative exposure are 

correlated. Thus, only the lagged cumulative exposure models are considered here. 

The extra risk estimates for breast cancer incidence in females from the lagged 

cumulative exposure and log cumulative exposure Cox regression models listed above are 

presented in Table 4-27. As can be seen in Table 4-27, the extra risk estimates for the lagged log 

cumulative exposure and cumulative exposure models differ substantially. Furthermore, the 

categorical Cox regression results for breast cancer incidence in the subcohort with interviews 

suggest that, for the lowest four exposure quintiles, the log cumulative exposure model 

overestimates the RR, while the cumulative exposure model generally underestimates the RR, 

with the categorical results largely falling between the RR estimates of those two models (see 

Figure 4-5). (The lowest four exposure quintiles represent individual worker exposures ranging 

from 0 to about 15,000 ppm × days, which covers the range of cumulative exposures for the 

occupational exposure scenarios of interest in this assessment, the maximum of which is 

12,775 ppm × days.) Therefore, the two-piece linear spline model (with a 15-year lag) (see 

Section 4.1.2.3) was also used to calculate the extra risk estimates. The two-piece linear spline 

model provides a better fit to the data than the log cumulative exposure or cumulative exposure 

Cox regression models, as indicated by a lower AIC value (1,950.9 for two-piece linear spline 

model vs. 1,956.2 for the log cumulative exposure Cox regression model and 1,956.8 for the 

cumulative exposure Cox regression model; Table 4-12 and Appendix D). In fact, the two-piece 

linear spline model provided the best fit to the breast cancer incidence data of all the models 

investigated in Section 4.1.2.3, and it provides the best representation of the categorical RR 

results, particularly for the range of cumulative exposures for the occupational exposure 

scenarios of interest (see Figures 4-5, 4-6, and 4-8). The extra risk estimates calculated using the 

two-piece linear spline model are also presented in Table 4-27 and are the preferred estimates 

because they are derived from the best-fitting model. 

In addition, extra risk estimates for breast cancer incidence in females from the 

continuous linear model (with a 15-year lag) (see Section 4.1.2.3) are presented in Table 4-27. 

This model, with an AIC of 1,952.3 (see Table 4-12), was the second-best-fitting model and also 

provided a good visual fit to the categorical data (see Figure 4-6). Moreover, the two best-fitting 

models (i.e., the continuous linear model and the two-piece linear spline model) span the range 

of RR estimates from the three best–fitting models investigated in Section 4.1.2.3 (the third 

being the two-piece log-linear spline model) over the range of cumulative exposures for the 

occupational exposure scenarios of interest in this assessment (see Figure 4-6). Comparing the 

results of the two best-fitting models shows that the extra risk estimates differ by just under 
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Table 4-27. Extra risk estimates for breast cancer incidence in females for various occupational exposure 

levelsa,b 

8-hr TWA 

(ppm) 

Log cumulative exposure Cox 

regression modelc 

Cumulative exposure Cox 

regression modelc 

Continuous linear modeld 

Two-piece linear spline modele 

MLE 95% UCL MLE 95% UCL MLE 95% UCLf MLE 95% UCLg 

0.1 0.055 0.11 0.0013 0.0023 0.0042 0.0081 0.016 0.031 

0.2 0.061 0.12 0.0026 0.0046 0.0084 0.016 0.032 0.061 

0.3 0.065 0.13 0.0040 0.0069 0.012 0.024 0.048 0.090 

0.4 0.068 0.14 0.0053 0.0092 0.017 0.032 0.063 0.118 

0.5 0.070 0.14 0.0067 0.012 0.021 0.040 0.075 0.139 

0.6 0.072 0.14 0.0081 0.014 0.025 0.048 0.081 0.150 

0.7 0.073 0.15 0.0095 0.017 0.029 0.055 0.086 0.157 

0.8 0.074 0.15 0.011 0.019 0.033 0.063 0.089 0.162 

0.9 0.076 0.15 0.012 0.022 0.037 0.070 0.093 0.167 

1.0 0.077 0.16 0.014 0.024 0.041 0.078 0.095 0.171 

a

Assuming a 35-yr exposure between ages 20 and 55 years. b

From incidence data for subcohort with interviews; invasive and in situ tumors (Steenland et al., 2003).
 c

Cox regression models from Steenland et al. (2003, Table 5), with 15-yr lag. d

Linear model with cumulative exposure as a continuous variable (see Section 4.1.2.3 and Section D.2 of Appendix D).
 e

Two-piece linear spline model results for occupational exposures use both spline segments (see Section D.2 of Appendix D), knot at 5,800 ppm × days; with
 

15-yr lag. For the 95% UCL, for exposures below the knot, RR = 1 + (β1 + 1.645 × SE1) × exposure; for exposures above the knot, RR = 1 + (β1 × exp + β2 ×
	

(exp-knot) + 1.645 × sqrt(exp2 × var1 + (exp-knot)2 × var2 + 2 × exp × (exp-knot) × covar)), where exp = cumulative exposure, var = variance, covar = 

covariance (see Section D.2 of Appendix D for the parameter values). f

Confidence intervals used in deriving the 95% UCLs were estimated employing the Wald approach. Confidence intervals for linear RR models, however, in
 

contrast to those for the log-linear RR models, may not be symmetrical. EPA also evaluated application of a profile likelihood approach for the linear RR models
 

(Langholz and Richardson, 2010), which allows for asymmetric CIs, for comparison with the Wald approach. Using the profile likelihood method, the resulting
 

extra risk estimates for breast cancer incidence for the continuous linear model would have been about 29% higher than those obtained using the Wald approach.
 
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Table 4-27. Extra risk estimates for breast cancer incidence in females for various occupational exposure 

levelsa,b (continued)
 

g

Confidence intervals used in deriving the 95% UCLs were estimated employing the Wald approach. Confidence intervals for linear RR models, however, in 

contrast to those for the log-linear RR models, may not be symmetrical. EPA also evaluated application of a profile likelihood approach for the linear RR models 

(Langholz and Richardson, 2010), which allows for asymmetric CIs, for comparison with the Wald approach. Using the profile likelihood method, the resulting 

extra risk estimates for breast cancer incidence for the low-exposure linear spline segment (i.e., below 0.4 ppm 8-hr TWA) would have been about 34% higher 

than those obtained using the Wald approach. Calculating the profile likelihood CIs in the region of the second spline segment is computationally difficult and 

was not pursued here. 

MLE: maximum likelihood estimates; UCL: (one-sided) upper confidence limit estimate. 
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fourfold at the lowest exposure level (0.1 ppm 8-hour TWA) and the difference tapers to just 

over twofold at the highest exposure level (1 ppm 8-hour TWA), with the estimates of the 

best-fitting model, the two-piece linear spline model, yielding the higher extra risk estimates 

across the range. 

Finally, for comparison, maximum likelihood estimates (MLEs) of extra risk for the log 

cumulative exposure Cox regression model with a 10-year lag were calculated. The model with 

a 10-year lag also provided a statistically significant fit to the data (p = 0.03), whereas, the 

models with 5- and 15-year lags did not. These estimates ranged from 0.067 for 0.1 ppm 

exposure to 0.094 for 1.0 ppm exposure. Thus, the MLEs of extra risk with a 10-year lag were 

about 20% higher than those with the 15-year lag. 

The continuous models (with a 15-year lag) considered for deriving the extra risk 

estimates for breast cancer incidence in females for the occupational exposure scenarios are 

displayed in Figure 4-8 over the range of occupational cumulative exposures of interest. 

