Pregnancy and Birth Outcomes Among Middle Eastern or North African Infants and Mothers in Quebec, Canada, 2008-2020
Bibliographic record
Abstract
OBJECTIVES: The health status of Middle Eastern or North African (MENA) populations in Western countries is poorly understood. We assessed whether MENA infants and mothers have a greater risk of adverse pregnancy and birth outcomes than non-MENA infants and mothers in Quebec, Canada. METHODS: We conducted a population-based observational study of 809 172 infants born to pregnant women in Quebec from 2008 through 2020. We identified infants in the MENA group based on self-reported mother tongue (Arabic or Turkish) and parents' country of birth (North African or Middle Eastern country). We compared infant and maternal outcomes, including gestational diabetes, cesarean delivery, preterm birth, severe maternal or neonatal morbidity, and other pregnancy and birth complications between the MENA and non-MENA groups. Using log-binomial regression models, we calculated risk ratios (RRs) and 95% CIs to measure the risk of adverse pregnancy and birth outcomes for the MENA group compared with the non-MENA group, adjusting for maternal age, comorbidity, and other patient characteristics. RESULTS: Compared with the non-MENA group (n = 716 387), the MENA group (n = 92 785) had an elevated risk of gestational diabetes (RR = 1.51; 95% CI, 1.48-1.55), postterm birth (RR = 1.24; 95% CI, 1.08-1.42), and short-stay neonatal intensive care unit admission (RR = 1.91; 95% CI, 1.82-1.99). However, MENA infants were 15% to 50% less likely than non-MENA infants to be born preterm, have severe neonatal morbidity, and have a mother with preeclampsia or severe maternal morbidity. CONCLUSIONS: Although findings among MENA infants and mothers in Quebec were reassuring overall, MENA infants and mothers may benefit from closer perinatal follow-up to improve complications of gestational diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".