What are the short-term annual cost savings associated with kidney transplantation?
Bibliographic record
Abstract
BACKGROUND: Kidney transplantation (KT) is often reported in the literature as associated with cost savings. However, existing studies differ in their choice of comparator, follow-up period, and the study perspective. Also, there may be unobservable heterogeneity in health care costs in the patient population which may divide the population into groups with differences in cost distributions. This study estimates the cost savings associated with KT from a payer perspective and identifies and characterizes both high and low patient cost groups. METHOD: The current study was a population-based retrospective before-and-after study. The timespan involved at most three years before and after KT. The sample included end-stage kidney disease patients in Nova Scotia, a province in Canada, who had a single KT between January 1, 2011, and December 31, 2018. Each patient served as their control. The primary outcome measure was total annual health care costs. We estimated cost savings using unadjusted and adjusted models, stratifying the analyses by donor type. We quantified the uncertainty around the estimates using non-parametric and parametric bootstrapping. We also used finite mixture models to identify data-driven cost groups based on patients' pre-transplantation annual inpatient costs. RESULTS: The mean annual cost savings per patient associated with KT was $19,589 (95% CI: $14,013, $23,397). KT was associated with a 24-29% decrease in mean annual health care costs per patient compared with the annual costs before KT. We identified and characterized patients in three cost groups made of 2.9% in low-cost (LC), 51.8% in medium-cost (MC) and 45.3% in high-cost (HC). Cost group membership did not change after KT. Comparing costs in each group before and after KT, we found that KT was associated with 17% mean annual cost reductions for the LC group, 24% for the MC group and 26% for the HC group. The HC group included patients more likely to have a higher comorbidity burden (Charlson comorbidity index ≥ 3). CONCLUSIONS: KT was associated with reductions in annual health care costs in the short term, even after accounting for costs incurred during KT.
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".