A

Radiation as a Carcinogen

A.1 RADIATION AS A CAUSE OF CANCER

At low doses of radiation, cells may be damaged. The main initiating event by which radiation damages the cells in the long term is damage to DNA in the cell nucleus. With well-orchestrated and efficient mechanisms, cells respond to the induced damage and attempt to repair it, but sometimes the damage cannot be repaired or is misrepaired, which may lead to mutations. The modifications induced by low levels of radiation dose may be transmitted to daughter cells and may lead to uncontrolled cell growth and consequently cancer, the health effect of primary concern in the context of radiation. Exposure to radiation is not the only way in which the DNA within a cell can be damaged and become cancerous. In fact, DNA damage can occur spontaneously or due to a number of other stressors such as chemical exposure (for example, smoking and lung cancer) and infectious agents (for example, hepatitis B virus and liver cancer). In other words, as ionizing radiation exposure induces DNA damage to the tissue, that tissue will already carry some damaged cells from other stressors.

Although small increases in the chance of developing cancer is the main health effect of low levels of radiation, such effects in individuals are probabilistic and known as stochastic effects. In other words, there appears to be no threshold below which effects do not occur, but the greater the exposure, the higher the probability that they will occur. Severity of the effects does not depend on dose. This is in contrast to the “deterministic” or “nonstochastic” radiation effects of high doses of radiation, that is, doses of several sieverts that can kill enough cells to cause injury such as skin reddening,



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A Radiation as a Carcinogen A.1 RADIATION AS A CAUSE OF CANCER At low doses of radiation, cells may be damaged. The main initiating event by which radiation damages the cells in the long term is damage to DNA in the cell nucleus. With well-orchestrated and efficient mechanisms, cells respond to the induced damage and attempt to repair it, but some- times the damage cannot be repaired or is misrepaired, which may lead to mutations. The modifications induced by low levels of radiation dose may be transmitted to daughter cells and may lead to uncontrolled cell growth and consequently cancer, the health effect of primary concern in the context of radiation. Exposure to radiation is not the only way in which the DNA within a cell can be damaged and become cancerous. In fact, DNA dam- age can occur spontaneously or due to a number of other stressors such as chemical exposure (for example, smoking and lung cancer) and infectious agents (for example, hepatitis B virus and liver cancer). In other words, as ionizing radiation exposure induces DNA damage to the tissue, that tissue will already carry some damaged cells from other stressors. Although small increases in the chance of developing cancer is the main health effect of low levels of radiation, such effects in individuals are probabilistic and known as stochastic effects. In other words, there appears to be no threshold below which effects do not occur, but the greater the exposure, the higher the probability that they will occur. Severity of the ef- fects does not depend on dose. This is in contrast to the “deterministic” or “nonstochastic” radiation effects of high doses of radiation, that is, doses of several sieverts that can kill enough cells to cause injury such as skin red- 271

