Assessing the migrant mortality advantage among foreign-born and interprovincial migrants in Manitoba, Canada
Bibliographic record
Abstract
OBJECTIVES: Studies on mortality differentials between international immigrants and non-immigrants produced mixed results. The mortality of interprovincial migrants has been less studied. Our objectives were to compare mortality risk between international immigrants, interprovincial migrants, and long-term residents of the province of Manitoba, Canada, and identify factors associated with mortality among migrants. METHODS: We conducted a retrospective matched-cohort study to examine all-cause and premature mortality of 355,194 international immigrants, interprovincial migrants, and long-term Manitoba residents (118,398 in each group) between January 1985 and March 2019 using linked administrative databases. Poisson regression was used to estimate adjusted incidence rate ratios (aIRR) with 95% confidence intervals (CI). RESULTS: The all-cause mortality risk of international immigrants (2.3 per 1000 person-years) and interprovincial migrants (4.4 per 1000) was lower than that of long-term Manitobans (5.6 per 1000) (aIRR: 0.43; 95% CI: 0.42, 0.45 and aIRR: 0.81; 95% CI: 0.80, 0.84, respectively). Compared with interprovincial migrants, international immigrants showed lower death risk (aIRR: 0.50; 95% CI: 0.47, 0.52). Similar trends were observed for premature mortality. Among international immigrants, higher mortality risk was observed for refugees, those from North America and Oceania, and those of low educational attainment. Among internal migrants, those from Eastern Canada had lower mortality risk than those migrating from Ontario and Western Canada. CONCLUSION: Migrants had a mortality advantage over non-migrants, being stronger for international immigrants than for interprovincial migrants. Among the two migrant groups, there was heterogeneity in the mortality risk according to migrants' characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".