Relationship Between COVID-19 Infection and Risk Perception, Knowledge, Attitude, and Four Nonpharmaceutical Interventions During the Late Period of the COVID-19 Epidemic in China: Online Cross-Sectional Survey of 8158 Adults

  1. Hong Xu
  2. Yong Gan
  3. Daikun Zheng
  4. Bo Wu
  5. Xian Zhu
  6. Chang Xu
  7. Chenglu Liu
  8. Zhou Tao
  9. Yaoyue Hu
  10. Min Chen
  11. Mingjing Li
  12. Zuxun Lu
  13. Jack Chen

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

So far, there have been no published population studies on the relationship between a COVID-19 infection and public risk perception, information source, knowledge, attitude, and behaviors during the COVID-19 outbreak in China.

Objective

This study aims to understand the relationships between COVID-19 infection; four personal nonpharmaceutical interventions (NPIs; handwashing, proper coughing habits, social distancing, and mask wearing); and public risk perception, knowledge, attitude, and other social demographic variables.

Methods

An online survey of 8158 Chinese adults between February 22 and March 5, 2020, was conducted. Bivariate associations between categorical variables were examined using Fisher exact test. We also explored the determinants of four NPIs as well as their association with COVID-19 infection using logistic regression.

Results

Of 8158 adults included, 57 (0.73%) were infected with COVID-19. The overwhelming majority of respondents showed a positive attitude (n=8094, 99.2%), positive risk perception (n=8146, 99.9%), and high knowledge levels that were among the strongest predictors of the four adopted NPIs (handwashing: n=7895, 96.8%; proper coughing: 5997/6444, 93.1%; social distancing: n=7104/8158, 87.1%; and mask wearing: 5011/5120, 97.9%). There was an increased risk of COVID-19 infection for those who did not wash their hands (2.28% vs 0.65%; risk ratio [RR] 3.53, 95% CI 1.53-8.15; P=.009), did not practice proper coughing (1.79% vs 0.73%; RR 2.44, 95% CI 1.15-5.15; P=.03), did not practice social distancing (1.52% vs 0.58%; RR 2.63, 95% CI 1.48-4.67; P=.002), and did not wear a mask (7.41% vs 0.6%; RR 12.38, 95% CI 5.81-26.36; P<.001). For those who did practice all other three NPIs, wearing a mask was associated with a significantly reduced risk of infection compared to those who did not wear a mask (0.6% vs 16.7%; P=.04). Similarly, for those who did not practice all or part of the other three NPIs, wearing a mask was also associated with a significantly reduced risk of infection. In a penalized logistic regression model including all four NPIs, wearing a mask was the only significant predictor of COVID-19 infection among the four NPIs (odds ratio 7.20, 95% CI 2.24-23.11; P<.001).

Conclusions

We found high levels of risk perception, positive attitude, desirable knowledge, as well as a high level of adopting the four NPIs. The relevant knowledge, risk perception, and attitude were strong predictors of adapting the four NPIs. Mask wearing, among the four personal NPIs, was the most effective protective measure against COVID-19 infection, with added preventive effect among those who practiced all or part of the other three NPIs.

Article activity feed

  1. Version published to 10.2196/21372
  2. Version published to 10.2196/preprints.21372
  3. ScreenIT

    SciScore for 10.1101/2020.06.02.20120808: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: The exclusion criteria were: 1) those who did not consent to participate; 2) those who did not answer all the questions; 3) the questionnaires completed in less than 2 minutes; 4) the repeated questionnaire from the same IP address.
    IRB: The Ethics Committee of Chongqing Medical University approved our study protocol.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data management and statistical analysis were done through SPSS v25 and StataTM v16.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study also has several limitations. First, our study sample have disproportionately more female, well-educated and less smokers, reflecting a typically young and healthy cohort in similar surveys. Thus, the frequencies of desirable knowledge levels and health behaviours may be over-estimated while less desirable outcomes (such as lower family monthly income) may be under-estimated. However, the modelling results may be less susceptible to these potential biases. Second, our study results were from a particular period of the outbreak and most of the respondents were from outside Hubei province. The generalization of the results to other settings and countries may be limited. Third, our study was a cross-sectional population survey and the association found between the predictors and outcomes should be interpreted with caution and further intervention studies are needed in confirming our findings. Fourth, despite the relatively large sample size, the total cases of COVID-19 infection were still small so that the relationship between NPIs and the covid-19 infection should be confirmed by other larger epidemiological studies. Fifth, the potential risk compensating effects of mask wearing against other NPIs should be considered as being of a hypothesis-generating in nature given the potential limitations outlined above. Common to any observational studies with multiple outcomes and modelled with different effective sample sizes, the interpretations and generalisation of the re...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.06.02.20120808v1 on medRxiv
  5. Version published to 10.1101/2020.06.02.20120808v2 on medRxiv