Evaluating a neonatal intensive care unit MRSA surveillance programme using agent-based network modelling

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Abstract

Background: Surveillance for meticillin-resistant Staphylococcus aureus (MRSA) in neonatal intensive care units (NICUs) is a commonplace infection prevention strategy, yet the optimal frequency with which to monitor the unit is unknown. Aim: To compare various surveillance frequencies using simulation modelling. Methods: One hundred NICU networks of 52 infants were simulated over a six-month period to assess MRSA transmission. Unit-wide surveillance occurred every N weeks where N={1,2,3,4}, and was compared with the current NICU policy of dynamic surveillance (i.e. weekly when at least one positive screen, otherwise every three weeks). For each surveillance period, colonized infants received a decolonization regimen (56% effective) and were moved to isolation rooms, if available. Findings: As the surveillance frequency increased, the mean number of MRSA-colonized infants decreased, from a high of 2.9 (four-weekly monitoring) to a low of 0.6 (weekly monitoring) detected per episode. The mean duration of colonization decreased from 307 h (four-weekly monitoring) to 61 h (weekly monitoring). Meanwhile, the availability of isolation rooms followed an inverse relationship: as surveillance frequency increased, the availability of isolation rooms decreased (61% isolation success rate for four-weekly monitoring vs 49% success rate for weekly monitoring). The dynamic policy performed similar to a biweekly programme. Conclusions: An effective MRSA surveillance programme needs to balance resource availability with potential for harm due to longer colonization periods and opportunity for development of invasive disease. While more frequent monitoring led to greater use of a decolonization regimen, it also reduced the likelihood of isolation rooms being available.

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APA

Goldstein, N. D., Jenness, S. M., Tuttle, D., Power, M., Paul, D. A., & Eppes, S. C. (2018). Evaluating a neonatal intensive care unit MRSA surveillance programme using agent-based network modelling. Journal of Hospital Infection, 100(3), 337–343. https://doi.org/10.1016/j.jhin.2018.05.002

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