Quantifying spillover benefits in value assessment: a case study of increased graft survival on the US kidney transplant waitlist. (1st January 2021)

• Record Type: Journal Article
• Authors: Dennen, Syvart; Díaz Espinosa, Oliver; Birch, Kelly; Cai, Jennifer; Sung, Jennifer C.; Machado, Paula G. P.; Shafrin, Jason
• Is Part Of: Journal of medical economics. Volume 24:Number 1(2021)
• Page Start: 918
• Page End: 928
• Publication Date: 2021-01-01
• Subjects: Kidney transplant waitlist -- cost-effectiveness analysis -- societal perspective -- graft failure -- immunosuppression; I18 -- I1 -- I -- I11 -- I31 -- I3 -- C63 -- C6 -- C; Medical care -- Cost control -- Periodicals; Medical economics -- Periodicals; 362.10941
• DOI: 10.1080/13696998.2021.1957287
• Languages: English
• ISSNs: 1369-6998

Abstract

Abstract: Aim: To quantify the wider impacts of increased graft survival on the size of the kidney transplant waitlist and health and economic outcomes. Materials and methods: The analysis employed known steady-state solutions to a double-queueing system as well as simulations of this system. Baseline input parameters were sourced from the Organ Procurement and Transplant Network and the United States Renal Data System. Three increased graft survival scenarios were modeled: decreases in repeat transplant candidates joining the waitlist of 25%, 50%, and 100%. Results: Under the three scenarios, we estimated that the US waitlist size would decrease from 91, 822 to 85, 461 (6.9% decrease), 80, 073 (12.8% decrease), and 69, 340 (24.4% decrease), respectively. Patient outcomes improved, with lifetime quality-adjusted life years (QALYs) for a 1-year cohort of transplant recipients increasing by 10, 010, 16, 888, and 43, 345 over the three scenarios. Discounted lifetime costs for the cohort in the new steady state were lower by $1.6 billion, $2.3 billion, and $9.0 billion for each scenario, respectively. Spillover impacts (i.e. benefits that accrued beyond the patients who directly experienced increased graft survival) accounted for 58–65% of the QALY gains and ranged from cost increases of 3.3% to decreases of 5.5%. Limitations: The model is a simplification of reality and does not account for the full degree of patient heterogeneity occurring in the real world. Health economic outcomes are extrapolated based on the assumption that the median patient is representative of the overall population. Conclusions: Increasing graft survival reduces demand from repeat transplants candidates, allowing additional candidates to receive transplants. These spillover impacts decrease waitlist size and shorten wait times, leading to improvements in graft and patient survival as well as quality-of-life. Cost-effectiveness analyses of treatments that increase kidney graft survival should incorporate spillover benefits that accrue beyond the direct recipient of an intervention.