Evidence map›Paper›PMID 37361145›Full record

ArticleFrontiers in public health2023

Analysis on the factors associated with COVID-19 infection among Chinese residents after the implementation of

Yunshu Li, Sunyi Wang, Nan Yang, Yuwen Shi, Yingxiao Yang, Zhixin Zhu, Xiuyang Li

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Yunshu LiDepartment of Big Data in Health Science and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, China.
Sunyi WangSchool of Public Health, College of Medicine, Zhejiang University, Hangzhou, China.
Nan YangSchool of Public Health, College of Medicine, Zhejiang University, Hangzhou, China.
Yuwen ShiSchool of Public Health, College of Medicine, Zhejiang University, Hangzhou, China.
Yingxiao YangSchool of Public Health, College of Medicine, Zhejiang University, Hangzhou, China.
Zhixin ZhuDepartment of Big Data in Health Science and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, China.
Xiuyang LiDepartment of Big Data in Health Science and Center for Clinical Big Data and Statistics, Second Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aimed to investigate the status of COVID-19 infection and the associated factors among Chinese residents after the implementation of Methods: Participants were recruited using convenience sampling. The study used self-filled questionnaires to examine COVID-19 infection and associated factors among Chinese residents, from December 29, 2022, to January 2, 2023. For the statistical analysis, descriptive and quantitative analyses were used. The potential risk factors for COVID-19 infection were identified by multivariable logistic regression analysis. Results: After the adjustments in control strategies against COVID-19, the infection rate of COVID-19 was high among respondents, and 98.4% of individuals who tested positive showed symptoms including cough, fever, fatigue, headache, sore throat, nasal congestion, sputum production, muscle and joint pain, and runny nose. The main problems respondents reported were the shortage of drugs and medical supplies, the increased burden on families, and the unreliable information source of COVID-19 infection. Logistic regression showed that isolating patients with COVID-19 at home was associated with a lower risk of COVID-19 infection (OR = 0.58, 95%CI: 0.42-0.81). Conclusion: COVID-19 infection among residents is closely related to age, gender, and epidemic prevention measures. The government needs to strengthen education for individuals and centrally manage and properly address difficulties that may arise during COVID-19.

Indexed as

COVID-19Cross-Sectional StudiesEast Asian PeopleHumansRisk FactorsSARS-CoV-2COVID-19 infectionlogistic regressionpolicy analysisregular epidemic prevention and controlrelated factors

Identifiers

PMID37361145
PMCPMC10285290

What OpenQuestion holds

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LicenceCC BY
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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.