Simulation Models Applied to HAIs: Much Promise but Unfulfilled So Far

December 30, 2019

Hybrid simulation models have a lot of potential to unlock the best approaches to combat healthcare-acquired infections.

Hybrid simulation models have a lot of potential to unlock the best approaches to combat healthcare-acquired infections (HAIs), but they still have a long way to go before they can be reliably counted on, according to investigators at the University of Strathclyde, Glasgow in the United Kingdom.

The complexity of simulation models for HAIs has evolved, taking more and more into account the details of healthcare settings, the interactions being modeled, and the methodological designs.

However, says the study in the American Journal of Infection Control, existing models focus predominately on the transmission of methicillin-resistant Staphylococcus aureus (MRSA) in hospitals in high-income countries (HICs) and, for the most part, ignore things like long-term-care facilities in low- and middle-income countries (LMICs).

The review of 68 studies aimed to consolidate and update the development and application of systems simulation modeling in studying HAIs. Investigators looked at how simulation models have been used to measure and track HAIs, how they may have evolved, gaps in their adoption, and how they can be better put to use in the future.

Nearly one-half of the studies (47%, 38 studies) investigated MRSA dynamics, followed by vancomycin-resistant Enterococci (VRE, 12%, 10 studies), and Clostridiodes difficile (7%, 6 studies). The simulation models that investigators analyzed included system dynamics (SD), discrete event simulation (DES), agent-based model (ABM), and hybrid simulation models.

SD models depict non-linear relationships, which derive from the existence of feedback processes that exist in which actors within a system will later be affected by their actions. ABM modelling is a bottom-up simulation method for modeling dynamic and adaptive systems with autonomous entities called agents and their environment, according to the study. DES models are process based simulation methods that look at a discrete sequence of activities and events in time. The hybrid method combines the methodological strengths of at least 2 of the other simulation modeling methods.

The study says that, historically, randomized controlled trials (RCTs) were used to investigate the epidemiology of HAIs, and cluster RCTs were used to examine methods for infection control.

“However, performing large cluster RCTs across various health facilities to achieve generalizability and sufficient power to address important research questions is difficult,” the study states.

Investigators said that’s one of the reasons simulation models have become more relied upon. “Simulation modeling provides a risk-free environment in which ideas on IPC strategies can be tested in a systematic manner without the time, costs, and risks associated with experiments conducted in a real-world setting,” the study states. “It is a valuable tool to guide the selection of the most appropriate empirical research to pursue and to examine the effects of IPC strategies…”

But it’s impossible to build a model that fully replicates the real world, says the study, “particularly when we describe a stochastic system as complex as infection transmission, which is influenced by human behavior, pathogen and host biological characteristics, and the health facility structure among many factors.”

Nonetheless, investigators say, simulation modeling can help to understand the relative effectiveness of different interventions, identify the risk of HAIs for different population groups, and provide confidence intervals on the epidemic behaviors and, therefore, aid with decision making. “IPC decision-makers using simulation models for decision-support must consider model assumptions and their relevance to the particular context in addition to carefully weighing the predicted benefits of interventions against the inconvenience, stigmatization, and costs they might engender,” the study states.