Bayesian inference of nosocomial meticillin-resistant Staphylococcus aureus transmission rates in an urban safety-net hospital.

backgroundMeticillin-resistant Staphylococcus aureus (MRSA) poses significant challenges for effective treatment and infection control within healthcare settings. Recent research suggests that the incidence of healthcare-associated (HA-)MRSA is higher among patients treated in safety-net hospitals when compared to other healthcare settings.

aimTo investigate HA-MRSA transmission patterns across nursing units in a Midwestern US safety-net hospital using Bayesian inference.

methodsUsing surveillance data from 2019 to 2023, a compartmental disease model was constructed and validated. Posterior probability distributions for MRSA transmission, isolation, and hospital discharge rates were computed using the Delayed Rejection Adaptive Metropolis (DRAM) Bayesian algorithm.

findingsAnalysis of 187,040 patient records revealed that inpatient nursing units exhibited the highest MRSA transmission rates in three out of the five years studied. Notable transmission rates were observed in certain inpatient and progressive care units (0.55 per individual per month; 0.018 per individual per day), and the Surgery Intensive Care Unit (ICU) (0.44 per individual per month; 0.015 per individual per day). In contrast, the Neonatal ICU and Medical ICU had the lowest transmission rates. Although MRSA transmission rates significantly declined across all units in 2021, these rates rebounded to pre-pandemic levels in subsequent years. Notably, outbreaks emerged in units such as ICUs and progressive care units that had not experienced prior MRSA outbreaks since 2019.

conclusionMRSA transmission significantly declined during the early phase of the pandemic but subsequently re-emerged within the safety-net hospital in later years.