Linking hospital and immigrant landing data to understand patterns of hospital use among refugees in Canada
Bibliographic record
Abstract
ABSTRACT
 ObjectivesIn Canada, refugees represent between 10% and 15% of the total immigrant annual in-take of immigrants. Refugees are generally admitted with different selection criteria and have different settlement challenges which may lead to higher health risk, compared to other categories of immigrants. The purpose of this study is to use linked hospital and immigrant landing data to report hospitalization rates among refugees arriving to Canada between 1980 and 2006 with special focus on those from refugee-dominant source areas, namely Poland and Vietnam, and the Middle East.
 ApproachData from the 2006/07-2008/09 Discharge Abstract Database (DAD) were linked to the 1980-2008 Immigrant Landing File (ILF) to identify hospitalizations among immigrants by category of admission. Age-standardized hospitalization rates (ASHR) for overall causes (excluding pregnancy), and for selected causes are derived for refugees overall and by specific source country. Rates are compared with those for the Canadian-born population and with economic class immigrants from the same source country.
 ResultsOverall, refugees had substantially lower overall ASHR compared with the Canadian-born population (494 per 10,000, 95% CI, 487, 500 vs 891 per 10,000, 95% CI, 890, 892, retrospectively). Among refugees, those who landed in Canada (refugee claimants) had the highest ASHR (539, 95% CI 524,555), especially for circulatory disease. Among refugee-dominant areas, Government-assisted refugees from the Middle East tended to have higher ASHR at levels comparable with that of the Canadian born population (for circulatory disease). Refugees had higher rates of hospitalization compared with their economic class counterparts, except among those from Poland.
 ConclusionRefugees, like other immigrants, generally had lower hospitalization rates compared with the Canadian born population, but the heterogeneity within groups reveals specific vulnerability for certain types of health care use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".