EPA/6 35/R-13/128b www.epa.gov/iris



## Evaluation of the Inhalation Carcinogenicity of Ethylene Oxide

APPENDICES

(CASRN 75

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- 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, DCDISCLAIMER

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.

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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. CONCLUSION S..................................................................................................... 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. Ba cterial 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. B REAST 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. HE MATOPOIETIC CANCER MORTALITY (ALL HEMATOPOIETIC CANCERS COMBINED) ...................................................................................... D-52 D.5. S UMMARY TABLE OF EC 01 S FOR DIFFERENT OUTCOMES, USING 2

­ PIECE SPLINE MODELS ..................................................................................... D-63 D.6. S ENSITIVITY 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. P OSSIBLE INFLUENCE OF EXPOSURE MIS

- MEASUREMENT ................... D-66 D.9. R EFERENCES ....................................................................................................... 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 O F 2007 EXTERNAL PEER REVIEW AND PUBLIC COMMENTS AND DISPOSITION ................................................................................. H-1

A PPENDIX I. LIST OF REFERENCES ADDED AFTER THE 2006 EXTERNAL REVIEW DRAFT ................................................................................................................I-1

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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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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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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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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 coefficient s for linear RR models, hematopoietic cancer mortality ..................................................................................... D-61

D-5a. Summary of EC 01 results (in ppm) in current analysis and previous EPA risk
assessment ..................................................................................................................... D-64

D-6a. Exposure-response c oefficients and EC 01 s 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 LEC 01 for lymphoid cancer incidence) using the weighted linear regression model based on the categorical cumulative exposure results of Steenland et al. (2004), reanalyzed by Stee nland f or 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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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

- lin ear 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 r estricted 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 dose

response 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 relati onship 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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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 categoric al 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-17LIST OF ABBREVIATION S

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 ma ximum likelihood estimate NCEA National Center for Environmental Assessment NHL non-Hodgkin lymphoma NIOSH National Institute for Occupational Safety and Health NTP National Toxicology Program O 6

- HEG O 6 -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 EVIDENC E

[EDITORIAL NOTE : P lease note that in this assessment document the responses to the 2007 external peer review and public co mments 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 (Ehrenbe rg 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 th ird 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 stud ies 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 Stee nland 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 2

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35 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., 1 995 ; 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 a l., 2010 ; Swaen et al.,
2009 ; Teta et al., 1999 ; Benson and Teta, 1993 ; Teta et al., 1993 ; Greenb erg 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., 19 91). These studie s have sufficient follow- up to analyze latent effects, and the cohorts appear to be large enough to test for small differences. In addition,

ex posure 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 occup ational 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 d eaths due to stomach cancer occurred versus only

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34 1.8 expected (SMR = 5.46). The results tend to agree with those from clastogenic and short- te rm 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 indu stry 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
cal culated 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 ).

I n 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

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1 bis- (2

- chloroethyl) ether, as well as other chemicals in the r espective plants. Although
170 women were present in the workforce, no gender differences in risk were analyzed separately by t he 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 mortali ty. 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
P lants 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 E tO 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 ris k 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 2
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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 th e 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 w oman 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 d ate. “Slight excesses” of deaths due to esophageal cancer (5 observed vs. 2.2 expected), lung c ancer (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 ye ars 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 hem atopoietic 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 2
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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 ex posures 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 incon sistent with
those of Hogstedt (1988) and Hogstedt et al. (1986). The possibility of a confounding e ffect
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 h ave 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 Rep ublic 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

co uld 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 late ncy 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 s hows only a 2
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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 c ategories 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 diffe rent 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 cutof f 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 s tudied 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 th us 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) 2
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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

. Th e healthy worker survivor effect bias c an 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 presumab ly 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 dea ths 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 ex pected, 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 surv ey 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 befor e 1976 (e.g., when EtO was in production), the authors developed a qualitative exposure categorization scheme with three cat egories of exposure (low, intermediate, and high) on the basis of the potential for exposure in each department . The number of work ers 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 p rior 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 2
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1 chl orohydrin department, were listed by the authors as having only low potential exposure to EtO. In contrast, among w orkers ever assigned to a department in the high exposure category, no leukemia deaths and only one pancreatic cancer death occurred.
The au thors 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 consi dered 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 a ll
salaried worker s, 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 2
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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 c ontrols 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 exposur e 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 enhanc ed 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, bas ed 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
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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 a ssignments; however, the results of these analyses are not fully presente d). According to Benson and Teta (1993), 112 of the
278 excluded wo rkers 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 t hey 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 dem onstrated no increased risk of overall cancer, or of leukemia, NHL, or cancers of the brain, pancrea s, 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 poten tial 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 2
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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 n ausea, dizziness, and vomiting were documented in the medical department in 1949. These acute ef fects occur in humans at exposures of several hundred ppm, according t o the authors. During the time periods under investigation, the esti mated 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 ). H owever, even when the potential confounding influence of the chlorohydrin process is removed, there remains the suggestion of a trend of an increasing r isk of leukemia and aleukemia with increasing duration of exposure to EtO in the remain ing 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 worker s. They also noted that they did not observe any significant trend of increa sing 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 analys e s w ere 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 d ata ( 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 mo dels. This analysis is superseded by the more 2
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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, therefor e, 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 e t al. (1990) , significant elevated risks of pancreatic
cancer and leukemia a nd 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 lympho hematopoietic 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 e t 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 o ut 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 pancreat ic cancer victims prior to diagnosis. However, this conclusion is disputed by Olsen et al. (1997) whose analy sis is
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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 statu s, 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 b rief, a regression model was developed, allowing the estimation of exposure levels for time periods, facilities, and operations for which industrial hygiene data were unavailable. The data for the model consisted of 2,700 individual time-weighted exposure values for workers’ personal breathing zones, acquired from 18 facilities between 1976 and 1985. The se 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 a rithmetic 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 expos ure 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 in dustry 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 hygie ne experts. Using the test data
as the standard , t he model estimates showed less bias (avera ge difference) than 9 of the
11 industrial hygienists and more precision (standard deviation of the differences) than all 11. Similarly, t he model outperformed the panel in terms of both bias and precision when the panel results were averaged

. Average e xposure level s, including early historical exposure level s, for the exposure categorie s 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 valu e 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 2
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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 subs tantial. 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 e xpected. Thus, in this highest exposure category, a statistical ly 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 th at 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 2
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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 expecte d 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 i n 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
dea ths 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 lymphopoiet ic 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. 2
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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 9 th 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 8 th rev ision. 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 a nd 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
license s 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 w as used as the reference group from which comparison death rates were obtained. 2
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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 w ithin 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 y oung 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 e t 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 em ployment 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. 2
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1 Because of a short follow-up period and the relative young ag e 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 a t 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 measuremen t 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 moni toring measurements were 2 to 3 times higher. The authors point ed 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 inductio n 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 e nough to evaluate morbidity. The strength of this study is the development of the cumulative exposure index as well as the 2
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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 th at 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 selecte d 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 expect ed), 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 c onsequence 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
detec t 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. 2
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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 matc hing 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. Verificatio n 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-contro l 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 emplo yees 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 act ive employment. However, given that there 2
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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 g roup.

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 suggest ed 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 report ed 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 to tal 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 can cer and lymphopoietic and 2
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31 hematopoietic cancer in ethylene chlorohydrin and propylene chlorohydrin workers. They
be lieved 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 pan creatic 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

g avage 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 t he 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 hist ories, chiefly because the exposure levels after the mid -1980s were very low (s ee 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 earlie r 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.)

1 Valdez

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l eukemia) (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

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– 10.37)
13,500+ 3.42 (1.09

– 10.73)

Source : Steenland et al. (2004 )

T able 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)

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– 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 ).

T he 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. Stee nland 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 ma les 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. Th e 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 w as 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 breas t 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). 2
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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

c ollected —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 i nto 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 earlie r 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 L ucas 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 calculat ed. Among the 11 deaths were 3 breast cancer deaths and 1 lymphoid leukemia death. The authors claim that their results confi rm “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 2
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increased risk of breast cancer 2

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A.2.18. Tompa et al. (1999) The authors reported a cluster of eight breast cancer cases and eight other malignant
tumor cases that develo ped 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 ga s sterilizer equipment. The authors report that EtO concentrations

we re in the neighborhood of 5 to 150 mg/m 3 . 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 t his 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
establis hed, the authors found that the risk of lymphatic and hematopoie tic 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 2
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2 Hungarian age

- standardized female cancer mortality rates reported by the International Agency for Research on Cancer ( http://eu

- cancer.iarc.fr/country

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- 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. This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 A-27 DRAFT

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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
Gr eat 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 work ers 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)
, identif ying 167 additional

wo rkers , and conducted mortality follow-up of the resulting c ohort 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 t he 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 th e 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 2
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Table A -4. Exposure assessment matrix from Swaen et al. (2009)—8- hour TWA exposures in ppm

Time p eriod Exposure potential category

Low (most EtO user departments) Medium (some EtO user

departments) High (EtO production

departments)

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– 1988 0.3 0.65 1

S ource : 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 es timate 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 fromthe 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 cumulat ive 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 e t 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, t he 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 lymphohematopoie tic cancer deaths (including 12 leukemias and 11 NHLs) were observed in the cohort. Internal Cox proportional hazards modeling was also done for some disease categ ories (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 re gression model. Year of hire wa s 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 a verage 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 2
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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 h ave developed the disease earlier. Meanwhile, having a n older cohort means that the background rate s 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 th e follow -up was extended well beyond the period of significant EtO exposures (exposure levels were considered very low by the mid -1980s).
Sw aen 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 y ears 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 becaus e 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 toget her

. 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 ca ncers, 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, 2
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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 refl ect 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

exp o sure -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 = e β×exposure ] for the lymphoid cancers from Steenland et al. (2004)]. Valdez -Flores et al. (2010) objected to the log c umulative 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 c umulative 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, n one o f 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 resu lts 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 2
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3 This pattern of findings from the NIOSH cohort data for males (i.e., statistically significant fits with log cumulative exposure but not wi th 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). This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 A-32 DRAFT

