COVID‐19 pandemic and population‐level pregnancy and neonatal outcomes: a living systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Conflicting reports of increases and decreases in rates of preterm birth (PTB) and stillbirth in the general population during the COVID-19 pandemic have surfaced. The objective of our study was to conduct a living systematic review and meta-analyses of studies reporting pregnancy and neonatal outcomes by comparing the pandemic and pre-pandemic periods. MATERIAL AND METHODS: We searched PubMed and Embase databases, reference lists of articles published up until 14 May 2021 and included English language studies that compared outcomes between the COVID-19 pandemic time period and pre-pandemic time periods. Risk of bias was assessed using the Newcastle-Ottawa scale. We conducted random-effects meta-analysis using the inverse variance method. RESULTS: Thirty-seven studies with low-to-moderate risk of bias, reporting on 1 677 858 pregnancies during the pandemic period and 21 028 650 pregnancies during the pre-pandemic period, were included. There was a significant reduction in unadjusted estimates of PTB (28 studies, unadjusted odds ratio [uaOR] 0.94, 95% confidence [CI] 0.91-0.98) but not in adjusted estimates (six studies, adjusted OR [aOR] 0.95, 95% CI 0.80-1.13). The reduction was noted in studies from single centers/health areas (uaOR 0.90, 95% CI 0.86-0.94) but not in regional/national studies (uaOR 0.99, 95% CI 0.95-1.03). There was reduction in spontaneous PTB (five studies, uaOR 0.89, 95% CI 0.82-0.98) and induced PTB (four studies, uaOR 0.90, 95% CI 0.81-1.00). There was no reduction in PTB when stratified by gestational age <34, <32 or <28 weeks. There was no difference in stillbirths between the pandemic and pre-pandemic time periods (21 studies, uaOR 1.08, 95% CI 0.94-1.23; four studies, aOR 1.06, 95% CI 0.81-1.38). There was an increase in birthweight (six studies, mean difference 17 g, 95% CI 7-28 g) during the pandemic period. There was an increase in maternal mortality (four studies, uaOR 1.15, 95% CI 1.05-1.26), which was mostly influenced by one study from Mexico. There was significant publication bias for the outcome of PTB. CONCLUSIONS: The COVID-19 pandemic time period may be associated with a reduction in PTB; however, referral bias cannot be excluded. There was no difference in stillbirth between the pandemic and pre-pandemic period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".