ESRD among Immigrants to Ontario, Canada: A Population-Based Study
Bibliographic record
Abstract
Background The epidemiology of ESRD requiring maintenance dialysis (ESRD-D) in large, diverse immigrant populations is unclear. Methods We estimated ESRD-D prevalence and incidence among immigrants in Ontario, Canada. Adults residing in Ontario in 2014 were categorized as long-term Canadian residents or immigrants according to administrative health and immigration datasets. We determined ESRD-D prevalence among these adults and calculated age-adjusted prevalence ratios (PRs) comparing immigrants to long-term residents. Among those who immigrated to Ontario between 1991 and 2012, age-adjusted ESRD-D incidence was calculated by world region and country of birth, with immigrants from Western nations as the referent group. Results Among 1,902,394 immigrants and 8,860,283 long-term residents, 1700 (0.09%) and 8909 (0.10%), respectively, presented with ESRD-D. Age-adjusted ESRD-D prevalence was higher among immigrants from sub-Saharan Africa (PR, 2.17; 95% confidence interval [95% CI], 1.84 to 2.57), Latin America and the Caribbean (PR, 2.11; 95% CI, 1.90 to 2.34), South Asia (PR, 1.45; 95% CI, 1.32 to 1.59), and East Asia and the Pacific (PR, 1.34; 95% CI, 1.22 to 1.46). Immigrants from Somalia (PR, 4.18; 95% CI, 3.11 to 5.61), Trinidad and Tobago (PR, 2.88; 95% CI, 2.23 to 3.73), Jamaica (PR, 2.88; 95% CI, 2.40 to 3.44), Sudan (PR, 2.84; 95% CI, 1.53 to 5.27), and Guyana (PR, 2.69; 95% CI, 2.19 to 3.29) had the highest age-adjusted ESRD-D PRs relative to long-term residents. Immigrants from these countries also exhibited higher age-adjusted ESKD-D incidence relative to Western Nations immigrants. Conclusions Among immigrants in Canada, those from sub-Saharan Africa and the Caribbean have the highest ESRD-D risk. Tailored kidney-protective interventions should be developed for these susceptible 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".