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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 increase s 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

expo sure 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

- Flore s 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 LEC 01 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 e xposure 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 observ able r ange of

responses (i.e., a response level that might reasonably be observed to have statistical significance 2
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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. T he
estimates also differ because V aldez -Flores et al. (2010) truncated their life

- table ana lysis at
70 years, wh ile EPA uses a cutoff of 85 years. A f urther reason for difference s 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 the se estimates were inappropriate. Sielken and Valdez-Flores (2009) suggest that, except a t 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. Moreo ver, 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 mortalit y 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 e xposure- 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). E PA 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 an alyses, 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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1 Valdez -Flores (2009) about using life- table analyses to derive incidence esti mates 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
i mpact 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 esti mate 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 inti mated 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 Sielk en 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

- than

lifetime 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 lympho hematopoietic syste m 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 epid emiological database. The main
effects and limitations in the epidemiological studies of EtO are presented in Table A-5. 2
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― D O NOT CITE OR QUOTEThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-36DRAFT―DO NOT CITE OR QUOTE Table A -5. Epidemiological studies of ethylene oxide and human cancer

Population/

I ndustry 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, be nzene, 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
Exposur e classification scheme vague, making it difficult to develop dose

response gradient

No exposure measurements prior to 1977, so individual exposure estimates were not made

Mixed exposure to several other chemicalsThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-37DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 lympho hematopoietic cancers vs. 12.9 expected (ICD

- 9 200

- 208)

11 breast cancers vs. 13.1 expected Ibid. Ibid. and, in addition, no latency evaluationThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-38DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 mortal ity

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

measur ements

No actual measurement data are given; dose

response analysis is not possibleThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-39DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 expect ed; 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 arom atic 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

c hlorohydrin, 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,

s ome of which may be carcinogenic

Lack of quantitative estimates of individual exposure levelsThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-40DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 chlorohydrin

exposed 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 chlorohydrin

exposed 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 cohortThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-41DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 lympho hematopoietic cancers vs. 30.4 expected

No statistically significant increases were observed for any cancer types

No statistically significant trends were observed for lympho id or leukemia cancer categories examined using Cox proportional hazards modelin g Same Same

Crude exposure assessment, especially for the early time periods

Small cohort; thus, small numbers of specific cancers even though long follow

- up timeThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-42DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 expe cted ( 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 lympho hematopoietic

cancer ; significant trend with increasing latency ( p < 0.03)

Signi ficantly increasing lympho hematopoietic 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 workersThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-43DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 i dentifiable 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 exposure

response data such as in Stayner et al. (1993 ) on the basis of individual cumulative exposure data but only on duration of employmentThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-44DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 20 8) : 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 fo r 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

- 20 8) ; 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 inter nal 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 lympho hematopoietic cancer risk with increase in exposure in womenThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-45DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 interna l nested case

control 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 potentia l nonresponse biases in the subcohort with interviews.

Exposure

- response trends not strictly monotonically increasingThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-46DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 lympho hematopoietic 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 lympho hematopoietic cancer deaths vs. 0.7 expected in group licensed to handle only EtO Toxic gases, dimethyl sulphat e, methylene chloride, carbon d isulphide, phosgene, chlorine, alk alic cyanides, s ulfur 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 indicateThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-47DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 case s vs. 1.25 expected (ICD -7 200+202)

2 leuke mia c ase s 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 for mate (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 laten cy analysisThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-48DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 confi rmed) and 200 controls; all male Cumulative exposure to EtO in cases was 500.2 ppm

- months vs. 60.2 ppm

months in controls 3 cases indicated exp osure 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 hypothesis

generating study; the authors were not looking for EtO exposure alone but for other chemical exposures as well to expla in 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 2 5- yr latency
, dichloride, chlorohydrin chemical s 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 pancreat ic cancer and lymphopoietic and hematopoietic cancersThis document is a draft for review purposes only and does not constitute Agency policy.7/2013A-49DRAFT―DO NOT CITE OR QUOTE Table A

- 5 . Epidemiological studies of ethylene oxide and human cancer (continued)

Population/

I ndustry 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 identif iable 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 m ales only, using log c umulative 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 q uar tile wa s stati stically 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 5

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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 significa nt 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 sign ificant 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. Estimate s 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 Joyn er (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 as sumed to be greater than for the later time periods, but the exposur e 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. The se authors used the Cox pr oportional 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 2
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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 lymphoh em a topoietic cancers and for lymphoid cancers in the NIOSH male workers, consistent with results reported by Steenland et al. (2004) . Statistically significan t 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 ex posed 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

a ppeared.
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 ob serve 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 sever al other studies (Coggon et al., 2004 ; Olsen et al., 1997 ; Swa en 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 in fluences, 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 y outhful or had not been followed for a sufficient number
of years to observe any effects from exposure to EtO. In the stu dy by Olsen et al. (1997), although a slight increase in the risk of cancer of the lymphopoietic and hematopoi etic 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 2
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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

res ult 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 s tudies, except that Benson and Teta (1993) suggested that exposure to thi s 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 i n 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 not ed 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
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1 The most compelli ng 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

cu mulative 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 r egression coefficient with log c umulative 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 al so 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 inter nal (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 studie s. The earlier significant risk of leukemia seen in the Hogstedt 2
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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 i n 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 th is time. For example, there was a lack of dose -response relationship in females, alt hough, 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 lymphohema topoietic 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 increase s the risk of breast cancer, based chiefly on the NIOSH studies [Steenland et al. (2004 ); Steenland et al. (2003)] discussed earlier
, with some corroborating s upport from the Norman et al. (1995) and Kardos et al. (2003) stud ies 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 ); St eenland 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.
T he 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 2
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― DO NOT CITE OR QUOTE1 ascertainment.” While these are not decisive limitations —exposure- response relationships are often not strictly monotonically increasing a cross finely dissected exposure categories, and the consistency of results between the full cohort (less nonresponse bias) and the subcohort with interviews (full case ascertainment) allevi ates some of the concerns about those potential
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— the evidence for a causal association between breast cancer and EtO exposure is less
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1 APPENDIX B. REFERENCES FOR FIGUR E 3-3

The references in this list correspond to the additional data that w ere 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 e xposure 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 hum an diploid fibroblast s. Environ M ol 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.

Rib eiro, 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 but adiene. 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 – F undam 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 Mu tagen 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; e t al. ( 1997 ) In vivo mutagenicity of ethylene oxide at the hprt locus in T

lymphocytes of B6C3F1 lacI transgenic mice following inhalation exposure. Mutat Res 392(3):211

– 222. 2

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― DO NOT CITE OR QUOTE1 2 Walker, VE; Wu, KY; Upton, PB; et al. ( 2000 ) Biomarkers of exposure and effect as indicators of potential carcinogeni c risk arising from in vivo metabolism of ethylene to ethylene oxide. Carcinogenesis 21(9):1661

– 1669.

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34 APPENDIX C. GENOTOXICITY AND MUTAGENICITY OF ETHYLENE OXIDE

A summary of the available genotoxicity and mutagenicity data for ethylene oxide (EtO) is pres ented 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 a re 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 an d 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 ( Wate rs 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 a gent, 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 a nd 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 SCE s 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 toThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-2 DRAFT

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34 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 s tudies 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 c hemical (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 a s S N 1- type or S N 2- type depending on the substitution nucleophilicity (S N ). The S N 1- type chemicals follow first

- order kinetics (e.g., ethylnitrosourea [ENU] and methylnitrosourea or [MNU]), while the S N 2- type agents exhibit an intermediate transition state (e.g., EtO and methyl methanesulfonate [MMS]).
EtO is a direct

- acting S N 2 (substitution-nucleophilic- bimolecular)

- type alkylating agent that forms adducts with cellular macromolecules such as proteins (e.g., hemoglobin) and DNA. The reactivity of an alk ylating 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 O 6 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 O 6 of guanine, N 1 , N 3 , and N 6 of adenine, and N 3 of cytosine, uracil and thymine are found in some instances (Segerbäck, 1994).5

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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),

32 P- postlabeling and electrochemical detection, with varying sensitivities for de tection of
EtO

- DNA adducts (Marsden et al., 2009 ; Huang e t 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., 19 95 ; 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 a gainst the imidazole ring of
N7 -alkyldeoxyguanosine, with the limits of detection being 5–10, 1–2, and 20 adducts per
10 6 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 10 6 nucleotides (van Delft et al., 1994). Kumar et al. (1995) developed a

32 P-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 e poxides as well as the endogenously formed 7- alkylguanine adducts. Bolt et al. (1997) develo ped 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, t he 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/10 8 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/10 8 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 2
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35 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/10 8 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), O 6

- hydroxyethyl -2’-deoxyguanosine (O 6

- HEG), N6

- hydroxyethyl

2’-deoxy adenosine (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
Veulem ans, 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 14 C- labeled EtO reacted in vitro with calf thymus DNA to produce N7-HEG adduct as the predominant adduct, with relatively low
amounts of O 6

- HEG and N3

- (2 -hydroxyethyl)adenine (N3-HEA) adducts. The levels of
N3

- HEA and O 6 -HEG are 4.4% and 0.5%, respectively, of the N7-HEG levels. Thus, the ratio
of N7

- HEG, N3

- HEA and O 6 -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 O 6

- HEG, which was 63% of the N7-HEG formed. The difference in reactivity towards the N7 and O 6 positions in guanine by these two alkylating agents was explai ned 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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― DO NOT CITE OR QUOTE 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 n ot detect the O 6

- 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 measur able using their LC-MS/MS analytical method, only the N7-H EG adduct was detectable at E tO 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
O 6 -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 N 6

- HEA adduct were reported. (N3

- HEA , N3

- HEC, and N3

- HET ad d ucts are not measurable by their method.)

