Chronic Obstructive Pulmonary Disease in Immigrants and Refugees to Ontario, Canada, 2002–2019
Bibliographic record
Abstract
Abstract Rationale Canada has welcomed millions of immigrants and refugees in the last decade, and little is known about their chronic obstructive pulmonary disease (COPD) burden. Objectives To determine the prevalence of COPD among immigrants to Ontario compared with nonimmigrants. Methods We conducted a population-based cross-sectional study of people aged 35 years and older living in Ontario between April 1, 2002, and March 30, 2020, using health administrative databases. The primary outcome was COPD prevalence as ascertained using a validated algorithm. The primary exposure was immigrant status; secondary exposure was refugee status. Results Twenty-three percent of Ontario’s population aged 35 years and older were immigrants in 2019. The overall prevalence of COPD was 12%. In adjusted analysis, immigrants <5 years, 5–14 years, and ⩾15 years from immigration were 76%, 54%, and 24%, respectively, less likely than nonimmigrants to have COPD. COPD prevalence slightly increased in immigrants over time. In comparison with nonrefugee immigrants, refugee immigrants had a higher prevalence of COPD (adjusted relative risk, 1.33; 95% confidence interval, 1.32–1.33). Conclusions Immigrants have a lower risk than nonimmigrants of having COPD; however, refugee immigrants had a higher risk than nonrefugee immigrants of COPD. The lower risk in immigrants may be explained by the “healthy immigrant effect,” in which immigrants may be generally healthier and younger than locally born individuals. In addition, COPD may be underdiagnosed or underreported in immigrants because of structural barriers to accessing healthcare services. Further research is needed into causes of the difference.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".