Análise espacial de casos prováveis de dengue no município de São Luís, Maranhão, Brasil / Spatial analysis of probable dengue cases in the municipality of São Luís, Maranhão, Brazil / Análisis espacial de los casos probables de dengue en el municipio de São Luís, Maranhão, Brasil

INTRODUÇÃO: A dengue é considerada uma das principais arboviroses mundiais, caracterizada no Brasil pelo aumento de casos graves e óbitos. OBJETIVO: realizar análise espacial dos casos prováveis de dengue em São Luís - MA. MÉTODOS: Estudo ecológico de base populacional dos casos prováveis de dengue, notificados no Sistema de Informação de Agravos de Notificação (SINAN) em 2015 e 2016, ocorridos no município de São Luís ­ MA. Foram georreferenciados 4.681 casos prováveis de dengue por setores censitários, calculadas as taxas de incidência e ajustadas através do estimador bayesiano empírico local. Foi utilizado o estimador de densidade de Kernel e Moran Global e Local para a análise espacial. RESULTADOS: Evidenciou-se através do estimador de densidade de Kernel, áreas quentes (alta-densidade) nos setores censitários da região noroeste do município. As taxas de incidência foram ajustadas pela aplicação do método bayesiano empírico local, identificando-se maior quantidade de setores com média e alta incidência. A partir do índice de Moran global foi evidenciada autocorrelação espacial positiva estatisticamente significativa para as taxas de incidência de dengue (I=0,69; p<0,001) e para as taxas de incidência ajustadas pelo método bayesiano (I=0,80; p<0,001). De acordo com o índice de Moran local, identificou-se clusters de setores de alta incidência de dengue em áreas com alta densidade populacional na região nordeste e noroeste do município. CONCLUSÃO: A pesquisa demonstrou que os estimadores bayesianos ajudaram a minimizar os problemas de subnotificação e da influência do tamanho populacional nos setores censitários.

INTRODUCTION: Dengue is considered one of the main arboviruses in the world, characterized in Brazil by the increase in severe cases and deaths. OBJECTIVE: to perform spatial analysis of probable dengue cases in São Luís - MA. METHODS: Population-based ecological study of probable dengue cases, reported in the Notifiable Diseases Information System (SINAN) in 2015 and 2016, which took place in the city of São Luís - MA. 4,681 probable dengue cases were georeferenced by census sectors, incidence rates were calculated and adjusted using the local empirical Bayesian estimator. The Kernel and Moran Global and Local density estimator was used for spatial analysis. RESULTS: Hot areas (high-density) in the census sectors of the northwest region of the municipality were evidenced through the Kernel density estimator. Incidence rates were adjusted by applying the local empirical Bayesian method, identifying a greater number of sectors with medium and high incidence. From the global Moran index, statistically significant positive spatial autocorrelation was evidenced for the dengue incidence rates (I = 0.69; p <0.001) and for the incidence rates adjusted by the Bayesian method (I = 0.80; p <0.001). According to the local Moran index, clusters of sectors with a high incidence of dengue were identified in areas with high population density in the northeast and northwest regions of the municipality. CONCLUSION: The research demonstrated that Bayesian estimators helped to minimize the problems of underreporting and the influence of population size on census tracts.

