Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.

Detalhes bibliográficos
Autor(a) principal: ALENCAR, Larissa Karla Barros de
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Biblioteca Digital de Teses e Dissertações da UFMA
Texto Completo: https://tedebc.ufma.br/jspui/handle/tede/tede/4753
Resumo: Introduction: COVID-19 is an infectious disease, caused by the SARS-CoV-2 virus, with high transmissibility and global distribution. Since it was first reported in the city of Wuhan, China, in December 2019, the world has been following the exponential growth of cases of the disease. Objective: To analyze the spatial distribution of COVID-19 cases and deaths in Maranhão and its relationship with socioeconomic and health indicators. Methodology: Ecological study of all cases and deaths of COVID-19 in the state of Maranhão notified until August 2022 at the Secretary of State for Health. Socioeconomic and health indicators were collected from the online sites of the Brazilian Institute of Geography and Statistics (IBGE), Institute of Applied Economic Research (IPEA) and e-Gestor Assistência Básica. The dependent variables used were: incidence, mortality and lethality of COVID-19, and the independent ones were: resident population of the municipalities of Maranhão, Gini Index, Municipal Human Development Index (MHDI), Social Vulnerability Index (SVI), income per capita, proportion of poor people, household crowding, illiteracy rate of people aged 15 years or over, proportion of households with a general water network, unemployment rate of the population aged 18 years or over and coverage of Primary Care (AB). The incidence, mortality and lethality rates of the 217 municipalities in Maranhão were estimated. The Global Moran Index (I) was used to assess the existence of spatial autocorrelation with the dependent variables, and the Local Moran Index to identify high and low risk areas (clusters). The maps were made using the QGIS software version 3.12.0. To calculate the global spatial autocorrelation indices, as well as the regression models, the GeoDa software, version 1.14, was used. Result: Until August 31, 2022, 468,943 cases and 11,524 deaths from COVID-19 were reported in Maranhão. The municipality of São Luís registered the highest number of cases and deaths, with 73,218 (15.61%) and 2,873 (24.93%), respectively, and the municipality of Boa Vista do Gurupi registered the lowest number, 16 cases (0.003 %). The municipality of São Francisco do Brejão did not record a death from COVID-19. The highest incidence rate was recorded in the municipality of Lagoa do Mato (25,957.44/100,000 inhab.) and the lowest rate was in Boa Vista do Gurupi (188.36/100,000 inhab.). The highest mortality rate was recorded in Imperatriz (374.25/100,000 inhab.) and the highest lethality rate was in Boa Vista do Gurupi (31.25%). The Moran I Index showed positive spatial autocorrelation for incidence, mortality and lethality in the studied period, making it possible to identify clusters of high and low risk for the dependent variables. The Ordinary Least Squares Estimation (OLS) regression model confirmed spatial autocorrelation with the dependent variables. Incidence showed a positive association with the Gini Index and AB coverage, and a negative association with IVS, MHDI and proportion of poor people. Mortality was positively associated with the Gini Index and illiteracy rate and negatively associated with the proportion of poor people and IVS. Regarding lethality, there was a positive correlation with household crowding and a negative correlation with primary care coverage and illiteracy rate. Conclusion: The spread of COVID-19 occurred heterogeneously, with wide variation between the municipalities of Maranhão, making it possible to identify areas of greater and lesser risk for the disease. Socioeconomic and health indicators influenced the evolution of the pandemic, and that such characteristics should be considered in the formulation of public policies to control the disease, as well as to reduce existing inequalities in the State.
