Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-30T21:42:23.006Z Has data issue: false hasContentIssue false

Interindividual Contacts and Carriage of Methicillin-Resistant Staphylococcus aureus: A Nested Case-Control Study

Published online by Cambridge University Press:  20 April 2015

Thomas Obadia*
Affiliation:
Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75013, Paris, France INSERM, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75013, Paris, France
Lulla Opatowski
Affiliation:
INSERM, UMR 1181 “Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases” (B2PHI), F-75015 Paris, France Institut Pasteur, UMR 1181, B2PHI, F-75015 Paris, France Univ. Versailles St Quentin, UMR 1181, B2PHI, F-78180 Montigny-le-Bretonneux, France
Laura Temime
Affiliation:
Laboratoire MESuRS, Conservatoire National des Arts et Métiers, 75003, Paris, France
Jean-Louis Herrmann
Affiliation:
INSERM, U1173, UFR Simone Veil, Versailles-Saint-Quentin University, 78180 Saint-Quentin en Yvelines, France AP-HP, Hôpital Raymond Poincaré, Service de Microbiologie, F-92380, Garches, France
Éric Fleury
Affiliation:
ENS de Lyon, Université de Lyon, Laboratoire de l’Informatique du Parallélisme (UMR CNRS 5668–ENS de Lyon–UCB Lyon 1), IXXI Rhône Alpes Complex Systems Institute, France INRIA–Institut National de Recherche en Informatique et en Automatique, France
Pierre-Yves Boëlle
Affiliation:
Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75013, Paris, France INSERM, UMR_S 1136, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75013, Paris, France AP-HP, Hôpital Saint-Antoine, Département de Santé Publique, F-75571, Paris, France
Didier Guillemot
Affiliation:
INSERM, UMR 1181 “Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases” (B2PHI), F-75015 Paris, France Institut Pasteur, UMR 1181, B2PHI, F-75015 Paris, France Univ. Versailles St Quentin, UMR 1181, B2PHI, F-78180 Montigny-le-Bretonneux, France AP-HP, Raymond Poincare Hospital, F-92380 Garches, France
*
Address correspondence to Thomas Obadia, INSERM U1136, 27 rue Chaligny, 75571 Paris CEDEX 12, France ([email protected]).

Abstract

BACKGROUND

Reducing the spread of multidrug-resistant bacteria in hospitals remains a challenge. Current methods are screening of patients, isolation, and adherence to hygiene measures among healthcare workers (HCWs). More specific measures could rely on a better characterization of the contacts at risk of dissemination.

OBJECTIVE

To quantify how close-proximity interactions (CPIs) affected Staphylococcus aureus dissemination.

DESIGN

Nested case-control study.

SETTING

French long-term care facility in 2009.

PARTICIPANTS

Patients (n=329) and HCWs (n=261).

METHODS

We recorded CPIs using electronic devices together with S. aureus nasal carriage during 4 months in all participants. Cases consisted of patients showing incident S. aureus colonization and were paired to 8 control patients who did not exhibit incident colonization at the same date. Conditional logistic regression was used to quantify associations between incidence and exposure to demographic, network, and carriage covariables.

RESULTS

The local structure of contacts informed on methicillin-resistant S. aureus (MRSA) carriage acquisition: CPIs with more HCWs were associated with incident MRSA colonization in patients (odds ratio [OR], 1.10 [95% CI, 1.04–1.17] for 1 more HCW), as well as longer CPI durations (1.03 [1.01–1.06] for a 1-hour increase). Joint analysis of carriage and contacts showed increased carriage acquisition in case of CPI with another colonized individual (OR, 1.55 [1.14–2.11] for 1 more HCW). Global network measurements did not capture associations between contacts and carriage.

CONCLUSIONS

Electronically recorded CPIs inform on the risk of MRSA carriage, warranting more study of in-hospital contact networks to design targeted intervention strategies.

Infect. Control Hosp. Epidemiol. 2015;36(8):922–929

Type
Original Articles
Copyright
© 2015 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.)

Footnotes

*

E.F., P.-Y.B., and D.G. equally contributed to this work.

Members of the Individual-Based Investigation of Resistance Dissemination(i-Bird) Study Group are listed at the end of the text.

