COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed

Detalhes bibliográficos
Autor(a) principal: Cavalcante, João Roberto
Data de Publicação: 2020
Outros Autores: Abreu, Ariane de Jesus Lopes de
Tipo de documento: preprint
Idioma: por
Título da fonte: SciELO Preprints
Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/130
Resumo: Objective: To analyze and describe the spatial distribution of the first confirmed cases and deaths of COVID-19 in Rio de Janeiro and its association with the Social Development Index (SDI). Methods: Data from confirmed cases and deaths of SARSCoV-2 between March 6 and April 10, 2020 were used. Incidence rates (IR), mortality (MR), lethality and excess risk were calculated. Results: The COVID- IR is 26.8, the MR 1.36 (both per 100 thousand inhabitants) and the lethality is 5%. The excess risk assessed that eight neighborhoods have an IR of 4-12 times greater than the municipality. Moran's I and LISA demonstrate global and local spatial autocorrelation. The regression demonstrated statistical significance between the SDI and the IR and MR. Conclusion: The elaboration of emergency plans against COVID-19 must consider the socioeconomic-cultural characteristics of the municipality.
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spelling COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmedCOVID-19 en la ciudad de Rio de Janeiro: distribución espacial de los primeros casos confirmados y muertesCOVID-19 no município Rio de Janeiro: distribuição espacial dos primeiros casos e óbitos confirmadosSARS-CoV-2COVID-19Análise EspacialEpidemiologiaSaúde ColetivaBrasilSARS-CoV-2COVID-19Spatial AnalysisEpidemiologyPublic HealthBrazilSARS-CoV-2COVID-19Análisis EspacialEpidemiologíaSalud PúblicaBrasilObjective: To analyze and describe the spatial distribution of the first confirmed cases and deaths of COVID-19 in Rio de Janeiro and its association with the Social Development Index (SDI). Methods: Data from confirmed cases and deaths of SARSCoV-2 between March 6 and April 10, 2020 were used. Incidence rates (IR), mortality (MR), lethality and excess risk were calculated. Results: The COVID- IR is 26.8, the MR 1.36 (both per 100 thousand inhabitants) and the lethality is 5%. The excess risk assessed that eight neighborhoods have an IR of 4-12 times greater than the municipality. Moran's I and LISA demonstrate global and local spatial autocorrelation. The regression demonstrated statistical significance between the SDI and the IR and MR. Conclusion: The elaboration of emergency plans against COVID-19 must consider the socioeconomic-cultural characteristics of the municipality.Objetivo: analizar y describir la distribución espacial de los primeros casos confirmados y muertes de COVID-19 en Río de Janeiro y su asociación con el Índice de Desarrollo Social (IDS). Métodos: Se utilizaron datos sobre casos confirmados de SARS-CoV-2 y muertes entre el 6 de marzo y el 10 de abril de 2020. Tasas de incidencia (TI), mortalidad (TM), letalidad y excess risk fueran calculadas. Resultados: La TI de COVID-19 es 26.8, la TM 1.36 (ambas por cada 100 mil habitantes) y la letalidad es 5%. El excess risk evaluó que ocho vecindarios tienen una TI de 4 a 12 veces mayor que la del municipio. El I de Moran y LISA demuestran autocorrelación espacial global y local. La regresión demostró significación estadística entre el IDS y el TI y TM. Conclusión: La creación de planes de emergencia contra COVID-19 debe considerar las características socioeconómicasculturales del municipio.Objetivo: Analisar e descrever a distribuição espacial dos primeiros casos e óbitos confirmados de COVID-19 do Rio de Janeiro e sua associação com o Índice de Desenvolvimento Social (IDS). Métodos: Foram utilizados dados de casos e óbitos confirmados de SARS-CoV-2 entre 6 de março e 10 de abril de 2020. Calculou-se as taxas de incidência (TI), mortalidade (TM), letalidade e excess risk. Resultados: A TI de COVID-19 é de 26,8, a TM 1,36 (ambas por 100 mil habitantes) e a letalidade é 5%. O excess risk avaliou que oito bairros possuem TI de 4-12 vezes maior que a do município. O I de Moran e o LISA demonstram uma autocorrelação espacial global e local. A regressão demonstrou significância estatística entre o IDS e as TI e TM. Conclusão: A criação de planos emergenciais contra a COVID-19 deve considerar as características sócio-econômicas-culturais do município.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-04-22info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/13010.1590/SciELOPreprints.130porhttps://preprints.scielo.org/index.php/scielo/article/view/130/154Copyright (c) 2020 João Roberto Cavalcante, Ariane de Jesus Lopes de Abreuhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCavalcante, João RobertoAbreu, Ariane de Jesus Lopes dereponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-04-22T04:10:43Zoai:ops.preprints.scielo.org:preprint/130Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-04-22T04:10:43SciELO Preprints - SciELOfalse
