Epidemiology is the study of diseases in populations of humans or other animals, specifically how, when and where they occur.

The first step in an epidemiological study is to strictly define exactly what requirements must be met in order to classify someone as a “case.” This seems relatively easy, and often is in instances where the outcome is either there or not there (a person is dead or alive). In other instances it can be very difficult, particularly if the experts disagree about the classification of the disease. This happens often with the diagnosis of particular types of cancer. In addition, it is necessary to verify that reported cases actually are cases, particularly when the survey relies on personal reports and recollections about the disease made by a variety of individuals.

Cohort Study

Case Control Study

Occupational Epidemiological Study

Cross-Sectional Study

Case Series


This chapter explains why and when epidemiologists prefer one type of study over another and describe strengths and weaknesses of each approach.

To begin an epidemiologic study, we decide what to study.

For this discussion, let’s say we want to study prenatal exposure to electric and magnetic fields and the effect on a baby’s birth weight. We look at the existing literature on birth weight to assess current knowledge and data. It is important to know if others have conducted similar studies in case they have uncovered specific design limitations or useful results, and this information is helpful in understanding the context of one’s own study.

We believe that known risks include prematurity, poor prenatal care, low socioeconomic status, non-white ethnicity, large size of the mother, younger or older mothers, smoking, alcohol consumption and a host of other factors. Electric and magnetic field exposures are not known risk factors but have not been studied extensively. Therefore we wish to study them.


The “What will happen to me?” study follows a group of healthy people with different levels of exposure and assesses what happens to their health over time. It is a desirable design because exposure precedes the health outcome — a condition necessary for causation — and is less subject to bias because exposure is evaluated before the health status is known. The cohort study is also expensive, time-consuming and the most logistically difficult of all the studies. It is most useful for relatively common diseases. To assess suitability, we find out the commonality of the disease we wish to study. Does it occur in 10 percent of all births, 1 percent of births, or 0.001 percent of births? For example, if low weight occurs in 10 percent or more of all births, then we might investigate a relatively small group of newborns, say 200 to 400, and characterize them with respect to their exposures during pregnancy. We would expect to see 20 to 40 low-weight babies in this group. We would want to know if, while pregnant, their mother’s exposure to electric and magnetic fields was different from the other 180 to 360 births.

The cohort study approach is good for our hypothetical study because we can identify a number of pregnant women, characterize their exposure during almost their entire pregnancies and assess the babies’ weights at birth. Thus we limit the possibility of investigator preferences, or “bias,” affecting the selection of study subjects. We must make sure nearly everyone selected for our study participates, because those not participating may be different from those who do, causing another type of bias. The assumption of risk (such as exposure to electric and magnetic fields during pregnancy) necessarily precedes the outcome (birth), and that is a necessary condition for inferring a cause.

Finally, because we selected our study subjects on the basis of their exposure only, the cohort approach enables us to look at other pregnancy outcomes such as birth defects, spontaneous abortion or increased mortality, in addition to birth weight.

An example of a cohort study would be the investigation of a cohort of smokers and non-smokers over time to estimate the incidence of lung cancer. The same 2×2 table is constructed as with the case control study. However, the point estimate generated is the relative risk (RR), which is the probability of disease for a person in the exposed group, Pe = A / (A + B) over the probability of disease for a person in the unexposed group, Pu = C / (C + D), i.e. RR = Pe / Pu.

ExposedAB(A + B)
UnexposedCD(C + D)

As with the OR, a RR greater than 1 shows association, where the conclusion can be read “those with the exposure were more likely to develop disease.”

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Prospective studies have many benefits over case control studies. The RR is a more powerful effect measure than the OR, as the OR is just an estimation of the RR, since true incidence cannot be calculated in a case control study where subjects are selected based on disease status. Temporality can be established in a prospective study, and confounders are more easily controlled for. However, they are more costly, and there is a greater chance of losing subjects to follow-up based on the long time period over which the cohort is followed.

Cohort studies also are limited by the same equation for number of cases as for cohort studies, but, if the base incidence rate in the study population is very low, the number of cases required is reduced by ½.


The “why me?” study investigates the prior exposure of individuals with a particular health condition and those without it to infer why certain subjects, the “cases,” become ill and others, the “controls,” do not. The main advantage of the case-control study is that it enables us to study rare health outcomes without having to follow thousands of people, and is therefore generally quicker, cheaper and easier to conduct than the cohort study.

