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慢性病共病轨迹方法学研究进展

魏琮菡 梁力中 曾志嵘

魏琮菡, 梁力中, 曾志嵘. 慢性病共病轨迹方法学研究进展[J]. 中华疾病控制杂志, 2024, 28(8): 969-975. doi: 10.16462/j.cnki.zhjbkz.2024.08.016
引用本文: 魏琮菡, 梁力中, 曾志嵘. 慢性病共病轨迹方法学研究进展[J]. 中华疾病控制杂志, 2024, 28(8): 969-975. doi: 10.16462/j.cnki.zhjbkz.2024.08.016
WEI Conghan, LIANG Lizhong, ZENG Zhirong. Methodological research progress on the trajectory of chronic disease multimorbidity[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(8): 969-975. doi: 10.16462/j.cnki.zhjbkz.2024.08.016
Citation: WEI Conghan, LIANG Lizhong, ZENG Zhirong. Methodological research progress on the trajectory of chronic disease multimorbidity[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(8): 969-975. 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
详细信息
    通讯作者:

    曾志嵘,E-mail:zengzrgdy@gdmu.edu.cn

  • 中图分类号: R181.2

Methodological research progress on the trajectory of chronic disease multimorbidity

More Information
  • 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。
  • 图  1  慢性病共病轨迹相关概念

    Figure  1.  Related concepts of chronic disease multimorbidity trajectories

    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

    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

    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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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

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