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spelling CALDAS, Arlene de Jesus Mendeshttp://lattes.cnpq.br/7214761052240294SOEIRO, Vanessa Moreira da Silvahttp://lattes.cnpq.br/2013273011748287CALDAS, Arlene de Jesus Mendeshttp://lattes.cnpq.br/7214761052240294SOEIRO, Vanessa Moreira da Silvahttp://lattes.cnpq.br/2013273011748287FERREIRA, Thaís Furtadohttp://lattes.cnpq.br/1542923855954206COUTINHO, Nair Portela Silvahttp://lattes.cnpq.br/0041085284657642http://lattes.cnpq.br/2963805947531518ALENCAR, Larissa Karla Barros de2023-06-06T16:53:04Z2023-04-12ALENCAR, Larissa Karla Barros de. Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão 2023. 70 f. Dissertação (Programa de Pós-Graduação em Enfermagem/CCBS) - Universidade Federal do Maranhão, São Luís, 2023.https://tedebc.ufma.br/jspui/handle/tede/tede/4753Introduction: COVID-19 is an infectious disease, caused by the SARS-CoV-2 virus, with high transmissibility and global distribution. Since it was first reported in the city of Wuhan, China, in December 2019, the world has been following the exponential growth of cases of the disease. Objective: To analyze the spatial distribution of COVID-19 cases and deaths in Maranhão and its relationship with socioeconomic and health indicators. Methodology: Ecological study of all cases and deaths of COVID-19 in the state of Maranhão notified until August 2022 at the Secretary of State for Health. Socioeconomic and health indicators were collected from the online sites of the Brazilian Institute of Geography and Statistics (IBGE), Institute of Applied Economic Research (IPEA) and e-Gestor Assistência Básica. The dependent variables used were: incidence, mortality and lethality of COVID-19, and the independent ones were: resident population of the municipalities of Maranhão, Gini Index, Municipal Human Development Index (MHDI), Social Vulnerability Index (SVI), income per capita, proportion of poor people, household crowding, illiteracy rate of people aged 15 years or over, proportion of households with a general water network, unemployment rate of the population aged 18 years or over and coverage of Primary Care (AB). The incidence, mortality and lethality rates of the 217 municipalities in Maranhão were estimated. The Global Moran Index (I) was used to assess the existence of spatial autocorrelation with the dependent variables, and the Local Moran Index to identify high and low risk areas (clusters). The maps were made using the QGIS software version 3.12.0. To calculate the global spatial autocorrelation indices, as well as the regression models, the GeoDa software, version 1.14, was used. Result: Until August 31, 2022, 468,943 cases and 11,524 deaths from COVID-19 were reported in Maranhão. The municipality of São Luís registered the highest number of cases and deaths, with 73,218 (15.61%) and 2,873 (24.93%), respectively, and the municipality of Boa Vista do Gurupi registered the lowest number, 16 cases (0.003 %). The municipality of São Francisco do Brejão did not record a death from COVID-19. The highest incidence rate was recorded in the municipality of Lagoa do Mato (25,957.44/100,000 inhab.) and the lowest rate was in Boa Vista do Gurupi (188.36/100,000 inhab.). The highest mortality rate was recorded in Imperatriz (374.25/100,000 inhab.) and the highest lethality rate was in Boa Vista do Gurupi (31.25%). The Moran I Index showed positive spatial autocorrelation for incidence, mortality and lethality in the studied period, making it possible to identify clusters of high and low risk for the dependent variables. The Ordinary Least Squares Estimation (OLS) regression model confirmed spatial autocorrelation with the dependent variables. Incidence showed a positive association with the Gini Index and AB coverage, and a negative association with IVS, MHDI and proportion of poor people. Mortality was positively associated with the Gini Index and illiteracy rate and negatively associated with the proportion of poor people and IVS. Regarding lethality, there was a positive correlation with household crowding and a negative correlation with primary care coverage and illiteracy rate. Conclusion: The spread of COVID-19 occurred heterogeneously, with wide variation between the municipalities of Maranhão, making it possible to identify areas of greater and lesser risk for the disease. Socioeconomic and health indicators influenced the evolution of the pandemic, and that such characteristics should be considered in the formulation of public policies to control the disease, as well as to reduce existing inequalities in the State.Introdução: A COVID-19 é uma doença infecciosa, causada pelo vírus SARS-CoV-2, de elevada transmissibilidade e distribuição global. Desde que foi relatada pela primeira vez na cidade de Wuhan, na China, em dezembro de 2019, o mundo acompanha o crescimento exponencial de casos da doença. Objetivo: Analisar a distribuição espacial dos casos e óbitos da COVID-19 no Maranhão e sua relação com indicadores socioeconômicos e de saúde. Metodologia: Estudo ecológico de todos os casos e óbitos da