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Estimating the Attributable Disease Burden and Effects of Interhospital Patient Sharing on Clostridium difficile Infections

Published online by Cambridge University Press:  12 April 2019

Daniel K. Sewell Open the ORCID record for Daniel K. Sewell [Opens in a new window] ,
Jacob E. Simmering ,
Samuel Justice ,
Sriram V. Pemmaraju ,
Alberto M. Segre  and
Philip M. Polgreen
Daniel K. Sewell*
Affiliation:
Department of Biostatistics, University of Iowa, Iowa City, Iowa
Jacob E. Simmering
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Samuel Justice
Affiliation:
Department of Statistics, University of Iowa, Iowa City, Iowa
Sriram V. Pemmaraju
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Alberto M. Segre
Affiliation:
Department of Computer Science, University of Iowa, Iowa City, Iowa
Philip M. Polgreen
Affiliation:
Department of Internal Medicine, University of Iowa, Iowa City, Iowafor the CDC MInD-Healthcare Program
*
Author for correspondence: Daniel K. Sewell, Email: [email protected]
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Abstract

Objective:

To estimate the burden of Clostridium difficile infections (CDIs) due to interfacility patient sharing at regional and hospital levels.

Design:

Retrospective observational study.

Methods:

We used data from the Healthcare Cost and Utilization Project California State Inpatient Database (2005–2011) to identify 26,878,498 admissions and 532,925 patient transfers. We constructed a weighted, directed network among the hospitals by defining an edge between 2 hospitals to be the monthly average number of patients discharged from one hospital and admitted to another on the same day. We then used a network autocorrelation model to study the effect of the patient sharing network on the monthly average number of CDI cases per hospital, and we estimated the proportion of CDI cases attributable to the network.

Results:

We found that 13% (95% confidence interval [CI], 7.6%–18%) of CDI cases were due to diffusion through the patient-sharing network. The network autocorrelation parameter was estimated at 5.0 (95% CI, 3.0–6.9). An increase in the number of patients transferred into and/or an increased CDI rate at the hospitals from which those patients originated led to an increase in the number of CDIs in the receiving hospital.

Conclusions:

A minority but substantial burden of CDI infections are attributable to hospital transfers. A hospital’s infection control may thus be nontrivially influenced by its neighboring hospitals. This work adds to the growing body of evidence that intervention strategies designed to minimize HAIs should be done at the regional rather than local level.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 40 , Issue 6 , June 2019 , pp. 656 - 661
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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Footnotes

PREVIOUS PRESENTATION: This work was presented in part at the International Conference on Emerging Infectious Diseases on August 27, 2018, in Atlanta, Georgia.

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