509. Spatio-Temporal Clustering of CDI Cases at the University of Iowa Hospitals and Clinics. (26th November 2018)

• Record Type: Journal Article
• Authors: Pai, Shreyas; Pemmaraju, Sriram; Polgreen, Philip M; Segre, Alberto Maria; Sewell, Daniel
• Is Part Of: Open forum infectious diseases. Volume 5(2018)Supplement 1
• Page Start: S188
• Page End: S189
• Publication Date: 2018-11-26
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofy210.518
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Understanding how C. diffcile infection (CDI) is acquired in healthcare settings is key to designing interventions to mitigate CDI. The goal of this research is apply statistical methods, typically used to investigate regional outbreaks, to study spatio-temporal clustering of in-hospital CDI incidence. Methods: We analyzed 1, 804 CDI cases (out of 241, 248 in-patient visits) at the University of Iowa Hospitals and Clinics (UIHC) during January 2005–December 2011. Letting T and D be time and space parameters, we constructed an observed CDI cluster graph by connecting pairs of CDI cases whose positive CDI tests occur within T days and D distance units of each other. In Experiment 1, for each CDI case, we replaced its actual time stamp by one picked uniformly at random from the time interval [January 2005, December 2011] and constructed a random CDI cluster graph. We tested the UIHC CDI case counts for seasonality and observed none, but did observe that the CDI counts increased significantly (weekly mean: 4.12–>8.11) starting in December 2009, when the C. diffcile Toxin A&B test was replaced by the C. diffcile Toxin PCR. So, we performed an Experiment 2 in which we sampled time stamps from a mixture of two uniform distributions, representing the periods of the two tests. Results: We report sizes of connected components in the table below, for 10, 000 trials of Experiments 1 and 2, for T = 14 days and varying D, a one setting in which D is set to the unit in which the CDI case occurs. The plots show the distribution of the mean and maximum component size (blue curves) for Experiment 2, for D = 2. Conclusion: Our analysis of the UIHC CDI cases shows significant spatio-temporal clustering in the observed CDI cluster graph. These results suggest that direct or environmental transmission may play a significant role in CDI acquisition at the UIHC. Funded by the CDC MInD-Healthcare. Disclosures: All authors: No reported disclosures.