Prevalence, incidence and outcomes of diabetes in Ontario First Nations children: a longitudinal population-based cohort study
Bibliographic record
Abstract
BACKGROUND: First Nations people are known to have a higher risk of childhood-onset type 2 diabetes, yet population-level data about diabetes in First Nations children are unavailable. In a partnership between Chiefs of Ontario and academic researchers, we describe the epidemiologic features and outcomes of diabetes in First Nations children in Ontario. METHODS: We created annual cohorts from 1995/96 to 2014/15 using data from the Registered Persons Database linked with the federal Indian Register. We used the Ontario Diabetes Database to identify children with all types of diabetes and calculated the prevalence and incidence for First Nations children and other children in Ontario. We describe glycemic control in First Nations children and other children in 2014. RESULTS: In 2014/15, there were 254 First Nations children and 10 144 other children with diagnosed diabetes in Ontario. From 1995/96 to 2014/15, the prevalence increased from 0.17 to 0.57 per 100 children, and the annual incidence increased from 37 to 94 per 100 000 per year among First Nations children. In 2014/15, the prevalence of diabetes was 0.62/100 among First Nations girls and 0.36/100 among other girls. The mean glycosylated hemoglobin level among First Nations children was 9.1% (standard deviation 2.7%) and for other children, 8.5% (standard deviation 2.1%). INTERPRETATION: First Nations children have substantially higher rates of diabetes than non-Aboriginal children in Ontario; this is likely driven by an increased incidence of type 2 diabetes and increased risk for diabetes among First Nations girls. There is an urgent need for strategies to address modifiable factors associated with the risk of diabetes, improve access to culturally sensitive diabetes care and improve outcomes for First Nations children.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".