Show simple item record

dc.contributor.authorRahmanian Haghighi, Mohammad Reza
dc.contributor.authorPallari, Chryso T.
dc.contributor.authorAchilleos, Souzana
dc.contributor.authorQuattrocchi, Annalisa
dc.contributor.authorGabel, John
dc.contributor.authorArtemiou, Andreas
dc.contributor.authorAthanasiadou, Maria
dc.contributor.authorPapatheodorou, Stefania
dc.contributor.authorLiu, Tianyu
dc.contributor.authorCernuda Martínez, José Antonio
dc.contributor.authorDenissov, Gleb
dc.contributor.authorŁyszczarz, Błażej
dc.contributor.authorHuang, Qian
dc.contributor.authorAthanasakis, Kostas
dc.contributor.authorBennett, Catherine M.
dc.date.accessioned2024-11-18T17:02:42Z
dc.date.available2024-11-18T17:02:42Z
dc.date.issued2024-11-11
dc.identifier.urihttps://hdl.handle.net/1721.1/157558
dc.description.abstractIntroduction The COVID-19 pandemic overwhelmed health systems, resulting in a surge in excess deaths. This study clustered countries based on excess mortality to understand their response to the pandemic and the influence of various factors on excess mortality within each cluster. Materials and Methods This ecological study is part of the COVID-19 MORtality (C-MOR) Consortium. Mortality data were gathered from 21 countries and were previously used to calculate weekly all-cause excess mortality. Thirty exposure variables were considered in five categories as factors potentially associated with excess mortality: population factors, health care resources, socioeconomic factors, air pollution, and COVID-19 policy. Estimation of Latent Class Linear Mixed Model (LCMM) was used to cluster countries based on response trajectory and Generalized Linear Mixture Model (GLMM) for each cluster was run separately. Results Using LCMM, two clusters were reached. Among 21 countries, Brazil, the USA, Georgia, and Poland were assigned to a separate cluster, with the mean of excess mortality z-score in 2020 and 2021 around 4.4, compared to 1.5 for all other countries assigned to the second cluster. In both clusters the population incidence of COVID-19 had the greatest positive relationship with excess mortality while interactions between the incidence of COVID-19, fully vaccinated people, and stringency index were negatively associated with excess mortality. Moreover, governmental variables (government revenue and government effectiveness) were the most protective against excess mortality. Conclusion This study highlighted that clustering countries based on excess mortality can provide insights to gain a broader understanding of countries' responses to the pandemic and their effectiveness.en_US
dc.publisherSpringer Netherlandsen_US
dc.relation.isversionofhttps://doi.org/10.1007/s44197-024-00320-7en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivsen_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceSpringer Netherlandsen_US
dc.titleExcess Mortality and its Determinants During the COVID-19 Pandemic in 21 Countries: An Ecological Study from the C-MOR Project, 2020 and 2021en_US
dc.typeArticleen_US
dc.identifier.citationRahmanian Haghighi, Mohammad Reza, Pallari, Chryso T., Achilleos, Souzana, Quattrocchi, Annalisa, Gabel, John et al. 2024. "Excess Mortality and its Determinants During the COVID-19 Pandemic in 21 Countries: An Ecological Study from the C-MOR Project, 2020 and 2021." Journal of Epidemiology and Global Health.
dc.relation.journalJournal of Epidemiology and Global Healthen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2024-11-17T04:24:26Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.embargo.termsN
dspace.date.submission2024-11-17T04:24:26Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record