Categorical results are also presented for comparison (deciles from the categorical linear model 

are presented because it had a better fit than the log-linear categorical model, as indicated by the 

AICs, which were 1,963.9 and 1,966.9, respectively; Appendix D). The recommended model is 

the two-piece linear spline model; this was the best-fitting continuous model of those evaluated 

in this assessment, and it provides the best visual fit in comparison to the categorical results in 

the range of the occupational exposure scenarios of interest. As shown in Figure 4-8, the log 

cumulative exposure Cox regression model is too flat across the range of exposures of interest, 

apparently overestimating the risks at lower exposures and underestimating those at higher 

exposures. It also appears from Figure 4-8 that the cumulative exposure Cox regression model 

and the linear model both underestimate risks across the range of exposures of interest. This is 

consistent with the analysis presented in Section D.1 of Appendix D showing the strong 

influence of the upper tail of cumulative exposures on the results of the cumulative exposure Cox 

regression model. The responses in the upper tail of exposures are relatively dampened, such 

that when the highest 5% of exposures (exposures > 27,500 ppm × days, which are well in 

excess of the exposures of corresponding to the occupational exposure scenarios considered 

here) are excluded, the slope of the Cox regression model is substantially increased (e.g., at 

10,000 ppm × days, the RR estimate increases from about 1.1 to almost 1.5; see Figure D-1d in 

Appendix D). This strong influence of the upper tail of exposures would similarly attenuate the 

slope of the (continuous) linear model, resulting in underestimation of the lower-exposure risks. 

The two-piece linear spline model, on the other hand, is more flexible, and the influence of the 

upper tail of exposures would be primarily on the upper spline segment; thus, the two-piece 

model is able to provide a better fit to the lower-exposure data. 
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Re lat ive Risk 

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1.6 

1.4 

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linear spline model 

log cumulative 

exposure Cox 

regression model 

cumulative exposure 

Cox regression model 

breast cancer deciles 

linear model 

1 ppm × 35 years 

0.8
 

0 2000 4000 6000 8000 10000 12000
 

Cumulative Exposure (ppm × days) 

Figure 4-8. RR estimates for breast cancer incidence from occupational EtO exposures (with 15-year lag). 

Breast cancer models (see Section 4.1.2.3): linear 2-piece spline model, with knot at 5,800 ppm × days; log cumulative 

exposure Cox regression model; (cumulative exposure) Cox regression model; continuous linear model; categorical results 

(deciles) from continuous linear model. 
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For the total cancer risk combined across the two cancer types, the MLE can be obtained 

directly by summing the MLEs for the individual cancer types. An upper bound can be 

approximated by summing the 95% UCL estimates for the individual cancer types; however, this 

will overestimate the corresponding 95% UCL on total cancer risk. 

Although there is model uncertainty, as discussed above, there is less overall uncertainty 

associated with the extra risk estimates for occupational exposure scenarios than with the unit 

risk estimates for environmental exposures. The extra risk estimates are derived for occupational 

exposure scenarios that yield cumulative exposures well within the range of the exposures in the 

NIOSH study. Moreover, the NIOSH study is a study of sterilizer workers who used EtO for the 

sterilization of medical supplies or spices (Steenland et al., 1991); thus, the results are directly 

applicable to workers in these occupations, and these are among the occupations of primary 

concern for current occupational EtO exposures. 

Calculation of Extra Risk Estimates for Other Occupational Exposure Scenarios: 

Some detailed guidance is provided here for calculating extra risk estimates outside of the 

range of occupational scenarios considered above. Note that for 35-year exposures to exposure 

levels between the exposure levels presented in Tables 4-26 and 4-27, e.g., 0.15 ppm, one could 

interpolate between the extra risk estimates presented for the closest exposure levels on either 

side. 

For occupational exposures with durations other than 35 years: 

Extra risk estimates for a 45-year exposure to the same exposure levels were nearly 

identical to those from the 35-year exposure for both lymphoid cancer in both sexes and breast 

cancer in females (results not shown). With the 15-year lag, the assumption of an additional 

10 years of exposure only negligibly affects the risks above age 70 and has little impact on 

lifetime risk. For exposure scenarios of 35−45 years but with 8-hour TWAs falling between 

those presented in the tables, one can estimate the extra risk by interpolation. For exposure 

scenarios with durations of exposure less than 30–35 years, one could roughly estimate extra 

risks by calculating the cumulative exposure and finding the extra risks for a similar cumulative 

exposure in Tables 4-26 and 4-27. For a more precise estimation, or for exposure scenarios of 

much shorter duration or for specific age groups, one should do the calculations using a life-table 

analysis, as presented in Appendix E but modified for the specific exposure scenarios. 

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For occupational exposures below 0.1 ppm: 

For lymphoid cancer, use of the log cumulative exposure Cox regression model is not 

advised below 0.1 ppm (× 35 years). Instead, the low-exposure continuation of the linear 

regression model presented in Table 4-26 of the assessment is recommended. For 35-year 

exposures, the following formulae would apply: 

95% UCL on extra risk for lymphoid cancer incidence ≈ (8-h TWA occ exp [in ppm]) × 

(0.016/0.1 ppm) = (8-h TWA occ exp [in ppm]) × (0.16/ppm) 

MLE of extra risk for lymphoid cancer incidence = (8-h TWA occ exp [in ppm]) × 

(0.007/0.1 ppm) = (8-h TWA occ exp [in ppm]) × (0. 07/ppm) 

If one is considering occupational exposure scenarios using a range of 8-h TWA 

exposure levels on both sides of 0.1 ppm, one might want to use the linear regression model for 

all the exposure levels up to about 0.6 ppm 8-h TWA (approximately where the linear regression 

model intersects the log cumulative exposure Cox regression model) to avoid the discontinuity 

between the two models below where they intersect. Note that the extra risk estimates from the 

different models differ by at most about 4.5-fold (at 0.1 ppm) and that there is model uncertainty 

in this range, so the use of either model could be justified. Above where the models intersect, 

only the log cumulative exposure Cox regression model should be used. 

For breast cancer, the low-exposure continuation of the two-piece linear spline model 

presented in Table 4-27 of the assessment is recommended. For 35-year exposures, the 

following formulae would apply: 

95% UCL on extra risk for breast cancer incidence ≈ (8-h TWA occ exp [in ppm]) × 

(0.031/0.1 ppm) = (8-h TWA occ exp [in ppm]) × (0. 31/ppm) 

MLE of extra risk for breast cancer incidence = (8-h TWA occ exp [in ppm]) × 

(0.016/0.1ppm) = (8-h TWA occ exp [in ppm]) × (0. 16/ppm) 
This document is a draft for review purposes only and does not constitute Agency policy. 

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1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

Above 0.1 ppm 8-h TWA, use the two-piece linear spline model results in Table 4-27 of the 

assessment. 

Alternatively, for exposures below 0.1 ppm, one could use the formulae presented below, 

which are based on the unit risk estimates presented in Tables 4-22 (for 95% UCLs) and 

Table 4−23 (for MLEs), with conversions for adjusting occupational to environmental exposures. 

Note, however, that the extra risk results for 35 years of exposure based on these unit risk values 

do not exactly match the values in Tables 4-26 and 4-27 for the linear models (the formulae 

below yield extra risk estimates that are 15−20% lower than the values in Tables 4-26 and 4-27 

for the low end of the exposure range [e.g., 0.1–0.4 ppm] where the comparison with the 

unit-risk-based estimates is appropriate). This is because the results in Tables 4-26 and 4-27 are 

based on life-table analyses, which take into account age-specific background rates of the 

cancers and ages of exposure (assumed to be from 20 to 55 years of age in these occupational 

exposure scenarios), whereas the formulae below are approximations that do not take 

age-specific considerations into account. The advantage of the formulae based on the unit risk 

values is that they can incorporate durations other than ~ 35 years. 

8-h TWA occ exp [in ppm] × (10 m3

/day/20 m3/day) × (240 days/year/365 days/year) × 

(35 years/70 years) = (continuous lifetime) env exp [in ppm] 

(Note that for exposure durations other than 35 years, replace 35 years with the alternate 

duration in the formula above.) 

_______________ 

95% UCL on extra risk for lymphoid cancer incidence ≈ 0.795/ppm × env exp [in ppm] 

MLE of extra risk for lymphoid cancer incidence = 0.356/ppm × env exp [in ppm] 

_______________ 

95% UCL on extra risk for breast cancer incidence (in females) = 1.50/ppm × env exp [in 

ppm] 

MLE of extra risk for breast cancer incidence (in females) = 0.776/ppm × env exp [in 

ppm] 
 

 

 

 

 

 

REFERENCES 

This reference list includes all the references cited in the document except for 

Appendix B, which is a reference list pertaining to Figure 3−3, and Appendix J, which includes 

some more recent references. References added after the 2007 external peer review are also 

listed separately in Appendix I. References identified in a May 2013 literature search but 

appearing after the 30 June 2010 cutoff date for literature inclusion into this carcinogenicity 

assessment are cited and discussed in Appendix J. 

