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Employment status at transplant influences ethnic disparities in outcomes after deceased donor kidney transplantation

Abstract

Background

African American (AA) recipients of deceased-donor (DD) kidney transplants (KT) have shorter allograft survival than recipients of other ethnic groups. Reasons for this disparity encompass complex interactions between donors and recipients characteristics.

Methods

Outcomes from 3872 AA and 19,719 European American (EA) DDs who had one kidney transplanted in an AA recipient and one in an EA recipient were analyzed. Four donor/recipient pair groups (DRP) were studied, AA/AA, AA/EA, EA/AA, and EA/EA. Survival random forests and Cox proportional hazard models were fitted to rank and evaluate modifying effects of DRP on variables associated with allograft survival. These analyses sought to identify factors contributing to the observed disparities in transplant outcomes among AA and EA DDKT recipients.

Results

Transplant era, discharge serum creatinine, delayed graft function, and DRP were among the top predictors of allograft survival and mortality among DDKT recipients. Interaction effects between DRP with the kidney donor risk index and transplant era showed significant improvement in allograft survival over time in EA recipients. However, AA recipients appeared to have similar or poorer outcomes for DDKT performed after 2010 versus before 2001; allograft survival hazard ratios (95% CI) were 1.15 (0.74, 1.76) and 1.07 (0.8, 1.45) for AA/AA and EA/AA, compared to 0.62 (0.54, 0.71) and 0.5 (0.41, 0.62) for EA/EA and AA/EA DRP, respectively. Recipient mortality improved over time among all DRP, except unemployed AA/AAs. Relative to DDKT performed pre-2001, employed AA/AAs had HR = 0.37 (0.2, 0.69) versus 0.59 (0.31, 1.11) for unemployed AA/AA after 2010.

Conclusion

Relative to DDKT performed before 2001, similar or worse overall DCAS was observed among AA/AAs, while EA/EAs experienced considerable improvement regardless of employment status, KDRI, and EPTS. AA recipients of an AA DDKT, especially if unemployed, had worse allograft survival and mortality and did not appear to benefit from advances in care over the past 20 years.

Peer Review reports

Background

Deceased donor (DD) kidney transplantation (KT) from African American (AA) donors is associated with shorter allograft survival compared to DDKT from donors of other races/ethnicities. Donor African ancestry is included as a risk factor in the calculation of the Kidney Donor Risk Index (KDRI), a measure of DD organ quality used to generate the Kidney Donor Profile Index in the US kidney allocation system [1, 2]. Similarly, AA recipients of DDKT have poorer outcomes, regardless of the race/ethnicity of the donor [3, 4].

Causes of ethnic differences in DDKT outcomes remain unclear; they are likely multifactorial, with inherited, environmental, and socioeconomic factors contributing to donor- and recipient-level effects. Several reports highlighted the adverse impact of genetics, poverty, geography, and lack of education on access to kidney transplantation and outcomes after engraftment [3, 5,6,7,8,9,10]. We demonstrated more rapid allograft failure after kidney transplantation from DDs with apolipoprotein L1 gene (APOL1) high-risk genotypes. We suggested that using APOL1 genotyping instead of DD race might refine the KDRI by increasing the number of good quality kidneys for waitlisted recipients [11,12,13,14,15]. We and others reported genetic variants that affect AA DDKT outcomes either independently or through their interaction with APOL1 kidney-risk variants [16,17,18,19]. Beyond APOL1, several biological factors independently contribute to, or interact with non-biological factors leading to poorer outcomes among AA DDKT recipients. For example, given fewer AA donors and greater allelic variation at the HLA locus, potential AA recipients are disadvantaged in an allocation system that includes HLA matching. Despite recognizing these limitations and related changes, AA wait longer for kidney transplantation, an important modifiable risk factor for adverse outcomes [20,21,22]. The situation is compounded by complex interactions between donor and recipient characteristics impacting long-term outcomes.

Herein, we attempt to measure the effects of recipient- and donor-specific factors and their interaction on observed racial/ethnic disparities by studying partner kidneys from DDs that are, by definition, genetically identical and were transplanted into recipients of different races. Analyses were restricted to AA and European American (EA) donors and recipients for ease of comparison. This approach provides better control for donor-level confounding factors, including donor-level genetic risk and race/ethnicity, on recipient outcomes after transplantation [1, 23].

