Abstract
Objective:
To evaluate the effect of the Affordable Care Act (ACA) Medicaid expansion on payor mix among patients on the kidney and liver transplant waitlist, and waitlist and post-transplant outcomes.
Design:
Using the Scientific Registry of Transplant Recipients, we performed a secondary data analysis of all patients on the kidney and liver transplant waitlist from 2007–2018. We described changes in payor mix by timing of state Medicaid expansion. We used competing risks models to estimate cause-specific hazard ratios for the effects of insurance and era on death/delisting and transplant. We used a Poisson regression model to estimate the effect of insurance and era on incidence rate ratio of inactivations on the waitlist. We used Cox proportional hazards models to estimate the effect of insurance and era on graft and patient survival.
Results:
A decade after implementation of the ACA, the prevalence of Medicaid beneficiaries listed for transplant increased by 2.5% (from 7.4% to 9.9%) for kidney, and by 2.6% (15.3% to 17.9%) for liver. Expansion states had greater increases than non-expansion states (kidney 3.8% vs. 0.6%, liver 5.3% vs. −1.8%). Among waitlisted patients, the magnitude of association of Medicaid insurance vs. private insurance with transplant decreased over time for kidney candidates (Era-1 SHR 0.62 [95% CI 0.60–0.64] vs. Era-3 SHR 0.77 [0.74–0.70]) but increased for liver candidates (Era-1 SHR 0.85 [0.83–0.90] vs. Era-3 SHR 0.79 [0.77–0.82]). Medicaid-insured kidney and liver recipients had greater hazards of graft failure; this did not change over time (kidney: HR 1.23 [1.06–1.44] liver:1.05[0.94–1.17])
Conclusions:
For the millions of patients with chronic kidney and liver diseases, implementation of the ACA has resulted in only modest increases in access to transplant for the publicly insured versus the privately insured.
Keywords: Medicaid, Insurance coverage, Organ transplantation, Survival analysis, Patient protection and affordable care act
Introduction
The Patient Protection and Affordable Care Act (ACA) included the expansion of Medicaid to include individuals with incomes below 138% of the federal poverty level.(1) However, a 2012 Supreme Court ruling that made Medicaid expansion optional and at the discretion of individual states, significantly reduced the magnitude of initial ACA impact, with only 30 US states and territories choosing to expand Medicaid prior to 2014. By the end of 2018, 19 states chose not to expand their Medicaid programs and did not implement changes. Moreover, Medicaid administration is state-specific, resulting in substantial variability in expanded beneficiary eligibility criteria.(2)
The ACA was anticipated to have significant impacts on access to organ transplantation, including modifying the number of low income patients referred for transplant, changing the billing and reimbursement structure of transplant centers, and improving waitlist and posttransplant outcomes.(3–5) Early analyses of the effects of the ACA on transplantation predicted 16 million new patients covered by Medicaid and a significant increase in patients listed for transplant who are covered by Medicaid insurance.(6, 7) Preliminary research following ACA implementation indicates that although the ACA has increased insurance coverage and healthcare access generally, expanded access has disparate effects on disparities in access to subspecialty care.(8, 9) Prior examination of the ACA on access to transplant-care demonstrated increased rates of Medicaid enrollment, but fewer performed transplantations than expected among Medicaid beneficiaries.(5, 10–12) Though proponents of the ACA’s Medicaid expansion have touted its potential to decrease these disparities, ten years after implementation, its full effect on access to transplantation remains unknown.
Solid organ transplantation remains the preferred treatment for patients with end-stage organ disease. However, the majority of patients with end stage kidney disease (ESKD) or end stage liver disease (ESLD) will not be placed on the transplant waiting list or die waiting. Previous data show the disparities within access-to and outcomes-of solid organ transplantation, including income, employment, wealth, and insurance status.(13–16) The goal of this study was to examine the effect of Medicaid expansion on access to kidney and liver transplantation as well as waitlist and post-transplant outcomes ten years after implementation of the ACA. We hypothesized that Medicaid expansion has increased the proportion of waitlisted patients covered by Medicaid, with a differential effect in expansion vs non-expansion states. We further posited that Medicaid expansion would improve waitlist and post-transplant outcomes for Medicaid insured kidney and liver transplant recipients.
Methods
Study Population
We identified adults (>18 years old) added to the kidney or liver transplantation waitlists (transplant candidates) from January 1, 2007, to December 31, 2018. When multiple listings per organ were recorded, we used the first listing in the observation period to construct cohorts with one record per patient. We examined outcomes by 3 distinct policy eras: January 1, 2007-December 31, 2010 (pre-ACA), January 1, 2011-December 31, 2014 (ACA implementation), and January 1, 2015- December 31, 2018 (post- or delayed implementation). We followed patients until September 1, 2019.
Data Sources and Variables
The Scientific Registry of Transplant Recipients (SRTR) data system includes data on all transplant donors, candidates, and recipients in the United States. These data are submitted by the members of the Organ Procurement and Transplantation Network (OPTN). The Health Resources and Services Administration, US Department of Health and Human Services provide oversight to the activities of the OPTN and SRTR contractors.
