J. T. Adler1,2, H. Yeh2,4, J. F. Markmann2,4, L. L. Nguyen1,3,4 1Brigham And Women’s Hospital,Center For Surgery And Public Health,Boston, MA, USA 2Massachusetts General Hospital,Transplant Surgery,Boston, MA, USA 3Brigham And Women’s Hospital,Vascular Surgery,Boston, MA, USA 4Harvard School Of Medicine,Brookline, MA, USA
Introduction: Prior work has demonstrated that the number of kidney transplants per population (KTP) is dependent on market factors such as competition and the number of transplant centers. Socioeconomic status (SES) also plays a role in KTP. We hypothesize that both of these important factors are spatially correlated in KTP, demonstrable by neighboring areas influencing each other more than remote areas.
Methods: The Herfindahl Hirschman Index (HHI), a standard measure of market competition, was calculated for each Health Service Area (HSA from Dartmouth Atlas) from 2000-2013 using zip code information of kidney transplant recipients from the Scientific Registry of Transplant Recipients. Global Moran’s I, a measure of spatial dependency, was used to test market competition for spatial autocorrelation. Areal interpolation was used to identify areas of concentrated market competition. Three standard spatial regression models were constructed to analyze the relationship between market competition and KTP adjusted for SES.
Results: Market competition exhibits moderate spatial autocorrelation (Global Moran’s I 0.27, P < 0.0001). It is unevenly distributed in the United States and mirrors the general population (Figure). The spatial lag model was the best fit by AIC criterion, suggesting a diffusion model among neighboring HSAs. Under the spatial lag model, market competition was strongly associated with an increase in KTP by 27.5 ± 1.7 (P < 0.0001). Markers of SES associated with an increase KTP included percent crowding (1.2 ± 0.2, P < 0.0001), percent with a college education or greater (0.39 ± 0.12, P = 0.0001), and percent unemployed (0.89 ± 0.21, P < 0.0001). Lower median property value (per ten thousand dollars) was associated with slightly decreased KTP (0.03 ± 0.0007, P < 0.0001).
Conclusions: Competition and SES effects diffuse among neighboring HSAs in KTP. This emphasizes a role understanding spatial autocorrelation in factors influencing KTP beyond market and SES factors. Efforts to improve access to kidney transplantation should consider such issues in planning transplant center location, organ allocation, and organ sharing.
