
doi: 10.1007/bf02602749
pmid: 8583267
To reinterpret epidemiologic information about the tuberculin test (purified protein derivative) in terms of modern approaches to test characteristics; to clarify why different cutpoints of induration should be used to define a positive test in different populations; and to calculate test characteristics of the intermediate-strength tuberculin skin test, the probability of Mycobacterium tuberculosis infection at various induration sizes, the area under the receiver operating characteristic (ROC) curve, and optimal cutpoints for positivity.Standard epidemiologic assumptions were used to distinguish M. tuberculosis-infected from -uninfected persons; also used were data from the U.S. Navy recruit and World Health Organization tuberculosis surveys; and Bayesian analysis.In the general U.S. population, the test's sensitivity is 0.59 to 1.0, the specificity is 0.95 to 1.0, and the positive predictive value is 0.44 to 1.0, depending on the cutpoint. Among tuberculosis patients, the sensitivity is nearly the same as in the general population; the positive predictive value is 1.0. The area under the ROC curve is 0.997. The probability of M. tuberculosis infection at each induration size varies widely, depending on the prevalence. The optimal cutpoint varies from 2 mm to 16 mm and is dependent on prevalence and the purpose for testing.The operating characteristics of the tuberculin test are superior to those of nearly all commonly used screening and diagnostic tests. The tuberculin test has an excellent ability to distinguish M. tuberculosis-infected from -uninfected persons. Interpretation requires consideration of prevalence and the purpose for testing. These findings support the recommendation to use different cutpoints for various populations. Even more accurate information can be gotten by interpreting induration size as indicating a probability of M. tuberculosis infection.
Adult, Male, Adolescent, Tuberculin Test, Bayes Theorem, Sensitivity and Specificity, United States, Military Personnel, ROC Curve, Predictive Value of Tests, Data Interpretation, Statistical, Humans, Tuberculosis, Naval Medicine, Probability
Adult, Male, Adolescent, Tuberculin Test, Bayes Theorem, Sensitivity and Specificity, United States, Military Personnel, ROC Curve, Predictive Value of Tests, Data Interpretation, Statistical, Humans, Tuberculosis, Naval Medicine, Probability
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