Methodological research progress on the trajectory of chronic disease multimorbidity
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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。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.
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Key words:
- Chronic disease /
- Comorbidity /
- Multimorbidity /
- Multimorbidity trajectories /
- Methodology
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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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