Pre-Omicron seroprevalence, seroconversion, and seroreversion of infection-induced SARS-CoV-2 antibodies among a cohort of children and teenagers in Montréal, Canada
Bibliographic record
Abstract
OBJECTIVES: To use serological testing to assess the pre-Omicron seroprevalence, seroconversion, and seroreversion of infection-induced SARS-CoV-2 antibodies in children and adolescents in Montréal, Canada. DESIGN: This analysis is from a prospective cohort study of children aged 2-17 years (at baseline) that included blood spots for antibody detection. The serostatus of participants was determined by enzyme-linked immunosorbent assays using the receptor-binding domain from the spike protein and the nucleocapsid protein as antigens. We estimated seroprevalence, seroconversion rates, and the likelihood of seroreversion at 6 months and 1 year. RESULTS: The baseline (October 2020 to April 2021) seroprevalence was 5.8% (95% confidence interval [CI] 4.8-7.1), which increased to 10.5% (May to September 2021) and 11.0% (November 2021 to March 2022) for the respective follow-ups (95% CI 8.6-12.7; 95% CI 8.8-13.5). The crude rate of seroconversion over the study period was 12.8 per 100 person-years (95% CI 11.0-14.7). The adjusted hazard rates of seroconversion by child characteristics showed higher rates in children who were female, whose parent identified as a racial or ethnic minority, and in households with incomes in the lowest tercile of our study population. The likelihood of remaining seropositive at 6 months was 68% (95% CI 60-77%) and dropped to 42% (95% CI 32-56%) at 1 year. CONCLUSION: Serological studies continue to provide valuable contributions for infection prevalence estimates and help us better understand the dynamics of antibody levels after infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".