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 suggesting 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 research 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 earliest 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 (Berrington 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 potential associations between cancer and living near a nuclear facility. The first thing that the epidemiologist questions is whether any observed association 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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