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. 2018 Jun;18(6):1415-1423.
doi: 10.1111/ajt.14622. Epub 2018 Jan 27.

Geographic disparity in kidney transplantation under KAS

Sheng Zhou  1 , Allan B Massie  1   2 , Xun Luo  1 , Jessica M Ruck  1 , Eric K H Chow  1 , Mary G Bowring  1 , Sunjae Bae  1   2 , Dorry L Segev  1   2 , Sommer E Gentry  1   3
Affiliations

Geographic disparity in kidney transplantation under KAS

Sheng Zhou et al. Am J Transplant. 2018 Jun.

Abstract

The Kidney Allocation System fundamentally altered kidney allocation, causing a substantial increase in regional and national sharing that we hypothesized might impact geographic disparities. We measured geographic disparity in deceased donor kidney transplant (DDKT) rate under KAS (6/1/2015-12/1/2016), and compared that with pre-KAS (6/1/2013-12/3/2014). We modeled DSA-level DDKT rates with multilevel Poisson regression, adjusting for allocation factors under KAS. Using the model we calculated a novel, improved metric of geographic disparity: the median incidence rate ratio (MIRR) of transplant rate, a measure of DSA-level variation that accounts for patient casemix and is robust to outlier values. Under KAS, MIRR was 1.75 1.811.86 for adults, meaning that similar candidates across different DSAs have a median 1.81-fold difference in DDKT rate. The impact of geography was greater than the impact of factors emphasized by KAS: having an EPTS score ≤20% was associated with a 1.40-fold increase (IRR = 1.35 1.401.45 , P < .01) and a three-year dialysis vintage was associated with a 1.57-fold increase (IRR = 1.56 1.571.59 , P < .001) in transplant rate. For pediatric candidates, MIRR was even more pronounced, at 1.66 1.922.27 . There was no change in geographic disparities with KAS (P = .3). Despite extensive changes to kidney allocation under KAS, geography remains a primary determinant of access to DDKT.

Keywords: Organ Procurement and Transplantation Network (OPTN); Scientific Registry for Transplant Recipients (SRTR); United Network for Organ Sharing (UNOS); kidney transplantation/nephrology; organ allocation; organ procurement and allocation; organ procurement organization; translational research/science.

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Conflict of interest statement

DISCLOSURE

The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

Figures

Figure 1
Figure 1. Distribution of IRRs and MIRR under KAS between June 1, 2015 and December 1, 2016
DDKT rates were estimated for each DSA using empirical Bayes estimation. For each pair of DSAs, IRRs were the ratio of the higher DDKT rate to the lower DDKT rate and were always larger than 1. MIRR was the median of all these IRRs.
Figure 2
Figure 2. Unadjusted (Observed) DDKT rate of DSAs Pre-KAS (6/1/2013–12/3/2014) and under KAS (6/1/2015–12/1/2016)
Each point represented an DSA. The diagonal line indicated identical DDKT rate pre-KAS and under KAS. Correlation between pre-KAS DDKT and under KAS DDKT was 0.70. DDKT rate was defined as number of DDKT per active person-years.
Figure 3
Figure 3. Unadjusted (Observed) DDKT Rate in the United States Pre-KAS (6/1/2013–12/3/2014) and under KAS (6/1/2015–12/1/2016), by DSA
We calculated unadjusted DDKT rates for each DSA pre-KAS (A) and under KAS (B) and displayed them using color-scaled maps. Darker colors represented higher DDKT rates, while lighter colors represented lower DDKT rates. DDKT rates were heterogeneous both pre-KAS and under KAS.
Figure 4
Figure 4. Unadjusted (Observed) DDKT Rate among Person-Years with CPRA ≥ 99% Pre-KAS (6/1/2013–12/3/2014) and under KAS (6/1/2015–12/1/2016), by DSA
Because there was evidence of increased sharing, which is likely due to increased imports for highly sensitized candidates (CPRA ≥99%), we examined unadjusted DDKT rates for each DSA pre-KAS (A) and under KAS (B) among this subgroup of candidates. For candidates with CPRA of 99 and 100%, the unadjusted DDKT rate increased for all DSAs from pre-KAS to under KAS.
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