Effects of control interventions on Clostridium difficile infection in England: an observational study

Kate E. Dingle*, Xavier Didelot, T. Phuong Quan, David W. Eyre, Nicole Stoesser, Tanya Golubchik, Rosalind M. Harding, Daniel J. Wilson, David Griffiths, Alison Vaughan, John M. Finney, David H. Wyllie, Sarah J. Oakley, Warren N. Fawley, Jane Freeman, Kirsti Morris, Jessica Martin, Philip Howard, Sherwood Gorbach, Ellie J.C. GoldsteinDiane M. Citron, Susan Hopkins, Russell Hope, Alan Johnson, Mark H. Wilcox, Timothy E.A. Peto, A. Sarah Walker, Derrick W. Crook, Carlos Del Ojo Elias, Charles Crichton, Vasiliki Kostiou, Adam Giess, Jim Davies

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Background The control of Clostridium difficile infections is an international clinical challenge. The incidence of C difficile in England declined by roughly 80% after 2006, following the implementation of national control policies; we tested two hypotheses to investigate their role in this decline. First, if C difficile infection declines in England were driven by reductions in use of particular antibiotics, then incidence of C difficile infections caused by resistant isolates should decline faster than that caused by susceptible isolates across multiple genotypes. Second, if C difficile infection declines were driven by improvements in hospital infection control, then transmitted (secondary) cases should decline regardless of susceptibility. Methods Regional (Oxfordshire and Leeds, UK) and national data for the incidence of C difficile infections and antimicrobial prescribing data (1998–2014) were combined with whole genome sequences from 4045 national and international C difficile isolates. Genotype (multilocus sequence type) and fluoroquinolone susceptibility were determined from whole genome sequences. The incidence of C difficile infections caused by fluoroquinolone-resistant and fluoroquinolone-susceptible isolates was estimated with negative-binomial regression, overall and per genotype. Selection and transmission were investigated with phylogenetic analyses. Findings National fluoroquinolone and cephalosporin prescribing correlated highly with incidence of C difficile infections (cross-correlations >0·88), by contrast with total antibiotic prescribing (cross-correlations <0·59). Regionally, C difficile decline was driven by elimination of fluoroquinolone-resistant isolates (approximately 67% of Oxfordshire infections in September, 2006, falling to approximately 3% in February, 2013; annual incidence rate ratio 0·52, 95% CI 0·48–0·56 vs fluoroquinolone-susceptible isolates: 1·02, 0·97–1·08). C difficile infections caused by fluoroquinolone-resistant isolates declined in four distinct genotypes (p<0·01). The regions of phylogenies containing fluoroquinolone-resistant isolates were short-branched and geographically structured, consistent with selection and rapid transmission. The importance of fluoroquinolone restriction over infection control was shown by significant declines in inferred secondary (transmitted) cases caused by fluoroquinolone-resistant isolates with or without hospital contact (p<0·0001) versus no change in either group of cases caused by fluoroquinolone-susceptible isolates (p>0·2). Interpretation Restricting fluoroquinolone prescribing appears to explain the decline in incidence of C difficile infections, above other measures, in Oxfordshire and Leeds, England. Antimicrobial stewardship should be a central component of C difficile infection control programmes. Funding UK Clinical Research Collaboration (Medical Research Council, Wellcome Trust, National Institute for Health Research); NIHR Oxford Biomedical Research Centre; NIHR Health Protection Research Unit on Healthcare Associated Infection and Antimicrobial Resistance (Oxford University in partnership with Public Health England [PHE]), and on Modelling Methodology (Imperial College, London in partnership with PHE); and the Health Innovation Challenge Fund.

Original languageEnglish
Pages (from-to)411-421
Number of pages11
JournalThe Lancet Infectious Diseases
Volume17
Issue number4
DOIs
Publication statusPublished - 1 Apr 2017

Bibliographical note

Funding Information:
This study was supported by the UK Clinical Research Collaboration (Wellcome Trust [grant 087646/Z/08/Z], Medical Research Council, National Institute for Health Research [NIHR grant G0800778]), NIHR Biomedical Research Centre, NIHR Oxford Health Protection Research Units on Healthcare Associated Infection and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (grant HPRU-2012-10041), NIHR Health Protection Unit in Modelling at Imperial College London in partnership with Public Health England (grant HPRU-2012-10080), and the Health Innovation Challenge Fund (a parallel funding partnership between the Wellcome Trust [grant WT098615/Z/12/Z] and the Department of Health [grants WT098615 and HICF-T5-358]). The authors acknowledge the research collaboration of IMS Health, within the HPRU grant HPRU-2012-10041. The funders had no role in the writing of the manuscript or the decision to submit it for publication. DWC and TEAP are NIHR senior investigators. DJW is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant 101237/Z/13/Z). The authors acknowledge the contribution of the Modernising Medical Microbiology Informatics Group comprising Carlos Del Ojo Elias, Charles Crichton, Vasiliki Kostiou, and Adam Giess (Nuffield Department of Clinical Medicine, University of Oxford, UK), and Jim Davies, (Department of Computer Science, University of Oxford, UK). This report presents independent research funded by the NlHR, Wellcome Trust, and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, Wellcome Trust, the Department of Health, or Public Health England.

Funding Information:
This study was supported by the UK Clinical Research Collaboration ( Wellcome Trust [grant 087646/Z/08/Z ], Medical Research Council, National Institute for Health Research [ NIHR grant G0800778] ), NIHR Biomedical Research Centre, NIHR Oxford Health Protection Research Units on Healthcare Associated Infection and Antimicrobial Resistance at University of Oxford in partnership with Public Health England (grant HPRU-2012-10041), NIHR Health Protection Unit in Modelling at Imperial College London in partnership with Public Health England ( grant HPRU-2012-10080 ), and the Health Innovation Challenge Fund (a parallel funding partnership between the Wellcome Trust [ grant WT098615/Z/12/Z ] and the Department of Health [ grants WT098615 and HICF-T5-358 ]). The authors acknowledge the research collaboration of IMS Health, within the HPRU grant HPRU-2012-10041. The funders had no role in the writing of the manuscript or the decision to submit it for publication. DWC and TEAP are NIHR senior investigators. DJW is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant 101237/Z/13/Z). The authors acknowledge the contribution of the Modernising Medical Microbiology Informatics Group comprising Carlos Del Ojo Elias, Charles Crichton, Vasiliki Kostiou, and Adam Giess (Nuffield Department of Clinical Medicine, University of Oxford, UK), and Jim Davies, (Department of Computer Science, University of Oxford, UK). This report presents independent research funded by the NlHR, Wellcome Trust, and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, Wellcome Trust, the Department of Health, or Public Health England.

Publisher Copyright:
© 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license

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