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

DOI: 10.1007/s11606-016-3869-x

Cite this article as:
Meddings, J., Reichert, H., Smith, S.N. et al. J GEN INTERN MED (2017) 32: 71. doi:10.1007/s11606-016-3869-x

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)

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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