Association Between Race, Neighborhood, and Medicaid Enrollment and Outcomes in Medicare Home Health Care
Open accessAbstract
Background/Objectives: More than 3 million Medicare beneficiaries use home health care annually, yet little is known about how vulnerable beneficiaries fare in the home health setting. This is particularly important given the recent launch of Medicare's Home Health Value‐Based Purchasing model. The objective of this study was to determine odds of adverse clinical outcomes associated with dual enrollment in Medicaid and Medicare as a marker of individual poverty, residence in a low‐income ZIP code tabulation area ( ZCTA ), and black race. Design: Retrospective observational study using individuals‐level logistic regression. Setting: Home health care. Participants: Fee‐for‐service Medicare beneficiaries from 2012 to 2014. Measurements: Thirty‐ and 60‐day clinical outcomes, including readmissions, admissions, and emergency department ( ED ) use. Results: Home health agencies serving a high proportion of dually enrolled, low‐income ZCTA , or black beneficiaries were less often high‐quality. Dually‐enrolled, low‐income ZCTA, and Black beneficiaries receiving home health care after hospitalization had higher risk‐adjusted odds of 30‐day readmission (odds ratio [OR] = 1.08, P < 0.001; OR = 1.03, P < 0.001; and OR = 1.02, P = 0.002 respectively) and 30‐day ED use (OR = 1.20, 1.07, and 1.15, P < 0.001 for each). Those receiving home health care without preceding hospitalization had higher 60‐day admission (OR = 1.06, P < 0.001; OR = 1.01, P = 0.002; and OR = 1.05, P < 0.001), and 60‐day ED use (OR = 1.16, 1.03, and 1.19, P < 0.001 for each). Differences were primarily within agencies rather than between the agencies where these beneficiaries sought care. Conclusion: Medicare beneficiaries receiving home health services who are dually enrolled, live in a low‐income neighborhood, or are black have higher rates of adverse clinical outcomes. These populations may be an important target for quality improvement under Home Health Value‐Based Purchasing.
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Medicare claims data sources of race/ethnicity information are commonly used in reports and studies of health disparities.
“Further, although Medicare claims that data sources of race/ethnicity information are commonly used in reports and studies of health disparities,35,36 Jarrín et al37 suggested high validity for Black patients and low sensitivity for Hispanic and Asian patients in Medicare beneficiary data files.”
From Disparities in SGLT2 Inhibitor or Glucagon-Like Peptide 1 Receptor Agonist Initiation Among Medicare-Insured Adults With CKD in the United States (2023) · cites this paper as [36]
Canagliflozin was included on <75% of Part D plan formularies in 2019 and 2020, but coverage without prior authorization and without step therapy requirements was only 53.2% in 2019 Part D plans.
“Canagliflozin was included on <75% of Part D plan formularies in 2019 and 2020,32,33 but coverage without prior authorization and without step therapy requirements was only 53.2% (95% CI, 49.1%-57.4%) in 2019 Part D plans.32”
From Disparities in SGLT2 Inhibitor or Glucagon-Like Peptide 1 Receptor Agonist Initiation Among Medicare-Insured Adults With CKD in the United States (2023) · cites this paper as [33]
The Discussion addresses disparities in Medicare home health care, not prescription-drug coverage. Searches of our copy for canagliflozin, Part D formularies, prior authorization, step therapy, and the 53.2% figure found no passage addressing the claim. (LLM, conf=0.78)
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Research works printed but not linked: 21
- Joynt KE, Jha AK. Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program. JAMA 2013;309:342-343.
- Gilman M, Adams EK, Hockenberry JM et al. Safety-net hospitals more likely than other hospitals to fare poorly under Medicare's value-based pur- chasing. Health Aff (Millwood) 2015;34:398-405.
- Ryan AM. Will value-based purchasing increase disparities in care? N Engl J Med 2013;369:2472-2474.
- Brega AG, Goodrich GK, Powell MC et al. Racial and ethnic disparities in the outcomes of elderly home care recipients. Home Health Care Serv Q 2005;24:1-21.
- Smith LM, Anderson WL, Kenyon A et al. Racial and ethnic disparities in patients' experience with skilled home health care services. Med Care Res Rev 2015;72:756-774.
- Chen HF, Homan S, Carlson E et al. The impact of race and neighborhood racial composition on preventable readmissions for diabetic Medicare home health beneficiaries. J Racial Ethn Health Disparities 2017;4:648-658.
- Eicheldinger C, Bonito A. More accurate racial and ethnic codes for Medi- care administrative data. Health Care Financ Rev 2008;29:27-42.
