COVID-19 Infection and Medicines in Pregnancy in Canada
Bibliographic record
Abstract
Introduction Although over 100 million pregnant women worldwide are at risk of infection with SARS-CoV-2, little data exists on the impact of COVID-19 and related treatments on maternal/neonatal health. Objectives 1) To quantify the prevalence of medication use in pregnancy to treat COVID-19; 2) To quantify and compare the risk of adverse pregnancy/neonatal outcomes in those with and without COVID-19. Methods In the Canadian Mother-Child population-based cohort (CAMCCO), two key sub-cohorts were identified using prospective data collection of medical services, prescription drugs, hospitalization archives data, and COVID-19 surveillance testing program (02/28/2020-2021). The first cohort included all pregnant women with at least one completed trimester of pregnancy during the study period regardless of pregnancy status (delivery, induced/planned or spontaneous abortion); this cohort was further stratified on COVID-19 status. The second cohort included all non-pregnant women (aged 15-45) with a positive COVID-19 test. COVID-19 infection in pregnant or non-pregnant women was assessed using COVID-19 test results or ICD-10CM codeU07.1 from hospital data. COVID-19 severity was categorized based on hospital admission. Women were considered exposed to COVID-19 medications if they filled at least one prescription for a medicine included in the WHO list in the 30 days pre- or 30 days post-COVID-19 positive test/diagnosis. Considering potential confounders, association between COVID-19 during pregnancy, treated vs not, and perinatal outcomes were quantified using log-binomial regression models. Results 150,345 pregnant women (3,464 (2.3%) had COVID-19), and 112,073 non-pregnant women with COVID-19 diagnoses were included. Pregnant women with COVID-19 were more likely to have severe infections compared to non-pregnant women with COVID-19 (11.4% vs 1.6%, p< 0.001). The most frequent medications used in pregnancy to treat COVID-19 were antibacterials (13.96%), psychoanaleptics (7.35%), and medicines for obstructive airway disease (3.20%). In pregnancy COVID-19 was associated with spontaneous abortions (adjRR 1.76, 95%CI 1.3, 2.25), gestational diabetes (adjRR 1.52, 95%CI 1.18, 1.97), prematurity (adjRR 1.30, 95%CI 1.01, 1.67), NICU admissions (adjRR 1.32, 95%CI 1.10, 1.59); COVID-19 severity was increasing these risks but COVID-19 treatment with study medications reduced all risks. Conclusions Severity of COVID-19 was greater in pregnancy. Antibacterials, psychoanaleptics, and medicines for obstructive airway disease were the most used overall. Severe COVID-19 in pregnancy was associated with higher risks of adverse maternal, and neonatal outcomes. Disclosure of Interest None Declared
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".