Análise espacial de dados geográficos

Souza-Santos, Reinaldo

doi:10.1590/s0102-311x2005000400034

Cited by 44 publications

(13 citation statements)

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“…Data from our study confirm the North region as the one with the highest detection and mortality rates from HBV in Brazil, and also suggest an increase in detection rates in some federated units of this region, particularly in the states of Acre, Amazonas, Rondônia and Roraima. This finding corroborates other studies that identified a high number of cases and mortality due to HBV in the North region [17][18][19] . Still in relation to HBV detection rates in our country, the global spatial correlation of this infection was clearly demonstrated through the analysis of the Global Moran's Index, which presented a positive spatial association.…”

Section: Discussionsupporting

confidence: 92%

“…Regions of high endemicity for HBV in our country, particularly in the North region, have already been identified by different authors [20][21][22][23] . Similarly, the description of other regions of high endemicity, as specific regions of the states of the South and Southeast, also appears in several studies [17][18][19] .…”

Section: Discussionmentioning

confidence: 74%

“…the value of the attribute in a region does not depend on the values of this same variable in different areas 16 . According to Druck et al, the global Moran's I represents stands for the autocorrelation considering only the first neighbor 18 .…”

Section: Methodsmentioning

confidence: 99%

“…This diagram is a scatter graph between the standardized values of the attributes (variables) z and the mean of the neighbors (also standardized) wz, which is divided into quadrants. According to Druck that in a region there are similar neighbors, whereas Q3 (positive values, negative means) and Q4 (negative values, positive means) indicate areas of negative spatial association, indicating locations with neighbors of distinct values 18 .…”

Section: Methodsmentioning

confidence: 99%

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Exploratory spatial analysis of HBV cases in Brazil between 2005 and 2017

Vivaldini

Pinto

Kohiyama

et al. 2019

Rev. bras. epidemiol.

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Objective: To analyze the pattern of spatial distribution of hepatitis B virus (HBV) cases and the mortality attributed to this disease throughout the Brazilian territory, in 2005, 2016 and 2017. Methods: This is an ecological study of spatial analysis, using data from the Information System for Notifiable Diseases and the Brazilian Mortality Information System. HBV detection and mortality rates were analyzed. The spatial analysis from 2005 to 2017 was held through the Global Moran’s Index (I) for global data and the Local Indicators of Spatial Association (LISA) for the 5,564 municipalities of the country. Results: The North region stands out with the highest HBV detection and mortality rates in the country. The Global Moran’s I showed a spatial correlation of HBV cases in Brazil, and the LISA Map evidenced the presence of hotspots or spatial clusters (high-high type), mainly in the North region and also in some municipalities of Santa Catarina, Paraná, Rio Grande do Sul, Espírito Santo, São Paulo and Rio de Janeiro. Conclusion: The spatial analysis of the HBV distribution pattern in Brazil shows areas with a large concentration of cases, particularly in the North of the country and in other points distributed throughout the national territory. These data reinforce the urgency of intervention actions related to prevention, diagnosis and treatment of hepatitis B.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 74%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Exploratory spatial analysis of HBV cases in Brazil between 2005 and 2017

Vivaldini

Pinto

Kohiyama

et al. 2019

Rev. bras. epidemiol.

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“…O índice de Moran Global avalia o padrão da distribuição espacial e fornece uma medida geral do grau de associação espacial presente no conjunto de dados, considerando-se a adjacência geográfica 25 . Ele pode variar de -1 a +1: índices próximos a +1 significam que áreas vizinhas apresentam comportamento semelhante (relação espacial positiva) e índices próximos a -1 retratam que municípios com valores baixos tendem a ficar próximos de vizinhos com valores altos e vice-versa (associação espacial negativa).…”

Section: Análise Espacialunclassified

Análise espacial da incidência da febre de Chikungunya e dos fatores socioeconômicos, demográficos e de infestação vetorial associados, em municípios de Pernambuco, Brasil, 2015–2021

Santos

Mendes

Passos

et al. 2023

Rev. bras. epidemiol.

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RESUMO Objetivo: Identificar, na Iᵃ Região de Saúde de Pernambuco (Iᵃ RSP), os padrões espaciais da febre de Chikungunya (CHIKF) e os fatores socioeconômicos, demográficos e de infestação vetorial associados. Métodos: Este estudo ecológico utilizou a análise espacial das Taxas Médias de Incidência (TMI) de casos prováveis da CHIKF notificados entre os residentes dos 19 municípios da Iᵃ RSP no período de 2015–2021. Os índices de Moran global (I) univariados e bivariados foram estimados. Das associações significativas (p<0,05), clusters foram localizados por meio do Índice de Moran Local e de mapas. Resultados: Identificou-se predominância das maiores TMI da CHIKF no leste. Entretanto, houve distribuição heterogênea das taxas dos municípios, o que pode ter contribuído para a ausência de autocorrelação espacial da CHIKF (I=0,03; p=0,294) no I univariado. O I bivariado revelou correlação espacial positiva entre a CHIKF e o Índice de Desenvolvimento Humano Municipal (IDHM) (I=0,245; p=0,038), porém com um cluster de cidades com baixas incidências e baixo IDHM no oeste. Não houve correlação espacial entre a CHIKF e as demais variáveis analisadas: densidade demográfica, Índice de Gini, Índice de Vulnerabilidade Social e Índice de Infestação Predial de Aedes aegypti. Conclusões: Os resultados sugerem que somente o IDHM influenciou na ocorrência da CHIKF na Iᵃ RSP, de forma que municípios do oeste demonstraram dependência espacial entre menores valores de IDHM e TMI. No entanto, essa correlação espacial pode ter ocorrido devido às possíveis subnotificações na área. Tais achados podem auxiliar na (re)orientação de recursos dos serviços de vigilância e assistência à saúde.

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