SARS-CoV-2 seroprevalence among public school staff in Metro Vancouver after the first Omicron wave in British Columbia, Canada
Bibliographic record
Abstract
Abstract Objective To determine the SARS-CoV-2 seroprevalence among school workers in the setting of full in-person schooling and the highly transmissible Omicron variants of concern. Design Cross-sectional study among school staff, comparing to period-, age-, sex- and postal code-weighted data from Canadian blood donors from the same community. Setting Three large school districts in the greater Vancouver metropolitan area, British Columbia, Canada, with serology sampling done between January 26, 2022 and April 8, 2022. Participants School staff actively working in the Vancouver, Richmond and Delta School Districts. Main outcome measure SARS-CoV-2 seroprevalence based on nucleocapsid (N)-protein testing, adjusted for the sensitivity and specificity of the assay. Results A majority (65.8%) of the 1845 school staff enrolled reported close contact with a COVID-19 case outside the household. Of those, about half reported close contact with a COVID-19 case at school either in a student (51.5%) or co-worker (54.9%). In a representative sample of 1620 (87.8%) school staff, the adjusted seroprevalence was 26.5% [95%CrI: 23.9 – 29.3%]. This compared to an age, sex and residency area-weighted seroprevalence of 32.4% [95%CrI: 30.6 – 34.5%] among 7164 blood donors. Conclusion Despite frequent COVID-19 exposures, the prevalence of SARS-CoV-2 infections among the staff of three main school districts in the Vancouver metropolitan area was no greater than a reference group of blood donors, even after the emergence of the more transmissible Omicron variant. What is already known on this subject? Earlier studies indicate that COVID-19 infection rates are not increased among school staff at previous stages of the pandemic compared to the community, yet controversy remains whether this will remain true after the emergence of the highly transmissible Omicron variant. What this study adds? Despite frequent COVID-19 exposures, this study identified no detectable increase in SARS-CoV-2 seroprevalence among school staff working in three metro Vancouver public school districts after the first Omicron wave in British Columbia, compared to a reference group of blood donors from the same age, sex and community area.
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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 | 0.000 | 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 | 0.003 | 0.001 |
| 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 | 0.000 |
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".