Severe maternal morbidity associated with maternal birthplace in three high-immigration settings
Bibliographic record
Abstract
BACKGROUND: Maternal mortality and morbidity vary substantially worldwide. It is unknown if these geographic differences translate into disparities in severe maternal morbidity among immigrants from various world regions. We assessed disparities in severe maternal morbidity between immigrant women from various world regions giving birth in three high-immigration countries. METHODS: We used population-based delivery data from Victoria; Australia and Ontario, Canada and national data from Denmark, in the most recent 10-year period ending in 2010 available to each participating centre. Each centre provided aggregate data according to standardized definitions of the outcome, maternal regions of birth and covariates for pooled analyses. We used random effects and stratified logistic regression to obtain odds ratios (ORs) with 95% confidence intervals (95% CIs), adjusted for maternal age, parity and comparability scores. RESULTS: We retrieved 2,322,907 deliveries in all three receiving countries, of which 479,986 (21%) were to immigrant women. Compared with non-immigrants, only Sub-Saharan African women were consistently at higher risk of severe maternal morbidity in all three receiving countries (pooled adjusted OR: 1.67; 95% CI: 1.43, 1.95). In contrast, both Western and Eastern European immigrants had lower odds (OR: 0.82; 95% CI: 0.70, 0.96 and OR: 0.64; 95% CI: 0.49, 0.83, respectively). The most common diagnosis was severe pre-eclampsia followed by uterine rupture, which was more common among Sub-Saharan Africans in all three settings. CONCLUSIONS: Immigrant women from Sub-Saharan Africa have higher rates of severe maternal morbidity. Other immigrant groups had similar or lower rates than the majority locally born populations.
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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 | 0.015 | 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 | 0.001 |
| 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".