<\/p>\n
\u00a0<\/p>\n
Excerpt from Methods section Of Guide To Clinical Preventive Services, The methodologic issues involved in evaluating screening tests require These two requirements of screening The “accuracy of a screening The use of screening tests with False-positive results can lead to A proper evaluation of a screening A population of 100,000 in which the prevalence of a Reliability<\/em>(reproducibility), Even if the test accurately detects It is often difficult to determine Length bias<\/em>can also result
\nSecond Edition<\/span><\/strong><\/p>\n
\nfurther elaboration. As mentioned above, a screening test must satisfy two
\nmajor requirements to be considered effective:<\/p>\n\n
\ndetect the target condition earlier than without screening and with
\nsufficient accuracy to avoid producing large numbers of false-positive and
\nfalse-negative results (accuracy of screening test<\/em>).<\/li>\n
\npersons with early disease should improve the likelihood of favorable
\nhealth outcomes (e.g., reduced disease-specific morbidity or mortality)
\ncompared to treating patients when they present with signs or symptoms of
\nthe disease (effectiveness of early detection<\/em>).<\/li>\n<\/ul>\n
\nare essential and therefore appear as headings in each of the 53 screening
\nchapters in this report.<\/p>\nAccuracy of Screening Tests.<\/h4>\n
\ntest” is used in this report to describe accuracy and reliability.
\nAccuracy is measured in terms of two indices: sensitivity and specificity (Table 2).<\/a> Sensitivity refers to the proportion of persons
\nwith a condition who correctly test “positive” when screened. A test
\nwith poor sensitivity will miss cases (persons with the condition) and will
\nproduce a large proportion of false-negative results true cases will be told
\nincorrectly that they are free of disease. Specificity refers to the proportion
\nof persons without the condition who correctly test “negative” when
\nscreened. A test with poor specificity will result in healthy persons being
\ntold that they have the condition (false positives). An accepted reference
\nstandard (“gold standard”) is essential to the empirical
\ndetermination of sensitivity and specificity, because it defines whether the
\ndisease is present and therefore provides the means for distinguishing between
\n“true” and “false” test results.<\/p>\n
\npoor sensitivity and\/or specificity is of special significance to the clinician
\nbecause of the potentially serious consequences of false-negative and
\nfalse-positive results. Persons who receive false-negative results may
\nexperience important delays in diagnosis and treatment. Some might develop a
\nfalse sense of security, resulting in inadequate attention to risk-reducing
\nbehaviors and delays in seeking medical care when warning symptoms become
\npresent.<\/p>\n
\nfollow-up testing that may be uncomfortable, expensive, and, in some cases,
\npotentially harmful. If follow-up testing does not disclose the error, the
\npatient may even receive unnecessary treatment. There may also be psychological
\nconsequences. Persons informed of an abnormal medical test that is falsely
\npositive may experience unnecessary anxiety until the error is corrected.
\nLabeling individuals with the results of screening tests may affect behavior for
\nexample, studies have shown that some persons with hypertension identified
\nthrough screening may experience altered behavior and decreased work
\nproductivity.2<\/a>,3<\/a><\/sup><\/p>\n
\ntest result must therefore include a determination of the likelihood that the
\npatient has the condition. This is done by calculating the positive
\npredictive value<\/em>(PPV) of test results in the population to be screened (Table 2). <\/a>The PPV is the proportion of positive test results
\nthat are correct (true positives). For any given sensitivity and specificity,
\nthe PPV increases and decreases in accordance with the prevalence of the target
\ncondition in the screened population. If the target condition is sufficiently
\nrare in the screened population, even tests with excellent sensitivity and
\nspecificity can have low PPV in these settings, generating more false-positive
\nthan true-positive results. This mathematical relationship is best illustrated
\nby an example (see Table 3):<\/a><\/p>\n
\nhypothetical cancer is 1% would have 1,000 persons with cancer and 99,000
\nwithout cancer. A screening test with 90% sensitivity and 90% specificity would
\ndetect 900 of the 1,000 cases, but would also mislabel 9,900 healthy persons.
\nThus, the PPV (the proportion of persons with positive test results who
\nactually had cancer) would be 900\/10,800, or 8.3%. If the same test were
\nperformed in a population with a cancer prevalence of 0.1%, the PPV would fall
\nto 0.9%, a ratio of 111 false positives for every true case of cancer detected.<\/p>\n
\nthe ability of a test to obtain the same result when repeated, is another
\nimportant consideration in the evaluation of screening tests measuring
\ncontinuous variables (e.g., cholesterol level). A test with poor reliability,
\nwhether due to differences in results obtained by different individuals or
\nlaboratories (interobserver variation<\/em>) or by the same observer (intraobserver
\nvariation<\/em>), may produce individual test results that vary widely from the
\ncorrect value, even though the average of the results approximates the true
\nvalue.<\/p>\nEffectiveness of Early Detection.<\/h4>\n
\nearly-stage disease, one must also question whether there is any benefit to the
\npatient in having done so. Early detection should lead to the implementation of
\nclinical interventions that can prevent or delay progression of the disorder.
\nDetection of the disorder is of little clinical value if the condition is not
\ntreatable. Thus, treatment efficacy is fundamental for an effective screening
\ntest. Even with the availability of an efficacious form of treatment, early
\ndetection must offer added benefit over conventional diagnosis and treatment if
\nscreening is to improve outcome. The effectiveness of a screening test is
\nquestionable if asymptomatic persons detected through screening have the same
\nhealth outcome as those who seek medical attention because of symptoms of the
\ndisease. Studies of the effectiveness of cancer screening tests, for example,
\ncan be influenced by lead-time and length biases.<\/p>\nLead-Time and Length Bias.<\/h5>\n
\nwith certainty whether early detection truly improves outcome, an especially common
\nproblem when evaluating cancer screening tests. For most forms of cancer,
\n5-year survival is higher for persons identified with early-s tage disease.
\nSuch data are often interpreted as evidence that early detection of cancer is
\neffective, because death due to cancer appears to be delayed as a result of
\nscreening and early treatment. Survival data do not constitute true proof of
\nbenefit, however, because they are easily influenced by lead-time bias:
\nsurvival can appear to be lengthened when screening simply advances the time of
\ndiagnosis, lengthening the period of time between diagnosis and death without
\nany true prolongation of life.4<\/a><\/sup><\/p>\n
\nin unduly optimistic estimates of the effectiveness of cancer screening. This
\nterm refers to the tendency of screening to detect a disproportionate number of
\ncases of slowly progressive disease and to miss aggressive cases that, by
\nvirtue of rapid progression, are present in the population only briefly. The
\n“window” between the time a cancer can be detected by screening and
\nthe time it will be found because of symptoms is shorter for rapidly growing cancers,
\nso they are less likely to be found by screening. As a result, persons with
\naggressive malignancies will be underrepresented in the cases detected by
\nscreening, and the patients found by screening may do better than unscreened
\npatients even if the screening itself does not influence outcome. Due to this
\nbias, the calculated survival of persons detected through screening could
\noverestimate the actual effectiveness of screening.4<\/a><\/sup><\/p>\nAssessing Population Benefits.<\/h5>\n