Between-Community Low-Income Status and Inclusion in Mandatory Bundled Payments in Medicare’s Comprehensive Care for Joint Replacement Model | Health Disparities | JAMA Network Open | JAMA Network
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Figure.  Association Between Dual-Eligibility Share and Comprehensive Care for Joint Replacement (CJR) Program Participation
Association Between Dual-Eligibility Share and Comprehensive Care for Joint Replacement (CJR) Program Participation

Graph shows that the likelihood of being a CJR market decreases as market dual-eligibility share increases (ie, as quartile increases). The dual-eligibility share is 2.8% to 12.3% for quartile 1, 12.3% to 15.9% for quartile 2, 15.9% to 20.6% for quartile 3, and 20.6% to 57.7% for quartile 4.

Table.  Dual-Eligibility Share and Other Characteristics by CJR and Non-CJR Markets
Dual-Eligibility Share and Other Characteristics by CJR and Non-CJR Markets
1.
Centers for Medicare and Medicaid Services. Comprehensive Care for Joint Replacement Model. Accessed October 5, 2020. https://innovation.cms.gov/innovation-models/cjr
2.
Barnett  ML, Wilcock  A, McWilliams  JM,  et al.  Two-year evaluation of mandatory bundled payments for joint replacement.   N Engl J Med. 2019;380(3):252-262. doi:10.1056/NEJMsa1809010PubMedGoogle ScholarCrossref
3.
Centers for Medicare and Medicaid Services. Radiation oncology model. Accessed October 5, 2020. https://innovation.cms.gov/innovation-models/radiation-oncology-model
4.
Yasaitis  LC, Pajerowski  W, Polsky  D, Werner  RM.  Physicians’ participation in ACOs is lower in places with vulnerable populations than in more affluent communities.   Health Aff (Millwood). 2016;35(8):1382-1390. doi:10.1377/hlthaff.2015.1635PubMedGoogle ScholarCrossref
5.
US Department of Health and Human Services; Office of the Assistant Secretary for Planning and Evaluation. Report to Congress: social risk factors and performance under Medicare’s value-based purchasing programs. Published December 21, 2016. Accessed October 6, 2020. https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs
6.
The Dartmouth Institute. Dartmouth atlas data. Accessed October 4, 2020. https://www.dartmouthatlas.org/data/
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    Research Letter
    Health Policy
    March 8, 2021

    Between-Community Low-Income Status and Inclusion in Mandatory Bundled Payments in Medicare’s Comprehensive Care for Joint Replacement Model

    Author Affiliations
    • 1Department of Medicine, University of Washington, Seattle
    • 2Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia
    • 3Population Health Sciences, Weill Cornell Medicine, New York, New York
    • 4Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 5Corporal Michael J. Cresencz VA Medical Center, Philadelphia, Pennsylvania
    JAMA Netw Open. 2021;4(3):e211016. doi:10.1001/jamanetworkopen.2021.1016
    Introduction

    Using a market-level mandate, Medicare’s Comprehensive Care for Joint Replacement (CJR) Model has required urban US hospitals to accept bundled payments for hip and knee surgery episodes. Among metropolitan statistical area (MSA) markets with above-average episode spending (196 of 384 MSAs), Medicare randomly selected 67 for inclusion.1 Given the 3% to 4% episode savings and stable quality achieved through CJR, Medicare has reinforced its commitment to MSA market-level mandates, using the approach in the forthcoming Radiation Oncology Model with another mandatory program planned in 2023.2,3

    One key advantage of mandatory over voluntary programs is mitigating physician or hospital self-selection that could lead to the exclusion of patients with low socioeconomic status (SES).4 This advantage can also enhance generalizability of program results, but only if regions in the program do not differ greatly from those not included. However, it remains unclear whether communities in CJR are representative of others nationwide with respect to residents’ SES.

    Methods

    This cohort study was approved by the University of Pennsylvania institutional review board with a waiver of informed consent because of the infeasibility of obtaining consent from a large retrospective claims data set. Our analysis followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

    We measured low SES using Medicaid and Medicare dual eligibility, which federal policy makers consider the most reliable measure of social risk among Medicare beneficiaries.5 We used 2016 Medicare data to identify CJR MSA markets and 2010 to 2012 Medicare data to define hospital service areas (HSAs), which are collections of zip codes whose residents receive the majority of hospitalizations from hospitals in that area, as communities within MSAs. We then measured community-level dual-eligibility share, which is the proportion of dual-eligible individuals in HSAs.6 The HSA-level dual-eligibility share was clustered at the MSA level, an approach that reflects the fact that although CJR participation was determined at the MSA level, the program incentivized care changes more narrowly among hospitals.

    We compared dual-eligibility share and other characteristics between CJR and non-CJR markets using Wilcoxon rank sum tests. We evaluated the association between dual-eligibility share, categorized into quartiles to allow for nonlinear associations, and CJR market status using multivariable linear regression on HSA-level data, controlling for market characteristics (shown in the Table) and clustered at the MSA level.

    Analyses were performed using SAS statistical software version 9.4 (SAS Institute). Statistical tests were 2-tailed and significant at α = .05. Data analysis was performed from October 2020 to January 2021.

