Mode of delivery and birth outcomes before and during COVID-19 –A population-based study in Ontario, Canada
Bibliographic record
Abstract
There is lack of clarity on whether pregnancies during COVID-19 resulted in poorer mode of delivery and birth outcomes in Ontario, Canada. We aimed to compare mode of delivery (C-section), birth (low birthweight, preterm birth, NICU admission), and health services use (HSU, hospitalizations, ED visits, physician visits) outcomes in pregnant Ontario women before and during COVID-19 (pandemic periods). We further stratified for pre-existing chronic diseases (asthma, eczema, allergic rhinitis, diabetes, hypertension). Deliveries before (Jun 2018-Feb 2020) and during (Jul 2020-Mar 2022) pandemic were from health administrative data. We used multivariable logistic regression analyses to estimate adjusted odds ratios (aOR) of delivery and birth outcomes, and negative binomial regression for adjusted rate ratios (aRR) of HSU. We compared outcomes between pre-pandemic and pandemic periods. Possible interactions between study periods and covariates were also examined. 323,359 deliveries were included (50% during pandemic). One in 5 (18.3%) women who delivered during the pandemic had not received any COVID-19 vaccine, while one in 20 women (5.2%) lab-tested positive for COVID-19. The odds of C-section delivery during the pandemic was 9% higher (aOR = 1.09, 95% CI: 1.08-1.11) than pre-pandemic. The odds of preterm birth and NICU admission were 15% (aOR = 0.85, 95% CI: 0.82-0.87) and 10% lower (aOR = 0.90, 95% CI: 0.88-0.92), respectively, during COVID-19. There was a 17% reduction in ED visits but a 16% increase in physician visits during the pandemic (aRR = 0.83, 95% CI: 0.81-0.84 and aRR = 1.16, 95% CI: 1.16-1.17, respectively). These aORs and aRRs were significantly higher in women with pre-existing chronic conditions. During the pandemic, healthcare utilization, especially ED visits (aRR = 0.83), in pregnant women was lower compared to before. Ensuring ongoing prenatal care during the pandemic may reduce risks of adverse mode of delivery and the need for acute care during pregnancy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".