Evidence map›Paper›PMID 35162149›Full record

ArticleInternational journal of environmental research and public health2022

Social Group Differences in the Exposure Risk of COVID-19: A Case Study in Beijing, China.

Chen Lu, Xiaodi Yi, Xiaocui Ren

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Chen LuSchool of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100190, China.
Xiaodi YiSino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China.
Xiaocui RenSino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China.

Funding

Guangdong Planning Office of Philosophy and Social Sciences GD18XSH04National Natural Science Foundation of China 41671124the University of Chinese Academy of Sciences Y95402CXX2
6 · The paper itself

Abstract

Taking Beijing as a case, this paper conducted a survey to collect the characteristics of residents' daily activities, including the mode of frequency and duration of travel, the type and environment of activities, and the duration and frequency of activities. We calculated the COVID-19 exposure risk of residents in different activities based on the exposure risk formula; the influencing factors of residents' exposure risk were analyzed by regression analysis. The variance of residents' COVID-19 exposure risk was calculated by coefficient of variation. The main conclusions are as follows: (1) There are differences in activity types of COVID-19 exposure risk, which are survival activity, daily activity and leisure activity from high to low. (2) There are differences in populations of COVID-19 exposure risk. Education level, occupation and income are the main factors affecting residents' COVID-19 exposure risk. (3) There is internal inequity in the risk of COVID-19 exposure. The exposure risk was higher on work days than on rest days. Health inequities at work are highest on both work days and rest days. Among the different population characteristics, male, 31-40 years old, married, with a high school education, income level of 20,001-25,000 yuan, with a non-local rural hukou, rental housing, farmers, three generations or more living together have a greater degree of COVID-19 exposure risk.

Indexed as

COVID-19AdultBeijingChinaHumansMaleSARS-CoV-2Surveys and QuestionnairesCOVID-19exposure riskgroup differencehealth inequity

Identifiers

PMID35162149
PMCPMC8834806

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.