We abstracted time periods of state Medicaid expansion from data provided by the Kaiser Family Foundation.(2) Insurance status was grouped based on primary payor at listing: Medicare was defined as Medicare Fee for Service, Medicare & Choice, or Medicare Unspecified. Medicaid was defined as Medicaid or Children’s Health Insurance Program, Government was defined as Department of Veterans’ affairs, other government, Foreign Government, or US/State Govt Agency. Self-pay was classified as other. Uninsured included Donation or Free Care. Patients were classified into eras according to the date they were added to the waitlist.
For our primary outcome, we quantified whether transplant candidates experienced either 1) transplantation or 2) a composite variable of waitlist mortality or delisting due to declining medical condition. Each incidence of an inactive waitlist status, which temporarily makes candidates ineligible to undergo transplantation and may be designated due to a range of factors including loss of insurance or temporary illness, was defined using the SRTR waitlist history records. We defined patient and graft survival from day of transplantation.
Analysis
We described the data using proportions for categorical variables and medians for continuous variables, stratified by insurance category and era. We stratified the analysis by kidney and liver transplant waitlists. Missing value indicators were created and included in the models. Interaction terms between era and insurance status were calculated for each model.
We used Cox proportional hazards models for all time-to-event outcomes, including death/delisting, transplant, and both graft and patient survival. We treated death/delisting and transplant as competing risks to estimate cause-specific hazard ratios for the effects of insurance and era on each of these outcomes. Multivariable models were adjusted for candidate illness and comorbidities at listing (diabetes, hypertension), age at listing, body mass index (BMI) at listing, and sex. Patient and graft survival models were additionally adjusted for comorbidities at transplant (diabetes, hypertension), age at transplant, BMI at transplant, and donor quality (age, BMI). Kidney models were additionally adjusted for albumin and dialysis status at listing. Liver models were adjusted for Model for End-Stage Liver Disease (MELD) score at listing.
To assess the association between insurance and era on waitlist inactivation, we considered two analytic approaches: (1) estimate the incidence rate ratios (IRR) of unique inactivations on the waitlist by insurance and era from a single Poisson regression model; this method counts the number of times a patient became inactive, generally indicating a perceived barrier to transplant and (2) estimate the average difference in total duration of inactivation in the first year on the waitlist by insurance and era from a linear model with robust standard error estimators to account for heteroscedasticity; this method measures the percent of the first year that is spent inactive, generally indicating the extent of the barriers to transplant. In putting together these approaches, we hoped to understand not only how many times patients were made inactive, but also how much time patients spend active versus inactive on the transplant waitlist. Multivariable models were adjusted for candidate demographics and comorbidities as described above.
To investigate for bias in either a missing indicator or multiple imputation approach, we ran a sensitivity analysis wherein we used multiple imputation (5 sets) and applied Rubin’s Rules (2004) to pool estimates across imputations. The results confirmed the observed associations between waitlist and post-transplant outcomes were robust to different methods for handling missing data under the missing at random assumption.
This study was approved by the Duke University Institutional Review Board. All statistical analysis was performed in SAS 9.4 (SAS Institute, Cary NC).
Results
Patient Characteristics
The study cohort was comprised of 416,231 kidney and 134,820 liver transplant candidates who were added to the waitlist between 2007 and 2018. Of the kidney transplant candidates, 66,148 were removed from the cohort as “multiple listings” and 1955 patients were excluded as their death or transplant date was prior to listing date, with 348,128 patients included in our final cohort (Figure 1a). Of the 134,820 liver transplant candidates, 12,927 were removed for “multiple listings” and 595 patients were excluded as their death or transplant date was prior to listing date, with 121,298 patients remaining in the final cohort (Figure 1b). The median age at listing was 52.0 (IQR 13.1) for kidney (table 1a) and 54.8 (IQR 10.5) for liver (table 1b). The proportion of Hispanic/Latino candidates on the kidney waitlist increased over time, while the proportion of Black/African American candidates decreased. On the liver waitlist, the proportion of women and Hispanic/Latino candidates increased over time.
