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Epidemiologic studies examine the relationship between exposures to agents of interest—uranium in this case—in a human population and the health outcomes seen in the population. The challenge of epidemiologic studies is to control for risk factors that are related to the exposures and health outcomes of interest by using study designs and statistical techniques that control for bias and confounding. Such studies can be used to generate hypotheses for future study or to test hypotheses posed by investigators.

A principal objective of epidemiology is to understand whether exposures to specific agents are associated with disease or other health outcomes and to evaluate whether such associations are potentially causal. Although they are often used synonymously by the general public, the terms association and causation have distinct meanings (Alpert and Goldberg, 2007).

Epidemiologic studies can establish statistical associations between exposures and health effects, and associations are generally expressed by using relative risks or odds ratios. To conclude that an association exists, it is necessary for an exposure to be followed by a health effect more frequently than would be expected by chance alone. Furthermore, confidence in an association rises when it is consistently observed in several studies. However, the results of separate studies are sometimes conflicting. It is possible to attribute discordant study results to differences in such characteristics as soundness of study design, quality of execution, and the influence of different forms of bias. Studies that result in a tight confidence interval around a statistically significant relative risk of association constitute stronger evidence of an effect. When the measure of association does not show a statistically significant effect, it is important to consider the size of the sample and whether the study had the power to detect an effect of a given size. Epidemiologic study designs differ in their ability to provide valid estimates of an association (Ellwood, 1998). Cross-sectional studies generally provide a lower level of evidence than cohort and case-control studies.

Determining whether a given statistical association rises to the level of causation requires inference (Hill, 1965). As discussed by the International Agency for Research on Cancer (IARC) in the preamble of its monographs on evaluating cancer risks (for example, IARC, 2004), a strong association is demonstrated by repeated observations in a number of studies, an increased risk of disease with increasing exposure or a decline in risk after cessation of exposure, and specificity of an effect. Those characteristics all strengthen the likelihood that an association seen in epidemiologic studies is a causal effect. Inferences from epidemiologic studies, however, are often limited to population or ecologic associations because of a lack of information on individual exposures. Exposures are rarely, if ever, controlled in epidemiologic studies, and there is usually large uncertainty in the assessment of exposure. To assess whether explanations other than causality are responsible for an observed association, one must bring together evidence from

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