Cooling towers influence in an urban environment: A predictive model to control and prevent Legionella risk and Legionellosis events

Cooling towers (CTs) are used to dissipate excess heat from water by evaporation, common in large facilities as hospital, companies, and hotels. The main risk attributed to CTs is represented by Legionella, a Gram-negative bacterium associated with a severe form of pneumonia known as Legionnaires' disease (LD). The infection route is by inhalation of aerosols reaching the lower respiratory tract. Despite several events associated with CTs, the knowledge in this field is still limited. The aim of this study was to develop a predictive model of bioaerosol dispersion using PM10 particles as a proxy, to generate risk maps of Legionella spread in the surrounding area in several weather and microbiological conditions. The Legionella contamination in the CT basin was 40938 ± 24523 cfu/L, with four peaks independent of the season, associated with an increase in air minimum temperature values (+1–2 °C) and a high relative humidity (66–100%) preceded by rainfall (0.2–30.6 mm/day). The model revealed that the most extensive bioaerosol spread is predicted in winter and summer, with an increase in Legionella risk at a distance of up to 1.5 km from the CT. This method represents a novel integrated approach for the prevention and management of LD risk in CTs

Combining Traditional and Molecular Techniques Supports the Discovery of a Novel Legionella Species During Environmental Surveillance in a Healthcare Facility

none9siLegionella surveillance plays a significant role not only to prevent the risk of infection but also to study the ecology of isolates, their characteristics, and how their prevalence changes in the environment. The difficulty in Legionella isolation, identification, and typing results in a low notification rate; therefore, human infection is still underestimated. In addition, during Legionella surveillance, the special attention given to Legionella pneumophila leads to an underestimation of the prevalence and risk of infection for other species. This study describes the workflow performed during environmental Legionella surveillance that resulted in the isolation of two strains, named 8cVS16 and 9fVS26, associated with the genus Legionella. Traditional and novel approaches such as standard culture technique, MALDI-TOF MS, gene sequencing, and whole-genome sequencing (WGS) analysis were combined to demonstrate that isolates belong to a novel species. The strain characteristics, the differences between macrophage infectivity potential (mip), RNA polymerase b subunit (rpoB), and reference gene sequences, the average nucleotide identity (ANI) of 90.4%, and the DNA–DNA digital hybridization (dDDH) analysis of 43% demonstrate that these isolates belong to a new Legionella species. The finding suggests that, during the culture technique, special attention should be paid to the characteristics of the isolates that are less associated with the Legionella genus in order to investigate the differences found using more sensitive methods. The characterization of the two newly discovered isolates based on morphological, biochemical, and microscopic characteristics is currently underway and will be described in another future study.openLuna Girolamini, Maria Rosaria Pascale, Marta Mazzotta, Simona Spiteri, Federica Marino, Silvano Salaris, Antonella Grottola, Massimiliano Orsini, Sandra CristinoLuna Girolamini, Maria Rosaria Pascale, Marta Mazzotta, Simona Spiteri, Federica Marino, Silvano Salaris, Antonella Grottola, Massimiliano Orsini, Sandra Cristin

Development of a cooling tower predictive aerosol dispersion model to prevent Legionella infection

Cooling towers (CTs) are widely used devices in hospitals, industries, and other settings to dissipate heat from water via evaporation. The use of large amounts of water, stagnant conditions and poor maintenance programs promote the proliferation of bacteria such as Legionella . Since the 1980s, Legionella -containing aerosols produced by CTs have been linked to a number of outbreaks. To date, no studies have been conducted to assess the risk associated with Legionella dispersion through the CTs circuit, much less the preventive models that trace the map of its spread. The aim of the study is to develop a predictive model of bioaerosol dispersion linked to PM10, to develop a map of Legionella risk in a radius of 5-10 km from the hospital’s CT. This model represents a novel approach to the Legionella risk in CTs. Furthermore, the model could already be used in the design and installation phases, in addition to managing the CTs maintenance required to avoid outbreaks

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)