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Temporal dynamic in the impact of COVID− 19 outbreak on cause-specific mortality in Guangzhou, China
BACKGROUND: Studies related to the SARS-CoV-2 spikes in the past few months, while there are limited studies on the entire outbreak-suppressed cycle of COVID-19. We estimate the cause-specific excess mortality during the complete circle of COVID-19 outbreak in Guangzhou, China, stratified by sociode...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105693/ https://www.ncbi.nlm.nih.gov/pubmed/33964914 http://dx.doi.org/10.1186/s12889-021-10771-3 |
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author | Li, Li Hang, Dong Dong, Han Yuan-Yuan, Chen Bo-Heng, Liang Ze-Lin, Yan Zhou, Yang Chun-Quan, Ou Peng-Zhe, Qin |
author_facet | Li, Li Hang, Dong Dong, Han Yuan-Yuan, Chen Bo-Heng, Liang Ze-Lin, Yan Zhou, Yang Chun-Quan, Ou Peng-Zhe, Qin |
author_sort | Li, Li |
collection | PubMed |
description | BACKGROUND: Studies related to the SARS-CoV-2 spikes in the past few months, while there are limited studies on the entire outbreak-suppressed cycle of COVID-19. We estimate the cause-specific excess mortality during the complete circle of COVID-19 outbreak in Guangzhou, China, stratified by sociodemographic status. METHODS: Guangzhou Center for Disease Control Prevention provided the individual data of deaths in Guangzhou from 1 January 2018 through 30 June 2020. We applied Poisson regression models to daily cause-specific mortality between 1 January 2018 and 20 January 2020, accounting for effects of population size, calendar time, holiday, ambient temperature and PM(2.5). Expected mortality was estimated for the period from 21 January through 30 June 2020 assuming that the effects of factors aforementioned remained the same as described in the models. Excess mortality was defined as the difference between the observed mortality and the expected mortality. Subgroup analyses were performed by place of death, age group, sex, marital status and occupation class. RESULTS: From 21 January (the date on which the first COVID-19 case occurred in Guangzhou) through 30 June 2020, there were three stages of COVID-19: first wave, second wave, and recovery stage, starting on 21 January, 11 March, and 17 May 2020, respectively. Mortality deficits were seen from late February through early April and in most of the time in the recovery stage. Excesses in hypertension deaths occurred immediately after the starting weeks of the two waves. Overall, we estimated a deficit of 1051 (95% eCI: 580, 1558) in all-cause deaths. Particularly, comparing with the expected mortality in the absence of COVID-19 outbreak, the observed deaths from pneumonia and influenza substantially decreased by 49.2%, while deaths due to hypertension and myocardial infarction increased by 14.5 and 8.6%, respectively. In-hospital all-cause deaths dropped by 10.2%. There were discrepancies by age, marital status and occupation class in the excess mortality during the COVID-19 outbreak. CONCLUSIONS: The excess deaths during the COVID-19 outbreak varied by cause of death and changed temporally. Overall, there was a deficit in deaths during the study period. Our findings can inform preparedness measures in different stages of the outbreak. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10771-3. |
format | Online Article Text |
id | pubmed-8105693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81056932021-05-10 Temporal dynamic in the impact of COVID− 19 outbreak on cause-specific mortality in Guangzhou, China Li, Li Hang, Dong Dong, Han Yuan-Yuan, Chen Bo-Heng, Liang Ze-Lin, Yan Zhou, Yang Chun-Quan, Ou Peng-Zhe, Qin BMC Public Health Research Article BACKGROUND: Studies related to the SARS-CoV-2 spikes in the past few months, while there are limited studies on the entire outbreak-suppressed cycle of COVID-19. We estimate the cause-specific excess mortality during the complete circle of COVID-19 outbreak in Guangzhou, China, stratified by sociodemographic status. METHODS: Guangzhou Center for Disease Control Prevention provided the individual data of deaths in Guangzhou from 1 January 2018 through 30 June 2020. We applied Poisson regression models to daily cause-specific mortality between 1 January 2018 and 20 January 2020, accounting for effects of population size, calendar time, holiday, ambient temperature and PM(2.5). Expected mortality was estimated for the period from 21 January through 30 June 2020 assuming that the effects of factors aforementioned remained the same as described in the models. Excess mortality was defined as the difference between the observed mortality and the expected mortality. Subgroup analyses were performed by place of death, age group, sex, marital status and occupation class. RESULTS: From 21 January (the date on which the first COVID-19 case occurred in Guangzhou) through 30 June 2020, there were three stages of COVID-19: first wave, second wave, and recovery stage, starting on 21 January, 11 March, and 17 May 2020, respectively. Mortality deficits were seen from late February through early April and in most of the time in the recovery stage. Excesses in hypertension deaths occurred immediately after the starting weeks of the two waves. Overall, we estimated a deficit of 1051 (95% eCI: 580, 1558) in all-cause deaths. Particularly, comparing with the expected mortality in the absence of COVID-19 outbreak, the observed deaths from pneumonia and influenza substantially decreased by 49.2%, while deaths due to hypertension and myocardial infarction increased by 14.5 and 8.6%, respectively. In-hospital all-cause deaths dropped by 10.2%. There were discrepancies by age, marital status and occupation class in the excess mortality during the COVID-19 outbreak. CONCLUSIONS: The excess deaths during the COVID-19 outbreak varied by cause of death and changed temporally. Overall, there was a deficit in deaths during the study period. Our findings can inform preparedness measures in different stages of the outbreak. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10771-3. BioMed Central 2021-05-08 /pmc/articles/PMC8105693/ /pubmed/33964914 http://dx.doi.org/10.1186/s12889-021-10771-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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. |
spellingShingle | Research Article Li, Li Hang, Dong Dong, Han Yuan-Yuan, Chen Bo-Heng, Liang Ze-Lin, Yan Zhou, Yang Chun-Quan, Ou Peng-Zhe, Qin Temporal dynamic in the impact of COVID− 19 outbreak on cause-specific mortality in Guangzhou, China |
title | Temporal dynamic in the impact of COVID− 19 outbreak on cause-specific mortality in Guangzhou, China |
title_full | Temporal dynamic in the impact of COVID− 19 outbreak on cause-specific mortality in Guangzhou, China |
title_fullStr | Temporal dynamic in the impact of COVID− 19 outbreak on cause-specific mortality in Guangzhou, China |
title_full_unstemmed | Temporal dynamic in the impact of COVID− 19 outbreak on cause-specific mortality in Guangzhou, China |
title_short | Temporal dynamic in the impact of COVID− 19 outbreak on cause-specific mortality in Guangzhou, China |
title_sort | temporal dynamic in the impact of covid− 19 outbreak on cause-specific mortality in guangzhou, china |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105693/ https://www.ncbi.nlm.nih.gov/pubmed/33964914 http://dx.doi.org/10.1186/s12889-021-10771-3 |
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