Management of Microbiological Contamination of the Water Network of a Newly Built Hospital Pavilion

Bacterial Infections: Surveillance, Prevention and Control

Bacteria play a vital role in maintaining human health, but they may also be responsible for many different serious infections and diseases [...].

Multi-criterion analysis of the effect of physico-chemical microbiological agents on Legionella detection in hotel water distribution systems in Crete

Introduction

Water distribution systems in hotels have been related to outbreaks caused by Legionella spp. Certain measures, including disinfection by chlorination, maintaining increased temperatures are usually undertaken to prevent Legionella outbreaks. However, these preventive strategies are not always effective, since there are several factors (e.g., synergistic interactions with other microbes, physico-chemical factors, biofilm formation, availability of nutrients) that promote survival and proliferation of the pathogen in water pipes., Accordingly, there is a need of a holistic approach in development of preventive models for Legionella outbreaks associated with water distribution systems.

Methods

Water samples were collected from hotel water systems and were tested for the presence of Legionella, E. coli, total coliforms, total mesophilic count and Pseudomonas. In each sample, temperature and chlorine were also tested. Other epidemiological factors were additionally recorded including number of rooms, stars, proximity of sampling point to the boiler, etc. Data were processed by generalized linear analysis, and modeling based on logistic regression analysis to identify independent predictive factors associated with the presence of Legionella in hotel water systems.

Results

According to the generalized linear model, temperature affected (p<0.05) the presence of Legionella regardless of the species or the water supply (hot or cold). Additionally, opportunistic (P. aeruginosa) or non-opportunistic (E. coli, coliforms) pathogens were significantly associated (p<0.05) with the presence of all Legionella species. Temperature also exhibited a positive effect to all pathogens tested except for Pseudomonas according to the linear model. Multivariate analysis showed that Pseudomonas, total coliforms, HPC and temperature had a statistically significant effect on the presence of Legionella. Based on a binomial model, cold water had a positive effect on Legionella. Type of sampling and proximity of the sample to the boiler seemed to pose different effect on Legionella depending on the cfu/L. The number of hotel stars and rooms did not appear to have any effect in all tested models.

Discussion

Collectively, these results indicate the need for development of individualized water safety plans tailored by the presence of other microbiological agents, and unique physico-chemical factors, which could facilitate the survival of Legionella.in hotel water systems.

Conceptual model to inform Legionella-amoebae control, including the roles of extracellular vesicles in engineered water system infections

Extracellular vesicles (EVs or exosomes) are well described for bacterial pathogens associated with our gastrointestinal system, and more recently as a novel mechanism for environmental persistence, dissemination and infection for human enteric viruses. However, the roles played by EVs in the ancient arms race that continues between amoebae and one of their prey, Legionella pneumophila, is poorly understood. At best we know of intracellular vesicles of amoebae containing a mix of bacterial prey species, which also provides an enhanced niche for bacteriophage infection/spread. Free-living amoeba-associated pathogens have recently been recognized to have enhanced resistance to disinfection and environmental stressors, adding to previously understood (but for relatively few species of) bacteria sequestered within amoebal cysts. However, the focus of the current work is to review the likely impacts of large numbers of respiratory-sized EVs containing numerous L. pneumophila cells studied in pure and biofilm systems with mixed prey species. These encapsulated pathogens are orders of magnitude more resistant to disinfection than free cells, and our engineered systems with residual disinfectants could promote evolution of resistance (including AMR), enhanced virulence and EV release. All these are key features for evolution within a dead-end human pathogen post lung infection. Traditional single-hit pathogen infection models used to estimate the probability of infection/disease and critical environmental concentrations via quantitative microbial risk assessments may also need to change. In short, recognizing that EV-packaged cells are highly virulent units for transmission of legionellae, which may also modulate/avoid human host immune responses. Key data gaps are raised and a previous conceptual model expanded upon to clarify where biofilm EVs could play a role promoting risk as well as inform a more wholistic management program to proactively control legionellosis.

Legionella anisa or Legionella bozemanii? Traditional and molecular techniques as support in the environmental surveillance of a hospital water network

Understanding the actual distribution of different Legionella species in water networks would help prevent outbreaks. Culture investigations followed by serological agglutination tests, with poly/monovalent antisera, still represent the gold standard for isolation and identification of Legionella strains. However, also MALDI-TOF and mip-gene sequencing are currently used. This study was conducted to genetically correlate strains of Legionella non pneumophila (L-np) isolated during environmental surveillance comparing different molecular techniques. Overall, 346 water samples were collected from the water system of four pavilions located in a hospital of the Apulia Region of Italy. Strains isolated from the samples were then identified by serological tests, MALDI-TOF, and mip-gene sequencing. Overall, 24.9% of water samples were positive for Legionella, among which the majority were Legionella pneumophila (Lpn) 1 (52.3%), followed by Lpn2-15 (20.9%), L-np (17.4%), Lpn1 + Lpn2-15 (7.1%), and L-np + Lpn1 (2.3%). Initially, L-np strains were identified as L. bozemanii by monovalent antiserum, while MALDI-TOF and mip-gene sequencing assigned them to L. anisa. More cold water than hot water samples were contaminated by L. anisa (p < 0.001). PFGE, RAPD, Rep-PCR, and SAU-PCR were performed to correlate L. anisa strains. Eleven out of 14 strains identified in all four pavilions showed 100% of similarity upon PFGE analysis. RAPD, Rep-PCR, and SAU-PCR showed greater discriminative power than PFGE.

Integrated approach for legionellosis risk analysis in touristic-recreational facilities

Legionellosis is a severe pneumonia caused by the inhalation of aerosols containing Legionella, Gram-negative bacteria present in the water systems of touristic-recreational facilities. The purpose of this study was to develop a scoring tool to predict the risk of both environmental contamination and Legionnaires' disease cases in such facilities in the Apulia region of southern Italy. We analyzed 47 structural and management parameters/risk factors related to the buildings, water systems, and air conditioning at the facilities. A Poisson regression model was used to compute an overall risk score for each facility with respect to three outcomes: water samples positive for Legionella (risk score range: 7-54), water samples positive for Legionella with an average load exceeding 1000 colony-forming units per liter (CFU/L) (risk score range: 22-179,871), and clinical cases of Legionnaire's disease (risk score range: 6-31). The cut-off values for three outcomes were determined by receiver operating characteristic curves (first outcome, samples positive for Legionella in a touristic-recreational facility: 19; second outcome, samples positive for Legionella in a touristic-recreational facility with an average load exceeding 1000 CFU/L: 2062; third outcome, clinical cases of Legionnaire's disease in a touristic-recreational facility: 22). Above these values, there was a significant probability of observing the outcome. We constructed this predictive model using 70% of a large dataset (18 years of clinical and environmental surveillance) and tested the model on the remaining 30% of the dataset to demonstrate its reliability. Our model enables the assessment of risk for a touristic facility and the creation of a conceptual framework to link the risk analysis with prevention measures.