Figure 1:
Study cohort – a: Kidney waiting list; b: Liver waiting list
Table 1a:
Waitlisted Kidney Cohort Characteristics by Era
| Characteristic | Waitlist Era | Total (N=348,128) | P value | ||
|---|---|---|---|---|---|
| Era 1:2007 – 2010 (N=118,283) | Era 2: 2011 – 2014 (N=115,272) | Era 3: 2015–2018 (N=114,573) | |||
| Race | <0.0011 | ||||
| White | 54,722 (46.3%) | 50,996 (44.2%) | 49,864 (43.5%) | 155,582 (44.7%) | |
| Black or African American | 35,099 (29.7%) | 33,691 (29.2%) | 31,693 (27.7%) | 100,483 (28.9%) | |
| American Indian or Alaska Native | 1,161 (1.0%) | 1,270 (1.1%) | 1,114 (1.0%) | 3,545 (1.0%) | |
| Asian | 7,170 (6.1%) | 7,943 (6.9%) | 8,563 (7.5%) | 23,676 (6.8%) | |
| Native Hawaiian, Pacific Islander | 444 (0.4%) | 548 (0.5%) | 570 (0.5%) | 1,562 (0.4%) | |
| Hispanic / Latino | 19,300 (16.3%) | 20,324 (17.6%) | 21,868 (19.1%) | 61,492 (17.7%) | |
| Multi-Racial | 387 (0.3%) | 500 (0.4%) | 901 (0.8%) | 1,788 (0.5%) | |
| Sex | <0.0011 | ||||
| Female | 46,931 (39.7%) | 44,606 (38.7%) | 43,414 (37.9%) | 134,951 (38.8%) | |
| Male | 71,352 (60.3%) | 70,666 (61.3%) | 71,159 (62.1%) | 213,177 (61.2%) | |
| Age at listing | <0.0012 | ||||
| Mean (SD) | 51.4 (13.1) | 52.2 (13.0) | 52.6 (13.1) | 52.0 (13.1) | |
| Median | 53.0 | 54.0 | 55.0 | 54.0 | |
| Q1, Q3 | 43.0, 61.0 | 44.0, 62.0 | 44.0, 63.0 | 43.0, 62.0 | |
| Range | (18.0–90.0) | (18.0–88.0) | (18.0–89.0) | (18.0–90.0) | |
| BMI at listing (kg/m2) | <0.0012 | ||||
| Missing | 1,979 (1.7%) | 311 (0.3%) | 433 (0.4%) | 2,723 (0.8%) | |
| Mean (SD) | 28.4 (5.7) | 28.9 (5.7) | 29.0 (5.5) | 28.8 (5.7) | |
| Median | 28.0 | 28.0 | 29.0 | 28.0 | |
| Q1, Q3 | 24.0, 32.0 | 25.0, 33.0 | 25.0, 33.0 | 25.0, 33.0 | |
| Range | (15.0–60.0) | (15.0–59.0) | (15.0–60.0) | (15.0–60.0) | |
| BMI at listing (kg/m2) | <0.0011 | ||||
| Missing | 1,983 (1.7%) | 311 (0.3%) | 437 (0.4%) | 2,731 (0.8%) | |
| [15–19) | 2,143 (1.8%) | 1,820 (1.6%) | 1,596 (1.4%) | 5,559 (1.6%) | |
| [19–25) | 29,025 (25.0%) | 25,715 (22.4%) | 24,405 (21.4%) | 79,145 (22.9%) | |
| [25–30) | 38,867 (33.4%) | 37,484 (32.6%) | 37,503 (32.9%) | 113,854 (33.0%) | |
| [30–40) | 42,201 (36.3%) | 45,735 (39.8%) | 47,330 (41.5%) | 135,266 (39.2%) | |
| [40–60) | 4,064 (3.5%) | 4,207 (3.7%) | 3,302 (2.9%) | 11,573 (3.4%) | |
| Dialysis Status at listing | <0.0011 | ||||
| No | 29,649 (25.1%) | 26,691 (23.2%) | 34,600 (30.2%) | 90,940 (26.1%) | |
| Yes | 88,634 (74.9%) | 88,581 (76.8%) | 79,973 (69.8%) | 257,188 (73.9%) | |
| Total Serum Albumin | <0.0012 | ||||
| Missing | 14,318 (12.1%) | 6,334 (5.5%) | 3,096 (2.7%) | 23,748 (6.8%) | |
| Mean (SD) | 3.8 (0.6) | 3.9 (0.6) | 3.9 (0.6) | 3.9 (0.6) | |
| Median | 3.9 | 3.9 | 4.0 | 3.9 | |
| Q1, Q3 | 3.5, 4.2 | 3.5, 4.3 | 3.6, 4.3 | 3.5, 4.3 | |
| Range | (0.5–9.9) | (0.5–9.8) | (0.5–9.8) | (0.5–9.9) | |
| Diabetes Status | <0.0011 | ||||
| Missing | 1,264 (1.1%) | 190 (0.2%) | 15 (0.0%) | 1,469 (0.4%) | |
| No | 68,336 (58.4%) | 63,426 (55.1%) | 62,984 (55.0%) | 194,746 (56.2%) | |
| Yes | 48,683 (41.6%) | 51,656 (44.9%) | 51,574 (45.0%) | 151,913 (43.8%) | |
| Hypertension | <0.0011 | ||||
| Missing | 28,150 (23.8%) | 19,171 (16.6%) | 109,896 (95.9%) | 157,217 (45.2%) | |
| No | 10,388 (11.5%) | 10,378 (10.8%) | 582 (12.4%) | 21,348 (11.2%) | |
| Yes | 79,745 (88.5%) | 85,723 (89.2%) | 4,095 (87.6%) | 169,563 (88.8%) | |
| States by Expansion Year | <0.0011 | ||||
| Missing | 162 (0.1%) | 177 (0.2%) | 263 (0.2%) | 602 (0.2%) | |
