Sławomir Tobis1, Krystyna Jaracz2, Sylwia Kropińska3, Dorota Talarska4, Juanita Hoe5, Katarzyna Wieczorowska-Tobis3, Aleksandra Suwalska6. 1. Department of Occupational Therapy, Poznan University of Medical Sciences, ul. Swiecickiego 6, 60-781, Poznan, Poland. stobis@ump.edu.pl. 2. Chair of Nursing, Poznan University of Medical Sciences, ul. Smoluchowskiego 11, 60-179, Poznan, Poland. 3. Geriatrics Unit, Chair and Department of Palliative Medicine, Poznan University of Medical Sciences, Poznan, Poland. 4. Chair of Preventive Medicine, Poznan University of Medical Sciences, ul. Swiecickiego 6, 60-781, Poznan, Poland. 5. Division of Nursing, School of Health Sciences, City, University of London, Northampton Square, London, EC1V 0HB, UK. 6. Department of Mental Health, Chair of Psychiatry, Poznan University of Medical Sciences, ul. Szpitalna 27/33, 60-572, Poznan, Poland.
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.
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.
Entities:
Keywords:
CANE; Care homes; Clusters; Long-term care; Needs
Authors: Peter Tanuseputro; Amy Hsu; Kerry Kuluski; Mathieu Chalifoux; Melissa Donskov; Sarah Beach; Peter Walker Journal: J Am Med Dir Assoc Date: 2017-04-01 Impact factor: 4.669
Authors: Mark D Martin; Geraldine A Hancock; Barbora Richardson; Peter Simmons; Cornelius Katona; Eleanor Mullan; Martin Orrell Journal: Int Psychogeriatr Date: 2002-12 Impact factor: 3.878