Examining the Impact of ICU Population Interaction Structure on Modeled Colonization Dynamics ofStaphylococcus aureus
Authors: Mietchen Matthew S. , Short Christopher T. , Samore Matthew , Lofgren Eric T. ,   the CDC Modeling Infectious Diseases in Healthcare Program (MInD-Healthcare)
Published: March 2022

Background

Complex transmission models of healthcare-associated infections provide insight for hospital epidemiology and infection control efforts; however, many are difficult to implement and come at high computational costs. Structuring more simplified models to incorporate the heterogeneity of the intensive care unit (ICU) patient-provider interactions, we explore how methicillin-resistant Staphylococcus aureus (MRSA) dynamics and acquisitions may be better represented and approximated.

Methods

Using a stochastic compartmental model of an 18-bed ICU, we compared the rates of MRSA acquisition across three ICU population interaction structures: a model with nurses and physicians as a single staff type (SST), 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. The proportion of time spent with the assigned patient group (γ) within the Metapopulation model was also varied.

Results

The SST, Nurse-MD, and Metapopulation models had a mean of 40.6, 32.2 and 19.6 annual MRSA acquisitions 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 ofγ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the metapopulation structure.

Discussion

Complex population interactions of a modeled hospital ICU 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 colonization rates across relatively similar populations. The non-linearity of the model’s response to differing values of a parameter gamma (γ) suggests only a narrow space of relatively dispersed nursing assignments where simple model approximations are appropriate.

Conclusion

Simplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions. In many, if not most cases, more complex models that represent population mixing with higher granularity are justified.

Author Summary

Some models of healthcare-associated infection assume random mixing between healthcare workers and patients – that is, all healthcare workers care for all patients in the model at equal frequency. This paper explores the impact of that assumption, creating a model of an 18-bed intensive care unit that compares a model with random mixing with one that segments patients into distinct groups with a single nurse assigned to them while a dedicated critical care physician continues to see all patients. Higher rates of segmentation result in lower rates of predicted acquisitions of methicillin-resistantStaphylococcus aureus, as well as more modest predicted impacts of interventions – represented by changes in parameter values. These findings suggest that the simpler random mixing models that are used for analytical tractability or computational speed may not be appropriate approximations in settings where healthcare workers are not uniformly distributed among patients due to scheduling, patient complexity or cohorting, the built environment, or hospital policy, and that balancing the computational costs with the trend toward more complex models in hospital epidemiology is justified.