Prevalence, awareness and treatment of hypercholesterolaemia in 32 populations: results from the WHO MONICA Project
Bibliographic record
Abstract
BACKGROUND: Several studies have been conducted to estimate the population prevalence of hypertension, or its diagnosis and treatment. There is no multinationally comparable information on the prevalence of hypercholesterolaemia, or its diagnosis and treatment, since individual studies are often not directly comparable. METHODS: Data from the WHO MONICA Project's final risk factor surveys were used. Data were collected using standardized methods between 1989 and 1997 for the 35-64 year age range in 32 populations, in 19 countries on 3 continents. RESULTS: The prevalence of hypercholesterolaemia (total cholesterol > or = 6.5 mmol/l or taking lipid-lowering drugs) varied across populations from 3% to 53% in men, and from 4% to 40% in women. Awareness of hypercholesterolaemia varied from 1% to 33% in men, and from 0% to 31% in women. In most populations, over 50% of men and women on lipid-lowering drugs had a cholesterol level < 6.5 mmol/l. CONCLUSIONS: There is wide variation in the prevalence, awareness, and treatment of hypercholesterolaemia between populations. For the planning and implementation of primary prevention programmes and for the development of health care systems, monitoring of changes, both within and between populations, is essential. To obtain reliable information on these changes, well-standardized methods must be applied.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".