dc.title.none.fl_str_mv COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed
COVID-19 en la ciudad de Rio de Janeiro: distribución espacial de los primeros casos confirmados y muertes
COVID-19 no município Rio de Janeiro: distribuição espacial dos primeiros casos e óbitos confirmados
title COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed
spellingShingle COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed
Cavalcante, João Roberto
SARS-CoV-2
COVID-19
Análise Espacial
Epidemiologia
Saúde Coletiva
Brasil
SARS-CoV-2
COVID-19
Spatial Analysis
Epidemiology
Public Health
Brazil
SARS-CoV-2
COVID-19
Análisis Espacial
Epidemiología
Salud Pública
Brasil
title_short COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed
title_full COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed
title_fullStr COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed
title_full_unstemmed COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed
title_sort COVID-19 in Rio de Janeiro municipality: spatial distribution of the first deaths and cases confirmed
author Cavalcante, João Roberto
author_facet Cavalcante, João Roberto
Abreu, Ariane de Jesus Lopes de
author_role author
author2 Abreu, Ariane de Jesus Lopes de
author2_role author
dc.contributor.author.fl_str_mv Cavalcante, João Roberto
Abreu, Ariane de Jesus Lopes de
dc.subject.por.fl_str_mv SARS-CoV-2
COVID-19
Análise Espacial
Epidemiologia
Saúde Coletiva
Brasil
SARS-CoV-2
COVID-19
Spatial Analysis
Epidemiology
Public Health
Brazil
SARS-CoV-2
COVID-19
Análisis Espacial
Epidemiología
Salud Pública
Brasil
topic SARS-CoV-2
COVID-19
Análise Espacial
Epidemiologia
Saúde Coletiva
Brasil
SARS-CoV-2
COVID-19
Spatial Analysis
Epidemiology
Public Health
Brazil
SARS-CoV-2
COVID-19
Análisis Espacial
Epidemiología
Salud Pública
Brasil
description Objective: To analyze and describe the spatial distribution of the first confirmed cases and deaths of COVID-19 in Rio de Janeiro and its association with the Social Development Index (SDI). Methods: Data from confirmed cases and deaths of SARSCoV-2 between March 6 and April 10, 2020 were used. Incidence rates (IR), mortality (MR), lethality and excess risk were calculated. Results: The COVID- IR is 26.8, the MR 1.36 (both per 100 thousand inhabitants) and the lethality is 5%. The excess risk assessed that eight neighborhoods have an IR of 4-12 times greater than the municipality. Moran's I and LISA demonstrate global and local spatial autocorrelation. The regression demonstrated statistical significance between the SDI and the IR and MR. Conclusion: The elaboration of emergency plans against COVID-19 must consider the socioeconomic-cultural characteristics of the municipality.
publishDate 2020
dc.date.none.fl_str_mv 2020-04-22
dc.type.driver.fl_str_mv info:eu-repo/semantics/preprint
info:eu-repo/semantics/publishedVersion
format preprint
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://preprints.scielo.org/index.php/scielo/preprint/view/130
10.1590/SciELOPreprints.130
url https://preprints.scielo.org/index.php/scielo/preprint/view/130
identifier_str_mv 10.1590/SciELOPreprints.130
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://preprints.scielo.org/index.php/scielo/article/view/130/154
dc.rights.driver.fl_str_mv Copyright (c) 2020 João Roberto Cavalcante, Ariane de Jesus Lopes de Abreu
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2020 João Roberto Cavalcante, Ariane de Jesus Lopes de Abreu
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv SciELO Preprints
SciELO Preprints
SciELO Preprints
publisher.none.fl_str_mv SciELO Preprints
SciELO Preprints
SciELO Preprints
dc.source.none.fl_str_mv reponame:SciELO Preprints
instname:SciELO
instacron:SCI
instname_str SciELO
instacron_str SCI
institution SCI
reponame_str SciELO Preprints
collection SciELO Preprints
repository.name.fl_str_mv SciELO Preprints - SciELO
repository.mail.fl_str_mv scielo.submission@scielo.org
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