EPA/635/R-13/128b 

www.epa.gov/iris 

Evaluation of the Inhalation Carcinogenicity of
 

Ethylene Oxide
 

APPENDICES
 

(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. 

7/2013 ii DRAFT―DO NOT CITE OR QUOTE 
CONTENTS
 

LIST OF TABLES ......................................................................................................................... vi
 

LIST OF FIGURES ....................................................................................................................... ix
 

LIST OF ABBREVIATIONS ........................................................................................................ xi
 

APPENDIX A. CRITICAL REVIEW OF EPIDEMIOLOGIC EVIDENCE ............................ A-1
 

A.1. BACKGROUND ...................................................................................................... A-1
 

A.2. INDIVIDUAL STUDIES ......................................................................................... A-2
 

A.2.1. Hogstedt (1988), Hogstedt et al. (1986)..................................................... A-2
 

A.2.2. Gardner et al. (1989) .................................................................................. A-5
 

A.2.3. Kiesselbach et al. (1990) ............................................................................ A-6
 

A.2.4. Greenberg et al. (1990) .............................................................................. A-7
 

A.2.5. Steenland et al. (1991) ............................................................................... A-9
 

A.2.6. Teta et al. (1993) ...................................................................................... A-11
 

A.2.7. Benson and Teta (1993) ........................................................................... A-13
 

A.2.8. Stayner et al. (1993) ................................................................................. A-14
 

A.2.9. Wong and Trent (1993)............................................................................ A-16
 

A.2.10. Bisanti et al. (1993) .................................................................................. A-17
 

A.2.11. Hagmar et al. (1995) and Hagmar et al. (1991) ....................................... A-18
 

A.2.12. Norman et al. (1995) ................................................................................ A-20
 

A.2.13. Swaen et al. (1996) .................................................................................. A-21
 

A.2.14. Olsen et al. (1997) .................................................................................... A-22
 

A.2.15. Steenland et al. (2004) ............................................................................. A-23
 

A.2.16. Steenland et al. (2003) ............................................................................. A-25
 

A.2.17. Kardos et al. (2003).................................................................................. A-26
 

A.2.18. Tompa et al. (1999) .................................................................................. A-27
 

A.2.19. Coggon et al. (2004) ................................................................................ A-27
 

A.2.20. Swaen et al. (2009) .................................................................................. A-28
 

A.3. SUMMARY ............................................................................................................ A-35
 

A.4. CONCLUSIONS..................................................................................................... A-54
 

APPENDIX B. REFERENCES FOR FIGURE 3-3 ....................................................................B-1
 

APPENDIX C. GENOTOXICITY AND MUTAGENICITY OF ETHYLENE OXIDE ...........C-1
 

C.1. DNA ADDUCTS .......................................................................................................C-2
 

C.1.1. Detection of EtO Adducts in In Vitro and In Vivo Systems.......................C-4
 

C.1.2. In Vitro DNA Binding Studies ...................................................................C-4
 

C.1.3. In Vivo Studies―Animal Experiments ......................................................C-5
 

C.1.4. In Vivo Studies―Human Subjects .............................................................C-9
 

C.1.5. DNA Adducts―Summary ........................................................................C-10
 

C.1.6. EtO-Hemoglobin Adducts ........................................................................C-10
 

C.2. GENE MUTATIONS ..............................................................................................C-11
 

C.2.1. Bacterial Systems ......................................................................................C-11
 

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C.2.2. Mammalian Systems .................................................................................C-12
 

C.2.3. Gene Mutations―Summary .....................................................................C-20
 

C.3. CHROMOSOMAL ABERRATIONS .....................................................................C-20
 

C.4. MICRONUCLEUS FORMATION .........................................................................C-23
 

C.5. SISTER CHROMATID EXCHANGES (SCEs) .....................................................C-24
 

C.6. OTHER ENDPOINTS (GENETIC POLYMORPHISM, SUSCEPTIBILITY) ......C-27
 

C.7. ENDOGENOUS PRODUCTION OF ETHYLENE AND EtO ..............................C-28
 

C.8. CONCLUSIONS......................................................................................................C-33
 

APPENDIX D. REANALYSES AND INTERPRETATION OF ETHYLENE OXIDE
 

EXPOSURE-RESPONSE DATA ..................................................................................... D-1
 

D.1. BREAST CANCER INCIDENCE BASED ON THE DATA WITH 

INTERVIEWS .......................................................................................................... D-4
 

D.2. BREAST CANCER MORTALITY ....................................................................... D-29
 

D.3. LYMPHOID CANCER MORTALITY (SUBSET OF ALL
 

HEMATOPOIETIC CANCERS COMBINED) (N = 18,235). .............................. D-41
 

D.4. HEMATOPOIETIC CANCER MORTALITY (ALL HEMATOPOIETIC
 

CANCERS COMBINED) ...................................................................................... D-52
 

D.5. SUMMARY TABLE OF EC01S FOR DIFFERENT OUTCOMES, USING 2­

PIECE SPLINE MODELS ..................................................................................... D-63
 

D.6. SENSITIVITY OF 2-PIECE SPLINE CURVES TO PLACEMENT OF
 

KNOT ..................................................................................................................... D-64
 

D.7. POSSIBLE INFLUENCE OF THE HEALTHY WORKER SURVIVOR
 

EFFECT .................................................................................................................. D-65
 

D.8. POSSIBLE INFLUENCE OF EXPOSURE MIS-MEASUREMENT ................... D-66
 

D.9. REFERENCES ....................................................................................................... D-68
 

APPENDIX E. LIFE-TABLE ANALYSIS .................................................................................E-1
 

APPENDIX F. EQUATIONS USED FOR WEIGHTED LINEAR REGRESSION ..................F-1
 

APPENDIX G. MODEL PARAMETERS IN THE ANALYSIS OF ANIMAL TUMOR
 

INCIDENCE ...................................................................................................................... G-1
 

APPENDIX H. SUMMARY OF 2007 EXTERNAL PEER REVIEW AND PUBLIC 

COMMENTS AND DISPOSITION ................................................................................. H-1
 

APPENDIX I. LIST OF REFERENCES ADDED AFTER THE 2006 EXTERNAL
 

REVIEW DRAFT ................................................................................................................I-1
 

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CONTENTS (continued) 

APPENDIX J. SUMMARY OF MAJOR NEW STUDIES SINCE THE LITERATURE 

CUTOFF DATE ..................................................................................................................J-1
 

J.1. SYSTEMATIC LITERATURE SEARCH .................................................................J-1
 

J.2. REVIEWS OF MAJOR NEW STUDIES PUBLISHED SINCE THE
 

LITERATURE CUTOFF DATE................................................................................J-4
 

J.2.1. Kiran et al. (2010) ........................................................................................J-4
 

J.2.2. Mikoczy et al. (2011) ...................................................................................J-6
 

J.3. REFERENCES .........................................................................................................J-12
 

APPENDIX K. DOCUMENTATION OF IMPLEMENTATIONS OF THE 2011 

NATIONAL RESEARCH COUNCIL RECOMMENDATIONS .................................... K-1
 

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

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LIST OF TABLES
 

A-1. Estimated 8-hour time-weighted average ethylene oxide exposure, Plant 3 .................. A-3
 

A-2. Cox regression results for hematopoietic cancer mortality (15-year lag) in males ...... A-24
 

A-3. Cox regression results for lymphoid cell line tumors (15-year lag) in males ............... A-24
 

A-4. Exposure assessment matrix from Swaen et al. (2009)—8-hour TWA exposures
 

in ppm ........................................................................................................................... A-29
 

A-5. Epidemiological studies of ethylene oxide and human cancer ..................................... A-36
 

C-1. Levels of endogenous (background) N7-HEG adducts in unexposed human and 

experimental rodent tissues ........................................................................................... C-30
 

D-1a. Distribution of cases in Cox regression for breast cancer morbidity analysis after
 

using a 15-year lag .......................................................................................................... D-6
 

D-1b. Categorical analysis of breast cancer incidence by deciles (log RR model) .................. D-7
 

D-1c. Fit of 2-piece log-linear model to breast cancer incidence data, Cox regression ......... D-14
 

D-1d. Fit of log-linear model to breast cancer incidence data, Cox regression (RR = e(β ×
	

exposure))........................................................................................................................... D-15
 

D-1e. Fit of the square root transformation log RR model to breast cancer incidence
 

data, Cox regression (RR = e(β × sqrt(exposure))) ................................................................. D-16
 

D-1f. Fit of the log transform model to breast cancer incidence data, Cox regression
 

(RR = e(β × ln(exposure))) ..................................................................................................... D-17
 

D-1g.	 Change in –2 log likelihood for log RR models for breast cancer incidence, with 

addition of exposure term(s) ......................................................................................... D-18
 

D-1h. Model fit statistics for linear RR models, breast cancer incidence ............................... D-20
 

D-1i. Model coefficients for linear RR models, breast cancer incidence .............................. D-21
 

D-1j. Supplemental Results: Parameter estimates for exposure variables for categorical
 

(decile) linear RR model (RR = 1 + β), breast cancer incidence .................................. D-22
 

D-2a. Distribution of cases in Cox regression analysis of breast cancer mortality after
 

using a 20-year lag ........................................................................................................ D-30
 

D-2b. Categorical output breast cancer mortality, 20-year lag (log RR model) ..................... D-35
 

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LIST OF TABLES (continued) 

D-2c. 2-piece log-linear spline, breast cancer mortality, 20-year lag, knot at 700 ppmdays ............................................................................................................................... D-36
 

D-2d. Log-linear model, breast cancer mortality, 20-year lag ................................................ D-37
 

D-2e. Log transform log RR model, breast cancer mortality, 20-year lag ............................. D-38
 

D-2f. 2-piece log-linear spline model, breast cancer mortality, 20-year lag, knot at
 

13,000 ppm-days ........................................................................................................... D-39
 

D-2g. Model results for breast cancer mortality, linear RR models ....................................... D-40
 

D-3a. Exposure categories and case distribution for lymphoid cancer mortality ................... D-42
 

D-3b. Lymphoid cancer mortality results by sex .................................................................... D-43
 

D-3c. Categorical results for lymphoid cancer mortality (log RR model), men and
 

women combined .......................................................................................................... D-46
 

D-3d. Results of 2-piece log-linear spline model for lymphoid cancer mortality, men and 

women combined, knot at 100 ppm-days ..................................................................... D-47
 

D-3e. Results of the log transform log RR model for lymphoid cancer mortality, both 

sexes combined ............................................................................................................. D-48
 

D-3f. Results of the log-linear model for lymphoid cancer mortality, both sexes
 

combined ....................................................................................................................... D-48
 

D-3g. Results of 2-piece log-linear spline model for lymphoid cancer mortality, men and 

women combined, knot at 1600 ppm-days ................................................................... D-49
 

D-3h. Supplemental Results: Model fit statistics and coefficients for linear RR models,
 

lymphoid cancer mortality ................................................................................................ 50
 

D-4a. Exposure categories and case distribution for hematopoietic cancer mortality ............ D-53
 

D-4b. All hematopoietic cancer mortality categorical results by sex (log RR model) ........... D-54
 

D-4c. Categorical results for all hematopoietic cancer mortality (log RR model), men
 

and women combined, cumulative exposure with a 15-year lag .................................. D-57
 

D-4d. Results of 2-piece log-linear spline model for all hematopoietic cancer mortality,
 

men and women combined, cumulative exposure with a 15-year lag .......................... D-58
 

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LIST OF TABLES (continued) 

D-4e. Results of log-transform log RR model for all hematopoietic cancer mortality, 

men and women combined, cumulative exposure with a 15-year lag .......................... D-59
 

D-4f. Results of log-linear model for all hematopoietic cancer morality, men and 

women combined, cumulative exposure with a 15-year lag ......................................... D-60
 

D-4g.	 Supplemental Results: Model fit statistics and coefficients for linear RR models, 

hematopoietic cancer mortality..................................................................................... D-61
 

D-5a. Summary of EC01 results (in ppm) in current analysis and previous EPA risk 

assessment ..................................................................................................................... D-64
 

D-6a. Exposure-response coefficients and EC01s based on selection of different knots, 

using 2-piece log-linear models .................................................................................... D-65
 

E-1. Extra risk calculation for environmental exposure to 0.0114 ppm (the LEC01 for 

lymphoid cancer incidence) using the weighted linear regression model based on 

the categorical cumulative exposure results of Steenland et al. (2004), reanalyzed 

by Steenland for both sexes combined with a 15-year lag, as described in
 

Section 4.1.1.....................................................................................................................E-2
 

G-1. Analysis of grouped data, NTP mice study (NTP, 1987); multistage model
 

parameters ....................................................................................................................... G-1
 

G-2. Analysis of grouped data, Lynch et al. (1984a); Lynch et al. (1984c) study of male
 

F344 rats; multistage model parameters ......................................................................... G-2
 

G-3. Analysis of grouped data, Garman et al. (1985) and Snellings et al. (1984) reports
 

on F344 rats; multistage model parameters .................................................................... G-2
 

G-4. Time-to-tumor analysis of individual animal data, NTP mice study (NTP, 1987);
 

multistage-Weibull model parameters ............................................................................ G-3
 

J-1. Disposition of 56 new references identified as potentially relevant ................................ J-2
 

J-2. New epidemiological studies of ethylene oxide and human cancer ................................ J-9
 

K-1. The EPA’s implementation of the National Research Council’s recommendations
 

in the ethylene oxide (EtO) carcinogenicity assessment................................................. K-2
 

K-2. National Research Council recommendations that the EPA is implementing in the
 

long term ......................................................................................................................... K-8
 

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LIST OF FIGURES
 

D-1a. Likelihoods vs knots, 2-piece linear log RR model for breast cancer morbidity. .......... D-9
 

D-1b. Breast cancer incidence. Plot of the dose-response relationship for continuous 

exposure generated using a 2-piece log-linear spline overlaid with a plot using 

restricted cubic (log RR) splines. .................................................................................. D-10
 

D-1c. Breast cancer incidence. Plot of a log-linear dose-response relationship overlaid 

with a dose-response relationship generated using restricted cubic log RR model 

with continuous exposure. ............................................................................................ D-11
 

D-1d. Breast cancer incidence. Comparison of log-linear curve (log RR = β × cumexp) 

with all the data and the log-linear curve (higher slope) after excluding those in 

the top 5% of exposure (>27,500 ppm-days). ............................................................... D-11
 

D-1e. Breast cancer incidence. Plot of a logarithmic transformation log RR doseresponse model (log RR = β × log(cumexp)) overlaid with a dose-response 

relationship generated using categorical log RR analyses (deciles) ............................. D-12
 

D-1f. Breast cancer incidence. Plot of a square-root transformation log RR dose– 

response model overlaid with a dose-response relationship generated using 

categorical log RR analyses (deciles) ........................................................................... D-13
 

D-1g.	 Breast cancer incidence exposure-response curves, linear RR models (units are 

ppm-days, 15-year lag). ................................................................................................ D-19
 

D-1h. Knot location for Figure D-1g above, 2-piece linear spline model, breast cancer
 

incidence (units are ppm-days, 15-year lag). ................................................................ D-20
 

D-2a. Likelihoods vs knots for the 2-piece log-linear model, breast cancer morality. ........... D-31
 

D-2b. Likelihoods vs knots for the 2-piece log-linear model, breast cancer morality. ........... D-32
 

D-2c. Plot of the dose-response relationship of continuous exposure (lagged 20 years) 

for breast cancer mortality, with the 2-piece linear spline, the categorical, and the 

linear log RR models .................................................................................................... D-33
 

D-2d. Plot of the dose-response relationship of continuous exposure (lagged 20 years) 

for breast cancer mortality, generated using a logarithmic transformation log RR 

model............................................................................................................................. D-34
 

D-2e. Linear RR models for breast cancer mortality. ............................................................. D-40
 

D-3a. Likelihoods vs knots for 2-piece log-linear model, lymphoid cancer mortality. .......... D-45
 

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LIST OF FIGURES (continued) 

D-3b. Plot of the exposure and lymphoid cancer mortality rate ratios generated using a 

2-piece log-linear spline model overlaid with other log RR curves and categorical 

log RR model points. .................................................................................................... D-45
 

D-3c. Linear RR models for lymphoid cancer. ....................................................................... D-50
 

D-4a. Likelihood vs knots for 2-piece log-linear model, all hematopoietic cancer................ D-55
 

D-4b. Plot of exposure and rate ratios for all hematopoietic cancer generated using a 2­

piece log-linear spline model and log transform, linear, and categorical log RR 

models. .......................................................................................................................... D-56
 

D-4c. Linear RR models for hematopoietic cancer mortality. ................................................ D-61
 

H-1. Induction of hprt mutations by EtO with data from ethylene (using estimated EtO
 

equivalents) shown........................................................................................................ H-16
 

H- 2. Induction of recessive lethal mutations by EtO in Drosophila (wild-type) .................. H-17
 
 

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 

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1 APPENDIX A. 

CRITICAL REVIEW OF EPIDEMIOLOGIC EVIDENCE 

[EDITORIAL NOTE: Please note that in this assessment document the responses to the 

2007 external peer review and public comments can be found in Appendix H.] 

A.1. BACKGROUND 

On the basis of studies indicating that EtO was a strong mutagen and that exposure to 

EtO produced increased chromosomal aberrations in human lymphocytes (Ehrenberg and 

Gustafsson, 1970; Ehrenberg and Hallstrom, 1967; Rapoport, 1948), Hogstedt and colleagues 

studied three small, independent cohorts of workers from Sweden. Reports on two of these 

cohorts (Hogstedt et al., 1984; Hogstedt et al., 1979b; Hogstedt et al., 1979a) were reviewed in 

the earlier health assessment document (U.S. EPA, 1985). These two small cohorts plus a third 

group of EtO-exposed workers from a third independent plant in Sweden were then combined 

and studied as one cohort (Hogstedt, 1988; Hogstedt et al., 1986). A review of this reconstituted 

cohort study and subsequent independent studies is presented in Section A.2. 

Shortly after the third Hogstedt study was completed, another independent study of 

EtO-exposed employees was completed (Gardner et al., 1989) on a cohort of workers from four 

companies and eight hospitals in Great Britain, and it was followed by a third independent study 

on a cohort of exposed workers in eight chemical plants from the Federal Republic of Germany 

(Kiesselbach et al., 1990). A follow-up study of the Gardner et al. (1989) cohort was recently 

conducted by Coggon et al. (2004). 

Greenberg et al. (1990) was the first in a series of studies of workers exposed to EtO at 

two chemical manufacturing facilities in the Kanawha Valley (South Charleston, WV). The 

workers at these two facilities were studied later by Teta et al. (1993), Benson and Teta (1993), 

Teta et al. (1999), and Swaen et al. (2009) and became the basis for several important 

quantitative risk assessment analyses (Valdez-Flores et al., 2010; EOIC, 2001; Teta et al., 1999). 

Another independent study of EtO-exposed workers in 14 sterilizing plants from across 

the United States was completed by the National Institute for Occupational Safety and Health 

(NIOSH) (Stayner et al., 1993; Steenland et al., 1991). The Stayner et al. (1993) paper presents 

the exposure-response analysis performed by the NIOSH investigators. These same workers 

were studied again from a different perspective by Wong and Trent (1993). The NIOSH 

investigators recently completed a follow-up of the mortality study Steenland et al. (2004) and a 

breast cancer incidence study based in the same cohort (Steenland et al., 2003). The results of 

the Steenland et al. (2004) and Steenland et al. (2003) analyses are the basis for the quantitative 

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assessment in this document, for reasons explained in the review and summary sections of this 

appendix. 

Several additional studies of lesser importance have been done on EtO-exposed cohorts 

of workers in Sweden (Hagmar et al., 1995; Hagmar et al., 1991), Italy (Bisanti et al., 1993), 

Belgium (Swaen et al., 1996) and western New York State (Norman et al., 1995), and other parts 

of the United States (Olsen et al., 1997). These studies are discussed in the following review, but 

they provide limited information to the overall discussion of whether EtO induces cancer in 

humans. 

The more important studies, which are discussed in detail in the summary, are those at 

two facilities in the Kanawha Valley 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) and at 

14 sterilizing plants around the country (Steenland et al., 2004; Steenland et al., 2003; Stayner et 

al., 1993; Steenland et al., 1991). These studies have sufficient follow-up to analyze latent 

effects, and the cohorts appear to be large enough to test for small differences. In addition, 

exposure estimates were derived for both cohorts, and attempts were made to assess 

dose-response relationships. 

A.2. INDIVIDUAL STUDIES 

A.2.1. Hogstedt (1988), Hogstedt et al. (1986) 

Hogstedt et al. (1986) combined workers from several cohorts for a total of 733 workers, 

including 378 workers from two separate and independent occupational cohort mortality studies 

by Hogstedt et al. (1979b) and Hogstedt et al. (1979a) and 355 employees from a third EtO 

production plant who had not been previously examined. The combined cohort was followed 

until the end of 1982. The first cohort comprised employees from a small technical factory in 

Sweden where hospital equipment was sterilized with EtO. The second was from a production 

facility where EtO was produced by the chlorohydrin method from 1940 to 1963. The third was 

from a production facility where EtO was made by the direct oxidation method from 1963 to 

1982. 

In the update of the 1986 occupational mortality report (Hogstedt, 1988), the cohort 

inexplicably was reduced to 709 employees (539 men; 170 women). Follow-up for mortality 

was extended to the end of 1985. The author reported that 33 deaths from cancer had occurred, 

whereas only 20 were expected in the combined cohort. The excesses that are significant are due 

mainly to an increased risk of stomach cancer at one plant and an excess of blood and lymphatic 

malignancies at all three. Seven deaths from leukemia occurred, whereas only 0.8 were expected 

(standard mortality ratio [SMR] = 9.2). Ten deaths due to stomach cancer occurred versus only 

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1.8 expected (SMR = 5.46). The results tend to agree with those from clastogenic and short-term 

tests on EtO ( Ehrenberg and Gustafsson, 1970). The authors believe that the large number of 

positive cytogenetic studies demonstrating increased numbers of chromosomal aberrations and 

sister chromatid exchanges at low-level exposure to EtO indicate that the lymphatic and 

hematopoietic systems are particularly sensitive to the genotoxic effects of EtO. They concluded 

that the induction of malignancies even at low-level and intermittent exposures to EtO should be 

“seriously considered by industry and regulating authorities.” 

The average air EtO concentrations in the three plants were as follows: In Plant 1 

(Hogstedt et al., 1979a) in 1977, levels ranged from 2 to 70 ppm in the storage hall. The average 

8-hour time-weighted average (TWA) concentration in the breathing zone of the employees was 

calculated as 20 ppm ± 10 ppm. Measured concentrations were 150 ppm on the floor outside of 

the sterilized boxes and 1,500 ppm inside. 

In Plant 2 (Hogstedt et al., 1979b), EtO was produced through the chlorohydrin process. 

Between 1941 and 1947, levels probably averaged about 14 ppm, with occasional exposures up 

to 715 ppm. Between 1948 and 1963, levels were in the range of 6 ppm to 28 ppm. After 1963, 

when production of EtO came to an end, levels ranged from less than 1 ppm to as much as 

6 ppm. 

In Plant 3 (Hogstedt et al., 1986), the 355 employees were divided into subgroups. 

Subgroup A had almost pure exposure to EtO. Subgroup B had principal exposure to EtO but 

also exposure to propylene oxide, amines, sodium nitrate, formaldehyde, and 1,2-butene oxide. 

Workers in the remaining subgroup C were maintenance and technical service personnel, who 

had multiple exposures, including EtO. Concentration levels in Plant 3 are shown in Table A-1. 

Table A-1. Estimated 8-hour time-weighted average ethylene oxide 

exposure, Plant 3 

Group 1963–1976 1977–1982 

A (n = 128) 5–8 ppm 1–2 ppm 

B (n = 69) 3 ppm 1 ppm 

C (n = 158) 1–3 ppm 0.4–1.6 ppm 

Source: Hogstedt et al. (1986).
 

In the earlier studies (Hogstedt et al., 1979b) and (Hogstedt et al., 1979a) of two of the 

plants that contributed workers to this cohort, the authors allude to the fact that there was 

exposure to benzene, ethylene dichloride, ethylene chlorohydrin, ethylene, and small amounts of 

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1 bis-(2-chloroethyl) ether, as well as other chemicals in the respective plants. Although 

170 women were present in the workforce, no gender differences in risk were analyzed 

separately by the investigators. Of 16 patients with tumors in the two exposed cohorts, there 

were three cases of leukemia (0.2 expected), six cases of alimentary tract cancer, and four cases 

of urogenital cancer. Of the 11 cancer cases in the full-time exposed cohort, 5.9 were expected 

(p < 0.05). This study was criticized by Divine and Amanollahi (1986) for several reasons. 

First, they believed that the study’s strongest evidence in support of a carcinogenic claim for EtO 

was only a “single case of leukemia” in subgroup C of Plant 3, where the workers had multiple 

chemical exposures; however, there were no cases in subgroups A or B of Plant 3. Hogstedt et 

al. (1986) countered that the expectation of leukemia in these two subgroups were 0.04 and 0.02, 

respectively, and that the appearance of a case could only happen if EtO had “outstanding 

carcinogenic properties at low levels.” Divine and Amanollahi also pointed out that a study 

(Morgan et al., 1981) of a cohort similar to that of Plant 3 found no leukemia cases or evidence 

of excessive mortality. Hogstedt et al. (1986) replied that Morgan et al. (1981) stated in their 

paper that the statistical power of their study to detect an increased risk of leukemia was not 

strong. 

Divine and Amanollahi (1986) also stated that the exposures to EtO were higher in 

Plants 1 and 2 than in Plant 3; therefore, combinations would “normally preclude comparisons 

between the plants for similar causes of adverse health.” This potential problem could be 

resolved by structuring exposure gradients to analyze risk. Furthermore, they noted Plant 1 was 

a nonproduction facility involved in sterilization of equipment. Plant 2 used the chlorohydrin 

process for making EtO, and Plant 3 used the direct oxygenation process. Although these 

conditions are obviously different, they “are grouped together as analogous.” This criticism 

would, in most instances, be valid only because the methods for producing EtO differ and there 

were differing exposures to multiple chemicals. 

However, these concerns are not supported by the evidence. In all three plants the 

leukemia risk was elevated, even if only slightly in Plant 3. This suggests that there may have 

been a common exposure, possibly to EtO, endemic to all three plants that was responsible for 

the measured excesses. Noteworthy is the elevated risk of leukemia seen in Plant 1 (3 observed 

vs. 0.14 expected), where the exposures were almost exclusively to EtO in the sterilization of 

equipment. The argument that Plant 1 leukemias form a “chance cluster,” as Shore et al. (1993) 

claim, and as such should be excluded from any analysis does not preclude the possibility that 

these cases are in reality the result of exposure to EtO. Hogstedt (1988) argues that earlier 

remarks by Ehrenberg and Gustafsson (1970) that EtO “constituted a potential cancer hazard” on 

the basis of a considerable amount of evidence other than epidemiologic should have served as a 

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1 warning that the increased risk seen in Plant 1 was not necessarily a “chance cluster,” and 

because the chlorohydrin process was not in use in Plant 1, it cannot be due to exposure to a 

chemical in the chlorohydrin process. 

A.2.2. Gardner et al. (1989) 

Gardner et al. (1989) completed a cohort study of 2,876 men and women who had 

potential exposure to EtO. The cohort was identified from employment records at four 

companies that had produced or used EtO since the 1950s and from eight hospitals that have had 

EtO clinical sterilizing units since the 1960s, and it was followed to December 31, 1987. All but 

1 of the 1,012 women and 394 of the men in the cohort worked at one of the hospitals. The 

remaining woman and 1,470 men made up the portion of the cohort from the four companies. 

By the end of the follow-up, 226 members (8% of the total cohort) had died versus 

258.8 expected. Eighty-five cancer deaths were observed versus 76.64 expected. 

No clear excess risk of leukemia (3 observed vs. 2.09 expected), stomach cancer 

(5 observed vs. 5.95 expected), or breast cancer (4 observed vs. 5.91 expected) was present as of 

the cutoff date. “Slight excesses” of deaths due to esophageal cancer (5 observed vs. 

2.2 expected), lung cancer (29 observed vs. 24.55 expected), bladder cancer (4 observed vs. 

2.04 expected), and non-Hodgkin lymphoma (NHL) (4 observed vs. 1.63 expected) were noted, 

although an adjustment made to reflect local “variations in mortality” reduced the overall cancer 

excess from 8 to only 3. According to the authors’ published tabulations, all three leukemias 

identified in this study fell into the longest latent category (20 years or longer), where only 

0.35 were expected. All three were in the chemical plants. This finding initially would seem to 

be consistent with experimental animal evidence demonstrating excess risks of hematopoietic 

cancer in animals exposed to EtO. But the authors note that since other known leukemogens 

were present in the workplace, the excess could have been due to a confounding effect. 

The hospitals began using EtO during or after 1962, whereas all of the chemical 

companies had handled EtO from or before 1960. In the hospitals there was occasional exposure 

to formaldehyde and carbon tetrachloride but few other confounding agents. On the other hand, 

the chemical workers were exposed to a wide range of compounds including chlorohydrin, 

propylene oxide, styrene, and benzene. The earliest industrial hygiene surveys in 1977 indicated 

that the TWA average exposures were less than 5 ppm in almost all jobs and less than 1 ppm in 

many. No industrial hygiene data were available for any of the facilities prior to 1977, although 

it is stated that peaks of exposure up to several hundred ppm occurred as a result of operating 

difficulties in the chemical plants and during loading and unloading of sterilizers in the hospitals. 

An odor threshold of 700 ppm was reported by both manufacturers and hospitals, according to 

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1 the authors. The authors assumed that past exposures were somewhat higher without knowing 

precisely what they were. An attempt was made to classify exposures into a finite number of 

subjectively derived categories (definite, possible, continual, intermittent, and unknown). This 

exercise produced no discernable trends in risk of exposure to EtO. However, the exposure 

status classification scheme was so vague as to be useless for determining risk by gradient of 

exposure to EtO. 

It is of interest that all three of the leukemia deaths entailed exposure to EtO, with very 

little or no exposure to benzene, according to the authors. The findings are not inconsistent with 

those of Hogstedt (1988) and Hogstedt et al. (1986). The possibility of a confounding effect 

other than benzene in these chemical workers cannot entirely be ruled out. Other cancers were 

slightly in excess, but overall there was little increased mortality from cancer in this cohort. It is 

possible that if very low levels of exposure to EtO had prevailed throughout the history of these 

hospitals and plants, the periods of observation necessary to observe an effect may not have been 

long enough. 

A follow-up study of this cohort conducted by Coggon et al. (2004) is discussed below. 

A.2.3. Kiesselbach et al. (1990) 

Kiesselbach et al. (1990) carried out an occupational cohort mortality study of 2,658 men 

from eight chemical plants in the Federal Republic of Germany (FRG) that were involved in the 

production of EtO. The method of production is not stated. At least some of the plants that were 

part of an earlier study by Thiess et al. (1981) were included. Each subject had to have been 

exposed to EtO for at least 1 year sometime between 1928 and 1981 before person-years at risk 

could start to accumulate. Most exposures occurred after 1950. By December 31, 1982, the 

closing date of the study, 268 men had died (about 10% of the total cohort), 68 from malignant 

neoplasms. The overall SMR for all causes was 0.87, and for total cancer the SMR was 0.97, 

based on FRG rates. The authors reported that this deficit in total mortality indicates a 

healthy-worker effect. 

The only remarkable findings here are slightly increased risks of death from stomach 

cancer (14 observed vs. 10.15 expected, SMR = 1.4), cancer of the esophagus (3 observed vs. 

1.5 expected, SMR = 2), and cancer of the lung (23 observed vs. 19.86 expected, SMR = 1.2). 

Although the authors claimed that they looked at latency, only stomach cancer and total 

mortality has a latency analysis included. This was accomplished by not counting the first 

10 years of follow-up in the parameter “years since first exposure.” This study is limited by the 

lack of further latency analyses at other cancer sites. The risk of stomach cancer shows only a 

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1 slight nonsignificant trend upward with increasing latency. Only two leukemias were recorded 

versus 2.35 expected. 

This is a largely unremarkable study, with few findings of any significance. No actual 

exposure estimates are available. The categories of exposure that the authors constructed are 

“weak,” “medium,” and “strong.” It is not known whether any of these categories is based on 

actual measurements. No explanation of how they were derived is provided except that the 

authors claim that the information is available on 67.2% of the members of the cohort. If the 

information was based on job categories, it should be kept in mind that exposures in jobs that 

were classified the same from one plant to the next may have produced entirely different 

exposures to EtO. The tabular data regarding these exposure categories shows that only 2.4% of 

all members of the cohort were considered “strongly” exposed to EtO. Although 71.6% were 

classified as “weak,” the remaining 26% were considered as having “medium” exposure to EtO. 

This is largely a study in progress, and further follow-up will be needed before any 

definite trends or conclusions can be drawn. The authors reported that only a median 15.5 years 

of follow-up had passed by the end of the cutoff date, whereas the median length of exposure 

was 9.6 years. Before any conclusions can be made from this study several additional years of 

follow-up would be needed with better characterization of exposure. 

A.2.4. Greenberg et al. (1990) 

Greenberg et al. (1990) retrospectively studied the mortality experience of 2,174 men 

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

Corporation (UCC) chemical plants in West Virginia. In 1970 and 1971, EtO production at the 

two plants was phased out, but EtO was still used in the plants for the production of other 

chemicals. SMRs were calculated in comparison with the general U.S. population and the 

regional population. Results based on regional population death rates were found to be similar to 

those based on the U.S. general population. Follow-up began either on January 1, 1940, if 

exposure to EtO began sooner, or on the date when exposure began, if it occurred after January 

1, 1940. Follow-up ended on December 31, 1978. Note that this cohort is thus a mixture of a 

prevalent cohort and an incident cohort, and the prevalent part of the cohort may be especially 

vulnerable to bias from the healthy worker survivor effect. The healthy worker survivor effect 

might have occurred if workers who were employed before 1940 and who were of greater 

susceptibility preferentially developed a disease of interest prior to 1940 and were no longer 

employed when cohort enumeration began. It appears that the chemical facilities began 

operating in 1925, so the maximum latency for the development of a disease of interest between 

the time of first exposure and cohort enumeration was 15 years; however, these early (pre-1940) 

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1 hires would also have had the highest EtO exposures (Swaen et al., 2009) and may thus have had 

short latency periods as well. The healthy worker survivor effect bias can also dampen 

exposure-response relationships (Applebaum et al., 2007). According to Greenberg et al. (1990), 

slightly over 10% of the cohort was comprised of prevalent hires (223 of 2,174). This is not a 

large proportion, but, as noted above, these early hires would also have had the highest exposures 

(Swaen et al., 2009). It is unknown how many workers employed before 1940 were no longer 

employed when cohort enumeration began. Two years of pre-1940 exposure were reportedly 

taken into account when categorizing the cohort into groups with ≥2 years exposure in the 

different potential exposure categories (see below); however, it is unclear how pre-1940 years of 

exposure were treated in other analyses, e.g., the analyses based on duration of exposure 

(although presumably they were taken into account for those analyses as well). 

Total deaths equaled 297 versus 375.9 expected (SMR = 0.79, p < 0.05). Only 60 total 

cancer deaths were observed versus 74.6 expected (SMR = 0.81). These deficits in mortality 

suggest a manifestation of the healthy-worker effect. In spite of this, nonsignificant elevated 

risks of cancer of the liver, unspecified and primary, (3 observed vs. 1.8 expected, SMR = 1.7), 

pancreas (7 observed vs. 4.1 expected, SMR = 1.7), and leukemia and aleukemia (7 observed vs. 

3.0 expected, SMR = 2.3) were noted. 

The authors also reported that in 1976, 3 years prior to the end of follow-up, an industrial 

hygiene survey found that 8-hour TWA EtO levels averaged less than 1 ppm, although levels as 

high as 66 ppm 8-hour TWA had been observed. In maintenance workers, levels averaged 

between 1 and 5 ppm 8-hour TWA. Because of the lack of information about exposures before 

1976 (e.g., when EtO was in production), the authors developed a qualitative exposure 

categorization scheme with three categories of exposure (low, intermediate, and high) on the 

basis of the potential for exposure in each department. The number of workers in each exposure 

category was not reported; however, it appears from Teta et al. (1993) (see below) that only 

425 workers were assigned to EtO production departments, which were apparently the only 

departments with high potential exposure. No significant findings of a dose-response 

relationship were discernable. 

Except for two cases of leukemia, all the victims of pancreatic cancer and leukemia 

began their work—and hence exposure to EtO—many years prior to their deaths. The leukemia 

and pancreatic cancer deaths were concentrated in the chlorohydrin production department. Four 

of the seven leukemia victims had been assigned to the chlorohydrin department; only 0.8 deaths 

(SMR = 5.0) would have been expected in this department of only 278 workers. Six pancreatic 

cancer victims were assigned to the chlorohydrin department, whereas only 0.98 deaths would 

have been expected to occur (SMR = 6.1). All seven leukemia victims, including the four in the 

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1 chlorohydrin department, were listed by the authors as having only low potential exposure to 

EtO. In contrast, among workers ever assigned to a department in the high exposure category, 

no leukemia deaths and only one pancreatic cancer death occurred. 

The authors hypothesized that the excesses in leukemia and pancreatic cancers were 

associated with production of ethylene chlorohydrin or propylene chlorohydrin or both in the 

chlorohydrin department. Some later follow-up studies (described below) were done of the 

cohort excluding the chlorohydrin production workers (Teta et al., 1993) and of the chlorohydrin 

production workers alone (Benson and Teta, 1993) to further examine this hypothesis. 

A.2.5. Steenland et al. (1991) 

In an industry-wide analysis by NIOSH, Steenland et al. (1991) studied EtO exposure in 

18,254 workers (55% female) identified from personnel files of 14 plants that had used EtO for 

sterilization of medical equipment, treating spices, or testing sterilizers. Each of the 14 plants 

(from 75 facilities surveyed) that were considered eligible for inclusion in the study had at least 

400 person-years at risk prior to 1978. Within each eligible facility, at least 3 months of 

exposure to EtO qualified an employee for inclusion in the cohort. Employees, including all 

salaried workers, who were “judged never to have been exposed to EtO” on the basis of 

industrial hygiene surveys were excluded. Follow-up ended December 31, 1987. The cohort 

averaged 16 years of latency. Approximately 86% achieved the 9-year latent point, but only 8% 

reached the 20-year latency category. The average year of first exposure was 1970, and the 

average length of exposure was 4.9 years. The workers’ average age at entry was not provided, 

nor was an age breakdown. Nearly 55% of the cohort were women. 

Some 1,137 workers (6.4%) were found to be deceased at the end of the study period, 

upon which the underlying cause of death was determined for all but 450. If a member was 

determined to be alive as of January 1, 1979, but not after and no death record was found in the 

National Death Index through December 31, 1987, then that member was assumed to be alive for 

the purposes of the life-table analysis and person-years were accumulated until the cutoff date. 

Altogether, 4.5% of the cohort fell into this category. This procedure would tend to increase the 

expected deaths and, as a consequence, potentially bias the risk ratio downward if a sizable 

number of deaths to such persons during this period remained undiscovered to the researchers. 

In the total cohort no significantly increased risks of death from any site-specific cancer 

were noted. Analyses by job categories and by duration of exposure indicated no excess risks of 

cancer when compared with the rate in the general population. However, there was an increased 

trend in the risk of hematopoietic cancers, all sites, with increasing lengths of time since first 

exposure. After 20 years latency, the SMR was 1.76, based on 13 cases. The test for trend was 

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1 significant at p = 0.03. For men (45%), without regard for latency, the SMR for hematopoietic 

cancer was a significant 1.55 (p < 0.05), based on 27 cases. Among men with long latency 

(greater than 20 years) and the longest duration of exposure (greater than 7 years) the SMR for 

hematopoietic cancers was 2.63, based on 7 deaths (p < 0.05). 

The authors pointed out that the SMR for leukemia among men was 3.45, based on 

5 deaths (p < 0.05), for deaths in the latter period of 1985 to 1987. For kidney cancer, the SMR 

was 3.27, based on six deaths (p < 0.05), after 20 years latency. The authors also reported on a 

significant excess risk (p < 0.05) of lymphosarcoma-reticulosarcoma in men (SMR = 2.6), based 

on seven deaths. Women had a lower nonsignificant rate. The risk of breast cancer was also 

nonsignificant (SMR = 0.85 based on 42 cases). The authors hypothesized that men were more 

heavily exposed to EtO than were women because “men have historically predominated in jobs 

with higher levels of exposure.” However, the lack of an association between EtO exposure and 

lymphohematopoietic cancer in females was also observed in the exposure-response analyses of 

this cohort, including in the highest exposure category, performed by Stayner et al. (1993) and 

discussed below. 

Industrial hygiene surveys indicated that sterilizer operators were exposed to an average 

personal 8-hour TWA EtO level of 4.3 ppm, whereas all other workers averaged only 2 ppm, 

based on 8-hour samples during the period 1976 to 1985. These latter employees primarily 

worked in production and maintenance, in the warehouse, and in the laboratory. This was during 

a time when engineering controls were being installed to reduce worker’s exposure to EtO; 

earlier exposures may have been somewhat higher. The authors reported that no evidence of 

confounding exposure to other occupational carcinogens was documented. 

The authors concluded that there was a trend toward an increased risk of death from 

hematopoietic cancer with increasing lengths of time since the first exposure to EtO. This trend 

might have been enhanced if the authors had added additional potential deaths identified from 

the 820 (4.5%) “untraceable” members of the cohort from 1979 to 1987. The authors felt that 

their results were not conclusive for the relatively rare cancers of a priori interest, based on the 

limited number of cases and the short follow-up. The cohort averaged 16 years of latency and 

86% had at least 9 years but only 8% reached the 20-year latent category. 

Exposure-response analyses were conducted by Stayner et al. (1993) and are discussed 

below. More recently, a follow-up mortality study (Steenland et al., 2004) and a breast cancer 

incidence study (Steenland et al., 2003) of this cohort were conducted; these are also discussed 

below. 

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1 A.2.6. Teta et al. (1993) 

In a follow-up analysis of the cohort of 2,174 male UCC workers studied by Greenberg et 

al. (1990), Teta and her colleagues excluded the 278 workers in the chlorohydrin unit in which 

Greenberg and colleagues found a high risk of leukemia and pancreatic cancer, thereby removing 

the potential confounding of the chlorohydrin production process. The 1,896 men in the 

remaining cohort were followed for an additional 10 years, through all of 1988. (Among the 

278 men who were excluded because they had worked in the chlorohydrin unit, 49 had also been 

assigned to EtO production departments, which were considered high potential ETO exposure 

departments, according to Greenberg et al. (1990). Data were reportedly examined with and 

without the inclusion of these 49 workers with overlapping assignments; however, the results of 

these analyses are not fully presented). According to Benson and Teta (1993), 112 of the 

278 excluded workers were employed before 1940, reducing the prevalent part of the remaining 

cohort to 111 of 1,896 workers, or just under 6%. (It is unclear how pre-1940 years of exposure 

were treated in the analyses based on duration of exposure, although presumably they were taken 

into account.) The update did not include additional work histories for the study subjects. Teta 

et al. (1993) note that duration of assignment to an EtO production unit was not affected by the 

update because EtO was no longer in production at the two plants; however, assignment to 

EtO-using departments might have been affected, and according to Greenberg et al. (1990), some 

of these departments had medium EtO exposure potential. 

Teta et al. (1993) reported that the average duration of exposure was more than 5 years 

and the average follow-up was 27 years. Furthermore, at least 10 years had elapsed since first 

exposure for all the workers. The reanalysis demonstrated no increased risk of overall cancer, or 

of leukemia, NHL, or cancers of the brain, pancreas, or stomach. The SMR for total deaths, 

based on comparison with mortality from the general population, was 0.79 (p < 0.01; 

observed = 431). The SMR for total cancer was 0.86 (observed = 110). No site-specific cancers 

were significantly elevated. Although the authors concluded that this study did not indicate any 

significant trends of increasing site-specific cancer risk with increasing duration of potential 

exposure to EtO, there appeared to be a nonsignificant increasing trend for leukemia and 

aleukemia (p = 0.28, based on five cases) as well as stomach cancer (p = 0.13; eight cases). 

According to Greenberg et al. (1990), 8-hour TWA EtO levels averaged less than 1 ppm, 

based on the 1976 monitoring (after EtO production at the plants had ceased), although levels as 

high as 66 ppm 8-hour TWA were reported. Teta et al. (1993) estimated that in the 1960s, 

exposure in the units producing EtO by direct oxidation ranged from 3 to 20 ppm 8-hour TWA, 

with peaks of several hundred ppm. These estimates were based on an industrial hygiene survey 

conducted at another UCC facility in Texas that used the same direct oxidation process as the 

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1 two plants in West Virginia from which the UCC EtO cohort was taken. Ethylene oxide was 

also produced via the chlorohydrin process in a closed building during the years 1925 to 1957. 

Levels of exposure to EtO would have been higher than in the direct oxidation production 

process because of start-up difficulties, fewer engineering controls, less complex equipment, and 

the enclosed building. Employee nausea, dizziness, and vomiting were documented in the 

medical department in 1949. These acute effects occur in humans at exposures of several 

hundred ppm, according to the authors. 

During the time periods under investigation, the estimated exposure ranges for 

departments using or producing EtO were >14 ppm from 1925 to 1939; 14 ppm from 1940 to 

1956; 5–10 ppm from 1957 to 1973; and <1 ppm from 1974 to 1988, with frequent peaks of 

several hundred ppm in the earliest period and some peaks of similar intensity in the 1940s to 

mid-1950s. In the absence of monitoring data prior to 1976, these estimates cannot be 

confirmed. Furthermore, workers were eliminated from the analysis if they had worked in the 

chlorohydrin unit because of the assumption that the increased risks of leukemia and pancreatic 

cancer were possibly due to exposure to something in the chlorohydrin process, as conjectured 

by Greenberg et al. (1990). However, even when the potential confounding influence of the 

chlorohydrin process is removed, there remains the suggestion of a trend of an increasing risk of 

leukemia and aleukemia with increasing duration of exposure to EtO in the remaining cohort 

members (p = 0.28, based on 5 cases). 

The authors indicated that their findings do not confirm the findings in experimental 

animal studies and are not consistent with the earliest results reported among EtO workers. They 

also noted that they did not observe any significant trend of increasing risks of stomach cancer 

(n = 8), leukemia (n = 5) or cancers of the pancreas or brain and nervous system with increasing 

duration of exposure. No lagged exposure or latency analyses were conducted in this study. 

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

the UCC data (Teta et al., 1993) and to the NIOSH data (Steenland et al., 1991). They reported 

that latency and lagging of dose did not appreciably affect the fitted models. Because Teta et al. 

(1999) did not present risk ratios for the categories used to model the dose-response 

relationships, the only comparison that could 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 dose, whereas the risk for the modeled NIOSH data increased as a function of dose. 

However, the models are based on small numbers of cases (16 [5 UCC, 11 NIOSH] for 

leukemia; 22 [3 UCC, 19 NIOSH] for lymphoid cancers), and no statistics are provided to assess 

model goodness of fit or to compare across models. This analysis is superseded by the more 

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

follow-up studies of these two cohorts [see discussion of the Swaen et al. (2009) study below]. 

A.2.7. Benson and Teta (1993) 

In a companion mortality study (Benson and Teta, 1993), the remaining 278 employees 

who were identified by Greenberg et al. (1990) as having worked at some time in the 

chlorohydrin unit and who were not included in the cohort of Teta et al. (1993) were followed to 

the end of 1988. Note that the prevalent part (i.e., those workers first employed before the cohort 

enumeration date of 1 January 1940) of this reduced cohort is 112 of the 278 workers, or 40%, 

and, therefore, the potential for bias from a healthy worker survivor effect, as discussed for the 

Greenberg et al. (1990) study above (see Section A.2.4), may be more pronounced in this study 

of the chlorohydrin unit workers. It is unknown how many chlorohydrin unit workers employed 

before 1940 were no longer employed when cohort enumeration began. 

Altogether, 40 cancer deaths occurred versus 30.8 expected (SMR = 1.3) in the subcohort 

of chlorohydrin workers. In Greenberg et al. (1990), significant elevated risks of pancreatic 

cancer and leukemia and aleukemia occurred in only those workers assigned to the chlorohydrin 

process. Benson and Teta (1993) noted a significantly increased risk of pancreatic cancer 

(SMR = 4.9, eight observed deaths, p < 0.05) in the same group and a significantly increased risk 

of cancer in the enlarged category of lymphohematopoietic cancer (SMR = 2.9, eight observed 

deaths, p < 0.05), which included leukemia and aleukemia, after an additional 10 years of 

follow-up. 

The authors concluded that these cancers were likely work-related and some exposure in 

the chlorohydrin unit, possibly to the chemical ethylene dichloride, was probably the cause. 

They pointed out that Greenberg et al. (1990) found that the chlorohydrin unit was likely to be a 

low-EtO exposure area in the West Virginia plants. The other possibility was bis-chloroethyl 

ether, which the authors pointed out is rated by the International Agency for Research on Cancer 

(IARC) as a group 3 (“not classifiable as to its carcinogenicity to humans”) chemical. 

Circumstantial evidence seems to support the authors’ contention that ethylene dichloride is the 

cause: IARC designated ethylene dichloride as a group 2B chemical (“possibly carcinogenic to 

humans”), exposure was likely heavier throughout the history of the facility, and plant medical 

records documented many accidental overexposures occurring to the pancreatic cancer victims 

prior to diagnosis. However, this conclusion is disputed by Olsen et al. (1997) whose analysis is 

discussed later. 

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1 A.2.8. Stayner et al. (1993) 

Stayner et al. (1993) provide an exposure-response analysis for the cohort study of EtO 

workers described by Steenland et al. (1991). Nothing was modified concerning the follow-up, 

cohort size, vital status, or cutoff date of the study. The exposure assessment and verification 

procedures were presented in Greife et al. (1988) and Hornung et al. (1994). In brief, 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. These data were divided 

into two sets, one for developing the regression model and the second (from six randomly 

selected plants) for testing it. Job titles were grouped into eight categories with similar potential 

for EtO exposure, and arithmetic mean exposure levels by facility, year, and exposure category 

were calculated from the data used for model development. The arithmetic means were 

logarithmically transformed, and weighted linear regression models were fitted. Seven out of 

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

(p ≤ 0.10) of EtO exposure and were included in the final model. This model predicted 85% of 

the variation in average EtO exposure levels in the test data. The model was also evaluated 

against estimates for the test data derived by a panel of 11 industrial hygienists familiar with EtO 

levels in the sterilization industry and provided with the values for the independent variables 

used in the model corresponding to the arithmetic means from the test data. The overall mean of 

the modeled estimates was not highly biased nor biased in one direction when compared to the 

overall mean exposure estimates of the individual industrial hygiene experts. Using the test data 

as the standard, the model estimates showed less bias (average difference) than 9 of the 

11 industrial hygienists and more precision (standard deviation of the differences) than all 11. 

Similarly, the model outperformed the panel in terms of both bias and precision when the panel 

results were averaged. 

Average exposure levels, including early historical exposure levels, for the exposure 

categories in the study plants were estimated using this industrial hygiene-based regression 

model. Then, the cumulative exposure for each worker was estimated by calculating the product 

of the average exposure in each job the worker held by the time spent in that job and then 

summing these over all the jobs held by that worker. This value became the cumulative 

exposure index for that employee and reflected the working lifetime total exposure to EtO. 

Stayner et al. (1993) generated SMRs based on standard life-table analysis. The three 

categories of cumulative exposure were less than 1,200 ppm-days, 1,200 to 8,500 ppm-days, and 

greater than 8,500 ppm-days. Additionally, the Cox proportional hazards model was used to 

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1 model the exposure-response relationship between EtO and various cancer types, using 

cumulative exposure as a continuous variable. 

Stayner and colleagues noted a marginally significant increase in the risk of 

hematopoietic cancers, with an increase in cumulative exposure by both the life-table analysis as 

well as the Cox model, although the magnitude of the increased risk was not substantial. At the 

highest level—greater than 8,500 ppm-days of exposure—the SMR was a nonsignificant 1.24, 

based on 13 cases. However, 12 of these cases were in males, whereas only 6.12 were expected. 

Thus, in this highest exposure category, a statistically significant (p < 0.05) SMR of 1.96 in 

males was produced. This dichotomy produced a deficit in females (1 observed vs. 4.5 expected, 

p < 0.05). 

The Cox analysis produced a significantly positive trend with respect to lymphoid cell 

tumors (combination of lymphocytic leukemia and NHL) when EtO exposures were lagged 

5 years. The authors stated that these data provide some support for the hypothesis that exposure 

to EtO increases the risk of mortality from lymphatic and hematopoietic neoplasms. They 

pointed out, however, that their data do not provide evidence for a positive association between 

exposure to EtO and cancer of the stomach, brain, pancreas, or kidney or leukemia as a group. 

Breast cancer was not analyzed in this report. 

This cohort was not updated with vital status information on the “untraceables” (4.5%), 

and cause of death information was not provided on deaths with unknown causes; thus, it lacks a 

complete follow-up and, therefore, the risk estimates may be understated. Another potential 

limiting factor is the information regarding industrial hygiene measurements of EtO that were 

completed in the plants. According to the authors, the median length of exposure to EtO of the 

cohort was 2.2 years and the median exposure was 3.2 ppm. It may be unreasonable to expect 

any findings of increased significant risks because follow-up was too short to allow the 

accumulation of mortality experience (average follow-up = 16 years; only 8% of cohort had 

>20 years follow-up). 

The authors also remind us that there is a lack of evidence for an exposure-response 

relationship among females or for a sex-specific carcinogenic effect of EtO in either laboratory 

animals or humans. In fact, the mortality rate from hematopoietic cancers among the women in 

this cohort was lower than that of the general U.S. population. Therefore the contrast seen here 

is unusual. 

The positive findings are somewhat affected by the presence in the cohort of one heavily 

exposed case (although the authors saw no reason to exclude it from the analysis), and there is a 

lack of definite evidence for an effect on leukemia as a group. Despite these limitations, the 

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1 authors believe that their data provide support for the hypothesis that exposure to EtO increases 

the risk of mortality from hematopoietic neoplasms. 

A.2.9. Wong and Trent (1993) 

This study is a reanalysis of the same cohort that was studied by Stayner et al. (1993) and 

Steenland et al. (1991), with some differences. The cohort was incremented without explanation 

by 474 to a total of 18,728 employees and followed one more year, to the end of December 1988. 

This change in the cohort resulted in the addition of 176 observed deaths and 392.2 expected 

deaths. The finding of more than twice as many expected deaths as observed deaths is baffling. 

A reduced total mortality of this magnitude suggests that many deaths may have been 

overlooked. This resulted in a further reduction of the overall SMR to a significant deficit of 

0.73. Sixty additional cancer deaths were added versus 65.9 expected, for an SMR = 0.9, based 

on 403 total cancer deaths observed versus 446.2 expected. 

The authors reported no significant increase in mortality at the cancer sites found to be of 

most interest in previous studies, that is, stomach, leukemia, pancreas, brain, and breast. They 

also reported the lack of a dose-response relationship and correlation with duration of 

employment or latency. They did report a statistically significant increased risk of NHL among 

men (SMR = 2.47; observed = 16, expected = 6.47; p < 0.05) that was not dose-related and a 

nonsignificant deficit of NHL among women (SMR = 0.32; observed = 2, expected = 6.27). The 

authors concluded that the increase in men was not related to exposure to EtO but could in fact 

have been related to the presence of acquired immune deficiency syndrome (AIDS) in the male 

population. When this explanation was offered in a letter to the editor (Wong, 1991) regarding 

the excess of NHL reported in Steenland et al. (1991), it was dismissed by Steenland and Stayner 

(1993) as pure speculation. Steenland and Stayner (1993) responded that most of the NHL 

deaths occurred prior to the AIDS epidemic, which began in the early 1980s. They also 

indicated that there was no reason to suspect that these working populations would be at a higher 

risk for AIDS than was the general population, the comparison group. 

Wong and Trent (1993) also reported a slightly increased risk of cancer in other 

lymphatic tissue (14 observed vs. 11.39 expected). In men, the risk was nonsignificantly higher 

(11 observed vs. 5.78 expected). Forty-three lymphopoietic cancers were observed versus 

42 expected. In men, the risk was higher (32 observed vs. 22.22 expected). Fourteen leukemia 

deaths were noted versus 16.2 expected. The authors did not derive individual exposure 

estimates for exposure-response analysis, such as in Stayner et al. (1993). Rather, they used 

duration of employment as a surrogate for exposure. 

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1 This study has many of the same limitations as the Stayner et al. (1993) study. The 

authors assumed that those individuals with an unknown vital status as of the cutoff date were 

alive for the purposes of the analysis, and they were unable to obtain cause-of-death information 

on 5% of the known deaths. 

The differences between this cohort study and that of Stayner et al. (1993) are in the 

methods of analysis. Stayner et al. (1993) used the 9th revision of the International Classification 

of Diseases (ICD) to develop their site-specific cancer categories for comparison with expected 

cancer mortality, whereas Wong and Trent (1993) used the 8th revision. This could account for 

some of the differences in the observed numbers of site-specific cancers, because minor 

differences in the coding of underlying cause of death could lead to a shifting of some unique 

causes from one site-specific category to another. Furthermore, Wong and Trent (1993) did not 

analyze separately the category “lymphoid” neoplasms, which includes lymphocytic leukemia 

and NHL, whereas Stayner et al. (1993) did. Stayner et al. (1993) further developed cumulative 

exposure information using exposure estimates, whereas Wong and Trent (1993) used length of 

employment as their surrogate for exposure but did not code detailed employment histories. 

Because Wong and Trent (1993) made no effort to quantify the exposures, as was the 

case in Stayner et al. (1993), this study is less useful in determining a exposure-response 

relationship. Furthermore, the assumption that a member of the cohort should be considered 

alive if a death indication could not be found will potentially tend to bias risk ratios downward if, 

in fact, a large portion of this group is deceased. In this study all untraceable persons were 

considered alive at the end of the follow-up; therefore, the impact of the additional person-years 

of risk cannot be gauged. 

A.2.10. Bisanti et al. (1993) 

These authors reported on a cohort mortality study of 1,971 male chemical workers 

licensed to handle EtO by the Italian government, whom they followed retrospectively from 

1940 to 1984. Altogether, 76 deaths had occurred in this group by the end of the study period, 

whereas 98.8 were expected. Of those, 43 were due to cancer versus 33 expected. The cause of 

one death remained unknown, and 16 workers were lost to follow-up. A group of 

637 individuals from this cohort was licensed to handle only EtO; the remaining 1,334 had 

licenses valid for handling other toxic gases as well. Date of licensing for handling EtO became 

the initiating point of exposure to EtO, although it is likely that some of these workers had been 

exposed previously to EtO. The regional population of Lombardia was used as the reference 

group from which comparison death rates were obtained. 

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1 Although there were excess risks from almost all cancers, one of the greatest SMRs was 

in the category known as “all hematopoietic cancers,” where 6 observed deaths occurred when 

only 2.4 were expected (SMR = 2.5). In the subgroup “lymphosarcoma, reticulosarcoma” there 

were 4 observed deaths whereas only 0.6 were expected (SMR = 6.7, p < 0.05); the remaining 

2 were leukemias. The authors note that five hematopoietic cancers occurred in the subgroup of 

workers who were licensed to handle only EtO but no other chemicals versus only 

0.7 hematopoietic cancers expected (SMR = 7.1, p < 0.05). These deaths occurred within 

10 years from date of licensing (latent period), which is consistent with the shorter latent period 

anticipated for this kind of cancer. According to the authors, all workers began their 

employment in this industry when the levels of EtO were high, although no actual measurements 

were available. The fact that this subgroup of workers was licensed only for handling EtO 

reduces the likelihood of a confounding chemical influence. 

The authors concluded that the excess risk of cancer of the lymphatic and hematopoietic 

tissues in these particular EtO cohort members support the suggested hypothesis of a higher risk 

of cancer found in earlier studies, but they added that the lack of exposure information on the 

other industrial chemicals in the group that had a license for handling other toxic chemicals made 

their findings inconclusive. 

This study was of a healthy young cohort, and most person-years were contributed in the 

latter years of observation. Many years of follow-up may be necessary in order to fully verify 

any trend of excess risks for the site-specific cancers of interest and to measure latent effects. 

Furthermore, the unusual deficit of total deaths versus expected contrasted with an excess of 

cancer deaths versus expected raises a question about the potential for selection bias when the 

members of this cohort were chosen for inclusion. Also, one of the study’s major limitations is 

the lack of exposure data. 

