Gender and Socioeconomic Differences in the Prevalence and Patterns of Multimorbidity among Middle-Aged and Older Adults in China

社会经济地位 中国 人口学 多发病率 人为因素与人体工程学 老年学 伤害预防 毒物控制 心理学 环境卫生 共病 地理 医学 精神科 社会学 人口 考古
作者
Yaqin Zhong,Hanqing Xi,Xiaojun Guo,Tiantian Wang,Yanan Wang,Jian Wang
出处
期刊:International Journal of Environmental Research and Public Health [Multidisciplinary Digital Publishing Institute]
卷期号:19 (24): 16956-16956 被引量:4
标识
DOI:10.3390/ijerph192416956
摘要

Multimorbidity has become a global public health concern. Knowledge about the prevalence and patterns of multimorbidity will provide essential information for public intervention and clinical management. This study aimed to examine gender and socioeconomic differences in the prevalence and patterns of multimorbidity among a nationally representative sample of middle-aged and older Chinese individuals.Data were obtained from the 2018 wave of the China Health and Retirement Longitudinal Study. Latent class analysis was conducted to discriminate among the multimorbidity patterns. Multinomial logit analysis was performed to explore gender and socioeconomic factors associated with various multimorbidity patterns.A total of 19,559 respondents over 45 years old were included in the study. The findings showed that 56.73% of the respondents reported multimorbidity, with significantly higher proportions among women. Four patterns, namely "relatively healthy class", "respiratory class", "stomach-arthritis class" and "vascular class", were identified. The women were more likely to be in the stomach-arthritis class. Respondents with a higher SES, including higher education, urban residence, higher consumption, and medical insurance, had a higher probability of being in the vascular class. Conclusions: Significant gender and socioeconomic differences were observed in the prevalence and patterns of multimorbidity. The examination of gender and socioeconomic differences for multimorbidity patterns has great implications for clinical practice and health policy. The results may provide insights to aid in the management of multimorbidity patients and improve health resource allocation.

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