Methods

These analyses used donor and recipient data in the Scientific Registry of Transplant Recipients (SRTR) for kidneys procured and transplanted between October 1, 1987, and June 30, 2016. Analyses were restricted to AA or EA DDs who had both partnered kidneys transplanted, one to an AA recipient and the other to an EA recipient, yielding four groups of donor/recipient pairs (DRP): AA/AA, AA/EA, EA/AA, and EA/EA. This matched design better controlled for confounding by donor-related genetic, organ-specific, or socioeconomic factors and facilitated comparison of recipient-level factors contributing to observed racial disparities in outcomes. Donors or recipients < 18 years of age were excluded.

The primary outcome was death-censored time to kidney allograft failure, determined by the interval between transplantation dates and allograft loss. In patients with a functioning allograft, the final observation date was censored for death with function or at last follow-up before March 5th, 2016. A secondary outcome treating death as a competing risk (CR) was also considered. In this case, the final observation date was censored at death for individuals who died with a functioning allograft or at the most recent follow-up before March 5th, 2016, for living individuals with functioning allografts.

A split-half hypothesis-free analysis approach was applied where a random survival forest (RSF) model was fit in a randomly selected subset of the data representing 50% of the data to rank variables and their interaction with DRP based on their variable importance (VIMP) measure [24, 25]. RSF models implementing the conditional VIMP measures are robust to multicollinearity between predictors and are well-suited to detect interaction effects, which are of particular importance here [26, 27]. Analyses were repeated on the second half of the data and then on the complete data after observing strong reliability between the results obtained in the two subsets. Therefore, effect sizes and interaction effects with the DRP were estimated in the combined dataset using the top-ranked variables based on VIMP. This approach minimized the loss of statistical power caused by splitting the data into subsets [28]. Cox Proportional Hazard (CPH) models were fitted for death-censored allograft survival (DCAS) and the Fine and Gray model when death was considered a CR to allograft survival to obtain effect size estimates. The sandwich estimator was used to obtain a robust estimation of the covariance matrix associated with the parameter estimates to account for the correlation between allograft failure rate and time to failure of kidneys donated by a single individual to two recipients. Lin and Wei reported that this sandwich estimator was consistent and robust to several misspecifications of the Cox model [29]. Proportional hazard assumptions were checked by visual inspection of the log-log curve and assessing the Schoenfeld and martingale residuals [30]. Models were fitted separately following missing data imputation, which was performed within the RSF framework because RSF based-imputations have demonstrated high degree of robustness even in the presence of non-random missingness patterns [31, 32]. Ten imputed datasets were created, and the result obtained with these datasets were combined using established approaches [33,34,35]. Analyses were performed in SAS 9.4 and R 4.1. The RandomForestSCR package was used to fit Random Forest models for DCAS and the competing risk model [36].

Results

The cohort consisted of 47,182 kidney transplants from 3872 AA and 19,719 EA DDs. Tables 1 and 2 display distributions of demographic variables and clinical characteristics for donors and recipients, respectively. Data are presented as median (Q1, Q3) for continuous and N (%) for categorical variables. All comparisons in these Tables were statistically significant (p < 0.0001).

Table 1 Demographic data for 23,591 deceased-donors (3872 African Americans and 19,719 European Americans)
Table 2 Demographic and clinical characteristics of deceased-donor kidney transplant recipients

AA and EA DDs had comparable body mass index (BMI) and KDRI. Relative to EA DDs, AA DDs were more likely to be male (64.6% vs. 59.1%), younger (median age 35.0 vs. 40.9 years), cytomegalovirus (CMV) IgG antibody-positive (75.3% vs. 56.2%), and diabetic (5.2% vs. 4.3%). However, AA DDs were less likely to be smokers (60.3% vs. 68.9%) or expanded-criteria donors (12% vs. 14.4%) (Table 1).

Independent of the race/ethnicity of the DD, AA recipients received their transplant at a younger age (median 48.0 vs. 51.0 years), were more likely to have been on dialysis (61.3% vs. 50.9%), and had longer dialysis vintage (4.2 vs. 3.1 years). In addition, AA recipients were less likely to have received a prior transplant (11.2% vs. 15.2%) ordie with a functioning allograft (17.1% vs. 23.8%), but more likely to experience DGF (30.5% vs.21.7%) and had higher rates of acute rejection (1.8% vs. 1.2%) (Table 2). However, rates of immunosuppression medication use and the proportion of KT recipients needing induction therapy were comparable. Supplementary Table 1 show the demographics and clinical characteristics distribution by donor and recipient race.

