Addressing the inverse care law in cardiac services
Bibliographic record
Abstract
BACKGROUND: Wide variation in rates of angiography and revascularization exist that are not explained by the level of need for these services. The National Service Framework for Coronary Heart Disease has set out a number of standards with the aim of increasing the number of revascularizations and reducing inequalities in access to care. In this study we aimed to investigate inequity in angiography and revascularization rates between the four Primary Care Group (PCG) areas in Camden and Islington Health Authority and to put in place measures to address the problems identified. METHODS: Routinely available data were collected on all residents within Camden and Islington Health Authority undergoing angiography, angioplasty (PTCA) or coronary artery bypass grafting (CABG) between 1997 and 2001. These were used to calculate intervention rates per million population for each of the three procedures within each PCG. Semi-structured interviews were carried out with a sample of clinicians to explore their views on the provision of revascularization services within the Health Authority. RESULTS: Angiography and revascularization rates varied widely between the four PCGs. In 2001 there was a two-fold difference for angiography and CABG and a 3.5-fold difference for PTCA. The variations were not explained by a measure of the level of need for these services. The highest rates were in the area with the lowest standardized mortality ratio for coronary heart disease. The interviews identified a number of possible explanations for the variations that related to differences in clinical behaviour atthe consultant level and barriers in access to interventional cardiology and cardiac services. Following this research, a further interventional cardiologist appointment is planned, joint protocols of care are being established and barriers to access are being addressed. CONCLUSIONS: The new strategic health authorities should make it a priority to assess inequity in the provision of services within their areas, investigate the possible causes and support the primary care trusts to implement plans to address them.
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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 | 0.032 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 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".