Prevalence and risk-factors of COVID-19 in pregnancy: Living systematic review and metaanalysis
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.127 |
| 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".