Fig. 1 displays unadjusted death-censored allograft survival for KT recipients by DRP. Figure 1A shows the unadjusted allograft survival; differences in allograft survival outcomes are apparent between recipients based on race; the top two curves represent DCAS in EA recipients, and the bottom two curves display DCAS in AA recipients. Hazard ratios (HRs) (95% CI) for EA/EA, AA/EA, and EA/AA DRPs, relative to AA/AA pairs, were 0.56 (0.53, 0.60), 0.65 (0.59, 0.70), and 0.96 (0.91, 1.02), respectively. Figure 1B shows unadjusted recipient survival, with mortality treated as a competing risk to allograft failure. At first glance, this graph suggests slightly higher recipient survival rates among AA/AA and EA/AA, compared to AA/EA and EA/EA DRP. However, it is important to keep in mind that AA recipients are approximately 3 years younger than EA recipients. Causes of graft failure did not vary between AA and EA recipients, except for the rate of non-compliance to immunosuppression medication, which was 11.9% among AA recipients, compared to 9.2% for EA recipients.

Fig. 1
figure 1

Distribution of allograft survival by type of donor-recipient pair

The five-year DCAS rate improved among all four DRPs during the observation period (Supplementary Table 2). Five-year allograft survival rates in transplants performed after 2010 vs. before 2001 were (0.74 (0.52, 0.90) vs. 0.64 (0.60, 0.67) for AA/AA DRPs, 0.85 (0.76, 0.94) vs. 0.74 (0.71, 0.77) for AA/EA, 0.83 (0.81, 0.86) vs. 0.64 (0.63, 0.65) for EA/AA, and 0.89 (0.87, 0.92) vs. 0.78 (0.77, 0.79) for EA/EA transplantations. Results of the random forest models, which inform the interaction tests that were subsequently performed can be found in Supplementary Table 3.

CPH models showed statistically significant interaction effects between the DRP with the transplant era (0.02), KDRI (p = 0.0009), and EPTS (p < 0.0001) for DCAS.

The CR analysis helped clarify these results; it showed statistically significant interactions between the DRP and KDRI (p < 0.001) for allograft survival, and between the DRP with the KDRI (p < 0.0001), EPTS (p = 0.009), employment status (p < 0.0001) and transplant era (p < 0.0001) with kidney recipient mortality. Table 3 shows HRs for overall DCAS according to employment status and assuming no change in KDRI and EPTS. With employment EA/EA DRPs saw consistent improvement over time; for transplantations performed after 2010, HRs ranged from 0.42 (0.37, 0.47) to 0.46 (0.41, 0.51) for employed recipients and from 0.52 (0.48, 0.58) to 0.57 (0.52, 063) for unemployed recipients. Similar improvements were also observed with AA/EA pairs. However, for EA/AA DRPs, significant improvement in the overall DCAS was observed only post-2010 DDKTs, and the overall improvement was significantly smaller; HRs were 0.78 (0.66, 0.92) for EA/AA DRPs, compared to 0.42 (0.38, 0.47) for EA/EA’s.

Table 3 Hazard ratio and 95% confidence interval (HR (95% CI)) for death-censored kidney allograft failure by DRP and transplant era, depending on employment status, and change in KDRI and EPTS score

Table 4 shows HRs for the effect of DRP, KDRI, EPTS, and transplant era and employment status on recipient mortality with allograft failure as a CR. For transplantations performed before 2001 and assuming no change in KDRI and EPTS over time, reductions in mortality were observed among all four DRPs for employed DDKT. HRs for the post 2010 transplant era were 0.24 (0.13, 0.43), 0.27 (0.17, 0.45), 0.20 (0.14, 0.28), 0.24 (0.19, 0.32) for AA/AA, AA/EA, EA/AA and AA/AA DRPs, respectively. In contrast, HRs for mortality were higher among unemployed recipients; 0.50 (0.29, 0.87), 0.55 (0.35, 0.87), 0.32 (0.24, 0.42), and 0.49 (0.43, 0.57) among these 4 DRPs, assuming no change in KDRI and EPTS. Figure 2 shows the disparity in recipient mortality according to employment status and DRP.

