Background: As populations age, frailty and the associated demand for health care increase. Evidence needed to inform planning and commissioning of services for older people living with frailty is scarce. Accurate information on incidence and prevalence of different levels of frailty and the consequences for health outcomes, service use and costs at population level is needed.
Objectives: To explore the incidence, prevalence, progression and impact of frailty within an ageing general practice population and model the dynamics of frailty-related healthcare demand, outcomes and costs, to inform the development of guidelines and tools to facilitate commissioning and service development.
Study design and methods: A retrospective observational study with statistical modelling to inform simulation (system dynamics) modelling using routine data from primary and secondary health care in England and Wales. Modelling was informed by stakeholder engagement events conducted in Hampshire, England. Data sources included the Royal College of General Practitioners Research and Surveillance Centre databank, and the Secure Anonymised Information Linkage Databank. Population prevalence, incidence and progression of frailty within an ageing cohort were estimated using the electronic Frailty Index tool, and associated service use and costs were calculated. Association of frailty with outcomes, service use and costs was explored with multistate and generalised linear models. Results informed development of a prototype system dynamics simulation model, exploring population impact of frailty and future scenarios over a 10-year time frame. Simulation model population projections were externally validated against retrospective data from Secure Anonymised Information Linkage.
Study population: The Royal College of General Practitioners Research and Surveillance Centre sample comprised an open cohort of the primary care population aged 50 + between 2006 and 2017 (approx. 2.1 million people). Data were linked to Hospital Episode Statistics data and Office for National Statistics death data. A comparable validation data set from Secure Anonymised Information Linkage was generated.
Baseline measures: Electronic Frailty Index score calculated annually and stratified into Fit, Mild, Moderate and Severe frailty categories. Other variables included age, sex, Index of Multiple Deprivation score, ethnicity and Urban/rural.
Outcomes: Frailty transitions, mortality, hospitalisations, emergency department attendances, general practitioner visits and costs.
Findings: Frailty is already present in people aged 50-64. Frailty incidence was 47 cases per 1000 person-years. Frailty prevalence increased from 26.5% (2006) to 38.9% (2017). Older age, higher deprivation, female sex, Asian ethnicity and urban location independently predict frailty onset and progression; 4.8% of 'fit' people aged 50-64 years experienced a transition to a higher frailty state in a year, compared to 21.4% aged 75-84. Individual healthcare use rises with frailty severity, but Mild and Moderate frailty groups have higher overall costs due to larger population numbers. Simulation projections indicate frailty will increase by 7.1%, from 41.5% to 48.7% between 2017 and 2027, and associated costs will rise by £5.8 billion (in England) over an 11-year period.
Conclusions: Simulation modelling indicates that frailty prevalence and associated service use and costs will continue to rise in the future. Scenario analysis indicates reduction of incidence and slowing of progression, particularly before the age of 65, has potential to substantially reduce future service use and costs, but reducing unplanned admissions in frail older people has a more modest impact. Study outputs will be collated into a commissioning toolkit, comprising guidance on drivers of frailty-related demand and simulation model outputs.
Study registration: This study is registered as NCT04139278 www.clinicaltrials.gov.
Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 16/116/43) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 44. See the NIHR Funding and Awards website for further award information.
Keywords: AGEING POPULATION; COHORT STUDY; COMPUTER SIMULATION MODELLING; FRAILTY; HEALTH; INCIDENCE; OLDER PEOPLE; PREVALENCE; SERVICE COSTS; SERVICE USE; SYSTEM DYNAMICS; TRANSITIONS.
More people are living longer with long-term medical conditions or disabilities. They are more likely to be admitted to hospital and need health care. People with these vulnerabilities are living with ‘frailty’, which can be mild, moderate or severe. Our research is aimed to produce information on how common frailty is, how it changes over time, what can influence it getting worse, and how it will impact our future population. We analysed two large data sets from England and Wales (2006–17) to find out the numbers of people aged 50 + living with frailty, their characteristics (e.g. age, sex, living in deprived areas) and how these influenced frailty occurring and worsening. We explored how often they used general practitioner/hospital services and how much that cost. This information was used in a computer model to predict what would happen in the future. The proportion of people with frailty increased from 26.5% in 2006 to 38.9% in 2017, including large increases in people with mild and moderate frailty. Older age, female sex, Asian ethnicity, and living in more deprived or urban areas, all increased the risk of someone becoming frail, and of their frailty worsening. The large numbers of people with mild and moderate frailty led to the highest costs overall. The computer model predicted that the proportion of people with frailty will increase by another 7.1% between 2017 (41.5%) and 2027 (48.7%), and associated costs will rise by £5.8 billion over an 11-year period. We have estimated how the number of people with frailty and their use of services will continue to rise in the future. Taking action to reduce people’s risk of becoming frail, particularly before age 65, and slowing frailty progression can reduce the need for services. We will report this information to people who plan health care so they can provide more effective care for people with frailty.