The Dynamics of Frailty in Older People: Modelling Impact on Health Care Demand and Outcomes to Inform Service Planning and Commissioning

The impact of frailty on demand for and outcomes of care has emerged as a significant issue for the National Health Service (NHS) in recent years. The association between frailty and adverse outcomes such as unplanned admission, transfer to residential care and high service use is well recognised (BGS 2014/15; NIHR 2017; Clegg et al. 2013). As the population ages, prevalence of frailty and associated demand for health care rise. In the context of reduced resources and rising demand for unplanned care, the delivery of appropriate services to support people with frailty will be key to providing cost-effective, quality care for older people. Recent consensus guidelines have emphasised the importance of identification and clinical management of frailty (BGS 2014; NICE 2016) and effective interventions are available, but capacity and resources for delivery are limited. There is recognition of an evidence gap in relation to the planning, commissioning and delivery of services for older people living with frailty (NIHR 2017). Questions remain about the incidence and prevalence different levels of frailty and the consequences for health outcomes, health and care service use and costs. Addressing these issues requires exploration of population trends in the development and impact of frailty, but this research has previously been limited because of the need for clinical assessment for the identification of frailty. The recent introduction of the electronic Frailty Index (eFI) (Clegg et al. 2016) allows routine primary care data to be used to identify the presence and severity of frailty in real-world populations. The eFI therefore facilitates the exploration of the dynamics of frailty and its impact at a population level. It enables stratification of the primary care population into robust, mild, moderate and severe frailty groups, so enabling comparison of trajectories of decline and pathways of care between these groups, which will be key to service development and commissioning.

In this study, the investigators will explore the incidence and prevalence, development and impact of frailty within the population using retrospective data from the Royal College of General Practitioners Research Surveillance Centre (RCGP RSC) database, which holds data for 1.8m patients from 230 practices. The eFI tool will be utilised to stratify a cohort of people aged 60 and over within the database between 2004-8 into robust, mild, moderate and severe frailty groups. Data will be extracted on frailty status, health care use, and outcomes for the subsequent 10 years, calculating key service use costs from these data. Outcomes will include mortality, unplanned hospital admission, Accident and Emergency (A&E) attendance and General Practitioner (GP) appointments. The RCGP RSC dataset will also provide data on socio-economic factors, practice size and location and residence. The cohort will be stratified by severity of frailty, and frailty status explored over time, determining incidence, prevalence and progression of frailty. The investigators will examine the relationships between factors such as age, deprivation, ethnicity, location and comorbidities of individuals in relation to development of, and deterioration in, frailty status. The influence of frailty on outcomes, service use and costs will be explored. Results from these analyses will be used to inform development of guidelines for service commissioners, developed in partnership with experts in service delivery, commissioning and Public Patient Involvement (PPI) representatives through stakeholder engagement. These analyses will inform the development of a prototype simulation model, which will use a System Dynamics (SD) based approach to explore the development and impact of frailty in the population and likely future scenarios over a 10-year timeframe. The simulation model population projections will be externally validated against retrospective data from the Leeds Data Model (LDM) dataset, which holds data for 810,000 primary care patients from 108 practices in the Leeds area. Residence data from RCGP RSC will be supplemented by data on residential care transitions and social care use by frailty status from the Secure Anonymised Information Linkage (SAIL) Databank, which holds primary and social care data on up to 30,000 individuals in Wales, to inform simulation of impacts and costs beyond the health care setting. Finally, 'what if' scenarios developed with the stakeholder engagement group (SEG) will be explored via simulation modelling.