Population‐level changes in perinatal death for pregnancies prior to and during the COVID‐19 pandemic: A pregnancy cohort analysis
Bibliographic record
Abstract
BACKGROUND: Results of population-level studies examining the effect of the COVID-19 pandemic on the risks of perinatal death have varied considerably. OBJECTIVES: To explore trends in the risk of perinatal death among pregnancies beginning prior to and during the pandemic using a pregnancy cohort approach. METHODS: This secondary analysis included data from singleton pregnancies ≥20 weeks' gestation in Alberta, Canada, beginning between 5 March 2017 and 4 March 2021. Perinatal death (i.e. stillbirth or neonatal death) was the primary outcome considered. The risk of this outcome was calculated for pregnancies with varying gestational overlap with the pandemic (i.e. none, 0-20 weeks, entire pregnancy). Interrupted time series analysis was used to further determine temporal trends in the outcome by time period of interest. RESULTS: There were 190,853 pregnancies during the analysis period. Overall, the risk of perinatal death decreased with increasing levels of pandemic exposure; this outcome was experienced in 1.0% (95% confidence interval [CI] 0.9, 1.0), 0.9% (95% CI 0.8, 1.1) and 0.8% (95% CI 0.7, 0.9) of pregnancies with no overlap, partial overlap and complete pandemic overlap respectively. Pregnancies beginning during the pandemic that had high antepartum risk scores less frequently led to perinatal death compared to those beginning prior; 3.3% (95% CI 2.7, 3.9) versus 5.7% (95% CI 5.0, 6.5) respectively. Interrupted time-series analysis revealed a decreasing temporal trend in perinatal death for pregnancies beginning ≤40 weeks prior to the start of the COVID-19 pandemic (i.e. with pandemic exposure), with no trend for pregnancies beginning >40 weeks pre-pandemic (i.e. no pandemic exposure). CONCLUSION: We observed a decrease in perinatal death for pregnancies overlapping with the COVID-19 pandemic in Alberta, particularly among those at high risk of these outcomes. Specific pandemic control measures and government response programmes in our setting may have contributed to this finding.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".