A.2.11. Hagmar et al. (1995) and Hagmar et al. (1991) 

Cancer incidence was studied in a cohort of 2,170 EtO-exposed workers from two plants 

in Sweden that produced disposable medical equipment. To fit the definition for inclusion, the 

subjects, 1,309 women and 861 men, had to have been employed for a minimum of 12 months 

and some part of that employment had to have been during the period 1970–1985 in the case of 

one plant and 1965–1985 in the case of the other. The risk ratios were not dichotomized by 

gender. No records of anyone who left employment or died before January 1, 1972 in one plant 

and January 1, 1975 in the other were included. Expected incidence rates were generated from 

the Southern Swedish Regional Tumor Registries. 

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1 Because of a short follow-up period and the relative young age of the cohort, little 

morbidity had occurred by the end of the cutoff date of December 31, 1990. Altogether, 

40 cancers occurred, compared with 46.3 expected. After 10 years latency, 22 cases of cancers 

were diagnosed versus 22.6 expected. However, 6 lymphohematopoietic cancers were observed 

versus 3.37 expected, and when latency is considered, this figure falls to 3 versus 1.51 expected. 

The authors pointed out that for leukemia the standard incidence ratio (SIR) is a nonsignificant 

7.14, based on 2 cases in 930 subjects having at least 0.14 ppm-years of cumulative exposure to 

EtO and a minimum of 10 years latency. The authors believed that the results provided some 

minor evidence to support an association between exposure to EtO and an increased risk of 

leukemia. However, for breast cancer, no increase in the risk was apparent for the total cohort 

(SIR = 0.46, OBS = 5). Even in the 10-years or more latency period, the risk was less than 

expected (SIR = 0.36, OBS = 2). 

The authors made a reasonably good attempt to determine exposure levels during the 

periods of employment in both plants for six job categories. Sterilizers in the years 1970–1972 

were exposed to an average 40 ppm in both plants. These levels gradually dropped to 0.75 ppm 

by 1985–1986. Packers and developmental engineers were the next highest exposed employees, 

with levels in 1970–1972 of 20 to 35 ppm and by 1985–1986 of less than 0.2 ppm. During the 

period 1964–1966 in the older plant, EtO levels averaged 75 ppm in sterilizers and 50 ppm in 

packers. Peak exposures were estimated to have ranged from 500 to 1,000 ppm during the 

unloading of autoclaves up to 1973. The levels gradually dropped to less than 0.2 ppm in both 

plants by 1985–1986 in all job categories (developmental engineers, laboratory technicians, 

repair men, store workers, controllers, foremen, and others) except sterilizers. 

These exposure estimates were verified by measurement of hydroxy ethyl adducts to 

N-terminal valine in hemoglobin in a sample of subjects from both plants. The adduct levels 

reflect the average exposure during the few months prior to the measurement of EtO. The results 

of this comparison were close except for sterilizers, whose air monitoring measurements were 

2 to 3 times higher. 

The authors pointed out two limitations in their study: a minority of subjects had a high 

exposure to EtO, and the follow-up (median 11.8 years) resulted in relatively few person-years at 

risk and was insufficient to assess the influence of a biologically relevant induction latency 

period. Although this study has good exposure information and the authors used this information 

to develop an exposure index per employee, they did not evaluate dose-response relationships 

that might have been present, nor did they follow the cohort long enough to evaluate morbidity. 

The strength of this study is the development of the cumulative exposure index as well as the 

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1 absence of any potential confounding produced by the chlorohydrin process, which was a 

problem in workers who produced and manufactured EtO in other similar studies. 

A.2.12. Norman et al. (1995) 

These authors conducted a mortality/incidence study in a cohort of 1,132 workers, mainly 

women (82%), who were exposed to EtO at some time during the period July 1, 1974, through 

September 30, 1980. Follow-up was until December 31, 1987. Ethylene oxide was used at the 

study plant to sterilize medical equipment and supplies that were assembled and packaged there. 

This plant was selected for the study because in an earlier small study at this plant (Stolley et al., 

1984) there was an indication that in a sample of workers the average number of sister chromatid 

exchanges was elevated over that of a control group selected from the nearby community. 

Cancer morbidity was measured by comparing cancers occurring in this cohort with those 

predicted from the National Cancer Institute’s Surveillance, Epidemiology, and End Results 

(SEER) Program for the period 1981–1985 and with average annual cancer incidence rates for 

western New York for 1979–1984. Observed cancers were compared to expected cancers using 

this method. 

Only 28 cancer diagnoses were reported in the cohort; 12 were for breast cancers. Breast 

cancer was the only cancer site in this study where the risk was significantly elevated, based on 

the SEER rates (SIR = 2.55, p < 0.05). No significant excesses were seen at other cancer sites of 

interest: leukemia (1 observed, 0.54 expected), brain (0 observed, 0.49 expected), pancreas 

(2 observed, 0.51 expected) and stomach (0 observed, 0.42 expected). The authors offered no 

explanation except chance as to why the risk of breast cancer was elevated in these workers. 

In 1980, three 2-hour samples from the plant provided 8-hour TWA exposures to 

sterilizer operators that ranged from 50 to 200 ppm. Corrective action reduced the levels to 5 to 

20 ppm. 

This study has little power to detect any significant risk of cancer at other sites because 

morbidity was small, chiefly as a consequence of the short follow-up period. The mean number 

of years from the beginning of follow-up to the end of the study was 11.4 years. In fact, the 

authors stated that breast cancer was the only cancer site for which there was adequate power to 

detect an increased relative risk. Additional weaknesses in this study include no historic 

exposure information and too short a period of employment in some cases (<1 month) to result in 

breast cancer. The authors maintained that their study was inconclusive. 

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1 A.2.13. Swaen et al. (1996) 

A significant cluster of 10 Hodgkin lymphoma cases in the active white male workforce 

of an unidentified large chemical manufacturing plant in Belgium led to a nested case-control 

study by Swaen et al. (1996) to determine which, if any, chemical agents within the plant may 

have led to the increase. By comparison with regional cancer incidence rates, the SIR for this 

disease was 4.97 (95% CI = 2.38–9.15) over a 23-year period, from 1966 to 1992. This 

suggested that an occupational exposure may have produced the significant excess risk of 

Hodgkin lymphoma seen in these workers. 

The investigators randomly selected 200 individuals from a computerized sampling frame 

of all men ever employed at the facility. From this list of 200, workers who were actively 

employed at the time of diagnosis of each case were chosen as controls. No age matching was 

done because the authors stated that age-specific incidence rates for Hodgkin lymphoma in the 

United States were relatively flat for men between ages 18 and 65. The investigators felt that a 

control could serve for more than one case. 

Verification of the 10 cases revealed that 1 case was, in reality, a large-cell anaplastic 

lymphoma. Two others could not be confirmed as Hodgkin lymphoma due to the lack of tissue. 

The remaining seven were confirmed as Hodgkin lymphoma. In the ensuing case-control 

analysis, significant odds ratios (ORs) for Hodgkin lymphoma were observed for five chemicals, 

ammonia (6 cases, OR = 5.6), benzene (5 cases, OR = 11), EtO (3 cases, OR = 8.5), NaOH 

(5 cases, OR = 8) and oleum (3 cases, OR = 6.9), based on the number of cases and controls 

known to be exposed to the chemicals in question. This does not mean they were exposed only 

to the chemical in question. 

The availability of exposure information made it possible to calculate cumulative 

exposure to the cases and controls of two chemicals, benzene and EtO. The cumulative exposure 

for benzene-exposed cases was 397.4 ppm-months versus an expected 99.7 ppm-months for the 

matched controls. The authors stated that one heavily exposed case was chiefly responsible for 

the high cumulative total for all the benzene-exposed cases; however, it was not statistically 

significant. Only a few studies have suggested that exposure to benzene could possibly be 

related to an increase in the risk of Hodgkin lymphoma. The cumulative total exposure to EtO 

for the cases was 500.2 ppm-months versus 60.2 for the matched controls, which was statistically 

significant, the significance being due to one extreme case. 

This study is limited because the authors enumerated only cases among active employees 

of the workforce; therefore, the distinct possibility exists that they could have missed potential 

cases in the inactive workers. It is possible that latent Hodgkin lymphoma cases could have been 

identified in the controls after the controls left active employment. However, given that there 

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1 were many different possible exposures to the chemicals produced in the workplaces of these 

employees, it is not likely that EtO or benzene could be considered solely responsible for the 

excess risk of Hodgkin lymphoma in this working group. 

A.2.14. Olsen et al. (1997) 

Olsen et al. (1997) studied 1,361 male employees of four plants in Texas, Michigan, and 

Louisiana who were employed a minimum of 1 month sometime during the period 1940 through 

1992 in the ethylene chlorohydrin and propylene chlorohydrin process areas. These areas were 

located within the EtO and propylene oxide production plants. Some 300 deaths had occurred by 

December 31, 1992. 

Plant A in Texas produced EtO beginning in 1941 and ceased production in 1967. 

Bis-chloroethyl ether, a byproduct of EtO continued to be produced at this plant until 1973. The 

plant was demolished in 1974. Plant B, which was nearby, manufactured EtO from 1951 to 1971 

and then again from 1975 until 1980. This plant continues to produce propylene oxide. The 

Louisiana plant produced EtO and propylene oxide through the propylene chlorohydrin process 

from 1959 until 1970, when it was converted to propylene oxide production. The Michigan plant 

produced ethylene chlorohydrin and subsequently EtO beginning in 1936 and continuing into the 

1950s. This plant produced propylene chlorohydrin and propylene oxide up to 1974. 

The authors suggested that exposure to EtO was possible at the plants studied in this 

report but that exposure was unlikely in the 278 chlorohydrin unit workers who were excluded 

from the cohort studied by Teta et al. (1993). Unfortunately, no actual airborne measurements 

were reported by Olsen et al. (1997), and thus only length of employment could be used as a 

surrogate for exposure. 

The SMR for all causes was 0.89 (300 observed). For total cancer the SMR was 0.94 

(75 observed, 79.7 expected). There were 10 lymphohematopoietic cancers versus 7.7 expected 

(SMR = 1.3). No significantly increased risks of any examined site-specific cancer (pancreatic, 

lymphopoietic, hematopoietic, and leukemia) were noted even after a 25-year induction latency 

period, although the SMR increased to 1.44 for lymphopoietic and hematopoietic cancer. When 

only the ethylene chlorohydrin process was examined after 25 years latency, the SMR increased 

to 1.94, based on six observed deaths. The data to support the latter observation by the authors 

were not presented in tabular form. 

The authors concluded that there was a weak, nonsignificant, positive association with 

duration of employment for lymphopoietic and hematopoietic cancer with Poisson regression 

modeling. They stated that the results of their study provide some assurance that their cohort has 

not experienced a significant increased risk for pancreatic cancer and lymphopoietic and 

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hematopoietic cancer in ethylene chlorohydrin and propylene chlorohydrin workers. They 

believed that this study contradicted the conclusions of Benson and Teta (1993) that ethylene 

dichloride, perhaps in combination with chlorinated hydrocarbons, appeared to be the causal 

agent in the increased risk of pancreatic cancer and hematopoietic cancer seen in their study. 

They pointed out that ethylene dichloride is readily metabolized and rapidly eliminated from the 

body after gavage or inhalation administration; therefore, they questioned whether experimental 

gavage studies (NCI, 1978) are appropriate for studying the effects of ethylene dichloride in 

humans. One study (Maltoni et al., 1980) found no evidence of tumor production in rats and 

mice chronically exposed to ethylene dichloride vapor concentrations up to 150 ppm for 7 hours 

a day. Also, because this chemical is a precursor in the production of vinyl chloride monomer, 

the authors wondered why an increase in these two site-specific cancers had not shown up in 

studies of vinyl chloride workers. However, they believe that an additional 5 to 10 years of 

follow-up of this cohort would be necessary to confirm the lack of risk for the two types of 

cancer described above. 

Another major weakness of this study is the lack of any actual airborne measurements of 

EtO and the chlorohydrin chemicals. 

A.2.15. Steenland et al. (2004) 

In an update of the earlier mortality studies of the same NIOSH cohort of workers 

exposed to EtO described by Steenland et al. (1991) and Stayner et al. (1993), an additional 

11 years of follow-up were added. This increased the number of deceased to 2,852. Work 

history data were originally gathered in the mid-1980s. Approximately 25% of the cohort 

continued working into the 1990s. Work histories on these individuals were extended to the last 

date employed. It was assumed that these employees continued in the job they last held in the 

1980s. Little difference was noted when cumulative exposure was calculated with and without 

the extended work histories, chiefly because the exposure levels after the mid-1980s were very 

low (see Section A.2.8 for a discussion of the NIOSH exposure assessment). Again, no excess 

risk of hematopoietic cancer was noted based on external rates. However, as in the earlier paper, 

exposure-response analyses reported positive trends for hematopoietic cancers limited to males 

(p = 0.02 for the log of cumulative exposure with a 15-year lag) using internal comparisons and 

Cox regression analysis.1 (See Table A-2 for the categorical exposure results.) 

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Valdez-Flores et al. (2010) suggest that Steenland et al. (2004) incorrectly used one degree of freedom in their 

evaluation of statistical significance and that a second degree of freedom should have been included for estimating 

the lag. However, Steenland et al. (2004) did not estimate the lag using the likelihood; rather, lagged exposure was 

treated as an alternate exposure metric. 

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The excess of these tumors was chiefly lymphoid (NHL, myeloma, lymphocytic 

leukemia) (see Table A-3), as in the earlier paper. A positive trend was also observed for 

Hodgkin lymphoma in males, although this was based on small numbers. 

Table A-2. Cox regression results for hematopoietic cancer mortality 

(15-year lag) in males 

Cumulative exposure (ppm-days) Odds ratio (95% CI) 

0 1 

>0–1,199 1.23 (0.32–4.73) 

1,200–3,679 2.52 (0.69–9.22) 

3,680–13,499 3.13 (0.95–10.37) 

13,500+ 3.42 (1.09–10.73) 

Source: Steenland et al. (2004) 

Table A-3. Cox regression results for lymphoid cell line tumors (15-year lag) 

in males 

Cumulative exposure (ppm-days) Odds ratio (95% CI) 

0 1 

>0–1,199 0.9 (0.16–5.24) 

1,200–3,679 2.89 (0.65–12.86) 

3,680–13,499 2.74 (0.65–11.55) 

13,500+ 3.76 (1.03–13.64) 

Source: Steenland et al. (2004). 

The hematopoietic cancer trends were somewhat weaker in this analysis than were those 

reported in the earlier studies of the same cohort. This is not unexpected because most of the 

cohort was not exposed after the mid-1980s, and the workers who were exposed in more recent 

years were exposed to much lower levels because EtO levels decreased substantially in the early 

1980s. No association was found in females, although average exposures were only twice as 

high in males (37.8 ppm-years) as in females (18.2 ppm-years), and there was enough variability 

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1 in female exposure estimates to expect to be able to see a similar trend if it existed. In later 

analyses conducted by Dr. Steenland and presented in Appendix D, the difference between the 

male and female results was found not to be statistically significant, and the same pattern of 

lymphohematopoietic cancer results observed for males by Steenland et al. (2004) was observed 

for the males and females combined (i.e., statistically significant positive trends for both 

hematopoietic and lymphoid cancers using log cumulative exposure and a 15-year lag). 

This study also reports a significant excess risk of breast cancer in the highest 

cumulative-exposure quartile, with a 20-year lag (SMR = 2.07, 95% CI 1.1–3.54, n = 13) in 

female employees. The results using internal Cox regression analyses with a 20-year lag time 

produced an OR = 3.13 (95% CI 1.42–6.92) in the highest cumulative-exposure quartile. The 

log of cumulative exposure with a 20-year lag was found to be the best model (p = 0.01) for the 

analyses of breast cancer. As for hematopoietic cancer in males, cumulative exposure 

untransformed showed a weaker trend (p = 0.16). A breast cancer incidence study of this cohort 

is discussed in Steenland et al. (2003). 

A.2.16. Steenland et al. (2003) 

In a companion study on breast cancer incidence in women employees of the same cohort 

discussed in Steenland et al. (2004), the authors elaborated on the breast cancer findings in a 

subgroup of 7,576 women from the cohort (76% of the original cohort). They had to be 

employed at least 1 year and exposed while employed in commercial sterilization facilities. The 

average length of exposure was 10.7 years. Breast cancer incidence analyses were based on 

319 cases identified via interview, death certificates, and cancer registries in the full cohort, 

including 20 in situ carcinomas. Interviews on 5,139 women (68% of the study cohort) were 

obtained (next-of-kin interviews were sought for the 18% of the cohort who were deceased); 

22% could not be located. Using external referent rates (SEER), the SIR was 0.87 for the entire 

cohort based on a 15-year lag time. When in situ cases were excluded, the overall SIR increased 

to 0.94. In the top quintile of cumulative exposure, with a 15-year lag time, the SIR was 1.27 

(95% CI 0.94–1.69, n = 48). A significant positive linear trend of increasing risk with increasing 

cumulative exposure was noted (p = 0.002) with a 15-year lag time. Breast cancer incidence was 

believed to be underascertained owing to incomplete response and a lack of coverage by regional 

cancer registries (68% were contacted directly and 50% worked in areas with cancer registries). 

An internal nested case-control analysis, which is less subject to concerns about 

underascertainment, produced a significant positive exposure-response with the log of 

cumulative exposure and a 15-year lag time (p = 0.05). The top quintile was significant with an 

OR of 1.74 (CI 1.16–2.65) based on all 319 cases (the entire cohort). 

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1 The authors also conducted separate analyses using the subcohort with interviews, for 

which there was complete case ascertainment and additional information on potential 

confounders. In the subcohort with interview data, the odds ratio for the top quintile equaled 

1.87 (CI 1.12–3.1), based on 233 cases in the 5,139 women and controlled for with respect to 

parity and breast cancer in a first-degree relative. Information on other risk factors was also 

collected—e.g., body mass index, SES, diet, age at menopause, age at menarche, breast cancer in 

a first-degree relative, and parity—but only parity and breast cancer in a first-degree relative 

were significant in the model. Continuous cumulative exposure, as well as the log cumulative 

exposure, lagged 15 years, produced p-values for the regression coefficient of 0.02 and 0.03, 

respectively, for the Cox regression model, taking into account age, race, year of birth, parity, 

and breast cancer in a first-degree relative. 

The authors concluded that their data suggest that exposure to EtO is associated with 

breast cancer, but because of inconsistencies in exposure-response trends and possible biases due 

to nonresponse and incomplete cancer ascertainment, the case for breast cancer is not conclusive. 

However, monotonically increasing trends in categorical exposure-response relationships are not 

always the norm owing to lack of precision in the estimates of exposure. Furthermore, positive 

trends were observed in both the full cohort and the subcohort with interviews, lessening 

concerns about nonresponse bias and case underascertainment. 

A.2.17. Kardos et al. (2003) 

These authors reported on a study completed earlier by Muller and Bertok (1995) of 

cancer among 299 female workers who were employed from 1976 to 1993 in a pediatric ward at 

the county hospital in Eger, Hungary, where gas sterilizers were used. Their observation period 

for cancer was begun in 1987 on the assumption that cancer deaths before 1987 were not due to 

EtO, based on a paper by Lucas and Teta (1996). Information about the Muller and Bertok 

(1995) study is unavailable because the paper is in Hungarian and no translated copy is available. 

Kardos and his colleagues evaluated mortality among these women and found a statistically 

significant excess of total cancer deaths (n = 11) in the period from 1987 to 1999 when compared 

with expected deaths generated from three different comparison populations (Hungary, n = 4.38; 

Heves County, n = 4.03; and city of Eger, n = 4.28). The SMRs are all significant at the 

p < 0.01 level. Site-specific rates were not calculated. Among the 11 deaths were 3 breast 

cancer deaths and 1 lymphoid leukemia death. The authors claim that their results confirm 

“predictions of an increased cancer risk for the Eger hospital staff.” They suggest an etiological 

role for EtO in the excess risk. The observation of 3 breast cancer deaths, with at most 4.4 (with 

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1 Hungarian national rates as the referent) total cancer deaths expected, is indicative of an 

increased risk of breast cancer 2 . 

A.2.18. Tompa et al. (1999) 

The authors reported a cluster of eight breast cancer cases and eight other malignant 

tumor cases that developed over a period of 12 years in 98 nurses who worked in a hospital in 

the city of Eger, Hungary, and were exposed to EtO. These nurses were exposed for 5 to 

15 years in a unit using gas sterilizer equipment. The authors report that EtO concentrations 

were in the neighborhood of 5 to 150 mg/m3 . The authors state that the high breast cancer 

incidence in the hospital in Eger indicates a combined effect of exposure to EtO and naturally 

occurring radioactive tap water, possibly due to the presence of radon. This case report study is 

discussed further in the genotoxicity section. 

A.2.19. Coggon et al. (2004) 

Descriptive information about this cohort is available from the earlier study (Gardner et 

al., 1989). In this update, the 1,864 men and 1,012 women described in the (Gardner et al., 

1989) study were followed to December 31, 2000. This added 13 more years of follow-up 

resulting in 565 observed deaths versus 607.6 expected. For total cancer, the observed number 

of deaths equaled 188 versus 184.2 expected. For NHL, 7 deaths were observed versus 

4.8 expected. For leukemia, 5 deaths were observed versus 4.6 expected. All 5 leukemia deaths 

fell into the subset with definite or continual exposure to EtO, where only 2.6 were expected. In 

fact, the total number of deaths classified to the lymphohematopoietic cancer category was 17 

with 12.9 expected. This increased risk was not significant. When definite exposure was 

established, the authors found that the risk of lymphatic and hematopoietic cancer was increased 

with 9 observed deaths versus 4.9 expected. Deaths from leukemia were also increased in 

chemical workers with 4 leukemia deaths versus 1.7 expected. No increase was seen in the risk 

of hematopoietic cancer in the hospital sterilizing unit workers, who are mostly female. Another 

finding of little significance was that of cancer of the breast. Only 11 deaths were recorded in 

this cohort up to the cutoff date versus 13.1 expected. Since there were no female workers in the 

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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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1 chemical industry, the results on breast cancer reflect only work in hospital sterilizing units. The 

researchers concluded that the risk of cancer must be low at the levels sustained by workers in 

Great Britain over the last 10 or 20 years. 

A.2.20. Swaen et al. (2009) 

Swaen et al. (2009) redefined and updated the cohort of 1,896 male UCC workers studied 

by Teta et al. (1993), which was itself a follow-up of the 2,174 UCC workers originally studied 

by Greenberg et al. (1990), excluding the 278 chlorohydrin unit workers because of potential 

confounding. (However, confounding by chlorohydrin production has not been established, and 

49 of those excluded workers were also employed in EtO production and thus had high potential 

EtO exposures.) Specifically, Swaen et al. (2009) extended the cohort enumeration period from 

the end of 1978 to the end of 1988 (workers hired after 1988 were not added to the cohort 

because they were considered to have no appreciable EtO exposure), 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 reportedly based on the qualitative categorizations of potential for EtO exposure in 

the different departments developed by Greenberg et al. (1990) and time-period exposure 

estimates from Teta et al. (1993). The exposure assessment matrix for the exposure estimates of 

Swaen et al. (2009) is presented in Table A-4 below. Cumulative exposures for the individual 

workers were estimated by multiplying the time (in months) a worker was assigned to a 

department by the estimated exposure level for the department and summing across the 

assignments. 

The exposure assessment used in this study was relatively crude, based on just a small 

number of department-specific and time-period-specific categories, and with exposure estimates 

for only a few of the categories derived from actual measurements. For the 1974–1988 time 

period, based on measurements from environmental monitoring conducted in the (West Virginia) 

plants since 1976, exposure estimates of 1 ppm and 0.3 ppm were chosen for the high- and 

low-exposure-potential departments, respectively, and the average of 0.65 ppm was taken for the 

medium-exposure-potential departments. For the 1957–1973 time period, exposure estimates 

were based on measurements from an air-sampling survey of three EtO direct-oxidation 

production units in a UCC plant in Texas in the early 1960s (during this 1957–1973 time period, 

direct oxidation was the only method used for EtO production at the West Virginia plants as 

well). The majority of the 8-hour TWA results in these units were between 3 and 20 ppm, with 

levels between 5 and 10 ppm for operators. Because the West Virginia plants and equipment 

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were much older than for the Texas facility, the high end of the range of values for operators 

(10 ppm) was selected as the exposure estimate for the high-exposure-potential departments, and 

the low end of the range (5 ppm) was selected for the low-exposure-potential departments (even 

though these were not EtO production departments). The average of 7.5 ppm was taken for the 

medium-exposure-potential departments. 

Table A-4. Exposure assessment matrix from Swaen et al. (2009)—8-hour 

TWA exposures in ppm 

Time period 

Exposure potential category 

Low 

(most EtO user 

departments) 

Medium 

(some EtO user 

departments) 

High 

(EtO production 

departments) 

1925–1939 17 28 70 

1940–1956 7 14 21 

1957–1973 5 7.5 10 

1974–1988 0.3 0.65 1 

Source: Swaen et al. (2009).
 

For the 1940–1956 time period, exposure estimates were derived from “rough” estimates 

of exposure reported by Hogstedt et al. (1986) for a chlorohydrin-based EtO production unit in 

an enclosed building, as was the West Virginia chlorohydrin-based EtO production. Hogstedt et 

al. (1986) reportedly suggested EtO exposures were probably below 14 ppm from 1941 to 1947, 

although much higher levels occasionally occurred, and levels from the 1950s to 1963 averaged 

5 to 25 ppm. Thus, based on these values, 14 ppm was selected as the exposure estimate for the 

medium-exposure-potential departments and values 50% higher (21 ppm) and 50% lower 

(7 ppm) were assigned to the high- and low-exposure-potential departments, respectively. For 

the 1925–1939 time period, it was assumed that exposures in this earlier, start-up period would 

have been higher than those in the subsequent 1940–1956 time period, so the 14 ppm estimate 

from the medium-exposure-potential departments in the 1940–1956 time period was used as the 

exposure estimate for the low-exposure-potential departments for the 1925–1939 time period. 

Then, the same ratio of 1:2 between the low- and medium-exposure-potential departments from 

the 1940–1956 time period was used to obtain an estimate of 28 ppm for the medium-exposurepotential departments for the 1925–1939 time period. A factor of 5 (half an order of magnitude) 

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1 was used between the low- and high-exposure-potential departments to obtain a highly uncertain 

exposure estimate of 70 ppm for the high-exposure-potential departments. Swaen et al. (2009) 

suggest that despite the high exposure estimates for the 1925–1939 time period, the contribution 

of this time period to cumulative exposure estimates is limited because only 98 workers (4.8% of 

the cohort) had employment histories before 1940. It appears, then, that pre-1940 employment 

histories may have been missing for 13 of the workers, because excluding the 112 pre-1940 

chlorohydrin production workers (Benson and Teta, 1993) from the original 223 pre-1940 

workers (Greenberg et al., 1990) leaves 111 pre-1940 workers in the cohort. 

At the end of the 2003 follow-up, 1,048 of the 2,063 workers had died and 23 were lost to 

follow-up. In comparison with general population U.S. mortality rates, 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 5 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. 

Internal Cox proportional hazards modeling was also done 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. Year of 

birth and year of hire were included as covariates in the Cox regression model. Year of hire was 

reportedly included to adjust for potential cohort effects; however, it is unclear whether or not 

this covariate was a statistically significant factor in the regression. Furthermore, because age at 

hire is often correlated with exposure, including it in the regression model could overadjust and 

attenuate the observed exposure-related effects. These internal analyses showed no evidence of 

an exposure-response relationship, although, again, these analyses rely on small numbers of 

cases and a crude exposure assessment, where there is a high potential for exposure 

misclassification. 

Swaen et al. (2009) note that one of the strengths of their study is the long average 

follow-up time of the workers. These authors further note that, because the UCC cohort is a 

much older population (50% deceased) than the NIOSH cohort (Steenland et al., 2004), the 

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1 number of expected deaths is less than 3 times larger for the NIOSH cohort even though the 

sample size is almost 9 times larger. However, the long follow-up and aged cohort might be a 

limitation, as well. Because the follow-up is extended well beyond the time period of 

nonnegligible exposures (pre-1989) for workers still employed and, especially, beyond the 

highest exposures (e.g., pre-1940 or pre-1956), the follow-up is likely observing workers at the 

high tail end of the distribution of latency times for EtO-associated lymphohematopoietic 

cancers. In other words, workers that were at risk of developing lymphohematopoietic cancer as 

a result of their EtO exposures would likely have developed the disease earlier. Meanwhile, 

having an older cohort means that the background rates of lymphohematopoietic cancers are 

higher, and thus, relative risks may be attenuated. Such attenuation was observed even in the 

younger NIOSH cohort between the 1987 follow-up (Steenland et al., 1991) and the 1998 

follow-up (Steenland et al., 2004), when the follow-up was extended well beyond the period of 

significant EtO exposures (exposure levels were considered very low by the mid-1980s). 

Swaen et al. (2009) also note that their estimate of the average cumulative exposure for 

the UCC cohort was more than twice the average cumulative exposure estimate for the NIOSH 

cohort. However, there are substantial uncertainties in the exposure assessment, especially for 

the early years when the highest exposures occurred. And despite the reported strengths of the 

Swaen et al. (2009) study in terms of follow-up, cohort age, and high exposures, a limitation of 

the study is the small cohort size. Based on data presented by Greenberg et al. (1990) and 

Benson and Teta (1993), it appears that fewer than 900 workers were hired before 1956 (1,104 of 

the original cohort were hired before 1960 and 233 of those were then excluded because they 

worked in the chlorohydrin unit) and would have been potentially exposed to the higher pre-1956 

exposures levels. In the full cohort of 2,063 men, only 27 lymphohematopoietic (17 lymphoid) 

cancers were observed. 

In alternate analyses of the UCC data, Valdez-Flores et al. (2010) fitted Cox proportional 

hazards models and conducted categorical exposure-response analyses using a larger set of 

cancer endpoints. These investigators also performed the same analyses using the data from the 

last follow-up of the NIOSH cohort (Steenland et al., 2004) and from the two cohorts combined, 

analyzing the sexes both separately and together. Valdez-Flores et al. (2010) 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). These investigators suggest that a review of the data from the 

NIOSH and UCC studies supports combining them, but it should be recognized that the exposure 

assessment conducted for the UCC cohort is much cruder, especially for the highest exposures, 

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1 (see above) than the NIOSH exposure assessment (which was based on a validated regression 

model; see A.2.8 above); thus, the results of exposure-response analyses of the combined cohort 

data are considered 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 

contributes 17 cases of lymphoid cancer to the 53 from the NIOSH cohort; however, as discussed 

above, it should also be noted that some of these UCC cases are occurring in older workers, with 

longer postexposure follow-up, and thus, may reflect background disease more than 

exposure-related disease). 

Notable differences between the Steenland et al. (2004) and the Valdez-Flores et al. 

(2010) analyses exist. A major difference is that Valdez-Flores et al. (2010) used only 

cumulative exposure in the Cox regression model, so they considered only a sublinear 

exposure-response relationship, whereas Steenland et al. (2004) also used log cumulative 

exposure, which provides a supralinear exposure-response relationship model structure [e.g., see 

Figure 4-1, illustrating the difference between the cumulative exposure and log cumulative 

exposure Cox regression models [RR = eβ×exposure] for the lymphoid cancers from Steenland et al. 

(2004)]. Valdez-Flores et al. (2010) objected to the log cumulative exposure model for a number 

of reasons, the primary one being that the use of log cumulative exposure forces the 

exposure-response relationship to be supralinear regardless of the observed data. This is correct 

but no different from the use of cumulative exposure imposing a sublinear exposure-response 

relationship. Moreover, Steenland et al. (2004) used log cumulative exposure specifically when 

the cumulative exposure Cox regression model did not yield a statistically significant fit to the 

exposure-response data and the categorical analyses suggested increases in risk that were more 

consistent with an underlying supralinear exposure-response relationship. With log cumulative 

exposure, Steenland et al. (2004) observed statistically significant fits to the exposure-response 

data for all lymphohematopoietic cancers in males, lymphoid cancers in males, and breast cancer 

in females, none of which yielded statistically significant fits with the cumulative exposure 

(sublinear exposure-response) model, supporting the apparent supralinearity of the data.3 

 

Another key difference between the Steenland et al. (2004) and the Valdez-Flores et al. 

(2010) analyses is that Valdez-Flores et al. (2010) present results only for unlagged analyses. 

Valdez-Flores et al. (2010) state that their Cox regression results with different lag times were 

similar to the unlagged results. Because the Valdez-Flores et al. (2010) categorical results are 

for unlagged analyses, however, their referent groups are different from those used by Steenland 

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This pattern of findings from the NIOSH cohort data for males (i.e., statistically significant fits with log cumulative 

exposure but not with cumulative exposure) was replicated for both the all lymphohematopoietic cancers and the 

lymphoid cancers when the NIOSH data on males and females were combined (see Appendix D). 

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1 et al. (2004). Valdez-Flores et al. (2010) used the lowest exposure quintile (providing there were 

sufficient data) as the referent group, whereas Steenland et al. (2004) used the no-exposure 

(lagged-out) group as the referent. Because the NIOSH cohort data have an underlying 

supralinear exposure-response relationship, the increased risk in the lowest exposure group is 

already notably elevated and using the lowest exposure quintile as a referent group would 

attenuate the relative risk. Nonetheless, Valdez-Flores et al. (2010) observed 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.4 

Although Valdez-Flores et al. (2010) found no statistically significant exposure-response 

relationships for any of the cohort/endpoint data sets that they analyzed using the cumulative 

exposure Cox regression model, these investigators derived risk estimates from the positive 

relationships for the purposes of comparing those estimates with EPA's 2006 draft risk estimates 

(U.S. EPA, 2006a). Valdez-Flores et al. (2010) report that their estimate of the exposure level 

associated with 10−6

 risk of lymphohematopoietic cancer based on the male NIOSH cohort data 

is 1,500 times larger than EPA's 2006 draft estimate (their exposure level estimate based on the 

NIOSH and UCC male and female data combined was a further 3 times higher). Most of the 

difference in magnitude between the Valdez-Flores et al. (2010) and the EPA 2006 draft 

estimates is attributable to the difference in the models used. The Valdez-Flores et al. (2010) 

estimate is based on the sublinear Cox regression model, which EPA rejected as not providing a 

good representation of the low-exposure data (EPA's 2006 draft risk estimate is based on a linear 

model). In addition, Valdez-Flores et al. (2010) used maximum likelihood estimates, while EPA 

uses upper bounds on risk (or lower bounds on exposure). Valdez-Flores et al. (2010) also 

modeled down to 10−6 risk, whereas EPA modeled to 10−2 risk and used the LEC01 as a point of 

departure (POD) for linear low-dose extrapolation. Valdez-Flores et al. (2010) suggest that 

PODs should be within the range of observed exposures, and they chose a 10−6 risk level because 

the corresponding exposure level was in the range of the observed occupational exposures 

(converted to equivalent environmental exposures). The intention of EPA's 2005 Guidelines for 

Carcinogen Risk Assessment (U.S. EPA, 2005a), however, is for the POD to be (or more 

specifically, to correspond to a response level) at the low end of the observable range of 

responses (i.e., a response level that might reasonably be observed to have statistical significance 

2 

3 

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14 

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19 

21 

22 

23 

24 

26 

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28 

29 

31 

32 

33 

4

In Dr. Steenland's analyses of the NIOSH cohort data for both sexes combined, presented in Appendix D, the 

categorical results for all lymphohematopoietic cancers were also statistically significantly increased. 

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5 

10 

15 

20 

25 

30 

1 with respect to background responses). The underlying assumption in this approach is that one 

can have relative confidence in an exposure-response model in the observable range, but there is 

less confidence in any empirical exposure-response model for much lower exposures. The 

estimates also differ because Valdez-Flores et al. (2010) truncated their life-table analysis at 

70 years, while EPA uses a cutoff of 85 years. 

A further reason for differences between the risk estimates of Valdez-Flores et al. (2010) 

and EPA's 2006 draft result is that Valdez-Flores et al. (2010) estimated mortality risks, while 

EPA estimates incidence risks. In a separate publication, Sielken and Valdez-Flores (2009) 

disagree with the assumption of similar exposure-response relationships for 

lymphohematopoietic cancer incidence and mortality used by EPA in deriving incidence 

estimates and assert that the methods used by EPA in calculating these estimates were 

inappropriate. Sielken and Valdez-Flores (2009) suggest that, except at high exposure levels, the 

exposure-response data on all lymphohematopoietic cancers in males in the NIOSH cohort are 

consistent with decreases in survival time as an explanation for the apparent increases in 

mortality. For two of the four exposure groups, however, the best-fitting survival times were 

0 years, which seems improbable. Moreover, Sielken and Valdez-Flores (2009) have not 

established that the excess mortality is due to decreased survival time; the data are also 

consistent with increased mortality resulting from increased incidence. Furthermore, the rodent 

bioassays show that EtO is a complete carcinogen (see Section 3.2), and the mechanistic data 

demonstrate that EtO is mutagenic (see Section 3.3.3), with sufficient evidence for a mutagenic 

mode of action (see Section 3.4). Thus, EtO can be expected to act as an initiator in 

carcinogenesis, and, consequently, be capable of inducing exposure-related increases in 

incidence. As for the methods used by EPA in calculating the incidence estimates, EPA used 

adjustments to the life-table analysis where warranted (U.S. EPA, 2006a). EPA did not adjust 

the all-cause mortality rates in the lymphohematopoietic cancer analyses, because “the 

lymphohematopoietic cancer incidence rates are small when compared with the all-cause 

mortality rates” (U.S. EPA, 2006a; Section 4.1.1.3) and, thus, the impact of taking into account 

lymphohematopoietic cancer incidence when calculating interval “survival” is negligible, as 

confirmed by Sielken and Valdez-Flores' own calculations, presented in their Table 2 where the 

“multiplier” = 1 (Sielken and Valdez-Flores, 2009). On the other hand, for the breast cancer 

incidence analyses, where incidence rates are higher, EPA adjusted the all-cause mortality rates 

to take into account breast cancer incidence, effectively redefining interval “survival” (and thus 

the resulting population at risk) as surviving the interval without developing an incident case of 

breast cancer (U.S. EPA, 2006a; Section 4.1.2.3). Therefore, the concerns raised by Sielken and 

2 

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7 

8 

9 

11 

12 

13 

14 

16 

17 

18 

19 

21 

22 

23 

24 

26 

27 

28 

29 

31 

32 

33 

34 

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30 

1 Valdez-Flores (2009) about using life-table analyses to derive incidence estimates do not apply 

to EPA's calculations. 

Finally, the risk estimates of Valdez-Flores et al. (2010) and EPA's 2006 draft also differ 

because Valdez-Flores et al. (2010), based on analyses in a separate publication by Sielken and 

Valdez Flores (2009), misinterpreted the application of the age-dependent adjustment factors 

(ADAFs) such that, even though they purported to apply the factors, this application had no 

impact on the risk estimate. The ADAFs are default adjustment factors intended to be applied 

directly to the unit risk estimates (i.e., risk per unit constant exposure, or “slope factors”) in 

conjunction with age-specific exposure level estimates (U.S. EPA, 2005b). For the purposes of 

applying the ADAFs, the unit risk estimate is parsed, as a proportion of an assumed 70-year 

lifespan, across age groups with different adjustment factors and/or exposure levels. The 

ADAFs were not designed to be applied in life-table analyses, as was done by Sielken and 

Valdez Flores (2009). In addition, the use of the 15-year lag in exposure in the life-table 

analyses does not mean that there is no risk from exposures before age 15 years, as intimated by 

Sielken and Valdez Flores (2009). Indeed, those exposures do not increase risk for cancer 

occurring before 15 years of age; however, they do contribute to lifetime risk. The assumption 

of increased early-life susceptibility that underlies the application of the ADAFs is that early-life 

exposure increases the lifetime risk of cancer, not just the risk of cancer in early life, so it is 

inappropriate to apply the ADAFs only to the age-specific hazard rates, as was done by Sielken 

and Valdez Flores (2009). One might conceivably incorporate the ADAFs into the lifetable 

analysis by weighting the age-specific exposures before they are aggregated into the cumulative 

exposure, but such an integrated approach does not allow for the risks associated with less-thanlifetime exposure scenarios to be calculated without redoing the lifetable analysis each time. 

A.3. SUMMARY 

The initial human studies by Hogstedt and colleagues [Hogstedt (1988); Hogstedt et al. 

(1986); Hogstedt et al. (1979b); Hogstedt et al. (1979a)], in which positive findings of leukemia 

and blood-related cancers suggested a causal effect, have been followed by studies that either do 

not indicate any increased risks of cancer or else suggest a dose-related increased risk of cancer 

at certain sites. These are chiefly cancers of the lymphohematopoietic system and include 

leukemia, lymphosarcoma, reticulosarcoma, and NHL. More recently, an association with breast 

cancer has also been suggested. However, the overall epidemiological evidence is not 

conclusive because of inadequacies and limitations in the epidemiological database. The main 

effects and limitations in the epidemiological studies of EtO are presented in Table A-5. 

2 

3 

4 

6 

7 

8 

9 

11 

12 

13 

14 

16 

17 

18 

19 

21 

22 

23 

24 

26 

27 

28 

29 

31 

32 

33 

34 

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Table A-5. Epidemiological studies of ethylene oxide and human cancer 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Sterilizers, 709 Plant 1: mean = 20 ppm in 33 cancer deaths vs. 20 Benzene, methyl formate, No personal exposure 

production (539 men, sterilizer room expected bis-(2-chloroethyl) ether, ethylene, information from which to 

workers, Sweden 

Hogstedt (1988); 

Hogstedt et al. 

(1986) 

170 women) 

Plant 2: mean = 14 ppm in 

early years, less than 6 ppm 

later 

Plant 3: less than 8 ppm in 

early years, less than 2 ppm 

later 

7 leukemia deaths vs. 0.8 

expected (ICD-8 204-207) 

9 lymphohematopoietic 

cancer deaths vs. 2.0 

expected (ICD-8 200-208) 

10 stomach cancer deaths 

vs. 1.8 expected 

ethylene chlorohydrin, ethylene 

dichloride, ethylene glycol, 

propylene oxide, amines, butylene 

oxide, formaldehyde, propylene, 

sodium 

estimate dose 

No latency analysis 

Mixed exposure to other 

chemicals 

Sterilizing workers 

in 8 hospitals and 

users in 4 

companies, Great 

Britain 

Gardner et al. 

(1989) 

2,876 

(1,864 men, 

1,012 

women) 

In early years, odor 

threshold of 700 ppm 

noted; in later years, 5 ppm 

or less was noted 

3 leukemia deaths vs. 2.1 

expected (ICD NS) 

3 leukemia deaths vs. 0.35 

expected (after 20+ years 

latency) 

4 NHL deaths vs. 1.6 

expected 

5 esophageal cancer deaths 

vs. 2.2 expected 

4 bladder cancer deaths vs. 

2.04 expected 

29 lung cancer deaths vs. 

24.6 expected 

Aliphatic and aromatic alcohols, 

amines, anionic surfactants, 

asbestos, butadiene, benzene, 

cadmium oxide, dimethylmine, 

ethylene, ethylene chlorohydrin, 

ethylene glycol, formaldehyde, 

heavy fuel oils, methanol, 

methylene chloride, propylene, 

propylene oxide, styrene, tars, white 

spirit, carbon tetrachloride 

Insufficient follow-up 

Exposure classification 

scheme vague, making it 

difficult to develop doseresponse gradient 

No exposure 

measurements prior to 

1977, so individual 

exposure estimates were 

not made 

Mixed exposure to several 

other chemicals 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Coggon et al. 

(2004) 

Update of Gardner 

et al. (1989) 

Same cohort 

followed 

additional 

13 years 

Ibid. 5 leukemia deaths vs. 4.6 

expected (ICD-9 204-208) 

5 leukemia deaths vs. 2.6 

expected (definite or 

continual exposure) 

7 NHL vs. 4.8 expected 

(ICD-9 200+202) 

17 lymphohematopoietic 

cancers vs. 12.9 expected 

(ICD-9 200-208) 

11 breast cancers vs. 13.1 

expected 

Ibid. Ibid. and, in addition, no 

latency evaluation 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Production workers 2,658 men No exposure information 2 leukemia deaths vs. 2.35 Beta-naphthylamine, 4-amino- Insufficient follow-up; 

(methods 

unspecified) from 8 

available expected (ICD-9 204-208) diphenyl, benzene, ethylene 

chlorohydrin, possibly alkylene 

few expected deaths in 

cancer sites of 

chemical plants in 5 lymphohematopoietic oxide (ethylene oxide/propylene significance with which to 

West Germany cancers vs. 5 expected oxide), based on inclusion of plants analyze mortality 

Kiesselbach et al. 

(ICD-9 200-208) that were part of a cohort study by 

Thiess et al. (1981). Production methods not 

(1990) 14 stomach cancer deaths stated; information vague 

vs. 10.1 expected on what these plants do 

3 esophageal cancer deaths Latency analysis given 

vs. 1.5 expected only for total cancer and 

stomach cancer mortality 

23 lung cancer deaths vs. 

19.9 expected Although categories of 

exposure are given, they 

are nonquantitative and 

are not based on actual 

measurements 

No actual measurement 

data are given; doseresponse analysis is not 

possible 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Production workers 

and users at 2 

chemical plants in 

West Virginia 

Greenberg et al. 

(1990) 

2,174 men Exposure prior to 1976 not 

known 

1976 survey: average 8-hr 

TWA exposure levels less 

than 1 ppm; 1−5 ppm 8-hr 

TWA for maintenance 

workers 

7 leukemia and aleukemia 

deaths vs. 3 expected; 

SMR = 2.3 (ICD NS) 

2 NHL vs. 2.4 expected 

9 lymphohematopoietic 

cancers vs. 7.5 expected 

3 liver cancer deaths vs. 1.8 

expected; SMR = 1.7 

7 pancreatic cancer deaths 

vs. 4.1 expected; SMR = 1.7 

Suggestion of increasing 

risk of stomach cancer and 

leukemia/aleukemia with 

cumulative duration of 

potential exposure 

Acetaldehyde, acetonitrile, acrolein, 

aldehydes, aliphatic and aromatic 

alcohols, alkanolamines, allyl 

chloride, amines, butadiene, 

benzene, bis-(chloroethyl) ether, 

ethylene dichloride, diethyl 

sulphate, dioxane, epichlorhydrin, 

ethylene, ethylene chlorohydrin, 

formaldehyde, glycol ethers, 

methylene chloride, propylene 

chlorohydrin, styrene, toluidine 

Low exposure levels: 

average 8-hr TWA 

exposure levels to EtO 

less than 1 ppm (from a 

1976 survey) 

No actual measurements 

of exposure to EtO for 

these plants exist prior to 

1976 

Exposure occurred to 

many other chemicals, 

some of which may be 

carcinogenic 

Lack of quantitative 

estimates of individual 

exposure levels 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Same cohort as 

Greenberg et al. 

(1990) minus all 

chlorohydrinexposed 

employees, 

followed an 

additional 10 years 

Teta et al. (1993) 

1,896 men Estimated exposure prior to 

1956: 14+ ppm; after 

1956: less than 10 ppm 

Prior to 1976, estimates 

were based on 

measurements taken at 

similar facilities 

5 leukemia and aleukemia 

deaths vs. 4.7 expected 

(ICD NS) 

2 lymphosarcoma and 

reticulosarcoma vs. 2.03 

expected 

7 lymphohematopoietic 

cancers vs. 11.8 expected 

Trend of increasing risk of 

leukemia and aleukemia 

death with increasing 

duration of exposure 

Same (except for chemicals specific 

to the chlorohydrin process) 

Same 

Only the 

chlorohydrinexposed employees 

from Greenberg et 

al. (1990) cohort, 

followed an 

additional 10 years 

Benson and Teta 

(1993) 

278 men Reported to be low 

exposure to EtO in the 

chlorohydrin process 

8 lymphohematopoietic 

cancer deaths vs. 2.72 

expected (p < 0.05) (ICD 

NS); SMR = 2.9 

4 leukemia and aleukemia 

deaths vs. 1.14 expected 

1 lymphosarcoma and 

reticulosarcoma vs. 0.50 

expected 

8 pancreatic cancer deaths 

vs. 1.63 expected (p < 0.05) 

Same Same, and, in addition, 

very small cohort 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Same cohort as for 

Teta et al. (1993) 

followed an 

additional 15 years 

plus cohort 

enumeration 

extended to end of 

1988 (an additional 

10 years), adding 

167 workers 

Swaen et al. (2009) 

2,063 men Individual exposure 

estimates derived from an 

exposure matrix based on 

potential EtO exposure 

categorizations developed 

by Greenberg et al. (1990) 

and time-period exposure 

estimates developed by 

Teta et al. (1993), which 

relied on measurements 

taken at other facilities and 

rough estimates for the 

time periods before 1974. 

11 leukemia deaths vs. 11.8 

expected (ICD NS) 

9 leukemia deaths in 

workers hired before 1956; 

SMR = 1.51 

12 NHL vs. 11.5 expected 

27 lymphohematopoietic 

cancers vs. 30.4 expected 

No statistically significant 

increases were observed for 

any cancer types 

No statistically significant 

trends were observed for 

lymphoid or leukemia 

cancer categories examined 

using Cox proportional 

hazards modeling 

Same Same 

Crude exposure 

assessment, especially for 

the early time periods 

Small cohort; thus, small 

numbers of specific 

cancers even though long 

follow-up time 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Sterilizers of 

medical equipment 

18,254 1938–1976 (estimated): 16 

ppm for sterilizer 

36 lymphohematopoietic 

cancer deaths vs. 33.8 

No identified exposures to other 

chemicals 

Potential bias due to lack 

of follow-up on 

and spices; and (45% male, operators, 5 ppm for expected (ICD NS) “untraceable” members 

manufacturers and 

testers of medical 

sterilization 

equipment, in 14 

plants in the United 

States 

Steenland et al. 

(1991); Stayner et 

al. (1993) 

55% female) remainder 

1977–1985 (mean): 4.3 for 

sterilizers, 2 ppm for 

remainder 

Individual cumulative 

exposure estimates 

calculated for workers in 

13 of the 14 facilities 

13 leukemia and aleukemia 

deaths vs. 13.5 expected 

8 lymphosarcoma and 

reticulosarcoma deaths vs. 

5.3 expected 

After 20+ years latency, 

SMR = 1.76 for 

lymphohematopoietic 

cancer; significant trend 

with increasing latency 

(p < 0.03) 

Significantly increasing 

lymphohematopoietic 

cancer and “lymphoid” 

cancer (ICD-9 200, 202, 

204) risks with cumulative 

exposure (Cox regression 

model) 

(4.5%) of the cohort 

Short duration of 

exposure and low median 

exposure levels 

Individual exposures were 

estimated prior to 1976 

before first industrial 

hygiene survey was 

completed 

Short follow-up for most 

members of the cohort; 

only 8% had attained 

20 years latency 

Little mortality (6.4%) 

had occurred in this large 

group of employees 

No exposure-response 

relationship among female 

workers 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Same cohort as 

Steenland et al. 

18,728 Same as Steenland et al. 

(1991) and Stayner et al. 

43 lymphohematopoietic 

cancer deaths observed vs. 

No identifiable exposures to other 

chemicals 

All of the limitations of 

Steenland et al. (1991) 

(1991) and Stayner (45% male, (1993) 42 expected (ICD-8 200­ apply here 

et al. (1993) plus 

474 additional 

members, followed 

1 more yr 

Wong and Trent 

(1993) 

55% female) 209) 

18 NHL deaths vs. 12.7 

expected (ICD-8 200+202) 

14 leukemia and aleukemia 

deaths vs. 16.2 expected 

(ICD-8 204-207) 

Although this group is the 

same as Steenland et al. 

(1991), an additional 

unexplained 474 

employees were added 

It is questionable that one 

additional yr of follow-up 

added 392.2 expected 

deaths but only 176 

observed deaths 

No effort was made to 

develop exposureresponse data such as in 

Stayner et al. (1993) on 

the basis of individual 

cumulative exposure data 

but only on duration of 

employment 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Steenland et al. 18,254 Same as Steenland et al. 79 lymphohematopoietic No identified exposures to other Potential bias due to lack 

(2004) (1991), with extension of cancer deaths (ICD-9 200­ chemicals of follow-up on 

(45% male, worker histories based on 208): SMR =1.00 “untraceable” members 

Update of 55% female) job held at end of initial (4.5% of the cohort) 

Steenland et al. exposure assessment for 31 NHL deaths (ICD-9 

(1991) and Stayner those still employed at end 200+202): SMR = 1.00 Individual exposures were 

et al. (1993) of 1991 study (25% of 

cohort) 29 leukemia deaths (ICD-9 

204-208); SMR = 0.99 

estimated prior to 1976 

before first industrial 

hygiene survey was 

In males, in internal Cox 

regression analyses, 

OR = 3.42 (p < 0.05) in 

highest cumulative exposure 

group, with 15-yr lag, for 

lymphohematopoietic 

cancer; significant 

regression coefficient for 

continuous log cumulative 

exposure (p = 0.02) 

Similar results for 

“lymphoid” cancers (ICD-9 

200, 202, 203, 204) in males 

For females, in internal Cox 

regression analyses, 

OR = 3.13 (p < 0.05) for 

breast cancer mortality in 

highest cumulative exposure 

group, with 20-yr lag; 

significant regression 

coefficient for continuous 

log cumulative exposure 

(p = 0.01) 

completed 

No increase in 

lymphohematopoietic 

cancer risk with increase 

in exposure in women 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Women employees 

from Steenland et 

al. (2004) 

employed in 

commercial 

sterilization 

facilities for at least 

1 yr 

Steenland et al. 

(2003) 

7,576 

women 

Same as in Steenland et al. 

(2004) 

Minimum of 1 yr 

SIR = 0.87 

319 cases of breast cancer 

SIR = 0.94 

20 in situ cases excluded 

A positive trend in SIRs 

with 15-yr lag time for 

cumulative exposure 

(p = 0.002) 

In internal nested casecontrol analysis, a positive 

exposure-response with log 

of cumulative exposure with 

15-yr lag; top quintile had 

OR = 1.74, p < 0.05 

Similar results in subcohort 

of 5,139 women with 

interviews (233 cases) 

Same as in Steenland et al. (2004), 

Stayner et al. (1993) 

Interviews were available 

for only 68% of the 

women; thus, there is 

underascertainment of 

cancer cases in full 

cohort. Also, there are 

potential nonresponse 

biases in the subcohort 

with interviews. 

Exposure-response trends 

not strictly monotonically 

increasing 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Chemical workers 

licensed to handle 

EtO and other toxic 

chemicals, Italy 

Bisanti et al. (1993) 

1,971 men Levels were said to be high 

at beginning of 

employment; no actual 

measurements were 

available 

637 workers were licensed 

only to handle EtO and no 

other toxic chemicals 

43 total cancer deaths vs. 33 

expected 

6 lymphohematopoietic 

cancer deaths vs. 2.4 

expected (ICD-9 200-208) 

4 lymphosarcoma and 

reticulosarcoma deaths vs. 

0.6 expected (ICD-9 200) 

2 leukemia deaths vs. 1.0 

expected (ICD-9 204-208) 

5 lymphohematopoietic 

cancer deaths vs. 0.7 

expected in group licensed 

to handle only EtO 

Toxic gases, dimethyl sulphate, 

methylene chloride, carbon 

disulphide, phosgene, chlorine, 

alkalic cyanides, sulfur dioxide, 

anhydrous ammonia, hydrocyanic 

acid 

Lack of exposure data 

Insufficient follow-up for 

this young cohort 

Potential selection bias 

Possible earlier exposure 

than date of licensing 

would indicate 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Two plants that 

produced 

disposable medical 

equipment, Sweden 

Hagmar et al. 

(1995); Hagmar et 

al. (1991) 

2,170 

(861 men, 

1,309 

women) 

1964–1966, 75 ppm in 

sterilizers, 50 ppm in 

packers 

1970–1972, 40 ppm in 

sterilizers, 20–35 ppm in 

packers and engineers 

By 1985, levels had 

dropped to 0.2 ppm in all 

categories except sterilizers 

and to 0.75 ppm in 

sterilizers 

6 lymphohematopoietic 

cancer cases vs. 3.37 

expected (ICD-7 200-209) 

2 NHL cases vs. 1.25 

expected (ICD-7 200+202) 

2 leukemia cases vs. 0.82 

expected (ICD-7 204-205) 

Among subjects with at 

least 0.14 ppm-years of 

cumulative exposure and 

10 years latency, the SIR for 

leukemia was 7.14, based 

on two cases 

5 breast cancer cases vs. 

10.8 expected 

Fluorochlorocarbons, methyl 

formate (1:1 mixture with EtO) 

Short follow-up period; 

authors recommend 

another 10 years of 

follow-up 

Youthful cohort—few 

cases and fewer deaths; 

unable to determine 

significance or 

relationships in categories 

Only a minority of 

subjects had high 

exposure to EtO 

Sterilizers of 

medical equipment 

1,132 In 1980, levels were 

50−200 ppm (8-hr TWA); 

Only 28 cancers were 

diagnosed 

No other chemical exposures cited Little power to detect any 

significant risk chiefly 

and supplies that (204 men, corrective action reduced because a short follow-up 

were assembled at 

this plant, New 

York 

Norman et al. 

(1995) 

928 women) levels to less than 20 ppm 1 leukemia case vs. 0.54 

expected 

12 breast cancer cases vs. 

4.6 to 7.0 expected 

(p ≤ 0.05) 

2 pancreatic cancer cases 

vs. 0.51 expected 

period produced few 

cancer cases 

Lack of exposure data 

Insufficient latency 

analysis 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Nested case-control 

study; cases and 

controls from a 

large chemical 

production plant, 

Belgium 

Swaen et al. (1996) 

10 cases of 

Hodgkin 

lymphoma 

(7 cases 

confirmed) 

and 200 

controls; all 

male 

Cumulative exposure to 

EtO in cases was 500.2 

ppm-months vs. 60.2 ppmmonths in controls 

3 cases indicated exposure 

to EtO, producing an 

OR = 8.5 (p < 0.05) 

Fertilizers, materials for synthetic 

fiber production, PVC, polystyrene, 

benzene, methane, acetone, 

ammonia, ammonium, sulfate, 

aniline, caprolactam, ethylene, 

Nah., oleum 

This was a hypothesisgenerating study; the 

authors were not looking 

for EtO exposure alone 

but for other chemical 

exposures as well to 

explain the excess risk 

Only one disease— 

Hodgkin lymphoma—was 

analyzed 

Four EtO 1,361 men No actual measurements 10 lymphohematopoietic Bis-chloroethyl ether, propylene No actual airborne 

production plants were taken cancer deaths vs. 7.7 oxide, ethylene chlorohydrin, measurements of EtO or 

in 3 states utilizing expected (ICD-8 200-209) propylene chlorohydrin, ethylene other chemicals such as 

the chlorohydrin After 25-yr latency, dichloride, chlorohydrin chemicals ethylene dichloride were 

process (both SMR = 1.44, based on 6 reported; only length of 

ethylene and deaths employment was used as a 

propylene) 

2 leukemia and aleukemia 

surrogate 

Olsen et al. (1997) deaths vs. 3.0 expected 

(ICD-8 204-207) 

No increase in pancreatic 

cancer (1 observed vs. 4.0 

expected) 

An additional 5 to 10 

years of follow-up is 

needed to confirm the 

presence or lack of risk of 

pancreatic cancer and 

lymphopoietic and 

hematopoietic cancers 
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Table A-5. Epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

Industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to which subjects 

were potentially exposed Limitations 

Female workers 

from pediatric 

clinic of hospital in 

Eger, Hungary 

Kardos et al. 

(2003) 

299 female 

employees 

EtO sterilizing units with 

unknown elevated 

concentrations 

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 

1 lymphoid leukemia death 

3 breast cancer deaths 

No identifiable exposures to other 

chemicals 

Underlying cause of death 

provided on all 11 cases 

but no expected deaths 

available by cause 

Possible exposure to 

natural radium, which is 

common in the region 

ICD NS: ICD codes not specified. 
Exposure information, where available, indicates that levels of EtO probably were not 

high in these study cohorts. If a causal relationship exists between exposure to EtO and cancer, 

the reported EtO levels may have been too low to produce a significant finding. Exposures in the 

earlier years (prior to 1970) in most of the companies, hospitals, and other facilities where EtO 

was made or used are believed to have been in the range of 20 ppm, with excursions many times 

higher, although few actual measurements are available during this period. (One exception is the 

environmental study by Joyner (1964), who sampled airborne levels of EtO from 1960 to 1962 in 

a Texas City facility owned by Union Carbide.) 

