The Association of Socioeconomic Status on Kidney Transplant Access and Outcomes: Cohort Studies of England and Northern Ireland
Bibliographic record
Abstract
BACKGROUND: While socioeconomic status (SES) is an established determinant of kidney transplant access and outcomes, less is known about how these disparities vary within universal healthcare systems. This study hypothesized that, despite shared healthcare and organ allocation systems, regional differences would be observed in the magnitude and pattern of the association between SES and transplant access and outcomes between England and Northern Ireland (NI). METHODS: We conducted a retrospective cohort study using national transplant registry data from England (n = 42 220) and NI (n = 1615) from 2000 to 2020. SES was measured using national deprivation indices. Outcomes included transplant incidence, preemptive and living donor transplantation, graft survival, and patient survival. Statistical analyses included Poisson regression, Cox proportional hazards models, and concentration indices to assess equity. RESULTS: In England, lower SES was significantly associated with reduced transplant access (incidence rate ratio for most versus least deprived quintile, 0.71; 95% confidence interval [CI], 0.69-0.73), lower rates of preemptive and living donor transplantation, and poorer graft (hazard ratio, 1.41; 95% CI, 1.32-1.50) and patient survival (hazard ratio, 1.49; 95% CI, 1.39-1.59). These disparities persisted across ethnic groups. In contrast, NI showed no significant SES-related differences in transplant access, despite a more deprived population overall. CONCLUSIONS: SES remains strongly associated with transplant access in England but not in NI, suggesting that regional models of healthcare delivery may mitigate or exacerbate inequities.These findings suggest a role of system design in promoting equity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".