References

REFERENCES

1. Pittet, D, Allegranzi, B, Storr, J, Donaldson, L. “Clean Care Is Safer Care”: the Global Patient Safety Challenge 2005–2006. Int J Infect Dis 2006;10:419424.Google Scholar
2. Pittet, D, Allegranzi, B, Sax, H, et al. Evidence-based model for hand transmission during patient care and the role of improved practices. Lancet Infect Dis 2006;6:641652.Google Scholar
3. Derde, LPG, Cooper, BS, Goossens, H, et al. Interventions to reduce colonisation and transmission of antimicrobial-resistant bacteria in intensive care units: an interrupted time series study and cluster randomised trial. Lancet Infect Dis 2014;14:3139.CrossRefGoogle ScholarPubMed
4. Ueno, T, Masuda, N. Controlling nosocomial infection based on structure of hospital social networks. J Theor Biol 2008;254:655666.CrossRefGoogle ScholarPubMed
5. Temime, L, Opatowski, L, Pannet, Y, Brun-Buisson, C, Boëlle, PY, Guillemot, D. Peripatetic health-care workers as potential superspreaders. Proc Natl Acad Sci 2009;106:1842018425.CrossRefGoogle ScholarPubMed
6. Hornbeck, T, Naylor, D, Segre, AM, Thomas, G, Herman, T, Polgreen, PM. Using sensor networks to study the effect of peripatetic healthcare workers on the spread of hospital-associated infections. J Infect Dis 2012;206:15491557.CrossRefGoogle Scholar
7. Cooper, BS, Kypraios, T, Batra, R, Wyncoll, D, Tosas, O, Edgeworth, JD. Quantifying type-specific reproduction numbers for nosocomial pathogens: evidence for heightened transmission of an Asian sequence type 239 MRSA clone. PLOS Comput Biol 2012; 8. Available at: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3325179/. Accessed October 5, 2012.CrossRefGoogle ScholarPubMed
8. Köser, CU, Holden, MTG, Ellington, MJ, et al. Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak. N Engl J Med 2012;366:22672275.Google Scholar
9. Gardy, JL, Johnston, JC, Sui, SJH, et al. Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. N Engl J Med 2011;364:730739.CrossRefGoogle ScholarPubMed
10. Salathé, M, Kazandjieva, M, Lee, JW, Levis, P, Feldman, MW, Jones, JH. A high-resolution human contact network for infectious disease transmission. Proc Natl Acad Sci 2010;107:2202022025.Google Scholar
11. Lucet, J-C, Laouenan, C, Chelius, G, et al. Electronic sensors for assessing interactions between healthcare workers and patients under airborne precautions. PLOS ONE 2012;7:e37893.CrossRefGoogle ScholarPubMed
12. Smieszek, T, Barclay, VC, Seeni, I, et al. How should social mixing be measured: comparing web-based survey and sensor-based methods. BMC Infect Dis 2014;14:136.Google Scholar
13. Obadia, T, Silhol, R, Opatowski, L, et al. Detailed contact data and the dissemination of Staphylococcus aureus in hospitals. PLOS Comput Biol 2015;11:e1004170.Google Scholar
14. High, KP, Bradley, SF, Gravenstein, S, et al. Clinical practice guideline for the evaluation of fever and infection in older adult residents of long-term care facilities: 2008 update by the Infectious Diseases Society of America. Clin Infect Dis 2009;48:149171.CrossRefGoogle ScholarPubMed
15. Manzur, A, Gudiol, F. Methicillin-resistant Staphylococcus aureus in long-term-care facilities. Clin Microbiol Infect 2009;15:2630.Google Scholar
16. Bradley, SF, Terpenning, MS, Ramsey, MA, et al. Methicillin-resistant Staphylococcus aureus: colonization and infection in a long-term care facility. Ann Intern Med 1991;115:417422.CrossRefGoogle ScholarPubMed
17. Bilavsky, E, Lerman, Y, Rabinovich, A, et al. Carriage of methicillin-resistant Staphylococcus aureus on admission to European rehabilitation centres—a prospective study. Clin Microbiol Infect 2012;18:E164E169.CrossRefGoogle ScholarPubMed
