Population Structure Drives Differential Methicillin-resistant Staphylococcus aureus Colonization Dynamics

Abstract

AbstractBackgroundUsing a model of methicillin-resistant Staphylococcus aureus (MRSA) within an intensive care unit (ICU), we explore how differing hospital population structures impact these infection dynamics.MethodsUsing a stochastic compartmental model of an 18-bed ICU, we compared the rates of MRSA acquisition across three potential population structures: a Single Staff Type (SST) model with nurses and physicians as a single staff type, a model with separate staff types for nurses and physicians (Nurse-MD model), and a Metapopulation model where each nurse was assigned a group of patients. By varying the proportion of time spent with the assigned patient group (γ) within the Metapopulation model, we explored whether simpler models may be acceptable approximations to more realistic patient-healthcare staff contact patterns.ResultsThe SST, Nurse-MD, and Metapopulation models had a mean annual number of cumulative MRSA acquisitions of 40.6, 32.2 and 19.6 respectively. All models were sensitive to the same parameters in the same direction, although the Metapopulation model was less sensitive. The number of acquisitions varied non-linearly by values of γ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the metapopulation structure.DiscussionThe population structure of a modeled hospital has considerable impact on model results, with the SST model having more than double the acquisition rate of the more structured Metapopulation model. While the direction of parameter sensitivity remained the same, the magnitude of these differences varied, producing different infection rates across relatively similar populations. The non-linearity of the model’s response to differing values of γ suggests only a narrow space of relatively dispersed nursing assignments where simple model approximations are appropriate.ConclusionSimplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions.