Frequency of use of terminal points as an important risk factor for Legionella colonization in hospitals [post]

2019 unpublished
Hospitals have a different structure than domestic buildings. Legionella is a bacterium that can colonize the hospital water causing many outbreaks. It's a Public Health problem and its prevention and control are essential to avoid nosocomial infections. The aims of this study are to evaluate several risk factors and parameters that can contribute to Legionella colonization in a hospital, thereby improving Legionella risk assessment process. Methods A total of 136 water samples (hot water and
more » ... es (hot water and cold water) was investigated from different points of a hospital water network. These samples were tested for Legionella sp by three laboratories using different diagnostic tests: culture, polymerase chain reaction (PCR) and a method based on immunomagnetic separation (IMS). The results for these three techniques were combined and interpreted by three microbiologists with the aim of defining a new standard index. A multivariate analysis was performed by logistic regression to estimate the adjusted risk associated with the type of water, length of the pipe, chlorine, temperature, type of terminal point, period of the year, type of health care (ambulatory or hospital) and the frequency of use of the terminal point. Results: There was no statistically significant difference, on the basis of the new standard index, neither between laboratories nor between diagnostic tests. Positive results of this index were significantly correlated with the outpatient medical consultations, shower, frequency of use of the terminal points, and temperature. Logistic regression model revealed that outpatient medical consultations (p=0.058), shower (p=0.007) and the frequency of use of the terminal points (p=0.001) are predictors of Legionella colonization. Conclusions: The inclusion of rapid techniques (IMS, PCR) in the development of a new standard index offer increased sensitivity of Legionella detection improving classical
doi:10.21203/rs.2.15735/v1 fatcat:hoq4aphvazhpllazinjbqitmmi