Kidney transplantation (KT) is the treatment of choice for patients with end-stage renal disease (ESRD) and has been associated with improved patient survival and quality of life compared with remaining on dialysis.1 African Americans (AA) are 4 times more likely than Whites (WH) to develop ESRD but only half as likely to receive KT.2 This disparity exists in every stage of the transplant process (from referral to receipt of KT).3 Although there have been numerous prior studies demonstrating AA versus WH racial disparities in referral for KT in dialysis patients, besides our work,4,5 only Sequist et al6 and Patzer et al7 examined disparities that occur after referral for KT (but before KT acceptance). Both found that minorities were less likely than WH to be placed on a waiting list and undergo KT. This finding speaks to the importance of our work focusing on disparities in processes occurring after referral to a transplant center rather than only on the referral itself. We aimed to identify factors and strategies that can be targeted in the transplant clinic to facilitate completing the transplant workup and increase the probability of waitlisting.
Medical factors such as medical comorbidities and HLA matching, as well as social determinants of health, including geography, insurance type, and socioeconomic status (SES),3-5,7-9 have been shown to impact racial disparity between WH and racial minorities (AA, Hispanics, etc) in KT. In 2003, the United Network for Organ Sharing/Organ Procurement and Transplantation Network changed the kidney allocation policy to eliminate points for HLA-B matching. This policy change resulted in a 23% reduction in the disparity of KT rates between AA and WH.10 The new kidney allocation system (KAS) implemented in December 2014 includes time on dialysis in the calculation of KT wait-time with the aim of increasing equity in allocation, particularly for ethnic minorities who have been disproportionately affected by delayed referrals.11,12 Because of this policy change, the difference in the waitlisting rates between AA and WH decreased from 19% pre-KAS to 12% post-KAS. This difference is mainly due to the decrease in early waitlisting of WH patients as there is no longer an incentive for early waitlisting in dialysis patients. Of note, racial disparity between WH and racial minorities (AA, Hispanics, etc) in kidney transplant waitlisting have persisted even under the new KAS13 suggesting that there are other factors that may be perpetuating racial disparity in kidney transplant waitlisting.
Using a biopsychosocial model14 to inform our conceptual approach (Figure 1) and informed by the Center for Health Equity Research and Promotion model15 of key potential determinants of health disparities within the healthcare system, we selected culturally related factors4,5 and psychosocial characteristics16 that have been shown to contribute to healthcare disparities across racial/ethnic groups. Although such factors have been studied in other clinical populations,17,18 ours was the first to use a prospective study design to examine these factors as potential contributors to racial disparity in KT waitlisting.19,20 Thus, we wanted to examine whether these variables account for the relationship between race and waitlisting.
Our previous pilot work demonstrated that psychosocial and cultural factors, including perceived racism, medical mistrust, religiosity, and family loyalty, were more prevalent in AA and were associated with longer time to waitlisting.5 That study, however, was limited by a small sample size. In the current study, we conducted a large-scale prospective cohort study of ESRD patients, who were being evaluated for KT, to assess whether racial disparity persists after accounting for culturally related factors, transplant-related beliefs and psychosocial characteristics (while controlling for demographics and medical factors).
MATERIALS AND METHODS
This prospective cohort study was conducted at the Starzl Transplant Institute at the University of Pittsburgh Medical Center (UPMC) kidney transplant clinic. All participants who were evaluated in clinic and provided informed consent were recruited in the study. Participants completed a semistructured telephone interview (≈1 h) shortly after their first KT evaluation appointment. The interview included several existing valid measures4,5 and was conducted by research interviewers from the Survey Research Program at the University of Pittsburgh Center for Social and Urban Research. We prospectively tracked participants via medical record until they were accepted, found ineligible for transplant, or the end of the follow-up period (08 of 18). Data analysis for this study was performed at the University of New Mexico. This study was approved by the Institutional Review Boards at the University of Pittsburgh (PRO09060113) and the University of New Mexico (17-084), and a data use agreement was signed between the 2 institutions. The study was conducted in accordance to the Declaration of Helsinki and is consistent with the Principles of the Declaration of Istanbul as outlined in the Declaration of Istanbul on Organ Trafficking and Transplant Tourism.
