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Use of Strain Typing Data to Estimate Bacterial Transmission Rates in Healthcare Settings

Published online by Cambridge University Press:  21 June 2016

Brian R. Jackson ,
Alun Thomas ,
Karen C. Carroll ,
Frederick R. Adler  and
Matthew H. Samore
Brian R. Jackson*
Affiliation:
Department of Medical Informatics, Salt Lake City, Utah
Alun Thomas
Affiliation:
Department of Medical Informatics and Center for High Performance Computing, Salt Lake City, Utah
Karen C. Carroll
Affiliation:
Department of Pathology, University of Utah; and ARUP Research Institute, Salt Lake City, Utah
Frederick R. Adler
Affiliation:
Departments of Mathematics and Biology, Salt Lake City, Utah
Matthew H. Samore
Affiliation:
Veteran's Administration Health Care and the Departments of Medical Informatics and Internal Medicine, University of Utah, Salt Lake City, Utah
*
ARUP Research Institute, 500 Chipeta Way, Salt Lake City, UT 84108
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Abstract

Objective:

To create an affordable and accurate method for continuously monitoring bacterial transmission rates in healthcare settings.

Design:

We present a discrete simulation model that relies on the relationship between in-hospital transmission rates and strain diversity. We also present a proof of concept application of this model to a prospective molecular epidemiology data set to estimate transmission rates for Pseudomonas aeruginosa and Staphylococcus aureus.

Setting:

Inpatient units of an academic referral center.

Patients:

All inpatients with nosocomial infections.

Intervention:

Mathematical model to estimate transmission rates.

Results:

Maximum likelihood estimates for transmission rates of these two species on different hospital units ranged from 0 to 0.36 transmission event per colonized patient per day.

Conclusions:

This approach is feasible, although estimates of transmission rates based solely on strain typed clinical cultures may be too imprecise for routine use in infection control. A modest level of surveillance sampling substantially improves the estimation accuracy. (Infect Control Hosp Epidemiol 2005;26:638-645)

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 26 , Issue 7 , July 2005 , pp. 638 - 645
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2005

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References

1.Chetchotisakd, P, Phelps, CL, Hartstein, AI. Assessment of bacterial cross-transmission as a cause of infections in patients in intensive care units. Clin Infect Dis 1994;18:929–937.Google Scholar
2.Bischoff, WE, Reynolds, TM, Hall, GO, Wenzel, RP, Edmond, MB. Molecular epidemiology of vancomycin-resistant Enterococcus faecium in a large urban hospital over a 5-year period. J Clin Microbiol 1999;37:3912–3916.Google Scholar
3.Almuneef, MA, Baltimore, RS, Farrel, PA, Reagan-Cirincione, R, Dembry, LM. Molecular typing demonstrating transmission of gram-negative rods in a neonatal intensive care unit in the absence of a recognized epidemic. Clin Infect Dis 2001;32:220–227.Google Scholar
4.Samore, MH, Bettin, KM, DeGirolami, PC, et al.Wide diversity of Clostridium difficile types at a tertiary referral hospital. J Infect Dis 1994;170:615–621.Google Scholar
5.Bauer, TM, Ofner, E, Just, HM, Daschner, FD. An epidemiologic study assessing the relative importance of airborne and direct contact transmission of microorganisms in a medical intensive care unit. J Hosp Infect 1990;15:301–309.CrossRefGoogle Scholar
6.Thuong, M, Arvaniti, K, Ruimy, R, et al.Epidemiology of Pseudomonas aeruginosa and risk factors for carriage acquisition in an intensive care unit. J Hosp Infect 2003;53:274–282.CrossRefGoogle Scholar
7.Cespedes, C, Said-Salim, B, Miller, M, et al.The clonality of Staphylococcus aureus nasal carriage. J Infect Dis 2005;191:444–452.Google Scholar
8.Peet, RK. The measurement of species diversity. Annual Review of Ecology and Systematics 1974;5:285–307.Google Scholar
9.Jernigan, JA, Pullen, AL, Flowers, L, Bell, M, Jarvis, WR. Prevalence of and risk factors for colonization with methicillin-resistant Staphylococcus aureus at the time of hospital admission. Infect Control Hosp Epidemiol 2003;24:409–414.CrossRefGoogle ScholarPubMed
10.Bonten, MJM, Bergmanns, DCJJ, Speijer, H, Stobberingh, EE. Characteristics of polyclonal endemicity of Pseudomonas aeruginosa colonization in intensive care units. Am J Respir Crit Care Med 1999;160:1212–1219.Google Scholar
11.Cinlar, E. Introduction to Stochastic Processes. Englewood Cliffs, NJ: Prentice-Hall; 1975:152.Google Scholar
12.Marjoram, P, Molitor, J, Plagnol, V, Tavare, S. Markov chain Monte Carlo without likelihoods. Proc Natl Acad Sci USA 2003; 100:15324–15328.Google Scholar
13.Fu, YX, Li, WH. Estimating the age of a common ancestor of a sample of DNA sequences. Mol Biol Evol 1997;14:195–199.Google Scholar
14.Weiss, G, von Haeseler, A. Inference of population history using a likelihood approach. Genetics 1998;149:1539–1546.Google Scholar
15.Pritchard, JK, Seielstad, MT, Perez-Lezaun, A, Feldman, MW. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. Mol Biol Evol 1999;16:1791–1798.Google Scholar
16.Hudson, DJ. Interval estimation from the likelihood function. Journal of the Royal Statistical Society 1971;33:256–262.Google Scholar
17.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
18.Sebille, V, Valleron, A-J.A computer simulation model for the spread of nosocomial infections caused by multidrug-resistant pathogens. Computers and Biomedical Research 1997;30:307–322.Google Scholar
19.Austin, DJ, Bonten, MJM, Weinstein, RA, Slaughter, S, Anderson, RM. Vancomycin-resistant enterococci in intensive-care hospital settings: transmission dynamics, persistence, and the impact on infection-control programs. Proc Natl Acad Sci USA 1999;96:6908–6913.Google Scholar
20.Pelupessy, I, Bonten, MJM, Diekmann, O. How to assess the relative importance of different colonization routes of pathogens within hospital settings. Proc Natl Acad Sci USA 2002;99:5601–5605.Google Scholar
21.Cooper, B, Lipsitch, M. The analysis of hospital infection data using hidden Markov models. Biostatistics 2004;5:223–237.Google Scholar
22.Anderson, RM, May, RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press; 1991.Google Scholar
23.Hilborn, R, Mangel, M. The Ecological Detective: Confronting Models With Data. Princeton, NJ: Princeton University Press; 1997:153–160.Google Scholar
24.Diekema, DJ, Pfaller, MA, Turnidge, J, et al.Genetic relatedness of multidrug-resistant, methicillin (oxacillin)-resistant Staphylococcus aureus bloodstream isolates from SENTRY Antimicrobial Resistance Surveillance Centers worldwide, 1998. Microb Drug Resist 2000;6:213–221.Google Scholar
25.Auranen, K, Arjas, E, Leino, T, Takala, AK. Transmission of pneumococcal carriage in families: a latent Markov process model for binary longitudinal data. Journal of the American Statistical Association 2000;95:1044–1053.Google Scholar
26.O'Neill, PD, Balding, DJ, Becker, NG, Eerola, M, Mollison, D. Analyses of infectious disease data from household outbreaks by Markov chain Monte Carlo methods. Applied Statistics 2000;49(part 4):517–542.Google Scholar