Identifying comorbidity patterns of mental health disorders in community-dwelling older adults: a cluster analysis

As global life expectancy increases, understanding mental health patterns and their associated risk factors in older adults becomes increasingly critical. Using data from the cross-sectional Trinity Ulster Department of Agriculture study (TUDA, 2008-2012; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>5186</mml:mn></mml:mrow> </mml:math> ; mean age 74.0 years) and a subset of participants followed-up longitudinally (TUDA 5+, 2014-2018; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>953</mml:mn></mml:mrow> </mml:math> ), we perform a multi-view co-clustering analysis to identify distinct mental health profiles and their relationships with potential risk factors. The TUDA multi-view dataset consists of five views: (1) mental health, measured with Center for Epidemiologic Studies Depression Scale [CES-D] and Hospital Anxiety and Depression Scale [HADS], (2) cognitive and neuropsychological function, (3) illness diagnoses and medical prescription history, (4) lifestyle and nutritional attainment, and (5) physical well-being. That is, each participant is described by five distinct sets of features. The mental health view serves as the target feature set, while the other four views are analyzed as potential contributors to mental health risks. Under the multi-view co-clustering framework, for each view data, the participants (rows) are partitioned into different row-clusters, and the features (columns) are partitioned into different column-clusters. Each row-cluster is most effectively explained by the features in one or two column-clusters. Notably, the row-clusterings across views are dependent. By analyzing the associations between row clusters in the mental health view and those in each of the other four views, we can identify which risk factors co-occur and contribute to an increased risk of poor mental health. We identify five distinct row-clusters in the mental-health view data, characterized by varying levels of depression and anxiety: Group 1, mild depressive symptoms and no symptoms of anxiety; Group 2, acute depression and anxiety; Group 3, less severe but persistent depression and anxiety symptoms; Group 4, symptoms of anxiety with no depressive symptoms; and Group 5, no symptoms of either depression or anxiety. Cross-view association analysis revealed the following key insights: Participants in Group 3 exhibit lower neuropsychological function, are older, more likely to live alone, come from more deprived regions, and have reduced physical independence. Contrasting Group 3, participants in Group 2 show better neuropsychological function, greater physical independence, and higher socioeconomic status. Participants in Group 5 report fewer medical diagnoses and prescriptions, more affluent backgrounds, less solitary living, and stronger physical independence. A significant portion of this group aligns with cognitive health row-clusters 1 and 3, suggesting a strong link between cognitive and mental health in older age. Participants with only depressive (Group 1) or anxiety symptoms (Group 4) exhibit notable differences. Those with anxiety symptoms are associated with healthier clusters across other views. The co-clustering methodology also categorizes the questions in the CES-D and HADS scales into meaningful clusters, providing valuable insights into the underlying dimensions of mental health assessment. In the CES-D scale, the questions are divided into four clusters: those related to loneliness and energy, those addressing feelings of insecurity, worthlessness, and fear, those concerning concentration and effort, and those focused on sleep disturbances. Similarly, the HADS questions are grouped into clusters that reflect themes such as a strong sense of impending doom, nervousness or unease, and feelings of tension or restlessness. By organizing the questions from both scales into these smaller groups, the methodology highlights distinct symptom patterns and their varying severity among participants. This approach could be leveraged to develop abridged versions of the assessment scales, enabling faster and more efficient triage in clinical practice.