Abstract
Importance The End-Stage Renal Disease Treatment Choices (ETC) model randomly selected 30% of US dialysis facilities to receive financial incentives based on their use of home dialysis, kidney transplant waitlisting, or transplant receipt. Facilities that disproportionately serve populations with high social risk have a lower use of home dialysis and kidney transplant raising concerns that these sites may fare poorly in the payment model.
Objective To examine first-year ETC model performance scores and financial penalties across dialysis facilities, stratified by their incident patients’ social risk.
Design, Setting, and Participants A cross-sectional study of 2191 US dialysis facilities that participated in the ETC model from January 1 through December 31, 2021.
Exposure Composition of incident patient population, characterized by the proportion of patients who were non-Hispanic Black, Hispanic, living in a highly disadvantaged neighborhood, uninsured, or covered by Medicaid at dialysis initiation. A facility-level composite social risk score assessed whether each facility was in the highest quintile of having 0, 1, or at least 2 of these characteristics.
Main Outcomes and Measures Use of home dialysis, waitlisting, or transplant; model performance score; and financial penalization.
Results Using data from 125 984 incident patients (median age, 65 years [IQR, 54-74]; 41.8% female; 28.6% Black; 11.7% Hispanic), 1071 dialysis facilities (48.9%) had no social risk features, and 491 (22.4%) had 2 or more. In the first year of the ETC model, compared with those with no social risk features, dialysis facilities with 2 or more had lower mean performance scores (3.4 vs 3.6, P = .002) and lower use of home dialysis (14.1% vs 16.0%, P < .001). These facilities had higher receipt of financial penalties (18.5% vs 11.5%, P < .001), more frequently had the highest payment cut of 5% (2.4% vs 0.7%; P = .003), and were less likely to achieve the highest bonus of 4% (0% vs 2.7%; P < .001). Compared with all other facilities, those in the highest quintile of treating uninsured patients or those covered by Medicaid experienced more financial penalties (17.4% vs 12.9%, P = .01) as did those in the highest quintile in the proportion of patients who were Black (18.5% vs 12.6%, P = .001).
Conclusions In the first year of the Centers for Medicare & Medicaid Services’ ETC model, dialysis facilities serving higher proportions of patients with social risk features had lower performance scores and experienced markedly higher receipt of financial penalties.
In the first year of CMS’ ETC model, dialysis facilities serving more patients in the highest quintile of social risk had lower performance scores and received markedly higher financial penalties. The performance differences were driven by lower home dialysis achievement, rather than transplant and transplant waitlisting, and occurred despite CMS’ incentives of rewarding improvement for the use of home dialysis and receipt of kidney transplants in addition to overall achievement. The higher financial penalization was most pronounced for facilities serving patient populations with the highest social risk. Social risk characteristics that were most highly associated with financial penalties and lower home dialysis use were the share of those who were uninsured or covered by Medicaid and were non-Hispanic Black patients. The share of patients from disadvantaged neighborhoods was most associated with lower transplant achievement and improvement. Finally, compared with for-profit facilities, not-for-profit facilities had lower use of home dialysis and were more likely to be financially penalized.
Other studies have demonstrated that pay-for-performance models can have the unintended consequence of disproportionately penalizing and transferring money away from safety-net facilities or clinicians in favor of those that serve populations with lower social risk.4,6,7,14,15,24–28 When payments are assigned without accounting for social risk, program performance may in part reflect socioeconomic case mix rather than the intended measures of quality.15 In kidney failure care, an evaluation of the 2012 ESRD Quality Incentive Program29 demonstrated that dialysis facilities that served high proportions of patients with dual Medicaid-Medicare coverage, living in low-income areas, or who were Black or Hispanic had poorer performance and were more frequently penalized than their counterparts serving lower proportions of such patients. In an effort to address these issues, CMS designed the ETC model to reward both overall achievement as well as improvement in use of home dialysis and kidney waitlisting and receipt of transplants. Our analysis of the first year of the ETC model finds that despite this approach, dialysis facilities serving patients with higher social risk experienced markedly higher receipt of financial penalties.
