Hostname: page-component-848d4c4894-wg55d Total loading time: 0 Render date: 2024-05-31T07:17:19.480Z Has data issue: false hasContentIssue false
  • English
  • Français

Integrating Time-Varying and Ecological Exposures into Multivariate Analyses of Hospital-Acquired Infection Risk Factors: A Review and Demonstration

Published online by Cambridge University Press:  16 February 2016

Kevin A. Brown ,
Nick Daneman ,
Vanessa W. Stevens ,
Yue Zhang ,
Tom H. Greene ,
Matthew H. Samore  and
Paul Arora
Kevin A. Brown*
Affiliation:
VA Salt Lake City Health Care System, Salt Lake City, Utah, USA Division of Epidemiology, University of Utah, Salt Lake City, United States Public Health Ontario, Toronto, Canada Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Canada
Nick Daneman
Affiliation:
Sunnybrook Health Sciences Center, University of Toronto, Canada
Vanessa W. Stevens
Affiliation:
VA Salt Lake City Health Care System, Salt Lake City, Utah, USA Department of Pharmacotherapy, University of Utah, Salt Lake City, Utah, United States
Yue Zhang
Affiliation:
Division of Epidemiology, University of Utah, Salt Lake City, United States
Tom H. Greene
Affiliation:
Division of Epidemiology, University of Utah, Salt Lake City, United States
Matthew H. Samore
Affiliation:
VA Salt Lake City Health Care System, Salt Lake City, Utah, USA Division of Epidemiology, University of Utah, Salt Lake City, United States
Paul Arora
Affiliation:
Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Canada Centre for Global Child Health, The Hospital for Sick Children, Toronto, Canada
*
Address correspondence to Kevin A. Brown, Public Health Ontario, Toronto, Canada M5G1V2 (kevin.brown@oahpp.ca).
Get access Rights & Permissions [Opens in a new window]

Abstract

OBJECTIVES

Hospital-acquired infections (HAIs) develop rapidly after brief and transient exposures, and ecological exposures are central to their etiology. However, many studies of HAIs risk do not correctly account for the timing of outcomes relative to exposures, and they ignore ecological factors. We aimed to describe statistical practice in the most cited HAI literature as it relates to these issues, and to demonstrate how to implement models that can be used to account for them.

METHODS

We conducted a literature search to identify 8 frequently cited articles having primary outcomes that were incident HAIs, were based on individual-level data, and used multivariate statistical methods. Next, using an inpatient cohort of incident Clostridium difficile infection (CDI), we compared 3 valid strategies for assessing risk factors for incident infection: a cohort study with time-fixed exposures, a cohort study with time-varying exposures, and a case-control study with time-varying exposures.

RESULTS

Of the 8 studies identified in the literature scan, 3 did not adjust for time-at-risk, 6 did not assess the timing of exposures in a time-window prior to outcome ascertainment, 6 did not include ecological covariates, and 6 did not account for the clustering of outcomes in time and space. Our 3 modeling strategies yielded similar risk-factor estimates for CDI risk.

CONCLUSIONS

Several common statistical methods can be used to augment standard regression methods to improve the identification of HAI risk factors.

Infect. Control Hosp. Epidemiol. 2016;37(4):411–419

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 37 , Issue 4 , April 2016 , pp. 411 - 419
Copyright
© 2016 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.)