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272 APPENDIX A dening, burns, organ damage, radiation sickness, and even death. Patients receiving radiation treatment for cancer often experience controlled acute radiation sickness because they receive relatively high levels of radiation. Infertility and cataract are two other examples of nonstochastic effects of radiation; cataract may not occur until several years after exposure. Doses to people near nuclear facilities are far below levels that would cause de- terministic effects. In the case of the effects of exposure to low levels of radiation (less than 0.1 Gy, or 100 mSv effective dose), the scientific uncertainty of radiation- induced cancer is considerable as there is little or no empirical knowledge. Despite the uncertainty, decisions are needed for use in setting standards for protection of individuals against the side effects of low-level radiation. Based on current scientific knowledge (or lack thereof), regulatory agencies in the United States currently use a model that describes radiation injury as a linear function of radiation dose that has no threshold; this is called the linear no-threshold (LNT) model. According to LNT, if a dose equal to 1 Gy gives a cancer risk X, the risk from a dose of 0.01 Gy is X/100, the risk from 0.00001 Gy is X/100,000, and so on. Thus, the risk of health effects including cancer risk is not zero regardless of how small the dose is. In the LNT model, data from high levels of exposure where radiogenic cancers have been observed are used to extrapolate risks at lower doses where cancers have not been observed, and if they exist they are beyond the current science to observe and measure. One result of following the LNT model is that a very small estimated risk, when multiplied by a large number such as the population of the United States, results in an estimate of a substantial number of cases or deaths, which in reality may not exist. Scientific groups such as the International Commission on Radiologi- cal Protection (ICRP), the National Council on Radiation Protection and Measurements (NCRP), and the National Research Council Committee on the Biological Effects of Ionizing Radiation (BEIR), repeatedly review and endorse the use of this model for assessing risk, which is used to set radia- tion protection standards and operating policies, such as the “as low as rea- sonably achievable” (ALARA) policy. This approach is often considered to be conservative and gives emphasis to public health. Data provided by the updated report of the atomic bombing survivors in Japan continue to be in support of the LNT model across the entire dose range. However, a concave curve was the best fit for data restricted to doses of 0-2 Gy. This resulted because risk estimates for exposure to 0.3-0.7 Gy were lower than those in the linear model (Ozasa et al., 2012). The finding was not explained. Not all countries support the LNT model at this time, but in general it is perceived that with so much uncertainty about the effects at low doses, it is appropriate to continue with the LNT model that has been in place for several decades for purposes of radiation protection.

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273 APPENDIX A A.2 BIOLOGICAL RESPONSES AT LOW DOSES A variety of different biological responses have been identified at low doses of radiation, although it is difficult to identify effects at doses that are close to those encountered from natural background radiation. It is highly unlikely that epidemiologic studies of populations around nuclear facilities will contribute toward knowledge of the effects of radiation at very low doses. Because of the epidemiologic limitations, efforts are directed toward improving understanding of the effects, response, and defense mechanisms to low-dose radiation at the cellular and molecular levels. The Department of Energy’s Low Dose Radiation Program is focused on understanding the effects of doses of radiation under 100 mSv by supporting research of the molecular and cellular responses to very-low-dose exposures. Some scien- tists have argued that DNA repair capabilities are effective at low doses, preventing the accumulation of DNA damage and mutations following low-dose exposures, while others have argued that low doses may be even more damaging per unit dose than high doses. Major discussion on the biological consequences of low-dose radiation despite being controversial has also led to the identification of pathways of radiation damage that are evident at low doses but difficult to measure at high doses in light of overwhelming DNA damage. Among these is the adaptive response, which would tend to dampen the potential adverse effects and perhaps even provide a beneficial (or hormetic) effect of radia- tion exposure at low doses. In most studies of adaptive responses, cells in vitro are given a “tickle” low dose of radiation (for example 20 cGy or 0.2 Gy) followed by a high dose of radiation (1 Gy). The administration of the “tickle” dose prevents some of the damaging effects of the high dose, including cell killing and chromosomal injury. In animal models a variety of investigators have documented that low doses of radiation can enhance immune responses (Cheng et al., 2010). There are also several damaging responses observed at low doses, in- cluding the bystander effect and delayed genomic instability. The bystander effect is defined as genetic changes (chromosome damage, mutations) in- duced in cells that are not directly hit by the radiation beam. The exact mechanism by which the bystander effect occurs is unclear, although data support both transmission of a factor either in conditioned medium (Sowa Resat and Morgan, 2004) or through gap junctions (Gaillard et al., 2009). Recent studies have documented that such bystander effects may occur in vivo as well (Singh et al., 2011). Delayed genomic instability has also been identified in irradiated cell populations where mutations do not occur in the irradiated cells themselves but rather in the progeny of these irradiated cells sometimes up to 13 generations later (Little et al., 1997; Morgan, 2003). Another detrimental effect of low-dose exposures (mostly in the cGy range) is low-dose hypersensitivity in which some cells in culture show an

