Excess All-Cause Deaths during Coronavirus Disease Pandemic, Japan, January–May 20201

Kawashima, Takayuki; Nomura, Shuhei; Tanoue, Yuta; Yoneoka, Daisuke; Eguchi, Akifumi; Ng, Chris Fook Sheng; Matsuura, Kentaro; Shi, Shoi; Makiyama, Koji; Uryu, Shinya; Kawamura, Yumi; Takayanagi, Shinichi; Gilmour, Stuart; Matsubara, Hiroaki; Sunagawa, Tomimasa; Takahashi, Takuri; Tsuchihashi, Yuuki; Kobayashi, Yusuke; Arima, Yuzo; Kanou, Kazuhiko; Suzuki, Motoi; Hashizume, Masahiro

doi:10.3201/eid2703.203925

Cited by 24 publications

(34 citation statements)

References 19 publications

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“…In addition, we note that the application results depend on the choice of the distance metrics, the associated kernel, and the prespecified parameter set (b, w). We (partially) show the results of the sensitivity analysis in terms of the choice of (b, w) in the previous studies, 18,36 and we welcome the re-evaluation of our method in other settings. In a rapid surveillance system, this novel algorithm can be used as a new tool for detecting outbreaks and further exploitative spatial analysis to identify the regional heterogeneity of excess death.…”

Section: Discussionmentioning

confidence: 78%

Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods

Yoneoka

Kawashima

Makiyama³

et al. 2021

Statistics in Medicine

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The demand for rapid surveillance and early detection of local outbreaks has been growing recently. The rapid surveillance can select timely and appropriate interventions toward controlling the spread of emerging infectious diseases, such as the coronavirus disease 2019 (COVID-19). The Farrington algorithm was originally proposed by Farrington et al (1996), extended by Noufaily et al (2012), and is commonly used to estimate excess death. However, one of the major challenges in implementing this algorithm is the lack of historical information required to train it, especially for emerging diseases. Without sufficient training data the estimation/prediction accuracy of this algorithm can suffer leading to poor outbreak detection. We propose a new statistical algorithm-the geographically weighted generalized Farrington (GWGF) algorithm-by incorporating both geographically varying and geographically invariant covariates, as well as geographical information to analyze time series count data sampled from a spatially correlated process for estimating excess death. The algorithm is a type of local quasi-likelihood-based regression with geographical weights and is designed to achieve a stable detection of outbreaks even when the number of time points is small. We validate the outbreak detection performance by using extensive numerical experiments and real-data analysis in Japan during COVID-19 pandemic.We show that the GWGF algorithm succeeds in improving recall without reducing the level of precision compared with the conventional Farrington algorithm.

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Section: Discussionmentioning

confidence: 78%

Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods

Yoneoka

Kawashima

Makiyama³

et al. 2021

Statistics in Medicine

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“…Some researchers in Japan have emphasized considerable excess mortality from all causes of death through June 2021 of around 49 thousand at maximum due to COVID-19 [21, 22] using the Farrington algorithm [23] and EuroMOMO [24], which was more than three times larger than the number of death confirmed by PCR test until June, 2021, 15 thousands. Their study measured excess mortalities as the gap between observation and beeline, not threshold as, in prefectures where observation was higher than threshold.…”

Section: Discussionmentioning

confidence: 99%

Huge Excess Mortality due to the delta strain of COVID-19 in Japan in August and September, 2021

Kurita¹,

Sugawara

Ohkusa

2020

Preprint

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Background: As of the end of June, 2020, the COVID−19 outbreak exhibited its highest peak on April 3. Nevertheless, no remarkable excess mortality attributable to COVID−19 has been observed. Object: We sought to quantify excess mortality in April using the National Institute of Infectious Diseases (NIID) model. Method: We applied the NIID model to deaths of all causes from 1987 up through April, 2020. Results: Results show no significant excess mortality in March or April, 2020, when the COVID−19 outbreak affected Japan most. Discussion and Conclusion: Because changes in application rule of the International Classification of Diseases in 2017 affected the number of pneumonia deaths drastically, we were unable to use pneumonia deaths to estimate excess mortality. it might be important to continue to monitor excess mortality of COVID−19 carefully after May 2020.

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“…The posterior median and the 95% upper bound were set as two thresholds. The range of differences between the nowcasted number of deaths and each of these thresholds was then reported as excess deaths as in previous studies [ 5 , 20 ]. All negative differences were assigned to zero.…”

Section: Methodsmentioning

confidence: 99%

“…Among the published studies on excess mortality in 2020 during the COVID-19 pandemic [ 16 – 21 ], few adjusted their estimates for reporting delays. Kawashima and colleagues [ 20 ] conducted such an adjustment for monthly all-cause deaths in Japan based on prompt vital statistics. By contrast, Weinberger and colleagues [ 21 ] analysed more granular data consisting of weekly counts across US jurisdictions and conducted nowcasting of deaths within a Bayesian framework [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of delay-adjusted all-cause excess mortality in the USA: March–December 2020

Akhmetzhanov

2021

Epidemiol. Infect.

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We estimate the delay-adjusted all-cause excess deaths across 53 US jurisdictions. Using provisional data collected from September through December 2020, we first identify a common mean reporting delay of 2.8 weeks, whereas four jurisdictions have prolonged reporting delays compared to the others: Connecticut (mean 5.8 weeks), North Carolina (mean 10.4 weeks), Puerto Rico (mean 4.7 weeks) and West Virginia (mean 5.5 weeks). After adjusting for reporting delays, we estimate the percent change in all-cause excess mortality from March to December 2020 with range from 0.2 to 3.6 in Hawaii to 58.4 to 62.4 in New York City. Comparing the March–December with September–December 2020 periods, the highest increases in excess mortality are observed in South Dakota (36.9–54.0), North Dakota (33.9–50.7) and Missouri (27.8–33.9). Our findings indicate that analysis of provisional data requires caution in interpreting the death counts in recent weeks, while one needs also to account for heterogeneity in reporting delays of excess deaths among US jurisdictions.

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Excess All-Cause Deaths during Coronavirus Disease Pandemic, Japan, January–May 20201

Cited by 24 publications

References 19 publications

Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods

Geographically weighted generalized Farrington algorithm for rapid outbreak detection over short data accumulation periods

Huge Excess Mortality due to the delta strain of COVID-19 in Japan in August and September, 2021

Estimation of delay-adjusted all-cause excess mortality in the USA: March–December 2020

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