All-cause Excess Mortality Cross-sectional Correlation Analysis at Global Scale during COVID-19, 2020-22

Research Article
Open access

All-cause Excess Mortality Cross-sectional Correlation Analysis at Global Scale during COVID-19, 2020-22

Xinxi Li 1* , Longfei Zhang 2
  • 1 The University of Melbourne    
  • 2 The University of Melbourne    
  • *corresponding author xinxil@student.unimelb.edu.au
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/16/20230991
AEMPS Vol.16
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-75-1
ISBN (Online): 978-1-915371-76-8

Abstract

The excess mortality during COVID-19 is significantly higher than the reported death due to COVID-19 infections. This study conducts a cross-sectional correlation analysis between excess mortality and reported COVID-19 death, aging rate, GHS (Global Health Security) index, and government response stringency index in the timeframe from January 2020 to September 2022. This study aims to explore the main contributors to excess mortality, hence to provide guidance on future diagnosis and control of the pandemic. A cross-sectional log-linear model is fitted with 79 observations to estimate the explanatory power of reported COVID-19 death, aging rate, GHS index and stringency index on excess mortality at a global scale. The correlation between excess mortality and reported COVID-19 death is also examined on a nation level by calculating the Spearman correlation coefficient on the time series of the two. The cross-sectional log-linear model has adjusted . The estimate for log of reported COVID death is ; aging rate is ; GHS index is ; stringency index is not significant. On a nation level, Brazil and United States have high correlations between reported and excess death (), the reported death takes up 85% and 86% respectively, of excess death. India and Japan, similarly, have high correlation coefficient (), but with COVID-19 death accounting 9% and 47% of excess death. Germany and China have relatively small correlations between reported and excess death (), but Germany has reported death taking up 89% of excess death, whereas in China the ratio is 4%. At a global scale, the reported COVID-19 death has the most explanatory power in excess mortality, which means that most of excess mortality is attributed to death from COVID-19 infections. Aging rate has a negative correlation between excess mortality, this is partly because of the preference of aged population to live in countries with better health care capacities. There is no significant explanatory power of stringency index on excess mortality, suggesting that the impact of government responses on COVID-19 vary by each nation. On a country level, the correlation between COVID-19 death and excess mortality can also vary, mostly due to the different levels of health care disruptions, efficiency and impact of government measures and other COVID-19 unrelated events such as wars and heat waves.

Keywords:

excess mortality, COVID-19, all-cause excess mortality, government response, population aging, health care disruption

Li,X.;Zhang,L. (2023). All-cause Excess Mortality Cross-sectional Correlation Analysis at Global Scale during COVID-19, 2020-22. Advances in Economics, Management and Political Sciences,16,127-135.
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References

[1]. A. Karlinsky, D. Kobak, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset, ELife. 10 (2021) e69336. https://doi.org/10.7554/eLife.69336.

[2]. World Health Statistics 2022, (n.d.). https://www.who.int/news/item/20-05-2022-world-health-statistics-2022 (accessed October 5, 2022).s

[3]. T. Economist, S.U. Solstad, The pandemic’s true death toll, The Economist. (2021). https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model (accessed October 5, 2022).

[4]. A. Karlinsky, World Mortality Dataset, (2022). https://github.com/akarlinsky/world_mortality (accessed October 5, 2022).

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[6]. National Bureau of Statistics of China, (n.d.). http://www.stats.gov.cn/english/ (accessed October 5, 2022).

[7]. Population ages 65 and above (% of total population) | Data, (n.d.). https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS?end=2021&most_recent_year_desc=true&start=1960&view=chart (accessed October 7, 2022).

[8]. The 2021 Global Health Security Index, GHS Index. (n.d.). https://www.ghsindex.org/ (accessed October 7, 2022).

[9]. Y. Chen, S.L. Klein, B.T. Garibaldi, H. Li, C. Wu, N.M. Osevala, T. Li, J.B. Margolick, G. Pawelec, S.X. Leng, Aging in COVID-19: Vulnerability, immunity and intervention, Ageing Res Rev. 65 (2021) 101205. https://doi.org/10.1016/j.arr.2020.101205.

[10]. L. Taylor, Covid-19: Why Peru suffers from one of the highest excess death rates in the world, BMJ. 372 (2021) n611. https://doi.org/10.1136/bmj.n611.

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[12]. COVID250 Regional Saftey Assessment, DKV. (n.d.). https://www.dkv.global/covid-safety-assessment-250-list (accessed October 5, 2022).

[13]. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21, Lancet. 399 (2022) 1513–1536. https://doi.org/10.1016/S0140-6736(21)02796-3.

[14]. Death rate, crude (per 1,000 people) | Data, (n.d.). https://data.worldbank.org/indicator/SP.DYN.CDRT.IN (accessed October 6, 2022).

[15]. A.M. dos Santos, B.F. de Souza, C.A. de Carvalho, M.A.G. Campos, B.L.C.A. de Oliveira, E.M. Diniz, M. dos R.F.C. Branco, R.C. de S. Queiroz, V.A. de Carvalho, W.R.M. Araújo, A.A.M. da Silva, Excess deaths from all causes and by COVID-19 in Brazil in 2020, Rev Saude Publica. 55 (n.d.) 71. https://doi.org/10.11606/s1518-8787.2021055004137.

