Abstract
BACKGROUND: Fasciola hepatica is a parasite with a significant impact on ruminant livestock production. Previous studies in north-west Europe have described its geographical distribution and determined potential predictors of fasciolosis using geographical information system (GIS) and regression modelling. In Sweden, however, information about the distribution of fasciolosis is limited. This study examined the geographical distribution of F. hepatica and identified high-risk areas for beef cattle in Sweden and sought to characterise potential predictors. Beef cattle serum samples were collected during winter 2006-2007 from 2135 herds which were examined for F. hepatica antibodies by enzyme-linked immunosorbent assay (ELISA). Fasciolosis distribution maps were created using GIS based on postcode location of seropositive herds. Spatial scan analysis (SaTScan) was performed to determine high-risk areas. Using datasets on animal density, temperature, precipitation and Corine land cover data, including soil type and soil mineral concentrations in Sweden, bivariate and multiple logistic regression analyses were carried out in R software to reveal potential predictors of F. hepatica infection. RESULTS: Overall herd seroprevalence of F. hepatica in beef cattle was 9.8 % (95 % CI: 8.6-11.1). An irregular spatial distribution of F. hepatica, with two main clusters, was observed in south-west Sweden. The most northerly occurrence of F. hepatica in the world was documented. The final model explained 15.8 % of the variation in F. hepatica distribution in study herds. Absence of coniferous forest was the variable with the highest predictive value. Precipitation in July-September, Dystric Cambisol, Dystric Regosol, and P and Cu concentrations in soil were other negative predictors. Beef cattle herd density, Dystric Leptosol and Fe concentration were positive predictors. CONCLUSIONS: The spatial distribution of F. hepatica in Swedish beef cattle herds is influenced by multi-factorial effects. Interestingly, absence of coniferous forest, herd density, specific soil type and concentration of some soil minerals are more important predictors than climate factors.