Uncertainties in Fatal Cancer Risk Estimates Used in Radiation Protection: Recommendations of the National Council on Radiation Protection and Measurements (N C R P Report)

NCRP REPORT No. 126

UNCERTAINTIES IN FATAL CANCER RISK ESTIMATES USED IN RADIATION PROTECTION Recommendations of the NATIONAL COUNCIL ON RADIATION PROTECTIONANDMEASUREMENTS

Issued October 17, 1997

National Council on Radiation Protection and Measurement 7910 Woodmont Avenue / Bethesda, Maryland 20814-3095

LEGAL NOTICE This Report was prepared by the National Council on Radiation Protection and Measurements (NCRP). The Council strives to provide accurate, complete and useful information in its documents. However, neither the NCRP, the members of NCRP, other persons contributing to or assisting in the preparation of this Report, nor any person acting on the behalf of any of these parties: (a) makes any warranty or representation, express or implied, with respect to the accuracy, completeness or usefulness of the information contained in this Report, or that the use of any information, method or process disclosed in this Report may not infringe on privately owned rights; or (b) assumes any liability with respect to the use of, or for damages resulting from the use of any information, method or process disclosed in this Report, under the Civil Rights Act of 1964, Section.701 et seq. a s amended 42 US.C. Section 2000e et seq. (Title VZZ) or any other statutory or common law theorygoverning liability.

Library of Congress Cataloging-in-PublicationData National Council on Radiation Protection and Measurements. Uncertainties in fatal cancer risk estimates used in radiation protection :recommendations of the National Council on Radiation Protection and Measurements. p. cm. -- (NCRP report ; no. 126) "Issued October 1997." Includes bibliographical references and index. ISBN 0-929600-57-6 1. Radiation carcinogenesis. 2. Cancer--Mortality 3. Cancer--Risk factors. 4. Health risk assessment. 5. Radiation--Dosimetry. I. Title. 11. Series. [DNLM: 1.Neoplasms, Radiation-Induced--etiology. 2. Neoplasms, Radiation -Induced--mortality. 3. Radiation Protection. 4. Risk Factors. 5. Radiation Dosage. QZ 200 N2745c 19971 RC269.55.N36 1997 616.99'4071--dc21 97-41391 CIP

Copyright O National Council on Radiation Protection and Measurements 1997 All rights reserved. This publication is protected by copyright. No part of this publication may be reproduced in any fonn or by any means, including photocopying, or utilized by any information storage and retrieval system without written permission from the copyrightowner,except for brief quotation in critical articles or reviews.

Preface In recent years, the practice of providing uncertainties when formulating estimates of dose and risk in human and environmental exposure circumstances has become recognized as an important step in expressing the degree of confidence appropriate to stated values. Knowledge of the magnitude of uncertainties in the nominal values of the coefficient for risk of fatal cancer per unit dose can be very helpful in providing perspective to those involved in radiation protection practice. In human cancer risk estimation, however, only rather tentative approaches to the evaluation of these uncertainties have been made, starting with the report of the NIH Ad Hoc Working Group on Radioepidemiological Tables in 1985 and with relatively brief attempts by the United Nations Scientific Committee on the Effects of Atomic Radiation and the National Academy of Sciences/National Research Council's Committee on the Biological Effects of Ionizing Radiation. The data on mortality from the Lifespan Study of the Japanese atomic-bomb survivors up to 1985 are virtually the sole numerical source used for risk estimates for low-LET radiation exposure today. (Later evaluations of the LSS data to 1987 and to 1990 are of wide interest in epidemiology but have not so far modified the risks recommended for use in radiation protection.) Other sources of risk information are used mainly to support and complement the data from the LSS. In the NCRP Taylor Lecture in 1993, it was pointed out that the singularity of the LSS as a source of low-LET risk information simplifies the assessment of uncertainties in the risk estimates. Because the LSS risk estimates depend on five distinct components, uncertainties overall can be evaluated by examining the uncertainties in each of these components. In the 1993 Taylor Lecture, the evaluation (and discussion) of the five components was quite limited, although an overall picture was outlined. The NCRP decided recently to build on that Taylor Lecture by looking at each of the five components in more detail and attempting to be more quantitative about their uncertainties. This Report is the result. It makes clear that the fundamental basis on which the evaluation of some of the components rests is, itself, uncertain and difficult to quantify. Nevertheless, the Report seeks not only to clarify the foundation of estimates of

iv / PREFACE uncertainty, but also to make a reasonable overall appraisal of the uncertainties in the average risk estimates presently used in low-LET radiation protection. Risk estimates for individual organs involve greater uncertainties than for total cancer and are not dealt with specifically in this Report. This Report was prepared by Scientific Committee 1-5 on Uncertainty in Risk Estimates. Serving on Scientific Committee 1-5 were: Warren K. Sinclair, Chairman National Council on Radiation Protection and Measurements Bethesda, Maryland Members

And& Bouville National Cancer Institute Bethesda, Maryland

Charles E. Land National Cancer Institute Bethesda, Maryland

NCRP Secretariat William M. Beckner, Senior StaffScientist Cindy L. O'Brien, Editorial Assistant The Council wishes to express its appreciation to the Committee members for the time and effort devoted to the preparation of this Report. Charles B. Meinhold President

Contents ...

Preface ............................................. 111 1 Intmdudion ....................................... 1 1.1 Risk Estimates for Radiation Protection .............. 1 1.2 Past Risk Estimates .............................. 2 1.3 Present Risk Estimates ...........................3 1.3.1 Age and Sex Dependence .....................3 1.3.2 Lifetime Risk ............................... 3 1.3.3 Risk Estimates for Low Dose and Dose Rate ..... 4 1.4 Uncertainties in Risk Estimates .................... 5 1.4.1 Past Uncertainty Evaluations ................. 5 1.4.2 NCRP Approach to Uncertainty in Risk Estimates for Radiation Protection ............. 6 1.4.3 City Differences ............................ 8 1.5 Dose Response ................................... 8 2 Epidemiological Uncertainties ..................... 10 2.1 Introduction .................................... 10 2.2 Specific Epidemiological Uncertainties .............. 13 2.3 Bias in Risk Estimates Due to Errors of Detection and Confirmation ............................... 16 2.4 Biases Affecting Risk Estimates of Cancer Morbidity ...................................... 18 2.5 Unrepresentative Population ...................... 20 2.6 Bias Deriving from City Differences ................ 21 2.7 Summary of Epidemiological Uncertainty ........... - 2 2 3 Dosimetrical Uncertainty .......................... 23 3.1 Random Errors and Biases ........................ 23 3.2 Bias Resulting from Random Errors in Dose ......... 24 3.3 Bias in Gamma-Ray Measurements Versus DS86 ..... 30 3.4 Uncertainty Due to Survivor Shielding Characterization in DS86 ......................... 32 3.5 Uncertainty Due to Neutron Weight (Relative Biological Effectiveness).......................... 32 3.6 Bias and Uncertainties Due to the Presence of Thermal Neutrons a t Hiroshima in Excess of Those Predicted by DS86 ............................... 34 3.7 Combination of Uncertainties and Bias for Dosimetry. . 37

.

.

.

vi / CONTENTS

.

4 Transfer of Risk Between Populations.............. 40 4.1 General Considerations .......................... 40 4.2 Factors Modifying Risk in Relation to Transfer

Between Populations ............................ 42

4.3 Site-Specific Evidence for Selecting the Transfer

Model

........................................-47

4.4 Uncertainty Due to Method of Transfer ............. 49 5 Projection to Lifetime Risk ........................ 51 5.1 Constant Relative Risk Projection Model 51 5.2 Considerations Regarding the Projection to Lifetime

.

.

5.3 5.4 5.5

............ Risks in the Lifespan Study ...................... 51 Attained Age Model ............................. 53 Lifetime Risk of Those Exposed a t Young Ages ....... 55 Uncertainty in Lifetime Risk ..................... 57

6 Extrapolation to Low Dose or Dose Rate ............ 6.1 Effect of Dose Rate and Dose in Radiobiology ......... 6.1.1 The Effect of Dose Rate 6.1.2 The Effect of Dose ......................... 6.2 Human Data and Dose-Rate Effects................ 6.3 The ICRP Choice of a Dose and Dose-Rate

60 60 ..................... 60 60 61

Effectiveness Factor

............................

63

6.4 The NCRP Position on the Application of a Dose

and Dose-Rate Effectiveness Factor ................ 64

6.5 UNSCEAR Evaluation of a Dose and Dose-Rate

Effectiveness Factor and Recent Studies ............ 64

6.6 Uncertainties in the Application of a Dose and

.

Dose-Rate Effectiveness Fador

...................

65

7 Combination of Uncertainties ..................... 67 7.1 Sources of Uncertainty .......................... 67 7.2 Method to Propagate Uncertainties ................ 69 7.3 Results ....................................... 71 7.3.1 Population of A11 Ages ...................... 71 7.3.2 Adult Worker Population ................... 73 7.4 Conclusions ................................... 74

Glossary............................................ 77 References ......................................... 83 TheNCRP .......................................... 92 NCRPPublications ................................. 100 Index ............................................. 109

1. Introduction 1.1 Risk Estimates for Radiation Protection This Report is concerned with the evaluation of uncertainties in the risk estimates of fatal cancer induced by low-LET radiation (see Glossary) as presently used in radiation protection, i.e., in estimates of the risk of fatal cancer following exposure of individuals or populations in occupational, environmental or domestic circumstances. These cancers are the main component of the health detriment following radiation exposure identified by the International Commission on Radiological Protection [ICRP (1991)l and the National Council on Radiation Protection and Measurements [NCRP (1993a)l as pertinent in low-LET radiation protection. Recent evaluations of the risk of fatal cancer induced by low-LET radiation are numerically based on the 1950 to 1985 mortality experience of the survivors of the atomic bombs dropped in Japan, as ascertained by the Lifespan Study (LSS) (EPA, 1994; ICRP, 1991; NASLNRC, 1990; NCRP, 1993a; NRPB, 1993; UNSCEAR, 1988). Other epidemiological studies, although they can be highly informative with regard to particular cancer sites, have served mainly to support the results from the LSS, and to show that the LSS results are not isolated, but are generally and broadly supported by these other sources of data. Later evaluations of induced fatal cancer risk in the LSS include the mortality and incidence data up to 1987 reviewed by the United Nations Scientific Committee on the Effects of Atomic Radiation [UNSCEAR (199411 and the more recent mortality evaluations up to 1990 (Pierce et al., 1996a). These new studies provide additional information, especially on cancer incidence, but they do not alter substantially the risk estimates derived in the 1988 to 1990 reports and, more especially, the derivation procedure, which is the source of the uncertainty considerations, remains the same. Not all aspects of radiation protection (notably those involving high-LET exposures)use risk estimates based on the LSS of the atomic-bomb survivors. For example, any consideration of radon exposure to workers or to the public uses risk estimates based on radon

2 1 1. INTRODUCTION

exposures to miners. However, many radiation protection situations in which risk is at issue will use the results of the LSS. The results of the.LSS indicate that lifetime risk coefficients for fatal cancer derived from the high-dose rate exposures of the SV-' for a population of atomic-bomb survivors are about 10 x all ages and about 8 x S V - ~ for an adult (worker) population. 1.2 Past Risk Estimates It is worth noting, [see Table 1.1,taken from ICRP (1991), Table B-101 that lifetime risk estimates for an acute exposure [i.e., no dose and dose-rate effectiveness factor (DDREF) applied1] have ranged over t,he period 1972 to 1990 from about 1 to about

Table 1.1-Excess lifetime mortality from all cancer, attributable to 1Gy (or 1Sv)acute uniform whole-body low-LET irradiation of the general population (ICRP, 1991). Probability of Death Source of Estimate NASNRC, 1972 UNSCEAR, 1977 NASNRC, 1980 Evans et al., 1985 UNSCEAR, 1988* NASNRC, 1990d Gilbert, 1991d

Additive Risk Projection Model

Multiplicative Risk Projection Model

6.2

-

2.3 to 5.0 5.2 7.0' to 11.0~ 8.gdP.f 7.1d

a Population

of Japan. Estimate based on age-specific coefficients of probability. 'Estimate based on constant (age-averaged) coefficient of probability. United States population. Modified multiplicative model. "LOW-dose"leukemia component multiplied by two.

or

low-LET radiation, the ratio between the biological effect of high-dose rate radiation to that of the effect of low-dose rate radiation a t the same dose is known as the dose and dose-rate effectiveness factor, DDREF.

