Predictors of Emergency Caesarean Births to Low-Risk Migrant Women
Bibliographic record
Abstract
Background The high number of caesareans performed in High Income Countries (HICs) is of concern due to associated risks. Recommendations to reduce caesarean rates include preventing emergency caesareans among low-risk women (i.e., vertex, singleton, term pregnancies). Pregnant migrant women from low or middle income countries (LMICs) may face conditions that exacerbate childbearing and delivery health risks. The objective of this study was to identify medical, migration, social and health service predictors associated with emergency caesareans in low-risk migrant women from LMICs. Methods Using a case-control research design, migrant women from LMICs, and living in Canada ≤ 8 years were recruited from the postpartum units of three hospitals in a major urban Canadian city between March 2014 and January 2015. Data were collected from medical records and by administration of the Migrant-Friendly Maternity Care questionnaire (available in 8 languages). Low risk women who delivered by emergency caesarean for discretionary indications (cases) or vaginally (controls) were included in analyses. Multi-variable logistic regression was performed to identify predictors of emergency caesarean. Results 233 cases and 1615 controls were analyzed. Predictors of emergency caesarean were: pre-pregnancy BMI ≥ 25 and/or excessive pregnancy weight gain (OR = 1.49, 95% CI 1.02-2.13), poor maternal health (OR = 1.38, 95% CI 0.95-1.98), admission to birthing centre < 4 cm dilated (OR = 6.48, 95% CI 3.50-12.01), maternal region of birth Sub-Saharan African/Caribbean (OR = 2.39, 95% CI 0.95-5.99), and length of time in Canada < 2 years (OR = 2.04, 95% CI 1.04-4.03). Among women < 2 years in Canada, gestational diabetes and/or hypertension (OR = 2.07, 95% CI 0.98-4.35), having a humanitarian migration classification (OR = 4.48, 95%CI 1.21-16.49), and admission to the birthing centre < 4 cm dilated (OR = 7.43, 95% CI 3.04-18.18) were important predictors. Conclusion There are important migration, medical, and health service predictors of emergency caesareans to migrant women from LMICs. Key messages Sub-Saharan African/Caribbean, recently-arrived, and humanitarian migrant women, are particularly vulnerable to having an emergency caesarean and this is not explained by medical factors BMI/pregnancy weight gain, gestational hypertension/diabetes and overall poor maternal health are predictive of emergency caesarean in low risk migrant women from LMICs
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".