| Initial expansion | 61,966 (52.5%) | 59,916 (52.1%) | 59,289 (51.9%) | 181,171 (52.1%) | |
| Late expansion | 14,622 (12.4%) | 13,651 (11.9%) | 13,057 (11.4%) | 41,330 (11.9%) | |
| Later/No expansion | 41,533 (35.2%) | 41,528 (36.1%) | 41,964 (36.7%) | 125,025 (36.0%) | |
Chi-Squared
ANOVA F-Test
Table 1b:
Waitlisted Liver Cohort Characteristics by Era
| Characteristic | Waitlist Era | Total (N=121,298) | P value | ||
|---|---|---|---|---|---|
| Era 1: 2007 – 2010 (N=38,575) | Era 2: 2011 – 2014 (N=39,656) | Era 3:2015–2018 (N=43,067) | |||
| Race | <0.0011 | ||||
| White | 27,203 (70.5%) | 27,778 (70.0%) | 29,920 (69.5%) | 84,901 (70.0%) | |
| Black or African American | 3,529 (9.1%) | 3,699 (9.3%) | 3,578 (8.3%) | 10,806 (8.9%) | |
| American Indian or Alaska Native | 257 (0.7%) | 300 (0.8%) | 438 (1.0%) | 995 (0.8%) | |
| Asian | 1,800 (4.7%) | 1,716 (4.3%) | 1,887 (4.4%) | 5,403 (4.5%) | |
| Native Hawaiian or Other Pacific Islander | 67 (0.2%) | 74 (0.2%) | 76 (0.2%) | 217 (0.2%) | |
| Hispanic / Latino | 5,571 (14.4%) | 5,925 (14.9%) | 6,941 (16.1%) | 18,437 (15.2%) | |
| Multi-Racial | 148 (0.4%) | 164 (0.4%) | 227 (0.5%) | 539 (0.4%) | |
| Sex | <0.0011 | ||||
| Female | 13,710 (35.5%) | 13,976 (35.2%) | 15,811 (36.7%) | 43,497 (35.9%) | |
| Male | 24,865 (64.5%) | 25,680 (64.8%) | 27,256 (63.3%) | 77,801 (64.1%) | |
| Calculated Candidate Age at Listing | <0.0012 | ||||
| Mean (SD) | 53.6 (10.2) | 55.1 (10.2) | 55.6 (10.9) | 54.8 (10.5) | |
| Median | 55.0 | 57.0 | 58.0 | 57.0 | |
| Q1, Q3 | 49.0, 60.0 | 51.0, 62.0 | 50.0, 64.0 | 50.0, 62.0 | |
| Range | (18.0–81.0) | (18.0–82.0) | (18.0–79.0) | (18.0–82.0) | |
| BMI at listing (kg/m2) | <0.0012 | ||||
| Missing | 244 (0.6%) | 127 (0.3%) | 151 (0.4%) | 522 (0.4%) | |
| Mean (SD) | 28.5 (5.7) | 28.8 (5.8) | 29.1 (6.0) | 28.8 (5.9) | |
| Median | 28.0 | 28.0 | 28.0 | 28.0 | |
| Q1, Q3 | 24.0, 32.0 | 25.0, 32.0 | 25.0, 33.0 | 25.0, 32.0 | |
| Range | (15.0–59.0) | (15.0–60.0) | (15.0–60.0) | (15.0–60.0) | |
| BMI of candidate at listing (kg/m2) | <0.0011 | ||||
| Missing | 244 (0.6%) | 130 (0.3%) | 156 (0.4%) | 530 (0.4%) | |
| [15–19) | 627 (1.6%) | 531 (1.3%) | 579 (1.3%) | 1,737 (1.4%) | |
| [19–25) | 9,117 (23.8%) | 9,024 (22.8%) | 9,420 (22.0%) | 27,561 (22.8%) | |
| [25–30) | 13,611 (35.5%) | 14,046 (35.5%) | 14,782 (34.4%) | 42,439 (35.1%) | |
| [30–40) | 13,440 (35.1%) | 14,065 (35.6%) | 15,637 (36.4%) | 43,142 (35.7%) | |
| [40–60) | 1,536 (4.0%) | 1,860 (4.7%) | 2,493 (5.8%) | 5,889 (4.9%) | |
| First SRTR MELD given | <0.0012 | ||||
| Mean (SD) | 17.4 (8.7) | 17.7 (8.9) | 18.1 (9.2) | 17.8 (9.0) | |
| Median | 15.0 | 15.0 | 16.0 | 15.0 | |
| Q1, Q3 | 11.0, 21.0 | 11.0, 22.0 | 11.0, 23.0 | 11.0, 22.0 | |
| Range | (6.0–40.0) | (6.0–40.0) | (6.0–40.0) | (6.0–40.0) | |
| Diabetes Status | <0.0011 | ||||
| Missing | 549 (1.4%) | 158 (0.4%) | 89 (0.2%) | 796 (0.7%) | |
| No | 28,214 (74.2%) | 28,647 (72.5%) | 29,992 (69.8%) | 86,853 (72.1%) | |
| Yes | 9,812 (25.8%) | 10,851 (27.5%) | 12,986 (30.2%) | 33,649 (27.9%) | |
| Hypertension | <0.0011 | ||||
| Missing | 10,420 (27.0%) | 8,661 (21.8%) | 41,503 (96.4%) | 60,584 (49.9%) | |
| No | 20,698 (73.5%) | 21,185 (68.3%) | 1,039 (66.4%) | 42,922 (70.7%) | |
| Yes | 7,457 (26.5%) | 9,810 (31.7%) | 525 (33.6%) | 17,792 (29.3%) | |
| States by Expansion Year | <0.0011 | ||||
| Missing | 191 (0.5%) | 236 (0.6%) | 169 (0.4%) | 596 (0.5%) | |
