SARS-CoV-2 cross-sectional seroprevalence study among public school staff in Metro Vancouver after the first Omicron wave in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: To determine the SARS-CoV-2 seroprevalence among school workers within the Greater Vancouver area, British Columbia, Canada, after the first Omicron wave. DESIGN: Cross-sectional study by online questionnaire, with blood serology testing. SETTING: Three main school districts (Vancouver, Richmond and Delta) in the Vancouver metropolitan area. PARTICIPANTS: Active school staff enrolled from January to April 2022, with serology testing between 27 January and 8 April 2022. Seroprevalence estimates were compared with data obtained from Canadian blood donors weighted over the same sampling period, age, sex and postal code distribution. PRIMARY AND SECONDARY OUTCOMES: SARS-CoV-2 nucleocapsid antibody testing results adjusted for test sensitivity and specificity, and regional variation across school districts using Bayesian models. RESULTS: Of 1850 school staff enrolled, 65.8% (1214/1845) reported close contact with a COVID-19 case outside the household. Of those close contacts, 51.5% (625/1214) were a student and 54.9% (666/1214) were a coworker. Cumulative incidence of COVID-19 positive testing by self-reported nucleic acid or rapid antigen testing since the beginning of the pandemic was 15.8% (291/1845). In a representative sample of 1620 school staff who completed serology testing (87.6%), the adjusted seroprevalence was 26.5% (95% CrI 23.9% to 29.3%), compared with 32.4% (95% CrI 30.6% to 34.5%) among 7164 blood donors. CONCLUSION: Despite frequent COVID-19 exposures reported, SARS-CoV-2 seroprevalence among school staff in this setting remained no greater than the community reference group. Results are consistent with the premise that many infections were acquired outside the school setting, even with Omicron.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".