Table 4 Hazard ratio and 95% confidence interval (HR (95% CI)) for mortality as a competing risk to allograft failure by DRP and transplant era, depending on employment status, and change in KDRI and EPTS score
Fig. 2
figure 2

Effect of employment on mortality by type of donor-recipient pair

Discussion

Donor characteristics contribute to racial disparities in outcomes following DDKT [2, 23, 37]. The present study evaluated recipient factors potentially affecting ethnic disparities in DDKT outcomes using a unique donor-matched design that controlled for genetic differences in transplanted kidneys, which allowed us to limit the impact of donor characteristics on DDKT outcomes, including many donor factors not available in the OPTN registry.

The analysis included 47,182 total kidney transplantations, 3872 involving AA DDs. As such, it is the most extensive analysis of its kind. Transplants resulting from the four possible DRPs had different DCAS, with EA recipients having better overall allograft survival than AA, independent from DD race/ethnicity. Analyses suggest that multiple factors contribute to kidney allograft outcomes. Some of the reported associations were described previously, including the well-known effects of DGF, serum creatinine at hospital discharge, recipient age, KDRI, EPTS, immunosuppressant medication, transplant era, donor age, etc. [7, 38, 39] However, these effects are not modified by the DRP.

Employment status, KDRI, and EPTS interacted with DRP to affect DDKT outcomes. Unemployed recipients had worse DDKT allograft survival and mortality. Employment status was obtained before kidney transplantation. Recipients who reported working a full-time or a part-time job was considered employed; all others were considered unemployed, independently of the reason for not working. The HR estimates among unemployed recipients were almost twice those observed among employed recipients for mortality, although there was a minor overlap between confidence intervals in some cases.

Employment status at transplantation was the only socioeconomic variable that showed significant interaction effects with the DRP. The absence of independent effects of educational attainment and insurance status probably reflects the careful screening process of potential recipients by transplant programs. In contrast, employment status is rarely invoked as a reason to preclude active status of wait-listed transplant candidates in the US, despite its potential adverse effect on the ability to afford medications or access health insurance, especially after expiration of the 36-month post-transplant coverage provided by the Center for Medicare and Medicaid Services. The newly passed Immuno Bill indefinitely extends Medicare coverage of immunosuppressive drugs for KT recipients and may help reduce disparities in long-term allograft survival. However, employment status may be a broader measure of social determinants of health with a clear association between unemployment, job loss, and retirement with poor outcomes.

In contrast, employment contributes to better physical health [40,41,42]. Unemployed individuals, independent of race/ethnicity, more often report feelings of depression and anxiety and high blood pressure, and tend to have higher rates of stroke, heart attack, and heart disease [43,44,45]. Unlike the composite scores considered in these analyses, employment status is a modifiable factor. Specific steps can be taken to understand how it affects outcomes among DDKT recipients and mitigate its effects.

Some measures reported in these analyses (e.g., KDRI and EPTS) are relatively new and were not previously part of the kidney allocation process. However, their utilization in these analyses ensures that comparisons across transplant eras are appropriate. KDRI includes donor race and other donor demographic and clinical characteristics. EPTS depends on recipient age, diabetes status, prior organ transplantations, and previous time on dialysis. Including these scores, the DRP, and the other variables in these models may have induced some collinearity. However, the random forests models are robust to multicollinearity. The KDRI score for AA donors is multiplied by a factor of 1.2, regardless of donor age, sex, and presence of other comorbidities. However, AA deceased donors were more likely to be younger and males such that the distributions of KDRI scores were comparable between AA and EA donors. The inclusion of these variables in the models was meant to help determine how socioeconomic and social determinants of health factors, which may interact with these scores, affect kidney transplant outcomes among AA and EA recipients.

Limitations of this report include potential underreporting in the SRTR database of various outcomes (e.g., DGF), mischaracterization of race and ethnicity, and viral infections, whose effects on KT outcomes were not initially recognized [46]. Analyses used registry data that were not collected for research purposes; therefore, some variables (e.g., employment status, medication use) may be incomplete and might not have been rigorously collected. However, it is unclear when the ongoing prospective APOL1 Long-term Kidney Transplantation Outcomes (APOLLO) study will accumulate enough events to address these questions [47]. These analyses provide some preliminary results that can be explored in other datasets.

