Spatial analysis of bovine cysticercosis in France in 2010

Abstract

Abstract Bovine cysticercosis is a zoonosis caused by the cestode Taenia saginata and involves cattle as the intermediate host and humans as the final host. This disease is both a public health issue and an economic concern for farmers. Cattle are infected after grazing on infected pasture. Humans are infected by the consumption of raw or under-cooked meat. This study aimed to identify geographical areas where animals are infected by bovine cysticercosis so as to implement adequate control measures and to provide a risk-based meat inspection process for improving disease detection. Considering both the long period of cyst development in cattle muscle and the complexity of cattle movements, a spatial analysis of slaughtered cattle found to be harboring viable and degenerated cysts was a challenge. Detection of clusters of bovine cysticercosis cases was performed using a spatial scan statistic with a discrete Poisson model adjusted for a variable combining age and sex. The novelty of this approach was that it used an animal-herd level weighted analysis to take into account the uncertainty of the location where animals became infected. This study included 4,557,593 (91.3%) cattle slaughtered in 2010 in France in 181 slaughterhouses. The meat inspection process enabled the detection of 6431 cattle harboring at least one bovine cysticercosis lesion and 603 harboring at least one viable cyst. Three significant clusters for cattle with all types of cysts were detected through the spatial analysis in north-western and eastern France. One significant cluster was detected in eastern France for cattle with viable cysts only. The difference in location of the clusters detected, when considering only cattle harboring viable cysts or cattle harboring all types of cysts, proved the relevancy of this novel approach. We identified areas in France with a higher risk of bovine cysticercosis in which investigations could be performed to identify the risk factors that explained this spatial distribution. These risk factors could then be used to suggest control measures in these areas and to implement a reinforced meat inspection protocol so as to increase the efficiency of the current meat inspection process.