Adám, B; Bárdos, H; Adány, R. (2005). Increased genotoxic susceptibility of breast epithelial cells to ethylene 

oxide. Mutat Res 585: 120-126. http://dx.doi.org/10.1016/j.mrgentox.2005.04.009 

Arias, E. (2007). United States life tables, 2004. Atlanta, GA: Centers of Disease Control and Prevention; National 

Center for Health Statistics. http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_09.pdf 

Bastlová, T; Andersson, B; Lambert, B; Kolman, A. (1993). Molecular analysis of ethylene oxide-induced mutations 

at the HPRT locus in human diploid fibroblasts. Mutat Res 287: 283-292. 

BEIR (Committee on the Biological Effects of Ionizing Radiation). (1988). Health risks of radon and other 

internally deposited alpha-emitters. Washington, DC: National Academy Press. 

Benson, LO; Teta, MJ. (1993). Mortality due to pancreatic and lymphopoietic cancers in chlorohydrin production 

workers. Br J Ind Med 50: 710-716. http://dx.doi.org/10.1136/oem.50.8.710 

Beranek, DT. (1990). Distribution of methyl and ethyl adducts following alkylation with monofunctional alkylating 

agents [Review]. Mutat Res 231: 11-30. 

Bisanti, L; Maggini, M; Raschetti, R; Alegiani, SS; Ippolito, FM; Caffari, B; Segnan, N; Ponti, A. (1993). Cancer 

mortality in ethylene oxide workers. Br J Ind Med 50: 317-324. 

Boffetta, P; van der Hel, O; Norppa, H; Fabianova, E; Fucic, A; Gundy, S; Lazutka, J; Cebulska-Wasilewska, A; 

Puskailerova, D; Znaor, A; Kelecsenyi, Z; Kurtinaitis, J; Rachtan, J; Forni, A; Vermeulen, R; Bonassi, S. (2007). 

Chromosomal aberrations and cancer risk: results of a cohort study from Central Europe. Am J Epidemiol 165: 36­

43. http://dx.doi.org/10.1093/aje/kwj367 

Bolt, HM. (1996). Quantification of endogenous carcinogens. The ethylene oxide paradox [Review]. Biochem 

Pharmacol 52: 1-5. 

Bonassi, S; Znaor, A; Ceppi, M; Lando, C; Chang, WP; Holland, N; Kirsch-Volders, M; Zeiger, E; Ban, S; Barale, 

R; Bigatti, MP; Bolognesi, C; Cebulska-Wasilewska, A; Fabianova, E; Fucic, A; Hagmar, L; Joksic, G; Martelli, A; 

Migliore, L; Mirkova, E; Scarfi, MR; Zijno, A; Norppa, H; Fenech, M. (2007). An increased micronucleus 

frequency in peripheral blood lymphocytes predicts the risk of cancer in humans. Carcinogenesis 28: 625-631. 

http://dx.doi.org/10.1093/carcin/bgl177 

Boogaard, PJ. (2002). Use of haemoglobin adducts in exposure monitoring and risk assessment [Review]. J 

Chromatogr B Analyt Technol Biomed Life Sci 778: 309-322. http://dx.doi.org/10.1016/S0378-4347(01)00445-5 

Brown, CD; Bahman, A; Turner, MJ; Fennell, TR. (1998). Ethylene oxide dosimetry in the mouse. Toxicol Appl 

Pharmacol 148: 215-222. http://dx.doi.org/10.1006/taap.1997.8349 

Brown, CD; Wong, BA; Fennell, TR. (1996). In vivo and in vitro kinetics of ethylene oxide metabolism in rats and 

mice. Toxicol Appl Pharmacol 136: 8-19. http://dx.doi.org/10.1006/taap.1996.0002 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-1 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

 

 

 

 

 

Chaganti, SR; Chen, W; Parsa, N; Offit, K; Louie, DC; Dalla-Favera, R; Chaganti, RS. (1998). Involvement of 

BCL6 in chromosomal aberrations affecting band 3q27 in B-cell non-Hodgkin lymphoma. Genes Chromosomes 

Cancer 23: 323-327. 

Christiansen, DH; Andersen, MK; Desta, F; Pedersen-Bjergaard, J. (2005). Mutations of genes in the receptor 

tyrosine kinase (RTK)/RAS-BRAF signal transduction pathway in therapy-related myelodysplasia and acute 

myeloid leukemia. Leukemia 19: 2232-2240. http://dx.doi.org/10.1038/sj.leu.2404009 

Christiansen, DH; Andersen, MK; Pedersen-Bjergaard, J. (2001). Mutations with loss of heterozygosity of p53 are 

common in therapy-related myelodysplasia and acute myeloid leukemia after exposure to alkylating agents and 

significantly associated with deletion or loss of 5q, a complex karyotype, and a poor prognosis. J Clin Oncol 19: 

1405-1413. 

Clare, MG; Dean, BJ; De Jong, G; Van Sittert, NJ. (1985). Chromosome analysis of lymphocytes from workers at 

an ethylene oxide plant. DNA Repair 156: 109-116. 

Clewell, HJ; Teeguarden, J; McDonald, T; Sarangapani, R; Lawrence, G; Covington, T; Gentry, R; Shipp, A. 

(2002). Review and evaluation of the potential impact of age- and gender-specific pharmacokinetic differences on 

tissue dosimetry [Review]. Crit Rev Toxicol 32: 329-389. http://dx.doi.org/10.1080/20024091064264 

Coggon, D; Harris, EC; Poole, J; Palmer, KT. (2004). Mortality of workers exposed to ethylene oxide: extended 

follow up of a British cohort. Occup Environ Med 61: 358-362. 

Csanady, GA; Denk, B; Putz, C; Kreuzer, PE; Kessler, W; Baur, C; Gargas, ML; Filser, JG. (2000). A physiological 

toxicokinetic model for exogenous and endogenous ethylene and ethylene oxide in rat, mouse, and human: 

formation of 2-hydroxyethyl adducts with hemoglobin and DNA. Toxicol Appl Pharmacol 165: 1-26. 

http://dx.doi.org/10.1006/taap.2000.8918 

Dellarco, VL; Generoso, WM; Sega, GA; Fowle, JR, III; Jacobson-Kram, D. (1990). Review of the mutagenicity of 

ethylene oxide [Review]. Environ Mol Mutagen 16: 85-103. 

Donner, EM; Wong, BA; James, RA; Preston, RJ. (2010). Reciprocal translocations in somatic and germ cells of 

mice chronically exposed by inhalation to ethylene oxide: implications for risk assessment. Mutagenesis 25: 49-55. 

http://dx.doi.org/10.1093/mutage/gep042 

Dunkelberg, H. (1982). Carcinogenicity of ethylene oxide and 1,2-propylene oxide upon intragastric administration 

to rats. Br J Cancer 46: 924-933. 

Ehrenberg, L; Hussain, S. (1981). Genetic toxicity of some important epoxides [Review]. DNA Repair 86: 1-113. 

EOIC (Ethylene Oxide Industry Council). (2001). Toxicological review of ethylene oxide in support of summary 

information on the integrated risk information system. Arlington, VA. 

Fennell, TR; Brown, CD. (2001). A physiologically based pharmacokinetic model for ethylene oxide in mouse, rat, 

and human. Toxicol Appl Pharmacol 173: 161-175. http://dx.doi.org/10.1006/taap.2001.9184 

Filser, JG; Denk, B; Tornqvist, M; Kessler, W; Ehrenberg, L. (1992). Pharmacokinetics of ethylene in man: body 

burden with ethylene oxide and hydroxyethylation of hemoglobin due to endogenous and environmental ethylene. 

Arch Toxicol 66: 157-163. 

Föst, U; Hallier, E; Ottenwälder, H; Bolt, HM; Peter, H. (1991). Distribution of ethylene oxide in human blood and 

its implications for biomonitoring. Hum Exp Toxicol 10: 25-31. 

