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An augmented data method for the analysis of nosocomial infection data
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Cooper, Ben S., Medley, Graham, Bradley, S. J. (Susan J.) and Scott, Geoffrey M.. (2008) An augmented data method for the analysis of nosocomial infection data. American Journal of Epidemiology, Vol.168 (No.5). pp. 548-557. ISSN 0002-9262
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Official URL: http://dx.doi.org/10.1093/aje/kwn176
Abstract
The analysis of nosocomial infection data for communicable pathogens is complicated by two facts. First, typical pathogens more commonly cause asymptomatic colonization than overt disease, so transmission can be only imperfectly observed through a sequence of surveillance swabs, which themselves have imperfect sensitivity. Any given set of swab results can therefore be consistent with many different patterns of transmission. Second, data are often highly dependent: the colonization status of one patient affects the risk for others, and, in some wards, repeated admissions are common. Here, the authors present a method for analyzing typical nosocomial infection data consisting of results from arbitrarily timed screening swabs that overcomes these problems and enables simultaneous estimation of transmission and importation parameters, duration of colonization, swab sensitivity, and ward- and patient-level covariates. The method accounts for dependencies by using a mechanistic stochastic transmission model, and it allows for uncertainty in the data by imputing the imperfectly observed colonization status of patients over repeated admissions. The approach uses a Markov chain Monte Carlo algorithm, allowing inference within a Bayesian framework. The method is applied to illustrative data from an interrupted time-series study of vancomycin-resistant enterococci transmission in a hematology ward.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics Q Science > QR Microbiology R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
| Divisions: | Faculty of Science > Life Sciences (2010- ) > Biological Sciences ( -2010) |
| Library of Congress Subject Headings (LCSH): | Cross infection, Drug resistance, Nosocomial infections, Enterococcus, Medical statistics, Stochastic processes |
| Journal or Publication Title: | American Journal of Epidemiology |
| Publisher: | Oxford University Press |
| ISSN: | 0002-9262 |
| Date: | 1 September 2008 |
| Volume: | Vol.168 |
| Number: | No.5 |
| Number of Pages: | 10 |
| Page Range: | pp. 548-557 |
| Identification Number: | 10.1093/aje/kwn176 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Restricted or Subscription Access |
| Funder: | Wellcome Trust (London, England) |
| Grant number: | 047918 (WT) |
| References: | 1. Becker NG, Britton N. Statistical studies of infectious disease incidence. J R Stat Soc Ser (B) 1999;61:287–307. 2. Cooper BS, Stone SP, Kibbler CC, et al. Isolation measures in the hospital management of methicillin resistant Staphylococcus aureus (MRSA): systematic review of the literature. BMJ 2004;329:533. 3. Cooper B, Lipsitch M. The analysis of hospital infection data using hidden Markov models. Biostatistics 2004;5:223–37. 4. Smith DL, Dushoff J, Perencevich EN, et al. Persistent colonization and the spread of antibiotic resistance in nosocomial pathogens: resistance is a regional problem. Proc Natl Acad Sci U S A 2004;101:3709–14. 5. Cooper BS, Medley GF, Stone SP, et al. Methicillin-resistant Staphylococcus aureus in hospitals and the community: stealth dynamics and control catastrophes. Proc Natl Acad Sci U S A 2004;101:10223–8. 6. Bootsma MC, Diekmann O, Bonten MJ. Controlling methicillinresistant Staphylococcus aureus: quantifying the effects of interventions and rapid diagnostic testing. Proc Natl Acad Sci U S A 2006;103:5620–5. 