1.3 PRESENT RISK ESTIMATES / 3

11x SV-lwith the type of projection model used being one of the largest contributorsto the variation of risk estimates. Risk estimates have been more consistent over time when the multiplicative risk projection model is used. UNSCEAR, BEIR I11 [Committee on the Biological Effects of Ionizing Radiation of the National Academy of ScienceslNational Research Council (NASINRC)],ICRP and NCRP, in the period 1977 to 1980, were all in substantial agreement about estimates of lifetime risk coefficients for fatal cancer that were several times lower SV-l, compared with 4 to than those in use now (about 1to 2 x 5x SV-~). Evident1y;risk values agreed upon today must still be considered subject to future change as different information comes forward on any of those aspects on which the estimates are based.

1.3 Present Risk Estimates The ICRP (1991) and the NCRP (1993b) have further derived the nominal values of risk to be used for (low-dose rate) radiation protection as 5 x SV-l for a population of all ages and 4 x ~o-~sv-' for adult workers, after dividing the average high-dose rate estimate by a DDREF of two. It is uncertainties in these estimates of risk, now widely used in radiation protection, with which this Report is concerned.

1.3.1 Age and Sex Dependence These risk estimates apply to the populations specified. If the age and sex of the population group is known in more detail, tables such as Table 1.2 can be used to apply risk estimates more accurately. More detail on age and sex dependencies is provided in references such as Land and Sinclair (1991) and Pierce et al. (1996a).

1.3.2 Lifetime Risk The term "lifetime risk estimate" is not a unique description of the risk resulting from exposure to a tumor inducing agent. For a detailed discussion of some of the issues relating to lifetime risk see Thomas et al. (1992) and UNSCEAR (1994). One point only with reference to lifetime risk estimates will be cited here. It concerns the choice of "risk of exposure-induced death* (REID) or the choice of "excess lifetime riskn (ELR) as a measure of radiation-related

4 / 1.INTRODUCTION

Table 1.2-Fatal cancer risk for different ages and sex after low SU-l) (Sinclair, 1992).a dose or low-dose rate exposure (x Age (Y)

Male

Female

Average

'United States population, average of multiplicative and NIH transfer models (Land and Sinclair, 1991).

population detriment. REID represents the probability of an "untimely" death due to exposure. ELR is the difference between the probability of a cancer death given a specific exposure history and the probability of a cancer death in the absence of the specific exposure history. Consequently, the REID includes the exposure-induced earlier deaths of those who would have later died of cancer without the exposure. Because about 20 percent of the population would be expected to die of cancer in the absence of radiation exposure, the REID is about 20 percent higher than the ELR for all cancer sites combined for uniform whole-body exposure. For exposure limited to a single organ (e.g.,salivary gland, for which lifetime mortality rates are considerably below one percent) the REID and the ELR are more closely comparable. It should also be pointed out that single values (point estimates) of lifetime risk coefficients do not convey the wealth of information already known about sex and age variations in risk, which should usually be accounted for when dealing with specific practical situations. Consequently, uncertainties in past estimates are only a beginning to the consideration of uncertainties in many risk circumstances. Furthermore, uncertainties in risk estimates for individual organ and tissue sites are also of practical importance and probably differ among themselves; but these will not be addressed in this Report. 1.3.3 Risk Estimates for Low Dose and Dose Rate

The lifetime risk coefficients presently recommended by ICRP and NCRP were derived from the LSS data of the atomic-bomb survivors taking into account the following evaluations: (1)the REID values given by UNSCEAR (1988)for the multiplicative projection model for the period of observation to the end of life and a Japanese

1.4 UNCERTAINTIES IN RISK ESTIMATES /

5

population2 (11x SV-'1, (2) the ELR values given by BEIR V (NAS/NRC, 1990) for a United States population3 (about 9x SV-'1, and (3) REID values derived by the ICRP for a n SV-l(ICRP, 1991). These valaverage of five populations, 9.5 x ues for high-dose rate exposures were averaged and rounded to 10 x 10" SV-land divided by a DDREF of two to obtain 5 x SV-' for a population of all ages and for the low-dose rate conditions of normal radiation protection (ICRP, 1991; NCRP, 1993b). The nominal lifetime risk value for workers was derived simiSV-' CUNSCEAR, 1988) larly from a high-dose rate value of 8 x SV-' for divided by a DDREF of two for a nominal value of 4 x adult workers (ICRP, 1991; NCRP, 1993b). Lifetime risk coefficients for individual organs were also derived by ICRP and NCRP for use in radiation protection (ICRP, 1991, Table 4; NCRP, 1993b, Table 7.2). These were also used to derive tissue weighting factors (rounded fractional health detriments) for estimating effective doses used in determining compliance with radiation protection limits (ICRP, 1991, Table 2; NCRP, 1993b, Table 5.1). The values are called nominal values because they apply to averages for the whole population and a worker population. They do not apply to a specific individual unless that individual can be considered to fit the average in all characteristics. Adjustments for age and sex have already been recommended, see Table 1.2. In this Report, low doses will refer to absorbed doses in the range 0 to 0.2 Gy and to equivalent doses of 0 to 0.2 Sv. Low-dose rates are those below 0.1 Gy d-' for all radiations.

1.4 Uncertainties in Risk Estimates 1.4.1 Past Uncertainty Evaluations I t is important to address uncertainties in risk estimates for radiation induced fatal cancer in a realistic manner. The first serious attempt to do so occurred in relation to the evaluation of probabilities of causation in given exposure circumstances, i.e., the production of the "NIH (National Institutes of Health) tables" (NIH, 1985). A very useful initial appraisal of uncertainties in the relative probability that a specific cancer was due to a given 2~apanesenational mortality patterns, 1980 (see UNSCEAR, 1988, Appendix F, Table 64). 3 ~ i t astatistics l of the United States, 1980 (PHs, 1984).

6 1 1. INTRODUCTION radiation exposure resulted. Our currently used estimates of the risk of cancer in radiation protection start with the evaluations by UNSCEAR in 1988. The UNSCEAR (1988) treatment of uncertainties dealt with such issues as confounding (by smoking for example), the "healthy worker effect," different and changing baseline cancer rates in different countries and the dose response pattern, but in a general rather than a specifically quantitative way. In the BEIR V Committee report of 1990 (NASNRC, 1990)the uncertainties were addressed in a more quantitative manner following broadly the approach of the NIH Committee in 1985 (NIH, 1985) and concentrating on individual tumor sites rather than total cancer risk. The features considered included not only random error associated with sampling variation in the fitted coefficients of the models used but uncertainties in dose estimates, certification of cause of death, population effects, the choice of risk versus time model, sex and age differences, and the shape of the dose response curve. Some results were provided in the form of geometrical standard deviations (GSD), a number greater than one (see Glossary). The range of uncertainty, expressed as a confidence interval is computed by dividing and multiplying the point estimate by a specified power of the GSD. For example, a 90 percent confidenceinterval for estimate E with GSD G has lower limit E / G ~ and . ~ upper ~ ~ limit E ~1.645 . While some GSD's provided by the BEIR V Committee for individual tumors, age groups and time after exposure were estimated to be as low as 1.24, others ranged up to more than three. Until now, neither ICRP nor NCRP has specifically addressed the issue of uncertainties in risk estimates recommended for use in radiation protection.

1.4.2 NCRP Approach to Uncertainty in Risk Estimates for Radiation Protection The question now arises, W h a t degree of confidence (or uncertainty) can be attached to the current nominal values of lifetime risk coefficients for all cancer used for radiation protection a t low doses and dose rates as recommended by ICRP and NCRP?" This Report examines individual uncertainties in the five modular components on which the lifetime risk coefficients are based (Table 1.3). The evaluation of overall uncertainty has been accomplished in the following way. For each of the five individual modular components, a probability distribution, a likeliest value (usually the 50th percentile), and a 90 percent confidence interval (5th to 95th

1.4 UNCERTAINTIES IN RISK ESTIMATES

/ 7

Table 1.34omponents of risk coeficient derivation from the LSS of the atomic-bomb survivors. Componenta Epidemiological uncertainties Dosimetrical uncertainties Population transfer model Projection to lifetime Extrapolation to low dose or low-dose rate exposure (DDREF)

Section 2 3 4 5 6

T h e first four of these components concerns the estimate of high dose, high-dose rate risks from the atomic-bomb survivors. The fiRh is the component for converting high dose and dose-rate risk to low dose and dose-rate risk.

percentile) have been subjectively selected. Other choices of confidence interval, e.g., 95 percent, could have been made but 90 percent is commonly used and is quite appropriate for our purposes. There is usually little or no information on the shapes of the probability distributions and therefore the choice has been largely subjective. A triangular distribution (such as shown later in Figure 3.2 for example) has been chosen when a degree of subjective confidence can only be attached to the likeliest value and to the possible range of values. Normal or lognormal distributions have been preferred for smooth, symmetric or right-skewed distributions. These two distributions are appropriate theoretically when there is no reason to believe that random error represents the sum or product of independent incremental components. However, the overall results for the combined uncertainties are not sensitive to the particular shapes of the probability distributions selected for each component provided the likeliest values and the 90 percent confidence intervals remain the same (IAEA, 1989; NCRP, 1996). Finally, the overall uncertainty in the risk estimate and its central value have been estimated using Monte Carlo methods which take into account all the uncertainty estimates for the individual modular components (Section 7). The text of this Report will rely on customary methods of uncertainty analysis as applied to uncertainty in environmental data and other circumstances. A useful source of information which includes many of the references to relevant principles and methods is NCRP Commentary No. 14 (NCRP,1996). The uncertainty evaluations addressed in this Report relate to the methods of derivation

8 / 1. INTRODUCTION of risk estimates and concern average or nominal values. They do not consider variability in the characteristics of individuals in the population which influences their risk and contributes to individual uncertainty. 1.4.3 City Differences In some ways, it might have been useful to examine the uncertainties in the risk of cancer derived from the exposures at Nagasaki separately from those derived from the exposures at Hiroshima. One reason for this is that the estimates of the dose according to the present DS86 system, may be relatively sound for Nagasaki (although this is not certain and there are some unique dosimetry problems with some specific groups from Nagasaki included in the analysis also), whereas more questions have continued to arise about Hiroshima, especially about the magnitude of the neutron component. This and other factors concerning city differences are discussed later (see Section 2.6). The sample of attributable cancers is small, 339 solid cancers and leukemia, in 5,936 cancer deaths altogether in the LSS up to 1985; consequently, subdividing the sample into Hiroshima (about two thirds of the sample) and Nagasaki (one third of the sample) is not considered desirable at this time. In this Report, uncertainties are considered collectively in the entire LSS sample. 1.5 Dose Response The evaluation of each of the five components (and in some cases subcomponents as well) of the uncertainty in risk estimates for radiation protection is described in Sections 2 through 6. Section 6 is of special impo~~tance because it discusses and accounts for the effects of dose and dose rate by using the DDREF. Inevitably, the choice of DDREF involves a choice in the shape of the dose response curve starting with a simple linear response (DDREF = 1)and proceeding to linear quadratic responses with initial linear portions of lower and lower slope as the DDREF increases. Values of DDREF from one up to five are considered in the distribution for the DDREF. Only if the DDREF went to infinity would the response be initially independent of dose, i.e., a threshold. Those responsible for the analysis of risks in the LSS state firmly "... the data for solid cancer, including tumor registry incidence data as well as cancer mortality data, are inconsistent with the notion of a threshold for

1.5 DOSERESPONSE /

9

radiation effects" (Pierce and Preston, 1996). Consequently, for the purposes of this Report, viz the evaluation of uncertainties in the risk coefficients derived from the LSS, the choices of DDREF will include all the reasonable linear and sublinear dose response models for the atomic-bomb survivor data. For the more general issue of linearity versus threshold for radiation effects, the NCRP has a committee addressing this question. It is noted that values of DDREF less than one, i.e., a supralinear response, have not been considered here either. If they had been, only a small value for the frequency could be assigned to a DDREF of say 0.5 or 0.3, and this would have only a very minor impact on the overall uncertainty.