Galloway, SM; Berry, PK; Nichols, WW; Wolman, SR; Soper, KA; Stolley, PD; Archer, P. (1986). Chromosome 

aberrations in individuals occupationally exposed to ethylene oxide, and in a large control population. Mutat Res 

Genet Toxicol 170: 55-74. http://dx.doi.org/10.1016/0165-1218(86)90082-0 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-2 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

 

 

Gardner, MJ; Coggon, D; Pannett, B; Harris, EC. (1989). Workers exposed to ethylene oxide: a follow up study. 

Occup Environ Med 46: 860-665. 

Garman, RH; Snellings, WM; Maronpot, RR. (1985). Brain tumors in F344 rats associated with chronic inhalation 

exposure to ethylene oxide. Neurotoxicology 6: 117-137. 

Garman, RH; Snellings, WM; Maronpot, RR. (1986). Frequency, size and location of brain tumours in F-344 rats 

chronically exposed to ethylene oxide. Food Chem Toxicol 24: 145-153. http://dx.doi.org/10.1016/0278­

6915(86)90349-2 

Garry, VF; Hozier, J; Jacobs, D; Wade, RL; Gray, DG. (1979). Ethylene oxide: evidence of human chromosomal 

effects. Environ Mutagen 1: 375-382. http://dx.doi.org/10.1002/em.2860010410 

Gelehrter, TD; Collins, FS; Ginsburg, D. (1990). Principles of medical genetics. Baltimore, MD: Williams and 

Wilkins. 

Generoso, WM; Cain, KT; Cornett, CV; Cacheiro, NLA; Hughes, LA. (1990). Concentration-response curves for 

ethylene-oxide-induced heritable translocations and dominant lethal mutations. Environ Mol Mutagen 16: 126-131. 

Godderis, L; Aka, P; Matecuca, R; Kirsch-Volders, M; Lison, D; Veulemans, H. (2006). Dose-dependent influence 

of genetic polymorphisms on DNA damage induced by styrene oxide, ethylene oxide and gamma-radiation. 

Toxicology 219: 220-229. http://dx.doi.org/10.1016/j.tox.2005.11.021 

Golberg, L. (1986). Chemical and physical properties. In Hazard assessment of ethylene oxide. Boca Raton, FL: 

CRC Press. 

Greenberg, HL; Ott, MG; Shore, RE. (1990). Men assigned to ethylene oxide production or other ethylene oxide 

related chemical manufacturing: A mortality study. Br J Ind Med 47: 221-230. 

http://dx.doi.org/10.1136/oem.47.4.221 

Greife, AL; Hornung, RW; Stayner, LG; Steenland, KN. (1988). Development of a model for use in estimating 

exposure to ethylene oxide in a retrospective cohort mortality study. Scand J Work Environ Health 1: 29-30. 

Hagmar, L; Mikoczy, Z; Welinder, H. (1995). Cancer incidence in Swedish sterilant workers exposed to ethylene 

oxide. Occup Environ Med 52: 154-156. http://dx.doi.org/10.1136/oem.52.3.154 

Hagmar, L; Stromberg, U; Bonassi, S; Hansteen, IL; Knudsen, LE; Lindholm, C; Norppa, H. (2004). Impact of 

types of lymphocyte chromosomal aberrations on human cancer risk: results from Nordic and Italian cohorts. Cancer 

Res 64: 2258-2263. http://dx.doi.org/10.1158/0008-5472.CAN-03-3360 

Hagmar, L; Welinder, H; Lindén, K; Attewell, R; Osterman-Golkar, S; Törnqvist, M. (1991). An epidemiological 

study of cancer risk among workers exposed to ethylene oxide using hemoglobin adducts to validate environmental 

exposure assessments. Int Arch Occup Environ Health 63: 271-277. 

Hansen, JP; Allen, J; Brock, K; Falconer, J; Helms, MJ; Shaver, GC; Strohm, B. (1984). Normal sister chromatid 

exchange levels in hospital sterilization employees exposed to ethylene oxide. J Occup Med 26: 29-32. 

Harada, H; Harada, Y; Tanaka, H; Kimura, A; Inaba, T. (2003). Implications of somatic mutations in the AML1 

gene in radiation-associated and therapy-related myelodysplastic syndrome/acute myeloid leukemia. Blood 101: 

673-680. http://dx.doi.org/10.1182/blood-2002-04-1010 

Harris, NL; Jaffe, ES; Diebold, J; Flandrin, G; Muller-Hermelink, HK; Vardiman, J; Lister, TA; Bloomfield, CD. 

(1999). World Health Organization classification of neoplastic diseases of the hematopoietic and lymphoid tissues: 

report of the Clinical Advisory Committee meeting--Airlie House, Virginia, November 1997. J Clin Oncol 17: 3835­

3849. 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-3 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

 

 

 

 

 

 

 

Haufroid, V; Merz, B; Hofmann, A; Tschopp, A; Lison, D; Hotz, P. (2007). Exposure to ethylene oxide in hospitals: 

biological monitoring and influence of glutathione S-transferase and epoxide hydrolase polymorphisms. Cancer 

Epidemiol Biomarkers Prev 16: 796-802. http://dx.doi.org/10.1158/1055-9965.EPI-06-0915 

Health Canada. (2001). Priority substances list assessment report: Ethylene oxide. (Cat. no. En40-215/51E). Ottawa, 

Ontario. http://www.hc-sc.gc.ca/ewh-semt/pubs/contaminants/psl2-lsp2/ethylene_oxide/index-eng.php 

Hill, AB. (1965). The environment and disease: Association or causation? Proc R Soc Med 58: 295-300. 

Högstedt, B; Gullberg, B; Hedner, K; Kolnig, AM; Mitelman, F; Skerfving, S; Widegren, B. (1983). Chromosome 

aberrations and micronuclei in bone marrow cells and peripheral blood lymphocytes in humans exposed to ethylene 

oxide. Hereditas 98: 105-113. 

Hogstedt, C. (1988). Epidemiologic studies on ethylene oxide and cancer: An updating. In Methods for detecting 

DNA damaging agents in humans: Applications in cancer epidemiology and prevention. Lyon, France: International 

Agency for Research on Cancer. 

Hogstedt, C; Aringer, L; Gustavsson, A. (1986). Epidemiologic support for ethylene oxide as a cancer-causing 

agent. JAMA 255: 1575-1578. 

Hong, HH; Houle, CD; Ton, TV; Sills, RC. (2007). K-ras mutations in lung tumors and tumors from other organs 

are consistent with a common mechanism of ethylene oxide tumorigenesis in the B6C3F1 mouse. Toxicol Pathol 35: 

81-85. http://dx.doi.org/10.1080/01926230601063839 

Horner, MJ; Ries, LAG; Krapcho, M; Neyman, N; Aminou, R; Howlader, N; Altekruse, SF; Feuer, EJ; Huang, L; 

Mariotto, A; Miller, BA; Lewis, DR; Eisner, MP; Stinchcomb, DG; Edwards, BK. (2009). SEER cancer statistics 

review, 1975-2006. Bethesda, MD: National Cancer Institute. http://seer.cancer.gov/csr/1975_2006/ 

Hornung, RW; Greife, AL; Stayner, LT; Steenland, NK; Herrick, RF; Elliott, LJ; Ringenburg, VL; Morawetz, J. 

(1994). Statistical model for prediction of retrospective exposure to ethylene oxide in an occupational mortality 

study. Am J Ind Med 25: 825-836. 

Houle, CD; Ton, TV; Clayton, N; Huff, J; Hong, HH; Sills, RC. (2006). Frequent p53 and H-ras mutations in 

benzene- and ethylene oxide-induced mammary gland carcinomas from B6C3F1 mice. Toxicol Pathol 34: 752-762. 

http://dx.doi.org/10.1080/01926230600935912 

IARC (International Agency for Research on Cancer). (1994a). Ethylene. In IARC monographs on the evaluation of 

carcinogenic risks to humans Some industrial chemicals (pp. 45-71). Lyon, France. 

http://monographs.iarc.fr/ENG/Monographs/vol60/volume60.pdf 

IARC (International Agency for Research on Cancer). (1994b). Ethylene oxide. In IARC monographs on the 

evaluation of carcinogenic risks to humans Some industrial chemicals (pp. 73-159). Lyon, France. 

http://monographs.iarc.fr/ENG/Monographs/vol60/volume60.pdf 

IARC (International Agency for Research on Cancer). (2008a). 1,3-butadiene, ethylene oxide and vinyl halides 

(vinyl fluoride, vinyl chloride and vinyl bromide). In IARC monographs on the evaluation of carcinogenic risks to 

humans (pp. 3-471). Lyon, France. http://monographs.iarc.fr/ENG/Monographs/vol97/mono97.pdf 

IARC (International Agency for Research on Cancer). (2008b). IARC monographs on the evaluation of carcinogenic 

risks to humans. Volume 97. 1,3-butadiene, ethylene oxide and vinyl halides (vinyl fluoride, vinyl chloride and vinyl 

bromide). 97: 3-471. 

