Rates of COVID-19-Associated Hospitalization in Immunocompromised Individuals in Omicron-era: A Population-Based Observational Study Using Surveillance Data in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background People with immune dysfunction have a higher risk for severe COVID-19 outcomes. Omicron variant is associated with a lower rate of hospitalization but higher vaccine escape. This population-based study quantifies COVID-19 hospitalization rate in the Omicron-dominant era among vaccinated people with immune dysfunction, identified as clinically extremely vulnerable (CEV) population before COVID-19 treatment was widely offered. Methods All COVID-19 cases were reported to the British Columbia Centre for Disease Control (BCCDC) between January 7, 2022 and March 14, 2022. Case and population hospitalization rates were estimated across CEV status, age groups and vaccination status. Cumulative rates of hospitalizations for the study period were also compared between CEV and non-CEV individuals matched by sex, age group, region, and vaccination characteristics. Findings A total of 5,591 COVID-19 reported cases and 1,153 hospitalizations among CEV individuals were included. A third vaccine dose with mRNA vaccine offered additional protection against severe illness in CEV individuals. Vaccinated CEV population still had a significantly higher breakthrough hospitalization rate compared with non-CEV individuals. Interpretation CEV population remains a higher risk group and may benefit from additional booster doses and pharmacotherapy. Funding BC Centre for Disease Control and Provincial Health Services Authority
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".