Elevated total cholesterol: its prevalence and population attributable fraction for mortality from coronary heart disease and ischaemic stroke in the Asia-Pacific region
Bibliographic record
Abstract
BACKGROUND: About half of the world's cases of cardiovascular disease occur in the Asia-Pacific region. The contribution of serum total cholesterol (TC) to this burden is poorly quantified. DESIGN: The most recent nationally representative data on TC distributions for countries in the region were sought. Individual participant data from 380,483 adults in the Asia Pacific Cohort Studies Collaboration were used to estimate associations between TC and cardiovascular disease. METHODS: High TC was defined as > or =6.2 mmol/l, and nonoptimal TC as > or =3.8 mmol/l. Hazard ratios for fatal coronary heart disease (CHD) and ischaemic stroke (IS) were found from Cox models. Sex-specific population attributable fractions for high TC and nonoptimal TC were estimated for each country. The former used conventional methods, based on single measures of TC and a fixed dichotomy of risk strata; the latter took account of the continuous positive association between TC and both CHD and IS and regression dilution. RESULTS: Data were available from 16 countries. Where reported, the prevalence of high TC ranged from 4 to 27%. The fraction of fatal CHD and IS attributable to high TC ranged from 0 to 14% and 0 to 15%, respectively. Although leaving the relative ranking of countries much the same, the fractions estimated for nonoptimal TC were typically at least twice as big, ranging from 0 to 47% and 0 to 35%, respectively. CONCLUSION: Conventional methods for estimating disease burden severely underestimate the effect of TC. Cholesterol-lowering strategies could have a tremendous effect in reducing cardiovascular deaths in this populous region.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".