Comparative analysis of perinatal health outcomes among refugee subgroups and economic immigrants in Canada (2000–2017)
Bibliographic record
Abstract
BACKGROUND: Refugees often face increased risks of poor perinatal health outcomes compared to native-born individuals and non-refugee immigrants. However, limited research has explored how birth outcomes vary across refugee subgroups in Canada, especially compared to economic immigrants and among refugee groups themselves. This study aimed to (1) compare the risk of preterm birth (PTB), small-for-gestational-age (SGA), large-for-gestational-age (LGA), stillbirth, and infant mortality between refugee subgroups and economic immigrants, and (2) examine differences among Government-Assisted Refugees (GARs), Privately Sponsored Refugees (PSRs), and In-Canada Refugees (ICRs). METHODS: This population-based study used data from the Migrant Maternal and Infant Morbidity and Mortality (MIMMM) dataset, including 706,620 singleton births from 2000 to 2017. Generalized estimating equation models calculated adjusted risk ratios (aRRs) for birth outcomes, accounting for maternal and immigration-related factors. RESULTS: All refugee subgroups had higher PTB (6.26-6.41 per 100 births) and LGA rates (8.65-9.17 per 100 births) but lower SGA rates (9.53-10.40 per 100 births) compared to economic immigrants (PTB: 5.95, LGA: 7.36, SGA: 10.96). After adjustment, GARs maintained higher PTB risks, and all refugee subgroups had lower SGA and higher LGA risks than economic immigrants. Within refugee subgroups, ICRs had higher SGA risks (aRR = 1.09; 95% CI: 1.04-1.14) than GARs, and PSRs (aRR = 1.22; 95% CI: 1.04-1.44) and ICRs (aRR = 1.28; 95% CI: 1.07-1.52) had higher stillbirth risks than GARs. CONCLUSION: Refugee women in Canada have higher risks of PTB and LGA births compared to economic immigrants. ICRs had higher risks of SGA births and stillbirths than other refugee subgroups but lower risks of SGA and stillbirths compared to economic immigrants. These disparities are partly explained by maternal and immigration-related factors. Further research is needed to better understand these factors and inform policies aimed at reducing health disparities among immigrant populations in Canada.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".