これは2022-05-02 09:17:47 UTCで公開された古いバージョンです。最新バージョンをお読みください。
プレプリント / バージョン1

Pathogenicity of the omicron variant strain comparison with delta variant strain and seasonal influenza in Japan

##article.authors##

  • Kurita, Junko Department of Nursing, Faculty of Sport and Health Science, Daito Bunka University
  • Tamie Sugawara Infectious Disease Surveillance Center, National Institute of Infectious Diseases
  • Yasushi Ohkusa Infectious Disease Surveillance Center, National Institute of Infectious Diseases

DOI:

https://doi.org/10.51094/jxiv.59

キーワード:

excess mortality、 COVID-19、 all cause death、 stochastic frontier estimation、 NIID model、 Tokyo、 Japan

抄録

Background: No remarkable mortality attributable to COVID-19 confirmed by PCR test has been observed in Japan.

Object: We sought to quantify excess mortality using the National Institute of Infectious Diseases (NIID) model.

Method: We applied the NIID model to deaths of all causes from 1987 up through the February 2022 for the whole of Japan and up through November, 2021 for Tokyo.

Results: Results in Japan show huge number of excess mortality, up to 10 thousands in the two months, in August and September, 2021. On the other hand, in Tokyo, we also substantial excess mortality in the same time, which corresponds to be approximately 9% of the baseline at that time. Moreover, we found the largest number of excess mortality in a month since COVID-19 emerging in February 2022 in the whole of Japan, when one month later since the number of newly confirmed patients with omicron variant strain reached the peak.

Discussion and Conclusion: The result in February 2022 may indicate the pathogenicity of the omicron variant strain was comparable delta variant strain and stronger than seasonal influenza. It will be stronger pathogenicity than delta variant strain if the excess mortality will be observed in n March or April, 2022.

ダウンロード *前日までの集計結果を表示します

ダウンロード実績データは、公開の翌日以降に作成されます。

引用文献

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.

US Center for Disease Control and Prevention. Excess Deaths Associated with COVID-19. https://www.cdc.gov/nchs/nvss/vsrr/covid19/excess_deaths.htm [accessed on January 15, 2022]

Japan Times. Japan's daily PCR test capacity tops 20,000. https://www.japantimes.co.jp/news/2020/05/16/national/japans-daily-pcr-test-capacity-20000/#.XuAwImeP6AA [accessed on January 15, 2022]

World Health Organization. Coronavirus disease (COVID-2019) situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ [accessed on January 12,2022]

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.

Excess and Exiguous Deaths Dashboard in Japan. https://exdeaths-japan.org/ [accessed on Januray 7, 2022]

Kawashima T, Nomura S, Tanoue Y, Yoneoka D, Eguchi A, Ng CFS, Matsuura K, Shi S, Makiyama K, Uryu S, Kawamura Y, Takayanagi S, Gilmour S, Miyata H, Sunagawa T, Takahashi T, Tsuchihashi Y, Kobayashi Y, Arima Y, Kanou K, Hashizume M.Excess All-Cause Deaths during Coronavirus Disease Pandemic, Japan, January-May 2020(1). Emerg Infect Dis. 2021 Mar;27(3):789-795. doi: 10.3201/eid2703.203925.

Center of Disease Control and Prevention, Excess Deaths Associated with COVID-19https://data.cdc.gov/NCHS/Excess-Deaths-Associated-with-COVID-19/xkkf-xrst [accessed on June 23, 2021]

EUROMOMO,EUROMOMO, https://www.euromomo.eu/ [accessed on June 23, 2021]

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

ダウンロード

研究分野
一般医学・社会医学・看護学