Also, the study compared DDKT outcomes over more than 30 years, such that the standard of care and ways that measurements were collected and reported to the SRTR may have changed over time. However, focusing on four transplant eras should reduce these effects and their likelihood for confounding. These analyses were performed in a non-random subset of the SRTR data that may not have provided a representative sample of the distribution of outcomes observed among all DDKT recipients. For multiple reasons, including a greater need for kidney transplants in AA, lower rate of living kidney donation among AA, higher rates of HLA matching among individuals with recent African ancestry, waitlisted AA are more likely to receive AA DDKTs. Therefore, AA/AA DRP represents a significant proportion of all DDKTs [7, 48, 49].

Conclusion

AA recipients of kidney transplants from AA DDs had significantly shorter kidney allograft survival than EA recipients of AA DD kidneys and AA recipients of EA DD kidneys. Mortality among DDKT recipients remains high, especially among unemployed recipients, and does not appear to have changed since the early 2000s among unemployed AA recipients. Unemployment is associated with poorer outcomes among DDKT recipients, independent of race/ethnicity; however, its effects appeared to be consistently worse for AA DDKT recipients. Thus, improving outcomes for transplant recipients will require an improved understanding of the mechanisms by which socioeconomic factors, such as unemployment, adversely affect outcomes in the United States.

Abbreviations

AA:

African American

APOL1:

Apolipoprotein L1

APOLLO:

APOL1 Long-term Kidney Transplantation Outcomes

BMI:

Body mass index

BP:

Blood pressure

CIT:

cold ischemia time

CIT:

Confidence interval

CKD:

Chronic kidney disease

CMV:

Cytomegalovirus

CPH:

Cox proportional hazard model

CR:

Competing risk

CVD:

Cardiovascular disease

DCAS:

Death-censored allograft survival

DD:

Deceased donors

DDKT:

Deceased donor kidney transplantation

DGF:

Delayed graft function

DNA:

Deoxyribonucleic acid

DRP:

Donor/recipient pairs

EA:

European American

EPTS:

Estimated post-transplant survival

ESKD:

End-stage kidney disease

HLA:

Human Leucocyte antigen

HR:

Hazard ratio

HRSA:

Health Resources and Services Administration

KDRI:

Kidney Donor Risk Index

KT:

Kidney transplant / kidney transplantation

NUDT7:

Nudix hydrolase 7 gene

OPTN:

Organ Procurement and Transplantation Network

PRA:

Panel reactive antibodies

RSF:

Random survival forest

SEC63:

Translocation protein SEC63 homolog

SNP:

Single nucleotide polymorphism

SRTR:

Scientific Registry for Transplant Outcomes

T2D:

Type 2 diabetes

UMOD:

Uromodulin

UNOS:

United Network for Organ Sharing

USRDS:

United States Renal Data System

VIMP:

Variable importance

References

  1. Callender CO, Cherikh WS, Traverso P, Hernandez A, Oyetunji T, Chang D. Effect of donor ethnicity on kidney survival in different recipient pairs: an analysis of the OPTN/UNOS database. Transplant Proc. 2009;41:4125–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Molnar MZ, Kovesdy CP, Bunnapradist S, Streja E, Krishnan M, Mucsi I, et al. Donor race and outcomes in kidney transplant recipients. Clin Transpl. 2013;27:37–51.

    Article  Google Scholar 

  3. Kasiske BL, Neylan JF III, Riggio RR, Danovitch GM, Kahana L, Alexander SR, et al. The effect of race on access and outcome in transplantation. N Engl J Med. 1991;324:302–7.

    Article  CAS  PubMed  Google Scholar 

  4. Young CJ, Gaston RS. Renal transplantation in black Americans. N Engl J Med. 2000;343:1545–52.

    Article  CAS  PubMed  Google Scholar 

  5. Mohan S, Mutell R, Patzer RE, Holt J, Cohen D, McClellan W. Kidney transplantation and the intensity of poverty in the contiguous United States. Transplantation. 2014;98:640–5.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hall YN. Understanding racial differences in deceased-donor kidney transplantation: geography, poverty, language, and health insurance coverage. Dial Transplant. 2011;40:401–6.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Malek SK, Keys BJ, Kumar S, Milford E, Tullius SG. Racial and ethnic disparities in kidney transplantation. Transpl Int. 2011;24:419–24.

