Simplifying the World Health Organization Protocol: 3 Steps Versus 6 Steps for Performance of Hand Hygiene in a Cluster-randomized Trial

BACKGROUND

In an experimental setting, a simplified, 3-step hand hygiene technique for applying alcohol-based hand rub was non inferior in terms of reduction of bacterial counts, as compared to the conventional World Health Organization 6-step technique. We therefore compared compliance and microbiological efficacy between both hand hygiene techniques in routine clinical practice.

METHODS

We performed a cluster-randomized trial from October-November 2015 at the University Hospital Basel, Switzerland: a tertiary, academic care center (ISRCTN45923734). We randomly assigned 12 wards to either the 3-step technique or the conventional 6-step technique of hand rubbing. The primary endpoints were compliance with the assigned technique and reduction of bacterial counts on the hands of health-care workers.

RESULTS

Overall, 2923 hand hygiene indications were observed, and compliance was 70.7% (2066/2923). Compliance with technique and indications was 51.7% (595/1151) and 75.9% (1151/1516) on wards assigned to the 3-step technique, respectively, as compared to 12.7% (116/915) and 65.0% (915/1407) on wards assigned to the 6-step technique (P < .001). The reduction factor (RF) of bacterial colony counts did not differ between techniques (median RF 0.97 log10 colony-forming units [CFU] [interquartile range 0.39-1.59] for the 3-step technique vs median RF 1.04 log10 CFU [interquartile range 0.49-1.52] for the 6-step technique; P = .629).

CONCLUSIONS

In a clinical setting, the simpler hand hygiene technique, consisting of 3 steps, resulted in higher compliance with both hand hygiene indications and technique, as compared to the 6 steps. As the results of the microbiological analyses exclude inferiority, the conventional 6 steps could be safely replaced by a simpler hand hygiene technique.

CLINICAL TRIALS REGISTRATION

ISRCTN45923734.

Cultivating Innovative Pragmatic Cluster-Randomized Registry Trials Embedded in Hemodialysis Care: Workshop Proceedings From 2018

Hemodialysis is a life-sustaining treatment for persons with kidney failure. However, those on hemodialysis still face a poor quality of life and a short life expectancy. High-quality research evidence from large randomized controlled trials is needed to identify interventions that improve the experiences, outcomes, and health care of persons receiving hemodialysis. With the support of the Canadian Institutes of Health Research and its Strategy for Patient-Oriented Research, the Innovative Clinical Trials in Hemodialysis Centers initiative brought together Canadian and international kidney researchers, patients, health care providers, and health administrators to participate in a workshop held in Toronto, Canada, on June 2 and 3, 2018. The workshop served to increase knowledge and awareness about the conduct of innovative, pragmatic, cluster-randomized registry trials embedded into routine hemodialysis care and provided an opportunity to discuss and build support for new trial ideas. The workshop content included structured presentations, facilitated group discussions, and expert panel feedback. Partnerships and promising trial ideas borne out of the workshop will continue to be developed to support the implementation of future large-scale trials.

Recommendations for choosing an analysis method that controls Type I error for unbalanced cluster sample designs with Gaussian outcomes

We used theoretical and simulation-based approaches to study Type I error rates for one-stage and two-stage analytic methods for cluster-randomized designs. The one-stage approach uses the observed data as outcomes and accounts for within-cluster correlation using a general linear mixed model. The two-stage model uses the cluster specific means as the outcomes in a general linear univariate model. We demonstrate analytically that both one-stage and two-stage models achieve exact Type I error rates when cluster sizes are equal. With unbalanced data, an exact size α test does not exist, and Type I error inflation may occur. Via simulation, we compare the Type I error rates for four one-stage and six two-stage hypothesis testing approaches for unbalanced data. With unbalanced data, the two-stage model, weighted by the inverse of the estimated theoretical variance of the cluster means, and with variance constrained to be positive, provided the best Type I error control for studies having at least six clusters per arm. The one-stage model with Kenward-Roger degrees of freedom and unconstrained variance performed well for studies having at least 14 clusters per arm. The popular analytic method of using a one-stage model with denominator degrees of freedom appropriate for balanced data performed poorly for small sample sizes and low intracluster correlation. Because small sample sizes and low intracluster correlation are common features of cluster-randomized trials, the Kenward-Roger method is the preferred one-stage approach.

Sample size determination for clustered count data

We consider the problem of sample size determination for count data. Such data arise naturally in the context of multicenter (or cluster) randomized clinical trials, where patients are nested within research centers. We consider cluster-specific and population-averaged estimators (maximum likelihood based on generalized mixed-effect regression and generalized estimating equations, respectively) for subject-level and cluster-level randomized designs, respectively. We provide simple expressions for calculating the number of clusters when comparing event rates of two groups in cross-sectional studies. The expressions we derive have closed-form solutions and are based on either between-cluster variation or intercluster correlation for cross-sectional studies. We provide both theoretical and numerical comparisons of our methods with other existing methods. We specifically show that the performance of the proposed method is better for subject-level randomized designs, whereas the comparative performance depends on the rate ratio for the cluster-level randomized designs. We also provide a versatile method for longitudinal studies. Three real data examples illustrate the results.

Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials

In cluster randomized trials (CRTs), identifiable clusters rather than individuals are randomized to study groups. Resulting data often consist of a small number of clusters with correlated observations within a treatment group. Missing data often present a problem in the analysis of such trials, and multiple imputation (MI) has been used to create complete data sets, enabling subsequent analysis with well-established analysis methods for CRTs. We discuss strategies for accounting for clustering when multiply imputing a missing continuous outcome, focusing on estimation of the variance of group means as used in an adjusted t-test or ANOVA. These analysis procedures are congenial to (can be derived from) a mixed effects imputation model; however, this imputation procedure is not yet available in commercial statistical software. An alternative approach that is readily available and has been used in recent studies is to include fixed effects for cluster, but the impact of using this convenient method has not been studied. We show that under this imputation model the MI variance estimator is positively biased and that smaller intraclass correlations (ICCs) lead to larger overestimation of the MI variance. Analytical expressions for the bias of the variance estimator are derived in the case of data missing completely at random, and cases in which data are missing at random are illustrated through simulation. Finally, various imputation methods are applied to data from the Detroit Middle School Asthma Project, a recent school-based CRT, and differences in inference are compared.