4

Epidemiologic Studies

This chapter addresses the second charge in the statement of task for this study (see Sidebar 1.1 in Chapter 1) on methodological approaches for assessing cancer risks in populations near U.S. Nuclear Regulatory Commission (USNRC)-licensed nuclear facilities. It is specifically intended to address the following issues:

  • Different epidemiological study designs and statistical assessment methods.
  • Geographic areas to use in the study.
  • Cancer types and health outcomes of morbidity and mortality.
  • Characteristics of the study populations.
  • Availability, completeness, and quality of cancer incidence and mortality data.
  • Approaches for overcoming potential methodological limitations arising from low statistical power, random clustering, changes in population characteristics over time, and other confounding factors.
  • Approaches for characterizing and communicating uncertainties.

4.1 BACKGROUND ON EPIDEMIOLOGIC STUDIES

Epidemiology is the study of the distribution of diseases and other health-related conditions in populations, and the application of this study to control health problems. The purpose of epidemiology is to understand what risk factors are associated with a specific disease, and how disease



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4 Epidemiologic Studies T his chapter addresses the second charge in the statement of task for this study (see Sidebar 1.1 in Chapter 1) on methodological ap- proaches for assessing cancer risks in populations near U.S. Nuclear Regulatory Commission (USNRC)-licensed nuclear facilities. It is specifi- cally intended to address the following issues: • Different epidemiological study designs and statistical assessment methods. • Geographic areas to use in the study. • Cancer types and health outcomes of morbidity and mortality. • Characteristics of the study populations. • Availability, completeness, and quality of cancer incidence and mortality data. • Approaches for overcoming potential methodological limitations arising from low statistical power, random clustering, changes in population characteristics over time, and other confounding factors. • Approaches for characterizing and communicating uncertainties. 4.1 BACKGROUND ON EPIDEMIOLOGIC STUDIES Epidemiology is the study of the distribution of diseases and other health-related conditions in populations, and the application of this study to control health problems. The purpose of epidemiology is to understand what risk factors are associated with a specific disease, and how disease 143

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144 ANALYSIS OF CANCER RISKS can be prevented in groups of individuals; due to the observational nature of epidemiology, it cannot provide answers to what caused a disease to a specific individual. Epidemiologic studies can be used for many reasons, commonly to estimate the frequency of a disease and find associations sug- gesting potential causes of a disease. To achieve these goals, measures of disease (incidence) or death (mortality) are made within population groups. Epidemiology is fundamentally multidisciplinary and it uses knowledge from biology, sociology, statistics, and other fields. The four types of epidemiologic studies commonly used in radiation re- search are cluster, ecologic, case-control, and cohort studies. An additional approach for estimating risk in radiation research—although strictly not an epidemiologic study—is risk-projection models. These models are used to predict excess cancer risks by combining population dose estimates with existing risk coefficients to transfer risks across populations with different baseline rates. This type of modeling approach is not new; one of the earli- est examples of its use was by the U.S. Federal Council Report, where 0 to 2000 leukemia deaths in the United States attributed to exposures to fallout from above-ground nuclear testing up to 1961 were estimated (Federal Radiation Council, 1962). As discussed in a comprehensive review (Ber- rington de González et al., 2011), recent applications of the risk-projection modeling have increased partly because of the publication of user-friendly risk estimates for U.S. populations in the BEIR VII report (NRC, 2005) and the increasing acceptance of the limitations of epidemiologic studies of low- dose radiation exposures, mainly owing to their limited statistical power. The study designs described in this chapter can provide clues for po- tential associations between cancer and living near a nuclear facility. The first thing that the epidemiologist questions is whether any observed asso- ciation is real, or if it is due to bias, confounding, or simply due to chance. “Bias”1 is a general term related to error in the measurement of a factor and can arise from a variety of sources such as the method of selection of cases and controls, or exposed and unexposed (selection bias), or due to the inaccurate information regarding either the disease or exposure status of the study participants (information bias). On the other hand, confounding refers specifically to the existence of some third variable, the “confounder,” that alters the degree of association between the exposure and the disease of interest. Confounding is a potential issue with all epidemiologic studies discussed here. 1 The term “bias” when used scientifically does not necessarily imply the researcher’s desire for a particular outcome, or any prejudice, as it is often implied with the conventional use of the term.

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145 EPIDEMIOLOGIC STUDIES 4.1.1 Cluster Studies A cancer cluster is an aggregation of a relatively unexpected high num- ber of cases. Clustering can be “spatial,” when the disease in question has a higher incidence rate in some places than in others, or “temporal,” when the incidence rate is higher at a specific time compared to other times. A disease cluster can also be “spatiotemporal.” Testing involves comparing the observed number of cases with the number expected, based on the size and age composition of the population. The scientific reason to examine disease clusters is to learn about the causes of the cluster and, by extension, gain insight toward the causes of disease. Epidemiologists and public health workers recognize the value of historic examples of cancer cluster examination which contributed to the recognition of human carcinogens in those situations. Typically, exposure was high, prolonged, and well defined. In contrast, most cluster reports involve exposures that are low and poorly defined, and the cases involved are a mix of unrelated, relatively common cancers. For these reasons there is skepticism regarding the scientific value of the investigation of reported clusters (Neutra, 1990; Rothman, 1990). In a rather provocative summary of the reasons why—with a few exceptions—there is little scientific or public health purpose to investigate individual disease clusters, Rothman (1990) explains that the boundaries of the space and time that encompass the cluster should be clearly defined before examination of the cluster and should not be defined after the fact to capture a population that has experienced the high disease rate. This interpretation has been described as the “Texas sharpshooter’s” procedure in which the shooter first fires his shots randomly at the side of the barn and then draws a bull’s eye around each of the bullet holes. This kind of process tends to produce clusters of causally unrelated cases of no etiologic interest. As noted by Rothman (1990), assigning statistical significance to a reported cluster requires clear definitions of the populations, regions, and/ or time periods under consideration, often a challenging undertaking. 4.1.2 Ecologic Studies An ecologic study (sometimes referred to as a geographic study or cor- relation study) evaluates the relationship between an exposure and a disease in some aggregate group of individuals, but not specific individuals, such as those living in a country, a county, a community, or a neighborhood. This is in contrast to case-control and cohort studies where the unit of analysis is the individual. In an ecologic study, average measures of exposure and disease frequency are obtained for each aggregate, and the analyses focus on determining whether or not the aggregates with high levels of exposure also display high disease rates. For example, in a study that uses counties as the