One primary disadvantage of a case-control study is a greater potential for bias. Since the health status is known before the exposure is determined, the study doesn’t allow for broader-based health assessments, because only one type of disease has been selected for study. If the condition we wish to study is rare — for instance, affecting less than 5 percent of the population — the cohort approach would not identify enough subjects from which to draw statistically reliable inferences, unless we looked at a very large number of subjects. For example, in our study of low birth weight, if between 500 and several thousand births would be needed to get 10 to 50 low-weight births, developing exposure information for all of those births would be cumbersome, expensive and time-consuming.

In the case-control design, we can review birth certificates of several thousand newborns and find a certain number that exhibited low birth weight — 50 to 100, for example — and a comparable number of normal birth weights, and compare them with respect to electric and magnetic field exposures. This approach has the advantage of identifying a sufficient number of cases of the rare outcome we wish to study out of a population of thousands of births. It then requires us to develop exposure and risk factor data only for the limited number of individuals in our study. Thus, it is quicker, easier and less expensive than the cohort design, which would require such information for all of the several thousand births. It is an approach commonly used for studies of cancers and other rare diseases. Also, because subjects were selected on the basis of outcome only, we can evaluate a variety of exposures, such as electric fields, magnetic fields, chemical exposures and so forth.

The case-control study has the disadvantage of selecting cases and controls after both the outcome and the assumption of risk have occurred. This makes substantial bias a possibility because we may inadvertently favor certain births for inclusion in our study, and because certain women who should have been eligible for our study were not (those pregnant mothers whose fetus spontaneously aborted, for instance). Once chosen on the basis of one outcome (low birth weight), our subjects cannot be analyzed for certain other outcomes (spontaneous abortion), as they could in a cohort study.

Another consideration in choosing an epidemiological design is the commonness of the risk factor. Common exposures can be studied by either the cohort or case-control design. Rare exposures are best studied by the cohort method since groups are selected on the basis of their exposure status.

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Consider our study at hand. Most of us are exposed at home to very low magnetic fields — under 10 milligrams (mg). But some homes score as high as 40 mg, or higher, and some occupations measure exposures in the hundreds or thousands of mg. Let’s define exposed houses as those having at least 10 mg. If, in our case-control study of low birth weight births, we were to compare the residential magnetic field exposure of the births, we likely would see few, if any, exposed houses and would not be able to draw any conclusions. On the other hand, if we conducted a cohort study, we could select houses with magnetic field exposures over 10 mg, and then compare the birth weights of babies in those houses with birth weights of babies in houses with magnetic fields less than 10 mg. Since low birth weight is far more common than high magnetic field exposure, the cohort design is more likely to produce a useful result.


The occupational study can be designed using any standard epidemiologic design, simply selecting working people with particular jobs or exposures as subjects. The main advantage of this approach is that workers often have substantially higher exposures to certain risk factors than the typical population, which increases our chances of detecting an effect if one truly exists. The main disadvantages are that workers with various jobs differ substantially from one another in terms of risks, and that the working population is substantially different from the nonworking one (such the rich, elderly or disabled), making it difficult to generalize to populations with some nonworking people. We usually look to occupational settings to exploit situations of high exposure. The number of eligible subjects in these settings is smaller than in the general population, but that is more than balanced by the extreme levels of exposures often seen in the workplace, which increase our chances of seeing effects.

There are two caveats to occupational epidemiological studies. First, in the workplace, people are exposed to a variety of risk factors that may affect results. For example, many workers are exposed to a variety of chemicals (such as solvents) that are known or suspected carcinogens; to a variety of electric and magnetic fields at different intensities and frequencies; and to other factors such as stress and poor ventilation or air quality.

Second, the number of people exposed to the risk factor we’re interested in may be much smaller in the workplace than in residences (for example, fewer workers service an electric power transmission line than live near the line), so in some cases it may be difficult to identify a sufficient number of exposed workers. And it may be difficult to identify a comparison population of workers not exposed to the risk factor (or exposed at a substantially lower level) who also have comparable characteristics with respect to other possible risks. For instance, we would not compare construction workers to business executives because of likely differences in lifestyle and occupational risk. We would expect to see differences in cancer rates between the two groups based on those factors alone, and it would be difficult to separate those risks from our primary interest in the study of exposures to electric and magnetic fields. It is preferable to examine cancer rates among groups of similar workers, say among all telephone line repair workers, pole climbers, van drivers, dispatchers and supervisors. That is still not ideal, as many of these workers perform more than one task, but the approach is better than comparing telephone workers with business executives.


