Cross-sectional study of COVID-19 knowledge, beliefs and prevention behaviours among adults in Senegal

  1. Matthew Kearney
  2. Marta Bornstein
  3. Marieme Fall
  4. Roch Nianogo
  5. Deborah Glik
  6. Philip Massey

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Abstract

The aim of the study was to explore COVID-19 beliefs and prevention behaviours in a francophone West African nation, Senegal.

Design

This was a cross-sectional analysis of survey data collected via a multimodal observational study.

Participants

Senegalese adults aged 18 years or older (n=1452).

Primary and secondary outcome measures

Primary outcome measures were COVID-19 prevention behaviours. Secondary outcome measures included COVID-19 knowledge and beliefs. Univariate, bivariate and multivariate statistics were generated to describe the sample and explore potential correlations.

Setting

Participants from Senegal were recruited online and telephonically between June and August 2020.

Results

Mask wearing, hand washing and use of hand sanitiser were most frequently reported. Social distancing and staying at home were also reported although to a lower degree. Knowledge and perceived risk of COVID-19 were very high in general, but risk was a stronger and more influential predictor of COVID-19 prevention behaviours. Men, compared with women, had lower odds (adjusted OR (aOR)=0.59, 95% CI 0.46 to 0.75, p<0.001) of reporting prevention behaviours. Rural residents (vs urban; aOR=1.49, 95% CI 1.12 to 1.98, p=0.001) and participants with at least a high school education (vs less than high school education; aOR=1.33, 95% CI 1.01 to 1.76, p=0.006) were more likely to report COVID-19 prevention behaviours.

Conclusions

In Senegal, we observed high compliance with recommended COVID-19 prevention behaviours among our sample of respondents, in particular for masking and personal hygiene practice. We also identified a range of psychosocial and demographic predictors for COVID-19 prevention behaviours such as knowledge and perceived risk. Stakeholders and decision makers in Senegal and across Africa can use place-based evidence like ours to address COVID-19 risk factors and intervene effectively with policies and programming. Use of both phone and online surveys enhances representation and study generalisability and should be considered in future research with hard-to-reach populations.

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  1. Version published to 10.1136/bmjopen-2021-057914
  2. ScreenIT

    SciScore for 10.1101/2021.09.29.21264312: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Potential participants were informed about the study and provided consent to participate in the survey (online) or interview (telephone).
    IRB: All study procedures and protocols were reviewed by and approved or determined to be exempt by institutional ethical review boards.
    Sex as a biological variablenot detected.
    RandomizationThe telephone survey used a random selection from a panel of phone numbers of individuals who lived in Senegal and agreed to participate in media-related research.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Forest plots were generated using the ‘forestplot’ package in RStudio software.[
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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:
    Limitations: The current study is not without limitations. First, our findings may not be generalizable beyond Senegal, or more specifically Senegalese adults. Second, our recruitment strategy increased the potential for selection bias because participants were recruited online and on-the-ground in Senegal; thus, all participants, by the nature of the recruitment methods, had access to the internet and/or a cell phone. Selection bias is common in online and social media recruitment because recruited participants tend to be more well-educated and younger than the general population.[2,42] To address potential confounding between recruitment methods, we controlled for recruitment method in our multivariate regression modelling. At the same time, we believe that our multi-modal recruitment strategy bolsters the representativeness of our sample and is a key strength of our study. Had we excluded online participants, then our sample’s demographic characteristics would likely have differed. Conversely, had telephone participants been excluded then our sample would have skewed older, female, and educated – social media sample biases that we addressed through dual data collection methods. Third and finally, we did not identify significant relationships for many factors in our regression model because our study was not statistically powered to detect significant differences. For example, COVID-19 knowledge was high in our sample and an insignificant predictor of prevention behaviors. ...

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  3. Version published to 10.1101/2021.09.29.21264312v1 on medRxiv