Evidence map›Paper›PMID 37361016›Full record

ArticleIJID regions2023

Case fatality rate considering the lag time from the onset of COVID-19 infection to related death from 2020 to 2022 in Japan.

Shuko Nojiri, Yuta Kawakami, Daisuke Nakamoto, Manabu Kuroki, Yuji Nishizaki

Abstract read
In one paragraph

Article in IJID regions, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shuko NojiriClinical Translational Science, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Yuta KawakamiClinical Translational Science, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Daisuke NakamotoClinical Translational Science, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Manabu KurokiFaculty of Engineering, Yokohama National University, Kanagawa, Japan.
Yuji NishizakiClinical Translational Science, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: On an ecological scale, the lag time between coronavirus disease 2019 (COVID-19) infection and related fatality has varied between epidemic waves and prefectures in Japan. The variability in lag time across areas of Japan during the seven distinct waves can help derive a more appropriate estimation of the weekly confirmed case fatality rate (CFR) of COVID-19. Objective: To estimate the 7-day moving average CFR across area block levels in Japan from February 2020 to July 2022 using the lag time between COVID-19 infection and related fatality. Main outcomes and measures: The 7-day moving average CFR of COVID-19 for area blocks in Japan considering the lag time between infection and death (total and subgroup analysis of elderly). Results: Lag time was found to vary substantially among prefectures in Japan from the first wave to the seventh wave of the COVID-19 epidemic. The estimated 7-day moving average CFR based on the lag time reflects the Japanese COVID-19 pandemic and related policy interventions (e.g. vaccination of elderly people) rather than other standard CFR estimations. Conclusions and relevance: The variation in estimated lag time across prefectures in Japan for different epidemic waves indicates that it is inadequate to use the clinical results of the period from the start of infection to death for evaluation of the ecological scale of the CFR. Moreover, the lag time between infection and related fatality was found to be either shorter or longer than the clinically reported period. This revealed that preliminary reports of CFR may be overestimated or underestimated, even if they consider the lag based on clinical reports.

Indexed as

Case fatality rateCOVID-19Ecological studyEpidemiologyJapanLag timeSARS-CoV-2

Identifiers

PMID37361016
PMCPMC10149354

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.