Journal of General Internal Medicine

, Volume 32, Issue 1, pp 71–80

The Impact of Disability and Social Determinants of Health on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort Study

  • Jennifer Meddings
  • Heidi Reichert
  • Shawna N. Smith
  • Theodore J. Iwashyna
  • Kenneth M. Langa
  • Timothy P. Hofer
  • Laurence F. McMahonJr.
Original Research

Abstract

Background

Readmission rates after pneumonia, heart failure, and acute myocardial infarction hospitalizations are risk-adjusted for age, gender, and medical comorbidities and used to penalize hospitals.

Objective

To assess the impact of disability and social determinants of health on condition-specific readmissions beyond current risk adjustment.

Design, Setting, and Participants

Retrospective cohort study of Medicare patients using 1) linked Health and Retirement Study-Medicare claims data (HRS-CMS) and 2) Healthcare Cost and Utilization Project State Inpatient Databases (Florida, Washington) linked with ZIP Code-level measures from the Census American Community Survey (ACS-HCUP). Multilevel logistic regression models assessed the impact of disability and selected social determinants of health on readmission beyond current risk adjustment.

Main Measures

Outcomes measured were readmissions ≤30 days after hospitalizations for pneumonia, heart failure, or acute myocardial infarction. HRS-CMS models included disability measures (activities of daily living [ADL] limitations, cognitive impairment, nursing home residence, home healthcare use) and social determinants of health (spouse, children, wealth, Medicaid, race). ACS-HCUP model measures were ZIP Code-percentage of residents ≥65 years of age with ADL difficulty, spouse, income, Medicaid, and patient-level and hospital-level race.

Key Results

For pneumonia, ≥3 ADL difficulties (OR 1.61, CI 1.079–2.391) and prior home healthcare needs (OR 1.68, CI 1.204–2.355) increased readmission in HRS-CMS models (N = 1631); ADL difficulties (OR 1.20, CI 1.063–1.352) and ‘other’ race (OR 1.14, CI 1.001–1.301) increased readmission in ACS-HCUP models (N = 27,297). For heart failure, children (OR 0.66, CI 0.437–0.984) and wealth (OR 0.53, CI 0.349–0.787) lowered readmission in HRS-CMS models (N = 2068), while black (OR 1.17, CI 1.056–1.292) and ‘other’ race (OR 1.14, CI 1.036-1.260) increased readmission in ACS-HCUP models (N = 37,612). For acute myocardial infarction, nursing home status (OR 4.04, CI 1.212–13.440) increased readmission in HRS-CMS models (N = 833); ‘other’ patient-level race (OR 1.18, CI 1.012–1.385) and hospital-level race (OR 1.06, CI 1.001–1.125) increased readmission in ACS-HCUP models (N = 17,496).

Conclusions

Disability and social determinants of health influence readmission risk when added to the current Medicare risk adjustment models, but the effect varies by condition.

KEY WORDS

readmission risk adjustment Medicare pneumonia heart failure 

Supplementary material

11606_2016_3869_MOESM1_ESM.docx (38 kb)
Appendix Table 1Summary statistics for HRS-CMS patients for readmission, demographics, disability, and social determinants of health variables, by cohort, N (%) (DOCX 37 kb)
11606_2016_3869_MOESM2_ESM.docx (42 kb)
Appendix Table 2Summary statistics for ACS-HCUP patients for readmission, demographics, disability, and social determinants of health variables, by cohort, N (%) (DOCX 41 kb)
11606_2016_3869_MOESM3_ESM.docx (40 kb)
Appendix Table 3HRS-CMS prevalence of clinical comorbidities by cohort, N (%) (DOCX 40 kb)
11606_2016_3869_MOESM4_ESM.docx (41 kb)
Appendix Table 4ACS-HCUP prevalence of clinical comorbidities by cohort, N (%) (DOCX 41 kb)
11606_2016_3869_MOESM5_ESM.docx (39 kb)
Appendix Table 5Average marginal effects for HRS-CMS models, by cohort (DOCX 38 kb)
11606_2016_3869_MOESM6_ESM.docx (295 kb)
Appendix Figure 1Study flow diagram for HRS-CMS pneumonia cohort (DOCX 294 kb)
11606_2016_3869_MOESM7_ESM.docx (295 kb)
Appendix Figure 2Study flow diagram for HRS-CMS heart failure cohort (DOCX 294 kb)
11606_2016_3869_MOESM8_ESM.docx (301 kb)
Appendix Figure 3Study flow diagram for HRS-CMS acute myocardial infarction cohort (DOCX 301 kb)
11606_2016_3869_MOESM9_ESM.docx (131 kb)
Appendix Figure 4Study flow diagram for ACS-HCUP pneumonia cohort (DOCX 130 kb)
11606_2016_3869_MOESM10_ESM.docx (132 kb)
Appendix Figure 5Study flow diagram for ACS-HCUP heart failure cohort (DOCX 131 kb)
11606_2016_3869_MOESM11_ESM.docx (134 kb)
Appendix Figure 6Study flow diagram for ACS-HCUP acute myocardial infarction cohort (DOCX 133 kb)
11606_2016_3869_MOESM12_ESM.docx (13.1 mb)
Appendix Figure 7Coefficient plots for acute myocardial infarction for both HRS-CMS and ACS-HCUP (DOCX 13.0 mb)

