Access to kidney transplantation in Mexico, 2007–2019: a call to end disparities in transplant care
Bibliographic record
Abstract
BACKGROUND: Access to kidney transplantation is limited to more than half of the Mexican population. A fragmented health system, gender, and sociocultural factors are barriers to transplant care. We analyzed kidney transplantation in Mexico and describe how public policies and sociocultural factors result in these inequities. METHODS: Kidney transplant data between 2007 to 2019 were obtained from the National Transplant Center database. Transplant rates and time spent on the waiting list, by age, gender, health system, and insurance status, were estimated. RESULTS: During the study period 34,931 transplants were performed. Recipients median age was 29 (IQR 22-42) years, 62.4% were males, and 73.9% were insured. 72.7% transplants were from living-donors. Annual transplant rates increased from 18.9 per million population (pmp) to 23.3 pmp. However, the transplant rate among the uninsured population remained low, at 9.3 transplants pmp. In 2019, 15,890 patients were in the waiting list; 60.6% were males and 88% were insured. Waiting time to transplant was 1.55 (IQR 0.56-3.14) years and it was shorter for patients listed in the Ministry of Health and private facilities, where wait lists are smaller, and for males. Deceased-organ donation rates increased modestly from 2.5 pmp to 3.9 pmp. CONCLUSIONS: In conclusion, access to kidney transplantation in Mexico is unequal and restricted to patients with medical insurance. An inefficient organ procurement program results in low rates of deceased-donor kidneys. The implementation of a comprehensive kidney care program, recognizing kidney transplantation as the therapy of choice for renal failure, offers an opportunity to correct these inequalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".