Brief Case Study Definition For Epidemiology

Case definition, in epidemiology, set of criteria used in making a decision as to whether an individual has a disease or health event of interest. Establishing a case definition is an imperative step in quantifying the magnitude of disease in a population. Case definitions are used in ongoing public health surveillance to track the occurrence and distribution of disease within a given area, as well as during outbreak investigations in field epidemiology.

A case definition must be clear, simple, and concise, allowing it to be easily applied to all individuals in the population of interest. It typically includes both clinical and laboratory characteristics, which are ascertained by one or many methods that might include diagnosis by a physician, completion of a survey, or routine population screening methods. Individuals meeting a case definition can be categorized as “confirmed,” “probable,” or “suspected.”

National and international organizations have published lists of uniform case definitions for the mandatory reporting of select diseases. Such lists provide explicit case definitions, enabling clinicians to report cases for diseases of interest to public health authorities in a standard and uniform way across geographic locations. This is particularly useful for studies that compare the prevalence of disease across regions, since they can use the same case definitions and, therefore, obtain a relatively accurate assessment of disease.

During an outbreak of disease, a case definition is developed at an early stage of the outbreak investigation, facilitating the identification of individual cases. While the same criteria apply for developing a case definition in routine public health surveillance, in an outbreak investigation a case definition may also include information regarding person, place, and time, in addition to clinical and laboratory characteristics. For example, a case definition developed for an outbreak of foodborne illness may include only those individuals who ate at a certain restaurant during a specified period of time. Furthermore, a case definition may be broadly defined in the initial stages of an outbreak investigation scenario in order to increase sensitivity, permitting the recording of as many cases as possible while also minimizing the possibility of overlooking cases. As the investigation continues and more knowledge is gained about the nature of the cases, the definition may be narrowed, making it more specific. This is particularly important for a newly emerging disease where a standard case definition does not yet exist.

The evidence for evidence-based medicine is all collected via research, which uses a variety of study designs.  You will be learning about "critical appraisal of the literature," and judging the quality of a study design is a central part of this.

Different study designs provide information of different quality.  Of course, we always try to use the best possible design, but sometimes this is not practical or ethically acceptable (you cannot do an experiment to expose some people to a harmful substance to see what effect it has).  Therefore, you need to understand the strengths and limitations of each type of study design, as applied to a particular research purpose.  The purposes we will consider include (1) describing the prevalence of health problems; (2) identifying causes of health problems (etiological research), and (3) evaluating therapy, including treatment and prevention. 

Types of Study Design

First, distinguish between observational and experimental studies. 


In observational studies, the researcher observes and systematically collects information, but does not try to change the people (or animals, or reagents) being observed.  In an experiment, by contrast,  the researcher intervenes to change something (e.g., gives some patients a drug) and then observes what happens.  In an observational study there is no intervention. 

Examples of observational studies:

  • a survey of drinking habits among students;

  • a researcher who joins a biker gang to study their lifestyle (note, as long as the researcher does not try to change their behavior, it's an observational study);

  • taking blood samples to measure blood alcohol levels during Monday morning lectures (yes, you are intervening to take the blood, but you are not trying to change the blood alcohol level: it's just a measurement).

Examples of experiments:

  • plying a law student with beer to see whether lawyers argue better when drunk;

  • encouraging bikers in one group to stop smoking those funny-looking cigarettes to see whether they get less belligerent;

  • warning one group of students that you are going to take blood alcohol levels next Monday to test for alcohol, and comparing their levels to another group that you did not warn.  

When do you do an observational study?

  • When you merely want to collect descriptive information: "Is the incidence of diabetes rising?"

  • When you want to report on the causes of a problem without disturbing the natural setting (I want to find out why students do not attend lectures)

  • When you can't do an experiment: "How fast does the earth move around the sun?"

  • When it's not acceptable to do an experiment: "How much does not wearing a condom increase the likelihood of  HIV infection?"

Observational Designs:

What types of observational study are there?  Lots, but you need to know about three main ones:

Cross-sectional surveys.  Example: what is the prevalence of diabetes in this community?  Here, you draw a random sample of people and record information about their health in a systematic manner.  You can also compare people with, and without, diabetes in terms of characteristics (such as being overweight) that may be associated with the disease.  The problem is that you cannot be sure which came first: the diabetes or the weight problem, so this is a very weak design for drawing conclusions about causes. 

  • Cohort, or "longitudinal", or "prospective"  studies.  These are like surveys, but extend over time.  This allows you to study changes and to establish the time-sequence in which things occur.  Therefore, you can use this to study causes.  For example, you could draw a sample of people (medical students, for example) who do not have the disease you are interested in, and collect information on the factor you have hypothesized to be a cause of the disease.  Maybe you want to see whether using a cell phone leads to brain cancer.  So, collect information on how many minutes each student uses their phone each week (you might get permission to obtain this from their phone company bills), and collect this information over a long time, and then eventually collect information on who gets brain cancer.  You could then see whether the cases of brain cancer arose among the people who used their cell phones most often.  In technical terms, you record the incidence of cancer among those who use their phones more than a pre-determined amount and compare this to the incidence in the non-users.  You could calculate the relative risk. 
    The advantages of this study design are that it can establish that the phone usage predates the cancer, and it allows for accurate collection of exposure information ('exposure' = their use of the phones). However, there are some problems with this design.  Brain cancer is rare, so you will need a very large cohort of students; you will also need to keep in contact with them for a very long time and you will probably get very bored waiting for the results.  We need a quicker solution.
    Link to ppt diagram of a cohort study

    Self-test question: cohort study
     Can you can estimate prevalence from a cohort study?


    You answered 'Yes', and in general this is not correct.

    Where a cohort study is designed to identify causal factors for a disease, you would begin by selecting a sample of people who do not have the disease (so, prevalence = zero). You would then follow them over time. Some incident cases would arise, and these would provide you with an estimate of prevalence. However, because you omitted the existing cases at the beginning of the study, your prevalence estimate would be biased: it would be too low. The only exception would be if you followed people for a long time, and if people die from the disease quickly, so none of the original cases would have survived anyway.


    Well done, this is correct. But there is a bit of a trick...

    You presumably recognize that a cohort study omits existing (prevalent) cases at the beginning, so the only estimate of prevalence will come from the new cases that accumulate. This will give you a low estimate of prevalence, unless you follow them for so long that all the prevalent cases you excluded will have died anyway. So, if the disease kills quickly and if you follow the cohort for a long time, you could get a good estimate of prevalence.

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