Associations Between Socioeconomic Status and Adverse Pregnancy Outcomes: A Greater Magnitude of Perinatal Inequities in Montreal Than in Brussels
Bibliographic record
Abstract
Abstract Objective This paper compares the associations between socioeconomic status (SES) and 1) low birth weight (LBW) and 2) preterm birth, in Brussels and Montreal, and discusses hypotheses that may explain the differences between these two regions. Methods This population-based study uses administrative databases from Belgian and Quebec birth records. The analysis is based on 97,844 and 214,620 singleton live births in Brussels and Montreal, respectively. Logistic regression models were developed for each region in order to estimate the relationship between SES (maternal education and income quintile) and pregnancy outcomes. The analyses were performed for all births according to the mother’s origin. Results SES is associated with LBW and preterm birth in both regions. This association varies according to the mother’s birth place; the impact of SES being greater for mothers born in Belgium or Canada than for those born abroad. The main difference between the two regions concerns the magnitude of perinatal inequalities, which is greater in Montreal than in Brussels, whether among the general population, native-born mothers, or immigrant mothers. Conclusion Significant differences in social inequalities in perinatal health are observed between Brussels and Montreal. The different characteristics of low-income and immigrant households between the two contexts help explain these results. In fact, the poor are relatively poorer in Quebec than in Belgium and live in a more unequal context.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".