| Initial expansion | 20,364 (53.1%) | 20,803 (52.8%) | 22,234 (51.8%) | 63,401 (52.5%) | |
| Later expansion | 5,024 (13.1%) | 4,774 (12.1%) | 5,025 (11.7%) | 14,823 (12.3%) | |
| Late/No expansion | 12,996 (33.9%) | 13,843 (35.1%) | 15,639 (36.5%) | 42,478 (35.2%) | |
Chi-Squared
ANOVA F-Test
Payor status
Between era 1(2007–2010) and era 3(2015–2018), the number of candidates with Medicaid listed for transplant increased by 2.5 percentage points (from 7.4% [n=8,713] to 9.9% [n=11,291]) for kidney, and by 2.6 percentage points (15.3% [n=5,915] to 17.9% [n=7,708]) for liver (Table 2). On average, candidates living in states with any Medicaid expansion had greater increases in Medicaid insurance on the transplant waitlist than states without expansion (kidney 3.8% vs. 0.6%, liver 5.3% vs. −1.8%). Candidates living in states with Medicaid expansion also had a higher overall prevalence of Medicaid insurance on the waitlist when averaged across all time periods compared to states without Medicaid expansion (kidney 11.4% vs. 3.2%, liver 20.4% vs. 11.6%).
Table 2.
Payor Status by Era for Waitlisted Kidney and Liver Transplant Candidates
| Kidney | Era 1: 2007 – 2010 (N=118,283) | Era 2: 2011 – 2014 (N=115,272) | Era 3:2015 – 2018 (N=114,573) | Total (N=348,128) | P value |
|---|---|---|---|---|---|
| Primary Payer | <0.0011 | ||||
| Private | 52,764 (44.6%) | 51,209 (44.4%) | 51,696 (45.1%) | 155,669 (44.7%) | |
| Medicare | 54,234 (45.9%) | 52,350 (45.4%) | 48,294 (42.2%) | 154,878 (44.5%) | |
| Medicaid | 8,713 (7.4%) | 9,055 (7.9%) | 11,291 (9.9%) | 29,059 (8.3%) | |
| Government | 2,161 (1.8%) | 2,432 (2.1%) | 3,155 (2.8%) | 7,748 (2.2%) | |
| Other | 337 (0.3%) | 154 (0.1%) | 113 (0.1%) | 604 (0.2%) | |
| Uninsured | 74 (0.1%) | 72 (0.1%) | 24 (0.0%) | 170 (0.0%) | |
| Liver | Era 1: 2007 – 2010 (N=38,575) | Era 2: 2011 – 2014 (N=39,656) | Era 3:2015 – 2018 (N=43,067) | Total (N=121,298) | P value |
| Primary Payer | <0.0011 | ||||
| Private | 23,274 (60.3%) | 21,714 (54.8%) | 22,127 (51.4%) | 67,115 (55.3%) | |
| Medicare | 7,794 (20.2%) | 9,450 (23.8%) | 11,306 (26.3%) | 28,550 (23.5%) | |
| Medicaid | 5,915 (15.3%) | 6,568 (16.6%) | 7,708 (17.9%) | 20,191 (16.6%) | |
| Government | 1,256 (3.3%) | 1,674 (4.2%) | 1,783 (4.1%) | 4,713 (3.9%) | |
| Other | 231 (0.6%) | 214 (0.5%) | 104 (0.2%) | 549 (0.5%) | |
| Uninsured | 105 (0.3%) | 36 (0.1%) | 39 (0.1%) | 180 (0.1%) |
Waitlist inactivation
Medicaid-enrolled kidney candidates had a lower incidence of waitlist inactivations when compared to privately insured candidates (unadjusted IRR 0.92 [95% CI 0.91–0.93], p<0.0001; adjusted IRR 0.94 [95% CI 0.93–0.96] p<0.0001) (SDCa); the magnitude of this association decreased over time (era-insurance interaction < 0.001). However, Medicaid-insured kidney candidates spent more days inactive in their first waitlist year compared to candidates with private insurance (unadjusted +6% [95% CI 6–7%], adjusted +8% [95% CI 0.07–0.08], p<0.0001). When allowing the association between insurance type and inactivation rate to vary by era, the magnitude of this association did not change over time. Medicaid-insured liver candidates had both a higher incidence of inactivation and more days spent inactive compared to those privately insured (inactivation IRR unadjusted 1.15 (95% CI 1.13–1.18) p<0.001, adjusted 1.09 (95% CI 1.06–1.11) p<0.0001 (SDCb); proportion of first year spent inactive +2% (95% CI 1–3%) p<0.0001, adjusted + 2% (95% CI 1–3%)).
