Ethnic and immigrant disparities in dialysis prevalence and chronic kidney disease trajectories in Toronto
Bibliographic record
Abstract
BACKGROUND: Large differences exist in chronic kidney disease (CKD) rates between countries, but differences within diverse populations living in the same setting with universal healthcare are not well understood. OBJECTIVES: To compare dialysis prevalence, CKD risk factors and control, and CKD progression by ethnicity and birth country in an ethnoculturally diverse setting with high rates of kidney disease and universal healthcare. SETTING: Scarborough, Toronto's most diverse region and site of Canada's largest regional dialysis programme. DESIGN AND PARTICIPANTS: Double observational cohort study of 2397 participants: a retrospective cohort of 1116 residents who received dialysis between 2016-2019, and a prospective cohort of 1281 individuals with non-dialysis CKD followed for 3 years between 2010-2015 in Scarborough. OUTCOME MEASUREMENTS: Dialysis prevalence, calculated by comparing frequencies of birth countries and ethnicities in the dialysis cohort with census-derived community frequencies. Secondary outcome measurements were traditional CKD risk factor prevalence (diabetes, hypertension, cardiovascular disease) and control (haemoglobin A1c, blood pressure); and CKD progression (estimated glomerular filtration rate decline, proteinuria) adjusted for socioeconomic status in the non-dialysis cohort. RESULTS: Dialysis prevalence was 4.2 times higher in immigrants (p<0.001), and highest in those born in the Caribbean, Southeast Asia and South Asia. Ethnicity-based differences were smaller, with rates up to 1.7-fold higher in Southeast Asian, Black and South Asian compared with White persons. Diabetes prevalence was highest in immigrants from South Asia, Southeast Asia and the Caribbean. Blood pressure and haemoglobin A1c were higher in Caribbean-born individuals. Kidney function declined fastest in patients born in the Caribbean, South Asia and East Asia. Proteinuria increased most rapidly in patients born in the Caribbean, Southeast Asia and South Asia. The year of immigration did not influence these secondary outcomes. CONCLUSIONS: Despite universal healthcare access, marked disparities in CKD risks and rates exist within ethnoculturally diverse immigrants living in this Canadian kidney disease hotspot. More focused research and tailored interventions are required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".