ReviewInterventions to control nosocomial infections: study designs and statistical issues
Introduction
The effective control of nosocomial infections (NIs) is one of the most important priorities in hospitals. There are many interventions that are effective at controlling infections including those caused by antibiotic resistant organisms. Examples include implementation of guidelines, controlled antibiotic stewardship, improved hygiene practices, isolation of infected patients, and universal screening at hospital admission. The use of strategic bundles of evidence-based procedures has had some success in reducing NIs – for instance, in controlling catheter-related bloodstream infections in the intensive care unit (ICU).1
Proving that an intervention is successful is rather challenging and requires at least one study with an appropriate design. Shardell et al. have produced an overview of quasi-experiments to antimicrobial resistance intervention studies.2 In this review, we extend their approach and discuss several aspects of randomization: randomization of the cluster (ICU or hospital) in a parallel design; randomization of the sequence in a cross-over design; and randomization of the timing in a stepped-wedge design.
There are several definitions of outcome in NI intervention studies. A well-established definition is the incidence rate of infection which is collected in monthly records [e.g. number of new meticillin-resistant Staphylococcus aureus (MRSA) infections per 1000 patient-days]. The most suitable denominator is often patient-days, but others are also suitable depending on the outcome of interest, e.g. number of patients, catheter-days for catheter-related bloodstream infections, or ventilation-days for ventilator-associated pneumonia. In the following, we use the term ‘infection rates’ for incidence rates of infection.
Even though the primary interest is in incidence rates of infections, one should keep in mind that discharge from the hospital and dying in the hospital without NI are competing events for NI.3, 4 Thus, the collection of monthly records of discharge or mortality rates without NI is necessary. Ideally, data should be available on the patient-individual rather than on the aggregated level.
Even though randomized trials at a patient level exist in this field, it is often not feasible to measure intervention effects at the individual patient level due to potentially complex transmission patterns.5 Therefore, we assume that trials are intended to evaluate interventions at the hospital or at the ICU level. In the following, we use the term ‘cluster’ for hospital or ICU. From a statistical point of view, clusters require special attention since individual patients within a hospital are correlated and thus not independent.
Intervention studies to control NIs have a specific challenge, namely the Hawthorne effect: healthcare workers might improve their behaviour (e.g. in hygiene practices) simply in response to being studied and not in response to the intervention. For instance, Kohli et al. explored the Hawthorne effect with respect to hand hygiene performance.6 This effect is a problem in all designs which only consider within-cluster comparisons. It could be addressed by adding a control group and assuming that the Hawthorne effect acts on the intervention as well as on the control group with the same intensity.
The choice of a control group can be inappropriate in the sense that the intervention and the control group are not comparable. This selection bias can be avoided if the groups are similar in all important respects such as the baseline infection rate, the size of cluster (number of beds in the ICU/hospital), specialty of the cluster (surgical or medical ICU), overall patient-days, average length of stay, and mortality rates.
Another challenge is to control for non-intervention factors which have an impact on the outcome (e.g. incidence rate of infection). Examples are a general better understanding of NI infections (which usually increases with time) and an implementation of new guidelines to control NI (which is independent of the intervention of interest). Thus, the minimum requirement to control for non-intervention factors is by adjusting for period effects.
Section snippets
Designs
Figure 1 presents the five designs: pre–post intervention, pre–post intervention with control, parallel, cross-over, and stepped-wedge cluster randomized.
Statistical issues
The statistical analysis relies on the chosen design. In this section, statistical issues which hold for all designs and which are specific for NI data are emphasized. The basic components of the model include the intervention effect and the period effect (and for cross-over studies the carry-over effect of the intervention from period 1 to period 2). Furthermore, there are the following statistical issues.
First, the risk of NI also depends on competing risks for NI (discharge or death without
Discussion
Five designs have been compared to examine the effect of interventions on NIs with respect to bias, control for non-intervention factors, different types of randomization, type of estimated intervention effect and generalizability. For the analysis, we discussed competing events, clustering, multiple time-scales and time-dependent period and intervention variables.
This overview may help to guide researchers when choosing the most suitable study design. A pre–post intervention design is often
Conflict of interest statement
None declared.
Funding sources
The IMPLEMENT project receives financial support from the European Commission as part of the Health Program (2008–2013), which is administered by the Executive Agency for Health and Consumers (EACH). Information on the progress of the IMPLEMENT Project is available on the project website at http://www.eu-implement.info. M. Wolkewitz receives funding from the Deutsche Forschungsgemeinschaft (DFG).
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