Sex Difference and Socioeconomic Inequity in Chinese People With Hypertension: National Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Sex differences in blood pressure (BP) levels and hypertension are important and the role of socioeconomic status (SES) in sex differences of hypertension remains unclear. OBJECTIVE: We aimed to evaluate the impact of SES on sex differences of hypertension in a nationally representative survey study. METHODS: A total of 98,658 participants aged ≥18 years who have lived in their current residence for ≥6 months were recruited from 162 study sites across mainland China. Sex was self-reported. Individual-level SES included the highest level of education and annual household income. Area-level SES included economic development status, urban/rural residency, and north/south location. Outcomes included levels of systolic and diastolic BP, and hypertension. Linear and Cox regression models were used to examine the associations between sex (women vs. men) and BP characteristics stratified by individual or combined SES indicators. RESULTS: Systolic and diastolic BP levels and prevalence of hypertension were higher in men than women. This sex difference was found across categories of SES with widened sex disparities in participants having more favorable SES. Significant multiplicative interaction effects of SES on the association of sex with BP characteristics were found. Women with improving SES were associated with lower BP and hypertension prevalence compared with men. For combined SES, a 9% (prevalence ratio (PR)=0.91, 95% confidence interval (CI)=0.83, 0.98) and a 30% lower probability (PR=0.70, 95% CI=0.63, 0.78) of having hypertension were found in women with an overall intermediate SES and high SES, respectively compare with low SES while no significant reduction was found in men. CONCLUSIONS: There are significant sex differences in BP characteristics and SES has a potent impact on the disparities. Sex-specific public health policies to alleviate socioeconomic inequalities, especially in women are important for the prevention of hypertension.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".