A method of probability diagnostic assignment that applies bayes theorem for use in serologic diagnostics, using an example of Neospora caninum infection in cattle.

Objective: To develop a method of probability diagnostic assignment (PDA) that uses continuous serologic measures and infection prevalence to estimate the probability of an animal being infected, using Neospora caninum as an example.

Animals: 196 N caninum-infected beef and dairy cattle and 553 cattle not infected with N caninum; 50 dairy cows that aborted and 50 herdmates that did not abort.

Procedure: Probability density functions corresponding to distributions of N caninum kinetic ELISA results from infected and uninfected cattle were estimated by maximum likelihood methods. Maximum likelihood methods also were used to estimate N caninum infection prevalence in a herd that had an excessive number of abortions. Density functions and the prevalence estimate were incorporated into Bayes formula to calculate the conditional probability that a cow with a particular ELISA value was infected with N caninum.

Results: Probability functions identified for infected and uninfected cattle were Weibull and inverse gamma functions, respectively. Herd prevalence was estimated, and probabilities of N caninum infection were determined for cows with various ELISA values.

Conclusions And Clinical Relevance: Use of PDA offers an advantage to clinicians and diagnosticians over traditional seronegative or seropositive classifications used as a proxy for infection status by providing an assessment of the actual probability of infection. The PDA permits use of all diagnostic information inherent in an assay, thereby eliminating a need for estimates of sensitivity and specificity. The PDA also would have general utility in interpreting results of any diagnostic assay measured on a continuous or discrete scale.