    Article  PubMed  Google Scholar 

  8. Axelrod DA, Dzebisashvili N, Schnitzler MA, Salvalaggio PR, Segev DL, Gentry SE, et al. The interplay of socioeconomic status, distance to center, and interdonor service area travel on kidney transplant access and outcomes. Clin J Am Soc Nephrol. 2010;5:2276–88.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Patzer RE, Amaral S, Wasse H, Volkova N, Kleinbaum D, McClellan WM. Neighborhood poverty and racial disparities in kidney transplant waitlisting. J Am Soc Nephrol. 2009;20:1333–40.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Patzer RE, Perryman JP, Schrager JD, Pastan S, Amaral S, Gazmararian JA, et al. The role of race and poverty on steps to kidney transplantation in the southeastern United States. Am J Transplant. 2012;12:358–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bostrom MA, Kao WH, Li M, Abboud HE, Adler SG, Iyengar SK, et al. Genetic association and gene-gene interaction analyses in African American dialysis patients with nondiabetic nephropathy. Am J Kidney Dis. 2012;59:210–21.

    Article  CAS  PubMed  Google Scholar 

  12. Freedman BI, Julian BA, Pastan SO, Israni AK, Schladt D, Gautreaux MD, et al. Apolipoprotein L1 gene variants in deceased organ donors are associated with renal allograft failure. Am J Transplant. 2015;15:1615–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Freedman BI, Pastan SO, Israni AK, Schladt D, Julian BA, Gautreaux MD, et al. APOL1 genotype and kidney transplantation outcomes from deceased African American donors. Transplantation. 2016;100:194–202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Reeves-Daniel AM, DePalma JA, Bleyer AJ, Rocco MV, Murea M, Adams PL, et al. The APOL1 gene and allograft survival after kidney transplantation. Am J Transplant. 2011;11:1025–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Julian BA, Gaston RS, Brown WM, Reeves-Daniel AM, Israni AK, Schladt DP, et al. Effect of replacing race with apolipoprotein L1 genotype in calculation of kidney donor risk index. Am J Transplant. 2017;17:1540–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ma J, Divers J, Palmer ND, Julian BA, Israni AK, Schladt D, et al. Deceased donor multidrug resistance protein 1 and caveolin 1 gene variants may influence allograft survival in kidney transplantation. Kidney Int. 2015;88:584–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Divers J, Ma L, Brown WM, Palmer ND, Choi Y, Israni AK, et al. GWAS for time to failure of kidney transplants from African American deceased donors. Clin Transpl. 2020;34(6):e13827.

  18. Moore J, McKnight AJ, Simmonds MJ, Courtney AE, Hanvesakul R, Brand OJ, et al. Association of Caveolin-1 gene polymorphism with kidney transplant fibrosis and allograft failure. JAMA. 2010;303:1282–7.

    Article  CAS  PubMed  Google Scholar 

  19. Cattaneo D, Ruggenenti P, Baldelli S, Motterlini N, Gotti E, Sandrini S, et al. ABCB1 genotypes predict cyclosporine-related adverse events and kidney allograft outcome. J Am Soc Nephrol. 2009;20:1404–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Roberts JP, Wolfe RA, Bragg-Gresham JL, Rush SH, Wynn JJ, Distant DA, et al. Effect of changing the priority for HLA matching on the rates and outcomes of kidney transplantation in minority groups. N Engl J Med. 2004;350:545–51.

    Article  CAS  PubMed  Google Scholar 

  21. Meier-Kriesche HU, Port FK, Ojo AO, Rudich SM, Hanson JA, Cibrik DM, et al. Effect of waiting time on renal transplant outcome. Kidney Int. 2000;58:1311–7.

    Article  CAS  PubMed  Google Scholar 

  22. Meier-Kriesche HU, Kaplan B. Waiting time on dialysis as the strongest modifiable risk factor for renal transplant outcomes: a paired donor kidney analysis. Transplantation. 2002;74:1377–81.

    Article  PubMed  Google Scholar 

  23. Locke JE, Warren DS, Dominici F, Cameron AM, Leffell MS, McRann DA, et al. Donor ethnicity influences outcomes following deceased-donor kidney transplantation in black recipients. J Am Soc Nephrol. 2008;19:2011–9.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ishwaran H, Kogalur UB, Gorodeski EZ, Minn AJ, Lauer MS. High-dimensional variable selection for survival data. J Am Stat Assoc. 2010;105:205–17.

    Article  CAS  Google Scholar 

  25. Behnamian A, Millard K, Banks SN, White L, Richardson M, Pasher J. A systematic approach for variable selection with random forests: achieving stable variable importance values. IEEE Geosci Remote Sens Lett. 2017;14:1988–92.

