More than half of confirmed swine disease cases — 23,846 of 45,310, or 53% — involved two or more distinct etiologies, according to a macroepidemiologic study of diagnostic data published in Porcine Health Management in 2026. The finding, drawn from tissue diagnoses recorded at the Iowa State University Veterinary Diagnostic Laboratory between January 2020 and September 2025, quantifies what practitioners in the barn already suspect: single-pathogen answers are the exception, not the rule.

The study, funded by the Swine Health Information Center through award 25-068 and led by Guilherme Cezar, then with Iowa State University, analyzed 58,170 cases in the ISU-VDL database, of which 45,310 met eligibility criteria. Individual co-diagnosis cases contained between two and nine distinct etiologies.

What does this change at the exam table?

The practical takeaway for practitioners: when investigating field cases, send multiple tissues and pursue a broad diagnostic workup rather than hunting a single pathogen. The data show that cases involving both bacterial and viral challenges carried significantly more distinct etiologies than cases involving neither. That association held even after researchers accounted for the two dominant agents — adjusting for PRRSV and Streptococcus suis changed model estimates by less than 10%.

Cases involving only bacteria, or only viruses, showed no statistically significant increase in etiology count. The added complexity appears specifically when viral and bacterial agents co-occur, and the most prevalent combinations within each anatomic system were typically bacterial-plus-viral pairs.

Which pigs carry the heaviest disease burden?

The distribution of co-diagnosis cases varied sharply by production phase. Wean-to-market pigs accounted for 77% of all cases in the database but 86% of co-diagnosis cases, a concentration the researchers say underscores that stage's vulnerability to multifactorial disease.

Age also shaped the anatomic picture:

• Respiratory system: involved in 82% of co-diagnosis cases, with diagnoses frequent from the start of the nursery period through grow-finish
• Systemic disease: 61%
• Digestive disease: 31%, with diagnoses clustered before three weeks of age
• Cardiovascular disease: 22%
• Nervous system disease: 5%

Which pathogen pairs dominated?

PRRSV appeared in each of the five most common pathogen combinations across the database:

• PRRSV + Streptococcus suis: 5,533 cases
• PRRSV + Pasteurella multocida: 3,085 cases
• PRRSV + Glaesserella parasuis: 2,899 cases
• PRRSV + influenza A virus: 2,560 cases
• PRRSV + Mycoplasma hyorhinis: 2,148 cases

The consistency of PRRSV across top combinations, alongside the persistent bacterial sequelae it enables, reinforces why PRRS control remains a leverage point for reducing multifactorial disease in grow-finish flows.

How was the data standardized?

The study's second objective was methodological: demonstrating that the standardized diagnostic coding system developed at ISU-VDL could transfer to another laboratory. The system goes beyond recording pathogen detection by PCR. Diagnosticians assign a final standardized code that incorporates the body system affected, type of challenge, lesion or tissue involved, and suspected or confirmed cause — integrating clinical history, pathology and laboratory testing from the full workup.

The project successfully ported the coding scheme to the Ohio Veterinary Diagnostic Laboratory and integrated both institutions' confirmed-diagnosis data with the SDRS (Swine Disease Reporting System), creating the first framework for multi-institutional integration of confirmed disease diagnosis data from veterinary diagnostic laboratories.

Why does surveillance integration matter?

For the swine industry, the framework converts case-level diagnostic output into population-level disease intelligence. The authors conclude that expanding and maintaining this data integration could provide a more holistic view of disease occurrence and support earlier recognition of changes in animal health — a population-health surveillance resource built from diagnostic records that laboratories already generate.

The researchers identify opportunities to extend the approach to additional laboratories and potentially other livestock species, positioning standardized diagnostic coding as a foundation for an ongoing multi-institutional database of confirmed tissue diagnoses.