Abstract Swine producers frequently encounter polymicrobial disease challenges, with co-infections exacerbating clinical disease and complicating response strategies. This study aimed to characterize co-diagnosis patterns in swine by integrating confirmed diagnosis cases using a standardize diagnosis system (Dx code) from the Iowa State University Veterinary Diagnostic Laboratory. As a secondary objective, the study developed the concept and implemented a framework for the technological transfer of the Dx code system from ISU-VDL to the Ohio Animal Disease Diagnostic Laboratory, and to gather Dx code data through an animal disease monitoring program, thereby creating a multi-institutional, confirmed tissue disease diagnosis database. The final collated database was harmonized and used to analyze 45,310 confirmed tissue diagnosis cases submitted between 2020 and 2025. Co-diagnosis was defined as the presence of two or more distinct etiologies within a single case. Overall, 52.62% of cases were co-diagnosed in 42 U.S. states, with a seasonal variation indicating reduced submissions and co-diagnosis rates during the summer months. The wean-to-market production phase accounted for 86.45% of co-diagnosed cases. The co-diagnosed cases were more abundant in respiratory and systemic anatomic systems, with porcine reproductive and respiratory syndrome virus (PRRSV) and Streptococcus suis being the predominant co-diagnosed pathogens. Age-specific trends revealed respiratory co-diagnoses peaking in nursery and grow-finish pigs, while digestive co-diagnoses were more common in suckling piglets. Statistical modeling using Conway-Maxwell-Poisson regression revealed that co-diagnosis cases involving bacterial and viral insults had significantly higher numbers of distinct etiologies (IRR = 2.48; CI: 1.66–3.66) compared with co-diagnosis cases without bacterial or viral involvement, with an expected count of 2.95 distinct etiologies per case. The study demonstrated the value of standardized diagnostic coding for epidemiological surveillance and highlights the complexity of co-infections in swine. Additionally, the findings underscore the importance of collaborative data sharing in enhancing swine health management strategies.
Cezar et al. (Thu,) studied this question.