Evidence map›Paper›PMID 33330788›Full record

ArticleGlobal health & medicine2020

Early cases of COVID-19 in Tokyo and occupational health.

Keisuke Kuwahara, Ai Hori, Norio Ohmagari, Tetsuya Mizoue

Abstract read
In one paragraph

Article in Global health & medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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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

4 authors.

Keisuke KuwaharaDepartment of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan.
Ai HoriUniversity of Tsukuba, Ibaraki, Japan.
Norio OhmagariAMR Clinical Reference Center, Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan.
Tetsuya MizoueDepartment of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) has spread rapidly across the globe, presenting severe challenges to societies. Gaining a better understanding of patient demographics is essential to develop measures to counteract such spreading. In this context, from a viewpoint of occupational health, we analyzed the publicly available data on patients diagnosed with COVID-19 in Tokyo, which reported the highest number of cases in Japan. A total of 243 cases aged 20 years or older (excluding students) were recorded between January 14 and March 27, 2020. Of 233 cases excluding 10 cases of the first cluster, 162 were men and 176 were of working age (20 to 69 years). Of 203 cases with valid information on employment status, 151 (74%) were workers: 114 employees, 31 self-employed, and 6 medical staff. Of the working patients, the majority were male: 72% in employed and 87% in self-employed. These data suggest the importance of occupational health in controlling the spread of COVID-19. In April 2020, a state of emergency was declared in response to a surge in the number of cases, especially in metropolitan areas. A working schedule associated with lower risks of infection, including telework and flexible working hours, should be rigorously promoted to minimize human-to-human contact. Such policies, along with the implementation of effective measures to protect essential workers from infection, overwork, and stigma, would ensure the smooth running of society amidst the present crisis.

Indexed as

COVID-19emerging infectious diseaseinfection prevention and controloccupational healthTokyo

Identifiers

PMID33330788
PMCPMC7731096

What OpenQuestion holds

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Registered trials

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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.