Needs of older persons living in long-term care institutions: on the usefulness of cluster approach

• Main Authors: Sławomir Tobis, Krystyna Jaracz, Sylwia Kropińska, Dorota Talarska, Juanita Hoe, Katarzyna Wieczorowska-Tobis, Aleksandra Suwalska
• Format: Article
• Language: English
• Published: BMC 2021-05-01
• Series: BMC Geriatrics
• Subjects: Needs; Care homes; Long-term care; Clusters; CANE
• Online Access: https://doi.org/10.1186/s12877-021-02259-x

Abstract Background Long-term care units’ residents do not constitute a homogeneous population. Providing effective care, tailored to individual needs, is crucial in this context. It can be facilitated by suitable tools and methods, which include needs assessment along with the physical, psychological and social aspects of care. We thus applied a cluster approach to identify their putative groupings to enable the provision of tailored care. Methods The needs of 242 residents of care homes in four Polish cities (Poznan, Wroclaw, Bialystok and Lublin), aged 75–102 years (184 females), with the Mini-Mental State Examination (MMSE) score ≥ 15 points, were assessed with the CANE (Camberwell Assessment of Need for the Elderly) questionnaire. Their independence in activities of daily living was evaluated by the Barthel Index (BI), and symptoms of depression by the Geriatric Depression Scale (GDS). The results of MMSE, BI and GDS were selected as variables for K-means cluster analysis. Results Cluster 1 (C1), n = 83, included subjects without dementia according to MMSE (23.7 ± 4.4), with no dependency (BI = 85.8 ± 14.4) and no symptoms of depression (GDS = 3.3 ± 2.0). All subjects of cluster 2 (C2), n = 87, had symptoms of depression (GDS = 8.9 ± 2.1), and their MMSE (21.0 ± 4.0) and BI (79.8 ± 15.1) were lower than those in C1 (p = 0.006 and p = 0.046, respectively). Subjects of cluster 3 (C3), n = 72, had the lowest MMSE (18.3 ± 3.1) and BI (30.6 ± 18,8, p < 0.001 vs. C1 & C2). Their GDS (7.6 ± 2.3) were higher than C1 (p < 0.001) but lower than C2 (p < 0.001). The number of met needs was higher in C2 than in C1 (10.0 ± 3.2 vs 8.2 ± 2.7, p < 0.001), and in C3 (12.1 ± 3.1) than in both C1 and C2 (p < 0.001). The number of unmet needs was higher in C3 than in C1 (1.2 ± 1.5 vs 0.7 ± 1.0, p = 0.015). There were also differences in the patterns of needs between the clusters. Conclusions Clustering seems to be a promising approach for use in long-term care, allowing for more appropriate and optimized care delivery. External validation studies are necessary for generalized recommendations regarding care optimization in various regional perspectives.