Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-19T01:11:12.169Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of Stochastic Epidemic Modeling to Quantify Transmission Rates of Colonization With Methicillin-Resistant Staphylococcus Aureus in an Intensive Care Unit

Published online by Cambridge University Press:  21 June 2016

Marie Forrester  and
Anthony N. Pettitt
Marie Forrester
Affiliation:
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
Anthony N. Pettitt*
Affiliation:
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
*
GPO Box 2434, Brisbane, Queensland, 4001, Australia.a.pettitt@qut.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

To consider statistical methods for estimating transmission rates for colonization of patients with methicillin-resistant Staphylococcus aureus (MRSA) in an intensive care unit (ICU) from three different sources: background contamination, non-isolated patients, and isolated patients.

Methods:

We developed statistical methods that allowed for the analysis of interval-censored, routine surveillance data and extended the general epidemic model for the flow of patients through the ICU.

Results:

Within this ICU, the rate of transmission to susceptible patients from a background source of MRSA (0.0092 case per day; 95% confidence interval [CI95], 0.0062–0.0126) is approximately double the rate of transmission from a non-isolated patient (0.0052 case per day; CI95, 0.0013–0.0096) and six times the rate of transmission from an isolated patient (0.0015 case per day; CI95, 0.0001–0.0043). We used the methodology to investigate whether transmission rates vary with workload.

Conclusion:

Our methodology has general application to infection by and transmission of pathogens in a hospital setting and is appropriate for quantifying the effect of infection control interventions. (Infect Control Hosp Epidemiol 2005;26:598-606)

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 26 , Issue 7 , July 2005 , pp. 598 - 606
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2005