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274 APPENDIX A enhanced response to the killing effects of x-rays at the very low doses (10- 60 cGy) than they do to slightly higher doses (1 Gy, for example). Whether this is really a low-dose hypersensitivity or an induced radiation resistance at the slightly higher doses (1 Gy) is not clear, and the mechanism for it has not been defined, although some attribute it to the need for a threshold number of double-strand breaks to induce cell-cycle arrest (Marples et al., 2004). Dose-rate factors are also important in considering the effects of low- dose radiation. Most studies have documented that low-dose-rate exposure is less damaging than similar doses administered at high rates, although these studies are limited, difficult to conduct, and predominantly in animal populations (Brooks, 2011; Vares et al., 2011). In long-term animal studies carried out at Argonne National Laboratory in 1960-1990, dogs and mice were exposed to doses of radiation daily with very low doses per day and equal doses given in a single exposure; these studies revealed that life short- ening and cancer incidence was significantly higher for animals given the high-dose-rate compared to the low-dose-rate exposures (Carnes and Fritz, 1991; Carnes et al., 1998). In other mouse strains (AKR), a lower incidence of cancer-induced thymic lymphoma was also found in mice exposed to low-dose-rate compared to high-dose-rate radiation (Shin et al., 2011), suggesting that there are significant differences in biological consequences (Uehara et al., 2010). Radiobiological data, some based on animal experiments, have been the basis of the dose and dose-rate effectiveness factors (DDREFs), that is, factors used to convert risk estimates from populations exposed in larger acute doses such as the atomic bombing survivors to populations who are exposed to lower low-rate doses. The ICRP derived estimates of the excess cancer risk after low-dose exposures and after exposures with higher doses but low-dose rates by reducing the corresponding risk value for the atomic bombing survivors by a DDREF of 2.0 (ICRP, 2007). The BEIR VII Com- mittee used a DDREF of 1.5 (National Research Council, 2005). It has been speculated that these DDREFs underestimate the risks from low-dose-rate exposures. For example, in a recent paper by Jacob et al. (2009), compari- sons of risks of radiation workers who receive chronic exposures with those of the atomic bombing survivors who received acute exposures indicated that risks among workers tended to be higher, contrary to expectations. A.3 BIOMARKERS Most individuals exposed to radiation do not wear physical dosimeters such as film badges or thermoluminescent dosimeters; therefore, recon- structing their exposure requires collecting information through interviews and available models and thus estimated exposures often contain a high

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275 APPENDIX A level of uncertainty. In an attempt to overcome this problem, biological markers are being developed as a useful tool for estimating the exposure and the effects of, or the response to, radiation. A biomarker is in general an end point that is objectively measured and can be used as an indicator of a biological state. Studies have highlighted the importance of biomarker research in radiation epidemiology specifically in assessing occupational exposure (Schneider et al., 1999), exposure following industrial accidents (Menz et al., 1997), as well as response to radiation therapy (Wickremese- kera et al., 2001). Two types of purpose-oriented categorizations of irradia- tion biomarkers have been proposed. Brooks segregates them into markers of exposure, sensitivity, and disease (Brooks, 1999), while others mention predictive, prognostic, diagnostic, and dosimetric markers (Okunieff et al., 2008). A single biomarker can often fit into several of these categories which serve different purposes. For example, biomarkers of effect mea- sure the biological responses in individuals who have been exposed to an agent (and also include elements of individual sensitivity to that agent); markers of exposure, on the other hand, do not necessarily indicate ef- fects. A methodology-focused categorization of radiation biomarkers would separate them into cytological and molecular markers, both with numerous subcategories. In addition, while cytological markers in radiation research are often very specific, molecular-based radiation biomarkers are often compendia of molecules rather than isolated molecular species. Today, the use of biomarkers in epidemiologic studies of low doses is unlikely to help with dose reconstruction, as the variability of the assays within a person and between persons is a major problem. However, the rapid advances in the research on biomarkers may in the future provide more sensitive tools that may also prove useful for epidemiologic purposes and significantly reduce the uncertainties related with current dose reconstruction models. A.4 EPIDEMIOLOGIC STUDIES OF IONIZING RADIATION A.4.1 Studies of Residents near Nuclear Facilities A British television program in 1983 reported a cluster of childhood leukemia in Seascale, a village 3 km from the nuclear fuel reprocessing facil- ity Sellafield on the Cumbrian coast, then known as Windscale. The televi- sion team discovered seven childhood leukemia cases over the previous 30 years, while less than one case was expected (Urquhart et al., 1984). Given the proximity of the village to the nuclear reprocessing plant, and in the ab- sence of any other obvious causative agent, a direct effect of environmental pollution with radioactive waste was hypothesized. The British government appointed an independent advisory group to investigate the claims. The group produced its report within seven months (Black, 1984), confirming

