COVID-19 and maternal and perinatal outcomes
Notice bibliographique
Résumé
We commend Chmielewska and colleagues for undertaking a timely and comprehensive systematic review on a topic of pivotal global health importance.1 The increase in maternal mortality and stillbirth during the COVID-19 pandemic, particularly in low-resource settings, is of considerable concern. Although a considerable number of studies were collated, many have substantial risk of bias. For example, of the 18 included studies assessing the link between the pandemic and preterm birth, only two had a quasi-experimental design, many lacked methodological detail, few adjusted for potential confounding, and only three included population-level data. Only one study accounted for time trends in preterm birth,2 which is important to ensure that any changes during the pandemic are independent of underlying temporal patterns. Of the 18 studies, this study also had the largest sample size and the maximum Newcastle-Ottawa score, indicating high quality. As systematic reviews serve an important role in summarising the best available evidence, it is remarkable that the current meta-analysis excluded this study. Using inverse-variance rather than Mantel-Haenszel weighting allows for its inclusion,3 with limited impact on the association between the COVID-19 pandemic and preterm birth (OR=0∙90 [95%CI:0∙83−0∙98; 13 studies; n=1,919,726 (Figure)], rather than 0∙91 [95%CI:0∙84−0∙99];1 12 studies; n=852,854). Thorough assessment of how the COVID-19 pandemic and lockdowns have affected maternal and perinatal outcomes is crucial and has important public health implications. Accordingly, more robust studies are needed based on high-quality longitudinal data. Ideally population-level data should be used, as the pandemic likely influenced health seeking behaviors and access to maternity care, leading to potential ascertainment bias if institutional-level data is relied on.4 Also, inclusion of both pregnancy and neonatal data (rather than just one or the other) is important to assess any disparate impact of the pandemic on competing events (e.g. stillbirth and preterm birth). Applying appropriate quasi-experimental designs to population-level maternity and birth data, accounting for underlying temporal trends in the outcomes of interest, has the highest potential to attribute causality and minimise confounding.<br/><br/>Now is the time as a perinatal research community to seize opportunities to collaboratively take advantage of the unique natural experiment provided by the COVID-19 pandemic to accelerate progress in maternal and child health globally. We call on researchers to undertake robust studies and contribute to joint international efforts such as the international Perinatal Outcomes in the Pandemic (iPOP) study.5 Together we can learn from recent experiences and start identifying mechanisms that may contribute toa healthier start for future generations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».