A Bayesian approach for estimating values for prevalence and diagnostic test characteristics of porcine cysticercosis

Several diagnostic techniques are used to estimate the prevalence of the zoonotic tapeworm Taenia solium in pigs, but none of these tests are perfect, making interpretation of results difficult. A Bayesian approach was used to estimate values for the prevalence and diagnostic test characteristic of porcine cysticercosis by combining results of four imperfect tests. Village pigs (N = 868), slaughtered in Lusaka (Zambia), were bled, and tongue and routine meat inspected-, and serum antibody and parasite antigen concentrations were determined by ELISA. A model. based on a multinomial distribution and including all possible interactions between the individual tests required 3 1 parameters to be estimated, but actually allowed only 15 parameters (i.e. had 15 degrees of freedom) to be estimated. Therefore, prior expert opinion on specificity and (in)-dependence of the tests was entered in the model, resulting in a reduction of the number of parameters to be estimated. 9 The estimated prevalence of porcine cysticercosis was 0.642 (95% confidence interval 0.54-0.91). The performances of the tests were (sensitivity (se) -specificity (sp)): tongue inspection (se 0.210-sp 1.000), meat inspection (se 0.221 -sp 1.000), Ab-ELISA (se 0.358-sp 0.917), Ag-ELISA (se 0.867-sp 0.947). To validate the estimates obtained from the model we performed a second study: 65 randomly purchased Zambian village pigs were bled for serum antibody and antigen determination, their tongue and meat inspected; and in addition, the carcasses were dissected for total cysticercus counts (gold standard). Cysticerci were found in 3 1 pigs (prevalence 0.477, 95% confidence interval 0.35-0.60). overlapping with the estimated prevalence in the first stud. Sensitivity and specificity values obtained for they aforementioned tests in this study were in agreement with those estimated. A Bayesian analysis framework offers the possibility to combine prior opinion with experimental data to more accurately estimate the