C.1.3. In Vivo Studies

― Animal Experiments
Sever al studie s 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 14 C- 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 conse cutive weeks (6 h ours/day, 5 days/w ee k) 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

5 The minor adducts may have been present at levels below the limits of detection, which were as follows: 0.001/10 6 nu cleotides for N7

- HEG and N1

- HEA; 0.016/10 6 nucleotides for O 6

- HEG; and 0.082/10 6 nucleotides for N3

- HEU ( Tompkins et al., 2009 ).This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-6 DRAFT

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- HEG formed by EtO confirms th e 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 relationship s. Using fluorescence -coupled HPLC, Walker et al. (1992a) measured N7

- HEG levels in DNA of target and nontarget tissues from male B6C3F 1 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 f or 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 fro m 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 add ucts 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 O 6

- 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 B6C3F 1 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. Rats5

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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 l ower 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/10 8 nucleot ides, respectively, while the mea n level in control rats was 2.6 adducts/10 8 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. (2 003) 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/10 6 nucleotides) over the entire dose range , as detected by

32 P-postlabeling assay. The N7- HEG adducts were undetectable in controls (i.e., below the
detection limit of 1 adduct/10 8 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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34 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 gly cosylase 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 t he 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 O 6

- 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 O 6

- 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 ( 14 C) N7 -HEG adducts (0.002 to 4 adducts/10 8 nucleotides) in spleen, liver and stomach DNA of F344 rats after exposure to low, occupationally relevant concentrations of ( 14 C)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 t he 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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dose were normalized to the TD 50 dose (the dose which results in 50% tumor incidence in a

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- year study). The calculated adduct level in mice associated with the hepatocellular TD 50 was 812 N7-HEG adducts/10 8 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 biomar kers 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/10 6 nucleotides, respectively, in EtO-exposed
individual s ( 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/10 7 nucleotides, respectively, with considerable interindividual variation (range : 1.6–241.3 adducts/10 7 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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C.1.5. DNA Adducts

― Summary In summary, EtO predominantly forms N7-HEG adducts. Minor adducts are O 6

- HEG adducts and reaction products with N 1, N 3 and N 6 of adenine and with N 3 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 O 6 -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 depurina tion 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 e xcision 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 exposu re, species, and tissue, which may be due to differences in formation,

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34 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 y ielded 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 in IARC,
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 a l., 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
B6C3F 1 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 observe d 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 S accharomyces
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.1
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strains. Ko lman (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 fiv e 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 ma ximum
forward -mutation frequency was between 10 and 100 ad-3 mutations per 10 6 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 ; Pres ton, 1999 ; Natarajan et al., 1995 ; Vogel and Natarajan, 1995 ; IARC, 1994b ; Dellarco et al., 1990). Only select in vitro studies of human cells will b e reviewed here. For reviews of other in vitro studies using mammalian cell cultures, see the aforementioned references.

C.2.2.1. In V itro S tudies 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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34 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/min ute 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, t he 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 persis ted 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 con ducted : 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
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34 mutation frequency. In Study 2, N1- HEA and O 6

- HEG adducts were also quantifiable, but at
lower levels than the N7-HEG adduct , an d 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- der ived adducts, t his 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 o f 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 interpret ation 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 th e 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 differ ent 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 ack nowledged 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 muta genic range is needed” (Tompkins et al., 2009).
Mo reover, 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.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-15 DRAFT

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34 C .2.2.2. In Vivo Studies

― Experimental Animals The results of in vivo studies on the mutageni city 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 inh alation 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 mutation s 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 B6C3F 1 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 o r 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 B6C3F 1 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 h ours, 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 exposur e, 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, aThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-16 DRAFT

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34 greater mutagenic efficiency (mutations per unit dose) was found at higher concentrations than at
lower concentrati ons 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 pre existing 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 B6C3F 1 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 w as significantly in creased 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 inhal ation 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 o r 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 a ssay. 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 causedThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-17 DRAFT

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34 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 -d ependent increase in mutant frequency.
In a study by Recio et al. (2004)
, male Big Blue ( LacI transgenic) B6C3F 1 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 ma rrow 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. Furthe rmore, 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 B6C3F 1 mice exposed to EtO. Hong e t 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 wasThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-18 DRAFT

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34 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-ind uced 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 a nd 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 lev els 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,

concur rent 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 mutati ons 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 mic e (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 asThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-19 DRAFT

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34 those that may occur in the workplace, may present a gre ater 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 s perm 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 inhalat ion 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 consecutiv e 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 ( Ta tes 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) pri or 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, chromoso mal 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 causalThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-20 DRAFT

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34 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/m 3 (2.7 ppm) in Budapest and 10 mg/m 3 (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 mutation s 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 chromo somal 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 severa l 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 aberr ations was observed from the chronic or acute exposures. In another study, by Kligerman et al. (1983) , no increase in chromosomalThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-21 DRAFT

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34 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 mic e, however, showed clear, statistically significant increases in chromosomal aberrations with longer durations of exposure (≥ 12 weeks).
Male B6C3F 1 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 wee ks 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 expos ure 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 lev els 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 a dministered 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).This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-22 DRAFT

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34 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 chromosom al 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. Majo r 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 an d 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 increa ses in chromosomal aberrations and SCEs in 66 Hungarian hospital nurses exposed to sterilizing gasesThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-23 DRAFT

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34 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 FORMATI ON 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 asso ciation 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 ; Tate s 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. Follo wing c hronic 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 sign ificant 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.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-24 DRAFT

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34 The frequency of micronuclei in peripheral blood cells was increased in workers exposed to relatively high (3.7–60.4 mg/m 3 ) 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/m 3 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
micron ucleated 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/m 3 ) 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 e t 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.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-25 DRAFT

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34 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 car ried 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
in creased 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 regime s (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. S tatistically s ignificant 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 inThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 C-26 DRAFT

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34 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 sign ificant increases in SCEs in the lymphocytes of the exposed workers.
Garr y 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 exac t 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 cumulat ive 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 stud ies 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 r anging 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 f actors, including smoking. A similar5

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34 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 stu dies 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 ob served to persist after exposure has ceased. The results of studies of individual workers exposed to very low levels (<0.9 mg/m 3 ) of
EtO have been mixed.

C.6. OTHER ENDPOINTS (GEN ETIC POLYMORPHISM, S USCEPTIBILITY)

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 c omet tail length and micronucleus frequencies in binucleated cells (MNBC). A dose-dependent increase in tail length (indicating DNA strand breaks) wa s observed in exposed individuals compared to controls. No change in MNBC was observed. None of the epoxide hydrolase or glutathione- S

transferase 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 hOGG1 326 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 XRCC3 241 Met allele. For the latter effect, there was a significant interaction between the XRCC3 241 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

- tr ansferase 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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34 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 incre ases in DNA single -strand breaks in peripheral blood mononuclear cells obtained from individuals exposed to EtO concentrations of 0.1

− 0.49 mg/m 3

, 0.5

− 2.0 mg/m 3 , and >2 mg/m 3 , respectively. Fuchs et al. (1994) further no ted 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 cervi cal 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 E TO 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) is1
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26 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 a l., 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 O 6

- 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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Spec ies Tissue Detection method Adduct levels reported Adducts/10 7 nucleotides * Reference

Human Lymphocytes GC/MS 8.5 pmol/mg DNA 28.05 ( Föst et al., 1989 )

Human WBC Immuno

- slotblot 0.34 adducts/10 6 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 a dducts per 10 7 nucleotides 2.0

– 19 ( Wu et al., 1999b )

Human WBC 32 P/TLC/HPLC 0.6 adducts/10 7 nucleotides 0.6 ( Zhao et al., 1999 )

Human WBC 32 P/TLC/HPLC 2.9 adducts/10 7 nucleotides 2.9 ( Zhao et al., 1999 )

Human Lung 32 P/TLC/HPLC 4.0 adducts/10 7 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 32 P/GC/MS 0.4 to 1.1 adducts/10 7 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 32 P/TLC/HPLC 0.6 to 0.9 adducts/10 7 nucleotides 0.6

– 0.9 ( Zhao et al., 1999 )

Rat Liver GC/MS 2.6 adducts/10 8 nucleotides 0.26 ( van Sittert et al., 2000 )

Rat Control tissues LC

- MS/MS 1.1

– 3.5 adducts/10 8 nucleotides 0.11

– 0.35 ( Marsden et al., 2007 )

Rat Liver HPLC/ESI TMS 8 adducts/10 8 normal nucleotides 0.8 ( Tompkins et al., 2008 )

Rat Spleen HPLC/LC

- MS/MS 0.08 adducts/10 10 nucleotides 0.00008 ( Marsden et al., 2009 )This document is a draft for review purposes only and does not constitute Agency policy.7/2013C-31DRAFT―DO NOT CITE OR QUOTE 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/10 7 normal nucleotides. GC/MS, gas chromatography mass spectrometry; HPLC, high performance liquid chromatography; 32 P, 32 P- postlabeling assay; TLC, thin

- layer chromatography; LC

- MS, liquid chromatography mass spectrometry; ESI TMS, electrospray ionization tandem mass spectrometry; GC/EC/NCI

- HRMS, gas chr omatography/electron capture/negative chemical ionization high

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lowest LOAEL from a n 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 observ e 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 genotoxicit y/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 m 3 /hour/kg. For a 6

- hour exposure, this results in an alveolar inhalation of 0.19 m 3 /kg. 10 ppm EtO is equivalent to 18.3 mg/m 3 , 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 t he 6

- hour exposure to 10 ppm EtO would approximate an absorbed daily dose of 1.7 mg/kg.1
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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

 lym phoid 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 m ay 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, a s 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).

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34 APPENDIX D. REANALYSES AND INTERPRETATION O F ETHYLENE OXIDE EXPOSURE

- RESPONSE DATA

Kyle Steenland May 27, 2010

(E DITORIAL 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 (excep t 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 ad dition, 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 re analyses 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 res ults 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 necessar y 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 includedThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-2 DRAFT

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34 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
smooth ly 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 = β 0 + β 1 × cumexp + β 2 × (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 β 1 + β 2 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 v isual 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 lo w 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.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-3 DRAFT

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- piece l inear 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 f rom 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 case

control risk set (which includes the case) (the denominator) and has the form

L ( β) = φ case (Z;β)/ Σ j cases and controls φ j (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 (z β)

.