Ingvarsson, S. (1999). Molecular genetics of breast cancer progression [Review]. Semin Cancer Biol 9: 277-288. 

http://dx.doi.org/10.1006/scbi.1999.0124 

Johanson, G; Filser, JG. (1992). Experimental data from closed chamber gas uptake studies in rodents suggest lower 

uptake rate of chemical than calculated from literature values on alveolar ventilation. Arch Toxicol 66: 291-295. 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-4 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

 

 

 

Kardos, L; Széles, G; Gombköto, G; Szeremi, M; Tompa, A; Adány, R. (2003). Cancer deaths among hospital staff 

potentially exposed to ethylene oxide: an epidemiological analysis. Environ Mol Mutagen 42: 59-60. 

http://dx.doi.org/10.1002/em.10167 

Kiesselbach, N; Ulm, K; Lange, HJ; Korallus, U. (1990). A multicentre mortality study of workers exposed to 

ethylene oxide. Br J Ind Med 47: 182-188. 

Kirman, CR; Sweeney, LM; Teta, MJ; Sielken, RL; Valdez-Flores, C; Albertini, RJ; Gargas, ML. (2004). 

Addressing nonlinearity in the exposure-response relationship for a genotoxic carcinogen: cancer potency estimates 

for ethylene oxide. Risk Anal 24: 1165-1183. http://dx.doi.org/10.1111/j.0272-4332.2004.00517.x 

Kolman, A; Chovanec, M. (2000). Combined effects of gamma-radiation and ethylene oxide in human diploid 

fibroblasts. Mutagenesis 15: 99-104. http://dx.doi.org/10.1093/mutage/15.2.99 

Kolman, A; Chovanec, M; Osterman-Golkar, S. (2002). Genotoxic effects of ethylene oxide, propylene oxide and 

epichlorohydrin in humans: update review (1990-2001) [Review]. DNA Repair 512: 173-194. 

Krewski, D; Crump, KS; Farmer, J; Gaylor, DW; Howe, R; Portier, C; Salsburg, D; Sielken, RL; Van Ryzin, J. 

(1983). A comparison of statistical methods for low dose extrapolation utilizing time-to-tumour data. Fundam Appl 

Toxicol 3: 140-160. http://dx.doi.org/10.1016/S0272-0590(83)80075-X 

Krishnan, K; Gargas, ML; Fennell, TR; Andersen, ME. (1992). A physiologically based description of ethylene 

oxide dosimetry in the rat. Toxicol Ind Health 8: 121-140. 

Lambert, B; Andersson, B; Bastlova, T; Hou, SM; Hellgren, D; Kolman, A. (1994). Mutations induced in the 

hypoxanthine phosphoribosyl transferase gene by three urban air pollutants: Acetaldehyde, benzo[a]pyrene 

diolepoxide, and ethylene oxide. Environ Health Perspect Suppl 102: 135-138. 

Langholz, B; Richardson, DB. (2010). Fitting general relative risk models for survival time and matched casecontrol analysis. Am J Epidemiol 171: 377-383. http://dx.doi.org/10.1093/aje/kwp403 

Laurent, C; Frederic, J; Léonard, AY. (1984). Sister chromatid exchange frequency in workers exposed to high 

levels of ethylene oxide, in a hospital sterilization service. Int Arch Occup Environ Health 54: 33-43. 

Lerda, D; Rizzi, R. (1992). Cytogenetic study of persons occupationally exposed to ethylene oxide. Mutat Res 281: 

31-37. 

Lewis, SE; Barnett, LB; Felton, C; Johnson, FM; Skow, LC; Cacheiro, N; Shelby, MD. (1986). Dominant visible 

and electrophoretically expressed mutations induced in male mice exposed to ethylene oxide by inhalation. Environ 

Mol Mutagen 8: 867-872. 

Liou, SH; Lung, JC; Chen, YH; Yang, T; Hsieh, LL; Chen, CJ; Wu, TN. (1999). Increased chromosome-type 

chromosome aberration frequencies as biomarkers of cancer risk in a blackfoot endemic area. Cancer Res 59: 1481­

1484. 

Lorenti Garcia, C; Darroudi, F; Tates, AD; Natarajan, AT. (2001). Induction and persistence of micronuclei, sisterchromatid exchanges and chromosomal aberrations in splenocytes and bone-marrow cells of rats exposed to 

ethylene oxide. Mutat Res Genet Toxicol Environ Mutagen 492: 59-67. http://dx.doi.org/10.1016/S1383­

5718(01)00149-8 

Lossos, IS; Levy, R. (2000). Mutation analysis of the 5' noncoding regulatory region of the BCL-6 gene in nonHodgkin lymphoma: evidence for recurrent mutations and intraclonal heterogeneity. Blood 95: 1400-1405. 

Lynch, DW; Lewis, TR; Moorman, WJ; Burg, JR; Groth, DH; Khan, A; Ackerman, LJ; Cockrell, BY. (1984a). 

Carcinogenic and toxicologic effects of inhaled ethylene oxide and propylene oxide in F344 rats. Toxicol Appl 

Pharmacol 76: 69-84. 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-5 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

 

 

 

 

 

 

Lynch, DW; Lewis, TR; Moorman, WJ; Burg, JR; Lal, JB; Setzer, JV; Groth, DH; Gulati, DK; Zavos, PM; 

Sabharwal, PS; Ackerman, LJ; Cockrell, BY; Sprinz, H. (1984b). Effects on monkeys and rats of long-term 

inhalation exposure to ethylene oxide: Major findings of the NIOSH study. In Inhospital ethylene oxide sterilization: 

Current issues in ethylene oxide toxicity and occupational exposure (pp. 7-10). (AAMI Technology Assessment 

Report No. 8-84). Arlington, VA: Association for the Advancement of Medical Instrumentation. 

Major, J; Jakab, MG; Tompa, A. (1996). Genotoxicological investigation of hospital nurses occupationally exposed 

to ethylene-oxide: I Chromosome aberrations, sister-chromatid exchanges, cell cycle kinetics, and UV-induced DNA 

synthesis in peripheral blood lymphocytes. Environ Mol Mutagen 27: 84-92. http://dx.doi.org/10.1002/(SICI)1098­

2280(1996)27:2<84::AID-EM2>3.0.CO;2-E 

Major, J; Jakab, MG; Tompa, A. (2001). Genotoxicological investigation of hospital nurses occupationally exposed 

to ethylene oxide. II. HPRT mutation frequencies. Central Eur J Occup Env Med 7: 195-208. 

Marsden, DA; Jones, DJ; Britton, RG; Ognibene, T; Ubick, E; Johnson, GE; Farmer, PB; Brown, K. (2009). Doseresponse relationships for N7-(2-hydroxyethyl)guanine induced by low-dose [14C]ethylene oxide: evidence for a 

novel mechanism of endogenous adduct formation. Cancer Res 69: 3052-3059. http://dx.doi.org/10.1158/0008­

5472.CAN-08-4233 

Mayer, J; Warburton, D; Jeffrey, AM; Pero, R; Walles, S; Andrews, L; Toor, M; Latriano, L; Wazneh, L; Tang, D; 

Tsai, WY; Kuroda, M; Perera, F. (1991). Biologic markers in ethylene oxide exposed workers and controls. Mutat 

Res 248: 163-176. http://dx.doi.org/10.1016/0027-5107(91)90098-9 

Memisoglu, A; Samson, L. (2000). Base excision repair in yeast and mammals [Review]. Mutat Res 451: 39-51. 

