The descriptive epidemiology of pre-omicron SARS-CoV-2 breakthrough infections and severe outcomes in Manitoba, Canada
Bibliographic record
Abstract
Introduction: Vaccination plays a key role in curbing severe outcomes resulting from COVID-19 disease. With the Omicron variant and the relaxing of public health protections breakthrough infections are increasingly common, and certain groups remain at higher risk for severe outcomes from breakthrough infections. We analysed population-based public health data from Manitoba, Canada to understand characteristics of those experiencing breakthrough infections and severe outcomes from breakthrough infections. Data from previous pandemic stages can provide valuable information regarding severe outcomes associated with breakthrough infection in the Omicron and future phases. Methods: Positive SARS-CoV-2 PCR tests from Cadham Provincial Laboratory were linked to case information from the population-based Public Health Information Management System. A retrospective design was used with time-to-event analyses to examine severe outcomes among those experiencing breakthrough infection. Results: Breakthrough cases were more likely to have 2 + chronic conditions, compared to age-, sex-, and time-period matched unvaccinated cases (24% vs. 17%), with hypertension (30%), diabetes (17%), and asthma (14%) being the most prevalent chronic conditions amongst breakthrough cases. Severe outcomes resulting from breakthrough infection was associated with age and chronic conditions, with those with 2 + chronic conditions at higher risk of severe outcomes (adjusted hazard ratio: 3.6, 95% confidence intervals: 2.0-6.4). Risk of severe outcomes varied by age group, with those 70 + years at over 13 times the risk of severe outcomes (95% CI: 4.5-39.8), compared to those 18-29 years of age. Discussion: Our results demonstrate the impact of chronic conditions on the likelihood of, and severity of outcomes from breakthrough infections. These findings underscore the importance of vaccination programs prioritizing vulnerable populations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 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".