Rates of obstetric intervention during birth and selected maternal and perinatal outcomes for low risk women born in Australia compared to those born overseas
Bibliographic record
Abstract
BACKGROUND: There are mixed reports in the literature about obstetric intervention and maternal and neonatal outcomes for migrant women born in resource rich countries. The aim of this study was to compare the risk profile, rates of obstetric intervention and selected maternal and perinatal outcomes for low risk women born in Australia compared to those born overseas. METHOD: A population-based descriptive study was undertaken in NSW of all singleton births recorded in the NSW Midwives Data Collection between 2000-2008 (n=691,738). Risk profile, obstetric intervention rates and selected maternal and perinatal outcomes were examined. RESULTS: Women born in Australia were slightly younger (30 vs 31 years), less likely to be primiparous (41% vs 43%), three times more likely to smoke (18% vs 6%) and more likely to give birth in a private hospital (26% vs 18%) compared to women not born in Australia. Among the seven most common migrant groups to Australia, women born in Lebanon were the youngest, least likely to be primiparous and least likely to give birth in a private hospital. Hypertension was lowest amongst Vietnamese women (3%) and gestational diabetes highest amongst women born in China (14%). The highest caesarean section (31%), instrumental birth rates (16%) and episiotomy rates (32%) were seen in Indian women, along with the highest rates of babies <10th centile (22%) and <3rd centile (8%). Lebanese women had the highest rates of stillbirth (7.2/1000). Similar trends were found in the different migrant groups when only low risk women were included. CONCLUSION: The results suggest there are significant differences in risk profiles, obstetric intervention rates and maternal and neonatal outcomes between Australian-born and women born overseas and these differences are seen overall and in low risk populations. The finding that Indian women (the leading migrant group to Australia) have the lowest normal birth rate and high rates of low birth weight babies is concerning, and attention needs to be focused on why there are disparities in outcomes and on effective models of care that might improve outcomes for this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".