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276 APPENDIX A the TV broadcast, but could not explain the finding in terms of radioactive discharges. In response, a governmental Committee on Medical Aspects of Radiation in the Environment (COMARE) was set up in 1985 and over the past 25 years has published several reports using data from the national registry of children’s tumors. The reports include an extensive investigation of the Sellafield area (COMARE, 1996) and the sites of Dounreay in Scot- land (COMARE, 1988), Aldermaston in Berkshire, and Burghfield in North Hampshire (COMARE, 1989). Reviews by COMARE of the discharges from the nuclear installations showed that the doses that the general public residing in the area were likely to have received were far too small to have caused increases in childhood leukemia (COMARE, 1988, 1989, 1996). In 2011, COMARE published an update on the issue as its fourteenth report (COMARE, 2011), undertaking a further review of the issues addressed in the tenth report that covered the years 1969-1993 (COMARE, 2005). The latest report covered the period 1969-2004 and found no significant evidence of an association between risk of childhood leukemia and living in proximity to a nuclear power plant (COMARE, 2011). The sequence of cluster or ecologic studies finding excess cancers around a nuclear site and more detailed examination following to confirm the findings and research the associations has been a common approach for many years. Studies from Great Britain, Germany, France, and the United States contribute the most to the literature. Childhood leukemia is primar- ily investigated as it is recognized to be a “sentinel indicator” for radiation effects occurring with a shorter time latency following exposure and with a stronger dose-risk relationship. Although initially mortality data were used to evaluate the possible impact of living near nuclear facilities under normal operating conditions, it was soon realized that, given the advances in cancer treatment and consequent improvements in survival, incidence data (the number of newly diagnosed cases in a given period of time) could provide more relevant estimates. Studies on the cancer risks associated with living near nuclear facilities have come to different conclusions, with some suggesting a positive as- sociation between living in proximity to a nuclear facility and cancer risk and others suggesting that there is not a risk, or that the risk is too small to be detected with the methodology used. The power of a study to detect an effect, if there is one, depends highly on the hypothesized strength of the association to be detected and the sample size. Neither of these variables is likely to be high in an epidemiologic study of cancer risks in populations near nuclear facilities: a. The size of the estimated risks from reported radioactive effluent releases from nuclear facilities is likely to be small. Consequently,

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277 APPENDIX A epidemiologic studies have a limited ability to discern associations between radiation exposure and cancer risk in these populations. b. The size of the populations most likely to be exposed (that is, those living in very close proximity to a nuclear facility, for ex- ample within a 5-10-km radius) is relatively small. This limits the expected number of informative (exposed) incident cases or deaths that will be available for study, especially for rare cancers such as those of childhood. Study conclusions are based on a very small local population size, which makes the risk estimations statistically unstable because a single ad- ditional case, or one less case, can change the rate estimate dramatically. For example, in the study in Germany with 23 years of follow-up, out of the 593 leukemia cases in children under 5 years old diagnosed in the study area, only 37 cases (6 percent) were observed in the risk zone (≤ 5 km from a facility) (Kaatsch et al., 2008). Similarly, in the recent COMARE report (2011) with 35 years of follow-up, out of the 430 leukemia cases in children under 5 diagnosed in an area up to 25 km from the nuclear power plants in Britain, only 20 (5 percent) were in the risk zone (Table A.1). It is ex- pected that a study in the United States would contain a larger number of exposed individuals than those in the European studies because the number of nuclear power plants in the United States is larger than that in any of the European countries. For this and other reasons related to differences in study design or analysis stages (results may be influenced, for example, by unrecognized bias in the data, the effect of other relevant factors, or by chance varia- tion; these need to be discussed by the investigators even if they cannot be quantified), interpretation of epidemiologic findings is not always easy and there are often subjective elements to their interpretation that experts may disagree upon. Evaluating well-designed studies that do not suggest the ex- istence of an association between a factor and a disease is equally important TABLE A.1 Number of Cases in the At-Risk Zone (≤ 5 km from a facility) in European Studies of Pediatric Cancers (children < 5 years old) Cases (≤ 5 km) Country Reference Study Years End Point Germany Spix et al., 2008 23 all cancers 77 Kaatsch et al., 2008 leukemia 37 France Sermage-Faure et al., 2012 17 leukemia 24 Britain COMARE, 2011 35 leukemia 20 Switzerland Spycher et al., 2011 24 all cancers 18 leukemia 8