2. Epidemiological Uncertainties 2.1 Introduction

"Epidemiological uncertainties" is a very genera1 term, used in this Report to refer to random error in observations, and also to systematic errors including the possibility that a model used to estimate risk may deviate from the actual (and unknown) pattern of excess risk in some important way. Confidence limits, standard errors, and p values for hypothesis tests all reflect random error in the context of a statistical model that is assumed to be true as specified. As a starting point, Table 2.1 gives the number of persons in the LSS sample as of the analysis of 1985 (Shimizu et al., 1988; 1990).

Table 2.1-LSS, atomic-bomb survivors (adapted from Shimizu et al., 1988; 1990). Total sample Total sample with DS86 Exposed Control Shielded Kerma (Gy) Dose groups

91,228 75,991 41,719 34,272 Number of People

2.1 INTRODUCTION

/ 11

Then, Table 2.2 summarizes the relationship between radiationdose and cancer mortality observed in the LSS sample over the years 1950 to 1985. The tabulated estimates resulted from linear regression of mortality rates on radiation dose and the associated confidence intervals reflect statistical uncertainties. Baseline (i.e., zero-dose) risk was allowed to depend upon city (Hiroshima or Nagasaki), age at exposure (i.e., in August 1945), attained age (i.e., age at diagnosis), and calendar year, but the slope of the line expressing excess risk as a function of radiation dose (in this case, shielded kerma? rather than organ or tissue dose), was assumed to be independent of city, sex, age and year. In fact, the estimates given in Table 2.2 are only average values and for many of these sites, excess risk is known to depend on sex, age at exposure, attained age andlor time following exposure. However, the average estimates in Table 2.2 are appropriate for our specific purpose. Excess risk was expressed in two ways: first, in relative terms in which the relative risk (RR) coefficient is given as a multiplier of baseline risk, i.e., a ratio without units and second, as an average number of deaths per lo4 person-year (PY), i.e., in the same units as baseline risk over the period of observation [excess absolute risk (EAR)]. For all cancers a RR of 1.39 times (average) baseline, was found while the EAR was estimated as 10.0 deaths per lo4 PY. Excess relative risk (ERR) is the RR - 1, or 0.39 in this example. Uncertainty about these estimates was expressed by confidence limits. Briefly, a pair of 90 percent confidence limits (i.e., a 90 percent confidence interval) for an unknown parameter a includes all numerical values % for which the null hypothesis, a = a. would not be rejected at significance level p = 0.10 in favor of the two-sided alternative hypothesis, a ;t ao.In most applications, it is also the set of values ole for which the null hypothesis would not be rejected at significance level p = 0.05 in favor of either of the two one-sided . or a > aO.Thus, if the EAR at 1Gy alternative hypotheses, a < a (EARlGy)for all cancers is estimated to be 10.0 per lo4 PY Gy with 90 percent confidence limits (8.36,11.8), that means that all values between 8.36 and 11.8 per lo4 PY Gy are consistent with the data, at the 90 percent confidence level; because the lower confidence limit is greater than zero, it also implies that the null hypothesis of no radiation effect (EARlGV= 0) is rejected at significance level 4~hielded kerma is the kinetic energy released per unit mass after the incident radiation has passed through intervening shielding material, but before entering the body.

12 /

2 . EPIDEMIOLOGICAL UNCERTAINTIES

Table 2.2-Summary measures of radiation dose response for cancer mortality by site:aBoth cities, both sexes (unless otherwise stated), all ages ATB~,1950 to 1985 (shielded kerma). Site of Cancer

Number Estimated Relative Excess Absolute Risk of per lo4 PYe Gy Risk a t 1Gy Deaths

All malignant neoplasms Leukemia All except leukemia Digestive organs and peritoneum Esophagus Stomach Colon Rectum Liver, primary Gallbladder and bile ducts Pancreas Other, unspecified Respiratory system Lung Female breaste Cervix uteri and uteruse Cervix uterie Ovarye Prostatee Urinary tract Malignant lymphoma Multiple myeloma Other aAdopted from Table 2a of Shimizuet al. (1988). Additional detail on individual organs is given in Table 2b of Shimizu et al. (1988). b~~~ = at the time of the bomb. 'PY = person years. d( ) Numbers i n parentheses indicate 90 percent confidence interval. Blanks in the Table indicate no lower confidence limit was provided. eRisk estimation for these sites is based on either males or females only.

2.2 SPECIFIC EPIDEMIOLOGICAL UNCERTAINTIES /

13

p = 0.05 in favor of the one-sided alternative of a positive effect

(EAR~G!,> 0). In science, generally a bias or systematic error is something that may invalidate the results of a study, but more often can be accounted for by a modification of the results, i.e., a correction. If, for example, smoking were more prevalent among high dose than among low-dose subjects, an analysis of radiation-induced lung cancer risks that did not adjust for smoking or an adequate surrogate, would be biased. A recognized bias can be corrected by modifying the statistical algorithm for estimation or, if that is not possible, by introducing a rationale for subjective adjustment with uncertainty factors contributing to the overall random error. Uncorrected biases should be included among random errors. Statistically, a biased estimate is one whose expected value is not equal to the value of the parameter being estimated. Thus, whether the estimate is biased or not may depend upon the use to which it is put. In the example of breast cancer mortality (Table 2.2), 1.02 excess deaths per lo4 PY at 1Gy, and a RR of 2.00 (ERRlGy = 1.00) are unbiased estimates of EAR and ERR, respectively, at 1Gy as a weighted average over all ages at exposure for the period 1950 to 1985. But they are biased estimates of the EAR and ERR following exposure at age 10, because other analyses in the same study show that both absolute and RR vary by exposure aze. An example of bias resulting from statistical random error occurs with respect to individual dose estimates, and is discussed later (see Section 3.2).

2.2 Specific Epidemiological Uncertainties The statistical uncertainty in the risks derived from the 1950 to 1985 LSS mortality data (assuming the doses are known correctly) is represented by the confidence intervals in the EAR coefficients, e.g., see Table 2.2 adapted from Table 2a of Shimizu et al. (1988). For all cancer deaths, the EAR coefficient is 10.0 per lo4 PY Gy at 1 Gy with 90 percent confidence interval, 8.36 to 11.8, i.e., the 90 percent confidence limits are within about 220 percent of the nominal value. For leukemia, it is 2.29 (1.89 to 2.73) per lo4 PY Gy (also about 220 percent), and for all solid tumors, 7.41 (5.83 to 9.08) per lo4 PY Gy or about z25 percent. Further data are available for some individual tumor sites such as stomach, colon, lung, breast, etc. with somewhat larger confidence intervals, often of the order of *50 percent (see Table 2.2).

14 / 2. EPIDEMIOLOGICAL UNCERTAINTIES In this Report, the nominal value of the lifetime risk coefficient RHN(Rm = Hiroshima and Nagasaki) for all cancers for high-dose and dose rate, is taken to be 10 x SV-' (see Section 7) for a population of all ages. For the purposes of the uncertainty analysis, Rm will be assumed to have the same relative statistical uncertainty due to sampling as for the solid tumors over the period of observation, viz 225 percent. Consequently, a factor, F(RHN),which takes into account the statistical uncertainties associated with RHN,will be assumed to be normally distributed (a reasonable assumption based on a linear response model), with an average of one and a 90 percent confidence interval from 0.75 to 1.25, corresponding to a standard deviation of 0.15. The probability distribution of F(RHN)is shown in Figure 2.1. The risk coefficients in Table 2.2, which summarizes the atomic-bomb survivor experience from 1950 to 1985,were obtained with a linear model, parameterized as follows: Risk = a + PD or Risk = a (1+ yD), where a represents the baseline rate, P the EAR coefficient, y the ERR coefficient, and D is the dose. If the EAR and ERR have the same value then p = acy but, if p and y have fixed values while a does not and instead varies with city, sex, age, etc., the absolute risk (AR) and RR models cannot predict the same risk for all combinations of these factors, i.e., p and ay cannot always be equal. A more usual practice, not followed in the calculations leading to the results in Table 2.2, is to model RR and then convert to age-specific AR by multiplying the estimated ERR by the age-specific baseline risk. The usual practice in estimating the risk coefficient (once the choice between relative and AR has been made) is to use the simplest dose-response model consistent with the data. Linear estimates are given in Table 2.2, at least partly because, for all solid cancers combined and most single organ sites, no statistically significant improvement in fit is obtained by adding dose-squared or higher power terms to the linear dose-response model. Sometimes, this occurs because linearity fits the data very well, and the estimated dose-squared coefficient, E, in a quadratic model, e.g., in Risk = a (1 + yD

+ dl2),

(2.3)

2.2 SPECIFIC EPIDEMIOLOGICAL UNCERTAINTIES / 15

1.25

0.75

Parameter Magnitude

Fig. 2.1. Distribution of statistical uncertainties in estimates of risk for solid tumors [F(RHN)].(The 90 percent confidence interval is shown by the arrows.)

is close to zero with fairly tight confidence intervals, while the value for the linear coefficient y is little different from its fitted value in the linear model. When that happens, there is usually little disagreement that a linear model estimate is appropriate. Where a linear-quadratic dose-response model is used for estimation, as in the case of leukemia (see, e.g., NAS/NRC, 1990), the relative contribution of the square of dose in Equation 2.3 relative to the linear term decreases with decreasing dose. The same is true at low-dose rate, since risk can be modeled as the sum of risks associated with low doses received during discrete time intervals. The DDREF is used when the available data do not support a multi-parameter model like that given by Equation 2.3 and a simpler, linear model is used by default, but for theoretical or other reasons it is believed that the true model is actually linearquadratic with a non-negligible coefficient for dose-squared. For low-dose exposures, or cumulative exposures obtained at a low-dose rate, the linear-model estimated risk is divided by the DDREF. This does not consider the problem of saturation, perhaps mainly from cell killing which is, however, reconcilable under certain conditions (Sinclair, 1993). In the case of leukemia, a linear quadratic model is often used to determine the initial slope of the response (e.g., NASMC, 1990). In this case, no DDREF needs to be applied.

16 / 2. EPIDEMIOLOGICAL UNCERTAINTIES

A simple linear response divided by a DDREF is the approach used in this Report. The DDREF will have a distribution of values determined by uncertainty considerations (Section 6) implying different dose response models. 2.3 Bias in Risk Estimates Due to Errors of Detection and Confirmation Cancer statistics s d e r from failures to detect cancer cases (detection error), and from erroneous classification of noncancer cases as cancer (confirmation error). Both errors lead to misclassification. This is clear for mortality data derived from the results of studies comparing autopsy findings with independently obtained death certificate diagnoses. It appears that, among LSS sample members, the frequencies of the two kinds of errors are independent of radiation dose, but depend heavily on cancer site and age at death. Also, some organs, such as the liver, are often targets cf metastases from cancers of other organs, and these metastatic cancers may be erroneously reported as primary cancers of the receiving organ; this kind of error does not involve confusion between benign and malignant disease, however. Errors of cancer detection affect absolute measures of risk but not usually RR. Consider that the cancer rates at 0 and 1Sv are both too low by 10 percent of their respective true values, their difference (an estimate of AR at 1Sv) is also too low by 10 percent, but their ratio (an estimate of RR at 1Sv) is unaffected. The BEIR I11 Committee (NASMRC, 1980) used a multiplying factor of 1.23 to correct its estimated AR coefficients, for all solid cancers, for errors of detection. Errors of confirmation, on the other hand, affect relative measures of risk: if the estimated rates at 0 and 1Sv are both inflated by the addition of, e.g., 12 cases per 100,000 PY due to misclassified benign disease unrelated to radiation dose, the difference between rates is unaffected but their ratio will be too low, provided that the actual RR for cancer is greater than unity. Sposto et al. (1992) in considering all these factors, concluded that estimates of ERR for total cancer mortality in the LSS sample should be multiplied by a factor of 1.13 to adjust for reporting errors. It should be remembered that this correction factor is itself based on data analysis, and is uncertain. The level of uncertainty is difficult to assess from the published paper because, in their sensitivity analysis, Sposto et al. (1992) concentrated on the problem of misclassification of cancer deaths as noncancer deaths, estimated from autopsy data to be 22 percent for all ages combined, rather than on the opposite form of

