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.
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.
26Introduction: Chlorhexidine gluconate and mupirocin are widely used to decolonize patients 27 with methicillin-resistant Staphylococcus aureus (MRSA) and reduce risks of infection in 28 hospitalized populations. The probability that a treated patient would be decolonized, which 29 we term per-application effectiveness, is difficult to directly measure. Quantifying the efficacy 30 of per-application effectiveness of CHG and mupirocin is important for studies evaluating 31 alternative decolonization strategies or schedules as well as identifying whether there is room 32 for improved decolonizing agents. 33 34 Methods: Using a stochastic compartmental model of an intensive care unit (ICU), the per-35 application effectiveness of chlorhexidine and mupirocin were estimated using approximate 36 Bayesian computation. Extended sensitivity analysis examined the potential impact of a latent 37 period between MRSA colonization and detection, the timing of decolonization administration, 38 and parameter uncertainty. 39 40 Results: The estimated per-application effectiveness of chlorhexidine was 0.15 (95% Credible 41Interval: 0.01, 0.42), while the estimated effectiveness of mupirocin was is 0.15 (95% CI: 0.01, 42 0.54). A lag in colonization detection markedly reduced both estimates, which were particularly 43 sensitive to the value to the modeled contact rate between nurses and patients. Gaps longer 44 than 24-hours in the administration of decolonizing agents still resulted in substantial reduction 45 of within-ICU MRSA transmission. 46 47 Discussion: The per-application effectiveness estimates for chlorhexidine and mupirocin suggest 48 there is room for substantial improvement in anti-MRSA disinfectants, either in the compounds 49 themselves, or in their delivery mechanism. Despite these estimates, these agents are robust to 50 delays in administration, which may help in alleviating concerns over patient comfort or 51 toxicity. 52 53 54 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. was not certified by peer review)
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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