Willingness of Nigerian residents to disclose COVID-19 symptoms and take COVID-19 test

  1. Victoria Oladoyin
  2. Oluyemi Okunlola
  3. Oluwaseyi Israel
  4. Demilade Ibirongbe
  5. Joy Osifo
  6. Taiwo Obembe
  7. Paulinus Omode
  8. Olugbenga Osunmakinwa

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Abstract

Background

An understanding of willingness of people to disclose coronavirus disease 2019 (COVID-19) symptoms and take the COVID-19 test will help provide important insight for motivators towards the self-surveillance and testing strategies recommended by the World Health Organization to curtail and halt the transmission of COVID-19.

Objectives

This study assessed willingness to disclose symptoms suggestive of COVID-19 and willingness to take COVID-19 test as well as their predictors.

Methods

A cross-sectional online survey of 524 Nigerian adults, aged ≥ 18 years, residing in Nigeria and who had not taken the COVID-19 test was conducted. Information on willingness to disclose COVID-19 symptoms, take COVID-19 test and possible predictors were collected. Data were analysed using descriptive and inferential statistics evaluated at 5% significance level.

Results

Mean age of respondents was 35.8 ± 10.7 years and 57.0% were males. Majority (85.8% and 86.2% respectively) were willing to disclose COVID-19 symptoms and take COVID-19 test. Self-risk perception of contracting COVID-19 predicted both willingness to disclose COVID-19 symptoms (aOR=3.236; 95%CI=1.836-5.704) and take COVID-19 test (aOR=3.174; 95%CI=1.570-6.419). Willingness to disclose COVID-19 symptoms (aOR=13.060; 95%CI= 6.253-27.276), knowledge of someone who had taken the test (aOR= 4.106; 95%CI= 1.179-14.299) and thought that it was important for people to know their COVID-19 status (aOR=3.123; 95%CI= 1.516-6.434) also predicted willingness to take COVID-19 test.

Conclusion

Nigerians are willing to disclose symptoms suggestive of COVID-19 and take the COVID-19 test. Investment in interventions developed based on the predicting factors will help speed up the finding and testing of suspected COVID-19 cases.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.02.20205914: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical considerations: Ethical approval to conduct the study was obtained from the Ondo State Health Research Ethics Committee with study protocol number OSHREC/28/05/20/268.
    Consent: The purpose of the study was also explained to the participants and written electronic consent was obtained from them before they could take the online survey.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data uploaded to the Google form server was downloaded in Microsoft Excel format before it was exported to SPSS software version 22 for analysis.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    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:
    A limitation of this study, which must be acknowledged, is the non-representativeness of the study population as most of our respondents were within the researchers’ social network. Despite this limitation, our study was able to add to the body of knowledge on COVID-19.

    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.

  2. Version published to 10.1101/2020.10.02.20205914v1 on medRxiv