Socio‐economic status and 1 year mortality among patients hospitalized for heart failure in China

Abstract

AimsThis study explored the association between socio-economic status (SES) and mortality among patients hospitalized for heart failure (HF) in China. Methods and resultsWe 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 (

Educational status affects prognosis of patients with heart failure with reduced ejection fraction: A post‐hoc analysis from the WARCEF trial

“…Our findings are consistent with several studies which focused on HF, 13,14,19 and further expand the evidence on the association between educational status and the risk of major outcomes in patients with HF. Indeed, our findings suggest that risk may be continuous across the education level, with lower risk in highly educated patients, and less differences between those with medium versus low educational levels.…”

Association of socioeconomic status with cardiovascular disease and cardiovascular risk factors: a systematic review and meta-analysis

“…Ten studies (Christensen et al 2011 ; Donyavi et al 2011 ; Masoudkabir et al 2012 ; Floud et al 2016 ; Kilpi et al 2016 , 2017 ; Kriegbaum et al 2019 ; Rosengren et al 2019 ; Hassen et al 2020 ; Ge et al 2022 ) assessed the effects of education on CVD outcomes. A random-effects model (I 2 = 82.8%, P = 0.000; I 2 = 67.8%, P = 0.003) was adopted to combine the effect sizes, and the analysis results revealed that the risk of CVD incidence in low- and middle-education groups was 44% and 20% higher than that in the high-education group, respectively [HR = 1.44, 95%CI (1.28, 1.63); HR = 1.20, 95%CI (1.11, 1.30)].…”

“…Ten studies (Christensen et al 2011 ; Donyavi et al 2011 ; Masoudkabir et al 2012 ; Floud et al 2016 ; Kilpi et al 2016 , 2017 ; Kriegbaum et al 2019 ; Rosengren et al 2019 ; Hassen et al 2020 ; Ge et al 2022 ) evaluated the association between education and CVD outcomes among LMICs/HICs. A random-effects model (I 2 = 77.6%, P = 0.000; I 2 = 85.4%, P = 0.000; I 2 = 53.7%, P = 0.035; I 2 = 60.4%, P = 0.056) was adopted to merge the effect sizes, and the analysis revealed that the risk of CVD incidence was 52% higher in the low-education group than that in the high-education group among LMICs [HR = 1.52, 95%CI (1.09, 2.11)].…”

Change in Depressive Symptoms During the First Month of Discharge and 1‐Year Clinical Outcomes in Patients Hospitalized for Heart Failure

“…Unemployed was defined as previously employed (including retirement, layoff, unable to work because of permanent illness or disability, unwilling to work, and the need to take care of family) or never employed. 20 Medical debt was defined as having borrowed money from others to pay for medical expenses during the past 12 months. Patients' health status at admission was measured by Kansas City Cardiomyopathy Questionnaire‐12 (KCCQ‐12).…”