COVID-19 and maternal and perinatal outcomes
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".