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.

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.

Figure 1

Map of hospitals (dots) and transfers (dark lines) in a force layout projection of the CA data. 3 major clusters in the CA network, colored separately (San Diego in orange, LA in purple and San Francisco and Northern CA in green) , were detected using network modularity clustering. This graph shows how tightly connected hospitals in CA are.

Figure 2

Map of hospitals (dots) and transfers (dark lines) on a map of CA. The same connections from Figure 1 are projected onto a map of CA. Major clusters are colored separately (San Diego in orange, LA in purple and San Francisco and Northern CA in green), and the map shows how transfers create close connect hospitals, despite geographic distance.

Figure 3

Increase in expected CDI incidence with a one unit increase in indegree (top) or weighted indegree (bottom), adjusted for the observed hospital characteristics. The x-axis represents the initial indegree/weighted indegree and the y-axis represents the increase in the number of expected cases of CDI per quarter for that hospital. The value is the difference between the predicted number at the starting value of indegree/weighted indegree and the predicted number of cases at 1 plus the starting value.