Age-stratified Bayesian analysis to estimate sensitivity and specificity of four diagnostic tests for the detection of Cryptosporidium spp. oocysts in neonatal calves

There is no gold standard diagnostic test for the detection of bovine cryptosporidiosis. Infection is usually the highest in two-week-old calves and these calves also excrete high numbers of oocysts. These factors may give rise to variations in the sensitivity and specificity of the various diagnostic tests used to detect infection in calves of varying ages. An age-stratified Bayesian analysis was carried out to determine the optimum diagnostic test to identify asymptomatic and clinical Cryptosporidium spp. infection in neonatal calves. Fecal samples collected from 82 calves at one week, two weeks, three weeks and four weeks of age were subjected to the following tests, microscopic examination of smears stained with either phenol-auramine O or FITC conjugated anti-Cryptosporidium monoclonal antibody, nested-PCR and quantitative real-time PCR. The results confirmed a high prevalence of Cryptosporidium spp. infection as well as a high level of oocysts excretion in two-week-old calves. The sensitivity of all the tests varied with the age of the calves. Quantitative real-time PCR proved to be the most sensitive and specific test for detecting infection irrespective of the age of the calf. The microscopic techniques were the least sensitive and only exhibited moderate efficiency with two-week-old calves excreting large numbers of oocysts and the majority of which were diarrheic. It was concluded that, when interpreting the results of routine tests for bovine cryptosporidiosis, cognisance should be taken of the sensitivity of the tests in relation to the age of the calves and stage of infection.

Publication year:2011

Keywords:Animal diseases, Cryptosporidiosis, Cryptosporidium, Cattle, Calves, Neonatal, Detection, Oocysts, Estimation, Rapid diagnostic tests, Bayes theorem, Sensitivity, Specificity, Comparison, Smear microscopy, Polymerase chain reaction, PCR, Real-time, Efficiency, Risk factors