Obesity as a predictor for adverse outcomes among COVID-19 patients: A meta-analysis

  1. Pranta Das
  2. Nandeeta Samad
  3. Abdul-Aziz Seidu
  4. Richard Gyan Aboagye
  5. Justice Kanor Tetteh
  6. Bright Opoku Ahinkorah

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Abstract

Background

This meta-analysis sought to determine the estimated association between obesity and adverse outcomes among COVID-19 patients.

Methods

We followed the recommended PRISMA guidelines. A systematic literature search was conducted in PubMed, Google Scholar, and ScienceDirect for published literature between December 1, 2019, and October 2, 2020. The data for the study were pooled from studies that contained the search terms “Obesity” AND (COVID-19 or 2019-nCoV or Coronavirus or SARS-CoV-2) AND (“ICU admission” OR “Hospitalization” OR “Disease severity” OR “Invasive mechanical ventilator” OR “Death” OR “Mortality”). All the online searches were supplemented by reference screening of retrieved studies for additional literature. The pooled odds ratio (OR) and confidence intervals (CI) from the retrieved studies were calculated using the random effect model (Inverse-Variance method).

Findings

Five studies with a combined sample size of 335,192 patients were included in the meta-analysis. The pooled OR from the final analysis showed that patients who are severely obese were more likely to experience adverse outcome (death or ICU admission or needing IMV or hospitalization) compared to the normal patients [OR = 2.81, 95% CI = 2.33 – 3.40, I 2 = 29%].

Conclusion

Severe obesity is a risk factor in developing adverse outcomes among COVID-19 patients. The finding of the study signifies promotive, preventive, and curative attention to be accorded patients diagnosed with severe obesity and COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.27.20239616: (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

    Software and Algorithms
    SentencesResources
    PubMed, Google Scholar and ScienceDirect for English language literature published between December 1, 2019 and October 2,2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    All the analysis was done using software STATA 12.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    Strength and limitations of the study: This meta-analysis strongly reiterates the evidence that obesity is a risk factor for severe COVID-19 outcomes. However, the cross-sectional nature of the studies included in the present meta-analysis could limit the findings’ generalisability as causal inference is not drawn. Also, the studies included were from the USA, France, and the UK, which could limit its applicability due to differences in races, patterns of the severity of COVID-19 infections, and mortality rate.

    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 10.1101/2020.11.27.20239616v1 on medRxiv