Associations between socioeconomic status and pregnancy outcomes: a greater magnitude of inequalities in perinatal health in Montreal than in Brussels
Bibliographic record
Abstract
BACKGROUND: Comparing health inequalities between countries helps us to highlight some factors specific to each context that contribute to these inequalities, thus contributing to the identification of courses of action likely to reduce them. This paper compares the associations between socioeconomic status (SES) and 1) low birth weight (LBW) and 2) preterm birth, in Brussels and Montreal (in general population, natives-born mothers, and immigrant mothers). METHODS: A population-based study examining associations between SES and pregnancy outcomes was conducted in each city, using administrative databases from Belgian and Quebec birth records (N = 97,844 and 214,620 births in Brussels and Montreal, respectively). Logistic regression models were developed in order to estimate the relationship between SES (maternal education and income quintile) and pregnancy outcomes, in each region. The analyses were first carried out for all births, then stratified according to the mother's origin. RESULTS: For the general population, SES is associated with LBW and preterm birth in both regions, except for income and preterm birth in Brussels. The association is stronger for mothers born in Belgium and Canada than for those born abroad. The main difference between the two regions concerns the magnitude of inequalities in perintal health, which is greater in Montreal than in Brussels among the general population. For native-born mothers, the magnitude of inequalities in perinatal health is also greater for mothers born in Canada than for those born in Belgium, except for the association between income and preterm birth. The socioeconomic gradient in perinatal health is less marked among immigrant mothers than native mothers. CONCLUSION: Significant differences in inequalities in perinatal health are observed between Brussels and Montreal. These differences can be explained by : on the one hand, the existence of greater social inequalities in Montreal than in Brussels and, on the other hand, the lower vulnerability of immigrants with low SES in Brussels. Future studies seeking to understand the mechanisms that lead to inequalities in health in different contexts should take into account a comparison of immigration and poverty contexts, as well as the public policies related to these factors.
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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) | 0.002 | 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".