INTRODUCCIÓN: El dengue es considerado una de las principales arbovirosis a nivel mundial, caracterizada en Brasil por el aumento de casos graves y muertes. OBJETIVO: Realizar un análisis espacial de los casos probables de dengue en São Luís - MA. MÉTODOS: Estudio ecológico de base poblacional de los casos probables de dengue, notificados en el Sistema de Informação de Agravos de Notificação (SINAN) en 2015 y 2016, ocurridos en el municipio de São Luís - MA. Se georreferenciaron 4.681 casos probables de dengue por sectores censales, se calcularon las tasas de incidencia y se ajustaron mediante el estimador empírico bayesiano local. Para el análisis espacial se utilizó el estimador de densidad Kernel y Moran global y local. RESULTADOS: Se evidenció a través del estimador de densidad Kernel, áreas calientes (de alta densidad) en los sectores censales de la región noroeste del municipio. Las tasas de incidencia se ajustaron mediante la aplicación del método bayesiano empírico local, identificándose una mayor cantidad de setores con incidencia media y alta. A partir del índice global de Moran se evidenció una autocorrelación espacial positiva estadísticamente significativa para las tasas de incidencia de dengue (I=0,69; p<0,001) y para las tasas de incidencia ajustadas por el método bayesiano (I=0,80; p<0,001). Según el índice local de Moran, se identificaron clusters de sectores de alta incidencia de dengue en áreas con alta densidad de población en las regiones noreste y noroeste del municipio. CONCLUSIÓN: La investigación demostró que los estimadores bayesianos ayudaron a minimizar los problemas de infradeclaración y la influencia del tamaño de la población en los sectores censales.

Spatial Analysis of Notified Zika Virus Congenital Syndrome, Maranhão, 2015 to 2018. / Análise espacial da taxa de detecção de casos suspeitos de síndrome congênita pelo vírus Zika, Maranhão, 2015 a 2018.

OBJECTIVE: To identify spatial patterns in cases of changes in growth and development related to Zika virus infection and other infectious etiologies (denominated Zika virus congenital syndrome in this study) reported in Maranhão from 2015 to 2018 and their relation with socioeconomic and demographic variables. METHODS: Ecological study of notified Zika virus congenital syndrome cases in the 217 cities of Maranhão, Brasil. Spatial autocorrelation was calculated using GeoDa 1.14 software and the local and global (I) Moran's index in univariate and bivariate analyses on Zika virus congenital syndrome incidence rate with Municipal Human Development Index (MHDI), population density, Gini coefficient and the cities' time of administrative political emancipation. Local Moran's Index was calculated to identify clusters with significant spatial autocorrelation. RESULTS: Spatial autocorrelation was checked in univariate analysis of the incidence rate of Zika virus congenital syndrome (I=0,494; p=0,001) and positive correlation in bivariate analysis of the incidence rate with Municipal Human Development Index (I=0,252; p=0,001), population density (I=0,338; p=0,001) and the cities' time of administrative political emancipation (I=0,134; p=0,001). The correlation between incidence rate with Gini coefficient was not significant (I= -0,033; p=0,131). Five high-incidence clusters were found in distinct areas of the state. CONCLUSIONS: Cities with higher MHDI, higher population density and more years of administrative political emancipation had more cases of Zika virus congenital syndrome notified.

OBJETIVO: Identificar padrões espaciais em casos de lactentes com alterações de crescimento e desenvolvimento relacionadas à infecção pelo vírus Zika e outras etiologias infecciosas (neste trabalho denominado de síndrome congênita pelo vírus Zika), notificados no Maranhão de 2015 a 2018 e sua relação com variáveis socioeconômicas e demográficas. MÉTODOS: Estudo ecológico de casos suspeitos notificados de síndrome congênita pelo vírus Zika nos 217 municípios do Maranhão, Brasil. Calculou-se a autocorrelação espacial pelos índices de Moran local e global (I) univariado e bivariado da taxa de detecção de casos suspeitos de síndrome congênita pelo vírus Zika com índice de desenvolvimento humano municipal, densidade demográfica, índice de Gini e tempo de emancipação político-administrativa dos municípios. O índice de Moran local foi calculado para localizar clusters com autocorrelação espacial significativa. RESULTADOS: Houve autocorrelação espacial na análise univariada da taxa municipal de detecção de casos suspeitos de síndrome congênita pelo vírus Zika (I=0,494; p=0,001) e, na análise bivariada, correlação positiva da taxa de detecção de casos suspeitos com índice de desenvolvimento humano municipal (I=0,252; p=0,001), densidade demográfica (I=0,338; p=0,001) e tempo de emancipação dos municípios (I=0,134; p=0,001). Não houve correlação significativa da taxa de detecção de casos suspeitos com o índice de Gini (I= -0,033; p=0,131). Cinco clusters de alta detecção de casos suspeitos foram encontrados em áreas distintas do estado. CONCLUSÕES: Os municípios com maior índice de desenvolvimento humano municipal, maior densidade demográfica e mais tempo de emancipação político-administrativa tiveram mais casos suspeitos notificados de síndrome congênita pelo vírus Zika.