Almost all actual measurements of EtO were taken in the 1970s and 1980s at most plants 

and facilities in the United States and Europe, and levels have generally fallen to 5 ppm and 

below. Some plants may have never sustained high levels of airborne EtO. Assuming that there 

is a true risk of cancer associated with exposure to EtO, then the risk is not evident at the levels 

that existed in these plants except under certain conditions, possibly due to a lack of sensitivity in 

the available studies to detect associated cancers at low exposures. 

The best evidence of an exposure-response relationship for lymphohematopoietic cancers 

comes from the large, diverse NIOSH study of sterilizer workers [Steenland et al. (2004); 

Steenland et al. (1991); Stayner et al. (1993)]. This study estimated cumulative exposure (i.e., 

total lifetime occupational exposure to EtO) in every member of the cohort. The investigators 

estimated exposures from the best available data on airborne levels of EtO throughout the history 

of the plants and used a regression model to estimate exposures for jobs/time periods where no 

measurements were available. This regression model predicted 85% of the variation in average 

EtO exposure levels. An added advantage to this study, besides its diversity, size, and 

comprehensive exposure assessment, is the absence of other known confounding exposures in 

the plants, especially benzene. 

In the recent follow-up of the NIOSH cohort, as in the earlier study, Steenland et al. 

(2004) observed no overall excess of hematopoietic cancers (ICD-9 codes 200–208). In internal 

analyses, however, they found a significant positive trend (p = 0.02) for hematopoietic cancers 

for males only, using log cumulative exposure and a 15-year lag, based on 37 male cases. In the 

Cox regression analysis using categorical cumulative exposure and a 15-year lag, a positive trend 

was observed and the OR in the highest exposure quartile was statistically significant 

(OR = 3.42; 95% CI 1.09–10.73). Similar results were obtained for the “lymphoid” category 

(lymphocytic leukemia, NHL, and myeloma). No evidence of a relationship between EtO 

exposure and hematopoietic cancers in females in this cohort was observed. In later analyses 

conducted by Dr. Steenland and presented in Appendix D, the difference between the male and 

female results was found not to be statistically significant, and the same pattern of 

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1 lymphohematopoietic cancer results observed for males by Steenland et al. (2004) was observed 

for the males and females combined (i.e., statistically significant positive trends for both 

hematopoietic [n = 74] and lymphoid [n = 53] cancers using log cumulative exposure and a 

15-year lag, as well as statistically significant ORs in the highest exposure quartile for both 

hematopoietic and lymphoid cancers). 

In the analysis by Swaen et al. (2009) of male UCC workers, the authors discussed the 

development of the exposure assessment matrix used in combination with worker histories to 

estimate cumulative exposures for each worker in West Virginia UCC cohort. The exposure 

matrix was based on the qualitative categorization of potential EtO exposure in the different 

departments developed by Greenberg et al. (1990) and the time-period exposure estimates from 

Teta et al. (1993). Eight-hour TWA concentrations (ppm) were estimated over four time periods 

(1925–1939, 1940–1956, 1957–1973, and 1974–1978) at the two facilities for three 

exposure-potential categories (high, medium, and low exposure departments). Average 

exposures in the latter time period (1974–1978) were based on industrial hygiene monitoring 

conducted at the locations where the study subjects worked. Estimates for the earlier time 

periods were inferred from data on airborne exposure levels in “similar” manufacturing 

operations during the time periods of interest. The estimates for the 1957–1973 time period were 

inferred from measurements reported for the EtO production facility at Texas City studied by 

Joyner (1964), and the estimates for the 1940–1956 time period were inferred from “rough” 

estimates of exposure reported for the Swedish company described by Hogstedt et al. (1979a). 

Exposures for the 1925–1939 time period were assumed to be greater than for the later time 

periods, but the exposure estimates for this period are largely guesses. 

This relatively crude exposure assessment formed the basis of the UCC 

exposure-response analyses of the UCC study described in Swaen et al. (2009). Swaen et al. 

(2009) conducted SMR analyses for the UCC workers stratified into those hired before and after 

December 31, 1956; for three subgroups of employment duration; and for three subgroups of 

cumulative exposure. These investigators also conducted Cox proportional hazards modeling for 

leukemia mortality and lymphoid malignancy mortality. No statistically significant excesses in 

cancer risk or positive trends were reported. Despite the long follow-up of the UCC cohort, its 

usefulness is limited by its small size (e.g., a total of 27 lymphohematopoietic cancer deaths were 

observed). 

Valdez-Flores et al. (2010) used the same exposure assessment to conduct further 

exposure-response modeling of the UCC data. These authors used the Cox proportional hazards 

model to model various cancer endpoints, using the UCC data, the NIOSH data (Steenland et al., 

2004), or the combined data from both cohorts. Using cumulative exposure as a continuous 

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1 variable, no statistically significant positive trends were observed from any of the analyses. 

Unlike Steenland et al. (2004), Valdez-Flores et al. (2010) rejected the log cumulative exposure 

model. Using cumulative exposure as a categorical variable, statistically significant increased 

risks in the highest exposure quintile were reported for all lymphohematopoietic cancers and for 

lymphoid cancers in the NIOSH male workers, consistent with results reported by Steenland et 

al. (2004). Statistically significant increased risks in the highest exposure quintile were also 

reported for NHL in the NIOSH male workers and for lymphoid cancers and NHL in both sexes 

combined in the NIOSH cohort. 

The many different analyses of the UCC data are weakened by the reliance on the crude 

exposure assessment. The NIOSH investigators, on the other hand, based their exposure 

estimates on a comprehensive, validated regression model. Furthermore, the NIOSH cohort was 

a much larger, more diversified group of workers who were exposed to fewer potential 

confounders. 

One other study that provides cumulative exposure estimates is the incidence study by 

Hagmar and colleagues [Hagmar et al. (1995); Hagmar et al. (1991)]. The short follow-up 

period and relative youthfulness of the cohort produced little morbidity by the end of the study, 

although some support for an excess risk of leukemia and lymphohematopoietic cancer had 

appeared. 

In a separate analysis of the NIOSH cohort by Wong and Trent (1993), duration of 

exposure to EtO was used as a surrogate for exposure. These authors did not find any positive 

exposure-response relationships. They did observe an elevated significant risk of “NHL” in 

males (SMR = 2.47, p < 0.05), based on 16 deaths, which was not dose related or time related. 

However, a deficit in females remained. 

Increases in the risk of hematopoietic cancers are also suggested in several other studies 

(Coggon et al., 2004; Olsen et al., 1997; Swaen et al., 1996; Norman et al., 1995; Bisanti et al., 

1993; Gardner et al., 1989). However, in all these studies the deaths were few and the risk ratios 

were mostly nonsignificant except at higher estimated exposures or after long observation 

periods. The findings were not robust and there were potentially confounding influences, such as 

exposure to benzene and/or chlorohydrin derivatives. 

In those plants with no detectable risks (Norman et al., 1995; Kiesselbach et al., 1990), 

the cohorts were generally relatively youthful or had not been followed for a sufficient number 

of years to observe any effects from exposure to EtO. In the study by Olsen et al. (1997), 

although a slight increase in the risk of cancer of the lymphopoietic and hematopoietic system 

was evident, the authors stated that their study provided some assurance that working in the 

chlorohydrin process had not produced significantly increased risks for pancreatic cancer or 

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1 lymphopoietic or hematopoietic cancer, thus contradicting the findings of Benson and Teta 

(1993). This study lacks any measurement of airborne exposure to any of the chemicals 

mentioned and the authors indicated that an additional 5 to 10 years of follow-up would be 

needed to confirm the lack of a risk for the cancers described in their study. 

Although the strongest evidence of a cancer risk is with cancer of the hematopoietic 

system, there are indications that the risk of stomach cancer may have been elevated in some 

studies (Teta et al., 1993; Kiesselbach et al., 1990; Hogstedt et al., 1986; Hogstedt et al., 1979b); 

however, it attained significance only in the study by Hogstedt et al. (1979b), with 9 observed 

versus 1.27 expected. It was reported by Shore et al. (1993) that this excess may have been due 

to the fact that early workers at this plant “tasted” the chemical reaction product to assess the 

result of the EtO synthesis. This reaction mix would have also contained ethylene dichloride, a 

suspected carcinogen, and other chemicals. This increased risk of stomach cancer was not 

supported by analyses of intensity or duration of exposure in the remaining studies, except that 

Benson and Teta (1993) suggested that exposure to this chemical increased the risk of pancreatic 

cancer and perhaps hematopoietic cancer but not stomach cancer. 

A significant risk of pancreatic cancer first reported by Morgan et al. (1981) was also 

reported by Greenberg et al. (1990) in his cohort of chemical workers, but only in those workers 

assigned to the ethylene chlorohydrin production process, where the authors reported that 

exposure to EtO was low. Benson and Teta (1993) attributed the increase in pancreatic cancer 

seen in Greenberg et al. (1990) to exposure to ethylene dichloride in the chlorohydrin process. 

However, Olsen et al. (1997) refuted this finding in their study. The pancreatic cancers from the 

study by Morgan et al. (1981) also occurred in workers in a chlorohydrin process of EtO 

production. The possibility that exposure to a byproduct chemical such as ethylene dichloride 

may have produced the elevated risks of pancreatic cancer seen in these workers cannot be ruled 

out. 

In addition to the cancer risks described above, some recent evidence indicates that 

exposure to EtO may increase the risk of breast cancer. The study by Norman et al. (1995) of 

women who sterilized medical equipment observed a significant twofold elevated risk of breast 

cancer, based on 12 cases. A study by Tompa et al. (1999) reported on a cluster of breast cancers 

occurring in Hungarian hospital workers exposed to EtO. In another Hungarian study of female 

hospital workers by Kardos et al. (2003), 3 breast cancers were noted out of 11 deaths reported 

by the authors. Although expected breast cancer deaths were not reported, the total expected 

deaths calculated was just slightly more than 4, making this a significant finding for cancer in 

this small cohort. 

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1 The most compelling evidence on breast cancer comes from the NIOSH cohort. In the 

recent update of this cohort, no overall excess of breast cancer mortality was observed in the 

female workers; however, a statistically significant SMR of 2.07 was observed in the highest 

cumulative exposure quartile, with a 20-year lag. In internal Cox regression analyses, a positive 

exposure-response (p = 0.01) was observed for log cumulative exposure with a 20-year lag, 

based on 103 cases. Similar evidence of an excess risk of breast cancer was reported in a breast 

cancer incidence study of a subgroup of 7,576 female workers from the NIOSH cohort who were 

exposed for 1 year or longer (Steenland et al., 2003). A significant (p = 0.002) linear trend in 

SIR was observed across cumulative exposure quintiles, with a 15-year lag. In internal Cox 

regression analyses, there was a significant regression coefficient with log cumulative exposure 

and a 15-year lag, based on 319 cases. Using categorical cumulative exposure, the OR of 1.74 

was statistically significant in the highest exposure quintile. In a subcohort of 5,139 women with 

interviews, similar results were obtained based on 233 cases, and the models for this subcohort 

were also able to take information on other potential risk factors for breast cancer into account. 

Additionally, the coefficient for continuous cumulative exposure was also significant (p = 0.02), 

with a 15-year lag. 

Several other studies with female employees in the defined cohorts reported no increased 

risks of breast cancer due to exposure to EtO (Coggon et al., 2004; Hagmar et al., 1995; Hagmar 

et al., 1991; Hogstedt et al., 1986). However, these studies have much lower statistical power 

than the NIOSH studies, as evidenced by the much lower numbers of breast cancer cases that 

they report. The largest number of cases in any of these other studies is 11 cases in the Coggon 

et al. (2004) study. Furthermore, none of these other studies conducted internal (or external) 

exposure-response analyses, which are the analyses that provided the strongest evidence in the 

NIOSH studies. 

A.4. CONCLUSIONS 

Experimental evidence demonstrates that exposure to EtO in rodents produces 

lymphohematopoietic cancers; therefore, an increase in the risk of lymphohematopoietic cancer 

in humans should not be unexpected. An increase in mammary gland carcinomas was also 

observed in mice. Although several human studies have indicated the possibility of a 

carcinogenic effect from exposure to EtO, especially for lymphohematopoietic cancers, the total 

weight of the epidemiologic evidence is not sufficient to support a causative determination. The 

causality factors of temporality, coherence, and biological plausibility are satisfied. There is also 

evidence of consistency and specificity in the elevated risk of lymphohematopoietic cancer as a 

single entity in the human studies. The earlier significant risk of leukemia seen in the Hogstedt 

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1 studies was supported in some studies and not in others. In fact, not all human studies of EtO 

have suggested an elevated risk of cancer and in those that do, the marginally elevated risks vary 

from one site to another within the lymphohematopoietic system. When combined under the 

rubric “lymphohematopoietic cancers,” this loosely defined combination of blood malignancies 

produces a slightly elevated risk of cancer in some studies but not in all. There is evidence of a 

biological gradient in the significant dose-response relationship seen in the large, high-quality 

Steenland et al. (2004) study. 

The best evidence of a carcinogenic effect produced by exposure to EtO is found in the 

NIOSH cohort of workers exposed to EtO in 14 sterilizer plants around the country (Steenland et 

al., 2004; Stayner et al., 1993; Steenland et al., 1991). A positive trend in the risk of 

lymphohematopoietic and “lymphoid” neoplasms with increasing log cumulative exposure to 

EtO with a 15-year lag is evident. But there are some limitations to concluding that this is a 

causal relationship at this time. For example, there was a lack of dose-response relationship in 

females, although, as presented in Appendix D, later calculations show that the difference in 

response between females and males is not statistically significant and that significant increases 

are also observed with both sexes combined. 

An elevated risk of lymphohematopoietic cancers from exposure to EtO is also apparent 

in several other studies. In some of these studies, confounding exposure to other chemicals 

produced in the chlorohydrin process concurrent with EtO may have been partially responsible 

for the excess risks. In other studies, where the chlorohydrin process was not present, there are 

no known confounding influences that would produce a positive risk of lymphohematopoietic 

cancer. Overall, the evidence on lymphohematopoietic cancers in humans is considered to be 

strong but not sufficient to support a causal association. 

There is also evidence that exposure to EtO increases the risk of breast cancer, based 

chiefly on the NIOSH studies [Steenland et al. (2004); Steenland et al. (2003)] discussed earlier, 

with some corroborating support from the Norman et al. (1995) and Kardos et al. (2003) studies 

of breast cancer in women exposed to EtO. The risk of breast cancer was analyzed in a few other 

studies (Coggon et al., 2004; Hagmar et al., 1991; Hogstedt, 1988; Hogstedt et al., 1986), and no 

increase in the risk of breast cancer was found. However, these studies had far fewer cases to 

analyze, did not have individual exposure estimates, and relied on external comparisons. The 

NIOSH studies [Steenland et al. (2004); Steenland et al. (2003)], 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 on internal exposure-response analyses. 

The authors suggest that the case is not conclusive of a causal association “due to inconsistencies 

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

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1 ascertainment.” While these are not decisive limitations—exposure-response relationships are 

often not strictly monotonically increasing across finely dissected exposure categories, and the 

consistency of results between the full cohort (less nonresponse bias) and the subcohort with 

interviews (full case ascertainment) alleviates some of the concerns about those potential 

biases—the evidence for a causal association between breast cancer and EtO exposure is less 

than conclusive at this time. 

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1 APPENDIX B. 

REFERENCES FOR FIGURE 3-3 

The references in this list correspond to the additional data that were added to Figure 3-3 

since the IARC (1994b) genetic toxicity profile was published. See the Figure 3-3 legend for 

details. 

de Serres, FJ; Brockman, HE. (1995) Ethylene oxide: induction of specific-locus mutations in the ad-3 region of 

heterokaryon 12 of Neurospora crassa and implications for genetic risk assessment of human exposure in the 

workplace. Mutat Res 328:31−47. 

Hengstler, JG; Fuchs, J; Gebhard, S; et al. (1994) Glycolaldehyde causes DNA-protein crosslinks: a new aspect of 

ethylene oxide genotoxicity. Mutat Res 304(2):229–234. 

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. 

Major, J; Jakab, MG; Tompa, A. (1999) The frequency of induced premature centromere division in human 

populations occupationally exposed to genotoxic chemicals. Mutat Res 445(2):241–249. 

Nygren, J; Cedervall, B; Eriksson, S; et al. (1994) Induction of DNA strand breaks by ethylene oxide in human 

diploid fibroblasts. Environ Mol Mutagen 24(3):161–167. 

Oesch, F; Hengstler, JG; Arand, M; et al. (1995) Detection of primary DNA damage: applicability to biomonitoring 

of genotoxic occupational exposure and in clinical therapy. Pharmacogenetics 5 Spec No:S118–S122. 

Ribeiro, LR; Salvadori, DM; Rios, AC; et al. (1994) Biological monitoring of workers occupationally exposed to 

ethylene oxide. Mutat Res 313:81–87. 

Sisk, SC; Pluta, LJ; Meyer, KG; et al. (1997) Assessment of the in vivo mutagenicity of ethylene oxide in the tissues 

of B6C3F1 lacI transgenic mice following inhalation exposure. Mutat Res 391(3):153–164. 

Swenberg, JA; Ham, A; Koc, H; et al. (2000) DNA adducts: effects of low exposure to ethylene oxide, vinyl 

chloride and butadiene. DNA Repair 464:77–86. 

Tates, AD; vanDam, FJ; Natarajan, AT; et al. (1999) Measurement of HPRT mutations in splenic lymphocytes and 

haemoglobin adducts in erythrocytes of Lewis rats exposed to ethylene oxide. DNA Repair 431(2):397–415. 

van Sittert, NJ; Boogaard, PJ; Natarajan, AT; et al. (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. 

Vogel, EW; Nivard, MJ. (1997) The response of germ cells to ethylene oxide, propylene oxide, propylene imine and 

methyl methanesulfonate is a matter of cell stage-related DNA repair. Environ Mol Mutagen 29(2):124–135. 

Vogel, EW; Nivard, MJ. (1998) Genotoxic effects of inhaled ethylene oxide, propylene oxide and butylene oxide on 

germ cells: sensitivity of genetic endpoints in relation to dose and repair status. Mutat Res 405(2):259–271. 

Walker, VE; Sisk, SC; Upton, PB; et al. (1997) In vivo mutagenicity of ethylene oxide at the hprt locus in Tlymphocytes of B6C3F1 lacI transgenic mice following inhalation exposure. Mutat Res 392(3):211–222. 

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Walker, VE; Wu, KY; Upton, PB; et al. (2000) Biomarkers of exposure and effect as indicators of potential 

carcinogenic risk arising from in vivo metabolism of ethylene to ethylene oxide. Carcinogenesis 21(9):1661–1669. 

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APPENDIX C. 

GENOTOXICITY AND MUTAGENICITY OF ETHYLENE OXIDE 

A summary of the available genotoxicity and mutagenicity data for ethylene oxide (EtO) 

is presented in Chapter 3 (see Section 3.3.3). This appendix provides further details on the 

available genotoxicity and mutagenicity data and on some of the studies that are briefly 

mentioned in Chapter 3. The genotoxic potential of EtO is a key component of the assessment of 

its carcinogenicity. The relationship between genotoxicity/mutagenicity and carcinogenicity is 

based on the observations that genetic alterations are observed in almost all cancers and that 

many of these alterations have been shown to play an important role in carcinogenesis. Exposure 

to EtO has been found to result in a number of genotoxic effects in laboratory animal studies and 

in studies of humans exposed in occupational settings. In particular, EtO has been shown to alter 

or damage genetic material in such a manner that the genetic alterations are transmissible during 

cell division. Evidence of genotoxicity/mutagenicity provides strong mechanistic support for 

potential carcinogenicity in humans (Waters et al., 1999). 

Since the first report of EtO’s role in inducing sex-linked recessive lethals in Drosophila 

(Rapoport, 1948), numerous papers have been published on the mutagenicity of EtO in 

biological systems, spanning a whole range of assay systems, from bacteriophage to higher 

plants and animals (see Figure 3-3 in Chapter 3). EtO, being a mono-functional alkylating agent, 

is DNA–reactive, capable of forming DNA adducts and inducing mutations at both the 

chromosome and gene levels under appropriate conditions, as evidenced in numerous in vitro 

and in vivo studies (reviewed in IARC, 2008; Kolman et al., 2002; Thier and Bolt, 2000; 

Natarajan et al., 1995; Vogel and Natarajan, 1995; Dellarco et al., 1990; Kolman et al., 1986). In 

prokaryotes (bacteria) and lower eukaryotes (yeasts and fungi), EtO induces DNA damage and 

gene mutations and conversions. In mammalian cells, EtO induces DNA adducts, unscheduled 

DNA synthesis, gene mutations, sister chromatid exchanges (SCEs), micronuclei, and 

chromosomal aberrations (IARC, 2008; Thier and Bolt, 2000; Natarajan et al., 1995; Preston et 

al., 1995; Dellarco et al., 1990; Walker et al., 1990; Ehrenberg and Hussain, 1981). The results 

of in vivo studies on the genotoxicity of EtO following ingestion, inhalation or injection have 

also been consistently positive (IARC, 2008, 1994b). Furthermore, in vivo exposure to 

EtO-induced gene mutations in the Hprt locus in mouse and rat splenic T-lymphocytes and SCEs 

in lymphocytes from rabbits, rats, and monkeys, in bone marrow cells from mice and rats, and in 

rat spleen. Increases in the frequency of gene mutation in the lung (LacI locus) (Recio et al., 

2004; Sisk et al., 1997) and in the Hprt locus in T-lymphocytes (Walker et al., 1997) in 

transgenic mice exposed to EtO via inhalation have been observed at concentrations similar to 
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those in carcinogenesis bioassays (NTP, 1987). EtO has also induced heritable mutations or 

effects in germ cells in rodents (Generoso et al., 1990; Lewis et al., 1986). In addition, 

significant increases in the frequency of SCEs and chromosomal aberrations in peripheral blood 

lymphocytes have been consistently reported in workers exposed to concentrations of EtO of 

greater than 5 ppm (TWA) (IARC, 2008, and references therein). Thus, there is consistent 

evidence that EtO interacts with the genome from both in vitro studies and in vivo studies of 

laboratory animals and occupationally exposed humans. Based on these observations, exposure 

to EtO is considered to cause cancer through a mutagenic mode of action (see Chapter 3, 

Section 3.4). 

The following sections provide further details on different genotoxicity test results 

regarding the mutagenic potential of EtO. 

C.1. DNA ADDUCTS 

Covalent binding of a chemical (direct-acting) or its electrophilic intermediates or 

metabolites (indirect-acting chemicals following metabolic activation) with the nucleophilic sites 

in DNA results in the formation of “DNA adducts,” which represent the biologically effective 

dose of the chemical agent in question. Alkylating agents, such as EtO, are direct-acting 

chemical agents which can transfer alkyl groups (e.g., ethyl groups) to nucleophilic sites in 

DNA, alkylating the nucleotide bases. Alkylating agents are classified as SN1-type or SN2-type 

depending on the substitution nucleophilicity (SN). The SN1-type chemicals follow first-order 

kinetics (e.g., ethylnitrosourea [ENU] and methylnitrosourea or [MNU]), while the SN2-type 

agents exhibit an intermediate transition state (e.g., EtO and methyl methanesulfonate [MMS]). 

EtO is a direct-acting SN2 (substitution-nucleophilic-bimolecular)-type alkylating agent that 

forms adducts with cellular macromolecules such as proteins (e.g., hemoglobin) and DNA. The 

reactivity of an alkylating agent can be estimated by its Swain Scott substrate constant (s-value), 

which ranges from 0 to 1 (Warwick, 1963). Alkylating agents such as EtO and MMS, which 

have high “s” values (0.96 and >0.83, respectively), target the nucleophilic centers of ring 

nitrogens (e.g., N7 of guanine and N3 of adenine) in DNA, while agents such as ENU with a low 

“s” values (0.26) target the less nucleophilic centers such as O6

 of guanine. EtO has a high 

substrate constant favoring efficient alkylation at N7 of guanine (Beranek, 1990; Golberg, 1986; 

Warwick, 1963). Due to the high nucleophilicity and steric availability of the N7 of guanine, 

EtO predominantly forms the N7-hydroxyethylguanine (N7-HEG) adduct, although minor 

adducts such as those forming at O6 of guanine, N1, N3, and N6 of adenine, and N3 of cytosine, 

uracil and thymine are found in some instances (Segerbäck, 1994). 
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1 Several methods have been developed since 1988 to detect EtO-induced DNA adducts in 

vitro and in vivo. However, sensitivity and specificity of these methods have been the main 

concern. These methods include immunochemical assays, fluorescence techniques, high 

pressure liquid chromatography (HPLC), gas chromatography/mass spectrometry (GC/MS), 

32P-postlabeling and electrochemical detection, with varying sensitivities for detection of 

EtO-DNA adducts (Marsden et al., 2009; Huang et al., 2008; Tompkins et al., 2008; Marsden et 

al., 2007; Bolt et al., 1997; Leclercq et al., 1997; Kumar et al., 1995; Saha et al., 1995; van Delft 

et al., 1994; van Delft et al., 1993; Uziel et al., 1992; Bolt et al., 1988). In the following 

paragraphs, a brief summary of available methods is provided to aid in the discussion of the 

DNA adduct data. 

van Delft et al. (1993) developed monoclonal antibodies against the imidazole ring of 

N7-alkyldeoxyguanosine, with the limits of detection being 5–10, 1–2, and 20 adducts per 

106 nucleotides, respectively, when used in the direct and competitive enzyme-linked 

immunosorbant assay and in immunofluorescence microscopy. Later the same authors 

developed an immunoslot-blot assay with increased sensitivity that detected 0.34 N7-HEG 

adducts per 106 nucleotides (van Delft et al., 1994). Kumar et al. (1995) developed a 

32P-postlabeling method using thin-layer chromatography (TLC) and HPLC, which detected 

0.1−1.0 fmol 7-alkylguanine adducts in rats exposed to different alkenes. Despite occasional 

inefficient labeling and poor recovery of adduct due to depurination, this method has potential 

for use in measuring human exposure to alkenes or their corresponding epoxides as well as the 

endogenously formed 7-alkylguanine adducts. 

Bolt et al. (1997) developed a HPLC method involving derivatization with phenylglyoxal 

and fluorescence detection, using 7-methylguanine as an internal standard, for measuring the 

physiological background of the N7-HEG adduct in DNA isolated from human blood. Using 

this method, the authors were able to detect N7-HEG levels in five individuals ranging between 

2.1 and 5.8 pmol/mg DNA (mean 3.2). Furthermore, Leclercq et al. (1997) developed a method 

based on DNA neutral thermal hydrolysis, adduct micro-concentration, and HPLC coupled to 

single-ion monitoring electrospray mass spectrometry which has a detection limit of 1 fmol 

(10−10 M), allowing the detection of 3 adducts/108 normal nucleotides. Using this method, 

Leclercq et al. (1997) detected a dose-response relationship for N7-HEG after exposing calf 

thymus DNA and blood samples to various doses of EtO. Marsden et al. (2007) used a highly 

sensitive LC-MS/MS assay with selected reaction monitoring that offers a limit of detection of 

0.1 fmol of N7-HEG to establish background levels of N7-HEG (1.1–3.5 adducts/108 

nucleotides) in tissues of rats. Huang et al. (2008) developed an isotope-dilution online solidphase extraction and liquid chromatography coupled with tandem mass spectrometry method 

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with reportedly excellent accuracy, sensitivity, and specificity to analyze N7-HEG in urine 

samples of nonsmokers. This method also demonstrated high-throughput capacity for detecting 

EtO-DNA adducts and may be particularly useful for future molecular epidemiology studies of 

individuals with low-dose EtO exposure. Tompkins et al. (2008) used a high-performance liquid 

chromatography/electrospray ionization tandem mass spectrometry and reported ~8 N7-HEG 

adducts/108

 nucleotides in the livers of control rats. This method was also capable of detecting 

the less prevalent but potentially more biologically significant N1-hydroxyethyl-2’­

deoxyadenosine (N1-HEA), O6-hydroxyethyl-2’-deoxyguanosine (O6-HEG), N6-hydroxyethyl2’-deoxyadenosine (N6-HEA) and N3-hydroxyethyl-2’-deoxyuridine (N3-HEU) adducts. 

However, these minor adducts were below the level of detection in control rat tissue DNA. 

Overall, the sensitivity of EtO adduct detection depends on the method used for analysis. 

Hence, use of appropriate methods is important when analyzing for these adducts and will be 

highlighted in the following discussion. 

C.1.1. Detection of EtO Adducts in In Vitro and In Vivo Systems 

Numerous studies have been conducted to investigate the formation of DNA adducts 

following EtO exposure, in a wide range of experimental models, including cell-free systems, 

bacteria, fungi, Drosophila and experimental animals, as well as in exposed human subjects. 

The following discussion is a review of the available studies of exposure to EtO and DNA adduct 

formation in in vitro systems, laboratory animals, and humans (Boysen et al., 2009; Pauwels and 

Veulemans, 1998; Bolt et al., 1988; Van Sittert and de Jong, 1985). 

C.1.2. In Vitro DNA Binding Studies 

The capacity of EtO to bind to DNA and form DNA adducts has been documented in a 

few in vitro studies. Segerbäck (1990) showed that 14C-labeled EtO reacted in vitro with calf 

thymus DNA to produce N7-HEG adduct as the predominant adduct, with relatively low 

amounts of O6

-HEG and N3-(2-hydroxyethyl)adenine (N3-HEA) adducts. The levels of 

N3-HEA and O6-HEG are 4.4% and 0.5%, respectively, of the N7-HEG levels. Thus, the ratio 

of N7-HEG, N3-HEA and O6-HEG produced in vitro was 200:8.8:1, respectively. In the same 

study, the in vitro reaction products of radiolabeled N-(2-hydroxyethyl)-N-nitrosourea 

(HOEtNU) with calf thymus DNA exhibited a higher relative amount of O6

-HEG, which was 

63% of the N7-HEG formed. The difference in reactivity towards the N7 and O6 positions in 

guanine by these two alkylating agents was explained by the difference in their “s” values. EtO, 

with an s-value of 0.9, has a greater relative preference for reacting with N rather than O atoms 

than does HOEtNU, with an s-values of 0.2. 

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In another study, Li et al. (1992) observed that EtO in aqueous solution incubated with 

calf thymus DNA in vitro for 10 hours produced several 2-hydroxyethyl (HE) DNA adducts 

whose relative yields (nmol/mg DNA) were in the descending order: N7-HEG (330) > N3-HEA 

(39) > N1-HEA (28), N6-HEA (6.2) > N3-HE-Cyt (3.1) > N3-HE-dThd (2.0) > N3-HEU (0.8). 

This in vitro study did not detect the O6-HEG adduct. 

Recently, Tompkins et al. (2009) treated pSP189 shuttle vector plasmid to a range of EtO 

concentrations in water and reported that, of the five 2-hydroxyethyl DNA adducts measurable 

using their LC-MS/MS analytical method, only the N7-HEG adduct was detectable at EtO 

concentrations up to 2,000 μM.5  At the 10 mM concentration, the level of N7-HEG adducts was 

about 19 times higher than that of N1-HEA adducts and about 1,000 times higher than that of 

O6-HEG adducts. At 30 mM, N3-HEU adducts were detectable, but this adduct was not 

quantifiable due to the lack of a suitable internal standard. Detection of the N3-HEU adduct 

implies that the N3-HEC adduct is also formed, as the former is the hydrolytic deamination 

product of the latter Tompkins et al. (2009). No results for the N6-HEA adduct were reported. 

(N3-HEA, N3-HEC, and N3-HET adducts are not measurable by their method.) 

C.1.3. In Vivo Studies―Animal Experiments 

Several studies evaluated N7-HEG levels following one or a range of doses with repeated 

exposures of EtO given by inhalation or intraperitoneal injection in laboratory animals. 

Segerback (1983) showed that in male CBA mice exposed by inhalation to 14C-labeled EtO 

N7-HEG adducts are formed in spleen, testes and liver with half-lives of 24, 20, and 12 hours, 

respectively. 

Walker et al. (1990) conducted a time-course study to investigate the formation and 

persistence of N7-HEG adducts in various tissues such as brain, kidney, liver, spleen, lung and 

kidney of male Fischer 344 rats exposed to one high dose of 300 ppm EtO by inhalation for 

4 consecutive weeks (6 hours/day, 5 days/week) and sacrificed 1–10 days after the end of 

exposure. The N7-HEG adduct was detectable in both target (brain, spleen and WBCs) and 

nontarget (kidney, liver, lung, and testis) tissues with maximum levels (1.5 times control levels) 

seen in brain compared to other tissues 1 day after exposure. The similarities in N7-HEG levels 

in various tissues are possibly due to efficient pulmonary uptake of EtO and rapid distribution by 

the circulatory system. The N7-HEG adduct levels increased linearly for 3–5 days followed by a 

slow removal from DNA with an apparent half-life of 7 days, suggesting that the adduct was 

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The minor adducts may have been present at levels below the limits of detection, which were as follows: 

0.001/106 nucleotides for N7-HEG and N1-HEA; 0.016/106 nucleotides for O6

-HEG; and 0.082/106 nucleotides for 

N3-HEU (Tompkins et al., 2009). 
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probably removed by spontaneous depurination. The calculated in vivo half-life for N7-HEG 

formed by EtO confirms the persistence of this adduct and is consistent with another study in rats 

exposed to another alkylating agent, N-nitrosomethyl-(2-hydroxyethyl)amine (Koepke et al., 

1988). Walker et al. (1990) suggested that the similarity in N7-HEG formation in the target as 

well as nontarget tissues could also be due to factors such as cell replication, location of the 

adducts in the genome, and tissue susceptibility genes, which might be critical determinants 

quantitatively affecting tissue-specific and/or dose-response relationships. 

Using fluorescence-coupled HPLC, Walker et al. (1992a) measured N7-HEG levels in 

DNA of target and nontarget tissues from male B6C3F1 mice and F344 rats exposed to 0, 3, 10, 

33, 100, or 300 (rats only) ppm EtO by inhalation for 4 weeks (6 hours/day, 5 days/week). 

Another group of mice was exposed to 100 ppm EtO for 1, 3, 7, 14, or 28 days (5 days/week). 

The authors reported linear dose-response relationships for N7-HEG in rat tissues following EtO 

exposures between 10 and 100 ppm, with the slope increasing for exposures above 100 ppm. In 

mice, only exposures to 100 ppm EtO resulted in significant increase in N7-HEG levels. Walker 

et al. (1992a) observed N7-HEG adduct levels of 2–6 pmols/mg DNA in control mice and rats, 

while in mice exposed to 100 ppm EtO, N7-HEG levels ranged from 17.5 ± 3.0 (testis) to 

32.9 ± 1.9 (lung) pmol/mg DNA after 4 weeks of exposure. Rats and mice concurrently exposed 

to 100 ppm EtO for 4 weeks showed two- to threefold lower N7-HEG levels in all tissues of 

mice compared to rats, suggesting species differences in the susceptibility to EtO-induced 

genotoxicity. The half-life of N7-HEG in mouse kidney DNA was 6.9 days, and in rat brain and 

lung it was 5.4–5.8 days. The half-lives of N7-HEG adducts in DNA from other tissues of 

mouse and rat were 1.0–2.3 days and 2.9–4.8 days, respectively. The authors suggested that the 

slow linear removal of N7-HEG adducts from the DNA was mainly due to chemical 

depurination, while the rapid removal was due to loss by depurination and DNA repair. Rats 

exposed to 300 ppm EtO showed O6-HEG adducts at a steady-state concentration of ~1 pmol/mg 

DNA. Based on the results from rats and mice, the authors suggested that DNA repair was 

saturated at the concentration of EtO used in the time-course studies and that repeated exposures 

to lower concentrations of EtO should lead to species- and tissue-specific differences in the 

levels of N7-HEG (Walker et al., 1992a). 

Wu et al. (1999a) analyzed DNA from liver, brain, lung and spleen of B6C3F1 mice and 

F344 rats for N7-HEG adducts after exposure to EtO (0, 3, 10, 33, or 100 ppm) for 4 weeks 

(6 h/day, 5 days/week). The authors observed tissue- and species-specific dose-response 

relationships of N7-HEG adducts in the EtO-exposed animals. Mice showed linear 

dose-response relationships for N7-HEG adducts in liver, brain and spleen at exposures between 

3 and 100 ppm, and sublinear responses in lung between 33 and 100 ppm EtO exposure. Rats 
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showed linear increases in adduct levels in liver and spleen DNA between 3 and 100 ppm EtO, 

and sublinear responses in the brain and lung between 33 and 100 ppm EtO exposure. Overall, 

rats and mice exposed to 3 ppm EtO showed 5.3- to 12.5- and 1.3- to 2.5-fold higher N7-HEG 

adducts, respectively, compared to the corresponding unexposed control animals. Thus, results 

from this study suggest species differences, with rats being more susceptible to adduct formation 

than mice, at lower levels of EtO exposure. This study also showed a clear difference in 

N7-HEG levels between unexposed and exposed mice at these lower exposure levels, unlike the 

study of Walker et al. (1992a) discussed above, which is possibly due to the use of a highly 

sensitive gas chromatography high-resolution mass spectrometry (GCHRS) assay in the Wu et 

al. (1999a) study. 

van Sittert et al. (2000) exposed Lewis rats to 50, 100 and 200 ppm EtO by inhalation 

(4 weeks, 5 days/week, 6 h/day) and measured N7-HEG adducts 5, 21, 35 and 49 days after 

cessation of exposure. The authors used mass spectrometry following neutral thermal hydrolysis 

of DNA to release the N7-HEG adducts, which clearly show a difference between control and 

EtO-exposed rats. The mean levels of liver N7-HEG immediately after cessation of exposure to 

50, 100, and 200 ppm were estimated by extrapolation to be 310, 558, and 

1,202 adducts/108 nucleotides, respectively, while the mean level in control rats was 

2.6 adducts/108 nucleotides. By 49 days postexposure, N7-HEG adducts had returned to near 

background levels. The N7-HEG levels in liver DNA showed a linear response between 0 and 

200 ppm EtO, suggesting that detoxification and DNA repair processes were not saturated up to 

the highest exposure level tested. The authors observed statistically significant linear 

relationships between mean N7-HEG levels at “day 0” postexposure and (1) Hprt mutant 

frequencies at expression times of 21/22 and 49/50 days postexposure, (2) SCEs at 5 days 

postexposure, or (3) high-frequency cells measured 5 days postexposure. The authors also 

observed that SCEs and high-frequency cells continued to be present at 21-days postexposure 

and significantly correlated with N7-HEG adducts at that time. However, induction of 

micronuclei, chromosome breaks or translocations did not show a dose-response relationship. 

Nivard et al. (2003) showed that in male Drosophila flies EtO exposure (2–1,000 ppm) 

by inhalation for 24 hours induced a linear dose-response relationship for N7-HEG adduct 

formation (0.15 to 105.4 adducts/106

 nucleotides) over the entire dose range, as detected by 

32P-postlabeling assay. The N7-HEG adducts were undetectable in controls (i.e., below the 

detection limit of 1 adduct/108 nucleotides). 

A study by Rusyn et al. (2005) tested the hypothesis that EtO exposure results in an 

accumulation of apurinic/apyrimidinic (AP) sites in DNA and induces changes in expression of 

genes involved in DNA base excision repair (BER). The authors exposed male F344 rats by 

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inhalation to 100 ppm EtO or ethylene (40 or 3,000 ppm) for 1, 3, or 20 days (6 h/day, 

5 days/week) and sacrificed them 2, 6, 24, or 72 hours after a single-day exposure. Brain and 

spleen were considered as target sites for EtO-induced carcinogenesis, and liver as a nontarget
 

organ. Rusyn et al. (2005) observed a time-dependent increase in N7-HEG in brain, spleen 

(target organs) and liver (nontarget organ) and in N-(2-hydroxyethyl)valine (HEVal) adducts in 

hemoglobin. However, they could not detect any increase in AP sites in control or EtO-exposed 

rats for any given duration or dose of exposure. Rats exposed to EtO for 1 day showed a 

threefold to sevenfold decrease in expression of the DNA repair enzyme 3-methyladenine-DNA 

glycosylase in the brain and spleen, while rats exposed to EtO for 20 days showed increased 

expression of hepatic 8-oxoguanine DNA glycosylase, 3-methyladenine-DNA glycosylase, AP 

endonuclease, polymerase beta, and alkylguanine methyltransferase by 20–100%. Levels of 

brain AP endonuclease and polymerase beta were increased by <20% only in rats exposed to 

3,000 ppm ethylene for 20 days. Results from this study suggest that EtO-induced DNA damage 

is repaired without accumulation of AP sites or involvement of the BER pathway in target 

organs. The authors conclude that accumulation of AP sites is not likely a primary mechanism 

for mutagenicity and carcinogenicity of EtO, and further suggest that minor DNA adducts such 

as O6

-HEG or N1-HEA are likely to be involved in mutagenicity. In fact, in a previous study 

from the same group (Walker et al., 1992a), steady-state concentrations of O6-HEG were 

reported after 4 weeks of exposure with 300 ppm EtO, a finding which warrants further 

investigation. 

Marsden et al. (2007) have shown that intraperitoneal administration of a single or three 

daily doses of EtO (0.01–1.0 mg/kg) induced dose-related increases in N7-HEG adduct levels in 

male F344 rats, except at the lowest dose (0.01 mg/kg), where N7-HEG levels were similar to 

endogenous levels detected in control animals. Further, they observed that N7-HEG adducts did 

not accumulate in rats given three daily doses of EtO. 

Recently, using a dual-isotope approach combining HPLC-accelerated mass spectrometry 

with LC-MS/MS analysis, Marsden et al. (2009) observed linear dose-response relationships for 

(

14C)N7-HEG adducts (0.002 to 4 adducts/108

 nucleotides) in spleen, liver and stomach DNA of 

F344 rats after exposure to low, occupationally relevant concentrations of (14C)EtO (0, 0.0001, 

0.0005, 0.001, 0.005, 0.01, 0.05, and 0.1 mg/kg daily for 3 consecutive days, with the rats killed 

4 h after the last exposure). These results suggest that by using a highly sensitive assay, it is 

possible to measure the N7-HEG adducts resulting from low EtO exposures above the 

background adduct levels. 

Otteneder and Lutz (1999) reviewed the quantitative relationship between DNA adduct 

levels and tumor incidence in rodents that received repeated administration of EtO. The authors 

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observed a correlation with tumor incidence when the DNA adduct levels measured at a given 

dose were normalized to the TD50 dose (the dose which results in 50% tumor incidence in a
 

two-year study). The calculated adduct level in mice associated with the hepatocellular TD50
 

was 812 N7-HEG adducts/108 normal nucleotides. 

C.1.4. In Vivo Studies―Human Subjects 

A few studies have examined the effect of EtO exposure on humans, particularly in 

occupational settings, and these have been comprehensively reviewed by Kolman et al. (2002). 

In that review, the authors examined the use of hemoglobin and DNA adducts as biomarkers of 

EtO exposure and the roles of genetic polymorphisms and confounding factors. Kolman et al. 

(2002) also described the genotoxic effects of EtO in mammalian cells and summarized the 

genotoxic and carcinogenic effects of EtO in humans. Some of the relevant studies in humans 

are briefly discussed below. 

An immunoslot blot assay was used to analyze N7-HEG levels in white blood cell DNA 

from individuals exposed to EtO (2–5 ppm) and from controls van Delft et al. (1994). The 

authors reported 0.1 and 0.065 N7-HEG adducts/106 nucleotides, respectively, in EtO-exposed 

individuals (n = 42) and controls (n = 29) by this method. However, these differences were not 

statistically significant. 

In a study involving 58 sterilizer operators exposed to low and high levels of EtO (≤32 

and >32 ppm-hour, respectively) and 6 nonexposed controls from different hospitals, Yong et al. 

(2007) examined N7-HEG adducts in granulocyte DNA. During the four-month study, the 

cumulative exposure to EtO (ppm-hour) was estimated before the blood sample collection. After 

adjusting for cigarette smoking and other potential confounders, the mean N7-HEG adduct levels 

in the nonexposed, low-, and high-exposure groups were 3.8, 16.3, and 

20.3 adducts/107 nucleotides, respectively, with considerable interindividual variation (range: 

1.6–241.3 adducts/107 nucleotides). However, these differences in mean adduct level were not 

statistically significant. The large variability across workers may reflect differences in their 

recent exposure patterns because granulocytes have a lifespan of less than a day. Also, the study 

did not find a significant correlation between the levels of N7-HEG adducts and HEVal adducts. 

Mayer et al. (1991) observed an apparent suppression of DNA repair capacity in 

EtO-exposed individuals as measured by the DNA repair index, i.e., the ratio of unscheduled 

DNA synthesis and N-acetoxy-2-acetylaminofluorene (NA-AAF)-DNA binding, (p < 0.01). In 

this study, 34 sterilization unit workers of a large university hospital and 23 controls working in 

the university library were used. Overall, this study demonstrates significant correlations 

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between EtO-induced hemoglobin adduct levels and SCEs and the number of high frequency 

cells, at low levels of EtO exposure (≤1 ppm), independent of smoking history. 

C.1.5. DNA Adducts―Summary 

In summary, EtO predominantly forms N7-HEG adducts. Minor adducts are O6

-HEG 

adducts and reaction products with N1, N3 and N6 of adenine and with N3 of cytosine, uracil and 

thymine in vitro. However, the minor adducts are not observed to the same extent in vivo, which 

may reflect a limitation in the sensitivity of the adduct assays available to date. Repeated 

inhalation exposure of EtO induces N7-HEG adducts in both target organs (brain, spleen, and 

white blood cells) and nontarget organs (kidney, liver, and lung) in rodents, with an apparent 

half-life of 3–6 days in rats and 1–3 days in mice (Walker et al., 1992a). The dose-response 

relationship of N7-HEG and EtO exposure is influenced by the analytical method used, which 

also affects the background (endogenous) levels of adducts observed in unexposed rodents. 

Steady-state levels of O6-HEG adducts (1 pmol/mg DNA) are detected in rats exposed by 

inhalation to high doses of EtO (300 ppm) which are ~250–300 times lower than the N7-HEG 

levels (Walker et al., 1992a). Although N7-HEG adducts are likely to be removed by 

depurination forming apurinic/apyrimidinic (AP) sites, Rusyn et al. (2005) showed that DNA 

damage induced by exposure to EtO is repaired without accumulation of AP sites and without 

affecting base excision repair (BER) in target organs of Fischer rats. There are only two studies 

available on EtO-induced DNA adducts in human populations. Although higher levels of 

N7-HEG DNA adducts were observed in human white blood cells (van Delft et al., 1994) and 

granulocytes (Yong et al., 2007) of exposed cases compared to controls, these differences were 

not statistically significant, possibly due to high interindividual variability. 

C.1.6. EtO-Hemoglobin Adducts 

Several studies have shown that EtO-induced hemoglobin adducts (e.g., HEVal) are good 

biomarkers of exposure for this compound in human studies and that predicted hemoglobin 

adduct levels resulting from exposure to ethylene or EtO are in agreement with measured values 

(Boogaard, 2002; Yong et al., 2001; Fennell et al., 2000; Tates et al., 1999; Walker et al., 1992a; 

Britton et al., 1991). Csanady et al. (2000) found a good agreement between the predicted and 

measured hemoglobin adduct levels in humans. However, in rodents, hemoglobin adducts were 

under-predicted by a factor of 2 to 3, while DNA adduct levels were comparable, suggesting 

inconsistencies between the two biomarkers. Walker et al. (1993) also observed that the 

relationships between HEVal and N7-HEG concentrations varied with length of exposure, 

interval since exposure, species, and tissue, which may be due to differences in formation, 

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persistence, repair, and chemical depurination of the DNA adduct. Thus, Walker et al. (1993) 

suggested that HEVal adducts do not provide accurate prediction of DNA adducts in specific 

tissues of humans under actual exposure conditions. In summary, HEVal adducts do not appear 

to be predictable markers for DNA adducts. 

C.2. GENE MUTATIONS 

EtO has consistently yielded positive results, at both the gene and chromosome levels, in 

a broad range of in vitro and in vivo mutational assays, including those performed in bacteria, 

fungi, yeast, insects, plants, Drosophila and rodents, in both repair-deficient and proficient 

organisms, and in mammalian cell cultures, including cells from humans (reviewed inIARC, 

2008; Kolman et al., 2002; Thier and Bolt, 2000; Natarajan et al., 1995; Vogel and Natarajan, 

1995; IARC, 1994b; Dellarco et al., 1990). 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 the lung (LacI locus) (Sisk et al., 

1997), in T-lymphocytes (Hprt locus) (Walker et al., 1997), and bone marrow and testes in 

B6C3F1 LacI transgenic mice (Recio et al., 2004) have been observed in mice exposed to EtO 

via inhalation at concentrations similar to those used in the carcinogenesis bioassays (NTP, 

1987), clearly documenting that EtO is a DNA-reactive mutagenic agent. Furthermore, 

occupational studies provide evidence for the genotoxic potential of EtO. 

C.2.1. Bacterial Systems 

Studies have been conducted to investigate the ability of EtO to induce gene mutations in 

bacterial systems. Victorin and Stahlberg (1988) treated Salmonella typhimurium strain TA100 

with EtO at concentrations of 1–200 ppm for 6 hours and demonstrated that EtO was mutagenic 

in this system. In another study, Agurell et al. (1991) compared EtO and propylene oxide (two 

alkylating agents) for genotoxic effectiveness in various test systems. The abilities of the two 

compounds to induce point mutations in S. typhimurium strains TA 100 and TA1535 were 

approximately equal. EtO induced a dose-dependent increase in the number of revertants in both 

tester strains. No toxic effects were observed under the conditions tested. 

In contrast, Agurell et al. (1991) found EtO to be 5–10 times more effective than 

propylene oxide with respect to gene conversion and reverse mutation in the Saccharomyces 

cerevisiae D7 and S. cerevisiae RS112 strains. The greater effectiveness of EtO over propylene 

oxide in inducing these types of mutations was probably due to the difference in these 

compounds' abilities to cause strand breaks via alkylation of DNA-phosphate groups. 
 

 

 

 

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Mutagenicity studies of EtO have also been conducted using different Escherichia coli 

strains. Kolman (1985) investigated the influence of the uvrB and umuC genes on the induction 

of LacI-mutants and nonsense mutants by EtO in the LacI gene of E. coli and found that uvrB 

gene mutation was associated with higher mutation frequencies whereas umuC mutation did not 

significantly affect the induction of LacI mutations. Thus, mutations induced by EtO were 

enhanced by a lack of excision repair but not influenced by changes in error-prone repair. In 

another study by the same group of authors (Kolman and Näslund, 1987), the mutagenicity of 

EtO in E. coli B strains with different repair capacities was investigated. Deficiencies in 

excision repair (uvrA, polA) led to considerable increases in mutation frequency compared to the 

wild-type strain and strains deficient in error-prone repair (recA, lexA). 

The induction of specific-locus mutations in the adenine-3 (ad-3) region of a 

two-component heterokaryon (H-12) of Neurospora crassa by EtO was studied by de Serres and 

Brockman (1995). The objective of this study was to compare EtO's mutational spectrum for 

induced specific-locus mutations with those of other chemical mutagens. Conidial suspensions 

were treated with five different concentrations of EtO (0.1–0.35%) for 3 hours. The results from 

these experiments showed (1) the dose-response curve for EtO-induced specific-locus mutations 

in the ad-3 region was linear, with an estimated slope of 1.49 ± 0.07, and (2) the maximum 

forward-mutation frequency was between 10 and 100 ad-3 mutations per 106

 survivors. The 

overall data demonstrate that EtO-induced ad-3 mutations were the result of a high percentage 

(96.9%) of gene/point mutations at the ad-3A and ad-3B loci. 

C.2.2. Mammalian Systems 

EtO has yielded positive results in virtually all in vitro mammalian cell culture systems 

tested, including human cells (IARC, 2008; Kolman et al., 2002; Thier and Bolt, 2000; Preston, 

1999; Natarajan et al., 1995; Vogel and Natarajan, 1995; IARC, 1994b; Dellarco et al., 1990). 

Only select in vitro studies of human cells will be reviewed here. For reviews of other in vitro 

studies using mammalian cell cultures, see the aforementioned references. 

C.2.2.1. In Vitro Studies 

Single base pair deletion and base substitution (both transitions and transversions) 

mutations were observed in the HPRT gene in human diploid fibroblasts exposed to EtO 

(Bastlová et al., 1993). Sequence analysis revealed that EtO induces many different kinds of 

HPRT mutations—several mutants had large HPRT gene deletions, a few mutants showed 

deletion of the entire HPRT gene, and other mutants had a truncated HPRT gene; overall, as 

many as 50% were large deletions. In another study by the same group of authors (Lambert et 

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al., 1994), comparisons of the HPRT mutations in human diploid fibroblasts were made for three 

urban air pollutants (acetaldehyde, benzo[a]pyrene, and EtO). Large genomic deletions in the 

HPRT gene were observed for acetaldehyde and EtO, whereas benzo[a]pyrene induced point 

mutations. The authors concluded that the HPRT locus could be a useful target for the study of 

chemical-specific mutational events (Lambert et al., 1994). 

The effect of EtO as a pretreatment or posttreatment to ionizing radiation was studied by 

Kolman and Chovanec (2000). Human diploid VH-10 fibroblasts were either preexposed to 

gamma rays (0.66 Gy/minute or 10 Gy/minute) and then treated with EtO (2.5 mMh) or 

pretreated with EtO and then exposed to gamma rays. Cell killing/cytotoxicity, DNA 

double-strand breakage, and mutagenicity were studied in both types of exposures. The results 

of the study indicate that preexposure of the cells to gamma radiation (1 Gy) followed by 

treatment with EtO (2.5 mMh) led to an additive interaction, irrespective of the dose rate. On the 

other hand, pretreatment with EtO followed by gamma ray exposure resulted in an antagonistic 

effect, which was most pronounced in the high-dose group (10 Gy/minute). In this group, the 

mutant frequency was half that of the sum of the mutant frequencies after the individual 

treatments. The authors suggest that one possible explanation for the difference in the results is 

that DNA damage induced by preexposure to gamma radiation persisted into the EtO treatment 

phase, and EtO might also prohibit DNA repair enzymes from operating, thus both treatments 

contributed to the mutant frequency. However, when cells were exposed to gamma radiation 

following EtO treatment, the cells may have been able to repair, at least in part, the promutagenic 

lesions induced by the gamma rays. 

Tompkins et al. (2009) investigated the mutagenicity of EtO-derived DNA adducts in a 

supF forward mutation assay. Aliquots of pSP189 plasmid containing the supF gene were 

exposed to various concentrations of EtO in water to induce the formation of DNA adducts. The 

plasmids were then transfected into human embryonic adenovirus-transformed kidney (Ad293) 

cells and allowed to replicate to propagate any mutations. Replicated plasmids were isolated and 

used to treat E. Coli indicator bacteria under conditions in which only bacteria containing the 

plasmid can grow; nonmutant colonies appear dark blue and mutant colonies appear white or 

pale blue. Two studies were conducted: Study 1, in which the plasmid was incubated with EtO 

concentrations ranging from 10–2,000 μM at 22oC for 4 hours, and Study 2, in which the 

plasmid was treated under “refined” conditions optimised to produce more of the minor 

2-hydroxyethyl adducts, which involved incubation of the plasmid with EtO concentrations 

ranging from 10–100 mM at 37oC for 24 hours. For Study 1, Tompkins et al. (2009) reported 

that N7-HEG was the only detectable adduct of the five they measured (before transfection; see 

Section C.1.2 above) and there was no clear exposure-response relationship for the relative 
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mutation frequency. In Study 2, N1-HEA and O6

-HEG adducts were also quantifiable, but at 

lower levels than the N7-HEG adduct, and there was an apparent exposure-response relationship 

for the relative mutation frequency for plasmids exposed to the 10 and 30 mM EtO 

concentrations. Plasmids exposed to higher concentrations of EtO failed to produce any E. Coli 

colonies; this was attributed to excessive strand breaks in the plasmid DNA at those 

concentrations. For the DNA damage induced by EtO-derived adducts, this limitation in the 

assay imposes a short response range for the relative mutation frequency for the mutations 

measured by the assay—the relative mutation frequency was 5.34 for plasmids exposed to 

30 mM and no E. Coli colonies were produced with plasmids exposed to the next highest EtO 

concentration of 50 mM, due to excessive DNA strand breaks. 

Tompkins et al. (2009) concluded that EtO is a relatively weak mutagen and that their 

results suggest that a certain level of total DNA adducts or of specific promutagenic adducts 

must be achieved before mutations become detectable above background levels. However, 

several issues pertaining to the study raise concerns about the interpretation of the results. For 

example, two solvent controls were used in the study—Solvent Control 1 was prepared in “a 

separate fume hood to totally exclude any possibility of [EtO] contamination” and Solvent 

Control 2 was prepared “alongside the [EtO] reactions.” Solvent Control 1 was used as the 

referent group for the relative mutation frequency determinations. In two replicates, Solvent 

Control 2 had a relative mutation frequency of 3.0 and 2.6 compared to Solvent Control 1. If this 

difference reflects a real difference between the two different solvent control preparations, it 

raises the possibility that cross-contamination may have been a problem and, if any 

cross-contamination also occurred across the different EtO concentrations, this could have 

dampened any exposure-response relationship. In addition, if the “refined conditions” for 

plasmid treatment used to produce more of the minor (more directly promutagenic) adducts in 

Study 2, which included incubation at a temperature more comparable to mammalian body 

temperatures, had also been used for Study 1, a different adduct profile, and different relative 

mutation frequencies, might have resulted. The authors themselves acknowledged that “[in] 

order to categorically determine whether a threshold exists for [EtO] in this system, a more 

detailed examination of the dose-response relationship using the optimised reaction protocol and 

including more concentrations around the mutagenic range is needed” (Tompkins et al., 2009). 

Moreover, there is uncertainty about the generalizability of mutagenicity results from this in vitro 

experimental system to the mutagenicity and genotoxicity induced by EtO exposure in vivo; for 

example, human embryonic adenovirus-transformed kidney cells were used for plasmid 

replication and mutation production, but embryonic kidneys are not a known target for EtO 

carcinogenesis. 
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C .2.2.2. In Vivo Studies―Experimental Animals 

The results of in vivo studies on the mutagenicity of EtO following ingestion, inhalation, 

or injection have also been consistently positive (e.g., Tates et al., 1999). For example, increases 

in the frequency of gene mutations in T-lymphocytes (Hprt locus) (Walker et 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 lung 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). These and 

other key in vivo studies are discussed in more detail below. 

An approach for determining mutational spectra in exon 3 of the Hprt gene in splenic 

T-lymphocytes of B6C3F1 mice was developed by Walker and Skopek (1993). Mice (12 days 

old) were given 2, 6, or 9 single intraperitoneal (i.p.) injections of 100 mg/kg EtO every other 

day or 30, 60, 90, or 120 mg/kg of EtO for 5 consecutive days to achieve different cumulative 

doses. In mice exposed every other day, cumulative doses of 200, 600, and 900 mg/kg produced 

average mutant frequencies of 15 × 10−6

, 45 × 10−6, and 73 × 10−6, respectively, 8 weeks after 

dosing began. However, in mice exposed daily, cumulative doses of 150, 300, 450, and 

600 mg/kg yielded average mutant frequencies of 4 × 10−6

, 8 × 10−6, 11 × 10−6, and 16 × 10−6 , 

20 weeks after initiation of dosing. Hprt mutants obtained from mice exposed to 600 or 

900 mg/kg EtO were isolated and analyzed for mutations, specifically in exon 3. DNA 

sequencing showed base-pair substitutions, transitions, and transversions. The results suggested 

both modified guanine and adenine bases being involved in EtO-induced mutagenesis. 

