Impact of Perinatal Exposure to SARS‐CoV‐2 Infection on Early Health Outcomes among Infants Born from 2020 to 2021 in British Columbia, Canada
Bibliographic record
Abstract
Background . The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pandemic has impacted healthcare services and outcomes. We aimed to investigate healthcare resource utilization and early health outcomes of infants born to mothers with perinatal SARS‐CoV‐2 infection. Methods . The study included all infants born alive between February 1, 2020, and April 30, 2021, in British Columbia. We used linked provincial population‐based databases including data on COVID‐19 testing, birth, and health information for up to one year from birth. Perinatal COVID‐19 exposure for infants was defined being born to mothers with a positive test for SARS‐CoV‐2 infection during pregnancy or at delivery. Cases of COVID‐19‐exposed infants were matched with up to four non‐exposed infants by birth month, sex, birthplace, and gestational age in weeks. Outcomes included hospitalizations, emergency department visits, and in‐/outpatient diagnoses. Outcomes were compared between groups using conditional logistic regression and linear mixed effects models including effect modification by maternal residence. Results . Among 52,711 live births, 484 infants had perinatal exposure to SARS‐CoV‐2, an incidence rate of 9.18 per 1000 live births. Exposed infants (54.6% male) had a mean gestational age of 38.5 weeks, and 99% were born in hospital. Proportions of infants requiring at least one hospitalization (8.1% vs. 5.1%) and at least one emergency department visit (16.9% vs. 12.9%) were higher among the exposed vs. unexposed infants, respectively. Among infants from the urban area, those with exposure were more likely to have respiratory infectious diseases (odds ratio: 1.74; 95% confidence intervals: 1.07, 2.84), compared with those without exposure. Interpretation . In our cohort, infants born to mothers with SARS‐CoV‐2 infection have increased healthcare demands in their early infancy, which warrants further investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".