Akhavnnezhad Sedigheh, Talebi Seyedeh Solmaz, Farkhani Ehsan Mosa, Rohani-Rasaf Marzieh
Student Research Committee, School of public health, Shahroud University of Medical Sciences, Shahroud, Iran.
Department of Epidemiology, School of Public Health, Shahroud University of Medical Sciences, Shahroud, Iran.
BMC Public Health. 2025 Apr 4;25(1):1272. doi: 10.1186/s12889-025-22523-8.
Epidemiological studies showed that Covid-19 patients with underlying diseases had higher rates of severe Covid-19. Previous studies focused on the presence of a single chronic disease but this study investigated the prevalence and patterns of multi-morbidities in patients with Covid-19 and its relationship with the severity of Covid-19.
This retrospective study focused on patients age 30 years and older with positive polymerase chain reaction (PCR) results in 24 hospitals of Mashhad in northeastern Iran from 20-3-2020 to 21-1-2022. The number of studied confirmed patients was 318,502. The underlying diseases were identified according to the International Classification of Diseases, and the severity of Covid-19, including death, need for ventilation, and need for treatment in the intensive care unit (ICU). The pattern of multi-morbidities in these confirmed cases was investigated using latent class analysis (LCA), and the relationship between this pattern and the severity of Covid-19 was determined by multivariate logistic regression.
The most common coexisting diseases were hypertension in 30,100 patients (9.5%), metabolic disorders in 23,798 (7.5%) and hyperlipidemia in 22,454 (7%). Different comorbidities were grouped into three classes by the LCA model. Class 1 was patients without multi-morbidities, or 83% people., Class 2, which included 9% patients, was patients with hypertension, diabetes, respiratory diseases, and mental behavioral disorders (HRMD class). Class 3, which included patients with metabolic diseases, for whom the probability of developing hypertension, hyperlipidemia, diabetes, and metabolic disorders was high, included 7% patients. The results of multivariate logistic regression showed that having HRMD and metabolic diseases compared to no multi-morbidity adjusted for some risk factors increased the odds of developing severe Covid-19 by 81% and 55%, respectively.
The classes identified in this study provided a clear view of different groups of Covid-19 patients with certain multi-morbidities and underscore the importance of considering these patterns, rather than individual comorbidities, in risk assessment and management of COVID-19 patients. This approach will guide clinical decision-making and resource allocation in the ongoing management of the COVID-19 pandemic.
流行病学研究表明，患有基础疾病的新冠病毒疾病（Covid-19）患者发生重症Covid-19的几率更高。以往研究聚焦于单一慢性病的存在情况，而本研究调查了Covid-19患者中多种疾病共存的患病率和模式及其与Covid-19严重程度的关系。
这项回顾性研究聚焦于2020年3月20日至2022年1月21日期间伊朗东北部马什哈德24家医院中年龄30岁及以上且聚合酶链反应（PCR）结果呈阳性的患者。研究的确诊患者数量为318,502例。根据国际疾病分类确定基础疾病，并确定Covid-19的严重程度，包括死亡、是否需要通气以及是否需要在重症监护病房（ICU）接受治疗。使用潜在类别分析（LCA）研究这些确诊病例中多种疾病共存的模式，并通过多因素逻辑回归确定这种模式与Covid-19严重程度之间的关系。
最常见的共存疾病为高血压，共30,100例患者（9.5%），代谢紊乱23,798例（7.5%），高脂血症22,454例（7%）。通过LCA模型将不同的合并症分为三类。第1类是无多种疾病共存的患者，占83%。第2类包括9%的患者，为患有高血压、糖尿病、呼吸系统疾病和精神行为障碍（HRMD类）的患者。第3类包括患有代谢性疾病的患者，这类患者发生高血压、高脂血症、糖尿病和代谢紊乱的概率较高，占7%。多因素逻辑回归结果显示，在调整了一些风险因素后，与无多种疾病共存相比，患有HRMD和代谢性疾病分别使发生重症Covid-19的几率增加81%和55%。
本研究确定的类别清晰呈现了患有特定多种疾病共存的不同Covid-19患者群体，并强调在Covid-19患者的风险评估和管理中考虑这些模式而非个体合并症的重要性。这种方法将为正在进行的Covid-19大流行管理中的临床决策和资源分配提供指导。
