RESEARCH PAPER
Geostatistics – a tool applied to the distribution of Legionella pneumophila in a hospital water system
 
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1
Department of Biomedical and Dental Sciences and Morphofunctional Imaging. Section of Biotechnologies and Medicine Preventive. University of Messina, Italy
2
Department of Public Health – Università Cattolica del Sacro Cuore – Rome, Italy
 
Ann Agric Environ Med. 2015;22(4):655–660
 
KEYWORDS
ABSTRACT
Introduction:
Legionnaires’ disease is normally acquired by inhalation of legionellae from a contaminated environmental source. Water systems of large buildings, such as hospitals, are often contaminated with legionellae and therefore represent a potential risk for the hospital population. The aim of this study was to evaluate the potential contamination of Legionella pneumophila (LP) in a large hospital in Italy through georeferential statistical analysis to assess the possible sources of dispersion and, consequently, the risk of exposure for both health care staff and patients.

Material and Methods:
LP serogroups 1 and 2–14 distribution was considered in the wards housed on two consecutive floors of the hospital building. On the basis of information provided by 53 bacteriological analysis, a ‘random’ grid of points was chosen and spatial geostatistics or FAIk Kriging was applied and compared with the results of classical statistical analysis.

Results:
Over 50% of the examined samples were positive for Legionella pneumophila. LP 1 was isolated in 69% of samples from the ground floor and in 60% of sample from the first floor; LP 2–14 in 36% of sample from the ground floor and 24% from the first. The iso-estimation maps show clearly the most contaminated pipe and the difference in the diffusion of the different L. pneumophila serogroups.

Conclusions:
Experimental work has demonstrated that geostatistical methods applied to the microbiological analysis of water matrices allows a better modeling of the phenomenon under study, a greater potential for risk management and a greater choice of methods of prevention and environmental recovery to be put in place with respect to the classical statistical analysis.

 
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