Development and evaluation of a new method to combine clinical impression survey data with existing laboratory data for veterinary syndromic surveillance with the Canada West Swine Health Intelligence Network (CWSHIN).

Abstract The Canada West Swine Health Intelligence Network (CWSHIN) is a surveillance system imbedded in an intelligence network. It has been conducting syndromic surveillance in the four western provinces of Canada since 2012. The quarterly activities include repeated clinical impression surveys, compilation of laboratory data, discussion of trends with an expert group (practitioners, laboratory diagnosticians) and swine health reports for producers and swine practitioners. However, due to the lack of standardized population identifiers across data sources usual methods of combining data could not be applied and the collated data were not being fully utilized and analysed. Therefore in 2019, CWSHIN underwent a substantial review resulting in the “Next Generation CWSHIN”. The objectives of this study were to develop and evaluate a new data merging method to combine CWSHIN’s clinical impression survey and laboratory data; and to provide examples of analyses and modeling based on these data. The data for analysis were restricted to repeated clinical impression surveys (2019–2020) from veterinary practitioners and laboratory diagnostic data (2016–2020). Merging surveillance data from existing sources can be challenging. Therefore, as an alternative to merge data using a hierarchy of population identifiers, we developed a Disease Map to link surveillance data from all our data-sources. The resulting Data Repository allowed monitoring of temporal trends of syndromes, clinical diseases, and laboratory identified organisms, but it cannot provide estimates of disease occurrence. Two main reasons were the lack of denominators and using existing data on routine diagnostic tests. Therefore, discussion in the expert group (veterinary practitioners, laboratory diagnosticians, swine health experts) was critical to the system’s success. Based on repeated clinical impression surveys a stochastic scenario tree model for freedom from Foot and Mouth Disease (CWSHIN Blister model) was also developed. In conclusion, the method to link existing data systems from multiple divergent sources by means of a Disease Map improved CWSHIN’s veterinary syndromic surveillance. Together the Data Repository and Disease map provided flexibility to monitor temporal trends, define populations and diseases, and allow analysis. However, it is critical that the surveillance is coupled with a good intelligence network that can help interpret the results and disseminate knowledge to veterinarians and producers.