Variant-of-concern-attributable health and health system-related outcomes: a population-level propensity-score matched cohort study
Bibliographic record
Abstract
Abstract Background As the transmission of SARS-CoV-2 variants intensifies globally, the burden of COVID-19 on the already strained health systems is becoming increasingly concerning. While there is growing literature on the effects of various variants-of-concern (VOC) on increased transmission, the extent to which VOCs may lead to more severe disease remains debated. Methods In the current analysis, we use a population-based propensity-score matched cohort study of all incident laboratory-confirmed COVID-19 cases with VOC testing in Ontario, Canada to estimate healthcare resource use and health outcomes attributable to VOCs introduced to Ontario between January 1 and April 9, 2021, relative to the previously circulating wild-type strain. Results We find that VOCs are associated with a higher odds of hospitalisation (odds ratio [OR], 2.25; 95% confidence interval [CI], 2.10-2.40) and ICU admission (OR, 3.31; 95%CI, 2.84-3.86); as well as with a higher odds of mortality for both the general COVID-19 population (OR 1.75; 1.47-2.09) and hospitalised cases (OR, 1.62; 95%CI, 1.23-2.15). Conclusion Taken together, these findings suggest that health systems may face increased demand for healthcare resources as VOCs predominate worldwide in view of low global vaccination coverage.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".