The same group of authors (Walker et al., 1997) studied the in vivo mutagenicity of EtO 

at the Hprt locus of T-lymphocytes following inhalation exposure of male B6C3F1 LacI 

transgenic mice. Big Blue mice at 6–8 and 8–10 weeks of age were exposed to 0, 50, 100, or 

200 ppm EtO for 4 weeks (6 h/day, 5 days/week). T-cells were isolated from the thymus and 

spleen and cultured in the presence of concanavalin A, IL-2, and 6-thioguanine. Mice were 

sacrificed at 2 hours, 2 weeks, and 8 weeks after exposure to 200 ppm EtO to determine a time 

course for the expression of Hprt-negative lymphocytes in the thymus. The results of this study 

showed that following 2 hours of exposure, the Hprt mutant frequency in the thymic 

lymphocytes of the exposed mice was increased and reached an average maximum mutant 

frequency of 7.5 ± 0.9 ×10−6

 at 2 weeks postexposure when compared to 2.3 ± 0.8 × 10−6 in the 

thymic lymphocytes of control mice. Dose-related increases in Hprt mutant frequency were 

found in thymic lymphocytes from mice exposed to 100 and 200 ppm EtO. Furthermore, a 
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greater mutagenic efficiency (mutations per unit dose) was found at higher concentrations than at 

lower concentrations of EtO in splenic T-cells. The average induced mutant frequencies in 

splenic T-cells were 1.6, 4.6, and 11.9 × 10−6 following exposures to 50, 100, or 200 ppm EtO, 

respectively. For the analysis of the LacI mutations, lymphocytes (both B- and T-cells) were 

isolated from the spleen in the same animals. Two of three EtO-exposed mice at the 200 ppm 

exposure level demonstrated an elevated LacI mutant frequency. The authors suggest that these 

elevations were probably due to the in vivo replication of preexisting mutants and not to the 

induction of new mutations associated with EtO exposure. The results of this study indicate that 

repeated inhalation exposures to high concentrations of EtO produce dose-related increases in 

mutations at the Hprt locus of T-lymphocytes in male LacI transgenic mice. 

LacI mutant frequencies as a result of exposure to EtO were further investigated by Sisk 

et al. (1997). Male transgenic LacI B6C3F1 mice (n = 15) were exposed to 0, 50, 100, or 

200 ppm EtO for 4 weeks (6 hours/day, 5 days/week) and were sacrificed at 0, 2, or 8 weeks 

after the last EtO exposure. To determine the LacI mutant frequency, the LacI transgene was 

recovered from several tissues, including lung, spleen, germ cells and bone marrow, selected 

because they were the target sites for tumor formation (particularly lung tumors and lymphomas) 

in chronic bioassays or germ cells. The results of this study indicate that the LacI mutant 

frequency in lung was significantly increased at 8 weeks postexposure to 200 ppm EtO. In 

contrast, no significant increase in the LacI mutant frequencies was observed in the spleen, bone 

marrow or germ cells at either 2 or 8 weeks following exposure. These results suggest that a 

4-week inhalation exposure to EtO is mutagenic in lung but not in other tissues examined under 

similar conditions. The authors predict that the lack of mutagenic response in other tissues 

examined is probably because of large deletions that were either not detected or recovered in the 

current lambda-based shuttle vector systems. Based on the above study, the authors also suggest 

that the primary mechanism of EtO-induced mutagenicity in vivo is likely through the induction 

of deletions. 

Tates et al. (1999) exposed rats to EtO via three routes: a single i.p. injection 

(10−80 mg/kg), ingestion of drinking water (4 weeks at concentrations of 2, 5, and 10 mM), or 

inhalation (50, 100, or 200 ppm for 4 weeks, 5 days/week, 6 hours/day). The goal of this study 

was to measure the induction of Hprt mutations in splenic lymphocytes using a cloning assay. 

Mutagenic effects of EtO following EtO administration via the three routes were compared in the 

Hprt assay based on blood doses, which were determined from HEVal adduct levels in 

hemoglobin. Exposure to EtO via both injection and ingestion of drinking water led to a 

statistically significant dose-dependent induction of mutations (up to 2.3- and 2.5-fold increases 

in mutant frequency compared to background, respectively). Exposure via inhalation also caused 
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a statistically significant increase in mutant frequency, although to a lesser extent (up to 1.4-fold 

over background). Plotting of the mutagenicity data for the three exposure routes against blood 

doses as a common denominator indicated that, at equal blood doses, the order of increased 

mutant frequency was i.p. injection > ingestion (drinking water) > inhalation. In the injection 

experiments, there was evidence for a saturation of detoxification processes at the highest doses, 

although such effects were not seen following subchronic administration. Taken together, the 

mutagenicity data from this study provide consistent results, showing that exposure to EtO gives 

rise to a linear dose-dependent increase in mutant frequency. 

In a study by Recio et al. (2004), male Big Blue (LacI transgenic) B6C3F1 mice were 

exposed to 0, 25, 50, 100, or 200 ppm EtO (6 hours per day, 5 days per week) for 12, 24, and 

48 weeks. An unambiguous mutagenic response in the bone marrow was observed only after 

48 weeks, with dose-related LacI mutant frequencies of 7.3 × 10−5, 11.3 × 10−5, 9.3 × 10−5 , 

14.1 × 10−5, and 30.3 × 10−5 . The mutagenic response in bone marrow is consistent with a linear 

exposure-response relationship, contrary to the assertion by Recio et al. (2004) which appears to 

be based on a misleading plotting scale. Mutant frequencies from testes (seminiferous tubules) 

were significantly greater than in controls at 25, 50, and 100 ppm (48-week exposure). No 

difference between the control and treated groups was observed in the LacI mutant frequency 

after 48 weeks of 200 ppm EtO exposure. The authors suggest that this was probably due to 

testicular toxicity. Furthermore, a mutation spectrum analysis of induced mutations in bone 

marrow indicated a decrease in mutations at G:C base pairs and an increase at A:T base pairs, 

exclusively in A:T to T:A transversions; however, the mutation spectrum from testes was similar 

to that of the untreated animals. The difference in mutation spectrum between the two tissues 

was probably due to differences in the repair of the DNA adducts formed. 

Mutations in oncogenes (Kras, Hras) and in the p53 tumor suppressor gene have been 

studied in tumor tissues of several types from B6C3F1 mice exposed to EtO. Hong et al. (2007) 

obtained tumor tissues from lung, harderian gland and uterus from a 2-year study (NTP, 1987) in 

which male and female mice were exposed to 0, 50, or 100 ppm EtO by inhalation 6 hours/day, 

5 days/week and from control mice from other NTP 2-year bioassays. The authors analyzed the 

tissues for Kras mutations in codons 12, 13, and 61. A high frequency of Kras mutations 

(23/23 examined, 100%) was observed in EtO-induced lung neoplasms compared to spontaneous 

lung neoplasms (27/108, 25%). EtO-induced lung neoplasms predominantly exhibited 

GGT-GTT mutations in codon 12 (21/23), a transversion that was rare in spontaneous lung 

tumors (1/108). A similar spectrum of Kras mutations was detected in EtO-induced lung 

neoplasms regardless of histological subtype (adenomas or carcinomas) or dose group. In the 

case of Harderian gland neoplasms, a high frequency (18/21, 86%) of Kras mutations was 
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detected in EtO-induced neoplasms compared to spontaneous tumors (2/27, 7%). The 

predominant mutations in EtO-induced harderian gland neoplasms consisted of GGC to CGC 

transversions at codon 13 and GGT to TGT transversions at codon 12, neither of which was 

observed in the spontaneous tumors. When the six EtO-induced uterine neoplasms were 

examined (there were no uterine tumors in the controls), the predominant mutation was a GGC to 

GGT transition in codon 13 (5/6, 83%). Based on the above results, the authors propose that the 

prominent targeting of guanine bases in the lung and harderian gland neoplasms suggests that the 

formation of N7-HEG adducts by EtO plays a role in the induction of these tumors. The authors 

further propose that EtO can specifically target the Kras gene in multiple types of tissues and that 

this is a critical component of EtO-induced tumorigenesis and is of potential relevance to 

humans. 

In an earlier study by the same group of authors (Houle et al., 2006), mammary 

carcinoma tissues from the same NTP study of mice exposed to EtO (0, 50, or 100 ppm) 

mentioned above were examined for p53 protein expression and for p53 (exons 5-8) and Hras 

(codon 61) mutations. The authors supplemented the number of spontaneous mammary 

carcinomas with tissues from female control mice in other NTP studies. P53 protein expression 

was detected in 67% (8/12) of the EtO-induced mammary carcinomas and 42% (8/19) of the 

spontaneous tumors; however, expression levels were about 6-times higher in the EtO-induced 

than in the spontaneous tumors. P53 mutations were observed in 67% (8/12) of the EtO-induced 

mammary carcinomas and 42% (8/19) of the spontaneous tumors. Hras mutations were detected 

in 33% (4/12) of the EtO-induced mammary carcinomas and 26% (5/19) of the spontaneous 

tumors of the samples. While the mutation levels for these two genes were not substantially 

elevated in the EtO-induced mammary carcinomas compared to the spontaneous tumors, a shift 

in the mutational spectrum was observed, with EtO-induced Hras mutations exhibiting a 

preference for A-to-G and A-to-T transversions while spontaneous Hras mutations exhibited a 

preference for C-to-A transversions and EtO-induced p53 mutations exhibiting a base preference 

for guanine while spontaneous p53 mutations exhibited a preference for cytosine. In addition, 

concurrent Hras and p53 mutations were more common in the EtO-induced tumors than in the 

spontaneous tumors. Based on the results of the above two studies, it is suggested that the purine 

bases serve as primary targets for mutations induced by EtO, while mutations of these genes 

involving cytosine appears to be a more common spontaneous event. 

In vivo exposure to EtO also induced heritable mutations or effects in germ cells in 

rodents (IARC, 1994b). EtO induces dominant lethal effects in mice and rats and heritable 

translocations in mice (Generoso et al., 1990; Lewis et al., 1986). Generoso et al. (1988); 

Generoso et al. (1986) have reported that short bursts of EtO at high concentrations, such as 
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those that may occur in the workplace, may present a greater risk to germ cell damage than 

cumulative, long-term exposure to lower levels. 

Dominant-lethal mutations were investigated by Generoso et al. (1986) by conducting 

two studies (dose response and dose rate) in mice exposed to different doses of EtO. 

Dominant-lethal responses were assessed based on matings involving sperm exposed as late 

spermatids and early spermatozoa, since these are the stages most sensitive to EtO exposure. In 

the dose-response study, male mice were exposed by inhalation to 300 ppm, 400 ppm, or 

500 ppm EtO, 6 hours per day, for 4 consecutive days. A dose-related increase in 

dominant-lethal mutations was observed. In the dose-rate study, mice were given a total 

exposure of 1,800 ppm × hours per day, also for 4 consecutive days, delivered either as 300 ppm 

in 6 hours, 600 ppm in 3 hours, or 1,200 ppm in 1.5 hours. Dominant-lethal responses increased 

with increasing concentration level, indicating a dose-rate effect for the production of 

dominant-lethal mutations. 

C.2.2.3. in Vivo Studies―Humans 

In humans, workers occupationally exposed to EtO have been studied using different 

physical and biological measures (Tates et al., 1991). Blood samples from 9 hospital workers 

and 15 factory workers engaged in sterilization of medical equipment with EtO and from 

matched controls were collected. Average exposure levels during 4 months (the lifespan of 

erythrocytes) prior to blood sampling were estimated from levels of HEVal adducts in 

hemoglobin. The adduct levels were significantly increased in hospital workers and factory 

workers and corresponded to a 40-hour time-weighted average of 0.025 ppm in hospital workers 

and 5 ppm in factory workers. Exposures were usually received in bursts, with EtO 

concentrations in air ranging from 22 to 72 ppm in hospital workers and 14 to 400 ppm in factory 

workers. All blood samples were analyzed for HPRT mutant frequencies, chromosomal 

aberrations, micronuclei and SCEs. Mutant frequencies were significantly increased in factory 

workers but not in hospital workers. The chromosomal aberration and SCE results are discussed 

in the respective sections below. 

The same authors (Tates et al., 1995) conducted another study of workers in an EtO 

production facility. HPRT mutations were measured in three exposed groups and one unexposed 

group (seven workers per group). Contrary to the earlier study, 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 was conducted to examine a possible causal 
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relationship between EtO exposure and a cluster of cancers (mostly breast) in nurses exposed to 

EtO in the Eger hospital. Controls were female hospital workers in the respective cities. The 

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. 

C.2.3. Gene Mutations―Summary 

In summary, there is sufficient evidence for mutagenicity of EtO in various organisms 

(prokaryotes, eukaryotes, in vitro and in vivo in rodents and in vitro in human cells) tested in a 

variety of mutational assays. In addition, increases in mutations in specific oncogenes and tumor 

suppressor genes in EtO-induced mouse tumors have been reported. Dominant-lethal mutations 

have also been observed in several in vivo studies. Although data in humans are limited, there is 

some evidence of increased frequencies of mutations from occupational studies. 

C.3. CHROMOSOMAL ABERRATIONS 

The induction and persistence of EtO-induced chromosomal alterations have been studied 

both in in vitro and in vivo systems in rodent and monkey models (Lorenti Garcia et al., 2001; 

Farooqi et al., 1993; Lynch et al., 1984b; Kligerman et al., 1983). In addition, several studies 

examined the association of chromosomal aberrations and EtO exposure in humans (WHO, 

2003; Lerda and Rizzi, 1992; Galloway et al., 1986; Clare et al., 1985; Sarto et al., 1984a; 

Stolley et al., 1984; Pero et al., 1981; Thiess et al., 1981). Chromosomal aberrations have been 

linked to an increased risk of cancer in several large prospective studies (e.g., Boffetta et al., 

2007; Rossner et al., 2005; Hagmar et al., 2004; Liou et al., 1999). This section discusses key 

studies on EtO and chromosomal aberrations. 

Lorenti Garcia et al. (2001) studied the effect of EtO on the formation of chromosomal 

aberrations in rat bone-marrow cells and splenocytes following in vivo exposure. Rats were 

exposed to EtO either chronically by inhalation (50–200 ppm, 4 weeks, 5 days/week, 

6 hours/day) or acutely by i.p. injection at dose levels of 50–100 ppm. Frequencies of both 

spontaneous and EtO-induced chromosomal aberrations (and other endpoints, such as 

micronucleus formation and SCEs, which are discussed in Sections 3.3.2.4 and 3.3.2.5) were 

determined in the splenocytes and bone-marrow cells following in vivo mitogen stimulation. No 

significant increase in chromosomal aberrations was observed from the chronic or acute 

exposures. In another study, by Kligerman et al. (1983) , no increase in chromosomal 
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aberrations was observed in peripheral blood lymphocytes from rats exposed to EtO by 

inhalation at concentrations of either 50, 150, or 450 ppm, for 6 hours per day, for 1 and 3 days. 

A recent study by Donner et al. (2010) in mice, however, showed clear, statistically 

significant increases in chromosomal aberrations with longer durations of exposure (≥12 weeks). 

Male B6C3F1 mice were exposed by inhalation to 0, 25, 50, 100, or 200 ppm EtO, 5 days/week, 

6 hours/day, for 6, 12, 24, or 48 weeks. The frequency of total chromosomal aberrations in 

peripheral blood lymphocytes was statistically significantly increased after 12 weeks exposure to 

100 or 200 ppm EtO. By 48 weeks, statistically significant increases were observed for all the 

exposure groups. In addition, reciprocal translocation frequencies were statistically significantly 

increased in spermatocytes for all the exposure groups at 48 weeks. Ribeiro et al. (1987) 

similarly observed chromosomal aberrations in mouse bone marrow cells and spermatocytes 

following 1-day and 2-week inhalation exposures to higher levels of EtO. Male Swiss Webster 

mice were exposed to 0, 200, 400, or 600 ppm EtO for 6 hours in 1 day or to 0, 200, or 400 ppm 

EtO for 6 hours/day, 5 days/week, for 2 weeks. Statistically significant increases in 

chromosomal aberrations were observed in bone marrow cells and in spermatocytes following a 

1-day exposure of 400 or 600 ppm EtO or a 2-week exposure of 200 or 400 ppm EtO. 

Chromosomal aberrations in bone marrow cells were also reported in a study of acute EtO 

exposure in mice (Farooqi et al., 1993). Female Swiss albino mice were administered single 

doses of EtO in the range of 30–150 mg/kg by i.p. injection. A dose-related increase in 

chromosomal aberrations in the bone marrow cells was observed. 

Chromosomal aberrations induced by long-term exposures to inhaled EtO were also 

investigated in the peripheral lymphocytes of cynomolgus monkeys (Lynch et al., 1984b). 

Groups of 12 adult male monkeys were exposed at 0, 50, or 100 ppm EtO (7 hours/day, 

5 days/week) for 2 years. Exposure to EtO at 100 ppm resulted in statistically significant 

increases in chromosome-type aberrations in monkey lymphocytes, and exposure at both 50 and 

100 ppm resulted in statistically significant increases in chromatid-type aberrations and in 

chromosome- and chromatid-type aberrations in combination. No differences in the number of 

gaps were found. 

Increases in chromosomal aberrations in peripheral blood lymphocytes have been 

consistently reported in studies of workers exposed to high occupational concentrations of EtO 

(>5 ppm, TWA). Effects observed at lower concentrations have been mixed (WHO, 2003). 

Chromosomal aberrations that have been detected in the peripheral blood lymphocytes of 

workers include breaks, gaps, and exchanges and supernumerary chromosomes (Lerda and Rizzi, 

1992; Galloway et al., 1986; Clare et al., 1985; Sarto et al., 1984a; Pero et al., 1981; Thiess et al., 

1981). 
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Clare et al. (1985) conducted chromosomal analyses of lymphocytes from 33 workers 

employed in the manufacture of EtO. A slightly higher frequency of chromatid aberrations was 

observed in workers exposed to EtO than in controls. Further, a positive correlation between 

length of employment in the EtO-exposed group and the number of aberrations was observed. In 

another study, Galloway et al. (1986) analyzed chromosomal aberration frequencies in 

61 employees potentially exposed to EtO. Three work sites (I, II and III) with different historical 

ambient levels of EtO were chosen for the study. Blood samples were drawn over a 24-month 

period and aberrations were analyzed in 100 cells per sample after culture for 48–51 hours. At 

work sites I and II, no consistent differences in aberration frequencies were found. However, at 

work site III, aberration frequencies in potentially exposed individuals were significantly 

increased when compared with controls. A previous study by the same group (Stolley et al., 

1984) showed an association between SCE frequency and EtO exposure. When the aberrations 

were compared with the levels of SCEs, the authors found a weak overall association. In 

addition, Lerda and Rizzi (1992) showed a significant increase in chromosomal aberration 

frequencies in EtO-exposed individuals when compared with controls. Major et al. (1996) 

studied hospital nurses exposed to low doses and high doses of EtO to identify changes in 

structural and numerical chromosomal aberrations. Chromosomal aberrations were found to be 

significantly elevated in both the low-dose and the high-dose exposure groups. Deletions and, to 

a lesser extent, chromatid exchanges and dicentrics were detected in the low-dose exposure 

group; however, in the high-dose group, in addition to the increased number of deletions, the 

frequencies of dicentrics and rings showed a significant excess when compared with controls. 

The authors suggest that a natural radioactivity from local tap water may have been a 

confounding factor. 

A study by Sarto et al. (1984a) showed significant increases in chromosomal aberrations 

after exposure to EtO. Chromosomal aberrations were detected in the peripheral lymphocytes of 

41 workers exposed to EtO in the sterilizing units of eight hospitals in the Venice region 

compared to 41 age- and smoking-matched controls. In another study of 28 EtO-exposed 

sterilizer workers and 20 unexposed controls, Högstedt et al. (1983) reported a statistically 

significant increase in total chromosomal aberrations and gaps, but not breaks, in the peripheral 

blood lymphocytes of the exposed workers, adjusted for age, smoking, drug intake, and exposure 

to ionizing radiation; no significant increases in chromosomal aberrations were observed in bone 

marrow cells. Tates et al. (1991) reported a significant increase in chromosomal aberrations in 

hospital workers and in factory workers (details of this study are provided in the section on gene 

mutations above). Tompa et al. (2006) reported statistically significant increases in 

chromosomal aberrations and SCEs in 66 Hungarian hospital nurses exposed to sterilizing gases 
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in uncontrolled environments compared to 94 nonexposed controls; however, it is difficult to sort 

out any effects of EtO exposure from possible effects from smoking or exposure to ionizing 

radiation or to formaldehyde or other possible sterilizing gases in this study. 

In summary, the above data clearly indicate that EtO is genotoxic and can cause a variety 

of chromosomal aberrations, including breaks, gaps and exchanges (reviewed in detail in 

Preston, 1999). Chromosomal aberrations have been observed in both in vitro and in vivo 

studies in rodent models and mammalian cells. Increases in chromosomal aberrations in 

peripheral blood lymphocytes have been consistently reported in studies of workers exposed to 

EtO. 

C.4. MICRONUCLEUS FORMATION 

Micronucleus formation also demonstrates the genotoxic effects of a chemical. When 

appropriate methods are used to identify the origin of the micronucleus (kinetochore-positive or 

kinetochore-negative), this assay can provide information about a chemical’s mechanism of 

action (e.g., if a chemical causes direct DNA damage resulting from strand breaks [clastogen] or 

indirect numerical changes [aneugen] resulting from spindle disruption). An association between 

increased micronucleus frequency and cancer risk has been reported in at least one large 

prospective study (Bonassi et al., 2007). Several in vitro and in vivo studies in both laboratory 

animals (Lorenti Garcia et al., 2001; Jenssen and Ramel, 1980; Appelgren et al., 1978) and 

humans (Ribeiro et al., 1994; Schulte et al., 1992; Mayer et al., 1991; Tates et al., 1991; Sarto et 

al., 1990; Högstedt et al., 1983) have been conducted to explore the induction of micronuclei as a 

result of exposure to EtO. 

Lorenti Garcia et al. (2001) studied the effect of EtO on the formation of micronuclei in 

rat bone marrow cells and splenocytes following in vivo exposure. Rats were exposed to EtO 

either subchronically by inhalation (50–200 ppm, 5 days/week, 6 hours/day, for 4 weeks) or 

acutely by i.p. injection at dose levels of 50 or 100 mg/kg. Spontaneous and induced frequencies 

of micronuclei were determined in the bone marrow cells (only for acute EtO exposure) and 

splenocytes following in vitro mitogen stimulation. Following chronic exposure, no significant 

increase in micronuclei was observed in rat splenocytes. Following acute exposure, micronuclei 

increased significantly in rat bone marrow cells as well as splenocytes. 

In the Högstedt et al. (1983) study of 28 EtO-exposed sterilizer workers and 

20 unexposed controls discussed in Section C.3, a statistically significant increase in micronuclei 

was observed in bone marrow cells (erythroblasts and polychromatic erythrocytes), but not in 

lymphocytes, in the exposed workers, adjusted for age, smoking, drug intake, and exposure to 

ionizing radiation. 
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The frequency of micronuclei in peripheral blood cells was increased in workers exposed 

to relatively high (3.7–60.4 mg/m3

) levels of EtO (Ribeiro et al., 1994; Tates et al., 1991). 

Schulte et al. (1992) did not observe increased micronuclei in the lymphocytes of hospital 

workers with low levels of EtO exposure (up to 2.5 mg/m3 8-hour TWAs). Sarto et al. (1990) 

studied micronucleus formation in human exfoliated cells of buccal and nasal cavities to monitor 

the genotoxic risk in a group of workers (n = 9) chronically exposed to EtO (concentrations 

lower than 0.38 ppm as time-weighted average). The mean frequencies of micronucleated 

buccal cells were similar to control values. The frequency of nasal micronucleated cells was 

higher than in controls (0.77 vs. 0.44); however, the difference was not statistically significant. 

In another group of three subjects that were acutely exposed (concentration not provided) to EtO, 

buccal cavity and nasal mucosa samples were taken 3, 9, or 16 days after acute exposure. The 

frequencies of micronucleated buccal cells did not change, while the frequencies of 

micronucleated nasal cells significantly increased. 

Peripheral blood cells of 34 EtO-exposed workers at a sterilization plant and 

23 unexposed controls were assessed for different biological markers, such as EtO-hemoglobin 

adducts, SCEs, micronuclei, chromosomal aberrations, DNA single-strand breaks and an index 

of DNA repair (Mayer et al., 1991). Neither chromosomal aberrations nor micronuclei differed 

significantly by exposure status, whether or not adjusted for smoking status. 

In summary, increases in the frequency of micronuclei have been observed in in vivo 

animal studies. The frequency of micronuclei in peripheral blood cells was also increased in 

workers exposed to relatively high (3.7–60.4 mg/m3

) levels of EtO (Ribeiro et al., 1994; Tates et 

al., 1991). However, in the majority of human studies involving exposures at lower levels, no 

effects on the frequency of micronuclei were observed. Apparent inconsistencies in the data 

could reflect the influence of peak exposures, differences in exposure measurement errors, 

duration of exposure and/or smoking status. 

C.5. SISTER CHROMATID EXCHANGES (SCES) 

There is a significant body of evidence for the induction of SCEs as a result of exposure 

to EtO. Studies have been conducted both in laboratory animals (Lorenti Garcia et al., 2001; 

Ong et al., 1993; Kelsey et al., 1988; Lynch et al., 1984b; Kligerman et al., 1983; Yager and 

Benz, 1982) and in humans (Agurell et al., 1991; Galloway et al., 1986; Laurent et al., 1984; 

Sarto et al., 1984a, b; Stolley et al., 1984; Yager et al., 1983; Garry et al., 1979). In particular, 

several occupational exposure studies have yielded positive results when EtO-exposed workers 

were studied. The following is a summary of both the animal and human studies. 
 

 

 

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Inhalation studies with rats have shown that exposures to EtO at 50 ppm or more for 

3 days result in an increase in SCEs in peripheral blood lymphocytes (Kligerman et al., 1983). 

Increased incidences of SCEs in the peripheral blood lymphocytes of monkeys exposed to EtO at 

500 or 100 ppm were also reported by Lynch et al. (1984b). A follow-up study in these same 

monkeys by Kelsey et al. (1988) indicated that the high SCE counts persisted for 6 years after 

exposure. 

Lorenti Garcia et al. (2001) studied the effect of EtO on the persistence of SCEs in rat 

bone marrow cells and splenocytes following in vivo exposure. Rats were exposed to EtO either 

chronically by inhalation (50–200 ppm, 5 days/week, 6 h/day, for 4 weeks) or acutely by i.p. 

injection at dose levels of 50 or 100 mg/kg. Frequencies of SCEs were determined in the bone 

marrow cells and splenocytes after in vitro mitogen stimulation. Following chronic exposure, 

cytogenetic analyses were carried out at days 5 and 21 in the splenocytes. In these experiments, 

EtO was effective in inducing SCEs, and marked increases in cells with high frequency SCEs 

were observed which persisted until day 21 postexposure. Following acute exposure, SCEs were 

increased significantly in rat bone marrow cells as well as splenocytes. 

New Zealand white male rabbits (n = 4) were exposed in inhalation chambers to 0, 10, 

50, and 250 ppm EtO for 6 hours a day, 5 days a week, for 12 weeks (Yager and Benz, 1982). 

Peripheral blood samples were drawn in three regimes (before the start of exposure, at intervals 

during exposure, and up to 15 weeks after the end of exposure) to measure SCE rates. No 

change in SCE rates was observed from exposure to 10 ppm; however, an increase was seen after 

exposure to 50 and 250 ppm. Above-baseline levels were observed even after 15 weeks 

postexposure, although the levels were not as high as during exposure. These results indicate 

that inhalation exposure to the EtO results in a dose-related increase in SCEs. 

The ability of long-term exposures to inhaled EtO to induce SCEs in peripheral 

lymphocytes of monkeys was investigated by Lynch et al. (1984b). Groups of 12 adult male 

cynomolgus monkeys were exposed at 0, 50, or 100 ppm EtO (7 hours/day, 5 days/week) for 

2 years. Statistically significant increases in SCE rates were observed in monkey lymphocytes in 

both exposure groups. Both exposure groups had increased numbers of SCEs/metaphase as 

compared to controls, and these numbers increased in a dose-dependent manner. 

In an in vitro study of human cells, peripheral lymphocyte cultures were exposed to 

methyl bromide, EtO, and propylene oxide, as well as diesel exhaust (Tucker et al., 1986). SCE 

frequency was measured, and the frequency more than doubled in the cultures treated with EtO. 

Agurell et al. (1991) also studied the effect of EtO on SCEs in human peripheral blood 

lymphocytes in vitro. An increase in SCE frequency was observed as a result of exposure 

(0−20 mMh) to EtO. Similarly, Hallier et al. (1993) observed that the frequency of SCEs in 
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human peripheral blood lymphocytes exposed in vitro to EtO was higher in cells isolated from 

individuals expressing low levels of glutathione S-transferase T1 than in cells from subjects 

expressing higher levels of this enzyme. 

Several studies of EtO-exposed workers have also reported an increased incidence of 

SCEs in peripheral lymphocytes (e.g., Schulte et al., 1992; Galloway et al., 1986; Sarto et al., 

1984a, b; Yager et al., 1983; Garry et al., 1979), although the Högstedt et al. (1983) study 

discussed in Sections C.3 and C.4 did not report significant increases in SCEs in the lymphocytes 

of the exposed workers. 

Garry et al. (1979) analyzed SCEs in lymphocytes cultured from EtO-exposed individuals 

as well as comparable controls. Significant increases in SCEs were observed at 3 weeks and at 

8 weeks following exposure. Although this study does not describe the exact exposure estimates, 

EtO was recognized as a mutagenic or genotoxic agent. Laurent et al. (1984) studied SCE 

frequency in workers exposed to high levels of EtO in a hospital sterilization service. Blood 

samples were obtained retrospectively from a group of 25 subjects exposed to high levels of EtO 

for a period of 2 years. A significant increase in SCEs was observed in the exposed group when 

compared with the control group. The authors concluded that the effect of exposure to EtO was 

sufficient to produce a cumulative and, in some cases, a persistent genetic change. 

Peripheral blood lymphocytes of nurses exposed to low and high concentrations of EtO 

were studied by Major et al. (1996). SCEs were slightly elevated in the low-exposure group but 

were significantly increased in the high-exposure group. Similarly, several studies by Sarto et al. 

(1991); Sarto et al. (1990); Sarto et al. (1987); Sarto et al. (1984a, b) showed significant 

increases in SCEs. 

Tates et al. (1991) studied workers occupationally exposed to EtO using different 

physical and biological measures. Blood samples from 9 hospital workers and 15 factory 

workers engaged in sterilization of medical equipment with EtO and from matched controls were 

collected. Exposures were usually received in bursts, with EtO concentrations in air ranging 

from 22 to 72 ppm in hospital workers and 14 to 400 ppm in factory workers. The mean 

frequency of SCEs was significantly elevated by 20% in hospital workers and by almost 100% in 

factory workers. In contrast, no significant increase in SCEs was observed in lymphocytes of 

workers who were accidentally exposed to high concentrations of EtO or of workers with low 

exposure concentrations (Tates et al., 1995). 

Schulte et al. (1992) observed a statistically significant increase in SCEs in 43 workers 

exposed to EtO in U.S. hospitals compared to 8 unexposed hospital workers. The frequency of 

SCEs was also significantly associated with cumulative EtO exposure in a regression analysis 

that controlled for various potential confounding factors, including smoking. A similar 
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relationship was not observed in 22 Mexican hospital workers. Schulte et al. (1992) 

hypothesized that the difference may have been due to longer shipping times of the Mexican 

specimens for the cytogenetic assays. 

In summary, significant increases in the frequency of SCEs were observed in rats and in 

monkeys both by inhalation and i.p. injection. In humans, multiple occupational studies have 

reported positive responses, with significant increases in frequency of SCEs in peripheral blood 

lymphocytes having been observed among individuals exposed to higher levels of EtO. In some 

studies, increases in the frequency of SCEs have been observed to persist after exposure has 

ceased. The results of studies of individual workers exposed to very low levels (<0.9 mg/m3

) of 

EtO have been mixed. 

C.6. OTHER ENDPOINTS (GENETIC POLYMORPHISM, SUSCEPTIBILITY) 

Dose-dependent effects of polymorphisms in the genes for epoxide hydrolase (EPHX1), 

different subfamilies of glutathione-S-transferase (GSTM1, GSTP1, GSTT1) and various DNA 

repair enzymes (hOGG1, XRCC1, XRCC3) on EtO-induced genotoxicity were evaluated by 

Godderis et al. (2006). Peripheral blood mononuclear cells from 20 individuals were exposed to 

3 doses of EtO (0.45, 0.67, 0.9 mM), and genotoxicity was evaluated by measuring comet tail 

length and micronucleus frequencies in binucleated cells (MNBC). A dose-dependent increase 

in tail length (indicating DNA strand breaks) was observed in exposed individuals compared to 

controls. No change in MNBC was observed. None of the epoxide hydrolase or glutathione-Stransferase polymorphisms had a significant influence on the tail length or MNBC results for any 

EtO dose. Further analysis revealed a significant contribution of the hOGG1 (involved in base 

excision repair) and XRCC3 (involved in repair of cross-links and chromosomal double-strand 

breaks) genotypes to the interindividual variability of EtO-induced increases in tail length. 

Homozygous hOGG1326 wild-type cells showed significantly lower effects of EtO on tail length 

compared to the heterozygous cells. Also, significantly higher tail lengths were found in 

EtO-exposed cells carrying at least one variant XRCC3241 Met allele. For the latter effect, there 

was a significant interaction between the XRCC3241 polymorphism and dose, signifying a greater 

impact of the polymorphism on DNA damage at higher doses. 

In contrast to the findings of no significant effect of glutathione-S-transferase 

polymorphisms on DNA breaks and micronuclei production by Godderis et al. (2006), Hallier et 

al. (1993) observed that the frequency of SCEs in human peripheral blood lymphocytes exposed 

in vitro to EtO was higher in cells isolated from individuals expressing low levels of GSTT1 than 

in cells from subjects expressing higher levels of this enzyme. Similarly, Yong et al. (2001) 

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measured approximately twofold greater EtO-hemoglobin adduct levels in occupationally 

exposed persons with a GSTT1-null genotype than in those with positive genotypes. 

In a study involving small numbers (n = 4−12 per group) of nonsmoking males and 

females exposed to EtO through the sterilization of medical equipment, Fuchs et al. (1994) 

reported 1.5-, 2.2-, and 1.5-fold increases in DNA single-strand breaks in peripheral blood 

mononuclear cells obtained from individuals exposed to EtO concentrations of 0.1−0.49 mg/m3 , 

0.5−2.0 mg/m3, and >2 mg/m3, respectively. Fuchs et al. (1994) further noted that these 

nonsmokers could be divided into two distinct susceptibility groups, with 67% of the subjects 

exhibiting approximately fivefold higher levels of DNA single-strand breaks in response to EtO 

exposure than the remaining subjects, and that the bimodal nature of the differential 

susceptibility suggested that the susceptibility was attributable to an unidentified polymorphism. 

Primary and secondary cultures of lymphoblasts, breast epithelial cells, peripheral blood 

lymphocytes, keratinocytes and cervical epithelial cells were exposed to 0–100 mM EtO, and 

DNA damage was measured using the comet assay (Adám et al., 2005). A dose-dependent 

increase in DNA damage was observed in all cell types without notable cytotoxicity. Breast 

epithelial cells (26% increase in tail length) were more sensitive than keratinocytes (5% increase) 

and cervical epithelial cells (5% increase) but less sensitive than lymphoblasts (51% increase) 

and peripheral lymphocytes (71% increase) at the same dose of 20 mM. 

C.7. ENDOGENOUS PRODUCTION OF ETHYLENE AND ETO 

Ethylene, a biological precursor of EtO, is ubiquitous in the environment as an air 

pollutant and is produced in plants, animals and humans (Abeles and Heggestad, 1973). 

Ethylene is generated in vivo endogenously during normal physiological processes such as (1) 

oxidation of methionine, (2) oxidation of hemoglobin, (3) lipid peroxidation, and (4) metabolism 

of intestinal bacteria (reviewed by Thier and Bolt, 2000; IARC, 1994a). Recently, Marsden et al. 

(2009) proposed that oxidative stress can induce the endogenous formation of ethylene, which 

can in turn be metabolized to EtO. Endogenous production of ethylene has been documented in 

laboratory animals and in humans (Filser et al., 1992; Shen et al., 1989; Ehrenberg et al., 1977; 

Chandra and Spencer, 1963). 

Shen et al. (1989) reported an endogenous production rate of 2.8 and 41 nmol/h ethylene 

in Sprague-Dawley rats and humans, respectively, with similar thermodynamic partition 

coefficients between the two species. Filser et al. (1992) reported a low degree of endogenous 

production of ethylene (32 ±12 nmol/h) in healthy volunteers based on exhalation data. The 

authors indicated that the endogenous levels of ethylene would account for ~66% of the 

background level of EtO-hemoglobin adducts (HEVal), while the remaining one-third (15 ppb) is 
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contributed by exogenous environmental ethylene exposure. Although the percentage of 

endogenous ethylene converted to EtO is not known, Törnqvist et al. (1989) have shown that in 

fruit-store workers exposed to 0.3 ppm ethylene, only 3% is metabolized to EtO. Thus, the 

amount of endogenous ethylene converted to EtO would be minimal. Furthermore, with 

inadequate laboratory animal and human evidence available for ethylene as a carcinogen (IARC, 

1994a), exogenous ethylene exposure may not produce enough EtO to contribute significantly to 

carcinogenicity under standard bioassay conditions (Walker et al., 2000). 

Ethylene formed from endogenous sources is converted to EtO by cytochrome 

P450-mediated metabolism (Törnqvist, 1996; IARC, 1994a). EtO formed from the endogenous 

conversion of ethylene leads to 2-hydroxyethylation of DNA and forms N7-HEG adducts 

contributing to the background levels of this adduct in unexposed humans and rodents. As 

shown in Table C-1, improvements in analytical methodology have led to the detection and 

quantification of background N7-HEG adducts in DNA of unexposed experimental animals and 

humans (Marsden et al., 2009; Swenberg et al., 2008; Tompkins et al., 2008; Marsden et al., 

2007; Swenberg et al., 2000; van Sittert et al., 2000; Walker et al., 2000; Eide et al., 1999; 

Farmer and Shuker, 1999; Wu et al., 1999b; Wu et al., 1999a; Zhao et al., 1999; Bolt et al., 1997; 

Zhao et al., 1997; Kumar et al., 1995; van Delft et al., 1994; Farmer et al., 1993; van Delft et al., 

1993; Leutbecher et al., 1992; Walker et al., 1992b; Cushnir et al., 1991; Föst et al., 1989). 

However, there is a wide variation in the levels of adducts detected in rodents and humans which 

appears to depend on the type of the analytical method used. Even with the most advanced 

techniques (Tompkins et al., 2008), minor DNA adducts such as O6

-HEG and N3-HEA were 

below the level of detection. Also, some researchers consistently demonstrated higher 

background levels of DNA adducts (Wu et al., 1999a; Walker et al., 1992b). However, the 

higher background levels in some of these studies are possibly due to the methodology used, 

which may have caused an artifactual increase in the adduct levels. 

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Table C-1. Levels of endogenous (background) N7-HEG adducts in unexposed human and experimental rodent 

tissues 

Species Tissue Detection method Adduct levels reported 

Adducts/107 

nucleotides* Reference 

Human Lymphocytes GC/MS 8.5 pmol/mg DNA 28.05 (Föst et al., 1989) 

Human WBC Immuno-slotblot 0.34 adducts/106 nucleotides 3.4 (van Delft et al., 1994) 

Human Blood HPLC-fluorescence 3.2 pmol/mg DNA 10.56 (Bolt et al., 1997) 

Human Lymphocytes GC/MS 2–19 adducts per 107 nucleotides 2.0–19 (Wu et al., 1999b) 

Human WBC 32P/TLC/HPLC 0.6 adducts/107 nucleotides 0.6 (Zhao et al., 1999) 

Human WBC 32P/TLC/HPLC 2.9 adducts/107 nucleotides 2.9 (Zhao et al., 1999) 

Human Lung 32P/TLC/HPLC 4.0 adducts/107 nucleotides 4 (Zhao et al., 1999) 

Rat Lymphocytes GC/MS 5.6 pmol/mg DNA 18.48 (Föst et al., 1989) 

Mice/Rats Control tissues HPLC-fluorescence 2–6 pmol/mg DNA 8.58 (Walker et al., 1992b) 

Rat Liver, kidney, spleen 32P/GC/MS 0.4 to 1.1 adducts/107 nucleotides 0.4–1.1 (Eide et al., 1999) 

Mice/Rats Spleen GC/EC/NCI-HRMS 0.2 to 0.3 pmol/mmol guanine (Wu et al., 1999a) 

Rat Control tissues 32P/TLC/HPLC 0.6 to 0.9 adducts/107 nucleotides 0.6–0.9 (Zhao et al., 1999) 

Rat Liver GC/MS 2.6 adducts/108 nucleotides 0.26 (van Sittert et al., 2000) 

Rat Control tissues LC-MS/MS 1.1–3.5 adducts/108 nucleotides 0.11–0.35 (Marsden et al., 2007) 

Rat Liver HPLC/ESI TMS 8 adducts/108 normal nucleotides 0.8 (Tompkins et al., 2008) 

Rat Spleen HPLC/LC-MS/MS 0.08 adducts/1010 nucleotides 0.00008 (Marsden et al., 2009)
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Table C-1. Levels of endogenous (background) N7-HEG adducts in unexposed human and experimental rodent 

tissues (continued) 

*Adduct levels are normalized using the formula: 1 pmol adducts/mg DNA = 3.3 adducts/107 normal nucleotides.
 

GC/MS, gas chromatography mass spectrometry; HPLC, high performance liquid chromatography; 32P, 32P-postlabeling assay; TLC, thin-layer chromatography;
 

LC-MS, liquid chromatography mass spectrometry; ESI TMS, electrospray ionization tandem mass spectrometry; GC/EC/NCI-HRMS, gas 

chromatography/electron capture/negative chemical ionization high-resolution mass spectrometry.
 
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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 F344 rats, Marsden et al. (2009) recently 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). 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. For example, an approximate calculation indicates 

that the low exposure level of 10 ppm for 6 hours/day used in the Snellings et al. (1984) bioassay 

of F344 rats is equivalent to a daily dose of about 1.7 mg/kg, which is over 10 times higher than 

the largest daily dose of 0.1 mg/kg used by Marsden et al. (2009). 6 

In summary, endogenous ethylene and EtO production, which contribute to background 

N7-HEG DNA adducts indicative of DNA damage, have been observed in unexposed rodents 

and humans. Although a constant reduction in DNA damage in vivo is carried out by DNA 

repair and DNA replicative synthesis, a certain steady-state background level of adducts is 

measurable at all times. The quantitative relationships between the background DNA damage 

and the spontaneous rates of mutation and cancer are not well established. Experimental 

evidence is needed that can unequivocally measure artifact-free levels of background DNA 

damage, including effects other than adducts, clearly establish mutagenic potency of such 

background lesions, and demonstrate the organ- and cell type-specific requirements for the 

primary DNA damage to be expressed as heritable genetic changes (Gupta and Lutz, 1999). 

Some investigators have posited that the high and variable background levels of 

endogenous EtO-induced DNA damage in the body may overwhelm any contribution from 

exogenous EtO exposure (Marsden et al., 2009; SAB, 2007). It is true that the existence of these 

high and variable background levels may 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 

Chapter 3, Section 3.5), and the genotoxicity/mutagenicity of EtO (Section 3.4) supports lowdose linear extrapolation of risk estimates from those studies (U.S. EPA, 2005a). In fact, as 

discussed above, 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 

 

6

This calculation uses the mean alveolar ventilation rate for rats of 52.9 mL/minute/100 g reported by Brown et al. 

(1998). Changing the units, this rate is equivalent to approximately 0.032 m3

/hour/kg. For a 6-hour exposure, this 

results in an alveolar inhalation of 0.19 m3

/kg. 10 ppm EtO is equivalent to 18.3 mg/m3, so a 6-hour exposure 

equates to about 3.48 mg/kg. IARC (2008) reports that measurements from Johanson and Filser (1992) indicate that 

only 50% of alveolar ventilation is available to be absorbed into the bloodstream, so the 6-hour exposure to 10 ppm 

EtO would approximate an absorbed daily dose of 1.7 mg/kg. 
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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). Furthermore, while the 

contributions to cancer risk 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) that exceed de minimis risk 

(e.g., >10−6

). 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%, 

whereas the background lifetime incidence risk for breast cancer is on the order of 15%. 

C.8. CONCLUSIONS 

The overall available data from in vitro studies, laboratory animal studies, and human 

studies indicate that EtO is both a mutagen and a genotoxicant. In addition, increases in 

mutations in specific oncogenes and tumor suppressor genes in EtO-induced mouse tumors have 

been reported. Stable translocations seen in human leukemias may arise from similar DNA 

adducts that produce chromosome breaks, micronuclei, SCEs, and even gene mutations observed 

in peripheral lymphocytes. Dominant lethal mutations, heritable translocations, chromosomal 

aberrations, DNA damage, and adduct formation in rodent sperm cells have been observed in a 

number of studies involving the exposure of rats and mice to EtO. Based upon the likely role for 

DNA alkylation in the production of the genotoxic effects in germ cells in laboratory animals 

exposed to EtO, as well as the lack of qualitative differences in the metabolism of EtO between 

humans and laboratory animals, EtO can also be considered a likely human germ cell mutagen 

(WHO, 2003). There is consistent evidence that EtO interacts with the genome of cells within 

the circulatory system in occupationally exposed humans and overwhelming evidence of 

carcinogenicity and genotoxicity in laboratory animals. Based on these considerations, there is a 

strong weight of evidence suggesting that EtO would be carcinogenic to humans (see Chapter 3, 

Section 3.4). 

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

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APPENDIX D. 

REANALYSES AND INTERPRETATION OF ETHYLENE OXIDE 

EXPOSURE-RESPONSE DATA 

Kyle Steenland 

May 27, 2010 

(EDITORIAL NOTE: This Appendix contains the report submitted by Dr. Steenland 

summarizing the results of analyses that he conducted under contract to U.S. EPA. The 

terminology originally used by Dr. Steenland to designate the different exposure-response 

model forms has been changed to be consistent with the terminology used in EPA’s Ethylene 

Oxide Carcinogenicity Assessment. Models that are linear in log RR and which were 

previously referred to as “linear” models have been renamed “log-linear” models (except 

where it is stated that they are log RR models), and models of the form RR = 1 + β × 

exposure, which were previously referred to as “excess relative risk” (ERR) models have 

been renamed “linear” models. In addition, section headings, figures, and tables have been 

renumbered for the table of contents. Finally, some supplemental results received from Dr. 

Steenland after the original completion of this Appendix have been inserted in the relevant 

sections.) 

This report contains the results of reanalyses of the National Institute for Occupational Safety 

and Health cohort of workers exposed to ethylene oxide conducted for the U.S. 

Environmental Protection Agency. The report begins with an overview of the modeling 

strategy used, followed by the results of reanalyses of the breast cancer incidence, breast 

cancer mortality, lymphoid cancer mortality, and, finally, hematopoietic cancer mortality 

databases. Various models were used for these reanalyses, as discussed in this report. The 

report concludes with the results of some sensitivity analyses and discussions of the possible 

influences of the healthy worker survivor effect and exposure mismeasurement. 

Introduction. Modeling strategy for ethylene oxide (ETO) risk assessment 

The modeling strategy adopted here for ETO risk assessment relies principally on the usual 

epidemiologic models in which the log of the rate ratio (RR) is some function of exposure, in 

this case cumulative exposure with a lag to reflect a length of time which is likely necessary 

before an exposure can result in (observable or fatal) cancer. We have relied primarily on 

Cox regression as a flexible method of modeling the log RR; however we have also included 
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some linear relative risk models. Cumulative exposure is typically the exposure metric of 

interest in predicting chronic disease. 

For breast cancer incidence, we have relied principally on 2-piece linear models, in which log 

RR (in the log-linear model) or RR (in the linear model) is a function of two lines which join 

smoothly at a single point of inflection. Two-piece linear models may also be thought of as 

linear splines with one knot, or point of inflection. They have been described as part of a 

general description of exposure-response modeling by Steenland and Deddens (2004) and 

have been used previously in risk assessment (e.g., see the risk assessment for dioxin by 

Steenland et al., 2001). The 2-piece log-linear model has the form log RR = β0 + β1 × 

cumexp + β2 × (max(0,cumexp-knot)), where cumexp is cumulative exposure, the last term 

equals either 0 or cumexp-knot, whichever is greater, and the knot is the point of inflection or 

point of change of slope for the 2 linear pieces. The slope of the last term is β1 + β2 for 

cumulative exposure values above the knot. 

Log RR models are not linear when the log RR function is transformed via exponentiation 

back to a nonlogarithmic function, but they are nearly so in the low dose region of interest. 

The splines are linear using the linear RR model. 

“Plateau-like” exposure-response curves, in which the exposure-response curve begins 

steeply but is attenuated at higher exposure, have been seen for many occupational 

carcinogens. This may occur for a variety of reasons, including depletion of susceptible 

subpopulations, mismeasurement at high exposure resulting in attenuation, and the healthy 

worker survivor effect (Stayner et al., 1993). Attenuation of the exposure-response 

relationship occurs for the breast cancer and (lympho) hematopoietic endpoints of interest for 

ETO. For these endpoints, a simple linear model (often considered the default model), where 

the log RR (for the log-linear model) or the RR increases linearly with cumulative exposure, 

does not fit the data well, based on simple visual inspection of the categorical data. 

Frequently, such plateau-like curves may be modeled by using the log of cumulative 

exposure rather than cumulative exposure itself, but this has the disadvantage that the curve 

is usually highly supra-linear at low doses. Two-piece linear spline models are particularly 

useful in modeling exposure-response relationships in which the log RR or RR increases 

initially with increasing exposure but then tends to increase less or plateau at high exposures. 
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The 2-piece linear models avoid this supra-linearity in the low-dose region (Steenland and 

Deddens, 2004). 

The shape of the 2-piece linear spline model, in particular the slope of the curve in the lowdose region, depends on the choice of the point of inflection where the two linear pieces are 

joined. Here we have chosen the point of inflection based on the best model likelihood, 

trying a range of points of inflection (knots) across the range of exposure starting from 0 and 

incrementing by 100 ppm-days (or 1000 ppm-days) intervals. The model likelihood often 

does not change much across these different points of inflection, but it does change some and 

we have chosen the point of inflection resulting in the best model likelihood. The model 

likelihood used to find the best fit in all models used in this analysis is the usual partial 

likelihood (Langholz and Richardson, 2010), as used with the Cox models, which maximizes 

the probability, across all the cases, that a case fails (the numerator) relative to its casecontrol risk set (which includes the case) (the denominator) and has the form 

L( β) = φcase (Z;β)/ Σj cases and controls φj (Zj;β), 

where φ(Z;β) is some function of a vector of covariates Z and the parameters of interest β. 

For example, for the linear RR model with only cumulative exposure in the model, 

φ(Z;β) = 1 + zβ, where z is cumulative exposure and β is the exposure-response coefficient of 

interest. For the log RR model, φ(Z;β) = e

(zβ). 

While the 2-piece models work well for ETO breast cancer incidence, they do not for 

hematopoietic cancer (and to a lesser extent for breast cancer mortality) because the best 

knots are at very low doses and the resulting slopes for the first piece of the 2-piece model 

are very steep, resulting in the same problem which occurs using log transform models (i.e., 

where the exposure metric is the log of cumulative exposure). Risk for hematopoietic cancer 

in fact increases quite steeply with very low exposure versus no exposure, and then plateaus 

at higher exposures. This may be partly a result of the relatively small numbers of 

hematopoietic cancers and the overall instability of the results. In this case, EPA's original 

approach of a weighted regression through categorical RRs is a reasonable alternative to both 

the log transform and 2-piece models. 
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D.1. BREAST CANCER INCIDENCE BASED ON THE DATA WITH INTERVIEWS 

a. Distribution of exposure among ETO-exposed women in breast cancer incidence 

cohort with interviews (n = 5139) 

The estimated daily exposure to ETO across different jobs and time periods ranged from 

0.05 ppm to 77 ppm. Exposure intensities from this broad range were multiplied by the 

length of time in different jobs to get estimates of cumulative exposure. The duration of 

exposure had a mean of 10.8 years (std dev 9.1), and a median of 7.4 years. The range was 

from 1.00 to 50.3 years. The 25th percentile was 2.8 years and the 75th percentile was 

17.6 years. Multiplying exposure intensity and exposure duration results in a wide range of 

cumulative exposures. 

Cumulative exposure at the end of follow-up, with no lag, had a mean of 13,524 ppm-days 

(37.0 ppm-years), with a standard deviation of 13,254 ppm-days. These data are highly 

skewed, with a range from 5 to 253,848 ppm-days. The 25th percentile is 926 ppm-days, 

while the 75th is 10,206 ppm-days. Log transformation of these data results in an 

approximately normal distribution of the data. 

As a caveat, it should be remembered that cumulative exposure at the end of follow-up may 

be misleading, as it is not relevant to standard analyses, all of which treat cumulative 

exposure as a time-dependent variable which must be assessed at specific points in time. For 

example, standard life-table analyses calculate cumulative exposure at different times during 

follow-up for each person. Subsequently, both person-time and disease events are put into 

categories of cumulative exposure. A given person may pass through many such categories, 

contributing person-time to each. Poisson regression, analogous to life-table analyses (and 

often based directly on output from life table programs), similarly relies on person-time (and 

disease occurrence) categorized by cumulative exposure. Both these types of analyses are 

inherently categorical. 

In the analyses presented here, we have used Cox regression in which age is the time 

variable. The basic approach is to compare each case to a set of 100 randomly chosen 

controls, whose exposure is evaluated at the same age at which the case fails (gets disease or 

dies of disease). Using 100 controls generally would be expected to give the same result as 

the full risk set and shortens analysis time (Steenland and Deddens, 1997). Hence, again 

cumulative exposure is time dependent. For the case who fails at an early age, the 
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cumulative exposure of the case and many of his or her controls at that same age may be low. 

For the case who fails late in life, the cumulative exposure of the case and his or her controls 

will be higher. When cumulative exposure is lagged so that no exposure is counted until 

after a lag period (e.g., 15 years) is fulfilled, many cases and their respective controls will be 

“lagged out” (i.e., will have no cumulative exposure, if the case fails at an early age). Note 

Note that Cox regression uses individual data, and there is no inherent categorization typical 

of life table analyses and Poisson regression, although categorical analyses can still be done 

in Cox regression and are often useful. 

For these reasons, it is difficult to describe the cumulative exposure distribution of all 

subjects in the Cox regression. Controls may appear more than once matched to different 

cases, and their cumulative exposure will differ each time depending on the age of the case. 

However, cases only appear once in the data and their exposure distribution can be easily 

presented. In our situation, we have used Cox regression with a 15-year lag to analyze breast 

cancer incidence. The exposure distribution of the cases, by deciles above the lagged out 

category, is shown below. Creating deciles such that cases are equally distributed is a good a 

priori way of creating categories in which rate ratios will have approximate equal variance, a 

desirable feature. The lagged out cases are women who got incident breast cancer within 

15 years of first exposure. 

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

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Table D-1a. Distribution of cases in Cox regression for breast cancer 

morbidity analysis after using a 15-year lag 

Cumulative exposure, 

15-year lag 

Mean cumulative exposure 

(ppm-days) 

Number of incident breast 

cancer cases 

0 (Lagged out) 62 

>0–355 ppm-days 157 17 

356–842 ppm-days 580 16 

843–1361 ppm-days 1097 17 

1362–2187 ppm-days 1725 17 

2188–3772 ppm-days 2899 17 

3773–5522 ppm-days 4546 18 

5523–7891 ppm-days 6554 16 

7892–14483 ppm-days 14384 17 

14484–25112 ppm-days 18859 17 

>25112 ppm-days 48807 18 

b.1. Results of Cox regression analysis of breast cancer incidence using a variety of (log 

RR) models 

Analyses used a case-control approach, with 100 controls per case, as in Steenland et al. 

(2003). Age was the time variable in proportional hazards (Cox) regression. For breast 

cancer incidence, family history of breast cancer, date of birth (quartiles), and parity were 

included in models along with exposure variables. For our exposure variable, we used 

cumulative exposure lagged 15 years, which was found in prior analyses to provide the best 

fit to the data (Steenland et al., 2003). 

Using log RR models, we used a categorical model, a linear model, a 2-piece linear model, a 

log transform model, a cubic spline model, and a square-root transform model. We also ran a 

number of analogous models using linear RR models. 

The categorical analysis (log RR model) used deciles, as indicated in Table D-1b. Deciles 

were used instead of the original quintiles from the publication (Steenland et al., 2003) 

because the relatively large sample size enabled more extensive categorization. Results of 

the categorical decile analysis are in Table D-1b below. 
 

 

 

 

 

 

 

 

 

 

 

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Table D-1b. Categorical analysis of breast cancer incidence by deciles (log 

RR model) 

Analysis of Maximum Likelihood Estimates 

Parameter Standard Hazard 

Variable Estimate Error Chi-Square Pr > ChiSq Ratio 

CAT1 -0.09015 0.29318 0.0945 0.7585 0.914 

CAT2 -0.08363 0.30341 0.0760 0.7828 0.920 

CAT3 0.18536 0.29757 0.3880 0.5333 1.204 

CAT4 0.12606 0.29995 0.1766 0.6743 1.134 

CAT5 0.07900 0.29968 0.0695 0.7921 1.082 

CAT6 0.37651 0.29675 1.6097 0.2045 1.457 

CAT7 0.38177 0.31168 1.5003 0.2206 1.465 

CAT8 0.25179 0.30640 0.6753 0.4112 1.286 

CAT9 0.57845 0.31120 3.4551 0.0631 1.783 

CAT10 0.80396 0.30766 6.8284 0.0090 2.234 

-2 LOG L 1936.910, df=15 (10 exposure terms, 5 covariates)

We then fit a cubic spline (restricted at the ends to be linear) which presents a description of 

the data similar to the categorical analyses but using a smooth curve. The exposure metric 

was cumulative exposure with a 15-year lag, which was found in earlier analyses to be the 

optimal lag (Steenland et al., 2003). Five knots for the cubic spline were chosen using every 

other midpoint from the categorical analysis (598, 1774, 4647, 11187, and 37668 ppm-days). 

We then ran a 2-piece linear (log RR) model. The knot, or inflection point, was chosen to be 

the one where the model likelihood was highest, which was at 5,800 ppm-days. To choose 

this knot we looked at possible inflection points over the range 100 to 15,000 ppm-days by 

100 ppm-day increments. Figure D-1a shows the –2 log likelihood graphed against the 

knots. In this figure the lower peak corresponds to the highest likelihood.7 

Figures D-1b and D-1c show the results of the 2-piece linear, the categorical, the linear, and 

the cubic spline (log RR) models. In these figures the categorical points are the mid-points 

of the categories in Table D-1a, with final category assigned the final cut point plus 50%. 

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Editorial note: -2 × (natural) log likelihood is reported because the difference in this value for any two models is the 

value of the test statistic commonly used to compare model fit (likelihood ratio test). Under certain assumptions, the 

probability distribution for this statistic is approximately chi-squared with degrees of freedom equal to the difference 

in degrees of freedom between the two (nested) models. 

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It appears that the two-piece log-linear curve in Figure D-1b approximates the shape of the 

exposure-response seen in the decile and cubic spline (log RR) analyses, better than the loglinear curve in Figure D-1c. 

The log-linear curve appears to have a low slope versus the other models, suggesting possible 

influential observations in the upper tail of exposure. To further explore this, we excluded 

from the analysis increasing amounts of the upper tail of the data using the log-linear model, 

i.e., via excluding the upper 1%, 2.5%, 5%, 10%, 15%, 20%, and 27% of exposure, based 

on the exposure distribution of the cases (the last amount, 27%, corresponds to excluding 

subjects with cumulative exposure above 6000 ppm-days, which was close to the knot in the 

2-piece log-linear model [5800 ppm-days]). The ratios of the slope (coefficient) for the 

linear term (log RR model) with these exclusions vs. the slope for the linear term (log RR 

model) with no exclusions were 1.5, 2.3, 3.2, 3.2, 2.5, 3.1, 6.1, 9.2, respectively. As 

expected, the slope increases markedly as the data are restricted to the lower range of 

exposure. For example, a modified log-linear curve after excluding the upper 5% of the data 

is seen in Figure D-1d, along with the full log-linear curve from Figure D-1c. Nonetheless, 

even the log-linear curve from these truncated data has a markedly lower slope in the lowexposure region than the 2-piece log-linear (or spline) curves. For example, inspection 

shows that the RR for 6000 ppm-days is about 1.2 for the log-linear curve from the truncated 

data and 1.6 from the 2-piece log-linear model. Use of the log-linear curve based on 

truncated data has the disadvantage of having to choose rather arbitrarily where to truncate 

the data. This disadvantage is avoided by using the 2-piece log-linear model. 