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278 APPENDIX A to evaluating studies that show an association. However, it is often harder to convince stakeholders of the validity of the so-called “negative” studies especially if there are flaws or inefficiencies in their design, methods, or analysis. A better term for flawed studies would be “uninformative.” In absence of biological plausibility, a positive or somewhat positive association may be underinterpreted. In studies that assess cancer risks as- sociated with releases from nuclear facilities, there are examples where in- vestigators are hesitant to conclude that evidence supported the hypothesis when they find a positive association between risk and exposure associated with nuclear facilities (Baker and Hoel, 2007; Hatch et al., 1990; Kaatsch et al., 2008; Nuclear Safety Council and the Carlos III Institute of Health, 2009), even though direct radiation measurements were not made. This phenomenon has led a researcher to emphasize the importance of having explicit study hypotheses (Wing et al., 2011) and to the question, “Why conduct a study if the results cannot be interpreted as providing evidence in support of the hypothesis?” (Wing, 2010). Of course, there is the opposite error, too—that of overinterpretation. A balanced “weight-of-evidence” approach is the most appropriate. It is important to be open to new information or novel interpretation and alternative hypotheses that can impact assumptions about exposure effects. A recent study from France demonstrated that children living in very close proximity to nuclear power plants are twice as likely to develop leukemia compared to those living farther away from the plants. However, analysis of the same population of children using a dose-based geographic zoning approach instead of distance, did not support the findings. The ab- sence of any association with the dose-based geographic zoning approach may indicate that the observed association with distance may be due to factors other than the releases from the nuclear power plants (Sermage- Faure et al., 2012). Among such potential factors are population mixing (Kinlen, 2011a), a hypothesis that could not be evaluated in this study, and exposures to agents including natural or manmade exposures to radiation not modeled in the study. From the reports published the past 4 years alone from Germany (Kaatsch et al., 2008), Finland (Heinavaara et al., 2010), Great Brit - ain (COMARE, 2011), Switzerland (Spycher et al., 2011), and France (Sermage-Faure et al., 2012), it is obvious that additional scientific resolu- tion to the question of whether living near a nuclear facility increases one’s risk of developing cancer remains. Authors have called for collaborative analysis of multisite studies conducted in various countries (Sermage-Faure et al., 2012). Similarly, the need for a well-conducted meta-analysis that would provide a more precise estimate of the risk remains. Two meta-analyses were conducted recently in an effort to provide more precise estimates of the possible risks associated with living near a

