ANÁLISE ESPACIAL DE CASOS PROVÁVEIS DE DENGUE, CHIKUNGUNYA E ZIKA NO MARANHÃO, BRASIL.

• Autor(a) principal: COSTA, Silmery da Silva Brito
• Data de Publicação: 2019
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFMA
• Texto Completo: https://tedebc.ufma.br/jspui/handle/tede/tede/2946
• topic: Dengue;; Chikungunya;; Zika;; Análise Espacial;; Fatores socioeconômicos;; Fatores sociodemográficos; Dengue;; Chikungunya;; Zika;; Spatial analysis;; Socioeconomic factors;; Sociodemographic Factors; Doenças Infecciosas e Parasitárias

Dengue, chikungunya and zika are extremely relevant arboviruses for world public health, given the damage they cause to the population and economic and social impacts in the affected countries. This ecological study used spatial analysis of probable cases of dengue, chikungunya and zika reported in the Notified Disease Information System (SINAN) in the State of Maranhão, Brazil, from 2015 to 2016. In the first article, the distribution of probable cases of dengue, chikungunya and zika in Maranhão was spatially analyzed, relating it to sociodemographic and economic factors, Unified Health System Performance Index (IDSUS) and vector infestation. The unit of analysis was the municipalities. Geodaversion 1.10 software was used to calculate Moran Global and Local indexes. In the univariate analysis, the Moran Global Index identified a significant autocorrelation of dengue (I = 0.10; p = 0.009) and Zika (I = 0.07; p = 0.03) incidence rates. In the bivariate analysis, there was a positive spatial correlation between dengue and population density (I = 0.31; p <0.001) and a negative correlation with IDSUS for primary care coverage (I = -0.08; p = 0.01). Regarding chikungunya, there were positive spatial correlations with population density (I = 0.06; p = 0.03) and the Municipal Human Development Index (MHDI) (I = 0.10; p = 0.002) and negative correlation with Gini index (I = -0.01; p <0.001) and IDSUS for primary care coverage (I = - 0.18; p <0.001). Finally, positive spatial correlations were identified between zika and population density (I = 0.13; p = 0.005) and MHDI (I = 0.12; p <0.001), as well as negative correlation with Gini index. (I = -0.11; p <0.001) and IDSUS by primary care coverage (I = - 0.05; p = 0.03). In the second article, we analyzed the spatial distribution of the cases of the three georeferenced diseases in the municipality of São Luís, Maranhão, from 2015 to 2016, relating it to socioenvironmental factors, economic and strategic points. The unit of analysis was the census sector. Arcgis version 10.4.1 software was used for georeferencing of disease cases, QGIS version 3.6.0 to aggregate cases by census sector, GeoDa 1.10 for the Global and Local Moran Index and spatial models, and for the classical model, the Stata software. ® 14.0. From the Moran Global Index, significant spatial autocorrelation of the incidence of the three arboviruses was identified (I = 0.55; p = 0.001). The model with the best performance was the SpatialLag, with the highest likelihood log value, the explanatory power (R2 = 0.508) and the Akaike information criterion (2059.28) and the Bayesian Schwarz criterion (2099; 46). In this model only the percentage variable of accumulated garbage in the surroundings remained with a statistically significant positive correlation (p = 0.03). The findings suggest that sociodemographic factors influenced the occurrence of dengue, chikungunya and zika in the state of Maranhão. In São Luís the improper disposal of solid waste had an impact on the occurrence of the three arboviruses.