A 2-piece log-linear model, then, is preferred for estimating risk parsimoniously in the lowexposure region. For comparison purposes, we also show the model using the logarithm of 

exposure (Figure D-1e), which we have not used for risk assessment because it is supralinear 

in the low-dose region. 

We also fit a square-root transformation (square root of cumulative exposure, 15-year lag) 

log RR model, which is shown in Figure D-1f. This model also fit the breast cancer 

morbidity well (it did not fit the other outcomes well and is not shown for them), and can be 

used for risk assessment, but with the disadvantage that it is not linear or approximately 

linear in the low-dose region. For this reason, we prefer the 2-piece log-linear curve, with is 

approximately linear in the low-dose region (and strictly linear in the linear RR models 

discussed below). Excess lifetime risk does not vary greatly between all these models (see 
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below), with the exception of the log RR model with a linear term for cumulative exposure,
 

which is below other excess risk estimates.
 

Figure D-1a. Likelihoods vs knots, 2-piece linear log RR model for breast 

cancer morbidity. 

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

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RR 

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1. 0 

1. 1 

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Figure D-1b. Breast cancer incidence. Plot of the dose-response relationship 

for continuous exposure generated using a 2-piece log-linear spline overlaid with 

a plot using restricted cubic (log RR) splines. Dots that represent the effect of 

exposure grouped in deciles (log RR categorical model) are also presented in the 

plot. Deciles formed by allocating cases approximately equally in ten groups, 

above lagged-out cases, see Table D-1a above. Y-axis is rate ratio, X-axis is 

cumulative exposure lagged 15 years, in ppm-days. 

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

7/2013 D-10 DRAFT―DO NOT CITE OR QUOTE
 
 

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

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RR 

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2. 2 

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Figure D-1c. Breast cancer incidence. Plot of a log-linear dose-response 

relationship overlaid with a dose-response relationship generated using restricted 

cubic log RR model with continuous exposure. Dots that represent the effect of 

exposure grouped in deciles (log RR categorical model) are also presented in the 

plot. Deciles formed by allocating cases approximately equally in ten groups, 

above lagged-out cases. 

Comparing log linear models, model with higher slope omits highest 5% of exposure 

RR 

2. 6 

2. 4 

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1. 0 

0 5000 10000 15000 20000 25000 

C U M EX P1 5 

Figure D-1d. Breast cancer incidence. Comparison of log-linear curve (log 

RR = β × cumexp) with all the data and the log-linear curve (higher slope) after 

excluding those in the top 5% of exposure (>27,500 ppm-days). 
This document is a draft for review purposes only and does not constitute Agency policy. 

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CUMEXP15 

-2 l og l i kel i hood i s 1944. 153 

Cat egor i cal anal yses over l ayed 

RR 

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Figure D-1e. Breast cancer incidence. Plot of a logarithmic transformation log 

RR dose-response model (log RR = β × log(cumexp)) overlaid with a doseresponse relationship generated using categorical log RR analyses (deciles). 

Deciles formed by allocating cases approximately equally in ten groups, above 

lagged-out cases. 
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-2 l og l i kel i hood i s 1941. 028 

Cat egor i cal anal yses over l ayed 

RR 

0. 9 

1. 0 

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1. 9 

2. 0 

2. 1 

2. 2 

2. 3 

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CUMEXP15 

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Figure D-1f. Breast cancer incidence. Plot of a square-root transformation log 

RR dose–response model overlaid with a dose-response relationship generated 

using categorical log RR analyses (deciles). Deciles formed by allocating cases 

approximately equally in ten groups, above lagged-out cases. 

Tables D-1c, D-1d, D-1e, and D-1f below present the model fit statistics for the 2-piece loglinear, the log-linear, the square root log RR model, and the log transform log RR model seen 

above. Table D-1g summarizes the goodness-of-fit data with regard to the exposure term. 

Table D-1g shows that the addition of exposure terms to the various models results in similar 

model fits. The exposure terms in the 2-piece log-linear improve model fit marginally better 

than those in the other models except the square root log RR model, with which the 2-piece 

log-linear model is tied. If one adds a degree of freedom to the chi-square test for the 2-piece 

log-linear model, on the assumption that the choice of the knot is equivalent to estimating 

another parameter, the p-value increases to 0.04, in the same range as the log-linear and logtransform log RR models. Our argument here, however, is not that the 2-piece log-linear 

model fits the data dramatically better than other models in purely statistical terms. Rather 

we believe that the fit conforms to the categorical and cubic spline models well in the lowexposure region of interest, and that the nearly linear exposure-response relationship in that 

region (strictly linear with the linear RR model) is a reason to prefer the 2-piece log-linear 

model to the other models. In particular, among the parametric models, the log transform 

and square root log RR models are supralinear in the low-exposure region. 
 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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The effects of these departures from linearity in the low-exposure region can be seen in the 

risk assessment results for the EC01 (estimate of effective concentration resulting in 1% extra 

risk) in the next sections (c, d, and e). In these sections we use some of the results from the 

exposure-response models to calculate EC01s. We restrict these calculations to models which 

appear most reasonable based on our results above, namely the 2-piece log-linear model, the 

square root transform log RR model, and the cubic spline log RR model. While we do not 

recommend the use of the cubic spline model for risk assessment due to its complexity, the 

EC01 based on the cubic spline model provides a good comparison to other parametric 

models. 

Table D-1c. Fit of 2-piece log-linear model to breast cancer incidence data, 

Cox regression8

Without With 

Criterion Covariates Covariates 

-2 LOG L 1967.813 1940.485 

AIC 1967.813 1954.485 

SBC 1967.813 1978.612 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 27.3281 7 0.0003 

Score 29.0949 7 0.0001 

Wald 28.4426 7 0.0002 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Variable Estimate Error Chi-Square Pr > ChiSq Ratio 

LIN_0 (β1) 0.0000770 0.0000317 5.4642 0.0194 1.000 

LIN_1 -0.0000724 0.0000334 4.1818 0.0409 1.000 

DOB1 0.08770 0.21805 0.1618 0.6875 1.092 

DOB2 0.41958 0.24430 2.9496 0.0859 1.521 

DOB3 0.55168 0.29096 3.5950 0.0580 1.736 

PARITY1 -0.23398 0.18793 1.5502 0.2131 0.791 

FREL_BR_CAN1 0.47341 0.17934 6.9686 0.0083 1.605 

Covariance lin0 and lin1 -1 × 10-9 

8

For environmental exposures, only exposures below the knot are of interest. Below the knot, RR = e(β1 * exposure). 

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

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Table D-1d. Fit of log-linear model to breast cancer incidence data, Cox 

regression (RR = e(β × exposure))

Without With 

Criterion Covariates Covariates 

-2 LOG L 1967.813 1944.675 

AIC 1967.813 1956.675 

SBC 1967.813 1977.356 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 23.1374 6 0.0008 

Score 25.8389 6 0.0002 

Wald 25.3594 6 0.0003 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Variable Estimate Error Chi-Square Pr > ChiSq Ratio 

CUMEXP15 (β) 9.54826E-6 4.09902E-6 5.4261 0.0198 1.000 

DOB1 0.13558 0.21676 0.3912 0.5316 1.145 

DOB2 0.53147 0.23741 5.0116 0.0252 1.701 

DOB3 0.74477 0.27425 7.3748 0.0066 2.106 

PARITY1 -0.23394 0.18882 1.5351 0.2154 0.791 

FREL_BR_CAN1 0.46449 0.17928 6.7126 0.0096 1.591 

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

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Table D-1e. Fit of the square root transformation log RR model to breast 

cancer incidence data, Cox regression (RR = e(β × sqrt(exposure)))

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 1967.813 1941.028 

AIC 1967.813 1953.028 

SBC 1967.813 1973.708 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 26.7851 6 0.0002 

Score 28.9446 6 <.0001 

Wald 28.5277 6 <.0001 

Analysis of Maximum Likelihood Estimates

Parameter Standard 

Variable DF Estimate Error Chi-Square Pr > ChiSq

dob1 1 0.09778 0.21756 0.2020 0.6531 

dob2 1 0.43872 0.24177 3.2929 0.0696 

dob3 1 0.58623 0.28404 4.2596 0.0390 

sqrtcumexp15 (β) 1 0.00349 0.00118 8.7489 0.0031 

PARITY1 1 -0.22539 0.18787 1.4393 0.2302 

FREL_BR_CAN1 1 0.46937 0.17922 6.8589 0.0088 

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

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Table D-1f. Fit of the log transform model to breast cancer incidence data, 

Cox regression (RR = e(β × ln(exposure)))

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 1967.813 1944.176 

AIC 1967.813 1956.176 

SBC 1967.813 1976.856 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 23.6371 6 0.0006 

Score 24.0044 6 0.0005 

Wald 23.5651 6 0.0006 

Analysis of Maximum Likelihood Estimates 

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

dob1 1 0.08605 0.21943 0.1538 0.6949 1.090 

dob2 1 0.38780 0.25363 2.3378 0.1263 1.474 

dob3 1 0.47303 0.31528 2.2509 0.1335 1.605 

LCUMEXP15 (β)

PARITY1 

1 

1 

0.04949 

-0.25908 

0.02288 

0.18638 

4.6787 

1.9322 

0.0305 

0.1645 

1.051 

0.772 

FREL_BR_CAN1 1 0.47620 0.17923 7.0595 0.0079 1.610 

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. 

D-18 DRAFT―DO NOT CITE OR QU

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Table D-1g. Change in –2 log likelihood for log RR models for breast cancer 

incidence, with addition of exposure term(s)a 

Log RR model Change (chi square) d.f. p-value 

Log transform 4.8 1 0.03 

Linear 4.2 1 0.04 

Categorical 12.0 10 0.29 

Cubic spline 8.8 4 0.07 

2-piece linear 8.4 2 0.01 

Square root 7.7 1 0.006 

a

All models had 3 variables for date of birth, 1 for family history, and 1 for parity.
 

b.2. Linear relative risk models for breast cancer incidence 

We also ran linear relative risk models for breast cancer incidence, using the techniques 

described recently by Langholz and Richardson (2010) to use SAS to fit these models, using 

the same data as used for the log RR models. The form of these linear RR models is 

RR = 1 + βx, where x can be cumulative dose, the log of cumulative dose, a 2-piece linear 

function of cumulative dose, etc. 

Figure D-1g below shows the different curves with the linear RR model, using cumulative 

exposure lagged 15 years as the exposure metric. The categorical points in Figure D-1g 

come from the published categorical results for the log RR model (Steenland et al., 2003). 

The midpoints for the 5 categories (above the lagged out referent, at 0 exposure) are the 

medians of cumulative exposure (lagged 15 years), which were 253, 1193, 3241, 7741, and 

26,597 ppm-days. 

Figure D-1h shows the likelihood profile for different possible knots for the 2-piece linear 

spline, with the search conducted by using increments of 100 ppm-days. For the 2-piece 

linear spline model the best knot was 5800 ppm-days, as was the case for the 2-piece loglinear model. 

Table D-1h shows the model fit statistics for the linear RR models. These models tend to fit 

slightly better than their log RR counterparts, although generally the improvement in the chi 

square does not attain significance at the 0.05 level. For the 2-piece linear model, the model 

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likelihood is 1936.9 vs a likelihood of 1940.5 for the 2-piece log-linear model. Among the linear 

RR models, the 2-piece spline model fits better than the other models, although not significantly 

so. Table D-1i gives the exposure parameter values for the linear RR models. Table D-1j 

presents the parameter estimates for the exposure variables for the categorical (decile) linear RR 

model. 

Figure D-1g. Breast cancer incidence exposure-response curves, linear RR 

models (units are ppm-days, 15-year lag). 

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

7/2013 D-19 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

 

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Figure D-1h. Knot location for Figure D-1g above, 2-piece linear spline 

model, breast cancer incidence (units are ppm-days, 15-year lag). 

Table D-1h. Model fit statistics for linear RR models, breast cancer 

incidencea 

Linear RR Model 

d.f. (full 

model)b 

–2 Log 

likelihood 

(full model) 

–2 LL (model 

without 

exposure) 

–2 LL 

(model 

without any 

covariates) 

p-value 

(full model) 

p-value 

(for addition 

of exposure 

terms)c 

CUMEXP15 6 1940.260 1949.06 1967.813 < 0.0001 0.0030 

Log(CUMEXP15) 6 1942.267 1949.06 1967.813 0.0003 0.0096 

Spline, knot = 5,800, 

CUMEXP15 7 1936.935 1949.06 1967.813 < 0.0001 0.0023 

a

For the linear RR models, all covariates were included linearly (i.e., additively). Including the nonexposure 

covariates in the model multiplicatively instead did not improve model fit (e.g., for the 2-piece spline model, 

inclusion of the non-exposure covariates multiplicatively instead of additively gave a -2 LL of 1940.4 (vs. 1936.9 

for additive inclusion). 

b

Degrees of freedom for full model. 

c

Based on change in likelihood for breast cancer incidence linear RR models with addition of exposure term(s) to 

model with date of birth, parity, and breast cancer in first degree relative. Degrees of freedom for addition of 

exposure terms is (degrees of freedom for the full model − 5). 

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

7/2013 D-20 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

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Table D-1i. Model coefficients for linear RR models, breast cancer incidence 

Linear RR Model Parameter(s) SE 

Profile likelihood 95% (onesided) upper and lower boundsc 

CUMEXP15 B = 0.000030402 SE = 0.000017549 UB = 0.0000745 

LB = 0.00000975 

Log(CUMEXP15) B = 0.071322 SE = 0.039227 

Spline, knot = 5,800, 

CUMEXP15a, b 

B1 = 0.000119, 

B2 = –0.000105 

SE1 = 0.000067727, 

SE2 = 0.000070478 

UB1 = 0.000309 

LB1 = 0.000032 

a

Covariance of 2 pieces of linear spline, –4.64 × 10–9 . b

For estimating risks from occupational exposures (Section 4.7 of the Carcinogenicity Assessment Document), both 

pieces of the 2-piece linear spline model were used. For the maximum likelihood estimate, for exposures below the 

knot, RR = 1 + (B1 × exp); for exposures above the knot, RR = 1 + (B1 × exp + B2 × (exp – knot)). For the 95% 

upper confidence limit, for exposures below the knot, RR = 1 + ((β1+ 1.645 × SE1) × exp); 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. 

c

Editorial note: As discussed in footnotes i and j of Table 4-7 in Section 4.1.2.3 of this assessment, 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 (Langholz and Richardson, 2010), which allows for asymmetric CIs, for selected linear 

RR models, for comparison with the Wald approach. 95% (one-sided) upper and lower bounds on the parameter 

estimate (regression coefficient) derived using the profile likelihood method are presented here. For the continuous 

linear model (CUMEXP15), the profile likelihood upper bound is about 29% higher than the upper bound obtained 

using the Wald approach. For the low-exposure segment of the linear spline model, the profile likelihood upper 

bound is about 34% higher than the upper bound obtained using the Wald approach. Calculating the profile 

likelihood bounds for the second spline segment parameter estimate is computationally difficult and was not 

pursued here. 

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

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Table D-1j. Supplemental Results: Parameter estimates for exposure 

variables for categorical (decile) linear RR model (RR = 1 + β), breast cancer

incidence 

Gradient
 

Approx Approx Objective
 

N Parameter Estimate Std Err t Value Pr > |t| Function
 

1 beta1a 0.039745 0.340310 0.116792 0.907133 2.4264E-10
 

2 beta1b 0.133788 0.371450 0.360177 0.719065 6.847537E-11 

3 beta1c 0.304056 0.438525 0.693361 0.488824 1.119129E-10 

4 beta1d 0.166744 0.402813 0.413950 0.679319 3.011577E-10 

5 beta1e -0.071617 0.347348 -0.206182 0.836842 7.617004E-11 

6 beta1f 0.749956 0.583672 1.284893 0.200200 9.568004E-11 

7 beta1g 0.919113 0.643333 1.428674 0.154536 7.337724E-11 

8 beta1h 0.487590 0.505453 0.964660 0.335789 5.850301E-11 

9 beta1i 0.985298 0.753167 1.308206 0.192187 1.207377E-10 

10 beta1j 1.575028 0.960886 1.639141 0.102633 1.341514E-10 

Value of Objective Function (log likelihood) = -966.9720784 

c. Risk assessment for breast cancer incidence using the 2-piece log-linear spline 

We used the 95% upper bound of the coefficient for the 1st piece of the 2-piece log-linear 

model from Table D-1c, which is 0.0000770 + 1.64 × 0.0000317 or 0.0001290, to calculate 

the LEC01 via the life-table analysis of excess risk used by EPA in Appendix C of their risk 

assessment. Here we used the same data on background breast cancer incidence and 

background all-cause mortality as used by EPA in their 2006 calculations. The rate ratio 

then, as a function of exposure, is RR = e(0.0001290 × cumexp15). Note that the 2-piece log-linear 

model is linear for the log RR. Once this is exponentiated, it is no longer strictly linear, but 

is still approximately so, as can be seen in Figure D-1a. 

Use of the function RR = e(0.0001290 × cumexp15) in the life-table analysis results in an excess risk 

of 0.01 when the daily exposure is 0.0090 ppm, which is the LEC01. This is slightly lower 

than the previous LEC01 of 0.0110 ppm in EPA's 2006 draft risk assessment (U.S. EPA, 

2006a, Table 14). 

Similar calculations were done for the EC01, which resulted in a value of 0.0152 ppm. 

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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d. Risk assessment for breast cancer incidence using the square root transformation log 

RR model 

Use of the 95% upper bound of the relative risk function, ie, RR = e((0.000349 + .00118 × 1.64) × square 

root(cumexp15)), in the life-table analysis results in an excess risk of 0.01 when the daily exposure 

is 0.00225 ppm, which is the LEC01. This is about 5 times lower than the previous LEC01 of 

0.0110 ppm in EPA's 2006 draft risk assessment (U.S. EPA, 2006a, Table 14). The EC01 is 

0.0060 ppm, which is about four times lower than the EPA's 2006 EC01. The reason these 

estimates are much lower than the EPA's is that the square root curve, as can be seen in 

Figure D-1d, rises very sharply (is supralinear) in the low-dose region. In this sense, it shares 

the disadvantage of the log transform model, and we recommend against using it as a basis 

for risk assessment for that reason. 

e. Risk assessment for breast cancer incidence using the cubic spline curve log RR 

model 

Risk assessment using the spline curve is more difficult due to the semi-parametric 

complicated nature of the restricted cubic spline function. The cubic spline function for the 

breast cancer incidence rate ratio is 

RR=exp((ns_0*cumexp15) + ns_1*(((cumexp15-598)**3)*(cumexp15>= 598) ­ 

((37668-598) /(37668-11187)) *(((cumexp15-11187)**3) *(cumexp15>= 11187)) + 

((11187 -598)/(37668 - 11187)) *(((cumexp15-37668 )**3) *(cumexp15>= 37668)) 

) + ns_2*(((cumexp15-1774)**3)*(cumexp15>= 1774) - ((37668-1774) /(37668­

11187)) *(((cumexp15-11187)**3) *(cumexp15>= 11187)) + ((11187 -1774) /(37668 

- 11187))*(((cumexp15-37668 )**3) *(cumexp15>= 37668)) ) + ns_3*(((cumexp15­

4647)**3)*(cumexp15>= 4647) - ((37668-4647) /(37668-11187)) *(((cumexp15­

11187)**3) *(cumexp15>= 11187)) + ((11187 -4647)/(37668 - 11187)) 

*(((cumexp15-37668 )**3) *(cumexp15>= 37668)) )). 

The coefficients ns_0, ns_1, ns_2, and ns_3 used in this function are 0.00008294999811, -

0.00000000000310 0.00000000000425, and -0.00000000000114, respectively. The 

expression “cumexp15>=” is a logical statement whereby the term is 0 when “cumexp” is less 

than the specified value. 

Here we calculate only the EC01 (without the LEC01) for comparison with the corresponding 

EC01 from the 2-piece log-linear model. The point is to show that the cubic spline log RR model 
This document is a draft for review purposes only and does not constitute Agency policy. 

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and the 2-piece log-linear spline give similar answers, not to use the cubic spline for risk 

assessment, given its relatively complicated formula above. Calculation of the LEC01 is also 

particularly complicated because to do it correctly one must use not only the standard error for 

four coefficients but also their covariances.
 

For breast cancer incidence, the EC01 using the cubic spline log RR model is 0.0138 ppm, similar 

to the value of 0.0152 ppm using the 2-piece log-linear model. 

f. Risk assessment for breast cancer incidence using the 2-piece linear spline model 

Use of the function RR = 1 + (0.000119 + 1.64 × 0.000067) × cumexp15 in the life-table 

analysis results in an excess risk of 0.01 when the daily exposure is 0.0052 ppm, which is the 

LEC01, which is about half of the value of 0.0110 ppm from the 2-piece log-linear spline 

model. The corresponding EC01 is 0.0100 ppm. 

g. Supplemental results: results for cumulative exposure and log cumulative exposure Cox 

regression models with different lag times (no lag, 5 years, 10 years, 15 years, and 20 years)   

(i) cumulative exposure model, no lag 

                                                 Without           With

                                Criterion     Covariates     Covariates

                               ‐ 2 LOG L        1967.813       1946.492

                                AIC             1967.813       1958.492

                                SBC             1967.813       1979.172

                                Testing Global Null Hypothesis: BETA=0

                        Test                 Chi‐Square       DF     Pr > ChiSq

                        Likelihood Ratio        21.3211        6         0.0016

                        Score                   22.2448        6         0.0011

                        Wald                    22.0301        6         0.0012

                                Analysis of Maximum Likelihood Estimates

                               Parameter      Standard                                  Hazard

        Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

        dob1             1       0.17056       0.21590        0.6241        0.4295       1.186

        dob2             1       0.59054       0.23671        6.2242        0.0126       1.805

        dob3             1       0.83494       0.27295        9.3573        0.0022       2.305

        CUMEXP           1    5.93879E‐6    3.52892E‐6        2.8321        0.0924       1.000

        PARITY1          1      ‐ 0.25022       0.18784        1.7746        0.1828       0.779

        FREL_BR_CAN1     1       0.47120       0.17920        6.9144        0.0086       1.602
6 

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(ii)cumulative exposure model, 5-year lag 

      

                                                 Without           With

                                Criterion     Covariates     Covariates

                               ‐ 2 LOG L        1967.813       1945.875

                                AIC             1967.813       1957.875

                                SBC             1967.813       1978.555

                                Testing Global Null Hypothesis: BETA=0

                        Test                 Chi‐Square       DF     Pr > ChiSq

                        Likelihood Ratio        21.9381        6         0.0012

                        Score                   23.1833        6         0.0007

                        Wald                    22.9563        6         0.0008

   

                             Analysis of Maximum Likelihood Estimates

                               Parameter      Standard                                  Hazard

        Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

        dob1             1       0.16362       0.21604        0.5736        0.4488       1.178

        dob2             1       0.57250       0.23698        5.8363        0.0157       1.773

        dob3             1       0.80642       0.27311        8.7184        0.0032       2.240

        CUMEXP5          1     6.8565E‐6    3.59626E‐6        3.6350        0.0566       1.000

        PARITY1          1      ‐ 0.24489       0.18810        1.6951        0.1929       0.783

        FREL_BR_CAN1     1       0.47063       0.17919        6.8981        0.0086       1.601

(iii) cumulative exposure model, 10-year lag 

                                                 Without           With

                                Criterion     Covariates     Covariates

                               ‐ 2 LOG L        1967.813       1945.521

                                AIC             1967.813       1957.521

                                SBC             1967.813       1978.201

                                Testing Global Null Hypothesis: BETA=0

                        Test                 Chi‐Square       DF     Pr > ChiSq

                        Likelihood Ratio        22.2922        6         0.0011

                        Score                   23.9807        6         0.0005

                        Wald                    23.6876        6         0.0006

                               Analysis of Maximum Likelihood Estimates

                               Parameter      Standard                                  Hazard

        Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

        dob1             1       0.15185       0.21633        0.4927        0.4827       1.164

        dob2             1       0.55144       0.23733        5.3986        0.0202       1.736

        dob3             1       0.77339       0.27377        7.9805        0.0047       2.167

        CUMEXP10         1    7.75726E‐6    3.80799E‐6        4.1498        0.0416       1.000

        PARITY1          1      ‐ 0.24110       0.18839        1.6379        0.2006       0.786

        FREL_BR_CAN1     1       0.46864       0.17921        6.8385        0.0089       1.598
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(iv)cumulative exposure model, 15-year lag 

                                                 Without           With

                                Criterion     Covariates     Covariates

                               ‐ 2 LOG L        1967.813       1944.675

                                AIC             1967.813       1956.675

                                SBC             1967.813       1977.356

                                Testing Global Null Hypothesis: BETA=0

                        Test                 Chi‐Square       DF     Pr > ChiSq

                        Likelihood Ratio        23.1374        6         0.0008

                        Score                   25.8389        6         0.0002

                        Wald                    25.3594        6         0.0003

                               Analysis of Maximum Likelihood Estimates

                               Parameter      Standard                                  Hazard

        Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

        dob1             1       0.13558       0.21676        0.3912        0.5316       1.145

        dob2             1       0.53147       0.23741        5.0116        0.0252       1.701

        dob3             1       0.74477       0.27425        7.3748        0.0066       2.106

        CUMEXP15         1    9.54826E‐6    4.09902E‐6        5.4261        0.0198       1.000

        PARITY1          1      ‐ 0.23394       0.18882        1.5351        0.2154       0.791

        FREL_BR_CAN1     1       0.46449       0.17928        6.7126        0.0096       1.591

(v) cumulative exposure model, 20-year lag     

                                                 Without           With

                                Criterion     Covariates     Covariates

                               ‐ 2 LOG L        1967.813       1946.040

                                AIC             1967.813       1958.040

                                SBC             1967.813       1978.720

                                Testing Global Null Hypothesis: BETA=0

                        Test                 Chi‐Square       DF     Pr > ChiSq

                        Likelihood Ratio        21.7730        6         0.0013

                        Score                   24.0576        6         0.0005

                        Wald                    23.5506        6         0.0006

                               Analysis of Maximum Likelihood Estimates

                               Parameter      Standard                                  Hazard

        Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

        dob1             1       0.13721       0.21682        0.4005        0.5268       1.147

        dob2             1       0.53985       0.23711        5.1839        0.0228       1.716

        dob3             1       0.76037       0.27371        7.7177        0.0055       2.139

        CUMEXP20         1     0.0000101    5.27041E‐6        3.6890        0.0548       1.000

        PARITY1          1     ‐ 0.23887       0.18905        1.5966        0.2064       0.788

        FREL_BR_CAN1     1       0.46310       0.17935        6.6673        0.0098       1.589
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(vi)log cumulative exposure model, no lag      

                                                Without           With

                               Criterion     Covariates     Covariates

                              ‐ 2 LOG L        1967.813       1943.662

                               AIC             1967.813       1955.662

                               SBC             1967.813       1976.343

                               Testing Global Null Hypothesis: BETA=0

                       Test                 Chi‐Square       DF     Pr > ChiSq

                       Likelihood Ratio        24.1508        6         0.0005

                       Score                   24.4372        6         0.0004

                       Wald                    24.1563        6         0.0005

                              Analysis of Maximum Likelihood Estimates

                              Parameter      Standard                                  Hazard

       Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

       dob1             1       0.17618       0.21596        0.6655        0.4146       1.193

       dob2             1       0.59516       0.23703        6.3045        0.0120       1.813

       dob3             1       0.83783       0.27359        9.3780        0.0022       2.311

       lcumexp          1       0.09294       0.04097        5.1458        0.0233       1.097

       PARITY1          1      ‐ 0.25682       0.18640        1.8984        0.1683       0.774

       FREL_BR_CAN1     1       0.47417       0.17923        6.9991        0.0082       1.607

(vii) log cumulative exposure model, 5-year lag    

                                                Without           With

                               Criterion     Covariates     Covariates

                              ‐ 2 LOG L        1967.813       1946.843

                               AIC             1967.813       1958.843

                               SBC             1967.813       1979.523

                               Testing Global Null Hypothesis: BETA=0

                       Test                 Chi‐Square       DF     Pr > ChiSq

                       Likelihood Ratio        20.9703        6         0.0019

                       Score                   21.0320        6         0.0018

                       Wald                    20.7379        6         0.0020

                              Analysis of Maximum Likelihood Estimates

                              Parameter      Standard                                  Hazard

       Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

       dob1             1       0.15082       0.21658        0.4850        0.4862       1.163

       dob2             1       0.53156       0.24038        4.8900        0.0270       1.702

       dob3             1       0.72413       0.28191        6.5981        0.0102       2.063

       LCUMEXP5         1       0.04458       0.03135        2.0222        0.1550       1.046

       PARITY1          1     ‐ 0.26745       0.18630        2.0608        0.1511       0.765

       FREL_BR_CAN1     1       0.47497       0.17922        7.0241        0.0080       1.608
  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

  

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(viii) log cumulative exposure model, 10-year lag 

                                               Without           With

                              Criterion     Covariates     Covariates

                             ‐ 2 LOG L        1967.813       1944.040

                              AIC             1967.813       1956.040

                              SBC             1967.813       1976.721

                              Testing Global Null Hypothesis: BETA=0

                      Test                 Chi‐Square       DF     Pr > ChiSq

                      Likelihood Ratio        23.7728        6         0.0006

                      Score                   23.5846        6         0.0006

                      Wald                    23.1565        6         0.0007

                             Analysis of Maximum Likelihood Estimates

                             Parameter      Standard                                  Hazard

      Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

      dob1             1       0.11282       0.21798        0.2679        0.6048       1.119

      dob2             1       0.43207       0.24800        3.0352        0.0815       1.540

      dob3             1       0.53777       0.30203        3.1702        0.0750       1.712

      LCUMEXP10        1       0.05654       0.02594        4.7498        0.0293       1.058

      PARITY1          1      ‐ 0.26063       0.18629        1.9573        0.1618       0.771

      FREL_BR_CAN1     1       0.47636       0.17921        7.0653        0.0079       1.610

(ix)log cumulative exposure model, 15-year lag 

                                               Without           With

                              Criterion     Covariates     Covariates

                             ‐ 2 LOG L        1967.813       1944.176

                              AIC             1967.813       1956.176

                              SBC             1967.813       1976.856

                              Testing Global Null Hypothesis: BETA=0

                      Test                 Chi‐Square       DF     Pr > ChiSq

                      Likelihood Ratio        23.6371        6         0.0006

                      Score                   24.0044        6         0.0005

                      Wald                    23.5651        6         0.0006

                             Analysis of Maximum Likelihood Estimates

                             Parameter      Standard                                  Hazard

      Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

      dob1             1       0.08605       0.21943        0.1538        0.6949       1.090

      dob2             1       0.38780       0.25363        2.3378        0.1263       1.474

      dob3             1       0.47303       0.31528        2.2509        0.1335       1.605

      LCUMEXP15        1       0.04949       0.02288        4.6787        0.0305       1.051

      PARITY1          1     ‐ 0.25908       0.18638        1.9322        0.1645       0.772

      FREL_BR_CAN1     1       0.47620       0.17923        7.0595        0.0079       1.610
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(x) log cumulative exposure model, 20-year lag    

                                               Without           With
 

                                Criterion     Covariates     Covariates
 

                               ‐ 2 LOG L        1967.813       1947.020

                                AIC             1967.813       1959.020

                                SBC             1967.813       1979.700

                                Testing Global Null Hypothesis: BETA=0

                        Test                 Chi‐Square       DF     Pr > ChiSq

                        Likelihood Ratio        20.7930        6         0.0020

                        Score                   21.5306        6         0.0015

                        Wald                    21.1847        6         0.0017

                               Analysis of Maximum Likelihood Estimates

                               Parameter      Standard                                  Hazard

        Variable        DF      Estimate         Error    Chi‐Square    Pr > ChiSq       Ratio

        dob1             1       0.10961       0.22008        0.2481        0.6184       1.116

        dob2             1       0.46136       0.25203        3.3509        0.0672       1.586

        dob3             1       0.61353       0.30969        3.9248        0.0476       1.847

        LCUMEXP20        1       0.02970       0.02151        1.9068        0.1673       1.030

        PARITY1          1      ‐ 0.26623       0.18642        2.0397        0.1532       0.766

        FREL_BR_CAN1     1       0.47060       0.17925        6.8927        0.0087       1.601

D.2. BREAST CANCER MORTALITY 

a. Exposure distribution among women and breast cancer deaths in the cohort 

mortality study (n = 9544) 

In the Cox regression analyses of Steenland et al. (2004), the data on breast cancer mortality 

was found to be fit best using cumulative exposure with a 20-year lag. Below is the 

distribution of the 102 breast cancer deaths used in the analysis. The cut points are those 

used in the published data (Steenland et al., 2004). 
 

 

 

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Table D-2a. Distribution of cases in Cox regression analysis of breast cancer 

mortality after using a 20-year lag 

Cumulative exposure, 

20-year laga 

Number of breast 

cancer deaths 

0 (Lagged out) 42 

>0–646 ppm-days 17 

647–2779 ppm-days 16 

2780–12321 ppm-days 15 

>12321 ppm-days 12 

a

Mean exposures for females with a 20-year lag for the categorical exposure quartiles 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. 

Regarding the women in the cohort as a whole, cumulative exposure at the end of follow-up, 

with no lag, had a mean of 8.2 ppm-years, with a standard deviation of 38.2. This
 

distribution was highly skewed; the median was 4.6 ppm-years.
 

b. Results of Cox regression analysis of breast cancer mortality using a variety of log 

RR models
 

Analyses used a case-control approach, with 100 controls per case, as in Steenland et al.
 

(2004). Age was the time variable in proportional hazards (Cox) regression. For breast
 

cancer mortality, only exposure variables were included in models. Cases and controls were
 

matched on sex (all female), date of birth, and race. 

Using log RR models, we used a categorical model, a linear model, a 2-piece linear model, a 

log transform model, and a cubic spline model. We also ran a number of analogous models
 

using linear RR models (Section D-2.c below).
 

The categorical log RR model for breast cancer mortality was run using the originally
 

published cut points to form four categories above the lagged-out group, as shown in 

Table D-2a. To graph the categorical points, each category was assigned the mid-point of the
 

category as its exposure level, except for the last one which was assigned 50% more than the 

last cut point 12,322 ppm-days.
 

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7/2013 D-30 DRAFT―DO NOT CITE OR QUOTE
 
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For the 2-piece log-linear model, the single knot was chosen at 700 ppm-days based on a 

comparison of likelihoods assessed every 100 ppm-days from 0 to 7,000 (Figure D-2a). We 

also explored knots beyond 7,000 ppm-days by looking at increments of 1,000 ppm-days 

from 0 to 25,000 (Figure D-2a shows the results for knots up to 15,000 ppm-days). None of 

these outperformed the knot at 700 ppm-days, although Figure D-2a' suggests a local 

maximum likelihood near 13,000 ppm-days. 

-2 log likelihood for different knots for breast cancer mortality 

 919.400 

 919.300

 919.200

 919.100

 919.000

 918.900

 918.800

 918.700

 918.600

 918.500

 918.400

 918.300

 918.200

 918.100

 918.000

0 1000 2000 3000 4000 5000 6000 7000 

KNOT 

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Figure D-2a. Likelihoods vs knots for the 2-piece log-linear model, breast 

cancer morality. 

In Figure D-2b below, we show the categorical and 2-piece log-linear spline models, as well 

as the log-linear model and the log-linear model after cutting out the top 5% of exposed 

subjects. 

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7/2013 D-31 DRAFT―DO NOT CITE OR QUOTE
 
 

-2 log likelihood for different knots for breast cancer mortality 

 919.400 

 919.300

 919.200

 919.100

 919.000

 918.900

 918.800

 918.700

 918.600

 918.500

 918.400

 918.300

 918.200

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 

KNOT 

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Figure D-2b. Likelihoods vs knots for the 2-piece log-linear model, breast 

cancer morality. 

The log-linear model was clearly highly sensitive to exclusion of the most highly exposed. 

As a sensitivity analysis, we excluded 1%, 2.5%, 5%, and 10% of the upper tail of exposure. 

The 5% cutoff was at 15,000 ppm-days, while the 10% cutoff was at 13,000 ppm-days. The 

slope of the linear exposure-response relationship increased by 1.2, 1.6, 5.9, and 4.5 times, 

respectively, with the exclusion of progressively more data. It would appear that the upper 

5% of the exposure range most affects the linear slope, and it is responsible for the 

attenuation seen in the exposure-response at high exposures. 

The 2-piece log-linear spline model in Figure D-2b fits reasonably well but appears to 

underestimate the categorical RRs at higher exposures. This may be due to the influence of 

the top 5% of the exposed, which appear to have a strong attenuating influence on the slope 

(see below). 

For comparison purposes, we also show the logarithmic transformation log RR model in 

Figure D-2c (which we have not used for risk assessment because it is supralinear in the low 

dose region). 

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Figure D-2c. Plot of the dose-response relationship of continuous exposure 

(lagged 20 years) for breast cancer mortality, with the 2-piece linear spline, 

the categorical, and the linear log RR models (labeled “log RR”). Also shown 

is the log-linear curve (log RR = β × cumexp20) after cutting out the top 5% of 

exposure subjects (‘log RR 95% cutoff’). 

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

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-2 l og l i kel i hood i s 917. 776 

Cat egor i cal anal yses over l ayed 

RR 

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CUMEXP20 

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Figure D-2d. Plot of the dose-response relationship of continuous exposure 

(lagged 20 years) for breast cancer mortality, generated using a logarithmic 

transformation log RR model. Dots that represent the effect of exposure 

grouped in categories are also presented in the plot. 

Outputs from the categorical, 2-piece linear spline, and linear log RR models are given 

below. The 2-piece log-linear model performed similarly to the log-linear model, but 

appeared to fit the categorical log RR model points and the cubic spline log RR model much 

better. The log-linear spline model is at the border of statistical significance (p = 0.07). In 

any case, models with relatively sparse data may not achieve conventional statistical 

significance (at the 0.05 level) but still provide a good fit to the data, judged by conformity 

with categorical and cubic spline analysis, and may still be useful for risk assessment. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Table D-2b. Categorical output breast cancer mortality, 20-year lag (log RR
 

model)
 

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 923.433 915.509 

AIC 923.433 923.509 

SBC 923.433 934.009 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 7.9244 4 0.0944 

Score 8.5160 4 0.0744 

Wald 8.3993 4 0.0780 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Variable DF Estimate Error Chi-Square Pr > ChiSq Ratio 

CUM201 1 0.56653 0.33920 2.7894 0.0949 1.762 

CUM202 1 0.57236 0.35505 2.5987 0.1070 1.772 

CUM203 1 0.67537 0.37632 3.2207 0.0727 1.965 

CUM204 1 1.14110 0.40446 7.9598 0.0048 3.130 

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

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Table D-2c. 2-piece log-linear spline, breast cancer mortality, 20-year lag, 

knot at 700 ppm-days 

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 923.433 918.037 

AIC 923.433 922.037 

SBC 923.433 927.287 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 5.3967 2 0.0673 

Score 6.0153 2 0.0494 

Wald 5.8857 2 0.0527 

Analysis of Maximum Likelihood Estimates 

Parameter Standard Hazard 

Parameter Estimate Error Chi-Square Pr > ChiSq Ratio 

LIN_0 

LIN_1 

0.0006877 

-0.0006782 

0.0004171 

0.0004188 

2.7178 

2.6229 

0.0992 

0.1053 

1.001 

0.999 

*covariance lin0 and lin1 -1.75 × 10-7 

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

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Table D-2d. Log-linear model, breast cancer mortality, 20-year lag 

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 923.433 920.647 

AIC 923.433 922.647 

SBC 923.433 925.272 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 2.7865 1 0.0951 

Score 3.7383 1 0.0532 

Wald 3.6046 1 0.0576 

Analysis of Maximum Likelihood Estimates 

Parameter Standard Hazard 

Variable Estimate Error Chi-Square Pr > ChiSq Ratio 

CUMEXP20 0.0000122 6.40812E-6 3.6046 0.0576 1.000 

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Table D-2e. Log transform log RR model, breast cancer mortality, 20-year 

lag 

Model Fit Statistics
 

Without With
 

Criterion Covariates Covariates
 

-2 LOG L 923.433 917.743
 

AIC 923.433 919.743 

SBC 923.433 922.368 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 5.6908 1 0.0171 

Score 5.7676 1 0.0163 

Wald 5.7688 1 0.0163 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

lcum20 1 0.08376 0.03487 5.7688 0.0163 1.087 

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Table D-2f. 2-piece log-linear spline model, breast cancer mortality, 20-year 

lag, knot at 13,000 ppm-days 

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 923.433 918.237 

AIC 923.433 922.237 

SBC 923.433 927.487 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 5.1963 2 0.0744 

Score 5.9044 2 0.0522 

Wald 5.7813 2 0.0555 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Variable Estimate Error Chi-Square Pr > ChiSq Ratio 

LIN_0 0.0000607 0.0000309 3.8539 0.0496 1.000 

LIN_1 -0.0000583 0.0000371 2.4761 0.1156 1.000 

c. Linear relative risk models for breast cancer mortality 

Finally, we also ran linear RR models for these data, as shown in Figure D-2d below 

(denoted “ERR” models), which also includes the RRs from the log RR categorical model as 

shown in other graphs. Again, the linear curve, highly influenced by the upper 5% tail of 

exposure, underestimates the categorical points, while the log transform and 2-piece spline 

capture better the initial increase in risk followed by an attenuation. Parameter estimates for 

these models can be found in Table D-2g. 

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Figure D-2e. Linear RR models for breast cancer mortality. 

Table D-2g. Model results for breast cancer mortality, linear RR modelsb 

Linear RR Model Parameter(s) SE –2 Log Likelihood 

CUMEXP20 B = 0.000026779 0.000021537 920.122 

Log(CUMEXP20) B = 0.122090 SE = 0.061659 917.841 

Spline, knot = 700, 

CUMEXP20a 

B1 = 0.000830, B2 = – 

0.000807 

SE1 = 0.000614, SE2 = 

0.000619 

918.058 

a

Covariance 2 pieces of spline, –3.80 × 10–7 . b

Editorial note: As discussed in footnotes i and j of Table 4-7 in Section 4.1.2.3, 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. For breast cancer incidence, 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 unit risk estimate for 

breast cancer mortality presented in this assessment does not rely on any of the linear RR models, thus 

revised CIs calculated using the profile likelihood method are not shown here. 

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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d. Risk assessment for breast cancer mortality using the 2-piece log-linear spline model 

We next used the 95% upper bound of the coefficient for the 1st piece of the 2-piece loglinear model from Table D-2c, which is 0.0006877 + 1.64 × 0.0004171, to calculate the 

LEC01 via the life-table analysis of excess risk used by EPA in Appendix C of their 2006 

draft risk assessment. Here we used the same data on background breast cancer mortality 

and background all cause mortality as used by EPA in their 2006 calculations. The rate ratio, 

then, as a function of exposure, is RR = e(0.00137 × cumexp20). Note that the 2-piece log-linear 

model is linear for the log of the rate ratio. Once this is exponentiated, it is no longer strictly 

linear, but is still approximately so, as can be seen in Figure D-2b. 

Use of this function in the life-table analysis results in an excess risk of 0.01 when the daily 

exposure is 0.0048 ppm, which is the LEC01. This is substantially lower than the previous 

LEC01 of 0.0195 ppm in EPA's 2006 draft risk assessment (U.S. EPA, 2006a, Table 12). 

Similar calculations were done to derive the EC01 which was 0.0095 ppm.
 

e. Risk assessment for breast cancer mortality using the 2-piece linear spline model.
 

The slope of the first segment of the 2-piece linear model was 21% higher than the slope of 

the corresponding 2-piece log-linear spline (knot at 700 ppm-days). The slope coefficient 

was 0.0008300, with a std. err. of 0.000614. The resulting EC01 and LEC01 were 0.0080 and 

0.0037 ppm, respectively. 

D.3. LYMPHOID CANCER MORTALITY (SUBSET OF ALL HEMATOPOIETIC 

CANCERS COMBINED) (N = 18,235). 

a. Exposure distribution in cohort and among lymphoid cases in the cohort mortality 

study 

The estimated daily exposure to ETO across different jobs and time periods ranged from 

0.05 to 77 ppm. Exposure intensities from this broad range were multiplied by the length of 

time in different jobs to get estimates of cumulative exposure. The duration of exposure for 

the full cohort at the end of follow-up had a mean of 8.7 years and a standard deviation of 

9.3 years. Cumulative exposure at the end of follow-up, with no lag, had a mean of 27 ppm-
 

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years and a median of 6 ppm-years, indicating that these data are highly skewed. Log
 

transformation of these data results in an approximately normal distribution of the data.
 

As noted in Section D.1.a, cumulative exposure at the end of follow-up may be misleading, 

as it is not relevant to standard analyses, all of which treat cumulative exposure as a timedependent variable which must be assessed at specific points in time. See Section D.1.a for
 

more discussion. 

In modeling lymphoid cancer, a subset of all (lympho)hematopoietic cancer, we used a 15­

year lag for cumulative exposure as in the prior publication (Steenland et al., 2004), and we 

also used the same cut points as in the publication. Lymphoid cancer consists of nonHodgkin 

lymphoma, lymphocytic leukemia, and myeloma (ICD-9 200, 202, 203, 204). The 

distribution of cases for lymphoid cancer mortality is seen below. 

Table D-3a. Exposure categories and case distribution for lymphoid cancer 

mortality 

Cumulative exposure, 

15-year laga 

Male lymphoid 

cancer deaths 

Female lymphoid 

cancer deaths 

Total lymphoid 

cancer deaths 

0 (Lagged out) 6 3 9 

>0–1200 ppm-days 2 8 10 

1201–3680 ppm-days 4 7 11 

3681–13,500 ppm-days 5 5 10 

>13,500 ppm-days 10 3 13 

a

The means of the categories were 0, 446, 2,143, 7,335, and 39,927 ppm-days, respectively. The medians were 

374, 1,985, 6,755, and 26,373 ppm-days, respectively. These values are for the full cohort. 

b. Results of Cox regression analysis of lymphoid cancer mortality using categorical, 2­

piece linear, log transform, and linear log RR models 

While the published results in Steenland et al. (2004) focused on males (Table 7 in Steenland 

et al., 2004), in fact males and females do not differ greatly in categorical results using a 15­

year lag. A formal chunk test for four interaction terms between exposure and gender is not 

close to significance (p = 0.58), although such tests are not very powerful in the face of 

sparse data such as these. Table D-3b below shows the categorical odds ratio results for men 

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

7/2013 D-42 DRAFT―DO NOT CITE OR QUOTE
 
 

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

7/2013 D-43 DRAFT―DO NOT CITE OR QUOTE
 

 

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and women separately and combined. In the analyses presented here, males and female are 

combined. 

Table D-3b. Lymphoid cancer mortality results by sex 

Cumulative exposure, 

15-year lag 

Odds ratios 

(95% CI) 

males 

Odds ratios 

(95% CI) 

females 

Odds ratios 

(95% CI) 

combined 

0 (Lagged out) 1.00 1.00 1.00 

>0–1200 ppm-days 0.91 (0.16–5.23) 2.25 (0.41−12.45) 1.75 (0.59−5.25) 

1,201−3,680 ppm-days 2.89 (0.65–12.86) 3.26 (0.56−18.98) 3.15 (1.04−9.49) 

3,681−13,500 ppm-days 2.71 (0.65–11.55) 2.16 (0.34−13.59) 2.44 (0.80−7.50) 

>13,500 ppm-days 3.76 (1.03–13.64) 1.83 (0.25−13.40) 3.00 (1.02−8.45) 

Analyses used a case-control approach, with 100 controls per case, as in Steenland et al. 

(2004). Age was the time variable in proportional hazards (Cox) regression. For lymphoid 

cancer mortality, only exposure variables were included in the model. Cases and controls 

were within risk sets matched on age, gender, and race. 

Using log RR models, we used a categorical model, a linear model, a 2-piece linear model, 

and a log transform model. We also ran a number of analogous models using linear RR 

models (Section D-3.c below). 

The categorical log RR model for lymphoid cancer mortality was run using the originally 

published cut points to form four categories above the lagged-out group, as shown in Table 

D-3b. To graph the categorical points, each category was assigned the mid-point of the 

category as its exposure level, except for the last one which was assigned 50% more than the 

last cut point. 

For the 2-piece log-linear model, the single knot was chosen at 100 ppm-days based on a 

comparison of likelihoods assessed every 100 ppm-day from 100 to 15,000. The best 

likelihood was at 100 ppm-days. Figure D-3a below shows the likelihood vs the knots. 

Figure D-3a also suggests a local maximum likelihood near 1600 ppm-days. Figure D-3b 

shows the log RR models. 
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Model results for the categorical and 2-piece linear log RR models are shown in Tables D-3c 

and D-3d. Tables D-3e and D-3f give the results for the log transform model and linear log 

RR models; the latter does not fit the data well. Table D-3g shows the model results for the 

2-piece log-linear spine model with the knot at the local maximum likelihood of 1600 ppmdays. 

Figure D-3b shows the graphical results for the categorical, 2-piece linear, and log transform 

log RR models. There is a very steep increase in risk at very low exposures. The knot for 

the 2-piece log-linear curve is a low 100 ppm-days. The steep slope at low exposures may be 

unrealistic as a basis for risk assessment, dependent as it is on relatively sparse data in the 

low-exposure region (e.g., only 19 cases in the non-exposed lagged-out referent group and 

the lowest cumulative exposure group, up to 1200 ppm-days, combined). 

We further explored the sensitivity of the log-linear model to high exposures, by excluding 

progressively more of the upper tail of exposure. We excluded 5%, 10%, 20%, 30%, 40%, 

and 55% of the upper tail of exposure. The 55% cutoff was at 2,000 ppm-days. The slope of 

the log-linear exposure-response model increased by 0.4, 1.7, 7.9, 5.6, 26.7, and 113.7 times, 

respectively, with the exclusion of progressively more data. It is clear that the curve changes 

substantially once the top 20% of the exposure range is truncated. 

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

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-2 log likelihood for different knots for lymphoid cancer mortality 

 461.000 

 460.000

 459.000

 458.000

 457.000

0 1000 2000 3000 4000 5000 6000 7000 

KNOT 

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Figure D-3a. Likelihoods vs knots for 2-piece log-linear model, lymphoid 

cancer mortality. 

Figure D-3b. Plot of the exposure and lymphoid cancer mortality rate ratios 

generated using a 2-piece log-linear spline model overlaid with other log RR 

curves and categorical log RR model points. 

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7/2013 D-45 DRAFT―DO NOT CITE OR QUOTE
 
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Table D-3c. Categorical results for lymphoid cancer mortality (log RR
 

model), men and women combined
 

Model Fit Statistics
 

Without With
 

Criterion Covariates Covariates
 

-2 LOG L 463.912 458.069
 

AIC 463.912 466.069 

SBC 463.912 473.950 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 5.8435 4 0.2111 

Score 5.7397 4 0.2195 

Wald 5.6220 4 0.2292 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Variable DF Estimate Error Chi-Square Pr > ChiSq Ratio 

CUM151 1 0.56036 0.55981 1.0020 0.3168 1.75 

CUM152 1 1.14581 0.56351 4.1344 0.0420 3.15 

CUM153 1 0.89001 0.57391 2.4049 0.1210 2.44 

CUM154 1 1.09998 0.55112 3.9837 0.0459 3.00 

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Table D-3d. Results of 2-piece log-linear spline model for lymphoid cancer 

mortality, men and women combined, knot at 100 ppm-days
 

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 463.912 457.847 

AIC 463.912 461.847 

SBC 463.912 465.787 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 6.0658 2 0.0482 

Score 5.9648 2 0.0507 

Wald 5.8246 2 0.0544 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Parameter Estimate Error Chi-Square Pr > ChiSq Ratio 

LIN_0 0.01010 0.00493 4.1997 0.0404 1.010 

LIN_1 -0.01010 0.00493 4.1959 0.0405 0.990 

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Table D-3e. Results of the log transform log RR model for lymphoid cancer 

mortality, both sexes combined

 Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 463.912 458.426 

AIC 463.912 460.426 

SBC 463.912 462.396 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 5.4868 1 0.0192 

Score 5.3479 1 0.0207 

Wald 5.2936 1 0.0214 

Analysis of Maximum Likelihood Estimates 

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

lcum15 1 0.11184 0.04861 5.2936 0.0214 1.118 

Table D-3f. Results of the log-linear model for lymphoid cancer mortality, 

both sexes combined

 Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 463.912 462.413 

AIC 463.912 464.413 

SBC 463.912 466.383 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 1.4998 1 0.2207 

Score 2.0403 1 0.1532 

Wald 1.9959 1 0.1577 

Analysis of Maximum Likelihood Estimates 

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

CUMEXP15 1 4.73679E-6 3.35285E-6 1.9959 0.1577 1.000 

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Table D-3g. Results of 2-piece log-linear spline model for lymphoid cancer 

mortality, men and women combined, knot at 1600 ppm-days 

Model Fit Statistics
 

Without With
 

Criterion Covariates Covariates
 

-2 LOG L 463.912 458.640 

AIC 463.912 462.640 

SBC 463.912 466.581 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 5.2722 2 0.0716 

Score 5.2666 2 0.0718 

Wald 5.1436 2 0.0764 

Analysis of Maximum Likelihood Estimates 

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

LIN_0 1 0.0004893 0.0002554 3.6713 0.0554 1.000 

LIN_1 1 -0.0004864 0.0002563 3.6014 0.0577 1.000 

c. Results for linear relative risk models 

Table D-3h shows the model fit statistics and coefficients for the linear RR models 

(Supplemental Results). Results for linear RR models are seen in Figure D-3c (denoted as 

“ERR” models). They are quite similar to the log RR results in Figure D-2b. Again there is 

a very steep rise in the exposure-response curve at very low exposures. The knot for the 2­

piece linear curve is again at 100 ppm-days. 

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Table D-3h. Supplemental Results: Model fit statistics and coefficients for 

linear RR models, lymphoid cancer mortality 

Linear RR Model 

–2 Log 

likelihood 

(full model) AIC p-valuea Parameter(s) SEd 

CUMEXP15 461.62 463.62 0.13 B = 0.00001226 SE = 0.00001214 

Log(CUMEXP15) 458.54 460.54 0.02 B = 0.2083 SE = 0.1434 

Spline, knot = 100, 

CUMEXP15b,c 457.48 461.48 0.04 B1 = 0.010090 B2 = –0.010086 

SE1 = 0.004458 

SE2 = 0.004458 

a

From likelihood ratio test.
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Covariance of 2 pieces of linear spline: −2.52 × 10-5 .
 c

For the maximum likelihood estimate, for exposures below the knot, RR = 1 + (B1 × exp); for exposures
 

above the knot, RR = 1 + (B1 × exp + B2 × (exp – knot)). For the 95% upper confidence limit, for
 

exposures below the knot, RR = 1 + ((β1+ 1.645 × SE1) × exp); 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.
 d

Editorial note: Confidence intervals for linear RR models, in contrast to those for the log-linear RR models,
 

may not be symmetrical. EPA did not apply the profile likelihood approach (Langholz and Richardson,
 

2010), which allows for asymmetric CIs, to develop CIs for this model because the model was not used
 

further in the assessment.
 

Figure D-3c. Linear RR models for lymphoid cancer. 

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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d. Risk assessment for all lymphoid cancer mortality using the 2-piece log-linear spline 

model 

We consider that none of the parametric models (either log RR or linear RR) generated for 

the lymphoid cancer data (and the same is true for all hematopoietic cancer) are suitable for 

EPA risk assessment because of the overly steep exposure-response relationship in the lowdose range for the 2-piece models and log transform models (highly influenced by the sparse 

number of deaths in the low-exposure region), and the overly shallow exposure-response 

relationship for the linear and log-linear models, which are influenced highly by the upper 

tail of exposures. A reasonable alternative approach is a weighted regression through the 

categorical points (excluding the highest exposure group), an approach adopted originally by 

EPA. 

Nonetheless, we have used the 2-piece log-linear model to calculate the LEC01 and the EC01, 

by way of illustrating the effect of the very steep exposure-response curve in the low-dose 

region. 

We used the 95% upper bound of the coefficient for the 1st piece of the 2-piece log-linear 

model from Table D-3d, which is 0.01010 + 1.64 × 0.00493, to calculate the LEC01 via the 

life-table analysis of excess risk used by EPA in Appendix C of their 2006 draft risk 

assessment. Here we used the same data on lymphoid cancer mortality and background allcause mortality as used by EPA in their 2006 calculations. The predicted rate ratio, then, as a 

function of exposure, is RR = e((0.01010 + 1.64 × 0.00493) × cumexp15). Use of this RR model in the 

life-table analysis results in an excess risk of 0.01 when the daily exposure (15-year lag) is 

0.0006 ppm, which is the LEC01. This is much lower than the previous LEC01 of 0.0165 ppm 

for lymphoid cancer mortality in EPA's 2006 draft risk assessment (U.S. EPA, 2006a, Table 

 9). 

A similar calculation was done for the EC01, which resulted in a value of 0.0012 ppm. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

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e. Supplemental results: results for log cumulative exposure Cox regression models with no 

lag 

Without With
 

Criterion Covariates Covariates
 

-2 LOG L 463.912 462.014 

AIC 463.912 464.014 

SBC 463.912 465.984 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 1.8987 1 0.1682 

Score 1.8589 1 0.1728 

Wald 1.8530 1 0.1734 

Analysis of Maximum Likelihood Estimates 

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

lcumexp 1 0.10230 0.07515 1.8530 0.1734 1.108 

D.4. HEMATOPOIETIC CANCER MORTALITY (ALL HEMATOPOIETIC CANCERS 

COMBINED) 

a. Exposure distribution in cohort and among all (lympho)hematopoietic cases in the 

cohort mortality study 

In modeling hematopoietic cancer, we used a 15-year lag for cumulative exposure, as in the 

prior publication (Steenland et al., 2004), and we also used the same cut points as in that 

publication. The distribution of cases for hematopoietic cancer mortality is seen below. 

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

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Table D-4a. Exposure categories and case distribution for hematopoietic 

cancer mortality 

Cumulative exposure, 

15 year lag 

Male hematopoietic 

cancer deaths 

Female 

hematopoietic cancer 

deaths 

Total 

hematopoietic 

cancer deaths 

0 (Lagged out) 9 4 13 

>0–1200 ppm-days 4 13 17 

1201–3680 ppm-days 5 10 15 

3681–13,500 ppm-days 8 7 15 

>13,500 ppm-days 11 3 14 

a

Mean exposures for both sexes combined with a 15-year lag for the categorical exposure quartiles 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.
 

b. Results of Cox regression analysis of hematopoietic cancer mortality using 

categorical, 2-piece linear, linear and log transform log RR models 

While the published results of these data in Steenland et al. (2004) focused on males (Table 8 

in Steenland et al., 2004), in fact males and females do not differ greatly in categorical results 

using a 15-year lag. A formal chunk test for four interaction terms between exposure and 

gender is not close to significance (chi square 4.5, 4 df; p = 0.34), although such tests are not 

very powerful in the face of sparse data such as these. Table D-4b below shows the 

categorical odds ratio results for men and women separately and combined. Males and 

females were combined in all analyses for hematopoietic cancer here. 

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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Table D-4b. All hematopoietic cancer mortality categorical results by sex 

(log RR model) 

Cumulative exposure, 

15 year lag 

Odds ratio 

(95% CI) 

males 

Odds ratio 

(95% CI) 

females 

Odds ratio 

(95% CI) 

combined 

0 (Lagged out) 1.00 1.00 1.00 

>0–1200 ppm-days 1.23 (0.32–4.74) 3.76 (1.01–17.23) 2.33 (0.93–5.86) 

1201–3680 ppm-days 2.53 (0.69–9.27) 4.93 (1.01–23.99) 3.46 (1.33–8.95) 

3681–13,500 ppm-days 3.14 (0.95–10.37) 3.31,(0.64–17.16) 3.02 (1.16–7.89) 

>13,500 ppm-days 3.42 (1.09–10.73) 2.11 (0.33–13.74) 2.96 (1.12–7.81) 

Analyses used a case-control approach, with 100 controls per case, as in Steenland et al. 

(2004). Age was the time variable in proportional hazards (Cox) regression. For lymphoid 

cancer mortality, only exposure variables were included in the model. Cases and controls 

were matched within risk sets on age, gender, and race. 

Using log RR models, we used a categorical model, a linear model, a 2-piece linear model, 

and a log transform model. We also ran a number of analogous models using linear RR 

models (Section D-4.c below). 

