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Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection

Published online by Cambridge University Press:  15 June 2015

Jacob E. Simmering
Department of Pharmacy Practice and Science, University of Iowa, Iowa City, Iowa
Linnea A. Polgreen
Department of Pharmacy Practice and Science, University of Iowa, Iowa City, Iowa
David R. Campbell
Department of Computer Science, University of Iowa, Iowa City, Iowa
Joseph E. Cavanaugh
Department of Biostatistics, University of Iowa, Iowa City, Iowa
Philip M. Polgreen*
Departments of Internal Medicine and Epidemiology, University of Iowa, Iowa City, Iowa
Address correspondence to Philip M. Polgreen, MD, MPH, Departments of Epidemiology and Internal Medicine, University of Iowa, 200 Hawkins Dr., Iowa City, IA 52242 (



To determine the effect of interhospital patient sharing via transfers on the rate of Clostridium difficile infections in a hospital.


Retrospective cohort.


Using data from the Healthcare Cost and Utilization Project California State Inpatient Database, 2005–2011, we identified 2,752,639 transfers. We then constructed a series of networks detailing the connections formed by hospitals. We computed 2 measures of connectivity, indegree and weighted indegree, measuring the number of hospitals from which transfers into a hospital arrive, and the total number of incoming transfers, respectively. Next, we estimated a multivariate model of C. difficile infection cases using the log-transformed network measures as well as covariates for hospital fixed effects, log median length of stay, log fraction of patients aged 65 or older, and quarter and year indicators as predictors.


We found an increase of 1 in the log indegree was associated with a 4.8% increase in incidence of C. difficile infection (95% CI, 2.3%–7.4%) and an increase of 1 in log weighted indegree was associated with a 3.3% increase in C. difficile infection incidence (1.5%–5.2%). Moreover, including measures of connectivity in our models greatly improved their fit.


Our results suggest infection control is not under the exclusive control of a given hospital but is also influenced by the connections and number of connections that hospitals have with other hospitals.

Infect. Control Hosp. Epidemiol. 2015;36(9):1031–1037

Original Articles
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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Presented in part: IDWeek 2013; San Francisco, California; October 5, 2013 (abstract 1214); and the 5th Biennial Conference of the American Society of Health Economists; University of Southern California; Los Angeles, CA; June 23, 2014; and the Midwest Social and Administrative Pharmacy Conference; Purdue University; Lafayette, IN; July 17, 2014.


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