The dynamics of frailty in older people: modelling impact on health care demand and outcomes to inform service planning and commissioning

STUDY AIMS AND PURPOSE:

Frailty has emerged as a significant issue for the National Health Service (NHS) in recent years. Frailty is associated with outcomes including unplanned admission, transfer to residential care and high levels of service use. As the population ages, the frailty becomes more common, and associated demand for health care increases. Where there are limited NHS resources but increasing demand, planning delivery of appropriate services to support people with frailty will be key to providing cost-effective, quality care for older people. However, detailed information about how many people develop frailty over a certain time, how common frailty is in different groups of people, how it progresses and how it impacts on need for health care is still lacking. We need this information to be able to plan, commission and delivery services for older people who are at risk of developing frailty, or who already have frailty.

To be able to do this, we need to explore the trends of frailty development and progression within large populations and understand the impact of frailty on patients and their use of NHS services. A useful tool, the electronic Frailty Index (eFI) has recently been introduced to the NHS. This tool uses data in the patients primary care medical record and looks for 36 different ‘deficits’ (e.g., clinical conditions or diagnoses, laboratory tests, limitations to mobility) which are used to calculate a score. A low score indicates that patients are ‘fit’, and higher scores indicate patients may have mild frailty, moderate frailty or severe frailty. The worse the frailty becomes, the more at-risk patients are from poor outcomes, as people are more likely to find it difficult to deal with small changes in their health or circumstances, so the effects of getting ill are worse than for other people.

Until recently, it was difficult for General Practitioners (GPs) to provide care for frail older people because it was hard to identify people who were frail without an assessment by a consultant. This meant that frail older people were not always receiving the care they needed. However, GPs can use the information from the eFI to improve care for patients with moderate and severe frailty.

University of Southampton have access to a pseudonymised extract of all individuals aged 50 years and above between 2006 and 2017 from the primary care records of participating RCGP RSC practices. An eFI score for each individual for each calendar year they are present in the cohort has been calculated from the RCGP RSC primary care data. The eFI score is then categorised into fit, mild, moderate and severe based on the cut-offs provided in Clegg (2016). Given the number of years individuals may be present in the dataset, it is likely the eFI category will vary during their follow-up time; University of Southamptons preliminary analyses of the existing primary care data (RCGP dataset) confirms this is the case. The data extract requested from NHS England will be pseudonymised and linked to the existing pseudonymised primary care data as per the processes described in section 5b. This linked dataset will allow University of Southampton to explore the relationship between frailty transitions and outcomes without having to identify individuals.

The request is for primary care data from RCGP RSC, including all relevant codes for the 36 variables used in calculating eFI. There is no use of the eFI scores coded within GP systems, which would not have been available for the years being requested. The eFI scores are generated from routinely collected primary care data and use Read or CTV3 codes, as specified in the method by Clegg (2016), which allows calculation of eFI for all ages (Read code algorithm). This method has been applied to retrospective primary care data to generate information on each of the 36 eFI deficits. The requested data will be linked to our pseudonymised primary care records using the processes described. This will allow University of Southampton to utilise the primary-care derived eFI scores to explore the relationship between frailty and secondary and urgent care use.

Within the primary care record, which are used to calculate the eFI score, the eFI uses clinical diagnoses which have already been made and recorded by GPs using Read or CTV3 codes in the patients’ records. No prospective clinical assessments or diagnoses are therefore required for the proposed work. No clinical diagnoses are being carried out for this study. The eFI is not the same as a clinical diagnosis of frailty, its development was based on the recognised cumulative deficit framework devised by Rockwood. The intention is to use eFI scores as a measure of potential frailty or frailty associated burden at population level, not as a clinical diagnostic tool. By diagnoses, it is meant existing diagnostic codes held by RCGP RSC.

Therefore, the overarching aim of this study is to explore trends in development and progression of frailty, and the dynamics of frailty related healthcare demand, outcomes, and costs in the older general practice population, to inform the development of guidelines and tools to facilitate commissioning and service development for this patient group.