The categorical log RR model for hematopoietic cancer mortality was run using the originally 

published cut points to form four categories above the lagged-out group, as shown in Table D4b. To graph the categorical points, each category was assigned the mid-point of the category as 

its exposure level, except for the last one which was assigned 50% more than the last cut point. 

For the 2-piece log-linear model, the single knot was chosen based on a comparison of 

likelihoods assessed every 100 ppm-days from 0 to 7,000 ppm-days. The best likelihood was 

at 500 ppm-days (Figure D-4a). In Figure D-4b below we show the categorical, 2-piece 

linear spline and log transform log RR model results. 

Model results for the categorical and 2-piece linear log RR models are shown in Tables D-4c 

and D-4d, and the results of the log transform and linear log RR models in Table D-4e and 

Table D-4f. Again the linear model appears to substantially underestimate the exposureresponse relationship and does not provide a good model fit. 
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We further explored the sensitivity of the log-linear model to high exposures by excluding 

progressively more of the upper tail of exposure. We excluded 5%, 10%, 20%, 30%, 40%, 

and 53% of the upper tail of exposure. The 53% cutoff was at 2,000 ppm-days. The slope of 

the log-linear exposure-response model increased by 0.8, 1.0, 9.3, 28.6, 58.2, and 191.4 

times, respectively, with the exclusion of progressively more data. It appears the curve is flat 

in the top 20% of exposure. 

-2 log like lihood for different knots for all hematopoetic cancer mortality 

 654.000 

 653.000

 652.000

 651.000

 650.000

 649.000

 648.000

 647.000

0 1000 2000 3000 4000 5000 6000 7000 

KNOT 

Figure D-4a. Likelihood vs knots for 2-piece log-linear model, all 

hematopoietic cancer. 

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

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Figure D-4b. Plot of exposure and rate ratios for all hematopoietic cancer 

generated using a 2-piece log-linear spline model and log transform, linear, 

and categorical log RR models.
 

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

7/2013 D-56 DRAFT―DO NOT CITE OR QUOTE
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Table D-4c. Categorical results for all hematopoietic cancer mortality (log 

RR model), men and women combined, cumulative exposure with a 15-year 

lag 

Model Fit Statistics
 

Without With
 

Criterion Covariates Covariates
 

-2 LOG L 655.643 647.806 

AIC 655.643 655.806 

SBC 655.643 665.022 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 7.8371 4 0.0977 

Score 7.3994 4 0.1162 

Wald 7.2354 4 0.1240 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Variable DF Estimate Error Chi-Square Pr > ChiSq Ratio 

CUM151 1 0.84746 0.46956 3.2573 0.0711 2.33 

CUM152 1 1.23989 0.48571 6.5166 0.0107 3.46 

CUM153 1 1.10664 0.48943 5.1126 0.0238 3.02 

CUM154 1 1.08360 0.49603 4.7723 0.0289 2.96 

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Table D-4d. Results of 2-piece log-linear spline model for all hematopoietic 

cancer mortality, men and women combined, cumulative exposure with a 15­

year lag 

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 655.643 647.581 

AIC 655.643 651.581 

SBC 655.643 656.189 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 8.0615 2 0.0178 

Score 7.5092 2 0.0234 

Wald 7.3467 2 0.0254 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

spl1 1 0.00201 0.0007731 6.7457 0.0094 1.002 

spl2 1 -0.00201 0.0007738 6.7249 0.0095 0.998 

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

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Table D-4e. Results of log-transform log RR model for all hematopoietic
 

cancer mortality, men and women combined, cumulative exposure with a 15­

year lag
 

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 655.643 648.825 

AIC 655.643 650.825 

SBC 655.643 653.129 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 6.8177 1 0.0090 

Score 6.6260 1 0.0100 

Wald 6.5593 1 0.0104 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

lcum15 1 0.10706 0.04180 6.5593 0.0104 1.113 

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

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Table D-4f. Results of log-linear model for all hematopoietic cancer 

morality, men and women combined, cumulative exposure with a 15-year lag 

Model Fit Statistics 

Without With 

Criterion Covariates Covariates 

-2 LOG L 655.643 654.922 

AIC 655.643 656.922 

SBC 655.643 659.226 

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 0.7213 1 0.3957 

Score 0.8783 1 0.3487 

Wald 0.8739 1 0.3499 

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard 

Parameter DF Estimate Error Chi-Square Pr > ChiSq Ratio 

CUMEXP15 1 3.26052E-6 3.48788E-6 0.8739 0.3499 1.000 

c. Results for linear relative risk models for hematopoietic cancer mortality 

For completeness, we also present the results of the linear RR models below (Table D-4g and 

Figure D-4c; linear RR models are denoted “ERR” models in the figure). They look much 

like their counterparts for the log RR models. Again, the high slope of the exposure-response 

relationship in the low-dose region for the 2-piece linear and log transform curves, and the 

low overall slope of the linear curve, call into question the use of these models for risk 

assessment. 

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Table D-4g. Supplemental Results: Model fit statistics and coefficients for 

linear RR models, hematopoietic cancer mortality 

Linear RR Model 

–2 Log 

likelihood 

(full model) AIC p-valuea Parameter(s) SEd 

CUMEXP15 654.64 656.64 0.32 B = 0.000006257 SE = 0.000008187 

Log(CUMEXP15) 648.13 650.13 0.006 B = 0.2322 SE = 0.1437 

Spline, knot = 100, 

CUMEXP15b,c 646.95 650.95 0.01 B1 = 0.003673 B2 = –0.003668 

SE1 = 0.002345 

SE2 = 0.002345 

a

From likelihood ratio test.
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Covariance of 2 pieces of linear spline: − 5.70 × 10-6 .
 c

For the maximum likelihood estimate, for exposures below the knot, RR = 1 + (B1 × exp); for exposures
 

above the knot, RR = 1 + (B1 × exp + B2 × (exp – knot)). For the 95% upper confidence limit, for
 

exposures below the knot, RR = 1 + ((β1+ 1.645 × SE1) × exp); 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.
 d

Editorial note: Confidence intervals for linear RR models, in contrast to those for the log-linear RR models,
 

may not be symmetrical. EPA did not apply the profile likelihood approach (Langholz and Richardson,
 

2010), which allows for asymmetric CIs, to develop CIs for this model because the model was not used
 

further in the assessment.
 

Figure D-4c. Linear RR models for hematopoietic cancer mortality. 

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 D-62 DRAFT―DO NOT CITE OR QUOTE
 

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d. Risk assessment for all hematopoietic cancer mortality using the 2-piece log-linear 

spline model 

As was the case for lymphoid cancer (which is a subset of the hematopoietic cancers), we 

consider that none of the parametric models (either log RR or ERR) generated for the 

hematopoietic cancer data are suitable for EPA risk assessment because of the overly steep 

exposure-response relationship in the low-dose range for the 2-piece models and the log 

transform models (highly influenced by the sparse number of deaths in the low-exposure 

region), and the overly shallow exposure-response relationship for the linear models, which 

are influenced highly by the upper tail of exposures. A reasonable alternative approach is a 

weighted regression through the categorical points (excluding the highest exposure group), 

an approach adopted originally by EPA. 

Nonetheless, we have used the 2-piece log-linear model to calculate the LEC01 and the EC01, 

by way of illustrating the effect of the very steep exposure-response curve in the low-dose 

region. 

We used the 95% upper bound of the coefficient for the 1st piece of the 2-piece log-linear 

model from Table D-4d, which is 0.00201 + 1.64 × 0.000773, or 0.003277, to calculate the 

predicted LEC01 via the life-table analysis of excess risk used by EPA in Appendix C of their 

2006 draft risk assessment. Again, here we used the data on hematopoietic cancer mortality 

and background all-cause mortality as used in EPA's 2006 calculations. The predicted RR, 

then, as a function of exposure, is RR = e(0.003277 × cumexp15) (up to the knot of 500 ppm-days). 

This results in an excess risk of 0.01 when the daily exposure (15-year lag) is 0.0032 ppm, 

which is the LEC01. This is notably lower than the previous LEC01 of 0.0109 ppm for 

hematopoietic cancer mortality in EPA's 2006 draft risk assessment (U.S. EPA, 2006a, 

Table 7). 

Similar calculations were done for the EC01, which resulted in a value of 0.0043 ppm. 
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D.5. SUMMARY TABLE OF EC01S FOR DIFFERENT OUTCOMES, USING 2-PIECE 

SPLINE MODELS 

Table D-5 below provides a summary of the current findings for EC01 and the prior EPA 

findings for EC01. 

In general, findings are similar. As described above, the EC01 values based on the 2-piece 

spline models were obtained by multiplying the background cancer rate by e(beta × cumexp) for 

log RR models or by (1 + beta × cumexp) for linear RR models, where the beta coefficient 

was for the first piece of the 2-piece linear models, and cumexp was determined such that a 

daily exposure would result in an excess risk of 1% above background, with risk calculated 

through age 85 years (BIER methodology, spreadsheet obtained from EPA). In the case of 

breast cancer incidence, following EPA’s methods in the risk assessment, the life-table 

values for all-cause mortality (within each 5-year age interval) were adjusted to account for 

incident cases being withdrawn from the pool at risk entering the next age interval, by adding 

the breast cancer incidence rate to the all-cause mortality rate and then subtracting breast 

cancer mortality rate so that fatal breast cancer cases are not “counted” twice in this 

adjustment. 

As noted above, we believe the 2-piece spline models (either log RR or linear RR versions 

are reasonable bases for risk assessment for the breast cancer incidence and mortality data. 

They also result in EC01 values that are lower than but in the ballpark of the previous EPA 

estimates using weighted regression for categorical points, excluding the highest exposure 

quintile. However, this is not the case for the hematopoietic/lymphoid cancer data. 

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

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Table D-5a. Summary of EC01 results (in ppm) in current analysis and 

previous EPA risk assessment 

U.S. EPA (2006a) 

EC01 a 

Steenlanda 

LEC01 

2-piece spline 

Steenland 

EC01 

2-piece spline 

Breast cancer incidenceb 

(log RR model, 15-year lag) 

0.0238 0.009 0.0152 

Breast cancer incidence (linear RR 

model, 15-year lag)b 

-­ 0.0052 0.0100 

Breast cancer mortality (log RR 

model, 20-year lag) 

0.0387 0.0048 0.0096 

Breast cancer mortality (linear RR 

model, 20-year lag) 

-­ 0.0037 0.0080 

Hematopoietic cancer mortality 

(log RR model, 15-yr lag)c 

0.0238 0.0032 0.0043d 

lymphoid cancer mortality (log RR 

model, 15-yr lag)c 

0.0427 0.0006 0.0012e 

a

EPA uses regression through categorical points (U.S. EPA, 2006a), Steenland uses 2-piece spline models.
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Breast cancer incidence for the subgroup with interviews, see Steenland et al. (2004).
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For hematopoietic and lymphoid cancer, EPA EC01 calculated for males only, Steenland includes both men and
 

women.
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Using at knot at 500 ppm-days. 2-piece linear RR model results similar but not presented.
 e

Using knot at 100 ppm-days. 2-piece linear RR model results similar but not presented. 

D.6. SENSITIVITY OF 2-PIECE SPLINE CURVES TO PLACEMENT OF KNOT 

By way of sensitivity analysis, we ran 2-piece log-linear models for all breast cancer incidence 

with knots chosen at 5000, 5800 (optimal) and 7000 ppm-days, and for hematopoietic cancer 

mortality for knots of 500 (optimal) and 1000. Results show the relatively large sensitivity to the 

knot placement in the EC01. 

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Table D-6a. Exposure-response coefficients and EC01s based on selection of 

different knots, using 2-piece log-linear models 

Coefficient first 

piece –2 log-likelihoodb EC01 

Breast cancer incidence knot at 5000 ppm-days 0.0000860 1940.6 0.0133 

Breast cancer incidence knot at 5800 ppm-daysa 0.0000770 1940.5 0.0151 

Breast cancer incidence knot at 7000 ppm-days 0.0000653 1940.7 0.0176 

Hematopoietic cancer mortality knot at 500 ppm-days 0.00201 647.6 0.0043 

Hematopoietic cancer mortality knot at 1000 ppm-days 0.00089 648.4 0.0098 

a

Knot used in analysis.
 b

Lower numbers equal better fit, linear RR model likelihoods not comparable to log RR likelihoods and are not
 

shown here.
 

D.7. POSSIBLE INFLUENCE OF THE HEALTHY WORKER SURVIVOR EFFECT 

The healthy worker survivor effect is the effect of healthy workers remaining in the 

workforce as sick workers leave, independently of any damaging effects of exposure. It is a 

selection bias via which healthier workers remain in the workforce. It tends to create a 

downward bias in exposure-response coefficients when the exposure metric is cumulative 

exposure, which is by definition correlated with duration of exposure and almost always with 

duration of employment Steenland et al. (1996). Given a true effect of exposure on disease 

incidence or mortality in the case of ethylene oxide, it is possible that the health worker 

survivor effect has caused some negative bias in observed exposure-response coefficients. 

However, there are no standard methods to correct for this bias, because leaving work is both 

a confounder and an intermediate variable on a pathway between exposure and disease. 

Therefore, standard analyses would need to adjust for employment status as a confounder, 

but should not adjust for it because it is an intermediate variable. Robins et al. (1992) has 

proposed some solutions using G-estimation to address this problem, but to date these 

solutions are not commonly used and can be difficult to implement. The degree to which the 

health worker survivor effect confounds measured exposure-response trends is not known, 

but it is likely that lagging exposure, as has been done here, diminishes such confounding 

(Arrighi and Hertz-Picciotto, 1994) 

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D.8. POSSIBLE INFLUENCE OF EXPOSURE MIS-MEASUREMENT 

Exposure estimation in the ETO studies considered here is subject to errors in measurement. 

The method for exposure estimation used here involved assigned estimated average 

exposures in a given job, at a given time period in a given plant, to each worker in that job. 

Estimated average exposures were taken from observed measurements in a given job, or 

estimated likely average exposures in that job derived from a regression model based on 

observed measurements (Hornung et al., 1994). Errors in measurement in this type of 

situation are typically errors of the Berkson type, rather than classical errors (Armstrong, 

1998, 1990). In Berkson errors, the model for errors is 

Exposuretrue = exposureobserved + error, 

and the error is independent of the observed exposure. The classical error model is 

Exposureobserved = exposuretrue + error, 

and the error is independent of the true exposure. Assuming the errors are unbiased, i.e., 

their expected value is 0, in the classical error model it is well known that measurement error 

will bias exposure-response coefficients towards the null in regression analyses. However, in 

the Berkson error model, exposure-response coefficients will be unbiased in linear regression 

models, although their variance may be increased. In log-linear regression models, such as 

used here, Berkson error in some instances may result in biased exposure-response estimates 

(Deddens and Hornung, 1994; Prentice, 1982). This may occur when the variance of the 

errors increases with the true exposure level, which is often the case in occupational studies, 

when the disease is relatively rare (also typical), and when the true exposure is distributed 

log-normally (again typical of occupational exposures). In this situation, (Steenland et al., 

2000) have shown that exposure-response coefficients using cumulative exposure can be 

biased either upward or downward. The direction and degree of bias depends on the degree 

of increase in the variance of exposure error as exposure level increases and on the variance 

of duration of exposure. When the standard deviation of duration of exposure is less than or 

equal to its mean, as is the case in the ETO cohort studied here, simulations have shown that 

the exposure-response coefficients are approximately unbiased (Steenland et al., 2000). An 

added complication not considered in the simulations conducted by (Steenland et al., 2000) is 

the possible correlation between measurement error and outcome. If this correlation is 
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strong, which may occur when there is a strong exposure-response relationship, it is 

important to take it into account. Estimating the effect of exposure measurement in the 

presence of this correlation can be done using Bayesian models and special software 

(WINBUGS), but the calculations are complex and require a good deal of time. 

Hornung et al. (1994) provide an estimate of the log-normal distribution of measured 

exposure based on personal samples, as well as the likely distribution of error in assigning 

the job-specific means to estimate individual exposures. Assignment of such job-specific 

means was shown to involve some bias as well as random error. This provides a rich source 

of information with which one could simulate the effect of measurement error on exposureresponse coefficients. Based on the exposure estimates used in the study, and some 

assumptions about the error of such measurement in terms of bias and random error, as well 

as the assumption of a Berkson error model, one could simulate what the true job-specific 

exposure means were likely to have been, and then in turn simulate likely true personal 

exposure distributions. Using the latter in exposure-response analysis, one could estimate the 

true exposure-response coefficient. However, such analyses are rather involved and beyond 

the scope of the current task. 

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D.9. REFERENCES 

Armstrong, B. (1990) The effects of measurement errors on relative risk regressions. Am J Epidemiol 

132:1176−1184. 

Armstrong, B. (1998) Effect of measurement error on epidemiological studies of environmental and occupational 

exposures [Review]. Occup Environ Med 55(10):651−656. 

Arrighi, HM; Hertz-Piccioto, I. (1994) The evolving concept of the healthy worker effect. Epidemiology 

5(2):189−196. 

Deddens, J; Hornung, R. (1994) Quantitative examples of continuous exposure measurement errors that bias risk 

estimates away from the null. In: CM Smith; DC Christiani; KT Kelsey; (Eds.) Chemical risk assessment of 

occupational health: current applications, limitations, and future prospects (pp.77−85). Westport, CT: Auburn 

House. 

Hornung, RW; Greife, AL; Stayner, LT; et al. (1994) Statistical model for prediction of retrospective exposure to 

ethylene oxide in an occupational mortality study. Am J Ind Med 25(6):825−836. 

Langholz, B; Richardson, DB. (2010) Fitting general relative risk models for survival time and matched case-control 

analysis. Am J Epidemiol 171:377−383. 

Prentice, RL. (1982) Covariate measurement errors and parameter estimation in a failure time regression model. 

Biometrika 69(2):331−341. 

Robins, J; Blevins, D; Ritter, G; et al. (1992) G-estimation of the effect of prophylaxis therapy for Pneumoocystis 

carinii pneumonia on the survival of AIDS patients. Epidemiology 3:319−335. 

Stayner, L; Steenland, K; Dosemeci, M; et al. (2003) Attenuation of exposure-response curves in occupational 

cohort studies at high exposure levels. Scand J Work Environ Health 29:317–324. 

Steenland, K; Deddens, J. (1997) Increased precision using counter-matching in nested case-control studies. 

Epidemiology 8(3):238−242. 

Steenland, K; Deddens, J. (2004) A practical guide to dose-response analyses and risk assessment in occupational 

epidemiology [Review]. Epidemiol 15:63−70. 

Steenland, K; Deddens, J; Piacitelli, L. (2001) Risk assessment for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCCD) 

based on an epidemiologic study. Am J Epidemiol 154:451–458. 

Steenland, K; Deddens, J; Salvan, A; et al. (1996) Negative bias in exposure-response trends in occupational studies: 

modeling the healthy worker effect. Am J Epi 143(2):202−210. 

Steenland, K; Deddens, J; Zhao, S. (2000) Biases in estimating the effect of cumulative exposure in log-linear 

models when estimated exposure levels are assigned. Scan J Work Environ Health 26:37−43. 

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; Whelan, E; Deddens, J; et al. (2003) Ethylene oxide and breast cancer incidence in a cohort study of 

7576 women (United States). Cancer Causes Control 14: 531–539. 

U.S. EPA (Environmental Protection Agency). (2006a). Evaluation of the carcinogenicity of ethylene oxide: 

external review draft [EPA Report]. (EPA/635/R-06/003). National Center for Environmental Assessment, 

Washington, DC. 
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APPENDIX E. 

LIFE-TABLE ANALYSIS 

A spreadsheet illustrating the extra risk calculation for the derivation of the LEC01 for 

lymphoid cancer incidence is presented in Table E-1. 

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Table E-1. Extra risk calculationa for environmental exposure to 0.0114 ppm (the LEC01 for lymphoid cancer 

incidence)b using the weighted linear regression model based on the categorical cumulative exposure results of 

Steenland et al. (2004), reanalyzed by Steenland for both sexes combined (see Appendix D of this assessment), 

with a 15-year lag, as described in Section 4.1.1 

A B C D E F G H I J K L M N O P 

Interval 

number 

(i) 

Age 

interval 

All cause 

mortality 

(×105/yr) 

lymphoid 

cancer 

incidence 

(×105/yr) 

All 

cause 

hazard 

rate 

(h*) 

Prob of 

surviving 

interval 

(q) 

Prob of 

surviving 

up to 

interval 

(S) 

lymphoid 

cancer 

hazard 

rate (h) 

Cond 

prob of 

lymphoid 

cancer 

incidence 

in 

interval 

(R0) 

Exp 

duration 

mid 

interval 

(xtime) 

Cum 

exp mid 

interval 

(xdose) 

Exposed 

lymphoid 

cancer 

hazard 

rate (hx) 

Exposed 

all cause 

hazard 

rate 

(h*x) 

Exposed 

prob of 

surviving 

interval 

(qx) 

Exposed 

prob of 

surviving 

up to 

interval 

(Sx) 

Exposed 

cond prob 

of 

lymphoid 

cancer in 

interval 

(Rx) 

1 <1 685.2 1.9 0.0069 0.9932 1.0000 0.0000 0.00002 0 0.00 0.00002 0.0069 0.9932 1.0000 0.00002 

2 1–4 29.9 8.1 0.0012 0.9988 0.9932 0.0003 0.00032 0 0.00 0.00032 0.0012 0.9988 0.9932 0.00032 

3 5–9 14.7 4.2 0.0007 0.9993 0.9920 0.0002 0.00021 0 0.00 0.00021 0.0007 0.9993 0.9920 0.00021 

4 10–14 18.7 3.2 0.0009 0.9991 0.9913 0.0002 0.00016 0 0.00 0.00016 0.0009 0.9991 0.9913 0.00016 

5 15–19 66.1 3.5 0.0033 0.9967 0.9903 0.0002 0.00017 2.5 31.64 0.00018 0.0033 0.9967 0.9903 0.00018 

6 20–24 94 3.2 0.0047 0.9953 0.9871 0.0002 0.00016 7.5 94.92 0.00017 0.0047 0.9953 0.9871 0.00017 

7 25–29 96 4.1 0.0048 0.9952 0.9824 0.0002 0.00020 12.5 158.20 0.00022 0.0048 0.9952 0.9824 0.00022 

8 30–34 107.9 6.0 0.0054 0.9946 0.9777 0.0003 0.00029 17.5 221.49 0.00034 0.0054 0.9946 0.9777 0.00033 

9 35–39 151.7 9.0 0.0076 0.9924 0.9725 0.0005 0.00044 22.5 284.77 0.00052 0.0077 0.9924 0.9724 0.00050 

10 40–44 231.7 13.2 0.0116 0.9885 0.9651 0.0007 0.00063 27.5 348.05 0.00079 0.0117 0.9884 0.9650 0.00075 

11 45–49 352.3 20.9 0.0176 0.9825 0.9540 0.0010 0.00099 32.5 411.33 0.00128 0.0179 0.9823 0.9538 0.00121 

12 50–54 511.7 32.5 0.0256 0.9747 0.9373 0.0016 0.00150 37.5 474.61 0.00205 0.0260 0.9743 0.9369 0.00190 

13 55–59 734.8 49.2 0.0367 0.9639 0.9137 0.0025 0.00221 42.5 537.90 0.00319 0.0375 0.9632 0.9128 0.00286 

14 60–64 1140.1 70.1 0.0570 0.9446 0.8807 0.0035 0.00300 47.5 601.18 0.00467 0.0582 0.9435 0.8793 0.00399 

15 65–69 1727.4 101.1 0.0864 0.9173 0.8319 0.0051 0.00403 52.5 664.46 0.00691 0.0882 0.9156 0.8296 0.00549 

16 70–74 2676.4 128.7 0.1338 0.8747 0.7631 0.0064 0.00460 57.5 727.74 0.00902 0.1364 0.8725 0.7595 0.00640 
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Table E-1. Extra risk calculationa for environmental exposure to 0.0114 ppm (the LEC01 for lymphoid cancer 

incidence)b using the weighted linear regression model based on the categorical cumulative exposure results of 

Steenland et al. (2004), reanalyzed by Steenland for both sexes combined (see Appendix D of this assessment), with 

a 15-year lag, as described in Section 4.1.1 (continued) 

A B C D E F G H I J K L M N O P 

Interval 

number 

(i) 

Age 

interval 

All cause 

mortality 

(×105/yr) 

lymphoid 

cancer 

incidence 

(×105/yr) 

All 

cause 

hazard 

rate 

(h*) 

Prob of 

surviving 

interval 

(q) 

Prob of 

surviving 

up to 

interval 

(S) 

lymphoid 

cancer 

hazard 

rate (h) 

Cond 

prob of 

lymphoid 

cancer 

incidence 

in 

interval 

(R0) 

Exp 

duration 

mid 

interval 

(xtime) 

Cum 

exp mid 

interval 

(xdose) 

Exposed 

lymphoid 

cancer 

hazard 

rate (hx) 

Exposed 

all cause 

hazard 

rate 

(h*x) 

Exposed 

prob of 

surviving 

interval 

(qx) 

Exposed 

prob of 

surviving 

up to 

interval 

(Sx) 

Exposed 

cond prob 

of 

lymphoid 

cancer in 

interval 

(Rx) 

17 75–59 4193.2 163.0 0.2097 0.8109 0.6675 0.0082 0.00491 62.5 791.02 0.01171 0.2132 0.8080 0.6627 0.00699 

18 80–84 6717.2 179.8 0.3359 0.7147 0.5412 0.0090 0.00413 67.5 854.31 0.01323 0.3401 0.7117 0.5354 0.00601 

Ro = 0.02797 Rx = 0.03769 

extra risk = (Rx−Ro)/(1−Ro) = 0.01001

Column A: interval index number (i). 

Column B: 5-yr age interval (except <1 and 1−4) up to age 85. 

Column C: all-cause mortality rate for interval i (× 105

/yr) (2004 data from NCHS). 

Column D: lymphoid cancer incidence rate for interval i (× 105

/yr) (2000−2004 SEER data).

c 

Column E: all-cause hazard rate for interval i (h*i) (= all-cause mortality rate × number of years in age interval).

d 

Column F: probability of surviving interval i (without being diagnosed with lymphoid cancer) (qi) [= exp(−h*i)]. This column is intended to represent the 

probability of surviving the interval without a diagnosis of lymphoid cancer; however, because lymphoid cancer incidence rates are negligible compared to 

all-cause mortality rates, no adjustment was made to the population at risk to account for the probability of a lymphoid cancer diagnosis. For breast cancer 

incidence, on the other hand, the age-specific “mortality” rates (representing the rates at which the population at risk is decreased in each interval) were adjusted 

to include the age-specific breast cancer incidence rates and to exclude the age-specific breast cancer mortality rates, this latter adjustment so that the probability 

of death from lymphoid cancer is not counted twice, i.e., both as an incident case and as a component of the all-cause mortality. 

Column G: probability of surviving up to interval i (without having been diagnosed with lymphoid cancer) (Si) (S1 = 1; Si = Si−1 × qi−1, for i > 1). 

Column H: lymphoid cancer incidence hazard rate for interval i (hi) (= lymphoid cancer incidence rate × number of years in interval). 

Column I: conditional probability of being diagnosed with lymphoid cancer in interval i [= (hi/h*i) × Si × (1−qi)], i.e., conditional upon surviving up to interval i 

(without having been diagnosed with lymphoid cancer) (Ro, the background lifetime probability of being diagnosed with lymphoid cancer = the sum of the 

conditional probabilities across the intervals). 
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Table E-1. Extra risk calculationa for environmental exposure to 0.0114 ppm (the LEC01 for lymphoid cancer 

incidence)b using the weighted linear regression model based on the categorical cumulative exposure results of 

Steenland et al. (2004), reanalyzed by Steenland for both sexes combined (see Appendix D of this assessment), 

with a 15-year lag, as described in Section 4.1.1 (continued) 

Column J: exposure duration at midinterval (taking into account 15-yr lag) (xtime).
 

Column K: cumulative exposure midinterval (xdose) (= exposure level (i.e., 0.0114 ppm) × 365/240 × 20/10 × xtime × 365) [365/240 × 20/10 converts
 

continuous environmental exposures to corresponding occupational exposures; xtime × 365 converts exposure duration in years to exposure duration in days).
 

Column L: lymphoid cancer incidence hazard rate in exposed people for interval i (hxi) (= hi × (1 + β × xdose), where β = 0.0002472 + (1.645 ×
 

0.0001854) = 0.0005522) (0.0002472 per ppm × day is the regression coefficient obtained from the weighted linear regression model of the categorical results
 

[see Section 4.1.1.2]). To estimate the LEC01, i.e., the 95% lower bound on the continuous exposure giving an extra risk of 1%, the 95% upper bound on the
 

regression coefficient is used, i.e., MLE + 1.645 × SE].
 

Column M: all-cause hazard rate in exposed people for interval i (h*xi) [= h*i + (hxi − hi)].
 

Column N: probability of surviving interval i (without being diagnosed with lymphoid cancer) for exposed people (qxi) [= exp(−h*xi)].
 

Column O: probability of surviving up to interval i (without having been diagnosed with lymphoid cancer) for exposed people (Sxi) (Sx1 = 1; Sxi = Sxi−1 × qxi-1, 

for i > 1).
 

Column P: conditional probability of being diagnosed with lymphoid cancer in interval i for exposed people [= (hxi/h*xi) × Sxi × (1−qxi)] (Rx, the lifetime
 

probability of being diagnosed with lymphoid cancer for exposed people = the sum of the conditional probabilities across the intervals).
 

a

Using the methodology of BEIR (1988). b

The estimated 95% lower bound on the continuous exposure level that gives a 1% extra lifetime risk of lymphoid cancer incidence.
 c

Background cancer incidence rates are used to estimate extra risks for cancer incidence under the assumption that the exposure-response relationship for cancer
 

incidence is the same as that for cancer mortality (see Section 4.1.1.3). d

For the cancer incidence calculation, the all-cause hazard rate for interval i should technically be the rate of either dying of any cause or being diagnosed with
 

the specific cancer during the interval, i.e., (the all-cause mortality rate for the interval + the cancer-specific incidence rate for the interval—the cancer-specific 

mortality rate for the interval [so that a cancer case isn’t counted twice, i.e., upon diagnosis and upon death]) × number of years in interval. For the lymphoid
 

cancer incidence calculations, this adjustment was ignored because the lymphoid cancer incidence rates are small when compared with the all-cause mortality
 

rates. For the breast cancer incidence calculations, on the other hand, this adjustment was made in the all-cause hazard rate (see Section 4.1.2.3).
 

MLE = maximum likelihood estimate, SE = standard error.
 
 

 

 

 

 

 

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APPENDIX F. 

EQUATIONS USED FOR WEIGHTED LINEAR REGRESSION 

(Source: Rothman (1986, p. 343−344))
 

linear model: RR = 1 + bX
 

where RR = rate ratio, X = exposure, and b = slope 

b can be estimated from the following equation: 

∑

n 

w x j RRˆ −∑

n 

wj x j j j 

j=2 j=2 ˆ

b = n 

∑wj x j 

2 

j=2 

(F-1) 

where j specifies the exposure category level and the reference category (j = 1) is ignored. 

the standard error of the slope can be estimated as follows: 

1 SE(b

ˆ) ≈ n 

∑wj x j 

2 

j=2 

(F-2) 

the weights, wj, are estimated from the confidence intervals (as the inverse of the variance): 

ˆ 2 ˆ 2 ln( RR j ) − ln( RR j )

2 

Var (RRˆ ) ≈ RR Var [ln( RRˆ 

j j j )] ≈ RR j ×   2 ×1.96  

(F-3) 

where RR j is the 95% upper bound on the RRj estimate (for the jth exposure category) and RRj is 

the 95% lower bound on the RRj estimate. 

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APPENDIX G. 

MODEL PARAMETERS IN THE ANALYSIS OF ANIMAL TUMOR INCIDENCE 

Table G-1. Analysis of grouped data, NTP mice study (NTP, 1987);a 

multistage model parameters 

Tumor 

Multistageb 

polynomial 

degree q0 

q1 

c 

(mg/m3

)

−1 

q2 

(mg/m3

)

−2 

q3 

(mg/m3

)

−2 

p value 

(chi-square 

goodness of 

fit) 

Males 

Lung adenomas 

plus carcinomas 

1 2.52 × 10−1 1.52 × 10−2 0.92 

Females 

Lung adenomas 

plus carcinomas 

2 3.87 × 10−2 0.0 4.80 × 10-4 0.39 

Malignant 

lymphoma 

3 1.74 × 10-1 0.0 0.0 1.13 × 10-5 0.18 

Uterine carcinoma 2 0.0 0.0 9.80 × 10-5 0.90 

Mammary 

carcinoma 

1d 2.27 × 10-2 1.09 × 10-2 – 

a

The exposure concentrations were at 0, 50 ppm, and 100 ppm. These were adjusted to continuous exposure.
 b

P(d) ∋ 1 − exp[−(q0 + q1d + q2d2 + ... + qkdk)], where d is inhaled ethylene oxide exposure concentration.
 c

Even though q1 is zero in some cases, the upper bound of q1 is nonzero.
 d

The 100-ppm dose was deleted; the fit was perfect with only two points to fit. 

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Table G-2. Analysis of grouped data, Lynch et al. (1984a); Lynch et al. 

(1984c) study of male F344 rats;a multistage model parameters 

Tumor 

Multistageb 

polynomial degree q0 

q1 

(mg/m3

)

−1 

p-value 

(chi-square 

goodness of fit) 

Splenic mononuclear cell leukemia 1c 3.12 × 10-1 1.48 × 10-2 – 

Testicular peritoneal 

mesothelioma 

1 3.54 × 10-2 6.30 × -3 0.34 

Brain mixed-cell glioma 1 0 1.72 × 10-4 0.96 

a

The exposure concentrations were at 0, 50 ppm, and 100 ppm. These were adjusted to continuous exposure.
 b

P(d) ∋ 1 − exp[−(q0 + q1d + q2d2 + ... + qkdk)], where d is inhaled ethylene oxide exposure concentration.
 c

The 100-ppm dose was deleted; the fit was perfect with only two points to fit.
 

Table G-3. Analysis of grouped data, Garman et al. (1985) and Snellings et 

al. (1984) reports on F344 rats;a multistage model parameters 

Tumor 

Multistageb 

polynomial degree q0 

q1 

(mg/m3

)

−1 

p-value 

(chi-square 

goodness of fit) 

Males 

Splenic mononuclear cell leukemia 1 1.63 × 10-1 8.56 × 10-3 0.34 

Testicular peritoneal mesothelioma 1 2.38 × 10-2 4.74 × 10-3 0.68 

Primary brain tumors 1 5.88 × 10-3 2.92 × 10-3 0.46 

Females 

Splenic mononuclear cell leukemia 1 1.08 × 10-1 2.37 × 10-2 0.75 

Primary brain tumors 1 5.94 × 10-3 1.65 × 10-3 0.80 

a

The exposure concentrations were at 0, 10 ppm, 33 ppm, and 100 ppm. These were adjusted to continuous 

exposure. 

b

P(d) ∋ 1 − exp[−(q0 + q1d + q2d2 + ... + qkdk)], where d is inhaled ethylene oxide exposure concentration. 

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Table G-4. Time-to-tumor analysis of individual animal data, NTP mice 

study (NTP, 1987);a multistage-Weibull modelb parameters 

Tumor 

Multistage 

polynomial degree q0 

q1 

(mg/m3

)

−1 z 

Males 

Lung adenomas plus 

carcinomas 

1 3.44 × 10-1 2.03 × 10-2 5.39 

Females 

Lung adenomas plus 

carcinomas 

1 5.35 × 10-2 1.76 × 10-2 7.27 

Malignant lymphoma 1 1.91 × 10-1 8.80 × 10-3 1.00 

Uterine carcinoma 1 0.0 3.81 × 10-3 3.93 

Mammary carcinoma 1 3.78 × 10-2 5.10 × 10-3 1.00 

a

The exposure concentrations were at 0, 50 ppm, and 100 ppm. These were adjusted to continuous exposure. b

P(d, t) = 1 − exp[−(q0 + q 2 k z 1 d + q2d + .... + qkd )*(t − t0) ], where d is inhaled ethylene oxide exposure 

concentration. 

The length of the study was 104 weeks. The times t and t0 as expressed in the above formula are scaled so that the 

length of the study is 1.0. Then, q0 is dimensionless, and the coefficients qk are expressed in units of (mg/m3

)

−k 

. 

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APPENDIX H. 

SUMMARY OF 2007 EXTERNAL PEER REVIEW AND 

PUBLIC COMMENTS AND DISPOSITION 

A draft of this assessment document entitled Evaluation of the Carcinogenicity of 

Ethylene Oxide (dated August 2006) (U.S. EPA, 2006a) was available for public comment and 

underwent a formal external peer review in accordance with EPA guidance on peer review (U.S. 

EPA, 2006b) At the request of EPA’s Office of Research and Development, the EPA Science 

Advisory Board (SAB) convened a panel of 15 experts external to the Agency to review the 

ethylene oxide (EtO) assessment document. An external peer review meeting was held in 

January 2007, and a Final Peer Review Report was released in December 2007 (SAB, 2007). 

The purpose of this assessment was to review and characterize the available data on the 

carcinogenicity of EtO and to estimate the lifetime unit cancer risk from inhalation exposure. 

The SAB panel was asked to comment primarily on three main issues including carcinogenic 

hazard, cancer risk estimation, and uncertainty associated with the hazard characterization and 

quantitative risk estimation. The SAB panel was charged with answering a number of specific 

questions that addressed key scientific issues relevant to the assessment. A summary of 

significant comments made by the panel in response to the charge questions and EPA’s response 

to these comments arranged by charge question are provided below. 

In addition, a number of comments from the public were received during the public 

comment period. A summary of the public comments and EPA’s responses are also included in 

a separate section of this appendix. 

SAB Panel Comments: 

The statement of the issues as contained in the Agency’s charge to the SAB panel are 

listed below in italics followed by (1) the Panel’s summary comments quoted directly from the 

Executive Summary of the Panel’s report (SAB, 2007) and (2) the Agency’s response to the 

comments. 

Issue 1: Carcinogenic Hazard (Section 3 and Appendix A of the EPA Draft Assessment) 

Do the available data and discussion in the draft document support the hazard conclusion that 

EtO is carcinogenic to humans based on the weight-of-evidence descriptors in EPA’s 

2005 Guidelines for Carcinogen Risk Assessment? In your response, please include 

consideration of the following: 
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1. a. EPA concluded that the epidemiological evidence on EtO carcinogenicity was strong, but 

less than completely conclusive. Does the draft document provide sufficient description of the 

studies, balanced treatment of positive and negative results, and a rigorous and transparent 

analysis of the data used to assess the carcinogenic hazard of ethylene oxide (EtO) to 

humans? Please comment on the EPA's characterization of the body of epidemiological data 

reviewed. Considerations include: a) the consistency of the findings, including the 

significance of differences in results using different exposure metrics, b) the utility of the 

internal (based on exposure category) versus external (e.g., SMR and SIR) comparisons of 

cancer rates, c) the magnitude of the risks, and d) the strength of the epidemiological evidence. 

SAB Panel Comment: A majority of the Panel agreed with the conclusion in the draft document 

that the available evidence supports a descriptor of “Carcinogenic to Humans” although some 

Panel members concluded that the descriptor “Likely to be Carcinogenic to Humans” was more 

appropriate. There was consensus that the epidemiological data regarding ethylene oxide 

carcinogenicity were not in and of themselves sufficient to provide convincing evidence of a 

causal association between human exposure and cancer. Differing views as to the appropriate 

descriptor for ethylene oxide were based on differences of opinion as to whether criteria 

necessary for designation as “Carcinogenic to Humans” in the absence of conclusive evidence 

from epidemiologic studies were met. The majority of Panel members thought that the 

combined weight of the epidemiological, experimental animal, and mutagenicity evidence was 

sufficient to conclude that EtO is carcinogenic to humans. 

The Panel concluded that the assessment would be improved by: (1) a better introduction 

to the hazard characterization section, including a brief description of the information that will be 

presented; (2) a clear articulation of the criteria by which epidemiologic studies were judged as 

to strengths and weaknesses; (3) addition of a more inclusive summary figure and/or table at the 

beginning of section 3.0; and (4) inclusion of material now provided in Appendix A of the draft 

assessment to within the main body of that assessment. 

The Panel agreed with the EPA in their reliance on “internal” estimates of cancer rates 

rather than “external” comparisons (SMR, SIR) due to well recognized limitations to the latter 

method of analysis. The Draft Assessment characterizes the magnitude of the unit risk estimate 

associated with EtO as “weak”. This finding is substantiated by the epidemiologic evidence 

where a relatively small number of excess cancers are found above background even among 

highly exposed individuals. However, the magnitude of risk suggested by the unit risk estimate 

is somewhat at odds with this concept. Subsequent recommendations in our report try to address 

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

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EPA Response: EPA is retaining the conclusion that the combined weight of the 

epidemiological, experimental animal, and mutagenicity evidence is sufficient to conclude that 

EtO is carcinogenic to humans, which was supported by the majority of the Panel. Some Panel 

members were of the opinion that the descriptor “Likely to be Carcinogenic to Humans” was 

more appropriate, as they found the epidemiological evidence to be weak and the data 

insufficient to conclude that key precursor events were observed in humans (SAB, 2007, p.10). 

EPA and the majority of the SAB Panel disagree that the epidemiological evidence is weak. 

EPA has strengthened the summary review of these data in the human evidence section (Section 

3.1) and in the hazard characterization section (Section 3.5.1). In addition, the assessment 

specifically addresses the precursor data for rodents and humans, and while the databases for 

humans and rodents contain different types of studies, EPA did not find any inconsistency and 

concluded that the data support a finding of a mutagenic mode of action (relevant to humans), a 

finding with which the SAB concurred. EPA has expanded the discussion of these data, 

specifically in Sections 3.3.3.2, 3.3.3.3, and 3.4.1. 

In response to the Panel recommendations, EPA has added an introduction at the 

beginning of Chapter 3 that provides a brief description of the information presented in the 

Chapter and has provided a clearer explanation of the criteria used to evaluate the strengths and 

weaknesses of epidemiological studies (at the beginning of Section 3.1). With respect to the 

recommendation to put material from Appendix A into the main body of the document, EPA has 

added two shorter summary tables of the lymphohematopoietic cancer (Table 3-1) and breast 

cancer (Table 3-2) findings in the various epidemiology studies to Section 3.1.1. EPA has also 

added a cross-reference to summary Table A-5 in Appendix A at the beginning of Section 3.1. 

The main body of the document provides a summary of the findings of all the epidemiological 

studies, referencing Appendix A for further details. EPA considered the recommendation to 

move more of the material in Appendix A of the draft assessment into the main body of the 

document, but judged that the in-depth level of detail in Appendix A was not appropriate for the 

main body of the document. 

EPA notes that the Panel agreed with EPA’s use of “internal” estimates rather than 

“external” comparisons. 

The Draft Assessment did not refer to or characterize the magnitude of the unit risk 

associated with EtO exposure as “weak.” Rather, it was with respect to the Hill considerations 

for causality (Hill, 1965) in the weight-of-evidence analysis for hazard characterization (Section 

3.5.1) that the Draft Assessment noted that there was little strength in the association, as reflected 

by the modest magnitude of the (relative) risk estimates from the epidemiology studies. The 

exposure-response models used to develop the unit risk estimates are derived from the NIOSH 
This document is a draft for review purposes only and does not constitute Agency policy. 

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data and are thus consistent with the results of the NIOSH epidemiology study, as can be seen in 

the figures depicting RR versus exposure for the various exposure-response models. The unit 

risk estimates are derived from these exposure-response models and are thus similarly consistent 

with the results of the NIOSH study, as long as they are used in the low-exposure range, as 

intended. Because the exposure-response relationships for the cancers of interest in the NIOSH 

study are generally supralinear, the unit risk estimates will overpredict the NIOSH results if 

applied to exposure levels that correspond to the region of the exposure-response relationships 

where the responses plateau. 

1.b. Are there additional key published studies or publicly available scientific reports that are 

missing from the draft document and that might be useful for the discussion of the 

carcinogenic hazard of EtO? 

SAB Panel Comment: The Panel agreed that the discussion of endogenous metabolic 

production of ethylene oxide and the formation of background adducts should be expanded. The 

Panel believed that the description of studies of DNA adduct formation resulting from EtO 

exposure appears incomplete and superficial. This discussion should be expanded—both in 

terms of the number of studies cited and the depth of the discussion. Since ethylene is 

metabolized to EtO, some members recommended the inclusion of the ethylene body of literature 

for consideration. Most members were hesitant about adding them to the document, but if added, 

they cautioned that a discussion of the caveats associated with their interpretation relative to 

ethylene oxide should be included. 

EPA Response: The discussion of endogenous metabolic production of EtO and its significance 

and contribution to the formation of background adducts in rodents and humans has been 

expanded (Sections 3.3.2 and 3.3.3.1 and Section C.7 of Appendix C). The discussion of DNA 

adduct formation resulting from EtO exposure has also been expanded to add depth and breadth 

(Section 3.3.3.1 and Section C.1 of Appendix C). Section C.1 of Appendix C includes 

discussion of general DNA adduct formation, sensitivity of the methods used to detect DNA 

adducts, and DNA adduct studies, both in vitro and in vivo, that have been conducted in animals 

and humans. A discussion of the endogenous production of ethylene during normal 

physiological processes and its metabolism to EtO under certain conditions has been added 

(Section C.7 of Appendix C). It should be noted that the endogenous production of EtO due to 

the metabolism of endogenous ethylene will be present in all test animals or subjects (including 

controls) and hence this factor is considered inherently in the analysis of effects of EtO exposure. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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EPA agrees with the majority of the Panel that data on (exogenous) ethylene should not 

be included in the assessment. One caveat provided on page 12 of the SAB report is that the 

ethylene bioassays administered ethylene concentrations with such low EtO equivalents that they 

would appear “to be below the limit of detection for a tumor response over the spontaneous 

background in the F344 rat.” Thus, the ethylene data would not be very informative for the EtO 

assessment, for which there are already adequate EtO bioassays. 

1.c. Do the available data and discussion in the draft document support the mode-of-action 

conclusions? 

SAB Panel Comment: The Panel agreed with the Draft Assessment conclusion of a mutagenic 

mode of action. However, an expanded discussion of the formation of DNA adducts and 

mutagenicity is warranted. 

EPA Response: EPA has expanded the discussion of DNA adduct formation (Section 3.3.3.1 

and Section C.1 of Appendix C) and mutagenicity (Section 3.3.3 and Sections C.2–C.5 of 

Appendix C) in the revised assessment document. 

1.d. Does the hazard characterization discussion for EtO provide a scientifically balanced and 

sound description that synthesizes the human, laboratory animal, and supporting (e.g., in 

vitro) evidence for human carcinogenic hazard? 

SAB Panel Comment: While some members of the Panel found the hazard characterization 

section of the Draft Assessment to be satisfactory, a majority expressed concerns that this section 

did not achieve the necessary level of rigor and balance. An issue in this characterization, 

particularly in the face of epidemiological data that are not strongly conclusive, is whether the 

presumed precursor events leading to cancer in animals, such as mutations and/or chromosomal 

aberrations, are observed in humans. This issue needs to be addressed in greater detail. 

EPA Response: The genotoxicity (Section 3.3.3 and Appendix C), mode of action (Section 

3.4.1), and hazard characterization (Section 3.5.1) sections have been revised to provide a more 

complete and balanced discussion of EtO-induced precursor events in animals and humans. As 

addressed in the EPA response under charge question 1.a above, while the databases for humans 

and rodents contain different types of studies, EPA did not find an inconsistency in EtO-induced 

precursor events (Sections 3.3.3.2, 3.3.3.3, 3.4.1, and 3.5.1). 
This document is a draft for review purposes only and does not constitute Agency policy. 

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Issue 2: Risk Estimation (Section 4 and Appendices C and D of the EPA Draft Assessment) 

Do the available data and discussion in the draft document support the approaches taken by 

EPA in its derivation of cancer risk estimates for EtO? In your response, please include 

consideration of the following: 

2.a. EPA concluded that the epidemiological evidence alone was strong but less than 

completely conclusive (although EPA characterized the total evidence—from human, 

laboratory animal, and in vitro studies—as supporting a conclusion that EtO is "carcinogenic 

to humans”). Is the use of epidemiological data, in particular the Steenland et al. (2004); 

Steenland et al. (2003) data set, the most appropriate for estimating the magnitude of the 

carcinogenic risk to humans from environmental EtO exposures? Are the scientific 

justifications for using this data set transparently described? Is the basis for selecting the 

Steenland et al. data over other available data (e.g., the Union Carbide data) for quantifying 

risk adequately described? 

SAB Panel Comment: The Panel concurred that the NIOSH cohort is the best single 

epidemiological data set with which to study the relationship of cancer mortality to the full range 

of occupational exposures to EtO. That said, the Panel encouraged the EPA to broadly consider 

all of the epidemiological data in developing its final Assessment. In particular, the Panel 

encourages the EPA to explore uses for the Greenberg et al. (1990) data including leukemia and 

pancreatic cancer mortality and EtO exposures for 2,174 Union Carbide workers from its two 

Kanawha Valley, West Virginia facilities. (Also described in Teta et al., 1999; Teta et al., 1993). 

The Panel encouraged the EPA to investigate potential instability that may result from 

interaction between the chosen time metric for the dose response model and the treatment of time 

in the estimated exposure (i.e., log cumulative exposure with 15 year lag) that is the independent 

variable in that dose-response model. 

EPA Response: EPA has revised the assessment to include an expanded discussion of study 

selection, including a summary table of important considerations, in Section 4.1, as well as 

expanded discussions of the exposure assessments for the Union Carbide (Appendix A, Section 

A.2.20) and NIOSH (Appendix A, Section A.2.8) studies. 

In regard to the possible use of other epidemiologic data, the assessment document 

includes a detailed discussion of the studies of workers at the Union Carbide facilities in West 

Virginia. The Greenberg et al. (1990) data are quite limited in terms of the number of cancers 
This document is a draft for review purposes only and does not constitute Agency policy. 

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observed. Teta et al. (1993) extended the follow-up of the Union Carbide cohort for an 

additional 10 years and excluded the 278 chlorohydrin unit workers, in which a significant threefold excess of lymphohematopoietic cancer was observed (8 vs. 2.7 expected, SMR 2.94, see 

Benson and Teta, 1993), on the grounds that the chlorohydrin unit workers were exposed to other 

potential carcinogens and likely had low exposures to EtO. Teta et al. (1993) studied the 

remaining 1,896 EtO production workers who did not work in the chlorohydrin unit. This cohort 

is about a tenth of the size of the NIOSH cohort. This cohort did not show an excess of 

lymphohematopoietic cancer (7 observed vs. 11.8 expected) but the cohort continues to be 

limited by small numbers (e.g., fewer than 6 expected deaths for non-Hodgkin lymphoma 

[NHL], although the exact number is not given). Furthermore, the Union Carbide study has a 

less extensive exposure assessment than the NIOSH study. In part, the deficiency is inherent in a 

chemical production setting, where it is difficult to find workers with relatively uniform work 

histories that involve relatively constant exposure to EtO. As such, the exposure assessment 

used in the Union Carbide study was relatively crude, based on just a small number of 

department-specific and time-period-specific categories, and with exposure estimates for only a 

few of the categories derived from actual measurements (see Section A.2.20 of Appendix A for 

the details). This is in contrast to sterilization plants, where the NIOSH study was done, where 

workers can be grouped into relatively common jobs/work zones, facilitating assignment of 

exposure. Furthermore, extensive sampling data (2,350 measurements from 1975 to 1986, 

reduced to 205 annual job-specific means, representing 80% of the data; another 20% were not 

included but used as a validation sample) were used in the NIOSH study to estimate exposure in 

different jobs and years. Such sampling data were not used in estimating exposures in the Union 

Carbide cohort. Finally, the NIOSH regression model for estimating EtO exposure included data 

not only on job/work zone, but also on variables such as size of sterilizer, type of product, 

freshness of product, and exhaust systems for sterilizers. This regression model explained 85% 

of the variance in the EtO validation data set. As a result, the exposure estimates in the NIOSH 

study are likely to be more accurate. Because of the lack of comparability in the exposure 

estimates across the two studies, it is not possible to group together the NIOSH cohort and the 

Union Carbide cohort for a rigorous combined quantitative exposure-response analysis. 

Teta et al. (1993) do not include any exposure-response analyses, but a later paper (Teta 

et al., 1999) does. Teta et al. (1999) divide exposure into high, medium, and low intensity of 

exposure and four time periods (1925–1939, 1940–1956, 1957–1973, and 1974–1988). The 

paper does not give the exposure level assigned to each of the resulting 12 cells, nor any 

justification for the chosen exposure levels. No published data describing how these estimates 

were derived could be found. 
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Teta et al. (1999) also do not provide the number of observed leukemia deaths, but 

models leukemia as a function of exposure using three categories of cumulative exposure and a 

variety of models using continuous exposure. Assuming, as indicated, that the data are the same 

as the 1988 follow-up reported by Teta et al. (1993), there are only five observed leukemia 

deaths which suggests that the extensive modeling of the data that was done is highly uncertain. 

The published (through 2006) Union Carbide data and analyses were not sufficient for 

dose-response assessment of lymphohematopoietic cancer due to small numbers and the inherent 

problem posed by the general assignment of exposure levels to subjects, adequate details of 

which were not provided. 

Since the peer review, follow-up of the Union Carbide cohort, without the chlorohydrin 

production workers, has now been extended through 2003, and analyses of the data have been 

published by Swaen et al. (2009) and Valdez-Flores et al. (2010). 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). These are the same generalized exposure estimates described above 

based on a small number of department-specific and time-period-specific categories, and with 

exposure estimates for only a few of the categories derived from actual measurements (additional 

detailed discussion is provided in Section A.2.20 of Appendix A of the final assessment 

document). At the end of the 2003 follow-up, only 27 lymphohematopoietic cancer deaths 

(including 12 leukemias and 11 NHLs) were observed in the cohort. Thus, even after extended 

follow-up, the number of cases is small compared to the NIOSH study, which had 74 

lymphohematopoietic cancer deaths, 53 from lymphoid cancers. More importantly, as discussed 

above, the exposure assessment is much more rudimentary than that used for the NIOSH cohort. 

The lack of comparability in the exposure estimates precludes a rigorous combined exposureresponse analysis of data from the two cohorts. 

EPA requested that Professor Kyle Steenland, the principal investigator of the NIOSH 

study, respond to the following excerpt from this comment from the SAB Panel: 

“The Panel encouraged the EPA to investigate potential instability that may result from 

interaction between the chosen time metric for the dose response model and the treatment of 

time in the estimated exposure (e.g. log cumulative exposure with 15 year lag) that is the 

independent variable in that dose-response model.” 
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Professor Steenland’s response: 

“This comment is difficult to understand, but appears to be a concern that the 15-year lag in the 

exposure metric, which discounts the most recent exposure, may cause an over-reliance in the 

exposure-response analysis on exposures which were estimated prior to 1979, which possibly are 

less accurate. The reason they may be less accurate is because the NIOSH exposure model 

assumed that the effect of calendar year was constant before 1979. There are a couple of 

comments to be made here. First, it is certain the much higher exposures took place before the 

early 1980s when engineering controls were implemented, and that these exposures are likely to 

compose the majority of the metric “cumulative exposure.” Second, such early exposures would 

often, but not always, also be more biologically relevant than later exposures, given that there is 

likely to be some latency period before a given exposure causes a cancer (the best fitting lag was 

15 years in the analysis), and cancers occurred during the period 1980–2004, so that later lower 

exposures were often discounted by the lag. But were such early exposures estimated 

appreciably worse than later exposures by the NIOSH regression model? The NIOSH regression 

model was based on seven variables, one of which had 8 levels (job), one of which had 5 levels 

(product types), and one of which was time or year. All these variables were statistically 

significant at the p < 0.05 level except one (aeration), which had a p value of 0.10. Given that 

engineering controls were included in the model, the effect of calendar year was thought to 

reflect improved work practices which got better year by year as employees and managers 

became more conscious of the dangers of exposure. The effect of year only began in 1979, and 

was not apparent in the period 1975–1978 when there much less concern about the dangers of 

EtO. It would seem logical that prior to 1975 (when there were no sampling data to include in 

the model), work practices also would have changed little year to year, given that worker and 

management concern about the dangers of EtO was minimal or nonexistent. Furthermore, data 

for the other variables in the model were available for years before 1979, and hence were able to 

play a role in prediction of EtO prior to 1979, independent of the year effect, which was constant 

prior to 1979. Hence, the model would be expected to perform reasonably well in the period 

before sampling data were available, i.e., prior to 1975, regardless of the assumption that 

calendar year had no effect independent of the other variables in the model.” 

“In summary, there is obviously more uncertainty about the estimation of exposures prior to 

1975 when there were no sampling data. This uncertainty is of some concern in the sense that 

the majority of cumulative exposure metric for most workers is probably contributed by earlier, 

higher exposures. The use of a 15-year lag does not, however, necessarily increase this 
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uncertainty, given that exposure in the lagged out period for most workers would be appreciably 

lower than exposure before the lag came into effect. Furthermore, while the validity of the 

NIOSH estimates before 1975 cannot be tested against sampling data, the NIOSH model would 

be expected to permit reasonable estimation of exposure prior to 1975 based on other variables in 

the model (job, type of product, size of sterilizer, exhaust of sterilizer, etc).” 

“What if exposures prior to 1975 were estimated poorly? This raises the general question of 

measurement error, which is more likely to have occurred in years before sampling data existed. 

Measurement error is a complicated issue and its effects cannot be easily predicted. It does not 

seem likely that the use of the 15-year lag, however, would appreciably increase whatever 

measurement error occurred for early years of exposure before 1975. While it is possible that the 

EPA should formally evaluate the likely effect of measurement error, this is a large task which 

would take considerable amount of time and would necessarily depend on a large number of 

assumptions about the error in the period before sampling data existed (as I have argued, it is 

also largely independent of the use of a 15-year lag).” 

2.b. Assuming that Steenland et al. (2004); Steenland et al. (2003) is the most appropriate 

data set, is the use of a linear regression model fit to Steenland et al.'s categorical results for 

all lymphohematopoietic cancer in males in only the lower exposure groups scientifically and 

statistically appropriate for estimating potential human risk at the lower end of the observable 

range? Is the use of the grouping of all lymphohematopoietic cancer for the purpose of 

estimating risk appropriate? Are there other appropriate analytical approaches that should be 

considered for estimating potential risk in the lower end of the observable range? Is EPA's 

choice of a preferred model adequately supported and justified? In particular, has EPA 

adequately explained its reasons for not using a quadratic model approach such as that of 

Kirman et al. (2004)? What recommendations would you make regarding low-dose 

extrapolation below the observed range? 

SAB Panel Comment: The Panel identified several important shortcomings in the linear 

regression modeling approach used to establish the point of departure for low dose extrapolation 

of cancer risk due to EtO. The Panel was unanimous in its recommendation that the EPA 

develop its risk models based on direct analysis of the individual exposure and cancer outcome 

data for the NIOSH cohort rather than the approach based on published grouped data that is 

presently used. The suggested analysis will require EPA to acquire or otherwise access 
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individual data and develop appropriate methods of analysis. The panel recommends that the 

Agency allocate the appropriate resources to conduct this analysis. 

The Panel was divided on whether low dose extrapolation of risk due to environmental 

EtO exposure levels should be linear (following Cancer Guideline defaults for carcinogenic 

agents operating via a mutagenic mode of action) or whether plausible biological mechanisms 

argued for a nonlinear form for the low dose response relationship. With appropriate discussion 

of the statistical and biological uncertainties, several Panel members strongly advocated that both 

linear and nonlinear calculations be considered in the final EtO Risk Assessment. 

In conjunction with its recommendation to use the individual NIOSH cohort data to 

model the relationship of cancer risk to exposures in the occupational range, the Panel 

recommended that the Agency explore the use of the full NIOSH data set to estimate the cancer 

slope coefficients that will in turn be used to extrapolate risk below the established point of 

departure. The use of different data to estimate different dose response curves should be avoided 

unless there is both strong biologic and statistical justification for doing so. The Panel believed 

this justification was not made in the Agency's draft assessment. 

Although the analysis based on total lymphohematopoietic (LH) cancers might have 

value as part of a complete risk assessment, the rationale for this aggregate grouping needs to be 

better justified. The Panel recommends that data be analyzed by subtype of LH cancers (e.g. 

lymphoid, myeloid) and strong consideration be given to these more biologically justified 

groupings as primary disease endpoints. 

