Retrospective and spatial analysis tools for integrated surveillance of cystic echinococcosis and bovine cysticercosis in hypo-endemic areas

Submitted: 10 December 2014
Accepted: 10 December 2014
Published: 1 May 2014
Abstract Views: 2253
PDF: 893
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Cystic echinococcosis (CE) and bovine cysticercosis (BC) are two important parasitic zoonoses, whose prevalence varies among European countries. Few data are available on prevalence and geographic distribution of these two diseases in Veneto region in north-eastern Italy, where they are generally perceived as minor public health problems. Available data from regional farms on cattle positive to CE and BC and slaughtered in the period 2006-2010 were analysed by spatial scan statistic using a Bernoulli probability model. Out of 576 bovines testing positive to CE, 467 were found to be autochthonous cases. Three significant CE clusters were identified, the most likely one (P <0.001) located in the eastern part of the Veneto region. As for BC, two clusters were identified from 148 animals testing positive, 91 of which were autochthonous. An epidemiological survey was conducted and the most likely CE cluster was centred, collecting faecal samples from 28 dogs living in the farms of the area. Out of five animals (all shepherd dogs) found positive for taenid eggs by copromicroscopy, one was confirmed positive for Echinococcus granulosus by means of polymerase chain reaction. The study demonstrates the usefulness of integration of slaughterhouse data and geographical coordinates of farms involved for effective surveillance of CE and BC. The reliability of the spatial analysis in the identification of clusters of EC cases was confirmed by the finding of one dog positive for E. granulosus.

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Cassini, R., Mulatti, P., Zanardello, C., Simonato, G., Signorini, M., Cazzin, S., Tambalo, P. G., Cobianchi, M., Pietrobelli, M., & Capelli, G. (2014). Retrospective and spatial analysis tools for integrated surveillance of cystic echinococcosis and bovine cysticercosis in hypo-endemic areas. Geospatial Health, 8(2), 509–515. https://doi.org/10.4081/gh.2014.40

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