Impact of COVID-19 on perinatal outcomes in First Nations and all other mothers and their offspring in Manitoba
Bibliographic record
Abstract
PURPOSE OF RESEARCH: COVID-19 substantially disrupted healthcare, but its impact on perinatal outcome in Manitoba remains unclear. METHODS: The present study examined perinatal outcomes in First Nations and all other women and infants in Manitoba during COVID-19 (March 2020-December 2021) compared to a control period (March 2018-March 2020) using a retrospective database study. PRINCIPAL RESULTS: First Nations pregnancies had significantly higher rates of gestational diabetes, pre-pregnancy diabetes (PPD), preterm birth, stillbirth, large-for-gestational-age infants, neonatal intensive care unit (NICU) admission and formula feeding, and lower rates of exclusive breastfeeding compared to all other pregnancies during COVID-19 or the control period. Age-adjusted odds ratio (aORs) for preterm birth (aOR 1⋅17, 95% confidence interval or CI: 1⋅06, 1⋅30) and shoulder dystocia (aOR 1⋅33, 95% CI: 1⋅08, 1⋅64) among First Nations, but not all other, newborns were increased during COVID-19 compared to the control period. The aORs for preeclampsia, eclampsia, and spontaneous abortion were increased in all other, but not First Nations, pregnancies during COVID-19 versus the control period. Logistic linear regression analyses indicated that PPD was the leading contributor to the increase of preterm birth, stillbirth, shoulder dystocia and NICU admission. CONCLUSIONS: The findings imply that COVID-19 caused more profound adverse neonatal outcomes in First Nations offspring. Maternal diabetes played an important role in the unfavorable neonatal outcomes. Prevention of maternal diabetes by strengthening perinatal education and care may help to reduce the risk of adverse neonatal outcomes in First Nations population in future pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".