Space-time description of dengue epidemic in Cruzeiro, SP, in 2006 and 2011

• Autor(a) principal: De Carvalho, Renata Marzzano
• Data de Publicação: 2014
• Outros Autores: Nascimento, Luiz Fernando Costa
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UNITAU
• Texto Completo: http://repositorio.unitau.br/jspui/handle/20.500.11874/2928
• topic: Dengue; Geographic Information Systems; Epidemiological Surveillance; Ecological Study; Spatial Distribution Of The Population; Southeastern Brazil; State; Medicine, General & Internal; General & Internal Medicine

Objective: to identify patterns in the spatial and temporal distribution of cases of dengue fever occurring in the city of Cruzeiro, state of Sao Paulo (SP). Methods: an ecological and exploratory study was undertaken using spatial analysis tools and data from dengue cases obtained on the SinanNet. The analysis was carried out by area, using the IBGE census sector as a unit. The months of March to June 2006 and 2011 were assessed, revealing progress of the disease. TerraView 3.3.1 was used to calculate the Global Moran's I, month to month, and the Kernel estimator. Results: in the year 2006, 691 cases of dengue fever (rate of 864.2 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (TM = 0.28; p = 0.01; I-M = 0.20; p = 0.01) with higher densities in the central, north, northeast and south regions. In the year 2011, 654 cases of dengue fever (rate of 886.8 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (I, = 0.28; p = 0.01; I-M = 0.16; p = 0.05) with densities in the same regions as 2006. The Global Moran's I is a global measure of spatial autocorrelation, which indicates the degree of spatial association in the set of information from the product in relation to the average. The I varies between -1 and +1 and can be attributed to a level of significance (p-value). The positive value points to a positive or direct spatial autocorrelation. Conclusion: we were able to identify patterns in the spatial and temporal distribution of dengue cases occurring in the city of Cruzeiro, SP, and locate the census sectors where the outbreak began and how it evolved.