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Impact of molecular testing on reported Clostridoides difficile infection rates

Published online by Cambridge University Press:  19 December 2019

Iulian Ilieş ,
James C. Benneyan Open the ORCID record for James C. Benneyan [Opens in a new window] ,
Tiago Barbieri Couto Jabur ,
Arthur W. Baker Open the ORCID record for Arthur W. Baker [Opens in a new window]  and
Deverick J. Anderson Open the ORCID record for Deverick J. Anderson [Opens in a new window]
Iulian Ilieş
Affiliation:
Healthcare Systems Engineering Institute, Northeastern University, Boston, Massachusetts
James C. Benneyan*
Affiliation:
Healthcare Systems Engineering Institute, Northeastern University, Boston, Massachusetts College of Engineering, Northeastern University, Boston, Massachusetts
Tiago Barbieri Couto Jabur
Affiliation:
Healthcare Systems Engineering Institute, Northeastern University, Boston, Massachusetts
Arthur W. Baker
Affiliation:
Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, North Carolina Division of Infectious Diseases, Duke University School of Medicine, Durham, North Carolina
Deverick J. Anderson
Affiliation:
Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, North Carolina Division of Infectious Diseases, Duke University School of Medicine, Durham, North Carolina
*
Author for correspondence: James C. Benneyan PhD, Healthcare Systems Engineering Institute, Northeastern University, 360 Huntington Avenue, Boston MA 02115, USA. E-mail: j.benneyan@northeastern.edu
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Abstract

Background:

The reported incidence of Clostridoides difficile infection (CDI) has increased in recent years, partly due to broadening adoption of nucleic acid amplification tests (NAATs) replacing enzyme immunoassay (EIA) methods. Our aim was to quantify the impact of this switch on reported CDI rates using a large, multihospital, empirical dataset.

Methods:

We analyzed 9 years of retrospective CDI data (2009–2017) from 47 hospitals in the southeastern United States; 37 hospitals switched to NAAT during this period, including 24 with sufficient pre- and post-switch data for statistical analyses. Poisson regression was used to quantify the NAAT-over-EIA incidence rate ratio (IRR) at hospital and network levels while controlling for longitudinal trends, the proportion of intensive care unit patient days, changes in surveillance methodology, and previously detected infection cluster periods. We additionally used change-point detection methods to identify shifts in the mean and/or slope of hospital-level CDI rates, and we compared results to recorded switch dates.

Results:

For hospitals that transitioned to NAAT, average unadjusted CDI rates increased substantially after the test switch from 10.9 to 23.9 per 10,000 patient days. Individual hospital IRRs ranged from 0.75 to 5.47, with a network-wide IRR of 1.75 (95% confidence interval, 1.62–1.89). Reported CDI rates significantly changed 1.6 months on average after switching to NAAT testing (standard deviation, 1.9 months).

Conclusion:

Hospitals that switched from EIA to NAAT testing experienced an average postswitch increase of 75% in reported CDI rates after adjusting for other factors, and this increase was often gradual or delayed.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 3 , March 2020 , pp. 306 - 312
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved.

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