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If interaction was not detected, pooled hazard ratios were calculated. For all Cox models, both patient- and zip-code level variables were considered as potential confounders. We evaluated confounding by comparing meaningful changes in point estimates from a full model containing all a priori covariates to all other potential models (18,19), and by examining directed acyclic graphs (20). We used the robust sandwich variance estimator using zip code as the cluster variable to examine neighborhood poverty and individual level covariates simultaneously while accounting for potential correlation of patients within neighborhoods (21). In multivariable analyses, we used the Markov Chain Monte Carlo method for multiple imputation http://www.selleck.cn/products/pd-1-pd-l1-inhibitor-2.html methods for missing covariate information. We included living donor transplant recipients in our main analyses and censored patients at the time of organ receipt. However, because death and living donor transplantation may preclude a patient from waitlisting or transplantation, we explored the effect of living donor transplant in several ways. We used competing risk Cox proportional hazards models to calculate cause-specific hazard ratios for living donor transplant and death (22). We also examined the effect of excluding all living donor transplant recipients from analyses and considering both living and deceased donor transplants as events of interest. To assess whether inactive waitlisting influenced access to deceased donor transplantation, we excluded patients who were listed inactive at any given time (n = 916). Finally, http://www.selleckchem.com/products/gsk1120212-jtp-74057.html we examined the impact of excluding patients with ��other�� health insurance (n = 1 199) since this group is a potentially heterogenous group of insured patients. Two-tailed p-values http://www.selleckchem.com/products/MDV3100.html mean age was 13.4 �� 6.2 years, 41.7% were white, 27.6% were Hispanic, 30.8% were black, 44.7% had public insurance and 28.0% lived in impoverished communities (Table 1). Racial differences in baseline clinical and demographic factors were evident. Minorities were significantly older compared to whites and less likely to receive ESAs prior to dialysis. Compared to whites, Hispanics and blacks were more likely to have public or no health insurance. Similarly, minorities had a fourfold higher likelihood of living in poor neighborhoods (p