Risk factors associated with severe outcomes of COVID-19: A systematic rapid review to inform national guidance on vaccine prioritization in Canada
Bibliographic record
Abstract
ABSTRACT Background To inform vaccine prioritization guidance in Canada, we systematically reviewed evidence on the magnitude of association between risk factors and severe outcomes of COVID-19. The urgent nature of this review necessitated an adapted methodology, which may serve as an exemplar for reviews undertaken under strict timelines. Methods We updated our existing review by searching online databases and websites for cohort studies providing multivariate adjusted associations. After piloting, one author screened studies and extracted data. Two authors estimated the magnitude of association between exposures and outcomes as little-to-no (odds, risk, or hazard ratio <2.0, or >0.50 for reduction), large (2.0-3.9, or 0.50-0.26 for reduction), or very large (≥4.0, or ≤0.25 for reduction), and rated the evidence certainty using GRADE. Results Of 11,734 unique records we included 134 reports. There is probably (moderate certainty) at least a large increase in mortality from COVID-19 among people aged 60-69 vs. <60 years (11 studies, n=517,217), with ≥2 vs. no comorbidities (4 studies, n=189,608), and for people with (vs. without): Down syndrome (1 study, n>8 million), type 1 and 2 diabetes (1 study, n>8 million), end-stage kidney disease (1 study, n>8 million), motor neuron disease, multiple sclerosis, myasthenia gravis, or Huntington’s disease (as a grouping; 1 study, n>8 million). The magnitude of association with mortality is probably very large for Down syndrome and may (low certainty) be very large for age 60-69 years, and diabetes. There is probably little-to-no increase in severe outcomes with several cardiovascular and respiratory conditions, and for adult males vs. females. Conclusion There is strong evidence to support at least a large increase in mortality from COVID-19 among older adults aged 60 to 69 years versus <60 years; people having two or more versus no comorbidities; and for people affected by several pre-existing conditions. The methodology employed in this review may provide an important exemplar for future syntheses undertaken under urgent timelines. Systematic review registration PROSPERO #CRD42021230185.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".