ABSTRACT
Importance: This study addresses the pressing clinical question of how variations in physician and nursing staffing levels influence methicillin-resistant Staphylococcus aureus (MRSA) rates, providing essential insights for optimizing staff allocation and improving patient outcomes in critical care settings. Objective: The main objective is to assess whether variations in staffing ratios and workload conceptualization significantly alter the rates of MRSA acquisitions in the ICU setting. Design: This simulation-based study utilizes stochastic compartmental mathematical modeling to explore the impact of staffing ratios and workload conceptualization on MRSA acquisitions in ICUs. Derived from a previously published model, the analysis involves running year-long stochastic simulations for each scenario 1000 times, varying nurse-to-patient ratios and intensivist staffing levels under infinite and finite workload conceptualizations. Our baseline model was a 3:1 nurse ratio with one intensivist. Main Outcome: MRSA acquisitions in ICUs, measured as median acquisitions per 1000 person-years. Results: Under baseline conditions, our model had a median of 8.2 MRSA acquisitions per 1000 person-years. Varying patient-to-nurse ratios and intensivist numbers showed substantial impacts. For infinite models, a 2:1 nurse ratio resulted in a 21% decrease, while a 1:1 nurse ratio led to a 65% reduction. Finite models demonstrated even larger effects, with a 48% decrease when having a 2:1 ratio, and an 83% reduction with a 1:1 nurse ratio. Reducing patient-to-nurse ratios in finite models increased acquisitions exponentially with a 348% increase for a 6:1 ratio. Intensivist variations had modest impacts. Conclusions and Relevance: Our study highlights the crucial role of optimizing staffing levels in ICUs for effective MRSA infection control. While intensivist variations have modest effects, bolstering nursing ratios significantly reduces MRSA acquisitions, underscoring the need for tailored staffing strategies, and recognizing the nuanced impact of workload conceptualization. Our findings offer practical insights for refining staffing protocols, emphasizing the dynamic nature of healthcare-associated infection outcomes.