COVID-19 do estado do Maranhão notificados até agosto de 2022 na Secretaria de Estado da Saúde. Os indicadores socioeconômicos e de saúde foram coletados dos sítios online do Instituto Brasileiro de Geografia e Estatística (IBGE), Instituto de Pesquisa Econômica Aplicada (IPEA) e e-Gestor Atenção Básica. As variáveis dependentes utilizadas foram: incidência, mortalidade e letalidade da COVID-19, e as independentes foram: população residente dos municípios do Maranhão, Índice de Gini, Índice de Desenvolvimento Humano Municipal (IDHM), Índice de Vulnerabilidade Social (IVS), renda per capita, proporção de pobres, aglomeração domiciliar, taxa de analfabetismo de pessoas com 15 anos ou mais, proporção de domicílio com rede geral de água, taxa de desocupação da população de 18 anos ou mais de idade e cobertura da Atenção Básica (AB). Foram estimadas as taxas de incidência, mortalidade e letalidade dos 217 municípios maranhenses. Utilizou-se o Índice de Moran Global (I) para avaliar a existência de autocorrelação espacial com as variáveis dependentes, e o Índice de Moran Local para identificar as áreas de alto e baixo risco (clusters). Os mapas foram confeccionados por meio do software QGIS versão 3.12.0. Para o cálculo dos índices de autocorrelação espacial global, assim como dos modelos regressivos, utilizou-se o software GeoDa, versão 1.14. Resultado: Até 31 de agosto de 2022 foram notificados 468.943 casos e 11.524 óbitos por COVID-19 no Maranhão. O município de São Luís registrou o maior número de casos e óbitos, com 73.218 (15,61%) e 2.873 (24,93%), respectivamente, e o município de Boa Vista do Gurupi registrou o menor número, 16 casos (0,003%). O município de São Francisco do Brejão não registrou óbito pela COVID-19. A maior taxa da incidência foi registrada no município de Lagoa do Mato (25.957,44/100mil hab.) e a menor taxa foi em Boa Vista do Gurupi (188,36/100mil hab.). A maior taxa de mortalidade foi registrada em Imperatriz (374,25/100mil hab.) e a maior de letalidade foi em Boa Vista do Gurupi (31,25%). O Índice de Moran I apontou autocorrelação espacial positiva para incidência, mortalidade e letalidade no período estudado, sendo possível identificar aglomerados de alto e baixo risco para as variáveis dependentes. O modelo de regressão Ordinary Least Squares Estimation (OLS) confirmou autocorrelação espacial com as variáveis dependentes. A incidência apresentou associação positiva com o Índice de Gini e cobertura AB, e negativa com IVS, IDHM e proporção de pobres. A mortalidade associação positiva com Índice de Gini e taxa de analfabetismo e negativa com proporção de pobres e IVS. Em relação a letalidade houve correlação positiva com aglomeração domiciliar e, negativa com cobertura de atenção básica e taxa de analfabetismo. Conclusão: A dispersão da COVID-19 ocorreu de forma heterogênea, com ampla variação entre os municípios maranhense, sendo possível identificar áreas de maior e menor risco para a doença. Os indicadores socioeconômicos e de saúde influenciaram na evolução da pandemia, e tais características devem ser consideradas na formulação de políticas públicas para o controle do agravo bem como reduzir as desigualdades existentes no Estado. Palavras-chave:Submitted by Daniella Santos (daniella.santos@ufma.br) on 2023-06-06T16:53:03Z No. of bitstreams: 1 Larissa_Karla_Alenca.pdf: 287682 bytes, checksum: b66b980535b3cadd287753aa423e72c1 (MD5)Made available in DSpace on 2023-06-06T16:53:04Z (GMT). No. of bitstreams: 1 Larissa_Karla_Alenca.pdf: 287682 bytes, checksum: b66b980535b3cadd287753aa423e72c1 (MD5) Previous issue date: 2023-04-12FAPEMAapplication/pdfporUniversidade Federal do MaranhãoPROGRAMA DE PÓS-GRADUAÇÃO EM ENFERMAGEM/CCBSUFMABrasilDEPARTAMENTO DE ENFERMAGEM/CCBSanálise espacial;covid-19;indicadores socioeconômicos;indicadores de saúde;spatial analysis;COVID-19;Socioeconomic indicators;health indicators.EnfermagemAnálise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.Spatial analysis of covid-19 cases and deaths and their relationship with indicators socioeconomic and health conditions in the state of Maranhão.Publicização parcial até 29/11/2023 (até publicação em periódico científico)info:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFMAinstname:Universidade Federal do Maranhão (UFMA)instacron:UFMAORIGINALLarissa_Karla_Alenca.pdfLarissa_Karla_Alenca.pdfapplication/pdf287682http://tedebc.ufma.br:8080/bitstream/tede/4753/2/Larissa_Karla_Alenca.pdfb66b980535b3cadd287753aa423e72c1MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82255http://tedebc.ufma.br:8080/bitstream/tede/4753/1/license.txt97eeade1fce43278e63fe063657f8083MD51tede/47532023-06-06 13:55:54.041oai:tede2: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Biblioteca Digital de Teses e Dissertaçõeshttps://tedebc.ufma.br/jspui/PUBhttp://tedebc.ufma.br:8080/oai/requestrepositorio@ufma.br||repositorio@ufma.bropendoar:21312023-06-06T16:55:54Biblioteca Digital de Teses e Dissertações da UFMA - Universidade Federal do Maranhão (UFMA)false
dc.title.por.fl_str_mv Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.
