More than 16,000 Transplant Recipients and Previous Living Donors at Risk for Poor Access to Reproductive Health Care
Bibliographic record
Abstract
Background: It is imperative that kidney transplant recipients (KTR) and living kidney donors (LKD) have access to reproductive health care. Since the overturning of Roe v Wade in 2022, 14 states have banned abortion in all or almost all circumstances and 7 states have significantly restricted access (between 6-18 weeks). It is unknown how many KTR and LKD are affected by these policies. Methods: We characterized the demographics of female KTR and LKD 18-45 years old in the United States as of May 1, 2024 from the OPTN dataset. Because policies are frequently changing, patients were stratified into 3 categories based on their home state policy for abortion as of May 1, 2024. P-values are calculated from Wilcoxon rank sum tests for continuous variables and Fisher's exact tests for categorical variables. Results: Overall 9,001/21,673 KTR (41%) and 7,476/18,988 LKD (39%) live in states with either a total or partial restriction on abortion. KTR who live in areas with less access to reproductive care were also more likely to have additional risk factors for pregnancy complication including Black race, high BMI, and diabetes as a cause of kidney failure and were less likely to have received a living donor transplant (Table 1). Conclusion: A significant proportion of KTR and LKD live in areas with decreased access to reproductive health care. Medical comorbidities and demographic risk factors can exacerbate the pre-existing pregnancy risk. These findings quantify the magnitude of the health challenge created by recent changes in state policies and may be useful in planning for alternative strategies and advocacy efforts to ensure KTR and past LKD receive access to essential reproductive health care. Demographics of Female KTR by abortion access in home state - Abortion Banned (1)n=5,333 (24.6%) Limited Abortion Access (2)n=3,688 (17%) Legal Access to Abortionn=12,652 (58.4%) p-value Recipient Current Age Mean (SD) 36.4 (30.5 - 41.3) 36.8 (30.7 - 41.4) 36.6 (30.9 - 41.3) 0.31 Recipient Race n (%) Black 1,683 (31.6%)White 1,980 (37.1%)Other 1,670 (31.3%) Black 1,362 (36.9%)White 1,280 (34.7%)Other 1,046 (28.4%) Black 2,656 (21.0%)White 4,985 (39.4%)Other 5,011 (39.6%) < 0.0001 Recipient BMIMean (SD) 26.4 (±7.1) 25.9 (±6.5) 25.3 (±6.6) < 0.0001 InsuranceN (%) Private 1,843 (34.6%)Public 3,456 (64.8%) Private 1,262 (34.2%)Public 2,411 (65.4%) Private 5,131 (40.6%)Public 7,462 (59.0%) < 0.0001 Diabetes as cause of ESRDN (%) 531 (10.0%) 290 (7.9%) 949 (7.5%) <0.0001 Transplant Vintage (years) Mean (SD) 8.1 (±5.3) 7.8 (±5.4) 8.5 (±5.5) < 0.0001 Transplant from Living Kidney DonorN (%) 1,885 (35.3%) 1,240 (33.6%) 5,625 (44.5%) < 0.0001 1. States where abortion is illegal in all or most circumstances AL, AR, ID, IN, KY, LA, MS, MO, ND, OK, SD, TN, TX, WV 2. States with restrictions on abortion (6-18 weeks) AZ, FL, GA, NE, NC, SC, UT
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".