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The Dynamics of Frailty in Older People

NCT ID: NCT04139278

Last Updated: 2020-12-21

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

300000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-07-21

Study Completion Date

2022-02-28

Brief Summary

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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. There is recognition of an evidence gap in relation to the planning, commissioning and delivery of services for older people living with frailty. Questions remain about the incidence and prevalence different levels of frailty and the consequences for health outcomes, health and care service use and costs.

In this study, the investigators will explore the incidence and prevalence, development and impact of frailty within the population using retrospective primary care data on patients aged 50 and over in 2006 within the database. The investigators will stratify the cohort by severity of frailty and explore frailty status over time, determining incidence, prevalence and progression of frailty. The relationships between factors such as age, deprivation, ethnicity, location and comorbidities of individuals in relation to development of, and deterioration in, frailty status will be examined. The influence of frailty on outcomes, service use and costs will be explored. These analyses will be used to 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. Finally, 'what if' scenarios developed with the stakeholder engagement group will be explored via simulation modelling.

Detailed Description

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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.

Conditions

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Frail Elderly Syndrome

Keywords

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Frail Elderly Syndrome

Study Design

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Observational Model Type

ECOLOGIC_OR_COMMUNITY

Study Time Perspective

RETROSPECTIVE

Interventions

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Observational

Observational retrospective study

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* aged 50 years and above
* registered with participating GP practices between 2006 and 2016

Exclusion Criteria

• aged less than 50 years
Minimum Eligible Age

50 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Institute for Health Research, United Kingdom

OTHER_GOV

Sponsor Role collaborator

University of Surrey

OTHER

Sponsor Role collaborator

University of Leeds

OTHER

Sponsor Role collaborator

Swansea University

OTHER

Sponsor Role collaborator

University of Southampton

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Bronagh Walsh, PhD

Role: PRINCIPAL_INVESTIGATOR

University of Southampton

Locations

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University of Southampton

Southampton, Hampshire, United Kingdom

Site Status RECRUITING

Countries

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United Kingdom

Central Contacts

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Bronagh Walsh, PhD

Role: CONTACT

Phone: +442380597991

Email: [email protected]

Francesca Lambert

Role: CONTACT

Phone: 02380595917

Email: [email protected]

Facility Contacts

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Bronagh Walsh, PhD

Role: primary

Francesca Lambert, CertHE

Role: backup

References

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Walsh B, Fogg C, England T, Brailsford S, Roderick P, Harris S, Fraser S, Clegg A, de Lusignan S, Zhu S, Lambert F, Barkham A, Patel H, Windle V. Impact of frailty in older people on health care demand: simulation modelling of population dynamics to inform service planning. Health Soc Care Deliv Res. 2024 Oct;12(44):1-140. doi: 10.3310/LKJF3976.

Reference Type DERIVED
PMID: 39487824 (View on PubMed)

Fogg C, Fraser SDS, Roderick P, de Lusignan S, Clegg A, Brailsford S, Barkham A, Patel HP, Windle V, Harris S, Zhu S, England T, Evenden D, Lambert F, Walsh B; Frailty Dynamics study team. The dynamics of frailty development and progression in older adults in primary care in England (2006-2017): a retrospective cohort profile. BMC Geriatr. 2022 Jan 6;22(1):30. doi: 10.1186/s12877-021-02684-y.

Reference Type DERIVED
PMID: 34991479 (View on PubMed)

Other Identifiers

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HS&DR16/116/43

Identifier Type: -

Identifier Source: org_study_id