Waitlist outcomes: transplant and death/de-listing
Among kidney or liver waitlist candidates, Medicaid coverage was consistently associated with both a lower hazard of transplant and a higher hazard of death/delisting when compared to private insurance (Table 3). While Medicaid was associated with lower hazard of kidney transplant compared to private insurance in Era 1, and this difference decreased over time (Era 1 SHR 0.62 [95% CI 0.60–0.64] vs. Era 3 SHR 0.77 [0.74–0.70]; p-value for interaction of era and insurance status p<0.001), it increased for liver candidates (Era 1 SHR 0.85 [0.83–0.90] vs. Era 3 SHR 0.79 [0.77–0.82]; p-value for interaction of era and insurance status p<0.001). Thus, the relative hazard of kidney transplants among Medicaid beneficiaries increased in Era 3 vs. Era 1 but remained below that of transplants in privately insured candidates. Medicaid associations with death/delisting did not change over time (kidney interaction p=0.46, liver p=0.36)
Table 3.
Waitlist Outcomes for Medicaid vs Privately Insured Transplant Candidates
| Era 1: 2007–2010 | Era 2: 2011–2014 | Era 3: 2015–2018 | |
|---|---|---|---|
| HR (95% CI) | HR (95% CI) | HR (95% CI) | |
| Kidney waitlist outcomes | |||
| Transplant | 0.62 (0.60–0.64) | 0.67 (0.65–0.69) | 0.77 (0.74–0.80) |
| Death/delisting | 1.18 (1.12–1.23) | 1.19 (1.13–1.25) | 1.11 (1.03–1.19) |
| Liver waitlist outcomes | |||
| Transplant | 0.86 (0.83–0.90) | 0.86 (0.83–0.89) | 0.79 (0.77–0.82) |
| Death/delisting | 1.27 (1.20–1.35) | 1.23 (1.17–1.30) | 1.19 (1.12–1.27) |
Results from multivariable competing risks regression models adjusted for patient characteristics. HR: Hazard Ratio, CI: Confidence Interval
Transplant & Post-transplant outcomes
The unadjusted rate of transplant recipients with Medicaid increased over time from Era 1 to Era 3 (kidney 6.4%, 7.0%, 8.6% consecutively; liver 14.5%, 15.6%, 16.7%). Transplant recipients with Medicaid had greater hazards of graft failure and mortality than those with private insurance (Table 4). This did not change over time (era vs. insurance interaction p > 0.5 for all).
Table 4.
Post-transplant outcomes for Medicaid vs Privately Insured Transplant Recipients
| Era 1: 2007–2010 HR (95% CI) | Era 2: 2011–2014 HR (95% CI) | Era 3: 2015–2018 HR (95% CI) | |
|---|---|---|---|
| Kidney transplant outcomes | |||
| Graft Failure | 1.27 (1.20–1.35) | 1.28 (1.16–1.40) | 1.23 (1.06–1.44) |
| Death | 1.24 (1.15–1.34) | 1.26(1.11–1.43) | 1.26 (1.03–1.55) |
| Liver transplant outcomes | |||
| Graft Failure | 1.20 (1.13–1.27) | 1.15 (1.07–1.25) | 1.05 (0.94–1.17) |
| Death | 1.22 (1.14–1.30) | 1.21 (1.11–1.31) | 1.12 (1.00–1.26) |
Results from multivariable competing risks regression models adjusted for recipient and donor characteristics. HR: Hazard Ratio, CI: Confidence Interval
Discussion
Organ transplant is fraught with disparities in access to care, and outcomes are heavily influenced by social determinants of health, including insurance status. In this study, we found Medicaid expansion was associated with an increased proportion of kidney and liver transplant candidates covered by Medicaid. Both waitlist-related and post-transplant outcomes for Medicaid beneficiaries, however, remained inferior to outcomes of the privately insured.
