Risk factors for hypertension in Canada.
Bibliographic record
Abstract
BACKGROUND: Hypertension (or high blood pressure) affects almost one in four adults in Canada. Quantifying risk factors associated with hypertension may help to inform prevention efforts. DATA AND METHODS: Data from the first four cycles of the Canadian Health Measures Survey (including 13,407 respondents) were used to identify hypertension status by systolic and diastolic blood pressure levels and the use of antihypertensive medications. Logistic regression analysis was employed to estimate the association between six cardiovascular risk factors (individually and as a composite score) and hypertension. RESULTS: Engaging in less than 150 minutes per week of moderate-to-vigorous physical activity, eating fruits and vegetables fewer than five times per day, being overweight or obese, having diabetes, and having chronic kidney disease were all independently associated with an increased risk of hypertension. When these factors were combined into a risk score, there was a linear increase in the predicted risk of hypertension with each additional risk factor. The predicted prevalence of hypertension for those with all six risk factors was 55% in women and 44% in men aged 20 to 39 years, and 80% in women and 76% in men aged 70 to 79 years. Being overweight or obese, consuming fruits and vegetables less often, being inactive, and having diabetes contributed to the largest attributable fractions for hypertension in the Canadian population. DISCUSSION: Physical activity, diet, body mass index, the presence of diabetes, and the presence of chronic kidney disease were strong risk factors for hypertension. Many of these risk factors are modifiable and highlight targets for future prevention strategies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".