Evidence map›Paper›PMID 34284821›Full record

ArticleInfectious diseases of poverty2021

Preventive behaviours and family inequalities during the COVID-19 pandemic: a cross-sectional study in China.

Yisheng Ye, Ruijun Wu, Yao Ge, Tao Wang, Xin Yao, Yao Yang, Chengxu Long, Fangfei Chen, Shangfeng Tang, Rui Huang

Abstract read
In one paragraph

Article in Infectious diseases of poverty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

10 authors.

Yisheng YeSchool of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, China.
Ruijun WuChina National Center for Biotechnology Development, Beijing, China.
Yao GeChina National Center for Biotechnology Development, Beijing, China.
Tao WangChina Science and Technology Exchange Center, Beijing, China.
Xin YaoHigh Technology Research and Development Center, Beijing, China.
Yao YangNational Center for Science and Technology Evaluation, Beijing, China.
Chengxu LongSchool of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, China.
Fangfei ChenSchool of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, China.
Shangfeng Tang *School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, China. sftang2018@hust.edu.cn.
Rui Huang *School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, China. hys19810612@163.com.ORCID http://orcid.org/0000-0003-4124-3896

Funding

Fundamental Research Funds for the Central Universities 2020kfyXGYJ071
6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) pandemic is an international public health threat, and people's participation in disease-related preventive behaviours is the key to controlling infectious diseases. This study aimed to assess the differences in adopting preventive behaviours among populations to explore potential individual and household factors and inequalities within families.

methodsThis online survey was conducted in April 2020. The directional stratified convenient sampling method was used to select 4704 participants from eight provinces in eastern, central, and western China. The questionnaire included demographic information, household variables, and five target prevention behaviours. The chi-squared test, binary multilevel model, and Mantel-Haenszel hierarchical analysis were used for data analysis in the study.

resultsApproximately 71.2% of the participants had appropriate outdoor prevention, and 32.9% of the participants had indoor protection in place. Sharing behaviours (P < 0.001) and education level (P < 0.001) were positively associated with adopting preventive measures. The inhibiting effect of household crowding and stimulating effect of high household income on preventive behaviours were determined in this study. Household size was negatively associated with living area (β = -0.057, P < 0.05) and living style (β = -0.077, P < 0.05). Household income was positively associated with age (β = 0.023, P < 0.05), and relationship with friends (β = 0.053, P < 0.05). Vulnerable groups, such as older adults or women, are more likely to have inadequate preventive behaviours. Older adults (OR = 1.53, 95% CI 1.09-2.15), women (OR = 1.37, 95% CI 1.15-1.64), and those with more than 2 suspected symptoms (OR = 1.85, 95% CI 1.07-3.19) were more likely to be affected by the inhibiting effect of household crowding, while the stimulating effect of high household income was limited in these groups.

conclusionsInequalities in COVID-19 prevention behaviours exist between families and inadequate adoption of prevention by vulnerable groups are noteworthy. This study expands the research perspective by emphasizing the role of household factors in preventive behaviours and by focusing on family inequalities. The government should use traditional media as a platform to enhance residents' public health knowledge. Targeted additional wage subsidies, investments in affordable housing, financial support for multigenerational households, and temporary relocation policies may deserve more attention. Communities could play a critical role in COVID-19 prevention.

Indexed as

Health BehaviorHealth Knowledge, Attitudes, PracticeAdolescentAdultChildChinaCommunicable Disease ControlCOVID-19Cross-Sectional StudiesCrowdingFamily CharacteristicsFemaleHumansMaleMiddle AgedPandemicsChinaCOVID-19HouseholdPreventive behaviours

Identifiers

PMID34284821
PMCPMC8290209

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.