Birth and postnatal outcomes among infants of immigrant parents of different admission categories and parents born in Canada: a population-based retrospective study
Bibliographic record
Abstract
BACKGROUND: Most studies of disparities in birth and postnatal outcomes by parental birthplace combine all immigrants into a single group. We sought to evaluate heterogeneity among immigrants in Canada by comparing birth and postnatal outcomes across different immigration categories. METHODS: We conducted a population-based retrospective study using Statistics Canada data on live births and stillbirths (1993-2017) and infant deaths (1993-2018), linked to parental immigration data (1960-2017). We classified birthing parents as born in Canada, economic-class immigrants, family-class immigrants, or refugees, and evaluated differences in preterm births, small-for-gestational-age (SGA) and large-for-gestational-age (LGA) births, stillbirths, and infant deaths among singleton births by group. RESULTS: Among 7 980 650 births, 1 715 050 (21.5%) were to immigrants, including 632 760 (36.9%) in the economic class, 853 540 (49.8%) in the family class, and 228 740 (13.4%) refugees. Compared with infants of Canadian-born birthing parents, infants of each of the 3 immigrant groups had higher risk of preterm birth, SGA birth, and stillbirth, but lower risk of LGA birth and neonatal death. Compared with infants of economic-class immigrants, infants of refugees had higher risk of early preterm birth (0.9% v. 0.8%, adjusted risk ratio [RR] 1.08, 95% confidence interval [CI] 1.01-1.15) and LGA birth (9.2% v. 7.5%, adjusted RR 1.12, 95% CI 1.10-1.15), but lower risk of SGA birth (10.2% v. 11.0%, adjusted RR 0.92, 95% CI 0.90-0.94), while infants of family-class immigrants had higher risk of SGA birth (12.2% v. 11.0%, adjusted RR 1.01, 95% CI 1.00-1.02). Risk of stillbirth, neonatal death, and overall infant death did not differ significantly among immigrant groups. INTERPRETATION: Heterogeneity exists in outcomes of infants born to immigrants to Canada across immigration categories. These results highlight the importance of disaggregating immigrant populations in studies of health disparities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".