Economic considerations for disease testing strategies (Proceedings)
Many veterinarians express frustration when trying to provide their clients with the best advice on which diagnostic tests to recommend for purchased cattle or the resident herd. The goal is to screen apparently healthy cattle to identify carriers of infectious disease that could cause reproductive losses and other health problems in the herd. To determine the economic return for diagnostic testing strategies, veterinarians need information on the accuracy (sensitivity and specificity) of available tests, commonness of the condition in question in the cattle population at large and specified sub-populations, disease dynamics such as reservoir, transmission pattern, incubation period, immune response, treatment efficacy, and negative or unintended consequences of diagnosis or treatment. In addition to the federal eradication programs for brucellosis and tuberculosis, the diseases with long-term carrier states that are most often considered for screening purchased cattle include: Trichomoniasis, Bovine Viral Diarrhea (persistent infection (PI) status), Bovine Leukosis, Anaplasmosis, and Johnes.
The most appropriate method to determine the economic value for diagnostic testing will vary depending on the condition in question, the time frame involved, and how the diagnostic information will be utilized to make decisions. The most straightforward method is by utilizing a partial budget. For rare conditions or events, it may be more appropriate to determine the cost of a negative outcome and the cost of intervention - and working with the client, determine his/her level of risk aversion, and together, determine the value of reducing the risk of a rare event.
Determining Diagnostic Test Usefulness and Diagnostic StrategyA valid question confronting veterinary practitioners is whether to use available diagnostic tests to screen a particular herd or purchased replacements (bulls, heifers, and cows) for a particular condition. The input needed to arrive at a logical conclusion includes epidemiologic data about the condition or disease, diagnostic test sensitivity and specificity data, disease or condition dynamics, and economic costs of the condition and its treatment.3 Literature review and mathematic aids, such as computer spreadsheets and expert systems, are the tools used to create the necessary outputs. These outputs include post-test predictive values of diagnostic tests, economic value of testing, sensitivity of the decision to the individual inputs, and the importance of individual inputs to the decision. These outputs are used to evaluate alternate diagnostic testing strategies in order to indicate the best testing strategy, and to identify the control points to be monitored for change that can trigger a re-evaluation of the decision.
Sensitivity and Specificity of Diagnostic Tests
Sensitivity and specificity are properties of a diagnostic test that are determined by comparing the test to a "gold standard". The gold standard is considered the true diagnosis, and may be made using a variety of such information as clinical examination, expert opinion, laboratory results, or postmortem results. Sensitivity is the proportion of known positive (gold standard-positive) samples that the test in question identifies as positive. Specificity is the proportion of known negative samples that the test in question identifies as negative. In other words, sensitivity answers the question, "How effective is the test at identifying animals with the condition?" and specificity answers the question, "How effective is the test at identifying animals without the condition?"
Because in almost every situation, there is overlap between the test results of truly negative bulls and truly positive bulls, it is generally impossible to have a test that is 100% accurate. Because diagnostic tests (both laboratory and clinical examination tests) use an arbitrary cut off to separate test-positive and test-negative populations, sensitivity and specificity are inversely related, and placing the cut off is always a trade-off between the impacts of false-negative and false-positive results.
Prevalence is the number of cases of a condition at a given time compared to the population size at that time. Each practitioner's judgment, based on history and clinical examination of both individuals and the population, aided by available prevalence information, is often all we have to estimate disease probability.
For a test with imperfect specificity, an increasing proportion of the test positives will be false positives as prevalence decreases. At low prevalence, the majority of test positives will be false positives, so that for an uncommon condition, even a highly accurate test will render results that must be interpreted with care when applied to the animal population as a whole. In other words, in the case of very rare conditions, most tests for that condition that appear to be positive are actually false-positive.