Higher SARS-CoV-2 antibody response following infection in children compared to adult members of the same family
Bibliographic record
Abstract
Abstract Background While children have had less severe clinical disease after SARS-CoV-2 infection (COVID-19), the cause of this remains unclear. The objective of this study was to describe the humoral immune response to COVID-19 in children vs. adult household contacts, and to identify predictors of the response over time. Methods Prospective cohort study of children with COVID-19 and their families at the Centre Hospitalier Universitaire Sainte-Justine (CHUSJ) in Montreal, Quebec, Canada, between August 2020 and July 2021. Children with a positive SARS-CoV-2 polymerase chain reaction (PCR) test (index case) were recruited along with their household contacts. Serum IgG antibodies against SARS-CoV-2 S1/S2 spike proteins were compared between children and adults at 6- and 12-months after infection. RESULTS 132 participants were enrolled, this included 54 index cases (children) and 78 household contacts from 36 families. Median SARS-CoV-2 antibody titer at 6 months post-infection was significantly higher in children vs. adults (92.7 AU/ml 23.8 AU/ml, p = 0.004). Significant predictors of lack of SARS-CoV-2 seropositivity were age ≥ 25 vs. <12 years (odds ratio [OR] = 0.23, p = 0.04), presence of comorbidities (vs. no adjusted OR = 0.23, p = 0.03), and immunosuppression (vs. immunocompetent, adjusted OR = 0.17, p = 0.02). While there were differences in the magnitude of median antibody titers by family, within families, children consistently had a higher antibody titer than adults. CONCLUSION Children produced a stronger humoral (anti-S1/S2 spike IgG) response to natural SARS-CoV-2 infection than their adult household contacts. These data reinforce the differences in the clinical and immunological responses to SARS-CoV-2 infection between children vs. adults.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".