2.3 BL4S IN RISK ESTIMATES /

17

rnisclassification, estimated to be 3.5 percent. However, parameter estimates, with standard errors, were given for the probability of misclassification as a function of age at death. Based on the point estimates, the average noncancer to cancer misclassification probability of 3.5 percent corresponds to death at age 72.2.If the estimates are assumed to correspond to normal random variables, approximate 90 percent confidence limits for the misclassification probability a t age 72.2 are 2.6 and 4.8 percent, which correspond to correction factors 1.09 and 1.18, respectively5 Finally, if the correction factor of 13 percent corresponding to a rnisclassification probability of 3.5 percent is interpolated linearly, 90 percent confidence limits for the correction factor of 13 percent are roughly 9.5 and 15.6 percent. For the purposes of the uncertainty analysis, the conversion factor corresponding to misclassification [F(R)], is taken to have a most probable value of 1.1 and a 90 percent subjective confidence interval from 1.02 to 1.18. The probability distribution of F(R), shown in Rgure 2.2, is assumed to be normal; a standard deviation of 0.05 corresponds to the 90 percent subjective confidence interval that has been selected. 9 h e s e limits were obtained by fitting corrected nonleukemia cancer mortality data from a modified 1950 to 1985 LSS distribution disk obtained from the RERF, stratified by city, sex, age ATB and attained age, to a dose response model linear in weighted intestinal dose [neutron weight = 10 (as used here, and as used in the publications of the RERF, "weightn is synonymous with the relative biological effectiveness or RBE)]. Let n,and n,, be the observed numbers in a given cell for nonleukemia cancer deaths and noncancer deaths, respectively, and let N, and N,,, denote the corresponding true values, after correction for misclassification. With misclassification probabilities of 22 percent for cancer to noncancer and 3.5 percent for noncancer to cancer, N, and N,, can be calculated by solving the following linear equations,

for No and N,,.The solution for N , is proportional to (1 - 0.035) n, - 0.035 n,,, which means that the estimated value of ERR,, is independent of the postulated rate of cancer to noncancer misclassification. Substituting the value of N, for that of n, in each cell of the data set yielded a regression coefficient of 0.442 compared to 0.392 based on n,, a 13 percent increase. The same procedure, using 0.026 and 0.048, respectively, instead of 0.035, gave regression coefficients 0.428 and 0.463.

18 /

2. EPIDEMIOLoGICAL UNCERTAINTIES

Parameter Magnitude

Fig. 2.2. Distribution of uncertainties due to misclassification of cancer deaths [F(R)I. (The 90 percent confidence interval is shown by the arrows.)

2.4 Biases Affecting Risk Estimates of Cancer Morbidity Estimates of radiation induced cancer mortality can also be derived from incidence estimates adjusted by suitable estimates of lethality fractions. There are some advantages to this approach, which, of course, requires the availability of incidence data. Epidemiological studies at the level of cancer incidence, which are performed through the RERF Tumor Registry, include ascertainment of nonfatal as well as fatal cases. Examination of clinical and pathology records, and pathology review of borrowed tissue samples and slides are used to refine case ascertainment, including that of cases identified from death certificates. To the extent that such supporting materials are available, the frequency of false positives can be substantially reduced. It is also possible, through aggressive pursuit of cases coded to conditions that are often confused with the diagnosis of interest (e.g., cancer of the minor salivary glands is often miscoded as cancer of the oral cavity),to reduce detection errors by identifying cases mistakenly diagnosed. Thus, studies at the level of incidence are inherently more accurate than mortality-based studies using death certificate diagnoses, in terms of classification of those cases that come to the attention of the investigators. In particular, bias due to false positives is a minor

2.4 BIASES AFFECTING RISK ESTIMATES OF CANCER MORBIDITY /

19

problem in incidence studies compared to estimates based on death certificate diagnoses. There is, however, no national system by which nonfatal cases of possible cancer can be identified by RERF among members of the LSS sample, many of whom migrated to other parts of Japan at sometime after the sample definition date of October 1,1950. Notification of deaths among members of the LSS sample, and access to their death certificates, is virtually complete because of special arrangements between RERF and the responsible Japanese government agencies. The RERF Tumor Registry, however, is based upon local tumor and tissue registries run by the medical associations of Hiroshima City, Hiroshima Prefecture, Nagasaki City and Nagasaki Prefecture, which cover current residents of their respective areas. A nonfatal cancer case diagnosed elsewhere may be reported to the RERF registry years later, if the patient returns to Hiroshima or Nagasaki and enters (or re-enters) the local medical care system, or the cancer may be mentioned on the patient's death certificate if he or she dies elsewhere in Japan. The problem with using such varied sources of case-finding information is in determining appropriate person or PY denorninators. Because of migration of LSS sample members throughout Japan and, to a much smaller extent, nonparticipation of some smaller hospitals and clinics in the local registries, there clearly is under-ascertainment of cancer morbidity. Migration is known to have been greater among younger survivors compared to older ones, among Nagasaki as compared to Hiroshima residents, and among LSS sample members who were not in the two cities at the time of the bombings, as compared to those who were present (i.e., the "exposed"). It does not, however, appear to depend upon radiation dose among those who were exposed. Thus, estimates of dose-related RR are largely unaffected by migration, whereas estimates of AR must be adjusted for underascertainment. The solution favored by RERF is to limit calculations of dose-related AR to cases diagnosed locally, using PY denominators adjusted to reflect the numbers of sample members actually resident in or near Hiroshima and Nagasaki at the times of diagnosis, and to consider only exposed cases when computing both absolute and RR estimates. No attempt is made here to evaluate uncertainties in the case where mortality data are derived from incidence data since this is not the basis of the current risk estimates used in radiation protection. Incidence data in the LSS (Mabuchi et al., 1994;Preston et al., 1994;Ron et al., 1994; Thompson et al., 1994)will eventually play a much larger role in radiation protection.

20 / 2. EPIDEMIOLOGICAL UNCERTAINTIES

2.5 Unrepresentative Population Another bias not often addressed is that of an unrepresentative population; unrepresentative, that is in terms of the normal state of a healthy population of all ages. Unlike most populations to which LSS-based risk estimates are likely to be applied, the LSS sample was drawn from a wartime Japanese population known to be suffering from various privations, including undernutrition and the possible stress (or benefit) of cigarette rationing both before and after the bombings. Some were also affected by blast and burn injuries as well as radiation. The population also was depleted of healthy men of military age, but this influence may be small, because in the known dependence of risk on age at exposure, young males are about "average" (Table 1.2), consequently, subtracting or adding them to a whole population has only a small effect. In fact, the population is unusually well represented by all age groups. It has been argued also that the survivors were healthier individuals (Stewart and Kneale, 1990). Little and Charles (1990) found some evidence of a selection effect but only in the first followup period of the survivors. They also analyzed the possible effects of the Stewart and Kneale hypothesis assuming it to be true and concluded the effect would underestimate the risk but at most by a small amount in the range 5 to 35 percent. The influences of these factors on radiation-related cancer risk years later are unknown, although they seem likely to be small. Thus, the population has certain unrepresentative features but overall these may be less important than those that exist in many other exposed populations (such as those in medical treatment groups) also used for risk estimation. Care must be taken, however, in applying LSS-based risk estimates to populations distributed differently with respect to age and sex. Risk coefficients often vary significantly by sex and by age a t exposure, and summary coefficients for the LSS population reflect its particular age and sex distribution. It is therefore important to apply risk coefficients on an age and sex-specific basis to other populations when an overall estimate is desired (Land and Sinclair, 1991) and see Table 1.2 given earlier. No allowance has otherwise been made here for possible nonrepresentative features of the population.

2.6 BIAS DERMNG FROM CITY DIFFERENCES / 21

2.6 Bias Deriving from City Differences Another possible source of bias is the fact that the LSS sample consists of survivors from two different types of atomic bombs dropped on two cities, Hiroshima and Nagasaki. The survivor populations in the two cities have different age distributions, Nagasaki survivors were, on the average, 5 y younger than Hiroshima survivors. They have different baseline cancer rates (depending on site) and are genetically somewhat different [for example, an HLA haplotype predisposing carriers to adult T-cell leukemia~lymphomais markedly more frequent in Nagasaki than in Hiroshima (Preston et al., 1994)l.Also the two cities have had very different histories of postwar industrial and economic development. These city differences would not be a serious problem for risk estimation, requiring only that each city be modeled in addition to age, sex, dose, etc., in order to produce separate risk estimates for each city. However, the Hiroshima and Nagasaki bombs were of different types (uranium versus plutonium) and construction, produced different radiation spectra, and were exploded over vastly different terrains. Any attempt to use the LSS data to estimate the weight of neutrons relative to gamma rays, given that neutrons made up more of the total dose in Hiroshima than in Nagasaki, is necessarily confounded with other city differences, making it difficult to tell to what extent possible neutron effects actually reflect differences in the epidemiological data between the cities. With the current DS86 dosimetry, neutron doses in both cities are generally so low that estimates of neutron weight (of acceptable uncertainty levels) cannot be derived from the epidemiological data, even ignoring the other city differences (see also Section 3.5). Another problem (Straume et al., 1992) is that the DS86 probably underestimates the neutron component at Hiroshima (see Section 3), but by an uncertain amount. It seems less likely, but not certain, that the same problem exists a t Nagasaki. It is difficult to use city differences in risk to contribute to the solution of this dosimetry problem because of the many confounding factors noted above and because there are other dosimetry problems a t Nagasaki involving the assignment of doses to certain individuals and groups. If eventual revisions in the dosimetry should increase the estimated neutron dose in Hiroshima to the extent that the LSS data could become informative about neutron weight, the problem of confounding due to other city differences will need to be addressed.

22 / 2. EPIDEMIOLOGICAL UNCERTAINTIES 2.7 Summary of Epidemiological Uncertainty Given accurate radiation doses, complete and accurate ascertainment of cases, and a risk model that corresponds to the true dose-response relationship and to the use to which the estimates are to be put, epidemiological uncertainty is adequately represented by confidence limits for sex- and age-specific risk coefficients of interest. Bayesian methods (Lindley, 1972)can be used to incorporate externally-derived information about details of the dose response that cannot be estimated adequately from the dose-response data at hand. An upward correction factor of 13 percent seems appropriate for dose-related ERR for all-site cancer mortality, to adjust for case ascertainment errors, mainly false positives. More information is needed about the effect of this adjustment upon uncertainty. For site-specific cancer mortality, different adjustment factors, which are yet to be determined, will be appropriate. For cancer incidence based on tumor registry data or site-specific incidence studies, any adjustment required should be considerably less than for mortality but such adjustments have not been evaluated here. Thus, for all cancer, the lifetime risk coefficient RHN for acute exposure, i.e., without DDREF, has a most probable value of SV-l.Statistical uncertainties are expressed by the factor 10 x [F(RHN)]which is normally distributed with the 90 percent confiS V - ~ corresponding to a standence interval from 7.5 to 12.5 x dard deviation of 0.15, as shown earlier in Figure 2.1. There is also a bias due to ascertainment errors or misclassification which is taken also to have a normal distribution F(R), a most probable value of 1.1and 90 percent confidence intervals from 1.02 to 1.18 as shown earlier in Figure 2.2. No account will be taken of possible confounding factors including those involved in city differences or the possible unrepresentative nature of the population.

Dosimetrical Uncertainty 3.1 Random Errors and Biases The random and systematic errors in the presently used dosimetry system iDS86) (Roesch, 1987) have been estimated to be represented by a coefficient of variation of the order of 25 to 40 percent in both the DS86 final report itself (Woolson et al., 1987) and also in later re-evaluations of the uncertainties in DS86 (Kaul, 1989; Kaul and Egbert, 1990~). In the report by Kaul and Egbert (1990)~ the uncertainty model is designed around the calculation of kerma (kinetic energy released per unit mass) in a specific organ of a single survivor. Under equilibrium conditions, kerma and absorbed dose are equal. The kerma in question is the sum of kermas from eight radiation components, as follows: prompt neutron kerma (Drip) fission product (delayed)neutron kerma illnd) early (prompt and airlground secondary)gamma-ray kerma (Dm) fission product (delayed)gamma-ray kerma (D*) prompt neutron-house secondary gamma-ray kerma (Dhp) delayed neutron-house secondary gamma-ray kerma (Dhd) prompt neutron-body secondary gamma-ray kerma (Dbp) delayed neutron-body secondary gamma-ray kerma (Dbd) There is little difference (if any) in the values of kerma and absorbed dose in most organs of the survivors. For many purposes it is convenient to express field quantities such as free in air kerma or shielded kerma in air, and to express localized energy deposition in terms of organ or tissue absorbed dose. In the remainder of the

'KAUL, D.C. and EGBERT,S.D.(1990). "DS86 uncertainty and bias analyses," unpublished (see reference list).

