Prevalence and geospatial distribution of bovine cysticercosis in the state of Mato Grosso, Brazil

• Published in: Preventive veterinary medicine 2016-08, Vol.130, p.94-98
• Main Authors: Rossi, Gabriel Augusto Marques, de Simoni, Heloisa Adélia Stefanoni, Lopes, Welber Daniel Zanetti, Almeida, Henrique Meiroz de Souza, Soares, Vando Edésio, Vidal, Ana Maria Centola, Ferraudo, Antonio Sergio, Mathias, Luis Antonio
• Format: Article
• Language: English
• Subjects: Abattoirs; Animals; Brazil - epidemiology; Cattle; Cattle Diseases - epidemiology; Cattle Diseases - parasitology; Cattle meat inspection; Cysticercosis - epidemiology; Cysticercosis - veterinary; Cysticercus - isolation & purification; Female; Food Inspection; Geographic information system (GIS); Geographic Information Systems; Humans; Logistic Models; Male; Meat - parasitology; Prevalence; Spatial Analysis; Taenia saginata; Zoonosis
• ISSN: 0167-5877; 1873-1716
• DOI: 10.1016/j.prevetmed.2016.06.008

•The bovine cysticerosis prevalence observed was 0.0873% (95% CI 0.0851–0.0897).•Some regions presented higher risk for the disease’s occurrence.•Cysticercosis prevalence is linked with human population density.•An origin risk-based model is suggested for the disease control. This study focused on estimating the prevalence and evaluating the geospatial distribution of bovine cysticercosis in the state of Mato Grosso, Brazil. To this, we used data of 6,200,497 animals slaughtered during the years of 2013 and 2014, and from 141 municipalities of the state. The prevalence observed for this period was 0.0873% (95% CI 0.0851–0.0897). Regarding the cysticerci detected, the calcified ones were the most frequent (74.43%). The high odds ratios were observed in animals reared in the Administrative Regions of Sinop, Barra do Garças, Água Boa, Cáceres, Barra do Bugres, Cuiabá, Pontes Lacerda, Rondonópolis, Matupa, São Félix do Araguaia and Lucas do Rio Verde, respectively. Furthermore, the results indicate the existence of a relation between the areas with high cysticercosis prevalence and human population density. We highlight the need of the development of a risk model based on the origin to improve cysticercosis detection in endemic areas.