Seroprevalence, seroconversion, and seroreversion of infection-induced SARS-CoV-2 antibodies among a cohort of children and adolescents in Montreal, Canada
Notice bibliographique
Résumé
ABSTRACT Importance Repeated serological testing for SARS-CoV-2 allows the monitoring of antibody dynamics in populations, including detecting infections that are missed by RT-PCR or antigen testing. Understanding the factors associated with seroconversion and seroreversion as well as the duration of infection-induced antibodies can also inform public health recommendations regarding disease prevention and mitigation efforts. Objective To use serological testing to assess the prevalence, seroconversion, and seroreversion of infection-induced SARS-CoV-2 antibodies in children and adolescents in Montreal, Canada. Design This analysis reports on three rounds of data collection from a prospective cohort study (Enfants et COVID-19: Étude de séroprévalence [EnCORE]). The study rounds occurred as follows: Round 1 October 2020-March 2021, Round 2 May to July 2021, and Round 3 November 2021 to January 2022. Most Round 3 samples were collected prior to the spread of the Omicron BA.1 variant in Quebec. Setting Population-based sample. Participants Children and adolescents aged 2 to 17 years in Montreal, Canada. Exposure Potential exposure to SARS-CoV-2. Main Outcomes and Measures Participants provided dried blood spots (DBS) for antibody detection and parents completed online questionnaires for sociodemographics and COVID-19 symptoms and testing history. The serostatus of participants was determined by enzyme-linked immunosorbent assays (ELISAs) using the receptor-binding domain (RBD) from the spike protein and the nucleocapsid protein (N) as antigens. We estimated seroprevalence for each round of data collection and by participant and household characteristics. Seroconversion rates were calculated as were the likelihoods of remaining seropositive at six months and one year. Results The study included DBS samples from 1 632, 936, and 723 participants in the first, second, and third rounds of data collection, respectively. The baseline seroprevalence was 5·8% (95% CI 4·8-7·1), which increased to 10·5% and 10·9% for the respective follow-ups (95% CI 8·6-12·7; 95% CI 8·8-13·5). The overall average crude rate of seroconversion over the study period was 12·7 per 100 person-years (95% CI 10·9-14·5). Adjusted hazard rates of seroconversion by child and household characteristics showed higher rates in children who were female, whose parent identified as a racial or ethnic minority, and in households with incomes less than 100K. The likelihood of remaining seropositive at six months was 67% (95% CI 59-76) and dropped to 19% (95% CI 11%-33%) at one year. Conclusions and Relevance The data reported here provide estimates of pre-Omicron seroprevalence, seroconversion rates and time to seroreversion in a population-based cohort of children and adolescents. Serological studies continue to provide valuable contributions for infection prevalence estimates and help us better understand the dynamics of antibody levels following infection. Continued study of seroconversion and seroreversion can inform public health recommendations such as COVID-19 vaccination and booster schedules. KEY POINTS Question What was the rate of seroconversion and time to seroreversion for SARS-CoV-2 antibodies among children and adolescents in Montreal between October 2020 to January 2022? Findings The overall average crude rate of seroconversion was 12·7 per 100 person-years (95% CI 10·9-14·5). We observed higher rates of seroconversion in children who were female, whose parent identified as a racial or ethnic minority, and in households with incomes less than 100K. Among all children who seroconverted, 71% had not been previously diagnosed with COVID-19. Median time to seroreversion was 7·5 months. Meaning Even before the emergence of the Omicron variants, we observed a high rate of seroconversion for infection-induced SARS-CoV-2 antibodies along with widespread antibody waning by one year. Many children and adolescents seroconverted despite not receiving a prior COVID-19 diagnosis, indicating that RT-PCR and antigen testing continue to underestimate true disease prevalence.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».