Would Achieving Healthy People 2010’s Targets Reduce Both Population Levels and Social Disparities in Heart Disease?
Bibliographic record
Abstract
BACKGROUND: The US Healthy People 2010 (HP2010) agenda set targets for major risk factors for coronary heart disease (CHD). However, the potential impact of achieving those risk factor reductions on both population levels and social disparities in CHD has not been quantified. METHODS AND RESULTS: Data on 10-year risk of CHD (from the First National Health and Nutrition Examination Epidemiological Follow-Up study 1971 to 1982), prevalence of major CHD risk factors (from the National Health and Nutrition Examination Survey 2003 to 2004), and HP2010 targets for CHD risk factors (reduction of smoking rate to 12%, hypertension to 14%, high cholesterol levels to 17%, diabetes to 2.5%, and obesity to 15%) were used to estimate effects of different scenarios on population levels and social disparities in CHD. Over a 10-year period, the largest relative reductions in population levels of CHD (20.0% in men; 23.9% in women) would be achieved if all social groups met the HP2010 targets. CHD disparities would be most reduced if the less educated (absolute disparities reduced by 66.1% in men; 56.3% in women) and the low income group (absolute disparities reduced by 93.7% in men; 94.3% in women) achieved the targets before the most advantaged. These reductions are larger than those expected if targets were achieved overall for the population but relative social group differences in risk factors remained, or under leveling-up approaches in which the least advantaged achieved the current levels of risk factors of the most advantaged. CONCLUSIONS: Interventions to reduce CHD risk factors to HP2010 targets that focus on all social groups would produce the best overall scenario for both population levels and disparities in CHD.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".