University of Illinois at Chicago
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Utility of Socioeconomic Status in Predicting 30-Day Outcomes After Heart Failure Hospitalization

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Version 2 2024-06-03, 16:30
Version 1 2023-12-18, 19:23
journal contribution
posted on 2024-06-03, 16:30 authored by A F Hernandez, C.W. Yancy, E.D. Peterson, G.C. Fonarow, L A McCoy, Marie Lynn MirandaMarie Lynn Miranda, R.M. Califf, Z Eapen
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 (CMS) beneficiaries hospitalized with heart failure (HF). Methods and Results—We linked clinical data on hospitalized HF patients in the Get With The Guidelines®-HF™ database (01/2005–12/2011) with CMS 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/or hospital characteristics to determine whether county-level SES data improved prediction or changed hospital rankings for 30-day allcause mortality and rehospitalization. After adjusting for patient and hospital characteristics, median household income (per $5,000 increase) was inversely associated with odds of 30-day mortality (OR 0.97, 95% CI 0.95–1.00, p=0.032), and the percentage of persons with at least a high school diploma (per 5 unit increase) was associated with lower odds of 30-day rehospitalization (OR 0.95, 95% CI 0.91–0.99). After adjustment for county-level SES data, relative to whites, Hispanic ethnicity (OR 0.70, 95% CI 0.58, 0.83) and black race (OR 0.57, 95% CI: 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 CMS beneficiaries hospitalized with HF, but do not improve risk adjustment models based on patient characteristics alone.

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