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

Jacob E Simmering; Linnea A Polgreen; David R Campbell; Joseph E Cavanaugh; Philip M Polgreen

doi:10.1017/ice.2015.130

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

Journal article

Peer reviewed

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

Jacob E Simmering, Linnea A Polgreen, David R Campbell, Joseph E Cavanaugh and Philip M Polgreen

Infection control and hospital epidemiology, Vol.36(9), pp.1031-1037

09/2015

DOI: 10.1017/ice.2015.130

PMCID: PMC4736722

PMID: 26072907

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Abstract

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.

Models, Theoretical

Enterocolitis, Pseudomembranous - epidemiology

Age Factors

Cross Infection - prevention & control

California - epidemiology

Humans

Risk Factors

Clostridium difficile

Incidence

Patient Transfer - organization & administration

Hospitals - statistics & numerical data

Aged

Patient Transfer - statistics & numerical data

Retrospective Studies

Enterocolitis, Pseudomembranous - prevention & control

Length of Stay - statistics & numerical data

Cross Infection - epidemiology

Details

Title: Subtitle: Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection
Creators: Jacob E Simmering - 1Department of Pharmacy Practice and Science,University of Iowa,Iowa City,Iowa
Linnea A Polgreen - 1Department of Pharmacy Practice and Science,University of Iowa,Iowa City,Iowa
David R Campbell - 2Department of Computer Science,University of Iowa,Iowa City,Iowa
Joseph E Cavanaugh - 3Department of Biostatistics,University of Iowa,Iowa City,Iowa
Philip M Polgreen - 4Departments of Internal Medicine and Epidemiology,University of Iowa,Iowa City,Iowa
Resource Type: Journal article
Publication Details: Infection control and hospital epidemiology, Vol.36(9), pp.1031-1037
DOI: 10.1017/ice.2015.130
PMID: 26072907
PMCID: PMC4736722
NLM abbreviation: Infect Control Hosp Epidemiol
ISSN: 1559-6834
eISSN: 1559-6834
Publisher: United States
Grant note: K25 HL122305 / NHLBI NIH HHS 1K25HL122305-01A1 / NHLBI NIH HHS
Language: English
Date published: 09/2015
Academic Unit: Statistics and Actuarial Science; Pulmonary, Critical Care, and Occupational Medicine; Infectious Diseases; Health Management and Policy; Epidemiology; Economics; Biostatistics; Pharmacy Practice and Science; Injury Prevention Research Center; Internal Medicine
Record Identifier: 9983985959602771

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