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

1.Bischoff, W, Wallis, ML, Tucker, K, Reboussin, B, Sherertz, R. Staphylococcus aureus nasal carriage in a student community: prevalence, clonal relationships, and risk factors. Infect Control Hosp Epidemiol 2004;25:485–491.CrossRefGoogle Scholar
2.Lee, Y, Cesario, X, Pax, A, Tran, C, Ghouri, A, Thrupp, L.Nasal colonization by Staphylococcus aureus in active, independent community seniors. Age Ageing 1999;28:229–232.CrossRefGoogle ScholarPubMed
3.Ayliffe, GA. The progressive intercontinental spread of methicillin-resistant Staphylococcus aureus. Clin Infect Dis 1997;24(suppl 1):S74–S79.CrossRefGoogle ScholarPubMed
4.Baird, VL, Hawley, R. Methicillin-resistant Staphylococcus aureus (MRSA): is there a need to change clinical practice? Intensive Crit Care Nurs 2000;16:357–366.CrossRefGoogle Scholar
5.Chaix, C, Durand-Zaleski, I, Alberti, C, Brun-Buisson, C.Control of endemic methicillin-resistant Staphylococcus aureus: a cost-benefit analysis in an intensive care unit. JAMA 1999;282:1745–1751.CrossRefGoogle Scholar
6.McDonald, M. The epidemiology of methicillin-resistant Staphylococcus aureus: surgical relevance 20 years on. The Australian and New Zealand Journal of Surgery 1997;67:682–685.CrossRefGoogle Scholar
7.McCullogh, J. Risk management in infection control. Nurs Stand 1999; 13:44–46.CrossRefGoogle Scholar
8.Oliveira, DC, Tomasz, A, de Lencastre, H. Secrets of success of a human pathogen: molecular evolution of pandemic clones of methicillin-resistant Staphylococcus aureus. Lancet Infect Dis 2002;2:180–189.CrossRefGoogle Scholar
9.Goetz, A, Posey, K, Fleming, H, Jacobs, S. Methicillin-resistant Staphylococcus aureus in the community: a hospital-based study. Infect Control Hosp Epidemiol 1999;20:689–691.CrossRefGoogle ScholarPubMed
10.Bonten, MJM, Austin, DJ, Lipsitch, M. Understanding the spread of antibiotic resistant pathogens in hospitals: mathematical models as tools for control. Healthcare Epidemiology 2001;33:1739–1746.Google ScholarPubMed
11.Escolano, S, Golmard, JL, Korinek, AM, Mallet, A. A multi-state model for evolution of intensive care unit patients: prediction of nosocomial infections and deaths. Stat Med 2000;19:3465–3482.3.0.CO;2-6>CrossRefGoogle ScholarPubMed
12.Theaker, C, Ormond-Walshe, S, B Azadian, NS. MRSA in the critically ill. J Hosp Infect 2001;48:98–102.CrossRefGoogle ScholarPubMed
13.Sébille, V, Chevret, S, Valleron, AJ. Modeling the spread of resistant nosocomial pathogens in an intensive care unit. Infect Control Hosp Epidemiol 1997;18:84–92.CrossRefGoogle Scholar
14.Albert, RK, Condie, F. Hand-washing patterns in medical intensive-care units. N Engl J Med 1981;304:1465–1466.CrossRefGoogle ScholarPubMed
15.Doebbeling, B, Stanley, G, Sheetz, C, et al.Comparative efficacy of alternative hand-washing agents in reducing nosocomial infections in intensive care units. N Engl J Med 1992;327:88–93.CrossRefGoogle ScholarPubMed
16.Cooper, BS, Medley, GF, Scott, GM. Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects. J Hosp Infect 1999;43:131–147.CrossRefGoogle ScholarPubMed
17.Grundmann, H, Hori, S, Winter, B, Tami, A, Austin, DJ. Risk factors for the transmission of methicillin-resistant Staphylococcus aureus in an adult intensive care unit: fitting a model to the data. J Infect Dis 2002;185:481–488.CrossRefGoogle Scholar
18.Austin, DJ, Kakehashi, M, Anderson, RM. The transmission dynamics of antibiotic-resistant bacteria: the relationship between resistance in commensal organisms and antibiotic consumption. Proceedings of the Royal Society of London Series B 1997;264:1629–1638.CrossRefGoogle ScholarPubMed
19.Austin, DJ, Kristinsson, KG, Anderson, RM. The relationship between the volume of antimicrobial consumption in human communities and the frequency of resistance. Proc Natl Acad Sci USA 1999;96:1152–1156.CrossRefGoogle ScholarPubMed
20.Austin, DJ, Bonten, MJM, Weinstein, RA, Slaughter, S, Anderson, RM. Vancomycin-resistant enterococci in intensive-care hospital settings: transmission dynamics, persistence, and the impact of infection control programs. Proc Natl Acad Sci USA 1999:96:6908–6913.CrossRefGoogle ScholarPubMed
21.Lipsitch, M, Bergstrom, CT, Levin, BR. The epidemiology of antibiotic-resistance in hospitals: paradoxes and prescriptions. Proc Natl Acad Sci USA 2000;97:1938–1943.CrossRefGoogle ScholarPubMed
22.Jernigan, JA, Titus, MG, Gröschel, DHM, Getchell-White, SI, Farr, BM. Effectiveness of contact isolation during a hospital outbreak of methicillin-resistant Staphylococcus aureus. Am J Epidemiol 1996;143:496–504.CrossRefGoogle ScholarPubMed
23.Cooper, B, Lipsitch, M. The analysis of hospital infection data using hidden Markov models. Biostatistics 2004;5:223–237.CrossRefGoogle ScholarPubMed
24.Pelupessy, I, Bonten, MJM, Diekmann, O. How to access the relative importance of different colonization routes of pathogens within hospital settings. Proc Natl Acad Sci USA 2002;99:5601–5605.CrossRefGoogle Scholar
25.Bailey, NTJ. The Mathematical Theory of Infectious Diseases and Its Applications, ed. 2. London: Griffin; 1975.Google Scholar
26.de Jong, MCM, Diekmann, O, Heesterbeek, H. How does transmission of infection depend on population size? In: Mollison, D, ed. Epidemic Models: Their Structure and Relation to Data. Cambridge: Cambridge University Press; 1995:84–94.Google Scholar
27.Spiegelhalter, D, Best, N, Carlin, B, van der Linde, A. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B, Statistical Methodology 2002;64:583–639.CrossRefGoogle Scholar
28.Cowles, MK, Carlin, BP. Markov chain Monte Carlo convergence diagnostics: a comparative review. J Am Stat Assoc 1996;91:883–904.CrossRefGoogle Scholar
29.Best, NG, Cowles, MK, Vines, SK. CODA Manual Version 0.30. Cambridge: MRC Biostatistics Unit; 1995.Google Scholar
30.McCullagh, P, Neider, JA. Generalized Linear Models, ed. 2. London: Chapman and Hall; 1989.CrossRefGoogle Scholar
31.Boyce, JM. Are the epidemiology and microbiology of methicillin-resistant Staphylococcus aureus changing? JAMA 1998;279:623–624.CrossRefGoogle ScholarPubMed
32.Graffunder, EM, Venezia, RA. Risk factors associated with nosocomial methicillin-resistant Staphylococcus aureus (MRSA) infection including previous use of antimicrobials. J Antimicrob Chemother 2002;49:999–1005.CrossRefGoogle ScholarPubMed
33.Ismail, NA. Statistical Methods for the Improvement of Health Care [PhD thesis], Brisbane, Queensland, Australia: Mathematical Sciences, Queensland University of Technology; 1999.Google Scholar
34.Gibson, GJ, Renshaw, E. Likelihood estimation for stochastic compart-mental models using Markov chain methods. Statistics and Computing 2001;11:347–358.CrossRefGoogle Scholar
35.O'Neill, PD, Roberts, GO. Bayesian inference for partially observed stochastic epidemics. Journal of the Royal Statistical Society Series A, Statistics in Society 1999;162:121–129.CrossRefGoogle Scholar
36.Lindsey, JC, Ryan, LM. Tutorial in biostatistics: methods for interval-censored data. Stat Med 1998;17:219–238.3.0.CO;2-O>CrossRefGoogle ScholarPubMed
37.Becker, NG, Britton, T. Statistical studies of infectious disease incidence. Journal of the Royal Statistical Society Series B, Statistical Methodology 1999;66:287–307.CrossRefGoogle Scholar
38.Collett, D. Modelling Survival Data in Medical Research. London: Chapman and Hall; 1994.CrossRefGoogle Scholar
39.Becker, NG. Analysis of Infectious Diseas Data. London: Chapman and Hall; 1989.Google Scholar
40.Gelman, A, Carlin, JB, Stern, HS, Rubin, DB. Bayesian Data Analysis. Boca Raton, FL: Chapman and Hall/CRC; 2000.Google Scholar