Utility of socioeconomic status in predicting 30-day outcomes after heart failure hospitalization

Circ Heart Fail. 2015 May;8(3):473-80. doi: 10.1161/CIRCHEARTFAILURE.114.001879. Epub 2015 Mar 6.

Abstract

Background: An individual's socioeconomic status (SES) is associated with health outcomes and mortality, yet it is unknown whether accounting for SES can improve risk-adjustment models for 30-day outcomes among Centers for Medicare & Medicaid Services beneficiaries hospitalized with heart failure.

Methods and results: We linked clinical data on hospitalized patients with heart failure in the Get With The Guidelines-Heart Failure database (January 2005 to December 2011) with Centers for Medicare & Medicaid Services claims and county-level SES data from the 2012 Area Health Resources Files. We compared the discriminatory capabilities of multivariable models that adjusted for SES, patient, and hospital characteristics to determine whether county-level SES data improved prediction or changed hospital rankings for 30-day all-cause mortality and rehospitalization. After adjusting for patient and hospital characteristics, median household income (per $5000 increase) was inversely associated with odds of 30-day mortality (odds ratio, 0.97; 95% confidence interval, 0.95-1.00; P=0.032) and the percentage of people with at least a high school diploma (per 5 U increase) was associated with lower odds of 30-day rehospitalization (odds ratio, 0.95; 95% confidence interval, 0.91-0.99). After adjustment for county-level SES data, relative to whites, Hispanic ethnicity (odds ratio, 0.70; 95% confidence interval, 0.58-0.83) and black race (odds ratio, 0.57; 95% confidence interval, 0.50-0.65) remained significantly associated with lower 30-day mortality, but had similar 30-day rehospitalization. County-level SES did not improve risk adjustment or change hospital rankings for 30-day mortality or rehospitalization.

Conclusions: County-level SES data are modestly associated with 30-day outcomes for Centers for Medicare & Medicaid Services beneficiaries hospitalized with heart failure, but do not improve risk adjustment models based on patient characteristics alone.

Keywords: heart failure; predictive models; risk stratification.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Aged
  • Aged, 80 and over
  • Centers for Medicare and Medicaid Services, U.S.
  • Chi-Square Distribution
  • Databases, Factual
  • Decision Support Techniques*
  • Female
  • Heart Failure / diagnosis
  • Heart Failure / mortality
  • Heart Failure / therapy*
  • Hospitalization*
  • Humans
  • Logistic Models
  • Male
  • Multivariate Analysis
  • Odds Ratio
  • Prognosis
  • Registries
  • Risk Assessment
  • Risk Factors
  • Socioeconomic Factors*
  • Time Factors
  • United States / epidemiology