Rodrigues Myanca, Rosic Tea, Babe Glenda, Dennis Brittany B, McEvoy Alannah, Perez Richard, de Oliveira Claire, Parpia Sameer, Samaan Zainab, Thabane Lehana
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada.
PLoS One. 2025 Jun 12;20(6):e0324548. doi: 10.1371/journal.pone.0324548. eCollection 2025.
Multimorbidity, the presence of two or more (2+) chronic conditions, presents significant challenges for healthcare delivery, particularly among populations with opioid use disorder (OUD). Multimorbidity patterns among individuals with OUD are not well established, and minimal research exists examining the impact of clustering methods on identifying these patterns.
Our study aimed to assess multimorbidity prevalence, explore associated sociodemographic and clinical characteristics, and determine multimorbidity patterns using hierarchical cluster analysis (HCA) and K-means clustering among people receiving treatment for OUD in Ontario, Canada between 2011 and 2021.
Data from two prospective cohort studies were merged and linked to Ontario provincial health administrative databases. We identified 16 chronic conditions, used in prior research examining multimorbidity in Ontario, using ICD-10-CA diagnostic codes and the diagnostic codes of physician billing claims using a 2-year lookback. Multimorbidity was defined as the presence of 2+ of the above conditions, excluding the diagnosis of OUD. We conducted a retrospective cohort study, following the participants for eight years in the data holdings to ascertain the prevalence of multimorbidity. Sociodemographic and clinical characteristics were analyzed using modified Poisson regression models, and multimorbidity patterns were identified through HCA and K-means clustering.
Among 3,430 people with OUD, 32.5% (n = 1,114, 95% confidence interval (CI)=30.9, 34.1) experienced multimorbidity over an eight-year period, with older age (Prevalence Ratio (PR)=3.39, 95% CI = 2.36, 4.87) and unemployment (PR = 1.31, 95% CI = 1.13, 1.54) associated with increased prevalence. HCA identified six distinct disease clusters, whereas K-means clustering identified four clusters. Both methods identified groupings of cardiovascular (coronary syndrome), cardiometabolic (diabetes, hypertension), and respiratory (chronic obstructive pulmonary disease) diseases, reflecting shared comorbidities among people with OUD.
Our findings highlight the substantial burden of multimorbidity among populations with OUD, and the importance of considering sociodemographic factors in understanding multimorbidity prevalence. Moreover, the choice of clustering method significantly influences the identification and interpretation of multimorbidity patterns, with HCA providing more clinically meaningful groupings compared to K-means clustering. Our findings highlight the need for clinicians to tailor care plans and for policymakers to prioritize integrated healthcare delivery strategies to address the complex health needs of people with OUD.
多种慢性病共存，即存在两种或更多（2种及以上）慢性疾病，给医疗服务带来了重大挑战，尤其是在患有阿片类物质使用障碍（OUD）的人群中。患有OUD的个体的多种慢性病共存模式尚未完全明确，并且几乎没有研究考察聚类方法对识别这些模式的影响。
我们的研究旨在评估多种慢性病共存的患病率，探索相关的社会人口学和临床特征，并使用层次聚类分析（HCA）和K均值聚类法确定2011年至2021年期间在加拿大安大略省接受OUD治疗的人群中的多种慢性病共存模式。
将两项前瞻性队列研究的数据合并，并与安大略省省级卫生行政数据库相链接。我们使用ICD-10-CA诊断代码以及医生计费索赔的诊断代码，通过回顾2年的记录，确定了先前在安大略省研究多种慢性病共存时所使用的16种慢性疾病。多种慢性病共存被定义为存在上述2种及以上疾病，但不包括OUD的诊断。我们进行了一项回顾性队列研究，在数据记录中对参与者随访8年以确定多种慢性病共存的患病率。使用修正的泊松回归模型分析社会人口学和临床特征，并通过HCA和K均值聚类法识别多种慢性病共存模式。
在3430名患有OUD的人群中，32.5%（n = 1114，95%置信区间（CI）= 30.9，34.1）在8年期间经历了多种慢性病共存，年龄较大（患病率比（PR）= 3.39，95% CI = 2.36，4.87）和失业（PR = 1.31，95% CI = 1.13，1.54）与患病率增加相关。HCA识别出六个不同的疾病簇，而K均值聚类法识别出四个簇。两种方法都识别出了心血管疾病（冠状动脉综合征）、心脏代谢疾病（糖尿病、高血压）和呼吸系统疾病（慢性阻塞性肺疾病）的分组，反映了患有OUD的人群中共同存在的合并症。
我们的研究结果突出了患有OUD的人群中多种慢性病共存的沉重负担，以及在理解多种慢性病共存患病率时考虑社会人口学因素的重要性。此外，聚类方法的选择对多种慢性病共存模式的识别和解释有显著影响，与K均值聚类法相比，HCA提供了更具临床意义的分组。我们的研究结果突出了临床医生制定个性化护理计划以及政策制定者优先考虑综合医疗服务提供策略以满足患有OUD的人群复杂健康需求的必要性。
