Prevalence and Patterns of Multimorbidity in a Nationally Representative Sample of Older Chinese: Results From the China Health and Retirement Longitudinal Study
Prevalence and Patterns of Multimorbidity in a Nationally Representative Sample of Older Chinese: Results From the China Health and Retirement Longitudinal Study
复制标题
中国老年人全国代表性样本中多种疾病的患病率和模式：中国健康与退休纵向研究的结果
DOI：
10.1093/gerona/glz185
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发表时间：
2020-10-01
影响因子：
5.1
通讯作者：
Xu, Beibei
中科院分区：
文献类型：
--
作者：
Yao, Shan-Shan;Cao, Gui-Ying;Xu, Beibei
背景：多发病已成为世界范围内的一个突出问题；然而，在中国老年多发病人群中进行的研究很少。本研究旨在调查具有全国代表性的中国老年人的多病患病率，并探索其常见模式。方法：本研究使用中国健康与退休纵向研究的数据，包括19,841名年龄在50岁以上的参与者。2011-2015年间，根据居住地区和性别，在整个队列中评估了个人慢性病和多发病的患病率。采用Logistic回归模型分析受试者的人口学特征与多发病的关系。结果：42.4%的受试者发生多病。女性(OR=1.31，95%可信区间[CI]：1.13~1.51)和城市居民(OR=1.14，95%CI：1.02~1.27)在考虑了年龄、教育程度、吸烟和饮酒的潜在混杂因素后，其多病患病率高于各自的相应人群。等级聚类分析揭示了四种常见的多发病模式：血管代谢集群、胃关节炎集群、认知情绪集群和肝肾集群。卒中和记忆相关疾病的分布存在地区差异。在关联规则挖掘中，系统聚类分析的多发病模式中的大多数条件组合和城乡差异也被观察到。结论：中国老年人多发病的患病率和模式因性别和居住地区而异。妇女和城市居民更容易患多发性疾病。未来的研究需要了解已确定的多发病模式背后的机制以及它们的政策和干预含义。
Background: Multimorbidity has become a prominent problem worldwide; however, few population-based studies have been conducted among older Chinese with multimorbidity. This study aimed to examine the prevalence of multimorbidity and explore its common patterns among a nationally representative sample of older Chinese.Methods: This study used data from the China Health and Retirement Longitudinal Study and included 19,841 participants aged at least 50 years. The prevalence of individual chronic diseases and multimorbidity during 2011-2015 were evaluated among the entire cohort and according to residential regions and gender. The relationships between participants' demographic characteristics and multimorbidity were examined using logistic regression model. Patterns of multimorbidity were explored using hierarchical cluster analysis and association rule mining.Results: Multimorbidity occurred in 42.4% of the participants. The prevalence of multimorbidity was higher among women (odds ratio [OR] = 1.31, 95% confidence interval [CI]: 1.13-1.51) and urban residents (OR = 1.14, 95% CI: 1.02-1.27) than their respective counterparts after accounting for potential confounders of age, education, smoking, and alcohol consumption. Hierarchical cluster analysis revealed four common multimorbidity patterns: the vascular-metabolic cluster, the stomach-arthritis cluster, the cognitive-emotional cluster, and the hepatorenal cluster. Regional differences were found in the distributions of stroke and memory-related disease. Most combinations of conditions and urban-rural difference in multimorbidity patterns from hierarchical cluster analysis were also observed in association rule mining.Conclusion: The prevalence and patterns of multimorbidity vary by gender and residential regions among older Chinese. Women and urban residents are more vulnerable to multimorbidity. Future studies are needed to understand the mechanisms underlying the identified multimorbidity patterns and their policy and interventional implications.