Prevalence and risk-factors of COVID-19 in pregnancy: Living systematic review and metaanalysis
Notice bibliographique
Résumé
Background Since the first report of COVID-19 in December 2019, there have been significant concerns regarding the effects of the disease on pregnant and recently pregnant women. Quantifying prevalence, and identifying risk factors for severe COVID-19 in this population is key to planning and providing effective clinical maternal care. Objectives To identify rates of COVID-19 amongst pregnant and recently pregnant women and to identify maternal risk factors for severe COVID-19 and worsening clinical outcomes. Design To address the objectives using the developing evidence base we are using a 'Living systematic review' study design. Methods A systematic search of various databases and sources was conducted, including: Medline, Embase, Cochrane database, WHO COVID-19 database, CNKI, Wanfang databases, preprint servers, social media, reference lists of guidelines and included studies until the 6th of October 2020. Quality assessment of prevalence studies was done using the risk of bias tool by Hoy et al. and comparative cohorts using the Newcastle Ottawa Scale. Data extraction was completed with a pre-piloted form by two independent reviewers. The analysis is undertaken monthly and findings are regularly updated. Results are disseminated through our website: https://www.birmingham.ac.uk/research/who-collabora ting-centre/pregcov/index.aspx. The living systematic review process and collated database has given rise to distinct review questions, and the authors of this focused on prevalence and maternal risk factors. Random effects meta-analysis was used to determine prevalence of COVID-19 and the maternal risk factors associated with severe COVID-19. Results 192 studies were included. Overall, 10% (95% confidence interval 7% to 12%;73 studies, 67 271 women) of pregnant and recently pregnant women attending or admitted to hospital for any reason were diagnosed as having suspected or confirmed COVID-19. Increased maternal age (1.82, 1.27 to 2.63;I2 = 30.1%;7 studies;3561 women), high body mass index (2.37, 1.83 to 3.07;I2 = 0%;6 studies;3380 women), pre-existing maternal comorbidity (1.81, 1.49 to 2.20;I2 = 0%;3 studies;2634 women), chronic hypertension (2.0, 1.14 to 3.48;I2 = 0%;2 studies;858 women), pre-existing diabetes (2.12, 1.62 to 2.78;I2 = 0%;3 studies;3333 women), and pre-eclampsia (4.21, 1.26 to 14.0;I2 = 0%;4 studies;274 women) were associated with severe COVID-19 in pregnancy. Conclusions 1 in 10 pregnant or recently pregnant women attending or admitted to hospital are estimated to have COVID-19. Pre-existing co-morbidities, chronic hypertension, pre-eclampsia, pre-existing diabetes, high maternal age, and high BMI are risk factors for severe COVID-19.
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 | 0,127 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».