Lupus nephritis (LN) develops in approximately half of systemic lupus erythematosus patients (1–3) and in 2010 accounted for 1.8% of prevalent end-stage renal disease (ESRD) cases (4). A greater than 40% reduction in 10-year renal survival has been reported for African-American (AF) compared with non-AF LN patients (5), which is felt to be the result of both biological and socioeconomic factors (5–12). Racial disparities have been reported after renal transplantation, with AF patients demonstrating decreased graft survival compared with other races in studies not limited to patients with ESRD from LN (13, 14). Decreased graft survival has been associated with lower income (15), less education (12, 16, 17), unemployment (18), residence in a poor rather than wealthy community (19), and type/amount of insurance coverage (14, 20), suggesting that socioeconomic variables may contribute to differences in transplant outcomes. The association between AF race and worse outcomes has been observed after kidney transplantation for ESRD secondary to LN (21–24). Contreras et al. (24) compared allograft and patient survival after transplantation for ESRD from LN in AF versus patients of different race/ethnicity and reported loss of statistical significance for differences in these endpoints with multivariate adjustment for known risk factors for allograft loss. Studies evaluating racial differences in outcomes for LN patients after kidney transplantation generally have not addressed the impact of socioeconomic factors, and an analysis of the effect of income on racial/ethnic disparities in transplant outcomes has not been reported.
In this study, we combined the United States Renal Data System (USRDS) database with U.S. Census data to investigate the impact of income on racial disparities in kidney transplant outcomes among both LN and non-LN cohorts.
We identified 4214 patients who had LN as the primary cause of ESRD in a retrospective cohort of 150,118 patients first transplanted from January 1, 1995 to September 29, 2006 followed until September 29, 2006. Demographic and risk characteristics associated with LN and non-LN cohorts on unadjusted analysis are shown in Table 1.
Kaplan–Meier curves as shown in Figures 1A and 2A demonstrate significant lower graft and patient survival among AF recipients with LN (vs. non-AF). At 5 years, graft survival (including death with functioning graft) for AF was 63.0% (95% confidence interval [CI], 60.1–65.8) versus 78.3% (95% CI, 76.4–80.0) in non-AF. Death-censored graft survival for AF was 69.3% (95% CI, 66.3–72.0) versus 84.4% (95% CI, 82.7–86.0) in non-AF. Therefore, death with functional graft accounted for 6.3% of graft survival for AF vs. 6.1% in non-AF at 5 years. Patient survival, at 5 years, was 85.0% (95% CI, 82.8–87.0) for AF versus 90.6% (95% CI, 89.2–91.8) for non-AF.
Unadjusted Cox regression model showed that AF recipients with LN had a 13% decreased risk of graft loss (adjusted hazard ratio [AHR], 0.87; 95% CI, 0.82–0.93) (Fig. 1B) and 20% decreased risk of death (AHR, 0.80; 95% CI, 0.73–0.89) for each increase in median household income (MHI) quintile (Fig. 2B). In comparison, time to graft loss and death among non-AF recipients with LN were not associated with MHI quintile levels (Fig. 1C and Fig. 2C).
In adjusted Cox regression analyses to include Hispanic ethnicity as a covariate, AF recipients with LN (vs. non-AF) had an increased risk of graft loss (AHR, 1.39) (Table 2; see Figure S1, SDC,https://links.lww.com/TP/A812) and death (AHR, 1.33) (Table 3; see Figure S2, SDC,https://links.lww.com/TP/A812). MHI quintiles were significantly associated with both outcomes. Interaction terms between MHI quintile levels and race are presented in these tables. AF recipients within the two lowest MHI quintile levels (1 and 2) had a significantly increased risk of graft loss and death compared with non-AF recipients within the lowest MHI quintile (index group).
