Q fever is a disease caused by bacteria Coxiella burnetii, which affects many animal species including cattle, sheep and goats and can be transmitted to humans. The infection of animals has economic consequences, since it can cause abortions. In humans, the disease is estimated to occur in approximately 40% of infections. It can then cause a severe flu syndrome, with the possibility of hepatic or pulmonary complications that can lead to hospitalizations. It can also occur in about 2% of those infected form chronic and cause hepatitis, pneumopathy and endocarditis. It is therefore important to monitor infection in ruminant farms, to curb its spread and prevent transmission to the human population. A study has just been published in the journal Veterinary Research, which compares the performances of the three serological tests marketed to diagnose the infection in ruminants. It was conducted as part of a thesis, which is the result of collaboration between VetAgro Sup, INRAE, and the ANSES units on animal Q fever (Sophia Antipolis laboratory), which is the national laboratory reference on this subject, and Epidemiology and surveillance support (Lyon laboratory). The National Federation of Health Defense Groups (GDS France), the CNRS and the Livestock Institute (IDELE) also took part in this work.
Different performances according to the tests and the species
These are Elisa tests, which detect the presence of antibodies against the bacterium in the serum of cattle, sheep and goats tested. This comparison made it possible to show that the three tests have a high specificity, that is, they correctly identify healthy animals. On the other hand, their sensitivity, which is the ability to reliably detect animals carrying antibodies against the bacterium, is lower and varies between tests and species (between 39.3% and 93.8%). The scientists also defined the optimal number of animals to test for detect infection at the herd level. Depending on the test and the species, this varies from three to more than twenty. Following this study, it is planned to develop an application to help analysis laboratories interpret the reliability of the results and therefore the probability that the positive or negative result obtained for an animal is correct.