Incident CKD among Canadian Immigrants: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: A “healthy immigrant effect” has been demonstrated for a number of chronic health conditions including cardiovascular disease, diabetes, and dementia; however, the link between immigrant status and chronic kidney disease (CKD) remains uncertain. We sought to compare the risk for incident CKD among Canadian immigrants and non-immigrants. Methods: We conducted a population level, observational cohort study of adult Ontario residents, including foreign-born immigrant Canadian citizens and non-immigrant Canadian citizens by birth, with normal baseline kidney function (outpatient estimated glomerular filtration rate [eGFR] ≥70 mL/min/1.73m2) between 2007-2020 utilizing provincial health administrative data. We used multivariable Cox proportional hazard regression modeling to evaluate the relationship between immigrant status and incident CKD (outpatient eGFR <60 mL/min/1.73m2). Results: The study cohort included 10,440,210 Ontario residents, consisting of 22% immigrants and 78% non-immigrants. The mean (SD) age and eGFR were 45 (17) years and 102 (16) mL/min/1.73m2, respectively, and 54% of individuals were female. A total of 117,028 immigrants (5%, 7 events per 1000 person years) and 984,277 non-immigrants (12%, 16 events per 1000 person years) developed incident CKD during follow-up. Immigrants experienced a 20% lower adjusted risk for incident CKD compared to non-immigrants (adjusted hazard ratio [HR] 0.80 [95% confidence interval [CI] 0.80-0.81]) - see Figure. Both refugee immigrants (adjusted HR 0.87 [95% CI 0.86-0.89]) and non-refugee immigrants (adjusted HR 0.79 [95% CI 0.79-0.80]) experienced a lower risk for incident CKD compared to non-immigrants. Conclusion: Immigrants experience a lower risk for incident CKD compared to non-immigrants. These findings provide evidence of a “healthy immigrant effect” in relation to kidney health.Adjusted Cumulative Incidence Curves for Chronic Kidney Disease among Immigrants Versus Non-Immigrants.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 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) | 0.001 | 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".