Frequency of bovine cysticercosis in the state of Rondônia, Brazil

2020 ◽  
Vol 20 ◽  
pp. 100375
Author(s):  
Rafaella P.M. Guimarães-Peixoto ◽  
Camila Franco ◽  
Paulo S.A. Pinto ◽  
Gabriel A.M. Rossi ◽  
Breno C. Cruz ◽  
...  
2017 ◽  
Vol 37 (9) ◽  
pp. 931-936 ◽  
Author(s):  
Wellington C. Alves ◽  
Gabriel A.M. Rossi ◽  
Welber D.Z. Lopes ◽  
Henrique M.S. Almeida ◽  
Luis A. Mathias ◽  
...  

ABSTRACT: This study focused on assessing the prevalence, geospatial distribution and risk factors for bovine cysticercosis in cattle from the state of Rondônia, Brazil, through the years 2012 to 2015. The prevalence established was 0.014% (95% C.I. 0.013-0.014), with a higher detection of unviable cysticerci (84.80%). The municipalities of Itapuã do Oeste, Candeias do Jamari, Nova Brasilândia D’Oeste, Pimenteiras do Oeste, Porto Velho, Nova Mamoré, Urupá and Guajará-Mirim had higher risk (OR>1; p<0.05) for cysticercosis occurrence compared with the municipality of Castanheiras (OR=1). The Administrative Regions of Porto Velho, Guajará-Mirim, Colorado D’Oeste, Cacoal, Ji-Paraná had higher risk (OR>1; p<0.05) for cysticercosis occurrence in the slaughtered animals than those reared in Ariquemes Administrative Region (OR=1). Some variables such as human population density (OR=2.15; 2.15-2.16), percentage of urban houses with inappropriate sewage system (OR=1.91, 1.91-.1.92) and percentage of inappropriate rural sewage system (OR=1.14, 1.14-1.14) were significantly associated (p<0.05) with the occurrence of bovine cysticercosis. In conclusion, the prevalence of bovine cysticercosis in the state of Rondônia was 0.014% (95% C.I. 0.013-0.014) and higher-risk areas were identified, providing useful information to Official Sanitary Inspection System in order to improve cysticercosis detection. Also, human population density and the lack of appropriate sewage system in urban and rural areas are closely related to bovine cysticercosis occurrence in this state.


Author(s):  
Marcella Nunes Pereira ◽  
Gabriel Augusto Marques Rossi ◽  
Welber Daniel Zanetti Lopes ◽  
Henrique Meiroz de Souza Almeida ◽  
Luis Antonio Mathias ◽  
...  

2016 ◽  
Vol 130 ◽  
pp. 94-98 ◽  
Author(s):  
Gabriel Augusto Marques Rossi ◽  
Heloisa Adélia Stefanoni de Simoni ◽  
Welber Daniel Zanetti Lopes ◽  
Henrique Meiroz de Souza Almeida ◽  
Vando Edésio Soares ◽  
...  

2017 ◽  
Vol 3 ◽  
Author(s):  
FERNANDA MARTINS DE AQUINO ◽  
VANDO EDÉSIO SOARES ◽  
GABRIEL AUGUSTO MARQUES ROSSI ◽  
LUIZ ANTÔNIO CARDOSO DANIN ◽  
JOÃO EDUARDO NICARETTA ◽  
...  

SUMMARY This study aimed to assess the prevalence and spatial distribution of bovine cysticercosis in the state of Goiás, Brazil; to verify its association with epidemiological variables, and to establish the economical losses for beef farms. A set of 23 255 979 bovines from 246 municipalities were slaughtered from 2007 through 2014. The prevalence of bovine cysticercosis was 0·53% [95% confidence interval (95% CI) 0·5295–0·5354]. The Central mesoregion showed a higher risk [odds ratio (OR) = 4·44; 95% CI 4·2936–4·5895] for detecting infected animals with cysticerci compared with those raised at North and Northeast mesoregion (OR = 1·02 and OR = 1·02). The microregion of Goiânia had a higher risk for bovine cysticercosis occurrence (OR = 11·05, 95% CI 10·6933–11·4099) compared with the microregion of São Miguel do Araguaia (OR = 1). None of the epidemiological variables evaluated in this study was significantly associated (P &gt; 0·05) with bovine cysticercosis prevalence. In conclusion, the prevalence of bovine cysticercosis in the state of Goiás, Brazil, was 0·53% and some mesoregions and microregions presented a higher risk for its occurrence. The economical losses due to its occurrence during the period ranged from US$9 260 728·57 to 11 313 816·67. These results highlighted the needs of adopting prophylactic measures and the development of political strategies in specific regions in order to control this zoonose and reduce the economical losses for beef production chain and the costs for public health.


2016 ◽  
Vol 1 (2) ◽  
pp. 116-123 ◽  
Author(s):  
Barbara Rauta de Avelar ◽  
Lazaro Corrêa Marcelino ◽  
Rafael Ferraço de Campos ◽  
Alexandre Rosa dos Santos ◽  
Isabella Vilhena Freire Martins

2021 ◽  
Vol 191 ◽  
pp. 105361
Author(s):  
Vinicius Cardoso Comin ◽  
Luis Antonio Mathias ◽  
Henrique Meiroz de Souza Almeida ◽  
Gabriel Augusto Marques Rossi

2017 ◽  
Vol 26 (2) ◽  
pp. 216-220
Author(s):  
Amanda Rafaela Alves Maia ◽  
Paulo Sérgio de Arruda Pinto ◽  
Rafaella Paola Meneguete dos Guimarães Peixoto ◽  
Letícia Ferreira da Silva ◽  
Leise Gomes Fernandes ◽  
...  

Abstract The aim of this survey was to identify spatial clustering of bovine cysticercosis-positive herds in the state of Paraíba. The state was divided into three sampling groups: sampling stratum 1 (Sertão mesoregion), sampling stratum 2 (Borborema mesoregion) and sampling stratum 3 (Zona da Mata and Agreste mesoregions), and 2382 cows aging ≥ 24 months from 474 farms were sampled. Serological diagnoses of bovine cysticercosis were initially done by means of indirect ELISA, and positive serum samples were confirmed by a immunoblot test. Herds were deemed positive for cysticercosis if they presented at least one positive animal in herds of up to 29 females, and two positive animals in herds with more than 29 females. The spatial clustering was assessed using the Cuzick-Edwards k-nearest neighbor method and spatial scan statistics. A significant clustering of positive herds was detected in the southern part of the Borborema mesoregion. Given that serological tests for bovine cysticercosis are not widely available, and also that replacement and maintenance of herds through animal purchases is common in the region, it can be concluded that prevention measures should be applied at herd level.


2020 ◽  
Vol 52 (6) ◽  
pp. 3373-3379
Author(s):  
Deise Janice Henckel ◽  
Vinicius Cardoso Comin ◽  
Henrique Meiroz de Souza Almeida ◽  
Luis Antonio Mathias ◽  
Gabriel Augusto Marques Rossi

2017 ◽  
Vol 142 ◽  
pp. 51-57 ◽  
Author(s):  
Amanda R.A. Maia ◽  
Leise G. Fernandes ◽  
Paulo S.A. Pinto ◽  
Rafaella P.M. Guimarães-Peixoto ◽  
Letícia F. Silva ◽  
...  

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