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.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-4 DRAFT

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34 D.1. BREAST CANCER INCIDENCE BASED ON THE DAT A 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 25 th percentile was 2.8 years and the 75 th percentile was 17.6 years. Multiplying e xposure 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 25 th percentile is 926 ppm-days,
while the 75 th 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 analyse s 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 a t 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, the1
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20 21 22 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 categori zation 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 th e 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 easi ly 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 equ ally 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.

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23 T able 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

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– 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.1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
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37 T able D -1b. Categorical analysis of breast cancer incidence by deciles (log RR m odel)

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)

W e 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 earl ier 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

F igure s 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%.

7 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. This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-7 DRAFT

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- piece log

- linear curve in Figure D -1b approximates the shape of theexposure-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 m odel [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. N onetheless, 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- line ar 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 t he logarithm of exposure (Figure D-1e), which we have not u sed 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

- li near 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 (see1
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5 6 7 8 below), with the exception of the log RR model with a l inear term for cumulative exposure,
which is below other excess risk estimates.

F igure 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. 7/2013 D-9 DRAFT

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0. 9 1. 0 1. 1 1. 2 1. 3 1. 4 1. 5 1. 6 1. 7 1. 8 1. 9 2. 0 2. 1 2. 2 2. 3

0 10000 20000 30000 40000

CUMEXP15
1 2 3 4 5 6 7 8 9 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 de ciles (log RR categorical model) are also presented in the plot. Deciles formed by allocating c ases 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

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CUMEXP15
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10 11 12 13 F igure 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

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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. 7/2013 D-12 DRAFT

― DO NOT CITE OR QUOTE CUMEXP15 -2 l og l i kel i hood i s 1944. 153 Cat egor i cal anal yses over l ayed

RR

0. 9 1. 0 1. 1 1. 2 1. 3 1. 4 1. 5 1. 6 1. 7 1. 8 1. 9 2. 0 2. 1 2. 2 2. 3

0 5000 10000 15000 20000 25000 30000 35000 40000

1 2 3 4 5 6 7 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). Decil es formed by allocating cases approximately equally in ten groups, above lagged

- out cases.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-13 DRAFT

― DO NOT CITE OR QUOTE -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 1. 1 1. 2 1. 3 1. 4 1. 5 1. 6 1. 7 1. 8 1. 9 2. 0 2. 1 2. 2 2. 3

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CUMEXP15
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23 Figure D -1f. Breast cancer incidence. Plot of a square-root transformation log RR dose–response model overlaid with a dose- respo nse 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 -pi ece log

linear, 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 vari ous 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 t he 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 parame tric models, the log transform and square root log RR models are supralinear in the low-exposure region.1
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10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 The effects of these departures from linearity in the low -exposure region can be seen in the
risk assessment results for the EC 01 (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 EC 01 s. 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
EC 01 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 regression 8

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 ( β 1) 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 e nvironmental exposures, only exposures below the knot are of interest. Below the knot, RR = e ( β1 * exposure)

. T

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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 T able 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

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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 T able D -1e. Fit of the square root transformation log RR model to breast

ca ncer 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

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35 T able D -1f. Fit of the log transform model to breas t 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

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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 0.0 06

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 dat a 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 log

linear 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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8 9 10 11 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 expos ure 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.

F igure D -1g. Breast cancer incidence exposure

- response curves, linear RR models (units are ppm -days, 15-year lag).

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9 10 11 12 13 14 15 16 17 18 19 F igure 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 incidence a

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

- p iece spline model, inclusion of the non

- e xposure 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 b irth, 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).

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3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 T able D -1i . Model coefficients for linear RR models, breast cancer incidence

Linear RR Model Parameter(s) SE Profile likelihood 95% (one

sided) upper and lower bounds c

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, CUMEXP15 a, 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

- p iece 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 – k not)). For the 95% upper confiden c e limit, for exposures below the knot, RR = 1 + ((β1+ 1.645 × SE1) × exp); for exposures above the knot, RR = 1 + (β1 × exp + β2 × (exp

- kn ot) + 1.645 × sqrt(exp 2 × 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

- l inear RR models, may not be symmetrical. EPA also evaluated application of a profile likelihood approach ( L angholz and Richardson, 2010 ) , which allows for asymmetric CIs, for selected linear RR models, for comparison with the Wald approach. 95% (one

- s ided) 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

- e xposure 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 b ounds for the second spline segment parameter estimate is computationally difficult and was not pursued here.

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41 T able D -1j . Supplemental Results: Parameter estimates for exposure

v ariables for categorical (decile) linear RR model (RR = 1 + β), breast cancer incidence

Gradient Approx Ap prox Objective N Parameter Estimate Std Err t Value Pr > |t| Function

1 beta1a 0.039745 0.340310 0.116792 0.907133 2.4264E

- 1 0 2 beta1b 0.133788 0.371450 0.360177 0.719065 6.847537E

- 1 1 3 beta1c 0.304056 0.438525 0.693361 0.488824 1.119129E

- 1 0 4 beta1d 0.166744 0.402813 0.413950 0.679319 3.011577E

- 1 0 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

- 1 1 7 beta1g 0.919113 0.643333 1.428674 0.154536 7.337724E

- 1 1 8 beta1h 0.487590 0.505453 0.964660 0.335789 5.850301E

- 1 1 9 beta1i 0.985298 0.753167 1.308206 0.192187 1.207377E

- 1 0 10 beta1j 1.575028 0.960886 1.639141 0.102633 1.341514E

- 1 0

Value of Objective Function (log likelihood) =

- 9 66.9720784

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 1 st 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 LEC 01 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 LEC 01 . This is slightly lower than the previous LEC 01 of 0.0110 ppm in EPA' s 2006 draft risk assessment (U.S. EPA, 2006a, Table 14).

Similar calculations were done for the EC 01 , which resulted in a value of 0.0152 ppm.

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37 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 LEC 01 . This is about 5 times lower than the previous LEC 01 of 0.0110 ppm in EPA's 2006 draft risk assessment (U.S. EPA, 2006a, Table 14). T he EC 01 is 0.0060 ppm, which is about four times lower than the EPA's 2006 EC 01 . 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 EC 01 (without the LEC 01 ) for comparison with the corresponding
EC 01 from the 2

- piece log -linear model. The point is to show that the cubic spline log RR modelThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-24 DRAFT

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52 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 LEC 01 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 EC 01 using the cubic spline log RR model is 0.0138 ppm, similar to the value of 0.0152 ppm using th e 2-piece log-linear model.

f. Risk assessment for b reast 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
LEC 01 , which is about half of the value of 0.0110 ppm from the 2-piece log-linear spline model. The corresponding EC 01 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 Covari ates Covariates

‐ 2 LOG L 1967.813 1946.492 AIC 1967.813 1958.492 SBC 1967.813 1979.172

Testing Global Nul l Hypothesis: BETA=0

Test Chi

‐ Sq uare DF Pr > ChiSq

Likelihood Ratio 21.3211 6 0.0016 Score 22.2448 6 0.0011 Wald 22.0301 6 0.0012

Analysis of Maximu m Likelihood Estimates

Parameter Standa rd Hazard Variable DF Estim ate Error Chi

‐ Square Pr > ChiSq Ratio

dob1 1 0.17 056 0.21590 0.6241 0.4295 1.186 dob2 1 0.59054 0.23671 6.2242 0.0126 1.805 dob3 1 0.83 494 0.27295 9.3573 0.0022 2.305 CUMEXP 1 5.93879 E

‐ 6 3.52892E

‐ 6 2.8321 0.0924 1.000 PARITY1 1 ‐ 0.2502 2 0.18784 1.7746 0.1828 0.779 FREL_BR_CAN1 1 0.47 120 0.17920 6.9144 0.0086 1.6026

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61 62 63 64
6 (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 0.16 362 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 Cr iterion 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 dob 2 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 1.598This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-26 DRAFT

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66 (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 0.13 558 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 Cr iterion 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 dob 2 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 1.58966

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61 62 63 64 (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 0.17 618 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 Cr iterion 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 dob 2 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 1.608This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-28 DRAFT

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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 0.11 282 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 Cr iterion 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 dob 2 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 1.6101 2 3 4 5 6 7 8 9 10 11 12 13

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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 br east cancer mortality was found to be fit best using cumu lative exposure with a 20-year lag. Below is the
distribution of the 102 breast cance r deaths used in the analys is. The cut points are those used in the published data (Steenland et al., 2004).1 2 3

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Cumulative exposure, 20

- year lag a Number of breast cancer deaths
0 (Lagged out) 42

> 0

– 64 6 ppm

- days 17

647

– 2779 ppm

- days 16

2780

– 12321 ppm

- days 15

> 1232 1 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 wa s 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 cate gorical 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 ru n 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 8 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 exp lored 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
maxi mum likelihood near 13,000 ppm- days.

-2 log likelihood for different knots for breast cancer mortality

919.400
919.300
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0 1000 2000 3000 4000 5000 6000 7000

KNOT 9 10 11 12 13 14
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17 F igure 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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919.000
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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 1 2 3 4 5 6
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22 23 24 F igure 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
th e 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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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7
8 F igure 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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― DO NOT CITE OR QUOTE -2 l og l i kel i hood i s 917. 776 Cat egor i cal anal yses over l ayed

RR 4

3

2

1

0 5000 10000 15000 20000 25000

CUMEXP20

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15 Figure D -2d. Plot of the dose- response r elationship 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 th e 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.1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 T able 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 CUM 202 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

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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 T able 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

Likelih ood 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

*covari ance lin0 and lin1

- 1.75 × 10 -7

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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 T able 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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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 T able 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
Wa ld 5.7688 1 0.0163

Analysis of Maximum Likelihood Estimates

Parameter Standard Hazard
Pa rameter DF Estima te Error Chi

- Square Pr > ChiSq Ratio

lcum20 1 0.08376 0.03487 5.7688 0.0163 1.087

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- linear spline model, breast cancer mortality, 20

- year l ag, 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 atten uation. Parameter estimates for
these models can be found in Table D- 2g.

