Differential COVID-19 infection rates in children, adults, and elderly: Systematic review and meta-analysis of 38 pre-vaccination national seroprevalence studies
Bibliographic record
Abstract
Background: Debate exists about whether extra protection of elderly and other vulnerable individuals is feasible in COVID-19. We aimed to assess the relative infection rates in the elderly vs the non-elderly and, secondarily, in children vs adults. Methods: We performed a systematic review and meta-analysis of seroprevalence studies conducted in the pre-vaccination era. We identified representative national studies without high risk of bias through SeroTracker and PubMed searches (last updated May 17, 2022). We noted seroprevalence estimates for children, non-elderly adults, and elderly adults, using cut-offs of 20 and 60 years (or as close to these ages, if they were unavailable) and compared them between different age groups. Results: We included 38 national seroprevalence studies from 36 different countries comprising 826 963 participants. Twenty-six of these studies also included pediatric populations and twenty-five were from high-income countries. The median ratio of seroprevalence in elderly vs non-elderly adults (or non-elderly in general, if pediatric and adult population data were not offered separately) was 0.90-0.95 in different analyses, with large variability across studies. In five studies (all in high-income countries), we observed significant protection of the elderly with a ratio of <0.40, with a median of 0.83 in high-income countries and 1.02 elsewhere. The median ratio of seroprevalence in children vs adults was 0.89 and only one study showed a significant ratio of <0.40. The main limitation of our study is the inaccuracies and biases in seroprevalence studies. Conclusions: Precision shielding of elderly community-dwelling populations before the availability of vaccines was indicated in some high-income countries, but most countries failed to achieve any substantial focused protection. Registration: Open Science Framework (available at: https://osf.io/xvupr).
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".