Cost-effectiveness of lipid-lowering treatment according to lipid level.
Bibliographic record
Abstract
BACKGROUND: Recent studies suggest that the benefit of lipid-lowering treatment for the primary and secondary prevention of cardiovascular disease (CVD) extends to individuals with average cholesterol levels, to women and to the elderly. However, the proportion of the general population for which treatment is cost-effective has not been evaluated. OBJECTIVES AND METHODS: Using data provided by the Canadian Heart Health Survey, the level of CVD risk was estimated for a random sample of the total population. A cost-effectiveness ratio for simvastatin was then calculated for each individual in the sample. Lastly, the proportion of the total population for which lipid-lowering therapy would be cost-effective for primary and secondary prevention of CVD was estimated according to total cholesterol (TC) levels. RESULTS: Among the surveyed individuals who were 30 to 74 years of age, 2212 had CVD and 12,982 did not. Among those with a TC level higher than 6.2 mmol/L, the proportions of individuals for which lipid-lowering therapy was cost-effective (at a level of less than 50,000 dollars per year of life saved) were 85.6% of men and 28.7% of women for primary prevention, and 99.8% of men and 86.1% of women for secondary prevention. The estimated cost of one year of lipid-lowering treatment for all individuals in the population with a TC level higher than 6.2 mmol/L and for all individuals regardless of TC levels for whom treatment would be cost-effective was $1 billion and 3.9 billion dollars, respectively. CONCLUSIONS: Lipid-lowering treatment for CVD prevention is cost-effective for a high proportion of the population, even for primary prevention. As a result, the cost of population-wide treatment for only one year is high even among individuals with a TC level higher than 6.2 mmol/L. Such costs should be considered in health care policy decisions.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".