Cardiovascular Risk and Statin Use in the United States
Bibliographic record
Abstract
PURPOSE: Statins reduce the risk of mortality and coronary artery disease in individuals at high cardiovascular risk. Using nationally representative data, we examined the relationships between statin use and cardiovascular risk, diagnosis of hyperlipidemia, and other risk factors. METHODS: We analyzed data from the 2010 Medical Expenditure Panel Survey, a nationally representative survey of the U.S. civilian noninstitutionalized population. The study sample had a total of 16,712 individuals aged 30 to 79 years. Those who reported filling at least 2 statin prescriptions were classified as statin users. We created multiple logistic regression models for statin use as the dependent variable, with cardiovascular risk factors and sociodemographic factors as independent variables. RESULTS: Overall, 58.2% (95% CI, 54.6%-61.7%) of individuals with coronary artery disease and 52.0% (95% CI, 49.4%-54.6%) of individuals with diabetes aged older than 40 years were statin users. After adjusting for cardiovascular risk factors and sociodemographic factors, the probability of being on a statin was significantly higher among individuals with both hyperlipidemia and coronary artery disease, at 0.44 (95% CI, 0.40-0.48), or hyperlipidemia only, at 0.32 (95% CI, 0.30-0.33), than among those with coronary artery disease only, at 0.11 (95% CI, 0.07-0.15). A similar pattern was seen in people with diabetes. CONCLUSIONS: In this nationally representative sample, many people at high risk for cardiovascular events, including those with coronary artery disease, diabetes, or both, were not receiving statins despite evidence that these agents reduce adverse events. This undertreatment appears to be related to placing too much emphasis on hyperlipidemia and not enough on cardiovascular risk. Recently released guidelines from the American College of Cardiology and the American Heart Association offer an opportunity to improve statin use by focusing on cardiovascular risk instead of lipid levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".