Prevalence of diabetic kidney disease by world region of birth among immigrants and long-term residents of Canada with type 2 diabetes
Bibliographic record
Abstract
AIMS: To measure the prevalence of diabetic kidney disease (DKD) among immigrants and long-term residents with type 2 diabetes (T2D). METHODS: We conducted a population-based retrospective cohort study in Ontario, Canada among adults aged 20-79 years with T2D. The exposure was world region of birth (immigrants); long-term residents were the comparison group. The outcome was DKD, defined by the Kidney Disease: Improving Global Outcomes (KDIGO) categories. We measured the age-sex standardized prevalence of DKD and constructed logistic regression models to compute adjusted odds ratios (OR) estimating the association between the exposure and outcome. RESULTS: We included 210,693 immigrants (mean age 59.8 [standard deviation 10.8] years, 54.3 % male) and 539,632 long-term residents (mean age 64.1 [10.4] years, 56.1 % male). Immigrants born in East Asia had the highest prevalence and adjusted odds of the KDIGO low-risk category (76.6 %, OR 1.59, 1.53-1.64). Immigrants born in Southeast Asia had the lowest prevalence of the KDIGO low-risk category (64.0 %), and the highest prevalence and adjusted odds of the moderately-increased, high, and very-high risk KDIGO categories (OR: 1.21, [1.18-1.25]; 1.20, 1.14-1.26; 1.18, 1.12-1.25) compared to long-term residents. CONCLUSIONS: There is substantial variation in the prevalence of DKD among immigrants according to world region of birth.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".