Primary and Secondary Cardiovascular Prevention Among First Nations Peoples With Type 2 Diabetes in Canada: Findings From the FORGE AHEAD Program
Bibliographic record
Abstract
Background First Nations (FN) peoples in Canada face spiraling rates of type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD). Data on the extent of CVD risk-factor management in FN peoples with T2DM in Canada are scarce. Methods A T2DM registry with data from 7 FN communities in Canada was utilized to identify individuals eligible for primary and secondary CVD prevention. Proportions of individuals meeting clinical practice guideline–specified targets (hemoglobin A1c ≤7.0%; blood pressure ≤130/80 mm Hg; low-density lipoprotein ≤2 mmol/L) were calculated. Prescription of recommended cardioprotective medications (antithrombotic medication, lipid-lowering agents, renin-angiotensin-aldosterone system inhibitors, and beta-blockers) among those with CVD was assessed. χ 2 tests were employed to evaluate differences between CVD prevention groups and sexes. Results Of the 2098 individuals in the registry, 18% had documented CVD (female: male = 1.12). Overall, <10% met all 3 clinical practice guideline targets. Attainment of hemoglobin A1c and blood pressure targets was comparable between primary and secondary CVD prevention groups, with<50% achieving targets. A greater proportion of the secondary prevention group met low-density lipoprotein targets compared to those without CVD (61.6% vs 40.9%, P < 0.01). In the secondary prevention group, beta-blockers were prescribed to only 20%, and <60% were prescribed antithrombotics, lipid-lowering medications, or agents targeting the renin-angiotensin-aldosterone system; <2% were prescribed medications from all 4 classes of cardioprotective medications. Conclusions Primary and secondary CVD prevention recommendations for individuals with T2DM are not being met for an alarmingly high proportion of FN peoples. These findings serve as an urgent call for proactive measures to reduce CVD events and related mortality in this high-risk population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".