Impact of inter-hospital transfers on the prevalence of resistant pathogens in a hospital–community system

M. J. Piotrowska, K. Sakowski*, A. Lonc, H. Tahir, M. E. Kretzschmar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

The spread of resistant bacteria in hospitals is an increasing problem worldwide. Transfers of patients, who may be colonized with resistant bacteria, are considered to be an important driver of promoting resistance. Even though transmission rates within a hospital are often low, readmissions of patients who were colonized during an earlier hospital stay lead to repeated introductions of resistant bacteria into hospitals. We developed a mathematical model that combines a deterministic model for within-hospital spread of pathogens, discharge to the community and readmission, with a hospital–community network simulation of patient transfers between hospitals. Model parameters used to create the hospital–community network are obtained from two health insurance datasets from Germany. For parameter values representing transmission of resistant Enterobacteriaceae, we compute estimates for the single admission reproduction numbers RA and the basic reproduction numbers R0 per hospital–community pair. We simulate the spread of colonization through the network of hospitals, and investigate how increasing connectedness of hospitals through the network influences the prevalence in the hospital–community pairs. We find that the prevalence in hospitals is determined by their RA and R0 values. Increasing transfer rates between network nodes tend to lower the overall prevalence in the network by diluting the high prevalence of hospitals with high R0 to hospitals where persistent spread is not possible. We conclude that hospitals with high reproduction numbers represent a continuous source of risk for importing resistant pathogens for hospitals with otherwise low levels of transmission. Moreover, high risk hospital–community nodes act as reservoirs of pathogens in a densely connected network.

Original languageEnglish
Article number100408
Pages (from-to)1-15
JournalEpidemics
Volume33
DOIs
Publication statusPublished - Dec 2020

Keywords

  • Basic reproduction number
  • Epidemiology
  • Healthcare associated infections
  • Healthcare network
  • Multidrug resistant Enterobacteriaceae

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