A population-based cohort study defined estimated glomerular filtration rate decline and kidney failure among Canadian immigrants
Bibliographic record
Abstract
The link between immigrant status, a key social determinant of health, and kidney disease remains uncertain. To evaluate this, we compared incident adverse kidney outcomes between immigrants and non-immigrants using Canadian provincial health administrative data. We conducted a population-based observational cohort study of all adult Ontario residents (immigrants and non-immigrants) with normal baseline kidney function (estimated glomerular filtration rate (eGFR) 70 mL/min/1.73m 2 or more). Multivariable Cox proportional hazard regression modeling was used to evaluate the relationship between immigrant status and the composite adverse kidney outcome of 40% eGFR decline or kidney failure. The study cohort included 10,440,210 individuals with 22% immigrants and 78% non-immigrants. The mean (Standard Deviation) age and eGFR were 45 (17) years and 102 (16) mL/min/1.73m 2 , respectively. Immigrants experienced a 27% lower hazard for the composite adverse kidney outcome (adjusted hazard ratio 0.73 [95% Confidence Interval 0.72-0.74]) compared to non-immigrants which was primarily driven by 40% eGFR decline. However, immigrants also experienced a 12% lower hazard for incident kidney failure (0.88 [0.84-0.93]) compared to non-immigrants. Results were consistent upon accounting for the competing risk of death and adjusting for baseline albuminuria. As has been demonstrated with other chronic diseases, these novel findings suggest that a "healthy immigrant effect" also extends to kidney disease. Differential kidney disease outcomes were identified among immigrants based on refugee status and world region of origin which may inform health policy decision-making toward targeted screening strategies and more cost-effective resource allocation for immigrant populations.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".