Ethnic differences in prediabetes incidence among immigrants to Canada: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Prediabetes appears to be increasing worldwide. This study examined the incidence of prediabetes among immigrants to Canada of different ethnic origins and the age at which ethnic differences emerged. METHODS: We assembled a cohort of Ontario adults (≥ 20 years) with normoglycemia based on glucose testing performed between 2002 and 2011 through a single commercial laboratory database (N = 1,772,180). Immigration data were used to assign ethnicity based on country of origin, mother tongue, and surname. Individuals were followed until December 2013 for the development of prediabetes, defined using either the World Health Organization/Diabetes Canada (WHO/DC) or American Diabetes Association (ADA) thresholds. Multivariate competing risk regression models were derived to examine the effect of ethnicity and immigration status on prediabetes incidence. RESULTS: After a median follow-up of 8.0 years, 337,608 individuals developed prediabetes. Using definitions based on WHO/DC, the adjusted cumulative incidence of prediabetes was 40% (HR 1.40, CI 1.38-1.41) higher for immigrants relative to long-term Canadian residents (21.2% vs 16.0%, p < 0.001) and nearly twofold higher among South Asian than Western European immigrants (23.6%; HR 1.95, CI1.87-2.03 vs 13.1%; referent). Cumulative incidence rates based on ADA thresholds were considerably higher (47.1% and 32.3% among South Asians and Western Europeans, respectively). Ethnic differences emerged at young ages. South Asians aged 20-34 years had a similar prediabetes incidence as Europeans who were 15 years older (35-49 years), regardless of which prediabetes definition was used (WHO/DC 14.4% vs 15.7%; ADA 38.0% vs 33.0%). CONCLUSION: Prediabetes incidence was substantially higher among non-European immigrants to Canada, highlighting the need for early prevention strategies in these populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".