Inclusion criteria were (1) age 18 and over; (2) English speaking; and (3) referred for KT. Patients were excluded if they had received a KT previously (to reflect national data on the majority of transplant recipients who are first-time recipients21 and prevent patients’ previous experience with KT from influencing current outcomes), had a cognitive or sensory impairment (such as blindness or deafness) that prevented them from completing an interview, or if they were judged by the clinic staff at the time of their initial clinic appointment to be ineligible to continue with the KT evaluation process (ie, because they were too ill—in these cases, patients were triaged from the clinic and never initiated transplant evaluation, thus we could not approach them to participate in the study).
In total, 1726 KT candidates were referred to UPMC transplant center for transplant evaluation. Of these, 389 were not eligible for participation (301 had a previous kidney transplant, 52 were determined to be ineligible for transplantation at the time of their initial clinic appointment and were triaged before initiating the KT evaluation process, 21 had a cognitive or sensory impairment, and 15 did not speak English). The remaining 1337 patients were eligible to participate in our study. Of these, 185 were not able to be enrolled (176 refused, 6 had no workable contact information and were thus lost, 2 were too ill and died before the first telephone interview, and 1 was inadvertently not approached by the transplant team personnel to gain permission for the research team to describe the study). Thus, 1152 patients (86.2% of those eligible) consented and completed the first interview. Due to small numbers (97; 8.42%) and significant heterogeneity within the other group, we excluded them from further analysis, leaving 1055 WH and AA in the remaining sample. Among eligible patients, those enrolled showed no large or significant differences from those not enrolled on any available demographic characteristic (age, sex, and race/ethnicity).
We provide extended descriptions, ranges, and psychometric properties of all predictor variables in Table 1.
Outcome Variable: Acceptance for Transplant Waitlisting
The primary outcome variable, acceptance for transplant waitlisting was determined by chart review. Patients who were not waitlisted at the end of follow-up (08 of 18) were censored. To determine patients’ outcome status, we accounted for all possible transplant outcomes, calculated time from evaluation to time of outcome, and identified 7 potential categories:
- Waitlisted = patients waitlisted after evaluation (endpoint = date of listing).
- Deceased before waitlist = patients who were enrolled in the study but passed away before completion of evaluation (endpoint = date of death).
- Closed patient choice before waitlist = patients who specifically verbalized desire not to pursue transplant any longer (endpoint = date record was closed).
- Closed due to incomplete evaluation = patients who started an evaluation but were closed before being accepted or rejected due to incomplete evaluation (endpoint = date of closure).
- Clinic rejected before waitlist = patients who started an evaluation but were rejected for transplant or closed for reasons other than patient choice or incomplete evaluation, that is, did not meet medical requirements, social requirements, etc (endpoint = date of rejection).
- Still undergoing evaluation (endpoint = date of last follow-up on August 2018).
- Transplanted at another center = a patient who did not complete their evaluation at UPMC because they received transplant from another center (endpoint = date of transplant).
Transplant at another center and death were considered as competing risks, and all other outcomes, other than waitlisting, were censored.
We compared descriptive statistics of baseline and outcome characteristics between the 2 racial groups using Wilcoxon rank-sum tests for continuous variables and chi-square tests for categorical variables. Before statistical modeling via time-to-waitlisting analysis with competing risks, we assessed for multicollinearity by computing correlation coefficients and multivariable variance inflation factors for all variables of interest. We identified no major concerns. We plotted cumulative incidence curves of time to acceptance for transplant waitlisting by race.
To address missing data in the multivariable analysis, we deleted individuals with missing data for any single variable from analysis. We verified this case-deletion strategy by including the data from all participants and incorporating missing data indicators. Because the results from both analyses were comparable, we reported the results from analyses on data from all participants.
