The shrinking health advantage: unintentional injuries among children and youth from immigrant families
Bibliographic record
Abstract
BACKGROUND: Immigrants typically arrive in good health. This health benefit can decline as immigrants adopt behaviours similar to native-born populations. Risk of injury is low in immigrants but it is not known whether this changes with increasing time since migration. We sought to examine the association between duration of residence in Canada and risk of unintentional injury. METHODS: Population-based cross-sectional study of children and youth 0 to 24 years in Ontario, Canada (2011-2012), using linked health and administrative databases. The main exposure was duration of Canadian residence (recent: 0-5 years, intermediate: 6-10 years, long-term: >10 years). The main outcome measure was unintentional injuries. Cause-specific injury risk by duration of residence was also evaluated. Poisson regression models estimated rate ratios (RR) for injuries. RESULTS: 999951 immigrants were included with 24.2% recent and 26.4% intermediate immigrants. The annual crude injury rates per 100000 immigrants were 6831 emergency department visits, 151 hospitalizations, and 4 deaths. In adjusted models, recent immigrants had the lowest risk of injury and risk increased over time (RR 0.79; 95% CI 0.77, 0.81 recent immigrants, RR 0.90; 95% CI 0.88, 0.92 intermediate immigrants, versus long-term immigrants). Factors associated with injury included young age (0-4 years, RR 1.30; 95% CI 1.26, 1.34), male sex (RR 1.52; 95% CI 1.49, 1.55), and high income (RR 0.93; 95% CI 0.89, 0.96 quintile 1 versus 5). Longer duration of residence was associated with a higher risk of unintentional injuries for most causes except hot object/scald burns, machinery-related injuries, non-motor vehicle bicycle and pedestrian injuries. The risk of these latter injuries did not change significantly with increasing duration of residence in Canada. Risk of drowning was highest in recent immigrants. CONCLUSIONS: Risk of all-cause and most cause-specific unintentional injuries in immigrants rises with increasing time since migration. This indicates the need to develop strategies for maintaining the immigrant health advantage over time while balancing the desire to support integration, active living, and healthy child development.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".