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. 2017 Jan;32(1):71-80.
doi: 10.1007/s11606-016-3869-x. Epub 2016 Nov 15.

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

Jennifer Meddings  1   2   3   4 , Heidi Reichert  5   6 , Shawna N Smith  5   7   6 , Theodore J Iwashyna  5   8   7   6 , Kenneth M Langa  5   8   7   9   6 , Timothy P Hofer  5   8   6 , Laurence F McMahon Jr  5   9   6
Affiliations

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

Jennifer Meddings et al. J Gen Intern Med. 2017 Jan.

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.

Keywords: Medicare; heart failure; pneumonia; readmission; risk adjustment.

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Conflict of interest statement

Compliance with Ethical Standards Funders This work was funded by the Agency for Healthcare Research and Quality (AHRQ; 2R01HS018334, 1K08HS019767). The funding sources were not involved in the conduct, interpretation, or reporting of the results, or the decision to submit the manuscript for publication. The Health and Retirement Study is funded by the National Institute on Aging (U01 AG009740), and performed at the Institute for Social Research, University of Michigan. Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent those of the sponsor, AHRQ, the US Government, or the Department of Veterans Affairs. Contributors We thank Jessica Ameling, MPH, and Laura Petersen, MHSA, for providing assistance with references and manuscript editing. We are very appreciative for the insights and expertise provided by Mary A.M. Rogers, PhD, for the development of the grant, use of HRS-CMS data, and analyses. Prior Presentations These analyses were presented in part as posters at the Society of General Internal Medicine Annual Meeting, Toronto, Ontario, on April 23, 2015; the AcademyHealth Annual Research Meeting, Minneapolis, Minnesota, on June 14, 2015; and the International Conference on Health Policy Statistics, Providence, Rhode Island, on October 8, 2015. Conflict of Interest All authors have completed and submitted the ICJME Form for Disclosure of Potential Conflicts of Interest. Dr. Meddings has reported receiving honoraria for lectures and teaching related to prevention and value-based purchasing policies involving catheter-associated urinary tract infection and hospital-acquired pressure ulcers. The remaining authors report no conflicts of interest.

Figures

Figure 1.
Figure 1.
Conceptual model for disability and social determinants of health as predictors of readmission. Data sources for the predictor variables of interest in this study are indicated in parentheses. PT, physical therapy; SNF, skilled nursing facility; CMS, Centers for Medicare and Medicaid Services; HRS, Health and Retirement Study; ACS, American Community Survey; HCUP, Healthcare Cost Utilization Project.
Figure 2.
Figure 2.
Coefficient plots for pneumonia for both HRS-CMS and ACS-HCUP. HRS-CMS refers to Health and Retirement Study data linked with administrative claims data from the Centers for Medicare and Medicaid Services. ACS-HCUP refers to merged Healthcare Cost Utilization Project State Inpatient Databases from Florida and Washington (2009–2012) and US Census American Community Survey 5-year ZIP Code Tabulation Area (ZCTA) data, 2008–2012. Individual predictor models reflect the addition of a single disability measure or social determinant of health to the current CMS risk adjustment, while the full models reflect the addition of all measures of interest in addition to the CMS risk adjustment. * p < 0.05.
Figure 3.
Figure 3.
Coefficient plots for heart failure for both HRS-CMS and ACS-HCUP. HRS-CMS refers to Health and Retirement Study data linked with administrative claims data from the Centers for Medicare and Medicaid Services. ACS-HCUP refers to merged Healthcare Cost Utilization Project State Inpatient Databases from Florida and Washington (2009–2012) and US Census American Community Survey 5-year ZIP Code Tabulation Area (ZCTA) data, 2008–2012. Individual predictor models reflect the addition of a single disability measure or social determinant of health to the current CMS risk adjustment, while the full models reflect the addition of all measures of interest in addition to the CMS risk adjustment. * p < 0.05.
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References

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