Diabetes prevalence, incidence and mortality in First Nations and other people in Ontario, 1995–2014: a population-based study using linked administrative data
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus is an established health concern in First Nations communities and is associated with complex influences of colonization. This study, a partnership between First Nations and academic researchers, was undertaken to determine patterns of diabetes prevalence, incidence and mortality in Ontario. METHODS: Using health services and population data from Ontario for 1995 to 2014, linked with the federal Indian Register, we calculated age- and sex-adjusted annual estimates of diabetes prevalence, incidence and mortality for First Nations people (living within and outside First Nations communities) and other people in Ontario. We also examined age- and sex-specific crude diabetes prevalence. RESULTS: Between 1995 and 2014, the prevalence of diabetes increased and the incidence decreased somewhat in all populations. Both prevalence and incidence were substantially higher among First Nations people than among other people in Ontario. In particular, First Nations women had higher prevalence than other women (4.2% v. 1.6% for ages 20-34 yr and 17.6% v. 6.0% for ages 35-49 yr). The lifetime risk of diabetes was higher among First Nations people than among other people in Ontario (57.0%, 95% confidence interval [CI] 56.3%-57.6% v. 44.5%, 95% CI 44.4%-44.6%). Over time, all-cause mortality for those with diabetes declined but remained consistently higher for First Nations people than for other people in Ontario. INTERPRETATION: Diabetes is more common among First Nations people than among other people in Ontario, particularly at younger ages and in women. First Nations-led approaches to address the high prevalence of diabetes in younger First Nations women have the potential to improve metabolic health across generations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".