Environmental surveillance and spatio-temporal analysis of Legionella spp. in a region of northeastern Italy (2002-2017).

Antonella Felice, Marinella Franchi, Stefano De Martin, Nicola Vitacolonna, Lucilla Iacumin, Marcello Civilini
Author Information
  1. Antonella Felice: Laboratory of Microbiology, ARPA - Regional Agency for Environmental Protection Friuli Venezia Giulia, Udine, Italy.
  2. Marinella Franchi: Laboratory of Microbiology, ARPA - Regional Agency for Environmental Protection Friuli Venezia Giulia, Udine, Italy.
  3. Stefano De Martin: Laboratory of Microbiology, ARPA - Regional Agency for Environmental Protection Friuli Venezia Giulia, Udine, Italy.
  4. Nicola Vitacolonna: Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy. ORCID
  5. Lucilla Iacumin: Department of Agriculture, Food, Environmental and Animal Sciences, University of Udine, Udine, Italy.
  6. Marcello Civilini: Department of Agriculture, Food, Environmental and Animal Sciences, University of Udine, Udine, Italy.

Abstract

Legionella spp. are considered an important cause of potentially preventable morbidity and mortality, making environmental surveillance a crucial component of risk assessment plans. In this work, 20,319 water samples were collected in 3,983 environmental surveys during a 16-year period by ARPA, the Regional Agency for Environmental Protection, Friuli Venezia Giulia, and the results were studied to better understand the diffusion mechanisms of Legionella. The data showed a strong seasonal signal, a prevalence of L. pneumophila serogroup 2-15 in most environments (63% of positive samples), a prevalence of L. pneumophila serogroup 1 in swimming pool-associated environments (82% of positive samples), a persistent presence of Legionella in hospitals and a recurrent presence of Legionella in other facilities such as hotels, possibly years after interventions, highlighting the difficulty of eradicating the bacteria. Retrospective spatio-temporal analyses on geocoded historical data were carried out with SaTScan using an ordinal model with risk as a covariate to identify potential clusters with an excess of cases in the higher-risk categories. Although no outbreaks occurred during the period of study, such analyses identified spatially restricted zones with unusual contamination, which sometimes were also areas in which several surveys triggered by notifications of clinical cases were performed. Simulations of periodic prospective analyses permitted the assessment of the efficacy of the method in early detection of such clusters. The proposed method may be a useful tool in environmental surveillance, prevention and control of Legionella.

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MeSH Term

Conservation of Natural Resources
Environmental Monitoring
Humans
Italy
Legionella pneumophila
Legionellosis
Risk Assessment
Spatio-Temporal Analysis
Swimming Pools
Water Microbiology
Water Supply

Word Cloud

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