A cross-national comparative study of blood pressure levels and hypertension prevalence in Canada and Hungary
Bibliographic record
Abstract
PURPOSE: Hungary has one of the highest cardiovascular (CV) mortality and stroke rates compared to other countries in Europe and North America. Data from two recent blood pressure (BP) screening projects in Hungary and Canada provided us with the opportunity to compare potential differences in the prevalence of hypertension between these countries. METHODS: From the Ontario Blood Pressure Survey, 880 white Canadians between 20 and 62 years old with white-collar occupation were selected and compared with a total of 1000 Hungarian bank employees in the same age range. Identical methods were employed for CV risk factor screening and BP measurements using the BpTRU instrument. Hypertension was defined by elevated BP measurement (SBP ≥140 mmHg and/or DBP ≥90 mmHg) or current intake of antihypertensive medication. RESULTS: Canadian participants were on average 10 years older with a higher rate of obesity, diabetes and high cholesterol. Smoking was more prevalent among Hungarians (29.4 vs. 22.5%, P < 0.001). Despite being younger, Hungarians exhibited significantly higher SBP (121.3 ± 4.3 vs. 111.6 ± 14.1, P < 0.001) and DBP (78.5 ± 10.5 vs. 70.8 ± 9.5, P < 0.001), which remained significant after adjustment for age and use of antihypertensive medication as well as sex and CV risk factors. Age-adjusted prevalence of hypertension was significantly higher and poorly controlled among Hungarians (P < 0.001). CONCLUSION: The increased prevalence of hypertension among young and middle-aged Hungarians compared with Canadians could represent an essential contributor to the high CV mortality and stroke rates in Hungary. BP awareness, treatment and control require improved medical attention and should be addressed early among young Hungarians.
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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 | 0.000 | 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".