Hostname: page-component-76fb5796d-r6qrq Total loading time: 0 Render date: 2024-04-30T00:20:23.772Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

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.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Freeman, J, Bauer, MP, Baines, SD, et al.The changing epidemiology of Clostridium difficile infections. Clin Microbiol Rev 2010;23:529–549.CrossRefGoogle ScholarPubMed
Reveles, KR, Lee, GC, Boyd, NK, Frei, CR.The rise in Clostridium difficile infection incidence among hospitalized adults in the United States: 2001–2010. Am J Infect Control 2014;42:1028–1032.CrossRefGoogle ScholarPubMed
Miller, BA, Chen, LF, Sexton, DJ, Anderson, DJ.Comparison of the burdens of hospital-onset, healthcare-facility–associated Clostridium difficile infection and of healthcare-associated infection due to methicillin-resistant Staphylococcus aureus in community hospitals. Infect Control Hosp Epidemiol 2011;32:387–390.CrossRefGoogle ScholarPubMed
Lessa, FC, Mu, Y, Bamberg, WM, et al.Burden of Clostridium difficile infection in the United States. N Engl J Med 2015;372:825–834.CrossRefGoogle ScholarPubMed
Martin, JSH, Monaghan, TM, Wilcox, MH.Clostridium difficile infection: epidemiology, diagnosis and understanding transmission. Nat Revs Gastroenterol Hepatol 2016;13:206.CrossRefGoogle ScholarPubMed
Postma, N, Kiers, D, Pickkers, P.The challenge of Clostridium difficile infection: overview of clinical manifestations, diagnostic tools and therapeutic options. Int J Antimicrob Agents 2015;46:S47–S50.CrossRefGoogle ScholarPubMed
Dubberke, ER, Reske, KA, Olsen, MA, McDonald, LC, Fraser, VJ.Short- and long-term attributable costs of Clostridium difficile–associated disease in nonsurgical inpatients. Clin Infect Dis 2008;46:497–504.CrossRefGoogle Scholar
Zimlichman, E, Henderson, D, Tamir, O, et al.Healthcare-associated infections: a meta-analysis of costs and financial impact on the US health care system. JAMA Intern Med 2013;173:2039–2046.CrossRefGoogle ScholarPubMed
Hospital-acquired condition (HAC) reduction program. Centers for Medicare and Medicaid Services website. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/HAC-Reduction-Program.html. Accessed November 20, 2018.Google Scholar
Humphries, RM, Uslan, DZ, Rubin, Z.Performance of Clostridium difficile toxin enzyme immunoassay and nucleic acid amplification tests stratified by patient disease severity. J Clin Microbiol 2013;51:869–873.CrossRefGoogle ScholarPubMed
Swindells, J, Brenwald, N, Reading, N, Oppenheim, B.Evaluation of diagnostic tests for Clostridium difficile infection. J Clin Microbiol 2010;48:606.CrossRefGoogle ScholarPubMed
Carroll, KC.Tests for the diagnosis of Clostridium difficile infection: the next generation. Anaerobe 2011;17:170–174.CrossRefGoogle Scholar
Chapin, KC, Dickenson, RA, Wu, F, Andrea, SB.Comparison of five assays for detection of Clostridium difficile toxin. J Molec Diagn 2011;13:395–400.CrossRefGoogle ScholarPubMed
Selvaraju, SB, Gripka, M, Estes, K, Nguyen, A, Jackson, MA, Selvarangan, R.Detection of toxigenic Clostridium difficile in pediatric stool samples: an evaluation of Quik Check Complete Antigen assay, BD GeneOhm Cdiff PCR, and ProGastro Cd PCR assays. Diagn Microbiol Infect Dis 2011;71:224–229.CrossRefGoogle ScholarPubMed
Loo, VG, Bourgault, A-M, Poirier, L, et al.Host and pathogen factors for Clostridium difficile infection and colonization. N Engl J Med 2011;365:1693–1703.CrossRefGoogle ScholarPubMed
McDonald, LC, Killgore, GE, Thompson, A, et al.An epidemic, toxin gene–variant strain of Clostridium difficile. N Engl J Med 2005;353:2433–2441.CrossRefGoogle ScholarPubMed
Warny, M, Pepin, J, Fang, A, et al.Toxin production by an emerging strain of Clostridium difficile associated with outbreaks of severe disease in North America and Europe. Lancet 2005;366:1079–1084.CrossRefGoogle ScholarPubMed
Moehring, RW, Lofgren, ET, Anderson, DJ.Impact of change to molecular testing for Clostridium difficile infection on healthcare facility–associated incidence rates. Infect Control Hosp Epidemiol 2013;34:1055–1061.CrossRefGoogle ScholarPubMed
