Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach

Adin Urtasun, Aritz; Congdon, P.; Santafé Rodrigo, Guzmán; Ugarte Martínez, María Dolores

Publication:
Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach

dc.contributor.author	Adin Urtasun, Aritz
dc.contributor.author	Congdon, P.
dc.contributor.author	Santafé Rodrigo, Guzmán
dc.contributor.author	Ugarte Martínez, María Dolores
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Institute for Advanced Materials and Mathematics - INAMAT2	en
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.date.accessioned	2022-04-07T12:10:19Z
dc.date.available	2022-04-07T12:10:19Z
dc.date.issued	2022
dc.description.abstract	The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.	en
dc.description.sponsorship	This work has been supported by Projects MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033). Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.	en
dc.format.extent	16 p.
dc.format.mimetype	application/pdf	en
dc.identifier.doi	10.1007/s00477-022-02175-5
dc.identifier.issn	1436-3240
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/42666
dc.language.iso	eng	en
dc.publisher	Springer
dc.relation.ispartof	Stochastic Environmental Research and Risk Assessment, 2022	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-82553-R/ES/	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/	en
dc.relation.publisherversion	https://doi.org/10.1007/s00477-022-02175-5
dc.rights	© The Author(s) 2022. Creative Commons Attribution 4.0 International	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.rights.accessRights	Acceso abierto / Sarbide irekia	es
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
dc.subject	Disease mapping	en
dc.subject	Ecological regression	en
dc.subject	INLA	en
dc.subject	Restricted regression	en
dc.subject	Smoothing	en
dc.title	Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach	en
dc.type	info:eu-repo/semantics/article
dc.type.version	info:eu-repo/semantics/publishedVersion	en
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dspace.entity.type	Publication
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