Epidemiology of diabetes mellitus among First Nations and non-First Nations adults
Bibliographic record
Abstract
BACKGROUND: First Nations people in Canada experience a disproportionate burden of type 2 diabetes mellitus. To increase our understanding of this evolving epidemic, we compared the epidemiology of diabetes between First Nations and non-First Nations adults in Saskatchewan from 1980 to 2005. METHODS: We used administrative databases to perform a population-based study of diabetes frequency, incidence and prevalence in adults by ethnic background, year, age and sex. RESULTS: We identified 8275 First Nations and 82,306 non-First Nations people with diabetes from 1980 to 2005. Overall, the incidence and prevalence of diabetes were more than 4 times higher among First Nations women than among non-First Nations women and more than 2.5 times higher among First Nations men than among non-First Nations men. The number of incident cases of diabetes was highest among First Nations people aged 40-49, while the number among non-First Nations people was greatest in those aged 70 or more years. The prevalence of diabetes increased over the study period from 9.5% to 20.3% among First Nations women and from 4.9% to 16.0% among First Nations men. Among non-First Nations people, the prevalence increased from 2.0% to 5.5% among women and from 2.0% to 6.2% among men. By 2005, almost 50% of First Nations women and more than 40% of First Nations men aged 60 or older had diabetes, compared with less than 25% of non-First Nations men and less than 20% of non-First Nations women aged 80 or older. INTERPRETATION: First Nations adults are experiencing a diabetes epidemic that disproportionately affects women during their reproductive years. This ethnicity-based pattern suggests diverse underlying mechanisms that may include differences in the diabetogenic impact of gestational diabetes.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 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".