The risk factors of COVID-19 in a longitudinal population-based study

  1. Hozhabr Jamali Atergeleh
  2. Mohammad Hassan Emamian
  3. Shahrbanoo Goli
  4. Marzieh Rohani-Rasaf
  5. Hassan Hashemi
  6. Akbar Fotouhi

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Abstract

The present longitudinal study aims to investigate the risk factors for getting COVID-19 in a population aged 50 to 74 years. Data were collected from Shahroud Eye Cohort Study and the electronic system of COVID-19 in Shahroud, northeast Iran. Participants were followed for about 13 months and predisposing factors for COVID-19 infection were investigated using log binomial model and by calculation of relative risks. From the beginning of the COVID-19 outbreak in Shahroud (February 20, 2020) to March 26, 2021, out of 4394 participants in the Eye Cohort Study, 271 (6.1%) were diagnosed with COVID-19 with a positive Reverse Transcription Polymerase Chain Reaction test on two nasopharyngeal and oropharyngeal swabs. Risk factors for getting COVID-19 were included male gender (Relative Risk (RR) = 1.51; 95% Confidence Intervals (CI), 1.15-1.99), BMI over 25 (RR = 1.03; 95% CI, 1.01-1.05) and diabetes (RR = 1.31; 95% CI, 1.02-1.67). Also, smoking (RR = 0.51; 95% CI, 0.28-0.93) and education (RR = 0.95; 95% CI, 0.92-0.98) had reverse associations. In conclusion men and diabetic patients and those who have BMI over 25, should be more alert to follow the health protocols related to COVID-19 and priority should be given to them considering COVID-19 vaccination.

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  1. ScreenIT

    SciScore for 10.1101/2021.04.12.21255369: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The Shahroud Eye Cohort Study and this study were approved by the ethics committee of Shahroud University of Medical Sciences.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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:
    Another limitation of this study is the lack of information about the level of knowledge and adherence to health protocols among the participants.

    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 10.1101/2021.04.12.21255369v1 on medRxiv
  3. Version published to 10.1515/em-2021-0024