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146 ANALYSIS OF CANCER RISKS unit of analysis, the data of interest are average values of exposure and ag- gregate counts of disease by county. However, the individuals who actually develop cancer in a county may be more or less exposed than the county average, so the association across county populations may not accurately reflect the association for the individuals who develop cancer. This issue is referred to as ecologic fallacy or ecologic bias and is the main limitation associated with ecologic studies. The magnitude of the ecologic bias is not measurable; therefore, conclusions need to be stated carefully and results interpreted with caution. One of the causes of ecologic fallacy is that average levels of poten- tial confounding variables across the geographic units may be subject to considerable measurement error, so trying to adjust for the geographically estimated confounding variables fails to control for confounding. This was illustrated in a study of the association of average county radon levels with lung cancer rates, with an attempt to characterize smoking levels by county (Cohen, 1995, 1997). The radon–lung cancer ecologic correlations were in the negative direction, whereas a series of studies using estimated individuals’ radon exposure have shown positive associations (Darby et al., 2005). This poor control for confounding is important mainly for potential variables that have strong association with the target disease (e.g., smoking and lung cancer) and is of lesser concern for weak confounding variables. However, when expected effects of exposure are themselves quite weak, then good control for confounding variables becomes especially important. 4.1.3 Case-Control Studies The aim of a case-control study is to determine whether the frequency of exposure to several possible risk factors is higher in the group of people with the disease of interest (cases) than in the group without the disease (controls). The proportion of cases with and without an exposure suspected to be linked with the disease is compared to the proportion of controls with and without the relevant exposure. If a certain exposure is associated with or causes a disease, then a higher proportion of past exposure among cases is expected compared to the proportion of past exposure among the controls. If the difference cannot be explained by chance, an association between the disease and the characteristic may be inferred. Cases can be selected from hospitals, registries, or other relevant sources. However, cases based on hospitals may be a biased sample; for example, those cases seen at referral hospitals may represent more serious or unusual cases. Therefore, population-based case ascertainment is the preferred study design. This may be possible through a cancer registry if the registry can provide complete information on diagnoses of cases. Control selection requires equal thought and consideration, because the controls

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147 EPIDEMIOLOGIC STUDIES must come from the same population base as the cases; subtle differences in the way cases and controls are selected may lead to selection bias. The major point is that the controls have to reflect the population from which the cases arose. For general-population case-control studies, various meth- ods are used to identify controls for study as discussed in Section 4.3.4. 4.1.4 Cohort Studies In a cohort study, the investigator typically selects a group of exposed and a group of unexposed individuals and follows both groups over time to determine disease occurrence in relation to the exposure. In the radia- tion epidemiology field, when individual exposures or doses are available, cohort studies typically examine gradients of exposure rather than just un- exposed and exposed groups. The data necessary for assessing disease diag- nosis can be obtained either directly by periodic examinations of individuals or by obtaining data from disease registrations, hospital records, and death certificates. For rare diseases or those that take a long time to become evi- dent, such as cancer, the investigator needs to start with a large number of exposed and unexposed individuals and follow them for a long period of time. Study participants may be lost to follow up in a cohort study because they do not wish to take part in the study, because they cannot be located, or because they have died. Minimizing these losses is crucial because they reduce the number of participants being followed. Also, participants that are lost to follow-up may differ in characteristics from those that remain enrolled in the study. When reporting the study design, it is important to note the percentage of and any available demographic information on sub- jects that are lost. A cohort study is considered to be a more scientifically rigorous study design compared to case-control, ecologic, or cluster studies. This is because cohort studies measure potential exposures before the disease has occurred and therefore can demonstrate that they may have caused the disease. Be- cause cohort studies most often look forward to the future, they are also referred to as prospective studies. However, a cohort study can also be retrospective if both exposures and outcomes have already occurred and accurate historical data are available when the study begins. Studies on radiation effects are often jointly retrospective and prospective; exposures occurred mainly in the past and disease ascertainment includes both past and prospective follow-up. 4.2 STUDY DESIGNS CONSIDERED Choosing from among different possible study designs to assess cancer risks in populations near nuclear facilities, or even deciding against mak-

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148 ANALYSIS OF CANCER RISKS ing a proposal for a particular study design, is based on answers to several difficult questions. Most of these questions are scientific, dosimetric, epi- demiologic, and statistical, and require technical knowledge and expertise. However, some are less technical and involve public concerns and percep- tions that may be difficult to quantify. The primary focus of this chapter is on technical issues, partly because they serve as a foundation for judgments that may involve additional public and stakeholder considerations. The committee considered the following general approaches to an epidemiologic study of cancers that might be undertaken by the USNRC: 1. Risk-projection models. 2. An ecologic study based on estimates of exposure levels at the census-tract level. 3. Cohort studies tracking estimates of individual exposure levels and recording case incidence within the cohort. Variations considered include: • prospective cohort study. A • retrospective cohort study. A 4. Case-control studies comparing estimates of individual exposure levels between cancer cases and controls. Variations considered include: • record-linkage-based case-control study with no direct contact A with cases and controls or their proxies. • de novo case-control study with direct contact with cases and A controls or their proxies. • uilding on existing studies and their associated data. B The discussions of these possible studies in the following sections are based primarily on the study characteristics summarized in Table 4.1. Sec- tion 4.2.1 of this chapter considers matters that affect most or all of these study designs; Section 4.2.2 describes each approach in some detail. These descriptions define the strengths and weaknesses of the recommended stud- ies, summarized in Section 4.2.3. Section 4.3 provides a summary of data sources for population counts, health outcomes, and other information required for the execution of the studies considered and recommended. 4.2.1 Issues Affecting Several Epidemiologic Study Designs In any of the studies considered, population sizes, estimated doses, and resulting risk estimates may be too low to demonstrate statistically signifi- cant increased cancer risks near nuclear facilities. As noted in Chapter 3, the dose received from living near a nuclear plant is estimated to be less than 0.01 mSv/yr (USEPA, 2007). This dose is much lower than doses from

