Cardiovascular Diseases and Risk‐Factor Burden in Urban and Rural Communities in High‐, Middle‐, and Low‐Income Regions of China: A Large Community‐Based Epidemiological Study
Bibliographic record
Abstract
Background Most cardiovascular diseases occur in low‐ and middle‐income regions of the world, but the socioeconomic distribution within China remains unclear. Our study aims to investigate whether the prevalence of cardiovascular diseases differs among high‐, middle‐, and low‐income regions of China and to explore the reasons for the disparities. Methods and Results We enrolled 46 285 individuals from 115 urban and rural communities in 12 provinces across China between 2005 and 2009. We recorded their medical histories of cardiovascular diseases and calculated the INTERHEART Risk Score for the assessment of cardiovascular risk‐factor burden, with higher scores indicating greater burden. The mean INTERHEART Risk Score was higher in high‐ and middle‐income regions than in low‐income regions (9.47, 9.48, and 8.58, respectively, P <0.0001). By contrast, the prevalence of total cardiovascular disease (stroke, ischemic heart disease, and other heart diseases that led to hospitalization) was lower in high‐ and middle‐income regions than in low‐income regions (7.46%, 7.42%, and 8.36%, respectively, P trend =0.0064). In high‐ and middle‐income regions, urban communities have higher INTERHEART Risk Score and higher prevalent rate than rural communities. In low‐income regions, however, the prevalence of total cardiovascular disease was similar between urban and rural areas despite the significantly higher INTERHEART Risk Score for urban settings. Conclusions We detected an inverse trend between risk‐factor burden and cardiovascular disease prevalence in urban and rural communities in high‐, middle‐, and low‐income regions of China. Such asymmetry may be attributed to the interregional differences in residents’ awareness, quality of healthcare, and availability and affordability of medical services.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".