The Cost of Transferring Dialysis Care From the Employer-Based Market to Medicare

Many patients with end-stage kidney disease (ESKD) retain their employer-based group health plan (EGHP) when they start dialysis.¹ Patients with EGHPs may obtain Medicare as a secondary payer, but the EGHP remains the primary payer for 30 months during what is known as a coordination period.² If patients prematurely drop their EGHPs (eg, if they become unemployed or if their employers or payers stop offering favorable plans), Medicare becomes the primary payer, which frees the EGHPs of their financial obligations. This cohort study investigated the frequency of premature switches, the characteristics of patients switching early, and the resulting Medicare spending.

The University of Southern California’s institutional review board approved this study. The US Renal Data System (USRDS) was used to identify all US adults (aged 62 years or younger) who had EGHP coverage when their dialysis for ESKD began between January 1, 2007, and December 31, 2014 (eAppendix in the Supplement). Patients were observed through the coordination period or death, with the latest follow-up being October 31, 2017. The USRDS is a deidentified registry of administrative data already collected by the Centers for Medicare & Medicaid Services, and thus patients did not provide informed consent. Analyses were conducted from June 2020 to January 2021 and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Premature switches to Medicare (ie, before the end of the EGHP coordination period) were identified, and the extra months of Medicare use were totaled. Patients were stratified into hospitalization probability quartiles to proxy for severity of illness. To estimate hospitalization probabilities, regression coefficients were applied from a multivariable logistic regression in adults aged 62 or younger who started dialysis with fee-for-service Medicare as the primary payer. The dependent variable was whether the patient was hospitalized in a 12-month period, and independent variables were patient characteristics and comorbidities, facility characteristics, and zip code sociodemographics (eTable in the Supplement).

A Cox proportional hazards model was used to estimate the likelihood of premature switching by hospitalization probability quartile. Ordinary least squares were used to compare total Medicare spending (as the primary or secondary payer) when switching prematurely, switching at the coordination period, and switching after the coordination period or never. Statistical significance was set at P = .05, and tests were 2-sided. Statistical analysis was performed using SAS version 9.4 (SAS Institute) and Stata version 14.0, MP edition (StataCorp).

A total of 113 693 US adults aged 62 years or younger (mean [SD] age, 50 [10] years; 69 357 [61%] men) who started dialysis with an EGHP were included in this study and followed for a mean of 789 (309) days (maximum of 1007 days). The study demographic characteristics included 71 406 (63%) who were White patients, 35 048 (31%) Black patients, 7239 (2%) other, which included Asian patients, Native American patients, or other patients in the database, and 14 511 (13%) Hispanic patients. Patients who switched from EGHP to Medicare prematurely (37 696 [33%]) contributed to 711 528 additional months of Medicare (mean [SD], 19 [12] additional months per patient who switched early) (Table 1). Patients with a higher hospitalization risk were more likely to switch from their EGHP to Medicare prematurely. For example, the third quartile of illness severity was 49% (95% CI, 45%-53%) more likely to switch relative to the quartile that included the patients least likely to be hospitalized within 12 months (Table 2).

For adjusted measurements, patients who prematurely switched to Medicare from their EGHP cost Medicare $81 000 (95% CI, $79 971-$82 029) more than patients who switched at the coordination period and $81 667 (95% CI, $80 611-$82 722) more than patients who switched late or never (Table 1). From 2007 to 2017, premature switches cost Medicare an additional $3.05 billion (95% CI, $3.01-$3.09 billion).

Nearly one-third of adults 62 years or younger who began dialysis with an EGHP switched to Medicare prematurely, resulting in more than $3 billion of additional Medicare spending from 2007 to 2017. Patients who were more likely to be hospitalized were also more likely to switch from their EGHP to Medicare prematurely.

While premature switches increase Medicare spending, many are likely unavoidable because of unemployment associated with the start of dialysis. Blanket prohibitions on early switching would be costly for patients who are unemployed and require Consolidated Omnibus Budget Reconciliation Act, or COBRA, coverage.^3,4 However, frequent premature switches to Medicare likely discourage EGHPs from funding cost-saving interventions that prevent ESKD in chronic kidney disease⁵ because switching to Medicare prematurely offloads more than $80 000 of financial risk per patient. More measured policies that share the risk of dialysis between EGHPs and Medicare, such as having Medicare pay EGHPs a capitated rate for premature switchers (similar in principle to Medicare Advantage), could incentivize EGHP investments aimed at preventing ESKD without increasing patients’ out-of-pocket spending.

This study was limited because of the inability to know why patients switched to Medicare or the employment status of the patients. Results were also subject to residual confounding and relied on the 2728 Form, which has incomplete comorbidity capture.⁶ Despite these limitations, there were large spending differences between premature switches to Medicare compared with switches to Medicare at or after the coordination period or never switching to Medicare. Policy makers should consider ways to share the financial risk of dialysis with EGHPs.

Accepted for Publication: January 21, 2021.

Published: March 18, 2021. doi:10.1001/jamanetworkopen.2021.2113

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Lin E. JAMA Network Open.

Corresponding Author: Eugene Lin, MD, MS, Division of Nephrology, Department of Medicine, University of Southern California, 1333 San Pablo St, MMR 622, Los Angeles, CA 90033 (eugeneli@usc.edu).

Author Contributions: Dr Lin had had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Lin.

Acquisition, analysis, or interpretation of data: Lin.

Drafting of the manuscript: Lin.

Critical revision of the manuscript for important intellectual content: Lin.

Statistical analysis: Lin.

Obtained funding: Lin.

Administrative, technical, or material support: Lin.

Supervision: Lin.

Conflict of Interest Disclosures: Dr Lin reported receiving personal fees from Acumen outside the submitted work.

Funding/Support: Dr Lin was partially supported by the National Institutes of Health through the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) under the award number NIDDK K08 DK118213 and the University Kidney Research Organization.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health. The data reported here have been supplied by the United States Renal Data System. The interpretation and reporting of these data are the responsibility of the author and in no way should be seen as an official policy or interpretation of the US government.

Additional Contributions: I thank Khristina I. Lung, MPH (Leonard D. Schaeffer Center for Health Policy and Economics at the University of Southern California), for her programming and analytic support and Paul Ginsburg, PhD (Leonard D. Schaeffer Center for Health Policy and Economics at the University of Southern California), for many conversations on this topic and his expert input. Ms Lung received salary compensation for her work constructing the study cohort and conducting the preliminary analyses as a research analyst.