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TABLE 4.1 Summary of the Characteristics of the Studies Considered Case-Control Cohort Risk Projection Hypothesis Testing Hypothesis Testing Models Ecologic Theoretical Hypothesis Prospective Retrospective Evaluation Generating Record Based Subject Contact (Subject Contact) (Record Based) Outcome Incidence/ Theoretical GU-based rates Individual level Individual level Individual level Individual level Mortality Time period Past, current or Past and current Fairly recent past Recent past and Future Fairly recent past and future and current current current Number of N/A Large, depending Limited to Limited to recent Limited to future Limited to cases Cases on availability relatively recent cases (and those cases and subject that are successfully of aggregated cases, depending that are alive), to length of linked via birth cancer incidence on available birth successfully traced, follow-up period records and mortality record and cancer and willing to data incidence data participate Cancer Types All All Limited, primarily Limited to one or a Limited to a few Limited, primarily suitable for few types relatively common suitable for childhood cancers types depending childhood cancers or or those due to on follow-up early exposures early exposures period Age All ages All ages Best for childhood Targeted ages All or targeted Best for childhood; cancers; limited for ages limited for adult adults 149 continued

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TABLE 4.1 Continued 150 Case-Control Cohort Risk Projection Hypothesis Testing Hypothesis Testing Models Ecologic Theoretical Hypothesis Prospective Retrospective Evaluation Generating Record Based Subject Contact (Subject Contact) (Record Based) Nondiseased N/A Census Requires selection Requires selection Participants would Participants would comparison denominators and study of a and study of a be nondiseased at be nondiseased at group comparison group comparison group entry, and number entry, and number of individuals of individuals developing disease developing disease during study during study period period would be would be determined determined Exposure Dosimetry GU-based GU-based Individual location Individual Individual Individual location at birth locations at birth Lifetime Can be Approximate, Limited primarily Complete lifetime Complete lifetime Lifetime exposure constructed for without to exposure at time residential history residential history residential history hypothetical information of birth derived from derived from derived from records, individuals based about residential interview data interview data but realistically will on residential changes be limited primarily history to exposure at time of birth

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Temporality Can fully Can fully Restricted to Restricted to Can include all Restricted to utilize historical utilize historical exposure at birth exposure prior to exposure prior exposure at birth variations in plant variations in location; limited diagnosis, limited to diagnosis, but location, dependent exposure levels plant exposure because must use because must use does not address on how far back in prior to each year levels prior to relatively recent recent cases only the higher past time birth records of interest diagnosis dates cases exposures with adequate information are available Potential Confounders Natural GU-based GU-based Residence based Residence based Residence based Residence based background and direct and direct radiation measurements measurements possible possible Socioeconomic GU-based GU-based Individual Individual level via Individual level Individual status level through questionnaires via questionnaires level through socioeconomic socioeconomic proxies insofar as proxies insofar as available in records available in records Urban/rural/ GU-based GU-based Individual level at Individual level Individual level Individual level at mixed birth complete history complete history birth residence Medical GU-based GU-based GU-based for Individual level via Individual level GU-based for exposures approximations approximations individual questionnaire via questionnaire individual birthplace birthplace Other risk GU-based GU-based Limited to Individual Individual Limited to factors information exposures and exposures and information available available on birth risk factors via risk factors via on birth records records interviews interviews 151 continued

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TABLE 4.1 Continued 152 Case-Control Cohort Risk Projection Hypothesis Testing Hypothesis Testing Models Ecologic Theoretical Hypothesis Prospective Retrospective Evaluation Generating Record Based Subject Contact (Subject Contact) (Record Based) Biases Selection bias In- and In- and Out-migration Out-migration, Lost to follow-up, Out-migration and out-migration out-migration and unsuccessful unsuccessful study dropouts unsuccessful linkage linkage linkage, and unlocatable study subjects Nonparticipation None None None Likely Likely None Response None None None Possible over or Possible over or None underreporting underreporting Assessment of N/A Requires Considered Considered Considered Considered causality confirmation using another study design NOTE: GU, geographic unit such as census tract; N/A, not applicable.

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153 EPIDEMIOLOGIC STUDIES natural background radiation and medical diagnostic procedures, which combined are estimated to be 6.2 mSv/yr for the average2 person in the United States (NCRP, 2009). Consequently, the attributed risk to exposure from radiation from a nuclear facility, if any, would be a small increase above the baseline lifetime risk of cancer occurrence in the general popula- tion in the United States, which is considered to be 42 percent (NRC, 2005). Statistical power calculations based on estimated exposure estimates indicate that extremely large sample sizes are required except under the following scenarios: A. Routine releases from the operating facilities have been far greater than those reported to the USNRC, or B. Sensitivity to radiation as characterized in most or all generally accepted risk models is either inappropriately low or simply irrel- evant to the populations living near nuclear facilities in the United States. Regarding scenario B, underestimation of risks associated with radia- tion could be perhaps a result of inaccurate models for interpolation to low doses. Translation of risk estimates from World War II atomic bombing survivors to the population in the United States may also be proven inac- curate, though there is reasonably good concordance of estimated risks for Japanese and Western populations (UNSCEAR, 2006, Annex A). Excep- tions are a few cancer sites with disparate background rates, such as stom- ach and liver cancer. (These cancers are more common among the Japanese compared to Western populations due to differences in risk factors such as diet and rate of infections.) Even if one or both of these scenarios are considered possible, the reliability of any proposed study still hinges on the technical issues of ac- curately characterizing doses received by the populations under study over the time of facility operations. Accurate estimation of those doses requires reasonably accurate measures of releases, modeling of exposure levels at various geographic locations, and biologic uptake and biokinetics for ra- dionuclide exposures (see Chapters 2 and 3). 4.2.1.1 Questions Addressed by the Studies Epidemiologic studies provide the most direct and relevant evidence for an association between a suspected risk factor and disease. Each of 2 This dose to the average person in the United States includes people who never had a medi- cal procedure that involves high-dose radiation, such as CT scan or a fluoroscopy procedure. For those individuals that have had such procedures, the annual dose is higher. For reference, the average dose received from a CT scan is 8 mSv.

