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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 ,
Linnea A. Polgreen ,
David R. Campbell ,
Joseph E. Cavanaugh  and
Philip M. Polgreen
Jacob E. Simmering
Affiliation:
Department of Pharmacy Practice and Science, University of Iowa, Iowa City, Iowa
Linnea A. Polgreen
Affiliation:
Department of Pharmacy Practice and Science, University of Iowa, Iowa City, Iowa
David R. Campbell
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Joseph E. Cavanaugh
Affiliation:
Department of Biostatistics, University of Iowa, Iowa City, Iowa
Philip M. Polgreen*
Affiliation:
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 ([email protected]).
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Abstract

OBJECTIVE

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

DESIGN

Retrospective cohort.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 36 , Issue 9 , September 2015 , pp. 1031 - 1037
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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Footnotes

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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