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Reduced mortality during the COVID-19 outbreak in Japan, 2020: a two-stage interrupted time-series design.
Onozuka, Daisuke; Tanoue, Yuta; Nomura, Shuhei; Kawashima, Takayuki; Yoneoka, Daisuke; Eguchi, Akifumi; Ng, Chris Fook Sheng; Matsuura, Kentaro; Shi, Shoi; Makiyama, Koji; Uryu, Shinya; Kawamura, Yumi; Takayanagi, Shinichi; Gilmour, Stuart; Hayashi, Takehiko I; Miyata, Hiroaki; Sera, Francesco; Sunagawa, Tomimasa; Takahashi, Takuri; Tsuchihashi, Yuuki; Kobayashi, Yusuke; Arima, Yuzo; Kanou, Kazuhiko; Suzuki, Motoi; Hashizume, Masahiro.
  • Onozuka D; Department of Medical Informatics and Clinical Epidemiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
  • Tanoue Y; Institute for Business and Finance, Waseda University, Tokyo, Japan.
  • Nomura S; Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan.
  • Kawashima T; Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan.
  • Yoneoka D; Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Eguchi A; Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan.
  • Ng CFS; Department of Mathematical and Computing Science, Tokyo Institute of Technology, Tokyo, Japan.
  • Matsuura K; Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan.
  • Shi S; Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Makiyama K; Graduate School of Public Health, St. Luke's International University, Tokyo, Japan.
  • Uryu S; Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan.
  • Kawamura Y; Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University, Chiba, Japan.
  • Takayanagi S; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
  • Gilmour S; Department of Management Science, Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan.
  • Hayashi TI; HOXO-M Inc., Tokyo, Japan.
  • Miyata H; Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Sera F; Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan.
  • Sunagawa T; HOXO-M Inc., Tokyo, Japan.
  • Takahashi T; Center for Environmental Biology and Ecosystem Studies, National Institute for Environmental Studies (NIES), Tokyo, Japan.
  • Tsuchihashi Y; RIKEN Center for Sustainable Resource Science, Saitama, Japan.
  • Kobayashi Y; HOXO-M Inc., Tokyo, Japan.
  • Arima Y; Graduate School of Public Health, St. Luke's International University, Tokyo, Japan.
  • Kanou K; Center for Health and Environmental Risk Research, National Institute for Environmental Studies, Ibaraki, Japan.
  • Suzuki M; Department of Health Policy and Management, School of Medicine, Keio University, Tokyo, Japan.
  • Hashizume M; Department of Statistics, Computer Science and Applications 'G. Parenti', University of Florence, Florence, Italy.
Int J Epidemiol ; 51(1): 75-84, 2022 02 18.
Article in English | MEDLINE | ID: covidwho-1493814
ABSTRACT

BACKGROUND:

Coronavirus disease 2019 (COVID-19) continues to be a major global health burden. This study aims to estimate the all-cause excess mortality occurring in the COVID-19 outbreak in Japan, 2020, by sex and age group.

METHODS:

Daily time series of mortality for the period January 2015-December 2020 in all 47 prefectures of Japan were obtained from the Ministry of Health, Labour and Welfare, Japan. A two-stage interrupted time-series design was used to calculate excess mortality. In the first stage, we estimated excess mortality by prefecture using quasi-Poisson regression models in combination with distributed lag non-linear models, adjusting for seasonal and long-term variations, weather conditions and influenza activity. In the second stage, we used a random-effects multivariate meta-analysis to synthesize prefecture-specific estimates at the nationwide level.

RESULTS:

In 2020, we estimated an all-cause excess mortality of -20 982 deaths [95% empirical confidence intervals (eCI) -38 367 to -5472] in Japan, which corresponded to a percentage excess of -1.7% (95% eCI -3.1 to -0.5) relative to the expected value. Reduced deaths were observed for both sexes and in all age groups except those aged <60 and 70-79 years.

CONCLUSIONS:

All-cause mortality during the COVID-19 outbreak in Japan in 2020 was decreased compared with a historical baseline. Further evaluation of cause-specific excess mortality is warranted.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Randomized controlled trials / Reviews Limits: Female / Humans / Male Country/Region as subject: Asia Language: English Journal: Int J Epidemiol Year: 2022 Document Type: Article Affiliation country: Ije

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Randomized controlled trials / Reviews Limits: Female / Humans / Male Country/Region as subject: Asia Language: English Journal: Int J Epidemiol Year: 2022 Document Type: Article Affiliation country: Ije