Diagnostic testing in small ruminant medicine

Disease diagnosis is a complex process, similar to hypothesis testing. Diagnostic tests should be considered ancillary measures, meaning they should complement a thorough process which culminates with a diagnosis, and includes a thorough history taking and/or proper data collection combined with, where necessary, clinical examination (Figure 1). These underlying steps will allow the clinician to formulate a comprehensive, yet relevant, list of differential diagnoses; diagnostic testing is then applied in a systematic, logical method to assess the list of differential diagnoses and confirm or exclude each possibility. Most importantly, diagnostic tests should not be used to stumble upon a diagnosis, but as a supportive tool in the decision-making process.

The fundamental, underlying question is, why do we need to perform diagnostic tests? The first reason will often be to confirm a clinical suspicion, either for personal interest or to apply the appropriate treatment and/or control measures. The classic example here is the development of anthelmintic resistance (Figure 2a); a very relevant topic in small ruminant medicine, where specific, updated guidelines are now available to guide diagnosis, providing reliable information for the selection of the appropriate anthelmintic class to use (Kaplan et al, 2023). The same applies in case of changes in routine management procedures or the application of preventive measures, where advice should be based on evidence of disease or on baseline measures to assess the impact of the proposed changes. For example, there are many vaccines available for small ruminants (Lacasta et al, 2015), therefore knowing which diseases are present on farm (or which have the highest impact), will be fundamental in selecting the most relevant vaccines to include in health management plans (Figure 2b). Finally, it is important to remember that, by submitting diagnostic samples to official laboratories (Figure 2c), clinicians can contribute to passive surveillance, providing data for monitoring of endemic diseases, as well as early detection of outbreaks and unknown diseases (Dijkstra et al, 2022).