Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection. (15th June 2015)

• Record Type: Journal Article
• Authors: Simmering, Jacob E.; Polgreen, Linnea A.; Campbell, David R.; Cavanaugh, Joseph E.; Polgreen, Philip M.
• Is Part Of: Infection control and hospital epidemiology. Volume 36:Number 9(2015)
• Page Start: 1031
• Page End: 1037
• Publication Date: 2015-06-15
• Subjects: Nosocomial infections -- Epidemiology -- Periodicals; Health facilities -- Sanitation -- Periodicals; Hospital buildings -- Sanitation -- Periodicals; Cross Infection -- Periodicals; Epidemiology -- Periodicals; Hospitals -- Periodicals; Infection Control -- Periodicals; 614.44
• DOI: 10.1017/ice.2015.130
• Languages: English
• ISSNs: 0899-823X

Abstract

<abstract abstract-type="normal"> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec id="abs1" sec-type="general"> <title>OBJECTIVE</title> <p>To determine the effect of interhospital patient sharing via transfers on the rate of <italic>Clostridium difficile</italic> infections in a hospital.</p> </sec> <sec id="abs2" sec-type="general"> <title>DESIGN</title> <p>Retrospective cohort.</p> </sec> <sec id="abs3" sec-type="methods"> <title>METHODS</title> <p>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 <italic>C. difficile</italic> 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.</p> </sec> <sec id="abs4" sec-type="results"> <title>RESULTS</title> <p>We found an increase of 1 in the log indegree was associated with a 4.8% increase in incidence of <italic>C. difficile</italic> infection (95% CI, 2.3%–7.4%) and an increase of 1 in log weighted indegree was associated with a 3.3% increase in <italic>C. difficile</italic> infection incidence (1.5%–5.2%). Moreover, including measures of connectivity in our models greatly improved their fit.</p> </sec> <sec id="abs5" sec-type="conclusions"> <title>CONCLUSIONS</title> <p>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.</p> <p> <italic>Infect. Control Hosp. Epidemiol.</italic> 2015;36(9):1031–1037</p> </sec> </abstract>