SPECIFIC STUDY OBJECTIVES AND RELATED WORKSTREAMS:

The study objectives are:

1. Identification of incidence and prevalence of frailty states in an ageing population (50 years and over)

2. Identification of frailty trajectories and transitions in severity in the older population over time

3. Exploration of drivers of progression of frailty, including clinical, socioeconomic, and demographic factors

4. Examination of the impact of frailty on service use, costs, and pathways of care

5. Exploration of the relationship between frailty status, socio-economic factors, practice factors and service use and outcomes (mortality, unplanned admissions, residential care use)

6. Prediction of trends in frailty, modelling of health and care demand and costs over time and in different service contexts

Workstreams:

To fulfil the above objectives, the project is divided into the following workstreams, of which workstreams 1 and 4 are relevant to this data request. There are two main aims for the project Workstream to which this data request relates. They are:

- the identification of key variables capable of predicting frailty development

- progression and assessment of the relationship between frailty status and key clinical outcomes (including mortality and unplanned admissions).

These analyses will then be used to inform the simulation modelling being conducted in Workstream 4.

This agreement relates to the linkage of HES and mortality data from NHS England to primary care data provided by RCGP RSC within Workstream 1 of the study. There is no data linkage between Hospital Episode Statistics (HES) and mortality data and SAIL.

• Workstream 1: statistical modelling of population trends, incidence and prevalence of frailty, stratification of frailty and related outcomes, resource use and costs – this data request specifically relates to providing secondary and urgent care service use data as a component of this workstream.

• Workstream 2: validation of the population model

• Workstream 3: stakeholder engagement.

• Workstream 4: simulation modelling to explore impact of different service and demographic scenarios on population trends, service demand and costs in the future – the analysis of data provided under this data request will inform this workstream, no further data is required for workstream 4.

The data requested under this agreement will provide the necessary hospital outcomes and mortality data to be able to fulfil Workstream 1, and the results of analyses in Workstream 1 will inform the simulation modelling in Workstream 4.

WHICH DATA IS BEING REQUESTED?

This study will use electronic data which is recorded during the routine care of NHS patients, where explicit consent has not been gained from participants. To be able to fulfil the aims of the study, healthcare data on an ageing cohort over a 12-year period will be needed, so that health outcomes can be explored over the medium to long term. We will use data which has already been collected (‘retrospective data’), as it would not be possible to do a large-scale representative study prospectively.

The dataset requested is minimised to a pre-defined cohort of approximately 2.2 million patients from the Royal College of GPs Research Surveillance Centre (RCGP RSC) dataset. The RCGP RSC dataset is an electronic health record (EHR) that collates routinely recorded primary care data from a population of 3 million nationwide from more than 400 GP practices. We are only requesting variables which are needed for analysis of our defined outcomes, in appropriate formats to minimise potential identifiers and reduce the risk of any inadvertent identification through combinations of variables. The RCGP Research Surveillance Centre team at University of Oxford and the study team at University of Southampton are requesting a unique study identifier (ID) only; NHS England data will be pseudonymised using a non-reversible hashing algorithm. The RCGP Research Surveillance Centre team will link the NHS England data to their primary care data (RCGP RSC dataset) using the pseudonymised Identifier.

To conduct this component of the research (Workstream 1), the research team from University of Southampton will work on a pseudonymised RCGP RSC data extract, linked to pseudonymised NHS England HES and Civil Registration Deaths data/Mortality data to determine the outcomes specified in this agreement. In total, for this workstream, the University of Southampton will obtain fully de-identified, pseudonymised data extracts from the following databanks:

• Royal College of General Practitioners Research Surveillance Centre (RCGP RSC) dataset: this primary care dataset will include demographic data, residence, long-term conditions diagnoses, frailty index domains, prescriptions, primary care service events

The primary care RCGP RSC dataset comprises the baseline characteristics of the patients and primary healthcare contacts over this period, in addition to frailty scores, the main predictor of interest in this study. Secondary care attendances and their outcomes (outpatient appointments, Accident and Emergency (A&E) visits, hospital admissions, critical care admissions) and deaths are key study outcomes of interest to understand how attendances and healthcare use varies between people with different frailty states. It is therefore important to have individual-level data to be able to analyse changes in healthcare use over the cohort period and examine predictors of secondary care use and deaths, hence the request for pseudonymised Hospital Episode Statistics (HES) and Civil Registration Deaths data/Mortality data, which will be linked to the RCGP RSC primary care dataset only.

For this study, the research team will need to link the de-identified RCGP RSC data extract with data on secondary care Hospital Episode Statistics (HES) and Civil Registration Deaths data/Mortality data. HES and mortality data are therefore being requested in the performance of a task in the public interest - Article 6(1)(e) i.e. processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller, and Article 9(2)(j) with regards to the processing being necessary for achieving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law. The public interest function of the proposed data linkage is evidenced in the acceptance of this study within the RCGP Research Surveillance Centre portfolio and its funding by the NIHR, where it is a part of their established programme of research in relation to management of frailty.

