Pools versus Queues: The Variable Dynamics of Stochastic “Steady States”

Lofgren, Eric

doi:10.1371/journal.pone.0130574

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…The ICU is considered a “closed ICU”, meaning physicians or other hospital staff from outside the ICU do not interact with patients. The ICU is also considered to be at 100% capacity at all times, therefore if a patient is discharged it is assumed another patient is admitted to the bed immediately 37 . A hand hygiene opportunity occurs after every direct care task or any contact between a healthcare provider and a patient, and in addition, personal protective equipment (PPE) such as gowns and gloves are changed on entry and exit from the rooms of all colonized patients.…”

Section: Methodsmentioning

confidence: 99%

Examining the Impact of ICU Population Interaction Structure on Modeled Colonization Dynamics ofStaphylococcus aureus

Mietchen

Short

Samore

et al. 2022

Preprint

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BackgroundComplex 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.MethodsUsing 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.ResultsThe 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.DiscussionComplex 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.ConclusionSimplifying 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 SummarySome 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-resistant Staphylococcus 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.

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Section: Methodsmentioning

confidence: 99%

Examining the Impact of ICU Population Interaction Structure on Modeled Colonization Dynamics ofStaphylococcus aureus

Mietchen

Short

Samore

et al. 2022

Preprint

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“…For each of these models, we consider the ICU to always be at capacity as a discharge will immediately lead to an admission, maintaining a steady-state population [ 8 ]. Further detail on the construction, implementation and parameterization of the models may be found in [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

Transient dynamics of infection transmission in a simulated intensive care unit

et al. 2022

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Healthcare-associated infections (HAIs) remain a serious public health problem. In previous work, two models of an intensive care unit (ICU) showed that differing population structures had markedly different rates of Staphylococcus aureus (MRSA) transmission. One explanation for this difference is the models having differing long-term equilbrium dynamics, resulting from different basic reproductive numbers, R0. We find in this system however that this is not the case, and that both models had the same value for R0. Instead, short-term, transient dynamics, characterizing a series of small, self-limiting outbreaks caused by pathogen reintroduction were responsible for the differences. These results show the importance of these short-term factors for disease systems where reintroduction events are frequent, even if they are below the epidemic threshold. Further, we examine how subtle changes in how a hospital is organized—or how a model assumes a hospital is organized—in terms of the admission of new patients may impact transmission rates. This has implications for both novel pathogens introduced into ICUs, such as Ebola, MERS or COVID-19, as well as existing healthcare-associated infections such as carbapenem-resistant Enterobacteriaceae.

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“…The ICU is considered a “closed ICU” – physicians from outside the ICU do not see patients. The ICU is also considered to be at 100% capacity – if a patient is discharged it is assumed another patient is admitted to the bed immediately [16]. A hand hygiene opportunity occurs after every direct care task and personal protective equipment such as gowns and gloves are changed on entry and exit from the rooms of colonized patients.…”

Section: Methodsmentioning

confidence: 99%

Population Structure Drives Differential Methicillin-resistantStaphylococcus aureusColonization Dynamics

Samore

2019

Preprint

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Background: Using 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. Methods: Using 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. Results: The 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. Discussion: The 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. Conclusion: Simplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions.

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Pools versus Queues: The Variable Dynamics of Stochastic “Steady States”

Cited by 6 publications

References 19 publications

Examining the Impact of ICU Population Interaction Structure on Modeled Colonization Dynamics ofStaphylococcus aureus

Examining the Impact of ICU Population Interaction Structure on Modeled Colonization Dynamics ofStaphylococcus aureus

Transient dynamics of infection transmission in a simulated intensive care unit

Population Structure Drives Differential Methicillin-resistantStaphylococcus aureusColonization Dynamics

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