Cardiovascular disease, mortality, and their associations with modifiable risk factors in a multi-national South Asia cohort: a PURE substudy
Bibliographic record
Abstract
AIM: To examine the incidence of cardiovascular disease (CVD), of death, and the comparative effects of 12 common modifiable risk factors for both outcomes in South Asia. METHODS AND RESULTS: Prospective study of 33 583 individuals 35-70 years of age from India, Bangladesh, or Pakistan. Mean follow-up period was 11 years. Age and sex adjusted incidence of a CVD event and mortality rates were calculated for the overall cohort, by urban or rural location, by sex, and by country. For each outcome, mutually adjusted population attributable fractions (PAFs) were calculated in 32 611 individuals without prior CVD to compare risks associated with four metabolic risk factors (hypertension, diabetes, abdominal obesity, high non-HDL cholesterol), four behavioural risk factors (tobacco use, alcohol use, diet quality, physical activity), education, household air pollution, strength, and depression. Hazard ratios were calculated using Cox regression models, and average PAFs were calculated for each risk factor or groups of risk factors. Cardiovascular disease was the most common cause of death (35.5%) in South Asia. Rural areas had a higher incidence of CVD (5.41 vs. 4.73 per 1000 person-years) and a higher mortality rate (10.27 vs. 6.56 per 1000 person-years) compared with urban areas. Males had a higher incidence of CVD (6.42 vs. 3.91 per 1000 person-years) and a higher mortality rate (10.66 vs. 6.85 per 1000 person-years) compared with females. Between countries, CVD incidence was highest in Bangladesh, while the mortality rate was highest in Pakistan. The modifiable risk factors studied contributed to approximately 64% of the PAF for CVD and 69% of the PAF for death. Largest PAFs for CVD were attributable to hypertension (13.1%), high non-HDL cholesterol (11.1%), diabetes (8.9%), low education (7.7%), abdominal obesity (6.9%), and household air pollution (6.1%). Largest PAFs for death were attributable to low education (18.9%), low strength (14.6%), poor diet (6.4%), diabetes (5.8%), tobacco use (5.8%), and hypertension (5.5%). CONCLUSION: In South Asia, both CVD and deaths are highest in rural areas and among men. Reducing CVD and premature mortality in the region will require investment in policies that target a broad range of health determinants.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".