Dengue em município do interior paulista: áreas de risco e relação com variáveis socioeconômicas, demográficas e ambientais

Abstract

Background – Spatial analysis technology can provide a better understanding of dengue virus transmission dynamics, thus allowing for the improvement of prevention and control of the disease. Objectives – To describe the occurrence of dengue fever in the city of Varzea Paulista, Southeastern Brazil, between 1998 and 2012 and to characterize the epidemic occurred in 2007; to identify, based on the epidemic’s data, the spatial and spatio temporal distribution of the risk of dengue; evaluating, based on the occurrence of the disease, the relationship between incidence rates and socioeconomic, demographic and environmental variables, taking as units of analysis, the census tracts. Methods – Data were obtained from Sistema de Informacao de Agravos de Notificacao (SINAN – Information System for Notifiable Diseases). Dengue fever cases registered in the city were geocoded by street names and grouped according to 165 census tracts, thus generating thematic maps. Incidence rates were calculated for the study period, as well as the identification of higher and lower-risk areas for space and space-time clusters of dengue fever. Geocoding, spatial analysis and generation of maps were performed using the softwares ArcGis, TerraView, and SatScan. Spatial relative risk was obtained from a generalized additive model for a case-control study. The association between socioeconomic variables and incidence rates using spatial regression analysis was tested in order to find the best explanatory model for the dengue fever epidemic in 2007. Results – The maps generated showed the spatial and space-time distribution of dengue fever in the city, the higher risk areas, and the course of the epidemic during the epidemiological weeks. Two clusters were identified in 2007. The high risk cluster was related to poor sanitary conditions whereas individuals in the low risk cluster showed the best socioeconomic indicators. Spatial, space-time and regression analyses provided similar results regarding the higher incidence and risk in areas with the worst socioeconomic indicators. Conclusions – The first dengue fever epidemic in the city can be explained by the immunological status of the population when a new serotype was introduced and the conditions of social vulnerability. The method has proven efficient for identification of risk areas, thus allowing for better planning and resource allocation from the government. Descriptors: Dengue; Geographic Information Systems; Spatial Analysis; Epidemiological Surveillance; Ecological Studies