Bazo-Alvarez Juan Carlos, Del Castillo Darwin, Piza Luis, Bernabé-Ortiz Antonio, Carrillo-Larco Rodrigo M, Smeeth Liam, Gilman Robert H, Checkley William, Miranda J Jaime
CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru.
Research Department of Primary Care and Population Health, University College London, London, UK.
Am J Epidemiol. 2024 Dec 19. doi: 10.1093/aje/kwae466.
Multimorbidity data is typically analysed by tallying disease counts, which overlooks nuanced relationships among conditions. We identified clusters of multimorbidity and subpopulations with varying risks and examined their association with all-cause mortality using a data-driven approach. We analysed 8-year follow-up data of people ≥35 years who were part of the CRONICAS Cohort Study, a multisite cohort from Peru. First, we used Partitioning Around Medoids and multidimensional scaling to identify multimorbidity clusters. We then estimated the association between multimorbidity clusters and all-cause mortality. Second, we identified subpopulations using finite mixture modelling. Our analysis revealed three clusters of chronic conditions: respiratory (cluster 1: bronchitis, COPD and asthma), lifestyle, hypertension, depression and diabetes (cluster 2), and circulatory (cluster 3: heart disease, stroke and peripheral artery disease). While only the cluster comprising circulatory diseases showed a significant association with all-cause mortality in the overall population, we identified two latent subpopulations (named I and II) exhibiting differential mortality risks associated with specific multimorbidity clusters. These findings underscore the importance of considering multimorbidity clusters and sociodemographic characteristics in understanding mortality risks. They also highlight the need for tailored interventions to address the unique needs of different subpopulations living with multimorbidity to reduce mortality risks effectively.
多重疾病数据通常通过统计疾病数量来分析，这忽略了各种疾病之间细微的关系。我们使用数据驱动的方法识别了多重疾病集群和具有不同风险的亚人群，并研究了它们与全因死亡率的关联。我们分析了来自秘鲁多中心队列研究CRONICAS队列中35岁及以上人群的8年随访数据。首先，我们使用围绕中心点划分法和多维缩放来识别多重疾病集群。然后，我们估计了多重疾病集群与全因死亡率之间的关联。其次，我们使用有限混合模型识别亚人群。我们的分析揭示了三类慢性病集群：呼吸系统疾病（集群1：支气管炎、慢性阻塞性肺疾病和哮喘）、生活方式相关疾病、高血压、抑郁症和糖尿病（集群2）以及循环系统疾病（集群3：心脏病、中风和外周动脉疾病）。虽然在总体人群中，只有包含循环系统疾病的集群与全因死亡率存在显著关联，但我们识别出了两个潜在亚人群（分别命名为I和II），它们与特定多重疾病集群相关的死亡风险存在差异。这些发现强调了在理解死亡风险时考虑多重疾病集群和社会人口学特征的重要性。它们还突出了需要采取针对性干预措施，以满足患有多重疾病的不同亚人群的独特需求，从而有效降低死亡风险。