24 / 3. DOSIMETRICAL UNCERTAINTY Report, the term "dose" will be used for absorbed dose and ICRP and NCRP define a mean absorbed dose in an organ and base the quantity equivalent dose (ICRP, 1991; NCRP, 1993b) on this mean absorbed dose times the value of the radiation weighting factor, w ~ , for the radiation in question. At RERF the term weighted dose is used to describe the gamma-ray absorbed dose plus 10 times the neutron absorbed dose. When the neutron component is small this is approximately equal to an equivalent dose. In the DS86 final report (Roesch, 1987),the most important contributors to the total dose are the prompt and delayed gamma-ray doses, Dypand Dd. In recent years, however, measurements have bolstered the case for a significant prompt neutron kerma (Drip) at Hiroshima, which remains to be confirmed (Straurne et al., 1992; 1994) (see Section 3.6). The calculation of dose for each organ in DS86 involves the source, a free field, and house-shielding and body-shielding transfer functions. Uncertainty and possible bias in the shielding, yield, air transport and phantom models receive special attention because of their dominance in the overall uncertainty. Table 3.1 presents estimates of the main sources of uncertainty in the doses for Hiroshima and Nagasaki (Kaul and Egbert, 1990). Random errors are very similar at Hiroshima and Nagasaki, and vary very little as a function of distance from the hypocenter. They also vary little if expressed on a relative scale. Table 3.2 presents recent estimates of overall uncertainties due to random errors in the assessment of exposures to survivors from both cities (Kaul and Egbert, 1990); for indoor locations, the fracticnal standard deviation (FSD) or coefficientsof variation are about 35 percent of the means, while the 90 percent confidence intervals indoors, assuming normal distributions, are estimated to be about 60 percent of the means. These random errors give rise to bias in the doses causing a need for an upward correction to the risk with increasing dose which is discussed in Section 3.2. 3.2 Bias Resulting from Random Errors in Dose Most risk estimates are not adjusted for random errors in dose estimates. The principal effect of such errors is a downward bias (Gilbert, 1982;Jablon, 1971;Pierce et al., 1990)in the slope of a fitted linear dose response, i.e., a lowering of the estimated risk coefficient. If the random error increases with increasing dose, as in a lognormal error model, a related effect is a bias toward less

26 /

3. DOSIMETRICAL UNCERTAINTY

Table 3.2-Uncertainties due to random errors in the assessment of exposure for survivors a t Hiroshima and Nagasaki a t various distances from the hypocenter (adapted from Kaul and Egbert, 1990). Open air Distance (m) Hiroshima 1,000 1,500 2,000 Nagasaki 1,000 1,500 2,000

Indoorsa

FSD

90%CI

FSD

(%I

(%)b

(%I

90%CI

21 21

34 34 34

56

20

35 35 33

25 24 22

41 39 36

36 36 35

59 59 58

c%)~

56 56

a Averages of

results obtained using several models. Assuming normal distributions.

positive, or more negative curvature of the fitted curve. (To see this, imagine that the individual doses are measured with less and less accuracy as the dose increases. In the limit, measured dose would have nothing to do with the response, the risk of excess cancers, i-e., the response to the measured dose is flat. However zero dose would still be zero, resulting in a curve rising steeply from zero excess at zero dose and quickly flattening out.) This type of bias can in principle be characterized, given an assumed distribution of the actual dose within the population and, for each possible value of the actual dose a statistical error model for the measured dose. Given these assumptions, and a measured dose value, a conditional probability distribution can be constructed for the unknown actual dose underlying that particular measurement; the correction for a linear response model consists of substituting the mean of that distribution for the measured dose (Pierce et al., 1990). These effects are illustrated by a numerical exercise calculated for this purpose and summarized in Table 3.3 in which RERF cancer mortality data for 1950 to 1985 (RERF, 1990; Shimizu et al., 1988; 1990) were regressed on unadjusted and adjusted dose. The left-hand side of the Table pertains to leukemia and bone marrow dose, while the right side pertains to solid tumors and intestinal dose. For each, models linear in weighted dose, Dw = D, + WD,, where W in this

28 1

3. DOSIMETRICAL UNCERTAINTY

example is 1or 10 or linear in Dw and D: (i-e.,quadratic in gamma dose), are summarized as fitted to DS86 or DS86 adjusted dose (i.e., adjusted for dose error) over the dose range 0 to 4 Gy (Dy= gamma dose; D, = neutron dose; W = neutron weight, i.e., neutron relative biological effectiveness (RBE);D, = total weighted dose). Thus, the tabulated values also illustrate the effects of dose adjustment on fit and parameter estimates for different dose-response models and neutron weights. This exercise is intended only to show effects of dose adjustment in some average sense: radiation related tumor risk also depends on sex, exposure age, attained age and time following exposure. The dose adjustment is a matter of scaling, which preserves the ordering of doses within cities and changes it very little for the combined cities. For any given model and W value, therefore, there is little difference between the degree of fit obtained using adjusted and unadjusted dose values, as measured by tabulated values for deviance [the lower the deviance, the better the fit (see Glossary)l, although changes in the parameter estimates are evident. The differences in fit between the linear and quadratic models were substantially higher for leukemia, but not for solid cancer, when the analysis was based on adjusted rather than unadjusted dose estimates. For example, the deviance difference for leukemia (W = 10) between the fitted linear and quadratic models was 849.09 - 840.33 = 8.76 based on unadjusted dose, but 851.29 - 839.99 = 11.30 based on adjusted dose. Moreover, using adjusted dose, the point estimate 5.15 for the parameter P for leukemia, and its lower confidence limit of 0.81, suggest a DDREF higher than the ICRP value of two which would correspond to a P value of about 0.67 if DDREF were determined only by the degree of curvature of the dose-response function for gamma rays. For solid cancers as a group, the corresponding analysis showed essentially no difference in fit between linear and quadratic models for either unadjusted or adjusted dose for either value of W. However, the 90 percent confidence limits for p using unadjusted doses, (-0.33, 0.72) when W = 1 and (-0.27, 0.69) when W = 10, while consistent with the value P = 0.67 corresponding to the ICRP DDREF value of two (P = 0.67, etc.), suggest a smaller value while the corresponding confidence limits obtained using adjusted dose, (-0.21, 1.59) for W = 1 and (-0.25, 1.55) for W = 10, are easily consistent with P = 0.67 and with values considerably higher. The dose adjustment recommended by Pierce et al. (1990) requires no change in the model for the variance of the estimated coefficient,that is, the adjustment itself is assumed to be without

3.2 BIAS RESULTING FROM RANDOM ERRORS IN DOSE 1

29

error. But different parametric assumptions about a lognormal model for dose measurement error would yield different values for adjusted dose, with consequent effects on calculated risk coefficients. It would appear that further work is needed to quantify this uncertainty. In the meantime, the proposed adjustment substantially corrects a known bias, and represents an important improvement in our ability to quantify radiation-related risk; the remaining, unresolved uncertainty, while not unimportant, seems relatively small in comparison. The estimated bias errors in the risk estimate to which random errors in the dosimetry give rise, are as follows: for the dose range 0 to 4 Gy, the bias errors cause an overestimate of the dose and an underestimate of the risk by 7 to 11percent for solid tumors and 4 to 7 percent for leukemia (Pierce et al., 1990). The overall underestimate of the risk coefficient due to random errors in the dosimetry, F(RE) is taken to be 10 percent on average (3to 15 percent with a subjective distribution assumed to be normal, as shown in Figure 3.1, with 90 percent confidence interval of 0 to 20 percent). These uncertainties do not include errors due to the magnitude of the neutron component and its weight, which are considered in Sections 3.5 to 3.7. In addition, other biases associated with the dosimetry system have been identified. The primary sources of other bias are the

1 .oo

1.20

Parameter Magnitude

Fig. 3.1. Distribution of uncertainties in risk from random errors in dosimetry [F(RE)I. (The 90 percent confidence interval is shown by the arrows.)

30 / 3. DOSIMETRICAL UNCERTAINTY errors in both the gamma ray and neutron fields and the survivor shielding models.

3.3 Bias in Gamma-Ray Measurements Versus DS86

Measurements by thermoluminescence dosimetry of ceramic bricks that were present and exposed at the time of the bombings allow a comparison to be made with estimates of gamma-ray free fields obtained using transport models (Maruyama et al., 1987). There is no substantive evidence that the Nagasaki gamma-ray free field, as estimated by DS86, is subject to any bias. On the other hand, it is very likely that the gamma-ray free field at Hiroshima, as currently described using DS86, systematically underestimates the true value, and that bias increases with distance from the hypocenter (Maruyama et al., 1987). It is probable that revised fission product gamma radiation and air/ground secondary gamma radiation calculations may result in very little change in the total gamma-ray free-field kerma at Hiroshima near the hypocenter but to increase by approximately 20 percent at 1,400 m. In this Report, the bias in the gamma-ray free field for the two cities F(Dy)is taken to range from 1to 1.4 with a most probable value of 1.1. This value and the distribution of values is triangular as shown in Figure 3.2. The 90 percent subjective confidence interval ranges from 1.04 to 1.32. Among the criticisms of DS86 is another paper which finds that the gamma-ray kerma (based on biological dosimetry) is overestimated at distances shorter than 0.8 k m (Scott, 1994).However, this is of little relevance for low-dose risk estimation since it refers to doses of 4 Gy and above. It may also be noted that the risk estimates derived from exposures at Hiroshima and Nagasaki are for relatively hard gamma rays, in the range 2 to 5 MeV. In radiation protection, more biologically effective lower energy gamma, x or beta rays may be encountered. No distinction or correction is usually made in radiation protection for differences in effect resulting from differences in the energy of the photons or beta particles. However, these differences in effects in some biological systems can be appreciable (e.g., Bond et al., 1978; Straume, 1995)although changes with energy just over 1MeV do not seem to be rapid in the plot given by ICRU Report 40,

3.3 BIAS IN GAMMA-RAY MEASUREMENTS VERSUS DSS6

1.04

/ 31

1.32 Parameter Magnitude

Fig. 3.2. Distribution of uncertainties due to bias in gamma-ray dose estimates [F(Dy)]. (The 90 percent confidence interval is shown by the arrows.)

Figure 3 (ICRU, 1986). No specific allowance is made for the effects of gamma-ray energy here. One additional uncertainty relates to the fact that the intestinal dose is used as a surrogate for the actual organ dose for each organ. Since cancers of the gastrointestinal tract constitute a large portion (45 percent) of the total risk (ICRP, 1991) this is not an unreasonable choice. However, it does introduce an additional uncertainty. The two principal components are the prompt and delayed gamma rays. In the case of prompt gamma rays (Hiroshima), the intestinal dose has a transmission factor relative to free-field kerma of about 0.80 (Roesch, 1987) and for other deep organs (e.g., bladder, bone marrow) it varies by 26 percent. For delayed gamma rays, this range is larger, of the order of k10 percent (Roesch, 1987).For shallow organs such as breast, the difference is larger too but the contribution of the breast to the overall risk is relatively small. When consideringthe exposure to and risk in the whole body, these differences will tend to balance out, consequently, the small additional uncertainty is not accounted for here. A more thorough evaluation would be more important in evaluating the uncertainties in the risk coefficients for individual organs.