dc.title.alternative.eng.fl_str_mv Spatial analysis of covid-19 cases and deaths and their relationship with indicators socioeconomic and health conditions in the state of Maranhão.
title Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.
spellingShingle Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.
ALENCAR, Larissa Karla Barros de
análise espacial;
covid-19;
indicadores socioeconômicos;
indicadores de saúde;
spatial analysis;
COVID-19;
Socioeconomic indicators;
health indicators.
Enfermagem
title_short Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.
title_full Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.
title_fullStr Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.
title_full_unstemmed Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.
title_sort Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão.
author ALENCAR, Larissa Karla Barros de
author_facet ALENCAR, Larissa Karla Barros de
author_role author
dc.contributor.advisor1.fl_str_mv CALDAS, Arlene de Jesus Mendes
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/7214761052240294
dc.contributor.advisor-co1.fl_str_mv SOEIRO, Vanessa Moreira da Silva
dc.contributor.advisor-co1Lattes.fl_str_mv http://lattes.cnpq.br/2013273011748287
dc.contributor.referee1.fl_str_mv CALDAS, Arlene de Jesus Mendes
dc.contributor.referee1Lattes.fl_str_mv http://lattes.cnpq.br/7214761052240294
dc.contributor.referee2.fl_str_mv SOEIRO, Vanessa Moreira da Silva
dc.contributor.referee2Lattes.fl_str_mv http://lattes.cnpq.br/2013273011748287
dc.contributor.referee3.fl_str_mv FERREIRA, Thaís Furtado
dc.contributor.referee3Lattes.fl_str_mv http://lattes.cnpq.br/1542923855954206
dc.contributor.referee4.fl_str_mv COUTINHO, Nair Portela Silva
dc.contributor.referee4Lattes.fl_str_mv http://lattes.cnpq.br/0041085284657642
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/2963805947531518
dc.contributor.author.fl_str_mv ALENCAR, Larissa Karla Barros de
contributor_str_mv CALDAS, Arlene de Jesus Mendes
SOEIRO, Vanessa Moreira da Silva
CALDAS, Arlene de Jesus Mendes
SOEIRO, Vanessa Moreira da Silva
FERREIRA, Thaís Furtado
COUTINHO, Nair Portela Silva
dc.subject.por.fl_str_mv análise espacial;
covid-19;
indicadores socioeconômicos;
indicadores de saúde;
spatial analysis;
COVID-19;
Socioeconomic indicators;
health indicators.
topic análise espacial;
covid-19;
indicadores socioeconômicos;
indicadores de saúde;
spatial analysis;
COVID-19;
Socioeconomic indicators;
health indicators.
