Moody, Dawn Karen (2016) Measurement of frailty: development of a primary care framework and validation of an electronic Frailty Index. Masters thesis, Keele University.

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Abstract

Frailty measurement in primary care and at transitions of care is needed to enable timely identification of frailty, guide interventions and support proactive, integrated care. However, there is currently no single tool for frailty measurement in these settings.
This thesis comprises a systematic review and two observational studies. The systematic review of frailty measurement tools in primary care and at transitions of care identified few studies for emerging frailty. It was used to develop a framework for a tool, which included components to reflect a multidimensional model, use of routinely collected primary care data and quality of life as a holistic outcome for frailty.
The observational studies used an electronic Frailty Index (eFI) applied to primary care consultation data over a 5-year period (2007-2012) to measure frailty in a population aged 40 years and over (n=9793), selected by osteoarthritis and cardiovascular disease status. The first study described frailty and frailty change over 2 years by socio-demographic and disease status characteristics, including comorbidity severity. The second study linked survey data regarding anxiety, depression, fatigue, social networks and quality of life at two time points 12 months apart for a subset of this population (n=2878). Multiple regression methods were used to investigate whether eFI predicted quality of life and whether this prediction could be improved by the addition of other explanatory factors.
Frailty increased with age, deprivation and comorbidity severity, and increasing frailty was associated with increased frailty change over 2 years. A model that included age, gender, deprivation and eFI was moderately predictive of quality of life at 12-months. This predictive ability was significantly improved by including anxiety, depression, fatigue and healthcare use as additional explanatory factors in the model, although adding social network data did not improve prediction. Further research is required on the lifecourse of frailty across care interfaces.

Item Type: Thesis (Masters)
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > Institute for Science and Technology in Medicine
Contributors: Kadam, UT (Thesis advisor)
Depositing User: Lisa Bailey
Date Deposited: 29 Jul 2022 16:00
Last Modified: 29 Jul 2022 16:00
URI: https://eprints.keele.ac.uk/id/eprint/11177

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