Disparities in pre‐eclampsia and eclampsia among immigrant women giving birth in six industrialised countries
Bibliographic record
Abstract
OBJECTIVE: To assess disparities in pre-eclampsia and eclampsia among immigrant women from various world regions giving birth in six industrialised countries. DESIGN: Cross-country comparative study of linked population-based databases. SETTING: Provincial or regional obstetric delivery data from Australia, Canada, Spain and the USA and national data from Denmark and Sweden. POPULATION: All immigrant and non-immigrant women delivering in the six industrialised countries within the most recent 10-year period available to each participating centre (1995-2010). METHODS: Data was collected using standardised definitions of the outcomes and maternal regions of birth. Pooled data were analysed with multilevel models. Within-country analyses used stratified logistic regression to obtain odds ratios (OR) with 95% confidence intervals (95% CI). MAIN OUTCOME MEASURES: Pre-eclampsia, eclampsia and pre-eclampsia with prolonged hospitalisation (cases per 1000 deliveries). RESULTS: There were 9,028,802 deliveries (3,031,399 to immigrant women). Compared with immigrants from Western Europe, immigrants from Sub-Saharan Africa and Latin America & the Caribbean were at higher risk of pre-eclampsia (OR: 1.72; 95% CI: 1.63, 1.80 and 1.63; 95% CI: 1.57, 1.69) and eclampsia (OR: 2.12; 95% CI: 1.61, 2.79 and 1.55; 95% CI: 1.26, 1. 91), respectively, after adjustment for parity, maternal age and destination country. Compared with native-born women, European and East Asian immigrants were at lower risk in most industrialised countries. Spain exhibited the largest disparities and Australia the smallest. CONCLUSION: Immigrant women from Sub-Saharan Africa and Latin America & the Caribbean require increased surveillance due to a consistently high risk of pre-eclampsia and eclampsia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".