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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).
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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 

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