Cardiovascular Risk Factors Among Low-Income Women: A Population-Based Study in China from 1991 to 2011
Bibliographic record
Abstract
BACKGROUND: Data on long-term trends in the prevalence and clustering of cardiovascular disease (CVD) risk factors among women in China are rare, especially among low-income women. The aim of this study was to investigate the secular trends in the prevalence of CVD risk factors among low-income women in northern China. MATERIALS AND METHODS: The prevalence and clustering of CVD risk factors, including hypertension, diabetes, obesity, current smoking status, and alcohol consumption, were assessed and compared in women aged 35-74 years in northern China in 1991 and 2011. RESULTS: The age-adjusted prevalence of cardiovascular risk factors among women was significantly higher in 2011 than in 1991, with increases of 31% (53.6% vs. 41.1%) for hypertension, 148% (20.9% vs. 8.4%) for obesity, 256% (11.7% vs. 3.3%) for diabetes, and 1634% (4.5% vs. 0.3%) for alcohol consumption. Over the 21-year period, there were significant differences in the prevalence of clustering of ≥1, ≥2, and 3 risk factors in all age groups. The greatest increase was observed among women aged 35-44 years, with a 7.3-fold increase in the prevalence of clustering of three risk factors. Simultaneously, the prevalence of clustering of ≥1 risk factors among women aged 35-44 years was 1.7-fold higher in 2011 than in 1991; the prevalence of clustering of ≥2 risk factors was raised by 5.5-fold among elderly women. CONCLUSIONS: Our findings suggest that it is crucial to emphasize the prevention and control of cardiovascular risk factors among young women in rural China to reduce the burden of CVDs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".