Mertes, I; Fleischmann, R; Glatt, H; Oesch, F. (1985). Interindividual variations in the activities of cytosolic and 

microsomal epoxide hydrolase in human liver. Carcinogenesis 6: 219-223. 

Miniño, AM; Arias, E; Kochanek, KD; Murphy, SL; Smith, BL. (2002). Deaths: Final Data for 2000. Hyattsville, 

MD: National Center for Health Statistics. http://www.cdc.gov/nchs/data/nvsr/nvsr50/nvsr50_15.pdf 

Natarajan, AT; Preston, RJ; Dellarco, V; Ehrenberg, L; Generoso, W; Lewis, S; Tates, AD. (1995). Ethylene oxide: 

evaluation of genotoxicity data and an exploratory assessment of genetic risk [Review]. DNA Repair 330: 55-70. 

NCI (National Cancer Institute). (2004). Breast cancer screening (PDQ): Health professional version. Washington, 

DC: U.S. Department of Health, Education, and Welfare, National Institutes of Health. 

http://www.cancer.gov/cancertopics/pdq/screening/breast/HealthProfessional/ 

Norman, SA; Berlin, JA; Soper, KA; Middendorf, BF; Stolley, PD. (1995). Cancer incidence in a group of workers 

potentially exposed to ethylene oxide. Int J Epidemiol 24: 276-284. 

NRC (National Research Council). (1983). Risk assessment in the federal government: Managing the process. 

Washington, DC: National Academies Press. http://www.nap.edu/openbook.php?record_id=366&page=R1 

NTP (National Toxicology Program). (1987). Toxicology and carcinogenesis studies of ethylene oxide (CAS no 75­

21-8) in B6C3F1 mice (inhalation studies). 

Olsen, GW; Lacy, SE; Bodner, KM; Chau, M; Arceneaux, TG; Cartmill, JB; Ramlow, JM; Boswell, JM. (1997). 

Mortality from pancreatic and lymphopoietic cancer among workers in ethylene and propylene chlorohydrin 

production. Occup Environ Med 54: 592-598. http://dx.doi.org/10.1136/oem.54.8.592 

Pauwels, W; Veulemans, H. (1998). Comparison of ethylene, propylene and styrene 7,8-oxide in vitro adduct 

formation on N-terminal valine in human haemoglobin and on N-7-guanine in human DNA. Mutat Res 418: 21-33. 

Paz-y-Miño, C; Pérez, JC; Fiallo, BF; Leone, PE. (2002). A polymorphism in the hMSH2 gene (gIVS12-6T>C) 

associated with non-Hodgkin lymphomas. Cancer Genet Cytogenet 133: 29-33. 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-6 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

 

Pedersen-Bjergaard, J; Christiansen, DH; Desta, F; Andersen, MK. (2006). Alternative genetic pathways and 

cooperating genetic abnormalities in the pathogenesis of therapy-related myelodysplasia and acute myeloid 

leukemia [Review]. Leukemia 20: 1943-1949. http://dx.doi.org/10.1038/sj.leu.2404381 

Pfeiffer, P; Goedecke, W; Obe, G. (2000). Mechanisms of DNA double-strand break repair and their potential to 

induce chromosomal aberrations [Review]. Mutagenesis 15: 289-302. 

Popp, W; Vahrenholz, C; Przygoda, H; Brauksiepe, A; Goch, S; Müller, G; Schell, C; Norpoth, K. (1994). DNAprotein cross-links and sister chromatid exchange frequencies in lymphocytes and hydroxyethyl mercapturic acid in 

urine of ethylene oxide-exposed hospital workers. Int Arch Occup Environ Health 66: 325-332. 

Preston, RJ. (1999). Cytogenetic effects of ethylene oxide, with an emphasis on population monitoring [Review]. 

Crit Rev Toxicol 29: 263-282. http://dx.doi.org/10.1080/10408449991349212 

Preston, RJ; Fennell, TR; Leber, AP; Sielken, RL; Swenberg, JA. (1995). Reconsideration of the genetic risk 

assessment for ethylene oxide exposures. Environ Mol Mutagen 26: 189-202. 

Preudhomme, C; Warot-Loze, D; Roumier, C; Grardel-Duflos, N; Garand, R; Lai, JL; Dastugue, N; Macintyre, E; 

Denis, C; Bauters, F; Kerckaert, JP; Cosson, A; Fenaux, P. (2000). High incidence of biallelic point mutations in the 

Runt domain of the AML1/PEBP2 alpha B gene in Mo acute myeloid leukemia and in myeloid malignancies with 

acquired trisomy 21. Blood 96: 2862-2869. 

Rapoport, IA. (1948). The effect of ethylene oxide, glycide and glycol on genetic mutations. Dokl Biochem Biophys 

60: 469-472. 

Recio, L; Donner, M; Abernethy, D; Pluta, L; Steen, AM; Wong, BA; James, A; Preston, RJ. (2004). In vivo 

mutagenicity and mutation spectrum in the bone marrow and testes of B6C3F1 lacI transgenic mice following 

inhalation exposure to ethylene oxide. Mutagenesis 19: 215-222. 

Ribeiro, LR; Salvadori, DM; Rios, AC; Costa, SL; Tates, AD; Törnqvist, M; Natarajan, AT. (1994). Biological 

monitoring of workers occupationally exposed to ethylene oxide. Mutat Res 313: 81-87. 

Richmond, GW; Abrahams, RH; Nemenzo, JH; Hine, CH. (1985). An evaluation of possible effects on health 

following exposure to ethylene oxide. Arch Environ Occup Health 40: 20-25. 

Ries, LAG; Eisner, MP; Kosary, CL; Hankey, BF; Miller, BA; Clegg, L; Mariotto, A; Feuer, EF; Edwards, BK. 

(2004). SEER (Surveillance Epidemiology and End Results) cancer statistics review, 1975-2001. Bethesda, MD: 

National Cancer Institute, U.S. Department of Health, Education, and Welfare, National Institutes of Health. 

http://seer.cancer.gov/csr/1975_2001 

Ries, LAG; Melbert, D; Krapcho, M; Mariotto, A; Miller, BA; Feuer, EJ; Clegg, L; Horner, MJ; Howlader, N; 

Eisner, MP. (2007). SEER (Surveillance Epidemiology and End Results) cancer statistics review, 19752004. 

Bethesda, MD: National Cancer Institute, U.S. Department of Health, Education, and Welfare, National Institutes of 

Health. http://seer.cancer.gov/csr/1975_2004 

Rossner, P; Boffetta, P; Ceppi, M; Bonassi, S; Smerhovsky, Z; Landa, K; Juzova, D; Sram, RJ. (2005). 

Chromosomal aberrations in lymphocytes of healthy subjects and risk of cancer. Environ Health Perspect 113: 517­

520. http://dx.doi.org/10.1289/ehp.6925 

Rothman, KJ. (1986). Modern epidemiology. Boston, MA: Little Brown & Co. 

Russo, J; Russo, IH. (1999). Cellular basis of breast cancer susceptibility [Review]. Oncol Res 11: 169-178. 

Rusyn, I; Asakura, S; Li, Y; Kosyk, O; Koc, H; Nakamura, J; Upton, PB; Swenberg, JA. (2005). Effects of ethylene 

oxide and ethylene inhalation on DNA adducts, apurinic/apyrimidinic sites and expression of base excision DNA 

repair genes in rat brain, spleen, and liver. DNA Repair 4: 1099-1110. 

http://dx.doi.org/10.1016/j.dnarep.2005.05.009 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-7 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

SAB (Science Advisory Board). (2007). Review of Office of Research and Development (ORD) draft assessment 

entitled "Evaluation of the carcinogenicity of ethylene oxide". Washington, DC: Science Advisory Board, U.S. 

Environmental Protection Agency. 

http://yosemite.epa.gov/sab/sabproduct.nsf/368203f97a15308a852574ba005bbd01/5D661BC118B527A3852573B8 

0068C97B/$File/EPA-SAB-08-004-unsigned.pdf 

Sarto, F; Clonfero, E; Bartolucci, GB; Franceschi, C; Chiricolo, M; Levis, AG. (1987). Sister chromatid exchanges 

and DNA repair capability in sanitary workers exposed to ethylene oxide: Evaluation of the dose-effect relationship. 

