Health selection among outmigrants, return migrants and non-migrants in three subcohorts of international, interprovincial migrants and non-migrants in Manitoba, Canada
Bibliographic record
Abstract
Objective and ApproachLinking national and provincial immigration registers with health care utilization datasets at the Manitoba Centre for Health Policy, we assembled a cohort of 816,185 adults who resided in Manitoba, Canada and were followed up for at least one year between 1985 to 2023 to outmigration, return migration or death. The cohort included three subcohorts of international immigrants (16.4%), interprovincial migrants (10.8%) and all other Manitobans (AOM) (72.8%). Within each subcohort, we matched ‘stayers’ who never migrated, outmigrants and returnees on sex, birth year and place of residence and compared their hospitalization rates and Charlson and Elixhauser comorbidity scores 1-year before outmigration and 1-year after return migration. ResultsOutmigrants had lower hospitalization rates than stayers among AOM [Adjusted Relative Rate (RR): 0.80; 95% confidence interval (CI): 0.78, 0.82] and in the other two subcohorts. Comorbidity scores were also consistently lower among outmigrants compared to stayers in all three subcohorts, even after restricting to hospitalized cases. Outmigrants whose destination was another country were healthier than those who migrated to other provinces. Returnees had better health status than stayers upon return in the AOM subcohort only but lower than those who did not return at the time of outmigration in all three subcohorts. ConclusionsMigration is associated with positive health selection among international immigrants, interprovincial migrants and the local population. ImplicationsContrary to common belief, health selection is not restricted to international immigrants. Selective migration may represent a source of bias in health-related population-based studies (e.g., sampling and informative censoring).
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".