Diabetes prevalence and demographics in 25 First Nations communities in northwest Ontario (2014–2017)
Bibliographic record
Abstract
INTRODUCTION: First Nations communities are known to have high rates of diabetes. The rural First Nations communities in northwest (NW) Ontario are particularly affected. Regional studies in 1985 and 1994 found a high prevalence of diabetes. More recently, they are estimated to have the highest prevalence in Ontario at 19%, double the provincial norm. The purpose of this study is to examine the epidemiology and prevalence of diabetes in the total population and cardiovascular comorbidities in the adult population of 25 First Nations communities in NW Ontario. METHODS: This retrospective diabetes prevalence study used primary care electronic medical record data for a 3-year period, 1 August 2014-31 July 2017. Diabetes prevalence was calculated for both the total and the adult (18+) populations and comorbid hypertension and dyslipidaemia were identified in adults. RESULTS: The age-adjusted diabetes prevalence for the total population was 15.1% versus a Canadian prevalence of 8.8%. The age-adjusted adult prevalence was 14.1%, double Canada's average of 7.1%. The average age of adults with diabetes was 52 years (±14.9); 57% were female. Comorbid hypertension (58%) and dyslipidaemia (73%) were common. Metformin was the most commonly used medication (58%), followed by insulin/analogues (23%) and sulphonylureas (13%). CONCLUSION: The diabetes prevalence in the First Nations population of NW Ontario is double Canada's norm. Addressing it will require addressing relevant social determinants of health, including poverty and food security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".