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Published on: August 5, 2020
Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus
Matthew S Mietchen1, Christopher T Short1, Matthew Samore2,3
1Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, Washington, United States of America.
Background:
Complex transmission models of healthcare-associated infections provide insight for hospital epidemiology and infection control efforts, but they 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 of γ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the Metapopulation structure.
Discussion:
Inclusion of complex population interactions within 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 simple model approximations are appropriate in only a narrow space of relatively dispersed nursing assignments.
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.
Insights
Simplified models of hospital infections may overestimate methicillin-resistant Staphylococcus aureus (MRSA) rates. More complex models representing patient-provider interactions offer a more accurate approximation of MRSA dynamics in intensive care units (ICUs).
Area of Science:
- Infectious disease modeling
- Hospital epidemiology
- Intensive care unit (ICU) patient-provider interactions
Background:
- Complex transmission models for healthcare-associated infections offer insights but are computationally intensive and difficult to implement.
- Simplified models are explored to better represent methicillin-resistant Staphylococcus aureus (MRSA) dynamics and acquisitions by incorporating ICU patient-provider interaction heterogeneity.
Purpose of the Study:
- To compare MRSA acquisition rates across different ICU population interaction structures using simplified models.
- To evaluate the impact of varying patient-group assignments for nurses on MRSA transmission dynamics.
Main Methods:
- A stochastic compartmental model of an 18-bed ICU was developed.
- Three interaction structures were compared: single staff type (SST), separate nurse and physician types (Nurse-MD), and a Metapopulation model with assigned patient groups.
- The proportion of time nurses spent with assigned patients (γ) was varied in the Metapopulation model.
Main Results:
- The Metapopulation model showed significantly lower annual MRSA acquisitions (19.6) compared to Nurse-MD (32.2) and SST (40.6) models.
- Model sensitivity to parameters was consistent across structures, but the Metapopulation model was less sensitive.
- MRSA acquisition varied non-linearly with γ, with values below 0.40 resembling Nurse-MD and above converging to the Metapopulation structure.
Conclusions:
- Complex population interactions significantly impact model results, with simplified models potentially overestimating infection rates.
- More granular models representing population mixing are often justified for accurate epidemiological insights.
- Simple model approximations are only appropriate under specific, limited conditions of patient assignment.
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