The Panel was divided in its views concerning the appropriateness of estimating the 

population unit risk for LH cancer based only on the NIOSH data for males. Several Panel 

members pointed out that a standard approach in cancer epidemiology and risk analysis begins 

by conducting separate dose-response analyses on males and females and combining the data 

only if the results are similar. Conducting separate analyses for males and females is also the 

standard practice when analyzing data from animal carcinogenicity bioassays. A second 

approach to dealing with the possibility of gender differences in response is to include gender as 

a fixed effect in the statistical modeling of the data and determine whether gender or its 

interaction with other predictors (e.g., gender × exposure) are significant explanatory variables. 

If so, the combined model with the estimated gender effects could be used directly or separate, 

gender-specific dose response analysis would be performed. If not, the gender effects could be 

dropped and the model re-estimated for the combined male and female data. In addition, the 

Agency should test whether the male/female differences are mitigated by use of alternate disease 

endpoints discussed in the previous paragraph. 
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EPA Response: The above comment from the Panel addresses a variety of issues and EPA’s 

responses to some of these issues are comparatively detailed; thus, EPA has subdivided its 

response into separately titled subsections to make it easier to read. 

EPA Response on the modeling of the individual-level data: In response to the SAB 

comments, EPA conducted considerable additional analysis using the individual-level 

(continuous) exposure and cancer outcome data for the NIOSH cohort. These analyses are 

described in Section 4.1.1.2 for lymphoid cancer modeling and Section 4.1.2.3 for breast cancer 

incidence modeling. These Sections also include summary tables of the key models examined 

(Table 4-4) and the factors considered in model selection (Table 4-12). More details on the 

various models and the model results are provided in Appendix D. 

The underlying problem that makes the EtO datasets from the NIOSH cohort difficult to 

model (for the purposes of environmental risk assessment) is that the exposure-response 

relationships, particularly for lymphoid cancer and breast cancer mortality, are supralinear, i.e., 

the responses rise relatively steeply at low exposures and then attenuate or “plateau.” 

Supralinear exposure-response relationships are inherently difficult to model for the purposes of 

environmental risk assessment, i.e., to estimate risk at low exposures, because the standard 

single-parameter exposure-response models tend to exaggerate the low-exposure slope in order 

to simultaneously fit the plateauing at higher exposures. One approach attempted by EPA, in 

consultation with Dr. Steenland, to address this difficulty was to use two-piece spline models, 

allowing for the lower exposure and higher exposure data to be fit with different spline segments. 

For the breast cancer incidence data, EPA was able to develop several continuous models 

that provided reasonable fits to the individual-level exposure data across the entire range of the 

data, consistent with the SAB recommendations. The best-fitting of these models, the two-piece 

linear spline model, now forms the basis for EPA’s unit risk estimate for breast cancer incidence 

(Section 4.1.2.3). 

For lymphoid cancer, however, despite the extensive modeling efforts, the various 

alternative continuous models investigated, including the two-piece spline models, proved 

problematic, as explained in detail in the text (Section 4.1.1.2). In particular, the adequately 

fitting models predicted extremely steep slopes in the low-dose region. In consideration of these 

results, EPA has retained the approach used in the Draft Assessment and has based the risk 

estimates for lymphoid cancer on a linear regression using the categorical data, excluding the 

highest exposure group. 

While EPA understood and appreciated the SAB’s recommendation and did much work to 

model the individual-level data for lymphoid cancer, it should be noted that modeling of grouped 
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data is also an important and well-recognized statistical methodology and its use is consistent with 

EPA guidance, policy, and past practice. For example, EPA’s 2005 Guidelines for Carcinogen Risk 

Assessment specifically recognize the use of linear modeling of grouped epidemiological data (“For 

epidemiologic studies, including those with grouped data, analysis by linear models in the range of 

observation is generally appropriate unless the fit is poor.”, p. 3–11). In addition, EPA's approach 

of using a weighted linear regression through the categorical relative risk estimates follows 

established statistical procedures (van Wijngaarden and Hertz-Picciotto, 2004; Rothman, 1986). 

With regard to modeling without the high-dose category, the lymphoid cancer data show 

a rise and then plateauing of response such that an overall linear relationship is not an 

appropriate description of the exposure-response relationship across the entire exposure range, in 

particular in the low-exposure region of interest for the derivation of low-exposure risk 

estimates. Restricting the linear regression to the lower categorical exposure groups provides a 

better representation of the exposure-response relationship in that lower exposure region. EPA's 

Benchmark Dose Technical Guidance (U.S. EPA, 2012) recognizes analyses omitting high-dose 

data points, when these data are not compatible with the development of suitable descriptive 

statistical analyses, as a viable analytical approach. 

The breast cancer mortality data displayed similar extreme supralinearity, and the twopiece spline model yielded an unrealistically steep low-dose slope estimate; thus, EPA again 

used a linear regression of the categorical data, excluding the highest exposure group (Section 

4.1.2.2). The breast cancer mortality data, however, are not critical to the assessment because 

the breast cancer incidence data set is preferred (Section 4.1.2.3). 

EPA Response on the use of a nonlinear approach to low-exposure extrapolation: 

EPA has given careful consideration to the range of perspectives provided in the SAB report on 

the issue of low-dose extrapolation, including the viewpoint expressed by several Panel members 

who advocated that both linear and nonlinear calculations be considered in the EtO assessment. 

It is EPA’s judgment, as detailed below, that the inclusion of a nonlinear approach is not 

warranted. 

As discussed in Chapter 3 of the assessment, EtO is a DNA-reactive, mutagenic, multisite carcinogen in humans and laboratory animal species; as such, it has the hallmarks of a 

compound for which low-dose linear extrapolation is strongly supported. EPA’s Guidelines for 

Carcinogen Risk Assessment (U.S. EPA, 2005a) specifically note the use of low-dose linear 

extrapolation for “agents that are DNA-reactive and have direct mutagenic activity.” By 

comparison, the Guidelines recommend that, “A nonlinear approach should be selected when 

there are sufficient data to ascertain the mode of action and conclude that it is not linear at low 
This document is a draft for review purposes only and does not constitute Agency policy. 

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doses and the agent does not demonstrate mutagenic or other activity consistent with linearity at 

low doses.” EPA’s analysis indicates that EtO does not meet any of those conditions. For EtO, 

there is sufficient weight of evidence to support a mutagenic/genotoxic MOA, without evidence 

of additional or alternative MOAs being operative (Section 3.4.1). 

EPA specifically considered a proposed 2-hit MOA hypothesized to support a (nonlinear) 

quadratic model for lymphohematopoietic cancer (leukemia specifically) and concluded that the 

evidence for this MOA was inadequate, as discussed in detail in Section 3.4 of the assessment. 

Appendix A of the SAB report also provides more general evidence for why a 2-hit process does 

not imply a quadratic exposure-response relationship for leukemia at low exposures. 

With regard to the particular comments of the SAB members advocating presentation of a 

nonlinear approach, the reasons for using such an approach presented in Appendix C of the SAB 

report 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, 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, as discussed in detail in the assessment (Section 3.3.3.1 

and 4.5), providing direct evidence against the first reason proposed in support of a nonlinear 

approach in Appendix C of the SAB report. In support of the second reason, Appendix C of the 

SAB report presents two EtO-specific mutation datasets; however, EPA’s analysis of these 

datasets, summarized below, finds that they are in fact consistent with low-dose linearity. In 

summary, EPA’s review of studies addressing dose-response patterns for adduct formation and 

mutagenesis by EtO finds these data to be supportive of the inferences made in the EtO 

assessment (and more broadly in EPA’s Guidelines for Carcinogen Risk Assessment) regarding 

the plausibility of linear, nonthreshold, low-dose dose-response relationships for the biological 

effects of EtO, which is mutagenic and directly damages DNA. 

EPA further notes that the supralinear exposure-response relationships from the NIOSH 

data at low occupational exposures argue against the existence of a “threshold,” practical or 

otherwise, at exposure levels anywhere near the POD. Also, the rodent bioassays do not suggest 

an absence of increased cancer risk at their lowest exposure levels. 

Analysis of the EtO mutagenicity datasets presented in Appendix C of the SAB Report: 

Appendix C in the SAB report provides slides (numbers 25 and 26) showing doseresponse data for hprt mutations in mice exposed to either EtO or to ethylene. For ethylene, a 
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model estimate of an EtO-equivalent concentration was used to represent metabolism of ethylene 

to EtO. In both cases, mutations at the hprt locus of T-cells isolated from spleens of Big Blue 

mice were quantified. The EtO study results are from Walker et al. (1997), and it appears that 

the ethylene results are derived from experiments presented in Walker et al. (2000). In the latter 

case, there are some differences in the estimated EtO equivalents and the hprt mutation 

frequencies between the values given in the slide and those reported by Walker et al. (2000). We 

performed statistical analyses using the data presented in slide 26 of Appendix C. 

To examine these data, we first analyzed the EtO dataset (Walker et al., 1997) using 

maximum likelihood estimation (MLE). We then looked at the consistency of the ethylene 

dataset (Walker et al., 2000) with the EtO dataset. The EtO data were fit with a linear model 

utilizing a log-normal distribution of the individual animal response measurements due to the 

low mutant frequency that causes skewness of the data. As shown in Figure H-1, this model 

provided an adequate fit to the EtO data (open circles represent individual animal data for the 

EtO exposures; model goodness-of-fit p = 0.09; variance fit assuming homogeneous variance in 

log scale, p = 0.64). The MLE of the model is plotted (geometric mean [solid line] as an 

estimation of the median response along with the lower and upper 2.5 percentiles of the model 

[dashed lines]). The second, ethylene-derived, dataset is plotted on the same graph (closed 

circles). The predicted EtO-equivalents from the ethylene dataset fall well below the lowest dose 

level used in the EtO experiment, a range in which the EtO-based model would predict only a 

small response (i.e., no more than a 25% increase in mutation rate above background, a level that 

cannot be expected to be detectable given the variability in the EtO experimental data; see Figure 

H-1). The fact that the ethylene results did not show measureable increases in hprt mutations is 

consistent with the modeled EtO results. 

Note, however, that all medians of the ethylene-derived data are at or below the EtObased model and one of the points is below the lower 2.5 percentile of the model, indicating that 

this point is unlikely to be consistent with the same model. To further investigate the 

compatibility of the data from the two experiments, we analyzed the combined dataset by 

including a term that represents the source of the data (the EtO vs. ethylene experiments) into the 

modeling (as above). This experimental variable was significant (p < 0.05), indicating that there 

is a systematic difference in response between the EtO and ethylene-derived data. As a further 

check, we refit the data using an exponential model that provided a MLE fit with a degree of 

upward curvature (but still having low-dose linear behavior). Using a categorical experimental 

variable within this experiment also indicated a systematic dependence of results on data source 

(EtO vs. ethylene), indicating that this finding was not dependent on the choice of a straight-line 

dose-response model. As an additional sensitivity analysis, we reran the modeling using the 
 

 

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values of EtO equivalents from ethylene exposure and hprt results directly from Walker et al. 

(2000) (rather than the values shown in the SAB Appendix C slide); the modeling results were 

essentially unchanged. Accordingly, we conclude that it is not appropriate to combine the 

ethylene data with EtO data in evaluating dose-response relationships for the hprt mutations. 

Figure H-1. Induction of hprt mutations by EtO (open circles and modeled 

fit) with data from ethylene (using estimated EtO equivalents) shown (solid 

circles). Source: SAB (2007) Appendix C (slides 25 and 26); original
 

experiments of Walker et al. (1997).
 

Slide 27 of the SAB report presents data from Nivard et al. (2003) on the frequency of
 

recessive lethal (RL) mutations in Drosophila exposed to EtO (full data set presented in Vogel
 

and Nivard, 1998). Plotting of mutation rate versus EtO concentration for wild-type Drosophila
 

on non-log-transformed axes shows a downward curving (supralinear) relationship indicating
 

greater potency of EtO (per unit exposure) at low exposures as compared with high exposures
 

(Figure H-2). These data are adequately fit by a Michaelis-Menten-type relationship (downward 

curving, linear at low dose); the fit is somewhat improved with a fractional power Hill model,
 

which would indicate even steeper low-dose response.
 

In conclusion, our review of the EtO mutagenicity data presented in Appendix C of the
 

SAB report finds that these data do not show a disproportionate fall-off of mutagenic effects at
 

low doses of EtO; that is, they do not indicate a low-dose nonlinear or threshold-type doseThis document is a draft for review purposes only and does not constitute Agency policy. 

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response pattern. Thus, our review finds these data to be supportive of the inferences made in 

the assessment (and more broadly in EPA’s 2005 Guidelines for Carcinogen Risk Assessment) 

regarding the plausibility of linear, nonthreshold, low-dose dose-response relationships for the 

carcinogenic effects of EtO, which is mutagenic and directly damages DNA. 0.0 0.5 1.0 1.5 2.0 Mutation frequency, percent 

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Ethylene oxide concentration, ppm

Data: Nivard (2003) / Vogel and Nivard (1998). Model: Michaelis Menten

Figure H- 2. Induction of recessive lethal mutations by EtO in Drosophila 

(wild-type). Standard deviations are calculated as the square root of the number 

of mutations, assuming a Poisson distribution, and plotted as ± (SD × percent 

mutation frequency). 

EPA response on using different data to estimate different dose-response curves: 

With respect to using different data to estimate different dose-response curves, that Panel 

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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comment pertains only to the occupational exposure scenarios. This is addressed in EPA’s 

response to the SAB comment on charge question 2.d below. 

EPA response on lymphohematopoietic cancer groupings: As recommend by the 

Panel, the primary risk estimates are now based on the lymphoid cancers (Section 4.1.1.2). 

Analysis based on total lymphohematopoietic cancers is also included for completeness and 

comparison purposes. 

EPA response on the use of only the male data for lymphohematopoietic cancers: 

Analyses by Dr. Steenland determined that there was not a statistically significant difference 

between the lymphohematopoietic cancer results for males and females. Thus, in the revised 

assessment, unit risk estimates based on lymphohematopoietic cancer in males only are not used. 

Unit risk estimates are now based on lymphoid cancers for males and females combined and 

breast cancer in females. 

The following additional comments on page 31 of the SAB Panel report under “2.b. 

Methods of Analysis,” “7. Statistical issues,” are quoted verbatim below followed by EPA’s 

responses: 

SAB Panel Comment: 

7. Statistical issues 

Pages 29–49 of the draft Evaluation outline the EPA’s proposed approach to estimation of the 

Inhalation Unit Risk for EtO. In addition to the general issues of estimation and model-based 

extrapolation described above, there are a number of statistical assumptions and methods used in 

this approach that deserve mention. Conditional on the cancer slope factor results from the 

weighted least squares regression analysis, the life table (BEIR IV) approach to the 

determination of the LEC01 is programmed correctly. The life table methodology that is the 

basis for the BEIR IV algorithm is designed to estimate excess mortality and is not readily 

adapted to modeling excess risk for events (incidence) that do not censor observation on the 

individual in population under study. The methodology for substituting the mortality slope to an 

excess risk computation for HL cancer incidence requires the assumption of a proportional rate 

of incidence/mortality across the cancer types that are included in the grouped analysis. This is 

generally not a viable assumption. The Panel therefore discourages the use of the BEIR IV 

algorithm for extrapolation of the cancer mortality algorithm to estimation of excess cancer 

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

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Several Panel members commented on the use of the upper confidence limit for the 

estimated slope coefficient as the basis for estimating an LEC01. The Panel encourages the EPA 

to present unit risk estimates based on the range of EC01 values corresponding to the lower 95% 

confidence limit, the point estimate, and the upper 95% confidence limit for the estimated cancer 

slope coefficients from the final dose-response models. 

EPA Response on using the BEIR approach to estimate incidence risks: In this assessment 

EPA is developing estimates of the risk of cancer incidence, not mortality, as the cancers 

associated with EtO exposure (lymphohematopoietic, in particular lymphoid, and breast cancers) 

have substantial survival rates. The breast cancer incidence estimates are not at issue here 

because they are based on incidence data. Regarding the lymphoid cancers, the SAB provided 

the relevant comment that mathematically the BEIR formula would apply to the case where there 

is a proportional rate of incidence/mortality across the cancer types that are included in the 

grouped analysis. EPA considered this in its application of the BEIR formula. The fact that the 

ratios of incidence to mortality are not strictly proportional contributes some uncertainty to the 

incidence estimates for the grouping of lymphoid cancers, but not a large amount. Uncertainties 

in using the life-table analysis approach to seek to develop reasonable estimates for incidence 

risk, including those noted by the SAB, are acknowledged in the assessment, and the impact of 

nonproportionality between cancer types is one of the uncertainties discussed (Section 4.1.1.3). 

As illustrated in the assessment, these uncertainties do not have a major impact on the final risk 

estimates. The incidence unit risk estimate is about 120% higher than (i.e., 2.2 times) the 

mortality-based estimate, which is consistent with the relatively high survival rates for lymphoid 

cancers. 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 rates for the other 

subtypes, which have higher incidence:mortality ratios, might inflate the incidence estimates). 

Multiple myelomas, however, constitute only 25% of the lymphoid cancer cases, and there is no 

evidence that multiple myeloma is driving the EtO-induced excess in lymphoid cancer mortality 

(25% is below the proportion of multiple myeloma deaths one would expect in the cohort based 

on age-adjusted background mortality rates of multiple myeloma, NHL, and chronic lymphocytic 

leukemia, and these 3 subtypes have the same pattern of mortality rates increasing as a function 

of age mostly above age 50, so the comparison with lifetime background rates is reasonable). 

Thus, using the total lymphoid cancer incidence rates is not expected to result in an 
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overestimation of the incidence risk estimates; if anything, the incidence risks would likely be 

diluted with the inclusion of the multiple myeloma rates. 

EPA decided that the Panel’s suggestion to not use the BEIR approach for development 

of cancer incidence estimates for lymphohematopoietic cancer would not allow EPA to develop 

the desired cancer incidence risk estimates. One possible alternative approach involving a crude 

survival adjustment to the mortality-based estimates would yield results with greater uncertainty 

than use of the BEIR approach. No alternative approaches were identified by the SAB. In the 

absence of an appropriate alternative approach to estimate risks of cancer incidence, EPA has 

retained the application of the BEIR approach, which it judges to provide a reasonable estimate 

of incidence risks. EPA recognizes the uncertainties and assumptions outlined by the Panel and 

has expanded the discussion of these in the carcinogenicity assessment (Section 4.1.1.3). 

However, EPA notes that deriving mortality estimates as the sole cancer risk estimates for 

lymphohematopoietic cancer would substantially underestimate cancer risk. In addition, EPA 

presents the mortality-based estimates as well for comparison, and as discussed above, the 

lymphoid cancer incidence unit risk estimate is about 120% higher than (i.e., 2.2 times) the 

mortality-based estimate, which is considered reasonable, given the high survival rates for 

lymphoid cancers. 

EPA Response on the use of upper and lower confidence limits: In the EtO assessment, EPA 

presents 95% (one-sided) lower bounds and central estimates of the EC01s as well as standard 

errors for the regression coefficients used in the modeling, which provide information about the 

variability in the modeled slope estimate. EPA’s Guidelines for Carcinogen Risk Assessment 

also recommend the calculation of a 95% upper bound on the central estimate (in this case the 

EC01) related to the POD “to the extent practicable” (U.S. EPA, 2005a, p. 1−14), and such a 

value has been added for the selected breast cancer incidence model (Section 4.1.2.3, Table 4-7, 

footnote i, based on the profile likelihood confidence limits for the regression coefficient). 

However, for the linear regression model used as the basis for the lymphoid cancer unit risk 

estimate, it was not practicable to calculate such a value, as it was undefined. Although there 

were models for lymphoid cancer from which upper bounds could have been calculated, the 

linear regression model was selected as the basis for the POD for the expressed purpose of 

obtaining a realistic slope estimate for the low-exposure region (Section 4.1.1.2) and not for 

providing a realistic upper bound estimate for the EC01. 

EPA considered the SAB Panel comment encouraging the EPA “to present unit risk 

estimates based on the range of EC01 values corresponding to the lower 95% confidence limit, 

the point estimate, and the upper 95% confidence limit.” However, as a consequence of the 
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2-step approach used by EPA to generate cancer potency estimates from a POD rather than 

directly from the statistical model used to estimate the POD, potency estimates below the 

response level corresponding to the POD are no longer associated with the statistical model. 

While linear extrapolation from a POD that is the 95% (one-sided) lower bound on the central 

estimate of the exposure concentration associated with the selected (benchmark) response level 

(e.g., the LEC01) might be generally expected to yield a reasonable upper bound on cancer risk 

for that data set (though not strictly a statistical “95%” upper bound), estimates involving a linear 

extrapolation from the upper bound on that central estimate are not generally meaningful and 

could be misleading if they are mistaken for lower bounds on potency, as the actual exposureresponse relationship may exhibit some sublinearity below the response level corresponding to 

the POD. Thus, it has not been EPA practice to develop such potency estimates, and EPA did 

not undertake to develop any for this assessment. 

2.c. Is the incorporation of age-dependent adjustment factors in the lifetime cancer unit risk 

estimate, in accordance with EPA’s Supplemental Guidance (U.S. EPA, 2005b), appropriate 

and transparently described? 

SAB Panel Comment: In accordance with EPA guidance, the Draft Assessment applied an Age 

Dependent Adjustment Factor (ADAF) to adjust the unit risk for early life exposure. While the 

majority of the Panel felt that the application of a default value by the Agency was appropriate 

due to lack of data, the description in the Draft Assessment was not adequate, particularly for 

those not familiar with the EPA’s Supplemental Guidance. 

EPA Response: EPA has added a new subsection (Section 4.4) detailing the application of the 

ADAFs. 

2.d. Is the use of different models for estimation of potential carcinogenic risk to humans 

from the higher exposure levels more typical of occupational exposures (versus the lower 

exposure levels typical of environmental exposures) appropriate and transparently described 

in Section 4.5? 

SAB Panel Comment: While the method was transparently described, most of the Panel did not 

agree with the estimation based on two different models for two different parts of the dose 

response curve (see response to 2b). The use of different data to estimate different dose response 
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models curves should be avoided unless there is both strong biological and statistical justification 

for doing so. The Panel believed this justification was not made in the Agency's draft report. 

EPA Response: For the breast cancer incidence risk estimates, a single model, the 2-piece linear 

model is now recommended for the occupational exposure scenarios. The 2-piece linear model 

is a unitary model comprised of two linear pieces or segments with different slopes that are 

joined at a point referred to as a “knot.” The 2-piece linear model has the flexibility to represent 

situations, such as with EtO, where the relationship between exposure level and response 

changes over the range of exposure. For lymphoid cancer risk estimates, two models are 

presented for the lower-exposure exposure scenarios, but just one of the models is recommended 

for the higher-exposure exposure scenarios; users have the option of using a single model across 

the range of exposure scenarios or of transitioning across models, depending on the exposure 

scenarios of interest, and some guidance on choice of approach is provided in Section 4.7 of the 

revised assessment. As discussed in the assessment, the log-cumulative exposure model, which 

provides a good fit to the data in the plateau and is suitable for exposure scenarios with 

cumulative exposures in that region, is not appropriate for the low-exposure region (i.e., below 

the range of the occupational scenarios presented in this assessment) because such a steep 

increase in slope is considered to be biologically implausible and the good statistical global fit of 

the model should not be over-interpreted to infer that the model provides a meaningful fit to the 

low-exposure region. Likewise, the linear regression used to model the lower-dose exposure 

groups is not intended to reflect the exposure-response relationship in the higher-exposure 

region. Hence, for lymphoid cancer, the use of both models may be required to cover a range of 

occupational exposure scenarios. Table 4-19 of the assessment shows how results from the two 

models compare over a range of exposure scenarios for which either model might be used. 

2.e. Are the methodologies used to estimate the carcinogenic risk based on rodent data 

appropriate and transparently described? Is the use of “ppm equivalence” adequate for 

interspecies scaling of EtO exposures from the rodent data to humans? 

SAB Panel Comment: The ppm equivalence method is a reasonable approach for interspecies 

scaling of EtO exposures from rodent data to humans. If the use of animal data becomes more 

important (i.e., the principal basis for the ethylene oxide unit risk value), more sophisticated 

approaches such as PBPK modeling should be considered. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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EPA Response: EPA notes the Panel’s support for the use of the ppm equivalence method. As 

the unit risk value is based on human data, the use of more sophisticated models is not necessary. 

Issue 3: Uncertainty (Sections 3 and 4 of the EPA Draft Assessment) 

EPA’s Risk Characterization Handbook requires that assessments address in a transparent 

manner a number of important factors. Please comment on how well this assessment clearly 

describes, characterizes and communicates the following: 

a. The assessment approach employed; 

b. The use of assumptions and their impact on the assessment; 

c. The use of extrapolations and their impact on the assessment; 

d. Plausible alternatives and the choices made among those alternatives; 

e. The impact of one choice versus another on the assessment; 

f. Significant data gaps and their implications for the assessment; 

g. The scientific conclusions identified separately from default assumptions and policy calls; 

h. The major risk conclusions and the assessor’s confidence and uncertainties in them; and 

i. The relative strength of each risk assessment component and its impact on the overall 

assessment. 

SAB Panel Comment: The Panel’s report contained specific responses to charge questions 1 

and 2. The report did not contain specific responses to question 3 and instead contained the 

following statements regarding question 3: 

“The Panel has responded to Charge Questions 1 and 2 and has tried to incorporate their 

comments regarding Charge Question 3 within those responses. A separate response for 

Charge Question 3 was not deemed necessary since issues of uncertainty were addressed 

in the responses to charge questions 1 and 2.” 

The following are detailed comments on the regression modeling used in the draft ethylene 

oxide assessment quoted from the SAB Ethylene Oxide Panel report and the EPA response: 

SAB Panel Comment: 

2. Linear regression model for categorical data 

The Panel identified several important shortcomings in the linear regression modeling 

approach used to establish the point of departure for low dose extrapolation of cancer risk due to 
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EtO. Based on its review of the methods and results presented at the January 17,18, 2007 

meeting, the Panel was unanimous in its recommendation that the EPA develop its risk models 

based on direct analysis of the individual exposure and cancer outcome data for the NIOSH 

cohort. The Panel understands that these data are available to EPA analysts upon request to the 

CDC/NIOSH. The Panel recognizes the burden that a reanalysis of the individual data places on 

the EPA ORD staff but given the important implications of the risk assessment, this burden is 

well justified to achieve the best scientific and statistical treatment of all the available 

epidemiological data. 

The following paragraphs present the statistical basis for the Panel's assessment of the 

linear regression model approach and the use of categorized exposure and outcome data. 

The approach described in the Draft Assessment uses a model based on categories 

defined by cumulative exposure ranges for male subjects in the NIOSH cohort. Steenland et al. 

identified several models that provide a significant (p < 0.05) fit to the exposure data; however, 

the EPA has elected to use model-based relative rate parameter estimates for categories of 15 

year lagged, cumulative exposure. In Steenland et al. (2004) this model was not one that 

provided a significant fit to the NIOSH data (p = 0.15 for the likelihood ratio test of β = [β1, β2, 

β3, β4] = 0). The use of the weighted least squares regression fit of a linear regression line 

through the three data points defined by the estimated rate ratios and mean cumulative exposures 

for the first three exposure categories of the Steenland et al. 15 year lag, cumulative exposure 

category model is not a robust application of this technique. The Panel identified four 

weaknesses in the approach. 

a) Model-based dependent variable: The dependent variables are model-based estimates 

of rate ratios for exposure categories. The rate ratio values used in the weighted least squares 

regression are derived from a cumulative exposure model (15 year lag) in which the estimated 

regression parameters in the proportional hazards regression model are not significantly different 

from 0 at α = 0.05 (p = 0.15). In Steenland et al. (2004), the only individually based 

(proportional hazards) model that fits the data for males in the NIOSH cohort is a model for log 

of individual exposure through t-15 years. 

b) Grouped data regression: The weighted least squares fit applies estimates of variance 

for the individual rate ratios under that assumption that these inverse weighting corrections 

correctly adjust for heteroscedasticity of residuals in the underlying regression model. 

Historically, models for grouped proportions applied adjustments of this type but it is by no 

means a preferred technique when the underlying individual data are available. The “ecological 

regression” model per Rothman (Rothman and Greenland, 1998) is subject to bias due to within 

group heterogeneity of predictors and unmeasured confounders. The heterogeneity in the 
 

 

 

 

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grouped model involves the range of exposures within the collapsed categories. The unmeasured 

confounders include variables (other than gender) that affect the potency of exposure or may 

have produced gross misclassification based on the original exposure model estimation for the 

individual (Hornung et al., 1994). 

c) The model fitting does not conform exactly to the Rothman (1986) procedure: The 

1998 (Second edition) of Rothman (Rothman and Greenland, 1998) describes the technique for 

estimating this risk from grouped data in Chapter 23. In that updated version of the original 

monograph the model that is fitted is: 

Expected Rate / Exposure ) = ˆ 

0 + Bˆ 1 ( B M* ( ean Exposure ) 

The objective is to estimate the rate ratio (for exposure 0=no, 1=yes, or equivalently for a one 

unit increase in the exposure metric). That estimator is then: 

ˆ ˆ rr =1+ B1 / B0 

The model estimated by the EPA method is: 

(/ ) = Bˆ 

1 

∗ Expected rr Exposure * Mean Exposure ( ) 

In the former, the variance in the estimation of the rate ratio is a function of the variance of the 

estimated slope and the variance in the estimated baseline hazard, represented by the estimated 

intercept. This variance is present in the estimation of the baseline hazard in the Steenland et al. 

(2004) estimation of the rate ratios but is not present in the EPA adaptation to the linear rate ratio 

model. The EPA approach permits no intercept (>0) for the background exposure or any 

allowance for an effect of true non-zero exposures in the internal control group (exposures less 

than 15 years). 

In general, the use of categorical exposure ranges is not the optimal strategy for using 

epidemiologic data. When continuous data are categorized and then used in dose response 

modeling, it amounts to starting with a full range of exposures, collapsing that range into 

somewhat arbitrary boundaries and then deriving a continuous dose response model for an even 

larger range of exposures. 

Categorizing continuous variables results in a host of issues: 

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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• Assumption that the risk within the category boundaries is constant. 

• It is not known whether a given categorization is representative of the data since there are many 

ways of categorizing. 

• Loss of power and precision by spending degrees of freedom on each category. 

• Misclassification at category boundaries (this can be minimized by choosing cutpoints 

where relatively few observations are present). 

• Categorizations can be manipulated to show the desired results. 

The Panel acknowledged that techniques such as the linear regression method described 

by Rothman and Greenland (1998) or Poisson regression may be the most appropriate techniques 

when only grouped or categorized data are available for estimating the dose/response model. 

However, the original NIOSH cohort data are available at the individual level and this permits 

the use of models such as the Cox regression models employed by Steenland et al. (2004) that 

utilize the full information in the individual observations. If categories of exposure (as opposed 

to individual exposure estimates) must be used, the crude rates should be computed for a large 

number of equally spaced exposure ranges and the Rothman and Greenland (1998) model fitted 

to these multiple points. 

EPA Response: EPA agrees that it may be preferable to develop risk models on the basis of 

direct analysis of individual exposure and cancer outcome data. In fact, the Draft Assessment 

document included the presentation of models based on fitting Cox regression models to 

individual exposure-outcome data for EtO. These models provided reasonable fits to the data, as 

described by Steenland et al. (2004) and in the Draft Assessment document. However, it was the 

judgment of EPA that these models represented exposure-response relationships that were 

excessively sensitive to changes in exposure level in the low-dose region and thus were not 

biologically realistic. That is, in the low-dose region, these models would yield extremely large 

changes in response for small changes in dose level. Accordingly, the judgment was that these 

models would not be suitable as the basis for low-dose unit risk values. This is what led EPA to 

use the regression methodology with the published grouped data. The grouped data regression 

methodology is considered to be a valid procedure for analysis of such data, and, as mentioned 

above with respect to charge question 2.b, EPA’s 2005 Guidelines for Carcinogen Risk Assessment 

specifically recognize the use of linear modeling of grouped epidemiological data (U.S. EPA, 

2005a); therefore, EPA has retained its use for some endpoints in the final assessment and 

implemented it as described by Rothman (1986) (also described in van Wijngaarden and HertzPicciotto, 2004). 
This document is a draft for review purposes only and does not constitute Agency policy. 

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EPA followed the Panel’s recommendation and performed additional analyses of the 

individual data in collaboration with Professor Steenland. The work performed by Professor 

Steenland is described in Appendix D of the final assessment. Working with Professor 

Steenland, alternative models based on direct analysis of all individual data using (1) linear 

relative risk models (Langholz and Richardson, 2010) and (2) two-piece linear and log-linear 

spline models (e.g., Rothman et al., 2008) were developed and evaluated. In the final 

assessment, linear low-dose risk estimates based on the two-piece linear spline model (using the 

Langholz-Richardson linear relative risk approach) were used for breast cancer incidence risk 

estimates. Additional responses to specific comments follow: 

a) Model-based dependent variable: The rate ratios for the exposure categories were not 

statistically significant, likely due to loss of power and the use of a cumulative Cox regression 

model within the categories that was not statistically significant across the full exposure range, as 

noted in the comment. Although the cumulative Cox regression model was not a good fit across 

the full range of exposures, the RR estimates based on bounded cumulative exposure ranges can 

still reflect the overall supralinear exposure-response relationship across the categories while 

representing a different subrelationship within the categories. It is doubtful that categorical 

results based on log cumulative exposure would have been better because, while a log 

cumulative exposure Cox regression model provided a statistically significant fit and made it 

possible to represent the supralinearity (e.g. the plateauing at high exposures) across the range of 

the data, one would not expect the exposure-response relationship within each discrete category 

to be supralinear. For example, for lymphoid cancer, the exposure-response relationship in the 

first exposure category is in the steeply increasing part of the overall exposure-response 

relationship and is not expected to have its own plateau (Figure 4-1). EPA used the cumulative 

exposure categorical results because they should provide adequate estimates of the RRs for the 

limited exposure ranges reflected in each category, particularly the three lowest quartiles (the 

highest exposure quartiles were excluded from the linear regression models), and this was the 

approach taken to obtain the categorical results that were reported in the peer-reviewed, 

published paper of Steenland et al. (2004). 

b) Grouped data regression: These comments correctly identify assumptions inherent in 

the method. The assumptions do not, however, preclude the use of the Rothman model in the 

context of the EtO cancer risk estimation. EPA disagrees with the suggestion that unmeasured 

confounders may have produced gross misclassification and somehow impaired the exposure 

model estimation for individuals. The estimation performed by NIOSH to estimate individual 

worker exposure (Hornung et al., 1994) was extensive and detailed. The resulting model used to 
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estimate worker exposure accounted for 85% of the variation in average EtO exposure (see 

Section 4.1 and Section A.2.8 of Appendix A). EPA agrees with the Panel that the exposure 

analysis of Hornung et al. (1994) is an example of an “exemplary quantitative analysis of likely 

errors in exposure estimates.” In response to the Panel’s suggestion that the Hornung analysis 

represents an “invaluable opportunity” for further analysis of the impact of possible errors in 

exposure estimation, EPA investigated the possible use of the “errors in variables” approach 

(page 27 of the Panel report). Professor Steenland visited the NIOSH offices in Cincinnati in 

order to review the data and assess whether it would support an “errors in variables” analysis. 

Unfortunately, the electronic data files used in the exposure analysis were no longer available, so 

that analysis based on the “errors in variables” approach was not possible. 

c) EPA reviewed the statistical procedure for modeling categorical data using the 

methodology in Rothman (1986). This review confirmed that the Rothman procedure was 

followed closely. The equations used, which are the same as those in Rothman (1986, pp. 

341−344), are described in Appendix F. The equations are also provided in van Wijngaarden 

and Hertz-Picciotto (2004). The Rothman (1986) procedure, which is appropriate for casecontrol data such as the NIOSH data, is based on estimating the effect at each response level 

relative to the reference or baseline level. Thus, the effect estimates are relative rates (odds 

ratios), not absolute rates as used in the approach of Rothman and Greenland (1998) cited by the 

SAB. The rate ratio in the referent group (i.e., those with cumulative exposure = 0) is 1.0, by 

definition, hence, there is no intercept term in the model. As described by Rothman (1986, page 

345), variability in the reference category is necessarily entrained in estimates of the slope. As 

Rothman points out, this can result in loss of estimation efficiency but nevertheless yields a valid 

estimate of trend. Thus, while it is true, as the comment states, that this procedure may not be 

optimal in a theoretical sense, it can provide a useful mechanism for estimating linear trend. The 

Panel acknowledges that a linear regression may be the most appropriate approach when only 

grouped data are available. EPA agrees but would add that when the objective is low-dose risk 

estimation, the approach may yield the most useful results from a pragmatic perspective. The 

availability of individual data does not preclude the use of the Rothman grouped data regression 

methodology. 

In the case of the EtO data, it was possible to derive theoretically correct models via 

direct analysis of the individual data. In the case of the breast cancer incidence data, this 

approach yielded a model that provided a suitable basis for risk estimation. For the other 

endpoints (breast cancer mortality, lymphoid cancer mortality), however, the models derived 

using all individual data were not suitable for risk estimation because of excessive sensitivity in 

the low-dose range. The large sensitivity of the models to small changes in low-dose values 
This document is a draft for review purposes only and does not constitute Agency policy. 

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results in unstable low-dose risk estimates lacking in biological plausibility and, thus, the 

Rothman procedure was used. 

Responses to SAB Panel ‘bullet’ comments: 

• Assumption that the risk within the category boundaries is constant. 

EPA Response: EPA is not assuming that within-category risk is constant. Instead, the 

assumption is that observed risk within a category may be averaged over a category even though 

there may be a trend within the category. This is a conventional approach in epidemiological 

analyses in which categorical analysis is used. 

• It is not known whether a given categorization is representative of the data since there are many 

ways of categorizing. 

EPA Response: The data groupings used in the EPA analyses were based on sound statistical 

principles and standard epidemiological practice and were subject to peer review through the 

publications of Steenland et al. (2004); Steenland et al. (2003). The categories were generally 

quartiles based on the distribution of cumulative exposures for the cases of the cancer of interest, 

resulting in essentially the same number of cancer cases per quartile, a typical approach in 

epidemiological studies. 

• Loss of power and precision by spending degrees of freedom on each category. 

EPA Response: There is some loss of power and precision in categorization. This can result in a 

failure to find a statistically significant effect when in fact there is a meaningful effect in the 

data. 

• Misclassification at category boundaries (this can be minimized by choosing cut points where 

relatively few observations are present) 

EPA Response: Misclassification can occur at category boundaries; however, this is expected to 

have a small impact on overall results. Moreover, the likely consequences of misclassification 

across boundaries are that if an RR is overestimated in one category, the RR in an adjacent 
This document is a draft for review purposes only and does not constitute Agency policy. 

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category will be underestimated. Using a linear regression model across the categories may 

serve to smooth out some of this misclassification, if there is any. 

• Categorizations can be manipulated to show the desired results. 

EPA Response: This may be possible, but no manipulation of the EtO data was performed to 

show “desired results.” The data categories used in the EPA analyses were established a priori in 

the Steenland et al. publications. The Panel’s recommendation to use “a large number of equally 

spaced exposure ranges” was not deemed feasible for lymphoid cancer because of the relatively 

small number of deaths. 

PUBLIC COMMENTS: 

A number of public comments were received that addressed a range of technical issues 

related to the inhalation carcinogenicity of EtO. A number of comments were also received that 

are generally directed at what are referred to as “Risk Management” issues and, as such, are not 

addressed here. In the following, summaries of comments on technical risk assessment issues 

submitted by the public are provided followed by EPA’s responses (note that some duplicate 

comments were omitted). 

Comment 1.0: The Draft Cancer Assessment Fails to Meet the Rigorous Standard of 

Quality Required Under the Information Quality Act and Cancer Guidelines. The Draft 

Cancer Assessment is “influential information” as set forth under the Information Quality Act 

(IQA) and therefore is subject to a rigorous standard of quality. EPA guidance and the 

Guidelines for Carcinogen Risk Assessment (Cancer Guidelines) require a rigorous standard of 

quality, which necessitates ensuring that the Draft Cancer Assessment uses scientifically 

defensible analytical and statistical methods and has a higher degree of transparency than 

information considered noninfluential, particularly regarding the application of uncertainty 

factors in EPA’s dose-response assessment and risk characterization. The Draft Cancer 

Assessment demonstrably fails to meet either the standard set forth under the IQA or the Cancer 

Guidelines. EPA must, therefore, substantially revise the assessment before the final EtO 

Integrated Risk Information System (IRIS) Risk Assessment (IRIS Assessment) is publicly 

disseminated or relied upon for any regulatory purposes. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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EPA RESPONSE: Comments received from the SAB and from the public have been addressed 

and the EtO carcinogenicity assessment has been revised. It is EPA’s position that as a result of 

the extensive development, review, reanalysis and revision, the final assessment follows EPA’s 

2005 Guidelines for Carcinogen Risk Assessment, uses scientifically defensible analytical and 

statistical methods, and meets a high standard of transparency. As such, the final assessment is 

consistent with Information Quality Guidelines. 

Comment 2.0: EPA failed to use all available epidemiologic data, including the Union Carbide 

Corporation (UCC) data and all the National Institute of Occupational Safety and Health 

(NIOSH) data that were available at the time EPA conducted its assessment. 

EPA RESPONSE: The assessment describes and considers all relevant epidemiological data 

available at the time the assessment was conducted, including all the NIOSH data and the UCC 

data. The Union Carbide data and the publications that this public commentator referred to were 

evaluated and included in the assessment. EPA also reviewed articles describing additional 

follow-up and analysis of the Union Carbide data that have been published after the Panel’s 

report was finalized. Ultimately, EPA came to the conclusion that the shortcomings inherent in 

the Union Carbide data, particularly the crude assignment of exposure levels to subjects in the 

UCC cohort, are fundamental, and, as a consequence, the data are not suitable for credible 

quantitative analysis of the carcinogenic risk due to exposure to EtO. In the NIOSH data, 

exposure estimates were based on a very large number of exposure measurements and a 

sophisticated modeling approach (Hornung et al., 1994) which took into account job category 

and other factors such as product type, exhaust controls, age of product, cubic feet of sterilizer, 

and degree of aeration. Hence, prediction and assignment of exposure levels for different 

workers in the NIOSH study would be expected to be much better than the crude assignment 

methods used in the Union Carbide study. Although the recent follow-up of the UCC cohort has 

now been reported, there still remains a rather small number of cancers (27 lymphohematopoietic 

cancers, vs. 79 in the NIOSH cohort, 12 vs. 31 NHLs). Consequently, for example, there was a 

50% excess of NHL in the 9+ years of employment category in the Union Carbide study (Swaen 

et al., 2009), but it was based on only five cases and was thus not statistically significant. Also, 

the UCC cohort is restricted to men, making impossible an analysis of breast cancer, which was 

seen to have a significant increase among those with high EtO exposures in the NIOSH cohort. 

In sum, the Union Carbide and NIOSH cohorts are not comparable on a number of levels, and 

the NIOSH cohort remains superior as a basis for risk assessment analyses. In the NIOSH 

cohort, exposure-response analyses are likely to involve much less misclassification of exposure 
This document is a draft for review purposes only and does not constitute Agency policy. 

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and are based on greater numbers, and thus would be expected to be more reliable. Analyses of 

the important breast cancer endpoint are only possible with the NIOSH cohort. See also EPA’s 

response to comments on charge question 2.a above. 

Comment 3.0: EPA inappropriately based its evaluation on summaries of statistics available in 

various publications, rather than the primary source data, review of which and reliance upon are 

essential to conduct valid dose-response modeling. EPA should have based its calculations on 

readily available NIOSH data for individual subjects from the cohort mortality study. 

EPA RESPONSE: The statistics used in draft assessment were obtained from published journal 

articles describing the analysis of the NIOSH data. They are summary and categorical statistics 

that are commonly used in epidemiological research. The methodology for using such 

categorical data to perform dose-response analysis is well established in the epidemiological 

literature and is described in Rothman (1986, pp. 343−344) and van Wijngaarden and HertzPicciotto (2004). The categorical and summary statistics used by EPA are constructed from the 

individual data in the NIOSH study. It is possible to perform analyses and construct models via 

direct analysis of the individual data and in some cases this is a preferable approach. In fact, the 

draft EPA assessment presented the results of such analyses in the form of the Cox regression 

models that were based on direct analysis of the individual data with exposure as a continuous 

variable. These models provided reasonable fits to the data. However, it was the judgment of 

EPA that these models generated estimates of risk in the low-dose region that were excessively 

sensitive to changes in exposure level and therefore would not be suitable as the basis for lowdose unit risk values. This is what led EPA to use the regression methodology with the 

published grouped data. EPA, in consultation with Professor Steenland, did perform analyses to 

fit additional models to the continuous NIOSH data. The work performed by Professor 

Steenland is described in Appendix D of the final assessment. Working with Professor 

Steenland, EPA developed and evaluated sets of models using the individual data, including (1) 

linear relative risk models (Langholz and Richardson, 2010) and (2) two-piece linear and loglinear spline models (e.g., Rothman et al., 2008). In the final assessment, linear low-dose 

estimates based on the two-piece spline model and using the Langholz-Richardson linear 

approach were used for breast cancer incidence risk estimates. See also EPA’s response to 

comments on charge question 2.b above. 

Comment 4.0: EPA Statistical Analysis of the Data Is Flawed and Other Incorrect 

Procedures Grossly Overestimate Risk. Key flaws include: 
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Comment 4.1: EPA’s risk assessments are invalid, based on linear regressions on odds ratios 

(ORs), rather than on individual subject data; 

EPA RESPONSE: The odds ratios referred to are summary statistics. Regression on 

categorical or summary statistics such as odds ratios is a valid statistical approach. See the 

response to comment 1.2 and response to the SAB Panel comment on this issue (charge question 

2.b above).
 

Comment 4.2: EPA fails to include all available epidemiologic data;
 

EPA RESPONSE: This comment refers to the Union Carbide data. See response to Comment 

2.0 and response to the SAB Panel comment on this issue (charge question 2.b above). 

Comment 4.3: EPA’s rationale and methodology for exclusion of the highest exposure group 

is inappropriate; 

EPA RESPONSE: EPA did not use the data from the highest exposure group in estimating the 

unit risk because it was evident that the relationship between exposure and response changed 

over the range of exposure. The general pattern in the data indicated a steep increase in response 

in the low exposure range with a leveling or plateau in the high exposure range. Inclusion of the 

data from the highest exposure levels in either a Cox regression model or a linear regression 

yielded overall estimated relationships that were not suitable for risk assessment. Although the 

Cox regression models with log cumulative exposure provided adequate fits to the data, 

estimates of risk in the low-dose region were overly sensitive to changes in dose level and thus 

not biologically realistic. In order to obtain a suitable result for risk estimation at low exposures, 

in the draft assessment, EPA used a linear regression model and excluded the highest exposure 

group. For the final assessment, EPA investigated the use of two-piece linear models that 

modeled the data as a combination of two linear relationships or segments, one that increased 

steeply in the lower dose region joined with a second that increased at a lower rate in the higher 

dose region. This approach has the advantage of including all the (individual) data and 

incorporating into the overall model the change in the relationship over the observed range of 

exposure. EPA's Benchmark Dose Technical Guidance (U.S. EPA, 2012) recognizes analyses 

omitting high-dose data points, when these data are not compatible with the development of 

suitable descriptive statistical analyses, as a viable analytical approach. 

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

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Comment 4.4: EPA’s use of the heterogeneous broad category of distinct diseases of 

lymphohematopoietic (LH) cancers as the response increases sample size at the expense of 

validity and, thereby, reduces the ability to identify a valid positive dose-response relationship. 

EPA RESPONSE: EPA uses the narrower category of lymphoid cancer data for the primary risk 

estimates in the final assessment. 

Comment 5.0: Certain Policy Decisions EPA Implements in the Draft Cancer Assessment 

Are Scientifically Unsupported, Overly Conservative, Inappropriate and Have Not Been 

Reviewed by a Science Advisory Board. EPA made several policy decisions that compounded 

greatly the inherent conservatism in the risk estimates. These include, among others: (1) EPA’s 

reliance on the lower bound of the point of departure, rather than the best estimate when using 

human data; (2) use of background incidence rates with mortality-based relative rates, thereby 

relying on unsupported assumptions that bias results; (3) EPA’s assumption of an 85-year 

lifetime of continuous exposure and cumulative risk, rather than the more traditional 70-year 

lifetime; and (4) the application of adjustment factors for early-life exposures. 

EPA RESPONSE: The EtO assessment has been reviewed by the SAB and EPA has responded 

to their comments and revised the assessment. With regard to: (1), use of the lower bound on the 

point of departure is consistent with EPA’s 2005 Guidelines for Carcinogen Risk Assessment 

(U.S. EPA, 2005a); (2), background incidence rates were used with mortality-based relative rates 

because EPA’s objective is to estimate incidence risk not mortality risk (see also EPA’s response 

to this issue under the further statistical issues subsection at the end of charge question 2.b 

above); (3), EPA did not assume an 85-year lifetime, rather exposures were considered up to age 

85 (i.e., actual age-specific mortality and disease rates to age 85 were used in a life-table 

analysis; because most individuals die before age 85 years, the overall average lifespan from the 

analysis is about 75 years); (4), EPA’s application of adjustment factors for early life exposures 

in the EtO assessment was in accordance with the recommendations in EPA's Supplemental 

Guidelines and the scientific data supporting the Supplemental Guidelines (U.S. EPA, 2005b). 

The application of these adjustment factors was endorsed by the SAB. 

Comment 6.0: EPA Improperly Relies Entirely on Males in Its Assessment of 

Lymphohematopoietic (LH) Cancer Mortality. To be scientifically defensible, EPA’s LH 

cancer risk characterization must include both males and females, consistent with a “weight-ofevidence” approach that relies on all relevant information. In the NIOSH retrospective study, 
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increased risks of LH cancer were observed in males but not females, even though the NIOSH 

cohort was large and diverse, and consisted of more women than men. EPA’s exclusive reliance 

on male data is scientifically unsound without a mechanistic justification for treating males and 

females differently with respect to LH, which the analysis lacks. 

EPA RESPONSE: In the final assessment, the lymphohematopoietic cancer unit risk estimates 

are based on data for both sexes. 

Comment 7.0: EPA’s Draft Risk Estimates for Occupational Exposure Levels Rely on 

Invalid and/or Inappropriate Models. The models used to estimate risks from occupational 

exposure are flawed because they generate supralinear results, regardless of the observed data. 

These estimates also suffer from the same invalid methodology used in the environmental risk 

estimates. EPA must employ a dose-response model that would generate results consistent with 

the observed data. 

EPA RESPONSE: It is the underlying data that indicate a supralinear exposure-response 

relationship, particularly for lymphohematopoietic cancer and breast cancer mortality, as 

suggested by the categorical results as well as by the poorer fits of the Cox regression models 

with untransformed cumulative exposure data. 

Comment 8.0: EtO is Considered by Many to be a Weak Mutagen and EPA Should 

Consider This in Proposing a Unit Risk Factor. A chemical’s mutagenic potency is 

necessarily related to its carcinogenic potency. If genotoxicity is considered the means by which 

a chemical induces cancer, it follows that it will not induce cancer under conditions where it does 

not induce mutations, at either the chromosome or gene level, thus providing a mechanistic basis 

for estimating carcinogenicity. EtO has been shown only to be a weak mutagen; therefore, it 

should not be automatically considered a human carcinogen and certainly not a potent 

carcinogen. In addition, no treatment-related tumors were observed in rats exposed to EtO, even 

at the 100 ppm concentration level, at the 18 month sacrifice, and the most sensitive tumor type 

(i.e., splenic mononuclear cell leukemia) did not significantly increase in the exposed rats until 

23 months, almost the end of their lifetime of exposures (Snellings et al., 1984). EPA’s analysis 

should have reconciled these findings with its estimation of EtO’s carcinogenic potency, but the 

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

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EPA RESPONSE: EPA does not consider the mutagenicity and carcinogenicity findings to be 

in conflict with the potency estimates. EtO is a relatively weak mutagen when compared to 

strong mutagens such as cancer chemotherapeutic agents and diepoxides but not necessarily 

when compared to other environmental mutagens. And EtO is clearly carcinogenic in mice and 

rats. The inhalation unit risk estimate based on human data is notably larger than that based on 

rodent data (about 23 times larger), and the reasons for this discrepancy are unknown; however, 

such species differences are not unusual. 

It is not surprising that that there was no statistically significant increase in tumors at 18 

months in the Snellings et al. (1984) study. Because of the latency for cancer development, 

tumors generally occur later in life. Furthermore, only 20 animals per sex per dose group were 

killed at 18 months (and tissues from the animals in the low- and mid-dose group only got 

microscopically examined in the presence of a gross lesion), so there is low power to detect an 

effect. 

Comment 9.0: EPA’s Risk Estimates Do Not Pass Simple Reality Checks. 

Comment 9.1: The results of the Draft Cancer Assessment (resulting in negligible risk only at 

levels less than a part per trillion), are not reasonable when compared with the results generated 

for other substances that are considered potent mutagens and/or potent carcinogens, and do not 

comport with the results of other assessments EPA has undertaken. 

EPA RESPONSE: The procedures used in this assessment comport with those used in other 

assessments EPA has undertaken. Differences in relative potency across chemicals based on 

exposure levels may reflect differences in absorption, distribution, metabolism, excretion, or 

pharmacodynamics of the chemicals. 

Comment 9.2: The Draft Cancer Assessment grossly over predicts the observed number of 

cancer mortalities in the study upon which it is based by more than 60-fold. 

EPA RESPONSE: The unit risk estimates are derived from, and are consistent with, the results 

of the NIOSH epidemiology study, as long as they are used in the low-exposure range, as 

intended. Because the exposure-response relationships for the cancers of interest in the NIOSH 

study are generally supralinear, the unit risk estimates will overpredict the NIOSH results if 

applied to the region of the exposure-response relationships where the responses plateau. The 

potency estimates derived in the assessment are constructed for use with low dose levels 
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consistent with environmental exposure and are not appropriate for use with exposures in 

occupational settings, as stated explicitly in the document. Occupational exposure scenarios are 

addressed in Section 4.7 of the assessment document. Extra risks associated with occupational 

exposures are in the “plateau” region of the exposure-response relationships and thus increase 

proportionately less than risks in the low-dose region. 

Comment 9.3: EPA’s de minimis value from the Draft Cancer Assessment is 2 to 3 orders of 

magnitude below the endogenous level of EtO that is produced naturally in humans. 

EPA RESPONSE: EPA's risk estimates are for risk above background. The issue of 

endogenous levels is addressed in the final assessment. See Section 4.5 for a discussion of the 

specific issue raised in this comment. 

Comment 9.4: EPA’s draft unit risk values for EtO are unreasonably large, given the evidence 

of carcinogenicity in a large body of epidemiology studies that is not conclusive, the weak 

mutagenicity data, and the lack of cancer response in rodents until very late in life. EPA must 

make the best use of all of the epidemiology, toxicology and genotoxicity data for EtO that 

provide valid information on the relationship between exposure and cancer response to improve 

the reasonableness of the unit risk values for EtO. 

EPA RESPONSE: EPA believes that it has made the best use of the available information in 

revising the assessment. EPA’s evaluation of the weight of evidence concludes that the 

epidemiological evidence is strong (Section 3.5.1). In addition, the unequivocal evidence of 

rodent carcinogenicity and the supporting mechanistic evidence add sufficient weight for the 

characterization of “carcinogenic to humans” (Section 3.5.1), which is beyond what is needed to 

support the derivation of quantitative risk estimates. This is thoroughly presented in the 

assessment and was supported by the SAB review. The unit risk estimates are derived from, and 

are consistent with, the results of the large, high-quality NIOSH epidemiology study. See also 

the response to Comment 8.0 above. 

Comment 10.0: The Draft Cancer Assessment Does Not Use the Best Available Science as 

Required under the Information Quality Act and Cancer Guidelines. 

Comment 10.1: EPA based its evaluation on summaries of statistics available in various 

publications. These data, however, are not sufficient to conduct valid dose-response modeling. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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EPA should have based its calculations on readily available National Institute of Occupational 

Safety and Health (NIOSH) data for individual subjects from the cohort mortality study. 

EPA RESPONSE: See response to Comment 3.0. 

Comment 10.2: EPA did not use all available epidemiologic data, including the Union Carbide 

Corporation (UCC) data and all NIOSH data that were available at the time EPA conducted its 

assessment. In particular, the Greenberg et al. (1990) UCC study reported the consistency of the 

death certificate diagnosis with a pathology review of medical records for leukemia cases, a 

validation not conducted for cases in the NIOSH study. 

EPA RESPONSE: EPA considered all the available epidemiological data, including NIOSH 

data and the Union Carbide data and the publications that the ACC Panel referred to in its 

comments. See response to Comment 2.0 for more details on why the UCC data were not used 

for the derivation of quantitative risk estimates. 

Comment 11.0: EPA Should Recognize That EtO Is Both a Weak Mutagen and Weak 

Animal Carcinogen. 

EPA RESPONSE: The full text of this comment was essentially the same as Comment 8.0 and 

is addressed in EPA’s response to that comment above. 

Comment 11.1: Among 26 alkylating agents studies by (Vogel and Nivard, 1998), EtO 

showed the second lowest carcinogenic potency. 

EPA RESPONSE: The (Vogel and Nivard, 1998) study is not relevant to EPA’s assessment of 

the carcinogenicity of EtO. Most of the substances considered by (Vogel and Nivard, 1998) are 

chemotherapeutic chemicals that are, by design, intended to be strong alkylating agents. 

Comment 11.2: Previous assessments of EtO inhalation time to tumor in rats showed that the 

increased risks observed at higher experimental doses did not extend to the lowest experimental 

dose. To comply with the Cancer Guidelines, EPA should include these and other relevant 

animal data in a weight-of-evidence characterization of EtO. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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EPA RESPONSE: The carcinogenicity data reviewed in Section 3.2 reveal that, of 13 exposureresponse relationships for the tumor types associated with EtO exposure from the 3 rodent 

bioassays, all but one show an increased incidence at the lowest exposure level, though not all 

the increases are statistically significant at that level. 

Comment 12.0: EPA’s Risk Estimates Do Not Pass Simple Reality Checks. 

Comment 12.1: [This was the same as Comment 9.1 above.] 

Comment 12.2: The results of the Draft Cancer Assessment are at odds with EPA’s conclusion 

that EtO is a potent (de minimis level < 1 ppt) human carcinogen and EtO’s potency seen in 

animal studies. 

EPA RESPONSE: The risk estimates based on the rodent data are over an order of magnitude 

lower than (~1/23) the estimate based on the human data, for unknown reasons, but species 

differences are not unusual and human data are generally preferred over rodent data for 

quantitative risk estimates because the uncertainties due to interspecies extrapolation are 

avoided. 

Comment 12.3: EPA’s draft unit risk values for EtO are not applicable to the general public. 

The Draft Cancer Assessment grossly over predicts the observed number of LH cancer 

mortalities in the study upon which it is based by more than 60-fold. Further, EPA’s de minimis 

value is about 50 times lower than the lowest ambient concentration found at remote coastal 

locations. Based upon PBPK simulations, endogenous concentrations of EtO in humans are 

approximately 400-1700 times greater than EPA’s proposed de minimis value of 0.00036 parts 

per billion. 

EPA RESPONSE: The unit risk estimates are derived from, and are consistent with, the results 

of the NIOSH epidemiology study, as long as they are used in the low-exposure range, as 

intended; see response to Comment 9.2 above. Endogenous and ambient concentrations of EtO 

could be contributing to background rates of lymphohematopoietic cancer and breast cancer 

incidences, which are appreciable. The EPA values are not implausible upper bound estimates. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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APPENDIX I. 

LIST OF REFERENCES ADDED AFTER THE 2006 EXTERNAL REVIEW DRAFT 

Note: These references were added to the Carcinogenicity Assessment in response to the 2007 

peer reviewers’ and public comments, and for completeness. The added references have not 

changed the overall qualitative or quantitative conclusions. These references are also included in 

the reference list at the end of the main body of the assessment or the reference list at the end of 

this appendix volume; see those reference lists for the HERO links. 

Abeles, FB; Heggestad, HE. (1973) Ethylene: an urban air pollutant. J Air Waste Manag Assoc 23:517−521. 

Ádám, B; Bárdos, H; Adány R. (2005) Increased genotoxic susceptibility of breast epithelial cells to ethylene oxide. 

Mutat Res 585(1−2):120−126. 

