社会经济地位
医学
人口学
队列
前瞻性队列研究
队列研究
人口
比例危险模型
环境卫生
老年学
内科学
社会学
作者
Qiuyu Cao,Mian Li,Ruizhi Zheng,Hong Lin,Yu Xu,Zhicheng Wang,Guijun Qin,Yan Li,Min Xu,Tiange Wang,Yu‐Hong Chen,Shuangyuan Wang,Zhiyun Zhao,Jie Zheng,Zhengnan Gao,Tianshu Zeng,Ruying Hu,Xuefeng Yu,Gang Chen,Qing Su
标识
DOI:10.1136/jech-2024-223570
摘要
Background Although socioeconomic inequality in mortality has long been a public health focus, the associations of area-level socioeconomic status (SES) and individual-level factors with mortality have not been well investigated, especially in China with rapid industrial development. Methods In this nationwide, population-based, prospective cohort study, adults aged over 40 from 29 counties were included in the China Cardiometabolic Disease and Cancer Cohort study. The composite area deprivation index of area-level SES was generated from national census data and categorised into tertiles. Cox proportional hazards models were fitted to calculate HRs and 95% CIs for area-level SES with the risk of mortality, and comprehensive individual socioeconomic, lifestyle, and metabolic factors were examined as potential mediators. Results A total of 174 004 participants were included. During a median follow-up of 10.1 years, low area-level SES was associated with 34% increased risk of all-cause mortality (95% CI 1.27 to 1.42), 76% increased risk of cardiovascular disease (CVD) mortality (95% CI 1.59 to 1.94) and 13% increased risk of non-CVD mortality (95% CI 1.05 to 1.21) compared with high area-level SES. The association between area-level SES and all-cause mortality was partly mediated by individual socioeconomic, lifestyle and metabolic factors, contributing 3.8%, 20.7% and 8.9%, respectively. Furthermore, individuals with low area-level SES and low individual SES, unhealthy lifestyles, or poor metabolic status had the highest risk of mortality. Conclusions Significant area-level socioeconomic inequalities in mortality exist in China. Comprehensive interventions targeting both area-level circumstances and individual socioeconomic, lifestyle and metabolic factors were key strategies to reduce these inequalities.
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