Pathogenicity of sublineage BA.5, omicron variant strain comparison with other variant strains of SARS-Cov-2 and seasonal influenza in Japan: Updated until April 2023
DOI:
https://doi.org/10.51094/jxiv.59Keywords:
excess mortality, COVID-19, all cause death, stochastic frontier estimation, NIID model, Tokyo, JapanAbstract
Background: Sublineage BA.5 of omicron variant strain recorded the highest peak of morbidity and mortality confirmed with test per day in August for the former and September for the latter, 2022.
Object: We sought to quantify excess mortality using the National Institute of Infectious Diseases (NIID) model for all cause of death to evaluate pathogenicity of sublineage BA.5 of omicron variant strain.
Method: We applied the NIID model to deaths of all causes from 1987 up through the April, 2023 for the whole of Japan and up through November, 2022 for Tokyo.
Results: Results in Japan show that 18845, 14133, 8300, 6702, 18426 and 18404 excess mortality was observed in August 2022 to January 2023,which means 16.8, 13.0,7.0, 13.7 and 12.7 % of the baseline and approximately 72% of total excess mortality during the COVID-19 pandemic. Even in Tokyo, it was 9150 and 74% of total of excess mortality in this pandemic, between February and December, 2022.
Discussion and Conclusion: Results may indicate that its pathogenicity was much stronger than other variant strain and seasonal influenza.
Conflicts of Interest Disclosure
No author has any conflict of interest, financial or otherwise, to declare in relation to this study.Downloads *Displays the aggregated results up to the previous day.
References
European Centre for Disease Prevention and Control. Epidemiological update: SARS-CoV-2 Omicron sub-lineages BA.4 and BA.5. https://www.ecdc.europa.eu/en/news-events/epidemiological-update-sars-cov-2-omicron-sub-lineages-ba4-and-ba5 [accessed on October 22, 2022]
National Intitute of Infectious Diseases. Updated Situation of COVID-19 Outbreak (July 13,2022) https://www.niid.go.jp/niid/ja/2019-ncov/11309-covid19-ab90th.html (in Japanese) [accessed on October 20, 2022]
Lin HC, Chiu HF, Ho SC, Yang CY. Association of influenza vaccination and reduced risk of stroke hospitalization among the elderly: a population-based case-control study. Int J Environ Res Public Health 2014; 11: 3639-49.
Asghar Z, Coupland C, Siriwardena N. Influenza vaccination and risk of stroke: Self-controlled case-series study. Vaccine. 2015; 33: 5458-63.
Riedmann EM. Influenza vaccination reduces risk of heart attack and stroke. Hum Vaccin Immunother 2013; 9: 2500.
Kwok CS, Aslam S, Kontopantelis E, Myint PK, Zaman MJ, Buchan I, Loke YK, Mamas MA. Influenza, influenza-like symptoms and their association with cardiovascular risks: a systematic review and meta-analysis of observational studies. Int J Clin Pract. 2015;69:928-37.
Muhammad S, Haasbach E, Kotchourko M, Strigli A, Krenz A, Ridder DA, Vogel AB, Marti HH, Al-Abed Y, Planz O, Schwaninger M. Influenza virus infection aggravates stroke outcome. Stroke. 201142 783-91.
Assad F, Cockburn WC, Sundaresan TK. Use of excess mortality from respiratory diseases in the study of influenza. Bull WHO 1973; 49: 219-33.
Sugawara T, Ohkusa Y. Comparison of Models for Excess Mortality of Influenza Applied to Japan. Journal of Biosciences and Medicines, 2019, 7, 13-23. doi:10.4236/jbm.2019.76002
Kurita J, Sugawara T, Ohkusa Y. Mobility data can explain the entire COVID-19 outbreak course in Japan. medRxiv 2020.04.26.20081315; doi: https://doi.org/10.1101/2020.04.26.20081315
Ministry of Health, Labour and Welfare. Preliminary statistics on demographicshttps://www.mhlw.go.jp/toukei/list/81-1a.html (in Japanese) [accessed on January 16, 2022]
Aiger AD, Lovell K, Schmitidt P. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 1977; 21-37.
Jondrow J, Lovell K, Materov S, Schmidt P. On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics 1982; 233-9.
Li T, Rosenman R. Cost inefficiency in Washington Hospitals: A stochastic frontier approach using panel data, Health Care Management Science 2001; 4: 73-81.
Newhouse JP. Frontier Estimation: How useful a tool for health economics? Journal of Health Economics 1994; 13: 317-22.
Shelton Brown H. Managed care and technical efficiency. Health Economics. 2003; 12: 149-58.
Jacobs R. Alternative methods to examine hospital efficiency: Data envelopment analysis and stochastic frontier analysis. Health Care Management Science. 2001; 4: 103-15
Rosko MD. Cost efficiency of US hospitals: A stochastic frontier approach. Health Economics. 2001; 539-51.
Ministry of Labour, Health and Welfare. Deta kara wakaru -shingata koronau uirusu kansenshou jouhou- Kansensha doukou (in Japanese) [Visualizing the data: information on COVID-19 infections: COVID-19 trend] https://covid19.mhlw.go.jp/ [accessed on April 14, 2023]
COVID-19 Excess Mortality Collaborators. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21. Lancet. 2022 ;399:1513-36.
Sugawara T, Ohkusa Y. Comparison of Models for Excess Mortality of Influenza Applied to Japan. Journal of Biosciences and Medicines, 2019, 7, 13-23. doi:10.4236/jbm.2019.76002
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Submitted: 2022-04-27 04:32:31 UTC
Published: 2022-05-02 09:17:47 UTC — Updated on 2023-07-04 10:08:51 UTC
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Reason(s) for revision
The study period has been extended.License
Copyright (c) 2022
Junko Kurita
Tamie Sugawara
Yasushi Ohkusa
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
