INVESTIGADORES
ALMIRON Walter Ricardo
artículos
Título:
Landscape determinants of Saint Louis encephalitis human infection in Córdoba city, Argentina, during 2010
Autor/es:
VERGARA CID C.; ESTALLO ELIZABET,; ALMIRÓN, WALTER R.; CONTIGIANI, MARTA; SPINSANTI, LORENA I
Revista:
ACTA TROPICA
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Lugar: Amsterdam; Año: 2013 vol. 125 p. 303 - 308
ISSN:
0001-706X
Resumen:
Saint Louis encephalitis virus (SLEV) is endemic in Argentina. During 2005 an outbreak ocurred in Córdoba. From January to April of 2010 a new outbreak occurred in Córdoba 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 Córdoba city. Geographical information systems and logistic regression 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 of human infections by SLEV. A map of probability of human infections in Córdoba 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.