Environmental Health and Preventive Medicine
Online ISSN : 1347-4715
Print ISSN : 1342-078X
ISSN-L : 1342-078X
COVID-19 fatality and DALYs, and associated metabolic disorders and ambient air pollutants in pre-Omicron era of the pandemic: an international comparative study
Nlandu-Roger Ngatu Daniel-Kuezina TonduanguEmmanuel MunyeshyakaTomohiro Hirao Georges-Matondo BalendaYusuke YamadoriTakayuki DeguchiBerthier Nsadi-FweneJose-Nzunzu LamiSteeve AkumwamiKanae KandaAkitsu MurakamiMarie-Claire YandjuDieudonne-Tshipukane NyembueAntoine TshimpiStanislas-Okitotsho Wembonyama
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2024 年 29 巻 p. 24

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Background: Air pollution and a number of metabolic disorders have been reported to increase the risk of severe COVID-19 outcomes. This study explored the association between severe COVID-19 outcomes, metabolic disorders and environmental air pollutants, at regional level, across 38 countries.

Methods: We conducted an ecological study using COVID-19 data related to countries of the Organization for Economic Cooperation and Development (OECD), with an estimated population of 1.4 billion. They were divided into 3 regions: 1. Europe & Middle east; 2. Americas (north, central & south America); 3. East-Asia & West Pacific. The outcome variables were: COVID-19 case-fatality rate (CFR) and disability-adjusted life years (DALYs) at regional level. Freely accessible datasets related to regional DALYs, demographics and other environmental pollutants were obtained from OECD, WHO and the World in Data websites. Generalized linear model (GLM) was performed to determine the regional determinants of COVID-19 CFR and DALYs using the aggregate epidemiologic data (Dec. 2019–Dec. 2021).

Results: Overall cumulative deaths were 65,000 per million, for mean CFR and DALYs of 1.31 (1.2)% and 17.35 (2.3) years, respectively. Globally, GLM analysis with adjustment for elderly population rate, showed that COVID-19 CFR was positively associated with atmospheric PM2.5 level (beta = 0.64(0.0), 95%CI: 0.06–1.35; p < 0.05), diabetes prevalence (beta = 0.26(0.1), 95%CI: 0.12–0.41; p < 0.001). For COVID-19 DALYs, positive associations were observed with atmospheric NOx level (beta = 0.06(0.0), 95%CI: 0.02–0.82; p < 0.05) and diabetes prevalence (beta = 0.32(0.2), 95%CI: 0.04–0.69; p < 0.05). At regional level, adjusted GLM analysis showed that COVID-19 CFR was associated with atmospheric PM2.5 level in the Americas and East-Asia & Western Pacific region; it was associated with diabetes prevalence for countries of Europe & Middle east and East-Asia & Western Pacific region. Furthermore, COVID-19 DALYs were positively associated with atmospheric PM2.5 and diabetes prevalence for countries of the Americas only.

Conclusion: These findings confirm that diabetes and air pollution increase the risk of disability and fatality due to COVID-19, with disparities in terms of their impact. They suggest that efficient preventive and management programs for diabetes and air pollution countermeasures would have curtailed severe COVID-19 outcome rates.

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© The Author(s) 2024.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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