Bayesian latent class analyses show that serological testing outperforms conventional diagnostic methods for the detection of Ascaris in (individual) pigs

Abstract

This study employed Bayesian latent class analyses to estimate the diagnostic accuracy of faecal egg count (FEC), milk spot detection and the ELISA for detection of Ascaris suum using matched samples from individual pigs in Australia. A total of 251 blood, faecal and liver samples were collected from finisher pigs from four Victorian pig farms. Matched samples (n = 189) were used to compare the three diagnostic tests. The ELISA detected a higher proportion of positive samples (56 %; 95 % CI 48–62 %) compared to milk spot detection (42 %; 95 % CI 35–49 %) and FEC (17 %; 95 % CI 12–23 %). Only the ELISA detected A. suum infections on two of the four farms, with 14 % and 41 % within-farm prevalence estimates. Agreement between diagnostic tests was moderate for FEC and milk spot detection (Cohen’s kappa 0.42; 95 % CI 0.30–0.53) and ELISA and milk spot detection (0.52; 0.41–0.64), while fair agreement was observed between FEC and ELISA (0.28; 0.19–0.37). Our latent class analyses identified a higher diagnostic sensitivity for the ELISA (0.92; 95 % CrI [credible interval] 0.86–0.96) than FEC (0.43; 0.34–0.53) and milk spot detection (0.86; 0.79–0.92). A strong association was observed between ELISA outcomes (optical density [OD] and OD ratio [ODr]) and milk spot grades (low, medium, high), with higher OD and ODr values corresponding to an increased number of milk spots on the liver. This study highlights the limitations of conventional A. suum detection methods. Quantitative estimates of the diagnostic sensitivity of the ELISA facilitate its use as a tool for assessing A. suum exposure in pig herds.