For our primary multivariable analyses, to determine the degree to which each of the patient characteristics was associated with transplant waitlisting, we used multivariable Fine and Gray proportional subdistribution hazards regression models.22 We included all baseline characteristics in the multivariable models that were previously statistically significant (P < 0.1) in bivariate models against time to waitlisting. To test our main hypothesis that social determinants of health account for the relationship between race and KT waitlisting in the presence of competing risks, we fit 3 nested models using an SAS macro.23,24 Model 1 was an unadjusted Fine and Gray model with race/ethnicity, for which we constructed a cumulative incidence curve. Model 2 was a multivariable Fine and Gray model including race/ethnicity, demographics, and medical factors. Model 3 included all variable in Model 2 as well as cultural, psychosocial, and transplant knowledge. Before testing our multivariable models, we assessed the proportional hazards modeling assumption. It is important to note that we are not making the case that social determinants of health operate differently for AA versus WH patients, as an interaction analysis would imply. Instead, we hypothesize that AA patients tend to have more of the variables that make KT waitlisting less likely and fewer of the variables that make KT more likely. Therefore, we hypothesize that differences in these key variables should account for differences in time to waitlisting. Thus, a test of interaction effect would not be appropriate for this analysis.
We found significant differences in the baseline characteristics between AA and WH in our sample (Table 2). In general, AA patients were younger, had lower status occupations, lower income, relied on public insurance, and were less likely to be married compared with WH patients. AA patients also had more comorbidities and were more likely to be on dialysis with higher dialysis vintage but had more potential donors at the time of evaluation compared to WH patients. Culturally, they reported experiencing more racism, discrimination in health care, and had higher medical mistrust and religious objections to living donor KT, although they had greater trust in physicians and family loyalty than WH. Psychosocially, AA reported less social support but greater internal and external locus of control than WH. AA candidates had lower transplant knowledge and spent less time engaging in fewer learning activities than WH. A higher percentage of AA patients were waitlisted after the implementation of the new KAS compared with WH.
Acceptance for Transplant Waitlisting
Figure 2 shows the cumulative incidence curves of the probability of being waitlisted for KT by race. AA patients were less likely to be waitlisted compared with WH patients (hazard ratio [HR], 0.56; 95% confidence interval [CI], 0.46-0.68; P < 0.001). Table 3 shows the Fine and Gray proportional subdistribution hazards models for time to waitlisting. The probability of waitlisting increased for AA patients after adjusting for demographic and medical factors (HR, 0.69; 95% CI, 0.55-0.84; P < 0.001) as well as for psychosocial factors (HR, 0.75; 95% CI, 0.59-0.96; P = 0.021), although the disparity persisted. Being older, having lower income, public insurance, more comorbidities, or being on dialysis at the time of kidney transplant evaluation decreased the probability of waitlisting while having more social support or more transplant knowledge increased the probability of waitlisting. We checked for proportionality of hazards assumptions for each model and found no issues.
This study is innovative because we offered a comprehensive prospective examination of the influence of social determinants of health on KT waitlisting (controlling for demographic and medical factors) in a large population being evaluated for KT. We found significant differences in the baseline characteristics between AA and WH patients, with AA being younger and having lower SES compared to WH. Our sample of KT candidates was older and had a larger proportion of WH patients than the US population of KT candidates but was equivalent in the proportion of women and those who were on dialysis.25 As found in other clinical and community-dwelling populations,26 AA reported more discrimination, perceived racism in healthcare, and medical mistrust compared with WH. Even after adjusting for these differences, racial disparity in KT waitlisting persisted.
The strength of this study lies in its large sample size and the use of a multipronged approach to understand the racial disparity in KT. The meticulous collection of data and chart reviews resulted in a significant amount of data available to test for the persistence of racial disparity in KT waitlisting after accounting for social determinants of health. Using the time of first KT evaluation as the starting point eliminated the barriers relating to transplant referral and allowed us to focus on racial disparity during the evaluation period, identify factors that impact waitlisting, and possibly channel resources to address these factors.
