ABSTRACT Objective: To detect spatial and spatiotemporal clusters of urban arboviruses and to investigate whether the social development index (SDI) and irregular waste disposal are related to the coefficient of urban arboviruses detection in São Luís, state of Maranhão, Brazil. Methods: The confirmed cases of Dengue, Zika and Chikungunya in São Luís, from 2015 to 2019, were georeferenced to the census tract of residence. The Bayesian Conditional Autoregressive regression model was used to identify the association between SDI and irregular waste disposal sites and the coefficient of urban arboviruses detection. Results: The spatial pattern of arboviruses pointed to the predominance of a low-incidence cluster, except 2016. For the years 2015, 2016, 2017, and 2019, an increase of one unit of waste disposal site increased the coefficient of arboviruses detection in 1.25, 1.09, 1.23, and 1.13 cases of arboviruses per 100 thousand inhabitants, respectively. The SDI was not associated with the coefficient of arboviruses detection. Conclusion: In São Luís, spatiotemporal risk clusters for the occurrence of arboviruses and a positive association between the coefficient of arbovirus detection and sites of irregular waste disposal were identified.

RESUMO Objetivo: Detectar aglomerados espaciais e espaço-temporais de arboviroses urbanas e investigar se o índice desenvolvimento social (IDS) e o descarte irregular de lixo estão relacionados ao coeficiente de detecção das arboviroses urbanas em São Luís, Maranhão. Métodos: Os casos confirmados de dengue, Zika e chikungunya em São Luís, no período de 2015 a 2019, foram georreferenciados para o setor censitário de residência. O modelo de regressão Autorregressivo Condicional Bayesiano foi utilizado para identificar a associação entre o coeficiente de detecção de arboviroses urbanas, IDS e pontos de descarte irregular de lixo. Resultados: O padrão espacial de arboviroses apontou para a predominância de cluster de baixo coeficiente de detecção, exceto em 2016. Para os anos de 2015, 2016, 2017 e 2019, o aumento de uma unidade de ponto de lixo aumenta o coeficiente de detecção de arboviroses em 1,25, 1,09, 1,23 e 1,13 casos de arboviroses por 100 mil habitantes, respectivamente. O IDS não foi associado ao coeficiente de detecção de arboviroses. Conclusão: Em São Luís foram identificados aglomerados espaço-temporais de risco para a ocorrência de arboviroses e a associação positiva entre o coeficiente de detecção de arboviroses e os pontos de descarte irregular de lixo.

RESUMO Objetivo: Detectar aglomerados espaciais e espaço-temporais de arboviroses urbanas e investigar se o índice desenvolvimento social (IDS) e o descarte irregular de lixo estão relacionados ao coeficiente de detecção das arboviroses urbanas em São Luís, Maranhão. Métodos: Os casos confirmados de dengue, Zika e chikungunya em São Luís, no período de 2015 a 2019, foram georreferenciados para o setor censitário de residência. O modelo de regressão Autorregressivo Condicional Bayesiano foi utilizado para identificar a associação entre o coeficiente de detecção de arboviroses urbanas, IDS e pontos de descarte irregular de lixo. Resultados: O padrão espacial de arboviroses apontou para a predominância de cluster de baixo coeficiente de detecção, exceto em 2016. Para os anos de 2015, 2016, 2017 e 2019, o aumento de uma unidade de ponto de lixo aumenta o coeficiente de detecção de arboviroses em 1,25, 1,09, 1,23 e 1,13 casos de arboviroses por 100 mil habitantes, respectivamente. O IDS não foi associado ao coeficiente de detecção de arboviroses. Conclusão: Em São Luís foram identificados aglomerados espaço-temporais de risco para a ocorrência de arboviroses e a associação positiva entre o coeficiente de detecção de arboviroses e os pontos de descarte irregular de lixo. Objetivo espaçotemporais espaço temporais (IDS Maranhão Métodos dengue 201 2019 residência Resultados 2016 125 1 25 1,25 109 09 1,09 123 23 1,2 113 13 1,1 10 habitantes respectivamente Conclusão 20 12 2 0 1,0 1, 11

ABSTRACT Objective: To detect spatial and spatiotemporal clusters of urban arboviruses and to investigate whether the social development index (SDI) and irregular waste disposal are related to the coefficient of urban arboviruses detection in São Luís, state of Maranhão, Brazil. Methods: The confirmed cases of Dengue, Zika and Chikungunya in São Luís, from 2015 to 2019, were georeferenced to the census tract of residence. The Bayesian Conditional Autoregressive regression model was used to identify the association between SDI and irregular waste disposal sites and the coefficient of urban arboviruses detection. Results: The spatial pattern of arboviruses pointed to the predominance of a low-incidence cluster, except 2016. For the years 2015, 2016, 2017, and 2019, an increase of one unit of waste disposal site increased the coefficient of arboviruses detection in 1.25, 1.09, 1.23, and 1.13 cases of arboviruses per 100 thousand inhabitants, respectively. The SDI was not associated with the coefficient of arboviruses detection. Conclusion: In São Luís, spatiotemporal risk clusters for the occurrence of arboviruses and a positive association between the coefficient of arbovirus detection and sites of irregular waste disposal were identified. Objective (SDI Luís Maranhão Brazil Methods Dengue 201 2019 residence Results lowincidence low incidence cluster 2016 2017 125 1 25 1.25 109 09 1.09 123 23 1.23 113 13 1.1 10 inhabitants respectively Conclusion identified 20 12 2 1.2 0 1.0 11 1.

ABSTRACT Dengue fever, chikungunya fever, and zika virus infections are increasing public health problems in the world, the last two diseases having recently emerged in Brazil. This ecological study employed spatial analysis of probable cases of dengue fever, chikungunya fever, and zika virus infections reported to the National Mandatory Reporting System (SINAN) in Maranhao State from 2015 to 2016. The software GeoDa version 1.10 was used for calculating global and local Moran indices. The global Moran index identified a significant autocorrelation of incidence rates of dengue (I=0.10; p=0.009) and zika (I=0.07; p=0.03). The study found a positive spatial correlation between dengue and the population density (I=0.31; p<0.001) and a negative correlation with the Performance Index of Unified Health System (PIUHS) by basic care coverage (I=-0.08; p=0.01). Regarding chikungunya fever, there were positive spatial correlations with the population density (I=0.06; p=0.03) and the Municipal Human Development Index (MHDI) (I=0.10; p=0.002), and a negative correlation with the Gini index (I=-0.01; p<0.001) and the PIUHS by basic care coverage (I=-0.18; p<0.001). Lastly, we found positive spatial correlations between Zika virus infections and the population density (I=0.13; p=0.005) and the MHDI (I=0.12; p<0.001), as well as a negative correlation with the Gini index (I=-0.11; p<0.001) and the PIUHS by basic care coverage (I=-0.05; p=0.03). Our results suggest that several socio-demographic factors influenced the occurrence of dengue fever, chikungunya fever, and zika virus infections in Maranhao State.