from the clinically affected animals, which are usually older, to animals that are silently infected with little pathological change or immunological response, which tend to be younger. Typically, there are far more of the latter than the former. If the group of study animals is biased toward those that are more severely affected, the estimate of test sensitivity will be biased upward. For establishing specificity, the infection-free animals must have been exposed to the cross-reacting or competing conditions at the same frequency as the groups in which the test will be used. For example, for immunologically based tests such exposures may be similar environmental mycobacteria or other bacteria with cross-reacting antigens. One of the difficulties of establishing test specificity is that the exposures that lower specificity are likely different in different geographic regions and different livestock species. Third, the appropriate gold standard, a definitive reference testing procedure with very high sensitivity and specificity, must be used to establish the infection status of the study animals. At present, the gold standard is necropsy followed by extensive culture and histological examination of multiple sections of lower small intestine and associated lymph nodes to reliably establish the infection status of each study animal.
Based on the assumption that acquisition of infection as an adult is rare, an alternative approach is to follow previously tested animals to slaughter, allowing the progression of the natural history of the disease to a more advanced and thus easier to detect state. Often, rather than such an intensive, expensive investigation, other strategies are used, such as using combinations of antemortem tests. The problem with this compromise is that it changes the assessment of test performance from an absolute measure to a relative measure of unknown bias. Given that the spectrum of disease is weighted toward those subclinical animals that are difficult to detect, this approach likely biases estimates of test performance upward. The result is that as technology improves, the estimations of conventional test performance have been moving downward.
It is also important to note that unless the gold standard test is applied, the resulting prevalence estimate is an apparent prevalence. Deriving a valid estimate of true prevalence from an apparent prevalence requires that both the disease spectrum and competing condition exposure be the same in the tested group as the group from which the estimates of test performance were derived. At best, these are tenuous assumptions.