32 / 3. DOSIMETRICAL UNCERTAINTY 3.4 Uncertainty Due to Survivor Shielding

Characterization in DS86 Doses could be lower or higher by 20 to 50 percent for survivors in certain classes of shielding (Roesch, 1987). For example, frontal shielding by a building external to the one occupied by the survivor is only taken into consideration in DS86 if the survivor and the building are separated by less than twice the building height. However, more refined calculations indicate that any building in the line-of-sight from survivor to hypocenter provides some shielding. Survivors for whom shielding by an external building was not considered may have received doses up to 30 percent less than estimated using DS86. This effect is compounded if there were several buildings in the line-of-sight from survivor to hypocenter. Also, it seems that the heights of internal walls were overestimated; this correction would lead to a dose increase of 4 to 10 percent for a single house. However, the DS86 model may have overestimated the dose received by the workers a t the Mitsubishi Heavy Industries Plant a t the Nagasaki Shipyard, located approximately 1,700 m from the hypocenter. In addition, changes in the shielding conditions may have occurred in the first several seconds following the detonation of the bomb; light Japanese houses may have been blown away, or, conversely, the collapse of solid walls may have provided more or less shielding. Since delayed radiation from the fireball makes a relatively large contribution to the total dose, the loss or gain of shielding as a result of the blast effect could be important for individuals who were indoors at the time of the detonation. Time-dose dependencies have not, however, been taken into account in DS86 (UNSCEAR, 1988). All of these sources of uncertainties in the shielding conditions affect individuals, or categories of individuals, and it is very difficult to estimate whether there is a bias and what the average bias would be. Thus, although additional uncertainty results, in this Report it has been assumed that there is no average bias in the shielding factor. 3.5 Uncertainty Due to Neutron Weight (Relative Biological Effectiveness) Changes in the neutron weight (from 1to 10) have little effect on the fit (as reflected in the deviance values) of dose-response models to the LSS leukemia and solid tumor data based on DS86 (Table 3.3). That is, these data provide very little information about or RBE, whose value must therefore be based neutron weight (W)

3.5 UNCERTAINTY DUE TO NEUTRON WEIGHT /

33

on other data. It must also be emphasized that, with such a small neutron component in DS86 for either Nagasaki or Hiroshima (nominally one to two percent of absorbed dose at Hiroshima, less at Nagasaki), even large differences in the values chosen for the W will have a relatively small effect on uncertainties in the total weighted dose. Let us consider that the neutron absorbed dose for given organs at a given location is as estimated in DS86 and consider the contribution of errors due to uncertainty in W.Note that values of W (i-e., RBE) and their relationship to values of Q (or wR) for neutrons have been reviewed in such texts as Rossi (1977), Sinclair (19851, ICRU (1986), Straume (1988) and NCRP (1990). Table 3-1-1 of Shimizu et al. (1988) provides neutron and gamma doses separately (for the combined sample, not Hiroshima and Nagasaki alone). For orientation purposes, consider the organ dose range 1.0 to 1.99 Gy: the average gamma dose to bone marrow is 1.369 Gy and the average neutron dose is 0.019 Gy or 1.4 percent of the total absorbed dose. A W of 10, which has been a preferred value in recent publications (Thompson et al., 1994; UNSCEAR, 1994), raises the equivalent dose due to neutrons to 12 percent of the total (0.19 Sv out of 1.559 Sv) and a W of 20 raises the neutron equivalent dose to 22 percent of the total (0.38 Sv out of 1.749 Sv), i.e., whether W is 1,10 or 20 causes an uncertainty of about k10 percent about the median value of the weighted dose for the value of W of 10. As noted earlier the weighted dose approximates the equivalent dose. Note that some estimates of weighted dose have been made by considering a W which is an increasing function of declining neutron dose (Shimizu et al., 1988; 1990). This subject has been discussed in some detail with regard to the incidence data by Thompson et al. (1994). The changes made by these more precise approaches to the problem are minor compared with a simple application of W = 10. Thus, it appears appropriate to apply a neutron W of 10 throughout the range of interest and an assumed uncertainty of +I0 percent in the total equivalent dose due to this assignment is reasonable. There have been suggestions that much larger values of W should be considered (e.g., Dobson et al., 1991; Rossi and Zaider, 1990; Zaider, 1991)but these are accompanied by very wide uncertainty bounds and occur only at very low doses of both neutrons and gamma rays at which the influence of uncertainties on risk estimates is very minor. A more important question is whether the shape of the dose-response curve is affected at low doses and

34 /

3. DOSIMETRICAL UNCERTAINTY

whether the slope of the linear portion of the gamma-ray curve is reduced by larger contributions from the neutrons (Straume, 1996). This possibility is dependent on the potential presence of more neutrons a t Hiroshima, a possibility that we have treated separately in Section 3.6. Until some of these questions are clarified by ongoing new studies and measurements, at which time a greater range of uncertainty in W may need to be considered, it seems appropriate to proceed as indicated earlier. Thus, finally, in this Report, the uncertainty in the nominal equivalent dose due to choice of W is taken into account by multiplying by a factor F(NR)ranging from 0.9 to 1.1with a most likely value of one (for W = 10). The distribution of values at F(NR) is assumed to be triangular, as shown in Figure 3.3. The 90 percent subjective confidence interval ranges from 0.93 to 1.07. 3.6 Bias and Uncertainties Due to the Presence of Thermal Neutrons at Hiroshima in Excess of Those Predicted by DS86

It was known prior to the completion of DS86, that there were some measurements of 6 0 ~ resulting o from neutron activation in steel that did not agree well with the calculations of neutron fluence used in DS86 (Chapter 5, Figure 3, Vol. 1of Roesch, 1987).The discrepancies between calculation and measurement were apparently present at both Hiroshima and Nagasaki. Subsequent examination of the 6 0 ~points o a t Nagasaki has resulted in revisions of the uncertain locations of the measurements which seem to cloud whether a discrepancy actually existed for that city. More recent et al., 1994)indicate good agreemeasurements with 3 6 ~(Straume 1 ment with calculations for Nagasaki, although the measurements do not extend beyond 1,261 m slant range. The neutron dose at Nagasaki is about 0.5 percent or less of the gamma dose. For Hiroshima, calculation and measurement of neutron activation and therefore presumably of neutron dose are in agreement at 800 m, but differ by up to a factor of 10 at about 1,600 m (Figure 3.4) (Straume et al., 1992). One scenario is that a modified Hiroshima spectrum (actually closer to a true fission spectrum) which will cause some increase in the estimated fast neutron kerma must be invoked in order to account for this. Precisely what combination of spectrum change and increase in fast neutron kerma will be required has not yet been determined and the dosimetry committees of the United States and Japan have yet to make a recommendation.

3.6 BIAS AND UNCERTAINTIES DUE TO THERMAL NEUTRONS

/ 35

Parameter Magnitude

Fig. 3.3. Distribution of uncertainties due to choice of W [F(NR)l. (The 90 percent confidence interval is shown by the arrows.) 100

1

'

1

.

1

.

,

.

,

.

,

.

1

.

=

1 5 2 ~ ~ 0 1 5 4 ~ ~ -0

0

9)

$

10:

0

Mco ~

0'

6

~

1

U

m 0

*50'

0

.

\

D

EJ

, :4'

0

,0°

0'

0

.

.

0

0'

0

0.1 400

1

600

.

1

800

,

,

,

1000

,

1200

,

1

1400

,

,

.

1600

1

.

1800

2000

Slant range (meters)

Fig. 3.4. Ratios of measured to calculated neutron activation in Hiroshima at various distances from the epicenter (slant range) (Straume et al.,1992).

Some authors have considered the potential impact on risk estimates of an increased estimate of the neutron kerma at Hiroshima. Straume (1993) used the measured response of chromosome aberrations induced by neutrons from a Hiroshima weapon replica in an in uitro system (Dobson et al., 1991) and applied it to the

36 / 3. DOSIMETRICAL UNCERTAINTY aberrations to be expected from the neutrons estimated to be at Hiroshima (Figure 3.4). He then estimated that the contribution of the neutrons to the total weighted dose will increase sharply with distance compared with DS86 where the neutron fraction is constant or declining. He also included dose dependent Wand DDREF in his analysis. The net effect is a large impact on the estimate of risk if this estimate depends mainly on doses at distances of 1,500 m or more. Preston et al. (1993) in the same issue of RERF update, questioned the approach used by Straume and used a simpler analysis to assess the impact on risk. They pointed out that the major contribution to the risk estimate comes from doses of about 1Gy at perhaps 1,100 to 1,200 m. Therefore, considering the whole range of doses and increasing the neutron component from about 1.5 percent in DS86 to about 5 percent (i.e.,about a factor of three), Preston et al. (1993) find that risk estimates based on all data and distances would be decreased by only 13 percent for a W of 10 and 22 percent for a W of 20. [For another useful discussion of the impact of changes in neutron component see Jablon (1993:l.l Straume (1996) and Rossi and Zaider (1996) both point to the possibility that at very low doses the relative effect of the neutrons is much greater than estimated by Preston et al. (1993)but some of the arguments made are countered in a response to Rossi and Zaider by Pierce et al. (1996b) (see also Rossi and Zaider, 1997). The situation is k r t h e r complicated by the fact that some Japanese workers, as explicated in a report to a joint meeting of the United States-Japan dosimetry committees in May 1996, believe that Nagasaki shows the same discrepancy in calculated versus measured neutron response as Hiroshima, based mainly on their measurements of europium radioactivity in soil. The situation requires further investigation of all the methods used by both United States and Japan workers. It may not get resolved until a recently proposed and potentially feasible technique of measuring fast neutrons directly in copper samples using the 6 3 ~ u ( n . p ) 6 3reaction ~n (Straume et al., 1996) gets implemented. In the mean time, calculations of the impact of increasing the neutron contribution to the weighted dose over that given in DS86 are made here using the RERF distribution disk (RERF, 1990). For example, using colon dose as the base, doubling the neutron weighted dose (W = 10) at Hiroshima, leads to a decrease in the risk estimate for all cancers and both cities, of 6.5 percent. Increasing further by a factor of four the neutron weighted dose to colon with W = 10, at Hiroshima, leads to a decrease in the risk estimate for

3.7 COMBINATION OF

UNCERTAIN^%^ AND BIAS FOR DOSIMETRY / 37

all cancers and both cities, of 17.7 percent. This is similar to the results obtained by Preston et al. (1993). Thus, a reasonable approach based on present knowledge is that the impact of the increased neutron contribution at Hiroshima (only) would seem to require a correction of about +13 percent in the estimate of equivalent dose and a new uncertainty from this cause which is probably at least of the order of k15 percent in the overall neutron dose. In this Report, the probability distribution of the correction for this bias, F(D,), is taken to be triangular, with a central value of 1.1 and a range from 1.0 to 1.3, as shown in Figure 3.5. The 90 percent confidence interval of the values is from 1.04 to 1.25. 3.7 Combination of Uncertainties and Bias for Dosimetry

Ideally, biases and random errors should be estimated separately and calculated for each individual in the cohort. Furthermore, conducting analyses of the atomic-bomb survivor data that fully account for all sources of error in dosimetry would be a very difficult task even if these errors could be fully characterized. Because such analyses have not yet been conducted, subjective estimates of average bias and random error have been assessed for

1.04

1.25

Parameter Magnitude

Fig. 3.5. Distribution of uncertainties in the risk due to uncertainty in the unresolved magnitude of the neutron component a t Hiroshima [F(D,)l. (The 90 percent confidence interval is shown by the arrows.)

38 / 3. DOSIMETRICAL UNCERTAINTY the entire cohort, along with estimates of the ranges within which the bias and the random error could vary. Dosimetry uncertainties in the risk [F(D)] appear to result from a combination of the four sources of uncertainty described above and summarized in Table 3.4, i.e.,

(for definition of terms, see Table 3.4). These biases and random errors were combined to provide a single distribution accounting for uncertainties in the risk due to uncertainties in dosimetry. The method used to obtain this single distribution is described later in Section 7. Assuming that there is Table 3.4-Dosimetrical uncertainties: Sources and subjective probability distributions. Range or Distribution Form

90%

Parameter

Symbol

Value of Parameter

Random errors (risk f a ~ t o r ) ~

F(RE)

1.1

Normal

1.0 to 1.2

Neutron weight (dose f a ~ t o r ) ~

F(NR)

1.00

Triangular

0.93 to 1.07

Neutron dose (dose f a c t ~ r ) ~

F(Dn)

1.1

Triangular

1.04 to 1.25

F(D$

1.1

Triangular

1.04 to 1.32

F(D)

0.84

Normal

0.69 to 1.0

Gamma-ray free field (dose factorlb Overall uncertainty in riskc

Confidence Interval

aRisk factor means the source of uncertainty impacts the risk. b ~ o s factor e means the source of uncertainty impacts the dose. T h e final uncertainty in the risk is given by the combination of all factors in Equation 3.1.

