Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain

After two years of the COVID-19 pandemic, there is extensive research on the spread of the virus and geostatistical analysis of spatial patterns. However, from the perspective of health geography, COVID-19 mortality is still under-studied. This research aims to provide a geographic profile of COVID-...

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Autores: Cos Guerra, Olga de|||0000-0002-2245-5378, Castillo Salcines, Valentín, Cantarero Prieto, David
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/30986
Acceso en línea:https://hdl.handle.net/10902/30986
Access Level:acceso abierto
Palabra clave:Geo-statistics
Spatial patterns
Emerging hot spots
Microdata
ArcGIS pro
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spelling Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North SpainCos Guerra, Olga de|||0000-0002-2245-5378Castillo Salcines, ValentínCantarero Prieto, DavidGeo-statisticsSpatial patternsEmerging hot spotsMicrodataArcGIS proAfter two years of the COVID-19 pandemic, there is extensive research on the spread of the virus and geostatistical analysis of spatial patterns. However, from the perspective of health geography, COVID-19 mortality is still under-studied. This research aims to provide a geographic profile of COVID-19 mortality, in terms of the space-time evolution and the relationship with individual and contextual variables. To this end, we geocoded the daily COVID-19 microdata of deceased persons provided by the Government of Cantabria (in northern Spain) from March 1, 2020 to March 31, 2022. The study also took cadastral variables, population records, and connections to geo-enrichment services accessed through ArcGIS Pro License (ESRI) into account. Using spatial statistics methods, such as 3D bins and emerging hot spots, local bivariate relationships, and ordinary least squares, we propose an exportable and scalable methodology to help policymakers cope with the current stage of living with the epidemic virus. Our results suggest that the spatial distribution of mortality is less clustered than that of contagion and shed light on differences in COVID-19 mortality profiles inside/outside nursing homes, such as higher age, and the temporal concentration of deaths in nursing homes. Spatial regimes showed hot spots of COVID-19 mortality in urban and metropolitan areas, with a pattern of repetition over time, such as sporadic hot spots that accounted for 36.28% of deaths in only 11.88% of the area with COVID-19 deaths. Despite immunization, periods of high contagion meant a subsequent increase in mortality, such as during the Omicron wave, where consecutive metropolitan hot spots accounted for 37.50% of the area and 51.45% of deaths were concentrated. Finally, there were interesting nuances in the significant local context variables of COVID-19 mortality compared with the explanatory factors of COVID-19 cases.This study is part of IDIVAL’s PRIMVAL-2021 research project (Code PRIMVAL21/01), entitled “Escenarios post-vacunación de la COVID-19: papel de la atención primaria ante la aparición de nuevos casos. Seguimiento y costes”. Spatial analysis methods are implemented using the Universidad de Cantabria’s ESRI geo-technological software (ArcGIS Pro) License.ElsevierUniversidad de Cantabria20242024-01-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttps://hdl.handle.net/10902/30986Applied Geography, 2024, 162, 103153reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/309862026-06-02T12:39:31Z
dc.title.none.fl_str_mv Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain
title Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain
spellingShingle Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain
Cos Guerra, Olga de|||0000-0002-2245-5378
Geo-statistics
Spatial patterns
Emerging hot spots
Microdata
ArcGIS pro
title_short Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain
title_full Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain
title_fullStr Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain
title_full_unstemmed Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain
title_sort Geodemographic profiles of COVID-19 mortality inside/outside nursing homes. Spatial analysis from microdata in North Spain
dc.creator.none.fl_str_mv Cos Guerra, Olga de|||0000-0002-2245-5378
Castillo Salcines, Valentín
Cantarero Prieto, David
author Cos Guerra, Olga de|||0000-0002-2245-5378
author_facet Cos Guerra, Olga de|||0000-0002-2245-5378
Castillo Salcines, Valentín
Cantarero Prieto, David
author_role author
author2 Castillo Salcines, Valentín
Cantarero Prieto, David
author2_role author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Geo-statistics
Spatial patterns
Emerging hot spots
Microdata
ArcGIS pro
topic Geo-statistics
Spatial patterns
Emerging hot spots
Microdata
ArcGIS pro
description After two years of the COVID-19 pandemic, there is extensive research on the spread of the virus and geostatistical analysis of spatial patterns. However, from the perspective of health geography, COVID-19 mortality is still under-studied. This research aims to provide a geographic profile of COVID-19 mortality, in terms of the space-time evolution and the relationship with individual and contextual variables. To this end, we geocoded the daily COVID-19 microdata of deceased persons provided by the Government of Cantabria (in northern Spain) from March 1, 2020 to March 31, 2022. The study also took cadastral variables, population records, and connections to geo-enrichment services accessed through ArcGIS Pro License (ESRI) into account. Using spatial statistics methods, such as 3D bins and emerging hot spots, local bivariate relationships, and ordinary least squares, we propose an exportable and scalable methodology to help policymakers cope with the current stage of living with the epidemic virus. Our results suggest that the spatial distribution of mortality is less clustered than that of contagion and shed light on differences in COVID-19 mortality profiles inside/outside nursing homes, such as higher age, and the temporal concentration of deaths in nursing homes. Spatial regimes showed hot spots of COVID-19 mortality in urban and metropolitan areas, with a pattern of repetition over time, such as sporadic hot spots that accounted for 36.28% of deaths in only 11.88% of the area with COVID-19 deaths. Despite immunization, periods of high contagion meant a subsequent increase in mortality, such as during the Omicron wave, where consecutive metropolitan hot spots accounted for 37.50% of the area and 51.45% of deaths were concentrated. Finally, there were interesting nuances in the significant local context variables of COVID-19 mortality compared with the explanatory factors of COVID-19 cases.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10902/30986
url https://hdl.handle.net/10902/30986
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Applied Geography, 2024, 162, 103153
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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