Bayesian inference of hospital-acquired infectious diseases and control measures given imperfect surveillance data

Biostatistics

Volumn 8, Issue 2, 2007, Pages 383-401

  Pettitt, A N ^a   Forrester, M L ^a   Gibson, G J ^b  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b HERIOT WATT UNIVERSITY   (United Kingdom)

Author keywords

Bayesian inference; False negatives; Imperfect detectability; Infectious diseases; Markov chain Monte Carlo methods; MRSA; Reversible jump methods; Screening; Sensitivity; Staphylococcus; Stochastic epidemic models

Indexed keywords

ARTICLE; AUSTRALIA; BAYES THEOREM; COMPUTER SIMULATION; CROSS INFECTION; DISEASE TRANSMISSION; EPIDEMIC; GROWTH, DEVELOPMENT AND AGING; HUMAN; INFECTION CONTROL; METHODOLOGY; MONTE CARLO METHOD; PENICILLIN RESISTANCE; PROBABILITY; STAPHYLOCOCCUS AUREUS; STAPHYLOCOCCUS INFECTION; STATISTICAL ANALYSIS; STATISTICAL MODEL;

BAYES THEOREM; COMPUTER SIMULATION; CROSS INFECTION; DATA INTERPRETATION, STATISTICAL; DISEASE OUTBREAKS; HUMANS; INFECTION CONTROL; MARKOV CHAINS; METHICILLIN RESISTANCE; MODELS, STATISTICAL; MONTE CARLO METHOD; QUEENSLAND; STAPHYLOCOCCAL INFECTIONS; STAPHYLOCOCCUS AUREUS;

BACTERIA (MICROORGANISMS); METHICILLIN RESISTANT STAPHYLOCOCCUS AUREUS; STAPHYLOCOCCUS;

EID: 34147173293     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxl017     Document Type: Article

References (54)

