Surface disinfection: You should know these trends
In recent years, ready-to-use wipes have become increasingly popular for surface disinfection. They are easy to work with... read more
As the use of digitalisation increases, collecting and analysing big data sets can greatly contribute to improving patient safety. Mathematical models can be of great value for estimating how effective hygiene measures are in reducing infection risk. A recent study estimates the effect of combined infection control interventions on virus concentration on the hands of medical staff.
Big data analytics can be a real game-changer in the fight against healthcare associated infections. Prevention measures are essential for successful infection control, and infection risk prediction can support such activities. By using existing data, mathematical models can be developed that faithfully predict microbial exposure in healthcare facilities. Such models also contribute to better understanding of how HAI are transmitted. Strategies that use current clinical data to forecast outcomes have valuable advantages as they help decrease the burden of data collection, reduce errors caused by poor inter-rater reliability, and increase the ability of tracking infection causes.1 In a study published in the American Journal of Infection Control in 2019, Amanda M. Wilson et al., report on the use of a mathematical model to assess the influence of hand hygiene compliance and surface disinfection on viral infection risk.2
Fomites, contaminated surfaces which can transfer pathogens to new hosts, represent a significant challenge for infection control. Viruses such as influenza can survive on surfaces for a prolongued period of time, acting as relevant source of infection for patients as well as healthcare workers.3 Understanding the influence of surface disinfecting interventions and hand hygiene measures on viral exposure is crucial to optimising hygiene intervention protocols.
In a preliminary study, Wilson AM et al. developed and validated a simulation model to predict virus concentration on nurses’ hands. The researchers used data from a bacteriophage tracer study conducted in an urgent care facility in Tucson, Arizona.4 In the study, they used this model to estimate rotavirus, rhinovirus, and influenza A virus quantity on nurses’ hands. They also calculated the infection risk depending on surface disinfecting events and hand hygiene compliance.2
Whereas a single surface disinfection event reduced the estimated infection risk by 7-17 per cent, with the greatest reduction observed for influenza A, two surface cleanings at a two-hour interval resulted in a reduction of 14-35 per cent. Also, a 15 per cent increase in hand hygiene compliance – from 36 to 51 per cent – reduced the infection risk by 7-20 per cent. A combination of increased compliance along with two cleaning events achieved the best results, lowering the infection risk by 21-48 per cent.
The results of the study confirm that hand hygiene and surface cleaning* procedures remain essential components of any infection control protocol. The publication also showed that mathematical modelling can be a very useful tool for predicting the efficacy of hygiene measures as well as for comparing different scenarios. Uncovering new insights thanks to big data analysis could prove essential for reducing the incidence of HAI and supporting patient safety in healthcare facilities.
Find the original publication here.