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


Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Eapen, ZJ; McCoy, LA; Fonarow, GC; Yancy, CW; Miranda, ML; Peterson, ED; Califf, RM; Hernandez, AF

Published Date

  • May 2015

Published In

Volume / Issue

  • 8 / 3

Start / End Page

  • 473 - 480

PubMed ID

  • 25747700

Pubmed Central ID

  • 25747700

Electronic International Standard Serial Number (EISSN)

  • 1941-3297

Digital Object Identifier (DOI)

  • 10.1161/CIRCHEARTFAILURE.114.001879


  • eng

Conference Location

  • United States