509. Spatio-Temporal Clustering of CDI Cases at the University of Iowa Hospitals and Clinics
Session: Poster Abstract Session: Healthcare Epidemiology: Updates in C. difficile
Thursday, October 4, 2018
Room: S Poster Hall

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.


We analyzed 1,804 CDI cases (out of 241,248 in-patient visits) at the Univ. of Iowa

Hosp. and Clinics (UIHC) during Jan 2005-Dec 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 [1-2005, 12-2011] 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 Dec 2009, when the C. diff Toxin A&B test

was replaced by the C. diff 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.


We report sizes of connected components in the table below, for 10,000 trials of Ex-

periments 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.




Mean Comp. Size

Experiment 1

Expected Mean Comp. Size (p-value)

Experiment 2

Expected Mean Comp. Size (p-value)

Observed Max. Comp. Size

Experiment 1

Expected Max. Comp. Size (p-value)

Experiment 2

Expected Max. Comp Size (p-value)



1.07 (0)

1.06 (0)


3.72 (0.01)

4.07 (0.05)



1.11 (0)

1.11 (0)


4.61 (0.03)

5.17 (0.1)



1.18 (0)

1.20 (0)


6.6 (0.01)

8.10 (0.1)



1.68 (0)

1.52 (0)


17.13 (0.02)

28.08 (0.4)



1.38 (0)

1.01 (0)


9.74 (0)

12.87 (0.01)


A close up of a map
Description generated with very high confidence


A screenshot of a cell phone
Description generated with very high confidence 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.

Shreyas Pai, B.Tech1, Sriram Pemmaraju, PhD2, Philip M. Polgreen, MD3,4, Alberto Maria Segre, PhD5,6 and Daniel Sewell, PhD4,7, (1)Computer Science, The University of Iowa, Iowa City, IA, (2)Computer Science, The University of Iowa/CDC MInD-Healthcare, Iowa City, IA, (3)Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, (4)CDC MInD-Healthcare, Iowa City, IA, (5)University of Iowa, Iowa City, IA, (6)CDC MInD-Healthcare, Iowa CIty, IA, (7)Biostatistics, University of Iowa, Iowa City, IA


S. Pai, None

S. Pemmaraju, None

P. M. Polgreen, None

A. M. Segre, None

D. Sewell, None

Findings in the abstracts are embargoed until 12:01 a.m. PDT, Wednesday Oct. 3rd with the exception of research findings presented at the IDWeek press conferences.