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279 APPENDIX A nuclear facility (Baker and Hoel, 2007; Greiser, 2009). Baker and Hoel combined and statistically analyzed studies of childhood leukemia around nuclear facilities published until 1999, but only included studies that calcu- lated standardized incidence ratios (SIRs) or standardized mortality ratios (SMRs) (see Sidebar A.1 for risk measures) for individual facilities. Studies that calculated rates for multiple sites or those that did not distinguish leu- kemia from lymphoma were excluded. Seventeen published studies (out of 37 individual studies published at the time) addressing 136 nuclear sites in 7 countries (Great Britain, Canada, France, United States, Germany, Japan, and Spain) met the criteria. Due to variability between study designs, eight separate analyses were performed stratified by age and zone. Meta-SMRs and meta-SIRs were all greater than the reference group, implying an in- crease in risk. More specifically, the overall estimated relative risk was 1.22 (95% CI=1.05-1.41) and the 0-9 age group accounted for the majority of the excess cases and deaths. Excluding the Aldermaston nuclear weapons plant and Amersham plant that produces radioisotopes (both in Britain) reduced the overall estimate to a nonsignificant 14 percent increase in risk (RR=1.14, 95% CI=0.98-1.33). The authors discuss that although the meta-analysis showed an increase in childhood leukemia near nuclear facilities, it “does not support a hypothesis to explain the excess” (Baker and Hoel, 2007). The meta-analysis by Baker and Hoel was criticized by authors of the German Kinderkrebs in der Umgebung von Kernkraftwerken (KiKK) study (Spix and Blettner 2009). The first issue they identified with the meta- analysis was the general problem of combining heterogeneous data such as different age groups (0-9 years or 0-25 years), the different types of nuclear facilities (nuclear power plants and other facilities), and the different expo- sure zone definitions (<10 km or county). Beyond that, there was criticism over the completeness of the publication search and lack of justification for excluding the 20 studies which were identified but did not fit the criteria for inclusion; possible selection bias resulting from the exclusion of sites with zero observed leukemia cases or deaths from leukemia; and a methodologi- cal problem with the confidence intervals presented in the forest plots which should be symmetric on a logarithmic scale but, contrary to expectation, were skewed (Spix and Blettner, 2009). The meta-analysis by Greiser included data from 80 nuclear power plants in five countries (Germany, France, Great Britain, United States, and Canada). Data were retrieved in the literature but also from cancer regis- tries. (Rather than relying on the data used in the Jablon et al. 1991 analysis of risks in nuclear facilities in the United States, the author retrieved cancer incidence data from cancer registries of Illinois, Pennsylvania, and Florida.) The incidence of leukemia was estimated to increase by 13 percent (95% CI = 10%-17%) relative to the corresponding average national or regional rate (Greiser, 2009). The latest COMARE report (2011) discusses the key

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280 APPENDIX A SIDEBAR A.1 Risk Measures, P Values, and Confidence Intervals Several types of estimates of relative risk (RR) are used in epidemiologic studies. RR is generically defined as the ratio of the risk of developing the disease or of dying of the disease among an exposed population compared to an unexposed population. A simple type of estimate of the RR is the standardized incidence ratio (SIR) or stan- dardized mortality ratio (SMR) for the exposed group. An SIR is the ratio of the number of cases observed in the exposed group in some time period to the number of cases expected if the group had the same disease occurrence rates as a standard population. The standard population is often the general population or a large reference population with characteristics similar to the study group except for the exposure of interest, and comparisons typically are based on cancer rates from population cancer registries. The ratio of observed to expected cases is often multiplied by 100 to yield results without decimals. Thus, an SIR of 100 indicates that the observed number of cases is the same as that expected in the standard population. Thus, an SIR of 140 indicates that incidence is 40 percent higher than expected, while an SIR of 80 indicates 20 percent fewer cases than expected. SIRs should be interpreted with caution as their significance partially depends on the number of cancer cases in the exposed group. Imagine a situation where 5 cases were expected and 6 were observed and a second situation where 500 cases were expected and 600 were observed. In both instances the SIR is 120; however, because in the second scenario the SIR is based on a greater number of cases, the estimate is more precise, and hence more meaningful. In other words, although the one excess case could have occurred due to chance alone, it is highly unlikely that an excess of 100 incident cases has occurred by chance. This is a common issue in interpreting studies of risks in populations near nuclear facilities where the number of excess can- cers in the exposed region is particularly small when rare diseases such as childhood leukemia are examined (see Table A.1). The SMR is similar to the SIR, except it is based on deaths due to some cause rather than cancer occurrences to draw conclusions regarding whether there is excess mortality. As age is one of the main determinants of mortality, and other factors such as gender and racial composition may influence the mortality or tumor rates, SMRs and SIRs are usually calculated by summing the observed and expected numbers of deaths or cancers across categories of gender, age, and sometimes race with the expected numbers calculated separately for each category. Results from cohort and ecologic studies are sometimes described in terms of SMRs or SIRs, but other techniques are often preferred which permit comparisons of disease rates (often called rate ratios) between exposed and unexposed study groups, usually with adjustment for gender, age, and perhaps other factors. More advanced techniques use some type of “regression analysis” to estimate exposure-effect asso- ciations, with study subgroups or individuals defined according to graded amounts of exposure. Case-control studies (which compare exposures observed in cases to those ob- served in control subjects) are typically unable to calculate actual disease rates since they lack appropriate population denominators, which means that SIRs, SMRs, and rate ratios cannot be used. However, for case-control studies the odds ratio (OR) can be calculated. The OR and relative risk are closely related (and are nearly identical for “rare” diseases). The OR indicates the ratio of the probability of exposure to the prob-