ROLE OF THE FUNDER AND OTHER ORGANISATIONS:

This project has been funded by the National Institute of Health Research (Health Services & Delivery Research funding stream, grant number 16/116/43) which commenced in March 2019 and is due to conclude in February 2022. NIHR are the funding body only, they will not determine the aims and objectives of this project nor will they have access to NHS England data.

As the main study is funded by the NIHR, an independent Study Steering Committee (SSC) comprising academics, service commissioners, public health experts and Public Patient Involvement (PPI) representatives provides oversight on behalf of NIHR. The SSC is independent of the study and their remit is to ensure that the project is delivered in line with the agreed protocol. The National Clinical Director for Older People and Person-Centred Integrated Care at NHS England is a member of the Study Steering Committee; the Director provides the NHS England perspective on commissioning of services for older people and guidance on dissemination and implementation of study findings.

The other organisations involved in the wider project with advisory roles include Southampton University Hospitals NHS Trust, Southern Health Foundation Trust, the University of Oxford, the University of Leeds, and public contributors. No staff from these organisations will have access to NHS England data. The Stakeholder Engagement Group (SEG) comprises a wide range of stakeholders, including representatives from service providers, commissioners, clinical experts, health, social care and voluntary organisation and patients/carers. The SEG role is to advise on the development of the simulation model and the scenarios to be tested by the simulation in Workstream 3 of the funded project.

DATA CONTROLLERS AND DATA PROCESSORS:

In this agreement, the University of Oxford and the University of Southampton are the joint Data Controllers. University of Oxford makes decisions about the processes for data processing and access. University of Southampton are data controllers as they are dictating the analysis that is being done.

The University of Oxford and University of Southampton are Data Processors. The RCGP RSC dataset is stored and managed at the University of Oxford. The University of Oxford has a contract with the RCGP to provide this surveillance, quality improvement and research platform. Under the 2018 Data Protection Act, the University of Oxford is identified as a processor of personal data for the Royal College of General Practitioners (RCGP). The RCGP Research Surveillance Centre has its secure data and analytics hub at University of Oxford, who will manage data governance, encryption, and access. NHS England data will be released to University of Oxford who will be carrying out the linkage with their primary care dataset before making the linked data available to University of Southampton. University of Southampton are Data Processor because University of Southampton staff will access NHS England data and carry out data analysis on the University of Oxford secure remote server.

DATA ANALYSIS METHODS AND USE OF THE RESULTS:

This study will explore the incidence and prevalence, development, and impact of frailty within the population using retrospective data from the RCGP Research Surveillance Centre databank. The eFI tool will be utilised to stratify a cohort of people aged 50 and over present in the database between 2006 and 2017 inclusive into fit, mild, moderate, and severe frailty groups. Data will be extracted on frailty status, health care use, and outcomes for the subsequent years, and the team at University of Southampton will calculate key service use costs from the linked RCGP RSC dataset and NHS England HES and mortality data. Outcomes will include mortality, unplanned hospital admission, A&E attendance, and GP appointments.

The RCGP RSC dataset will provide data on socio-economic factors, practice size and location and residence. The research team at University of Southampton will use the eFI to stratify the RCGP RSC dataset cohort by severity of frailty and explore frailty status over time, determining incidence, prevalence, and progression of frailty. The University of Southampton research team will use descriptive statistics to estimate baseline prevalence, burden of frailty and transition rates between frailty states in population aged 50 and over.

The research team at the University of Southampton will use the RCGP RSC dataset to 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 epidemiology of frailty will also be described, calculating prevalence, incidence and describing trajectories of decline. Relationships between demographics, practice characteristics, outcomes, service use and costs will be explored for frailty (eFI score) strata (robust, mild, moderate, and severe). The influence of frailty on outcomes, service use and costs will be explored. With the linked HES and mortality data, the University of Southampton team will also explore the relationship between frailty and secondary care outcomes and costs and mortality. Multi-state models (models which take account of the ‘level’ of frailty a patient has at any one time – i.e. fit, mild, moderate or severe) will be used to determine what clinical, demographic, and socio-economic variables are able to stratify frailty progression. Time-dependent Cox models will be used to examine the relationship between frailty state and key binary clinical outcomes (including mortality and service use). Mixed-effects negative binomial models will be used to examine the relationship between frailty state and count based clinical outcomes, such as the number of A&E attendances and unplanned hospitalisations.

