Immunosorbent Assay (ELISA), a positive test result might be the appearance of a particular color marker of sufficient intensity (optical density) that it is above a cutoff value. In this example, a negative test result would be when the color intensity is below the cutoff value. Further, because of the many variables affecting the test mechanics, the actual cutoff value for each run usually has to be calculated from control results within that test run. The mechanics of many of these tests are sufficiently complex and resource intensive that their performance is often laboratory- and operator-dependent, making protocol standardization difficult at best.

When test outcomes are classified as positive or negative, present or absent, the result can be wrong in two ways. A positive result can be a true positive, correct, or a false positive, wrong. A false positive occurs when the condition being tested for is actually not present in the animal, but the test indicates that it is. For example, in the case of fecal bacterial culture, a false positive could occur when organisms in contaminated feed or water are consumed by an animal, and then pass through the animal rather than infect it (e.g., Sweeney et al., 1992a). Alternatively, false positive cultures could occur from accidental laboratory contamination or other error. In the case of a serologic test such as ELISA, the false positive result could occur because the animal responded to an antigen in their environment that is immunologically similar to the target antigen from Map that is used in the test.

A negative test result can be a true negative or a false negative. A false negative test means that the condition is present in the animal but the test indicates that it is not. In the case of fecal culture, a false negative could occur because the number of organisms in the fecal specimen is too few to be detected, but the animal is infected (e.g., Whitlock et al., 2000b). In the case of serologic tests, the test could be a false negative because an infected animal has not mounted the particular immune response that the test detects, or has mounted a weak immune response that is below the threshold of detection as a positive result.

The measure that is most useful when interpreting an uncertain test result is the predictive value, which is the likelihood that a test result is correct. The positive predictive value (PPV) is defined as the likelihood that a positive test result is a true positive and the negative predictive value (NPV) is defined as the likelihood that a negative test result is a true negative (Last, 1995). These predictive values depend on the prevalence of the condition being tested for in the population being tested, meaning both PPV and NPV are different when the test is used in an uninfected group compared to when the test is used in an infected group with a high prevalence. Intuitively, a positive test is more likely to be a false positive in a herd without any history of having any animals diagnosed with the infection. Similarly, a negative test is more likely to be a false negative in a herd with a history of having many animals with confirmed disease. As a consequence, predictive values are not useful for comparing test performance across groups of animals with significantly different infection prevalences.



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