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242 ANALYSIS OF CANCER RISKS 5. Available, costly contact of individuals: locating services, public appeal. Motor vehicle licensing records and directories proved the most useful in tracing individuals. The investigators note that, at the time their study was conducted, the use of internet and email was not as widespread as it is today. These two options could potentially improve the tracing response rate. As methods of recruiting participants are also relevant for retaining participants in a longitudinal study, research on retaining participants em- phasizes this point (Davis et al., 2008; Robinson et al., 2007). An average of five sources was required to locate an individual. An extensive effort was required before a cohort member was declared “unlocated” by the team of supervisory staff. Another example of a study with satisfactory response rate of 75 per- cent used 14 sources to locate 230 parents of sudden infant death syndrome infants and 255 parents of healthy living infants in Southern California (Klonoff-Cohen, 1996). Possible reasons for the lower success rate com- pared to the Hanford study is that case parents were relatively young and transient without an established credit history and, therefore, harder to be traced through tax assessor records, and the fact that the Human Subjects Committee required at least a 1-year waiting period to contact the parents of the deceased infant, during which period the parents may have moved. The Northern California Childhood Leukemia Study, which enrolled birth registry controls aged 0-14 years reported a contact rate of 80 percent (Ma et al., 2004). A case-control study of birth defects based in seven Texas counties aimed to contact mothers and interview them by telephone 4 years after the births of their children. Case mothers were more likely than control mothers to be located (44 percent versus 30 percent, respectively) and, of those that were located, to be interviewed (43 percent versus 31 percent, respectively). Young maternal age and black race decreased the likelihood of locating mothers (Gilboa et al., 2006). Nationwide studies in- clude the Pregnancy Risk and Monitoring System, which contacts mothers between 2 and 6 months after giving birth in 23 states. The study achieved a contact rate of 82 percent in 2001 (Shulman et al., 2006). As expected, age affects the effort required to trace children, with less efforts needed for birth certificate controls aged 0-4 years than for those aged 5-14 years (Ma et al., 2004). 4.3.6 Data on Population Characteristics As discussed in Section 4.3.1, the U.S. Census is a source for informa- tion regarding the population characteristics such as age, gender, and race/ ethnicity. Surveillance systems that collect information on population char-

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243 EPIDEMIOLOGIC STUDIES acteristics over time, including lifestyle factors, are important for tracking such things as chances in the incidence of cancer or other chronic disease, and risk behavior prevalence. In the context of this report, surveillance systems are important as they could be a source of information on the char- acteristics of the populations compared and thus provide clues on potential confounders in an ecologic study. The committee found that three national surveillance systems might be relevant: The National Health Interview Survey (NHIS), the National Health and Nutrition Examination Survey (NHANES), and the Behavioral Risk Factor Surveillance System (BRFSS). All three surveys are managed by CDC. However, none of these surveys are directly applicable for the present task, as they do not contain information about behavioral data at the census-tract level. Technical and methodologi- cal details for the surveys are available online and briefly summarized here. Sources of health care information are also discussed, but again information from these sources is not directly applicable for the present task. 4.3.6.1 The National Health Interview Survey (NHIS) The NHIS is a large-scale face-to-face household interview survey of a random sample of households in the United States. The main objective of the NHIS is to monitor the health of the population in the United States and track progress toward national health objectives. Interviewers of the U.S. Census Bureau have conducted the survey for the NCHS continuously since 1957. Each year, interviewers visit 35,000 to 40,000 households across the county and collect data for about 75,000 to 100,000 individuals. The annual questionnaire consists of three components: the family core, the sample adult core, and the sample child core. The family core collects infor- mation on everyone in the family, including family composition, and basic demographic characteristics such as age, race, gender, income, and health insurance coverage. In addition, one adult and one child, if applicable, from each household are randomly selected and information on each is collected. In 2007, participation rates for the survey were 68 percent. As noted above, the goal of the NHIS is to collect summaries of health at the national, and perhaps state level, not at the fine geographic scale of census tracts. 4.3.6.2 The National Health and Nutrition Examination Survey (NHANES) NCHS also conducts NHANES, a survey that aims to assess the health and nutritional status of adults and children in the United States. The NHANES program began in the early 1960s. In 1999 the survey became a continuous program that examines a nationally representative sample of about 5,000 persons each year. Although substantially smaller than either

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244 ANALYSIS OF CANCER RISKS NHIS or BRFSS, NHANES is unique because it combines information from interviews with a physical examination and some laboratory tests. The NHANES interview includes demographic, socioeconomic, dietary, and health related questions while the physical examination component consists of medical and dental measurements. In the 2005-2006 survey, participa- tion rates were 80 percent. Again, the goals are estimates at the national and perhaps state level, not at the fine geographic resolution desired for the studies under consideration. NHANES, like NHIS, is based on cluster sampling. 4.3.6.3 The Behavioral Risk Factor Surveillance System (BRFSS) In 1984, the CDC recognized the importance to disease prevention of monitoring personal health behaviors in the general population and estab- lished the BRFSS in 15 states. A decade later, this system was in place na- tionwide. In contrast to NHIS and NHANES, BRFSS is a telephone-based survey conducted by state and territorial health departments with technical and methodological assistance provided by the National Center for Chronic Disease Prevention and Health Promotion of CDC. Each state works with CDC to develop a sampling protocol to select households and one adult (age >18 years) is selected from each household and is interviewed. BRFSS is the only one of these three surveillance systems that can generate state- or territorial-based estimates on a variety of health measures. BRFSS col- lects data from approximately 210,000 people in 50 states, the District of Columbia, Puerto Rico, the U.S. Virgin Islands and Guam. Self-reports of health-related variables (e.g., weight) have not matched measurements from the other surveillance systems that do not rely on self-reports (Carl- son et al., 2009). Perhaps the largest challenge in using BRFSS data is that the response rates for BRFSS have declined from 72 percent in 1993 to 51 percent in 2007. The low, and apparently biased, participation rates pro- duce different estimates in some outcome measures compared to NHIS and NHANES, both of which have higher participation rates. The consequences have been estimated to be minimum in some cases and unknown in oth- ers (Fahimi et al., 2008). Finally, BRFSS provides design-based state and national estimates and some research has considered extensions to county level. However, the data are not sufficient to support design-based estimates at the census-tract level. 4.3.6.4 Health Care Surveys NCHS performs the National Health Care Survey to answer questions on the use and quality of health care, the impact of medical technology, and disparities in health care services provided to population subgroups in the

