Are children and adolescents living with HIV in Europe and South Africa at higher risk of SARS-CoV-2 and poor COVID-19 outcomes?
Bibliographic record
Abstract
Abstract Children, adolescents, and young people living with HIV (CALWHIV), including those in resource-limited settings, may be at increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, poorer coronavirus disease 2019 (COVID-19) outcomes, and multisystem inflammatory syndrome (MIS). We conducted a repeat SARS-CoV-2 seroprevalence survey among CALWHIV in Europe (n = 493) and South Africa (SA, n = 307), and HIV-negative adolescents in SA (n = 100), in 2020–2022. Blood samples were tested for SARS-CoV-2 antibody, questionnaires collected data on SARS-CoV-2 risk factors and vaccination status, and clinical data were extracted from health records. SARS-CoV-2 seroprevalence (95% CI) was 55% (50%–59%) in CALWHIV in Europe, 67% (61%–72%) in CALWHIV in SA, and 85% (77%–92%) among HIV-negative participants in SA. Among those unvaccinated at time of sampling (n = 769, 85%), seroprevalence was 40% (35%–45%), 64% (58%–70%), and 81% (71%–89%), respectively. Few participants (11% overall) had a known history of SARS-CoV-2-positive PCR or self-reported COVID-19. Three CALWHIV were hospitalized, two with COVID-19 (nonsevere disease) and one young adult with MIS. Although SARS-CoV-2 seroprevalence was high across all settings, even in unvaccinated participants, it was broadly comparable to general population estimates, and most infections were mild/asymptomatic. Results support policy decisions excluding CALWHIV without severe immunosuppression from high-risk groups for COVID-19.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".