Setting clinically relevant thresholds for the notification of canine disease outbreaks to veterinary practitioners: an exploratory qualitative interview study - Tamayo Cuartero, Carmen; Szilassy, Eszter; Radford, Alan D.; Newton, J. Richard; Sánchez-Vizcaíno, Fernando (Frontiers in Veterinary Science, 2024)
Introduction: The Small Animal Veterinary Surveillance Network (SAVSNET) has developed mathematical models to analyse veterinary practice and diagnostic laboratory data to detect genuine outbreaks of canine disease in the UK. There are, however, no validated methods available to establish the clinical relevance of these genuine statistical outbreaks before their formal investigation is conducted. The aim of this study was to gain actionable understanding of veterinary practitioner's preferences regarding which outbreak scenarios have a substantial impact in veterinary practice for six priority canine diseases in the UK.Methodology: An intensity sampling approach was followed to recruit veterinary practitioners according to their years of experience and the size of their practice. In depth semi-structured and structured interviews were conducted to describe outbreak notification and outbreak response thresholds for six canine endemic diseases, exotic diseases and syndromes. These thresholds reflected participants' preferred balance between levels of excess case incidence and predictive certainty of the detection system. Interviews were transcribed and a thematic analysis was performed using NVivo 12.Results: Seven interviews were completed. Findings indicate higher preferred levels of predictive certainty for endemic diseases than for exotic diseases, ranging from 95-99% and 80-90%, respectively. Excess case incidence levels were considered clinically relevant at values representing an increase of two to four times the normal case incidence expectancy for endemic agents like parvovirus, and where they indicated a single case in the practice's catchment area for exotic diseases like leishmaniosis and babesiosis.Conclusion: This study's innovative methodology uses veterinary practitioners' opinion to inform the selection of a notification threshold value in real world applications of stochastic canine outbreak detection models. The clinically relevant thresholds derived from participants' needs will be used by SAVSNET to inform its outbreak detection system and to improve its response to canine disease outbreaks in the UK.