Access to the transplant waitlist
Ten years after ACA implementation, Medicaid expansion states had nearly double the rate of listing when compared to non-expansion states for liver transplant candidates, and more than three times the rate of listing for kidney candidates. These findings are consistent with earlier work showing that Medicaid expansion states had an increase in the proportion of waitlisted Medicaid beneficiaries from 2010–2016 with no change in non-expansion states.(10, 17) The differential effect of Medicaid expansion by organ is also consistent with prior work by Oliveira et al.(18) which demonstrated that post-ACA listing for heart transplants among Medicaid beneficiaries increased by 17%, but liver transplants only increased by 2% and kidney transplants by 1%. While Oliveira et al. found no difference in listings based on state expansion status, we found expansion states showed a greater increase in listed Medicaid beneficiaries. These findings underscore the potential for state Medicaid expansion to contribute to increased access to transplantation, but differ from our hypothesis that a longer observation period after ACA implementation would demonstrate an increased access to the waitlist for Medicaid beneficiaries. In addition, we found the magnitude of increase modest relative to estimates of newly eligible adults in expansion states along with prior research demonstrating 25.2% increase in the probability of having an office-based physician specialist visit among newly eligible adults.(19) The relatively modest rates of increased waitlist additions should prompt further investigation into the interaction between state-specific Medicaid expansion and disparities in access to the waitlist based on social determinants of health.
Waitlist outcomes
We found that compared to the privately insured, Medicaid-insured transplant candidates remained less likely to receive a kidney or liver transplant and more likely to die or be delisted for clinical decline. While the magnitude of differences in these outcomes between Medicaid insured and others decreased over time, differences persisted throughout the observation time-period. Pre-ACA, liver transplant candidates with Medicaid insurance were disproportionately more likely to die while still on the waitlist when compared to candidates with other types of insurance, consistent with data showing that liver transplant evaluation and referral are delayed for Medicaid beneficiaries.(14, 20)
Substantial disparities in access to the transplant waitlist and post-transplant outcomes persist when comparing Medicaid beneficiaries to candidates with private insurance. This may be due in part to the inability of Medicaid beneficiaries to maintain preventative care while on the waitlist, leading to inactivations due to lapses in care. Also contributing is the labor-intensive nature of transplant waitlist maintenance: with most centers requiring patients to be seen annually and have some of their workup, like cardiac testing and age-appropriate cancer screening, repeatedly up to date. Patients with substantial social barriers or financial constraints, including lack of resources to travel for required follow-up and additional testing may be less likely to maintain their active status on the waitlist. We found that Medicaid beneficiaries spent a larger portion of their first waitlist year inactive than the privately insured. While this association decreased over time in the kidney candidate population, it did not consistently decrease in the liver candidate population—likely because of the morbidity and mortality associated with delay of liver transplant. Consistent and frequent contact with patients on the waitlist allows transplant centers to maintain accurate health records and assist with social barriers to transplant that may arise, including lack of transportation or caregivers. These processes vary by center, but lapses in communication may lead to avoidable waitlist inactivations that affect clinical outcomes, as waitlist inactivation is associated with lower likelihood of receiving a transplant and lower waitlist survival.(21, 22) Additional work is needed to investigate waitlist maintenance practices among transplant centers with high proportions of disadvantaged patients. There is single-center evidence that waitlist inactivation is often not due to contraindications to transplantation, and structured ongoing review of waitlist inactivations can potentially increase transplant rates(23) and effectively reduce the insurance-related disparities we observed in our analysis.
Social Determinants of Patient and Graft Outcomes
Despite an increase in the number of kidney and liver transplant candidates with Medicaid expansion, we found no significant improvement in post-transplant patient and graft survival in Medicaid beneficiaries vs. the privately insured, consistent with work done in other clinical disciplines and subspecialities.(24, 25) These findings suggest that improvements in access to care may not be associated with improved outcomes. Social determinants of health are strongly associated with health outcomes independent of insurance coverage, and Medicaid beneficiaries, who often face substantial social barriers, have inferior health outcomes in several settings when compared to the privately insured. (3, 10, 26–33) The increased waitlisting rates but persistently inferior outcomes likely reflect the fact that other social determinants of health besides insurance affect post-transplant care and outcomes.(34–36) Additional work will be necessary to elucidate causal pathways between post-transplant outcomes and social barriers such as geographic isolation, food insecurity, access to transportation and information technology, caregiving burden, among others.
It is also possible that in the same way population mix influences waitlist maintenance, hospitals caring for large populations of poor, underserved, or disenfranchised patients may face unique challenges that affect their resources to provide post-transplant care.(37) However, prior work has shown that the hospitals located in expansion states had increased Medicaid revenue, lower uncompensated care costs, and greater profit margins compared with hospitals located in the 25 non-expansion states.(38) As such, transplant centers in expansion states may have different regional market characteristics and benefit from additional resources for programs to mitigate patient barriers and improve post-transplant outcomes than transplant centers in non-expansion states. Future work distinguishing between early and late post-transplant outcomes among Medicaid beneficiaries may help improve our understanding of disparities based on insurance status. A critical component of this future work will be investigations of decision making about graft quality for patients with social disadvantage. Equally important will be development of interventions that improve education and communication for newly insured and publicly insured patients across the transplant care continuum.
