Resumo Objetivou-se realizar uma análise espacial da taxa de mortalidade hospitalar (TMH) por síndrome respiratória aguda grave (SRAG) atribuída à COVID-19 em crianças e adolescentes no Brasil no período de 2020 a 2021. Utilizou-se o método de cluster para agrupar as unidades federativas (UFs) com base na TMH. Em 2020, clusters com altas TMHs foram formados por UFs Norte/Nordeste. Em 2021, houve redução na TMH. Os clusters com maiores taxas permaneceram na região N/NE. Diferenças regionais foram observadas nas TMHs. Os achados podem refletir as desigualdades sociais e o acesso à atenção hospitalar, principalmente na faixa etária de menores de 1 ano pela gravidade da doença neste grupo.

Abstract The objective was to perform a spatial analysis of the hospital mortality rate (HMR) due to severe acute respiratory syndrome (SARS) attributed to COVID-19 among children and adolescents in Brazil from 2020 to 2021. A cluster method was used to group federal units (FUs) based on HMR. In 2020, clusters with high HMRs were formed by north/northeast FUs. In 2021, there was a reduction in HMR. Clusters with higher rates remained in the N/NE region. Regional differences were observed in the HMR. The findings may reflect social inequalities and access to hospital care, especially in the under 1-year-old age group due to the severity of the disease in this group.

Abstract The objective was to perform a spatial analysis of the hospital mortality rate (HMR) due to severe acute respiratory syndrome (SARS) attributed to COVID-19 among children and adolescents in Brazil from 2020 to 2021. A cluster method was used to group federal units (FUs) based on HMR. In 2020, clusters with high HMRs were formed by north/northeast FUs. In 2021, there was a reduction in HMR. Clusters with higher rates remained in the N/NE region. Regional differences were observed in the HMR. The findings may reflect social inequalities and access to hospital care, especially in the under 1-year-old age group due to the severity of the disease in this group.

Resumo Objetivou-se realizar uma análise espacial da taxa de mortalidade hospitalar (TMH) por síndrome respiratória aguda grave (SRAG) atribuída à COVID-19 em crianças e adolescentes no Brasil no período de 2020 a 2021. Utilizou-se o método de cluster para agrupar as unidades federativas (UFs) com base na TMH. Em 2020, clusters com altas TMHs foram formados por UFs Norte/Nordeste. Em 2021, houve redução na TMH. Os clusters com maiores taxas permaneceram na região N/NE. Diferenças regionais foram observadas nas TMHs. Os achados podem refletir as desigualdades sociais e o acesso à atenção hospitalar, principalmente na faixa etária de menores de 1 ano pela gravidade da doença neste grupo.

Abstract The objective was to perform a spatial analysis of the hospital mortality rate (HMR) due to severe acute respiratory syndrome (SARS) attributed to COVID-19 among children and adolescents in Brazil from 2020 to 2021. A cluster method was used to group federal units (FUs) based on HMR. In 2020, clusters with high HMRs were formed by north/northeast FUs. In 2021, there was a reduction in HMR. Clusters with higher rates remained in the N/NE region. Regional differences were observed in the HMR. The findings may reflect social inequalities and access to hospital care, especially in the under 1-year-old age group due to the severity of the disease in this group.

Resumo Objetivou-se realizar uma análise espacial da taxa de mortalidade hospitalar (TMH) por síndrome respiratória aguda grave (SRAG) atribuída à COVID-19 em crianças e adolescentes no Brasil no período de 2020 a 2021. Utilizou-se o método de cluster para agrupar as unidades federativas (UFs) com base na TMH. Em 2020, clusters com altas TMHs foram formados por UFs Norte/Nordeste. Em 2021, houve redução na TMH. Os clusters com maiores taxas permaneceram na região N/NE. Diferenças regionais foram observadas nas TMHs. Os achados podem refletir as desigualdades sociais e o acesso à atenção hospitalar, principalmente na faixa etária de menores de 1 ano pela gravidade da doença neste grupo.

Abstract The objective was to perform a spatial analysis of the hospital mortality rate (HMR) due to severe acute respiratory syndrome (SARS) attributed to COVID-19 among children and adolescents in Brazil from 2020 to 2021. A cluster method was used to group federal units (FUs) based on HMR. In 2020, clusters with high HMRs were formed by north/northeast FUs. In 2021, there was a reduction in HMR. Clusters with higher rates remained in the N/NE region. Regional differences were observed in the HMR. The findings may reflect social inequalities and access to hospital care, especially in the under 1-year-old age group due to the severity of the disease in this group.

Resumo Objetivou-se realizar uma análise espacial da taxa de mortalidade hospitalar (TMH) por síndrome respiratória aguda grave (SRAG) atribuída à COVID-19 em crianças e adolescentes no Brasil no período de 2020 a 2021. Utilizou-se o método de cluster para agrupar as unidades federativas (UFs) com base na TMH. Em 2020, clusters com altas TMHs foram formados por UFs Norte/Nordeste. Em 2021, houve redução na TMH. Os clusters com maiores taxas permaneceram na região N/NE. Diferenças regionais foram observadas nas TMHs. Os achados podem refletir as desigualdades sociais e o acesso à atenção hospitalar, principalmente na faixa etária de menores de 1 ano pela gravidade da doença neste grupo.

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.

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.

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.

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.