Maternal and early childhood health and social outcomes of migrants in high-income countries and the impact of policies that restrict access to healthcare; a systematic review and meta-analysis
Bibliographic record
Abstract
• Migrant women and children face mixed maternal and child health outcomes in HICs • Emergency caesarean, low Apgar, and perinatal mental health risks are elevated • Restrictive healthcare policies may contribute to adverse perinatal outcomes • Evidence from countries with inclusive policies and on child health is limited • Further research is needed to inform equitable care and policy for migrant families The “healthy migrant effect” suggests migrants experience better health than local populations despite socioeconomic disadvantage. Its relevance to maternal and child health is uncertain. This systematic review and meta-analysis examined outcomes among migrant women and children in high-income countries (HICs), and the impact of restrictive healthcare policies. Studies published between 2014–2024 comparing outcomes for foreign-born migrant women and children (up to five years) with local-born populations were included. Quality was assessed using the Newcastle-Ottawa Scale. Pooled odds ratios (ORs) were calculated using random-effects meta-analyses. Fifty-one moderate- or high-quality studies (67,471,879 participants across 16 HICs) were included. Migrant women were more likely to be from minority ethnic groups, have lower educational and socioeconomic status, and be older and multiparous. Migrants had higher odds of emergency caesarean birth (OR=1.24, 95%CI=1.16–1.33), food insecurity (OR=2.49, 95%CI=1.24–5.96), perinatal depression/anxiety (OR=1.67, 95%CI=1.10–2.54), intimate partner violence (OR=2.20, 95%CI=1.31–3.72), and low Apgar scores (OR=1.37, 95%CI=1.19–1.56). Odds of low birth weight were slightly lower (OR=0.95, 95%CI=0.90–1.00). Associations persisted under restrictive healthcare policies. No significant differences were found in maternal mortality, severe maternal morbidity, preterm birth, fetal loss, neonatal intensive care use, or vaccination coverage. There is a notable lack of evidence on longer-term child health outcomes. The “healthy migrant effect” may not apply during the perinatal period. Migrant women face significant health inequities, exacerbated by exclusionary policies. Further research, particularly into long-term child outcomes and in inclusive healthcare settings, is needed to inform equitable policy and practice.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".