Gender disparities in midlife hypertension: a review of the evidence on the Arab region
Bibliographic record
Abstract
While gender differences in hypertension and increased prevalence rates among women at midlife have been documented in multiple settings, the evidence on the Arab world has not been systematically examined. This review summarizes the evidence related to gender disparities in midlife hypertension in this region. We searched MEDLINE and Social Sciences Citation Index (SSCI) databases for studies, published between January 2000 and August 2015, on hypertension in the 22 countries of the Arab region. We abstracted information on the prevalence of hypertension among women and men, in general populations during midlife. Nineteen studies provided data on the prevalence of hypertension by gender and age in the Arab world. Higher rates of hypertension were found among Arab women at midlife in most countries. In studies that included subjects younger than 35 years old, a decrease in sex ratios (M/F) at midlife was observed in all countries except Palestine. Higher female prevalence rates are observed in the 4 th decade of life in most countries of the region, almost two decades earlier than in other parts of the world. This review highlights the need for more systematic examinations of hypertension in the Arab region, its risk factors, and the reasons for the particular patterns of gender differences that are observed. Such research would have considerable implications for prevention, treatment, and improved well-being.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".