7. Robotham JV, Scarff CA, Jenkins DR, et al. Methicillinresistant Staphylococcus aureus (MRSA) in hospitals and the community: model predictions based on the UK situation. J Hosp Infect 2007;65(suppl 2):93–9. 8. Jernigan JA, Titus MG, Groschel DH, et al. Effectiveness of contact isolation during a hospital outbreak of methicillinresistant Staphylococcus aureus. Am J Epidemiol 1996;143: 496–504. 9. Williams R, NobleW, Jevons M, et al. Isolation for the control of staphylococcal infection in surgical wards. Br Med J 1962; 2:275–82. 10. Shooter R, Thom B, Dunkerley D, et al. Pre-operative segregation of patients in a surgical ward. Br Med J 1963;2:1567–9. 11. Lidwell O, Davies J, Payne R, et al. Nasal acquisition of Staphylococcus aureus in partly divided wards. J Hyg (Camb) 1971;69:113–23. 12. Bradley S, Wilson A, Allen M, et al. The control of hyperendemic glycopeptide-resistant Enterococcus spp. on a haematology unit by controlling antibiotic usage. J Antimicrob Chemother 1999;43:261–6. 13. Bradley S, Kaufmann M, Happy C, et al. The epidemiology of glycopeptide-resistant enterococci on a haematology unit—analysis by pulsed-field gel electrophoresis. Epidemiol Infect 2002;129:57–64. 14. Becker N. Analysis of infectious disease data. London, United Kingdom: Chapman and Hall, 1989. 15. Green P. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 1995; 82:711–32. 16. Spiegelhalter D, Best N, Carlin B, et al. Bayesian measures of model complexity and fit. J R Stat Soc Ser (B) 2002;64:583–616. 17. Gelman A, Carlin J, Stern H, et al. Bayesian data analysis. 2nd ed. Boca Raton, FL: Chapman and Hall, 2004. 18. Gamerman D. Markov chain Monte Carlo: stochastic simulation for Bayesian inference. London, United Kingdom: Chapman and Hall, 1997. 19. Gilks W, Richardson S, Spiegelhalter D, eds. Markov chain Monte Carlo in practice. London, United Kingdom: Chapman and Hall/CRC, 1996. 20. Smith B. Bayesian Output Analysis program (boa), version 1.1.5. 2005. (http://www.public-health.uiowa.edu/boa). 21. Gibson G, Renshaw E. Estimating parameters in stochastic compartmental models using Markov chain methods. IMA J Math Appl Med Biol 1998;15:19–40. 22. O’Neill PD, Roberts GO. Bayesian inference for partially observed stochastic epidemics. J R Stat Soc Ser (A) 1999;162: 121–9. 23. Auranen K, Arjas E, Leino T, et al. Transmission of pneumococcal carriage in families: a latent Markov process model for binary longitudinal data. J Am Stat Assoc 2000;95: 1044–53. 24. Forrester ML, Pettitt AN, Gibson GJ. Bayesian inference of hospital-acquired infectious diseases and control measures given imperfect surveillance data. Biostatistics 2007;8: 383–401. 25. Pelupessy I, Bonten MJ, Diekmann O. How to assess the relative importance of different colonization routes of pathogens within hospital settings. Proc Natl Acad Sci U S A 2002;99: 5601–5. 26. McBryde ES, Pettitt AN, Cooper BS, et al. Characterizing an outbreak of vancomycin-resistant enterococci using hidden Markov models. J R Soc Interface 2007;4:745–54. 27. Bootsma MC, Bonten MJ, Nijssen S, et al. An algorithm to estimate the importance of bacterial acquisition routes in hospital settings. Am J Epidemiol 2007;166:841–51. 28. Donskey C, Hanrahan J, Hutton R, et al. Effect of parenteral antibiotic administration on persistence of vancomycin-resistant Enterococcus faecium in the mouse gastrointestinal tract. J Infect Dis 1999;180:384–90. 29. Harbarth S, Cosgrove S, Carmeli Y. Effects of antibiotics on nosocomial epidemiology of vancomycin-resistant enterococci. Antimicrob Agents Chemother 2002;46:1619–28. |
| URI: | http://wrap.warwick.ac.uk/id/eprint/29457 |
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