[16]. A.M. dos Santos, B.F. de Souza, C.A. de Carvalho, M.A.G. Campos, B.L.C.A. de Oliveira, E.M. Diniz, M. dos R.F.C. Branco, R.C. de S. Queiroz, V.A. de Carvalho, W.R.M. Araújo, A.A.M. da Silva, Excess deaths from all causes and by COVID-19 in Brazil in 2020, Rev Saude Publica. 55 (n.d.) 71. https://doi.org/10.11606/s1518-8787.2021055004137.

[17]. J. Bao, L. Zhou, G. Liu, J. Tang, X. Lu, C. Cheng, Y. Jin, J. Bai, Current state of care for the elderly in China in the context of an aging population, BioScience Trends. 16 (2022) 107–118. https://doi.org/10.5582/bst.2022.01068.

Cite this article

Li,X.;Zhang,L. (2023). All-cause Excess Mortality Cross-sectional Correlation Analysis at Global Scale during COVID-19, 2020-22. Advances in Economics, Management and Political Sciences,16,127-135.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Business and Policy Studies

ISBN：978-1-915371-75-1(Print) / 978-1-915371-76-8(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://2023.confbps.org/
Conference date: 26 February 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.16
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. A. Karlinsky, D. Kobak, Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset, ELife. 10 (2021) e69336. https://doi.org/10.7554/eLife.69336.

[2]. World Health Statistics 2022, (n.d.). https://www.who.int/news/item/20-05-2022-world-health-statistics-2022 (accessed October 5, 2022).s

[3]. T. Economist, S.U. Solstad, The pandemic’s true death toll, The Economist. (2021). https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model (accessed October 5, 2022).

[4]. A. Karlinsky, World Mortality Dataset, (2022). https://github.com/akarlinsky/world_mortality (accessed October 5, 2022).

[5]. COVID-19 Government Response Tracker, (n.d.). https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker (accessed October 4, 2022).

[6]. National Bureau of Statistics of China, (n.d.). http://www.stats.gov.cn/english/ (accessed October 5, 2022).

[7]. Population ages 65 and above (% of total population) | Data, (n.d.). https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS?end=2021&most_recent_year_desc=true&start=1960&view=chart (accessed October 7, 2022).

[8]. The 2021 Global Health Security Index, GHS Index. (n.d.). https://www.ghsindex.org/ (accessed October 7, 2022).

[9]. Y. Chen, S.L. Klein, B.T. Garibaldi, H. Li, C. Wu, N.M. Osevala, T. Li, J.B. Margolick, G. Pawelec, S.X. Leng, Aging in COVID-19: Vulnerability, immunity and intervention, Ageing Res Rev. 65 (2021) 101205. https://doi.org/10.1016/j.arr.2020.101205.

[10]. L. Taylor, Covid-19: Why Peru suffers from one of the highest excess death rates in the world, BMJ. 372 (2021) n611. https://doi.org/10.1136/bmj.n611.

[11]. Third round of the global pulse survey on continuity of essential health services during the COVID-19 pandemic, (n.d.). https://www.who.int/publications-detail-redirect/WHO-2019-nCoV-EHS_continuity-survey-2022.1 (accessed October 5, 2022).

[12]. COVID250 Regional Saftey Assessment, DKV. (n.d.). https://www.dkv.global/covid-safety-assessment-250-list (accessed October 5, 2022).

[13]. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21, Lancet. 399 (2022) 1513–1536. https://doi.org/10.1016/S0140-6736(21)02796-3.

[14]. Death rate, crude (per 1,000 people) | Data, (n.d.). https://data.worldbank.org/indicator/SP.DYN.CDRT.IN (accessed October 6, 2022).

[15]. A.M. dos Santos, B.F. de Souza, C.A. de Carvalho, M.A.G. Campos, B.L.C.A. de Oliveira, E.M. Diniz, M. dos R.F.C. Branco, R.C. de S. Queiroz, V.A. de Carvalho, W.R.M. Araújo, A.A.M. da Silva, Excess deaths from all causes and by COVID-19 in Brazil in 2020, Rev Saude Publica. 55 (n.d.) 71. https://doi.org/10.11606/s1518-8787.2021055004137.

[16]. A.M. dos Santos, B.F. de Souza, C.A. de Carvalho, M.A.G. Campos, B.L.C.A. de Oliveira, E.M. Diniz, M. dos R.F.C. Branco, R.C. de S. Queiroz, V.A. de Carvalho, W.R.M. Araújo, A.A.M. da Silva, Excess deaths from all causes and by COVID-19 in Brazil in 2020, Rev Saude Publica. 55 (n.d.) 71. https://doi.org/10.11606/s1518-8787.2021055004137.

[17]. J. Bao, L. Zhou, G. Liu, J. Tang, X. Lu, C. Cheng, Y. Jin, J. Bai, Current state of care for the elderly in China in the context of an aging population, BioScience Trends. 16 (2022) 107–118. https://doi.org/10.5582/bst.2022.01068.

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