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Socio-economic status and 1 year mortality among patients hospitalized for heart failure in China
Yilan Ge1, Lihua Zhang1, Yan Gao1
1National Clinical Research Center for Cardiovascular Diseases, NHC Key Laboratory of Clinical Research for Cardiovascular Medications, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, People's Republic of China.
Insights
Socio-economic status (SES) significantly impacts heart failure (HF) patient mortality in China. Lower income, education, employment, and partner status increase death risk, highlighting the need for targeted interventions for vulnerable HF patients.
Area of Science:
- Cardiology
- Public Health
- Health Services Research
Background:
- Heart failure (HF) is a major cause of hospitalization and mortality worldwide.
- Socio-economic status (SES) is increasingly recognized as a determinant of health outcomes.
- The prognostic impact of SES on HF mortality in China remains underexplored.
Purpose of the Study:
- To investigate the association between SES and 1-year all-cause mortality in hospitalized HF patients in China.
- To determine if SES provides incremental prognostic value beyond traditional risk factors for HF mortality.
- To inform the development of targeted interventions for socio-economically disadvantaged HF patients.
Main Methods:
- Analysis of data from the China Patient-centred Evaluative Assessment of Cardiac Events-Prospective Heart Failure Study (China PEACE 5p-HF Study) (2016-2018).
- SES assessed via income, employment, education, and partner status, with individual socio-economic risk factor (SERF) scores.
- Cox proportional hazards models and Harrell c-statistic used to evaluate mortality risk and prognostic improvement.
Main Results:
- Low/middle income, unemployment, low education, and unpartnered status were independently associated with increased 1-year all-cause mortality.
- Patients with more SERFs exhibited substantially higher mortality risk (e.g., 4 SERFs: 3.20-fold increased risk).
- Inclusion of SES in a clinical model significantly improved mortality prediction (c-statistic increment of 0.01, P < 0.01).
Conclusions:
- SES is a critical predictor of mortality in Chinese patients hospitalized for HF.
- Addressing socio-economic disparities is essential for improving outcomes in HF management.
- Clinical interventions should be tailored to mitigate the excess mortality risk in socio-economically deprived HF populations.
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.
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