3.3-O6Is the socioeconomic status of immigrant mothers in Brussels relevant to predict their risk of adverse pregnancy outcomes?
Bibliographic record
Abstract
Background: Understanding and tackling the social inequalities in perinatal health require taking into account the interaction between socioeconomic status (SES) and migration. Studies show that among certain migrant groups, education is not associated with the risk of adverse pregnancy outcomes. We aim to extend this analysis to further dimensions of SES and to other settings. Objectives: a) describe the socioeconomic profile of mothers living in Brussels; b) identify the sociodemographic characteristics associated with adverse pregnancy outcomes, by maternal country of birth. Methods: We analysed all singleton live births in Brussels between 2005-2010 (n = 97 844). The data arise from the linkage between three administrative registers: births and deaths, national security, and the population register. Four groups of women were included according to country of birth: Belgium, EU, North Africa, and Sub-Saharan Africa. For each group, logistic regression was carried out to estimate the odds ratios of low birthweight (LBW) and small for gestation age according to: household income, maternal occupation, maternal education, and single parenthood. Results 25% of children were born into a household under the poverty threshold. This proportion was much higher for mothers born outside of the EU. For North African immigrants, three SES indicators (education, occupation, and income) didn’t influence the pregnancy outcomes, whereas their risk of LBW increased with single parenthood. For Sub-Saharan African mothers the risk of LBW increased with low household income. Conclusion: In a region where immigrant mothers are at high poverty risk, we observe a classic social gradient in perinatal outcomes only for mothers born in Belgium or the EU; in the other groups, SES influences perinatal outcomes less systematically. Main message: To develop interventions to reduce inequalities from birth, it’s important to identify the determinants of perinatal health among immigrants and to understand the underlying mechanisms in different contexts.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".