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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first appeared in December 2019 in Wuhan, China, and has rapidly led to a global pandemic (1). Globally, accurate figures on deaths caused [...]

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. [ABSTRACT FROM AUTHOR]

Strong lockdowns to control COVID-19 pandemic have been enforced globally and strongly restricted social activities with consequent negative effects on mental health. Japan has effectively implemented a unique voluntary policy to control COVID-19, but the mental health impact of the policy has not been examined on a large scale. In this study, we examined the effect of the first declaration on the mental health of affected residents. We used population-level questionnaire data of 17,400 people living under the state of emergency and 9208 who were not through a social-networking-service app and applied a difference-in-differences regression model to estimate the causal effect of the declaration of the state of emergency on psychological wellbeing, stratified by job category. No statistically significant effect of the declaration was observed among all job categories. This suggests that residents' psychological situation has gradually changed, possibly influenced by other factors such as the surrounding environment, rather than the declaration itself. Given that Japan has a unique policy to control COVID-19 instead of a strict lockdown, our results showed the Japanese-style policy may serve as a form of harm reduction strategy, to control the epidemic with minimal psychological harm, and enable a policy that balances disease control and mental health. Caution is necessary that this study used self-reported data from a limited time period before and after the first declaration in April 2020. [ABSTRACT FROM AUTHOR]

As people move around using public transportation networks such as train and airplanes, it is expected that emerging infectious diseases will spread on the network. The scan statistics approach has been frequently applied to identify high-risk locations, and the results are widely used for making a clinical decision in a timely manner. However, they are not optimally designed for modeling the spread and might not effectively work in the emergency situation where computational time is essentially important. We propose a new scan statistics approach for the public transportation network, called PTNS (Public Transportation Network Scan). PTNS utilizes the available network structure to construct potential candidates of clusters, and thus it can work well especially in situations where public transportation is the main medium of the infection spread. Further, it is designed for rapid surveillance. Lastly, PTNS is generalized to detect space-time clusters by customizing the iteration for potential clusters creation. Using the simulation data generated with a real railway network, we showed that, PTNS outperformed the conventional methods, including Circular- and Flex-scan approaches, in terms of the detection performance, while the computational time is feasible. [ABSTRACT FROM AUTHOR]

As people move around using public transportation networks such as train and airplanes, it is expected that emerging infectious diseases will spread on the network. The scan statistics approach has been frequently applied to identify high-risk locations, and the results are widely used for making a clinical decision in a timely manner. However, they are not optimally designed for modeling the spread and might not effectively work in the emergency situation where computational time is essentially important. We propose a new scan statistics approach for the public transportation network, called PTNS (Public Transportation Network Scan). PTNS utilizes the available network structure to construct potential candidates of clusters, and thus it can work well especially in situations where public transportation is the main medium of the infection spread. Further, it is designed for rapid surveillance. Lastly, PTNS is generalized to detect space-time clusters by customizing the iteration for potential clusters creation. Using the simulation data generated with a real railway network, we showed that, PTNS outperformed the conventional methods, including Circular- and Flex-scan approaches, in terms of the detection performance, while the computational time is feasible. [ABSTRACT FROM AUTHOR]

In Japan, in response to the spread of the new coronavirus disease (COVID-19), a 'new normal' in the era of the COVID-19 was proposed by the government, which calls for thorough wearing of masks as an infection control measure in the era of the COVID-19, but related heat illness has been a great concern this summer. We applied quasi-Poisson regression models to the daily number of emergency transportations due to heat illness from 2008 to 2020 from the Fire and Disaster Management Agency, Ministry of Internal Affairs and Communications, Japan, to estimate the expected weekly number of emergency transportations from heat illness, with adjustment for their long-term trend and the weather conditions, including temperatures. We found that, at the national level, the number of heat illness emergency transports did not significantly increase or decrease from the annual trend in 2020. By prefecture, on the other hand, there were some prefectures in which the number of heat illness emergency transports was less than the average year, and most of them were in the week of August 10–16. By age group, the number of heat illness emergency transports in the 0–17 and 18–64 age groups was particularly low in some prefectures, and by severity, those in mild cases was particularly low. A caution is necessary that there is a possibility that a decrease in cases possibly associated with COVID-19 measures, such as, outdoor activity restrictions at schools/universities and cancellation of public events, may offset the possible increase in heat illness cases occurring elsewhere associated with wearing masks. Given that the end of the COVID-19 pandemic is not expected yet, continuous and appropriate awareness-raising activities to prevent heat-related illness remain important. Unlabelled Image • Under the 'new normal' lifestyle calling for the mask wearing as a COVID-19 control measure, heat illness is a concern. • We estimated the expected weekly number of emergency transportations from heat illness. • By prefecture, age, and severity, there were some weeks where decreased cases were observed. • The decrease in the cases was possibly associated with COVID-19 measures, such as, outdoor activity restrictions. • Continuous and appropriate awareness-raising activities to prevent heat-related illness remain important. [ABSTRACT FROM AUTHOR]