Healthcare Worker Staffing Ratios Affect Methicillin-Resistant Staphylococcus aureus Acquisition

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. Question How does the conceptualization of ICU healthcare worker tasks in models—whether infinite or finite— impact the results of changes in staffing ratios affecting methicillin-resistant Staphylococcus aureus (MRSA) acquisition? Findings In this compartmental mathematical model approach that included 15 different models, the trends of the impact of staffing ratios were consistent between the Infinite and Finite tasks models. However, both the absolute and relative values were markedly different, with the infinite task models having a much more linear effect on MRSA acquisitions while the number of MRSA cases in the finite model continued to rise exponentially as the number of nurses decreased. Meaning It is essential when considering model generalizability, to state the assumptions made about how workload and contact patterns within a hospital work, and to ensure these are appropriately tailored for the specific setting being modeled. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the CDC U01CK0006730100 Healthcare, Infectious Diseases, Research (HIRe) Modeling Fellowship (to S.S.J.), the National Institutes of Health (R35GM147013 to E.T.L.) and the Advancing Science in America Fellowship (to S.S.J) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at https://github.com/epimodels/StaffRatios