    Results

    Our sample consisted of 67 CJR markets containing 389 HSAs and 306 non-CJR markets containing 915 HSAs (Table). The mean (SD) dual-eligibility share was 17.5% (8.4%) of the population among CJR markets and 17.2% (7.2%) of the population among non-CJR markets. There were small differences between CJR and non-CJR markets with respect to population sex, age, and racial/ethnic mix, as well as other characteristics, such as total number of hospital beds and Medicare Advantage penetration.

    In multivariable analysis, market-level dual-eligibility share was inversely associated with the likelihood of being a CJR market (Figure). The probability of being a CJR market decreased from the lowest quartile (quartile 1) of dual-eligibility share to quartile 2 (−4.5 percentage point probability; 95% CI, −8.3 to −0.7 percentage point probability; P = .02) and quartile 3 (−8.3 percentage point probability; 95% CI, −14.6 to −2.0 percentage point probability; P = .01). An increase from the lowest to highest quartile (quartile 4) of dual-eligibility share was associated with a −14.1 percentage point probability of being a CJR market (95% CI, −22.2 to −6.0 percentage point probability; P < .001).

    Discussion

    Markets that were more likely to have a higher burden of adverse outcomes through social risk factors were less likely to be selected for CJR. A limitation of this study is the observational design; however, this study underscores the need to ensure that expanded or additional market-level mandates do not inadvertently perpetuate SES disparities. Policy makers should urgently address this concern by directly considering community social factors when selecting markets in forthcoming mandatory bundled payment programs.

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    Article Information

    Accepted for Publication: January 15, 2021.

    Published: March 8, 2021. doi:10.1001/jamanetworkopen.2021.1016

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Liao JM et al. JAMA Network Open.

    Corresponding Author: Amol S. Navathe, MD, PhD, Department of Medical Ethics and Health Policy, University of Pennsylvania, 1108 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19104 (amol@pennmedicine.upenn.edu).

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

    Concept and design: Liao, Huang, Ibrahim, Navathe.

    Acquisition, analysis, or interpretation of data: Liao, Huang, Ibrahim, Connolly, Cousins, Zhu.

    Drafting of the manuscript: Liao, Huang, Ibrahim, Cousins.

    Critical revision of the manuscript for important intellectual content: Liao, Huang, Ibrahim, Connolly, Zhu, Navathe.

    Statistical analysis: Huang, Zhu.

    Obtained funding: Liao, Ibrahim, Cousins, Navathe.

    Administrative, technical, or material support: Huang, Connolly, Cousins, Navathe.

    Supervision: Liao.

    Conflict of Interest Disclosures: Dr. Liao reported receiving textbook royalties from Wolters Kluwer, honoraria from Wolters Kluwer and the Journal of Clinical Pathways, and personal fees from Kaiser Permanente Washington Health Research Institute outside the submitted work. Dr Navathe reported receiving grants from Hawaii Medical Service Association, Anthem Public Policy Institute, Commonwealth Fund, Oscar Health, Cigna Corporation, Robert Wood Johnson Foundation, Donaghue Foundation, Pennsylvania Department of Health, Ochsner Health System, United Healthcare, Blue Cross Blue Shield of North Carolina, and Blue Shield of California; personal fees from Navvis Healthcare, Agathos, Inc, YNHHSC/CORE, Maine Health Accountable Care Organization, Maine Department of Health and Human Services, National University Health System–Singapore, Ministry of Health–Singapore, Elsevier Press, Medicare Payment Advisory Commission, Cleveland Clinic, VIBD Health, and Analysis Group; and personal fees and equity from Navahealth and Embedded Healthcare; and reported serving as an uncompensated board member for Integrated Services, Inc outside the submitted work. No other conflicts were reported.

    Funding/Support: This work was supported by grant 1R01MD013859-01 from the National Institutes of Health.

    Role of the Funder/Sponsor: The funder 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.

    References
    1.
    Centers for Medicare and Medicaid Services. Comprehensive Care for Joint Replacement Model. Accessed October 5, 2020. https://innovation.cms.gov/innovation-models/cjr
    2.
    Barnett  ML, Wilcock  A, McWilliams  JM,  et al.  Two-year evaluation of mandatory bundled payments for joint replacement.   N Engl J Med. 2019;380(3):252-262. doi:10.1056/NEJMsa1809010PubMedGoogle ScholarCrossref
    3.
    Centers for Medicare and Medicaid Services. Radiation oncology model. Accessed October 5, 2020. https://innovation.cms.gov/innovation-models/radiation-oncology-model
    4.
    Yasaitis  LC, Pajerowski  W, Polsky  D, Werner  RM.  Physicians’ participation in ACOs is lower in places with vulnerable populations than in more affluent communities.   Health Aff (Millwood). 2016;35(8):1382-1390. doi:10.1377/hlthaff.2015.1635PubMedGoogle ScholarCrossref
    5.
    US Department of Health and Human Services; Office of the Assistant Secretary for Planning and Evaluation. Report to Congress: social risk factors and performance under Medicare’s value-based purchasing programs. Published December 21, 2016. Accessed October 6, 2020. https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs
    6.
    The Dartmouth Institute. Dartmouth atlas data. Accessed October 4, 2020. https://www.dartmouthatlas.org/data/
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