ANÁLISE ESPACIAL DOS CASOS PROVÁVEIS DE DENGUE NO MUNICÍPIO DE SÃO LUÍS, MARANHÃO – BRASIL.

• Ano de defesa: 2019
• Autor(a) principal: COSTA, Flávia Regina Vieira da
• Orientador(a): BRANCO, Maria dos Remédios Freitas Carvalho
• Banca de defesa: BRANCO, Maria dos Remédios Freitas Carvalho , RODRIGUES, Zulimar Márita Ribeiro , MEDEIROS, Maria Nilza Lima , QUEIROZ, Rejane Christine de Sousa , GONÇALVES, Eloisa da Graça do Rosário
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM SAÚDE E AMBIENTE/CCBS
• Departamento: DEPARTAMENTO DE PATOLOGIA/CCBS
• País: Brasil
• Palavras-chave em Português: Dengue; Vigilância em Saúde Pública; Análise Espacial
• Área do conhecimento CNPq: Ecologia Aplicada.
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/2757

Dengue is considered one of the main arboviruses worldwide, characterized in Brazil by the increase of serious cases and deaths. The objective of this study was to perform a spatial analysis of the probable cases of dengue fever in São Luís-MA. A population - based ecological study of the probable cases of dengue, reported in the Information System for Notifiable Diseases (SINAN) in 2015 and 2016, occurred in the city of São Luís-MA. A descriptive analysis of gender and age group was performed. Were georeferenced, by comun census tracts, 4,681 probable cases of dengue, incidence rates were calculated and adjusted using the local empirical Bayesian estimator. The density estimator of Kernel and Moran Global and Local was used for spatial analysis. There was a higher concentration of dengue cases in the female sex (57.59%) and the age group in the adult phase of 20-35 years (35.65%). It was evidenced by the Kernel density estimator, hot areas (high-density) in the census tracts of the northwest region of the municipality. The incidence rates were adjusted by the application of the local empirical Bayesian model, identifying a larger number of sectors with medium and high incidence. From the global Moran index, a statistically significant positive spatial autocorrelation was observed for the dengue incidence rates (I = 0.69, p <0.001) and for the incidence rates adjusted by the Bayesian method (I = 0.80; <0.001). The overall Moran index identified a significant spatial correlation for the local Bayesian rate and population density (I = 0.05; p <0.05). It was evidenced with the Moran Local index that in the map of the empiric Bayesian rate there was a reduction of sectors classified as not significant and an increase of the areas considered of low incidence in relation to the map of the incidence rate. For both rates were observed clusters of sectors with high incidence and their neighbors also with high incidence of dengue in the northeast and northwest regions of the municipality. According to the local Moran index, we identified clusters of sectors with a high incidence of dengue in areas with high population density in the northeast and northwest regions of the municipality. There was heterogeneity in the distribution of cases in the census tracts, identifying areas with a higher concentration of cases - information that if analyzed in a timely manner by epidemiological surveillance could impact vector control and reduce the number of infected individuals.