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7 8 9 10 11 12 13 14 15 16 F igure D -2e . Linear RR models for breast cancer mortality.

Table D -2g . Model results for breast cancer mortali ty, linear RR models b

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, CUMEXP20 a 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

- l inear RR models, may not be symmetrical. For breast cancer incidence, EPA also evaluated application of a profile likelihood approach for the linear RR models ( L angholz and Richardson, 2010 )
, which allows for asymmetric CIs, for comparison with the Wald approach. The unit risk estimate for breast cancer mortality presented in thi s a ssessment does not rely on any of the linear RR models, thus revised CIs calculated using the profile likelihood method are not shown here.

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. 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 1 st pi ece of the 2

- piece log

linear model from Table D-2c, which is 0.0006877 + 1.64 × 0 .0004171 , to calculate the
LEC 01 via the life -table analysis of excess risk used by EPA in Appendix C of their 2006
draft risk a ssessment. 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-pie ce 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 LEC 01 . This is substantially lower than the previous
LEC 01 of 0.0195 ppm in EPA' s 2006 draft risk assessment (U.S. EPA, 2006a, Table 12).

Similar calculations were done to derive the EC 01 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 EC 01 and LEC 01 were 0.0080 and 0.0037 ppm, respect ively.

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 devi ation of
9.3 years. Cumulative exposure at the end of follow-up, with no lag, had a mean of 27 ppm-1
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31 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 time

dependent 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 ca ses for lymphoid cancer mortality is seen below.

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

Cumulative exposure, 15

- year lag a 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
, 98 5, 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

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29 an d women separately and combined. In the analyses presented here, males and female are
combined.

T able D -3b. Lymphoid cancer mortality results by sex

Cumulative exposure, 15

- year lag Odds ratio s (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 m odels, 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 r un 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.1
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and D-3d. Tables D-3e and D-3f give the results for the log transform model and linear log RR models; the latte r 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 t ransform 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.

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― DO NOT CITE OR QUOTE-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 1
2 3 4 5

6 7 8 9 10 F igure D -3a. Likelihoods vs knots for 2- piece log -linear model, lymphoid cancer mortality.

F igure 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

curve s and categorical log RR model points.

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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 T able D -3c. Categorical results for lymphoid cancer mortality (log RR model), men and women combined

Model Fit Statistic s

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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36 T able D -3d. Results of 2- piece log

- linear spline model for lympho id 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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31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 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 This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 D-48 DRAFT

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- linear spline model for lymphoid cancer

m ortality, 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 C hi

- Square DF P r > ChiSq

Likelihood Ratio 5.2722 2 0.0716 Score 5.2 666 2 0.0718 Wald 5.1 436 2 0.0764

Analysis of Maximum Likelihood Estimates

Parameter Standard Ha zard Parameter DF Estimate Error Chi

- S quare 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 sta tistics 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 th e 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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Linear RR Model – 2 Log likelihood (full model) AIC p - value a Parameter(s) SE d

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, CUMEXP15 b,c 457.48 461.48 0.04 B1 = 0.010090 B2 =

– 0.010086 SE1 = 0.004458 SE2 = 0.004458

a From likelihood ratio test.
b 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 – k not)). 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

- k not) + 1.645 × sqrt(exp 2 × 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. E PA 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.

F igure D -3c . Linear RR models for lymphoid cancer.

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31 d. Risk assessment for all lymphoid canc er mortality using the 2

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- 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 assessm ent 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
rel ationship 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 LEC 01 and the EC 01 , 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 coeff icient for the 1 st piece of the 2

- piece log

- linear
model from Table D-3d, which is 0.01010 + 1.64 × 0.00493, to calculate the LEC 01 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 LEC 01 . This is much lower than the previous LEC 01 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 EC 01 , which resulted in a value of 0.0012 ppm.1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
31 32
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41 e. Supplemental results: results for log cumulative exposure Cox regression models with no l ag

Without With Criterion Covariates Covariates

- 2 LOG L 463.912 462.014 AIC 46 3.912 464.014 SBC 46 3.912 465.984

Testin g Global Null Hypothesis: BETA=0

Test Chi

- Sq uare 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 H azard 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 point s as in that publication. The distribution of cases for hematopoietic cancer mortality is seen below.

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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.

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Cumulative exposure, 15 year lag Od ds 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 catego ry 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 Table s 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.1
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10 11 12 13 14 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 appear s 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

F igure D -4a. Likelihood vs knots for 2- piece log

- linear model, all hematopoietic cancer.

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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 F igure 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.

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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 T able 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 Crite rion 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 Ch i- 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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- piece log

- linear spline model for all hematopoietic

ca ncer 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

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― DO NOT CITE OR QUOTE1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 T able 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

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m orality, 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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Linear RR Model – 2 Log likelihood (full model) AIC p - value a Parameter(s) SE d

CUMEXP15 654.64 656.64 0.32 B = 0.0000 06257 SE = 0.0000 08187 Log(CUMEXP15) 648.13 650.13 0.006 B = 0.2 322 SE = 0.143 7 Spline, knot = 100, CUMEXP15 b,c 646.95 650.95 0.01 B1 = 0.0 03673 B2 =

– 0.0 03668 SE1 = 0.00 2345 SE2 = 0.00 2345

a From likelihood ratio test.
b 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 – k not)). For the 95% upper confidence limit, for exposures below the knot, RR = 1 + ((β1+ 1.645 × SE1) × ex p); for exposures above the knot, RR = 1 + (β1 × exp + β2 × (exp

- k not) + 1.645 × sqrt(exp 2 × 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. E PA 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.

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

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31 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 LE C 01 and the EC 01 , 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 1 st 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 LEC 01 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 hematopoi etic cancer mortality

an d 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- ye ar lag) is 0.0032 ppm,
which is the LEC 01 . This is notably lower than the previous LEC 0.0109 ppm 01 of for hematopoietic cancer mortality in EPA's 2006 draft risk assessment (U.S. EPA, 2006a,
Table 7).

Similar calculations were done for the EC 01 , which resulted in a value of 0.0043 ppm.1 2
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25 D.5. SUMMARY TABLE OF EC 01 S FOR DIFFERENT OUTCOMES, USING 2

- PIECE SPLINE MODELS

Table D-5 below provides a summary of the current findings for EC 01 and the prior EPA findings for EC 01 .

In general, findings are similar. As described above, the EC 01 values based on the 2- piece spline models were obtained by multiplying the background cancer rate by e (beta × cumexp) for log R R 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 w ithdrawn 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 resu lt in EC 01 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.

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19 T a ble D -5a. Summary of EC 01 results (in ppm) in current analysis and previous EPA risk assessment

U.S. EPA (2006a ) EC 01 a Steenland a LEC 01 2

- piece spline Steenland EC 01 2

- piece spline

Breast cancer incidence b (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 0.0043 d

lymphoid cancer mortality (log RR model, 15

- yr lag) c 0.0427 0.0006 0.0012 e

a EPA uses regression through categorical points ( U.S. EPA, 2006a ) , Steenland uses 2

- piece spline models.
b Breast cancer incidence for the subgroup with interviews, see Steenland et al. (2004 ).

c For hematopoietic and lymphoid cancer, EPA EC 01 calculated for males only, Steenland includes both men and

women. d 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 rela tively large sensitivity to the knot placement in the EC 01 .

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- piece log

- linear models

Coefficient first piece

– 2 log

- like lihood b EC 01

B reast cancer incidence knot at 5000 ppm

- days 0.0000860 1940.6 0.0133

B reast cancer incidence knot at 5800 ppm

- days a 0.0000770 1940.5 0.0151

B reast 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 K not used in analysis

. b L ower numbers equal better fit, linear RR model likelihoods not comparable to log RR likel ihoods 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 anal yses 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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- 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 t hat 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

Exposure true = exposure observed + error,

and the error is independent of the observed exposure. The classical error model is

Exposure observed = exposure true + 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 me asurement 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 in crease 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 is1
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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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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.

A rrighi, HM; Hertz

- Piccioto, I. (1994) The evolving concept of the healthy w orker 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 I nd Med 25(6):825−836.
Langholz, B; Richardson, DB. (2010) Fitting general relative risk models for survival time and matched case

- contro l analysis. Am J E pidemiol 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 –4 58.

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 E pi 143(2):202−210.
Steenland, K; Deddens, J; Zhao, S. (2000) Biases in estimating the effec t 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 (Environment al 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.1 2

3
4 APPENDIX E. LIFE

- TABLE ANALYSIS

A spreadsheet illustrating the extra risk calculation for the derivation of the LEC 01 for lymphoid cancer incidence is presented in Table E-1.