References

  1. 1.
    Accounting for Social Risk Factors in Medicare Payment: Identifying Social Risk Factors. Washington, DC: National Academies of Sciences, Engineering, and Medicine. 2016. Available at http://www.nap.edu/catalog/21858/accounting-for-social-risk-factors-in-medicare-payment-identifying-social. Accessed September 9, 2016.
  2. 2.
    Boccuti C, Casillas G. Aiming for Fewer Hospital U-turns: The Medicare Hospital Readmission Reduction Program. Menlo Park, CA: The Henry J. Kaiser Family Foundation; 2015. Available at http://slcsuperiorhomecare.com/wp-content/uploads/2015/06/Kaiser-Readmission-paper.pdf. Accessed September 9, 2016.Google Scholar
  3. 3.
    Krumholz H, Normand SL, Keenan P, et al. Hospital 30-day Heart Failure Readmission Measure: Methodology. New Haven, CT: Yale New Haven Health Services Corporation/Center for Outcomes Research & Evaluation (YNHHSC/CORE); 2008. Available at http://www.qualitynet.org/. Accessed September 9, 2016.Google Scholar
  4. 4.
    Krumholz HM, Normand SL, Keenan PS, et al. Hospital 30-Day Pneumonia Readmission Measure: Methodology. New Haven, CT: Yale New Haven Health Services Corporation/Center for Outcomes Research & Evaluation (YNHHSC/CORE); 2008. Available at http://www.qualitynet.org/. Accessed September 9, 2016.Google Scholar
  5. 5.
    Krumholz HM, Normand SL, Keenan PS, et al. Hospital 30-Day Acute Myocardial Infarction Readmission Measure: Methodology. New Haven, CT: Yale New Haven Health Services Corporation/Center for Outcomes Research & Evaluation (YNHHSC/CORE); 2008. Available at http://www.qualitynet.org/. Accessed September 9, 2016.Google Scholar
  6. 6.
    Grady JN, Lin Z, Wang C, et al. Measures Updates and Specifications Report: Hospital-Level 30-Day Risk-Standardization Readmission Measures for Acute Myocardial Infarction, Heart Failure, and Pneumonia. New Haven, CT: Yale New Haven Health Services Corporation/Center for Outcomes Research & Evaluation (YNHHSC/CORE); 2013. Available at http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/Measure-Methodology.html. Accessed September 9, 2016.Google Scholar
  7. 7.
    Yale New Haven Health Services Corporation/Center for Outcomes Research & Evaluation (YNHHSC/CORE). 2013 Measures Updates and Specifications Report: Hospital-Level 30-Day Risk-Standardized Readmission Measures for Acute Myocardial Infarction, Heart Failure, and Pneumonia (Version 6.0). 2013. Available at https://www.qualitynet.org/dcs/ContentServer?cid=1228774371008&pagename=QnetPublic%2FPage%2FQnetTier4&c=Page. Accessed September 9, 2016.
  8. 8.
    H.R. 4994 (113th): IMPACT Act of 2014. Pub.L. 113-185. Signed October 6, 2014. Available at https://www.govtrack.us/congress/bills/113/hr4994, accessed September 9, 2016.
  9. 9.
    Medicare Payment Advisory Commission. Refining the hospital readmissions reduction program. Washington, D.C: Medicare Payment Advisory Commission; 2013. Available at http://www.medpac.gov/docs/default-source/reports/jun13_ch04.pdf. Accessed September 9, 2016.Google Scholar
  10. 10.
    Calvillo–King L, Arnold D, Eubank KJ, et al. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med. 2013;28(2):269–82.CrossRefPubMedGoogle Scholar
  11. 11.
    Chin MH, Goldman L. Correlates of early hospital readmission or death in patients with congestive heart failure. Am J Cardiol. 1997;79(12):1640–4.CrossRefPubMedGoogle Scholar
  12. 12.
    Coleman EA, Min SJ, Chomiak A, Kramer AM. Post-hospital care transitions: patterns, complications, and risk identification. Health Serv Res. 2004;39(5):1449–65.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688–98.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Smith DM, Katz BP, Huster GA, Fitzgerald JF, Martin DK, Freeman JA. Risk factors for non-elective hospital readmissions. J Gen Intern Med. 1996;11(12):762–4.CrossRefPubMedGoogle Scholar
