慢性病共病轨迹方法学研究进展

魏琮菡; 梁力中; 曾志嵘

doi:10.16462/j.cnki.zhjbkz.2024.08.016

慢性病共病轨迹方法学研究进展

doi: 10.16462/j.cnki.zhjbkz.2024.08.016

1.
广东医科大学公共卫生学院流行病与卫生统计学系, 东莞 523808
2.
广东医科大学医保研究院, 湛江 524023
3.
广东医科大学公共卫生与健康研究院, 湛江 524023

详细信息

通讯作者:
曾志嵘，E-mail：zengzrgdy@gdmu.edu.cn

中图分类号: R181.2
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- 文章访问数: 94
- HTML全文浏览量: 23
- PDF下载量: 18
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-01
- 修回日期: 2024-05-15
- 网络出版日期: 2024-09-29
- 刊出日期: 2024-08-10

Methodological research progress on the trajectory of chronic disease multimorbidity

1.
Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan 523808, China
2.
Institute of Healthcare Security, Guangdong Medical University, Zhanjiang 524023, China
3.
Institute of Public Health and Wellness, Guangdong Medical University, Zhanjiang 524023, China

More Information

Corresponding author: ZENG Zhirong, E-mail: zengzrgdy@gdmu.edu.cn

摘要

摘要: 随着人口老龄化加剧和人群疾病谱变化，慢性病共病日趋复杂，明确共病时间序列上的进展规律，将为更深入的研究提供切入点。本研究通过对慢性病共病轨迹相关概念进行分析，采用文献可视化系统梳理和以变量为中心、以个体为中心、以疾病诊断为中心3种研究视角对其方法学进展及研究发现具体总结。慢性病共病轨迹研究的开展，将为提早发现疾病风险并实施干预、延缓、控制疾病进展提供证据支持，对找出共病发生机制和优化卫生资源配置具有重要参考意义。
- 慢性病 /
- 共病 /
- 多病 /
- 共病轨迹 /
- 方法学
Abstract: As the aging of the population intensifies and the spectrum of diseases in the population changes, the complexity of multimorbidities of chronic diseases is increasing. Clarifying the progression patterns of multimorbidities over time will provide points for further research. This study analyzes the concepts related to chronic disease multimorbidity trajectories, systematically reviews the literature visualization, and summarizes the methodological advances and research findings from three research perspectives: variable-centered, individual-centered, and disease diagnosis-centered. The development of chronic disease multimorbidity trajectory research will provide evidence to early detect disease risks, implement interventions, delay and control disease progression, and have important reference significance in identifying the mechanisms of multimorbidity occurrence and optimizing health resource allocation.
- Chronic disease /
- Comorbidity /
- Multimorbidity /
- Multimorbidity trajectories /
- Methodology

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图 1 慢性病共病轨迹相关概念

Figure 1. Related concepts of chronic disease multimorbidity trajectories

下载: 全尺寸图片幻灯片

图 2 慢性病共病轨迹研究国家(地区)合作网络图谱

Figure 2. Mapping of national (regional) cooperation networks for research on chronic disease multimorbidity trajectories

下载: 全尺寸图片幻灯片

图 3 慢性病共病轨迹研究关键词时序图

Figure 3. Keyword timing diagram for research on chronic disease multimorbidity trajectories

下载: 全尺寸图片幻灯片

表 1 慢性病共病轨迹3种研究视角

Table 1. Three research perspectives of chronic disease multimorbidity trajectories

项目Program	以变量为中心 Variable-centered	以个体为中心 Individual-centered	以疾病诊断为中心 Disease diagnosis-centered
目的Objective	探讨变量间关系 Exploring the relationship between variables	识别同质个体 Identifying Homogeneous Individuals	疾病对间因果推断 Diseases causality inference
假设Hypothesis	样本和总体间有同质性 Homogeneity between sample and population	样本存在异质性, 考虑变量综合作用 There is heterogeneity in the sample, considering the combined effect of variables	疾病间存在诊断相关性 Diagnostic correlation between diseases
方法Methods	描述性、相关性、回归和生存分析 Descriptives, correlations, regression and survival analysis	聚类、序列分析、潜变量轨迹模型 Cluster, sequence analysis, latent class trajectory modeling	概率、图形算法 Probabilistic, graph algorithms
优势Strengths	变量相对独立, 模型可解释性较高 Relative independence of variables and high interpretability of the model	整合患者特征进行群体比较 Integration of patient characteristics for group comparisons	综合利用诊断和相关影响因素 Integration of diagnostic and relevant influencing factors
不足Weaknesses	变量有限且缺乏交互作用；统计推断泛化性较差 Limited variables and lack of interactions; poor generalisation of statistical inference	亚组较多且依赖模型寻找潜在特征, 可解释性相对较差 Interpretability is relatively poor with more subgroups and reliance on the model to find underlying features	常见疾病关联较强；诊断和时间存在偏倚 Stronger association of common diseases; bias in diagnosis and time

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