Our findings support previous work examining the effects of discrimination and medical mistrust on referral for KT,27-29 although ours is the first to examine these variables as predictors of KT waitlisting. Although significant differences were noted in perceived discrimination and medical mistrust between the 2 groups, these factors did not account for the racial disparity in KT waitlisting, which contradicted the findings of our pilot work.5 This finding could be explained by the smaller sample size (n = 127) used in the pilot study resulting in potential sampling bias. The study may have been underpowered to detect a significant difference in racial disparity, hence justifying the need to conduct this current larger study.
In the current study, we confirmed previous work that racial disparity exists in KT waitlisting between AA and WH.3,5,8,30 Consistent with previous studies that showed that patients with lower SES are less likely to be referred for transplant, waitlisted, or receive a KT,3,5,8 we found that being older, AA, having lower income, being on public insurance, having more comorbidities, or being on dialysis at the time of kidney transplant evaluation negatively impacted waitlisting. We believe these variables can be considered risk factors for taking longer to complete the KT evaluation process. Because these are basic assessments done at the first KT evaluation clinic appointment, these patients can be flagged for increased assistance to complete the evaluation process. Identifying this at-risk population will allow us to channel more resource including the use of peer navigators,31,32 fast-track clinics33 as well as education programs34 to help these patients complete their work up and be waitlisted.
Similarly, we found that greater social support and transplant knowledge positively impacted waitlisting. Although it may not be feasible to assess these variables in transplant centers nationwide, we believe knowing that these factors have a positive influence on KT waitlisting helps transplant centers identify ways to improve patients’ likelihood of completing evaluation; namely, targeting efforts at improving KT education for all patients at initiation of evaluation and assessing for and ensuring strong social support during the evaluation period from the transplant team’s social worker.
The results of this study differed from our previously published Veterans Affairs (VA) study. In the VA study, we looked at the effects of the same set of predictors on race differences in KT waitlisting among Veterans but did not find any racial disparity in the time to waitlisting.4 This finding could be explained by the difference in the evaluation process used by VA. At VA facilities, patients are referred to the transplant centers only after the required transplant workup has been completed, hence eliminating patients who have yet to complete their work up and cannot be waitlisted. In contrast, at UPMC (and most non-VA transplant centers), the work up starts only after the first transplant clinic evaluation. Furthermore, the Veteran patient population with ESRD tends to be more homogenous than non-Veterans (eg, age, income, education, SES, and comorbidities). Despite the uniqueness of the Veteran population, the success of the VA system in eliminating racial disparity in waitlisting should serve as an example to the non-VA transplant centers, especially in light of the current study findings of persistent racial disparity. The VA KT evaluation process provides full coverage for (1) all ancillary testing that can be scheduled and tracked within the same electronic health record; (2) all transport and lodging in connection with KT evaluation for patients and their primary caregiver; and (3) posttransplant immunosuppression. Thus, extending the Medicare ESRD entitlement to cover these expenses may help reduce disparities in non-VA transplant centers where racial disparities exists.3,8,35
The results of this study should be interpreted in light of some limitations. First, this was a single-center study. Every transplant center has its unique evaluation process and patient population; the barriers noted in this study may not apply to other transplant centers. However, this study highlighted the need to focus on social determinants of health to reduce racial disparity. This finding will likely be applicable to transplant centers that experience racial disparity regardless of racial/ethnic composition. Another limitation of this study is the persistence of racial disparity in spite of adjustments for all measured variables, suggesting that there may be variables that we did not measure and adjust for accounting for the persistent disparity. Novel factors will need to be considered in future studies on racial disparity.
In conclusion, we found that racial disparity exists in transplant waitlisting even after correcting for social determinants of health. Efforts to identify novel factors that continue to contribute to racial disparity are needed. In the meantime, clinically, the identification of high-risk KT candidates in this study (older patients, AA patients, patients with lower SES, patients on dialysis, or who have multiple comorbidities) will allow us to target patients on whom to intensify interventions that facilitate the completion of their work up promptly. At the same time, increasing transplant education programs36-38 to enhance transplant knowledge and ensuring that patients have adequate social support will increase the probability that patients complete their work up and get waitlisted for transplant.
The Scientific Registry of Transplant Recipient (SRTR) data reported here have been supplied by the Minneapolis Medical Research Foundation as the contractor for the SRTR. The 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.
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