Prevalence of SARS-CoV-2 antibodies and associated factors in the adult population of Belgium: a general population cohort study between March 2021 and April 2022
Bibliographic record
Abstract
Abstract Background This cohort study assessed seroprevalence trends of SARS-CoV-2 antibodies in the general Belgian population between March 2021 and April 2022, and explored factors associated with seropositivity among the vaccinated and unvaccinated population. Seroreversion and its potential determinants were also examined. Methods A random sample of the adult population in Belgium was invited to provide a saliva sample and to complete a survey questionnaire. Participants were followed up twice for a new saliva sample and updated information. Antibodies were assessed with a semi-quantitative measure of anti-RBD (Receptor Binding Domain) IgG ELISA. Seven time periods were defined for estimating SARS-CoV-2 antibody prevalence using post stratification weights to match the population distribution. Seroreversion was defined as passing from a positive to a negative antibody test from one data collection point to the next. Potential determinants of seropositivity were assessed through hierarchical multiple logistic regressions. Results In total 6,178 valid observations were obtained from 2,768 individuals. SARS-CoV-2 antibody prevalence increased from 25.1% in the beginning of the study period to 92.3% in the end. Among the vaccinated population, factors significantly associated with a higher seropositivity were being younger, having a bachelor diploma, living with others, having had a vaccine in the last 3 months and having received a nucleic-acid vaccine or a combination. Lower seropositivity rates were observed among vaccinated people with a neurological disease and transplant patients. Factors significantly associated with a higher seropositivity rate among the unvaccinated population were having non-O blood type and being non-smoker. Among fully vaccinated people the seroreversion rate was much lower (0.3%) among those who had received their latest vaccine in the last 3 months compared to those who had received their latest vaccine more than 3 months ago (2.7%). Conclusions The rapid increase in antibody seropositivity in the general adult population in Belgium during the study period was driven by the vaccination campaign which ran at full speed during this period. Factors associated with higher and lower seropositivity were identified among the vaccinated and unvaccinated people.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".