SeriesUses and abuses of screening tests
Section snippets
What are the potential harms of screening?
Screening differs from the traditional clinical use of tests in several important ways. Ordinarily, patients consult with clinicians about complaints or problems; this prompts testing to confirm or exclude a diagnosis.8 Because the patient is in pain and requests our help, the risk and expense of tests are usually deemed acceptable by the patient. By contrast, screening engages apparently healthy individuals who are not seeking medical help (and who might prefer to be left alone).
If a test is available, should it be used?
The availability of a screening test does not imply that it should be used. Indeed, before screening is done, the strategy must meet several stringent criteria. One checklist separates criteria in three parts: the disease, the policy, and the test.1 The disease should be medically important and clearly defined, and its prevalence reasonably well known. The natural history should be known, and an effective intervention must exist. Concerning policy, the screening programme must be cost
Is the test valid?
For over half a century,16 four indices of test validity have been widely used: sensitivity, specificity, and predictive values of positive and negative. Although clinically useful (and far improved over clinical hunches), these terms are predicated on an assumption that is often clinically unrealistic—ie, that all people can be dichotomised as ill or well. (Indeed, one definition of an epidemiologist is a person who sees the entire world in a 2×2 table.) Often, those tested simply do not fit
Where should the cut-off for abnormal be?
The ideal test would perfectly discriminate between those with and without the disorder. The distributions of test results for the two groups would not overlap. More commonly in human biology, test values for those with and without a disease overlap, sometimes widely.18 Where one puts the cut-off defining normal versus abnormal determines the sensitivity and specificity. For any continuous outcome measurement—for example, blood pressure, intraocular pressure, or blood glucose—the sensitivity
Can test results be trusted?
A badly understood feature of screening is the potent effect of disease prevalence on predictive values. Clinicians must know the approximate prevalence of the condition of interest in the population being tested; if not, reasonable interpretation is impossible. Consider a new PCR test for chlamydia, with a sensitivity of 0·98 and specificity of 0·97 (a superb test). As shown in the left panel of figure 3, a doctor uses the test in a municipal sexually transmitted disease clinic, where the
Should a follow-up test be done?
Clinicians rarely use tests in isolation. Few tests have high sensitivity and specificity, so a common approach is to do tests in sequence. In the instance of syphilis, a sensitive (but not specific) reagin test is the initial screen. Those who test positive then get a second, more specific test, a diagnostic treponemal test. Only those who test positive on both receive the diagnosis. This strategy generally increases the specificity compared with a single test and limits the use of the more
Does a screening programme really improve health?
Even worthless screening tests seem to have benefit.2 This cruel irony underlies many inappropriate screening programmes used today. Two common pitfalls lead to the conclusion that screening improves health; one is an artifact and the other a reflection of biology.
Lead-time bias
Lead-time bias refers to a spurious increase in longevity associated with screening. For example, assume that mammography screening leads to cancer detection 2 years earlier than would have ordinarily occurred, yet the screening does
Conclusion
Screening can promote or impair health, depending on its application. Unlike a diagnostic test, a screening test is done in apparently healthy people, which raises unique ethical concerns. Sensitivity and specificity tend to be inversely related, and choice of the cut-off point for abnormal should indicate the implications of incorrect results. Even very good tests have poor predictive value positive when applied to low-prevalence populations.
Lead-time and length bias exaggerate the apparent
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