Evidence map›Paper›PMID 32351037›Full record

ArticleTransboundary and emerging diseases2020

Epidemiological and clinical characteristics of 333 confirmed cases with coronavirus disease 2019 in Shanghai, China.

Xiao Yu, Xiaodong Sun, Peng Cui, Hao Pan, Sheng Lin, Ruobing Han, Chenyan Jiang, Qiwen Fang, Dechuan Kong, Yiyi Zhu and 7 more

Open access · bronzeAbstract read
In one paragraph

Article in Transboundary and emerging diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 12 pooled it
2.4field-weighted citation impact, top 6% of its field
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

40 citing papers in PubMed, 12 syntheses or guidelines pooled it, 96 citations in OpenAlex.

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  20. Gender-based differences in COVID-19.New microbes and new infections · 2021
    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

17 authors at 1 institution in 1 country.

Xiao YuShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Xiaodong SunShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Peng CuiShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Hao PanShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Sheng LinShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Ruobing HanShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Chenyan JiangShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Qiwen FangShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Dechuan KongShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Yiyi ZhuShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Yaxu ZhengShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Xiaohuan GongShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Wenjia XiaoShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Shenghua MaoShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Bihong JinShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Huanyu WuShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Chen FuShanghai Municipal Center for Disease Control and Prevention, Shanghai, China.ORCID https://orcid.org/0000-0003-0646-9048
Shanghai Municipal Center For Disease Control Prevention · CN

Funding

Natural Science Foundation of Shanghai 20411950100
6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease first identified in Wuhan City, Hubei Province, China. As of 19 February 2020, there had been 333 confirmed cases reported in Shanghai, China. This study elaborates on the epidemiological and clinical characteristics of COVID-19 based on a descriptive study of the 333 patients infected with COVID-19 in Shanghai for the purpose of probing into this new disease and providing reference. Among the 333 confirmed cases in Shanghai, 172 (51.7%) were males and 161 (48.3%) were females, with a median age of 50 years. 299 (89.8%) cases presented mild symptoms. 139 (41.7%) and 111 (33.3%) cases were infected in Wuhan and Shanghai, respectively. 148 (44.4%) cases once had contact with confirmed cases before onset, while 103 (30.9%) cases had never contacted confirmed cases but they had a sojourn history in Wuhan. The onset date of the first case in Shanghai was 28 December, with the peak appearing on 27 January. The median incubation period of COVID-19 was estimated to be 7.2 days. 207 (62.2%) cases had fever symptoms at the onset, whereas 273 (82.0%) cases experienced fever before hospitalization. 56 (18.6%) adults experienced a decrease in white blood cell and 84 (42.9%) had increased C-reactive protein after onset. Elderly, male and heart disease history were risk factors for severe or critical pneumonia. These findings suggest that most cases experienced fever symptoms and had mild pneumonia. Strengthening the health management of elderly men, especially those with underlying diseases, may help reduce the incidence of severe and critical pneumonia. Time intervals from onset to visit, hospitalization and diagnosis confirmed were all shortened after Shanghai's first-level public health emergency response. Shanghai's experience proves that COVID-19 can be controlled well in megacities.

Indexed as

BetacoronavirusChinaCoronavirus InfectionsCOVID-19FemaleFeverHumansMaleMiddle AgedPandemicsPneumonia, ViralRisk FactorsSARS-CoV-2clinical characteristicsCOVID-19epidemiologyShanghai

Identifiers

PMID32351037
PMCPMC7267440
OpenAlexW3017464525

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.