3.7 COMBINATION OF UNCERTAINTIES AND BIAS FOR DOSlMETRY

/ 39

no correlation between the various sources of error, the dosimetric uncertainties are found to result in an average correction. factor in the risk coefficient of 0.84, with an approximately normal distribution and a 90 percent confidence interval from 0.69 to 1.0 (standard deviation: 0.11) as shown in Figure 3.6.

Parameter Magnitude

Fig. 3.6. Distribution of uncertainties in the risk estimate due to overall uncertainties in the dosimetry from all sources combined [F(D)]. (The 90 percent confidence interval is shown by the arrows.)

4. Transfer of Risk Between Populations 4.1 General Considerations

We have only limited understanding of how to apply estimates of excess cancer risk, based on observations on an irradiated subset of one population, to predict the risk that would follow exposure of another population that has very different baseline cancer rates. This is unfortunate, because the Japanese atomic-bomb survivors constitute our primary experience, and for non-Japanese populations, transferred estimates are the basis for much of our risk calculations. For example, between Japan and the United States, baseline rates differ by factors of four or more for cancers of the stomach, colon and female breast. Although the analysis here is concerned only with "all cancers" for which the transfer problem is less than for individual tumors, some detail will be forwarded on individual tumors to illustrate the transfer problem and perhaps to point the way for future uncertainties assessments. There are two transfer models that are commonly used, and they are both very simple. The additive model for transfer is based on the assumption that the average yearly excess risk to be expected in any population, assuming the same proportional distributions by dose, sex and age at exposure, will be the same for similar follow-up periods. It was used by BEIR I11 (NAS/NRC, 1980) and by an ad hoc committee of NIH (1985) to compute excess relative risks for the United States population, using United States baseline values. These excess relative risks pertained to the period of observation in the LSS sample following the Hiroshima and Nagasaki atomic bombings, and were assumed to apply to the remainder of life as well, i.e., a constant RR. This additive model followed by constant RR projection is sometimes called the NIH model. The multiplicative model, as applied to transfer, was used by UNSCEAR (1988) and BEIR V (NASNRC, 1990);it is based on the assumption that the ratio of excess risk to baseline risk, at any age, is invariant over populations with different baseline risks.

4.1 GENERAL CONSIDERATIONS

/ 41

The two simple transfer models each fit biologically plausible ideas of multistage carcinogenesis, which in the past have been summarized in terms of cancer "initiation" and "promotion." For most solid cancer sites affected by exposure to ionizing radiation, excess risk appears following a latent period of a few years or, if exposure occurred at a young age, may not be visible until attained ages have been reached at which background risk is appreciable. Once seen, it tends to increase over time in rough proportion to age-specific background cancer rates. Thus, radiation carcinogenesis appears in most cases to involve a radiation-induced, early-stage event, most likely a somatic gene mutation, that may eventually progress to cancer if certain later events also occur. If background rates for a cancer differ between the United States and Japan mainly because of differential exposure to events leading to early-stage events like those caused by radiation exposure, then the effects of those differential exposures and radiation should be additive, and the additive model should apply for transfer of radiation-related risk estimates between countries. The multiplicative transfer model, on the other hand, would apply if background rates differ because of differential levels of later, age-related events required before an early-stage change can progress to cancer. By this reasoning, the additive model might be appropriate for transferring breast and stomach estimates from the LSS sample to the United States population, because Japanese immigrants to the United States, even when immigration occurred at very young ages, have tended somewhat to retain the low breast cancer and high stomach cancer rates characteristic of Japan, while rates among their American-born descendants are more similar to those of the general United States population (Buell, 1973; Haenszel, 1982;Haenszel and Kurihara, 1968;Haenszel et al., 1972;Ziegler et al., 1993).The multiplicative model might be more appropriate for colon cancer, because migrants to the United States and Australia from low-risk countries have tended to develop risks characteristic of the host country, suggesting that differential late-stage effects account for the between-country differences in baseline rates. It is, of course, naive to assume that things are so simple; it could be that Japanese immigrants to the United States have developed behavior patterns that cause them in later life to avoid exposure to influences associated with late-stage events in the progression to breast or stomach cancer, and that their colon cancer rates increase because of mutations caused by exposures during adult life in the adopted country.

In the meantime, it is important to recognize that, when the two countries' rates differ markedly for a given cancer site, risk estimates based on Japanese data also differ markedly depending upon how they are calculated. Table 4.1 contains lifetime risk estimates of site specific, excess cancer morbidity and, for all solid tumors only, mortality for a United States population. These coefficients are calculated from the LSS Tumor Registry data for 1958 to 1987 (Thompson et al., 1994) and transferred using the additive and multiplicative models. Table 4.2 contains ratios and differences of estimates obtained by the two methods. Two- and three-fold ratios are common; 10- and 20-fold ratios occur for a few sites, notably stomach, skin and female breast, depending upon sex and age at exposure. For certain sites and ages at exposure (all solid cancers as a group, and female breast cancer following exposure at age 10) the absolute difference in estimated lifetime risk can be substantial even if the ratio is less than two (see also Land, 1990), for comparisons based on mortality coefficients.

4.2 Factors Modifying Risk in Relation to Transfer Between Populations For most organ sites, the RR of radiation-induced cancer decreases with increasing age at exposure (Table 4.1). This trend is rather drastic for some sites, like the female breast and the thyroid gland, while for others, like the lung, it is less remarkable. For the breast, the trend appears to be like a three-step staircase: ERR is high for exposure before age 20, substantially lower for exposure at ages 20 to 39 and barely visible for exposure after 40. Increased cancer risk is not evident immediately following radiation exposure, but takes 5,10or more years to appear; furthermore, it is evident from experimental investigations (Clifton and Crowley, 1978; Clifton et al., 1975) and from studies of breast cancer in atomic-bomb survivors (Land et al., 1994),that risk can be modified by events that intervene between radiation exposure and cancer diagnosis. Radiation exposure during the third or fourth decades of a woman's life is likely to have been preceded or closely followed by a full-term pregnancy accompanied by major irreversible terminal differentiation of ductal cells, which then may be less capable of neoplastic transformation. Female exposure after age 40 is likely to have been preceded or closely followed by menopause, which results in a drastic reduction in levels of estrogen, an important breast cancer promoting agent. Exposure during the first and

4.3 SITE-SPECIFICEVIDENCE FOR THE: TRANSFER MODEL /

47

second decades of life, on the other hand, is followed by years of estrogenic stimulation of undifferentiated cells. All these factors increase the complexity of transfers of risk between populations for individual cancer sites. 4.3 Site-Specific Evidence for Selecting the

Transfer Model There may not be a single transfer method that is equally suitable for all cancers or situations. Preston's analysis of breast cancer data from irradiated populations in the United States and Japan suggests that whatever is responsible for the four- to five-fold difference between the two countries in breast cancer rates is additive with respect to radiation dose as a cause of breast cancer (Land, 1995).But if a difference between countries were due to a tendency for women in one country to have a first child at age 18 while in the second the usual age a t first birth was 30, and no other factors were involved, then a multiplicative transfer model probably would be more appropriate given the results of a recent case-control study of interactions between radiation dose and reproductive history among atomic-bomb survivors. There are only a few data sets that are informative about the validity of different transfer models. Comparisons of breast cancer risks among different irradiated populations (Boice et al., 1979; Land, 1995; Land et al., 1980; UNSCEAR, 1994) strongly support the additive transfer model, despite the conclusion to the contrary by BEIR V (NASLNRC, 1990). Stomach cancer data, however, now appear to conform to the multiplicative model. Incidence of stomach cancer has been estimated to be increased by 54 percent of baseline per Sv in a large case-control study of European and North American women treated by radiation or surgery for cervical cancer (Boice et al., 1988);this compares with a very similar increase in stomach cancer incidence among women in the LSS sample a t all exposure ages (UNSCEAR, 1994), and an estimated ERRls, = 0.31 (90 percent CI = 0.12 to 0.56) for women exposed after age 30 (calculation based on RERF distribution disk of tumor registry data, 1958 to 1987). Estimates of EAR, on the other hand, are very different: 0.37 per lo4 PY Sv (0.03, 1.0) for the cervical cancer series, versus 6.2 per lo4 PY Sv (2.2, 11.0) for female atomic-bomb survivors exposed after age 30. Another comparison involves stomach cancer mortality among peptic ulcer patients treated by x radiation and surgery a t the University of Chicago (Griem et al., 1994). An ERR of 0.15 a t 1Sv was

48 / 4. TRANSFER OF RISK BETWEEN POPULATIONS estimated by the authors [the UNSCEAR estimate was 0.09 (0.05 to 0.14)l which may be compared with the LSS sample estimates, based on mortality for 1950 to 1985, of ERRlsv = 0.14 SV-' for males exposed after age 30 and 0.29 SV-' for females. The foregoing estimates were calculated from the RERF distribution disk data (RERF, 1990) using the estimated weighted dose to the stomach (W= 10) adjusted for random error in individual dose estimates according to the algorithm of Pierce et al. ('1990). Eighty-one percent of the peptic ulcer patients were males, giving a weighted LSS estimate of ERRlsv = 0.17. Griem et al. (1994)used a somewhat different method to derive an LSS-based estimate of 0.13 at 1 Gy. They also estimated 0.25 excess stomach cancer deaths per lo4 PY Gy in their series; the UNSCEAR 1994 estimate of EARlSv (Annex A, Table 8, part 11)for irradiated peptic ulcer patients is 0.43 lo4 PY Sv (0.2, 0.7), compared to 2.02 lod PY Sv (0.5 to 3.5) for the atomic-bomb survivors. Two studies of women irradiated for treatment of benign gynecological disease, giving stomach doses averaging less than 0.25 Sv, are also summarized by UNSCEAR (1994); they, however, have little power to discriminate between transfer models. The most that can be said about data for the stomach at this time, is that the results tend, on balance, to favor multiplicative transfer of the stomach cancer risk coefficient between the LSS sample and the United States population. The colon is another site for which Japanese and United States rates differ considerably. For that site, UNSCEAR (1994, Annex A, Table 8, part 111)gave ERRls, estimates of 0.47 (0.2,0.7) for Inskip et al. (1990) and 0.13 (0.03, 0.3) for Darby et al. (1994) studies of United States and United Kingdom women treated by irradiation for benign gynecological disease. The average colon doses in these studies were 1.3 and 3.2 Sv, respectively The corresponding estimates of EAR per lo4 PY Sv were 2.81 (1.5,4.4) and 0.93 (0.2,1.8), respectively. Based on the distributions of exposure age in these two studies, estimates were computed from the 1950 to 1985 LSS mortality data for women >35 ATB (at time of bomb): ERRlSv = 0.63 (-0.004, 1.69) and EARlSv = 0.92 (-0.55,2.42) lo4 PY. This is a case where one comparison is more consistent with the multiplicative model (Inskip et al, 1990) and the other is more consistent with the additive model (Darby et al., 1994) and no overall conclusion can be reached.

4.4 UNCERTAINTY DUE TO METHOD OF TRANSFER /

49

4.4 Uncertainty Due to Method of Transfer

The uncertainties associated with the choice of transfer model can be even greater than those associated with time projection or low-dose extrapolation of risk. The problem is, however, much greater for certain specific sites (such as stomach) for which baseline rates vary widely by population, than it is for all solid cancers as a group, for which baseline rates vary much less. One approach is to assume that the additive and multiplicative transfer models represent extremes, and that the truth must lie somewhere between them. Land and Sinclair (1991) used additive transfer for leukemia and the arithmetic average of the results using the additive and multiplicative models for solid cancer sites. The EPA (1994) used the geometric mean of the two approaches, and NCRP Scientific Committee 75 (NCRP, 1997) has used additive transfer for leukemia and female breast cancer, and the arithmetic average for other sites. A weighted average, arithmetic or geometric, might be used to take advantage of whatever epidemiological or theoretical information is available. One way to perform an additive transfer is to calculate a value for ERRlSv adjusted for differences in population rates and life tables between the two populations. The multiplicative transfer value (call it hdt) is the unchanged ERRlSv calculated from the LSS mortality data for 1950 to 1985. Suppose BLSs is the estimated baseline rate for the LSS cohort to which the coefficient applies, and B,, is the corresponding value for the new population. Then the adjusted ERRlsv (call it Radd)can be calculated as

Radd = h u l t

Bnew / B ~ ~ ~ .

