348 Developing a Frailty Index: Does the Composition of Functional and Disease or Risk Items Influence Frailty Estimates?. (16th September 2019)

• Record Type: Journal Article
• Authors: O'Donovan, Mark; Sezgin, Duygu; Liew, Aaron; O'Caoimh, Rónán
• Is Part Of: Age and ageing. Volume 48(2019)Supplement 3
• Page Start: iii1
• Page End: iii16
• Publication Date: 2019-09-16
• Subjects: Aging -- Periodicals; Geriatrics -- Periodicals; 618.97
• DOI: 10.1093/ageing/afz102.72
• Languages: English
• ISSNs: 0002-0729

Abstract

Abstract: Background: Frailty is a multi-factorial, age-related syndrome, often defined in terms of accumulation of deficits. This approach uses the proportion of a list of age-associated items (signs, symptoms, diseases, impairments, etc.) available to identify frailty. Some frailty indexes (FI) include more functional aspects than others. This study examines whether functional components and diseases or risk factors predict the same phenomena. Methods: Data were included from The Survey of Health, Ageing and Retirement in Europe (SHARE) wave seven for participants age ≥50 years. A 52-item FI was constructed from physical health data with 26 functional and 26 non-functional items. Functional items were obtained from activities of daily living questions (ph048 & ph049), and health-related activity limitation (gali question). Non-functional items were obtained from questions on BMI, self-perceived health, disease/risks (ph006), medication use (sleep, anxiety/depression, osteoporosis), and frailty related symptoms (ph048). Participants missing one or more of the 52 items were excluded. Data were analysed at individual and country-level to assess for significant differences. Results: In total, 73, 510 (97.3%) participants had complete frailty data available and were included. The mean score for the FI-52 was 0.12 (95% CI: 0.11-0.12). Mean FI scores for the functional and non-functional components were 0.12 (95% CI:0.12-0.12) and 0.11 (95% CI:0.11-0.11), respectively, representing a significant difference (p<0.001). For a cut-off of 0.25 for frailty this difference was 5.4% (95% CI:3.9%-6.2%). Spearman's correlation between these components was 0.66 for individual participants and 0.72 for country-level mean scores, indicating moderate to strong correlation. These were similar using a cut-off of 0.25, at 0.45 and 0.71, respectively. Conclusion: Findings suggest that having more functional items in a frailty index results in significantly higher frailty estimates but this only amounted to an approximately 5% difference. Although moderate-strong correlation was observed, further research is needed to investigate if the proportion of functional components influences risk-prediction at population-level.