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245 EPIDEMIOLOGIC STUDIES United States. The National Health Care Survey is built upon the merging and expansion of separate record-based surveys: • National Ambulatory Medical Care Survey • National Hospital Ambulatory Medical Care Survey • National Survey of Ambulatory Surgery • National Nursing Home Survey • National Hospital Care Survey • National Nursing Assistant Survey • National Home and Hospice Care Survey • National Home Health Aide Survey • National Survey of Residential Care Facilities The combined surveys use provider-based information which depend- ing on the setting in which the care is delivered, may come from a record of the patient’s most recent visit, the hospital discharge form, or review of the entire medical record. Information on the sample design for each of the component surveys can be found at http://www.cdc.gov/nchs/dhcs.htm. Overall, the design is such to permit monitoring of the delivery of specific health care services and understanding the characteristics of the patients that receive different types of services. The National Hospital Discharge Survey (NHDS) is briefly described here as an example to demonstrate the relation of the different health care surveys and the potential for linkage with other national data sets. NHDS is a national probability survey that was initiated in 1965 and was the first survey of medical care delivery conducted by the NCHS to collect information on inpatient use of short-stay nonfederal hospitals in the United States (Dennison and Pokras, 2000). The survey was redesigned in 1987 to improve on its sampling and link with the design of NHIS and to use automated retrieval of data, among other reasons. In 1988 the survey collected data on diagnoses, procedures, length of stay, and patient charac- teristics from a sample of approximately 250,000 discharges from over 500 hospitals. NHDS was conducted annually since its inception until 2010, when it was integrated into the National Hospital Care Survey together with data from the emergency department, outpatient department, and ambulatory surgery center data collected by the National Hospital Ambu- latory Medical Care Survey (NHAMCS). (NHAMCS was conducted since 1973 and data were collected from the physician who would be randomly be assigned a 1-week reporting period.) The integration of these two sur- veys along with the collection of patient identifiers will permit linkage of care provided in different departments. It will also be possible to link the survey data to the NDI and Medicaid and Medicare data to obtain a more complete picture of patient care.

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246 ANALYSIS OF CANCER RISKS Important to the committee’s task and many times reiterated is the need for a source of information on medical diagnostic procedures that use radia- tion, especially those that use high doses such as CT scans. The main data source for aggregate counts on medical diagnostic procedures that involve radiation by body part is IMV.19 IMV is a market research and database provider founded in 1977 which, using a variety of survey methods, tracks diagnostic medical procedures. While IMV surveys have high participation rates and cover a large number of imaging facilities (IMV data were the main source for the NCRP Report 160 [NCRP, 2009]), they do not have a detailed categorization of procedures and therefore are unable to capture the variation in radiation doses and protocols. Detailed data on counts of procedures for large populations are also available from administrative claims such as Medicare. However, information is restricted to those that are age 65 or over and use this social insurance program. Neither IMV nor Medicare data are directly applicable for the present task, as they do not contain information about medical diagnostic imaging at the census-tract level. 4.4 FINDINGS AND RECOMMENDATIONS This chapter provides the committee’s assessment of methodological approaches for carrying out a cancer epidemiology study. Based on this assessment, the committee finds that: 1. The statistical power of an epidemiologic study of cancer risks in populations near nuclear facilities is likely to be low because (a) the size of the estimated risks from the reported radioactive efflu- ent releases from nuclear facilities is likely to be small and (b) the size of the populations most likely to be exposed (that is, those in close proximity to a nuclear facility, for example, within an 8-km radius) is relatively small. This implies that a large-scale multisite study with as many years of observations as possible is needed to reliably assess the potential risks. 2. Centralized cancer registries such as SEER and NPCR (for cancer incidence) or national offices such as NCHS (for cancer mortal- ity) can only release data that are aggregated across geographic areas such as counties. Cancer incidence and mortality data for more refined geographic areas can be released only by individual states upon submission and approval of a research proposal. In general, cancer mortality data are available since about 1970, but individual address at time of death is not captured until much 19 http://www.imvinfo.com.

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247 EPIDEMIOLOGIC STUDIES later in some states. Moreover, mortality data are not consistently geocoded at the census-tract level. Cancer incidence data of known quality are available from about 1995. These data include address at time of diagnosis and have been widely geocoded. 3. Large-scale studies that rely on contacting individuals are likely to be subject to selection and information biases due to difficulties related to tracing individuals, low (and declining) participation rates of cases and especially controls in epidemiologic studies, and the risk of collecting inaccurate information via interviews and questionnaires. Alternatively, studies that rely on information in existing records are more practical and free of the biases mentioned above, although other limitations exist. 4. Studies of pediatric cancers could take advantage of existing link- ages of cancer registration and birth records in at least six states that include more than 30 percent of the U.S. pediatric population. In light of these findings, the committee recommends that, should the USNRC decide to proceed with an epidemiologic study of cancer risks in populations near nuclear facilities (Phase 2), two studies be carried out to assess cancer risks in populations near nuclear facilities: (a) an ecologic study of multiple cancer types that would provide an assessment of cancer incidence and mortality in populations living within approximately 50 km of nuclear facilities and (b) a record-linkage-based case-control study of childhood cancer that would provide an assessment of early life exposure to radiation during more recent operating periods of nuclear facilities. The strengths and limitations of the recommended studies are described in Sec- tion 4.2.3. Specifying up front the hypotheses to be tested and the analysis plan is the responsibility of the Phase 2 committee. The committee judges that additional information and analyses beyond the scope of this Phase 1 activity are needed to assess the feasibility of carry- ing out the recommended studies that could be performed by a pilot study. The purpose of the pilot study is to evaluate the feasibility of the methods proposed, and to develop the specific operational procedures and data col- lection methods needed for a full study. The purpose of the pilot study is not to perform a small-scale preliminary assessment of risks, the results of which would be used for or against moving forward with the full study. As discussed in Chapter 3, seven facilities were selected collaboratively by the dosimetry and epidemiology experts of this committee and include Dresden (Illinois), Millstone (Connecticut), Oyster Creek (New Jersey), Haddam Neck (Connecticut), Big Rock Point (Michigan), San Onofre (Cali- fornia), and Nuclear Fuel Services (Tennessee). The reasons of selection of these facilities with regards to dosimetry are discussed in Chapter 3. These facilities are also good candidates to evaluate the feasibility of the studies

