Research Department, United Network for Organ Sharing, Richmond, VA, USA.
Chief Medical Officer, United Network for Organ Sharing, Richmond, VA, USA.
Am J Transplant. 2018 Aug;18(8):1924-1935. doi: 10.1111/ajt.14922. Epub 2018 Jun 1.
The Organ Procurement and Transplantation Network monitors progress toward strategic goals such as increasing the number of transplants and improving waitlisted patient, living donor, and transplant recipient outcomes. However, a methodology for assessing system performance in providing equity in access to transplants was lacking. We present a novel approach for quantifying the degree of disparity in access to deceased donor kidney transplants among waitlisted patients and determine which factors are most associated with disparities. A Poisson rate regression model was built for each of 29 quarterly, period-prevalent cohorts (January 1, 2010-March 31, 2017; 5 years pre-kidney allocation system [KAS], 2 years post-KAS) of active kidney waiting list registrations. Inequity was quantified as the outlier-robust standard deviation (SD ) of predicted transplant rates (log scale) among registrations, after "discounting" for intentional, policy-induced disparities (eg, pediatric priority) by holding such factors constant. The overall SD declined by 40% after KAS implementation, suggesting substantially increased equity. Risk-adjusted, factor-specific disparities were measured with the SD after holding all other factors constant. Disparities associated with calculated panel-reactive antibodies decreased sharply. Donor service area was the factor most associated with access disparities post-KAS. This methodology will help the transplant community evaluate tradeoffs between equity and utility-centric goals when considering new policies and help monitor equity in access as policies change.
器官获取和移植网络监测着向战略目标的进展,如增加移植数量和改善候补患者、活体供者和移植受者的结果。然而,缺乏评估系统在提供移植机会公平性方面的绩效的方法。我们提出了一种量化候补患者获得已故供体肾脏移植机会差异程度的新方法,并确定了哪些因素与差异最相关。为每个活跃的肾脏候补登记 29 个季度性、时期性流行群组(2010 年 1 月 1 日至 2017 年 3 月 31 日;5 年预肾脏分配系统[KAS],2 年后 KAS)构建了泊松率回归模型。不平等程度通过在保持其他因素不变的情况下,对有意的、政策诱导的差异(如儿科优先权)进行“折扣”,量化为登记处之间预测移植率(对数尺度)的异常稳健标准差(SD)。KAS 实施后,总体 SD 下降了 40%,表明公平性大大提高。通过保持所有其他因素不变,衡量了具有风险调整和因素特异性的差异。与计算面板反应性抗体相关的差异急剧下降。KAS 后,捐赠服务区是与准入差异最相关的因素。这种方法将帮助移植界在考虑新政策时评估公平性和以效用为中心的目标之间的权衡,并有助于在政策变化时监测准入公平性。