Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study.

Affiliation

Tahir H(1), López-Cortés LE(2), Kola A(3), Yahav D(4), Karch A(5), Xia H(6), Horn J(6), Sakowski K(7)(8), Piotrowska MJ(7), Leibovici L(9), Mikolajczyk RT(6), Kretzschmar ME(1).
Author information:
(1)Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
(2)Unidad Clínica de Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen Macarena, Sevilla, Spain.
(3)Institute of Institute of Hygiene and Environmental Medicine, Charité- University Medicine Berlin, Berlin, Germany.
(4)Infectious Diseases Unit, Rabin Medical Center, Beilinson Hospital, Petah-Tiqva, Israel.
(5)Institute for Epidemiology and Social Medicine, University of Münster, Münster, Germany.
(6)Institute for Medical Epidemiology, Biometry, and Informatics
(IMEBI), Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle, Germany.
(7)Institute of Applied Mathematics and Mechanics, University of Warsaw, Warsaw, Poland.
(8)Institute of High Pressure Physics, Polish Academy of Sciences, Warsaw, Poland.
(9)Department of Medicine E; Rabin Medical Center, Beilinson Hospital, Petah-Tiqva, Israel.

Abstract

The aim of this study is to analyze patient movement patterns between hospital departments to derive the underlying intra-hospital movement network, and to assess if movement patterns differ between patients at high or low risk of colonization. For that purpose, we analyzed patient electronic medical record data from five hospitals to extract information on risk stratification and patient intra-hospital movements. Movement patterns were visualized as networks, and network centrality measures were calculated. Next, using an agent-based model where agents represent patients and intra-hospital patient movements were explicitly modeled, we simulated the spread of multidrug resistant enterobacteriacae (MDR-E) inside a hospital. Risk stratification of patients according to certain ICD-10 codes revealed that length of stay, patient age, and mean number of movements per admission were higher in the high-risk groups. Movement networks in all hospitals displayed a high variability among departments concerning their network centrality and connectedness with a few highly connected departments and many weakly connected peripheral departments. Simulating the spread of a pathogen in one hospital network showed positive correlation between department prevalence and network centrality measures. This study highlights the importance of intra-hospital patient movements and their possible impact on pathogen spread. Targeting interventions to departments of higher (weighted) degree may help to control the spread of MDR-E. Moreover, when the colonization status of patients coming from different departments is unknown, a ranking system based on department centralities may be used to design more effective interventions that mitigate pathogen spread.