SARS-CoV-2 Seroprevalence among Canadian Blood Donors: The Advance of Omicron
Bibliographic record
Abstract
With the emergence of the SARS-CoV-2 Omicron variant in late 2021, Canadian public health case/contact testing was scaled back due to high infection rates with milder symptoms in a highly vaccinated population. We monitored the seroprevalence of SARS-CoV-2 nucleocapsid (anti-N) and spike protein (anti-S) antibodies in blood donors across Canada from September 2021 to June 2022 in 202,123 randomly selected samples. Multivariable logistic regression of anti-N positivity with month, age, sex, racialization, region, material and social deprivation (based on postal code) identified as independent predictors. Piece-wise logistic regression analysed the association between anti-S concentration and month, and anti-N/anti-S positivity. Infection-related seroprevalence (anti-N positive) was 4.38% (95% CI: 3.96, 4.81) in September reaching 50.70% (50.15, 52.16) in June; nearly 100% were anti-S positive throughout. Anti-N positivity was associated with younger age, male sex, the Alberta and Prairies regions, greater material deprivation and less social deprivation (p < 0.001). Anti-S concentration was high initially (3306 U/mL, IQR 4280 U/mL), increased to (13,659 U/mL, IQR 28,224 U/mL) by June (p < 0.001), following the pattern of deployment of the third and fourth vaccine doses and was higher in those that were anti-N positive (p < 0.001). Despite already high vaccination-related seroprevalence, infection-related seroprevalence increased dramatically with the emergence of the Omicron SARS-CoV-2 variant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".