    Article  Google Scholar 

  26. van der Laan MJ. Statistical inference for variable importance. Int J Biostat. 2006;2:1–33.

  27. Strobl C, Boulesteix A-L, Kneib T, Augustin T, Zeileis A. Conditional variable importance for random forests. BMC Bioinformatics. 2008;9:307.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Steyerberg EW, Uno H, Ioannidis JPA, van Calster B. Poor performance of clinical prediction models: the harm of commonly applied methods. J Clin Epidemiol. 2018;98:133–43.

    Article  PubMed  Google Scholar 

  29. Lin DY, Wei LJ. The robust inference for the cox proportional hazards model. J Am Stat Assoc. 1989;84:1074–8.

    Article  Google Scholar 

  30. Zhou B, Latouche A, Rocha V, Fine J. Competing risks regression for stratified data. Biometrics. 2011;67:661–70.

    Article  PubMed  Google Scholar 

  31. Tang F, Ishwaran H. Random Forest missing data algorithms. Stat Anal Data Min. 2017;10:363–77.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kokla M, Virtanen J, Kolehmainen M, Paananen J, Hanhineva K. Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study. BMC Bioinformatics. 2019;20:492.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New York: John Wiley & Sons; 2002.

    Book  Google Scholar 

  34. Schafer JL. Analysis of incomplete multivariate data. New York: CRC Press; 1997.

  35. Li KH, Raghunathan TE, Rubin DB. Large-sample significance levels from multiply imputed data using moment-based statistics and an F reference distribution. J Am Stat Assoc. 1991;86:1065–73.

    Google Scholar 

  36. Ishwaran H, Gerds TA, Kogalur UB, Moore RD, Gange SJ, Lau BM. Random survival forests for competing risks. Biostatistics. 2014;15:757–73.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Cannon RM, Brock GN, Marvin MR, Slakey DP, Buell JF. The contribution of donor quality to differential graft survival in African American and Caucasian renal transplant recipients. Am J Transplant. 2012;12:1776–83.

    Article  CAS  PubMed  Google Scholar 

  38. Mannon RB. Delayed graft function: the AKI of kidney transplantation. Nephron. 2018;140:94–8.

    Article  PubMed  Google Scholar 

  39. Joshi S, Gaynor JJ, Bayers S, Guerra G, Eldefrawy A, Chediak Z, et al. Disparities among blacks, Hispanics, and whites in time from starting dialysis to kidney transplant waitlisting. Transplantation. 2013;95:309–18.

    Article  PubMed  Google Scholar 

  40. Hergenrather K, Zeglin R, McGuire-Kuletz M, Rhodes S. Employment as a social determinant of health: a systematic review of longitudinal studies exploring the relationship between employment status and physical health. Rehabil Res. 2015;29.

  41. van der Noordt M, IJzelenberg H, Droomers M, Proper KI. Health effects of employment: a systematic review of prospective studies. Occup Environ Med. 2014;71:730–6.

    Article  PubMed  Google Scholar 

  42. Olesen SC, Butterworth P, Leach LS, Kelaher M, Pirkis J. Mental health affects future employment as job loss affects mental health: findings from a longitudinal population study. BMC Psychiatry. 2013;13:144.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Doede MS. Black jobs matter: racial inequalities in conditions of employment and subsequent health outcomes. Public Health Nurs. 2016;33:151–8.

    Article  PubMed  Google Scholar 

  44. Zagożdżon P, Parszuto J, Wrotkowska M, Dydjow-Bendek D. Effect of unemployment on cardiovascular risk factors and mental health. Occup Med (Lond). 2014;64:436–41.

    Article  Google Scholar 

  45. Schultz WM, Kelli HM, Lisko JC, Varghese T, Shen J, Sandesara P, et al. Socioeconomic status and cardiovascular outcomes: challenges and interventions. Circulation. 2018;137:2166–78.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Israni AK, Li N, Cizman BB, Snyder J, Abrams J, Joffe M, et al. Association of donor inflammation- and apoptosis-related genotypes and delayed allograft function after kidney transplantation. Am J Kidney Dis. 2008;52:331–9.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Freedman BI, Moxey-Mims MM, Alexander AA, Astor BC, Birdwell KA, Bowden DW, et al. APOL1 long-term kidney transplantation outcomes network (APOLLO): design and rationale. Kidney Int Rep. 2020;5:278–88.

