Background: The increasing prevalence of multimorbidity has created a serious global public health problem in aging populations. Certain multimorbidity patterns across different age ranges and their association with health status remain unclear. The main aim of this study is to identify multimorbidity patterns discrepancies and associated health status between younger-old and oldest-old.
Methods: The Ethics Committee of Nanjing Medical University approved the study protocol (No.2019-473). Convenience sampling method was used to recruit older adults aged ≥ 60 years with multimorbidity from July to December 2021 from 38 Landsea long-term care facilities in China. The multimorbidity patterns were analyzed using network analysis and two-step cluster analysis. One-Way ANOVA was utilized to explore their association with health status including body function, activity of daily living, and social participation. A Sankey diagram visualized the flow of health status within different multimorbidity patterns. This study is reported following the STROBE guidelines.
Results: A total of 214 younger-old (60-84 years) and 173 oldest-old (≥ 85 years) were included. Leading coexisting diseases were cardiovascular disease (CD), metabolic and endocrine disease (MED), neurological disease (ND), and orthopedic disease (OD). Cluster 1 (53, 24.8%) of CD-ND (50, 94.3%; 31, 58.8%), cluster 2 (39, 18.2%) of MED-ND-CD (39, 100%; 39, 100%; 37, 94.9%), cluster 3 (37, 17.3%) of OD-CD-MED-ND (37, 100%; 33, 89.2%; 27, 73.0%; 16, 43.2%), and cluster 4 (34, 15.9%) of CD-MED (34, 100%; 34, 100%) were identified in the younger-old. In the oldest-old, the primary multimorbidity patterns were: cluster 1 (33, 19.1%) of CD-respiratory disease-digestive disease-urogenital disease (CD-RD-DSD-UD) (32, 97.0%; 9, 27.3%; 8, 24.2%; 7, 21.2%), cluster 2 (42, 24.3%) of ND-CD-MED (42, 100%; 35, 83.3%; 14, 33.3%), cluster 3 (28, 16.2%) of OD-CD-MED (28, 100%; 25, 89.3%; 18, 64.3%), and cluster 4 (35, 20.2%) of CD-MED (35, 100%; 35, 100%). Younger-old with CD-ND or MED-ND-CD, and oldest-old with ND-CD-MED have worse health status compared with other multimorbidity patterns (e.g., CD-MED and OD-CD-MED).
Conclusion: Discrepancies in common patterns of multimorbidity across age groups suggest that caregivers in long-term care facilities should consider changes in multimorbidity patterns with ageing when developing prevention plans for individualized management. Neurological disease concurrent with other diseases was the major determinant of health status, especially for the oldest-old. Interventions targeting multimorbidity need to be focused, yet generic. It is essential to assess complex needs and health outcomes that arise from different multimorbidity patterns and manage them through an interdisciplinary approach and consider their priorities to gain high-quality primary care for older adults living in long-term care facilities.
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http://dx.doi.org/10.1186/s12877-023-04507-8 | DOI Listing |
Syst Rev
January 2025
School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, UK.
Background: Multimorbidity, the co-existence of two or more conditions within an individual at any one time, is globally increasing and forecasted to rise. This poses a significant challenge for current models of healthcare delivery, which are now ill-equipped to meet the future population health needs. Interprofessional collaborative practice is a specific way professionals work closely together and with patients and their families to improve patient outcomes.
View Article and Find Full Text PDFBMC Public Health
January 2025
Indian Council of Medical Research, New Delhi, India.
Background: Cardiometabolic multimorbidity (CMM), characterized by the coexistence of diabetes, hypertension, and cardiovascular disease, poses a major health challenge in India, particularly in rural areas with limited healthcare resources. Lifestyle interventions can manage cardiometabolic risk factors, yet adherence remains suboptimal. Mobile health (mHealth) interventions offer a scalable approach for managing CMM by promoting behaviour change and medication adherence.
View Article and Find Full Text PDFZhonghua Yu Fang Yi Xue Za Zhi
December 2024
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei230032 MOE Key Laboratory of Population Health Across Life Cycle/Anhui Provincial Key Laboratory of Environment and Population Health across the Life Course/Center for Big Data and Population Health, Institute of Health and Medicine, Hefei Comprehensive National Science Center, Hefei230032.
Multimorbidity of common diseases in children and adolescents refers to the coexistence of two or more common diseases or chronic health problems in the same individual. In recent years, the multimorbidity of common diseases in children and adolescents has become increasingly serious, which has enormously increased disease burden and socio-economic losses. These diseases typically share similar influencing factors, such as adverse environmental factors, unhealthy diets, lack of outdoor activities and physical exercise, sedentary lifestyle, excessive use of electronic devices, and disturbed sleep rhythms.
View Article and Find Full Text PDFZhonghua Yu Fang Yi Xue Za Zhi
December 2024
School of Public Health, Peking University/Institute of Child and Adolescent Health, Peking University, Beijing100191, China.
The main health problems faced by children and adolescents in China are constantly changing. Myopia, overweight and obesity, abnormal spinal curvature, and mental health issues have become the main health problems for children and adolescents. However, multidimensional health problems such as infectious diseases, chronic non-communicable diseases and injuries still coexist and present regional imbalances.
View Article and Find Full Text PDFZhonghua Yu Fang Yi Xue Za Zhi
December 2024
Institute of Environmental and Public Health, Tianjin Center of Disease Control and Prevention, Tianjin300011, China.
To understand the occurrence of different patterns of multimorbidity among children and adolescents aged 9-18 in Tianjin and analyze the cumulative effects of lifestyle on these patterns of multimorbidity. From September to November 2022, a stratified cluster random sampling method was used to select students from primary schools, junior high schools, general high schools, and vocational schools in 16 districts of Tianjin to screen for height, weight, blood pressure, distant vision, and diopter. One year later, a follow-up measurement and questionnaire survey were conducted.
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