Mortality, Cardiovascular Disease, and Their Associations With Risk Factors in Southeast Asia
Bibliographic record
Abstract
Background: The drivers of cardiovascular disease (CVD) and all-cause mortality may differ around the world. Regional-level prospective data can help guide policies to reduce CVD and all-cause mortality. Objectives: This study examined the incidence of CVD and mortality in Malaysia and the Philippines and estimated the population-level risks attributable to common risk factors for each outcome. Methods: This prospective cohort study included 20,272 participants from Malaysia and the Philippines. The mean follow-up was 8.2 years. The incidences of CVD and mortality rates were calculated for the overall cohort and in key subgroups. For each outcome, population-attributable fractions (PAFs) were calculated to compare risks associated with 12 modifiable risk factors. Results: The mean age of the cohort was 51.8 years (59% women). Leading causes of mortality were CVD (37.9%) and cancer (12.4%). The incidence of CVD (per 1,000 person-years) was higher in the Philippines (11.0) than Malaysia (8.3), and CVD contributed to a higher proportion of deaths in the Philippines (58% vs 36%). By contrast, all-cause mortality rates were higher in Malaysia (14.1) than in the Philippines (10.9). Approximately 78% of the PAF for CVD and 68% of the PAF for all-cause mortality were attributable to 12 modifiable risk factors. For CVD, the largest PAF was from hypertension (24.2%), whereas for all-cause mortality, the largest PAF was from low education (18.4%). Conclusions: CVD and cancer account for one-half of adult mortality in Malaysia and the Philippines. Hypertension was the largest population driver of CVD, whereas low education was associated with the largest burden of overall mortality.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".