Severe maternal morbidity among migrants with insecure residency status in Sweden 2000–2014: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Migrants with insecure residency status (i.e., undocumented migrants and asylum-seekers, who are denied or waiting for authorized residency) often experience social and psychosocial adversities and limited access to health care. Nonetheless, they have not been profiled on the risk of severe maternal morbidity (SMM), a sentinel measure of maternal health and maternity care. METHODS: = 1,570,472). Lacking a maternal personal identification number was used as an indicator for insecure residency status (1.3% of all births). We used Poisson regression models to estimate risk ratios of SMM in migrant women with insecure residency status compared to the Swedish-born or migrant women with long-term residency, adjusting for the calendar year of birth, maternal age, and parity. RESULTS: Overall SMM rate among migrant women with insecure residency status was 21.5/1000 and 14.7/1000 among Swedish-born women. Compared to Swedish-born, migrants with insecure residency status had 50% higher risk of overall SMM (adjusted risk ratio (aRR)=1.54 [1.37-1.74]) and over 80% higher risk of SMM excluding transfusion-only cases (aRR=1.88 [1.37-2.57]). When compared to migrant women with long-term residency, migrant women with insecure residency also had a higher risk of SMM (overall SMM aRR=1.42 [1.26,1.61]; SMM excluding transfusion only cases aRR=1.43 [1.04,1.97]), suggesting that insecure residency conferred additional risks of SMM beyond migration. CONCLUSION: Migrant women with insecure residency status had increased risk of severe maternal morbidity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".