- Chatterjee P, Joynt KE, Orav EJ et al. Patient experience in safety-net hos- pitals: Implications for improving care and value-based purchasing. Arch Intern Med 2012;172:1204-1210.
- Gilman M, Hockenberry JM, Adams EK et al. The financial effect of value-based purchasing and the hospital readmissions reduction program on safety-net hospitals in 2014: A cohort study. Ann Intern Med 2015;163:427-436.
- Cabin W, Himmelstein DU, Siman ML et al. For-profit medicare home health agencies' costs appear higher and quality appears lower compared to nonprofit agencies. Health Aff (Millwood) 2014;33:1460-1465.
- Tsai TC, Orav EJ, Joynt KE. Disparities in surgical 30-day readmission rates for Medicare beneficiaries by race and site of care. Ann Surg 2014;259:1086-1090.
- Joynt KE, Orav E, Jha AK. Thirty-day readmission rates for Medicare ben- eficiaries by race and site of care. JAMA 2011;305:675-681.
- Bach PB, Pham HH, Schrag D et al. Primary care physicians who treat blacks and whites. N Engl J Med 2004;351:575-584.
- Kangovi S, Barg FK, Carter T et al. Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Aff (Millwood) 2013;32:1196-1203.
- Arnett MJ, Thorpe RJ Jr, Gaskin DJ et al. Race, medical mistrust, and seg- regation in primary care as usual source of care: Findings from the Explor- ing Health Disparities in Integrated Communities Study. J Urban Health 2016;93:456-467.
- Ballard DW, Price M, Fung V et al. Validation of an algorithm for catego- rizing the severity of hospital emergency department visits. Med Care 2010;48:58-63.
- Kahlon S, Pederson J, Majumdar SR et al. Association between frailty and 30-day outcomes after discharge from hospital. Can Med Assoc J 2015;187:799-804.
- Arbaje AI, Wolff JL, Yu Q et al. Postdischarge environmental and socioe- conomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. Gerontologist 2008;48:495- 504.
- Mukamel DB, Ladd H, Li Y et al. Have racial disparities in ambulatory care sensitive admissions abated over time? Med Care 2015;53:931-939.
- Dresden SM, Feinglass JM, Kang R et al. Ambulatory care sensitive hospi- talizations through the emergency department by payer: Comparing 2003 and 2009. J Emerg Med 2016;50:135-142.
- Wright B, Potter AJ, Trivedi A. Federally qualified health center use among dual eligibles: Rates of hospitalizations and emergency department visits. Health Aff (Millwood) 2015;34:1147-1155.
Cited by
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Joynt Maddox, K. E., Reidhead, M., Hu, J., Kind, A. J. H., Zaslavsky, A. M., Nagasako, E. M., & Nerenz, D. R. (2019) · Health Services Research · doi:10.1111/1475-6773.13133 · Cited by 214 · see citing works
Zhao, J. Z., Weinhandl, E. D., Carlson, A. M., & St. Peter, W. L. (2023) · Kidney Medicine · doi:10.1016/j.xkme.2022.100564 · Cited by 33 · see citing works
- Authors
- Joynt Maddox, K. E., Chen, L. M., Zuckerman, R., & Epstein, A. M.
- Affiliations
- Washington University School of Medicine St. Louis Missouri
- University of Michigan (US)
- Office of the Assistant Secretary for Planning and Evaluation (US)
- Brigham and Women's Hospital (US)
- Venue
- Journal of the American Geriatrics Society
- Publisher
- Wiley
- Published
- 2017-10-04
- Volume
- 66
- Issue
- 2
- Pages
- 239-246
- ISSN
- 0002-8614 (ISSN-L)
- 1532-5415
- OpenAlex
- W2763433724
- Cited by
- 66
- References
- 0
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 8 · 2023: 9 · 2022: 8 · 2021: 12 · 2020: 7 · 2019: 7 · 2018: 2 · 2017: 1
- FWCI
- 8.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Geriatrics Society (publishedVersion)
- PubMed (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- U.S. Department of Health and Human Services
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Health Sciences › Health Professions › General Health Professions › Geriatric Care and Nursing Homes
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- Medicare home health care, racial disparities, low-income communities, ZIP code tabulation areas, poverty, hospital readmission, emergency department utilization, hospitalization, vulnerable populations
- MeSH terms
- Aged, Black or African American, Female, Medicare, Home Care Services, Hospitalization, Humans, Male, Medicaid, Patient Readmission, Poverty, Quality of Health Care, Residence Characteristics, Retrospective Studies, Time Factors, United States, Outcome Assessment, Health Care, Black People, Healthcare Disparities
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