Distribuição espacial da dengue e sua coerência geográfica no município do Rio de Janeiro nos anos 2000 a 2019

• Autor(a) principal: Valente, Bárbara Campos Silva
• Data de Publicação: 2022
• Outros Autores: [e-mail]
• 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/1/18711
• topic: Dengue; Spatial analisys; Epidemiology; Spatio-temporal analisys; Wavelet analisys; Dengue; Análise espacial; Epidemiologia; Análise espaço-temporal; Análise de Wavelet; CIENCIAS DA SAUDE::SAUDE COLETIVA

Dengue is a major public health problem in the municipality of Rio de Janeiro since its reintroduction in Brazil in the 1980s and several factors have been pointed out as suitable for the persistence of the disease in the municipality, such as the hot and humid climate, social vulnerability in much of the territory with deficient supply and access of the population to essential goods and services, creating conditions conducive to the development and maintenance of the cycle of the vector Aedes aegypti. Moreover, the wide dissemination of the vector throughout the city and the co-circulation of various serotypes of the virus (DEN-1, DEN-2, DEN-3, and DEN 4) make its control difficult. According to the rapid index survey for Aedes aegypti (LIRAa) of August 2019, Rio de Janeiro was one of the municipalities in the state of Rio de Janeiro that presented regions classified in the risk stratum, reflecting the fragility of vector control in the municipality. In this context, it becomes relevant the use of spatial and temporal analysis tools, to understand the dynamics of dengue in the city of Rio de Janeiro during the 20 years of study. For this, an ecological study was conducted based on secondary data of dengue notification in the period from 2000 to 2019, where, initially, the movement of the geographic trajectory of the disease during the study period was described, by months and neighborhoods, in addition to characterizing areas of high and low risk for dengue. Incidence rates smoothed by the local empirical Bayes method and local indicators of spatial autocorrelation were calculated for the years studied. The location of neighborhood centroids and smoothed incidence rates were used to plot the monthly and annual trajectory of the geographic center of dengue occurrence. Subsequently, a geographic coherence analysis was performed using wavelet analysis in order to understand the spread of the disease in the municipality over time in its Administrative Regions. We analyzed the monthly incidence rates of dengue in the period from 2000 to 2019 in the municipality of Rio de Janeiro to understand the behavior between time series from different areas and identify possible relationships between them. Power spectrum analysis was performed for the time series of each Administrative Region, which allows to verify the periodicity of the series, its predominant signal, and the average power. Phase analysis was also performed for each Administrative Region and phase extraction of the epidemic years in the annual period. The results suggest that dengue occurs in a heterogeneous manner in the city of Rio de Janeiro, with clusters of high incidence migrating to the West Zone of the city over the two decades analyzed. Regarding the consistency of dengue the results suggested predominantly annual periodicity across regions and with large peaks of 3, 4, and 5 years, with differences between regions and most series moved together over the years. Our findings suggest that in epidemic years, some Administrative Regions in the North Zone and West Zone were approximately 2 months to 1 month ahead of the other regions. This lag may be a potential for control in areas that were delayed, considering this time for planning assistance, surveillance, and control actions. The trajectory of dengue and the variation of its spatial distribution over time reveal a complex system of occurrence, potentially dependent on the interaction between the levels of immunity acquired by the population and changes in socioenvironmental and demographic factors. Analyses that incorporate spatial and temporal dimensions may be useful to inform more intensive, integrated, and targeted control strategies for specific regions.