HIV and SARS-CoV-2 infection in postpartum Kenyan women and their infants
Bibliographic record
Abstract
ABSTRACT Background HIV may increase SARS-CoV-2 infection risk and COVID-19 severity generally, but data are limited about its impact on postpartum women and their infants. As such, we characterized SARS-CoV-2 infection among mother-infant pairs in Nairobi, Kenya. Methods We conducted a nested study of 53 HIV-uninfected and 51 healthy women living with HIV, as well as their HIV-exposed uninfected (N=41) and HIV-unexposed (N=48) infants, participating in a prospective cohort. SARS-CoV-2 serology was performed on plasma collected between 1 May-31 December 2020 to determine the incidence, risk factors, and symptoms of infection. SARS-CoV-2 RNA PCR and sequencing was also performed on stool samples from seropositive participants. Results SARS-CoV-2 seropositivity was found in 38% of the 104 mothers and in 17% of the 89 infants. There was no significant association between SARS-CoV-2 infection and maternal HIV (Hazard Ratio [HR]=1.51, 95% CI: 0.780-2.94) or infant HIV exposure (HR=1.48, 95% CI: 0.537-4.09). Maternal SARS-CoV-2 was associated with a >10-fold increased risk of infant infection (HR=10.3, 95% CI: 2.89-36.8). Twenty percent of participants had symptoms, but no participant experienced severe COVID-19 or death. Seroreversion occurred in ∼30% of mothers and infants. SARS-CoV-2 sequences obtained from stool were related to contemporaneously circulating variants. Conclusions These data indicate that postpartum Kenyan women and their infants were at high risk for SARS-CoV-2 infection in 2020, and that antibody responses waned rapidly. However, most cases were asymptomatic and healthy women living with HIV did not have a substantially increased risk of infection or severe 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".