Effect of the COVID-19 Pandemic on Stillbirths in Canada and the United States
Bibliographic record
Abstract
OBJECTIVE: There is uncertainty regarding the effect of the COVID-19 pandemic on population rates of stillbirth. We quantified pandemic-associated changes in stillbirth rates in Canada and the United States. METHODS: We carried out a retrospective study that included all live births and stillbirths in Canada and the United States from 2015 to 2020. The primary analysis was based on all stillbirths and live births at ≥20 weeks gestation. Stillbirth rates were analyzed by month, with March 2020 considered to be the month of pandemic onset. Interrupted time series analyses were used to determine pandemic effects. RESULTS: The study population included 18 475 stillbirths and 2 244 240 live births in Canada and 134 883 stillbirths and 22 963 356 live births in the United States (8.2 and 5.8 stillbirths per 1000 total births, respectively). In Canada, pandemic onset was associated with an increase in stillbirths at ≥20 weeks gestation of 1.01 (95% confidence interval [CI] 0.56-1.46) per 1000 total births and an increase in stillbirths at ≥28 weeks gestation of 0.35 (95% CI 0.16-0.54) per 1000 total births. In the United States, pandemic onset was associated with an increase in stillbirths at ≥20 weeks gestation of 0.48 (95% CI 0.22-0.75) per 1000 total births and an increase in stillbirths at ≥28 weeks gestation of 0.22 (95% CI 0.12-0.32) per 1000 total births. The increase in stillbirths at pandemic onset returned to pre-pandemic levels in subsequent months. CONCLUSION: The COVID-19 pandemic's onset was associated with a transitory increase in stillbirth rates in Canada and the United States.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".