Question

You are an investigator conducting social/behavioral epidemiological study. Formulate and design ONE hypothetical epidemiological study that falls within the scope of social or behavioral epidemiology. Justify your study and describe your study in detail.

Answer 1

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.

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 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.

Cluster Studies

A cancer cluster is an aggregation of a relatively unexpected high number 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.

Ecologic Studies

An ecologic study (sometimes referred to as a geographic study or correlation 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 unit of analysis, the data of interest are average values of exposure and aggregate 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 potential 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.

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 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.

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