Identificação de áreas sob maior risco para leishmaniose visceral na cidade de Teresina, Piauí, Brasil

• Autor(a) principal: Almeida, Andréa Sobral de
• Data de Publicação: 2011
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UERJ
• Texto Completo: http://www.bdtd.uerj.br/handle/1/4552
• topic: Visceral leishmaniasis; Epidemiology. Public health surveillance; Public health; Geographic Information Systems (GIS); Remote sensing; Multivariate analysis; Preditive models; Leishmaniose visceral; Epidemiologia; Vigilância em saúde pública; Saúde pública; Sistema de Informação Geográfica (SIG); Sensoriamento remoto; Análise multivariada; Modelos preditivos; CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA::EPIDEMIOLOGIA

The objective of this Thesis was to detect and characterize areas under high risk of visceral leishmaniasis (VL) and to describe patterns of occurrence and diffusion of the disease, between the years of 1993 1996 and 2001 the 2006, in Teresina, Piauí, Brazil, using statistical methods for spatial data analysis, geographic information systems and remote sensing. The results of this study are presented in three manuscripts. The first used spatial analysis to identify areas at greatest risk of VL in the urban area of Teresina, Brazil from 2001 to 2006. The results from kernel ratios showed that peripheral census tracts were the most heavily affected. Local spatial analysis showed that in the beginning of the study period local clusters of high incidence of VL were mostly located in the South and Northeast parts of the city, but in the following years those clusters appeared also in the North region of the city, suggesting that the pattern of VL occurrence is not static and disease may occasionally spread out to other areas of the municipality. The second study aimed to characterize and predict areas at high risk for occurrence of VL in Teresina, based on socioeconomic and environmental indicators obtained by remote sensing. The results of object-oriented classification indicate the expansion of the urban area to the periphery of the city, where larger areas covered by vegetation were usually seen. The model developed was able to discriminate 15 subsets of census tracts (SC) with different probabilities of containing SC with high risk of VL occurrence. The subset with higher probability of containing SC with high risk of VL (92%) encompassed SC with percentage of literate heads-of-households less than the median (≤64.2%), with greater area covered by dense vegetation, with percentage of up to 3 people per household above the third quartile (>31.6%). The model presented, respectively, in the validation and training samples, sensitivity of 79% and 54%, specificity of 75% and 71% global accuracy of 75% and 67% and area under the ROC curve of 83% and 66%. The third manuscript aimed to evaluate the applicability of object-oriented classification in searching for possible indicators of "land cover" related to the occurrence of VL on the urban environment. Accuracy indices were high for both images (>90%). In the correlation analysis, the incidence of VL was found to be positively correlated with dense vegetation, vegetation and exposed soil and negatively correlated with water, dense urban and green urban indicators, all statistically significant. The results of this thesis reveal that the occurrence of VL on the periphery of Teresina is intensely related to inadequate socioeconomic conditions and environmental modifications arising from the process of urban expansion, both favoring the occurrence of the vector (Lutzomyia longipalpis) in these regions.