Análise epidemiológica e espacial da COVID-19 no Estado do Piauí

Abstract

Estudo ecológico, analítico e exploratório que objetivou realizar análises epidemiológicas e espaciais da distribuição dos casos confirmados e dos óbitos por doença do Coronavírus 2019 (do inglês, Coronavírus Disease 2019 - COVID-19) que ocorreram no ano de 2020, no estado do Piauí, Brasil. As variáveis elencadas foram obtidas via banco de dados do IBGE e SESAPI. Nas análises epidemiológicas foram realizadas estatísticas descritivas e nas análises espaciais, foram construídos mapas utilizando técnicas de geoprocessamento, através das análises estatísticas de Moran Global e Local. Os resultados evidenciaram que, no ano de 2020, foram notificados 143.179 casos confirmados e 2.240 óbitos causados por COVID-19 no Piauí. Observou-se maior percentual de casos em indivíduos do sexo feminino, adultos jovens, e os óbitos em idosos, do sexo masculino, com doenças crônicas. Houve formação de clusters no estado, elementos a serem considerados na organização e fortalecimento dos serviços de saúde para o desenvolvimento de estratégias que busquem minimizar a transmissão durante a emergência da COVID-19.

Potential Epidemic Vulnerability and Socioepidemiological Profile of SARS-CoV2 in the Brazilian Northeast Region

“…In this study, the p-value that returned for all the tested data was statistically significant. Next, Moran's local autocorrelation (IML) and spatial clusters were evaluated using the local indicators of spatial association (LISA) method alongside the GeoDa 0.9.9.10 software [26,27]. For the mensuration of spatial autocorrelation, generating a geographic/spatial proximity matrix was necessary.…”

“…To this end, univariate cluster maps were constructed for the dependent variable (total number of COVID-19 cases in the neighborhoods) and the independent variables (PEVI and indicators in Table 1). In this analysis, the cartograms show the outline of four types of spatial clusters: high-high (regions formed by neighborhoods with high frequencies of the variable, surrounded by regions of high frequency); low-low (regions formed by neighborhoods with low frequencies of the variable, surrounded by regions of low frequency), high-low (regions formed by neighborhoods with high frequencies of the variable, surrounded by regions of low frequency), and low-high (regions formed by neighborhoods with low frequencies of the variable, surrounded by regions of high frequency) [26,27].…”

“…Likewise, the LISA method was used to conduct the bivariate analysis to assess the spatial correlation between the dependent and independent variables. In this case, we categorized five spatial cluster interpretations: non-significant (territories that did not form clusters, as their differences were not significant); high-high (regions formed by neighborhoods with high frequencies of the dependent variable and high frequencies of the independent variable); low-low (regions formed by neighborhoods with low frequencies of the dependent variable and low frequencies of the independent variable), high-low (regions formed by neighborhoods with high frequencies of the dependent variable and low frequencies of the independent variable) and low-high (regions formed by neighborhoods with low frequencies of the dependent variable and high frequencies of the independent variable) [26,27].…”

Geographic information system in assessing bruxism among school children

“…Geographic Information Systems (GIS) is a system increasingly accepted and used in health science and epidemiological studies (Barros et al, 2020;Dias et al, 2020;Ferronato et al, 2021;Pieri et al, 2021;Macedo et al, 2021;Nayak et al, 2021;Jardine et al, 2022). Studies using this type of strategy have been increasing in dental research and showing the importance of the use of GIS also in oral health promotion (Pereira et al, 2010).…”