Risk factors for severity of COVID-19: a rapid review to inform vaccine prioritisation in Canada

  1. Aireen Wingert
  2. Jennifer Pillay
  3. Michelle Gates
  4. Samantha Guitard
  5. Sholeh Rahman
  6. Andrew Beck
  7. Ben Vandermeer
  8. Lisa Hartling

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Abstract

Rapid review to determine the magnitude of association between potential risk factors and severity of COVID-19, to inform vaccine prioritisation in Canada.

Setting

Ovid MEDLINE(R) ALL, Epistemonikos COVID-19 in L·OVE Platform, McMaster COVID-19 Evidence Alerts and websites were searched to 15 June 2020. Eligible studies were conducted in high-income countries and used multivariate analyses.

Participants

After piloting, screening, data extraction and quality appraisal were performed by a single experienced reviewer. Of 3740 unique records identified, 34 were included that reported on median 596 (range 44–418 794) participants, aged 42–84 years. 19/34 (56%) were good quality.

Outcomes

Hospitalisation, intensive care unit admission, length of stay in hospital or intensive care unit, mechanical ventilation, severe disease, mortality.

Results

Authors synthesised findings narratively and appraised the certainty of the evidence for each risk factor–outcome association. There was low or moderate certainty evidence for a large (≥2-fold) magnitude of association between hospitalisation in people with COVID-19, and: obesity class III, heart failure, diabetes, chronic kidney disease, dementia, age >45 years, male gender, black race/ethnicity (vs non-Hispanic white), homelessness and low income. Age >60 and >70 years may be associated with large increases in mechanical ventilation and severe disease, respectively. For mortality, a large magnitude of association may exist with liver disease, Bangladeshi ethnicity (vs British white), age >45 years, age >80 years (vs 65–69 years) and male gender among 20–64 years (but not older). Associations with hospitalisation and mortality may be very large (≥5-fold) for those aged ≥60 years.

Conclusions

Increasing age (especially >60 years) may be the most important risk factor for severe outcomes. High-quality primary research accounting for multiple confounders is needed to better understand the magnitude of associations for severity of COVID-19 with several other factors.

PROSPERO registration number

CRD42020198001.

Article activity feed

  1. Version published to 10.1136/bmjopen-2020-044684
  2. ScreenIT

    SciScore for 10.1101/2020.08.27.20183434: (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
    We supplemented the Medline search by hand-searching Epistemonikos COVID-19 in L^OVE Platform (https://app.iloveevidence.com/topics) and McMaster COVID-19 Evidence Alerts (https://plus.mcmaster.ca/COVID-19/) for relevant prognosis or aetiology studies up to 12 June 2020.
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    Study Selection: All records retrieved by the searches were exported to a Microsoft Office Excel (Microsoft Corporation, Redmond, WA) spreadsheet for screening.
    Microsoft Office Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Quality Assessments: To expedite quality assessments, we did not use a formal tool; instead we focused on key variables that were considered to be most relevant to the topic, and that would allow for meaningful stratification of studies by quality.
    Quality Assessments
    suggested: None

    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:
    The Equity Matrix applied to COVID-19 with evidence to-date can be found elsewhere.[11] Strengths and Limitations: The expedited methods used in this review allowed for a rapid but comprehensive synthesis of the highest quality evidence available on multiple risk factors associated with severe COVID-19 outcomes that is applicable to OECD countries. Generalizationsto other countries should be made with caution, as high risk groups in these populations may differ. We excluded studies only examining patients with severe COVID-19 (i.e., in ICU settings), and therefore our findings for mechanical ventilation and mortality are applicable to people with COVID-19 or in general populations, but not necessarily all those with severe infection. Most studies of patients in the ICU setting that we located were relatively small and descriptive in nature, such that many would have been excluded due to lack of adjustment or only have been able to provide low or very low certainty evidence due to their lack of precision. As described previously, many available studies do not control for any important confounding variables which limited the number of studies and risk factors included in this review. Given the rapid emergence of new evidence on the topic, potential associations (or lack of association) for which only low or very low certainty of evidence is available should continue to be reviewed as new primary research is published. There is a need for high quality primary research (accountin...

    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.

  3. Version published to 10.1101/2020.08.27.20183434v1 on medRxiv