Likelihood confidence limits for ERRlsv are derived from its likelihood contour (Preston et al., 1992), i.e., as the abcissa values under the intersection of a horizontal line at a given ordinate (likelihood) value, such that the proportion of the total area under that part of the curve corresponding to the interval agrees with the specified confidence interval. The likelihood contour for Radd is obtained by applying a change of scale to the contour for ERRlsv (i.e., multiplying by BneJBLSS). Thus, for example if BneJBLSS = 0.34 and if ERRlsv = 1.1with 90 percent confidence limits 0.2 and 4.3, then a mixture such as Qix = 0.5 Radd + 0.5 Rmult is equal to (0.5 x 0.34 + 0.5 x 1)x ERRlsv = 0.67 ERRlSv with point estimate 0.74 and confidence limits 0.13 and 2.88. Since both Radd and hult are defined in terms of ERRlsv any linear mixture of Radd and

50 / 4.TRANSFER OF RISK BETWEEN POPULATIONS

Quit is also a multiple of ERRlsv and its likelihood contour is obtained by scaling that for ERRlsv Fortunately, the differencesobserved in total cancer rates transferred by either the multiplicative or NIH additive models are much smaller than the large uncertainties that are associated with the cancer rates in some organs. For this reason, the confidence limits on the total cancer risk due to uncertainty in transfer can be relatively narrow, especially when confining the comparison to the United States population. For the United States population, the SV-l, the same value of the total cancer risk estimate is 10 x as for the rounded average of the ICRP populations. The two methods of transfer (additive and multiplicative) differ by about 30 percent for the risk estimates in the United States population. In this Report, the uncertainty due to transfer [F(T)I from the 1945 Japanese population to the current United States population is taken to have a most likely value of one and a GSD of 1.3.The probability distribution of F(T) is assumed to be lognormal as shown in Figure 4.1.The 90 percent subjective confidence interval of the values of F(T) are from 0.70 to 1.65.

1.$5

0.70 Parameter Magnitude

Fig. 4.1. Distribution of uncertainties due to transfer of risk from the Japanese to the United States population [F(T)].(The 90 percent confidence interval is shown by the arrows.)

5. Projection to Lifetime Risk 5.1 Constant Relative Risk Projection Model In the LSS sample at the time of the major evaluation of the mortality data up to 1985 (Shimizu et ad., 1988; 1990) only 39 percent of the survivors had died. Lifetime risks have to account for the remaining 61 percent of the population by some method of projecting to the end of the sample's life. Over a rather long period (more than 20 y) the time relationships indicate so far that the RR is reasonably constant for both total solid cancers and many individual cancers. Consequently, UNSCEAR (1988) used a constant RR model to project the remainder of the current sample to lifetime (as well as an additive projection which is not now favored).BEIR V (NASINRC, 1990) used time relationships, modified in different ways for different tissue groups but overall, on the average, the modifications were not much different (except for breast) from constant RR.

5.2 Considerations Regarding the Projection to Lifetime Risks in the Lifespan Study For any particular health outcome, lifetime risk is estimated by describingthe age-specific variation in excess risk over time following exposure during the period for which data are available, and evaluating the implications of any discernible patterns for age-time blocks not adequately covered by the data. For example, by 1985 nearly all of the lifetime cancer mortality risk had been observed for LSS sample members who were 40 y of age or older at the time of the bombings, whereas those who were under 20 in 1945were yet to experience most of their lifetime risk. The fact is that we have essentially no data on the level of radiation-related cancer risk at advanced ages among persons exposed at young ages, and until such data have been obtained there will be great uncertainty about lifetime risk following exposure at young ages.

For lifetime risk estimation, a simple model, in which radiationrelated ERR is constant over time following exposure, with a latent period of 5 to 10 y, generally fits LSS cancer mortality and morbidity data for survivors exposed at age 30 or older (UNSCEAR, 1994). m a t is, for survivors in this range of age at exposure, there is little evidence that radiation-related RR for all cancer deaths depends upon age at observation or time following exposure. There is, however, some uncertainty about risk during the early years when many of the older survivorshad already reached ages at which cancer is an important contributor to morbidity and mortality. The LSS sample includes only persons alive on October 1, 1950, and there is no observed LSS mortality before then. The period 1950 to 1958 predates the establishment of the Hiroshima and Nagasaki tumor registries, which is a disadvantage for studies of cancer incidence in the older cohorts; also, death certificate diagnoses were probably less accurate then than they are today. Although over 80 percent of the cancer deaths observed through 1985 in the LSS sample occurred among survivors over age 30 ATB, observations are relatively few at the oldest attained ages due to population decrement from all causes of death, and the signal-to-noiseratio is low overall because ERR tends to decrease with increasing age at exposure. The bottom line is that estimated ERR coefficients for the older age at exposure cohorts are appropriate for expressing the average risk over the period of observation and for lifetime risk calculations, but may be especially subject to statistical uncertainty and errors of case ascertainment. For younger survivors, and particularly those under age 20 ATB, a relatively small proportion of ELR was observed for most cancers by 1985 (leukemia is the principal exception), and much of the early follow-up period was empty of both baseline and excess cancers. For some sites, like the lung, it is still difficult to tell whether there actually is a radiation-related cancer risk associated with exposure before age 10. For others, like the stomach, thyroid gland, and female breast, and for all solid cancers as a group, there is a clear ERR associated with childhood exposure, and it is substantially higher than that among survivorsexposed during adult life. Shimizu et al. (1988, Table 6) present evidence suggesting that the RR of mortality from all cancers except leukemia, considered as a group, decreased with increasing age at death among survivors under 10 y of age ATE (p < 0.05 for trend). For ages ATB 10 to 19, 20 to 29,40 to 49, and 50+, no evidence was found for trend with attained age, but for the 30 to 39 ATB cohort there was suggestive

5.3 ATTAINED AGE MODEL

/ 53

evidence (p < 0.10) of an increasing trend with age. One question is the extent to which the observed pattern with attained age may reflect changing mixtures of different cancer sites with different dose responses and age-specific baseline rates, resulting in patterns that would not appear in data for a single cancer site. Another question is whether the observed decline in RR is a continuing one which might, by its apparent momentum, be expected to continue in the future, or merely reflects a contrast between early-onset and later-onset cancer and not a continuing decline. An example of the latter pattern is LSS breast cancer incidence for 1950 to 1985 (Tokunaga et al., 1994). For women exposed before age 20, there was a six-fold difference in ERR between incidence before and after age 35 (ERRlsv = 13.5 versus 2.1, p < 0.05), but ERRlsv was essentially flat as a function of attained age thereafter. The reason for the early versus late-onset difference is unclear, but it may reflect variation in genetic susceptibility to radiation-induced breast cancer within the LSS population; in any case, the estimate for early-onset cancer is based on relatively small numbers and would seem to have little to do with risk at older ages. Examples of gradual and continuing decline over time or with increasing age are leukemia following exposure during childhood, adolescence, or youn adulthood (Preston et al., 1994), and osteosarcoma following '24Ra injection (Mays and Spiess, 1983). Decreases in lung cancer mortality risk have been observed among uranium miners (NAS/NRC, 1988) and, less clearly, among British patients treated by 250 kVp x rays for ankylosing spondylitis (Darby et al., 1987); no such pattern is evident however, for lung cancer among LSS subjects, even in terms of RR. 5.3 Attained Age Model

Kellerer and Barclay (1992) have argued that a model in which ERRlS, depends only upon attained age fits the LSS cancer mortality data as well as a model dependent only on age at exposure, even though lifetime risk projections according to the two models differ by a factor of two, the attained age model yielding a lower lifetime risk (see Figure 5.1). To some extent, this may merely reflect a correlation between age at exposure and age a t observation in studies with follow-up limited to only 40 y following exposure. Tokunaga et al. (1994) obtained the same result in their analysis of LSS breast cancer incidence data for 1950 to 1985 although, within cohorts defined by exposure age, ERRlsv did not vary by attained age except for early-onset cancer.

54 /

5. PROJECTION TO LIFETIME RISK

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There is little in the way of theoretical guidance by which to limit the variety of models for the conditional distribution of time to cancer diagnosis or death from cancer, given that a cancer was in fact caused by a given radiation exposure. If the process depended only upon exposure, then one might expect the distribution of time to (radiation-induced) tumor to reflect tumor growth kinetics. Such a pattern is characteristic of osteosarcomas occurring among patients treated by injected 2 2 4 ~for a trabecular tuberculosis or ankylosing spondylitis (Mays and Spiess, 1983), for which time to tumor closely follows a lognormal distribution (Chmelevsky et al., 1988; Land, 1987),and it could be the case for some leukemia types (NIH, 1985). If it is assumed that radiation a d s primarily as a cancer initiator, then subsequent exposure to competing initiators over a lifetime might be expected to decrease ERRlsv by increasing the level of risk unrelated to radiation. If, on the other hand, the tendency for cancer rates to increase as a power of age reflects mainly the action following cancer initiation of promoter/progressor agents and events related to aging, one might expect ERRls, to be fairly stable over time following exposure (Land, 1987). The decline in ERR over time among uranium miners might reflect a promoting effect of alpha particles but possible influences of changes in smoking habits among miners should not be ignored.

5.4

LIFETIME RISK OF THOSE EXPOSED AT YOUNG AGES / 55

Excess leukemia risk among survivors exposed during childhood appears to have increased rather rapidly during the first few years after exposure and to have declined more slowly afterward, and to have essentially disappeared by 1985. The initial increase is poorly characterized because, although case ascertainment was reasonably efficient, many of the cases occurred before October 1, 1950, the date of recruitment into the LSS sample, and it is difficult to define appropriate PY denominators for them. Estimates of early risk have incorporated ad hoc extrapolations partially derived from obsewations of smaller, medically exposed populations like the United Kingdom ankylosing spondylitis patients. Risk increased and declined more slowly among survivors exposed a t older ages, and among the oldest female survivors, who were over 80 y of age in 1985, it cannot be said to have declined a t all (Preston et al., 1994). Temporal patterns of risk appear to depend upon histological cell type as well a s exposure age. For various reasons, therefore, it is difficult to characterize the temporal distribution of leukemia risk following exposure, but since that risk is essentially over, extrapolation to the remaining lifetimes of the survivors is not a problem.

5.4 Lifetime Risk of Those Exposed at Young Ages For solid cancers among persons exposed a t age 30 and older, the choice of a projection model has little effect on estimates of lifetime risk. For persons exposed a t younger ages, the choice of model may have a pronounced effect, a s illustrated in the UNSCEAR (1994) report (see Figure 5.2). The RR model constant over time following exposure was compared with alternatives in which, beginning 40 y after exposure, ERRlSv declined linearly from its estimated value for 1950 to 1985 to a value a t attained age 90 equal to (1)the estimated ERRlsv for males 50 y of age ATB or (2) zero illustrated for solid tumors in males in Figure 5.2. Alternative (1) yielded a lifetime risk estimate for the entire LSS cohort that was 16 percent less than the constant RR model estimate, while the estimate according to alternative (2) was 30 percent less than the constant RR model estimate. I t should be kept in mind, however, that a low ERRls, might also increase with further follow-up; a t the time of the 1980 BEIR I11 report, there were no data suggesting a n excess breast cancer risk among survivors 0 to 9 y of age ATB, whereas a t present the estimated risk is a t least as high as that

56 / 5. PROJECTION TO LIFETIME RISK

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among women ages 10 to 19 ATB, and higher than that in the older cohorts. Obviously, there is uncertainty in using any projection model to estimate lifetime risk for persons exposed at young ages. Even more uncertainty may occur, however, with estimates based on time trends within the period of observation, because of the multiplier effect as corrections reflecting relatively few deaths or cases at young ages are applied to the much greater n ~ m b e r anticipated s at older ages. For example, the estimated values for ERRlGy (kerma) presented in Shimizu et al. (1990) for nonleukemia cancer mortality among survivors ages 0 to 9 y ATB were 69 at attained ages