The Purpose of Epidemiology

The purpose of epidemiology is to establish associations which may be causative or may reveal clues to causation. Does the presence of a particular factor lead to greater risk of disease? Does a treatment lead to greater chance of a good outcome? In answering these questions using animal or cell culture models, one can have almost complete control over experimental conditions; in humans however, one cannot have the same degree of experimental control for ethical reasons. Epidemiology can be seen, then, as the science of inferring association or causation in humans under so-called “messy” real-world conditions, when one can only observe (observational study designs) or intervene to a limited degree (interventional study designs), rather than manipulate experimentally. There are different study designs in research conducted in humans.

Observational Study Designs

To study causes or exposures known to be harmful, it is not ethical nor feasible to use an experimental design; for example, one cannot ask one group to start smoking and another to abstain from smoking to study if smoking causes age-related macular degeneration (AMD). Observational studies do not interfere in human subjects’ choice of exposure and assess outcomes in subjects who were exposed or not exposed to the factors of interest; these are surveys, case-control, cohort studies (all with controls) or cases series (without controls).

Observational Study Designs

To study causes or exposures known to be harmful, it is not ethical nor feasible to use an experimental design; for example, one cannot ask one group to start smoking and another to abstain from smoking to study if smoking causes age-related macular degeneration (AMD). Observational studies do not interfere in human subjects’ choice of exposure and assess outcomes in subjects who were exposed or not exposed to the factors of interest; these are surveys, case-control, cohort studies (all with controls) or cases series (without controls).

Surveys (Cross-Sectional Studies)

In surveys, exposures and disease outcomes are assessed at the same time, that is, cross-sectionally. Surveys simultaneously collect data on multiple exposures and outcomes for exploration of associations. Associations assessed should be guided by sound hypotheses and should be seen as hypothesis generating. A major drawback of surveys is that temporality (the exposure must precede the effect or outcome), a key component of causation, cannot be established.

Surveys, if conducted in representative population-based samples, such as the baseline surveys of the Beaver Dam Eye Study, Rotterdam Study, or the Blue Mountains Eye Study, can provide estimates of frequency of the diseases at a particular point in time, regardless of when the diseases developed; this is termed prevalence . It is calculated as the proportion of subjects with the disease at a particular point in time out of the total number of subjects who were surveyed at that time. Prevalence differs from incidence, which can be provided only by longitudinal studies and refers to the proportion of subjects in whom the disease develops over a defined period, from the total number of subjects who were free of the disease at the beginning of the period.

Case-Control Studies

When studying rare diseases or diseases with long latency, it makes sense to start with groups who do (cases) and do not (controls) have the outcome of interest and to investigate the exposures retrospectively. The advantage of this design is also its biggest drawback: in assessing exposures retrospectively, cases may overreport exposures relative to controls (recall bias). Where and how to select the appropriate control group for a series of cases also may affect the study findings (potential selection bias).

Cohort (Longitudinal) Studies

The drawbacks of case-control studies can be addressed by using cohort studies. Cohort studies are appropriate for study questions about disease causes or prognosis. Disease incidence or prognosis can be assessed during follow-up among subjects with and without the exposures of interest.

Cohort designs are not feasible where the disease incidence is rare or the latency to disease is long. Failure to follow-up a large number of study subjects likely introduces selection bias; for example, subjects with better or worse outcomes may be more likely to be followed up than others (differential loss in follow-up).

Table 1 provides a comparison of case-control and cohort study designs. Case-control studies can be nested within population-based surveys or cohort studies. This hybrid study design incorporates the advantage of population-based sampling (minimized selection bias) and the cost-effectiveness of investigating associations for specific diseases with exposures, using all cases and randomly selected or matched controls from the study sample.

Measures of associations provided by case-control studies are odds ratios, which can be interpreted as relative risk if the disease is rare (< 10% in prevalence) or if the effect is not too extreme, for example, odds ratio less than 2 to 3. True relative risk can be provided only by cohort studies and is an estimate of the difference in the incidence (or risk) associated with an exposure compared with the absence of the exposure. The odds ratio is the ratio of the likelihood of being exposed among the cases compared with the likelihood of being exposed among the controls. The odds are not a proportion, but rather are the probability that an event occurs ( p ) relative to the probability that the event does not occur (1 − p ), calculated as p /(1 − p ).