Trends in 10-Year Predicted Risk of Cardiovascular Disease in the United States, 1976 to 2004
Bibliographic record
Abstract
BACKGROUND: There have been significant bidirectional changes in the prevalence of cardiovascular (CV) risk factors over time in the United States, making the net trend in risk for incident CV disease unknown. We assessed these trends by applying the Framingham Heart Study prediction model to national data. METHODS AND RESULTS: The National Health and Nutrition Examination Survey (NHANES) II (1976-1980), NHANES III (1988-1994), and NHANES 1999-2004 are cross-sectional representative samples of the noninstitutionalized population of the United States. We excluded people with a history of CV disease, pregnant women, participants with missing CV risk factors data, and individuals outside the Framingham age range of 30 to 74 years. The Framingham risk function was used to estimate the 10-year risk for incident symptomatic CV disease. We calculated the slope of change or rate of change per year between NHANES II and III, and between NHANES III and 1999-2004. The difference between slopes was calculated and compared to zero. The average age-adjusted 10-year CV risk between NHANES II and III decreased from 10.0% to 7.9% between NHANES II and III, with a statistically significant slope (P<0.001). However, the average age-adjusted CV risk decreased at a lesser magnitude between NHANES III and NHANES 1999-2004 from 7.9% to 7.4% (P<0.001). These slopes were significantly different (P<0.0001). In women and middle-aged participants, CV risk did not change between NHANES III and NHANES 1999-2004 (P=0.40). CONCLUSIONS: The estimated net risk for CV disease in the US population decreased from 1976-1980 to 1988-1994 but has changed minimally from 1988-1994 to 1999-2004, particularly in women and middle-aged people.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".