The contribution of primary prevention medication and dietary change in coronary mortality reduction in England between 2000 and 2007: a modelling study
Bibliographic record
Abstract
OBJECTIVE: To analyse the falls in coronary heart disease (CHD) mortality in England between 2000 and 2007 and quantify the relative contributions from preventive medications and population-wide changes in blood pressure (BP) and cholesterol levels, particularly by exploring socioeconomic inequalities. DESIGN: A modelling study. SETTING: Sources of data included controlled trials and meta-analyses, national surveys and official statistics. PARTICIPANTS: English population aged 25+ in 2000-2007. MAIN OUTCOME MEASURES: Number of deaths prevented or postponed (DPPs) in 2007 by socioeconomic status. We used the IMPACTSEC model which applies the relative risk reduction quantified in previous randomised controlled trials and meta-analyses to partition the mortality reduction among specific treatments and risk factor changes. RESULTS: Between 2000 and 2007, approximately 20 400 DPPs were attributable to reductions in BP and cholesterol in the English population. The substantial decline in BP was responsible for approximately 13 000 DPPs. Approximately 1800 DPPs came from medications and some 11 200 DPPs from population-wide changes. Reduction in population BP prevented almost twofold more deaths in the most deprived quintile compared with the most affluent. Reduction in cholesterol resulted in approximately 7400 DPPs; approximately 5300 DPPs were attributable to statin use and approximately 2100 DPPs to population-wide changes. Statins prevented almost 50% more deaths in the most affluent quintile compared with the most deprived. Conversely, population-wide changes in cholesterol prevented threefold more deaths in the most deprived quintile compared with the most affluent. CONCLUSIONS: Population-wide secular changes in systolic blood pressure (SBP) and cholesterol levels helped to substantially reduce CHD mortality and the associated socioeconomic disparities. Mortality reductions were, in absolute terms, greatest in the most deprived quintiles, mainly reflecting their bigger initial burden of disease. Statins for high-risk individuals also made an important contribution but maintained socioeconomic inequalities. Our results strengthen the case for greater emphasis on preventive approaches, particularly population-based policies to reduce SBP and cholesterol.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".