Infant mortality among the Canadian-born offspring of immigrants and non-immigrants in Canada: a population-based study
Bibliographic record
Abstract
BACKGROUND: Adult immigrants in Canada have a survival advantage over their Canadian-born counterparts. It is unknown whether migrants are able to transmit their survival advantage to their Canadian-born children. METHODS: Neonatal and postneonatal mortality between the Canadian-born population and 12 immigrant subgroups were compared using 1990-2005 linked birth-infant death records. Age-at-death specific mortality rates and rate differences were calculated by nativity status and maternal birthplace. A chi-square statistic was used to compare group differences in maternal sociodemographic characteristics. Multivariate survival analysis was used to estimate the effect of maternal birthplace on neonatal and postneonatal mortality, net of maternal sociodemographic and infant characteristics. RESULTS: Overall, immigrants had lower rates of neonatal and postneonatal mortality than the Canadian-born population. But the adjusted risk of neonatal mortality was higher for Sub-Saharan African (hazard ratio [HR] = 1.32; 95 % confidence interval [CI] = 1.05, 1.66), Haitian (HR = 2.29, 95 % CI = 1.90, 2.76), non-Spanish Caribbean (HR = 1.38; 95 % CI = 1.01, 1.89), and Pakistani (HR = 1.87; 95 % CI = 1.31, 2.68) migrants relative to Canadian-born women. There were fewer significant disparities in postneonatal death, with higher adjusted risks of mortality observed for Pakistani (HR = 2.67, 95 % CI = 1.77, 4.02) and Haitian (HR = 1.41, 95 % CI = 1.02, 1.97) migrants only. CONCLUSION: Inequalities in infant mortality are more concentrated in the neonatal period. Contingent on surviving the first 27 days after birth, the infants of most immigrants (except those from Haiti and Pakistan) have the same chances of survival as the infants of Canadian-born women. Improvements in prenatal care and access to postpartum care may reduce disparities in infant mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".