Understanding Antihypertensive Medication Use after Living Kidney Donation through Linked National Registry and Pharmacy Claims Data
Bibliographic record
Abstract
BACKGROUND: Use of antihypertensive medications (AHM) after living kidney donation is not well described. METHODS: We examined a database wherein national transplant registry data for 4,650 living kidney donors in 1987-2007 were linked to pharmacy claims from a US private health insurer (2000-2007 claims) to identify post-donation AHM fills. Cox regression with left- and right-censoring was used to estimate the frequencies and relative likelihood (adjusted hazards ratios, aHR) of post-donation AHM fills according to donor demographic traits. Medication possession ratio (MPRs), defined as (days of AHM dispensed)/(days observed), were also compared among donors and non-donor general beneficiaries. RESULTS: Overall, 17.8% of the sample filled at least one AHM by 5 years post-donation. As compared with White living donors, African-Americans had 37% higher relative likelihood of any AHM use after donation (aHR 1.37, p < 0.0007), including significantly higher likelihoods of filling diuretics (aHR 2.25, p < 0.0001), ACEi/ARBs (aHR 1.46, p < 0.01), calcium channel blockers (aHR 1.56, p = 0.03), and vasodilators/other agents (aHR 2.17, p = 0.03). MPRs for any AHM and subcategories were lower among donors compared with age- and sex-matched non-donors. However, AHM MPRs rose in donors with multiple hypertension diagnoses, and prescription fill exposure for all AHM classes except diuretics was similar among donors and general non-donors with ≥ 3 hypertension diagnoses. CONCLUSIONS: While AHM requirements are lower after kidney donation than among unscreened general persons, racial variation in AHM use occurs in privately insured donors. Demonstration of pharmaceutical care needs of insured donors supports the need for long-term follow-up and healthcare access for all donors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".