The research team at the University of Southampton will use results from these analyses to inform development of guidelines for service commissioners, developed in partnership with experts in service delivery, commissioning and the study PPI representatives through stakeholder engagement. The key clinical, demographic and socio-economic drivers that are identified as significant predictors of frailty progression and/or associated with outcomes or service use patterns of interest will also be used to inform the development of a prototype simulation model. The simulation model 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 12-year timeframe. SD is a computer simulation modelling approach whose purpose is to analyse changes over time in complex, interacting systems and is ideally suited for health and care systems. The statistical analyses will be used to stratify the SD model and to inform potential ‘what if’ scenarios for simulation developed with the Stakeholder Engagement Group.

WHAT WILL THE SIMULATION MODEL DO?

An SD model consists of stocks (accumulations) of material, and flows between them, analogous to a series of water tanks connected by pipes. The rate of flow along each pipe is governed by valves that can be turned up or down. A stock-flow model will be developed, depicting patient transitions between different states. In this case, the “material” is frail patients, and the stocks are the numbers of patients in different health and social care states. These states will be further broken down by those characteristics identified in Workstream 1 as significantly impacting on demand for services, or strongly associated with specific outcomes. Potential candidate characteristics include age, gender, long-term condition (LTC) diagnoses and Index of Multiple Deprivation (IMD) scores.

The model does not follow individual patients, but uses the results obtained in Workstream 1 to calculate monthly transition probabilities between states (stocks). The model will use data from Workstream 1 to capture the key clinical and demographic differences that influence these transitions, as well as information about the costs and outcomes associated with each state. Data from Workstream 1 will be used to populate the simulation model to enable accurate simulation of population trends, service use and costs.

The anticipated time horizon for running the model is ten years (2018-2027). This length of time is required to capture fully the population dynamics and the evolution of frailty. While the demographic predictions thus derived will be robust, it is recognised that any cost calculations more than two or three years into the future can only be indicative, given that service delivery modalities and health and social care organisational structures are unlikely to remain fixed for the whole period.

Moreover, there is bound to be considerable local variation. The simulation model can easily take this into account by modifying the relevant parameters. The key benefit of using simulation is that a wide range of “what-if” scenarios can be tested and compared, including demographic trends and changes to prevalence and progression rates, in addition to service delivery scenarios developed with the SEG in Workstream 3. The model outputs, which will enable comparison between scenarios, will include:

• The number of patients, and proportion of the population, in each stock over time

• Demand for services over time, aggregated or broken down by patient category

• A range of health outcome measures, aggregated or broken down by patient category

• Mortality, total or cumulative, aggregated or broken down by patient category

To develop the simulation model to allow prediction of future trends and population health burden, retrospective analysis of a number of years of population-level data is required. As frailty is a slowly developing condition and is much more prevalent in the oldest old, this study requires analysis of transitions in frailty states and health outcomes for a large ageing cohort on an individual level over at least a 10-year period. This will allow the study to capture transitions in frailty development and important health outcomes during this period.

In line with the public interest basis for this request, the data requested from NHS England is to provide additional longitudinal data required for delivery of this National Institute of Health Research (NIHR) funded project, specifically linked hospital outpatient, emergency department, health economic and mortality data for a cohort of primary care patients identified from the RCGP RSC dataset. The simulation modelling study will be at population level (England) for which a representative cohort has been obtained from RCGP Research Surveillance Centre. This cohort primary care data covers a range of geographical areas, urban and rural locations, and the range of deprivation levels. This data request is for linked NHS EnglandHES and Civil Registration Deaths data/Mortality data, so necessarily covers the same geographical range as the primary care data.

As per the definition of ‘controller’ in the General Data Protection Regulation (1), both University of Southampton and the RCGP RSC research group based at the University of Oxford determine the purposes and means of the processing of personal data. The Principal Investigator (PI) at University of Southampton determines the study aims and objectives, the personal data that will be processed and the analyses that will be carried out. The University of Southampton staff working on Workstream 1 will also have access to the pseudonymised, linked primary care, HES and mortality data provided by RCGP RSC. In this case, University of Southampton are controllers in that they determine why the personal data are processed. The RCGP Research Surveillance Centre based at the University of Oxford determines the means of processing the data and approves its purpose; the University of Oxford therefore has oversight of study aims and objectives via the RCGP Research Surveillance Centre. University of Southampton and the University of Oxford are therefore joint Data Controllers.