  15. 15.
    DePalma G, Xu H, Covinsky KE, et al. Hospital readmission among older adults who return home with unmet need for ADL disability. Gerontologist. 2012;53(3):454–61.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Fisher SR, Kuo YF, Sharma G, et al. Mobility after hospital discharge as a marker for 30-day readmission. J Gerontol A Biol Sci Med Sci. 2013;68(7):805–10.CrossRefPubMedGoogle Scholar
  17. 17.
    Garcia-Perez L, Linertova R, Lorenzo-Riera A, Vazquez-Diaz JR, Duque-Gonzalez B, Sarria-Santamera A. Risk factors for hospital readmissions in elderly patients: a systematic review. QJM. 2011;104(8):639–51.CrossRefPubMedGoogle Scholar
  18. 18.
    Shih SL, Gerrard P, Goldstein R, et al. Functional status outperforms comorbidities in predicting acute care readmissions in medically complex patients. J Gen Intern Med. 2015;30(11):1688–95.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Barnett ML, Hsu J, McWilliams JM. Patient characteristics and differences in hospital readmission rates. JAMA Intern Med. 2015;175(11):1803–12.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    McCoy JL, Davis M, Hudson RE. Geographic patterns of disability in the United States. Soc Secur Bull. 1994;57(1):25–36.PubMedGoogle Scholar
  21. 21.
    Oddone EZ, Weinberger M, Horner M, et al. Classifying general medicine readmissions. Are they preventable? Veterans Affairs Cooperative Studies in Health Services Group on Primary Care and Hospital Readmissions. J Gen Intern Med. 1996;11(10):597–607.CrossRefPubMedGoogle Scholar
  22. 22.
    Joynt KE, Jha AK. Thirty-day readmissions - truth and consequences. N Engl J Med. 2012;366(15):1366–9.CrossRefPubMedGoogle Scholar
  23. 23.
    Greysen SR, Cenzer IS, Auerbach AD, Covinsky KE. Functional impairment and hospital readmission in Medicare seniors. JAMA Intern Med. 2015;175(4):559–65.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Berenson J, Shih A. Higher Readmissions at Safety-Net Hospitals and Potential Policy Solutions. New York, NY: The Commonwealth Fund; 2012. Available at http://www.commonwealthfund.org/publications/issue-briefs/2012/dec/higher-readmissions-and-potential-policy-solutions. Accessed September 9, 2016.Google Scholar
  25. 25.
    Risk Adjustment for Socioeconomic Status or Other Sociodemographic Factors Washington, DC: National Quality Forum. 2014. Available at http://www.qualityforum.org/Publications/2014/08/Risk_Adjustment_for_Socioeconomic_Status_or_Other_Sociodemographic_Factors.aspx. Accessed September 9, 2016.
  26. 26.
    To amend title XVIII of the Social Security Act to improve the risk adjustment under the Medicare Advantage program, and for other purposes., House of Representatives. 2015.Google Scholar
  27. 27.
    Health and Retirement Study: An Introduction. 2016. Available at http://hrsonline.isr.umich.edu/index.php?p=intro. Accessed September 9, 2016.
  28. 28.
    Health and Retirement Study. Sample Sizes and Response Rates. Ann Arbor, MI. 2011. Available at http://hrsonline.isr.umich.edu/sitedocs/sampleresponse.pdf. Accessed September 9, 2016.
  29. 29.
    Langa KM, Chernew ME, Kabeto MU, et al. National estimates of the quantity and cost of informal caregiving for the elderly with dementia. J Gen Intern Med. 2001;16(11):770–8.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Langa KM, Larson EB, Karlawish JH, et al. Trends in the prevalence and mortality of cognitive impairment in the United States: is there evidence of a compression of cognitive morbidity? Alzheimers Dement. 2008;4(2):134–44.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Langa KM, Plassman BL, Wallace RB, et al. The Aging, Demographics, and Memory Study: study design and methods. Neuroepidemiology. 2005;25(4):181–91.CrossRefPubMedGoogle Scholar
  32. 32.