The “Am I like my neighbors?” study compares groups in terms of their current health and exposure status and assesses their similarities. The main advantage is that the cross-sectional study is a particularly easy study to conduct, as we do not have to wait for the health outcome to occur or estimate what the level of exposure was likely to have been years ago. Its main disadvantage is that a cause can’t be inferred, because only current health and exposure are being studied. The cross-sectional study is the one in which we assess a group’s health status and exposure status simultaneously. We might inquire about recent health problems (including breast cancer diagnosed in the past year) and assess the current electric and magnetic fields exposures in people’s homes as part of the same survey. An important limitation of this approach is that it does not allow for changes over time, and thus cannot accommodate diseases that take time to develop. For example, someone exposed today to ionizing radiation may be diagnosed with leukemia (or another cancer) five, 10, or even 20 years from now, even though the leukemia may not be evident today, next week or next month. Therefore, much information may be lost by contemporaneous evaluation.

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Implicit in the cross-sectional study is the assumption that the study population has been exposed for a long time and will continue to be exposed unless some intervention is affected. Although such studies can be used to identify possible associations and suggest worthwhile case-control or cohort studies for follow up, the cross-sectional study may not necessarily confirm causes.


Case-series may refer to the qualitative study of the experience of a single patient, or small group of patients with a similar diagnosis, or to a statistical technique comparing periods during which patients are exposed to some factor with the potential to produce illness with periods when they are unexposed.

The former type of study is purely descriptive and cannot be used to make inferences about the general population of patients with that disease. These types of studies, in which an astute clinician identifies an unusual feature of a disease or a patient’s history, may lead to formulation of a new hypothesis. Using the data from the series, analytic studies could be done to investigate possible causal factors. These can include case control studies or prospective studies. A case control study would involve matching comparable controls without the disease to the cases in the series. A prospective study would involve following the case series over time to evaluate the disease’s natural history.

The latter type, more formally described as self-controlled case-series studies, divide individual patient follow-up time into exposed and unexposed periods and use fixed-effects Poisson regression processes to compare the incidence rate of a given outcome between exposed and unexposed periods. This technique has been extensively used in the study of adverse reactions to vaccination, and has been shown in some circumstances to provide statistical power comparable to that available in cohort studies.


Retrospective and prospective studies have been extremely valuable in discovering links between chemical exposure and disease. Perhaps the best example, again, is the association Reference

Epidemiology has helped develop methodology used in clinical research, public health studies and, to a lesser extent, basic research in the biological sciences.[1]

Major areas of epidemiological study include disease etiology, outbreak investigation, disease surveillance and screening, biomonitoring, and comparisons of treatment effects such as in clinical trials. Epidemiologists rely on other scientific disciplines like biology to better understand disease processes, statistics to make efficient use of the data and draw appropriate conclusions, social sciences to better understand proximate and distal causes, and engineering for exposure assessment.



Miquel Porta (2008). A Dictionary of Epidemiology. Oxford         University   Press. pp. 10–11. ISBN 978-0-19-531450-2.        Retrieved 11 July         2012.

Nutter, Jr., F.W. (1999). “Understanding the interrelationships    between botanical, human, and veterinary epidemiology: the Ys   and Rs of it all”. Ecosys Health 5 (3): 131–40.    doi:10.1046/j.1526-0992.1999.09922.x.

Hippocrates. (~200BC). Airs, Waters, Places.

Carol Buck, Alvaro Llopis, Enrique Nájera, Milton Terris. (1998). The   Challenge of Epidemiology: Issues and Selected Readings.   Scientific    Publication No. 505. Pan American Health Organization. Washington, DC. p3.

Alfredo Morabia (2004). A history of epidemiologic methods and         concepts. Birkhäuser. p. 93. ISBN 3-7643-6818-7.

Historical Developments in Epidemiology. Chapter 2. Jones & Bartlett Learning LLC.

Ray M. Merrill (2010). Introduction to Epidemiology. Jones & Bartlett   Learning. p. 24. ISBN 0-7637-6622-4. of cigarette smoking and lung cancer and emphysema.

Steps of an Outbreak Investigation, EXCITE | Epidemiology in the     Classroom | Outbreak Steps

Public Health Agency of Canada. “Canada’s Food-borne Illness   Outbreak Response Protocol (FIORP) 2010: To guide a multi- jurisdictional response”. Retrieved 22 March 2013.


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