Risk and protective factors of SARS-CoV-2 infection – Meta-regression of data from worldwide nations

  1. Hisato Takagi
  2. Toshiki Kuno
  3. Yujiro Yokoyama
  4. Hiroki Ueyama
  5. Takuya Matsushiro
  6. Yosuke Hari
  7. Tomo Ando

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Abstract

Although it has been reported that coexistent chronic diseases are strongly associated with COVID-19 severity, investigations of predictors for SARS-CoV-2 infection itself have been seldom performed. To screen potential risk and protective factors for SARS-CoV-2 infection, meta-regression of data from worldwide nations were herein conducted. We extracted total confirmed COVID-19 cases in worldwide 180 nations (May 31, 2020), nation total population, population ages 0-14/≥65, GDP/GNI per capita, PPP, life expectancy at birth, medical-doctor and nursing/midwifery-personnel density, hypertension/obesity/diabetes prevalence, annual PM2.5 concentrations, daily ultraviolet radiation, population using safely-managed drinking-water/sanitation services and hand-washing facility with soap/water, inbound tourism, and bachelor’s or equivalent (ISCED 6). Restricted maximum-likelihood meta-regression in the random-effects model was performed using Comprehensive Meta-Analysis version 3. To adjust for other covariates, we conducted the hierarchical multivariate models. A slope (coefficient) of the meta-regression line for the COVID-19 prevalence was significantly negative for population ages 0-14 (–0.0636; P = .0021) and positive for obesity prevalence (0.0411; P = .0099) and annual PM2.5 concentrations in urban areas (0.0158; P = .0454), which would indicate that the COVID-19 prevalence decreases significantly as children increase and that the COVID-19 prevalence increases significantly as the obese and PM2.5 increase. In conclusion, children (negatively) and obesity/PM2.5 (positively) may be independently associated with SARS-CoV-2 infection.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.06.20124016: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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

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  2. Version published to 10.1101/2020.06.06.20124016v1 on medRxiv