    Ofstedal MB, Fisher GG, Herzog AR. Documentation of cognitive functioning measures in the Health and Retirement Study. Ann Arbor, MI: University of Michigan; 2005.CrossRefGoogle Scholar
  33. 33.
    Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long-term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010;304(16):1787–94.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Chien S, Campbell N, Hayden O, et al. RAND HRS Data Documentation, Version N. Labor & Population Program, RAND Center for the Study of Aging. 2014.Google Scholar
  35. 35.
    Bureau of Labor Statistics. CPI Inflation Calculator. 2016. Available at http://www.bls.gov/data/inflation_calculator.htm. Accessed September 9, 2016.
  36. 36.
    U.S. Census Bureau. American Community Survey: Sample Size and Data Quality. 2015. Available at http://www.census.gov/acs/www/methodology/sample-size-and-data-quality/response-rates/. Accessed September 9, 2016.
  37. 37.
    ReadmissionAMI_HF_PN_SASPACK. 1 ed. Baltimore, MD: Centers for Medicare & Medicaid Services; 2013.Google Scholar
  38. 38.
    StataCorp. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP; 2013.Google Scholar
  39. 39.
    Allman RM, Goode PS, Patrick MM, Burst N, Bartolucci AA. Pressure ulcer risk factors among hospitalized patients with activity limitation. JAMA. 1995;273(11):865–70.CrossRefPubMedGoogle Scholar
  40. 40.
    Bergquist S, Frantz R. Pressure ulcers in community-based older adults receiving home health care. Prevalence, incidence, and associated risk factors. Adv Wound Care. 1999;12(7):339–51.PubMedGoogle Scholar
  41. 41.
    De Brauwer I, Lepage S, Yombi JC, Cornette P, Boland B. Prediction of risk of in-hospital geriatric complications in older patients with hip fracture. Aging Clin Exp Res. 2012;24(1):62–7.CrossRefPubMedGoogle Scholar
  42. 42.
    Holicky R, Charlifue S. Ageing with spinal cord injury: The impact of spousal support. Disabil Rehabil. 1999;21(5/6):250–7.PubMedGoogle Scholar
  43. 43.
    Joynt KE, Orav EJ, Jha AK. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. J Am Med Assoc. 2011;305(7):675–81.CrossRefGoogle Scholar
  44. 44.
    Andrews R. The quality of reporting on race & ethnicity in US hospital discharge abstract data. Agency for Healthcare Research and Quality; June 10, 2008.Google Scholar
  45. 45.
    Defining Categorization Needs for Race and Ethnicity Data. Rockville, MD: Agency for Healthcare Research and Quality. October 2014. Available at http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata3.html. Accessed September 9, 2016.
  46. 46.
    Centers for Medicare & Medicaid Services. Medicare Current Beneficiary Survey. 2016. Available at https://www.cms.gov/Research-Statistics-Data-and-Systems/Research/MCBS/index.html?redirect=/mcbs. Accessed September 9, 2016.

Copyright information

© Society of General Internal Medicine 2016

Authors and Affiliations

  • Jennifer Meddings
    • 1
    • 2
    • 3
    • 6
  • Heidi Reichert
    • 1
    • 6
  • Shawna N. Smith
    • 1
    • 4
    • 6
  • Theodore J. Iwashyna
    • 1
    • 3
    • 4
    • 6
  • Kenneth M. Langa
    • 1
    • 3
    • 4
    • 5
    • 6
  • Timothy P. Hofer
    • 1
    • 3
    • 6
  • Laurence F. McMahonJr.
    • 1
    • 5
    • 6
  1. 1.Department of Internal Medicine, Division of General MedicineUniversity of Michigan Medical SchoolAnn ArborUSA
  2. 2.Department of Pediatrics and Communicable Diseases, Division of General PediatricsUniversity of Michigan Medical SchoolAnn ArborUSA
  3. 3.Ann Arbor VA Medical CenterAnn ArborUSA
  4. 4.University of Michigan Institute for Social ResearchAnn ArborUSA
  5. 5.University of Michigan School of Public HealthAnn ArborUSA
  6. 6.Institute for Healthcare Policy & InnovationUniversity of MichiganAnn ArborUSA

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