Journal of Epidemiology
Online ISSN : 1349-9092
Print ISSN : 0917-5040
ISSN-L : 0917-5040
Statistical Data
Excess Mortality From Suicide During the Early COVID-19 Pandemic Period in Japan: A Time-Series Modeling Before the Pandemic
Tatsuhiko AnzaiKeisuke FukuiTsubasa ItoYuri ItoKunihiko Takahashi
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Supplementary material

2021 Volume 31 Issue 2 Pages 152-156

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Abstract

Background: Suicide amidst the coronavirus disease (COVID-19) pandemic is an important issue. In Japan, the number of suicides in April 2020 decreased by nearly 20% from that in 2019. To assess the impact of an infectious disease pandemic, excess mortality is often discussed. Our main purpose was evaluating excess mortality from suicide in Japan during the early pandemic period.

Methods: We used data on suicides collected by the National Police Agency of Japan until June 2020. We estimated excess mortality during the early pandemic period (March–June 2020) using a time-series model of the number of suicides before the pandemic. A quasi-Poisson model was employed for the estimation. We evaluated excess mortalities by the categories of age and sex, and by prefecture.

Results: No significant excess mortality was observed throughout the early pandemic; instead, a downward trend in the number of suicides for both sexes was noted. For males, negative values of excess mortalities below the lower bound of the 95% prediction interval were observed in April and May. All numbers of females during the period were included in the interval, and the excess mortalities in June were positive and higher than those in April and May. In Tokyo, the number of suicides was below the lower bound throughout the period.

Conclusion: Our results suggest that various changes, such as communication, and social conditions amid the early COVID-19 pandemic induced a decrease in suicides in Japan. However, continuous monitoring is needed to evaluate the long-term effects of the pandemic on suicides.

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© 2020 Tatsuhiko Anzai et al. This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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