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― DO NOT CITE OR QUOTEThis document is a draft for review purposes only and does not constitute Agency policy.7/2013E-2DRAFT―DO NOT CITE OR QUOTE Table E -1. Extra risk calculation a for environmental exposure to 0.0114 ppm (the LEC 01 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 Steen land 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 (×10 5 /yr) lymphoid

cancer incidence (×10 5 /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 0.00640This document is a draft for review purposes only and does not constitute Agency policy.7/2013E-3DRAFT―DO NOT CITE OR QUOTE Table E

- 1. Extra risk calculation a for environmental exposure to 0.0114 ppm (the LEC 01 for lymphoid cancer incidence) b using the weighted linear regression model based on the categorical cumulative exposure r esults 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 (×10 5 /yr) lymphoid

cancer incidence (×10 5 /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

C olumn A: interval index number (i)

. C olumn B: 5- yr age interval (except <1 and 1

− 4) up to age 85

. C olumn C: all

- cause mortality rate for interval i (× 10 5 / yr ) ( 2004 data from NCHS)

. C olumn D: lympho id cancer incidence rate for interval i (× 10 5 / yr ) (2000

− 2004 SEER data)

. c C olumn E: all

- cause hazard rate for interval i (h* i ) (= all

- cause mortality rate × number of years in age interval)

. d C olumn F: probability of surviving interval i ( without being diagnosed with lympho id cancer ) (q i ) [ = exp(

− h* i )]. This column is intended to represent the probability of surviving the interval without a diagnosis of lymphoid cancer; however, b e cause lymph o id 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 d iagnosis. 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 adjust ed to include the age

- specific breast cancer incidence rates and to exclude the age

- specific breast cancer mortality rates, th is 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. C olumn G: probability of surviving up to interval i ( without having been diagnosed with lympho id cancer ) (S i ) (S 1 = 1; S i = S i

− 1 × q i

− 1 , for i > 1)

. C olumn H: lympho id cancer incidence hazard rate for interval i (h i ) (= lympho id cancer incidence rate × number of years in interval)

. C olumn I: conditional probability of being dia gnosed with lympho id cancer in interval i [= (h i /h* i ) × S i × (1

− q i )] , i.e., conditional upon surviving up to interval i ( without having been diagnosed with lympho id cancer ) ( Ro, the background lifetime probability of being diagnosed with lympho id cancer = the sum of the conditional probabilities across the intervals ).This document is a draft for review purposes only and does not constitute Agency policy.7/2013E-4DRAFT―DO NOT CITE OR QUOTE Table E-1. Extra risk calculation a for environmental exposure to 0.0114 ppm (the LEC 01 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)

C olumn J: exposure duration at midinterval (taking into account 15

- yr lag) (xtime)

. C olumn K: cumulative exposure midinterval (xdose) (= exposure level (i.e., 0.0 114 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 y ears to exposure duration in days ). C olumn L: lympho id cancer incidence hazard rate in exposed people for interval i (hx i ) (= h i × (1 + β × xdose), where β = 0.000 2472 + (1.645 × 0.0001854 ) = 0.0005522 ) ( 0.0002472 per ppm × day is the regression coefficient obtained f rom the weighted linear regression model of the categorical results [ see Section 4. 1 .1. 2] ). T o estimate the LEC 01 , 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]

. C olumn M: all -c ause hazard rate in exposed people for interval i (h*x i ) [ = h* i + (hx i − h i )]. C olumn N: probability of surviving interval i ( without being diagnosed with lympho id cancer ) for exposed people (qx i ) [ = exp(

− h*x i )]. C olumn O: probability of surviving up to interval i ( without having been diagnosed with lympho id cancer ) for exposed people (Sx i ) (Sx 1 = 1; Sx i = Sx i

− 1 × qx i-1 , for i > 1)

. C olumn P: conditional probability of being diagnosed with lympho id cancer in interval i for e xposed people [ = (hx i /h*x i ) × Sx i × (1

− qx i )] ( Rx, the lifetime probability of being diagnosed with lympho id cancer for exposed people = the sum of the conditional probabilities across the intervals ).

a U sing the methodology of BEIR (1988 ). b T he estimated 95% lower bound on the continuous exposure level that gives a 1% extra lifetime risk of lympho id 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 F or 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. F or the lympho id cancer incidence calculations, this adjustment was ignored because the lympho id canc er incidence rates are small when compared with the all

- cause mortality
rates. F or 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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25 APPENDIX F. EQUATIONS USED FOR WEIGHTED LINEAR REGRE SSION

(S ource: R othman (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 

### RR ˆ

− 

# ∑ n 

### w j 

### x j j j j = 2 j = 2 

### ˆ b = n 

# ∑ w j 

### x j 2

j = 2 (F

- 1)

wh ere j specifies the exposure category level and the reference c ategory ( j = 1) is ignored.

the standard error of the slope can be estimated as follows:

1 SE ( b ˆ ) ≈ n 

# ∑ w j x j 2

j = 2 (F

- 2)

t he weights, w j , are estimated from the confidence intervals (as the inverse of the variance):

ˆ 2 ˆ 2  ln( RR j )

− ln( RR j )  2 Var ( RR ˆ ) ≈ RR Var [ln( RR ˆ )] ≈ RR ×   j j j j 2 × 1.96     (F

- 3)

wh ere 

### RR j is the 95% upper bound on the RR j estimate (for the jth exposure category) and RR j is
the 95% lower bound on the RR j estimate.

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6 7 8 9 10 11 AP PENDIX G. MODEL PARAMETERS IN THE ANALYSIS OF ANIMAL TUMOR INCIDENCE

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

m ultistage model parameters

Tumor Multistage b polynomial

degree q 0 q 1 c (mg/m 3 ) − 1 q 2 (mg/m 3 ) − 2 q 3 (mg/m 3 ) − 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 1 d 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 expo sure. b P(d) ∋ 1 − exp[−(q 0 + q 1 d + q 2 d 2 + ... + q k d k )], where d is inhaled ethylene oxide exposure concentration.

c Even though q 1 is zero in some cases, the upper bound of q 1 is nonzero.
d The 100

- ppm dose was deleted; the fit was perfect with only two points to fit.

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13 14 15 16 17 18 19 T able G -2. Analysis of grouped data, Lynch et al. (1984a ); Lynch et al. (1984c) study of male F344 rats; a multistage model parameters

Tumor Multistage b polynomial degree q 0 q 1 (mg/m 3 ) − 1 p - value ( chi

- square goodness of fit)
Splenic mononuclear cell leukemia 1 c 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[−(q 0 + q 1 d + q 2 d 2 + ... + q k d k )], 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.

T able G -3. Analysis of grouped data, Garman et al. (1985) and Snellings et al. (1984) reports on F344 rats; a multistage model parameters

Tumor Multistage b polynomial degree q 0 q 1 (mg/m 3 ) − 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[−(q 0 + q 1 d + q 2 d 2 + ... + q k d k )], where d is inhaled ethylene oxide exposure concentration.

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4 5 6 7 8 9 10 T able G -4. Time

- to -tumor analysis of individual animal data, NTP mice

s tudy (NTP, 1987); a multistage

- Weibull model b parameters

Tumor Multistage polynomial degree q 0 q 1 (mg/m 3 ) − 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[−(q 0 + q 2 k z 1 d + q 2 d + .... + q k d )*(t − t 0 ) ], where d is inhaled ethylene oxide exposure concentration. The length of the study w as 104 weeks. The times t and t 0 as expressed in the above formula are scaled so that the length of the study i s 1.0. Then, q 0 is dimensionless, and the coefficients q k are expressed in units of (mg/m 3 ) − k

.

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34 APPENDIX H. SUMMARY OF 2007 EXTERNAL PEER REVIEW AND PUBLI C COMMENTS AND DISPOSITION

A draft of this assessment document entitled Evaluation of the Ca rcinogenicity of Ethylene Oxide (dated August 2006) (U.S. EPA, 2006a) wa s available for public comment and underwent a formal external peer review in acco rdance 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 expe rts 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 Repor t 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 lifetim e 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 as sociated with the hazard characterization and quantitative risk estimation. The SAB panel was charged with answering a number of specific questions that addressed key sc ientific issues relevant to the assessment. A summary of significant comments made by the panel in response to the char ge questions and EPA’s response to these comments arranged by char ge question are provided below. In addition, a number of comments from th e public were received during the public comment period. A summary of the public comment s 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 Pane l’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 a nd Appendix A of the EPA Draft Assessment)
Do the available data and discussion in the dr aft document support the hazard conclusion that EtO is carcinogenic to humans based on th e weight-of-evidence descriptors in EPA’s 2005 Guidelines for Carcinogen Risk Assessmen t? In your response, please include consideration of the following:This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-2 DRAFT

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34 1. a. EPA concluded that the epidemiological ev idence on EtO carcinogenicity was strong, but less than completely conclusive. Does the draf t 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 carcin ogenic hazard of ethy lene oxide (EtO) to
humans? Please comment on the EPA's characte rization of the body of epidemiological data reviewed. Considerations include: a) the consistency of the findings, including the significance of differences in results using di fferent 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 descript or of “Carcinogenic to Humans” although some Panel members concluded that the descriptor “Lik ely to be Carcinogenic to Humans” was more appropriate. There was consensus that the ep idemiological data re garding ethylene oxide carcinogenicity were not in and of themselves su fficient 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 di fferences of opinion as to whether criteria necessary for designation as “Car cinogenic 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, experi mental animal, and mutagenicity evidence was
sufficient to conclude that Et O is carcinogenic to humans.
The Panel concluded that the assessment would be improved by: (1) a better introduction
to the hazard characterization sect ion, including a brief description of the information that will be presented; (2) a clear articulati on of the criteria by which epidemiologic studies were judged as to strengths and weaknesses; (3) addition of a more inclusive summ ary figure and/or table at the beginning of section 3.0; and (4) inclusion of ma terial 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 reli ance on “internal” estimates of cancer rates rather than “external” comparis ons (SMR, SIR) due to well rec ognized limitations to the latter method of analysis. The Draft Assessment charact erizes 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 exce ss cancers are found above background even among highly exposed individuals. However, the magnit ude of risk suggested by the unit risk estimate is somewhat at odds with this concept. Subseq uent 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. 7/2013 H-3 DRAFT

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34 EPA Response: EPA is retaining the conclusion that the combined weight of the epidemiological, experimental animal, and mutag enicity 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, t he 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

st udies, referencing Appendix A for further details. EPA considered the recommendation to

move more of the material in Appendix A of the draft assessment in to 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 magnitu d e of the (relative) risk estimates from the epidemiology studies. The exposure-response models used to develop the unit risk estimates are derived from the NIOSHThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-4 DRAFT

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34 data and are thus con s istent 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 correspo nd 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 rela tive 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

expan d ed (Section s 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 animal s 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. 7/2013 H-5 DRAFT

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34 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 Pane l 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 characterizat ion, 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 human s and rodents contain different types of studies, EPA did not find an inconsistency in EtO-induced

precursor even ts (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. 7/2013 H-6 DRAFT

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34 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 characterize d 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 c umulative 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. T he Greenberg et al. (1990) data are quite limited in terms of the number of canc ersThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-7 DRAFT

