Pregnancy Outcomes During the COVID-19 Pandemic in Canada, March to August 2020
Bibliographic record
Abstract
OBJECTIVE: Several studies have documented changes in the rates preterm birth and stillbirth during the COVID-19 pandemic. We carried out a study to examine obstetric intervention, preterm birth, and stillbirth rates in Canada from March to August 2020. METHODS: The study included all singleton hospital deliveries in Canada (excluding Québec) from March to August 2020 (and March to August for the years 2015-2019) with information obtained from the Canadian Institute for Health Information. Data for Ontario were examined separately because this province had the highest rates of COVID-19 in the study population. Rates and odds ratios with 95% confidence intervals (CIs) were used to quantify pregnancy-related outcomes. RESULTS: There were 136,445 and 717,905 singleton hospital deliveries in Canada (excluding Quebéc) in from March to August 2020 and between March and August 2015-2019, respectively. Rates of obstetric intervention declined in early gestation in 2020. Odds ratios for labour induction and cesarean delivery at <32 weeks gestation for March-August 2020 versus March-August in 2015 to 2019 were 0.84 (95% CI 0.74-0.95) and 0.92 (95% CI 0.85-1.00), respectively. Preterm birth rates increased in Canada (excluding Québec) from 6.42% in March-August 2015 to 6.74% in March-August 2019 but were unchanged in March-August 2020 (6.74%). Stillbirth rates were stable between March-August 2015 and March-August 2020. However, stillbirth rates peaked in Ontario in April 2020 due to higher rates of stillbirths at 20-27 and 37-41 weeks gestation. CONCLUSION: Changes in labour induction and cesarean delivery at early gestation and other perinatal outcomes during the period of March to August 2020 highlight the need to reconsider the use and impact of obstetric services in pandemics as well as the need for timely perinatal surveillance.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".