18. Frénay, HME, Bunschoten, AE, Schouls, LM, et al. Molecular typing of methicillin-resistant Staphylococcus aureus on the basis of protein A gene polymorphism. Eur J Clin Microbiol Infect Dis 1996;15:6064.Google Scholar
19. Keith, RA, Granger, CV, Hamilton, BB, Sherwin, FS. The functional independence measure: a new tool for rehabilitation. Adv Clin Rehabil 1987;1:618.Google Scholar
20. Jaccard, P. The distribution of the flora in the Alpine zone. New Phytol 1912;11:3750.CrossRefGoogle Scholar
21. Albrich, WC, Harbarth, S. Health-care workers: source, vector, or victim of MRSA? Lancet Infect Dis 2008;8:289301.Google Scholar
22. Blok, HEM, Troelstra, A, Kamp‐Hopmans, TEM, et al. Role of healthcare workers in outbreaks of methicillin‐resistant Staphylococcus aureus: a 10‐year evaluation from a Dutch university hospital. Infect Control Hosp Epidemiol 2003;24:679685.CrossRefGoogle ScholarPubMed
23. Ben-David, D, Mermel, LA, Parenteau, S. Methicillin-resistant Staphylococcus aureus transmission: the possible importance of unrecognized health care worker carriage. Am J Infect Control 2008;36:9397.Google Scholar
24. Cookson, B, Peters, B, Webster, M, Phillips, I, Rahman, M, Noble, W. Staff carriage of epidemic methicillin-resistant Staphylococcus aureus . J Clin Microbiol 1989;27:14711476.Google Scholar
25. McBryde, ES, Pettitt, AN, McElwain, DLS. A stochastic mathematical model of methicillin resistant Staphylococcus aureus transmission in an intensive care unit: predicting the impact of interventions. J Theor Biol 2007;245:470481.Google Scholar
26. Meyers, LA, Pourbohloul, B, Newman, MEJ, Skowronski, DM, Brunham, RC. Network theory and SARS: predicting outbreak diversity. J Theor Biol 2005;232:7181.Google Scholar
27. Newman, MEJ. Spread of epidemic disease on networks. Phys Rev E 2002;66:016128.Google Scholar
28. Harris, AD, Samore, MH, Lipsitch, M, Kaye, KS, Perencevich, E, Carmeli, Y. Control-group selection importance in studies of antimicrobial resistance: examples applied to Pseudomonas aeruginosa, enterococci, and Escherichia coli . Clin Infect Dis 2002;34:15581563.Google Scholar
29. Pittet, D, Simon, A, Hugonnet, S, Pessoa-Silva, CL, Sauvan, V, Perneger, TV. Hand hygiene among physicians: performance, beliefs, and perceptions. Ann Intern Med 2004;141:18.Google Scholar
30. Eckmanns, T, Schwab, F, Bessert, J, et al. Hand rub consumption and hand hygiene compliance are not indicators of pathogen transmission in intensive care units. J Hosp Infect 2006;63:406411.Google Scholar
31. Boyce, JM, Pittet, D. Guideline for hand hygiene in health‐care settings: recommendations of the Healthcare Infection Control Practices Advisory Committee and the HICPAC/SHEA/APIC/IDSA Hand Hygiene Task Force. Infect Control Hosp Epidemiol 2002;23:S3S40.Google Scholar
32. Eveillard, M, Hitoto, H, Raymond, F, et al. Measurement and interpretation of hand hygiene compliance rates: importance of monitoring entire care episodes. J Hosp Infect 2009;72:211217.Google Scholar
33. Borg, MA, Benbachir, M, Cookson, BD, et al. Self‐protection as a driver for hand hygiene among healthcare workers. Infect Control Hosp Epidemiol 2009;30:578580.CrossRefGoogle ScholarPubMed
34. Lau, T, Tang, G, Mak, K, Leung, G. Moment-specific compliance with hand hygiene. Clin Teach 2014;11:159164.Google Scholar
35. Lee, BY, Bartsch, SM, Wong, KF, et al. The importance of nursing homes in the spread of methicillin-resistant Staphylococcus aureus (MRSA) among hospitals. Med Care 2013;51:205215.Google Scholar
Supplementary material: File

Obadia supplementary material S1

Obadia supplementary material

Download Obadia supplementary material S1(File)
File 897 KB