Automated hand hygiene compliance monitoring – comparison with traditional methods
The World Health Organization considers observation the gold standard for determining hand hygiene compliance. However, human observation has a number of limitations, including reproducibility, timing of auditing being dependent upon staff availability, and the ‘Hawthorne effect’, where workers are more likely to be compliant when they know they are being watched. McCalla S, et al. (Am J Infect Control 2017; 45:492-497) compared an automated system for monitoring with traditional methods of auditing hand hygiene compliance in an ICU and step-down facility at a US centre.
During 2014, hand hygiene compliance was recorded manually by human observers on a sporadic, part-time basis. In 2015, an automated hand hygiene compliance system was implemented. A wearable device prompted healthcare workers to perform hand hygiene (electronic badges glowed yellow, then red until hand hygiene was performed). The system differentiated between healthcare workers categories. A chemical sensor in the badge detected the presence of alcohol, confirming that the badge-wearer had sanitised their hands. Upon completing a shift, healthcare workers removed badges for re-charging at a base station, enabling data entry to a central repository.
During 2014 and 2015, patient characteristics in the study ICU and step-down unit were comparable. Median stays in the unit were 3.06 and 2.88 in 2014 and 2015, respectively. Human observation of hand hygiene in 2014 revealed compliance for 167/169 opportunities (98.8%) in ICU and 308/311 opportunities (99.0%) in the ICU step-down unit. Continual automated data collection during 2015 revealed compliance for 210648/221396 opportunities (95.2%) in ICU and 397476/411008 opportunities (96.7%) in the ICU step-down unit. Compared with human observers, automated monitoring showed statistically significant lower compliance (p<0.05).
Findings are valuable in informing future and novel hand hygiene monitoring programs. While automated methods have previously been trialled, few have compared with manually-collected data. It is noteworthy that automated monitoring revealed lower compliance than manual methods, and this is consistent with a Hawthorne effect. Looking ahead, prospective tandem data (manual and automated) would be of value, in addition to linking automated compliance monitoring with incidence of healthcare-associated infections.
Candida auris in US healthcare facilities
Candida auris was first described in association with otitis media infections in 2009. Over the last 12-months, an increased number of invasive infections have been reported, particularly bloodstream infections. This fungus is notable for resistance to fluconazole, as well as variable susceptibility to other azoles, amphotericin B & echinocandins. Tsay S, et al. (MMWR May 19 2017; 66:514-515) recently reported clustering and possible transmission of C. auris in US healthcare facilities.
As of May, 2017 a total of 77 U.S. clinical cases of C. auris had been reported to the Centers for Disease Control (CDC) from seven states. All cases were identified through cultures taken as part of routine clinical care. Screening of close contacts of these patients (e.g. same ward as index cases) identified an additional 45 patients with C. auris colonisation. Whole-genome sequencing revealed four distinct clades, and isolates within each state were highly related.
Median age of patients with infection was 70 years (range 21–96), and 55% were male. C. auris was cultured from the following sites: blood (n=45), urine (n=11), respiratory tract (n=8), bile fluid (n=4), wounds (n=4), CVC tip (n=2), bone (n=1), ear (n=1), and a jejunal biopsy (n=1). Antifungal susceptibility testing of the first 35 clinical isolates revealed 30 (86%) isolates to be resistant to fluconazole (minimum inhibitory concentration [MIC] >32), 15 (43%) to be resis¬tant to amphotericin B (MIC ≥2), and one (3%) was resistant to echinocandins (MIC >4). Previous studies have confirmed the presence of C. auris in environmental swabs collected from patient rooms.
Recommendations have been drafted by the CDC to prevent the spread of C. auris in healthcare, including: (i) use of standard and contact precautions, (ii) isolation (or cohorting) of patients, (iii) daily and terminal cleaning of room with a disinfectant active against Clostridium difficile spores, and (iv) notification of receiving health care facilities when a patient with C. auris colonisation/infection is transferred.