Are all immigrant mothers really at risk of low birth weight and perinatal mortality? The crucial role of socio-economic status
Bibliographic record
Abstract
BACKGROUND: Increasing studies show that immigrants have different perinatal health outcomes compared to native women. Nevertheless, we lack a systematic examination of the combined effects of immigrant status and socioeconomic factors on perinatal outcomes. Our objectives were to analyse national Belgian data to determine 1) whether socioeconomic status (SES) modifies the association between maternal nationality and perinatal outcomes (low birth weight and perinatal mortality); 2) the effect of adopting the Belgian nationality on the association between maternal foreign nationality and perinatal outcomes. METHODS: This study is a population-based study using the data from linked birth and death certificates from the Belgian civil registration system. Data are related to all singleton births to mothers living in Belgium between 1998 and 2010. Perinatal mortality and low birth weight (LBW) were estimated by SES (maternal education and parental employment status) and by maternal nationality (at her own birth and at her child's birth). We used logistic regression to estimate the odds ratios for the associations between nationality and perinatal outcomes after adjusting for and stratifying by SES. RESULTS: The present study includes, for the first time, all births in Belgium; that is 1,363,621 singleton births between 1998 and 2010. Compared to Belgians, we observed an increased risk of perinatal mortality in all migrant groups (p < 0.0001), despite lower rates of LBW in some nationalities. Immigrant mothers with the Belgian nationality had similar rates of perinatal mortality to women of Belgian origin and maintained their protection against LBW (p < 0.0001). After adjustment, the excess risk of perinatal mortality among immigrant groups was mostly explained by maternal education; whereas for sub-Saharan African mothers, mortality was mainly affected by parental employment status. After stratification by SES, we have uncovered a significant protective effect of immigration against LBW and perinatal mortality for women with low SES but not for high SES. CONCLUSIONS: Our results show a protective effect of migration in relation to perinatal mortality and LBW among women of low SES. Hence, the study underlines the importance of taking into account socioeconomic status in order to understand more fully the relationship between migration and perinatal outcomes. Further studies are needed to analyse more finely the impact of socio-economic characteristics on perinatal outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".