title: Hospital Transfer Network Structure as a Risk Factor for Clostridium difficile Infection.
abstract: OBJECTIVE
  To determine the effect of interhospital patient sharing via transfers on the rate of Clostridium difficile infections in a hospital.
  
  
  METHODS
  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.
authors.affiliation: 1Department of Pharmacy Practice and Science,University of Iowa,Iowa City,Iowa.
authors.forename: Jacob E
authors.initials: JE
authors.lastname: Simmering
authors.affiliation: 1Department of Pharmacy Practice and Science,University of Iowa,Iowa City,Iowa.
authors.forename: Linnea A
authors.initials: LA
authors.lastname: Polgreen
authors.affiliation: 2Department of Computer Science,University of Iowa,Iowa City,Iowa.
authors.forename: David R
authors.initials: DR
authors.lastname: Campbell
authors.affiliation: 3Department of Biostatistics,University of Iowa,Iowa City,Iowa.
authors.forename: Joseph E
authors.initials: JE
authors.lastname: Cavanaugh
authors.affiliation: 4Departments of Internal Medicine and Epidemiology,University of Iowa,Iowa City,Iowa.
authors.forename: Philip M
authors.initials: PM
authors.lastname: Polgreen
mesh_terms: D000367:Age Factors; D000368:Aged; D002140:California / Q000453:epidemiology; D016360:Clostridioides difficile; D003428:Cross Infection / Q000453:epidemiology / Q000517:prevention & control; D004761:Enterocolitis, Pseudomembranous / Q000453:epidemiology* / Q000517:prevention & control; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D007902:Length of Stay / Q000706:statistics & numerical data; D008962:Models, Theoretical; D010360:Patient Transfer / Q000458:organization & administration / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D012307:Risk Factors