Department of Drug Statistics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway.
Emerging Technologies Advisory Group, ISACA, Chicago, IL, United States.
Front Public Health. 2024 May 16;12:1349723. doi: 10.3389/fpubh.2024.1349723. eCollection 2024.
The presence of multiple chronic conditions, also referred to as multimorbidity, is a common finding in adults. Epidemiologic research can help identify groups of individuals with similar clinical profiles who could benefit from similar interventions. Many cross-sectional studies have revealed the existence of different multimorbidity patterns. Most of these studies were focused on the older population. However, multimorbidity patterns begin to form at a young age and can evolve over time following distinct multimorbidity trajectories with different impact on health. In this study, we aimed to identify multimorbidity patterns and trajectories in adults 18-65 years old.
We conducted a retrospective longitudinal epidemiologic study in the EpiChron Cohort, which includes all inhabitants of Aragón (Spain) registered as users of the Spanish National Health System, linking, at the patient level, information from electronic health records from both primary and specialised care. We included all 293,923 patients 18-65 years old with multimorbidity in 2011. We used cluster analysis at baseline (2011) and in 2015 and 2019 to identify multimorbidity patterns at four and eight years of follow-up, and we then created alluvial plots to visualise multimorbidity trajectories. We performed age- and sex-adjusted logistic regression analysis to study the association of each pattern with four- and eight-year mortality.
We identified three multimorbidity patterns at baseline, named , , and . The pattern, found in one out of every four patients was associated with a higher likelihood of four- and eight-year mortality (age- and sex-adjusted odds ratio 1.11 and 1.16, respectively) compared to the pattern. Baseline patterns evolved into different patterns during the follow-up.
Well-known preventable cardiovascular risk factors were key elements in most patterns, highlighting the role of hypertension and obesity as risk factors for higher mortality. Two out of every three patients had a cardiovascular profile with chronic conditions like diabetes and obesity that are linked to low-grade systemic chronic inflammation. More studies are encouraged to better characterise the relatively large portion of the population with an unspecific disease pattern and to help design and implement effective and comprehensive strategies towards healthier ageing.
多种慢性疾病的存在,也被称为多种共病,在成年人中很常见。流行病学研究可以帮助确定具有相似临床特征的人群,这些人群可能受益于类似的干预措施。许多横断面研究揭示了不同的多种共病模式的存在。这些研究大多集中在老年人群。然而,多种共病模式在年轻时就开始形成,并随着时间的推移沿着不同的多种共病轨迹发展,对健康的影响也不同。在这项研究中,我们旨在确定 18-65 岁成年人的多种共病模式和轨迹。
我们在 EpiChron 队列中进行了一项回顾性纵向流行病学研究,该队列包括阿拉贡(西班牙)的所有居民,他们作为西班牙国家卫生系统的用户进行登记,在患者层面上,将初级和专科保健的电子健康记录信息进行链接。我们纳入了 2011 年患有多种共病的 293923 名 18-65 岁的患者。我们在基线(2011 年)和 2015 年和 2019 年使用聚类分析来识别四年和八年随访时的多种共病模式,然后创建了冲积图来可视化多种共病轨迹。我们进行了年龄和性别调整的逻辑回归分析,以研究每种模式与四年和八年死亡率的关联。
我们在基线时识别出三种多种共病模式,分别命名为 、 、 。每四个患者中就有一个的 模式与四年和八年死亡率的增加相关(年龄和性别调整的优势比分别为 1.11 和 1.16),与 模式相比。基线模式在随访期间演变成不同的模式。
众所周知的可预防心血管危险因素是大多数模式的关键因素,突出了高血压和肥胖作为导致更高死亡率的危险因素的作用。每三个患者中就有两个有心血管疾病的特征,有糖尿病和肥胖等慢性疾病,这些疾病与低度系统性慢性炎症有关。鼓励进行更多的研究,以更好地描述具有非特异性疾病模式的人群的相对较大部分,并帮助设计和实施针对更健康老龄化的有效和全面的策略。