The association of COVID-19 lockdowns with adverse birth and pregnancy outcomes in 28 high-income countries: a systematic review and meta-analysis
Bibliographic record
Abstract
We conducted a systematic review and meta-analysis to review the association of lockdowns with adverse birth and pregnancy outcomes (ABPOs) and related inequalities, in high-income countries (HICs). Databases (EMBASE, MEDLINE/PubMed and Web of Science) were searched from 1 January 2019 to 22 June 2023 for original observational studies based in HICs that compared the rates of ABPOs, before and during lockdowns. The risk of bias was assessed using the Newcastle-Ottawa tool for cohort studies. We ran random-effects meta-analyses and subgroup analyses per region, lockdown period, ethnicity group and deprivation level and adjusted for underlying temporal trends. A total of 132 studies were meta-analysed from 28 HICs. Reduced rates of preterm birth (reported by 26 studies) were associated with the first lockdown (relative risk 0.96, 95% confidence interval 0.93-0.99), 11 studies adjusted for long-term trends and the association remained (0.97, 0.95-0.99), and subgroup analysis found that this association varied by continental region. Ten studies reported positive screening rates for possible depression antenatally, and lockdown was associated with increases in positive screening rates (1.37, 1.06-1.78). No other ABPOs were associated with lockdowns. Investigation of inequalities was limited due to data availability and heterogeneity; further research is warranted on the effect of lockdowns on health inequalities. This study was funded by the National Institute of Health Research, School of Primary Care Research and registered on PROSPERO (CRD42022327448).
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| 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".