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248 ANALYSIS OF CANCER RISKS from the epidemiologic perspective as they represent both currently oper- ating and decommissioned facilities in six states, that started operation in different time points and with some variation in (a) the population size in close proximity, (b) quality and maturation of the state’s cancer registration, and (c) level of complexity for registry’s research approval processes and research support. Actions specific to the recommended studies to be taken during the piloting activity are the following: • Retrieve cancer incidence and mortality data at the census-tract level within 50 km of selected facilities to assess feasibility of the recommended ecologic study. • Confer with investigators conducting linkages of cancer and birth registration data to identify eligible cases of pediatric cancers and matched controls to assess feasibility of the recommended record- linkage-based case-control study in the selected facilities. In states with the necessary capabilities, but without such linkages in place, link birth registration and cancer incidence data. REFERENCES Bernstein, L. (2006). Control recruitment in population-based case-control studies. Epidemiol- ogy 17(3):255-257. Bernstein, J. L., R. W. Haile, M. Stovall, J. D. Boice, Jr., R. E. Shore, B. Langholz, D. C. Thomas, L. Bernstein, C. F. Lynch, J. H. Olsen, K. E. Malone, L. Mellemkjaer, A.-L. Borrensen-Dale, B. S. Rosenstein, S. N. Teraoka, T. A. Diep, S. A. Smith, M. Capanu, A. S. Reiner, X. Liang, et al. (2010). Radiation exposure, the ATM gene, and contralat- eral breast cancer in the Women’s Environmental Cancer and Radiation Epidemiology Study. J Natl Cancer Inst. 102:475-483. Berrington de Gonzalez, A., M. Mahesh, et al. (2009). Projected cancer risks from com- puted tomographic scans performed in the United States in 2007. Arch. Intern. Med. 169(22):2071-2077. Berrington de González, A. B., A. Brenner, P. Hartge, C. Lee, L. Morton, and P. Rajaraman. (2011). Evolving strategies in epidemiologic research on radiation and cancer. Radiat. Res. 176(4):527-532. Epub Aug. 8, 2011. Bithell, J. F., T. J. Keegan, et al. (2008). Childhood leukaemia near British nuclear installations: Methodological issues and recent results. Radiat. Prot. Dosim. 132(2):191-197. Boice, J. D., Jr., W. L. Bigbee, et al. (2003). Cancer incidence in municipalities near two for- mer nuclear materials processing facilities in Pennsylvania. Health Phys. 85(6):678-690. Boice, J. D. Jr., S. S. Cohen, M. T. Mumma, E. D. Ellis, K. F. Eckerman, R. W. Leggett, B. B. Boecker, A. B. Brill, and B. E. Henderson (2011). Updated mortality analysis of radiation workers at Rocketdyne (Atomics International), 1948-2008. Radiat. Res. 176(2):244-258. Brenner, D. J., R. Doll, et al. (2003). Cancer risks attributable to low doses of ionizing radia- tion: Assessing what we really know. Proc. Natl. Acad. Sci. U S A 100(24):13761-13766. Bunin, G. R., L. G. Spector, et al. (2007). Secular trends in response rates for controls selected by random digit dialing in childhood cancer studies: A report from the Children’s Oncol- ogy Group. Am. J. Epidemiol. 166(1):109-116.

OCR for page 143
249 EPIDEMIOLOGIC STUDIES Carlson, S. A., D. Densmore, et al. (2009). Differences in physical activity prevalence and trends from 3 U.S. surveillance systems: NHIS, NHANES, and BRFSS. J. Phys. Act. Health 6(Suppl 1):S18-S27. Centers for Disease Control and Prevention (2011). State-specific trends in lung cancer inci- dence and smoking—United States, 1999-2008. MMWR 60(36):1243-1247. Chokkalingam, A. P., K. Bartley, et al. (2011). Haplotypes of DNA repair and cell cycle con- trol genes, X-ray exposure, and risk of childhood acute lymphoblastic leukemia. Cancer Causes Control 22(12):1721-1730. Cohen, B. L. (1995). Test of the linear-no threshold theory of radiation carcinogenesis for inhaled radon decay products. Health Phys. 68(2):157-174. Cohen, B. L. (1997). Lung cancer rate vs. mean radon level in U.S. counties of various char- acteristics. Health Phys. 72(1):114-119. Curado, M. P., B. Edwards, H. R. Shin, H. Storm, J. Ferlay, M. Heanue, and P. Boyle. (2007). Cancer incidence in five continents. Volume IX, IARC Scientific Publications No. 160. Darby, S., D. Hill, et al. (2005). Radon in homes and risk of lung cancer: collaborative analysis of individual data from 13 European case-control studies. BMJ 330(7485):223. Das, B., L. X. Clegg, et al. (2008). A new method to evaluate the completeness of case ascer- tainment by a cancer registry. Cancer Causes Control 19(5):515-525. Davis, S., L. Onstad, et al. (2008). Locating members of a cohort identified retrospectively from limited data in 50-year-old records: successful approaches employed by the Hanford Thyroid Disease Study. Ann. Epidemiol. 18(3):187-195. Dennison, C., and R. Pokras (2000). Design and operation of the National Hospital Discharge Survey: 1988 redesign. Vital Health Stat 1(39):1-42. Dufault, B., and N. Klar (2011). The quality of modern cross-sectional ecologic studies: A bibliometric review. Am. J. Epidemiol. 174(10):1101-1107. Evrard, A. S., D. Hemon, et al. (2006). Childhood leukaemia incidence around French nuclear installations using geographic zoning based on gaseous discharge dose estimates. Br. J. Cancer 94(9):1342-1347. Fahimi, M., M. Link, et al. (2008). Tracking chronic disease and risk behavior prevalence as survey participation declines: statistics from the behavioral risk factor surveillance system and other national surveys. Prev. Chronic Dis. 5(3):A80. Federal Radiation Council (1962). Health implications of fallout from nuclear weapons testing through 1961. Federal Guidance Report No 3. Washington, DC. Fell, D. B., L. Dodds, and W. D. King (2004). Residential mobility during pregnancy. Paediatr. Perinat. Epidemiol. 18(6):408-414. German, R. R., A. K. Fink, et al. (2011). The accuracy of cancer mortality statistics based on death certificates in the United States. Cancer Epidemiol. 35(2):126-131. Gilboa, S. M., P. Mendola, et al. (2006). Characteristics that predict locating and interviewing mothers identified by a state birth defects registry and vital records. Birth Defects Res. A Clin. Mol. Teratol. 76(1):60-65. Hartge, P. (2006). Participation in population studies. Epidemiology 17(3):252-254. Hays, J., J. R. Hunt, et al. (2003). The Women’s Health Initiative recruitment methods and results. Ann. Epidemiol. 13(9 Suppl):S18-S77. Heath, C. W. Jr., P. D. Bond, D. G. Hoel, and C. B. Meinhold (2004). Residential radon ex- posure and lung cancer risk: commentary on Cohen’s county-based study. Health Phys. 87(6):647-655; discussion 656-658. Hunt, J. R., and E. White (1998). Retaining and tracking cohort study members. Epidemiol. Rev. 20(1):57-70. Jablon, S., Z. Hrubec, J. D. Boice, Jr., and B. J. Stone (1990). Cancer in populations living near nuclear facilities, Vols. 1-3, NIH Publication No. 90-874. Jablon, S., Z. Hrubec, et al. (1991). Cancer in populations living near nuclear facilities. A survey of mortality nationwide and incidence in two states. JAMA 265(11):1403-1408.

