Risk factors for severity of COVID-19: a rapid review to inform vaccine prioritisation in Canada
Bibliographic record
Abstract
OBJECTIVES: 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".