Measuring equity in cancer care quality: Critical review of national frameworks and lessons learnt
Bibliographic record
Abstract
Abstract Issue/problem Cancer inequalities persist across Europe, with inequitable access and delivery of care playing a key role. Quality indicators (QIs) are essential to assess inequalities experienced by migrants, ethnic minorities and socioeconomically disadvantaged groups, and to evaluate the impacts of policy. While national QI frameworks for cancer exist in many countries, their adequacy from a health equity perspective is unclear. Description of the problem We conducted a critical review to describe how cancer care quality is defined and measured, and assess how well current methods address migrant, ethnic and socioeconomic inequalities. We searched Pubmed and grey literature for national QI frameworks in G7 countries. We focused on those using the Donabedian model to assess performance in breast and colorectal cancer, as two common cancers for which highly effective treatments are available. We explored gaps in current practice and measures addressing these. Results Fourteen frameworks were found from five countries: the US (n = 5), UK (n = 5), Canada (n = 2), France (n = 1), and Germany (n = 1), in addition to one international framework. No eligible frameworks were found from Japan or Italy. We extracted 437 QIs spanning structural, process and outcome domains. Most were process measures derived from clinical guidelines. 39 thematic domains were identified. Equity-specific indicators were rare or absent. Methods addressing gaps included performance metrics specific to systemic therapy such as dose intensity and treatment toxicity; patient reported outcomes and experience surveys; and composite QIs such as opportunity scoring, textbook outcomes, and measures of pathway concordance. These consistently revealed inequity in treatment quality. Lessons To improve treatment equity, standardised equity-focused indicators should be embedded in national QI frameworks, with administrative data offering a promising approach for national and comparative assessment across Europe. Key messages • Inequalities in cancer care quality are well documented; addressing them will help reduce inequalities in cancer mortality and patient experience. • Equity-focused indicators should be developed to enable European countries to improve cancer outcomes for all.
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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 | 0.175 | 0.393 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.031 | 0.024 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".