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34 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 three

fold excess of ly mpho hematopoietic 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 coho rt. Th is cohort did not show an excess of lympho hematopoietic 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, th e 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, t he 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 samp le) 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 E tO 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 comb ined 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.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-8 DRAFT

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33 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 lymphohematopoi etic 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 request ed 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 bet ween 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.”This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-9 DRAFT

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34 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 prio r 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 c ontrols 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 in cluded 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 ap parent 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 effec t 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 c umulative exposure metric for most workers is probably contributed by earlier, higher exposures. The use of a 15- year lag does not, however, necessarily increase thisThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-10 DRAFT

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34 uncertainty, given that exposure in the lagged out period for most workers would be appr eciably 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 h ave 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 err or occurred for early y ears 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, i s 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 lo wer 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 accessThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-11 DRAFT

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34 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 lin ear (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 estimatin g 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.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-12 DRAFT

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34 EPA Response : The above comment from the Panel addresses a variety of issues and EP A’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 mod e ling of the individual

- level data : In response to the SAB

comment s, 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 ca ncer 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 ). M ore 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 ri sk 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 , t he 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, d espite the extensive modeling efforts , the various
alternative continuous models investigated , including the two

- piece spline mo dels, 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 groupedThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-13 DRAFT

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34 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-exposu re 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 two

piece spline model yielded an unrealistically steep low -dose slop e 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 se t 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, multi

site 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 lowThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-14 DRAFT

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34 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 M OAs 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, t he reason s 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 endogenous ly 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 i n 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 i n 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 mi ce exposed to either EtO or to ethylene. For ethylene, aThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-15 DRAFT

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34 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 w ere 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 hpr t 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

m aximum 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 M LE 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 r esults. 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 w as not dependent on the choice of a straight- line dose-response model. As an additional sensitivity analysis, we reran the modeling using the1
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23 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 slid e); 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).

S lide 27 of the SAB report pr esents data from Nivard et al. (2003) on the frequency of recessive lethal (RL) mutations in Drosophila exposed to EtO ( full d ata 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 (F igure 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 atlow 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. 7/2013 H-16 DRAFT

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14 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- respo nse relationships for the carcinogenic effects of EtO , which is mutagenic and directly damages DNA.0.0 0.5 1.0 1.5 2.0Mutation frequency, percent

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Ethylene oxide concentration, ppm Data: Nivard ( 2003 ) / Vo g el 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 : W ith 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. 7/2013 H-17 DRAFT

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34 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 purpo ses

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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 co mments 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 l ife 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. 7/2013 H-19 DRAFT

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34 Several Panel members commented on the use of the upper confid ence limit for the estimated slope coefficient as the basis for estimating an LEC 01

. The Panel encourages the EPA to present unit risk estimates based on the range of EC 01 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 incidenc e, 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 typ es 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

mor tality

- 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 subtyp e 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 anThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-20 DRAFT

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34 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 recogn izes 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 uppe r and lower confidence limits: In the EtO assessment, EPA presents 95% (one-sided) lower bounds and central estimates of the EC 01 s as well as standard

errors for the regression coefficients used in the modeling, which provide information about the
variabil ity 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
EC 01 ) 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 EC 01 . EPA considered the SAB Panel comment enco urag ing the EPA “to present unit risk estimates based on the range of EC 01 values corresponding to the lower 95% confidence limit, the point estimate, and the upper 95% confidence limit.” However, as a consequence of theThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-21 DRAFT

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34 2-step approach used by EPA to gen erate 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.
W hile linear extra polation 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 LEC 01 ) 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 subsec tion (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 level s 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 responseThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-22 DRAFT

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34 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 a cross models, depending on the exposure scenarios of interest, and some guidance on choice of approach is provided in Section 4.7 of the
revised assessmen t. 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 sophist icated approaches such as PBPK modeling should be considered.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-23 DRAFT

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34 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 cal ls;
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 toThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-24 DRAFT

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34 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 avai lable 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 outc ome 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 St eenland 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 throug h 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 P anel 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 r egression model. Historically, models for grouped proportions applied adjustments of this type but it is by no means a preferred technique when the underlying i ndividual 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 the1
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35 g rouped model involves the range of exposures within the collapsed categ ories. 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 con form 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 + B ˆ 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 + B 1 / B 0

The model estimated by the EPA method is:

(/ ) = B ˆ 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. Th is 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 c ategorical 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 a rbitrary 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. 7/2013 H-25 DRAFT

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. • 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 av ailable 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 Dr aft 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 regre ssion 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 R isk 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. 7/2013 H-27 DRAFT

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34 EPA foll owed 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
Steen land, 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

assess ment, 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 f ull 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 exposur e- response relationship across the categories while represent ing a different subrelationship within the categories. I t is doubtful that categorical results based on log cumulative exposure would have been better because, while a log
cumulat ive exposure Cox regression model provided a statistically significant fit and made it possible to represent the supr alinearity (e.g. the plateauing at high exposures) across the range of the data, one would not expect the exposure-response relationship within each discr ete category to be supralinear. For example, for lymphoid cancer, the exposure -response relationship in the first exposure category is in the steeply increasing pa rt 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 (t he highest exposure quartil es 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 est imation. 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 toThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-28 DRAFT

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34 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 rate s (odds ratios) , not absolute rates as used in the approach of Rothman and Greenland (1998) cited by the
SAB. T he 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 sl ope. 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 ap proach may y ield 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 valuesThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-29 DRAFT

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34 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

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• 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 expec ted 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 adjacentThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-30 DRAFT

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34 category will be underestimated. Using a linear regression m odel 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 lymphoi d cancer because of the relatively
small number of deaths

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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 respon ses (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 in formation” 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 ne cessitates 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 be fore the final E t O 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. 7/2013 H-31 DRAFT

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34 EPA RESPONSE: Comments received from the SAB and from the public have been addressed
and t he 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 revie wed 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 , particula rly the crude assignment of exposure levels to subjects in the
UCC cohort, are fundamental
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, 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 lympho hematopoietic cancers, vs. 79 in the NIOSH cohort, 12 vs. 31 NHL s). Consequently, f or 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 exposureThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-32 DRAFT

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34 and are based on greater numbers, and thus would be expected to b e 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 excessivelysensitive 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 log

linear 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:5

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34 C omment 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 appro ach. 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 assessm ent. 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 exclude d 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 the se 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. 7/2013 H-33 DRAFT

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34 Comment 4.4: EPA’s use of the heterogeneous broad category of distinct diseases of lymphohematopoiet ic (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 Sci entifically 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 character ization must include both males and females, consistent with a “weight-ofevidence” approach that relies on all relevant information. In the NIOSH retrospective study,This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-35 DRAFT

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34 increased risks of LH cancer were observed in males but not females, even though the NI OSH 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 m odels 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 mus t 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: Et O is Considered by Many to be a Weak Mutagen and EPA Should Consider This in Proposi ng 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., s plenic 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 hav e reconciled these findings with its estimation of E tO’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. 7/2013 H-36 DRAFT

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34 EPA RESPONSE : EPA does not cons ider 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 gr oup 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 i n 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 as sessments 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 inter est 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 c onstructed for use with low dose levelsThis document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-37 DRAFT

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34 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 fi nal assessment . S ee Section 4.5 for a discussion of the specific issue raised in this comment

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Comment 9.4 : EPA’s draft unit risk values for Et O 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 Et O that provide valid information on the relationship between exposure and cancer response to improve
the reasonableness of the unit risk values for Et O.

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 human s” (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. 7/2013 H-38 DRAFT

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34 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 fo r 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

comm ents. 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 E tO 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), Et O 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 E tO 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 we ight

- of

- evidence characterization of E t O.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 H-39 DRAFT

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32 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 E tO 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 stud y 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

ap proximately 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 val ues are not implausible upper bound estimates.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 I-1 DRAFT

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37 38 APPENDIX I. LIST OF REFERENCES A DDED AFTER THE 2006 EXTERNAL REVIEW DRAF T

Note : These references were added to the Carcinogenicity Assessment in response to the 2007 peer reviewers’ and public comments
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Zhao, C; Tyndyk, M; Eide, I; et al. (1999) Endogenous and background DNA a dducts 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.This document is a draft for review purposes only and does not constitute A gency policy. 7/2013 J-1 DRAFT

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26 AP PENDIX J. S UMMARY OF MAJOR NEW STUDIES SINCE THE LI TERATURE CUTOFF DATE

T he 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
id entified 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, W eb 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 studies 10 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 o f the 372 references by first author, the first reference is: Agarwal, A., Unfer, R. and Malla pragada, 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), synthesi zed 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.
10 This refers to general exposure studies; exposure studies related to any of the epidemiol ogical studies of EtO would be considered further.This document is a draft for review purposes only and does not constitute A gency policy. 7/2013 J-2 DRAFT

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

Category References a 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 ) Sne deker (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.7/2013 J-3 DRAFT

― DO NOT CITE OR QUOTE Table J

- 1. Disposition of 56 new references identified as potentially relevant (continued)

Category References a Disposition
Genotoxicity/ Mutagenicity studies Donner et al. (2010 ) Godderis et al. (2006 ) Hong et al. (2007 ) Houle et al. (2006 ) Marsd en 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 impli cations of endogenous adducts for risk
assessment

— this issue is already addressed in the assessment (e.g., in the respon ses to SAB comments in Appendix H). This document is a draft for review purposes only and does not constitute A gency policy.This document is a draft for review purposes only and does not constitute A gency policy. 7/2013 J-4 DRAFT

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37 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 a ppendix are provided in Se ction 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, a nd 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 case

control study of lymphoma in six European countri es (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 a ll
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 sp ecific 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.This document is a draft for review purposes only and does not constitute A gency policy. 7/2013 J-5 DRAFT

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34 Exposure was evaluated in each center by specially trained i ndustrial 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 wer e 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 lymph oma 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 e ither 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 EtOThis document is a draft for review purposes only and does not constitute A gency policy. 7/2013 J-6 DRAFT

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33 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 e t 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 a nd 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 y ears 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 hygie ne 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 90 th 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

11 Without 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 (H agmar et al., 1995 ; 1991 ).This document is a draft for review purposes only and does not constitute A gency policy. 7/2013 J-7 DRAFT

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- , 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 al so 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 th e 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 grou p, which could itself dampen relative risk estimate s. 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 repor ted. 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 highes t 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 evide nce for these cancers. For breast cancer incidence,1
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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 ( s ee 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 i n 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.