There was a trend toward lower AHR for both outcomes among AF recipients with LN and higher MHI quintile levels (Levels 3–5), but this was not statistically significant. In comparison, non-AF recipients with LN had no significant association between MHI quintile levels and transplant outcomes. The AHR of AF recipients in the top fifth MHI quintile approximated the risk of non-AF recipients in the bottom fifth quintile for both graft loss (AHR, 1.065 vs. 1.0; P=0.762) and death (AHR, 0.887 vs. 1.0; P=0.727). Furthermore, Hispanic ethnicity was not a significant predictor of transplant outcomes in Cox regression analysis to include AF and MHI as covariates (Tables 2 and 3). In a separate adjusted Cox regression analysis of Hispanic recipients that excluded AF, there was no consistent trend of AHR for graft loss and death based on levels of MHI quintiles (data not shown). Employment status and educational level were not associated with either graft loss or death.
Similar adjusted Cox regressions were performed in patients with non-LN causes of ESRD. In this non-LN cohort, AF had an AHR of 1.32 (95% CI, 1.29–1.36) for graft loss and AHR of 1.02 (95% CI, 0.99–1.05) for death. Thus, the racial disparities (AF versus non-AF) for both graft loss and death outcomes among the LN cohort were greater than among the cohort without LN (AHR, 1.39 vs. 1.32 for graft loss; P<0.001 and AHR, 1.33 vs. 1.02 for death; P<0.001). Furthermore, unlike non-AF with LN, MHI quintiles were significantly associated with graft loss among non-AF recipients without LN, except for quintile level 2 (Table 4). The top two MHI quintile levels among non-AF without LN were significantly associated with lower risk for death.
Since January 1, 2000, Medicare had extended the coverage of immunosuppression medications from 3 years to lifelong for kidney transplant recipients ages more than 65 years or disabled. Thus, our analysis of income disparities was adjusted for this difference in medication coverage for those transplanted before versus after 2000 and demonstrated that income was still significantly associated with graft loss and death.
We found that AF recipients with LN had an increased risk of graft loss and death compared with non-AF in both unadjusted and adjusted analyses. Among AF recipients with LN, those in the lowest two income quintiles had significantly higher risk of graft loss and death in comparison with non-AF in the lowest quintile level. There was also a trend of lower risk of graft loss and death in AF recipients with LN among higher income levels (middle, upper middle, and top fifth quintiles); however, this association was not significant. Among non-AF recipients with LN, no association of income with graft loss or death was significant.
We demonstrated that the racial disparities (AF versus non-AF) for both graft loss and death outcomes among the LN cohort were significantly greater than among the cohort without LN. Similar to the LN cohort, AF recipients with non-LN also had significantly higher AHR for graft loss at lower quintile levels. Unlike the LN cohort, however, non-AF recipients due to other causes of ESRD had significant interactions with income for graft loss, with a trend toward lower risk of death as MHI quintile increased. Therefore, among the non-AF recipients without LN, there were significant interactions with MHI quintiles in effecting transplant outcomes, in contrast to the absence of interactions among non-AF recipients with LN.
To the extent that education and employment status were assessed in the USRDS, we did not find these variables to be significantly associated with allograft or death outcome in recipients with LN. It is possible that there was collinearity and thus excessive correlation between income and other overlapping variables such as education and employment status. On the contrary, that income was still significant when both education and employment status were included in the model implied a strong association.
The findings of our study are consistent with those of Contreras et al. (24) who also found that AF recipients of kidney transplants with LN had high allograft failure risk. The intriguing findings of this study are the association of low income with poorer transplant outcomes among AF recipients with LN but not among non-AF recipients with LN. Previous studies have often assumed such racial disparities could be accounted for by differences in income; however, few studies have used the MHI based on the ZIP code to account for these disparities.