References

REFERENCES

1. Porta, M, Vandenbroucke, JP, Ioannidis, JPA, et al. Trends in citations to books on epidemiological and statistical methods in the biomedical literature. PLoS One 2013;8:e61837.Google Scholar
2. Breslow, NE, Day, NE. Statistical methods in cancer research. Volume I—The analysis of case-control studies. IARC Sci Publ 1980:5–338.Google Scholar
3. Mantel, N, Haenszel, W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 1959;22:719–748.Google Scholar
4. Weber, DJ, Rutala, WA, Miller, MB, Huslage, K, Sickbert-Bennett, E. Role of hospital surfaces in the transmission of emerging health care-associated pathogens: norovirus, Clostridium difficile, and Acinetobacter species. Am J Infect Control 2010;38:S25–S33.Google Scholar
5. Ajao, AO, Harris, AD, Roghmann, M-C, et al. Systematic review of measurement and adjustment for colonization pressure in studies of methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, and Clostridium difficile acquisition. Infect Control Hosp Epidemiol 2011;32:481–489.CrossRefGoogle ScholarPubMed
6. de Irala-Estévez, J, Martínez-Concha, D, Díaz-Molina, C, Masa-Calles, J, Serrano del Castillo, A, Fernández-Crehuet Navajas, R. Comparison of different methodological approaches to identify risk factors of nosocomial infection in intensive care units. Intensive Care Med 2001;27:1254–1262.CrossRefGoogle ScholarPubMed
7. Samore, MH, Harbarth, SI. A Methodologically Focused Review of the Literature in Healthcare Epidemiology and Infection Control. In: Hospital Epidemiology and Infection Control 4th ed. Philadelphia, PA: Lippincott Williams & Wilkins, 2012, Pp. 1320–1328.Google Scholar
8. Vonesh, EF, Schaubel, DE, Hao, W, Collins, AJ. Statistical methods for comparing mortality among ESRD patients: examples of regional/international variations. Kidney Int 2000;57:19–27.Google Scholar
9. Greenland, S, Thomas, DC. On the need for the rare disease assumption in case-control studies. Am J Epidemiol 1982;116:547–553.Google Scholar
10. van Walraven, C, Davis, D, Forster, AJ, Wells, GA. Time-dependent bias was common in survival analyses published in leading clinical journals. J Clin Epidemiol 2004;57:672–682.Google Scholar
11. Therneau, TM, Grambsch, PM. Modeling Survival Data: Extending the Cox Model. New York: Springer-Verlag; 2000.Google Scholar
12. Abrahamowicz, M, Beauchamp, M-E, Sylvestre, M-P. Comparison of alternative models for linking drug exposure with adverse effects. Stat Med 2012;31:1014–1030.Google Scholar
13. Gasparrini, A, Armstrong, B, Kenward, MG. Distributed lag non-linear models. Stat Med 2010;29:2224–2234.Google Scholar
14. Brown, KA, Fisman, DN, Moineddin, R, Daneman, N. The magnitude and duration of Clostridium difficile infection risk associated with antibiotic therapy: a hospital cohort study. PLoS One 2014;9:e105454.Google Scholar
15. Susser, M. The logic in ecological: I. The logic of analysis. Am J Public Health 1994;84:825–829.Google Scholar
16. Blakely, TA, Woodward, AJ. Ecological effects in multi-level studies. J Epidemiol Community Health 2000;54:367–374.Google Scholar
17. 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.Google Scholar
18. Diez Roux, AV, Aiello, AE. Multilevel analysis of infectious diseases. J Infect Dis 2005;191:S25–S33.Google Scholar
19. Burton, P, Gurrin, L, Sly, P. Extending the simple linear regression model to account for correlated responses: an introduction to generalized estimating equations and multi-level mixed modelling. Stat Med 1998;17:1261–1291.Google Scholar
20. Bolker, BM, Brooks, ME, Clark, CJ, et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol 2009;24:127–135.Google Scholar
21. Hanley, JA, Negassa, A, Edwardes, MD deB, Forrester, JE. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol 2003;157:364–375.Google Scholar
22. Brown, K, Valenta, K, Fisman, D, Simor, A, Daneman, N. Hospital ward antibiotic prescribing and the risks of Clostridium difficile infection. JAMA Intern Med 2015;175:626–633.CrossRefGoogle ScholarPubMed
