Outcomes in pregnant Ontarians with asthma and allergic diseases during COVID-19
Bibliographic record
Abstract
Background: There is lack of clarity on whether pregnancies during COVID-19 resulted in poorer delivery and birth outcomes. Aim: Compare delivery and birth outcomes in pregnant Ontario women before and during COVID-19 (pandemic periods). Methods: Deliveries before (Jun 2018–Feb 2020) and during (Jul 2020–Mar 2022) COVID-19 were identified from health administrative data. We used logistic regressions to estimate odds ratios (OR) of delivery and birth outcomes and negative binomial regression for rate ratios (RR) of health services use (HSU). We compared those with prevalent chronic respiratory diseases (asthma, allergic rhinitis, or eczema) to those without these diseases. All regressions were adjusted for pandemic periods and other confounders. Results: 323,359 deliveries were included (50% during pandemic). 1 in 5 (18.3%) women who delivered during the pandemic had not received any COVID-19 vaccine. While overall HSU rates were lower during the pandemic compared to before, pregnant women with respiratory conditions had higher all-cause hospitalization (RR=1.15, 95%CI: 1.10-1.19) and ED visit rates (RR=1.18, 95%CI: 1.16-1.20). They also had significantly higher odds of anxiety (OR=1.49, 95%CI: 1.45-1.54), preterm deliveries (OR=1.09, 95%CI: 1.05-1.12), but lower odds of C-section (OR=0.96, 95%CI: 0.95-0.98). These observed differences were not explained by vaccination status nor pandemic periods. Conclusions: Women with prevalent respiratory conditions had higher odds of adverse delivery and birth outcomes and higher acute HSU rates. Ensuring ongoing prenatal care during the pandemic may reduce risks of adverse delivery outcomes and the need for acute care during pregnancy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".