在使用网络方法研究多病症时需要小心:使用加拿大长度老龄化研究数据的插图
Lauren E Griffith1, Alberto Brini2, Graciela Muniz-Terrera3
1Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada.
Journal of clinical epidemiology
|June 20, 2024
概括
网络分析的选择显著影响了多病症集群的识别. 老年人的疾病集群是不可预测的,需要个性化护理策略.
科学领域:
- 老年学与公共卫生
- 计算流行病学计算流行病学
- 网络科学 网络科学
背景情况:
- 多病性,即多种慢性疾病的同时发生,是越来越严重的公共卫生问题,特别是在老龄化人口中.
- 网络分析越来越多地用于了解疾病共发生模式,但方法选择可能会影响结果.
- 具体分析决策对识别多病症集群的影响仍未得到充分研究.
研究的目的:
- 调查不同集群方法和关联措施如何影响多病症集群的识别.
- 将网络分析衍生集群与临床专家识别的集群进行比较.
主要方法:
- 对30 097名加拿大成年人 (45-85岁) 关于24种疾病的自我报告数据的横截面分析.
- 在网络分析中采用了5种聚类方法和11种关联措施.
- 利用调整后的兰德指数 (ARI) 来量化集群相似性,并将结果与临床医生定义的集群进行比较.
主要成果:
- 在不同分析组合中观察到集群数量 (1-24) 和集群内的疾病相似性的显著变化.
- 在将网络衍生集群与临床医生衍生集群进行比较时,调整后的兰德指数值从-0.02到0.24不等,表明同意程度低.
- 选择集群算法和关联测量方法大大改变了已识别的多病症集群的结构和组成.
结论:
- 对二进制集群数据 (如疾病) 的网络分析方法进行系统评估至关重要.
- 在老年人中,疾病的同时发生模式可能不一致,这表明通用集群方法的局限性.
- 这些发现强调了需要个性化护理策略来管理老年人多病症的需要.
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