Data flow into NHS England will consist of hashed identifiers for the study cohort (adults aged 50 and above registered with an RCGP practice at any year from 2006 to 2017 inclusive). The hashing of identifiable data for the Clinical Informatics and outcomes Research Group (RCGP Research Surveillance Centre) is conducted by the Salt Service of the University of Oxford Central IT team, so that the holder of the pseudonymised data is separated from the service that holds the non-reversible hash key. This avoids pseudonymised data becoming identifiable data.

NHS England will hash their NHS numbers using the same pseudonymisation algorithm (SHA-512). NHS England will undertake data linkage via the hashed NHS numbers in both sets of data. This process has been used for previous projects linking different sets of data, and the linkage has been successful. Records for each study participant containing information from HES and mortality data, together with hashed NHS numbers will be sent to the University of Oxford.

All individual-level data will then be stored and analysed at the University of Oxford. There will be no subsequent flows of individual level data from the University of Oxford. Aggregate analyses will be shared with the wider study team and used in dissemination of the research.

Apollo Medical Software Solutions, an approved third-party provider, has formal service agreements and service specifications with RCGP Research Surveillance Centre and with individual participating GP practices to conduct data collection and secure web transfer. (Copies of these formal agreements and technical details were shared with NHS England in the last IGTK assessment and were deemed satisfactory and are available to legitimate requests).

Each unique patient within the RCGP RSC databank is de-identified at source before data is extracted from individual practices using a computer-generated patient identifier created by Apollo Medical Software Solutions. This de-identification of records includes production of a hashed NHS number using pseudonymisation algorithm (SHA-512).

Pseudonymised record-level HES data will be processed and stored at the University of Oxford. Patient level databases (such as this study’s RCGP RSC dataset) are held in the database server within the RCGP Research Surveillance Centre Research Group's secure network. The Research Group's dedicated secure network is sited behind a firewall within the University's network. It is a standalone, independent network, all in-bounded connections are block, but out-bounded connections are allowed. All staff members of the research group working within the team base work from secure workstations or secure laptops with encrypted drive. Only substantive employees of the University of Oxford will have access to the data and only for the purposes described in this document. The data will be used solely for the "Dynamics of frailty in older people" study.

The University of Oxford will send the hashed NHS numbers to NHS England. The following flow of hashed NHS numbers will be undertaken.

The study group is the cohort of patients aged 50 years and over in registered in RCGP Research Surveillance Centre network practices 2006. University of Oxford will identify the study patients for the cohort above from primary care records in the RCGP Research Surveillance Centre practices and send the hashed NHS numbers of the cohort under study to NHS England to link to HES/ Civil Registration Data (CRD).

No other GP data will be sent to NHS England.

The process of linkage is as follows:

• University of Oxford’s senior SQL developer will submit the cohort to NHS England, with hashed NHS numbers using the pseudonymisation algorithm SHA-512

• NHS England will hash their NHS numbers using the same pseudonymisation algorithm (SHA-512) as used by the RCGP Research Surveillance Centre.

• NHS England will undertake data linkage via the hashed NHS numbers in both sets of data. This process has been used for previous projects linking different sets of data, and the linkage has been successful

• NHS England extract all HES and CRD records for which there are matched primary care records.

• NHS England will send the extract of HES and CRD records with the hashed NHS number to the University of Oxford.

• University of Oxford will link the HES and CRD records together with GP data from the primary care records from RCGP Research Surveillance Centre network practices with the same hashed NHS numbers.

• Records for each study participant will when fully linked contain information from HES and CRD, together with information from RCGP Research Surveillance Centre network primary care practices.

Each unique patient within the RCGP RSC dataset is de-identified at source before data is extracted from individual practices using a computer-generated patient ID. The University of Oxford holds no identifiable data and only hashed NHS number.

Only pseudonymised data with direct patient identifiers removed will be used. The research team will not seek individual patient identifiers; where required, data linkage will be achieved through ‘hashing’ algorithms to generate non-identifiable, unique IDs from identifiable data. As a further protection, non-reversible, pseudonymised ID numbers held be database organisations will be converted to unique study IDs, the keys to which will not be accessible to the research team; and, when using these data the research team will suppress small numbers in reporting and avoid the presentation of data that can potentially be used to reveal identities. Data extracts and aggregate analyses will be pseudonymised as described.

