Risk factors for COVID-19 hospitalization or death during the first Omicron surge in adults: a large population-based case-control study
Bibliographic record
Abstract
Abstract Background Description of risk factors of severe acute COVID-19 outcomes with the consideration of vaccination status in the era of the Omicron variant of concern are limited. Objectives To examine the association of age, sex, underlying medical conditions, and COVID-19 vaccination with hospitalization, intensive-care unit (ICU) admission, or death due to the disease, using data from a period when Omicron was the dominant strain. Methods A population-based case-control study based on administrative health data, that included confirmed COVID-19 patients during January (2022) in Alberta, Canada. Patients who were non-residents, without the provincial healthcare insurance coverage, or ≤18 years of age were excluded. Patients with any severe outcome were the cases; and those without any hospitalization, ICU admission, or death were controls. Adjusted odds ratios, of the explanatory factors of a severe outcome, were estimated using a logistic regression model. Results There were 90,989 COVID-19 patients included in the analysis; 2% had severe outcomes and 98% were included in the control group. Overall, more COVID patients were found in the younger age-groups (72.0% ≤49 years old), females (56.5%), with no underlying conditions (59.5%), and fully vaccinated patients (90.4%). However, the adjusted odds ratios were highest in the 70–79 age group (28.32; 95% CI 20.6–38.9) or among ≥80 years old (29.8; 21.6–41.0), males (1.4; 1.3–1.6); unvaccinated (16.1; 13.8–18.8), or patients with ≥3 underlying conditions (13.1; 10.9–15.8). Conclusion Higher risk of severe acute COVID-19 outcomes were associated with older age, the male sex, and increased number of underlying medical conditions. Unvaccination or undervaccination remained as the greatest modifiable risk factor in prevention of severe COVID outcomes. These findings help inform medical decisions and allocation of scarce healthcare resources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".