Impacts of COVID-19 on mothers’ and newborns’ health outcomes in regional Canada: A cross-sectional analysis
Bibliographic record
Abstract
Background: COVID-19 infection and pandemic-related stressors (e.g., socioeconomic challenges, isolation) resulted in significant concerns for the health of mothers and their newborns during the perinatal period. Therefore, the primary objective of this study was to compare the health outcomes of pregnant mothers and their newborns one year prior to and one year into the pandemic period in Alberta, Canada. Secondary objectives included investigating: 1) predictors of admission to neonatal intensive care units (NICU) and to compare NICU-admitted newborn health outcomes between the two time periods; 2) hospital utilization between the two time periods; and 3) the health outcomes of mothers and their newborns following infection with COVID-19. Methods: This analytical cross-sectional study used a large administrative dataset (n = 32,107) obtained from provincial regional hospitals and homebirths in Alberta, Canada, from April 15, 2019, to April 14, 2021. Descriptive statistics characterized the samples. Chi-squares and two-sample t-tests statistically compared samples. Multivariable logistic regression identified predictor variables. Results: General characteristics, pregnancy and labor complications, and infant outcomes were similar for the two time periods. Preterm birth and low birthweight predicted NICU admission. During the pandemic, prevalence of hospital visits and rehospitalization after discharge decreased for all infants and hospital visits after discharge decreased for NICU-admitted neonates. The odds of hospital revisits and rehospitalization after discharge were higher among newborns with COVID-19 at birth. Conclusions: Most of the findings are contextualized on pandemic-related stressors (rather than COVID-19 infection) and are briefly compared with other countries. Hospitals in Alberta appeared to adapt well to COVID-19 since health conditions were comparable between the two time periods and COVID-19 infection among mothers or newborns resulted in few observable impacts. Further investigation is required to determine causal reasons for changes in hospital utilization during the pandemic and greater birthweight among pandemic-born infants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".