Social Risk and Dialysis Facility Performance in the First Year of the ESRD Treatment Choices Model

JAMA. 2024 Jan 9;331(2):124-131. doi: 10.1001/jama.2023.23649.

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Aged
  • Black People / statistics & numerical data
  • Black or African American / statistics & numerical data
  • Cross-Sectional Studies
  • Female
  • Healthcare Disparities* / economics
  • Healthcare Disparities* / ethnology
  • Healthcare Disparities* / statistics & numerical data
  • Hispanic or Latino / statistics & numerical data
  • Humans
  • Kidney Failure, Chronic* / economics
  • Kidney Failure, Chronic* / epidemiology
  • Kidney Failure, Chronic* / ethnology
  • Kidney Failure, Chronic* / therapy
  • Kidney Transplantation / statistics & numerical data
  • Male
  • Medicaid / economics
  • Medicaid / statistics & numerical data
  • Medically Uninsured / statistics & numerical data
  • Models, Economic
  • Reimbursement, Incentive* / economics
  • Reimbursement, Incentive* / statistics & numerical data
  • Renal Dialysis* / economics
  • Renal Dialysis* / methods
  • Renal Dialysis* / statistics & numerical data
  • Self Care* / economics
  • Self Care* / methods
  • Self Care* / statistics & numerical data
  • Social Determinants of Health* / economics
  • Social Determinants of Health* / ethnology
  • Social Determinants of Health* / statistics & numerical data
  • United States / epidemiology
  • Vulnerable Populations / statistics & numerical data
  • Waiting Lists