Elsa Dent1, Eleonora Dal Grande2, Kay Price3, Anne W Taylor4. 1. Centre for Research in Geriatric Medicine, The University of Queensland, Brisbane, Australia; School of Public Health, The University of Adelaide, Adelaide, Australia. Electronic address: elsa.dent@adelaide.edu.au. 2. School of Medicine, The University of Adelaide, Adelaide, Australia. Electronic address: eleonora.dalgrande@adelaide.edu.au. 3. School of Nursing and Midwifery, University of South Australia, Adelaide, Australia. Electronic address: Kay.Price@unisa.edu.au. 4. School of Medicine, The University of Adelaide, Adelaide, Australia. Electronic address: anne.taylor@adelaide.edu.au.
Abstract
OBJECTIVES: Little is known about frailty and its impact on health-care systems. Using large-scale population health surveillance data, this study determined the prevalence of frailty, its associated factors, and the impact it places on health care services. STUDY DESIGN: A cross-sectional snapshot of the 2013-2015 South Australian Monitoring and Surveillance System (SAMSS) database was used, focusing on individuals aged ≥65years. Frailty was assessed by the Frailty Index (FI), and classified as robust (scores≤0.1), pre-frail (>0.1 to ≤0.25), and frail (>0.25). RESULTS: 7207 people (53.7% female) were included; mean (SD) age was 74.8 (7.17) years. The mean (SD) FI score was 0.23 (0.11), with a 99% upper limit of 0.53. Over a third (36.3% (95% CI 34.8-37.9)) were classified as frail and over half (53.6% (95% CI 52.0-55.1)) as pre-frail. Frailty was less common in rural areas, and was associated with age, lower education level, and higher socioeconomic disadvantage. After adjustment for confounders, multivariable analyses showed a gradient effect by frailty classification with regard to both hospital- and non-hospital-based services. Frail older adults were more likely to present to hospital Emergency Departments (EDs) than their pre-frail or robust counterparts, yet visited the GP at the same rate as older adults with pre-frailty. CONCLUSION: Frail older adults were higher users of health care services, with the exception of GPs. Knowledge of the health service usage patterns of frail older adults can be used to direct public health policy and plan future GP provision.
OBJECTIVES: Little is known about frailty and its impact on health-care systems. Using large-scale population health surveillance data, this study determined the prevalence of frailty, its associated factors, and the impact it places on health care services. STUDY DESIGN: A cross-sectional snapshot of the 2013-2015 South Australian Monitoring and Surveillance System (SAMSS) database was used, focusing on individuals aged ≥65years. Frailty was assessed by the Frailty Index (FI), and classified as robust (scores≤0.1), pre-frail (>0.1 to ≤0.25), and frail (>0.25). RESULTS: 7207 people (53.7% female) were included; mean (SD) age was 74.8 (7.17) years. The mean (SD) FI score was 0.23 (0.11), with a 99% upper limit of 0.53. Over a third (36.3% (95% CI 34.8-37.9)) were classified as frail and over half (53.6% (95% CI 52.0-55.1)) as pre-frail. Frailty was less common in rural areas, and was associated with age, lower education level, and higher socioeconomic disadvantage. After adjustment for confounders, multivariable analyses showed a gradient effect by frailty classification with regard to both hospital- and non-hospital-based services. Frail older adults were more likely to present to hospital Emergency Departments (EDs) than their pre-frail or robust counterparts, yet visited the GP at the same rate as older adults with pre-frailty. CONCLUSION: Frail older adults were higher users of health care services, with the exception of GPs. Knowledge of the health service usage patterns of frail older adults can be used to direct public health policy and plan future GP provision.
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Authors: E Dent; J E Morley; A J Cruz-Jentoft; L Woodhouse; L Rodríguez-Mañas; L P Fried; J Woo; I Aprahamian; A Sanford; J Lundy; F Landi; J Beilby; F C Martin; J M Bauer; L Ferrucci; R A Merchant; B Dong; H Arai; E O Hoogendijk; C W Won; A Abbatecola; T Cederholm; T Strandberg; L M Gutiérrez Robledo; L Flicker; S Bhasin; M Aubertin-Leheudre; H A Bischoff-Ferrari; J M Guralnik; J Muscedere; M Pahor; J Ruiz; A M Negm; J Y Reginster; D L Waters; B Vellas Journal: J Nutr Health Aging Date: 2019 Impact factor: 4.075
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