Am J Ind Med 12: 625-637. http://dx.doi.org/10.1002/ajim.4700120515 

Sarto, F; Cominato, I; Pinton, AM; Brovedani, PG; Faccioli, CM; Bianchi, V; Levis, AG. (1984). Cytogenetic 

damage in workers exposed to ethylene oxide. Mutat Res 138: 185-195. 

Sarto, F; Tomanin, R; Giacomelli, L; Iannini, G; Cupiraggi, AR. (1990). The micronucleus assay in human 

exfoliated cells of the nose and mouth: application to occupational exposures to chromic acid and ethylene oxide. 

Mutat Res 244: 345-351. 

Sarto, F; Törnqvist, MA; Tomanin, R; Bartolucci, GB; Osterman-Golkar, SM; Ehrenberg, L. (1991). Studies of 

biological and chemical monitoring of low-level exposure to ethylene oxide. Scand J Work Environ Health 17: 60­

64. 

Schulte, PA; Boeniger, M; Walker, JT; Schober, SE; Pereira, MA; Gulati, DK; Wojciechowski, JP; Garza, A; 

Froelich, R; Strauss, G. (1992). Biologic markers in hospital workers exposed to low levels of ethylene oxide. Mutat 

Res 278: 237-251. 

Segerbäck, D. (1990). Reaction products in hemoglobin and DNA after in vitro treatment with ethylene oxide and 

N-(2-hydroxyethyl)-N-nitrosourea. Carcinogenesis 11: 307-312. 

Sielken, RL; Valdez-Flores, C. (2009). Life-table calculations of excess risk for incidence versus mortality: ethylene 

oxide case study. Regul Toxicol Pharmacol 55: 82-89. http://dx.doi.org/10.1016/j.yrtph.2009.06.003 

Sisk, SC; Pluta, LJ; Meyer, KG; Wong, BC; Recio, L. (1997). Assessment of the in vivo mutagenicity of ethylene 

oxide in the tissues of B6C3F1 lacI transgenic mice following inhalation exposure. Mutat Res 391: 153-164. 

Snellings, WM; Weil, CS; Maronpot, RR. (1984). A two-year inhalation study of the carcinogenic potential of 

ethylene oxide in Fischer 344 rats. Toxicol Appl Pharmacol 75: 105-117. 

Stayner, L; Steenland, K; Dosemeci, M; Hertz-Picciotto, I. (2003). Attenuation of exposure-response curves in 

occupational cohort studies at high exposure levels. Scand J Work Environ Health 29: 317-324. 

Stayner, L; Steenland, K; Greife, A; Hornung, R; Hayes, RB; Nowlin, S; Morawetz, J; Ringenburg, V; Elliot, L; 

Halperin, W. (1993). Exposure-response analysis of cancer mortality in a cohort of workers exposed to ethylene 

oxide. Am J Epidemiol 138: 787-798. 

Steenland, K; Deddens, J; Piacitelli, L. (2001). Risk assessment for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) 

based on an epidemiologic study. Am J Epidemiol 154: 451-458. 

Steenland, K; Deddens, JA. (2004). A practical guide to dose-response analyses and risk assessment in occupational 

epidemiology [Review]. Epidemiology 15: 63-70. http://dx.doi.org/10.1097/01.ede.0000100287.45004.e7 

Steenland, K; Stayner, L; Deddens, J. (2004). Mortality analyses in a cohort of 18 235 ethylene oxide exposed 

workers: follow up extended from 1987 to 1998. Occup Environ Med 61: 2-7. 

Steenland, K; Stayner, L; Greife, A; Halperin, W; Hayes, R; Hornung, R; Nowlin, S. (1991). Mortality among 

workers exposed to ethylene oxide. N Engl J Med 324: 1402-1407. 

http://dx.doi.org/10.1056/NEJM199105163242004 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-8 DRAFT―DO NOT CITE OR QUOTE
 
Steenland, K; Whelan, E; Deddens, J; Stayner, L; Ward, E. (2003). Ethylene oxide and breast cancer incidence in a 

cohort study of 7576 women (United States). Cancer Causes Control 14: 531-539. 

Stolley, PD; Soper, KA; Galloway, SM; Nichols, WW; Norman, SA; Wolman, SR. (1984). Sister-chromatid 

exchanges in association with occupational exposure to ethylene oxide. Mutat Res 129: 89-102. 

http://dx.doi.org/10.1016/0027-5107(84)90127-1 

Swaen, GM; Burns, C; Teta, JM; Bodner, K; Keenan, D; Bodnar, CM. (2009). Mortality study update of ethylene 

oxide workers in chemical manufacturing: a 15 year update. J Occup Environ Med 51: 714-723. 

http://dx.doi.org/10.1097/JOM.0b013e3181a2ca20 

Swaen, GM; Slangen, JM; Ott, MG; Kusters, E; Van Den Langenbergh, G; Arends, JW; Zober, A. (1996). 

Investigation of a cluster of ten cases of Hodgkin's disease in an occupational setting. Int Arch Occup Environ 

Health 68: 224-228. 

Swenberg, JA; Fedtke, N; Fennell, TR; Walker, VE. (1990). Relationships between carcinogen exposure, DNA 

adducts and carcinogenesis. In DB Clayson; IC Munro; P Shubik; JA Swenberg (Eds.), Progress in predictive 

toxicology (pp. 161-184). New York, NY: Elsevier. 

Tates, AD; Boogaard, PJ; Darroudi, F; Natarajan, AT; Caubo, ME; Van Sittert, NJ. (1995). Biological effect 

monitoring in industrial workers following incidental exposure to high concentrations of ethylene oxide. DNA 

Repair 329: 63-77. 

Tates, AD; Grummt, T; Törnqvist, M; Farmer, PB; van Dam, FJ; van Mossel, H; Schoemaker, HM; OstermanGolkar, S; Uebel, C; Tang, YS; Zwinderman, AH; Natarajan, AT; Ehrenberg, L. (1991). Biological and chemical 

monitoring of occupational exposure to ethylene oxide. Mutat Res 250: 483-497. http://dx.doi.org/10.1016/0027­

5107(91)90205-3 

Tates, AD; Van Dam, FJ; Natarajan, AT; Van Teylingen, CMM; De Zwart, FA; Zwinderman, AH; Van Sittert, NJ; 

Nilsen, A; Nilsen, OG; Zahlsen, K; Magnusson, AL; Tornqvist, M. (1999). Measurement of HPRT mutations in 

splenic lymphocytes and haemoglobin adducts in erythrocytes of Lewis rats exposed to ethylene oxide. DNA Repair 

431: 397-415. 

Teta, MJ; Benson, LO; Vitale, JN. (1993). Mortality study of ethylene oxide workers in chemical manufacturing: a 

10 year update. Br J Ind Med 50: 704-709. 

Teta, MJ; Sielken, RL; Valdez-Flores, C. (1999). Ethylene oxide cancer risk assessment based on epidemiological 

data: application of revised regulatory guidelines. Risk Anal 19: 1135-1155. 

Thier, R; Bolt, HM. (2000). Carcinogenicity and genotoxicity of ethylene oxide: new aspects and recent advances 

[Review]. Crit Rev Toxicol 30: 595-608. http://dx.doi.org/10.1080/10408440008951121 

Tompa, A; Major, J; Jakab, MG. (1999). Is breast cancer cluster influenced by environmental and occupational 

factors among hospital nurses in Hungary. Pathol Oncol Res 5: 117-121. 

Tompkins, EM; McLuckie, KI; Jones, DJ; Farmer, PB; Brown, K. (2009). Mutagenicity of DNA adducts derived 

from ethylene oxide exposure in the pSP189 shuttle vector replicated in human Ad293 cells. Mutat Res 678: 129­

137. http://dx.doi.org/10.1016/j.mrgentox.2009.05.011 

Törnqvist, M. (1996). Ethylene oxide as a biological reactive intermediate of endogenous origin [Review]. Adv Exp 

Med Biol 387: 275-283. 