23. McDonald, LC, Coignard, B, Dubberke, E, Song, X, Horan, T, Kutty, PK. Recommendations for surveillance of Clostridium difficile-associated disease. Infect Control Hosp Epidemiol 2007;28:140–145.Google Scholar
24. Brown, KA, Daneman, N, Arora, P, Moineddin, R, Fisman, DN. The co-seasonality of pneumonia and influenza with Clostridium difficile infection in the United States, 1993–2008. Am J Epidemiol 2013;178:118–125.Google Scholar
25. Fielding, A, Goldstein, H. Cross-classified and multiple membership structures in multilevel models: an introduction and review. Nottingham, Great Britain, Department for Education and Skills website. http://dera.ioe.ac.uk/6469/. Published 2006. Accessed December 4, 2014.Google Scholar
26. Fox, J. Bootstrapping Regression Models. In: Applied Regression Analysis and Generalized Linear Models, 2nd ed. Los Angeles: Sage, 2008.Google Scholar
27. Dubberke, ER, Reske, KA, Yan, Y, Olsen, MA, McDonald, LC, Fraser, VJ. Clostridium difficile-associated disease in a setting of endemicity: identification of novel risk factors. Clin Infect Dis 2007;45:1543–1549.Google Scholar
28. Simmering, JE, Polgreen, LA, Campbell, DR, Cavanaugh, JE, Polgreen, PM. Hospital transfer network structure as a risk factor for Clostridium difficile infection. Infect Control Hosp Epidemiol 2015;36:1031–1037.Google Scholar
29. Siler, K, Lee, K, Bero, L. Measuring the effectiveness of scientific gatekeeping. Proc Natl Acad Sci 2015;112:360–365.CrossRefGoogle ScholarPubMed
30. Thomas, A, Redd, A, Khader, K, Leecaster, M, Greene, T, Samore, M. Efficient parameter estimation for models of healthcare-associated pathogen transmission in discrete and continuous time. Math Med Biol 2015;32:81–100.Google Scholar
31. Zaas, AK, Song, X, Tucker, P, Perl, TM. Risk factors for development of vancomycin-resistant enterococcal bloodstream infection in patients with cancer who are colonized with vancomycin-resistant enterococci. Clin Infect Dis 2002;35:1139–1146.Google Scholar
32. Blumberg, HM, Jarvis, WR, Soucie, JM, et al. Risk factors for candidal bloodstream infections in surgical intensive care unit patients: the NEMIS prospective multicenter study. The National Epidemiology of Mycosis Survey. Clin Infect Dis 2001;33:177–186.Google Scholar
33. Loo, VG, Poirier, L, Miller, MA, et al. A predominantly clonal multi-institutional outbreak of Clostridium difficile-associated diarrhea with high morbidity and mortality. N Engl J Med 2005;353:2442–2449.Google Scholar
34. Padiglione, AA, Wolfe, R, Grabsch, EA, et al. Risk factors for new detection of vancomycin-resistant enterococci in acute-care hospitals that employ strict infection control procedures. Antimicrob Agents Chemother 2003;47:2492–2498.Google Scholar
35. Srinivasan, A, Song, X, Ross, T, Merz, W, Brower, R, Perl, TM. A prospective study to determine whether cover gowns in addition to gloves decrease nosocomial transmission of vancomycin-resistant enterococci in an intensive care unit. Infect Control Hosp Epidemiol 2002;23:424–428.Google Scholar
36. Muto, CA, Pokrywka, M, Shutt, K, et al. A Large Outbreak of Clostridium difficile‐associated disease with an unexpected proportion of deaths and colectomies at a teaching hospital following increased fluoroquinolone use. Infect Control Hosp Epidemiol 2005;26:273–280.Google Scholar
37. Pépin, J, Saheb, N, Coulombe, M-A, et al. Emergence of fluoroquinolones as the predominant risk factor for Clostridium difficile-associated diarrhea: a cohort study during an epidemic in Quebec. Clin Infect Dis 2005;41:1254–1260.Google Scholar
38. Bell, BA, Morgan, GB, Kromrey, JD, Ferron, JM. The impact of small cluster size on multilevel models: a Monte Carlo examination of two-level models with binary and continuous predictors. JSM Proc Surv Res Methods Sect 2010:4057–4067.Google Scholar
39. Moineddin, R, Matheson, FI, Glazier, RH. A simulation study of sample size for multilevel logistic regression models. BMC Med Res Methodol 2007;7:34.Google Scholar