Assessing the relationship between sex, gender, and hypertension: A federated analysis of European and Canadian Public Health Surveys
Bibliographic record
Abstract
While gendered psycho-socio-cultural factors are recognized as major determinants of cardiovascular health, their contribution to our understanding of their effect on hypertension (HTN) in each country is poorly understood. Therefore, we investigated the role of these factors in HTN prevalence, focusing on sex- and gender-specific differences across countries. Data from the Canadian Community Health Survey (2015-2016, N = 109,659, women: 56.6%) and the European Health Interview Survey (2013-2015, N = 316,333, women: 51.3%) were analyzed. Primary endpoint was defined as HTN prevalence within 1-year. Relationship and interaction between sex, gender, and country with HTN prevalence were assessed using multivariate models. Federated analysis was conducted using DataShield. Prevalence of HTN was higher in Canada compared to Europe (30.1% vs 22.4%, P < .001). Amongst European countries, living in the Central-East region was associated with a greater risk of developing HTN. Women in the southern and central-east regions had higher prevalence of HTN. There was a significant interaction between socioeconomic status and sex in country-stratified analysis. This was more evident in central-east and southern countries compared to northern, western nations and Canada, where women with lower socioeconomic status, income, and education had a greater risk of developing HTN. Similar trends were observed regardless of country in women who were divorced or widowed. While immigrants were at higher risk of HTN, those in northern and southern Europe were at lower risk compared to central-east region. Sex- and gender-related factors and country should be considered in the prevention and control of HTN.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".