The association between quality of primary care, deprivation and cardiovascular outcomes: a cross-sectional study using data from the UK Quality and Outcomes Framework
Bibliographic record
Abstract
BACKGROUND: The Quality and Outcomes Framework, a financial incentive scheme for general practitioners in the UK, seems to have improved the quality of primary care and reduced inequalities in primary care delivery. It remains unclear, however, whether higher-quality primary care improves health outcomes or reduces health inequalities. METHODS: We conducted a cross-sectional study examining the association between quality of cardiovascular care and coronary heart disease (CHD) outcomes in 1531 general practices in London. We calculated CHD quality achievement scores (ranging from 0 to 100) for each practice using the 2006-2007 data from the Quality and Outcomes Framework. We used weighted linear regression models to assess the practice-level association between the CHD quality score and CHD admissions and deaths. FINDINGS: Overall, practices with higher CHD quality achievement scores had better CHD outcomes. Each one point increase in the CHD quality achievement score was associated with 4.28 (95% CI 1.19 to 7.38; p=0.007) fewer admissions per 100,000 for practices serving highly deprived populations and 2.11 (95% CI 0.68 to 3.55; p=0.004) fewer admissions per 100 000 for practices serving populations of average deprivation. There was no association between the CHD quality achievement score and the CHD admissions for practices serving affluent populations (p=0.906). We observed a similar deprivation-dependent gradient between quality achievement and CHD deaths. INTERPRETATION: High-quality primary care is associated with improved health outcomes. This association is strongest in deprived areas, suggesting that high-quality primary care may play an important role in reducing health inequalities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".