Prevention and control of health care-associated waterborne infections in health care facilities

A cluster-adjusted sample size algorithm for proportions was developed using a beta-binomial model

OBJECTIVE

The objective of the paper was to design a computer algorithm to calculate sample sizes for estimating proportions incorporating clustered sampling units using a beta-binomial model when information concerning the intraclass correlation is not available.

STUDY DESIGN AND SETTING

A computer algorithm was written in FORTRAN and evaluated for a hypothetical sample size situation.

RESULTS

The developed algorithm was able to incorporate clustering in estimated sample sizes through the specification of a beta distribution to account for within-cluster correlation. In a hypothetical example, the usual normal approximation method for estimation of a proportion ignoring the clustered sampling design resulted in a calculated sample size of 107, whereas the developed algorithm suggested that 208 sampling units would be necessary.

CONCLUSION

It is important to incorporate cluster adjustment in sample size calculations when designing epidemiologic studies for estimation of disease burden and other population proportions in the situation of correlated data even when information concerning the intraclass correlation is not available. Beta-binomial models can be used to account for clustering, and design effects can be estimated by generating beta distributions that encompass within-cluster correlation.

A plasma expander-related Pseudomonas aeruginosa outbreak

Nine patients developed P. aeruginosa bloodstream infection (BSI) in a neurosurgical intensive care unit (NSICU) during a 2-month period. Environmental strains were isolated from a plasma-expander solution and tap water. All clinical and plasma-expander strains displayed the same PFGE pattern, whereas the water strains were unrelated to the outbreak.

Bacterial biofilms within the clinical setting: what healthcare professionals should know

Bacterial biofilm formation is the prevailing microbial lifestyle in natural and manmade environments and occurs on all surface types. Biofilm formation develops in several phases and is influenced by various parameters, both environmental and inherent to the attaching cell. Biofilms also serve as protective niches for particular pathogens when outside a host. Although it is accepted that biofilms are ubiquitous in nature, the significance of biofilms in clinical settings, especially with regard to their role in medical-related infections, is often underestimated. It has been found that several aspects of human pathogenesis within a clinical context are directly related to biofilm development. Various types of surfaces in clinical settings are prone to biofilm development and an increased risk of disease may be a direct consequence of their formation. This review describes the process of biofilm formation, highlights the importance of bacterial associations with surfaces in clinical settings and describes various methods for biofilm visualization and control.