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Since the first occurrence of infection with this new orthobunyavirus in late summer 2011, the Schmallenberg virus (SBV) infection has spread rapidly in Germany and in many other European countries. A multitude of epidemiologic aspects of the SBV infection had initially been unexplored. As for many other firstly unexplored infectious diseases, an identification and estimation of risk factors for the spread of this very disease is required in order to develop efficient measures to decrease the risk of infection.
In the context of this case-control study of SBV infection in Germany, data has been analyzed retrospectively from November 2011 to February 2013 from 7 federal states in order to identify potential risk and protective factors of SBV infection on cattle and sheep farms. During farm visits, standardized surveys with questionnaires were used to generate data on farm management, veterinary visits and monitoring, herd visits by people with close animal contacts, occurring diseases and potential causes of invasion or retransmission. Furthermore, samples were taken to determine the seroprevalence. After the match of the serologic results had led to falsely categorised control cases, the case definitions had to be adjusted to achieve a better distinction between cases and controls. The fast spread and retransmission of this new disease in Germany was also reflected in the difficulty of finding SBV-free farms at the time of the visits and in the distribution of cases and controls within the investigation area. Accordingly, farms close to the epidemiologic centre showed high intraherd prevalence, whereas farms in peripheral regions were less exposed to SBV.
In the bivariate and multivariate analyses of risk factors of this study, 73 variables were tested from 7 control and 33 case farms in the cattle sector, while 63 variables were checked from 16 control and 29 case farms in the sheep sector. Bivariate analyses based on the Fisher exact test resulted in 7 variables for cattle and 5 variables for sheep showing statistically significant differences between case and control farms. From the multivariate analyses, 3 models were derived for each of the two species. Its underlying variables played an important role to explain the target variable by bivariate analysis and using logistic regression models.
For cattle farms, the variables “temporary indoor housing”, “own bull” and “purchase of new animals” revealed positive associations with SBV infection. The variables “migrating sheep herds” and “all-year indoor housing” showed a protective effect on a statistically significant level. The best multivariate model explaining the occurrence of SBV infection consisted of the variables “purchase of new animals”, “migrating sheep herds” and “all-year indoor housing”. For sheep farms, the variables „keeping poultry“, “keeping hair sheep” and “all-year coverage” revealed statistically significant differences between case and control farms in the bivariate analysis. In the multivariate analysis, the combination of “fertility disturbances of ewes”, “regular veterinary care” and “keeping of poultry” explained the target variable in the best way.
This case-control study did not indicate a correlation between SBV infection and the proximity of the farms to wetlands or to the use of repellents and insecticides.