    Article  PubMed  Google Scholar 

  48. Lentine KL, Mandelbrot D. Addressing disparities in living donor kidney transplantation. Clin J Am Soc Nephrol. 2018;13:1909.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Rodrigue JR, Kazley AS, Mandelbrot DA, Hays R, LaPointe RD, Baliga P. Living donor kidney transplantation: overcoming disparities in live kidney donation in the US—recommendations from a consensus conference. Clin J Am Soc Nephrol. 2015;10:1687.

    Article  PubMed  PubMed Central  Google Scholar 

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Availability and of data and materials

We do not have permission from SRTR to release the data used in these analyses. However, these data can be generated by obtaining access from SRTR and following the study design and analysis plan outlined in this manuscript.

Disclaimer

The data reported here have been supplied by the Hennepin Healthcare Research Institute (HHRI) as the Scientific Registry of Transplant Recipients (SRTR) contractor. Interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the SRTR or the US Government.

Funding

This work was supported, in part, by grants from the National Institutes of Health R01 DK070941 (BIF), R01 DK084149 (BIF), R01 MD009055 (JD, BIF, BAJ), and Genomics of Transplantation U19-AI070119 (AKI, BAJ). The data reported here have been supplied by the Minneapolis Medical Research Foundation (MMRF) as the Scientific Registry of Transplant Recipients (SRTR) contractor. Interpretation and reporting of these data are the responsibilities of the authors, and in no way should be considered an official policy of, or interpretation by the SRTR or the United States Government. In addition, Dr. Mohan is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK114893 and U01116066) and the National Institute of Minority Health and Health Disparities (R01 MD014161).

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Authors and Affiliations

Authors

Contributions

Jasmin Divers designed the study, performed the statistical analyses, and drafted the manuscript. W. Mark Brown created the analysis dataset, contributed to the statistical analyses and interpretation of results. Sumit Mohan, Stephen Pastan, Ajay Israni, Robert S. Gaston, Robert Bray, Natalia V. Sakhovskaya, Alejandra M. Mena-Gutierrez, Amber M. Reeves-Daniel, and Bruce A. Julian contributed to the interpretation of results and preparation of the manuscript; Shahidul Islam contributed to the statistical analyses and interpretation of results; and Barry I Freedman designed the study, contributed in the statistical analyses, interpretation of results, and helped draft the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Jasmin Divers.

Ethics declarations

Ethics approval and consent to participate

This study used data from the SRTR that includes data on all donors, wait-listed candidates, and transplant recipients in the US, submitted by the Organ Procurement and Transplantation Network (OPTN) members. The Health Resources and Services Administration (HRSA) in the US Department of Health and Human Services provides oversight to the activities of the OPTN and SRTR contractors. Clinical and research activities are consistent with the Principles of the Declaration of Istanbul as outlined in the Declaration of Istanbul on Organ Trafficking and Transplant Tourism.

The NYU Langone Institutional Review Board granted an exemption from requiring ethics approval on the ground that these analyses are conducted on de-identified data curated by the SRTR. Therefore, written consent was not required for this study based on the granted exemption.

Consent for publication

Not applicable.

Competing interests

Wake Forest University Health Sciences and Dr. Freedman have rights to an issued United States patent related to APOL1 genetic testing. In addition, Dr. Freedman receives research support from and is a consultant for AstraZeneca and RenalytixAI Pharmaceuticals. Dr. Mohan is a member of the Scientific Advisory Board for Angion Biomedica and is the deputy editor for Kidney International Reports. The other authors of this manuscript have no conflict of interest to disclose. Results presented in this paper have not been published previously in whole or part, except in abstract format.

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Supplementary Information

Additional file 1: Supplementary Table 1.

Demographic and clinical characteristics by race/ethnicity of the donor-recipient pair. Supplementary Table 2. Five‐year death‐censored kidney allograft survival probability and 95% confidence interval by DRP and transplant era. Supplementary Table 3. Predictor ranking based on variable importance for death-censored kidney allograft survival and allograft survival with mortality as a competing risk.

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Divers, J., Mohan, S., Brown, W.M. et al. Employment status at transplant influences ethnic disparities in outcomes after deceased donor kidney transplantation. BMC Nephrol 23, 6 (2022). https://doi.org/10.1186/s12882-021-02631-4

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