Made available in DSpace on 2011-05-04T12:36:15Z (GMT). No. of bitstreams: 0 Previous issue date: 2009 / Este estudo seguiu desenho ecológico analítico e teve por objetivo analisar a distribuição espacial e temporal da dengue no município do Rio de Janeiro, investigando possíveis associações entre a incidência de dengue e os níveis médios de exposição a variáveis ambientais, sócio-demográficas e à infestação pelo vetor. (...) / This study is an ecological analysis design, aimed to examine the spatial and temporal distribution of dengue in Rio de Janeiro, in order to investigating possible associations between the incidence of dengue and the average levels of exposure to evironmental nde socio-demographic variables, and infestation by vecto. Dengue is a reemerging disease, ande nowadays is the most important human viral disease transmitted by mosquito. In Brazil, the vector hs found, since its reintroduction in 1976, fvorable conditions to their dispersion, ande the accelerated process of urbanization and population growth combined with the inefficiency of the vector-control programs. There were seven outbreaks of dengue in Rio de Janeiro: 1986/1987, 1990/1991, 1995, 1998, 2001/2002, 2006 and 2007/2008. In this study, was created a time series and was analyzed the spatial distribution of disease in the city, during the period 1996 to 2006. The samples was represented by reported neighborhood. the data was obtained from SINAN (National Surveillance System). We created an indicator of the incidence rate per 100.000 in habitants. The following variables were employed: rain index, Breteau index (for Aedes aegypti and Aedes albopictus), Gini index, ande IDS, synthetic indicator for social developmente. Thematic maps were built, using the Kernel estimator of intensity for spatial smoothing. The spatial autocorrelation of dengue between neighbors was calculated by the global and the locl Morn Index. For modeling, we used the generalized linear model and the spatial model CAR (Conditionl Auto Regressive). It was found positive spatial autocorretion bya Moran Global, and some clusters were identified by graphical analysis of the Local Morn. The final GLM model pointed out direct association between the incidence of dengue and rainfall; the time-lag of a month for rainfall; the Gini index for Aedes aegypt IB. Direct association was observed also for the months February, March, April and May, and inverse association for Octuber and November. CAR spatial models were conducted separately for each month. We found direct association with the rainfall in January, March, May and June; with the time-lag of rainfall in July, and with the Gini index in February.