Population health impact of statin treatment in Canada.
Bibliographic record
Abstract
BACKGROUND: Statins are prescribed to treat dyslipidemia (abnormal amount of lipids such as cholesterol and/or fat in the blood) and reduce cardiovascular disease (CVD) risk. This study describes the CVD risk profile of Canadians aged 20 to 79, compares current treatment patterns with guideline recommendations, and investigates the population health impact of statin treatment. DATA AND METHODS: The baseline CVD risk of the Canadian population aged 20 to 79 was estimated by applying population-weighted risk factor data from the 2007 to 2011 Canadian Health Measures Survey (CHMS) to the Framingham Risk Score. Estimates of statin effectiveness from the literature were applied to baseline risk to assess the number of CVD events avoided owing to actual (CHMS-reported) and recommended (2012 Canadian Cardiovascular Society guidelines) statin treatment. RESULTS: An estimated 2.8 million Canadian adults (about 1 in 10) were treated with statin drugs. The mean 10-year CVD risk of those treated was 27%. Assuming optimal adherence, it was estimated that statin treatment avoided around 18,900 CVD events annually and yielded a number-needed-to-treat (average number of patients treated to prevent one additional CVD event) of 15 over 10 years. In comparison, 6.5 million Canadian adults (about 1 in 4) were recommended for treatment under the 2012 guidelines. The mean 10-year CVD risk of those recommended for treatment was 24%, which translates into a number-needed-to-treat of 17 over 10 years, or approximately 38,600 CVD events avoided annually. The largest gaps in treatment and potential CVD events avoided were among people at high and intermediate risk for CVD. INTERPRETATION: Canadians' CVD risk could be lessened with enhanced targeting of statin treatment to individuals at high and intermediate risk. Such a strategy would likely require additional investments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".