environments; characteristics of potentially harmful factors (viruses, poverty, metabolic disturbances, high cholesterol, or radiation) or beneficial factors (including new medication, surgery, medical devices, health education, income, and housing); or measures of health status (mortality rates, cholesterol levels, or disease incidence). Notice that one factor can be at once a characteristic, risk factor, and outcome. A key distinction between epidemiologic and experimental data is that epidemiologic studies usually are not designed experiments with purebred animal subjects randomized to be exposed or not exposed. Rather, one makes use of exposure situations that have occurred for various reasons to learn what one can. This is essential in situations such as the study of CROSSROADS participation where a randomized design is impossible retrospectively.
It is important to understand that while epidemiology seeks to understand causal pathways, it cannot prove causation. Epidemiology uses judgment, statistics, and skepticism to reach descriptions and interpretations of relationships and associations. It is both a practical technique and an intellectual framework for considering the possibilities of causal relationships. It is the approach we have taken in this study.
Epidemiologists compare groups. The key to making sound comparisons is in choosing groups that are alike in all ways except for the matter being studied. This selection of comparison groups is where the science, mathematics, and art of good epidemiology are blended. For example, because age and sex are associated with health risks and conditions, data regarding age and sex are collected, making it possible in the analysis to either compare like age distributions and sexes or statistically adjust the data to account for known differences.
In studying CROSSROADS participants, comparison group options include the development of a specific control group, internal comparisons by level of exposure, and use of national statistics. Each carries useful and restrictive elements.
If, for example, one wants to study the effect of something on lung cancer, knowing what we do about cigarette smoking and lung cancer, we would want to pick two groups to compare that do not differ in smoking practices, for that difference could mask the true causal relationship we are looking to explore. In studies of military participants, it helps to use a reference group that is also military. After checking age and sex, we rest a bit more comfortably that the two groups are rather likely to be similar on a host of unmeasured characteristics—such as smoking behavior. If, however, we chanced to compare the woodwind section of the Navy band (good breathers) with an average group of smokers, we could encounter differences attributable to smoking behavior.