U.S. EPA (U.S. Environmental Protection Agency). (1986). Guidelines for carcinogen risk assessment. Fed Reg 51: 

33993-34003. 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-9 DRAFT―DO NOT CITE OR QUOTE
 
U.S. EPA (U.S. Environmental Protection Agency). (1994). Methods for derivation of inhalation reference 

concentrations and application of inhalation dosimetry [EPA Report]. (EPA/600/8-90/066F). Research Triangle 

Park, NC. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=71993 

U.S. EPA (U.S. Environmental Protection Agency). (1997). Chemical and radiation leukemogenesis in humans and 

rodents and the value of rodent models for assessing risks of lymphohematopoietic cancers [EPA Report]. 

(EPA/600/R-97/090). Washington, DC: National Center for Environmental Assessment, Office of Research and 

Development. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12181 

U.S. EPA (U.S. Environmental Protection Agency). (2000). Science policy council handbook: Risk characterization 

[EPA Report]. (EPA 100-B-00-002). Washington, D.C.: Office of Science Policy, Office of Research and 

Development. http://www.epa.gov/osa/spc/pdfs/rchandbk.pdf 

U.S. EPA (U.S. Environmental Protection Agency). (2005a). Guidelines for carcinogen risk assessment [EPA 

Report]. (EPA/630/P-03/001F). Washington, DC: Risk Assessment Forum. http://www.epa.gov/cancerguidelines/ 

U.S. EPA (U.S. Environmental Protection Agency). (2005b). Supplemental guidance for assessing susceptibility 

from early-life exposure to carcinogens. (EPA/630/R-03/003F). Washington, DC: U.S. Environmental Protection 

Agency, Risk Assessment Forum. http://www.epa.gov/raf/publications/pdfs/childrens_supplement_final.pdf. 

U.S. EPA (U.S. Environmental Protection Agency). (2006a). Evaluation of the carcinogenicity of ethylene oxide: 

external review draft [EPA Report]. (EPA/635/R-06/003). Washington, DC: National Center for Environmental 

Assessment, Office of Research and Development. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=157664 

U.S. EPA (U.S. Environmental Protection Agency). (2006b). Science policy council peer review handbook 3rd 

edition [EPA Report]. (100B06002). Science Policy Council. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=157664 

U.S. EPA (U.S. Environmental Protection Agency). (2012). Benchmark dose technical guidance. (EPA/100/R12/001). Washington, DC: Risk Assessment Forum. 

http://www.epa.gov/raf/publications/pdfs/benchmark_dose_guidance.pdf 

Valdez-Flores, C; Sielken, RL; Teta, MJ. (2010). Quantitative cancer risk assessment based on NIOSH and UCC 

epidemiological data for workers exposed to ethylene oxide. Regul Toxicol Pharmacol 56: 312-320. 

http://dx.doi.org/10.1016/j.yrtph.2009.10.001 

van Delft, JH; van Winden, MJ; Luiten-Schuite, A; Ribeiro, LR; Baan, RA. (1994). Comparison of various 

immunochemical assays for the detection of ethylene oxide-DNA adducts with monoclonal antibodies against 

imidazole ring-opened N7-(2-hydroxyethyl)guanosine: application in a biological monitoring study. Carcinogenesis 

15: 1867-1873. 

van Sittert, NJ; Beulink, GD; van Vliet, EW; van der Waal, H. (1993). Monitoring occupational exposure to 

ethylene oxide by the determination of hemoglobin adducts. Environ Health Perspect 99: 217-220. 

van Sittert, NJ; Boogaard, PJ; Natarajan, AT; Tates, AD; Ehrenberg, LG; Törnqvist, MA. (2000). Formation of 

DNA adducts and induction of mutagenic effects in rats following 4 weeks inhalation exposure to ethylene oxide as 

a basis for cancer risk assessment. Mutat Res-Fundam Mol Mech Mutagen 447: 27-48. 

http://dx.doi.org/10.1016/S0027-5107(99)00208-0 

Van Sittert, NJ; de Jong, G. (1985). Biomonitoring of exposure to potential mutagens and carcinogens in industrial 

populations. Food Chem Toxicol 23: 23-31. 

van Wijngaarden, E; Hertz-Picciotto, I. (2004). A simple approach to performing quantitative cancer risk assessment 

using published results from occupational epidemiology studies. Sci Total Environ 332: 81-87. 

http://dx.doi.org/10.1016/j.scitotenv.2004.04.005 

This document is a draft for review purposes only and does not constitute Agency policy. 

7/2013 R-10 DRAFT―DO NOT CITE OR QUOTE
 
Walker, VE; Fennell, TR; Boucheron, JA; Fedtke, N; Ciroussel, F; Swenberg, JA. (1990). Macromolecular adducts 

of ethylene oxide: a literature review and a time-course study on the formation of 7-(2-hydroxyethyl)guanine 

following exposures of rats by inhalation [Review]. DNA Repair 233: 151-164. 

Walker, VE; Fennell, TR; Upton, PB; MacNeela, JP; Swenberg, JA. (1993). Molecular dosimetry of DNA and 

hemoglobin adducts in mice and rats exposed to ethylene oxide. Environ Health Perspect 99: 11-17. 

Walker, VE; Fennell, TR; Upton, PB; Skopek, TR; Prevost, V; Shuker, DEG; Swenberg, JA. (1992a). Molecular 

dosimetry of ethylene oxide: formation and persistence of 7-(2-hydroxyethyl)guanine in DNA following repeated 

exposures of rats and mice. Cancer Res 52: 4328-4334. 

Walker, VE; MacNeela, JP; Swenberg, JA; Turner, MJ, Jr; Fennell, TR. (1992b). Molecular dosimetry of ethylene 

oxide: formation and persistence of N-(2-hydroxyethyl)valine in hemoglobin following repeated exposures of rats 

and mice. Cancer Res 52: 4320-4327. 

Walker, VE; Sisk, SC; Upton, PB; Wong, BA; Recio, L. (1997). In vivo mutagenicity of ethylene oxide at the hprt 

locus in T-lymphocytes of B6C3F1 lacI transgenic mice following inhalation exposure. Mutat Res 392: 211-222. 

Walker, VE; Skopek, TR. (1993). A mouse model for the study of in vivo mutational spectra: sequence specificity 

of ethylene oxide at the hprt locus. Mutat Res 288: 151-162. http://dx.doi.org/10.1016/0027-5107(93)90216-3 

Warwick, GP. (1963). The mechanism of action of alkylating agents [Review]. Cancer Res 23: 1315-1333. 

WHO (World Health Organization). (2003). Concise International Chemical Assessment Document: Ethylene oxide. 

Geneva: International Programme on Chemical Safety (IPCS). 

http://www.inchem,org/documents/cicads/cicad54.htm 

Wong, O; Trent, LS. (1993). An epidemiological study of workers potentially exposed to ethylene oxide. Occup 

Environ Med 50: 308-316. 

Yager, JW; Hines, CJ; Spear, RC. (1983). Exposure to ethylene oxide at work increases sister chromatid exchanges 

in human peripheral lymphocytes. Science 219: 1221-1223. http://dx.doi.org/10.1126/science.6828851 

Yong, LC; Schulte, PA; Kao, CY; Giese, RW; Boeniger, MF; Strauss, GH; Petersen, MR; Wiencke, JK. (2007). 

DNA adducts in granulocytes of hospital workers exposed to ethylene oxide. Am J Ind Med 50: 293-302. 

http://dx.doi.org/10.1002/ajim.20443 

Yong, LC; Schulte, PA; Wiencke, JK; Boeniger, MF; Connally, LB; Walker, JT; Whelan, EA; Ward, EM. (2001). 

Hemoglobin adducts and sister chromatid exchanges in hospital workers exposed to ethylene oxide: effects of 

glutathione S-transferase T1 and M1 genotypes. Cancer Epidemiol Biomarkers Prev 10: 539-550. 

Zharlyganova, D; Harada, H; Harada, Y; Shinkarev, S; Zhumadilov, Z; Zhunusova, A; Tchaizhunusova, NJ; 

Apsalikov, KN; Kemaikin, V; Zhumadilov, K; Kawano, N; Kimura, A; Hoshi, M. (2008). High frequency of 

AML1/RUNX1 point mutations in radiation-associated myelodysplastic syndrome around Semipalatinsk nuclear 

test site. J Radiat Res (Tokyo) 49: 549-555. 

This document is a draft for review purposes only and does not constitute Agency policy. 

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