Temporal Trends in Cardiometabolic Control Among Canadian Adults: A Comparative Analysis of Hypertension and Diabetes Using the Canadian Health Measures Survey (CHMS) 2008–2019
Bibliographic record
Abstract
BACKGROUND: Hypertension and diabetes remain major contributors to cardiovascular morbidity and mortality in Canada. Monitoring their trends is critical to evaluating public health progress in prevention and disease management. OBJECTIVE: To descriptively summarize published Canadian Health Measures Survey (CHMS) estimates on the prevalence, awareness, treatment, and control of hypertension and diabetes among Canadian adults aged 20-79 years across the 2008-2019 survey cycles. METHODS: Descriptive analysis was conducted using aggregated CHMS combined-cycle data (2008-2011, 2012-2015, and 2016-2019). Weighted proportions and absolute counts published by Statistics Canada were organized and analyzed in Stata version 18. Data were stratified by sex and age group, and graphical visualization was applied to highlight temporal and demographic patterns. RESULTS: Hypertension prevalence remained stable over time, accompanied by modest improvements in treatment and control rates. Conversely, diabetes prevalence continued to rise, with limited gains in glycemic control. These patterns suggest differential progress in managing cardiometabolic conditions among Canadian adults. CONCLUSION: National strategies appear to have improved hypertension outcomes but less effectively addressed diabetes control. Strengthening preventive interventions, enhancing primary care integration, and improving access to chronic disease management resources remain essential for advancing cardiometabolic health in Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".