Incident diabetes, hypertension and dyslipidemia in a Manitoba First Nation
Bibliographic record
Abstract
BACKGROUND: Diabetes and diabetes complications are substantially higher among Canadian First Nations populations compared with the general Canadian population. However, incidence data using detailed individual assessments from a population-based cohort have not been undertaken. OBJECTIVE: We sought to describe incident diabetes, hypertension and dyslipidemia in a population-based cohort from a Manitoba Ojibway First Nation community. DESIGN: Study data were from 2 diabetes screening studies in Sandy Bay First Nation in Manitoba, Canada, collected in 2002/2003 and 2011/2012. The cohort comprised of respondents to both screening studies (n=171). Health and demographic data were collected using a questionnaire. Fasting blood samples, blood pressure and anthropometric data were also collected objectively. Incident diabetes, hypertension and dyslipidemia were determined. Generalized linear models with Poisson distribution were used to estimate risk of incident diabetes and cardiometabolic conditions according to age and sex. RESULTS: There were 35 (95% CI: 26, 45) new cases of diabetes among 128 participants without diabetes at baseline (27 or 3.3% per year). While participants who were 50 years and older at baseline had a significantly higher risk of incident diabetes at follow-up compared with participants aged 18-29 at baseline (p=0.012), more than half of the incident cases of diabetes occurred among participants aged less than 40 at baseline. There were 28 (95% CI: 20, 37) new cases of dyslipidemia at follow-up among 112 without dyslipidemia at baseline (25%). There were 36 (95% CI: 31, 42) new cases of hypertension among 104 participants without hypertension at baseline (34.6%). Women had half the risk of developing hypertension compared with men (p=0.039). CONCLUSIONS: Diabetes incidence is very high, and the number of new cases among those younger than 40 is a concern. Additional public health and primary care efforts are needed to address the diabetes burden in this First Nation community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".