Agurell, E; Cederberg, H; Ehrenberg, L; et al. (1991) Genotoxic effects of ethylene oxide and propylene oxide: a 

comparative study. Mutat Res 250(1−2):229−237. 

Applebaum, KM; Malloy, EJ; Eisen, EA. (2007) Reducing healthy worker survivor bias by restricting date of hire in 

a cohort of Vermont granite workers. Occup Environ Med 64:681–687. 

Applegren, LE; Eneroth, G; Grant, C; et al. (1978) Testing of ethylene oxide for mutagenicity using the 

micronucleus test in mice and rats. Act Pharmacol Toxicol 43:69−71. 

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; et al. (1993) Molecular analysis of ethylene oxide-induced mutations at the 

HPRT locus in human diploid fibroblasts. Mutat Res 287:283−292. 

Boffetta, P; van der Hel, O; Norppa, H; et al. (2007) Chromosomal aberrations and cancer risk: results of a cohort 

study from Central Europe. Am J Epidemiol 165:36−43. 

Bolt, HM; Leutbecher, M; Golka, K. (1997) A note on the physiological background of the ethylene oxide adduct 

7-(2-hydroxyethyl) guanine in DNA from human blood [Letter]. Arch Toxicol 71(11):719−721. 

Bolt, HM; Peter, H; Föst, U. (1988) Analysis of macromolecular ethylene oxide adducts [Review]. Int Arch Occup 

Environ Health 60:141−144. 

Bonassi, S; Znaor, A; Ceppi, M; et al. (2007) An increased micronucleus frequency in peripheral blood lymphocytes 

predicts the risk of cancer in humans [Review]. Carcinogenesis 28:625−631. 

Boogaard, PJ. (2002) Use of haemoglobin adducts in exposure monitoring and risk assessment. J Chromatogr B 

Analyt Technol Biomed Life Sci 778(1−2):309−322. 

Boysen, G; Pachkowski, BF; Nakamura, J; et al. (2009) The formation and biological significance of N7-guanine 

adducts [Review]. Mutat Res 678:76–94. 

Britton, DW; Törnqvist, M; van Sittert, NJ; et al. (1991) Immunochemical and GC/MS analysis of protein adducts: 

dosimetry studies with ethylene oxide [Review]. Prog Clin Biol Res 372:99−106. 

Chandra, GR; Spencer, M. (1963) A micro apparatus for absorption of ethylene and its use in determination of 

ethylene in exhaled gases from human subjects. Biochim Biophys Acta 69:423−425. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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Christiansen, DH; Andersen, MK; Desta, F; et al. (2005) Mutations of genes in the receptor tyrosine kinase 

(RTK)/RAS-BRAF signal transduction pathway in therapy-related myelodysplasia and acute myeloid leukemia. 

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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. 

Cushnir, JR; Lamb, JH; Parry, A; et al. (1991) Tandem mass spectrometric approaches for determining exposure to 

alkylating agents. IARC Sci Publ 105:107−12. 

de Serres, FJ; Brockman, HE. (1995) Ethylene oxide: induction of specific-locus mutations in the ad-3 region of 

heterokaryon 12 of Neurospora crassa and implications for genetic risk assessment of human exposure in the 

workplace. Mutat Res 328:31–47. 

Donner, EM; Wong, BA; James, RA; et al. (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. 

Ehrenberg, L; Osterman-Golkar, S; Segerbäck, D; et al. (1977) Evaluation of genetic risks of alkylating agents. III. 

alkylation of haemoglobin after metabolic conversion of ethene to ethene oxide in vivo. Mutat Res 45(2):175−184. 

Eide, I; Zhao, C; Kumar, R; et al. (1999) Comparison of (32)P-postlabeling and high-resolution GC/MS in 

quantifying N7-(2-Hydroxyethyl)guanine adducts. Chem Res Toxicol 12(10):979−984. 

Farmer, PB; Bailey, E; Naylor, S; et al. (1993) Identification of endogenous electrophiles by means of mass 

spectrometric determination of protein and DNA adducts [Review]. Environ Health Perspect 99:19−24. 

Farmer, PB; Shuker, DE. (1999) What is the significance of increases in background levels of carcinogen-derived 

protein and DNA adducts? Some considerations for incremental risk assessment [Review]. Mutat Res 

424(1−2):275−286. 

Farooqi, Z; Törnqvist, M; Ehrenberg, L; et al. (1993) Genotoxic effects of ethylene oxide and propylene oxide in 

mouse bone marrow cells. Mutat Res 288(2):223−228. 

Fennell, TR; MacNeela, JP; Morris, RW; et al. (2000) Hemoglobin adducts from acrylonitrile and ethylene oxide in 

cigarette smokers: effects of glutathione S-transferase T1-null and M1-null genotypes. Cancer Epidemiol 

Biomarkers Prev 9(7):705−712. 

Föst, U; Marczynski, B; Kasemann, R; et al. (1989) Determination of 7-(2-hydroxyethyl)guanine with gas 

chromatography/mass spectrometry as a parameter for genotoxicity of ethylene oxide. Arch Toxicol Suppl 

13:250−253. 

Fost, U; Hallier, E; Ottenwalder, H; et al. (1991) Distribution of ethylene oxide in human blood and its implications 

for biomonitoring. Hum Exp Toxicol 10:25−31. 

Fuchs, J; Wullenweber, U; Hengstler, JG; et al. (1994) Genotoxic risk for humans due to workplace exposure to 

ethylene oxide: remarkable individual differences in susceptibility. Arch Toxicol 68(6):343−348. 

Generoso, WM; Cain, KT; Hughes, LA; et al. (1986) Ethylene oxide dose and dose-rate effects in the mouse 

dominant-lethal test. Environ Mol Mutagen 8(1):1−7. 

Generoso, WM; Rutledge, JC; Cain, KT; et al. (1988) Mutagen-induced fetal anomalies and death following 

treatment of females within hours after mating. DNA Repair 199:175−181. 

Generoso, WM; Cain, KT; Cornett, CV; et al. (1990) Concentration-response curves for ethylene-oxide-induced 

heritable translocations and dominant lethal mutations. Environ Mol Mutagen 16(2):126−131. 
This document is a draft for review purposes only and does not constitute Agency policy. 

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Godderis, L; Aka, P; Matecuca, R; et al. (2006) Dose-dependent influence of genetic polymorphisms on DNA 

damage induced by styrene oxide, ethylene oxide and gamma-radiation. Toxicology 219(1−3):220−229. 

Golberg, L. (1986) Chemical and physical properties. In Hazard assessment of ethylene oxide. Boca Raton, FL: 

CRC Press. 

Gupta, RC; Lutz, WK. (1999) Background DNA damage for endogenous and unavoidable exogenous carcinogens: a 

basis for spontaneous cancer incidence? DNA Repair 424(1−2):1−8. 

Hallier, E; Langhof, T; Dannappel, D; et al. (1993) Polymorphism of glutathione conjugation of methyl bromide, 

ethylene oxide and dichloromethane in human blood: influence on the induction of sister chromatid exchanges 

(SCE) in lymphocytes. Arch Toxicol 67(3):173−178. 

Harris, NL: Jaffe, ES; Diebold, J; et al. (1999) World Health Organization classification of neoplastic diseases of the 

hematopoietic and lymphoid tissues report of the Clinical Advisory Committee—Airlee House, Virginia, November 

1997. J Clin Oncol 17:3835−3849. 

Haufroid, V; Merz, B; Hofmann, A; et al. (2007) Exposure to ethylene oxide in hospitals: biological monitoring and 

influence of glutathione S-transferase and epoxide hydrolase polymorphisms. Cancer Epidemiol Biomarkers Prev 

16(4):796−802. 

Hong, HH; Houle, CD; Ton, TV; et al. (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. 

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/ 

Houle, CD; Ton, TV; Clayton, N; et al. (2006) Frequent p53 and H-ras mutations in benzene- and ethylene oxideinduced mammary gland carcinomas from B6C3F1 mice. Toxicol Pathol 34(6):752−762. 

Huang, CC; Shih, WC; Wu, CF; et al. (2008) Rapid and sensitive on-line liquid chromatographic/tandem mass 

spectrometric determination of an ethylene oxide-DNA adduct, N7-(2-hydroxyethyl)guanine, in urine of 

nonsmokers. Rapid Commun Mass Spectrom 22(5):706−710. 

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evaluation of carcinogenic risks to humans. Vol. 97. 1,3-Butadiene, ethylene oxide, and vinyl halides (vinyl fluoride, 

vinyl chloride and vinyl bromide). Lyon, France: World Health Organization; pp. 185−311. 

Ingvarsson, S. (1999) Molecular genetics of breast cancer progression [Review]. Semin Cancer Biol 9(4):277–288. 

Jenssen, D; Ramel, C. (1980) The micronucleus test as part of a short-term mutagenicity test program for the 

prediction of carcinogenicity evaluated by 143 agents tested. Mutat Res 75(2):191−202. 

Kelsey, KT; Wiencke, JK; Eisen, EA; et al. (1988) Persistently elevated sister chromatid exchanges in ethylene 

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lymphocytes of rats exposed to ethylene oxide by inhalation. Mutat Res Lett 120:37−44. 
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Koepke, SR; Kroeger-Koepke, MB; Bosan, W; et al. (1988) Alkylation of DNA in rats by 

N-nitrosomethyl-(2-hydroxyethyl)amine: dose response and persistence of the alkylated lesions in vivo. Cancer Res 

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Kolman, A; Chovanec, M. (2000) Combined effects of gamma-radiation and ethylene oxide in human diploid 

fibroblasts. Mutagenesis 15(2):99−104. 

Kolman, A; Näslund, M. (1987) Mutagenicity testing of ethylene oxide in Escherichia coli strains with different 

repair capacities. Environ Mol Mutagen 10(3):311−315. 

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cancer [Review]. Carcinogenesis 7(8):1245−1250. 

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epichlorohydrin in humans: update review (1990−2001) [Review]. DNA Repair 512(2−3):173−194. 

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Lambert, B; Andersson, B; Bastlova, T; et al. (1994) Mutations induced in the hypoxanthine phosphoribosyl 

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Langholz, B; Richardson, DB. (2010) Fitting general relative risk models for survival time and matched case-control 

analysis. Am J Epidemiol 171:377−383. 

Leclercq, L; Laurent, C; De Pauw, E. (1997) High-performance liquid chromatography/electrospray mass 

spectrometry for the analysis of modified bases in DNA: 7-(2-hydroxyethyl)guanine, the major ethylene oxide-DNA 

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to biological monitoring. Arch Toxicol Suppl 15:289. 

Lewis, SE; Barnett, LB; Felton, C; et al. (1986) Dominant visible and electrophoretically expressed mutations 

induced in male mice exposed to ethylene oxide by inhalation. Environ Mol Mutagen 8(6):867−872. 

Li, F; Segal, A; Solomon, JJ. (1992) In vitro reaction of ethylene oxide with DNA and characterization of DNA 

adducts. Chem Biol Interact 83(1):35−54. 

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Cancer Res 69(7):3052−3059. 
 

 

 

 

 

 

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Marsden, DA; Jones, DJ; Lamb, JH; et al. (2007) Determination of endogenous and exogenously derived 

N7-(2-hydroxyethyl)guanine adducts in ethylene oxide-treated rats. Chem Res Toxicol. 20(2):290−299.
	

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the detection of ethylene oxide-DNA adducts with monoclonal antibodies against imidazole ring-opened 

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environmental carcinogens [Review]. Mutat Res 437(1):21−49. 

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N7-(2-hydroxyethyl) guanine formation in tissues of rodents. Carcinogenesis 20(9):1787−1792. 

Wu, KY; Scheller, N; Ranasinghe, A; et al. (1999b) A gas chromatography/electron capture/negative chemical 

ionization high-resolution mass spectrometry method for analysis of endogenous and exogenous 

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12(8):722−729. 

Yager, JW; Benz, RD. (1982) Sister chromatid exchanges induced in rabbit lymphocytes by ethylene oxide after 

inhalation exposure. Environ Mutagen 4(2):121−134. 

Yong, LC; Schulte, PA; Kao, CY; et al. (2007) DNA adducts in granulocytes of hospital workers exposed to 

ethylene oxide. Am J Ind Med 50(4):293−302. 

Zhao, C; Tyndyk, M; Eide, I; et al. (1999) Endogenous and background DNA adducts by methylating and 

2-hydroxyethylating agents. Mutat Res 424(1−2):117−125. 

Zharlyganova, D; Harada, H; Harada, Y; et al. (2008) High frequency of AML1/RUNX1 point mutations in 

radiation-associated myelodysplastic syndrome around Semipalatinsk nuclear test site. J Radiat Res (Tokyo) 

49(5):549−555. 
 

 

 

 

 

 

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APPENDIX J.
 

SUMMARY OF MAJOR NEW STUDIES SINCE THE LITERATURE CUTOFF DATE
 

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. No 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 this Appendix for transparency and 

completeness. The Appendix first provides a description of the systematic literature search that 

was conducted to identify relevant new studies and then provides the reviews of the two major 

new studies. 

J.1. SYSTEMATIC LITERATURE SEARCH 

A systematic literature search was conducted in May 2013, covering the time frame from 

January 2006 to May 2013. The search was conducted using the LitSearch tool in EPA’s HERO 

database, and the following three literature databases were searched: PubMed, Web of Science, 

and ToxNet. The search terms involved Ethylene Oxide AND (carcinogenicity OR cancer OR 

mutagenicity OR mutation OR genotoxicity). 

The search identified 372 references, of which 56 were determined to be potentially 

relevant 9 . The disposition of the 56 potentially relevant references is summarized in Table J-1. 

In brief, for the purposes of this carcinogenicity assessment, 26 references that were primarily 

discussions of methods studies or exposure studies10 or were reviews or other secondary source 

material were not generally considered further. The remaining 30 references were given further 

consideration to see if they represented major new studies. No new studies were identified that 

9

In this first part of the screening, any references of potential relevance to the carcinogenicity assessment of ethylene 

oxide were identified. References that pertained to other things and that were inadvertently captured in the literature 

search were excluded. For example, in an alphabetical listing of the 372 references by first author, the first 

reference is: Agarwal, A., Unfer, R. and Mallapragada, S. K. (2007), Investigation of in vitro biocompatibility of 

novel pentablock copolymers for gene delivery. J. Biomed. Mater. Res., 81A: 24–39. This reference discusses some 

copolymers of various chemicals, including poly(ethylene oxide), synthesized as vectors for gene delivery and tested 

in some cancer cell lines; this reference was not relevant to the assessment and was excluded from further 

consideration. 

10This refers to general exposure studies; exposure studies related to any of the epidemiological studies of EtO 

would be considered further. 
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would impact the assessment’s major conclusions. Two references were identified as highly 

pertinent studies, and these are reviewed briefly in Section J.2 of this Appendix. 

Table J-1. Disposition of 56 new references identified as potentially relevant 

Category Referencesa Disposition 

Exposure studies Davis et al. (2006) 

Lin et al. (2007) 

Tateo and Bononi (2006) 

Not considered further. 

Methods studies Ahn and Shin (2006) 

Tretyakova et al. (2012) 

Wu et al. (2011) 

Not considered further. 

Reviews or other 

secondary source 

material 

Brown et al. (2012) 

Butterworth and Chapman (2007) 

Chan et al. (2006) 

Farmer and Singh (2008) 

Hoenerhoff et al. (2009) 

Jarabek et al. (2009) 

Keshava et al. (2006a) 

Keshava et al. (2006b) 

(Manservigi et al., 2010) 

McCarthy et al. (2009) 

Mosavi-Jarrahi et al. (2009) 

Okada et al. (2012) 

Smith-Bindman (2012) 

Snedeker (2006) 

Steinhausen et al. (2012) 

Weiderpass et al. (2011) 

Won (2010) 

WHO, 2008 (same as IARC, 2008) 

Not considered further. 

IARC (2008) Already cited in the assessment. 

Cancer studies Kiran et al. (2010) 

Mikoczy et al. (2011) 

Reviewed in Section J.2. 

Swaen et al. (2009) Already cited in the assessment. 

van Balen et al. (2006) Not considered further. Primarily a study of risks to 

farmers. EtO left out of analysis because too few study 

subjects were exposed to it. Subjects were part of the 

EPILYMPH study analyzed by Kiran et al. (2010) (see 

Section J.2.1). 

Fondelli et al. (2007) Not considered further. No EtO-specific results. 

Kim et al. (2011) Not considered further. Case report study of 7 cases of 

malignant lymphohematopoietic disorders found in 2 

semiconductor plants. Various carcinogens suspected 

of causing lymphohematopoietic cancers were 

investigated; EtO not found in cases’ processes. 
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Table J-1. Disposition of 56 new references identified as potentially relevant 

(continued) 

Category Referencesa Disposition 

Genotoxicity/ 

Mutagenicity studies 

Donner et al. (2010) 

Godderis et al. (2006) 

Hong et al. (2007) 

Houle et al. (2006) 

Marsden et al. (2007) 

Marsden et al. (2009) 

Tompkins et al. (2008) 

Yong et al. (2007) 

Already cited in the assessment. 

Tompa et al. (2006) 

Tompkins et al. (2009) 

Citations added to the assessment. 

Huang et al. (2011) Not considered a major new study. Largely an 

exposure study; examined use of urinary N7-HEG as a 

biomarker of EtO exposure in EtO-exposed workers 

and smokers in Taiwan. 

Lindberg et al. (2010) Not considered further. This study examined utility of 

a micronucleus assay for detecting genotoxic damage 

in mouse alveolar Type II and Clara cells—EtO was 

used as a test agent but at a high concentration (>3 

times higher than the highest exposure concentration 

used in the mouse cancer bioassay). 

Mazon et al. (2009) 

Mazon et al. (2010) 

Not considered further. Focused on a specific repair 

gene product in E. Coli. 

Parsons et al. (2012) 

Tompkins et al. (2006) 

Not considered further. Published abstracts, not full 

papers. 

Other Sielken and Valdez-Flores (2009) 

Sielken and Valdez Flores (2009) 

Swenberg et al. (2008) 

Valdez-Flores et al. (2010) 

Already cited in the assessment. 

Haufroid et al. (2007) Citation added. 

Kensler et al. (2012) Not relevant; focused on chemoprevention. 

Steenland et al. (2011) Not considered further. Peer-reviewed publication of 

analyses already in the assessment. 

Valdez-Flores et al. (2011) Not considered further. Quantitative risk assessment 

for occupational exposures—issues pertaining to the 

Valdez-Flores et al. risk assessment approach are 

already addressed in the assessment in discussions of 

the 2010 paper by the same authors (Valdez-Flores et 

al., 2010). 

Swenberg et al. (2011) Not considered further. Largely a review; focused on 

implications of endogenous adducts for risk 

assessment—this issue is already addressed in the 

assessment (e.g., in the responses to SAB comments in 

Appendix H). 

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Table J-1. Disposition of 56 new references identified as potentially relevant 

(continued) 

EtO: ethylene oxide. 

N7-HEG: N7-(2-hydroxyethyl)guanine. 

a

Full citations for references cited elsewhere in the assessment are provided in the Reference section of the 

assessment; full citations for references appearing only in this appendix are provided in Section J.3 of the appendix. 

J.2. REVIEWS OF MAJOR NEW STUDIES PUBLISHED SINCE THE LITERATURE 

CUTOFF DATE 

As discussed in Section J.1, a systematic literature search was conducted in part to 

determine whether any significant new studies had been published since 30 June 2010, the cutoff 

date for literature inclusion into this assessment. No new studies were identified that would 

impact the assessment’s major conclusions. Nonetheless, two studies of high pertinence to the 

assessment have been published since the cutoff date for literature inclusion. The two studies are 

epidemiology studies of occupational exposure to ethylene oxide (EtO). These studies are 

reviewed briefly here for transparency and completeness, and key features of the studies are 

summarized in Table J-2. 

J.2.1. Kiran et al. (2010) 

Kiran et al. (2010) investigated occupational exposure to EtO in a population-based casecontrol study of lymphoma in six European countries (the “EPILYMPH study”). Cases 

(n = 2,347) were consecutive adult patients with a first diagnosis of lymphoma, classified under 

the 2001 WHO lymphoma classification system, in 1998−2004 at 22 centers in the six countries. 

Controls from Germany and Italy were randomly selected from the general population, matched 

on sex, 5-year age group, and residence area. Controls from the Czech Republic, France, 

Ireland, and Spain were matched hospital controls with diagnoses other than cancer, infectious 

diseases, and immunodeficient diseases (total controls = 2,463). Participation rates were 88% in 

cases, 81% in hospital controls, and 52% in population controls. All study subjects were 

interviewed in person using the same structured questionnaire, which included questions on 

sociodemographic factors, lifestyle, health history, and complete work history (including all 

full-time jobs held for ≥1 year). For each job, information was collected on type of industry, 

tasks performed, machines used, and exposure to 35 specific agents (or groups of agents) of 

interest, including EtO. Supplemental questionnaire modules for specific occupations were used 

to get additional details on jobs and exposures of interest. 
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Exposure was evaluated in each center by specially trained industrial hygienists who 

reviewed all the questionnaires and assessed frequency and intensity of exposure to each agent 

on a 4-point scale (unexposed and low, medium and high exposures) as well as confidence in the 

assessment (low, medium, or high). Most of the exposed cases and controls were classified with 

medium or high confidence, although a greater proportion of cases than controls were thus 

classified (23/31 versus 15/27). Because of the low prevalence (1.2%) of EtO exposure in the 

study, the medium and high categories of exposure frequency and intensity were combined in the 

statistical analyses. A cumulative exposure score for EtO was also developed for each study 

subject, integrating duration, frequency, and intensity of exposure; these scores were then 

categorized as above or below the median score among exposed subjects. 

Risk was assessed for all lymphoma, B-cell lymphoma (which represented 80% of all the 

lymphoma cases), and the most common subtypes of B-cell lymphoma. The OR was calculated 

using unconditional logistic regression, adjusting for age, sex, and center. Including education, 

farm work, and exposure to solvents in the model, reportedly did not change the risk estimates 

(results not shown). Linear trends for the exposure metrics were calculated using the Wald test 

for trend. 

Because of the low prevalence of EtO exposure in the study (1.2%), the number of 

exposed cases and controls was limited (31 and 27, respectively), so the study power was not 

large, especially for analyses of lymphoma subtypes. Results for all lymphoma for ever exposed 

and for the highest exposure category for each of the different exposure metrics are presented in 

Table J-2. Increased risks were observed for ever exposed and for the highest exposure category 

for each of the exposure metrics, and the OR for medium or high frequency of exposure was 

statistically significant (4.3; 95% CI 1.4, 13.0). However, none of the trend tests was statistically 

significant. The overall association appeared to be stronger using hospital controls; however, 

when considering only subjects whose EtO exposures were assessed with medium or high 

confidence, the increased ORs were similar using either hospital or population controls. Results 

were similar when only B-cell lymphoma, which represented the majority of all lymphomas, was 

evaluated. The strongest associations were observed for chronic lymphocytic leukemia, and 

p-values for trend were ≤0.051 for all the exposure metrics for that lymphoma subtype. The 

investigators note that while random variation related to the low prevalence might account for 

some positive results, their combined probability test (Fischer method) indicated that the chance 

probability of an upward trend in chronic lymphocytic leukemia across the four metrics assumed 

to be independent (confidence, frequency, intensity, and duration) was 0.003. 

In conclusion, this study adds further support to the weight-of-evidence finding obtained 

in Chapter 3 of strong, but less than conclusive, evidence of a causal association between EtO 
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exposure and lymphohematopoietic cancers in humans. Because only categorical exposures 

were assessed, no quantitative risk estimates can be derived from this study. 

J.2.2. Mikoczy et al. (2011) 

This study is an update of the Hagmar et al. (1991) and Hagmar et al. (1995) studies 

discussed in Section 3.1 of the assessment and in Section A.2.11 of Appendix A. The first 

update (Hagmar et al., 1995) had a median follow-up time of only 11.8 years; this update extends 

the follow-up period through 2006, providing an additional 16 years of follow-up. The cohort 

consists of 2,171 (1,309 females and 862 males 11), employed for at least 1 year prior to 1986, at 

two Swedish facilities that sterilized medical equipment using EtO (Plant A sterilization 

operations ran from 1970 to 1994; Plant B sterilization operations ran from 1964 to 2002). Vital 

status and emigration data at the end of follow-up were obtained from the Swedish population 

registry; cause of death for 1972−2006 was obtained from Statistics Sweden; and malignant 

tumor data for 1972−2006 were obtained from the Swedish Cancer Registry. At the end of 

follow-up, the mean age of the cohort was 56 years and the cohort had contributed 

58,305 person-years of risk; 171 cohort members had died (7.9%) and 126 (5.8%) had emigrated 

and were of unknown vital status. Mean duration of employment in the cohort was 6.3 years. 

In the original study (Hagmar et al., 1991), individual cumulative exposure estimates 

were derived based on job-exposure matrices for each plant and exposure level estimates 

determined up to 1986. While exposure levels were high in the early years of the operations 

(e.g., peak levels of 75 ppm in 1964 in Plant B and exposure levels up to 40 ppm in 1970 in 

Plant A), 8-hour TWA levels had decreased to below 1 ppm by 1985 (See Hagmar et al., 1991, 

and Section A.2.11 of Appendix A for more details on the original exposure assessment). For 

this update, worker histories for the 1,303 workers who were still employed at the two plants at 

the end of the original study (1986) were extended up until the cessation of sterilization 

operations in the plants, and exposure estimates for the follow-up period were determined from 

yearly statutory industrial hygiene measurements of EtO from 1986 on. Because of the low 

exposure levels after 1985, the impact of updating the cumulative exposure estimates was low 

(the largest impact was reportedly on the 90th percentile, which changed from 1.17 to 1.29 ppm 

× years). The mean and median cumulative exposures for the 2,020 cohort members for whom 

job titles were available were 2.92 ppm × years and 0.13 ppm × years, respectively. 

Standardized mortality and incidence ratios (SMRs and SIRs) were obtained by 

comparing the number of deaths or incident cases observed to the number expected based on 

 

11Without explanation, there is one additional male in the update; the 1991 and 1995 papers both reported 

2,170 workers, including 861 males, in the cohort (Hagmar et al., 1995; 1991). 
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cause-, calendar year-, sex-, and 5-year age group-specific rates in the Swedish population 

(external referents). For cancer incidence (but not mortality), internal analyses were also 

conducted using Poisson regression analyses, adjusted for age group and calendar period, with 

no induction (latency) period. In the internal analyses, incidence rate ratios were calculated by 

comparing the incidence rates for the two highest cumulative exposure quartiles with that for the 

50% of workers with cumulative exposures below the median of 0.13 ppm × years (internal 

referents). Internal analyses are generally preferred over external analyses because the referents 

are from the same cohort as the exposed subjects, potentially reducing confounding as well as the 

healthy worker effect, which can mask an increase in risk; however, in this study, some of the 

advantages of internal analyses may be mitigated by the absence of an unexposed referent group, 

which could itself dampen relative risk estimates. 

Results for cancer mortality and incidence for the cancer types of interest (i.e., 

lymphohematopoietic cancers and female breast cancer) are summarized in Table J-2. For 

lymphohematopoietic cancers, nonsignificant increases in SMRs and SIRs were reported. For 

the incidence data, the internal analysis shows no exposure-related association for 

lymphohematopoietic cancers, although this analysis is relatively uninformative for these 

cancers, given the small number of cases (five cases in each of the two highest exposure quartiles 

and seven cases in the referent group of workers with cumulative exposures below the median), 

the generally low estimated cumulative exposures, and the absence of an unexposed referent 

group. It should also be noted that data were not reported or analyzed for the subgrouping of 

“lymphoid” cancers. 

For breast cancer mortality (results not shown), a “slight but nonsignificant decrease” in 

the SMR was reported. With a 15-year induction period included, the SMR for breast cancer 

was reportedly “somewhat increased.” For workers with cumulative exposures above the 

median, with a 15-year induction period, a “higher than expected” SMR, which was not 

statistically significant, was reported. 

For breast cancer incidence (41 incident cases), SIRs were nonsignificantly decreased, 

both with and without a 15-year induction period. Internal analyses resulted in statistically 

significant increases in the incidence rate ratios for the two highest cumulative exposure quartiles 

as compared to the 50% of workers with cumulative exposures below the median (see Table J-2), 

despite having a low-exposed rather than an unexposed referent group. 

In conclusion, the nonsignificant increases in SMRs and SIRs for lymphohematopoietic 

cancers reported in this study are consistent with an increase in lymphohematopoietic cancer risk 

but, overall, the study is underpowered for the analysis of lymphohematopoietic cancers and 

contributes little to the weight of evidence for these cancers. For breast cancer incidence, 
 

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however, the statistically significant exposure-related increases in breast cancer incidence in 

internal analyses add support to the weight-of-evidence finding obtained in Chapter 3 of strong, 

but less than conclusive, evidence of a causal association between EtO exposure and female 

breast cancer in humans. The cumulative exposure estimates for this study were very low 

compared to those in other studies. For example, in the Swaen et al. (2009) study of the UCC 

cohort of male EtO production workers, the average cumulative exposure was 67.16 ppm 

× years. In the more comparable NIOSH cohort of sterilization workers, cumulative exposures at 

the end of follow-up for the full cohort, which included workers with <1 year of employment, 

had a mean of 27 ppm × years and median of 6 ppm × years (see Appendix D, Section D.1), and 

in particular, the mean cumulative exposure at the end of follow-up in the breast cancer 

incidence study cohort, which only included workers with ≥1 year of employment, was 37.0 ppm 

× years. Yet, the breast cancer incidence RRs for the categorical exposure groups reported in 

Steenland et al. (2003) for the NIOSH breast cancer incidence study were lower than those 

observed in the Mikoczy et al. (2011) study. 

Thus, if unit risk estimates were derived based on the Mikoczy et al. (2011) study, they 

would be higher than the estimates calculated from the NIOSH study. However, no such 

estimates were derived based on the Mikoczy et al. (2011) study because, in comparison to the 

NIOSH study, the Mikoczy et al. (2011) study had limitations that would have made the 

estimates more uncertain than those based on the NIOSH study. In particular, there was greater 

uncertainty about the exposure estimates [e.g., measurement data were available only from 1973 

for one plant and 1975 for the other; for earlier exposures, estimates were constructed taking into 

account information on changes in production methods and environmental controls, subjective 

memories, and time trends (Hagmar et al., 1991), but this is a less sophisticated approach than 

that of the NIOSH exposure assessment, which used a detailed, validated regression model]; 

there were many fewer breast cancer cases (41 incident cases vs. 319 cases in NIOSH’s full 

incidence study cohort and 233 in the subcohort with interviews); and there was no information 

on potential breast cancer risk factors, as was available for the NIOSH subcohort. 

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Table J-2. New epidemiological studies of ethylene oxide and human cancer 

Population/ 

industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to 

which subjects were 

potentially exposed Limitations 

Population-based 

case-control study 

involving 

22 centers in 

6 European 

countries (Czech 

Republic, France, 

Germany, Italy, 

Ireland, Spain) 

[EPILYMPH 

study] 

Kiran et al. (2010) 

2,347 cases 

(1,314 male, 

1,033 female); 

2,463 controls 

(1,321 male, 

1,142 female), 

matched on 

sex, age 

group, and 

residence area 

1.2% of study population 

defined as ever-exposed 

(31 cases, 27 controls) 

All lymphoma 

(# cases/# controls) OR (95% CI) 

Unexposed (2,316/2,436) 1.0 

[referent category] 

Ever exposed (31/27) 1.3 (0.7, 2.1) 

Confidence in exposure classification 

low (8/12) 0.8 (0.3, 1.9) 

med or high (23/15) 1.6 (0.8, 3.1) 

p-trend = 0.242 

Exposure frequency (no. working hr) 

1−5% (16/23) 0.8 (0.4, 1.4) 

>5% (15/4) 4.3 (1.4, 13.0) 

p-trend = 0.107 

Exposure intensity (ppm) 

≤0.5 (15/19) 0.9 (0.4, 1.7) 

>0.5 (16/8) 2.2 (0.9, 5.1) 

p-trend = 0.197 

Duration (years) 

≤10 (18/16) 1.2 (0.6, 2.4) 

>10 (13/11) 1.3 (0.6, 3.0) 

p-trend = 0.441 

Cumulative exposure score 

≤median (13/16) 0.9 (0.4, 1.8) 

>median (18/11) 1.8 (0.8, 3.9) 

p-trend = 0.246 

Would vary by 

individual participant 

since not industrybased study; however, 

inclusion of farm 

work and 

occupational 

exposure to solvents 

in the regression 

model did not affect 

the risk estimates 

Low exposure prevalence 

in study population, so 

small numbers of exposed 

cases and controls 

Lymphoma subtype 

analyses, in particular, 

limited by small numbers 

Participation rate only 

52% in population 

controls, but the positive 

association was observed 

across centers with 

different control types 
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Table J-2. New epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to 

which subjects were 

potentially exposed Limitations 

Two plants that 2,171 Exposure levels were up Lymphohematopoietic cancers: Fluorochlorocarbons, Still a youthful cohort 

produced (862 men, to 75 ppm in 1964 in methyl formate (mean age 56 years), with 

disposable medical 

equipment, Sweden 

Mikoczy et al. 

(2011) 

Same cohort as 

Hagmar et al. 

(1995); Hagmar et 

al. (1991), followed 

an additional 

16 years 

1,309 women) Plant B and up to 40 ppm 

in 1970 in Plant A. 

By 1985, levels had 

dropped to below 1 ppm. 

For the 2,020 cohort 

members for whom job 

titles were available, the 

median was 0.13 ppm 

× years; the 

75th percentile was 

0.22 ppm × years; and 

the 90th percentile was 

1.29 ppm × years. 

Mortality (results not shown): 

Nonsignificant increases of deaths from 

leukemia and lymphoma were reported; 

with a 15-yr induction period, these 

increases were lowered; with a 15-yr 

induction period and restriction to workers 

with cumulative exposure estimates above 

the median; nonsignificant increases in 

leukemia deaths were reported 

Incidence: 

Cancer (ICD-7) [cases] SIR (95% CI) 

All lymphohematopoietic 

(200−209) [18] 1.25 (0.74, 1.98) 

NHL (200+202) [9] 1.44 (0.66, 2.73) 

Leukemia (204−205) [5] 1.40 (0.45, 3.26) 

Internal analysis of lymphohematopoietic 

cancers: 

Cum exp gp 

ppm × years [cases] IIR (95% CI) 

0−0.13 [7] 1.00 

0.14−0.21 [5] 1.17 (0.36, 3.78) 

≥0.22 [5] 0.92 (0.28, 3.05) 

(1:1 mixture with 

EtO) 

small numbers of events 

for the study of the 

incidence and mortality of 

specific cancer types— 

203 total cancer cases 

(9.4%) and 171 total 

deaths (7.9%) 

Estimated cumulative 

exposures were generally 

low 

There was no unexposed 

referent group; internal 

analyses involved 

comparison of responses 

in the top quartiles of 

cumulative exposure to 

those in the lower 50% of 

cumulative exposures 
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Table J-2. New epidemiological studies of ethylene oxide and human cancer (continued) 

Population/ 

industry 

Number of 

subjects 

Extent of exposure to 

ethylene oxide Health outcomes 

Other chemicals to 

which subjects were 

potentially exposed Limitations 

(continued from previous page) 

Female breast cancer: 

mortality (results not shown): 

Slight but nonsignificant decrease in the 

SMR was reported. With a 15-yr 

induction period included, the SMR for 

breast cancer was “somewhat increased.” 

For workers with cumulative exposures 

above the median, with a 15-yr induction 

period, a “higher than expected” SMR, 

which was not statistically significant, was 

reported. 

Incidence: 

41 female breast cancer cases vs. 

50.9 expected (ICD−7 170); 

SIR = 0.81 (95% CI = 0.58, 1.09) 

Internal analysis: 

Cum exp gp 

ppm × yrs [cases] IIR (95% CI) 

0−0.13 [10] 1.00 

0.14−0.21 [14] 2.76 (1.20, 6.33) 

≥0.22 [17] 3.55 (1.58, 7.93) 
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J.3. REFERENCES 

Full citations for references cited elsewhere in the assessment are provided in the 

Reference section of the assessment; full citations for references appearing only in this appendix 

are provided below. 

Ahn, H; Shin, H. (2006) Determination of ethylene oxide-hemoglobin adduct by silylation and gas chromatographyelectron impact-mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 843(2): 202−208. 

Brown, T; Rushton, L; the British Occupational Cancer Burden Study Group. (2012) Occupational cancer in Britain 

Haematopoietic malignancies: leukaemia, multiple myeloma, non-Hodgkins lymphoma. Br J Cancer 107: S41−S48. 

Butterworth, BE; Chapman, JR. (2007) Exposure of hematopoietic stem cells to ethylene oxide during processing 

represents a potential carcinogenic risk for transplant recipients. Regul Toxicol Pharmacol 49(3): 149−153. 

Chan, C; Shie, R; Chang, T. (2006) Workers' exposures and potential health risks to air toxics in a petrochemical 

complex assessed by improved methodology. Int Arch Occup Environ Health 79(2): 135−142. 

Davis, FG; Erdal, S; Williams, L; Bigner, D. (2006) Work exposures to animal neurocarcinogens. Int J Occup 

Environ Health 12(1): 16−23. 

Farmer, PB; Singh, R. (2008) Use of DNA adducts to identify human health risk from exposure to hazardous 

environmental pollutants: the increasing role of mass spectrometry in assessing biologically effective doses of 

genotoxic carcinogens [Review]. Mutat Res 659(1-2): 68−76. 

Fondelli, MC; Costantini, AS; Ercolanelli, M; et al. (2007) Exposure to carcinogens and mortality in a cohort of 

restoration workers of water-damaged library materials following the River Arno flooding in Florence, 4 November 

1966. Med Lav 98(5): 422−431. 

Grosse, Y; Baan, R; Straif, K; et al. (2007) Carcinogenicity of 1,3-butadiene, ethylene oxide, vinyl chloride, vinyl 

fluoride, and vinyl bromide. Lancet Oncol 8(8): 679−680. 

Hoenerhoff, MJ; Hong, HH; Ton, TV; et al. (2009) A review of the molecular mechanisms of chemically induced 

neoplasia in rat and mouse models in National Toxicology Program bioassays and their relevance to human cancer 

[Review]. Toxicol Pathol 37(7): 835−848. 

Huang, CC; Wu, CF; Shih, WC; et al. (2011) Comparative analysis of urinary N7-(2-hydroxyethyl)guanine for 

ethylene oxide- and non-exposed workers. Toxicol Lett 202(3): 237−243. 

Jarabek, AM; Pottenger, LH; Andrews, LS; et al. (2009) Creating context for the use of DNA adduct data in cancer 

risk assessment: I. Data organization [Review]. Crit Rev Toxicol 39(8): 659−678. 

Kensler, TW; Ng, D; Carmella, SG; et al. (2012) Modulation of the metabolism of airborne pollutants by 

glucoraphanin-rich and sulforaphane-rich broccoli sprout beverages in Qidong, China. Carcinogenesis 33(1): 

101−107. 

Keshava, N; Jinot, J; Sonawane, B. (2006a) An evaluation of mutagenic mode of action for carcinogenicity: 

ethylene oxide. Environ Mol Mutagen 47(6): 442 [abstract]. 

Keshava, N; Woodall, GM; Rice, S; et al. (2006b) An evaluation of the mutagenic mode of action for four 

environmental carcinogens. Toxicol Sci 90(1−S). 

Kim, EA; Lee, HE; Ryu, HW; et al. (2011) Cases series of malignant lymphohematopoietic disorder in korean 

semiconductor industry. Saf Health Work 2(2): 122−134. 
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Kiran, S; Cocco, P; Mannetje, A; et al. (2010) Occupational exposure to ethylene oxide and risk of lymphoma. 

Epidemiology 21(6):905−910. 

Lin, JS; Chuang, KT; Huang, MS; Wei, KM. (2007) Emission of ethylene oxide during frying of foods in soybean 

oil. Food Chem Toxicol 45(4): 568−574. 

Lindberg, HK; Falck, GC; Catalán, J; et al. (2010) Micronucleus assay for mouse alveolar Type II and Clara cells. 

Environ Mol Mutagen 51(2): 164−172. 

Manservigi, M; Tibaldi, E; Soffritti, M. (2010) Toxic and carcinogenic effects of ethylene and its ethylene oxyde 

metabolite. Eur J Oncol 15(1): 5−23. 

Mazon, G; Philippin, G; Cadet, J; et al. (2009) The alkyltransferase-like ybaZ gene product enhances nucleotide 

excision repair of O(6)-alkylguanine adducts in E. coli. DNA Repair 8(6): 697−703. 

Mazon, G; Philippin, G; Cadet, J; et al. (2010) Alkyltransferase-like protein (eATL) prevents mismatch repairmediated toxicity induced by O6-alkylguanine adducts in Escherichia coli. PNAS 107(42): 18050−18055. 

McCarthy, MC; O'Brien, TE; Charrier, JG; Hafner, HR. (2009) Characterization of the chronic risk and hazard of 

hazardous air pollutants in the United States using ambient monitoring data. Environ Health Perspect 117(5): 

790−796. 

Mikoczy, Z; Tinnerberg, H; Björk, J; Albin, M. (2011) Cancer incidence and mortality in Swedish sterilant workers 

exposed to ethylene oxide: updated cohort study findings 1972−2006. Int J Environ Res Public Health 

8(6):2009−2019. 

Mosavi-Jarrahi, AM; Mohagheghi, MA; Kalaghchi, B; et al. (2009) Estimating the incidence of leukemia 

attributable to occupational exposure in Iran. Asian Pac J Cancer Prev 10(1): 67−70. 

Okada, Y; Nakagoshi, A; Tsurukawa, M; et al. (2012) Environmental risk assessment and concentration trend of 

atmospheric volatile organic compounds in Hyogo Prefecture, Japan. Environ Sci Pollut Res Int 19(1): 201−213. 

Parsons, BL; Manjanatha, MG; Myers, MB; et al. (2012) Induction of cII and K-Ras mutation in lung DNA of Big 

Blue mice exposed to ethylene oxide by inhalation. Environ Mol Mutagen 53: S62 [abstract]. 

Smith-Bindman, R. (2012) Environmental causes of breast cancer and radiation from medical imaging: findings 

from the Institute of Medicine report. Arch Intern Med 172(13): 1023−1027. 

Snedeker, SM. (2006) Chemical exposures in the workplace: effect on breast cancer risk among women [Review]. 

AAOHN 54(6): 270−279. 

Steenland, K; Seals, R; Klein, M; et al. (2011) Risk estimation with epidemiologic data when response attenuates at 

high-exposure levels. Environ Health Perspect 119(6): 831−837. 

Steinhausen, M; Van Gelder, R; Gabriel, S. (2012) Work-related exposure to carcinogenic, mutagenic and 

reprotoxic substances in Germany - Part 2: Substances with exposure-risk relationships according to BekGS 910. 

Gefahrstoffe - Reinhaltung der Luft (Air Quality Control) 72(9): 347−358. 

Swenberg, JA; Lu, K; Moeller, BC; et al. (2011) Endogenous versus exogenous DNA adducts: their role in 

carcinogenesis, epidemiology, and risk assessment [Review]. Toxicol Sci 120 Suppl 1: S130−S145. 

Tateo, F; Bononi, M. (2006) Determination of ethylene chlorohydrin as marker of spices fumigation with ethylene 

oxide. J Food Compos Anal 19(1): 83−87. 

Tompkins, EM; Jones, DJL; McLuckie, KIE; et al. (2006) Weak mutagenicity of DNA adducts derived from 

ethylene oxide exposure. Mutagenesis 21(4): 292 [abstract]. 
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Tretyakova, N; Goggin, M; Sangaraju, D; Janis, G. (2012) Quantitation of DNA adducts by stable Isotope dilution 

mass spectrometry. Chem Res Toxicol 25(10): 2007−2035. 

Valdez-Flores, C; Sielken, RL; Teta, MJ. (2011) Quantitative cancer risk assessment for ethylene oxide inhalation in 

occupational settings. Arch Toxicol 85(10): 1189−1193. 

van Balen, E; Font, R; Cavalle, N; et al. (2006) Exposure to non-arsenic pesticides is associated with lymphoma 

among farmers in Spain. Occup Environ Med 63(10): 663−668. 

Weiderpass, E; Meo, M; Vainio, H. (2011) Risk factors for breast cancer, including occupational exposures. Saf 

Health Work 2(1): 1−8. 

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53(6): 474−482. 

Wu, KY; Chiang, SY; Shih, WC; et al. (2011) The application of mass spectrometry in molecular dosimetry: 

ethylene oxide as an example. Mass Spectrom Rev 30(5):733−756. 

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APPENDIX K. 

DOCUMENTATION OF IMPLEMENTATIONS OF THE 

2011 NATIONAL RESEARCH COUNCIL RECOMMENDATIONS 

Background: On December 23, 2011, The Consolidated Appropriations Act, 2012, was 

signed into law.12 The report language included direction to EPA for the Integrated Risk 

Information System (IRIS) Program related to recommendations provided by the National 

Research Council (NRC) in its review of EPA’s draft IRIS assessment of formaldehyde.13 The 

report language included the following: 

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

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. 

The NRC’s recommendations, provided in Chapter 7 of the review report, offered 

suggestions to EPA for improving the development of IRIS assessments. 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 is provided in the tables below. 

Where necessary, the documentation includes an explanation for why certain recommendations 

were not incorporated. 

The IRIS Program’s implementation of the NRC recommendations is following a phased 

approach that is consistent with the NRC’s “Roadmap for Revision” as described in Chapter 7 of 

the formaldehyde review report. The NRC stated that, “the committee recognizes that the 

changes suggested would involve a multiyear process and extensive effort by the staff at the 

National Center for Environmental Assessment and input and review by the EPA Science 

Advisory Board and others.” 

The IRIS ethylene oxide carcinogenicity assessment is in Phase 1 of implementation, 

which focuses on a subset of the short-term recommendations, such as editing and streamlining 

 

12Pub. L. No. 112−74, Consolidated Appropriations Act, 2012. 13National Research Council, (2011). Review of the Environmental Protection Agency’s Draft IRIS Assessment of 

Formaldehyde. 
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documents, increasing transparency and clarity, and using more tables, figures, and appendices to 

present information and data in assessments. Phase 1 also focuses on assessments near the end 

of the development process and close to final posting. Chemical assessments in Phase 2 of the 

implementation will address all of the short-term recommendations from Table K-1. Chemical 

assessments in Phase 3 of implementation will incorporate the longer-term recommendations 

made by the NRC as outlined below in Table K-2. On May 16, 2012, EPA announced14 that as a 

part of a review of the IRIS Program’s assessment development process, the NRC will also 

review current methods for weight-of-evidence analyses and recommend approaches for 

weighing scientific evidence for chemical hazard identification. This effort is included in 

Phase 3 of EPA’s implementation plan. 

Table K-1. The EPA’s implementation of the National Research Council’s 

recommendations in the ethylene oxide (EtO) carcinogenicity assessment 

NRC recommendations that the EPA is 

implementing in the short term 

Implementation in the EtO carcinogenicity 

assessment 

General recommendations for completing the IRIS formaldehyde assessment that the EPA will adopt 

for all IRIS assessments (p. 152) 

1. To enhance the clarity of the document, the 

draft IRIS assessment needs rigorous editing to 

reduce the volume of text substantially and 

address redundancies and inconsistencies. 

Long descriptions of particular studies should 

be replaced with informative evidence tables. 

When study details are appropriate, they could 

be provided in appendices. 

Partially Implemented. EtO is a post-peer review, 

Phase 1 chemical; as such, implementation has 

focused on a subset of the short-term 

recommendations, such as editing and streamlining, 

increasing transparency and clarity, and using more 

tables, figures, and appendices to present information 

and data. For example, the main body of the EtO 

Assessment contains only abbreviated study 

summaries and study summary tables. The longer 

descriptions of the epidemiology studies and the 

genotoxicity studies are contained in appendices 

(Appendices A and C, respectively), along with a 

detailed summary table of the epidemiology studies 

in Appendix A. The main text of the hazard 

identification chapter (see Chapter 3) is 

comparatively streamlined. 

14EPA Announces NAS’ Review of IRIS Assessment Development Process (www.epa.gov/iris). 

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1 

Table K-1. The EPA’s implementation of the National Research Council’s 

recommendations in the ethylene oxide (EtO) carcinogenicity assessment 

(continued) 

NRC recommendations that the EPA is 

implementing in the short term 

Implementation in the EtO carcinogenicity 

assessment 

2. Chapter 1 needs to be expanded to describe 

more fully the methods of the assessment, 

including a description of search strategies used 

to identify studies with the exclusion and 

inclusion criteria articulated and a better 

description of the outcomes of the searches and 

clear descriptions of the weight-of-evidence 

approaches used for the various noncancer 

outcomes. The committee emphasizes that it is 

not recommending the addition of long 

descriptions of the EPA guidelines to the 

introduction, but rather clear concise statements 

of criteria used to exclude, include, and 

advance studies for derivation of the reference 

concentrations (RfCs) and unit risk estimates. 

Partially Implemented. The EPA’s literature search 

strategy is described briefly in Chapter 2 of the EtO 

Assessment. To update the Assessment, a systematic 

literature search was done covering the time span 

from 2006 (the year of the 1st external review draft) 

to May 2013; this search is described in detail in 

Appendix J. 

In addition, the text has been expanded to include 

more description of the considerations made in 

evaluating the epidemiology studies (p. 3-1) and in 

selecting the study that formed the basis for the 

quantitative cancer risk estimates (p. 4-1−4-3). 

3. Standardized evidence tables for all health 

outcomes need to be developed. If there were 

appropriates tables, long text descriptions of 

studies could be moved to an appendix or 

deleted. 

Partially Implemented. This assessment was 

largely finalized before the release of the NRC 

recommendations; thus, the tables herein may not be 

consistent with current standardizations. However, 

the EtO Assessment contains a detailed summary 

table of the epidemiology studies in Appendix A (see 

Table A-5) along with the longer text study 

descriptions. Less detailed tables of the results are 

presented in the main text (see Tables 3-1 and 3-2). 

4. All critical studies need to be thoroughly 

evaluated with standardized approaches that are 

clearly formulated and based on the type of 

research: for example, observational 

epidemiologic or animal bioassays. The 

findings of the reviews might be presented in 

tables to ensure transparency. 

Partially Implemented. All critical studies were 

thoroughly evaluated in Chapter 3 and Appendix A. 

As discussed above, the text has been expanded to 

include more description of the considerations made 

in evaluating the epidemiology studies (see p. 3-1), 

and the epidemiology studies are summarized in a 

detailed table in Appendix A (see Table A-5). 

Standardized approaches for evaluating studies are 

under development as part of Phases 2 and 3. 
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Table K-1. The EPA’s implementation of the National Research Council’s 

recommendations in the ethylene oxide (EtO) carcinogenicity assessment 

(continued) 

NRC recommendations that the EPA is 

implementing in the short term 

Implementation in the EtO carcinogenicity 

assessment 

5. The rationales for the selection of the studies Implemented. As discussed above, the text has been 

that are advanced for consideration in expanded to include more description of the 

calculating the RfCs and unit risks need to be considerations made in selecting the study that 

expanded. All candidate RfCs should be formed the basis for the quantitative cancer risk 

evaluated together with the aid of graphic estimates (see p. 4-1−4-3). The selection 

displays that incorporate selected information considerations are also summarized in a table (see 

on attributes relevant to the database. Table 4-1). The EtO Assessment is a carcinogenicity 

assessment; thus, no RfCs or reference doses (RfDs) 

are derived. 

6. Strengthened, more integrative and more Implemented. The weight-of-evidence discussion in 

transparent discussions of weight of evidence the EtO Assessment has been substantially enhanced 

are needed. The discussions would benefit (see Section 3.5.1), and two tables have been added 

from more rigorous and systematic coverage of addressing consistency in the epidemiology study 

the various determinants of weight of evidence, results (see Table 3-1 for lymphohematopoietic 

such as consistency. cancer and Table 3-2 for breast cancer). 

General Guidance for the Overall Process (see p. 164) 

7. Elaborate an overall, documented, and 

quality-controlled process for IRIS 

assessments. 

Partially Implemented. A team approach was used 

for the development of the EtO Assessment to help 

ensure that the necessary disciplinary expertise was 

available for assessment development and review. 

Because EtO is a post-peer review, phase one 

chemical, the EtO team did not have access to the 

“overall, documented, and quality-controlled 

process” that is now being developed in response to 

the NRC recommendations. 

8. Ensure standardization of review and 

evaluation approaches among contributors and 

teams of contributors; for example, include 

standard approaches for reviews of various 

types of studies to ensure uniformity. 

9. Assess disciplinary structure of teams needed 

to conduct the assessments. 

Evidence Identification: Literature Collection and Collation Phase (see p. 164) 

10. Select outcomes on the basis of available Implemented. The EtO Assessment has detailed 

evidence and understanding of mode of action. discussions of genotoxicity (see Section 3.3.3 and 

Appendix C) and mode of action (see Section 3.4), 

and EPA concludes that the weight of evidence 

supports a mutagenic mode of action for EtO 

carcinogenicity. The cancer outcomes selected are 

consistent with that mode-of-action finding as well as 

the available hazard evidence. 
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Table K-1. The EPA’s implementation of the National Research Council’s 

recommendations in the ethylene oxide (EtO) carcinogenicity assessment 

(continued) 

NRC recommendations that the EPA is 

implementing in the short term 

Implementation in the EtO carcinogenicity 

assessment 

11. Establish standard protocols for evidence 

identification. 

Partially Implemented. This is being implemented 

by the IRIS program as part of Phase 2. The EPA’s 

literature search strategy is described briefly in 

Chapter 2 of the EtO Assessment. More details of 

the original search are no longer available for this 

Assessment, which was largely finalized before the 

release of the NRC recommendations. To update the 

Assessment, a systematic literature search was done 

covering the time span from 2006 (the year of the 1st 

external review draft) to May 2013; this search is 

described in detail in Appendix J. 

12. Develop a template for description of the 

search approach. 

This is being implemented by the IRIS program as 

part of Phase 2. 

13. Use a database, such as the Health and Implemented. HERO links were incorporated for all 

Environmental Research Online (HERO) citations. 

database, to capture study information and 

relevant quantitative data. 

Evidence Evaluation: Hazard Identification and Dose-Response Modeling (see p. 165) 

14. Standardize the presentation of reviewed Partially Implemented. This Assessment was 

studies in tabular or graphic form to capture largely finalized before the release of the NRC 

the key dimensions of study characteristics, recommendations; thus, the tables herein may not be 

weight of evidence, and utility as a basis for consistent with current standardizations. The EtO 

deriving reference values and unit risks. Assessment does include a detailed summary table of 

key characteristics of the epidemiology studies (see 

Table A-5 of Appendix A) and a table summarizing 

the considerations made in selecting the study that 

formed the basis for the quantitative cancer risk 

estimates (see Table 4-1). 

15. Develop templates for evidence tables, forest 

plots, or other displays. 

This is being implemented by the IRIS program as 

part of Phase 2. 

16. Establish protocols for review of major types 

of studies, such as epidemiologic and 

bioassay. 

Partially Implemented. This is being implemented 

by the IRIS program as part of Phase 2. The study 

review process was not revised for this assessment 

because EtO is a Phase 1 chemical. However, this 

assessment was developed using standard protocols 

for evidence evaluation that are provided in existing 

EPA guidance. 
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Table K-1. The EPA’s implementation of the National Research Council’s 

recommendations in the ethylene oxide (EtO) carcinogenicity assessment 

(continued) 

NRC recommendations that the EPA is 

implementing in the short term 

Implementation in the EtO carcinogenicity 

assessment 

Selection of Studies for Derivation of Reference Values and Unit Risks (see p. 165) 

17. Establish clear guidelines for study selection. 

a. Balance strengths and weaknesses. 

b. Weigh human vs. experimental evidence. 

c. Determine whether combining estimates 

among studies is warranted. 

Partially Implemented. As discussed above, the 

text has been expanded to include more description of 

the considerations made in selecting the study that 

formed the basis for the quantitative cancer risk 

estimates (see p. 4-1−4-3). The selection 

considerations are also summarized in a table (see 

Table 4-1). Consideration was given to combining 

data from the Union Carbide Cohort (UCC) and 

NIOSH cohort studies, and discussion is provided for 

why the UCC data were ultimately not used (see 

Section 4.1). The EtO Assessment is a 

carcinogenicity assessment; thus, no RfCs or RfDs 

are derived. 

Calculation of Reference Values and Unit Risks (see pp. 165−166) 

18. Describe and justify assumptions and models Implemented. The EtO Assessment has a detailed 

used. This step includes review of dosimetry discussion of model selection for the epidemiological 

models and the implications of the models for data sets (see Section 4.1) and the laboratory animal 

uncertainty factors; determination of data sets (see Section 4.2), including a discussion of 

appropriate points of departure (such as cross-species scaling (see Section 4.2.2). The EtO 

benchmark dose, no-observed-adverse-effect Assessment is a carcinogenicity assessment; thus, no 

level, and lowest observed-adverse-effect reference values are derived. 

level), and assessment of the analyses that 

underlie the points of departure. 

19. Provide explanation of the risk-estimation Implemented. The EtO Assessment has a detailed 

modeling processes (for example, a statistical discussion of model selection for the epidemiological 

or biologic model fit to the data) that are used data sets (see Section 4.1) and the laboratory animal 

to develop a unit risk estimate. data sets (see Section 4.2). 
 

 

 

 

1 

2 

Table K-1. The EPA’s implementation of the National Research Council’s 

recommendations in the ethylene oxide (EtO) carcinogenicity assessment 

(continued) 

NRC recommendations that the EPA is 

implementing in the short term 

Implementation in the EtO carcinogenicity 

assessment 

20. Provide adequate documentation for Implemented. The EtO Assessment includes, as an 

conclusions and estimation of reference values appendix (see Appendix D), more detailed fit 

and unit risks. As noted by the committee statistics and modeling results for the epidemiological 

throughout the present report, sufficient cancer data sets. Appendix G provides results of the 

support for conclusions in the formaldehyde laboratory animal tumor modeling. The EtO 

draft IRIS assessment is often lacking. Given Assessment is a carcinogenicity assessment; thus, no 

that the development of specific IRIS reference values are derived. 

assessments and their conclusions are of 

interest to many stakeholders, it is important 

that they provide sufficient references and 

supporting documentation for their 

conclusions. Detailed appendixes, which 

might be made available only electronically, 

should be provided, when appropriate. 

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1 

2 

3 

 

 

 

 

 

 

 

 

4 

Table K-2. National Research Council recommendations that the EPA is generally 

implementing in the long term 

NRC recommendations that the EPA is implementing 

in the long term 

Implementation in the EtO Carcinogenicity 

Assessment 

Weight-of-Evidence Evaluation: Synthesis of Evidence 

for Hazard Identification (see p. 165) 

1. Review use of existing weight-of-evidence 

guidelines. 

2. Standardize approach to using weight-of-evidence 

guidelines. 

3. Conduct agency workshops on approaches to 

implementing weight-of-evidence guidelines. 

4. Develop uniform language to describe strength of 

evidence on noncancer effects. 

5. Expand and harmonize the approach for 

characterizing uncertainty and variability. 

6. To the extent possible, unify consideration of 

outcomes around common modes of action rather 

than considering multiple outcomes separately. 

As indicated above, Phase 3 of EPA’s 

implementation plan will incorporate the 

longer-term recommendations made by the 

NRC. On May 16, 2012, EPA announced that 

as a part of a review of the IRIS Program’s 

assessment development process, the NRC will 

also review current methods for weight-ofevidence analyses and recommend approaches 

for weighing scientific evidence for chemical 

hazard identification. In addition, EPA will 

hold a workshop on August 26, 2013, on issues 

related to weight of evidence to inform future 

assessments. 

Calculation of Reference Values and Unit Risks (see 

pp. 165−166) 

7. Assess the sensitivity of derived estimates to model 

assumptions and end points selected. This step 

should include appropriate tabular and graphic 

displays to illustrate the range of the estimates and 

the effect of uncertainty factors on the estimates. 

Implemented. The EtO Assessment is a 

carcinogenicity assessment; thus, no reference 

values are derived. Chapter 4 presents 

derivations of unit risk estimates for multiple 

data sets, species, and models. Many of these 

derivations are summarized in tables and 

figures; for example, for the breast cancer 

incidence subcohort, Figure 4-5 depicts the 

range of relative risk estimates for different 

exposure-response models considered and 

Table 4-13 summarizes unit risk estimates 

derived from different models. 

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