Severe Maternal Morbidity Associated With Maternal Birthplace: A Population-Based Register Study
Bibliographic record
Abstract
OBJECTIVE: This study sought to quantify the risk of severe maternal morbidity (SMM) according to maternal country of birth in Canada. METHODS: The study analyzed 1 252 543 in-hospital deliveries of Ontario residents discharged between April 1, 2002, and March 31, 2012. The main outcome measure was a composite indicator of SMM used for surveillance. The top 10 most common component conditions were also evaluated. Maternal country of birth and other immigration characteristics were obtained through linkage with official immigration records. We used modified Poisson regression with generalized estimating equations to assess associations according to maternal country of birth. RESULTS: Overall, immigrant women (N = 335 544) did not differ from Canadian-born women (n = 916 999) in SMM rates (12.1 vs. 12.0 cases per 1000 deliveries, respectively). However, SMM varied substantially according to maternal region of birth, from 9.2 cases per 1000 deliveries among immigrants from Western countries to 23.0 cases per 1000 deliveries among immigrants from Sub-Saharan Africa. Even larger variations were found when immigrants were categorized by their specific countries of birth. The top 10 contributing conditions to SMM among Canadian-born women were also the main contributors among immigrant subgroups. The notable exception was HIV infection, the top contributor among immigrants from Sub-Saharan Africa, whose rate of HIV infection was 43 times that of Canadian-born women (95% CI 34.39-55.23). After excluding HIV cases, disparities in SMM were largely reduced among Sub-Saharan African women but did not disappear. CONCLUSION: There is large heterogeneity in SMM and its component conditions among Canadian immigrants depending on country of origin.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".