New World Screwworm: Fact Sheet & SHIC Article 6/10/2026 & USDA NWS Website & SHIC/AASV NWS Webinar (8/4/2026)
New World Screwworm: Fact Sheet & SHIC Article 6/10/2026 & USDA NWS Website & SHIC/AASV NWS Webinar (8/4/2026)
Multiple pathogens are frequently detected within the same diagnostic investigation case, complicating diagnosis, disease management, and efforts to understand disease patterns across the US swine population. A study funded by the Swine Health Information Center and led by Dr. Guilherme Cezar, then with Iowa State University, provides a broad look at these co-diagnosis patterns while demonstrating how standardized veterinary diagnostic data can strengthen swine health surveillance.
Published in Porcine Health Management in 2026, the study, “Macroepidemiologic assessment of swine disease co-occurrences in the United States of America,” analyzed confirmed tissue diagnosis data from the ISU Veterinary Diagnostic Laboratory collected between 2020 and 2025. The project was funded through SHIC award 25-068 to investigators Drs. Giovani Trevisan and Daniel Linhares.
Looking beyond single-pathogen diagnoses
The study had two objectives. First, researchers sought to characterize patterns of co-diagnosis in swine and determine how those patterns varied according to anatomic system, animal age, geographic location, and season. Second, they sought to demonstrate that the standardized diagnostic coding system developed at ISU-VDL could be transferred to another veterinary diagnostic laboratory, the Ohio Veterinary Diagnostic Laboratory in this case, and incorporated into an animal disease monitoring program. The goal was to establish a foundation for an ongoing multi-institutional database of confirmed tissue diagnoses.
The standardized Dx code system goes beyond simply recording whether a pathogen was detected, for instance by PCR-based assays. The system incorporates the body system affected, type of challenge, lesion or tissue involved, and suspected or confirmed cause. Diagnosticians use information from the broader diagnostic workup, including clinical history, pathology, and laboratory testing, to assign a final standardized code. This creates a more consistent way to examine disease patterns across cases and over time.
More than half of cases involved multiple etiologies
The scale of the resulting datasetenabled the examinationof disease complexity across a large number of confirmed tissue diagnoses. Of 58,170 cases in the database between January 2020 and September 2025, 45,310 met the study’s eligibility criteria and were analyzed.
Of those cases, 23,846 (53%) were classified as co-diagnosis cases, meaning two or more distinct etiologies were identified within a case. Individual co-diagnosis cases included between two and nine distinct etiologies.
The finding demonstrates the frequency with which veterinary diagnostic cases involve more than a single disease agent. It also illustrates why understanding the broader disease context can be important when interpreting diagnostic results.
Wean-to-market pigs accounted for most co-diagnosis cases
The distribution of co-diagnosis cases varied considerably by production phase. Wean-to-market pigs accounted for 77% of all cases in the database but represented 86% of the co-diagnosis cases. Researchers noted that this concentration underscores the vulnerability of pigs during this stage to multifactorial disease processes.
The anatomic distribution also showed a strong respiratory component. The respiratory system was involved in 82% of co-diagnosis cases, followed by systemic disease at 61%, digestive disease at 31%, cardiovascular disease at 22% and nervous system disease at 5%.
Respiratory diagnoses were frequent in beginning of the nursery period and continued through grow-finish, while digestive diagnoses were more prominent earlier in life, particularly before three weeks of age.
PRRSV and Streptococcus suis were prominent in complex cases
As expected, PRRSV and S. suis emerged as particularly important components of co-diagnosed cases. PRRSV appeared in each of the five most common pathogen combinations identified across the database:
Across the individual anatomic systems, the most prevalent combinations also typically involved a bacterial and viral agent, highlighting the frequency with which these two types of challenges occurred together.
Viral and bacterial involvement was associated with greater complexity
Cases involving both bacterial and viral challenges had significantly more distinct etiologies than cases involving neither. In contrast, cases involving bacteria without viruses, or viruses without bacteria, did not show statistically significant increases in the number of distinct etiologies. The researchers also found that accounting for PRRSV and S. suis changed the model estimates by less than 10%, indicating that the primary association was not explained solely by those two prominent pathogens.
Together, these findings reinforce the complexity of disease cases involving both viral and bacterial agents and support the need for diagnostic approaches that consider potential co-infections rather than focusing exclusively on a single pathogen. As an example, practicing veterinarians should consider sending multiple tissues and consider a broad investigation, rather than looking for single pathogens when investigating field cases.
Turning diagnostic data into disease intelligence
The study demonstrates two complementary outcomes of SHIC-supported research. First, it provides a clearer picture of how frequently multiple etiologies occur in confirmed swine disease cases and how those patterns vary across production stages, anatomic systems, seasons, and locations. Second, it demonstrates a framework for turning standardized diagnostic information from individual veterinary diagnostic laboratories into a broader population-health surveillance resource.
This work also provided the first framework for integrating confirmed disease diagnosis data from veterinary diagnostic laboratories, in which two institutions, i.e., ISU VDL and OH VDL, use a standard way to record confirmed disease diagnosis results with the SDRS. The authors conclude that expanding and maintaining this type of data integration could provide a more holistic view of disease occurrence and support earlier recognition of changes in animal health. They also identify opportunities to expand the approach to additional laboratories and potentially other livestock species.
The project demonstrates how consistent interpretation and coding of diagnostic information can help move disease surveillance from individual cases toward a broader understanding of disease complexity and trends—providing information that can support more informed swine health decisions.
The Swine Health Information Center, launched in 2015 with Pork Checkoff funding, protects and enhances the health of the US swine herd by minimizing the impact of emerging disease threats through preparedness, coordinated communications, global disease monitoring, analysis of swine health data, and targeted research investments. As a conduit of information and research, SHIC encourages sharing of its publications and research. Forward, reprint, and quote SHIC material freely. For more information, visit https://www.swinehealth.org or contact Dr. Lisa Becton at [email protected].