Excess Mortality and its Determinants During the COVID-19 Pandemic in 21 Countries: An Ecological Study from the C-MOR Project, 2020 and 2021.

IF 3.8 4区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Journal of Epidemiology and Global Health Pub Date : 2024-11-11 DOI:10.1007/s44197-024-00320-7
Mohammad Reza Rahmanian Haghighi, Chryso Th Pallari, Souzana Achilleos, Annalisa Quattrocchi, John Gabel, Andreas Artemiou, Maria Athanasiadou, Stefania Papatheodorou, Tianyu Liu, José Antonio Cernuda Martínez, Gleb Denissov, Błażej Łyszczarz, Qian Huang, Kostas Athanasakis, Catherine M Bennett, Claudia Zimmermann, Wenjing Tao, Serge Nganda Mekogo, Terje P Hagen, Nolwenn Le Meur, Jackeline Christiane Pinto Lobato, Giuseppe Ambrosio, Ivan Erzen, Binyamin Binyaminy, Julia A Critchley, Lucy P Goldsmith, Olesia Verstiuk, Jideofor Thomas Ogbu, Laust H Mortensen, Levan Kandelaki, Marcin Czech, Joseph Cutherbertson, Eva Schernhammer, Catharina Vernemmen, Antonio José Leal Costa, Tamar Maor, Dimos Alekkou, Bo Burström, Antonis Polemitis, Andreas Charalambous, Christiana A Demetriou
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引用次数: 0

Abstract

Introduction: 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.

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21 个国家 COVID-19 大流行期间的超额死亡率及其决定因素:2020 年和 2021 年 C-MOR 项目的生态研究。
导言:COVID-19 大流行使卫生系统不堪重负,导致超额死亡人数激增。本研究根据超额死亡率对各国进行了分组,以了解各国对这一流行病的反应以及各分组内各种因素对超额死亡率的影响:这项生态研究是 COVID-19 MORtality(C-MOR)联盟的一部分。从 21 个国家收集的死亡率数据曾用于计算每周全因超额死亡率。30 个暴露变量被视为与超额死亡率潜在相关的五类因素:人口因素、医疗资源、社会经济因素、空气污染和 COVID-19 政策。采用潜类线性混合模型(LCMM)进行估计,根据反应轨迹对国家进行分组,并对每个分组分别运行广义线性混合模型(GLMM):结果:利用 LCMM,得出了两个聚类。在 21 个国家中,巴西、美国、格鲁吉亚和波兰被归入一个单独的群组,其 2020 年和 2021 年的超额死亡率 Z 值平均值约为 4.4,而被归入第二个群组的所有其他国家的 Z 值平均值为 1.5。在这两个群组中,COVID-19 的人口发病率与超额死亡率的正相关关系最大,而 COVID-19 发病率、完全接种疫苗的人口和严格指数之间的交互作用与超额死亡率呈负相关。此外,政府变量(政府收入和政府效率)对超额死亡率的保护作用最大:本研究强调,根据超额死亡率对国家进行分组可以使人们更广泛地了解各国应对大流行病的措施及其有效性。
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来源期刊
CiteScore
10.70
自引率
1.40%
发文量
57
审稿时长
19 weeks
期刊介绍: The Journal of Epidemiology and Global Health is an esteemed international publication, offering a platform for peer-reviewed articles that drive advancements in global epidemiology and international health. Our mission is to shape global health policy by showcasing cutting-edge scholarship and innovative strategies.
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