Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal

Detalhes bibliográficos
Autor(a) principal: Almendra, Ricardo
Data de Publicação: 2021
Outros Autores: Santana, Paula, Costa, Cláudia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/103792
https://doi.org/10.21138/bage.3160
Resumo: COVID-19 hit the world in a sudden and uneven way. Scientific community has provided strong evidence about socioeconomic characteristics of the territory associated with the geographical pattern of COVID-19 incidence. Still, the role played by these factors differs between study areas. Geographically Weighted Regression (GWR) models were applied to explore the spatially varying association between age-standardized COVID-19 incidence rate in 2020 and socioeconomic conditions in Portugal, at the municipality level. The spatial context was defined as a function of the number of neighbours; the bandwidth was determined through AIC. Prior, the validity of the GWR was assessed through ordinary least squares models. Border proximity, proportion of overcrowded living quarters, persons employed in manufacturing establishments and persons employed in construction establishments were found to be significant predictors. It was possible to observe that municipalities are affected differently by the same factor, and that this varying influence has identifiable geographical patterns, the role of each analysed factor varies importantly across the country. This study provides useful insights for policymakers for targeted interventions and for proper identification of risk factors.
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spelling Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in PortugalCOVID-19geographical patternssocioeconomic disparitiesspatial analysisCOVID-19patrones geográficosdisparidades socioeconómicasanálisis espacialCOVID-19 hit the world in a sudden and uneven way. Scientific community has provided strong evidence about socioeconomic characteristics of the territory associated with the geographical pattern of COVID-19 incidence. Still, the role played by these factors differs between study areas. Geographically Weighted Regression (GWR) models were applied to explore the spatially varying association between age-standardized COVID-19 incidence rate in 2020 and socioeconomic conditions in Portugal, at the municipality level. The spatial context was defined as a function of the number of neighbours; the bandwidth was determined through AIC. Prior, the validity of the GWR was assessed through ordinary least squares models. Border proximity, proportion of overcrowded living quarters, persons employed in manufacturing establishments and persons employed in construction establishments were found to be significant predictors. It was possible to observe that municipalities are affected differently by the same factor, and that this varying influence has identifiable geographical patterns, the role of each analysed factor varies importantly across the country. This study provides useful insights for policymakers for targeted interventions and for proper identification of risk factors.COVID-19 golpeó al mundo de manera repentina y desigual. La comunidad científica ha aportado pruebas sobre las características socioeconómicas del territorio asociadas al patrón geográfico de incidencia de COVID-19. Se aplicaron modelos de regresión ponderada geográficamente (GWR) para explorar la asociación espacialmente variable entre la tasa de incidencia de COVID-19 estandarizada por edad y las condiciones socioeconómicas (viviendas superpobladas, capacidad en unidades de atención social para ancianos, trabajadores de la construcción y manufactura, proximidad de la frontera y personas que se desplazan para un municipio). El contexto espacial se definió en función del número de vecinos; el ancho de banda se determinó mediante AIC. Previamente se evaluó el GWR mediante modelos de mínimos cuadrados ordinarios. La proximidad de la frontera, la proporción de viviendas superpobladas, las personas empleadas en establecimientos manufactureros y las personas empleadas en establecimientos de construcción resultan ser predictores significativos. Se pudo observar que los municipios se ven afectados diferentemente por el mismo factor y que esta influencia variable tiene patrones geográficos identificables, el papel de cada factor analizado varía de manera importante a lo largo del país. Este estudio proporciona información útil para los formuladores de políticas para intervenciones específicas y para la identificación adecuada de factores de riesgo.Asociacion Espanola de Geografia2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/103792http://hdl.handle.net/10316/103792https://doi.org/10.21138/bage.3160eng2605-33220212-9426Almendra, RicardoSantana, PaulaCosta, Cláudiainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-11-28T21:39:02ZPortal AgregadorONG
dc.title.none.fl_str_mv Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal
title Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal
spellingShingle Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal
Almendra, Ricardo
COVID-19
geographical patterns
socioeconomic disparities
spatial analysis
COVID-19
patrones geográficos
disparidades socioeconómicas
análisis espacial
title_short Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal
title_full Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal
title_fullStr Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal
title_full_unstemmed Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal
title_sort Spatial inequalities of COVID-19 incidence and associated socioeconomic risk factors in Portugal
author Almendra, Ricardo
author_facet Almendra, Ricardo
Santana, Paula
Costa, Cláudia
author_role author
author2 Santana, Paula
Costa, Cláudia
author2_role author
author
dc.contributor.author.fl_str_mv Almendra, Ricardo
Santana, Paula
Costa, Cláudia
dc.subject.por.fl_str_mv COVID-19
geographical patterns
socioeconomic disparities
spatial analysis
COVID-19
patrones geográficos
disparidades socioeconómicas
análisis espacial
topic COVID-19
geographical patterns
socioeconomic disparities
spatial analysis
COVID-19
patrones geográficos
disparidades socioeconómicas
análisis espacial
description COVID-19 hit the world in a sudden and uneven way. Scientific community has provided strong evidence about socioeconomic characteristics of the territory associated with the geographical pattern of COVID-19 incidence. Still, the role played by these factors differs between study areas. Geographically Weighted Regression (GWR) models were applied to explore the spatially varying association between age-standardized COVID-19 incidence rate in 2020 and socioeconomic conditions in Portugal, at the municipality level. The spatial context was defined as a function of the number of neighbours; the bandwidth was determined through AIC. Prior, the validity of the GWR was assessed through ordinary least squares models. Border proximity, proportion of overcrowded living quarters, persons employed in manufacturing establishments and persons employed in construction establishments were found to be significant predictors. It was possible to observe that municipalities are affected differently by the same factor, and that this varying influence has identifiable geographical patterns, the role of each analysed factor varies importantly across the country. This study provides useful insights for policymakers for targeted interventions and for proper identification of risk factors.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/103792
http://hdl.handle.net/10316/103792
https://doi.org/10.21138/bage.3160
url http://hdl.handle.net/10316/103792
https://doi.org/10.21138/bage.3160
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2605-3322
0212-9426
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Asociacion Espanola de Geografia
publisher.none.fl_str_mv Asociacion Espanola de Geografia
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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