Análise espacial da taxa de detecção de casos suspeitos de síndrome congênita pelo vírus Zika, Maranhão, 2015 a 2018 / Spatial Analysis of Notified Zika Virus Congenital Syndrome, Maranhão, 2015 to 2018

RESUMO: Objetivo: Identificar padrões espaciais em casos de lactentes com alterações de crescimento e desenvolvimento relacionadas à infecção pelo vírus Zika e outras etiologias infecciosas (neste trabalho denominado de síndrome congênita pelo vírus Zika), notificados no Maranhão de 2015 a 2018 e sua relação com variáveis socioeconômicas e demográficas. Métodos: Estudo ecológico de casos suspeitos notificados de síndrome congênita pelo vírus Zika nos 217 municípios do Maranhão, Brasil. Calculou-se a autocorrelação espacial pelos índices de Moran local e global (I) univariado e bivariado da taxa de detecção de casos suspeitos de síndrome congênita pelo vírus Zika com índice de desenvolvimento humano municipal, densidade demográfica, índice de Gini e tempo de emancipação político-administrativa dos municípios. O índice de Moran local foi calculado para localizar clusters com autocorrelação espacial significativa. Resultados: Houve autocorrelação espacial na análise univariada da taxa municipal de detecção de casos suspeitos de síndrome congênita pelo vírus Zika (I=0,494; p=0,001) e, na análise bivariada, correlação positiva da taxa de detecção de casos suspeitos com índice de desenvolvimento humano municipal (I=0,252; p=0,001), densidade demográfica (I=0,338; p=0,001) e tempo de emancipação dos municípios (I=0,134; p=0,001). Não houve correlação significativa da taxa de detecção de casos suspeitos com o índice de Gini (I= -0,033; p=0,131). Cinco clusters de alta detecção de casos suspeitos foram encontrados em áreas distintas do estado. Conclusões: Os municípios com maior índice de desenvolvimento humano municipal, maior densidade demográfica e mais tempo de emancipação político-administrativa tiveram mais casos suspeitos notificados de síndrome congênita pelo vírus Zika.

ABSTRACT: Objective: To identify spatial patterns in cases of changes in growth and development related to Zika virus infection and other infectious etiologies (denominated Zika virus congenital syndrome in this study) reported in Maranhão from 2015 to 2018 and their relation with socioeconomic and demographic variables. Methods: Ecological study of notified Zika virus congenital syndrome cases in the 217 cities of Maranhão, Brasil. Spatial autocorrelation was calculated using GeoDa 1.14 software and the local and global (I) Moran's index in univariate and bivariate analyses on Zika virus congenital syndrome incidence rate with Municipal Human Development Index (MHDI), population density, Gini coefficient and the cities' time of administrative political emancipation. Local Moran's Index was calculated to identify clusters with significant spatial autocorrelation. Results: Spatial autocorrelation was checked in univariate analysis of the incidence rate of Zika virus congenital syndrome (I=0,494; p=0,001) and positive correlation in bivariate analysis of the incidence rate with Municipal Human Development Index (I=0,252; p=0,001), population density (I=0,338; p=0,001) and the cities' time of administrative political emancipation (I=0,134; p=0,001). The correlation between incidence rate with Gini coefficient was not significant (I= -0,033; p=0,131). Five high-incidence clusters were found in distinct areas of the state. Conclusions: Cities with higher MHDI, higher population density and more years of administrative political emancipation had more cases of Zika virus congenital syndrome notified.

Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil.