This document is a draft for review purposes only and does not constitute A gency policy. 7/2013 J-8 DRAFT

― DO NOT CITE OR QUOTEThis document is a draft for review purposes only and does not constitute Agency policy.7/2013J-9DRAFT―DO NOT CITE ORQUOTE Table J -2. New e pidemiological studies of ethylene oxide and human cancer

Population/

i ndustry Number of subjects Extent of exposure to ethylene oxide Health outcomes Other chemicals to which subjects were potentially exposed Limitations

P opulation

- 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) A ll lymphoma (# cases/# controls ) OR (95% CI)

U nexposed (2
, 316/2
, 436 ) 1.0 [referent category]

E ver exposed (31/27 ) 1.3 (0.7, 2.1)

C onfidence 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

E xposure 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

E xposure 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

D uration (years)

≤10 (18/16 ) 1.2 (0.6, 2.4) >10 (13/11 ) 1.3 (0.6, 3.0)
p- trend = 0.441

C umulative exposure score

≤median (13/16 ) 0.9 (0.4, 1.8) >median (18/11 ) 1.8 (0.8, 3.9)
p

- trend = 0.246 W ould vary by individual participant since not industry

based 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 pop ulation, 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 typesThis document is a draft for review purposes only and does not constitute Agency policy.7/2013J-10DRAFT―DO NOT CITE ORQUOTE Table J

- 2

. New epidemiological studies of ethylene oxide and human cancer (continued)

Population/

i ndustry 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 L ymphohematopoietic cancers: Fluorochlorocarbons, Still a youth ful 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 75 th percentile was 0.22 ppm × years; and the 90 th percentile was 1.29 ppm × years. M ortality (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 estim ates above the median; nonsignificant increases in leukemia deaths were reported

I ncidence:

C ancer (ICD

- 7) [cases] SIR (95% CI) A ll lymphohematopoietic

(200

− 209) [18] 1.25 (0.74, 1.98) NHL (200+202) [9] 1.44 (0.66, 2.73) L eukemia (204

− 205) [5] 1.40 (0.45, 3.26)

Internal analysis of lymphohematopoietic

cancers: Cum exp gp p pm × 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 un exposed referent group; internal analyses involved comparison of responses in the top quartiles of cumulative exposure to those in the lower 50% of cumulative exposuresThis document is a draft for review purposes only and does not constitute Agency policy.7/2013J-11DRAFT―DO NOT CITE ORQUOTE Table J

- 2

. New epidemiological studies of ethylene oxide and human cancer (continued)

Population/

i ndustry 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 s ignificant, was reported.

I ncidence:

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 × yr s [c ases] 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)This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 J-12 DRAFT

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38 39 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 s ilylation and gas chromatography

electron 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 ma lignancies: 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 carcinoge nic 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 F lorence, 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.

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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 semicondu ctor industry. Saf Health Work 2 (2): 122−134.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 J-13 DRAFT

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34 APPENDIX K. DOCUMENTATION OF IMP LEMENTATIONS 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 a n 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 re view 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

12 Pub. L. No. 1 12

− 74, Con s oli dat ed A ppr o priations Ac t, 201 2.
13 National Rese a rch Coun cil, ( 2011 ). Re v iew of t he Env iron me ntal Prot e ction A g en c y’s Draft IRIS Assess m ent of For m alde h yde.1
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11 12 13 14 15 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 addres s 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 announced 14 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 implemen ting 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 appr opriate, 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, t he 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 cont ained in appendices (Appendices A and C, respectively), along with a detailed summary table of the e pidemiology studies in Appendix A. The main text of the hazard identification chapter ( see Chapter 3) is compa ratively streamlined.

14 E PA An no u nc e s N A S’ Re v iew of IR IS Asses sm ent De v el opm ent Process (ww w.epa.go v/ iris)

. This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 K-2 DRAFT

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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
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 description s 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 lite rature 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 1 st external review draft) to May 2013; this search is desc ribed 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 e pidemiology studies in Appendix A ( see Tabl e 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 i n 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 ep idemiology 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.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 K-4 DRAFT

― DO NOT CITE OR QUOTE 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 selecti on 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 c onsiderations 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 enhanc ed 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 A ssessment 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 recomm endations. 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 Imple mented. 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 consi stent with that mode

- of

- action finding as well as the available hazard evidence.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 K-5 DRAFT

― DO NOT CITE OR QUOTE 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 th e 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 1 st 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 th e IRIS program as part of Phase 2.

13. Use a database, such as the Health and Implemented. HERO links were in corporated 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 standa rdizations. The EtO deriving r eference 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 quan titative cancer risk estimates ( see Table 4

- 1).

15. Develop templates for evidence tables, forest plots, or other displays. This is being implemented by th e IRIS program as part of Phase 2.

16. Establish protocols for review of major types of studies, such as ep idemiologic and bioassay. Partially Implemented. This is being implemented by th e 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 usi ng standard protocols for evidence evaluation that are provided in existing EPA guidance.This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 K-6 DRAFT

― DO NOT CITE OR QUOTE 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 Sec tion 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 a nd modeling results for the epidemiological throughout the present report, sufficient cancer data set s. 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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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- ev idence 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 selecte d. 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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― DO NOT CITE OR QUOTEREFERENCES

This reference list includes all the references cited in the document except for Appendix B, which is a reference list pertaining to Figure 3−3, and Appendix J, which includes some more recent references. References added after the 2007 external peer review are also listed separately in Appendix I. References identified in a May 2013 lite rature search but appearing after the 30 June 2010 cutoff date for literature inclusion into this carcinogenicity assessment are cited and discussed in Appendix J.

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Agurell, E; Cederberg, H; Ehrenberg, L; Lindahl

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Ahn, HS; Shin, HS. (2006). Determ ination of ethylene oxide

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- electron impact

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Armstrong, B. (1998). Effect of measurement error on epidemiological studies of environmental and occupational exposures [Review]. Occup Environ Med 55: 651

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Arrighi, HM; Hertz

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Bastl ová, T; Andersson, B; Lambert, B; Kolman, A. (1993). Molecular analysis of ethylene oxide

- induced mutations at the HPRT locus in human diploid fibroblasts. Mutat Res 287: 283

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BEIR (Committee on the Biological Effects of Ionizing Radiation). (1988). Health risks of radon and other internally deposited alpha

- emitters. Washington, DC: National Academy Press.

Benson, LO; Teta, MJ. (1993). Mortality due to pancreatic and lymphopoietic cancers in chlorohydrin production workers. Br J Ind Med 50: 710

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Beranek, DT. (1990). Distribution of methyl and ethyl adducts following alkylation with monofunctional alkylating agents [Review] . Mutat Res 231: 11

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Bisanti, L; Maggini, M; Raschetti, R; Alegiani, SS; Ippolito, FM; Caffari, B; Segnan, N; Ponti, A. (1993). Cancer mortality in ethylen e oxide workers. Br J Ind Med 50: 317

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This document is a draft for review purposes only and does not constitute Agency policy. 7/2013 R-1 DRAFT

― DO NOT CITE OR QUOTEBoffetta, P; van der Hel, O; Norppa, H; Fabianova, E; Fucic, A; Gundy, S; Lazutka, J; Cebulska

- Wasilewska, A; Puskaile rova, D; Znaor, A; Kelecsenyi, Z; Kurtinaitis, J; Rachtan, J; Forni, A; Vermeulen, R; Bonassi, S. (2007). Chromosomal aberrations and cancer risk: results of a cohort study from Central Europe. Am J Epidemiol 165: 36

­ 43. http://dx.doi.org/10.1093/aje/kwj367

Bolt, HM; 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: 719

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Bolt, HM; Peter, H; Föst, U. (1988). Analysis of macromolecular ethylene oxide adducts [Review]. Int Arch Occup Environ Health 60: 141

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Bonassi, S; Znaor, A; Ceppi, M; Lando, C; Chang, WP; Holland, N; Kirsch

- Volders, M; Zeiger, E; Ban, S; Barale, R; Bigatti, MP; Bolognesi, C; Cebulska

- Wasilewska, A; Fabianova, E; Fucic, A; Hagmar, L; Joksic, G; Martelli, A; Migliore, L; Mirkova, E ; Scarfi, MR; Zijno, A; Norppa, H; Fenech, M. (2007). An increased micronucleus frequency in peripheral blood lymphocytes predicts the risk of cancer in humans. Carcinogenesis 28: 625

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Boogaard, PJ. (2002). Use of haemoglobin adducts in exposure monitoring and risk assessment [Review]. J Chromatogr B An alyt Technol Biomed Life Sci 778: 309

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Boysen, G ; Pachkowski, BF; Nakamura, J; Swenberg, JA. (2009). The formation and biological significance of N7

­ guanine adducts [Review]. Mutat Res 678: 76

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Britton, DW; Törnqvist, M; van Sittert, NJ; Watson, WP; Wraith, MJ; Wright, AS. (1991). Immunochemical and GC/MS analysis of protein adducts: dosimetry studies with ethylene oxide [Review]. Prog Clin Biol Res 372: 99

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Brown, CD; Bahman, A; Turner, MJ; Fennell, TR. (1998). Ethylene oxide dosimetry in the mouse. Toxicol Appl Pharmacol 148: 215

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Brown, T; Rushton, L; Group, tBOCBS. (2012). Occupational cancer in Britain Haematopoietic malignancies: leukaemia, multiple myeloma, non

- Hodgkins lymphoma. Br J Cancer 107: S41

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Butterworth, BE; Chapman, JR. (2007). Exposure of hematopoietic stem cells to ethylene oxide during processing represents a potential carcinogenic risk for tr ansplant recipients. Regul Toxicol Pharmacol 49: 149

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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: 135

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