Characterizing risk of type 2 diabetes in First Nations people living in First Nations communities in Ontario: a population-based analysis using cross-sectional survey data
Bibliographic record
Abstract
BACKGROUND: Population-based planning tools are important for informing diabetes-prevention efforts in First Nations communities. We used the Diabetes Population Risk Tool (DPoRT) to predict 10-year diabetes risk and describe the factors that contribute to diabetes risk in First Nations adults living in Ontario First Nations communities. METHODS: We examined population data from adult (≥ 20 yr) respondents to the First Nations Regional Health Survey (RHS) phase 3, a representative cohort of First Nations people living in Ontario First Nations communities. We applied the DPoRT to risk factor information in the survey to predict the distribution of 10-year type 2 diabetes incidence and number of new diabetes cases from 2015/16 to 2025/26. RESULTS: There were 993 respondents to the RHS phase 3 adult survey, of whom 936 (708 without diabetes and 228 with a diagnosis of type 2 diabetes) were eligible for inclusion. The DPoRT predicted a type 2 diabetes risk of 9.6% (confidence interval [CI] 8.3-10.8) between 2015/16 and 2025/26, corresponding to 3501 (95% CI 2653-4348) new diabetes cases. Diabetes cases were predicted to occur disproportionately among those experiencing food insecurity, low income, overweight, obesity and physical inactivity. Reduced diabetes risk was predicted among those who reported connections to Indigenous culture, as measured by eating traditional vegetative foods a few times or often in the previous 12 months. INTERPRETATION: Socioeconomic conditions and known risk factors for type 2 diabetes are important determinants of diabetes risk in First Nations communities. Culturally appropriate policies, programming and services that address socioeconomic disadvantage and other diabetes risk factors in First Nations communities likely have an important role for diabetes prevention in First Nations adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".