Abstract
AIMS: This study explored the association between socio-economic status (SES) and mortality among patients hospitalized for heart failure (HF) in China. METHODS AND RESULTS: We used data from the China Patient-centred Evaluative Assessment of Cardiac Events-Prospective Heart Failure Study (China PEACE 5p-HF Study), which enrolled patients hospitalized primarily for HF from 52 hospitals between 2016 and 2018. SES was measured using the income, employment status, educational attainment, and partner status. Individual socio-economic risk factor (SERF) scores were assigned based on the number of coexisting SERFs, including low income, unemployed status, low education, and unpartnered status. We assessed the effects of SES on 1 year all-cause mortality using Cox models. We used the Harrell c statistic to investigate whether SES added incremental prognostic information for mortality prediction. A total of 4725 patients were included in the analysis. The median (interquartile range) age was 67 (57-76) years; 37.6% were women. In risk-adjusted analyses, patients with low/middle income [low income: hazard ratio (HR) 1.61, 95% confidence interval (CI) 1.21-2.14; middle income: HR 1.32, 95% CI 1.00-1.74], unemployment status (HR 1.43, 95% CI 1.10-1.86), low education (HR 1.25, 95% CI 1.03-1.53), and unpartnered status (HR 1.22, 95% CI 1.03-1.46) had a higher risk of death than patients with high income, who were employed, who had a high education level, and who had a partner, respectively. Compared with the patients without SERFs, those with 1, 2, 3, and 4 SERFs had 1.52-, 2.01-, 2.45-, and 3.20-fold increased risk of death, respectively. The addition of SES to fully adjusted model improved the mortality prediction, with increments in c statistic of 0.01 (P < 0.01). CONCLUSIONS: In a national Chinese cohort of patients hospitalized for HF, low income, unemployment status, low education, and unpartnered status were all associated with a higher risk of death 1 year following discharge. In addition, incorporating SES into a clinical-based model could better identify patients at risk for death. Tailored clinical interventions are needed to mitigate the excess risk experienced by those socio-economic deprived HF patients.