Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus

Abstract

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.

Healthcare Worker Staffing Ratios Affect Methicillin-ResistantStaphylococcus aureusAcquisition

“…Our baseline model of MRSA transmission is based on a previously published model of a 18-bed medical ICU, staffed by six nurses (a 3:1 patient-to-nurse ratio) and one dedicated intensivist (see Figure 1) 4,6,7 . The 18-bed size was chosen for coherence with previous studies and for ease of creating a variety of whole numbered patient: staff ratios 4,17 .…”

“…The 18-bed size was chosen for coherence with previous studies and for ease of creating a variety of whole numbered patient: staff ratios 4,17 . Our parameters came from previously published models of ICU-based MRSA transmission and are shown in Table 1 6,7,16,21 .…”

“…Only one state, California, has a mandated ICU paCent-to-nurse raCo of 2:1 5 . Other states have lej this up to hospitals and healthcare systems, leading to a naConal average of 3:1 that is used as a baseline for many modeling studies 2,[6][7][8] .…”

“…These models can help criCcal care teams and infecCon control professionals understand the intricate interplay between their own ward's behaviors and healthcare-associated infecCons (HAIs) [9][10][11][12] . Modeling studies have emerged as indispensable tools, especially in scenarios or with pathogens where conducCng randomized controlled trials would be cost-prohibiCve, labor-intensive or the pathogens cause sporadic and self-limiCng epidemics 7,9,10,[13][14][15] .…”

“…This study focuses on understanding how one structures a model affects the results a healthcare team might be expected to see. This was done by modifying a previously published model of an ICU, and adjusting the staffing ratios of nurses and physicians to obtain the number of yearly MRSA acquisitions based on staffing 7 . In addition, we adjusted the models to have nurses and physicians have varying amount on contact with patients.…”