Enfermagem
dc.subject.cnpq.fl_str_mv Enfermagem
description Introduction: COVID-19 is an infectious disease, caused by the SARS-CoV-2 virus, with high transmissibility and global distribution. Since it was first reported in the city of Wuhan, China, in December 2019, the world has been following the exponential growth of cases of the disease. Objective: To analyze the spatial distribution of COVID-19 cases and deaths in Maranhão and its relationship with socioeconomic and health indicators. Methodology: Ecological study of all cases and deaths of COVID-19 in the state of Maranhão notified until August 2022 at the Secretary of State for Health. Socioeconomic and health indicators were collected from the online sites of the Brazilian Institute of Geography and Statistics (IBGE), Institute of Applied Economic Research (IPEA) and e-Gestor Assistência Básica. The dependent variables used were: incidence, mortality and lethality of COVID-19, and the independent ones were: resident population of the municipalities of Maranhão, Gini Index, Municipal Human Development Index (MHDI), Social Vulnerability Index (SVI), income per capita, proportion of poor people, household crowding, illiteracy rate of people aged 15 years or over, proportion of households with a general water network, unemployment rate of the population aged 18 years or over and coverage of Primary Care (AB). The incidence, mortality and lethality rates of the 217 municipalities in Maranhão were estimated. The Global Moran Index (I) was used to assess the existence of spatial autocorrelation with the dependent variables, and the Local Moran Index to identify high and low risk areas (clusters). The maps were made using the QGIS software version 3.12.0. To calculate the global spatial autocorrelation indices, as well as the regression models, the GeoDa software, version 1.14, was used. Result: Until August 31, 2022, 468,943 cases and 11,524 deaths from COVID-19 were reported in Maranhão. The municipality of São Luís registered the highest number of cases and deaths, with 73,218 (15.61%) and 2,873 (24.93%), respectively, and the municipality of Boa Vista do Gurupi registered the lowest number, 16 cases (0.003 %). The municipality of São Francisco do Brejão did not record a death from COVID-19. The highest incidence rate was recorded in the municipality of Lagoa do Mato (25,957.44/100,000 inhab.) and the lowest rate was in Boa Vista do Gurupi (188.36/100,000 inhab.). The highest mortality rate was recorded in Imperatriz (374.25/100,000 inhab.) and the highest lethality rate was in Boa Vista do Gurupi (31.25%). The Moran I Index showed positive spatial autocorrelation for incidence, mortality and lethality in the studied period, making it possible to identify clusters of high and low risk for the dependent variables. The Ordinary Least Squares Estimation (OLS) regression model confirmed spatial autocorrelation with the dependent variables. Incidence showed a positive association with the Gini Index and AB coverage, and a negative association with IVS, MHDI and proportion of poor people. Mortality was positively associated with the Gini Index and illiteracy rate and negatively associated with the proportion of poor people and IVS. Regarding lethality, there was a positive correlation with household crowding and a negative correlation with primary care coverage and illiteracy rate. Conclusion: The spread of COVID-19 occurred heterogeneously, with wide variation between the municipalities of Maranhão, making it possible to identify areas of greater and lesser risk for the disease. Socioeconomic and health indicators influenced the evolution of the pandemic, and that such characteristics should be considered in the formulation of public policies to control the disease, as well as to reduce existing inequalities in the State.
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-06-06T16:53:04Z
dc.date.issued.fl_str_mv 2023-04-12
dc.type.driver.fl_str_mv Publicização parcial até 29/11/2023 (até publicação em periódico científico)
info:eu-repo/semantics/masterThesis
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dc.identifier.citation.fl_str_mv ALENCAR, Larissa Karla Barros de. Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão 2023. 70 f. Dissertação (Programa de Pós-Graduação em Enfermagem/CCBS) - Universidade Federal do Maranhão, São Luís, 2023.
dc.identifier.uri.fl_str_mv https://tedebc.ufma.br/jspui/handle/tede/tede/4753
identifier_str_mv ALENCAR, Larissa Karla Barros de. Análise espacial dos casos e óbitos da covid-19 e sua relação com indicadores socioeconômicos e de saúde no estado do Maranhão 2023. 70 f. Dissertação (Programa de Pós-Graduação em Enfermagem/CCBS) - Universidade Federal do Maranhão, São Luís, 2023.
url https://tedebc.ufma.br/jspui/handle/tede/tede/4753
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dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv Universidade Federal do Maranhão
dc.publisher.program.fl_str_mv PROGRAMA DE PÓS-GRADUAÇÃO EM ENFERMAGEM/CCBS
dc.publisher.initials.fl_str_mv UFMA
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv DEPARTAMENTO DE ENFERMAGEM/CCBS
publisher.none.fl_str_mv Universidade Federal do Maranhão
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