All data processing will be carried out by staff employed by the University of Oxford. Data analysis will be carried by staff employed by the University of Southampton and the University of Oxford. All staff have received Information Governance training on an annual basis and have all passed the NHS Information Governance on-line test for the current year.

Access to the data will limited to either:

1) Researchers with substantive employee contracts with the University of Oxford

2) Senior researchers from the University of Southampton. Southampton researchers will be required to complete training and sign the relevant agreements to be able to access the data on the University of Oxford secure environment. No individual-level study data can leave this environment, and all aggregated results data is reviewed prior to export.

The Research Group at the University of Oxford has conducted a risk assessment of the physical security of the offices and servers where patient level data is kept. The Research Group of Department of Clinical and Experimental Medicine at the University of Oxford has worked with routinely collected healthcare data in several research and evaluation projects over the last 15 years. The Research Group works within the Research and Information Governance team at the University of Oxford.

No data is stored outside of the secure computer system hosted at the University of Oxford.

There will be several outputs by the end of the study. The target groups and individuals for the outputs will include:

• academic

• scientific

• professional

• policy makers (both political and professional) involved in deciding future health policies

The main report will be delivered by 30th March 2022 delivered to the study funder and academic papers and seminars will be delivered in the following year.

The immediate project outputs will be:

• statistical and economic analyses - in the form of aggregate data tables, graphs, reports and submissions to peer reviewed journals, with any small numbers suppressed (in line with the HES Analysis Guide)

• oral and poster presentations at a minimum of two national and one international conference focusing on care of older people and aiming for the widest possible audience. The research team will submit at least two academic papers to high impact open access journals.

• Guidance for providers/commissioners

• Algorithms, simulation model and interactive dashboard for simulation

These outputs will form the core of guidance for NHS commissioners and planners to aid resource planning in relation to frailty. Study Patient and Public Involvement (PPI) representatives, drawn from the core study team and the School of Health Sciences Ageing & Dementia Research PPI Panel at the University of Southampton, will advise on dissemination and implementation through the SEG events and a study dissemination planning event.

The study team, Study Engagement Group and collaborators includes senior stakeholders relevant to development of frailty services and use of the electronic Frailty Index (eFI), including from provider Trusts, National Health Service England (NHSE) Older People Team and Clinical Commissioning Groups (CCGs).

The study team will use the established networks to share findings with leaders in implementation and commissioning of frailty services nationally. The team will work with the study PPI lead and SEG (including the Age UK representative) to plan dissemination to NHS staff 'on the ground' and also the local and wider body of patient/carers, with a focus on making the results 'accessible' to the wider public, both in writing and verbally, through presentations at workshops/team meetings/to patient groups. The study team will run a dissemination planning event, to which NHS commissioners and frailty leaders will be invited, to review findings, consider their implications and implementation and explore key messages and strategies for dissemination.

HOW DOES SHARING THIS DATA BENEFIT HEALTHCARE PROVISION?

The clinical management of frailty will become increasingly important as the population ages, with prevalence of frailty rising from 10% of people aged over 65, to up to 50% in those aged over 85. Despite the scale of this patient group, research indicates that half of patients with frailty are not receiving effective health care interventions. In Fit for Frailty Part 2 (British Geriatric Society, 2015), it is noted that there is potential for significant harm to frail patients if they receive inappropriate interventions. However, many services across the health and care system do not take adequate account of individuals’ frailty and so opportunities to improve quality of care are missed. Attention to the needs of older people living with frailty could, therefore, be more effective in reducing acute bed use and improving quality of care than focusing on those at high risk of admission. At the individual patient level, guidance for patient management exists and there is general agreement about the features of good quality care. There are, however, gaps in the evidence relating to the organisation and delivery of interventions and services to optimise provision of high-quality individualised patient management across the frail older population. The improved understanding of population needs offered by this study hopes to inform appropriate service planning and delivery, giving direct benefit for patients through provision of timely and appropriate care.

This study, with its emphasis on whole-system population dynamics of frailty, will explore the issues around population need, service configurations and clinical interventions highlighted above. Data from Workstream 1 (including the shared data) will be analysed and the results used to inform the simulation model in Workstream 4. A strength of the simulation modelling approach is that it allows for identification of different trajectories of care and key transition points, projection of future demand and rapid testing of the impact of different service configuration scenarios to aid decision-making. The proposed study hopes to impact on patient care directly, for example, by identifying features of people with frailty who are more likely to have adverse outcomes, identifying risk factors for frailty progression and informing targeted prevention through identification of trajectories of frailty, so enabling better targeting of interventions and services. Indirect impact may also be important, for example, through allowing commissioners to understand different care trajectories, and therefore the likely scale and nature of service demand, or service providers to identify cost-effective approaches for their specific population and facilitating the integration of health and social care.