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281 APPENDIX A ability of nonexposure among those with the disease of interest divided by the similar ratio of probabilities among those without the disease. A value greater than 1 means that the odds of disease are greater among the exposed than the unexposed. A value less than 1 means that the odds are higher in the unexposed than in the exposed. Similar to all the other statistics mentioned, the number of disease cases with exposure has a major influence on the precision and statistical significance of the OR. A useful measure of risk in epidemiologic studies is that of “excess” risk associated with an exposure. Excess risk can be expressed as excess relative risk (ERR) or ex- cess absolute risk (EAR). The ERR and EAR in principle are estimates of the amount of risk due to the exposure of interest when the effects of other risk factors are removed. Statistically, ERR = RR-1 and EAR = RE – RU , where RE is the rate of occurrence of disease or death in the exposed group in a specified period, and RU is the correspond- ing rate of occurrence in the unexposed group, which is the baseline rate. In contrast to ERR, which represents the ratio of the excess rate associated with exposure to the baseline rate, the EAR represents the additional rate of a disease due to the exposure of interest over a given period of time. As baseline disease rates depend on a number of factors, excess risks can vary not only with radiation dose but also with age at ex- posure, time after exposure, age at risk (attained age), gender, and other factors such as smoking. Therefore, risk estimates are usually reported for a specified combination of these factors. ERR and EAR estimates can best be calculated in a cohort study, although ecologic studies can sometimes permit such estimates to be made. A statistic analogous to the ERR estimate can be calculated as OR-1 for case-control studies, but usually EAR estimates cannot be obtained from a case-control study due to the lack of population denominators. By describing the excess number of people affected by the disease of interest, EAR is a better descriptor than the ERR of the public health impact that an exposure may have in a population. For example, in the Life Span Study (LSS) follow-up of the Japa- nese atomic bombing survivors the ERR for leukemia is the highest among the various cancer effects of radiation exposure (RR approximately 5 for a dose of 1 Gy, which translates into an ERR of about 4), and the total number of radiation-related cases of leukemia among the LSS survivors is estimated to be about 90-100. In contrast, the ERR for solid cancers is much smaller (RR approximately 1.5 at 1 Gy, or an ERR of about 0.5), yet the total number of LSS survivors who have developed solid cancers due to the bombing is estimated to be about 850. This is because of the relative rarity of leukemias compared to the group of cancers described as solid cancers. Common cancers may appear to have a low ERR in an epidemiologic study, but the risk may translate to a large number of cases, or a large EAR. One can say that the ERR is an appropriate measure to assess disease etiology, whereas the EAR is useful for estimat- ing the extent of a health problem. Applying ERR or EAR estimates derived from individuals in one population to those in another population sometimes has substantial uncertainties. Since most types of cancer vary substantially in their baseline frequency according to age, both ERR and EAR estimates can be affected by differences in the age distributions of populations being compared. For instance, it would be inappropriate to compare radiation-related leukemia risk of children in one population with adult leukemia risk in another popula- tion. Sometimes there also are differences in the baseline rates of disease in different populations even with the same age distributions. For example, the Japanese have historically had much higher rates of stomach and liver cancers than in the United States. It is therefore uncertain as to how to extrapolate stomach or liver cancer ERR continued

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