Landscape determinants of Saint Louis encephalitis human infections in Cordoba city, Argentina during 2010

被引:14
作者
Vergara Cid, Carolina [1 ]
Lilia Estallo, Elizabet [2 ]
Ricardo Almiron, Walter [2 ]
Silvia Contigiani, Marta [1 ]
Ivana Spinsanti, Lorena [1 ]
机构
[1] Univ Nacl Cordoba, Fac Ciencias Med, Inst Virol Dr JM Vanella, RA-5016 Cordoba, Argentina
[2] Univ Nacl Cordoba, Fac Ciencias Exactas Fis & Nat, Ctr Invest Entomol Cordoba, Inst Invest Biol & Tecnol IIByT CONICET, RA-5016 Cordoba, Argentina
关键词
Saint Louis encephalitis virus; Argentina; Geographical information systems; Human infections; Landscape elements; WEST-NILE-VIRUS; AEDES-AEGYPTI; OUTBREAK; ASSOCIATION; MOSQUITOS; HABITATS; COUNTY;
D O I
10.1016/j.actatropica.2012.12.005
中图分类号
R38 [医学寄生虫学]; Q [生物科学];
学科分类号
07 ; 0710 ; 09 ; 100103 ;
摘要
Saint Louis encephalitis virus (SLEV) is endemic in Argentina. During 2005 an outbreak occurred in Cordoba. From January to April of 2010 a new outbreak occurred in Cordoba city with a lower magnitude than the one reported in 2005. Understanding the association of different landscape elements related to SLEV hosts and vectors in urban environments is important for identifying high risk areas for human infections, which was here evaluated. The current study uses a case-control approach at a household geographical location, considering symptomatic and asymptomatic human infections produced by SLEV during 2010 in Cordoba city. Geographical information systems and logistic regression analysis were used to study the distribution of infected human cases and their proximity to water bodies, vegetation abundance, agricultural fields and housing density classified as high/low density urban constructions. Population density at a neighborhood level was also analyzed as a demographic variable. Logistic regression analysis revealed vegetation abundance was significantly (p < 0.01) associated with the presence of human infections by SLEV. A map of probability of human infections in Cordoba city was derived from the logistic model. The model highlights areas that are more likely to experience SLEV infections. Landscape variables contributing to the outbreak were the proximity to places with vegetation abundance (parks, squares, riversides) and the presence of low density urban constructions, like residential areas. The population density analysis shows that SLEV infections are more likely to occur when population density by neighborhood is lower. These findings and the predictive map developed could be useful for public health surveillance and to improve prevention of vector-borne diseases. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:303 / 308
页数:6
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