Polage, CR, Gyorke, CE, Kennedy, MA, et al.Overdiagnosis of Clostridium difficile infection in the molecular test era. JAMA Intern Med 2015;175:1792–1801.CrossRefGoogle ScholarPubMed
McDonald, LC, Gerding, DN, Johnson, S, et al.Clinical practice guidelines for Clostridium difficile infection in adults and children: 2017 update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA). Clin Infect Dis 2018;66:987–994.CrossRefGoogle Scholar
Hinkley, DV.Inference about the change-point in a sequence of random variables. Biometrika 1970;57:1–17.CrossRefGoogle Scholar
Kander, Z, Zacks, S.Test procedures for possible changes in parameters of statistical distributions occurring at unknown time points. Ann Math Statist 1966;37:1196–1210.CrossRefGoogle Scholar
Page, ES.On problems in which a change in a parameter occurs at an unknown point. Biometrika 1957;44:248–252.CrossRefGoogle Scholar
Picard, D.Testing and estimating change-points in time series. Adv Appl Prob 1985;17:841–867.CrossRefGoogle Scholar
Anderson, DJ, Miller, BA, Chen, LF, et al.The network approach for prevention of healthcare-associated infections: long-term effect of participation in the Duke Infection Control Outreach Network. Infect Control Hosp Epidemiol 2011;32:315–322.CrossRefGoogle Scholar
Durkin, MJ, Baker, AW, Dicks, KV, et al.A comparison between National Healthcare Safety Network laboratory-identified event reporting versus traditional surveillance for Clostridium difficile infection. Infect Control Hosp Epidemiol 2015;36:125–131.CrossRefGoogle ScholarPubMed
Campbell, DT, Stanley, JC.Experimental and quasi-experimental designs for research. In: Gage, NL, ed. Handbook of Research on Teaching. Chicago: Rand McNally; 1963:171–246.Google Scholar
McDowall, D, McCleary, R, Meidinger, EE, Hay, RA Jr. Interrupted time-series analysis. Thousand Oaks, CA: Sage; 1980.CrossRefGoogle Scholar
Cook, TD, Campbell, DT.Quasi-experimentation: Design and Analysis Issues for Field Settings. Boston, MA: Houghton Mifflin; 1979.Google Scholar
Quandt, RE.The estimation of the parameters of a linear regression system obeying two separate regimes. J Am Statist Assoc 1958;53:873–880.CrossRefGoogle Scholar
Quandt, RE.Tests of the hypothesis that a linear regression system obeys two separate regimes. J Am Statist Assoc 1960;55:324–330.CrossRefGoogle Scholar
Page, ES.A test for a change in a parameter occurring at an unknown point. Biometrika 1955;42:523–527.CrossRefGoogle Scholar
Hsu, DA.Tests for variance shift at an unknown time point. J Roy Statist Soc C (Appl Statist) 1977;26:279–284.Google Scholar
Wichern, DW, Miller, RB, Hsu, D-A.Changes of variance in first-order autoregressive time series models—with an application. J Roy Statist Soc C (Appl Statist) 1976;25:248–256.Google Scholar
Horváth, L.The maximum likelihood method for testing changes in the parameters of normal observations. Ann Statist 1993;21:671–680.CrossRefGoogle Scholar
Horváth, L.Detecting changes in linear regressions. Statistics 1995;26:189–208.CrossRefGoogle Scholar
Longtin, Y, Trottier, S, Brochu, G, et al.Impact of the type of diagnostic assay on Clostridium difficile infection and complication rates in a mandatory reporting program. Clin Infect Dis 2013;56:67–73.CrossRefGoogle Scholar
Dudeck, MA, Weiner, LM, Malpiedi, P, Edwards, J, Peterson, K, Sievert, D.Risk Adjustment for Healthcare Facility-Onset C. difficile and MRSA Bacteremia Laboratory-Identified Event Reporting in NHSN. Atlanta, GA: Centers for Disease Control and Prevention; 2013.Google Scholar
Goldenberg, SD, Price, NM, Tucker, D, Wade, P, French, GL.Mandatory reporting and improvements in diagnosing Clostridium difficile infection: an incompatible dichotomy? J Infect 2011;62:363–370.CrossRefGoogle ScholarPubMed
Texier, G, Farouh, M, Pellegrin, L, et al.Outbreak definition by change point analysis: a tool for public health decision? BMC Med Info Dec Making 2016;16:33.CrossRefGoogle ScholarPubMed