Figure 8.1. A model of disease natural history showing the phases of disease, passage of time, clinical detection, and outcomes.

and disease natural history (Fletcher et al, 1996). The distinction made is that clinical course describes the evolution of disease that has come under medical care with its consequent interventions that may impact the course of events. The course of an illness not under medical care is then referred to as "disease natural history.'' There are any number of reasons why a disorder may go untreated, including lack of symptoms, lack of detection, or failure to seek medical attention. The examples are plentiful in the literature, most prominent among which are found in the cardiovascular field and in mental illnesses.

The pattern of utilization of services itself may be a clue to the natural course of disease or help identify those who may otherwise go undetected. In addition, those under treatment with the therapy of interest bring with them the combined risks and benefits of this treatment, concomitant illness and their treatments, as well as the risks associated directly with the illness. Under these circumstances, the attribution of risk to the medication of interest in combination with treatments for concomitant illness can become quite difficult if we do not have information about the events that occur with each level or combination of therapy.

There are many who seek medical attention for their symptoms who fail to receive the proper diagnosis or treatment (for example, Stang and Von Korff, 1994). In many circumstances, they are still "cases" who may be contrasted with those who clearly do not suffer the target indication but receive treatment anyway. This reality makes pharmacovigilance more difficult. Patients receiving therapy who do not suffer the target indication may not carry with them the biological or clinical "burden" of that disease. If extensive medication use falls into this category, additional information may need to be provided to patients and physicians to place the potential risks and benefits in a context that differs from the intended target population. Nonetheless, the capture of information on the spectrum of illnesses, their associated demographics and illness are the foundation used for the interpretation of adverse event signals within a spectrum of uses.

For studies that depend on clinical detection of disease or events, the researcher should be wary of any potential distortion in the identification of disease or events (detection bias) as it may be that only those who undergo "screening" or have the opportunity to present with symptoms will likely be detected which leaves one to speculate about the status of those who were not screened. This may be particularly true for symptoms, rather than illnesses, as the natural history of symptoms of a particular illness are even more difficult to ascertain since those with the target disorder may report a symptom more frequently. This is the case in pain syndromes. An interesting example of this was seen in a study of the systematic assessment of chest pain among migraine sufferers (Sternfeld et al, 1995).

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