1. ANDERSSON, H. AND BRITTON, T. (2000). Stochastic Epidemic Models and Their Statistical Analysis, Volume 151. Lecture notes in statistics. New York: Springer.
2. ARJAS, E. AND GASBARRA, D. (1997). On prequential model assessment in life history analysis. Biometrika 84, 505-22.
3. ASLANIDOU, H., DEY, D. AND SINHA, D. (1998). Bayesian analysis of multivariate survival data using Monte Carlo methods. Canadian Journal of Statistics 26, 33-48.
4. AUSTIN, D. J., BONTEN, M. J. M., WEINSTEIN, R. A., SLAUGHTER, S. AND ANDERSON, R. M. (1999). Vancomycin-resistant enterococci in intensive-care hospital settings: transmission dynamics, persistence, and the impact of infection control programs. Proceedings of the National Academy of Sciences USA 96, 6908-13.
5. BAILEY, N. T. J. (1975). The Mathematical Theory of Infectious Diseases and Its Applications, 2nd edition. London: Griffin.
6. BAYARRI, M. AND BERGER, J. (1998). Quantifying surprise in the data and model verification. In: Bernardo, J., Berger, J., Dawid, A., Smith, A. (eds), Bayesian Statistics 6. Oxford, UK: Oxford University Press, pp 53-82.
7. BECKER, N. G. AND BRITTON, T. (1999). Statistical studies of infectious disease incidence. Journal of the Royal Statistical Society Series B Statistical Methodology 61, 287-307.
8. BONTEN, M. J. M., AUSTIN, D. J. AND LIPSITCH, M. (2001). Understanding the spread of antibiotic resistant pathogens in hospitals: mathematical models as tools for control. Clinical Infectious Diseases 33, 1739-46.
9. BROOKS, S., CATCHPOLE, E. AND MORGAN, B. (2000). Bayesian animal survival estimation. Statistical Science 15, 357-76.
10. BROOKS, S. P. AND GIUDICI, P. (1998). Convergence assessment for reversible jump MCMC simulations. In: Bernardo, J., Berger, J., Dawid, A., Smith, A. (eds), Bayesian Statistics 6. Oxford, UK: Oxford University Press, pp 733-42.
11. CARLIN, B. AND LOUIS, T. A. (2000). Bayes and Empirical Bayes methods for data analysis, 2nd edition. Chapman & Hall texts in statistical science series. Boca Raton: Chapman & Hall.
12. CASTELLOE, J. M. AND ZIMMERMAN, D. L. (2002). Convergence assessment for reversible jump MCMC samplers. Technical Report 313. Cary, NC: SAS Institute.
13. COELLO, R., JIMÉNEZ, J., GARCÍA, M., ARROYO, P., MINGUEZ, D., FERNÁNDEZ, C., CRUZET, F. AND GASPAR, C. (1994). Prospective study of infection, colonization and carriage of methicillin-resistant Staphylococcus aureus in an outbreak affecting 990 patients. European Journal of Clinical Microbiology and Infectious Diseases 13, 74-81.
14. COOPER, B. (2000). The transmission dynamics of methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci in hospital wards, [dissertation]. Coventry, UK: University of Warwick.
15. COOPER, B. AND LIPSITCH, M. (2004). The analysis of hospital infection data using hidden Markov models. Biostatistics 5, 223-37.
16. COOPER, B. S., MEDLEY, G. F. AND SCOTT, G. M. (1999). Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects. Journal of Hospital Infection 43, 131-47.
17. COOPER, B. S., STONE, S. P., KIBBLER, C. C., COOKSON, B. D., ROBERTS, J. A., MEDLEY, G. F., DUCKWORTH, G. J., LAI, R. AND EBRAHIM, S. (2003). Systematic review of isolation policies in the hospital management of methicillin resistant Staphylococcus aureus: a review of the literature with epidemiological and economic modelling. Health Technology Assessment 7, 1-194.
18. COWLES, M. K. AND CARLIN, B. P. (1996). Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 91, 883-904.
19. DAVISON, A. (2003). Statistical models, Chapter 6. Stochastic models. Cambridge series in statistical and probabilistic mathematics. Cambridge, UK: Cambridge University Press, pp 274-91.
20. DE JONG, M. C. M., DIEKMANN, O. AND HEESTERBEEK, H. (1995). How does transmission of infection depend on population size? In: Mollison, D. (editor), Epidemic Models: Their Structure & Relation to Data, No. 5 in Publications of the Newton Institute. Cambridge, UK: Cambridge University Press, pp 84-94.
21. DEY, D., GELFAND, A., SWARTZ, T. AND VLACHOS, P. (1995). A simulation-intensive based model checking for hierarchical methods. Technical Report 95-29. Department of Statistics, University of Connecticut.
22. EVEILLARD, M., LANCIEN, E., BARNAUD, G., HIDRI, N., GABA, S., BENLOLO, J. AND JOLY-GIULLOU, M. L. (2005). Impact of screening for MRSA carriers at hospital admission on risk-adjusted indicators according to the imported MRSA colonization pressure. Journal of Hospital Infection 59, 254-8.
23. FORRESTER, M. AND PETTITT, A. N. (2005). Use of stochastic epidemic modeling to quantify transmission rates of colonization with methicillin-resistant Staphylococcus aureus in an intensive care unit. Infection Control and Hospital Epidemiology 26, 598-606.
24. GELFAND, A., DEY, D. AND CHANG, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods. In: Bernardo, J., Berger, J., Dawid, A., Smith, A. (eds), Bayesian Statistics 4. Oxford, UK: Oxford University Press, pp 147-67.
25. GELFAND, A. AND SMITH, A. (1990). Sampling-based approaches to calculating marginal densities. Journal of American Statistical Association 85, 398-409.
