Ethnic variation in the conversion of prediabetes to diabetes among immigrant populations relative to Canadian-born residents: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare absolute and relative rates of conversion from prediabetes to diabetes among non-European immigrants to Europeans and Canadian-born residents, overall, and by age and level of glycemia. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort population-based study using administrative health databases from Ontario, Canada, to identify immigrants (n=23 465) and Canadian born (n=1 11 085) aged ≥20 years with prediabetes based on laboratory tests conducted between 2002 and 2011. Individuals were followed until 31 December 2013 for the development of diabetes using a validated algorithm. Immigration data was used to assign ethnicity based on country of origin, mother tongue, and surname. Fine and Gray's survival models were used to compare diabetes incidence across ethnic groups overall and by age and glucose category. RESULTS: Over a median follow-up of 5.2 years, 8186 immigrants and 39 722 Canadian-born residents developed diabetes (7.1 vs 6.1 per 100 person-years, respectively). High-risk immigrant populations such as South Asians (HR: 1.72, 95% CI 1.55 to 1.99) and Southeast Asians (HR: 1.65, 95% CI 1.46 to 1.86) had highest risk of converting to diabetes compared with Western Europeans (referent). Among immigrants aged 20-34 years, the adjusted cumulative incidence ranged from 18.4% among Eastern Europeans to 52.3% among Southeast Asians. Conversion rates increased with age in all groups but were consistently high among South Asians, Southeast Asians and Sub-Saharan African/Caribbeans after the age of 35 years. On average, South Asians converted to diabetes 3.1-4.6 years earlier than Western Europeans and at an equivalent rate of conversion to Western Europeans who had a 0.5 mmol/L higher baseline fasting glucose value. CONCLUSIONS: High-risk ethnic groups converted to diabetes more rapidly, at younger ages, and at lower fasting glucose values than European populations, leading to a shorter window for diabetes prevention.
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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) | 0.000 | 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 | 0.000 |
| 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".