Department of Preventive Medicine and Public Health, Universidad Autónoma de Madrid, Madrid, Spain.
CIBER of Epidemiology and Public Health, Madrid, Spain.
Gerontology. 2023;69(6):716-727. doi: 10.1159/000529406. Epub 2023 Feb 1.
While some condition clusters represent the chance co-occurrence of common individual conditions, others may represent shared causal factors. The aims of this study were to identify multimorbidity patterns in older adults and to explore the relationship between social variables, lifestyle behaviors, and the multimorbidity patterns identified.
This was a cross-sectional design. Data came from 3,273 individuals aged ≥65 from the Seniors-ENRICA-2 cohort; information on 60 chronic disease categories, categorized according to the 2nd edition of the International Classification of Primary Care and the 10th edition of the International Classification of Diseases, was obtained from clinical record linkage. To identify multimorbidity patterns, an exploratory factor analysis was conducted over chronic disease categories with a prevalence >5%, using Oblimin rotation and Kaiser's eigenvalues-greater-than-one rule. The association between multimorbidity patterns and their potential determinants was assessed with multivariable linear regression.
The three-factor solution (Musculoskeletal diseases and mental disorders, Cardiometabolic diseases, and Cardiopulmonary diseases) explained 64.5% of the total variance. Being older, lower occupational category, higher levels of loneliness, lower levels of physical activity, and higher body mass index were associated with higher scores in the multimorbidity patterns identified. Female sex was linked to the Musculoskeletal diseases and mental disorders pattern, while being male was revealed to the two remaining multimorbidity patterns. A high diet quality was inversely related to Cardiometabolic diseases, while optimal sleep duration was inversely related to Cardiopulmonary diseases.
Three multimorbidity patterns were identified in older adults. Multimorbidity patterns were differently associated with social variables and lifestyles behavioral factors.
虽然某些病症集群代表常见个体病症的偶然共同发生，但其他病症集群可能代表共同的因果因素。本研究旨在确定老年人的多重病症模式，并探索社会变量、生活方式行为与所确定的多重病症模式之间的关系。
这是一项横断面设计。数据来自 Seniors-ENRICA-2 队列中≥65 岁的 3273 名个体；通过临床记录链接，获得了 60 种慢性疾病类别的信息，这些疾病类别根据国际初级保健分类第 2 版和国际疾病分类第 10 版进行了分类。为了确定多重病症模式，对患病率>5%的慢性疾病类别进行了探索性因子分析，使用 Oblimin 旋转和 Kaiser 的特征值大于 1 的规则。使用多变量线性回归评估多重病症模式及其潜在决定因素之间的关联。
三因素解决方案（肌肉骨骼疾病和精神障碍、心血管代谢疾病和心肺疾病）解释了总方差的 64.5%。年龄较大、职业类别较低、孤独感较高、身体活动水平较低以及身体质量指数较高与所确定的多重病症模式得分较高有关。女性与肌肉骨骼疾病和精神障碍模式有关，而男性与两种剩余的多重病症模式有关。饮食质量较高与心血管代谢疾病呈负相关，而睡眠时长最佳与心肺疾病呈负相关。
在老年人中确定了三种多重病症模式。多重病症模式与社会变量和生活方式行为因素的关联不同。
