Abstract 080: SEX AND GENDER ASPECTS IN HYPERTENSION PREVALENCE OF CANADIAN AND EUROPEAN POPULATIONS
Bibliographic record
Abstract
Introduction: Gendered-psycho-socio-cultural factors have been shown to play a significant role in disease manifestation, control and management of hypertension (HTN), and their relationship varies in males and females. We investigated the role of sex and gender in HTN prevalence and country-level differences in Canadian and European populations. Methods: Data from the Canadian Community Health Survey (CCHS, 2015-16, N=109,659, Females:56.6%) and the European Health Interview Survey (E-HIS, 2013-2015, N=316,333, females: 51.3%) were analyzed. Primary endpoint was defined as having a diagnosed HTN made by a health professional in the past 12 months. Relationship between gender variables and HTN prevalence and interaction with sex was assessed in a multivariable model. Federated analysis was conducted using the R package and DataShield which allows international data pooling by only exposing aggregated results. Results: The prevalence of HTN was greater in Canada compared with Europe (CCHS: 30.1% vs EHIS: 22.4%, P<0.001). Amongst European countries, Southern (SEU) and Central East (CEU) region had greater prevalence of HTN and more significant sex-differences (greater prevalence in females) compared to Northern (NEU) and Western (WEU) regions. In the multivariable model for assessing the role of gender variables in prevalence of HTN, female sex, older age, greater BMI, married or divorced/widowed status, and lower income were associated with higher risk of HTN, while greater household size, higher level of education, and living in European countries compared to Canada were associated with lower risk. There was a significant interaction between socioeconomic status (income, education) and sex in country stratified analysis. Within European countries, this was more evident in CEU, and SEU compared to NEU and WEU, where women with lower socioeconomic status had greater risk of HTN. There were significant country-level differences with being an immigrant and risk of having HTN. While living in NEU and SEU was associated with lower risk of having HTN, living in CEU was associated with a greater risk. Conclusion: The findings of the study demonstrate the importance of gender related factors and particularly the differences amongst various countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".