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Utility of Socioeconomic Status in Predicting 30-Day Outcomes After Heart Failure Hospitalization

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.

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