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J Hosp Infect. 2014 Feb;86(2):77-82. doi: 10.1016/j.jhin.2013.09.015. Epub 2013 Oct 18.

Interventions to control nosocomial infections: study designs and statistical issues.

Author information

1
Institute of Medical Biometry and Medical Informatics, University of Freiburg, Freiburg, Germany. Electronic address: wolke@imbi.uni-freiburg.de.
2
Institute of Health and Biomedical Innovation and School of Public Health and Social Work, Queensland University of Technology, Queensland, Australia.
3
Hospital Arnau de Vilanova, Lleida, Universitat Autonoma de Barcelona, Barcelona, Spain.
4
Department of Infectiology, Heidelberg University Hospital, Heidelberg, Germany.
5
Institute of Medical Biometry and Medical Informatics, University of Freiburg, Freiburg, Germany.

Abstract

There is a wide range of potential study designs for intervention studies to decrease nosocomial infections in hospitals. The analysis is complex due to competing events, clustering, multiple timescales and time-dependent period and intervention variables. This review considers the popular pre-post quasi-experimental design and compares it with randomized designs. Randomization can be done in several ways: randomization of the cluster [intensive care unit (ICU) or hospital] in a parallel design; randomization of the sequence in a cross-over design; and randomization of the time of intervention in a stepped-wedge design. We introduce each design in the context of nosocomial infections and discuss the designs with respect to the following key points: bias, control for non-intervention factors, and generalizability. Statistical issues are discussed. A pre-post-intervention design is often the only choice that will be informative for a retrospective analysis of an outbreak setting. It can be seen as a pilot study with further, more rigorous designs needed to establish causality. To yield internally valid results, randomization is needed. Generally, the first choice in terms of the internal validity should be a parallel cluster randomized trial. However, generalizability might be stronger in a stepped-wedge design because a wider range of ICU clinicians may be convinced to participate, especially if there are pilot studies with promising results. For analysis, the use of extended competing risk models is recommended.

KEYWORDS:

Cluster; Competing risks; Quasi-experiment; Randomization; Stepped-wedge design; Study design; Timescale

PMID:
24286854
DOI:
10.1016/j.jhin.2013.09.015
[Indexed for MEDLINE]

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