The study outputs may have a direct benefit for commissioners; commissioning is a complex cycle involving assessment and understanding of population health needs, planning services to meet those needs, procuring appropriate and cost-effective services, and monitoring their delivery and impact. The outputs of this study has the potential to contribute at each of these stages but may have most impact in relation to assessment of population health needs. The planning stage of the commissioning cycle is often limited by a lack of reliable data on demand, particularly data which allows for forward projections; this study will address this need in relation to older people with frailty. The simulation modelling approach proposed in this study is particularly well-positioned to support commissioning, with its recent shifts towards more local commissioning, joint working and context specific (or ‘place-based’) commissioning and a focus on integrated systems of care. Integrated care organisations and commissioners may need to become more focused on needs of patients with multiple morbidity and functional problems (consistent with the presenting problems encountered in frailty) rather than disease-specific approaches.

The study team anticipate that realisable benefits from the proposed work will include guidance for commissioners and service providers on service configurations and the development of a customisable simulation model for local exploration of service demand and configurations.

The immediate project outputs from Workstreams 1 and 4 are the statistical and economic analyses, algorithms and simulation model, which will form the core of guidance for NHS commissioners and planners to aid resource planning in relation to frailty.

SPECIFIC OUTPUTS AND DISSEMINATION:

The study team will use a range of dissemination approaches to reach the various target audiences for this research. The dissemination strategy will be guided by study PPI representatives and other key stakeholders on the SEG, including carer organisations and Age UK. The study team, SEG and collaborators include senior stakeholders relevant to development of frailty services and use of the eFI, including from provider Trusts, NHS England (NHSE) Older People Team and CCGs. The study team will use their established networks to share findings with leaders in implementation and commissioning of frailty services. The team will work with the study PPI lead and SEG to plan dissemination to NHS staff ‘on the ground’ and the local and wider body of patient/carers, with a focus on making the results ‘accessible’ to the wider public, both in writing and verbally, through presentations at workshops/team meetings/to patient groups. The study team will run a dissemination planning event, to which NHS commissioners and frailty leaders will be invited, to review findings, consider their implications and implementation and explore key messages and strategies for dissemination. The study team will use their established formal social media networks to promote project outputs, and for dissemination. The team will share the results of the study with the public and staff in the relevant health, local and third sectors a public/patient friendly way by use of infographics, using plain English, and via use of local and national media and social media. In addition, the study team will summarise the findings of the work via professional journals (e.g. the Health Service Journal (HSJ)) and health service networks and professional organisations (Health Services Research Network, British Geriatrics Society).

In addition to the above, the core study team will lead on other study outputs, including academic journal papers. They will submit abstracts for oral and poster presentations at a minimum of two national and one international conference focusing on care of older people and aiming for the widest possible audience. They will submit at least two academic papers to high impact open access journals. These will be focused on the dynamics of frailty within the population and the impact of frailty on health care demand and outcomes. This analyses from Workstream 1 will provide data on incidence and prevalence of frailty, stratified by severity, in a typical older, primary care population, and the associated outcomes including emergency department use, hospitalisation and deaths. The long-term impact of frailty on outcomes and service demand and costs will be modelled. The simulation model could allow local and regional service planners and commissioners to explore a range of scenarios relevance to their specific contexts, so aiding decisions on service commissioning and design. The study team will collate the outputs of the study into a commissioning toolkit, comprising guidance on drivers of frailty-related demand and outputs from the Workstream 4 simulation model that can be used for prediction of future demand and exploration of different scenarios. The simulation model could be capable of adaptation for exploration of different service and demographic contexts. The simulation model algorithms may also be transferable to modelling of other chronic conditions that are common within the ageing population.