OCR for page 143
250 ANALYSIS OF CANCER RISKS Johnson, K. J., S. E. Carozza, et al. (2009). Parental age and risk of childhood cancer: a pooled analysis. Epidemiology 20(4):475-483. Kaatsch, P., C. Spix, et al. (2008). Leukaemia in young children living in the vicinity of German nuclear power plants. Int. J. Cancer 122(4):721-726. Kelsey, J. L., A. S. Whittemore, et al. (1996). Methods in Observational Epidemiology. New York and Oxford: Oxford University Press. Kinlen, L. (1988). Evidence for an infective cause of childhood leukaemia: Comparison of a Scottish new town with nuclear reprocessing sites in Britain. Lancet 2(8624):1323-1327. Kinlen, L. (2011). Childhood leukaemia, nuclear sites, and population mixing. Br. J. Cancer 104(1):12-18. Kirby, R. S., and H. M. Salihu (2006). Back to the future? A critical commentary on the 2003 U.S. National standard certificate of live birth. Birth 33(3):238-244. Klonoff-Cohen, H. (1996). Tracking strategies involving fourteen sources for locating a tran- sient study sample: Parents of sudden infant death syndrome infants and control infants. Am. J. Epidemiol. 144(1):98-101. Land, C. E. (1980). Estimating cancer risks from low doses of ionizing radiation. Science 209(4462):1197-1203. Land, C. E. (2002). Uncertainty, low-dose extrapolation and the threshold hypothesis. J. Radiol. Prot. 22(3A):A129-A135. Last, J. M. (1995). A dictionary of epidemiology. New York: Oxford University Press. Law, G. R. (2008). Host, family and community proxies for infections potentially associated with leukaemia. Radiat. Prot. Dosim. 132(2):267-272. Little, M. P., and J. D. Boice, Jr. (1999). Comparison of breast cancer incidence in the Mas- sachusetts tuberculosis fluoroscopy cohort and in the Japanese atomic bomb survivors. Radiat Res. 151(2):218-224. Liu, L., M. Krailo, et al. (2003). Childhood cancer patients’ access to cooperative group cancer programs: A population-based study. Cancer 97(5):1339-1345. Ma, X., P. A. Buffler, et al. (2002). Daycare attendance and risk of childhood acute lympho- blastic leukaemia. Br. J. Cancer 86(9):1419-1424. Ma, X., P. A. Buffler, et al. (2004). Control selection strategies in case-control studies of child- hood diseases. Am. J. Epidemiol. 159(10):915-921. Machado, C. J. (2004). A literature review of record linkage procedures focusing on infant health outcomes. Cad. Saude Publica 20(2):362-371. Malone, K. E., C. B. Begg, R. W. Haile, A. Borg, P. Concannon, L. X. Tellhed, S. Teraoka, L. Bernstein, M. Capanu, A. S. Reiner, E. R. Riedel, D. C. Thomas, L. Mellemkjaer, C. F. Lynch, J. D. Boice. Jr., H. Anton-Culver, and J. L. Bernstein (2010). Population-based study of the risk of second primary contralateral breast cancer associated with carrying a mutation in BRCA1 or BRCA2. J. Clin. Oncol. 28(14):2404-2410. McKenzie, M., J. P. Tulsky, et al. (1999). Tracking and follow-up of marginalized populations: A review. J. Health Care Poor Underserved 10(4):409-429. McLaughlin, C. C., M. S. Baptiste, et al. (2006). Maternal and infant birth characteristics and hepatoblastoma. Am. J. Epidemiol. 163(9):818-828. Morin, A., and J. Backe. (2002). Programme environnement et santé 1999. Une estimation de l’exposition du public due aux rejets radioactifs des centrales nucléaires (in French). Technical Note SEGR/SAER/02–51 Indice 1. Institut de Radioprotection et de Sûreté Nucléaire, Fontenay-aux-Roses (July). Morton, L. M., J. Cahill, et al. (2006). Reporting participation in epidemiologic studies: A survey of practice. Am. J. Epidemiol. 163(3):197-203. Mueller, B. A., E. J. Chow, et al. (2009). Pregnancy outcomes in female childhood and ado- lescent cancer survivors: A linked cancer-birth registry analysis. Arch. Pediatr. Adolesc. Med. 163(10):879-886. NCRP (National Council on Radiation Protection and Measurements) (2009). Ionizing radia- tion exposure of the populations of the United States. Report 160.