26. GELMAN, A., CARLIN, J. B., STERN, H. S. AND RUBIN, D. B. (2000). Bayesian Data Analysis. Boca Raton: Chapman & Hall.
27. GELMAN, A., MENG, X. L. AND STERN, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica 6, 733-807.
28. GELMAN, A. AND RUBIN, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science 7, 457-511.
29. GEMAN, S. AND GEMAN, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721-41.
30. GEWEKE, J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: Bernardo, J., Berger, J., Dawid, A., Smith, A. (eds), Bayesian Statistics 4. Oxford, UK: Oxford University Press, pp 169-93.
31. GIBSON, G. J. AND RENSHAW, E. (1998). Estimating parameters in stochastic compartmental models using Markov chain methods. IMA Journal of Mathematics Applied in Medicine and Biology 15, 19-40.
32. GIBSON, G. J. AND RENSHAW, E. (2001). Likelihood estimation for stochastic compartmental models using Markov chain methods. Statistical Computation 11, 347-58.
33. GREEN, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711-32.
34. GREEN, P. J. (2003). Trans-dimensional Markov chain Monte Carlo. In: Green, P. J., Hjort, N. L. and Richardson, S. (editors), Highly Structured Stochastic Systems, Volume 27 of Oxford Statistical Science. Oxford, UK: Oxford University Press, pp 179-98.
35. HASTINGS, W. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57, 97-109.
36. HOPE, W. W., MORTON, A. P., LOOKE, D. F. M., SCHOONEVELDT, J. M. AND NIMMO, G. R. (2004). A PCR method for the identification of methicillin resistant Staphylococcus aureus (MRSA) from screening swabs. Pathology 36, 265-8.
37. IBRAHIM, J. G., CHEN, M. H. AND SINHA, D. (2001). Bayesian Survival Analysis. Springer series in statistics. New York: Springer.
38. JERNIGAN, J. A., TITUS, M. G., GRÖSCHEL, D. H. M., GETCHELL-WHITE, S. I. AND FARR, B. M. (1996). Effectiveness of contact isolation during a hospital outbreak of methicillin-resistant Staphylococcus aureus. American Journal of Epidemiology 143, 496-504.
39. KEENE, A., AD M-H LEE, P. V., FINNERTY, K., NICOLLS, D., CESPEDES, C., QUAGLIARELLO, B., CHIASSON, M., CHONG, D. AND LOWY, F. D. (2005). Staphylococcus aureus colonization and the risk of infection in critically ill patients. Infection Control and Hospital Epidemiology 26, 622-8.
40. LIPSITCH, M., BERGSTROM, C. T. AND LEVIN, B. R. (2000). The epidemiology of antibiotic resistance in hospitals: paradoxes and prescriptions. Proceedings of the National Academy of Sciences USA 97, 1938-43.
41. LUCET, J. C., CHEVRET, S., DURAND- ZALESKI, I., CHASTANG, C. AND RÉ GNIER, B. (2003). Prevalence and risk factors for carriage of methicillin-resistant Staphylococcus aureus at admission to the Intensive Care Unit. Archives of Internal Medicine 163, 181-8.
42. MARSHALL, C., HARRINGTON, G., WOLFE, R., FAIRLEY, C. K., WESSELINGH, S. AND SPELMEN, D. (2003). Acquisition of methicillin-resistant Staphylococcus aureus in a large intensive care unit. Infection Control and Hospital Epidemiology 24, 322-6.
43. METROPOLIS, N., ROSENBLUTH, A., ROSENBLUTH, M., TELLER, A. AND TELLER, E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics 21, 1087-92.
44. O'NEILL, P. D. AND ROBERTS, G. O. (1999). Bayesian inference for partially observed stochastic epidemics. Journal of the Royal Statistical Society Series A 162, 121-9.
45. PELUPESSY, I., BONTEN, M. J. M. AND DIEKMANN, O. (2002). How to assess the relative importance of different colonization routes of pathogens within hospital settings. Proceedings of the National Academy of Sciences USA 99, 5601-5.
46. RENSHAW, E. (1999). Stochastic effects in population models. In: McGlade, J. (ed), Advanced Ecological Theory: Principles & Applications, Chapter 2. Malden, Mass: Blackwell Science, pp 23-63.
47. RUBIN, D. (1984). Bayesianly justifiable and relevant frequency calculations for the applied statistician. Annals of Statistics 12, 1151-72.
48. SÉBILLE, V., CHEVRET, S. AND VALLERON, A. J. (1997). Modeling the spread of resistant nosocomial pathogens in an Intensive Care Unit. Infection Control and Hospital Epidemiology 18, 84-92.
49. SINHA, D. AND DEY, D. (1997). Semiparametric Bayesian analysis of survival data. Journal of the American Statistical Association 92, 1195-212.
50. SMITH, T. AND VOUNATSOU, P. (2003). Estimation of infection and recovery rates for highly polymorphic parasites when detectability is imperfect, using hidden Markov models. Statistical Medicine 22, 1709-24.
51. STREFTARIS, G. AND GIBSON, G. J. (2004). Bayesian inference for stochastic epidemics in closed populations. Statistical Model 4 63-75.
52. THOMPSON, D. (2004). Methicillin-resistant Staphylococcus aureus in a general intensive care unit. Journal Royal Society of Medicine 97, 521-6.
53. TROCHE, G., JOLY, L. M., GUIBERT, M. AND ZAZZO J. F. (2005). Detection and treatment of antibiotic-resistant bacterial carriage in a surgical intensive care unit: a 6-year prospective study. Infection Control and Hospital Epidemiology 26, 161-5.
54. TROTTER, C. AND GAY, N. (2003). Analysis of longitudinal bacterial carriage studies accounting for sensitivity of swabbing: an application to Neisseria meningitidis. Epidemiology and Infection 130, 201-5.