Racial disparities
We found no increase in the proportion of Black/African American waitlist candidates over time and observed a decrease in the proportion of Black/African American kidney and liver transplant recipients. These findings add to previous data reporting the conflicting results on the effects of the ACA among national and transplant-specific data sources. Prior work has shown that increased insurance access leads to better access to care and facilitates ongoing economic recovery.(39, 40) When fully implemented at the state level, the ACA reduced racial differences in insurance coverage by 23%, age disparity by 36%, and income group disparity by 43%.(41) Increased coverage through the ACA has not consistently resulted in increased receipt of appropriate medical or surgical care, or improved outcomes.(42) Specific to transplantation, however, Medicaid expansion was associated with disproportionately higher increases in new preemptive listings for kidney transplantation and listings for heart and liver transplantation among racial and ethnic minorities with Medicaid coverage than among white listings.(12, 43–45) These inconsistent results may reflect the inability of our study to account for nuances in the transplant evaluation and selection process that may disadvantage racial and ethnic minorities even when they present with adequate insurance. It is important to note that racial disparities were not the focus of this analysis, our study was not designed to examine racial inequities, and additional work examining center variation in protocols for evaluation and selection of Medicaid beneficiaries may help clarify this interaction.
Limitations
We acknowledge several important limitations to this study. Medicaid expansion was not standardized among participating states, leading to potential variability in state-level effects. Our study design defined candidates by the first transplant listing and may misclassify transplant recipients with multiple listings who ultimately receive their transplant in another state. However, this is rare and should not substantially bias our findings. Our study period overlaps with implementation of the Kidney Allocation System (KAS) on December 4, 2014. As KAS was implemented by UNOS/OPTN immediately and uniformly, it is unlikely to have masked the state-level trends of interest here. Finally, we cannot address the transplant referral or evaluation processes, neither of which are recorded in SRTR. Substantial variability and bias have been reported in the referral and evaluation of patients with end-stage organ disease, and this may also have been affected by Medicaid expansion.(46–48)
Conclusions
Full implementation of the ACA, which includes several key policy reforms at the state level, has been associated with an overall increase in insurance coverage, increases in the use of primary care and treatment for chronic conditions, and reductions in catastrophic medical spending, increases in elective surgical care, and decreased all-cause mortality.(24, 49) For the millions of patients with chronic liver and kidney disease, this should theoretically include improved access to transplantation. In the United States, less than 1% of transplant recipients are uninsured, while the national rate of uninsured in the past decade has ranged 8–16%.(50–52) Thus, increases of 3.8 and 5.3% for kidney and liver waitlist registrants with Medicaid, respectively, indicate that Medicaid expansion is a necessary but insufficient step to improve access for the under-insured. Further work is needed to understand additional barriers faced by Medicaid beneficiaries that may be encountered along the continuum of transplant care.
| SDCa: Number of Inactivations in the Kidney Cohort | ||||
|---|---|---|---|---|
|
| ||||
| Insurance | Unadjusted | Adjusted | ||
|
| ||||
| IRR (95% CI) | P-Value* | IRR (95% CI) | P-Value* | |
| Private | ref | ref | ||
| Government | 1.04 (1.01, 1.07) | 1.00 (0.97, 1.02) | ||
| Medicaid | 0.92 (0.91, 0.93) | <0.0001 | 0.94 (0.93, 0.96) | <0.0001 |
| Medicare | 1.01 (1.00, 1.02) | 1.02 (1.01, 1.02) | ||
| Other | 1.08 (0.99, 1.18) | 1.13 (1.04, 1.23) | ||
| Uninsured | 1.09 (0.93, 1.26) | 1.15 (0.99, 1.34) | ||
|
| ||||
| SDCb: Number of Inactivations in the Liver Coho | ||||
|
| ||||
| Insurance | Unadjusted | Adjusted | ||
|
| ||||
| IRR (95% CI) | P-Value* | IRR (95% CI) | P-Value* | |
|
| ||||
| Private | ref | ref | ||
| Government | 0.92 (0.88, 0.97) | 0.89 (0.84, 0.93) | ||
| Medicaid | 1.15 (1.13, 1.18) | <0.0001 | 1.09 (1.06, 1.11) | <0.0001 |
| Medicare | 1.23 (1.20, 1.25) | 1.11 (1.08, 1.13) | ||
| Other | 1.37 (1.19, 1.59) | 1.16 (1.00, 1.34) | ||
| Uninsured | 2.32 (1.86, 2.89) | 2.41 (1.93, 3.00) | ||
Acknowledgements and financial support statements:
The data reported here have been supplied by the Hennepin Healthcare Research Institute (HHRI) as the contractor for the Scientific Registry of Transplant Recipients (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 U.S. Government. Research reported in this publication was supported by the National Institute on Minority Health and Health Disparities under Award Number U54MD012530. LM salary is supported by 5T32CA093245-13, the Robert Wood Johnson Foundation. The content is solely the responsibility of the authors and does not represent the official views of the Duke University, the National Institutes of Health, and the Department of Veterans Affairs.
Financial support statement:
Footnotes
Conflict of Interest
Disclosure: The authors declare no conflicts of interest.
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