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Epidemiological Studies

As mentioned earlier, epidemiologists used several different types of studies. Simply speaking, these can be classified as either experimental, where the epidemiologists have control over the circumstances from the start, or observational, where they do not. Vaccine efficacy trials are a good example of experimental studies because investigators control who gets the vaccine and who doesn't. Observational studies can be further subdivided into descriptive and analytical studies. In a descriptive study, the epidemiologist collects information to characterize and summarize the health event or problem. In an analytical study, the epidemiologist relies on comparisons between groups to determine the role of various risk factors in causing the problem. Descriptive epidemiology is the most basic of the these categories and is fundamental to the work of an epidemiologists.

Another way of comparing descriptive and analytical epidemiology is to say that in the descriptive process, we are concerned with "person" (Who was affected?), "place" (Where were they affected?), and time (When were they affected?). Once we know the answers to these questions, we can enter the realm of analytical epidemiology and ask how and why these people were affected.

A descriptive study of fatalities associated with the use of farm tractors illustrates the usefulness of person, place, and time for drawing inferences about health problems. The study was conducted in the early 1980s, using data from death certificate records, which are a readily available surveillance system. Take a moment to study the graphics below and consider what the data might mean, then click the "Inferences" bar for an explanation.

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Deaths associated with tractor injuries, by month of death, Georgia 1971-1981

Deaths associated with tractor injuries, by month of death, Georgia 1971-1981

Deaths peaked in the spring and fall months, so they may be related to planting and harvest.

Deaths associated with tractor injuries, by time of day, Georgia 1971-1981

Deaths associated with tractor injuries, by time of day, Georgia 1971-1981

Because deaths peaked just before lunch and during the late afternoon and decrease from 12:00-1:00, they might be related to fatigue. The dip at noon could be because most people are taking a break for lunch. The fact that children are home from school by 4:00 could contribute to the peak in the afternoon.

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Deaths associated with tractor injuries, by place, Georgia 1971-1981

Deaths associated with tractor injuries, by place, Georgia 1971-1981

Most of the deaths occurred in northern Georgia, which is hilly and mountainous; south-central Georgia, where fewer deaths occurred, is much flatter. This distribution might implicate the rugged terrain, but because the map shows numbers of deaths, not rates, we don't know whether the numbers could instead reflect differences in population size or even perhaps the number of tractors being used. As for the prevalence of tractors, south-central Georgia is more agrarian than northern Georgia, so the number of tractors probably isn't a factor. Another possible association is differences in experience and skill in using tractors.

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Deaths associated with tractor injuries, by age group, Georgia 1971-1981

Deaths associated with tractor injuries, by age group, Georgia 1971-1981 (Not Available)

The number of deaths is clearly higher in the older age group. This could mean that tractor users are predominantly older, but it could also indicate that older users are less likely to survive an accident.

Any inferences you make are likely bases for hypotheses, which would then have to be tested using one of three analytical study designs: cross-sectional, cohort, and case-control. In all three types, the epidemiologist is attempting to discover the relationship between an exposure or risk factor and a health outcome. For example: Did the chicken salad at the company picnic cause the salmonella outbreak? Does cigarette smoke cause lung cancer? Are alcohol use and motor vehicle crashes related? Does the supplement L-tryptophan cause EMS?

The first type of design, a cross-sectional study, is basically the same as a survey. In this type of study, the epidemiologist defines the population to be studied and then collects information from members of the group about their disease and exposure status. Since the data represent a point in time, it's like taking a "snapshot" of the population. Cross-sectional studies are good for examining the relationship between a variable and a disease, but not for determining cause and effect, which requires data over time. Cohort studies and case-control studies are much better suited to examining cause-and-effect relationships.

In a cohort study, you select the study population according to their exposure, regardless of whether they have the disease or health outcome you're studying. You then determine the outcomes and compare them on the basis of the individuals' exposures. Cohort studies are often referred to as prospective studies because they follow the study population forward in time, from suspected cause to effect. An example would be dividing a group of people on the basis of their smoking status and following them for 20 years to see if they develop lung cancer. Cohort studies are also used to investigate outbreaks in small, well-defined populations. For example, you would use a cohort study to answer the question posed earlier regarding the cause of a salmonella outbreak at a company picnic. In this situation, you would ask each attendee about their exposure (e.g., what foods and beverages they consumed at the picnic) and whether they became ill afterward. The relationship between exposure and outcome in a cohort study is quantified by calculating the relative risk for the exposure.

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Cohort studies have several advantages:

  • You can examine multiple outcomes for a single exposure. For instance, if you select a group based on their smoking exposure, you can look not only at the incidence of lung cancer, but also at the incidence of cardiovascular disease, emphysema, burns, other smoking-related outcomes.

  • Cohort studies are very useful in examining rare exposures, such as asbestos exposure and lung cancer.

  • You can directly calculate the incidence of disease for each of the exposure groups.

  • The temporal sequence is logical: you are starting with exposure and following forward in time to the development of disease.

Disadvantages of cohort studies are that they are costly in time and resources and, if the disease is rare, you need a large number of subjects. Also, because you are following forward in time, logistical problems may develop and subjects can be lost to follow-up. (Cohort studies are discussed in more detail in How to Investigate an Outbreak.)

In a case-control study, the epidemiologist is working backward, from the effect to the suspected cause. For this reason, case-control studies are often referred to as retrospective studies. Participants are selected on the basis of the presence or absence of the disease or outcome in question, so that you have one group of people (case-subjects) with the health problem and one without (controls). These groups are then compared to determine the presence of specific exposures or risk factors. For example, you could pick a group of people with lung cancer and a group without and then compare them for their history of exposure to smoking. The relationship between exposure and outcome in a case-control study is quantified by calculating the odds ratio.

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Case-control studies have three primary advantages:

  • You can examine multiple exposures for a single outcome.

  • They are well suited for studying rare diseases and those with long latency periods.

  • They require fewer case-subjects and are generally quicker and less expensive to conduct than cohort studies, which makes them well suited for the conditions of an outbreak investigation.

The disadvantages of case-control studies are that they aren't suitable for studying rare exposures;

they are subject to bias because of the method used to select controls; and they don't allow you to directly measure the incidence of disease. Also, because they look backward, case-control studies may create uncertainty about the temporal relationship between exposure and disease. (Case-control studies are discussed in more detail in How to Investigate an Outbreak.)

One important point must be made about analytical epidemiologic studies: In these studies, we are attempting to answer the how and why questions, and we are able to quantify the relationship between an exposure and an outcome. However, a mathematical, or quantitative, relationship between the two is not enough to establish causation. We can demonstrate increased relative risk of lung cancer for smokers in a cohort study, or we can demonstrate in a case-control study that people with lung cancer are much more likely to have smoked in the past; but that alone doesn't establish cause and effect.

In general, five criteria must be met to establish a cause-and-effect relationship:

  • Strength of association—the relationship must be clear.

  • Consistency—observation of the association must be repeatable in different populations at different times.

  • Temporality—the cause must precede the effect.

  • Plausibility—the explanation must make sense biologically.

  • Biological gradient—there must be a dose-response relationship.

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