Ferris Jennifer K, Wagar Brandon, Choi Alex, Simkin Jonathan, Sbihi Hind, Harder Kari, Smolina Kate
BC Centre for Disease Control, Provincial Health Services Authority, Vancouver, BC, Canada.
Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada.
BMC Med. 2025 Jul 1;23(1):370. doi: 10.1186/s12916-025-04184-x.
Multimorbidity is analytically and clinically complex, involving multiple interactions between diseases each with unique implications for health. Identifying disease co-occurrence patterns at the population level could aid in disease prevention, management, and care delivery.
Here, we analyzed multimorbidity patterns using linked administrative data from a longitudinal cohort of 1,347,820 individuals with multimorbidity over 20 years in British Columbia, Canada. A directed network-based approach was used to assess disease patterns in multimorbidity by frequency (prevalence) and non-random association (lift). We applied a community detection algorithm to identify multimorbidity disease clusters.
Mood and anxiety disorders and hypertension were the most common disease predecessors in prevalence networks, with differences between age groups. Lift networks revealed non-random disease associations. Some indicate potential etiological disease relationships (e.g., breast cancer preceding heart disease in young women), shared risk profiles (e.g., chronic obstructive pulmonary disease and lung cancer), or overlapping disease constructs (e.g., Parkinsonism and dementia). Disease clusters often centered around a single disease as a common predecessor or consequence, representing potential multimorbidity profiles, which may be relevant for patient subgrouping or management.
Insights from these analyses can complement traditional chronic disease surveillance methods, flagging disease patterns for further interrogation into their impacts on function, mortality, and health service utilization.
共病在分析和临床方面都很复杂，涉及多种疾病之间的多重相互作用，每种疾病对健康都有独特的影响。在人群层面识别疾病共现模式有助于疾病预防、管理和医疗服务提供。
在此，我们使用来自加拿大不列颠哥伦比亚省一个长达20年的1347820名患有共病个体的纵向队列的关联行政数据，分析共病模式。一种基于网络的定向方法被用于通过频率（患病率）和非随机关联（提升度）评估共病中的疾病模式。我们应用社区检测算法来识别共病疾病集群。
情绪和焦虑障碍以及高血压是患病率网络中最常见的疾病前驱因素，不同年龄组之间存在差异。提升度网络揭示了非随机的疾病关联。一些表明潜在的病因学疾病关系（例如年轻女性中乳腺癌先于心脏病）、共同的风险特征（例如慢性阻塞性肺疾病和肺癌）或重叠的疾病结构（例如帕金森症和痴呆症）。疾病集群通常围绕一种单一疾病作为共同的前驱因素或后果，代表潜在的共病概况，这可能与患者亚组划分或管理相关。
这些分析的见解可以补充传统的慢性病监测方法，标记疾病模式以便进一步探究它们对功能、死亡率和医疗服务利用的影响。
