Generation of an Etiology Index Based on Confirmed Tissue Diagnosis Data to Assess Endemic Swine Etiology Activity in the United States of America

Global pork production has increased substantially over the past few decades, making swine a critical source of animal protein. However, endemic diseases in pigs continue to pose significant challenges to animal health, productivity, and food security. Several etiologies affect farm‐level performance and have broader implications for zoonotic risks and public health. Many diagnostic cases are submitted to veterinary diagnostic laboratories, and their aggregation can yield insights into etiological activity. Therefore, this study aimed to develop a composite etiology index using confirmed tissue diagnosis data from the Iowa State University Veterinary Diagnostic Laboratory (ISU‐VDL) to assess endemic etiology activity across the United States. A total of 59,950 porcine tissue cases from 2020 to 2024 were analyzed, focusing on 81 etiologies of bacterial, viral, parasitic, and metabolic/intoxication origin. Four normalized variables: disease occurrence, codiagnosis, state occurrence, and Early Aberration Reporting System (EARS) alarms were integrated using the CompidexR package to generate a weighted index ranging from 0.01 to 1. Temporal consistency was evaluated using the Manhattan distance, Spearman’s correlation, and the Wilcoxon signed‐rank test. Also, a bootstrap resampling method was developed to detect anomalies in the distribution of etiologies. The index demonstrated strong year‐over‐year stability, with porcine reproductive and respiratory syndrome virus (PRRSV) and Streptococcus suis consistently receiving yearly highest scores. Reemerging viruses like porcine sapovirus (PSaV) and astrovirus (AsV) showed notable increases in index values, reflecting rising diagnostic activity and geographic spread. Bootstrap analysis showed that over 55% of etiologies fell within expected confidence intervals (CIs) and had low root mean square errors (RMSEs), detecting anomalies such as PCV2 occurrences in 2024. The index summaries were visualized through an interactive Power BI dashboard, enabling dynamic exploration of etiology trends. This framework offers a scalable, reproducible tool for monitoring endemic swine diseases using routine diagnostic data. The ability to generate information on endemic etiology rankings can support decision‐makers with evidence‐based disease management and control. The developed model has flexibility and can be adapted to other species and disease systems. The index provided a robust foundation for enhancing surveillance of endemic pathogens in swine populations.