COVID-19 Infection and Medicines in Pregnancy in Canada
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».