OCR for page 143
251 EPIDEMIOLOGIC STUDIES Neglia, J. P., D. L. Friedman, Y. Yasui, A. C. Mertens, S. Hammond, M. Stovall, S. S. Donald- son, A. T. Meadows, and L. L. Robison (2001). Second malignant neoplasms in five-year survivors of childhood cancer: Childhood cancer survivor study. J. Natl. Cancer Inst. 93(8):618-629. Neutra RR. (1990) Counterpoint from a cluster buster., Am J Epidemiol. 132(1):1-8. NRC (National Research Council) (2005). Health Risks From Exposure to Low Levels, of Ion- izing Radiation: BEIR VII—Phase 2. Committee to Assess Health Risks from Exposure to Low Levels of Ionizing Radiation. Washington, DC: The National Academies Press. Nuclear Safety Council and the Carlos III Institute of Health (2009). Epidemiological study of the possible effect of ionizing radiations deriving from the operation of Spanish nuclear fuel cycle facilities on the health of the population living in their vicinity, Spain. Parker, D. M., S. L. Whelan, and J. Ferlay (2002). Cancer Incidence in Five Continents. Vol. VIII, IARC Scientific Publications No. 122. Pawel, D. J. (2005). Can confounding by smoking explain the ecologic correlation between lung cancer and radon? Health Phys. 89(2):181-182; author reply 182. Pierce, D. A., Y. Shimizu, et al. (1996). Studies of the mortality of atomic bomb survivors. Report 12, Part I. Cancer: 1950-1990. Radiat. Res. 146(1):1-27. Pierce, D. A., G. B. Sharp, et al. (2005). Joint effects of radiation and smoking on lung cancer risk among atomic bomb survivors. Radiat. Res. 163(6):694-695. Podvin, D., C. M. Kuehn, et al. (2006). Maternal and birth characteristics in relation to child- hood leukaemia. Paediatr. Perinat. Epidemiol. 20(4):312-322. Preston, D. L., A. Mattsson, et al. (2002). Radiation effects on breast cancer risk: A pooled analysis of eight cohorts. Radiat. Res. 158(2):220-235. Preston, D. L., Y. Shimizu, et al. (2003). Studies of mortality of atomic bomb survivors. Report 13: Solid cancer and noncancer disease mortality: 1950-1997. Radiat. Res. 160(4):381-407. Preston, D. L., E. Ron, et al. (2007). Solid cancer incidence in atomic bomb survivors: 1958- 1998. Radiat. Res. 168(1):1-64. Puumala, S. E., J. T. Soler, et al. (2008). Birth characteristics and Wilms tumor in Minnesota. Int. J. Cancer 122(6):1368-1373. Reynolds, P., J. Von Behren, et al. (2002). Birth characteristics and leukemia in young children. Am. J. Epidemiol. 155(7):603-613. Richardson, D., H. Sugiyama, et al. (2009). Ionizing radiation and leukemia mortality among Japanese Atomic Bomb Survivors, 1950-2000. Radiat. Res. 172(3):368-382. Robinson, K. A., C. R. Dennison, et al. (2007). Systematic review identifies number of strate- gies important for retaining study participants. J. Clin. Epidemiol. 60(8):757-765. Ron, E., J. H. Lubin, et al. (1995). Thyroid cancer after exposure to external radiation: A pooled analysis of seven studies. Radiat. Res. 141(3):259-277. Rosenbaum, P. F., G. M. Buck, et al. (2000). Early child-care and preschool experiences and the risk of childhood acute lymphoblastic leukemia. Am. J. Epidemiol. 152(12):1136-1144. Ross, J. A., L. G. Spector, et al. (2004). Invited commentary: Birth certificates—a best control scenario? Am. J. Epidemiol. 159(10):922-924; discussion 925. Rothman, K. J. (1990). A sobering start for the cluster busters’ conference. Am. J. Epidemiol. 132(1 Suppl):S6-S13. Rothman, K. J., and S. Greenland (1998). Modern Epidemiology. Philadelphia: Lippincott Williams & Wilkins. Satten, G. A., and L. L. Kupper (1990). Sample size requirements for interval estimation of the odds ratio. Am. J. Epidemiol. 131(1):177-184. Savitz, D. A., and A. F. Olshan (1995). Multiple comparisons and related issues in the inter- pretation of epidemiologic data. Am. J. Epidemiol. 142(9):904-908. Sermage-Faure, C., D. Laurier, S. Goujon-Bellec, M. Chartier, A. Guyot-Goubin, J. Rudant, D. Hémon, and J. Clavel (2012). Childhood leukemia around French nuclear power plants—the Geocap study, 2002-2007. Int. J. Cancer, Epub Feb. 20.

OCR for page 143
252 ANALYSIS OF CANCER RISKS Shore, R. E., V. Iyer, et al. (1992). Use of human data in quantitative risk assessment of car- cinogens: Impact on epidemiologic practice and the regulatory process. Regul. Toxicol. Pharmacol. 15(2 Pt 1):180-221. Shulman, H. B., B. C. Gilbert, et al. (2006). The Pregnancy Risk Assessment Monitoring Sys- tem (PRAMS): Current methods and evaluation of 2001 response rates. Public Health Rep. 121(1):74-83. Socolow, E. L., A. Hashizume, et al. (1963). Thyroid carcinoma in man after exposure to ionizing radiation. A summary of the findings in Hiroshima and Nagasaki. N. Engl. J. Med. 268:406-410. Spector, L. G., J. A. Ross, et al. (2007). Feasibility of nationwide birth registry control selection in the United States. Am. J. Epidemiol. 166(7):852-856. Spycher, B. D., M. Feller, et al. (2011). Childhood cancer and nuclear power plants in Swit- zerland: A census-based cohort study. Int. J. Epidemiol., Epub Jul. 12. Steele, J. R., A. S. Wellemeyer, et al. (2006). Childhood cancer research network: A North Amer- ican Pediatric Cancer Registry. Cancer Epidemiol. Biomarkers Prev. 15(7):1241-1242. Tromp, M., J. B. Reitsma, et al. (2006). Record linkage: Making the most out of errors in linking variables. AMIA Annu. Symp. Proc. 779-783. Trott, K. R., and M. Rosemann (2000). Molecular mechanisms of radiation carcinogenesis and the linear, non-threshold dose response model of radiation risk estimation. Radiat. Environ. Biophys. 39(2):79-87. UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation) (2006). Sources and Effects of Ionizing Radiation, Volume I, Annex A—Epidemiological studies of radiation and cancer. USEPA (U.S. Environmental Protection Agency) (2007). Radiation Risks and Realities. http:// www.epa.gov/rpdweb00/docs/402-k-07-006.pdf. Von Behren, J., L. G. Spector, et al. (2011). Birth order and risk of childhood cancer: A pooled analysis from five US States. Int. J. Cancer 128(11):2709-2716. Walker, K. M., S. Carozza, et al. (2007). Childhood cancer in Texas counties with moderate to intense agricultural activity. J. Agric. Saf. Health 13(1):9-24. Wanebo, C. K., K. G. Johnson, et al. (1968). Breast cancer after exposure to the atomic bomb- ings of Hiroshima and Nagasaki. N. Engl. J. Med. 279(13):667-671. White-Koning, M. L., D. Hemon, et al. (2004). Incidence of childhood leukaemia in the vicin- ity of nuclear sites in France, 1990-1998. Br. J. Cancer 91(5):916-922. Willett, W. C., M. J. Stampfer, et al. (1987). Dietary fat and the risk of breast cancer. N. Engl. J. Med. 316(1):22-28. Winkler, W. E. (1995) Matching and record linkage, in Business Survey Methods, edited by B. G. Cox, D. A. Binder, B. N. Chinnappa, A. Christianson, M. J. Colledge and P. S. Kott. Hoboken, New Jersey: John Wiley & Sons, doi: 10.1002 /9781118150504.ch20