There are several limitations of this study, which are inherent to using USRDS research as reported previously (25). We used ZIP code–based MHI as a surrogate for patient income due to the unavailability of individual patient or household income data. Our model attempts to account for some potential confounders such as education level and employment status. However, additional confounders may exist, such as varying levels of healthcare access (including proximity to healthcare, which may be disproportionately important for low-income patients who rely on public transportation) or healthcare quality between residents of higher versus lower MHI ZIP codes. Disparity in income levels may not fully account for poorer outcomes in AF patients. Variables such as medication adherence and psychosocial issues may be contributory. Medication adherence is an important contributor to graft and patient outcomes after transplantation (26). Our study did not specifically evaluate the effect of income disparity on nonadherence. However, one can speculate that people living in lower MHI ZIP codes have poorer access to quality healthcare and longer distances to travel for their healthcare needs.
In conclusion, we confirmed the finding of others that AF with LN had an increased risk of graft loss and mortality versus non-AF after kidney transplantation. We also found that disparity in income was a strong factor in graft loss and death among AF, but not non-AF, recipients with LN. Furthermore, the racial disparity for transplant outcomes in patient with LN was greater than those found in the ESRD population at large. A recent study showed that healthcare providers seem to spend less time with AF patients with instructions than with non-AF (27). Evidence is accumulating that healthcare providers can partially overcome the adverse effects of income disparity by paying more attention to their interaction with AF patients to improve their adherence and understanding of medications and to provide better follow-up care (28, 29). However, this hypothesis will need to be tested in a prospective trial.
MATERIALS AND METHODS
Patients and Sources
This study used the USRDS, which incorporates baseline and follow-up demographic and clinical data on all patients accessing the Medicare ESRD program in the United States. We used a cohort consisting of patients who underwent a first kidney transplantation between January 1, 1995 and July 1, 2006 followed up until 29 September 2006. We defined the LN population using the code (7100) for “lupus erythematosus, (SLE nephritis)” from the variable PDIS (primary disease causing ESRD) (30). Other variables assessed are listed in Table 1.
The primary outcomes were allograft loss (including death with functioning graft) and all-cause mortality. The secondary outcome was death-censored graft survival.
We merged databases from the USRDS and the U.S. Census to assess the relationship between income and renal allograft survival as described previously (31). As a surrogate measure of each recipient’s income, MHI based on the ZIP code in the 2000 U.S. Census (32) was merged to the ZIP code of each patient’s residence in the USRDS. We assigned MHI into five quintiles. Income data were available for 96.2% of patients with valid gender data (31, 32). Thus, missing value imputation was not performed. Analyses of interactions between each MHI quintile and race (AF or non-AF) were performed as shown in Tables 2 to 4.
Other Predictor Variables
Race was defined as either AF or non-AF, thus representing mutually exclusive groups. Data on Hispanic ethnicity are derived from the USRDS and are not a mutually exclusive group because Hispanic recipients can be either AF or non-AF race. Also derived from the USRDS are the “empcur” variable, representing employment status at the time of the Medicare Evidence Form 2728 filing and the “educ” variable representing patient highest education level at listing time.
Analyses were performed using Stata 12.0 (College Station, TX). Files were converted to Stata files using DBMS/Copy (Conceptual Software, Houston, TX). Unadjusted analyses were performed with chi-square test for categorical variables (Fisher’s exact test used for violations of Cochrane’s assumptions; i.e., <5 observations in a category) and Student’s t test for continuous variables (Mann–Whitney test used for nonnormally distributed variables); P<0.05 was considered statistically significant. Variables with P<0.10 in unadjusted analysis were entered into a multivariate analysis as covariates. Kaplan–Meier analyses were used to construct survival plots of event times. The log-rank test was used for significance. Patients were censored at loss to follow-up or the end of the study period. Cox regression was used to model factors associated with events controlling for covariates. We tested the proportional hazards assumption based on the graphical method of plotting estimates of -ln[-ln(survival probability)] versus ln(analysis time) and based on testing of Schoenfeld residuals (global test Prob>chi2 of 0.589 for graft loss and 0.544 for death). Thus, we found no evidence that our specification violated the proportional hazards assumption.
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