TY - JOUR
T1 - Impact of inter-hospital transfers on the prevalence of resistant pathogens in a hospital–community system
AU - Piotrowska, M. J.
AU - Sakowski, K.
AU - Lonc, A.
AU - Tahir, H.
AU - Kretzschmar, M. E.
N1 - Funding Information:
This publication was made possible by grants from following national funding agencies: National Science Centre, Poland , Unisono: 2016/22/Z/ST1/00690 (University of Warsaw, Faculty of Mathematics, Informatics and Mechanics, Institute of Applied Mathematics and Mechanics); and the ZonMw Netherlands grant number 547001005 (Julius Centre, University Medical Centre Utrecht) within the 3rd JPI-EC-AMR framework (Joint Programming Initiative on Antimicrobial Resistance) cofound grant no 681055 for the consortium EMerGE-Net (Effectiveness of infection control strategies against intra- and inter-hospital transmission of MultidruG-resistant Enterobacteriaceae). We thank the AOK Lower Saxony and AOK Plus for providing anonymized record data. We would like also to express our thanks to Prof. Rafael Mikolajczyk, Prof. Andre Karch and Dr. Johannes Horn for substantive discussions.
Publisher Copyright:
© 2020 The Authors
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Basic reproduction number
KW - Epidemiology
KW - Healthcare associated infections
KW - Healthcare network
KW - Multidrug resistant Enterobacteriaceae
UR - http://www.scopus.com/inward/record.url?scp=85094612464&partnerID=8YFLogxK
U2 - 10.1016/j.epidem.2020.100408
DO - 10.1016/j.epidem.2020.100408
M3 - Article
C2 - 33128935
AN - SCOPUS:85094612464
SN - 1755-4365
VL - 33
SP - 1
EP - 15
JO - Epidemics
JF - Epidemics
M1 - 100408
ER -