Objectives: This study presents demographic and temporal trends in the isolation of Staphylococcus aureus in Vermont clinical microbiology laboratories and explores the use of statistical algorithms and multi-resistance phenotypes to improve outbreak detection.Methods: Routine microbiology test results downloaded from Vermont clinical laboratory information systems were used to monitor S. aureus antimicrobial resistance trends. The integrated WHONET-SaTScan software used multi-resistance phenotypes to identify possible acute outbreaks with the space-time permutation model and slowly emerging geographic clusters using the spatial-only multinomial model.Results: Data were provided from seven hospital laboratories from 2012 to 2018 for 19,224 S. aureus isolates from 14,939 patients. Statistically significant differences (p ≤ 0.05) in methicillin-resistant S. aureus (MRSA) isolation were seen by age group, specimen type, and health-care setting. Among MRSA, multi-resistance profiles permitted the recognition and tracking of 6 common and 21 rare 'phenotypic clones.' We identified 43 acute MRSA clusters and 7 significant geographic clusters (p ≤ 0.05).Conclusions: There was significant heterogeneity in MRSA strains between facilities and the use of multi-resistance phenotypes facilitated the recognition of possible outbreaks. Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging resistance threats.
Keywords: Staphylococcus aureus; Vermont; WHONET; antimicrobial resistance surveillance; laboratory-based surveillance.