The study team will produce a final research report for NIHR detailing the work undertaken and results alongside an abstract, executive summary and technical appendices. The executive summary will be suitable for use as a briefing paper for NHS managers and commissioners. In addition, they will prepare a short Powerpoint presentation to present the main findings to NHS organisations. The slides will be made available, alongside the full report, on the HS&DR programme web pages and, where possible, as additional linked material with other publications. They will also work closely with the University communications team and ensure that members of the study team are given appropriate support and training in handling enquiries from the media.

i) Development of guidance and commissioning toolkit for service providers and commissioners to inform planning over a 15 year+ period

ii) Development of a simulation model that may allow service planners and commissioners to explore scenarios and trends tailored to local and regional populations

iii) Future development of the simulation model of population trends into a workforce planning tool

iv) Future adaptation of the simulation model algorithms to explore health care demand and mitigation scenarios in relation to other conditions within the ageing population

Better understanding of the development and dynamics of frailty over time could facilitate service and workforce planning and commissioning. Outputs of the study will include guidance for commissioners, a simulation model to facilitate prediction of service demand associated with frailty and the potential for development of these resources into a workforce planning toolkit. The simulation model architecture, and the know-how relating to populating and operationalising the model may be transferable to prediction of demand for other populations and conditions with a high population prevalence (e.g., dementia, obesity, mental health problems). As the models are based on national-level data, the application of results and the ability to adapt the model to geographical locations means that the impact may be nationwide within the UK, and the information may also be adapted on an international level.

HOW THE BENEFITS WILL BE ACHIEVED, AND TIMELINES

The study team (including researchers at the University of Oxford and Southampton) will achieve the benefit, working together with the NIHR to ensure appropriate dissemination and with third parties such as NHS Commissioners to realise the benefits.

The Stakeholder Engagement Group (SEG) includes representation from local Strategic Transformation Partnership (STP), including from Clinical Commissioning Groups (CCGs), local authorities, and provider organisations, in addition to national commissioning representatives. The SEG also includes the PPI lead, PPI representatives from the Ageing & Dementia PPI panel and representation from third sector organisations, including Age UK. This will ensure that the results from the analyses and simulation model are discussed with the right people to make the appropriate changes to the healthcare system.

The study outputs will be monitored by the independent Study Steering Committee (SSC) according to the study Gannt chart, publication plan and dissemination activities. For example, milestones such as simulation model production, analysis of scenarios, commissioning guidance and toolkit and dissemination and implementation events will all be reviewed by the SSC.

Epidemiological analysis of the primary care and linked HES/mortality data is expected to be complete within 9 months of data delivery, enabling provision of aggregate data to inform the simulation model and scenario development. The simulation model and related outputs including scenarios is projected to complete by the end of 2022.

FEB 2024 ACR UPDATE:

The study team have completed the main analyses of the project and data has contributed to the following publications:

Fogg C, England T, Zhu S, Jones J, de Lusignan S, Fraser SDS, Roderick P, Clegg A, Harris S, Brailsford S, Barkham A, Patel HP, Walsh B. Primary and secondary care service use and costs associated with frailty in an ageing population: longitudinal analysis of an English primary care cohort of adults aged 50 and over, 2006-2017. Age Ageing. 2024 Feb 1;53(2):afae010. doi: 10.1093/ageing/afae010. PMID: 38337044; PMCID: PMC10857897.

Key points:

• Use of primary and secondary care services escalated with increasing frailty severity at all ages.

• After adjusting for age and other sociodemographic factors, frailty remained the main driver of service use and costs.

• Adjusted annual cost estimates doubled in people with mild frailty versus fit, trebled in moderate and quadrupled in severe frailty.

• Higher numbers of people with mild and moderate frailty led to higher population level costs as compared to severe frailty.

• Early, targeted intervention to prevent frailty onset and manage patient outcomes is key to reduce healthcare use and costs.

Walsh B, Fogg C, Harris S, Roderick P, de Lusignan S, England T, Clegg A, Brailsford S, Fraser SDS. Frailty transitions and prevalence in an ageing population: longitudinal analysis of primary care data from an open cohort of adults aged 50 and over in England, 2006-2017. Age Ageing. 2023 May 1;52(5):afad058. doi: 10.1093/ageing/afad058. PMID: 37140052; PMCID: PMC10158172.

Key points:

• Frailty is already present in the population before age 65.

• Longer times spent in moderate and severe frailty suggest extended burden of disease.

• Frailty progresses more rapidly with increasing age, resulting in high prevalence.

• Frailty transitions are associated with increasing age, higher deprivation, female sex, Asian ethnicity and urban dwelling.

• Strategies to reduce the burden of frailty need to consider health inequalities.

The study has two other publications in progress which use these results to create the simulation model to predict future demand for services by older people with frailty.

The study team are currently presenting and discussing these results with the public and partners and working with health and care partners to establish how the simulation model can be further developed and used in practice.