INTRODUCTION: Dengue, chikungunya, and Zika are a growing global health problem. This study analyzed the spatial distribution of dengue, chikungunya, and Zika cases in São Luís, Maranhão, from 2015 to 2016 and investigated the association between socio-environmental and economic factors and hotspots for mosquito proliferation. METHODS: This was a socio-ecological study using data from the National Information System of Notifiable Diseases. The spatial units of analysis were census tracts. The incidence rates of the combined cases of the three diseases were calculated and smoothed using empirical local Bayes estimates. The spatial autocorrelation of the smoothed incidence rate was measured using Local Moran's I and Global Moran's I. Multiple linear regression and spatial autoregressive models were fitted using the log of the smoothed disease incidence rate as the dependent variable and socio-environmental factors, demographics, and mosquito hotspots as independent variables. RESULTS: The findings showed a significant spatial autocorrelation of the smoothed incidence rate. The model that best fit the data was the spatial lag model, revealing a positive association between disease incidence and the proportion of households with surrounding garbage accumulation. CONCLUSIONS: The distribution of dengue, chikungunya, and Zika cases showed a significant spatial pattern, in which the high-risk areas for the three diseases were explained by the variable "garbage accumulated in the surrounding environment," demonstrating the need for an intersectoral approach for vector control and prevention that goes beyond health actions.

Beriberi in Brazil: A Disease That Affects Indigenous People.

BACKGROUND: Beriberi is the clinical manifestation of thiamine deficiency. It is multicausal and typically associated with poverty and food insecurity among vulnerable populations, such as indigenous people. OBJECTIVE: The objective of this study was to carry out a spatial analysis of reported cases of beriberi among indigenous people in Brazil. METHODS: Cross-sectional study using time series data on suspected cases of beriberi reported to the Ministry of Health via the FormSUS between July 2013 and September 2018. Indigenous villages were georeferenced, and Kernel density estimation was used to identify patterns of the spatial distribution of beriberi cases. RESULTS: A total of 414 cases of beriberi were reported in the country of which 210 (50.7%) were indigenous people. All the cases in indigenous people occurred in states located in the Legal Amazon (Maranhão, Roraima, and Tocantins). Kernel density estimation showed high-density areas in Tocantins and Roraima. CONCLUSIONS: This is the first nationwide study of reported cases of beriberi. The findings can be used to guide actions that contribute to the monitoring and prevention of beriberi among indigenous people.

Autoregressive spatial modeling of possible cases of dengue, chikungunya, and Zika in the capital of Northeastern Brazil

Abstract INTRODUCTION: Dengue, chikungunya, and Zika are a growing global health problem. This study analyzed the spatial distribution of dengue, chikungunya, and Zika cases in São Luís, Maranhão, from 2015 to 2016 and investigated the association between socio-environmental and economic factors and hotspots for mosquito proliferation. METHODS: This was a socio-ecological study using data from the National Information System of Notifiable Diseases. The spatial units of analysis were census tracts. The incidence rates of the combined cases of the three diseases were calculated and smoothed using empirical local Bayes estimates. The spatial autocorrelation of the smoothed incidence rate was measured using Local Moran's I and Global Moran's I. Multiple linear regression and spatial autoregressive models were fitted using the log of the smoothed disease incidence rate as the dependent variable and socio-environmental factors, demographics, and mosquito hotspots as independent variables. RESULTS: The findings showed a significant spatial autocorrelation of the smoothed incidence rate. The model that best fit the data was the spatial lag model, revealing a positive association between disease incidence and the proportion of households with surrounding garbage accumulation. CONCLUSIONS: The distribution of dengue, chikungunya, and Zika cases showed a significant spatial pattern, in which the high-risk areas for the three diseases were explained by the variable "garbage accumulated in the surrounding environment," demonstrating the need for an intersectoral approach for vector control and prevention that goes beyond health actions.

Spatial analysis of probable cases of dengue fever, chikungunya fever and zika virus infections in Maranhao State, Brazil.

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.