Extensive research has demonstrated significant disparities in access to optimal kidney failure care across the US.1,4,30–32 Structural barriers such as institutional and interpersonal racism, residential segregation, and neighborhood poverty are influential in driving these care gaps.1,33 Furthermore, non-Hispanic Black, uninsured, Medicaid-covered, and socioeconomically disadvantaged patients are highly concentrated within dialysis facilities that have lower use of home dialysis and receipt of transplants. Although the financial margins on Medicare dialysis payments are highly variable (approximately <0.5% on average),29,34 facilities that serve high proportions of patients who are uninsured or are covered by Medicaid likely operate on particularly narrow margins. The magnitude of the ETC model’s 5% to 10% penalty on all Medicare reimbursements, larger than previous quality kidney failure programs,29 may threaten the solvency of these safety-net centers. Their closures would likely have harmful consequences, including extended travel and missed sessions.
Beginning in 2022, CMS applied a new health equity adjustment to the modality performance score, which accounts for the proportion of Medicare beneficiaries per aggregation group who are dually insured or low income.16 Our analysis demonstrates social risk factors like race, ethnicity, and neighborhood disadvantage are associated with model performance yet will not be directly addressed in the new scoring system. Furthermore, this study found that only 57 facilities (2.6%) in the sample may be eligible for the future health equity adjustment, when approximated using a proxy for the stratum cutoff proposed by CMS (>50% dually insured or low income). Only about 10% of the cohort with the highest social risk score (≥2) and 17.6% of those currently penalized met this threshold. These findings suggest that relatively few facilities serving patients with substantial social disadvantage may benefit from this adjustment, given that the 50% or higher threshold is substantially greater than the national proportion of patients with kidney failure who are eligible for both Medicaid and Medicare.5 CMS could consider defining a broader construct of social risk, rather than exclusively focusing on eligibility for Medicaid or low-income subsidies or revising the proposed stratum cutoff to be the median proportion of patients who are dually insured or low income. In 2021, the first year after the 21st Century Cures Act expanded Medicare Advantage enrollment to persons with kidney failure, nearly 1 in 4 dual beneficiaries with kidney failure switched to Medicare Advantage coverage.35 Given this disproportionate shift ,36 smaller proportions of dialysis facilities may qualify for CMS’ health equity scoring adjustment going forward.
This study has 4 limitations. First, the use of historical (2017-2020) data on incident patients to characterize facilities’ 2021 social risk may lead to misclassification. However, it is likely that the sociodemographic characteristics of a facility’s population in 2021 are strongly associated with the composition of patients who initiated dialysis in that facility over the prior 3 years. Second, the approach to identifying facilities eligible for the health equity scoring adjustment used the proportion of patients who were uninsured or were covered by Medicaid at initiation, but CMS uses the proportion of prevalent traditional Medicare patients with dual coverage or who are eligible for a low-income subsidy. Third, CMS assesses outcomes and provides data at the aggregation-group level and then attributes performance and applies payment adjustments to all member facilities. Therefore, although the analyses in this study align with CMS’ approach, it was not possible to assess facility-level variations in home dialysis and transplant waitlisting or transplant rates. Fourth, although not-for-profit facilities had received substantially higher financial penalization driven by lower use of home dialysis, the small number of such facilities (n = 130) precludes more extensive analyses. The experience of not-for-profit facilities under the ETC model should be closely monitored in future work.
In this observational study of 2191 dialysis facilities, those serving higher proportions of patients who were non-Hispanic Black or Hispanic, living in a highly disadvantaged neighborhood, or were uninsured or covered by Medicaid at dialysis initiation received lower performance scores and had experienced more financial penalization, driven primarily by lower use of home dialysis. These findings, coupled with the escalation of penalties to as much as 10% in future years, support monitoring the ETC model’s continued impact on dialysis facilities that disproportionately serve patients with social risk factors, as well as its influence on outcomes and disparities in care among patients treated in these sites.
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