Knowledge, risk perceptions, and behaviors related to the COVID-19 pandemic in Malawi

  1. Jethro Banda
  2. Albert Dube
  3. Sarah Brumfield
  4. Abena Amoah
  5. Amelia Crampin
  6. Georges Reniers
  7. Stephane Helleringer

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.4054/demres.2021.44.20
  2. Version published to 10.1101/2020.06.16.20133322v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.16.20133322: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: We sought the oral consent of respondents prior to participation in the study.
    IRB: The protocol for this study was approved by institutional review boards at the Malawi Ministry of Health (National Health Sciences Research Committee), at the Johns Hopkins University School of Public Health, and at the London School of Hygiene and Tropical Medicine.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableThe oldest age of KHDSS residents and KHDSS migrants recruited during pre-COVID-19 mortality studies was 49 years old for women and 54 years old for men, similar to the age eligibility criteria used during Demographic and Health Surveys [17].

    Table 2: Resources

    No key resources detected.

    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 has several limitations. First, it is based on a sample of individuals who could be reached by mobile phone. As a result, it might exclude members of the more impoverished households of the study communities. If the knowledge and behaviors of population members who do not have access to mobile phones differs from those of other members, then our study results might be biased. Second, our sampling frame was constituted from lists of participants in studies conducted in 2019/2020. Only approximately 45% of the sample was randomly selected (HDSS migrants and HDSS residents). Other respondents were referred to this study by their (randomly selected) siblings. As such, our sample is not representative of a specific population in Karonga District or elsewhere in Malawi. In particular, whereas our sample included respondents in a number of cities of the country (e.g., Lilongwe, Mzuzu), it was not representative of urban areas in Malawi. In appendix A2, however, we documented how our study sample differs from representative samples of households in rural and urban areas. The most salient difference was the limited number of respondents aged 55 years and older in our study sample. Since members of such age groups are at an increased risk of adverse COVID-19 outcomes [24], future studies of knowledge and behaviors related to the pandemic in African countries should ensure that they are adequately represented. Third, we did not investigate other determinants of the adoption of...

    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 10.1101/2020.06.16.20133322v1 on medRxiv